"Authors","Author full names","Author(s) ID","Title","Year","Source title","Volume","Issue","Art. No.","Page start","Page end","Page count","Cited by","DOI","Link","Affiliations","Authors with affiliations","Abstract","Author Keywords","Index Keywords","Molecular Sequence Numbers","Chemicals/CAS","Tradenames","Manufacturers","Funding Details","Funding Texts","References","Correspondence Address","Editors","Publisher","Sponsors","Conference name","Conference date","Conference location","Conference code","ISSN","ISBN","CODEN","PubMed ID","Language of Original Document","Abbreviated Source Title","Document Type","Publication Stage","Open Access","Source","EID"
"Exarchos K.P.; Aggelopoulou A.; Oikonomou A.; Biniskou T.; Beli V.; Antoniadou E.; Kostikas K.","Exarchos, Konstantinos P. (24398705700); Aggelopoulou, Agapi (57387336500); Oikonomou, Aikaterini (56656256400); Biniskou, Theodora (57387527100); Beli, Vasiliki (57386221200); Antoniadou, Eirini (57386221300); Kostikas, Konstantinos (6602272047)","24398705700; 57387336500; 56656256400; 57387527100; 57386221200; 57386221300; 6602272047","Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease","2022","IEEE Journal of Biomedical and Health Informatics","26","5","","2331","2338","7","25","10.1109/JBHI.2021.3135838","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121797480&doi=10.1109%2fJBHI.2021.3135838&partnerID=40&md5=44c6f29c3fee56636de287ee9013ab71","University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece","Exarchos K.P., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Aggelopoulou A., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Oikonomou A., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Biniskou T., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Beli V., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Antoniadou E., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; Kostikas K., University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece","Background: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. Objective: The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. Methods: We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. Results: The resulting articles were assessed and organized into four basic contextual categories, namely: i) 'COPD diagnosis', ii) 'COPD prognosis', iii) 'Patient classification', iv) 'COPD management', and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. Conclusions: We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.  © 2013 IEEE.","Artificial intelligence; chronic bronchitis; chronic obstructive pulmonary disease; data mining; emphysema; machine learning","Artificial Intelligence; Chronic Disease; Delivery of Health Care; Forecasting; Humans; Pulmonary Disease, Chronic Obstructive; Bioinformatics; Computer aided diagnosis; Job analysis; Learning systems; Pulmonary diseases; Artificial intelligence techniques; Chronic bronchitis; Chronic conditions; Chronic obstructive lung disease; Chronic obstructive pulmonary disease; Disease research; Emphysema; Healthcare domains; Machine-learning; Task analysis; acceleration; adoption; algorithm; Article; artificial intelligence; bioinformatics; chronic bronchitis; chronic obstructive lung disease; classifier; clinical decision support system; computer assisted tomography; convolutional neural network; data mining; decision tree; disease severity; electronic health record; emphysema; fuzzy c means clustering; gene expression; hospital readmission; human; learning algorithm; length of stay; lung function test; machine learning; medical history; noninvasive positive pressure ventilation; oxygen therapy; patient coding; physician; prognosis; quantitative analysis; random forest; sensitivity and specificity; systematic review; chronic disease; chronic obstructive lung disease; forecasting; health care delivery; Data mining","","","","","","","Jiang F., Et al., Artificial intelligence in healthcare: Past, present and future, Stroke Vasc Neurol., 2, 4, pp. 230-243, (2017); Angelini E., Dahan S., Shah A., Unravelling machine learning: Insights in respiratory medicine, Eur. Respir. J., 54, 6, (2019); Topalovic M., Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur. Respir. J., 53, 4, (2019); John Gibson G., Loddenkemper R., Sibille Y., Lundback B., The European Lung White Book: Respiratory Health and Disease in Europe, (2013); Global Initiative for Chronic Obstructive Lung Disease, Pocket Guide to COPD Diagnosis, Management and Prevention: A Guide for Healthcare., (2017); Exarchos K.P., Beltsiou M., Votti C.-A., Kostikas K., Artificial intelligence techniques in asthma: A systematic review and critical appraisal of the existing literature, Eur. Respir. J., (2020); Tang L.Y.W., Coxson H.O., Lam S., Leipsic J., Tam R.C., Sin D.D., Towards large-scale case-finding: Training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit. Health, 2, 5, pp. e259-e267, (2020); Mostafaei S., Et al., Identification of novel genes in human airway epithelial cells associated with chronic obstructive pulmonary disease (COPD) usingmachine-based learning algorithms, Sci. Rep., 8, 1, (2018); Leidyet Al N.K., Insight into best variables forCOPDcase identification: A random forests analysis, Int. J. Chron. Obstruct. Pulmon. Dis., 3, 1, pp. 406-418, (2016); Cheng Y.-T., Lin Y.-F., Chiang K.-H., Tseng V.S., Mining sequential risk patterns from large-scale clinical databases for early assessment of chronic diseases: A case study on chronic obstructive pulmonary disease, Ieee J. Biomed. Health Informat., 21, 2, pp. 303-311, (2017); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informat. J., 25, 3, pp. 811-827, (2019); Ying J., Et al., Classification of exacerbation frequency in the COPDGene cohort using deep learning with deep belief networks, Ieee J. Biomed. Health Informat., 24, 6, pp. 1805-1813, (2020); Swaminathan S., Et al., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLoS One, 12, 11, (2017); Goto T., Jo T., Matsui H., Fushimi K., Hayashi H., Yasunaga H., Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease, COPD, 16, 5-6, pp. 338-343, (2019); Merone M., Pedone C., Capasso G., Incalzi R.A., Soda P., A decision support system for tele-monitoring COPD-related worrisome events, Ieee J. Biomed. Health Informat., 21, 2, pp. 296-302, (2017); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: Identification and prediction using a digital health system, J. Med. Internet Res., 19, 3, (2017); Gonzalez G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Amer. J. Respir. Crit. Care Med., 197, 2, pp. 193-203, (2018); Peng L., Et al., Classification and quantification of emphysema using a multi-scale residual network, Ieee J. Biomed. Health Informat., 23, 6, pp. 2526-2536, (2019); Moghadas-Dastjerdi H., Et al., Lung CT image based automatic technique for COPD GOLD stage assessment, Expert Syst. with Appl., 85, pp. 194-203, (2017); Pikoula M., Quint J.K., Nissen F., Hemingway H., Smeeth L., Denaxas S., Identifying clinically importantCOPDsub-Types using data-driven approaches in primary care population based electronic health records, Bmc Med. Informat. Decis. Mak., 19, 1, (2019); Lin S., Zhang Q., Chen F., Luo L., Chen L., Zhang W., Smooth Bayesian network model for the prediction of future high-cost patients with COPD, Int. J. Med. Informat., 126, pp. 147-155, (2019); Pinto-Plata V., Et al., Plasma metabolomics and clinical predictors of survival differences in COPD patients, Respir. Res., 20, 1, (2019); Lee S., Et al., Reducing COPD readmissions: A causal Bayesian network model, Ieee Robot. Automat. Lett., 3, 4, pp. 4046-4053, (2018); Weng Y., Fang Y., Yan H., Yang Y., Hong W., Bayesian nonparametric classification with tree-based feature transformation for NIPPV efficacy prediction in COPD patients, Ieee Access, 7, pp. 177774-177783, (2019); Lu W., Yan Z., An improved fuzzy C-means clustering algorithm for assisted therapy of chronic bronchitis, Technol. Health Care, 23, 6, pp. 699-713, (2015); Kourou K., Exarchos T.P., Exarchos K.P., Karamouzis M.V., Fotiadis D.I., Machine learning applications in cancer prognosis and prediction, Comput. Struct. Biotechnol. J., 13, pp. 8-17, (2015); Kourou K., Exarchos K.P., Papaloukas C., Sakaloglou P., Exarchos T., Fotiadis D.I., Applied machine learning in cancer research: A systematic reviewfor patient diagnosis, classification and prognosis, Comput. Struct. Biotechnol. J., 19, pp. 5546-5555, (2021); Labaki W.W., Et al., The role of chest computed tomography in the evaluation and management of the patient with chronic obstructive pulmonary disease, Amer. J. Respir. Crit. Care Med., 196, 11, pp. 1372-1379, (2017); Xu Y., Et al., Deep learning predicts lung cancer treatment response from serial medical imaging, Clin.Cancer Res., 25, 11, pp. 3266-3275, (2019); Hardy M., Harvey H., Artificial intelligence in diagnostic imaging: Impact on the radiography profession, Br. J. Radiol., 93, 1108, (2020); Oren O., Gersh B.J., Bhatt D.L., Artificial intelligence in medical imaging: Switching from radiographic pathological data to clinically meaningful endpoints, Lancet Digit. Health, 2, 9, pp. e486-e488, (2020); Panayides A.S., Et al., AI in medical imaging informatics: Current challenges and future directions, Ieee J Biomed. Health Informat., 24, 7, pp. 1837-1857, (2020); Xu C., Et al., DCT-MIL: Deep CNN transferredmultiple instance learning for COPD identification using CT images, Phys. Med. Biol., 65, 14, (2020); Fischer A.M., Et al., Comparison of artificial intelligence-based fully automatic chest CT emphysema quantification to pulmonary function testing, Ajr Amer. J. Roentgenol., 214, 5, pp. 1065-1071, (2020); De Fauw J., Et al., Clinically applicable deep learning for diagnosis and referral in retinal disease, Nat. Med., 24, 9, pp. 1342-1350, (2018); Litjens G., Et al., Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Sci. Rep., 6, (2016); Esteva A., Et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 7639, pp. 115-118, (2017); Altan G., Kutlu Y., Allahverdi N., Deep learning on computerized analysis of chronic obstructive pulmonary disease, Ieee J. Biomed. Health Informat., 24, 5, pp. 1344-1350, (2019); Garcia-Ordas M.T., Et al., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Tang C., Et al., A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes, Bmc Med. Informat. Decis. Mak., 19, 8, (2019); Viegi G., Pistelli F., Sherrill D.L., Maio S., Baldacci S., Carrozzi L., Definition, epidemiology and natural history of COPD, Eur. Respir. J., 30, 5, pp. 993-1013, (2007); Tan P.-N., Steinbach M., Karpatne A., Kumar V., Introduction to Data Mining, (2013)","K.P. Exarchos; University of Ioannina, Respiratory Medicine Department, School of Medicine, Ioannina, 45500, Greece; email: kexarcho@gmail.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","34914601","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85121797480"
"Zhang Y.; Xia R.; Lv M.; Li Z.; Jin L.; Chen X.; Han Y.; Shi C.; Jiang Y.; Jin S.","Zhang, Yuepeng (57220074337); Xia, Rongyao (57554949600); Lv, Meiyu (57221950204); Li, Zhiheng (57221964932); Jin, Lingling (57555414800); Chen, Xueda (57554017300); Han, Yaqian (57668790300); Shi, Chunpeng (57671883800); Jiang, Yanan (55538395400); Jin, Shoude (55340154400)","57220074337; 57554949600; 57221950204; 57221964932; 57555414800; 57554017300; 57668790300; 57671883800; 55538395400; 55340154400","Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease","2022","Frontiers in Immunology","13","","740513","","","","34","10.3389/fimmu.2022.740513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127303007&doi=10.3389%2ffimmu.2022.740513&partnerID=40&md5=6a63339f4ed4b0b91b27cc015ccae2e1","Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; Department of Respiratory Medicine, The Second Hospital of Harbin Medical University, Harbin, China; Department of Medical Oncology, The Fourth Hospital of Harbin Medical University, Harbin, China; School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; Department of Pharmacology, State-Province Key Laboratories of Biomedicine- Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education, College of Pharmacy, Harbin Medical University, Harbin, China; Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China","Zhang Y., Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; Xia R., Department of Respiratory Medicine, The Second Hospital of Harbin Medical University, Harbin, China; Lv M., Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; Li Z., Department of Medical Oncology, The Fourth Hospital of Harbin Medical University, Harbin, China; Jin L., Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; Chen X., Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; Han Y., School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China; Shi C., Department of Pharmacology, State-Province Key Laboratories of Biomedicine- Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education, College of Pharmacy, Harbin Medical University, Harbin, China; Jiang Y., Department of Pharmacology, State-Province Key Laboratories of Biomedicine- Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education, College of Pharmacy, Harbin Medical University, Harbin, China, Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China; Jin S., Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China","Objective: This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis. Methods: The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results. Results: 80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells. Conclusion: STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD. Copyright © 2022 Zhang, Xia, Lv, Li, Jin, Chen, Han, Shi, Jiang and Jin.","COPD; immune infiltration; LASSO; SLC27A3; STAU1; SVM-RFE","Algorithms; Biomarkers; Cytoskeletal Proteins; Genes, MHC Class I; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; RNA-Binding Proteins; amino acid; biological marker; CD19 antigen; CD20 antigen; CD4 antigen; CD57 antigen; CD8 antigen; cholecystokinin octapeptide; cigarette smoke; interleukin 1beta; interleukin 6; major histocompatibility antigen class 1; telomerase; tumor necrosis factor; biological marker; cytoskeleton protein; RNA binding protein; STAU1 protein, human; algorithm; animal experiment; animal model; animal tissue; antigen presentation; Article; BEAS-2B cell line; biological phenomena and functions concerning the entire organism; biosynthesis; cell infiltration; chronic obstructive lung disease; clinical article; cohort analysis; controlled study; correlation analysis; diagnostic test accuracy study; differential gene expression; DNA microarray; eosinophil; functional enrichment analysis; genetic transfection; histology; human; human cell; human tissue; immunocompetent cell; immunohistochemistry; immunotherapy; machine learning; macrophage; mast cell; memory B lymphocyte; metabolism; mouse; natural killer cell; nonhuman; pathogenesis; phagocytosis; plasma cell; real time polymerase chain reaction; receiver operating characteristic; support vector machine; Western blotting; algorithm; gene; genetics; machine learning","","amino acid, 65072-01-7; cholecystokinin octapeptide, 25126-32-3; Human immunodeficiency virus reverse transcriptase, ; RNA directed DNA polymerase, ; telomerase, ; Biomarkers, ; Cytoskeletal Proteins, ; RNA-Binding Proteins, ; STAU1 protein, human, ","","","Fourth Hospital of Harbin Medical University; Major Program of Natural Science Foundation of Heilongjiang Province, (ZD2016014); Harbin Applied Technology Research and Development Project, (2016RQXYJ116); Harbin Applied Technology Research and Development Project; National Natural Science Foundation of China, NSFC, (81670028); National Natural Science Foundation of China, NSFC","Funding text 1: This work was funded by the National Science Foundation of China (81670028), the Major Program of Natural Science Foundation of Heilongjiang Province (ZD2016014), Harbin City Applied Technology Research and Development Project (2016RQXYJ116). ; Funding text 2: We acknowledge the National Science Foundation of China (81670028), the Major Program of Natural Science Foundation of Heilongjiang Province (ZD2016014), Harbin City Applied Technology Research and Development Project (2016RQXYJ116). We acknowledge the support from The Fourth Hospital of Harbin Medical University. 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Jin; Department of Respiratory Medicine, The Fourth Hospital of Harbin Medical University, Harbin, China; email: jinshoude@163.com; Y. Jiang; Department of Pharmacology, State-Province Key Laboratories of Biomedicine- Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education, College of Pharmacy, Harbin Medical University, Harbin, China; email: jiangyanan@hrbmu.edu.cn","","Frontiers Media S.A.","","","","","","16643224","","","35350787","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127303007"
"Meng Q.; Wang J.; Cui J.; Li B.; Wu S.; Yun J.; Aschner M.; Wang C.; Zhang L.; Li X.; Chen R.","Meng, Qingtao (57001986700); Wang, Jiajia (55930900700); Cui, Jian (57199423816); Li, Bin (57208579582); Wu, Shenshen (57001489600); Yun, Jun (57210157821); Aschner, Michael (7006089061); Wang, Chengshuo (57204929951); Zhang, Luo (36068675900); Li, Xiaobo (35741076500); Chen, Rui (59492944500)","57001986700; 55930900700; 57199423816; 57208579582; 57001489600; 57210157821; 7006089061; 57204929951; 36068675900; 35741076500; 59492944500","Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning","2022","Environment International","168","","107469","","","","23","10.1016/j.envint.2022.107469","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136677649&doi=10.1016%2fj.envint.2022.107469&partnerID=40&md5=7f400836adef16fef5a00bbdf6f8b55f","Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China; Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87, Ding Jia Qiao Road, Nanjing, 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China; Department of Molecular Pharmacology, Albert Einstein College of Medicine, Forchheimer 209, 1300 Morris Park Avenue, Bronx, 10461, NY, United States; Department of Otolaryngology, Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, 100730, China; Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, 100005, China; Department of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, 100005, China; Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, China; Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, 100005, China; School of Public Health, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China; Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou, 511436, China","Meng Q., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China; Wang J., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China; Cui J., Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87, Ding Jia Qiao Road, Nanjing, 210009, China, Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China; Li B., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China; Wu S., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China; Yun J., Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China; Aschner M., Department of Molecular Pharmacology, Albert Einstein College of Medicine, Forchheimer 209, 1300 Morris Park Avenue, Bronx, 10461, NY, United States; Wang C., Department of Otolaryngology, Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, 100730, China, Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, 100005, China; Zhang L., Department of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, 100005, China, Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, 100005, China; Li X., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China, Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China; Chen R., Beijing Key Laboratory of Environmental Toxicology, School of Public Health, Capital Medical University, Beijing, 100069, China, School of Public Health, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100069, China, Institute for Chemical Carcinogenesis, Guangzhou Medical University, Guangzhou, 511436, China","Ambient fine particulate matter (PM2.5) is linked to an increased risk of chronic obstructive pulmonary disease (COPD) exacerbations, which significantly increase the risk of mortality in COPD patients. Identifying the subtype of COPD patients who are sensitive to environmental aggressions is necessary. Using in vitro and in vivo PM2.5 exposure models, we demonstrate that exosomal hsa_circ_0005045 is upregulated by PM2.5 and binds to the protein cargo peroxiredoxin2, which functionally aggravates hallmarks of COPD by recruiting neutrophil elastase and triggering in situ release of tumor necrosis factor (TNF)-α by inflammatory cells. The biological function of hsa_circ_0005045 associated with aggravation of COPD is validated using exosome-transplantation and conditional circRNA-knockdown murine models. By sorting the major components of PM2.5, we find that PM2.5-bound heavy metals, which are distinguishable from the components of cigarette smoke, trigger the elevation of exosomal hsa_circ_0005045. Finally, using machine learning models in a cohort with 327 COPD patients, the PM2.5 exposure-sensitive COPD patients are characterized by relatively high hsa_circ_0005045 expression, non-smoking, and group C (mMRC 0–1 (or CAT < 10) and ≥ 2 exacerbations (or ≥ 1 exacerbation leading to hospital admission) in the past year). Thus, our results suggest that environmental reduction in PM2.5 emission provides a targeted approach to protecting non-smoking COPD patients against air pollution-related disease exacerbation. © 2022 The Author(s)","Air pollution; circRNA; COPD; Machine learning; PM<sub>2.5</sub>","Air Pollutants; Air Pollution; Animals; Environmental Exposure; Humans; Mice; Particulate Matter; Pulmonary Disease, Chronic Obstructive; RNA, Circular; Tumor Necrosis Factor-alpha; Air pollution; Cell death; Heavy metals; Hospitals; Pulmonary diseases; Smoke; cigarette smoke; circular ribonucleic acid; leukocyte elastase; peroxiredoxin 2; tumor necrosis factor; circular ribonucleic acid; tumor necrosis factor; Acute exacerbations; Ambients; Chronic obstructive pulmonary disease; Circrna; Fine particulate matter; In-vitro; Machine-learning; PM 2.5; Profile learning; Vitro and in vivo; atmospheric pollution; chronic obstructive pulmonary disease; health risk; machine learning; mortality; particulate matter; pollution exposure; prediction; RNA; adult; aged; air pollution; animal experiment; animal model; animal tissue; Article; C57BL 6 mouse; chronic obstructive lung disease; cohort analysis; controlled study; disease exacerbation; environmental exposure; environmental factor; exosome; female; human; in vitro study; in vivo study; machine learning; major clinical study; male; mouse; nonhuman; particulate matter 2.5; prediction; protein expression; upregulation; air pollutant; analysis; animal; particulate matter; Machine learning","","leukocyte elastase, 109968-22-1; Air Pollutants, ; Particulate Matter, ; RNA, Circular, ; Tumor Necrosis Factor-alpha, ","","","Guangdong Provincial Natural Science Foundation Team Project, (2018B030312005); National Institute of Environmental Health Sciences, NIEHS, (R01 ES020852, R01 ES07331, R01ES010563); National Institute of Environmental Health Sciences, NIEHS; National Natural Science Foundation of China, NSFC, (81730088); National Natural Science Foundation of China, NSFC; National Science Fund for Distinguished Young Scholars, (81973084, 82003498, 82003499, 82025031, 91943301); National Science Fund for Distinguished Young Scholars","This work was supported by the State Key Program of the National Natural Science Foundation of China ( 81730088 ), the National Science Fund for Distinguished Young Scholars (82025031), the National Natural Science Foundation of China (81973084, 82003498, 82003499, 91943301), and Guangdong Provincial Natural Science Foundation Team Project, China (2018B030312005). MA was supported by the National Institute of Environmental Health Sciences (NIEHS) (R01 ES10563, R01 ES07331, and R01 ES020852). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors approved the manuscript. ","Adibi A., Sin D.D., Safari A., Johnson K.M., Aaron S.D., FitzGerald J.M., Sadatsafavi M., The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study, Lancet Respir. Med., 8, pp. 1013-1021, (2020); Amaral J.L.M., Lopes A.J., Faria A.C.D., Melo P.L., Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease, Comput. Methods Programs Biomed., 118, 2, pp. 186-197, (2015); Annesi-Maesano I., Air Pollution and Chronic Obstructive Pulmonary Disease Exacerbations: When Prevention Becomes Feasible, Am. J. Respir. Crit. 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Psychiatry, (2018); Zhong Y., Wang Y., Zhang C., Hu Y., Sun C., Liao J., Wang G., Identification of long non-coding RNA and circular RNA in mice after intra-tracheal instillation with fine particulate matter, Chemosphere, 235, pp. 519-526, (2019); Zhou L., Wu B., Yang J., Wang B., Pan J., Xu D., Du C., Knockdown of circFOXO3 ameliorates cigarette smoke-induced lung injury in mice, (2021); Zhu L., Sun H.T., Wang S., Huang S.L., Zheng Y., Wang C.Q., Hu B.Y., Qin W., Zou T.T., Fu Y., Et al., (2020)","L. Zhang; Beijing Institute of Otolaryngology, Beijing, No. 17, HouGouHuTong, Dongcheng District, 100005, China; email: dr.luozhang@139.com","","Elsevier Ltd","","","","","","01604120","","ENVID","36041244","English","Environ. Int.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85136677649"
"Su J.G.; Barrett M.A.; Combs V.; Henderson K.; Van Sickle D.; Hogg C.; Simrall G.; Moyer S.S.; Tarini P.; Wojcik O.; Sublett J.; Smith T.; Renda A.M.; Balmes J.; Gondalia R.; Kaye L.; Jerrett M.","Su, Jason G (11840142100); Barrett, Meredith A (23567765400); Combs, Veronica (57202003064); Henderson, Kelly (57193166166); Van Sickle, David (12140909800); Hogg, Chris (57193161542); Simrall, Grace (57202004149); Moyer, Sarah S (57202009410); Tarini, Paul (57202022749); Wojcik, Oktawia (35751013500); Sublett, James (6603679185); Smith, Ted (57205444364); Renda, Andrew M (56989051900); Balmes, John (7005041892); Gondalia, Rahul (55701668900); Kaye, Leanne (49961411000); Jerrett, Michael (57204339574)","11840142100; 23567765400; 57202003064; 57193166166; 12140909800; 57193161542; 57202004149; 57202009410; 57202022749; 35751013500; 6603679185; 57205444364; 56989051900; 7005041892; 55701668900; 49961411000; 57204339574","Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors","2022","International Journal of Epidemiology","51","1","","213","224","11","15","10.1093/ije/dyab187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121757506&doi=10.1093%2fije%2fdyab187&partnerID=40&md5=5e314e1f003ef72673c3e926b6de9a0e","Division of Environmental Health Sciences, School of Public Health, University of California at Berkeley, Berkeley, CA, United States; Propeller Health, San Francisco, CA, United States; Center for Healthy Air, Water and Soil University of Louisville, Louisville, KY, United States; Propeller Health, Madison, WI, United States; Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States; Louisville Metro, Office of Civic Innovation, Louisville, KY, United States; Louisville Metro, Department of Public Health and Wellness, Louisville, KY, United States; Robert Wood Johnson Foundation, Princeton, NJ, United States; Family Allergy and Asthma, Louisville, KY, United States; Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, United States; Humana, Louisville, KY, United States; Fielding School of Public Health, University of California, Los Angeles, CA, United States","Su J.G., Division of Environmental Health Sciences, School of Public Health, University of California at Berkeley, Berkeley, CA, United States; Barrett M.A., Propeller Health, San Francisco, CA, United States; Combs V., Center for Healthy Air, Water and Soil University of Louisville, Louisville, KY, United States; Henderson K., Propeller Health, San Francisco, CA, United States; Van Sickle D., Propeller Health, Madison, WI, United States, Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States; Hogg C., Propeller Health, San Francisco, CA, United States; Simrall G., Louisville Metro, Office of Civic Innovation, Louisville, KY, United States; Moyer S.S., Louisville Metro, Department of Public Health and Wellness, Louisville, KY, United States; Tarini P., Robert Wood Johnson Foundation, Princeton, NJ, United States; Wojcik O., Robert Wood Johnson Foundation, Princeton, NJ, United States; Sublett J., Family Allergy and Asthma, Louisville, KY, United States; Smith T., Center for Healthy Air, Water and Soil University of Louisville, Louisville, KY, United States, Envirome Institute, School of Medicine, University of Louisville, Louisville, KY, United States; Renda A.M., Humana, Louisville, KY, United States; Balmes J., Division of Environmental Health Sciences, School of Public Health, University of California at Berkeley, Berkeley, CA, United States; Gondalia R., Propeller Health, San Francisco, CA, United States; Kaye L., Propeller Health, San Francisco, CA, United States; Jerrett M., Fielding School of Public Health, University of California, Los Angeles, CA, United States","Objective tracking of asthma medication use and exposure in real-time and space has not been feasible previously. Exposure assessments have typically been tied to residential locations, which ignore exposure within patterns of daily activities. Methods: We investigated the associations of exposure to multiple air pollutants, derived from nearest air quality monitors, with space-time asthma rescue inhaler use captured by digital sensors, in Jefferson County, Kentucky. A generalized linear mixed model, capable of accounting for repeated measures, over-dispersion and excessive zeros, was used in our analysis. A secondary analysis was done through the random forest machine learning technique. Results: The 1039 participants enrolled were 63.4% female, 77.3% adult (>18) and 46.8% White. Digital sensors monitored the time and location of over 286 980 asthma rescue medication uses and associated air pollution exposures over 193 697 patient-days, creating a rich spatiotemporal dataset of over 10 905 240 data elements. In the generalized linear mixed model, an interquartile range (IQR) increase in pollutant exposure was associated with a mean rescue medication use increase per person per day of 0.201 [95% confidence interval (CI): 0.189-0.214], 0.153 (95% CI: 0.136-0.171), 0.131 (95% CI: 0.115-0.147) and 0.113 (95% CI: 0.097-0.129), for sulphur dioxide (SO2), nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3), respectively. Similar effect sizes were identified with the random forest model. Time-lagged exposure effects of 0-3 days were observed. Conclusions: Daily exposure to multiple pollutants was associated with increases in daily asthma rescue medication use for same day and lagged exposures up to 3 days. Associations were consistent when evaluated with the random forest modelling approach.  © 2021 The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.","Asthma; digital sensor; environmental trigger; mobile health; short-acting beta agonist","Adult; Air Pollutants; Air Pollution; Asthma; Environmental Exposure; Female; Humans; Male; Nitrogen Dioxide; Ozone; Particulate Matter; nitrogen dioxide; ozone; sulfur dioxide; nitrogen dioxide; ozone; asthma; atmospheric pollution; pollution exposure; sensor; symplectite; symptom; adult; aged; air monitoring; air pollution; air quality; Article; asthma; child; controlled study; daily life activity; effect size; environmental exposure; female; human; machine learning; major clinical study; male; particulate matter 2.5; PM2.5 exposure; preschool child; random forest; seasonal variation; secondary analysis; spatiotemporal analysis; symptom; young adult; adverse event; air pollutant; asthma; particulate matter","","nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; Air Pollutants, ; Nitrogen Dioxide, ; Ozone, ; Particulate Matter, ","","","Institute for Healthy Air, Water and Soil, Louisville Metro; National Institute for Occupational Safety and Health, NIOSH, (T42OH008429); National Institute for Occupational Safety and Health, NIOSH; Community Foundation of Louisville, CFL","We would like to acknowledge the network of local partners that made this programme possible, including the Institute for Healthy Air, Water and Soil, Louisville Metro, the Community Foundation of Louisville and all the AIR Louisville participants. 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Su; Division of Environmental Health Sciences, School of Public Health, University of California at Berkeley, Berkeley, 2121 Berkeley Way West, 94720-7360, United States; email: jasonsu@berkeley.edu","","Oxford University Press","","","","","","03005771","","IJEPB","34664072","English","Int. J. Epidemiol.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85121757506"
"Mohamed I.; Fouda M.M.; Hosny K.M.","Mohamed, Israa (57192916377); Fouda, Mostafa M. (35955834000); Hosny, Khalid M. (57205214086)","57192916377; 35955834000; 57205214086","Machine Learning Algorithms for COPD Patients Readmission Prediction: A Data Analytics Approach","2022","IEEE Access","10","","","15279","15287","8","19","10.1109/ACCESS.2022.3148600","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124233695&doi=10.1109%2fACCESS.2022.3148600&partnerID=40&md5=368ac303ecc9588f2b6f059a78d1c789","Faculty of Engineering and Computer Sciences, King Salman International University, Ras Sedr, Egypt; Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt; Department of Electrical and Computer Engineering, Idaho State University, Pocatello, 83209, ID, United States","Mohamed I., Faculty of Engineering and Computer Sciences, King Salman International University, Ras Sedr, Egypt, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt; Fouda M.M., Department of Electrical and Computer Engineering, Idaho State University, Pocatello, 83209, ID, United States; Hosny K.M., Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt","Patients' readmission can be considered as a critical factor affecting cost reduction while maintaining a high-quality treatment of patients. Therefore, predicting and controlling patients' readmission rates would significantly improve the healthcare service. In this study, we aim at predicting the readmission of COPD (Chronic Obstructive Pulmonary Disease) patients through the deployment of machine learning algorithms. Area Under Curve (AUC) and ACCuracy (ACC) were considered as the main criteria for evaluating models' prediction power in each time frame. Then, the importance of the variables for each outcome was explicitly identified, and defined important variables have then been differentiated. Our study could achieve the highest accuracy in predicting readmission with %91 ACC.  © 2013 IEEE.","Classification algorithms; COPD readmission; Data mining; Decision support systems; Healthcare data analytics","Cost reduction; Data mining; Decision support systems; Forecasting; Learning algorithms; Patient treatment; Predictive analytics; Pulmonary diseases; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease readmission; Classification algorithm; Data analytics; Healthcare data analytic; Machine learning algorithms; Medical services; Predictive models; Support vectors machine; Support vector machines","","","","","","","Yang K., Li X., Liu H., Mei J., Xie G., Zhao J., Xie B., Wang F., TaGiTeD: Predictive task guided tensor decomposition for representation learning from electronic health records, Proc. 31st AAAI Conf. Artif. Intell. 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Thoracic Surg., 92, 2, pp. 520-527, (2011); Oztekin A., Kong Z.J., Delen D., Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations, Decis. Support Syst., 51, 1, pp. 155-166, (2011); Kilic A., Weiss E.S., George T.J., Arnaoutakis G.J., Yuh D.D., Shah A.S., Conte J.V., What predicts long-term survival after heart transplantation? An analysis of 9, 400 ten-year survivors, Ann. Thoracic Surg., 93, 3, pp. 699-704, (2012); Nakayama N., Oketani M., Kawamura Y., Inao M., Nagoshi S., Fujiwara K., Tsubouchi H., Mochida S., Algorithm to determine the outcome of patients with acute liver failure: A data-mining analysis using decision trees, J. Gastroenterol., 47, 6, pp. 664-677, (2012); Quinlan R.J., C4. 5: Programs for Machine Learning, (1994); Han J., Kamber M., Pei J., Data mining concepts and techniques third edition, Morgan Kaufmann Ser. Data Manage. Syst., 5, 4, pp. 83-124, (2011); Helm J.E., Alaeddini A., Stauffer J.M., Bretthauer K.M., Skolarus T.A., Reducing hospital readmissions by integrating empirical prediction with resource optimization, Prod. Oper. Manage., 25, 2, pp. 233-257, (2016); Yu S., Farooq F., Van Esbroeck A., Fung G., Anand V., Krishnapuram B., Predicting readmission risk with institution-specific prediction models, Artif. Intell. Med., 65, 2, pp. 89-96, (2015)","K.M. Hosny; Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519, Egypt; email: k_hosny@yahoo.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85124233695"
"Joumaa H.; Sigogne R.; Maravic M.; Perray L.; Bourdin A.; Roche N.","Joumaa, Hassan (57218339617); Sigogne, Raphaël (57894353100); Maravic, Milka (55635768400); Perray, Lucas (57254613800); Bourdin, Arnaud (7801311848); Roche, Nicolas (55629754700)","57218339617; 57894353100; 55635768400; 57254613800; 7801311848; 55629754700","Artificial intelligence to differentiate asthma from COPD in medico-administrative databases","2022","BMC Pulmonary Medicine","22","1","357","","","","21","10.1186/s12890-022-02144-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138217730&doi=10.1186%2fs12890-022-02144-2&partnerID=40&md5=1bab3ac677ecac4cf0d8080cc6eaf03d","Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France; IQVIA, La Défense, France; Hôpital Lariboisière, Rhumatologie, Paris, France; PhyMedExp, INSERM U1046, CNRS UMR 9214, University of Montpellier, Montpellier, France; Department of Respiratory Medicine, Arnaud de Villeneuve Hospital, CHU Montpellier, Montpellier, France; University Paris Descartes (EA2511), Paris, France","Joumaa H., Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France; Sigogne R., IQVIA, La Défense, France; Maravic M., IQVIA, La Défense, France, Hôpital Lariboisière, Rhumatologie, Paris, France; Perray L., IQVIA, La Défense, France; Bourdin A., PhyMedExp, INSERM U1046, CNRS UMR 9214, University of Montpellier, Montpellier, France, Department of Respiratory Medicine, Arnaud de Villeneuve Hospital, CHU Montpellier, Montpellier, France; Roche N., Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France, University Paris Descartes (EA2511), Paris, France","Introduction: Discriminating asthma from chronic obstructive pulmonary disease (COPD) using medico-administrative databases is challenging but necessary for medico-economic analyses focusing on respiratory diseases. Artificial intelligence (AI) may improve dedicated algorithms. Objectives: To assess performance of different AI-based approaches to distinguish asthmatics from COPD patients in medico-administrative databases where the clinical diagnosis is absent. An “Asthma COPD Overlap” category was defined to further test whether AI can detect complexity. Methods: This study included 178,962 patients treated by two “R03” treatment prescriptions at least from January 2016 to December 2018 and managed by either a general practitioner and/or a pulmonologist participating in a permanent longitudinal observatory of prescription in ambulatory medicine (LPD). Clinical diagnoses are available in this database and were used as gold standards to develop diagnostic rules. Three types of AI approaches were explored using data restricted to demographics and treatment dispensations: multinomial regression, gradient boosting and recurrent neural networks (RNN). The best performing model (based on metric properties) was then applied to estimate the size of asthma and COPD populations based on a database (LRx) of treatment dispensations between July, 2018 and June, 2019. Results: The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Performance metrics were better for asthma than COPD. Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Asthma patients were younger than COPD patients (mean, 49.9 vs. 72.1 years); COPD occurred mostly in men (68%) compared to asthma (33%). Conclusion: AI can provide models with acceptable accuracy to distinguish between asthma, ACO and COPD in medico-administrative databases where the clinical diagnosis is absent. Deep learning and machine learning (RNN) had similar performances in this regard. © 2022, The Author(s).","Algorithms; Asthma; Chronic obstructive pulmonary disease; COPD; Epidemiology; Healthcare administrative databases; ICD code; Prevalence","Algorithms; Artificial Intelligence; Asthma; Databases, Factual; Humans; Male; Pulmonary Disease, Chronic Obstructive; administrative health data; adult; aged; algorithm; Article; artificial intelligence; asthma; chronic obstructive lung disease; female; human; International Classification of Diseases; major clinical study; male; multinomial logistic regression; performance indicator; recurrent neural network; artificial intelligence; asthma; factual database","","","","","Laboratoire de Probabilités; Statistique et Modélisation; Université Paris Diderot","We thank Stéphane Gaiffas (Université Paris Diderot, Laboratoire de Probabilités, Statistique et Modélisation, Paris France) and Emmanuel Bacry (CEREMADE Université Paris-Dauphine, Paris, France) for their expertise and assistance throughout the methodological approach of this study.","Reddel H.K., FitzGerald J.M., Bateman E.D., Et al., GINA 2019: a fundamental change in asthma management: Treatment of asthma with short-acting bronchodilators alone is no longer recommended for adults and adolescents, Eur Respir J, 53, 6, (2019); Postma D.S., Reddel H.K., ten Hacken N.H.T., van den Berge M., Asthma and chronic obstructive pulmonary disease: similarities and differences, Clin Chest Med, 35, 1, pp. 143-156, (2014); Yawn B.P., Wollan P.C., Knowledge and attitudes of family physicians coming to COPD continuing medical education, Int J Chron Obstruct Pulmon Dis, 3, 2, pp. 311-318, (2008); Boer L.M., van der Heijden M., van Kuijk N.M., Et al., Validation of ACCESS: an automated tool to support self-management of COPD exacerbations, Int J Chron Obstruct Pulmon Dis, 13, pp. 3255-3267, (2018); Badnjevic A., Gurbeta L., Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Sci Rep, 8, 1, (2018); Feng Y., Wang Y., Zeng C., Mao H., Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease, Int J Med Sci, 18, 13, pp. 2871-2889, (2021); Mohktar M.S., Redmond S.J., Antoniades N.C., Et al., Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data, Artif Intell Med, 63, 1, pp. 51-59, (2015); Badnjevic A., Cifrek M., Koruga D., Osmankovic D., Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease, BMC Med Inform Decis Mak, 15, (2015); Walia N., Tiwari S.K., Malhotra R., Design and identification of tuberculosis using fuzzy based decision support system, Adv Comput Sci Inf Technol, 2, 8, (2015); He J., Baxter S.L., Xu J., Xu J., Zhou X., Zhang K., The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25, 1, pp. 30-36, (2019); Bowles M., Machine learning in python: essential techniques for predictive analysis, (2015); Yang Q., Zhou Z.-H., Gong Z., Zhang M.-L., Huang S.-J., Advances in Knowledge Discovery and Data Mining: 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14–17, 2019, Proceedings, Springer, (2019); Maravic M., Hincapie N., Pilet S., Flipo R.-M., Liote F., Persistent clinical inertia in gout in 2014: an observational French longitudinal patient database study, Joint Bone Spine, 85, 3, pp. 311-315, (2018); Vilcu A.-M., Blanchon T., Sabatte L., Et al., Cross-validation of an algorithm detecting acute gastroenteritis episodes from prescribed drug dispensing data in France: comparison with clinical data reported in a primary care surveillance system, winter seasons 2014/15 to 2016/17, BMC Med Res Methodol, 19, 1, (2019); Price D.B., Yawn B.P., Jones R.C.M., Improving the differential diagnosis of chronic obstructive pulmonary disease in primary care, Mayo Clin Proc, 85, 12, pp. 1122-1129, (2010); Miravitlles M., Andreu I., Romero Y., Sitjar S., Altes A., Anton E., Difficulties in differential diagnosis of COPD and asthma in primary care, Br J Gen Pract, 62, 595, pp. e68-e75, (2012); (2017); Leung J.M., Sin D.D., Asthma-COPD overlap syndrome: pathogenesis, clinical features, and therapeutic targets, BMJ, (2017); Alshabanat A., Zafari Z., Albanyan O., Dairi M., FitzGerald J.M., Asthma and COPD overlap syndrome (ACOS): a systematic review and meta analysis, PLoS ONE, 10, 9, (2015); Abramson M.J., Perret J.L., Dharmage S.C., McDonald V.M., McDonald C.F., Distinguishing adult-onset asthma from COPD: a review and a new approach, Int J Chron Obstruct Pulmon Dis, 9, pp. 945-962, (2014); Soler X., Ramsdell J.W., Are asthma and COPD a continuum of the same disease?, J Allergy Clin Immunol Pract, 3, 4, pp. 489-495, (2015); Agusti A., Bel E., Thomas M., Et al., Treatable traits: toward precision medicine of chronic airway diseases, Eur Respir J, 47, 2, pp. 410-419, (2016); Buist A.S., Similarities and differences between asthma and chronic obstructive pulmonary disease: treatment and early outcomes, Eur Respir J, 21, pp. 30S-35s, (2003); Chambliss J.M., Sur S., Tripple J.W., Asthma versus chronic obstructive pulmonary disease, the Dutch versus British hypothesis, and role of interleukin-5, Curr Opin Allergy Clin Immunol, 18, 1, pp. 26-31, (2018); Gothe H., Rajsic S., Vukicevic D., Et al., Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review, BMC Health Serv Res, 19, 1, (2019); Delmas M.-C., Fuhrman C., L’asthme en France: synthèse des données épidémiologiques descriptives, Rev Mal Respir, 27, 2, pp. 151-159, (2010); Giraud V., Ameille J., Chinet T., Épidémiologie de la bronchopneumopathie chronique obstructive en France, La Presse Médicale, 37, 3, pp. 377-384, (2008); 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Asthma and Chronic Obstructive Pulmonary Disease, Epidemiol Prev, 43, 4S2, pp. 75-87, (2019); Gershon A.S., Wang C., Guan J., Vasilevska-Ristovska J., Cicutto L., To T., Identifying patients with physician-diagnosed asthma in health administrative databases, Can Respir J, 16, 6, pp. 183-188, (2009); Toelle B.G., Peat J.K., Salome C.M., Mellis C.M., Woolcock A.J., Toward a definition of asthma for epidemiology, Am Rev Respir Dis, 146, 3, pp. 633-637, (1992); Pearson M., Ayres J.G., Sarno M., Massey D., Price D., Diagnosis of airway obstruction in primary care in the UK: the CADRE (COPD and Asthma Diagnostic/management REassessment) programme 1997–2001, Int J Chron Obstruct Pulmon Dis, 1, 4, pp. 435-443, (2006)","H. Joumaa; Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France; email: hassan.joumaa@aphp.fr","","BioMed Central Ltd","","","","","","14712466","","BPMMB","36127649","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138217730"
"Shamji M.H.; Ollert M.; Adcock I.M.; Bennett O.; Favaro A.; Sarama R.; Riggioni C.; Annesi-Maesano I.; Custovic A.; Fontanella S.; Traidl-Hoffmann C.; Nadeau K.; Cecchi L.; Zemelka-Wiacek M.; Akdis C.A.; Jutel M.; Agache I.","Shamji, Mohamed H. (57210446924); Ollert, Markus (7004161698); Adcock, Ian M. (7007066538); Bennett, Oscar (57223086358); Favaro, Alberto (57218612536); Sarama, Roudin (57479069100); Riggioni, Carmen (57204845939); Annesi-Maesano, Isabella (56229296400); Custovic, Adnan (7006755479); Fontanella, Sara (57193973057); Traidl-Hoffmann, Claudia (6507511715); Nadeau, Kari (35305427400); Cecchi, Lorenzo (57193526705); Zemelka-Wiacek, Magdalena (55508841400); Akdis, Cezmi A. (7007154667); Jutel, Marek (7004260631); Agache, Ioana (57201020933)","57210446924; 7004161698; 7007066538; 57223086358; 57218612536; 57479069100; 57204845939; 56229296400; 7006755479; 57193973057; 6507511715; 35305427400; 57193526705; 55508841400; 7007154667; 7004260631; 57201020933","EAACI guidelines on environmental science in allergic diseases and asthma – Leveraging artificial intelligence and machine learning to develop a causality model in exposomics","2023","Allergy: European Journal of Allergy and Clinical Immunology","78","7","","1742","1757","15","35","10.1111/all.15667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148294815&doi=10.1111%2fall.15667&partnerID=40&md5=330254adbc558e6e5072d173d39b4b85","National Heart and Lung Institute, Imperial College London, London, United Kingdom; NIHR Imperial Biomedical Research Centre, London, United Kingdom; Department of Infection and Immunity, Luxembourg Institute of Health (LIH), Esch-sur-Alzette, Luxembourg; Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, Denmark; Faculty Science Limited, London, United Kingdom; Pediatric Allergy and Clinical Immunology Service, Institut de Reserca Sant Joan de Deú, Barcelona, Spain; Research Director and Deputy DIrector of Institut Desbrest of Epidemiology and Public Health (IDESP) French NIH (INSERM) and University of Montpellier, Montpellier, France; Environmental Medicine Faculty of Medicine University of Augsburg, Augsburg, Germany; CK-CARE, Christine Kühne Center for Allergy Research and Education, Davos, Switzerland; Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA, United States; SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy; Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland; Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland; ALL-MED Medical Research Institute, Wroclaw, Poland; Faculty of Medicine, Transylvania University, Brasov, Romania","Shamji M.H., National Heart and Lung Institute, Imperial College London, London, United Kingdom, NIHR Imperial Biomedical Research Centre, London, United Kingdom; Ollert M., Department of Infection and Immunity, Luxembourg Institute of Health (LIH), Esch-sur-Alzette, Luxembourg, Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, Denmark; Adcock I.M., National Heart and Lung Institute, Imperial College London, London, United Kingdom, NIHR Imperial Biomedical Research Centre, London, United Kingdom; Bennett O., Faculty Science Limited, London, United Kingdom; Favaro A., Faculty Science Limited, London, United Kingdom; Sarama R., National Heart and Lung Institute, Imperial College London, London, United Kingdom, NIHR Imperial Biomedical Research Centre, London, United Kingdom; Riggioni C., Pediatric Allergy and Clinical Immunology Service, Institut de Reserca Sant Joan de Deú, Barcelona, Spain; Annesi-Maesano I., Research Director and Deputy DIrector of Institut Desbrest of Epidemiology and Public Health (IDESP) French NIH (INSERM) and University of Montpellier, Montpellier, France; Custovic A., National Heart and Lung Institute, Imperial College London, London, United Kingdom, NIHR Imperial Biomedical Research Centre, London, United Kingdom; Fontanella S., National Heart and Lung Institute, Imperial College London, London, United Kingdom, NIHR Imperial Biomedical Research Centre, London, United Kingdom; Traidl-Hoffmann C., Environmental Medicine Faculty of Medicine University of Augsburg, Augsburg, Germany, CK-CARE, Christine Kühne Center for Allergy Research and Education, Davos, Switzerland; Nadeau K., Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, CA, United States; Cecchi L., SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy; Zemelka-Wiacek M., Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland; Akdis C.A., Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland; Jutel M., Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland, ALL-MED Medical Research Institute, Wroclaw, Poland; Agache I., Faculty of Medicine, Transylvania University, Brasov, Romania","Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health. © 2023 European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","allergy; artificial intelligence; asthma; environment; exposome","Artificial Intelligence; Asthma; Environmental Science; Humans; Hypersensitivity; Machine Learning; carbon monoxide; nitrogen dioxide; sulfur dioxide; air pollution; allergic disease; allergy; Article; artificial intelligence; asthma; atopic dermatitis; climate change; clinical outcome; contact dermatitis; convolutional neural network; environmental exposure; environmental science; exposome; exposomics; food allergy; health care access; human; machine learning; mass cytometry; Parietaria; particulate matter 10; particulate matter 2.5; personalized medicine; pollen; practice guideline; prediction; primary prevention; quality of life; random forest; secondary prevention; thunderstorm asthma; Urtica; Urticaceae; wildfire; artificial intelligence; asthma; hypersensitivity; machine learning","","carbon monoxide, 630-08-0; nitrogen dioxide, 10102-44-0; sulfur dioxide, 7446-09-5","","","","","Smith M.T., de la Rosa R., Daniels S.I., Using exposomics to assess cumulative risks and promote health, Environ Mol Mutagen, 56, 9, pp. 715-723, (2015); Agache I., Miller R., Gern J.E., Et al., Emerging concepts and challenges in implementing the exposome paradigm in allergic diseases and asthma: a Practall document, Allergy, 74, 3, pp. 449-463, (2019); Bycroft C., Freeman C., Petkova D., Et al., The UK biobank resource with deep phenotyping and genomic data, Nature, 562, 7726, pp. 203-209, (2018); UK Biobank; NIH All of Us Research Program; Goldstein B.A., Navar A.M., Carter R.E., Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges, Eur Heart J, 38, 23, pp. 1805-1814, (2017); Sammut S.J., Crispin-Ortuzar M., Chin S.F., Et al., Multi-omic machine learning predictor of breast cancer therapy response, Nature, 601, 7894, pp. 623-629, (2022); 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ACM SIGKDD Workshop on Health Informatics, (2014); Peng J., Jury E.C., Donnes P., Ciurtin C., Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: applications and challenges, Front Pharmacol, 12, (2021); Gille F., Jobin A., Lenca M., What we talk about when we talk about trust: theory of trust for AI in healthcare, Intelligence-Based Med, 1-2, (2020); 3TR Home; Rahman M.M., Davis D., Machine learning-based missing value imputation method for clinical datasets, IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, 229, pp. 245-257, (2013); Retrieved December 7, 2022; Pineau J., Vincent-Lamarre P., Sinha K., Improving reproducibility in machine learning research, J Mach Learn Res, 22, pp. 1-20, (2021); Kinkorova J., Topolcan O., Biobanks in the era of big data: objectives, challenges, perspectives, and innovations for predictive, preventive, and personalised medicine, EPMA J, 11, 3, pp. 333-341, (2020)","M.H. Shamji; National Heart and Lung Institute, Imperial College London, London, United Kingdom; email: m.shamji99@imperial.ac.uk; M. Jutel; Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland; email: marek.jutel@all-med.wroclaw.pl; I. Agache; Faculty of Medicine, Transylvania University, Brasov, Romania; email: ibrumaru@unitbv.ro","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","36740916","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85148294815"
"Dash T.K.; Chakraborty C.; Mahapatra S.; Panda G.","Dash, Tusar Kanti (57196949091); Chakraborty, Chinmay (7005340424); Mahapatra, Satyajit (57139765100); Panda, Ganapati (7005294702)","57196949091; 7005340424; 57139765100; 7005294702","Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19","2022","IEEE Journal of Biomedical and Health Informatics","26","11","","5364","5371","7","39","10.1109/JBHI.2022.3197910","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136093539&doi=10.1109%2fJBHI.2022.3197910&partnerID=40&md5=8baaecd8e3c7249777c53164a76b1254","C V Raman Global University, Electronics and Communications Engineering, Bhubaneswar, 752054, India; Birla Institute of Technology, Electronics and Communication Engineering, Mesra, 835215, India; VIT Bhopal University, School of Electrical and Electronics Engineering, Bhopal, 466114, India","Dash T.K., C V Raman Global University, Electronics and Communications Engineering, Bhubaneswar, 752054, India; Chakraborty C., Birla Institute of Technology, Electronics and Communication Engineering, Mesra, 835215, India; Mahapatra S., VIT Bhopal University, School of Electrical and Electronics Engineering, Bhopal, 466114, India; Panda G., C V Raman Global University, Electronics and Communications Engineering, Bhubaneswar, 752054, India","In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians. © 2013 IEEE.","COVID-19 detection; feature fusion; health informatics; LightGBM; speech classification","COVID-19; Humans; Pandemics; Speech; Cost effectiveness; Diagnosis; Feature extraction; Pulmonary diseases; Respiratory system; Speech recognition; Boosting; COVID-19 detection; Disease detection; Features extraction; Features fusions; Gradient boosting; Health informatics; Lightgbm; Noise levels; Speech classification; Article; controlled study; coronavirus disease 2019; diagnostic test accuracy study; false positive result; feature extraction; gradient boosting; human; k fold cross validation; k nearest neighbor; machine learning; medical informatics; receiver operating characteristic; reverse transcription polymerase chain reaction; speech discrimination; support vector machine; virus detection; pandemic; speech; COVID-19","","","","","","","Trancoso I., Correia J., Teixeira F., Raj B., Abad A., Analysing speech for clinical applications, Proc. Int. Conf. Stat. Lang. Speech Process., pp. 3-6, (2018); Chakraborty C., Abougreen A.N., Intelligent Internet of Things and advanced machine learning techniques for COVID-19, EAI Endorsed Trans. Pervasive Health Technol., 7, 26; Rezaee K., Zadeh H.G., Chakraborty C., Khosravi M.R., Jeon G., Smart visual sensing for overcrowding in COVID-19 infected cities using modified deep transfer learning, IEEE Trans. Ind. Informat.; Piccialli F., Somma V.D., Giampaolo F., Cuomo S., Fortino G., A survey on deep learning in medicine: Why, how and when?, Inf. Fusion, 66, pp. 111-137; World Health Organization/Diseases/Coronavirus disease (COVID-19); Ramdas K., Darzi A., Jain S., Test, re-test, re-test’: Using inaccurate tests to greatly increase the accuracy of COVID-19 testing, Nat. Med., 26, 6, pp. 810-811, (2020); Orlandic L., Teijeiro T., Atienza D., The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms, Sci.Data, 8, 1, pp. 1-10, (2021); Han J., Et al., An early study on intelligent analysis of speech under COVID-19: Severity, sleep quality, fatigue, and anxiety, Proc. Interspeech, 2020, pp. 4946-4950; Sharma N., Et al., Coswara A database of breathing, cough, and voice sounds for COVID-19 diagnosis, Proc. Interspeech, 2020, pp. 4811-4815; Brown C., Et al., Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2020, pp. 3474-3484; Ritwik K.V.S., Kalluri S.B., Vijayasenan D., COVID-19 patient detection from telephone quality speech data; Stasak B., Huang Z., Razavi S., Joachim D., Epps J., Automatic detection of COVID-19 based on short-duration acoustic smartphone speech analysis, J. Healthcare Informat. Res., 5, 2, pp. 201-217, (2021); Dash T.K., Mishra S., Panda G., Satapathy S.C., Detection of COVID-19 from speech signal using bio-inspired based cepstral features, Pattern Recognit, 117; Pahar M., Klopper M., Warren R., Niesler T., COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features, Comput. Biol. Med., 141; Verde L., Pietro G.D., Ghoneim A., Alrashoud M., Al-Mutib K.N., Sannino G., Exploring the use of artificial intelligence techniques to detect the presence of coronavirus COVID-19 through speech and voice analysis, IEEE Access, 9, pp. 65750-65757, (2021); Ponomarchuk A., Et al., Project achoo: A practical model and application for COVID-19 detection from recordings of breath, voice, and cough, IEEE J. Sel. Topics Signal Process., 16, 2, pp. 175-187; Sun L., Et al., Adaptive feature selection guided deep forest for covid-19 classification with chest CT, IEEE J. Biomed. Health Informat., 24, 10, pp. 2798-2805, (2020); Ravi V., Narasimhan H., Chakraborty C., Pham T.D., Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images, Multimedia Syst, 28, 4, pp. 1401-1415; Bhuyan H.K., Chakraborty C., Shelke Y., Pani S.K., COVID-19 diagnosis system by deep learning approaches, Expert Syst, 39, 3; Gumaei A., Et al., A decision-level fusion method for COVID-19 patient health prediction, Big Data Res, 27; Wong K.K., Fortino G., Abbott D., Deep learning-based cardiovascular image diagnosis: A promising challenge, Future Gener. Comput. Syst., 110, pp. 802-811, (2020); Ke G., Et al., Lightgbm: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst., 30, pp. 3149-3157, (2017); Chen C., Zhang Q., Ma Q., Yu B., LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion, Chemometrics Intell. Lab. Syst., 191, pp. 54-64, (2019); Kostic Z., Jevremovic A., What image features boost housing market predictions?, IEEE Trans. Multimedia, 22, 7, pp. 1904-1916; Wang D., Meng Q., Chen D., Zhang H., Xu L., Automatic detection of arrhythmia based on multi-resolution representation of ECG signal, Sensors, 20, 6; Chaudhari G., Et al., Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough; Keerio A., Mitra B.K., Birch P., Young R., Chatwin C., On preprocessing of speech signals, Int. J. Signal Process., 5, 3, pp. 216-222, (2009); Nass C., Lee K.M., Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction, J. Exp. Psychol.: Appl., 7, 3, pp. 171-181, (2001); Prell C.G.L., Clavier O.H., Effects of noise on speech recognition: Challenges for communication by service members, Hear. 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Process., 23, 11, pp. 1904-1916, (2015); MATLAB and audio toolbox; Matlab: AudioFeatureExtractor function, (2020); Vermeulen A.F., Industrial machine learning, Using Artificial Intelligence as a Transformational Disruptor, pp. 137-180, (2020); Sun X., Liu M., Sima Z., A novel cryptocurrency price trend forecasting model based on LightGBM, Finance Res. Lett., 32; Pao C., Yeh C., Lin, A comparative study of different weighting schemes on KNN-based emotion recognition in mandarin speech, Proc. Int. Conf. Intell. Comput., pp. 997-1005, (2007); Lever J., Krzywinski M., Altman N., Points of significance: Model selection and overfitting, Nature Methods, 13, 9, pp. 703-705, (2016); Wong T.T., Parametric methods for comparing the performance of two classification algorithms evaluated by k-fold cross validation on multiple data sets, Pattern Recognit, 65, pp. 97-107, (2017)","C. Chakraborty; Birla Institute of Technology, Electronics and Communication Engineering, Mesra, 835215, India; email: cchakrabarty@bitmesra.ac.in","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","35947565","English","IEEE J. Biomedical Health Informat.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85136093539"
"AlSaad R.; Malluhi Q.; Janahi I.; Boughorbel S.","AlSaad, Rawan (55635996300); Malluhi, Qutaibah (35616323300); Janahi, Ibrahim (6603098146); Boughorbel, Sabri (6504801269)","55635996300; 35616323300; 6603098146; 6504801269","Predicting emergency department utilization among children with asthma using deep learning models","2022","Healthcare Analytics","2","","100050","","","","16","10.1016/j.health.2022.100050","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148462435&doi=10.1016%2fj.health.2022.100050&partnerID=40&md5=5501b77a47bcc40a85678877be7237a0","College of Engineering, Qatar University, Doha, Qatar; Department of Pediatric Pulmonology, Sidra Medicine, Doha, Qatar; Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar","AlSaad R., College of Engineering, Qatar University, Doha, Qatar; Malluhi Q., College of Engineering, Qatar University, Doha, Qatar; Janahi I., Department of Pediatric Pulmonology, Sidra Medicine, Doha, Qatar; Boughorbel S., Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar","Pediatric asthma is a leading cause of emergency department (ED) utilization, which is expensive and often preventable. Therefore, development of ED utilization predictive models that can accurately predict patients at high-risk of frequent ED use and subsequently steering their treatment pathway towards more personalized interventions, has high clinical utility. In this paper, we investigate the extent to which deep learning models, specifically recurrent neural networks (RNNs), coupled with routinely collected electronic health record (EHR) clinical data can predict the frequency of emergency department utilization among children with asthma. We use retrospective longitudinal EHR data of 87,413 children with asthma aged 0–18 years, who were attributed to one or more healthcare facility for at least 2 consecutive years between 2000–2013. The models were trained for the task of predicting the frequency of emergency department visits in the next 12 months. We compared prediction results of three recurrent neural network (RNN) models: bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and reverse time attention model (RETAIN), to a baseline multinomial logistic regression model. We assessed the predictive accuracy of the models using receiver operating characteristic curve (AUC–ROC), precision–recall curve (AUC-PR), and F1-score. The results indicated that all RNN models have similar performances reaching AUC–ROC: 0.85, AUC-PR: 0.74, and F1-score: 0.61, compared to AUC–ROC: 0.81, AUC-PR: 0.69, and F1-score: 0.56 for a baseline multinomial logistic regression. Predictive models created from large routinely available EHR data using RNN models can accurately identify children with asthma at high-risk of repeated ED visits, without interacting with the patient or collecting information beyond the patient's EHR. © 2022 The Author(s)","Asthma; Deep learning; Electronic health record; Emergency medicine; Predictive models","adolescent; adult; Article; asthma; child; clinical feature; cohort analysis; comparative study; controlled study; deep learning; electronic health record; emergency ward; health care facility; health care utilization; human; long short term memory network; longitudinal study; major clinical study; medical information; medical record review; recurrent neural network; retrospective study; task performance","","","","","","","Hunt K.A., Weber E.J., Showstack J.A., Colby D.C., Callaham M.L., Characteristics of frequent users of emergency departments, Anna. Emerg. Med., 48, 1, pp. 1-8, (2006); Fuda K.K., Immekus R., Frequent users of massachusetts emergency departments: A statewide analysis, Anna. Emerg. Med., 48, 1, pp. 16.e1-16.e8, (2006); Hooker E.A., Mallow P.J., Oglesby M.M., Characteristics and trends of emergency department visits in the united states (2010–2014), J. Emerg. Med., 56, 3, pp. 344-351, (2019); Patel P.B., Combs M.A., Vinson D.R., Reduction of admit wait times: The effect of a leadership-based program, Acad. Emerg. Med., 21, 3, pp. 266-273, (2014); Crilly J., Bost N., Thalib L., Timms J., Gleeson H., Patients who present to the emergency department and leave without being seen, Eur. J. Emerg. Med., 20, 4, pp. 248-255, (2013); Wu D.J., Hipolito E., Bilderback A., Okelo S.O., Garro A., Predicting future emergency department visits and hospitalizations for asthma using the pediatric asthma control and communication instrument – emergency department version (PACCI-ED), J. Asthma, 53, 4, pp. 387-391, (2016); Milbrett P., Halm M., Characteristics and predictors of frequent utilization of emergency services, J. Emerg. Nurs., 35, 3, pp. 191-198, (2009); Zuckerman S., Shen Y.-C., Characteristics of occasional and frequent emergency department users, Med. Care, 42, 2, pp. 176-182, (2004); Blank F.S., Li H., Henneman P.L., Smithline H.A., Santoro J.S., Provost D., Maynard A.M., A descriptive study of heavy emergency department users at an academic emergency department reveals heavy ED users have better access to care than average users, J. Emerg. Nurs., 31, 2, pp. 139-144, (2005); Doupe M.B., Palatnick W., Day S., Chateau D., Soodeen R.-A., Burchill C., Derksen S., Frequent users of emergency departments: Developing standard definitions and defining prominent risk factors, Anna. Emerg. Med., 60, 1, pp. 24-32, (2012); Drewek R., Mirea L., Rao A., Touresian P., Adelson P.D., Asthma treatment and outcomes for children in the emergency department and hospital, J. Asthma, 55, 6, pp. 603-608, (2017); LaCalle E., Rabin E., Frequent users of emergency departments: The myths, the data, and the policy implications, Anna. Emerg. Med., 56, 1, pp. 42-48, (2010); Stewart J., Sprivulis P., Dwivedi G., Artificial intelligence and machine learning in emergency medicine, Emerg. Med. Australas., 30, 6, pp. 870-874, (2018); Ehrlich H., McKenney M., Elkbuli A., The niche of artificial intelligence in trauma and emergency medicine, Am. J. Emerg. Med., 45, pp. 669-670, (2021); Grant K., McParland A., Mehta S., Ackery A.D., Artificial intelligence in emergency medicine: Surmountable barriers with revolutionary potential, Anna. Emerg. Med., 75, 6, pp. 721-726, (2020); Kirubarajan A., Taher A., Khan S., Masood S., Artificial intelligence in emergency medicine: A scoping review, J. Am. Coll. Emerg. Physicians Open, 1, 6, pp. 1691-1702, (2020); Patel S.J., Chamberlain D.B., Chamberlain J.M., A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage, Acad. Emerg. Med., 25, 12, pp. 1463-1470, (2018); Hond A.D., Raven W., Schinkelshoek L., Gaakeer M., Avest E.T., Sir O., Lameijer H., Hessels R.A., Reijnen R., Jonge E.D., Steyerberg E., Nickel C.H., Groot B.D., Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope?, Int. J. Med. Inf., 152, (2021); Abedi V., Goyal N., Tsivgoulis G., Hosseinichimeh N., Hontecillas R., Bassaganya-Riera J., Elijovich L., Metter J.E., Alexandrov A.W., Liebeskind D.S., Alexandrov A.V., Zand R., Novel screening tool for stroke using artificial neural network, Stroke, 48, 6, pp. 1678-1681, (2017); Sterling N.W., Patzer R.E., Di M., Schrager J.D., Prediction of emergency department patient disposition based on natural language processing of triage notes, Int. J. Med. Inf., 129, pp. 184-188, (2019); Yadav K., Sarioglu E., Choi H.-A., Cartwright W.B., Hinds P.S., Chamberlain J.M., Automated outcome classification of computed tomography imaging reports for pediatric traumatic brain injury, Acad. Emerg. Med., 23, 2, pp. 171-178, (2016); Zhang X., Kim J., Patzer R.E., Pitts S.R., Patzer A., Schrager J.D., Prediction of emergency department hospital admission based on natural language processing and neural networks, Methods Inf. 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Janahi; Department of Pediatric Pulmonology, Sidra Medicine, Doha, Qatar; email: ijanahi@sidra.org","","Elsevier Inc.","","","","","","27724425","","","","English","Healthc. Anal.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85148462435"
"Lu J.Q.; Lu J.Y.; Wang W.; Liu Y.; Buczek A.; Fleysher R.; Hoogenboom W.S.; Zhu W.; Hou W.; Rodriguez C.J.; Duong T.Q.","Lu, Joyce Q. (57222992421); Lu, Justin Y. (57224454485); Wang, Weihao (57459029500); Liu, Yuhang (58872426000); Buczek, Alexandra (57458240600); Fleysher, Roman (6603340002); Hoogenboom, Wouter S. (55345772900); Zhu, Wei (57037437700); Hou, Wei (55490989800); Rodriguez, Carlos J. (7401788260); Duong, Tim Q. (7102106094)","57222992421; 57224454485; 57459029500; 58872426000; 57458240600; 6603340002; 55345772900; 57037437700; 55490989800; 7401788260; 7102106094","Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19: Predictors of acute cardiac injury recovery in COVID-19","2022","eBioMedicine","76","","103821","","","","52","10.1016/j.ebiom.2022.103821","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124884169&doi=10.1016%2fj.ebiom.2022.103821&partnerID=40&md5=8be8dd3bda0ebcc37eaa9f271f287a61","Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States; Department of Family and Preventive Medicine, Stony Brook University, Stony Brook, NY, United States; Department of Medicine, Cardiology Division, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States","Lu J.Q., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Lu J.Y., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Wang W., Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States; Liu Y., Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States; Buczek A., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Fleysher R., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Hoogenboom W.S., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Zhu W., Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States; Hou W., Department of Family and Preventive Medicine, Stony Brook University, Stony Brook, NY, United States; Rodriguez C.J., Department of Medicine, Cardiology Division, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States; Duong T.Q., Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, United States","Background: Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. Methods: This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. Results: There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. Interpretation: Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. Funding: None © 2022 The Authors","acute myocardial injury; heart failure; Machine learning; SARS-CoV-2","Aged; Aged, 80 and over; COVID-19; Female; Heart Injuries; Hospital Mortality; Humans; Incidence; L-Lactate Dehydrogenase; Logistic Models; Lymphocyte Count; Male; Middle Aged; New York; Patient Discharge; Retrospective Studies; SARS-CoV-2; Troponin I; alanine aminotransferase; aspartate aminotransferase; brain natriuretic peptide; C reactive protein; creatine kinase; creatinine; D dimer; ferritin; lactate dehydrogenase; sodium; troponin T; lactate dehydrogenase; troponin I; adult; aged; Article; asthma; cardiovascular disease; cerebrovascular accident; chronic obstructive lung disease; clinical outcome; coronary artery disease; coronavirus disease 2019; creatinine blood level; demographics; ethnicity; female; follow up; heart failure; heart injury; heart rate; hematocrit; hospitalization; human; hypertension; inflammation; insulin dependent diabetes mellitus; intensive care unit; laboratory test; length of stay; leukocyte count; liver disease; lymphocyte; lymphocyte count; machine learning; major clinical study; male; non insulin dependent diabetes mellitus; observational study; retrospective study; risk assessment; Severe acute respiratory syndrome coronavirus 2; urea nitrogen blood level; complication; heart injury; hospital discharge; hospital mortality; incidence; isolation and purification; metabolism; middle aged; mortality; New York; pathology; statistical model; very elderly; virology","","alanine aminotransferase, 9000-86-6, 9014-30-6; aspartate aminotransferase, 9000-97-9; brain natriuretic peptide, 114471-18-0; C reactive protein, 9007-41-4; creatine kinase, 9001-15-4; creatinine, 19230-81-0, 60-27-5; ferritin, 9007-73-2; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; sodium, 7440-23-5; troponin T, 60304-72-5; troponin I, 77108-40-8; L-Lactate Dehydrogenase, ; Troponin I, ","","","","","Huang C., Wang Y., Li X., Et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395, 10223, pp. 497-506, (2020); Wang D., Hu B., Hu C., Et al., Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China, JAMA, 323, 11, pp. 1061-1069, (2020); Guo T., Fan Y., Chen M., Et al., Cardiovascular Implications of Fatal Outcomes of Patients With Coronavirus Disease 2019 (COVID-19), JAMA Cardiol, 5, 7, pp. 811-818, (2020); Lippi G., Lavie C.J., Sanchis-Gomar F. 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Saadati M., Bagheri A., Analysing First Birth Interval by A CART Survival Tree, Int J Fertil Steril, 14, 3, pp. 247-255, (2020); Therneau T.M., Atkinson E.J., An Introduction to Recursive Partitioning Using the RPART Routines, (1997); Livingston F., Implementation of Breiman's Random Forest Machine Learning Algorithm, ECE591Q Machine Learning Journal Paper, (2005); FaF G., NeuralNet S., Training of Neural Networks, The R Journal, 2, pp. 30-38, (2010); Ni W., Yang X., Liu J., Et al., Acute Myocardial Injury at Hospital Admission Is Associated With All-Cause Mortality in COVID-19, J Am Coll Cardiol, 76, 1, pp. 124-125, (2020); Lu J.Y., Hou W., Duong T.Q., Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study, Infection, (2021); Lu J.Y., Babatsikos I., Fisher M.C., Hou W., Duong T.Q., Longitudinal Clinical Profiles of Hospital vs. Community-Acquired Acute Kidney Injury in COVID-19, Front Med (Lausanne), 8, (2021); Lu J.Y., Anand H., Frager S.Z., Hou W., Duong T.Q., Longitudinal progression of clinical variables associated with graded liver injury in COVID-19 patients, Hepatol Int, (2021); 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Daniels L.B., Clopton P., deFilippi C.R., Et al., Serial measurement of N-terminal pro-B-type natriuretic peptide and cardiac troponin T for cardiovascular disease risk assessment in the Multi-Ethnic Study of Atherosclerosis (MESA), Am Heart J, 170, 6, pp. 1170-1183, (2015); Hirsch J.S., Ng J.H., Ross D.W., Et al., Acute kidney injury in patients hospitalized with COVID-19, Kidney Int, 98, 1, pp. 209-218, (2020); Pelayo J., Lo K.B., Bhargav R., Et al., Clinical Characteristics and Outcomes of Community- and Hospital-Acquired Acute Kidney Injury with COVID-19 in a US Inner City Hospital System, Cardiorenal Med, 10, 4, pp. 223-231, (2020); Lu J.Y., Buczek A., Fleysher R., Et al.; Musheyev B., Borg L., Janowicz R., Et al.; Musheyev B., Janowicz R., Borg L., Et al.","T.Q. Duong; Albert Einstein College of Medicine and Montefiore Medical Center, Department of Radiology, Bronx, 1300 Morris Park Avenue, 10461, United States; email: tim.duong@einsteinmed.org","","Elsevier B.V.","","","","","","23523964","","","35144887","English","eBioMedicine","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124884169"
"Kothalawala D.M.; Kadalayil L.; Curtin J.A.; Murray C.S.; Simpson A.; Custovic A.; Tapper W.J.; Arshad S.H.; Rezwan F.I.; Holloway J.W.","Kothalawala, Dilini M. (57216360716); Kadalayil, Latha (6506439135); Curtin, John A. (7101961514); Murray, Clare S. (7402491950); Simpson, Angela (7402780427); Custovic, Adnan (7006755479); Tapper, William J. (57208118395); Arshad, S. Hasan (7004353614); Rezwan, Faisal I. (24537660300); Holloway, John W. (57221220827)","57216360716; 6506439135; 7101961514; 7402491950; 7402780427; 7006755479; 57208118395; 7004353614; 24537660300; 57221220827","Integration of Genomic Risk Scores to Improve the Prediction of Childhood Asthma Diagnosis","2022","Journal of Personalized Medicine","12","1","75","","","","16","10.3390/jpm12010075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122725957&doi=10.3390%2fjpm12010075&partnerID=40&md5=19e4d734799a9174d156bd93704b1750","Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, United Kingdom; National Heart and Lung Institute, Imperial College of Science, Technology, and Medicine, London, SW3 6LY, United Kingdom; The David Hide Asthma and Allergy Research Centre, St. Mary’s Hospital, Isle of Wight, PO30 5TG, United Kingdom; Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, United Kingdom","Kothalawala D.M., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Kadalayil L., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; Curtin J.A., Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, United Kingdom; Murray C.S., Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, United Kingdom; Simpson A., Division of Infection, Immunity, and Respiratory Medicine, School of Biological Sciences, Manchester University Hospital NHS Foundation Trust, University of Manchester, Manchester Academic Health Science Centre, Manchester, M13 9PL, United Kingdom; Custovic A., National Heart and Lung Institute, Imperial College of Science, Technology, and Medicine, London, SW3 6LY, United Kingdom; Tapper W.J., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; Arshad S.H., NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, The David Hide Asthma and Allergy Research Centre, St. Mary’s Hospital, Isle of Wight, PO30 5TG, United Kingdom; Rezwan F.I., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, United Kingdom; Holloway J.W., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom","Genome-wide and epigenome-wide association studies have identified genetic variants and differentially methylated nucleotides associated with childhood asthma. Incorporation of such genomic data may improve performance of childhood asthma prediction models which use phenotypic and environmental data. Using genome-wide genotype and methylation data at birth from the Isle of Wight Birth Cohort (n = 1456), a polygenic risk score (PRS), and newborn (nMRS) and childhood (cMRS) methylation risk scores, were developed to predict childhood asthma diagnosis. Each risk score was integrated with two previously published childhood asthma prediction models (CAPE and CAPP) and were validated in the Manchester Asthma and Allergy Study. Individually, the genomic risk scores demonstrated modest-to-moderate discriminative performance (area under the receiver operating characteristic curve, AUC: PRS = 0.64, nMRS = 0.55, cMRS = 0.54), and their integration only marginally improved the performance of the CAPE (AUC: 0.75 vs. 0.71) and CAPP models (AUC: 0.84 vs. 0.82). The limited predictive performance of each genomic risk score individually and their inability to substantially improve upon the performance of the CAPE and CAPP models suggests that genetic and epigenetic predictors of the broad phenotype of asthma are unlikely to have clinical utility. Hence, further studies predicting specific asthma endotypes are warranted. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Asthma; Childhood; Data integration; Machine learning; Methylation risk score; Polygenic risk score; Prediction","CpG oligodeoxynucleotide; Article; asthma; child; cohort analysis; controlled study; diagnostic test accuracy study; DNA methylation; epigenetics; epigenome; genetic risk score; genetic susceptibility; genetic variability; genome-wide association study; genotype; human; major clinical study; newborn; phenotype; quality control; receiver operating characteristic; sensitivity and specificity; single nucleotide polymorphism; wheezing","","","","","National Institute for Health Research, NIHR; BMA James Trust; University of Southampton; NIHR Southampton Biomedical Research Centre; JP Moulton Charitable Foundation; North west Lung Centre Charity; Manchester Biomedical Research Centre, BRC; Wight Health Authority; UK Research and Innovation, UKRI; Medical Research Council, MRC, (MR/L012693/1, G0601361, MR/K002449/1); National Asthma Campaign, (364); Asthma UK, (04/014, 01/012); UNICORN, (MR/S025340/1)","Funding text 1: This research was funded by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and a University of Southampton Presidential Research Studentship. Replication analysis in MAAS was supported by the Medical Research Council as part of UNICORN (Unified Cohorts Research Network): Disaggregating asthma MR/S025340/1. Angela Simpson and Clare Murray are supported by the NIHR Manchester Biomedical Research Centre.; Funding text 2: Acknowledgments: The authors would like to acknowledge the help of all the staff at the David Hide Asthma and Allergy Research Centre in undertaking the assessments of the Isle of Wight birth cohort. The authors would also like to thank the IOWBC and MAAS study participants and their parents for their continued support and enthusiasm. Recruitment and initial assessment for the first 4 years of age for the IOWBC was supported by the Isle of Wight Health Authority. The 10-year follow-up of the IOWBC was funded by the National Asthma Campaign, UK (Grant No 364). MAAS was supported by the Asthma UK Grants No 301 (1995\u20131998), No 362 (1998\u20132001), No 01/012 (2001\u20132004), No 04/014 (2004\u20132007), BMA James Trust (2005) and The JP Moulton Charitable Foundation (2004-current), The North west Lung Centre Charity (1997-current) and the Medical Research Council (MRC) G0601361 (2007\u20132012), MR/K002449/1 (2013\u20132014) and MR/L012693/1 (2014\u20132018). UNICORN (Unified Cohorts Research Network): Disaggregating asthma MR/S025340/1. The authors would also like to acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.; Funding text 3: Funding: This research was funded by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and a University of Southampton Presidential Research Studentship. Replication analysis in MAAS was supported by the Medical Research Council as part of UNICORN (Unified Cohorts Research Network): Disaggregating asthma MR/S025340/1. Angela Simpson and Clare Murray are supported by the NIHR Manchester Biomedical Research Centre.","Martinez F.D., Wright A.L., Taussig L.M., Holberg C.J., Halonen M., Morgan W.J., Asthma and Wheezing in the First Six Years of Life, N. Engl. J. Med, 332, pp. 133-138, (1995); Ullmann N., Mirra V., Di Marco A., Pavone M., Porcaro F., Negro V., Onofri A., Cutrera R., Asthma: Differential Diagnosis and Comorbidities, Front. Pediatr, 6, (2018); Kothalawala D.M., Kadalayil L., Weiss V.B.N., Kyyaly M.A., Arshad S.H., Holloway J.W., Rezwan F.I., Prediction models for childhood asthma: A systematic review, Allergy Immunol, 31, pp. 616-627, (2020); Patel D., Hall G.L., Broadhurst D., Smith A., Schultz A., Foong R.E., Does machine learning have a role in the prediction of asthma in children?, Paediatr. Respir. 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Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85122725957"
"Yuan W.; Thiboutot J.; Park H.-C.; Li A.; Loube J.; Mitzner W.; Yarmus L.; Brown R.H.; Li X.","Yuan, Wu (16320234900); Thiboutot, Jeffrey (36509422900); Park, Hyeon-Cheol (36246544700); Li, Ang (57217861168); Loube, Jeffrey (57205449234); Mitzner, Wayne (7006266169); Yarmus, Lonny (35182298600); Brown, Robert H. (58533151900); Li, Xingde (55718082900)","16320234900; 36509422900; 36246544700; 57217861168; 57205449234; 7006266169; 35182298600; 58533151900; 55718082900","Direct Visualization and Quantitative Imaging of Small Airway Anatomy Using Deep Learning Assisted Diffractive OCT","2023","IEEE Transactions on Biomedical Engineering","70","1","","238","246","8","14","10.1109/TBME.2022.3188173","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134236449&doi=10.1109%2fTBME.2022.3188173&partnerID=40&md5=11b695a6e6c0ae11e58db0b7765b15eb","Johns Hopkins University, United States; Department of Biomedical Engineering, Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong; Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, United States; Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States; Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States","Yuan W., Johns Hopkins University, United States, Department of Biomedical Engineering, Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong; Thiboutot J., Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, United States; Park H.-C., Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States; Li A., Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States; Loube J., Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, United States; Mitzner W., Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, United States; Yarmus L., Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, United States; Brown R.H., Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States; Li X., Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, 21205, MD, United States","Objective/background: In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo. Methods: We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 μm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy. Results: The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method. Conclusion: The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D. Significance: These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.  © 2022 IEEE.","airway smooth muscle; deep learning; Optical coherence tomography; quantitative imaging; small airway disease","Deep learning; Image enhancement; Image resolution; Image segmentation; Muscle; Optical tomography; Pulmonary diseases; Three dimensional computer graphics; Three dimensional displays; Airway smooth muscles; Airway walls; Deep learning; Direct visualization; Images segmentations; In-vivo; Quantitative imaging; Small airway disease; Small airways; Three-dimensional display; airway; animal experiment; animal tissue; Article; automation; bandwidth; basement membrane; deep learning; endoscopy; image segmentation; in vivo study; nonhuman; optical coherence tomography; quantitative analysis; sheep; smooth muscle; three-dimensional imaging; Microstructure","","","BF-P40, Olympus","Olympus","National Heart, Lung, and Blood Institute, NHLBI, (T32HL007534)","","Hogg J.C., Et al., The nature of small-airway obstruction in chronic obstructive pulmonary disease, New England J. 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Physiol., 69, pp. 849-860, (1990); Quirk B.C., Et al., Optofluidic needle probe integrating targeted delivery of fluid with optical coherence tomography imaging, Opt. Lett., 39, pp. 2888-2891, (2014); Listewnik P., Et al., Porous phantoms mimicking tissues-Investigation of optical parameters stability over time, Materials, 14, (2021)","X. Li; Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, 21205, United States; email: xingde@jhu.edu; R.H. Brown; Department of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, 21205, United States; email: rbrown@jhmi.edu","","IEEE Computer Society","","","","","","00189294","","IEBEA","35786546","English","IEEE Trans. Biomed. Eng.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85134236449"
"Davies M.P.A.; Sato T.; Ashoor H.; Hou L.; Liloglou T.; Yang R.; Field J.K.","Davies, Michael P.A. (12771675200); Sato, Takahiro (57194716867); Ashoor, Haitham (55542361800); Hou, Liping (57220750322); Liloglou, Triantafillos (7003702760); Yang, Robert (57220775674); Field, John K. (57647345000)","12771675200; 57194716867; 55542361800; 57220750322; 7003702760; 57220775674; 57647345000","Plasma protein biomarkers for early prediction of lung cancer","2023","eBioMedicine","93","","104686","","","","29","10.1016/j.ebiom.2023.104686","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164717731&doi=10.1016%2fj.ebiom.2023.104686&partnerID=40&md5=b297b367f41035fe2ca0b2e52441c822","Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom; World Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, 02142, MA, United States; Population Analytics & Insights, Data Science, Janssen R&D, 1400 McKean Rd, Spring House, 19477, PA, United States; Faculty of Health, Social Care & Medicine, Edge Hill University, St Helens Road, Lancashire, Ormskirk, L39 4QP, United Kingdom","Davies M.P.A., Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom; Sato T., World Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, 02142, MA, United States; Ashoor H., World Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, 02142, MA, United States; Hou L., Population Analytics & Insights, Data Science, Janssen R&D, 1400 McKean Rd, Spring House, 19477, PA, United States; Liloglou T., Faculty of Health, Social Care & Medicine, Edge Hill University, St Helens Road, Lancashire, Ormskirk, L39 4QP, United Kingdom; Yang R., World Without Disease Accelerator, Johnson & Johnson, 10th Floor 255 Main St, Cambridge, 02142, MA, United States; Field J.K., Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom","Background: Individual plasma proteins have been identified as minimally invasive biomarkers for lung cancer diagnosis with potential utility in early detection. Plasma proteomes provide insight into contributing biological factors; we investigated their potential for future lung cancer prediction. Methods: The Olink® Explore-3072 platform quantitated 2941 proteins in 496 Liverpool Lung Project plasma samples, including 131 cases taken 1–10 years prior to diagnosis, 237 controls, and 90 subjects at multiple times. 1112 proteins significantly associated with haemolysis were excluded. Feature selection with bootstrapping identified differentially expressed proteins, subsequently modelled for lung cancer prediction and validated in UK Biobank data. Findings: For samples 1–3 years pre-diagnosis, 240 proteins were significantly different in cases; for 1–5 year samples, 117 of these and 150 further proteins were identified, mapping to significantly different pathways. Four machine learning algorithms gave median AUCs of 0.76–0.90 and 0.73–0.83 for the 1–3 year and 1–5 year proteins respectively. External validation gave AUCs of 0.75 (1–3 year) and 0.69 (1–5 year), with AUC 0.7 up to 12 years prior to diagnosis. The models were independent of age, smoking duration, cancer histology and the presence of COPD. Interpretation: The plasma proteome provides biomarkers which may be used to identify those at greatest risk of lung cancer. The proteins and the pathways are different when lung cancer is more imminent, indicating that both biomarkers of inherent risk and biomarkers associated with presence of early lung cancer may be identified. Funding: Janssen Pharmaceuticals Research Collaboration Award; Roy Castle Lung Cancer Foundation. © 2023 The Authors","Early-detection; Lung cancer prediction; Plasma; Proteins; Proteomics","Biomarkers; Biomarkers, Tumor; Blood Proteins; Early Detection of Cancer; Humans; Lung Neoplasms; Proteome; Smoking; biological marker; plasma protein; biological marker; protein blood level; proteome; tumor marker; adult; aged; algorithm; area under the curve; Article; biobank; biological pathway; bootstrapping; cancer diagnosis; chronic obstructive lung disease; cohort analysis; controlled study; diagnostic test accuracy study; differential expression analysis; differentially expressed protein; feature selection; female; gene set enrichment analysis; histology; human; human tissue; ICD-10; k nearest neighbor; learning algorithm; longitudinal study; lung cancer; machine learning; major clinical study; male; pathway enrichment analysis; prediction; receiver operating characteristic; sensitivity and sensibility; smoking; support vector machine; validation process; early cancer diagnosis; lung tumor; metabolism","","Biomarkers, ; Biomarkers, Tumor, ; Blood Proteins, ; Proteome, ","","","Janssen Pharmaceuticals; Roy Castle Lung Cancer Foundation, RCLCF; University of Liverpool, UoL","This work was supported through a Research Collaboration Agreement between Janssen Pharmaceuticals and the University of Liverpool . The Liverpool Lung Project has also been funded by the Roy Castle Lung Cancer Foundation , including a Senior Research fellowship for Dr Michael Davies. We would like to thank the Liverpool Lung Project participants for their invaluable contribution to this project and LLP staff past and present, especially Nial Hodge and Stephanie Tate for data and sample curation. We acknowledge staff at the Janssen World Without Disease Accelerator in Leiden who instigated this collaboration. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. ","Sung H., Ferlay J., Siegel R.L., Et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA A Cancer J Clin, 71, 3, pp. 209-249, (2021); Miller K.D., Nogueira L., Mariotto A.B., Et al., Cancer treatment and survivorship statistics, 2019, CA A Cancer J Clin, 69, 5, pp. 363-385, (2019); Nicholson A.G., Chansky K., Crowley J., Et al., The international association for the study of lung cancer lung cancer staging project: proposals for the revision of the clinical and pathologic staging of small cell lung cancer in the forthcoming eighth edition of the TNM classification for lung cancer, J Thorac Oncol, 11, 3, pp. 300-311, (2016); de Koning H.J., van der Aalst C.M., de Jong P.A., Et al., Reduced lung-cancer mortality with volume CT screening in a randomized trial, N Engl J Med, 382, 6, pp. 503-513, (2020); Field J.K., Vulkan D., Davies M.P.A., Et al., Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis, Lancet Reg Health Eur, 10, (2021); National Lung Screening Trial Research T., Aberle D.R., Adams A.M., Et al., Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, 5, pp. 395-409, (2011); Ten Haaf K., van der Aalst C.M., de Koning H.J., Kaaks R., Tammemagi M.C., Personalising lung cancer screening: an overview of risk-stratification opportunities and challenges, Int J Cancer, 149, 2, pp. 250-263, (2021); Seijo L.M., Peled N., Ajona D., Et al., Biomarkers in lung cancer screening: achievements, promises, and challenges, J Thorac Oncol, 14, 3, pp. 343-357, (2019); Dama E., Colangelo T., Fina E., Et al., Biomarkers and lung cancer early detection: state of the art, Cancers (Basel), 13, 15, (2021); Ostrin E.J., Sidransky D., Spira A., Hanash S.M., Biomarkers for lung cancer screening and detection, Cancer Epidemiol Biomarkers Prev, 29, 12, pp. 2411-2415, (2020); Fahrmann J.F., Marsh T., Irajizad E., Et al., Blood-based biomarker panel for personalized lung cancer risk assessment, J Clin Oncol, 40, 8, pp. 876-883, (2022); Guida F., Sun N., Et al., Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins, JAMA Oncol, 4, 10, (2018); Dagnino S., Bodinier B., Guida F., Et al., Prospective identification of elevated circulating CDCP1 in patients years before onset of lung cancer, Cancer Res, 81, 13, pp. 3738-3748, (2021); Field J.K., Smith D.L., Duffy S., Cassidy A., The Liverpool lung project research protocol, Int J Oncol, 27, 6, pp. 1633-1645, (2005); Olink, Pre-analytical variation in protein biomarker research, (2020); Sun B.B., Chiou J., Traylor M., Et al., Genetic regulation of the human plasma proteome in 54,306 UK Biobank participants, bioRxiv, (2022); Zou H., Hastie T., Regularization and variable selection via the elastic net, J Roy Stat Soc B, 67, 2, pp. 301-320, (2005); Tin Kam H., Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition; 1995 14-16 Aug, 1, pp. 278-282, (1995); Cortes C., Vapnik V., Support-vector networks, Mach Learn, 20, 3, pp. 273-297, (1995); Chen T., Guestrin C., XGBoost: a scalable tree boosting system, Kdd, 16, pp. 785-794, (2016); Ridker P.M., MacFadyen J.G., Thuren T., Et al., Effect of interleukin-1beta inhibition with canakinumab on incident lung cancer in patients with atherosclerosis: exploratory results from a randomised, double-blind, placebo-controlled trial, Lancet, 390, 10105, pp. 1833-1842, (2017); Klein E.A., Richards D., Cohn A., Et al., Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set, Ann Oncol, 32, 9, pp. 1167-1177, (2021); Mathios D., Johansen J.S., Cristiano S., Et al., Detection and characterization of lung cancer using cell-free DNA fragmentomes, Nat Commun, 12, 1, (2021); Cohen J.D., Li L., Wang Y., Et al., Detection and localization of surgically resectable cancers with a multi-analyte blood test, Science, 359, 6378, pp. 926-930, (2018); Abbosh C., Birkbak N.J., Wilson G.A., Et al., Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution, Nature, 545, 7655, pp. 446-451, (2017); Ooki A., Maleki Z., Tsay J.J., Et al., A panel of novel detection and prognostic methylated DNA markers in primary non-small cell lung cancer and serum DNA, Clin Cancer Res, 23, 22, pp. 7141-7152, (2017); Integrative Analysis of Lung Cancer E, Risk Consortium for Early Detection of Lung C, Guida F, et al. Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins, JAMA Oncol, 4, 10, (2018); Wang G., Qiu M., Xing X., Et al., Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis, Sci Transl Med, 14, 630, (2022); Montani F., Marzi M.J., Dezi F., Et al., miR-Test: a blood test for lung cancer early detection, J Natl Cancer Inst, 107, 6, (2015); Sozzi G., Boeri M., Rossi M., Et al., Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study, J Clin Oncol, 32, 8, pp. 768-773, (2014); Pastorino U., Boeri M., Sestini S., Et al., Baseline computed tomography screening and blood microRNA predict lung cancer risk and define adequate intervals in the BioMILD trial, Ann Oncol, 33, 4, pp. 395-405, (2022); Robbins H.A., Alcala K., Moez E.K., Et al., Design and methodological considerations for biomarker discovery and validation in the integrative analysis of lung cancer etiology and risk (INTEGRAL) program, Ann Epidemiol, 77, pp. 1-12, (2023)","J.K. Field; Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, William Henry Duncan Building, 6 West Derby Street, L7 8TX, United Kingdom; email: J.K.Field@liverpool.ac.uk","","Elsevier B.V.","","","","","","23523964","","","37379654","English","eBioMedicine","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164717731"
"Su S.-H.; Sundhar N.; Kuo W.-W.; Lai S.-C.; Kuo C.-H.; Ho T.-J.; Lin P.-Y.; Lin S.-Z.; Shih C.Y.; Lin Y.-J.; Huang C.-Y.","Su, San-Hua (57286585700); Sundhar, Navaneethan (57420198300); Kuo, Wei-Wen (57131311300); Lai, Shang-Chih (35215483100); Kuo, Chia-Hua (26660722900); Ho, Tsung-Jung (15048044800); Lin, Pi-Yu (57420370400); Lin, Shinn-Zong (8417052700); Shih, Cheng Yen (57843406900); Lin, Yu-Jung (57221539659); Huang, Chih-Yang (59174481700)","57286585700; 57420198300; 57131311300; 35215483100; 26660722900; 15048044800; 57420370400; 8417052700; 57843406900; 57221539659; 59174481700","Artemisia argyi extract induces apoptosis in human gemcitabine-resistant lung cancer cells via the PI3K/MAPK signaling pathway","2022","Journal of Ethnopharmacology","299","","115658","","","","32","10.1016/j.jep.2022.115658","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138452758&doi=10.1016%2fj.jep.2022.115658&partnerID=40&md5=ff3bb43fadcbd93c736a1f46470d64cb","Department of Chinese Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Department of Biological Science and Technology, China Medical University, Taichung, Taiwan; School of Post-Baccalaureate Chinese Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan; School of Medicine, Tzu Chi University, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Department of Sports Sciences, University of Taipei, Taipei, Taiwan; Department of Chinese Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien, Taiwan; Integration Center of Traditional Chinese and Modern Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Buddhist Tzu Chi Charity Foundation, Hualien, 970, Taiwan; Bioinnovation Center, Buddhist Tzu Chi Medical Foundation, 970, Hualien, Taiwan; Department of Neurosurgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, 970, Hualien, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan; Department of Biotechnology, Asia University, Taichung, Taiwan; Center of General Education, Buddhist Tzu Chi Medical Foundation, Tzu Chi University of Science and Technology, 970, Hualien, Taiwan","Su S.-H., Department of Chinese Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Sundhar N., Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Kuo W.-W., Department of Biological Science and Technology, China Medical University, Taichung, Taiwan; Lai S.-C., School of Post-Baccalaureate Chinese Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan, School of Medicine, Tzu Chi University, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Kuo C.-H., Department of Sports Sciences, University of Taipei, Taipei, Taiwan; Ho T.-J., School of Post-Baccalaureate Chinese Medicine, College of Medicine, Tzu Chi University, Hualien, Taiwan, Department of Chinese Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien, Taiwan, Integration Center of Traditional Chinese and Modern Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Lin P.-Y., Buddhist Tzu Chi Charity Foundation, Hualien, 970, Taiwan; Lin S.-Z., Bioinnovation Center, Buddhist Tzu Chi Medical Foundation, 970, Hualien, Taiwan, Department of Neurosurgery, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, 970, Hualien, Taiwan; Shih C.Y., Buddhist Tzu Chi Charity Foundation, Hualien, 970, Taiwan; Lin Y.-J., Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan; Huang C.-Y., Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan, Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan, Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan, Department of Biotechnology, Asia University, Taichung, Taiwan, Center of General Education, Buddhist Tzu Chi Medical Foundation, Tzu Chi University of Science and Technology, 970, Hualien, Taiwan","Ethnopharmacological relevance: Artemisia argyi H. Lév. & Vaniot (Asteraceae), also called “Chinese mugwort”, is frequently used as a herbal medicine in China, Japan, Korea, and eastern parts of Russia. It is known as “ai ye” in China and “Gaiyou” in Japan. In ancient China, the buds and leaves of A. argyi were commonly consumed before and after Tomb-sweeping Day. It is used to treat malaria, hepatitis, cancer, inflammatory diseases, asthma, irregular menstrual cycle, sinusitis, and pathologic conditions of the kidney and liver. Although A. argyi extract (AAE) has shown anti-tumor activity against various cancers, the therapeutic effect and molecular mechanism of AAE remains to be further studied in lung cancer. Aim of the study: This study aimed to demonstrate the anti-tumor effect of AAE and its associated biological mechanisms in CL1-0 parent and gemcitabine-resistant (CL1-0-GR) lung cancer cells. Experimental procedure: Human lung cancer cells CL1-0 and CL1-0-GR cells were treated with AAE. Cell viability was assessed using the MTT, colony, and spheroid formation assays. Migration, invasion, and immunofluorescence staining were used to determine the extent of epithelial– mesenchymal transition (EMT). JC-1 and MitoSOX fluorescent assays were performed to investigate the effect of AAE on mitochondria. Apoptosis was detected using the TUNEL assay and flow cytometry with Annexin V staining. Result: We found that A. argyi significantly decreased cell viability and induced apoptosis, accompanied by mitochondrial membrane depolarization and increased ROS levels in both parent cells (CL1-0) and gemcitabine-resistant lung cancer cells (CL1-0-GR). AAE-induced apoptosis is regulated via the PI3K/AKT and MAPK signaling pathways. It also prevents CL1-0 and CL1-0-GR cancer cell invasion, migration, EMT, colony formation, and spheroid formation. In addition, AAE acts cooperative with commercial chemotherapy drugs to enhance tumor spheroid shrinkage. Conclusion: Our study provides the first evidence that A. argyi treatment suppresses both parent and gemcitabine-resistant lung cancer cells by inducing ROS, mitochondrial membrane depolarization, and apoptosis, and reducing EMT. Our finding provides insights into the anti-cancer activity of A. argyi and suggests that A. argyi may serve as a chemotherapy adjuvant that potentiates the efficacy of chemotherapeutic agents. © 2022 Elsevier B.V.","Adjuvant chemotherapy; Artemisia argyi; Gemcitabine resistance; Lung cancer; Traditional Chinese medicine","Annexin A5; Apoptosis; Artemisia; Cell Line, Tumor; Deoxycytidine; Humans; Lung Neoplasms; Phosphatidylinositol 3-Kinases; Plant Extracts; Proto-Oncogene Proteins c-akt; Reactive Oxygen Species; Signal Transduction; antineoplastic agent; Artemisia argyi extract; doxorubicin; gemcitabine; irinotecan; mitogen activated protein kinase; oxaliplatin; paclitaxel; phosphatidylinositol 3 kinase; plant extract; unclassified drug; doxecitine; gemcitabine; lipocortin 5; phosphatidylinositol 3 kinase; plant extract; protein kinase B; reactive oxygen metabolite; antineoplastic activity; apoptosis; Artemisia; Article; cell invasion; cell migration; cell viability; colony formation; controlled study; drug mechanism; drug potentiation; epithelial mesenchymal transition; flow cytometry; fluorescence analysis; gemcitabine-resistant cell line; human; human cell; immunofluorescence; in vitro study; lung cancer; MAPK signaling; mitochondrial membrane; mitochondrion; MTT assay; nonhuman; Pi3K/Akt signaling; tumor spheroid; TUNEL assay; apoptosis; lung tumor; metabolism; signal transduction; tumor cell line","","doxorubicin, 23214-92-8, 25316-40-9; gemcitabine, 103882-84-4, 95058-81-4; irinotecan, 100286-90-6, 97682-44-5; mitogen activated protein kinase, 142243-02-5; oxaliplatin, 61825-94-3; paclitaxel, 33069-62-4; phosphatidylinositol 3 kinase, 115926-52-8; doxecitine, 951-77-9, 56905-41-0; lipocortin 5, 111237-10-6; protein kinase B, 148640-14-6; Annexin A5, ; Deoxycytidine, ; gemcitabine, ; Plant Extracts, ; Proto-Oncogene Proteins c-akt, ; Reactive Oxygen Species, ","","","Department of Medicine Research; Buddhist Tzu Chi Medical Foundation; Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation","We would like to acknowledge the core facilities provided by Advanced Instrumentation Centre of Department of Medicine Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.","Ardalani H., Avan A., Ghayour-Mobarhan M., Podophyllotoxin: a novel potential natural anticancer agent, Avicenna J. 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Lett., 24, (2019); Zimmermann-Klemd A.M., Reinhardt J.K., Morath A., Schamel W.W., Steinberger P., Leitner J., Huber R., Hamburger M., Grundemann C., Immunosuppressive activity of artemisia argyi extract and isolated compounds, Front. Pharmacol., 11, (2020)","C.-Y. Huang; Cardiovascular and Mitochondrial Related Disease Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University of Science and Technology, Hualien, Taiwan; email: cyhuang@mail.cmu.edu.tw","","Elsevier Ireland Ltd","","","","","","03788741","","JOETD","36075273","English","J. Ethnopharmacol.","Article","Final","","Scopus","2-s2.0-85138452758"
"Wu C.-T.; Wang S.-M.; Su Y.-E.; Hsieh T.-T.; Chen P.-C.; Cheng Y.-C.; Tseng T.-W.; Chang W.-S.; Su C.-S.; Kuo L.-C.; Chien J.-Y.; Lai F.","Wu, Chia-Tung (57211695064); Wang, Ssu-Ming (57219353749); Su, Yi-En (57917125200); Hsieh, Tsung-Ting (55276617200); Chen, Pei-Chen (59042158700); Cheng, Yu-Chieh (57205059680); Tseng, Tzu-Wei (57189221037); Chang, Wei-Sheng (57917125300); Su, Chang-Shinn (57916549700); Kuo, Lu-Cheng (8161056900); Chien, Jung-Yien (7202435260); Lai, Feipei (7202559775)","57211695064; 57219353749; 57917125200; 55276617200; 59042158700; 57205059680; 57189221037; 57917125300; 57916549700; 8161056900; 7202435260; 7202559775","A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning","2022","IEEE Journal of Translational Engineering in Health and Medicine","10","","2700414","","","","20","10.1109/JTEHM.2022.3207825","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139344017&doi=10.1109%2fJTEHM.2022.3207825&partnerID=40&md5=4c1afff27f2f4d577965e249120cb558","National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; National Taiwan University Hospital, National Taiwan University, College of Medicine, Department of Internal Medicine, Taipei, 10617, Taiwan","Wu C.-T., National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan; Wang S.-M., Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Su Y.-E., National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan; Hsieh T.-T., Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Chen P.-C., Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Cheng Y.-C., Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Tseng T.-W., National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan; Chang W.-S., National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan; Su C.-S., Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Kuo L.-C., National Taiwan University Hospital, National Taiwan University, College of Medicine, Department of Internal Medicine, Taipei, 10617, Taiwan; Chien J.-Y., National Taiwan University Hospital, National Taiwan University, College of Medicine, Department of Internal Medicine, Taipei, 10617, Taiwan; Lai F., National Taiwan University, Department of Computer Science and Information Engineering, Taipei, 10617, Taiwan","This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time monitoring of lifestyle and environmental factors is implemented by integrating wearable devices, open environmental data, indoor air quality sensing devices, a location-based smartphone app, and an AI-assisted telecare platform. The AI-assisted telecare platform provided comprehensive insight into patients' clinical, lifestyle, and environmental data, and generated reliable predictions of future acute exacerbation events. All data from 1,667 patients were collected prospectively during a 24-month follow-up period, resulting in the detection of 386 abnormal episodes. Machine learning algorithms and deep learning algorithms were used to train modular chronic disease models. The modular chronic disease prediction models that have passed external validation include obesity, panic disorder, and chronic obstructive pulmonary disease, with an average accuracy of 88.46%, a sensitivity of 75.6%, a specificity of 93.0%, and an F1 score of 79.8%. Compared with previous studies, we establish an effective way to collect lifestyle, life trajectory, and symptom records, as well as environmental factors, and improve the performance of the prediction model by adding objective comprehensive data and feature selection. Our results also demonstrate that lifestyle and environmental factors are highly correlated with patient health and have the potential to predict future abnormal events better than using only questionnaire data. Furthermore, we have constructed a cost-effective model that needs only a few features to support the prediction task, which is helpful for deploying real-world modular prediction models. © 2013 IEEE.","artificial intelligence; chronic obstructive pulmonary disease; panic disorder; Precision health; wearable device","Chronic Disease; Cohort Studies; Deep Learning; Humans; Machine Learning; Precision Medicine; Wearable Electronic Devices; Air quality; Cost effectiveness; Deep learning; Forecasting; Indoor air pollution; Interactive computer systems; Learning algorithms; Pulmonary diseases; Ubiquitous computing; Wearable computers; Chronic disease; Chronic obstructive pulmonary disease; Environmental factors; Medical services; Modulars; Panic disorder; Precision health; Predictive models; Real - Time system; Wearable devices; apple; Article; chronic disease; chronic obstructive lung disease; cohort analysis; cost effectiveness analysis; deep learning; disease control; disease exacerbation; environmental factor; feature selection; follow up; health promotion; health service; human; information processing; life; lifestyle; machine learning; obesity; panic; personalized medicine; predictive model; prospective study; retrospective study; sensitivity and specificity; symptom; telecare; chronic disease; electronic device; machine learning; personalized medicine; Real time systems","","","","","","","Schwaederle M., Et al., Impact of precision medicine in diverse cancers: A meta-analysis of phase II clinical trials, J. Clin. Oncol., 33, 32, pp. 3817-3825, (2015); Duffy D.J., Problems, challenges and promises: Perspectives on precision medicine, Briefings Bioinf, 17, 3, pp. 494-504, (2016); Hekler E., Tiro J.A., Hunter C.M., Nebeker C., Precision health: The role of the social and behavioral sciences in advancing the vision, Ann. Behav. Med., 54, 11, pp. 805-826; Muller P., The prospective lynch syndrome database reports enable evidence-based personal precision health care, Hereditary Cancer Clin. Pract., 18, 1, pp. 1-7; Gambhir S.S., Ge T.J., Vermesh O., Spitler R., Toward achieving precision health, Sci. Transl. Med., 10, 430, (2018); Ryan J.C., Viana J.N., Sellak H., Gondalia S., O'Callaghan N., Defining precision health: A scoping review protocol, BMJ Open, 11, 2; Gambhir S.S., Ge T.J., Vermesh O., Spitler R., Gold G.E., Continuous health monitoring: An opportunity for precision health, Sci. Transl. Med., 13, 597; Goldstein J.R., Lee R.D., Demographic perspectives on the mortality of COVID-19 and other epidemics, Proc. Nat. Acad. Sci. USA, 117, 36, pp. 22035-22041; Krausz M., Westenberg J.N., Vigo D., Spence R.T., Ramsey D., Emergency response to COVID-19 in Canada: Platform development and implementation for eHealth in crisis management, JMIR Public Health Surveill, 6, 2, (2020); Watson A.R., Wah R., Thamman R., The value of remote monitoring for the COVID-19 pandemic, Telemedicine E-Health, 26, 9, pp. 1110-1112; Louvardi M., Pelekasis P., Chrousos G.P., Darviri C., Mental health in chronic disease patients during the COVID-19 quarantine in Greece, Palliative Supportive Care, 18, 4, pp. 394-399, (2020); Alqahtani J.S., Et al., Prevalence, severity and mortality associated with COPD and smoking in patients with COVID-19: A rapid systematic review and meta-analysis, PLoS ONE, 15, 5, (2020); Kwok S., Et al., Obesity: A critical risk factor in the COVID-19 pandemic, Clin. Obesity, 10, 6; Martin A.B., Et al., National health care spending in 2019: Steady growth for the fourth consecutive year: Study examines national health care spending for 2019, Health Affairs, 40, 1, pp. 14-24, (2021); Hajat C., Stein E., The global burden of multiple chronic conditions: A narrative review, Preventive Med. Rep., 12, pp. 284-293, (2018); Goto T., Camargo C.A., Faridi M.K., Yun B.J., Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, Amer. J. Emergency Med., 36, 9, pp. 1650-1654, (2018); Peng J., Chen C., Zhou M., Xie X., Zhou Y., Luo C.-H., A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators, Sci. Rep., 10, 1, pp. 1-9; Lueken U., Et al., Separating depressive comorbidity from panic disorder: A combined functional magnetic resonance imaging and machine learning approach, J. Affect. Disorders, 184, pp. 182-192, (2015); Butler E.M., Et al., A prediction model for childhood obesity in new Zealand, Sci. Rep., 11, 1, pp. 1-9; Clarke M., de Folter J., Verma V., Gokalp H., Interoperable end-to-end remote patient monitoring platform based on IEEE 11073 PHD and ZigBee health care profile, IEEE Trans. Biomed. Eng., 65, 5, pp. 1014-1025, (2018); Yang G., Et al., An IoT-enabled stroke rehabilitation system based on smart wearable armband and machine learning, IEEE J. Transl. Eng. Health Med., 6, pp. 1-10, (2018); Zeadally S., Bello O., Harnessing the power of Internet of Things based connectivity to improve healthcare, Internet Things, 14; McPadden J., Et al., Health care and precision medicine research: Analysis of a scalable data science platform, J. Med. Internet Res., 21, 4, (2019); Aida A., Et al., Using mHealth to provide mobile app users with visualization of health checkup data and educational videos on lifestyle-related diseases: Methodological framework for content development, JMIR mHealth uHealth, 8, 10; Lozano R., Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the global burden of disease study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Bandelow B., Michaelis S., Epidemiology of anxiety disorders in the 21st century, Dialogues Clin. Neurosci., 17, 3, pp. 327-335, (2015); Cawley J., Et al., Direct medical costs of obesity in the United States and the most populous states, J. Managed Care Specialty Pharmacy, 27, 3, pp. 354-366, (2021); Michou M., Panagiotakos D.B., Costarelli V., Low health literacy and excess body weight: A systematic review, Central Eur. J. 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"Nguyen T.; Pernkopf F.","Nguyen, Truc (57212284066); Pernkopf, Franz (6602606395)","57212284066; 6602606395","Lung Sound Classification Using Co-Tuning and Stochastic Normalization","2022","IEEE Transactions on Biomedical Engineering","69","9","","2872","2882","10","91","10.1109/TBME.2022.3156293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126335936&doi=10.1109%2fTBME.2022.3156293&partnerID=40&md5=d4cae838782936cbc3d9e2abbee6eb25","Graz University of Technology, Signal Processing and Speech Communication Lab., Graz, 8010, Austria","Nguyen T., Graz University of Technology, Signal Processing and Speech Communication Lab., Graz, 8010, Austria; Pernkopf F., Graz University of Technology, Signal Processing and Speech Communication Lab., Graz, 8010, Austria","Computational methods for lung sound analysis are beneficial for computer-aided diagnosis support, storage and monitoring in critical care. In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The learned representation of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we introduce spectrum correction to account for the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.  © 1964-2012 IEEE.","Adventitious lung sound classification; co-tuning for transfer learning; crackles; ICBHI dataset; respiratory disease classification; stochastic normalization; wheezes","Diagnosis, Computer-Assisted; Humans; Lung; Respiratory Sounds; Biological organs; Classification (of information); Computer aided analysis; Computer aided diagnosis; Digital storage; Frequency domain analysis; Random processes; Stochastic models; Stochastic systems; Time domain analysis; Adventitious lung sound classification; Co-tuning for transfer learning; Crackle; Disease classification; ICBHI dataset; Lung; Lung sounds; Normalisation; Respiratory disease classification; Sound classification; Stochastic normalization; Stochastics; Task analysis; Transfer learning; Wheeze; abnormal respiratory sound; Article; asthma; audio recording; breathing pattern; bronchiectasis; bronchiolitis; chronic obstructive lung disease; classification; co tuning normalization technique; computer assisted diagnosis; crackle; data base; deep learning; feature extraction; hidden Markov model; human; information processing; intensive care; lower respiratory tract infection; lung auscultation; lung disease; pneumonia; residual neural network; spectrum; stochastic normalization technique; support vector machine; transfer of learning; upper respiratory tract infection; vanilla fine tuning; wheezing; abnormal respiratory sound; computer assisted diagnosis; lung; COVID-19","","","","","","","WHO Coronavirus (COVID-19) Dashboard, (2021); Barbosa M.T., Et al., The big five lung diseases inCOVID-19 pandemica Google trends analysis, Pulmonology, 27, 1, pp. 71-72, (2021); Chen N., Et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia inWuhan, China:Adescriptive study, Lancet, 395, pp. 507-513, (2020); Sarkar M., Et al., Auscultation of the respiratory system, Ann. 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Health Informat, pp. 39-43, (2017); Serbes G., Et al., An automated lung sound preprocessing and classification system based onspectral analysismethods, Proc. Int. Conf. Biomed. Health Informat., pp. 45-49, (2017); Garia-Ordas M.T., Et al., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Shuvo S.B., Et al., AlightweightCNNmodel for detecting respiratory diseases from lung auscultation sounds usingEMD-CWT-based hybrid scalogram, IEEE J. Biomed. Health Informat., 25, 7, pp. 2595-2603, (2021); Demir F., Et al., Classification of lung sounds with CNN model using parallel pooling structure, IEEE Access, 8, pp. 105376-105383, (2020)","T. Nguyen; Graz University of Technology, Signal Processing and Speech Communication Lab., Graz, 8010, Austria; email: t.k.nguyen@tugraz.at","","IEEE Computer Society","","","","","","00189294","","IEBEA","35254969","English","IEEE Trans. Biomed. Eng.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85126335936"
"Bucholc M.; Bradley D.; Bennett D.; Patterson L.; Spiers R.; Gibson D.; Van Woerden H.; Bjourson A.J.","Bucholc, Magda (57188975350); Bradley, Declan (50961090800); Bennett, Damien (7401541002); Patterson, Lynsey (57209093781); Spiers, Rachel (57205180761); Gibson, David (7401626445); Van Woerden, Hugo (35390385900); Bjourson, Anthony J. (6603965630)","57188975350; 50961090800; 7401541002; 57209093781; 57205180761; 7401626445; 35390385900; 6603965630","Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19","2022","Scientific Reports","12","1","17313","","","","19","10.1038/s41598-022-20176-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139855858&doi=10.1038%2fs41598-022-20176-w&partnerID=40&md5=b42ac56fc356d97e0739f60d005d7bc1","Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, BT48 7JL, United Kingdom; Public Health Agency, Belfast, BT2 8BS, United Kingdom; Centre for Public Health, Queen’s University Belfast, Belfast, BT12 6BA, United Kingdom; Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry, BT47 6SB, United Kingdom; Institute of Nursing and Health Research, Ulster University, Coleraine, BT52 1SA 3, United Kingdom; Division of Rural Health and Wellbeing, University of the Highlands and Islands, Inverness, IV2 3JH, United Kingdom","Bucholc M., Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, BT48 7JL, United Kingdom, Public Health Agency, Belfast, BT2 8BS, United Kingdom; Bradley D., Public Health Agency, Belfast, BT2 8BS, United Kingdom, Centre for Public Health, Queen’s University Belfast, Belfast, BT12 6BA, United Kingdom; Bennett D., Public Health Agency, Belfast, BT2 8BS, United Kingdom; Patterson L., Public Health Agency, Belfast, BT2 8BS, United Kingdom; Spiers R., Public Health Agency, Belfast, BT2 8BS, United Kingdom; Gibson D., Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry, BT47 6SB, United Kingdom; Van Woerden H., Public Health Agency, Belfast, BT2 8BS, United Kingdom, Institute of Nursing and Health Research, Ulster University, Coleraine, BT52 1SA 3, United Kingdom, Division of Rural Health and Wellbeing, University of the Highlands and Islands, Inverness, IV2 3JH, United Kingdom; Bjourson A.J., Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry, BT47 6SB, United Kingdom","We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence of multi morbidity on the risk of COVID-19-related death using a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted using Patient Administration System Admissions and Discharges data. The International Classification of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64–4.74), metastatic cancer (OR, 2.17; 95% CI,1.25–3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16–2.41), liver disease (OR: 2.50, 95% CI,1.53–4.07), congestive heart failure (OR, 1.69; 95% CI,1.32–2.15), chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18–1.72), obesity (OR, 5.28; 95% CI,2.92–9.52), renal disease (OR, 1.81; 95% CI,1.51–2.19), and dementia (OR, 1.44; 95% CI,1.17–1.76) were at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared to non-asthma controls (OR, 0.60; 95% CI,0.42–0.86). Individuals with two (OR, 1.79; 95% CI, 1.47–2.20; P < 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43–2.27; P < 0.001) were at increasingly higher risk of death when compared to those with no underlying conditions. Furthermore, multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different combinations of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster 2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest mortality of 29.4% was reported in Cluster 6. © 2022, The Author(s).","","Asthma; Cardiovascular Diseases; Comorbidity; COVID-19; Hospital Mortality; Humans; Multimorbidity; Neoplasms; Preexisting Condition Coverage; Retrospective Studies; asthma; cardiovascular disease; comorbidity; hospital mortality; human; insurance; multiple chronic conditions; neoplasm; retrospective study","","","","","","","Guan W.J., Et al., Clinical characteristics of coronavirus disease 2019 in China, N. Engl. J. Med., 382, 18, pp. 1708-1720, (2020); Sun P., Et al., Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis, J. Med. Virol., 92, 6, pp. 612-617, (2020); Williamson E.J., Et al., Factors associated with COVID-19-related death using open safely, Nature, 584, 7821, pp. 430-436, (2020); Deng G., Yin M., Chen X., Zeng F., Clinical determinants for fatality of 44,672 patients with COVID-19, Crit. Care, 24, pp. 1-3, (2020); Docherty A.B., Et al., Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO clinical characterisation protocol: Prospective observational cohort study, BMJ, 369, (2020); Bae S., Et al., Impact of cardiovascular disease and risk factors on fatal outcomes in patients with COVID-19 according to age: A systematic review and meta-analysis, Heart, 107, 5, pp. 373-380, (2021); Chen T., Et al., Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study, BMJ, (2020); Yang J., Et al., Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: A systematic review and meta-analysis, Int. J. Infect. Dis., 94, pp. 91-95, (2020); Meng Y., Et al., Cancer history is an independent risk factor for mortality in hospitalized COVID-19 patients: A propensity score-matched analysis, J. Hematol. Oncol., 13, 1, (2020); Henry B.M., Lippi G., Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection, Int. Urol. Nephrol., 52, 6, pp. 1193-1194, (2020); Lighter J., Et al., Obesity in patients younger than 60 years is a risk factor for Covid-19 hospital admission, Clin. Infect. 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Rev., 21, 11, (2020); Simonnet A., Et al., High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation, Obesity., 28, 1195, (2020); Gerayeli F.V., Et al., COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis, EClinicalMedicine, 33, (2021); Alqahtani J.S., Et al., Prevalence, severity and mortality associated with COPD and smoking in patients with COVID-19: A rapid systematic review and meta-analysis, Plos One, 15, 5, (2020); Rabbani G., Et al., Pre-existing COPD is associated with an increased risk of mortality and severity in COVID-19: A rapid systematic review and meta-analysis, Expert Rev. Respir. 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Neurol., 14, 3, (2018); Ueno M., Et al., Blood-brain barrier damage in vascular dementia, Neuropathology, 36, 2, pp. 115-124, (2016); Reilev M., Et al., Characteristics and predictors of hospitalization and death in the first 11 122 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: A nationwide cohort, Int. J. Epidemiol., 49, 5, pp. 1468-1481, (2020); Tomasoni D., Et al., COVID-19 and heart failure: From infection to inflammation and angiotensin II stimulation. Searching for evidence from a new disease, Eur. J. Heart Fail., 22, 6, pp. 957-966, (2020); Hirsch J.S., Et al., Acute kidney injury in patients hospitalized with COVID-19, Kidney Int., 98, 1, pp. 209-218, (2020); Yang R., Gui X., Zhang Y., Xiong Y., The role of essential organ-based comorbidities in the prognosis of COVID-19 infection patients, Expert Rev. Respir. 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Publica., 44, (2020); Iaccarino G., Et al., Age and multimorbidity predict death among COVID-19 patients: Results of the SARS-RAS study of the italian society of hypertension, Hypertension, 76, 2, pp. 366-372, (2020); Kim L., Et al., Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the US coronavirus disease 2019 (COVID-19)-associated hospitalization surveillance network (COVID-NET), Clin. Infect. Dis., 72, 9, pp. e206-e214, (2021); McQueenie R., Et al., Multimorbidity, polypharmacy, and COVID-19 infection within the UK Biobank cohort, Plos One, 15, 8, (2020); Sin D.D., Man S.P., Why are patients with chronic obstructive pulmonary disease at increased risk of cardiovascular diseases? the potential role of systemic inflammation in chronic obstructive pulmonary disease, Circulation, 107, 11, pp. 1514-1519, (2003); Maclay J.D., MacNee W., Cardiovascular disease in COPD: Mechanisms, Chest, 143, 3, pp. 798-807, (2013); Silva S.T.D., Ribeiro R.D.C.L., Rosa C.D.O.B., Cotta R.M.M., Cognitive capacity in individuals with chronic kidney disease: Relation to demographic and clinical characteristics, Braz. J. Nephrol., 36, pp. 163-170, (2014); Radic J., Et al., The possible impact of dialysis modality on cognitive function in chronic dialysis patients, Neth. J. Med., 68, 4, pp. 153-157, (2010); Cheong K.C., Et al., Association of metabolic syndrome with risk of cardiovascular disease mortality and all-cause mortality among Malaysian adults: A retrospective cohort study, BMJ Open, 11, 8, (2021); Karajamaki A.J., Et al., Non-alcoholic fatty liver disease with and without metabolic syndrome: Different long-term outcomes, Metabolism, 66, pp. 55-63, (2017); De Hert M., Detraux J., Vancampfort D., The intriguing relationship between coronary heart disease and mental disorders, Dialogues Clin. Neurosci., 20, 1, pp. 31-40, (2022); Hawkins M.L., Et al., Endocrine and metabolic diseases among colorectal cancer survivors in a population-based cohort, JNCI J. Natl. Cancer Inst., 112, 1, pp. 78-86, (2020); Galiero R., Et al., Impact of chronic liver disease upon admission on COVID-19 in-hospital mortality: Findings from COVOCA study, Plos One, 15, 12, (2020); Lee L.Y., Et al., COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: A prospective cohort study, Lancet Oncol., 21, 10, pp. 1309-1316, (2020); Doolub G., Et al., Impact of COVID-19 on inpatient referral of acute heart failure: A single-centre experience from the south-west of the UK, ESC Heart Fail., 8, 2, pp. 1691-1695, (2021); de Lusignan S., Et al., Risk factors for SARS-CoV-2 among patients in the Oxford Royal college of general practitioners research and surveillance centre primary care network: A cross-sectional study, Lancet Infect. Dis, 20, 9, pp. 1034-1042, (2020); Griffith G.J., Et al., Collider bias undermines our understanding of COVID-19 disease risk and severity, Nat. commun., 11, 1, pp. 1-12, (2020); The Northern Ireland Statistics and Research Agency, Covid-19 Related Deaths and Pre-Existing Conditions in Northern Ireland, (2021); Raleigh V.S., Ethnic differences in covid-19 death rates, BMJ, (2022); Bosworth M.L., Et al., Deaths involving COVID-19 by self-reported disability status during the first two waves of the COVID-19 pandemic in England: A retrospective, population-based cohort study, Lancet Public Health, 6, 11, pp. e817-e825, (2021); Cifuentes M.P., Rodriguez-Villamizar L.A., Rojas-Botero M.L., Alvarez-Moreno C.A., Fernandez-Nino J.A., Socioeconomic inequalities associated with mortality for COVID-19 in Colombia: A cohort nationwide study, J. Epidemiol. Community Health., 75, 7, pp. 610-615, (2021); Paakkari L., Okan O., COVID-19: Health literacy is an underestimated problem, Lancet Public Health., 5, 5, pp. e249-e250, (2020); McKeigue P.M., Et al., Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: The REACT-SCOT case-control study, BMC Med., 19, 1, pp. 1-11, (2021); Quan H., Et al., Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data, Med. Care, 43, 11, pp. 1130-1139, (2005); van Walraven C., Austin P.C., Jennings A., Quan H., Forster A.J., A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data, Med. Care, 47, 6, pp. 626-633, (2009); Huang Z., A fast clustering algorithm to cluster very large categorical data sets in data mining, KDD: Techniques and Applications, pp. 21-34, (1997); Shalev-Shwartz S., Ben-David S., Understanding Machine Learning from Theory to Algorithms, (2014); Vandenbroucke J.P., Et al., Strobe initiative. Strengthening the reporting of observational studies in epidemiology (STROBE): Explanation and elaboration, Plos Med., 4, 10, (2007)","M. Bucholc; Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, BT48 7JL, United Kingdom; email: m.bucholc@ulster.ac.uk","","Nature Research","","","","","","20452322","","","36243878","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139855858"
"Sun J.; Liao X.; Yan Y.; Zhang X.; Sun J.; Tan W.; Liu B.; Wu J.; Guo Q.; Gao S.; Li Z.; Wang K.; Li Q.","Sun, Jiaxing (57230240100); Liao, Ximing (57226169730); Yan, Yusheng (57464166800); Zhang, Xin (57221515014); Sun, Jian (59282461100); Tan, Weixiong (57221684842); Liu, Baiyun (57195219563); Wu, Jiangfen (57005902900); Guo, Qian (57215590117); Gao, Shaoyong (57220896463); Li, Zhang (59157723300); Wang, Kun (55537784200); Li, Qiang (57219134438)","57230240100; 57226169730; 57464166800; 57221515014; 59282461100; 57221684842; 57195219563; 57005902900; 57215590117; 57220896463; 59157723300; 55537784200; 57219134438","Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach","2022","European Radiology","32","8","","5319","5329","10","37","10.1007/s00330-022-08632-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125145596&doi=10.1007%2fs00330-022-08632-7&partnerID=40&md5=acae8a126691fb9c4e7be426e3746199","Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China; Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China; Department of Pulmonary and Critical Care Medicine, Changsha First Hospital, Changsha, China; Department of Pulmonary and Critical Care Medicine, People’s Liberation Army Joint Logistic Support Force 920th Hospital, Yunnan, Kunming, China; Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital, Jinan, China; Infervision, Beijing, China; College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China","Sun J., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China, Department of Pulmonary and Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China; Liao X., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China; Yan Y., Department of Pulmonary and Critical Care Medicine, Changsha First Hospital, Changsha, China; Zhang X., Department of Pulmonary and Critical Care Medicine, People’s Liberation Army Joint Logistic Support Force 920th Hospital, Yunnan, Kunming, China; Sun J., Department of Pulmonary and Critical Care Medicine, Shandong Provincial Hospital, Jinan, China; Tan W., Infervision, Beijing, China; Liu B., Infervision, Beijing, China; Wu J., Infervision, Beijing, China; Guo Q., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China; Gao S., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China; Li Z., College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China; Wang K., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China; Li Q., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, No. 150 Jimo Road, Pudong, Shanghai, China","Objectives: Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. Methods: A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. Results: The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. Conclusions: The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. Key Points: • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)–based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation. © 2022, The Author(s), under exclusive licence to European Society of Radiology.","Chronic obstructive pulmonary disease; Deep learning; Mass screening; Spirometry; Tomography, X-ray computed","Deep Learning; Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Spirometry; Tomography, X-Ray Computed; chronic obstructive lung disease; disease exacerbation; human; procedures; retrospective study; spirometry; x-ray computed tomography","","","","","National Natural Science Foundation of China, NSFC, (81870064, 82070086, 82100089); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2018YFC1313700); National Key Research and Development Program of China, NKRDPC; Wuxi Health and Family Planning Commission, (PWYgy2018-06); Wuxi Health and Family Planning Commission","This work was supported by the National Key R&D Program (2018YFC1313700), the National Natural Science Foundation of China (grant nos. 82100089, 81870064, and 82070086), and the “Gaoyuan” project of Pudong Health and Family Planning Commission (PWYgy2018-06). ","Disease G.B.D., Injury I., Prevalence C., Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet, 388, pp. 1545-1602, (2016); Halpin D.M.G., Criner G.J., Papi A., Et al., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 GOLD Science Committee report on COVID-19 and chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 203, pp. 24-36, (2021); Jithoo A., Enright P.L., Burney P., Et al., Case-finding options for COPD: results from the Burden of Obstructive Lung Disease study, Eur Respir J, 41, pp. 548-555, (2013); Perez-Padilla R., Thirion-Romero I., Guzman N., Underdiagnosis of chronic obstructive pulmonary disease: should smokers be offered routine spirometry tests?, Expert Rev Respir Med, 12, pp. 83-85, (2018); Lamprecht B., Soriano J.B., Studnicka M., Et al., Determinants of underdiagnosis of COPD in national and international surveys, Chest, 148, pp. 971-985, (2015); Wang C., Xu J., Yang L., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Miller M.R., Levy M.L., Chronic obstructive pulmonary disease: missed diagnosis versus misdiagnosis, BMJ, 351, (2015); Estepar R.S., Kinney G.L., Black-Shinn J.L., Et al., Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications, Am J Respir Crit Care Med, 188, pp. 231-239, (2013); McDonald M.L., Diaz A.A., Ross J.C., Et al., Quantitative computed tomography measures of pectoralis muscle area and disease severity in chronic obstructive pulmonary disease. A cross-sectional study, Ann Am Thorac Soc, 11, pp. 326-334, (2014); Bhatt S.P., Washko G.R., Hoffman E.A., Et al., Imaging advances in chronic obstructive pulmonary disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, Am J Respir Crit Care Med, 199, pp. 286-301, (2019); Park J., Hobbs B.D., Crapo J.D., Et al., Subtyping COPD using visual and quantitative CT features, Chest, (2019); Washko G.R., Parraga G., COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging, Eur Respir J, 52, (2018); Kauczor H.U., Bonomo L., Gaga M., Et al., ESR/ERS white paper on lung cancer screening, Eur Radiol, 25, pp. 2519-2531, (2015); Lathan C., Frank D.A., ACP Journal Club. Review: Low-dose CT screening reduces lung cancer and mortality in current or former smokers, Ann Intern Med, 159, JC3, (2013); Chassagnon G., Vakalopolou M., Paragios N., Revel M.P., Deep learning: definition and perspectives for thoracic imaging, Eur Radiol, 30, pp. 2021-2030, (2020); Philbrick K.A., Yoshida K., Inoue D., Et al., What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images, AJR Am J Roentgenol, 211, pp. 1184-1193, (2018); Kermany D.S., Goldbaum M., Cai W., Et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, pp. 1122-1131 e1129, (2018); Cho Y.H., Lee S.M., Seo J.B., Et al., Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: associations with pulmonary function test and survival in the KOLD cohort, Eur J Radiol, 108, pp. 276-282, (2018); Lynch D.A., Moore C.M., Wilson C., Et al., CT-based visual classification of emphysema: association with mortality in the COPDGene study, Radiology, 288, pp. 859-866, (2018); Peng L., Lin L., Hu H., Et al., Classification and quantification of emphysema using a multi-scale residual network, IEEE J Biomed Health Inform, 23, pp. 2526-2536, (2019); Nambu A., Zach J., Schroeder J., Et al., Quantitative computed tomography measurements to evaluate airway disease in chronic obstructive pulmonary disease: relationship to physiological measurements, clinical index and visual assessment of airway disease, Eur J Radiol, 85, pp. 2144-2151, (2016); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, pp. 193-203, (2018); Hatt C.R., Galban C.J., Labaki W., Kazerooni E.A., Han M.L., Convolutional neural network based COPD and emphysema classifications are predictive of lung cancer diagnosis, Image Analysis for Moving Organ, Breast, and Thoracic Images, (2018); Tang L.Y.W., Coxson H.O., Lam S., Leipsic J., Tam R.C., Sin D.D., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, pp. e259-e267, (2020); Aberle D.R., Adams A.M., Et al., Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, pp. 395-409, (2011); Xu C., Qi S., Feng J., Et al., DCT-MIL: deep CNN transferred multiple instance learning for COPD identification using CT images, Phys Med Biol, 65, (2020); Yan X., Tao M., Feng Q., Zhong P., Chang I.C., Deep learning of feature representation with multiple instance learning for medical image analysis, IEEE International Conference on Acoustics, (2014); Shen Y., Wu N., Phang J., Et al., An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization, Med Image Anal, 68, (2021); Ilse M., Tomczak J.M., Welling M., Attention-based deep multiple instance learning, The 35Th International Conference on Machine Learning, (2018); Alom M.Z., Yakopcic C., Hasan M., Taha T.M., Asari V.K., Recurrent residual U-Net for medical image segmentation, J Med Imaging (Bellingham), 6, (2019); Fluss R., Faraggi D., Reiser B., Estimation of the Youden Index and its associated cutoff point, Biom J, 47, pp. 458-472, (2005); Young K.A., Strand M., Ragland M.F., Et al., Pulmonary subtypes exhibit differential global initiative for chronic obstructive lung disease spirometry stage progression: the COPDGene(R) study, Chronic Obstr Pulm Dis, 6, pp. 414-429, (2019); Kinney G.L., Santorico S.A., Young K.A., Et al., Identification of chronic obstructive pulmonary disease axes that predict all-cause mortality: the COPDGene study, Am J Epidemiol, 187, pp. 2109-2116, (2018); Woodruff P.G., Barr R.G., Bleecker E., Et al., Clinical significance of symptoms in smokers with preserved pulmonary function, N Engl J Med, 374, pp. 1811-1821, (2016); Lowe K.E., Regan E.A., Anzueto A., Et al., COPDGene((R)) 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr Pulm Dis, 6, pp. 384-399, (2019); Esteva A., Robicquet A., Ramsundar B., Et al., A guide to deep learning in healthcare, Nat Med, 25, pp. 24-29, (2019); Pino Pena I., Cheplygina V., Paschaloudi S., Et al., Automatic emphysema detection using weakly labeled HRCT lung images, PLoS One, 13, (2018)","K. Wang; Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, No. 150 Jimo Road, Pudong, China; email: Dr_Wangk@tongji.edu.cn; Q. Li; Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, No. 150 Jimo Road, Pudong, China; email: liqressh1962@163.com","","Springer Science and Business Media Deutschland GmbH","","","","","","09387994","","EURAE","35201409","English","Eur. Radiol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85125145596"
"Nadarajah R.; Wu J.; Hogg D.; Raveendra K.; Nakao Y.M.; Nakao K.; Arbel R.; Haim M.; Zahger D.; Parry J.; Bates C.; Cowan C.; Gale C.P.","Nadarajah, Ramesh (8672636400); Wu, Jianhua (57194400210); Hogg, David (7103188000); Raveendra, Keerthenan (58113673500); Nakao, Yoko M (56719154000); Nakao, Kazuhiro (35741449000); Arbel, Ronen (57140322100); Haim, Moti (7004459681); Zahger, Doron (7004143560); Parry, John (7202279139); Bates, Chris (57059890400); Cowan, Campbel (59622144800); Gale, Chris P (35837808000)","8672636400; 57194400210; 7103188000; 58113673500; 56719154000; 35741449000; 57140322100; 7004459681; 7004143560; 7202279139; 57059890400; 59622144800; 35837808000","Prediction of short-term atrial fibrillation risk using primary care electronic health records","2023","Heart","109","14","","1072","1079","7","21","10.1136/heartjnl-2022-322076","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148666780&doi=10.1136%2fheartjnl-2022-322076&partnerID=40&md5=2a92794ecb02a8a0d4c763b8201c7383","Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom; Department of Dentistry, University of Leeds, Leeds, United Kingdom; School of Computing, University of Leeds, Leeds, United Kingdom; Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom; Maximizing Health Outcomes Research Lab, Sapir College, Hof Ashkelon, Israel; Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel; Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel; Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; Cardiology, Soroka Medical Center, Beer Sheva, Israel; The Phoenix Partnership, Leeds, United Kingdom; Cardiology, Leeds General Infirmary, Leeds, United Kingdom","Nadarajah R., Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom; Wu J., Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Department of Dentistry, University of Leeds, Leeds, United Kingdom; Hogg D., School of Computing, University of Leeds, Leeds, United Kingdom; Raveendra K., Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom; Nakao Y.M., Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom; Nakao K., Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom; Arbel R., Maximizing Health Outcomes Research Lab, Sapir College, Hof Ashkelon, Israel, Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel; Haim M., Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel; Zahger D., Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel, Cardiology, Soroka Medical Center, Beer Sheva, Israel; Parry J., The Phoenix Partnership, Leeds, United Kingdom; Bates C., The Phoenix Partnership, Leeds, United Kingdom; Cowan C., Cardiology, Leeds General Infirmary, Leeds, United Kingdom; Gale C.P., Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom, Cardiology, Leeds General Infirmary, Leeds, United Kingdom","Objective Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). Methods We used primary care electronic health record data from individuals aged ≥30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA 2 DS 2 -VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C 2 HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age ≥75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. Results Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA 2 DS 2 -VASc (0.784, 0.773 to 0.794) and C 2 HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). Conclusions FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF. © 2023 BMJ Publishing Group. All rights reserved.","atrial fibrillation; biostatistics; electronic health records","Aged; Atrial Fibrillation; Electronic Health Records; Heart Failure, Systolic; Humans; Hypertension; Primary Health Care; Risk Assessment; Risk Factors; Stroke; adult; age; aged; algorithm; Article; atrial fibrillation; C2HEST score; cardiovascular risk; cerebrovascular accident; CHA2DS2-VASc score; chronic obstructive lung disease; clinical assessment tool; clinical research; cohort analysis; comorbidity; conceptual framework; controlled study; coronary artery disease; diagnostic test accuracy study; electronic health record; ethnicity; female; heart failure; high risk population; human; hypertension; hyperthyroidism; incidence; information processing; machine learning; major clinical study; male; mortality rate; prediction; primary medical care; proportional hazards model; random forest; randomized controlled trial; receiver operating characteristic; sensitivity and specificity; sex; systolic heart failure; thromboembolism; Youden index; cerebrovascular accident; complication; electronic health record; hypertension; primary health care; risk assessment; risk factor; systolic heart failure","","","","","British Heart Foundation, BHF, (FS/20/12/34789); British Heart Foundation, BHF","RN is supported by the British Heart Foundation Clinical Research Training Fellowship (FS/20/12/34789). ","Wu J., Nadarajah R., Nakao Y.M., Et al., Temporal trends and patterns in atrial fibrillation incidence: A population-based study of 3·4 million individuals, Lancet Reg Health Eur, 17, (2022); Svennberg E., Engdahl J., Al-Khalili F., Et al., Mass screening for untreated atrial fibrillation: The STROKESTOP study, Circulation, 131, pp. 2176-2184, (2015); Kamel H., Cryptogenic stroke and atrial fibrillation, N Engl J Med, 371, pp. 1261-1262, (2014); Ruff C.T., Giugliano R.P., Braunwald E., Et al., Comparison of the efficacy and safety of new oral anticoagulants with warfarin in patients with atrial fibrillation: A meta-analysis of randomised trials, Lancet, 383, pp. 955-962, (2014); Kirchhof P., Camm A.J., Goette A., Et al., Early rhythm-control therapy in patients with atrial fibrillation, N Engl J Med, 383, pp. 1305-1316, (2020); Cardiovascular Disease, (2019); Hindricks G., Potpara T., Dagres N., Et al., 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the european association for cardio-thoracic surgery (EACTS): The task force for the diagnosis and management of atrial fibrillation of the european society of cardiology (ESC) developed with the special contribution of the european heart rhythm association (EHRA) of the ESC, Eur Heart J, 42, pp. 373-498, (2021); Herrett E., Gallagher A.M., Bhaskaran K., Et al., Data resource profile: Clinical practice research Datalink (CPRD), Int J Epidemiol, 44, pp. 827-836, (2015); Himmelreich J.C.L., Lucassen W.A.M., Harskamp R.E., Et al., CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: Validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening, Open Heart, 8, (2021); Nadarajah R., Alsaeed E., Hurdus B., Et al., Prediction of incident atrial fibrillation in community-based electronic health records: A systematic review with meta-analysis, Heart, 108, pp. 1020-1029, (2022); Hill N.R., Ayoubkhani D., McEwan P., Et al., Predicting atrial fibrillation in primary care using machine learning, PLoS One, 14, (2019); Ruigomez A., Johansson S., Wallander M.A., Et al., Incidence of chronic atrial fibrillation in general practice and its treatment pattern, J Clin Epidemiol, 55, pp. 358-363, (2002); Collins G.S., Reitsma J.B., Altman D.G., Et al., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. the TRIPOD group, Circulation, 131, pp. 211-219, (2015); Kotecha D., Asselbergs F.W., Achenbach S., Et al., CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research, BMJ, 378, (2022); Breiman L., Random forests, Mach Learn, 45, pp. 5-32, (2001); Routen A., Akbari A., Banerjee A., Et al., Strategies to record and use ethnicity information in routine health data, Nat Med, 28, pp. 1338-1342, (2022); Groenwold R.H.H., Informative missingness in electronic health record systems: The curse of knowing, Diagn Progn Res, 4, (2020); Sakamoto Y., Ishiguro M., Kitagawa G., Akaike information criterion statistics, Dordrecht Netherlands D Reidel, 81, (1986); Szymanski T., Ashton R., Sekelj S., Et al., Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients, Europace, 24, pp. 1240-1247, (2022); Attia Z.I., Noseworthy P.A., Lopez-Jimenez F., Et al., An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: A retrospective analysis of outcome prediction, Lancet, 394, pp. 861-867, (2019); Van Smeden M., Heinze G., Van Calster B., Et al., Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease, Eur Heart J, 43, pp. 2921-2930, (2022); Ibrahim H., Liu X., Zariffa N., Et al., Health data poverty: An assailable barrier to equitable digital health care, Lancet Digit Health, 3, pp. e260-e265, (2021); Middeldorp M.E., Pathak R.K., Meredith M., Et al., Prevention and regressive effect of weight-loss and risk factor modification on atrial fibrillation: The REVERSE-AF study, Europace, 20, pp. 1929-1935, (2018); Jones N.R., Taylor C.J., Hobbs F.D.R., Et al., Screening for atrial fibrillation: A call for evidence, Eur Heart J, 41, pp. 1075-1085, (2020); Uittenbogaart S.B., Verbiest-Van Gurp N., Lucassen W.A.M., Et al., Opportunistic screening versus usual care for detection of atrial fibrillation in primary care: Cluster randomised controlled trial, BMJ, 370, (2020); Svennberg E., Friberg L., Frykman V., Et al., Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): A multicentre, parallel group, unmasked, randomised controlled trial, Lancet, 398, pp. 1498-1506, (2021); Svendsen J.H., Diederichsen S.Z., Hojberg S., Et al., Implantable loop recorder detection of atrial fibrillation to prevent stroke (the loop study): A randomised controlled trial, Lancet, 398, pp. 1507-1516, (2021)","R. Nadarajah; Leeds Institute for Data Analytics, University of Leeds, Leeds, LS2 9JT, United Kingdom; email: r.nadarajah@leeds.ac.uk","","BMJ Publishing Group","","","","","","13556037","","HEARF","36759177","English","Heart","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85148666780"
"Ayus I.; Natarajan N.; Gupta D.","Ayus, Ishan (57226357254); Natarajan, Narayanan (57193261612); Gupta, Deepak (57198645913)","57226357254; 57193261612; 57198645913","Comparison of machine learning and deep learning techniques for the prediction of air pollution: a case study from China","2023","Asian Journal of Atmospheric Environment","17","1","4","","","","21","10.1007/s44273-023-00005-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160253496&doi=10.1007%2fs44273-023-00005-w&partnerID=40&md5=0acaa318c49b0048dabd6731accf5690","Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Odisha, Bhubaneswar, India; Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, 211004, India","Ayus I., Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Odisha, Bhubaneswar, India; Natarajan N., Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; Gupta D., Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, 211004, India","The adverse effect of air pollution has always been a problem for human health. The presence of a high level of air pollutants can cause severe illnesses such as emphysema, chronic obstructive pulmonary disease (COPD), or asthma. Air quality prediction helps us to undertake practical action plans for controlling air pollution. The Air Quality Index (AQI) reflects the degree of concentration of pollutants in a locality. The average AQI was calculated for the various cities in China to understand the annual trends. Furthermore, the air quality index has been predicted for ten major cities across China using five different deep learning techniques, namely, Recurrent Neural Network (RNN), Bidirectional Gated Recurrent unit (Bi-GRU), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network BiLSTM (CNN-BiLSTM), and Convolutional BiLSTM (Conv1D-BiLSTM). The performance of these models has been compared with a machine learning model, eXtreme Gradient Boosting (XGBoost) to discover the most efficient deep learning model. The results suggest that the machine learning model, XGBoost, outperforms the deep learning models. While Conv1D-BiLSTM and CNN-BiLSTM perform well among the deep learning models in the estimation of the air quality index (AQI), RNN and Bi-GRU are the least performing ones. Thus, both XGBoost and neural network models are capable of capturing the non-linearity present in the dataset with reliable accuracy. © 2023, The Author(s).","AQI; Bidirectional GRU; Bidirectional LSTM; CNN BiLSTM; Conv1D BiLSTM","China; air quality; atmospheric pollution; bidirectional reflectance; comparative study; data set; machine learning; nonlinearity; performance assessment; pollution monitoring","","","","","","","The CIA World Factbook 2011, (2011); Al-Janabi S., Mohammad M., Al-Sultan A., A new method for prediction of air pollution based on intelligent computation, Soft Computing, 1, pp. 661-680, (2019); Athira V., Geetha P., Vinakumar R., Soman J.P., DeepAirNet: Applying recurrent networks for air quality prediction, Procedia Computer Science, 132, pp. 1394-1403, (2018); Biancofiore F., Busilacchio M., Verdecchia M., Tomassetti B., Aruffo E., Bianco S., Di Tommaso S., Colangeli C., Rosatelli G., Di Carlo P., Recursive neural network model for analysis and forecast of PM<sub>10</sub> and PM<sub>2.5</sub>, Atmospheric Pollution Research, 4, pp. 652-659, (2017); 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Khaniabadi Y.O., Daryanoosh S.M., Hopke P.K., Ferrante M., De Marco A., Sicard P., Oliveri Conti G., Goudarzi G., Basiri H., Mohammadi M.J., Keishams F., Acute myocardial infarction and COPD attributed to ambient SO<sub>2</sub> in Iran, Environmental Research, 156, pp. 683-687, (2017); Kumar R.P., Perumpully S.J., Samuel C., Gautam S., Exposure and health: A progress update by evaluation and scientometric analysis, Stochastic Environmental Research and Risk Assessment, (2022); Li X., Peng L., Yao X., Cui S., Hu Y., You C., Chi T., Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation, Environmental Pollution, 231, pp. 997-1004, (2017); Li Q., Li S., Hu J., Zhang S., Hu J., Tourism review sentiment classification using a bidirectional recurrent neural network with an attention mechanism and topic-enriched word vectors, Sustainability, 9, (2018); Li M., He B., Guo R., Li Y., Chen Y., Fan Y., Study on population distribution pattern at the county level of China, Sustainability, 10, (2018); Lin B., Zhu J., Changes in urban air quality during urbanization in China, Journal of Cleaner Production, 188, pp. 312-321, (2018); Liu W., Xu Z., Yang T., Health effects of air pollution in China, International Journal of Environmental Research and Public Health, 15, 7, (2018); Lu W., Li J., Li Y., Sun A., Wang J., A CNN-LSTM-based model to forecast stock prices, Complexity, 2020, pp. 1-10, (2020); Minmin L., He B., Guo R., Li Y., Chen Y., Fan Y., Study on population distribution pattern at the county level of China, Sustainability, 10, (2018); Navares R., Aznarte J.L., Predicting air quality with deep learning LSTM: Towards comprehensive models, Ecological Informarics, 55, (2020); Nejadettehad A., Mahini H., Bahrak B., Short-term demand forecasting for online car-hailing services using recurrent neural networks, Applied Artifificial Intelligence, 9, pp. 674-689, (2020); Ni X.Y., Huang H., Du W.P., Relevance analysis and short-term prediction of PM<sub>2.5</sub> concentrations in Beijing based on multi-source data, Atmospheric Environment, 150, pp. 146-161, (2017); 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Zhou X., Xu J., Zeng P., Meng X., Air pollutant concentration prediction based on GRU method, Journal of Physics: Conference Series, (2019); Zhu S., Lian X., Liu H., Hu J., Wang Y., Che J., Daily air quality index forecasting with hybrid models: A case in China, Environmental Pollution, 231, pp. 1232-1244, (2017); Zou B., You J., Lin Y., Duan X., Zhao X., Fang X., Campen M.J., Li S., Air pollution intervention and life-saving effect in China, Environment International, 125, pp. 529-541, (2019)","N. Natarajan; Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India; email: itsrajan2002@yahoo.co.in","","Springer","","","","","","19766912","","","","English","Asian J. Atmos. Environ.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85160253496"
"Kumar S.; Bhagat V.; Sahu P.; Chaube M.K.; Behera A.K.; Guizani M.; Gravina R.; Di Dio M.; Fortino G.; Curry E.; Alsamhi S.H.","Kumar, Santosh (57192413856); Bhagat, Vijesh (57952222600); Sahu, Prakash (57812042000); Chaube, Mithliesh Kumar (36243062700); Behera, Ajoy Kumar (57209582741); Guizani, Mohsen (7004750176); Gravina, Raffaele (34869586200); Di Dio, Michele (57209800483); Fortino, Giancarlo (6602895297); Curry, Edward (12790805000); Alsamhi, Saeed Hamood (56159911000)","57192413856; 57952222600; 57812042000; 36243062700; 57209582741; 7004750176; 34869586200; 57209800483; 6602895297; 12790805000; 56159911000","A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases","2024","Computer Methods and Programs in Biomedicine","243","","107911","","","","21","10.1016/j.cmpb.2023.107911","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177076971&doi=10.1016%2fj.cmpb.2023.107911&partnerID=40&md5=59eee8b47739dda8df0bdeb607797535","Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India; Department of Mathematical Sciences, IIIT-Naya Raipur, Chhattisgarh, India; Department of Pulmonary Medicine & TB, All India Institute of Medical Sciences (AIIMS), Chhattisgarh, Raipur, India; Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates; Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, Rende, 87036, Italy; Annunziata Hospital Cosenza, Italy; Insight Centre for Data Analytics, University of Galway, Galway, Ireland; Faculty of Engineering, IBB University, Ibb, Yemen","Kumar S., Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India; Bhagat V., Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India; Sahu P., Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India; Chaube M.K., Department of Mathematical Sciences, IIIT-Naya Raipur, Chhattisgarh, India; Behera A.K., Department of Pulmonary Medicine & TB, All India Institute of Medical Sciences (AIIMS), Chhattisgarh, Raipur, India; Guizani M., Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates; Gravina R., Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, Rende, 87036, Italy; Di Dio M., Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, Rende, 87036, Italy, Annunziata Hospital Cosenza, Italy; Fortino G., Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, Rende, 87036, Italy; Curry E., Insight Centre for Data Analytics, University of Galway, Galway, Ireland; Alsamhi S.H., Insight Centre for Data Analytics, University of Galway, Galway, Ireland, Faculty of Engineering, IBB University, Ibb, Yemen","Background and objective: Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study. Method: The proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients. Results: The proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model. Conclusion: Finally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis. © 2023 Elsevier B.V.","Chronic Obstructive Pulmonary Disease (COPD); Classification; Computed Tomography (CT); Deep learning; Lung sound; Texture","Cough; Early Diagnosis; Humans; Lung Diseases; Pulmonary Disease, Chronic Obstructive; Respiratory Sounds; Tomography, X-Ray Computed; Audio acoustics; Benchmarking; Biological organs; Classification (of information); Computer aided diagnosis; Computerized tomography; Deep learning; Image classification; Learning algorithms; Learning systems; Medical imaging; Pulmonary diseases; Accurate prediction; Chronic obstructive pulmonary disease; Computed tomography; Deep learning; Early diagnosis; Lung sounds; Machine learning techniques; Multimodal frameworks; Scan images; accuracy; adult; Article; chronic obstructive lung disease; classifier; computer assisted tomography; convolutional neural network; coughing; cross validation; deep learning; disease classification; early diagnosis; feature extraction; feature selection; female; human; image analysis; machine learning; male; recall; respiratory tract disease; sound analysis; unsupervised machine learning; abnormal respiratory sound; chronic obstructive lung disease; diagnostic imaging; early diagnosis; lung disease; procedures; x-ray computed tomography; Textures","","","","","Science Foundation Ireland, SFI, (SFI/12/RC/2289_P2); Science Foundation Ireland, SFI; Science and Engineering Research Board, SERB, (EEQ/2022/000617, SERB/F/11403/2022-2023); Science and Engineering Research Board, SERB; Ministero della Salute, (CUP: H53C22000650006); Ministero della Salute; All-India Institute of Medical Sciences, AIIMS","Funding text 1: This publication has emanated from research conducted with the financial support of the Science and Engineering Research Board (SERB) India under award numbers (Sanction Order No. EEQ/2022/000617 ), (SERB Finance No: SERB/F/11403/2022-2023 ). Furthermore, this publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number SFI/12/RC/2289_P2 . Partially supported by the POS RADIOAMICA project funded by the Italian Minister of Health (CUP: H53C22000650006 ). ; Funding text 2: This publication has emanated from research conducted with the financial support of the Science and Engineering Research Board (SERB) India under award numbers (Sanction Order No. EEQ/2022/000617), (SERB Finance No: SERB/F/11403/2022-2023). Furthermore, this publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number SFI/12/RC/2289_P2. Partially supported by the POS RADIOAMICA project funded by the Italian Minister of Health (CUP:H53C22000650006). The authors would like to thank experts and staff of Department of Pulmonary Medicine & TB, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattishgarh, India, for their guidance and collaboration, and for coordination of the pulmonary specialists and patients for this research work.","Exarchos K.P., Aggelopoulou A., Oikonomou A., Biniskou T., Beli V., Antoniadou E., Kostikas K., Review of artificial intelligence techniques in chronic obstructive lung disease, IEEE J. Biomed. Health Inform., 26, 5, pp. 2331-2338, (2021); Sahu H.K., Kumar S., Alsamhi S.H., Chaube M.K., Curry E., Novel framework for Alzheimer early diagnosis using inductive transfer learning techniques, 2022 2nd International Conference on Emerging Smart Technologies and Applications (eSmarTA), pp. 1-7, (2022); Zhao Q., Li J., Zhao L., Zhu Z., Knowledge guided feature aggregation for the prediction of chronic obstructive pulmonary disease with Chinese EMRs, IEEE/ACM Trans. Comput. Biol. 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"Pinnock H.; Murphie P.; Vogiatzis I.; Poberezhets V.","Pinnock, Hilary (6701815935); Murphie, Phyllis (38961662100); Vogiatzis, Ioannis (6603579064); Poberezhets, Vitalii (57197871548)","6701815935; 38961662100; 6603579064; 57197871548","Telemedicine and virtual respiratory care in the era of COVID-19","2022","ERJ Open Research","8","3","00111-2022","","","","24","10.1183/23120541.00111-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138528639&doi=10.1183%2f23120541.00111-2022&partnerID=40&md5=795b08c1f24b07e4f090e2d5e3e8d97f","Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; NHS Dumfries and Galloway, Scotland, United Kingdom; Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom; Department of Propedeutics of Internal Medicine, National Pirogov Memorial Medical University, Vinnytsya, Ukraine","Pinnock H., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; Murphie P., NHS Dumfries and Galloway, Scotland, United Kingdom; Vogiatzis I., Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University Newcastle, Newcastle upon Tyne, United Kingdom; Poberezhets V., Department of Propedeutics of Internal Medicine, National Pirogov Memorial Medical University, Vinnytsya, Ukraine","The World Health Organization defines telemedicine as „an interaction between a health care provider and a patient when the two are separated by distance‟. The COVID-19 pandemic has forced a dramatic shift to telephone and video consulting for follow up and routine ambulatory care for reasons of infection control. Short Message Service („text‟) messaging has proved a useful adjunct to remote consulting allowing transfer of photographs and documents. Maintaining non-communicable diseases care is a core component of pandemic preparedness and telemedicine has developed to enable (for example) remote monitoring of sleep apnoea, telemonitoring of chronic obstructive pulmonary disease, digital support for asthma self-management, remote delivery of pulmonary rehabilitation. There are multiple exemplars of telehealth instigated rapidly to provide care for people with COVID-19, to manage the spread of the pandemic, or to maintain safe routine diagnostic or treatment services. Despite many positive examples of equivalent functionality and safety, there remain questions about the impact of remote delivery of care on rapport and the longer-term impact on patient/professional relationships. Although telehealth has the potential to contribute to universal health coverage by providing cost-effective accessible care, there is a risk of increasing social health inequalities if the „digital divide‟ excludes those most in need of care. As we emerge from the pandemic, the balance of remote versus face-to-face consulting, and the specific role of digital health in different clinical and healthcare contexts will evolve. What is clear is that telemedicine in one form or another will be part of the „new norm‟. © The authors 2022. All rights reserved.","","ambulatory care; Article; artificial intelligence; asthma; chronic obstructive lung disease; coronavirus disease 2019; follow up; health care personnel; health care system; health disparity; health practitioner; hospitalization; human; interview; lung cancer; non communicable disease; palliative therapy; pandemic; public health; pulmonary rehabilitation; randomized controlled trial (topic); remote sensing; respiratory care; sleep apnea syndromes; social distancing; teleconsultation; telehealth; telemedicine; telemonitoring; telerehabilitation; tobacco dependence; videoconferencing; World Health Organization","","","","","","","Global diffusion of eHealth: making universal health coverage achievable, (2019); Telemedicine: opportunities and developments in Member States: Report on the second global survey on eHealth, (2009); Digital respiratory medicine – realism vs futurism; Report on the WHO Symposium on the Future of Digital Health Systems in the European Region; Latulippe K, Hamel C, Giroux D., Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies, J Med Internet Res, 19, (2017); Poly TN, Islam MM, Li YC, Et al., Application of artificial intelligence for screening covid-19 patients using digital images: Meta-analysis, JMIR Med Inform, 9, (2021); Hwang J, Lee T, Lee H, Et al., A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study, J Med Intern Res, 24, (2022); Wu CT, Li GH, Huang CT, Et al., Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study, JMIR mHealth uHealth, 9, (2021); Kataoka Y, Takemura T, Sasajima M, Et al., Development and Early Feasibility of Chatbots for Educating Patients With Lung Cancer and Their Caregivers in Japan: Mixed Methods Study, JMIR Cancer, 7, (2021); Gama F, Tyskbo D, Nygren J, Et al., Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review, J Med Intern Res, 24, (2022); Kaplan A, Haenlein M., Siri, Siri, in my hand: Who‟s the fairest in the land? 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Cottrell M, Burns CL, Jones A, Et al., Sustaining allied health telehealth services beyond the rapid response to COVID-19: Learning from patient and staff experiences at a large quaternary hospital, J Telemed Telecare, 27, pp. 615-624, (2021); Ellis LA, Meulenbroeks I, Churruca K, Et al., The Application of e-Mental Health in Response to COVID-19: Scoping Review and Bibliometric Analysis, JMIR Mental Health, 8, (2021); Xu Z, Su C, Xiao Y, Et al., AI for COVID-19: Battling the pandemic with computational intelligence, Intell Med, 9, (2021); Alam MU, Rahmani R., Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application, Sensors, 21, (2021); Sadre R, Sundaram B, Majumdar S, Et al., Validating deep learning inference during chest X-ray classification for COVID-19 screening, Sci rep, 11, (2021); Delli Pizzi A, Chiarelli AM, Chiacchiaretta P, Et al., Radiomics-based machine learning differentiates ""ground-glass"" opacities due to COVID-19 from acute non-COVID-19 lung disease, Sci rep, 11, (2021); Haq AU, Li JP, Ahmad S, Et al., Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare, Sensors, 21, (2021); Dhont J, Wolfs C, Verhaegen F., Automatic COVID‐ 19 diagnosis based on chest radiography and deep learning - success story or dataset bias?, Med Phys, 49, pp. 978-987, (2022); Jungmann F, Muller L, Hahn F, Et al., Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?, Eur Radiology, (2021); Miller EA., The technical and interpersonal aspects of telemedicine: effects on doctor–patient communication, J Telemed Telecare, 9, pp. 1-7, (2003); Turner A, Scott A, Horwood J, Et al., Maintaining face-to-face contact during the COVID-19 pandemic: a longitudinal qualitative investigation in UK primary care, BJGP Open, 5, (2021); Boers SN, Jongsma KR, Lucivero F, Et al., Part 2: Clinical implementation of eHealth in Primary Care: addressing the ethical dimensions, Eur J Gen Pract, 26, pp. 26-32, (2020); Orlando JF, Beard M, Kumar S., Systematic review of patient and caregivers‟ satisfaction with telehealth videoconferencing as a mode of service delivery in managing patients‟ health, PloS One, 14, (2019); Funderskov KF, Boe Danbjorg D, Jess M, Et al., Telemedicine in specialised palliative care: Healthcare professionals’ and their perspectives on video consultations - A qualitative study, J Clin Nurse, 28, pp. 3966-3976, (2019); Data and analytics: taking the pulse of the information society; Hernandez K, Roberts T., Leaving No One Behind in a Digital World; Connected Women The Mobile Gender Gap Report 2021; Data Ethics Framework 2020; Jakobsen AS, Laursen LC, Rydahl-Hansen S, Et al., Home-based telehealth hospitalization for exacerbation of chronic obstructive pulmonary disease: findings from “the virtual hospital” trial, Telemed J E Health, 21, pp. 364-373, (2015); Polgar O, Patel S, Walsh JA, Et al., Digital habits of pulmonary rehabilitation service-users following the COVID-19 pandemic, Chronic Respir Dis, 19, (2022); Joy M, McGagh D, Jones N, Et al., Reorganisation of primary care for older adults during COVID-19: a cross-sectional database study in the UK, Br J Gen Pract, 70, pp. e540-e547, (2020); Johnston S, MacDougall M, McKinstry B., The use of video consulting in general practice: semistructured interviews examining acceptability to patients, J Innov Health Inform, 23, pp. 493-500, (2016)","H. Pinnock; Usher Institute, The University of Edinburgh, Medical School, Edinburgh, Doorway 3 Teviot Place, EH8 9AG, United Kingdom; email: hilary.pinnock@ed.ac.uk","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138528639"
"Sethi A.; Taylor D.L.; Ruby J.G.; Venkataraman J.; Sorokin E.; Cule M.; Melamud E.","Sethi, Anurag (8251254100); Taylor, D. Leland (12765167500); Ruby, J. Graham (15136724800); Venkataraman, Jagadish (57221462511); Sorokin, Elena (57202654923); Cule, Madeleine (57214626051); Melamud, Eugene (57214804878)","8251254100; 12765167500; 15136724800; 57221462511; 57202654923; 57214626051; 57214804878","Calcification of the abdominal aorta is an under-appreciated cardiovascular disease risk factor in the general population","2022","Frontiers in Cardiovascular Medicine","9","","1003246","","","","20","10.3389/fcvm.2022.1003246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140468126&doi=10.3389%2ffcvm.2022.1003246&partnerID=40&md5=3e0d84a71528f19be076838e4deb4aa7","Calico Life Sciences LLC, South San Francisco, CA, United States","Sethi A., Calico Life Sciences LLC, South San Francisco, CA, United States; Taylor D.L., Calico Life Sciences LLC, South San Francisco, CA, United States; Ruby J.G., Calico Life Sciences LLC, South San Francisco, CA, United States; Venkataraman J., Calico Life Sciences LLC, South San Francisco, CA, United States; Sorokin E., Calico Life Sciences LLC, South San Francisco, CA, United States; Cule M., Calico Life Sciences LLC, South San Francisco, CA, United States; Melamud E., Calico Life Sciences LLC, South San Francisco, CA, United States","Calcification of large arteries is a high-risk factor in the development of cardiovascular diseases, however, due to the lack of routine monitoring, the pathology remains severely under-diagnosed and prevalence in the general population is not known. We have developed a set of machine learning methods to quantitate levels of abdominal aortic calcification (AAC) in the UK Biobank imaging cohort and carried out the largest to-date analysis of genetic, biochemical, and epidemiological risk factors associated with the pathology. In a genetic association study, we identified three novel loci associated with AAC (FGF9, NAV9, and APOE), and replicated a previously reported association at the TWIST1/HDAC9 locus. We find that AAC is a highly prevalent pathology, with ~ 1 in 10 adults above the age of 40 showing significant levels of hydroxyapatite build-up (Kauppila score > 3). Presentation of AAC was strongly predictive of future cardiovascular events including stenosis of precerebral arteries (HR~1.5), myocardial infarction (HR~1.3), ischemic heart disease (HR~1.3), as well as other diseases such as chronic obstructive pulmonary disease (HR~1.3). Significantly, we find that the risk for myocardial infarction from elevated AAC (HR ~1.4) was comparable to the risk of hypercholesterolemia (HR~1.4), yet most people who develop AAC are not hypercholesterolemic. Furthermore, the overwhelming majority (98%) of individuals who develop pathology do so in the absence of known pre-existing risk conditions such as chronic kidney disease and diabetes (0.6% and 2.7% respectively). Our findings indicate that despite the high cardiovascular risk, calcification of large arteries remains a largely under-diagnosed lethal condition, and there is a clear need for increased awareness and monitoring of the pathology in the general population. Copyright © 2022 Sethi, Taylor, Ruby, Venkataraman, Sorokin, Cule and Melamud.","abdominal aorta calcification; cardiovascular disease(s); Dual-Energy X-ray Absorptiometry (DEXA); genome-wide association study (GWAS); machine learning; myocardial infarction","acetylsalicylic acid; cholesterol; cystatin C; hemoglobin A1c; high density lipoprotein; low density lipoprotein; triacylglycerol; abdominal aorta; abdominal aortic aneurysm; adult; angina pectoris; aortic calcification; arterial wall thickness; Article; atherosclerosis; body mass; calcium blood level; cardiovascular disease; cardiovascular risk; chronic kidney failure; chronic obstructive lung disease; cohort analysis; creatinine blood level; diabetes mellitus; diagnostic test accuracy study; dual energy X ray absorptiometry; echocardiography; emphysema; end stage renal disease; estimated glomerular filtration rate; female; follow up; gene mutation; genetic analysis; genetic association; genetic risk; genetic susceptibility; genome-wide association study; glomerulus filtration rate; heart failure; heart infarction; human; human cell; human tissue; hypercholesterolemia; hyperlipidemia; hypertension; image analysis; ischemic heart disease; machine learning; major clinical study; male; non insulin dependent diabetes mellitus; phosphate blood level; predictive value; risk factor; scoring system; single nucleotide polymorphism; systolic blood pressure","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; cholesterol, 57-88-5; hemoglobin A1c, 62572-11-6","","","Calico Life Sciences LLC; Cohorts for Heart and Aging Research in Genomic; National Institutes of Health, NIH, (R01 HL105756, U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, UL1 TR000128); National Institutes of Health, NIH; National Institute on Aging, NIA; National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIAMS; National Center for Advancing Translational Sciences, NCATS","Funding text 1: This work was supported by Calico Life Sciences LLC. Support for the CHARGE consortium infrastructure was provided by the NIH grant R01 HL105756 (B Psaty). Support for establishing and curation of the dbGaP CHARGE Summary site (phs000930) was provided by the University of Virginia. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. ; Funding text 2: The authors would like to thank Nick van Bruggen, Garret Fitzgerald, Amoolya Singh, Aarif Khakoo, David Kelley, Magdalena Lopez, Tian-Quan Cai, and Dan Eaton for discussions and inputs regarding the manuscript. This research has been conducted using the UK Biobank Resource application number 18448. The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. ; Funding text 3: The authors would like to thank Nick van Bruggen, Garret Fitzgerald, Amoolya Singh, Aarif Khakoo, David Kelley, Magdalena Lopez, Tian-Quan Cai, and Dan Eaton for discussions and inputs regarding the manuscript. This research has been conducted using the UK Biobank Resource application number 18448. The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. Summary statistics were obtained from dbGaP accession phs000930.v9.p1. The authors acknowledge the essential role of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium in the development and support of this research. (See http://web.chargeconsortium.com for more details) The authors thank the investigators, the staff, and the participants of each contributing cohort in the CHARGE consortium publication from which these results were obtained.","Giachelli C.M., Vascular calcification mechanisms, J Am Soc Nephrol, 15, pp. 2959-2964, (2004); Lanzer P., Boehm M., Sorribas V., Thiriet M., Janzen J., Zeller T., Et al., Medial vascular calcification revisited: review and perspectives, Eur Heart J, 35, pp. 1515-1525, (2014); Amann K., Media calcification and intima calcification are distinct entities in chronic kidney disease, Clin J Am Soc Nephrol, 3, pp. 1599-1605, (2008); Kelkar Anita A., Schultz William M., Faisal K., Joshua S., O'Hartaigh Briain W.J., Heidi G., Et al., Long-term prognosis after coronary artery calcium scoring among low-intermediate risk women and men, Circ Cardiovasc Imaging, 9, (2016); Prabhakaran S., Singh R., Zhou X., Ramas R., Sacco R.L., Rundek T., Presence of calcified carotid plaque predicts vascular events: the Northern Manhattan Study, Atherosclerosis, 195, pp. e197-e201, (2007); Budoff M.J., Shaw L.J., Liu S.T., Weinstein S.R., Mosler T.P., Tseng P.H., Et al., Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients, J Am Coll Cardiol, 49, pp. 1860-1870, (2007); Keyes K.M., Westreich D., Uk, Biobank, big data, and the consequences of non-representativeness, Lancet, 393, (2019); Petersen S.E., Matthews P.M., Bamberg F., Bluemke D.A., Francis J.M., Friedrich M.G., Et al., Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches, J Cardiovasc Magn Reson, 15, (2013); Kauppila L.I., Polak J.F., Cupples L.A., Hannan M.T., Kiel D.P., Wilson P.W., New indices to classify location, severity and progression of calcific lesions in the abdominal aorta: a 25-year follow-up study, Atherosclerosis, 132, pp. 245-250, (1997); Denny J.C., Ritchie M.D., Basford M.A., Pulley J.M., Bastarache L., Brown-Gentry K., Et al., PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations, Bioinformatics, 26, pp. 1205-1210, (2010); Cox D.R., Regression models and life-tables, J R Stat Soc Series B Stat Methodol, 34, pp. 187-220, (1972); Hoffmann U., Massaro J.M., D'Agostino RB S., Kathiresan S., Fox C.S., O'Donnell C.J., Cardiovascular event prediction and risk reclassification by coronary, aortic, and valvular calcification in the framingham heart study, J Am Heart Assoc, (2016); Levitzky Y.S., Cupples L.A., Murabito J.M., Kannel W.B., Kiel D.P., Wilson P.W.F., Et al., Prediction of intermittent claudication, ischemic stroke, and other cardiovascular disease by detection of abdominal aortic calcific deposits by plain lumbar radiographs, Am J Cardiol, 101, pp. 326-331, (2008); Walsh C.R., Cupples L.A., Levy D., Kiel D.P., Hannan M., Wilson P.W.F., Et al., Abdominal aortic calcific deposits are associated with increased risk for congestive heart failure: the Framingham Heart Study, Am Heart J, 144, pp. 733-739, (2002); Wilson P.W., Kauppila L.I., O'Donnell C.J., Kiel D.P., Hannan M., Polak J.M., Et al., Abdominal aortic calcific deposits are an important predictor of vascular morbidity and mortality, Circulation, 103, pp. 1529-1534, (2001); Linefsky J.P., O'Brien K.D., Katz R., de Boer I.H., Barasch E., Jenny N.S., Et al., Association of serum phosphate levels with aortic valve sclerosis and annular calcification, J Am Coll Cardiol, 58, pp. 291-297, (2011); Szulc P., Blackwell T., Schousboe J.T., Bauer D.C., Cawthon P., Lane N.E., Et al., High hip fracture risk in men with severe aortic calcification: MrOS study, J Bone Miner Res, 29, pp. 968-975, (2014); Nissen S.E., Tuzcu E.M., Schoenhagen P., Crowe T., Sasiela W.J., Tsai J., Et al., Statin therapy, LDL cholesterol, C-reactive protein, and coronary artery disease, N Engl J Med, 352, pp. 29-38, (2005); Malhotra R., Mauer A.C., Lino Cardenas C.L., Guo X., Yao J., Zhang X., Et al., HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype, Nat Genet, 51, pp. 1580-1587, (2019); Psaty B.M., O'Donnell C.J., Gudnason V., Lunetta K.L., Folsom A.R., Rotter J.I., Et al., Cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts, Circ Cardiovasc Genet, 2, pp. 73-80, (2009); Malik R., Chauhan G., Traylor M., Sargurupremraj M., Okada Y., Mishra A., Et al., Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes, Nat Genet, (2018); Lancet Neurol, (2016); von Berg J., van der Laan S.W., McArdle P.F., Malik R., Kittner S.J., Mitchell B.D., Et al., Alternate approach to stroke phenotyping identifies a genetic risk locus for small vessel stroke, Eur J Hum Genet, 28, pp. 963-972, (2020); van der Harst P., Verweij N., Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease, Circ Res, (2018); Klarin D., Lynch J., Aragam K., Chaffin M., Assimes T.L., Huang J., Et al., Genome-wide association study of peripheral artery disease in the Million Veteran Program, Nat Med, 25, pp. 1274-1279, (2019); Nelson C.P., Goel A., Butterworth A.S., Kanoni S., Webb T.R., Marouli E., Et al., Association analyses based on false discovery rate implicate new loci for coronary artery disease, Nat Genet, (2017); Giri A., Hellwege J.N., Keaton J.M., Park J., Qiu C., Warren H.R., Et al., Trans-ethnic association study of blood pressure determinants in over 750,000 individuals, Nat Genet, (2019); Takeuchi F., Akiyama M., Matoba N., Katsuya T., Nakatochi M., Tabara Y., Et al., Interethnic analyses of blood pressure loci in populations of East Asian and European descent, Nat Commun, (2018); Kato N., Loh M., Takeuchi F., Verweij N., Wang X., Zhang W., Et al., Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation, Nat Genet, (2015); Hoffmann T.J., Ehret G.B., Nandakumar P., Ranatunga D., Schaefer C., Kwok P.-Y., Et al., Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation, Nat Genet, 49, pp. 54-64, (2017); Duan L., Wei L., Tian Y., Zhang Z., Hu P., Wei Q., Et al., Novel susceptibility loci for moyamoya disease revealed by a genome-wide association study, Stroke, (2018); Jones G.T., Tromp G., Kuivaniemi H., Gretarsdottir S., Baas A.F., Giusti B., Et al., Meta-analysis of genome-wide association studies for abdominal aortic aneurysm identifies four new disease-specific risk, Loci Circ Res, 120, pp. 341-353, (2017); Sebastien Di T., David M.Z., Solena L.S., Romain C., Nathalie G., Sidwell R., Et al., Genetic association analyses highlight IL6, ALPL, and NAV1 as 3 new susceptibility genes underlying calcific aortic valve stenosis, Circ Genom Precis Med, 12, (2019); El-Saed A., Curb J.D., Kadowaki T., Okamura T., Sutton-Tyrrell K., Masaki K., Et al., The prevalence of aortic calcification in Japanese compared to white and Japanese-American middle-aged men is confounded by the amount of cigarette smoking, Int J Cardiol, 167, pp. 134-139, (2013); O'Donnell C.J., Chazaro I., Wilson P.W.F., Fox C., Hannan M.T., Kiel D.P., Et al., Evidence for heritability of abdominal aortic calcific deposits in the Framingham Heart Study, Circulation, 106, pp. 337-341, (2002); Leow K., Szulc P., Schousboe J.T., Kiel D.P., Teixeira-Pinto A., Shaikh H., Et al., Prognostic value of abdominal aortic calcification: a systematic review and meta-analysis of observational studies, J Am Heart Assoc, 10, (2021); Dhingra R., Sullivan L.M., Fox C.S., Wang T.J., D'Agostino RB S., Gaziano J.M., Et al., Relations of serum phosphorus and calcium levels to the incidence of cardiovascular disease in the community, Arch Intern Med, 167, pp. 879-885, (2007); Lanzer P., Hannan F.M., Lanzer J.D., Janzen J., Raggi P., Furniss D., Et al., Medial arterial calcification: JACC state-of-the-art review, J Am Coll Cardiol, 78, pp. 1145-1165, (2021); Sage A.P., Tintut Y., Demer L.L., Regulatory mechanisms in vascular calcification, Nat Rev Cardiol, 7, pp. 528-536, (2010); Burton D.G.A., Matsubara H., Ikeda K., Pathophysiology of vascular calcification: Pivotal role of cellular senescence in vascular smooth muscle cells, Exp Gerontol, 45, pp. 819-824, (2010); Nurnberg S.T., Guerraty M.A., Wirka R.C., Rao H.S., Pjanic M., Norton S., Et al., Genomic profiling of human vascular cells identifies TWIST1 as a causal gene for common vascular diseases, PLoS Genet, 16, (2020); Raggi Paolo B., David B., Jordi B., Mariano R., Markus K., Et al., Slowing progression of cardiovascular calcification with SNF472 in patients on hemodialysis, Circulation, 141, pp. 728-739, (2020); Pirruccello J.P., Chaffin M.D., Fleming S.J., Arduini A., Lin H., Khurshid S., Et al., Deep learning enables genetic analysis of the human thoracic aorta, Nat Genet, 54, pp. 40-51, (2020); Eichner J.E., Dunn S.T., Perveen G., Thompson D.M., Stewart K.E., Stroehla B.C., Apolipoprotein E polymorphism and cardiovascular disease: a HuGE review, Am J Epidemiol, 155, pp. 487-495, (2002)","M. Cule; Calico Life Sciences LLC, South San Francisco, United States; email: cule@calicolabs.com; E. Melamud; Calico Life Sciences LLC, South San Francisco, United States; email: eugene@calicolabs.com","","Frontiers Media S.A.","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140468126"
"Petmezas G.; Cheimariotis G.-A.; Stefanopoulos L.; Rocha B.; Paiva R.P.; Katsaggelos A.K.; Maglaveras N.","Petmezas, Georgios (57218829579); Cheimariotis, Grigorios-Aris (55978320200); Stefanopoulos, Leandros (57195564646); Rocha, Bruno (56604765600); Paiva, Rui Pedro (7003358437); Katsaggelos, Aggelos K. (7102711302); Maglaveras, Nicos (7005468952)","57218829579; 55978320200; 57195564646; 56604765600; 7003358437; 7102711302; 7005468952","Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function","2022","Sensors","22","3","1232","","","","98","10.3390/s22031232","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123930567&doi=10.3390%2fs22031232&partnerID=40&md5=1f5aca384856608e67c7cd45a4223263","Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece; Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, 3030-290, Portugal; Department of Electrical and Computer Engineering, Northwestern University, Evanston, 60208, IL, United States","Petmezas G., Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece; Cheimariotis G.-A., Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece; Stefanopoulos L., Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece; Rocha B., Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, 3030-290, Portugal; Paiva R.P., Centre for Informatics and Systems of the University of Coimbra (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, 3030-290, Portugal; Katsaggelos A.K., Department of Electrical and Computer Engineering, Northwestern University, Evanston, 60208, IL, United States; Maglaveras N., Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece","Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Asthma; CNN; COPD; Crackles; Focal loss; LSTM; Lung sounds; STFT; Wheezes","Auscultation; Humans; Lung; Neural Networks, Computer; Quality of Life; Respiratory Sounds; Biological organs; Convolutional neural networks; Diagnosis; Patient monitoring; Pulmonary diseases; Statistical methods; Asthma; Convolutional neural network; COPD; Crackle; Focal loss; Lung sounds; Memory network; Short time Fourier transforms; Sound classification; Wheeze; abnormal respiratory sound; auscultation; diagnostic imaging; human; lung; quality of life; Long short-term memory","","","","","Ci?ncia e Tecnologia; Fundação para a Ciência e Tecnologia; Horizon 2020 Framework Programme, H2020, (825572); Horizon 2020 Framework Programme, H2020; Fundação para a Ciência e a Tecnologia, FCT, (2020.04927, SFRH/BD/135686/2018); Fundação para a Ciência e a Tecnologia, FCT","Funding text 1: This work was supported in part by the EU-WELMO project (project number 210510516) and by Funda??o para a Ci?ncia e Tecnologia (FCT) Ph.D. scholarships SFRH/BD/135686/2018 and 2020.04927.BD.; Funding text 2: Funding: This work was supported in part by the EU-WELMO project (project number 210510516) and by Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia (FCT) Ph.D. scholarships SFRH/BD/135686/2018 and 2020.04927.BD.","The Global Impact of Respiratory Disease—Second Edition, (2017); Monitoring Health for The Sdgs, Sustainable Development Goals, (2021); Rocha B.M., Pessoa D., Marques A., Carvalho P., Paiva R.P., Automatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?, Sensors, 21, (2020); Padilla-Ortiz A.L., Ibarra D., Padilla A., Lung and Heart Sounds Analysis: State-of-the-Art and Future Trends, Crit. 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Maglaveras; Laboratory of Computing, Medical Informatics and Biomedical—Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, GR 54124, Greece; email: nicmag@auth.gr","","MDPI","","","","","","14248220","","","35161977","English","Sensors","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85123930567"
"Delgado-Dolset M.I.; Obeso D.; Rodríguez-Coira J.; Tarin C.; Tan G.; Cumplido J.A.; Cabrera A.; Angulo S.; Barbas C.; Sokolowska M.; Barber D.; Carrillo T.; Villaseñor A.; Escribese M.M.","Delgado-Dolset, María Isabel (57209777212); Obeso, David (57192282186); Rodríguez-Coira, Juan (57203717299); Tarin, Carlos (24777020300); Tan, Ge (55991169200); Cumplido, José A. (25622337100); Cabrera, Ana (57368085300); Angulo, Santiago (14059458200); Barbas, Coral (7101761748); Sokolowska, Milena (24081481900); Barber, Domingo (54407867900); Carrillo, Teresa (7003526269); Villaseñor, Alma (36487099200); Escribese, María M. (9736253800)","57209777212; 57192282186; 57203717299; 24777020300; 55991169200; 25622337100; 57368085300; 14059458200; 7101761748; 24081481900; 54407867900; 7003526269; 36487099200; 9736253800","Understanding uncontrolled severe allergic asthma by integration of omic and clinical data","2022","Allergy: European Journal of Allergy and Clinical Immunology","77","6","","1772","1785","13","28","10.1111/all.15192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120849910&doi=10.1111%2fall.15192&partnerID=40&md5=105cc4289bb9b79ad05a2740c6db3fe7","Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Zurich, Switzerland; Hospital Universitario de Gran Canaria Doctor Negrin, Las Palmas de Gran Canaria, Spain; Department of Applied Mathematics and Statistics, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain","Delgado-Dolset M.I., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain, Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Obeso D., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain, Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Rodríguez-Coira J., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain, Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Zurich, Switzerland; Tarin C., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Tan G., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Zurich, Switzerland; Cumplido J.A., Hospital Universitario de Gran Canaria Doctor Negrin, Las Palmas de Gran Canaria, Spain; Cabrera A., Hospital Universitario de Gran Canaria Doctor Negrin, Las Palmas de Gran Canaria, Spain; Angulo S., Department of Applied Mathematics and Statistics, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain; Barbas C., Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Sokolowska M., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Zurich, Switzerland; Barber D., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Carrillo T., Hospital Universitario de Gran Canaria Doctor Negrin, Las Palmas de Gran Canaria, Spain; Villaseñor A., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; Escribese M.M., Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain","Background: Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. Methods: Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms. Results: Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. Conclusions: UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype. © 2021 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","allergy; asthma; machine learning; metabolomics; proteomics","Animals; Antigens, Dermatophagoides; Asthma; Humans; Hypersensitivity; Pyroglyphidae; Quality of Life; amino acid; antihistaminic agent; beta adrenergic receptor stimulating agent; bile acid; biological product; bronchodilating agent; CD4 antigen; cholinergic receptor blocking agent; corticosteroid; fatty acid; gamma interferon; immunoglobulin E; interleukin 15; leukotriene receptor blocking agent; lysophospholipid; macrophage inflammatory protein 3beta; monocyte chemotactic protein 4; omalizumab; phospholipid; sphingolipid; theophylline; tumor necrosis factor receptor superfamily member 12A; vitamin; house dust allergen; adult; allergic asthma; Article; clinical feature; comparative study; controlled study; corticosteroid therapy; disease control; disease severity; female; human; immunoglobulin blood level; immunotherapy; inflammation; lymphocyte activation; machine learning; major clinical study; male; medical history; metabolic fingerprinting; metabolomics; pathway enrichment analysis; phenotype; prescription; proteomics; receptor down regulation; receptor upregulation; T lymphocyte activation; treatment response; animal; asthma; hypersensitivity; Pyroglyphidae; quality of life","","amino acid, 65072-01-7; gamma interferon, 82115-62-6; immunoglobulin E, 37341-29-0; macrophage inflammatory protein 3beta, 181030-14-8; monocyte chemotactic protein 4, 173146-42-4, 177346-98-4; omalizumab, 242138-07-4; theophylline, 58-55-9, 5967-84-0, 8055-07-0, 8061-56-1, 99007-19-9; Antigens, Dermatophagoides, ","","","CEU Universities; Centre of Metabolomics and Bioanalysis; FPI-CEU; FPI‐CEU; Las Palmas de Gran Canaria, Spain; SIAF; SNFS, (310030_189334/1); Swiss Institute of Allergy and Asthma Research; Ministerio de Ciencia, Innovación y Universidades, MCIU, (PCI2018‐092930); Ministerio de Ciencia, Innovación y Universidades, MCIU; Allergy Therapeutics; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Federación Española de Enfermedades Raras, FEDER; Instituto de Salud Carlos III, ISCIII, (PI18/01467, PI19/00044); Instituto de Salud Carlos III, ISCIII; European Regional Development Fund, ERDF, (RD16/0006/0015, RTI2018‐095166‐B‐I00); European Regional Development Fund, ERDF; Universidad San Pablo - CEU, USPCEU","Funding text 1: We would like to thank all institutions involved: Institute of Applied Molecular Medicine (IMMA, Universidad CEU San Pablo, CEU Universities, Madrid), Centre of Metabolomics and Bioanalysis (CEMBIO, Universidad CEU San Pablo, CEU Universities, Madrid), and Swiss Institute of Allergy and Asthma Research (SIAF); as well as the Hospital Universitario de Gran Canaria Dr Negrín (Las Palmas de Gran Canaria, Spain). This work was supported by ISCIII (PI18/01467 and PI19/00044), cofounded by FEDER ‘Investing in your future’ for the thematic network and co‐operative research centres ARADyAL RD16/0006/0015; as well as by the grant from Ministerio de Ciencia, Innovación y Universidades co‐financed with FEDER RTI2018‐095166‐B‐I00. This work was also supported by the Ministry of Science, Innovation and Universities in Spain (PCI2018‐092930) co‐funded by the European program ERA HDHL ‐ Nutrition & the Epigenome, project Dietary Intervention in Food Allergy: Microbiome, Epigenetic and Metabolomic interactions DIFAMEM. M.I.D.D. and J.R‐C. are supported by FPI‐CEU predoctoral fellowships, D.O. is funded by JPI‐DIFAMEM PCI2018‐092930 and A.V. is funded by a postdoctoral research fellowship from ARADyAL. MS is funded by the Swiss National Science Foundation (SNFS) grant 310030_189334/1. ; Funding text 2: We would like to thank all institutions involved: Institute of Applied Molecular Medicine (IMMA, Universidad CEU San Pablo, CEU Universities, Madrid), Centre of Metabolomics and Bioanalysis (CEMBIO, Universidad CEU San Pablo, CEU Universities, Madrid), and Swiss Institute of Allergy and Asthma Research (SIAF); as well as the Hospital Universitario de Gran Canaria Dr Negrín (Las Palmas de Gran Canaria, Spain). This work was supported by ISCIII (PI18/01467 and PI19/00044), cofounded by FEDER ‘Investing in your future’ for the thematic network and co-operative research centres ARADyAL RD16/0006/0015; as well as by the grant from Ministerio de Ciencia, Innovación y Universidades co-financed with FEDER RTI2018-095166-B-I00. This work was also supported by the Ministry of Science, Innovation and Universities in Spain (PCI2018-092930) co-funded by the European program ERA HDHL - Nutrition & the Epigenome, project Dietary Intervention in Food Allergy: Microbiome, Epigenetic and Metabolomic interactions DIFAMEM. M.I.D.D. and J.R-C. are supported by FPI-CEU predoctoral fellowships, D.O. is funded by JPI-DIFAMEM PCI2018-092930 and A.V. is funded by a postdoctoral research fellowship from ARADyAL. MS is funded by the Swiss National Science Foundation (SNFS) grant 310030_189334/1.; Funding text 3: Delgado‐Dolset has nothing to disclose. Dr. Obeso has nothing to disclose. Rodriguez‐Coira has nothing to disclose. Dr. Tarín has nothing to disclose. Dr. Tan has nothing to disclose. Dr. Cumplido Bonny has nothing to disclose. Dr. Cabrera Santana has nothing to disclose. Dr. Angulo Díaz‐Parreño has nothing to disclose. Dr. Barbas reports grants from Ministerio de Ciencia, Innovación y Universidades co‐financed with FEDER RTI2018‐095166‐B‐I00, during the conduct of the study. Dr. Sokolowska reports grants from Swiss National Science Foundation (SNSF), grants from GSK, outside the submitted work. Dr. Barber reports grants from ALK, Allero Therapeutics, personal fees from ALK, AIMMUNE, outside the submitted work and the grant from the Ministry of Science, Innovation and Universities in Spain (PCI2018‐092930) co‐funded by the European program ERA HDHL ‐ Nutrition & the Epigenome, project Dietary Intervention in Food Allergy: Microbiome, Epigenetic and Metabolomic interactions DIFAMEM. Dr. Carrillo has nothing to disclose. Dr. Villaseñor has nothing to disclose. Dr. Escribese has nothing to disclose. 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Mendelson K., Evans T., Hla T., Sphingosine 1-phosphate signalling, Development, 141, pp. 5-9, (2014); Kertys M., Grendar M., Kosutova P., Mokra D., Mokry J., Plasma based targeted metabolomic analysis reveals alterations of phosphatidylcholines and oxidative stress markers in guinea pig model of allergic asthma, Biochim Biophys Acta - Mol Basis Dis, 1866, (2020); Comhair S.A.A., McDunn J., Bennett C., Fettig J., Erzurum S.C., Kalhan S.C., Metabolomic endotype of asthma, J Immunol, 195, pp. 643-650, (2015); Jung J., Kim S.-H., Lee H.-S., Et al., Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis, Clin Exp Allergy, 43, pp. 425-433, (2013); Farraia M., Cavaleiro Rufo J., Paciencia I., Et al., Metabolic interactions in asthma, Eur Ann Allergy Clin Immunol, 51, (2019); Ananieva E.A., Powell J.D., Hutson S.M., Leucine metabolism in T cell activation: mTOR signaling and beyond, Adv Nutr Int Rev J, 7, pp. 798S-805S, (2016); Garcia-Zepeda E.A., Combadiere C., Rothenberg M.E., Et al., Human monocyte chemoattractant protein (MCP)-4 is a novel CC chemokine with activities on monocytes, eosinophils, and basophils induced in allergic and nonallergic inflammation that signals through the CC chemokine receptors (CCR)-2 and -3, J Immunol, 157, pp. 5613-5626, (1996); Li C.W., Zhang K.K., Li T.Y., Et al., Expression profiles of regulatory and helper T-cell-associated genes in nasal polyposis, Allergy, 67, pp. 732-740, (2012); Morris S.R., Chen B., Mudd J.C., Et al., ‘Inflammescent’ CX3CR1+CD57+ CD8 T cells are generated and expanded by IL-15, JCI Insight, 5, (2020); Grabstein K., Eisenman J., Shanebeck K., Et al., Cloning of a T cell growth factor that interacts with the beta chain of the interleukin-2 receptor, Science, 264, pp. 965-968, (1994); He H., Suryawanshi H., Morozov P., Et al., Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis, J Allergy Clin Immunol, 145, pp. 1615-1628, (2020); Kaiser A., Donnadieu E., Abastado J.-P., Trautmann A., Nardin A., CC chemokine ligand 19 secreted by mature dendritic cells increases naive T cell scanning behavior and their response to rare cognate antigen, J Immunol, 175, pp. 2349-2356, (2005); Takamura K., Fukuyama S., Nagatake T., Et al., Regulatory role of lymphoid chemokine CCL19 and CCL21 in the control of allergic rhinitis, J Immunol, 179, pp. 5897-5906, (2007); Mendez-Barbero N., Yuste-Montalvo A., Nunez-Borque E., Et al., The TNF-like weak inducer of the apoptosis/fibroblast growth factor–inducible molecule 14 axis mediates histamine and platelet-activating factor–induced subcutaneous vascular leakage and anaphylactic shock, J Allergy Clin Immunol, 145, pp. 583-596.e6, (2020); Suwanpradid J., Shih M., Pontius L., Et al., Arginase1 deficiency in monocytes/macrophages upregulates inducible nitric oxide synthase to promote cutaneous contact hypersensitivity, J Immunol, 199, pp. 1827-1834, (2017); Oberst A., Dillon C.P., Weinlich R., Et al., Catalytic activity of the caspase-8-FLIP L complex inhibits RIPK3-dependent necrosis, Nature, 471, pp. 363-368, (2011); Bell B.D., Leverrier S., Weist B.M., Et al., FADD and caspase-8 control the outcome of autophagic signaling in proliferating T cells, Proc Natl Acad Sci USA, 105, pp. 16677-16682, (2008); Venables W.N., Ripley B.D., Modern Applied Statistics with S-Plus, (2013)","M.M. Escribese; Institute of Applied Molecular Medicine (IMMA), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Urbanización Montepríncipe, Madrid, Spain; email: mariamarta.escribesealonso@ceu.es","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","34839541","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85120849910"
"Moslemi A.; Makimoto K.; Tan W.C.; Bourbeau J.; Hogg J.C.; Coxson H.O.; Kirby M.","Moslemi, Amir (57737896400); Makimoto, Kalysta (57738337000); Tan, Wan C. (13403886200); Bourbeau, Jean (34567907500); Hogg, James C. (7201452328); Coxson, Harvey O. (6603681233); Kirby, Miranda (35174507500)","57737896400; 57738337000; 13403886200; 34567907500; 7201452328; 6603681233; 35174507500","Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD","2023","Academic Radiology","30","4","","707","716","9","14","10.1016/j.acra.2022.05.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131825492&doi=10.1016%2fj.acra.2022.05.009&partnerID=40&md5=fd0f45dd87a4119053b1af81f5acea5e","Department of Physics, Toronto Metropolitan University, Toronto, M5B 2K3, ON, Canada; Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada","Moslemi A., Department of Physics, Toronto Metropolitan University, Toronto, M5B 2K3, ON, Canada; Makimoto K., Department of Physics, Toronto Metropolitan University, Toronto, M5B 2K3, ON, Canada; Tan W.C., Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Bourbeau J., Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada, Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada; Hogg J.C., Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Coxson H.O., Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Kirby M., Department of Physics, Toronto Metropolitan University, Toronto, M5B 2K3, ON, Canada, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada","Rationale: Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an important goal for improving patient management. Objective: Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. Materials and Methods: In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov, NCT00920348). Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measurements were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utilization. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). Results: Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% ± 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% ± 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% ± 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. Conclusion: The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD. © 2022 The Association of University Radiologists","Computed tomography; COPD; Hospitalization; Machine learning; Quantitative imaging","Canada; Emergency Service, Hospital; Hospitalization; Humans; Lung; Machine Learning; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Tomography, X-Ray Computed; aged; area under the curve; Article; chronic obstructive lung disease; cohort analysis; comparative study; computer assisted tomography; controlled study; demography; diagnostic accuracy; diffusing capacity for carbon monoxide; disease severity; emergency ward; feature selection; feature selection algorithm; female; follow up; forced expiratory volume; functional residual capacity; health care utilization; hospitalization; human; hypertension; lung function; lung function test; machine learning; major clinical study; male; quantitative analysis; random digit dialing; receiver operating characteristic; retrospective study; support vector machine; total lung capacity; Canada; chronic obstructive lung disease; diagnostic imaging; hospital emergency service; hospitalization; lung; machine learning; procedures; x-ray computed tomography","","","","","Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada, NSERC","M. Kirby acknowledges support from the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, the Early Researchers Award Program, and the Canada Research Chair Program (Tier II).","Yeatts K.B., Lippmann S.J., Waller A.E., Et al., Population-based burden of COPD-related visits in the ED: Return ED visits, hospital admissions, and comorbidity risks, Chest, 144, 3, pp. 784-793, (2013); Menzin J., Boulanger L., Marton J., Et al., The economic burden of chronic obstructive pulmonary disease (COPD) in a U.S. Medicare population, Respir Med, 102, 9, pp. 1248-1256, (2008); Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med, 3, 11, pp. 2011-2030, (2006); Han M.L.K., Kazerooni E.A., Lynch D.A., Et al., Chronic obstructive pulmonary disease exacerbations in the COPDGene study: Associated radiologic phenotypes, Radiology, 261, 1, pp. 274-282, (2011); Johannessen A., Skorge T.D., Grydeland T.B., Et al., Mortality by level of emphysema and airway wall thickness, ATS Journals, 187, 6, pp. 602-608, (2013); Goto T., Camargo Jr C., Faridi M., Et al., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, Am J Emerg Med, 36, 9, pp. 1650-1654, (2008); Bourbeau J., Tan W.C., Benedetti A., Et al., Canadian Cohort Obstructive Lung Disease (CanCOLD): Fulfilling the need for longitudinal observational studies in COPD, COPD, 11, 2, pp. 125-132, (2014); Rabe K., Hurd S., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 176, 6, pp. 532-555, (2007); Baker C., Zou K., Su J., Risk assessment of readmissions following an initial COPD-related hospitalization, Int J Chron Obstruct Pulmon Dis, 8, pp. 551-559, (2012); Hurst J.R., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Yii A., Loh C., Tiew P., Et al., A clinical prediction model for hospitalized COPD exacerbations based on “treatable traits, Int J Chron Obstruct Pulmon Dis, 14, pp. 719-728, (2019); Miller M.R., Hankinson J., Brusasco V., Et al., Standardisation of spirometry, Eur Respir J, 26, 2, pp. 319-338, (2005); Wanger J., Clausen J.L., Coates A., Et al., Standardisation of the measurement of lung volumes, Eur Respir J, 26, 3, pp. 511-522, (2005); Macintyre N., Crapo R.O., Viegi G., Et al., Standardisation of the single-breath determination of carbon monoxide uptake in the lung, Eur Respir J, 26, 4, pp. 720-735, (2005); Morris M.A., Jacobson S.R., Kinney G.L., Et al., Original research marijuana use associations with pulmonary symptoms and function in tobacco smokers enrolled in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), Chronic Obstr Pulm Dis, 5, 1, pp. 46-56, (2018); Gevenois P.A., Zanen J., de Maertelaer V., Et al., Macroscopic assessment of pulmonary emphysema by image analysis, J Clin Pathol, 48, 4, pp. 318-322, (1995); Muller N.L., Staples C.A., Miller R.R., Et al., Density mask”. An objective method to quantitate emphysema using computed tomography, Chest, 94, 4, pp. 782-787, (1988); Dirksen A., Dijkman J.H., Madsen F., Et al., A randomized clinical trial of alpha(1)-antitrypsin augmentation therapy, Am J Respir Crit Care Med, 160, 5, pp. 1468-1472, (1999); Mishima M., Hirai T., Itoh H., Et al., Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease, Proc Natl Acad Sci U S A, 96, 16, pp. 8829-8834, (1999); Jain N., Covar R.A., Gleason M.C., Et al., Quantitative computed tomography detects peripheral airway disease in asthmatic children, Pediatr Pulmonol, 40, 3, pp. 211-218, (2005); Kirby M., Yin Y., Tschirren J., Et al., A novel method of estimating small airway disease using inspiratory-to-expiratory computed tomography, Respiration, 94, 4, pp. 336-345, (2017); Shikata H., Hoddman E., Sonka M., Automated segmentation of pulmonary vascular tree from 3D CT images, Medical Imaging, 5369, pp. 107-116, (2004); Kirby M., Tanabe N., Tan W.C., Et al., Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. Findings from a population-based study, Am J Respir Crit Care Med, 197, 1, pp. 56-65, (2018); Grydeland T.B., Dirksen A., Coxson H.O., Et al., Quantitative computed tomography measures of emphysema and airway wall thickness are related to respiratory symptoms, Am J Respir Crit Care Med, 181, 4, pp. 353-359, (2010); Smith B.M., Hoffman E.A., Rabinowitz D., Et al., Comparison of spatially matched airways reveals thinner airway walls in COPD. The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study and the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS), Thorax, 69, 11, pp. 987-996, (2014); Ebrahimpour M., Zare M., Eftekhari M., Et al., Occam's razor in dimension reduction: Using reduced row Echelon form for finding linear independent features in high dimensional microarray datasets, Eng Appl Artif Intell, 62, pp. 214-221, (2017); Braga P.L., Meira S., Petr P., Et al., A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation, Proceedings of the 2008 ACM symposium on Applied computing, pp. 1788-1792, (2008); Lanclus M., Clukers J., van H.C., Et al., Machine learning algorithms utilizing functional respiratory imaging may predict COPD exacerbations, Acad Radiol, 26, 9, pp. 1191-1199, (2019); Martinez-Garcia M.-A., de La D., Carrillo R., Et al., Prognostic value of bronchiectasis in patients with moderate-to-severe chronic obstructive pulmonary disease, ATS Journals, 187, 8, pp. 823-831, (2013); Zemanick E.T., Taylor-Cousar J.L., Davies J., Et al., A phase 3 open-label study of elexacaftor/tezacaftor/ivacaftor in children 6 through 11 years of age with cystic fibrosis and at least one F508del allele, Am J Respir Crit Care Med, 203, 12, pp. 1522-1532, (2021); Mummy D.G., Kruger S.J., Zha W., Et al., Ventilation defect percent in Helium-3 MRI as a biomarker of severe outcomes in asthma, J Allergy Clin Immunol, 141, 3, pp. 1140-1141, (2018); Hurst J.R., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Kirby M., Pike D., Coxson H.O., Et al., Hyperpolarized 3He ventilation defects used to predict pulmonary exacerbations in mild to moderate chronic obstructive pulmonary disease, Radiological, 273, 3, pp. 887-896, (2014); Wei X., Ma Z., Yu N., Et al., Risk factors predict frequent hospitalization in patients with acute exacerbation of COPD, Int J Chron Obstruct Pulmon Dis, 13, pp. 121-129, (2018); Hurst J.R., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Kirby M., Tanabe N., Vasilesc D.M., Et al., Computed tomography total airway count is associated with the number of micro-computed tomography terminal bronchioles, Am J Respir Crit Care Med, 201, 5, pp. 613-615, (2020); Han M., Quibrera P., Carretta E., Et al., Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort, Lancet Respir Med, 5, 8, pp. 619-626, (2017); Schroeder J.D., Mckenzie A.S., Zach J.A., Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease, AJR Am J Roentgenol, 201, 3, pp. 460-470, (2013); Nakano Y., Muro S., Sakai H., Et al., Computed tomographic measurements of airway dimensions and emphysema in smokers correlation with lung function, Am J Respir Crit Care Med, 162, pp. 1102-1108, (2000); Aysola R., Hoffman E., Gierada D., Et al., Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology, Chest, 134, 6, pp. 1183-1191, (2008); Siddiqui S., Gupta S., Cruse G., Et al., Airway wall geometry in asthma and nonasthmatic eosinophilic bronchitis, Allergy, 64, 6, pp. 951-958, (2009); Gupta S., Siddiqui S., Haldar P., Et al., Quantitative analysis of high-resolution computed tomography scans in severe asthma subphenotypes, Thorax, 65, 9, pp. 775-781, (2010)","M. Kirby; Department of Physics, Toronto Metropolitan University, Toronto, M5B 2K3, Canada; email: Miranda.Kirby@ryerson.ca","","Elsevier Inc.","","","","","","10766332","","ARADF","35690537","English","Acad. Radiol.","Article","Final","","Scopus","2-s2.0-85131825492"
"Yang Y.; Cao Y.; Han X.; Ma X.; Li R.; Wang R.; Xiao L.; Xie L.","Yang, Yuwei (58097971100); Cao, Yan (56997922500); Han, Xiaobo (57222039287); Ma, Xihui (49561544700); Li, Rui (57955438300); Wang, Rentao (56712133200); Xiao, Li (54413532800); Xie, Lixin (36653380900)","58097971100; 56997922500; 57222039287; 49561544700; 57955438300; 56712133200; 54413532800; 36653380900","Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis","2023","Computers in Biology and Medicine","154","","106621","","","","17","10.1016/j.compbiomed.2023.106621","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147797532&doi=10.1016%2fj.compbiomed.2023.106621&partnerID=40&md5=7e1190b1275362489400823e6bce2906","College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China; Hebei North Universit, Zhangjiakou, 075000, China","Yang Y., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Cao Y., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Han X., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Ma X., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Li R., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Hebei North Universit, Zhangjiakou, 075000, China; Wang R., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Xiao L., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China; Xie L., College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China, Beijing Key Laboratory of OTIR, Beijing, 100091, China","Chronic obstructive pulmonary disease is a kind of chronic lung disease characterized by persistent air flow obstruction, which was the third leading cause of death in China. The incidence of COPD is steadily and increasing and has been a globally sever disease. Accordingly, it is urgently needed to explore how to diagnose and treat COPD timely. This study aims to find key genes to diagnose COPD as soon as possible to avoid COPD processing and analyze immune cell infiltration between COPD early stage and late stage. Two GEO datasets were merged as the merge data for analyses. 157 DEGs were used for GSEA analysis to find the pathway between COPD early stage and late stage. Above all, gene EXPH5 stood out from the screen as the most likely candidate diagnosis biomarker of COPD indicating the late-stage by least LASSO and SVM-RFE. ROC curves of EXPH5 were applied to represent the discriminatory ability through the area under the curve which is the gold standard to evaluate the accuracy of diagnosis and survival rate. The CIBERSORT algorithm was used to assess the distribution of tissue-infiltrating immune cells between two COPD stages. The diagnosis biomarker, gene EXPH5 had a positive correlation with NK cells resting; mast cell resting, eosinophils, and negative correlation with T cell gamma delta, macrophages M1, which underscore the role of gene and immune cell infiltration. To make results more reliable, we further analyzed the gene EXPH5 expression in single-cell transcriptome data and showed again that EXPH5 genes significantly downregulated in the late stage of COPD especially in the main lung cell types AT1 and AT2. In a word, our study identified genes EXPH5 as a marker gene, which adds to the knowledge for clinical diagnosis and pharmaceutical design of COPD. © 2023 The Authors","Bioinformatics analysis; COPD; Differential expressed genes; EXPH5; Machine learning","Adaptor Proteins, Signal Transducing; Algorithms; Biomarkers; Drug Design; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; Biological organs; Biomarkers; Cytology; Genes; Pulmonary diseases; Support vector machines; T-cells; transcriptome; biological marker; EXPH5 protein, human; signal transducing adaptor protein; Bioinformatics analysis; Cell infiltration; COPD; Differential expressed gene; EXPH5; Expressed genes; Immune cells; Late stage; Machine-learning; On-machines; adult; Article; cell infiltration; cell population; chronic obstructive lung disease; clinical article; cohort analysis; controlled study; dendritic cell; diagnostic accuracy; diagnostic value; differential expression analysis; differential gene expression; down regulation; eosinophil; exph5 gene; extracellular matrix; female; functional enrichment analysis; gamma delta T lymphocyte; gene; gene expression level; gene identification; gene ontology; gene set enrichment analysis; genetic transcription; gold standard; human; immunocompetent cell; least absolute shrinkage and selection operator; lung alveolus cell; M1 macrophage; M2 macrophage; machine learning; male; marker gene; mast cell; middle aged; natural killer cell; neutrophil; protein expression; protein expression level; recursive feature elimination; single cell analysis; support vector machine; survival rate; tubulogenesis; algorithm; drug design; genetics; Diagnosis","","Adaptor Proteins, Signal Transducing, ; Biomarkers, ; EXPH5 protein, human, ","","","Capital's Fund for Health Improvement and Research, (CFH2022-2-5092); National Natural Science Foundation of China, NSFC, (81701974)","This work was supported by China Key Scientific Grant [ 2021YFC0122500 ], the National Natural Science Foundation of China [grant number 81701974 ]; Capital's Fund for Health Improvement and Research [grant number CFH2022-2-5092 ]. 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Invest., 130, 7, pp. 3919-3935, (2020); Bare Y., Chan G.K., Hayday T., Et al., Slac 2-b coordinates extracellular vesicle secretion to regulate keratinocyte adhesion and migration, J. Invest. Dermatol., 141, 3, pp. 523-532 e522, (2021); Hamill K.J., Hopkinson S.B., Skalli O., Et al., Actinin-4 in keratinocytes regulates motility via an effect on lamellipodia stability and matrix adhesions, Faseb. J., 27, 2, pp. 546-556, (2013); Kirkham P.A., Caramori G., Casolari P., Et al., Oxidative stress-induced antibodies to carbonyl-modified protein correlate with severity of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 184, 7, pp. 796-802, (2011); Huang M.N., Nicholson L.T., Batich K.A., Et al., Antigen-loaded monocyte administration induces potent therapeutic antitumor T cell responses, J. Clin. Invest., 130, 2, pp. 774-788, (2020)","L. Xie; College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; email: xielx301@126.com; L. Xiao; College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; email: xiaolilab309@163.com; R. Wang; College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; email: rtwang@126.com","","Elsevier Ltd","","","","","","00104825","","CBMDA","36746116","English","Comput. Biol. Med.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147797532"
"Davies H.J.; Bachtiger P.; Williams I.; Molyneaux P.L.; Peters N.S.; Mandic D.P.","Davies, Harry J. (57213609195); Bachtiger, Patrik (57200376259); Williams, Ian (57206923926); Molyneaux, Philip L. (43661539300); Peters, Nicholas S. (7202298487); Mandic, Danilo P. (7006513328)","57213609195; 57200376259; 57206923926; 43661539300; 7202298487; 7006513328","Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD","2022","IEEE Transactions on Biomedical Engineering","69","7","","2390","2400","10","33","10.1109/TBME.2022.3145688","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123705701&doi=10.1109%2fTBME.2022.3145688&partnerID=40&md5=3b6dd47a60c35ac57475b0ddc122e204","Imperial College London, Department of Electrical Engineering, London, SW7 2AZ, United Kingdom; Imperial College London, Department of Electrical Engineering, United Kingdom; Imperial College London, Faculty of Medicine, United Kingdom","Davies H.J., Imperial College London, Department of Electrical Engineering, London, SW7 2AZ, United Kingdom; Bachtiger P., Imperial College London, Faculty of Medicine, United Kingdom; Williams I., Imperial College London, Department of Electrical Engineering, United Kingdom; Molyneaux P.L., Imperial College London, Faculty of Medicine, United Kingdom; Peters N.S., Imperial College London, Faculty of Medicine, United Kingdom; Mandic D.P., Imperial College London, Department of Electrical Engineering, United Kingdom","An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.  © 1964-2012 IEEE.","biomedical signal processing; empirical mode decomposition; machine learning; photoplethysmography; Wearable sensors","Heart Rate; Humans; Photoplethysmography; Pulmonary Disease, Chronic Obstructive; Respiratory Rate; Signal Processing, Computer-Assisted; Wearable Electronic Devices; Photoplethysmography; Pulmonary diseases; Wearable technology; Blood; Ear; Ear canal; Healthy subjects; Idiopathic pulmonary fibrosis; Intensity pulse; Lung; Pulse amplitude; Respiratory variation; Waveforms; adult; aged; amplitude modulation; Article; artificial ventilation; auditory canal; breathing; breathing rate; chronic obstructive lung disease; classification; clinical article; comparative study; controlled study; diagnostic accuracy; disease classification; empirical mode decomposition; female; fibrosing alveolitis; human; inspiratory capacity; male; multivariate analysis; photoelectric plethysmography; sensitivity and specificity; spirometry; waveform; electronic device; heart rate; photoelectric plethysmography; procedures; signal processing; Data mining","","","Arduino Uno, Arduino, United States; MAX30101; SFM3200","Arduino, United States","","","Looney D., Hemakom A., Mandic D.P., Intrinsic multi-scale analysis: A multi-variate empirical mode decomposition framework, Proc. 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Syst., pp. 7-8, (2007); Looney D., Park C., Xia Y., Kidmose P., Ungstrup M., Mandic D.P., Towards estimating selective auditory attention from EEG using a novel time-frequency-synchronisation framework, Proc. Int. Joint Conf.Neural Netw., pp. 1-5, (2010); Madhav K.V., Ram M.R., Krishna E.H., Komalla N.R., Reddy K.A., Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition, Proc. IEEE Int. Instrum. Meas. Technol. Conf., pp. 1-4, (2011); Ur Rehman N., Park C., Huang N.E., Mandic D.P., EMDviaMEMD: Multivariate noise-aided computation of standard EMD, Adv. Adaptive Data Anal., 5, 2, (2013); Rehman N., Mandic D.P., Multivariate empirical mode decomposition, Proc. Roy. Soc. A: Math., Phys. Eng. Sci., 466, 2117, pp. 1291-1302, (2010); Pedregosa F., Et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res., 12, pp. 2825-2830, (2011)","H.J. Davies; Imperial College London, Department of Electrical Engineering, London, SW7 2AZ, United Kingdom; email: harry.davies14@imperial.ac.uk","","IEEE Computer Society","","","","","","00189294","","IEBEA","35077352","English","IEEE Trans. Biomed. Eng.","Article","Final","","Scopus","2-s2.0-85123705701"
"Collin C.B.; Gebhardt T.; Golebiewski M.; Karaderi T.; Hillemanns M.; Khan F.M.; Salehzadeh-Yazdi A.; Kirschner M.; Krobitsch S.; Kuepfer L.","Collin, Catherine Bjerre (57219895376); Gebhardt, Tom (57200688908); Golebiewski, Martin (16229843100); Karaderi, Tugce (26424286300); Hillemanns, Maximilian (57266399600); Khan, Faiz Muhammad (55760421500); Salehzadeh-Yazdi, Ali (53864219400); Kirschner, Marc (23047051600); Krobitsch, Sylvia (8836934300); Kuepfer, Lars (56095324700)","57219895376; 57200688908; 16229843100; 26424286300; 57266399600; 55760421500; 53864219400; 23047051600; 8836934300; 56095324700","Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation","2022","Journal of Personalized Medicine","12","2","166","","","","53","10.3390/jpm12020166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124959672&doi=10.3390%2fjpm12020166&partnerID=40&md5=2c239a7904874c3a05c59a8a3e3d2a05","Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, N Copenhagen, 2200, Denmark; Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18057, Germany; Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, 69118, Germany; Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, N Copenhagen, 2200, Denmark; Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, 37077, Germany; Forschungszentrum Jülich GmbH, Project Management Jülich, Jülich, 52425, Germany; Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, 52074, Germany","Collin C.B., Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, N Copenhagen, 2200, Denmark; Gebhardt T., Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18057, Germany; Golebiewski M., Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, 69118, Germany; Karaderi T., Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, N Copenhagen, 2200, Denmark, Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, N Copenhagen, 2200, Denmark; Hillemanns M., Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18057, Germany; Khan F.M., Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18057, Germany; Salehzadeh-Yazdi A., Max-Planck-Institute for Multidisciplinary Sciences, Göttingen, 37077, Germany; Kirschner M., Forschungszentrum Jülich GmbH, Project Management Jülich, Jülich, 52425, Germany; Krobitsch S., Forschungszentrum Jülich GmbH, Project Management Jülich, Jülich, 52425, Germany; Kuepfer L., Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, 52074, Germany","The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas. © 2022 by the authors.","Clinical translation; Computational models; Data integration; Ethical and legal requirements; Guidelines and recommendations; Model validation; Personalized medicine","C reactive protein; prostaglandin D2; succinic acid; Alzheimer disease; Article; artificial intelligence; asthma; atherosclerosis; atrial fibrillation; autoimmune disease; biology; biometry; blood pressure; boolean model; breast cancer; clinical practice; computer assisted tomography; computer model; consort artificial intelligence; constraint based model; convolutional neural network; coronary artery disease; coronavirus disease 2019; data integration; decision making; deep learning; deep neural network; degenerative disease; depression; diabetic retinopathy; flow cytometry; gain of function mutation; genetic risk score; genome-wide association study; glomerulus filtration rate; human; inflammation; inflammatory bowel disease; intensive care unit; k nearest neighbor; loss of function mutation; machine learning; mass spectrometry; mechanistic model; molecular interaction; molecular interaction maps; neoplasm; non insulin dependent diabetes mellitus; nonalcoholic fatty liver; obesity; personalized medicine; pharmacogenomics; pharmacokinetic model; phenotype; Pi3K/Akt signaling; pneumonia; practice guideline; quantitative model; random forest; rheumatoid arthritis; schizophrenia; software; spirit artificial intelligence; support vector machine; systematic review; thorax radiography; validation process","","C reactive protein, 9007-41-4; prostaglandin D2, 41598-07-6; succinic acid, 110-15-6","","","European Commission, EC; Horizon 2020 Framework Programme, H2020, (825843); Bundesministerium für Bildung und Forschung, BMBF, (FKZ 01ZX1903B); Novo Nordisk Foundation Data Science Investigator, (NNF20OC0062294)","Funding text 1: The authors of this article are part of the EU-STANDS4PM consortium (www.eu-stands4pm.eu) that is funded by the European Union Horizon 2020 framework programme of the European Commission under Grant Agreement # 825843. Faiz Muhammad Khan received additional funding from The German Federal Ministry of Education and Research (BMBF) and the SASKit project (FKZ 01ZX1903B). Tugce Karaderi is supported by the Novo Nordisk Foundation Data Science Investigator grant (NNF20OC0062294).; Funding text 2: Funding: The authors of this article are part of the EU-STANDS4PM consortium (www.eu-stands4 pm.eu) that is funded by the European Union Horizon 2020 framework programme of the European Commission under Grant Agreement # 825843. Faiz Muhammad Khan received additional funding from The German Federal Ministry of Education and Research (BMBF) and the SASKit project (FKZ; Funding text 3: 01ZX1903B). Tugce Karaderi is supported by the Novo Nordisk Foundation Data Science Investigator grant (NNF20OC0062294).","Wolkenhauer O., Auffray C., Brass O., Clairambault J., Deutsch A., Drasdo D., Gervasio F., Preziosi L., Maini P., Marciniak-Czochra A., Et al., Enabling multiscale modeling in systems medicine, Genome Med, 6, (2014); Apweiler R., Beissbarth T., Berthold M.R., Bluthgen N., Burmeister Y., Dammann O., Deutsch A., Feuerhake F., Franke A., Hasenauer J., Et al., Whither systems medicine?, Exp. Mol. Med, 50, (2018); Pison C., The CASyM roadmap-Implementation of Systems Medicine across Europe, (2014); Morrison T.M., Pathmanathan P., Adwan M., Margerrison E., Advancing Regulatory Science with Computational Modeling for Medical Devices at the FDA’s Office of Science and Engineering Laboratories, Front. 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Kuepfer; Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, 52074, Germany; email: lkuepfer@ukaachen.de","","MDPI","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124959672"
"Oliveros G.; Wallace C.H.; Chaudry O.; Liu Q.; Qiu Y.; Xie L.; Rockwell P.; Figueiredo-Pereira M.E.; Serrano P.A.","Oliveros, Giovanni (57223882639); Wallace, Charles H. (57223897805); Chaudry, Osama (57223898343); Liu, Qiao (57193611768); Qiu, Yue (57209653936); Xie, Lei (57198836096); Rockwell, Patricia (7004474204); Figueiredo-Pereira, Maria E. (6701441169); Serrano, Peter A. (7006156406)","57223882639; 57223897805; 57223898343; 57193611768; 57209653936; 57198836096; 7004474204; 6701441169; 7006156406","Repurposing ibudilast to mitigate Alzheimer's disease by targeting inflammation","2023","Brain","146","3","","","","","30","10.1093/brain/awac136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149176616&doi=10.1093%2fbrain%2fawac136&partnerID=40&md5=6953b3e408162b11c8598017e7996bb4","Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States; Department of Biological Sciences, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Department of Computer Science, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Program in Biology, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States; Program in Computer Science and Biochemistry, Graduate Center, 365 5th Ave, New York, 10016, NY, United States; Helen and Robert Appel Alzheimer's disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, 411 E 69th St, New York, 10021, NY, United States; Department of Psychology, Hunter College, 695 Park Ave, New York, 10065, NY, United States","Oliveros G., Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States; Wallace C.H., Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States; Chaudry O., Department of Biological Sciences, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Liu Q., Department of Computer Science, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Qiu Y., Program in Biology, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States; Xie L., Department of Computer Science, Hunter College, 695 Park Ave, New York, 10065, NY, United States, Program in Biology, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States, Program in Computer Science and Biochemistry, Graduate Center, 365 5th Ave, New York, 10016, NY, United States, Helen and Robert Appel Alzheimer's disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, 411 E 69th St, New York, 10021, NY, United States; Rockwell P., Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States, Department of Biological Sciences, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Figueiredo-Pereira M.E., Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States, Department of Biological Sciences, Hunter College, 695 Park Ave, New York, 10065, NY, United States; Serrano P.A., Program in Biochemistry, Graduate Center, CUNY, 365 5th Ave, New York, 10016, NY, United States, Department of Psychology, Hunter College, 695 Park Ave, New York, 10065, NY, United States","Alzheimer's disease is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. Hence, a multi-target drug would improve treatment efficacy. We applied a new multi-scale predictive modelling framework that integrates machine learning with biophysics and systems pharmacology to screen drugs for Alzheimer's disease using patients' tissue samples. Our predictive modelling framework identified ibudilast as a drug with repurposing potential to treat Alzheimer's disease. Ibudilast is a multi-target drug, as it is a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist. In addition, we predict that ibudilast inhibits off-target kinases (e.g. IRAK1 and GSG2). In Japan and other Asian countries, ibudilast is approved for treating asthma and stroke due to its anti-inflammatory potential. Based on these previous studies and on our predictions, we tested for the first time the efficacy of ibudilast in Fisher transgenic 344-AD rats. This transgenic rat model is unique as it exhibits hippocampal-dependent spatial learning and memory deficits and Alzheimer's disease pathology, including hippocampal amyloid plaques, tau paired-helical filaments, neuronal loss and microgliosis, in a progressive age-dependent manner that mimics the pathology observed in Alzheimer's disease patients. Following long-term treatment with ibudilast, transgenic rats were evaluated at 11 months of age for spatial memory performance and Alzheimer's disease pathology. We demonstrate that ibudilast-treatment of transgenic rats mitigated hippocampal-dependent spatial memory deficits, as well as hippocampal (hilar subregion) amyloid plaque and tau paired-helical filament load, and microgliosis compared to untreated transgenic rat. Neuronal density analysed across all hippocampal regions was similar in ibudilast-treated transgenic compared to untreated transgenic rats. Interestingly, RNA sequencing analysis of hippocampal tissue showed that ibudilast-treatment affects gene expression levels of the TLR and ubiquitin-proteasome pathways differentially in male and female transgenic rats. Based on the TLR4 signalling pathway, our RNA sequencing data suggest that ibudilast-treatment inhibits IRAK1 activity by increasing expression of its negative regulator IRAK3, and/or by altering TRAF6 and other TLR-related ubiquitin ligase and conjugase levels. Our results support that ibudilast can serve as a repurposed drug that targets multiple pathways including TLR signalling and the ubiquitin/proteasome pathway to reduce cognitive deficits and pathology relevant to Alzheimer's disease. © 2022 The Author(s). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.","drug repurposing; machine learning; polypharmacology; systems pharmacology; TLR and ubiquitin-proteasome pathways","Alzheimer Disease; Amyloid beta-Peptides; Animals; Disease Models, Animal; Drug Repositioning; Female; Inflammation; Male; Memory Disorders; Mice; Mice, Transgenic; Plaque, Amyloid; Proteasome Endopeptidase Complex; Rats; Rats, Transgenic; Toll-Like Receptor 4; Ubiquitins; gamma glutamyl hydrolase; ibudilast; interleukin 1 receptor associated kinase 1; interleukin 1 receptor associated kinase 3; proteasome; toll like receptor 4; tumor necrosis factor receptor associated factor 6; ubiquitin; ubiquitin protein ligase; amyloid beta protein; ibudilast; adult; age; Alzheimer disease; amnesia; amyloid plaque; animal experiment; animal model; animal tissue; Article; asthma; cerebrovascular accident; climate model; cognitive defect; controlled study; drug efficacy; drug repositioning; enzyme activity; female; gene expression; gene expression level; gliosis; hippocampal tissue; hippocampus; human; immunohistochemistry; inflammation; Japan; learning disorder; long term care; machine learning; male; nonhuman; paired helical filament; polypharmacology; prediction; protein expression; rat; RNA sequencing; signal transduction; spatial learning; spatial memory; systems pharmacology; TLR signaling; transgenic rat; Alzheimer disease; animal; disease model; drug repositioning; inflammation; memory disorder; metabolism; mouse; pathology; transgenic mouse","","gamma glutamyl hydrolase, 55326-32-4, 9074-87-7; ibudilast, 50847-11-5; proteasome, 140879-24-9; toll like receptor 4, 203811-83-0; ubiquitin, 60267-61-0; ubiquitin protein ligase, 134549-57-8; amyloid beta protein, 109770-29-8; Amyloid beta-Peptides, ; ibudilast, ; Proteasome Endopeptidase Complex, ; Toll-Like Receptor 4, ; Ubiquitins, ","","","National Institutes of Health, NIH; National Institute on Aging, NIA, (R01AG057555, RISE 5R25GM060665); National Institute of General Medical Sciences, NIGMS, (R01GM122845); City University of New York, CUNY","This work was supported in part by NIGMS R01GM122845 of NIH and NIA R01AG057555 of NIH to L.X., NIGMS Training Grant RISE 5R25GM060665 to G.O. and O.C. and the City University of New York (Ph.D. program in Biochemistry, Graduate Center). 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Serrano; Program in Biochemistry, Graduate Center, CUNY, New York, 365 5th Ave, 10016, United States; email: serrano@genectr.hunter.cuny.edu","","Oxford University Press","","","","","","00068950","","BRAIA","35411386","English","Brain","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85149176616"
"Perez-Garcia J.; González-Carracedo M.; Espuela-Ortiz A.; Hernández-Pérez J.M.; González-Pérez R.; Sardón-Prado O.; Martin-Gonzalez E.; Mederos-Luis E.; Poza-Guedes P.; Corcuera-Elosegui P.; Callero A.; Sánchez-Machín I.; Korta-Murua J.; Pérez-Pérez J.A.; Villar J.; Pino-Yanes M.; Lorenzo-Diaz F.","Perez-Garcia, Javier (57215905983); González-Carracedo, Mario (56911150700); Espuela-Ortiz, Antonio (57211444166); Hernández-Pérez, José M. (9939197900); González-Pérez, Ruperto (7403455167); Sardón-Prado, Olaia (8679204900); Martin-Gonzalez, Elena (57218948729); Mederos-Luis, Elena (57218955679); Poza-Guedes, Paloma (6506765396); Corcuera-Elosegui, Paula (24464865000); Callero, Ariel (55496501600); Sánchez-Machín, Inmaculada (6506940514); Korta-Murua, Javier (23034822900); Pérez-Pérez, José A. (57220177006); Villar, Jesús (55236061500); Pino-Yanes, Maria (57189630546); Lorenzo-Diaz, Fabian (6506188328)","57215905983; 56911150700; 57211444166; 9939197900; 7403455167; 8679204900; 57218948729; 57218955679; 6506765396; 24464865000; 55496501600; 6506940514; 23034822900; 57220177006; 55236061500; 57189630546; 6506188328","The upper-airway microbiome as a biomarker of asthma exacerbations despite inhaled corticosteroid treatment","2023","Journal of Allergy and Clinical Immunology","151","3","","706","715","9","14","10.1016/j.jaci.2022.09.041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144351556&doi=10.1016%2fj.jaci.2022.09.041&partnerID=40&md5=a92dbc89c03c1387b92d22cbc78e970c","Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain; Department of Biochemistry, Microbiology, Cell Biology and Genetics, ULL, Tenerife, La Laguna, Spain; Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Tenerife, La Laguna, Spain; Pulmonary Medicine Service, Hospital Universitario de NS de Candelaria, Tenerife, La Laguna, Spain; Pulmonary Medicine Section, Hospital Universitario de La Palma, La Palma, Spain; Severe Asthma Unit, Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain; Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Allergy Service, Hospital Universitario NS de Candelaria, Tenerife, La Laguna, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Multidisciplinary Organ Dysfunction Evaluation Research Network (MODERN), Research Unit, Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain; Li Ka Shing Knowledge Institute at the St Michael's Hospital, Toronto, ON, Canada; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, Tenerife, La Laguna, Spain","Perez-Garcia J., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain; González-Carracedo M., Department of Biochemistry, Microbiology, Cell Biology and Genetics, ULL, Tenerife, La Laguna, Spain, Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Tenerife, La Laguna, Spain; Espuela-Ortiz A., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain; Hernández-Pérez J.M., Pulmonary Medicine Service, Hospital Universitario de NS de Candelaria, Tenerife, La Laguna, Spain, Pulmonary Medicine Section, Hospital Universitario de La Palma, La Palma, Spain; González-Pérez R., Severe Asthma Unit, Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Sardón-Prado O., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain, Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain; Martin-Gonzalez E., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain; Mederos-Luis E., Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Poza-Guedes P., Severe Asthma Unit, Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Corcuera-Elosegui P., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Callero A., Allergy Service, Hospital Universitario NS de Candelaria, Tenerife, La Laguna, Spain; Sánchez-Machín I., Allergy Department, Hospital Universitario de Canarias, Tenerife, La Laguna, Spain; Korta-Murua J., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain, Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain; Pérez-Pérez J.A., Department of Biochemistry, Microbiology, Cell Biology and Genetics, ULL, Tenerife, La Laguna, Spain, Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Tenerife, La Laguna, Spain; Villar J., CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain, Multidisciplinary Organ Dysfunction Evaluation Research Network (MODERN), Research Unit, Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain, Li Ka Shing Knowledge Institute at the St Michael's Hospital, Toronto, ON, Canada; Pino-Yanes M., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain, CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain, Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, Tenerife, La Laguna, Spain; Lorenzo-Diaz F., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), Tenerife, La Laguna, Spain, Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Tenerife, La Laguna, Spain","Background: The response to inhaled corticosteroids (ICS) in asthma is affected by the interplay of several factors. Among these, the role of the upper-airway microbiome has been scarcely investigated. We aimed to evaluate the association between the salivary, pharyngeal, and nasal microbiome with asthma exacerbations despite receipt of ICS. Methods: Samples from 250 asthma patients from the Genomics and Metagenomics of Asthma Severity (GEMAS) study treated with ICS were analyzed. Control/case subjects were defined by the absence/presence of asthma exacerbations in the past 6 months despite being treated with ICS. The bacterial microbiota was profiled by sequencing the V3-V4 region of the 16S rRNA gene. Differences between groups were assessed by PERMANOVA and regression models adjusted for potential confounders. A false discovery rate (FDR) of 5% was used to correct for multiple comparisons. Classification models of asthma exacerbations despite ICS treatment were built with machine learning approaches based on clinical, genetic, and microbiome data. Results: In nasal and saliva samples, case subjects had lower bacterial diversity (Richness, Shannon, and Faith indices) than control subjects (.007 ≤ P ≤ .037). Asthma exacerbations accounted for 8% to 9% of the interindividual variation of the salivary and nasal microbiomes (.003 ≤ P ≤ .046). Three, 4, and 11 bacterial genera from the salivary, pharyngeal, and nasal microbiomes were differentially abundant between groups (4.09 × 10−12 ≤ FDR ≤ 0.047). Integrating clinical, genetic, and microbiome data showed good discrimination for the development of asthma exacerbations despite receipt of ICS (AUCtraining: 0.82 and AUCvalidation: 0.77). Conclusion: The diversity and composition of the upper-airway microbiome are associated with asthma exacerbations despite ICS treatment. The salivary microbiome has a potential application as a biomarker of asthma exacerbations despite receipt of ICS. © 2022 The Authors","16S rRNA; asthma; biomarker; exacerbations; inhaled corticosteroids; microbiota; nasal; pharyngeal; precision medicine; saliva","Administration, Inhalation; Adrenal Cortex Hormones; Anti-Asthmatic Agents; Asthma; Biomarkers; Humans; Microbiota; RNA, Ribosomal, 16S; antibiotic agent; corticosteroid; RNA 16S; antiasthmatic agent; biological marker; corticosteroid; RNA 16S; adult; antibiotic therapy; area under the curve; Article; asthma; bacterial flora; bacterial microbiome; body mass; comparative study; controlled study; corticosteroid therapy; Corynebacterium; disease exacerbation; disease severity; DNA extraction; false discovery rate; female; forced expiratory volume; genomics; hospitalization; human; lung microbiota; machine learning; major clinical study; male; metagenomics; middle aged; Neisseria; Prevotella; RNA sequencing; seasonal variation; second-degree relative; single nucleotide polymorphism; Staphylococcus; Streptococcus; Veillonella; inhalational drug administration; microflora","","Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ; Biomarkers, ; RNA, Ribosomal, 16S, ","","","Asociación Canaria de Neumología y Cirugía Torácica; Consorcio Centro de Investigación Biomédica en Red; NEUMOCAN; Ramón y Cajal Program, (RYC-2015-17205); Spanish Ministry of Science, Innovation and Universities and Universidad de La Laguna; Spanish Ministry of Universities; Spanish National Cancer Research Centre, (PT17/0019); Center for International Business Education and Research, University of Illinois at Urbana-Champaign, CIBER; Ministerio de Ciencia, Innovación y Universidades, MCIU; Universidad de La Laguna, ULL; GlaxoSmithKline España, GSK; Instituto de Salud Carlos III, ISCIII; Ministerio de Ciencia e Innovación, MICINN, (MCIN/AEI/10.13039/501100011033, SAF2017-83417R); Ministerio de Ciencia e Innovación, MICINN; European Social Fund, ESF, (FPU19/02175); European Social Fund, ESF; European Regional Development Fund, ERDF, (CB06/06/1088); European Regional Development Fund, ERDF; Fundación Canaria Instituto de Investigación Sanitaria de Canarias, FIISC","Funding text 1: This study was funded by the Spanish Ministry of Science and Innovation , grant SAF2017-83417R awarded by MCIN/AEI/10.13039/501100011033, and European Regional Developmental Fund (ERDF) “A way of making Europe,” to M.P.-Y. and F.L.-D. M.P.-Y. and J.V. were also supported by CIBER–Consorcio Centro de Investigación Biomédica en Red (CIBERES), Instituto de Salud Carlos III (ISCIII), and the European Regional Development Fund (CB06/06/1088). This project was also partially funded by grant support from GlaxoSmithKline (Spain) through an agreement with Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC) to M.P.-Y. and J.M.H.-P., Asociación Canaria de Neumología y Cirugía Torácica (NEUMOCAN), and an agreement between the Spanish Ministry of Science, Innovation and Universities and Universidad de La Laguna . M.P.-Y. was also supported by a grant from the Ramón y Cajal Program (RYC-2015-17205) by MCIN/AEI/10.13039/501100011033 and by the European Social Fund “ESF Investing in your future.” J.P.-G. was funded by the fellowship FPU19/02175 (Formación Profesorado Universitario Program) from the Spanish Ministry of Universities. A.E.-O. reports funding from the Spanish Ministry of Science, Innovation, and Universities (MICIU) and Universidad de La Laguna (ULL), under the M-ULL program. Genotyping was performed at the Spanish National Cancer Research Centre, in the Human Genotyping lab, a member of CeGen, PRB3, and is supported by grant PT17/0019, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing the report. ; Funding text 2: This study was funded by the Spanish Ministry of Science and Innovation, grant SAF2017-83417R awarded by MCIN/AEI/10.13039/501100011033, and European Regional Developmental Fund (ERDF) “A way of making Europe,” to M.P.-Y. and F.L.-D. M.P.-Y. and J.V. were also supported by CIBER–Consorcio Centro de Investigación Biomédica en Red (CIBERES), Instituto de Salud Carlos III (ISCIII), and the European Regional Development Fund (CB06/06/1088). This project was also partially funded by grant support from GlaxoSmithKline (Spain) through an agreement with Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC) to M.P.-Y. and J.M.H.-P., Asociación Canaria de Neumología y Cirugía Torácica (NEUMOCAN), and an agreement between the Spanish Ministry of Science, Innovation and Universities and Universidad de La Laguna. M.P.-Y. was also supported by a grant from the Ramón y Cajal Program (RYC-2015-17205) by MCIN/AEI/10.13039/501100011033 and by the European Social Fund “ESF Investing in your future.” J.P.-G. was funded by the fellowship FPU19/02175 (Formación Profesorado Universitario Program) from the Spanish Ministry of Universities. A.E.-O. reports funding from the Spanish Ministry of Science, Innovation, and Universities (MICIU) and Universidad de La Laguna (ULL), under the M-ULL program. Genotyping was performed at the Spanish National Cancer Research Centre, in the Human Genotyping lab, a member of CeGen, PRB3, and is supported by grant PT17/0019, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. 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Pino-Yanes; Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), Santa Cruz de Tenerife, Apartado 456, La Laguna, 38200, Spain; email: mdelpino@ull.edu.es","","Elsevier Inc.","","","","","","00916749","","JACIB","36343772","English","J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85144351556"
"Dunn J.L.M.; Rothenberg M.E.","Dunn, Julia L.M. (57189948443); Rothenberg, Marc E. (7101631875)","57189948443; 7101631875","2021 year in review: Spotlight on eosinophils","2022","Journal of Allergy and Clinical Immunology","149","2","","517","524","7","16","10.1016/j.jaci.2021.11.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121755908&doi=10.1016%2fj.jaci.2021.11.012&partnerID=40&md5=62c4c53a106195dc700227c06aca8aed","Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States","Dunn J.L.M., Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States; Rothenberg M.E., Division of Allergy and Immunology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States","This review highlights recent advances in the understanding of eosinophils and eosinophilic diseases, particularly eosinophilic gastrointestinal diseases during the last year. The increasing incidence of diseases marked by eosinophilia has been documented and highlighted the need to understand eosinophil biology and eosinophilic contributions to disease. Significant insight into the nature of eosinophilic diseases has been achieved using next-generation sequencing technologies, proteomic analysis, and machine learning to analyze tissue biopsies. These technologies have elucidated mechanistic underpinnings of eosinophilic inflammation, delineated patient endotypes, and identified patient responses to therapeutic intervention. Importantly, recent clinical studies using mAbs that interfere with type 2 cytokine signaling or deplete eosinophils point to multiple and complex roles of eosinophils in tissues. Several studies identified distinct activation features of eosinophils in different tissues and disease states. The confluence of these studies supports a new paradigm of tissue-resident eosinophils that have pro- and anti-inflammatory immunomodulatory roles in allergic disease. Improved understanding of unique eosinophil activation states is now poised to identify novel therapeutic targets for eosinophilic diseases. © 2021 American Academy of Allergy, Asthma & Immunology","asthma; biologics; chronic rhinosinusitis; endotypes; eosinophilic gastrointestinal diseases; Eosinophils","Enteritis; Eosinophilia; Eosinophilic Esophagitis; Eosinophils; Gastritis; Genome-Wide Association Study; High-Throughput Nucleotide Sequencing; Humans; Immunomodulation; benralizumab; cendakimab; cytokine; glucocorticoid; lirentelimab; mepolizumab; monoclonal antibody; omalizumab; allergic disease; Article; basophil; biopsy; blood; cell function; cell interaction; confocal microscopy; coronavirus disease 2019; corticosteroid therapy; cytokine signaling; cytology; drug targeting; eosinophil; eosinophilia; eosinophilic esophagitis; eosinophilic gastritis; eosinophilic gastrointestinal disorder; genome-wide association study; high throughput sequencing; human; immunomodulation; lipidomics; lung; lymphocyte; machine learning; mast cell; metabolomics; neutrophil; proteomics; treatment response; upper respiratory tract; drug effect; enteritis; eosinophil; eosinophilia; eosinophilic esophagitis; gastritis; genetics; immunology; physiology","","benralizumab, 1044511-01-4; cendakimab, 2151032-62-9; lirentelimab, 2283348-97-8; mepolizumab, 196078-29-2; omalizumab, 242138-07-4","","","Cincinnati Children's Hospital; Mapi Research Trust; UpToDate; National Institutes of Health, NIH, (AI045898, U54 AI117804); National Institute of Allergy and Infectious Diseases, NIAID, (U19AI070235)","Funding text 1: We are grateful to Shawna Hottinger for editorial assistance. This publication was supported in part by awards of the National Institutes of Health (AI045898, U19 AI070235, and U54 AI117804 to M.E.R.). Disclosure of potential conflict of interest: M. E. Rothenberg is a consultant for Pulm One, Spoon Guru, ClostraBio, Serpin Pharm, Allakos, Celgene, Astra Zeneca, Adare/Ellodi Pharma, GlaxoSmithKline, Regeneron/Sanofi, Revolo Biotherapeutics, Celldex, and Guidepoint, has an equity interest in the first 5 listed, and royalties from reslizumab (Teva Pharmaceuticals), PEESSv2 (Mapi Research Trust), and UpToDate, and is an inventor of patents owned by Cincinnati Children's Hospital. J. L. M. Dunn has no financial interests to disclose.; Funding text 2: This publication was supported in part by awards of the National Institutes of Health (AI045898, U19 AI070235, and U54 AI117804 to M.E.R.). 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Rothenberg; Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, 3333 Burnet Ave, 45229, United States; email: Rothenberg@cchmc.org","","Elsevier Inc.","","","","","","00916749","","JACIB","34838883","English","J. Allergy Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85121755908"
"Custovic A.; Siddiqui S.; Saglani S.","Custovic, Adnan (7006755479); Siddiqui, Salman (16242465600); Saglani, Sejal (6603099922)","7006755479; 16242465600; 6603099922","Considering biomarkers in asthma disease severity","2022","Journal of Allergy and Clinical Immunology","149","2","","480","487","7","22","10.1016/j.jaci.2021.11.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123368930&doi=10.1016%2fj.jaci.2021.11.021&partnerID=40&md5=1e051f6ce602677dc660499d03b4d487","National Heart and Lung Institute, Imperial College London, London, United Kingdom; Department of Respiratory Sciences, University of Leicester and NIHR Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom","Custovic A., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Siddiqui S., Department of Respiratory Sciences, University of Leicester and NIHR Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom; Saglani S., National Heart and Lung Institute, Imperial College London, London, United Kingdom","Among patients with asthma, reliance on the type/dose of prescribed medication and symptom control does not adequately capture those at risk of adverse outcomes, and we need biomarkers for risk and treatment stratification that are consistently accurate, readily quantifiable, and reproducible. Most patients with severe asthma, regardless of age, have predominant type-2 inflammation-mediated disease, making airway/blood eosinophils, fractional exhaled nitric oxide, periostin, and/or allergic sensitization potentially important biomarkers for severe disease. In both adult and pediatric asthma, there is scope to improve prediction of severe attacks by using a composite type-2 biomarker of blood eosinophils and fractional exhaled nitric oxide. Technological advances in component-resolved diagnostics microarray technologies coupled with the development of interpretation software offer a possibility to use component-resolved diagnostics as biomarkers of asthma severity among sensitized patients with asthma. Genetic predisposition and polygenic risk scores of relevant traits (eg, lung function, host immune responses, biomarkers of exposure from the indoor and outdoor environment, infection, and microbial dysbiosis) may also contribute to prediction algorithms. We challenge the idea that asthma can be accurately defined in an individual patient by a discrete and static “endotype” (eg, type-2–high asthma). As we traverse the new era of molecular endotyping in asthma, we need to understand how relevant mechanisms impact patient outcomes, and in parallel develop new tools and approaches to stratify therapies and define individual patient trajectories. © 2021","allergic sensitization; eosinophils; FENO; immune responses; microbial dysbiosis; Severe asthma; T2 asthma","Asthma; Biomarkers; Breath Tests; Dysbiosis; Eosinophils; Genetic Predisposition to Disease; Humans; Severity of Illness Index; biological marker; biological marker; adverse outcome; Article; asthma; asthmatic state; clinical assessment; contact sensitization; disease exacerbation; disease severity; dysbiosis; eosinophil; fractional exhaled nitric oxide; genetic susceptibility; host; human; immune response; infection; inflammation; machine learning; meta analysis (topic); patient identification; patient risk; phase 2 clinical trial (topic); phase 3 clinical trial (topic); prescription; protein targeting; risk assessment; risk factor; sensitization; severe asthma; symptom; treatment indication; treatment outcome; asthma; breath analysis; genetic predisposition; immunology; severity of illness index","","Biomarkers, ","","","","","Anderson G.P., Endotyping asthma: new insights into key pathogenic mechanisms in a complex, heterogeneous disease, Lancet, 372, pp. 1107-1119, (2008); Lotvall J., Akdis C.A., Bacharier L.B., Bjermer L., Casale T.B., Custovic A., Et al., Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome, J Allergy Clin Immunol, 127, pp. 355-360, (2011); Custovic A., Ainsworth J., Arshad H., Bishop C., Buchan I., Cullinan P., Et al., The Study Team for Early Life Asthma Research (STELAR) consortium ‘Asthma e-lab’: team science bringing data, methods and investigators together, Thorax, 70, pp. 799-801, (2015); Chung K.F., Wenzel S.E., Brozek J.L., Bush A., Castro M., Sterk P.J., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, pp. 343-373, (2014); 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Custovic; National Heart & Lung Institute, Imperial College London, London, United Kingdom; email: a.custovic@imperial.ac.uk","","Elsevier Inc.","","","","","","00916749","","JACIB","34942235","English","J. Allergy Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85123368930"
"Afrash M.R.; Kazemi-Arpanahi H.; Shanbehzadeh M.; Nopour R.; Mirbagheri E.","Afrash, Mohammad Reza (57204183233); Kazemi-Arpanahi, Hadi (57202931591); Shanbehzadeh, Mostafa (57216971321); Nopour, Raoof (57220634861); Mirbagheri, Esmat (57210713236)","57204183233; 57202931591; 57216971321; 57220634861; 57210713236","Predicting hospital readmission risk in patients with COVID-19: A machine learning approach","2022","Informatics in Medicine Unlocked","30","","100908","","","","27","10.1016/j.imu.2022.100908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130111759&doi=10.1016%2fj.imu.2022.100908&partnerID=40&md5=84bafbb8a7f009fcdf10baa9b193a438","Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran; Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran; Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran; Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran; Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran","Afrash M.R., Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Kazemi-Arpanahi H., Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran, Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran; Shanbehzadeh M., Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran; Nopour R., Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran; Mirbagheri E., Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran","Introduction: The Coronavirus 2019 (COVID-19) epidemic stunned the health systems with severe scarcities in hospital resources. In this critical situation, decreasing COVID-19 readmissions could potentially sustain hospital capacity. This study aimed to select the most affecting features of COVID-19 readmission and compare the capability of Machine Learning (ML) algorithms to predict COVID-19 readmission based on the selected features. Material and methods: The data of 5791 hospitalized patients with COVID-19 were retrospectively recruited from a hospital registry system. The LASSO feature selection algorithm was used to select the most important features related to COVID-19 readmission. HistGradientBoosting classifier (HGB), Bagging classifier, Multi-Layered Perceptron (MLP), Support Vector Machine ((SVM) kernel = linear), SVM (kernel = RBF), and Extreme Gradient Boosting (XGBoost) classifiers were used for prediction. We evaluated the performance of ML algorithms with a 10-fold cross-validation method using six performance evaluation metrics. Results: Out of the 42 features, 14 were identified as the most relevant predictors. The XGBoost classifier outperformed the other six ML models with an average accuracy of 91.7%, specificity of 91.3%, the sensitivity of 91.6%, F-measure of 91.8%, and AUC of 0.91%. Conclusion: The experimental results prove that ML models can satisfactorily predict COVID-19 readmission. Besides considering the risk factors prioritized in this work, categorizing cases with a high risk of reinfection can make the patient triaging procedure and hospital resource utilization more effective. © 2022","Artificial intelligent; Coronavirus; COVID-19; Machine learning; Readmission","C reactive protein; adolescent; adult; age; aged; area under the curve; Article; asthma; bagging classifier; cerebrovascular disease; child; classifier; congestive heart failure; coronary artery disease; coronavirus disease 2019; cross validation; demographics; diagnostic accuracy; extreme gradient boosting; feature selection algorithm; female; histgradientboosting classifier; hospital patient; hospital readmission; human; infant; k fold cross validation; least absolute shrinkage and selection operator; length of stay; machine learning; major clinical study; male; metastasis; multi layered perceptron; newborn; oxygen therapy; patient selection; perceptron; prediction; predictive model; retrospective study; risk; risk factor; sensitivity and specificity; support vector machine","","C reactive protein, 9007-41-4","","","Ilam University of Medical Sciences","This article was extracted from the project with the Ethics code: IR.MEDILAM.REC.1399.294. 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Clinical features of and risk factors for 30-day readmission after an initial hospitalization with COVID-19, Open forum infectious diseases, (2021); Green H., Yahav D., Eliakim-Raz N., Karny-Epstein N., Kushnir S., Shochat T., Et al., Risk-factors for re-admission and outcome of patients hospitalized with confirmed COVID-19, Sci Rep, 11, 1, (2021)","M. Shanbehzadeh; Department of Health Information Technology, Ilam University of Medical Sciences, Ilam, Iran; email: shanbehzadeh-m@medilam.ac.ir","","Elsevier Ltd","","","","","","23529148","","","","English","Inform. Med. Unlocked","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130111759"
"Choi Y.; Lee H.","Choi, Youngjin (57726404200); Lee, Hongchul (8959866800)","57726404200; 8959866800","Interpretation of lung disease classification with light attention connected module","2023","Biomedical Signal Processing and Control","84","","104695","","","","20","10.1016/j.bspc.2023.104695","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149411472&doi=10.1016%2fj.bspc.2023.104695&partnerID=40&md5=ef94aa85120115a6301315dd6163583f","School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea","Choi Y., School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea; Lee H., School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea","Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts’ opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes. © 2023","Attention; ECA-Net; eXplainable AI; Grad-CAM; Lung disease; Respiratory sound","Cams; Computer aided diagnosis; Deep learning; Pulmonary diseases; Activation mapping; Attention; Disease classification; ECA-net; Explainable AI; Gradient-weighted class activation mapping; Intelligence models; Medical practitioner; Performance; Respiratory sounds; adult; aged; Article; artificial intelligence; asthma; bronchiectasis; chronic obstructive lung disease; classification algorithm; clinical article; computer vision; controlled study; convolutional neural network; coronavirus disease 2019; crackle; cross validation; data interpretation; deep learning; diagnostic accuracy; diagnostic test accuracy study; dimensionality reduction; disease classification; empirical mode decomposition; feature extraction; human; interstitial lung disease; light attention connected module; lung disease; measurement precision; pneumonia; segmentation algorithm; sensitivity and specificity; thorax radiography; transfer of learning; wheezing; Biological organs","","","Python 3.8; Ryzen 7, amd; TensorFlow 2.4.0","amd","Korea University, KU, (2022, k2209271)","This work was supported by the Korea University Grant [No. k2209271, 2022] and in part by Brain Korea 21 FOUR. This paper is an extended version of “Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification” by Choi et al, published in IEEE Access.","pp. 1689-1699, (2021); Petmezas G., Cheimariotis G.-A., Stefanopoulos L., Rocha B., Paiva R.P., Katsaggelos A.K., Maglaveras N., Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function, Sensors, 22, (2022); Leung J.M., Niikura M., Yang C.W.T., Sin D.D., Covid-19 and COPD, Eur. Respir. J., 56, (2020); Ma Y., Xu X., Li Y., LungRN+ NL: an improved adventitious lung sound classification using non-local block ResNet neural network with mixup data augmentation, Interspeech, pp. 2902-2906, (2020); Rocha B.M., Filos D., Mendes L., Vogiatzis I., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Α respiratory sound database for the development of automated classification, Int. Conf. Biomed. Heal. 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Comput., 13, pp. 4759-4771, (2021); Grooby E., Sitaula C., Fattahi D., Sameni R., Tan K., Zhou L., King A., Ramanathan A., Malhotra A., Dumont G.A., Real-time multi-level neonatal heart and lung sound quality assessment for telehealth applications, IEEE Access., 10, pp. 10934-10948, (2022); Dar J.A., Srivastava K.K., Lone S.A., Spectral features and optimal hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals, Biomed. Signal Process. Control., 78, (2022); Aykanat M., Kilic O., Kurt B., Saryal S., Classification of lung sounds using convolutional neural networks, EURASIP J. Image Video Proc., 2017, (2017); Stephen O., Sain M., Maduh U.J., Jeong D.-U., An efficient deep learning approach to pneumonia classification in healthcare, J. Health. Eng., 2019, (2019); Park K.J., Choi Y.J., Lee H.C., COVID-19 CXR classification: applying domain extension transfer learning and deep learning, Appl. 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Med., 370, pp. 744-751, (2014); Woo S., Park J., Lee J.-Y., Kweon I.S., pp. 3-19, (2018); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data Br., 35, (2021); Park C., Awadalla A., Kohno T., Patel S., Reliable and trustworthy machine learning for health using dataset shift detection, Adv. Neural Inf. Process. Syst., 34, pp. 3043-3056, (2021); Tripathy R.K., Dash S., Rath A., Panda G., Pachori R.B., Automated detection of pulmonary diseases from lung sound signals using fixed-boundary-based empirical wavelet transform, IEEE Sensors Lett., 6, pp. 1-4, (2022); Soni P.N., Shi S., Sriram P.R., Ng A.Y., Rajpurkar P., Contrastive learning of heart and lung sounds for label-efficient diagnosis, Patterns, 3, (2022); Altan G., Deep OCT: an explainable deep learning architecture to analyze macular edema on OCT images, Eng. Sci. Technol. an Int. J., 34, (2022); Kim J.K., Jung S., Park J., Han S.W., Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization, Biomed. Signal Process. Control., 73, (2022)","H. Lee; School of Industrial Management Engineering, Korea University, Seoul, 145 Anam-ro, Seongbuk-gu, 02841, South Korea; email: hclee@korea.ac.kr","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85149411472"
"Marchetti P.; Miotti J.; Locatelli F.; Antonicelli L.; Baldacci S.; Battaglia S.; Bono R.; Corsico A.; Gariazzo C.; Maio S.; Murgia N.; Pirina P.; Silibello C.; Stafoggia M.; Torroni L.; Viegi G.; Verlato G.; Marcon A.","Marchetti, Pierpaolo (35240765700); Miotti, Jessica (57948865300); Locatelli, Francesca (57218553590); Antonicelli, Leonardo (55906545600); Baldacci, Sandra (7003668400); Battaglia, Salvatore (35434499600); Bono, Roberto (55502646900); Corsico, Angelo (7003664779); Gariazzo, Claudio (56281048300); Maio, Sara (23009647000); Murgia, Nicola (8894561000); Pirina, Pietro (57189226636); Silibello, Camillo (22136612400); Stafoggia, Massimo (13608072800); Torroni, Lorena (57204444615); Viegi, Giovanni (7007103216); Verlato, Giuseppe (7006872229); Marcon, Alessandro (13614044300)","35240765700; 57948865300; 57218553590; 55906545600; 7003668400; 35434499600; 55502646900; 7003664779; 56281048300; 23009647000; 8894561000; 57189226636; 22136612400; 13608072800; 57204444615; 7007103216; 7006872229; 13614044300","Long-term residential exposure to air pollution and risk of chronic respiratory diseases in Italy: The BIGEPI study","2023","Science of the Total Environment","884","","163802","","","","23","10.1016/j.scitotenv.2023.163802","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153851261&doi=10.1016%2fj.scitotenv.2023.163802&partnerID=40&md5=57d5e37183eaa1a6a5f75e5f114394a6","Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Allergy Unit, Ospedali Riuniti, Ancona, Italy; Pulmonary Environmental Epidemiology Unit, CNR Institute of Clinical Physiology (IFC), Pisa, Italy; Dipartimento PROMISE, University of Palermo, Palermo, Italy; Department of Public Health and Pediatrics, University of Turin, Torino, Italy; Respiratory Diseases Division, IRCCS Policlinico San Matteo Foundation, Pavia, Italy; Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy; Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Roma, Italy; Department of Environmental and Prevention Sciences, University of Ferrara, Italy; Respiratory Unit, Sassari University, Sassari, Italy; ARIANET s.r.l., Milano, Italy; Department of Epidemiology, Lazio Regional Health Service ASL Roma 1, Roma, Italy","Marchetti P., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Miotti J., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Locatelli F., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Antonicelli L., Allergy Unit, Ospedali Riuniti, Ancona, Italy; Baldacci S., Pulmonary Environmental Epidemiology Unit, CNR Institute of Clinical Physiology (IFC), Pisa, Italy; Battaglia S., Dipartimento PROMISE, University of Palermo, Palermo, Italy; Bono R., Department of Public Health and Pediatrics, University of Turin, Torino, Italy; Corsico A., Respiratory Diseases Division, IRCCS Policlinico San Matteo Foundation, Pavia, Italy, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy; Gariazzo C., Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers' Compensation Authority (INAIL), Roma, Italy; Maio S., Pulmonary Environmental Epidemiology Unit, CNR Institute of Clinical Physiology (IFC), Pisa, Italy; Murgia N., Department of Environmental and Prevention Sciences, University of Ferrara, Italy; Pirina P., Respiratory Unit, Sassari University, Sassari, Italy; Silibello C., ARIANET s.r.l., Milano, Italy; Stafoggia M., Department of Epidemiology, Lazio Regional Health Service ASL Roma 1, Roma, Italy; Torroni L., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Viegi G., Pulmonary Environmental Epidemiology Unit, CNR Institute of Clinical Physiology (IFC), Pisa, Italy; Verlato G., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Marcon A., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy","Long-term exposure to air pollution has adverse respiratory health effects. We investigated the cross-sectional relationship between residential exposure to air pollutants and the risk of suffering from chronic respiratory diseases in some Italian cities. In the BIGEPI project, we harmonised questionnaire data from two population-based studies conducted in 2007–2014. By combining self-reported diagnoses, symptoms and medication use, we identified cases of rhinitis (n = 965), asthma (n = 328), chronic bronchitis/chronic obstructive pulmonary disease (CB/COPD, n = 469), and controls (n = 2380) belonging to 13 cohorts from 8 Italian cities (Pavia, Turin, Verona, Terni, Pisa, Ancona, Palermo, Sassari). We derived mean residential concentrations of fine particulate matter (PM10, PM2.5), nitrogen dioxide (NO2), and summer ozone (O3) for the period 2013–2015 using spatiotemporal models at a 1 km resolution. We fitted logistic regression models with controls as reference category, a random-intercept for cohort, and adjusting for sex, age, education, BMI, smoking, and climate. Mean ± SD exposures were 28.7 ± 6.0 μg/m3 (PM10), 20.1 ± 5.6 μg/m3 (PM2.5), 27.2 ± 9.7 μg/m3 (NO2), and 70.8 ± 4.2 μg/m3 (summer O3). The concentrations of PM10, PM2.5, and NO2 were higher in Northern Italian cities. We found associations between PM exposure and rhinitis (PM10: OR 1.62, 95%CI: 1.19–2.20 and PM2.5: OR 1.80, 95%CI: 1.16–2.81, per 10 μg/m3) and between NO2 exposure and CB/COPD (OR 1.22, 95%CI: 1.07–1.38 per 10 μg/m3), whereas asthma was not related to environmental exposures. Results remained consistent using different adjustment sets, including bi-pollutant models, and after excluding subjects who had changed residential address in the last 5 years. We found novel evidence of association between long-term PM exposure and increased risk of rhinitis, the chronic respiratory disease with the highest prevalence in the general population. Exposure to NO2, a pollutant characterised by strong oxidative properties, seems to affect mainly CB/COPD. © 2023","Air quality; Asthma; Chronic bronchitis; Epidemiology; Public health; Rhinitis","Air Pollutants; Air Pollution; Asthma; Drug-Related Side Effects and Adverse Reactions; Environmental Exposure; Environmental Pollutants; Humans; Italy; Nitrogen Dioxide; Particulate Matter; Pulmonary Disease, Chronic Obstructive; Respiration Disorders; Rhinitis; Ancona; Italy; Lombardy; Marche; Palermo [Sicily]; Pavia; Piedmont [Italy]; Pisa [Tuscany]; Sardinia; Sassari; Sicily; Terni; Torino [Piedmont]; Turin; Tuscany; Umbria; Veneto; Verona; Climate models; Diagnosis; Health risks; Housing; Nitrogen oxides; Population statistics; Public health; Pulmonary diseases; nitrogen dioxide; ozone; Air pollutants; Asthma; Chronic bronchitis; Diagnose symptoms; Health effects; Long term exposure; PM 10; PM 2.5; Questionnaire data; Rhinitis; air quality; asthma; atmospheric pollution; chronic obstructive pulmonary disease; epidemiology; nitrogen dioxide; ozone; particulate matter; pollution exposure; adult; aged; air pollutant; air pollution; air quality; allergic rhinitis; Article; asthma; body mass; case control study; chronic bronchitis; chronic cough; chronic obstructive lung disease; chronic respiratory tract disease; climate; cohort analysis; controlled study; dyspnea; environmental exposure; environmental monitoring; female; fever; human; long term exposure; machine learning; major clinical study; male; multicenter study; obesity; oxidation; particulate matter; particulate matter 10; particulate matter 2.5; pollution; prevalence; public health; questionnaire; random forest; residence characteristics; residential area; rhinitis; risk assessment; sensitivity analysis; smoking; sneezing; solar radiation; spirometry; summer; wheezing; adverse drug reaction; air pollutant; asthma; breathing disorder; chronic obstructive lung disease; Italy; pollutant; rhinitis; Air quality","","nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; Air Pollutants, ; Environmental Pollutants, ; Nitrogen Dioxide, ; Particulate Matter, ","","","Italian Workers' Compensation Authority; Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro, INAIL, (04/2016, 46/2019)","Funding: this research was funded by the Italian Workers' Compensation Authority ( INAIL ) within the BEEP project (project No. 04/2016 ) and the BIGEPI project (project No. 46/2019 ). 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Marcon; Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; email: alessandro.marcon@univr.it","","Elsevier B.V.","","","","","","00489697","","STEVA","37127163","English","Sci. Total Environ.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85153851261"
"Yin C.; Udrescu M.; Gupta G.; Cheng M.; Lihu A.; Udrescu L.; Bogdan P.; Mannino D.M.; Mihaicuta S.","Yin, Chenzhong (57219020532); Udrescu, Mihai (8841844000); Gupta, Gaurav (57194176138); Cheng, Mingxi (57201587061); Lihu, Andrei (35100587500); Udrescu, Lucretia (36467301400); Bogdan, Paul (6602660680); Mannino, David M. (7005919220); Mihaicuta, Stefan (6602901111)","57219020532; 8841844000; 57194176138; 57201587061; 35100587500; 36467301400; 6602660680; 7005919220; 6602901111","Fractional Dynamics Foster Deep Learning of COPD Stage Prediction","2023","Advanced Science","10","12","2203485","","","","17","10.1002/advs.202203485","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148653394&doi=10.1002%2fadvs.202203485&partnerID=40&md5=339d56f2a126bebd5671860dd49fe096","Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States; Department of Computer and Information Technology, Politehnica University of Timisoara, 2 Vasile Parvan Blvd., Timişoara, 300223, Romania; Department I – Drug Analysis, “Victor Babeş”, University of Medicine and Pharmacy Timişoara, 2 Eftimie Murgu Sq., Timişoara, 300041, Romania; College of Medicine, University of Kentucky, Lexington, KY, United States; Department of Pulmonology, Center for Research and Innovation in Precision Medicine of Respiratory Diseases, “Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., Timişoara, 300041, Romania","Yin C., Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States; Udrescu M., Department of Computer and Information Technology, Politehnica University of Timisoara, 2 Vasile Parvan Blvd., Timişoara, 300223, Romania; Gupta G., Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States; Cheng M., Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States; Lihu A., Department of Computer and Information Technology, Politehnica University of Timisoara, 2 Vasile Parvan Blvd., Timişoara, 300223, Romania; Udrescu L., Department I – Drug Analysis, “Victor Babeş”, University of Medicine and Pharmacy Timişoara, 2 Eftimie Murgu Sq., Timişoara, 300041, Romania; Bogdan P., Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States; Mannino D.M., College of Medicine, University of Kentucky, Lexington, KY, United States; Mihaicuta S., Department of Pulmonology, Center for Research and Innovation in Precision Medicine of Respiratory Diseases, “Victor Babes” University of Medicine and Pharmacy, 2 Eftimie Murgu Sq., Timişoara, 300041, Romania","Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages—from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals. © 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.","chronic obstructive pulmonary disease (COPD); deep learning; fractional analysis","Deep Learning; Humans; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Spirometry; Diagnosis; Learning systems; Physiology; Pulmonary diseases; Causes of death; Chronic obstructive pulmonary disease; Deep learning; Fractional analyse; Fractional dynamics; Fractional order; Learning models; Physiological signals; Stage prediction; artificial neural network; chronic obstructive lung disease; deep learning; human; spirometry; Deep neural networks","","","","","Intel Corporation; Autoritatea Natională pentru Cercetare Stiintifică; Northrop Grumman; Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare, CCCDI; Corporation for National and Community Service, CNCS; U.S. Department of Defense, DOD; Horizon 2020 Framework Programme, H2020, (965417); National Science Foundation, NSF, (1932620, MCB‐1936775, CCF‐1837131, CMMI‐1936624, 1453860, 1936775, 1932620, 1453860); Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI, (PN‐III‐P2‐2.1‐PED‐2016‐1145); Army Research Office, ARO, (W911NF‐17‐1‐0076); Defense Advanced Research Projects Agency, DARPA, (N66001‐17‐1‐4044)","Funding text 1: P.B., C.Y., M.C., and G.G. gratefully acknowledge the support by the National Science Foundation under the Career Award CPS/CNS-1453860, the NSF award under Grant Numbers CCF-1837131, MCB-1936775, CMMI-1936624, and CNS-1932620, the U.S. Army Research Office (ARO) under Grant No. W911NF-17-1-0076 and the DARPA Young Faculty Award and DARPA Director Award, under Grant Number N66001-17-1-4044, a 2021 USC Stevens Center Technology Adcancement Grant (TAG) award, an Intel faculty award and a Northrop Grumman grant. M.U. A.L., L.U. and S.M. gratefully acknowledge that this work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI\u2013UEFISCDI, project number PN-III-P2-2.1-PED-2016-1145, within PNCDI III project number 31 PED/2017: INCEPTION. There was no additional external funding received for this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing official views or policies, either expressed or implied by the Defense Advanced Research Projects Agency, the Department of Defense or the National Science Foundation.; Funding text 2: P.B., C.Y., M.C., and G.G. gratefully acknowledge the support by the National Science Foundation under the Career Award CPS/CNS\u20101453860, the NSF award under Grant Numbers CCF\u20101837131, MCB\u20101936775, CMMI\u20101936624, and CNS\u20101932620, the U.S. Army Research Office (ARO) under Grant No. W911NF\u201017\u20101\u20100076 and the DARPA Young Faculty Award and DARPA Director Award, under Grant Number N66001\u201017\u20101\u20104044, a 2021 USC Stevens Center Technology Adcancement Grant (TAG) award, an Intel faculty award and a Northrop Grumman grant. M.U. A.L., L.U. and S.M. gratefully acknowledge that this work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI\u2013UEFISCDI, project number PN\u2010III\u2010P2\u20102.1\u2010PED\u20102016\u20101145, within PNCDI III project number 31 PED/2017: INCEPTION. There was no additional external funding received for this study. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing official views or policies, either expressed or implied by the Defense Advanced Research Projects Agency, the Department of Defense or the National Science Foundation. 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"Passarelli-Araujo H.; Passarelli-Araujo H.; Urbano M.R.; Pescim R.R.","Passarelli-Araujo, Hemanoel (57204240897); Passarelli-Araujo, Hisrael (57219163538); Urbano, Mariana R. (35099890600); Pescim, Rodrigo R. (35099239900)","57204240897; 57219163538; 35099890600; 35099239900","Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil","2022","Smart Health","26","","100323","","","","14","10.1016/j.smhl.2022.100323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139738611&doi=10.1016%2fj.smhl.2022.100323&partnerID=40&md5=10dba84b3fffa40f32662f138ba358cf","Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, MG, Belo Horizonte, Brazil; Departamento de Demografia, Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais, MG, Belo Horizonte, Brazil; Departamento de Estatística, Universidade Estadual de Londrina, PR, Londrina, Brazil","Passarelli-Araujo H., Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, MG, Belo Horizonte, Brazil; Passarelli-Araujo H., Departamento de Demografia, Faculdade de Ciências Econômicas, Universidade Federal de Minas Gerais, MG, Belo Horizonte, Brazil; Urbano M.R., Departamento de Estatística, Universidade Estadual de Londrina, PR, Londrina, Brazil; Pescim R.R., Departamento de Estatística, Universidade Estadual de Londrina, PR, Londrina, Brazil","The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision. © 2022","Co-occurrence analysis; Epidemiology; Network density; Risk-factors; SARS-CoV-2","abdominal pain; adolescent; adult; aged; ageusia; Article; asthma; Brazil; child; clinical feature; comorbidity; controlled study; coronavirus disease 2019; coughing; decision tree; demographics; diabetes mellitus; diarrhea; Down syndrome; dyspnea; fatigue; feature selection algorithm; female; fever; heart disease; hematologic disease; hospital admission; hospital mortality; hospital patient; human; infant; invasive ventilation; kidney disease; liver disease; logistic regression analysis; lung disease; machine learning; major clinical study; male; middle aged; network analysis; neurologic disease; newborn; nonhuman; obesity; puerperium; random forest; retrospective study; sore throat; support vector machine; survivor; very elderly","","","","","","","Aktar S., Talukder A., Ahamad M.M., Kamal A.H.M., Khan J.R., Protikuzzaman M., Hossain N., Azad A.K.M., Quinn J.M.W., Summers M.A., Liaw T., Eapen V., Moni M.A., Machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in COVID-19, Diagnostics (Basel), 11, 8, (2021); 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Espinosa O.A., Zanetti A.D.S., Antunes E.F., Longhi F.G., Matos T.A., Battaglini P.F., Prevalence of comorbidities in patients and mortality cases affected by SARS-CoV2: A systematic review and meta-analysis, Revista do Instituto de Medicina Tropical de Sao Paulo, 62, (2020); Ferri J., Pavel P., Hatef M., Comparative study of techniques for large-scale feature selection, pattern recognition in practice, IV: Multiple paradigms, comparative studies and hybrid systems, (2001); Freaney P.M., Shah S.J., Khan S.S., COVID-19 and heart failure with preserved ejection fraction, JAMA, 324, 15, pp. 1499-1500, (2020); Ge E., Li Y., Wu S., Candido E., Wei X., Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: A population-based cohort study, PLoS One, 16, 10, (2021); Gili T., Benelli G., Buscarini E., Canetta C., La Piana G., Merli G., Scartabellati A., Vigano G., Sfogliarini R., Melilli G., Assandri R., Cazzato D., Rossi D.S., Usai S., Caldarelli G., Tramacere I., Pellegata G., Lauria G., SARS-COV-2 comorbidity network and outcome in hospitalized patients in Crema, Italy, PLoS One, 16, 3, (2021); 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Lim Z.J., Subramaniam A., Ponnapa Reddy M., Blecher G., Kadam U., Afroz A., Billah B., Ashwin S., Kubicki M., Bilotta F., Curtis J.R., Rubulotta F., Case fatality rates for patients with COVID-19 requiring invasive mechanical ventilation. A meta-analysis, American Journal of Respiratory and Critical Care Medicine, 203, 1, pp. 54-66, (2021); Li B., Yang J., Zhao F., Zhi L., Wang X., Liu L., Bi Z., Zhao Y., Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China, Clinical Research in Cardiology, 109, 5, pp. 531-538, (2020); Lundberg S., Lee S., A unified approach to interpreting model predictions, 31st conference on neural information processing systems, (2017); Lu R., Zhao X., Li J., Niu P., Yang B., Wu H., Wang W., Song H., Huang B., Zhu N., Bi Y., Ma X., Zhan F., Wang L., Hu T., Zhou H., Hu Z., Zhou W., Zhao L., Tan W., Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding, Lancet, 395, 10224, pp. 565-574, (2020); Mason K.E., Maudsley G., McHale P., Pennington A., Day J., Barr B., Age-adjusted associations between comorbidity and outcomes of COVID-19: A review of the evidence from the early stages of the pandemic, Frontiers in Public Health, 9, (2021); Guia de vigilância epidemiológica: Emergência de saúde pública de importância nacional pela doença pelo coronavírus 2019 – covid-19, (2022); Passarelli-Araujo H., Pott-Junior H., Susuki A.M., Olak A.S., Pescim R.R., Tomimatsu M., Urbano M.R., The impact of COVID-19 vaccination on case fatality rates in a city in Southern Brazil, American Journal of Infection Control, 50, pp. 491-496, (2022); R Core Team, R: A language and environment for statistical computing, (2018); Richardson S., Hirsch J.S., Narasimhan M., Crawford J.M., McGinn T., Davidson K.W., the Northwell C.-R.C., Barnaby D.P., Becker L.B., Chelico J.D., Cohen S.L., Cookingham J., Coppa K., Diefenbach M.A., Dominello A.J., Duer-Hefele J., Falzon L., Gitlin J., Hajizadeh N., Zanos T.P., Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area, JAMA, 323, 20, pp. 2052-2059, (2020); Rossum V., Drake F., Python 3 reference manual, CreateSpace, Scotts Valley, (2009); Veech A.J., A probabilistic model for analysing species co-occurrence, Global Ecology and Biogeography, (2012); Yang D., Leibowitz J.L., The structure and functions of coronavirus genomic 3' and 5' ends, Virus Research, 206, pp. 120-133, (2015); Zirpe K.G., Tiwari A.M., Gurav S.K., Deshmukh A.M., Suryawanshi P.B., Wankhede P.P., Kapse U.S., Bhoyar A.P., Khan A.Z., Malhotra R.V., Kusalkar P.H., Chavan K.J., Naik S.A., Bhalke R.B., Bhosale N.N., Makhija S.V., Kuchimanchi V.N., Jadhav A.S., Deshmukh K.R., Kulkarni G.S., Timing of invasive mechanical ventilation and mortality among patients with severe COVID-19-associated acute respiratory distress syndrome, Indian Journal of Critical Care Medicine, 25, 5, pp. 493-498, (2021)","H. Passarelli-Araujo; Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; email: passarelli@ufmg.br; R.R. Pescim; Departamento de Estatística, Universidade Estadual de Londrina, Londrina, PR, Brazil; email: rrpescim@uel.br","","Elsevier B.V.","","","","","","23526483","","","","English","Smart Health","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85139738611"
"Bhosale Y.H.; Patnaik K.S.","Bhosale, Yogesh H. (57772765000); Patnaik, K. Sridhar (24438167800)","57772765000; 24438167800","PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates","2023","Biomedical Signal Processing and Control","81","","104445","","","","58","10.1016/j.bspc.2022.104445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144056056&doi=10.1016%2fj.bspc.2022.104445&partnerID=40&md5=498cb1874b19fecd0eb936eae2701b23","Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India","Bhosale Y.H., Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India; Patnaik K.S., Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India","Background and Objective: In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods: Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results: PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion: The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality. © 2022 Elsevier Ltd","Biomedical engineering; Chronic Obstructive Pulmonary Diseases (COPD); Convolution neural networks (CNN); COVID-19; Diagnosis & Classification; Ensemble deep learning; Medical Imaging; Transfer learning","Biological organs; Biomedical engineering; Classification (of information); Computer aided diagnosis; Convolution; Convolutional neural networks; Deep neural networks; Forecasting; Image classification; Learning systems; Medical imaging; Pulmonary diseases; Transfer learning; Chest X-ray image; Chronic obstructive pulmonary disease; Convolution neural network; Convolutional neural network; Diagnose & classification; Ensemble deep learning; Learning models; Transfer learning; accuracy; algorithm; Article; atelectasis; bacterial pneumonia; cardiomegaly; chronic disease; chronic lung disease; chronic obstructive lung disease; convolutional neural network; coronavirus disease 2019; deep neural network; disease classification; disease severity; effusion; human; mortality rate; performance indicator; pneumothorax; prediction; sensitivity and specificity; thorax radiography; transfer of learning; virus pneumonia; COVID-19","","","","","","","(2022); Albawi S., Mohammed T.A., Al-Zawi S., Understanding of a convolutional neural network, Proc. Int. Conf, pp. 1-6, (2017); Cao Z., Liao T., Song W., Chen Z., Li C., Detecting the shuttlecock for a badminton robot: A YOLO based approach, Expert Systems with Applications, 164, (2021); Ma C., Liu Z., Cao Z., Song W., Zhang J., Zeng W., Cost-sensitive deep forest for price prediction, Pattern Recognition, 107, (2020); Chen S., Dobriban E., Lee J.H., (1907); Jakubovitz D., Giryes R., Rodrigues M.R., pp. 153-193, (2019); Polikar R., “Ensemble learning,” in Ensemble Machine Learning, Ensemble Machine Learning, pp. 1-34, (2012); Chowdhury M.E.H., Rahman T., Khandakar A., Mazhar R., Kadir M.A., Mahbub Z.B., Islam K.R., Khan M.S., Iqbal A., Emadi N.A., Reaz M.B.I., Islam M.T., Can AI Help in Screening Viral and COVID-19 Pneumonia?, IEEE Access, 8, pp. 132665-132676, (2020); Svetnik V., Wang T., Tong C., Liaw A., Sheridan R.P., Song Q., “Boosting: An ensemble learning tool for compound classification and QSAR modeling, J. Chem. Inf. 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Virol., 92, 7, pp. 903-908, (2020); Huang G., Li Y., Pleiss G., Liu Z., Hopcroft J.E., Weinberger K.Q., (2017); Geron A., In: Hands-on machine learning with scikit-learn, keras, and TensorFlow, (2019); Ledezma C.A., Zhou X., Rodriguez B., Tan P.J., Diaz-Zuccarini V., A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG, PLoS ONE, 14, 8, (2019); Semenzato L., Botton J., Drouin J., Cuenot F., Dray-Spira R., Weill A., Zureik M., Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people, The Lancet Regional Health - Europe, 8, (2021); Geng J., Yu X., Bao H., Feng Z., Yuan X., Zhang J., Chen X., Chen Y., Li C., Yu H., Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review with Cumulative Meta-Analysis, Front. Med., 8, (2021); Maghdid H.S., Asaad A.T., Ghafoor K.Z., Sadiq A.S., Khan M.K., (2004); Wang L., Lin Z.Q., Wong A., COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images, Sci Rep, 10, 1, (2020); Abdel-Basset M., Chang V., Hawash H., Chakrabortty R.K., Ryan M., FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection, Knowledge-Based Systems, 212, (2021); Hall L.O., Paul R., Goldgof D.B.; Singh G.A.P., Gupta P., Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans, Neural Computing Applications, 31, 10, pp. 6863-6877, (2019); Klang E., Deep learning and medical imaging, J Thorac Dis, 10, 3, pp. 1325-1328, (2018); Shan F., Gao Y., Wang J., Shi W., Shi N., Han M., Et al.; Liu J., Pan Y., Li M., Chen Z., Tang L., Lu C., Et al., Applications of deep learning to mri images: asurvey, Big Data Min Anal, 1, 1, pp. 1-18, (2018); Rajaraman S., Siegelman J., Alderson P.O., Folio L.S., Folio L.R., Antani S.K., Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays, IEEE Access, 8, pp. 115041-115050, (2020); Aveyard P., Gao M., Lindson N., Hartmann-Boyce J., Watkinson P., Young D., Coupland C.A.C., Tan P.S., Clift A.K., Harrison D., Gould D.W., Pavord I.D., Hippisley-Cox J., Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study, The Lancet Respiratory Medicine, 9, 8, pp. 909-923, (2021); Rahimzadeh M., Attar A., (2020); Alqudah A.M., Qazan S., Alquran H.H., Alquran H., Qasmieh I.A., Alqudah A., (2020); Hemdan E.E.-D., Shouman M.A., Karar M.E., (2020); El Asnaoui K., Chawki Y., Using X-ray images and deep learning for automated detection of coronavirus disease, J Biomol Struct Dyn, 39, 10, pp. 3615-3626, (2021); (2020); Narin A., Kaya C., Pamuk Z., Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal Applic, 24, 3, pp. 1207-1220, (2021); Ucar F., Korkmaz D., (2020); Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Rajendra Acharya U., Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput. Biol. Med., 121, (2020); Loey M., Smarandache F., Khalifa N.E.M., Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning, Symmetry, 12, (2020); Tang S., Wang C., Nie J., Kumar N., Zhang Y., Xiong Z., Barnawi A., EDL-COVID: Ensemble Deep Learning For COVID-19 Cases Detection From Chest X-ray Iimages. IEEE Transactions On Industrial, Info, 17, 9, (2021); Zhou T., Lu H.L., Et al.; Tahamtan A., Ardebili A., Real-time RT-PCR in COVID-19 detection: Issues affecting the results, Expert Rev. Mol. Diagn., 10, 5, pp. 453-454, (2020); Bhosale Y.H., Patnaik K.S.; Bhosale Y.H., Patnaik K.S., pp. 1-6; Bhosale Y.H., Et al., pp. 1398-1402; Ilhan H.O., Serbes G., Aydin N., Decision and feature level fusion of deep features extracted from public COVID-19 data-sets, Applied Intelligence, 52, 8, pp. 8551-8571, (2022)","Y.H. Bhosale; Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Mesra, 835215, India; email: yogeshbhosale988@gmail.com","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85144056056"
"Luo X.; Gandhi P.; Storey S.; Huang K.","Luo, Xiao (57195635280); Gandhi, Priyanka (57210155622); Storey, Susan (12799570500); Huang, Kun (57206831361)","57195635280; 57210155622; 12799570500; 57206831361","A Deep Language Model for Symptom Extraction From Clinical Text and its Application to Extract COVID-19 Symptoms From Social Media","2022","IEEE Journal of Biomedical and Health Informatics","26","4","","1737","1748","11","19","10.1109/JBHI.2021.3123192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118587974&doi=10.1109%2fJBHI.2021.3123192&partnerID=40&md5=987f2cd414231be1610bc40a71ac6f8d","Department Of Computer Information Technology, Iupui, Indianapolis, 46202, IN, United States; Department Of Computer Information Science, Iupui, Indianapolis, 46202, IN, United States; School Of Nursing, Indiana University, Indianapolis, 46202, IN, United States; Indiana University, School Of Medicine, Indianapolis, 46202, IN, United States","Luo X., Department Of Computer Information Technology, Iupui, Indianapolis, 46202, IN, United States; Gandhi P., Department Of Computer Information Science, Iupui, Indianapolis, 46202, IN, United States; Storey S., School Of Nursing, Indiana University, Indianapolis, 46202, IN, United States; Huang K., Indiana University, School Of Medicine, Indianapolis, 46202, IN, United States","Patients experience various symptoms when they haveeither acute or chronic diseases or undergo some treatments for diseases. Symptoms are often indicators of the severity of the disease and the need for hospitalization. Symptoms are often described in free text written as clinical notes in the Electronic Health Records (EHR) and are not integrated with other clinical factors for disease prediction and healthcare outcome management. In this research, we propose a novel deep language model to extract patient-reported symptoms from clinical text. The deep language model integrates syntactic and semantic analysis for symptom extraction and identifies the actual symptoms reported by patients and conditional or negation symptoms. The deep language model can extract both complex and straightforward symptom expressions. We used a real-world clinical notes dataset to evaluate our model and demonstrated that our model achieves superior performance compared to three other state-of-the-art symptom extraction models. We extensively analyzed our model to illustrate its effectiveness by examining each component's contribution to the model. Finally, we applied our model on a COVID-19 tweets data set to extract COVID-19 symptoms. The results show that our model can identify all the symptoms suggested by the Center for Disease Control (CDC) ahead of their timeline and many rare symptoms.  © 2013 IEEE.","COVID-19; deep language model; Natural language processing; social media; symptom extraction","COVID-19; Electronic Health Records; Humans; Language; Natural Language Processing; Social Media; Clinical research; Computational linguistics; Data mining; Diseases; Extraction; Natural language processing systems; Social networking (online); Syntactics; Unified Modeling Language; Clinical notes; COVID-19; Deep language model; ITS applications; Language model; Pain; Patient experiences; Social media; Symptom extraction; anxiety; Article; asthma; coronavirus disease 2019; data mining; disease control; dyspnea; electronic health record; entropy; headache; heart failure; hospitalization; human; language test; machine learning; medical informatics; mental disease; natural language processing; paresthesia; prevalence; psychiatrist; social media; support vector machine; World Health Organization; language; natural language processing; Semantics","","","","","National Institute of General Medical Sciences, NIGMS, (R15GM139094); National Institute of General Medical Sciences, NIGMS","","Molarius A., Janson S., Self-rated health, chronic diseases, and symptoms among middle-aged and elderly men and women, J. 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Informat., 6, 4, (2018); Emadzadeh E., Sarker A., Nikfarjam A., Gonzalez G., Hybrid se785 mantic analysis for mapping adverse drug reaction mentions in tweets to 786 medical terminology, Proc. Amer. Med. Informat. Assoc. Annu. Symp., 2017, pp. 679-688, (2017); Dandala B., Joopudi V., Devarakonda M., Adverse drug events detection in clinical notes by jointly modeling entities and relations using neural networks, Drug Saf., 42, 1, pp. 135-146, (2019); Armengol-Estape J., Soares F., Marimon M., Krallinger M., PharmacoNER tagger: A deep learning-based tool for automatically finding chemicals and drugs in spanishmedical texts, Genomic. Informat., 17, 2, pp. 1-7, (2019); Cai X., Dong S., Hu J., A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records, BMC Med. Informat. Decis. 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"Ibrahim Z.M.; Bean D.; Searle T.; Qian L.; Wu H.; Shek A.; Kraljevic Z.; Galloway J.; Norton S.; Teo J.T.; Dobson R.J.B.","Ibrahim, Zina M. (57211618186); Bean, Daniel (55971358800); Searle, Thomas (55922066200); Qian, Linglong (57226188126); Wu, Honghan (34874014100); Shek, Anthony (57221445250); Kraljevic, Zeljko (57217635988); Galloway, James (37016394700); Norton, Sam (15837698200); Teo, James T (11940769800); Dobson, Richard Jb (8931612400)","57211618186; 55971358800; 55922066200; 57226188126; 34874014100; 57221445250; 57217635988; 37016394700; 15837698200; 11940769800; 8931612400","A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes from Electronic Health Records Data","2022","IEEE Journal of Biomedical and Health Informatics","26","1","","423","435","12","16","10.1109/JBHI.2021.3089287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112215778&doi=10.1109%2fJBHI.2021.3089287&partnerID=40&md5=879678c124428ff29e9541f53a9a4fec","Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Institute of Health Inforamtics, University College London, London, United Kingdom; Centre for Rheumatic Diseases, King's College London, London, United Kingdom; Department of Psychology, The Department of Inflammation Biology, King's College London, London, United Kingdom; NHS Foundation Trust, King's College Hospital, London, United Kingdom","Ibrahim Z.M., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Bean D., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Searle T., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Qian L., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Wu H., Institute of Health Inforamtics, University College London, London, United Kingdom; Shek A., Institute of Health Inforamtics, University College London, London, United Kingdom; Kraljevic Z., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; Galloway J., Centre for Rheumatic Diseases, King's College London, London, United Kingdom; Norton S., Department of Psychology, The Department of Inflammation Biology, King's College London, London, United Kingdom; Teo J.T., NHS Foundation Trust, King's College Hospital, London, United Kingdom; Dobson R.J.B., Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom, Institute of Health Inforamtics, University College London, London, United Kingdom","The ability to perform accurate prognosis is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission and readmission from time-series of vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked ensemble platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction by incorporating static features. The model is used to assess a patient's risk of adversity and provides visual justifications of its prediction. Results of three case studies show that the model outperforms existing platforms in ICU and general ward settings, achieving average Precision-Recall Areas Under the Curve (PR-AUCs) of 0.891 (95% CI: 0.878-0.939) for mortality and 0.908 (95% CI: 0.870-0.935) in predicting ICU admission and readmission.  © 2013 IEEE.","Clinical Outcome Prediction; Ensemble Learning; Gradient Boost; Imbalanced time-series; Long Short Term Memory networks (LSTM); Machine Learning; Mortality Prediction; Outlier Detection; Stacked Ensemble","Electronic Health Records; Hospitalization; Humans; Length of Stay; Machine Learning; Retrospective Studies; ROC Curve; Decision making; Diagnosis; Distillation; Distilleries; Intensive care units; Learning systems; Long short-term memory; Predictive analytics; Risk assessment; Time series; Areas under the curves; Clinical decision making; Dynamic signals; Electronic health record; Gradient boosting; Hospital admissions; Outcome prediction; Resource management; aged; area under the curve; Article; asthma; chronic kidney failure; chronic obstructive lung disease; clinical outcome; controlled study; coronavirus disease 2019; diagnostic test accuracy study; dyspnea; electronic health record; female; heart arrest; heart failure; hospital admission; hospital readmission; hospitalization; human; ICD-9; intensive care unit; long short term memory network; machine learning; major clinical study; male; mortality; pneumonia; prediction; receiver operating characteristic; reverse transcription polymerase chain reaction; sensitivity and specificity; Severe acute respiratory syndrome coronavirus 2; thorax pain; hospitalization; length of stay; machine learning; retrospective study; Forecasting","","","","","University College London Hospitals Biomedical Research Centre, UCLH BRC; Medical Research Council, MRC; National Institute for Health and Care Research, NIHR; King's College London; Manchester Biomedical Research Centre, BRC; King's College Hospital NHS Foundation Trust; King’s Medical Research Trust, (AI4VBH); UK Research and Innovation, UKRI, (MR/S00310X/1); Horizon 2020 Framework Programme, H2020, (116074); HDR, (MR/S004149/1); Wellcome Trust, WT, (PIII054)","Funding text 1: The work of Zina M. Ibrahim and Richard JB Dobson was supported by in part by the NIHR Biomedical Research Centre at SLaM, in part by Kings College London, London, U.K., and in part by the NIHR University College London Hospitals Biomedical Research Centre. The work of Richard JB Dobson was also supported in part by Health Data Research (HDR), U.K., and in part by The BigData@Heart Consortium under Grant 116074. The work of Daniel Bean was supported by a UKRI Innovation Fellowship as part of Health Data Research U.K. under Grant MR/S00310X/1. The work of Honghan Wu was supported by MRC and HDR U.K. under Grant MR/S004149/1, and in part by the Wellcome Institutional Translation Partnership Award (PIII054). The work of Anthony Shek was supported by a King\u2019s Medical Research Trust studentship. The work of JTHT was supported by the London AI Medical Imaging Centre for Value-Based Healthcare (AI4VBH), and in part by the NIHR Applied Research Collaboration South London at King\u2019s College Hospital NHS Foundation Trust.; Funding text 2: Manuscript received November 18, 2020; revised March 26, 2021 and May 22, 2021; accepted June 6, 2021. Date of publication June 15, 2021; date of current version January 5, 2022. The work of Zina M. Ibrahim and Richard JB Dobson was supported by in part by the NIHR Biomedical Research Centre at SLaM, in part by Kings College London, London, U.K., and in part by the NIHR University College London Hospitals Biomedical Research Centre. The work of Richard JB Dobson was also supported in part by Health Data Research (HDR), U.K., and in part by The BigData@Heart Consortium under Grant 116074. The work of Daniel Bean was supported by a UKRI Innovation Fellowship as part of Health Data Research U.K. under Grant MR/S00310X/1. The work of Honghan Wu was supported by MRC and HDR U.K. under Grant MR/S004149/1, and in part by the Wellcome Institutional Translation Partnership Award (PIII054). The work of Anthony Shek was supported by a King\u2019s Medical Research Trust studentship. The work of JTHT was","Aczon M., Et al., Dynamic mortality risk predictions in pediatric critical care using recurrent neural networks, CoRR, (2017); Armstrong R., Et al., The incidence of cardiac arrest in the intensive care unit: A systematic review and meta-analysis, Pediatr. Crit. Care Med., 20, 2, pp. 144-154, (2019); Audit: Key Statistics from the Case Mix Programme, (2013); Awad A., Et al., Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach, Int. J. Med. Informat., 108, pp. 185-195, (2017); Bai T., Zhang S., Egleston B., Vucetic S., Interpretable representation learning for healthcare via capturing disease progression through time, Proc. ACM SIGKDD Int. Conf. Knowl. 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Technol., pp. 23-27, (2017); Yadaw A., Et al., Clinical features of COVID-19 mortality: Development and validation of a clinical predictionmodel, Lancet Digit. Health, 2, 1, pp. E516-E525, (2020); Yan L., Zhang H., Yuan Y., An interpretable mortality prediction model for COVID-19 patients, Nature Mach. Intell., 2, pp. 283-288, (2020)","Z.M. Ibrahim; Department of Biostatistics and Health Informatics, King's College London, London, United Kingdom; email: zina.ibrahim@kcl.ac.uk","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","34129509","English","IEEE J. Biomedical Health Informat.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85112215778"
"Mayampurath A.; Ajith A.; Anderson-Smits C.; Chang S.-C.; Brouwer E.; Johnson J.; Baltasi M.; Volchenboum S.; Devercelli G.; Ciaccio C.E.","Mayampurath, Anoop (8605166600); Ajith, Aswathy (57205302855); Anderson-Smits, Colin (37074125400); Chang, Shun-Chiao (57915995100); Brouwer, Emily (15755339900); Johnson, Julie (57225011356); Baltasi, Michael (57915995200); Volchenboum, Samuel (6506930602); Devercelli, Giovanna (6508248484); Ciaccio, Christina E. (14631476700)","8605166600; 57205302855; 37074125400; 57915995100; 15755339900; 57225011356; 57915995200; 6506930602; 6508248484; 14631476700","Early Diagnosis of Primary Immunodeficiency Disease Using Clinical Data and Machine Learning","2022","Journal of Allergy and Clinical Immunology: In Practice","10","11","","3002","3007.e5","","15","10.1016/j.jaip.2022.08.041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139324132&doi=10.1016%2fj.jaip.2022.08.041&partnerID=40&md5=cd9061ec70e98dd39ceef003e1ae485f","Department of Pediatrics, University of Chicago, Chicago, Ill; Center for Research Informatics, University of Chicago, Chicago, Ill; Takeda Development Center Americas, Inc., Cambridge, Mass, United States","Mayampurath A., Department of Pediatrics, University of Chicago, Chicago, Ill; Ajith A., Center for Research Informatics, University of Chicago, Chicago, Ill; Anderson-Smits C., Takeda Development Center Americas, Inc., Cambridge, Mass, United States; Chang S.-C., Takeda Development Center Americas, Inc., Cambridge, Mass, United States; Brouwer E., Takeda Development Center Americas, Inc., Cambridge, Mass, United States; Johnson J., Center for Research Informatics, University of Chicago, Chicago, Ill; Baltasi M., Center for Research Informatics, University of Chicago, Chicago, Ill; Volchenboum S., Department of Pediatrics, University of Chicago, Chicago, Ill; Devercelli G., Takeda Development Center Americas, Inc., Cambridge, Mass, United States; Ciaccio C.E., Department of Pediatrics, University of Chicago, Chicago, Ill","Background: Primary immunodeficiency diseases (PIDD) are a group of immune-related disorders that have a current median delay of diagnosis between 6 and 9 years. Early diagnosis and treatment of PIDD has been associated with improved patient outcomes. Objective: To develop a machine learning model using elements within the electronic health record data that are related to prior symptomatic treatment to predict PIDD. Methods: We conducted a retrospective study of patients with PIDD identified using inclusion criteria of PIDD-related diagnoses, immunodeficiency-specific medications, and low immunoglobulin levels. We constructed a control group of age-, sex-, and race-matched patients with asthma. The primary outcome was the diagnosis of PIDD. We considered comorbidities, laboratory tests, medications, and radiological orders as features, all before diagnosis and indicative of symptom-related treatment. Features were presented sequentially to logistic regression, elastic net, and random forest classifiers, which were trained using a nested cross-validation approach. Results: Our cohort consisted of 6422 patients, of whom 247 (4%) were diagnosed with PIDD. Our logistic regression model with comorbidities demonstrated good discrimination between patients with PIDD and those with asthma (c-statistic: 0.62 [0.58-0.65]). Adding laboratory results, medications, and radiological orders improved discrimination (c-statistic: 0.70 vs 0.62, P < .001), sensitivity, and specificity. Extending to the advanced machine learning models did not improve performance. Conclusions: We developed a prediction model for early diagnosis of PIDD using historical data that are related to symptomatic care, which has potential to fill an important need in reducing the time to diagnose PIDD, leading to better outcomes for immunodeficient patients. © 2022 The Authors","Common variable immunodeficiency (CVID); Electronic health record (EHR); Immunodeficiency; Machine learning; Primary immunodeficiency diseases (PIDD); Specific antibody deficiency","Asthma; Early Diagnosis; Humans; Immunologic Deficiency Syndromes; Machine Learning; Primary Immunodeficiency Diseases; Retrospective Studies; alanine aminotransferase; albumin; alkaline phosphatase; aspartate aminotransferase; bilirubin; creatinine; hemoglobin A1c; potassium; protein; sodium; adult; agammaglobulinemia; anion gap; area under the curve; Article; asthma; calcium blood level; case control study; chloride blood level; clinical study; cohort analysis; common variable immunodeficiency; comorbidity; computer assisted tomography; controlled study; cross validation; demographics; diagnostic test accuracy study; discrimination learning; early diagnosis; elastic tissue; electronic health record; ethnicity; female; glucose blood level; hematocrit; hemoglobin blood level; human; immune deficiency; immunoglobulin deficiency; laboratory test; lactate blood level; leukocyte count; logistic regression analysis; machine learning; major clinical study; male; neutrophil count; observational study; platelet count; race; random forest; receiver operating characteristic; retrospective study; sensitivity and specificity; sodium blood level; stem cell; thorax radiography; urea nitrogen blood level; asthma; complication; early diagnosis; immune deficiency; machine learning","","alanine aminotransferase, 9000-86-6, 9014-30-6; alkaline phosphatase, 9001-78-9; aspartate aminotransferase, 9000-97-9; bilirubin, 18422-02-1, 635-65-4; creatinine, 19230-81-0, 60-27-5; hemoglobin A1c, 62572-11-6; potassium, 7440-09-7; protein, 67254-75-5; sodium, 7440-23-5","","","National Institutes of Health, NIH, (K01HL148390); Food Allergy Research and Education, FARE; AbbVie; Takeda Pharmaceutical Company, TPC","Funding text 1: Conflicts of interest: A. Mayampurath is supported by the National Institutes of Health (NIH) K01HL148390 and reports personal fees from Litmus Health outside of submitted work. S. Volchenboum reported personal fees from Sanford Health, CVS Accordant, and AbbVie, and stock in Litmus Health all outside the submitted work. C. E. Ciaccio receives research grant support from the NIH, Food Allergy Research and Education (FARE), Paul and Mary Yovovich, and Takeda and has served as a medical consultant/advisor for Aimmune Therapeutics, Genentech, Novartis, ALK, DBV Technologies, Siolta, Clostrabio, and FARE. C. Anderson-Smits, S.-C. Chang, E. Brouwer, and G. Devercelli are employees of Takeda Development Center Americas, Inc., and have stock ownership in Takeda. The rest of the authors declare that they have no relevant conflicts of interest. Takeda Development Center Americas, Inc. provided funding for this collaborative research project.; Funding text 2: Conflicts of interest: A. Mayampurath is supported by the National Institutes of Health (NIH) K01HL148390 and reports personal fees from Litmus Health outside of submitted work. S. Volchenboum reported personal fees from Sanford Health, CVS Accordant, and AbbVie, and stock in Litmus Health all outside the submitted work. C. E. Ciaccio receives research grant support from the NIH, Food Allergy Research and Education (FARE), Paul and Mary Yovovich, and Takeda and has served as a medical consultant/advisor for Aimmune Therapeutics , Genentech , Novartis , ALK, DBV Technologies , Siolta, Clostrabio, and FARE. C. Anderson-Smits, S.-C. Chang, E. Brouwer, and G. Devercelli are employees of Takeda Development Center Americas, Inc., and have stock ownership in Takeda. The rest of the authors declare that they have no relevant conflicts of interest. ","Kobrynski L., Powell R.W., Bowen S., Prevalence and morbidity of primary immunodeficiency diseases, United States 2001-2007, J Clin Immunol, 34, pp. 954-961, (2014); Cunningham-Rundles C., Bodian C., Common variable immunodeficiency: clinical and immunological features of 248 patients, Clin Immunol, 92, pp. 34-48, (1999); Slade C.A., Bosco J.J., Binh Giang T., Kruse E., Stirling R.G., Cameron P.U., Et al., Delayed diagnosis and complications of predominantly antibody deficiencies in a cohort of Australian adults, Front Immunol, 9, (2018); Rider N.L., Miao D., Dodds M., Modell V., Modell F., Quinn J., Et al., Calculation of a primary immunodeficiency “risk vital sign” via population-wide analysis of claims data to aid in clinical decision support, Front Pediatr, 7, (2019); Buckley R.H., Schiff R.I., The use of intravenous immune globulin in immunodeficiency diseases, N Engl J Med, 325, pp. 110-117, (1991); Gardulf A., Nicolay U., Asensio O., Bernatowska E., Bock A., Carvalho B.C., Et al., Rapid subcutaneous IgG replacement therapy is effective and safe in children and adults with primary immunodeficiencies—a prospective, multi-national study, J Clin Immunol, 26, pp. 177-185, (2006); Orange J.S., Grossman W.J., Navickis R.J., Wilkes M.M., Impact of trough IgG on pneumonia incidence in primary immunodeficiency: a meta-analysis of clinical studies, Clin Immunol, 137, pp. 21-30, (2010); van Wilder P., Odnoletkova I., Mouline M., de Vries E., Immunoglobulin replacement therapy is critical and cost-effective in increasing life expectancy and quality of life in patients suffering from common variable immunodeficiency disorders (CVID): a health-economic assessment, PLoS One, 16, (2021); Rider N.L., Srinivasan R., Khoury P., Artificial intelligence and the hunt for immunological disorders, Curr Opin Allergy Clin Immunol, 20, pp. 565-573, (2020); Rider N.L., Cahill G., Motazedi T., Wei L., Kurian A., Noroski L.M., Et al., PI prob: a risk prediction and clinical guidance system for evaluating patients with recurrent infections, PLoS One, 16, (2021); Elixhauser comorbidity software, (2017); DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, pp. 837-845, (1988); Stray-Pedersen A., Sorte H.S., Samarakoon P., Gambin T., Chinn I.K., Akdemir Z.H.C., Et al., Primary immunodeficiency diseases—genomic approaches delineate heterogeneous Mendelian disorders, J Allergy Clin Immunol, 139, pp. 232-245, (2017); Vorsteveld E.E., Hoischen A., van der Made C.I., Next-generation sequencing in the field of primary immunodeficiencies: current yield, challenges, and future perspectives, Clinic Rev Allerg Immunol, 61, pp. 212-225, (2021)","C.E. Ciaccio; Department of Pediatrics, University of Chicago, Chicago, 5841 South Maryland Ave, MS 5042, 60637; email: cciaccio@bsd.uchicago.edu","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36108921","English","J. Allergy Clin. Immunol. Pract.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85139324132"
"Kranthi Kumar L.; Alphonse P.J.A.","Kranthi Kumar, Lella (57225192891); Alphonse, P.J.A. (57200760920)","57225192891; 57200760920","COVID-19 disease diagnosis with light-weight CNN using modified MFCC and enhanced GFCC from human respiratory sounds","2022","European Physical Journal: Special Topics","231","18-20","","3329","3346","17","34","10.1140/epjs/s11734-022-00432-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123462489&doi=10.1140%2fepjs%2fs11734-022-00432-w&partnerID=40&md5=7df3949f2f8ff5b40551906bd12c2e17","Health Analytics Research Labs, Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu, Tiruchirappalli, 620015, India","Kranthi Kumar L., Health Analytics Research Labs, Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu, Tiruchirappalli, 620015, India; Alphonse P.J.A., Health Analytics Research Labs, Department of Computer Applications, NIT Tiruchirappalli, Tamil Nadu, Tiruchirappalli, 620015, India","In the last 2 years, medical researchers and clinical scientists have paid close attention to the problem of respiratory sound classification to classify COVID-19 disease symptoms. In the physical world, very few AI-based (Artificial Intelligence) techniques are often used to detect COVID-19/SARS-CoV-2 respiratory disease symptoms from the human respiratory system-generated acoustic sounds such as acoustic voice sound, breathing (inhale and exhale) sounds, and cough sound. We propose a light-weight Convolutional Neural Network (CNN) with Modified-Mel-frequency Cepstral Coefficient (M-MFCC) using different depths and kernel sizes to classify COVID-19 and other respiratory sound disease symptoms such as Asthma, Pertussis, and Bronchitis. The proposed network outperforms conventional feature extraction models and existing Deep Learning (DL) models for COVID-19/SARS-CoV-2 classification accuracy in the range of 4–10%. The model’s performance is compared with the COVID-19 crowdsourced benchmark dataset and gives a competitive performance. We applied different receptive fields and depths in the proposed model to get different contextual information that should aid in classification. And our experiments suggested 1 × 12 receptive fields and a depth of 5-Layer for the light-weight CNN to extract and identify the features from respiratory sound data. The model is also trained and tested with different modalities of data to showcase its effectiveness in classification. © 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature.","","","","","","","","","Coronavirus Disease (COVID-19), (2019); Wang Y., Hu M., Li Q., Abnormal Respiratory Patterns Classifier May Contribute to Large-Scale Screening of People Infected with COVID-19 in an Accurate and Unobtrusive Manner, (2020); Jiang Z., Hu M., Lei F., Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections Such as COVID-19 on Portable Device, (2020); Imran A., Posokhova I., Qureshi H.N., Et al., AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app, Inform. Med. Unlocked, 20, (2020); Shuja J., Alanazi E., Alasmary W., Et al., COVID-19 open source data sets: a comprehensive survey, Appl. Intell., 21, pp. 1-30, (2020); Rasheed J., Jamil A., Hameed A.A., Et al., A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic, Chaos Solitons Fractals, 141, (2020); Alafif T., Tehame A.M., Bajaba S., Et al., Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions, Int. J. Environ. Res. Public Health, 18, (2021); Ritwik K.V.S., Shareef B.K., Deepu V., COVID-19 patient detection from telephone quality speech data, Arxiv, (2020); Kranthi Kumar L., Alphonse P.J.A., A literature review on COVID-19 disease diagnosis from respiratory sound data, AIMS Bioeng., 8, 2, pp. 140-153, (2021); Easwaramoorthy D., Gowrisankar A., Manimaran A., Nandhini S., Rondoni L., Banerjee S., An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion, Nonlinear Dyn., 2021, pp. 1-21, (2021); Kavitha C., Gowrisankar A., Banerjee S., The second and third waves in India: when will the pandemic be culminated?, Eur. Phys. J. Plus, 136, (2021); Gowrisankar A., Rondoni L., Banerjee S., Can India develop herd immunity against COVID-19?, Eur. Phys. J. Plus, 135, 6, (2020); SreeJagadeesh M., Alphonse P.J.A., COVID-19 outbreak: an ensemble pre-trained deep learning model for detecting informative tweets,, Appl. Soft Comput., 107, (2021); KranthiKumar L., Alphonse P.J.A., Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice, AIMS Public Health, 8, 2, pp. 240-264, (2021); Huang Y., Meng S., Zhang Y., Et al., The Respiratory Sound Features of COVID-19 Patients Fill Gaps between Clinical Data and Screening Methods, (2020); Rebecca N., Et al., Symptom-based screening tool for asthma syndrome among young children in Uganda, NPJ Prim. Care Respir. Med., 30, (2020); Shi J., Zheng X., Li Y., Et al., Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer’s disease, IEEE J. Biomed. Health Inform., 22, pp. 173-183, (2018); Brabenec L., Mekyska J., Galaz Z., Et al., Speech disorders in Parkinson’s disease: early diagnostics and effects of medication and brain stimulation, J. Neural. Transm. (Vienna), 124, pp. 303-334, (2017); Erdogdu S.B., Serbes G., Sakar C.O., Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson’s disease, PLoS ONE, 12, (2017); Klara V., Viktor I., Krisztina M., Voice Disorder Detection on the Basis of Continuous Speech, (2011); Liu R., Cai S., Zhang K., Hu N., InDetection of Adventitious Respiratory Sounds based on Convolutional Neural Network, 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 298-303, (2019); Pasterkamp H., Kraman S.S., Wodicka G.R., Respiratory sounds: advances beyond the stethoscope, Am. J. Respir. Crit. Care Med., 156, 3, pp. 974-987, (1997); KranthiKumar L., Alphonse P.J.A., A literature review on COVID-19 disease diagnosis from respiratory sound data, AIMS Bioeng., 8, 2, pp. 140-153, (2021); Brown C., Chauhan J., Grammenos A., Et al., Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data, Proceedings of the 26Th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2020); Lara O., Et al., The COUGHVID crowdsourcing dataset: A corpus for the study of large scale cough analysis algorithms, (2020); Wang Y., Abnormal Respiratory Patterns Classifier May Contribute to Large-Scale Screening of People Infected With COVID-19 in an Accurate and Unobtrusive Manner, Arxiv, 5534, (2020); Imran A., AI4COVID-19: AI-Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App, Arxiv:2004.01275V6 [Eess.As], (2020); Bader M., Et al., Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC, Arxiv:2010.08770V1, (2020); Jiang X., Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough, 2011, (2020); Al Ismail M., Detection of COVID-19 through the Analysis of Vocal Fold Oscillations, Arxiv, (2020); Laguarta J., Hueto F., Subirana B., COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings, IEEE Open Journal of Engineering in Medicine and Biology, (2020); Hassan A., Shahin I., Alsabek M.B., COVID-19 Detection System using Recurrent Neural Networks, 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Sharjah, United Arab Emirates, pp. 1-5, (2020); Quartieri T.F., Talker T., Palmer J.S., A framework for biomarkers of COVID-19 based on coordination of speech-production subsystems, IEEE Open J. Eng. Med. Biol., 1, pp. 203-206, (2020); Lella K.K., Pja A., Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice, AIMS Public Health, 8, 2, pp. 240-264, (2021); Lella K.K., Pja A., Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath. Alexandria Eng, J. ISSN, pp. 1110-0168, (2021); Han J., Qian K., Song M., An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety, Arxiv, 2005, (2020); Susanta Sarangi M., Sahidullah G.S., Optimization of data-driven filterbank for automatic speaker verification, Dig. Signal Process., 104, (2020); Dua M., Aggarwal R.K., Performance evaluation of Hindi speech recognition system using optimized filterbanks, Eng. Sci. Technol. Int. J., 21, 3, pp. 389-398, (2018); Adiga A., Magimai M., Seelamantula C.S., Gammatone wavelet Cepstral Coefficients for robust speech recognition, 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013), pp. 1-4, (2013); Krobba A., Debyeche M., Selouani S.A., Mixture linear prediction Gammatone Cepstral features for robust speaker verification under transmission channel noise, Multimed. Tools Appl., 79, pp. 18679-18693, (2020); Kranthi Kumar L., COVID-19 disease diagnosis with light-weight CNN. Figshare, Journal Contribution, (2022)","","","Springer Science and Business Media Deutschland GmbH","","","","","","19516355","","","","English","Eur. Phys. J.: Spec. Top.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85123462489"
"Smith L.A.; Oakden-Rayner L.; Bird A.; Zeng M.; To M.-S.; Mukherjee S.; Palmer L.J.","Smith, Luke A (57907889900); Oakden-Rayner, Lauren (57605502700); Bird, Alix (57906903200); Zeng, Minyan (57208244186); To, Minh-Son (55669090500); Mukherjee, Sutapa (7401816826); Palmer, Lyle J (7202347641)","57907889900; 57605502700; 57906903200; 57208244186; 55669090500; 7401816826; 7202347641","Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis","2023","The Lancet Digital Health","5","12","","e872","e881","9","21","10.1016/S2589-7500(23)00177-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177784980&doi=10.1016%2fS2589-7500%2823%2900177-2&partnerID=40&md5=7e7a1171cfaeb2a13d5eda566cd76de4","Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia; Health Data and Clinical Trials, Flinders University, Bedford Park, SA, Australia; South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia; Department of Respiratory and Sleep Medicine, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia; Adelaide Institute for Sleep Health/Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia","Smith L.A., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia, School of Public Health, University of Adelaide, Adelaide, SA, Australia; Oakden-Rayner L., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia, School of Public Health, University of Adelaide, Adelaide, SA, Australia; Bird A., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia, School of Public Health, University of Adelaide, Adelaide, SA, Australia; Zeng M., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia, School of Public Health, University of Adelaide, Adelaide, SA, Australia; To M.-S., Health Data and Clinical Trials, Flinders University, Bedford Park, SA, Australia, South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia; Mukherjee S., Department of Respiratory and Sleep Medicine, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia, Adelaide Institute for Sleep Health/Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia; Palmer L.J., Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia, School of Public Health, University of Adelaide, Adelaide, SA, Australia","Background: Machine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps. Methods: We conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18–90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052). Findings: We identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69–0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74–0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias. Interpretation: There is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility. Funding: None. © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license","","Adult; Deep Learning; Humans; Prognosis; Pulmonary Disease, Chronic Obstructive; Quality of Life; Reproducibility of Results; Diagnosis; Forecasting; Learning systems; Pulmonary diseases; Risk assessment; Chronic obstructive pulmonary disease; Conventional machines; Disease severity; Learning models; Machine-learning; Meta-analysis; Performance; Predictive models; Prognostic modeling; Systematic Review; Article; chronic obstructive lung disease; clinical feature; confidence interval; deep learning; disease exacerbation; disease severity; forced expiratory volume; human; machine learning; meta analysis; mortality risk; prediction; predictive model; prognosis; receiver operating characteristic; research gap; risk factor; systematic review; treatment outcome; adult; chronic obstructive lung disease; prognosis; quality of life; reproducibility; Deep learning","","","","","Australian Government Research Training Program","LAS, AB, and MZ were supported by the Australian Government Research Training Program scholarship. This work was not supported by a specific grant from any funding agencies in the public, commercial, or not-for-profit sectors. We thank Mary Filsell for her advice and guidance regarding strategies for literature searches. ","Chronic obstructive pulmonary disease (COPD); Global strategy for prevention, diagnosis and management of COPD: 2023 report; Zafari Z., Li S., Eakin M.N., Bellanger M., Reed R.M., Projecting long-term health and economic burden of COPD in the United States, Chest, 159, pp. 1400-1410, (2021); Bellou V., Belbasis L., Konstantinidis A.K., Tzoulaki I., Evangelou E., Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal, BMJ, 367, (2019); Austin P.C., Steyerberg E.W., Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable, BMC Med Res Methodol, 12, (2012); Lane N.D., Gillespie S.M., Steer J., Bourke S.C., Uptake of clinical prognostic tools in COPD exacerbations requiring hospitalisation, COPD, 18, pp. 406-410, (2021); Jordan M.I., Mitchell T.M., Machine learning: trends, perspectives, and prospects, Science, 349, pp. 255-260, (2015); Chauhan N.K., Singh K., (2018); Litjens G., Kooi T., Bejnordi B.E., Et al., A survey on deep learning in medical image analysis, Med Image Anal, 42, pp. 60-88, (2017); Tufail A.B., Ma Y.-K., Kaabar M.K.A., Et al., Deep learning in cancer diagnosis and prognosis prediction: a minireview on challenges, recent trends, and future directions, Comput Math Methods Med, 2021, (2021); Motwani M., Dey D., Berman D.S., Et al., Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis, Eur Heart J, 38, pp. 500-507, (2017); Washko G.R., The role and potential of imaging in COPD, Med Clin North Am, 96, pp. 729-743, (2012); Riva J.J., Malik K.M., Burnie S.J., Endicott A.R., Busse J.W., What is your research question? 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Moons K.G.M., Wolff R.F., Riley R.D., Et al., PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration, Ann Intern Med, 170, pp. W1-33, (2019); Fellicious C., Weissgerber T., Granitzer M., (2020); Siontis G.C.M., Tzoulaki I., Castaldi P.J., Ioannidis J.P.A., External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination, J Clin Epidemiol, 68, pp. 25-34, (2015); Steyerberg E.W., Moons K.G.M., van der Windt D.A., Et al., Prognosis research strategy (PROGRESS) 3: prognostic model research, PLoS Med, 10, (2013); Jang J.-H., Choi J., Roh H.W., Et al., Deep learning approach for imputation of missing values in actigraphy data: algorithm development study, JMIR Mhealth Uhealth, 8, (2020); Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: a simple way to prevent neural networks from overfitting, JMLR, 15, pp. 1929-1958, (2014); Ioffe S., Szegedy C., (2015); Shorten C., Khoshgoftaar T.M., A survey on image data augmentation for deep learning, J Big Data, 6, (2019); Belkin M., Hsu D., Mitra P., (2018); Dransfield M.T., Davis J.J., Gerald L.B., Bailey W.C., Racial and gender differences in susceptibility to tobacco smoke among patients with chronic obstructive pulmonary disease, Respir Med, 100, pp. 1110-1116, (2006); Mamary A.J., Stewart J.I., Kinney G.L., Et al., Race and gender disparities are evident in COPD underdiagnoses across all severities of measured airflow obstruction, Chronic Obstr Pulm Dis (Miami), 5, pp. 177-184, (2018); Gichoya J.W., Banerjee I., Bhimireddy A.R., Et al., AI recognition of patient race in medical imaging: a modelling study, Lancet Digit Health, 4, pp. e406-e414, (2022); Lopez-Campos J.L., Tan W., Soriano J.B., Global burden of COPD, Respirology, 21, pp. 14-23, (2016); McCradden M.D., Anderson J.A.A., A Stephenson E., Et al., A research ethics framework for the clinical translation of healthcare machine learning, Am J Bioeth, 22, pp. 8-22, (2022); Singhal K., Azizi S., Tu T., Et al., Large language models encode clinical knowledge, Nature, 620, pp. 172-180, (2023); Wei J., Bosma M., Vincent, Et al., Finetuned language models are zero-shot learners, arXiv, (2021); Keane P.A., Topol E.J., With an eye to AI and autonomous diagnosis, NPJ Digit Med, 1, (2018)","L.A. Smith; Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia; email: luke.a.smith@adelaide.edu.au","","Elsevier Ltd","","","","","","25897500","","","38000872","English","Lancet Digit. Heal.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85177784980"
"Barbieri S.; Mehta S.; Wu B.; Bharat C.; Poppe K.; Jorm L.; Jackson R.","Barbieri, Sebastiano (36622164700); Mehta, Suneela (37108022500); Wu, Billy (57039386100); Bharat, Chrianna (56592998300); Poppe, Katrina (20735112100); Jorm, Louisa (6701859739); Jackson, Rod (36506553200)","36622164700; 37108022500; 57039386100; 56592998300; 20735112100; 6701859739; 36506553200","Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach","2022","International Journal of Epidemiology","51","3","","931","944","13","23","10.1093/ije/dyab258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129587585&doi=10.1093%2fije%2fdyab258&partnerID=40&md5=ad889e6f273384d7877f6ba236ddd0db","Centre for Big Data Research in Health, University of New South Wales, Level 2, Sydney, 2052, NSW, Australia; Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand; National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia","Barbieri S., Centre for Big Data Research in Health, University of New South Wales, Level 2, Sydney, 2052, NSW, Australia; Mehta S., Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand; Wu B., Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand; Bharat C., National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia; Poppe K., Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand; Jorm L., Centre for Big Data Research in Health, University of New South Wales, Level 2, Sydney, 2052, NSW, Australia; Jackson R., Section of Epidemiology and Biostatistics, University of Auckland, Auckland, New Zealand","Background: Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets. Methods: Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30-74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables. Results: First CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001). Conclusions: Deep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.  © 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.","Cardiovascular diseases; deep learning; health planning; machine learning; population health; primary prevention; risk assessment; survival analysis","Cardiovascular Diseases; Deep Learning; Female; Heart Disease Risk Factors; Humans; Male; Proportional Hazards Models; Risk Assessment; Risk Factors; Survival Analysis; amfebutamone; anticoagulant agent; antihypertensive agent; antilipemic agent; antithrombocytic agent; cilazapril; felodipine; furosemide; glyceryl trinitrate; insulin; ipratropium bromide; ipratropium bromide plus salbutamol; malathion; nicotine; quinapril; salbutamol; simvastatin; smoking cessation agent; tiotropium bromide; varenicline; algorithm; cardiovascular disease; chronic obstructive pulmonary disease; health risk; health services; public health; risk factor; adult; aged; Article; atrial fibrillation; cardiovascular disease; cardiovascular risk; chronic kidney failure; chronic obstructive lung disease; cohort analysis; comparative study; controlled study; data base; deep learning; diabetes mellitus; essential hypertension; female; follow up; general anesthesia; hazard ratio; human; major clinical study; male; New Zealander; non insulin dependent diabetes mellitus; predictor variable; proportional hazards model; public health; respiratory tract infection; sex difference; survival analysis; systemic disease; thorax pain; tobacco use; cardiovascular disease; risk assessment; risk factor; survival analysis","","amfebutamone, 31677-93-7, 34911-55-2, 144445-76-1; cilazapril, 88768-40-5; felodipine, 72509-76-3; furosemide, 54-31-9; glyceryl trinitrate, 55-63-0, 80738-44-9; insulin, 9004-10-8; ipratropium bromide, 22254-24-6, 66985-17-9; malathion, 121-75-5; nicotine, 54-11-5; quinapril, 82586-55-8, 85441-61-8; salbutamol, 18559-94-9, 35763-26-9; simvastatin, 79902-63-9; tiotropium bromide, 136310-93-5; varenicline, 249296-44-4, 375815-87-5","","","","","Damen JA, Hooft L, Schuit E, Et al., Prediction models for cardiovascular disease risk in the general population: systematic review, BMJ, 353, (2016); Usher-Smith JA, Silarova B, Schuit E, Moons KG, Griffin SJ., Impact of provision of cardiovascular disease risk estimates to healthcare professionals and patients: A systematic review, BMJ Open, 5, (2015); Manuel DG, Rosella LC, Hennessy D, Sanmartin C, Wilson K., Predictive risk algorithms in a population setting: An overview, J Epidemiol Community Health, 66, pp. 859-865, (2012); Manuel DG, Rosella LC., Commentary: Assessing population (baseline) risk is a cornerstone of population health planning looking forward to address new challenges, Int J Epidemiol, 39, pp. 380-382, (2010); Mehta S, Jackson R, Pylypchuk R, Poppe K, Wells S, Kerr AJ., Development and validation of alternative cardiovascular risk prediction equations for population health planning: A routine health data linkage study of 1.7 million New Zealanders, Int J Epidemiol, 47, pp. 1571-1584, (2018); Cox DR., Regression models and life-Tables, J R Stat Soc Ser B Methodol, 34, pp. 187-202, (1972); Bzdok D, Altman N, Krzywinski M., Points of significance: statistics versus machine learning, Nat Methods, 15, (2018); Kvamme H, Borgan O, Scheel I., Time-To-event prediction with neural networks and Cox regression, J Mach Learn Res, 20, (2019); National Health Index Data Dictionary (Version 5.3) [Internet], (2009); Cardiovascular Disease Risk Assessment. Updated 2013 (New Zealand Primary Care Handbook 2012), (2013); Mehta S, Jackson R, Wells S, Harrison J, Exeter DJ, Kerr AJ., Cardiovascular medication changes over 5 years in a national data linkage study: implications for risk prediction models, Clin Epidemiol, 10, (2018); Alpaydm E., Combined 5 _ 2 CV F test for comparing supervised classification learning algorithms, Neural Comput, 11, (1999); Kingma DP, Ba J, Adam: A method for stochastic optimization, (2014); Fort S, Hu H, Lakshminarayanan B, Deep ensembles: A loss landscape perspective; Breslow NE., Discussion of the paper by D. R. Cox, J R Stat Soc B, 34, (1972); Royston P, Sauerbrei W., A new measure of prognostic separation in survival data, Stat Med, 23, pp. 723-748, (2004); Harrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA., Evaluating the yield of medical tests, JAMA, 247, (1982); Graf E, Schmoor C, Sauerbrei W, Schumacher M., Assessment and comparison of prognostic classification schemes for survival data, Stat Med, 18, pp. 2529-2545, (1999); Blattenberger G, Lad F., Separating the Brier score into calibration and refinement components: A graphical exposition, Am Stat, 39, (1985); Ambale-Venkatesh B, Yang X, Wu CO, Et al., Cardiovascular event prediction by machine learning: The multi-ethnic study of atherosclerosis, Circ Res, 121, pp. 1092-1101, (2017); Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, van der Schaar M., Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants, PLoS One, 14, (2019); Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N., Can machinelearning improve cardiovascular risk prediction using routine clinical data?, PLoS One, 12, (2017); Quesada JA, Lopez-Pineda A, Gil-Guill_en VF, Et al., Machine learning to predict cardiovascular risk, Int J Clin Pract, 73, (2019); Du Z, Yang Y, Zheng J, Et al., Accurate prediction of coronary heart disease for patients with hypertension from electronic health records with big data and machine-learning methods: model development and performance evaluation, JMIR Med Inform, 8, (2020); Zhao J, Feng Q, Wu P, Et al., Learning from longitudinal data in electronic health record and genetic data to improve cardiovascular event prediction, Sci Rep, 9, (2019); Li Y, Sperrin M, Ashcroft DM, van Staa TP., Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar, BMJ, 371, (2020); Strobl C, Boulesteix A-L, Zeileis A, Hothorn T., Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinformatics, 8, (2007); Nasejje JB, Mwambi H, Dheda K, Lesosky M., A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-To-event data, BMC Med Res Methodol, 17, (2017); Maas AH, Appelman YE., Gender differences in coronary heart disease, Neth Heart J, 18, pp. 598-603, (2010); Luo Y, Peng J, Ma J., When causal inference meets deep learning, Nat Mach Intell, 2, (2020)","S. Barbieri; Centre for Big Data Research in Health, University of New South Wales, Sydney, Level 2, 2052, Australia; email: s.barbieri@unsw.edu.au","","Oxford University Press","","","","","","03005771","","IJEPB","34910160","English","Int. J. Epidemiol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85129587585"
"Li L.; Ayiguli A.; Luan Q.; Yang B.; Subinuer Y.; Gong H.; Zulipikaer A.; Xu J.; Zhong X.; Ren J.; Zou X.","Li, Li (58741116300); Ayiguli, Alimu (57705860100); Luan, Qiyun (57705578900); Yang, Boyi (55613822000); Subinuer, Yilamujiang (57705303400); Gong, Hui (57273604800); Zulipikaer, Abudureherman (57705579000); Xu, Jingran (57273604700); Zhong, Xuemei (57201186191); Ren, Jiangtao (7403083639); Zou, Xiaoguang (57201185196)","58741116300; 57705860100; 57705578900; 55613822000; 57705303400; 57273604800; 57705579000; 57273604700; 57201186191; 7403083639; 57201185196","Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods","2022","Frontiers in Public Health","10","","881234","","","","15","10.3389/fpubh.2022.881234","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130626784&doi=10.3389%2ffpubh.2022.881234&partnerID=40&md5=1bd8e176c551f88b706f61d0e7194ffa","Department of Respiratory and Critical Care Medicine, First People's Hospital of Kashi, Kashi, China; Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; State Key Laboratory of Pathogenesis, Prevention, Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University, Ürümqi, China; Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China; Department of Software, Sun Yat-sen University, Guangzhou, China","Li L., Department of Respiratory and Critical Care Medicine, First People's Hospital of Kashi, Kashi, China, Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China, State Key Laboratory of Pathogenesis, Prevention, Treatment of High Incidence Diseases in Central Asia, Xinjiang Medical University, Ürümqi, China; Ayiguli A., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Luan Q., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Yang B., Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, China; Subinuer Y., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Gong H., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Zulipikaer A., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Xu J., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; Zhong X., Department of Respiratory and Critical Care Medicine, First People's Hospital of Kashi, Kashi, China; Ren J., Department of Software, Sun Yat-sen University, Guangzhou, China; Zou X., Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China","Objective: Based on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians. Methods: The method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with “jieba” word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information. Results: The precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease. Copyright © 2022 Li, Ayiguli, Luan, Yang, Subinuer, Gong, Zulipikaer, Xu, Zhong, Ren and Zou.","convolutional neural network; long-short-term memory network; medical records; predictive diagnosis; respiratory disease","Machine Learning; Memory, Short-Term; Neural Networks, Computer; machine learning; short term memory","","","","","International Science Editing; State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, (SKL-HIDCA-2020-KS1)","Funding text 1: The authors thank to the First Peoples Hospital of Kashi for their vigorous cooperation, and the participants for whom these studies were created and who generously volunteer time in completing the tasks. We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.; Funding text 2: This work was supported by the State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (SKL-HIDCA-2020-KS1). State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (SKL-HIDCA-2020-10). Tianshan Innovation Team Plan of Autonomous Region (2020D14013). 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Zou; Department of Clinical Research Center of Infectious Diseases (Pulmonary Tuberculosis), First People's Hospital of Kashi, Kashi, China; email: ZXGKashi@yeah.net; J. Ren; Department of Software, Sun Yat-sen University, Guangzhou, China; email: issrjt@mail.sysu.edu.cn","","Frontiers Media S.A.","","","","","","22962565","","","35602136","English","Front. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130626784"
"Chen F.; Zhang W.; Mfarrej M.F.B.; Saleem M.H.; Khan K.A.; Ma J.; Raposo A.; Han H.","Chen, Fu (57203586164); Zhang, Wanyue (58815263000); Mfarrej, Manar Fawzi Bani (57208142916); Saleem, Muhammad Hamzah (58674229900); Khan, Khalid Ali (56709204600); Ma, Jing (56393390600); Raposo, António (55257860600); Han, Heesup (21233360400)","57203586164; 58815263000; 57208142916; 58674229900; 56709204600; 56393390600; 55257860600; 21233360400","Breathing in danger: Understanding the multifaceted impact of air pollution on health impacts","2024","Ecotoxicology and Environmental Safety","280","","116532","","","","14","10.1016/j.ecoenv.2024.116532","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195261549&doi=10.1016%2fj.ecoenv.2024.116532&partnerID=40&md5=4486f88b1b015b0cf6bb6a4749fa66f4","School of Public Administration, Hohai University, Nanjing, 211100, China; Department of Environmental Sciences and Sustainability, College of Natural and Health Sciences, Zayed University, Abu Dhabi, 144534, United Arab Emirates; Office of Academic Research, Office of VP for Research & Graduate Studies, Qatar University, Doha, 2713, Qatar; Applied College, Center of Bee Research and its Products, Unit of Bee Research and Honey Production, and Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia; CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisboa, 1749-024, Portugal; College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul, 143-747, South Korea","Chen F., School of Public Administration, Hohai University, Nanjing, 211100, China; Zhang W., School of Public Administration, Hohai University, Nanjing, 211100, China; Mfarrej M.F.B., Department of Environmental Sciences and Sustainability, College of Natural and Health Sciences, Zayed University, Abu Dhabi, 144534, United Arab Emirates; Saleem M.H., Office of Academic Research, Office of VP for Research & Graduate Studies, Qatar University, Doha, 2713, Qatar; Khan K.A., Applied College, Center of Bee Research and its Products, Unit of Bee Research and Honey Production, and Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha, 61413, Saudi Arabia; Ma J., School of Public Administration, Hohai University, Nanjing, 211100, China; Raposo A., CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, Lisboa, 1749-024, Portugal; Han H., College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul, 143-747, South Korea","Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike, poses significant risks to human health, exacerbating respiratory conditions, triggering cardiovascular problems, and contributing to a myriad of other health complications across diverse populations worldwide. This article delves into the multifarious impacts of air pollution, utilizing cutting-edge research methodologies and big data analytics to offer a comprehensive overview. It highlights the emergence of new pollutants, their sources, and characteristics, thereby broadening our understanding of contemporary air quality challenges. The detrimental health effects of air pollution are examined thoroughly, emphasizing both short-term and long-term impacts. Particularly vulnerable populations are identified, underscoring the need for targeted health risk assessments and interventions. The article presents an in-depth analysis of the global disease burden attributable to air pollution, offering a comparative perspective that illuminates the varying impacts across different regions. Furthermore, it addresses the economic ramifications of air pollution, quantifying health and economic losses, and discusses the implications for public policy and health care systems. Innovative air pollution intervention measures are explored, including case studies demonstrating their effectiveness. The paper also brings to light recent discoveries and insights in the field, setting the stage for future research directions. It calls for international cooperation in tackling air pollution and underscores the crucial role of public awareness and education in mitigating its impacts. This comprehensive exploration serves not only as a scientific discourse but also as a clarion call for action against the invisible but insidious threat of air pollution, making it a vital read for researchers, policymakers, and the general public. © 2024 The Authors","Air quality monitoring; Environmental health; Pollution control strategies; Public health policy; Sustainable urban planning; Technological innovations in air purification","Air Pollutants; Air Pollution; Cardiovascular Diseases; Environmental Exposure; Environmental Monitoring; Humans; Particulate Matter; Risk Assessment; nitrate; organic carbon; sulfate; air quality; atmospheric pollution; health impact; health policy; innovation; policy approach; pollution control; pollution monitoring; public health; purification; urban planning; acquired immune deficiency syndrome; air pollution; air quality; anxiety; Article; artificial intelligence; asthma; awareness; behavior change; big data; bioinformatics; breathing; breathing disorder; bronchitis; cardiovascular disease; chronic obstructive lung disease; climate change; conceptual framework; coughing; degenerative disease; depression; disease burden; dyspnea; education; emphysema; environmental aspects and related phenomena; environmental health; environmental monitoring; fatigue; gas; global health; greenhouse effect; greenhouse gas; health care cost; health care policy; health disparity; health hazard; health promotion; heart disease; human; Human immunodeficiency virus infection; human impact (environment); indoor air pollution; industrialization; information technology; life expectancy; machine learning; mineral dust; morbidity; mortality; outcome assessment; particulate matter; physiology; pollution; pollution control; public health; quality control; quality of life; respiratory function disorder; respiratory tract disease; risk assessment; risk factor; satellite imagery; social determinants of health; socioeconomics; sore throat; sustainable development; technology; thorax pain; total quality management; traffic and transport; virus load; wheezing; adverse event; air pollutant; environmental exposure; epidemiology; etiology","","nitrate, 14797-55-8; sulfate, 14808-79-8; Air Pollutants, ; Particulate Matter, ","","","Deanship of Scientific Research, King Khalid University, (RGP2/360/44, RGP2/328/45); Deanship of Scientific Research, King Khalid University; National Natural Science Foundation of China, NSFC, (41907405, 42377465, 52374170); National Natural Science Foundation of China, NSFC","Funding text 1: This work was supported by the National Natural Science Foundation of China (No. 42377465, No.52374170). The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University Saudi Arabia for funding this work through Large Groups Project under grant number RGP2/360/44.; Funding text 2: This work was supported by the National Natural Science Foundation of China (No. 41907405, No. 52374170). The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University Saudi Arabia for funding this work through Large Groups Project under grant number RGP2/328/45.","Abhijith K.V., Kukadia V., Kumar P., Investigation of air pollution mitigation measures, ventilation, and indoor air quality at three schools in London, Atmos. 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"Lin Y.-T.; Chu C.-Y.; Hung K.-S.; Lu C.-H.; Bednarczyk E.M.; Chen H.-Y.","Lin, Yi-Ting (57917428700); Chu, Chao-Yu (57672420900); Hung, Kuo-Sheng (55659088300); Lu, Chi-Hua (57223106082); Bednarczyk, Edward M. (7003753019); Chen, Hsiang-Yin (8570269200)","57917428700; 57672420900; 55659088300; 57223106082; 7003753019; 8570269200","Can machine learning predict pharmacotherapy outcomes? An application study in osteoporosis","2022","Computer Methods and Programs in Biomedicine","225","","107028","","","","16","10.1016/j.cmpb.2022.107028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139374547&doi=10.1016%2fj.cmpb.2022.107028&partnerID=40&md5=1551fd54977314a74dd0a955b0d8bea4","Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, No. 250 Wuxing St., Xinyi District, Taipei, 11031, Taiwan; Department of Neurosurgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Pharmacy Practice, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, United States; Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan","Lin Y.-T., Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, No. 250 Wuxing St., Xinyi District, Taipei, 11031, Taiwan; Chu C.-Y., Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, No. 250 Wuxing St., Xinyi District, Taipei, 11031, Taiwan; Hung K.-S., Department of Neurosurgery, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; Lu C.-H., Department of Pharmacy Practice, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, United States; Bednarczyk E.M., Department of Pharmacy Practice, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, NY, United States; Chen H.-Y., Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, No. 250 Wuxing St., Xinyi District, Taipei, 11031, Taiwan, Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan","Background and objective: The specific aim of this study is to develop machine learning models as a clinical approach for personalized treatment of osteoporosis. The model performance on outcome prediction was compared between four machine learning algorithms. Methods: Retrospective, electronic clinical data for patients with suspected or confirmed osteoporosis treated at Wan Fang Hospital between 2011 to 2018 were used as inputs for building the following predictive machine learning models,i.e., artificial neural network (ANN), random forest (RF), support vector machine (SVM) and logistic regression (LR) models. The predicted outcome was defined as an increase/decrease in T-score after treatment. A genetic algorithm was employed to select relevant variables as input features for each model; the leave-one-out method was applied for model building and internal validation. The model with best performance was selected by a separate set of testing. Area under the receiver operating characteristic curve, accuracy, precision, sensitivity and F1 score were calculated to evaluate model performance. Main analysis for all the patients with subclinical or confirmed osteoporosis and subgroup analysis for the patients with confirmed osteoporosis (T score < -2.5) were carried out in this study. Results: A genetic algorithm was employed to select 12 to 18 features from all 33 variables for the four models. No difference was found in accuracy (ANN, 71.7%; LR, 70.0%; RF, 75.0%; SVM, 66.7%), precision (ANN, 80.0%; LR, 59.3%; RF, 70.0%; SVM, 63.6%), and AUC (ANN, 0.709; LR, 0.731; RF, 0.719; SVM, 0.702) among the ANN, LR, RF and SVM models. Main analysis in performance revealed significant recall in the LR model, as compared to ANN and SVM model; while subgroup revealed significant recall in ANN model, compared to LR and SVM model. Conclusions: Machine learning-based models hold potential in forecasting the outcomes of treatment for osteoporosis via early initiation of first-line therapy for patients with subclinical disease; or a switch to second-line treatment for patients with a high risk of impending treatment failure. This convenient approach can assist clinicians in adjusting treatment tailored to individual patient for prevention of disease progression or ineffective therapy. © 2022","Artificial neural network; Genetic algorithm; Logistic regression; Machine learning; Osteoporosis; Random forest; Support vector machine","Humans; Logistic Models; Machine Learning; Neural Networks, Computer; Osteoporosis; Retrospective Studies; Decision trees; Disease control; Diseases; Feature extraction; Forecasting; Forestry; Genetic algorithms; Learning systems; Logistic regression; Neural networks; Patient treatment; Random forests; alendronic acid; anticonvulsive agent; antidepressant agent; parathyroid hormone[1-34]; raloxifene; sex hormone; steroid; Logistic Regression modeling; Logistics regressions; Machine learning models; Machine-learning; Modeling performance; Osteoporosis; Performance; Random forests; Support vector machine models; Support vectors machine; aged; Article; artificial neural network; asthma; body height; body weight; bone density; cardiovascular disease; chronic kidney failure; chronic liver disease; chronic obstructive lung disease; clinical outcome; cohort analysis; comorbidity; comparative study; dementia; diabetes mellitus; diagnostic accuracy; dual energy X ray absorptiometry; endocrine disease; epilepsy; feature selection; female; forecasting; fracture; fracture risk assessment; fragility fracture; genetic algorithm; hip fracture; hormone substitution; human; ICD-9; k nearest neighbor; leave one out cross validation; logistic regression analysis; machine learning; major clinical study; male; malignant neoplasm; medical decision making; osteoporosis; Parkinson disease; personalized medicine; practice guideline; prediction; random forest; receiver operating characteristic; retrospective study; rheumatoid arthritis; support vector machine; systemic lupus erythematosus; validation study; osteoporosis; statistical model; Support vector machines","","alendronic acid, 66376-36-1; parathyroid hormone[1-34], 12583-68-5, 52232-67-4, 99294-94-7; raloxifene, 82640-04-8, 84449-90-1","","","Ministry of Education, MOE","This work was supported by the research grant (DP2-107-21121-A-05, DP2-108-21121-01-A-06) awarded by the Higher Education Sprout projected by the Ministry of Education (MOE) in Taiwan. 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Chen; Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, No. 250 Wuxing St., Xinyi District, 11031, Taiwan; email: shawn@tmu.edu.tw","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","35930862","English","Comput. Methods Programs Biomed.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85139374547"
"Siddiqui H.U.R.; Saleem A.A.; Bashir I.; Zafar K.; Rustam F.; Diez I.D.L.T.; Dudley S.; Ashraf I.","Siddiqui, Hafeez Ur Rehman (58580612500); Saleem, Adil Ali (57214224665); Bashir, Imran (57904720400); Zafar, Kainat (57903695900); Rustam, Furqan (57211950161); Diez, Isabel de la Torre (55665183400); Dudley, Sandra (7006033464); Ashraf, Imran (57195478761)","58580612500; 57214224665; 57904720400; 57903695900; 57211950161; 55665183400; 7006033464; 57195478761","Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning","2022","Electronics (Switzerland)","11","18","2875","","","","18","10.3390/electronics11182875","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138718264&doi=10.3390%2felectronics11182875&partnerID=40&md5=483cd88a3b06eb9adeb7aa024c75a2c3","Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Pulmonology Department, Sheik Zayed Hospital, Rahim Yar Khan, 64200, Pakistan; School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland; Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea","Siddiqui H.U.R., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Saleem A.A., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Bashir I., Pulmonology Department, Sheik Zayed Hospital, Rahim Yar Khan, 64200, Pakistan; Zafar K., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Rustam F., School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland; Diez I.D.L.T., Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Dudley S., School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom; Ashraf I., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea","COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach. © 2022 by the authors.","chronic obstructive pulmonary disease; impulse radio radar; machine learning; non-invasive disease prediction; respiration rate","","","","","","European University of the Atlantic","This research was supported by the European University of the Atlantic.","Laniado-Laborin R., Smoking and chronic obstructive pulmonary disease (COPD). Parallel epidemics of the 21st century, Int. J. Environ. Res. 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Physiol, 50, pp. 95-104, (1982); Tiinanen S., Kiviniemi A., Tulppo M., Seppanen T., RSA component extraction from cardiovascular signals by combining adaptive filtering and PCA derived respiration, 2010 Computing in Cardiology, pp. 73-76, (2010); Kircher M., Lenis G., Dossel O., Separating the effect of respiration from the heart rate variability for cases of constant harmonic breathing, Curr. Dir. Biomed. Eng, 1, pp. 46-49, (2015); Kim S.H., Geem Z.W., Han G.T., A novel human respiration pattern recognition using signals of ultra-wideband radar sensor, Sensors, 19, (2019); Bhattacharjee S., Saha B., Bhattacharyya P., Saha S., Classification of obstructive and non-obstructive pulmonary diseases on the basis of spirometry using machine learning techniques, J. Comput. Sci, 63, (2022); Haider N.S., Behera A., Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybern. Biomed. Eng, 42, pp. 42-59, (2022)","I.D.L.T. Diez; Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Valladolid, Paseo de Belén 15, 47011, Spain; email: isator@tel.uva.es; I. Ashraf; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; email: imranashraf@ynu.ac.kr","","MDPI","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138718264"
"Wen J.; Wang C.; Giri M.; Guo S.","Wen, Jun (57660679400); Wang, Changfen (58134509000); Giri, Mohan (57189391243); Guo, Shuliang (7403650631)","57660679400; 58134509000; 57189391243; 7403650631","Association between serum folate levels and blood eosinophil counts in American adults with asthma: Results from NHANES 2011–2018","2023","Frontiers in Immunology","14","","1134621","","","","14","10.3389/fimmu.2023.1134621","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149707614&doi=10.3389%2ffimmu.2023.1134621&partnerID=40&md5=226f128f81d9a494e8db2e14cf88a5df","Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China","Wen J., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Wang C., Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Giri M., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Guo S., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China","Background: To date, many researches have investigated the correlation of folate and asthma occurrence. Nevertheless, few studies have discussed whether folate status is correlated with dis-ease severity, control or progression of asthma. So, we explored the correlation of serum folate and blood eosinophil counts in asthmatic adults to gain the role of folate in the control, progression, and treatment of asthma. Methods: Data were obtained from the 2011–2018 NHANES, in which serum folate, blood eosinophils, and other covariates were measured among 2332 asthmatic adults. The regression model, XGBoost algorithm model, and generalized linear model were used to explore the potential correlation. Moreover, we conducted stratified analyses to determine certain populations. Results: Among three models, the multivariate regression analysis demonstrated serum folate levels were negatively correlated with blood eosinophil counts among asthmatic adults with statistical significance. And we observed that blood eosinophil counts decreased by 0.20 (-0.34, -0.06)/uL for each additional unit of serum folate (nmol/L) after adjusting for confounders. Moreover, we used the XGBoost Algorithm model to identify the relative significance of chosen variables correlated with blood eosinophil counts and observed the linear relationship between serum folate levels and blood eosinophil counts by constructing the generalized linear model. Conclusions: Our study indicated that serum folate levels were inversely associated with blood eosinophil counts in asthmatic adult populations of America, which indicated serum folate might be correlated with the immune status of asthmatic adults in some way. We suggested that serum folate might affect the control, development, and treatment of asthma. Finally, we hope more people will recognize the role of folate in asthma. Copyright © 2023 Wen, Wang, Giri and Guo.","asthma; eosinophil; folate; machine learning; national health and nutrition examination survey","Adult; Asthma; Eosinophils; Humans; Leukocyte Count; Nutrition Surveys; Regression Analysis; 5 methyltetrahydrofolate homocysteine methyltransferase; 5,10 methenyltetrahydrofolate; cotinine; creatinine; folic acid; mecobalamin; retinol; tetrahydrofolate synthase; unclassified drug; vitamin D; adult; algorithm; Article; asthma; basophil count; body mass; correlation analysis; disease association; disease exacerbation; disease severity; educational status; eosinophil count; ethnicity; female; folic acid blood level; hemolysate; high performance liquid chromatography; human; hypertension; immune status; leukocyte differential count; limit of detection; machine learning; major clinical study; male; marriage; mass spectrometry; middle aged; national health and nutrition examination survey; poverty; public health; quantitative analysis; questionnaire; smoking; tandem mass spectrometry; vitamin supplementation; eosinophil; leukocyte count; nutrition; regression analysis","","5 methyltetrahydrofolate homocysteine methyltransferase, 9033-23-2; cotinine, 486-56-6; creatinine, 19230-81-0, 60-27-5; folic acid, 59-30-3, 6484-89-5; mecobalamin, 13422-55-4; retinol, 68-26-8, 82445-97-4; tetrahydrofolate synthase, 63363-84-8","","","Chongqing Talents, Teachers and Masters","Chongqing Talents, Teachers and Masters (SG).","Wenzel S., Severe asthma: From characteristics to phenotypes to endotypes, Clin Exp Allergy J Br Soc Allergy Clin Immunol, 42, 5, (2012); Chung K.F., Wenzel S.E., Brozek J.L., Bush A., Castro M., Sterk P.J., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, 2, (2014); Soriano J.B., Abajobir A.A., Abate K.H., Abera S.F., Agrawal A., Ahmed M.B., Et al., Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: A systematic analysis for the global burden of disease study 2015, Lancet Respir Med, 5, 9, pp. 691-706, (2017); Wang H., Naghavi M., Allen C., Barber R.M., Bhutta Z.A., Carter A., Et al., Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: A systematic analysis for the global burden of disease study 2015, Lancet (London England), 388, (2016); Pate C.A., Zahran H.S., Qin X., Johnson C., Hummelman E., Malilay J., Asthma surveillance - united states, 2006-2018, Morbidity mortality weekly Rep Surveillance Summ. 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Guo; Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; email: guosl999@sina.com","","Frontiers Media S.A.","","","","","","16643224","","","36911740","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149707614"
"Zeng S.; Arjomandi M.; Tong Y.; Liao Z.C.; Luo G.","Zeng, Siyang (57193919325); Arjomandi, Mehrdad (10045533200); Tong, Yao (57208420959); Liao, Zachary C. (57296820700); Luo, Gang (7401536289)","57193919325; 10045533200; 57208420959; 57296820700; 7401536289","Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study","2022","Journal of Medical Internet Research","24","1","e28953","","","","16","10.2196/28953","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122511906&doi=10.2196%2f28953&partnerID=40&md5=eae915732f76542b0af3a38e030bdb3f","Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States; Department of Medicine, University of California, San Francisco, CA, United States","Zeng S., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Arjomandi M., Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States, Department of Medicine, University of California, San Francisco, CA, United States; Tong Y., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Liao Z.C., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Luo G., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Background: Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes. Objective: The aim of this study is to develop a more accurate model to predict severe COPD exacerbations. Methods: We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD. Results: The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347). Conclusions: Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes. © 2022 Journal of Medical Internet Research. All rights reserved.","Chronic obstructive pulmonary disease; Forecasting; Machine learning; Patient care management; Symptom exacerbation","Disease Progression; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; ROC Curve; beta 2 adrenergic receptor stimulating agent; corticosteroid; muscarinic receptor blocking agent; phosphodiesterase IV inhibitor; adult; aged; Article; binary classification; chronic obstructive lung disease; classification algorithm; clinical practice; cohort analysis; corticosteroid therapy; diagnostic accuracy; diagnostic test accuracy study; disease exacerbation; disease severity; female; high risk patient; human; machine learning; major clinical study; male; middle aged; open source software; performance indicator; predictive value; receiver operating characteristic; retrospective study; secondary analysis; sensitivity and specificity; Waikato Environment for Knowledge Analysis; chronic obstructive lung disease; disease exacerbation; machine learning","","","","","California Tobacco-Related Disease Research Program, (T29IR0715); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (R01HL142503); U.S. National Library of Medicine, NLM, (T15LM007442); Flight Attendant Medical Research Institute, FAMRI, (CIA190001)","GL and SZ were partially supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01HL142503. SZ was also partially supported by the National Library of Medicine Training Grant under award number T15LM007442. MA was partially supported by grants from the Flight Attendant Medical Research Institute (CIA190001) and the California Tobacco-Related Disease Research Program (T29IR0715). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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Luo; Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, Seattle, 850 Republican Street, 98195, United States; email: gangluo@cs.wisc.edu","","JMIR Publications Inc.","","","","","","14388871","","","34989686","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85122511906"
"Ali F.; Kumar H.; Patil S.; Ahmed A.; Banjar A.; Daud A.","Ali, Farman (57224837156); Kumar, Harish (57396853200); Patil, Shruti (57208826757); Ahmed, Aftab (57188683550); Banjar, Ameen (57209376523); Daud, Ali (28267686800)","57224837156; 57396853200; 57208826757; 57188683550; 57209376523; 28267686800","DBP-DeepCNN: Prediction of DNA-binding proteins using wavelet-based denoising and deep learning","2022","Chemometrics and Intelligent Laboratory Systems","229","","104639","","","","25","10.1016/j.chemolab.2022.104639","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136575651&doi=10.1016%2fj.chemolab.2022.104639&partnerID=40&md5=eef7f6d2a847c56846757c759d634699","Department of Elementary and Secondary Education, Khyber Pakhtunkhwa, Peshawar, Pakistan; Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia; Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia; Abu Dhabi School of Management, Abu Dhabi, United Arab Emirates","Ali F., Department of Elementary and Secondary Education, Khyber Pakhtunkhwa, Peshawar, Pakistan; Kumar H., Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia; Patil S., Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India; Ahmed A., Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan; Banjar A., Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia; Daud A., Abu Dhabi School of Management, Abu Dhabi, United Arab Emirates","DNA-binding proteins (DBPs) are highly concerned with several types of cancers (lung, breast, and liver), other fatal diseases (AIDS/HIV, asthma), and are used in the designing of drug. A series of predictors were constructed for identification of DBPs. However, a more accurate computational predictor is still essential for further performance improvement. In this work, a deep learning-based predictor (DBP-DeepCNN) is proposed for improving DBPs prediction. The salient features are derived by a novel method, namely, R-PSSM-DWT (Reduced position-specific scoring matrix-discrete wavelet transform) as well as Lead-BiPSSM (Lead-bigram-position specific scoring matrix), PSSM-DPC (Position specific scoring matrix-dipeptide composition), ED-PSSM (Evolutionary difference position specific scoring matrix), and F-PSSM (Filtered position specific scoring matrix). Further, the models are trained with 2D CNN (two-dimensional convolutional neural network), XGB (eXtreme gradient boosting), Adaboost, and ERT (extremely randomized trees). 2D CNN-based model with R-PSSM-DWT produced 6.92% and 1.32% higher accuracies than existing approach on training and independent datasets, respectively. These outcomes verified the superlative success rate of our novel predictor over the existing studies. In addition to being a promising method for large scale prediction of DBPs. DBP-DeepCNN would be fruitful for establishing more promising therapeutic strategies for chronic disease treatment. © 2022 Elsevier B.V.","Convolutional neural network; DNA-binding proteins; Extremely randomized trees","DNA binding protein; Article; benchmarking; convolutional neural network; deep learning; denoising; image processing; imaging algorithm; imaging and display; prediction","","","","","Deanship of Scientific Research, King Faisal University, DSR, KFU, (RGP.2/198/43); Deanship of Scientific Research, King Faisal University, DSR, KFU","The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number RGP.2/198/43 .","Ahmed S., Kabir M., Ali Z., Arif M., Ali F., Yu D.-J., An integrated feature selection algorithm for cancer classification using gene expression data, Comb. Chem. 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Syst., (2022); Akbar S., Ahmad A., Hayat M., Rehman A.U., Khan S., Ali F., iAtbP-hyb-EnC: prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model, Comput. Biol. Med., (2021); Akbar S., Hayat M., Kabir M., Iqbal M., iAFP-gap-SMOTE: an efficient feature extraction scheme gapped dipeptide composition is coupled with an oversampling technique for identification of antifreeze proteins, Lett. Org. Chem., 16, pp. 294-302, (2019); Akbar S., Khan S., Ali F., Hayat M., Qasim M., Gul S., iHBP-DeepPSSM: identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach, Chemometr. Intell. Lab. Syst., 204, (2020); Ali F., Akbar S., Ali G., Maher Z.A., Unar A., Talpur D.B., AFP-CMBPred: computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information, Comput. Biol. 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Biol., 384, pp. 78-83, (2015); Ullah M., Iltaf A., Hou Q., Ali F., Liu C., A foreground extraction approach using convolutional neural network with graph cut, 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), pp. 40-44, (2018); Hu J., Zhou X.-G., Zhu Y.-H., Yu D.-J., Zhang G.-J., TargetDBP: accurate DNA-binding protein prediction via sequence-based multi-view feature learning, IEEE ACM Trans. Comput. Biol. Bioinf, 17, pp. 1419-1429, (2019); Du X., Diao Y., Liu H., Li S., MsDBP: exploring DNA-binding proteins by integrating multiscale sequence information via Chou's five-step rule, J. Proteome Res., 18, pp. 3119-3132, (2019)","F. Ali; Department of Elementary and Secondary Education, Peshawar, Khyber Pakhtunkhwa, Pakistan; email: farman335@yahoo.com; S. Patil; Symbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India; email: shruti.patil@sitpune.edu.in; A. Daud; Abu Dhabi School of Management, Abu Dhabi, United Arab Emirates; email: alimsdb@gmail.com","","Elsevier B.V.","","","","","","01697439","","CILSE","","English","Chemometr. Intelligent Lab. Syst.","Article","Final","","Scopus","2-s2.0-85136575651"
"Kor C.-T.; Li Y.-R.; Lin P.-R.; Lin S.-H.; Wang B.-Y.; Lin C.-H.","Kor, Chew-Teng (56641745300); Li, Yi-Rong (56075115400); Lin, Pei-Ru (57270094600); Lin, Sheng-Hao (55772851400); Wang, Bing-Yen (36095181200); Lin, Ching-Hsiung (36018426500)","56641745300; 56075115400; 57270094600; 55772851400; 36095181200; 36018426500","Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease","2022","Journal of Personalized Medicine","12","2","228","","","","25","10.3390/jpm12020228","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124345006&doi=10.3390%2fjpm12020228&partnerID=40&md5=3e3ba8905e29aaa13af5083aa6b2f00a","Big Data Center, Changhua Christian Hospital, Changhua, 500, Taiwan; Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua, 500, Taiwan; Thoracic Medicine Research Center, Changhua Christian Hospital, Changhua, 500, Taiwan; Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, 500, Taiwan; Division of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua, 500, Taiwan; Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan; Department of Recreation and Holistic Wellness, MingDao University, Changhua, 523, Taiwan; Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, 500, Taiwan","Kor C.-T., Big Data Center, Changhua Christian Hospital, Changhua, 500, Taiwan, Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua, 500, Taiwan; Li Y.-R., Thoracic Medicine Research Center, Changhua Christian Hospital, Changhua, 500, Taiwan; Lin P.-R., Big Data Center, Changhua Christian Hospital, Changhua, 500, Taiwan; Lin S.-H., Thoracic Medicine Research Center, Changhua Christian Hospital, Changhua, 500, Taiwan, Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, 500, Taiwan; Wang B.-Y., Division of Thoracic Surgery, Department of Surgery, Changhua Christian Hospital, Changhua, 500, Taiwan; Lin C.-H., Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, 500, Taiwan, Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan, Department of Recreation and Holistic Wellness, MingDao University, Changhua, 523, Taiwan, Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, 500, Taiwan","Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Acute exacerbation; COPD; Explainable machine learning; Local explanation; SHapley Additive exPlanations (SHAP)","antibiotic agent; bronchodilating agent; methylxanthine; accuracy; aged; anxiety; area under the curve; Article; body mass; breathing rate; case control study; chronic lung disease; chronic obstructive lung disease; congestive heart failure; controlled study; COPD assessment test; coughing; diastolic blood pressure; disease exacerbation; dyspnea; eosinophil; eosinophil count; erythrocyte count; female; human; hypertension; leukocyte count; lymphocyte; machine learning; major clinical study; male; monocyte; monocyte count; neutrophil lymphocyte ratio; outcome assessment; physician; platelet volume; pneumonia; prediction; pulse rate; retrospective study; risk assessment; sensitivity and specificity; sleep disorder; systolic blood pressure; wheezing","","methylxanthine, 28109-92-4","","","Changhua Christian Hospital, CCH, (109-CCH-IRP-032)","Author Contributions: Conceptualization, C-T. K., Y-R. L. and C-H. L.; Data curation, C-T. K., Y-R. L. and P-R. L.; Formal analysis, Kor C-T. K.; Funding acquisition, Kor C-T. K. and C-H. L.; Investigation, C-T. K., Y-R. L. P-R. L. and C-H. L.; Methodology, C-T. K. Y-R. L. and Y-R. L.; Project administration, C-T. K. and C-H. L.; Resources, Y-R. L.; Software, P-R. L.; Supervision, C-H. L.; Validation, P-R. L., S-H. L. and C-H. L.; Visualization, Kor C-T. K.; Writing – original draft, Kor C-T. K., Y-R. L. and C-H. L.; Writing—review & editing, B-Y. W. and C-H. L. All authors have read and agreed to the published version of the manuscript Funding: This research was funded by Changhua Christian Hospital, grant number 109-CCH-IRP-032.","Lopez-Campos J.L., Tan W., Soriano J.B., Global burden of COPD, Respirology, 21, pp. 14-23, (2016); Blasi F., Cesana G., Conti S., Chiodini V., Aliberti S., Fornari C., Mantovani L.G., The Clinical and Economic Impact of Exacerbations of Chronic Obstructive Pulmonary Disease: A Cohort of Hospitalized Patients, PLoS ONE, 9, (2014); Global Strategy for The Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2021 Report) 2020, (2020); Agusti A., Calverley P.M., Decramer M., Stockley R.A., Wedzicha J.A., Prevention of Exacerbations in Chronic Obstructive Pulmonary Disease: Knowns and Unknowns, Chronic Obstr Pulm Dis, 1, pp. 166-184, (2014); Jiang L., Gershon A.S., Using Health Administrative Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations, Ann. Am. Thorac. 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"Honap S.; Jairath V.; Danese S.; Peyrin-Biroulet L.","Honap, Sailish (39461364600); Jairath, Vipul (16679040000); Danese, Silvio (57210684024); Peyrin-Biroulet, Laurent (15830165800)","39461364600; 16679040000; 57210684024; 15830165800","Navigating the complexities of drug development for inflammatory bowel disease","2024","Nature Reviews Drug Discovery","23","7","","546","562","16","16","10.1038/s41573-024-00953-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193938831&doi=10.1038%2fs41573-024-00953-0&partnerID=40&md5=9b1bdfcfc5923ea22c77c46aa75deeaf","Department of Gastroenterology, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom; School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom; INFINY Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France; Division of Gastroenterology, Department of Medicine, Schulich School of Medicine, Western University, London, ON, Canada; Lawson Health Research Institute, Western University, London, ON, Canada; Department of Epidemiology and Biostatistics, Western University, London, ON, Canada; Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy; Department of Gastroenterology, Nancy University Hospital, Vandœuvre-lès-Nancy, France; INSERM, NGERE, University of Lorraine, Nancy, France; FHU-CURE, Nancy University Hospital, Vandœuvre-lès-Nancy, France; Groupe Hospitalier privé Ambroise Paré — Hartmann, Paris IBD Center, Neuilly sur Seine, France; Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, QC, Canada","Honap S., Department of Gastroenterology, St George’s University Hospitals NHS Foundation Trust, London, United Kingdom, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom, INFINY Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France; Jairath V., Division of Gastroenterology, Department of Medicine, Schulich School of Medicine, Western University, London, ON, Canada, Lawson Health Research Institute, Western University, London, ON, Canada, Department of Epidemiology and Biostatistics, Western University, London, ON, Canada; Danese S., Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Hospital, Vita-Salute San Raffaele University, Milan, Italy; Peyrin-Biroulet L., INFINY Institute, Nancy University Hospital, Vandœuvre-lès-Nancy, France, Department of Gastroenterology, Nancy University Hospital, Vandœuvre-lès-Nancy, France, INSERM, NGERE, University of Lorraine, Nancy, France, FHU-CURE, Nancy University Hospital, Vandœuvre-lès-Nancy, France, Groupe Hospitalier privé Ambroise Paré — Hartmann, Paris IBD Center, Neuilly sur Seine, France, Division of Gastroenterology and Hepatology, McGill University Health Centre, Montreal, QC, Canada","Inflammatory bowel disease (IBD) — consisting of ulcerative colitis and Crohn’s disease — is a complex, heterogeneous, immune-mediated inflammatory condition with a multifactorial aetiopathogenesis. Despite therapeutic advances in this arena, a ceiling effect has been reached with both single-agent monoclonal antibodies and advanced small molecules. Therefore, there is a need to identify novel targets, and the development of companion biomarkers to select responders is vital. In this Perspective, we examine how advances in machine learning and tissue engineering could be used at the preclinical stage where attrition rates are high. For novel agents reaching clinical trials, we explore factors decelerating progression, particularly the decline in IBD trial recruitment, and assess how innovative approaches such as reconfiguring trial designs, harmonizing end points and incorporating digital technologies into clinical trials can address this. Harnessing opportunities at each stage of the drug development process may allow for incremental gains towards more effective therapies. © Springer Nature Limited 2024.","","Animals; Biomarkers; Clinical Trials as Topic; Colitis, Ulcerative; Crohn Disease; Drug Development; Humans; Inflammatory Bowel Diseases; Machine Learning; adalimumab; biological marker; biosimilar agent; calgranulin; corticosteroid; cyclosporine; filgotinib; fluvoxamine; guselkumab; infliximab; interleukin 10; interleukin 17; ivarmacitinib; metronidazole; mongersen; monoclonal antibody; oxazolone; ozanimod; risankizumab; ritlecitinib; secukinumab; tofacitinib; tumor necrosis factor; ustekinumab; vedolizumab; abdominal abscess; animal model; Article; artificial intelligence; asthma; bioinformatics; breast cancer; Caco-2 cell line; clinical decision making; coculture; colectomy; colonoscopy; computer assisted tomography; Crohn disease; digital technology; disease activity; disease severity; drug approval; drug development; drug metabolism; drug transport; dysbiosis; endoscopy; enteropathy; fatigue; feces analysis; gene expression; histology; hospitalization; human; hydrogen bond; ileostomy; immunogenicity; immunosuppressive treatment; inflammation; inflammatory bowel disease; inflammatory disease; irritable colon; machine learning; maximum tolerated dose; mesenchymal stem cell; neural stem cell; nonhuman; obsessive compulsive disorder; papillomavirus infection; personalized medicine; phase 3 clinical trial (topic); prevalence; quality of life; questionnaire; systematic review; T lymphocyte; telemedicine; tissue engineering; ulcerative colitis; animal; clinical trial (topic); drug development; drug therapy; immunology; metabolism; procedures","","adalimumab, 331731-18-1, 1446410-95-2; cyclosporine, 59865-13-3, 63798-73-2, 79217-60-0; filgotinib, 1206161-97-8, 1540859-07-1, 1802998-75-9, 1206101-20-3; fluvoxamine, 54739-18-3; guselkumab, 1350289-85-8; infliximab, 170277-31-3; ivarmacitinib, 1639419-51-4, 1445987-21-2; metronidazole, 39322-38-8, 443-48-1, 69198-10-3; mongersen, 1443994-46-4, 1443994-98-6; ozanimod, 1306760-87-1, 1618636-37-5; risankizumab, 1612838-76-2; ritlecitinib, 1792180-81-4, 2140301-97-7, 2192215-81-7; secukinumab, 875356-43-7, 875356-44-8, 1229022-83-6; tofacitinib, 477600-75-2, 540737-29-9; ustekinumab, 815610-63-0, 949907-93-1; vedolizumab, 943609-66-3; Biomarkers, ","","","","","Ng S.C., Et al., Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies, Lancet, 390, pp. 2769-2778, (2017); Kaplan G.G., Windsor J.W., The four epidemiological stages in the global evolution of inflammatory bowel disease, Nat. 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"Le Goallec A.; Diai S.; Collin S.; Prost J.-B.; Vincent T.; Patel C.J.","Le Goallec, Alan (57207815892); Diai, Samuel (57224087524); Collin, Sasha (57224069221); Prost, Jean-Baptiste (57224081347); Vincent, Théo (57224091573); Patel, Chirag J. (36098277500)","57207815892; 57224087524; 57224069221; 57224081347; 57224091573; 36098277500","Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images","2022","Nature Communications","13","1","1979","","","","25","10.1038/s41467-022-29525-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128141471&doi=10.1038%2fs41467-022-29525-9&partnerID=40&md5=1ddb7013a5693a2bebac0417edc93699","Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States; Department of Systems, Synthetic and Quantitative Biology, Harvard University, Cambridge, 02118, MA, United States","Le Goallec A., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States, Department of Systems, Synthetic and Quantitative Biology, Harvard University, Cambridge, 02118, MA, United States; Diai S., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States; Collin S., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States; Prost J.-B., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States; Vincent T., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States; Patel C.J., Department of Biomedical Informatics, Harvard Medical School, Boston, 02115, MA, United States","With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both predict abdominal age and identify risk factors for accelerated abdominal age may ultimately lead to advances that will delay the onset of these diseases. We build an abdominal age predictor by training convolutional neural networks to predict abdominal age (or “AbdAge”) from 45,552 liver magnetic resonance images [MRIs] and 36,784 pancreas MRIs (R-Squared = 73.3 ± 0.6; mean absolute error = 2.94 ± 0.03 years). Attention maps show that the prediction is driven by both liver and pancreas anatomical features, and surrounding organs and tissue. Abdominal aging is a complex trait, partially heritable (h_g2 = 26.3 ± 1.9%), and associated with 16 genetic loci (e.g. in PLEKHA1 and EFEMP1), biomarkers (e.g body impedance), clinical phenotypes (e.g, chest pain), diseases (e.g. hypertension), environmental (e.g smoking), and socioeconomic (e.g education, income) factors. © 2022, The Author(s).","","Deep Learning; Image Processing, Computer-Assisted; Liver; Magnetic Resonance Imaging; Neural Networks, Computer; Pancreas; serine protease HTRA1; age; diabetes; machine learning; prediction; risk factor; adult; age related macular degeneration; aged; alcohol consumption; arms2 gene; Article; asthma; atrial fibrillation; biological age; body weight control; body weight gain; body weight loss; bone density; bone radiography; calcaneus; cardiovascular disease; chronic disease; chronic obstructive lung disease; controlled study; convolutional neural network; deep learning; densitometry; diastolic blood pressure; diet composition; digit symbol substitution test; disability; disease association; dyspnea; echography; educational status; efemp1 gene; employment status; feature detection; gene; gene locus; genetic association; genetic correlation; genome-wide association study; genotype phenotype correlation; grip strength; hand grip; heart atrium flutter; hepatography; heritability; home environment; human; hypertension; image analysis; impedance; ischemic heart disease; mean absolute error; mean arterial pressure; measurement accuracy; medical history; nuclear magnetic resonance; pancreatography; physical activity; plekha1 gene; pleura effusion; predictive value; pulse wave; root mean squared error; sexual intercourse; single nucleotide polymorphism; smoking; social support; sport; sun exposure; systolic blood pressure; thorax pain; transfer of learning; travel; wakefulness; walking difficulty; working time; diagnostic imaging; image processing; liver; nuclear magnetic resonance imaging; pancreas; procedures","","proprotein convertase 9, ; serine protease HTRA1, ; serine proteinase, ","","","National Science Foundation, NSF, (163870); National Science Foundation, NSF; National Institute of Allergy and Infectious Diseases, NIAID, (R01 AI127250); National Institute of Allergy and Infectious Diseases, NIAID; National Institute of Environmental Health Sciences, NIEHS, (R01ES032470); National Institute of Environmental Health Sciences, NIEHS; Harvard Medical School, HMS, (52887); Harvard Medical School, HMS; Massachusetts Life Sciences Center, MLSC","We would like to thank Raffaele Potami from Harvard Medical School research computing group for helping us utilize O2’s computing resources. We thank HMS RC for computing support. We also want to acknowledge UK Biobank for providing us with access to the data they collected. The UK Biobank project number is 52887. Funding sources include NIAID R01 AI127250 (C.J.P., A.G.), NIEHS R01 ES032470 (C.J.P.), NSF 163870 (C.J.P., A.G.), Massachusetts Life Science Center (C.J.P., A.G.), Sanofi (C.J.P.). The funders had no role in the study design or drafting of the manuscript(s).","Meier J.M., Et al., Assessment of age-related changes in abdominal organ structure and function with computed tomography and positron emission tomography, Semin. Nucl. Med., 37, pp. 154-172, (2007); Kim I.H., Kisseleva T., Brenner D.A., Aging and liver disease, Curr. Opin. Gastroenterol., 31, pp. 184-191, (2015); Schmucker D.L., Age-related changes in liver structure and function: Implications for disease?, Exp. Gerontol., 40, pp. 650-659, (2005); Matsuda Y., Age-related pathological changes in the pancreas, Front. 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Commun.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128141471"
"Gomathi S.; Kohli R.; Soni M.; Dhiman G.; Nair R.","Gomathi, S. (55324593200); Kohli, Rashi (56828926500); Soni, Mukesh (57202986134); Dhiman, Gaurav (23049229800); Nair, Rajit (57203125821)","55324593200; 56828926500; 57202986134; 23049229800; 57203125821","Pattern analysis: predicting COVID-19 pandemic in India using AutoML","2022","World Journal of Engineering","19","1","","21","28","7","28","10.1108/WJE-09-2020-0450","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097310236&doi=10.1108%2fWJE-09-2020-0450&partnerID=40&md5=f7bca99bb9a397f2b36f47a5ffda7ed2","UK International Qualifications Ltd, Coimbatore, India; Senior member IEEE, Pooler, United States; Department of Computer Engineering, Smt Sr Patel Engineering College, Shihi, India; Government Bikram College of Commerce, Patiala, India; Jagran Lakecity University, Bhopal, India","Gomathi S., UK International Qualifications Ltd, Coimbatore, India; Kohli R., Senior member IEEE, Pooler, United States; Soni M., Department of Computer Engineering, Smt Sr Patel Engineering College, Shihi, India; Dhiman G., Government Bikram College of Commerce, Patiala, India; Nair R., Jagran Lakecity University, Bhopal, India","Purpose: Since December 2019, global attention has been drawn to the rapid spread of COVID-19. Corona was discovered in India on 30 January 2020. To date, in India, 178,014 disease cases were reported with 14,011 deaths by the Indian Government. In the meantime, with an increasing spread speed, the COVID-19 epidemic occurred in other countries. The survival rate for COVID-19 patients who suffer from a critical illness is efficiently and precisely predicted as more fatal cases can be affected in advanced cases. However, over 400 laboratories and clinically relevant survival rates of all present critically ill COVID-19 patients are estimated manually. The manual diagnosis inevitably results in high misdiagnosis and missed diagnosis owing to a lack of experience and prior knowledge. The chapter presents an option for developing a machine-based prognostic model that exactly predicts the survival of individual severe patients with clinical data from different sources such as Kaggle data.gov and World Health Organization with greater than 95% accuracy. The data set and attributes are shown in detail. The reasonableness of such a mere three elements may depend, respectively, on their representativeness in the indices of tissue injury, immunity and inflammation. The purpose of this paper is to provide detailed study from the diagnostic aspect of COVID-19, the work updates the cost-effective and prompt criticality classification and prediction of survival before the targeted intervention and diagnosis, in particular the triage of the vast COVID-19 explosive epidemic. Design/methodology/approach: Automated machine learning (ML) provides resources and platforms to render ML available to non-ML experts, to boost efficiency in ML and to accelerate research in machine learning. H2O AutoML is used to generate the results (Dulhare et al., 2020). ML has achieved major milestones in recent years, and it is on which an increasing range of disciplines depend. But this performance is crucially dependent on specialists in human ML to perform the following tasks: preprocess the info and clean it; choose and create the appropriate apps; choose a family that fits the pattern; optimize hyperparameters for layout; and models of computer learning post processes. Review of the findings collected is important. Findings: These days, the concept of automated ML techniques is being used in every field and domain, for example, in the stock market, education institutions, medical field, etc. ML tools play an important role in harnessing the massive amount of data. In this paper, the data set relatively holds a huge amount of data, and appropriate analysis and prediction are necessary to track as the numbers of COVID cases are increasing day by day. This prediction of COVID-19 will be able to track the cases particularly in India and might help researchers in the future to develop vaccines. Researchers across the world are testing different medications to cure COVID; however, it is still being tested in various labs. This paper highlights and deploys the concept of AutoML to analyze the data and to find the best algorithm to predict the disease. Appropriate tables, figures and explanations are provided. Originality/value: As the difficulty of such activities frequently goes beyond non-ML-experts, the exponential growth of ML implementations has generated a market for off-the-shelf ML solutions that can be used quickly and without experience. We name the resulting work field which is oriented toward the radical automation of AutoML machine learning. The third class is that of the individuals who have illnesses such as diabetes, high BP, asthma, malignant growth, cardiovascular sickness and so forth. As their safe frameworks have been undermined effectively because of a common ailment, these individuals become obvious objectives. Diseases experienced by the third classification of individuals can be lethal (Shinde et al., 2020). Examining information is fundamental in having the option to comprehend the spread and treatment adequacy. The world needs a lot more individuals investigating the information. The understanding from worldwide data on the spread of the infection and its conduct will be key in limiting the harm. The main contributions of this study are as follows: predicting COVID-19 pandemic in India using AutoML; analyzing the data set predicting the patterns of the virus; and comparative analysis of predictive algorithms. The organization of the paper is as follows, Sections I and II describe the introduction and the related work in the field of analyzing the COVID pandemic. Section III describes the workflow/framework for AutoML using the components with respect to the data set used to analyze the patterns of COVID-19 patients. © 2020, Emerald Publishing Limited.","AutoML; COVID-19; Data analysis; Data processing; India; Kaggle; Laboratory; Machine learning; Pandemic WHO","","","","","","","","Ait-Sahalia Y., Xiu D., Principal component analysis of high-frequency data, Journal of the American Statistical Association, 114, 525, pp. 287-303, (2019); BarstuganOzkaya M., Ozturk S., Coronavirus (covid-19) classification using ct images by machine learning methods, (2020); Chen X., Gupta A., Webly supervised learning of convolutional networks, Proceedings of the IEEE International Conference on Computer Vision, pp. 1431-1439, (2015); Cui Z., Li F., Zhang W., Bat algorithm with principal component analysis, International Journal of Machine Learning and Cybernetics, 10, 3, pp. 603-622, (2019); Dulhare U.N., Mubeen A., Ahmad K., Hands‐on H2O machine learning tool, Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications, 1, pp. 423-453, (2020); El ShawiMaher R., Sakr S., Automated machine learning: state-of-the-art and open challenges, (2019); Elaziz M.A., Hosny K.M., Salah A., Darwish M.M., Lu S., Sahlol A.T., New machine learning method for image-based diagnosis of COVID-19, PLoS One, 15, 6, (2020); Elmousalami H.H., Hassanien A.E., Day level forecasting for coronavirus disease (COVID-19) spread: analysis, modeling and recommendations, (2020); Ezzat D., Ella H.A., GSA-DenseNet121-COVID-19: a hybrid deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization algorithm, (2020); Feurer M., Eggensperger K., Falkner S., Lindauer M., Hutter F., Practical automated machine learning for the automl challenge 2018, Proc. 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Soni; Department of Computer Engineering, Smt Sr Patel Engineering College, Shihi, India; email: soni.mukesh15@gmail.com","","Emerald Group Holdings Ltd.","","","","","","17085284","","","","English","World J. Eng.","Article","Final","","Scopus","2-s2.0-85097310236"
"Liu Y.; Teo S.M.; Méric G.; Tang H.H.F.; Zhu Q.; Sanders J.G.; Vázquez-Baeza Y.; Verspoor K.; Vartiainen V.A.; Jousilahti P.; Lahti L.; Niiranen T.; Havulinna A.S.; Knight R.; Salomaa V.; Inouye M.","Liu, Yang (57214949165); Teo, Shu Mei (37035201300); Méric, Guillaume (55252598900); Tang, Howard H.F. (57189972187); Zhu, Qiyun (57200983709); Sanders, Jon G. (23390171900); Vázquez-Baeza, Yoshiki (55580465100); Verspoor, Karin (12772581800); Vartiainen, Ville A. (24333790400); Jousilahti, Pekka (7005647985); Lahti, Leo (8679063700); Niiranen, Teemu (12446050400); Havulinna, Aki S. (13403065100); Knight, Rob (57202526255); Salomaa, Veikko (7004714461); Inouye, Michael (22953271700)","57214949165; 37035201300; 55252598900; 57189972187; 57200983709; 23390171900; 55580465100; 12772581800; 24333790400; 7005647985; 8679063700; 12446050400; 13403065100; 57202526255; 7004714461; 22953271700","The gut microbiome is a significant risk factor for future chronic lung disease","2023","Journal of Allergy and Clinical Immunology","151","4","","943","952","9","23","10.1016/j.jaci.2022.12.810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147261209&doi=10.1016%2fj.jaci.2022.12.810&partnerID=40&md5=785eefbb19464be06806d787515cf93e","Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; School of Life Sciences, Arizona State University, Tempe, Ariz, United States; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, Ariz, United States; Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, United States; Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, Calif, United States; School of Computing Technologies, RMIT University, Melbourne, Australia; School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia; Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland; Department of Computing, University of Turku, Turku, Finland; Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland; Institute for Molecular Medicine Finland, FIMM-HiLIFE, University of Helsinki, Helsinki, Finland; Department of Computer Science and Engineering, University of California San Diego, La Jolla, Calif, United States; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, Calif, United States; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom; The Alan Turing Institute, London, United Kingdom; Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom","Liu Y., Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia, Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia; Teo S.M., Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia, Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; Méric G., Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia; Tang H.H.F., Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia, Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Zhu Q., School of Life Sciences, Arizona State University, Tempe, Ariz, United States, Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, Ariz, United States; Sanders J.G., Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, United States; Vázquez-Baeza Y., Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, Calif, United States; Verspoor K., School of Computing Technologies, RMIT University, Melbourne, Australia, School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia; Vartiainen V.A., Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland, Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland, Department of Pulmonary Medicine, Heart and Lung Center, Helsinki University Hospital, Helsinki, Finland; Jousilahti P., Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; Lahti L., Department of Computing, University of Turku, Turku, Finland; Niiranen T., Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland, Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland; Havulinna A.S., Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland, Institute for Molecular Medicine Finland, FIMM-HiLIFE, University of Helsinki, Helsinki, Finland; Knight R., Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, Calif, United States, Department of Computer Science and Engineering, University of California San Diego, La Jolla, Calif, United States, Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, Calif, United States; Salomaa V., Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland; Inouye M., Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia, Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Australia, Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom, British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom, Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom, The Alan Turing Institute, London, United Kingdom, Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom","Background: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. Objective: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). Methods: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register–derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. Results: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. Conclusions: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors. © 2023 The Authors","asthma; COPD; Gut; metagenomics; microbiome","Adult; Asthma; Gastrointestinal Microbiome; Humans; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Risk Factors; adult; age; area under the curve; Article; asthma; Bacteroides; body mass; chronic obstructive lung disease; cohort analysis; controlled study; current smoker; decision tree; education; Eubacterium; Faecalibacterium; feces; female; gastrointestinal tract; human; income; intestine flora; machine learning; major clinical study; male; metagenomics; microbial community; microbiome; middle aged; morbidity; nonhuman; onset age; people by smoking status; prediction; predictive model; predictive value; Prevotella; prospective study; quality control; risk factor; sex; taxon; asthma; intestine flora; risk factor","","","","","Health Data Research UK; Department for Employment and Learning, Northern Ireland, DEL, NI; Wellcome Trust, WT; Chief Scientist Office, Scottish Government Health and Social Care Directorate, CSO; Medical Research Council, MRC; Engineering and Physical Sciences Research Council, EPSRC; Economic and Social Research Council, ESRC; National Institute for Health and Care Research, NIHR; British Heart Foundation, BHF, (RG/13/13/30194, RG/18/13/33946); British Heart Foundation, BHF; Department of Health and Social Care, DH; Public Health Agency, PHA; Academy of Finland, AKA, (295741, 321356, 328791); Academy of Finland, AKA; Juho Vainion Säätiö; Emil Aaltosen Säätiö; Sydäntutkimussäätiö; Sigrid Juséliuksen Säätiö, (321351); Sigrid Juséliuksen Säätiö; Health and Social Care Research and Development Division, HCS R&D; NIHR Cambridge Biomedical Research Centre, (BRC-1215-20014); NIHR Cambridge Biomedical Research Centre; Health Data Research UK, HDR UK","Funding text 1: V.S. was supported by the Finnish Foundation for Cardiovascular Research and by Juho Vainio Foundation . M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention. A.S.H. was supported by the Academy of Finland (grant no. 321356). L.L. was supported by the Academy of Finland (grant nos. 295741 and 328791). T.N. was supported by the Emil Aaltonen Foundation , the Finnish Foundation for Cardiovascular Research , the Sigrid Jusélius Foundation, and the Academy of Finland (grant no. 321351). This study was supported by the Victorian government’s Operational Infrastructure Support program and by core funding from the British Heart Foundation (grant nos. RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (grant no. BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council , the Engineering and Physical Sciences Research Council , the Economic and Social Research Council , the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division ( Welsh Government ), the Public Health Agency (Northern Ireland), the British Heart Foundation , and Wellcome. ; Funding text 2: V.S. was supported by the Finnish Foundation for Cardiovascular Research and by Juho Vainio Foundation. M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention. A.S.H. was supported by the Academy of Finland (grant no. 321356). L.L. was supported by the Academy of Finland (grant nos. 295741 and 328791). T.N. was supported by the Emil Aaltonen Foundation, the Finnish Foundation for Cardiovascular Research, the Sigrid Jusélius Foundation, and the Academy of Finland (grant no. 321351). This study was supported by the Victorian government's Operational Infrastructure Support program and by core funding from the British Heart Foundation (grant nos. RG/13/13/30194 and RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (grant no. BRC-1215-20014). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division (Welsh Government), the Public Health Agency (Northern Ireland), the British Heart Foundation, and Wellcome.","Halpin D.M.G., Criner G.J., Papi A., Singh D., Anzueto A., Martinez F.J., Et al., Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. 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Liu; Department of Clinical Pathology, The University of Melbourne, Melbourne, Parkville, VIC 3010, Australia; email: yang.liu2@baker.edu.au; M. Inouye; Department of Public Health and Primary Care, The University of Cambridge, Worts Causeway, Cambridge, CB1 8RN, United Kingdom; email: minouye@baker.edu.au","","Elsevier Inc.","","","","","","00916749","","JACIB","36587850","English","J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147261209"
"Angelini E.D.; Yang J.; Balte P.P.; Hoffman E.A.; Manichaikul A.W.; Sun Y.; Shen W.; Austin J.H.M.; Allen N.B.; Bleecker E.R.; Bowler R.; Cho M.H.; Cooper C.S.; Couper D.; Dransfield M.T.; Garcia C.K.; Han M.K.; Hansel N.N.; Hughes E.; Jacobs D.R.; Kasela S.; Kaufman J.D.; Kim J.S.; Lappalainen T.; Lima J.; Malinsky D.; Martinez F.J.; Oelsner E.C.; Ortega V.E.; Paine R.; Post W.; Pottinger T.D.; Prince M.R.; Rich S.S.; Silverman E.K.; Smith B.M.; Swift A.J.; Watson K.E.; Woodruff P.G.; Laine A.F.; Barr R.G.","Angelini, Elsa D. (57452134800); Yang, Jie (57192455652); Balte, Pallavi P. (55875257300); Hoffman, Eric A. (58000586800); Manichaikul, Ani W. (57216593567); Sun, Yifei (57144927700); Shen, Wei (55574196325); Austin, John H.M. (7402093289); Allen, Norrina B. (23003203100); Bleecker, Eugene R. (7004832308); Bowler, Russell (56773748500); Cho, Michael H. (57219307474); Cooper, Christopher S. (7403318967); Couper, David (7004067300); Dransfield, Mark T. (6603516755); Garcia, Christine Kim (7401486381); Han, Meilan K. (57221229257); Hansel, Nadia N. (58533627100); Hughes, Emlyn (35227373700); Jacobs, David R. (57200715827); Kasela, Silva (55342945000); Kaufman, Joel Daniel (57214957383); Kim, John Shinn (57190683762); Lappalainen, Tuuli (57206704371); Lima, Joao (7202778154); Malinsky, Daniel (57016670700); Martinez, Fernando J. (7402221202); Oelsner, Elizabeth C. (6507490337); Ortega, Victor E. (16835212100); Paine, Robert (7102460664); Post, Wendy (57221279716); Pottinger, Tess D. (55920714700); Prince, Martin R. (7101821221); Rich, Stephen S. (57216593979); Silverman, Edwin K. (57213401294); Smith, Benjamin M. (55476100800); Swift, Andrew J. (57211595965); Watson, Karol E. (7201554483); Woodruff, Prescott G. (35418932500); Laine, Andrew F. (26643433600); Barr, R Graham (24330708900)","57452134800; 57192455652; 55875257300; 58000586800; 57216593567; 57144927700; 55574196325; 7402093289; 23003203100; 7004832308; 56773748500; 57219307474; 7403318967; 7004067300; 6603516755; 7401486381; 57221229257; 58533627100; 35227373700; 57200715827; 55342945000; 57214957383; 57190683762; 57206704371; 7202778154; 57016670700; 7402221202; 6507490337; 16835212100; 7102460664; 57221279716; 55920714700; 7101821221; 57216593979; 57213401294; 55476100800; 57211595965; 7201554483; 35418932500; 26643433600; 24330708900","Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans","2023","Thorax","78","11","","1067","1079","12","23","10.1136/thorax-2022-219158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164437748&doi=10.1136%2fthorax-2022-219158&partnerID=40&md5=3211e4fd6301ba4a5caf9f0635aba476","Department of Biomedical Engineering, Columbia University, New York, NY, United States; LTCI, Institut Polytechnique de Paris, Telecom Paris, Palaiseau, France; NIHR, Imperial Biomedical Research Centre, ITMAT, Data Science Group, Imperial College, London, United Kingdom; Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States; Departments of Radiology Medicine and Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; Department of Biostatistics, Columbia University, Irving Medical Center, New York, NY, United States; Department of Pediatrics, Institute of Human Nutrition, Columbia University, Irving Medical Center, New York, NY, United States; Columbia Magnetic Resonance Research Center (CMRRC), Columbia University, Irving Medical Center, New York, NY, United States; Department of Radiology, Columbia University, Irving Medical Center, New York, NY, United States; Institute for Public Health and Medicine (IPHAM), Center for Epidemiology and Population Health, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States; Department of Medicine, University of Arizona, Health Sciences, Tucson, AZ, United States; Department of Medicine, National Jewish Health, Denver, CO, United States; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Medicine, University of California, Los Angeles, CA, United States; Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States; Lung Health Center, University of Alabama, Birmingham, AL, United States; Department of Medicine, University of Michigan, Ann Arbor, MI, United States; Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; Department of Physics, Columbia University, New York, NY, United States; Division of Epidemiology and Community Public Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States; Department of Systems Biology, Columbia University, Irving Medical Center, New York, NY, United States; New York Genome Center, New York, NY, United States; Departments of Environmental & Occupational Health Sciences Medicine, and Epidemiology, University of Washington, Seattle, WA, United States; Department of Medicine, University of Virginia, School of Medicine, Charlottesville, VA, United States; Department of Medicine, Cornell University, Joan and Sanford i Weill Medical College, New York, NY, United States; Department of Pulmonary Medicine, Mayo Clinic, Phoenix, AZ, United States; Department of Medicine, University of Utah, Salt Lake City, UT, United States; Department of Radiology, Cornell University, Joan and Sanford i Weill Medical College, New York, NY, United States; Department of Medicine, Research Institute, The McGill University, Health Centre, Montreal, QC, Canada; Department of Infection Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Department of Medicine, University of California, San Francisco, CA, United States; Department of Epidemiology, Columbia University, Irving Medical Center, New York, NY, United States","Angelini E.D., Department of Biomedical Engineering, Columbia University, New York, NY, United States, LTCI, Institut Polytechnique de Paris, Telecom Paris, Palaiseau, France, NIHR, Imperial Biomedical Research Centre, ITMAT, Data Science Group, Imperial College, London, United Kingdom; Yang J., Department of Biomedical Engineering, Columbia University, New York, NY, United States; Balte P.P., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States; Hoffman E.A., Departments of Radiology Medicine and Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Manichaikul A.W., Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; Sun Y., Department of Biostatistics, Columbia University, Irving Medical Center, New York, NY, United States; Shen W., Department of Pediatrics, Institute of Human Nutrition, Columbia University, Irving Medical Center, New York, NY, United States, Columbia Magnetic Resonance Research Center (CMRRC), Columbia University, Irving Medical Center, New York, NY, United States; Austin J.H.M., Department of Radiology, Columbia University, Irving Medical Center, New York, NY, United States; Allen N.B., Institute for Public Health and Medicine (IPHAM), Center for Epidemiology and Population Health, Northwestern University, Feinberg School of Medicine, Chicago, IL, United States; Bleecker E.R., Department of Medicine, University of Arizona, Health Sciences, Tucson, AZ, United States; Bowler R., Department of Medicine, National Jewish Health, Denver, CO, United States; Cho M.H., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Cooper C.S., Department of Medicine, University of California, Los Angeles, CA, United States; Couper D., Department of Biostatistics, University of North Carolina, Chapel Hill, NC, United States; Dransfield M.T., Lung Health Center, University of Alabama, Birmingham, AL, United States; Garcia C.K., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States; Han M.K., Department of Medicine, University of Michigan, Ann Arbor, MI, United States; Hansel N.N., Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; Hughes E., Department of Physics, Columbia University, New York, NY, United States; Jacobs D.R., Division of Epidemiology and Community Public Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States; Kasela S., Department of Systems Biology, Columbia University, Irving Medical Center, New York, NY, United States, New York Genome Center, New York, NY, United States; Kaufman J.D., Departments of Environmental & Occupational Health Sciences Medicine, and Epidemiology, University of Washington, Seattle, WA, United States; Kim J.S., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States, Department of Medicine, University of Virginia, School of Medicine, Charlottesville, VA, United States; Lappalainen T., Department of Systems Biology, Columbia University, Irving Medical Center, New York, NY, United States; Lima J., Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; Malinsky D., Department of Biostatistics, Columbia University, Irving Medical Center, New York, NY, United States; Martinez F.J., Department of Medicine, Cornell University, Joan and Sanford i Weill Medical College, New York, NY, United States; Oelsner E.C., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States; Ortega V.E., Department of Pulmonary Medicine, Mayo Clinic, Phoenix, AZ, United States; Paine R., Department of Medicine, University of Utah, Salt Lake City, UT, United States; Post W., Department of Medicine, Johns Hopkins University, Baltimore, MD, United States; Pottinger T.D., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States; Prince M.R., Department of Radiology, Cornell University, Joan and Sanford i Weill Medical College, New York, NY, United States; Rich S.S., Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; Silverman E.K., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Smith B.M., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States, Department of Medicine, Research Institute, The McGill University, Health Centre, Montreal, QC, Canada; Swift A.J., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States, Department of Infection Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Watson K.E., Department of Medicine, University of California, Los Angeles, CA, United States; Woodruff P.G., Department of Medicine, University of California, San Francisco, CA, United States; Laine A.F., Department of Biomedical Engineering, Columbia University, New York, NY, United States, Columbia Magnetic Resonance Research Center (CMRRC), Columbia University, Irving Medical Center, New York, NY, United States, Department of Radiology, Columbia University, Irving Medical Center, New York, NY, United States; Barr R.G., Department of Medicine, Columbia University, Irving Medical Center, New York, NY, United States, Department of Epidemiology, Columbia University, Irving Medical Center, New York, NY, United States","Background Treatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations. Methods New CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case-control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined. Results The algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91-1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10 -8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome. Conclusion Large-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD. © 2023 BMJ Publishing Group. All rights reserved.","COPD epidemiology; Emphysema; Imaging/CT MRI etc","Case-Control Studies; Emphysema; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Tomography, X-Ray Computed; Unsupervised Machine Learning; dopamine 1 receptor; mucin; adult; age; aged; airway obstruction; Article; body weight; case control study; chronic bronchitis; chronic obstructive lung disease; computer assisted tomography; controlled study; correlation coefficient; disease association; DRD1 gene; female; gene; genetic association; genetic variation; genome-wide association study; hospitalization; human; image analysis; information processing; lung emphysema; lung fibrosis; lung function; major clinical study; male; middle aged; mortality; prognosis; reproducibility; respiratory tract disease; single nucleotide polymorphism; spirometry; symptom; unsupervised machine learning; vanishing lung syndrome; chronic obstructive lung disease; diagnostic imaging; emphysema; genetics; lung; lung emphysema; unsupervised machine learning; x-ray computed tomography","","","","","; National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (N02-HL-64278, R01-HL142028, R01-HL093081, R01-HL130506, R01-HL077612, R01-HL121270, R01-HL131565, R01-HL103676, T32-HL144442); National Heart, Lung, and Blood Institute, NHLBI","This work was supported by NIH/NHLBI R01-HL121270, R01-HL077612, R01-HL093081, R01-HL142028, R01-HL130506, R01-HL131565, R01-HL103676 and T32-HL144442. MESA and the MESA SHARe project are conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts HHSN268201500003I, N01-HC-95159-69, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1-TR-001881 and DK063491. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. SPIROMICS was supported by contracts from NIH/NHLBI (HHSN268200900013C-20C), which were supplemented by contributions made through the Foundation for the NIH and COPD Foundation from AstraZeneca; Bellerophon Pharmaceuticals; Boehringer-Ingelheim Pharmaceuticals; Chiesi Farmaceutici SpA; Forest Research Institute; GSK; Grifols Therapeutics; Ikaria Nycomed; Takeda Pharmaceutical Company; Novartis Pharmaceuticals Corporation; Regeneron Pharmaceuticals and Sanofi. The COPD Gene Study was supported by NIH grants K12HL120004, R01HL113264, U01HL089856 and P01HL105339. 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Haghighi B., Choi S., Choi J., Et al., Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: The subpopulations and intermediate outcome measures in COPD study (SPIROMICS), Respir Res, 20, (2019); Binder P., Batmanghelich N.K., Estepar R.S.J., Et al., Unsupervised discovery of emphysema subtypes in a large clinical cohort In:, International Workshop on Machine Learning in Medical Imaging. Springer, pp. 180-187, (2016)","R.G. Barr; Department of Medicine, Columbia University, Irving Medical Center, New York, 10032, United States; email: rgb9@columbia.edu","","BMJ Publishing Group","","","","","","00406376","","THORA","37268414","English","Thorax","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85164437748"
"Ananthajothi K.; Rajasekar P.; Amanullah M.","Ananthajothi, K. (57201257630); Rajasekar, P. (56835457000); Amanullah, M. (56432596900)","57201257630; 56835457000; 56432596900","Enhanced U-Net-based segmentation and heuristically improved deep neural network for pulmonary emphysema diagnosis","2023","Sadhana - Academy Proceedings in Engineering Sciences","48","1","33","","","","15","10.1007/s12046-023-02092-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149042493&doi=10.1007%2fs12046-023-02092-5&partnerID=40&md5=ffe17c4e220630d63baac3a09e032d8c","Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, 602105, India; Department of Data Science and Business Systems, S.R.M Institute of Science and Technology, Kattankulathur, 603203, India; Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602117, India","Ananthajothi K., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, 602105, India; Rajasekar P., Department of Data Science and Business Systems, S.R.M Institute of Science and Technology, Kattankulathur, 603203, India; Amanullah M., Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602117, India","Pulmonary emphysema is a main part of chronic obstructive pulmonary disease and lung cancer. Though, quantitative emphysema severity prediction is essential in patients with unclear lung cancer categories. Thus, diagnosis of pulmonary emphysema at the early stage is more significant and could save human life. Moreover, non-invasive positive pressure ventilation is a life-saving method that focuses on reducing the complexities in patients. When failure occurs in non-invasive positive pressure ventilation, there are more chances for mortality, which shows the significance of rational diagnosis. The delay in endotracheal intubation is avoided by developing different approaches, which leads to more demand. This paper plans to develop an enhanced system for pulmonary emphysema diagnosis using deep learning-based segmentation and classification. Initially, the detection model considers pre-processing with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE) and average filtering. Moreover, lung segmentation is the major part of pulmonary emphysema diagnosis. Here, the enhanced U-Net model is utilized for lung segmentation by considering the multi-objective function with the developed algorithm. Then, the feature extraction is done using the local tri-directional weber pattern and local directional pattern descriptors. The extracted features are classified by the heuristically improved deep neural network based on a new algorithm, which will optimally diagnose the severity of pulmonary emphysema. Both segmentation and classification will be enhanced by proposing a new Electric Fish-based Grey Wolf Optimization (EF-GWO). Here, various performance metrics, like accuracy, precision, specificity, etc., on the public dataset by comparing with other models. The accuracy of the EF-GWO with E-UNet is 96%. The accuracy of the suggested developed EF-GWO based on HI-DNN is 97%. Hence, it verifies the superior performance of the recommended pulmonary emphysema disease diagnosis method with improved segmentation and classification techniques compared to other existing methods. © 2023, Indian Academy of Sciences.","chronic obstructive pulmonary disease; electric fish-based grey wolf optimization; enhanced U-Net model; feature extraction; heuristically improved deep neural network; Pulmonary emphysema disease diagnosis","Biological organs; Computer aided diagnosis; Deep neural networks; Extraction; Fish; Optimization; Pulmonary diseases; Chronic obstructive pulmonary disease; Disease diagnosis; Electric fish; Electric fish-based gray wolf optimization; Enhanced U-net model; Features extraction; Gray wolves; Heuristically improved deep neural network; Net model; Optimisations; Pulmonary emphysema; Pulmonary emphysema disease diagnose; Feature extraction","","","","","","","Guo H.-M., Du J., Huang L., Application of model based on R language in predicting incidence of patients with acute exacerbation of chronic obstructive pulmonary disease, (in Chinese), Health Stat., 34, 2, pp. 288-289, (2017); Hakim M.A., Garden F.L., Jennings M.D., Dobler C.C., Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease, Clin. Epidemiol., 10, (2017); Himes B.E., Dai Y., Kohane I.S., Weiss S.T., Ramoni M.F., Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records, J. Am. Med. Inform. Assoc., 16, 3, pp. 371-379, (2016); Lu G., Li D., Zhang L., Clinical investigation of depression in elderly patients with chronic obstructive pulmonary disease, China Med. Pharmacy, 3, 1, pp. 12-14, (2013); Sanchez-Morillo D., Fernandez-Granero M.A., Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review, Chronic Respir. Dis., 13, 3, pp. 264-283, (2016); Danielsson P., Olafsdottir I.S., Benediktsdottir B., Gislason T., Janson C., The prevalence of chronic obstructive pulmonary disease in Uppsala, Sweden-The burden of obstructive lung disease (BOLD) study: Cross-sectional population-based study, Clin. Respir. J., 6, 2, pp. 120-127, (2012); Borne Y., Ashraf W., Zaigham S., Frantz S., Socioeconomic circumstances and incidence of chronic obstructive pulmonary disease (COPD) in an urban population in Sweden, COPD, J. Chronic Obstr. Pulm. Dis., 16, 1, pp. 51-57, (2019); Jensen M.H., Cichosz S.L., Dinesen B., Hejlesen O.K., Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare, J. Telemed. Telecare, 18, 2, pp. 99-103, (2012); Burton C., Pinnock H., McKinstry B., Changes in telemonitored physiological variables and symptoms prior to exacerbations of chronic obstructive pulmonary disease, J. Telemed. Telecare, 21, 1, pp. 29-36, (2015); Ohayon M.M., Chronic obstructive pulmonary disease and its association with sleep and mental disorders in the general population, J. Psychiatric Res., 54, pp. 79-84, (2014); Zhu Y., Zhang W., Discussion on the treatment of chronic obstructive pulmonary disease from lung, spleen, and kidney, Shanxi J. Tradit. Chin. Med., 31, 7, pp. 1-2, (2015); Zhong N., Cai C., Relationship between chronic obstructive pulmonary disease and anxiety depression, Cont. Med. Educ., 21, 16, pp. 17-19, (2006); Tee A., Chronic obstructive pulmonary disease (COPD): Not a cigarette only pulmonary disease, Ann. Acad. Med., 46, 11, pp. 415-416, (2017); Dranseld M.T., Kunisaki K.M., Strand M.J., Anzueto A., Bhatt S.P., Bowler R.P., Criner G.J., Curtis J.L., Hanania N.A., Nath H., Acute exacerbations and lung function loss in smokers with and without chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 195, 3, pp. 324-330, (2017); Obi J., Mehari A., Gillum R., Mortality related to chronic obstructive pulmonary disease and co-morbidities in the United States, a multiple causes of death analysis, COPD, J. Chronic Obstr. Pulm. Dis., 15, 2, pp. 200-205, (2018); Raju B.M., Jotkar S., Prathyusha M., Goswami S., Dube M., Singh A., Effectiveness of non-invasive positive pressure ventilation for acute exacerbation of chronic obstructive pulmonary disease, Int. J., 5, 2, (2018); Kohnlein T., Windisch W., Kohler D., Drabik A., Geiseler J., Hartl S., Karg O., Laier-Groeneveld G., Nava S., Schonhofer B., Noninvasive positive pressure ventilation for the treatment of severe stable chronic obstructive pulmonary disease: A prospective, multicentre, randomised, controlled clinical trial, Lancet Respir. Med., 2, 9, pp. 698-705, (2014); Meinel F.G., Schwab F., Schleede S., Bech M., Herzen J., Achterhold K., Auweter S., Bamberg F., Yildirim A.O., Bohla A., Eickelberg O., Loewen R., Gifford M., Ruth R., Reiser M.F., Pfeiffer F., Nikolaou K., Diagnosing and Mapping Pulmonary Emphysema on X-Ray Projection Images: Incremental Value of Grating-Based X-Ray Dark-Field Imaging, PLOS ONE, 8, 3, (2013); Chan K.-S., Jiao F., Mikulski M.A., Gerke A., Guo J., Newell J.D., Hoffman E.A., Thompson B., Lee C.H., Fuortes L.J., Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) Versus Normal Lung, Academic Radiology, 23, 3, pp. 304-314, (2016); Fang Y., Wang H., Wang L., Di R., Song Y., Diagnosis of COPD Based on a Knowledge Graph and Integrated Model, IEEE Access, 7, pp. 46004-46013, (2019); Lee S.J., Yoo J.W., Ju S., Cho Y.J., Kim J.D., Kim S.H., Jang I.-S., Jeong B.K., Lee G.-W., Jeong Y.Y., Kim H.C., Bae K., Jeon K.N., Lee J.D., Quantitative severity of pulmonary emphysema as a prognostic factor for recurrence in patients with surgically resected non‐small cell lung cancer, Thoracic Cancer, 10, 3, pp. 421-427, (2018); Boer E., Nijholt I.M., Jansen S., Edens M.A., Walen S., Berg J.W.K., Boomsma M.F., Optimization of pulmonary emphysema quantification on CT scans of COPD patients using hybrid iterative and post processing techniques: Correlation with pulmonary function tests, Insights into Imaging, (2019); Weng Y., Fang Y., Yan H., Yang Y., Hong W., Bayesian Non-Parametric Classification With Tree-Based Feature Transformation for NIPPV Efficacy Prediction in COPD Patients, IEEE Access, 7, pp. 177774-177783, (2019); Isaac A., Nehemiah H.K., Isaac A., Kannan A., Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms, Computers in Biology and Medicine, (2020); Wang Q., Wang H., Wang L., Yu F., Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning, IEEE Access, 8, pp. 47370-47383, (2020); Ma J., Fan X., Yang S.X., Zhang X., Zhu X., Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement, International Journal of Pattern Recognition and Artificial Intelligence, 32, 7, (2017); Qu J., Li Y., Dong W., Fusion of hyperspectral and panchromatic images using an average filter and a guided filter, Journal of Visual Communication and Image Representation, 52, pp. 151-158, (2018); Hambarde P., Talbar S., Mahajan A., Chavan S., Thakur M., Sable N., Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net, Biocybernetics and Biomedical Engineering, 40, 4, pp. 1421-1435, (2020); Jabid T., Kabir H., Chae O., Local Directional Pattern (LDP)—A Robust Image Descriptor for Object Recognition, Advanced Video and Signal Based Surveillance, (2010); Gangavarapu V.S.K., Pillutla G.K.M., Local Tri-directional Weber Patterns: A New Descriptor for Texture and Face Image Retrieval, International Journal of Computer Science and Information Technologies, 7, 3, pp. 1571-1577, (2016); Yilmaz S., Sen S., Electric fish optimization: a new heuristic algorithm inspired by electrolocation, Neural Computing and Applications, 32, pp. 11543-11578, (2020); Mirjalili S., Mirjalili S.M., Lewis A., Grey Wolf Optimizer, Advances in Engineering Software, 69, pp. 46-61, (2014); Beno M.M., Valarmathi I.R., Swamy S.M., Rajakumar B.R., Threshold prediction for segmenting tumour from brain MRI scans, International Journal of Imaging Systems and Technology, 24, 2, pp. 129-137, (2014); Ramesh S., Vydeki D., Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm, Information Processing in Agriculture, 7, 2, pp. 249-260, (2020); Bonyadi M.R., Michalewicz Z., Analysis of Stability, Local Convergence, and Transformation Sensitivity of a Variant of the Particle Swarm Optimization Algorithm, IEEE Transactions on Evolutionary Computation, 20, 3, pp. 370-385, (2016); Wang T., Yang L., Liu Q., Beetle Swarm Optimization Algorithm: Theory and Application, (2018); Tsang S., Kao B., Yip K.Y., Ho W., Lee S.D., Decision Trees for Uncertain Data, IEEE Transactions on Knowledge and Data Engineering, 23, 1, pp. 64-78, (2011); Wu J., Yang H., Linear Regression-Based Efficient SVM Learning for Large-Scale Classification, IEEE Transactions on Neural Networks and Learning Systems, 26, 10, pp. 2357-2369, (2015); Wu D., Et al., Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 8, pp. 1583-1597, (2016); Dehmeshki J., Amin H., Valdivieso M., Ye X., Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach, IEEE Transactions on Medical Imaging, 27, 4, pp. 467-480, (2008); Li B.N., Qin J., Wang R., Wang M., Li X., Selective Level Set Segmentation Using Fuzzy Region Competition, IEEE Access, 4, pp. 4777-4788, (2016); Tareef A., Song Y., Huang H., Feng D., Chen M., Wang Y., Cai W., Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells, IEEE Transactions on Medical Imaging, 37, 9, pp. 2044-2059, (2018); Kalavathi P., Brain tissue segmentation in MR brain images using multiple Otsu's thresholding technique, Computer Science & Education, pp. 639-642, (2013)","K. Ananthajothi; Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, 602105, India; email: kanandjothime@gmail.com","","Springer","","","","","","02562499","","SAPSE","","English","Sadhana","Article","Final","","Scopus","2-s2.0-85149042493"
"Radzikowska U.; Baerenfaller K.; Cornejo-Garcia J.A.; Karaaslan C.; Barletta E.; Sarac B.E.; Zhakparov D.; Villaseñor A.; Eguiluz-Gracia I.; Mayorga C.; Sokolowska M.; Barbas C.; Barber D.; Ollert M.; Chivato T.; Agache I.; Escribese M.M.","Radzikowska, Urszula (56538726700); Baerenfaller, Katja (23059607800); Cornejo-Garcia, José Antonio (6507113740); Karaaslan, Cagatay (14017933300); Barletta, Elena (57202358404); Sarac, Basak Ezgi (57221106639); Zhakparov, Damir (57218600823); Villaseñor, Alma (36487099200); Eguiluz-Gracia, Ibon (55213874000); Mayorga, Cristobalina (7004417105); Sokolowska, Milena (24081481900); Barbas, Coral (7101761748); Barber, Domingo (54407867900); Ollert, Markus (7004161698); Chivato, Tomas (6701522636); Agache, Ioana (57201020933); Escribese, Maria M. (9736253800)","56538726700; 23059607800; 6507113740; 14017933300; 57202358404; 57221106639; 57218600823; 36487099200; 55213874000; 7004417105; 24081481900; 7101761748; 54407867900; 7004161698; 6701522636; 57201020933; 9736253800","Omics technologies in allergy and asthma research: An EAACI position paper","2022","Allergy: European Journal of Allergy and Clinical Immunology","77","10","","2888","2908","20","40","10.1111/all.15412","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133037361&doi=10.1111%2fall.15412&partnerID=40&md5=f4b26a54c77e7c268d39ee7685ada769","Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland; Christine-Kühne Center for Allergy Research and Education (CK-CARE), Davos, Switzerland; Swiss Institute of Bioinformatics (SIB), Davos, Switzerland; Research Laboratory, IBIMA, ARADyAL Instituto de Salud Carlos III, Regional University Hospital of Málaga, UMA, Málaga, Spain; Department of Biology, Molecular Biology Section, Faculty of Science, Hacettepe University, Ankara, Turkey; Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain; Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; Allergy Unit, Hospital Regional Universitario de Málaga, Málaga, Spain; Allergy Research Group, Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain; Andalusian Centre for Nanomedicine and Biotechnology – BIONAND, Málaga, Spain; Department of Infection and Immunity, Luxembourg Institute of Healthy, Esch-sur-Alzette, Luxembourg; Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, Odense University Hospital, University of Southern Denmark, Odense, Denmark; Department of Clinic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; Transylvania University, Brasov, Romania","Radzikowska U., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland, Christine-Kühne Center for Allergy Research and Education (CK-CARE), Davos, Switzerland; Baerenfaller K., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland, Swiss Institute of Bioinformatics (SIB), Davos, Switzerland; Cornejo-Garcia J.A., Research Laboratory, IBIMA, ARADyAL Instituto de Salud Carlos III, Regional University Hospital of Málaga, UMA, Málaga, Spain; Karaaslan C., Department of Biology, Molecular Biology Section, Faculty of Science, Hacettepe University, Ankara, Turkey; Barletta E., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland, Swiss Institute of Bioinformatics (SIB), Davos, Switzerland; Sarac B.E., Department of Biology, Molecular Biology Section, Faculty of Science, Hacettepe University, Ankara, Turkey; Zhakparov D., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland, Swiss Institute of Bioinformatics (SIB), Davos, Switzerland; Villaseñor A., Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain, Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; Eguiluz-Gracia I., Allergy Unit, Hospital Regional Universitario de Málaga, Málaga, Spain, Allergy Research Group, Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain; Mayorga C., Allergy Unit, Hospital Regional Universitario de Málaga, Málaga, Spain, Allergy Research Group, Instituto de Investigación Biomédica de Málaga-IBIMA, Málaga, Spain, Andalusian Centre for Nanomedicine and Biotechnology – BIONAND, Málaga, Spain; Sokolowska M., Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland, Christine-Kühne Center for Allergy Research and Education (CK-CARE), Davos, Switzerland; Barbas C., Centre for Metabolomics and Bioanalysis (CEMBIO), Department of Chemistry and Biochemistry, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Madrid, Spain; Barber D., Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; Ollert M., Department of Infection and Immunity, Luxembourg Institute of Healthy, Esch-sur-Alzette, Luxembourg, Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, Odense University Hospital, University of Southern Denmark, Odense, Denmark; Chivato T., Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain, Department of Clinic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; Agache I., Transylvania University, Brasov, Romania; Escribese M.M., Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain","Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force “Omics technologies in allergic research” broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients’ stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted. © 2022 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","allergy; biomarker; omic; precision medicine; systems biology","Asthma; Biomarkers; Genomics; Humans; Hypersensitivity; Metabolomics; biological marker; biological marker; allergy; amino acid sequence; Article; asthma; data mining; electrospray; epigenetics; exposome; exposomics; genomics; human; machine learning; medical research; metabolomics; metagenomics; personalized medicine; prognosis; protein degradation; protein purification; proteomics; single cell analysis; systems biology; transcriptomics; asthma; genetics; genomics; hypersensitivity; metabolomics; procedures","","Biomarkers, ","","","Clinical Immunology Board Secretary; Swiss canton of Grisons; GlaxoSmithKline España, GSK; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; European Academy of Allergy and Clinical Immunology, EAACI","Funding text 1: AV is a Secretary of the EAACI IG on Allergy Diagnosis & Systems Medicine. CK is a member of the EAACI working group Genomics and Proteomics. IA is an Associate Editor of Allergy and CTA. KB reports: The Center for Precision Proteomics providing partial salary funding is funded through the Swiss canton of Grisons. Salary payments are made through the institution and are part of the regular salary. KB has been funded by the Earth Vision nonprofit corporation for chairing an event, performing a study and manuscript writing. The funding has been made to SIAF; the resulting manuscript has been published https://doi.org/10.3390/microorganisms8040498; the topic has no overlap with the study presented here. KB is a member of the board of directors of the Swiss Institute of Bioinformatics, member of the EAACI working group Genomics and Proteomics and president of the bioinformatics intersection of LS2 and member of LS2. MME is a Chair of EAACI WG on Genomics and Proteomics and report lecture fees from Diater and Stallergenes. MS reports grants from SNSF, GSK, Novartis; payments from AstraZeneca, and position of European Academy of Allergy and Clinical Immunology (EAACI) Basic and Clinical Immunology Board Secretary. MO reports personal consulting honoraria received from Hycor Biomedical; Member‐at‐Large 2019‐2022, Executive Committee, European Academy of Allergy and Clinical Immunology (EAACI). UR is a Secretary of the EAACI WG on Genomics and Proteomics. 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Abdel-Aziz M.I., Brinkman P., Vijverberg S.J.H., Et al., eNose breath prints as a surrogate biomarker for classifying patients with asthma by atopy, J Allergy Clin Immunol, 146, 5, pp. 1045-1055, (2020); Ghosh D., Ding L., Bernstein J.A., Mersha T.B., The utility of resolving asthma molecular signatures using tissue-specific transcriptome data, G3 (Bethesda), 10, 11, pp. 4049-4062, (2020); Fontanella S., Frainay C., Murray C.S., Simpson A., Custovic A., Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: a cross-sectional analysis within a population-based birth cohort, PLoS Med, 15, 11, (2018); Kuruvilla R., Scott K., Pirmohamed S.M., Pharmacogenomics of drug hypersensitivity: technology and translation, Immunol Allergy Clin North Am, 42, 2, pp. 335-355, (2022); Farzan N., Vijverberg S.J., Hernandez-Pacheco N., Et al., 17q21 variant increases the risk of exacerbations in asthmatic children despite inhaled corticosteroids use, Allergy, 73, 10, pp. 2083-2088, (2018); 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D'Adamo G.L., Widdop J.T., Giles E.M., The future is now? Clinical and translational aspects of ""Omics"" technologies, Immunol Cell Biol, 99, 2, pp. 168-176, (2021); Krassowski M., Das V., Sahu S.K., Misra B.B., State of the field in multi-omics research: from computational needs to data mining and sharing, Front Genet, 11, (2020); Wang S., Cheng Q., Microarray Analysis in Drug Discovery and Clinical Applications, Bioinformatics and Drug Discovery, pp. 49-65, (2006); McCarthy M.I., Abecasis G.R., Cardon L.R., Et al., Genome-wide association studies for complex traits: consensus, uncertainty and challenges, Nat Rev Genet, 9, 5, pp. 356-369, (2008); Ioannidis J.P., Thomas G., Daly M.J., Validating, augmenting and refining genome-wide association signals, Nat Rev Genet, 10, 5, pp. 318-329, (2009); Lamprecht A.-L., Microarray Data Analysis Pipelines, User-Level Workflow Design, pp. 119-137, (2013); Loewe R.P., Nelson P.J., Microarray Bioinformatics, Biological Microarrays: Methods and Protocols, pp. 295-320, (2011); Bjornsdottir U.S., Holgate S.T., Reddy P.S., Et al., Pathways activated during human asthma exacerbation as revealed by gene expression patterns in blood, PLoS One, 6, 7, (2011)","M.M. Escribese; Institute of Applied Molecular Medicine Nemesio Diaz (IMMAND), Department of Basic Medical Sciences, Facultad de Medicina, Universidad San Pablo CEU, CEU Universities, Madrid, Spain; email: mariamarta.escribesealonso@ceu.es","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","35713644","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85133037361"
"Moslemi A.; Kontogianni K.; Brock J.; Wood S.; Herth F.; Kirby M.","Moslemi, Amir (57737896400); Kontogianni, Konstantina (23094529400); Brock, Judith (57220577571); Wood, Susan (59608990500); Herth, Felix (57207907661); Kirby, Miranda (35174507500)","57737896400; 23094529400; 57220577571; 59608990500; 57207907661; 35174507500","Differentiating COPD and asthma using quantitative CT imaging and machine learning","2022","European Respiratory Journal","60","3","2103078","","","","26","10.1183/13993003.03078-2021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136219389&doi=10.1183%2f13993003.03078-2021&partnerID=40&md5=d74199741e862e3202e5b2168e86a25f","Dept of Physics, Ryerson University, Toronto, ON, Canada; Dept of Pneumology and Critical Care Medicine, Thoraxklinik, Translational Lung Research Center (TLRCH), University of Heidelberg, Heidelberg, Germany; Vida Diagnostics Inc., Coralville, IA, United States","Moslemi A., Dept of Physics, Ryerson University, Toronto, ON, Canada; Kontogianni K., Dept of Pneumology and Critical Care Medicine, Thoraxklinik, Translational Lung Research Center (TLRCH), University of Heidelberg, Heidelberg, Germany; Brock J., Dept of Pneumology and Critical Care Medicine, Thoraxklinik, Translational Lung Research Center (TLRCH), University of Heidelberg, Heidelberg, Germany; Wood S., Vida Diagnostics Inc., Coralville, IA, United States; Herth F., Dept of Pneumology and Critical Care Medicine, Thoraxklinik, Translational Lung Research Center (TLRCH), University of Heidelberg, Heidelberg, Germany; Kirby M., Dept of Physics, Ryerson University, Toronto, ON, Canada","Background There are similarities and differences between chronic obstructive pulmonary disease (COPD) and asthma patients in terms of computed tomography (CT) disease-related features. Our objective was to determine the optimal subset of CT imaging features for differentiating COPD and asthma using machine learning. Methods COPD and asthma patients were recruited from Heidelberg University Hospital (Heidelberg, Germany). CT was acquired and 93 features were extracted: percentage of low-attenuating area below -950 HU (LAA950), low-attenuation cluster (LAC) total hole count, estimated airway wall thickness for an idealised airway with an internal perimeter of 10 mm (Pi10), total airway count (TAC), as well as airway inner/outer perimeters/areas and wall thickness for each of five segmental airways, and the average of those five airways. Hybrid feature selection was used to select the optimum number of features, and support vector machine learning was used to classify COPD and asthma. Results 95 participants were included (n=48 COPD and n=47 asthma); there were no differences between COPD and asthma for age ( p=0.25) or forced expiratory volume in 1 s ( p=0.31). In a model including all CT features, the accuracy and F1 score were 80% and 81%, respectively. The top features were: LAA950, outer airway perimeter, inner airway perimeter, TAC, outer airway area RB1, inner airway area RB1 and LAC total hole count. In the model with only CT airway features, the accuracy and F1 score were 66% and 68%, respectively. The top features were: inner airway area RB1, outer airway area LB1, outer airway perimeter, inner airway perimeter, Pi10, TAC, airway wall thickness RB1 and TAC LB10. Conclusion COPD and asthma can be differentiated using machine learning with moderate-to-high accuracy by a subset of only seven CT features. Copyright ©The authors 2022.","","Asthma; Forced Expiratory Volume; Humans; Lung; Machine Learning; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; adult; age; aged; Article; asthma; chronic obstructive lung disease; computer assisted tomography; controlled study; diagnostic accuracy; differential diagnosis; feature extraction; feature selection; forced expiratory volume; Germany; human; image analysis; machine learning; major clinical study; middle aged; prediction; sensitivity and specificity; spirometry; support vector machine; asthma; chronic obstructive lung disease; diagnostic imaging; lung; machine learning; procedures; x-ray computed tomography","","","Emotion 6, Siemens Healthineers, Germany; SOMATOM Definition AS, Siemens Healthineers, Germany","Siemens Healthineers, Germany; Siemens Healthineers, Germany","Olympus Medical Systems; Chiesi Farmaceutici; Boehringer Ingelheim; Government of Ontario; Natural Sciences and Engineering Research Council of Canada, NSERC; European Commission, EC; Deutsche Forschungsgemeinschaft, DFG; Canada Research Chairs; Bundesministerium für Bildung und Forschung, BMBF; Klaus Tschira Stiftung, KTS","Funding text 1: Acknowledgements: M. Kirby was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, the Early Researchers Awards (ERA) programme from the Government of Ontario, and holds a Canada Research Chair in Quantitative Imaging (Tier 2).; Funding text 2: Conflict of interest: S. Wood is a CEO and shareholder of VIDA Diagnostics, a company commercialising lung image analysis software. F. Herth is affiliated with, or has received grants or research support from, the German Federal Ministry of Education and Research (BMBF), BMG Pharma, Broncus-Uptake Medical, Deutsche Forschungsgemeinschaft (DFG), European Union, Klaus Tschirra Stiftung, Olympus Medical Systems, Pulmonx and Roche Diagnostics; honoraria or consulting fees from AstraZeneca, Berlin-Chemie, Boehringer Ingelheim, Chiesi Farmaceutici SpA, Erbe China, Novartis, MedUpdates, Pulmonx, Roche Diagnostics, Uptake Medical, Boston Scientific, Broncus-Uptake Medical, Dinova Pharmaceutical Inc., Erbe Medical, Free Flow Medical, Johnson & Johnson, Karger Publishers, LAK Medical, Nanovation and Olympus Medical. All other authors do not have any potential conflicts of interest to declare.","Global Strategy for Asthma Management and Prevention, (2021); Global Strategy for the Diagnosis, Management and Prevention of COPD, (2020); Smolonska J, Koppelman GH, Wijmenga C, Et al., Common genes underlying asthma and COPD? 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The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study and the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS), Thorax, 69, pp. 987-996, (2014); Kim J-O, Mueller CW., Factor Analysis: Statistical Methods and Practical Issues 14 (Quantitative Applications in the Social Sciences), (1978); Vieira SM, Mendonca LF, Farinha GJ, Et al., Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients, Appl Soft Comput J, 13, pp. 3494-3504, (2013); Hua J, Xiong Z, Lowey J, Et al., Optimal number of features as a function of sample size for various classification rules, Bioinformatics, 21, pp. 1509-1515, (2005); Badnjevic A, Gurbeta L, Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Sci Rep, 8, (2018); Maghsoudloo M, Azimzadeh Jamalkandi S, Najafi A, Et al., An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases, Genomics, 112, pp. 3284-3293, (2020); Zarrin PS, Wenger C., Implementation of Siamese-based few-shot learning algorithms for the distinction of COPD and asthma subjects, Lect Notes Comput Sci, 12396, pp. 431-440, (2020); Hsu HH, Hsieh CW, Da Lu M., Hybrid feature selection by combining filters and wrappers, Expert Syst Appl, 38, pp. 8144-8150, (2011); Mondonedo JR, Sato S, Oguma T, Et al., CT imaging-based low-attenuation super clusters in three dimensions and the progression of emphysema, Chest, 155, (2019); Williamson JP, James AL, Phillips MJ, Et al., Quantifying tracheobronchial tree dimensions: methods, limitations and emerging techniques, Eur Respir J, 34, pp. 42-55, (2009); James AL, Pare PD, Hogg JC., Effects of lung volume, bronchoconstriction, and cigarette smoke on morphometric airway dimensions, J Appl Physiol, 64, pp. 913-919, (1988); Hackx M, Bankier AA, Gevenois PA., Chronic obstructive pulmonary disease: CT quantification of airways disease, Radiology, 265, pp. 34-48, (2012); Nakano Y, Muro S, Sakai H, Et al., Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function, Am J Respir Crit Care Med, 162, pp. 1102-1108, (2000); Aysola RS, Hoffman EA, Gierada D, Et al., Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology, Chest, 134, pp. 1183-1191, (2008); Siddiqui S, Gupta S, Cruse G, Et al., Airway wall geometry in asthma and nonasthmatic eosinophilic bronchitis, Allergy, 64, (2009); Gupta S, Siddiqui S, Haldar P, Et al., Quantitative analysis of high-resolution computed tomography scans in severe asthma subphenotypes, Thorax, 65, pp. 775-781, (2010); Pornsuriyasak P, Suwatanapongched T, Thaipisuttikul W, Et al., Assessment of proximal and peripheral airway dysfunction by computed tomography and respiratory impedance in asthma and COPD patients with fixed airflow obstruction, Ann Thorac Med, 13, pp. 212-219, (2018); Boser SR, Park H, Perry SF, Et al., Fractal geometry of airway remodeling in human asthma, Am J Respir Crit Care Med, 172, pp. 817-823, (2005); Livraghi-Butrico A, Kelly EJ, Klem ER, Et al., Mucus clearance, MyD88-dependent and MyD88-independent immunity modulate lung susceptibility to spontaneous bacterial infection and inflammation, Mucosal Immunol, 5, pp. 397-408, (2012); Zach JA, Newell JD, Schroeder J, Et al., Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults, Invest Radiol, 47, pp. 596-602, (2012); Sheshadri A, Rodriguez A, Chen R, Et al., Effect of reducing field of view on multidetector quantitative computed tomography parameters of airway wall thickness in asthma, J Comput Assist Tomogr, 39, pp. 584-590, (2015); Oguma T, Hirai T, Niimi A, Et al., Limitations of airway dimension measurement on images obtained using multi-detector row computed tomography, PLoS One, 8, (2013)","F. Herth; Dept of Pneumology and Critical Care Medicine, Thoraxklinik, Translational Lung Research Center (TLRCH), University of Heidelberg, Heidelberg, Germany; email: Felix.Herth@med.uni-heidelberg.de","","European Respiratory Society","","","","","","09031936","","ERJOE","35210316","English","Eur. Respir. J.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85136219389"
"Alam M.Z.; Simonetti A.; Brillantino R.; Tayler N.; Grainge C.; Siribaddana P.; Nouraei S.A.R.; Batchelor J.; Rahman M.S.; Mancuzo E.V.; Holloway J.W.; Holloway J.A.; Rezwan F.I.","Alam, Md. Zahangir (58572115300); Simonetti, Albino (57224951652); Brillantino, Raffaele (57725273500); Tayler, Nick (56536862500); Grainge, Chris (57192248350); Siribaddana, Pandula (56083201700); Nouraei, S. A. Reza (9938825200); Batchelor, James (57190586326); Rahman, M. Sohel (55457931100); Mancuzo, Eliane V. (16507341400); Holloway, John W. (57221220827); Holloway, Judith A. (7202050142); Rezwan, Faisal I. (24537660300)","58572115300; 57224951652; 57725273500; 56536862500; 57192248350; 56083201700; 9938825200; 57190586326; 55457931100; 16507341400; 57221220827; 7202050142; 24537660300","Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach","2022","Frontiers in Digital Health","4","","750226","","","","15","10.3389/fdgth.2022.750226","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131258791&doi=10.3389%2ffdgth.2022.750226&partnerID=40&md5=8ce8a38e40cc25aac8d4f5c4ee88ceab","Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh; Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy; Peter Doherty Institute, The University of Melbourne, Melbourne, VIC, Australia; Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia; Department of Respiratory Medicine, John Hunter Hospital, Newcastle, NSW, Australia; Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka; Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom; Robert White Centre for Airway Voice and Swallowing, Poole Hospital, Poole, United Kingdom; Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; National Institute for Health Research, Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; MSc Allergy, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom","Alam M.Z., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh; Simonetti A., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy; Brillantino R., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, Department of Information and Electrical Engineering and Applied Mathematics/DIEM, University of Salerno, Fisciano, Italy; Tayler N., Peter Doherty Institute, The University of Melbourne, Melbourne, VIC, Australia; Grainge C., Hunter Medical Research Institute, The University of Newcastle, Newcastle, NSW, Australia, Department of Respiratory Medicine, John Hunter Hospital, Newcastle, NSW, Australia; Siribaddana P., Postgraduate Institute of Medicine, University of Colombo, Colombo, Sri Lanka; Nouraei S.A.R., Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom, Robert White Centre for Airway Voice and Swallowing, Poole Hospital, Poole, United Kingdom; Batchelor J., Clinical Informatics Research Unit, University of Southampton, Southampton, United Kingdom; Rahman M.S., Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh; Mancuzo E.V., Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Holloway J.W., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, National Institute for Health Research, Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, United Kingdom; Holloway J.A., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, MSc Allergy, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Rezwan F.I., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom","Introduction: To self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients. Methods: A threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples. Results: The RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%). Conclusion: Our machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma. Copyright © 2022 Alam, Simonetti, Brillantino, Tayler, Grainge, Siribaddana, Nouraei, Batchelor, Rahman, Mancuzo, Holloway, Holloway and Rezwan.","asthma; breathe; FEV<sub>1</sub>; human voice; machine learning; pulmonary function; speech","allergic asthma; Article; breathing; clinical article; controlled study; disease severity; exploratory research; false positive result; feature extraction; forced expiratory volume; human; intermethod comparison; linear regression analysis; lung function; machine learning; measurement accuracy; predictive model; predictive value; random forest; receiver operating characteristic; regression model; root mean squared error; speech; support vector machine; voice analysis","","","","","","","World Health Organization, WHO Scope: Asthma, (2021); Asthma statistics, (2021); Asthma Facts Statistics, (2021); Petsky H.L., Cates C.J., Lasserson TJ Li A.M., Turner C., Kynaston J.A., Et al., A systematic review and meta-analysis: tailoring asthma treatment on eosinophilic markers (exhaled nitric oxide or sputum eosinophils), Thorax, 67, pp. 199-208, (2012); Larson E.C., Goel M., Boriello G., Heltshe S., Rosenfeld M., Patel S.N., SpiroSmart: using a microphone to measure lung function on a mobile phone, Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12, (2012); Bell D., Layton A.J., Gabbay J., Use of a guideline based questionnaire to audit hospital care of acute asthma, BMJ, 302, pp. 1440-1443, (1991); Lai C.K.W., Beasley R., Crane J., Foliaki S., Shah J., Weiland S., Et al., Global variation in the prevalence and severity of asthma symptoms: phase three of the International Study of Asthma and Allergies in Childhood (ISAAC), Thorax, 64, pp. 476-483, (2009); McCormack M.C., Enright P.L., Making the diagnosis of asthma, Respir Care, 53, pp. 590-592, (2008); Veiga J., Lopes A.J., Jansen J.M., de Melo P.L., Within-breath analysis of respiratory mechanics in asthmatic patients by forced oscillation, Clin São Paulo Braz, 64, pp. 649-656, (2009); Detterbeck F., Gat M., Miller D., Force S., Chin C., Fernando H., Et al., A new method to predict postoperative lung function: quantitative breath sound measurements, Ann Thorac Surg, 95, pp. 968-975, (2013); Westhoff M., Herth F., Albert M., Dienemann H., Eberhardt R., A new method to predict values for postoperative lung function and surgical risk of lung resection by quantitative breath sound measurements, Am J Clin Oncol, 36, pp. 273-278, (2013); Rao M.V.A., Kausthubha N.K., Yadav S., Gope D., Krishnaswamy U.M., Ghosh P.K., Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity, 2017 25th European Signal Processing Conference (EUSIPCO), pp. 41-45, (2017); Sharan R.V., Abeyratne U.R., Swarnkar V.R., Claxton S., Hukins C., Porter P., Predicting spirometry readings using cough sound features and regression, Physiol Meas, 39, (2018); Rudraraju G., Palreddy S., Mamidgi B., Sripada N.R., Sai Y.P., Vodnala N.K., Et al., Cough sound analysis and objective correlation with spirometry and clinical diagnosis, Inform Med Unlocked, 19, (2020); Tong J.Y., Sataloff R.T., Respiratory function and voice: The role for airflow measures, J Voice; Yadav S., Nk K., Gope D., Krishnaswamy U.M., Ghosh P.K., Comparison of cough, wheeze and sustained phonations for automatic classification between healthy subjects and asthmatic patients, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1400-1403, (2018); Tayler N., Grainge C., Gove K., Howarth P., Holloway J., Clinical assessment of speech correlates well with lung function during induced bronchoconstriction, Npj Prim Care Respir Med, 25, (2015); Saleheen N., Ahmed T., Rahman M.M., Nemati E., Nathan V., Vatanparvar K., Et al., Lung function estimation from a monosyllabic voice segment captured using smartphones, 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1-11, (2020); Chun K.S., Nathan V., Vatanparvar K., Nemati E., Rahman M.M., Blackstock E., Et al., Towards passive assessment of pulmonary function from natural speech recorded using a mobile phone, 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom) [Internet], pp. 1-10, (2020); Global Initiative for Asthma - GINA, (2021); Grainge C.L., Lau L.C.K., Ward J.A., Dulay V., Lahiff G., Wilson S., Et al., Effect of bronchoconstriction on airway remodeling in asthma, N Engl J Med, 364, pp. 2006-2015, (2011); British Thoracic Society guidelines on diagnostic flexible bronchoscopy, Thorax, 56, pp. i1-i21, (2001); McFee B., Raffel C., Liang D., Ellis D., McVicar M., Battenberg E., Et al., Librosa: v0.4.0. Zenodo, (2015); Audacity(R): Free Audio Editor and Recorder, (2012); Johnson J.D., Theurer W.M., A stepwise approach to the interpretation of pulmonary function tests, Am Fam Physician, 89, pp. 359-366, (2014); Alam M.Z., Rahman M.S., Rahman M.S., A Random Forest based predictor for medical data classification using feature ranking, Inform Med Unlocked, 15, (2019); Alam M.Z., Masud M.M., Rahman M.S., Cheratta M., Nayeem M.A., Rahman M.S., Feature-ranking-based ensemble classifiers for survivability prediction of intensive care unit patients using lab test data, Inform Med Unlocked, 22, (2021); Pellegrino R., Interpretative strategies for lung function tests, Eur Respir J, 26, pp. 948-968, (2005); Wiechern B., Liberty K.A., Pattemore P., Lin E., Effects of asthma on breathing during reading aloud, Speech Lang Hear, 21, pp. 30-40, (2018); Forgacs P., Breath sounds, Thorax, 33, pp. 681-683, (1978); Laguarta J., Hueto F., Subirana B., COVID-19 Artificial intelligence diagnosis using only cough recordings, IEEE Open J Eng Med Biol, 1, pp. 275-281, (2020)","F.I. Rezwan; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; email: f.rezwan@aber.ac.uk","","Frontiers Media S.A.","","","","","","2673253X","","","","English","Front. Digit. Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85131258791"
"Soni M.; Gomathi S.; Kumar P.; Churi P.P.; Mohammed M.A.; Salman A.O.","Soni, Mukesh (57202986134); Gomathi, S. (55324593200); Kumar, Pankaj (57226822516); Churi, Prathamesh P. (57188831747); Mohammed, Mazin Abed (57192089894); Salman, Akbal Omran (57202601697)","57202986134; 55324593200; 57226822516; 57188831747; 57192089894; 57202601697","Hybridizing Convolutional Neural Network for Classification of Lung Diseases","2022","International Journal of Swarm Intelligence Research","13","2","","","","","40","10.4018/IJSIR.287544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123202152&doi=10.4018%2fIJSIR.287544&partnerID=40&md5=bef23c6c54cb971a699d29f03e4da1e6","Jagran Lakecity University, India; UK International Qualifications, Ltd., India; Noida Institute of Engineering and Technology, Greater Noida, India; NMIMS University, India; University of Anbar, Iraq; Middle Technical University, Iraq","Soni M., Jagran Lakecity University, India; Gomathi S., UK International Qualifications, Ltd., India; Kumar P., Noida Institute of Engineering and Technology, Greater Noida, India; Churi P.P., NMIMS University, India; Mohammed M.A., University of Anbar, Iraq; Salman A.O., Middle Technical University, Iraq","Pulmonary disease is widespread worldwide. There is persistent blockage of the lungs, pneumonia, asthma, TB, etc. It is essential to diagnose the lungs promptly. For this reason, machine learning models were developed. For lung disease prediction, many deep learning technologies, including the CNN and the capsule network, are used. The fundamental CNN has low rotating, inclined, or other irregular image orientation efficiency. Therefore, by integrating the space transformer network (STN) with CNN, the authors propose a new hybrid deep learning architecture named STNCNN. The new model is implemented on the dataset from the Kaggle repository for an NIH chest x-ray image. STNCNN has an accuracy of 69% in respect of the entire dataset, while the accuracy values of vanilla grey, vanilla RGB, hybrid CNN are 67.8%, 69.5%, and 63.8%, respectively. When the sample data set is applied, STNCNN takes much less time to train at the cost of a slightly less reliable validation. Therefore, both specialist and physician jobs are simplified by the proposed STNCNN system for the diagnosis of lung disease. Copyright © 2022, IGI Global.","Capsule network; Convolution neural network; Epoch; Loss; Max-pooling","Convolution; Convolutional neural networks; Deep learning; Diagnosis; Capsule network; Chest X-ray image; Convolution neural network; Convolutional neural network; Epoch; Image orientation; Learning architectures; Learning technology; Machine learning models; Max-pooling; Biological organs","","","","","","","Afshar P., Heidarian S., Naderkhani F., Oikonomou A., Plataniotis K. N., Mohammadi A., COVIDCAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images, Pattern Recognition Letters, 138, pp. 638-643, (2020); Babitha Ismail, Chowdhury Govindaraj, Prakash, Automated road safety surveillance system using hybrid cnn-lstm approach, Int. J. Adv. Trends Comput. Sci. Eng, (2020); Behzadi-khormouji H., Rostami H., Salehi S., Derakhshande-Rishehri T., Masoumi M., Salemi S., Keshavarz A., Gholamrezanezhad A., Assadi M., Batouli A., Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images, Computer Methods and Programs in Biomedicine, 185, (2020); Bhandary A., Prabhu G. A., Rajinikanth V., Thanaraj K. P., Satapathy S. C., Robbins D. E., Shasky C., Zhang Y.-D., Tavares J. M. R. S., Raja N. S. M., Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images, Pattern Recognition Letters, 129, pp. 271-278, (2020); Bharati S., Podder P., Mondal M. R. H., Hybrid deep learning for detecting lung diseases from X-ray images, Informatics Med. 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S., Solomon K., Analysis of the IoT sensors and networks with big data and sharing the data through cloud platform, International Journal of Innovative Technology and Exploring Engineering, (2019); Chowdhury S., Mayilvahanan P., Govindaraj R., Optimal feature extraction and classification-oriented medical insurance prediction model: Machine learning integrated with the internet of things, International Journal of Computers and Applications, pp. 1-13, (2020); Dehghani M., Montazeri Z., Dhiman G., Malik O. P., Morales-Menendez R., Ramirez-Mendoza R. A., Dehghani A., Guerrero J. M., Parra-Arroyo L., A Spring Search Algorithm Applied to Engineering Optimization Problems, Applied Sciences, 10, 18, (2020); Dhiman G., An Innovative Approach for Face Recognition Using Raspberry Pi, Artificial Intelligence Evolution, (2020); Dhiman G., MOSHEPO: A hybrid multi-objective approach to solve economic load dispatch and micro grid problems, Applied Intelligence, 50, 1, pp. 119-137, (2020); Dhiman G., Garg M., MoSSE: A novel hybrid multi-objective meta-heuristic algorithm for engineering design problems, Soft Computing, pp. 1-20, (2020); Dhiman G., Garg M., Nagar A. K., Kumar V., Dehghani M., A novel algorithm for global optimization: Rat swarm optimizer, Journal of Ambient Intelligence and Humanized Computing, (2020); Dhiman G., Kaur A., HKn-RVEA: A novel many-objective evolutionary algorithm for car side impact bar crashworthiness problem, International Journal of Vehicle Design, 80, 2-4, pp. 257-284, (2019); Dhiman G., Kumar V., Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Advances in Engineering Software, 114, pp. 48-70, (2017); Dhiman G., Kumar V., Emperor penguin optimizer: A bio-inspired algorithm for engineering problems, Knowledge-Based Systems, 159, pp. 20-50, (2018); Dhiman G., Kumar V., Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems, Knowledge-Based Systems, 165, pp. 169-196, (2019); Dhiman G., Singh K. K., Slowik A., Chang V., Yildiz A. R., Kaur A., Garg M., EMoSOA: A new evolutionary multi-objective seagull optimization algorithm for global optimization, International Journal of Machine Learning and Cybernetics, pp. 1-26, (2020); Dhiman G., Singh K. K., Soni M., Nagar A., Dehghani M., Slowik A., Kaur A., Sharma A., Houssein E. H., Cengiz K., MOSOA: A new multi-objective seagull optimization algorithm, Expert Systems with Applications, (2020); Dhiman G., Soni M., Pandey H. M., Slowik A., Kaur H., A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization, Engineering with Computers, (2020); Dickman S. L., Himmelstein D. U., Woolhandler S., Inequality and the health-care system in the USA, Lancet, 389, 10077, pp. 1431-1441, (2017); Garg M., Dhiman G., Deep convolution neural network approach for defect inspection of textured surfaces, Journal of the Institute of Electronics and Computer, 2, 1, pp. 28-38, (2020); Garg M., Dhiman G., A novel content based image retrieval approach for classification using glcm features and texture fused lbp variants, Neural Computing & Applications, (2020); Gomathi S., Kohli R., Soni M., Dhiman G., Nair R., Pattern analysis: predicting COVID-19 pandemic in India using AutoML, World Journal of Engineering, (2020); Gu Y., Lu X., Yang L., Zhang B., Yu D., Zhao Y., Gao L., Wu L., Zhou T., Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs, Computers in Biology and Medicine, 103, pp. 220-231, (2018); He Gkioxari, Dollar, Girshick, Mask R-CNN, IEEE Trans. Pattern Anal. Mach. Intell, (2020); Irvin J., CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison, 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, (2019); Kallianos K., Mongan J., Antani S., Henry T., Taylor A., Abuya J., Kohli M., How far have we come? Artificial intelligence for chest radiograph interpretation, Clinical Radiology, 74, 5, pp. 338-345, (2019); Kaur S., Awasthi L. K., Sangal A. L., Dhiman G., Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Engineering Applications of Artificial Intelligence, 90, (2020); Kong W., Hong J., Jia M., Yao J., Cong W., Hu H., Zhang H., YOLOv3-DPFIN: A Dual-Path Feature Fusion Neural Network for Robust Real-Time Sonar Target Detection, IEEE Sensors Journal, 20, 7, pp. 3745-3756, (2020); Kumar N., Kharkwal N., Kohli R., Choudhary S., Ethical aspects and future of artificial intelligence, 2016 1st International Conference on Innovation and Challenges in Cyber Security, ICICCS 2016, (2016); Liang C. H., Liu Y. C., Wu M. T., Garcia-Castro F., Alberich-Bayarri A., Wu F. Z., Identifying pulmonary nodules or masses on chest radiography using deep learning: External validation and strategies to improve clinical practice, Clinical Radiology, 75, 1, pp. 38-45, (2020); Limbasiya T., Soni M., Mishra S. K., Advanced formal authentication protocol using smart cards for network applicants, Computers & Electrical Engineering, 66, pp. 50-63, (2018); Long J., Shelhamer E., Darrell T., Fully convolutional networks for semantic segmentation, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2015); Radiologist-Level COVID-19 detection using CT scans with detail-oriented capsule networks, (2020); Nair, Bhagat, Genes expression classification using improved deep learning method, Int. J. Emerg. Technol, (2019); Nair R., Bhagat A., Feature selection method to improve the accuracy of classification algorithm, International Journal of Innovative Technology and Exploring Engineering, (2019); Nair R., Gupta S., Soni M., Shukla P. K., Dhiman G., An approach to minimize the energy consumption during blockchain transaction, Materials Today: Proceedings, (2020); Nair R., Vishwakarma S., Soni M., Patel T., Joshi S., Detection of COVID-19 cases through X-ray images using hybrid deep neural network, World Journal of Engineering, (2021); Nasrullah N., Sang J., Alam M. S., Mateen M., Cai B., Hu H., Automated lung nodule detection and classification using deep learning combined with multiple strategies, Sensors, (2019); Pandey H. MDhiman, GSoni MSlowik, AKaur H., A Novel Hybrid Evolutionary Algorithm based on Hypervolume Indicator and Reference Vector Adaptation Strategies for Many-Objective Optimization, Engineering with Computers, (2020); Patel M., Rami D., Soni M., Next Generation Web for Alumni Web Portal, Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, 33, (2020); Prakash Nazeer, Vadla, Chowdhury, Layered programming model for resource provisioning in fog computing using yet another fog simulator, Int. J. Emerg. Trends Eng. Res, (2020); Ren He, Girshick, Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Trans. Pattern Anal. Mach. Intell, (2017); Ronneberger O., Fischer P., Brox T., U-Net: Convolutional Networks for Biomedical Image Segmentation, International Conference on Medical image computing and computer-assisted intervention, (2015); Sabour S., Frosst N., Hinton G. E., Dynamic routing between capsules, Advances in Neural Information Processing Systems, (2017); Setio A. A. A., Traverso A., de Bel T., Berens M. S. N., Bogaard C., Cerello P., Chen H., Dou Q., Fantacci M. E., Geurts B., Gugten R., Heng P. A., Jansen B., de Kaste M. M. J., Kotov V., Lin J. Y.-H., Manders J. T. M. C., Sonora-Mengana A., Garcia-Naranjo J. C., Jacobs C., Et al., Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge, Medical Image Analysis, 42, pp. 1-13, (2017); Soni Barot, Gomathi, A review on Privacy-Preserving Data Preprocessing, Journal of Cybersecurity and Information Management, 4, 2, pp. 16-30; Soni, Gomathi, Cotton Leaf Spot Disease Detection using Multi-Class SVM, International Journal of Research in Engineering and Advanced Technology, 8, 5, (2020); Soni, Singh, Median First Tournament Sort, International Journal of Computer Science Engineering and Information Technology Research, 2, 1, pp. 35-52; Soni M., Privacy Preserving Authentication and Key management protocol for health information System, Data Protection and Privacy in Healthcare: Research and Innovations, (2021); Soni M., Chauhan S., Bajpai B., Puri T., An Approach To Enhance Fall Detection Using Machine Learning Classifier, 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 229-233, (2020); Soni M., Gomathi S., Bhupendra Kumar Adhyaru Y., Natural Language Processing for the Job Portal Enhancement, 2020 7th International Conference on Smart Structures and Systems (ICSSS), pp. 1-4, (2020); Soni M., Jain A., Secure Communication and Implementation Technique for Sybil Attack in Vehicular Ad-Hoc Networks, 2018 Second International Conference on Computing Methodologies and Communication (ICCMC), pp. 539-543, (2018); Soni M., Jain A., Patel T., Human Movement Identification Using Wi-Fi Signals, 2018 3rd International Conference on Inventive Computation Technologies (ICICT), pp. 422-427, (2018); Soni M., Kumar D., Wavelet Based Digital Watermarking Scheme for Medical Images, 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 403-407, (2020); Soni M., Patel T., Systematic investigation on LargeScale simulations in big data systems, 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 684-688, (2018); Soni M., Patel T., Jain A., Security Analysis on Remote User Authentication Methods, Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, 31, (2020); Soni M., Rajput B. S., Security and Performance Evaluations of QUIC Protocol, Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, 52, (2021); Soni M., Rajput B. S., Patel T., Parmar N., Lightweight Vehicle-to-Infrastructure Message Verification Method for VANET, Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, 52, (2021); Soni M., Singh D. K., Blockchain-based security & privacy for biomedical and healthcare information exchange systems, Materials Today: Proceedings, (2021); Toraman S., Alakus T. B., Turkoglu I., Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks, Chaos, Solitons, and Fractals, 140, (2020); Brain pollution: Evidence builds that dirty air causes Alzheimer’s, dementia, Science, (2017); Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R. M., ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, (2017); Yang Y., Islam M. S., Wang J., Li Y., Chen X., Traditional Chinese medicine in the treatment of patients infected with 2019-new coronavirus (SARS-CoV-2): A review and perspective, International Journal of Biological Sciences, 16, 10, pp. 1708-1717, (2020); Yuvaraj N., Srihari K., Chandragandhi S., Arshath Raja R., Analysis of protein-ligand interactions of SARS-Cov-2 against selective drug using deep neural networks, Big Data Mining and Analytics, (2020); Yuvaraj N., Srihari K., Gaurav Dhiman K., Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking, Mathematical Problems in Engineering, (2021)","","","IGI Global","","","","","","19479263","","","","English","Int. J. Swarm Intelligence Res.","Article","Final","","Scopus","2-s2.0-85123202152"
"Zhang K.; Li Z.; Zhang J.; Zhao D.; Pi Y.; Shi Y.; Wang R.; Chen P.; Li C.; Chen G.; Lei I.M.; Zhong J.","Zhang, Kaijun (57750143000); Li, Zhaoyang (57219972094); Zhang, Jianfeng (57211345877); Zhao, Dazhe (57749606000); Pi, Yucong (57749875300); Shi, Yujun (57204119038); Wang, Renkun (57749875400); Chen, Peisheng (57921452000); Li, Chaojie (57921191000); Chen, Gangjin (7406541235); Lei, Iek Man (57203536143); Zhong, Junwen (54782087800)","57750143000; 57219972094; 57211345877; 57749606000; 57749875300; 57204119038; 57749875400; 57921452000; 57921191000; 7406541235; 57203536143; 54782087800","Biodegradable Smart Face Masks for Machine Learning-Assisted Chronic Respiratory Disease Diagnosis","2022","ACS Sensors","7","10","","3135","3143","8","36","10.1021/acssensors.2c01628","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139556869&doi=10.1021%2facssensors.2c01628&partnerID=40&md5=8d863555ef66ec6c404493cf44779961","Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Laboratory of Electret & Its Application, Hangzhou Dianzi University, Hangzhou, 310018, China; Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, Zhuhai, 519000, China","Zhang K., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Li Z., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Zhang J., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao, Laboratory of Electret & Its Application, Hangzhou Dianzi University, Hangzhou, 310018, China; Zhao D., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Pi Y., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Shi Y., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Wang R., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Chen P., Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, Zhuhai, 519000, China; Li C., Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, Zhuhai, 519000, China; Chen G., Laboratory of Electret & Its Application, Hangzhou Dianzi University, Hangzhou, 310018, China; Lei I.M., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; Zhong J., Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao","Utilizing smart face masks to monitor and analyze respiratory signals is a convenient and effective method to give an early warning for chronic respiratory diseases. In this work, a smart face mask is proposed with an air-permeable and biodegradable self-powered breath sensor as the key component. This smart face mask is easily fabricated, comfortable to use, eco-friendly, and has sensitive and stable output performances in real wearable conditions. To verify the practicability, we use smart face masks to record respiratory signals of patients with chronic respiratory diseases when the patients do not have obvious symptoms. With the assistance of the machine learning algorithm of the bagged decision tree, the accuracy for distinguishing the healthy group and three groups of chronic respiratory diseases (asthma, bronchitis, and chronic obstructive pulmonary disease) is up to 95.5%. These results indicate that the strategy of this work is feasible and may promote the development of wearable health monitoring systems. © 2022 American Chemical Society. All rights reserved.","biodegradable; chronic respiratory disease diagnosis; machine learning; self-powered sensors; smart face mask","Humans; Machine Learning; Masks; Monitoring, Physiologic; Decision trees; Diagnosis; Learning algorithms; Machine learning; Wearable sensors; Biodegradable; Chronic respiratory disease diagnose; Disease diagnosis; Early warning; Face masks; Machine-learning; Respiratory signals; Self-powered; Self-powered sensor; Smart face mask; human; machine learning; mask; physiologic monitoring; Pulmonary diseases","","","","","Universidade de Macau, UM, (MYRG2022-00003-FST, SRG2021-00001-FST); Fundo para o Desenvolvimento das Ciências e da Tecnologia, FDCT, (0040/2021/A1, 0059/2021/AFJ)","We acknowledge the funding support from the Science and Technology Development Fund, Macau SAR (FDCT) (file no. 0059/2021/AFJ, 0040/2021/A1), Start Research Grant from the University of Macau (SRG2021-00001-FST), and Multi-Year Research Grant (MYRG2022-00003-FST).","Brooks J.T., Butler J.C., Effectiveness of Mask Wearing to Control Community Spread of SARS-CoV-2, J. Am. Med. Assoc., 325, pp. 998-999, (2021); Cheng K.K., Lam T.H., Leung C.C., Wearing Face Masks in the Community during the COVID-19 Pandemic: Altruism and Solidarity, Lancet, 399, pp. e39-e40, (2022); Tabatabaeizadeh S.A., Airborne Transmission of COVID-19 and the Role of Face Mask to Prevent It: A Systematic Review and Meta-Analysis, Eur. J. Med. Res., 26, pp. 1-6, (2021); Kissler S.M., Tedijanto C., Goldstein E., Grad Y.H., Lipsitch M., Projecting the Transmission Dynamics of SARS-CoV-2 through the Postpandemic Period, Science, 368, pp. 860-868, (2020); Techasatian L., Lebsing S., Uppala R., Thaowandee W., Chaiyarit J., Supakunpinyo C., Panombualert S., Mairiang D., Saengnipanthkul S., Wichajarn K., Kiatchoosakun P., Kosalaraksa P., The Effects of the Face Mask on the Skin Underneath: A Prospective Survey during the COVID-19 Pandemic, J. Prim. Care Community Health, 11, (2020); Zhong J., Li Z., Takakuwa M., Inoue D., Hashizume D., Jiang Z., Shi Y., Ou L., Nayeem M.O.G., Umezu S., Fukuda K., Someya T., Smart Face Mask Based on an Ultrathin Pressure Sensor for Wireless Monitoring of Breath Conditions, Adv. Mater., 34, (2022); Escobedo P., Fernandez-Ramos M.D., Lopez-Ruiz N., Moyano-Rodriguez O., Martinez-Olmos A., Perez De Vargas-Sansalvador I.M., Carvajal M.A., Capitan-Vallvey L.F., Palma A.J., Smart facemask for wireless CO2 monitoring, Nat. Commun., 13, (2022); Ferkol T., Schraufnagel D., The Global Burden of Respiratory Disease, Ann. Am. Thorac. Soc., 11, pp. 404-406, (2014); Gibson G.J., Loddenkemper R., Lundback B., Sibille Y., Respiratory Health and Disease in Europe: The New European Lung White Book, Eur. Respir. J., 42, pp. 559-563, (2013); Pan L., Wang C., Jin H., Li J., Yang L., Zheng Y., Wen Y., Tan B.H., Loh X.J., Chen X., Lab-on-Mask for Remote Respiratory Monitoring, ACS Mater. 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C, 6, pp. 4549-4554, (2018); Duan Z., Jiang Y., Yan M., Wang S., Yuan Z., Zhao Q., Sun P., Xie G., Du X., Tai H., Facile, Flexible, Cost-Saving, and Environment-Friendly Paper-Based Humidity Sensor for Multifunctional Applications, ACS Appl. Mater. Interfaces, 11, pp. 21840-21849, (2019); Ma L., Wu R., Patil A., Zhu S., Meng Z., Meng H., Hou C., Zhang Y., Liu Q., Yu R., Wang J., Lin N., Liu X.Y., Full-Textile Wireless Flexible Humidity Sensor for Human Physiological Monitoring, Adv. Funct. Mater., 29, (2019); Duan Z., Jiang Y., Tai H., Recent Advances in Humidity Sensors for Human Body Related Humidity Detection, J. Mater. Chem. C, 9, pp. 14963-14980, (2021); Ghosh R., Song M.S., Park J., Tchoe Y., Guha P., Lee W., Lim Y., Kim B., Kim S.-W., Kim M., Yi G.-C., Fabrication of Piezoresistive Si Nanorod-Based Pressure Sensor Arrays: A Promising Candidate for Portable Breath Monitoring Devices, Nano Energy, 80, (2021); Massaroni C., Di Tocco J., Lo Presti D., Longo U.G., Miccinilli S., Sterzi S., Formica D., Saccomandi P., Schena E., Smart Textile Based on Piezoresistive Sensing Elements for Respiratory Monitoring, IEEE Sens. J., 19, pp. 7718-7725, (2019); Park S.W., Das P.S., Chhetry A., Park J.Y., A Flexible Capacitive Pressure Sensor for Wearable Respiration Monitoring System, IEEE Sens. J., 17, pp. 6558-6564, (2017); Min S.D., Yun Y., Shin H., Simplified Structural Textile Respiration Sensor Based on Capacitive Pressure Sensing Method, IEEE Sens. J., 14, pp. 3245-3251, (2014); Liu Z., Zhang S., Jin Y.M., Ouyang H., Zou Y., Wang X.X., Xie L.X., Li Z., Flexible Piezoelectric Nanogenerator in Wearable Self-Powered Active Sensor for Respiration and Healthcare Monitoring, Semicond. Sci. Technol., 32, (2017); Maity K., Garain S., Henkel K., Schmeisser D., Mandal D., Self-Powered Human-Health Monitoring through Aligned PVDF Nanofibers Interfaced Skin-Interactive Piezoelectric Sensor, ACS Appl. Polym. Mater., 2, pp. 862-878, (2020); Ning C., Cheng R., Jiang Y., Sheng F., Yi J., Shen S., Zhang Y., Peng X., Dong K., Wang Z.L., Helical Fiber Strain Sensors Based on Triboelectric Nanogenerators for Self-Powered Human Respiratory Monitoring, ACS Nano, 16, pp. 2811-2821, (2022); Zhao Z., Yan C., Liu Z., Fu X., Peng L.-M., Hu Y., Zheng Z., Machine-Washable Textile Triboelectric Nanogenerators for Effective Human Respiratory Monitoring through Loom Weaving of Metallic Yarns, Adv. Mater., 28, pp. 10267-10274, (2016); Shen S., Xiao X., Xiao X., Chen J., Triboelectric Nanogenerators for Self-Powered Breath Monitoring, ACS Appl. Energy Mater., 5, pp. 3952-3965, (2022); Lin S., Wang S., Yang W., Chen S., Xu Z., Mo X., Zhou H., Duan J., Hu B., Huang L., Trap-Induced Dense Monocharged Perfluorinated Electret Nanofibers for Recyclable Multifunctional Healthcare Mask, ACS Nano, 15, pp. 5486-5494, (2021); Cheng Y., Wang C., Zhong J., Lin S., Xiao Y., Zhong Q., Jiang H., Wu N., Li W., Chen S., Wang B., Zhang Y., Zhou J., Electrospun Polyetherimide Electret Nonwoven for Bi-Functional Smart Face Mask, Nano Energy, 34, pp. 562-569, (2017); Dinh T., Nguyen T., Phan H.-P., Nguyen N.-T., Dao D.V., Bell J., Stretchable Respiration Sensors: Advanced Designs and Multifunctional Platforms for Wearable Physiological Monitoring, Biosens. Bioelectron., 166, (2020); Bokka N., Karhade J., Sahatiya P., Deep Learning Enabled Classification of Real-Time Respiration Signals Acquired by MoSSe Quantum Dot-Based Flexible Sensors, J. Mater. Chem. B, 9, pp. 6870-6880, (2021); Chen G., Zhang J., Shi X., Peng H., Chen X., Charge Trapped Mechanism for Semi-Crystalline Polymer Electrets: Quasi-Dipole Model, IET Nanodielectr., 3, pp. 81-87, (2020); Zhang J., Chen G., Bhat G.S., Azari H., Pen H., Electret Characteristics of Melt-Blown Polylactic Acid Fabrics for Air Filtration Application, J. Appl. Polym. Sci., 137, (2020); Leal Ferreira G.F., Figueiredo M.T., Corona Charging of Electrets: Models and Results, IEEE Trans. Electr. Insul., 27, pp. 719-738, (1992); Fan F.R., Tang W., Wang Z.L., Flexible Nanogenerators for Energy Harvesting and Self-Powered Electronics, Adv. Mater., 28, pp. 4283-4305, (2016); Zhong J., Ma Y., Song Y., Zhong Q., Chu Y., Karakurt I., Bogy D.B., Lin L., A Flexible Piezoelectret Actuator/Sensor Patch for Mechanical Human-Machine Interfaces, ACS Nano, 13, pp. 7107-7116, (2019); Li W., Wu N., Zhong J., Zhong Q., Zhao S., Wang B., Cheng X., Li S., Liu K., Hu B., Zhou J., Theoretical Study of Cellular Piezoelectret Generators, Adv. Funct. Mater., 26, pp. 1964-1974, (2016); Mhetre M.R., Abhyankar H.K., Human Exhaled Air Energy Harvesting with Specific Reference to PVDF Film, Eng. Sci. Technol., 20, pp. 332-339, (2017); Fadare O.O., Okoffo E.D., Covid-19 Face Masks: A Potential Source of Microplastic Fibers in the Environment, Sci. Total Environ., 737, (2020); Dharmaraj S., Ashokkumar V., Hariharan S., Manibharathi A., Show P.L., Chong C.T., Ngamcharussrivichai C., The COVID-19 Pandemic Face Mask Waste: A Blooming Threat to the Marine Environment, Chemosphere, 272, (2021); Zaaba N.F., Jaafar M., A Review on Degradation Mechanisms of Polylactic Acid: Hydrolytic, Photodegradative, Microbial, and Enzymatic Degradation, Polym. Eng. Sci., 60, pp. 2061-2075, (2020); Farah S., Anderson D.G., Langer R., Physical and Mechanical Properties of PLA, and Their Functions in Widespread Applications─A Comprehensive Review, Adv. Drug Deliver Rev., 107, pp. 367-392, (2016); Elsawy M.A., Kim K.-H., Park J.-W., Deep A., Hydrolytic Degradation of Polylactic Acid (PLA) and Its Composites, Renewable Sustainable Energy Rev., 79, pp. 1346-1352, (2017); Krzywinski M., Altman N., Classification, and regression trees, Nat. Methods, 14, pp. 757-758, (2017); Askari H., Xu N., Groenner Barbosa B.H., Huang Y., Chen L., Khajepour A., Chen H., Wang Z.L., Intelligent Systems Using Triboelectric, Piezoelectric, and Pyroelectric Nanogenerators, Mater. Today, 52, pp. 188-206, (2022)","J. Zhong; Department of Electromechanical Engineering, Centre for Artificial Intelligence and Robotics, University of Macau, SAR, 999078, Macao; email: junwenzhong@um.edu.mo","","American Chemical Society","","","","","","23793694","","","36196484","English","ACS Sensors","Article","Final","","Scopus","2-s2.0-85139556869"
"Chmiel F.P.; Burns D.K.; Pickering J.B.; Blythin A.; Wilkinson T.M.A.; Boniface M.J.","Chmiel, Francis P. (57191830751); Burns, Dan K. (57221713567); Pickering, John Brian (25654353500); Blythin, Alison (57219667389); Wilkinson, Thomas M.A. (7202351234); Boniface, Michael J. (55794460800)","57191830751; 57221713567; 25654353500; 57219667389; 7202351234; 55794460800","Prediction of Chronic Obstructive Pulmonary Disease Exacerbation Events by Using Patient Self-reported Data in a Digital Health App: Statistical Evaluation and Machine Learning Approach","2022","JMIR Medical Informatics","10","3","e26499","","","","19","10.2196/26499","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128155354&doi=10.2196%2f26499&partnerID=40&md5=b5e505c0d3ca2dfeb6020685b7f27944","School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; my mHealth Limited, Bournemouth, United Kingdom; National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton, Southampton, United Kingdom; Faculty of Medicine, University of Southampton, Southampton, United Kingdom","Chmiel F.P., School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; Burns D.K., School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; Pickering J.B., School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom; Blythin A., my mHealth Limited, Bournemouth, United Kingdom; Wilkinson T.M.A., my mHealth Limited, Bournemouth, United Kingdom, National Institute for Health Research Applied Research Collaboration Wessex, University of Southampton, Southampton, United Kingdom, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Boniface M.J., School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom","Background: Self-reporting digital apps provide a way of remotely monitoring and managing patients with chronic conditions in the community. Leveraging the data collected by these apps in prognostic models could provide increased personalization of care and reduce the burden of care for people who live with chronic conditions. This study evaluated the predictive ability of prognostic models for the prediction of acute exacerbation events in people with chronic obstructive pulmonary disease by using data self-reported to a digital health app. Objective: The aim of this study was to evaluate if data self-reported to a digital health app can be used to predict acute exacerbation events in the near future. Methods: This is a retrospective study evaluating the use of symptom and chronic obstructive pulmonary disease assessment test data self-reported to a digital health app (myCOPD) in predicting acute exacerbation events. We include data from 2374 patients who made 68,139 self-reports. We evaluated the degree to which the different variables self-reported to the app are predictive of exacerbation events and developed both heuristic and machine learning models to predict whether the patient will report an exacerbation event within 3 days of self-reporting to the app. The model's predictive ability was evaluated based on self-reports from an independent set of patients. Results: Users self-reported symptoms, and standard chronic obstructive pulmonary disease assessment tests displayed correlation with future exacerbation events. Both a baseline model (area under the receiver operating characteristic curve [AUROC] 0.655, 95% CI 0.689-0.676) and a machine learning model (AUROC 0.727, 95% CI 0.720-0.735) showed moderate ability in predicting exacerbation events, occurring within 3 days of a given self-report. Although the baseline model obtained a fixed sensitivity and specificity of 0.551 (95% CI 0.508-0.596) and 0.759 (95% CI 0.752-0.767) respectively, the sensitivity and specificity of the machine learning model can be tuned by dichotomizing the continuous predictions it provides with different thresholds. Conclusions: Data self-reported to health care apps designed to remotely monitor patients with chronic obstructive pulmonary disease can be used to predict acute exacerbation events with moderate performance. This could increase personalization of care by allowing preemptive action to be taken to mitigate the risk of future exacerbation events. © 2022 JMIR Publications Inc.. All Rights Reserved.","chronic disease; COPD; digital applications; digital health; exacerbation events; health care applications; machine learning; mHealth; mobile health; myCOPD; remote monitoring","","","","","","","","McLean S, Hoogendoorn M, Hoogenveen RT, Feenstra TL, Wild S, Simpson CR, Et al., Projecting the COPD population and costs in England and Scotland: 2011 to 2030, Sci Rep, 6, (2016); Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Miravitlles M, Vogelmeier C, Roche N, Halpin D, Cardoso J, Chuchalin AG, Et al., A review of national guidelines for management of COPD in Europe, Eur Respir J, 47, 2, pp. 625-637, (2016); Rodriguez-Roisin R., Toward a consensus definition for COPD exacerbations, Chest, 117, 5, pp. 398S-401S, (2000); Hoogendoorn M, Feenstra TL, Hoogenveen RT, Al M, Molken MRV., Association between lung function and exacerbation frequency in patients with COPD, Int J Chron Obstruct Pulmon Dis, 5, pp. 435-444, (2010); Donaldson GC, Seemungal TAR, Bhowmik A, Wedzicha JA., Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease, Thorax, 57, 10, pp. 847-852, (2002); Donaldson GC, Wedzicha JA., COPD exacerbations .1: Epidemiology, Thorax, 61, 2, pp. 164-168, (2006); Wilkinson TMA, Donaldson GC, Hurst JR, Seemungal TAR, Wedzicha JA., Early Therapy Improves Outcomes of Exacerbations of Chronic Obstructive Pulmonary Disease, Am J Respir Crit Care Med, 169, 12, pp. 1298-1303, (2004); Wedzicha JA, Seemungal TAR., COPD exacerbations: defining their cause and prevention, Lancet, 370, 9589, pp. 786-796, (2007); Adibi A, Sin DD, Safari A, Johnson KM, Aaron SD, FitzGerald JM, Et al., The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study, Lancet Respir Med, 8, 10, pp. 1013-1021, (2020); Guerra B, Gaveikaite V, Bianchi C, Puhan MA., Prediction models for exacerbations in patients with COPD, Eur Respir Rev, 26, 143, (2017); Sobnath DD, Philip N, Kayyali R, Nabhani-Gebara S, Pierscionek B, Vaes AW, Et al., Features of a Mobile Support App for Patients With Chronic Obstructive Pulmonary Disease: Literature Review and Current Applications, JMIR Mhealth Uhealth, 5, 2, (2017); Velardo C, Shah SA, Gibson O, Clifford G, Heneghan C, Rutter H, Digital health system for personalised COPD long-term management, BMC Med Inform Decis Mak, 17, 1, (2017); North M, Bourne S, Green B, Chauhan AJ, Brown T, Winter J, Et al., A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial, NPJ Digit Med, 3, (2020); Jones PW, Harding G, Berry P, Wiklund I, Chen W, Kline Leidy N., Development and first validation of the COPD Assessment Test, Eur Respir J, 34, 3, pp. 648-654, (2009); Dodd JW, Hogg L, Nolan J, Jefford H, Grant A, Lord VM, Et al., The COPD assessment test (CAT): response to pulmonary rehabilitation. A multicentre, prospective study, Thorax, 66, 5, pp. 425-429, (2011); Gupta N, Pinto LM, Morogan A, Bourbeau J., The COPD assessment test: a systematic review, Eur Respir J, 44, 4, pp. 873-884, (2014); Bergstra J, Bardenet R, Bengio Y, Kegl B., Algorithms for hyper-parameter optimization, Advances in neural information processing systems -2554, 2546, (2011); Bergstra J, Yamins D, Cox D., Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures, ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning, pp. 115-123, (2013); Youden WJ., Index for rating diagnostic tests, Cancer, 3, 1, pp. 32-35, (1950); Varol Y, Ozacar R, Balci G, Usta L, Taymaz Z., Assessing the effectiveness of the COPD Assessment Test (CAT) to evaluate COPD severity and exacerbation rates, COPD, 11, 2, pp. 221-225, (2014); Halpin DM, Miravitlles M, Metzdorf N, Celli B., Impact and prevention of severe exacerbations of COPD: a review of the evidence, Int J Chron Obstruct Pulmon Dis, 12, pp. 2891-2908, (2017); Global strategy for the diagnosis, management and prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD), (2020); Wilkinson TMA, Donaldson GC, Johnston SL, Openshaw PJM, Wedzicha JA., Respiratory syncytial virus, airway inflammation, and FEV1 decline in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 173, 8, pp. 871-876, (2006); Tomasic I, Tomasic N, Trobec R, Krpan M, Kelava T., Continuous remote monitoring of COPD patients-justification and explanation of the requirements and a survey of the available technologies, Med Biol Eng Comput, 56, 4, pp. 547-569, (2018); Siafakas N, Vermeire P, Pride N, Paoletti P, Gibson J, Howard P, Et al., Optimal assessment and management of chronic obstructive pulmonary disease (COPD). The European Respiratory Society Task Force, Eur Respir J, 8, 8, pp. 1398-1420, (1995); Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Et al., Explainable machine-learning predictions for the prevention of hypoxaemia during surgery, Nat Biomed Eng, 2, 10, pp. 749-760, (2018); Walters EH, Walters J, Wills KE, Robinson A, Wood-Baker R., Clinical diaries in COPD: compliance and utility in predicting acute exacerbations, Int J Chron Obstruct Pulmon Dis, 7, pp. 427-435, (2012); Stone AA, Shiffman S., Capturing momentary, self-report data: a proposal for reporting guidelines, Ann Behav Med, 24, 3, pp. 236-243, (2002); Bradbury K, Morton K, Band R, van Woezik A, Grist R, McManus RJ, Et al., Using the Person-Based Approach to optimise a digital intervention for the management of hypertension, PLoS One, 13, 5, (2018)","F.P. Chmiel; School of Electronics and Computer Science, University of Southampton, Southampton, University Road, SO17 1BJ, United Kingdom; email: F.P.Chmiel@soton.ac.uk","","JMIR Publications Inc.","","","","","","22919694","","","","English","JMIR Med. Inform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128155354"
"Gonsard A.; AbouTaam R.; Prévost B.; Roy C.; Hadchouel A.; Nathan N.; Taytard J.; Pirojoc A.; Delacourt C.; Wanin S.; Drummond D.","Gonsard, Apolline (58003356800); AbouTaam, Rola (16023887000); Prévost, Blandine (57216654289); Roy, Charlotte (57221695386); Hadchouel, Alice (25027171700); Nathan, Nadia (35280062400); Taytard, Jessica (55877665300); Pirojoc, Alexandra (58003041100); Delacourt, Christophe (7006682738); Wanin, Stéphanie (35111924000); Drummond, David (54909495200)","58003356800; 16023887000; 57216654289; 57221695386; 25027171700; 35280062400; 55877665300; 58003041100; 7006682738; 35111924000; 54909495200","Children’s views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study","2023","European Journal of Pediatrics","182","2","","877","888","11","16","10.1007/s00431-022-04754-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143887837&doi=10.1007%2fs00431-022-04754-8&partnerID=40&md5=767337b2c4e33ca757cdbacaab51048e","Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France; Department of Pediatric Pulmonology, University Hospital Armand Trousseau, Paris, AP-HP, France; Université Paris Cité, Paris, France; UMRS1158 Neurophysiologie Respiratoire Expérimentale Et Clinique, Sorbonne Université, INSERM, Paris, France; Paris Cité Necker–Cochin Clinical Research Unit, Paris, France; Department of Pediatric Allergology, University Hospital Armand Trousseau, APHP, Paris, France; Inserm UMR 1138, Centre de Recherche Des Cordeliers, HeKA Team, Paris, 75006, France","Gonsard A., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France; AbouTaam R., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France; Prévost B., Department of Pediatric Pulmonology, University Hospital Armand Trousseau, Paris, AP-HP, France; Roy C., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France; Hadchouel A., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France, Université Paris Cité, Paris, France; Nathan N., Department of Pediatric Pulmonology, University Hospital Armand Trousseau, Paris, AP-HP, France; Taytard J., Department of Pediatric Pulmonology, University Hospital Armand Trousseau, Paris, AP-HP, France, UMRS1158 Neurophysiologie Respiratoire Expérimentale Et Clinique, Sorbonne Université, INSERM, Paris, France; Pirojoc A., Paris Cité Necker–Cochin Clinical Research Unit, Paris, France; Delacourt C., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France, Université Paris Cité, Paris, France; Wanin S., Department of Pediatric Allergology, University Hospital Armand Trousseau, APHP, Paris, France; Drummond D., Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, Paris, 75015, France, Université Paris Cité, Paris, France, Inserm UMR 1138, Centre de Recherche Des Cordeliers, HeKA Team, Paris, 75006, France","New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children’s home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether children and adolescents with asthma would be ready to use such DTS. A mixed-method study was conducted with 104 asthma patients aged 8 to 17 years. The potential advantages and disadvantages associated with AI and the use of DTS were collected in semi-structured interviews. Children were then asked whether they would agree to use a DTS for the daily management of their asthma. The strength of their decision was assessed as well as the factors determining their choice. The main advantages of DTS identified by children were the possibility to be (i) supported in managing their asthma (ii) from home and (iii) in real time. Technical issues and the risk of loss of humanity were the main drawbacks reported. Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. Those with the best computer skills were more likely to choose the DTS, while those who placed a high value on the physician–patient relationship were less likely to do so. Conclusions: The majority of children were ready to use a DTS for the management of their asthma, particularly if it was more effective than current care. The results of this study support the development of DTS for childhood asthma and the evaluation of their effectiveness in clinical trials.What is Known:• New technologies enable the creation of digital twin systems (DTS) for children with asthma.• Acceptance of these DTSs by children with asthma is unknown.What is New:• Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective.•Children identified the ability to be supported from home and in real time as the main benefits of DTS. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Automated decision system; Digital twins; Internet of things; Paediatrics; Telemonitoring","Adolescent; Artificial Intelligence; Asthma; Child; Humans; adolescent; Article; artificial intelligence; asthma; child; comparative effectiveness; digital twin; female; human; internet of things; major clinical study; male; pediatrics; physician; semi structured interview; skill; telemonitoring; asthma","","","","","","","Drummond D., Between competence and warmth: the remaining place of the physician in the era of artificial intelligence, NPJ Digit Med, 4, (2021); Exarchos K.P., Beltsiou M., Votti C.A., Kostikas K., Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature, Eur Respir J, 56, (2020); Popa E.O., van Hilten M., Oosterkamp E., Bogaardt M.-J., The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks, Life Sci Soc Policy, 17, (2021); Laubenbacher R., Sluka J.P., Glazier J.A., Using digital twins in viral infection, Science, 371, pp. 1105-1106, (2021); Drummond D., Coulet A., Technical, Ethical, legal, and societal challenges with digital twin systems for the management of chronic diseases in children and young people, J Med Internet Res, 24, (2022); Drummond D., Digital tools for remote monitoring of asthma patients: gadgets or revolution?, Rev Mal Respir, 39, pp. 241-257, (2022); Hammer S.C., Et al., Actual asthma control in a paediatric outpatient clinic population: do patients perceive their actual level of control?, Pediatr Allergy Immunol, 19, pp. 626-633, (2008); Guilleminault L., Et al., Personalised medicine in asthma: From curative to preventive medicine, Eur Respir J, 26, (2017); Abdoul C., Et al., Parents’ views on artificial intelligence for the daily management of childhood asthma: a survey, J Allergy Clin Immunol Pract, 9, pp. 1728-1730.e3, (2021); Kallio H., Pietila A.-M., Johnson M., Kangasniemi M., Systematic methodological review: developing a framework for a qualitative semi-structured interview guide, J Adv Nurs, 72, pp. 2954-2965, (2016); Lederer D.J., Et al., Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals, Ann Am Thorac Soc, 16, pp. 22-28, (2019); Diemer E.W., Hudson J.I., Javaras K.N., More (adjustment) is not always better: how directed acyclic graphs can help researchers decide which covariates to include in models for the causal relationship between an exposure and an outcome in observational research, Psychother Psychosom, 90, pp. 289-298, (2021); Textor J., van der Zander B., Gilthorpe M.S., Liskiewicz M., Ellison G.T., Robust causal inference using directed acyclic graphs: the R package ‘dagitty’, Int J Epidemiol, 45, pp. 1887-1894, (2016); Ripley B., Et al., Package ‘mass’, Cran r, 538, pp. 113-120, (2013); Young A.T., Amara D., Bhattacharya A., Wei M.L., Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review, The Lancet Digital Health, 3, pp. e599-e611, (2021); Adolescent Perspectives on Artificial Intelligence., (2021); Karimi M., Et al., National Survey Trends in Telehealth Use in 2021: Disparities in Utilization and Audio Vs. Video Services, (2022); Predmore Z.S., Roth E., Breslau J., Fischer S.H., Uscher-Pines L., Assessment of patient preferences for telehealth in post–COVID-19 pandemic health care, JAMA Netw Open, 4, (2021)","D. Drummond; Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, Paris, 149 Rue de Sèvres, 75015, France; email: david.drummond@aphp.fr","","Springer Science and Business Media Deutschland GmbH","","","","","","03406199","","EJPED","36512148","English","Eur. J. Pediatr.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85143887837"
"Wang X.; Ren H.; Ren J.; Song W.; Qiao Y.; Ren Z.; Zhao Y.; Linghu L.; Cui Y.; Zhao Z.; Chen L.; Qiu L.","Wang, Xuchun (57222429561); Ren, Hao (57201028229); Ren, Jiahui (57667435000); Song, Wenzhu (57667129400); Qiao, Yuchao (57669426700); Ren, Zeping (57656488600); Zhao, Ying (58083476800); Linghu, Liqin (57669755200); Cui, Yu (57876925500); Zhao, Zhiyang (57876734600); Chen, Limin (57201033578); Qiu, Lixia (56673436700)","57222429561; 57201028229; 57667435000; 57667129400; 57669426700; 57656488600; 58083476800; 57669755200; 57876925500; 57876734600; 57201033578; 56673436700","Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data","2023","Computer Methods and Programs in Biomedicine","230","","107340","","","","34","10.1016/j.cmpb.2023.107340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147089974&doi=10.1016%2fj.cmpb.2023.107340&partnerID=40&md5=9de4654521bb6fac49784c5e48b76a18","Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; The Fifth Hospital (Shanxi People's Hospital) of Shanxi Medical University, No. 29, Shuangtaji Street, Shanxi, Taiyuan, 030012, China","Wang X., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Ren H., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Ren J., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Song W., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Qiao Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Ren Z., Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; Zhao Y., Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; Linghu L., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China, Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; Cui Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Zhao Z., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China; Chen L., The Fifth Hospital (Shanxi People's Hospital) of Shanxi Medical University, No. 29, Shuangtaji Street, Shanxi, Taiyuan, 030012, China; Qiu L., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Shanxi, Taiyuan, 030001, China","Background and objective: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. Methods: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. Results: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. Conclusions: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD. © 2023 The Author(s)","Chronic obstructive pulmonary disease; Disease risk prediction; Imbalanced data; Machine learning","Adolescent; Humans; Logistic Models; Machine Learning; Pulmonary Disease, Chronic Obstructive; Support Vector Machine; Adaptive boosting; Balancing; Classification (of information); Decision trees; Feature Selection; Forestry; Logistic regression; Patient treatment; Pulmonary diseases; Random forests; Support vector machines; salbutamol; Chronic obstructive pulmonary disease; Disease risk prediction; Disease risks; Imbalanced data; Logistics regressions; Machine learning methods; Machine-learning; Risk predictions; Stackings; Synthetic minority over-sampling techniques; accuracy; adult; aged; airway obstruction; anthropometry; area under the curve; Article; Chinese; chronic obstructive lung disease; controlled study; correlation coefficient; coughing; data mining; dimensionality reduction; early diagnosis; feature selection; female; human; least absolute shrinkage and selection operator; lightgbm; logistic regression analysis; lung function; lung function test; machine learning; major clinical study; male; middle aged; minority group; ngboost; performance; performance indicator; practice guideline; prediction; predictive model; random forest; receiver operating characteristic; ridge regression; risk; sampling; sensitivity and specificity; stacking; stratified sample; support vector machine; synthetic minority oversampling technique; thorax radiography; underdiagnosis; adolescent; chronic obstructive lung disease; machine learning; statistical model; Forecasting","","salbutamol, 18559-94-9, 35763-26-9","","","National Natural Science Foundation of China, NSFC, (81973155); Shanxi Medical University","Funding text 1: This research is supported by a grant from the National Natural Science Foundation of China (Grant No: 81973155 ). ; Funding text 2: This research is supported by a grant from the National Natural Science Foundation of China (Grant No: 81973155).This research is supported by a grant from the National Natural Science Foundation of China (Grant No: 81973155). We thank all teachers in the statistical research office of Shanxi medical university. The authors would also like to acknowledge all interviewers for survey data collection work.","Lopez-Campos J.L., Tan W., Soriano J.B., Global burden of COPD, Respirology, 21, 1, pp. 14-23, (2016); Berlin L., Medical errors, malpractice, and defensive medicine: an ill-fated triad, Iagnosis, 4, 3, pp. 133-139, (2017); Wang C., Xu J., Yang L., Xu Y., Zhang X., Bai C., Kang J., Ran P., Shen H., Wen F., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet, 391, 10131, pp. 1706-1717, (2018); Jensen M.H., Cichosz S.L., Dinesen B., Hejlesen O.K., Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare, J. Telemed. Telecare, 18, 2, pp. 99-103, (2012); van der Heijden M., Lucas P.J., Lijnse B., Heijdra Y.F., Schermer T.R., An autonomous mobile system for the management of COPD, J. Biomed. Inform., 46, 3, pp. 458-469, (2013); Burton C., Pinnock H., McKinstry B., Changes in telemonitored physiological variables and symptoms prior to exacerbations of chronic obstructive pulmonary disease, J. Telemed. Telecare, 21, 1, pp. 29-36, (2015); Amaral J.L., Lopes A.J., Jansen J.M., Faria A.C., Melo P.L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Comput. Methods Progr. Biomed., 105, 3, pp. 183-193, (2012); Bodduluri S., Newell J.D., Hoffman E.A., Reinhardt J.M., Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework, Acad. Radiol., 20, 5, pp. 527-536, (2013); Yu H., Zhao J., Liu D., Chen Z., Sun J., Zhao X., Multi-channel lung sounds intelligent diagnosis of chronic obstructive pulmonary disease, BMC Pulm. Med., 21, 1, (2021); Levy J., Alvarez D., Del Campo F., Behar J.A., Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers, Physiol. Meas., 42, 5, (2021); Murgia N., Brisman J., Claesson A., Muzi G., Olin A.C., Toren K., Validity of a questionnaire-based diagnosis of chronic obstructive pulmonary disease in a general population-based study, BMC Pulm. Med., 14, (2014); Feinstein L., Wilkerson J., Salo P.M., MacNell N., Bridge M.F., Fessler M.B., Thorne P.S., Mendy A., Cohn R.D., Curry M.D., Et al., Validation of questionnaire-based case definitions for chronic obstructive pulmonary disease, Epidemiology, 31, 3, pp. 459-466, (2020); Pauwels R.A., Buist A.S., Calverley P.M., Jenkins C.R., Hurd S.S., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO global initiative for chronic obstructive lung disease (GOLD) workshop summary, Am. J. Respir. Crit. Care Med., 163, 5, pp. 1256-1276, (2001); Liu X., Li Y., Li L., Zhang L., Ren Y., Zhou H., Cui L., Mao Z., Hu D., Wang C., Prevalence, awareness, treatment, control of type 2 diabetes mellitus and risk factors in Chinese rural population: the ruraldiab study, Sci. Rep., 6, (2016); Huang X., Zhou Z., Liu J., Song W., Chen Y., Liu Y., Zhang M., Dai W., Yi Y., Zhao S., Prevalence, awareness, treatment, and control of hypertension among China's Sichuan Tibetan population: a cross-sectional study, Clin. Exp. Hypertens., 38, 5, pp. 457-463, (2016); Lanera C., Berchialla P., Sharma A., Minto C., Gregori D., Baldi I., Screening PubMed abstracts: is class imbalance always a challenge to machine learning?, Syst. Rev., 8, 1, (2019); Sui Y., Wei Y., Zhao D., Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE, Comput. Math. Methods Med., 2015, (2015); Sun T., Haifeng W.U., Liang Z., Wen H.E., Zhang L., Pingxin L.V., Application of SMOTE arithmetic for unbalanced data, Beijing Biomed. Eng., 31, pp. 528-530, (2012); Zadrozny B., Langford J., Abe N., Cost-sensitive learning by cost-proportionate example weighting, Proceedings of the IEEE International Conference on Data Mining, (2003); Wang L., Wang Y., Chang Q., Feature selection methods for big data bioinformatics: a survey from the search perspective, Methods, 111, pp. 21-31, (2016); Tibshirani R.J., Regression shrinkage and selection via the LASSO, J. R. Stat. Soc. Ser. B Methodol., 73, 1, pp. 273-282, (1996); Hui Z., The adaptive lasso and its oracle properties, J. Am. Stat. Assoc., 101, 476, pp. 1418-1429, (2006); Friedman J.H., Fast sparse regression and classification, Int. J. Forecast., 28, 3, pp. 722-738, (2012); Basili V.R., Briand L.C., A validation of object-oriented design metrics as quality indicators, IEEE Trans. Softw. Eng., 22, 10, pp. 751-761, (1996); Cortes C., Support-vector networks, Mach. Learn., 20, pp. 273-297, (1995); Liu Y., Wang Y., Zhang J., New machine learning algorithm: random forest, Proceedings of the International Conference on Information Computing & Applications, (2012); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System, (2016); Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu. LightGBM T.Y., a highly efficient gradient boosting decision tree, Advances in neural information processing systems., 30, (2017); Duan T., Avati A., Ding D.Y., Basu S., Ng A.Y., Schuler A., NGBoost: natural gradient boosting for probabilistic prediction, Proceedings of the International Conference on Machine Learning (PMLR)., 119, pp. 2690-2700, (2020); Wolpert DH: stacked generalization, Neural Netw., 5, 2, pp. 241-259, (1992); Nusinovici S., Tham Y.C., Chak Yan M.Y., Wei Ting D.S., Li J., Sabanayagam C., Wong T.Y., Cheng C.Y., Logistic regression was as good as machine learning for predicting major chronic diseases, J. Clin. Epidemiol., 122, pp. 56-69, (2020); Raghavan N., Lam Y.M., Webb K.A., Guenette J.A., Amornputtisathaporn N., Raghavan R., Tan W.C., Bourbeau J., O'Donnell D.E., Components of the COPD assessment test (CAT) associated with a diagnosis of COPD in a random population sample, COPD, 9, 2, pp. 175-183, (2012); Swaminathan S., Qirko K., Smith T., Corcoran E., Wysham N.G., Bazaz G., Kappel G., Gerber A.N., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLoS One, 12, 11, (2017); Austin P.C., Tu J.V., Ho J.E., Levy D., Lee D.S., Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes, J. Clin. Epidemiol., 66, 4, pp. 398-407, (2013); Christodoulou E., Ma J., Collins G.S., Steyerberg E.W., Verbakel J.Y., Van Calster B., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J. Clin. Epidemiol., 110, pp. 12-22, (2019); van der Ploeg T., Austin P.C., Steyerberg E.W., Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints, BMC Med. Res. Methodol., 14, (2014); Steyerberg E.W., van der Ploeg T., Van Calster B., Risk prediction with machine learning and regression methods, Biom. J., 56, 4, pp. 601-606, (2014)","L. Chen; The Fifth Hospital (Shanxi People's Hospital) of Shanxi Medical University, Taiyuan, No. 29, Shuangtaji Street, Shanxi, 030012, China; email: sxchenlimin@163.com","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","36640604","English","Comput. Methods Programs Biomed.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147089974"
"Gabaldón-Figueira J.C.; Keen E.; Giménez G.; Orrillo V.; Blavia I.; Doré D.H.; Armendáriz N.; Chaccour J.; Fernandez-Montero A.; Bartolomé J.; Umashankar N.; Small P.; Lapierre S.G.; Chaccour C.","Gabaldón-Figueira, Juan C. (57190818099); Keen, Eric (56962542700); Giménez, Gerard (57746805200); Orrillo, Virginia (57226521699); Blavia, Isabel (57226521295); Doré, Dominique Hélène (57226522638); Armendáriz, Nuria (57748414500); Chaccour, Juliane (57222267834); Fernandez-Montero, Alejandro (46761091200); Bartolomé, Javier (57226521875); Umashankar, Nita (37113946900); Small, Peter (35407414200); Lapierre, Simon Grandjean (57189581172); Chaccour, Carlos (36131095300)","57190818099; 56962542700; 57746805200; 57226521699; 57226521295; 57226522638; 57748414500; 57222267834; 46761091200; 57226521875; 37113946900; 35407414200; 57189581172; 36131095300","Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence","2022","ERJ Open Research","8","2","00053-2022","","","","16","10.1183/23120541.00053-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132364416&doi=10.1183%2f23120541.00053-2022&partnerID=40&md5=8510f4e080574cef6eb335cd4f39be87","Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain; ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain; Research and Development Dept, Hyfe Inc, Wilmington, DE, United States; School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, QC, Canada; Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain; Dept of Occupational Medicine – COVID-19 Area, Clinica Universidad de Navarra, Pamplona, Spain; Fowler College of Business, San Diego State University, San Diego, CA, United States; Dept of Global Health, University of Washington, Seattle, WA, United States; Dept of Microbiology, Infectious Diseases and Immunology, Research Center of the University of Montreal Hospital Center, Montreal, QC, Canada; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Madrid, Spain","Gabaldón-Figueira J.C., Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain, ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain; Keen E., Research and Development Dept, Hyfe Inc, Wilmington, DE, United States; Giménez G., Research and Development Dept, Hyfe Inc, Wilmington, DE, United States; Orrillo V., School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; Blavia I., School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; Doré D.H., Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, QC, Canada; Armendáriz N., Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain; Chaccour J., Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain; Fernandez-Montero A., Dept of Occupational Medicine – COVID-19 Area, Clinica Universidad de Navarra, Pamplona, Spain; Bartolomé J., Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain; Umashankar N., Fowler College of Business, San Diego State University, San Diego, CA, United States; Small P., Research and Development Dept, Hyfe Inc, Wilmington, DE, United States, Dept of Global Health, University of Washington, Seattle, WA, United States; Lapierre S.G., Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, QC, Canada, Dept of Microbiology, Infectious Diseases and Immunology, Research Center of the University of Montreal Hospital Center, Montreal, QC, Canada; Chaccour C., Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain, ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain, Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Madrid, Spain","Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs·h−1; p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring. © The authors 2022.","","acoustic analysis; adult; aged; Article; artificial intelligence; asthma; bronchitis; chronic cough; chronic obstructive lung disease; cohort analysis; consultation; coronavirus disease 2019; coughing; disease surveillance; epidemic; female; gastroesophageal reflux; human; incidence; influenza; lifestyle modification; major clinical study; male; mobile application; observational study; outcome assessment; perception; pharyngitis; pneumonia; prospective study; questionnaire; respiratory syncytial virus infection; respiratory tract disease; smoking; social media","","","","","Patrick J. McGovern Foundation; Fonds de Recherche du Québec - Santé, FRQS; Generalitat de Catalunya; Ministerio de Ciencia e Innovación, MICINN, (CEX2018-000806-S); Ministerio de Ciencia e Innovación, MICINN","Support statement: This study was funded by the Patrick J. McGovern Foundation (grant name: “Early diagnosis of COVID-19 by utilising Artificial Intelligence and Acoustic Monitoring”). S. Grandjean Lapierre received salary support from the Fonds de Recherche en Santé Québec. ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019–2023” Programme (grant number: CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA programme. Funding information for this article has been deposited with the Crossref Funder Registry.","Chowdhury R, Luhar S, Khan N, Et al., Long-term strategies to control COVID-19 in low and middle-income countries: an options overview of community-based, non-pharmacological interventions, Eur J Epidemiol, 35, pp. 743-748, (2020); Grant MC, Geoghegan L, Arbyn M, Et al., The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries, PLoS One, 15, (2020); Agrawal A, Bhardwaj R., Probability of COVID-19 infection by cough of a normal person and a super-spreader, Phys Fluids, 33, (2021); Grandjean Lapierre S., Making cough count in tuberculosis care, Commun Med, (2022); Cho PSP, Birring SS, Fletcher HV, Et al., Methods of cough assessment, J Allergy Clin Immunol Pract, 7, pp. 1715-1723, (2019); Pramono RXA, Imtiaz SA, Rodriguez-Villegas E., A cough-based algorithm for automatic diagnosis of pertussis, PLoS One, 11, (2016); Lee KK, Matos S, Ward K, Et al., Sound: a non-invasive measure of cough intensity, BMJ Open Respir Res, 4, (2017); Korpas J, Sadlonova J, Vrabec M., Analysis of the cough sound: an overview, Pulm Pharmacol, 9, pp. 261-268, (1996); Liu J, You M, Wang Z, Et al., Cough detection using deep neural networks, 2014 IEEE International Conference on Bioinformatics and Biomedicine, pp. 560-563, (2014); Kvapilova L, Boza V, Dubec P, Et al., Continuous sound collection using smartphones and machine learning to measure cough, Digit Biomark, 3, pp. 166-175, (2019); Gabaldon-Figueira JC, Brew J, Dore DH, Et al., Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study, BMJ Open, 11, (2021); Larson S, Comina G, Gilman RH, Et al., Validation of an automated cough detection algorithm for tracking recovery of pulmonary tuberculosis patients, PLoS One, 7, (2012); Ryan NM, Birring SS, Gibson PG., Gabapentin for refractory chronic cough: a randomised, double-blind, placebo-controlled trial, Lancet, 380, pp. 1583-1589, (2012); Smith JA, Kitt MM, Morice AH, Et al., Gefapixant, a P2X3 receptor antagonist, for the treatment of refractory or unexplained chronic cough: a randomised, double-blind, controlled, parallel-group, phase 2b trial, Lancet Respir Med, 8, pp. 775-785, (2020); Hsu JY, Stone RA, Logan-Sinclair RB, Et al., Coughing frequency in patients with persistent cough: assessment using a 24 hour ambulatory recorder, Eur Respir J, 7, pp. 1246-1253, (1994); Positivos Covid-19 por PCR Distribuidos por Municipio. [PCR-positive Cases of COVID-19 Distributed by Municipality in Navarra, Spain], (2021); Lauer SA, Grantz KH, Bi Q, Et al., The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application, Ann Intern Med, 172, pp. 577-582, (2020); Toussaert S., Upping uptake of COVID contact tracing apps, Nat Hum Behav, 5, pp. 183-184, (2021); Lee KK, Birring SS., Cough and sleep, Lung, 188, pp. S91-S94, (2010); Gabaldon-Figueira JC, Keen E, Rudd M, Et al., Longitudinal passive cough monitoring and its implications for detecting changes in clinical status, ERJ Open Res, 8, pp. 00001-2022, (2022)","J.C. Gabaldón-Figueira; Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain; email: juancarlos.gabaldon@isglobal.org","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85132364416"
"Tully J.L.; Zhong W.; Simpson S.; Curran B.P.; Macias A.A.; Waterman R.S.; Gabriel R.A.","Tully, Jeffrey L. (56076138100); Zhong, William (58143950000); Simpson, Sierra (57203637383); Curran, Brian P. (57217132349); Macias, Alvaro A. (57196865797); Waterman, Ruth S. (35084455900); Gabriel, Rodney A. (56462222800)","56076138100; 58143950000; 57203637383; 57217132349; 57196865797; 35084455900; 56462222800","Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing","2023","Journal of Medical Systems","47","1","71","","","","15","10.1007/s10916-023-01966-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164297530&doi=10.1007%2fs10916-023-01966-9&partnerID=40&md5=dc3b1575e945db26fb7d0166dd5864a2","Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, United States","Tully J.L., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Zhong W.; Simpson S., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Curran B.P., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Macias A.A., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Waterman R.S., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States; Gabriel R.A., Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, United States, Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, United States","The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization. © 2023, The Author(s).","Artificial intelligence; Machine learning; Outpatient surgery; Perioperative informatics; Perioperative resource management","Ambulatory Surgical Procedures; Anesthesia Recovery Period; Humans; Length of Stay; Machine Learning; Operating Rooms; adult; alcohol abuse; ambulatory surgery; American Society of Anaesthesiologists score; anxiety disorder; Article; asthma; body mass; chronic kidney failure; chronic obstructive lung disease; chronic pain; classifier; comorbidity; comparative study; coronary artery disease; current smoker; depression; diabetes mellitus; feed forward neural network; female; gastroesophageal reflux; heart arrhythmia; human; hypertension; hypothyroidism; ICD-10; ICD-9; k fold cross validation; length of stay; logistic regression analysis; machine learning; major clinical study; male; operation duration; patient risk; prediction; predictive model; quality improvement study; random forest; recovery room; sleep apnea syndromes; xgboost model; anesthetic recovery; length of stay; machine learning","","","","","","","Hollenbeck B.K., Dunn R.L., Suskind A.M., Strope S.A., Zhang Y., Hollingsworth J.M., Ambulatory Surgery Centers and Their Intended Effects on Outpatient Surgery, Health Serv Res. Wiley Online Library, 50, pp. 1491-1507, (2015); Chazapis M., Gilhooly D., Smith A.F., Myles P.S., Haller G., Grocott M.P.W., Et al., Perioperative structure and process quality and safety indicators: a systematic review, Br J Anaesth. Elsevier, 120, pp. 51-66, (2018); D'Errico C., Voepel-Lewis T.D., Siewert M., Malviya S., Prolonged recovery stay and unplanned admission of the pediatric surgical outpatient: an observational study, J Clin Anesth. Elsevier, 10, pp. 482-487, (1998); Seago J.A., Weitz S., Walczak S., Factors influencing stay in the postanesthesia care unit: a prospective analysis, J Clin Anesth. Elsevier, 10, pp. 579-587, (1998); Ganter M.T., Blumenthal S., Dubendorfer S., Brunnschweiler S., Hofer T., Klaghofer R., Et al., The length of stay in the post-anaesthesia care unit correlates with pain intensity, nausea and vomiting on arrival, Perioper Med (Lond). Perioperativemedicinejourn, 3, (2014); Waddle J.P., Evers A.S., Piccirillo J.F., Postanesthesia care unit length of stay: Quantifying and assessing dependent factors, Anesth Analg. Journals.Lww.Com, 87, pp. 628-633, (1998); McLaren J.M., Reynolds J.A., Cox M.M., Lyall J.S., McCarthy M., McNoble E.M., Et al., Decreasing the length of stay in phase I postanesthesia care unit: an evidence-based approach, J Perianesth Nurs. Elsevier, 30, pp. 116-123, (2015); Samad K., Khan M., Hameedullah K.F.A., Hamid M., Khan F.H., Unplanned prolonged postanaesthesia care unit length of stay and factors affecting it, J Pak Med Assoc. Ecommons.Aku.Edu, 56, pp. 108-112, (2006); Schulz E.B., Phillips F., Waterbright S., Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists, Br J Anaesth. Elsevier, 125, pp. 1079-1087, (2020); Gabriel R.A., Waterman R.S., Kim J., Ohno-Machado L., A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries, Anesth Analg. Ingentaconnect.Com, 124, pp. 1529-1536, (2017); Ogrinc G., Mooney S.E., Estrada C., Foster T., Goldmann D., Hall L.W., Et al., The SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for quality improvement reporting: Explanation and elaboration, Qual Saf Health Care. Qualitysafety.Bmj.Com, 17, pp. i13-i32, (2008); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Et al., Scikit-learn: Machine learning in Python, The Journal of Machine Learning Research. JMLR. Org, 12, pp. 2825-2830, (2011); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: Synthetic Minority Over-sampling Technique, J Artif Intell Res. Jair.Org, 16, pp. 321-357, (2002); Dillon J.V., Langmore I., Tran D., Brevdo E., Vasudevan S., Moore D., Tensorflow Distributions, (2017); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System, Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Breiman L., Random Forests. Mach Learn. Springer, 45, pp. 5-32, (2001); Lundberg S., Lee S.-I., A Unified Approach to Interpreting Model Predictions, (2017); Gabriel R.A., Harjai B., Simpson S., Goldhaber N., Curran B.P., Waterman R.S., Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center, Anesth Analg, (2022); Cao B., Li L., Su X., Zeng J., Guo W., Development and validation of a nomogram for determining patients requiring prolonged postanesthesia care unit length of stay after laparoscopic cholecystectomy, Ann Palliat Med., 10, pp. 5128-5136, (2021); Elsharydah A., Walters D.R., Somasundaram A., Bryson T.D., Minhajuddin A., Gabriel R.A., Et al., A preoperative predictive model for prolonged post-anaesthesia care unit stay after outpatient surgeries, J Perioper Pract. Journals.Sagepub.Com, 30, pp. 91-96, (2020); Childers C.P., Maggard-Gibbons M., Understanding Costs of Care in the Operating Room, JAMA Surg. Jamanetwork.Com, 153, (2018); Song D., Chung F., Ronayne M., Ward B., Yogendran S., Sibbick C., Fast-tracking (bypassing the PACU) does not reduce nursing workload after ambulatory surgery, Br J Anaesth., 93, pp. 768-774, (2004); White P.F., Rawal S., Nguyen J., Watkins A., PACU fast-tracking: an alternative to “bypassing” the PACU for facilitating the recovery process after ambulatory surgery, J Perianesth Nurs., 18, pp. 247-253, (2003); Rice A.N., Muckler V.C., Miller W.R., Vacchiano C.A., Fast-tracking ambulatory surgery patients following anesthesia, J Perianesth Nurs., 30, pp. 124-133, (2015); Macario A., Glenn D., Dexter F., What can the postanesthesia care unit manager do to decrease costs in the postanesthesia care unit?, J Perianesth Nurs., 14, pp. 284-293, (1999); Manzia T.M., Quaranta C., Filingeri V., Toti L., Anselmo A., Tariciotti L., Et al., Feasibility and cost effectiveness of ambulatory laparoscopic cholecystectomy. A retrospective cohort study, Ann Med Surg (Lond). Elsevier, 55, pp. 56-61, (2020); Rider C.M., Hong V.Y., Westbrooks T.J., Wang J., Sheffer B.W., Kelly D.M., Et al., Surgical Treatment of Supracondylar Humeral Fractures in a Freestanding Ambulatory Surgery Center is as Safe as and Faster and More Cost-Effective Than in a Children’s Hospital, Journal of Pediatric Orthopaedics. Journals.Lww.Com, 38, (2018); Ford M.C., Walters J.D., Mulligan R.P., Dabov G.D., Mihalko W.M., Mascioli A.M., Et al., Safety and Cost-Effectiveness of Outpatient Unicompartmental Knee Arthroplasty in the Ambulatory Surgery Center: A Matched Cohort Study, Orthop Clin North Am. Orthopedic.Theclinics.Com, 51, pp. 1-5, (2020); Alonso S., Du A.L., Waterman R.S., Gabriel R.A., Body Mass Index Is Not an Independent Factor Associated With Recovery Room Length of Stay for Patients Undergoing Outpatient Surgery, J Patient Saf., 18, pp. 742-746, (2022)","J.L. Tully; Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, United States; email: jtully@health.ucsd.edu","","Springer","","","","","","01485598","","JMSYD","37428267","English","J. Med. Syst.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85164297530"
"Cosentino J.; Behsaz B.; Alipanahi B.; McCaw Z.R.; Hill D.; Schwantes-An T.-H.; Lai D.; Carroll A.; Hobbs B.D.; Cho M.H.; McLean C.Y.; Hormozdiari F.","Cosentino, Justin (57218718503); Behsaz, Babak (57225017322); Alipanahi, Babak (57222596097); McCaw, Zachary R. (57190581018); Hill, Davin (57787051300); Schwantes-An, Tae-Hwi (36523963000); Lai, Dongbing (57208772732); Carroll, Andrew (7202435548); Hobbs, Brian D. (56305051300); Cho, Michael H. (57219307474); McLean, Cory Y. (25637321300); Hormozdiari, Farhad (57209971734)","57218718503; 57225017322; 57222596097; 57190581018; 57787051300; 36523963000; 57208772732; 7202435548; 56305051300; 57219307474; 25637321300; 57209971734","Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models","2023","Nature Genetics","55","5","","787","795","8","26","10.1038/s41588-023-01372-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153067155&doi=10.1038%2fs41588-023-01372-4&partnerID=40&md5=7abb7cefa3976e4f74a7ae3b0b2c017e","Google Health AI, Palo Alto, CA, United States; Google Health AI, Cambridge, MA, United States; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States; Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States; Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States","Cosentino J., Google Health AI, Palo Alto, CA, United States; Behsaz B., Google Health AI, Cambridge, MA, United States; Alipanahi B., Google Health AI, Palo Alto, CA, United States; McCaw Z.R., Google Health AI, Palo Alto, CA, United States; Hill D., Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States, Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Schwantes-An T.-H., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States, Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States; Lai D., Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States; Carroll A., Google Health AI, Palo Alto, CA, United States; Hobbs B.D., Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States, Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Cho M.H., Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States, Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; McLean C.Y., Google Health AI, Cambridge, MA, United States; Hormozdiari F., Google Health AI, Cambridge, MA, United States","Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case–control status from high-dimensional raw spirograms and use the model’s predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design. © 2023, The Author(s), under exclusive licence to Springer Nature America, Inc.","","Deep Learning; Genetic Loci; Genome-Wide Association Study; Humans; Polymorphism, Single Nucleotide; Pulmonary Disease, Chronic Obstructive; Article; body composition; body mass; chronic obstructive lung disease; controlled study; convolutional neural network; cross validation; deep learning; disease exacerbation; forced expiratory volume; forced vital capacity; gene linkage disequilibrium; gene locus; genetic correlation; genome-wide association study; human; International Classification of Diseases; lung flow volume curve; machine learning; overall survival; peak expiratory flow; people by smoking status; phenotype; risk model; self report; spirography; spirometry; deep learning; gene locus; genetics; procedures; single nucleotide polymorphism","","","","","Google Health AI; National Institutes of Health, NIH, (K08 HL136928, R01 HL155749, U01 HL089856, U01 HL089897); National Institutes of Health, NIH; Alpha-1 Foundation, A1F, (2T32HL007427-41, R01HL089856, R01HL147148, R01HL149861, R01HL153248); Alpha-1 Foundation, A1F; Google; COPD Foundation","Funding text 1: Genotypes and phenotypes are available for approved projects through the UKB study ( https://www.ukbiobank.ac.uk ). The full ML-based COPD GWAS summary statistics are currently available on our GitHub repository page ( https://github.com/Google-Health/genomics-research/tree/main/ml-based-copd ) and in the GWAS catalog (accession number GCST90244098 ). The raw ML-based COPD liability scores will be returned to UKB. This research has been conducted under Application Number 65275. We used the GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ) for replication analysis. This research used data generated by the COPDGene study (dbGaP accession phs000179.v6.p2 ), which was supported by NIH grants U01 HL089856 and U01 HL089897. The COPDGene project is also supported by the COPD Foundation through contributions made by an Industry Advisory Board comprised of Pfizer, AstraZeneca, Boehringer-Ingelheim, Novartis and Sunovion. ICGC genome-wide association summary statistics were obtained from dbGaP under accession phs000179.v5.p2 . SpiroMeta summary statistics were obtained from LDHub ( https://ldsc.broadinstitute.org/ldhub ). ; Funding text 2: We thank T. Yun (Google Health AI) for helpful discussions and H. Yang (Google Health AI) for project management assistance. B.D.H. is supported by NIH K08 HL136928, U01 HL089856, R01 HL155749 and a Research Grant from the Alpha-1 Foundation. M.H.C. is supported by R01HL153248, R01HL149861, R01HL147148 and R01HL089856. D.H. was supported by NIH 2T32HL007427-41. This study was funded by Google. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.; Funding text 3: We thank T. Yun (Google Health AI) for helpful discussions and H. Yang (Google Health AI) for project management assistance. B.D.H. is supported by NIH K08 HL136928, U01 HL089856, R01 HL155749 and a Research Grant from the Alpha-1 Foundation. M.H.C. is supported by R01HL153248, R01HL149861, R01HL147148 and R01HL089856. D.H. was supported by NIH 2T32HL007427-41. This study was funded by Google. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. ","MacNee W., ABC of chronic obstructive pulmonary disease: pathology, pathogenesis, and pathophysiology, BMJ, 332, pp. 1202-1204, (2006); Ingebrigtsen T., Genetic influences on chronic obstructive pulmonary disease—a twin study, Respir. Med., 104, pp. 1890-1895, (2010); Zhou J.J., Et al., Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers, Am. J. Respir. Crit. Care Med., 188, pp. 941-947, (2013); Jorgen V., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 187, pp. 347-365, (2013); Brian L.G., Et al., Standardization of spirometry 2019 update. An official American Thoracic Society and European Respiratory Society technical statement, Am. J. Respir. Crit. Care Med., 200, pp. e70-e88, (2019); Mannino D.M., Buist A.S., Global burden of COPD: risk factors, prevalence, and future trends, Lancet, 370, pp. 765-773, (2007); Hobbs B.D., Et al., Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis, Nat. Genet., 49, pp. 426-432, (2017); Sakornsakolpat P., Et al., Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations, Nat. Genet., 51, pp. 494-505, (2019); Wain L.V., Et al., Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank, Lancet Respir. Med., 3, pp. 769-781, (2015); Nick S., Et al., New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries, Nat. Genet., 51, pp. 481-493, (2019); Regan E.A., Et al., Clinical and radiologic disease in smokers with normal spirometry, JAMA Intern. Med., 175, pp. 1539-1549, (2015); Woodruff P.G., Et al., Clinical significance of symptoms in smokers with preserved pulmonary function, N. Engl. J. Med., 374, pp. 1811-1821, (2016); Anzueto A., Et al., COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr. Pulm. Dis., 6, pp. 384-399, (2019); Han M.K., Et al., From GOLD 0 to pre-COPD, Am. J. Respir. Crit. Care Med., 203, pp. 414-423, (2021); Silverman E.K., Genetics of COPD, Annu. Rev. Physiol., 82, pp. 413-431, (2020); Babak A., Et al., Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology, Am. J. Hum. Genet., 108, pp. 1217-1230, (2021); Xikun H., Et al., Automated AI labeling of optic nerve head enables insights into cross-ancestry glaucoma risk and genetic discovery in >280,000 images from UKB and CLSA, Am. J. Hum. Genet., 108, pp. 1204-1216, (2021); LeCun Y., Et al., Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, pp. 541-551, (1989); He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778, (2016); He T., Et al., Bag of tricks for image classification with convolutional neural networks, (2019); Nay A., Et al., Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function, Nat. Genet., 54, pp. 783-791, (2022); Joo J., Hobbs B., Cho M., Himes B., Trait insights gained by comparing genome-wide association study results using different chronic obstructive pulmonary disease definitions, AMIA Jt. 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Genet., 46, pp. 646-651, (2014); Tilley A.E., Walters M.S., Shaykhiev R., Crystal R.G., Cilia dysfunction in lung disease, Annu. Rev. Physiol., 77, pp. 379-406, (2015); Qiao D., Et al., Whole exome sequencing analysis in severe chronic obstructive pulmonary disease, Hum. Mol. Genet., 27, pp. 3801-3812, (2018); Wootton R.E., Et al., Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study, Psychol. Med., 50, pp. 2435-2443, (2019); Lehmann M., Baarsma H.A., Konigshoff M., WNT signaling in lung aging and disease, Ann. Am. Thorac. Soc., 13, pp. S411-S416, (2016); Morrow J.D., Et al., Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue, Sci. Rep., 7, (2017); Conlon T.M., Et al., Inhibition of LTβR signalling activates WNT-induced regeneration in lung, Nature, 588, pp. 151-156, (2020); Shrine N., Et al., Multi-ancestry genome-wide association study improves resolution of genes, pathways and pleiotropy for lung function and chronic obstructive pulmonary disease, Nat. Genet., 55, pp. 410-422, (2022); Cloonan S.M., Et al., Mitochondrial iron chelation ameliorates cigarette smoke–induced bronchitis and emphysema in mice, Nat. Med., 22, pp. 163-174, (2016); Routhier J., Et al., An innate contribution of human nicotinic receptor polymorphisms to COPD-like lesions, Nat. Commun., 12, (2021); Golovin D., Et al., Google vizier, pp. 1487-1495, (2017); Frazier P.I., (2018); Lakshminarayanan B., Pritzel A., Blundell C., Simple and scalable predictive uncertainty estimation using deep ensembles, Adv. Neural Inf. Process Syst., 30, pp. 6405-6416, (2017); Mbatchou J., Et al., Computationally efficient whole-genome regression for quantitative and binary traits, Nat. Genet., 53, pp. 1097-1103, (2021); Cosentino J., Google-Health/genomics-research: ML-based COPD v0.2.0, Zenodo, (2023)","J. Cosentino; Google Health AI, Palo Alto, United States; email: jtcosentino@google.com; F. Hormozdiari; Google Health AI, Cambridge, United States; email: fhormoz@google.com","","Nature Research","","","","","","10614036","","NGENE","37069358","English","Nat. Genet.","Article","Final","","Scopus","2-s2.0-85153067155"
"Lee H.; Cho J.K.; Park J.; Lee H.; Fond G.; Boyer L.; Kim H.J.; Park S.; Cho W.; Lee H.; Lee J.; Yon D.K.","Lee, Hojae (58403500500); Cho, Joong Ki (58678379500); Park, Jaeyu (57092892800); Lee, Hyeri (58889654100); Fond, Guillaume (57192192554); Boyer, Laurent (8951031500); Kim, Hyeon Jin (58065396200); Park, Seoyoung (58889684100); Cho, Wonyoung (58146983800); Lee, Hayeon (58288501400); Lee, Jinseok (57314523000); Yon, Dong Keon (57193675906)","58403500500; 58678379500; 57092892800; 58889654100; 57192192554; 8951031500; 58065396200; 58889684100; 58146983800; 58288501400; 57314523000; 57193675906","Machine Learning-Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts","2024","Journal of Medical Internet Research","26","1","e51473","","","","17","10.2196/51473","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185207852&doi=10.2196%2f51473&partnerID=40&md5=56d45761a16d180766572a7a6cc81a7a","Department of Regulatory Science, Kyung Hee University, Seoul, South Korea; Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States; Assistance Publique-Hôpitaux de Marseille, Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France; Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea; Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, South Korea; Department of Pediatrics, Kyung Hee University College of Medicine, Seoul, South Korea","Lee H., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea, Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Cho J.K., Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States; Park J., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea, Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Lee H., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea, Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Fond G., Assistance Publique-Hôpitaux de Marseille, Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France; Boyer L., Assistance Publique-Hôpitaux de Marseille, Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France; Kim H.J., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea, Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea; Park S., Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea; Cho W., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea; Lee H., Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea, Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea; Lee J., Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea, Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, South Korea; Yon D.K., Department of Regulatory Science, Kyung Hee University, Seoul, South Korea, Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea, Department of Pediatrics, Kyung Hee University College of Medicine, Seoul, South Korea","Background: Given the additional risk of suicide-related behaviors in adolescents with allergic rhinitis (AR), it is important to use the growing field of machine learning (ML) to evaluate this risk. Objective: This study aims to evaluate the validity and usefulness of an ML model for predicting suicide risk in patients with AR. Methods: We used data from 2 independent survey studies, Korea Youth Risk Behavior Web-based Survey (KYRBS; n=299,468) for the original data set and Korea National Health and Nutrition Examination Survey (KNHANES; n=833) for the external validation data set, to predict suicide risks of AR in adolescents aged 13 to 18 years, with 3.45% (10,341/299,468) and 1.4% (12/833) of the patients attempting suicide in the KYRBS and KNHANES studies, respectively. The outcome of interest was the suicide attempt risks. We selected various ML-based models with hyperparameter tuning in the discovery and performed an area under the receiver operating characteristic curve (AUROC) analysis in the train, test, and external validation data. Results: The study data set included 299,468 (KYRBS; original data set) and 833 (KNHANES; external validation data set) patients with AR recruited between 2005 and 2022. The best-performing ML model was the random forest model with a mean AUROC of 84.12% (95% CI 83.98%-84.27%) in the original data set. Applying this result to the external validation data set revealed the best performance among the models, with an AUROC of 89.87% (sensitivity 83.33%, specificity 82.58%, accuracy 82.59%, and balanced accuracy 82.96%). While looking at feature importance, the 5 most important features in predicting suicide attempts in adolescent patients with AR are depression, stress status, academic achievement, age, and alcohol consumption. Conclusions: This study emphasizes the potential of ML models in predicting suicide risks in patients with AR, encouraging further application of these models in other conditions to enhance adolescent health and decrease suicide rates. © Hojae Lee, Joong Ki Cho, Jaeyu Park, Hyeri Lee, Guillaume Fond, Laurent Boyer, Hyeon Jin Kim, Seoyoung Park, Wonyoung Cho, Hayeon Lee, Jinseok Lee, Dong Keon Yon.","allergic rhinitis; machine learning; prediction; random forest; suicidality","Adolescent; Humans; Machine Learning; Nutrition Surveys; Rhinitis, Allergic; Suicidal Ideation; Suicide; academic achievement; adolescent; adolescent health; age; alcohol consumption; allergic rhinitis; Article; asthma; atopic dermatitis; body mass; clinical feature; clinical outcome; cohort analysis; controlled study; cross validation; data accuracy; depression; feature selection; female; health survey; hopelessness; household income; human; machine learning; major clinical study; male; people by smoking status; physiological stress; prediction; predictive model; random forest; receiver operating characteristic; risk assessment; risk behavior; risk factor; sadness; sensitivity and specificity; South Korea; suicidal behavior; suicidal ideation; suicide attempt; underage drinking; validation study; machine learning; nutrition; suicide","","","","","Ministry of Health and Welfare, MOHW, (HV22C0233); Ministry of Health and Welfare, MOHW; Korea Health Industry Development Institute, KHIDI","This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant HV22C0233). The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.","Koo MJ, Kwon R, Lee SW, Choi YS, Shin YH, Rhee SY, Et al., National trends in the prevalence of allergic diseases among Korean adolescents before and during COVID-19, 2009-2021: a serial analysis of the national representative study, Allergy, 78, 6, pp. 1665-1670, (2023); Lee K, Lee H, Kwon R, Shin YH, Yeo SG, Lee YJ, Et al., Global burden of vaccine-associated anaphylaxis and their related vaccines, 1967-2023: a comprehensive analysis of the international pharmacovigilance database, Allergy (Forthcoming), (2023); Shin YH, Hwang J, Kwon R, Lee SW, Kim MS, GBD 2019 Allergic Disorders Collaborators; et al. Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019, Allergy, 78, 8, pp. 2232-2254; Brown T., Diagnosis and management of allergic rhinitis in children, Pediatr Ann, 48, 12, pp. e485-e488, (2019); Yon DK, Hwang S, Lee SW, Jee HM, Sheen YH, Kim JH, Et al., Indoor exposure and sensitization to formaldehyde among inner-city children with increased risk for asthma and rhinitis, Am J Respir Crit Care Med, 200, 3, pp. 388-393, (2019); Noh H, An J, Kim MJ, Sheen YH, Yoon J, Welsh B, Et al., Sleep problems increase school accidents related to allergic diseases, Pediatr Allergy Immunol, 31, 1, pp. 98-103, (2020); Timonen M, Jokelainen J, Hakko H, Silvennoinen-Kassinen S, Meyer-Rochow VB, Herva A, Et al., Atopy and depression: results from the Northern Finland 1966 Birth Cohort Study, Mol Psychiatry, 8, 8, pp. 738-744, (2003); Guzman A, Tonelli LH, Roberts D, Stiller JW, Jackson MA, Soriano JJ, Et al., Mood-worsening with high-pollen-counts and seasonality: a preliminary report, J Affect Disord, 101, 1-3, pp. 269-274, (2007); Timonen M, Jokelainen J, Silvennoinen-Kassinen S, Herva A, Zitting P, Xu B, Et al., Association between skin test diagnosed atopy and professionally diagnosed depression: a Northern Finland 1966 Birth Cohort study, Biol Psychiatry, 52, 4, pp. 349-355, (2002); Postolache TT, Stiller JW, Herrell R, Goldstein MA, Shreeram SS, Zebrak R, Et al., Tree pollen peaks are associated with increased nonviolent suicide in women, Mol Psychiatry, 10, 3, pp. 232-235, (2005); Marshall PS, O'Hara C, Steinberg P., Effects of seasonal allergic rhinitis on fatigue levels and mood, Psychosom Med, 64, 4, pp. 684-691, (2002); Woo HG, Park S, Yon H, Lee SW, Koyanagi A, Jacob L, Et al., National trends in sadness, suicidality, and COVID-19 pandemic-related risk factors among South Korean adolescents from 2005 to 2021, JAMA Netw Open, 6, 5, (2023); Amritwar AU, Lowry CA, Brenner LA, Hoisington AJ, Hamilton R, Stiller JW, Et al., Mental health in allergic rhinitis: depression and suicidal behavior, Curr Treat Options Allergy, 4, 1, pp. 71-97, (2017); Kim N, Song JY, Yang H, Kim MJ, Lee K, Shin YH, Et al., National trends in suicide-related behaviors among youths between 2005-2020, including COVID-19: a Korean representative survey of one million adolescents, Eur Rev Med Pharmacol Sci, 27, 3, pp. 1192-1202, (2023); Lee KH, Yon DK, Suh DI., Prevalence of allergic diseases among Korean adolescents during the COVID-19 pandemic: comparison with pre-COVID-19 11-year trends, Eur Rev Med Pharmacol Sci, 26, 7, pp. 2556-2568, (2022); Franklin JC, Ribeiro JD, Fox KR, Bentley KH, Kleiman EM, Huang X, Et al., Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research, Psychol Bull, 143, 2, pp. 187-232, (2017); Large M, Kaneson M, Myles N, Myles H, Gunaratne P, Ryan C., Meta-analysis of longitudinal cohort studies of suicide risk assessment among psychiatric patients: heterogeneity in results and lack of improvement over time, PLoS One, 11, 6, (2016); Burke TA, Ammerman BA, Jacobucci R., The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: a systematic review, J Affect Disord, 245, pp. 869-884, (2019); Park S, Yon H, Ban CY, Shin H, Eum S, Lee SW, Et al., National trends in alcohol and substance use among adolescents from 2005 to 2021: a Korean serial cross-sectional study of one million adolescents, World J Pediatr, 19, 11, pp. 1071-1081, (2023); Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, Et al., Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES), Int J Epidemiol, 43, 1, pp. 69-77, (2014); Ren Y, Wu D, Tong Y, Lopez-DeFede A, Gareau S., Issue of data imbalance on low birthweight baby outcomes prediction and associated risk factors identification: establishment of benchmarking key machine learning models with data rebalancing strategies, J Med Internet Res, 25, (2023); Lee SW., Methods for testing statistical differences between groups in medical research: statistical standard and guideline of life cycle committee, Life Cycle, 2, (2022); Koo JH, Park YH, Kang DR., Factors predicting older people's acceptance of a personalized health care service app and the effect of chronic disease: cross-sectional questionnaire study, JMIR Aging, 6, (2023); 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Akdis SH., Allergy and the immunologic basis of atopic disease, Nelson Textbook of Pediatrics: Expert Consult, pp. 388-393, (2019); Mukundan TH., Screening for allergic disease in a child with sleep disorder and screening for sleep disturbance in allergic disease, Allergy and Sleep: Basic Principles and Clinical Practice, pp. 77-85, (2023); Kim HJ, Kim YJ, Lee SH, Yu J, Jeong SK, Hong SJ., Effects of Lactobacillus rhamnosus on allergic march model by suppressing Th2, Th17, and TSLP responses via CD4(+)CD25(+)Foxp3(+) Tregs, Clin Immunol, 153, 1, pp. 178-186, (2014); Gadani SP, Cronk JC, Norris GT, Kipnis J., IL-4 in the brain: a cytokine to remember, J Immunol, 189, 9, pp. 4213-4219, (2012); Zanno AE, Romer MA, Fox L, Golden T, Jaeckle-Santos L, Simmons RA, Et al., Reducing Th2 inflammation through neutralizing IL-4 antibody rescues myelination in IUGR rat brain, J Neurodev Disord, 11, 1, (2019); Thompson A, Sardana N, Craig TJ., Sleep impairment and daytime sleepiness in patients with allergic rhinitis: the role of congestion and inflammation, Ann Allergy Asthma Immunol, 111, 6, pp. 446-451, (2013); Jackson-Cowan L, Cole EF, Arbiser JL, Silverberg JI, Lawley LP., TH2 sensitization in the skin-gut-brain axis: how early-life Th2-mediated inflammation may negatively perpetuate developmental and psychologic abnormalities, Pediatr Dermatol, 38, 5, pp. 1032-1039, (2021); Lee H, Park J, Lee M, Kim HJ, Kim M, Kwon R, Et al., National trends in allergic rhinitis and chronic rhinosinusitis and COVID-19 pandemic-related factors in South Korea, from 1998 to 2021, Int Arch Allergy Immunol, 2024, pp. 1-7; Kwon R, Lee H, Kim MS, Lee J, Yon DK., Machine learning-based prediction of suicidality in adolescents during the COVID-19 pandemic (2020-2021): derivation and validation in two independent nationwide cohorts, Asian J Psychiatr, 88, (2023); Kang J, Park J, Lee H, Lee M, Kim S, Koyanagi A, Et al., National trends in depression and suicide attempts and COVID-19 pandemic-related factors, 1998-2021: a nationwide study in South Korea, Asian J Psychiatr, 88, (2023); Berthelot E, Etchecopar-Etchart D, Thellier D, Lancon C, Boyer L, Fond G., Fasting interventions for stress, anxiety and depressive symptoms: a systematic review and meta-analysis, Nutrients, 13, 11, (2021); Fond G, Young AH, Godin O, Messiaen M, Lancon C, Auquier P, Et al., Improving diet for psychiatric patients: high potential benefits and evidence for safety, J Affect Disord, 265, pp. 567-569, (2020)","D.K. Yon; Department of Regulatory Science, Kyung Hee University, Seoul, 23 Kyungheedae-ro, Dongdaemun-gu, 02447, South Korea; email: yonkkang@gmail.com","","JMIR Publications Inc.","","","","","","14388871","","","38354043","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85185207852"
"Mujahid M.; Rustam F.; Álvarez R.; Luis Vidal Mazón J.; Díez I.T.; Ashraf I.","Mujahid, Muhammad (57258056900); Rustam, Furqan (57211950161); Álvarez, Roberto (57280741100); Luis Vidal Mazón, Juan (57720010800); Díez, Isabel de la Torre (55665183400); Ashraf, Imran (57195478761)","57258056900; 57211950161; 57280741100; 57720010800; 55665183400; 57195478761","Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network","2022","Diagnostics","12","5","1280","","","","76","10.3390/diagnostics12051280","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131087657&doi=10.3390%2fdiagnostics12051280&partnerID=40&md5=efa3c2e5852018b7533a31563d511382","Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Department of Software Engineering, University of Management and Technology, Lahore, 54770, Pakistan; Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, C/Isabel Torres 21, Santander, 39011, Spain; Department of Project Management, Universidad Internacional Iberoamericana, Campeche, C.P. 24560, Mexico; Project Department, Universidade Internacional do Cuanza Bairro Kaluanda, Cuito EN 250, Bié, Angola; Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea","Mujahid M., Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Rustam F., Department of Software Engineering, University of Management and Technology, Lahore, 54770, Pakistan; Álvarez R., Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, C/Isabel Torres 21, Santander, 39011, Spain, Department of Project Management, Universidad Internacional Iberoamericana, Campeche, C.P. 24560, Mexico; Luis Vidal Mazón J., Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, C/Isabel Torres 21, Santander, 39011, Spain, Project Department, Universidade Internacional do Cuanza Bairro Kaluanda, Cuito EN 250, Bié, Angola; Díez I.T., Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain; Ashraf I., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea","Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen’s kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","chest X-ray; deep learning; ensemble learning; pneumonia","Article; controlled study; convolutional neural network; coronavirus disease 2019; deep learning; deep neural network; disease classification; human; immune system; k fold cross validation; machine learning; major clinical study; pneumonia; thorax radiography; transfer of learning","","","","","","","Pneumonia, KEY Facts, (2021); Pneumonia, (2021); McAllister D.A., Liu L., Shi T., Chu Y., Reed C., Burrows J., Adeloye D., Rudan I., Black R.E., Campbell H., Et al., Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: A systematic analysis, Lancet Glob. 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Inform, 14, pp. 4224-4231, (2018); Jmour N., Zayen S., Abdelkrim A., Convolutional neural networks for image classification, Proceedings of the 2018 International Conference on Advanced Systems and ELECTRIC technologies (IC_ASET), pp. 397-402, (2018); Albawi S., Mohammed T.A., Al-Zawi S., Understanding of a convolutional neural network, Proceedings of the 2017 International Conference on Engineering and Technology (ICET), pp. 1-6, (2017); Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z., Rethinking the inception architecture for computer vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, (2016); Farooq M., Hafeez A., Covid-resnet: A deep learning framework for screening of covid19 from radiographs, (2020); Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, (2014); Zhou T., Lu H., Yang Z., Qiu S., Huo B., Dong Y., The ensemble deep learning model for novel COVID-19 on CT images, Appl. Soft Comput, 98, (2021); Rupapara V., Rustam F., Shahzad H.F., Mehmood A., Ashraf I., Choi G.S., Impact of SMOTE on imbalanced text features for toxic comments classification using RVVC model, IEEE Access, 9, pp. 78621-78634, (2021)","I.T. Díez; Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Valladolid, Paseo de Belén 15, 47011, Spain; email: isator@tel.uva.es; I. Ashraf; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; email: imranashraf@ynu.ac.kr","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85131087657"
"Yan J.; Zhai W.; Li Z.; Ding L.L.; You J.; Zeng J.; Yang X.; Wang C.; Meng X.; Jiang Y.; Huang X.; Wang S.; Wang Y.; Li Z.; Zhu S.; Wang Y.; Zhao X.; Feng J.","Yan, Jing (57197817369); Zhai, Weiqi (57665783400); Li, Zhaoxia (57191700187); Ding, LingLing (57193908264); You, Jia (57667047600); Zeng, Jiayi (57665150000); Yang, Xin (57328793500); Wang, Chunjuan (57218359472); Meng, Xia (57218359252); Jiang, Yong (57211868906); Huang, Xiaodi (58830948200); Wang, Shouyan (55660820300); Wang, Yilong (57219932873); Li, Zixiao (57205356183); Zhu, Shanfeng (8940145500); Wang, Yongjun (8320879700); Zhao, Xingquan (7407574974); Feng, Jianfeng (7403884410)","57197817369; 57665783400; 57191700187; 57193908264; 57667047600; 57665150000; 57328793500; 57218359472; 57218359252; 57211868906; 58830948200; 55660820300; 57219932873; 57205356183; 8940145500; 8320879700; 7407574974; 7403884410","ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage","2022","Journal of Translational Medicine","20","1","193","","","","16","10.1186/s12967-022-03389-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129415965&doi=10.1186%2fs12967-022-03389-5&partnerID=40&md5=08801bb03a12b5cb0f40d858a06f806b","Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Chinese Institute for Brain Research, Beijing, China; Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China; School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, 2640, NSW, Australia; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China; MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China; Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China","Yan J., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Zhai W., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China, Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China, MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China, Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China; Li Z., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Ding L.L., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; You J., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China, Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China, MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China, Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China; Zeng J., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; Yang X., China National Clinical Research Center for Neurological Diseases, Beijing, China; Wang C., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Meng X., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Jiang Y., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Huang X., School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, 2640, NSW, Australia; Wang S., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China, Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China, MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China, Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China; Wang Y., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China; Li Z., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China, Chinese Institute for Brain Research, Beijing, China, Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China; Zhu S., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China, Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China, MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China, Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China; Wang Y., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China, Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China; Zhao X., Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China, China National Clinical Research Center for Neurological Diseases, Beijing, China, Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China; Feng J., Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China, Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Shanghai, 200433, China, MOE Frontiers Center for Brain Science and Shanghai Institute of Artificial Intelligence Algorithms, Fudan University, Shanghai, 200433, China, Zhangjiang Fudan International Innovation Center, Shanghai, 200433, China","Purpose: We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH). Method: We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups – 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC). Results: The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739–0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694–0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762–0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774–0.794]. Conclusion: ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases. © 2022, The Author(s).","","Blood Glucose; C-Reactive Protein; Cerebral Hemorrhage; Humans; Intracranial Hemorrhages; Pneumonia; Prognosis; Prospective Studies; Risk Factors; Stroke; C reactive protein; C reactive protein; adult; aged; area under the curve; Article; blood pressure; brain hemorrhage; cerebrovascular accident; chronic obstructive lung disease; clinical practice; computer assisted tomography; controlled study; diagnostic accuracy; diagnostic test accuracy study; dysphagia; family income; fasting blood glucose level; fever; Glasgow coma scale; glucose blood level; high risk patient; high risk population; human; laboratory test; leukocyte count; logistic regression analysis; low risk population; machine learning; male; middle aged; National Institutes of Health Stroke Scale; nuclear magnetic resonance imaging; pneumonia; prediction; Rankin scale; receiver operating characteristic; respiratory tract infection; risk assessment; risk factor; risk stratification; Sequential Organ Failure Assessment Score; smoking; sputum culture; statistical model; stratification; thorax radiography; transient ischemic attack; uric acid blood level; validation process; very elderly; brain hemorrhage; cerebrovascular accident; complication; pneumonia; prognosis; prospective study; randomized controlled trial","","C reactive protein, 9007-41-4; Blood Glucose, ; C-Reactive Protein, ","","","Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences, (2019-I2M-5–029); Shanghai Municipal Science and Technology, (2017SHZDZX01); National Natural Science Foundation of China, NSFC, (92046016); National Natural Science Foundation of China, NSFC; Chinese Academy of Sciences, CAS; Science and Technology Commission of Shanghai Municipality, STCSM, (2018SHZDZX01); Science and Technology Commission of Shanghai Municipality, STCSM; Ministry of Science and Technology, MOST; Natural Science Foundation of Beijing Municipality, (Z200016); Natural Science Foundation of Beijing Municipality; Ministry of Health of the People's Republic of China, MOH; Higher Education Discipline Innovation Project, (B18015); Higher Education Discipline Innovation Project","Funding text 1: This work has been supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2019-I2M-5–029); Beijing Natural Science Foundation (Z200016), National Natural Science Foundation of China (92046016). This work has been also supported by Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), 111 Project (No. B18015), ZJ Lab, and Shanghai Center for Brain Science and Brain-Inspired Technology. SZ has been supported by the Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01) and Information Technology Facility, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences. ; Funding text 2: Participating hospitals received either healthcare quality assessment and research approval to collect data in the CSCA project without requiring individual patient informed consent under the common rule or a waiver of authorization and exemption from subsequent review by their Institutional Review Board. The CNSR is funded by the Ministry of Science and Technology and the Ministry of Health of the People’s Republic of China. The Grant Numbers are 2006BA101A11 and 2009CB521905. 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Li; Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; email: lizixiao2008@hotmai.com; X. Zhao; Vascular Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; email: zxq@vip.163.com; S. Zhu; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; email: zhusf@fudan.edu.cn","","BioMed Central Ltd","","","","","","14795876","","","35509104","English","J. Transl. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85129415965"
"Khoury P.; Srinivasan R.; Kakumanu S.; Ochoa S.; Keswani A.; Sparks R.; Rider N.L.","Khoury, Paneez (36164232100); Srinivasan, Renganathan (57219717332); Kakumanu, Sujani (57208045805); Ochoa, Sebastian (57220547974); Keswani, Anjeni (24780930600); Sparks, Rachel (57190585039); Rider, Nicholas L. (15056496800)","36164232100; 57219717332; 57208045805; 57220547974; 24780930600; 57190585039; 15056496800","A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee","2022","Journal of Allergy and Clinical Immunology: In Practice","10","5","","1178","1188","10","23","10.1016/j.jaip.2022.01.047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126388265&doi=10.1016%2fj.jaip.2022.01.047&partnerID=40&md5=1936a5bd2c23603806755d133d0aad11","Laboratory of Allergic Diseases, NIAID, Md, Bethesda; The Vancouver Clinic, Vancouver, Wash, United States; Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health and the William S. Middleton Veterans Memorial Hospital, Madison, Wisc, United States; National Institutes of Infectious and Allergic Diseases, Md, Bethesda; Laboratory of Clinical Immunology and Microbiology, NIAID, Md, Bethesda; Division of Allergy/Immunology, George Washington University School of Medicine and Health Sciences, DC, Washington; Laboratory of Immune System Biology, NIAID, Md, Bethesda; Section of Immunology, Allergy and Retrovirology and the William T. Shearer Center for Human Immunobiology, Baylor College of Medicine, Texas Children's Hospital, Texas, Houston","Khoury P., Laboratory of Allergic Diseases, NIAID, Md, Bethesda; Srinivasan R., The Vancouver Clinic, Vancouver, Wash, United States; Kakumanu S., Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin School of Medicine and Public Health and the William S. Middleton Veterans Memorial Hospital, Madison, Wisc, United States; Ochoa S., National Institutes of Infectious and Allergic Diseases, Md, Bethesda, Laboratory of Clinical Immunology and Microbiology, NIAID, Md, Bethesda; Keswani A., Division of Allergy/Immunology, George Washington University School of Medicine and Health Sciences, DC, Washington; Sparks R., National Institutes of Infectious and Allergic Diseases, Md, Bethesda, Laboratory of Immune System Biology, NIAID, Md, Bethesda; Rider N.L., Section of Immunology, Allergy and Retrovirology and the William T. Shearer Center for Human Immunobiology, Baylor College of Medicine, Texas Children's Hospital, Texas, Houston","Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists. © 2022","Artificial intelligence; Asthma; Atopic dermatitis; Augmented intelligence; Clinical decision support; Electronic health records; Equity; Machine learning; Medical education; Natural language processing; Primary immunodeficiency","allergy; Article; artificial intelligence; augmented reality; bioinformatics; biological monitoring; clinical practice; deep learning; drug repositioning; expiratory flow rate; exposomics; flow cytometry; gene expression profiling; health education; health equity; immune deficiency; immunology; immunophenotyping; machine learning; medical informatics; natural language processing; nuclear magnetic resonance spectroscopy; phenotype; proteomics; support vector machine; telemonitoring; transcriptomics","","","","","Consortium of Eosinophilic Gastrointestinal Researchers/National Center for Advancing Translational Sciences and American Partnership For Eosinophilic Disorders; Horizon Therapeutics; National Institutes of Health, NIH, (1R21AI164100-01A1); National Institute of Allergy and Infectious Diseases, NIAID, (ZIEAI001148); Jeffrey Modell Foundation, JMF; Division of Intramural Research, National Institute of Allergy and Infectious Diseases, DIR, NIAID; Health Services Research and Development, HSR&D; Takeda Pharmaceutical Company, TPC","Funding text 1: The work was funded in part by the Division of Intramural Research, National Institutes of Allergic and Infectious Diseases, National Institutes of Health . ; Funding text 2: Conflicts of interest: P. Khoury has received research grant funding from Consortium of Eosinophilic Gastrointestinal Researchers/National Center for Advancing Translational Sciences and American Partnership For Eosinophilic Disorders. S. Kakumanu has received royalties from UpToDate and federal grant funding from VA HSR&D. N. L. Rider receives grant funding from the National Institutes of Health (1R21AI164100-01A1), The Jeffrey Modell Foundation, Takeda Pharmaceuticals, and Pharma Healthcare; is a consultant to Horizon Therapeutics, Takeda Pharmaceuticals, and Pharming Healthcare; and receives royalties for topic contributions to UptoDate from Wolters Kluwer. The rest of the authors declare that they have no relevant conflicts of interest. 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"Jokic S.; Cleres D.; Rassouli F.; Steurer-Stey C.; Puhan M.A.; Brutsche M.; Fleisch E.; Barata F.","Jokic, Stefan (57468082100); Cleres, David (57226129083); Rassouli, Frank (56668637700); Steurer-Stey, Claudia (14044007900); Puhan, Milo A. (56103449300); Brutsche, Martin (7004585886); Fleisch, Elgar (6602498499); Barata, Filipe (57191505036)","57468082100; 57226129083; 56668637700; 14044007900; 56103449300; 7004585886; 6602498499; 57191505036","TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning","2022","IEEE Journal of Biomedical and Health Informatics","26","6","","2746","2757","11","15","10.1109/JBHI.2022.3152944","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125362063&doi=10.1109%2fJBHI.2022.3152944&partnerID=40&md5=6b87b9a459ffc0308bf91f6eab8d5f70","Eth Zurich, Department of Computer Science, Zurich, 8092, Switzerland; Eth Zurich, Centre for Digital Health Interventions, Zurich, 8092, Switzerland; Cantonal Hospital St. Gallen, Lung Center, St. Gallen, 9007, Switzerland; University of Zurich, Epidemiology, Biostatistics and Prevention Institute, Zurich, 8006, Switzerland; MediX Group Practice, Zurich, 8037, Switzerland; Eth Zurich and University of St. Gallen, Centre for Digital Health Interventions, Zurich, 8092, Switzerland","Jokic S., Eth Zurich, Department of Computer Science, Zurich, 8092, Switzerland; Cleres D., Eth Zurich, Centre for Digital Health Interventions, Zurich, 8092, Switzerland; Rassouli F., Cantonal Hospital St. Gallen, Lung Center, St. Gallen, 9007, Switzerland; Steurer-Stey C., University of Zurich, Epidemiology, Biostatistics and Prevention Institute, Zurich, 8006, Switzerland, MediX Group Practice, Zurich, 8037, Switzerland; Puhan M.A., University of Zurich, Epidemiology, Biostatistics and Prevention Institute, Zurich, 8006, Switzerland; Brutsche M., Cantonal Hospital St. Gallen, Lung Center, St. Gallen, 9007, Switzerland; Fleisch E., Eth Zurich and University of St. Gallen, Centre for Digital Health Interventions, Zurich, 8092, Switzerland; Barata F., Eth Zurich, Centre for Digital Health Interventions, Zurich, 8092, Switzerland","Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracyof 88$\%$ ($\pm$ 10$\%$ SD) on two-way identification tests with 12 enrollment samples and accuracy of 80$\%$ and an equal error rate (EER) of 20$\%$ on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.  © 2013 IEEE.","Cough monitoring; metric learning; mobile sensing; remote patient monitoring; speaker identification; speaker verification; triplet network","Algorithms; Cough; Humans; Neural Networks, Computer; Respiration Disorders; Smartphone; Acceptance tests; Audio recordings; Diagnosis; Network architecture; Neural networks; Consumer-centric; Contact free; Convolutional neural network; Detection algorithm; Disease progression; Embeddings; Metric learning; Monitoring system; Smart phones; Verification tests; adult; Article; asthma; audio recording; controlled study; convolutional neural network; coughing; deep learning; detection algorithm; embedding; female; human; human experiment; learning; male; validation process; algorithm; breathing disorder; coughing; smartphone; Smartphones","","","","","","","Benich J.J., Carek P.J., Evaluation of the patientwith chronic cough, Amer. Fam. 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Representations, (2015); Wu C., Manmatha R., Smola A.J., Krahenbuhl P., Sampling matters in deep embedding learning, Proc. IEEE Int. Conf. Comput. Vis., pp. 2859-2867, (2017); Musgrave K., Belongie S., Lim S.-N., A metric learning reality check, Eur. Conf. Comput. Vis., Springer, pp. 681-699, (2020); Kingma D.P., Ba J., Adam: A method for stochastic optimization, 3rd Int. Conf. Learn. Representations, (2015); Glorot X., Bengio Y., Understanding the difficulty of training deep feedforward neural networks, J.Mach. Learn. Res.-Proc. Track, 9, pp. 249-256, (2010); Abadi M., Tensorflow: A system for large-scale machine learning, Proc. 12th USENIX Conf. Operating Syst. Des. Implementation, ser. OSDI'16, USA: USENIX Association, pp. 265-283, (2016); Lane N.D., DeepX: A software accelerator for low-power deep learning inference on mobile devices, Proc. 15th ACM/IEEE Int. Conf. Inf. Process. Sensor Netw., pp. 1-12, (2016)","F. Barata; Eth Zurich, Centre for Digital Health Interventions, Zurich, 8092, Switzerland; email: fbarata@ethz.ch","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","35196248","English","IEEE J. Biomedical Health Informat.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85125362063"
"Nam J.G.; Kang H.-R.; Lee S.M.; Kim H.; Rhee C.; Goo J.M.; Oh Y.-M.; Lee C.-H.; Park C.M.","Nam, Ju Gang (57201464949); Kang, Hye-Rin (57193886298); Lee, Sang Min (57204539234); Kim, Hyungjin (57207824165); Rhee, Chanyoung (57917453800); Goo, Jin Mo (7006253916); Oh, Yeon-Mok (7402125922); Lee, Chang-Hoon (56150043400); Park, Chang Min (16234023200)","57201464949; 57193886298; 57204539234; 57207824165; 57917453800; 7006253916; 7402125922; 56150043400; 16234023200","Deep Learning Prediction of Survival in Patients with Chronic Obstructive Pulmonary Disease Using Chest Radiographs","2022","Radiology","305","1","","199","208","9","21","10.1148/radiol.212071","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138615149&doi=10.1148%2fradiol.212071&partnerID=40&md5=c25587b71655af72674da79c49548ef4","The Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea; Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Division of Pulmonary Medicine, Department of Internal Medicine, Veteran Health Service Medical Center, Seoul, South Korea; Department of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Research Institute of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea; Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, South Korea","Nam J.G., The Department of Radiology, Seoul National University Hospital, Seoul, South Korea, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Kang H.-R., The Department of Radiology, Seoul National University Hospital, Seoul, South Korea, Division of Pulmonary Medicine, Department of Internal Medicine, Veteran Health Service Medical Center, Seoul, South Korea; Lee S.M., Department of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea, Research Institute of Radiology, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Kim H., The Department of Radiology, Seoul National University Hospital, Seoul, South Korea, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Rhee C., Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Goo J.M., The Department of Radiology, Seoul National University Hospital, Seoul, South Korea, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea; Oh Y.-M., Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea, Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan, College of Medicine, Seoul, South Korea; Lee C.-H., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Park C.M., The Department of Radiology, Seoul National University Hospital, Seoul, South Korea, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea, Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, South Korea","Background: Preexisting indexes for predicting the prognosis of chronic obstructive pulmonary disease (COPD) do not use radiologic information and are impractical because they involve complex history assessments or exercise tests. Purpose: To develop and to validate a deep learning–based survival prediction model in patients with COPD (DLSP) using chest radiographs, in addition to other clinical factors. Materials and Methods: In this retrospective study, data from patients with COPD who underwent postbronchodilator spirometry and chest radiography from 2011–2015 were collected and split into training (n = 3475), validation (n = 435), and internal test (n = 315) data sets. The algorithm for predicting survival from chest radiographs was trained (hereafter, DLSPCXR), and then age, body mass index, and forced expiratory volume in 1 second (FEV1) were integrated within the model (hereafter, DLSPinteg). For external test, three independent cohorts were collected (n = 394, 416, and 337). The discrimination performance of DLSPCXR was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) at 5-year survival. Goodness of fit was assessed by using the Hosmer-Lemeshow test. Using one external test data set, DLSPinteg was compared with four COPD-specific clinical indexes: BODE, ADO, COPD Assessment Test (CAT), and St George’s Respiratory Questionnaire (SGRQ). Results: DLSPCXR had a higher performance at predicting 5-year survival than FEV1 in two of the three external test cohorts (TD AUC: 0.73 vs 0.63 [P = .004]; 0.67 vs 0.60 [P = .01]; 0.76 vs 0.77 [P = .91]). DLSPCXR demonstrated good calibration in all cohorts. The DLSPinteg model showed no differences in TD AUC compared with BODE (0.87 vs 0.80; P = .34), ADO (0.86 vs 0.89; P = .51), and SGRQ (0.86 vs 0.70; P = .09), and showed higher TD AUC than CAT (0.93 vs 0.55; P , .001). Conclusion: A deep learning model using chest radiographs was capable of predicting survival in patients with chronic obstructive pulmonary disease. © RSNA, 2022.","","Deep Learning; Forced Expiratory Volume; Humans; Pulmonary Disease, Chronic Obstructive; Radiography; Respiratory Function Tests; Retrospective Studies; bronchodilating agent; adult; age; aged; Article; body mass; calibration; chronic obstructive lung disease; controlled study; COPD assessment test; deep learning; diagnostic test accuracy study; female; follow up; forced expiratory volume; forced vital capacity; human; major clinical study; male; prediction; receiver operating characteristic; retrospective study; sensitivity and specificity; spirometry; St. George Respiratory Questionnaire; survival; thorax radiography; chronic obstructive lung disease; diagnostic imaging; lung function test; radiography","","","","","VHS Medical Research Center; GlaxoSmithKline, GSK; Ministry of Science, ICT and Future Planning, MSIP, (NRF-2018R1A5A1060031); Ministry of Science, ICT and Future Planning, MSIP; National Research Foundation of Korea, NRF","Funding text 1: From the Department of Radiology (J.G.N., H.K., J.M.G., C.M.P.) and Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L.), Seoul National University Hospital, Seoul, Republic of Korea; Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., H.K., C.R., J.M.G., C.H.L., C.M.P.); Division of Pulmonary Medicine, Department of Internal Medicine, Veteran Health Service Medical Center, Seoul, Republic of Korea (H.R.K.); Department of Radiology (S.M.L.), Research Institute of Radiology (S.M.L.), Department of Pulmonary and Critical Care Medicine (Y.M.O.), and Clinical Research Center for Chronic Obstructive Airway Diseases (Y.M.O.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Institute of Radiation Medicine (J.M.G., C.M.P.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea. Received August 16, 2021; revision requested October 26; final revision received March 3, 2022; accepted March 29. Address correspondence to C.M.P. (email: cmpark.morphius@gmail.com). Supported by a National Research Foundation of Korea grant, funded by the Ministry of Science and ICT (NRF-2018R1A5A1060031). * J.G.N. and H.R.K. contributed equally to this work. ** C.H.L. and C.M.P. are co-senior authors. Conflicts of interest are listed at the end of this article.; Funding text 2: Disclosures of conflicts of interest: J.G.N. Research grants from Vuno. H.R.K. Research grants from VHS Medical Research Center. S.M.L. No relevant relationships. H.K. Research grant from Lunit; holds stock in Medical IP. C.R. No relevant relationships. J.M.G. Research grants from INFINITT Healthcare, Dong-kook Lifescience, and LG Electronics; member of the Radiology editorial board. Y.M.O. No relevant relationships. C.H.L. Research funding from GSK. C.M.P. 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Park; The Department of Radiology, Seoul National University Hospital, Seoul, South Korea; email: cmpark.morphius@gmail.com","","Radiological Society of North America Inc.","","","","","","00338419","","RADLA","35670713","English","Radiology","Article","Final","","Scopus","2-s2.0-85138615149"
"Alam M.S.; Sultana A.; Sun H.; Wu J.; Guo F.; Li Q.; Ren H.; Hao Z.; Zhang Y.; Wang G.","Alam, Md Shahin (57675671800); Sultana, Adiba (57219903630); Sun, Hongyang (57201480548); Wu, Jin (57222735208); Guo, Fanfan (57932313000); Li, Qing (57217854344); Ren, Haigang (7202793945); Hao, Zongbing (57072455300); Zhang, Yi (59071181200); Wang, Guanghui (56381739500)","57675671800; 57219903630; 57201480548; 57222735208; 57932313000; 57217854344; 7202793945; 57072455300; 59071181200; 56381739500","Bioinformatics and network-based screening and discovery of potential molecular targets and small molecular drugs for breast cancer","2022","Frontiers in Pharmacology","13","","942126","","","","17","10.3389/fphar.2022.942126","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140086757&doi=10.3389%2ffphar.2022.942126&partnerID=40&md5=590673083100af162995ad502c915833","Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Department of Pharmacology, College of Pharmaceutical Science, Soochow University, Jiangsu, Suzhou, China; Department of Gastroenterology, the First People’s Hospital of Taicang, Taicang Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China","Alam M.S., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Sultana A., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Sun H., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Wu J., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Guo F., Department of Pharmacology, College of Pharmaceutical Science, Soochow University, Jiangsu, Suzhou, China; Li Q., Department of Gastroenterology, the First People’s Hospital of Taicang, Taicang Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China; Ren H., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Hao Z., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China; Zhang Y., Department of Pharmacology, College of Pharmaceutical Science, Soochow University, Jiangsu, Suzhou, China; Wang G., Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Jiangsu, Suzhou, China","Accurate identification of molecular targets of disease plays an important role in diagnosis, prognosis, and therapies. Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study was to accurately identify a set of molecular targets and small molecular drugs that might be effective for BC diagnosis, prognosis, and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages, LIMMA and clusterProfiler, in R were used to identify overlapping differential expressed genes (oDEGs) and significant GO and KEGG enrichment terms. We constructed a PPI (protein–protein interaction) network through the STRING database and identified eight key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using six topological measures, betweenness, closeness, eccentricity, degree, MCC, and MNC, in the Analyze Network tool in Cytoscape. Three online databases GSCALite, Network Analyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks, and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm support vector machine (SVM). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and posttranscriptional regulators of KGs. Finally, we proposed 16 candidate repurposing drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, dasatinib, fedratinib, dabrafenib, methotrexate, trametinib, tubastatin A, BIX02189, CP466722, afatinib, and belinostat for BC through molecular docking analysis. Using BC cell lines, we validated that masitinib inhibits the mTOR signaling pathway and induces apoptotic cell death. Therefore, the proposed results might play an effective role in the treatment of BC patients. Copyright © 2022 Alam, Sultana, Sun, Wu, Guo, Li, Ren, Hao, Zhang and Wang.","apoptotic cell death; bioinformatics and network-based discovery; Breast cancer; drug repurposing; gene expression profiles; molecular docking analysis; molecular targets","2 (2 difluoromethylbenzimidazol 1 yl) 4,6 dimorpholino 1,3,5 triazine; 2 [4 (2 dimethylaminoethoxy)phenyl] 4 (1 hydroxyimino 5 indanyl) 5 (4 pyridinyl) 1h imidazole; afatinib; antineoplastic agent; baculoviral IAP repeat containing protein 5; belinostat; bix 02189; cp 466722; cyclin dependent kinase 1; dabrafenib; dasatinib; epidermal growth factor receptor; fedratinib; gsk 1070916; lapatinib; mammalian target of rapamycin; masitinib; methotrexate; microRNA; microRNA 1 3p; microRNA 16 5p; microRNA 23b 3p; microRNA 34a 5p; Myc protein; omipalisib; protein KDM5B; protein TP63; trametinib; transcription factor; transcription factor EZH2; transcription factor Sox2; tubastatin A; unclassified drug; ym 201636; Alzheimer disease; apoptosis; Article; asthma; AURKA gene; bioinformatics; BIRC5 gene; breast cancer; breast cancer cell line; cancer chemotherapy; cancer prognosis; CDK1 gene; chronic myeloid leukemia; controlled study; coronavirus disease 2019; differential expression analysis; drug design; drug efficacy; drug repositioning; drug screening; EGFR gene; enzyme inhibition; EZH2 gene; FN1 gene; gastrointestinal cancer; gene ontology; genetic analysis; genetic transcription; human; human cell; human tissue; KEGG; kidney cancer; liver cancer; liver cell carcinoma; liver metastasis; mastocytosis; melanoma; MET gene; metastatic melanoma; molecular docking; molecularly targeted therapy; mTOR signaling; multiple myeloma; myelofibrosis; myeloid metaplasia; myeloproliferative neoplasm; non small cell lung cancer; oncogene; pancreas cancer; peripheral T cell lymphoma; polycythemia vera; protein protein interaction; rheumatoid arthritis; solid malignant neoplasm; support vector machine; TOP2A gene","","2 [4 (2 dimethylaminoethoxy)phenyl] 4 (1 hydroxyimino 5 indanyl) 5 (4 pyridinyl) 1h imidazole, 405554-55-4; afatinib, 439081-18-2, 850140-72-6, 850140-73-7; baculoviral IAP repeat containing protein 5, 195263-98-0; belinostat, 414864-00-9, 866323-14-0; dabrafenib, 1195765-45-7, 1195768-06-9; dasatinib, 302962-49-8, 863127-77-9; epidermal growth factor receptor, 79079-06-4; fedratinib, 936091-26-8, 1374744-69-0; lapatinib, 231277-92-2, 388082-78-8, 437755-78-7; masitinib, 790299-79-5; methotrexate, 15475-56-6, 59-05-2, 7413-34-5, 7532-09-4, 6745-93-3, 51865-79-3, 60388-53-6; omipalisib, 1086062-66-9; trametinib, 1187431-43-1, 871700-17-3","bix 02189; cp 466722; gsk 1070916; gsk 2126458; sb 590885; tg 101348; ym 201636; zstk 474","","Key Project of Natural Science Foundation of Jiangsu Provincial Higher Education Institutions, (21KJA180003); National Natural Science Foundation of China, NSFC, (32000676, 32070970, 81973352); National Natural Science Foundation of China, NSFC; China Postdoctoral Science Foundation, (249658); China Postdoctoral Science Foundation; Tangshan Science and Technology Bureau, (TC2018JCYL20); Tangshan Science and Technology Bureau; Priority Academic Program Development of Jiangsu Higher Education Institutions, PAPD","This work was supported by the National Natural Science Foundation of China (No. 32070970, 81973352, and 32000676), the Taicang Science and Technology Bureau (TC2018JCYL20), the Key Project of Natural Science Foundation of Jiangsu Provincial Higher Education Institutions (No. 21KJA180003), the China Postdoctoral Science Foundation (249658), and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions. 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Zhang; Department of Pharmacology, College of Pharmaceutical Science, Soochow University, Suzhou, Jiangsu, China; email: zhangyi@suda.edu.cn","","Frontiers Media S.A.","","","","","","16639812","","","","English","Front. Pharmacol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140086757"
"Yu G.; Tabatabaei M.; Mezei J.; Zhong Q.; Chen S.; Li Z.; Li J.; Shu L.; Shu Q.","Yu, Gang (57212830901); Tabatabaei, Mohammad (57580722900); Mezei, József (31367547700); Zhong, Qianhui (59589730700); Chen, Siyu (59616407500); Li, Zheming (57218365043); Li, Jing (57226369583); Shu, LiQi (57204555568); Shu, Qiang (57210698907)","57212830901; 57580722900; 31367547700; 59589730700; 59616407500; 57218365043; 57226369583; 57204555568; 57210698907","Improving chronic disease management for children with knowledge graphs and artificial intelligence","2022","Expert Systems with Applications","201","","117026","","","","15","10.1016/j.eswa.2022.117026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128476469&doi=10.1016%2fj.eswa.2022.117026&partnerID=40&md5=af8fc4254158b68a59405772d2274a73","Department of IT Center, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, China; National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310052, China; Department of Neurology, The Warren Alpert Medical School of Brown University, 593 Eddy St, Providence, 02903, RI, United States; Avaintec Oy, Itämerenkatu 1, Helsinki, 00180, Finland; Å bo Akademi University, Tuomiokirkontori 3, Turku, 20500, Finland; Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China","Yu G., Department of IT Center, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, China, National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310052, China, Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China; Tabatabaei M., Avaintec Oy, Itämerenkatu 1, Helsinki, 00180, Finland; Mezei J., Avaintec Oy, Itämerenkatu 1, Helsinki, 00180, Finland, Å bo Akademi University, Tuomiokirkontori 3, Turku, 20500, Finland, Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China; Zhong Q., Avaintec Oy, Itämerenkatu 1, Helsinki, 00180, Finland, Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China; Chen S., Avaintec Oy, Itämerenkatu 1, Helsinki, 00180, Finland; Li Z., Department of IT Center, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, China, National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310052, China, Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China; Li J., Department of IT Center, The Children's Hospital, Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou, 310052, China, National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310052, China, Sino-Finland Joint AI Laboratory, 3333 Binsheng Road, Hangzhou, 310052, China; Shu L., Department of Neurology, The Warren Alpert Medical School of Brown University, 593 Eddy St, Providence, 02903, RI, United States; Shu Q., National Clinical Research Center for Child Health, 3333 Binsheng Road, Hangzhou, 310052, China","Chronic diseases for children pose serious challenges from a health management perspective. When not implemented in a well-designed manner, an inefficient management platform can have a significant negative impact on patients and the utilization of health care resources. Innovations of recent years in information technology, artificial intelligence and machine learning provide possibilities to design and implement knowledge-based systems and platforms that follow-up, monitor and advise child patients with a chronic disease in an automated manner. In this article we propose the Artificial Intelligence Chronic Management System that combines artificial intelligence, knowledge graph, big data and internet of things in a platform to offer an optimized solution from the perspective of treatment and utilization of resources. The system includes patient and hospital clients, data storage and analytic tools for decision support relying on AI-based services. We illustrate the functionality of the system through different situations frequently occurring in pediatric wards. To assess the feasibility of the AI component, we utilize real life health care data from a hospital in China to develop a classification model for patients with asthma. To provide a more qualitative assessment at the same time, we discuss how the Artificial Intelligence Chronic Management System conforms to the requirements set forth by the standard Chronic Care Model. © 2022 The Authors","Artificial intelligence; Big data; Chronic disease management; Health care application; Machine learning","Big data; Decision support systems; Digital storage; Diseases; Health care; Hospitals; Information management; Knowledge graph; Chronic disease; Chronic disease management; Design and implements; Health care application; Health management; Healthcare resources; Knowledge graphs; Knowledge-based systems; Management platforms; Management systems; Machine learning","","","","","National Natural Science Foundation of China, NSFC, (62076218); National Natural Science Foundation of China, NSFC; Zhejiang Province Public Welfare Technology Application Research Project, (LGF18H260004); Zhejiang Province Public Welfare Technology Application Research Project; National Key Research and Development Program of China, NKRDPC, (2019YFE0126200); National Key Research and Development Program of China, NKRDPC","The authors are grateful to the editor and referees for their valuable comments and suggestions for improving the paper. 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Mezei; Turku, Tuomiokirkontori 3, 20500, Finland; email: jmezei@abo.fi; Q. Shu; Hangzhou, No. 3333 Binsheng Road, Zhejiang, 310052, China; email: shuqiang@zju.edu.cn","","Elsevier Ltd","","","","","","09574174","","ESAPE","","English","Expert Sys Appl","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85128476469"
"Roy A.; Satija U.","Roy, Arka (58073475200); Satija, Udit (55253538100)","58073475200; 55253538100","RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds","2023","IEEE Transactions on Instrumentation and Measurement","72","","4008813","","","","23","10.1109/TIM.2023.3292953","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164409428&doi=10.1109%2fTIM.2023.3292953&partnerID=40&md5=2cdd3d29e759fdfbacce5d97e173a993","Indian Institute of Technology Patna, Department of Electrical Engineering, Bihar, Patna, 801106, India","Roy A., Indian Institute of Technology Patna, Department of Electrical Engineering, Bihar, Patna, 801106, India; Satija U., Indian Institute of Technology Patna, Department of Electrical Engineering, Bihar, Patna, 801106, India","Respiratory diseases are the world's third leading cause of mortality. Early detection is critical in dealing with respiratory diseases, as it improves the effectiveness of intervention, including treatment and reducing the spread. The main aim of this article is to propose a novel lightweight inception network to classify a wide spectrum of respiratory diseases using lung sound signals. The proposed framework consists of three stages: 1) preprocessing; 2) mel spectrogram extraction and conversion into a three-channel image; and 3) classification of the mel spectrogram images into different pathological classes using the proposed lightweight inception network, namely, respiratory disease lightweight inception network (RDLINet). Utilizing the proposed architecture, we have achieved a high classification accuracy of 96.6%, 99.6%, and 94.0% for seven-class classification, six-class classification, and healthy versus asthma classification. To the best of our knowledge, this is the first work on seven-class respiratory disease classification using lung sounds. Whereas, our proposed network outperforms all the existing published works for six-class and binary classifications. The suggested framework makes use of deep-learning methods and offers a standardized evaluation with strong categorization capabilities. In order to distinguish between a wide range of respiratory diseases, our study is a pioneering one that focuses exclusively on lung sounds. The proposed framework can be translated into real-time clinical application, which will facilitate the prospect of automated respiratory health screening using lung sounds.  © 1963-2012 IEEE.","Lightweight inception network; lung auscultation; lung sounds; mel spectrogram; respiratory disease classification","Biological organs; Deep learning; Diagnosis; Spectrographs; Chronic obstructive pulmonary disease; Deep learning; Disease classification; Lightweight inception network; Lung; Lung auscultation; Lung sounds; Mel spectrogram; Recording; Respiratory disease classification; Spectrograms; Pulmonary diseases","","","","","Ministry of Education, India, MoE, (2702854)","This work was supported by the Ministry of Education (MoE), Government of India, through the Prime Minister Research Fellowship (PMRF) Program under Grant 2702854.","Pham L., Phan H., Palaniappan R., Mertins A., McLoughlin I., CNN-MoE based framework for classification of respiratory anomalies and lung disease detection, Ieee J. 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(BIBM), pp. 2109-2113, (2018); Garcia-Ordas M.T., Benitez-Andrades J.A., Garcia-Rodriguez I., Benavides C., Alaiz-Moreton H., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Saini M., Satija U., Upadhayay M.D., DSCNN-CAU: Deeplearning-based mental activity classification for IoT implementation toward portable BCI, Ieee Internet Things J., 10, 10, pp. 8944-8957, (2023); Sivapalan G., Nundy K.K., Dev S., Cardiff B., John D., ANNet: A lightweight neural network for ECG anomaly detection in IoT edge sensors, Ieee Trans. Biomed. Circuits Syst., 16, 1, pp. 24-35, (2022); Altan G., Kutlu Y., Pekmezci A.O., Nural S., The Diagnosis of Asthma Using Hilbert-Huang Transform and Deep Learning on Lung Sounds, (2021); Tripathy R.K., Dash S., Rath A., Panda G., Pachori R.B., Automated detection of pulmonary diseases from lung sound signals using fixed-boundary-based empirical wavelet transform, Ieee Sensors Lett., 6, 5, pp. 1-4, (2022); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data Brief, 35, (2021); Satija U., Ramkumar B., Manikandan M.S., Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring, Ieee Internet Things J., 4, 3, pp. 815-823, (2017); Driedger J., Muller M., A review of time-scale modification of music signals, Appl. Sci., 6, 2, (2016); Salamon J., Bello J.P., Deep convolutional neural networks and data augmentation for environmental sound classification, Ieee Signal Process. 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(CVPR), pp. 1-9, (2015); Howard A.G., Et al., MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, (2017); Bazarevsky V., Kartynnik Y., Vakunov A., Raveendran K., Grundmann M., BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs, (2019); Chakraborty M., Dhavale S.V., Ingole J., Corona-nidaan: Lightweight deep convolutional neural network for chest X-ray based COVID-19 infection detection, Int. J. Speech Technol., 51, 5, pp. 3026-3043, (2021); Lin M., Chen Q., Yan S., Network in Network, (2013); Shazeer N., Glu Variants Improve Transformer, (2020); Saini M., Satija U., Upadhayay M.D., One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram, Biomed. Signal Process. Control, 74, (2022); Prabhakararao E., Dandapat S., Multi-scale convolutional neural network ensemble for multi-class arrhythmia classification, Ieee J. Biomed. Health Informat., 26, 8, pp. 3802-3812, (2022); Shankar A., Dandapat S., Barma S., Seizure types classification by generating input images with in-depth features from decomposed EEG signals for deep learning pipeline, Ieee J. Biomed. Health Informat., 26, 10, pp. 4903-4912, (2022); Kautz T., Eskofier B.M., Pasluosta C.F., Generic performance measure for multiclass-classifiers, Pattern Recognit., 68, pp. 111-125, (2017); Maaten Der L.Van, Hinton G., Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 11, pp. 2579-2605, (2008); Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-CAM: Visual explanations from deep networks via gradient-based localization, Proc. Ieee Int. Conf. Comput. Vis. (ICCV), pp. 618-626, (2017); Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-scale Image Recognition, (2014); Ma N., Zhang X., Zheng H.-T., Sun J., ShuffleNet v2: Practical guidelines for efficient CNN architecture design, Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 116-131, (2018); Huzaifah M., Comparison of Time-frequency Representations for Environmental Sound Classification Using Convolutional Neural Networks, (2017); Chen J., Sun H., Xu B., Improvement of empirical mode decomposition based on correlation analysis, Social Netw. Appl. Sci., 1, 9, pp. 1-16, (2019)","U. Satija; Indian Institute of Technology Patna, Department of Electrical Engineering, Patna, Bihar, 801106, India; email: udit@iitp.ac.in","","Institute of Electrical and Electronics Engineers Inc.","","","","","","00189456","","IEIMA","","English","IEEE Trans. Instrum. Meas.","Article","Final","","Scopus","2-s2.0-85164409428"
"Avian C.; Mahali M.I.; Putro N.A.S.; Prakosa S.W.; Leu J.-S.","Avian, Cries (57216978643); Mahali, Muhammad Izzuddin (57205281644); Putro, Nur Achmad Sulistyo (57194874971); Prakosa, Setya Widyawan (57193220552); Leu, Jenq-Shiou (8976241800)","57216978643; 57205281644; 57194874971; 57193220552; 8976241800","Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals","2022","Computers in Biology and Medicine","148","","105913","","","","18","10.1016/j.compbiomed.2022.105913","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135857067&doi=10.1016%2fj.compbiomed.2022.105913&partnerID=40&md5=9de47e92375365e1fa99512d56104a1a","Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Department of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia; Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia","Avian C., Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Mahali M.I., Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan, Department of Electronics and Informatics Engineering, Faculty of Engineering, Universitas Negeri Yogyakarta, Indonesia; Putro N.A.S., Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan, Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia; Prakosa S.W., Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; Leu J.-S., Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan","As one of the most reliable and significant indicators, Chronic Obstructive Pulmonary Disease (COPD) becomes a robust predictor of lung cancer early detection, the world's leading cause of cancer death. One of the methods is to analyze the Volatile Organic Compounds (VOCs) in exhaled breath using electronic noses (E-noses), which have become emerging tools for analyzing breath because of their potential and promising technology for diagnosing. However, the signal processing of the E-Nose sensor becomes vital in exposing information about the subject condition, which most researchers strive to accomplish. We proposed a Convolutional Neural Network (CNN) architecture to classify COPD in smokers and non-smokers, healthy subjects, and smokers from E-Nose signals to contribute to this field. Two models were constructed following E-Nose signal processing state-of-the-arts. One was by combined feature extraction and classifier, and the second was by CNN, which directly processed the raw signal. In addition, various feature extraction and classifier (Machine Learning and CNN) used in prior research were investigated. Using 3K and 5K Fold cross-validation results demonstrated that our proposed models outperformed in Kernel Principal Component Analysis (KPCA) with Fx-ConvNet and Pure-ConvNet. They all reached maximum F1-Score with zero standard deviation values indicating a consistent result. Further experiments also showed that KPCA contributed to the increasing performance of some classifiers with average F1-Score 0.933 and 0.068 as standard deviation values. © 2022 Elsevier Ltd","Chronic obstructive pulmonary disease (COPD); Convolutional neural network; Electronic nose; Kernel principal component analysis; Lung cancer","Breath Tests; Electronic Nose; Exhalation; Healthy Volunteers; Humans; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Biological organs; Classification (of information); Convolution; Convolutional neural networks; Diagnosis; Electronic nose; Extraction; Network architecture; Principal component analysis; Pulmonary diseases; Signal processing; Statistics; Volatile organic compounds; Chronic obstructive pulmonary disease; Convolutional neural network; Feature classifiers; Features extraction; Healthy subjects; Kernel principal component analyses (KPCA); Lung Cancer; Neural network architecture; Signal-processing; aged; Article; Bayesian learning; chronic obstructive lung disease; classifier; clinical article; controlled study; convolutional neural network; decision tree; discriminant analysis; discrimination learning; feature extraction; female; human; k fold cross validation; k nearest neighbor; male; non-smoker; principal component analysis; random forest; signal processing; smoking; support vector machine; breath analysis; electronic nose; exhalation; normal human; Feature extraction","","","Xeon, Intel","Intel","Artificial Neural Network, (5,38,45); CNN, (5,59, 60,61); Cat Boost; Kernel PCA; Kernelized PCA; Naïve Bayes, (ME-C6H6, NAP-55A); Random Forest; Pancreatic Cancer Action, PCA","An Electronic Nose (E-Nose) is a device that mimics the functionality of biological noses [34]. This device can measure Volatile Organic Compounds (VOC) and determine the type and density of odors constructed from several gas sensor arrays, signal processing, and control circuits. The chosen gas sensor depends on the application of this device and is primarily different in the types of sensors [32, 40–42], especially in COPD research. For instance, the work developed by Ren et al. [43] used Aenoses, Tirzïte et al. [37] used Cyranose 320 sensor, Tan et al. [36] used chemiresistor-based alkane sensor, and Shlomi et al. [44] used Nanoarray sensors based E-Nose. Besides, the other researcher was eager to build their E-Nose system by utilizing the commercial sensor to reach a low-cost screening system [5,34], such as the MOS gas sensor. Aside from the previous matter, further motivation is to develop the signal processing method, which has become a critical point to establish in any E-Nose devices. Machine Learning (ML) became the common method used in processing this signal as it is easy to use from a prior research perspective which focuses on discriminating between COPD, lung cancer, healthy subject, and smokers. The various development of signal processing research, such as work conducted by Binson et al. [38], used five gas sensor arrays consisting of the TGS sensor family. Principal Component Analysis (PCA) and various methods of Supervised Machine Learning were deployed. The other Machine Learning (ML) used such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), also used as a method for comparison. The evaluation was done by testing the PCA and ML combination model using 3, 5, and 10 K-Fold validation. The result showed that KNN reached the maximum accuracy of 91.3% compared to the other method. Chang et al. [39] also worked in the same case and used a different method to apply seven gas sensor arrays from the TGS sensor family. Their research used PCA and Multilayer Perceptron (MLP) and achieved an accuracy of 75%. Binson et al. [5], in their other study, continued to conduct their previous work with used other gas sensors such as MR 516, ME-C6H6, NAP-55A, and the previous TGS sensor in the same case to predict COPD using Electronic Nose. SVM and Extreme Gradient Boosting (XGBoost) were used to process eight sensor gas arrays with various feature extraction explored, such as LDA, Independent Component Analysis (ICA), PCA, and Kernelized PCA (KPCA). The accuracy of this method reached 91.74% using KPCA and XGBoost. Hendrick et al. [45] developed nine sensor gas arrays and used signal preprocessing Standard Scaler, feature extraction, PCA, and various ML such as SVM, Artificial Neural Network (ANN), XGBoost, and Random Forest (RF). The researcher reached the best accuracy by using ANN with 94.87%. The latest study led by Chen et al. [34] developed a model that discriminates lung cancer with its stage level and healthy subjects. KPCA and XGboost achieved 96% for COPD vs. lung cancer and 93.59% for lung cancer vs. healthy subjects. Based on the initial literature review, numerous Machine Learning approaches have been applied to classify E-Nose signals. It has attracted much attention due to the relatively fast training time and the ease of applying the algorithm. Unfortunately, the effectiveness of a machine-learning algorithm is strongly dependent on the integrity of the input-data representation [46]. An appropriate data representation gives better performance than a poor data representation. Thus, feature engineering has been a prominent study trend in machine learning for many years to seek the best method such as PCA, KPCA, and many more features in different cases to extract the information. Furthermore, in prior research, the main problem is not only lie in the feature extraction method but also in ML itself. Therefore, feature extraction and ML algorithm are commonly combined to generalize the problem and find the best combination [5,38,45]. Not all feature engineering promises a good result since each ML has different approaches and leads to different results.The first is by utilizing distinguish feature extraction, then processed by the classifier. The second is by direct process the raw signal. The feature extractions used for the first scheme are Principal Component Analysis (PCA), Kernel PCA (KPCA), and Mean Absolute Value (MAV) combined with Variance (VAR). Then for the classifier, apart from using the proposed and prior developed CNN model, we also used Machine Learning (ML) as a comparison. The type of ML used type is K-Nearest Neighbour (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Cat Boost (CB), Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM). Comparing the two approaches will demonstrate that the constructed CNN performs better than the ML method on extracted featured data (partial crafted feature, first scheme) and raw data (full crafted feature, second scheme). Furthermore, we also adapted and analyzed the previously utilized CNN architecture from different cases to compare the designed CNN performance in this case. All models will validate by using 3K and 5K Fold cross-validation to check the consistency of classification from the subject condition in the open dataset for COPD detection. There are two main contributions from this work. First, propose a CNN architecture for COPD detection from smokers and healthy subjects to achieve high and consistent accuracy by utilizing KFold cross-validation. Second, investigating the potential combination of feature extraction, Machine Learning, and CNN architecture for E-Nose on the specific case from the COPD dataset. In addition, time training and inference training were also provided to identify the time aspect and potential drawbacks in implementation. The rest of the paper consists of the following sections: experimental setups described in section 2, which report detailed datasets and different methods used; data analysis, evaluation scheme, and interpretation presented in section 3; results discussed in section 4; and finally, conclusions in section 5.The first type of model classification consisted of four processing blocks: sensor, preprocessing signal, feature extraction, and classification. The first one is the signal generated by gas sensors, producing a signal with 4000 samples x 8 channels (number of the gas sensor). That signal is then processed using signal preprocessing, which is a standard scaler. The following process extracts the specific signal into meaningful information to avoid overfitting and burdening calculation. Therefore, this block reduces the signal size to 1 sample x 8 channels for one subject. There are three kinds of feature extraction used. The types of feature extraction are PCA, KPCA with Radial Basis Function as kernel, and a combination of MAV and VAR. After the signal is extracted, the classification process runs with two classifier types. The first type is Supervised Machine Learning, such as KNN (K-Nearest Neighbour), DT (Decision Tree), LDA (Linear Discriminant Analysis), NB (Naïve Bayes), RF (Random Forest), SVM (Support Vector Machine), CB (Cat Boost), GB (Gradient Boosting), LGBM (Light Gradient Boosting Machine). Then the second classifier is Convolutional Neural Network (CNN), which utilizes feature extraction for the input. For illustrating the first model of the classifier block, is depicted in Fig. 4.This study deployed different types of Supervised Machine Learning algorithms. The various Machine Learning has been chosen from existing work [5,59] and the related trend algorithm in Machine Learning [60,61], achieving robust results. Those Machine Learning used are K-Nearest Neighbour (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Cat Boost (CB), Gradient Boosting (GB), Light Gradient Boosting Machine (LGBM), XGBoost (eXtreme Gradient Boosting). The machine is first trained with a labeled dataset to test the above algorithm. After that, the models are loaded and tested to predict a non-labeled data test to recognize which classes of the input signal. 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Big Data., 6, (2019)","J.-S. Leu; Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taiwan; email: jsleu@mail.ntust.edu.tw","","Elsevier Ltd","","","","","","00104825","","CBMDA","35940164","English","Comput. Biol. Med.","Article","Final","","Scopus","2-s2.0-85135857067"
"Niu Q.; Li H.; Tong L.; Liu S.; Zong W.; Zhang S.; Tian S.; Wang J.; Liu J.; Li B.; Wang Z.; Zhang H.","Niu, Qikai (57901494300); Li, Hongtao (57221832155); Tong, Lin (55207192700); Liu, Sihong (57220027308); Zong, Wenjing (58717210900); Zhang, Siqi (58066563300); Tian, SiWei (58264564700); Wang, Jingai (58264385000); Liu, Jun (55841036000); Li, Bing (57054235600); Wang, Zhong (55935246900); Zhang, Huamin (58938941300)","57901494300; 57221832155; 55207192700; 57220027308; 58717210900; 58066563300; 58264564700; 58264385000; 55841036000; 57054235600; 55935246900; 58938941300","TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms","2023","Briefings in Bioinformatics","24","3","bbad102","","","","18","10.1093/bib/bbad102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159757140&doi=10.1093%2fbib%2fbbad102&partnerID=40&md5=d32841c5acba7d9095e9a0a5c8de3b23","The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; The Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China; The Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China","Niu Q., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Li H., The Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China; Tong L., The Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China; Liu S., The Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China; Zong W., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Zhang S., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Tian S., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Wang J., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Liu J., The Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China; Li B., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; Wang Z., The Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China; Zhang H., The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China","Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development. © The Author(s) 2023. Published by Oxford University Press. All rights reserved.","genetic algorithm; herb combination; herbal formula; network pharmacology; traditional Chinese medicine (TCM)","Artificial Intelligence; Asthma; Drugs, Chinese Herbal; Humans; Medicine, Chinese Traditional; Supervised Machine Learning; herbaceous agent; artificial intelligence; asthma; Chinese medicine; human; procedures; supervised machine learning","","Drugs, Chinese Herbal, ","","","Fundamental Research Funds for the Central public Welfare Research Institutes, (ZXKT21024); Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences, (CI2021A05052, CI2021B015); National Natural Science Foundation of China, NSFC, (81803966, 81873199); National Natural Science Foundation of China, NSFC","National Natural Science Foundation of China (No. 81873199 to H.Z. and No. 81803966 to B.L.); Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences (Nos. CI2021A05052 and CI2021B015 to B.L.); Fundamental Research Funds for the Central public Welfare Research Institutes (ZXKT21024 to B.L.). 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Li; The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; email: bli@icmm.ac.cn; Z. Wang; The Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China; email: zhonw@vip.sina.com; H. Zhang; The Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China; email: hmzhang@icmm.ac.cn","","Oxford University Press","","","","","","14675463","","","36941113","English","Brief. Bioinform.","Article","Final","","Scopus","2-s2.0-85159757140"
"Ooka T.; Raita Y.; Fujiogi M.; Freishtat R.J.; Gerszten R.E.; Mansbach J.M.; Zhu Z.; Camargo C., Jr.; Hasegawa K.","Ooka, Tadao (57212649509); Raita, Yoshihiko (57206848664); Fujiogi, Michimasa (55802188100); Freishtat, Robert J. (6506843276); Gerszten, Robert E. (6603803894); Mansbach, Jonathan M. (6506455627); Zhu, Zhaozhong (56142038700); Camargo, Carlos A. (34567876000); Hasegawa, Kohei (47061524200)","57212649509; 57206848664; 55802188100; 6506843276; 6603803894; 6506455627; 56142038700; 34567876000; 47061524200","Proteomics endotyping of infants with severe bronchiolitis and risk of childhood asthma","2022","Allergy: European Journal of Allergy and Clinical Immunology","77","11","","3350","3361","11","16","10.1111/all.15390","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131654011&doi=10.1111%2fall.15390&partnerID=40&md5=b22a256d786411d837328927a3b786c3","Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Health Science, University of Yamanashi, Chuo, Japan; Center for Genetic Medicine Research and Division of Emergency Medicine Children's National Hospital, Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, United States; Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States","Ooka T., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, Department of Health Science, University of Yamanashi, Chuo, Japan; Raita Y., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Fujiogi M., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Freishtat R.J., Center for Genetic Medicine Research and Division of Emergency Medicine Children's National Hospital, Department of Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, DC, United States; Gerszten R.E., Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States; Mansbach J.M., Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States; Zhu Z., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Camargo C., Jr., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Hasegawa K., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States","Background: Bronchiolitis is the leading cause of hospitalization in U.S. infants and a major risk factor for childhood asthma. Growing evidence supports clinical heterogeneity within bronchiolitis. We aimed to identify endotypes of infant bronchiolitis by integrating clinical, virus, and serum proteome data, and examine their relationships with asthma development. Methods: This is a multicenter prospective cohort study of infants hospitalized for physician-diagnosis of bronchiolitis. We identified bronchiolitis endotypes by applying unsupervised machine learning (clustering) approaches to integrated clinical, virus (respiratory syncytial virus [RSV], rhinovirus [RV]), and serum proteome data measured at hospitalization. We then examined their longitudinal association with the risk for developing asthma by age 6 years. Results: In 140 infants hospitalized with bronchiolitis, we identified three endotypes: (1) clinicalatopicvirusRVproteomeNFκB-dysregulated, (2) clinicalnon-atopicvirusRSV/RVproteomeTNF-dysregulated, and (3) clinicalclassicvirusRSVproteomeNFκB/TNF-regulated endotypes. Endotype 1 infants were characterized by high proportion of IgE sensitization and RV infection. These endotype 1 infants also had dysregulated NFκB pathways (FDR < 0.001) and significantly higher risks for developing asthma (53% vs. 22%; adjOR 4.04; 95% CI, 1.49–11.0; p = 0.006), compared with endotype 3 (clinically resembling “classic” bronchiolitis). Likewise, endotype 2 infants were characterized by low proportion of IgE sensitization and high proportion of RSV or RV infection. These endotype 2 infants had dysregulated tumor necrosis factor (TNF)-mediated signaling pathway (FDR <0.001) and significantly higher risks for developing asthma (44% vs. 22%; adjOR 2.71; 95% CI, 1.03–7.11, p = 0.04). Conclusion: In this multicenter cohort, integrated clustering of clinical, virus, and proteome data identified biologically distinct endotypes of bronchiolitis that have differential risks of asthma development. © 2022 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.","asthma; bronchiolitis; endotyping; infants; proteome","Asthma; Bronchiolitis; Child; Humans; Immunoglobulin E; Infant; Prospective Studies; Proteome; Proteomics; Respiratory Syncytial Virus Infections; Respiratory Syncytial Virus, Human; Rhinovirus; Risk Factors; Viruses; corticosteroid; immunoglobulin E; proteome; tumor necrosis factor; immunoglobulin E; proteome; Article; asthma; bronchiolitis; child hospitalization; cohort analysis; controlled study; female; human; Human respiratory syncytial virus; infant; major clinical study; male; nonhuman; prospective study; proteomics; Rhinovirus; sensitization; TNF signaling; unsupervised machine learning; asthma; child; clinical trial; complication; Human respiratory syncytial virus; multicenter study; proteomics; respiratory syncytial virus infection; risk factor; virus","","immunoglobulin E, 37341-29-0; Immunoglobulin E, ; Proteome, ","","","National Institutes of Health, NIH; National Institute of Allergy and Infectious Diseases, NIAID, (K01AI153558)","This study was supported by grants (UG3/UH3 OD‐023253, R01 AI‐127507, R01 AI‐134940, and R01 AI‐137091) from the National Institutes of Health (Bethesda, MD). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding organization was not involved in the collection, management, or analysis of the data; preparation or approval of the manuscript; or decision to submit the manuscript for publication ","Fujiogi M., Goto T., Yasunaga H., Et al., Trends in bronchiolitis hospitalizations in the United States: 2000–2016, Pediatrics, 144, 6, (2019); Hasegawa K., Piedra P.A., Bauer C.S., Et al., Nasopharyngeal CCL5 in infants with severe bronchiolitis and risk of recurrent wheezing: A multi-center prospective cohort study, Clin Exp Allergy, 48, 8, pp. 1063-1067, (2018); Dumas O., Hasegawa K., Mansbach J.M., Sullivan A.F., Piedra P.A., Camargo C.A., Severe bronchiolitis profiles and risk of recurrent wheeze by age 3 years, J Allergy Clin Immunol, 143, 4, pp. 1371-1379.e1377, (2019); Midulla F., Nicolai A., Ferrara M., Et al., Recurrent wheezing 36 months after bronchiolitis is associated with rhinovirus infections and blood eosinophilia, Acta Paediatr, 103, 10, pp. 1094-1099, (2014); Regnier S.A., Huels J., Association between respiratory syncytial virus hospitalizations in infants and respiratory sequelae: systematic review and meta-analysis, Pediatr Infect Dis J, 32, 8, pp. 820-826, (2013); 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Bowler R.P., Wendt C.H., Fessler M.B., Et al., New strategies and challenges in lung proteomics and metabolomics. An official American Thoracic Society workshop report, Ann Am Thorac Soc, 14, 12, pp. 1721-1743, (2017); Ooka T., Raita Y., Ngo D., Et al., Proteome signature difference between respiratory viruses is associated with severity of bronchiolitis, Pediatr Allergy Immunol, 32, 8, pp. 1869-1872, (2021); Schofield J.P.R., Burg D., Nicholas B., Et al., Stratification of asthma phenotypes by airway proteomic signatures, J Allergy Clin Immunol, 144, 1, pp. 70-82, (2019); Ejaz S., F-u-H N., Ashraf M., Ahmad S., Serum proteome profiling to identify proteins promoting pathogenesis of non-atopic asthma, Protein Pept Lett, 25, 10, pp. 933-942, (2018); Xu P., Wang L., Chen D., Et al., The application of proteomics in the diagnosis and treatment of bronchial asthma, Ann Transl Med, 8, 4, (2020); Rubner F.J., Jackson D.J., Evans M.D., Et al., Early life rhinovirus wheezing, allergic sensitization, and asthma risk at adolescence, J Allergy Clin Immunol, 139, 2, pp. 501-507, (2017); Jackson D.J., Gangnon R.E., Evans M.D., Et al., Wheezing rhinovirus illnesses in early life predict asthma development in high-risk children, Am J Respir Crit Care Med, 178, 7, pp. 667-672, (2008); Kusel M.M., Kebadze T., Johnston S.L., Holt P.G., Sly P.D., Febrile respiratory illnesses in infancy and atopy are risk factors for persistent asthma and wheeze, Eur Respir J, 39, 4, pp. 876-882, (2012); Kusel M.M., de Klerk N.H., Kebadze T., Et al., Early-life respiratory viral infections, atopic sensitization, and risk of subsequent development of persistent asthma, J Allergy Clin Immunol, 119, 5, pp. 1105-1110, (2007); Das J., Chen C.H., Yang L., Cohn L., Ray P., Ray A., A critical role for NF-kappa B in GATA3 expression and TH2 differentiation in allergic airway inflammation, Nat Immunol, 2, 1, pp. 45-50, (2001); Park S.J., Lee K.S., Lee S.J., Et al., l-2-oxothiazolidine-4-carboxylic acid or α-lipoic acid attenuates airway remodeling: involvement of nuclear factor-κB (NF-κB), nuclear factor rrythroid 2p45-related factor-2 (Nrf2), and hypoxia-inducible factor (HIF), Int J Mol Sci, 13, 7, pp. 7915-7937, (2012); Yuan F., Liu R., Hu M., Et al., JAX2, an ethanol extract of Hyssopus cuspidatus Boriss, can prevent bronchial asthma by inhibiting MAPK/NF-κB inflammatory signaling, Phytomedicine, 57, pp. 305-314, (2019); Athari S.S., Targeting cell signaling in allergic asthma, Signal Transduct Target Ther, 4, 1, (2019); Raita Y., Perez-Losada M., Freishtat R.J., Et al., Integrated omics endotyping of infants with respiratory syncytial virus bronchiolitis and risk of childhood asthma, Nat Commun, 12, 1, (2021); Cakebread J.A., Haitchi H.M., Xu Y., Holgate S.T., Roberts G., Davies D.E., Rhinovirus-16 induced release of IP-10 and IL-8 is augmented by Th2 cytokines in a pediatric bronchial epithelial cell model, PLoS One, 9, 4, (2014); Webster J.D., Vucic D., The balance of TNF mediated pathways regulates inflammatory cell death signaling in healthy and diseased tissues, Front Cell Dev Biol, 8, (2020); Bradley J., TNF-mediated inflammatory disease, J Pathol, 214, 2, pp. 149-160, (2008); Kikuchi S., Kikuchi I., Hagiwara K., Kanazawa M., Nagata M., Association of tumor necrosis factor-α and neutrophilic inflammation in severe asthma, Allergol Int, 54, 4, pp. 621-625, (2005); Silvestri M., Bontempelli M., Giacomelli M., Et al., High serum levels of tumour necrosis factor-α and interleukin-8 in severe asthma: markers of systemic inflammation?, Clin Exp Allergy, 36, 11, pp. 1373-1381, (2006); Kyriakopoulos C., Gogali A., Bartziokas K., Kostikas K., Identification and treatment of T2-low asthma in the era of biologics, ERJ Open Res, 7, 2, pp. 00309-02020, (2021); Davies E.R., Perotin J.-M., Kelly J.F.C., Et al., Involvement of the epidermal growth factor receptor in IL-13-mediated corticosteroid-resistant airway inflammation, Clin Exp Allergy, 50, 6, pp. 672-686, (2020); Ostling J., van Geest M., Schofield J.P.R., Et al., IL-17-high asthma with features of a psoriasis immunophenotype, J Allergy Clin Immunol, 144, 5, pp. 1198-1213, (2019)","T. Ooka; Department of Emergency Medicine, Massachusetts General Hospital, Boston, 125 Nashua Street, Suite 920, 02114-1101, United States; email: tooka@mgh.harvard.edu","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","35620861","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85131654011"
"Kadirvelu B.; Burcea G.; Quint J.K.; Costelloe C.E.; Faisal A.A.","Kadirvelu, Balasundaram (57195373284); Burcea, Gabriel (57193451859); Quint, Jennifer K. (16507541000); Costelloe, Ceire E. (24780732400); Faisal, A. Aldo (6602900233)","57195373284; 57193451859; 16507541000; 24780732400; 6602900233","Variation in global COVID-19 symptoms by geography and by chronic disease: A global survey using the COVID-19 Symptom Mapper","2022","eClinicalMedicine","45","","101317","","","","16","10.1016/j.eclinm.2022.101317","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125628719&doi=10.1016%2fj.eclinm.2022.101317&partnerID=40&md5=fb88e3a1533ce3e799add734cf648a08","Brain & Behaviour Lab, Dept. Of Computing, Imperial College London, UK, London, United Kingdom; Global Digital Health Unit, School of Public Health, Imperial College London, UK, London, United Kingdom; National Heart and Lung Institute, Imperial College London, UK, London, United Kingdom; Brain & Behaviour Lab, Dept. Of Computing & Dept. Of Bioengineering, UKRI Centre for Doctoral Training in AI for Healthcare, and the Global Covid Observatory, Imperial College London, UK and MRC London Institute for Medical Sciences, UK, London, United Kingdom; Institute for Artificial & Human Intelligence, University of Bayreuth, Bayreuth, Germany","Kadirvelu B., Brain & Behaviour Lab, Dept. Of Computing, Imperial College London, UK, London, United Kingdom; Burcea G., Global Digital Health Unit, School of Public Health, Imperial College London, UK, London, United Kingdom; Quint J.K., National Heart and Lung Institute, Imperial College London, UK, London, United Kingdom; Costelloe C.E., Global Digital Health Unit, School of Public Health, Imperial College London, UK, London, United Kingdom; Faisal A.A., Brain & Behaviour Lab, Dept. Of Computing & Dept. Of Bioengineering, UKRI Centre for Doctoral Training in AI for Healthcare, and the Global Covid Observatory, Imperial College London, UK and MRC London Institute for Medical Sciences, UK, London, United Kingdom, Institute for Artificial & Human Intelligence, University of Bayreuth, Bayreuth, Germany","Background: COVID-19 is typically characterised by a triad of symptoms: cough, fever and loss of taste and smell, however, this varies globally. This study examines variations in COVID-19 symptom profiles based on underlying chronic disease and geographical location. Methods: Using a global online symptom survey of 78,299 responders in 190 countries between 09/04/2020 and 22/09/2020, we conducted an exploratory study to examine symptom profiles associated with a positive COVID-19 test result by country and underlying chronic disease (single, co- or multi-morbidities) using statistical and machine learning methods. Findings: From the results of 7980 COVID-19 tested positive responders, we find that symptom patterns differ by country. For example, India reported a lower proportion of headache (22.8% vs 47.8%, p<1e-13) and itchy eyes (7.3% vs. 16.5%, p=2e-8) than other countries. As with geographic location, we find people differed in their reported symptoms if they suffered from specific chronic diseases. For example, COVID-19 positive responders with asthma (25.3% vs. 13.7%, p=7e-6) were more likely to report shortness of breath compared to those with no underlying chronic disease. Interpretation: We have identified variation in COVID-19 symptom profiles depending on geographic location and underlying chronic disease. Failure to reflect this symptom variation in public health messaging may contribute to asymptomatic COVID-19 spread and put patients with chronic diseases at a greater risk of infection. Future work should focus on symptom profile variation in the emerging variants of the SARS-CoV-2 virus. This is crucial to speed up clinical diagnosis, predict prognostic outcomes and target treatment. Funding: We acknowledge funding to AAF by a UKRI Turing AI Fellowship and to CEC by a personal NIHR Career Development Fellowship (grant number NIHR-2016-090-015). JKQ has received grants from The Health Foundation, MRC, GSK, Bayer, BI, Asthma UK-British Lung Foundation, IQVIA, Chiesi AZ, and Insmed. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Imperial College London is grateful for the support from the Northwest London NIHR Applied Research Collaboration. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. © 2022","Comorbidities; COVID symptom profile; COVID symptoms mapper; COVID symptoms survey; COVID-19; COVID-19 symptoms","adult; ageusia; anosmia; Article; asymptomatic coronavirus disease 2019; chronic disease; clinical outcome; comorbidity; controlled study; coronavirus disease 2019; coughing; dyspnea; female; fever; genetic variability; geographic distribution; geographic mapping; geography; headache; human; India; infection risk; machine learning; major clinical study; male; multiple chronic conditions; ocular pruritus; online system; prediction; prognosis; public health message; questionnaire; Severe acute respiratory syndrome coronavirus 2; statistical analysis","","","","","UK Research and Innovation Industrial Strategy Challenge Fund; Medical Research Council, MRC; IQVIA; Chiesi AZ; BREATHE; GlaxoSmithKline, GSK; Health Foundation; Lung Foundation; UK Research and Innovation, UKRI, (MC_PC_19004); Jonathon Carr-Brown, (NIHR-2016-090-015)","Funding text 1: We acknowledge funding to AAF (UKRI Turing AI Fellowship), CEC by a personal NIHR Career Development Fellowship (grant number NIHR-2016-090-015). JKQ has received grants from The Health Foundation, MRC, GSK, Bayer, BI, Asthma UK-British Lung Foundation, IQVIA, Chiesi AZ, and Insmed. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Imperial College London is grateful for the support from the Northwest London NIHR Applied Research Collaboration. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. ; Funding text 2: JKQ reports grants from MRC, grants from GSK, grants and personal fees from AZ, grants and personal fees from BI, grants and personal fees from Chiesi, grants from The Health Foundation, grants from Bayer, grants from Asthma UK, outside the submitted work. Other authors declare no competing interests. ; Funding text 3: We thank Your.MD (now known as Healthily), Matteo Berlucchi and his team, for sharing their data with the public freely. We thank Your.MD, Jonathon Carr-Brown, Hai Nguyen, Jas Singh and Sally Anne Creamer for study design, data collection and sharing. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We acknowledge funding to AAF (UKRI Turing AI Fellowship), CEC by a personal NIHR Career Development Fellowship (grant number NIHR-2016-090-015). JKQ has received grants from The Health Foundation, MRC, GSK, Bayer, BI, Asthma UK-British Lung Foundation, IQVIA, Chiesi AZ, and Insmed. This work is supported by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Imperial College London is grateful for the support from the Northwest London NIHR Applied Research Collaboration. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The data is provided on request for free from Your.MD (now known as Healthily).","Munsch N., Martin A., Gruarin S., Et al., Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study, Journal of medical Internet research, 22, 10, (2020); Novosad P., Jain R., Campion A., Asher S., COVID-19 mortality effects of underlying health conditions in India: a modelling study, BMJ Open, 10, (2020); Beigel J.H., Tomashek K.M., Dodd L.E., Et al., Remdesivir for the treatment of Covid-19—preliminary report, The New England journal of medicine, (2020); Docherty A.B., Harrison E.M., Green C.A., Et al., Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study, BMJ, 369, (2020); Mahase E., Covid-19: What new variants are emerging and how are they being investigated?, British Medical Journal Publishing Group, (2021); (2021); Lumsden J., Morgan W., Online-questionnaire design: Establishing guidelines and evaluating existing eupport, 2005 Information Resources Management Association International Conference, IGI Global, (2005); Regmi P.R., Waithaka E., Paudyal A., Simkhada P., Van Teijlingen E., Guide to the design and application of online questionnaire surveys, Nepal journal of epidemiology, 6, 4, (2016); Menni C., Valdes A.M., Freidin M.B., Et al., Real-time tracking of self-reported symptoms to predict potential COVID-19, Nature Medicine, 26, 7, pp. 1037-1040, (2020); Tanimoto T.T., (1958); Nowakowska M., Zghebi S.S., Ashcroft D.M., Et al., The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort, BMC Medicine, 17, 1, (2019); Kelley K., Clark B., Brown V., Sitzia J., Good practice in the conduct and reporting of survey research, International Journal for Quality in health care, 15, 3, pp. 261-266, (2003); Davalbhakta S., Advani S., Kumar S., Et al., A Systematic Review of Smartphone Applications Available for Corona Virus Disease 2019 (COVID19) and the Assessment of their Quality Using the Mobile Application Rating Scale (MARS), J Med Syst, 44, 9, (2020); Struyf T., Deeks J.J., Dinnes J., Et al., Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19, Cochrane Database of Systematic Reviews, 2, (2021); Ahmed A., Ali A., Hasan S., Comparison of Epidemiological Variations in COVID-19 Patients Inside and Outside of China-A Meta-Analysis, Front Public Health, 8, (2020); Singh A.K., Agrawal B., Sharma A., Sharma P., COVID-19: Assessment of knowledge and awareness in Indian society, J Public Aff, (2020); Moreira R.D.S., Latent class analysis of COVID-19 symptoms in Brazil: results of the PNAD-COVID19 survey, Cad Saude Publica, 37, 1, (2021); Fernandez-Rojas M.A., Luna-Ruiz Esparza M.A., Campos-Romero A., Et al., Epidemiology of COVID-19 in Mexico: Symptomatic profiles and presymptomatic people, International Journal of Infectious Diseases, 104, pp. 572-579, (2021); NHS U.K., (2021); Elliott J., Whitaker M., Bodinier B., Et al., Symptom reporting in over 1 million people: community detection of COVID-19, medRxiv, (2021); Grant M.C., Geoghegan L., Arbyn M., Et al., The prevalence of symptoms in 24,410 adults infected by the novel coronavirus (SARS-CoV-2; COVID-19): a systematic review and meta-analysis of 148 studies from 9 countries, PloS one, 15, 6, (2020)","A.A. Faisal; Brain & Behaviour Lab, Dept. Of Computing & Dept. Of Bioengineering, UKRI Centre for Doctoral Training in AI for Healthcare, and the Global Covid Observatory, Imperial College London, UK, United Kingdom; email: A.FAISAL@IMPERIAL.AC.UK","","Elsevier Ltd","","","","","","25895370","","","","English","eClinicalMedicine","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85125628719"
"Victor Ikechukwu A.; Murali S.","Victor Ikechukwu, Agughasi (58663521900); Murali, S. (58739639200)","58663521900; 58739639200","CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis","2023","Machine Learning: Science and Technology","4","2","025021","","","","17","10.1088/2632-2153/acd2a5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160307912&doi=10.1088%2f2632-2153%2facd2a5&partnerID=40&md5=2eff2633322234637d834064d4d4b005","Department of CSE, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","Victor Ikechukwu A., Department of CSE, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India; Murali S., Department of CSE, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","Automatic identification of salient features in large medical datasets, particularly in chest x-ray (CXR) images, is a crucial research area. Accurately detecting critical findings such as emphysema, pneumothorax, and chronic bronchitis can aid radiologists in prioritizing time-sensitive cases and screening for abnormalities. However, traditional deep neural network approaches often require bounding box annotations, which can be time-consuming and challenging to obtain. This study proposes an explainable ensemble learning approach, CX-Net, for lung segmentation and diagnosing lung disorders using CXR images. We compare four state-of-the-art convolutional neural network models, including feature pyramid network, U-Net, LinkNet, and a customized U-Net model with ImageNet feature extraction, data augmentation, and dropout regularizations. All models are trained on the Montgomery and VinDR-CXR datasets with and without segmented ground-truth masks. To achieve model explainability, we integrate SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) techniques, which enable a better understanding of the decision-making process and provide visual explanations of critical regions within the CXR images. By employing ensembling, our outlier-resistant CX-Net achieves superior performance in lung segmentation, with Jaccard overlap similarity of 0.992, Dice coefficients of 0.994, precision of 0.993, recall of 0.980, and accuracy of 0.976. The proposed approach demonstrates strong generalization capabilities on the VinDr-CXR dataset and is the first study to use these datasets for semantic lung segmentation with semi-supervised localization. In conclusion, this paper presents an explainable ensemble learning approach for lung segmentation and diagnosing lung disorders using CXR images. Extensive experimental results show that our method efficiently and accurately extracts regions of interest in CXR images from publicly available datasets, indicating its potential for integration into clinical decision support systems. Furthermore, incorporating SHAP and Grad-CAM techniques further enhances the interpretability and trustworthiness of the AI-driven diagnostic system. © 2023 The Author(s). Published by IOP Publishing Ltd.","chest x-ray; COPD; CX-Net; ensemble learning; SHAP and Grad-CAM; VinDR-chest x-ray","Automation; Biological organs; Decision making; Decision support systems; Deep neural networks; Diagnosis; Large dataset; Learning systems; Medical imaging; Semantic Segmentation; Activation mapping; Chest X-ray image; Chest x-rays; COPD; CX-net; Ensemble learning; Shapley; Shapley additive explanation and gradient-weighted class activation mapping; VinDR-chest x-ray; Semantics","","","","","","","Antonelli M, Et al., The medical segmentation decathlon, (2021); Lehman T M, Brendo J, Strategies to configure image analysis algorithms for clinical usage, J. Am. Med. Inform. Assoc, 12, pp. 497-504497, (2005); Geldermann I, Grouls C, Kuhl C, Deserno T M, Spreckelsen C, Black box integration of computer-aided diagnosis into PACS deserves a second chance: results of a usability study concerning bone age assessment, J. Digit. 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"Alhamdan F.; Greulich T.; Daviaud C.; Marsh L.M.; Pedersen F.; Thölken C.; Pfefferle P.I.; Bahmer T.; Potaczek D.P.; Tost J.; Garn H.","Alhamdan, Fahd (57202217319); Greulich, Timm (25824986000); Daviaud, Christian (7801359810); Marsh, Leigh M. (15849174800); Pedersen, Frauke (51261392700); Thölken, Clemens (57190878626); Pfefferle, Petra Ina (23670815100); Bahmer, Thomas (55843546500); Potaczek, Daniel P. (8594671600); Tost, Jörg (6602954348); Garn, Holger (7004463403)","57202217319; 25824986000; 7801359810; 15849174800; 51261392700; 57190878626; 23670815100; 55843546500; 8594671600; 6602954348; 7004463403","Identification of extracellular vesicle microRNA signatures specifically linked to inflammatory and metabolic mechanisms in obesity-associated low type-2 asthma","2023","Allergy: European Journal of Allergy and Clinical Immunology","78","11","","2944","2958","14","21","10.1111/all.15824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165429080&doi=10.1111%2fall.15824&partnerID=40&md5=05abba2712141866f6b434677fab357f","Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Member of the German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Medical Faculty, Philipps University of Marburg, Marburg, Germany; Department of Medicine, Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Medicine, Pulmonary and Critical Care Medicine, German Center for Lung Research (DZL), University Medical Center Giessen and Marburg, Marburg, Germany; Laboratory for Epigenetics & Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, Université Paris-Saclay, France; Division of Physiology and Pathophysiology, Ludwig Boltzmann Institute for Lung Vascular Research and Otto Loewi Research Center, Medical University of Graz, Graz, Austria; Lungen Clinic Großhansdorf GmbH, Member of the German Center for Lung Research (DZL), Airway Research Center North (ARCN), Großhansdorf, Germany; Institute of Medical Bioinformatics and Biostatistics, Medical Faculty, Philipps University of Marburg, Marburg, Germany; Comprehensive Biobank Marburg (CBBMR), German Biobank Alliance (GBA), German Center for Lung Research (DZL), Medical Faculty, Philipps University of Marburg, Marburg, Germany; Department for Internal Medicine I, Airway Research Center North (ARCN), German Center for Lung Research (DZL), University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Center for Infection and Genomics of the Lung (CIGL), German Center for Lung Research (DZL) and Universities of Giessen and Marburg Lung Center (UGMLC), Justus Liebig University of Giessen, Giessen, Germany; Bioscientia MVZ Labor Mittelhessen GmbH, Gießen, Germany","Alhamdan F., Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Member of the German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Medical Faculty, Philipps University of Marburg, Marburg, Germany, Department of Medicine, Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Greulich T., Department of Medicine, Pulmonary and Critical Care Medicine, German Center for Lung Research (DZL), University Medical Center Giessen and Marburg, Marburg, Germany; Daviaud C., Laboratory for Epigenetics & Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, Université Paris-Saclay, France; Marsh L.M., Division of Physiology and Pathophysiology, Ludwig Boltzmann Institute for Lung Vascular Research and Otto Loewi Research Center, Medical University of Graz, Graz, Austria; Pedersen F., Lungen Clinic Großhansdorf GmbH, Member of the German Center for Lung Research (DZL), Airway Research Center North (ARCN), Großhansdorf, Germany; Thölken C., Institute of Medical Bioinformatics and Biostatistics, Medical Faculty, Philipps University of Marburg, Marburg, Germany; Pfefferle P.I., Comprehensive Biobank Marburg (CBBMR), German Biobank Alliance (GBA), German Center for Lung Research (DZL), Medical Faculty, Philipps University of Marburg, Marburg, Germany; Bahmer T., Lungen Clinic Großhansdorf GmbH, Member of the German Center for Lung Research (DZL), Airway Research Center North (ARCN), Großhansdorf, Germany, Department for Internal Medicine I, Airway Research Center North (ARCN), German Center for Lung Research (DZL), University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany; Potaczek D.P., Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Member of the German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Medical Faculty, Philipps University of Marburg, Marburg, Germany, Center for Infection and Genomics of the Lung (CIGL), German Center for Lung Research (DZL) and Universities of Giessen and Marburg Lung Center (UGMLC), Justus Liebig University of Giessen, Giessen, Germany, Bioscientia MVZ Labor Mittelhessen GmbH, Gießen, Germany; Tost J., Laboratory for Epigenetics & Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, Université Paris-Saclay, France; Garn H., Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Member of the German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Medical Faculty, Philipps University of Marburg, Marburg, Germany","Rationale and Objective: Plasma extracellular vesicles (EVs) represent a vital source of molecular information about health and disease states. Due to their heterogenous cellular sources, EVs and their cargo may predict specific pathomechanisms behind disease phenotypes. Here we aimed to utilize EV microRNA (miRNA) signatures to gain new insights into underlying molecular mechanisms of obesity-associated low type-2 asthma. Methods: Obese low type-2 asthma (OA) and non-obese low type-2 asthma (NOA) patients were selected from an asthma cohort conjointly with healthy controls. Plasma EVs were isolated and characterised by nanoparticle tracking analysis. EV-associated small RNAs were extracted, sequenced and bioinformatically analysed. Results: Based on EV miRNA expression profiles, a clear distinction between the three study groups could be established using a principal component analysis. Integrative pathway analysis of potential target genes of the differentially expressed miRNAs revealed inflammatory cytokines (e.g., interleukin-6, transforming growth factor-beta, interferons) and metabolic factors (e.g., insulin, leptin) signalling pathways to be specifically associated with OA. The miR-17–92 and miR-106a–363 clusters were significantly enriched only in OA. These miRNA clusters exhibited discrete bivariate correlations with several key laboratory (e.g., C-reactive protein) and lung function parameters. Plasma EV miRNA signatures mirrored blood-derived CD4+ T-cell transcriptome data, but achieved an even higher sensitivity in identifying specifically affected biological pathways. Conclusion: The identified plasma EV miRNA signatures and particularly the miR-17–92 and -106a–363 clusters were capable to disentangle specific mechanisms of the obesity-associated low type-2 asthma phenotype, which may serve as basis for stratified treatment development. © 2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","asthma endotypes; asthma phenotypes; extracellular vesicles; microRNA clusters; obesity-associated asthma","Cytokines; Extracellular Vesicles; Humans; Interleukin-6; MicroRNAs; Obesity; C reactive protein; interferon; interleukin 6; microRNA; microRNA 106a; microRNA 17; nanoparticle; transcriptome; transforming growth factor beta; unclassified drug; cytokine; interleukin 6; microRNA; adult; area under the curve; Article; asthma; bioinformatics; blood sampling; CD4+ T lymphocyte; clinical article; cohort analysis; controlled study; correlation coefficient; epithelial mesenchymal transition; exosome; false discovery rate; female; forced expiratory volume; forced vital capacity; gene duplication; hierarchical clustering; human; lung function; machine learning; male; obesity; phenotype; principal component analysis; RNA sequencing; sensitivity and specificity; signal transduction; complication; metabolism; obesity","","C reactive protein, 9007-41-4; Cytokines, ; Interleukin-6, ; MicroRNAs, ","","","HMWK; Hessen State Ministry for Higher Education, Research, Science and the Arts; World University Service; Deutscher Akademischer Austauschdienst, DAAD, (91726294); Deutsches Zentrum für Lungenforschung, DZL","This study was supported by the German Center for Lung Research (DZL); the German Academic Exchange Service (DAAD; F.A., personal reference number: 91726294); the HessenFonds, World University Service (WUS; F.A.); the Hessen State Ministry for Higher Education, Research, Science and the Arts (HMWK; F.A.). 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Li H., Li T., Wang S., Et al., MiR-17-5p and miR-106a are involved in the balance between osteogenic and adipogenic differentiation of adipose-derived mesenchymal stem cells, Stem Cell Res, 10, 3, pp. 313-324, (2013); Kastle M., Bartel S., Geillinger-Kastle K., Et al., microRNA cluster 106a~363 is involved in T helper 17 cell differentiation, Immunology, 152, 3, pp. 402-413, (2017); Mathelier A., Carbone A., Large scale chromosomal mapping of human microRNA structural clusters, Nucleic Acids Res, 41, 8, pp. 4392-4408, (2013); Jevnikar Z., Ostling J., Ax E., Et al., Epithelial IL-6 trans-signaling defines a new asthma phenotype with increased airway inflammation, J Allergy Clin Immunol, 143, 2, pp. 577-590, (2019); Richard A.J., Stephens J.M., The role of JAK-STAT signaling in adipose tissue function, Biochim Biophys Acta Mol Basis Dis, 1842, 3, pp. 431-439, (2014); Majoros A., Platanitis E., Kernbauer-Holzl E., Rosebrock F., Muller M., Decker T., Canonical and non-canonical aspects of JAK-STAT signaling: lessons from interferons for cytokine responses, Front Immunol, 8, (2017); Rojas J.M., Alejo A., Martin V., Sevilla N., Viral pathogen-induced mechanisms to antagonize mammalian interferon (IFN) signaling pathway, Cell Mol Life Sci, 78, 4, pp. 1423-1444, (2021); Dixon A.E., Que L.G., Interplay between immune and airway smooth muscle cells in obese asthma, Am J Respir Crit Care Med, 207, 4, pp. 388-389, (2023); Yon C., Thompson D.A., Jude J.A., Panettieri R.A., Rastogi D., Crosstalk between CD4+ T cells and airway smooth muscle in pediatric obesity-related asthma, Am J Respir Crit Care Med, 207, 4, pp. 461-474, (2023)","H. Garn; Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Member of the German Center for Lung Research (DZL), Universities of Giessen and Marburg Lung Center (UGMLC), Medical Faculty, Philipps University of Marburg, Marburg, Germany; email: garn@staff.uni-marburg.de","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","37486026","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85165429080"
"Jabbour S.; Fouhey D.; Kazerooni E.; Wiens J.; Sjoding M.W.","Jabbour, Sarah (57221148269); Fouhey, David (36622345400); Kazerooni, Ella (57216996382); Wiens, Jenna (37110475500); Sjoding, Michael W (56426422500)","57221148269; 36622345400; 57216996382; 37110475500; 56426422500","Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure","2022","Journal of the American Medical Informatics Association","29","6","","1060","1068","8","17","10.1093/jamia/ocac030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130005195&doi=10.1093%2fjamia%2focac030&partnerID=40&md5=93eb10e6947929d0c2a98901b5b42bb2","Department of Electrical Engineering and Computer Science, Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States; Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, United States; Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, G027W Building 16 NCRC, Ann Arbor, 48109, MI, United States","Jabbour S., Department of Electrical Engineering and Computer Science, Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States; Fouhey D., Department of Electrical Engineering and Computer Science, Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States; Kazerooni E., Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, United States; Wiens J., Department of Electrical Engineering and Computer Science, Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, United States; Sjoding M.W., Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, G027W Building 16 NCRC, Ann Arbor, 48109, MI, United States","Objective: When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients. Materials and Methods: Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort. Results: The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77-0.79), image model AUROC was 0.74 (0.72-0.75), and EHR model AUROC was 0.74 (0.70-0.76). For heart failure, combined: 0.83 (0.77-0.84), image: 0.80 (0.71-0.81), and EHR: 0.79 (0.75-0.82). For COPD, combined: AUROC = 0.88 (0.83-0.91), image: 0.83 (0.77-0.89), and EHR: 0.80 (0.76-0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia. Conclusions: Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.  © 2022 The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.","acute respiratory failure; chest X-ray; electronic health record; machine learning","Electronic Health Records; Heart Failure; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; Respiratory Insufficiency; X-Rays; acute respiratory failure; adult; aged; Article; chronic obstructive lung disease; cohort analysis; controlled study; electronic health record; female; heart failure; human; machine learning; major clinical study; male; pneumonia; thorax radiography; chronic obstructive lung disease; diagnostic imaging; heart failure; respiratory failure; X ray","","","","","U.S. National Library of Medicine, NLM, (R01LM013325)","","Kempker JA, Abril MK, Chen Y, Et al., The epidemiology of respiratory failure in the United States 2002-2017: A serial cross-sectional study, Crit Care Explor, 2, 6, (2020); Stefan MS, Shieh M-S, Pekow PS, Et al., Epidemiology and outcomes of acute respiratory failure in the United States, 2001 to 2009: A national survey, J Hosp Med, 8, 2, pp. 76-82, (2013); Healthcare Cost and Utilization Project (HCUP), (2021); Roberts E, Ludman AJ, Dworzynski K, Et al., The diagnostic accuracy of the natriuretic peptides in heart failure: systematic review and diagnostic metaanalysis in the acute care setting, BMJ, 350, (2015); Lien CT, Gillespie ND, Struthers AD, McMurdo ME., Heart failure in frail elderly patients: diagnostic difficulties, co-morbidities, polypharmacy and treatment dilemmas, Eur J Heart Fail, 4, 1, pp. 91-98, (2002); Daniels LB, Clopton P, Bhalla V, Et al., How obesity affects the cut-points for B-Type natriuretic peptide in the diagnosis of acute heart failure. Results from the Breathing Not Properly Multinational Study, Am Heart J, 151, 5, pp. 999-1005, (2006); Levitt JE, Vinayak AG, Gehlbach BK, Et al., Diagnostic utility of B-Type natriuretic peptide in critically ill patients with pulmonary edema: A prospective cohort study, Crit Care, 12, 1, (2008); Zwaan L, Thijs A, Wagner C, van der Wal G, Timmermans DR., Relating faults in diagnostic reasoning with diagnostic errors and patient harm, Acad Med, 87, pp. 149-156, (2012); Improving Diagnosis in Health Care, (2015); Irvin J, Rajpurkar P, Ko M, Et al., Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison, AAAI, 33, (2019); Huang S-C, Pareek A, Seyyedi S, Banerjee I, Lungren MP., Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines, NPJ Digit Med, 3, (2020); Johnson A, Bulgarelli L, Pollard T., MIMIC-IV, (2020); Johnson AEW, Pollard TJ, Berkowitz SJ, Et al., MIMIC-CXR, a deidentified publicly available database of chest radiographs with free-Text reports, Sci Data, 6, 1, (2019); Johnson AEWP, Tom J, Berkowitz S, Greenbaum, Et al., MIMIC-CXR Database, (2019); Goldberger AL, Amaral LA, Glass L, Et al., PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation, 101, 23, (2000); Wells JM, Washko GR, Han MK, Et al., Pulmonary arterial enlargement and acute exacerbations of COPD, N Engl J Med, 367, 10, pp. 913-921, (2012); Landis JR, Koch GG., The measurement of observer agreement for categorical data, Biometrics, 33, 1, pp. 159-174, (1977); Feinstein AR, Cicchetti DV., High agreement but low kappa: I. 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E-DIAL (Early DIAgnosis of obstructive lung disease) study group, Respir Med, 143, (2018); Carey SA, Bass K, Saracino G, Et al., Probability of accurate heart failure diagnosis and the implications for hospital readmissions, Am J Cardiol, 119, 7, (2017); Albaum MN, Hill LC, Murphy M, Et al., Interobserver reliability of the chest radiograph in community-Acquired pneumonia. PORT Investigators, Chest, 110, 2, pp. 343-350, (1996); Croskerry P., The importance of cognitive errors in diagnosis and strategies to minimize them, Acad Med, 78, pp. 775-780, (2003); Rabe KF, Hurd S, Anzueto A, Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am J Respir Crit Care Med, 176, 6, pp. 532-555, (2007); Ciccarese F, Poerio A, Stagni S, Et al., Saber-sheath trachea as a marker of severe airflow obstruction in chronic obstructive pulmonary disease, Radiol Med, 119, 2, (2014); Seah JCY, Tang JSN, Kitchen A, Gaillard F, Dixon AF., Chest radiographs in congestive heart failure: visualizing neural network learning, Radiology, 290, 2, pp. 514-522, (2019); Ray P, Birolleau S, Lefort Y, Et al., Acute respiratory failure in the elderly: etiology, emergency diagnosis and prognosis, Crit Care, 10, 3, (2006); Wiens J, Saria S, Sendak M, Et al., Do no harm: A roadmap for responsible machine learning for health care, Nat Med, 25, 9, pp. 1337-1340, (2019); O Malley KJ, Cook KF, Price MD, Et al., Measuring diagnoses: ICD code accuracy, Health Serv Res, 40, 5, (2005); Wang Z, Dai Z, Poczos B, Carbonell J., Characterizing and avoiding negative transfer, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2019)","M.W. Sjoding; Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, G027W Building 16 NCRC, 48109, United States; email: msjoding@umich.edu","","Oxford University Press","","","","","","10675027","","JAMAF","35271711","English","J. Am. Med. Informatics Assoc.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85130005195"
"Roy A.; Satija U.","Roy, Arka (58073475200); Satija, Udit (55253538100)","58073475200; 55253538100","A Novel Melspectrogram Snippet Representation Learning Framework for Severity Detection of Chronic Obstructive Pulmonary Diseases","2023","IEEE Transactions on Instrumentation and Measurement","72","","4003311","","","","20","10.1109/TIM.2023.3256468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151346059&doi=10.1109%2fTIM.2023.3256468&partnerID=40&md5=a67737a9bd26600b699b0ae300d2b782","Indian Institute of Technology Patna, Department of Electrical Engineering, Patna, 801106, India","Roy A., Indian Institute of Technology Patna, Department of Electrical Engineering, Patna, 801106, India; Satija U., Indian Institute of Technology Patna, Department of Electrical Engineering, Patna, 801106, India","A chronic obstructive pulmonary disease (COPD) is a major public health concern across the world. Since it is an incurable disease, early detection and accurate diagnosis are very crucial for preventing the progression of the disease. Lung sounds provide reliable and accurate prognoses for identifying respiratory diseases. Recently, Altan et al. recorded 12-channel real-time lung sound dataset, namely, RespiratoryDatabase@TR, for five different severity levels of COPD at the Antakya State Hospital, Turkey, and proposed deep learning frameworks for two-class COPD classification and five-class classification using a deep belief network (DBN) classifier and an extreme learning machine (ELM) classifier, respectively. The classification accuracies (ACC) of 95.84% and 94.31% were achieved for two classes and five classes, respectively. In this article, we have proposed a melspectrogram snippet representation learning framework for both two-class and five-class COPD classification. The proposed framework consists of the following stages: data augmentation and preprocessing, melspectrogram snippet representation generation from lung sound, and fine-tuning of a pretrained YAMNet. An experimental analysis on the RespiratoryDatabase@TR dataset demonstrates that the proposed framework achieves the accuracies of 99.25% and 96.14% for binary and multiclass COPD severity classification, respectively, which are superior to the only existing methods proposed by Altan et al. for severity analysis of COPD using lung sounds. © 2022 IEEE.","Chronic obstructive pulmonary disease (COPD); lung sounds; transfer learning; YAMNet","Biological organs; Classification (of information); Deep learning; Diagnosis; Learning systems; Transfer learning; Chronic obstructive pulmonary disease; Health concerns; Incurable disease; Learning frameworks; Lung sounds; Pulmonary disease classification; Real- time; Transfer learning; YAMNet; Pulmonary diseases","","","","","","","Cruz A.A., Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach, (2007); Guessoum S., Laskri M.T., Lieber J., RespiDiag: A case-based reasoning system for the diagnosis of chronic obstructive pulmonary disease, Exp. 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Satija; Indian Institute of Technology Patna, Department of Electrical Engineering, Patna, 801106, India; email: udit@iitp.ac.in","","Institute of Electrical and Electronics Engineers Inc.","","","","","","00189456","","IEIMA","","English","IEEE Trans. Instrum. Meas.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85151346059"
"Mengash H.A.; Hussain L.; Mahgoub H.; Al-Qarafi A.; Nour M.K.; Marzouk R.; Qureshi S.A.; Hilal A.M.","Mengash, Hanan Abdullah (56201890800); Hussain, Lal (56038783200); Mahgoub, Hany (57063016800); Al-Qarafi, A. (57202536897); Nour, Mohamed K (56027613700); Marzouk, Radwa (35749022100); Qureshi, Shahzad Ahmad (18038208100); Hilal, Anwer Mustafa (57202837434)","56201890800; 56038783200; 57063016800; 57202536897; 56027613700; 35749022100; 18038208100; 57202837434","Smart Cities-Based Improving Atmospheric Particulate Matters Prediction Using Chi-Square Feature Selection Methods by Employing Machine Learning Techniques","2022","Applied Artificial Intelligence","36","1","2067647","","","","17","10.1080/08839514.2022.2067647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130133395&doi=10.1080%2f08839514.2022.2067647&partnerID=40&md5=ec9f9d11624705382a6d9c1e25f26104","Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan; Department of Computer Science IT, Neelum Campus, University of Azad Jammu and Kashmir Athmuqam, Azad Kashmir, Pakistan; Department of Computer Science, College of Science Art at Mahayil, King Khalid University, Abha, Saudi Arabia; Faculty of Computers and Information, Computer Science Department, Menoufia University, Menofia Governorat, Egypt; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia; Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca, Saudi Arabia; Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia","Mengash H.A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; Hussain L., Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan, Department of Computer Science IT, Neelum Campus, University of Azad Jammu and Kashmir Athmuqam, Azad Kashmir, Pakistan; Mahgoub H., Department of Computer Science, College of Science Art at Mahayil, King Khalid University, Abha, Saudi Arabia, Faculty of Computers and Information, Computer Science Department, Menoufia University, Menofia Governorat, Egypt; Al-Qarafi A., College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia; Nour M.K., Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Mecca, Saudi Arabia; Marzouk R., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; Qureshi S.A., Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Hilal A.M., Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia","Particulate matter is emitted from diverse sources and affect the human health very badly. Dust particles exposure from the stated environment can affect our heart and lungs very badly. The particle pollution exposure creates a variety of problems including nonfatal heart attacks, premature deaths in people with lung or heart disease, asthma, difficulty in breathing, etc. In this article, we developed an automated tool by computing multimodal features to capture the diverse dynamics of ambient particulate matter and then applied the Chi-square feature selection method to acquire the most relevant features. We also optimized parameters of robust machine learning algorithms to further improve the prediction performance such as Decision Tree, SVM with Linear and Regression, Naïve Bayes (NB), Random Forest (RF), Ensemble Classifier, K-Nearest Neighbor, and XGBoost for classification. The classification results with and without feature selection methods yielded the highest detection performance with random forest, and GBM yielded 100% of accuracy and AUC. The results revealed that the proposed methodology is more robust to provide an efficient system that will detect the particulate matters automatically and will help the individuals to improve their lifestyle and comfort. The concerned department can monitor the individual’s healthcare services and reduce the mortality risk. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.","","Classification (of information); Diseases; Feature Selection; Learning systems; Nearest neighbor search; Particles (particulate matter); Random forests; Smart city; Support vector machines; Atmospheric particulate matter; Dust particle; Feature selection methods; Heart attack; Human health; Machine learning techniques; Particle pollution; Particulate Matter; Premature death; Random forests; Decision trees","","","","","Deanship of Scientific Research at Umm Al-Qura University, (22UQU4310373DSR22); Princess Nourah Bint Abdulrahman University, PNU; Deanship of Scientific Research, King Faisal University, DSR, KFU, (RGP 2/46/43); Deanship of Scientific Research, King Faisal University, DSR, KFU","Funding text 1: The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/46/43). Princess Nourah bint Abdulrahman University Researchers supported Project number (PNURSP2022R114), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the ‎Deanship of Scientific Research at Umm Al-Qura University ‎for supporting this work by Grant Code: (22UQU4310373DSR22).; Funding text 2: The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number (RGP 2/46/43). ; Funding text 3: The authors would like to thank the ‎Deanship of Scientific Research at Umm Al-Qura University ‎for supporting this work by Grant Code: (22UQU4310373DSR22). 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Information Forensics and Security, 8, 1, pp. 5-15, (2013); Zhao Z., Liu H., Spectral feature selection for supervised and unsupervised learning, Proceedings of the 24th International Conference on Machine Learning - ICML ’07, pp. 1151-1157, (2007)","L. Hussain; Department of Computer Science and Information Technology, King Abdullah Campus, University of Azad Jammu and Kashmir, Azad Kashmir, Muzaffarabad, 13100, Pakistan; email: lall_hussain2008@live.com","","Taylor and Francis Ltd.","","","","","","08839514","","AAINE","","English","Appl Artif Intell","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85130133395"
"Gunasekar S.; Joselin Retna Kumar G.; Pius Agbulu G.","Gunasekar, S. (57694798600); Joselin Retna Kumar, G. (56656643000); Pius Agbulu, G. (57215563127)","57694798600; 56656643000; 57215563127","Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM","2022","Computer Systems Science and Engineering","43","3","","1271","1284","13","15","10.32604/csse.2022.024303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130158554&doi=10.32604%2fcsse.2022.024303&partnerID=40&md5=a7409618e64490890da178aec46c8f1d","Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Tamilnadu, Chennai, 603203, India","Gunasekar S., Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Tamilnadu, Chennai, 603203, India; Joselin Retna Kumar G., Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Tamilnadu, Chennai, 603203, India; Pius Agbulu G., Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Tamilnadu, Chennai, 603203, India","Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air quality leads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminated air comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI). These pollutant ingredients are very harmful to human’s health and also leads to death. So, it is necessary to develop a prediction model for air quality as regular on the basis of monthly or seasonaly. In this work, a new hybrid model for air quality prediction (AQP) is developed by using reed deer metaheuristic optimized Long Short Term Memory (LSTM) Deep Learning network. To overcome the drawback of the existing autoregressive integrated moving average model (ARIMA) model, the residual errors are processed by using an optimized LSTM network. The red deer optimization (RDO) is a new type of metaheuristic method which is motivated by the mating behaviour of Red Deer. The proposed model is better in terms of all prediction performance parameters when compared with other models. © 2022 CRL Publishing. All rights reserved.","Air quality; ARIMA; prediction; RDO","Air quality; Diseases; Long short-term memory; Nitrogen oxides; Optimization; Population statistics; Sulfur dioxide; Urban growth; Urban transportation; Air quality prediction; Auto-regressive; Autoregressive integrated moving average model; Growth activity; Metaheuristic; Moving average model; Optimisations; Population growth; Red deer optimization; Urban areas; Forecasting","","","","","","","Akimoto H., Global air quality and pollution, Science, 302, 5651, pp. 1716-1719, (2003); Gurjar B. R., Nagpure A. S., Kumar P., Pollutant emissions from road vehicles in megacity Kolkat, India: Past and present trends, Indian Journal of Air Pollution Control, 10, 2, pp. 18-30, (2010); Kampa M., Castanas E., Human health effects of air pollution, Environmental Pollution, 151, 2, pp. 632-367, (2008); Gu K., Qiao J., Lin W., Recurrent air quality predictor based on meteorology- and pollution-related factors, IEEE Transactions on Industrial Informatics, 14, 9, pp. 3946-3955, (2018); Yang H. L., Lin H.-C., An integrated model combined ARIMA, EMD with SVR for stock indices forecasting, International Journal on Artificial Intelligence Tools, 25, 2, pp. 165-172, (2016); Li C., Chiang T., Complex neurofuzzy ARIMA forecasting—A new approach using complex fuzzy sets, IEEE Transactions on Fuzzy Systems, 21, 3, pp. 567-584, (2013); Khashei M., Bijari M., Ardali G. A. R., Improvement of autoregressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs), Neurocomputing, 72, 4, pp. 956-967, (2009); Ordonez C., Lasheras F. S., Roca-Pardinas J., de Cos Juez F. J., A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines, Journal of Computational and Applied Mathematics, 346, 5, pp. 184-191, (2019); Chen K. Y., Wang C.-H., A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Systems with Applications, 32, 1, pp. 254-264, (2007); Kumar U., An integrated SSA-ARIMA approach to make multiple day ahead forecasts for the daily maximum ambient O3 concentration, Aerosol and Air Quality Research, 15, 1, pp. 208-219, (2014); Chen K., Zhou Y., Dai F., A LSTM-based method for stock returns prediction: A case study of China stock market, 2015 IEEE Int. Conf. on Big Data (Big Data), pp. 2823-3024, (2015); Siami Namini S., Namin A. S., Forecasting economics and financial time series: Arima vs. lstm, Machine Learning, 6, 5, pp. 1012-1019, (2018); Choi H. K., Stock price correlation coefficient prediction with ARIMA-LSTM hybrid model, Computational Engineering, Finance, and Science, 3, 1, pp. 613-625, (2018); Bukhari A. H., Raja M. A. Z., Sulaiman M., Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting, IEEE Access, 8, 5, pp. 71326-71338, (2020); Song S., Lam J. C. K., Han Y., ResNet-LSTM for real-time PM2.5 and PM10 estimation using sequential smartphone images, IEEE Access, 8, 5, pp. 220069-220082, (2020); Gilik A., Ogrenci A. S., Ozmen A., Air quality prediction using CNN+LSTM-based hybrid deep learning architecture, Environ. Sci. Pollut. Res, 5, 7, pp. 115-123, (2021); Chang Y. S., Chiao H. T., Abimannan S., An LSTM-based aggregated model for air pollution forecasting, Atmospheric Pollution Research, 11, 8, pp. 1451-1463, (2020); Mythili K., A swarm based bi-directional LSTM-enhanced elman recurrent neural network algorithm for better crop yield in precision agriculture, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12, 10, pp. 7497-7510, (2020)","S. Gunasekar; Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Chennai, Tamilnadu, 603203, India; email: gs9063@srmist.edu.in","","Tech Science Press","","","","","","02676192","","CSSEE","","English","Comput Syst Sci Eng","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85130158554"
"Vollmer A.; Vollmer M.; Lang G.; Straub A.; Shavlokhova V.; Kübler A.; Gubik S.; Brands R.; Hartmann S.; Saravi B.","Vollmer, Andreas (57219931103); Vollmer, Michael (57217490036); Lang, Gernot (57193254888); Straub, Anton (57219967155); Shavlokhova, Veronika (57204099712); Kübler, Alexander (7005589434); Gubik, Sebastian (57222371826); Brands, Roman (55572048000); Hartmann, Stefan (55572531900); Saravi, Babak (57190620092)","57219931103; 57217490036; 57193254888; 57219967155; 57204099712; 7005589434; 57222371826; 55572048000; 55572531900; 57190620092","Associations between Periodontitis and COPD: An Artificial Intelligence-Based Analysis of NHANES III","2022","Journal of Clinical Medicine","11","23","7210","","","","19","10.3390/jcm11237210","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143608044&doi=10.3390%2fjcm11237210&partnerID=40&md5=4a77c021c4c79544647853bdb7abcdbe","Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Department of Oral and Maxillofacial Surgery, Tuebingen University Hospital, Osianderstrasse 2-8, Tuebingen, 72076, Germany; Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, 79106, Germany; Division of Medicine, Department of Oral and Maxillofacial Surgery, University Hospital Ruppin-Brandenburg, Brandenburg Medical School, Fehrbelliner Straße 38, Neuruppin, 16816, Germany","Vollmer A., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Vollmer M., Department of Oral and Maxillofacial Surgery, Tuebingen University Hospital, Osianderstrasse 2-8, Tuebingen, 72076, Germany; Lang G., Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, 79106, Germany; Straub A., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Shavlokhova V., Division of Medicine, Department of Oral and Maxillofacial Surgery, University Hospital Ruppin-Brandenburg, Brandenburg Medical School, Fehrbelliner Straße 38, Neuruppin, 16816, Germany; Kübler A., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Gubik S., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Brands R., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Hartmann S., Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; Saravi B., Department of Orthopedics and Trauma Surgery, Medical Centre-Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Freiburg, 79106, Germany","A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results. © 2022 by the authors.","artificial intelligence; bone loss; COPD; gingivitis; machine learning; model; periodontitis; prediction","adolescent; adult; aged; area under the curve; Article; artificial intelligence; artificial neural network; binary classification; chronic obstructive lung disease; controlled study; convolutional neural network; deep learning; demographics; dental examination; diagnostic accuracy; female; forced expiratory volume; forced vital capacity; gingiva bleeding; gingiva disease; human; k means clustering; k nearest neighbor; kernel method; logistic regression analysis; low risk patient; lung function test; major clinical study; male; multilayer perceptron; periodontitis; radial basis function neural network; random forest; social status; support vector machine","","","","","","","Petersen P.E., Ogawa H., The Global Burden of Periodontal Disease: Towards Integration with Chronic Disease Prevention and Control, Periodontology 2000, 60, pp. 15-39, (2012); Hegde R., Awan K.H., Effects of Periodontal Disease on Systemic Health, Dis.-A-Mon, 65, pp. 185-192, (2019); Sudhakara P., Gupta A., Bhardwaj A., Wilson A., Oral Dysbiotic Communities and Their Implications in Systemic Diseases, Dent. J, 6, (2018); Sharma S., Gupta A., Verma A.K., Pathak A., Verma S., Chaudhary S.C., Kaushal S., Lal N., Kant S., Verma U.P., Impact of Non-Surgical Periodontal Therapy on Pulmonary Functions, Periodontal Health and Salivary Matrix Metalloproteinase-8 of COPD Patients with Chronic Periodontitis: A Clinico-Biochemical Study, Turk. Thorac. J, 22, pp. 324-332, (2021); Monsarrat P., Blaizot A., Kemoun P., Ravaud P., Nabet C., Sixou M., Vergnes J.-N., Clinical Research Activity in Periodontal Medicine: A Systematic Mapping of Trial Registers, J. Clin. Periodontol, 43, pp. 390-400, (2016); Okuda K., Ishihara K., Nakagawa T., Hirayama A., Inayama Y., Okuda K., Detection of Treponema Denticola in Atherosclerotic Lesions, J. Clin. Microbiol, 39, pp. 1114-1117, (2001); Bialowas K., Radwan-Oczko M., Dus-Ilnicka I., Korman L., Swierkot J., Periodontal Disease and Influence of Periodontal Treatment on Disease Activity in Patients with Rheumatoid Arthritis and Spondyloarthritis, Rheumatol. Int, 40, pp. 455-463, (2020); Liccardo D., Cannavo A., Spagnuolo G., Ferrara N., Cittadini A., Rengo C., Rengo G., Periodontal Disease: A Risk Factor for Diabetes and Cardiovascular Disease, Int. J. Mol. Sci, 20, (2019); Nazir M.A., Prevalence of Periodontal Disease, Its Association with Systemic Diseases and Prevention, Int. J. Health Sci. (Qassim), 11, pp. 72-80, (2017); Soriano J.B., Rodriguez-Roisin R., Chronic Obstructive Pulmonary Disease Overview: Epidemiology, Risk Factors, and Clinical Presentation, Proc. Am. Thorac. Soc, 8, pp. 363-367, (2011); Su Y.-C., Jalalvand F., Thegerstrom J., Riesbeck K., The Interplay between Immune Response and Bacterial Infection in COPD: Focus Upon Non-Typeable Haemophilus Influenzae, Front. Immunol, 9, (2018); Huertas A., Palange P., COPD: A Multifactorial Systemic Disease, Adv. Respir. Dis, 5, pp. 217-224, (2011); Fabbri L.M., Luppi F., Beghe B., Rabe K.F., Complex Chronic Comorbidities of COPD, Eur. Respir. J, 31, pp. 204-212, (2008); Shi Q., Zhang B., Xing H., Yang S., Xu J., Liu H., Patients with Chronic Obstructive Pulmonary Disease Suffer from Worse Periodontal Health—Evidence from a Meta-Analysis, Front. Physiol, 9, (2018); Gomes-Filho I.S., Cruz S.S.D., Trindade S.C., Passos-Soares J.S., Carvalho-Filho P.C., Figueiredo A.C.M.G., Lyrio A.O., Hintz A.M., Pereira M.G., Scannapieco F., Periodontitis and respiratory diseases: A systematic review with meta-analysis, Oral Dis, 26, (2020); Ouyang Y., Liu J., Wen S., Xu Y., Zhang Z., Pi Y., Chen D., Su Z., Liang Z., Wang Y., Et al., Association between Chronic Obstructive Pulmonary Disease and Periodontitis: The Common Role of Innate Immune Cells?, Cytokine, 158, (2022); Sapey E., Yonel Z., Edgar R., Parmar S., Hobbins S., Newby P., Crossley D., Usher A., Johnson S., Walton G.M., Et al., The Clinical and Inflammatory Relationships between Periodontitis and Chronic Obstructive Pulmonary Disease, J. Clin. Periodontol, 47, pp. 1040-1052, (2020); Punceviciene E., Rovas A., Puriene A., Stuopelyte K., Vitkus D., Jarmalaite S., Butrimiene I., Investigating the Relationship between the Severity of Periodontitis and Rheumatoid Arthritis: A Cross-Sectional Study, Clin. Rheumatol, 40, pp. 3153-3160, (2021); Suzuki R., Kamio N., Kaneko T., Yonehara Y., Imai K., Fusobacterium Nucleatum Exacerbates Chronic Obstructive Pulmonary Disease in Elastase-induced Emphysematous Mice, FEBS Open Bio, 12, pp. 638-648, (2022); Apessos I., Voulgaris A., Agrafiotis M., Andreadis D., Steiropoulos P., Effect of Periodontal Therapy on COPD Outcomes: A Systematic Review, BMC Pulm. Med, 21, (2021); Scannapieco F.A., Ho A.W., Potential Associations between Chronic Respiratory Disease and Periodontal Disease: Analysis of National Health and Nutrition Examination Survey III, J. Periodontol, 72, pp. 50-56, (2001); Gaeckle N.T., Pragman A.A., Pendleton K.M., Baldomero A.K., Criner G.J., The Oral-Lung Axis: The Impact of Oral Health on Lung Health, Respir. Care, 65, pp. 1211-1220, (2020); Reddi K., Meghji S., Wilson M., Henderson B., Comparison of the Osteolytic Activity of Surface-Associated Proteins of Bacteria Implicated in Periodontal Disease, Oral Dis, 1, pp. 26-31, (1995); Cardoso E.M., Reis C., Manzanares-Cespedes M.C., Chronic Periodontitis, Inflammatory Cytokines, and Interrelationship with Other Chronic Diseases, Postgrad. Med, 130, pp. 98-104, (2018); Birkedal-Hansen H., Role of Cytokines and Inflammatory Mediators in Tissue Destruction, J. Periodontal Res, 28, pp. 500-510, (1993); Jaedicke K.M., Preshaw P.M., Taylor J.J., Salivary Cytokines as Biomarkers of Periodontal Diseases, Periodontology 2000, 70, pp. 164-183, (2016); Zekeridou A., Giannopoulou C., Cancela J., Courvoisier D., Mombelli A., Effect of Initial Periodontal Therapy on Gingival Crevicular Fluid Cytokine Profile in Subjects with Chronic Periodontitis, Clin. Exp. Dent. Res, 3, pp. 62-68, (2017); Bruzzaniti S., Bocchino M., Santopaolo M., Cali G., Stanziola A.A., D'Amato M., Esposito A., Barra E., Garziano F., Micillo T., Et al., An Immunometabolic Pathomechanism for Chronic Obstructive Pulmonary Disease, Proc. Natl. Acad. Sci. USA, 116, pp. 15625-15634, (2019); Anand P.S., Jadhav P., Kamath K.P., Kumar S.R., Vijayalaxmi S., Anil S., A Case-Control Study on the Association between Periodontitis and Coronavirus Disease (COVID-19), J. Periodontol, 93, pp. 584-590, (2022); Miller D.D., Brown E.W., Artificial Intelligence in Medical Practice: The Question to the Answer?, Am. J. Med, 131, pp. 129-133, (2018); Khanagar S.B., Al-ehaideb A., Maganur P.C., Vishwanathaiah S., Patil S., Baeshen H.A., Sarode S.C., Bhandi S., Developments, Application, and Performance of Artificial Intelligence in Dentistry—A Systematic Review, J. Dent. Sci, 16, pp. 508-522, (2021); Waring J., Lindvall C., Umeton R., Automated Machine Learning: Review of the State-of-the-Art and Opportunities for Healthcare, Artif. Intell. Med, 104, (2020); Prosperi M., Min J.S., Bian J., Modave F., Big Data Hurdles in Precision Medicine and Precision Public Health, BMC Med. Inf. Decis. Mak, 18, (2018); Shen T.-C., Chang P.-Y., Lin C.-L., Chen C.-H., Tu C.-Y., Hsia T.-C., Shih C.-M., Hsu W.-H., Sung F.-C., Kao C.-H., Risk of Periodontal Diseases in Patients With Chronic Obstructive Pulmonary Disease, Medicine, 94, (2015); Peter K.P., Mute B.R., Doiphode S.S., Bardapurkar S.J., Borkar M.S., Raje D.V., Association Between Periodontal Disease and Chronic Obstructive Pulmonary Disease: A Reality or Just a Dogma?, J. Periodontol, 84, pp. 1717-1723, (2013); Oztekin G., Baser U., Kucukcoskun M., Tanrikulu-Kucuk S., Ademoglu E., Isik G., Ozkan G., Yalcin F., Kiyan E., The Association between Periodontal Disease and Chronic Obstructive Pulmonary Disease: A Case Control Study, COPD J. Chronic Obstr. Pulm. Dis, 11, pp. 424-430, (2014); Zeng X.-T., Tu M.-L., Liu D.-Y., Zheng D., Zhang J., Leng W., Periodontal Disease and Risk of Chronic Obstructive Pulmonary Disease: A Meta-Analysis of Observational Studies, PLoS ONE, 7, (2012); Fitzgerald B.P., Hawley C.E., Harrold C.Q., Garrett J.S., Polson A.M., Rams T.E., Reproducibility of Manual Periodontal Probing Following a Comprehensive Standardization and Calibration Training Program, J. Oral Biol. (Northborough), 8, (2022); Farook F.F., Alodwene H., Alharbi R., Alyami M., Alshahrani A., Almohammadi D., Alnasyan B., Aboelmaaty W., Reliability Assessment between Clinical Attachment Loss and Alveolar Bone Level in Dental Radiographs, Clin. Exp. Dent. Res, 6, pp. 596-601, (2020); Saravi B.E., Putz M., Patzelt S., Alkalak A., Uelkuemen S., Boeker M., Marginal Bone Loss around Oral Implants Supporting Fixed versus Removable Prostheses: A Systematic Review, Int. J. Implant Dent, 6, (2020); Chen H., Zhang X., Luo J., Dong X., Jiang X., The Association between Periodontitis and Lung Function: Results from the National Health and Nutrition Examination Survey 2009 to 2012, J. Periodontol, 93, pp. 901-910, (2022); Saravi B., Hassel F., Ulkumen S., Zink A., Shavlokhova V., Couillard-Despres S., Boeker M., Obid P., Lang G.M., Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models, J. Pers. Med, 12, (2022); Lee W.-C., Fu E., Li C.-H., Huang R.-Y., Chiu H.-C., Cheng W.-C., Chen W.-L., Association between Periodontitis and Pulmonary Function Based on the Third National Health and Nutrition Examination Survey (NHANES III), J. Clin. Periodontol, 47, pp. 788-795, (2020); Hobbins S., Chapple I.L., Sapey E., Stockley R.A., Is Periodontitis a Comorbidity of COPD or Can Associations Be Explained by Shared Risk Factors/Behaviors?, Int. J. Chronic Obstr. Pulm. Dis, 12, pp. 1339-1349, (2017); Chung J.H., Hwang H.-J., Kim S.-H., Kim T.H., Associations between Periodontitis and Chronic Obstructive Pulmonary Disease: The 2010 to 2012 Korean National Health and Nutrition Examination Survey, J. Periodontol, 87, pp. 864-871, (2016); Azarpazhooh A., Leake J.L., Systematic Review of the Association between Respiratory Diseases and Oral Health, J. Periodontol, 77, pp. 1465-1482, (2006); Yildirim E., Kormi I., Basoglu O.K., Gurgun A., Kaval B., Sorsa T., Buduneli N., Periodontal Health and Serum, Saliva Matrix Metalloproteinases in Patients with Mild Chronic Obstructive Pulmonary Disease, J. Periodontal Res, 48, pp. 269-275, (2013); Baldomero A.K., Siddiqui M., Lo C.-Y., Petersen A., Pragman A.A., Connett J.E., Kunisaki K.M., Wendt C.H., The Relationship between Oral Health and COPD Exacerbations, COPD, 14, pp. 881-892, (2019); Sundh J., Tanash H., Arian R., Neves-Guimaraes A., Broberg K., Lindved G., Kern T., Zych K., Nielsen H.B., Halling A., Et al., Advanced Dental Cleaning Is Associated with Reduced Risk of COPD Exacerbations—A Randomized Controlled Trial, Int. J. Chronic Obstr. Pulm. Dis, 16, pp. 3203-3215, (2021); Murphy T.F., Sethi S., Bacterial Infection in Chronic Obstructive Pulmonary Disease, Am. Rev. Respir. Dis, 146, pp. 1067-1083, (1992); Whittemore A.S., Perlin S.A., DiCiccio Y., Chronic Obstructive Pulmonary Disease in Lifelong Nonsmokers: Results from NHANES, Am. J. Public Health, 85, pp. 702-706, (1995); Lorenz K.A., Weiss P.J., Capnocytophagal Pneumonia in a Healthy Man, West. J. Med, 160, pp. 79-80, (1994); Goolam Mahomed A., Feldman C., Smith C., Promnitz D.A., Kaka S., Does Primary Streptococcus Viridans Pneumonia Exist?, S. Afr. Med. J, 82, pp. 432-434, (1992); Morris J.F., Sewell D.L., Necrotizing Pneumonia Caused by Mixed Infection with Actinobacillus Actinomycetemcomitans and Actinomyces Israelii: Case Report and Review, Clin. Infect. Dis, 18, pp. 450-452, (1994); Demmer R.T., Trinquart L., Zuk A., Fu B.C., Blomkvist J., Michalowicz B.S., Ravaud P., Desvarieux M., The Influence of Anti-Infective Periodontal Treatment on C-Reactive Protein: A Systematic Review and Meta-Analysis of Randomized Controlled Trials, PLoS ONE, 8, (2013); Demmer R.T., Breskin A., Rosenbaum M., Zuk A., LeDuc C., Leibel R., Paster B., Desvarieux M., Jacobs Jr D.R., Papapanou P.N., The Subgingival Microbiome, Systemic Inflammation and Insulin Resistance: The Oral Infections, Glucose Intolerance and Insulin Resistance Study, J. Clin. Periodontol, 44, pp. 255-265, (2017); Teeuw W.J., Slot D.E., Susanto H., Gerdes V.E.A., Abbas F., D'Aiuto F., Kastelein J.J.P., Loos B.G., Treatment of Periodontitis Improves the Atherosclerotic Profile: A Systematic Review and Meta-Analysis, J. Clin. Periodontol, 41, pp. 70-79, (2014); Kataoka S., Kimura M., Yamaguchi T., Egashira K., Yamamoto Y., Koike Y., Ogawa Y., Fujiharu C., Namai T., Taguchi K., Et al., A Cross-Sectional Study of Relationships between Periodontal Disease and General Health: The Hitachi Oral Healthcare Survey, BMC Oral Health, 21, (2021)","A. Vollmer; Department of Oral and Maxillofacial Plastic Surgery, University Hospital of Würzburg, Würzburg, 97070, Germany; email: vollmer_a@ukw.de","","MDPI","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85143608044"
"Lugogo N.L.; Depietro M.; Reich M.; Merchant R.; Chrystyn H.; Pleasants R.; Granovsky L.; Li T.; Hill T.; Brown R.W.; Safioti G.","Lugogo, Njira L. (12780337600); Depietro, Michael (56901296000); Reich, Michael (57916559900); Merchant, Rajan (57040685500); Chrystyn, Henry (7005136151); Pleasants, Roy (56661122300); Granovsky, Lena (57219460027); Li, Thomas (57743121600); Hill, Tanisha (57633611600); Brown, Randall W. (7408434122); Safioti, Guilherme (56716323900)","12780337600; 56901296000; 57916559900; 57040685500; 7005136151; 56661122300; 57219460027; 57743121600; 57633611600; 7408434122; 56716323900","A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors","2022","Journal of Asthma and Allergy","15","","","1623","1637","14","26","10.2147/JAA.S377631","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141539535&doi=10.2147%2fJAA.S377631&partnerID=40&md5=db262d35331b1978720833e2fe89e13c","Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, United States; Teva Branded Pharmaceutical Products R&D Inc, Parsippany, NJ, United States; Teva Pharmaceutical Industries Ltd, Tel Aviv, Israel; Woodland Clinic Medical Group, Allergy Department, Dignity Health, Woodland, CA, United States; Inhalation Consultancy Ltd, Leeds, United Kingdom; Population Health, University of Michigan, Ann Arbor, MI and Division of Pulmonary Disease and Critical Care Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, United States; Teva Pharmaceuticals Europe B.V, Amsterdam, Netherlands","Lugogo N.L., Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI, United States; Depietro M., Teva Branded Pharmaceutical Products R&D Inc, Parsippany, NJ, United States; Reich M., Teva Pharmaceutical Industries Ltd, Tel Aviv, Israel; Merchant R., Woodland Clinic Medical Group, Allergy Department, Dignity Health, Woodland, CA, United States; Chrystyn H., Inhalation Consultancy Ltd, Leeds, United Kingdom; Pleasants R., Population Health, University of Michigan, Ann Arbor, MI and Division of Pulmonary Disease and Critical Care Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC, United States; Granovsky L., Teva Pharmaceutical Industries Ltd, Tel Aviv, Israel; Li T., Teva Branded Pharmaceutical Products R&D Inc, Parsippany, NJ, United States; Hill T., Teva Branded Pharmaceutical Products R&D Inc, Parsippany, NJ, United States; Brown R.W., Teva Branded Pharmaceutical Products R&D Inc, Parsippany, NJ, United States; Safioti G., Teva Pharmaceuticals Europe B.V, Amsterdam, Netherlands","Purpose: Machine learning models informed by sensor data inputs have the potential to provide individualized predictions of asthma deterioration. This study aimed to determine if data from an integrated digital inhaler could be used to develop a machine learning model capable of predicting impending exacerbations. Patients and Methods: Adult patients with poorly controlled asthma were enrolled in a 12-week, open-label study using ProAir® Digihaler®, an electronic multi-dose dry powder inhaler (eMDPI) with integrated sensors, as reliever medication (albuterol, 90 µg/dose; 1–2 inhalations every 4 hours, as needed). Throughout the study, the eMDPI recorded inhaler use, peak inspiratory flow (PIF), inhalation volume, inhalation duration, and time to PIF. A model predictive of impending exacerbations was generated by applying machine learning techniques to data downloaded from the inhalers, together with clinical and demographic information. The generated model was evaluated by receiver operating characteristic area under curve (ROC AUC) analysis. Results: Of 360 patients included in the predictive analysis, 64 experienced a total of 78 exacerbations. Increased albuterol use preceded exacerbations; the mean number of inhalations in the 24-hours preceding an exacerbation was 7.3 (standard deviation 17.3). The machine learning model, using gradient-boosting trees with data from the eMDPI and baseline patient character-istics, predicted an impending exacerbation over the following 5 days with an ROC AUC of 0.83 (95% confidence interval: 0.77–0.90). The feature of the model with the highest weight was the mean number of daily inhalations during the 4 days prior to the day the prediction was made. Conclusion: A machine learning model to predict impending asthma exacerbations using data from the eMDPI was successfully developed. This approach may support a shift from reactive care to proactive, preventative, and personalized management of chronic respiratory diseases. © 2022 Lugogo et al.","digital inhalers; machine learning; personalized medicine; predictive modeling","fluticasone propionate; leukotriene receptor blocking agent; salbutamol; adult; area under the curve; Article; asthma; body mass; clinical decision making; controlled study; demographics; diagnostic test accuracy study; double blind procedure; female; health care cost; human; inhalation; lung ventilation; machine learning; major clinical study; male; multicenter study; open study; peak inspiratory flow; prediction; receiver operating characteristic","","fluticasone propionate, 80474-14-2; salbutamol, 18559-94-9, 35763-26-9","","","Teva Branded Pharmaceutical Products R&D Inc.","We thank the patients, caregivers, health care providers, and research staff who participated in this study. We also thank Thomas Ferro, Dilip Chary, Dan Buck, Enric Calderon and Mark Milton-Edwards for important contributions to the conception, design, and direction of the study, and Shai Fine and Shahar Cohen for their important contributions to the statistical analysis and machine learning modeling. We also acknowledge Melanie Francis, MSc, of Ashfield MedComms, an Inizio company, for her medical writing support in the preparation of the manuscript, under the direction of the authors and funded by Teva Branded Pharmaceutical Products R&D Inc. Data from this paper were presented as poster presentations at the 2019 Annual Scientific Meeting of the American College of Allergy, Asthma and Immunology [https://doi.org/10.1016/j.","Global strategy for asthma management and prevention, (2020); Melani AS, Bonavia M, Cilenti V, Et al., Inhaler mishandling remains common in real life and is associated with reduced disease control, Respir Med, 105, pp. 930-938, (2011); Lavorini F, Usmani OS., Correct inhalation technique is critical in achieving good asthma control, Prim Care Respir J, 22, pp. 385-386, (2013); Wright J, Brocklebank D, Ram F., Inhaler devices for the treatment of asthma and chronic obstructive airways disease (COPD), Qual Saf Health Care, 11, pp. 376-382, (2002); Usmani OS, Lavorini F, Marshall J, Et al., Critical inhaler errors in asthma and COPD: a systematic review of impact on health outcomes, Respir Res, 19, (2018); Price DB, Roman-Rodriguez M, McQueen RB, Et al., Inhaler errors in the CRITIKAL study: type, frequency, and association with asthma outcomes, J Allergy Clin Immunol Pract, 5, pp. 1071-1081, (2017); 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Ghahramani Z., Probabilistic machine learning and artificial intelligence, Nature, 521, 7553, pp. 452-459, (2015); Deo RC., Machine learning in medicine, Circulation, 132, pp. 1920-1930, (2015); Kingsford C, Salzberg SL., What are decision trees?, Nat Biotechnol, 26, pp. 1011-1013, (2008); Friedman JH., Greedy function approximation: a gradient boosting machine, Ann Stat, 29, pp. 1189-1232, (2001); Bateman ED, Buhl R, O'Byrne PM, Et al., Development and validation of a novel risk score for asthma exacerbations: the risk score for exacerbations, J Allergy Clin Immunol, 135, 1457–1464.e4, pp. 1457-1464, (2015); Finkelstein J, Jeong IC., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann N Y Acad Sci, 1387, pp. 153-165, (2017); Messinger AI, Deterding RR, Szefler SJ., Bringing technology to day-to-day asthma management, Am J Respir Crit Care Med, 198, pp. 291-292, (2018); Korn S, Both J, Jung M, Hubner M, Taube C, Buhl R., Prospective evaluation of current asthma control using ACQ and ACT compared with GINA criteria, Ann Allergy Asthma Immunol, 107, pp. 474-479, (2011); 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Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD); Cosgriff CV, Celi LA, Sauer CM., Boosting clinical decision-making: machine learning for intensive care unit discharge, Ann Am Thorac Soc, 15, 7, pp. 804-805, (2018); Patel M, Pilcher J, Reddel HK, Et al., Predictors of severe exacerbations, poor asthma control, and β-agonist overuse for patients with asthma, J Allergy Clin Immunol Pract, 2, pp. 751-758, (2014); Mahler DA, Waterman LA, Gifford AH., Prevalence and COPD phenotype for a suboptimal peak inspiratory flow rate against the simulated resistance of the Diskus dry powder inhaler, J Aerosol Med Pulm Drug Deliv, 26, pp. 174-179, (2013)","N.L. Lugogo; Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan, Ann Arbor, 300 North Ingalls St, Suite 2C40, 48109, United States; email: nlugogo@med.umich.edu","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85141539535"
"Makimoto K.; Hogg J.C.; Bourbeau J.; Tan W.C.; Kirby M.","Makimoto, Kalysta (57738337000); Hogg, James C. (7201452328); Bourbeau, Jean (34567907500); Tan, Wan C. (13403886200); Kirby, Miranda (35174507500)","57738337000; 7201452328; 34567907500; 13403886200; 35174507500","CT Imaging With Machine Learning for Predicting Progression to COPD in Individuals at Risk","2023","Chest","164","5","","1139","1149","10","16","10.1016/j.chest.2023.06.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171981461&doi=10.1016%2fj.chest.2023.06.008&partnerID=40&md5=2be94c11746acd9a6e09158234d78dbf","Toronto Metropolitan University, Toronto, ON, Canada; Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada","Makimoto K., Toronto Metropolitan University, Toronto, ON, Canada; Hogg J.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Bourbeau J., Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada, Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada; Tan W.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Kirby M., Toronto Metropolitan University, Toronto, ON, Canada","Background: Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions. Research Question: Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning? Study Design and Methods: Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n = 95), and established quantitative CT scan (n = 8), as well as demographic (n = 5) and spirometry (n = 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models. Results: Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42% female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P < .05) or CT imaging features and spirometry to demographics (AUC, 0.877; P < .05) significantly improved the performance for predicting progression to COPD. Interpretation: Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD. © 2023 American College of Chest Physicians","COPD; CT scan; lung; machine learning; quantitative imaging; radiomics","Aged; Canada; Female; Humans; Lung; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; aged; Article; chronic obstructive lung disease; cohort analysis; computer assisted tomography; controlled study; demographics; disease exacerbation; feature extraction; feature selection; female; follow up; forced expiratory volume; high risk population; human; intermethod comparison; lung function; machine learning; major clinical study; male; measurement precision; population research; predictive value; radiomics; receiver operating characteristic; risk factor; smoking; spirometry; Canada; diagnostic imaging; epidemiology; lung; machine learning; middle aged; procedures; x-ray computed tomography","","","","","Darcy Marciniuk; Joe Comeau; John Hopkins School of Public Health, Baltimore, MD; Jonathon Leipsic; Milo Puhan; Ron Clemens; UBC James Hogg Research Center; Yvan Fortier and Mina Dligui; University of Saskatchewan, UOS; Université de Sherbrooke, UdeS; Canadian Institutes of Health Research, IRSC; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs; Queen's University; University of Toronto, U of T; University of South China, USC","Funding text 1: Author contributions: J. C. H. J. B. W. C. T. and M. K. had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. K. M. contributed substantially to the study design, data analysis and interpretation, and writing of the manuscript. M. K. had final approval of the version to be published. ∗CanCOLD Collaborative Research Group collaborators: Jonathon Samet (Keck School of Medicine of USC, Los Angeles, CA); Milo Puhan (John Hopkins School of Public Health, Baltimore, MD); Qutayba Hamid, Carolyn Baglole, Palmina Mancino, Pei-Zhi Li, Zhi Song, Dennis Jensen, and Benjamin Mcdonald Smith (McGill University, Montreal, QC, Canada); Yvan Fortier and Mina Dligui (Sherbrooke University, Sherbrooke, QC, Canada); Kenneth Chapman, Jane Duke, Andrea S. Gershon, and Teresa To (University of Toronto, Toronto, ON, Canada); J. Mark Fitzgerald and Mohsen Sadatsafavi (University of British Columbia, Vancouver, BC, Canada); Christine Lo, Sarah Cheng, Elena Un, Michael Cheng, Cynthia Fung, Nancy Haynes, Liyun Zheng, LingXiang Zou, Joe Comeau, Jonathon Leipsic, and Cameron Hague (UBC James Hogg Research Center, Vancouver, BC, Canada); Brandie L. Walker and Curtis Dumonceaux (University of Calgary, Calgary, AB, Canada); Paul Hernandez and Scott Fulton (University of Dalhousie, Halifax, NS, Canada); Shawn Aaron and Kathy Vandemheen (University of Ottawa, Ottawa, ON, Canada); Denis O'Donnell, Matthew McNeil, and Kate Whelan (Queen's University, Kingston, ON, Canada); Francois Maltais and Cynthia Brouillard (University of Laval, Quebec City, QC, Canada); and Darcy Marciniuk, Ron Clemens, and Janet Baran (University of Saskatchewan, Saskatoon, SK, Canada). Role of sponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript. Additional information: The e-Tables are available online under “Supplementary Data.”; Funding text 2: M. K. acknowledges support from the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, the Early Researchers Award Program, and the Canada Research Chair Program (Tier II). K. M. acknowledges support from the Canadian Institutions of Health Research (CIHR) Masters Award. 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Park Y.S., Seo J.B., Kim N., Et al., Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: comparison with density-based quantification and correlation with pulmonary function test, Invest Radiol, 43, 6, pp. 395-402, (2008); Sorensen L., Nielsen M., Petersen J., Pedersen J.H., Dirksen A., de Bruijne M., Chronic obstructive pulmonary disease quantification using CT texture analysis and densitometry: results from the Danish Lung Cancer Screening Trial, AJR Am J Roentgenol, 214, 6, pp. 1269-1279, (2020); Krist A.H., Davidson K.W., Mangione C.M., Et al., Screening for lung cancer: US Preventive Services Task Force recommendation statement, JAMA, 325, 10, pp. 962-970, (2021); ten Haaf K., Jeon J., Tammemagi M.C., Et al., Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study, PLoS Med, 14, 4, (2017); Siu A.L., Bibbins-Domingo K., Grossman D.C., Et al., Screening for chronic obstructive pulmonary disease: US Preventive Services Task Force recommendation statement, JAMA, 315, 13, pp. 1372-1377, (2016); 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Kirby; Toronto Metropolitan University, Toronto, Canada; email: miranda.kirby@torontomu.ca","","Elsevier Inc.","","","","","","00123692","","CHETB","37421974","English","Chest","Article","Final","","Scopus","2-s2.0-85171981461"
"Wu Y.; Du R.; Feng J.; Qi S.; Pang H.; Xia S.; Qian W.","Wu, Yanan (57394724800); Du, Ran (57215774797); Feng, Jie (57214107623); Qi, Shouliang (36572483500); Pang, Haowen (57781616300); Xia, Shuyue (7202893268); Qian, Wei (36842193500)","57394724800; 57215774797; 57214107623; 36572483500; 57781616300; 7202893268; 36842193500","Deep CNN for COPD identification by Multi-View snapshot integration of 3D airway tree and lung field","2023","Biomedical Signal Processing and Control","79","","104162","","","","20","10.1016/j.bspc.2022.104162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137634155&doi=10.1016%2fj.bspc.2022.104162&partnerID=40&md5=5467e75b439aa952ddffc3195d7b3ed0","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China; Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China","Wu Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Du R., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Feng J., School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China; Qi S., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Pang H., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Xia S., Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Qian W., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China","Background: Chronic obstructive pulmonary disease (COPD) is a complex and irreversible respiratory disease with potential morphological abnormalities of the airway and lung fields. To date, whether and how these abnormalities can be used to identify COPD is unknown. This study developed a deep convolutional neural network (CNN) integrating the airway tree and lung field morphologies to identify COPD. Methods: We represent 3D airway and lung fields through multi-view 2D snapshots and their integration via deep CNN, to estimate the possibility of COPD. We constructed two datasets named Dataset 1 including 380 participants (190 COPD and 190 healthy controls) for training and validation and Dataset 2 including 201 participants (101 COPD and 100 healthy controls) for testing. First, the 3D airway tree and lung field are automatically extracted from computed tomography (CT) images, and 2D snapshots in nine views are captured. Second, the proposed ResNet-26 is trained with each view of snapshots as input. Finally, majority voting of nine models is performed to identify COPD. Results: The accuracy (ACC) of the single-view ResNet-26 model (ventral, dorsal, and isometric view of airway; front, rear, left, right, top, and bottom view of lung field) is 0.900, 0.873, 0.889, 0.868, 0.824, 0.876, 0.861, 0.839, and 0.884, respectively. For the multi-view ResNet-26 model of airway tree and lung field, the ACC is 0.913 and 0.895, respectively. For the model integrating all nine views, the ACC eventually reaches as high as 0.947. Conclusions: The deep CNN model identifies COPD through integrating morphology of the airway tree and lung field extracted from CT images. A different view of 2D snapshots represents various characteristics of the 3D airway tree and lung field. The integration of multiple views can improve the performance of COPD prediction. The CNN model provides a potential method of identifying COPD via CT scans. © 2022 Elsevier Ltd","Airway tree; Chronic obstructive pulmonary disease (COPD); computed tomography (CT); Deep learning; Image classification; Lung field","Biological organs; Computerized tomography; Convolution; Convolutional neural networks; Deep neural networks; Integration; Neural network models; Pulmonary diseases; Statistical tests; Airway trees; Chronic obstructive pulmonary disease; Computed tomography; Convolutional neural network; Deep learning; Healthy controls; Images classification; Lung fields; Multi-views; adult; aged; airway; Article; chronic obstructive lung disease; controlled study; convolutional neural network; cross validation; diagnostic accuracy; diagnostic value; female; human; image segmentation; lung; lung development; major clinical study; male; middle aged; morphology; pathophysiology; prediction; predictive model; residual neural network; three-dimensional imaging; tracheobronchial tree; x-ray computed tomography; Image classification","","","","","Key R&D Program Guidance Projects in Liaoning Province, (2019JH8/10300051); National Natural Science Foundation of China, NSFC, (61672146, 81671773, 82072008); Fundamental Research Funds for the Central Universities, (N2119010, N2124006‑3)","This work was partly supported by the National Natural Science Foundation of China under Grant (Nos. 82072008, 81671773, and 61672146), Key R&D Program Guidance Projects in Liaoning Province (2019JH8/10300051), and the Fundamental Research Funds for the Central Universities (N2119010, N2124006‑3). 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"Plaza Moral V.; Alobid I.; Álvarez Rodríguez C.; Blanco Aparicio M.; Ferreira J.; García G.; Gómez-Outes A.; Garín Escrivá N.; Gómez Ruiz F.; Hidalgo Requena A.; Korta Murua J.; Molina París J.; Pellegrini Belinchón F.J.; Plaza Zamora J.; Praena Crespo M.; Quirce Gancedo S.; Sanz Ortega J.; Soto Campos J.G.","Plaza Moral, Vicente (57188991363); Alobid, Isam (6603835520); Álvarez Rodríguez, Cesáreo (36182900500); Blanco Aparicio, Marina (6603249462); Ferreira, Jorge (57193906398); García, Gabriel (55108800300); Gómez-Outes, Antonio (6506531653); Garín Escrivá, Noé (58654762400); Gómez Ruiz, Fernando (36350693900); Hidalgo Requena, Antonio (35237308400); Korta Murua, Javier (23034822900); Molina París, Jesús (59157751900); Pellegrini Belinchón, Francisco Javier (58654655900); Plaza Zamora, Javier (55805961500); Praena Crespo, Manuel (6506029105); Quirce Gancedo, Santiago (56186264200); Sanz Ortega, José (7003578102); Soto Campos, José Gregorio (6602151116)","57188991363; 6603835520; 36182900500; 6603249462; 57193906398; 55108800300; 6506531653; 58654762400; 36350693900; 35237308400; 23034822900; 59157751900; 58654655900; 55805961500; 6506029105; 56186264200; 7003578102; 6602151116","GEMA 5.3. Spanish Guideline on the Management of Asthma; [GEMA 5.3. Guía Española para el Manejo del Asma]","2023","Open Respiratory Archives","5","4","100277","","","","28","10.1016/j.opresp.2023.100277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174453907&doi=10.1016%2fj.opresp.2023.100277&partnerID=40&md5=e859d01af7ef301af44e890684778f90","Neumología, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Otorrinolaringología, Hospital Clinic de Barcelona, Spain; Medicina de Urgencias, Hospital de Verín, Orense, Spain; Neumología, Complejo Hospitalario Universitario, A Coruña, Spain; Hospital de São Sebastião – CHEDV, Santa Maria da Feira, Portugal; Neumonología, Hospital Rossi La Plata, Argentina; Farmacología clínica, Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain; Farmacia Hospitalaria, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Medicina de familia, Centro de Salud de Bargas, Toledo, Spain; Medicina de familia, Centro de Salud Lucena I, Córdoba, Lucena, Spain; Neumología Pediátrica, Hospital Universitario Donostia, Sebastián, Donostia-San, Spain; Medicina de familia, semFYC, Centro de Salud Francia, Fuenlabrada, Dirección Asistencial Oeste, Madrid, Spain; Pediatría, Centro de Salud de Pizarrales, Salamanca, Spain; Farmacia comunitaria, Farmacia Dr, Javier Plaza Zamora, Mazarrón, Murcia, Spain; Centro de Salud La Candelaria, Sevilla, Spain; Alergología, Hospital Universitario La Paz, Madrid, Spain; Alergología Pediátrica, Hospital Católico Universitario Casa de Salud, Valencia, Spain; Neumología, Hospital Universitario de Jerez, Jerez de la Frontera, Spain","Plaza Moral V., Neumología, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Alobid I., Otorrinolaringología, Hospital Clinic de Barcelona, Spain; Álvarez Rodríguez C., Medicina de Urgencias, Hospital de Verín, Orense, Spain; Blanco Aparicio M., Neumología, Complejo Hospitalario Universitario, A Coruña, Spain; Ferreira J., Hospital de São Sebastião – CHEDV, Santa Maria da Feira, Portugal; García G., Neumonología, Hospital Rossi La Plata, Argentina; Gómez-Outes A., Farmacología clínica, Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain; Garín Escrivá N., Farmacia Hospitalaria, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Gómez Ruiz F., Medicina de familia, Centro de Salud de Bargas, Toledo, Spain; Hidalgo Requena A., Medicina de familia, Centro de Salud Lucena I, Córdoba, Lucena, Spain; Korta Murua J., Neumología Pediátrica, Hospital Universitario Donostia, Sebastián, Donostia-San, Spain; Molina París J., Medicina de familia, semFYC, Centro de Salud Francia, Fuenlabrada, Dirección Asistencial Oeste, Madrid, Spain; Pellegrini Belinchón F.J., Pediatría, Centro de Salud de Pizarrales, Salamanca, Spain; Plaza Zamora J., Farmacia comunitaria, Farmacia Dr, Javier Plaza Zamora, Mazarrón, Murcia, Spain; Praena Crespo M., Centro de Salud La Candelaria, Sevilla, Spain; Quirce Gancedo S., Alergología, Hospital Universitario La Paz, Madrid, Spain; Sanz Ortega J., Alergología Pediátrica, Hospital Católico Universitario Casa de Salud, Valencia, Spain; Soto Campos J.G., Neumología, Hospital Universitario de Jerez, Jerez de la Frontera, Spain","The Spanish Guideline on the Management of Asthma, better known by its acronym in Spanish GEMA, has been available for more than 20 years. Twenty-one scientific societies or related groups both from Spain and internationally have participated in the preparation and development of the updated edition of GEMA, which in fact has been currently positioned as the reference guide on asthma in the Spanish language worldwide. Its objective is to prevent and improve the clinical situation of people with asthma by increasing the knowledge of healthcare professionals involved in their care. Its purpose is to convert scientific evidence into simple and easy-to-follow practical recommendations. Therefore, it is not a monograph that brings together all the scientific knowledge about the disease, but rather a brief document with the essentials, designed to be applied quickly in routine clinical practice. The guidelines are necessarily multidisciplinary, developed to be useful and an indispensable tool for physicians of different specialties, as well as nurses and pharmacists. Probably the most outstanding aspects of the guide are the recommendations to: establish the diagnosis of asthma using a sequential algorithm based on objective diagnostic tests; the follow-up of patients, preferably based on the strategy of achieving and maintaining control of the disease; treatment according to the level of severity of asthma, using six steps from least to greatest need of pharmaceutical drugs, and the treatment algorithm for the indication of biologics in patients with severe uncontrolled asthma based on phenotypes. And now, in addition to that, there is a novelty for easy use and follow-up through a computer application based on the chatbot-type conversational artificial intelligence (ia-GEMA). © 2023","Asthma; diagnosis; practical guidelines; treatment","beclometasone; benralizumab; budesonide; budesonide plus formoterol; ciclesonide; dupilumab; fluticasone; formoterol; immunoglobulin E; ipratropium bromide; mepolizumab; mometasone furoate; omalizumab; reslizumab; salbutamol; tezepelumab; air pollution; airborne particle; Article; asthma; differential diagnosis; disease exacerbation; disease severity; drug approval; drug mechanism; environmental factor; food industry; fractional exhaled nitric oxide; grass pollen; human; lung function; nonhuman; pathogenesis; practice guideline; quality of life; respiratory tract infection; textile industry; workplace","","beclometasone, 4419-39-0; benralizumab, 1044511-01-4; budesonide, 51333-22-3, 51372-29-3; budesonide plus formoterol, 150693-37-1, 150693-38-2; ciclesonide, 126544-47-6; dupilumab, 1190264-60-8; fluticasone, 90566-53-3; formoterol, 73573-87-2; immunoglobulin E, 37341-29-0; ipratropium bromide, 22254-24-6, 66985-17-9; mepolizumab, 196078-29-2; mometasone furoate, 83919-23-7, 105102-22-5; omalizumab, 242138-07-4; reslizumab, 241473-69-8; salbutamol, 18559-94-9, 35763-26-9; tezepelumab, 1572943-04-4","","","","","(2015); (2009); (2007); Guyatt G.H., Oxman A.D., Vist G.E., Kunz R., Falck-Ytter Y., Alonso-Coello P., Et al., GRA- DE: an emerging consensus on rating quality of evidence and strength of recommendations, BMJ., 336, pp. 924-926, (2008); Alonso-Coello P., Rigau D., Juliana Sanabria A., Plaza V., Miravitlles M., Martinez L., Calidad y fuerza: el sistema GRADE para la formulación de recomendaciones en las guías de práctica clínica, Arch Bronconeumol., 49, pp. 261-267, (2013); (2019); Variations in the prevalence of respiratory symptoms, self-reported asthma attacks, and use of asthma medication in the European Community Respiratory Health Survey, Eur Respir, 9, pp. 687-695, (1996); The European Community Respiratory Health Survey II, EurRespir J., 20, pp. 1071-1079, (2002); Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet Respir Med., 5, pp. 691-706, (2017); Lundback B., Backman H., Lotvall J., Ronmark E., Is asthma prevalence still increasing?, Expert Rev Respir Med., 10, pp. 39-51, (2016); Estudio europeo del asma Prevalencia de hiperreactividad bronquial y asma en jóvenes en 5 regiones de España, Med Clin (Barc)., 106, pp. 761-767, (1996); Alvarez N., Guillen F., Aguinaga I., Hermoso de Mendoza J., Marin B., Serrano I., Et al., Estudio de prevalencia y asociación entre síntomas de asma y obesidad en la población pediátrica de Pamplona, Nutr Hosp., 30, pp. 519-525, (2014); Elizalde I., Guillen F., Aguinaga I., Factores asociados al asma en los niños y adolescentes de la zona rural de Navarra (España), Aten Primaria., 50, pp. 332-339, (2018); Lopez P., Gandarilla A.M., Diez L., Ordobas M., Evolución de la prevalencia de asma y factores demográficos y de salud asociados en población de 18-64 años de la Comunidad de Madrid (1996-2013), Rev Esp Salud Pública., 91, pp. e1-e14, (2017); Vila-Rigat R., Panades R., Hernandez E., Sivecas J., Blanche X., Munoz-Ortiz L., Et al., Prevalence of Work-Related Asthma and its Impact in Primary Health Care, Arch Bronconeumol., 51, pp. 449-455, (2015); Arias S.J., Neffen H., Bossio J.C., Calabrese C.A., Videla A.J., Armando G.A., Et al., Prevalencia y características clínicas del asma en adultos jóvenes en zonas urbanas de Argentina, Arch Bronconeumol., 54, pp. 134-139, (2018); Arbes S.J., Gergen P.J., Vaughn B., Zeldin D.C., Asthma cases attributable to atopy: results from the Third National Health and Nutrition Examination Survey, J Allergy Clin Immunol., 120, pp. 1139-1145, (2007); Minelli C., van der Plaat D.A., Leynaert B., Granell R., Amaral A.F.S., Pereira M., Et al., Age at puberty and risk of asthma: A Mendelian randomisation study, PLoS Med., 15, (2018); Egan K.B., Ettinger A.S., Bracken M.B., Childhood body mass index and subsequent physiciandiagnosed asthma: a systematic review and meta-analysis of prospective cohort studies, BMC Pediatr., 13, (2013); Carey V.J., Weiss S.T., Tager I.B., Leeder S.R., Speizer F.E., Airways responsiveness, wheeze onset, and recurrent asthma episodes in Young adolescents. 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Plaza Moral; Neumología, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; email: vplaza@santpau.cat","","Elsevier Espana S.L.U","","","","","","26596636","","","","English","Open Respirat. Arc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85174453907"
"Nadarajah R.; Alsaeed E.; Hurdus B.; Aktaa S.; Hogg D.; Bates M.G.D.; Cowan C.; Wu J.; Gale C.P.","Nadarajah, Ramesh (8672636400); Alsaeed, Eman (57219100876); Hurdus, Ben (57191894328); Aktaa, Suleman (57204447089); Hogg, David (7103188000); Bates, Matthew G.D. (36450083400); Cowan, Campbel (59622144800); Wu, Jianhua (57194400210); Gale, Chris P. (35837808000)","8672636400; 57219100876; 57191894328; 57204447089; 7103188000; 36450083400; 59622144800; 57194400210; 35837808000","Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis","2022","Heart","108","13","","1020","1029","9","16","10.1136/heartjnl-2021-320036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131902460&doi=10.1136%2fheartjnl-2021-320036&partnerID=40&md5=338b9190a32c6a04c871e1203fca3cbb","Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom; Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; School of Computing, University of Leeds, Leeds, United Kingdom; Department of Cardiology, South Tees Hospitals NHS Foundation Trust, Middlesbrough, United Kingdom; School of Dentistry, University of Leeds, Leeds, United Kingdom","Nadarajah R., Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom, Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Alsaeed E., Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom; Hurdus B., Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Aktaa S., Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom, Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Hogg D., School of Computing, University of Leeds, Leeds, United Kingdom; Bates M.G.D., Department of Cardiology, South Tees Hospitals NHS Foundation Trust, Middlesbrough, United Kingdom; Cowan C., Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom; Wu J., Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom, School of Dentistry, University of Leeds, Leeds, United Kingdom; Gale C.P., Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom, Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom","Objective Atrial fibrillation (AF) is common and is associated with an increased risk of stroke. We aimed to systematically review and meta-analyse multivariable prediction models derived and/or validated in electronic health records (EHRs) and/or administrative claims databases for the prediction of incident AF in the community. Methods Ovid Medline and Ovid Embase were searched for records from inception to 23 March 2021. Measures of discrimination were extracted and pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using Prediction model Risk Of Bias ASsessment Tool and certainty in effect estimates by Grading of Recommendations, Assessment, Development and Evaluation. Results Eleven studies met inclusion criteria, describing nine prediction models, with four eligible for meta-analysis including 9 289 959 patients. The CHADS (Congestive heart failure, Hypertension, Age>75, Diabetes mellitus, prior Stroke or transient ischemic attack) (summary c-statistic 0.674; 95% CI 0.610 to 0.732; 95% PI 0.526–0.815), CHA2DS2-VASc (Congestive heart failure, Hypertension, Age>75 (2 points), Stroke/transient ischemic attack/ thromboembolism (2 points), Vascular disease, Age 65–74, Sex category) (summary c-statistic 0.679; 95% CI 0.620 to 0.736; 95% PI 0.531–0.811) and HATCH (Hypertension, Age, stroke or Transient ischemic attack, Chronic obstructive pulmonary disease, Heart failure) (summary c-statistic 0.669; 95% CI 0.600 to 0.732; 95% PI 0.513–0.803) models resulted in a c-statistic with a statistically significant 95% PI and moderate discriminative performance. No model met eligibility for inclusion in meta-analysis if studies at high risk of bias were excluded and certainty of effect estimates was’low’. Models derived by machine learning demonstrated strong discriminative performance, but lacked rigorous external validation. Conclusions Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.","","Aged; Atrial Fibrillation; Bayes Theorem; Electronic Health Records; Heart Failure; Humans; Hypertension; Ischemic Attack, Transient; Risk Assessment; Risk Factors; Stroke; adult; aged; Article; atrial fibrillation; community; electronic medical record; female; human; incidence; major clinical study; male; model; prediction; systematic review; atrial fibrillation; Bayes theorem; cerebrovascular accident; complication; electronic health record; heart failure; hypertension; meta analysis; procedures; risk assessment; risk factor; transient ischemic attack","","","","","British Heart Foundation, BHF, (FS/20/12/34789); British Heart Foundation, BHF","Funding RN is supported by the British Heart Foundation Clinical Research Training Fellowship (FS/20/12/34789).","Wolf P.A., Abbott R.D., Kannel W.B., Atrial fibrillation as an independent risk factor for stroke: the Framingham study, Stroke, 22, pp. 983-988, (1991); Hindricks G., Potpara T., Dagres N., Et al., 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European association for Cardio-Thoracic surgery (EACTS): the task force for the diagnosis and management of atrial fibrillation of the European Society of cardiology (ESC) developed with the special contribution of the European heart rhythm association (EHRA) of the ESC, Eur Heart J, 42, pp. 373-498, (2021); 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Berlin J.A., Santanna J., Schmid C.H., Et al., Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head, Stat Med, 21, pp. 371-387, (2002); Collins G.S., de Groot J.A., Dutton S., Et al., External validation of multivariable prediction models: a systematic review of methodological conduct and reporting, BMC Med Res Methodol, 14, pp. 1-11, (2014); Morrison A., Polisena J., Husereau D., Et al., The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies, Int J Technol Assess Health Care, 28, pp. 138-144, (2012); Alonso A., Krijthe B.P., Aspelund T., Et al., Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF Consortium, J Am Heart Assoc, 2, (2013)","R. Nadarajah; Leeds Institute of Data Analytics, University of Leeds, Leeds, United Kingdom; email: r.nadarajah@leeds.ac.uk","","BMJ Publishing Group","","","","","","13556037","","HEARF","34607811","English","Heart","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85131902460"
"Jabbour S.; Fouhey D.; Shepard S.; Valley T.S.; Kazerooni E.A.; Banovic N.; Wiens J.; Sjoding M.W.","Jabbour, Sarah (57221148269); Fouhey, David (36622345400); Shepard, Stephanie (58773791700); Valley, Thomas S. (54386099800); Kazerooni, Ella A. (57216996382); Banovic, Nikola (54794750000); Wiens, Jenna (37110475500); Sjoding, Michael W. (56426422500)","57221148269; 36622345400; 58773791700; 54386099800; 57216996382; 54794750000; 37110475500; 56426422500","Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study","2023","JAMA","330","23","","2275","2284","9","45","10.1001/jama.2023.22295","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180293804&doi=10.1001%2fjama.2023.22295&partnerID=40&md5=14440c78d2eb1ceaf390be6fa5c95f7e","Computer Science and Engineering, University of Michigan, Ann Arbor, United States; Now with Computer Science Courant Institute, New York University, New York, United States; Now with Electrical and Computer Engineering Tandon School of Engineering, New York University, New York, United States; Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States; Department of Radiology, University of Michigan Medical School, Ann Arbor, United States","Jabbour S., Computer Science and Engineering, University of Michigan, Ann Arbor, United States; Fouhey D., Computer Science and Engineering, University of Michigan, Ann Arbor, United States, Now with Computer Science Courant Institute, New York University, New York, United States, Now with Electrical and Computer Engineering Tandon School of Engineering, New York University, New York, United States; Shepard S., Computer Science and Engineering, University of Michigan, Ann Arbor, United States; Valley T.S., Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States; Kazerooni E.A., Department of Radiology, University of Michigan Medical School, Ann Arbor, United States; Banovic N., Computer Science and Engineering, University of Michigan, Ann Arbor, United States; Wiens J., Computer Science and Engineering, University of Michigan, Ann Arbor, United States; Sjoding M.W., Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States","Importance: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established. Objectives: To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors. Design, Setting, and Participants: Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants. Interventions: Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient's acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions. Main Outcomes and Measures: Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease. Results: Median participant age was 34 years (IQR, 31-39) and 241 (57.7%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians' baseline diagnostic accuracy was 73.0% (95% CI, 68.3% to 77.8%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95% CI, 0.5 to 5.2) and by 4.4 percentage points (95% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95% CI, -2.7 to 7.2) compared with the systematically biased AI model. Conclusions and Relevance: Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect. Trial Registration: ClinicalTrials.gov Identifier: NCT06098950. © 2023 American Medical Association. All rights reserved.","","Adult; Artificial Intelligence; Female; Heart Failure; Humans; Male; Pneumonia; Pulmonary Disease, Chronic Obstructive; Respiratory Insufficiency; acute respiratory failure; adult; Article; artificial intelligence; chronic obstructive lung disease; diagnostic accuracy; evaluation study; female; heart failure; hospitalization; human; male; nurse practitioner; physical examination; physician; physician assistant; pneumonia; prediction; thorax radiography; United States; vignette; artificial intelligence; chronic obstructive lung disease; controlled study; heart failure; pneumonia; randomized controlled trial; respiratory failure","","","","","National Science Foundation, NSF; U.S. Department of Energy, USDOE; National Heart, Lung, and Blood Institute, NHLBI; Toyota Research Institute, TRI","Funding text 1: Conflict of Interest Disclosures: Dr Banovic reported receiving grants from the US Department of Energy, Toyota Research Institute, and National Science Foundation outside the submitted work. Dr Wiens reported receiving grants from the Alfred P. Sloan Foundation during the conduct of the study and serving on the advisory board of Machine Learning for Healthcare, a nonprofit organization that hosts a yearly academic conference. Dr Sjoding reported receiving royalties for a patent from Airstrip outside the submitted work. No other disclosures were reported. ; Funding text 2: Funding/Support: This work was supported by grant R01 HL158626 from the National Heart, Lung, and Blood Institute (NHLBI) . ","Tschandl P., Rinner C., Apalla Z., Human-computer collaboration for skin cancer recognition, Nat Med, 26, 8, pp. 1229-1234, (2020); Gulshan V., Peng L., Coram M., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 22, pp. 2402-2410, (2016); Van Der Laak J., Litjens G., Ciompi F., Deep learning in histopathology: The path to the clinic, Nat Med, 27, 5, pp. 775-784, (2021); Kather J.N., Weis C.-A., Bianconi F., Multi-class texture analysis in colorectal cancer histology, Sci Rep, 6, 1, (2016); Jabbour S., Fouhey D., Kazerooni E., Sjoding M.W., Wiens J., Deep learning applied to chest x-rays: Exploiting and preventing shortcuts, Proc Mach Learn Res, 126, pp. 750-782, (2020); Gichoya J.W., Banerjee I., Bhimireddy A.R., AI recognition of patient race in medical imaging: A modelling study, Lancet Digit Health, 4, 6, pp. e406-e414, (2022); Obermeyer Z., Powers B., Vogeli C., Mullainathan S., Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366, 6464, pp. 447-453, (2019); Beery T.A., Gender bias in the diagnosis and treatment of coronary artery disease, Heart Lung, 24, 6, pp. 427-435, (1995); Gaube S., Suresh H., Raue M., Do as AI say: Susceptibility in deployment of clinical decision-aids, NPJ Digit Med, 4, 1, (2021); Bucinca Z., Malaya M.B., Gajos K.Z., To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making; Vasconcelos H., Jorke M., Grunde-Mclaughlin M., Gerstenberg T., Bernstein M.S., Krishna R., Explanations Can Reduce Overreliance on AI Systems during Decision-making; Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff, (2022); Bhatt U., Xiang A., Sharma S., Explainable machine learning in deployment, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, (2020); Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-CAM: Visual Explanations from Deep Networks Via Gradient-based Localization; Kempker J.A., Abril M.K., Chen Y., Kramer M.R., Waller L.A., Martin G.S., The epidemiology of respiratory failure in the United States 2002-2017: A serial cross-sectional study, Crit Care Explor, 2, 6, (2020); Zwaan L., Thijs A., Wagner C., Van Der Wal G., Timmermans D.R.M., Relating faults in diagnostic reasoning with diagnostic errors and patient harm, Acad Med, 87, 2, pp. 149-156, (2012); Vasconcelos H., Jorke M., Grunde-Mclaughlin M., Gerstenberg T., Bernstein M., Krishna R., Explanations Can Reduce Overreliance on AI Systems during Decision-making, (2022); Jabbour S., Fouhey D., Kazerooni E., Wiens J., Sjoding M.W., Combining chest x-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure, J Am Med Inform Assoc, 29, 6, pp. 1060-1068, (2022); Ray P., Birolleau S., Lefort Y., Acute respiratory failure in the elderly: Etiology, emergency diagnosis and prognosis, Crit Care, 10, 3, (2006); Clayton D.G., Gilks W.R., Richardson S., Spiegelhalter D.J., Generalized linear mixed models, Markov Chain Monte Carlo in Practice, pp. 275-302, (1996); Oehlert G.W., A note on the delta method, Am Stat, 46, 1, pp. 27-29, (1992); Degrave A.J., Janizek J.D., Lee S.-I., AI for radiographic COVID-19 detection selects shortcuts over signal, Nat Mach Intell, 3, 7, pp. 610-619, (2021); Ray P., Birolleau S., Lefort Y., Acute respiratory failure in the elderly: Etiology, emergency diagnosis and prognosis, Crit Care, 10, 3, (2006); Bai B., Liang J., Zhang G., Li H., Bai K., Wang F., Why Attentions May Not Be Interpretable?; Banovic N., Yang Z., Ramesh A., Liu A., Being trustworthy is not enough: How untrustworthy artificial intelligence (AI) can deceive the end-users and gain their trust, Proc ACM Hum Comput Interact, 7, CSCW1, pp. 1-17, (2023); Long D., Magerko B., What is AI literacy? competencies and design considerations, Proc Conf Hum Factors Comput Syst, pp. 1-16, (2020); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Proc 31st Int Conf Neural Info Process Systems, pp. 4768-4777, (2017); Ribeiro M.T., Singh S., Guestrin C., Model-agnostic Interpretability of Machine Learning.; Pazzani M., Soltani S., Kaufman R., Qian S., Hsiao A., Expert-informed, user-centric explanations for machine learning, Proc AAAI Conf Art Intel, 36, 11, pp. 12280-12286, (2022); Shachar C., Gerke S., Prevention of bias and discrimination in clinical practice algorithms, JAMA, 329, 4, pp. 283-284, (2023); Nondiscrimination in health programs and activities: Final rule, Fed Regist, 87, pp. 47824-47920, (2022); Blueprint for An AI Bill of Rights: Making Automated Systems Work for the American People, (2023); Otles E., James C.A., Lomis K.D., Woolliscroft J.O., Teaching artificial intelligence as a fundamental toolset of medicine, Cell Rep Med, 3, 12, (2022); Sendak M.P., Gao M., Brajer N., Balu S., Presenting machine learning model information to clinical end users with model facts labels, NPJ Digit Med, 3, 1, (2020); Ryskina K.L., Shultz K., Unruh M.A., Jung H.-Y., Practice trends and characteristics of US hospitalists from 2012 to 2018, JAMA Health Forum, 2, 11, pp. e213524-e213524, (2021); Bubeck S., Chandrasekaran V., Eldan R., Sparks of Artificial General Intelligence: Early Experiments with GPT-4, (2023)","J. Wiens; Computer Science and Engineering, University of Michigan, Ann Arbor, 3749 Beyster Bldg, 2260 Haward St, 48109, United States; email: wiensj@umich.edu; M.W. Sjoding; Internal Medicine, Ann Arbor, G020W Bldg 16 NCRC, 2800 Plymouth Rd, SPC 2800, 48109, United States; email: msjoding@umich.edu","","American Medical Association","","","","","","00987484","","JAMAA","38112814","English","JAMA","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85180293804"
"Xing F.; Luo R.; Liu M.; Zhou Z.; Xiang Z.; Duan X.","Xing, Fei (56471337600); Luo, Rong (57213527077); Liu, Ming (57198348950); Zhou, Zongke (9940265100); Xiang, Zhou (7102139053); Duan, Xin (12645250900)","56471337600; 57213527077; 57198348950; 9940265100; 7102139053; 12645250900","A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures","2022","Frontiers in Medicine","9","","829977","","","","20","10.3389/fmed.2022.829977","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130693842&doi=10.3389%2ffmed.2022.829977&partnerID=40&md5=1da29d7bd991f05d8651958b5bb23768","Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China","Xing F., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; Luo R., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; Liu M., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; Zhou Z., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; Xiang Z., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; Duan X., Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China","Background: Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients. Methods: This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model. Results: Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively. Conclusions: Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year. Copyright © 2022 Xing, Luo, Liu, Zhou, Xiang and Duan.","hip fracture; machine learning; mortality; prediction model; random forest","albumin; hemoglobin; adult; age distribution; aged; area under the curve; Article; blood transfusion; cohort analysis; feature selection; female; geriatric patient; hip fracture; human; least absolute shrinkage and selection operator; logistic regression analysis; machine learning; major clinical study; male; observational study; obstructive lung disease; perioperative period; prediction; random forest; receiver operating characteristic; retrospective study; risk factor; surgical mortality","","hemoglobin, 9008-02-0","","","National Clinical Research Center for Geriatrics; National Natural Science Foundation of China, NSFC, (31870961, 81501879); National Natural Science Foundation of China, NSFC; Department of Science and Technology of Sichuan Province, SPDST, (2015HH0049, 2017SZ0127, 2022YFS0099); Department of Science and Technology of Sichuan Province, SPDST; Sichuan University, SCU, (Z2018A11); Sichuan University, SCU; Chinesisch-Deutsche Zentrum für Wissenschaftsförderung, CDZ, (GZ1219); Chinesisch-Deutsche Zentrum für Wissenschaftsförderung, CDZ","This work was supported by the National Natural Science Foundation of China (31870961 and 81501879), the Sino-German Center for Research Promotion (GZ1219), the Science and Technology Department of Sichuan Province (Grant Nos. 2015HH0049, 2017SZ0127, and 2022YFS0099), and the National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2018A11). 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Zanetti M., Cappellari G.G., Ratti C., Ceschia G., Murena L., De Colle P., Et al., Poor nutritional status but not cognitive or functional impairment per se independently predict 1 year mortality in elderly patients with hip-fracture, Clin Nutr, 38, pp. 1607-1612, (2019); de Luise C., Brimacombe M., Pedersen L., Sorensen H.T., Chronic obstructive pulmonary disease and mortality following hip fracture: a population-based cohort study, Eur J Epidemiol, 23, pp. 115-122, (2008); Cha Y.-H., Ha Y.-C., Park H.-J., Lee Y.-K., Jung S.-Y., Kim J.-Y., Et al., Relationship of chronic obstructive pulmonary disease severity with early and late mortality in elderly patients with hip fracture, Injury, 50, pp. 1529-1533, (2019); Yombi J.C., Putineanu D.C., Cornu O., Lavand'homme P., Cornette P., Castanares-Zapatero D., Low haemoglobin at admission is associated with mortality after hip fractures in elderly patients, Bone Joint J, 101-B, pp. 1122-1128, (2019); Wilson J.M., Lunati M.P., Grabel Z.J., Staley C.A., Schwartz A.M., Schenker M.L., Hypoalbuminemia Is an independent risk factor for 30-day mortality, postoperative complications, readmission, and reoperation in the operative lower extremity orthopaedic Trauma Patient, J Orthop Trauma, 33, pp. 284-291, (2019); Lizaur-Utrilla A., Gonzalez-Navarro B., Vizcaya-Moreno M.F., Miralles Munoz F.A., Gonzalez-Parreno S., Lopez-Prats F.A., Reasons for delaying surgery following hip fractures and its impact on one year mortality, Int Orthop, 43, pp. 441-448, (2019); Ozturk B., Johnsen S.P., Rock N.D., Pedersen L., Pedersen A.B., Impact of comorbidity on the association between surgery delay and mortality in hip fracture patients: a Danish nationwide cohort study, Injury, 50, pp. 424-431, (2019); Smeets S.J.M., Verbruggen J.P.A.M., Poeze M., Effect of blood transfusion on survival after hip fracture surgery, Eur J Orthop Surg Traumatol, 28, pp. 1297-1303, (2018); Arshi A., Lai W.C., Iglesias B.C., McPherson E.J., Zeegen E.N., Stavrakis A.I., Et al., Blood transfusion rates and predictors following geriatric hip fracture surgery, HIP Int, 31, pp. 272-279, (2021); Dubljanin-Raspopovic E., Markovic-Denic L., Marinkovic J., Nedeljkovic U., Bumbasirevic M., Does early functional outcome predict 1-year mortality in elderly patients with hip fracture?, Clin Orthop Relat Res, 8, pp. 2703-2710, (2013)","X. Duan; Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China; email: dxbaal@hotmail.com","","Frontiers Media S.A.","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130693842"
"Zolbanin H.M.; Davazdahemami B.; Delen D.; Zadeh A.H.","Zolbanin, Hamed M. (56016604600); Davazdahemami, Behrooz (57189870930); Delen, Dursun (55887961100); Zadeh, Amir Hassan (57142063200)","56016604600; 57189870930; 55887961100; 57142063200","Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases","2022","Information and Management","59","5","103282","","","","31","10.1016/j.im.2020.103282","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079834062&doi=10.1016%2fj.im.2020.103282&partnerID=40&md5=d41a48ecadb9f1d2a3ee20fc2cb227ec","Department of MIS, Operations Management, and Decision Sciences, University of Dayton, Dayton, OH, United States; Department of Information Technology and Supply Chain Management, College of Business and Economics, University of Wisconsin Whitewater, Whitewater, 53190, WI, United States; Department of Management Science and Information Systems, Center for Health Systems Innovation, Spears School of Business, Oklahoma State University, Tulsa, 74106, OK, United States; Department of Information Systems and Supply Chain Management, Raj Soin College of Business, Wright State University, Dayton, 45435, OH, United States","Zolbanin H.M., Department of MIS, Operations Management, and Decision Sciences, University of Dayton, Dayton, OH, United States; Davazdahemami B., Department of Information Technology and Supply Chain Management, College of Business and Economics, University of Wisconsin Whitewater, Whitewater, 53190, WI, United States; Delen D., Department of Management Science and Information Systems, Center for Health Systems Innovation, Spears School of Business, Oklahoma State University, Tulsa, 74106, OK, United States; Zadeh A.H., Department of Information Systems and Supply Chain Management, Raj Soin College of Business, Wright State University, Dayton, 45435, OH, United States","Various factors are behind the forces that drive hospitals toward more sustainable operations. Hospitals contracting with Medicare, for instance, are reimbursed for the procedures performed, regardless of the number of days that patients stay in the hospital. This reimbursement structure has incentivized hospitals to use their resources (such as their beds) more efficiently to maximize revenues. One way hospitals can improve bed utilization is by predicting patients’ length of stay (LOS) at the time of admission, the benefits of which extend to employees, communities, and the patients themselves. In this paper, we employ a data analytics approach to develop and test a deep learning neural network to predict LOS for patients with chronic obstructive pulmonary disease (COPD) and pneumonia. The theoretical contribution of our effort is that it identifies variables related to patients’ prior admissions as important factors in the prediction of LOS in hospitals, thereby revising the current paradigm in which patients’ medical histories are rarely considered for the prediction of LOS. The methodological contributions of our work include the development of a data engineering methodology to augment the data sets, prediction of LOS as a numerical (rather than a binary) variable, temporal evaluation of the training and validation data sets, and a significant improvement in the accuracy of predicting LOS for COPD and pneumonia inpatients. Our evaluations show that variables related to patients’ previous admissions are the main driver of the deep network's superior performance in predicting the LOS as a numerical variable. Using the assessment criteria introduced in prior studies (i.e., ±2 days and ±3 days tolerance), our models are able to predict the length of hospital stay with 86 % and 91 % accuracy for the COPD data set, and with 74 % and 85 % accuracy for the pneumonia data set. Hence, our effort could help hospitals serve a larger number of patients with a fixed amount of resources, thereby reducing their environmental footprint while increasing their revenue, as well as their patients’ satisfaction. © 2020 Elsevier B.V.","Data analytics; Deep learning; Length of hospital stay; Sustainability; Temporal evaluation","Data Analytics; Deep learning; Deep neural networks; Digital storage; Forecasting; Health insurance; Personnel training; Pulmonary diseases; Sustainable development; Chronic obstructive pulmonary disease; Environmental footprints; Learning neural networks; Length of hospital stays; Methodological contributions; Numerical variables; Sustainable operations; Temporal evaluation; Hospitals","","","","","Center for Health Systems Innovation; Cerner Corporation; Oklahoma State University, OSU; Center for Health System Innovation, University of Southern California, CHSI, USC","Funding text 1: This study was conducted with the data provided by, and the support from, the Center for Health Systems Innovation (CHSI) at Oklahoma State University (OSU) and the Cerner Corporation. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of CHSI, OSU or the Cerner Corporation.; Funding text 2: This study was conducted with the data provided by, and the support from, the Center for Health Systems Innovation (CHSI) at Oklahoma State University (OSU) and the Cerner Corporation. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of CHSI, OSU or the Cerner Corporation.  ","Becker's Healthcare, How Predicting Patient Length of Stay Enables Hospitals to Save Millions, (2018); Kozma C.M., Dickson M., Raut M.K., Mody S., Fisher A.C., Schein J.R., Mackowiak J.I., Economic benefit of a 1-day reduction in hospital stay for community-acquired pneumonia (CAP), J. Med. 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Zolbanin; University of Dayton, Department of MIS, OM, and DS, Dayton, 300 College Park, 45469-2130, United States; email: hmzolbanin@udayton.edu","","Elsevier B.V.","","","","","","03787206","","IMAND","","English","Inf Manage","Article","Final","","Scopus","2-s2.0-85079834062"
"Bhosale Y.H.; Patnaik K.S.","Bhosale, Yogesh H. (57772765000); Patnaik, K. Sridhar (24438167800)","57772765000; 24438167800","Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review","2023","Multimedia Tools and Applications","82","25","","39157","39210","53","16","10.1007/s11042-023-15029-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150043981&doi=10.1007%2fs11042-023-15029-1&partnerID=40&md5=657efa87e2d33289fdb26a6d58ab8524","Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India","Bhosale Y.H., Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India; Patnaik K.S., Computer Science and Engineering Department, Birla Institute of Technology, Mesra, Ranchi, India","This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists’ arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","Bio-medical imaging; Chronic obstructive pulmonary diseases (COPD); Classification; CT; Deep machine learning; Deep neural network; Diagnosis; ECG); Pattern / feature extraction; Radiography imaging(X-ray; Ultrasound","Computer aided diagnosis; Computerized tomography; Electrocardiography; Learning systems; Medical imaging; Pulmonary diseases; Ultrasonic applications; Bio-medical; Bio-medical imaging; Chronic obstructive pulmonary disease; CT; Deep machine learning; ECG); Machine-learning; Pattern feature extraction; Radiography imaging(X-ray; Deep neural networks","","","","","","","Available; Abdani S.R., Zulkifley M.A., Zulkifley N.H., A Lightweight Deep Learning Model for COVID-19 Detection, In 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA), TBD, Malaysia, pp. 1-5, (2020); 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Dashboard (Online), (2023); Wang Z., Tang K., Combating COVID-19: health equity matters, Nat Med, 26, 4, (2020); Wang X., Deng X., Fu Q., Zhou Q., Feng J., Ma H., Liu W., Zheng C., A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT, IEEE Trans Med Imaging, 39, 8, pp. 2615-2625, (2020); Wang S., Et al., A deep learning algorithm using CT images to screen for coronavirus disease (COVID-19), Eur Radiol, (2021); Wang Y., Feng Z., Song L., Liu X., Liu S., Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning”, Computat Math Methods Med, 12, (2021); Xu X., Jiang X., Ma C., du P., Li X., Lv S., Yu L., Ni Q., Chen Y., Su J., Lang G., Li Y., Zhao H., Liu J., Xu K., Ruan L., Sheng J., Qiu Y., Wu W., Liang T., Li L., A deep learning system to screen novel coronavirus disease 2019 pneumonia, Engineering, 6, 10, pp. 1122-1129, (2020); Yan Q., Wang B., Gong D., Luo C., Zhao W., Shen J., Ai J., Shi Q., Zhang Y., Jin S., Zhang L., You Z., COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations, IEEE Trans Big Data, 7, 1, pp. 13-24, (2021); Yang M., Liu M., Chen Y., Et al., Diagnostic efficacy of ultrasound combined with magnetic resonance imaging in diagnosis of deep pelvic endometriosis under deep learning, J Supercomput, 77, pp. 7598-7619, (2021); Zebin T., Rezvy S., COVID-19 detection and disease progression visualization: deep learning on chest X-rays for classification and coarse localization, Appl Intell, 51, 2, pp. 1010-1021, (2021); Zhang H., Zhang J.S., Zhang H.H., Nan Y.D., Zhao Y., Fu E.Q., Xie Y.H., Liu W., Li W.P., Zhang H.J., Jiang H., Li C.M., Li Y.Y., Ma R.N., Dang S.K., Gao B.B., Zhang X.J., Zhang T., Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software, Eur J Nucl Med Mol Imaging, 47, 11, pp. 2525-2532, (2020)","Y.H. Bhosale; Computer Science and Engineering Department, Birla Institute of Technology, Ranchi, Mesra, India; email: yogeshbhosale988@gmail.com","","Springer","","","","","","13807501","","MTAPF","","English","Multimedia Tools Appl","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85150043981"
"Makimoto K.; Au R.; Moslemi A.; Hogg J.C.; Bourbeau J.; Tan W.C.; Kirby M.","Makimoto, Kalysta (57738337000); Au, Ryan (57405301900); Moslemi, Amir (57737896400); Hogg, James C. (7201452328); Bourbeau, Jean (34567907500); Tan, Wan C. (13403886200); Kirby, Miranda (35174507500)","57738337000; 57405301900; 57737896400; 7201452328; 34567907500; 13403886200; 35174507500","Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features","2023","Academic Radiology","30","5","","900","910","10","18","10.1016/j.acra.2022.07.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136776662&doi=10.1016%2fj.acra.2022.07.016&partnerID=40&md5=7b21248ca625ddd2ce7d65bdd750cd75","Toronto Metropolitan University, Kerr Hall South Bldg. Room – KHS-344, 350 Victoria St., Toronto, M5B 2K3, ON, Canada; Western University, London, ON, Canada; Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, Québec, Canada; Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, Québec, Canada","Makimoto K., Toronto Metropolitan University, Kerr Hall South Bldg. Room – KHS-344, 350 Victoria St., Toronto, M5B 2K3, ON, Canada; Au R., Western University, London, ON, Canada; Moslemi A., Toronto Metropolitan University, Kerr Hall South Bldg. Room – KHS-344, 350 Victoria St., Toronto, M5B 2K3, ON, Canada; Hogg J.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Bourbeau J., Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, Québec, Canada, Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, Québec, Canada; Tan W.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Kirby M., Toronto Metropolitan University, Kerr Hall South Bldg. Room – KHS-344, 350 Victoria St., Toronto, M5B 2K3, ON, Canada","Rationale: Texture-based radiomics analysis of lung computed tomography (CT) images has been shown to predict chronic obstructive pulmonary disease (COPD) status using machine learning models. However, various approaches are used and it is unclear which provides the best performance. Objectives: To compare the most commonly used feature selection and classification methods and determine the optimal models for classifying COPD status in a mild, population-based COPD cohort. Materials and Methods: CT images from the multi-center Canadian Cohort Obstructive Lung Disease (CanCOLD) study were pre-processed by resampling the image to a 1mm isotropic voxel volume, segmenting the lung and removing the airways (VIDA Diagnostics Inc.), and applying a threshold of -1000HU-to-0HU. A total of 95 texture features were then extracted from each CT image. Combinations of 17 feature selection methods and 9 classifiers were tested and evaluated. In addition, the role of data cleaning (outlier removal and highly correlated feature removal) was evaluated. The area under the curve (AUC) from the receiver operating characteristic curve was used to evaluate model performance. Results: A total of 1204 participants were evaluated (n = 602 no COPD, n = 602 COPD). There were no significant differences between the groups for female sex (no COPD = 46.3%; COPD = 38.5%; p = 0.77), or body mass index (no COPD = 27.7 kg/m2; COPD = 27.4 kg/m2; p = 0.21). The highest AUC value for predicting COPD status (AUC = 0.78 [0.73, 0.84]) was obtained following data cleaning and feature selection using Elastic Net with the Linear-SVM classifier. Conclusion: In a population-based cohort, the optimal combination for radiomics-based prediction of COPD status was Elastic Net as the feature selection method and Linear-SVM as the classifier. © 2022 The Association of University Radiologists","AI; COPD; CT imaging; Machine learning; Quantitative imaging; Radiomics","Canada; Female; Humans; Lung; Machine Learning; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; aged; Article; body mass; chronic obstructive lung disease; classifier; cohort analysis; comparative study; controlled study; feature selection; female; forced expiratory volume; forced vital capacity; human; image segmentation; linear support vector machine; lung function; lung function test; major clinical study; male; prediction; prospective study; radiomics; smoking; spirometry; x-ray computed tomography; Canada; chronic obstructive lung disease; diagnostic imaging; lung; machine learning; procedures; x-ray computed tomography","","","","","Darcy Marciniuk, Ron Clemens; Francois Maltais; John Hopkins School of Public Health, Baltimore; UBC James Hogg Research Center; Yvan Fortier and Mina Dligui; University of Calgary, U of C; Keck School of Medicine of USC; University of Saskatchewan, UOS; Université de Sherbrooke, UdeS; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs; Dalhousie University; University of Toronto, U of T","Funding text 1: M. Kirby acknowledges support from the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, the Early Researchers Award Program, and the Canada Research Chair Program (Tier II). The authors would also like to thank the men and women who participated in the study and individuals in the *CanCOLD Collaborative research Group: Jonathon Samet (the Keck School of Medicine of USC, California, USA); Milo Puhan (John Hopkins School of Public Health, Baltimore, USA); Qutayba Hamid, Carolyn Baglole, Palmina Mancino, Pei-Zhi Li, Zhi Song, Dennis Jensen, Benjamin Mcdonald Smith (McGill University, Montreal, QC, Canada); Yvan Fortier and Mina Dligui (Sherbrooke University, Sherbrooke, QC, Canada); Kenneth Chapman, Jane Duke, Andrea S Gershon, Teresa To, (University of Toronto, Toronto, ON Canada); J Mark Fitzgerald, Mohsen Sadatsafavi (University of British Columbia, Vancouver, BC); Christine Lo, Sarah Cheng, Elena Un, Michael Cheng, Cynthia Fung, Nancy Haynes, Liyun Zheng, LingXiang Zou, Joe Comeau, Jonathon Leipsic, Cameron Hague (UBC James Hogg Research Center, Vancouver, BC, Canada); Brandie L Walker, Curtis Dumonceaux, (University of Calgary, Calgary, AB, Canada); Paul Hernandez, Scott Fulton, (University of Dalhousie, Halifax, NS, Canada); Shawn Aaron, Kathy Vandemheen, (University of Ottawa, Ottawa, ON, Canada); Denis O'Donnell, Matthew McNeil, Kate Whelan (Queen's University, Kingston, ON, Canada); Francois Maltais, Cynthia Brouillard (University of Laval, Quebec City, QC, Canada); Darcy Marciniuk, Ron Clemens, Janet Baran (University of Saskatchewan, Saskatoon, SK, Canada). ; Funding text 2: M. Kirby acknowledges support from the Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, the Early Researchers Award Program, and the Canada Research Chair Program (Tier II). The authors would also like to thank the men and women who participated in the study and individuals in the *CanCOLD Collaborative research Group: Jonathon Samet (the Keck School of Medicine of USC, California, USA); Milo Puhan (John Hopkins School of Public Health, Baltimore, USA); Qutayba Hamid, Carolyn Baglole, Palmina Mancino, Pei-Zhi Li, Zhi Song, Dennis Jensen, Benjamin Mcdonald Smith (McGill University, Montreal, QC, Canada); Yvan Fortier and Mina Dligui (Sherbrooke University, Sherbrooke, QC, Canada); Kenneth Chapman, Jane Duke, Andrea S Gershon, Teresa To, (University of Toronto, Toronto, ON Canada); J Mark Fitzgerald, Mohsen Sadatsafavi (University of British Columbia, Vancouver, BC); Christine Lo, Sarah Cheng, Elena Un, Michael Cheng, Cynthia Fung, Nancy Haynes, Liyun Zheng, LingXiang Zou, Joe Comeau, Jonathon Leipsic, Cameron Hague (UBC James Hogg Research Center, Vancouver, BC, Canada); Brandie L Walker, Curtis Dumonceaux, (University of Calgary, Calgary, AB, Canada); Paul Hernandez, Scott Fulton, (University of Dalhousie, Halifax, NS, Canada); Shawn Aaron, Kathy Vandemheen, (University of Ottawa, Ottawa, ON, Canada); Denis O'Donnell, Matthew McNeil, Kate Whelan (Queen's University, Kingston, ON, Canada); Francois Maltais, Cynthia Brouillard (University of Laval, Quebec City, QC, Canada); Darcy Marciniuk, Ron Clemens, Janet Baran (University of Saskatchewan, Saskatoon, SK, Canada).","Barnes P.J., Celli B.R., Systemic manifestations and comorbidities of COPD, Eur Respir J, 33, 5, pp. 1165-1185, (2009); Shaker S.B., Stavngaard T., Laursen L.C., Et al., Rapid fall in lung density following smoking cessation in COPD, J Chronic Obstr Pulm Dis, 8, 1, pp. 2-7, (2011); Gietema H.A., Muller N.L., Nasute Fauerbach P.V., Et al., Quantifying the extent of emphysema: factors associated with radiologists’ estimations and quantitative indices of emphysema severity using the ECLIPSE cohort, Acad Radiol, 18, 6, pp. 661-671, (2011); Virdee S., Tan W.C., Hogg J.C., Et al., Spatial dependence of ct emphysema in chronic obstructive pulmonary disease quantified by using join-count statistics, Radiology, 301, 3, pp. 702-709, (2021); Kirby M., Smith B.M., Tanabe N., Et al., Computed tomography total airway count predicts progression to COPD in at-risk smokers, ERJ Open Res, 7, 4, pp. 00307-02021, (2021); Charbonnier J.P., Pompe E., Moore C., Et al., Airway wall thickening on CT: relation to smoking status and severity of COPD, Respir Med, 146, pp. 36-41, (2019); Moslemi A., Makimoto K., Tan W.C., Et al., Quantitative CT lung imaging and machine learning improves prediction of emergency room visits and hospitalizations in COPD, Acad Radiol, (2022); Kirby M., Hatt C., Obuchowski N., Et al., Inter- and intra-software reproducibility of computed tomography lung density measurements, Med Phys, 47, 7, pp. 2962-2969, (2020); Muller N.L., Staples C.A., Miller R.R., Et al., “Density mask”: an objective method to quantitate emphysema using computed tomography, Chest, 94, 4, pp. 782-787, (1988); Zwanenburg A., Vallieres M., Abdalah M.A., Et al., (2020); Sun P., Wang D., Mok V.C., Et al., Comparison of feature selection methods and machine learning classifiers for radiomics analysis in glioma grading, IEEE Access, 7, pp. 102010-102020, (2019); Parmar C., Grossmann P., Bussink J., Et al., Machine learning methods for quantitative radiomic biomarkers, Sci Rep, 5, 1, pp. 1-11, (2015); Krajnc D., Papp L., Nakuz T.S., Et al., Breast tumor characterization using [18F]FDG-PET/CT imaging combined with data preprocessing and radiomics, Cancers, 13, (2021); Li Z., Liu L., Zhang Z., Et al., A novel CT-based radiomics features analysis for identification and severity staging of COPD, Acad Radiol, 29, 5, pp. 663-673, (2022); Almuallim H., Dietterich T.G., Hall D., Learning with many irrelevant features, AAAI, 91, pp. 547-552, (1991); Bluma A.L., Langley P., Artificial intelligence selection of relevant features and examples in machine, Artif Intell, 97, pp. 245-271, (1997); John G.H., Kohavi R., Pfleger K., Irrelevant features and the subset selection problem, Machine Learning Proceedings, pp. 121-129, (1994); Efron B., The efficiency of logistic regression compared to normal discriminant analysis, J Am Stat Assoc, 70, 352, pp. 892-898, (1975); Ho T., Random decision forests, IEEE, 1, pp. 278-282, (1995); Cortes C., Vapnik V., Support-vector networks, Mach Learn, 20, 3, pp. 273-297, (1995); Altman N.S., An introduction to kernel and nearest-neighbor nonparametric regression, Am Stat, 46, 3, pp. 175-185, (1992); Langley P., Induction of selective Bayesian classifiers, Uncertainty Proceedings, pp. 399-406, (1994); Hopfield J., Neural networks and physical systems with emergent collective computational abilities, Natl Acad Sci, 79, pp. 2554-2558, (1982); Bourbeau J., Tan W.C., Benedetti A., Et al., Canadian Cohort Obstructive Lung Disease (CanCOLD): fulfilling the need for longitudinal observational studies in COPD, J Chronic Obstr Pulm Dis, 11, 2, pp. 125-132, (2014); Vestbo J., Hurd S.S., Agusti A.G., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, American journal of respiratory and critical care medicine, 187, 4, pp. 347-365, (2013); Dis A.T.S.-A.R.R., (2022); Au R.C., Tan W.C., Bourbeau J., Et al., Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease, Phys Med Biol, 66, (2021); Thibault G., Angulo J., Meyer F., Advanced statistical matrices for texture characterization: application to cell classification, IEEE Trans Biomed Eng, 61, 3, pp. 630-637, (2014); Galloway M.M., Texture analysis using gray level run lengths, Comput Graph Image Process, 4, 2, pp. 172-179, (1975); Sun C., Wee W.G., Neighboring gray level dependence matrix for texture classification, Comput Vis Graph Image Process, 23, 3, pp. 341-352, (1983); Amadasun M., King R., Texural features corresponding to texural properties, IEEE Trans Syst Man Cybern, 19, 5, pp. 1264-1274, (1989); Li W., Mo W., Zhang X., Et al., Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging, J Biomed Opt, 20, 12, (2015); Christodoulou E., Ma J., Collins G.S., Et al., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, pp. 12-22, (2019); Young R.P., Hopkins R.J., Christmas T., Et al., COPD prevalence is increased in lung cancer, independent of age, sex and smoking history, Eur Respir J, 34, 2, pp. 380-386, (2009); Yasaka K., Akai H., Mackin D., Et al., Precision of quantitative computed tomography texture analysis using image filtering: a phantom study for scanner variability, Medicine (Baltimore), 96, 21, (2017); Khalid S., Nasreen S., Khalil T., A survey of feature selection and feature extraction techniques in machine learning, 2014 Science and Information Conference, pp. 372-378, (2014); Wu C.T., Li G.H., Huang C.T., Et al., Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study, JMIR Mhealth Uhealth, 9, 5, (2021)","M. Kirby; Toronto Metropolitan University, Toronto, Kerr Hall South Bldg. Room – KHS-344, 350 Victoria St., M5B 2K3, Canada; email: Miranda.Kirby@ryerson.ca","","Elsevier Inc.","","","","","","10766332","","ARADF","35965158","English","Acad. Radiol.","Article","Final","","Scopus","2-s2.0-85136776662"
"Prakash S.P.; Dhivya P.; Vinitha R.; Yogeshwaran A.; Natarajan V.P.","Prakash, S.P. (56801319300); Dhivya, P. (58312567900); Vinitha, R. (59396141700); Yogeshwaran, A. (57164021900); Natarajan, Vignesh Prasanna (56095540300)","56801319300; 58312567900; 59396141700; 57164021900; 56095540300","Chronic Lower Respiratory Diseases detection based on Deep Recursive Convolutional Neural Network","2024","International Journal of Computational and Experimental Science and Engineering","10","4","","744","752","8","8","10.22399/ijcesen.513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208185158&doi=10.22399%2fijcesen.513&partnerID=40&md5=ed076b2bc4e557922ee186708fcc2966","Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, 638 401, India; Department of Biomedical Engineering, Sri Shanmugha College of Engineering and Technology, Tamil Nadu, Salem, 637304, India; Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, 641021, India; Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan Engineering College (Autonomous), Tamilnadu, Perambalur, 621212, India; Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600054, India","Prakash S.P., Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, 638 401, India; Dhivya P., Department of Biomedical Engineering, Sri Shanmugha College of Engineering and Technology, Tamil Nadu, Salem, 637304, India; Vinitha R., Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore, 641021, India; Yogeshwaran A., Department of Electronics and Communication Engineering, Dhanalakshmi Srinivasan Engineering College (Autonomous), Tamilnadu, Perambalur, 621212, India; Natarajan V.P., Department of ECE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, 600054, India","Recently, symptoms of Chronic Obstructive Pulmonary Disease (COPD) have been identified concerning long-term continuous treatment. Furthermore, predicting the life probability of patients with COPD is crucial for formative ensuing treatment and conduct plans. Additionally, it plays a vital role in providing complementary solutions using technologies such as Deep Learning (DL) to address experiments in the medical field. Early and timely analysis of clinical images can improve prognostic accuracy. These include COPD, pneumonia, asthma, tuberculosis and fibrosis. Conventional methods of diagnosing COPD often rely on physical exams and tests such as spirometers, chest and genetic analysis. However, respiratory diseases pose an enormous comprehensive health burden for many patients. Thus these methods are not always accurate or obtainable. However, succeeding in their accuracy involves a nonspecific diagnosis rate, time-consuming manual procedures, and extensive clinical imaging knowledge of the radiologist. To solve this problem, we use a Deep Recursive Convolutional Neural Network (DRCNN) method to detect chronic lower respiratory disease. Initially, we collected the images from the Kaggle repository, and evaluate the result based on the following stage. The first stage is pre-processing using a Gaussian filter to reduce noise and detect the edges. The second stage is segmentation used on Image Threshold Based Segmentation (ITBS), used for counting the binary image and separating the regions. In the third stage, we use the chi-square test to select the best features and evaluate the image values for each feature and threshold. Finally, classification using DRCNN detects CLRD classifying better than the previous method. In synthesis, CLRD can be detected by many staging measures, such as sensitivity, specificity, accuracy, precision, and Recall. © IJCESEN.","Chi-square test; COPD; DRCNN; Gaussian filter; ITBS; Precision and Recall","","","","","","","","Mieloszyk R. J., Verghese G. C., Krauss B. S., Heldt T., Model-Based Estimation of Respiratory Parameters from Capnography, With Application to Diagnosing Obstructive Lung Disease, IEEE Transactions on Biomedical Engineering, 64, 12, pp. 2957-2967, (2017); Computer-Aided Diagnosis System for Chronic Obstructive Pulmonary Disease Using Empirical Wavelet Transform on Auscultation Sounds, The Computer Journal, 64, 11, pp. 1775-1783, (2019); Roy, Satija U., A Novel Melspectrogram Snippet Representation Learning Framework for Severity Detection of Chronic Obstructive Pulmonary Diseases, IEEE Transactions on Instrumentation and Measurement, 72, 1-11, (2023); Demir F., Sengur A., Bajaj V., Convolutional neural networks based efficient approach for classification of lung diseases, Health Inf. Sci. Syst, 8, 1, (2020); Fang Y., Wang H., Wang L., Et al., Feature-maximum-dependency-based fusion diagnosis method for COPD, Multimed Tools Appl, 79, pp. 15191-15208, (2020); Altan G., Kutlu Y., Gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds, TURKISH J. Electr. Eng. Comput. Sci, 28, 5, pp. 2979-2996, (2020); Altan G., Kutlu Y., Allahverdi N., Deep learning on computerized analysis of chronic obstructive pulmonary disease, IEEE J. Biomed. Health Information, 24, 5, pp. 1344-1350, (2020); Sugimori H., Shimizu K., Makita H., Suzuki M., Konno S., A comparative evaluation of computed tomography images for the classification of spirometric severity of the chronic obstructive pulmonary disease with deep learning, Diagnostics, 11, 6, (2021); Fan K. G., Mandel J., Agnihotri P., Tai-Seale M., Remote patient monitoring technologies for predicting chronic obstructive pulmonary disease exacerbations: Review and comparison, JMIR mHealth uHealth, 8, 5, (2020); Khatri K. L., Tamil L. S., Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks, IEEE Journal of Biomedical and Health Informatics, 22, 1, pp. 285-290, (2018); Exarchos K. P., Et al., Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease, IEEE Journal of Biomedical and Health Informatics, 26, 5, pp. 2331-2338, (2022); Pham L., Phan H., Palaniappan R., Mertins A., McLoughlin I., CNN-MoE based framework for classification of respiratory anomalies and lung disease detection, IEEE J. Biomed. Health Information, 25, 8, pp. 2938-2947, (2021); Garcia-Ordas M. T., Benitez-Andrades J. A., Garcia-Rodiguez I., Benavides C., Alaiz-Moreton H., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Syed N., Road J. D., Ryerson C. J., Guenette J. A., Evaluation of the Vibe Actigraph in Patients With Chronic Obstructive Pulmonary Disease: A Pilot Study, IEEE Journal of Translational Engineering in Health and Medicine, 8, 1-8, (2020); Wang Q., Wang H., Wang L., Yu F., Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning, IEEE Access, 8, pp. 47370-47383, (2020); Xu Q., Et al., Intelligent Syndrome Differentiation of Traditional Chinese Medicine by ANN: A Case Study of Chronic Obstructive Pulmonary Disease, IEEE Access, 7, pp. 76167-76175, (2019); Ritchie AI, Wedzicha JA., Definition, Causes, Pathogenesis, and Consequences of Chronic Obstructive Pulmonary Disease Exacerbations, Clin Chest Med, 41, 3, pp. 421-438, (2020); Shuvo S. B., Ali S. N., Swapnil S. I., Hasan T., Bhuiyan M. I. H., A Lightweight CNN Model for Detecting Respiratory Diseases From Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram, IEEE Journal of Biomedical and Health Informatics, 25, 7, pp. 2595-2603, (2021); Nousias S., Et al., A mHealth System for Monitoring Medication Adherence in Obstructive Respiratory Diseases Using Content-Based Audio Classification, IEEE Access, 6, pp. 11871-11882, (2018); Lin X., Et al., A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data, Tsinghua Science and Technology, 28, 3, pp. 525-540, (2023); URAL A., KILIMCI Z. H., The Prediction of Chiral Metamaterial Resonance using Convolutional Neural Networks and Conventional Machine Learning Algorithms, International Journal of Computational and Experimental Science and Engineering, 7, 3, pp. 156-163, (2021); BACAK A., SENEL M., GUNAY O., Convolutional Neural Network (CNN) Prediction on Meningioma, Glioma with Tensorflow, International Journal of Computational and Experimental Science and Engineering, 9, 2, pp. 197-204, (2023); Jha K., Srivastava Sumit, Jain Aruna, A Novel Texture based Approach for Facial Liveness Detection and Authentication using Deep Learning Classifier, International Journal of Computational and Experimental Science and Engineering, 10, 3, pp. 323-331, (2024); Radhi M., Tahseen I., An Enhancement for Wireless Body Area Network Using Adaptive Algorithms, International Journal of Computational and Experimental Science and Engineering, 10, 3, pp. 388-396, (2024); Sreetha E S, Naveen Sundar G, Narmadha D, Enhancing Food Image Classification with Particle Swarm Optimization on NutriFoodNet and Data Augmentation Parameters, International Journal of Computational and Experimental Science and Engineering, 10, 4, pp. 718-730, (2024)","S.P. Prakash; Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Erode, 638 401, India; email: prakashsp@bitsathy.ac.in","","Prof.Dr. İskender AKKURT","","","","","","21499144","","","","English","Int. J. Comput. Exp. Sci. Eng.","Article","Final","","Scopus","2-s2.0-85208185158"
"Sousa-Pinto B.; Sá-Sousa A.; Vieira R.J.; Amaral R.; Pereira A.M.; Anto J.M.; Klimek L.; Czarlewski W.; Mullol J.; Pfaar O.; Bedbrook A.; Brussino L.; Kvedariene V.; Larenas-Linnemann D.E.; Okamoto Y.; Ventura M.T.; Ansotegui I.J.; Bosnic-Anticevich S.; Canonica G.W.; Cardona V.; Cecchi L.; Chivato T.; Cingi C.; Costa E.M.; Cruz A.A.; Del Giacco S.; Devillier P.; Fokkens W.J.; Gemicioglu B.; Haahtela T.; Ivancevich J.C.; Kuna P.; Kaidashev I.; Kraxner H.; Laune D.; Louis R.; Makris M.; Monti R.; Morais-Almeida M.; Mösges R.; Niedoszytko M.; Papadopoulos N.G.; Patella V.; Pham-Thi N.; Regateiro F.S.; Reitsma S.; Rouadi P.W.; Samolinski B.; Sheikh A.; Sova M.; Taborda-Barata L.; Toppila-Salmi S.; Sastre J.; Tsiligianni I.; Valiulis A.; Yorgancioglu A.; Zidarn M.; Zuberbier T.; Fonseca J.A.; Bousquet J.","Sousa-Pinto, Bernardo (55982726300); Sá-Sousa, Ana (36627453600); Vieira, Rafael José (57189456989); Amaral, Rita (56067841600); Pereira, Ana Margarida (57222642430); Anto, Josep M. (57218106928); Klimek, Ludger (7005088080); Czarlewski, Wienczyslawa (57203909023); Mullol, Joaquim (55972415400); Pfaar, Oliver (9744229500); Bedbrook, Anna (55253648100); Brussino, Luisa (6701711701); Kvedariene, Violeta (14056134900); Larenas-Linnemann, Desirée E. (23492386700); Okamoto, Yoshitaka (57211783510); Ventura, Maria Teresa (7201760331); Ansotegui, Ignacio J. (6701538864); Bosnic-Anticevich, Sinthia (7801624496); Canonica, G. Walter (55412658800); Cardona, Victoria (18133374200); Cecchi, Lorenzo (57193526705); Chivato, Tomas (6701522636); Cingi, Cemal (6507661698); Costa, Elísio M. (7402527214); Cruz, Alvaro A. (55512188000); Del Giacco, Stefano (55828150411); Devillier, Philippe (55160889400); Fokkens, Wytske J. (35355799700); Gemicioglu, Bilun (6505921956); Haahtela, Tari (7004531314); Ivancevich, Juan Carlos (23091137000); Kuna, Piotr (7006421186); Kaidashev, Igor (6603855774); Kraxner, Helga (6507277473); Laune, Daniel (6602589396); Louis, Renaud (55556102200); Makris, Michael (26643105100); Monti, Riccardo (57526330800); Morais-Almeida, Mario (6602654955); Mösges, Ralph (55617900200); Niedoszytko, Marek (6603308598); Papadopoulos, Nikolaos G. (57945263200); Patella, Vincenzo (7004792918); Pham-Thi, Nhân (57193002159); Regateiro, Frederico S. (8859661600); Reitsma, Sietze (57195635560); Rouadi, Philip W. (6507628553); Samolinski, Boleslaw (57194529979); Sheikh, Aziz (7202522962); Sova, Milan (36549506300); Taborda-Barata, Luis (15128160500); Toppila-Salmi, Sanna (14631330000); Sastre, Joaquin (14326067900); Tsiligianni, Ioanna (8315152700); Valiulis, Arunas (57204694158); Yorgancioglu, Arzu (57210951407); Zidarn, Mihaela (57205729265); Zuberbier, Torsten (7004554588); Fonseca, Joao A. (45661083200); Bousquet, Jean (55156315400)","55982726300; 36627453600; 57189456989; 56067841600; 57222642430; 57218106928; 7005088080; 57203909023; 55972415400; 9744229500; 55253648100; 6701711701; 14056134900; 23492386700; 57211783510; 7201760331; 6701538864; 7801624496; 55412658800; 18133374200; 57193526705; 6701522636; 6507661698; 7402527214; 55512188000; 55828150411; 55160889400; 35355799700; 6505921956; 7004531314; 23091137000; 7006421186; 6603855774; 6507277473; 6602589396; 55556102200; 26643105100; 57526330800; 6602654955; 55617900200; 6603308598; 57945263200; 7004792918; 57193002159; 8859661600; 57195635560; 6507628553; 57194529979; 7202522962; 36549506300; 15128160500; 14631330000; 14326067900; 8315152700; 57204694158; 57210951407; 57205729265; 7004554588; 45661083200; 55156315400","Cutoff Values of MASK-air Patient-Reported Outcome Measures","2023","Journal of Allergy and Clinical Immunology: In Practice","11","4","","1281","1289.e5","","12","10.1016/j.jaip.2022.12.005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146860940&doi=10.1016%2fj.jaip.2022.12.005&partnerID=40&md5=3e83390f05ff15e53e9ad15789a2948f","MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal; RISE—Health Research Network, University of Porto, Porto, Portugal; Department of Research & Development, ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Department of Medical Research & Environment, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Department of Epidemiology of Asthma, Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Department of Otolaryngology, Head and Neck Surgery, Universitätsmedizin Mainz, Mainz, Germany; Center for Rhinology and Allergology, Wiesbaden, Germany; Medical Consulting Czarlewski, Levallois and MASK-air, Montpellier, France; Rhinology Unit and Smell Clinic, ENT Department, Hospital Clínic, Clinical and Experimental Respiratory Immunoallergy, IDIBAPS, CIBERES, University of Barcelona, Barcelona, Spain; Department of Otorhinolaryngology, Head and Neck Surgery, Section of Rhinology and Allergy, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany; Department of Allergy, ARIA & MASK-air, Montpellier, France; Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino and Mauriziano Hospital, Torino, Italy; Department of Pathology, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania; Institute of Clinical Medicine, Clinic of Chest Diseases and Allergology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania; Center of Excellence in Asthma and Allergy, Médica Sur Clinical Foundation and Hospital, México City, Mexico; Department of Otorhinolaryngology, Chiba University Hospital and Chiba Rosai Hospital, Chiba, Japan; Unit of Geriatric Immunoallergology, University of Bari Medical School and Institute of Sciences of Food Production, National Research Council (Ispa-Cnr), Bari, Italy; Department of Allergy and Immunology, Hospital Quironsalud Bizkaia, Bilbao, Spain; Quality Use of Respiratory Medicines Group, Woolcock Institute of Medical Research, Sydney, Sydney Pharmacy School, The University of Sydney, Sydney Local Health District, Sydney, NSW, Australia; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy; Allergy Section, Department of Internal Medicine, Hospital Vall d'Hebron & ARADyAL research network, Barcelona, Spain; SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy; School of Medicine, University CEU San Pablo, Madrid, Spain; ENT Department, Eskisehir Osmangazi University, Medical Faculty, Eskisehir, Turkey; UCIBIO, REQUINTE, Faculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing), Porto, Portugal; Fundaçao ProAR, Federal University of Bahia and GARD/WHO Planning Group, Bahia, Salvador, Brazil; Department of Medical Sciences and Public Health and Unit of Allergy and Clinical Immunology, University Hospital “Duilio Casula”, University of Cagliari, Cagliari, Italy; VIM Suresnes, UMR 0892, Pôle des Maladies des Voies Respiratoires, Hôpital Foch, Université Paris-Saclay, Suresnes, France; Department of Otorhinolaryngology, Amsterdam University Medical Centres, AMC, Amsterdam, Netherlands; Department of Pulmonary Diseases, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Istanbul, Turkey; Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Servicio de Alergia e Immunologia, Clinica Santa Isabel, Buenos Aires, Argentina; Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Lodz, Poland; Department of Internal Medicine, Poltava State Medical University, Poltava, Ukraine; Department of Otorhinolaryngology, Head and Neck Surgery, Semmelweis University, Budapest, Hungary; Department Recherches & Développement, KYomed INNOV, Montpellier, France; Department of Pulmonary Medicine, CHU Liege, and GIGA I3 research group, University of Liege, Liege, Belgium; Allergy Unit “D Kalogeromitros”, 2nd Department of Dermatology and Venereology, National and Kapodistrian University of Athens, “Attikon” University Hospital, Athens, Greece; Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; Allergy Center, CUF Descobertas Hospital, Lisbon, Portugal; IMSB, Medical Faculty, University at Cologne, ClinCompetence Cologne GmbH, Cologne, Germany; Department of Allergology, Medical University of Gdańsk, Gdansk, Poland; Allergy Department, 2nd Pediatric Clinic, University of Athens, Athens, Greece; Division of Allergy and Clinical Immunology, Department of Medicine, Agency of Health ASL Salerno, “Santa Maria della Speranza” Hospital, Salerno, Battipaglia, Italy; Ecole Polytechnique Palaiseau, IRBA (Institut de Recherche bio-Médicale des Armées), Bretigny, France; Allergy and Clinical Immunology Unit, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Institute of Immunology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Coimbra Institute for Clinical and Biomedical Research (ICBR), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Department of Otolaryngology-Head and Neck Surgery, Eye and Ear University Hospital, Beirut, Lebanon; Department of Otolaryngology-Head and Neck Surgery, Dar Al Shifa Hospital, Salmiya, Kuwait; Department of Prevention of Environmental Hazards, Allergology and Immunology, Medical University of Warsaw, Poland; Usher Institute, the University of Edinburgh, Edinburgh, United Kingdom; Department of Respiratory Medicine and Tuberculosis, University Hospital, Brno, Czech Republic; UBIAir—Clinical & Experimental Lung Centre, University of Beira Interior, Covilhã and CICS-Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal; Department of Immunoallergology, Cova da Beira University Hospital Centre, Covilhã, Portugal; Fundacion Jimenez Diaz, CIBERES, Faculty of Medicine, Autonoma University of Madrid, Madrid, Spain; Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece; International Primary Care Respiratory Group IPCRG, Aberdeen, United Kingdom; Institute of Clinical Medicine and Institute of Health Sciences, Vilnius, Lithunia; Medical Faculty of Vilnius University, Vilnius, Lithuania; Department of Pulmonary Diseases, Celal Bayar University, Faculty of Medicine, Manisa, Turkey; University Clinic of Respiratory and Allergic Diseases, Golnik & University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia; Institute of Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany; Department of Pneumology, University Hospital, Montpellier, France","Sousa-Pinto B., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Sá-Sousa A., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Vieira R.J., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Amaral R., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Pereira A.M., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Anto J.M., Department of Research & Development, ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain, Department of Medical Research & Environment, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain, Department of Epidemiology of Asthma, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Klimek L., Department of Otolaryngology, Head and Neck Surgery, Universitätsmedizin Mainz, Mainz, Germany, Center for Rhinology and Allergology, Wiesbaden, Germany; Czarlewski W., Medical Consulting Czarlewski, Levallois and MASK-air, Montpellier, France; Mullol J., Rhinology Unit and Smell Clinic, ENT Department, Hospital Clínic, Clinical and Experimental Respiratory Immunoallergy, IDIBAPS, CIBERES, University of Barcelona, Barcelona, Spain; Pfaar O., Department of Otorhinolaryngology, Head and Neck Surgery, Section of Rhinology and Allergy, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany; Bedbrook A., Department of Allergy, ARIA & MASK-air, Montpellier, France; Brussino L., Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino and Mauriziano Hospital, Torino, Italy; Kvedariene V., Department of Pathology, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania, Institute of Clinical Medicine, Clinic of Chest Diseases and Allergology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania; Larenas-Linnemann D.E., Center of Excellence in Asthma and Allergy, Médica Sur Clinical Foundation and Hospital, México City, Mexico; Okamoto Y., Department of Otorhinolaryngology, Chiba University Hospital and Chiba Rosai Hospital, Chiba, Japan; Ventura M.T., Unit of Geriatric Immunoallergology, University of Bari Medical School and Institute of Sciences of Food Production, National Research Council (Ispa-Cnr), Bari, Italy; Ansotegui I.J., Department of Allergy and Immunology, Hospital Quironsalud Bizkaia, Bilbao, Spain; Bosnic-Anticevich S., Quality Use of Respiratory Medicines Group, Woolcock Institute of Medical Research, Sydney, Sydney Pharmacy School, The University of Sydney, Sydney Local Health District, Sydney, NSW, Australia; Canonica G.W., Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy, Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy; Cardona V., Allergy Section, Department of Internal Medicine, Hospital Vall d'Hebron & ARADyAL research network, Barcelona, Spain; Cecchi L., SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy; Chivato T., School of Medicine, University CEU San Pablo, Madrid, Spain; Cingi C., ENT Department, Eskisehir Osmangazi University, Medical Faculty, Eskisehir, Turkey; Costa E.M., UCIBIO, REQUINTE, Faculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing), Porto, Portugal; Cruz A.A., Fundaçao ProAR, Federal University of Bahia and GARD/WHO Planning Group, Bahia, Salvador, Brazil; Del Giacco S., Department of Medical Sciences and Public Health and Unit of Allergy and Clinical Immunology, University Hospital “Duilio Casula”, University of Cagliari, Cagliari, Italy; Devillier P., VIM Suresnes, UMR 0892, Pôle des Maladies des Voies Respiratoires, Hôpital Foch, Université Paris-Saclay, Suresnes, France; Fokkens W.J., Department of Otorhinolaryngology, Amsterdam University Medical Centres, AMC, Amsterdam, Netherlands; Gemicioglu B., Department of Pulmonary Diseases, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Istanbul, Turkey; Haahtela T., Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Ivancevich J.C., Servicio de Alergia e Immunologia, Clinica Santa Isabel, Buenos Aires, Argentina; Kuna P., Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Lodz, Poland; Kaidashev I., Department of Internal Medicine, Poltava State Medical University, Poltava, Ukraine; Kraxner H., Department of Otorhinolaryngology, Head and Neck Surgery, Semmelweis University, Budapest, Hungary; Laune D., Department Recherches & Développement, KYomed INNOV, Montpellier, France; Louis R., Department of Pulmonary Medicine, CHU Liege, and GIGA I3 research group, University of Liege, Liege, Belgium; Makris M., Allergy Unit “D Kalogeromitros”, 2nd Department of Dermatology and Venereology, National and Kapodistrian University of Athens, “Attikon” University Hospital, Athens, Greece; Monti R., Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; Morais-Almeida M., Allergy Center, CUF Descobertas Hospital, Lisbon, Portugal; Mösges R., IMSB, Medical Faculty, University at Cologne, ClinCompetence Cologne GmbH, Cologne, Germany; Niedoszytko M., Department of Allergology, Medical University of Gdańsk, Gdansk, Poland; Papadopoulos N.G., Allergy Department, 2nd Pediatric Clinic, University of Athens, Athens, Greece; Patella V., Division of Allergy and Clinical Immunology, Department of Medicine, Agency of Health ASL Salerno, “Santa Maria della Speranza” Hospital, Salerno, Battipaglia, Italy; Pham-Thi N., Ecole Polytechnique Palaiseau, IRBA (Institut de Recherche bio-Médicale des Armées), Bretigny, France; Regateiro F.S., Allergy and Clinical Immunology Unit, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal, Institute of Immunology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal, Coimbra Institute for Clinical and Biomedical Research (ICBR), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Reitsma S., Department of Otorhinolaryngology, Amsterdam University Medical Centres, AMC, Amsterdam, Netherlands; Rouadi P.W., Department of Otolaryngology-Head and Neck Surgery, Eye and Ear University Hospital, Beirut, Lebanon, Department of Otolaryngology-Head and Neck Surgery, Dar Al Shifa Hospital, Salmiya, Kuwait; Samolinski B., Department of Prevention of Environmental Hazards, Allergology and Immunology, Medical University of Warsaw, Poland; Sheikh A., Usher Institute, the University of Edinburgh, Edinburgh, United Kingdom; Sova M., Department of Respiratory Medicine and Tuberculosis, University Hospital, Brno, Czech Republic; Taborda-Barata L., UBIAir—Clinical & Experimental Lung Centre, University of Beira Interior, Covilhã and CICS-Health Sciences Research Centre, University of Beira Interior, Covilhã, Portugal, Department of Immunoallergology, Cova da Beira University Hospital Centre, Covilhã, Portugal; Toppila-Salmi S., Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Sastre J., Fundacion Jimenez Diaz, CIBERES, Faculty of Medicine, Autonoma University of Madrid, Madrid, Spain; Tsiligianni I., Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece, International Primary Care Respiratory Group IPCRG, Aberdeen, United Kingdom; Valiulis A., Institute of Clinical Medicine and Institute of Health Sciences, Vilnius, Lithunia, Medical Faculty of Vilnius University, Vilnius, Lithuania; Yorgancioglu A., Department of Pulmonary Diseases, Celal Bayar University, Faculty of Medicine, Manisa, Turkey; Zidarn M., University Clinic of Respiratory and Allergic Diseases, Golnik & University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia; Zuberbier T., Institute of Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany; Fonseca J.A., MEDCIDS—Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal, CINTESIS—Center for Health Technology and Services Research, University of Porto, Porto, Portugal, RISE—Health Research Network, University of Porto, Porto, Portugal; Bousquet J., Institute of Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany, Department of Pneumology, University Hospital, Montpellier, France","Background: In clinical and epidemiological studies, cutoffs of patient-reported outcome measures can be used to classify patients into groups of statistical and clinical relevance. However, visual analog scale (VAS) cutoffs in MASK-air have not been tested. Objective: To calculate cutoffs for VAS global, nasal, ocular, and asthma symptoms. Methods: In a cross-sectional study design of all MASK-air participants, we compared (1) approaches based on the percentiles (tertiles or quartiles) of VAS distributions and (2) data-driven approaches based on clusters of data from 2 comparators (VAS work and VAS sleep). We then performed sensitivity analyses for individual countries and for VAS levels corresponding to full allergy control. Finally, we tested the different approaches using MASK-air real-world cross-sectional and longitudinal data to assess the most relevant cutoffs. Results: We assessed 395,223 days from 23,201 MASK-air users with self-reported allergic rhinitis. The percentile-oriented approach resulted in lower cutoff values than the data-driven approach. We obtained consistent results in the data-driven approach. Following the latter, the proposed cutoff differentiating “controlled” and “partly-controlled” patients was similar to the cutoff value that had been arbitrarily used (20/100). However, a lower cutoff was obtained to differentiate between “partly-controlled” and “uncontrolled” patients (35 vs the arbitrarily-used value of 50/100). Conclusions: Using a data-driven approach, we were able to define cutoff values for MASK-air VASs on allergy and asthma symptoms. This may allow for a better classification of patients with rhinitis and asthma according to different levels of control, supporting improved disease management. © 2022 American Academy of Allergy, Asthma & Immunology","Asthma; Conjunctivitis; Cutoffs; MASK-air; Rhinitis","Asthma; Cross-Sectional Studies; Humans; Patient Reported Outcome Measures; Rhinitis; Rhinitis, Allergic; beta adrenergic receptor stimulating agent; corticosteroid; muscarinic receptor blocking agent; adult; allergic rhinitis; Article; asthma; conjunctivitis; cross-sectional study; disease duration; female; human; longitudinal study; machine learning; male; patient-reported outcome; questionnaire; reliability; sensitivity analysis; sleep; validity; visual analog scale; allergic rhinitis; asthma; patient-reported outcome; rhinitis","","","","","American Heart Association, AHA, (H2020); American Heart Association, AHA; Novartis; EIT Health; Charité – Universitätsmedizin Berlin","MASK-air has been supported by Charité Universitätsmedizin Berlin , EU grants (EU Structural and Development Funds; POLLAR, EIT Health, Twinning, EIP on AHA and H2020) and educational grants from Mylan-Viatris, ALK, GSK, Novartis , and Uriach. 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Proceedings of the 29th International Conference on Machine Learning: Edinburgh, Scotland; 2012; Wu J., Cluster analysis and k-means clustering: an introduction, Advances in K-means Clustering. Springer Theses, pp. 1-16, (2012); Yuan C., Yang H., Research on k-value selection method of k-means clustering algorithm, 2, pp. 227-235, (2019); Vandenplas O., Vinnikov D., Blanc P.D., Agache I., Bachert C., Bewick M., Et al., Impact of rhinitis on work productivity: a systematic review, J Allergy Clin Immunol Pract, 6, (2018); Vandenplas O., Suarthana E., Rifflart C., Lemiere C., Le Moual N., Bousquet J., The impact of work-related rhinitis on quality of life and work productivity: a general workforce-based survey, J Allergy Clin Immunol Pract, 8, (2020); Devillier P., Bousquet J., Salvator H., Naline E., Grassin-Delyle S., de Beaumont O., In allergic rhinitis, work, classroom and activity impairments are weakly related to other outcome measures, Clin Exp Allergy, 46, pp. 1456-1464, (2016); Leger D., Annesi-Maesano I., Carat F., Rugina M., Chanal I., Pribil C., Et al., Allergic rhinitis and its consequences on quality of sleep: an unexplored area, Arch Intern Med, 166, pp. 1744-1748, (2006); Liu J., Zhang X., Zhao Y., Wang Y., The association between allergic rhinitis and sleep: a systematic review and meta-analysis of observational studies, PLoS One, 15, (2020); Munoz-Cano R., Ribo P., Araujo G., Giralt E., Sanchez-Lopez J., Valero A., Severity of allergic rhinitis impacts sleep and anxiety: results from a large Spanish cohort, Clin Transl Allergy, 8, (2018); Hanley J.A., Receiver operating characteristic (ROC) methodology: the state of the art, Crit Rev Diagn Imaging, 29, pp. 307-335, (1989); Del Cuvillo A., Santos V., Montoro J., Bartra J., Davila I., Ferrer M., Et al., Allergic rhinitis severity can be assessed using a visual analogue scale in mild, moderate and severe, Rhinology, 55, pp. 34-38, (2017); Vieira R., Sousa-Pinto B., Anto J., Sheikh A., Klimek L., Zuberbier T., Et al., Usage patterns of oral H1-antihistamines in 10 European countries: a study using MASK-air® and Google Trends real-world data, World Allergy Organ J, 15, (2022); Sousa-Pinto B., Schunemann H.J., Sa-Sousa A., Vieira R.J., Amaral R., Anto J.M., Et al., Comparison of rhinitis treatments using MASK-air(R) data and considering the minimal important difference, Allergy, 77, pp. 3002-3014, (2022); Fonseca J.A., Nogueira-Silva L., Morais-Almeida M., Azevedo L., Sa-Sousa A., Branco-Ferreira M., Et al., Validation of a questionnaire (CARAT10) to assess rhinitis and asthma in patients with asthma, Allergy, 65, pp. 1042-1048, (2010)","J. Bousquet; Charité – Universitätsmedizin Berlin Institute of Allergology - Campus Benjamin Franklin, Haus II, Hindenburgdamm 30, Berlin, 12203, Germany; email: jean.bousquet@orange.fr","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36566778","English","J. Allergy Clin. Immunol. Pract.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85146860940"
"Emeryk A.; Derom E.; Janeczek K.; Kuźnar-Kamińska B.; Zelent A.; Łukaszyk M.; Grzywalski T.; Pastusiak A.; Biniakowski A.; Szarzyński K.; Botteldooren D.; Kociński J.; Hafke-Dys H.","Emeryk, Andrzej (6602148262); Derom, Eric (7004084873); Janeczek, Kamil (57211005857); Kuźnar-Kamińska, Barbara (6504754903); Zelent, Anna (36991180700); Łukaszyk, Mateusz (56009627500); Grzywalski, Tomasz (54389074300); Pastusiak, Anna (57208226923); Biniakowski, Adam (57189685244); Szarzyński, Krzysztof (57207583386); Botteldooren, Dick (6701666696); Kociński, Jędrzej (8725367600); Hafke-Dys, Honorata (57195298396)","6602148262; 7004084873; 57211005857; 6504754903; 36991180700; 56009627500; 54389074300; 57208226923; 57189685244; 57207583386; 6701666696; 8725367600; 57195298396","Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope","2023","Annals of Family Medicine","21","6","","517","525","8","8","10.1370/afm.3039","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178373729&doi=10.1370%2fafm.3039&partnerID=40&md5=bdd48b41d61a049cd55d65efdc701542","Department of Paediatric Pulmonology and Rheumatology, Faculty of Medicine, Medical University of Lublin, Lublin, Poland; Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Department of Pulmonology, Allergology, and Respiratory Oncology, Poznań University of Medical Sciences, Poznań, Poland; Department of Pediatric Pneumonology, Allergology, and Clinical Immunology, Poznań University of Medical Sciences, Poznań, Poland; 1st Department of Lung Diseases and Tuberculosis, Faculty of Medicine, Medical University of Bialystok, Białystok, Poland; StethoMe Sp. z o.o., Poznań, Poland; WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium; Department of Acoustics, Faculty of Physics, Adam Mickiewicz University, Poznań, Poland","Emeryk A., Department of Paediatric Pulmonology and Rheumatology, Faculty of Medicine, Medical University of Lublin, Lublin, Poland; Derom E., Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Janeczek K., Department of Paediatric Pulmonology and Rheumatology, Faculty of Medicine, Medical University of Lublin, Lublin, Poland; Kuźnar-Kamińska B., Department of Pulmonology, Allergology, and Respiratory Oncology, Poznań University of Medical Sciences, Poznań, Poland; Zelent A., Department of Pediatric Pneumonology, Allergology, and Clinical Immunology, Poznań University of Medical Sciences, Poznań, Poland; Łukaszyk M., 1st Department of Lung Diseases and Tuberculosis, Faculty of Medicine, Medical University of Bialystok, Białystok, Poland; Grzywalski T., StethoMe Sp. z o.o., Poznań, Poland, WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium; Pastusiak A., StethoMe Sp. z o.o., Poznań, Poland, Department of Acoustics, Faculty of Physics, Adam Mickiewicz University, Poznań, Poland; Biniakowski A., StethoMe Sp. z o.o., Poznań, Poland; Szarzyński K., StethoMe Sp. z o.o., Poznań, Poland; Botteldooren D., WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium; Kociński J., StethoMe Sp. z o.o., Poznań, Poland, Department of Acoustics, Faculty of Physics, Adam Mickiewicz University, Poznań, Poland; Hafke-Dys H., StethoMe Sp. z o.o., Poznań, Poland, Department of Acoustics, Faculty of Physics, Adam Mickiewicz University, Poznań, Poland","PURPOSE The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection? METHODS A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.5 months, with increased frequency during exacerbations) performed self-examinations using 3 devices: an artificial intelligence (AI)-aided home stethoscope (providing wheezes, rhonchi, and coarse and fine crackles intensity; respiratory and heart rate; and inspiration-to-expiration ratio), a peripheral capillary oxygen saturation (SpO2) meter, and a peak expiratory flow (PEF) meter and filled out a health state survey. The resulting 6,029 examinations were evaluated by physicians for the presence of exacerbations. For each registered parameter, a machine learning model was trained, and the area under the receiver operating characteristic curve (AUC) was calculated to assess its utility in exacerbation detection. RESULTS The best single-parameter discriminators of exacerbations were wheezes intensity for young children (AUC 84% [95% CI, 82%-85%]), rhonchi intensity for older children (AUC 81% [95% CI, 79%-84%]), and survey answers for adults (AUC 92% [95% CI, 89%-95%]). The greatest efficacy (in terms of AUC) was observed for a combination of several parameters. CONCLUSIONS The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier. © 2023, Annals of Family Medicine, Inc. All rights reserved.","AI-aided medical device; asthma exacerbation; asthma monitoring; childhood asthma; home health care","Adolescent; Adult; Artificial Intelligence; Asthma; Child; Child, Preschool; Humans; Machine Learning; Respiratory Sounds; Stethoscopes; abnormal respiratory sound; adolescent; adult; artificial intelligence; asthma; child; human; machine learning; preschool child","","","","","Narodowe Centrum Badań i Rozwoju, NCBR, (POIR.01.01.01-00-0648/20); Narodowe Centrum Badań i Rozwoju, NCBR","Conflicts of interest: K.J., B.K.-K., A.Z, M.Ł., T. G., omasz Grzywalski, A.P., A.B., K.S., J.K., and H.H.-D. were paid for their work by the National Centre for Research and Development (grant no. POIR.01.01.01-00-0648/20). T.G., A.P., A.B., and K.S. are employees of StethoMe Sp. z o.o. J.K. and H.H.-D. are employees and shareholders of StethoMe Sp. z o.o. All other authors report none.","Global Strategy for Asthma Management and Prevention, (2022); Asher I, Pearce N., Global burden of asthma among children, Int J Tuberc Lung Dis, 18, 11, pp. 1269-1278, (2014); van den Akker-van Marle ME, Bruil J, Detmar SB., Evaluation of cost of disease:assessing the burden to society of asthma in children in the European Union, Allergy, 60, 2, pp. 140-149, (2005); Selroos O, Kupczyk M, Kuna P, Et al., National and regional asthma programmes in Europe, Eur Respir Rev, 24, 137, pp. 474-483, (2015); Persons reporting a chronic disease, by disease, sex, age and broad group of citizenship; Bateman ED, Boushey HA, Bousquet J, Et al., Can guideline-defined asthma control be achieved? 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Pediatric acute asthma scoring systems:a systematic review and survey of UK practice, J Am Coll Emerg Physicians Open, 1, 5, pp. 1000-1008, (2020); Grzywalski T, Piecuch M, Szajek M, Et al., Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination, Eur J Pediatr, 178, 6, pp. 883-890, (2019); Hafke-Dys H, Kuznar-Kaminska B, Grzywalski T, Maciaszek A, Szarzynski K, Kocinski J., Artificial intelligence approach to the monitoring of respiratory sounds in asthmatic patients, Front Physiol, 12, (2021); Kevat A, Kalirajah A, Roseby R., Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes, Respir Res, 21, 1, (2020); Kevat A, Kalirajah A, Roseby R., Late breaking abstract-Accuracy of artificial intelligence in detecting pathological breath sounds in children using digital stethoscopes, Eur Respir J, 56, (2020); Menard J, Bui S, Galode F, Et al., Evaluation of an artificial intelligence (AI)-based electronic stethoscope (ES) in pediatric lung diseases, Pediatr Pulmonol, 57, S2; Park SH, Goo JM, Jo CH., Receiver operating characteristic (ROC) curve:practical review for radiologists, Korean J Radiol, 5, 1, pp. 11-18, (2004); Hajian-Tilaki K., Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation, Caspian J Intern Med, 4, 2, pp. 627-635, (2013); Polo TCF, Miot HA., Use of ROC curves in clinical and experimental studies, J Vasc Bras, 19, (2020); Van der Schouw YT, Verbeek AL, Ruijs JH., ROC curves for the initial assessment of new diagnostic tests, Fam Pract, 9, 4, pp. 506-511, (1992); Obuchowski NA, Bullen JA., Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine, Phys Med Biol, 63, 7, (2018); Nahm FS., Receiver operating characteristic curve:overview and practical use for clinicians, Korean J Anesthesiol, 75, 1, pp. 25-36, (2022); Mandrekar JN., Receiver operating characteristic curve in diagnostic test assessment, J Thorac Oncol, 5, 9, pp. 1315-1316, (2010); Rottier BL, Eber E, Hedlin G, Et al., Monitoring asthma in childhood:management-related issues, Eur Respir Rev, 24, 136, pp. 194-203, (2015); Cane RS, Ranganathan SC, McKenzie SA., What do parents of wheezy children understand by “wheeze”?, Arch Dis Child, 82, 4, pp. 327-332, (2000); Munoz-Lopez F., Usefulness and limitations of PEF, Allergol Immunopathol (Madr), 26, 2, pp. 39-41, (1998); Brand PL, Duiverman EJ, Waalkens HJ, van Essen-Zandvliet EE, Kerrebijn KF, Peak flow variation in childhood asthma:correlation with symptoms, airways obstruction, and hyperresponsiveness during long-term treatment with inhaled corticosteroids, Thorax, 54, 2, pp. 103-107, (1999); Rodrigo GJ, Neffen H., Assessment of acute asthma severity in the ED:are heart and respiratory rates relevant?, Am J Emerg Med, 33, 11, pp. 1583-1586, (2015); Priftis K, Karadag B, van Aalderen W., Importance of detecting wheezing in young children to minimise asthma exacerbation, EMJ Respir, 8, 1, pp. 44-49, (2020); Grandinetti R, Fainardi V, Caffarelli C, Et al., Risk factors affecting development and persistence of preschool wheezing:consensus document of the Emilia-Romagna Asthma (ERA) study group, J Clin Med, 11, 21, (2022); Garcia-Marcos L, Edwards J, Kennington E, Et al., Priorities for future research into asthma diagnostic tools:a PAN-EU consensus exercise from the European asthma research innovation partnership (EARIP), Clin Exp Allergy, 48, 2, pp. 104-120, (2018); Mirra V, Montella S, Santamaria F., Pediatric severe asthma:a case series report and perspectives on anti-IgE treatment, BMC Pediatr, 18, 1, (2018); Persaud YK., Using telemedicine to care for the asthma patient, Curr Allergy Asthma Rep, 22, 4, pp. 43-52, (2022); Codispoti CD, Greenhawt M, Oppenheimer J., The role of access and cost-effectiveness in managing asthma:a systematic review, J Allergy Clin Immunol Pract, 10, 8, pp. 2109-2116, (2022); Gilkey MB, Kong WY, Kennedy KL, Et al., Leveraging telemedicine to reduce the financial burden of asthma care, J Allergy Clin Immunol Pract, 10, 10, pp. 2536-2542, (2022)","K. Janeczek; Department of Paediatric Pulmonology and Rheumatology, Faculty of Medicine, Medical University of Lublin, Lublin, ul. Prof. Gębali 6, 20-093, Poland; email: kamil.janeczek@umlub.pl","","Annals of Family Medicine, Inc","","","","","","15441709","","","38012028","English","Ann. Fam. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85178373729"
"Haba R.; Singer G.; Naftali S.; Kramer M.R.; Ratnovsky A.","Haba, Rotem (58142286700); Singer, Gonen (7102795609); Naftali, Sara (6506214530); Kramer, Mordechai R. (35459711900); Ratnovsky, Anat (6506070805)","58142286700; 7102795609; 6506214530; 35459711900; 6506070805","A remote and personalised novel approach for monitoring asthma severity levels from EEG signals utilizing classification algorithms","2023","Expert Systems with Applications","223","","119799","","","","10","10.1016/j.eswa.2023.119799","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150017156&doi=10.1016%2fj.eswa.2023.119799&partnerID=40&md5=fc0a5d0f67d1628d582252e2f11b6935","Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel; Pulmonary Division, Rabin Medical Center, Petach Tikva, Israel","Haba R., Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; Singer G., Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; Naftali S., School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel; Kramer M.R., Pulmonary Division, Rabin Medical Center, Petach Tikva, Israel; Ratnovsky A., School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel","Asthma is a complex respiratory disorder in which structural changes in the conducting airway cause variable airflow limitation. The excessive narrowing of the airway lumen underlies the morbidity and the mortality that are attributable to the disease, which reduces quality of life in people of all ages. Monitoring of the disease progression to gather data continuously as an objective marker of morbidity and to support therapy is essential. Currently, patients with asthma are examined only a few times a year, which prevents continuous monitoring of the disease progression and any advance in precision medicine. Remote non-invasive monitoring tools will enable the collection of data with minimal effort from patients. To date, however, assessment and monitoring of asthma based on electroencephalogram (EEG) signals have not been studied. The objective of this research was to develop a general approach for identifying asthma severity levels based on EEG signals for personalised remote and non-invasive monitoring of patients with asthma. Simultaneous measurements of EEG and respiration motion signals were acquired from adults with suspected asthma, during the entire methacholine challenge test. The EEG segments were categorized into three classes, each representing a level of asthma severity based on participant's spirometry score. Three artificial intelligence (AI) methodologies were designed and examined: the first aimed to identify a subject's asthma severity levels based on their known data, the second based on mixed data comprising data from all subjects including the subject's personal data, and the third methodology based on the datasets of all other subjects, reflecting a situation of a new patient. To overcome multi-subject variations in the third methodology, the probabilities of being at each one of the possible asthma severity levels in previous breathing cycles, as inputs for predictions of asthma severity levels in the current breathing cycle, was used. The classification was done based on ordinal and non-ordinal classification algorithms. In the first and second methodologies, an ensemble approach was also applied to enhance predictive performance. Good performance measures were obtained to identify asthma severity level using both the first and second methodologies, especially by the ensemble model and XGBoost classifier. In the third methodology, the combination of random forest and ordinal random forest in different stages of the methodology, yielded the best improvement in performance measures. The results of all three AI methodologies demonstrate that they may be employed for personalised home asthma monitoring and management that do not depend on patients exerting an effort. © 2023 Elsevier Ltd","Asthma; Electroencephalogram; Ensemble learning; Machine learning; Ordinal classification","Biomedical signal processing; Disease control; Diseases; Forestry; Learning systems; Machine learning; Patient monitoring; Asthma; Breathing cycle; Classification algorithm; Disease progression; Electroencephalogram signals; Ensemble learning; Machine-learning; Non-invasive monitoring; Ordinal classification; Performance measure; Electroencephalography","","","","","Israeli Ministry of Innovation, Science, and Technology, (0004323)","This research was supported by the Israeli Ministry of Innovation, Science, and Technology (Grant No. 0004323).","Akesdotter C., Kentta G., Eloranta S., Franck J., The prevalence of mental health problems in elite athletes, Journal of Science and Medicine in Sport, 23, 4, pp. 329-335, (2020); 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Tomita K., Nagao R., Touge H., Ikeuchi T., Sano H., Yamasaki A., Tohda Y., Deep learning facilitates the diagnosis of adult asthma, Allergology International, 68, 4, pp. 456-461, (2019); Tuomisto L.E., Ilmarinen P., Kankaanranta H., Prognosis of new-onset 5 asthma diagnosed at adult age, Respiratory Medicine, 109, 8, pp. 944-954, (2015); Vimala V., Ramar K., Ettappan M., An intelligent sleep apnea classification system based on EEG signals, Journal of Medical Systems, 43, 2, pp. 1-9, (2019); Warwick M., Gallagher R., Chenoweth L., Stein-Parbury J., Self-management and symptom monitoring among older adults with chronic obstructive pulmonary disease, Journal of Advanced Nursing, 66, 4, pp. 784-793, (2010); Wensley D.C., Silverman M., The quality of home spirometry in school children with asthma, Thorax, 56, 3, pp. 183-185, (2001); Xue J.Z., Zhang H., Zheng C.X., Yan X.G., Wavelet packet transform for feature extraction of EEG during mental tasks, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), 1, pp. 360-363, (2003); Zhan J., Chen W., Cheng L., Wang Q., Han F., Cui Y., Diagnosis of asthma based on routine blood biomarkers using machine learning, Computational Intelligence and Neuroscience, (2020); Zhao X., Wang X., Yang T., Ji S., Wang H., Wang J., Wang Y., (2021); Zhu J., Zhou A., Gong Q., Zhou Y., Huang J., Chen Z., Detection of sleep apnea from electrocardiogram and pulse oximetry signals using random forest, Applied Sciences, 12, 9, (2022)","A. Ratnovsky; School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel; email: ratnovskya@afeka.ac.il","","Elsevier Ltd","","","","","","09574174","","ESAPE","","English","Expert Sys Appl","Article","Final","","Scopus","2-s2.0-85150017156"
"Hogan A.H.; Brimacombe M.; Mosha M.; Flores G.","Hogan, Alexander H. (57211041050); Brimacombe, Michael (6603904814); Mosha, Maua (57211631084); Flores, Glenn (24301253900)","57211041050; 6603904814; 57211631084; 24301253900","Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission","2022","Academic Pediatrics","22","1","","55","61","6","14","10.1016/j.acap.2021.07.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114718096&doi=10.1016%2fj.acap.2021.07.015&partnerID=40&md5=acd1c2a6c88a075830bfd9d0a415d174","Division of Hospital Medicine, Connecticut Children's Medical Center, Hartford, Conn, United States; Department of Pediatrics, University of Connecticut School of Medicine, Farmington, Conn, United States; Health Services Research Institute, Connecticut Children's Medical Center, Hartford, Conn, United States; Department of Pediatrics, University of Miami Miller School of Medicine, and Holtz Children's Hospital, Jackson Health System, Miami, Fla, United States","Hogan A.H., Division of Hospital Medicine, Connecticut Children's Medical Center, Hartford, Conn, United States, Department of Pediatrics, University of Connecticut School of Medicine, Farmington, Conn, United States; Brimacombe M., Health Services Research Institute, Connecticut Children's Medical Center, Hartford, Conn, United States; Mosha M., Health Services Research Institute, Connecticut Children's Medical Center, Hartford, Conn, United States; Flores G., Department of Pediatrics, University of Miami Miller School of Medicine, and Holtz Children's Hospital, Jackson Health System, Miami, Fla, United States","Objective: To identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling. Methods: This retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve. Results: Of 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network. Conclusions: Different methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks. © 2021 Academic Pediatric Association","artificial neural network; asthma; machine learning; rehospitalization","Adolescent; Artificial Intelligence; Asthma; Child; Child, Preschool; Humans; Patient Readmission; Retrospective Studies; Risk Factors; alcohol; adolescent; adult; area under the curve; Article; artificial intelligence; artificial neural network; asthma; child; chronic disease; cohort analysis; comparative study; controlled study; disease association; female; health insurance; hospital readmission; hospitalization; human; length of stay; logistic regression analysis; major clinical study; male; outcome assessment; pediatrics; proportional hazards model; receiver operating characteristic; retrospective study; risk assessment; risk factor; seasonal variation; teaching hospital; artificial intelligence; asthma; preschool child","","alcohol, 64-17-5","","","Academic Pediatric Association; Connecticut Institute for Clinical and Translational Science; University of Connecticut","Financial statement: Dr Hogan was supported by the Connecticut Institute for Clinical and Translational Science at the University of Connecticut , and an Academic Pediatric Association's Young Investigator Award. The study sponsors had no role in study design, the collection, analysis, interpretation of data, the writing of the report, or the decision to submit the paper for publication. The content is solely the responsibility of the authors, and does not necessarily represent the official views of either awarding institution.","Kamble S., Bharmal M., Incremental direct expenditure of treating asthma in the United States, J Asthma, 46, pp. 73-80, (2009); Chang L.V., Shah A.N., Hoefgen E.R., Et al., Lost earnings and nonmedical expenses of pediatric hospitalizations, Pediatrics, 142, (2018); Hain P.D., Gay J.C., Berutti T.W., Et al., Preventability of early readmissions at a children's hospital, Pediatrics, 131, pp. e171-e181, (2013); Parikh K., Berry J., Hall M., Et al., Racial and ethnic differences in pediatric readmissions for common chronic conditions, J Pediatr, 186, pp. 158-164, (2017); Bergert L., Patel S.J., Kimata C., Et al., Linking patient-centered medical home and asthma measures reduces hospital readmission rates, Pediatrics, 134, pp. e249-e256, (2014); Lu S., Kuo D.Z., Hospital charges of potentially preventable pediatric hospitalizations, Acad Pediatr, 12, pp. 436-444, (2012); Parikh K., Keller S., Ralston S., Inpatient quality improvement interventions for asthma: a meta-analysis, Pediatrics, 141, (2018); Kansagara D., Englander H., Salanitro A., Et al., Risk prediction models for hospital readmission: a systematic review, JAMA, 306, pp. 1688-1698, (2011); (2020); Veeranki S.P., Ohabughiro M.U., Moran J., Et al., National estimates of 30-day readmissions among children hospitalized for asthma in the United States, J Asthma, 55, pp. 695-704, (2018); Morosco G., Kiley J., Expert panel report 3 (EPR-3): guidelines for the diagnosis and management of asthma-summary report 2007, J Allergy Clin Immunol, 120, pp. S94-S138, (2007); Pantell M.S., Kaiser S.V., Torres J.M., Et al., Associations between social factor documentation and hospital length of stay and readmission among children, Hosp Pediatr, 10, pp. 12-19, (2020); (2020); Feudtner C., Feinstein J.A., Zhong W., Et al., Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation, BMC Pediatr, 14, (2014); 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Liu S.Y., Pearlman D.N., Hospital readmissions for childhood asthma: the role of individual and neighborhood factors, Public Health Rep, 124, pp. 65-78, (2009); Beck A.F., Simmons J.M., Huang B., Et al., Geomedicine: area-based socioeconomic measures for assessing risk of hospital reutilization among children admitted for asthma, Am J Public Health, 102, pp. 2308-2314, (2012); Beck A.F., Huang B., Auger K.A., Et al., Explaining racial disparities in child asthma readmission using a causal inference approach, JAMA Pediatr, 170, pp. 695-703, (2016); Kenyon C.C., Melvin P.R., Chiang V.W., Et al., Rehospitalization for childhood asthma: timing, variation, and opportunities for intervention, J Pediatr, 164, pp. 300-305, (2014); Rushworth R.L., Rob M.I., Readmissions to hospital: the contribution of morbidity data to the evaluation of asthma management, Aust J Public Health, 19, pp. 363-367, (1995); Flores G., Bridon C., Torres S., Et al., Improving asthma outcomes in minority children: a randomized, controlled trial of parent mentors, Pediatrics, 124, pp. 1522-1532, (2009); Kenyon C.C., Rubin D.M., Zorc J.J., Et al., Childhood asthma hospital discharge medication fills and risk of subsequent readmission, J Pediatr, 166, pp. 1121-1127, (2015); Kenyon C.C., Melvin P.R., Chiang V.W., Et al., Rehospitalization for childhood asthma: timing, variation, and opportunities for intervention, J Pediatr, 164, pp. 300-305, (2014); Chen Y., Dales R., Stewart P., Et al., Hospital readmissions for asthma in children and young adults in Canada, Pediatr Pulmonol, 36, pp. 22-26, (2003); Auger K.A., Kahn R.S., Davis M.M., Et al., Pediatric asthma readmission: asthma knowledge is not enough?, J Pediatr, 166, pp. 101-108, (2015); Andrews A.L., Brinton D.L., Simpson K.N., Et al., A longitudinal examination of the asthma medication ratio in children with Medicaid, J Asthma, 57, pp. 1083-1091, (2020); Silber J.H., Rosenbaum P.R., Even-shoshan O., Et al., Length of stay, conditional length of stay, and prolonged stay in pediatric asthma, Health Serv Res, 38, pp. 867-886, (2003); Lara M., Akinbami L., Flores G., Et al., Heterogeneity of childhood asthma among Hispanic children: Puerto Rican children bear a disproportionate burden, Pediatrics, 117, pp. 43-53, (2006); Hogan A.H., Flores G., Social determinants of health and the hospitalized child, Hosp Pediatr, 10, pp. 101-103, (2020); Flores G., Hollenbach J.P., Hogan A.H., To eliminate racial and ethnic disparities in child health care, more needs to be addressed than just social determinants, Lancet Respir Med, 7, pp. 842-843, (2019); Nakamura M.M., Toomey S.L., Zaslavsky A.M., Et al., Measuring pediatric hospital readmission rates to drive quality improvement, Acad Pediatr, 14, pp. S39-S46, (2014)","A.H. Hogan; Connecticut Children's Medical Center, Hartford, 282 Washington St, 06106, United States; email: AHogan@connecticutchildrens.org","","Elsevier Inc.","","","","","","18762859","","","34329757","English","Acad. Pediatr.","Article","Final","","Scopus","2-s2.0-85114718096"
"Zhang P.; Swaminathan A.; Uddin A.A.","Zhang, Pinzhi (58700021400); Swaminathan, Alagappan (57248226200); Uddin, Ahmed Abrar (58699387900)","58700021400; 57248226200; 58699387900","Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks","2023","Frontiers in Medicine","10","","1269784","","","","9","10.3389/fmed.2023.1269784","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177046234&doi=10.3389%2ffmed.2023.1269784&partnerID=40&md5=ffa310743460b5396f5afa8dbe1d9b1b","College of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States; College of Computing, Georgia Institute of Technology, Atlanta, GA, United States","Zhang P., College of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Swaminathan A., College of Computing, Georgia Institute of Technology, Atlanta, GA, United States; Uddin A.A., College of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States","Introduction: In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis. Methods: Our proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM). Results: The aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97. Discussion: The LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time. Copyright © 2023 Zhang, Swaminathan and Uddin.","artificial intelligence; audio parsing; lung disease; machine learning; neural networks; predictive analytics; pulmonary diagnostics","abnormal respiratory sound; Article; asthma; audio recording; breathing; breathing pattern; bronchiectasis; bronchiolitis; chronic obstructive lung disease; convolutional neural network; coughing; crackle; data base; deep learning; diagnostic accuracy; diagnostic test accuracy study; disease classification; false negative result; false positive result; feature extraction; Fourier transform; human; hyperpnea; long short term memory network; lower respiratory tract infection; lung disease; major clinical study; oscillation; pneumonia; prediction; recall; short time Fourier transform; upper respiratory tract infection; wheezing","","","","","National Science Foundation Innovation Corps Sites, (AWD-101499)","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Partial funding was provided by National Science Foundation Innovation Corps Sites Grant AWD-101499. 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Dalianis H., Evaluation metrics and evaluation, Clinical Text Mining, pp. 45-53, (2018); Greff K., Srivastava R.K., Koutnik J., Steunebrink B.R., Schmidhuber J., LSTM: a search space odyssey, IEEE Trans Neural Netw Learn Syst, 28, pp. 2222-2232, (2017); Bubeck S., Sellke M., A universal law of robustness via isoperimetry, arXiv preprint arxiv:2105.12806, (2021); Purwins H., Li B., Virtanen T., Schluter J., Chang S.Y., Sainath T., Deep learning for audio signal processing, arXiv preprint arxiv:1905.00078, (2019); Staudemeyer R.C., Rothstein Morris E., Understanding LSTM–a tutorial into long short-term memory recurrent neural networks, arXiv preprint arxiv:1909.09586, (2019)","A. Swaminathan; College of Computing, Georgia Institute of Technology, Atlanta, United States; email: aswaminathan44@gatech.edu","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85177046234"
"Li Z.; Huang K.; Liu L.; Zhang Z.","Li, Zongli (55771845900); Huang, Kewu (23005052100); Liu, Ligong (57447676000); Zhang, Zuoqing (57210591633)","55771845900; 23005052100; 57447676000; 57210591633","Early detection of COPD based on graph convolutional network and small and weakly labeled data","2022","Medical and Biological Engineering and Computing","60","8","","2321","2333","12","13","10.1007/s11517-022-02589-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132794736&doi=10.1007%2fs11517-022-02589-x&partnerID=40&md5=f3a9b9a596fae170ae035625ef8a8c7f","Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China; Beijing Institute of Respiratory Medicine, Beijing, 100020, China; Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, China; Department of Enterprise Management, China Energy Engineering Corporation Limited, Beijing, 100022, China","Li Z., Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China, Beijing Institute of Respiratory Medicine, Beijing, 100020, China, Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, China; Huang K., Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China, Beijing Institute of Respiratory Medicine, Beijing, 100020, China; Liu L., Department of Enterprise Management, China Energy Engineering Corporation Limited, Beijing, 100022, China; Zhang Z., Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, China","Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection. Graphical abstract: [Figure not available: see fulltext.]. © 2022, International Federation for Medical and Biological Engineering.","Chronic obstructive pulmonary disease; Deep learning; Early detection; Graph convolution network","Biological organs; Computerized tomography; Deep learning; Diagnosis; Graphic methods; Image segmentation; Pulmonary diseases; Topology; Chronic obstructive pulmonary disease; Convolutional networks; Deep learning; Disease detection; Early detection; Graph convolution network; Network models; Region-of-interest; Regions of interest; Structure information; Article; binary classification; chronic obstructive lung disease; controlled study; convolutional neural network; decision tree; diagnostic accuracy; early diagnosis; entropy; human; lung parenchyma; measurement accuracy; measurement precision; nonlinear system; quantitative analysis; receiver operating characteristic; support vector machine; Convolution","","","","","Beijing Municipal Science and Technology Commission, BMSTC, (Z18110001718053)","This research is supported by the Special Project of Beijing Science and Technology Commission “Application Research of Capital Clinical Characteristics” (Z18110001718 053). ","Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med, 3, 11, (2006); Zhong N., Wang C., Yao W., Et al., Prevalence of chronic obstructive pulmonary disease in China, Am J Respir Crit Care Med, 176, 8, pp. 753-760, (2007); Mapel D.W., Dalal A.A., Blanchette C.M., Et al., Severity of COPD at initial spirometry-confirmed diagnosis: data from medical charts and administrative claims, Int J Chron Obstruct Pulmon Dis, 6, pp. 573-581, (2011); Bellamy D., Smith J., Role of primary care in early diagnosis and effective management of COPD, Int J Clin Pract, 61, pp. 1380-1389, (2007); Gurney J.W., Jones K.K., Robbins R.A., Et al., Regional distribution of emphysema: correlation of high-resolution CT with pulmonary function tests in unselected smokers, Radiology, 183, 2, pp. 457-463, (1992); Lynch D.A., Austin J.H., Hogg J.C., Et al., CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society, Radiology, 277, 1, pp. 192-205, (2015); Kauczor H.U., Wielputz M.O., Jobst B.J., Et al., Computed tomography imaging for novel therapies of chronic obstructive pulmonary disease, J Thorac Imaging, 34, 3, pp. 202-213, (2019); Ostridge K., Wilkinson T.M., Present and future utility of computed tomography scanning in the assessment and management of COPD, Eur Respir J, 48, 1, pp. 216-228, (2016); Feragen A., Petersen J., Grimm D., Et al., Geometric tree kernels: classification of COPD from airway tree geometry, Inf Process Med Imaging, 23, pp. 171-183, (2013); Bodduluri S., Newell J.D., Hoffman E.A., Et al., Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework, Acad Radiol, 20, 5, pp. 527-536, (2013); Cheplygina V., Sorensen L., Et al., Classification of COPD with Multiple Instance Learning in 2014 22Nd International Conference on Pattern Recognition, (2014); Cheplygina V., Pena I.P., Pedersen J.H., Et al., Transfer learning for multicenter classification of chronic obstructive pulmonary disease, IEEE J Biomed Health Inform, 22, 5, pp. 1486-1496, (2018); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018); Hatt C., Galban C., Labaki W., Kazerooni E., Lynch D., Han M., Convolutional neural network based COPD and emphysema classifications are predictive of lung cancer diagnosis, Image Analysis for Moving Organ, Breast, and Thoracic Images, 11040, (2018); Tang L., Coxson H.O., Lam S., Et al., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, 5, pp. e259-e267, (2020); Ju J., Li R., Gu S., Et al., Impact of emphysema heterogeneity on pulmonary function, PLoS ONE, 9, 11, (2014); Ahmed J., Vesal S., Durlak F., Et al., COPD Classification in CT Images Using a 3D Convolutional Neural Network. Arxiv, 2001, (2020); Ho T.T., Kim T., Kim W.J., Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci Rep, 11, 1, (2021); Liu J., Tan G., Lan W., Et al., Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks, BMC Bioinformatics, 21, (2020); Zhang X., He L., Chen K., Et al., Multi-view graph convolutional network and its applications on neuroimage analysis for Parkinson’s disease, AMIA Annu Symp Proc, 5, 2018, pp. 1147-1156, (2018); Ta Song S., Roy Chowdhury F., Graph convolutional neural networks for Alzheimer’s disease classification, Proc IEEE Intsymp Biomed Imaging, pp. 414-417, (2019); Jiang H., Cao P., Xu M., Et al., Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction, Comput Biol Med, 127, 1, (2020); Liang X., Zhang Y., Wang J., Ye Q., Liu Y., Tong J., Diagnosis of COVID-19 pneumonia based on graph convolutional network, Front Med, 7, (2021); Wang S.H., Govindaraj V.V., Gorriz J.M., Et al., Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network, Inf Fusion, 67, pp. 208-229, (2021); Li Y., Chen J., Xue P., Et al., Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images, IEEE Trans Med Imaging, 39, 11, pp. 3403-3415, (2020); Ye H., Wang D.H., Li J., Et al., Improving histopathological image segmentation and classification using graph convolution network, ICCPR ’19: 2019 8Th International Conference on Computing and Pattern Recognition, (2019); Zhou Y., Graham S., Koohbanani N.A., Et al., CGC-net: Cell graph convolutional network for grading of colorectal cancer histology images, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), pp. 388-398, (2019); Pedersen J.H., Ashraf H., Dirksen A., Et al., The Danish randomized lung cancer ct screening trial—overall design and results of the prevalence round, J Thorac Oncol, 4, 5, pp. 608-614, (2019); Vogelmeier C.F., Criner G.J., Martinez F.J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report, GOLD executive summary, 53, 3, pp. 128-149, (2017); Bruna J., Zaremba W., Szlam A., Et al., Spectral networks and locally connected networks on graphs, Computer Science. 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Arxiv, 1606, (2016); Wang Z., Zheng L., Li Y., Et al., Linkage based face clustering via graph convolution network, (2019); Niepert M., Ahmed M., Kutzkov K., Learning convolutional neural networks for graphs, (2016); Li Q., Han Z., Wu X.M., Deeper insights into graph convolutional networks for semi-supervised learning, (2018); Lin T.Y., Goyal P., Girshick R., Et al., Focal loss for dense object detection, IEEE Trans Pattern Anal Mach Intell, 42, 2, pp. 318-327, (2020); Shin H.C., Roth H.R., Gao M., Et al., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans Med Imaging, 35, 5, pp. 1285-1298, (2016); Tajbakhsh N., Shin J.Y., Gurudu S.R., Et al., Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans Med Imaging, 35, 5, pp. 1299-1312, (2016); Kipf T.N., Welling M., Semi-supervised classification with graph convolutional networks, (2016); Sorensen L., Nielsen M., Lo P., Et al., Texture-based analysis of COPD: a data-driven approach, IEEE Trans Med Imaging, 31, 1, pp. 70-78, (2012); Shuman D.I., Narang S.K., Frossard P., Et al., The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Process Magazine, 30, 3, pp. 83-98, (2013); Li R.Y., Yao J.W., Zhu X.L., Et al., Graph CNN for survival analysis on whole slide pathological images, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, (2018); Chen Z.M., Wei X.S., Wang P., Et al., Multi-label image recognition with graph convolutional networks, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019); Xu C., Qi S., Feng J., Et al., DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images, Phys Med Biol, 65, 14, (2020)","K. Huang; Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China; email: kewu_huang@126.com","","Springer Science and Business Media Deutschland GmbH","","","","","","01400118","","MBECD","","English","Med. Biol. Eng. Comput.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85132794736"
"Dong Y.; Zhou S.; Xing L.; Chen Y.; Ren Z.; Dong Y.; Zhang X.","Dong, Yao (35111107100); Zhou, Shaoze (57932412400); Xing, Li (57207050158); Chen, Yumeng (57932551600); Ren, Ziyu (57931710600); Dong, Yongfeng (24922854500); Zhang, Xuekui (16053655400)","35111107100; 57932412400; 57207050158; 57932551600; 57931710600; 24922854500; 16053655400","Deep learning methods may not outperform other machine learning methods on analyzing genomic studies","2022","Frontiers in Genetics","13","","992070","","","","9","10.3389/fgene.2022.992070","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140082763&doi=10.3389%2ffgene.2022.992070&partnerID=40&md5=0c42c1df1b72886affa9fc3d6af713a7","School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China; Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada; Hebei Province Key Laboratory of Big Data Computing, Tianjin, China; Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatoon, Canada","Dong Y., School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China, Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada, Hebei Province Key Laboratory of Big Data Computing, Tianjin, China; Zhou S., Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada; Xing L., Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Saskatoon, Canada; Chen Y., School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China, Hebei Province Key Laboratory of Big Data Computing, Tianjin, China; Ren Z., School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China, Hebei Province Key Laboratory of Big Data Computing, Tianjin, China; Dong Y., School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China, Hebei Province Key Laboratory of Big Data Computing, Tianjin, China; Zhang X., Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada","Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful in image processing. Recently, many DL methods have been applied to analyze genomic studies. However, genomic data usually has too small a sample size to fit a complex network. They do not have common structural patterns like images to utilize pre-trained networks or take advantage of convolution layers. The concern of overusing DL methods motivates us to evaluate DL methods’ performance versus popular non-deep Machine Learning (ML) methods for analyzing genomic data with a wide range of sample sizes. In this paper, we conduct a benchmark study using the UK Biobank data and its many random subsets with different sample sizes. The original UK Biobank data has about 500k participants. Each patient has comprehensive patient characteristics, disease histories, and genomic information, i.e., the genotypes of millions of Single-Nucleotide Polymorphism (SNPs). We are interested in predicting the risk of three lung diseases: asthma, COPD, and lung cancer. There are 205,238 participants have recorded disease outcomes for these three diseases. Five prediction models are investigated in this benchmark study, including three non-deep machine learning methods (Elastic Net, XGBoost, and SVM) and two deep learning methods (DNN and LSTM). Besides the most popular performance metrics, such as the F1-score, we promote the hit curve, a visual tool to describe the performance of predicting rare events. We discovered that DL methods frequently fail to outperform non-deep ML in analyzing genomic data, even in large datasets with over 200k samples. The experiment results suggest not overusing DL methods in genomic studies, even with biobank-level sample sizes. The performance differences between DL and non-deep ML decrease as the sample size of data increases. This suggests when the sample size of data is significant, further increasing sample sizes leads to more performance gain in DL methods. Hence, DL methods could be better if we analyze genomic data bigger than this study. Copyright © 2022 Dong, Zhou, Xing, Chen, Ren, Dong and Zhang.","deep learning; disease prediction; genomic analysis; hit curve; imbalance data; machine learning","adult; aged; Article; asthma; biobank; chronic obstructive lung disease; decision tree; deep learning; deep neural network; female; forced expiratory volume; genotype; human; learning algorithm; long short term memory network; lung cancer; lung disease; machine learning; male; people by smoking status; recurrent neural network; single nucleotide polymorphism; support vector machine","","","","","Chinese State Scholarship Fund, (202108130108); Science, Technology Research Project of Higher Education in Hebei Province of China, (QN2021213); Compute Canada; Natural Sciences and Engineering Research Council of Canada, NSERC, (RGPIN-2017-04722, RGPIN-2021-03530); Canada Research Chairs, (950-231363); National Key Research and Development Program of China, NKRDPC, (2019YFC1904601)","Funding text 1: This research was enabled in part by support provided by WestGrid [ https://www.westgrid.ca/ ] and Compute Canada [ https://www.computecanada.ca/ ]. ; Funding text 2: This work was supported by the Natural Sciences and Engineering Research Council Discovery Grants (RGPIN-2017-04722 PI:XZ, RGPIN-2021-03530 PI:LX), the Canada Research Chair (950-231363, PI:XZ), Chinese State Scholarship Fund (202108130108), Chinese National Key Research and Development Program (2019YFC1904601), Science, Technology Research Project of Higher Education in Hebei Province of China (QN2021213). ","Chao H., Shan H., Homayounieh F., Singh R., Khera R., Guo H., Et al., Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography, Nat. Commun, 12, (2021); Deberneh H.M., Kim I., Prediction of type 2 diabetes based on machine learning algorithm, Int. J. Environ. Res. Public Health, 18, (2021); Elsheikh A.H., Saba A.I., Elaziz M.A., Lu S., Shanmugan S., Muthuramalingam T., Et al., Deep learning-based forecasting model for Covid-19 outbreak in Saudi Arabia, Process Saf. Environ. Prot, 149, pp. 223-233, (2021); Fan S., Zhao Z., Zhang Y., Yu H., Zheng C., Huang X., Et al., Probability calibration-based prediction of recurrence rate in patients with diffuse large b-cell lymphoma, BioData Min, 14, (2021); Hussain A., Choi H.-E., Kim H.-J., Aich S., Saqlain M., Kim H.-C., Forecast the exacerbation in patients of chronic obstructive pulmonary disease with clinical indicators using machine learning techniques, Diagnostics, 11, (2021); Jin C., Yu H., Ke J., Ding P., Yi Y., Jiang X., Et al., Predicting treatment response from longitudinal images using multi-task deep learning, Nat. Commun, 12, (2021); Lin S., Li Z., Fu B., Chen S., Li X., Wang Y., Et al., Feasibility of using deep learning to detect coronary artery disease based on facial photo, Eur. Heart J, 41, pp. 4400-4411, (2020); Ma B., Yan G., Chai B., Hou X., Xgblc: An improved survival prediction model based on XGBoost, Bioinformatics, 38, pp. 410-418, (2021); Park Y.M., Lee B.-J., Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence, Sci. Rep, 11, (2021); Placek K., Benatar M., Wuu J., Rampersaud E., Hennessy L., Van Deerlin V.M., Et al., Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis, EMBO Mol. Med, 13, (2021); Rowlands C.F., Baralle D., Ellingford J.M., Machine learning approaches for the prioritization of genomic variants impacting pre-mrna splicing, Cells, 8, (2019); Sun Y., Milne S., Jaw J.E., Yang C., Xu F., Li X., Et al., Bmi is associated with fev1 decline in chronic obstructive pulmonary disease: A meta-analysis of clinical trials, Respir. Res, 20, (2019); Wang G., Zhang Y., Li S., Zhang J., Jiang D., Li X., Et al., A machine learning-based prediction model for cardiovascular risk in women with preeclampsia, Front. Cardiovasc. Med, 8, (2021); Ye J., Wang S., Yang X., Xianjun T., Gene prediction of aging-related diseases based on dnn and mashup, BMC Bioinforma, 22, (2021); Zhou H., Li L., Liu Z., Zhao K., Chen X., Lu M., Et al., Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images, Eur. Radiol, 31, pp. 3931-3940, (2020); Zou H., Hastie T., Regularization and variable selection via the elastic net, J. R. Stat. Soc. B, 67, pp. 301-320, (2005)","Y. Dong; School of Artifcial Intelligence, Hebei University of Technology, Tianjin, China; email: dongyf@hebut.edu.cn; X. Zhang; Department of Mathematics and Statistics, University of Victoria, Victoria, Canada; email: xuekui@uvic.ca","","Frontiers Media S.A.","","","","","","16648021","","","","English","Front. Genet.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140082763"
"Ding X.; Qin J.; Huang F.; Feng F.; Luo L.","Ding, Xuexuan (58191127400); Qin, Jingtong (58191127500); Huang, Fangfang (57216902164); Feng, Fuhai (58190948600); Luo, Lianxiang (57193721943)","58191127400; 58191127500; 57216902164; 58190948600; 57193721943","The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma","2023","Inflammation Research","72","5","","1099","1119","20","8","10.1007/s00011-023-01732-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153046825&doi=10.1007%2fs00011-023-01732-0&partnerID=40&md5=05a8e0af69c6b9fd3ac797357354b659","The First Clinical College, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Graduate School, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; The Marine Biomedical Research Institute, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, Guangdong, Zhanjiang, 524023, China","Ding X., The First Clinical College, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Qin J., The First Clinical College, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Huang F., Graduate School, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Feng F., The First Clinical College, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Luo L., The Marine Biomedical Research Institute, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China, The Marine Biomedical Research Institute of Guangdong Zhanjiang, Guangdong, Zhanjiang, 524023, China","Background: Lipids, significant signaling molecules, regulate a multitude of cellular responses and biological pathways in asthma which are closely associated with disease onset and progression. However, the characteristic lipid genes and metabolites in asthma remain to be explored. It is also necessary to further investigate the role of lipid molecules in asthma based on high-throughput data. Objective: To explore the biomarkers and molecular mechanisms associated with lipid metabolism in asthma. Methods: In this study, we selected three mouse-derived datasets and one human dataset (GSE41665, GSE41667, GSE3184 and GSE67472) from the GEO database. Five machine learning algorithms, LASSO, SVM-RFE, Boruta, XGBoost and RF, were used to identify core gene. Additionally, we used non-negative matrix breakdown (NMF) clustering to identify two lipid molecular subgroups and constructed a lipid metabolism score by principal component analysis (PCA) to differentiate the subtypes. Finally, Western blot confirmed the altered expression levels of core genes in OVA (ovalbumin) and HDM+LPS (house dust mite+lipopolysaccharide) stimulated and challenged BALB/c mice, respectively. Results of non-targeted metabolomics revealed multiple differentially expressed metabolites in the plasma of OVA-induced asthmatic mice. Results: Cholesterol 25-hydroxylase (CH25H) was finally localized as a core lipid metabolism gene in asthma and was verified to be highly expressed in two mouse models of asthma. Five-gene lipid metabolism constructed from CYP2E1, CH25H, PTGES, ALOX15 and ME1 was able to distinguish the subtypes effectively. The results of non-targeted metabolomics showed that most of the aberrantly expressed metabolites in the plasma of asthmatic mice were lipids, such as LPC 16:0, LPC 18:1 and LPA 18:1. Conclusion: Our findings imply that the lipid-related gene CH25H may be a useful biomarker in the diagnosis of asthma. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.","Asthma; CH25H; Lipid metabolism; Lipid metabolism-related genes; Machine learning; Untargeted metabolomics","Animals; Asthma; Biomarkers; Humans; Lipid Metabolism; Lipids; Metabolomics; Mice; arachidonate 15 lipoxygenase; cholesterol; cholesterol 25 hydroxylase; cytochrome P450 2E1; house dust allergen; lipid; lipopolysaccharide; ovalbumin; oxygenase; prostaglandin E synthase 1; unclassified drug; biological marker; cholesterol 25-hydroxylase; lipid; animal experiment; animal model; animal tissue; arachidonic acid metabolism; Article; asthma; cell infiltration; chemokine signaling; controlled study; core gene; cytokine signaling; differential gene expression; environmental factor; eosinophil; feature selection; functional enrichment analysis; gene ontology; gene set enrichment analysis; glutathione metabolism; immunocompetent cell; inflammatory cell; KEGG; least absolute shrinkage and selection operator; lipid metabolism; machine learning; marker gene; metabolomics; mouse; mucus secretion; neutrophil; nonhuman; protein expression; protein expression level; protein protein interaction; random forest; upregulation; weighted gene co expression network analysis; Western blotting; animal; genetics; human; metabolomics; procedures","","arachidonate 15 lipoxygenase, 82249-77-2; cholesterol, 57-88-5; lipid, 66455-18-3; ovalbumin, 77466-29-6; oxygenase, 9037-29-0, 9046-59-7; Biomarkers, ; cholesterol 25-hydroxylase, ; Lipids, ","","","Guangdong Provincial Department of Education Research Project, (2022KTSCX); Public Service Platform of South China Sea for R&D Marine Biomedicine Resources; Science and Technology Special Project of Zhanjiang, (2022A01034)","Funding text 1: This study was supported by the Science and Technology Special Project of Zhanjiang (2022A01034); The Guangdong Provincial Department of Education Research Project (2022KTSCX). ; Funding text 2: We thank the Public Service Platform of South China Sea for R&D Marine Biomedicine Resources for support.","Mims J.W., Asthma: definitions and pathophysiology, Int Forum Allergy Rhinol, 5, pp. S2-S6, (2015); Masoli M., Fabian D., Holt S., Beasley R., The global burden of asthma: executive summary of the GINA Dissemination Committee report, Allergy, 59, pp. 469-478, (2004); Braido F., Failure in asthma control: reasons and consequences, Scientifica (Cairo), 2013, (2013); Rothe T., Spagnolo P., Bridevaux P.O., Clarenbach C., Eich-Wanger C., Meyer F., Et al., Diagnosis and management of asthma - the swiss guidelines, Respiration, 95, pp. 364-380, (2018); Lambrecht B.N., Hammad H., The immunology of asthma, Nat Immunol, 16, pp. 45-56, (2015); D'Amato G., Liccardi G., Noschese P., Salzillo A., D'Amato M., Cazzola M., Anti-IgE monoclonal antibody (omalizumab) in the treatment of atopic asthma and allergic respiratory diseases, Curr Drug Targets Inflamm Allergy, 3, pp. 227-229, (2004); 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Lee-Sarwar K.A., Kelly R.S., Lasky-Su J., Zeiger R.S., O'Connor G.T., Sandel M.T., Et al., Integrative analysis of the intestinal metabolome of childhood asthma, J Allergy Clin Immunol, 144, pp. 442-454, (2019); Yoder M., Zhuge Y., Yuan Y., Holian O., Kuo S., van Breemen R., Et al., Bioactive lysophosphatidylcholine 16:0 and 18:0 are elevated in lungs of asthmatic subjects, Allergy Asthma Immunol Res, 6, pp. 61-65, (2014); Arbibe L., Koumanov K., Vial D., Rougeot C., Faure G., Havet N., Et al., Generation of lyso-phospholipids from surfactant in acute lung injury is mediated by type-II phospholipase A2 and inhibited by a direct surfactant protein A-phospholipase A2 protein interaction, J Clin Invest, 102, pp. 1152-1160, (1998); Kim S.J., Moon H.G., Park G.Y., The roles of autotaxin/lysophosphatidic acid in immune regulation and asthma, Biochim Biophys Acta Mol Cell Biol Lipids, 1865, (2020); Lee Y.J., Im D.S., Efficacy comparison of LPA(2) antagonist H2L5186303 and agonist GRI977143 on ovalbumin-induced allergic asthma in BALB/c mice, Int J Mol Sci, 23, (2022); Ohtsu H., Pathophysiologic role of histamine: evidence clarified by histidine decarboxylase gene knockout mice, Int Arch Allergy Immunol, 158, pp. 2-6, (2012); Chang C., Guo Z.G., He B., Yao W.Z., Metabolic alterations in the sera of Chinese patients with mild persistent asthma: a GC-MS-based metabolomics analysis, Acta Pharmacol Sin, 36, pp. 1356-1366, (2015); Loureiro C.C., Duarte I.F., Gomes J., Carrola J., Barros A.S., Gil A.M., Et al., Urinary metabolomic changes as a predictive biomarker of asthma exacerbation, J Allergy Clin Immunol, 133, (2014); Quan-Jun Y., Jian-Ping Z., Jian-Hua Z., Yong-Long H., Bo X., Jing-Xian Z., Et al., Distinct metabolic profile of inhaled budesonide and salbutamol in asthmatic children during acute exacerbation, Basic Clin Pharmacol Toxicol, 120, pp. 303-311, (2017)","L. Luo; The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China; email: luolianxiang321@gdmu.edu.cn","","Springer Science and Business Media Deutschland GmbH","","","","","","10233830","","INREF","37081162","English","Inflamm. Res.","Article","Final","","Scopus","2-s2.0-85153046825"
"Cilluffo G.; Fasola S.; Ferrante G.; Licari A.; Marseglia G.R.; Albarelli A.; Marseglia G.L.; La Grutta S.","Cilluffo, Giovanna (56998681700); Fasola, Salvatore (37037266700); Ferrante, Giuliana (55360312800); Licari, Amelia (15057979700); Marseglia, Giuseppe Roberto (56102727300); Albarelli, Andrea (24479348800); Marseglia, Gian Luigi (26422377200); La Grutta, Stefania (6701854558)","56998681700; 37037266700; 55360312800; 15057979700; 56102727300; 24479348800; 26422377200; 6701854558","Machine learning: A modern approach to pediatric asthma","2022","Pediatric Allergy and Immunology","33","S27","","34","37","3","9","10.1111/pai.13624","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123525998&doi=10.1111%2fpai.13624&partnerID=40&md5=b39b75bc516e219a7ee0b973fea92075","Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy; Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy","Cilluffo G., Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy; Fasola S., Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy; Ferrante G., Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy; Licari A., Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Marseglia G.R., Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy; Albarelli A., Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy; Marseglia G.L., Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; La Grutta S., Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy","Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma. © 2022 The Authors. Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","","Asthma; Child; Humans; Machine Learning; Phenotype; Article; asthma; child; clinical practice; computer prediction; disease exacerbation; human; machine learning; phenotype; asthma; machine learning","","","","","","","Deliu M., Yavuz T.S., Sperrin M., Et al., Features of asthma which provide meaningful insights for understanding the disease heterogeneity, Clin Exp Allergy, 48, 1, pp. 39-47, (2018); Su M.W., Lin W.C., Tsai C.H., Et al., Childhood asthma clusters reveal neutrophil-predominant phenotype with distinct gene expression, Allergy, 73, 10, pp. 2024-2032, (2018); Fitzpatrick A.M., Bacharier L.B., Jackson D.J., Et al., Heterogeneity of mild to moderate persistent asthma in children: confirmation by latent class analysis and association with 1-year outcomes, J Allergy Clin Immunol Pract, 8, 8, pp. 2617-2627, (2020); Fitzpatrick A.M., Teague W.G., Meyers D.A., Et al., Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program, J Allergy Clin Immunol, 127, 2, pp. 382-389, (2011); Just J., Gouvis-Echraghi R., Rouve S., Wanin S., Moreau D., Annesi-Maesano I., Two novel, severe asthma phenotypes identified during childhood using a clustering approach, Eur Respir J, 40, 1, pp. 55-60, (2012); Sottile G., Ferrante G., Cilluffo G., Et al., A model-based approach for assessing bronchodilator responsiveness in children: the conventional cutoff revisited, J Allergy Clin Immunol, 147, 2, pp. 769-772, (2021); Bose S., Kenyon C.C., Masino A.J., Personalized prediction of early childhood asthma persistence: a machine learning approach, PLoS One, 16, 3, (2021); Navanandan N., Hatoun J., Celedon J.C., Liu A.H., Predicting severe asthma exacerbations in children: blueprint for today and tomorrow, J Allergy Clin Immunol Pract, 9, 7, pp. 2619-2626, (2021); Luo G., He S., Stone B.L., Et al., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, (2020); Sills M.R., Ozkaynak M., Jang H., Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning, Int J Med Inform, 151, (2021)","A. Licari; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; email: amelia.licari@unipv.it","","John Wiley and Sons Inc","","","","","","09056157","","PALUE","35080316","English","Pediatr. Allergy Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85123525998"
"Bhowmik R.T.; Most S.P.","Bhowmik, Rohan T. (57221911070); Most, Sam P. (6601986888)","57221911070; 6601986888","A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks","2022","Electronics (Switzerland)","11","16","2562","","","","11","10.3390/electronics11162562","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136731765&doi=10.3390%2felectronics11162562&partnerID=40&md5=5f0f9e5accdb29e8de05c32dc52e4437","School of Medicine, Stanford University, Stanford, 94305, CA, United States; The Harker School, San Jose, 95129, CA, United States","Bhowmik R.T., School of Medicine, Stanford University, Stanford, 94305, CA, United States, The Harker School, San Jose, 95129, CA, United States; Most S.P., School of Medicine, Stanford University, Stanford, 94305, CA, United States","Chronic respiratory diseases, such as the Chronic Obstructive Pulmonary Disease (COPD) and asthma, are a serious health crisis, affecting a large number of people globally and inflicting major costs on the economy. Current methods for assessing the progression of respiratory symptoms are either subjective and inaccurate, or complex and cumbersome, and do not incorporate environmental factors to track individualized risks. Lacking predictive assessments and early intervention, unexpected exacerbations often lead to hospitalizations and high medical costs. This work presents a multi-modal solution for predicting the exacerbation risks of respiratory diseases, such as COPD, based on a novel spatio-temporal machine learning architecture for real-time and accurate respiratory events detection, and tracking of local environmental and meteorological data and trends. The proposed new neural network model blends key attributes of both convolutional and recurrent neural architectures, allowing extraction of the salient spatial and temporal features encoded in respiratory sounds, thereby leading to accurate classification and tracking of symptoms. Combined with the data from environmental and meteorological sensors, and a predictive model based on retrospective medical studies, this solution can assess and provide early warnings of respiratory disease exacerbations, thereby potentially reducing hospitalization rates and medical costs. © 2022 by the authors.","artificial intelligence; multi-modal; personalized medicine; respiratory exacerbation; sensor network","","","","","","","","The Global Impact of Respiratory Disease, (2017); Syamlal G., Bhattacharya A., Dodd K.E., Medical Expenditures Attributed to Asthma and Chronic Obstructive Pulmonary Disease Among Workers—United States, 2011–2015, Morb. Mortal. Wkly. Rep, 69, pp. 809-814, (2020); Diab N., Gershon A.S., Sin D.D., Tan W.C., Bourbeau J., Boulet L.P., Aaron S.D., Underdiagnosis and Overdiagnosis of Chronic Obstructive Pulmonary Disease, Am. J. Respir. Crit. Care Med, 198, pp. 1130-1139, (2018); Christenson S.A., Smith B.M., Bafadhel M., Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Camac E.R., Stumpf N.A., Voelker H.K., Criner G.J., Short-Term Impact of the Frequency of COPD Exacerbations on Quality of Life, Chronic Obstr. Pulm. Dis, 9, pp. 298-308, (2022); Tomasic I., Tomasic N., Trobec R., Krpan M., Kelava T., Continuous remote monitoring of COPD patients—Justification and explanation of the requirements and a survey of the available technologies, Med. Biol. Eng. Comput, 56, pp. 547-569, (2018); Bentsen S.B., Rustoen T., Miaskowski C., Differences in subjective and objective respiratory parameters in patients with chronic obstructive pulmonary disease with and without pain, Int. J. Chronic Obstr. Pulm. Dis, 7, pp. 137-143, (2012); Ho T., Cusack R.P., Chaudhary N., Satia I., Kurmi O.P., Under- and over-diagnosis of COPD: A global perspective, Breathe, 15, pp. 24-35, (2019); De Miguel-Diez J., Hernandez-Vazquez J., Lopez-de-Andres A., Alvaro-Meca A., Hernandez-Barrera V., Jimenez-Garcia R., Analysis of environmental risk factors for chronic obstructive pulmonary disease exacerbation: A case-crossover study (2004–2013), PLoS ONE, 14, (2019); Smith J., Woodcock A., Cough and its importance in COPD, Int. J. Chronic Obstr. Pulm. Dis, 1, pp. 305-314, (2006); Barry S.J., Dane A.D., Morice A.H., Walmsley A.D., The automatic recognition and counting of cough, Cough, 2, (2006); Liu J.M., You M., Wang Z., Li G.Z., Xu X., Qiu Z., Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2014): Medical Informatics and Decision Making, BMC Med. Inform. Decis. Mak, 15, (2015); Wang H.H., Liu J.M., You M.Y., Li G.Z., Audio signals encoding for cough classification using convolutional neural networks: A comparative study, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, pp. 442-445; Amoh J., Odame K., Deep Neural Networks for Identifying Cough Sounds, IEEE Trans. Biomed. Circuits Syst, 10, pp. 1003-1011, (2016); Elfaramawy T., Fall C.L., Morissette M., Lellouche F., Gosselin B., Wireless respiratory monitoring and coughing detection using a wearable patch sensor network, Proceedings of the 15th IEEE International New Circuits and Systems Conference, pp. 197-200; Drugman T., Urbain J., Dutoit T., Objective study of sensor relevance for automatic cough detection, Proceedings of the 19th European Signal Processing Conference, pp. 1289-1293; Solinski M., Lepek M., Koltowski L., Automatic cough detection based on airflow signals for portable spirometry system, arXiv, (2019); Mesaros A., Heittola T., Virtanen T., Plumbley M.D., Sound Event Detection: A tutorial, IEEE Signal Process. Mag, 38, pp. 67-83, (2021); Cakir E., Parascandolo G., Heittola T., Huttunen H., Virtanen T., Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection, IEEE/ACM Trans. Audio Speech Lang. Process, 25, pp. 1291-1303, (2017); Sang J., Park S., Lee J., Convolutional Recurrent Neural Networks for Urban Sound Classification Using Raw Waveforms, Proceedings of the 26th European Signal Processing Conference, pp. 2444-2448; Deshmukh S., Raj B., Singh R., Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection, arXiv, (2020); Analytics Vidhya, (2020); Sanjeevan K., Hung T., UrbanSound Classification Using Convolutional Recurrent Networks in PyTorch, (2020); Parikh S., Henderson K., Gondalia R., Kaye L., Remmelink E., Thompson A., Barrett M., Perceptions of Environmental Influence and Environmental Information-Seeking Behavior among People with Asthma and COPD, Front. Digit. Health, 4, (2022); Patel N., Kinmond K., Jones P., Birks P., Spiteri M.A., Validation of COPDPredict™: Unique Combination of Remote Monitoring and Exacerbation Prediction to Support Preventative Management of COPD Exacerbations, Int. J. Chron. Obstruct. Pulmon. Dis, 16, pp. 1887-1899, (2021); Lecun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proc. IEEE, 86, pp. 2278-2324, (1998); (2018); Jo E.J., Song W.J., Environmental triggers for chronic cough, Asia Pac. Allergy, 9, (2019); National Ambient Air Quality Standards for Particle Pollution; NAAQS Table; Real-Time Air Quality Monitoring; Khoshrounejad F., Hamednia M., Mehrjerd A., Pichaghsaz S., Jamalirad H., Sargolzaei M., Hoseini B., Aalaei S., Telehealth-Based Services During the COVID-19 Pandemic: A Systematic Review of Features and Challenges, Front. Public Health, 19, (2021); Gajarawala S.N., Pelkowski J.N., Telehealth Benefits and Barriers, J. Nurse Pract, 17, pp. 218-221, (2021)","R.T. Bhowmik; School of Medicine, Stanford University, Stanford, 94305, United States; email: rbhowmik@stanford.edu","","MDPI","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85136731765"
"Benedict S.","Benedict, Shajulin (23990271800)","23990271800","Shared Mobility Intelligence Using Permissioned Blockchains for Smart Cities","2022","New Generation Computing","40","4","","1009","1027","18","10","10.1007/s00354-021-00147-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123860017&doi=10.1007%2fs00354-021-00147-x&partnerID=40&md5=5e30d2a121dd28c3691ef7da66eb11e0","Indian Institute of Information Technology Kottayam, Kerala, Kottayam, India","Benedict S., Indian Institute of Information Technology Kottayam, Kerala, Kottayam, India","Suggesting tourists/residents about the pollution-free locations and controlling the number of passengers in a shareable vehicle have become crucial tasks to smart city officials as they plummet health issues such as asthma or COVID-19. Recently, city authorities, transport logistic designers, and policymakers have tasked researchers/entrepreneurs to innovate in shared mobility systems. This paper proposes a Blockchain-Enabled Shared Mobility (BESM) architecture that allocates seats to residents/tourists in a shareable vehicle based on air quality and COVID-19 information of traveling locations. BESM involves smart city authorities, vehicle owners, hospital authorities, and residents using permissioned-blockchains to collaboratively decide on allocating travel seats. Experiments were carried out at the IoT Cloud research laboratory to manifest the allocation of seats. For instance, BESM excluded in allocating seats to asthma patients and limited the number of travelers in the cities where COVID-19 cases or pollution levels were higher in numbers using BESM. The pollution levels of cities were monitored using air quality monitoring sensors or predicted using a few prediction algorithms such as Random Forests (RF), Linear Regression (LR), Quantile Regression (QR), Ridge Regression (RR), Lasso Regression (LaR), ElasticNet Regression (ER), Support Vector Machine (SVM), and Recursive Partitioning (RP). In succinct, the article unfolded the primordial importance of the proposed BESM architecture for promoting efficient shared mobility aspects in smart cities. © 2021, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.","Blockchain; Intelligence; Machine learning; Smart city; Smart transportation; Tourism","Air quality; Blockchain; Diseases; Logistic regression; Research laboratories; Smart city; Support vector machines; Vehicles; Block-chain; Health issues; Hospital authorities; Intelligence; Machine-learning; Mobility systems; Policy makers; Pollution level; Smart transportation; Transport logistics; Decision trees","","","","","American Institute of Mathematics, AIM; Belarusian Republican Foundation for Fundamental Research, BRFFR, (2020, 4891/1641/PC/CRL Sep 4); Belarusian Republican Foundation for Fundamental Research, BRFFR","This work is partially supported by the BEL consultancy work and AIM grants. 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Intell. Transport. Syst., 20, 5, pp. 1837-1846, (2018); Chin- I., Tse-Chih H., Hsiang-Chih H., Jing-Ya L., Ontology-based tourism recommendation system, 4Th Int. Conf. of Industrial Engineering and Applications, pp. 376-379, (2017); Dataset for Kerala State of India; Daniel B., Philipp S., Constantinos S., Concept of interlinking mobility services for urban transport towards intermodal mobility including private and shared electromobility, Fourteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), pp. 1-7; Fernandez-Llorca D., Quintero Minguez R.A.I., Lopez C., Cristina, Assistive Intelligent Transportation Systems: The Need for User Localization and Anonymous Disability Identification, IEEE Intelligent Transportation Systems Magazine, 9, pp. 25-40, (2017); David M., Evgenia D., Kurt H., Andreas W., Friedrich L., Chih-Chung C., Chih-Chen L.; Dominic P., Thomas F., Harald W., Christian S., VeLink - A Blockchain-based Shared Mobility Platform for Private and Commercial Vehicles utilizing ERC-721 Tokens, IEEE 5Th International Conference on Cryptography, Security and Privacy (CSP), pp. 62-67, (2021); Energy Efficient Mobility Systems; Emissions in the Automotive Sector, In; Ferdowsi A., Challita U., Saad W., Deep learning for reliable mobile edge analytics in intelligent transportation systems: an overview, IEEE Veh. Technol. Mag., 14, 1, pp. 62-70, (2019); Forbes on Shared Mobility, In; McKenzie G., Urban mobility in the sharing economy: A spatiotemporal comparison of shared mobility services, Comput. Environ. 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Accepted, IEEE Transactions on Computational Social Systems; Stephens T.S., Gonder J., Chen Y., Lin Z., Liu C., Gohlke D., Estimated bounds and important factors for fuel use and consumer costs of connected and automated vehicles, TR/NREL/TP-5400-67216, pp. 1-58, (2016); Terry T., Beth A., Brian R., Package rpart – Recursive Partitioning for Classification, Regression, and Survival Trees; Tianqi Z., Jian S., Yongjun R., Sai J., Security and Communication Networks, pp. 1-8, (2021); Li W., Kamargianni M., An integrated choice and latent variable model to explore the influence of attitudinal and perceptual factors on shared mobility choices and their value of time estimation, Transport. Sci., (2019); Yue G., Anuradha M.A., Eric Tseng H., Cumulative prospect theory based dynamic pricing for shared mobility on demand services, Proc. of IEEE 58Th Conference on Decision and Control (CDC), France, pp. 2239-2244, (2019); Zhongju Z., Daoqin T., Wencong C., Consumer behavior choice in the era of shared mobility: The role of proximity, competition, and quality, Proceedings of the 53Rd Hawaii International Conference on System Sciences, pp. 814-821, (2020)","S. Benedict; Indian Institute of Information Technology Kottayam, Kottayam, Kerala, India; email: shajulin@iiitkottayam.ac.in","","Springer","","","","","","02883635","","NGCOE","","English","New Gener Comput","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85123860017"
"Bhattacharjee S.; Saha B.; Bhattacharyya P.; Saha S.","Bhattacharjee, Sudipto (57271468400); Saha, Banani (7202946091); Bhattacharyya, Parthasarathi (7101802939); Saha, Sudipto (55554063800)","57271468400; 7202946091; 7101802939; 55554063800","Classification of obstructive and non-obstructive pulmonary diseases on the basis of spirometry using machine learning techniques","2022","Journal of Computational Science","63","","101768","","","","9","10.1016/j.jocs.2022.101768","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133909218&doi=10.1016%2fj.jocs.2022.101768&partnerID=40&md5=8208c62dc93ff3907609c4bbea4ebd61","Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India; Institute of Pulmocare and Research, DG-8, Action Area-1, New Town, Kolkata, 700156, India; Division of Bioinformatics, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata, 700091, India","Bhattacharjee S., Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India; Saha B., Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India; Bhattacharyya P., Institute of Pulmocare and Research, DG-8, Action Area-1, New Town, Kolkata, 700156, India; Saha S., Division of Bioinformatics, Bose Institute, EN 80, Sector V, Bidhan Nagar, Kolkata, 700091, India","Background: The symptomatic similarities between the two categories of pulmonary diseases, obstructive and non-obstructive, make the early diagnosis difficult for clinicians. Spirometry is a popular lung investigation that is performed in the early diagnostic stages to understand the mechanics of lungs. This work aims to develop machine learning models to classify obstructive and non-obstructive pulmonary diseases on the basis of spirometry data. Method: Supervised learning models were developed with support vector machine (SVM), random forest (RF), Naive Bayes (NB) and multi-layer perceptron (MLP) algorithms. Models were trained with spirometry data of 1163 patients using 5-fold cross validation (CV) and further validated with a blind dataset of 151 patients for external validation. Results: The MLP model performed optimally with an accuracy of 83.7% and Matthew's correlation coefficient of 0.682 with 5-fold CV. All the models performed well while validating the blind dataset. The disease-specific prediction of COPD and DPLD, as obstructive and non-obstructive respectively, achieved ~90% accuracy in the training dataset. The MLP model was stored in a web server for use in a web application. Conclusions: The machine learning models were able to predict obstructive and non-obstructive pulmonary diseases with good accuracy, based on spirometry data. The web application can be used by clinicians and patients as a tool for early prediction. © 2022 Elsevier B.V.","Naive Bayes; Neural network; Pulmonary diseases; Random forest; Spirometry; Support vector machine","Barium compounds; Classifiers; Decision trees; Diagnosis; Forecasting; Learning systems; Neural networks; Pulmonary diseases; Random forests; Cross validation; Machine learning models; Multilayers perceptrons; Naive bayes; Neural-networks; Random forests; Spirometry; Support vectors machine; WEB application; Web applications; Support vector machines","","","","","Indian Council of Medical Research, ICMR","This work is supported by Indian Council of Medical Research [Project ID: 2019–0075 ]. ","(2017); Buist A.S., Similarities and differences between asthma and chronic obstructive pulmonary disease: treatment and early outcomes, Eur. Respir. J. Suppl., 39, pp. 30s-35s, (2003); Gilbert R., Auchincloss J.H., What is a “restrictive” defect?, Arch. Intern. Med., 146, pp. 1779-1781, (1986); Crapo R.O., Pulmonary-function testing, N. Engl. J. Med., 331, pp. 25-30, (1994); Pino Pena I., Cheplygina V., Paschaloudi S., Vuust M., Carl J., Weinreich U.M., Ostergaard L.R., de Bruijne M., Automatic emphysema detection using weakly labeled HRCT lung images, PLoS One, 13, (2018); Bermejo-Pelaez D., Ash S.Y., Washko G.R., San Jose Estepar R., Ledesma-Carbayo M.J., Classification of interstitial lung abnormality patterns with an ensemble of deep convolutional neural networks, Sci. Rep., 10, (2020); Parveen N.R.S., Sathik M.M., Detection of pneumonia in chest X-ray images, J. Xray. Sci. Technol., 19, pp. 423-428, (2011); Lakhani P., Sundaram B., Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks, Radiology, 284, pp. 574-582, (2017); Feng P.-H., Lin Y.-T., Lo C.-M., A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings, Med. Phys., 45, pp. 5509-5514, (2018); Lynch C.M., Abdollahi B., Fuqua J.D., de Carlo A.R., Bartholomai J.A., Balgemann R.N., van Berkel V.H., Frieboes H.B., Prediction of lung cancer patient survival via supervised machine learning classification techniques, Int. J. Med. Inform., 108, pp. 1-8, (2017); Swaminathan S., Qirko K., Smith T., Corcoran E., Wysham N.G., Bazaz G., Kappel G., Gerber A.N., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLoS One, 12, (2017); Aneja S., Lal S., Effective asthma disease prediction using naive Bayes — Neural network fusion technique, pp. 137-140, (2014); Mani S., Chen Y., Elasy T., Clayton W., Denny J., (2012); Hussain L., Ahmed A., Saeed S., Rathore S., Awan I.A., Shah S.A., Majid A., Idris A., Awan A.A., Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies, Cancer Biomark., 21, pp. 393-413, (2018); Ranu H., Wilde M., Madden B., Pulmonary function tests, Ulst. Med. J., 80, pp. 84-90, (2011); Cortes C., Vapnik V., Support-vector networks, Mach. Learn., 20, pp. 273-297, (1995); Breiman L., Random Forests, Mach. Learn., 45, pp. 5-32, (2001); Zhang H., The Optimality of Naïve Bayes, Proc. Seventeenth Int. Florida Artif. Intell. Res. Soc. Conf. (FLAIRS 2004), pp. 562-567, (2004); Rumelhart D.E., Hinton G.E., Williams R.J., Learning representations by back-propagating errors, Nature, 323, pp. 533-536, (1986); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, pp. 2825-2830, (2011); Chicco D., Jurman G., The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genom., 21, (2020); Boughorbel S., Jarray F., El-Anbari M., Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric, PLoS One, 12, (2017); Diaz-Guzman E., McCarthy K., Siu A., Stoller J.K., Frequency and causes of combined obstruction and restriction identified in pulmonary function tests in adults, Respir. Care., 55, (2010); Gardner Z.S., Ruppel G.L., Kaminsky D.A., Grading the severity of obstruction in mixed obstructive-restrictive lung disease, Chest, 140, pp. 598-603, (2011)","S. Saha; Division of Bioinformatics, Bose Institute, Unified Academic Campus, Bose Institute, Kolkata, EN 80, Sector V, Bidhan Nagar, 700091, India; email: ssaha4@jcbose.ac.in","","Elsevier B.V.","","","","","","18777503","","","","English","J. Comput. Sci.","Article","Final","","Scopus","2-s2.0-85133909218"
"Abraham V.M.; Booth G.; Geiger P.; Balazs G.C.; Goldman A.","Abraham, Vivek Mathew (57823519300); Booth, Greg (57222308965); Geiger, Phillip (57205322372); Balazs, George Christian (56542924400); Goldman, Ashton (57118873000)","57823519300; 57222308965; 57205322372; 56542924400; 57118873000","Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty","2022","Clinical Orthopaedics and Related Research","480","11","","2137","2145","8","10","10.1097/CORR.0000000000002276","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140273157&doi=10.1097%2fCORR.0000000000002276&partnerID=40&md5=2cf71f91e6cc06e94f59e781bc32f1aa","Department of Orthopaedic Surgery, Bone and Joint Sports Medicine Center, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Department of Anesthesiology and Pain Medicine, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Naval Biotechnology Group, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Uniformed University of the Health Sciences, Bethesda, MD, United States","Abraham V.M., Department of Orthopaedic Surgery, Bone and Joint Sports Medicine Center, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Booth G., Department of Anesthesiology and Pain Medicine, Naval Medical Center Portsmouth, Portsmouth, VA, United States, Naval Biotechnology Group, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Geiger P., Department of Anesthesiology and Pain Medicine, Naval Medical Center Portsmouth, Portsmouth, VA, United States, Naval Biotechnology Group, Naval Medical Center Portsmouth, Portsmouth, VA, United States; Balazs G.C., Department of Orthopaedic Surgery, Bone and Joint Sports Medicine Center, Naval Medical Center Portsmouth, Portsmouth, VA, United States, Uniformed University of the Health Sciences, Bethesda, MD, United States; Goldman A., Department of Orthopaedic Surgery, Bone and Joint Sports Medicine Center, Naval Medical Center Portsmouth, Portsmouth, VA, United States, Uniformed University of the Health Sciences, Bethesda, MD, United States","BackgroundAseptic revision THA and TKA are associated with an increased risk of adverse outcomes compared with primary THA and TKA. Understanding the risk profiles for patients undergoing aseptic revision THA or TKA may provide an opportunity to decrease the risk of postsurgical complications. There are risk stratification tools for postoperative complications after aseptic revision TKA or THA; however, current tools only include nonmodifiable risk factors, such as medical comorbidities, and do not include modifiable risk factors.Questions/purposes(1) Can machine learning predict 30-day mortality and complications for patients undergoing aseptic revision THA or TKA using a cohort from the American College of Surgeons National Surgical Quality Improvement Program database? (2) Which patient variables are the most relevant in predicting complications?MethodsThis was a temporally validated, retrospective study analyzing the 2014 to 2019 National Surgical Quality Improvement Program database, as this database captures a large cohort of aseptic revision THA and TKA patients across a broad range of clinical settings and includes preoperative laboratory values. The training data set was 2014 to 2018, and 2019 was the validation data set. Given that predictive models learn expected prevalence of outcomes, this split allows assessment of model performance in contemporary patients. Between 2014 and 2019, a total of 24,682 patients underwent aseptic revision TKA and 17,871 patients underwent aseptic revision THA. Of those, patients with CPT codes corresponding to aseptic revision TKA or THA were considered as potentially eligible. Based on excluding procedures involving unclean wounds, 78% (19,345 of 24,682) of aseptic revision TKA procedures and 82% (14,711 of 17,871) of aseptic revision THA procedures were eligible. Ten percent of patients in each of the training and validation cohorts had missing predictor variables. Most of these missing data were preoperative sodium or hematocrit (8% in both the training and validation cohorts). No patients had missing outcome data. No patients were excluded due to missing data. The mean patient was age 66 ± 12 years, the mean BMI was 32 ± 7 kg/m2, and the mean American Society of Anesthesiologists (ASA) Physical Score was 3 (56%). XGBoost was then used to create a scoring tool for 30-day adverse outcomes. XGBoost was chosen because it can handle missing data, it is nonlinear, it can assess nuanced relationships between variables, it incorporates techniques to reduce model complexity, and it has a demonstrated record of producing highly accurate machine-learning models. Performance metrics included discrimination and calibration. Discrimination was assessed by c-statistics, which describe the area under the receiver operating characteristic curve. This quantifies how well a predictive model discriminates between patients who have the outcome of interest versus those who do not. Relevant ranges for c-statistics include good (0.70 to 0.79), excellent (0.80 to 0.89), and outstanding (> 0.90). We estimated 95% confidence intervals (CIs) for c-statistics by 500-sample bootstrapping. Calibration curves quantify reliability of model predictions. Reliable models produce prediction probabilities for outcomes that are similar to observed probabilities of those outcomes, so a well-calibrated model should demonstrate a calibration curve that does not deviate substantially from a line of slope 1 and intercept 0. Calibration curves were generated on the 2019 validation data. Shapley Additive Explanations (SHAP) visualizations were used to investigate feature importance to gain insight into how models made predictions. The models were built into an online calculator for ongoing testing and validation. The risk calculator, which is freely available (http://nb-group.org/rev2/), allows a user to input patient data to calculate postoperative risk of 30-day mortality, cardiac, and respiratory complications after aseptic revision TKA or THA. A post hoc analysis was performed to assess whether using data from 2020 would improve calibration on 2019 data.ResultsThe model accurately predicted mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA, with c-statistics of 0.88 (95% CI 0.83 to 0.93), 0.80 (95% CI 0.75 to 0.84), and 0.78 (95% CI 0.74 to 0.82), respectively, on internal validation and 0.87 (95% CI 0.77 to 0.96), 0.70 (95% CI 0.61 to 0.78), and 0.82 (95% CI 0.75 to 0.88), respectively, on temporal validation. Calibration curves demonstrated slight over-confidence in predictions (most predicted probabilities were higher than observed probabilities). Post hoc analysis of 2020 data did not yield improved calibration on the 2019 validation set. Important risk factors for all models included increased age and higher ASA, BMI, hematocrit level, and sodium level. Hematocrit and ASA were in the top three most important features for all models. The factor with the strongest association for mortality and cardiac complication models was age, and for the respiratory model, chronic obstructive pulmonary disease. Risk related to sodium followed a U-shaped curve. Preoperative hyponatremia and hypernatremia predicted an increased risk of mortality and respiratory complications, with a nadir of 138 mmol/L; hyponatremia was more strongly associated with mortality than hypernatremia. A hematocrit level less than 36% predicted an increased risk of all three adverse outcomes. A BMI less than 24 kg/m2 - and especially less than 20 kg/m2 - predicted an increased risk of all three adverse outcomes, with little to no effect for higher BMI.ConclusionThis temporally validated model predicted 30-day mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA with c-statistics ranging from 0.78 to 0.88. This freely available risk calculator can be used preoperatively by surgeons to educate patients on their individual postoperative risk of these specific adverse outcomes. Unanswered questions that remain include whether altering the studied preoperative patient variables, such as sodium or hematocrit, would affect postoperative risk of adverse outcomes; however, a prospective cohort study is needed to answer this question.  Copyright © 2022 by the Association of Bone and Joint Surgeons.","","Aged; Arthroplasty, Replacement, Hip; Humans; Hypernatremia; Hyponatremia; Machine Learning; Middle Aged; Postoperative Complications; Prospective Studies; Reproducibility of Results; Retrospective Studies; Risk Assessment; Risk Factors; Sodium; Time Factors; sodium; sodium; adverse outcome; aged; arthroplasty; Article; body mass; cardiovascular disease; clinical outcome; cohort analysis; comorbidity; controlled study; Current Procedural Terminology; deep vein thrombosis; female; hematocrit; human; hypernatremia; hyponatremia; lung complication; lung embolism; machine learning; major clinical study; male; medical society; mortality; mortality rate; preoperative complication; receiver operating characteristic; retrospective study; revision arthroplasty; risk factor; surgical infection; total hip replacement; total knee arthroplasty; venous thromboembolism; adverse event; hip replacement; hypernatremia; hyponatremia; middle aged; postoperative complication; prospective study; reproducibility; risk assessment; time factor","","sodium, 7440-23-5; Sodium, ","","","","","ACS NSQIP participant use data file; Bellamy J.L., Runner R.P., Vu C.C.L., Schenker M.L., Bradbury T.L., Roberson J.R., Modified frailty index is an effective risk assessment tool in primary total hip arthroplasty, J Arthroplasty, 32, pp. 2963-2968, (2017); Chen T., Guestrin C., XGBoost: a scalable tree boosting system; Engh C.A., Ho H., Padgett D.E., The surgical options and clinical evidence for treatment of wear or corrosion occurring with THA or TKA, Clin Orthop Relat Res, 472, pp. 3674-3686, (2014); Funk G.C., Lindner G., Druml W., Et al., Incidence and prognosis of dysnatremias present on ICU admission, Intensive Care Med, 36, pp. 304-311, (2010); Gu A., Chen A.Z., Selemon N.A., Et al., Preoperative anemia independently predicts significantly increased odds of short-term complications following aseptic revision hip and knee arthroplasty, J Arthroplasty, 36, pp. 1719-1728, (2021); Gu A., Chen F.R., Chen A.Z., Et al., Preoperative hyponatremia is an independent risk factor for postoperative complications in aseptic revision hip and knee arthroplasty, J Orthop, 20, pp. 224-227, (2020); Harris A.H.S., Kuo A.C., Weng Y., Trickey A.W., Bowe T., Giori N.J., Can machine learning methods produce accurate and easy-to-use prediction models of 30-day complications and mortality after knee or hip arthroplasty?, Clin Orthop Relat Re s, 477, pp. 452-460, (2019); Haynes J.A., Stambough J.B., Sassoon A.A., Johnson S.R., Clohisy J.C., Nunley R.M., Contemporary surgical indications and referral trends in revision total hip arthroplasty: a 10-year review, J Arthroplasty, 31, pp. 622-625, (2016); Hosmer D.W., Lemeshow S., Applied Logistic Regression, (2000); Jafari S.M., Coyle C., Mortazavi S.M., Sharkey P.F., Parvizi J., Revision hip arthroplasty: infection is the most common cause of failure, Clin Orthop Relat Res, 468, pp. 2046-2051, (2010); Le D.H., Goodman S.B., Maloney W.J., Huddleston J.I., Current modes of failure in TKA: infection, instability, and stiffness predominate, Clin Orthop Relat Res, 472, pp. 2197-2200, (2014); Liodakis E., Bergeron S.G., Zukor D.J., Huk O.L., Epure L.M., Antoniou J., Perioperative complications and length of stay after revision total hip and knee arthroplasties: an analysis of the NSQIP database, J Arthroplasty, 30, pp. 1868-1871, (2015); Lu M., Sing D.C., Kuo A.C., Hansen E.N., Preoperative anemia independently predicts 30-day complications after aseptic and septic revision total joint arthroplasty, J Arthroplasty, 32, pp. S197-S201, (2017); Pathak N., Kahlenberg C.A., Moore H.G., Sculco P.K., Grauer J.N., Thirty-day readmissions after aseptic revision total hip arthroplasty: rates, predictors, and reasons vary by surgical indication, J Arthroplasty, 35, pp. 3673-3678, (2020); Prange F., Seifert A., Piakong P., Et al., Short-term mortality after primary and revision total joint arthroplasty: a single-center analysis of 103,560 patients, Arch Orthop Trauma Surg, 141, pp. 517-525, (2021); Raad M., Amin R., Puvanesarajah V., Et al., The CARDE-B scoring system predicts 30-day mortality after revision total joint arthroplasty, J Bone Joint Surg Am, 103, pp. 424-431, (2021); Rodriguez-Perez R., Bajorath J., Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values, J Med Chem, 63, pp. 8761-8777, (2020); Runner R.P., Bellamy J.L., Vu C.C.L., Erens G.A., Schenker M.L., Guild G.N., Modified frailty index is an effective risk assessment tool in primary total knee arthroplasty, J Arthroplasty, 32, pp. S177-S182, (2017); Shapley I.S., Kuhn H.W., Tucker A.W., A value for N-person games. Contributions to the theory of games, Annals of Mathematical Studies, pp. 307-317, (1953); Shen T.S., Gu A., Bovonratwet P., Ondeck N.T., Sculco P.K., Su E.P., Etiology and complications of early aseptic revision total hip arthroplasty within 90 days, J Arthroplasty, 36, pp. 1734-1739, (2021); Subramaniam S., Aalberg J.J., Soriano R.P., Divino C.M., New 5-factor modified frailty index using American College of Surgeons NSQIP data, J Am Coll Surg, 226, pp. 173-181e8, (2018); Traven S.A., Reeves R.A., Sekar M.G., Slone H.S., Walton Z.J., New 5-factor modified frailty index predicts morbidity and mortality in primary hip and knee arthroplasty, J Arthroplasty, 34, pp. 140-144, (2019)","V.M. Abraham; Department of Orthopaedic Surgery, Bone and Joint Sports Medicine Center, Portsmouth, 620 John Paul Jones Circle, 23708, United States; email: vivabraham@gmail.com","","Wolters Kluwer Health Inc","","","","","","0009921X","","CORTB","35767804","English","Clin. Orthop. Relat. Res.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85140273157"
"Granell R.; Curtin J.A.; Haider S.; Kitaba N.T.; Mathie S.A.; Gregory L.G.; Yates L.L.; Tutino M.; Hankinson J.; Perretti M.; Vonk J.M.; Arshad H.S.; Cullinan P.; Fontanella S.; Roberts G.C.; Koppelman G.H.; Simpson A.; Turner S.W.; Murray C.S.; Lloyd C.M.; Holloway J.W.; Custovic A.","Granell, Raquel (23469333600); Curtin, John A. (7101961514); Haider, Sadia (57201130476); Kitaba, Negusse Tadesse (57217228100); Mathie, Sara A. (24329737000); Gregory, Lisa G. (7006214273); Yates, Laura L. (36538294300); Tutino, Mauro (57769393200); Hankinson, Jenny (24481058800); Perretti, Mauro (16454399500); Vonk, Judith M. (7006334144); Arshad, Hasan S. (57209874416); Cullinan, Paul (57203056609); Fontanella, Sara (57193973057); Roberts, Graham C. (14047151700); Koppelman, Gerard H. (6603894882); Simpson, Angela (7402780427); Turner, Steve W. (57215035261); Murray, Clare S. (7402491950); Lloyd, Clare M. (7202193210); Holloway, John W. (57221220827); Custovic, Adnan (7006755479)","23469333600; 7101961514; 57201130476; 57217228100; 24329737000; 7006214273; 36538294300; 57769393200; 24481058800; 16454399500; 7006334144; 57209874416; 57203056609; 57193973057; 14047151700; 6603894882; 7402780427; 57215035261; 7402491950; 7202193210; 57221220827; 7006755479","A meta-analysis of genome-wide association studies of childhood wheezing phenotypes identifies ANXA1 as a susceptibility locus for persistent wheezing","2023","eLife","12","","","","","","12","10.7554/eLife.84315","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163585262&doi=10.7554%2feLife.84315&partnerID=40&md5=96286cdbcd1a8ba2ce03ec30da33f31f","MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; William Harvey Research Institute, Barts and The London School of Medicine Queen Mary University of London, London, United Kingdom; Department of Epidemiology, University of Groningen, University Medical Center Groningen\, Groningen, Netherlands; University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, Netherlands; NIHR Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, United Kingdom; David Hide Asthma and Allergy Research Centre, Isle of Wight, United Kingdom; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Department of Pediatric Pulmonology and Pediatric Allergology, University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, Groningen, Netherlands; Child Health, University of Aberdeen, Aberdeen, United Kingdom","Granell R., MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; Curtin J.A., Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Haider S., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Kitaba N.T., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Mathie S.A., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Gregory L.G., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Yates L.L., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Tutino M., Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Hankinson J., Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Perretti M., William Harvey Research Institute, Barts and The London School of Medicine Queen Mary University of London, London, United Kingdom; Vonk J.M., Department of Epidemiology, University of Groningen, University Medical Center Groningen\, Groningen, Netherlands, University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, Netherlands; Arshad H.S., NIHR Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, United Kingdom, David Hide Asthma and Allergy Research Centre, Isle of Wight, United Kingdom, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Cullinan P., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Fontanella S., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Roberts G.C., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, United Kingdom, David Hide Asthma and Allergy Research Centre, Isle of Wight, United Kingdom; Koppelman G.H., University of Groningen, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, Netherlands, Department of Pediatric Pulmonology and Pediatric Allergology, University of Groningen, University Medical Center Groningen, Beatrix Children's Hospital, Groningen, Netherlands; Simpson A., Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Turner S.W., Child Health, University of Aberdeen, Aberdeen, United Kingdom; Murray C.S., Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Lloyd C.M., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Holloway J.W., Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, United Kingdom; Custovic A., National Heart and Lung Institute, Imperial College London, London, United Kingdom","Background: Many genes associated with asthma explain only a fraction of its heritability. Most genome-wide association studies (GWASs) used a broad definition of 'doctor-diagnosed asthma', thereby diluting genetic signals by not considering asthma heterogeneity. The objective of our study was to identify genetic associates of childhood wheezing phenotypes. Methods: We conducted a novel multivariate GWAS meta-analysis of wheezing phenotypes jointly derived using unbiased analysis of data collected from birth to 18 years in 9568 individuals from five UK birth cohorts. Results: Forty-four independent SNPs were associated with early-onset persistent, 25 with pre-school remitting, 33 with mid-childhood remitting, and 32 with late-onset wheeze. We identified a novel locus on chr9q21.13 (close to annexin 1 [ANXA1], p<6.7 × 10-9), associated exclusively with early-onset persistent wheeze. We identified rs75260654 as the most likely causative single nucleotide polymorphism (SNP) using Promoter Capture Hi-C loops, and then showed that the risk allele (T) confers a reduction in ANXA1 expression. Finally, in a murine model of house dust mite (HDM)-induced allergic airway disease, we demonstrated that anxa1 protein expression increased and anxa1 mRNA was significantly induced in lung tissue following HDM exposure. Using anxa1-/- deficient mice, we showed that loss of anxa1 results in heightened airway hyperreactivity and Th2 inflammation upon allergen challenge. Conclusions: Targeting this pathway in persistent disease may represent an exciting therapeutic prospect. Funding: UK Medical Research Council Programme Grant MR/S025340/1 and the Wellcome Trust Strategic Award (108818/15/Z) provided most of the funding for this study. © 2023, Granell et al.; Three-quarters of children hospitalized for wheezing or asthma symptoms are preschool-aged. Some will continue to experience breathing difficulties through childhood and adulthood. Others will undergo a complete resolution of their symptoms by the time they reach elementary school. The varied trajectories of young children with wheezing suggest that it is not a single disease. There are likely different genetic or environmental causes. Despite these differences, wheezing treatments for young children are ‘one size fits all.’ Studying the genetic underpinnings of wheezing may lead to more customized treatment options. Granell et al. studied the genetic architecture of different patterns of wheezing from infancy to adolescence. To do so, they used machine learning technology to analyze the genomes of 9,568 individuals, who participated in five studies in the United Kingdom from birth to age 18. The experiments found a new genetic variation in the ANXA1 gene linked with persistent wheezing starting in early childhood. By comparing mice with and without this gene, Granell et al. showed that the protein encoded by ANXA1 controls inflammation in the lungs in response to allergens. Animals lacking the protein develop worse lung inflammation after exposure to dust mite allergens. Identifying a new gene linked to a specific subtype of wheezing might help scientists develop better strategies to diagnose, treat, and prevent asthma. More studies are needed on the role of the protein encoded by ANXA1 in reducing allergen-triggered lung inflammation to determine if this protein or therapies that boost its production may offer relief for chronic lung inflammation.","ALSPAC; ANXA1; epidemiology; genetics; genomics; global health; GWAS; human; MAAS; meta-analysis; wheezing phenotypes","Animals; Annexins; Asthma; Genome-Wide Association Study; Hypersensitivity; Mice; Phenotype; Respiratory Sounds; annexin; abnormal respiratory sound; animal; asthma; genetics; genome-wide association study; hypersensitivity; meta analysis; mouse; phenotype","","Annexins, ","","","","","","","","NLM (Medline)","","","","","","2050084X","","","37227431","English","Elife","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85163585262"
"Zhuang Y.; Xing F.; Ghosh D.; Hobbs B.D.; Hersh C.P.; Banaei-Kashani F.; Bowler R.P.; Kechris K.","Zhuang, Yonghua (59057320700); Xing, Fuyong (38461688800); Ghosh, Debashis (57201786914); Hobbs, Brian D. (56305051300); Hersh, Craig P. (6701515147); Banaei-Kashani, Farnoush (6602315096); Bowler, Russell P. (56773748500); Kechris, Katerina (6507018968)","59057320700; 38461688800; 57201786914; 56305051300; 6701515147; 6602315096; 56773748500; 6507018968","Deep learning on graphs for multi-omics classification of COPD","2023","PLoS ONE","18","4 April","e0284563","","","","12","10.1371/journal.pone.0284563","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153551769&doi=10.1371%2fjournal.pone.0284563&partnerID=40&md5=fab6bf6af6162bf0917b65f9dfab4551","Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Biostatistics Shared Resource, University of Colorado Cancer Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Department of Pediatrics, School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States; National Jewish Health, Denver, CO, United States","Zhuang Y., Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States, Biostatistics Shared Resource, University of Colorado Cancer Center, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States, Department of Pediatrics, School of Medicine, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Xing F., Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Ghosh D., Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Hobbs B.D., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Hersh C.P., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States, Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Banaei-Kashani F., Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States; Bowler R.P., National Jewish Health, Denver, CO, United States; Kechris K., Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States","Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). However despite recent advances, we remain limited in our ability to incorporate protein-protein interaction (PPI) network information with omics data for disease prediction. New deep learning methods including convolution Graph Neural Network (ConvGNN) has shown great potential for disease classification using transcriptomics data and known PPI networks from existing databases. In this study, we first reconstructed the COPD-associated PPI network through the AhGlasso (Augmented High-Dimensional Graphical Lasso Method) algorithm based on one independent transcriptomics dataset including COPD cases and controls. Then we extended the existing ConvGNN methods to successfully integrate COPD-associated PPI, proteomics, and transcriptomics data and developed a prediction model for COPD classification. This approach improves accuracy over several conventional classification methods and neural networks that do not incorporate network information. We also demonstrated that the updated COPD-associated network developed using AhGlasso further improves prediction accuracy. Although deep neural networks often achieve superior statistical power in classification compared to other methods, it can be very difficult to explain how the model, especially graph neural network(s), makes decisions on the given features and identifies the features that contribute the most to prediction generally and individually. To better explain how the spectral-based Graph Neural Network model(s) works, we applied one unified explainable machine learning method, SHapley Additive exPlanations (SHAP), and identified CXCL11, IL-2, CD48, KIR3DL2, TLR2, BMP10 and several other relevant COPD genes in subnetworks of the ConvGNN model for COPD prediction. Finally, Gene Ontology (GO) enrichment analysis identified glycosaminoglycan, heparin signaling, and carbohydrate derivative signaling pathways significantly enriched in the top important gene/proteins for COPD classifications. Copyright: © 2023 Zhuang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Algorithms; Bone Morphogenetic Proteins; Deep Learning; Humans; Multiomics; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; bone morphogenetic protein; CD48 antigen; CXCL11 chemokine; interleukin 2; killer cell immunoglobulin like receptor 3DL2; toll like receptor 2; BMP10 protein, human; bone morphogenetic protein; accuracy; aged; algorithm; Article; artificial neural network; case control study; chronic obstructive lung disease; controlled study; decision making; deep learning; deep neural network; disease classification; female; gene; gene ontology; human; machine learning; major clinical study; male; multiomics; prediction; protein analysis; protein protein interaction; proteomics; transcriptomics; validation process; genetics; multiomics","","interleukin 2, 85898-30-2; toll like receptor 2, 203811-81-8; BMP10 protein, human, ; Bone Morphogenetic Proteins, ","","","","","Lee JH, Cho MH, McDonald MLN, Hersh CP, Castaldi PJ, Crapo JD, Et al., Phenotypic and genetic heterogeneity among subjects with mild airflow obstruction in COPDGene, Respiratory medicine, 108, 10, pp. 1469-1480, (2014); Himes BE, Dai Y, Kohane IS, Weiss ST, Ramoni MF., Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records, Journal of the American Medical Informatics Association, 16, 3, pp. 371-379, (2009); Macaulay D, Sun SX, Sorg RA, Yan SY, De G, Wu EQ, Et al., Development and validation of a claims-based prediction model for COPD severity, Respiratory medicine, 107, 10, pp. 1568-1577, (2013); Humphries SM, Notary AM, Centeno JP, Strand MJ, Crapo JD, Silverman EK, Et al., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, 2, pp. 434-444, (2020); Schroeder JD, Lanfredi RB, Li T, Chan J, Vachet C, Paine R, Et al., Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data, International Journal of Chronic Obstructive Pulmonary Disease, 15, (2020); Li X, Liu L, Zhou J, Wang C., Heterogeneity analysis and diagnosis of complex diseases based on deep learning method, Scientific reports, 8, 1, pp. 1-8, (2018); Sun YV, Hu YJ., Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases, Advances in genetics, 93, pp. 147-190, (2016); Liu Y, Millsap RE, West SG, Tein JY, Tanaka R, Grimm KJ., Testing measurement invariance in longitudinal data with ordered-categorical measures, Psychological methods, 22, 3, (2017); Zhuang Y, Hobbs BD, Hersh CP, Kechris K., Identifying miRNA-mRNA Networks Associated With COPD Phenotypes, Frontiers in genetics, (2021); Chang Y, Glass K, Liu YY, Silverman EK, Crapo JD, Tal-Singer R, Et al., COPD subtypes identified by network-based clustering of blood gene expression, Genomics, 107, 2-3, pp. 51-58, (2016); Li CX, Wheelock CE, Skold CM, Wheelock AM., Integration of multi-omics datasets enables molecular classification of COPD, European Respiratory Journal, 51, 5, (2018); Defferrard M, Bresson X, Vandergheynst P., Convolutional neural networks on graphs with fast localized spectral filtering, Advances in neural information processing systems, pp. 3844-3852, (2016); Kipf TN, Welling M., Semi-supervised classification with graph convolutional networks, (2016); Hamilton WL, Ying R, Leskovec J., Representation learning on graphs: Methods and applications, (2017); Rhee S, Seo S, Kim S., Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. 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Zhuang; Department of Biostatistics and Informatics, University of Colorado, Aurora, Anschutz Medical Campus, United States; email: Yonghua.Zhuang@cuanschutz.edu; K. Kechris; Department of Biostatistics and Informatics, University of Colorado, Aurora, Anschutz Medical Campus, United States; email: Katerina.Kechris@cuanschutz.edu","","Public Library of Science","","","","","","19326203","","POLNC","37083575","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85153551769"
"Ramar M.; Yano N.; Fedulov A.V.","Ramar, Mohankumar (57203169482); Yano, Naohiro (7005828502); Fedulov, Alexey V. (8947483000)","57203169482; 7005828502; 8947483000","Intra-Airway Treatment with Synthetic Lipoxin A4 and Resolvin E2 Mitigates Neonatal Asthma Triggered by Maternal Exposure to Environmental Particles","2023","International Journal of Molecular Sciences","24","7","6145","","","","9","10.3390/ijms24076145","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152321176&doi=10.3390%2fijms24076145&partnerID=40&md5=f94df382bea635c2efa4face87145028","Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Alpert Medical School of Brown University, 593 Eddy Street, Providence, 02903, RI, United States","Ramar M., Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Alpert Medical School of Brown University, 593 Eddy Street, Providence, 02903, RI, United States; Yano N., Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Alpert Medical School of Brown University, 593 Eddy Street, Providence, 02903, RI, United States; Fedulov A.V., Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Alpert Medical School of Brown University, 593 Eddy Street, Providence, 02903, RI, United States","Particulate matter in the air exacerbates airway inflammation (AI) in asthma; moreover, prenatal exposure to concentrated urban air particles (CAPs) and diesel exhaust particles (DEPs) predisposes the offspring to asthma and worsens the resolution of AI in response to allergens. We previously tested the hypothesis that such exposure impairs the pathways of specialized proresolving mediators that are critical for resolution and found declined Lipoxin A4 (LxA4) and Resolvin E2 (RvE2) levels in the “at-risk” pups of exposed mothers. Here, we hypothesized that supplementation with synthetic LxA4 or RvE2 via the airway can ameliorate AI after allergen exposure, which has not been tested in models with environmental toxicant triggers. BALB/c newborns with an asthma predisposition resultant from prenatal exposure to CAPs and DEPs were treated once daily for 3 days with 750 ng/mouse of LxA4 or 300 ng/mouse of RvE2 through intranasal instillation, and they were tested with the intentionally low-dose ovalbumin protocol that elicits asthma in the offspring of particle-exposed mothers but not control mothers, mimicking the enigmatic maternal transmission of asthma seen in humans. LxA4 and RvE2 ameliorated the asthma phenotype and improved AI resolution, which was seen as declining airway eosinophilia, lung tissue infiltration, and proallergic cytokine levels. © 2023 by the authors.","asthma; concentrated urban air particles (CAPs); diesel exhaust particles (DEPs); Lipoxin A4; maternal transmission of asthma; Resolvin E2","Animals; Asthma; Female; Humans; Infant, Newborn; Inflammation; Maternal Exposure; Mice; Pregnancy; Prenatal Exposure Delayed Effects; Vehicle Emissions; allergen; antiasthmatic agent; cytokine; interleukin 13; interleukin 4; interleukin 5; lipid; lipoxin A; ovalbumin; pentobarbital; resolvin e2; unclassified drug; lipoxin A; resolvin E2; allergic airway inflammation; animal experiment; animal model; animal tissue; Article; asthma; controlled study; diesel particulate matter; disease predisposition; environmental exposure; eosinophil count; eosinophil percentage; female; gene expression; histopathology; lung parenchyma; maternal exposure; maternal fetal transmission; mother; mouse; newborn; nonhuman; particulate matter; perinatal exposure; phenotype; prenatal exposure; progeny; quality control; supplementation; adverse event; animal; exhaust gas; genetics; human; inflammation; maternal exposure; pregnancy; prenatal exposure; toxicity","","interleukin 13, 148157-34-0; lipid, 66455-18-3; lipoxin A, 89663-86-5, 94292-80-5; ovalbumin, 77466-29-6; pentobarbital, 57-33-0, 76-74-4; lipoxin A4, ; resolvin E2, ; Vehicle Emissions, ","","","National Institute of Environmental Health Sciences, NIEHS, (R01 ES030227)","This research was funded by NIEHS, grant number R01 ES030227.","Kim D., Chen Z., Zhou L.F., Huang S.X., Air pollutants and early origins of respiratory diseases, Chronic Dis. 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Fedulov; Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, 593 Eddy Street, 02903, United States; email: alexey@brown.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","16616596","","","37047118","English","Int. J. Mol. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152321176"
"De Nucci S.; Zupo R.; Donghia R.; Castellana F.; Lofù D.; Aresta S.; Guerra V.; Bortone I.; Lampignano L.; De Pergola G.; Lozupone M.; Tatoli R.; Sborgia G.; Tirelli S.; Panza F.; Di Noia T.; Sardone R.","De Nucci, Sara (57226638204); Zupo, Roberta (57202731009); Donghia, Rossella (57201426844); Castellana, Fabio (57209715279); Lofù, Domenico (57218171697); Aresta, Simona (57481534300); Guerra, Vito (7006789326); Bortone, Ilaria (56347065600); Lampignano, Luisa (57204292926); De Pergola, Giovanni (7006784116); Lozupone, Madia (35753443500); Tatoli, Rossella (57205676699); Sborgia, Giancarlo (7004056387); Tirelli, Sarah (57223795900); Panza, Francesco (57190310649); Di Noia, Tommaso (6508366184); Sardone, Rodolfo (57192418289)","57226638204; 57202731009; 57201426844; 57209715279; 57218171697; 57481534300; 7006789326; 56347065600; 57204292926; 7006784116; 35753443500; 57205676699; 7004056387; 57223795900; 57190310649; 6508366184; 57192418289","Dietary profiling of physical frailty in older age phenotypes using a machine learning approach: the Salus in Apulia Study","2023","European Journal of Nutrition","62","3","","1217","1229","12","8","10.1007/s00394-022-03066-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143548505&doi=10.1007%2fs00394-022-03066-9&partnerID=40&md5=d97dbfbf1f59747b180a7e9d861e433e","Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Unit of Geriatrics and Internal Medicine, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy","De Nucci S., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Zupo R., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Donghia R., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Castellana F., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Lofù D., Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Aresta S., Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Guerra V., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Bortone I., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Lampignano L., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; De Pergola G., Unit of Geriatrics and Internal Medicine, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Lozupone M., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy; Tatoli R., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Sborgia G., Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy; Tirelli S., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; Panza F., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy; Di Noia T., Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Sardone R., Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy","Purpose: Growing awareness of the biological and clinical value of nutrition in frailty settings calls for further efforts to investigate dietary gaps to act sooner to achieve focused management of aging populations. We cross-sectionally examined the eating habits of an older Mediterranean population to profile dietary features most associated with physical frailty. Methods: Clinical and physical examination, routine biomarkers, medical history, and anthropometry were analyzed in 1502 older adults (65 +). CHS criteria were applied to classify physical frailty, and a validated Food Frequency Questionnaire to assess diet. The population was subdivided by physical frailty status (frail or non-frail). Raw and adjusted logistic regression models were applied to three clusters of dietary variables (food groups, macronutrients, and micronutrients), previously selected by a LASSO approach to better predict diet-related frailty determinants. Results: A lower consumption of wine (OR 0.998, 95% CI 0.997–0.999) and coffee (OR 0.994, 95% CI 0.989–0.999), as well as a cluster of macro and micronutrients led by PUFAs (OR 0.939, 95% CI 0.896–0.991), zinc (OR 0.977, 95% CI 0.952–0.998), and coumarins (OR 0.631, 95% CI 0.431–0.971), was predictive of non-frailty, but higher legumes intake (OR 1.005, 95%CI 1.000–1.009) of physical frailty, regardless of age, gender, and education level. Conclusions: Higher consumption of coffee and wine, as well as PUFAs, zinc, and coumarins, as opposed to legumes, may work well in protecting against a physical frailty profile of aging in a Mediterranean setting. Longitudinal investigations are needed to better understand the causal potential of diet as a modifiable contributor to frailty during aging. © 2022, The Author(s).","Dietary habits; Food intake; Mediterranean diet; Older population; Physical frailty; Salus in Apulia Study","Aged; Coffee; Diet; Frail Elderly; Frailty; Humans; Phenotype; Physical Examination; C reactive protein; calcifediol; calcium; coumarin derivative; flavanone; flavanone derivative; furocoumarin; glucose; hemoglobin A1c; high density lipoprotein cholesterol; interleukin 6; low density lipoprotein cholesterol; naphthoquinone; polyphenol; trace element; triacylglycerol; tumor necrosis factor; zinc; aged; aging; anthropometry; Article; asthma; body weight loss; cholesterol blood level; chronic obstructive lung disease; clinical assessment; clinical examination; cognitive defect; controlled study; diabetes mellitus; diastolic blood pressure; dyslipidemia; fasting blood glucose level; female; food frequency questionnaire; food intake; frailty; hearing impairment; human; hypertension; Italy; legume; machine learning; macronutrient; major clinical study; male; medical history; Mediterranean diet; Mini Mental State Examination; multiple chronic conditions; nutritional assessment; nutritional status; phenotype; physical activity; physical examination; systolic blood pressure; vegetarian diet; visual impairment; weakness; coffee; diet; frail elderly; frailty","","C reactive protein, 9007-41-4; calcifediol, 19356-17-3; calcium, 7440-70-2, 14092-94-5; flavanone, 487-26-3; glucose, 50-99-7, 84778-64-3, 8027-56-3; hemoglobin A1c, 62572-11-6; polyphenol, 37331-26-3; zinc, 7440-66-6, 14378-32-6; Coffee, ","OMRON M6","","National Institutes of Health, NIH; Ministero della Salute","This manuscript is the result of the research work on frailty undertaken by the “Italia Longeva: Research Network on Aging” team, supported by the resources of the Italian Ministry of Health—Research Networks of National Health Institutes. We thank the General Practitioners of Castellana Grotte, for the fundamental role in the recruitment of participants to this studies: Campanella Cecilia Olga Maria, Daddabbo Annamaria, Dell’aera Giosue’, Giustiniano Rosalia Francesca, Guzzoni Iudice Massimo, Lomuscio Savino, Lucarelli Rocco, Mazzarisi Antonio, Palumbo Mariana, Persio Maria Teresa, Pesce Rosa Vincenza, Puzzovivo Gabriella, Romano Pasqua Maria, Sgobba Cinzia, Simeone Francesco, Tartaglia Paola, Tauro Nicola.","Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the global burden of disease study 2015, Lancet, 388, pp. 1459-1544, (2016); Eurostat, Population Structure and Ageing, (2016); Mitnitski A.B., Mogilner A.J., Rockwood K., Accumulation of deficits as a proxy measure of aging, Sci World J, 1, pp. 323-336, (2001); Fried L.P., Tangen C.M., Walston J., Et al., Frailty in older adults: evidence for a phenotype, J Gerontol A Biol Sci Med Sci, 56, pp. M146-M156, (2001); 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Zupo; Unit of Data Sciences and Technology Innovation for Population Health, National Institute of Gastroenterology “Saverio de Bellis,” Research Hospital, Castellana Grotte, Bari, Italy; email: zuporoberta@gmail.com","","Springer Science and Business Media Deutschland GmbH","","","","","","14366207","","EJNUF","36484807","English","Eur. J. Nutr.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85143548505"
"Singh D.; Hurst J.R.; Martinez F.J.; Rabe K.F.; Bafadhel M.; Jenkins M.; Salazar D.; Dorinsky P.; Darken P.","Singh, Dave (57188632439); Hurst, John R. (57201513306); Martinez, Fernando J. (35374549400); Rabe, Klaus F. (7102576614); Bafadhel, Mona (35336030900); Jenkins, Martin (36981563100); Salazar, Domingo (57789676900); Dorinsky, Paul (7003756845); Darken, Patrick (6603368953)","57188632439; 57201513306; 35374549400; 7102576614; 35336030900; 36981563100; 57789676900; 7003756845; 6603368953","Predictive modeling of COPD exacerbation rates using baseline risk factors","2022","Therapeutic Advances in Respiratory Disease","16","","","","","","14","10.1177/17534666221107314","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133666075&doi=10.1177%2f17534666221107314&partnerID=40&md5=59b282656d093c89e7037dce5e9dd6ce","Medicines Evaluation Unit, University of Manchester, Manchester University NHS Foundation Hospitals Trust, Manchester, M23 9QZ, United Kingdom; UCL Respiratory, University College London, London, United Kingdom; Joan and Sanford I, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States; LungenClinic Grosshansdorf and Christian-Albrechts University Kiel, Airway Research Center North, German Center for Lung Research (DZL), Grosshansdorf, Germany; Respiratory Medicine Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom; AstraZeneca, Cambridge, United Kingdom; Formerly of AstraZeneca, Durham, NC, United States; AstraZeneca, Gaithersburg, MD, United States","Singh D., Medicines Evaluation Unit, University of Manchester, Manchester University NHS Foundation Hospitals Trust, Manchester, M23 9QZ, United Kingdom; Hurst J.R., UCL Respiratory, University College London, London, United Kingdom; Martinez F.J., Joan and Sanford I, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, United States; Rabe K.F., LungenClinic Grosshansdorf and Christian-Albrechts University Kiel, Airway Research Center North, German Center for Lung Research (DZL), Grosshansdorf, Germany; Bafadhel M., Respiratory Medicine Unit, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom; Jenkins M., AstraZeneca, Cambridge, United Kingdom; Salazar D., AstraZeneca, Cambridge, United Kingdom; Dorinsky P., Formerly of AstraZeneca, Durham, NC, United States; Darken P., AstraZeneca, Gaithersburg, MD, United States","Background: Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual’s rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD. Methods: Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation. Results: Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use. Conclusion: These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients. Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878). © The Author(s), 2022.","chronic obstructive pulmonary disease; exacerbations; ICS/LAMA/LABA; machine learning; prediction model; triple therapy","Administration, Inhalation; Adrenal Cortex Hormones; Adrenergic beta-2 Receptor Agonists; Bronchodilator Agents; Disease Progression; Drug Therapy, Combination; Forced Expiratory Volume; Humans; Pulmonary Disease, Chronic Obstructive; Risk Factors; budesonide; budesonide plus formoterol; budesonide plus formoterol plus glycopyrronium; formoterol fumarate; formoterol fumarate plus glycopyrronium bromide; glycopyrronium; placebo; beta 2 adrenergic receptor stimulating agent; bronchodilating agent; corticosteroid; adult; aged; Article; chronic obstructive lung disease; clinical study; controlled study; COPD assessment test; disease exacerbation; disease severity; eosinophil count; female; forced expiratory volume; geographic distribution; human; human cell; machine learning; major clinical study; male; middle aged; monotherapy; people by smoking status; phase 3 clinical trial; prediction; predictive value; randomized controlled trial; receiver operating characteristic; risk benefit analysis; risk factor; treatment response; triplet chemotherapy; very elderly; chronic obstructive lung disease; combination drug therapy; disease exacerbation; inhalational drug administration; risk factor","","budesonide, 51333-22-3, 51372-29-3; budesonide plus formoterol, 150693-37-1, 150693-38-2; budesonide plus formoterol plus glycopyrronium, 2588185-76-4; formoterol fumarate, 43229-80-7, 183814-30-4; glycopyrronium, 596-51-0, 1624259-25-1, 740028-90-4, 13283-82-4, 51186-83-5, 873295-46-6; Adrenal Cortex Hormones, ; Adrenergic beta-2 Receptor Agonists, ; Bronchodilator Agents, ","Aerosphere, Astra Zeneca; Symbicort Turbuhaler, Astra Zeneca","Astra Zeneca; Astra Zeneca","CMC Connect; McCann Health Medical Communications; AstraZeneca; GlaxoSmithKline, GSK; Novartis; Boehringer Ingelheim; Sunovion; Manchester Biomedical Research Centre, BRC; Pearl Therapeutics; National Institute for Health and Care Research, NIHR; NIHR Imperial Biomedical Research Centre, BRC","Funding text 1: We thank all the patients and their families, and the team of investigators and research staff involved in the studies that contributed to this work. We thank Mattis Gottlow, Aline Gendrin Brokmann, David Svensson, Bart Willigers, and Jack Nyberg for their valuable contributions to these analyses. Dave Singh is supported by the National Institute for Health Research (NIHR) Manchester Biomedical Research Centre (BRC). This study, as well as the studies that contributed to the development of the prediction models, were supported by AstraZeneca. Medical writing support, under the direction of the authors, was provided by Julia King, PhD, CMC Connect, McCann Health Medical Communications, funded by AstraZeneca in accordance with Good Publication Practice (GPP3) guidelines. ; Funding text 2: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: DSi reports personal fees from AstraZeneca during the conduct of the study and personal fees from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, Genentech, GlaxoSmithKline, Glenmark, Gossamerbio, Menarini, Mundipharma, Novartis, Peptinnovate, Pfizer, Pulmatrix, Theravance, and Verona, outside the submitted work. JRH reports personal fees from AstraZeneca during the conduct of the study and personal fees from AstraZeneca, Boehringer Ingelheim, Chiesi, and Novartis, outside the submitted work. FJM reports grants, personal fees, and non-financial support from AstraZeneca during the conduct of the study; grants, personal fees, and non-financial support from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Novartis, Pearl Therapeutics, Sunovion, Theravance, and Verona; grants and personal fees from Sanofi; personal fees from Circassia, Innoviva, and Mylan; and grants from Altavant, outside the submitted work. KFR reports grants and personal fees from AstraZeneca and Boehringer Ingelheim; personal fees from Chiesi, Novartis, Regeneron, Roche, and Sanofi, outside the submitted work. MB reports grants from AstraZeneca; honoraria from AstraZeneca, Chiesi, and GlaxoSmithKline; and is on the scientific advisory board for AlbusHealth and ProAxsis. MJ, DSa, and PDa are employees of AstraZeneca and hold stock and/or stock options in the company. PDo is a former employee of AstraZeneca and previously held stock and/or stock options in the company. ","Halpin D.M.G., Decramer M., Celli B.R., Et al., Effect of a single exacerbation on decline in lung function in COPD, Respir Med, 128, pp. 85-91, (2017); Suissa S., Dell'Aniello S., Ernst P., Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality, Thorax, 67, pp. 957-963, (2012); Seemungal T.A.R., Donaldson G.C., Paul E.A., Et al., Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 157, pp. 1418-1422, (1998); Blasi F., Cesana G., Conti S., Et al., The clinical and economic impact of exacerbations of chronic obstructive pulmonary disease: a cohort of hospitalized patients, PLOS ONE, 9, (2014); Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, (2021); Mullerova H., Shukla A., Hawkins A., Et al., Risk factors for acute exacerbations of COPD in a primary care population: a retrospective observational cohort study, BMJ Open, 4, (2014); Santibanez M., Garrastazu R., Ruiz-Nunez M., Et al., Predictors of hospitalized exacerbations and mortality in chronic obstructive pulmonary disease, PLOS ONE, 11, (2016); Hurst J.R., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, New Engl J Med, 363, pp. 1128-1138, (2010); Bafadhel M., Peterson S., De Blas M.A., Et al., Predictors of exacerbation risk and response to budesonide in patients with chronic obstructive pulmonary disease: a post-hoc analysis of three randomised trials, Lancet Respir Med, 6, pp. 117-126, (2018); Pascoe S., Barnes N., Brusselle G., Et al., Blood eosinophils and treatment response with triple and dual combination therapy in chronic obstructive pulmonary disease: analysis of the IMPACT trial, Lancet Respir Med, 7, pp. 745-756, (2019); Pascoe S., Locantore N., Dransfield M.T., Et al., Blood eosinophil counts, exacerbations, and response to the addition of inhaled fluticasone furoate to vilanterol in patients with chronic obstructive pulmonary disease: a secondary analysis of data from two parallel randomised controlled trials, Lancet Respir Med, 3, pp. 435-442, (2015); Ferguson G.T., Rabe K.F., Martinez F.J., Et al., Triple therapy with budesonide/glycopyrrolate/formoterol fumarate with co-suspension delivery technology versus dual therapies in chronic obstructive pulmonary disease (KRONOS): a double-blind, parallel-group, multicentre, phase 3 randomised controlled trial, Lancet Respir Med, 6, pp. 747-758, (2018); Rabe K.F., Martinez F.J., Ferguson G.T., Et al., Triple inhaled therapy at two glucocorticoid doses in moderate-to-very-severe COPD, New Engl J Med, 383, pp. 35-48, (2020); Hoogendoorn M., Feenstra T.L., Boland M., Et al., Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources, Int J Chron Obstruct Pulmon Dis, 12, pp. 3183-3194, (2017); Adibi A., Sin D.D., Safari A., Et al., The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study, Lancet Respir Med, 8, pp. 1013-1021, (2020); Annavarapu S., Goldfarb S., Gelb M., Et al., Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data, Int J Chron Obstruct Pulmon Dis, 13, pp. 2121-2130, (2018); Yii A.C.A., Loh C.H., Tiew P.Y., Et al., A clinical prediction model for hospitalized COPD exacerbations based on ‘treatable traits’, Int J Chron Obstruct Pulmon Dis, 14, pp. 719-728, (2019); Ichinose M., Fukushima Y., Inoue Y., Et al., Long-term safety and efficacy of budesonide/glycopyrrolate/formoterol fumarate metered dose inhaler formulated using co-suspension delivery technology in Japanese patients with COPD, Int J Chron Obstruct Pulmon Dis, 14, pp. 2993-3002, (2019); Kerwin E.M., Ferguson G.T., Mo M., Et al., Bone and ocular safety of budesonide/glycopyrrolate/formoterol fumarate metered dose inhaler in COPD: a 52-week randomized study, Respir Res, 20, (2019); Ferguson G.T., Papi A., Anzueto A., Et al., Budesonide/formoterol MDI with co-suspension delivery technology in COPD: the TELOS study, Eur Respir J, 52, (2018); Hanania N.A., Papi A., Anzueto A., Et al., Efficacy and safety of two doses of budesonide/formoterol fumarate metered dose inhaler in COPD, ERJ Open Res, 6, pp. 00187-2019, (2020); Martinez F.J., Rabe K.F., Ferguson G.T., Et al., Efficacy and safety of glycopyrrolate/formoterol metered dose inhaler formulated using co-suspension delivery technology in patients with COPD, Chest, 151, pp. 340-357, (2017); Hanania N.A., Tashkin D.P., Kerwin E.M., Et al., Long-term safety and efficacy of glycopyrrolate/formoterol metered dose inhaler using novel Co-Suspension™ Delivery Technology in patients with chronic obstructive pulmonary disease, Respir Med, 126, pp. 105-115, (2017); Lipworth B.J., Collier D.J., Gon Y., Et al., Improved lung function and patient-reported outcomes with co-suspension delivery technology glycopyrrolate/formoterol fumarate metered dose inhaler in COPD: a randomized Phase III study conducted in Asia, Europe, and the USA, Int J Chron Obstruct Pulmon Dis, 13, pp. 2969-2984, (2018); Friedman J.H., Stochastic gradient boosting, Comput Stat Data An, 38, pp. 367-378, (2002); Foster J.C., Taylor J.M., Ruberg S.J., Subgroup identification from randomized clinical trial data, Stat Med, 30, pp. 2867-2880, (2011); Lipkovich I., Dmitrienko A., D'Agostino B.R., Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials, Stat Med, 36, pp. 136-196, (2017); Hothorn T., Zeileis A., partykit: a modular toolkit for recursive partytioning in R, J Mach Learn Res, 16, pp. 3905-3909, (2015); Zeileis A., Hothorn T., Hornik K., Model-based recursive partitioning, J Comput Graph Stat, 17, pp. 492-514, (2008); Zeileis A., Hothorn T., Parties, models, mobsters: a new implementation of model-based recursive partitioning in R; Loh W.Y., He X., Man M., A regression tree approach to identifying subgroups with differential treatment effects, Stat Med, 34, pp. 1818-1833, (2015); Loh W.Y., Man M., Wang S., Subgroups from regression trees with adjustment for prognostic effects and postselection inference, Stat Med, 38, pp. 545-557, (2019); Hastie T., Tibshirani R., Friedman J., The elements of statistical learning: data mining, inference and prediction, (2001); Halpin D.M.G., Dransfield M.T., Han M.K., Et al., The effect of exacerbation history on outcomes in the IMPACT trial, Eur Respir J, 55, (2020); Kerkhof M., Freeman D., Jones R., Et al., Predicting frequent COPD exacerbations using primary care data, Int J Chron Obstruct Pulmon Dis, 10, pp. 2439-2450, (2015); Bertens L.C., Reitsma J.B., Moons K.G., Et al., Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease, Int J Chron Obstruct Pulmon Dis, 8, pp. 493-499, (2013); Stanford R.H., Nag A., Mapel D.W., Et al., Claims-based risk model for first severe COPD exacerbation, Am J Manag Care, 24, (2018); Stallberg B., Lisspers K., Larsson K., Et al., Predicting hospitalization due to COPD exacerbations in Swedish primary care patients using machine learning – based on the ARCTIC study, Int J Chron Obstruct Pulmon Dis, 16, pp. 677-688, (2021); Tavakoli H., Chen W., Sin D.D., Et al., Predicting severe chronic obstructive pulmonary disease exacerbations. Developing a population surveillance approach with administrative data, Ann Am Thorac Soc, 17, pp. 1069-1076, (2020)","D. Singh; Medicines Evaluation Unit, University of Manchester, Manchester University NHS Foundation Hospitals Trust, Manchester, M23 9QZ, United Kingdom; email: dsingh@meu.org.uk","","SAGE Publications Ltd","","","","","","17534658","","","35815359","English","Ther. Adv. Respir. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85133666075"
"Wanasinghe T.; Bandara S.; Madusanka S.; Meedeniya D.; Bandara M.; Diez I.D.L.T.","Wanasinghe, Thinira (58882322100); Bandara, Sakuni (58882464600); Madusanka, Supun (57211141564); Meedeniya, Dulani (24779848000); Bandara, Meelan (58119179000); Diez, Isabel De La Torre (55665183400)","58882322100; 58882464600; 57211141564; 24779848000; 58119179000; 55665183400","Lung Sound Classification with Multi-Feature Integration Utilizing Lightweight CNN Model","2024","IEEE Access","12","","","21262","21276","14","11","10.1109/ACCESS.2024.3361943","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184820500&doi=10.1109%2fACCESS.2024.3361943&partnerID=40&md5=6691d7ddc377c96f1f76618b0ecc4f4d","University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; University of Valladolid, Department of Signal Theory and Communications, and Telematics Engineering, Valladolid, 47011, Spain","Wanasinghe T., University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; Bandara S., University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; Madusanka S., University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; Meedeniya D., University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; Bandara M., University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; Diez I.D.L.T., University of Valladolid, Department of Signal Theory and Communications, and Telematics Engineering, Valladolid, 47011, Spain","Detecting respiratory diseases is of utmost importance, considering that respiratory ailments represent one of the most prevalent categories of diseases globally. The initial stage of lung disease detection involves auscultation conducted by specialists, relying significantly on their expertise. Therefore, automating the auscultation process for the detection of lung diseases can yield enhanced efficiency. Artificial intelligence (AI) has shown promise in improving the accuracy of lung sound classification by extracting features from lung sounds that are relevant to the classification task and learning the relationships between these features and the different pulmonary diseases. This paper utilizes two publicly available respiratory sound recordings namely, ICBHI 2017 challenge dataset and another lung sound dataset available at Mendeley Data. Foremost in this paper, we provide a detailed exposition about employing a Convolutional Neural Network (CNN) that utilizes feature extraction from Mel spectrograms, Mel frequency cepstral coefficients (MFCCs), and Chromagram. The highest accuracy achieved in the developed classification is 91.04% for 10 classes. Extending the contribution, this paper elaborates on the explanation of the classification model prediction by employing Explainable Artificial Intelligence (XAI). The novel contribution of this study is a CNN model that classifies lung sounds into 10 classes by combining audio-specific features to enhance the classification process.  © 2013 IEEE.","Artificial intelligence; explainability; respiratory diseases; sound processing","Audio acoustics; Biological organs; Classification (of information); Extraction; Neural networks; Pulmonary diseases; Speech recognition; Acoustic application; Asthma; Convolutional neural network; Explainability; Features extraction; Lung; Lung sounds; Recording; Sound classification; Sound processing; Feature extraction","","","","","Senate Research Committee; University of Moratuwa","This work was supported in part by the domain experts and in part by the University of Moratuwa, Sri Lanka, under the Senate Research Committee (SRC), Conference and Publishing Grant.","Soriano J.B., Kendrick P.J., Paulson K.R., Gupta V., Abrams E.M., Adedoyin R.A., Adhikari T.B., Advani S.M., Agrawal A., Ahmadian E., Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: A systematic analysis for the global burden of disease study 2017, Lancet Respiratory Med., 8, 6, pp. 585-596, (2020); Tariq Z., Shah S.K., Lee Y., Feature-based fusion using CNN for lung and heart sound classification, Sensors, 22, 4, (2022); Brunese L., Mercaldo F., Reginelli A., Santone A., A neural networkbased method for respiratory sound analysis and lung disease detection, Appl. Sci., 12, 8, (2022); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. Biomed. Circuits Syst., 14, 3, pp. 535-544, (2020); Kim Y., Hyon Y., Jung S.S., Lee S., Yoo G., Chung C., Ha T., Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning, Sci. Rep., 11, 1, (2021); Wijethilake N., Meedeniya D., Chitraranjan C., Perera I., Islam M., Ren H., Glioma survival analysis empowered with data engineering-A survey, IEEE Access, 9, pp. 43168-43191, (2021); Nguyen M.T., Lin W.W., Huang J.H., Heart sound classification using deep learning techniques based on log-mel spectrogram, Circuits, Syst., Signal Process., 42, 1, pp. 344-360, (2023); Meedeniya D., Kumarasinghe H., Kolonne S., Fernando C., Diez I.D.L.T., Marques G., Chest X-ray analysis empowered with deep learning: A systematic review, Appl. Soft Comput., 126, (2022); Xiang M., Zang J., Wang J., Wang H., Zhou C., Bi R., Zhang Z., Xue C., Research of heart sound classification using twodimensional features, Biomed. Signal Process. Control, 79, (2023); Gamage L., Isuranga U., De Silva S., Meedeniya D., Melanoma skin cancer classification with explainability, Proc. 3rd Int. Conf. Adv. Res. Comput. (ICARC), pp. 30-35, (2023); Meedeniya D., Rubasinghe I., A review of supportive computational approaches for neurological disorder identification, Interdisciplinary Approaches to Altering Neurodevelopmental Disorders, pp. 271-302, (2020); Faruqui N., Yousuf M.A., Whaiduzzaman M., Azad A.K.M., Barros A., Moni M.A., LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data, Comput. Biol. Med., 139, (2021); Fraiwan L., Hassanin O., Fraiwan M., Khassawneh B., Ibnian A.M., Alkhodari M., Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers, Biocybern. Biomed. Eng., 41, 1, pp. 1-14, (2021); Zhao Z., Gong Z., Niu M., Ma J., Wang H., Zhang Z., Li Y., Automatic respiratory sound classification via multi-branch temporal convolutional network, Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), pp. 9102-9106, (2022); Huang D.-M., Huang J., Qiao K., Zhong N.-S., Lu H.-Z., Wang W.-J., Deep learning-based lung sound analysis for intelligent stethoscope, Mil. Med. Res., 10, 1, (2023); Tripathi M., Image Processing Using CNN: Beginner's Guide to Image Processing, (2021); Dimoulas C.A., Audiovisual spatial-audio analysis by means of sound localization and imaging: A multimedia healthcare framework in abdominal sound mapping, IEEE Trans. Multimedia, 18, 10, pp. 1969-1976, (2016); Zhu H., Luo M.-D., Wang R., Zheng A.-H., He R., Deep audio-visual learning: A survey, Int. J. Autom. Comput., 18, 3, pp. 351-376, (2021); Choi Y., Lee H., Interpretation of lung disease classification with light attention connected module, Biomed. Signal Process. Control, 84, (2023); Basu V., Rana S., Respiratory diseases recognition through respiratory sound with the help of deep neural network, Proc. 4th Int. Conf. Comput. Intell. Netw. (CINE), pp. 1-6, (2020); Demir F., Ismael A.M., Sengur A., Classification of lung sounds with CNN model using parallel pooling structure, IEEE Access, 8, pp. 105376-105383, (2020); ICBHI 2017 Challenge; Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chestwall using an electronic stethoscope, Data Brief, 35, (2021); Shuvo S.B., Ali S.N., Swapnil S.I., Hasan T., Bhuiyan M.I.H., A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid Scalogram, IEEE J. Biomed. Health Informat., 25, 7, pp. 2595-2603, (2021); Islam M.A., Bandyopadhyaya I., Bhattacharyya P., Saha G., Multichannel lung sound analysis for asthma detection, Comput. Methods Programs Biomed., 159, pp. 111-123, (2018); Ma Y., Xu X., Li Y., LungRN+NL: An improved adventitious lung sound classification using non-local block ResNet neural network with mixup data augmentation, Proc. Interspeech, pp. 2902-2906, (2020); Srivastava A., Jain S., Miranda R., Patil S., Pandya S., Kotecha K., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput. Sci., 7, (2021); Serbes G., Ulukaya S., Kahya Y.P., An automated lung sound preprocessing and classification system based onspectral analysis methods, Proc. Int. Conf. Biomed. Health Inform., Thessaloniki, Greece., pp. 45-49, (2017); Dubey R., Bodade R.M., A review of classification techniques based on neural networks for pulmonary obstructive diseases, Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (RAITEA), (2019); Perna D., Tagarelli A., Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks, Proc. IEEE 32nd Int. Symp. Comput.-Based Med. Syst. (CBMS), pp. 50-55, (2019); Ancona M., Ceolini E., Oztireli C., Gross M., Towards better understanding of gradient-based attribution methods for deep neural networks, (2017); Du M., Liu N., Hu X., Techniques for interpretable machine learning, Commun. ACM, 63, 1, pp. 68-77, (2019); Topaloglu I., Barua P.D., Yildiz A.M., Keles T., Dogan S., Baygin M., Gul H.F., Tuncer T., Tan R.-S., Acharya U.R., Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds, Eng. Appl. Artif. Intell., 126, (2023); Meedeniya D., Deep Learning: A Beginners' Guide, (2023); Feature Extraction Librosa 0.10.1 Documentation; Kaplun D., Voznesensky A., Romanov S., Andreev V., Butusov D., Classification of hydroacoustic signals based on harmonic wavelets and a deep learning artificial intelligence system, Appl. Sci., 10, 9, (2020); Monaco A., Amoroso N., Bellantuono L., Pantaleo E., Tangaro S., Bellotti R., Multi-time-scale features for accurate respiratory sound classification, Appl. Sci., 10, 23, (2020); Messner E., Fediuk M., Swatek P., Scheidl S., Smolle-Juttner F.-M., Olschewski H., Pernkopf F., Multi-channel lung sound classification with convolutional recurrent neural networks, Comput. Biol. Med., 122, (2020); Lella K.K., Pja A., Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath, Alexandria Eng. J., 61, 2, pp. 1319-1334, (2022); Fernando C., Kolonne S., Kumarasinghe H., Meedeniya D., Chest radiographs classification using multi-model deep learning: A comparative study, Proc. 2nd Int. Conf. Adv. Res. Comput. (ICARC), pp. 165-170, (2022); Nguyen T., Pernkopf F., Lung sound classification using snapshot ensemble of convolutional neural networks, Proc. 42nd Annu. Int. Conf. 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Control, 82, (2023); Tanveer M., Pachori R.B., Machine Intelligence and Signal Analysis, 748, (2019); Bhatt D., Patel C., Talsania H., Patel J., Vaghela R., Pandya S., Modi K., Ghayvat H., CNN variants for computer vision: History, architecture, application, challenges and future scope, Electronics, 10, 20, (2021); Ho Y., Wookey S., The real-world-weight cross-entropy loss function: Modeling the costs of mislabeling, IEEE Access, 8, pp. 4806-4813, (2020); Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-CAM: Visual explanations from deep networks via gradient-based localization, Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 618-626, (2017); Simonyan K., Vedaldi A., Zisserman A., Deep inside convolutional networks: Visualising image classification models and saliency maps, (2013); Kapoor S., Narayanan A., Leakage and the reproducibility crisis in machine-learning-based science, Patterns, 4, 9, (2023); Chambres G., Hanna P., Desainte-Catherine M., Automatic detection of patient with respiratory diseases using lung sound analysis, Proc. Int. Conf. Content-Based Multimedia Indexing (CBMI), pp. 1-6, (2018); Chen H., Yuan X., Pei Z., Li M., Li J., Triple-classification of respiratory sounds using optimized S-Transform and deep residual networks, IEEE Access, 7, pp. 32845-32852, (2019); Riley R.D., Archer L., Snell K.I.E., Ensor J., Dhiman P., Martin G.P., Bonnett L.J., Collins G.S., Evaluation of clinical prediction models (Part 2): How to undertake an external validation study, Brit. Med. J., 384, (2024)","D. Meedeniya; University of Moratuwa, Department of Computer Science and Engineering, Moratuwa, 10400, Sri Lanka; email: dulanim@cse.mrt.ac.lk","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85184820500"
"Kanwade A.B.; Sardey M.P.; Panwar S.A.; Gajare M.P.; Chaudhari M.N.; Upreti K.","Kanwade, Archana B. (57063273500); Sardey, Mohini P. (57211499410); Panwar, Sarika A. (57223011085); Gajare, Milind P. (57211001296); Chaudhari, Monali N. (57192096775); Upreti, Kamal (57202706345)","57063273500; 57211499410; 57223011085; 57211001296; 57192096775; 57202706345","Combined weighted feature extraction and deep learning approach for chronic obstructive pulmonary disease classification using electromyography","2024","International Journal of Information Technology (Singapore)","16","3","","1485","1494","9","9","10.1007/s41870-023-01498-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173783641&doi=10.1007%2fs41870-023-01498-y&partnerID=40&md5=e82dfdaaa28cb933a8480e8db8eb421d","Marathawada Mitra Mandal College of Engineering, Pune, India; AISSMS’s Institute of Information Technology, Pune, India; Vivekanand Education Society’s Institute of Technology Chembur, Mumbai, India; CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India","Kanwade A.B., Marathawada Mitra Mandal College of Engineering, Pune, India; Sardey M.P., AISSMS’s Institute of Information Technology, Pune, India; Panwar S.A., AISSMS’s Institute of Information Technology, Pune, India; Gajare M.P., AISSMS’s Institute of Information Technology, Pune, India; Chaudhari M.N., Vivekanand Education Society’s Institute of Technology Chembur, Mumbai, India; Upreti K., CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India","The COVID-19 outbreak has led to a rise in respiratory disease-related deaths, including Chronic Obstructive Pulmonary Disease (COPD). Early diagnosis of COPD is crucial, but it can be challenging to distinguish between different chronic pulmonary diseases due to their similar symptoms, leading to misdiagnosis and time-consuming manual inspections. To address this issue, this paper explores the use of a deep learning model to differentiate COPD from other lung diseases using lung sound captured during Electromyography (EMG). The model includes steps such as noise removal, data augmentation, combined weighted feature extraction, and learning. The model's efficacy was evaluated using various metrics, including accuracy, precision, recall, F1-score, kappa coefficient, and Matthew’s correlation coefficient (MCC), with and without augmentation. The results show that the model achieved 93% accuracy and outperformed other existing state-of-the-art deep learning models, increasing the robustness of clinical decision-making. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023.","Classification; COPD; Deep learning; Electromyography; Respiratory diseases","","","","","","","","Lozano R., Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, The Lancet, 380, 9859, pp. 2095-2128, (2012); Singh D., Singh B.K., Behera A.K., A real-time correlation model between lung sounds & clinical data for asthmatic patients, Int J Inf Technol, 15, 1, pp. 39-44, (2023); Senior R.M., Anthonisen N.R., Chronic obstructive pulmonary disease (COPD), Am J Respir Crit Care Med, 157, 4, pp. S139-S147, (1998); Crim C., Et al., Respiratory system impedance with impulse oscillometry in healthy and COPD subjects: ECLIPSE baseline results, Respir Med, 105, 7, pp. 1069-1078, (2011); Brashier B., Salvi S., Measuring lung function using sound waves: role of the forced oscillation technique and impulse oscillometry system, Breathe, 11, 1, pp. 57-65, (2015); Oostveen E., Et al., The forced oscillation technique in clinical practice: methodology, recommendations and future developments, Eur Respir J, 22, 6, pp. 1026-1041, (2003); Tarannum S., Jabin S., Prioritizing severity level of COVID-19 using correlation coefficient and intuitionistic fuzzy logic, Int J Inf Technol, 14, 5, pp. 2469-2475, (2022); Pattnaik S., Rout N., Sabut S., Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features, Int J Inf Technol, 14, 7, pp. 3495-3505, (2022); Nayak S.R., Mishra J., Pyarimohan J., Fractal analysis of image sets using differential box counting techniques, Int J Inf Technol, 10, pp. 39-47, (2018); Rizal A., Hidayat R., Nugroho H.A., Entropy measurement as features extraction in automatic lung sound classification, In: ICCREC 2017—2017 International Conference on Control, Electronics, Renewable Energy, and Communications, Proceedings, pp. 93-97, (2017); koshti R., Et al., Improvement in spectrum sensing of wireless regional area network with empirical mode decomposition, Int J Inf Technol, 15, 1, pp. 79-86, (2023); Xu Q., Et al., An adaptive algorithm for the determination of the onset and offset of muscle contraction by EMG signal processing, IEEE Trans Neural Syst Rehabil Eng, 21, 1, pp. 65-73, (2012); Kumar A., Singh G.K., Anurag S., An optimized cosine-modulated nonuniform filter bank design for subband coding of ECG signal, J King Saud Univ-Eng Sci, 27, 2, pp. 158-169, (2015); Fatma T., Et al., Automatic detection of non-convulsive seizures: a reduced complexity approach, J King Saud Univ-Comput Inform Sci, 28, 4, pp. 407-415, (2016); Kanwade A.B., Bairagi V., Analysis of inspiratory muscle of respiration in COPD patients, Advances in Signal Processing and Intelligent Recognition Systems., 425, (2016); Pasinetti S., Et al., A novel algorithm for EMG signal processing and muscle timing measurement, IEEE Trans Instrum Measur, 64, 11, pp. 2995-3004, (2015); Bairagi V.K., Kanwade A.B., Classification of chronic obstructive pulmonary disease (COPD) using electromyography, Sādhanā, 45, 1, pp. 1-17, (2020); Norali A.N., Classification of human breathing task based on electromyography signal of respiratory muscles, In: 2017 IEEE 13Th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE, (2017); Sarkar S., Et al., A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration, Australas Phys Eng Sci Med, 42, 4, pp. 1011-1024, (2019); Melese E.A., Deep learning based algorithms for detecting chronic obstructive pulmonary disease, In: 2022 Ist-Africa Conference (Ist-Africa)., (2022); Sarlabous L., Et al., Electromyography-based respiratory onset detection in COPD patients on non-invasive mechanical ventilation, Entropy, 21, 3, (2019); Odajiu I., Et al., Peripheral neuropathy: a neglected cause of disability in COPD–a narrative review, Respir Med, 201, (2022); Rocha B.M., Α respiratory sound database for the development of automated classification, In: Precision Medicine Powered by Phealth and Connected Health: ICBHI 2017, Thessaloniki, Greece, 18–21 November 2017, (2017); Sangeetha B., Periyasamy R., Empirical mode decomposition, S-method and steepest gradient-based reconstruction algorithm for denoising lung sound, 2022 IEEE 7Th International Conference for Convergence in Technology (I2CT), (2022); Li J., Wang X., Wang X., Qiao S., Zhou Y., Improving The ResNet-based respiratory sound classification systems with focal loss, In: 2022 IEEE Biomedical Circuits and Systems Conference (Biocas), Taipei, Taiwan, 2022, pp. 223-227, (2022); Kim Y., Et al., Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning, Sci Rep, 11, 1, (2021); Demir F., Sengur A., Bajaj V., Convolutional neural networks based efficient approach for classification of lung diseases, Health Inf Sci Syst, 8, 1, (2020); Saraiva A.A., Fonseca Ferreira N.M., de Sousa L.L., Costa N.C., Sousa J.V.M., Santos D.B.S., Soares S., Classification of images of childhood pneumonia using convolutional neural networks. In: BIOIMAGING 2019–6th International Conference on Bioimaging, Proceedings; Part of 12Th International Joint Conference on Biomedical Engineering Systems and Technologies, (2019); Perna D., Tagarelli A., Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks, . In: Proceedings-Ieee Symposium on Computer-Based Medical Systems, pp. 50-55, (2019); Liu R., Cai S., Zhang K., Hu N., Detection of adventitious respiratory sounds based on convolutional neural network, ICIIBMS 2019–4th International Conference on Intelligent Informatics and Biomedical Sciences, pp. 298-303, (2019); Kanwade A., Bairagi V.K., Classification of COPD and normal lung airways using feature extraction of electromyographic signals, J King Saud Univ-Comput Inform Sci, 31, 4, pp. 506-513, (2019); Dabla P.K., Upreti K., Singh D., Singh A., Sharma J., Dabas A., Gruson D., Gouget B., Bernardini S., Homsak E., Stankovic S., Target association rule mining to explore novel paediatric illness patterns in emergency settings, Scand J Clin Lab Invest, 82, 7-8, pp. 595-600, (2022); Bhatnagar S., Dayal M., Singh D., Upreti S., Upreti K., Kumar J., Block-Hash Signature (BHS) for transaction validation in smart contracts for security and privacy using blockchain, JMM, 19, 4, pp. 935-962, (2023)","K. Upreti; CHRIST (Deemed to Be University), Delhi NCR, Ghaziabad, India; email: kamalupreti1989@gmail.com","","Springer Science and Business Media B.V.","","","","","","25112104","","","","English","Int. J. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85173783641"
"de Hond A.A.H.; Kant I.M.J.; Honkoop P.J.; Smith A.D.; Steyerberg E.W.; Sont J.K.","de Hond, Anne A. H. (57205398709); Kant, Ilse M. J. (57195222927); Honkoop, Persijn J. (36876526900); Smith, Andrew D. (55557337400); Steyerberg, Ewout W. (7006417148); Sont, Jacob K. (7003820342)","57205398709; 57195222927; 36876526900; 55557337400; 7006417148; 7003820342","Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations","2022","Scientific Reports","12","1","20363","","","","10","10.1038/s41598-022-24909-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142814333&doi=10.1038%2fs41598-022-24909-9&partnerID=40&md5=3567ffde009294313048560c02caaf8a","Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Department of Respiratory Medicine, University Hospital Wishaw, 50 Netherton Street, Wishaw, ML2 0DP, United Kingdom","de Hond A.A.H., Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands, Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands, Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Kant I.M.J., Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands, Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands, Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Honkoop P.J., Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Smith A.D., Department of Respiratory Medicine, University Hospital Wishaw, 50 Netherton Street, Wishaw, ML2 0DP, United Kingdom; Steyerberg E.W., Clinical AI Implementation and Research Lab, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands, Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands; Sont J.K., Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2300 RC, Netherlands","Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82–0.87) and 0.88 (0.86–0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity. © 2022, The Author(s).","","Asthma; Humans; Logistic Models; Machine Learning; Sensitivity and Specificity; Time Factors; asthma; human; machine learning; sensitivity and specificity; statistical model; time factor","","","","","","","Malasinghe L.P., Ramzan N., Dahal K., Remote patient monitoring: A comprehensive study, J. Ambient. Intell. Humaniz. Comput., 10, pp. 57-76, (2019); Honkoop P.J., Taylor D.R., Smith A.D., Snoeck-Stroband J.B., Sont J.K., Early detection of asthma exacerbations by using action points in self-management plans, Eur. Respir. J., 41, pp. 53-59, (2013); Fine M.J., Et al., A prediction rule to identify low-risk patients with community-acquired pneumonia, N. Engl. J. Med., 336, pp. 243-250, (1997); Wells P.S., Et al., Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: Increasing the models utility with the SimpliRED d-dimer, Thromb. Haemost., 83, pp. 416-420, (2000); British Guideline on the Management of Asthma, (2019); Mak R.H., Et al., Use of crowd innovation to develop an artificial intelligence-based solution for radiation therapy targeting, JAMA Oncol., 5, pp. 654-661, (2019); Esteva A., Et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, pp. 115-118, (2017); McKinney S.M., Et al., International evaluation of an AI system for breast cancer screening, Nature, 577, pp. 89-94, (2020); Cearns M., Hahn T., Baune B.T., Recommendations and future directions for supervised machine learning in psychiatry, Transl. Psychiatry, 9, (2019); Neuhaus A.H., Popescu F.C., Sample size, model robustness, and classification accuracy in diagnostic multivariate neuroimaging analyses, Biol. Psychiatry, 84, pp. e81-e82, (2018); Chen P.-H.C., Liu Y., Peng L., How to develop machine learning models for healthcare, Nat. Mater., 18, pp. 410-414, (2019); Altman D.G., Vergouwe Y., Royston P., Moons K.G.M., Prognosis and prognostic research: Validating a prognostic model, BMJ, 338, (2009); Wynants L., Smits L.J.M., Van Calster B., Demystifying AI in healthcare, BMJ, 370, (2020); Tsang K.C.H., Pinnock H., Wilson A.M., Shah S.A., 2020 42Nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 5673-5677; Christodoulou E., Et al., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J. Clin. Epidemiol., 110, pp. 12-22, (2019); Gravesteijn B.Y., Et al., Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury, J. Clin. Epidemiol., 122, pp. 95-107, (2020); Nusinovici S., Et al., Logistic regression was as good as machine learning for predicting major chronic diseases, J. Clin. Epidemiol., 122, pp. 56-69, (2020); Martin A., Et al., Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data, J. Asthma, 57, pp. 1339-1346, (2020); Sanders S., Doust J., Glasziou P., A systematic review of studies comparing diagnostic clinical prediction rules with clinical judgment, PLoS ONE, 10, (2015); Satici C., Et al., Performance of pneumonia severity index and CURB-65 in predicting 30-day mortality in patients with COVID-19, Int. J. Infect. Dis., 98, pp. 84-89, (2020); Obradovic D., Et al., Correlation between the Wells score and the Quanadli index in patients with pulmonary embolism, Clin. Respir. J., 10, pp. 784-790, (2016); Winters B.D., Et al., Technological distractions (Part 2): A summary of approaches to manage clinical alarms with intent to reduce alarm fatigue, Crit. Care Med., 46, pp. 130-137, (2018); Mori T., Uchihira N., Balancing the trade-off between accuracy and interpretability in software defect prediction, Empir. Softw. Eng., 24, pp. 779-825, (2019); Johansson U., Sonstrod C., Norinder U., Bostrom H., Trade-off between accuracy and interpretability for predictive in silico modeling, Future Med. Chem., 3, pp. 647-663, (2011); Wallace B.C., Dahabreh I.J., Improving class probability estimates for imbalanced data, Knowl. Inf. Syst., 41, pp. 33-52, (2014); Van Calster B., Et al., Calibration: The Achilles heel of predictive analytics, BMC Med., 17, (2019); Honkoop P.J., Et al., MyAirCoach: The use of home-monitoring and mHealth systems to predict deterioration in asthma control and the occurrence of asthma exacerbations; study protocol of an observational study, BMJ Open, 7, (2017); Finkelstein J., Jeong I.C., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann. N. Y. Acad. Sci., 1387, pp. 153-165, (2017); Sanchez-Morillo D., Fernandez-Granero M.A., Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review, Chron. Respir. Dis., 13, pp. 264-283, (2016); Smith A.D., Cowan J.O., Brassett K.P., Herbison G.P., Taylor D.R., Use of exhaled nitric oxide measurements to guide treatment in chronic asthma, N. Engl. J. Med., 352, pp. 2163-2173, (2005); Taylor D.R., Et al., Asthma control during long-term treatment with regular inhaled salbutamol and salmeterol, Thorax, 53, pp. 744-752, (1998); Smith A.E., Nugent C.D., McClean S.I., Evaluation of inherent performance of intelligent medical decision support systems: Utilising neural networks as an example, Artif. Intell. Med., 27, pp. 1-27, (2003); Nielsen D., Tree boosting with xgboost-why does xgboost win"" every"" machine learning competition?, NTNU, (2016); Ma J., Perkins S., Proceedings of the International Joint Conference on Neural Networks, pp. 1741-1745, (2003); Schober P., Vetter T.R., Logistic regression in medical research, Anesth. Analg., 132, pp. 365-366, (2021); Steyerberg E.W., Clinical Prediction Models, (2009)","A.A.H. de Hond; Department of Information Technology and Digital Innovation, Leiden University Medical Centre, Leiden, Albinusdreef 2, 2300 RC, Netherlands; email: a.a.h.de_hond@lumc.nl","","Nature Research","","","","","","20452322","","","36437306","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85142814333"
"Zang C.; Hou Y.; Schenck E.J.; Xu Z.; Zhang Y.; Xu J.; Bian J.; Morozyuk D.; Khullar D.; Nordvig A.S.; Shenkman E.A.; Rothman R.L.; Block J.P.; Lyman K.; Zhang Y.; Varma J.; Weiner M.G.; Carton T.W.; Wang F.; Kaushal R.","Zang, Chengxi (57190986705); Hou, Yu (58508188500); Schenck, Edward J. (56989074500); Xu, Zhenxing (57210798752); Zhang, Yongkang (56941929100); Xu, Jie (58966963300); Bian, Jiang (7103200005); Morozyuk, Dmitry (57747953100); Khullar, Dhruv (37021679200); Nordvig, Anna S. (55428486900); Shenkman, Elizabeth A. (6603877456); Rothman, Russell L. (7201810893); Block, Jason P. (55551008400); Lyman, Kristin (57223081565); Zhang, Yiye (55635153300); Varma, Jay (57210676985); Weiner, Mark G. (7402253836); Carton, Thomas W. (35602711900); Wang, Fei (56177292700); Kaushal, Rainu (7005295324)","57190986705; 58508188500; 56989074500; 57210798752; 56941929100; 58966963300; 7103200005; 57747953100; 37021679200; 55428486900; 6603877456; 7201810893; 55551008400; 57223081565; 55635153300; 57210676985; 7402253836; 35602711900; 56177292700; 7005295324","Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohorts","2024","Communications Medicine","4","1","130","","","","8","10.1038/s43856-024-00549-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201726848&doi=10.1038%2fs43856-024-00549-0&partnerID=40&md5=5e564b94fab2345150a91c753648df35","Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Division of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, New York, NY, United States; Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States; Department of Neurology, Weill Cornell Medicine, New York, NY, United States; Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, United States; Louisiana Public Health Institute, New Orleans, LA, United States","Zang C., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Hou Y., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Schenck E.J., Division of Pulmonary and Critical Care Medicine, Weill Cornell Department of Medicine, New York, NY, United States; Xu Z., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Zhang Y., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Xu J., Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States; Bian J., Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States; Morozyuk D., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Khullar D., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Nordvig A.S., Department of Neurology, Weill Cornell Medicine, New York, NY, United States; Shenkman E.A., Department of Health Outcomes Biomedical Informatics, University of Florida, Gainesville, FL, United States; Rothman R.L., Center for Health Services Research, Vanderbilt University Medical Center, Nashville, TN, United States; Block J.P., Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, United States; Lyman K., Louisiana Public Health Institute, New Orleans, LA, United States; Zhang Y., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Varma J., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Weiner M.G., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Carton T.W., Louisiana Public Health Institute, New Orleans, LA, United States; Wang F., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States; Kaushal R., Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States","Background: SARS-CoV-2-infected patients may develop new conditions in the period after the acute infection. These conditions, the post-acute sequelae of SARS-CoV-2 infection (PASC, or Long COVID), involve a diverse set of organ systems. Limited studies have investigated the predictability of Long COVID development and its associated risk factors. Methods: In this retrospective cohort study, we used electronic healthcare records from two large-scale PCORnet clinical research networks, INSIGHT (~1.4 million patients from New York) and OneFlorida+ (~0.7 million patients from Florida), to identify factors associated with having Long COVID, and to develop machine learning-based models for predicting Long COVID development. Both SARS-CoV-2-infected and non-infected adults were analysed during the period of March 2020 to November 2021. Factors associated with Long COVID risk were identified by removing background associations and correcting for multiple tests. Results: We observed complex association patterns between baseline factors and a variety of Long COVID conditions, and we highlight that severe acute SARS-CoV-2 infection, being underweight, and having baseline comorbidities (e.g., cancer and cirrhosis) are likely associated with increased risk of developing Long COVID. Several Long COVID conditions, e.g., dementia, malnutrition, chronic obstructive pulmonary disease, heart failure, PASC diagnosis U099, and acute kidney failure are well predicted (C-index > 0.8). Moderately predictable conditions include atelectasis, pulmonary embolism, diabetes, pulmonary fibrosis, and thromboembolic disease (C-index 0.7–0.8). Less predictable conditions include fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions: This observational study suggests that association patterns between investigated factors and Long COVID are complex, and the predictability of different Long COVID conditions varies. However, machine learning-based predictive models can help in identifying patients who are at risk of developing a variety of Long COVID conditions. © The Author(s) 2024.","","","","","","","National Institutes of Health, NIH, (OTA OT2HL161847, EHR-01-21); National Institutes of Health, NIH; Patient-Centered Outcomes Research Institute, PCORI, (RI-CORNELL-01-MC); Patient-Centered Outcomes Research Institute, PCORI","This research was funded by the National Institutes of Health (NIH) Agreement OTA OT2HL161847 (contract number EHR-01-21) as part of the Researching COVID to Enhance Recovery (RECOVER) research program. The PCORnet\u00AE Study reported in this work was conducted using PCORnet\u00AE, the National Patient-Centered Clinical Research Network. PCORnet\u00AE has been developed with funding from the Patient-Centered Outcomes Research Institute\u00AE (PCORI\u00AE). This work was conducted through the use of data from the INSIGHT Clinical Research Network and supported in part by the Patient-Centered Outcomes Research Institute (PCORI) PCORnet grant to the INSIGHT Clinical Research Network (Grant # RI-CORNELL-01-MC). The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the views of other organizations participating in, collaborating with, or funding PCORnet\u00AE or of the Patient-Centered Outcomes Research Institute\u00AE (PCORI\u00AE). ","WHO Coronavirus (COVID-19) Dashboard., (2022); Nalbandian A., Et al., Post-acute COVID-19 syndrome, Nat. Med, 27, pp. 601-615, (2021); Al-Aly Z., Xie Y., Bowe B., High-dimensional characterization of post-acute sequelae of COVID-19, Nature, 594, pp. 259-264, (2021); Xie Y., Xu E., Bowe B., Al-Aly Z., Long-term cardiovascular outcomes of COVID-19, Nat. Med., pp. 1-8; Xie Y., Xu E., Al-Aly Z., Risks of mental health outcomes in people with covid-19: cohort study, BMJ, 376, (2022); Zhang H., Et al., Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes, Nat. Med., pp. 1-10; Zang C., Et al., Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative, Nat. Commun, 14, (2023); Williamson E.J., Et al., Factors associated with COVID-19-related death using OpenSAFELY, Nature, 584, pp. 430-436, (2020); Antonelli M., Et al., Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study, Lancet Infect. Dis, 22, pp. 43-55, (2022); Asadi-Pooya A.A., Et al., Risk factors associated with long COVID syndrome: a retrospective study, Iran J. Med. 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Public Health, 19, (2022); Rubio-Rivas M., Et al., WHO ordinal scale and inflammation risk categories in COVID-19. comparative study of the severity scales, J. Gen. Intern. Med, 37, pp. 1980-1987, (2022); Zang C., . Calvin-zcx/pasc_phenotype: Code for risk factors and predictive modeling for Long COVID","F. Wang; Department of Population Health Sciences, Weill Cornell Medicine, New York, United States; email: few2001@med.cornell.edu","","Springer Nature","","","","","","2730664X","","","","English","Commun. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85201726848"
"Zhu Z.; Zhao S.; Li J.; Wang Y.; Xu L.; Jia Y.; Li Z.; Li W.; Chen G.; Wu X.","Zhu, Zecheng (58541573300); Zhao, Shunjin (57205766416); Li, Jiahui (58990996500); Wang, Yuting (57201335581); Xu, Luopiao (58887679400); Jia, Yubing (58888095300); Li, Zihan (58485804600); Li, Wenyuan (56724853200); Chen, Gang (57114035800); Wu, Xifeng (8236942400)","58541573300; 57205766416; 58990996500; 57201335581; 58887679400; 58888095300; 58485804600; 56724853200; 57114035800; 8236942400","Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease","2024","Respiratory Research","25","1","167","","","","8","10.1186/s12931-024-02793-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190679454&doi=10.1186%2fs12931-024-02793-3&partnerID=40&md5=f869eb797e7467cfa51d470263f23d65","Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People’s Hospital), Zhejiang, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Zhejiang, Hangzhou, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Zhejiang, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang, Hangzhou, China","Zhu Z., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Zhao S., Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People’s Hospital), Zhejiang, Hangzhou, China; Li J., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Wang Y., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Xu L., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Jia Y., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Li Z., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Li W., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Chen G., College of Computer Science and Technology, Zhejiang University, Zhejiang, Hangzhou, China; Wu X., Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China, National Institute for Data Science in Health and Medicine, Zhejiang University, Zhejiang, Hangzhou, China, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang, Hangzhou, China","Background: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. Methods: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). Results: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. Conclusion: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. Trial registration: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants. © The Author(s) 2024.","COPD; Data fusion; Deep learning; Diagnostic","Area Under Curve; Deep Learning; Humans; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; ROC Curve; accuracy; adult; area under the curve; Article; chronic obstructive lung disease; clinical feature; computer assisted tomography; deep learning; early diagnosis; epidemiological monitoring; female; human; major clinical study; male; questionnaire; radiomics; scoring system; artificial neural network; chronic obstructive lung disease; diagnostic imaging; receiver operating characteristic; retrospective study","","","","","Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, (2020C03002, 2019R01007); National Natural Science Foundation of China, NSFC, (2023-3-001); National Natural Science Foundation of China, NSFC; Key Research and Development Program of Zhejiang Province, (82203984, K20230085); Key Research and Development Program of Zhejiang Province","This study was supported in part by grants 2020E10004 from the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, 2019R01007 from the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang, 2020C03002 from the Key Research and Development Program of Zhejiang Province, K20230085 from the Healthy Zhejiang One Million People Cohort, 82203984 from the National Natural Science Foundation for Young Scientists of China, and 2023-3-001 from the Major Research Plan of Jinhua. The funding source had no role in study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit the article for publication. 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Wu; Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; email: xifengw@zju.edu.cn; G. Chen; College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China; email: cg@zju.edu.cn","","BioMed Central Ltd","","","","","","14659921","","RREEB","38637823","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85190679454"
"Xue M.; Jia S.; Chen L.; Huang H.; Yu L.; Zhu W.","Xue, Mengfan (55768119800); Jia, Shishen (57965825700); Chen, Ling (57223966283); Huang, Hailiang (57965825800); Yu, Lijuan (55686827100); Zhu, Wentao (36516078000)","55768119800; 57965825700; 57223966283; 57965825800; 55686827100; 36516078000","CT-based COPD identification using multiple instance learning with two-stage attention","2023","Computer Methods and Programs in Biomedicine","230","","107356","","","","11","10.1016/j.cmpb.2023.107356","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146652766&doi=10.1016%2fj.cmpb.2023.107356&partnerID=40&md5=d69ace6a984484e1b443c42a5b602ef5","School of Automation, Hangzhou Dianzi University, Zhejiang, Hangzhou, 310018, China; Zhejiang Lab, Zhejiang, Hangzhou, 311121, China; Hainan Cancer Hospital, Hainan, Haikou, 570312, China","Xue M., School of Automation, Hangzhou Dianzi University, Zhejiang, Hangzhou, 310018, China, Zhejiang Lab, Zhejiang, Hangzhou, 311121, China; Jia S., School of Automation, Hangzhou Dianzi University, Zhejiang, Hangzhou, 310018, China; Chen L., Zhejiang Lab, Zhejiang, Hangzhou, 311121, China; Huang H., Zhejiang Lab, Zhejiang, Hangzhou, 311121, China; Yu L., Hainan Cancer Hospital, Hainan, Haikou, 570312, China; Zhu W., Zhejiang Lab, Zhejiang, Hangzhou, 311121, China","Background and objective: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide. However, COPD remains underdiagnosed globally. Spirometry is currently the primary tool for diagnosing COPD, but it has unneglected difficulties in detecting mild COPD. Chest computed tomography (CT) has been validated for COPD diagnosis and quantification. Whereas many CT-based deep learning approaches have been developed to identify COPD, it remains challenging to characterize CT-based pathological alternations of COPD which are multidimensional and highly spatially heterogeneous, and the diagnosis performance still needs to be improved. Methods: A multiple instance learning (MIL) with two-stage attention (TSA-MIL) is proposed to identify COPD using CT images. Based on transfer learning, a Resnet-50 model pre-trained on natural images is used to extract multicomponent and multidimensional features of COPD abnormalities, in which a pseudo-color method is designed to transfer single-channel CT slices to RGB-like three channels and meanwhile increase the richness of feature representations. To generate more robust attention score for each instance, a two-stage attention module is utilized with the first stage aiming at discovering the key instance while the second stage correcting the attention score for each instance by calculating its average relative distance to the key instances; besides, an instance-level clustering over feature domain is exploited to further improve feature separability and therefore facilitate the subsequent attention module. CT scans, spirometry and demographic data of a total of 800 participants were collected from a large public hospital, with 720 and 80 participants used for model development and evaluation, respectively. In addition, data of 260 participants from another large hospital were also collected for external validation. Results and Conclusions: The proposed TSA-MIL approach outperforms not only most of the advanced MIL models, but also other up-to-date COPD identification methods, with an accuracy of 0.9200 and an area under curve (AUC) of 0.9544 on the test set, and with an accuracy of 0.8115 and an AUC of 0.8737 on the external validation set without multicenter effect reduction, which is clinically acceptable. Therefore, this approach is promising to be a powerful tool for COPD diagnosis in clinical practice. © 2023 Elsevier B.V.","Attention; COPD; CT image; Multiple instance learning","Cluster Analysis; Humans; Pulmonary Disease, Chronic Obstructive; Spirometry; Tomography, X-Ray Computed; Computerized tomography; Diagnosis; Hospitals; Learning systems; Pulmonary diseases; bronchodilating agent; Attention; Chronic obstructive pulmonary disease; Computed tomography images; Diagnosis performance; Disease diagnosis; Learning approach; Multicomponents; Multiple-instance learning; Natural images; Transfer learning; aged; area under the curve; Article; attention network; chronic obstructive lung disease; classification algorithm; clinical evaluation; clinical practice; computer assisted tomography; conceptual framework; controlled study; deep learning; deep neural network; demographics; diagnostic accuracy; feature extraction; female; human; image analysis; major clinical study; male; multiple instance learning two stage attention; public hospital; receiver operating characteristic; recurrent neural network; residual neural network; spirometry; transfer of learning; chronic obstructive lung disease; cluster analysis; diagnostic imaging; procedures; x-ray computed tomography; Deep learning","","","","","Key R&D Program Projects in Hainan Province, (ZDYF2021SHFZ244); Natural Science Foundation of Zhejiang Province, ZJNSF, (LZ23F030002); Natural Science Foundation of Zhejiang Province, ZJNSF","This work was supported by the Key R&D Program Projects in Hainan Province (No. ZDYF2021SHFZ244) and the Zhejiang Provincial Natural Science Foundation of China (No. LZ23F030002).","Labaki W.W., Han M.K., Improving detection of early chronic obstructive pulmonary disease, Ann. Am. Thorac. Soc., 15, (2018); Li J., Jing G., Fink J.B., Et al., Airborne particulate concentrations during and after pulmonary function testing, Chest, 159, 4, pp. 1570-1574, (2021); HELGESON S.A., LIM K.G., LEE A.S., Et al., Aerosol generation during spirometry, Ann. Am. Thorac. Soc., 17, 12, pp. 1637-1639, (2020); Perez-Padilla R., Thirion-Romero I., Guzman N., Underdiagnosis of chronic obstructive pulmonary disease: should smokers be offered routine spirometry tests?, Expert Rev. Respir. Med., 12, 2, pp. 83-85, (2018); Macnee W., Computed tomography-derived pathological phenotypes in COPD, Eur. Respirat. Soc., pp. 10-13, (2016); Ho T.T., Kim T., Kim W.J., Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci. Rep., 11, 1, pp. 1-12, (2021); Du R., Qi S., Feng J., Et al., Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN, IEEE Access, 8, pp. 38907-38919, (2020); Tajbakhsh N., Shin J.Y., Gurudu S.R., Et al., Convolutional neural networks for medical image analysis: full training or fine tuning?, IEEE Trans. Med. Imaging, 35, 5, pp. 1299-1312, (2016); Bhatt S.P., Washko G.R., Hoffman E.A., Et al., Imaging advances in chronic obstructive pulmonary disease. Insights from the genetic epidemiology of chronic obstructive pulmonary disease (COPDGene) study, Am. J. Respir. Crit. Care Med., 199, 3, pp. 286-301, (2019); Lecun Y., Bengio Y., Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015); Kermany D.S., Goldbaum M., Cai W., Et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, 5, pp. 1122-1131, (2018); Gulshan V., Peng L., Coram M., Et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 22, pp. 2402-2410, (2016); Ardila D., Kiraly A.P., Bharadwaj S., Et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nat. Med., 25, 6, pp. 954-961, (2019); Gonzalez G., Ash S Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am. J. Respir. Crit. 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Big Data, 3, 1, pp. 1-40, (2016); Selvaraju R.R., Cogswell M., Das A., Et al., Grad-cam: visual explanations from deep networks via gradient-based localization, Proceedings of the Proceedings of the IEEE International Conference on Computer Vision, (2017); Qi C.R., Su H., Mo K., Et al., Pointnet: deep learning on point sets for 3d classification and segmentation, Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2017); Liu G., Wu J., Zhou Z.-H., Key instance detection in multi-instance learning, Proceedings of the Asian Conference on Machine Learning, (2012); Refaeilzadeh P., Tang L., Liu H., Cross-validation, Encycl. Database Syst., 5, pp. 532-538, (2009); Campanella G., Hanna M.G., Geneslaw L., Et al., Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat. Med., 25, 8, pp. 1301-1309, (2019); Tang L.Y., Coxson H.O., Lam S., Et al., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit. Health, 2, pp. e259-ee67, (2020); Washko G.R., Coxson H.O., O'Donnell D.E., Et al., CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise, Lancet Respirat. Med., 5, 11, pp. 903-908, (2017); Altan G., Kutl U.Y., Gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning onmulti-channel lung sounds, Turk. J. Electric. Eng. Comput. Sci., 28, 5, pp. 2979-2996, (2020); Ying J., Dutta J., Guo N., Et al., Classification of exacerbation frequency in the COPDGene cohort using deep learning with deep belief networks, IEEE J. Biomed. Health Inform., 24, 6, pp. 1805-1813, (2016); Ghaffarian S., Valente J., Van Der Voor T.M., Et al., Effect of attention mechanism in deep learning-based remote sensing image processing: a systematic literature review, Remote Sens., 13, 15, (2021)","W. Zhu; Zhejiang Lab, Hangzhou, Zhejiang, 311121, China; email: wentao.zhu@zhejianglab.com","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","36682106","English","Comput. Methods Programs Biomed.","Article","Final","","Scopus","2-s2.0-85146652766"
"Brennan V.; Martin-Grace J.; Greene G.; Heverin K.; Mulvey C.; McCartan T.; Lombard L.; Walsh J.; Hale E.M.; Srinivasan S.; O'Reilly M.W.; Thompson C.J.; Costello R.W.; Sherlock M.","Brennan, Vincent (57222597272); Martin-Grace, Julie (56572836200); Greene, Garrett (57193514494); Heverin, Karen (57786170000); Mulvey, Christopher (57205671884); McCartan, Tom (57192913330); Lombard, Lorna (57194574553); Walsh, Joanne (57214140881); Hale, Elaine Mac (57162774300); Srinivasan, Shari (57787363000); O'Reilly, Michael W. (9243776300); Thompson, Chris J. (57206741339); Costello, Richard W. (7101602656); Sherlock, Mark (57216064887)","57222597272; 56572836200; 57193514494; 57786170000; 57205671884; 57192913330; 57194574553; 57214140881; 57162774300; 57787363000; 9243776300; 57206741339; 7101602656; 57216064887","The Contribution of Oral and Inhaled Glucocorticoids to Adrenal Insufficiency in Asthma","2022","Journal of Allergy and Clinical Immunology: In Practice","10","10","","2614","2623","9","9","10.1016/j.jaip.2022.05.031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133582978&doi=10.1016%2fj.jaip.2022.05.031&partnerID=40&md5=6f7fd3b03940cabc859df444567bc56c","Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Department of Clinical Biochemistry, Beaumont Hospital, Dublin, Ireland; Department of Endocrinology, Beaumont Hospital, Dublin, Ireland; Department of Respiratory Medicine, Beaumont Hospital, Dublin, Ireland","Brennan V., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Martin-Grace J., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Greene G., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Heverin K., Department of Clinical Biochemistry, Beaumont Hospital, Dublin, Ireland; Mulvey C., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; McCartan T., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Lombard L., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Walsh J., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Hale E.M., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland; Srinivasan S., Department of Clinical Biochemistry, Beaumont Hospital, Dublin, Ireland; O'Reilly M.W., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland, Department of Endocrinology, Beaumont Hospital, Dublin, Ireland; Thompson C.J., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland, Department of Endocrinology, Beaumont Hospital, Dublin, Ireland; Costello R.W., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland, Department of Respiratory Medicine, Beaumont Hospital, Dublin, Ireland; Sherlock M., Department of Medicine, Royal College of Surgeons in Ireland Beaumont Campus, Dublin, Ireland, Department of Endocrinology, Beaumont Hospital, Dublin, Ireland","Background: Exposure to any form of glucocorticoid preparation is associated with a risk of adrenal insufficiency (AI). Objective: To establish the contribution of oral corticosteroid (OCS) and inhaled corticosteroid (ICS) exposure to the risk of AI in a cohort of patients (n = 80) with severe, uncontrolled asthma. Methods: We compiled individualized cumulative OCS and ICS exposure data using a combination of health care records and electronic inhaler monitoring using an Inhaler Compliance Assessment device and estimated the risk of AI for each participant using a morning serum cortisol concentration. Results: The predicted prevalence of AI based on morning cortisol concentrations was 25% (20 of 80). Participants on maintenance OCS therapy had the highest risk of AI at 60% (6 of 10) compared with 17% (11 of 65) in those with no recent OCS exposure. Morning serum cortisol correlated negatively with both OCS exposure (mg/kg prednisolone) (r = −0.4; P < .0002) and ICS exposure (mg/kg fluticasone propionate) (r = −0.26; P = .019). Logistic regression of risk of AI against the number of standard treatment courses of OCS demonstrated a positive relationship although this did not reach statistical significance (odds ratio, 1.41; 95% CI, 0.97-2.05; P = .073). Logistic regression analysis, categorizing patients as high-risk AI (cortisol <130 nmol/L) or not (cortisol >130 nmol/L), showed that cumulative ICS exposure remained a significant predictor of AI, even when exposure to OCS was controlled for (odds ratio, 2.17 per 1 mg/kg increase in cumulative fluticasone propionate exposure; 95% CI, 1.06-4.42; P = .033). Conclusions: Our data suggest that AI is common among patients with asthma and highlights that the risk of AI is associated with both high-dose ICS therapy and intermittent treatment courses of OCS. © 2022 American Academy of Allergy, Asthma & Immunology","Adrenal insufficiency; Corticosteroids; Cortisol; Inhaled glucocorticoids; Oral glucocorticoids; Synacthen","Administration, Inhalation; Adrenal Cortex Hormones; Adrenal Insufficiency; Anti-Asthmatic Agents; Asthma; Fluticasone; Glucocorticoids; Humans; Hydrocortisone; Prednisolone; fluticasone propionate; hydrocortisone; prednisolone; antiasthmatic agent; corticosteroid; fluticasone; glucocorticoid; hydrocortisone; prednisolone; adrenal function; adrenal insufficiency; adult; Article; asthma; clinical assessment; cohort analysis; controlled study; disease burden; disease control; disease severity; drug exposure; drug safety; female; high risk patient; human; hydrocortisone blood level; maintenance therapy; major clinical study; male; medical record; middle aged; patient coding; patient monitoring; patient participation; patient selection; prediction; prevalence; quality of life; randomized controlled trial; risk factor; undiagnosed disease; adrenal insufficiency; asthma; inhalational drug administration","","fluticasone propionate, 80474-14-2; hydrocortisone, 50-23-7; prednisolone, 50-24-8; fluticasone, 90566-53-3; Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ; Fluticasone, ; Glucocorticoids, ; Hydrocortisone, ; Prednisolone, ","Diskus","","Mater Private Hospital; GlaxoSmithKline, GSK; Novartis; European Respiratory Society, ERS; Health Research Board, HRB, (ECSA-2020-001); Royal College of Surgeons in Ireland, RCSI; Irish Endocrine Society, IES","Funding text 1: Conflicts of interest: M. W. O'Reilly received the Health Research Board Emerging Clinician Scientist Award 2020 and speaking fees from Sanofi, Bayer, and the Mater Private Hospital. R. W. Costello received funding from Health Research Board, GlaxoSmithKline, and Cross-Border Healthcare Intervention Trials in Ireland Network; speaker fees from GlaxoSmithKline, Astra Zeneca, and Novartis; holds patents on methods to quantify adherence, methods to assess adherence, and methods to predict exacerbations; and serves as the current Education Chair of the European Respiratory Society. The rest of the authors declare that they have no relevant conflicts of interest. Funding was provided by GlaxoSmithKline, Ireland to the Royal College of Surgeons in Ireland to support the INCA SUN trial. J. Martin-Grace receives funding from her fellowship in the Royal College of Surgeons in Ireland/Beacon Hospital Strategic Academic Recruitment programme and the Irish Endocrine Society Clinical Science Award. M. W. O'Reilly is funded by an HRB Emerging Clinician Scientist Award (ECSA-2020-001).; Funding text 2: Funding was provided by GlaxoSmithKline, Ireland to the Royal College of Surgeons in Ireland to support the INCA SUN trial. J. Martin-Grace receives funding from her fellowship in the Royal College of Surgeons in Ireland/Beacon Hospital Strategic Academic Recruitment programme and the Irish Endocrine Society Clinical Science Award. M. W. O'Reilly is funded by an HRB Emerging Clinician Scientist Award (ECSA-2020-001). ","Arlt W., Allolio B., Adrenal insufficiency, Lancet, 361, pp. 1881-1893, (2003); Broersen L.H., Pereira A.M., Jorgensen J.O., Dekkers O.M., Adrenal insufficiency in corticosteroids use: systematic review and meta-analysis, J Clin Endocrinol Metab, 100, pp. 2171-2180, (2015); Brodlie M., Cunningham S., Compliance with inhaled corticosteroids is important when considering adrenal suppression, Arch Dis Childhood, 92, (2007); Mebrahtu T.F., Morgan A.W., Keeley A., Baxter P.D., Stewart P.M., Pujades-Rodriguez M., Dose dependency of iatrogenic glucocorticoid excess and adrenal insufficiency and mortality: a cohort study in England, J Clin Endocrinol Metab, 104, pp. 3757-3767, (2019); Wei L., MacDonald T.M., Walker B.R., Taking glucocorticoids by prescription is associated with subsequent cardiovascular disease, Ann Intern Med, 141, pp. 764-770, (2004); Todd G.R.G., Acerini C.L., Ross-Russell R., Zahra S., Warner J.T., McCance D., Survey of adrenal crisis associated with inhaled corticosteroids in the United Kingdom, Arch Dis Childhood, 87, pp. 457-461, (2002); Drake A.J., Howells R.J., Shield J.P., Prendiville A., Ward P.S., Crowne E.C., Symptomatic adrenal insufficiency presenting with hypoglycaemia in children with asthma receiving high dose inhaled fluticasone propionate, BMJ, 324, pp. 1081-1082, (2002); Donaldson M.D., Morrison C., Lees C., McNeill E., Howatson A.G., Paton J.Y., Et al., Fatal and near-fatal encephalopathy with hyponatraemia in two siblings with fluticasone-induced adrenal suppression, Acta Paediatr, 96, pp. 769-772, (2007); Woods C.P., Argese N., Chapman M., Boot C., Webster R., Dabhi V., Et al., Adrenal suppression in patients taking inhaled glucocorticoids is highly prevalent and management can be guided by morning cortisol, Eur J Endocrinol, 173, pp. 633-642, (2015); Taylor T.E., Zigel Y., Egan C., Hughes F., Costello R.W., Reilly R.B., Objective assessment of patient inhaler user technique using an audio-based classification approach, Sci Rep, 8, (2018); Taylor T.E., Zigel Y., De Looze C., Sulaiman I., Costello R.W., Reilly R.B., Advances in audio-based systems to monitor patient adherence and inhaler drug delivery, Chest, 153, pp. 710-722, (2018); Taylor T.E., LacalleMuls H., Costello R.W., Reilly R.B., Estimation of inhalation flow profile using audio-based methods to assess inhaler medication adherence, PLoS One, 13, (2018); D'Arcy S., MacHale E., Seheult J., Holmes M.S., Hughes C., Sulaiman I., Et al., A method to assess adherence in inhaler use through analysis of acoustic recordings of inhaler events, PLoS One, 9, (2014); Mokoka M.C., Lombard L., MacHale E.M., Walsh J., Cushen B., Sulaiman I., Et al., In patients with severe uncontrolled asthma, does knowledge of adherence and inhaler technique using electronic monitoring improve clinical decision making? A protocol for a randomised controlled trial, BMJ Open, 7, (2017); Liu D., Ahmet A., Ward L., Krishnamoorthy P., Mandelcorn E.D., Leigh R., Et al., A practical guide to the monitoring and management of the complications of systemic corticosteroid therapy, Allergy Asthma Clin Immunol, 9, (2013); Daley-Yates P.T., Inhaled corticosteroids: potency, dose equivalence and therapeutic index, Br J Clin Pharmacol, 80, pp. 372-380, (2015); Derendorf H., Nave R., Drollmann A., Cerasoli F., Wurst W., Relevance of pharmacokinetics and pharmacodynamics of inhaled corticosteroids to asthma, Eur Respir J, 28, pp. 1042-1050, (2006); Wilson A., Lipworth B., Systemic dose–response relationships with oral and inhaled corticosteroid in asthmatics, Thorax, 52, (1997); Wilson A.M., Lipworth B.J., Short-term dose-response relationships for the relative systemic effects of oral prednisolone and inhaled fluticasone in asthmatic adults, Br J Clin Pharmacol, 48, pp. 579-585, (1999); Lipworth B.J., Systemic adverse effects of inhaled corticosteroid therapy: a systematic review and meta-analysis, Arch Intern Med, 159, pp. 941-955, (1999); Dineen R., Mohamed A., Gunness A., Rakovac A., Cullen E., Barnwell N., Et al., Outcomes of the short Synacthen test: what is the role of the 60 min sample in clinical practice?, Postgrad Med J, 96, pp. 67-72, (2020); Sbardella E., Isidori A.M., Woods C.P., Argese N., Tomlinson J.W., Shine B., Et al., Baseline morning cortisol level as a predictor of pituitary-adrenal reserve: a comparison across three assays, Clin Endocrinol (Oxf), 86, pp. 177-184, (2017); Nanzer A.M., Chowdhury A., Raheem A., Roxas C., Fernandes M., Thomson L., Et al., Prevalence and recovery of adrenal insufficiency in steroid-dependent asthma patients receiving biologic therapy, Eur Respir J, 56, (2020); Borresen S.W., Klose M., Locht H., Laursen T., Jensen B., Hilsted L., Et al., Predictive baseline morning P-cortisol levels for the response to a Synacthen test in prednisolone treated patients, Endocrine Abstr, 49, (2017); Nathan R.A., Sorkness C.A., Kosinski M., Schatz M., Li J.T., Marcus P., Et al., Development of the asthma control test: a survey for assessing asthma control, J Allergy Clin Immunol, 113, pp. 59-65, (2004); Juniper E.F., Buist A.S., Cox F.M., Ferrie P.J., King D.R., Validation of a standardized version of the Asthma Quality of Life Questionnaire, Chest, 115, pp. 1265-1270, (1999); EuroQol—a new facility for the measurement of health-related quality of life, Health Policy, 16, pp. 199-208, (1990); Salehmohamed M.R., Griffin M., Branigan T., Cuesta M., Thompson C.J., Patients treated with immunosuppressive steroids are less aware of sick-day rules than those on endocrine replacement therapy and may be at greater risk of adrenal crisis, Ir J Med Sci, 187, pp. 69-74, (2018); Prete A., Bancos I., Glucocorticoid induced adrenal insufficiency, BMJ, 374, (2021); Aulinas A., Webb S.M., Health-related quality of life in primary and secondary adrenal insufficiency, Expert Rev Pharmacoecon Outcomes Res, 14, pp. 873-888, (2014); Sherlock M., Ayuk J., Tomlinson J.W., Toogood A.A., Aragon-Alonso A., Sheppard M.C., Et al., Mortality in patients with pituitary disease, Endocr Rev, 31, pp. 301-342, (2010); Bergthorsdottir R., Leonsson-Zachrisson M., Oden A., Johannsson G., Premature mortality in patients with Addison's disease: a population-based study, J Clin Endocrinol Metab, 91, pp. 4849-4853, (2006); Dima A.L., Hernandez G., Cunillera O., Ferrer M., de Bruin M., Asthma inhaler adherence determinants in adults: systematic review of observational data, Eur Respir J, 45, pp. 994-1018, (2015); Erskine D., Simpson H., Exogenous steroids treatment in adults. Adrenal insufficiency and adrenal crisis—who is at risk and how should they be managed safely; Simpson H., Tomlinson J., Wass J., Dean J., Arlt W., Guidance for the prevention and emergency management of adult patients with adrenal insufficiency, Clin Med, 20, pp. 371-378, (2020); Woodcock T., Barker P., Daniel S., Fletcher S., Wass J.A.H., Tomlinson J.W., Et al., Guidelines for the management of glucocorticoids during the peri-operative period for patients with adrenal insufficiency: guidelines from the Association of Anaesthetists, the Royal College of Physicians and the Society for Endocrinology UK, Anaesthesia, 75, pp. 654-663, (2020); Schatz M., Kosinski M., Yarlas A.S., Hanlon J., Watson M.E., Jhingran P., The minimally important difference of the Asthma Control Test, J Allergy Clin Immunol, 124, pp. 719-723.e1, (2009)","M. Sherlock; Department of Endocrinology, Beaumont Hospital/Royal College of Surgeons in Ireland Beaumont Hospital, Dublin 9, Ireland; email: marksherlock@rcsi.ie","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","35697207","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85133582978"
"Ramadan M.N.A.; Ali M.A.H.; Khoo S.Y.; Alkhedher M.; Alherbawi M.","Ramadan, Montaser N.A. (58067779100); Ali, Mohammed A.H. (57199279259); Khoo, Shin Yee (55845633900); Alkhedher, Mohammad (57220976699); Alherbawi, Mohammad (55453951100)","58067779100; 57199279259; 55845633900; 57220976699; 55453951100","Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment","2024","Ecotoxicology and Environmental Safety","283","","116856","","","","12","10.1016/j.ecoenv.2024.116856","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201432385&doi=10.1016%2fj.ecoenv.2024.116856&partnerID=40&md5=5d90bf54db2994ea4f089ee65402b33f","Mechanical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Mechanical and Industrial Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates; Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar","Ramadan M.N.A., Mechanical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Ali M.A.H., Mechanical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Khoo S.Y., Mechanical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; Alkhedher M., Mechanical and Industrial Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates; Alherbawi M., Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar","Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings. © 2024 The Authors","Air Pollution; Chrome Plating; Industrial Environment; IoT; Long short-term memory (LSTM); Random Forest and Linear regression; Real-time analysis","Air Pollutants; Air Pollution; Artificial Intelligence; Environmental Monitoring; Forecasting; Industry; Internet of Things; Particulate Matter; Volatile Organic Compounds; ammonia; carbon dioxide; carbon monoxide; chromium; formaldehyde; hydrogen sulfide; methane; nitrogen dioxide; ozone; sulfur dioxide; trioxygen; unclassified drug; volatile organic compound; volatile organic compound; artificial intelligence; atmospheric pollution; forecasting method; industrial emission; Internet; occupational exposure; pollution monitoring; real time; regression analysis; air pollution; air quality; Article; asthma; cost effectiveness analysis; decision making; deep learning; environmental monitoring; forecasting; humidity; industry; internet of things; long short term memory network; lung cancer; machine learning; mathematical model; particulate matter; particulate matter 10; particulate matter 2.5; policy; pollution control; respiratory tract disease; temperature; workplace; air pollutant; artificial intelligence; environmental monitoring; industry; internet of things; particulate matter; procedures","","ammonia, 14798-03-9, 51847-23-5, 7664-41-7; carbon dioxide, 124-38-9, 58561-67-4; carbon monoxide, 630-08-0; chromium, 16065-83-1, 7440-47-3, 14092-98-9; formaldehyde, 50-00-0; hydrogen sulfide, 15035-72-0, 7783-06-4; methane, 74-82-8; nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; Air Pollutants, ; Particulate Matter, ; Volatile Organic Compounds, ","","","Universiti Malaya, UM; Ministry of Higher Education, Malaysia, MOHE, (FRGS/1/2023/TK10/UM/02/3); Ministry of Higher Education, Malaysia, MOHE","The authors would like to thank Universiti Malaya (UM) and Ministry of Higher Education (MOHE) for providing the research grant and facilities for this project. This research is supported by Universiti Malaya (UM) and Ministry of Higher Education (MOHE) under FRGS Research Grant No. FRGS/1/2023/TK10/UM/02/3.","Agrawal M., Agrawal S.B., Effects of air pollution on plant diversity, Environmental Pollution and Plant Responses, pp. 137-152, (2023); Alekhya K., Sravya P.D., Naik N.C., LakshmiNarayana B.J., pp. 1-5; Ansari M., Alam M., An intelligent IoT-cloud-based air pollution forecasting model using univariate time-series analysis, Arab J. Sci. Eng., 49, 3, pp. 3135-3162, (2024); Bertrand J.-M., Meleux F., Ung A., Descombes G., and A. Colette, Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques, Atmos. Chem. 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Interact., 19, (2023); Martinez A., Hernandez-Rodriguez E., Hernandez L., Schalm O., Gonzalez-Rivero R.A., Alejo-Sanchez D., Design of a low-cost system for the measurement of variables associated with air quality, IEEE Embed Syst. Lett., 15, 2, pp. 105-108, (2022); Mendez M., Merayo M.G., Nunez M., Machine learning algorithms to forecast air quality: a survey, Artif. Intell. Rev., 56, 9, pp. 10031-10066, (2023); Merlo A., Leonard G., Magnetron sputtering vs. Electrodeposition for hard chrome coatings: a comparison of environmental and economic performances, Materials, 14, 14, (2021); Michalik J., Machaczka O., Jirik V., (2022); Naik U.U., Salgaokar S.R., Jambhale S., (2023); Parri L., Et al., A distributed IoT air quality measurement system for high-risk workplace safety enhancement, Sensors, 23, 11, (2023); Pilat M.J., Pegnam R.C., Particle emissions from chrome plating, Aerosol Sci. 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Med, 217, (2023); Singh A.A., Eram R., Agrawal M., Agrawal S.B., Air pollution: sources and its effects on humans and plants, Int. J. Plant Environ., 8, 1, pp. 10-24, (2022); Singh A., Singh K.K., An overview of the environmental and health consequences of air pollution, Iran. J. Energy Environ., 13, 3, pp. 231-237, (2022); Tanasa I., Cazacu M., Sluser B., Air quality integrated assessment: Environmental impacts, risks and human health hazards, Appl. Sci., 13, 2, (2023); Vajs I., Drajic D., Cica Z., Data-driven machine learning calibration propagation in a hybrid sensor network for air quality monitoring, Sensors, 23, 5, (2023); Veerani M., Dwivedi S., Das J., Gnana D., Air quality monitoring system, Proc. Adv. Electron. Commun. Eng., (2022); Velez-Guerrero A.C., Callejas-Cuervo M., Alarcon-Aldana A.C., The Evolution of air quality monitoring: measurement techniques and instruments, J. Hunan Univ. Nat. Sci., 50, 5, (2023); Wei Y., Jang-Jaccard J., Xu W., Sabrina F., Camtepe S., Boulic M., LSTM-autoencoder-based anomaly detection for indoor air quality time-series data, IEEE Sens J., 23, 4, pp. 3787-3800, (2023); Zaidan M.A., Et al., Intelligent air pollution sensors calibration for extreme events and drifts monitoring, IEEE Trans. Ind. Inf., 19, 2, pp. 1366-1379, (2022)","M.A.H. Ali; Mechanical Engineering Department, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia; email: hashem@um.edu.my","","Academic Press","","","","","","01476513","","EESAD","39151373","English","Ecotoxicol. Environ. Saf.","Article","Final","","Scopus","2-s2.0-85201432385"
"Yang F.; Kong J.; Zong Y.; Li Z.; Lyu M.; Li W.; Li W.; Zhu H.; Chen S.; Zhao X.; Wang J.","Yang, Fan (57007495500); Kong, Jingwei (57219434874); Zong, Yuhan (57226029706); Li, Zhuqing (57218164181); Lyu, Mingsheng (57218937637); Li, Wanyang (57710487500); Li, Wenle (57710995000); Zhu, Haoyue (57222016383); Chen, Shunqi (57709732000); Zhao, Xiaoshan (55595516700); Wang, Ji (54797082000)","57007495500; 57219434874; 57226029706; 57218164181; 57218937637; 57710487500; 57710995000; 57222016383; 57709732000; 55595516700; 54797082000","Autophagy-Related Genes Are Involved in the Progression and Prognosis of Asthma and Regulate the Immune Microenvironment","2022","Frontiers in Immunology","13","","897835","","","","9","10.3389/fimmu.2022.897835","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130762466&doi=10.3389%2ffimmu.2022.897835&partnerID=40&md5=0026281e4bf51959caa5d2deeb699df5","College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China; National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Center of Respiratory, Beijing University of Chinese Medicine Affiliated Dongzhimen Hospital, Beijing, China; Department of Respiratory, The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China; Department of Clinical Nutrition, Chinese Academy of Medical Sciences - Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China; Beijing Hospital of Traditional Chinese Medicine (TCM), Capital Medical University, Beijing, China; School of Chinese Medicine, Southern Medical University, Guangzhou, China","Yang F., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Kong J., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Zong Y., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Li Z., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Lyu M., Center of Respiratory, Beijing University of Chinese Medicine Affiliated Dongzhimen Hospital, Beijing, China, Department of Respiratory, The Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing, China; Li W., Department of Clinical Nutrition, Chinese Academy of Medical Sciences - Peking Union Medical College, Peking Union Medical College Hospital, Beijing, China; Li W., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Zhu H., Beijing Hospital of Traditional Chinese Medicine (TCM), Capital Medical University, Beijing, China; Chen S., College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; Zhao X., School of Chinese Medicine, Southern Medical University, Guangzhou, China; Wang J., National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China","Background: Autophagy has been proven to play an important role in the pathogenesis of asthma and the regulation of the airway epithelial immune microenvironment. However, a systematic analysis of the clinical importance of autophagy-related genes (ARGs) regulating the immune microenvironment in patients with asthma remains lacking. Methods: Clustering based on the k-means unsupervised clustering method was performed to identify autophagy-related subtypes in asthma. ARG-related diagnostic markers in low-autophagy subtypes were screened, the infiltration of immune cells in the airway epithelium was evaluated by the CIBERSORT, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. On the basis of the expression of ARGs and combined with asthma control, a risk prediction model was established and verified by experiments. Results: A total of 66 differentially expressed ARGs and 2 subtypes were identified between mild to moderate and severe asthma. Significant differences were observed in asthma control and FEV1 reversibility between the two subtypes, and the low-autophagy subtype was closely associated with severe asthma, energy metabolism, and hormone metabolism. The autophagy gene SERPINB10 was identified as a diagnostic marker and was related to the infiltration of immune cells, such as activated mast cells and neutrophils. Combined with asthma control, a risk prediction model was constructed, the expression of five risk genes was supported by animal experiments, was established for ARGs related to the prediction model. Conclusion: Autophagy plays a crucial role in the diversity and complexity of the asthma immune microenvironment and has clinical value in treatment response and prognosis. Copyright © 2022 Yang, Kong, Zong, Li, Lyu, Li, Li, Zhu, Chen, Zhao and Wang.","asthma; autophagy-related genes; diagnostic model; immune cell; prognosis","Animals; Asthma; Autophagy; Autophagy-Related Protein 5; Epithelium; Humans; Prognosis; Serpins; 15 hydroxy 11alpha,9alpha epoxymethanoprosta 5,13 dienoic acid; autophagy related protein; calgranulin B; fluprostenol; iloprost; long untranslated RNA; membrane cofactor protein; picotamide; prostaglandin E2; autophagy related protein 5; serine proteinase inhibitor; SERPINB10 protein, human; ACBD5 gene; animal experiment; animal model; animal tissue; Article; asthma; autophagy (cellular); CD8+ T lymphocyte; cell infiltration; controlled study; differential gene expression; disease exacerbation; energy metabolism; female; forced expiratory volume; functional enrichment analysis; gene; gene expression regulation; gene ontology; gene set enrichment analysis; hormone metabolism; human; human cell; immunocompetent cell; immunohistochemistry; KEGG; least absolute shrinkage and selection operator; male; MAP2K7 gene; mast cell; mouse; neutrophil; nonhuman; prognosis; PTK6 gene; real time reverse transcription polymerase chain reaction; recursive feature elimination; SERPINB10 gene; support vector machine; unsupervised machine learning; animal; asthma; autophagy; epithelium; genetics; metabolism; prognosis","","15 hydroxy 11alpha,9alpha epoxymethanoprosta 5,13 dienoic acid, 56985-40-1; fluprostenol, 40666-16-8, 55028-71-2; iloprost, 78919-13-8, 82889-99-4; picotamide, 32828-81-2; prostaglandin E2, 363-24-6; Autophagy-Related Protein 5, ; SERPINB10 protein, human, ; Serpins, ","","","Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine, (ZYYCXTD-C-202001); National Natural Science Foundation of China, NSFC, (81973715, 82174243); National Natural Science Foundation of China, NSFC; Natural Science Foundation of Beijing Municipality, (7202110); Natural Science Foundation of Beijing Municipality; National Key Research and Development Program of China, NKRDPC, (2020YFC2003100, 2020YFC2003101); National Key Research and Development Program of China, NKRDPC","This work was supported by the National Key R&D Program of China (2020YFC2003100, 2020YFC2003101), National Natural Science Foundation of China (No. 82174243, No. 81973715), General project of Beijing Natural Science Foundation (No. 7202110), Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No. ZYYCXTD-C-202001). 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Zhao X., Sun J., Yuan Y., Lin S., Lin J., Mei X., Zinc Promotes Microglial Autophagy Through Nlrp3 Inflammasome Inactivation Via Xist/Mir-374a-5p Axis in Spinal Cord Injury, Neurochem Res, 47, 2, (2021); Theofani E., Semitekolou M., Samitas K., Mais A., Galani I.E., Triantafyllia V., Et al., Tfeb Signaling Attenuates Nlrp3-Driven Inflammatory Responses in Severe Asthma, Allergy, (2022); Reed M., Morris S.H., Jang S., Mukherjee S., Yue Z., Lukacs N.W., Autophagy-Inducing Protein Beclin-1 in Dendritic Cells Regulates Cd4 T Cell Responses and Disease Severity During Respiratory Syncytial Virus Infection, J Immunol (Baltimore Md 1950), 191, 5, (2013); Liu H.X., Yan H.Y., Qu W., Wen X., Hou L.F., Zhao W.H., Et al., Inhibition of Thymocyte Autophagy-Associated Cd4(+)T Thymopoiesis Is Involved in Asthma Susceptibility in Mice Exposed to Caffeine Prenatally, Arch Toxicol, 93, 5, (2019); Al-Ramli W., Prefontaine D., Chouiali F., Martin J.G., Olivenstein R., Lamiere C., Et al., T(H)17-Associated Cytokines (Il-17a and Il-17f) in Severe Asthma, J Allergy Clin Immunol, 123, 5, (2009); Mo Y.Q., Zhang K., Feng Y.C., Yi L.L., Jiang Y.X., Wu W.L., Et al., Epithelial Serpinb10, a Novel Marker of Airway Eosinophilia in Asthma, Contributes to Allergic Airway Inflammation, Am J Physiol-Lung Cell Mol Physiol, 316, 1, (2019); Mo Y.Q., Ye L., Cai H., Zhu G.P., Wang J., Zhu M.C., Et al., Serpinb10 Contributes to Asthma by Inhibiting the Apoptosis of Allergenic Th2 Cells, Respir Res, 22, 1, (2021); Subramanian H., Hashem T., Bahal D., Kammala A.K., Thaxton K., Das R., Ruxolitinib Ameliorates Airway Hyperresponsiveness and Lung Inflammation in a Corticosteroid-Resistant Murine Model of Severe Asthma, Front Immunol, 12, (2021); Guo Y., Gao F., Wang X., Pan Z.Z., Wang Q., Xu S.Y., Et al., Spontaneous Formation of Neutrophil Extracellular Traps Is Associated With Autophagy, Sci Rep, 11, 1, (2021); Chen X., Luo Y., Wang M., Sun L., Huang K., Li Y., Et al., Wuhu Decoction Regulates Dendritic Cell Autophagy in the Treatment of Respiratory Syncytial Virus (Rsv)-Induced Mouse Asthma by Ampk/Ulk1 Signaling Pathway, Med Sci Monit Int Med J Exp Clin Res, 25, (2019); Mulugeta T., Ayele T., Zeleke G., Tesfay G., Asthma Control and Its Predictors in Ethiopia: Systematic Review and Meta-Analysis, PloS One, 17, 1, (2022); Quoc Q.L., Choi Y., Bich T.C.T., Yang E.M., Shin Y.S., Park H.S., S100a9 in Adult Asthmatic Patients: A Biomarker for Neutrophilic Asthma, Exp Mol Med, 53, 7, (2021); Zayed H., Novel Comprehensive Bioinformatics Approaches to Determine the Molecular Genetic Susceptibility Profile of Moderate and Severe Asthma, Int J Mol Sci, 21, 11, (2020); Tsai Y.G., Wen Y.S., Wang J.Y., Yang K.D., Sun H.L., Liou J.H., Et al., Complement Regulatory Protein Cd46 Induces Autophagy Against Oxidative Stress-Mediated Apoptosis in Normal and Asthmatic Airway Epithelium, Sci Rep, 8, (2018); Safholm J., Abma W., Liu J.L., Balgoma D., Fauland A., Kolmert J., Et al., Prostaglandin D-2 Inhibits Mediator Release and Antigen Induced Bronchoconstriction in the Guinea Pig Trachea by Activation of Dp1 Receptors, Eur J Pharmacol, 907, (2021); Urbano A., Plaza J., Turon S., Pujol A., Costa-Farre C., Marco A., Et al., Transgenic Mice Overexpressing the Pge(2) Receptor Ep(2) on Mast Cells Exhibit a Protective Phenotype in a Model of Allergic Asthma, Allergy, 76, 10, (2021)","J. Wang; National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China; email: doctorwang2009@126.com; X. Zhao; School of Chinese Medicine, Southern Medical University, Guangzhou, China; email: zhaoxs0609@163.com","","Frontiers Media S.A.","","","","","","16643224","","","35619697","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130762466"
"Hoffman J.S.; Viswanath V.K.; Tian C.; Ding X.; Thompson M.J.; Larson E.C.; Patel S.N.; Wang E.J.","Hoffman, Jason S. (57223826585); Viswanath, Varun K. (57382589600); Tian, Caiwei (58608721900); Ding, Xinyi (57205168723); Thompson, Matthew J. (57202482305); Larson, Eric C. (55636314693); Patel, Shwetak N. (8450420300); Wang, Edward J. (56963164200)","57223826585; 57382589600; 58608721900; 57205168723; 57202482305; 55636314693; 8450420300; 56963164200","Smartphone camera oximetry in an induced hypoxemia study","2022","npj Digital Medicine","5","1","146","","","","13","10.1038/s41746-022-00665-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138258825&doi=10.1038%2fs41746-022-00665-y&partnerID=40&md5=081f7378b2c5659bc34edd4c584222ce","Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States; The Design Lab, University of California San Diego, La Jolla, CA, United States; Department of Computer Science, Southern Methodist University, Dallas, TX, United States; Department of Family Medicine, University of Washington, Seattle, WA, United States; Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States","Hoffman J.S., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States; Viswanath V.K., Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States, The Design Lab, University of California San Diego, La Jolla, CA, United States; Tian C., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States; Ding X., Department of Computer Science, Southern Methodist University, Dallas, TX, United States; Thompson M.J., Department of Family Medicine, University of Washington, Seattle, WA, United States; Larson E.C., Department of Computer Science, Southern Methodist University, Dallas, TX, United States; Patel S.N., Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States, Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States; Wang E.J., Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States, The Design Lab, University of California San Diego, La Jolla, CA, United States","Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO2) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO2 while identifying positive cases of low SpO2 < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work. © 2022, The Author(s).","","Blood; Cameras; COVID-19; Deep learning; Diagnosis; mHealth; Noninvasive medical procedures; Open source software; Oximeters; Blood oxygen saturation; Camera-based; Clinical development; Hypoxemia; Leading indicators; Medical conditions; Oximetry; Pulse oximeters; Smart-phone cameras; Software updates; adult; Article; blood oxygen tension; convolutional neural network; deep learning; female; fraction of inspired oxygen; human; human experiment; hypoxemia; leave one out cross validation; male; normal human; pulse oximetry; young adult; Smartphones","","","Radical-7, Masimo","Masimo","University of Washington, UW","The authors thank Clinimark for conducting the study. The authors thank the University of Washington for gift funding, which supported the study.","Carni D.L., Grimaldi D., Sciammarella P.F., Lamonaca F., Spagnuolo V., Setting-up of ppg scaling factors for spo2% evaluation by smartphone, 2016 IEEE International Symposium on Medical Measurements and Applications (Memea), pp. 1-5; Bui N., Et al., Smartphone-based spo2 measurement by exploiting wavelengths separation and chromophore compensation, ACM Trans. Sensor Netw., 16, (2020); Ding X., Nassehi D., Larson E.C., Measuring oxygen saturation with smartphone cameras using convolutional neural networks, IEEE J. Biomed. Health Informatics, 23, (2018); Mendelson Y., Ochs B.D., Noninvasive pulse oximetry utilizing skin reflectance photoplethysmography, IEEE Trans. Biomed. Eng., 35, (1988); Tayfur I., Afacan M.A., Reliability of smartphone measurements of vital parameters: A prospective study using a reference method, Am. J. Emerg. 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Optics Expr., 12, (2021); Kateu F., Jakllari G., Chaput E., Chaput, Smartphox: Smartphone-based pulse oximetry using a meta-region of interest, 2022 IEEE International Conference on Pervasive Computing and Communications (Percom) (IEEE, pp. 130-140, (2022); Luks A.M., Swenson E.R., Pulse oximetry for monitoring patients with covid-19 at home. potential pitfalls and practical guidance, Ann. Am. 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Methods Med. Res., 8, (1999)","J.S. Hoffman; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, United States; email: jasonhof@cs.washington.edu","","Nature Research","","","","","","23986352","","","","English","npj Digit. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138258825"
"Pyrros A.; Fernandez J.R.; Borstelmann S.M.; Flanders A.; Wenzke D.; Hart E.; Horowitz J.M.; Nikolaidis P.; Willis M.; Chen A.; Cole P.; Siddiqui N.; Muzaffar M.; Muzaffar N.; McVean J.; Menchaca M.; Katsaggelos A.K.; Koyejo S.; Galanter W.","Pyrros, Ayis (6507737642); Fernandez, Jorge Rodriguez (59272287000); Borstelmann, Stephen M. (57212140126); Flanders, Adam (7006675016); Wenzke, Daniel (55434067000); Hart, Eric (7102601833); Horowitz, Jeanne M. (36876183100); Nikolaidis, Paul (7004366925); Willis, Melinda (57224577634); Chen, Andrew (57224578037); Cole, Patrick (57224201800); Siddiqui, Nasir (36182478400); Muzaffar, Momin (59270243200); Muzaffar, Nadir (57224562840); McVean, Jennifer (24437334300); Menchaca, Martha (57203723166); Katsaggelos, Aggelos K. (7102711302); Koyejo, Sanmi (57846623900); Galanter, William (6603094520)","6507737642; 59272287000; 57212140126; 7006675016; 55434067000; 7102601833; 36876183100; 7004366925; 57224577634; 57224578037; 57224201800; 36182478400; 59270243200; 57224562840; 24437334300; 57203723166; 7102711302; 57846623900; 6603094520","Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19","2022","PLOS Digital Health","1","8","e0000057","","","","10","10.1371/journal.pdig.0000057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143572459&doi=10.1371%2fjournal.pdig.0000057&partnerID=40&md5=f802e5a7915b5d6e689f5ac5cc849844","Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; Department of Neurology, University of Illinois at Chicago, Chicago, IL, United States; Department of Radiology, University of Central Florida, Orlando, FL, United States; Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, United States; Department of Radiology, NorthShore University HealthSystem, Evanston, IL, United States; Department of Radiology, Northwestern University, Chicago, IL, United States; Department of Computer Science, University of Illinois at Urbana- Champaign, Urbana-Champaign, IL, United States; Medtronic, Minneapolis, MN, United States; Department of Radiology, University of Illinois at Chicago, Chicago, IL, United States; Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States; Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States","Pyrros A., Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; Fernandez J.R., Department of Neurology, University of Illinois at Chicago, Chicago, IL, United States; Borstelmann S.M., Department of Radiology, University of Central Florida, Orlando, FL, United States; Flanders A., Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, United States; Wenzke D., Department of Radiology, NorthShore University HealthSystem, Evanston, IL, United States; Hart E., Department of Radiology, Northwestern University, Chicago, IL, United States; Horowitz J.M., Department of Radiology, Northwestern University, Chicago, IL, United States; Nikolaidis P., Department of Radiology, Northwestern University, Chicago, IL, United States; Willis M., Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; Chen A., Department of Computer Science, University of Illinois at Urbana- Champaign, Urbana-Champaign, IL, United States; Cole P., Department of Computer Science, University of Illinois at Urbana- Champaign, Urbana-Champaign, IL, United States; Siddiqui N., Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; Muzaffar M., Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; Muzaffar N., Department of Radiology, Duly Health and Care, Hinsdale, IL, United States; McVean J., Medtronic, Minneapolis, MN, United States; Menchaca M., Department of Radiology, University of Illinois at Chicago, Chicago, IL, United States; Katsaggelos A.K., Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States; Koyejo S., Department of Computer Science, University of Illinois at Urbana- Champaign, Urbana-Champaign, IL, United States; Galanter W., Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States","We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model’s performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85–0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79–0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making. © 2022 Pyrros et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","","","","","","National Institute of Biomedical Imaging and Bioengineering, NIBIB; National Institutes of Health, NIH, (75N92020C00008, 75N92020C00021); National Institutes of Health, NIH; Center for Clinical and Translational Science, University of Illinois at Chicago, CCTS, UIC, (ULTR002003); Center for Clinical and Translational Science, University of Illinois at Chicago, CCTS, UIC","AP, NS and SK were funded by the Medical Imaging Data Resource Center, which is supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under contracts 75N92020C00008 and 75N92020C00021. JR-F and WG received funding from the University of Illinois at Chicago Center for Clinical and Translational Science (CCTS) award ULTR002003. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Brady AP, Bello JA, Derchi LE, Fuchsjager M, Goergen S, Krestin GP, Et al., Radiology in the era of value-based healthcare: A multi-society expert statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA, Radiology, 298, 3, pp. 486-491, (2021); Rubin GD., Costing in radiology and health care: Rationale, relativity, rudiments, and realities, Radiology, 282, 2, pp. 333-347, (2017); Juhnke C, Bethge S, Muhlbacher AC., A review on methods of risk adjustment and their use in integrated healthcare systems, International Journal of Integrated Care, 16, 4, (2016); King JT, Yoon JS, Rentsch CT, Tate JP, Park LS, Kidwai-Khan F, Et al., Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (Vaco) Index, PLOS ONE, 15, 11, (2020); Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 395, 10223, pp. 497-506, (2020); 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Tallam H, Elton DC, Lee S, Wakim P, Pickhardt PJ, Summers RM., Fully automated abdominal ct biomarkers for type 2 diabetes using deep learning, Radiology, (2022); Gupta RK, Marks M, Samuels THA, Luintel A, Rampling T, Chowdhury H, Et al., Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study, European Respiratory Journal, 56, 6, (2020)","A. Pyrros; Department of Radiology, Duly Health and Care, Hinsdale, United States; email: ayis@ayis.org","","Public Library of Science","","","","","","27673170","","","","English","PLOS Digit. Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85143572459"
"Mehrotra R.; Agrawal R.; Ansari M.A.","Mehrotra, Rajat (57216484704); Agrawal, Rajeev (57192985539); Ansari, M.A. (57211649852)","57216484704; 57192985539; 57211649852","Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography","2022","Multimedia Tools and Applications","81","6","","7625","7649","24","12","10.1007/s11042-021-11748-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123849496&doi=10.1007%2fs11042-021-11748-5&partnerID=40&md5=a5c3fd5cb0e5cfc234083b825830e829","Department of Electrical & Electronics Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India; Department of Electronics & Communication Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India; Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Gr. Noida, India","Mehrotra R., Department of Electrical & Electronics Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India; Agrawal R., Department of Electronics & Communication Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India; Ansari M.A., Department of Electrical Engineering, School of Engineering, Gautam Buddha University, Gr. Noida, India","Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual’s health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","Chest x-ray (CXR); Chronic pulmonary disorders; CNN; COVID-19; Deep learning; Pneumonia","Biological organs; Convolutional neural networks; Deep learning; Diagnosis; Image classification; Image segmentation; Pulmonary diseases; X ray radiography; 'current; Chest x-ray; Chest X-ray image; Chest x-rays; Chronic pulmonary disorder; Classifieds; CNN; COVID-19; Deep learning; Pneumonia; Convolution","","","","","","","Apostolopoulos I.D., Mpesiana T.A., Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks, Phys Eng Sci Med, (2020); Baker J.A., Rosen E.L., Lo J.Y., Gimenez E.I., Walsh R., Soo M.S., Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion, Am J Roentgenol, 181, 4, pp. 1083-1088, (2003); Barrientos F., Roman-Gonzalez A., Barrientos R., Solis L., Alva A., Correa M., Oberhelman R., (2016); Barrientos R., Roman-Gonzalez A., Barrientos F., Solis L., Correa M., Pajuelo M., Checkley W., (2016); Behzadi-khormouji H., Et al., Deep learning, reusable and problem based architectures for detection of consolidation on chest X-ray images, Comput Methods Progr Biomed, (2019); Abhir B., Et al., Deep-learning framework to detect lung abnormality – a study with chest X-Ray and lung CT scan images, Pattern Recogn Lett, 129, pp. 271-278, (2020); Bharati S., Podder P., Paul P.K., Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data, Int J Hybrid Intell Syst, 15, 2, pp. 91-100, (2019); Cheng J.Z., Ni D., Chou Y.H., Qin J., Tiu C.M., Chang Y.C., Chen C.M., Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans, Sci Rep, 6, 1, pp. 1-13, (2016); Cheng Y.T., Lin Y.F., Chiang K.H., Tseng V.S., Mining sequential risk patterns from large-scale clinical databases for early assessment of chronic diseases: a case study on chronic obstructive pulmonary disease, IEEE J Biomed Health Inf, 21, pp. 303-311, (2017); Chouhan V., Et al., A novel transfer learning based approach for pneumonia detection in chest X-ray images, Appl Sci, 10, 2, (2020); Cisneros-Velarde P., Correa M., Mayta H., Anticona C., Pajuelo M., Oberhelman R., Lavarello R., (2016); Datta P., Gupta A., Agrawal R., Statistical Modeling of B-Mode Clinical Kidney Images, pp. 222-229, (2014); Doi K., Computer-aided diagnosis in medical imaging: historical review, current status and future potential, Comput Med Imaging Graph, 31, 4-5, pp. 198-211, (2007); Eshaghi H., Ziaee V., Khodabande M., Safavi M., Haji Esmaeil Memar E., Clinical Misdiagnosis of COVID-19 Infection with Confusing Clinical Course, Case Rep Infect Dis, 2021, (2021); Gu Y., Lu X., Yang L., Zhang B., Yu D., Zhao Y., Gao L., Wu L., Zhou T., Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs, Comput Biol Med, 103, pp. 220-231, (2018); He K., Gkioxari G., Dollar P., Girshick R., Mask r-cnn, Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969, (2017); Hemdan E.E.D., Shouman M.A., Karar M.E., Covidx-Net: A Framework of Deep Learning Classifiers to Diagnose Covid-19 in X-Ray Images, (2020); Hina K., Khalid S., Akbar M.U., A Review on Automatic Tuberculosis Screening Using Chest Radiographs, pp. 285-289, (2016); Horvath G., Orban G., Horvath A., Simko G., Pataki B., Maday P., Juhasz S., A cad system for screening x-ray chest radiography, World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany, pp. 210-213, (2009); Huang J., Rathod V., Sun C., Zhu M., Korattikara A., Fathi A., Fischer I., Wojna Z., Song Y., Guadarrama S., Et al., Speed/accuracy trade-offs for modern convolutional object detectors, pp. 7310-7311, (2017); Irvin J., Rajpurkar P., Ko M., Yu Y., Ciurea-Ilcus S., Chute C., Marklund H., Haghgoo B., Ball R., Shpanskaya K., (2019); Jaiswal A., Gianchandani N., Singh D., Kumar V., Kaur M., Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning, J Biomol Struct Dyn, pp. 1-8, (2020); Kallianos K., Mongan J., Antani S., Et al., How far have we come? Artificial intelligence for chest radiograph interpretation, Clin Radiol, 74, 5, pp. 338-345, (2019); Karargyris A., Antani S., Thoma G., Segmenting anatomy in chest x-rays for tuberculosis screening, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7779-7782, (2011); Khan A.I., Shah J.L., Bhat M.M., CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images, Comput Methods Programs Biomed, 196, (2020); Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Commun ACM, 60, 6, pp. 84-90, (2017); Kuan K., Ravaut M., Manek G., Chen H., Lin J., Nazir B., Chen C., Howe T.C., Zeng Z., Chandrasekhar V., (2017); Liang C.-H., Et al., Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice, Clin Radiol, (2019); Liu J., Pan Y., Li M., Chen Z., Tang L., Lu C., Wang J., Applications of deep learning to MRI images: A survey, Big Data Mining and Analytics, 1, 1, pp. 1-18, (2018); Lundervold A.S., Lundervold A., An overview of deep learning in medical imaging focusing on MRI, Z Med Phys, 29, 2, pp. 102-127, (2019); Mehrotra R., Ansari M.A., Agrawal R., Anand R.S., A Transfer Learning approach for AI-based classification of brain tumors, Mach Learn Appl, 2, (2020); Mohammed M.A., Abdulkareem K.H., Garcia-Zapirain B., Mostafa S.A., Maashi M.S., Al-Waisy A.S., Le D.N., A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of covid-19 based on x-ray images, Comput Mater Continua, 66, 3, (2020); Mondal M.R.H., Bharati S., Podder P., Podder P., Data Analytics for Novel Coronavirus Disease; Murray C.J.L., Lopez A.D., Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study, Lancet, 349, pp. 1498-1504, (1997); Nasrullah N., Sang J., Alam M.S., Xiang H., (2019); Nielsen K.G., Bisgaard H., The effect of inhaled budesonide on symptoms, lung function, and cold air and methacholine responsiveness in 2- to 5-year-old asthmatic children, Am J Respir Crit Care Med, 162, pp. 1500-1506, (2005); ] NIH Sample Chest X-Rays Dataset; Pan S.J., Yang Q., A survey on transfer learning, IEEE Trans Knowl Data Eng, 22, 10, pp. 1345-1359, (2009); Ren S., He K., Girshick R., Sun J., (2015); Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, 9351, pp. 234-241, (2015); Sethy P.K., Behera S.K., Detection of coronavirus disease (covid-19) based on deep features, Preprints, (2020); Setio A.A.A., Traverso A., de Bel T., Berens M.S.N., van den Bogaard C., Cerello P., Chen H., Dou Q., Fantacci M.E., Geurts B., Et al., Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge, Med Image Anal, 42, pp. 1-13, (2017); Shelhamer E., Long J., Darrell T., Fully convolutional networks for semantic segmentation, IEEE Trans Pattern Anal Mach Intell, 39, 4, pp. 640-651, (2017); Shrivastava A., Sukthankar R., Malik J., Gupta A. 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Arxiv Preprint Arxiv, 2003, (2020); World Lung Day 2019: Healthy Lungs for All - Global Initiative for Chronic Obstructive Lung Disease – GOLD; Yamashita R., Nishio M., Do R.K.G., Togashi K., Convolutional neural networks: an overview and application in radiology, Insights Imaging, 9, 4, pp. 611-629, (2018); Zenteno O., Castaneda B., Lavarello R., Spectral-based pneumonia detection tool using ultrasound data from pediatric populations, 2016 38Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4129-4132, (2016); Zhou B., Khosla A., Lapedriza A., Olivatorralba A., Learning deep features for discriminative localization, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929, (2016); Zhu W., Liu C., Fan W., Xie X., DeepLung. Deep 3D dual path nets for automated pulmonary nodule detection and classification, Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV); 12–15 March, pp. 673-681, (2018)","R. Mehrotra; Department of Electrical & Electronics Engineering, GL Bajaj Institute of Technology & Management, Gr. Noida, India; email: rajjatmehrootra@gmail.com","","Springer","","","","","","13807501","","MTAPF","","English","Multimedia Tools Appl","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85123849496"
"Sagheb E.; Wi C.-I.; Yoon J.; Seol H.Y.; Shrestha P.; Ryu E.; Park M.; Yawn B.; Liu H.; Homme J.; Juhn Y.; Sohn S.","Sagheb, Elham (57211215840); Wi, Chung-Il (56182827700); Yoon, Jungwon (55259661400); Seol, Hee Yun (57208402180); Shrestha, Pragya (56976028700); Ryu, Euijung (24077625100); Park, Miguel (8293342300); Yawn, Barbara (7005036463); Liu, Hongfang (7409753328); Homme, Jason (6602338882); Juhn, Young (6507775791); Sohn, Sunghwan (7101646425)","57211215840; 56182827700; 55259661400; 57208402180; 56976028700; 24077625100; 8293342300; 7005036463; 7409753328; 6602338882; 6507775791; 7101646425","Artificial Intelligence Assesses Clinicians’ Adherence to Asthma Guidelines Using Electronic Health Records","2022","Journal of Allergy and Clinical Immunology: In Practice","10","4","","1047","1056.e1","","10","10.1016/j.jaip.2021.11.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121988684&doi=10.1016%2fj.jaip.2021.11.004&partnerID=40&md5=cf351df7738cb9ef839666f186aa1731","Department of Artificial Intelligence and Informatics, Mayo Clinic, Minn, Rochester; Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minn, Rochester; Department of Pediatrics, Myongji Hospital, Goyang, South Korea; Pusan National University, Yangsan Hospital, Yangsan, South Korea; Department of Health Sciences Research, Mayo Clinic, Minn, Rochester; Division of Allergic Diseases, Mayo Clinic, Minn, Rochester; Department of Family and Community Health, University of Minnesota, Minneapolis, Minn, United States","Sagheb E., Department of Artificial Intelligence and Informatics, Mayo Clinic, Minn, Rochester; Wi C.-I., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minn, Rochester; Yoon J., Department of Pediatrics, Myongji Hospital, Goyang, South Korea; Seol H.Y., Pusan National University, Yangsan Hospital, Yangsan, South Korea; Shrestha P., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minn, Rochester; Ryu E., Department of Health Sciences Research, Mayo Clinic, Minn, Rochester; Park M., Division of Allergic Diseases, Mayo Clinic, Minn, Rochester; Yawn B., Department of Family and Community Health, University of Minnesota, Minneapolis, Minn, United States; Liu H., Department of Artificial Intelligence and Informatics, Mayo Clinic, Minn, Rochester; Homme J., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minn, Rochester; Juhn Y., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Minn, Rochester; Sohn S., Department of Artificial Intelligence and Informatics, Mayo Clinic, Minn, Rochester","Background: Clinicians’ asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians’ adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data. Objective: This study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines. Methods: This is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text. Results: Natural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions. Conclusions: Natural language processing technologies may enable the automated assessment of clinicians’ documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms. © 2021 American Academy of Allergy, Asthma & Immunology","Adherence to asthma guidelines; Automated chart review; Documentation variation; National asthma education and prev4ention program; Natural language processing","Algorithms; Artificial Intelligence; Asthma; Child; Cross-Sectional Studies; Electronic Health Records; Humans; Retrospective Studies; adolescent; Article; artificial intelligence; asthma; birth cohort; child; cohort analysis; cross-sectional study; diagnostic test accuracy study; electronic health record; female; health care quality; health personnel attitude; human; major clinical study; male; medical record review; medication compliance; natural language processing; practice guideline; predictive value; retrospective study; sensitivity and specificity; algorithm; artificial intelligence; asthma","","","","","National Institutes of Health?funded; National Institutes of Health, NIH, (R01 HL126667, R21 AI142702); GlaxoSmithKline, GSK","Funding text 1: This study was supported by National Institutes of Health–funded R21 Grant R21 AI142702 and R01 Grant R01 HL126667. ; Funding text 2: We thank Mrs Kelly Okeson for her administrative assistance. E. Sagheb conceptualized and designed the study, developed the algorithms, collected and interpreted the data, drafted the initial manuscript, and reviewed and revised the manuscript. S. Sohn conceptualized and designed the study; supervised data collection, algorithm development, and analysis; drafted the initial manuscript; interpreted the data; and reviewed and finalized the manuscript. Y. Juhn conceptualized and designed the study; supervised data collection, algorithm development, and analysis; interpreted the data; and reviewed and finalized the manuscript. C-I Wi, J. Yoon, Y. Seol, and P. Shrestha conceptualized and designed the study, collected and interpreted the data, and reviewed and revised the manuscript. J. Homme, B. Yawn, M. Park, H. Liu, and E. Ryu analyzed and interpreted the data and reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. This study was supported by National Institutes of Health?funded R21 Grant R21 AI142702 and R01 Grant R01 HL126667. Y. Juhn is Principal Investigator of the Respiratory Syncytial Virus incidence study supported by GlaxoSmithKline, United Kingdom. The rest of the authors declare that they have no relevant conflicts of interest.","Vital signs: asthma prevalence, disease characteristics, and self-management education: United States, 2001–2009, MMWR Morb Mortal Wkly Rep, 60, pp. 547-552, (2011); Lethbridge-Cejku M., Vickerie J.L., Summary health statistics for US adults; National Health Interview Survey, 2004, Vital Health Stat, 10, pp. 1-64, (2006); Stanton M.W., Rutherford M., The high concentration of US health care expenditures, (2006); Kim C.H., Gee K.A., Byrd R.S., Excessive absenteeism due to asthma in California elementary schoolchildren, Acad Pediatr, 20, pp. 950-957, (2020); Yawn B.P., Wollan P., Kurland M., Scanlon P., A longitudinal study of the prevalence of asthma in a community population of school-age children, J Pediatr, 140, pp. 576-581, (2002); Zhong W., Finnie D.M., Shah N.D., Wagie A.E., St Sauver J.L., Jacobson D.J., Et al., Effect of multiple chronic diseases on health care expenditures in childhood, J Prim Care Community Health, 6, pp. 2-9, (2015); National Heart, Lung, and Blood Institute, Expert Panel Report 3: guidelines for the diagnosis and management of asthma. 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Sohn S., Ye Z., Liu H., Chute C., Kullo I., Identifying abdominal aortic aneurysm cases and controls using natural language processing of radiology reports, AMIA Jt Summits Transl Sci Proc, 2013, pp. 249-253, (2013); Sohn S., Torii M., Li D., Wagholikar K., Wu S., Liu H., A hybrid approach to sentiment sentence classification in suicide notes, Biomed Inform Insights, 5, pp. 43-50, (2012); 2020 Minnesota Statutes: Disclosure of Health Records for External Research, (2020); Yawn B.P., Enright P.L., Lemanske R.F., Israel E., Pace W., Wollan P., Et al., Spirometry can be done in family physicians' offices and alters clinical decisions in management of asthma and COPD, Chest, 132, pp. 1162-1168, (2007); Rim K.; Liu H., Bielinski S., Sohn S., Murphy S., Wagholikar K., Jonnalagadda S., Et al., An information extraction framework for cohort identification using electronic health records, AMIA Jt Summits Transl Sci Proc, 2013, pp. 149-153, (2013); Sohn S., Wagholikar K.B., Li D., Jonnalagadda S.R., Tao C., Elayavilli R.K., Et al., Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification, J Am Med Inform Assoc, 20, pp. 836-842, (2013); Devlin J., Chang M.-W., Lee K., Toutanova K.B., (2019); Sohn S., Wi C., Juhn Y., Liu H., Analysis of clinical variations in asthma care documented in electronic health records between staff and resident physicians, Stud Health Technol Inform, 245, pp. 1170-1174, (2017); Weber G.M., Kohane I.S., Extracting physician group intelligence from electronic health records to support evidence based medicine, PLoS One, 8, (2013); Schuur J.D., Baugh C.W., Hess E.P., Hilton J.A., Pines J.M., Asplin B.R., Critical pathways for post–emergency outpatient diagnosis and treatment: tools to improve the value of emergency care, Acad Emerg Med, 18, pp. e52-e63, (2011); Wennberg J.E., Unwarranted variations in healthcare delivery: implications for academic medical centres, Br Med J, 325, pp. 961-964, (2002); Akinbami L.J., Salo P.M., Cloutier M.M., Wilkerson J.C., Elward K.S., Mazurek J.M., Et al., Primary care clinician adherence with asthma guidelines: the National Asthma Survey of Physicians, J Asthma, 57, pp. 543-555, (2020); Cloutier M.M., Akinbami L.J., Salo P.M., Schatz M., Simoneau T., Wilkerson J.C., Et al., Use of national asthma guidelines by allergists and pulmonologists: a national survey, J Allergy Clin Immunol Pract, 8, pp. 3011-3020.e2, (2020); Mold J.W., Fox C., Wisniewski A., Lipman P.D., Krauss M.R., Harris D.R., Et al., Implementing asthma guidelines using practice facilitation and local learning collaboratives: a randomized controlled trial, Ann Fam Med, 12, pp. 233-240, (2014); Yee A.B., Fagnano M., Halterman J.S., Preventive asthma care delivery in the primary care office: missed opportunities for children with persistent asthma symptoms, Acad Pediatr, 13, pp. 98-104, (2013); Hemnes A.R., Bertram A., Sisson S.D., Impact of medical residency on knowledge of asthma, J Asthma, 46, pp. 36-40, (2009); Carroll A.E., Tarczy-Hornoch P., O'Reilly E., Christakis D.A., Resident documentation discrepancies in a neonatal intensive care unit, Pediatrics, 111, pp. 976-980, (2003); Carroll A.E., Tarczy-Hornoch P., O'Reilly E., Christakis D.A., The effect of point-of-care personal digital assistant use on resident documentation discrepancies, Pediatrics, 113, pp. 450-454, (2004); (2019); Sohn S., Wang Y., Wi C.-I., Krusemark E.A., Ryu E., Ali M.H., Et al., Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions, J Am Med Inform Assoc, 25, pp. 353-359, (2018); Stetson P.D., Johnson S.B., Scotch M., Hripcsak G., The sublanguage of cross-coverage, Proc AMIA Symp, pp. 742-746, (2002); Friedman C., Kra P., Rzhetsky A., Two biomedical sublanguages: a description based on the theories of Zellig Harris, J Biomed Inform, 35, pp. 222-235, (2002); Wu Y., Denny J.C., Rosenbloom S.T., Miller R.A., Giuse D.A., Wang L., Et al., A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD), J Am Med Inform Assoc, 24, pp. e79-e86, (2017); Xu H., Stetson P.D., Friedman C., Methods for building sense inventories of abbreviations in clinical notes, J Am Med Inform Assoc, 16, pp. 103-108, (2009)","Y. Juhn; Professor of Pediatrics, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, 200 1st Street SW, Minn, 55905; email: Juhn.young@mayo.edu; S. Sohn; Associate Professor of Biomedical Informatics, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, 200 1st St SW, Minn, 55905; email: sohn.sunghwan@mayo.edu","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","34800704","English","J. Allergy Clin. Immunol. Pract.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85121988684"
"Palanichamy N.; Haw S.-C.; Subramanian S.; Murugan R.; Govindasamy K.","Palanichamy, Naveen (57215301642); Haw, Su-Cheng (57885692500); Subramanian, S. (58460160000); Murugan, Rishanti (57224445184); Govindasamy, Kuhaneswaran (57224454807)","57215301642; 57885692500; 58460160000; 57224445184; 57224454807","Machine learning methods to predict particulate matter PM2.5","2022","F1000Research","11","","406","","","","12","10.12688/f1000research.73166.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140611279&doi=10.12688%2ff1000research.73166.1&partnerID=40&md5=fc0dc311914ed277c30bf568f7428b5f","Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; Department of Electrical Engineering, Annamalai University, Chidambaram Tamil Nadu, 608002, India","Palanichamy N., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; Haw S.-C., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; Subramanian S., Department of Electrical Engineering, Annamalai University, Chidambaram Tamil Nadu, 608002, India; Murugan R., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; Govindasamy K., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia","Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions. © 2022 Palanichamy N et al.","Air Pollution; Artificial Neural Network; Long Short-Term Memory; Particulate Matter (PM2.5); Random Forest","Air Pollutants; Air Pollution; Environmental Monitoring; Machine Learning; Particulate Matter; adverse event; air pollution; air quality; Article; artificial neural network; classifier; concentration (parameter); data analysis; data base; data extraction; decision tree; experimental study; factor analysis; forecasting; intermethod comparison; long short term memory network; Malaysia; measurement accuracy; particulate matter 2.5; peer review; prediction; random forest; random sample; statistical model; supervised machine learning; urban area; air pollutant; air pollution; environmental monitoring; machine learning; particulate matter; procedures","","Air Pollutants, ; Particulate Matter, ","","","Department of the Environment, Australian Government","We would like to thank Department of Environment for providing us the dataset to complete this research successfully.","Sentian J., Herman F., Yin C.Y., Et al., Long-term air pollution trend analysis in Malaysia, International Journal of Environmental Impacts, 2, 4, pp. 309-324, (2019); Ameer S., Ali Shah M., Khan A., Et al., Comparative Analysis of Machine Learning Techniques For Predicting Air Quality in Smart Cities, Urban Computing and Intelligence, 7, (2017); Mahalingam U., Elangovan K., Dobhal H., Et al., A Machine Learning Model to Air Quality Prediction for Smart Cities, (2019); Suleiman A., Tight M.R., Quinn A.D., Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2. 5), Atmos. Pollut. Res, 10, 1, pp. 134-144, (2019); Shahriar S.A., Kayes I., Hasan K., Et al., Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for Atmospheric PM2. 5 Forecasting in Bangladesh, Atmos, 12, 1, (2021); Danesh Yazdi M., Kuang Z., Dimakopoulou K., Et al., Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods, Remote Sens, 12, 6, (2020); Murugan R., Palanichamy N., Smart City Air Quality Prediction using Machine Learning. 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1048-1054, (2002); Yang G., Lee H., Lee G., A hybrid deep learning model to forecast particulate matter concentration levels in Seoul, South Korea, Atmos, 11, 4, (2020); Karimian H., Li Q., Wu C., Et al., Evaluation of different machine learning approaches to forecasting PM2. 5 mass concentrations, Aerosol Air Qual. Res, 19, 6, pp. 1400-1410, (2019); Zhang B., Zhang H., Zhao G., Et al., Constructing a PM2. 5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks, Environ. Model Softw, 124, (2020)","N. Palanichamy; Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; email: p.naveen@mmu.edu.my","","F1000 Research Ltd","","","","","","20461402","","","36531254","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140611279"
"Weiss J.; Raghu V.K.; Bontempi D.; Christiani D.C.; Mak R.H.; Lu M.T.; Aerts H.J.W.L.","Weiss, Jakob (56335154300); Raghu, Vineet K. (56335925700); Bontempi, Dennis (57216159062); Christiani, David C. (57216584687); Mak, Raymond H. (8597064200); Lu, Michael T. (14629103500); Aerts, Hugo J.W.L. (16479697600)","56335154300; 56335925700; 57216159062; 57216584687; 8597064200; 14629103500; 16479697600","Deep learning to estimate lung disease mortality from chest radiographs","2023","Nature Communications","14","1","2797","","","","10","10.1038/s41467-023-37758-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159471060&doi=10.1038%2fs41467-023-37758-5&partnerID=40&md5=b94d5d7217572daefe223258e95aa591","Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States; Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, 02115, MA, United States; Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, Freiburg, 79106, Germany; Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Boston, 02114, United States; Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, Maastricht, 6229 ER, Netherlands; Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, 02115, MA, United States; Pulmonary and Critical Care Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, 02114, MA, United States","Weiss J., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, 02115, MA, United States, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, Freiburg, 79106, Germany, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Boston, 02114, United States; Raghu V.K., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Boston, 02114, United States; Bontempi D., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Universiteitssingel 40, Maastricht, 6229 ER, Netherlands; Christiani D.C., Department of Environmental Health, Harvard T.H. Chan School of Public Health, 655 Huntington Ave., Boston, 02115, MA, United States, Pulmonary and Critical Care Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, 02114, MA, United States; Mak R.H., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, 02115, MA, United States; Lu M.T., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Boston, 02114, United States; Aerts H.J.W.L., Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, 77 Avenue Louis Pasteur, Boston, 02115, MA, United States, Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis Street and 450 Brookline Avenue, Boston, 02115, MA, United States, Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Boston, 02114, United States, Pulmonary and Critical Care Division, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, 02114, MA, United States","Prevention and management of chronic lung diseases (asthma, lung cancer, etc.) are of great importance. While tests are available for reliable diagnosis, accurate identification of those who will develop severe morbidity/mortality is currently limited. Here, we developed a deep learning model, CXR Lung-Risk, to predict the risk of lung disease mortality from a chest x-ray. The model was trained using 147,497 x-ray images of 40,643 individuals and tested in three independent cohorts comprising 15,976 individuals. We found that CXR Lung-Risk showed a graded association with lung disease mortality after adjustment for risk factors, including age, smoking, and radiologic findings (Hazard ratios up to 11.86 [8.64–16.27]; p < 0.001). Adding CXR Lung-Risk to a multivariable model improved estimates of lung disease mortality in all cohorts. Our results demonstrate that deep learning can identify individuals at risk of lung disease mortality on easily obtainable x-rays, which may improve personalized prevention and treatment strategies. © 2023, The Author(s).","","Deep Learning; Humans; Lung; Lung Diseases; Radiography, Thoracic; Thorax; disease; disease treatment; health risk; learning; mortality; radiography; adult; aged; Article; cancer mortality; cancer staging; cardiovascular risk factor; cohort analysis; controlled study; deep learning; female; groups by age; human; lung cancer; lung disease; major clinical study; male; mortality risk; obesity; race; risk assessment; smoking; thorax radiography; diagnostic imaging; lung; procedures; thorax; thorax radiography","","","","","European Commission, EC; National Cancer Institute, NCI; Cancer Imaging Program grants; European Research Council, ERC; NIH-USA, (U24CA194354, U01CA190234, R35CA22052, U01CA209414); National Academy of Medicine Healthy Longevity Grand Challenge, (2000011734); Horizon 2020 Framework Programme, H2020, (866504); National Institutes of Health, NIH, (5U01CA209414)","The authors thank the study participants, the investigators, and the NCI for data collected in the PLCO and NLST trials. Original data collection for the ACRIN 6654 trial (NLST) was supported by NCI Cancer Imaging Program grants. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by the above organizations. The authors further acknowledge financial support from NIH (NIH (NCI) 5U01CA209414, DC; NIH-USA U24CA194354, HA; NIH-USA U01CA190234, HA; NIH-USA U01CA209414, HA; and NIH-USA R35CA22052, HA), the European Union - European Research Council (866504; HA) and the National Academy of Medicine Healthy Longevity Grand Challenge (2000011734; VR, JW, ML).","Halpern M.T., Stanford R.H., Borker R., The burden of COPD in the U.S.A.: results from the Confronting COPD survey, Respir. Med., 97, pp. S81-S89, (2003); Ford E.S., Et al., COPD Surveillance—United States, 1999-2011, Chest, 144, pp. 284-305, (2013); Moorman J.E., Et al., National surveillance of asthma: United States, 2001-2010, Vital-. 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Imaging, 33, pp. 1041-1046, (2020); Ganaie M.A., Hu M., Malik A.K., Tanveer M., Suganthan P.N., Ensemble deep learning: A review, (2021); Cao Y., Geddes T.A., Yang J.Y.H., Yang P., Ensemble deep learning in bioinformatics, Nat. Mach. Intell., 2, pp. 500-508, (2020); He K., Zhang X., Ren S., Sun J., Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR, (2016); Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z., Rethinking the Inception Architecture for Computer Vision., (2016); Raghu M., Zhang C., Kleinberg J., Bengio S., Transfusion: Understanding transfer learning for medical imaging, Adv. Neural Inf. Process. Syst, 32, (2019); Wenzel F., Snoek J., Tran D., Jenatton R., Hyperparameter Ensembles for Robustness and Uncertainty Quantification, Arxiv [Cs.Lg], (2020); Actuarial Life Table, Website","H.J.W.L. Aerts; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, 77 Avenue Louis Pasteur, 02115, United States; email: Hugo_Aerts@DFCI.harvard.edu","","Nature Research","","","","","","20411723","","","37193717","English","Nat. Commun.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85159471060"
"Jiao T.; Schnitzer M.E.; Forget A.; Blais L.","Jiao, Tianze (57222193759); Schnitzer, Mireille E. (37034990300); Forget, Amélie (7005752890); Blais, Lucie (7004693956)","57222193759; 37034990300; 7005752890; 7004693956","Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model","2022","Respiratory Medicine","198","","106866","","","","9","10.1016/j.rmed.2022.106866","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130171736&doi=10.1016%2fj.rmed.2022.106866&partnerID=40&md5=88f2283163b5079e944f4c1e094b0c67","Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL, United States; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Faculty of Pharmacy, Université de Montréal, Montréal, Québec, Canada; Department of Social and Preventive Medicine, Université de Montréal, Canada; Research Center, Hôpital Du Sacré-Coeur de Montréal, Montréal, Québec, Canada","Jiao T., Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL, United States, Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States; Schnitzer M.E., Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada, Faculty of Pharmacy, Université de Montréal, Montréal, Québec, Canada, Department of Social and Preventive Medicine, Université de Montréal, Canada; Forget A., Faculty of Pharmacy, Université de Montréal, Montréal, Québec, Canada, Research Center, Hôpital Du Sacré-Coeur de Montréal, Montréal, Québec, Canada; Blais L., Faculty of Pharmacy, Université de Montréal, Montréal, Québec, Canada, Research Center, Hôpital Du Sacré-Coeur de Montréal, Montréal, Québec, Canada","Background: Tools capable of predicting the risk of asthma exacerbations can facilitate asthma management in clinical practice. However, existing tools require additional data from patients beyond electronic medical records. Objective: To predict asthma exacerbation in an upcoming year using electronically accessible data conditional on past adherence to asthma medications. Methods: This retrospective cohort study included patients with ≥1 hospitalization or ≥2 medical claims for asthma within 2 consecutive years between 2002 and 2015 in Quebec administrative databases. Cohort entry (CE) was defined as the date of the first asthma-related ambulatory visit on or after meeting the operational definition of asthma. Adherence to each controller medication and use of each rescue medication was measured in the year prior to CE. Elastic-net regularized logistic regression was applied. Results: Among 98,823 patients, the mean age was 55.9 years and 36.2% were men. The area under the curve for prediction was 0.708. In the model, the use of long-acting anticholinergic or long-acting β2-agonists in the year prior to CE increased the odds of exacerbation by 24% and 21%, respectively. Among patients who received rescue medication, low and high adherence to controller medications increased the odds by 2%–5% compared with patients with medium adherence. Patients with a predicted risk of ≥0.20 were more likely to develop future exacerbation. Conclusion: This risk prediction indicated that asthma-related medication use increased the risk of asthma exacerbation. A potential U-shaped relationship between adherence to controller medications and the risk of exacerbation was identified among users of rescue medications. © 2022 Elsevier Ltd","Asthma exacerbation; Asthma management; Elastic-net regression; Machine learning; Medication adherence; Prediction model","Administration, Inhalation; Anti-Asthmatic Agents; Asthma; Female; Humans; Machine Learning; Male; Middle Aged; Retrospective Studies; beta 2 adrenergic receptor stimulating agent; cholinergic receptor blocking agent; corticosteroid; leukotriene receptor blocking agent; methylxanthine; omalizumab; antiasthmatic agent; adult; aged; ambulatory care; area under the curve; Article; asthma; cohort analysis; controlled study; disease exacerbation; elastic tissue; female; hospitalization; human; machine learning; major clinical study; male; medication compliance; patient compliance; Quebec; retrospective study; asthma; inhalational drug administration; machine learning; middle aged","","methylxanthine, 28109-92-4; omalizumab, 242138-07-4; Anti-Asthmatic Agents, ","","","Teva Pharmaceutical Industries; GlaxoSmithKline, GSK; Canadian Institutes of Health Research, IRSC, (PCG 155450, CIHR-425772)","This study was funded by the Canadian Institutes of Health Research (CIHR; grant number PCG 155450 ). Dr. Jiao was funded by a project grant from the CIHR (grant number CIHR-425772 ). Dr. Schnitzer is a CIHR Canada Research Chair in Causal Inference and Machine Learning in Health Sciences and going to receive personal fees from Carebook Technologies Inc, outside the submitted work. Dr. Blais receives grants and personal fees from AstraZeneca , grants from TEVA, grants from GlaxoSmithKline , and is going to receive personal fees from Carebook Technologies Inc., outside the submitted work. Ms. Forget has no conflict of interest to report. 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J., 28, 6, pp. 1145-1155, (2006); Osborne M.L., Pedula K.L., O'Hollaren M., Ettinger K.M., Stibolt T., Buist A.S., Et al., Assessing future need for acute care in adult asthmatics: the Profile of Asthma Risk Study: a prospective health maintenance organization-based study, Chest, 132, 4, pp. 1151-1161, (2007); Peters D., Chen C., Markson L.E., Allen-Ramey F.C., Vollmer W.M., Using an asthma control questionnaire and administrative data to predict health-care utilization, Chest, 129, 4, pp. 918-924, (2006); Sato R., Tomita K., Sano H., Ichihashi H., Yamagata S., Sano A., Et al., The strategy for predicting future exacerbation of asthma using a combination of the Asthma Control Test and lung function test, J. Asthma, 46, 7, pp. 677-682, (2009); Yurk R.A., Diette G.B., Skinner E.A., Dominici F., Clark R.D., Steinwachs D.M., Et al., Predicting patient-reported asthma outcomes for adults in managed care, Am. J. Manag. Care, 10, 5, pp. 321-328, (2004)","L. Blais; Faculty of Pharmacy, University of Montréal. Pavillon Jean-Coutu. 2940, chemin de la Polytechnique. Bureau 2125-1, Montréal QC, H3T 1J4, Canada; email: lucie.blais@umontreal.ca","","W.B. Saunders Ltd","","","","","","09546111","","RMEDE","35594754","English","Respir. Med.","Article","Final","","Scopus","2-s2.0-85130171736"
"Tran-Anh D.; Vu N.H.; Nguyen-Trong K.; Pham C.","Tran-Anh, Dat (57873062100); Vu, Nam Hoai (57201356510); Nguyen-Trong, Khanh (57466775300); Pham, Cuong (36647896300)","57873062100; 57201356510; 57466775300; 36647896300","Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare","2022","Pervasive and Mobile Computing","86","","101685","","","","12","10.1016/j.pmcj.2022.101685","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137181221&doi=10.1016%2fj.pmcj.2022.101685&partnerID=40&md5=0ea88bb55312bd22f47a63b2573e5861","Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam","Tran-Anh D., Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam; Vu N.H., Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam; Nguyen-Trong K., Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam; Pham C., Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam","With the emergence of many grave Chronic obstructive pulmonary diseases (COPDs) and the COVID-19 pandemic, there is a need for timely detection of abnormal respiratory sounds, such as deep and heavy breaths. Although numerous efficient pervasive healthcare systems have been proposed for tracking patients, few studies have focused on these breaths. This paper presents a method that supports physicians in monitoring in-hospital and at-home patients by monitoring their breath. The proposed method is based on three deep neural networks in audio analysis: RNNoise for noise suppression, SincNet - Convolutional Neural Network, and Residual Bidirectional Long Short-Term Memory for breath sound analysis at edge devices and centralized servers, respectively. We also developed a pervasive system with two configurations: (i) an edge architecture for in-hospital patients; and (ii) a central architecture for at-home ones. Furthermore, a dataset, named BreathSet, was collected from 27 COPD patients being treated at three hospitals in Vietnam to verify our proposed method. The experimental results demonstrated that our system efficiently detected and classified breath sounds with F1-scores of 90% and 91% for the tiny model version on low-cost edge devices, and 90% and 95% for the full model version on central servers, respectively. The proposed system was successfully implemented at hospitals to help physicians in monitoring respiratory patients in real time1. © 2022 Elsevier B.V.","Breath sound detection and classification; BreathSet dataset; Pervasive deep learning; Residual BiLSTM; RNNoise; SincNet-CNN","Audio acoustics; Classification (of information); Convolutional neural networks; Deep neural networks; Learning systems; Network architecture; Patient treatment; Pulmonary diseases; Breath sound detection and classification; Breath sound detections; Breathset dataset; Chronic obstructive pulmonary disease; Multitask learning; Pervasive deep learning; Residual BiLSTM; Rnnoise; Sincnet-CNN; Sound classification; Hospitals","","","","","Ministry of Science and Technology, MOST, (DTDLCN-16/18)","This work was funded by National Independent Research Program, Ministry of Science and Technology of Vietnam - Grant no. DTDLCN-16/18 .","Pramono R.X.A., Bowyer S., Rodriguez-Villegas E., Automatic adventitious respiratory sound analysis: A systematic review, PLoS One, (2017); Forgacs P., Nathoo A.R., Richardson H.D., pp. 288-295, (1971); Bardou D., Zhang K., Ahmad S.M., Lung sounds classification using convolutional neural networks, Artif. Intell. Med., 88, pp. 58-69, (2018); Niggemann B., Functional symptoms confused with allergic disorders in children and adolescents, Pediatric Allergy Immunol., 13, 5, pp. 312-318, (2002); Chen Y., Wang J., Huang M., Yu H., Cross-position activity recognition with stratified transfer learning, Pervasive Mob. Comput., 57, pp. 1-13, (2019); Al-Khalidi F.Q., Saatchi R., Burke D., Elphick H.E., Tan S.N., Respiration rate monitoring methods: A review, Pediatr. Pulmonol., 46, (2011); Vanegas E., Igual R., Plaza I., Sensing systems for respiration monitoring: A technical systematic review, Sensors, 20, 18, (2020); Di Tocco J., Sabbadini R., Raiano L., Fani F., Ripani S., Schena E., Formica D., Massaroni C., Breath-jockey: Development and feasibility assessment of a wearable system for respiratory rate and kinematic parameter estimation for gallop athletes, Sensors, 21, 1, (2021); Wang T., Zhang D., Wang L., Zheng Y., Gu T., Dorizzi B., Zhou X., Contactless respiration monitoring using ultrasound signal with off-the-shelf audio devices, IEEE Internet Things J., 6, 2, pp. 2959-2973, (2019); Jagadev P., Naik S., Giri L.I., Contactless monitoring of human respiration using infrared thermography and deep learning, Physiol. Meas., 43, 2, (2022); Zhai Q., Han X., Han Y., Yi J., Wang S., Liu T., A contactless on-bed radar system for human respiration monitoring, IEEE Trans. Instrum. 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Discov., 9, 1, (2019); Islam B., Rahman M.M., Ahmed T., Ahmed M.Y., Hasan M.M., Nathan V., Vatanparvar K., Nemati E., Kuang J., Gao J.A., (2021); Shih C.-H.I., Tomita N., Lukic Y.X., (2019); Accurate detection of sleep apnea with long short-term memory network based on RR interval signals, Knowl.-Based Syst., 212, (2021); Ramachandran A., Karuppiah A., A survey on recent advances in machine learning based sleep apnea detection systems, Healthcare, 9, 7, (2021); Rahman M.A., Hossain M.S., An internet of medical things-enabled edge computing framework for tackling COVID-19, IEEE Internet Things J., (2021); Yan J., Song Y., Guo W., Dai L.-R., McLoughlin I., Chen L., A region based attention method for weakly supervised sound event detection and classification, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 755-759, (2019); Thung K.-H., Wee C.-Y., Multimedia tools and applications a brief review on multi-task learning, (2018); Rocha B.M.M., Filos D., Mendes L., Vogiatzis I.M., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Paiva R.P., Chouvarda I., de Carvalho P., Maglaveras N.K., A respiratory sound database for the development of automated classification, BHI 2017, (2017); Soh J., Singh P., Introduction to azure machine learning, Data Science Solutions on Azure, pp. 117-148, (2020); Gairola S., Tom F., Kwatra N., Jain M., RespireNet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting, (2021); Kressbach M., Breath work: mediating health through breathing apps and wearable technologies, 16 and 2018 - Issue 2: Breath: Image and Sound and Tandfonline, (2018)","K. Nguyen-Trong; Posts and Telecommunications Institute of Technology, Hanoi, Viet Nam; email: khanhnt@ptit.edu.vn","","Elsevier B.V.","","","","","","15741192","","","","English","Pervasive Mob. Comput.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85137181221"
"Burke H.; Freeman A.; O'Regan P.; Wysocki O.; Freitas A.; Dushianthan A.; Celinski M.; Batchelor J.; Phan H.; Borca F.; Sheard N.; Williams S.; Watson A.; Fitzpatrick P.; Landers D.; Wilkinson T.","Burke, Hannah (55292903000); Freeman, Anna (55566806200); O'Regan, Paul (57353591200); Wysocki, Oskar (57204597866); Freitas, Andre (36631806600); Dushianthan, Ahilanandan (27067711700); Celinski, Michael (6602435081); Batchelor, James (57190586326); Phan, Hang (57221752673); Borca, Florina (57205351226); Sheard, Natasha (57219175634); Williams, Sarah (57218384462); Watson, Alastair (56996651600); Fitzpatrick, Paul (57221711087); Landers, Dónal (56911970700); Wilkinson, Tom (7202351234)","55292903000; 55566806200; 57353591200; 57204597866; 36631806600; 27067711700; 6602435081; 57190586326; 57221752673; 57205351226; 57219175634; 57218384462; 56996651600; 57221711087; 56911970700; 7202351234","Biomarker identification using dynamic time warping analysis: A longitudinal cohort study of patients with COVID-19 in a UK tertiary hospital","2022","BMJ Open","12","2","e050331","","","","10","10.1136/bmjopen-2021-050331","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124679891&doi=10.1136%2fbmjopen-2021-050331&partnerID=40&md5=7087eccae636694b52780f6ff7a66434","Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, University of Manchester, Manchester, United Kingdom; Clinical Informatics Research Unit, University of Southampton Faculty of Medicine, Southampton, United Kingdom; University of Southampton, Southampton, United Kingdom; Institute for Life Sciences, University of Southampton, Southampton, United Kingdom; University Hospital Southampton Nhs Foundation Trust, Southampton, United Kingdom; University of Manchester, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, Manchester, United Kingdom; Digital Experimental Cancer Medicine Team, University of Manchester, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, Alderley Edge, Cheshire, United Kingdom","Burke H., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Freeman A., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; O'Regan P., Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, University of Manchester, Manchester, United Kingdom; Wysocki O., Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, University of Manchester, Manchester, United Kingdom; Freitas A., Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, University of Manchester, Manchester, United Kingdom; Dushianthan A., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Celinski M., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Batchelor J., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Phan H., Clinical Informatics Research Unit, University of Southampton Faculty of Medicine, Southampton, United Kingdom, University of Southampton, Southampton, United Kingdom; Borca F., Institute for Life Sciences, University of Southampton, Southampton, United Kingdom; Sheard N., University Hospital Southampton Nhs Foundation Trust, Southampton, United Kingdom; Williams S., University Hospital Southampton Nhs Foundation Trust, Southampton, United Kingdom; Watson A., Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Fitzpatrick P., University of Manchester, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, Manchester, United Kingdom; Landers D., Digital Experimental Cancer Medicine Team, University of Manchester, Cancer Biomarker Centre, Cancer Research Uk Manchester Institute, Alderley Edge, Cheshire, United Kingdom; Wilkinson T., Faculty of Medicine, University of Southampton, Southampton, United Kingdom","Objectives COVID-19 is a heterogeneous disease, and many reports have described variations in demographic, biochemical and clinical features at presentation influencing overall hospital mortality. However, there is little information regarding longitudinal changes in laboratory prognostic variables in relation to disease progression in hospitalised patients with COVID-19. Design and setting This retrospective observational report describes disease progression from symptom onset, to admission to hospital, clinical response and discharge/death among patients with COVID-19 at a tertiary centre in South East England. Participants Six hundred and fifty-one patients treated for SARS-CoV-2 between March and September 2020 were included in this analysis. Ethical approval was obtained from the HRA Specific Review Board (REC 20/HRA/2986) for waiver of informed consent. Results The majority of patients presented within 1 week of symptom onset. The lowest risk patients had low mortality (1/45, 2%), and most were discharged within 1 week after admission (30/45, 67%). The highest risk patients, as determined by the 4C mortality score predictor, had high mortality (27/29, 93%), with most dying within 1 week after admission (22/29, 76%). Consistent with previous reports, most patients presented with high levels of C reactive protein (CRP) (67% of patients >50 mg/L), D-dimer (98%>upper limit of normal (ULN)), ferritin (65%>ULN), lactate dehydrogenase (90%>ULN) and low lymphocyte counts (81%<lower limit of normal (LLN)). Increases in platelet counts and decreases in CRP, neutrophil:lymphocyte ratio (p<0.001), lactate dehydrogenase, neutrophil counts, urea and white cell counts (all p<0.01) were each associated with discharge. Conclusions Serial measurement of routine blood tests may be a useful prognostic tool for monitoring treatment response in hospitalised patients with COVID-19. Changes in other biochemical parameters often included in a â € COVID-19 bundle' did not show significant association with outcome, suggesting there may be limited clinical benefit of serial sampling. This may have direct clinical utility in the context of escalating healthcare costs of the pandemic.  © ","COVID-19; respiratory infections; respiratory medicine (see thoracic medicine)","Biomarkers; Cohort Studies; COVID-19; Humans; Longitudinal Studies; Retrospective Studies; SARS-CoV-2; Tertiary Care Centers; United Kingdom; antibiotic agent; anticoagulant agent; antifungal agent; antivirus agent; biological marker; C reactive protein; D dimer; dexamethasone; ferritin; lactate dehydrogenase; urea; biological marker; adult; aged; Article; artificial intelligence; asthma; blood cell count; blood examination; breathing rate; chronic obstructive lung disease; cohort analysis; comorbidity; controlled study; coronavirus disease 2019; death; dementia; diabetes mellitus; disease course; disease exacerbation; dynamic time warping; England; female; Glasgow coma scale; heart disease; high risk population; hospital admission; hospital discharge; hospital patient; human; Human immunodeficiency virus infection; illness trajectory; in-hospital mortality; intermediate risk population; kidney disease; laboratory test; leukocyte count; longitudinal study; low risk patient; low risk population; lung lavage; lymphocyte count; major clinical study; male; malignant neoplasm; mortality; mortality rate; mortality risk; nasopharyngeal swab; neurologic disease; neutrophil count; neutrophil lymphocyte ratio; nonhuman; obesity; observational study; oxygen saturation; pandemic; platelet count; prognosis; real time reverse transcription polymerase chain reaction; Severe acute respiratory syndrome coronavirus 2; tertiary care center; thromboembolism; treatment duration; treatment response; retrospective study; United Kingdom","","C reactive protein, 9007-41-4; dexamethasone, 50-02-2; ferritin, 9007-73-2; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; urea, 57-13-6; Biomarkers, ","","","CRUK Manchester Institute, (C147/A25254); NIHR Southampton CRF; Office of Development and Alumni Relations; AstraZeneca, (119106); AstraZeneca; National Institute for Health Research, NIHR; Cancer Research UK, CRUK, (A29374); Cancer Research UK, CRUK; University of Southampton","Funding The REACT platform has been supported by the digital Experimental Cancer Medicine Team free of charge (grant number not applicable). The biobanking sub-cohort is supported the NIHR Southampton CRF and NIHR Southampton Biomedical Research Centre at University Hospital Southampton NHS Foundation Trust and as part of a broader effort (Enabling New Treatment Approaches for COVID-19 Treatment) by the University of Southampton (UoS) charity (Office of Development and Alumni Relations) (grant number not applicable). In addition, the Clinical Informatics Research Unit, UoS has supported infrastructure costs (Grant number not applicable). The support described previously was not provided from a specific award or grant. The digital Experimental Cancer Medicine Team are supported by the AstraZeneca iDECIDE Programme (grant number: 119106), awarded to Manchester Cancer Research Centre and by Cancer Research UK via an Accelerator Award (award number: A29374) through the CRUK Manchester Institute (award number: C147/A25254).","Guan W.-J., Liang W.-H., Zhao Y., Et al., Comorbidity and its impact on 1590 patients with COVID-19 in China: A nationwide analysis, Eur Respir J, 55, (2020); W-J G., Z-Y N., Hu Y., Clinical characteristics of coronavirus disease 2019 in China, New England Journal of Medicine, 382, pp. 1708-1720, (2020); Thanh Le T., Andreadakis Z., Kumar A., Et al., The COVID-19 vaccine development landscape, Nat Rev Drug Discov, 19, pp. 305-306, (2020); Ramasamy M.N., Minassian A.M., Ewer K.J., Safety and immunogenicity of ChAdOx1 nCoV-19 vaccine administered in a prime-boost regimen in young and old adults (COV002): A single-blind, randomised, controlled, phase 2/3 trial, Lancet, (2020); Docherty A.B., Harrison E.M., Green C.A., Et al., Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: Prospective observational cohort study, Bmj, 369, (2020); Watson A., Oberg L., Angermann B., Et al., Dysregulation of COVID-19 related gene expression in the COPD lung, Respir Res, 22, pp. 1-13, (2021); Clift A.K., Coupland C.A.C., Keogh R.H., Et al., Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: National derivation and validation cohort study, Bmj, 371, (2020); Knight S.R., Ho A., Pius R., Et al., Risk stratification of patients admitted to hospital with covid-19 using the ISARIC who clinical characterisation protocol: Development and validation of the 4C mortality score, Bmj, 370, (2020); Moutchia J., Pokharel P., Kerri A., Et al., Clinical laboratory parameters associated with severe or critical novel coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis, PLoS One, 15, (2020); Williams S., Sheard N., Stuart B., Et al., Comparisons of early and late presentation to hospital in COVID-19 patients, Respirology, 26, pp. 204-205, (2021); Wu C., Chen X., Cai Y., Et al., Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China, Jama Intern Med, 180, pp. 934-943, (2020); Zhou F., Yu T., Du R., Et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study, Lancet, 395, pp. 1054-1062, (2020); Burke H., Freeman A., Cellura D.C., Et al., Inflammatory phenotyping predicts clinical outcome in COVID-19, Respir Res, 21, (2020); Stubinger J., Schneider L., Epidemiology of coronavirus COVID-19: Forecasting the future incidence in different countries, Healthcare, 8, (2020); Jin Q., Time Warping Clustering for the Forecast and Analysis of COVID-19, (2020); Freeman A., Watson A., O'Regan P., Et al., Wave comparisons of clinical characteristics and outcomes of COVID-19 admissions-Exploring the impact of treatment and strain dynamics, J Clin Virol, 146, (2022); Burke H., Freeman A., Dushianthan A., Et al., Research evaluation alongside clinical treatment in COVID-19 (react COVID-19): An observational and biobanking study, Bmj Open, 11, (2021); Cuturi M., Blondel M., Soft-DTW: A differentiable loss function for time-series. 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Watson A., Madsen J., Clark H.W., SP-A and SP-D: Dual functioning immune molecules with antiviral and immunomodulatory properties, Front Immunol, 11, (2020)","A. Freeman; Faculty of Medicine, University of Southampton, Southampton, United Kingdom; email: a.freeman@soton.ac.uk","","BMJ Publishing Group","","","","","","20446055","","","35168965","English","BMJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124679891"
"Medvedev A.; Sharma S.M.; Tsatsorin E.; Nabieva E.; Yarotsky D.","Medvedev, Aleksandr (57666993200); Sharma, Satyarth Mishra (57218318707); Tsatsorin, Evgenii (57226769343); Nabieva, Elena (10045123000); Yarotsky, Dmitry (36832975400)","57666993200; 57218318707; 57226769343; 10045123000; 36832975400","Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models","2022","PLoS ONE","17","8 August","e0273293","","","","9","10.1371/journal.pone.0273293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137136507&doi=10.1371%2fjournal.pone.0273293&partnerID=40&md5=933654f94de062cacdebccc4e793c082","Skolkovo Institute of Science and Technology, Moscow, Russian Federation","Medvedev A., Skolkovo Institute of Science and Technology, Moscow, Russian Federation; Sharma S.M., Skolkovo Institute of Science and Technology, Moscow, Russian Federation; Tsatsorin E., Skolkovo Institute of Science and Technology, Moscow, Russian Federation; Nabieva E., Skolkovo Institute of Science and Technology, Moscow, Russian Federation; Yarotsky D., Skolkovo Institute of Science and Technology, Moscow, Russian Federation","Genotype-to-phenotype prediction is a central problem of human genetics. In recent years, it has become possible to construct complex predictive models for phenotypes, thanks to the availability of large genome data sets as well as efficient and scalable machine learning tools. In this paper, we make a threefold contribution to this problem. First, we ask if stateof- the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. We find that this is indeed the case if model features include a sufficiently rich set of covariates, but probably not otherwise. Second, we ask if the conventional selection of single nucleotide polymorphisms (SNPs) by genome wide association studies (GWAS) can be replaced by a more efficient procedure, taking into account information in previously selected SNPs. We propose such a procedure, based on a sequential feature importance estimation with decision trees, and show that this approach indeed produced informative SNP sets that are much more compact than when selected with GWAS. Finally, we show that the highest prediction accuracy can ultimately be achieved by ensembling individual linear and nonlinear models. To the best of our knowledge, for some of the phenotypes that we consider (asthma, hypothyroidism), our results are a new state-of-the-art. © 2022 Medvedev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","","","","","","","","Mancinelli L, Cronin M, Sadee W., Pharmacogenomics: the promise of personalized medicine, Aaps Pharmsci, 2, 1, pp. 29-41, (2000); Jannink JL, Lorenz AJ, Iwata H., Genomic selection in plant breeding: from theory to practice, Briefings in functional genomics, 9, 2, pp. 166-177, (2010); Tibshirani R., Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society Series B (Methodological), 58, 1, pp. 267-288, (1996); Qian J, Tanigawa Y, Du W, Aguirre M, Chang C, Tibshirani R, Et al., A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank, PLOS Genetics, 16, 10, (2020); Prive F, Vilhjalmsson BJ, Aschard H., Fitting penalized regressions on very large genetic data using snpnet and bigstatsr, bioRxiv, (2020); 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Hunter DJ, De Lange M, Andrew T, Snieder H, MacGregor AJ, Spector TD., Genetic variation in bone mineral density and calcaneal ultrasound: A study of the influence of menopause using female twins, Osteoporosis International, 12, 5, pp. 406-411, (2001); Kemp JP, Morris JA, Medina-Gomez C, Forgetta V, Warrington NM, Youlten SE, Et al., Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis, Nature Genetics, 49, 10, pp. 1468-1475, (2017); Ambrozio B, Longo L, Rizzo L., LightGWAS: A Novel Machine Learning Procedure for Genome-Wide Association Study, (2020); Behravan H, Hartikainen JM, Tengstrom M, Pylkas K, Winqvist R, Kosma VM, Et al., Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls; Johnsen PV, Riemer-Sorensen S, DeWan AT, Cahill ME, Langaas M., A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values, BMC Bioinformatics, 22, 1, pp. 1-29, (2021)","A. Medvedev; Skolkovo Institute of Science and Technology, Moscow, Russian Federation; email: aleksandr.medvedev@skoltech.ru","","Public Library of Science","","","","","","19326203","","POLNC","36044406","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85137136507"
"Pu Y.; Zhou X.; Zhang D.; Guan Y.; Xia Y.; Tu W.; Lu Y.; Zhang W.; Fu C.-C.; Fang Q.; de Bock G.H.; Liu S.; Fan L.","Pu, Yu (57205611901); Zhou, Xiuxiu (57204916826); Zhang, Di (57206456966); Guan, Yu (55524064000); Xia, Yi (55523286100); Tu, Wenting (57202852701); Lu, Yang (57850888900); Zhang, Weidong (57917992900); Fu, Chi-Cheng (57217491423); Fang, Qu (57217493486); de Bock, Geertruida H. (7004025636); Liu, Shiyuan (9232762100); Fan, Li (56611135400)","57205611901; 57204916826; 57206456966; 55524064000; 55523286100; 57202852701; 57850888900; 57917992900; 57217491423; 57217493486; 7004025636; 9232762100; 56611135400","Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models","2022","International Journal of COPD","17","","","2471","2483","12","9","10.2147/COPD.S369904","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139403700&doi=10.2147%2fCOPD.S369904&partnerID=40&md5=50ebdb757c3797e93ee643490d87af22","Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Department of Epidemiology, University Medical Center Groningen, Groningen, Netherlands","Pu Y., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Zhou X., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Zhang D., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Guan Y., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Xia Y., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Tu W., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Lu Y., Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Zhang W., Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Fu C.-C., Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Fang Q., Department of Scientific Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China; de Bock G.H., Department of Epidemiology, University Medical Center Groningen, Groningen, Netherlands; Liu S., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Fan L., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China","Purpose: To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model. Patients and Methods: A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes. To identify a more reasonable thresholds of FEV1% predicted value for distinguishing high-risk COPD patients from the normal, 80 thresholds from 50% to 129% were taken with a partition of 1% to establish a random forest classification model under each threshold, such that novel PFT-parameter-based high-risk criteria would be more consistent with the PRM-based machine learning classification model. Results: Machine learning-based PRM showed that consistency between PRM parameters and PFT was better able to distinguish high-risk COPD from the normal, with an AUC of 0.84 when the threshold was 72%. When the threshold was 80%, the AUC was 0.72 and when the threshold was 95%, the AUC was 0.64. Conclusion: Machine learning-based PRM is feasible for redefining high-risk COPD, and setting the optimal FEV1% predicted value lays the foundation for redefining high-risk COPD diagnosis. © 2022 Pu et al.","artificial intelligence; chronic obstructive pulmonary disease; computed tomography; pulmonary function test; quantitative imaging","Humans; Lung; Machine Learning; Pulmonary Disease, Chronic Obstructive; Respiratory Function Tests; Retrospective Studies; aged; airway remodeling; algorithm; Article; artificial intelligence; asthma; chronic obstructive lung disease; computer assisted tomography; emphysema; female; forced expiratory volume; forced vital capacity; human; image analysis; lung function test; machine learning; major clinical study; male; parameter response mapping; quantitative analysis; questionnaire; random forest; receiver operating characteristic; risk assessment; sensitivity and specificity; spirometry; diagnostic imaging; lung; machine learning; retrospective study","","","A-VIEW; Brilliance iCT, Philips Medical Systems, Netherlands; HI-801, Chest, Japan","Chest, Japan; Philips Medical Systems, Netherlands","Pyramid Talent Project of Shanghai Changzheng Hospital; Science and Technology Innovation Action Plan of Shanghai Municipality, (19411951300); Shanghai Changzheng Hospital, (2020YLCYJ-Y24); Shanghai Sailing Program, (20YF1449000); National Natural Science Foundation of China, NSFC, (81871321, 81930049, 82171926); National Natural Science Foundation of China, NSFC; Science and Technology Commission of Shanghai Municipality, STCSM, (21DZ2202600); Science and Technology Commission of Shanghai Municipality, STCSM; National Key Research and Development Program of China, NKRDPC, (2022YFC2010000, 2022YFC2010002); National Key Research and Development Program of China, NKRDPC","This work was supported by the the National Natural Science Foundation of China [grant number 81871321, 81930049, 82171926], National Key R&D Program of China [grant number 2022YFC2010002, 2022YFC2010000], The clinical Innovative Project of Shanghai Changzheng Hospital [grant number 2020YLCYJ-Y24]; The program of Science and Technology Commission of Shanghai Municipality [grant number 21DZ2202600], The program of Science and Technology Innovation Action Plan of Shanghai Municipality [grant number 19411951300], Shanghai Sailing Program [grant number 20YF1449000] and Pyramid Talent Project of Shanghai Changzheng Hospital.","Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 392, 10159, pp. 1736-1788, (2018); Rabe KF, Watz H., Chronic obstructive pulmonary disease, Lancet, 389, 10082, pp. 1931-1940, (2017); Celli BR, Agusti A., COPD: time to improve its taxonomy?, ERJ Open Res, 4, 1, pp. 00132-2017, (2018); Koo HK, Vasilescu DM, Booth S, Et al., Small airways disease in mild and moderate chronic obstructive pulmonary disease: a cross-sectional study, Lancet Respir Med, 6, 8, pp. 591-602, (2018); Jobst BJ, Weinheimer O, Buschulte T, Et al., Longitudinal airway remodeling in active and past smokers in a lung cancer screening population, Eur Radiol, 29, 6, pp. 2968-2980, (2019); Takayanagi S, Kawata N, Tada Y, Et al., Longitudinal changes in structural abnormalities using MDCT in COPD: do the CT measurements of airway wall thickness and small pulmonary vessels change in parallel with emphysematous progression?, Int J Chron Obstruct Pulmon Dis, 12, pp. 551-560, (2017); Guan ZHUBJ. HB., The application of quantitative CT in the diagnosis and treatment of COPD, J Pract Radio, 34, (2018); Regan EA, Lynch DA, Curran-Everett D, Et al., Clinical and radiologic disease in smokers with normal spirometry, JAMA Intern Med, 175, 9, pp. 1539-1549, (2015); Colak Y, Afzal S., Prognostic significance of chronic respiratory symptoms in individuals with normal spirometry, Eur Respir J, 55, 1, (2020); Woodruff PG, Barr RG, Bleecker E, Et al., Clinical significance of symptoms in smokers with preserved pulmonary function, N Engl J Med, 374, 19, pp. 1811-1821, (2016); Chen S, Wang C, Li B, Et al., Risk factors for FEV1 decline in mild COPD and high-risk populations, Int J Chron Obstruct Pulmon Dis, 12, pp. 435-442, (2017); Wan ES, Castaldi PJ, Cho MH, Et al., Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene, Respir Res, 15, 1, (2014); Xia Y, Guan Y, Fan L, Et al., Dynamic contrast enhanced magnetic resonance perfusion imaging in high-risk smokers and smoking-related COPD: correlations with pulmonary function tests and quantitative computed tomography, COPD, 11, 5, pp. 510-520, (2014); Fan L, Xia Y, Guan Y, Et al., Capability of differentiating smokers with normal pulmonary function from COPD patients: a comparison of CT pulmonary volume analysis and MR perfusion imaging, Eur Radiol, 23, 5, pp. 1234-1241, (2013); Kirby M, Yin Y, Tschirren J, Et al., A novel method of estimating small airway disease using inspiratory-to-expiratory computed tomography, Respiration, 94, 4, pp. 336-345, (2017); Galban CJ, Han MK, Boes JL, Et al., Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression, Nat Med, 18, 11, pp. 1711-1715, (2012); Pedregosa F, Varoquaux G, Gramfort A, Et al., Scikit-learn: machine learning in Python, J Machine Res, 12, pp. 2825-2830, (2011); Stringer WW, Porszasz J, Bhatt SP, McCormack MC, Make BJ, Casaburi R., Physiologic insights from the COPD genetic epidemiology study, Chronic Obstr Pulm Dis, 6, 3, pp. 256-266, (2019); Young KA, Strand M, Ragland MF, Et al., Pulmonary subtypes exhibit differential global initiative for chronic obstructive lung disease spirometry stage progression: the COPDGene® study, Chronic Obstr Pulm Dis, 6, 5, pp. 414-429, (2019); Lowe KE, Regan EA, Anzueto A, Et al., COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr Pulm Dis, 6, 5, pp. 384-399, (2019); Balkissoon R., Journal Club-COPD2020 update. global initiative for chronic obstructive lung disease 2020 report and the journal of the COPD foundation special edition, moving to a new definition for COPD: ”COPDGene® 2019”, Chronic Obstr Pulm Dis, 6, 4, pp. 64-72, (2019); Bhatt SP, Soler X, Wang X, Et al., Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 194, 2, pp. 178-184, (2016); Kim SJ, Lee J, Park YS, Et al., Age-related annual decline of lung function in patients with COPD, Int J Chron Obstruct Pulmon Dis, 11, pp. 51-60, (2015); Grabicki M, Kuznar-Kaminska B, Rubinsztajn R, Et al., COPD course and comorbidities: are there gender differences?, Adv Exp Med Biol, 1113, pp. 43-51, (2019); Zhou Y, Wang D, Liu S, Et al., The association between BMI and COPD: the results of two population-based studies in Guangzhou, China, COPD, 10, 5, pp. 567-572, (2013); Ran PX, Wang C, Yao WZ, Et al., A study on the correlation of body mass index with chronic obstructive pulmonary disease and quality of life, Zhonghua Jie He He Hu Xi Za Zhi, 30, 1, pp. 18-22, (2007)","S. Liu; Department of Radiology, ChangZheng Hospital, Naval Medical University, Shanghai, No. 415 Fengyang Road, 200003, China; email: cjr.liushiyuan@vip.163.com; L. Fan; Department of Radiology, ChangZheng Hospital, Naval Medical University, Shanghai, No. 415 Fengyang Road, 200003, China; email: fanli0930@163.com","","Dove Medical Press Ltd","","","","","","11769106","","","36217330","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139403700"
"Weikert T.; Friebe L.; Wilder-Smith A.; Yang S.; Sperl J.I.; Neumann D.; Balachandran A.; Bremerich J.; Sauter A.W.","Weikert, Thomas (57204439485); Friebe, Liene (57857321600); Wilder-Smith, Adrian (57437223600); Yang, Shan (55861065500); Sperl, Jonathan I. (22981426200); Neumann, Dominik (53866980300); Balachandran, Abishek (57219691796); Bremerich, Jens (55894138900); Sauter, Alexander W. (8504352000)","57204439485; 57857321600; 57437223600; 55861065500; 22981426200; 53866980300; 57219691796; 55894138900; 8504352000","Automated quantification of airway wall thickness on chest CT using retina U-Nets – Performance evaluation and application to a large cohort of chest CTs of COPD patients","2022","European Journal of Radiology","155","","110460","","","","8","10.1016/j.ejrad.2022.110460","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136604699&doi=10.1016%2fj.ejrad.2022.110460&partnerID=40&md5=9ba8920d4eba1d552e02ddc480e3fb47","Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Siemens Healthineers, Henkestrasse 127, Erlangen, 91052, Germany; Siemens Healthineers, Joggers Ln 2, Karnataka/Bangalore, 560100, India","Weikert T., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Friebe L., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Wilder-Smith A., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Yang S., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Sperl J.I., Siemens Healthineers, Henkestrasse 127, Erlangen, 91052, Germany; Neumann D., Siemens Healthineers, Henkestrasse 127, Erlangen, 91052, Germany; Balachandran A., Siemens Healthineers, Joggers Ln 2, Karnataka/Bangalore, 560100, India; Bremerich J., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland; Sauter A.W., Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, Basel, 4031, Switzerland","Purpose: Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). Materials and methods: This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019–12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D- U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3–8 (AWT3-8) across the lungs. Mean AWT3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. Results: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). Conclusion: Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. Clinical relevance statement: Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease. © 2022","Airway wall thickness; Computed tomography; COPD; Deep learning; Imaging biomarker; U-Net","DEET; Emphysema; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Retina; Retrospective Studies; Tomography, X-Ray Computed; diethyltoluamide; aged; Article; artificial intelligence; chronic obstructive lung disease; cohort analysis; computer assisted tomography; controlled study; deep learning; female; human; image analysis; image segmentation; major clinical study; male; retrospective study; validation study; diagnostic imaging; emphysema; lung; lung emphysema; procedures; retina; x-ray computed tomography","","diethyltoluamide, 134-62-3, 26545-51-7; DEET, ","","","","","O'Donnell D.E., Laveneziana P., Physiology and consequences of lung hyperinflation in COPD, Eur. 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J., 20, pp. 15-22, (2011); Pompe E., Strand M., van Rikxoort E.M., Hoffman E.A., Barr R.G., Charbonnier J.P., Humphries S., Han M.L.K., Hokanson J.E., Make B.J., Regan E.A., Silverman E.K., Crapo J.D., Lynch D.A., Five-year progression of emphysema and air trapping at ct in smokers with and those without chronic obstructive pulmonary disease: Results from the COPDGene study, Radiology., 295, pp. 218-226, (2020); Nakano Y., Wong J.C., de Jong P.A., Buzatu L., Nagao T., Coxson H.O., Elliott W.M., Hogg J.C., Pare P.D., The Prediction of Small Airway Dimensions Using Computed Tomography, Am. J. Respir. Crit. Care Med., 171, 2, pp. 142-146, (2005); Matsuoka S., Uchiyama K., Shima H., Ueno N., Oish S., Nojiri Y., Bronchoarterial ratio and bronchial wall thickness on high-resolution CT in asymptomatic subjects: correlation with age and smoking, AJR, Am. J. 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J., 34, pp. 858-865, (2009); Kim V., Desai P., Newell J.D., Make B.J., Washko G.R., Silverman E.K., Crapo J.D., Bhatt S.P., Criner G.J., Airway wall thickness is increased in COPD patients with bronchodilator responsiveness, Respir. Res., 15, pp. 1-9, (2014); Konietzke P., Wielputz M.O., Wagner W.L., Wuennemann F., Kauczor H.U., Heussel C.P., Eichinger M., Eberhardt R., Gompelmann D., Weinheimer O., Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval, Eur. Radiol., 30, pp. 2502-2512, (2020); Kim N., Joon B.S., Song K.S., Eun J.C., Kang S.H., Semi-Automatic Measurement of the Airway Dimension by Computed Tomography Using the Full-Width-Half-Maximum Method: a Study on the Measurement Accuracy according to the CT Parameters and Size of the Airway, Korean J. Radiol., 9, pp. 226-235, (2008); Peters C.M., Molgat-Seon Y., Dominelli P.B., Lee A.M.D., Lane P., Lam S., Sheel A.W., Fiber optic endoscopic optical coherence tomography (OCT) to assess human airways: The relationship between anatomy and physiological function during dynamic exercise, Physiol. Rep., 9, (2021); Johannessen A., Skorge T.D., Bottai M., Grydeland T.B., Nilsen R.M., Coxson H., Dirksen A., Omenaas E., Gulsvik A., Bakke P., Mortality by Level of Emphysema and Airway Wall Thickness, Am. J. Respir. Crit. Care Med., 187, 6, pp. 602-608, (2013); Willemink M.J., Persson M., Pourmorteza A., Pelc N.J., Fleischmann D., Photon-counting CT: Technical principles and clinical prospects, Radiology., 289, pp. 293-312, (2018); Barnes P.J., Asthma-COPD Overlap, Chest., 149, pp. 7-8, (2016); Oelsner E.C., Smith B.M., Hoffman E.A., Kalhan R., Donohue K.M., Kaufman J.D., Nguyen J.N., Manichaikul A.W., Rotter J.I., Michos E.D., Jacobs D.R., Burke G.L., Folsom A.R., Schwartz J.E., Watson K., (2018)","T. Weikert; Department of Radiology, University Hospital Basel, University of Basel, Basel, Petersgraben 4, 4031, Switzerland; email: thomas.weikert@usb.ch","","Elsevier Ireland Ltd","","","","","","0720048X","","EJRAD","35963191","English","Eur. J. Radiol.","Article","Final","","Scopus","2-s2.0-85136604699"
"Hurst J.H.; Zhao C.; Hostetler H.P.; Ghiasi Gorveh M.; Lang J.E.; Goldstein B.A.","Hurst, Jillian H. (23050605700); Zhao, Congwen (57303304300); Hostetler, Haley P. (57222982815); Ghiasi Gorveh, Mohsen (57612148900); Lang, Jason E. (35366602100); Goldstein, Benjamin A. (57203232935)","23050605700; 57303304300; 57222982815; 57612148900; 35366602100; 57203232935","Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models","2022","BMC Medical Informatics and Decision Making","22","1","108","","","","11","10.1186/s12911-022-01847-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128792040&doi=10.1186%2fs12911-022-01847-0&partnerID=40&md5=93cd96eb90414d864d2955056829fe7a","Department of Pediatrics, Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, United States; Department of Pediatrics, Children’s Health and Discovery Initiative, Duke University School of Medicine, Durham, NC, United States; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States; Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, United States; Duke Clinical Research Institute, Duke University, Durham, NC, United States; Department of Pediatrics, Division of Pulmonary and Sleep Medicine, Duke University School of Medicine, Durham, NC, United States","Hurst J.H., Department of Pediatrics, Division of Infectious Diseases, Duke University School of Medicine, Durham, NC, United States, Department of Pediatrics, Children’s Health and Discovery Initiative, Duke University School of Medicine, Durham, NC, United States; Zhao C., Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States; Hostetler H.P., Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, United States; Ghiasi Gorveh M., Duke Clinical Research Institute, Duke University, Durham, NC, United States; Lang J.E., Department of Pediatrics, Division of Pulmonary and Sleep Medicine, Duke University School of Medicine, Durham, NC, United States; Goldstein B.A., Department of Pediatrics, Children’s Health and Discovery Initiative, Duke University School of Medicine, Durham, NC, United States, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States, Duke Clinical Research Institute, Duke University, Durham, NC, United States","Background: Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations. Methods: We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30–180 day time horizons, and evaluated the contributions of different data types to model performance. Results: Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730–0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes. Conclusions: EHR data-based models perform moderately wellover a 30–180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful. Trial Registration: Not applicable. © 2022, The Author(s).","Asthma; Environmental data; Machine learning; Pediatrics; Predictive modeling","Asthma; Child; Electronic Health Records; Humans; Machine Learning; Retrospective Studies; ROC Curve; asthma; child; electronic health record; human; machine learning; receiver operating characteristic; retrospective study","","","","","National Heart, Lung, and Blood Institute, NHLBI, (R21HL145415); National Center for Advancing Translational Sciences, NCATS, (UL1TR001117)","This project was supported by the Translating Duke Health Children’s Health and Discovery Initiative and grants from the National Heart, Lung, and Blood Institute (5R21HL145415-02) and the National Center for Advancing Translational Sciences (UL1TR001117). ","Centers for Disease Control and Prevention. Most Recent National Asthma Data., (2021); AsthmaStats: Asthma Attacks among People with Current Asthma, Centers for Disease Control and Prevention, pp. 2014-2017; Quickstats:Percentage* of All Emergency Department (ED) Visits Made by Patients with Asthma, by Sex and Age Group—National Hospital Ambulatory Medical Care Survey, United States, (2014); Hogan A.H., Carroll C.L., Iverson M.G., Hollenbach J.P., Philips K., Saar K., Et al., Risk factors for pediatric asthma readmissions: a systematic review, J Pediatr, S0022–3476, 21, pp. 00438-448, (2021); Anise A., Hasnain-Wynia R., Patient-centered outcomes research to improve asthma outcomes, J Allergy Clin Immunol, 138, pp. 1503-1510, (2016); Goldstein B.A., Navar A.M., Pencina M.J., Ioannidis J.P.A., Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review, J Am Med Inform Assoc, 24, pp. 198-208, (2017); Stolte A., Merli M.G., Hurst J.H., Liu Y., Wood C.T., Goldstein B.A., Using Electronic Health Records to understand the population of local children captured in a large health system in Durham County, NC, USA, and implications for population health research, Soc Sci Med, 296, (2022); Hurst J.H., Liu Y., Maxson P.J., Permar S.R., Boulware L.E., Goldstein B.A., Development of an electronic health records datamart to support clinical and population health research, J Clin Transl Sci, 5, (2020); Tang M., Goldstein B.A., He J., Hurst J.H., Lang J.E., Performance of a computable phenotype for pediatric asthma using the problem list, Ann Allergy Asthma Immunol, 125, pp. 611-613.e1, (2020); Lang J.E., Tang M., Zhao C., Hurst J., Wu A., Goldstein B.A., Well-child care attendance and risk of asthma exacerbations, Pediatrics, 146, (2020); Bonito A., Bann C., Eicheldinger C., Carpenter L., Creation of New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators for Medicare Beneficiaries, (2013); He J., Ghorveh M.G., Hurst J.H., Tang M., Alhanti B., Lang J.E., Et al., Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data, BMC Public Health, 20, (2020); Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Softw, 33, pp. 1-22, (2010); Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R | Journal of Statistical Software, (2022); Greenwell B., Boehmke B.J., R Package Version., 2, 5, (2019); ROCR: Visualizing Classifier Performance in R | Bioinformatics | Oxford Academic, (2022); R Core Team. R: A language and environment for statistical computing, R Foundation for Statistical Computing; Cohen H.A., Blau H., Hoshen M., Batat E., Balicer R.D., Seasonality of asthma: a retrospective population study, Pediatrics, 133, pp. e923-e932, (2014); Haselkorn T., Zeiger R.S., Chipps B.E., Mink D.R., Szefler S.J., Simons E.R., Et al., Recent asthma exacerbations predict future exacerbations in children with severe or difficult-to-treat asthma, J Allergy Clin Immunolo., 124, pp. 921-927, (2009); Wu A.C., Tantisira K., Li L., Schuemann B., Weiss S.T., Fuhlbrigge A.L., Predictors of symptoms are different from predictors of severe exacerbations from asthma in children, Chest, 140, pp. 100-107, (2011); Price D.B., Rigazio A., Campbell J.D., Bleecker E.R., Corrigan C.J., Thomas M., Et al., Blood eosinophil count and prospective annual asthma disease burden: a UK cohort study, Lancet Respir Med, 3, pp. 849-858, (2015); Miller M.K., Lee J.H., Miller D.P., Wenzel S.E., Recent asthma exacerbations: a key predictor of future exacerbations, Respir Med, 101, pp. 481-489, (2007); Covar R.A., Szefler S.J., Zeiger R.S., Sorkness C.A., Moss M., Mauger D.T., Et al., Factors associated with asthma exacerbations during a long-term clinical trial of controller medications in children, J Allergy Clin Immunol, 122, pp. 741-747.e4, (2008); Peters M.C., Mauger D., Ross K.R., Phillips B., Gaston B., Cardet J.C., Et al., Evidence for exacerbation-prone asthma and predictive biomarkers of exacerbation frequency, Am J Respir Crit Care Med, 202, pp. 973-982, (2020); Hoch H.E., Calatroni A., West J.B., Liu A.H., Gergen P.J., Gruchalla R.S., Et al., Can we predict fall asthma exacerbations? Validation of the seasonal asthma exacerbation index, J Allergy Clin Immunol, 140, pp. 1130-1137.e5, (2017); Luo G., He S., Stone B.L., Nkoy F.L., Johnson M.D., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, (2020); Finkelstein J., Jeong I.C., Machine learning approaches to personalize early prediction of asthma exacerbations: personalized prediction of asthma exacerbation, Ann NY Acad Sci, 1387, pp. 153-165, (2017); Bhavsar N.A., Gao A., Phelan M., Pagidipati N.J., Goldstein B.A., Value of neighborhood socioeconomic status in predicting risk of outcomes in studies that use electronic health record data, JAMA Netw Open, 1, (2018); Schuler A., O'Suilleabhain L., Rinetti-Vargas G., Kipnis P., Barreda F., Liu V.X., Et al., Assessment of value of neighborhood socioeconomic status in models that use electronic health record data to predict health care use rates and mortality, JAMA Netw Open, 3, (2020); Stevens E.L., Rosser F., Han Y.-Y., Forno E., Acosta-Perez E., Canino G., Et al., Traffic-related air pollution, dust mite allergen, and childhood asthma in puerto ricans, Am J Respir Crit Care Med, 202, pp. 144-146, (2020); Brandt S.J., Perez L., Kunzli N., Lurmann F., McConnell R., Costs of childhood asthma due to traffic-related pollution in two California communities, Eur Respir J, 40, pp. 363-370, (2012); Rodriguez-Villamizar L.A., Berney C., Villa-Roel C., Ospina M.B., Osornio-Vargas A., Rowe B.H., The role of socioeconomic position as an effect-modifier of the association between outdoor air pollution and children’s asthma exacerbations: an equity-focused systematic review, Rev Environ Health, 31, pp. 297-309, (2016); Zheng X., Ding H., Jiang L., Chen S., Zheng J., Qiu M., Et al., Association between air pollutants and asthma emergency room visits and hospital admissions in time series studies: a systematic review and meta-analysis, PLoS ONE, 10, (2015); Witonsky J., Abraham R., Toh J., Desai T., Shum M., Rosenstreich D., Et al., The association of environmental, meteorological, and pollen count variables with asthma-related emergency department visits and hospitalizations in the Bronx, J Asthma, 56, pp. 927-937, (2019); Goldstein B.A., Pencina M.J., Montez-Rath M.E., Winkelmayer W.C., Predicting mortality over different time horizons: which data elements are needed?, J Am Med Inform Assoc, 24, pp. 176-181, (2017)","B.A. Goldstein; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, United States; email: ben.goldstein@duke.edu","","BioMed Central Ltd","","","","","","14726947","","","35459216","English","BMC Med. Informatics Decis. Mak.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128792040"
"Kuluozturk M.; Kobat M.A.; Barua P.D.; Dogan S.; Tuncer T.; Tan R.-S.; Ciaccio E.J.; Acharya U.R.","Kuluozturk, Mutlu (57008328900); Kobat, Mehmet Ali (55213695300); Barua, Prabal Datta (36993665100); Dogan, Sengul (25653093400); Tuncer, Turker (37062172100); Tan, Ru-San (7201984906); Ciaccio, Edward J. (7004846604); Acharya, U Rajendra (7004510847)","57008328900; 55213695300; 36993665100; 25653093400; 37062172100; 7201984906; 7004846604; 7004510847","DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis","2022","Medical Engineering and Physics","110","","103870","","","","10","10.1016/j.medengphy.2022.103870","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136257817&doi=10.1016%2fj.medengphy.2022.103870&partnerID=40&md5=120ec3e282023b0b7b3c30728b199f6e","Department of Pulmonology, Firat University Hospital, Elazig, Turkey; Department of Cardiology, Firat University Hospital, Elazig, Turkey; School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; Department of Medicine, Columbia University Irving Medical Center, United States; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan","Kuluozturk M., Department of Pulmonology, Firat University Hospital, Elazig, Turkey; Kobat M.A., Department of Cardiology, Firat University Hospital, Elazig, Turkey; Barua P.D., School of Management & Enterprise, University of Southern Queensland, Australia, Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; Dogan S., Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Tuncer T., Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Tan R.-S., Department of Cardiology, National Heart Centre Singapore, Singapore, Duke-NUS Medical School, Singapore; Ciaccio E.J., Department of Medicine, Columbia University Irving Medical Center, United States; Acharya U.R., Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore, Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan","Problem: Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. Aim: In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: ‘Covid-19’, ‘heart failure’, ‘acute asthma’, and ‘healthy’, and used it to train, validate, and test a novel model designed for automatic detection. Method: The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. Results: The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. Conclusions: The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis. © 2022","acute asthma; cough sound; Covid-19; Directed knight pattern; DKPNet41; heart failure; multiple pooling","Asthma; Cough; COVID-19; Humans; Machine Learning; Support Vector Machine; Cardiology; Classification (of information); Computer aided diagnosis; Diseases; Iterative methods; Large dataset; Nearest neighbor search; Signal processing; Statistical tests; Acute asthma; Automatic Detection; Cough sounds; Covid-19; Directed knight pattern; Disease diagnosis; Dkpnet41; Features vector; Heart failure; Multiple pooling; Article; artificial neural network; clinical feature; coughing; directed knight pattern network; disease classification; feature selection; human; k nearest neighbor; risk factor; validation study; asthma; coughing; machine learning; support vector machine; Vectors","","","","","Firat Üniversitesi, FU","We gratefully acknowledge the Non-Invasive Ethics Committee, Firat University for data transcription.","Velavan T.P., Meyer C.G., The COVID-19 epidemic, Tropical medicine & international health, 25, (2020); Ciotti M., Ciccozzi M., Terrinoni A., Jiang W.-C., Wang C.-B., Bernardini S., The COVID-19 pandemic, Critical reviews in clinical laboratory sciences, 57, pp. 365-388, (2020); Dua S., Acharya U.R., Dua P., Machine learning in healthcare informatics, (2014); Ksiazek W., Abdar M., Acharya U.R., Plawiak P., A novel machine learning approach for early detection of hepatocellular carcinoma patients, Cognitive Systems Research, 54, pp. 116-127, (2019); Acharya U.R., Oh S.L., Hagiwara Y., Tan J.H., Adeli H., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in biology and medicine, 100, pp. 270-278, (2018); Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Acharya U.R., Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in biology and medicine, 121, (2020); Pal A., Sankarasubbu M., Pay attention to the cough: Early diagnosis of covid-19 using interpretable symptoms embeddings with cough sound signal processing, Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 620-628, (2021); 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Islam M.Z., Islam M.M., Asraf A., A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, Informatics in medicine unlocked, 20, (2020); Feng C., Elazab A., Yang P., Wang T., Zhou F., Hu H., Et al., Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM, IEEE Access, 7, pp. 63605-63618, (2019); Tuncer T., Dogan S., Acharya U.R., Automated EEG signal classification using chaotic local binary pattern, Expert Systems with Applications, (2021); Aydemir E., Tuncer T., Dogan S., Gururajan R., Acharya U.R., Automated major depressive disorder detection using melamine pattern with EEG signals, Applied Intelligence, pp. 1-18, (2021); Tuncer T., Dogan S., Tan R.-S., Acharya U.R., Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals, Information Sciences, 565, pp. 91-104, (2021); Yildirim O., Plawiak P., Tan R.-S., Acharya U.R., Arrhythmia detection using deep convolutional neural network with long duration ECG signals, Computers in biology and medicine, 102, pp. 411-420, (2018); Zhou J., Zhang Q., Zhang B., An automatic multi-view disease detection system via Collective Deep Region-based Feature Representation, Future Generation Computer Systems, 115, pp. 59-75, (2021); Narin A., Kaya C., Pamuk Z., Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, Pattern Analysis and Applications, pp. 1-14, (2021); Dogan S., Akbal E., Tuncer T., Acharya U.R., Application of substitution box of present cipher for automated detection of snoring sounds, Artificial Intelligence in Medicine, (2021); Rahman A.U., Saeed M., Mohammed M.A., Krishnamoorthy S., Kadry S., Eid F., An Integrated Algorithmic MADM Approach for Heart Diseases’ Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting, Life, 12, (2022); Wah T.Y., Mohammed M.A., Iqbal U., Kadry S., Majumdar A., Thinnukool O., Novel DERMA Fusion Technique for ECG Heartbeat Classification, Life, 12, (2022); Ristoski P., Bizer C., Paulheim H., Mining the web of linked data with rapidminer, Journal of Web Semantics, 35, pp. 142-151, (2015); Yunus R., Ulfa U., Safitri M.D., Application of the K-Nearest Neighbors (K-NN) Algorithm for Classification of Heart Failure, Journal of Applied Intelligent System, 6, pp. 1-9, (2021); Asuncion A., Newman D., UCI machine learning repository. Irvine, CA, USA, (2007); Al-Khassaweneh M., Re B.A., A signal processing approach for the diagnosis of asthma from cough sounds, Journal of medical engineering & technology, 37, pp. 165-171, (2013); Belkacem A.N., Ouhbi S., Lakas A., Benkhelifa E., Chen C., End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework, Frontiers in Medicine, 8, (2021); Brown C., Chauhan J., Grammenos A., Han J., Hasthanasombat A., Spathis D., Et al., Exploring automatic diagnosis of covid-19 from crowdsourced respiratory sound data, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3474-3484, (2020); Islam M.A., Bandyopadhyaya I., Bhattacharyya P., Saha G., Multichannel lung sound analysis for asthma detection, Computer methods and programs in biomedicine, 159, pp. 111-123, (2018); Badnjevic A., Gurbeta L., Cifrek M., Marjanovic D., Classification of asthma using artificial neural network, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 387-390, (2016); Hassan A., Shahin I., Alsabek M.B., Covid-19 detection system using recurrent neural networks, 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), pp. 1-5, (2020); Sabour S., Frosst N., Hinton G.E., Dynamic routing between capsules, Advances in neural information processing systems, (2017); Tuncer T., Dogan S., Ozyurt F., Belhaouari S.B., Bensmail H. Novel multi center and threshold ternary pattern based method for disease detection method using voice, IEEE Access, 8, pp. 84532-84540, (2020); Maillo J., Ramirez S., Triguero I., Herrera F., kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data, Knowledge-Based Systems, 117, pp. 3-15, (2017); Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Communications of the ACM, 60, pp. 84-90, (2017); Tolstikhin I.O., Houlsby N., Kolesnikov A., Beyer L., Zhai X., Unterthiner T., Et al., MLP-Mixer: An all-MLP Architecture for Vision, Advances in Neural Information Processing Systems, 34, pp. 24261-24272, (2021); Frazier P.I., A tutorial on Bayesian optimization, arXiv preprint, (2018); Amrulloh Y., Abeyratne U., Swarnkar V., Triasih R., Cough sound analysis for pneumonia and asthma classification in pediatric population, 2015 6th International Conference on Intelligent Systems, Modelling and Simulation, pp. 127-131, (2015); Hee H.I., Balamurali B., Karunakaran A., Herremans D., Teoh O.H., Lee K.P., Et al., Development of machine learning for asthmatic and healthy voluntary cough Sounds: a proof of concept study, Applied Sciences, 9, (2019); Yadav S., Keerthana M., Gope D., Ghosh P.K., Analysis of acoustic features for speech sound based classification of asthmatic and healthy subjects, ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6789-6793, (2020); Mouawad P., Dubnov T., Dubnov S., Robust Detection of COVID-19 in Cough Sounds: Using Recurrence Dynamics and Variable Markov Model, Sn Computer Science, 2, (2021); Knocikova J., Korpas J., Vrabec M., Javorka M., Wavelet analysis of voluntary cough sound in patients with respiratory diseases, J Physiol Pharmacol, 59, pp. 331-340, (2008); Loey M., Mirjalili S., COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models, Computers in Biology and Medicine, 139, (2021); Islam R., Abdel-Raheem E., Tarique M., A study of using cough sounds and deep neural networks for the early detection of COVID-19, Biomedical Engineering Advances, 3, (2022); Chowdhury N.K., Kabir M.A., Rahman M.M., Islam S.M.S., Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method, Computers in Biology and Medicine, 145, (2022); Bagad P., Dalmia A., Doshi J., Nagrani A., Bhamare P., Mahale A., Et al., Cough against covid: Evidence of covid-19 signature in cough sounds, arXiv preprint, (2020); Pahar M., Klopper M., Warren R., Niesler T., COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings, Computers in Biology and Medicine, 135, (2021)","S. Dogan; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; email: sdogan@firat.edu.tr","","Elsevier Ltd","","","","","","13504533","","MEPHE","35989223","English","Med. Eng. Phys.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85136257817"
"Siebert J.N.; Hartley M.-A.; Courvoisier D.S.; Salamin M.; Robotham L.; Doenz J.; Barazzone-Argiroffo C.; Gervaix A.; Bridevaux P.-O.","Siebert, Johan N. (57189618479); Hartley, Mary-Anne (58038546000); Courvoisier, Delphine S. (25221247700); Salamin, Marlène (58300579300); Robotham, Laura (58300544700); Doenz, Jonathan (58300507900); Barazzone-Argiroffo, Constance (55667090500); Gervaix, Alain (7004668790); Bridevaux, Pierre-Olivier (6506429571)","57189618479; 58038546000; 25221247700; 58300579300; 58300544700; 58300507900; 55667090500; 7004668790; 6506429571","Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case–control study","2023","BMC Pulmonary Medicine","23","1","191","","","","8","10.1186/s12890-022-02255-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160968219&doi=10.1186%2fs12890-022-02255-w&partnerID=40&md5=5ca74de33062f1fbe7894237ca181343","Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, Geneva 14, 1211, Switzerland; Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland; Division of Pulmonology, Hospital of Valais, Sion, Switzerland; Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Switzerland","Siebert J.N., Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, Geneva 14, 1211, Switzerland, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Hartley M.-A., Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Courvoisier D.S., Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Salamin M., Division of Pulmonology, Hospital of Valais, Sion, Switzerland; Robotham L., Division of Pulmonology, Hospital of Valais, Sion, Switzerland; Doenz J., Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Barazzone-Argiroffo C., Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Gervaix A., Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, Geneva 14, 1211, Switzerland, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Bridevaux P.-O., Division of Pulmonology, Hospital of Valais, Sion, Switzerland","Background: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. Methods: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. Discussion: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. ClinicalTrials.gov Identifier: NCT05318599. © 2023, The Author(s).","Artificial intelligence; Auscultation; Deep learning; Idiopathic interstitial pneumonias; Idiopathic pulmonary fibrosis; Lung diseases, Interstitial; Pulmonary disease, Chronic obstructive; Respiratory sounds; Ultrasonography","Adult; Artificial Intelligence; Auscultation; Case-Control Studies; Clinical Protocols; Deep Learning; Humans; Idiopathic Interstitial Pneumonias; Idiopathic Pulmonary Fibrosis; Lung; Lung Diseases, Interstitial; Observational Studies as Topic; Pulmonary Disease, Chronic Obstructive; Quality of Life; Respiratory Sounds; Ultrasonography; abnormal respiratory sound; algorithm; Article; case control study; chronic obstructive lung disease; computer assisted diagnosis; computer assisted tomography; controlled study; convolutional neural network; deep learning; differential diagnosis; human; interstitial lung disease; interstitial pneumonia; long short term memory network; lung auscultation; lung function test; observational study; point of care ultrasound; prospective study; quality of life; receiver operating characteristic; Switzerland; adult; artificial intelligence; auscultation; chronic obstructive lung disease; clinical protocol; complication; diagnostic imaging; echography; fibrosing alveolitis; interstitial lung disease; interstitial pneumonia; lung; pathology","","","Eko CORE digital stethoscope, Eko Devices, United States","Eko Devices, United States","Ligue Pulmonaire Valaisanne; Promotion Santé Valais","The present study has financial support from the Promotion Santé Valais, Ligue Pulmonaire Valaisanne, a branch of the Swiss Pulmonary League. The funders of the study had no role in study design and will have no role in data collection, data analysis, data interpretation, or writing of the report. This study is not commercially funded. 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Siebert; Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva 14, 47 Avenue de la Roseraie, 1211, Switzerland; email: Johan.Siebert@hcuge.ch","","BioMed Central Ltd","","","","","","14712466","","BPMMB","37264374","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85160968219"
"Barata F.; Cleres D.; Tinschert P.; Shih C.-H.I.; Rassouli F.; Boesch M.; Brutsche M.; Fleisch E.","Barata, Filipe (57191505036); Cleres, David (57226129083); Tinschert, Peter (57204220320); Shih, Chen-Hsuan Iris (57218604272); Rassouli, Frank (56668637700); Boesch, Maximilian (55056474500); Brutsche, Martin (7004585886); Fleisch, Elgar (6602498499)","57191505036; 57226129083; 57204220320; 57218604272; 56668637700; 55056474500; 7004585886; 6602498499","Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study","2023","JMIR Formative Research","7","","e38439","","","","11","10.2196/38439","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149144075&doi=10.2196%2f38439&partnerID=40&md5=6c395c905083f9bd4335b7e392fdfb6c","Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland; Resmonics AG, Zurich, Switzerland; Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland; Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland","Barata F., Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland; Cleres D., Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland; Tinschert P., Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland, Resmonics AG, Zurich, Switzerland; Shih C.-H.I., Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland, Resmonics AG, Zurich, Switzerland; Rassouli F., Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland; Boesch M., Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland; Brutsche M., Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland; Fleisch E., Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Switzerland, Center for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland","Background: Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, noninvasive ward monitoring and allow the detection of subtle changes in vital signs. Cough can be effectively monitored through mobile technologies in the ward, as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. Objective: This exploratory study involved the validation of an automated smartphone-based monitoring system for continuous cough counting in 2 different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real time. Methods: Automated cough counts were measured consistently on devices and on computers and compared with cough and noncough sounds counted manually over 8-hour long nocturnal recordings in 9 patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble convolutional neural network developed and trained on asthmatic cough data. Results: In this validation study, a total of 72 hours of recordings from 9 participants with pneumonia, 4 of whom were infected with SARS-CoV-2, were analyzed. All the recordings were subjected to manual analysis by 2 blinded raters. The proposed system yielded a sensitivity and specificity of 72% and 99% on the device and 82% and 99% on the computer, respectively, for detecting coughs. The mean differences between the automated and human rater cough counts were -1.0 (95% CI -12.3 to 10.2) and -0.9 (95% CI -6.5 to 4.8) coughs per hour within subject for the on-device and on-computer modes, respectively. Conclusions: The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward. © 2023 Authors. All rights reserved.","convolutional neural network; cough monitoring; COVID-19; machine learning; mobile phone; mobile sensing; ward monitoring","","","","","","","","Khanna AK, Hoppe P, Saugel B., Automated continuous noninvasive ward monitoring: future directions and challenges, Crit Care, 23, 1, (2019); Andersen L, Berg K, Chase M, Cocchi M, Massaro J, Donnino M, Acute respiratory compromise on inpatient wards in the United States: incidence, outcomes, and factors associated with in-hospital mortality, Resuscitation, 105, pp. 123-129, (2016); Perman SM, Stanton E, Soar J, Berg RA, Donnino MW, Mikkelsen ME, Et al., Location of in-hospital cardiac arrest in the United States-variability in event rate and outcomes, J Am Heart Assoc, 5, 10, (2016); Michard F, Sessler DI., Ward monitoring 3.0, Br J Anaesth, 121, 5, pp. 999-1001, (2018); Morrison L, Neumar R, Zimmerman J, Link M, Newby L, McMullan JP, Et al., Strategies for improving survival after in-hospital cardiac arrest in the United States: 2013 consensus recommendations, Circulation, 127, 14, pp. 1538-1563, (2013); Pearse R, Moreno R, Bauer P, Pelosi P, Metnitz P, Spies C, Et al., Mortality after surgery in Europe: a 7 day cohort study, Lancet, 380, 9847, pp. 1059-1065, (2012); Leuvan CH, Mitchell I., Missed opportunities?. An observational study of vital sign measurements, Crit Care Resusc, 10, 2, pp. 111-115, (2008); Lee L, Caplan R, Stephens L, Posner K, Terman G, Voepel-Lewis T, Et al., Postoperative opioid-induced respiratory depression: a closed claims analysis, Anesthesiology, 122, 3, pp. 659-665, (2015); Jones D, Mitchell I, Hillman K, Story D., Defining clinical deterioration, Resuscitation, 84, 8, pp. 1029-1034, (2013); Al Hossain F, Lover AA, Corey GA, Reich NG, Rahman T., FluSense: a contactless syndromic surveillance platform for influenza-like illness in hospital waiting areas, Proc ACM Interact Mob Wearable Ubiquitous Technol, 4, 1, (2020); Irwin R, French C, Lewis S, Diekemper R, Gold P, Overview of the management of cough: CHEST Guideline and Expert Panel Report, Chest, 146, 4, pp. 885-889, (2014); Morice AH, Fontana GA, Belvisi MG, Birring SS, Chung KF, Dicpinigaitis PV, ERS guidelines on the assessment of cough, Eur Respir J, 29, 6, pp. 1256-1276, (2007); Smith J, Earis J, Woodcock A., Establishing a gold standard for manual cough counting: video versus digital audio recordings, Cough, 2, (2006); Ekberg-Aronsson M, Pehrsson K, Nilsson J, Nilsson PM, Lofdahl CG., Mortality in GOLD stages of COPD and its dependence on symptoms of chronic bronchitis, Respir Res, 6, 1, (2005); Niimi A., Cough and asthma, Curr Respir Med Rev, 7, 1, pp. 47-54, (2011); Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, Et al., Predictors of COVID-19 severity: a literature review, Rev Med Virol, 31, 1, pp. 1-10, (2021); Larson E, Lee T, Liu S, Rosenfeld M, Patel S., Accurate and privacy preserving cough sensing using a low-cost microphone, Proceedings of the 13th international conference on Ubiquitous computing. 2011 Presented at: Ubicomp '11: The 2011 ACM Conference on Ubiquitous Computing; Amoh J, Odame K., DeepCough: a deep convolutional neural network in a wearable cough detection system, Proceedings of the 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). 2015 Presented at: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS); Barata F, Tinschert P, Rassouli F, Steurer-Stey C, Fleisch E, Puhan MA, Et al., Automatic recognition, segmentation, and sex assignment of nocturnal asthmatic coughs and cough epochs in smartphone audio recordings: observational field study, J Med Internet Res, 22, 7, (2020); McGuinness K, Holt K, Dockry R, Smith J., P159 validation of the VitaloJAK™24 hour ambulatory cough monitor: abstract P159 table 1, Thorax, 67, (2012); Birring S, Fleming T, Matos S, Raj A, Evans D, Pavord I., The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough, Eur Respir J, 31, 5, pp. 1013-1018, (2008); Vizel E, Yigla M, Goryachev Y, Dekel E, Felis V, Levi H, Et al., Validation of an ambulatory cough detection and counting application using voluntary cough under different conditions, Cough, 6, 1, (2010); Coyle MA, Keenan DB, Henderson LS, Watkins ML, Haumann BK, Mayleben DW, Et al., Evaluation of an ambulatory system for the quantification of cough frequency in patients with chronic obstructive pulmonary disease, Cough, 1, 1, (2005); Leconte S, Liistro G, Lebecque P, Degryse JM., The objective assessment of cough frequency: accuracy of the LR102 device, Cough, 7, 1, (2011); Sohrabi K, Gross V, Fischer P, Weissflog A, Hildebrandt O, Koehler U., Validation of the LEOSound cough detection algorithm, Research Square, (2019); Larson S, Comina G, Gilman RH, Tracey BH, Bravard M, Lopez JW., Validation of an automated cough detection algorithm for tracking recovery of pulmonary tuberculosis patients, PLoS One, 7, 10, (2012); Proano A, Bravard MA, Tracey BH, Lopez JW, Comina G, Zimic M, Protocol for studying cough frequency in people with pulmonary tuberculosis, BMJ Open, 6, 4, (2016); Koehler U, Brandenburg U, Weissflog A, Sohrabi K, Gross V., LEOSound, an innovative procedure for acoustic long-term monitoring of asthma symptoms (wheezing and coughing) in children and adults, Pneumologie, 68, 4, pp. 277-281, (2014); Turner RD, Bothamley GH., How to count coughs?. Counting by ear, the effect of visual data and the evaluation of an automated cough monitor, Respir Med, 108, 12, pp. 1808-1815, (2014); Barry SJ, Dane AD, Morice AH, Walmsley AD., The automatic recognition and counting of cough, Cough, 2, 1, (2006); Barata F, Kipfer K, Weber M, Tinschert P, Fleisch E, Kowatsch T., Towards device-agnostic mobile cough detection with convolutional neural networks, Proceedings of the 2019 IEEE International Conference on Healthcare Informatics (ICHI). 2019 Presented at: 2019 IEEE International Conference on Healthcare Informatics (ICHI); Tinschert P, Rassouli F, Barata F, Steurer-Stey C, Fleisch E, Puhan M, Et al., Prevalence of nocturnal cough in asthma and its potential as a marker for asthma control (MAC) in combination with sleep quality: protocol of a smartphone-based, multicentre, longitudinal observational study with two stages, BMJ Open, 9, 1, (2019); McFee B, Raffel C, Liang D, Ellis DP, McVicar M, Battenberg E, Et al., librosa: audio and music signal analysis in Python, Proceedings of the 14th Python in Science Conference, pp. 18-25, (2015); Hagos T., Android studio, Learn Android Studio 3, (2018); Fernandez A, Garcia S, Galar M, Prati R, Krawczyk B, Herrera F., Learning from Imbalanced Data Sets, (2018); Bakdash JZ, Marusich LR., Repeated measures correlation, Front Psychol, 8, (2017); Martin Bland J, Altman D., Statistical methods for assessing agreement between two methods of clinical measurement, Lancet, 327, 8476, pp. 307-310, (1986); Bewick V, Cheek L, Ball J., Statistics review 7: correlation and regression, Crit Care, 7, 6, pp. 451-459, (2003); Giavarina D., Understanding Bland Altman analysis, Biochem Med, 25, 2, pp. 141-151, (2015); Zou G., Confidence interval estimation for the Bland-Altman limits of agreement with multiple observations per individual, Stat Methods Med Res, 22, 6, pp. 630-642, (2013); Welch P., The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms, IEEE Trans Audio Electroacoust, 15, 2, pp. 70-73, (1967); Jokic S, Cleres D, Rassouli F, Steurer-Stey C, Puhan MA, Brutsche M, Et al., TripletCough: cougher identification and verification from contact-free smartphone-based audio recordings using metric learning, IEEE J Biomed Health Inform, 26, 6, pp. 2746-2757, (2022); Hall JI, Lozano M, Estrada-Petrocelli L, Birring S, Turner R., The present and future of cough counting tools, J Thorac Dis, 12, 9, pp. 5207-5223, (2020); Monge-Alvarez J, Hoyos-Barcelo C, Lesso P, Casaseca-de-la-Higuera P., Robust Detection of Audio-Cough Events Using Local Hu Moments, IEEE J Biomed Health Inform, 23, 1, pp. 184-196, (2019)","F. Barata; Center for Digital Health Interventions, Department of Management Technology and Economics, ETH Zurich, Zurich, Weinbergstrasse 56/58, 8092, Switzerland; email: fbarata@ethz.ch","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149144075"
"Luo Y.-L.; Cheng Y.-Q.; Zhou Z.-Q.; Fan M.-Y.; Chen D.-F.; Chen Y.; Chen X.-B.; Zhong C.-H.; Tang C.-L.; Li S.-Y.; Su Z.-Q.","Luo, Yu-Long (55652227300); Cheng, Yan-Qiuzi (57203954851); Zhou, Zi-Qing (57192195570); Fan, Ming-Yue (57226791109); Chen, Di-Fei (57206581577); Chen, Yu (7601446257); Chen, Xiao-Bo (35344579500); Zhong, Chang-Hao (56598311100); Tang, Chun-Li (56443115500); Li, Shi-Yue (55780805100); Su, Zhu-Quan (56157041100)","55652227300; 57203954851; 57192195570; 57226791109; 57206581577; 7601446257; 35344579500; 56598311100; 56443115500; 55780805100; 56157041100","A clinical and canine experimental study in small-airway response to bronchial thermoplasty: Role of the neuronal effect","2022","Allergology International","71","1","","66","72","6","11","10.1016/j.alit.2021.07.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112676894&doi=10.1016%2fj.alit.2021.07.011&partnerID=40&md5=c800f79ec4afa0a455bb163fafeaaeb5","State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Shunyi Women's and Children's Hospital, Beijing Children's Hospital, Beijing, China","Luo Y.-L., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Cheng Y.-Q., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, Shunyi Women's and Children's Hospital, Beijing Children's Hospital, Beijing, China; Zhou Z.-Q., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Fan M.-Y., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Chen D.-F., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Chen Y., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Chen X.-B., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zhong C.-H., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Tang C.-L., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Li S.-Y., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Su Z.-Q., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Centre for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China","Background: The effects of bronchial thermoplasty (BT) on smooth muscle (SM) and nerves in small airways are unclear. Methods: We recruited 15 patients with severe refractory asthma, who received BT treatment. Endobronchial optical-coherence tomography (EB-OCT) was performed at baseline, 3 weeks' follow-up and 2 years' follow-up to evaluate the effect of BT on airway structure. In addition, we divided 12 healthy beagles into a sham group and a BT group, the latter receiving BT on large airways (inner diameter >3 mm) of the lower lobe. The dogs’ lung lobes were resected to evaluate histological and neuronal changes of the treated large airways and untreated small airways 12 weeks after BT. Results: Patients receiving BT treatment had significant improvement in Asthma Control Questionnaire (ACQ) scores and significant reduction in asthma exacerbations. EB-OCT results demonstrated a notable increase in inner-airway area (Ai) and decrease in airway wall area percentage (Aw%) in both large (3rd-to 6th-generation) and small (7th-to 9th-generation) airways. Furthermore, the animal study showed a significant reduction in the amount of SM in BT-treated large airways but not in untreated small airways. Protein gene product 9.5 (PGP9.5)–positive nerves and muscarinic receptor 3 (M3 receptor) expression in large and small airways were both markedly decreased throughout the airway wall 12 weeks after BT treatment. Conclusions: BT significantly reduced nerves, but not SM, in small airways, which might shed light on the mechanism of lung denervation by BT. © 2021 Japanese Society of Allergology","Asthma; Bronchial thermoplasty; Nerve; Small airway; Smooth muscle","Adult; Animals; Asthma; Bronchi; Bronchial Thermoplasty; Disease Progression; Dogs; Female; Humans; Male; Middle Aged; corticosteroid; muscarinic M3 receptor; adult; airway; Article; asthma; Asthma Control Questionnaire; beagle; bronchial thermoplasty; clinical article; controlled study; data analysis software; denervation; experimental study; female; follow up; gene expression; histology; human; lung lobe; male; optical coherence tomography; smooth muscle; animal; asthma; bronchial thermoplasty; bronchus; disease exacerbation; dog; middle aged; pathology; procedures","","","","","Distinctive Innovation Project of Colleges and Universities of Guangdong, (2014KTSCX097); Foundation of the State Key Laboratory of Respiratory Diseases, (SKLRD-QN-201910); Zhongnanshan Medical Foundation of Guangdong Province, (ZNSA-2020003); National Natural Science Foundation of China, NSFC, (81770017, 81900032)","This study was supported by National Natural Science Foundation of China (No. 81770017 and No. 81900032 ), Distinctive Innovation Project of Colleges and Universities of Guangdong (No. 2014KTSCX097 ), Zhongnanshan Medical Foundation of Guangdong Province (No. ZNSA-2020003 ) and Foundation of the State Key Laboratory of Respiratory Diseases (No. SKLRD-QN-201910 ). ","Global Initiative for Asthma, Global Strategy for Asthma Management and Prevention, (2019); van der Velden V.H., Hulsmann A.R., Autonomic innervation of human airways: structure, function, and pathophysiology in asthma, Neuroimmunomodulator, 6, pp. 145-159, (1999); Bonta P.I., Chanez P., Annema J.T., Shah P.L., Niven R., Bronchial thermoplasty in severe asthma: best practice recommendations from an expert panel, Respiration, 95, pp. 289-300, (2018); Facciolongo N., Di Stefano A., Pietrini V., Galeone C., Bellanova F., Menzella F., Et al., Nerve ablation after bronchial thermoplasty and sustained improvement in severe asthma, Bmc Pulm Med, 18, (2018); Pretolani M., Bergqvist A., Thabut G., Dombret M.C., Knapp D., Hamidi F., Et al., Effectiveness of bronchial thermoplasty in patients with severe refractory asthma: clinical and histopathologic correlations, J Allergy Clin Immunol, 139, pp. 1176-1185, (2017); Hogg J.C., Macklem P.T., Thurlbeck W.M., Site and nature of airway obstruction in chronic obstructive lung disease, N Engl J Med, 278, pp. 1355-1360, (1968); Su Z.Q., Guan W.J., Li S.Y., Feng J.X., Zhou Z.Q., Chen Y., Et al., Evaluation of the normal airway morphology using optical coherence tomography, Chest, 156, pp. 915-925, (2019); Pisi R., Tzani P., Aiello M., Martinelli E., Marangio E., Nicolini G., Et al., Small airway dysfunction by impulse oscillometry in asthmatic patients with normal forced expiratory volume in the 1st second values, Allergy Asthma Proc, 34, pp. e14-e20, (2013); Takeda T., Oga T., Niimi A., Matsumoto H., Ito I., Yamaguchi M., Et al., Relationship between small airway function and health status, dyspnea and disease control in asthma, Respiration, 80, pp. 120-126, (2010); Telenga E.D., van den Berge M., Ten H.N., Riemersma R.A., van der Molen T., Postma D.S., Small airways in asthma: their independent contribution to the severity of hyperresponsiveness, Eur Respir J, 41, pp. 752-754, (2013); Hamid Q., Song Y., Kotsimbos T.C., Minshall E., Bai T.R., Hegele R.G., Et al., Inflammation of small airways in asthma, J Allergy Clin Immunol, 100, pp. 44-51, (1997); Goldin J.G., McNitt-Gray M.F., Sorenson S.M., Johnson T.D., Dauphinee B., Kleerup E.C., Et al., Airway hyperreactivity: assessment with helical thin-section CT, Radiology, 208, pp. 321-329, (1998); Donovan G.M., Elliot J.G., Green F., James A.L., Noble P.B., Unraveling a clinical paradox: why does bronchial thermoplasty work in asthma?, Am J Respir Cell Mol Biol, 59, pp. 355-362, (2018); Costello R.W., Evans C.M., Yost B.L., Belmonte K.E., Gleich G.J., Jacoby D.B., Et al., Antigen-induced hyperreactivity to histamine: role of the vagus nerves and eosinophils, Am J Physiol, 276, pp. L709-L714, (1999); Kistemaker L.E., Bos S.T., Mudde W.M., Hylkema M.N., Hiemstra P.S., Wess J., Et al., Muscarinic M(3) receptors contribute to allergen-induced airway remodeling in mice, Am J Respir Cell Mol Biol, 50, pp. 690-698, (2014); Sato E., Koyama S., Okubo Y., Kubo K., Sekiguchi M., Acetylcholine stimulates alveolar macrophages to release inflammatory cell chemotactic activity, Am J Physiol, 274, pp. L970-L979, (1998); Lipworth B., Manoharan A., Anderson W., Unlocking the quiet zone: the small airway asthma phenotype, Lancet Respir Med, 2, pp. 497-506, (2014); Oguma T., Hirai T., Niimi A., Matsumoto H., Muro S., Shigematsu M., Et al., Limitations of airway dimension measurement on images obtained using multi-detector row computed tomography, PLoS One, 8, (2013); Tanabe N., Oguma T., Sato S., Kubo T., Kozawa S., Shima H., Et al., Quantitative measurement of airway dimensions using ultra-high resolution computed tomography, Respir Investig, 56, pp. 489-496, (2018); Chen Y., Ding M., Guan W.J., Wang W., Luo W.Z., Zhong C.H., Et al., Validation of human small airway measurements using endobronchial optical coherence tomography, Respir Med, 109, pp. 1446-1453, (2015); Goorsenberg A., D'Hooghe J., de Bruin D.M., van den Berk I., Annema J.T., Bonta P.I., Bronchial Thermoplasty-Induced acute airway effects assessed with optical coherence tomography in severe asthma, Respiration, 96, pp. 564-570, (2018); Su Z.Q., Guan W.J., Li S.Y., Ding M., Chen Y., Jiang M., Et al., Significances of spirometry and impulse oscillometry for detecting small airway disorders assessed with endobronchial optical coherence tomography in COPD, Int J Chron Obstruct Pulmon Dis, 13, pp. 3031-3044, (2018); Pavord I.D., Thomson N.C., Niven R.M., Corris P.A., Chung K.F., Cox G., Et al., Safety of bronchial thermoplasty in patients with severe refractory asthma, Ann Allergy Asthma Immunol, 111, pp. 402-407, (2013); Thomson N.C., Rubin A.S., Niven R.M., Corris P.A., Siersted H.C., Olivenstein R., Et al., Long-term (5 year) safety of bronchial thermoplasty: asthma Intervention Research (AIR) trial, Bmc Pulm Med, 11, (2011); Wechsler M.E., Laviolette M., Rubin A.S., Fiterman J., Lapa E.S.J., Shah P.L., Et al., Bronchial thermoplasty: long-term safety and effectiveness in patients with severe persistent asthma, J Allergy Clin Immunol, 132, pp. 1295-1302, (2013); Danek C.J., Lombard C.M., Dungworth D.L., Cox P.G., Miller J.D., Biggs M.J., Et al., Reduction in airway hyperresponsiveness to methacholine by the application of RF energy in dogs, J Appl Physiol, 97, pp. 1946-1953, (2004); Thomen R.P., Sheshadri A., Quirk J.D., Kozlowski J., Ellison H.D., Szczesniak R.D., Et al., Regional ventilation changes in severe asthma after bronchial thermoplasty with (3)He MR imaging and CT, Radiology, 274, pp. 250-259, (2015); Chernyavsky I.L., Russell R.J., Saunders R.M., Morris G.E., Berair R., Singapuri A., Et al., In vitro, in silico and in vivo study challenges the impact of bronchial thermoplasty on acute airway smooth muscle mass loss, Eur Respir J, 51, (2018); Undem B.J., Carr M.J., The role of nerves in asthma, Curr Allergy Asthma Rep, 2, pp. 159-165, (2002); Buels K.S., Fryer A.D., Muscarinic receptor antagonists: effects on pulmonary function, Handb Exp Pharmacol, pp. 317-341, (2012); Zhao C.M., Hayakawa Y., Kodama Y., Muthupalani S., Westphalen C.B., Andersen G.T., Et al., Denervation suppresses gastric tumorigenesis, Sci Transl Med, 6, pp. 115r-250, (2014); Slebos D.J., Klooster K., Koegelenberg C.F., Theron J., Styen D., Valipour A., Et al., Targeted lung denervation for moderate to severe COPD: a pilot study, Thorax, 70, pp. 411-419, (2015); Valipour A., Shah P.L., Pison C., Ninane V., Janssens W., Perez T., Et al., Safety and dose study of targeted lung denervation in Moderate/Severe COPD patients, Respiration, 98, pp. 329-339, (2019); Thien F., Measuring and imaging small airways dysfunction in asthma, Asia Pac Allergy, 3, pp. 224-230, (2013); Mayse M., Johnson P., Streeter J., Deem M., Hummel J., Targeted lung denervation in the healthy sheep model - a potential treatment for COPD, Eur Respir J, 44, (2014); Duarte A.G., Myers A.C., Cough reflex in lung transplant recipients, Lung, 190, pp. 23-27, (2012)","Z.-Q. Su; Sate Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 151 Yanjiang Road, 510120, China; email: py1011xiaoquan@163.com; S.-Y. Li; Sate Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 151 Yanjiang Road, 510120, China; email: lishiyue@188.com","","Japanese Society of Allergology","","","","","","13238930","","ALINF","34400075","English","Allergol. Int.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85112676894"
"Sood A.; Petersen H.; Liu C.; Myers O.; Shore X.W.; Gore B.A.; Vazquez-Guillamet R.; Cook L.S.; Meek P.; Tesfaigzi Y.","Sood, Akshay (57203164460); Petersen, Hans (7203049207); Liu, Congjian (57565286300); Myers, Orrin (6701591264); Shore, Xin Wang (57208084373); Gore, Bobbi A. (57566266700); Vazquez-Guillamet, Rodrigo (55656007100); Cook, Linda S. (56501376000); Meek, Paula (7004594248); Tesfaigzi, Yohannes (6701917029)","57203164460; 7203049207; 57565286300; 6701591264; 57208084373; 57566266700; 55656007100; 56501376000; 7004594248; 6701917029","Racial and Ethnic Minorities Have a Lower Prevalence of Airflow Obstruction than Non-Hispanic Whites","2022","COPD: Journal of Chronic Obstructive Pulmonary Disease","19","1","","61","68","7","8","10.1080/15412555.2022.2029384","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127796823&doi=10.1080%2f15412555.2022.2029384&partnerID=40&md5=a5a27232e2467f7ee15e04635f9dfcb6","Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States; Black Lung Program, Miners Colfax Medical Center, Raton, NM, United States; COPD Program, Lovelace Respiratory Research Institute, Albuquerque, NM, United States; Department of Internal Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Department of Family and Community Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States; Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, United States; Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO, United States; PhD Program, University of Utah College of Nursing, Salt Lake City, UT, United States","Sood A., Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States, Black Lung Program, Miners Colfax Medical Center, Raton, NM, United States; Petersen H., COPD Program, Lovelace Respiratory Research Institute, Albuquerque, NM, United States; Liu C., Department of Internal Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Myers O., Department of Family and Community Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States; Shore X.W., Department of Family and Community Medicine, University of New Mexico School of Medicine, Albuquerque, NM, United States; Gore B.A., Black Lung Program, Miners Colfax Medical Center, Raton, NM, United States; Vazquez-Guillamet R., Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, United States; Cook L.S., Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO, United States; Meek P., PhD Program, University of Utah College of Nursing, Salt Lake City, UT, United States; Tesfaigzi Y., COPD Program, Lovelace Respiratory Research Institute, Albuquerque, NM, United States, Department of Internal Medicine, Brigham and Women’s Hospital, Boston, MA, United States","Racial and ethnic disparities in chronic obstructive pulmonary disease (COPD) are not well-studied. Our objective was to examine differences in limited COPD-related outcomes between three minority groups—African Americans (AAs), Hispanics, and American Indians (AIs) versus non-Hispanic Whites (NHWs), as the referent group, in separate cohorts. Separate cross-sectional evaluations were performed of three US-based cohorts of subjects at risk for COPD: COPDGene Study with 6,884 NHW and 3,416 AA smokers; Lovelace Smokers’ Cohort with 1,598 NHW and 378 Hispanic smokers; and Mining Dust Exposure in the United States Cohort with 2,115 NHW, 2,682 Hispanic, and 2,467 AI miners. Prebronchodilator spirometry tests were performed at baseline visits using standard criteria. The primary outcome was the prevalence of airflow obstruction. Secondary outcomes were self-reported physician diagnosis of COPD, chronic bronchitis, and modified Medical Research Council dyspnea score. All minority groups had a lower prevalence of airflow obstruction than NHWs (adjusted ORs varied from 0.29 in AIs to 0.85 in AAs; p < 0.01 for all analyses). AAs had a lower prevalence of chronic bronchitis than NHWs. In our study, all minority groups had a lower prevalence of airflow obstruction but a greater level of self-reported dyspnea than NHWs, and covariates did not explain this association. A better understanding of racial and ethnic differences in smoking-related and occupational airflow obstruction may improve prevention and therapeutic strategies. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.","African Americans; American Indians; chronic bronchitis; COPD; emphysema; Hispanic Americans; miners; racial and ethnic differences; spirometric diagnosis of airflow obstruction","Bronchitis, Chronic; Cross-Sectional Studies; Dyspnea; Ethnic and Racial Minorities; Humans; Prevalence; Pulmonary Disease, Chronic Obstructive; United States; adult; African American; airway obstruction; American Indian; Article; Caucasian; chronic bronchitis; chronic obstructive lung disease; cohort analysis; cross-sectional study; dust exposure; dyspnea; emphysema; ethnic difference; ethnic group; female; Hispanic; human; longitudinal study; major clinical study; male; medical research; middle aged; mining; minority group; odds ratio; prevalence; race; self report; smoking; spirometry; United States; chronic bronchitis; chronic obstructive lung disease; dyspnea; epidemiology","","","","","State of New Mexico; Tobacco Settlement Fund; National Institutes of Health, NIH, (RO1 HL068111); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (R01HL140839); National Heart, Lung, and Blood Institute, NHLBI; Health Resources and Services Administration, HRSA, (2H1GRH27375, D04RH31788, H37RH0057); Health Resources and Services Administration, HRSA","This work was supported by funding by the State of New Mexico (appropriation from the Tobacco Settlement Fund) and the National Institutes of Health (RO1 HL068111 and HL140839 to YT). This work was supported by HRSA (2H1GRH27375, H37RH0057, D04RH31788 to CP, AS). Role of the sponsors: The sponsors played no role in developing the research and manuscript. Guarantor statement : AS takes responsibility for (is the guarantor of) the content of the manuscript, including the data and analysis. Author contributions : AS, HP, RVG, LSC, PM, YT made substantial contributions to the conception or design of the work and CP, OM, XWS, HP made substantial contributions to the acquisition, analysis, or interpretation of data for the work. AS, HP, CL, RVG, LSC, PM, YT, CP, OM, XWS made substantial contribution toward drafting the work or revising it critically for important intellectual content. AS, HP, CL, RVG, LSC, PM, YT, CP, OM, XWS provided the final approval of the version to be published. AS, HP, CL, RVG, LSC, PM, YT, CP, OM, XWS agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.","(2017); Balmes J., Becklake M., Blanc P., Et al., American thoracic society statement: occupational contribution to the burden of airway disease, Am J Respir Crit Care Med, 167, 5, pp. 787-797, (2003); Dransfield M.T., Bailey W.C., COPD: racial disparities in susceptibility, treatment, and outcomes, Clin Chest Med, 27, 3, pp. 463-471, (2006); Colby S.L., Ortman J.M., Projections of the size and composition of the U.S. population: 2014 to 2060, current population reports, (2014); Bruse S., Sood A., Petersen H., Et al., New Mexican Hispanic smokers have lower odds of chronic obstructive pulmonary disease and less decline in lung function than non-Hispanic whites, Am J Respir Crit Care Med, 184, 11, pp. 1254-1260, (2011); Sood A., Stidley C.A., Picchi M.A., Et al., Difference in airflow obstruction between Hispanic and non-Hispanic white female smokers, COPD, 5, 5, pp. 274-281, (2008); Regan E.A., Hokanson J.E., Murphy J.R., Et al., Genetic epidemiology of COPD (COPDGene) study design, COPD, 7, 1, pp. 32-43, (2010); Evans K., Lerch S., Boyce T.W., Et al., An innovative approach to enhancing access to medical screening for miners using a mobile clinic with telemedicine capability, J Health Care Poor Underserved, 27, 4A, pp. 62-72, (2016); Shumate A.M., Yeoman K., Victoroff T., Et al., Morbidity and health risk factors among New Mexico miners: a comparison across mining sectors, J Occup Environ Med, 59, 8, pp. 789-794, (2017); Ferris B.G., Epidemiology Standardization Project (American Thoracic Society, Am Rev Respir Dis, 118, 6, pp. 1-120, (1978); Standardization of Spirometry, 1994 Update. 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Mehari A., Gillum R.F., Chronic obstructive pulmonary disease in African- and European-American women: morbidity, mortality and healthcare utilization in the USA, Expert Rev Respir Med, 9, 2, pp. 161-170, (2015); Hansel N.N., Washko G.R., Foreman M.G., Et al., Racial differences in CT phenotypes in COPD, COPD, 10, 1, pp. 20-27, (2013); Foreman M.G., Zhang L., Murphy J., Et al., Early-onset chronic obstructive pulmonary disease is associated with female sex, maternal factors, and African American race in the COPDGene study, Am J Respir Crit Care Med, 184, 4, pp. 414-420, (2011); Rice K.L., Leimer I., Kesten S., Et al., Responses to tiotropium in African-American and Caucasian patients with chronic obstructive pulmonary disease, Transl Res, 152, 2, pp. 88-94, (2008); Shaya F.T., Maneval M.S., Gbarayor C.M., Et al., Burden of COPD, asthma, and concomitant COPD and asthma among adults: racial disparities in a medicaid population, Chest, 136, 2, pp. 405-411, (2009); Hardin M., Silverman E.K., Barr R.G., Et al., The clinical features of the overlap between COPD and asthma, Respir Res, 12, 1, (2011); Putcha N., Han M.K., Martinez C.H., Et al., Comorbidities of COPD have a major impact on clinical outcomes, particularly in African Americans, Chronic Obstr Pulm Dis, 1, 1, pp. 105-114, (2014); Vaz Fragoso C.A., McAvay G., Gill T.M., Et al., Ethnic differences in respiratory impairment, Thorax, 69, 1, pp. 55-62, (2014); Kinney G.L., Thomas D.S., Cicutto L., Et al., The protective effect of Hispanic ethnicity on chronic obstructive pulmonary disease mortality is mitigated by smoking behavior, J Pulm Respir Med, 4, 6, (2014); Samet J.M., Wiggins C.L., Key C.R., Et al., Mortality from lung cancer and chronic obstructive pulmonary disease in New Mexico, 1958–82, Am J Public Health, 78, 9, pp. 1182-1186, (1988); Diaz A.A., Come C.E., Mannino D.M., Et al., Obstructive lung disease in Mexican Americans and non-Hispanic whites: an analysis of diagnosis and survival in the National Health and Nutritional Examination Survey III follow-up study, Chest, 145, 2, pp. 282-289, (2014); Kurth L., Doney B., Halldin C., Prevalence of airflow obstruction among ever-employed US adults aged 18–79 years by longest held occupation group: National Health and Nutrition Examination Survey 2007–2008, Occup Environ Med, 73, 7, pp. 482-486, (2016); Ruiz J.M., Steffen P., Smith T.B., Hispanic mortality paradox: a systematic review and meta-analysis of the longitudinal literature, Am J Public Health, 103, 3, pp. e52-e60, (2013); Chen W., Brehm J.M., Boutaoui N., Et al., Native American ancestry, lung function, and COPD in Costa Ricans, Chest, 145, 4, pp. 704-710, (2014); Chen W., Brehm J.M., Manichaikul A., Et al., A genome-wide association study of chronic obstructive pulmonary disease in Hispanics, Ann Am Thorac Soc, 12, 3, pp. 340-348, (2015); Diaz A.A., Petersen H., Meek P., Et al., Differences in health-related quality of life between New Mexican Hispanic and non-Hispanic White smokers, Chest, 150, 4, pp. 869-876, (2016); Diaz A.A., Rahaghi F.N., Doyle T.J., Et al., Differences in respiratory symptoms and lung structure between Hispanic and non-Hispanic white smokers: a comparative study, Chronic Obstr Pulm Dis, 4, 4, pp. 297-304, (2017); Dwyer-Lindgren L., Bertozzi-Villa A., Stubbs R.W., Et al., Trends and patterns of differences in chronic respiratory disease mortality among US counties, 1980–2014, Jama, 318, 12, pp. 1136-1149, (2017); Pahwa P., Karunanayake C.P., Rennie D.C., Et al., Prevalence and associated risk factors of chronic bronchitis in first nations people, BMC Pulm Med, 17, 1, (2017); Bird Y., Moraros J., Mahmood R., Et al., Prevalence and associated factors of COPD among aboriginal peoples in Canada: a cross-sectional study, Int J Chronic Obstr Pulm Dis, 12, pp. 1915-1922, (2017); Konrad S., Hossain A., Senthilselvan A., Et al., Chronic bronchitis in aboriginal people—prevalence and associated factors, Chronic Dis Inj Can, 33, 4, pp. 218-225, (2013); Hwang Y.I., Kim C.H., Kang H.R., Et al., Comparison of the prevalence of chronic obstructive pulmonary disease diagnosed by lower limit of normal and fixed ratio criteria, J Korean Med Sci, 24, 4, pp. 621-626, (2009); Schluger N.W., Dozor A.J., Jung Y.E.G., Rethinking the race adjustment in pulmonary function testing, Ann Am Thorac Soc, (2021); Linares-Perdomo O., Hegewald M., Collingridge D.S., Et al., Comparison of NHANES III and ERS/GLI 12 for airway obstruction classification and severity, Eur Respir J, 48, 1, pp. 133-141, (2016); Braun L., Wolfgang M., Dickersin K., Defining race/ethnicity and explaining difference in research studies on lung function, Eur Respir J, 41, 6, pp. 1362-1370, (2013); Young R.P., Hopkins R.J., A review of the Hispanic paradox: time to spill the beans?, Eur Respir Rev, 23, 134, pp. 439-449, (2014); Leng S., Picchi M.A., Tesfaigzi Y., Et al., Dietary nutrients associated with preservation of lung function in hispanic and non-Hispanic white smokers from New Mexico, Int J Chron Obstruct Pulmon Dis, 12, pp. 3171-3181, (2017); Perez-Stable E.J., Marin B.V., Marin G., Et al., Apparent underreporting of cigarette consumption among Mexican American smokers, Am J Public Health, 80, 9, pp. 1057-1061, (1990)","A. Sood; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, 1 University of New Mexico, MSC 10 5550, 87131, United States; email: asood@salud.unm.edu","","Taylor and Francis Ltd.","","","","","","15412555","","","35099333","English","COPD J. Chronic Obstructive Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127796823"
"Verstraete K.; Gyselinck I.; Huts H.; Das N.; Topalovic M.; De Vos M.; Janssens W.","Verstraete, Kenneth (57216490136); Gyselinck, Iwein (57211536554); Huts, Helene (58289017500); Das, Nilakash (57200823166); Topalovic, Marko (55931197500); De Vos, Maarten (57202389126); Janssens, Wim (8866170000)","57216490136; 57211536554; 58289017500; 57200823166; 55931197500; 57202389126; 8866170000","Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials","2023","Thorax","78","10","","983","989","6","8","10.1136/thorax-2022-219382","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160274987&doi=10.1136%2fthorax-2022-219382&partnerID=40&md5=5e47205a1a0ecc84956d74d1be20a891","Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium; ArtiQ, Leuven, Belgium; Department of Development and Regeneration, KU Leuven, Leuven, Belgium","Verstraete K., Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium; Gyselinck I., Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; Huts H., Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium; Das N., Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium; Topalovic M., ArtiQ, Leuven, Belgium; De Vos M., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Janssens W., Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, Belgium","Rationale Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention. Objectives We aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates. Methods We used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal inference models. We then validated the methodology on 5990 subjects from the InforMing the PAthway of COPD Treatment (IMPACT) trial (NCT02164513) to estimate the ITE of FF/umeclidinium/VI (FF/UMEC/VI) versus UMEC/VI on exacerbation rate. We used Causal Forest as causal inference model. Results In SUMMIT, Causal Forest was optimised on the training set (n=5705) and tested on 2446 subjects (Q-score 0.61). In IMPACT, Causal Forest was optimised on 4193 subjects in the training set and tested on 1797 individuals (Q-score 0.21). In both trials, the quantiles of patients with the strongest ITE consistently demonstrated the largest reductions in observed exacerbations rates (0.54 and 0.53, p<0.001). Poor lung function and blood eosinophils, respectively, were the strongest predictors of ITE. Conclusions This study shows that ML models for causal inference can be used to identify individual response to different COPD treatments and highlight treatment traits. Such models could become clinically useful tools for individual treatment decisions in COPD. © 2023 Authors. All rights reserved.","COPD Exacerbations","Administration, Inhalation; Androstadienes; Benzyl Alcohols; Bronchodilator Agents; Chlorobenzenes; Double-Blind Method; Drug Combinations; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Randomized Controlled Trials as Topic; Treatment Outcome; fluticasone furoate plus umeclidinium plus vilanterol; fluticasone furoate plus vilanterol; placebo; umeclidinium plus vilanterol; androstane derivative; benzyl alcohol derivative; bronchodilating agent; chlorobenzene; aged; Article; chronic obstructive lung disease; controlled study; disease exacerbation; drug response; eosinophil; female; human; human cell; lung function; machine learning; major clinical study; male; morbidity; mortality; personalized medicine; placebo effect; prediction; randomized controlled trial (topic); therapy effect; validation process; double blind procedure; drug combination; inhalational drug administration; lung; randomized controlled trial (topic); treatment outcome","","chlorobenzene, 108-90-7; Androstadienes, ; Benzyl Alcohols, ; Bronchodilator Agents, ; Chlorobenzenes, ; Drug Combinations, ","","","Artificial Intelligence, (G0C9623N); AstraZeneca KU Leuven, (LSASZ7-O2010); Onderzoeksprogramma Artificiële Intelligentie; Fonds Wetenschappelijk Onderzoek, FWO, (11N3922N); Fonds Wetenschappelijk Onderzoek, FWO; Vlaamse regering","Funding text 1: KV has nothing to disclose. IG receives personal funding from Research Foundation Flanders (FWO). HH has nothing to disclose. ND has nothing to disclose. MT is CEO and co-founder of ArtiQ but received no payments related to the manuscript. MDV received funding from the AI in Flanders project. WJ received grants from AstraZeneca and Chiesi and obtained fees from AstraZeneca, Chiesi and GlaxoSmithKline. He is chairman of Board of Flemish Society for TBC prevention and board member of ArtiQ. ; Funding text 2: This research received funding from the Flemish Government under the ‘Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen’ program, from AstraZeneca KU Leuven Chair in Respiratory Diseases (LSASZ7-O2010) and FWO Research Project:‘Artificial Intelligence (AI) for data-driven personalised medicine’ (G0C9623N). IG is funded by Research Foundation—Flanders (FWO, 11N3922N). WJ is supported as senior investigator of the Research Foundation—Flanders (FWO, 1800715N|1800720N). ","Holland P.W., Statistics and causal inference, J Am Stat Assoc, 81, pp. 945-960, (1986); Pearl J., Causal diagrams for empirical research, Biometrika, 82, pp. 669-688, (1995); Wright S., The method of path coefficients, Ann Math Statist, 5, pp. 161-215, (1934); Haavelmo T., The statistical implications of a system of simultaneous equations, Econometrica, 11, (1943); Heckman J., Pinto R., Causal analysis after Haavelmo, Econ Theory, 31, pp. 115-151, (2015); Splawa-Neyman J., Dabrowska D.M., Speed T.P., On the application of probability theory to agricultural experiments. Essay on principles. Section 9, Statist Sci, 5, pp. 465-472, (1990); Rubin D.B., Estimating causal effects of treatments in randomized and nonrandomized studies, J Educ Psychol, 66, pp. 688-701, (1974); Rubin D.B., Matched Sampling for Causal Effects. 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Janssens; Laboratory of Respiratory Diseases and Thoracic Surgery (BREATHE), KU Leuven, Leuven, 3000, Belgium; email: wim.janssens@uzleuven.be","","BMJ Publishing Group","","","","","","00406376","","THORA","37012070","English","Thorax","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85160274987"
"Mietus-Snyder M.; Suslovic W.; Delaney M.; Playford M.P.; Ballout R.A.; Barber J.R.; Otvos J.D.; DeBiasi R.L.; Mehta N.N.; Remaley A.T.","Mietus-Snyder, Michele (6603021444); Suslovic, William (57203653809); Delaney, Meghan (57190804857); Playford, Martin P. (6603652509); Ballout, Rami A. (56561215100); Barber, John R. (57208385581); Otvos, James D. (7005594127); DeBiasi, Roberta L. (57198182572); Mehta, Nehal N. (56281239400); Remaley, Alan T. (7006511029)","6603021444; 57203653809; 57190804857; 6603652509; 56561215100; 57208385581; 7005594127; 57198182572; 56281239400; 7006511029","Changes in HDL cholesterol, particles, and function associate with pediatric COVID-19 severity","2022","Frontiers in Cardiovascular Medicine","9","","1033660","","","","9","10.3389/fcvm.2022.1033660","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140605322&doi=10.3389%2ffcvm.2022.1033660&partnerID=40&md5=0dee38dfa183db3596998694f6bc5941","Children's National Hospital, Washington, DC, United States; The Children's National Clinical and Translational Science Institute, Washington, DC, United States; Division of Cardiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Department of Pediatrics, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Division of Clinical and Laboratory Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Cardiovascular and Pulmonary Branch, National Institutes of Health, Bethesda, MD, United States; Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States; Division of Infectious Diseases, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Department of Microbiology, Immunology and Tropical Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Clinical Center, Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States","Mietus-Snyder M., Children's National Hospital, Washington, DC, United States, The Children's National Clinical and Translational Science Institute, Washington, DC, United States, Division of Cardiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States, Department of Pediatrics, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Suslovic W., Children's National Hospital, Washington, DC, United States; Delaney M., Children's National Hospital, Washington, DC, United States, Department of Pediatrics, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States, Division of Clinical and Laboratory Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Playford M.P., Cardiovascular and Pulmonary Branch, National Institutes of Health, Bethesda, MD, United States; Ballout R.A., Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States; Barber J.R., The Children's National Clinical and Translational Science Institute, Washington, DC, United States; Otvos J.D., Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States; DeBiasi R.L., Children's National Hospital, Washington, DC, United States, The Children's National Clinical and Translational Science Institute, Washington, DC, United States, Department of Pediatrics, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States, Division of Infectious Diseases, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States, Department of Microbiology, Immunology and Tropical Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States; Mehta N.N., Cardiovascular and Pulmonary Branch, National Institutes of Health, Bethesda, MD, United States; Remaley A.T., Lipoprotein Metabolism Section, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States, Clinical Center, Department of Laboratory Medicine, National Institutes of Health, Bethesda, MD, United States","Background: Myriad roles for high-density lipoprotein (HDL) beyond atheroprotection include immunologic functions implicated in the severity of coronavirus disease-2019 (COVID-19) in adults. We explored whether there is an association between HDL and COVID-19 severity in youth. Methods: A pediatric cohort (N = 102), who tested positive for COVID-19 across a range of disease manifestations from mild or no symptoms, to acute severe symptoms, to the multisystem inflammatory syndrome of children (MIS-C) was identified. Clinical data were collected from the medical record and reserve plasma aliquots were assessed for lipoproteins by NMR spectroscopy and assayed for HDL functional cholesterol efflux capacity (CEC). Findings were compared by COVID-19 status and symptom severity. Lipoprotein, NMR spectroscopy and CEC data were compared with 30 outpatient COVID negative children. Results: Decreasing HDL cholesterol (HDL-c), apolipoprotein AI (ApoA-I), total, large and small HDL particles and HDL CEC showed a strong and direct linear dose-response relationship with increasing severity of COVID-19 symptoms. Youth with mild or no symptoms closely resembled the uninfected. An atypical lipoprotein that arises in the presence of severe hepatic inflammation, lipoprotein Z (LP-Z), was absent in COVID-19 negative controls but identified more often in youth with the most severe infections and the lowest HDL parameters. The relationship between HDL CEC and symptom severity and ApoA-I remained significant in a multiply adjusted model that also incorporated age, race/ethnicity, the presence of LP-Z and of GlycA, a composite biomarker reflecting multiple acute phase proteins. Conclusion: HDL parameters, especially HDL function, may help identify youth at risk of more severe consequences of COVID-19 and other novel infectious pathogens. Copyright © 2022 Mietus-Snyder, Suslovic, Delaney, Playford, Ballout, Barber, Otvos, DeBiasi, Mehta and Remaley.","HDL function; HDL subspecies; HDL-cholesterol; lipoprotein Z; NMR lipoprotein analysis; pediatric COVID-19 severity","acute phase protein; alanine aminotransferase; analgesic agent; anticoagulant agent; apolipoprotein A1; apolipoprotein B; aspartate aminotransferase; biological marker; C reactive protein; cholesterol; corticosteroid; creatinine; high density lipoprotein; high density lipoprotein cholesterol; lipoprotein; low density lipoprotein cholesterol; SARS-CoV-2 antibody; triacylglycerol; adult; Article; asthma; blood pressure; body mass; child; clinical assessment; clinical study; cohort analysis; controlled study; coronavirus disease 2019; diabetes mellitus; disease severity; dose response; electronic medical record; epilepsy; ethnicity; female; hematocrit; hepatitis; hospitalization; human; immunosuppressive treatment; infectious agent; inflammation; laboratory test; length of stay; leukocyte count; major clinical study; male; medical record; mental disease; nuclear magnetic resonance spectroscopy; outpatient; pediatric multisystem inflammatory syndrome; pediatric patient; platelet count; polymerase chain reaction; protein function; red blood cell distribution width; retrospective study; seizure; sickle cell anemia; urea nitrogen blood level; vaccination","","alanine aminotransferase, 9000-86-6, 9014-30-6; aspartate aminotransferase, 9000-97-9; C reactive protein, 9007-41-4; cholesterol, 57-88-5; creatinine, 19230-81-0, 60-27-5","","","National Heart, Lung, and Blood Institute, NHLBI; National Center for Advancing Translational Sciences, NCATS; NHLBI Division of Intramural Research, DIR","This study was supported by Award Number UL1TR001876 from the NIH National Center for Advancing Translational Sciences. Research by Playford, Ballout, Mehta, and Remaley was supported by DIR intramural research funds from the National Heart, Lung and Blood Institute. The other authors received no additional funding. 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Mietus-Snyder; Children's National Hospital, Washington, United States; email: mmsnyder@childrensnational.org","","Frontiers Media S.A.","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140605322"
"Chandran U.; Reps J.; Yang R.; Vachani A.; Maldonado F.; Kalsekar I.","Chandran, Urmila (36666126300); Reps, Jenna (43061445600); Yang, Robert (57220775674); Vachani, Anil (8625677900); Maldonado, Fabien (23012782200); Kalsekar, Iftekhar (6602997722)","36666126300; 43061445600; 57220775674; 8625677900; 23012782200; 6602997722","Machine Learning and Real-World Data to Predict Lung Cancer Risk in Routine Care","2023","Cancer Epidemiology Biomarkers and Prevention","32","3","","337","343","6","10","10.1158/1055-9965.EPI-22-0873","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149999298&doi=10.1158%2f1055-9965.EPI-22-0873&partnerID=40&md5=8609f1bd97312f9cccb84e779e18302d","Johnson & Johnson Global Epidemiology, Titusville, NJ, United States; Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States; University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, United States; Vanderbilt University, Nashville, TN, United States","Chandran U., Johnson & Johnson Global Epidemiology, Titusville, NJ, United States, Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States; Reps J., Johnson & Johnson Global Epidemiology, Titusville, NJ, United States; Yang R., Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States; Vachani A., University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, United States; Maldonado F., Vanderbilt University, Nashville, TN, United States; Kalsekar I., Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States","Background: This study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population. Methods: Over 4.7 million individuals, aged 45 to 65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in Optum's de-identified Electronic Health Record (EHR) dataset.Aleast absolute shrinkage and selection operator model was fit using all available data in the 365 days prior. Temporal validation was assessed with recent data. External validation was assessed with data from Mercy Health Systems EHR and Optum's de-identified Clinformatics Data Mart Database. Racial inequities in model discrimination were assessed with xAUCs. Results: The model AUC was 0.76. Top predictors included age, smoking, race, ethnicity, and diagnosis of chronic obstructive pulmonary disease. The model identified a high-risk group with lung cancer incidence 9 times the average cohort incidence, representing 10% of patients with lung cancer. Model performed well temporally and externally, while performance was reduced for Asians and Hispanics. Conclusions: A high-dimensional model trained using big data identified a subset of patients with high lung cancer risk. The model demonstrated transportability toEHRand claims data, while underscoring the need to assess racial disparities when using machine learning methods. Impact: This internally and externally validated real-world databased lung cancer prediction model is available on an open-source platform for broad sharing and application. Model integration into an EHR system could minimize physician burden by automating identification of high-risk patients. © 2022 The Authors.","","Early Detection of Cancer; Electronic Health Records; Humans; Incidence; Lung Neoplasms; Machine Learning; Pulmonary Disease, Chronic Obstructive; adult; aged; area under the curve; Article; Asian; cancer diagnosis; cancer incidence; cancer risk; cancer screening; cancer therapy; chronic obstructive lung disease; cohort analysis; electronic health record; ethnicity; ex-smoker; female; follow up; high risk patient; high risk population; Hispanic; human; least absolute shrinkage and selection operator; low risk population; lung cancer; machine learning; major clinical study; male; middle aged; never smoker; patient care; people by smoking status; predictive value; race; racial disparity; receiver operating characteristic; retrospective study; smoking; chronic obstructive lung disease; early cancer diagnosis; incidence; lung tumor; machine learning","","","","","Johnson & Johnson LLC; Johnson & Johnson, LLC; MagArray Inc.; Optellum Ltd., Gordon and Betty Moore Foundation; Precyte Inc.","Funding text 1: U. Chandran reports receiving salary and compensation from Johnson & Johnson LLC; the submitted work was funded by Johnson & Johnson LLC. J. Reps reports being an employee of Janssen R&D and shareholder of Johnson & Johnson. R. Yang reports other support from Johnson & Johnson outside the submitted work. A. Vachani reports personal fees from Johnson & Johnson during the conduct of the study; grants from Precyte Inc., MagArray Inc., Optellum Ltd., Gordon and Betty Moore Foundation; and grants from Lungevity outside the submitted work. F. Maldonado received fees from Johnson & Johnson for preparation of this manuscript. I. Kalsekar reports other support from Johnson & Johnson outside the submitted work.; Funding text 2: This work was funded by Johnson & Johnson, LLC.","US Cancer Statistics Data Visualizations Tool, based on 2021 submission data (1999-2019), (2022); Siegel RL, Miller KD, Fuchs HE, Jemal A., Cancer statistics, 2022, CA Cancer J Clin, 72, pp. 7-33, (2022); Fedewa SA, Bandi P, Smith RA, Silvestri GA, Jemal A., Lung cancer screening rates during the COVID-19 pandemic, Chest, 161, pp. 586-589, (2022); Wang Y, Midthun DE, Wampfler JA, Deng B, Stoddard SM, Zhang S, Et al., Trends in the proportion of patients with lung cancer meeting screening criteria, JAMA, 313, pp. 853-855, (2015); Clinician summary of USPSTF recommendation: screening for lung cancer 2021; Faselis C, Nations JA, Morgan CJ, Antevil J, Roseman JM, Zhang S, Et al., Assessment of lung cancer risk among smokers for whom annual screening is not recommended, JAMA Oncol, 2022; Gould MK, Huang BZ, Tammemagi MC, Kinar Y, Shiff R., Machine learning for early lung cancer identification using routine clinical and laboratory data, Am J Respir Crit Care Med, 204, pp. 445-453, (2021); Wang X, Zhang Y, Hao S, Zheng L, Liao J, Ye C, Et al., Prediction of the 1-year risk of incident lung cancer: prospective study using electronic health records from the state of Maine, J Med Internet Res, 21, (2019); Cancer Stat Facts: Lung and Bronchus Cancer: National Cancer Institute, (2022); Collins GS, Reitsma JB, Altman DG, Moons KG., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMC Med, 13, (2015); Setoguchi S, Solomon DH, Glynn RJ, Cook EF, Levin R, Schneeweiss S., Agreement of diagnosis and its date for hematologic malignancies and solid tumors between Medicare claims and cancer registry data, Cancer Causes Control, 18, pp. 561-569, (2007); Goldsbury D, Weber M, Yap S, Banks E, O'Connell DL, Canfell K., Identifying incident colorectal and lung cancer cases in health service utilization databases in Australia: a validation study, BMC Med Inform Decis Mak, 17, (2017); Berquist SL, Brooks GA, Keating NL, Landrum MB, Rose S., Classifying lung cancer severity with ensemble machine learning in health care claims data, Proc Mach Learn Res, 68, pp. 25-38, (2017); Turner RM, Chen YW, Fernandes AW., Validation of a case-finding algorithm for identifying patients with non-small cell lung cancer (NSCLC) in administrative claims databases, Front Pharmacol, 8, (2017); Hardin J, Reps JM., Evaluating the impact of covariate lookback times on performance of patient-level prediction models, BMC Med Res Methodol, 21, (2021); Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR., Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data, J Am Med Inform Assoc, 25, pp. 969-975, (2018); Reps JM, Williams RD, You SC, Falconer T, Minty E, Callahan A, Et al., Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation, BMC Med Res Methodol, 20, (2020); Reps JM, Ryan P, Rijnbeek PR., Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data, BMJ Open, 11, (2021); Saito T, Rehmsmeier M., The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS One, 10, (2015); Kallus N, Zhou A., The fairness of risk scores beyond classification: bipartite ranking and the xAUC metric; Khalid S, Yang C, Blacketer C, Duarte-Salles T, Fernandez-Bertolin S, Kim C, Et al., A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data, Comput Methods Programs Biomed, 211, (2021); Ten Haaf K, Bastani M, Cao P, Jeon J, Toumazis I, Han SS, Et al., A comparative modeling analysis of risk-based lung cancer screening strategies, J Natl Cancer Inst, 112, pp. 466-479, (2020); Reps JM, Ryan PB, Rijnbeek PR, Schumie MJ., Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets, J Big Data, 8, pp. 1-18, (2021); Rojas JC, Fahrenbach J, Makhni S, Cook SC, Williams JS, Umscheid CA, Et al., Framework for integrating equity into machine learning models: a case study, Chest, 161, pp. 1621-1627, (2022); Pinsky P., Electronic health records and machine learning for early detection of lung cancer and other conditions: thinking about the path ahead, Am J Respir Crit Care Med, 204, pp. 389-390, (2021)","","","American Association for Cancer Research Inc.","","","","","","10559965","","CEBPE","36576991","English","Cancer Epidemiol. Biomarkers Prev.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85149999298"
"Parks J.; McLean K.E.; McCandless L.; de Souza R.J.; Brook J.R.; Scott J.; Turvey S.E.; Mandhane P.J.; Becker A.B.; Azad M.B.; Moraes T.J.; Lefebvre D.L.; Sears M.R.; Subbarao P.; Takaro T.K.","Parks, Jaclyn (57214988762); McLean, Kathleen E. (55189294200); McCandless, Lawrence (16064418900); de Souza, Russell J. (7102470762); Brook, Jeffrey R. (7202925211); Scott, James (57202359575); Turvey, Stuart E. (12778474700); Mandhane, Piush J. (23992736900); Becker, Allan B. (7401943668); Azad, Meghan B. (51664461300); Moraes, Theo J. (6602867764); Lefebvre, Diana L. (56463655200); Sears, Malcolm R. (35822534200); Subbarao, Padmaja (57373689300); Takaro, Tim K. (7006248568)","57214988762; 55189294200; 16064418900; 7102470762; 7202925211; 57202359575; 12778474700; 23992736900; 7401943668; 51664461300; 6602867764; 56463655200; 35822534200; 57373689300; 7006248568","Assessing secondhand and thirdhand tobacco smoke exposure in Canadian infants using questionnaires, biomarkers, and machine learning","2022","Journal of Exposure Science and Environmental Epidemiology","32","1","","112","123","11","14","10.1038/s41370-021-00350-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108779292&doi=10.1038%2fs41370-021-00350-4&partnerID=40&md5=96a9b287570dbe56950c43d6a387b783","Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada; BC Centre for Disease Control, Vancouver, BC, Canada; Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Department of Pediatrics, University of Alberta, Edmonton, AB, Canada; Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada; Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Hospital for Sick Children, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto, ON, Canada; Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada","Parks J., Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada; McLean K.E., BC Centre for Disease Control, Vancouver, BC, Canada; McCandless L., Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada; de Souza R.J., Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Brook J.R., Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Scott J., Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Turvey S.E., Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada; Mandhane P.J., Department of Pediatrics, University of Alberta, Edmonton, AB, Canada; Becker A.B., Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada; Azad M.B., Department of Pediatrics and Child Health, University of Manitoba, Winnipeg, MB, Canada, Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada; Moraes T.J., Hospital for Sick Children, Toronto, ON, Canada, Department of Pediatrics, University of Toronto, Toronto, ON, Canada; Lefebvre D.L., Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Sears M.R., Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Subbarao P., Hospital for Sick Children, Toronto, ON, Canada, Department of Pediatrics, University of Toronto, Toronto, ON, Canada; Takaro T.K., Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada","Background: As smoking prevalence has decreased in Canada, particularly during pregnancy and around children, and technological improvements have lowered detection limits, the use of traditional tobacco smoke biomarkers in infant populations requires re-evaluation. Objective: We evaluated concentrations of urinary nicotine biomarkers, cotinine and trans-3’-hydroxycotinine (3HC), and questionnaire responses. We used machine learning and prediction modeling to understand sources of tobacco smoke exposure for infants from the CHILD Cohort Study. Methods: Multivariable linear regression models, chosen through a combination of conceptual and data-driven strategies including random forest regression, assessed the ability of questionnaires to predict variation in urinary cotinine and 3HC concentrations of 2017 3-month-old infants. Results: Although only 2% of mothers reported smoking prior to and throughout their pregnancy, cotinine and 3HC were detected in 76 and 89% of the infants’ urine (n = 2017). Questionnaire-based models explained 31 and 41% of the variance in cotinine and 3HC levels, respectively. Observed concentrations suggest 0.25 and 0.50 ng/mL as cut-points in cotinine and 3HC to characterize SHS exposure. This cut-point suggests that 23.5% of infants had moderate or regular smoke exposure. Significance: Though most people make efforts to reduce exposure to their infants, parents do not appear to consider the pervasiveness and persistence of secondhand and thirdhand smoke. More than half of the variation in urinary cotinine and 3HC in infants could not be predicted with modeling. The pervasiveness of thirdhand smoke, the potential for dermal and oral routes of nicotine exposure, along with changes in public perceptions of smoking exposure and risk warrant further exploration. © 2021, The Author(s).","Biomarker; Childhood asthma; Cotinine; Secondhand smoke; Thirdhand smoke; Variable importance","Biomarkers; Canada; Cohort Studies; Cotinine; Female; Humans; Infant; Machine Learning; Pregnancy; Surveys and Questionnaires; Tobacco Smoke Pollution; biological marker; cotinine; Canada; cohort analysis; epidemiology; female; human; infant; machine learning; passive smoking; pregnancy; questionnaire","","cotinine, 486-56-6; Biomarkers, ; Cotinine, ; Tobacco Smoke Pollution, ","","","AllerGen NCE; Asthma Canada; Asthma-Canada; Environment Network of Centers of Excellence; Canadian Institutes of Health Research, CIHR; Canada Research Chairs; Simon Fraser University, SFU","No financial assistance was received in support of this secondary analysis study. The Canadian Institutes of Health Research (CIHR), and the Allergy, Genes, and Environment (AllerGen) Network of Centres of Excellence (NCE) provided core funding for CHILD. Additional support for the CHILD Study has been provided by Health Canada, Environment Canada, Canada Mortgage and Housing Corporation, the Sick Children’s Hospital Foundation, Don & Debbie Morrison, the Silver Thread Foundation, and the Childhood Asthma Foundation. JP has received graduate student research award funding from Asthma-Canada and AllerGen NCE, as well as graduate scholarships from Simon Fraser University in support of this project. MBA is supported by the Canada Research Chairs program. This work has been funded in part by the Canadian Institutes of Health Research (CIHR), Allergy, Genes, and Environment Network of Centers of Excellence (AllerGen NCE), Asthma Canada, and Simon Fraser University.","Chilmonczyk B.A., Salmun L.M., Megathlin K.N., Neveux L.M., Palomaki G.E., Knight G.J., Et al., Association between exposure to environmental tobacco smoke and exacerbations of asthma in children, N Engl J Med, 328, pp. 1665-1669, (1993); Bird Y., Staines-Orozco H., Pulmonary effects of active smoking and secondhand smoke exposure among adolescent students in Juárez, Mexico, Int J Chronic Obstr Pulm Dis, 11, pp. 1459-1467, (2016); Zhou S., Rosenthal D.G., Sherman S., Zelikoff J., Gordon T., Weitzman M., Physical, behavioral, and cognitive effects of prenatal tobacco and postnatal secondhand smoke exposure, Curr Probl Pediatr Adolesc Health Care, 44, pp. 219-241, (2014); Lee M., Ha M., Hong Y.-C., Park H., Kim Y., Kim E.-J., Et al., Exposure to prenatal secondhand smoke and early neurodevelopment: Mothers and Children’s Environmental Health (MOCEH) study, Environ Health, 18, (2019); Bruin J.E., Gerstein H.C., Holloway A.C., Long-term consequences of fetal and neonatal nicotine exposure: a critical review, Toxicological Sci, 116, pp. 364-374, (2010); Jacob P., Benowitz N.L., Destaillats H., Gundel L., Hang B., Martins-Green M., Et al., Thirdhand smoke: new evidence, challenges, and future directions, Chem Res Toxicol, 30, pp. 270-294, (2017); Silvestri M., Franchi S., Pistorio A., Petecchia L., Rusconi F., Smoke exposure, wheezing, and asthma development: a systematic review and meta-analysis in unselected birth cohorts, Pediatr Pulmonol, 50, pp. 353-362, (2015); Hukkanen J., Metabolism and disposition kinetics of nicotine, Pharmacol Rev, 57, pp. 79-115, (2005); Benowitz N.L., Hukkanen J., Jacob P., Nicotine chemistry, metabolism, kinetics and biomarkers, Handb Exp Pharmacol, 192, pp. 29-60, (2009); Dempsey D.A., Sambol N.C., Jacob P., Hoffmann E., Tyndale R.F., Fuentes-Afflick E., Et al., CYP2A6 genotype but not age determines cotinine half-life in infants and children, Clin Pharmacol Therapeutics, 94, pp. 400-406, (2013); Biomonitoring Summaries – Cotinine, (2016); Al-Sahab B., Saqib M., Hauser G., Tamim H., Prevalence of smoking during pregnancy and associated risk factors among Canadian women: a national survey, BMC Pregnancy Childbirth, 10, (2010); Cawkwell P.B., Lee L., Shearston J., Sherman S.E., Weitzman M., The difference a decade makes: smoking cessation counseling and screening at pediatric visits, Nicotine Tob Res, 18, (2016); Asbridge M., Public place restrictions on smoking in Canada: assessing the role of the state, media, science and public health advocacy, Soc Sci Med, 58, pp. 13-24, (2004); Noar S.M., Hall M.G., Francis D.B., Ribisl K.M., Pepper J.K., Brewer N.T., Pictorial cigarette pack warnings: a meta-analysis of experimental studies, Tob Control, 25, pp. 341-354, (2016); Hammond D., Fong G.T., Mcdonald P.W., Impact of the graphic Canadian warning labels on adult smoking behaviour, Tob Control, 12, pp. 391-395, (2003); Millar W.J., Hill G., Pregnancy and smoking, Health Rep, 15, pp. 53-56, (2004); Sanchez-Rodriguez J.E., Bartolome M., Canas A.I., Huetos O., Navarro C., Rodriguez A.C., Et al., Anti-smoking legislation and its effects on urinary cotinine and cadmium levels, Environ Res, 136, pp. 227-233, (2015); Connor S.K., McIntyre L., The sociodemographic predictors of smoking cessation among pregnant women in Canada. 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Takaro; Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada; email: ttakaro@sfu.ca","","Springer Nature","","","","","","15590631","","","34175887","English","J. Expos. Sci. Environ. Epidemiol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85108779292"
"Maniaci A.; Saibene A.M.; Calvo-Henriquez C.; Vaira L.; Radulesco T.; Michel J.; Chiesa-Estomba C.; Sowerby L.; Lobo Duro D.; Mayo-Yanez M.; Maza-Solano J.; Lechien J.R.; La Mantia I.; Cocuzza S.","Maniaci, Antonino (57194709764); Saibene, Alberto Maria (57204816220); Calvo-Henriquez, Christian (57188724822); Vaira, Luigi (57192231779); Radulesco, Thomas (56222224600); Michel, Justin (54965595700); Chiesa-Estomba, Carlos (56161942700); Sowerby, Leigh (35201965200); Lobo Duro, David (16550154100); Mayo-Yanez, Miguel (57192719804); Maza-Solano, Juan (56480757100); Lechien, Jerome Rene (36637838000); La Mantia, Ignazio (6602939790); Cocuzza, Salvatore (7004626904)","57194709764; 57204816220; 57188724822; 57192231779; 56222224600; 54965595700; 56161942700; 35201965200; 16550154100; 57192719804; 56480757100; 36637838000; 6602939790; 7004626904","Is generative pre-trained transformer artificial intelligence (Chat-GPT) a reliable tool for guidelines synthesis? A preliminary evaluation for biologic CRSwNP therapy","2024","European Archives of Oto-Rhino-Laryngology","281","4","","2167","2173","6","9","10.1007/s00405-024-08464-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184392400&doi=10.1007%2fs00405-024-08464-9&partnerID=40&md5=099db50979355c371863aaadbd158daa","Faculty of Medicine and Surgery, “Kore” University of Enna, Enna, Italy; Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France; Otolaryngology Unit, Santi Paolo E Carlo Hospital, Department of Health Sciences, Università Degli Studi Di Milano, Milan, Italy; Service of Otolaryngology, Hospital Complex of Santiago de Compostela, Santiago de Compostela, Spain; Maxillofacial Surgery Unit, University Hospital of Sassari, Sassari, Italy; ENT Surgeon, Department of Oto-Rhino-Laryngology Head and Neck Surgery, La Conception University Hospital, Aix-Marseille Univesity, 147 Bd Baille, Marseille, 13005, France; ENT-Head and Neck Department, Hospital Universitario Donostia, San Sebastián, Spain; Department of Otolaryngology - Head and Neck Surgery, Schulich School of Medicine, Western University, London, United Kingdom; Rhinology, Endoscopic Sinus and Skull Base Surgery, Hospital Universitario Marqués de Valdecilla, Santander, Spain; Department of Otorhinolaryngology, A Coruña University Hospital Complex, A Coruña, 15006, Spain; Servicio de Otorrinolaringología, Hospital Universitario Virgen Macarena, Sevilla. Hospital Quirónsalud Sagrado Corazón, Seville. Departamento de Cirugía, Universidad de Sevilla, Seville, Spain; Department of Anatomy and Experimental Oncology, Mons School of Medicine, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Deparment of Medical, Surgical Sciences and Advanced Technologies G.F.Ingrassia, University of Catania, Catania, 95123, Italy; Valdecilla Biomedical Research Institute, IDIVAL, Santander, Spain","Maniaci A., Faculty of Medicine and Surgery, “Kore” University of Enna, Enna, Italy, Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France; Saibene A.M., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Otolaryngology Unit, Santi Paolo E Carlo Hospital, Department of Health Sciences, Università Degli Studi Di Milano, Milan, Italy; Calvo-Henriquez C., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Service of Otolaryngology, Hospital Complex of Santiago de Compostela, Santiago de Compostela, Spain; Vaira L., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Maxillofacial Surgery Unit, University Hospital of Sassari, Sassari, Italy; Radulesco T., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, ENT Surgeon, Department of Oto-Rhino-Laryngology Head and Neck Surgery, La Conception University Hospital, Aix-Marseille Univesity, 147 Bd Baille, Marseille, 13005, France; Michel J., ENT Surgeon, Department of Oto-Rhino-Laryngology Head and Neck Surgery, La Conception University Hospital, Aix-Marseille Univesity, 147 Bd Baille, Marseille, 13005, France; Chiesa-Estomba C., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, ENT-Head and Neck Department, Hospital Universitario Donostia, San Sebastián, Spain; Sowerby L., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Department of Otolaryngology - Head and Neck Surgery, Schulich School of Medicine, Western University, London, United Kingdom; Lobo Duro D., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Rhinology, Endoscopic Sinus and Skull Base Surgery, Hospital Universitario Marqués de Valdecilla, Santander, Spain, Valdecilla Biomedical Research Institute, IDIVAL, Santander, Spain; Mayo-Yanez M., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Department of Otorhinolaryngology, A Coruña University Hospital Complex, A Coruña, 15006, Spain; Maza-Solano J., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Servicio de Otorrinolaringología, Hospital Universitario Virgen Macarena, Sevilla. Hospital Quirónsalud Sagrado Corazón, Seville. Departamento de Cirugía, Universidad de Sevilla, Seville, Spain; Lechien J.R., Rhynology Study Group of the Young-Otolaryngologists of the International Federations of Otorhino-Laryngological Societies (YO-IFOS), Paris, France, Department of Anatomy and Experimental Oncology, Mons School of Medicine, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; La Mantia I., Deparment of Medical, Surgical Sciences and Advanced Technologies G.F.Ingrassia, University of Catania, Catania, 95123, Italy; Cocuzza S., Deparment of Medical, Surgical Sciences and Advanced Technologies G.F.Ingrassia, University of Catania, Catania, 95123, Italy","Introduction: Biologic therapies for Chronic Rhinosinusitis with Nasal Polyps (CRSwNP) have emerged as an auspicious treatment alternative. However, the ideal patient population, dosage, and treatment duration are yet to be well-defined. Moreover, biologic therapy has disadvantages, such as high costs and limited access. The proposal of a novel Artificial Intelligence (AI) algorithm offers an intriguing solution for optimizing decision-making protocols. Methods: The AI algorithm was initially programmed to conduct a systematic literature review searching for the current primary guidelines on biologics' clinical efficacy and safety in treating CRSwNP. The review included a total of 12 studies: 6 systematic reviews, 4 expert consensus guidelines, and 2 surveys. Simultaneously, two independent human researchers conducted a literature search to compare the results. Subsequently, the AI was tasked to critically analyze the identified papers, highlighting strengths and weaknesses, thereby creating a decision-making algorithm and pyramid flow chart. Results: The studies evaluated various biologics, including monoclonal antibodies targeting Interleukin-5 (IL-5), IL-4, IL-13, and Immunoglobulin E (IgE), assessing their effectiveness in different patient populations, such as those with comorbid asthma or refractory CRSwNP. Dupilumab, a monoclonal antibody targeting the IL-4 receptor alpha subunit, demonstrated significant improvement in nasal symptoms and quality of life in patients with CRSwNP in several randomized controlled trials and systematic reviews. Similarly, mepolizumab and reslizumab, which target IL-5, have also shown efficacy in reducing nasal polyp burden and improving symptoms in patients with CRSwNP, particularly those with comorbid asthma. However, additional studies are required to confirm the long-term efficacy and safety of these biologics in treating CRSwNP. Conclusions: Biologic therapies have surfaced as a promising treatment option for patients with severe or refractory CRSwNP; however, the optimal patient population, dosage, and treatment duration are yet to be defined. The application of AI in decision-making protocols and the creation of therapeutic algorithms for biologic drug selection, could offer fascinating future prospects in the management of CRSwNP. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.","Artificial intelligence; Biologic CRSwNP therapy; Generative pre-trained transformer (GPT); Guidelines synthesis; Preliminary evaluation","Artificial Intelligence; Asthma; Biological Products; Biological Therapy; Chronic Disease; Humans; Interleukin-5; Nasal Polyps; Quality of Life; Rhinitis; Sinusitis; benralizumab; dupilumab; immunoglobulin E; interleukin 13; interleukin 4; interleukin 5; mepolizumab; reslizumab; biological product; interleukin 5; algorithm; Article; artificial intelligence; chronic rhinosinusitis with nasal polyp therapy; data analysis; data base; data synthesis; decision making; drug dosage form; drug therapy; evaluation study; eye inflammation; fatigue; fever; headache; health survey; human; inflammation; information processing; leukocyte; literature; population; practice guideline; quality of life; questionnaire; rhinosinusitis; safety; artificial intelligence; asthma; biological therapy; chronic disease; complication; rhinitis; sinonasal polyp; sinusitis","","benralizumab, 1044511-01-4; dupilumab, 1190264-60-8; immunoglobulin E, 37341-29-0; interleukin 13, 148157-34-0; mepolizumab, 196078-29-2; reslizumab, 241473-69-8; Biological Products, ; Interleukin-5, ","","","","","Banoub R.G., Hoehle L.P., Phillips K.M., Et al., Depressed Mood Modulates Impact of Chronic Rhinosinusitis Symptoms on Quality of Life, J Allergy Clin Immunol Pract, 6, 6, pp. 2098-2105, (2018); De Corso E., Pipolo C., Cantone E., Et al., Survey on use of local and systemic corticosteroids in the management of chronic rhinosinusitis with nasal polyps: identification of unmet clinical needs, J Pers Med, 12, 6, (2022); Rank M.A., Chu D.K., Bognanni A., Oykhman P., Bernstein J.A., Ellis A.K., Golden D.B.K., Greenhawt M., Horner C.C., Ledford D.K., Lieberman J., Luong A.U., Orlandi R.R., Samant S.A., Shaker M.S., Soler Z.M., Stevens W.W., Stukus D.R., Wang J., Peters A.T., The Joint Task Force on Practice Parameters GRADE guidelines for the medical management of chronic rhinosinusitis with nasal polyposis, J Allergy Clin Immunol, 151, 2, pp. 386-398, (2023); Haxel B.R., Hummel T., Fruth K., Et al., Real-world-effectiveness of biological treatment for severe chronic rhinosinusitis with nasal polyps, Rhinology, 60, 6, pp. 435-443, (2022); La Mantia I., Grigaliute E., Ragusa M., Cocuzza S., Radulesco T., Saibene A.M., Calvo-Henriquez C., Fakhry N., Michel J., Maniaci A., Effectiveness and rapidity on olfatory fuction recovery in CRS patients treated with Dupilumab: a real life prospective controlled study, Eur Arch Otorhinolaryngol, (2023); Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., Chou R., Glanville J., Grimshaw J.M., Hrobjartsson A., Lalu M.M., Li T., Loder E.W., Mayo-Wilson E., McDonald S., McGuinness L.A., Stewart L.A., Thomas J., Tricco A.C., Welch V.A., Whiting P., Moher D., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews, BMJ, 29, 372, (2021); Yang S.K., Cho S.H., Kim D.W., Interpretation of clinical efficacy of biologics in chronic rhinosinusitis with nasal polyps via understanding the local and systemic pathomechanisms, Allergy Asthma Immunol Res, 14, 5, pp. 465-478, (2022); Bachert C., Zhang N., Cavaliere C., Weiping W., Gevaert E., Krysko O., Biologics for chronic rhinosinusitis with nasal polyps, J Allergy Clin Immunol, 145, 3, pp. 725-739, (2020); Bachert C., Han J.K., Wagenmann M., Hosemann W., Lee S.E., Backer V., Mullol J., Gevaert P., Klimek L., Prokopakis E., Knill A., Cavaliere C., Hopkins C., Hellings P., EUFOREA expert board meeting on uncontrolled severe chronic rhinosinusitis with nasal polyps (CRSwNP) and biologics: Definitions and management, J Allergy Clin Immunol, 147, 1, pp. 29-36, (2021); Fokkens W.J., Lund V., Bachert C., Mullol J., Bjermer L., Bousquet J., Canonica G.W., Deneyer L., Desrosiers M., Diamant Z., Han J., Heffler E., Hopkins C., Jankowski R., Joos G., Knill A., Lee J., Lee S.E., Marien G., Pugin B., Senior B., Seys S.F., Hellings P.W., EUFOREA consensus on biologics for CRSwNP with or without asthma, Allergy, 74, 12, pp. 2312-2319, (2019); Fokkens W.J., Viskens A.S., Backer V., Conti D., De Corso E., Gevaert P., Scadding G.K., Wagemann M., Bernal-Sprekelsen M., Chaker A., Heffler E., Han J.K., Van Staeyen E., Hopkins C., Mullol J., Peters A., Reitsma S., Senior B.A., Hellings P.W., EPOS/EUFOREA update on indication and evaluation of Biologics in Chronic Rhinosinusitis with Nasal Polyps 2023, Rhinology, (2023); Maza-Solano J., Biadsee A., Sowerby L.J., Calvo-Hernandez C., Tucciarone M., Rocha T., Maniaci A., Saibene A.M., Chiesa-Estomba C.M., Radulesco T., Metwaly O., Lechien J.R., Alobid I., Locatello L.G., Chronic rhinosinusitis with nasal polyps management in the biologic therapy era: an international YO-IFOS survey, Eur Arch Otorhinolaryngol, 280, 5, pp. 2309-2316, (2023); Rampi A., Vinciguerra A., Tanzini U., Bussi M., Trimarchi M., Comparison of guidelines for prescription and follow-up of biologics for chronic rhinosinusitis with nasal polyps, Eur Arch Otorhinolaryngol, 280, 1, pp. 39-46, (2023); Agache I., Song Y., Alonso-Coello P., Vogel Y., Rocha C., Sola I., Santero M., Akdis C.A., Akdis M., Canonica G.W., Chivato T., Del Giacco S., Eiwegger T., Fokkens W., Georgalas C., Gevaert P., Hopkins C., Klimek L., Lund V., Naclerio R., O'Mahony L., Palkonen S., Pfaar O., Schwarze J., Soyka M.B., Wang Y., Zhang L., Canelo-Aybar C., Palomares O., Jutel M., Efficacy and safety of treatment with biologicals for severe chronic rhinosinusitis with nasal polyps: A systematic review for the EAACI guidelines, Allergy, 76, 8, pp. 2337-2353, (2021); Hopkins C., McKenzie J.L., Anari S., Carrie S., Ramakrishnan Y., Kara N., Philpott C., Hobson J., Qureishi A., Stew B., Bhalla R., Gane S., Walker A., Harries P., Hathorn I., Lund V., British Rhinological Society Consensus Guidance on the use of biological therapies for chronic rhinosinusitis with nasal polyps, Clin Otolaryngol, 46, 5, pp. 1037-1043, (2021); Al-Ahmad M., Alsaleh S., Al-Reefy H., Al Abduwani J., Nasr I., Al Abri R., Alamadi A.M.H., Fraihat A.A., Alterki A., Abuzakouk M., Marglani O., Rand H.A., Expert Opinion on Biological Treatment of Chronic Rhinosinusitis with Nasal Polyps in the Gulf Region, J Asthma Allergy, 4, 15, pp. 1-12, (2022); Scadding G.K., Scadding G.W., Biologics for chronic rhinosinusitis with nasal polyps (CRSwNP), J Allergy Clin Immunol, 49, 3, pp. 895-897, (2022); Tversky J., Lane A.P., Azar A., Benralizumab effect on severe chronic rhinosinusitis with nasal polyps (CRSwNP): A randomized double-blind placebo-controlled trial, Clin Exp Allergy, 51, 6, pp. 836-844, (2021); Bachert C., Han J.K., Desrosiers M.Y., Et al., Efficacy and safety of benralizumab in chronic rhinosinusitis with nasal polyps: A randomized, placebo-controlled trial, J Allergy Clin Immunol, 149, 4, pp. 1309-1317.e12, (2022); Takabayashi T., Asaka D., Okamoto Y., Et al., A Phase II, Multicenter, Randomized, Placebo-Controlled Study of Benralizumab, a Humanized Anti-IL-5R Alpha Monoclonal Antibody, in Patients With Eosinophilic Chronic Rhinosinusitis, Am J Rhinol Allergy, 35, 6, pp. 861-870, (2021); Canonica G.W., Harrison T.W., Chanez P., Et al., Benralizumab improves symptoms of patients with severe, eosinophilic asthma with a diagnosis of nasal polyposis, Allergy, 77, 1, pp. 150-161, (2022); Weinstein S.F., Katial R.K., Bardin P., Et al., Effects of Reslizumab on Asthma Outcomes in a Subgroup of Eosinophilic Asthma Patients with Self-Reported Chronic Rhinosinusitis with Nasal Polyps, J Allergy Clin Immunol Pract, 7, 2, pp. 589-596.e3, (2019); Wong G., Greenhalgh T., Westhorp G., Pawson R., Development of methodological guidance, publication standards and training materials for realist and meta-narrative reviews: The RAMESES (Realist And Meta-narrative Evidence Syntheses – Evolving Standards) project, Southampton (UK): NIHR Journals Library, (2014); Lechien J.R., Maniaci A., Gengler I., Hans S., Chiesa-Estomba C.M., Vaira L.A., Validity and reliability of an instrument evaluating the performance of intelligent chatbot: the Artificial Intelligence Performance Instrument (AIPI), Eur Arch Otorhinolaryngol, (2023)","A. Maniaci; Faculty of Medicine and Surgery, “Kore” University of Enna, Enna, Italy; email: tnmaniaci29@gmail.com","","Springer Science and Business Media Deutschland GmbH","","","","","","09374477","","EAOTE","38329526","English","Eur. Arch. Oto-Rhino-Laryngol.","Article","Final","","Scopus","2-s2.0-85184392400"
"Bergmann K.-C.; Skowasch D.; Timmermann H.; Lindner R.; Virchow J.C.; Schmidt O.; Koschel D.; Neurohr C.; Heck S.; Milger K.","Bergmann, Karl-Christian (35429595700); Skowasch, Dirk (6603928258); Timmermann, Hartmut (57211859930); Lindner, Robert (57221977579); Virchow, Johann Christian (57210783795); Schmidt, Olaf (56517631100); Koschel, Dirk (22980028800); Neurohr, Claus (6508182224); Heck, Sebastian (55759924400); Milger, Katrin (36925704400)","35429595700; 6603928258; 57211859930; 57221977579; 57210783795; 56517631100; 22980028800; 6508182224; 55759924400; 36925704400","Prevalence of Patients with Uncontrolled Asthma Despite NVL/GINA Step 4/5 Treatment in Germany","2022","Journal of Asthma and Allergy","15","","","897","906","9","14","10.2147/JAA.S365967","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135005041&doi=10.2147%2fJAA.S365967&partnerID=40&md5=e8a154b1732e410375997131f8af7ace","Institute for Allergology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin Institute of Health, Berlin, Germany; Department of Internal Medicine II – Pneumology, University Hospital Bonn, Bonn, Germany; Schwerpunktpraxis Colonnaden, Hamburg, Germany; IQVIA Commercial GmbH & Co. OHG, Frankfurt Am Main, Germany; Universitätsmedizin Rostock - Zentrum für Innere Medizin, Medizinische Klinik I, Abteilung Pneumologie & Interdisziplinäre Internistische Intensivmedizin, Rostock, Germany; Pneumologische Gemeinschaftspraxis und Studienzentrum KPPK, Koblenz, Germany; Fachkrankenhaus Coswig, Lung Centre, Coswig, and Division of Pulmonology, Medical Department I, University Hospital Carl Gustav Carus, Dresden, Germany; Abteilung für Pneumologie und Beatmungsmedizin, Robert-Bosch-Krankenhaus Lungenzentrum, Stuttgart, Germany; GlaxoSmithKline GmbH & Co. KG, Munich, Germany; Department of Medicine V, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany","Bergmann K.-C., Institute for Allergology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, Berlin Institute of Health, Berlin, Germany; Skowasch D., Department of Internal Medicine II – Pneumology, University Hospital Bonn, Bonn, Germany; Timmermann H., Schwerpunktpraxis Colonnaden, Hamburg, Germany; Lindner R., IQVIA Commercial GmbH & Co. OHG, Frankfurt Am Main, Germany; Virchow J.C., Universitätsmedizin Rostock - Zentrum für Innere Medizin, Medizinische Klinik I, Abteilung Pneumologie & Interdisziplinäre Internistische Intensivmedizin, Rostock, Germany; Schmidt O., Pneumologische Gemeinschaftspraxis und Studienzentrum KPPK, Koblenz, Germany; Koschel D., Fachkrankenhaus Coswig, Lung Centre, Coswig, and Division of Pulmonology, Medical Department I, University Hospital Carl Gustav Carus, Dresden, Germany; Neurohr C., Abteilung für Pneumologie und Beatmungsmedizin, Robert-Bosch-Krankenhaus Lungenzentrum, Stuttgart, Germany; Heck S., GlaxoSmithKline GmbH & Co. KG, Munich, Germany; Milger K., Department of Medicine V, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL), Munich, Germany","Purpose: Asthma is one of the most prevalent chronic diseases in Germany affecting 4–5% of all adults and 10% of children. Despite the availability of biologicals in recent years, studies show patients with inadequately controlled severe asthma in real life. The aim of the current study was to characterize and estimate the number of patients with NVL/GINA level 4 or 5 asthma and signs of poor control in Germany. Patients and Methods: In 2021, we retrospectively analyzed data collected during 2019 using the IQVIA™ LRx and IQVIA™ Disease Analyzer databases which contain anonymized longitudinal data covering approximately 80% of statutory health insurance (GKV) prescriptions in Germany with most relevant information about prescriptions, basic patient demographics or location of the prescriber; the IQVIA™ Disease Analyzer anonymized electronic medical records from a representative sample of office-based GPs and specialists. An expert committee of pulmonologists from different hospitals and expert practices supported the study. Asthma patients treated according to NVL/GINA 4/5 who used SABAs frequently (≥3 on days with no ICS-containing prescriptions/year) and/or received prescriptions for oral corticosteroids (OCS) (score of ≥2/year, a pulmonologist prescription scored 1.0, GP 0.75) were classified as severe, uncontrolled asthma. Results: In 2019, 3.4 million patients received at least two prescriptions of respiratory medications and 2.4 million patients on maintenance respiratory treatment have asthma. A total of 625,000 asthma patients were treated according to NVL/GINA step 4 or 5. Among these, 54,000 were uncontrolled according to the pre-defined OCS and/or SABA use, which corresponds to approximately 15% of patients in certain regions. Conclusion: In 2019, approximately 54,000 patients in Germany treated according to NVL/GINA step 4/5 had evidence suggestive for poor asthma control, up to 15% of patients in certain regions. Yet, only 12,000 patients overall were being treated with biologicals suggesting a possible treatment gap that requires further investigation. © 2022 Bergmann et al. This work is published and licensed by Dove Medical Press Limited.","disease analyzer; OCS; oral corticosteroid; prescription database; SABA; short-acting β2-agonist; uncontrolled asthma","benralizumab; cloprednol; dupilumab; mepolizumab; methylprednisolone; omalizumab; prednisolone; prednisone; reslizumab; triamcinolone; adult; Article; asthma; child; chronic bronchitis; cohort analysis; demographics; disease exacerbation; female; Germany; health insurance; human; machine learning; maintenance therapy; male; prediction; prescription; prevalence; retrospective study","","benralizumab, 1044511-01-4; cloprednol, 5251-34-3; dupilumab, 1190264-60-8; mepolizumab, 196078-29-2; methylprednisolone, 6923-42-8, 83-43-2; omalizumab, 242138-07-4; prednisolone, 50-24-8; prednisone, 53-03-2; reslizumab, 241473-69-8; triamcinolone, 124-94-7","","","MEDA; Mundipharma; Schwarz-Pharma; Universitätsmedizin Rostock; GlaxoSmithKline, GSK; Novartis; Roche; Sanofi; Teva Pharmaceutical Industries; Meso Scale Diagnostics, MSD; Merck KGaA; UCB; Cilag; Deutsche Forschungsgemeinschaft, DFG","KCB and OS have no relevant competing interests to disclose. SH is a GSK employee and shareholder. KM reports speaker and/ or advisory fees from AstraZeneca, GSK, Novartis, Sanofi. CN received honoraria and advisory board fees from GSK, Sanofi, AstraZeneca and Novartis. DS received honoraria for lectures and/or consultancy from AstraZeneca, Bayer, Berlin-Chemie, Boehringer, Chiesi, GSK, Janssen, Novartis, Pfizer. RL is an employee of IQVIA. IQVIA is a technology service provider that carried out the database studies within the scope of a commercial engagement with GSK. DK reports personal fees from GSK. JCV has lectured for and received honoraria from AstraZeneca, Avontec, Bayer, Bencard, Bionorica, Boehringer-Ingelheim, Chiesi, Essex/Schering-Plough, GSK, Janssen-Cilag, Leti, MEDA, Merck, MSD, Mundipharma, Novartis, Nycomed/Altana, Pfizer, Revotar, Sandoz-Hexal, Stallergens, TEVA, UCB/ Schwarz-Pharma, Zydus/Cadila and has participated in advisory boards for Avontec, Boehringer-Ingelheim, Chiesi, Essex/Schering-Plough, GSK, Janssen-Cilag, MEDA, MSD, Mundipharma, Novartis, Regeneron, Revotar, Roche, Sanofi-Aventis, Sandoz-Hexal, TEVA, UCB/Schwarz-Pharma and has received research grants from Deutsche Forschungsgesellschaft, Land Mecklenburg-Vorpommern, GSK, MSD and is a full time employee of the Universitätsmedizin Rostock. The authors report no other conflicts of interest in this work.","Virchow JC., Asthma - historical development, current status and perspectives, Pneumologie, 64, 9, pp. 541-549, (2010); Papaioannou AI, Kostikas K, Zervas E, Kolilekas L, Papiris S, Gaga M., Control of asthma in real life: still a valuable goal?, Eur Respir Rev, 24, 136, pp. 361-369, (2015); Reddel HK, Boulet LP, Global strategy for asthma management and prevention, (2021); Chung KF, Wenzel SE, Brozek JL, Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, pp. 343-373, (2014); Pepper AN, Renz H, Casale TB, Garn H., Biologic therapy and novel molecular targets of severe asthma, J Allergy Clin Immunol Pract, 5, 4, pp. 909-916, (2017); Summary of product characteristics benralizumab; Del Giacco SR, Bakirtas A, Bel E, Et al., Allergy in severe asthma, Allergy, 72, pp. 207-220, (2017); Kaur R, Chupp G., Phenotypes and endotypes of adult asthma: moving toward precision medicine, J Allergy Clin Immunol, 144, pp. 1-12, (2019); Taube C, Bramlage P, Hofer A, Anderson D., Prevalence of oral corticosteroid use in the German severe asthma population, ERJ Open Res, 5, 4, pp. 00092-2019, (2019); Rathmann W, Bongaerts B, Carius H, Kruppert Y, Kostev K., Basic characteristics and representativeness of the German Disease Analyzer database, Int J Clin Pharmacol Ther, 56, pp. 459-466, (2018); Steppuhn H, Kuhnert R, Scheidt-Nave C., 12-Monats-Prävalenz von Asthma bronchiale bei Erwachsenen in Deutschland, J Health Monit, 2, 3, (2017); Worth H, Criee CP, Vogelmeier CF, Et al., Prevalence of overuse of short-acting beta-2 agonists (SABA) and associated factors among patients with asthma in Germany, Respir Res, 22, 1, (2021); Janson C, Menzies-Gow A, Nan C, Nuevo J, Papi A, Quint J., SABINA: an overview of short-acting β2-agonist use in asthma in European countries, Adv Ther, 37, pp. 1124-1135, (2020); Lommatzsch M, Wilmer C, Sauerbeck IS., Prevalence of the use of oral corticosteroids in asthma-a 3-year analysis in Germany, Eur Respir J, 54, pp. 00092-2019, (2019); Hekking PPW, Wener RR, Amelink M, Zwinderman AH, Bouvy ML, Bel EH., The prevalence of severe refractory asthma, J Allergy Clin Immunol, 135, 4, pp. 896-902, (2015)","K.-C. Bergmann; Institute for Allergology-Charité, Berlin, Luisenstraße 2, 10117, Germany; email: karlchristianbergmann@gmail.com","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85135005041"
"Levetin E.; Pityn P.J.; Ramon G.D.; Pityn E.; Anderson J.; Bielory L.; Dalan D.; Codina R.; Rivera-Mariani F.E.; Bolanos B.","Levetin, Estelle (7003424893); Pityn, Peter J. (56718515900); Ramon, German D. (55617014100); Pityn, Elaine (58115966700); Anderson, Jim (57212280546); Bielory, Leonard (58452484600); Dalan, Dan (36007991900); Codina, Rosa (7005833651); Rivera-Mariani, Felix E. (36349534700); Bolanos, Benjamin (6602565088)","7003424893; 56718515900; 55617014100; 58115966700; 57212280546; 58452484600; 36007991900; 7005833651; 36349534700; 6602565088","Aeroallergen Monitoring by the National Allergy Bureau: A Review of the Past and a Look Into the Future","2023","Journal of Allergy and Clinical Immunology: In Practice","11","5","","1394","1400","6","8","10.1016/j.jaip.2022.11.026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148743165&doi=10.1016%2fj.jaip.2022.11.026&partnerID=40&md5=ce90c569de5054501422e1f9977184d7","Department of Biological Science, University of Tulsa, Tulsa, Okla; OSHTECH Incorporated, London, ON, Canada; Instituto de Alergia e Inmunología del Sur, Hospital Italiano Regional del Sur, Bahía Blanca, Argentina; Medicine, Allergy, Immunology and Ophthalmology Department, Hackensack Meridian School of Medicine, Nutley, NJ; Rutgers University Center for Environmental Prediction, New Brunswick, NJ, United States; Department of Medicine, Thomas Jefferson University Sidney Kimmel School of Medicine, Philadelphia, Pa, United States; MercyOne Health Care, Allergy and Immunology, Waterloo, Iowa, United States; Allergen Science & Consulting, Lenoir, NC, United States; Division of Allergy and Immunology, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Fla, United States; Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, Fla, United States; Department of Microbiology and Medical Zoology, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico","Levetin E., Department of Biological Science, University of Tulsa, Tulsa, Okla; Pityn P.J., OSHTECH Incorporated, London, ON, Canada; Ramon G.D., Instituto de Alergia e Inmunología del Sur, Hospital Italiano Regional del Sur, Bahía Blanca, Argentina; Pityn E., OSHTECH Incorporated, London, ON, Canada; Anderson J., OSHTECH Incorporated, London, ON, Canada; Bielory L., Medicine, Allergy, Immunology and Ophthalmology Department, Hackensack Meridian School of Medicine, Nutley, NJ, Rutgers University Center for Environmental Prediction, New Brunswick, NJ, United States, Department of Medicine, Thomas Jefferson University Sidney Kimmel School of Medicine, Philadelphia, Pa, United States; Dalan D., MercyOne Health Care, Allergy and Immunology, Waterloo, Iowa, United States; Codina R., Allergen Science & Consulting, Lenoir, NC, United States, Division of Allergy and Immunology, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, Fla, United States; Rivera-Mariani F.E., Department of Public Health Sciences, University of Miami, Miller School of Medicine, Miami, Fla, United States; Bolanos B., Department of Microbiology and Medical Zoology, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico","Monitoring aeroallergens has a long history within the American Academy of Allergy, Asthma & Immunology. The Aeroallergen Network of the National Allergy Bureau is composed mainly of members of the American Academy of Allergy, Asthma & Immunology, whose objectives are to enhance the knowledge of aerobiology and its relationship to allergy, increase the number of certified stations, maintain the standardization and quality of aerobiology data, improve the alert and forecast reporting system, and increase ties with other scientific entities inside and outside the United States. The public has a keen interest in pollen counts and pollen forecasts, as do many health professionals in the allergy community. In this review, we explore the past, present, and future of allergen monitoring with a focus on methods used for sampling, the training of those performing the analysis, and emerging technologies in the field. Although the development of automated samplers with machine intelligence offers great promise for meeting the goal of a fully automated system, there is still progress to be made regarding reliability and affordability. © 2022 American Academy of Allergy, Asthma & Immunology","Aerobiology; Allergy; Artificial intelligence; National Allergy Bureau; Pollen","Allergens; Asthma; Humans; Hypersensitivity; Pollen; Reproducibility of Results; United States; allergen; allergen; aerospace medicine; air sampling; allergy; ambient air; Article; artificial intelligence; artificial neural network; automation; data analysis; fluorescence; fungus spore; human; nonhuman; pattern recognition; pollen; pollen analysis; United States; asthma; hypersensitivity; reproducibility","","Allergens, ","","","Pfizer","By the early 1990s, all network stations were using volumetric sampling instruments, significantly increasing the accuracy of pollen data. In the 1992 Pollen and Spore Report, 17 most stations used Rotorod samplers and five stations operated Hirst-type spore traps. During the mid-1990s, the National Allergy Bureau (NAB) was established, providing more structure for network organization. 1 In 1997, the AAAAI was able to acquire 100 Burkard spore traps, made possible by a generous grant from Pfizer, Inc. These instruments would allow network stations to improve the capture and analysis of airborne fungal spores. The Burkard samplers were distributed between 1998 and 1999; and in 2000, the NAB no longer accepted fungal spore data from Rotorod samplers. 18 ","Portnoy J., Barnes C., Barnes C.S., The National Allergy Bureau: pollen and spore reporting today, J Allergy Clin Immunol, 114, pp. 1235-1238, (2004); Cohen S.G., The American Academy of Allergy: an historical review. I. Forward, J Allergy Clin Immunol, 64, pp. 332-333, (1979); Durham O.C., Cooperative studies in ragweed pollen incidence. Atmospheric data from twenty-two cities, J Allergy, 1, pp. 12-21, (1929); Durham O.C., The pollen content of the air in North America, J Allergy, 6, pp. 128-149, (1935); Mitman G., A history of pollen mapping and surveillance: the relations between natural history and clinical allergy, J Allergy Clin Immunol, 114, pp. 1230-1235, (2004); Mitman G., Oren C. Durham, J. Allergy Clin Immunol, 114, pp. 1229-1230, (2004); Durham O.C., The volumetric incidence of atmospheric allergens. IV. A proposed standard method of gravity sampling, counting, and volumetric interpolation of results, J Allergy, 17, pp. 79-86, (1946); Ogden E.C., Raynor G.S., Field evaluation of ragweed pollen samplers, J Allergy, 31, pp. 307-316, (1960); Ogden E.C., Raynor G.S., Hayes J.V., Lewis D.M., Haines J.H., Manual for sampling airborne pollen, (1974); Hirst J.M., An automatic volumetric spore trap, Ann Appl Biol, 39, pp. 257-265, (1952); Pady S.M., Kramer C.L., Sampling airborne fungi in Kansas for diurnal periodicity, Rev Palaeobot Palynol, 4, pp. 227-232, (1967); Zerboni R., Manfredi M., Campi P., Arrigoni P.V., Correlation between aerobiological and phytogeographical investigations in the Florence area, Aerobiologia, 2, pp. 2-13, (1986); Statistical report of the pollen and mold committee of the American Academy of Allergy, 1974, (1974); Statistical report of the pollen and mold committee of theAmerican Academy of Allergy, 1979, (1979); Statistical report of the pollen and mold committee of the American Academy of Allergy, (1984); Chapman J., Burge H., Boogaard M., Muilenberg M., Quality control of the AAAI Pollen/Mold counting network, J Allergy Clin Immunol, 81, (1988); Academy of Allergy and Immunology pollen and spore report, (1993); 2000 AAAAI pollen and spore report, (2001); Zhang Y., Bielory L., Mi Z., Cai T., Robock A., Georgopoulos P., Allergenic pollen season variations in the past two decades under changing climate in the United States, Glob Chang Biol, 21, pp. 1581-1589, (2015); Anderegg W.R.L., Abatzoglou J.T., Anderegg L.D.L., Bielory L., Kinney P.L., Ziska L., Anthropogenic climate change is worsening North American pollen seasons, Proc Natl Acad Sci U S A, 118, (2021); Damialis A., Gilles S., Sofiev M., Sofieva V., Kolek F., Bayr D., Et al., Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe, Proc Natl Acad Sci U S A, 118, (2021); Tummon F., Arboledas L.A., Bonini M., Guinot B., Hicke M., Jacob C., Et al., The need for Pan-European automatic pollen and fungal spore monitoring: a stakeholder workshop position paper, Clin Transl Allergy, 11, (2021); Buters J.T.M., Antunes C., Galveias A., Bergmann K.C., Thibaudon M., Galan C., Et al., Pollen and spore monitoring in the world, Clin Transl Allergy, 8, (2018); European Standards. CSN EN 16868. Ambient air – Sampling and analysis of airborne pollen grains and fungal spores for networks related to allergy – Volumetric Hirst method; Maya-Manzano J.M., Smith M., Markey E., Hourihane Clancy J., Sodeau J., O'Connor D.J., Recent developments in monitoring and modelling airborne pollen, a review, Grana, 60, pp. 1-19, (2021); Huffman J.A., Perring A.E., Savage N.J., Clot B., Crouzy B., Tummon F., Et al., Real-time sensing of bioaerosols: review and current perspectives, Aerosol Sci Technol, 54, pp. 465-495, (2020); Suanno C., Aloisi I., Fernandez-Gonzalez D., del Duca S., Monitoring techniques for pollen allergy risk assessment, Environ Res, 197, (2021); Hund Wetzlar. Pollen monitor BAA500. Data sheet; Oteros J., Weber A., Kutzora S., Rojo J., Heinze S., Herr C., Et al., An operational robotic pollen monitoring network based on automatic image recognition, Environ Res, 191, (2020); Crawford I., Topping D., Evans J., Realtime pollen detection – a UK first; Sauliene I., Sukiene L., Daunys G., Valiulis G., Vaitkevicius L., Matavulj P., Et al., Automatic pollen recognition with the Rapid-E particle counter: the first-level procedure, experience and next steps, Atmos Meas Tech, 12, pp. 3435-3452, (2019); Kawashima S., Thibaudon M., Matsuda S., Fujita T., Lemonis N., Clot B., Et al., Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen, Aerobiologia, 33, (2017); Sauvageat E., Zeder Y., Auderset K., Calpini B., Clot B., Crouzy B., Et al., Real-time pollen monitoring using digital holography, Atmos Meas Tech, 13, pp. 1539-1550, (2020); Oteros J., Pusch G., Weichenmeier I., Heimann U., Moller R., Roseler S., Et al., Automatic and online pollen monitoring, Int Arch Allergy Immunol, 167, pp. 158-166, (2015); Tummon F., Adamov S., Clot B., Crouzy B., Gysel-Beer M., Kawashima S., Et al., A first evaluation of multiple automatic pollen monitors run in parallel; Healy D.A., Huffman J.A., O'Connor D.J., Pohlker C., Poschl U., Sodeau J.R., Ambient measurements of biological aerosol particles near Killarney, Ireland: a comparison between real-time fluorescence and microscopy techniques, Atmos Chem Phys, 14, pp. 8055-8069, (2014); Perring A.E., Schwarz J.P., Baumgardner D., Hernandez M.T., Spracklen D.V., Heald C.L., Et al., Airborne observations of regional variation in fluorescent aerosol across the United States, J Geophys Res, 120, pp. 1153-1170, (2015); Crouzy B., Stella M., Konzelmann T., Calpini B., Clot B., All-optical automatic pollen identification: towards an operational system, Atmos Environ, 140, pp. 202-212, (2016); Heimann U., Haus J., Zuehlke D., Fully automated pollen analysis and counting: The pollen monitor BAA500. SENSOR+TEST Conference, Conf Proceedings of OPTO, 2009 (P3), pp. 125-128, (2009); Plaza M.P., Kolek F., Leier-Wirtz V., Brunner J.O., Traidl-Hoffmann C., Damialis A., Detecting airborne pollen using an automatic, real-time monitoring system: evidence from two sites, Int J Environ Res Public Health, 19, (2022); Niederberger E., Abt R., Burch P., Zeder Y., First experience with the newly-developed Swisens Pollen Monitor, Technical Conference on Meteorological and Environmental Instruments and Methods. (Abst O2_9), (2018); Hund Wetzlar. Pollen monitoring systems; MeteoSwiss: Federal Office of Meteorology and Climatology, Switzerland. Pollen monitoring network; Interreg IPA Cross-border Cooperation Programme Croatia-Bosnia and Herzegovnia-Montenegro. Real-time measurements and forecasting for successful prevention and management of seasonal allergies in Croatia-Serbia cross-border region. Consortium of BioSensе Institute – Research Institute for Information Technologies in Biosystems (Serbia, Lead Partner), J. J. Strossmayer University of Osijek, Department of Mathematics (Croatia), University of Novi Sad Faculty of Sciences (Serbia) and City of Osijek (Croatia); Japanese Ministry of the Environment; Endo Otolaryngology and Allergy Clinic. Pollen information in Tokyo; Dalan D., Bunderson L., Anderson J., Lucas R., Results of a beta test evaluating automated pollen identification during ragweed pollen season, J Allergy Clin Immunol, 145, (2020); Lucas R., Bunderson L., Anderson J., Dalan D., Visual machine learning and artificial intelligence application in aeroallergen identification during spring, summer, and fall pollen season, J Allergy Clin Immunol, 147, (2021); Jiang C., Wang W., Du L., Huang G., McConaghy C., Fineman S., Et al., Field evaluation of an automated pollen sensor, Int J Environ Res Public Health, 19, (2022); Langford M., Taylor G.E., Flenley J.R., Computerized identification of pollen grains by texture analysis, Rev Palaeobot Palynol, 64, pp. 197-203, (1990); Sanchez-Mesa J.A., Galan C., Martinez-Heras J.A., Hervas-Martinez C., The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula, Clin Exp Allergy, 32, pp. 1606-1612, (2002)","G.D. Ramon; Instituto de Alergia e Inmunologia del Sur, Buenos Aires, 25 de Mayo 44, Bahia Blanca, 8000, Argentina; email: germanramon2004@hotmail.com","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36473626","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85148743165"
"Tanabe N.; Sakamoto R.; Kozawa S.; Oguma T.; Shima H.; Shiraishi Y.; Koizumi K.; Sato S.; Nakamoto Y.; Hirai T.","Tanabe, Naoya (37018840600); Sakamoto, Ryo (57350902200); Kozawa, Satoshi (57191492108); Oguma, Tsuyoshi (7005166362); Shima, Hiroshi (58865879400); Shiraishi, Yusuke (8573318200); Koizumi, Koji (57200615335); Sato, Susumu (36037634900); Nakamoto, Yuji (57317897300); Hirai, Toyohiro (7402768436)","37018840600; 57350902200; 57191492108; 7005166362; 58865879400; 8573318200; 57200615335; 36037634900; 57317897300; 7402768436","Deep learning-based reconstruction of chest ultra-high-resolution computed tomography and quantitative evaluations of smaller airways","2022","Respiratory Investigation","60","1","","167","170","3","9","10.1016/j.resinv.2021.10.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119693249&doi=10.1016%2fj.resinv.2021.10.004&partnerID=40&md5=7e8db7be12cb9c32dbe3b06b7716454d","Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan","Tanabe N., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Sakamoto R., Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Kozawa S., Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Oguma T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Shima H., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Shiraishi Y., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Koizumi K., Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Sato S., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Nakamoto Y., Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan; Hirai T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan","The full-iterative model reconstruction generates ultra-high-resolution computed tomography (U-HRCT) images comprising a 1024 × 1024 matrix and 0.25 mm thickness while suppressing image noises, allowing evaluating small airways 1–2 mm in diameter. However, this technique imposes huge computational burdens and requires a long reconstruction time. This study evaluated whether a recently-established deep learning-based reconstruction, Advanced intelligent Clear-IQ Engine (AiCE), allows quantitative morphological analyses of smaller airways with equal or better quality than the full-iterative model reconstruction while shortening the reconstruction time. In phantom tubes mimicking small airways, the measurement error of 0.5-mm-thickness wall was smaller on the AiCE-based than the full-iterative model-based U-HRCT. Moreover, in five patients with chronic obstructive pulmonary disease, the AiCE-based U-HRCT decreased the reconstruction time approximately by 90% with a modest improvement in image noise, contrast, and sharpness compared to the full-iterative model-based U-HRCT. Therefore, the AiCE-based U-HRCT can be readily used clinically for morphologically evaluating peripheral small airways. © 2021 The Japanese Respiratory Society","Airway; Asthma; Chronic obstructive pulmonary disease; Deep learning; Ultra-high-resolution computed tomography","Algorithms; Deep Learning; Humans; Phantoms, Imaging; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted; Tomography, X-Ray Computed; airway; Article; asthma; chronic obstructive lung disease; contrast; deep learning; high resolution computer tomography; human; image quality; iterative reconstruction; measurement error; quantitative analysis; signal noise ratio; algorithm; computer assisted diagnosis; imaging phantom; radiation dose; x-ray computed tomography","","","Aquilion Precision, Canon, Japan","Canon, Japan","Canon Medical Systems Corporation; Japan Society for the Promotion of Science, KAKEN, (19K08624); Japan Society for the Promotion of Science, KAKEN","Funding text 1: Yuji Nakamoto and Ryo Sakamoto received a research grant from Canon Medical Systems Corporation. The other authors have no conflicts of interest. ; Funding text 2: This work was partially supported by the Japan Society for the Promotion of Science ( JSPS ) [Grants-in-Aid for scientific research 19K08624 ].","Adeloye D., Chua S., Lee C., Basquill C., Papana A., Theodoratou E., Et al., Global and regional estimates of COPD prevalence: systematic review and meta-analysis, J Glob Health, 5, (2015); Hogg J.C., Pare P.D., Hackett T.L., The contribution of small airway obstruction to the pathogenesis of chronic obstructive pulmonary disease, Physiol Rev, 97, pp. 529-552, (2017); Tanabe N., Shimizu K., Terada K., Sato S., Suzuki M., Shima H., Et al., Central airway and peripheral lung structures in airway disease-dominant COPD, ERJ Open Res, 7, (2021); Tanabe N., Oguma T., Sato S., Kubo T., Kozawa S., Shima H., Et al., Quantitative measurement of airway dimensions using ultra-high resolution computed tomography, Respir Investig, 56, pp. 489-496, (2018); Usui Y., Kurokawa R., Maeda E., Mori H., Amemiya S., Sato J., Et al., Evaluation of peripheral bronchiole visualization using model-based iterative reconstruction in quarter-detector computed tomography, PLoS One, 15, (2020); Oostveen L.J., Boedeker K.L., Brink M., Prokop M., de Lange F., Sechopoulos I Physical evaluation of an ultra-high-resolution CT scanner, Eur Radiol, 30, pp. 2552-2560, (2020); Nishiyama Y., Tada K., Nishiyama Y., Mori H., Maruyama M., Katsube T., Et al., Effect of the forward-projected model-based iterative reconstruction solution algorithm on image quality and radiation dose in pediatric cardiac computed tomography, Pediatr Radiol, 46, pp. 1663-1670, (2016); Tanabe N., Shima H., Sato S., Oguma T., Kubo T., Kozawa S., Et al., Direct evaluation of peripheral airways using ultra-high-resolution CT in chronic obstructive pulmonary disease, Eur J Radiol, 120, (2019); Akagi M., Nakamura Y., Higaki T., Narita K., Honda Y., Zhou J., Et al., Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT, Eur Radiol, 29, pp. 6163-6171, (2019); Hata A., Yanagawa M., Yoshida Y., Miyata T., Kikuchi N., Honda O., Et al., The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting, Clin Radiol, 76, pp. 155 e15-e23, (2021)","N. Tanabe; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 54 Kawahara-cho, Shogoin, Sakyo-ku, 606-8507, Japan; email: ntana@kuhp.kyoto-u.ac.jp","","Elsevier B.V.","","","","","","22125345","","","34824028","English","Respir. Invest.","Article","Final","","Scopus","2-s2.0-85119693249"
"Cedeno Jimenez J.R.; Pugliese Viloria A.D.J.; Brovelli M.A.","Cedeno Jimenez, Jesus Rodrigo (57221997379); Pugliese Viloria, Angelly de Jesus (58168806000); Brovelli, Maria Antonia (6602533891)","57221997379; 58168806000; 6602533891","Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements","2023","ISPRS International Journal of Geo-Information","12","3","107","","","","8","10.3390/ijgi12030107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151563779&doi=10.3390%2fijgi12030107&partnerID=40&md5=903f43121d4c38ab170d87ebee79b704","Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy","Cedeno Jimenez J.R., Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy; Pugliese Viloria A.D.J., Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy; Brovelli M.A., Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy","Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO (Formula presented.), which is one of the most harmful pollutants, has the potential to cause diseases such as Chronic Obstructive Pulmonary Disease (COPD). Unfortunately, not all countries have local atmospheric pollutant monitoring networks to perform ground measurements (especially Low- and Middle-Income Countries). Although some alternatives, such as satellite technologies, provide a good approximation for tropospheric NO (Formula presented.), these do not measure concentrations at the ground level. In this work, we aim to provide an alternative to ground sensor measurements. We used a combination of ground meteorological measurements with satellite Sentinel-5P observations to estimate ground NO (Formula presented.). For this task, we used state-of-the-art Machine Learning models, linear regression models, and feature selection algorithms. From the results obtained, we found that a Multi-layer Perceptron Regressor and Kriging in combination with a Random Forest feature selection algorithm achieved the lowest RMSE (2.89 µg/m (Formula presented.)). This result, in comparison with the real data standard deviation and the models using only satellite data, represented an RMSE decrease of 55%. Future work will focus on replacing the use of meteorological ground sensors with only satellite-based data. © 2023 by the authors.","atmospheric pollution; machine learning; nitrogen dioxide","","","","","","Horizon 2020","This work is partially funded by the project Harmonia (identifier: H2020-LC-CLA-2018-2019-2020), an EU Horizon 2020 project dedicated to the development of a support system for improved resilience and sustainable urban areas to cope with climate change and urban areas.","Health Impacts of Air Pollution in Europe, 2021, (2021); The 17 Sustainable Development Goals; Trushna T., Tiwari R.R., Establishing the National Institute for Research in Environmental Health, India, Bull. World Health Organ, 100, pp. 281-285, (2022); Zhang Z., Wang J., Lu W., Exposure to Nitrogen Dioxide and Chronic Obstructive Pulmonary Disease (COPD) in Adults: A Systematic Review and Meta-Analysis, Environ. Sci. Pollut. Res, 25, pp. 15133-15145, (2018); Tyagi S., Chaudhary M., Ambedkar A.K., Sharma K., Gautam Y.K., Singh B.P., Metal Oxide Nanomaterials based sensors for monitoring environmental NO<sub>2</sub> and its impact on plant ecosystem: A Review, Sens. Diagn, 1, pp. 106-129, (2022); Emissions from Road Traffic and Domestic Heating behind Breaches of EU Air Quality Standards across Europe, (2022); Pruitt E., Review of the primary national ambient air quality standards for oxides of nitrogen, Fed. Regist, 83, pp. 17226-17278, (2018); Piccoli A., Agresti V., Balzarini A., Bedogni M., Bonanno R., Collino E., Colzi F., Lacavalla M., Lanzani G., Pirovano G., Et al., Modeling the Effect of COVID-19 Lockdown on Mobility and NO<sub>2</sub> Concentration in the Lombardy Region, Atmosphere, 11, (2020); Air Quality Standards, (2021); Copernicus in Detail, (2019); Reimann S., Wegener R., Claude A., Sauvage S., Updated Measurement Guideline for NOx and VOCs, (2018); Kramer H.J., Publication Title: Copernicus: Sentinel-5P—Satellite Missions—eoPortal Directory, European Space Agency, Paris, France, (2012); Pinder R.W., Klopp J.M., Kleiman G., Hagler G.S.W., Awe Y., Terry S., Opportunities and challenges for filling the air quality data gap in low- and middle-income countries, Atmos. 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Learn, 108, pp. 971-992, (2019); Mao F., Zhou X., Song Y., Environmental and Human Data-Driven Model Based on Machine Learning for Prediction of Human Comfort, IEEE Access, 7, pp. 132909-132922, (2019); Unser M., Aldroubi A., Eden M., B-spline signal processing. I. Theory, IEEE Trans. Signal Process, 41, pp. 821-833, (1993); Krivoruchko K., Empirical Bayesian Kriging, (2012); Oxoli D., Cedeno Jimenez J.R., Brovelli M.A., Assessment of Sentinel-5P Performance for Ground-Level Air Quality Monitoring: Preparatory Experiments over the COVID-19 Lockdown Period, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, XLIV-3/W1-2020, pp. 111-116, (2020); Mathieu E., Ritchie H., Rodes-Guirao L., Appel C., Giattino C., Hasell J., Macdonald B., Dattani S., Beltekian D., Ortiz-Ospina E., Et al., Coronavirus Pandemic (COVID-19), Our World in Data, (2020)","J.R. Cedeno Jimenez; Department of Civil and Environmental Engineering, Politecnico di Milano, Milano, Piazza Leonardo da Vinci 32, 20133, Italy; email: jesusrodrigo.cedeno@polimi.it","","MDPI","","","","","","22209964","","","","English","ISPRS Int. J. Geo-Inf.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85151563779"
"Karthick G.S.; Pankajavalli P.B.","Karthick, G.S. (55876514600); Pankajavalli, P.B. (39362190600)","55876514600; 39362190600","Chronic obstructive pulmonary disease prediction using Internet of things-spiro system and fuzzy-based quantum neural network classifier","2023","Theoretical Computer Science","941","","","55","76","21","13","10.1016/j.tcs.2022.08.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137409711&doi=10.1016%2fj.tcs.2022.08.021&partnerID=40&md5=303ccd821f2a008921c46ca39370572b","Department of Software Systems, PSG College of Arts and Science, Tamil Nadu, Coimbatore, India; Department of Computer Science, Bharathiar University, Tamil Nadu, Coimbatore, India","Karthick G.S., Department of Software Systems, PSG College of Arts and Science, Tamil Nadu, Coimbatore, India, Department of Computer Science, Bharathiar University, Tamil Nadu, Coimbatore, India; Pankajavalli P.B., Department of Computer Science, Bharathiar University, Tamil Nadu, Coimbatore, India","Chronic Obstructive Pulmonary Disease (COPD) is a multifarious progressive disease that increases the mortality and morbidity ratio as well as becoming a life-threatening issue of an individual. Accurate and cost-effective diagnosis of diseases plays a primary role in the medical domain and a wide range of research has been carried out on disease prediction using sensory approaches along with the assistance of machine learning techniques. The traditional disease diagnosis procedures are invasive, costlier and the decision support systems were unreliable most of the time. The human exhaled breath discharged from the body is composed of various Volatile Organic Compounds (VOCs) which can be influenced by metabolic and disease activities. Hence, the analysis of VOCs in exhaled breath has an incredible potentiality for COPD diagnosis and can rapidly decrease the mortality rate. In this research, IoT-Spiro System is designed and an intelligent machine learning forecasting framework (IMLFF) has been proposed. IoT-Spiro System perceives the various VOCs patterns available in exhaled breath and that real-time parameter has been analyzed using IMLFF. The proposed framework incorporates a hybrid Genetic Big Bang-Big Crunch (GBB-BC) algorithm for selecting the optimal features from the real-time dataset and a Fuzzy-based Quantum Neural Network (F-QNN) classifier for diagnosing COPD. The experimental results illustrate that IMLFF outperforms when compared to recent existing approaches concerning various statistical parameters and performance metrics. From the result analysis, it has been determined that IoT-Spiro System and IMLFF framework can serve as an efficient assisting model to the medical practitioner for diagnosing COPD. © 2022 Elsevier B.V.","COPD diagnosis; Exhaled breath; Feature selection; IoT Spiro-system; Keywords volatile organic compounds; Machine learning classifiers","Classification (of information); Cost effectiveness; Decision support systems; Diagnosis; Forecasting; Fuzzy inference; Internet of things; Machine learning; Pulmonary diseases; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease diagnose; Disease diagnosis; Exhaled breaths; Features selection; IoT spiro-system; Keyword volatile organic compound; Learning classifiers; Machine learning classifier; Machine-learning; Volatile organic compounds","","","","","DST-ICPS, (DST/ICPS/CPS-Individual/2018/193); Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी","Funding: This work has been supported by the Department of Science and Technology—Interdisciplinary Cyber-Physical System ( DST-ICPS ), New Delhi, India, under Grant DST/ICPS/CPS-Individual/2018/193 . ","Halpin D.M.G., Criner G.J., Papi A., Singh D., Anzueto A., Martinez F.J., Agusti A.A., Vogelmeier C.F., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 GOLD science committee report on COVID-19 and chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 203, 1, pp. 24-36, (2021); Boulet L.P., FitzGerald J.M., Levy M.L., Cruz A.A., Pedersen S., Haahtela T., A guide to the translation of the global initiative for asthma (GINA) strategy into improved care, Eur. Respir. J., 39, pp. 1220-1229, (2012); Schnabel R., Fijten R., Smolinska A., Analysis of volatile organic compounds in exhaled breath to diagnose ventilator-associated pneumonia, Sci. Rep., 5, (2015); Xiang L., Wu S., Hua Q., Bao C., Liu H., Volatile organic compounds in human exhaled breath to diagnose gastrointestinal cancer: a meta-analysis, Front. Oncol., 11, (2021); (2014); Eswari T., Sampath P., Lavanya S., Predictive methodology for diabetic data analysis in big data, Proc. Comput. Sci., 50, pp. 203-208, (2015); D'Souza S., Prema K.V., Balaji S., Feature selection and modeling using statistical and machine learning methods, 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 18-22, (2020); Wang Z., Lin Z., Optimal feature selection for learning-based algorithms for sentiment classification, Cogn. Comput., 12, pp. 238-248, (2020); Yang G., Et al., A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box, IEEE Trans. Ind. Inform., 10, 4, pp. 2180-2191, (2014); Yan Y., Li Q., Li H., Zhang X., Wang L., Open Access: a home-based health information acquisition system, Health Inf. Sci. 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Appl., 95, (2017); Beer K., Bondarenko D., Farrelly T., Et al., Training deep quantum neural networks, Nat. Commun., 11, (2020); Narain R., Saxena S., Goyal A., A novel heart disease prediction system based on quantum neural network using clinical parameters, Annu. Res. Rev. Biol., 14, pp. 1-10, (2017)","G.S. Karthick; Department of Software Systems, PSG College of Arts and Science, Coimbatore, Tamil Nadu, India; email: karthick@psgcas.ac.in","","Elsevier B.V.","","","","","","03043975","","TCSCD","","English","Theor Comput Sci","Article","Final","","Scopus","2-s2.0-85137409711"
"Yu T.; Shen R.; You G.; Lv L.; Kang S.; Wang X.; Xu J.; Zhu D.; Xia Z.; Zheng J.; Huang K.","Yu, Tao (56537461200); Shen, Runnan (57216646484); You, Guochang (57221305181); Lv, Lin (57760297900); Kang, Shimao (57390346700); Wang, Xiaoyan (57913856600); Xu, Jiatang (57913008600); Zhu, Dongxi (57390361100); Xia, Zuqi (57221305556); Zheng, Junmeng (57198759946); Huang, Kai (55246075200)","56537461200; 57216646484; 57221305181; 57760297900; 57390346700; 57913856600; 57913008600; 57390361100; 57221305556; 57198759946; 55246075200","Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study","2022","Frontiers in Cardiovascular Medicine","9","","990788","","","","8","10.3389/fcvm.2022.990788","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139103852&doi=10.3389%2ffcvm.2022.990788&partnerID=40&md5=e1ae5a1b4e701a31e2c1316a680ff878","Department of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China","Yu T., Department of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Shen R., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; You G., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Lv L., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Kang S., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Wang X., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Xu J., Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Zhu D., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Xia Z., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; Zheng J., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China, Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Huang K., Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China, Department of Cardiovascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China","Background: Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months. Materials and methods: The clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold. Results: In total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points. Conclusion: Machine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery. Copyright © 2022 Yu, Shen, You, Lv, Kang, Wang, Xu, Zhu, Xia, Zheng and Huang.","deep vein thrombosis; endovascular; machine learning; post-thrombotic syndrome; prognosis","anticoagulant agent; antivitamin K; low molecular weight heparin; urokinase; adult; aged; algorithm; angina pectoris; area under the curve; Article; asthma; body mass; calibration; cardiovascular disease assessment; catheter directed thrombolysis; Chinese medicine; chronic obstructive lung disease; clinical assessment; clinical study; clinical variable; cohort analysis; congestive heart failure; continuous infusion; controlled study; decision tree; deep vein thrombosis; diabetes mellitus; diagnostic test accuracy study; Doppler ultrasonography; endovascular surgery; external validation; feature selection; female; high risk population; hospitalization; human; hypercholesterolemia; hypertension; incidence; kernel method; least absolute shrinkage and selection operator; logistic regression analysis; lung embolism; machine learning; major clinical study; male; phlebography; postthrombosis syndrome; prediction; probability; random forest; receiver operating characteristic; risk assessment; scoring system; sensitivity and specificity; software; support vector machine; validation process; validation study; venous clinical severity score; villalta score","","urokinase, 139639-24-0","","","National Natural Science Foundation of China, NSFC, (81800420); Guangzhou Municipal Science and Technology Project, (201803010030)","Funding text 1: This research was funded by National Natural Science Foundation of China (Project approval number is 81800420) and Guangzhou Science and Technology Plan Project (Project approval number is 201803010030).; Funding text 2: This research was supported by the grants (31872587 and 3201101951) from the National Natural Science Foundation of China. 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Zheng; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; email: zhengjm27@mail.sysu.edu.cn; K. Huang; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China; email: huangk37@mail.sysu.edu.cn","","Frontiers Media S.A.","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139103852"
"Weber R.; Streckenbach B.; Welti L.; Inci D.; Kohler M.; Perkins N.; Zenobi R.; Micic S.; Moeller A.","Weber, Ronja (57242374000); Streckenbach, Bettina (57210863105); Welti, Lara (58193538500); Inci, Demet (22134686800); Kohler, Malcolm (8843819000); Perkins, Nathan (57205740891); Zenobi, Renato (7005407859); Micic, Srdjan (57242222400); Moeller, Alexander (7006926256)","57242374000; 57210863105; 58193538500; 22134686800; 8843819000; 57205740891; 7005407859; 57242222400; 7006926256","Online breath analysis with SESI/HRMS for metabolic signatures in children with allergic asthma","2023","Frontiers in Molecular Biosciences","10","","1154536","","","","13","10.3389/fmolb.2023.1154536","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153386420&doi=10.3389%2ffmolb.2023.1154536&partnerID=40&md5=6562a952b2f3392999c586f978a1b7f3","Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland; Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland; Division of Clinical Chemistry and Biochemistry, University Children’s Hospital Zurich, Zurich, Switzerland","Weber R., Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; Streckenbach B., Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland; Welti L., Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; Inci D., Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; Kohler M., Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland; Perkins N., Division of Clinical Chemistry and Biochemistry, University Children’s Hospital Zurich, Zurich, Switzerland; Zenobi R., Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland; Micic S., Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; Moeller A., Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland","Introduction: There is a need to improve the diagnosis and management of pediatric asthma. Breath analysis aims to address this by non-invasively assessing altered metabolism and disease-associated processes. Our goal was to identify exhaled metabolic signatures that distinguish children with allergic asthma from healthy controls using secondary electrospray ionization high-resolution mass spectrometry (SESI/HRMS) in a cross-sectional observational study. Methods: Breath analysis was performed with SESI/HRMS. Significant differentially expressed mass-to-charge features in breath were extracted using the empirical Bayes moderated t-statistics test. Corresponding molecules were putatively annotated by tandem mass spectrometry database matching and pathway analysis. Results: 48 allergic asthmatics and 56 healthy controls were included in the study. Among 375 significant mass-to-charge features, 134 were putatively identified. Many of these could be grouped to metabolites of common pathways or chemical families. We found several pathways that are well-represented by the significant metabolites, for example, lysine degradation elevated and two arginine pathways downregulated in the asthmatic group. Assessing the ability of breath profiles to classify samples as asthmatic or healthy with supervised machine learning in a 10 times repeated 10-fold cross-validation revealed an area under the receiver operating characteristic curve of 0.83. Discussion: For the first time, a large number of breath-derived metabolites that discriminate children with allergic asthma from healthy controls were identified by online breath analysis. Many are linked to well-described metabolic pathways and chemical families involved in pathophysiological processes of asthma. Furthermore, a subset of these volatile organic compounds showed high potential for clinical diagnostic applications. Copyright © 2023 Weber, Streckenbach, Welti, Inci, Kohler, Perkins, Zenobi, Micic and Moeller.","allergic asthma; breath analysis; children; metabolites; SESI/HRMS; volatile organic compounds (VOCs)","","","","","","Heidi Ras Stiftung; Lotte und Adolf Hotz-Sprenger Stiftung; University Children’s Hospital Zurich; Zurich Foundation; Evi Diethelm-Winteler Foundation; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (177101, 326030_177101/1)","This work was supported by the Swiss National Science Foundation (SNSF) [grant number 326030_177101/1]; the Evi Diethelm-Winteler Foundation; the Childhood Research Center of the University Children\u2019s Hospital Zurich; the Heidi Ras Stiftung; the Zurich Foundation; and the Lotte and Adolf Hotz-Sprenger Stiftung.","Barcik W., Boutin R.C.T., Sokolowska M., Finlay B.B., The role of lung and gut microbiota in the pathology of asthma, Immunity, 52, pp. 241-255, (2020); Barnes M.A., Carson M.J., Nair M.G., Non-traditional cytokines: How catecholamines and adipokines influence macrophages in immunity, metabolism and the central nervous system, Cytokine, 72, pp. 210-219, (2015); Benjamini Y., Hochberg Y., Controlling the false discovery rate: A practical and powerful approach to multiple testing, J. 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Nutr, 110, pp. 685-690, (2019)","A. Moeller; Department of Respiratory Medicine, University Children’s Hospital Zurich, Zurich, Switzerland; email: alexander.moeller@kispi.uzh.ch","","Frontiers Media S.A.","","","","","","2296889X","","","","English","Front. Mol. Biosci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85153386420"
"Henschke C.; Huber R.; Jiang L.; Yang D.; Cavic M.; Schmidt H.; Kazerooni E.; Zulueta J.J.; Sales dos Santos R.; Ventura L.; Viola L.; Mohan A.; Lee C.-T.; dos Santos R.S.; Kerpel-Fronius A.; Sozzi G.; Tammemägi M.; Lam S.","Henschke, Claudia (7005884727); Huber, Rudolf (7402557775); Jiang, Long (57203459155); Yang, Dawei (57217462285); Cavic, Milena (39760938900); Schmidt, Heidi (57189358308); Kazerooni, Ella (57216996382); Zulueta, Javier J. (6701785243); Sales dos Santos, Ricardo (55346568500); Ventura, Luigi (57980975700); Viola, Lucia (57217027497); Mohan, Anant (57205861694); Lee, Choon-Taek (7410162518); dos Santos, Ricardo Sales (7201375222); Kerpel-Fronius, Anna (57195369381); Sozzi, Gabriella (7005301493); Tammemägi, Martin (6508015967); Lam, Stephen (55193690600)","7005884727; 7402557775; 57203459155; 57217462285; 39760938900; 57189358308; 57216996382; 6701785243; 55346568500; 57980975700; 57217027497; 57205861694; 7410162518; 7201375222; 57195369381; 7005301493; 6508015967; 55193690600","Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee","2024","Journal of Thoracic Oncology","19","4","","565","580","15","9","10.1016/j.jtho.2023.11.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180312764&doi=10.1016%2fj.jtho.2023.11.013&partnerID=40&md5=d01a4bc5216137ee6b585263e2c2c5ad","Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, United States; Division of Respiratory Medicine and Thoracic Oncology, Department of Medicine, University of Munich – Campus Innenstadt, Ziemssenstrabe, Munich, Germany; Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China; Department of Pulmonary Medicine and Critical Care, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia; Department of Medical Imaging, Toronto General Hospital, Toronto, Canada; Division of Cardiothoracic Radiology and Internal Medicine, University of Michigan Medical School, Frankel Cardiovascular Center, Ann Arbor, MI, United States; Department of Medicine, Mount Sinai Morningside, New York, New York, United States; Department of Minimally Invasive Thoracic and Robotic Surgery, Albert Einstein Israeli Hospital, Sao Paulo, Brazil; Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy","Henschke C., Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, United States; Huber R., Division of Respiratory Medicine and Thoracic Oncology, Department of Medicine, University of Munich – Campus Innenstadt, Ziemssenstrabe, Munich, Germany; Jiang L., Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China; Yang D., Department of Pulmonary Medicine and Critical Care, Zhongshan Hospital, Fudan University, Shanghai, China; Cavic M., Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia; Schmidt H., Department of Medical Imaging, Toronto General Hospital, Toronto, Canada; Kazerooni E., Division of Cardiothoracic Radiology and Internal Medicine, University of Michigan Medical School, Frankel Cardiovascular Center, Ann Arbor, MI, United States; Zulueta J.J., Department of Medicine, Mount Sinai Morningside, New York, New York, United States; Sales dos Santos R., Department of Minimally Invasive Thoracic and Robotic Surgery, Albert Einstein Israeli Hospital, Sao Paulo, Brazil; Ventura L., Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy; Viola L.; Mohan A.; Lee C.-T.; dos Santos R.S.; Kerpel-Fronius A.; Sozzi G.; Tammemägi M.; Lam S.","Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches. © 2023 International Association for the Study of Lung Cancer","Abnormal LDCT findings; Diagnostics; Early detection; Low-dose computed tomography; Lung cancer; Screening","Early Detection of Cancer; Humans; Lung; Lung Neoplasms; Mass Screening; Smoking Cessation; Tomography, X-Ray Computed; annual repeat interim diagnosed lung cancer; annual repeat screen diagnosed lung cancer; Article; artificial intelligence; baseline screen diagnosed lung cancer; cancer classification; cancer screening; cancer therapy; clinical protocol; disease assessment; early cancer diagnosis; human; information processing; invasive procedure; invasive workup; low-dose computed tomography; lung cancer; lung nodule; management system; measurement; medical ethics; nodule growth assessment; nodule size; non invasive procedure; noninvasive workup; nuclear magnetic resonance imaging; positron emission tomography-computed tomography; process optimization; repeat screening; screening; screening interval; size; diagnostic imaging; lung; lung tumor; mass screening; procedures; smoking cessation; x-ray computed tomography","","","","","Cornell Research Foundation","Disclosure: Dr. Henschke is a named inventor on a number of patents and patent applications relating to the evaluation of pulmonary nodules on computed tomography scans of the chest which are owned by Cornell Research Foundation. Since 2009, Dr. Henschke does not accept any financial benefit from these patents including royalties and any other proceeds related to the patents or patent applications owned by Cornell Research Foundation. Dr. Henschke is the President and serves on the board of the Early Diagnosis and Treatment Research Foundation. Dr. Henschke reports not receiving compensation from the Foundation. The Foundation is established to provide grants for projects, conferences, and public databases for research on early diagnosis and treatment of diseases. Recipients include, I-ELCAP, among others. The funding comes from a variety of sources including philanthropic donations, grants and contracts with agencies (federal and nonfederal), and imaging and pharmaceutical companies relating to image processing assessments. The various sources of funding exclude any funding from tobacco companies or tobacco-related sources. She is also on the advisory board for Lunglife AI. Dr. Kazerooni reports having leadership roles on the National Lung Cancer Roundtable at the American Cancer Society. Dr. Zulueta reports receiving consulting fees from heart lung technologies and median technologies and having stock or stock options in VisionGate. Dr. Santos reports receiving grants or contracts from Bristol-Myers Squibb and Roche; having advisory board participation for AstraZeneca; and having equipment or gifts from Ethicon. 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A meta-analysis of the choices for treating stage I non-small-cell lung cancer, Eur J Cardiothorac Surg, 51, pp. 203-210, (2017); Berlin E., Buckstein M., Yip R., Et al., Definitive radiation for Stage I lung cancer in a screened population: results from the I-ELCAP, Int J Radiat Oncol Biol Phys, 104, pp. 122-126, (2019); Tomita N., Okuda K., Osaga S., Miyakawa A., Nakanishi R., Shibamoto Y., Surgery versus stereotactic body radiotherapy for clinical stage I non-small-cell lung cancer: propensity score-matching analysis including the ratio of ground glass nodules, Clin Transl Oncol, 23, pp. 638-647, (2021); de Ruiter J.C., van Diessen J.N.A., Smit E.F., Et al., Minimally invasive lobectomy versus stereotactic ablative radiotherapy for stage I non-small cell lung cancer, Eur J Cardio Thorac Surg, 62, (2022); Henschke C.I., Yip R., Sun Q., Et al., Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a-b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery, J Thorac Oncol, 19, 3, pp. 476-490, (2024); Veterans Affairs lung cancer or stereotactic radiotherapy (VALOR); Vitzthum von Eckstaedt H., Kitts A.B., Swanson C., Hanley M., Krishnaraj A., Patient-centered radiology reporting for lung cancer screening, J Thorac Imaging, 35, pp. 85-90, (2020); Cavic M., Kerpel-Fronius A., Viola L., Et al., P1.02-02 current status, challenges and perspectives of lung cancer screening in low- and middle-income countries World Conference on Lung Cancer, (2022); Ryska A., Buiga R., Fakirova A., Et al., Non-small cell lung cancer in countries of Central and southeastern Europe: diagnostic procedures and treatment reimbursement surveyed by the Central European Cooperative Oncology Group, Oncologist, 23, (2018); Saul E.E., Guerra R.B., Saul M.E., Et al., The challenges of implementing low-dose computed tomography for lung cancer screening in low- and middle-income countries, Nat Cancer, 1, pp. 1140-1152, (2020)","C. Henschke; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, One Gustave L. Levy Place, United States; email: cihenschke@gmail.com","","Elsevier Inc.","","","","","","15560864","","","37979778","English","J. Thorac. Oncol.","Article","Final","","Scopus","2-s2.0-85180312764"
"Yang Y.; Li W.; Guo Y.; Zeng N.; Wang S.; Chen Z.; Liu Y.; Chen H.; Duan W.; Li X.; Zhao W.; Chen R.; Kang Y.","Yang, Yingjian (57218501666); Li, Wei (57221637991); Guo, Yingwei (57218502342); Zeng, Nanrong (57671655900); Wang, Shicong (57670751600); Chen, Ziran (57670751700); Liu, Yang (57222473378); Chen, Huai (55205182900); Duan, Wenxin (57670149700); Li, Xian (57191970686); Zhao, Wei (57670149800); Chen, Rongchang (57200034537); Kang, Yan (57213821412)","57218501666; 57221637991; 57218502342; 57671655900; 57670751600; 57670751700; 57222473378; 55205182900; 57670149700; 57191970686; 57670149800; 57200034537; 57213821412","Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier","2022","Mathematical Biosciences and Engineering","19","8","","7826","7855","29","13","10.3934/mbe.2022366","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131723136&doi=10.3934%2fmbe.2022366&partnerID=40&md5=38fcc89ddb8ce09b7a142f6e8096c757","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Medical Engineering, Liaoning Provincial Corps Hospital, the Chinese People’s Armed Police Force, Shenyang, 110141, China; Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China; The Second Clinical Medical College, Jinan University, Shenzhen, 518001, China; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518001, China; Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Yang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Li W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Guo Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Zeng N., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Wang S., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Chen Z., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Liu Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Chen H., Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Duan W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Li X., Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Zhao W., Medical Engineering, Liaoning Provincial Corps Hospital, the Chinese People’s Armed Police Force, Shenyang, 110141, China; Chen R., Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China, The Second Clinical Medical College, Jinan University, Shenzhen, 518001, China, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518001, China; Kang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, Engineering Research Center of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%). ©2022 the Author(s), licensee AIMS Press.","chest HRCT images; classification; convolutional neural networks (CNN); COPD stage (GOLD); feature combination; Lasso; machine learning (ML); radiomics","Humans; Lung; Machine Learning; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; Biological organs; Computerized tomography; Convolution; Convolutional neural networks; Diagnosis; Image classification; Image segmentation; Machine learning; Multilayer neural networks; Pulmonary diseases; Chest high-resolution CT image; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease stage (GOLD); Convolutional neural network; CT Image; Feature combination; High resolution CT; Lasso; Machine learning; Radiomic; chronic obstructive lung disease; diagnostic imaging; human; lung; machine learning; x-ray computed tomography; Classification (of information)","","","","","Department of Radiology of the First Affiliated Hospital of Guangzhou Medical University; Scientific Research Fund of Liaoning Province, (JL201919); Special Program for Key Fields of Colleges and Universities in Guangdong Province, (2021ZDZX2008); Stable Support Plan for Colleges and Universities in Shenzhen, China, (SZWD2021010); National Natural Science Foundation of China, NSFC, (62071311); National Natural Science Foundation of China, NSFC; Natural Science Foundation of Guangdong Province, (2019A1515011382); Natural Science Foundation of Guangdong Province","Thanks to the Department of Radiology of the First Affiliated Hospital of Guangzhou Medical University for providing the data set, and to the National Natural Science Foundation of China (62071311), Natural Science Foundation of Guangdong Province, China (2019A1515011382), Stable Support Plan for Colleges and Universities in Shenzhen, China (SZWD2021010), Scientific Research Fund of Liaoning Province, China (JL201919) and the Special Program for Key Fields of Colleges and Universities in Guangdong Province (biomedicine and health) of China (2021ZDZX2008) for the funding support.","Mathioudakis A. 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Cardiol, 21, pp. 686-694, (2014); Torrey L., Shavlik J., Transfer learning, Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, IGI global, pp. 242-264, (2010)","W. Li; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; email: liwei2@sztu.edu.cn; H. Chen; Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; email: chenhuai1977@163.com; R. Chen; Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China; email: chenrc@vip.163.com; Y. Kang; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; email: kangyan@sztu.edu.cn","","American Institute of Mathematical Sciences","","","","","","15471063","","","35801446","English","Math. Biosci. Eng.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85131723136"
"Shanthappa P.M.; Kumar R.","Shanthappa, Pallavi M. (56595021200); Kumar, Rakshitha (57893519500)","56595021200; 57893519500","ProAll-D: protein allergen detection using long short term memory - a deep learning approach","2022","ADMET and DMPK","10","3","","231","240","9","11","10.5599/admet.1335","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138182589&doi=10.5599%2fadmet.1335&partnerID=40&md5=056902bb197cf518cfd7858e26df2f15","Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India","Shanthappa P.M., Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India; Kumar R., Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India","Background: An allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens. Method: This work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration. Results: The Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %. Conclusion: As the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data. © 2022 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).","Acc transformation; Ada boost; Allergen prediction; Bagging classifier; Classifier; Extra tree classifier; Gaussian naive bayes; Linear discriminant analysis; Lstm model; Quadratic discriminant analysis","allergen; allergenicity; amino acid sequence; Article; Bayesian learning; controlled study; dispersion; hydrophilicity; intermethod comparison; long short term memory network; measurement accuracy; protein intake; protein structure","","","","","","","Stadler M.B., Stadler B.M., Allergenicity prediction by protein sequence, FASEB J, 17, (2003); Poms R.E., Anklam E., Akuhn M., Polymerase chain reaction techniques for food allergen detection, Journal of AOAC International, 87, pp. 1391-1397, (2004); Saha S., Raghava G.S., AlgPred: prediction of allergenic proteins and mapping of IgE epitopes, Nucleic acids research, 34, pp. W202-W209, (2006); Muh H.C., Tong J.C., AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins, PloS one, 4, (2009); Mohabatkar H., Mohammad B.M., Abdolahi K., Mohsenzadeh S., Prediction of allergenic proteins by means of the concept of Chou's pseudo amino acid composition and a machine learning approach, Medicinal Chemistry, 9, pp. 133-137, (2013); Vijayakumar S., Lakshmi P.T.V., IEEE International Conference on Bioinformatics and Biomedicine, A fuzzy inference system for predicting allergenicity and allergic cross-reactivity in proteins, pp. 49-52, (2013); Dimitrov I., Flower D.R., Doytchinova I., BMC Bioinformatics, AllerTop – a server for in silico prediction of allergens, pp. 1-9, (2013); Dimitrov I., Naneva L., Doytchinova I., Bangov I., AllergenFP: allergenicity prediction by descriptor fingerprints, Bioinformatics, 30, pp. 846-851, (2014); Dimitrov I., Naneva L., Bangov I., Doytchinova I., Allergenicity prediction by artificial neural networks, Journal of Chemometrics, 28, pp. 282-286, (2014); Dimitrov I., Bangov I., Flower D.R., Doytchinova I., AllerTOP v.2 – a server for in silico prediction of allergens, Journal of molecular modelling, 20, pp. 1-6, (2014); Dang Ha. X., Lawrence C.B., Allerdictor: fast allergen prediction using text classification techniques, Bioinformatics, 30, pp. 1120-1128, (2014); Negi S.S., Braun W., Cross-React: a new structural bioinformatics method for predicting allergen crossreactivity, Bioinformatics, 33, pp. 1014-1020, (2017); Ladics G.S., Assessment of the potential allergenicity of genetically-engineered food crops, Journal of Immunotoxicology, 16, pp. 43-53, (2019); Maurer-Stroh S., Krutz N.L., Kern P.S., Gunalan V., Nguyen M.N., Limviphuvadh V., Eisenhaber F., Gerberick G.F., AllerCatPro-prediction of protein allergenicity potential from the protein sequence, Bioinformatics, 35, pp. 3020-3027, (2019); Pallavi M.S., Pramod Kumar H.S., In-silico Analysis to Determine the Efficient Drug for Malignant Melanoma using Molecular Dynamics, Biomedical and Pharmacology Journal, 13, pp. 1463-1470, (2020); Dimitrov I., Atanasova M., AllerScreener – a server for allergenicity and cross-reactivity prediction, Cybernetics and information technologies, 20, pp. 175-184, (2020); Sharma N., Patiyal S., Dhall A., Pande A., Arora C., Raghava G.P.S, AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes, Briefings in Bioinformatics, 22, (2021); Wang L., Niu D., Zhao X., Wang X., Hao M., Che H., A Comparative Analysis of Novel Deep Learning and Ensemble Learning Models to Predict the Allergenicity of Food Proteins, Foods, 10, (2021); Venkatarajan M.S., Braun W., New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical–chemical properties, Molecular modeling annual, 7, (2001); Doytchinova I.A., Flower D.R., VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines, BMC Bioinformatics, 8, pp. 1-7, (2007)","P.M. Shanthappa; Department of Computer Science, Amrita School of Arts and Sciences, Mysuru Campus, Amrita Vishwa Vidyapeetham, India; email: palls.ms@gmail.com","","International Association of Physical Chemists","","","","","","18487718","","","","English","ADMET DMPK","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138182589"
"Heitmann J.; Glangetas A.; Doenz J.; Dervaux J.; Shama D.M.; Garcia D.H.; Benissa M.R.; Cantais A.; Perez A.; Müller D.; Chavdarova T.; Ruchonnet-Metrailler I.; Siebert J.N.; Lacroix L.; Jaggi M.; Gervaix A.; Hartley M.-A.; Hugon F.; Fassbind D.; Barro M.; Bediang G.; Hafidi N.E.L.; Bouskraoui M.; Ba I.","Heitmann, Julien (58303915700); Glangetas, Alban (57221410503); Doenz, Jonathan (58300507900); Dervaux, Juliane (58303567400); Shama, Deeksha M. (57222592926); Garcia, Daniel Hinjos (58303915800); Benissa, Mohamed Rida (57205005510); Cantais, Aymeric (56189534300); Perez, Alexandre (59286310800); Müller, Daniel (58380992800); Chavdarova, Tatjana (56453406500); Ruchonnet-Metrailler, Isabelle (14012522300); Siebert, Johan N. (57189618479); Lacroix, Laurence (57212879583); Jaggi, Martin (35190508300); Gervaix, Alain (7004668790); Hartley, Mary-Anne (58038546000); Hugon, Florence (56444332600); Fassbind, Derrick (56856824100); Barro, Makura (58303214700); Bediang, Georges (36647266700); Hafidi, N.E.L. (6507322616); Bouskraoui, M. (7004108384); Ba, Idrissa (57192347660)","58303915700; 57221410503; 58300507900; 58303567400; 57222592926; 58303915800; 57205005510; 56189534300; 59286310800; 58380992800; 56453406500; 14012522300; 57189618479; 57212879583; 35190508300; 7004668790; 58038546000; 56444332600; 56856824100; 58303214700; 36647266700; 6507322616; 7004108384; 57192347660","DeepBreath—automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries","2023","npj Digital Medicine","6","1","104","","","","13","10.1038/s41746-023-00838-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161034857&doi=10.1038%2fs41746-023-00838-3&partnerID=40&md5=09d64f906b9a1bb94623464734f24693","Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Pediatric Emergency Department, Hospital University of Saint Etienne, Saint Etienne, France; Center for Intelligent Systems (CIS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Division of Pediatric Emergency Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Geneva University Hospitals, Geneva, Switzerland; Department of Pediatrics, Hospital da Crianca Santo Antonio, Porto Allegre, Brazil; Department of Pediatrics, University Hospital Souro Sano, Bobo Dioulasso, Burkina Faso; Faculty of Medicine and Biomedical Sciences, University of Yaounde 1, Yaounde, Cameroon; University Children Hospital, Rabat, Morocco; Faculty of Medicine, University Cadi Ayyad, Marrakech, Morocco; Faculty of Medicine, University Cheick Anta Diop, Dakar, Senegal","Heitmann J., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Glangetas A., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Doenz J., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Dervaux J., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Shama D.M., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Garcia D.H., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Benissa M.R., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Cantais A., Pediatric Emergency Department, Hospital University of Saint Etienne, Saint Etienne, France; Perez A., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Müller D., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Chavdarova T., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Ruchonnet-Metrailler I., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Siebert J.N., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Lacroix L., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Jaggi M., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; Gervaix A., Division of Pediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals (HUG), University of Geneva, Switzerland, Geneva, Switzerland; Hartley M.-A., Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, Center for Intelligent Systems (CIS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, Division of Pediatric Emergency Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Hugon F., Geneva University Hospitals, Geneva, Switzerland; Fassbind D., Department of Pediatrics, Hospital da Crianca Santo Antonio, Porto Allegre, Brazil; Barro M., Department of Pediatrics, University Hospital Souro Sano, Bobo Dioulasso, Burkina Faso; Bediang G., Faculty of Medicine and Biomedical Sciences, University of Yaounde 1, Yaounde, Cameroon; Hafidi N.E.L., University Children Hospital, Rabat, Morocco; Bouskraoui M., Faculty of Medicine, University Cadi Ayyad, Marrakech, Morocco; Ba I., Faculty of Medicine, University Cheick Anta Diop, Dakar, Senegal","The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] ± 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 ± 0.10), wheezing disorders (AUROC 0.91 ± 0.03), and bronchiolitis (AUROC 0.94 ± 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology. © 2023, The Author(s).","","Biological organs; Convolutional neural networks; Deep learning; Diseases; Learning systems; Pathology; Pediatrics; Physiological models; Acute respiratory illness; Automated detection; Bronchiolitis; Computer-aided analysis; Convolutional neural network; Healthy controls; Learning models; Logistic regression classifier; Receiver operator characteristics; Respiratory pathology; age; Article; asthma; automation; Brazil; breathing pattern; breathing rate; bronchiolitis; bronchitis; Cameroon; child; classifier; cohort analysis; controlled study; convolutional neural network; cross validation; deep learning; diagnostic test accuracy study; external validity; female; human; logistic regression analysis; lung auscultation; major clinical study; male; model; Morocco; observational study; outpatient; pneumonia; prediction; receiver operating characteristic; respiratory tract disease; Senegal; Switzerland; wheezing; Computer aided analysis","","","Littmann 3200, 3M, United States; Littmann StethAssist v.1.3","3M, United States","Fondation Privée des HUG; Ligue Pulmonaire Genevoise; Service de la Solidarité internationale du Canton de Genève; Université de Genève, UNIGE; Gertrude von Meissner-Stiftung","We sincerely thank the patients who contributed their data to this study and the clinicians at each site for their meticulous acquisition. This work was supported the following grants: • Service de la Solidarité internationale du Canton de Genève (A.Ge.) https://www.geneve-int.ch/fr/service-de-la-solidarit-internationale-2 ; no grant number. • Fondation Gertrude Von Meissner (A.Ge.) https://www.unige.ch/medecine/fr/recherche/appels-a-projets/fondation-gertrude-von-meissner-appel-a-projets2/ ; no grant number. • Ligue Pulmonaire Genevoise (M.R.B.) https://www.lpge.ch/ ; no grant number. • Fondation Privée des HUG (J.S.) https://www.fondationhug.org/ ; no grant number. • Université de Genève (A.Ge.) https://www.unige.ch/unitec/en/presentation/innogap-proof-of-principle-fund/ ; no grant number.","Hafke-Dys H., Breborowicz A., Kleka P., Kocinski J., Biniakowski A., The accuracy of lung auscultation in the practice of physicians and medical students, PloS One, 14, (2019); Sarkar M., Madabhavi I., Niranjan N., Dogra M., Auscultation of the respiratory system, Ann. Thorac. Med., 10, (2015); Abdel-Hamid O., Et al., Convolutional neural networks for speech recognition, IEEE/ACM Trans Audio Speech, Language Process, 22, pp. 1533-1545, (2014); Kong Q., Et al., PANNs: Large-scale pretrained audio neural networks for audio pattern recognition, IEEE/ACM Transac Audio Speech Language Processing, 28, pp. 2880-2894, (2020); Hershey S., (2017); Gurung A., Scrafford C.G., Tielsch J.M., Levine O.S., Checkley W., Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis, Respir. Med., 105, pp. 1396-1403, (2011); Pinho C., Oliveira A., Jacome C., Rodrigues J.M., Marques A., Integrated approach for automatic crackle detection based on fractal dimension and box filtering, IJRQEH, 5, pp. 34-50, (2016); Andres E., Gass R., Charloux A., Brandt C., Hentzler A., Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0, J. Med. Life, 11, (2018); Respirenet: A deep neural network for accurately detecting abnormal lung sounds in limited data setting, In 2021 43Rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC, pp. 527-530, (2021); Jung S.-Y., Liao C.-H., Wu Y.-S., Yuan S.-M., Sun C.-T., Efficiently classifying lung sounds through depthwise separable cnn models with fused stft and mfcc features, Diagnostics, 11, (2021); Pramono R.X.A., Bowyer S., Rodriguez-Villegas E., Automatic adventitious respiratory sound analysis: a systematic review, PLOS ONE, 12, (2017); Grzywalski T., Et al., Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination, Eur. J. Pediatrics, 178, pp. 883-890, (2019); Fernando T., Sridharan S., Denman S., Ghaemmaghami H., Fookes C., Robust and interpretable temporal convolution network for event detection in lung sound recordings, Arxiv Preprint Arxiv, 2106, (2021); . Distinguishing between asthma and pneumonia through automated lung sound analysis, In Proceedings of the IEEE 31St Annual Northeast Bioengineering Conference, 2005, pp. 241-243, (2005); Rocha B., A respiratory sound database for the development of automated classification, In International Conference on Biomedical and Health Informatics, pp. 33-37, (2017); Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks, In 2019 IEEE 32Nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 50-55, (2019); Srivastava A., Et al., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput. Sci., 7, (2021); Finlayson S.G., Et al., The Clinician and Dataset Shift in Artificial Intelligence, N. Eng. J. Med., 385, pp. 283-286, (2021); Zech J.R., Et al., Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study, PLoS Med., 15, (2018); Kavanagh J., Jackson D.J., Kent B.D., Over-and under-diagnosis in asthma, Breathe, 15, pp. e20-e27, (2019); Obidike E., Et al., Misdiagnosis of pneumonia, bronchiolitis and reactive airway disease in children: A retrospective case review series in South East, Nigeria, Curr. Pediatr. Res., 22, pp. 166-171, (2018); Gong H., Et al., (1990); Barret K.E., Boitano S., (2012); Park D.S., Et al., Specaugment: A Simple Data Augmentation Method for Automatic Speech Recognition. Arxiv Preprint Arxiv, 1904, (2019); Kong Q., Et al., Cross-Task Learning for Audio Tagging, Sound Event Detection and Spatial Localization: Dcase 2019 Baseline Systems. Arxiv Preprint Arxiv, 1904, (2019); Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, pp. 1929-1958, (2014); Loshchilov I., Hutter F., Decoupled weight decay regularization, . Arxiv Preprint Arxiv, 1711, (2017); Clopper C.J., Pearson E.S., The use of confidence or fiducial limits illustrated in the case of the binomial, Biometrika, 26, pp. 404-413, (1934); DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, pp. 837-845, (1988)","M.-A. Hartley; Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland; email: mary-anne.hartley@epfl.ch","","Nature Research","","","","","","23986352","","","","English","npj Digit. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85161034857"
"Tessema B.A.; Nemomssa H.D.; Simegn G.L.","Tessema, Biruk Abera (57697873700); Nemomssa, Hundessa Daba (57212000960); Simegn, Gizeaddis Lamesgin (57204930723)","57697873700; 57212000960; 57204930723","Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases","2022","Medical Devices: Evidence and Research","15","","","89","102","13","11","10.2147/MDER.S362407","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130224100&doi=10.2147%2fMDER.S362407&partnerID=40&md5=cfb7a267df327032ce9e93f6d9e8c58f","School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; School of Medicine, Haramaya University College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia","Tessema B.A., School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia, School of Medicine, Haramaya University College of Health and Medical Sciences, Haramaya University, Harar, Ethiopia; Nemomssa H.D., School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; Simegn G.L., School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia","Purpose: Lung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician’s experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease. Methods: An electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronch-iolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques. Results: A test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier. Conclusion: Our experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited. © 2022 Abera Tessema et al.","auscultation; classification; denoising; discrete wavelet transform; feature extraction; lung diseases; lung sounds","Audition; Biological organs; Biomedical signal processing; Classification (of information); Computer aided diagnosis; Decision support systems; Discrete wavelet transforms; Learning systems; Pulmonary diseases; Support vector machines; Auscultation; Causes of death; Classification accuracy; De-noising; Discrete-wavelet-transform; Features extraction; Gaussians; Lung sound signals; Lung sounds; Respiratory tract infections; abnormal respiratory sound; adolescent; adult; aged; analysis of variance; Article; asthma; auscultation; Bayesian optimization technique; bronchiectasis; bronchiolitis; child; chronic lung disease; classification accuracy; classifier; Clearance factor; computerized respiratory sound analysis; controlled study; Crest factor; diagnostic accuracy; discrete wavelet transform; efficacy parameters; feature extraction; feature selection; feature visualization; female; human; impulse factor; infant; Kurtosis; learning algorithm; lower respiratory tract infection; lung acquisition; machine learning; major clinical study; male; middle aged; Naive Bayes algorithm; pneumonia; preschool child; probability; recording; school child; signal acquisition; signal noise ratio; Skewness; time frequency signal analysis; Total harmonic distortion; training; upper respiratory tract infection; validation study; wavelet multiresolution analysis; young adult; Feature extraction","","","","","Jimma University Medical Center; Jimma University, JU","The resources required for this research were provided by the school of Biomedical Engineering, Jimma institute of Technology, Jimma University and Jimma University Medical Center (JUMC). We would like to acknowledge our clinical collaborators Prof. Yoo, Dr. Assefa, Dr. Bekela, and Dr. Aneso, who are the medical doctors working in JUMC, Department of Internal Medicine, Pulmonology Unit, for their valuable advice and guidance from clinical perspectives.","Prasad B., Chronic Obstructive Pulmonary Disease (COPD), IJPRT, 10, 1, pp. 67LP-71, (2020); Vestbo J., COPD: definition and phenotypes, Clin Chest Med, 35, 1, pp. 1-6, (2014); Calverley PMA, Georgopoulos D., Chronic obstructive pulmonary disease: symptoms and signs, Eur Respir Monogr, 3, 7, pp. 6-24, (1998); Kirenga BJ, Schwartz JI, De Jong C, van der Molen T, Okot-Nwang M., Guidance on the diagnosis and management of asthma among adults in resource limited settings, Afr Health Sci, 15, 4, (2015); Kim H, Mazza J., Asthma, Allergy Asthma Clin Immunol, 7, (2011); Cukic V, Lovre V, Dragisic D, Ustamujic A., Asthma and Chronic Obstructive Pulmonary Disease (COPD) – differences and similarities, Mater Sociomed, 24, 2, (2012); Grief SN, Loza JK., Guidelines for the evaluation and treatment of pneumonia, Prim Care, 45, 3, pp. 485-503, (2018); Cotton MF, Innes S, Jaspan H, Madide A, Rabie H., Management of upper respiratory tract infections in children, S Afr Fam Pract, 50, 2, (2008); Rohilla A, Sharma VK, Kumar S., Upper respiratory tract infections: an overview, Int J Curr Pharmaceut Res, 5, pp. 1-3, (2013); Mirkarimi M, Alisamir M, Saraf S, Heidari S, Barouti S, Mohammadi S., Clinical and epidemiological determinants of lower respiratory tract infections in hospitalized pediatric patients, Int J Pediatr, 2020, pp. 1-7, (2020); Rademacher J, Welte T., Bronchiectasis, Dtsch Arztebl Int, 108, 48, pp. 809-815, (2011); Islam MA, Bandyopadhyaya I, Bhattacharyya P, Saha G., Classification of normal, Asthma and COPD subjects using multichannel lung sound signals, Proc 2018 IEEE Int Conf Commun Signal Process ICCSP, 2018, pp. 290-294, (2018); Dukic L, Kopcinovic LM, Dorotic A, Barsic I., Blood gas testing and related measurements: national recommendations on behalf of the Croatian Society of Medical Biochemistry and Laboratory Medicine, Biochem Medica, 26, 3, pp. 318-336, (2016); Altan G, Kutlu Y, Garbi Y, Pekmezci AO, Nural S, Altan G., Multimedia respiratory database (RespiratoryDatabase@TR): auscultation sounds and Chest X-rays, Nat Eng Sci, (2021); Demir F, Sengur A, Bajaj V., Convolutional neural networks based efficient approach for classification of lung diseases, Heal Inf Sci Syst, 8, 1, (2020); Andres E, Gass R, Charloux A, Brandt C, Hentzler A., Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0, J Med Life, 11, 2, pp. 89-106, (2018); Gogus FZ, Karlik B, Harman G., Identification of pulmonary disorders by using different spectral analysis methods, Int J Comput Intell Syst, 9, 4, pp. 595-611, (2016); Gokcen A., Computer-aided diagnosis system for chronic obstructive pulmonary disease using empirical wavelet transform on auscultation sounds, Comput J, 64, 11, pp. 1775-1783, (2021); Aykanat M, Kilic O, Kurt B, Saryal S., Classification of lung sounds using convolutional neural networks, Eurasip J Image Video Process, 2017, 1, pp. 1-9, (2017); Haider NS, Singh BK, Periyasamy R, Behera AK., Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm, J Med Syst, 43, 8, (2019); Vora S, Shah PC., COPD classification using machine learning algorithms, Int Res J Eng Technol, 6, pp. 608-611, (2019); Osisanwo FY, Akinsola JE, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J., Supervised machine learning algorithms: classification and comparison, Int J Comput Trends Technol, 48, 3, pp. 128-138, (2017); Do Q, Son TC, Chaudri J., Classification of asthma severity and medication using tensorflow and multilevel databases, Procedia Comput Sci, 113, pp. 344-351, (2017); Rao A, Huynh E, Royston TJ, Kornblith A, Roy S., Acoustic methods for pulmonary diagnosis, IEEE Rev Biomed Eng, 12, (2019); Verma N, Verma AK., Performance analysis of wavelet thresholding methods in denoising of audio signals of some Indian musical instruments, Int J Eng Sci Technol, 4, pp. 2040-2045, (2012); Devnath L, Kumer S, Nath D, Das AK, Islam R., Selection of wavelet and thresholding rule for denoising the ECG signals, Annals of Pure and Applied Mathematics, 10, 1, pp. 65-73, (2015); Elssied NOF, Ibrahim O, Osman AH., A novel feature selection based on one-way ANOVA F-test for e-mail spam classification, Res J Appl Sci Eng Technol, 7, 3, pp. 625-638, (2014); Neili Z, Fezari M, Redjati A., ELM and K-nn machine learning in classification of breath sounds signals, Int J Electr Comput Eng, 10, 4, pp. 3528-3536, (2020); Gambella C, Ghaddar B, Naoum-Sawaya J., Optimization problems for machine learning: a survey, Eur J Oper Res, 290, 3, pp. 807-828, (2021); Don S., Random subset feature selection and classification of lung sound, Procedia Comput Sci, 167, 2019, pp. 313-322, (2020); Sengupta N, Sahidullah M, Saha G., Lung sound classification using cepstral-based statistical features, Comput Biol Med, 75, pp. 118-129, (2016); Mondal A, Bhattacharya P, Saha G., Detection of lungs status using morphological complexities of respiratory sounds, Sci World J, 2014, pp. 1-9, (2014); Rizal A, Hidayat R, Nugroho HA., Multiscale tsallis entropy for pulmonary crackle detection, Int J Adv Intell Informatics, 4, 3, pp. 192-201, (2018); Rizal A, Hidayat R, Nugroho HA., Comparison of multiscale entropy techniques for lung sound classification, Indones J Electr Eng Comput Sci, 12, 3, pp. 984-994, (2018); Adhi Pramono RX, Imtiaz SA, Rodriguez-Villegas E., Evaluation of features for classification of wheezes and normal respiratory sounds, PLoS One, 14, 3, (2019); Alsheref FK, Gomaa WH., Blood diseases detection using classical machine learning algorithms, Int J Adv Comput Sci Appl, 10, 7, pp. 77-81, (2019); Hosseini M, Ren H, Rashid H-A, Mazumder AN, Prakash B, Mohsenin T., Neural networks for pulmonary disease diagnosis using auditory and demographic information, (2020)","H.D. Nemomssa; School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; email: hundedb@gmail.com","","Dove Medical Press Ltd","","","","","","11791470","","","","English","Med. Devices Evid. Res.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85130224100"
"Zuo Z.; Li Y.; Peng K.; Li X.; Tan Q.; Mo Y.; Lan Y.; Zeng W.; Qi W.","Zuo, Z. (57220084265); Li, Y. (57210254281); Peng, K. (57901043400); Li, X. (59651343400); Tan, Q. (57220091378); Mo, Y. (57373347900); Lan, Y. (57205334873); Zeng, W. (57663318800); Qi, W. (57219781556)","57220084265; 57210254281; 57901043400; 59651343400; 57220091378; 57373347900; 57205334873; 57663318800; 57219781556","CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study","2022","Clinical Radiology","77","3","","e215","e221","6","10","10.1016/j.crad.2021.11.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121257908&doi=10.1016%2fj.crad.2021.11.008&partnerID=40&md5=1e28b95b3b15b08fa7a42755425d0ef6","Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China","Zuo Z., Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Li Y., Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; Peng K., Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Li X., Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Tan Q., Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Mo Y., Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Lan Y., Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China; Zeng W., Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, China; Qi W., Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China","AIM: To develop a nomogram based on computed tomography (CT) texture analysis for the preoperative prediction of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma. MATERIALS AND METHODS: A dataset of chest CT containing lung nodules was collected from two institutions, and all surgically resected nodules were classified pathologically based on the presence of visceral pleural invasion. Each nodule on the CT image was segmented automatically by artificial-intelligence software and its CT texture features were extracted. The dataset was divided into training and external validation cohorts according to the institution, and a nomogram for predicting visceral pleural invasion was developed and validated. RESULTS: Of a total of 313 patients enrolled from two independent institutions, 63 were diagnosed with visceral pleural invasion. Three-dimensional (3D) CT long diameter, skewness, and sphericity, and chronic obstructive pulmonary disease were identified as independent predictors for visceral pleural invasion by multivariable logistic regression. The nomogram based on multivariable logistic regression showed great discriminative ability, as indicated by a C-index of 0.890 (95% confidence interval [CI]: 0.867–0.914) and 0.864 (95% CI: 0.817–0.911) for the training and external validation cohorts, respectively. Additionally, calibration of the nomogram revealed good predictive ability, as indicated by the Brier score (0.108 and 0.100 for the training and external validation cohorts, respectively). CONCLUSIONS: A nomogram was developed that could compute the probability of visceral pleural invasion in patients with cT1N0M0 lung adenocarcinoma with good calibration and discrimination. The nomogram has potential as a reliable tool for clinical evaluation and decision-making. © 2021 The Royal College of Radiologists","","Adenocarcinoma of Lung; Aged; Clinical Decision-Making; Cohort Studies; Confidence Intervals; Female; Humans; Logistic Models; Lung Neoplasms; Male; Middle Aged; Multiple Pulmonary Nodules; Neoplasm Invasiveness; Nomograms; Pleura; Preoperative Period; Retrospective Studies; Tomography, X-Ray Computed; adult; aged; Article; artificial intelligence; calibration; cancer staging; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; discriminant validity; female; human; lung adenocarcinoma; lung nodule; major clinical study; male; multidetector computed tomography; nomogram; pleura metastasis; predictive validity; preoperative evaluation; quantitative analysis; retrospective study; sensitivity and specificity; texture analysis; three-dimensional imaging; validation study; clinical decision making; clinical trial; confidence interval; diagnostic imaging; lung adenocarcinoma; lung tumor; middle aged; multicenter study; multiple pulmonary nodules; pathology; pleura; preoperative period; statistical model; tumor invasion; x-ray computed tomography","","","Deepwise 20201130fix1a; MX16, Philips Healthcare, Netherlands; Revolution, GE Healthcare, United States; uCT550; uCT760","GE Healthcare, United States; Philips Healthcare, Netherlands","","","Ferlay J., Colombet M., Soerjomataram I., Et al., Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods, Int J Cancer, 144, 8, pp. 1941-1953, (2019); Jiang L., Liang W., Shen J., Et al., The impact of visceral pleural invasion in node-negative non-small cell lung cancer: a systematic review and meta-analysis, Chest, 148, 4, pp. 903-911, (2015); Wo Y., Zhao Y., Qiu T., Et al., Impact of visceral pleural invasion on the association of extent of lymphadenectomy and survival in stage I non-small cell lung cancer, Cancer Med, 8, 2, pp. 669-678, (2019); Zeng Y., Mayne N., Yang C.J., Et al., A nomogram for predicting cancer-specific survival of TNM 8th edition stage I non-small-cell lung cancer, Ann Surg Oncol, 26, 7, pp. 2053-2062, (2019); Detterbeck F.C., Boffa D.J., Kim A.W., Et al., The eighth edition lung cancer stage classification, Chest, 151, 1, pp. 193-203, (2017); Kamigaichi A., Tsutani Y., Kagimoto A., Et al., Comparing segmentectomy and lobectomy for clinical stage IA solid-dominant lung cancer measuring 2.1 to 3 cm, Clin Lung Cancer, 21, 6, pp. e528-e538, (2020); Suzuki K., Saji H., Aokage K., Et al., Comparison of pulmonary segmentectomy and lobectomy: safety results of a randomized trial, J Thorac Cardiovasc Surg, 158, 3, pp. 895-907, (2019); Takizawa H., Kondo K., Kawakita N., Et al., Autofluorescence for the diagnosis of visceral pleural invasion in non-small-cell lung cancer, Eur J Cardiothorac Surg, 53, 5, pp. 987-992, (2018); Mizuno T., Arimura T., Kuroda H., Et al., P2.16-34 visceral pleural invasion is closely associated with nodal spread in cStage IA lung adenocarcinoma, J Thorac Oncol, 13, (2018); Ahn S.Y., Park C.M., Jeon Y.K., Et al., Predictive CT features of visceral pleural invasion by T1-sized peripheral pulmonary adenocarcinomas manifesting as subsolid nodules, AJR Am J Roentgenol, 209, 3, pp. 561-566, (2017); Hsu J.S., Han I.T., Tsai T.H., Et al., Pleural tags on CT scans to predict visceral pleural invasion of non-small cell lung cancer that does not abut the pleura, Radiology, 279, 2, pp. 590-596, (2016); Imai K., Minamiya Y., Ishiyama K., Et al., Use of CT to evaluate pleural invasion in non-small cell lung cancer: measurement of the ratio of the interface between tumour and neighboring structures to maximum tumour diameter, Radiology, 267, 2, pp. 619-626, (2013); Yang S., Yang L., Teng L., Et al., Visceral pleural invasion by pulmonary adenocarcinoma ≤3 cm: the pathological correlation with pleural signs on computed tomography, J Thorac Dis, 10, 7, pp. 3992-3999, (2018); Hong H., Hahn S., Matsuguma H., Et al., Pleural recurrence after transthoracic needle lung biopsy in stage I lung cancer: a systematic review and individual patient-level meta-analysis, Thorax, 76, 6, pp. 582-590, (2021); Gu Y., She Y., Xie D., Et al., A texture analysis-based prediction model for lymph node metastasis in stage IA lung adenocarcinoma, Ann Thorac Surg, 106, 1, pp. 214-220, (2018); Qiu Z.B., Zhang C., Chu X.P., Et al., Quantifying invasiveness of clinical stage IA lung adenocarcinoma with computed tomography texture features, J Thorac Cardiovasc Surg, 30, (2020); Shimomura M., Iwasaki M., Ishihara S., Et al., Volume-based consolidation-to-tumour ratio is a useful predictor for postoperative upstaging in stage I and II lung adenocarcinomas, Thorac Cardiovasc Surg, (2019); Travis W.D., Brambilla E., Rami-Porta R., Et al., Visceral pleural invasion: pathologic criteria and use of elastic stains: proposal for the 7th edition of the TNM classification for lung cancer, J Thorac Oncol, 3, 12, pp. 1384-1390, (2008); Iizuka S., Kawase A., Oiwa H., Et al., A risk scoring system for predicting visceral pleural invasion in non-small lung cancer patients, Gen Thorac Cardiovasc Surg, 67, 10, pp. 876-879, (2019); Hsu J.S., Jaw T.S., Yang C.J., Et al., Convex border of peripheral non-small cell lung cancer on CT images as a potential indicator of pleural invasion, Medicine (Baltimore), 96, 42, (2017); Kim H., Goo J.M., Kim Y.T., Et al., CT-defined visceral pleural invasion in T1 lung adenocarcinoma: lack of relationship to disease-free survival, Radiology, 292, 3, pp. 741-749, (2019); Choi H., Kim H., Hong W., Et al., Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs, Eur Radiol, 31, 5, pp. 2866-2876, (2021); Li X., Zhang W., Yu Y., Et al., CT features and quantitative analysis of subsolid nodule lung adenocarcinoma for pathological classification prediction, BMC Cancer, 20, 1, (2020); Ikeda K., Awai K., Mori T., Et al., Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification, Chest, 132, 3, pp. 984-990, (2007); Sanchez-Salcedo P., Zulueta J.J., Lung cancer in chronic obstructive pulmonary disease patients, it is not just the cigarette smoke, Curr Opin Pulm Med, 22, 4, pp. 344-349, (2016); Mouronte-Roibas C., Leiro-Fernandez V., Fernandez-Villar A., Et al., COPD, emphysema and the onset of lung cancer. A systematic review, Cancer Lett, 382, 2, pp. 240-244, (2016); Parris B.A., O'Farrell H.E., Fong K.M., Et al., Chronic obstructive pulmonary disease (COPD) and lung cancer: common pathways for pathogenesis, J Thorac Dis, 11, pp. S2155-S2172, (2019); Lim C.G., Shin K.M., Lim J.K., Et al., Emphysema is associated with the aggressiveness of COPD-related adenocarcinomas, Clin Respir J, 14, 4, pp. 405-412, (2020)","W. Qi; Department of Radiology, The Affiliated Hospital of Southwest Medical University, 646000, China; email: qiwanyin0508@163.com","","W.B. Saunders Ltd","","","","","","00099260","","CLRAA","34916048","English","Clin. Radiol.","Article","Final","","Scopus","2-s2.0-85121257908"
"Huang T.; Yang R.; Shen L.; Feng A.; Li L.; He N.; Li S.; Huang L.; Lyu J.","Huang, Tao (57225870307); Yang, Rui (57223084369); Shen, Longbin (57213618230); Feng, Aozi (57215380389); Li, Li (57211232765); He, Ningxia (57710827400); Li, Shuna (57215905857); Huang, Liying (57560713900); Lyu, Jun (57194336419)","57225870307; 57223084369; 57213618230; 57215380389; 57211232765; 57710827400; 57215905857; 57560713900; 57194336419","Deep transfer learning to quantify pleural effusion severity in chest X-rays","2022","BMC Medical Imaging","22","1","100","","","","13","10.1186/s12880-022-00827-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130713241&doi=10.1186%2fs12880-022-00827-0&partnerID=40&md5=a89d98c0df05a3302e61badc17f1b3bc","Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangdong, Guangzhou, China","Huang T., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Yang R., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Shen L., Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Feng A., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Li L., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; He N., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Li S., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Huang L., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Lyu J., Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China, Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangdong, Guangzhou, China","Purpose: The detection of pleural effusion in chest radiography is crucial for doctors to make timely treatment decisions for patients with chronic obstructive pulmonary disease. We used the MIMIC-CXR database to develop a deep learning model to quantify pleural effusion severity in chest radiographs. Methods: The Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) dataset was divided into patients ‘with’ or ‘without’ chronic obstructive pulmonary disease (COPD). The label of pleural effusion severity was obtained from the extracted COPD radiology reports and classified into four categories: no effusion, small effusion, moderate effusion, and large effusion. A total of 200 datasets were randomly sampled to manually check each item and determine whether the tags are correct. A professional doctor re-tagged these items as a verification cohort without knowing their previous tags. The learning models include eight common network structures including Resnet, DenseNet, and GoogleNET. Three data processing methods (no sampling, downsampling, and upsampling) and two loss algorithms (focal loss and cross-entropy loss) were used for unbalanced data. The Neural Network Intelligence tool was applied to train the model. Receiver operating characteristic curves, Area under the curve, and confusion matrix were employed to evaluate the model results. Grad-CAM was used for model interpretation. Results: Among the 8533 patients, 15,620 chest X-rays with clearly marked pleural effusion severity were obtained (no effusion, 5685; small effusion, 4877; moderate effusion, 3657; and large effusion, 1401). The error rate of the manual check label was 6.5%, and the error rate of the doctor’s relabeling was 11.0%. The highest accuracy rate of the optimized model was 73.07. The micro-average AUCs of the testing and validation cohorts was 0.89 and 0.90, respectively, and their macro-average AUCs were 0.86 and 0.89, respectively. The AUC of the distinguishing results of each class and the other three classes were 0.95 and 0.94, 0.76 and 0.83, 0.85 and 0.83, and 0.87 and 0.93. Conclusion: The deep transfer learning model can grade the severity of pleural effusion. © 2022, The Author(s).","Chest radiographs; Deep learning; MIMIC-CXR; Pleural effusion; Severity; X-rays","Humans; Machine Learning; Pleural Effusion; Pulmonary Disease, Chronic Obstructive; Radiography; Radiography, Thoracic; X-Rays; chronic obstructive lung disease; diagnostic imaging; human; machine learning; pleura effusion; procedures; radiography; thorax radiography; X ray","","","","","Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, (2021B1212040007)","This study was supported by Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization (2021B1212040007). 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In: Intelligent Information Processing and Web Mining, pp. 107-116, (2004); Blackmore C.C., Black W.C., Dallas R.V., Crow H.C., Pleural fluid volume estimation: a chest radiograph prediction rule, Acad Radiol, 3, 2, pp. 103-109, (1996)","J. Lyu; Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; email: lyujun2020@jnu.edu.cn","","BioMed Central Ltd","","","","","","14712342","","BMIMA","35624426","English","BMC Med. Imaging","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130713241"
"Tu J.-B.; Liao W.-J.; Liu W.-C.; Gao X.-H.","Tu, Jun-Bo (58917871200); Liao, Wei-Jie (57222326025); Liu, Wen-Cai (57222319399); Gao, Xing-Hua (58917229800)","58917871200; 57222326025; 57222319399; 58917229800","Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data","2024","Scientific Reports","14","1","5245","","","","9","10.1038/s41598-024-56114-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186631849&doi=10.1038%2fs41598-024-56114-1&partnerID=40&md5=9008e0a55b74a7954857bc93e4881969","Department of Orthopaedics, Xinfeng County People’s Hospital, Xinfeng, Jiangxi, 341600, China; Department of ICU, GanZhou People’s Hospital, Jiangxi, GanZhou, 341000, China; Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China; Department of Orthopaedics, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China","Tu J.-B., Department of Orthopaedics, Xinfeng County People’s Hospital, Xinfeng, Jiangxi, 341600, China; Liao W.-J., Department of ICU, GanZhou People’s Hospital, Jiangxi, GanZhou, 341000, China; Liu W.-C., Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China; Gao X.-H., Department of Orthopaedics, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China","Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies. © The Author(s) 2024.","Chronic disease; Machine learning; Osteoporosis; Predict; Stacker","Chronic Disease; Humans; Machine Learning; Neoplasms; Osteoporosis; Pulmonary Disease, Chronic Obstructive; chronic disease; chronic obstructive lung disease; human; machine learning; neoplasm; osteoporosis","","","","","","","Kanis J.A., McCloskey E.V., Johansson H., Cooper C., Rizzoli R., Reginster J.-Y., European guidance for the diagnosis and management of osteoporosis in postmenopausal women, Osteoporos. Int, 24, 1, pp. 23-57, (2013); Wright N.C., Looker A.C., Saag K.G., Curtis J.R., Delzell E.S., Randall S., Et al., The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine, J. Bone Miner. Res, 29, 11, pp. 2520-2526, (2014); Svedbom A., Hernlund E., Ivergard M., Compston J., Cooper C., Stenmark J., Et al., Osteoporosis in the European Union: A compendium of country-specific reports, Arch. Osteoporos, 8, pp. 1-218, (2013); Weitzmann M.N., Pacifici R., Estrogen deficiency and bone loss: An inflammatory tale, J. Clin. Invest, 116, 5, pp. 1186-1194, (2006); Ruffing J., Cosman F., Zion M., Tendy S., Garrett P., Lindsay R., Et al., Determinants of bone mass and bone size in a large cohort of physically active young adult men, Nutr. Metab, 3, 1, pp. 1-10, (2006); Rajkomar A., Dean J., Kohane I., Machine learning in medicine, N. Eng. J. 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Med, 25, 1, pp. 24-29, (2019); Liu W.C., Li M.X., Wu S.N., Tong W.L., Li A.A., Sun B.L., Et al., Using machine learning methods to predict bone metastases in breast infiltrating ductal carcinoma patients, Front. Public Health, 10, (2022); Suh B., Yu H., Kim H., Lee S., Kong S., Kim J.-W., Et al., Interpretable deep-learning approaches for osteoporosis risk screening and individualized feature analysis using large population-based data: Model development and performance evaluation, J. Med. Internet Res, 25, (2023); Jacob L., Breuer J., Kostev K., Prevalence of chronic diseases among older patients in German general practices, GMS German Med. Sci., 14, (2016); Zheng Z., Cai Y., Li Y., Oversampling method for imbalanced classification, Comput. Inform, 34, 5, pp. 1017-1037, (2015); Chen X.-W., Jeong J.C., Enhanced recursive feature elimination, Sixth International Conference on Machine Learning and Applications (ICMLA 2007, (2007); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Et al., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011); Marcilio W.E., Eler D.M., From explanations to feature selection: Assessing SHAP values as feature selection mechanism, 2020 33Rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), (2020); Sozen T., Ozisik L., Basaran N.C., An overview and management of osteoporosis, Eur. J. Rheumatol, 4, 1, (2017); Coughlan T., Dockery F., Osteoporosis and fracture risk in older people, Clin. Med, 14, 2, pp. 187-191, (2014); Clynes M.A., Harvey N.C., Curtis E.M., Fuggle N.R., Dennison E.M., Cooper C., The epidemiology of osteoporosis, Br. Med. Bull, 133, 1, pp. 105-117, (2020); Yu E.W., Tsourdi E., Clarke B.L., Bauer D.C., Drake M.T., Osteoporosis management in the era of COVID-19, J. Bone Mineral Res, 35, 6, pp. 1009-1013, (2020); Miller P.D., Management of severe osteoporosis, Expert Opin. Pharmacother, 17, 4, pp. 473-488, (2016); Smets J., Shevroja E., Hugle T., Leslie W.D., Hans D., Machine learning solutions for osteoporosis—a review, J. Bone Miner. Res, 36, 5, pp. 833-851, (2021); Kanis J.A., Oden A., Johansson H., Borgstrom F., Strom O., McCloskey E., FRAX® and its applications to clinical practice, Bone, 44, 5, pp. 734-743, (2009); Li C., Alike Y., Hou J., Long Y., Zheng Z., Meng K., Et al., Machine learning model successfully identifies important clinical features for predicting outpatients with rotator cuff tears, Knee Surg. Sports Traumatol. Arthrosc, 31, 7, pp. 2615-2623, (2023); Wang L., Lu H., Chen H., Jin S., Wang M., Shang S., Development of a model for predicting the 4-year risk of symptomatic knee osteoarthritis in China: A longitudinal cohort study, Arthritis Res. Ther, 23, 1, (2021); Grinsztajn L., Oyallon E., Varoquaux G., Why do tree-based models still outperform deep learning on typical tabular data?, Adv. Neural Inform. Process. Syst, 35, pp. 507-520, (2022); Hanley J.A., McNeil B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 1, pp. 29-36, (1982); Meng Y., Speier W., Ong M., Arnold C.W., HCET: Hierarchical clinical embedding with topic modeling on electronic health records for predicting future depression, IEEE J. Biomed. Health Inform, 25, 4, pp. 1265-1272, (2021); Chen T., Guestrin C., Xgboost: A scalable tree boosting system, Proceedings of the 22Nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, (2016); Li M.P., Liu W.C., Sun B.L., Zhong N.S., Liu Z.L., Huang S.H., Et al., Prediction of bone metastasis in non-small cell lung cancer based on machine learning, Front. Oncol, 12, (2022); Li W., Zhou Q., Liu W., Xu C., Tang Z.R., Dong S., Et al., A machine learning-based predictive model for predicting lymph node metastasis in patients with ewing's sarcoma, Front. Med, 9, (2022); Polikar R., Polikar R., Ensemble based systems in decision making, IEEE Circuit Syst. Mag, 6, 21-45, (2006); Wolpert D.H., Stacked generalization, Neural Netw, 5, 2, pp. 241-259, (1992); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Adv. Neural Inform. Process. Syst., 30, (2017); Kanis J., Johnell O., Oden A., Sernbo I., Redlund-Johnell I., Dawson A., Et al., Long-term risk of osteoporotic fracture in Malmö, Osteoporos. Int, 11, pp. 669-674, (2000); Riggs B.L., Khosla S., Melton L.J., Sex steroids and the construction and conservation of the adult skeleton, Endocr. Rev, 23, 3, pp. 279-302, (2002); Zolfaroli I., Ortiz E., Garcia-Perez M.-A., Hidalgo-Mora J.J., Tarin J.J., Cano A., Positive association of high-density lipoprotein cholesterol with lumbar and femoral neck bone mineral density in postmenopausal women, Maturitas, 147, pp. 41-46, (2021); Ackert-Bicknell C.L., HDL cholesterol and bone mineral density: Is there a genetic link?, Bone, 50, 2, pp. 525-533, (2012); Hadji P., Aapro M.S., Body J.-J., Gnant M., Brandi M.L., Reginster J.Y., Et al., Management of aromatase inhibitor-associated bone loss (AIBL) in postmenopausal women with hormone sensitive breast cancer: Joint position statement of the IOF, CABS, ECTS, IEG, ESCEO, IMS, and SIOG, J. Bone Oncol, 7, pp. 1-12, (2017); Body J.-J., Terpos E., Tombal B., Hadji P., Arif A., Young A., Et al., Bone health in the elderly cancer patient: A SIOG position paper, Cancer Treat. Rev, 51, pp. 46-53, (2016); Graat-Verboom L., Wouters E., Smeenk F., Van Den Borne B., Lunde R., Spruit M., Current status of research on osteoporosis in COPD: A systematic review, Eur. Respir. J, 34, 1, pp. 209-218, (2009); Cappuccio F.P., Meilahn E., Zmuda J.M., Cauley J.A., High blood pressure and bone-mineral loss in elderly white women: A prospective study, Lancet, 354, 9183, pp. 971-975, (1999); Carbone L., Buzkova P., Fink H.A., Lee J.S., Chen Z., Ahmed A., Et al., Hip fractures and heart failure: Findings from the cardiovascular health study, Eur. Heart J, 31, 1, pp. 77-84, (2010); Tanko L.B., Christiansen C., Cox D.A., Geiger M.J., McNabb M.A., Cummings S.R., Relationship between osteoporosis and cardiovascular disease in postmenopausal women, J. Bone Mineral Res, 20, 11, pp. 1912-1920, (2005); Jha V., Garcia-Garcia G., Iseki K., Li Z., Naicker S., Plattner B., Et al., Chronic kidney disease: Global dimension and perspectives, Lancet, 382, 9888, pp. 260-272, (2013)","W.-C. Liu; Department of Orthopaedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 600 Yishan Road, 200233, China; email: liuwencaincu@163.com; X.-H. Gao; Department of Orthopaedics, Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China; email: hurryman1999@sina.com","","Nature Research","","","","","","20452322","","","38438569","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85186631849"
"Schmidt S.; Kinne J.; Lautenbach S.; Blaschke T.; Lenz D.; Resch B.","Schmidt, Sebastian (57411329300); Kinne, Jan (57200553140); Lautenbach, Sven (14825445000); Blaschke, Thomas (7005427503); Lenz, David (57204172286); Resch, Bernd (24528725900)","57411329300; 57200553140; 14825445000; 7005427503; 57204172286; 24528725900","Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from satellite data, a plant-level firm database and web text mining","2022","Science of the Total Environment","835","","155512","","","","8","10.1016/j.scitotenv.2022.155512","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129452483&doi=10.1016%2fj.scitotenv.2022.155512&partnerID=40&md5=9eb696466932d74a8bdd0eb4255277c9","Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, 5020, Austria; ISTARI.AI, Mannheim, 68163, Germany; Department of Economics of Innovation and Industrial Dynamics, Centre for European Economic Research, Mannheim, 68161, Germany; Heidelberg Institute for Geoinformation Technology at Heidelberg University, Heidelberg, 69118, Germany; GIScience department, Heidelberg University, Heidelberg, 69120, Germany; Department of Statistics and Econometrics, Justus-Liebig-University, Giessen, 35394, Germany; Center for Geographic Analysis, Harvard University, Cambridge, 9VGM+R8, United States","Schmidt S., Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, 5020, Austria, ISTARI.AI, Mannheim, 68163, Germany; Kinne J., ISTARI.AI, Mannheim, 68163, Germany, Department of Economics of Innovation and Industrial Dynamics, Centre for European Economic Research, Mannheim, 68161, Germany; Lautenbach S., Heidelberg Institute for Geoinformation Technology at Heidelberg University, Heidelberg, 69118, Germany, GIScience department, Heidelberg University, Heidelberg, 69120, Germany; Blaschke T., Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, 5020, Austria; Lenz D., ISTARI.AI, Mannheim, 68163, Germany, Department of Statistics and Econometrics, Justus-Liebig-University, Giessen, 35394, Germany; Resch B., Department of Geoinformatics – Z_GIS, University of Salzburg, Salzburg, 5020, Austria, Center for Geographic Analysis, Harvard University, Cambridge, 9VGM+R8, United States","This study deals with the issue of greenwashing, i.e. the false portrayal of companies as environmentally friendly. The analysis focuses on the US metal industry, which is a major emission source of sulfur dioxide (SO2), one of the most harmful air pollutants. One way to monitor the distribution of atmospheric SO2 concentrations is through satellite data from the Sentinel-5P programme, which represents a major advance due to its unprecedented spatial resolution. In this paper, Sentinel-5P remote sensing data was combined with a plant-level firm database to investigate the relationship between the US metal industry and SO2 concentrations using a spatial regression analysis. Additionally, this study considered web text data, classifying companies based on their websites in order to depict their self-portrayal on the topic of sustainability. In doing so, we investigated the topic of greenwashing, i.e. whether or not a positive self-portrayal regarding sustainability is related to lower local SO2 concentrations. Our results indicated a general, positive correlation between the number of employees in the metal industry and local SO2 concentrations. The web-based analysis showed that only 8% of companies in the metal industry could be classified as engaged in sustainability based on their websites. The regression analyses indicated that these self-reported ”sustainable” companies had a weaker effect on local SO2 concentrations compared to their “non-sustainable” counterparts, which we interpreted as an indication of the absence of general greenwashing in the US metal industry. However, the large share of firms without a website and lack of specificity of the text classification model were limitations to our methodology. © 2022 The Authors","Air pollution; Natural language processing; Sentinel-5P; Spatial regression","Air Pollutants; Air Pollution; Data Mining; Environmental Monitoring; Humans; Industry; Metals; Regression Analysis; Sulfur Dioxide; United States; Air pollution; Classification (of information); Metal analysis; Metals; Natural language processing systems; Regression analysis; Remote sensing; Support vector machines; Sustainable development; Text processing; Websites; carbon monoxide; formaldehyde; metal oxide; methane; nitrogen dioxide; nitrogen oxide; ozone; sulfur dioxide; metal; sulfur dioxide; Greenwashing; Language processing; Metal industries; Natural language processing; Natural languages; Plant level; Satellite data; Sentinel-5p; SO 2 concentration; Spatial regression; atmospheric pollution; concentration (composition); data mining; database; industrial emission; iron and steel industry; regression analysis; remote sensing; satellite data; Sentinel; sulfur dioxide; air pollutant; air pollution; air pollution control; algorithm; aquatic environment; Article; asthma; bronchitis; controlled study; data mining; Differential Optical Absorption Spectroscopy; ecosystem health; energy consumption; exhaust gas; green chemistry; greenwashing; heating; human; human impact (environment); lung carcinoma; machine learning; metal industry; muliticollinearity; population density; regression model; remote sensing; spatial regression; spectroscopy; transfer of learning; Variance Inflation Factor; Web text mining; air pollutant; air pollution; environmental monitoring; industry; regression analysis; Sulfur dioxide","","carbon monoxide, 630-08-0; formaldehyde, 50-00-0; methane, 74-82-8; nitrogen dioxide, 10102-44-0; nitrogen oxide, 11104-93-1; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; Air Pollutants, ; Metals, ; Sulfur Dioxide, ","","","Klaus-Tschirra Stiftung; Universität Salzburg; University of G?ttingen; Dorian Arifi; , (105145)","We want to thank Hannah Kemper (WFP), Theresa Keller (BfG), Tobias Hellmundt (University of G?ttingen) and Dorian Arifi (University of Salzburg) for their helpful input. 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"Sarabadani S.; Baruah G.; Fossat Y.; Jeon J.","Sarabadani, Sarah (57201450392); Baruah, Gaurav (56286403400); Fossat, Yan (57203245215); Jeon, Jouhyun (57203225438)","57201450392; 56286403400; 57203245215; 57203225438","Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study","2022","Journal of Medical Internet Research","24","2","e33959","","","","11","10.2196/33959","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124800764&doi=10.2196%2f33959&partnerID=40&md5=9b89c679cfc50d0d5518f2568a9e5c9a","Applied Sciences, Klick Inc, Toronto, ON, Canada","Sarabadani S., Applied Sciences, Klick Inc, Toronto, ON, Canada; Baruah G., Applied Sciences, Klick Inc, Toronto, ON, Canada; Fossat Y., Applied Sciences, Klick Inc, Toronto, ON, Canada; Jeon J., Applied Sciences, Klick Inc, Toronto, ON, Canada","Background: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Many studies have been conducted to understand the clinical characteristics of COVID-19, and recently postinfection sequelae of this disease have begun to be investigated. However, there is little consensus on the longitudinal changes of lasting physical or psychological symptoms from prior COVID-19 infection. Objective: This study aims to investigate and analyze public social media data from Reddit to understand the longitudinal impact of COVID-19 symptoms before and after recovery from COVID-19. Methods: We collected 22,890 Reddit posts that were generated by 14,401 authors from March 14 to December 16, 2020. Using active learning and intensive manual inspection, 292 (2.03%) active authors, who were infected by COVID-19 and frequently reported disease progress on Reddit, along with their 2213 (9.67%) longitudinal posts, were identified. Machine learning tools to extract biomedical information were applied to identify COVID-19 symptoms mentioned in the Reddit posts. We then examined longitudinal changes in individual physiological and psychological characteristics before and after recovery from COVID-19 infection. Results: In total, 58 physiological and 3 psychological symptoms were identified in social media before and after recovery from COVID-19 infection. From the analyses, we found that symptoms of patients with COVID-19 lasted 2.5 months. On average, symptoms appeared around a month before recovery and remained for 1.5 months after recovery. Well-known COVID-19 symptoms, such as fever, cough, and chest congestion, appeared relatively earlier in patient journeys and were frequently observed before recovery from COVID-19. Meanwhile, mental discomfort or distress, such as brain fog or stress, fatigue, and manifestations on toes or fingers, were frequently mentioned after recovery and remained as intermediate- and longer-term sequelae. Conclusions: In this study, we showed the dynamic changes in COVID-19 symptoms during the infection and recovery phases of the disease. Our findings suggest the feasibility of using social media data for investigating disease states and understanding the evolution of the physiological and psychological characteristics of COVID-19 infection over time. ©Sarah Sarabadani, Gaurav Baruah, Yan Fossat, Jouhyun Jeon.","COVID-19; Diagnosis; Longitudinal; Machine learning; Observational; Reddit; Social media; Symptom; Treatment","COVID-19; Disease Outbreaks; Female; Humans; Machine Learning; Pregnancy; SARS-CoV-2; Social Media; elasomeran; SARS-CoV-2 vaccine; tozinameran; adult; ageusia; anemia; anosmia; arthritis; Article; asthma; autoimmune disease; chest tightness; chill; clouding of consciousness; confusion; constipation; coronavirus disease 2019; coughing; distress syndrome; dyspnea; epilepsy; false positive result; fatigue; female; fever; finger; headache; human; immune deficiency; kidney pain; longitudinal study; machine learning; major clinical study; male; mental disease; muscle spasm; myalgia; nausea; observational study; panic; polymerase chain reaction; respiratory distress; shock; social media; symptom; toe; epidemic; pregnancy","","elasomeran, 2430046-03-8, 2457298-05-2; tozinameran, 2417899-77-3","","","UK Research and Innovation, UKRI, (103568)","","Guan W, Ni Z, Hu Y, Liang W, Ou C, He J, China Medical Treatment Expert Group for Covid-19. 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Yousefinaghani S, Dara R, Mubareka S, Sharif S., Prediction of COVID-19 waves using social media and google search: a case study of the US and Canada, Front Public Health, 9, (2021); Shen C, Chen A, Luo C, Zhang J, Feng B, Liao W., Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in Mainland China: observational infoveillance study, J Med Internet Res, 22, 5, (2020); Park Y., Developing a COVID-19 crisis management strategy using news media and social media in big data analytics, Soc Sci Comput Rev, (2021); Dominguez M, Sapina L., Pediatric cancer and the internet: exploring the gap in doctor-parents communication, J Cancer Educ, 30, 1, pp. 145-151, (2015); Oh J, Kim JA., Information-seeking behavior and information needs in patients with amyotrophic lateral sclerosis: analyzing an online patient community, Comput Inform Nurs, 35, 7, pp. 345-351, (2017); Hargreaves S, Bath PA, Duffin S, Ellis J., Sharing and empathy in digital spaces: qualitative study of online health forums for breast cancer and motor neuron disease (amyotrophic lateral sclerosis), J Med Internet Res, 20, 6, (2018); Hartzler A, Huh J., Level 3: patient power on the web: the multifaceted role of personal health wisdom, Consumer Health Informatics: New Services, Roles, and Responsibilities, pp. 134-146, (2016); Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A., Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions, BMJ, 328, 7449, (2004); Esquivel A, Meric-Bernstam F, Bernstam EV., Accuracy and self correction of information received from an internet breast cancer list: content analysis, BMJ, 332, 7547, pp. 939-942, (2006)","J. Jeon; Applied Sciences Klick Inc, Toronto, 175 Bloor Street East, Suite 300, M4W 3R8, Canada; email: cjeon@klick.com","","JMIR Publications Inc.","","","","","","14388871","","","35076400","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124800764"
"Tsang K.C.H.; Pinnock H.; Wilson A.M.; Salvi D.; Shah S.A.","Tsang, Kevin Cheuk Him (57219008423); Pinnock, Hilary (6701815935); Wilson, Andrew M (35477762200); Salvi, Dario (16176012000); Shah, Syed Ahmar (56424513100)","57219008423; 6701815935; 35477762200; 16176012000; 56424513100","Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol","2022","BMJ Open","12","10","e064166","","","","14","10.1136/bmjopen-2022-064166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139121573&doi=10.1136%2fbmjopen-2022-064166&partnerID=40&md5=4dac88a41eb13fbbd06e4b7706758c0f","Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Norwich Medical School, University of East Anglia, Norwich, United Kingdom; Norwich University Hospital Foundation Trust, Colney Lane, Norwich, United Kingdom; Internet of Things and People Research Centre, Malmo University, Malmo, Sweden","Tsang K.C.H., Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom, Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Pinnock H., Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Wilson A.M., Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom, Norwich Medical School, University of East Anglia, Norwich, United Kingdom, Norwich University Hospital Foundation Trust, Colney Lane, Norwich, United Kingdom; Salvi D., Internet of Things and People Research Centre, Malmo University, Malmo, Sweden; Shah S.A., Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom, Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom","Introduction Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback. We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. Methods and analysis A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device). Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. Ethics and dissemination Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh. Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.  © ","Asthma; Health informatics; Information technology; World Wide Web technology","Adrenal Cortex Hormones; Asthma; Humans; Machine Learning; Nebulizers and Vaporizers; Observational Studies as Topic; Smartphone; beclometasone dipropionate plus formoterol fumarate; bronchodilating agent; budesonide; corticosteroid; salbutamol; salbutamol sulfate; corticosteroid; adult; Article; asthma; asthmatic state; classification algorithm; clinical article; corticosteroid therapy; female; hospital; human; lung function; machine learning; male; medical society; observational study; peak expiratory flow; prediction; prescription; self care; self report; severe asthma; social media; asthma; machine learning; nebulizer; smartphone","","beclometasone dipropionate plus formoterol fumarate, 959587-70-3; budesonide, 51333-22-3, 51372-29-3; salbutamol, 18559-94-9, 35763-26-9; salbutamol sulfate, 51022-70-9; Adrenal Cortex Hormones, ","airomir; budiair; fostair; salamol; ventolin","","Asthma and Lung UK; Asthma UK Centre for Applied Research, AUKCAR, (AUK-AC-2018-01)","This work was supported by Asthma + Lung UK as part of the Asthma UK Centre for Applied Research grant number AUK-AC-2018-01. Support from Malmö University is co-funded by the Knowledge Foundation KK-stiftelsen. ","Asthma facts and statistics; Iacobucci G., Asthma deaths rise 33% in past decade in England and Wales, BMJ, 366, (2019); Pinnock H., Parke H.L., Panagioti M., Et al., Systematic meta-review of supported self-management for asthma: a healthcare perspective, BMC Med, 15, (2017); SIGN 158 British guideline on the management of asthma, (2019); Juniper E.F., O'Byrne P.M., Guyatt G.H., Et al., Development and validation of a questionnaire to measure asthma control, Eur Respir J, 14, pp. 902-907, (1999); Tsang K.C.H., Pinnock H., Wilson A.M., Et al., Application of machine learning to support self-management of asthma with mHealth, Annu Int Conf IEEE Eng Med Biol Soc, 2020, pp. 5673-5677, (2020); Su J.G., Barrett M.A., Henderson K., Et al., Feasibility of deploying inhaler sensors to identify the impacts of environmental triggers and built environment factors on asthma short-acting bronchodilator use, Environ Health Perspect, 125, pp. 254-261, (2017); Tinschert P., Jakob R., Barata F., Et al., The potential of mobile Apps for improving asthma self-management: a review of publicly available and Well-Adopted asthma Apps, JMIR Mhealth Uhealth, 5, (2017); The asthma APP for managing your symptoms; Asthma control iPhone APP | AsthmaMD; Tsang K.C.H., Pinnock H., Wilson A.M., Et al., Application of machine learning algorithms for asthma management with mHealth: a clinical review, J Asthma Allergy, 15, pp. 855-873, (2022); Finkelstein J., Jeong I.C., Cheol J.I., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann N Y Acad Sci, 1387, pp. 153-165, (2017); Castner J., Jungquist C.R., Mammen M.J., Et al., Prediction model development of women's daily asthma control using fitness tracker sleep disruption, Heart Lung, 49, pp. 548-555, (2020); Huffaker M.F., Carchia M., Harris B.U., Et al., Passive nocturnal physiologic monitoring enables early detection of exacerbations in children with asthma, A proof-of-concept study. 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Tsang; Asthma Uk Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; email: k.c.h.tsang@sms.ed.ac.uk","","BMJ Publishing Group","","","","","","20446055","","","36192103","English","BMJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139121573"
"Vhaduri S.; Dibbo S.V.; Kim Y.","Vhaduri, Sudip (35173933500); Dibbo, Sayanton V. (57190847645); Kim, Yugyeong (57221323020)","35173933500; 57190847645; 57221323020","Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings","2023","IEEE Open Journal of Engineering in Medicine and Biology","4","","","55","66","11","11","10.1109/OJEMB.2023.3271457","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159663964&doi=10.1109%2fOJEMB.2023.3271457&partnerID=40&md5=2e9792c63ad72991d9e08a39563af4b0","Purdue University, Department of Computer and Information Technology, West Lafayette, 47907, IN, United States; Dartmouth College, Department of Computer Science, Hanover, 03755, NH, United States; Lg Electronics, Field Service Department, Englewood Cliffs, 07632, NJ, United States","Vhaduri S., Purdue University, Department of Computer and Information Technology, West Lafayette, 47907, IN, United States; Dibbo S.V., Dartmouth College, Department of Computer Science, Hanover, 03755, NH, United States; Kim Y., Lg Electronics, Field Service Department, Englewood Cliffs, 07632, NJ, United States","Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment.  © 2020 IEEE.","Audio analytics; COPD; cough; COVID-19; microphone-sensing","Audio recordings; Diagnosis; Learning systems; Microphones; Pulmonary diseases; Smartphones; Support vector machines; Adaptation models; Adaptive machine learning; Audio analytic; COPD; Cough; Generic modeling; Microphone-sensing; Performances evaluation; Smart phones; Support vectors machine; COVID-19","","","","","","","WHO coronavirus disease (COVID-19) dashboard, (2023); COPD-Key Facts, (2022); Asthma facts and figures, (2023); CDC: Symptoms of COVID-19, (2022); Controlled coughing for COPD patients, (2023); Asthma-symptoms and causes-mayo clinic, (2022); Simpson C.B., Amin M.R., Chronic cough: State-of-the-art review, Otolaryngology-Head Neck Surg., 21, pp. 693-700, (2006); CDC: COVID-19 testing, (2022); COPD symptoms and diagnosis| American lung association, (2023); Pneumonia | Disease or condition of the week| CDC, (2021); Asthma: Steps in testing and diagnosis-mayo clinic, (2022); Vhaduri S., Kessel T.V., Ko B., Wood D., Wang S., Brunschwiler T., Nocturnal cough and snore detection in noisy environments using smartphone-microphones, Proc. 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Summit Smart City 360?, pp. 179-191, (2015); Vhaduri S., Poellabauer C., Design factors of longitudinal smartphone-based health surveys, J. Healthcare Inform. Res., 1, 1, pp. 52-91, (2017); Vhaduri S., Cho J., Meng K., Predicting unreliable response patterns in smartphone health surveys:Acase study with themood survey, Elsevier Smart Health J., 28, (2023); Vhaduri S., Ali A., Sharmin M., Hovsepian K., Kumar S., Estimating drivers' stress from GPS traces, Proc. 6th Int. Conf. Automot. User Interfaces Interactive Veh. Appl., pp. 1-8, (2014)","S. Vhaduri; Purdue University, Department of Computer and Information Technology, West Lafayette, 47907, United States; email: svhaduri@purdue.edu","","Institute of Electrical and Electronics Engineers Inc.","","","","","","26441276","","","","English","IEEE open J. Eng. Med. Biol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85159663964"
"Yang Y.; Wang S.; Zeng N.; Duan W.; Chen Z.; Liu Y.; Li W.; Guo Y.; Chen H.; Li X.; Chen R.; Kang Y.","Yang, Yingjian (57218501666); Wang, Shicong (57670751600); Zeng, Nanrong (57671655900); Duan, Wenxin (57670149700); Chen, Ziran (57670751700); Liu, Yang (57222473378); Li, Wei (57221637991); Guo, Yingwei (57218502342); Chen, Huai (55205182900); Li, Xian (57191970686); Chen, Rongchang (57200034537); Kang, Yan (57213821412)","57218501666; 57670751600; 57671655900; 57670149700; 57670751700; 57222473378; 57221637991; 57218502342; 55205182900; 57191970686; 57200034537; 57213821412","Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network","2022","Diagnostics","12","10","2274","","","","12","10.3390/diagnostics12102274","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140767309&doi=10.3390%2fdiagnostics12102274&partnerID=40&md5=97bebb07f198482dfb2d7d01f77e2991","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China; The Second Clinical Medical College, Jinan University, Guangzhou, 518001, China; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518001, China; Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Yang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Wang S., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Zeng N., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Duan W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Chen Z., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Liu Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Li W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Guo Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Chen H., Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Li X., Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China; Chen R., Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China, The Second Clinical Medical College, Jinan University, Guangzhou, 518001, China, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, 518001, China; Kang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China, Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification. © 2022 by the authors.","auto-metric graph neural network (AMGNN); chest HRCT image; COPD stage (GOLD); generalized linear model (GLM); Lasso algorithm; lung radiomics features; multi-classification","adult; Article; chronic obstructive lung disease; cohort analysis; controlled study; convolutional neural network; disease classification; feature selection; high resolution computer tomography; human; lung parenchyma; machine learning; major clinical study; radiomics; risk factor; statistical model","","","","","Scientific Research Fund of Liaoning Province of China, (JL201919); Stable Support Plan for Colleges and Universities in Shenzhen of China, (SZWD2021010); National Natural Science Foundation of China, NSFC, (62071311); Natural Science Foundation of Guangdong Province, (2019A1515011382); special program for key fields of colleges and universities in Guangdong Province, (2021ZDZX2008)","This research was funded by the National Natural Science Foundation of China, grant number 62071311; the Stable Support Plan for Colleges and Universities in Shenzhen of China, grant number SZWD2021010; the Scientific Research Fund of Liaoning Province of China, grant number JL201919; the Natural Science Foundation of Guangdong Province of China, grant number 2019A1515011382; the special program for key fields of colleges and universities in Guangdong Province (biomedicine and health) of China, grant number 2021ZDZX2008.","Singh D., Agusti A., Anzueto A., Barnes P.J., Bourbeau J., Celli B.R., Criner G.J., Frith P., Halpin D.M.G., Han M., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: The GOLD science committee report 2019, Eur. 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Kang; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; email: kangyan@sztu.edu.cn; R. Chen; Shenzhen Institute of Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, 518001, China; email: chenrc@vip.163.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140767309"
"Henderson H.I.; Napravnik S.; Kosorok M.R.; Gower E.W.; Kinlaw A.C.; Aiello A.E.; Williams B.; Wohl D.A.; Van Duin D.","Henderson, Heather I (56556147600); Napravnik, Sonia (57202594359); Kosorok, Michael R (7003879907); Gower, Emily W (57207585464); Kinlaw, Alan C (53264079300); Aiello, Allison E (57203233119); Williams, Billy (58386871800); Wohl, David A (7004696106); Van Duin, David (11241301400)","56556147600; 57202594359; 7003879907; 57207585464; 53264079300; 57203233119; 58386871800; 7004696106; 11241301400","Predicting Risk of Multidrug-Resistant Enterobacterales Infections Among People With HIV","2022","Open Forum Infectious Diseases","9","10","ofac487","","","","11","10.1093/ofid/ofac487","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145036279&doi=10.1093%2fofid%2fofac487&partnerID=40&md5=9b4cf3eca56e6f04d0e8c1eac5a13865","Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Division of Pharmaceutical Outcomes and Policy, University of North Carolina School of Pharmacy, Chapel Hill, NC, United States; Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Clinical Microbiology Laboratory, University of North Carolina Hospitals, Chapel Hill, NC, United States","Henderson H.I., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Napravnik S., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Kosorok M.R., Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Gower E.W., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Kinlaw A.C., Division of Pharmaceutical Outcomes and Policy, University of North Carolina School of Pharmacy, Chapel Hill, NC, United States, Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Aiello A.E., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Williams B., Clinical Microbiology Laboratory, University of North Carolina Hospitals, Chapel Hill, NC, United States; Wohl D.A., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; Van Duin D., Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States","Background: Medically vulnerable individuals are at increased risk of acquiring multidrug-resistant Enterobacterales (MDR-E) infections. People with HIV (PWH) experience a greater burden of comorbidities and may be more susceptible to MDR-E due to HIV-specific factors. Methods: We performed an observational study of PWH participating in an HIV clinical cohort and engaged in care at a tertiary care center in the Southeastern United States from 2000 to 2018. We evaluated demographic and clinical predictors of MDR-E by estimating prevalence ratios (PRs) and employing machine learning classification algorithms. In addition, we created a predictive model to estimate risk of MDR-E among PWH using a machine learning approach. Results: Among 4734 study participants, MDR-E was isolated from 1.6% (95% CI, 1.2%-2.1%). In unadjusted analyses, MDR-E was strongly associated with nadir CD4 cell count ≤200 cells/mm3 (PR, 4.0; 95% CI, 2.3-7.4), history of an AIDS-defining clinical condition (PR, 3.7; 95% CI, 2.3-6.2), and hospital admission in the prior 12 months (PR, 5.0; 95% CI, 3.2-7.9). With all variables included in machine learning algorithms, the most important clinical predictors of MDR-E were hospitalization, history of renal disease, history of an AIDS-defining clinical condition, CD4 cell count nadir ≤200 cells/mm3, and current CD4 cell count 201-500 cells/mm3. Female gender was the most important demographic predictor. Conclusions: PWH are at risk for MDR-E infection due to HIV-specific factors, in addition to established risk factors. Early HIV diagnosis, linkage to care, and antiretroviral therapy to prevent immunosuppression, comorbidities, and coinfections protect against antimicrobial-resistant bacterial infections.  © 2022 The Author(s). Published by Oxford University Press on behalf of Infectious Diseases Society of America.","Enterobacterales; gram-negative; HIV; machine learning; multidrug resistance","acquired immune deficiency syndrome; adult; antibiotic sensitivity; antiretroviral therapy; Article; asthma; bacterial infection; cardiovascular disease; CD4 lymphocyte count; chronic obstructive lung disease; cohort analysis; coinfection; comorbidity; controlled study; demography; diabetes mellitus; diagnostic test accuracy study; disorders of lipid and lipoprotein metabolism; drug dependence; electronic health record; ethnicity; female; health care system; hospital admission; hospitalization; human; Human immunodeficiency virus infected patient; hypertension; immunosuppressive treatment; kidney disease; learning algorithm; logistic regression analysis; machine learning; major clinical study; male; mental disease; multidrug resistant Enterobacteriaceae; observational study; prevalence ratio; receiver operating characteristic; risk factor; sexually transmitted disease; support vector machine; tertiary care center","","","","","National Institutes of Health-funded, (P30 AI50410); National Institutes of Health, NIH, (TL1TR002491); National Institutes of Health, NIH; National Institute of Allergy and Infectious Diseases, NIAID, (AI007001, T32 AI070114); National Institute of Allergy and Infectious Diseases, NIAID; National Center for Advancing Translational Sciences, NCATS; Center for AIDS Research, University of North Carolina at Chapel Hill, UNC CFAR","This work was supported by the University of North Carolina at Chapel Hill Center for AIDS Research, a National Institutes of Health-funded program (grant number P30 AI50410); the National Center for Advancing Translational Sciences, National Institutes of Health (grant number TL1TR002491); and the National Institute of Allergy and Infectious Diseases (grant numbers T32 AI070114 and AI007001 to H.I.H.).","Antimicrobial resistance: global report on surveillance 2014; Laxminarayan R, Duse A, Wattal C, Et al., Antibiotic resistance-the need for global solutions, Lancet Infect Dis, 13, pp. 1057-1098, (2013); The biggest antibiotic-resistant threats in the U.S, (2019); Thaden JT, Fowler VG, Sexton DJ, Anderson DJ., Increasing incidence of extended-spectrum β-lactamase-producing Escherichia coli in community hospitals throughout the Southeastern United States, Infect Control Hosp Epidemiol, 37, pp. 49-54, (2016); van Duin D, Paterson DL., Multidrug-resistant bacteria in the community: an update, Infect Dis Clin North Am, 34, pp. 709-722, (2020); da Silva Winter J, dos Santos RP, de Azambuja AZ, Cechinel AB, Goldani LZ., Microbiologic isolates and risk factors associated with antimicrobial resistance in patients admitted to the intensive care unit in a tertiary care hospital, Am J Infect Control, 41, pp. 846-848, (2013); Lim CJ, Cheng AC, Kong DC, Peleg AY., Community-onset bloodstream infection with multidrug-resistant organisms: a matched case-control study, BMC Infect Dis, 14, (2014); Neuner EA, Yeh J-Y, Hall GS, Et al., Treatment and outcomes in carbapenem- resistant Klebsiella pneumoniae bloodstream infections, Diagn Microbiol Infect Dis, 69, pp. 357-362, (2011); Nouvenne A, Ticinesi A, Lauretani F, Et al., Comorbidities and disease severity as risk factors for carbapenem-resistant Klebsiella pneumoniae colonization: report of an experience in an internal medicine unit, PLoS One, 9, (2014); Richter SE, Miller L, Needleman J, Et al., Risk factors for development of carbapenem resistance among gram-negative rods, Open Forum Infect Dis, 6, pp. XXX-XXX, (2019); van Duin D, Strassle PD, DiBiase LM, Et al., Timeline of healthcare-associated infections and pathogens after burn injuries, Am J Infect Control, 44, pp. 1511-1516, (2016); Ben-Ami R, Rodriguez-Bano J, Arslan H, Et al., A multinational survey of risk factors for infection with extended-spectrum beta-lactamase-producing Enterobacteriaceae in nonhospitalized patients, Clin Infect Dis, 49, pp. 682-690, (2009); Gasink LB, Edelstein PH, Lautenbach E, Synnestvedt M, Fishman NO., Risk factors and clinical impact of Klebsiella pneumoniae carbapenemase-producing K. pneumoniae, Infect Control Hosp Epidemiol, 30, pp. 1180-1185, (2009); Patel G, Huprikar S, Factor SH, Jenkins SG, Calfee DP., Outcomes of carbapenem- resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies, Infect Control Hosp Epidemiol, 29, pp. 1099-1106, (2008); Perez F, van Duin D., Carbapenem-resistant Enterobacteriaceae: a menace to our most vulnerable patients, Cleve Clin J Med, 80, pp. 225-233, (2013); Sultana ZZ, Hoque FU, Beyene J, Et al., HIV infection and multidrug resistant tuberculosis: a systematic review and meta-analysis, BMC Infect Dis, 21, (2021); Madhi SA, Petersen K, Madhi A, Khoosal M, Klugman KP., Increased disease burden and antibiotic resistance of bacteria causing severe community-acquired lower respiratory tract infections in human immunodeficiency virus type 1-infected children, Clin Infect Dis, 31, pp. 170-176, (2000); Marbou WJT, Kuete V., Bacterial resistance and immunological profiles in HIV-infected and non-infected patients at Mbouda AD LUCEM hospital in Cameroon, J Infect Public Health, 10, pp. 269-276, (2017); Reinheimer C, Keppler OT, Stephan C, Wichelhaus TA, Friedrichs I, Kempf VAJ., Elevated prevalence of multidrug-resistant gram-negative organisms in HIV positive men, BMC Infect Dis, 17, (2017); Olaru ID, Ferrand RA, Chisenga M, Et al., Prevalence of ESBL-producing Escherichia coli in adults with and without HIV presenting with urinary tract infections to primary care clinics in Zimbabwe, JAC Antimicrob Resist, 3, (2021); Henderson HI, Napravnik S, Gower EW, Et al., Resistance in Enterobacterales is higher among people with HIV, Clin Infect Dis, 75, pp. 28-34, (2022); Kaplan-Lewis E, Aberg JA, Lee M., Aging with HIV in the ART era, Semin Diagn Pathol, 34, pp. 384-397, (2017); Aberg JA., Aging, inflammation, and HIV infection, Top Antivir Med, 20, pp. 101-105, (2012); Wada N, Jacobson LP, Cohen M, French A, Phair J, Munoz A., Cause-specific life expectancies after 35 years of age for human immunodeficiency syndrome-infected and human immunodeficiency syndrome-negative individuals followed simultaneously in long-term cohort studies, 1984-2008, Am J Epidemiol, 177, pp. 116-125, (2013); Causes of death in HIV-1-infected patients treated with antiretroviral therapy, 1996-2006: collaborative analysis of 13 HIV cohort studies, Clin Infect Dis, 50, pp. 1387-1396, (2010); Guaraldi G, Orlando G, Zona S, Et al., Premature age-related comorbidities among HIV-infected persons compared with the general population, Clin Infect Dis, 53, pp. 1120-1126, (2011); Serrano-Villar S, Gutierrez F, Miralles C, Et al., Human immunodeficiency virus as a chronic disease: evaluation and management of non-acquired immune deficiency syndrome-defining conditions, Open Forum Infect Dis, 3, pp. XXX-XXX, (2016); Shiels MS, Pfeiffer RM, Engels EA., Age at cancer diagnosis among people with AIDS in the United States, Ann Intern Med, 153, pp. 452-460, (2010); Egwuatu CC, Iwuafor AA, Egwuatu TO, Et al., Effect of trimethoprim- sulfamethoxazole prophylaxis on faecal carriage rates of resistant isolates of Escherichia coli in HIV-infected adult patients in Lagos, Afr J Infect Dis, 10, pp. 156-163, (2016); Martin JN, Rose DA, Hadley WK, Perdreau-Remington F, Lam PK, Gerberding JL., Emergence of trimethoprim-sulfamethoxazole resistance in the AIDS era, J Infect Dis, 180, pp. 1809-1818, (1999); Sibanda EL, Weller IVD, Hakim JG, Cowan FM., Does trimethoprim- sulfamethoxazole prophylaxis for HIV induce bacterial resistance to other antibiotic classes? Results of a systematic review, Clin Infect Dis, 52, pp. 1184-1194, (2011); Napravnik S, Eron JJ, McKaig RG, Heine AD, Menezes P, Quinlivan E., Factors associated with fewer visits for HIV primary care at a tertiary care center in the Southeastern U.S, AIDS Care, 18, pp. 45-50, (2006); M100 | Performance Standards for Antimicrobial Susceptibility Testing, (2018); Magiorakos A-P, Srinivasan A, Carey RB, Et al., Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance, Clin Microbiol Infect Dis, 18, pp. 268-281, (2012); Clinical Classifications Software Refined (CCSR) for ICD-10-CM diagnoses; van der Laan MJ, Polley EC, Hubbard AE., Super learner, Stat Appl Genet Mol Biol, 6, (2007); Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP., SMOTE: synthetic minority over-sampling technique, J Artif Intell Res, 16, pp. 321-357, (2002); Lundberg SM, Lee S-I., A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems, (2017); Ribeiro ABDTM, Heimesaat MM, Bereswill S., Changes of the intestinal microbiome-host homeostasis in HIV-infected individuals - a focus on the bacterial gut microbiome, Eur J Microbiol Immunol, 7, pp. 158-167, (2017); Bandera A, De Benedetto I, Bozzi G, Gori A., Altered gut microbiome composition in HIV infection: causes, effects and potential intervention, Curr Opin HIV AIDS, 13, pp. 73-80, (2018); Nance RM, Delaney JAC, Simoni JM, Et al., HIV viral suppression trends over time among HIV-infected patients receiving care in the United States, 1997 to 2015: a cohort study, Ann Intern Med, 169, pp. 376-384, (2018); Schouten J, Wit FW, Stolte IG, Et al., Cross-sectional comparison of the prevalence of age-associated comorbidities and their risk factors between HIV-infected and uninfected individuals: the AGEhIV cohort study, Clin Infect Dis, 59, pp. 1787-1797, (2014); Althoff KN, Gange SJ, Klein MB, Et al., Late presentation for human immunodeficiency virus care in the United States and Canada, Clin Infect Dis, 50, pp. 1512-1520, (2010); Kitahata MM, Gange SJ, Abraham AG, Et al., Effect of early versus deferred antiretroviral therapy for HIV on survival, N Engl J Med, 360, pp. 1815-1826, (2009); Survival of HIV-positive patients starting antiretroviral therapy between 1996 and 2013: a collaborative analysis of cohort studies, Lancet HIV, 4, pp. e349-e356, (2017); Davy-Mendez T, Napravnik S, Eron JJ, Et al., Current and past immunodeficiency are associated with higher hospitalization rates among persons on virologically suppressive antiretroviral therapy for up to 11 years, J Infect Dis, 224, pp. 657-666, (2021); Mugavero MJ, Norton WE, Saag MS., Health care system and policy factors influencing engagement in HIV medical care: piecing together the fragments of a fractured health care delivery system, Clin Infect Dis, 52, pp. S238-S246, (2011)","H.I. Henderson; Bioinformatics Building, Chapel Hill, 130 Mason Farm Road, 27599, United States; email: henderh@email.unc.edu","","Oxford University Press","","","","","","23288957","","","","English","Open Forum Infect. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145036279"
"Revel M.-P.; Abdoul H.; Chassagnon G.; Canniff E.; Durand-Zaleski I.; Wislez M.","Revel, Marie-Pierre (56045263500); Abdoul, Hendy (24490770200); Chassagnon, Guillaume (56155698000); Canniff, Emma (57212084620); Durand-Zaleski, Isabelle (55641699500); Wislez, Marie (6701766796)","56045263500; 24490770200; 56155698000; 57212084620; 55641699500; 6701766796","Lung CAncer SCreening in French women using low-dose CT and Artificial intelligence for DEtection: The CASCADE study protocol","2022","BMJ Open","12","12","e067263","","","","10","10.1136/bmjopen-2022-067263","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143887835&doi=10.1136%2fbmjopen-2022-067263&partnerID=40&md5=ae43b1ab8d980746b7784ca7bc69b6e6","Université Paris Cité, Paris, France; Assistance Publique- Hopitaux de Paris, Cochin Hospital Radiology Department, Paris, France; Assistance Publique-Hopitaux de Paris, URC Necker/Cochin, Paris, France; Assistance Publique- Hopitaux de Paris, Cochin Hospital, Pulmonology Department, Paris, France; Pulmonology Department, Cochin Hospital, Assistance Publique - Hopitaux de Paris, Paris, France","Revel M.-P., Université Paris Cité, Paris, France, Assistance Publique- Hopitaux de Paris, Cochin Hospital Radiology Department, Paris, France; Abdoul H., Assistance Publique-Hopitaux de Paris, URC Necker/Cochin, Paris, France; Chassagnon G., Université Paris Cité, Paris, France, Assistance Publique- Hopitaux de Paris, Cochin Hospital Radiology Department, Paris, France; Canniff E., Assistance Publique- Hopitaux de Paris, Cochin Hospital Radiology Department, Paris, France; Durand-Zaleski I., Université Paris Cité, Paris, France, Assistance Publique- Hopitaux de Paris, Cochin Hospital, Pulmonology Department, Paris, France; Wislez M., Université Paris Cité, Paris, France, Pulmonology Department, Cochin Hospital, Assistance Publique - Hopitaux de Paris, Paris, France","Introduction Lung cancer screening (LCS) using low-dose CT has been demonstrated to reduce lung cancer-related mortality in large randomised controlled trials. Moving from trials to practice requires answering practical questions about the level of expertise of CT readers, the need for double reading as in trials and the potential role of artificial intelligence (AI). In addition, most LCS studies have predominantly included male participants with women being under-represented, even though the benefit of screening is greater for them. Thus, this study aims to compare the performance of a single CT reading by general radiologists trained in LCS using AI as a second reader to that of a double reading by expert thoracic radiologists, in a campaign for low-dose CT screening in high-risk women. Methods and analysis This observational cohort study will recruit 2400 asymptomatic women aged between 50 and 74 years, current or former smokers with at least a 20 pack-year smoking history, in 4 different French district areas. Assistance with smoking cessation will be offered to current smokers. An initial low-dose CT scan will be performed, with subsequent follow-ups at 1 year and 2 years. The primary objective is to compare CT scan readings by a single LCS-trained, AI-assisted radiologist to that of an expert double reading. The secondary objectives are: to evaluate the performance of AI as a stand-alone reader; the adherence to screening of female participants; the influence on smoking cessation; the psychological consequences of screening; the detection of chronic obstructive pulmonary disease (COPD), coronary artery disease and osteoporosis on low-dose CT scans and the costs incurred by screening. Ethics and dissemination Ethics approval was obtained from the Comité de Protection des Personnes Sud-Est 1 (ethics approval number: 2021-A02265-36 with an amendment on 15 July 2022). Trial results will be disseminated at conferences, through relevant patient groups and published in peer-reviewed journals. Trial registration number NCT05195385. © 2022 Author(s). Published by BMJ.","Adult oncology; Chest imaging; Clinical trials; Computed tomography; Diagnostic radiology; Respiratory tract tumours","Aged; Artificial Intelligence; Early Detection of Cancer; Female; Humans; Lung Neoplasms; Male; Middle Aged; Observational Studies as Topic; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; adult; aged; Article; artificial intelligence; cancer diagnosis; cancer screening; chronic obstructive lung disease; cigarette smoking; clinical protocol; clinical trial; cohort analysis; controlled study; coronary artery disease; cost benefit analysis; current smoker; ex-smoker; female; follow up; France; high risk patient; human; low-dose computed tomography; lung cancer; major clinical study; medical education; medical expert; medical history; observational study; osteoporosis; outcome assessment; psychological aspect; radiologist; smoking cessation; artificial intelligence; chronic obstructive lung disease; complication; diagnostic imaging; early cancer diagnosis; lung tumor; male; middle aged; procedures; x-ray computed tomography","","","","","French Ministry of Health financement dérogatoire SERI 2020; Institut National Du Cancer, INCa, (INCA_14771)","This work was supported by Institut National du Cancer grant number INCA_14771 and by the French Ministry of Health financement dérogatoire SERI 2020 ","Fitzmaurice C., Dicker D., The global burden of cancer 2013, JAMA Oncol, 1, (2015); Levi F., Bosetti C., Fernandez E., Et al., Trends in lung cancer among young European women: The rising epidemic in France and Spain, Int J Cancer, 121, pp. 462-465, (2007); Pujol J.-L., Thomas P.-A., Giraud P., Et al., Lung cancer in France, J Thorac Oncol, 16, pp. 21-29, (2021); Zang E.A., Wynder E.L., Differences in lung cancer risk between men and women: Examination of the evidence, J Natl Cancer Inst, 88, pp. 183-192, (1996); Debieuvre D., Molinier O., Falchero L., Et al., Lung cancer trends and tumor characteristic changes over 20 years (2000-2020): Results of three French consecutive nationwide prospective cohorts' studies, Lancet Reg Health Eur, 22, (2022); Bar J., Urban D., Amit U., Et al., Long-term survival of patients with metastatic non-small-cell lung cancer over five decades, J Oncol, 2021, pp. 1-10, (2021); Aberle D.R., Adams A.M., Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, pp. 395-409, (2011); De Koning H.J., Van Der Aalst C.M., De Jong P.A., Et al., Reduced lung-cancer mortality with volume CT screening in a randomized trial, N Engl J Med, 382, pp. 503-513, (2020); Pastorino U., Silva M., Sestini S., Et al., Prolonged lung cancer screening reduced 10-year mortality in the mild trial: New confirmation of lung cancer screening efficacy, Ann Oncol, 30, (2019); Field J.K., Vulkan D., Davies M.P.A., Et al., Lung cancer mortality reduction by LDCT screening: UKLS randomised trial results and international meta-analysis, Lancet Reg Health Eur, 10, (2021); Becker N., Motsch E., Trotter A., Et al., Lung cancer mortality reduction by LDCT screening-Results from the randomized German LUSI trial, Int J Cancer, 146, pp. 1503-1513, (2020); Martini K., Chassagnon G., Frauenfelder T., Et al., Ongoing challenges in implementation of lung cancer screening, Transl Lung Cancer Res, 10, pp. 2347-2355, (2021); Field J.K., Dekoning H., Oudkerk M., Et al., Implementation of lung cancer screening in Europe: Challenges and potential solutions: Summary of a multidisciplinary roundtable discussion, ESMO Open, 4, (2019); Field J.K., Duffy S.W., Baldwin D.R., Et al., The UK lung cancer screening trial: A pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer, Health Technol Assess, 20, pp. 1-146, (2016); Lopes Pegna A., Picozzi G., Mascalchi M., Et al., Design, recruitment and baseline results of the Italung trial for lung cancer screening with low-dose CT, Lung Cancer, 64, pp. 34-40, (2009); Pedersen J.H., Ashraf H., Dirksen A., Et al., The Danish randomized lung cancer CT screening trial - Overall design and results of the prevalence round, J Thorac Oncol, 4, pp. 608-614, (2009); Infante M., Lutman F.R., Cavuto S., Et al., Lung cancer screening with spiral CT, Lung Cancer, 59, pp. 355-363, (2008); Pastorino U., Rossi M., Rosato V., Et al., Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the mild trial, Eur J Cancer Prev, 21, pp. 308-315, (2012); Nair A., Gartland N., Barton B., Et al., Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial, Br J Radiol, 89, (2016); Zhao Y., De Bock G.H., Vliegenthart R., Et al., Performance of computer-aided detection of pulmonary nodules in low-dose CT: Comparison with double reading by nodule volume, Eur Radiol, 22, pp. 2076-2084, (2012); Ardila D., Kiraly A.P., Bharadwaj S., Et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nat Med, (2019); Nasrullah N., Sang J., Alam M.S., Et al., Automated lung nodule detection and classification using deep learning combined with multiple strategies, Sensors, 19, (2019); Trajanovski S., Mavroeidis D., Swisher C.L., Et al., Towards radiologist-level cancer risk assessment in CT lung screening using deep learning, Comput Med Imaging Graph, 90, (2021); Mastouri R., Khlifa N., Neji H., Et al., Deep learning-based CAD schemes for the detection and classification of lung nodules from CT images: A survey, J Xray Sci Technol, 28, pp. 591-617, (2020); Oudkerk M., Devaraj A., Vliegenthart R., Et al., European position statement on lung cancer screening, Lancet Oncol, 18, pp. e754-e766, (2017); Von Elm E., Altman D.G., Egger M., Et al., The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies, J Clin Epidemiol, 61, pp. 344-349, (2008); Horeweg N., Van Rosmalen J., Heuvelmans M.A., Et al., Lung cancer probability in patients with CT-detected pulmonary nodules: A prespecified analysis of data from the Nelson trial of low-dose CT screening, Lancet Oncol, 15, pp. 1332-1341, (2014)","M.-P. Revel; Université Paris Cité, Paris, France; email: marie-pierre.revel@aphp.fr","","BMJ Publishing Group","","","","","","20446055","","","36600392","English","BMJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85143887835"
"Wang Z.; Wang Q.; Duan S.; Zhang Y.; Zhao L.; Zhang S.; Hao L.; Li Y.; Wang X.; Wang C.; Zhang N.; Bachert C.; Zhang L.; Lan F.","Wang, Zaichuan (58041474000); Wang, Qiqi (57221354983); Duan, Su (37121842700); Zhang, Yuling (57222608587); Zhao, Limin (36996043600); Zhang, Shujian (57226881528); Hao, Liusiqi (58041451100); Li, Yan (57211569149); Wang, Xiangdong (56140396700); Wang, Chenshuo (58041474200); Zhang, Nan (56982196400); Bachert, Claus (7102663930); Zhang, Luo (36068675900); Lan, Feng (56637201700)","58041474000; 57221354983; 37121842700; 57222608587; 36996043600; 57226881528; 58041451100; 57211569149; 56140396700; 58041474200; 56982196400; 7102663930; 36068675900; 56637201700","A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion","2022","Frontiers in Immunology","13","","1054201","","","","9","10.3389/fimmu.2022.1054201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145503565&doi=10.3389%2ffimmu.2022.1054201&partnerID=40&md5=614a0a5c601285ffcab6070ff12133f1","Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China; Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Department of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Upper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University, Ghent, Belgium","Wang Z., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Wang Q., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China; Duan S., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Zhang Y., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Zhao L., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Zhang S., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Hao L., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Li Y., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China; Wang X., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Wang C., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Zhang N., Upper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University, Ghent, Belgium; Bachert C., Upper Airways Research Laboratory, Department of Otorhinolaryngology, Ghent University, Ghent, Belgium; Zhang L., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China, Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China, Department of Allergy, Beijing TongRen Hospital, Capital Medical University, Beijing, China; Lan F., Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China","Background: Predicting type 2 chronic rhinosinusitis with nasal polyps (CRSwNP) may help for selection of appropriate surgical procedures or pharmacotherapies in advance. However, an accurate non-invasive method for diagnosis of type 2 CRSwNP is presently unavailable. Methods: To optimize the technique for collecting nasal secretion (NasSec), 89 CRSwNP patients were tested using nasal packs made with four types of materials. Further, Th2low and Th2highCRSwNP defined by clustering analysis in another 142 CRSwNP patients using tissue biomarkers, in the meanwhile, inflammatory biomarkers were detected in NasSec of the same patients collected by the selected nasal pack. A diagnostic model was established by machine learning algorithms to predict Th2highCRSwNP using NasSecs biomarkers. Results: Considering the area under receiver operating characteristic curve (AUC) for IL-5 in NasSec, nasal pack in polyvinyl alcohol (PVA) was superior to other materials for NasSec collection. When Th2low and Th2highCRSwNP clusters were defined, logistic regression and decision tree model for prediction of Th2highCRSwNP demonstrated high AUCs values of 0.92 and 0.90 respectively using biomarkers of NasSecs. Consequently, the pre-pruned decision tree model; based on the levels of IL-5 in NasSec (≤ 15.04 pg/mL), blood eosinophil count (≤ 0.475*109/L) and absence of comorbid asthma; was chosen to define Th2lowCRSwNP from Th2highCRSwNP for routine clinical use. Conclusions: Taken together, a decision tree model based on a combination of NasSec biomarkers and clinical features can accurately define type 2 CRSwNP patients and therefore may be of benefit to patients in receiving appropriate therapies in daily clinical practice. Copyright © 2022 Wang, Wang, Duan, Zhang, Zhao, Zhang, Hao, Li, Wang, Wang, Zhang, Bachert, Zhang and Lan.","blood eosinophils; chronic rhinosinusitis with nasal polyps; diagnostic model; IL-5; nasal secretion","Biomarkers; Chronic Disease; Humans; Interleukin-5; Nasal Polyps; Rhinitis; Sinusitis; biological marker; eotaxin 2; eotaxin 3; gamma interferon; immunoglobulin E; interleukin 17; interleukin 5; polymer; polyvinyl alcohol sponge; polyvinyl fluoride sponge; RANTES; transcription factor RUNX2; unclassified drug; biological marker; interleukin 5; adult; Article; blood cell count; blood sampling; bright field microscopy; chronic rhinosinusitis; clinical feature; controlled study; cross-sectional study; diagnostic model; diagnostic test accuracy study; eosinophil count; eosinophilia; female; histology; human; human tissue; inflammation; learning algorithm; machine learning; major clinical study; male; middle aged; nasal packing; nose polyp; nose secretion; predictive model; predictive value; receiver operating characteristic; sensitivity and specificity; visual analog scale; chronic disease; nose polyp; rhinitis; sinusitis","","gamma interferon, 82115-62-6; immunoglobulin E, 37341-29-0; polyvinyl alcohol sponge, 63148-64-1; Biomarkers, ; Interleukin-5, ","ivalon, Fabco, United States; merocel, Medtronic, United States","Fabco, United States; Medtronic, United States","Beijing Hospitals Authority Youth Programme, (QML20180201); Beijing Nova Program of Science and Technology, (Z191100001119117); Public Welfare Development, (2019-10); Young top-notch talent, (2018000021223ZK12); National Natural Science Foundation of China, NSFC, (81970851, 82271140); National Natural Science Foundation of China, NSFC; Chinese Academy of Meteorological Sciences, CAMS, (2019-I2M-5-022); Chinese Academy of Meteorological Sciences, CAMS; National Key Research and Development Program of China, NKRDPC, (2022YFC2504100); National Key Research and Development Program of China, NKRDPC","This work was supported by grants from the National Nature Science Foundation of China (81970851, 82271140), the Beijing Nova Program of Science and Technology (Z191100001119117); the Young top-notch talent (2018000021223ZK12); the Beijing Hospitals Authority Youth Programme (QML20180201); CAMS Innovation Fund for Medical Sciences (2019-I2M-5-022); Public Welfare Development and Reform Pilot Project (2019-10) and the National Key Research and Development Program of China (2022YFC2504100). Acknowledgments ","Bachert C., Mannent L., Naclerio R.M., Mullol J., Ferguson B.J., Gevaert P., Et al., Effect of subcutaneous dupilumab on nasal polyp burden in patients with chronic sinusitis and nasal polyposis: A randomized clinical trial, JAMA, 315, 5, (2016); Alsharif S., Jonstam K., van Zele T., Gevaert P., Holtappels G., Bachert C., Endoscopic sinus surgery for type-2 crs wnp: An endotype-based retrospective study, Laryngoscope, 129, 6, (2019); Van Zele T., Gevaert P., Watelet J.B., Claeys G., Holtappels G., Claeys C., Et al., Staphylococcus aureus colonization and ige antibody formation to enterotoxins is increased in nasal polyposis, J Allergy Clin Immunol, 114, 4, (2004); Wang X., Zhang N., Bo M., Holtappels G., Zheng M., Lou H., Et al., Diversity of Th cytokine profiles in patients with chronic rhinosinusitis: A multicenter study in Europe, Asia, and Oceania, J Allergy Clin Immunol, 138, 5, (2016); DeConde A.S., Mace J.C., Levy J.M., Rudmik L., Alt J.A., Smith T.L., Prevalence of polyp recurrence after endoscopic sinus surgery for chronic rhinosinusitis with nasal polyposis, Laryngoscope, 127, 3, (2017); Bachert C., Marple B., Schlosser R.J., Hopkins C., Schleimer R.P., Lambrecht B.N., Et al., Adult chronic rhinosinusitis, Nat Rev Dis Primers, 6, 1, (2020); Jonstam K., Alsharif S., Bogaert S., Suchonos N., Holtappels G., Jae-Hyun Park J., Et al., Extent of inflammation in severe nasal polyposis and effect of sinus surgery on inflammation, Allergy, 76, 3, (2021); Legrand F., Klion A.D., Biologic therapies targeting eosinophils: Current status and future prospects, J Allergy Clin Immunol Pract, 3, 2, (2015); Bachert C., Zinreich S.J., Hellings P.W., Mullol J., Hamilos D.L., Gevaert P., Et al., Dupilumab reduces opacification across all sinuses and related symptoms in patients with crswnp, Rhinology, 58, 1, (2020); Bachert C., Sousa A.R., Lund V.J., Scadding G.K., Gevaert P., Nasser S., Et al., Reduced need for surgery in severe nasal polyposis with mepolizumab: Randomized trial, J Allergy Clin Immunol, 140, 4, pp. 1024-31.e14, (2017); Tomassen P., Vandeplas G., Van Zele T., Cardell L.O., Arebro J., Olze H., Et al., Inflammatory endotypes of chronic rhinosinusitis based on cluster analysis of biomarkers, J Allergy Clin Immunol, 137, 5, pp. 1449-56.e4, (2016); Hoggard M., Waldvogel-Thurlow S., Zoing M., Chang K., Radcliff F.J., Wagner Mackenzie B., Et al., Inflammatory endotypes and microbial associations in chronic rhinosinusitis, Front Immunol, 9, (2018); Liao B., Liu J.X., Li Z.Y., Zhen Z., Cao P.P., Yao Y., Et al., Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes, Allergy, 73, 7, (2018); Bachert C., Han J.K., Wagenmann M., Hosemann W., Lee S.E., Backer V., Et al., Euforea expert board meeting on uncontrolled severe chronic rhinosinusitis with nasal polyps (Crswnp) and biologics: Definitions and management, J Allergy Clin Immunol, 147, 1, pp. 29-36, (2021); Zhang Y., Gevaert E., Lou H., Wang X., Zhang L., Bachert C., Et al., Chronic rhinosinusitis in Asia, J Allergy Clin Immunol, 140, 5, (2017); Watelet J.B., Gevaert P., Holtappels G., Van Cauwenberge P., Bachert C., Collection of nasal secretions for immunological analysis, Eur Arch Otorhinolaryngol, 261, 5, (2004); Jonstam K., Swanson B.N., Mannent L.P., Cardell L.O., Tian N., Wang Y., Et al., Dupilumab reduces local type 2 pro-inflammatory biomarkers in chronic rhinosinusitis with nasal polyposis, Allergy, 74, 4, (2019); Gilles-Stein S., Beck I., Chaker A., Bas M., McIntyre M., Cifuentes L., Et al., Pollen derived low molecular compounds enhance the human allergen specific immune response in vivo, Clin Exp Allergy, 46, 10, (2016); Berings M., Arasi S., De Ruyck N., Perna S., Resch Y., Lupinek C., Et al., Reliable mite-specific ige testing in nasal secretions by means of allergen microarray, J Allergy Clin Immunol, 140, 1, pp. 301-3.e8, (2017); Kramer M.F., Burow G., Pfrogner E., Rasp G., In vitro diagnosis of chronic nasal inflammation, Clin Exp Allergy, 34, 7, (2004); Riechelmann H., Deutschle T., Friemel E., Gross H.J., Bachem M., Biological markers in nasal secretions, Eur Respir J, 21, 4, (2003); Fokkens W.J., Lund V.J., Mullol J., Bachert C., Alobid I., Baroody F., Et al., Epos 2012: European position paper on rhinosinusitis and nasal polyps 2012. a summary for otorhinolaryngologists, Rhinology, 50, 1, pp. 1-12, (2012); Gevaert P., Calus L., Van Zele T., Blomme K., De Ruyck N., Bauters W., Et al., Omalizumab is effective in allergic and nonallergic patients with nasal polyps and asthma, J Allergy Clin Immunol, 131, 1, (2013); Lund V.J., Mackay I.S., Staging in rhinosinusitus, Rhinology, 31, 4, (1993); Howarth P.H., Persson C.G., Meltzer E.O., Jacobson M.R., Durham S.R., Silkoff P.E., Objective monitoring of nasal airway inflammation in rhinitis, J Allergy Clin Immunol, 115, (2005); Zhang N., Holtappels G., Claeys C., Huang G., van Cauwenberge P., Bachert C., Pattern of inflammation and impact of staphylococcus aureus enterotoxins in nasal polyps from southern China, Am J Rhinol, 20, 4, (2006); Chung Y.W., Cha J., Han S., Chen Y., Gucek M., Cho H.J., Et al., Apolipoprotein e and periostin are potential biomarkers of nasal mucosal inflammation. a parallel approach of in vitro and in vivo secretomes, Am J Respir Cell Mol Biol, 62, 1, pp. 23-34, (2020); Yao Y., Yang C., Yi X., Xie S., Sun H., Comparative analysis of inflammatory signature profiles in eosinophilic and noneosinophilic chronic rhinosinusitis with nasal polyposis, Biosci Rep, 40, 2, (2020); Soler Z.M., Schlosser R.J., Bodner T.E., Alt J.A., Ramakrishnan V.R., Mattos J.L., Et al., Endotyping chronic rhinosinusitis based on olfactory cleft mucus biomarkers, J Allergy Clin Immunol, 147, 5, pp. 1732-41.e1, (2021); Christodoulou E., Ma J., Collins G.S., Steyerberg E.W., Verbakel J.Y., Van Calster B., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, pp. 12-22, (2019); Takeda K., Sakakibara S., Yamashita K., Motooka D., Nakamura S., El Hussien M.A., Et al., Allergic conversion of protective mucosal immunity against nasal bacteria in patients with chronic rhinosinusitis with nasal polyposis, J Allergy Clin Immunol, 143, 3, pp. 1163-75.e15, (2019); Workman A.D., Nocera A.L., Mueller S.K., Otu H.H., Libermann T.A., Bleier B.S., Translating transcription: Proteomics in chronic rhinosinusitis with nasal polyps reveals significant discordance with messenger rna expression, Int Forum Allergy Rhinol, 9, 7, (2019)","L. Zhang; Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China; email: dr.luozhang@139.com; F. Lan; Beijing Key Laboratory of Nasal Disease, Beijing Institute of Otolaryngology, Beijing, China; email: fenglanent@126.com","","Frontiers Media S.A.","","","","","","16643224","","","36618395","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145503565"
"Al-Issa Y.; Alqudah A.M.; Alquran H.; Issa A.A.","Al-Issa, Yazan (56520179300); Alqudah, Ali Mohammad (57189294287); Alquran, Hiam (56198738900); Issa, Ahmed Al (57695693000)","56520179300; 57189294287; 56198738900; 57695693000","Pulmonary Diseases Decision Support System Using Deep Learning Approach","2022","Computers, Materials and Continua","73","1","","311","326","15","8","10.32604/cmc.2022.025750","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130169984&doi=10.32604%2fcmc.2022.025750&partnerID=40&md5=dc5871e66c05c5deec2a6dc4f73c0825","Department of Computer Engineering, Yarmouk University, Irbid, 21163, Jordan; Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan; Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan; Pediatrician, Mediclinic Hospital, Al Ain, 14444, United Arab Emirates","Al-Issa Y., Department of Computer Engineering, Yarmouk University, Irbid, 21163, Jordan; Alqudah A.M., Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan; Alquran H., Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan, Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan; Issa A.A., Pediatrician, Mediclinic Hospital, Al Ain, 14444, United Arab Emirates","Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of our knowledge, this is the first published study to ever attempt to distinguish all four cases normal, pneumonia, COVID-19 and lung opacity from Chest-X-Ray (CXR) images. All models were trained using Chest-X-Ray (CXR) images, and statistically tested using 5-fold cross validation. Using individual models, XceptionNet outperformed all other models with a 94.775% accuracy and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) of 99.84%. On the other hand, DarkNet19 represents a good compromise between accuracy, fast convergence, resource utilization, and near real time detection (0.33 s). Using a collection of models, the 97.79% accuracy achieved by Ensemble Features was the highest among all surveyed methods, but it takes the longest time to predict an image (5.68 s). An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time, such a dependable system can be used in rural areas and various healthcare sectors. © 2022 Tech Science Press. All rights reserved.","classification; deep learning; ensemble features; lung opacity; majority voting; Pulmonary diseases","Biological organs; Decision support systems; Deep learning; Developing countries; Image processing; Pulmonary diseases; Chest radiographs; Chest X-ray image; Chronic obstructive pulmonary disease; Deep learning; Ensemble feature; Image processing technique; Intelligence models; Learning approach; Lung opacity; Majority voting; Opacity","","","","","","","Li C., Zhao C., Bao J., Tang B., Wang Y., Gu B., Laboratory diagnosis of coronavirus disease-2019 (COVID-19), Clinica Chimica Acta; International Journal of Clinical Chemistry, 51, pp. 35-46, (2020); Huang C., Wang Y., Li X., Ren L., Zhao J., Et al., Clinical features of patients infected with 2019 novel coronavirus in wuhan, China, The Lancet, 395, pp. 497-506, (2020); COVID-19Worldwide Statistics, (2021); West C. P., Montori V. M., Sampathkumar P., COVID-19 testing: The threat of false-negative results, Mayo Clinic Proceedings, 95, 6, pp. 1127-1129, (2020); Guyatt G., Rennie D., Meade M., Cook D., Users’ guides to the medical literature: A manual for evidence-based clinical practice, IEEE Conf. on Computer Vision and Pattern Recognition, 706, pp. 6517-6525, (2002); Liu N., Wan L., Zhang Y., Zhou T., Huo H., Et al., Exploiting convolutional neural networks with deeply local description for remote sensing image classification, IEEE Access, 6, pp. 11215-11228, (2018); Zu Z. Y., Jiang M. D., Xu P. P., Chen W., Ni W., Et al., Coronavirus disease 2019 “(COVID-19): A perspective from China, Radiology, 296, 2, pp. 15-25, (2020); Yari Y., Nguyen T. V., Nguyen H., Accuracy improvement in detection of COVID-19 in chest radiography, 2020 14th Int. Conf. on Signal Processing and Communication Systems (ICSPCS), pp. 1-6, (2020); Pranav J. V., Anand R., Shanthi T., Manju K., Veni S., Et al., Detection and identification of COVID-19 based on chest medical image by using convolutional neural networks, International Journal of Intelligent Networks, 1, pp. 112-118, (2020); Khan I. U., Aslam N., A Deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images, Information, 11, 9, pp. 419-432, (2020); Lacruz F., Vidarte R., Analysis of deep learning models for COVID-19 diagnosis from X-ray chest images, Researchgate, (2020); Zhang W., Pogorelsky B., Loveland M., Wolf T., Classification of COVID-19 X-ray images using a combination of deep and handcrafted features, (2021); Fontanellaz M., Ebner L., Huber A., Peters A., Lobelenz L., Et al., A Deep-learning diagnostic support system for the detection of COVID-19 using chest radiographs: A multireader validation study, Investigative Radiology, 56, 6, pp. 348-356, (2021); Oyelade O. N., Ezugwu A. E., Chiroma H., Covframenet: An enhanced deep learning framework for COVID-19 detection, IEEE Access, 9, pp. 77905-77919, (2021); Pham T. D., Classification of COVID-19 chest X-rays with deep learning: New models or fine tuning?, Health Information Science and Systems, 9, 1, pp. 1-11, (2021); Alquran H., Alsleti M., Alsharif R., Qasmieh I. A., Alqudah A. M., Et al., Employing texture features of chest X-ray images and machine learning in COVID-19 detection and classification, Mendel, 27, 1, pp. 9-17, (2021); Alsharif R., Al-Issa Y., Alqudah A. M., Qasmieh I. A., Mustafa W. A., Et al., PneumoniaNet: Automated detection and classification of pediatric pneumonia using chest x-ray images and cnn approach, Electronics, 10, 23, pp. 2949-2962, (2021); Deng J., Dong W., Socher R., Li L. J., Li K., Et al., Imagenet: A large-scale hierarchical image database, 2009 IEEE Conf. on Computer Vision and Pattern Recognition, pp. 248-255, (2009); Huang G., Liu Z., Van Der Maaten L., Weinberger K. Q., Densely connected convolutional networks, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 4700-4708, (2017); Chollet F., Xception: Deep learning with depthwise separable convolutions, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1251-1258, (2019); Redmon J., Farhadi A., YOLO9000: Better, faster, stronger, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 7263-727, (2017); Simonyan K., Zisserman A., Very deep convolutional networks for large-scale image recognition, (2014); Alqudah A., Alqudah A. M., Sliding window based deep ensemble system for breast cancer classification, Journal of Medical Engineering and Technology, 45, 4, pp. 313-323, (2021); Alqudah A., Alqudah A. M., Sliding window based support vector machine system for classification of breast cancer using histopathological microscopic images, IETE Journal of Research, (2019); Alqudah A. M., Qazan S., Alquran H., Qasmieh I. A., Alqudah A., Covid-19 detection from x-ray images using different artificial intelligence hybrid models, Jordan Journal of Electrical Engineering, 6, 2, pp. 168-178, (2020); Chowdhury M. E. H., Rahman T., Khandakar A., Mazhar R., Kadir M. A., Et al., Can AI help in screening viral and COVID-19 pneumonia?, IEEE Access, 8, pp. 132665-132676, (2020); Rahman T., Khandakar A., Qiblawey Y., Tahir A., Kiranyaz S., Et al., Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images, Computers in Biology and Medicine, 132, pp. 104319-104319, (2020); Alqudah A. M., Towards classifying non-segmented heart sound records using instantaneous frequency based features, Journal of Medical Engineering and Technology, 43, 7, pp. 418-430, (2019); Wong T. T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recognition, 48, 9, pp. 2839-2846, (2015)","A.M. Alqudah; Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid, 21163, Jordan; email: ali_qudah@hotmail.com","","Tech Science Press","","","","","","15462218","","","","English","Comput. Mater. Continua","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85130169984"
"Zafari H.; Langlois S.; Zulkernine F.; Kosowan L.; Singer A.","Zafari, Hasan (35070621500); Langlois, Sarah (57221391930); Zulkernine, Farhana (22735783200); Kosowan, Leanne (57200563762); Singer, Alexander (41862365300)","35070621500; 57221391930; 22735783200; 57200563762; 41862365300","AI in predicting COPD in the Canadian population","2022","BioSystems","211","","104585","","","","10","10.1016/j.biosystems.2021.104585","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121119588&doi=10.1016%2fj.biosystems.2021.104585&partnerID=40&md5=a4c73a41ac218cb5b7bb412225233f14","School of Computing, Queen's University, Kingston, ON, Canada; Department of Family Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada","Zafari H., School of Computing, Queen's University, Kingston, ON, Canada; Langlois S., School of Computing, Queen's University, Kingston, ON, Canada; Zulkernine F., School of Computing, Queen's University, Kingston, ON, Canada; Kosowan L., Department of Family Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Singer A., Department of Family Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada","Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used Electronic Medical Record (EMR) data from primary care clinics in seven provinces across Canada to develop predictive models to identify COPD in the Canadian population. The comprehensive nature of this primary care EMR data containing structured numeric, categorical, hybrid, and unstructured text data, enables the predictive models to capture symptoms of COPD and discriminate it from diseases with similar symptoms. We applied two supervised machine learning models, a Multilayer Neural Networks (MLNN) model and an Extreme Gradient Boosting (XGB) to identify COPD patients. The XGB model achieved an accuracy of 86% in the test dataset compared to 83% achieved by the MLNN. Utilizing feature importance, we identified a set of key symptoms from the EMR for diagnosing COPD, which included medications, health conditions, risk factors, and patient age. Application of this XGB model to primary care structured EMR data can identify patients with COPD from others having similar chronic conditions for disease surveillance, and improve evidence-based care delivery. © 2021","Bag of words model; COPD; EMR data; Extreme gradient boosting; Feature importance; Machine learning; Medical diagnosis; Text classification","Algorithms; Artificial Intelligence; Canada; Datasets as Topic; Electronic Health Records; Humans; Pulmonary Disease, Chronic Obstructive; Canada; chronic obstructive pulmonary disease; health care; machine learning; public health; risk factor; symptom; algorithm; artificial intelligence; Canada; chronic obstructive lung disease; electronic health record; human; information processing","","","","","International Business Machines Corporation, IBM; Queen's University; Canadian Institute for Military and Veteran Health Research, CIMVHR","Funding for this study was provided by an Advanced Analytics Grant from IBM and Canadian Institute for Military and Veteran Health Research (CIMVHR) , and from the Queen's University Research Initiation Grant. 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Cybern., 1, 1-4, pp. 43-52, (2010)","H. Zafari; School of Computing, Queen's University, Kingston, Canada; email: hasan.zafari@queensu.ca","","Elsevier Ireland Ltd","","","","","","03032647","","BSYMB","34864143","English","BioSystems","Article","Final","","Scopus","2-s2.0-85121119588"
"Thomas H.M.T.; Hippe D.S.; Forouzannezhad P.; Sasidharan B.K.; Kinahan P.E.; Miyaoka R.S.; Vesselle H.J.; Rengan R.; Zeng J.; Bowen S.R.","Thomas, Hannah M. T. (57193696943); Hippe, Daniel S. (26967805400); Forouzannezhad, Parisa (57193922765); Sasidharan, Balu Krishna (55643250700); Kinahan, Paul E. (7006626834); Miyaoka, Robert S. (7006640096); Vesselle, Hubert J. (6603786501); Rengan, Ramesh (6507828284); Zeng, Jing (36706880400); Bowen, Stephen R. (27168962700)","57193696943; 26967805400; 57193922765; 55643250700; 7006626834; 7006640096; 6603786501; 6507828284; 36706880400; 27168962700","Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics?","2022","Discover Oncology","13","1","85","","","","10","10.1007/s12672-022-00548-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137580810&doi=10.1007%2fs12672-022-00548-4&partnerID=40&md5=1cb2450eee246ea28ff6b76d69a3c819","Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States; Department of Radiation Oncology, Christian Medical College Vellore, Tamil Nadu, Vellore, India; Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States","Thomas H.M.T., Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States, Department of Radiation Oncology, Christian Medical College Vellore, Tamil Nadu, Vellore, India; Hippe D.S., Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States; Forouzannezhad P., Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States; Sasidharan B.K., Department of Radiation Oncology, Christian Medical College Vellore, Tamil Nadu, Vellore, India; Kinahan P.E., Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States; Miyaoka R.S., Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States; Vesselle H.J., Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States; Rengan R., Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States; Zeng J., Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States; Bowen S.R., Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, 98195, WA, United States, Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States","Background: Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC. Methods: Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout. Results: Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69–12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59–0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65–0.88]) or shape feature classes (c-index 0.79 [0.66–0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59–0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R2 = 0.84–1.00), representing surrogates rather than independent predictors of pneumonitis risk. Conclusions: In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted. © 2022, The Author(s).","Functional lung imaging; Immunotherapy; Machine learning; Pneumonitis; Radiation therapy; Radiomics; SPECT","","","","","","Washington Cancer Consortium, (P30 CA015704); National Institutes of Health, NIH; National Cancer Institute, NCI, (NCT02773238, R01CA204301, R01CA258997); National Cancer Institute, NCI","Funding text 1: This investigation was supported by NIH/NCI R01CA204301. The authors gratefully acknowledge the patients who participated in the FLARE-RT clinical trial (NCT02773238). This research was also supported by the Biostatistics Shared Resource of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704). We thank Priya Vissamraju and Christina Lo for coordinating FLARE-RT protocol imaging and curating the study database. We acknowledge the efforts of Nuclear Medicine, Radiation Oncology, and Proton Center staff during SPECT/CT acquisitions, radiation therapy planning, and image-guided radiation therapy delivery.; Funding text 2: This investigation was supported by NIH/NCI R01CA204301. The authors gratefully acknowledge the patients who participated in the FLARE-RT clinical trial (NCT02773238). This research was also supported by the Biostatistics Shared Resource of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704). We thank Priya Vissamraju and Christina Lo for coordinating FLARE-RT protocol imaging and curating the study database. We acknowledge the efforts of Nuclear Medicine, Radiation Oncology, and Proton Center staff during SPECT/CT acquisitions, radiation therapy planning, and image-guided radiation therapy delivery. ; Funding text 3: This investigation was supported by NIH/NCI R01CA204301 and R01CA258997. 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Bowen; Department of Radiation Oncology, University of Washington School of Medicine, Seattle, 1959 NE Pacific St, Box 356043, 98195, United States; email: srbowen@uw.edu","","Springer Science and Business Media B.V.","","","","","","27306011","","","","English","Discov. Oncol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85137580810"
"Squillacioti G.; Bellisario V.; Ghelli F.; Marcon A.; Marchetti P.; Corsico A.G.; Pirina P.; Maio S.; Stafoggia M.; Verlato G.; Bono R.","Squillacioti, Giulia (57205739431); Bellisario, Valeria (55499738800); Ghelli, Federica (57215589872); Marcon, Alessandro (13614044300); Marchetti, Pierpaolo (35240765700); Corsico, Angelo G. (7003664779); Pirina, Pietro (57189226636); Maio, Sara (23009647000); Stafoggia, Massimo (13608072800); Verlato, Giuseppe (7006872229); Bono, Roberto (55502646900)","57205739431; 55499738800; 57215589872; 13614044300; 35240765700; 7003664779; 57189226636; 23009647000; 13608072800; 7006872229; 55502646900","Air pollution and oxidative stress in adults suffering from airway diseases. Insights from the Gene Environment Interactions in Respiratory Diseases (GEIRD) multi-case control study","2024","Science of the Total Environment","909","","168601","","","","10","10.1016/j.scitotenv.2023.168601","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177168331&doi=10.1016%2fj.scitotenv.2023.168601&partnerID=40&md5=f08abfbe57b80b8f01b470941157efa5","Department of Public Health and Pediatrics, University of Turin, Via Santena 5 bis, Turin, 10126, Italy; Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy; SC Pneumologia, Fondazione IRCCS Policlinico San Matteo, Italy; Clinical and Interventional Pulmonology, University Hospital Sassari (AOU), Sassari, Italy; Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy; Institute of Clinical Physiology, National Research Council, Pisa, Italy; Department of Epidemiology of the Lazio Region Health Service, ASL Roma 1, Rome, Italy","Squillacioti G., Department of Public Health and Pediatrics, University of Turin, Via Santena 5 bis, Turin, 10126, Italy; Bellisario V., Department of Public Health and Pediatrics, University of Turin, Via Santena 5 bis, Turin, 10126, Italy; Ghelli F., Department of Public Health and Pediatrics, University of Turin, Via Santena 5 bis, Turin, 10126, Italy; Marcon A., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Marchetti P., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Corsico A.G., Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy, SC Pneumologia, Fondazione IRCCS Policlinico San Matteo, Italy; Pirina P., Clinical and Interventional Pulmonology, University Hospital Sassari (AOU), Sassari, Italy, Department of Medical, Surgical and Experimental Sciences, University of Sassari, Sassari, Italy; Maio S., Institute of Clinical Physiology, National Research Council, Pisa, Italy; Stafoggia M., Department of Epidemiology of the Lazio Region Health Service, ASL Roma 1, Rome, Italy; Verlato G., Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy; Bono R., Department of Public Health and Pediatrics, University of Turin, Via Santena 5 bis, Turin, 10126, Italy","Air pollution is a leading risk factor for global mortality and morbidity. Oxidative stress is a key mechanism underlying air-pollution-mediated health effects, especially in the pathogenesis/exacerbation of airway impairments. However, evidence lacks on subgroups at higher risk of developing more severe outcomes in response to air pollution. This multi-centre study aims to evaluate the association between air pollution and oxidative stress in healthy adults and in patients affected by airway diseases from the Italian GEIRD (Gene Environment Interactions in Respiratory Diseases) multi-case control study. Overall, 1841 adults (49 % females, 20–83 years) were included from four Italian centres: Pavia, Sassari, Turin, and Verona. Following a 2-stage screening process, we identified 1273 cases of asthma, chronic bronchitis, rhinitis, or COPD and 568 controls. Systemic oxidative stress was quantified by urinary 8-isoprostane and 8-OH-dG. Individual residential exposures to NO2, PM10, PM2.5, and O3 were derived using an innovative five-stage machine-learning-based approach. Linear mixed regression models tested the association between oxidative stress biomarkers and air pollution tertiles, adjusting by age, sex, BMI, smoking, education and season, with recruiting centres as random intercept. Only cases exhibited higher levels of log-transformed 8-isoprostane and 8-OH-dG in association with NO2 (β: 0.30 95 % CI: 0.08–0.52 and 0.20 95 % CI: 0.03–0.37), PM10 (0.34 95 % CI: 0.12–0.55 and 0.21 95 % CI: 0.05–0.37) and PM2.5 (0.27 95 % CI: 0.09–0.49 and 0.18 95 % CI: 0.02–0.34) as compared to the first tertile of exposure. No significant associations were observed for summer O3. Our findings suggest that exposure to air pollution may increase systemic oxidative stress levels in people suffering from airway diseases. This introduces a potential novel approach available for future epidemiological studies and Public Health for effective prevention strategies oriented at the quantification of early biological effects in susceptible people, whose additional risk level might be currently underrated. Air-pollution-mediated exacerbations, driven by oxidative stress, still deserve our attention. © 2023 The Authors","Air pollution; Asthma; Chronic bronchitis; COPD; Oxidative stress biomarkers; Rhinitis","8-Hydroxy-2'-Deoxyguanosine; Adult; Air Pollutants; Air Pollution; Case-Control Studies; Environmental Exposure; Female; Gene-Environment Interaction; Humans; Male; Nitrogen Dioxide; Oxidative Stress; Particulate Matter; Respiration Disorders; Respiratory Tract Diseases; Italy; Lombardy; Pavia; Piedmont [Italy]; Sardinia; Sassari; Torino [Piedmont]; Turin; Veneto; Verona; Air pollution; Association reactions; Biomarkers; Cardiology; Diagnosis; Disease control; Genes; Health risks; Neurodegenerative diseases; Nitrogen oxides; Pulmonary diseases; Regression analysis; 8 hydroxydeoxyguanosine; 8 isoprostane; biological marker; nitrogen dioxide; 8 hydroxydeoxyguanosine; Asthma; Case-control study; Chronic bronchitis; COPD; Gene-environment interaction; Isoprostanes; OH -; Oxidative stress biomarkers; PM 10; Rhinitis; adult; asthma; atmospheric pollution; biomarker; chronic obstructive pulmonary disease; gene; genotype-environment interaction; oxidative stress; pollution exposure; adult; age; aged; air pollution; Article; asthma; biological activity; body mass; case control study; chronic bronchitis; chronic obstructive lung disease; controlled study; disease association; education; environmental exposure; female; genotype environment interaction; human; machine learning; major clinical study; male; oxidative stress; particulate matter 10; particulate matter 2.5; PM10 exposure; PM2.5 exposure; respiratory tract disease; rhinitis; season; sex; smoking; urine level; urine sampling; air pollutant; analysis; breathing disorder; clinical trial; genotype environment interaction; multicenter study; oxidative stress; particulate matter; respiratory tract disease; Oxidative stress","","nitrogen dioxide, 10102-44-0; 8-Hydroxy-2'-Deoxyguanosine, ; Air Pollutants, ; Nitrogen Dioxide, ; Particulate Matter, ","","","Biostatistics of the Catholic University of the Sacred Heart of Rome; Università Cattolica del Sacro Cuore, UCSC; Università degli Studi di Perugia","We thank Prof. Stefania Boccia and Dr. Roberta Pastorino from the Catholic University of the Sacred Heart of Rome, Prof. Chiara De Waure from the University of Perugia, and Prof. Giovanni Capelli and Prof. Bruno Federico and all the other Professors of the Professional Master's Program in Epidemiology and Biostatistics of the Catholic University of the Sacred Heart of Rome, for their valuable support. 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Squillacioti; Department of Public Health and Pediatrics, University of Turin, Turin, Via Santena 5 bis, 10126, Italy; email: giulia.squillacioti@unito.it","","Elsevier B.V.","","","","","","00489697","","STEVA","37977381","English","Sci. Total Environ.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85177168331"
"Izquierdo J.L.; Almonacid C.; Campos C.; Morena D.; Benavent M.; González-De-olano D.; Rodríguez J.M.","Izquierdo, J.L. (7102685483); Almonacid, Carlos (15519447700); Campos, C. (57612415400); Morena, D. (57215197044); Benavent, M. (58458005900); González-De-olano, D. (23034327200); Rodríguez, J.M. (58881557500)","7102685483; 15519447700; 57612415400; 57215197044; 58458005900; 23034327200; 58881557500","Systemic Corticosteroids in Patients With Bronchial Asthma: A Real-Life Study","2023","Journal of Investigational Allergology and Clinical Immunology","33","1","","30","38","8","12","10.18176/jiaci.0765","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136726709&doi=10.18176%2fjiaci.0765&partnerID=40&md5=510b89a79f3c16d64d2e75f224957f5e","Department of Medicine and Medical Specialties, University of Alcalá, Madrid, Spain; University Hospital of Guadalajara, Guadalajara, Spain; University Hospital of Toledo, Toledo, Spain; SAVANA, Spain; Allergy Department, University Hospital Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain; University Hospital Príncipe de Asturias, Madrid, Alcalá de Henares, Spain","Izquierdo J.L., Department of Medicine and Medical Specialties, University of Alcalá, Madrid, Spain, University Hospital of Guadalajara, Guadalajara, Spain; Almonacid C., University Hospital of Toledo, Toledo, Spain; Campos C., University Hospital of Guadalajara, Guadalajara, Spain; Morena D., University Hospital of Guadalajara, Guadalajara, Spain; Benavent M., SAVANA, Spain; González-De-olano D., Allergy Department, University Hospital Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria, Madrid, Spain; Rodríguez J.M., Department of Medicine and Medical Specialties, University of Alcalá, Madrid, Spain, University Hospital Príncipe de Asturias, Madrid, Alcalá de Henares, Spain","Objective: The objective of the present study was to determine the use of systemic corticosteroids (SCs) in patients with bronchial asthma using big data analysis. Methods: We performed an observational, retrospective, noninterventional study based on secondary data captured from free text in the electronic health records. This study was performed based on data from the regional health service of Castille-La Mancha (SESCAM), Spain. We performed the analysis using big data and artificial intelligence via Savana® Manager version 3.0. Results: During the study period, 103 667 patients were diagnosed with and treated for asthma at different care levels. The search was restricted to patients aged 10 to 90 years (mean age, 43.5 [95%CI, 43.4-43.7] years). Of these, 59.8% were women. SCs were taken for treatment of asthma by 58 745 patients at some point during the study period. These patients were older, with a higher prevalence of hypertension, dyslipidemia, diabetes, obesity, depression, and hiatus hernia. SCs are used frequently in the general population with asthma (31.4% in 2015 and 39.6% in 2019). SCs were prescribed mainly in primary care (59%), allergy (13%), and pulmonology (20%). The frequency of prescription of SCs had a direct impact on the main associated adverse effects. Conclusion: In clinical practice, SCs are frequently prescribed to patients with asthma, especially in primary care. Use of SCs is associated with a greater number of adverse events. It is necessary to implement measures to reduce prescription of SCs to patients with asthma, especially in primary care. © 2023 Esmon Publicidad.","Artificial intelligence; Asthma; Big data; Systemic corticosteroids","Adolescent; Adrenal Cortex Hormones; Adult; Aged; Aged, 80 and over; Anti-Asthmatic Agents; Asthma; Child; Electronic Health Records; Female; Humans; Male; Middle Aged; Retrospective Studies; Spain; Young Adult; corticosteroid; antiasthmatic agent; corticosteroid; adolescent; adult; aged; algorithm; allergy; Article; artificial intelligence; asthma; child; clinical practice; coronavirus disease 2019; data analysis; depression; diabetes mellitus; dyslipidemia; electronic health record; female; hiatus hernia; human; hypertension; information retrieval; major clinical study; male; natural language processing; obesity; observational study; pandemic; prescription; prevalence; primary medical care; pulmonology; retrospective study; very elderly; drug therapy; epidemiology; middle aged; Spain; young adult","","Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ","","","Universidad de Alcalá, UAH","Project funded by the NEUMOMADRID awards and the Chair of Inflammatory Diseases of the Airways, University of Alcalá.","The Global Asthma Report, (2018); Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet, 396, pp. 1204-1222, (2020); Pereira PL, Grande AMG, Ganan LD, Ordobas Gavin M., Evolution of Asthma Prevalence and Sociodemographic and Health Factors Associated in Madrid Region (1996-2013), Rev Esp Salud Publica, 91, (2017); Encuesta Nacional de Salud de España 2017. Problemas o enfermedades crónicas o de larga evolución en los últimos 12 meses en población adulta, (2017); Urrutia I, Aguirre U, Sunyer J, Plana E, Muniozguren N, Martinez-Moratalla J, Et al., Changes in the prevalence of asthma in the Spanish cohort of the European Community Respiratory Health Survey (ECRHS-II), Arch Bronconeumol, 43, pp. 425-430, (2007); Chen W, Marra CA, Lynd LD, FitzGerald JM, Zafari Z, Sadatsafavi M., The natural history of severe asthma and influences of early risk factors: a population-based cohort study, Thorax, 71, pp. 267-275, (2016); Quirce S, Plaza V, Picado C, Vennera M, Casafont J., Prevalence of uncontrolled severe persistent asthma in pneumology and allergy hospital units in Spain, J Investig Allergol Clin Immunol, 21, pp. 466-471, (2011); Lundback B, Backman H, Lotvall J, Ronmark E., Is asthma prevalence still increasing?, Expert Rev Respir Med, 10, pp. 39-51, (2016); Rabe KF, Adachi M, Lai CKW, Soriano JB, Vermeire PA, Weiss KB, Et al., Worldwide severity and control of asthma in children and adults: the global asthma insights and reality surveys, J Allergy Clin Immunol, 114, pp. 40-47, (2004); Global Strategy for Asthma Management and Prevention, (2021); Guía española para el manejo del asma 5.1, (2021); Deo RC., Machine Learning in Medicine, Circulation, 132, pp. 1920-1930, (2015); Ho LV, Ledbetter D, Aczon M, Wetzel R., The Dependence of Machine Learning on Electronic Medical Record Quality, AMIA Annu Symp Proc, 2017, pp. 883-891, (2017); Shenoy VN, Aalami OO., Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition, AMIA Annu Symp Proc, 2017, pp. 1564-1570, (2017); Espinosa L, Tello J, Pardo A, Medrano I, Urena A, Salcedo I, Et al., Savana: A Global Information Extraction and Terminology Expansion Framework in the Medical Domain, Procesamiento del Lenguaje Natural, 57, pp. 23-30, (2016); Medrano IH, Guijarro JT, Belda C, Belda C, Urena A, Salcedo I, Et al., Savana: Re-using Electronic Health Records with Artificial Intelligence, Int J Interact Multi, 4, pp. 8-12, (2018); Benson T., Using SNOMED and HL7 Together, Principles of Health Interoperability HL7 and SNOMED, pp. 217-225; Baeza-Yates R, Ribeiro-Neto B., Modern Information Retrieval, (1999); Izquierdo JL, Almonacid C, Gonzalez Y, Del Rio-Bermudez C, Ancochea J, Cardena R, Et al., The impact of COVID-19 on patients with asthma, Eur Respir J, 57, (2021); Graziani D, Soriano JB, Del Rio-Bermudez C, Morena D, Diaz T, Castillo M, Et al., Characteristics and Prognosis of COVID-19 in Patients with COPD, J Clin Med, 9, (2020); Perez de Llano L, Martinez-Moragon E, Plaza Moral V, Trisan Alonso A, Almonacid Sanchez C, Callejas FJ, Et al., Unmet therapeutic goals and potential treatable traits in a population of patients with severe uncontrolled asthma in Spain. ENEAS study, Respir Med, 151, pp. 49-54, (2019); Bleecker ER, Menzies-Gow AN, Price DB, Bourdin A, Sweet S, Martin AL, Et al., Systematic Literature Review of Systemic Corticosteroid Use for Asthma Management, Am J Respir Crit Care Med, 201, pp. 276-293, (2020); Tse K, Chen L, Tse M, Zuraw B, Christiansen S., Effect of catastrophic wildfires on asthmatic outcomes in obese children: breathing fire, Ann Allergy Asthma Immunol, 114, pp. 308-311, (2015); Allen-Ramey FC, Nelsen LM, Leader JB, Mercer D, Kirchner HL, Jones JJ., Electronic health record-based assessment of oral corticosteroid use in a population of primary care patients with asthma: an observational study, Allergy Asthma Clin Immunol, 9, (2013); Chipps BE, Haselkorn T, Paknis B, Ortiz B, Bleecker ER, Kianifard F, Et al., More than a decade follow-up in patients with severe or difficult-to-treat asthma: The Epidemiology and Natural History of Asthma: Outcomes and Treatment Regimens (TENOR) II, J Allergy Clin Immunol, 141, pp. 1590-1597, (2018); O'Neill S, Sweeney J, Patterson CC, Menzies-Gow A, Niven R, Mansur AH, Et al., The cost of treating severe refractory asthma in the UK: an economic analysis from the British Thoracic Society Difficult Asthma Registry, Thorax, 70, pp. 376-378, (2015); Moore WC, Panettieri RA, Trevor J, Ledford DK, Lugogo N, Soong W, Et al., Biologic and maintenance systemic corticosteroid therapy among US subspecialist-treated patients with severe asthma, Ann Allergy Asthma Immunol, 125, pp. 294-303, (2020); Zeiger R, Sullivan P, Chung Y, Kreindler JL, Zimmerman NM, Tkacz J., Systemic Corticosteroid-Related Complications and Costs in Adults with Persistent Asthma, J Allergy Clin Immunol Pract, 8, pp. 3455-3465, (2020); Sullivan PW, Ghushchyan VH, Skoner DP, LeCocq J, Park S, Zeiger RS., Complications and Health Care Resource Utilization Associated with Systemic Corticosteroids in Children and Adolescents with Persistent Asthma, J Allergy Clin Immunol Pract, 9, pp. 1541-1551, (2021); Price D, Castro M, Bourdin A, Fucile S, Altman P., Short-course systemic corticosteroids in asthma: striking the balance between efficacy and safety, Eur Respir Rev, 29, (2020); Matsunaga K, Adachi M, Nagase H, Okoba T, Hayashi N, Tohda Y., Association of low-dosage systemic corticosteroid use with disease burden in asthma, NPJ Prim Care Respir Med, 30, (2020); Bloechliger M, Reinau D, Spoendlin J, Chang SC, Kuhlbusch K, Haeney LG, Et al., Adverse events profile of oral corticosteroids among asthma patients in the UK: cohort study with a nested case-control analysis, Respir Res, 19, (2018); Bhargava S, Prakash A, Rehan HS, Gupta LK., Effect of systemic corticosteroids on serum apoptotic markers and quality of life in patients with asthma, Allergy Asthma Proc, 36, pp. 275-282, (2015); Suehs CM, Menzies-Gow A, Price D, Bleecker ER, Canonica GW, Gurnell M, Et al., Expert Consensus on the Tapering of Oral Corticosteroids for the Treatment of Asthma. A Delphi Study, Am J Respir Crit Care Med, 203, pp. 871-881, (2021); Dalal AA, Duh MS, Gozalo L, Robitaille MN, Albers F, Yancey S, Et al., Dose-Response Relationship Between Long-Term Systemic Corticosteroid Use and Related Complications in Patients with Severe Asthma, J Manag Care Spec Pharm, 22, pp. 833-847, (2016); Broersen LHA, Pereira AM, Jorgensen JOL, Dekkers OM., Adrenal Insufficiency in Corticosteroids Use: Systematic Review and Meta-Analysis, J Clin Endocrinol Metab, 100, pp. 2171-2180, (2015)","C. Almonacid; Pulmonology Department, Hospital Universitario de Toledo, Toledo, Avda Río Guadiana, 45007, Spain; email: caralmsan@gmail.com","","ESMON Publicidad S.A.","","","","","","10189068","","JIAIE","34779775","English","J. Invest. Allergol. Clin. Immunol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85136726709"
"Agache I.; Shamji M.H.; Kermani N.Z.; Vecchi G.; Favaro A.; Layhadi J.A.; Heider A.; Akbas D.S.; Filipaviciute P.; Wu L.Y.D.; Cojanu C.; Laculiceanu A.; Akdis C.A.; Adcock I.M.","Agache, Ioana (57201020933); Shamji, Mohamed H. (57210446924); Kermani, Nazanin Zounemat (57195464650); Vecchi, Giulia (56624089400); Favaro, Alberto (57218612536); Layhadi, Janice A. (55178544500); Heider, Anja (57219093062); Akbas, Didem Sanver (57954502000); Filipaviciute, Paulina (57954502100); Wu, Lily Y.D. (57953123300); Cojanu, Catalina (57213605386); Laculiceanu, Alexandru (57209748215); Akdis, Cezmi A. (7007154667); Adcock, Ian M. (7007066538)","57201020933; 57210446924; 57195464650; 56624089400; 57218612536; 55178544500; 57219093062; 57954502000; 57954502100; 57953123300; 57213605386; 57209748215; 7007154667; 7007066538","Multidimensional endotyping using nasal proteomics predicts molecular phenotypes in the asthmatic airways","2023","Journal of Allergy and Clinical Immunology","151","1","","128","137","9","10","10.1016/j.jaci.2022.06.028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141327767&doi=10.1016%2fj.jaci.2022.06.028&partnerID=40&md5=17ae16a57246540bf559f01394a0655b","Faculty of Medicine, Transylvania University, Brasov, Romania; Theramed Healthcare, Brasov, Romania; National Heart and Lung Institute, Imperial College London, United Kingdom; NIHR Biomedical Research Centre, London, United Kingdom; Data Science Institute, Imperial College London, United Kingdom; Faculty Science Limited, London, United Kingdom; Christine Kühne-Center for Allergy Research and Education, Davos, Switzerland; Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland","Agache I., Faculty of Medicine, Transylvania University, Brasov, Romania, Theramed Healthcare, Brasov, Romania; Shamji M.H., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom; Kermani N.Z., National Heart and Lung Institute, Imperial College London, United Kingdom, Data Science Institute, Imperial College London, United Kingdom; Vecchi G., Faculty Science Limited, London, United Kingdom; Favaro A., Faculty Science Limited, London, United Kingdom; Layhadi J.A., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom; Heider A., Christine Kühne-Center for Allergy Research and Education, Davos, Switzerland; Akbas D.S., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom; Filipaviciute P., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom; Wu L.Y.D., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom; Cojanu C., Faculty of Medicine, Transylvania University, Brasov, Romania, Theramed Healthcare, Brasov, Romania; Laculiceanu A., Faculty of Medicine, Transylvania University, Brasov, Romania, Theramed Healthcare, Brasov, Romania; Akdis C.A., Christine Kühne-Center for Allergy Research and Education, Davos, Switzerland, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland; Adcock I.M., National Heart and Lung Institute, Imperial College London, United Kingdom, NIHR Biomedical Research Centre, London, United Kingdom","Background: Unsupervised clustering of biomarkers derived from noninvasive samples such as nasal fluid is less evaluated as a tool for describing asthma endotypes. Objective: We sought to evaluate whether protein expression in nasal fluid would identify distinct clusters of patients with asthma with specific lower airway molecular phenotypes. Methods: Unsupervised clustering of 168 nasal inflammatory and immune proteins and Shapley values was used to stratify 43 patients with severe asthma (endotype of noneosinophilic asthma) using a 2 “modeling blocks” machine learning approach. This algorithm was also applied to nasal brushings transcriptomics from U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes). Feature reduction and functional gene analysis were used to compare proteomic and transcriptomic clusters. Gene set variation analysis provided enrichment scores of the endotype of noneosinophilic asthma protein signature within U-BIOPRED sputum and blood. Results: The nasal protein machine learning model identified 2 severe asthma endotypes, which were replicated in U-BIOPRED nasal transcriptomics. Cluster 1 patients had significant airway obstruction, small airways disease, air trapping, decreased diffusing capacity, and increased oxidative stress, although only 4 of 18 were current smokers. Shapley identified 20 cluster-defining proteins. Forty-one proteins were significantly higher in cluster 1. Pathways associated with proteomic and transcriptomic clusters were linked to TH1, TH2, neutrophil, Janus kinase-signal transducer and activator of transcription, TLR, and infection activation. Gene set variation analysis of the nasal protein and gene signatures were enriched in subjects with sputum neutrophilic/mixed granulocytic asthma and in subjects with a molecular phenotype found in sputum neutrophil-high subjects. Conclusions: Protein or gene analysis may indicate molecular phenotypes within the asthmatic lower airway and provide a simple, noninvasive test for non–type 2 immune response asthma that is currently unavailable. © 2022","biomarkers; endotypes; machine learning; nasal proteomics; Severe asthma; T2 asthma; trascriptome-associated cluster","Asthma; Biomarkers; Gene Expression Profiling; Humans; Phenotype; Proteomics; Sputum; biological marker; Janus kinase; protein; proteome; biological marker; adult; airway obstruction; algorithm; Article; asthma; blood; cohort analysis; cross validation; current smoker; data clustering; female; gene; gene set variation analysis; granulocyte; human; infection; lower respiratory tract; lung diffusion capacity; machine learning; major clinical study; male; middle aged; neutrophil; nose mucus; oxidative stress; pathway analysis; phenotype; protein expression; proteomics; quantitative analysis; severe asthma; small airway disease; sputum; Th1 cell; Th2 cell; transcriptomics; gene expression profiling; metabolism; phenotype","","Janus kinase, 161384-16-3; protein, 67254-75-5; Biomarkers, ","","","UK MRC; Wellcome Trust, WT, (208340/Z/17/Z, PN-II-RU-TE-2014-4-2303); Wellcome Trust, WT; Seventh Framework Programme, FP7, (FP7/2007-2013); Seventh Framework Programme, FP7; European Federation of Pharmaceutical Industries and Associations, EFPIA; Medical Research Council, MRC, (MR/M016579/1, MR/T010371/1); Medical Research Council, MRC; Engineering and Physical Sciences Research Council, EPSRC, (EP/T003189/1, EP/V052462/1); Engineering and Physical Sciences Research Council, EPSRC; National Institute for Health and Care Research, NIHR; Innovative Medicines Initiative, IMI, (115010); Innovative Medicines Initiative, IMI; NIHR Imperial Biomedical Research Centre, BRC","This study was funded, in part, by the PN-II-RU-TE-2014-4 programme, the UK Medical Research Council (MRC) (grant nos. MR/T010371/1 and MR/M016579/1), and the National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre (BRC). I.M.A. is supported by the Engineering and Physical Sciences Research Council ( EPSRC ; grant nos. EP/T003189/1 and EP/V052462/1), the UK MRC (grant nos. MR/T010371/1 and MR/M016579/1), and the Wellcome Trust (grant no. 208340/Z/17/Z). N.Z.K. is supported by the UK MRC (MR/T010371/1 and MR/M016579/1). The clinical trial performed by Ioana Agache was funded through the PN-II-RU-TE-2014-4-2303 ENDANA project. All aspects of the research performed at Imperial College London were funded by the BRC and Imperial College Trust . Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes was supported by an Innovative Medicines Initiative Joint Undertaking (no. 115010), resources from the European Union’s Seventh Framework Programme (FP7/2007-2013), and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in-kind contribution ( www.imi.europa.eu ). 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Agache; Faculty of Medicine, Transylvania University, Brasov, 2A, Pictor Ion Andreescu, 500051, Romania; email: ibrumaru@unitbv.ro; M.H. Shamji; National Heart and Lung Institute, Sir Alexander Fleming Building, Imperial College London, London, SW7 2AZ, United Kingdom; email: m.shamji@imperial.ac.uk","","Elsevier Inc.","","","","","","00916749","","JACIB","36154846","English","J. Allergy Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85141327767"
"Panaitescu C.; Haidar L.; Buzan M.R.; Grijincu M.; Spanu D.E.; Cojanu C.; Laculiceanu A.; Bumbacea R.; Agache I.","Panaitescu, Carmen (35739107400); Haidar, Laura (57222864458); Buzan, Maria Roxana (57462025600); Grijincu, Manuela (57462039400); Spanu, Daniela Elena (57219718415); Cojanu, Catalina (57213605386); Laculiceanu, Alexandru (57209748215); Bumbacea, Roxana (9232969100); Agache, Ioana (57201020933)","35739107400; 57222864458; 57462025600; 57462039400; 57219718415; 57213605386; 57209748215; 9232969100; 57201020933","Precision medicine in the allergy clinic: the application of component resolved diagnosis","2022","Expert Review of Clinical Immunology","18","2","","145","162","17","8","10.1080/1744666X.2022.2034501","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125020318&doi=10.1080%2f1744666X.2022.2034501&partnerID=40&md5=5738d7491594942fd0e38ce5d800e541","Department of Functional Sciences, Physiology, Center of Immuno-Physiology and Biotechnologies (Cifbioteh), “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timisoara, Romania; Centre for Gene and Cellular Therapies in the Treatment of Cancer–OncoGen, “Pius Brinzeu” Clinical Emergency Hospital, Timisoara, Romania; Transylvania University Brasov, Faculty of Medicine, Brasov, Romania; Department of Allergy, “Carol Davila” University of Medicine and Pharmacy Bucharest, Romania","Panaitescu C., Department of Functional Sciences, Physiology, Center of Immuno-Physiology and Biotechnologies (Cifbioteh), “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timisoara, Romania, Centre for Gene and Cellular Therapies in the Treatment of Cancer–OncoGen, “Pius Brinzeu” Clinical Emergency Hospital, Timisoara, Romania; Haidar L., Department of Functional Sciences, Physiology, Center of Immuno-Physiology and Biotechnologies (Cifbioteh), “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timisoara, Romania; Buzan M.R., Department of Functional Sciences, Physiology, Center of Immuno-Physiology and Biotechnologies (Cifbioteh), “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timisoara, Romania, Centre for Gene and Cellular Therapies in the Treatment of Cancer–OncoGen, “Pius Brinzeu” Clinical Emergency Hospital, Timisoara, Romania; Grijincu M., Department of Functional Sciences, Physiology, Center of Immuno-Physiology and Biotechnologies (Cifbioteh), “Victor Babeș” University of Medicine and Pharmacy Timișoara, Timisoara, Romania, Centre for Gene and Cellular Therapies in the Treatment of Cancer–OncoGen, “Pius Brinzeu” Clinical Emergency Hospital, Timisoara, Romania; Spanu D.E., Transylvania University Brasov, Faculty of Medicine, Brasov, Romania; Cojanu C., Transylvania University Brasov, Faculty of Medicine, Brasov, Romania; Laculiceanu A., Transylvania University Brasov, Faculty of Medicine, Brasov, Romania; Bumbacea R., Department of Allergy, “Carol Davila” University of Medicine and Pharmacy Bucharest, Romania; Agache I., Transylvania University Brasov, Faculty of Medicine, Brasov, Romania","Introduction: A precise diagnosis is key for the optimal management of allergic diseases and asthma. In vivo or in vitro diagnostic methods that use allergen extracts often fail to identify the molecules eliciting the allergic reactions. Areas covered: Component-resolved diagnosis (CRD) has solved most of the limitations of extract-based diagnostic procedures and is currently valuable tool for the precision diagnosis in the allergy clinic, for venom and food allergy, asthma, allergic rhinitis, and atopic dermatitis. Its implementation in daily practice facilitates: a) the distinction between genuine multiple sensitizations and cross-reactive sensitization in polysensitized patients; b) the prediction of a severe, systemic reaction in food or insect venom allergy; c) the optimal selection of allergen immunotherapy based on the patient sensitization profile. This paper describes its main advantages and disadvantages, cost-effectiveness and future perspectives. Expert opinion: The diagnostic strategy based on CRD is part of the new concept of precision immunology, which aims to improve the management of allergic diseases. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","Allergen immunotherapy; allergic diseases; asthma; component resolved diagnosis; molecular allergens","Allergens; Desensitization, Immunologic; Food Hypersensitivity; Humans; Precision Medicine; Rhinitis, Allergic; albumin; allergen; bee venom; immunoglobulin; immunoglobulin E; insect venom; lipid transfer protein; profilin; recombinant protein; tropomyosin; wasp venom; allergic asthma; allergic rhinitis; allergy; Article; artificial intelligence; atopic dermatitis; atopic march; basophil; clinical practice; component resolved diagnosis; cost effectiveness analysis; cross allergy; cross reaction; desensitization; diagnostic procedure; drug hypersensitivity; food allergy; food induced anaphylaxis; grass pollen; honeybee; house dust allergy; human; Hymenoptera venom allergy; immunotherapy; multicenter study (topic); multiplex polymerase chain reaction; nonhuman; pathogenesis; personalized medicine; prediction; predictor variable; prevalence; protein microarray; quality adjusted life year; quality of life; respiratory tract allergy; risk assessment; singleplex polymerase chain reaction; allergic rhinitis; food allergy; personalized medicine; procedures","","immunoglobulin, 9007-83-4; immunoglobulin E, 37341-29-0; profilin, 131383-86-3; tropomyosin, 72067-79-9; Allergens, ","","","","","Brozek J.L., Bousquet J., Agache I., Et al., Allergic rhinitis and its impact on asthma (ARIA) guidelines—2016 revision, J Allergy Clin Immunol, 140, 4, pp. 950-958, (2017); Agache I., Akdis C.A., Chivato T., Et al., EAACI white paper on research, innovation and quality care published by the european academy of allergy and clinical immunology, Zurich Switzerland, 152, (2018); Asher M.I., Stewart A.W., Wong G., Et al., Changes over time in the relationship between symptoms of asthma, rhinoconjunctivitis and eczema: a global perspective from the international study of asthma and allergies in childhood (ISAAC), Allergol Immunopathol, 40, 5, pp. 267-274, (2012); 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Agache; Transylvania University, Brasov, 2A, Pictor Ion Andreescu, 500051, Romania; email: ibrumaru@unitbv.ro","","Taylor and Francis Ltd.","","","","","","1744666X","","","35078387","English","Expert Rev. Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85125020318"
"Moran I.; Altilar D.T.; Ucar M.K.; Bilgin C.; Bozkurt M.R.","Moran, Inanc (10641888700); Altilar, Deniz Turgay (57211191927); Ucar, Muhammed Kursad (56779734300); Bilgin, Cahit (8967819100); Bozkurt, Mehmet Recep (48761063800)","10641888700; 57211191927; 56779734300; 8967819100; 48761063800","Deep Transfer Learning for Chronic Obstructive Pulmonary Disease Detection Utilizing Electrocardiogram Signals","2023","IEEE Access","11","","","40629","40644","15","12","10.1109/ACCESS.2023.3269397","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158907168&doi=10.1109%2fACCESS.2023.3269397&partnerID=40&md5=38966da8c1696b7eb74136a711bb6502","Istanbul Technical University, Computer Engineering Department, Istanbul, 34467, Turkey; Sakarya University, Electrical and Electronics Engineering Department, Sakarya, 54050, Turkey; Sakarya University, Faculty of Medicine, Sakarya, 54050, Turkey","Moran I., Istanbul Technical University, Computer Engineering Department, Istanbul, 34467, Turkey; Altilar D.T., Istanbul Technical University, Computer Engineering Department, Istanbul, 34467, Turkey; Ucar M.K., Sakarya University, Electrical and Electronics Engineering Department, Sakarya, 54050, Turkey; Bilgin C., Sakarya University, Faculty of Medicine, Sakarya, 54050, Turkey; Bozkurt M.R., Sakarya University, Electrical and Electronics Engineering Department, Sakarya, 54050, Turkey","The motivation of this research is to introduce the first research on automated Chronic Obstructive Pulmonary Disease (COPD) diagnosis using deep learning and the first annotated dataset in this field. The primary objective and contribution of this research is the development and design of an artificial intelligence system capable of diagnosing COPD utilizing only the heart signal (electrocardiogram, ECG) of the patient. In contrast to the traditional way of diagnosing COPD, which requires spirometer tests and a laborious workup in a hospital setting, the proposed system uses the classification capabilities of deep transfer learning and the patient's heart signal, which provides COPD signs in itself and can be received from any modern smart device. Since the disease progresses slowly and conceals itself until the final stage, hospital visits for diagnosis are uncommon. Hence, the medical goal of this research is to detect COPD using a simple heart signal before it becomes incurable. Deep transfer learning frameworks, which were previously trained on a general image data set, are transferred to carry out an automatic diagnosis of COPD by classifying patients' electrocardiogram signal equivalents, which are produced by signal-to-image transform techniques. Xception, VGG-19, InceptionResNetV2, DenseNet-121, and 'trained-from-scratch' convolutional neural network architectures have been investigated for the detection of COPD, and it is demonstrated that they are able to obtain high performance rates in classifying nearly 33.000 instances using diverse training strategies. The highest classification rate was obtained by the Xception model at 99%. This research shows that the newly introduced COPD detection approach is effective, easily applicable, and eliminates the burden of considerable effort in a hospital. It could also be put into practice and serve as a diagnostic aid for chest disease experts by providing a deeper and faster interpretation of ECG signals. Using the knowledge gained while identifying COPD from ECG signals may aid in the early diagnosis of future diseases for which little data is currently available. © 2013 IEEE.","Biomedical signal analysis; chronic obstructive pulmonary disease; deep transfer learning; ECG signal classification; stockwell transform; wavelet transform","Biological organs; Biomedical signal processing; Cardiology; Classification (of information); Deep learning; Electrocardiography; Hospitals; Network architecture; Neural networks; Pulmonary diseases; Signal analysis; Biomedical signal analysis; Chronic obstructive pulmonary disease; Deep transfer learning; Disease detection; Electrocardiogram signal; Electrocardiogram signal classifications; Heart signal; Stockwell transform; Transfer learning; Wavelets transform; Wavelet transforms","","","","","","","Jones P.W., Vogelmeier C., Vestbo J., Hurd S.S., Agusti A.G., Anzueto A., Barnes P.J., Fabbri L.M., Martinez F.J., Pulmonary perspective global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease gold executive summary, Amer. 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Mag, 14, 2, pp. 167-173, (1995); Wang G., Li W., Zuluaga M.A., Pratt R., Patel P.A., Aertsen M., Doel T., David A.L., Deprest J., Ourselin S., Vercauteren T., Interactive medical image segmentation using deep learning with image-specific fine tuning, IEEE Trans. Med. Imag, 37, 7, pp. 1562-1573, (2018); Stockwell R.G., Mansinha L., Lowe R.P., Localization of the complex spectrum: The S transform, IEEE Trans. Signal Process, 44, 4, pp. 998-1001, (1996); Raj S., Phani T.C.K., Dalei J., Power quality analysis using modified S-transform on ARM processor, Proc. 6th Int. Symp. Embedded Comput. Syst. Design (ISED), pp. 116-170, (2016); Zhao Z., Wang S., Zhang W., Xie Y., A novel automatic modulation classification method based on stockwell-transform and energy entropy for underwater acoustic signals, Proc. IEEE Int. Conf. Signal Process., Commun. Comput. (ICSPCC), pp. 1-6, (2016); Yang Y., Yan L.-F., Zhang X., Han Y., Nan H.-Y., Hu Y.-C., Hu B., Yan S.-L., Zhang J., Cheng D.-L., Ge X.-W., Cui G.-B., Zhao D., Wang W., Glioma grading on conventional MR images: A deep learning study with transfer learning, Frontiers Neurosci, 12, pp. 1-10, (2018); Shin H.-C., Roth H.R., Gao M., Lu L., Xu Z., Nogues I., Yao J., Mollura D., Summers R.M., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imag, 35, 5, pp. 1285-1298, (2016); Nokoff N.J., Spack N.P., Shumer D.E., Deep learning and radiomics in precision medicine, Precis. Med. Drug Develop, 176, 2, pp. 139-148, (2017)","I. Moran; Istanbul Technical University, Computer Engineering Department, Istanbul, 34467, Turkey; email: moran@itu.edu.tr","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85158907168"
"Kessler R.; Philipp J.; Wilfer J.; Kostev K.","Kessler, Roman (57217163435); Philipp, Jos (58294060300); Wilfer, Joanna (58294320700); Kostev, Karel (25637745400)","57217163435; 58294060300; 58294320700; 25637745400","Predictive Attributes for Developing Long COVID—A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany","2023","Journal of Clinical Medicine","12","10","3511","","","","11","10.3390/jcm12103511","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160570479&doi=10.3390%2fjcm12103511&partnerID=40&md5=7de063f8bdbfe9855d5f3e6882c65285","Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany; IQVIA, Frankfurt, 60549, Germany","Kessler R., Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany; Philipp J., IQVIA, Frankfurt, 60549, Germany; Wilfer J., IQVIA, Frankfurt, 60549, Germany; Kostev K., IQVIA, Frankfurt, 60549, Germany","(1) In the present study, we used data comprising patient medical histories from a panel of primary care practices in Germany to predict post-COVID-19 conditions in patients after COVID-19 diagnosis and to evaluate the relevant factors associated with these conditions using machine learning methods. (2) Methods: Data retrieved from the IQVIATM Disease Analyzer database were used. Patients with at least one COVID-19 diagnosis between January 2020 and July 2022 were selected for inclusion in the study. Age, sex, and the complete history of diagnoses and prescription data before COVID-19 infection at the respective primary care practice were extracted for each patient. A gradient boosting classifier (LGBM) was deployed. The prepared design matrix was randomly divided into train (80%) and test data (20%). After optimizing the hyperparameters of the LGBM classifier by maximizing the F2 score, model performance was evaluated using several test metrics. We calculated SHAP values to evaluate the importance of the individual features, but more importantly, to evaluate the direction of influence of each feature in our dataset, i.e., whether it is positively or negatively associated with a diagnosis of long COVID. (3) Results: In both the train and test data sets, the model showed a high recall (sensitivity) of 81% and 72% and a high specificity of 80% and 80%; this was offset, however, by a moderate precision of 8% and 7% and an F2-score of 0.28 and 0.25. The most common predictive features identified using SHAP included COVID-19 variant, physician practice, age, distinct number of diagnoses and therapies, sick days ratio, sex, vaccination rate, somatoform disorders, migraine, back pain, asthma, malaise and fatigue, as well as cough preparations. (4) Conclusions: The present exploratory study describes an initial investigation of the prediction of potential features increasing the risk of developing long COVID after COVID-19 infection by using the patient history from electronic medical records before COVID-19 infection in primary care practices in Germany using machine learning. Notably, we identified several predictive features for the development of long COVID in patient demographics and their medical histories. © 2023 by the authors.","COVID-19; gradient boosting classifier; long COVID; machine learning","antihistaminic agent; bronchodilating agent; adult; Article; asthma; backache; classifier; coughing; exploratory research; fatigue; female; general practitioner; Germany; human; ICD-10; long COVID; machine learning; major clinical study; malaise; male; medical history; migraine; prescription; primary medical care; sensitivity and specificity; Severe acute respiratory syndrome coronavirus 2; somatoform disorder; vaccination; virus strain","","","","","","","Chen C., Haupert S.R., Zimmermann L., Shi X., Fritsche L.G., Mukherjee B., Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review, J. Infect. Dis, 226, pp. 1593-1607, (2022); Chen H., Zhang L., Zhang Y., Chen G., Wang D., Chen X., Wang Z., Wang J., Che X., Horita N., Et al., Prevalence and clinical features of long COVID from omicron infection in children and adults, J. Infect, 86, pp. e97-e99, (2023); Cisterna-Garcia A., Guillen-Teruel A., Caracena M., Perez E., Jimenez F., Francisco-Verdu F.J., Reina G., Gonzalez-Billalabeitia E., Palma J., Sanchez-Ferrer A., Et al., A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study, Sci. Rep, 12, (2022); Gupta H., Verma O.P., Vaccine hesitancy in the post-vaccination COVID-19 era: A machine learning and statistical analysis driven study, Evol. Intell, 16, pp. 739-757, (2023); Jimenez-Solem E., Petersen T.S., Hansen C., Hansen C., Lioma C., Igel C., Boomsma W., Krause O., Lorenzen S., Selvan R., Et al., Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients, Sci. Rep, 11, (2021); Sudre C.H., Murray B., Varsavsky T., Graham M.S., Penfold R.S., Bowyer R.C., Pujol J.C., Klaser K., Antonelli M., Canas L.S., Et al., Attributes and Predictors of Long COVID, Nat. Med, 27, pp. 626-631, (2021); Pfaff E.R., Girvin A.T., Bennett T.D., Bhatia A., Brooks I.M., Deer R.R., Dekermanjian J.P., Jolley S.E., Kahn M.G., Kostka K., Et al., Identifying who has long COVID in the USA: A machine learning approach using N3C data, Lancet Digit. Health, 4, pp. e532-e541, (2022); Rathmann W., Bongaerts B., Carius H.-J., Kruppert S., Kostev K., Basic characteristics and representativeness of the German Disease Analyzer database, Int. J. Clin. Pharmacol. Ther, 56, pp. 459-466, (2018); Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme, 10. Revision, German Modification, Version 2023; Anzahl und Anteile von VOC und VOI in Deutschland; Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.Y., Lightgbm: A highly efficient gradient boosting decision tree, Advances in Neural Information Processing Systems, pp. 3146-3154, (2017); Grinsztajn L., Oyallon E., Varoquaux G., Why do tree-based models still outperform deep learning on typical tabular data?, arXiv, (2022); Scholer D., Kostev K., Peters M., Zamfir C., Wolk A., Roderburg C., Loosen S.H., Machine Learning Can Predict the Probability of Biologic Therapy in Patients with Inflammatory Bowel Disease, J. Clin. Med, 11, (2022); Csizmadia G., Liszkai-Peres K., Ferdinandy B., Miklosi A., Konok V., Human activity recognition of children with wearable devices using LightGBM machine learning, Sci. Rep, 12, (2022); Rahman S., Irfan M., Raza M., Moyeezullah Ghori K., Yaqoob S., Awais M., Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living, Int. J. Environ. Res. Public Health, 17, (2020); Sasaki Y., The Truth of the F-Measure, (2007); Lundberg S., Lee S., A Unified Approach to Interpreting Model Predictions, Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017); O'Sullivan C., SHAP for Categorical Features; Aktar S., Ahamad M.M., Rashed-Al-Mahfuz M., Azad A., Uddin S., Kamal A., A Alyami S., Lin P.-I., Islam S.M.S., Quinn J.M., Et al., Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development, JMIR Med. Inform, 9, (2021); Du M., Ma Y., Deng J., Liu M., Liu J., Comparison of Long COVID-19 Caused by Different SARS-CoV-2 Strains: A Systematic Review and Meta-Analysis, Int. J. Environ. Res. Public Health, 19, (2022); Kostev K., Smith L., Koyanagi A., Jacob L., Prevalence of and Factors Associated with Post-Coronavirus Disease 2019 (COVID-19) Condition in the 12 Months After the Diagnosis of COVID-19 in Adults Followed in General Practices in Germany, Open Forum Infect. Dis, 9, (2022); Peghin M., Palese A., Venturini M., De Martino M., Gerussi V., Graziano E., Bontempo G., Marrella F., Tommasini A., Fabris M., Et al., Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients, Clin. Microbiol. Infect, 27, pp. 1507-1513, (2021); Fernandez-De-Las-Penas C., Martin-Guerrero J.D., Pellicer-Valero O.J., Navarro-Pardo E., Gomez-Mayordomo V., Cuadrado M.L., Arias-Navalon J.A., Cigaran-Mendez M., Hernandez-Barrera V., Arendt-Nielsen L., Female Sex Is a Risk Factor Associated with Long-Term Post-COVID Related-Symptoms but Not with COVID-19 Symptoms: The LONG-COVID-EXP-CM Multicenter Study, J. Clin. Med, 11, (2022); Thompson E.J., Williams D.M., Walker A.J., Mitchell R.E., Niedzwiedz C.L., Yang T.C., Huggins C.F., Kwong A.S.F., Silverwood R.J., Di Gessa G., Et al., Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records, Nat. Commun, 13, (2022); Yong S.J., Long COVID or post-COVID-19 syndrome: Putative pathophysiology, risk factors, and treatments, Infect. Dis, 53, pp. 737-754, (2021); Tsampasian V., Elghazaly H., Chattopadhyay R., Debski M., Naing T.K.P., Garg P., Clark A., Ntatsaki E., Vassiliou V.S., Risk Factors Associated with Post−COVID-19 Condition: A Systematic Review and Meta-analysis, JAMA Intern. Med, (2023); Schou T.M., Joca S., Wegener G., Bay-Richter C., Psychiatric and neuropsychiatric sequelae of COVID-19—A systematic review, Brain Behav. Immun, 97, pp. 328-348, (2021); Plywaczewska-Jakubowska M., Chudzik M., Babicki M., Kapusta J., Jankowski P., Lifestyle, course of COVID-19, and risk of Long-COVID in non-hospitalized patients, Front. Med, 9, (2022); Wilk P., Ruiz-Castell M., Moran V., Noel Pi Alperin M., Bohn T., Fagherazzi G., Suhrcke M., How multimorbidity and socio-economic factors affect Long COVID: Evidence from European Countries, Eur. J. Public Health, 32, (2022); Hayhoe B.W., Powell R.A., Barber S., Nicholls D., Impact of COVID-19 on individuals with multimorbidity in primary care, Br. J. Gen. Pract, 72, pp. 38-39, (2021); Notarte K.I., Catahay J.A., Velasco J.V., Pastrana A., Ver A.T., Pangilinan F.C., Peligro P.J., Casimiro M., Guerrero J.J., Gellaco M.M.L., Et al., Impact of COVID-19 vaccination on the risk of developing long-COVID and on existing long-COVID symptoms: A systematic review, eClinicalMedicine, 53, (2022)","R. Kessler; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany; email: rkesslerx@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85160570479"
"Hallinan C.M.; Habibabadi S.K.; Conway M.; Ann Bonomo Y.","Hallinan, Christine Mary (14123155400); Habibabadi, Sedigheh Khademi (59016411900); Conway, Mike (7103044028); Ann Bonomo, Yvonne (6603093250)","14123155400; 59016411900; 7103044028; 6603093250","Social media discourse and internet search queries on cannabis as a medicine: A systematic scoping review","2023","PLoS ONE","18","1 January","e0269143","","","","9","10.1371/journal.pone.0269143","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146673103&doi=10.1371%2fjournal.pone.0269143&partnerID=40&md5=35de90929a6d39a617bbbec5f26efa97","Faculty of Medicine, Department of General Practice, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Faculty of Medicine, Department of General Practice, Health & Biomedical Research Information Technology Unit (HaBIC R2), Melbourne Medical School, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Centre for Digital Transformation of Health, Victorian Comprehensive Cancer Centre, The University of Melbourne, Melbourne, VIC, Australia; St Vincent's Health, Department of Addiction Medicine, Melbourne, VIC, Australia; Faculty of Medicine, St Vincent's Clinical School, Melbourne Medical School, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia","Hallinan C.M., Faculty of Medicine, Department of General Practice, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia, Faculty of Medicine, Department of General Practice, Health & Biomedical Research Information Technology Unit (HaBIC R2), Melbourne Medical School, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Habibabadi S.K., Faculty of Medicine, Department of General Practice, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Conway M., Centre for Digital Transformation of Health, Victorian Comprehensive Cancer Centre, The University of Melbourne, Melbourne, VIC, Australia; Ann Bonomo Y., St Vincent's Health, Department of Addiction Medicine, Melbourne, VIC, Australia, Faculty of Medicine, St Vincent's Clinical School, Melbourne Medical School, Dentistry & Health Sciences, The University of Melbourne, Melbourne, VIC, Australia","The use of cannabis for medicinal purposes has increased globally over the past decade since patient access to medicinal cannabis has been legislated across jurisdictions in Europe, the United Kingdom, the United States, Canada, and Australia. Yet, evidence relating to the effect of medical cannabis on the management of symptoms for a suite of conditions is only just emerging. Although there is considerable engagement from many stakeholders to add to the evidence base through randomized controlled trials, many gaps in the literature remain. Data from real-world and patient reported sources can provide opportunities to address this evidence deficit. This real-world data can be captured from a variety of sources such as found in routinely collected health care and health services records that include but are not limited to patient generated data from medical, administrative and claims data, patient reported data from surveys, wearable trackers, patient registries, and social media. In this systematic scoping review, we seek to understand the utility of online user generated text into the use of cannabis as a medicine. In this scoping review, we aimed to systematically search published literature to examine the extent, range, and nature of research that utilises user-generated content to examine to cannabis as a medicine. The objective of this methodological review is to synthesise primary research that uses social media discourse and internet search engine queries to answer the following questions: (i) In what way, is online user-generated text used as a data source in the investigation of cannabis as a medicine? (ii) What are the aims, data sources, methods, and research themes of studies using online user-generated text to discuss the medicinal use of cannabis. We conducted a manual search of primary research studies which used online user-generated text as a data source using the MEDLINE, Embase, Web of Science, and Scopus databases in October 2022. Editorials, letters, commentaries, surveys, protocols, and book chapters were excluded from the review. Forty-two studies were included in this review, twenty-two studies used manually labelled data, four studies used existing meta-data (Google trends/geo-location data), two studies used data that was manually coded using crowdsourcing services, and two used automated coding supplied by a social media analytics company, fifteen used computational methods for annotating data. Our review reflects a growing interest in the use of user-generated content for public health surveillance. It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. This systematic scoping review has shown that user-generated content as a data source for studying cannabis as a medicine provides another means to understand how cannabis is perceived and used in the community. As such, it provides another potential 'tool' with which to engage in pharmacovigilance of, not only cannabis as a medicine, but also other novel therapeutics as they enter the market. © 2023 Hallinan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Cannabis; Delivery of Health Care; Humans; Medicine; Social Media; United Kingdom; cannabidiol; cannabinoid; cannabis; Article; asthma; attention deficit hyperactivity disorder; autism; computer model; construct validity; Embase; glaucoma; headache; health service; human; Internet; machine learning; Medline; mental disease; migraine; opiate addiction; posttraumatic stress disorder; Preferred Reporting Items for Systematic Reviews and Meta-Analyses; prevalence; public health surveillance; Scopus; search engine; sentiment analysis; sleep disorder; social media; systematic review; Web of Science; health care delivery; medicine; United Kingdom","","cannabidiol, 13956-29-1; cannabis, 8001-45-4, 8063-14-7","","","Australian Centre for Cannabinoid Clinical and Research Excellence; ACRE; National Health and Medical Research Council, NHMRC, (CRE APP1135054, 1135054); China Scholarship Council, CSC, (NHMRCCREAPP1135054)","Funding text 1: This systematic scoping review was supported by the Australian Centre for Cannabinoid Clinical and Research Excellence (ACRE), funded by the National Health and Medical Research Council (NHMRC) through the Centre of Research Excellence scheme (NHMRC CRE APP1135054) The funders had no role in study design, data; Funding text 2: Funding:Thissystematicscopingreviewwas supportedbytheAustralianCentreforCannabinoid ClinicalandResearchExcellence(ACRE),funded bytheNationalHealthandMedicalResearch Council(NHMRC)throughtheCentreofResearch Excellencescheme(NHMRCCREAPP1135054) Thefundershadnoroleinstudydesign,data","Russo EB., History of cannabis and its preparations in saga, science, and sobriquet, Chemistry and Biodiversity, 4, 8, pp. 1614-1648, (2007); Bridgeman MB, Abazia DT., Medicinal cannabis: history, pharmacology, and implications for the acute care setting, Pharmacy and therapeutics, 42, 3, (2017); Grinspoon L, Bakalar JB., Marihuana: the forbidden medicine, (1993); Kalant H., Medicinal use of cannabis: history and current status, Pain Research and Management, 6, 2, pp. 80-91, (2001); Taylor S., Medicalizing cannabis-Science, medicine and policy, 1950-2004: An overview of a work in progress, Drugs: Education, Prevention and Policy, 15, 5, pp. 462-474, (2008); Musto DF., The marihuana tax act of 1937, Archives of General Psychiatry, 26, 2, pp. 101-108, (1972); Cannabis policy: status and recent developments Lisbon, (2021); Shover CL, Humphreys K., Six policy lessons relevant to cannabis legalization, Am J Drug Alcohol Abuse, 45, 6, pp. 698-706, (2019); Home Office Circular 1 November 2018: Rescheduling of cannabis-based products for medicinal use in humans, (2018); The health effects of cannabis and cannabinoids: the current state of evidence and recommendations for research, (2017); Hall W., Medical use of cannabis and cannabinoids: questions and answers for policymaking, (2018); Aguilar S, Gutierrez V, Sanchez L, Nougier M., Medicinal cannabis policies and practices around the world, International Drug Policy Consortium, (2018); Access to medicinal cannabis products Canberra, Australia: Therapeutic Goods Administration2021; The health effects of cannabis and cannabinoids: the current state of evidence and recommendations for research, (2017); Pearlson G., Medical marijuana and clinical trials, Weed Science: Cannabis Controversies and Challenges Academic Press, pp. 243-260, (2020); Sarris J, Sinclair J, Karamacoska D, Davidson M, Firth J., Medicinal cannabis for psychiatric disorders: A clinically-focused systematic review, BMC Psychiatry, 20, 1, pp. 1-14, (2020); Suraev AS, Marshall NS, Vandrey R, McCartney D, Benson MJ, McGregor IS, Et al., Cannabinoid therapies in the management of sleep disorders: A systematic review of preclinical and clinical studies, Sleep Medicine Reviews, 53, (2020); Whiting PF, Wolff RF, Deshpande S, Di Nisio M, Duffy S, Hernandez AV, Et al., Cannabinoids for medical use: A systematic review and meta-analysis, JAMA-Journal of the American Medical Association, 313, 24, pp. 2456-2473, (2015); Schulze-Schiappacasse C, Duran J, Bravo-Jeria R, Verdugo-Paiva F, Morel M, Rada G., Are Cannabis, Cannabis-Derived Products, and Synthetic Cannabinoids a Therapeutic Tool for Rheumatoid Arthritis? A Friendly Summary of the Body of Evidence, JCR: Journal of Clinical Rheumatology, pp. 1-5, (2021); Bonomo Y, Souza JDS, Jackson A, Crippa JAS, Solowij N., Clinical issues in cannabis use, British Journal of Clinical Pharmacology, 84, 11, pp. 2495-2498, (2018); Owens B., The professionalization of cannabis growing, Nature, 572, 7771, (2019); Hallinan CM, Bonomo YA., The Rise and Rise of Medicinal Cannabis, What Now? 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Hallinan; Faculty of Medicine, Department of General Practice, Dentistry & Health Sciences, The University of Melbourne, Melbourne, Australia; email: hallinan@unimelb.edu.au","","Public Library of Science","","","","","","19326203","","POLNC","36662832","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85146673103"
"Makrinioti H.; Fainardi V.; Bonnelykke K.; Custovic A.; Cicutto L.; Coleman C.; Eiwegger T.; Kuehni C.; Moeller A.; Pedersen E.; Pijnenburg M.; Pinnock H.; Ranganathan S.; Tonia T.; Subbarao P.; Saglani S.","Makrinioti, Heidi (54785912100); Fainardi, Valentina (24491509000); Bonnelykke, Klaus (57202732511); Custovic, Adnan (57226203185); Cicutto, Lisa (6701381827); Coleman, Courtney (57194194103); Eiwegger, Thomas (8068873400); Kuehni, Claudia (57216109291); Moeller, Alexander (7006926256); Pedersen, Eva (57192213128); Pijnenburg, Marielle (6603923338); Pinnock, Hilary (6701815935); Ranganathan, Sarath (7102368342); Tonia, Thomy (8665754600); Subbarao, Padmaja (57373689300); Saglani, Sejal (6603099922)","54785912100; 24491509000; 57202732511; 57226203185; 6701381827; 57194194103; 8068873400; 57216109291; 7006926256; 57192213128; 6603923338; 6701815935; 7102368342; 8665754600; 57373689300; 6603099922","European Respiratory Society statement on preschool wheezing disorders: updated definitions, knowledge gaps and proposed future research directions","2024","European Respiratory Journal","64","3","2400624","","","","12","10.1183/13993003.00624-2024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203292177&doi=10.1183%2f13993003.00624-2024&partnerID=40&md5=6318d4fd371afaf7a1193a11bed4ea54","Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Medicine and Surgery, Paediatric Clinic, University of Parma, Parma, Italy; Department of Pediatrics, University of Copenhagen, Copenhagen, Denmark; National Heart and Lung Institute, Imperial College London, Imperial NIHR Biomedical Research Centre, Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom; Community Research Department, National Jewish Health, University of Colorado, Denver, CO, United States; Patient Involvement and Engagement, European Lung Foundation, Sheffield, United Kingdom; Department of Pediatric and Adolescent Medicine, University Hospital St Pölten, St Pölten, Austria; Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria; Translational Medicine Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada; Department of Immunology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Institute of Social and Preventive Medicine, Bern, Switzerland; Department of Respiratory Medicine, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus University Medical Center, Rotterdam, Netherlands; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Murdoch Childrens Research Institute, Melbourne, Australia; SickKids Research Institute, Toronto, ON, Canada","Makrinioti H., Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Fainardi V., Department of Medicine and Surgery, Paediatric Clinic, University of Parma, Parma, Italy; Bonnelykke K., Department of Pediatrics, University of Copenhagen, Copenhagen, Denmark; Custovic A., National Heart and Lung Institute, Imperial College London, Imperial NIHR Biomedical Research Centre, Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom; Cicutto L., Community Research Department, National Jewish Health, University of Colorado, Denver, CO, United States; Coleman C., Patient Involvement and Engagement, European Lung Foundation, Sheffield, United Kingdom; Eiwegger T., Department of Pediatric and Adolescent Medicine, University Hospital St Pölten, St Pölten, Austria, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria, Translational Medicine Program, Research Institute, Hospital for Sick Children, Toronto, ON, Canada, Department of Immunology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Kuehni C., Institute of Social and Preventive Medicine, Bern, Switzerland; Moeller A., Department of Respiratory Medicine, University Children’s Hospital Zurich, University of Zurich, Zurich, Switzerland; Pedersen E., Institute of Social and Preventive Medicine, Bern, Switzerland; Pijnenburg M., Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus University Medical Center, Rotterdam, Netherlands; Pinnock H., Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Ranganathan S., Murdoch Childrens Research Institute, Melbourne, Australia; Tonia T., Institute of Social and Preventive Medicine, Bern, Switzerland; Subbarao P., SickKids Research Institute, Toronto, ON, Canada; Saglani S., National Heart and Lung Institute, Imperial College London, Imperial NIHR Biomedical Research Centre, Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom","Since the publication of the European Respiratory Society (ERS) task force reports on the management of preschool wheezing in 2008 and 2014, a large body of evidence has accumulated suggesting that the clinical phenotypes that were proposed (episodic (viral) wheezing and multiple-trigger wheezing) do not relate to underlying airway pathology and may not help determine response to treatment. Specifically, using clinical phenotypes alone may no longer be appropriate, and new approaches that can be used to inform clinical care are needed for future research. This ERS task force reviewed the literature published after 2008 related to preschool wheezing and has suggested that the criteria used to define wheezing disorders in preschool children should include age of diagnosis (0 to <6 years), confirmation of wheezing on at least one occasion, and more than one episode of wheezing ever. Furthermore, diagnosis and management may be improved by identifying treatable traits, including inflammatory biomarkers (blood eosinophils, aeroallergen sensitisation) associated with type-2 immunity and differential response to inhaled corticosteroids, lung function parameters and airway infection. However, more comprehensive use of biomarkers/treatable traits in predicting the response to treatment requires prospective validation. There is evidence that specific genetic traits may help guide management, but these must be adequately tested. In addition, the task force identified an absence of caregiver-reported outcomes, caregiver/self-management options and features that should prompt specialist referral for this age group. Priorities for future research include a focus on identifying 1) mechanisms driving preschool wheezing; 2) biomarkers of treatable traits and efficacy of interventions in those without allergic sensitisation/eosinophilia; 3) the need to include both objective outcomes and caregiver-reported outcomes in clinical trials; 4) the need for a suitable action plan for children with preschool wheezing; and 5) a definition of severe/difficult-to-treat preschool wheezing. ©The authors 2024.","","Advisory Committees; Asthma; Biomarkers; Child; Child, Preschool; Europe; Humans; Infant; Infant, Newborn; Phenotype; Pulmonary Medicine; Respiratory Function Tests; Respiratory Sounds; Societies, Medical; antibiotic agent; biological marker; bronchodilating agent; corticosteroid; eosinophil cationic protein; hydrocortisone; immunoglobulin E; influenza vaccine; interleukin 5; macrolide; myeloid differentiation factor 88; nitric oxide; Pneumococcus vaccine; thymic stromal lymphopoietin; transcription factor RUNX2; vasculotropin; airway obstruction; Article; artificial intelligence; bone density; breathing rate; bronchodilatation; cardiometabolic risk; caregiver; cell differentiation; chest tightness; child; chronic obstructive lung disease; clinical decision making; dyspnea; eosinophil count; eosinophilia; forced expiratory volume; forced vital capacity; fractional exhaled nitric oxide; gene expression; genetic susceptibility; Haemophilus influenzae; health care system; hospital admission; human; innate immunity; knowledge gap; lung function; maternal nutrition; nasopharyngeal aspiration; noninvasive ventilation; peak expiratory flow; pregnancy; preschool child; prevalence; quality of life; questionnaire; respiratory tract allergy; respiratory tract infection; respiratory tract inflammation; self care; single nucleotide polymorphism; spirometry; Staphylococcus aureus; Th2 cell; wheezing; wound healing; abnormal respiratory sound; advisory committee; asthma; blood; diagnosis; Europe; infant; lung function test; medical society; newborn; phenotype; pulmonology","","hydrocortisone, 50-23-7; immunoglobulin E, 37341-29-0; nitric oxide, 10102-43-9; vasculotropin, 127464-60-2; Biomarkers, ","","","NIHR Imperial Biomedical Research Centre, BRC","Acknowledgements: Infrastructure support for this research was provided by the NIHR Imperial Biomedical Research Centre.","Brand PL, Baraldi E, Bisgaard H, Et al., Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach, Eur Respir J, 32, pp. 1096-1110, (2008); Brand PL, Caudri D, Eber E, Et al., Classification and pharmacological treatment of preschool wheezing: changes since 2008, Eur Respir J, 43, pp. 1172-1177, (2014); Spycher BD, Cochrane C, Granell R, Et al., Temporal stability of multitrigger and episodic viral wheeze in early childhood, Eur Respir J, 50, (2017); van Wonderen KE, Geskus RB, van Aalderen WM, Et al., Stability and predictiveness of multiple trigger and episodic viral wheeze in preschoolers, Clin Exp Allergy, 46, pp. 837-847, (2016); Schultz A, Devadason SG, Savenije OE, Et al., The transient value of classifying preschool wheeze into episodic viral wheeze and multiple trigger wheeze, Acta Paediatr, 99, pp. 56-60, (2010); Robinson PFM, Fontanella S, Ananth S, Et al., Recurrent severe preschool wheeze: from prespecified diagnostic labels to underlying endotypes, Am J Respir Crit Care Med, 204, pp. 523-535, (2021); Patel SP, Jarvelin MR, Little MP., Systematic review of worldwide variations of the prevalence of wheezing symptoms in children, Environ Health, 7, (2008); Martinez FD, Wright AL, Taussig LM, Et al., Asthma and wheezing in the first six years of life. 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Beydon N, Nguyen TT, Amsallem F, Et al., Interrupter resistance to measure dose-response to salbutamol in wheezy preschool children, Pediatr Pulmonol, 53, pp. 1252-1259, (2018); Safavi S, Dai R, Breton VL, Et al., Lung clearance index predicts persistence of preschool wheeze, Pediatr Allergy Immunol, 33, (2022); Castro-Rodriguez JA, Beckhaus AA, Forno E., Efficacy of oral corticosteroids in the treatment of acute wheezing episodes in asthmatic preschoolers: systematic review with meta-analysis, Pediatr Pulmonol, 51, pp. 868-876, (2016); Panickar J, Lakhanpaul M, Lambert PC, Et al., Oral prednisolone for preschool children with acute virus-induced wheezing, N Engl J Med, 360, pp. 329-338, (2009); Lezmi G, Deschildre A, Abou Taam R, Et al., Remodelling and inflammation in preschoolers with severe recurrent wheeze and asthma outcome at school age, Clin Exp Allergy, 48, pp. 806-813, (2018); Saglani S, Malmstrom K, Pelkonen AS, Et al., Airway remodeling and inflammation in symptomatic infants with reversible airflow obstruction, Am J Respir Crit Care Med, 171, pp. 722-727, (2005); Saglani S, Payne DN, Zhu J, Et al., Early detection of airway wall remodeling and eosinophilic inflammation in preschool wheezers, Am J Respir Crit Care Med, 176, pp. 858-864, (2007); Oommen A, Patel R, Browning M, Et al., Systemic neutrophil activation in acute preschool viral wheeze, Arch Dis Child, 88, pp. 529-531, (2003); Guiddir T, Saint-Pierre P, Purenne-Denis E, Et al., Neutrophilic steroid-refractory recurrent wheeze and eosinophilic steroid-refractory asthma in children, J Allergy Clin Immunol Pract, 5, pp. 1351-1361, (2017); Chung HL, Lee EJ, Park HJ, Et al., Increased epidermal growth factor in nasopharyngeal aspirates from infants with recurrent wheeze, Pediatr Pulmonol, 50, pp. 841-847, (2015); Carlsson CJ, Rasmussen MA, Pedersen SB, Et al., Airway immune mediator levels during asthma-like symptoms in young children and their possible role in response to azithromycin, Allergy, 76, pp. 1754-1764, (2021); Turato G, Barbato A, Baraldo S, Et al., Nonatopic children with multitrigger wheezing have airway pathology comparable to atopic asthma, Am J Respir Crit Care Med, 178, pp. 476-482, (2008); Iosifidis T, Sutanto EN, Buckley AG, Et al., Aberrant cell migration contributes to defective airway epithelial repair in childhood wheeze, JCI Insight, 5, (2020); Ahdieh M, Vandenbos T, Youakim A., Lung epithelial barrier function and wound healing are decreased by IL-4 and IL-13 and enhanced by IFN-γ, Am J Physiol Cell Physiol, 281, pp. C2029-C2038, (2001); Fayon M, Beaufils F, Esteves P, Et al., Bronchial remodeling-based latent class analysis predicts exacerbations in severe preschool wheezers, Am J Respir Crit Care Med, 207, pp. 416-426, (2023); Coverstone AM, Wilson B, Burgdorf D, Et al., Recurrent wheezing in children following human metapneumovirus infection, J Allergy Clin Immunol, 142, pp. 297-301, (2018); Cuthbertson L, Oo SWC, Cox MJ, Et al., Viral respiratory infections and the oropharyngeal bacterial microbiota in acutely wheezing children, PloS One, 14, (2019); Lemanske RF, Jackson DJ, Gangnon RE, Et al., Rhinovirus illnesses during infancy predict subsequent childhood wheezing, J Allergy Clin Immunol, 116, pp. 571-577, (2005); Jartti T, Gern JE., Role of viral infections in the development and exacerbation of asthma in children, J Allergy Clin Immunol, 140, pp. 895-906, (2017); Bisgaard H, Hermansen MN, Bonnelykke K, Et al., Association of bacteria and viruses with wheezy episodes in young children: prospective birth cohort study, BMJ, 341, (2010); Bonnelykke K, Coleman AT, Evans MD, Et al., Cadherin-related family member 3 genetics and rhinovirus C respiratory illnesses, Am J Respir Crit Care Med, 197, pp. 589-594, (2018); Hasegawa K, Mansbach JM, Ajami NJ, Et al., Association of nasopharyngeal microbiota profiles with bronchiolitis severity in infants hospitalised for bronchiolitis, Eur Respir J, 48, pp. 1329-1339, (2016); Miller EK, Lu X, Erdman DD, Et al., Rhinovirus-associated hospitalizations in young children, J Infect Dis, 195, pp. 773-781, (2007); Saraya T, Kurai D, Ishii H, Et al., Epidemiology of virus-induced asthma exacerbations: with special reference to the role of human rhinovirus, Front Microbiol, 5, (2014); Gu W, Jiang W, Zhang X, Et al., Refractory wheezing in Chinese children under 3 years of age: bronchial inflammation and airway malformation, BMC Pediatr, 16, (2016); Schwerk N, Brinkmann F, Soudah B, Et al., Wheeze in preschool age is associated with pulmonary bacterial infection and resolves after antibiotic therapy, PLoS One, 6, (2011); Caliskan M, Bochkov YA, Kreiner-Moller E, Et al., Rhinovirus wheezing illness and genetic risk of childhood-onset asthma, N Engl J Med, 368, pp. 1398-1407, (2013); Bonnelykke K, Sleiman P, Nielsen K, Et al., A genome-wide association study identifies CDHR3 as a susceptibility locus for early childhood asthma with severe exacerbations, Nat Genet, 46, pp. 51-55, (2014); Bisgaard H, Bonnelykke K, Sleiman PM, Et al., Chromosome 17q21 gene variants are associated with asthma and exacerbations but not atopy in early childhood, Am J Respir Crit Care Med, 179, pp. 179-185, (2009); Bouzigon E, Corda E, Aschard H, Et al., Effect of 17q21 variants and smoking exposure in early-onset asthma, N Engl J Med, 359, pp. 1985-1994, (2008); Loss GJ, Depner M, Hose AJ, Et al., The early development of wheeze. Environmental determinants and genetic susceptibility at 17q21, Am J Respir Crit Care Med, 193, pp. 889-897, (2016); Tutino M, Granell R, Curtin JA, Et al., Dog ownership in infancy is protective for persistent wheeze in 17q21 asthma-risk carriers, J Allergy Clin Immunol, 151, pp. 423-430, (2023); van der Valk RJ, Duijts L, Kerkhof M, Et al., Interaction of a 17q12 variant with both fetal and infant smoke exposure in the development of childhood asthma-like symptoms, Allergy, 67, pp. 767-774, (2012)","S. Saglani; National Heart and Lung Institute, Imperial College London, Imperial NIHR Biomedical Research Centre, Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom; email: s.saglani@imperial.ac.uk","","European Respiratory Society","","","","","","09031936","","ERJOE","38843917","English","Eur. Respir. J.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85203292177"
"Chiu W.-T.; Chung C.-C.; Huang C.-H.; Chien Y.-S.; Hsu C.-H.; Wu C.-H.; Wang C.-H.; Chiu H.-W.; Chan L.","Chiu, Wei-Ting (57072553500); Chung, Chen-Chih (56666475300); Huang, Chien-Hua (36063485900); Chien, Yu-san (57216816497); Hsu, Chih-Hsin (16063882300); Wu, Cheng-Hsueh (55548447300); Wang, Chen-Hsu (56026203900); Chiu, Hung-Wen (55649778700); Chan, Lung (35221093600)","57072553500; 56666475300; 36063485900; 57216816497; 16063882300; 55548447300; 56026203900; 55649778700; 35221093600","Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks","2022","Journal of the Formosan Medical Association","121","2","","490","499","9","8","10.1016/j.jfma.2021.07.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111285529&doi=10.1016%2fj.jfma.2021.07.004&partnerID=40&md5=bf3de4e696d6f643a72a5e8402b3f24e","Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan; Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan; Taipei Neuroscience Institute, Taipei Medical University, Taiwan; Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Department of Emergency Medicine, National Taiwan University Medical College and Hospital, Taipei, Taiwan; Cardiovascular Division, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, Taiwan; Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei Branch, Taiwan; Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital Dou Liou Branch, College of Medicine, National Cheng Kung University, Taiwan; Department of Critical Care Medicine, Taipei Veterans General Hospital, National Yang-Ming University, Taipei, Taiwan; Attending Physician, Coronary Care Unit, Cardiovascular Center, Cathay General Hospital, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan","Chiu W.-T., Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan, Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan, Taipei Neuroscience Institute, Taipei Medical University, Taiwan, Division of Critical Care Medicine, Department of Emergency and Critical Care Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan; Chung C.-C., Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan, Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan, Taipei Neuroscience Institute, Taipei Medical University, Taiwan, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Huang C.-H., Department of Emergency Medicine, National Taiwan University Medical College and Hospital, Taipei, Taiwan, Cardiovascular Division, Department of Internal Medicine, National Taiwan University College of Medicine and Hospital, Taiwan; Chien Y.-S., Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei Branch, Taiwan; Hsu C.-H., Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital Dou Liou Branch, College of Medicine, National Cheng Kung University, Taiwan; Wu C.-H., Department of Critical Care Medicine, Taipei Veterans General Hospital, National Yang-Ming University, Taipei, Taiwan; Wang C.-H., Attending Physician, Coronary Care Unit, Cardiovascular Center, Cathay General Hospital, Taipei, Taiwan; Chiu H.-W., Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan; Chan L., Department of Neurology, Shuang Ho Hospital, Taipei Medical University, Taiwan, Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taiwan, Taipei Neuroscience Institute, Taipei Medical University, Taiwan","Background: To identify the outcome-associated predictors and develop predictive models for patients receiving targeted temperature management (TTM) by artificial neural network (ANN). Methods: The derived cohort consisted of 580 patients with cardiac arrest and ROSC treated with TTM between January 2014 and August 2019. We evaluated the predictive value of parameters associated with survival and favorable neurologic outcome. ANN were applied for developing outcome prediction models. The generalizability of the models was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The parameters associated with survival were age, duration of cardiopulmonary resuscitation, history of diabetes mellitus (DM), heart failure, end-stage renal disease (ESRD), systolic blood pressure (BP), diastolic BP, body temperature, motor response after ROSC, emergent coronary angiography or percutaneous coronary intervention (PCI), and the cooling methods. The parameters associated with the favorable neurologic outcomes were age, sex, DM, chronic obstructive pulmonary disease, ESRD, stroke, pre-arrest cerebral-performance category, BP, body temperature, motor response after ROSC, emergent coronary angiography or PCI, and cooling methods. After adequate training, ANN Model 1 to predict survival achieved an AUC of 0.80. Accuracy, sensitivity, and specificity were 75.9%, 71.6%, and 79.3%, respectively. ANN Model 4 to predict the favorable neurologic outcome achieved an AUC of 0.87, with accuracy, sensitivity, and specificity of 86.7%, 77.7%, and 88.0%, respectively. Conclusion: The ANN-based models achieved good performance to predict the survival and favorable neurologic outcomes after TTM. The models proposed have clinical value to assist in decision-making. © 2021","Artificial neural network; Cardiac arrest; Outcome; Prediction; Targeted temperature management","Cardiopulmonary Resuscitation; Heart Arrest; Humans; Hypothermia, Induced; Neural Networks, Computer; Out-of-Hospital Cardiac Arrest; Percutaneous Coronary Intervention; neuromuscular blocking agent; sedative agent; adult; aged; Article; artificial neural network; asthma; body temperature; cerebral performance category scale; cerebrovascular accident; chronic obstructive lung disease; clinical feature; clinical outcome; cohort analysis; controlled study; coronary angiography; cross validation; demography; diabetes mellitus; diastolic blood pressure; electrocardiography; end stage renal disease; female; Glasgow coma scale; heart arrest; heart failure; human; induced hypothermia; machine learning; major clinical study; male; middle aged; out of hospital cardiac arrest; outcome assessment; percutaneous coronary intervention; predictive model; resuscitation; return of spontaneous circulation; sensitivity and specificity; survival; systolic blood pressure; targeted temperature management; treatment outcome; heart arrest; out of hospital cardiac arrest; percutaneous coronary intervention; resuscitation","","","","","","","Koltowski L., Et al., Predicting survival in out-of-hospital cardiac arrest patients undergoing targeted temperature management: The Polish Hypothermia Registry Risk Score. LID, Cardiol J, (2021); Myat A., Song K.J., Rea T., Out-of-hospital cardiac arrest: current concepts, Lancet, 391, 10124, pp. 970-979, (2018); Mozaffarian D., Et al., Heart disease and stroke statistics-2016 update: a report from the American heart association, Circulation, 133, 4, pp. e38-e360, (2016); Stanger D.E., Fordyce C.B., The cost of care for cardiac arrest, (2018); Girotra S., Chan P.S., Bradley S.M., Post-resuscitation care following out-of-hospital and in-hospital cardiac arrest, Heart, 101, 24, pp. 1943-1949, (2015); Binks A., Nolan J.P., Post-cardiac arrest syndrome, Minerva Anestesiol, 76, 5, pp. 362-368, (2010); Mild therapeutic hypothermia to improve the neurologic outcome after cardiac arrest, N Engl J Med, 346, 8, pp. 549-556, (2002); Bernard S.A., Et al., Treatment of comatose survivors of out-of-hospital cardiac arrest with induced hypothermia, N Engl J Med, 346, 8, pp. 557-563, (2002); Song S.S., Lyden P.D., Overview of therapeutic hypothermia, Curr Treat Options Neurol, 14, 6, pp. 541-548, (2012); Chiu W.T., Et al., Post-cardiac arrest care and targeted temperature management: a consensus of scientific statement from the taiwan society of emergency & critical care medicine, taiwan society of critical care medicine and taiwan society of emergency medicine, J Formos Med Assoc, 120, 1, pp. 569-587, (2020); Kim S.I., Et al., Apache II score immediately after cardiac arrest as a predictor of good neurological outcome in out-of-hospital cardiac arrest patients receiving targeted temperature management, Acute Crit Care, 33, 2, pp. 83-88, (2018); Kalra R., Et al., Targeted temperature management after cardiac arrest: systematic review and meta-analyses, Anesth Analg, 126, 3, pp. 867-875, (2018); Amato F., Et al., Artificial neural networks in medical diagnosis, J Appl Biomed, 11, 2, pp. 47-58, (2013); Agatonovic-Kustrin S., Beresford R., Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J Pharmaceut Biomed Anal, 22, 5, pp. 717-727, (2000); Lisboa P.J., A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Netw, (2002); Chung C.C., Et al., Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks, J Neurol Sci, 410, (2020); Chung C.-C., Et al., Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death, Sci Rep, 10, 1, (2020); Chang H.C., Et al., Factors affecting outcomes in patients with cardiac arrest who receive target temperature management: the multi-center TIMECARD registry, J Formos Med Assoc, (2021); Rittenberger J.C., Et al., Association between cerebral performance category, modified rankin scale, and discharge disposition after cardiac arrest, Resuscitation, 82, 8, pp. 1036-1040, (2011); Breiman L., Random Forests, Mach Learn, 45, 1, pp. 5-32, (2001); Lin J.-J., Et al., Targeted temperature management and emergent coronary angiography are associated with improved outcomes in patients with prehospital return of spontaneous circulation, J Formos Med Assoc, 119, 8, pp. 1259-1266, (2020); Chan P.S., Et al., Association between therapeutic hypothermia and survival after in-hospital cardiac arrest, JAMA, 316, 13, pp. 1375-1382, (2016); Andersen L.W., Et al., In-hospital cardiac arrest: a review, JAMA, 321, 12, pp. 1200-1210, (2019); Laver S., Et al., Mode of death after admission to an intensive care unit following cardiac arrest, Intensive Care Med, 30, 11, pp. 2126-2128, (2004); Arrich J., Et al., Hypothermia for neuroprotection in adults after cardiopulmonary resuscitation, Cochrane Database Syst Rev, 2, (2016); Schenone A.L., Et al., Therapeutic hypothermia after cardiac arrest: a systematic review/meta-analysis exploring the impact of expanded criteria and targeted temperature, Resuscitation, (2016); Soar J., Et al., Part 4: advanced life support: 2015 International consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations, Resuscitation, 95, pp. e71-e120, (2015); Panchal A.R., Et al., 2019 American heart association focused update on advanced cardiovascular life support: use of advanced airways, vasopressors, and extracorporeal cardiopulmonary resuscitation during cardiac arrest: an update to the American heart association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care, Circulation, 140, 24, pp. e881-e894, (2019); Nolan J.P., Et al., European resuscitation council and European society of intensive care medicine guidelines for post-resuscitation care 2015: section 5 of the European resuscitation council guidelines for resuscitation 2015, Resuscitation, 95, pp. 202-222, (2015); Chan P.S., Et al., A validated prediction tool for initial survivors of in-hospital cardiac arrest, Arch Intern Med, 172, 12, pp. 947-953, (2012); Adrie C., Et al., Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score, Eur Heart J, 27, 23, pp. 2840-2845, (2006); Aschauer S., Et al., A prediction tool for initial out-of-hospital cardiac arrest survivors, Resuscitation, 85, 9, pp. 1225-1231, (2014); Rossetti A.O., Et al., Prognostication after cardiac arrest and hypothermia: a prospective study, Ann Neurol, 67, 3, pp. 301-307, (2010); Greer D., Et al., Clinical MRI interpretation for outcome prediction in cardiac arrest, Neurocritical Care, 17, 2, pp. 240-244, (2012); Jiang F., Et al., Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, 4, pp. 230-243, (2017); Su P.I., Et al., Improvement of consciousness before initiating targeted temperature management, Resuscitation, 148, pp. 83-89, (2020); Chung C.C., Et al., Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks, J Neurol Sci, 425, (2021)","L. Chan; Department of Neurology, Shuang Ho Hospital, New Taipei City, No. 291, Zhongzheng Rd., Zhonghe District, 23561, Taiwan; email: 12566@s.tmu.edu.tw","","Elsevier B.V.","","","","","","09296646","","JFASE","34330620","English","J. Formos. Med. Assoc.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85111285529"
"Kong W.; Wu Q.; Chen Y.; Ren Y.; Wang W.; Zheng R.; Deng H.; Yuan T.; Qiu H.; Wang X.; Luo X.; Huang X.; Yang Q.; Zhang G.; Zhang Y.","Kong, Weifeng (57204705507); Wu, Qingwu (57220847367); Chen, Yubin (52463298900); Ren, Yong (57214465775); Wang, Weihao (57211414216); Zheng, Rui (56009276900); Deng, Huiyi (57203397488); Yuan, Tian (57204707992); Qiu, Huijun (57214657095); Wang, Xinyue (58500561600); Luo, Xin (57464877200); Huang, Xuekun (37115514700); Yang, Qintai (9745178600); Zhang, Gehua (7405273940); Zhang, Yana (55265881900)","57204705507; 57220847367; 52463298900; 57214465775; 57211414216; 56009276900; 57203397488; 57204707992; 57214657095; 58500561600; 57464877200; 37115514700; 9745178600; 7405273940; 55265881900","Chinese Central Compartment Atopic Disease: The Clinical Characteristics and Cellular Endotypes Based on Whole-Slide Imaging","2022","Journal of Asthma and Allergy","15","","","341","352","11","9","10.2147/JAA.S350837","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127193974&doi=10.2147%2fJAA.S350837&partnerID=40&md5=8045541a7ea69db95ee0337f8f2f66b1","Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, the Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China","Kong W., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Wu Q., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Chen Y., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Ren Y., Guangdong Provincial Key Laboratory of Digestive Cancer Research, the Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China; Wang W., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Zheng R., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Deng H., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Yuan T., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Qiu H., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Wang X., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Luo X., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Huang X., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Yang Q., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Zhang G., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; Zhang Y., Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China","Purpose: Histopathologic characterizations of central compartment atopic disease (CCAD) by whole-slide imaging remains lacking. We aim to study clinical presentations and cellular endotyping diagnosis of Chinese CCAD using artificial intelligence (AI). Methods: A total of 72 patients diagnosed with chronic rhinosinusitis with nasal polyps (CRSwNP) were enrolled. CCAD was defined by positive result of serology specific IgE, endoscopic and radiological findings. The aeroallergen sensitization status, endoscopic results, radiological findings, and symptoms were evaluated and compared between patients with CCAD (n=14), eosinophilic CRSwNP (ENP, n=32) and non-eosinophilic CRSwNP (NENP, n=26). The cellular endotypes including eosinophils, neutrophils, lymphocytes, and plasma cells were analyzed by the AI chronic rhinosinusitis evaluation platform 2.0. Results: CCAD was most common in male (71.43%). The positive rate of aeroallergen in patients with CCAD is 100%, which is much higher than those in patients with ENP (40.63%) and NENP (23.08%). Allergic rhinitis incidence was found to be 57.14% in Chinese CCAD subjects, which is obviously higher when compared with those in patients with ENP (21.88%) or NENP (0.00%). The presence of asthma was not significantly different between groups. Chinese CCAD population demonstrated mild symptoms and lower endoscopic and radiological scores than those in patients with ENP and NENP. For cellular endotypes in CCAD subjects, the median of eosinophils, neutrophils, lymphocytes, and plasma cells was 26.55%, 0.49%, 60.85%, and 7.33%, respectively. The proportion of eosinophils in nasal tissue and peripheral blood mononuclear cells from the CCAD group is between the proportions in those patients with ENP and NENP. Conclusion: Chinese CCAD was associated with aeroallergen sensitivity, and displayed an eosinophil-dominant inflammatory pattern. Thus, proper management with allergy control and topical steroids could be recommended for CCAD treatment. © 2022Kong et al. This workis published and licensed byDove Medical Press Limited.","aeroallergen; central compartment atopic disease; chronic rhinosinusitis with nasal polyps; deep learning; eosinophil","immunoglobulin E; adult; allergic rhinitis; allergy test; Article; artificial intelligence; asthma; atopy; blood; Chinese; chronic rhinosinusitis; computer assisted tomography; endoscopy; eosinophil; histology; histopathology; human; human tissue; incidence; lymphocyte; major clinical study; male; neutrophil; nose polyp; phenotype; plasma cell; qualitative analysis; radiology; serology; smoking","","immunoglobulin E, 37341-29-0","","","Guangdong Provincial Key Laboratory of Digestive Cancer Research, (2021B1212040006); National Natural Science Foundation of China, NSFC, (81870704, 82000958, 82171114, U20A20399); National Natural Science Foundation of China, NSFC; Sun Yat-sen University, SYSU, (2019006); Sun Yat-sen University, SYSU; Natural Science Foundation of Guangdong Province, (2021A1515011764); Natural Science Foundation of Guangdong Province; Special Project for Research and Development in Key areas of Guangdong Province, (2020B0101130015); Special Project for Research and Development in Key areas of Guangdong Province","This work was supported by the National Natural Science Foundation of China (No. 82171114, 82000958, U20A20399 and 81870704), the Key-area Research and Development Program of Guangdong Province (No. 2020B0101130015), the Guangdong Provincial Key Laboratory of Digestive Cancer Research (No. 2021B1212040006), Sun Yat-sen University Clinical Research 5010 Program (No. 2019006) and The Natural Science Foundation of Guangdong Province (No. 2021A1515011764).","Cao PP, Li HB, Wang BF, Et al., Distinct immunopathologic characteristics of various types of chronic rhinosinusitis in adult Chinese, J Allergy Clin Immunol, 124, 3, pp. 478-484, (2009); DelGaudio JM, Loftus PA, Hamizan AW, Harvey RJ, Wise SK., Central compartment atopic disease, Am J Rhinol Allergy, 31, 4, pp. 228-234, (2017); DelGaudio JM., Central compartment atopic disease: the missing link in the allergy and chronic rhinosinusitis with nasal polyps saga, Int Forum Allergy Rhinol, 10, 10, pp. 1191-1192, (2020); 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McHugh T, Snidvongs K, Xie M, Banglawala S, Sommer D., High tissue eosinophilia as a marker to predict recurrence for eosinophilic chronic rhinosinusitis: a systematic review and meta-analysis, Int Forum Allergy Rhinol, 8, 12, pp. 1421-1429, (2018); Nakayama T, Sugimoto N, Okada N, Et al., JESREC score and mucosal eosinophilia can predict endotypes of chronic rhinosinusitis with nasal polyps, Auris Nasus Larynx, 46, 3, pp. 374-383, (2019); Lou H, Meng Y, Piao Y, Et al., Cellular phenotyping of chronic rhinosinusitis with nasal polyps, Rhinology, 54, 2, pp. 150-159, (2016); Ahn SH, Lee EJ, Ha JG, Et al., Comparison of olfactory and taste functions between eosinophilic and non-eosinophilic chronic rhinosinusitis, Auris Nasus Larynx, 47, 5, pp. 820-827, (2020); Liang Z, Yan B, Liu C, Tan R, Wang C, Zhang L., Predictive significance of arachidonate 15-lipoxygenase for eosinophilic chronic rhinosinusitis with nasal polyps, Allergy Asthma Clin Immunol, 16, (2020); Kim DK, Kim JY, Han YE, Et al., Elastase-Positive Neutrophils Are Associated With Refractoriness of Chronic Rhinosinusitis With Nasal Polyps in an Asian Population, Allergy Asthma Immunol Res, 12, 1, pp. 42-55, (2020); De Corso E, Settimi S, Tricarico L, Et al., Predictors of Disease Control After Endoscopic Sinus Surgery Plus Long-Term Local Corticosteroids in CRSwNP, Am J Rhinol Allergy, 35, 1, pp. 77-85, (2021); Kim DK, Lim HS, Eun KM, Et al., Subepithelial neutrophil infiltration as a predictor of the surgical outcome of chronic rhinosinusitis with nasal polyps, Rhinology, 59, 2, pp. 173-180, (2021); Bousquet J, Lockey R, Malling HJ., Allergen immunotherapy: therapeutic vaccines for allergic diseases. 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Zhang; Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; email: zhanggeh@mail.sysu.edu.cn; Y. Zhang; Department of Otolaryngology-Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China; email: zhangyn95@mail.sysu.edu.cn","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127193974"
"McDowell A.; Kang J.; Yang J.; Jung J.; Oh Y.-M.; Kym S.-M.; Shin T.-S.; Kim T.-B.; Jee Y.-K.; Kim Y.-K.","McDowell, Andrea (57204660167); Kang, Juwon (57913964800); Yang, Jinho (57205889547); Jung, Jihee (57913964900); Oh, Yeon-Mok (7402125922); Kym, Sung-Min (57190249541); Shin, Tae-Seop (35277671400); Kim, Tae-Bum (57206927697); Jee, Young-Koo (7005104108); Kim, Yoon-Keun (7410211755)","57204660167; 57913964800; 57205889547; 57913964900; 7402125922; 57190249541; 35277671400; 57206927697; 7005104108; 7410211755","Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects","2022","Experimental and Molecular Medicine","54","9","","1586","1595","9","9","10.1038/s12276-022-00846-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139133400&doi=10.1038%2fs12276-022-00846-5&partnerID=40&md5=d0612fde32ae250c9d823f4597cb15ca","Institute of MD Healthcare, Inc, Seoul, South Korea; Department of Pulmonary and Critical Care Medicine, and Clinical Research Center for Chronic Obstructive Airway Disease, Asan Medical Center, Seoul, South Korea; Department of Internal Medicine, Inje University Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea; Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Internal Medicine, Dankook University College of Medicine, Cheonan, South Korea","McDowell A., Institute of MD Healthcare, Inc, Seoul, South Korea; Kang J., Institute of MD Healthcare, Inc, Seoul, South Korea; Yang J., Institute of MD Healthcare, Inc, Seoul, South Korea; Jung J., Institute of MD Healthcare, Inc, Seoul, South Korea; Oh Y.-M., Department of Pulmonary and Critical Care Medicine, and Clinical Research Center for Chronic Obstructive Airway Disease, Asan Medical Center, Seoul, South Korea; Kym S.-M., Department of Internal Medicine, Inje University Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea; Shin T.-S., Institute of MD Healthcare, Inc, Seoul, South Korea; Kim T.-B., Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Jee Y.-K., Department of Internal Medicine, Dankook University College of Medicine, Cheonan, South Korea; Kim Y.-K., Institute of MD Healthcare, Inc, Seoul, South Korea","Although mounting evidence suggests that the microbiome has a tremendous influence on intractable disease, the relationship between circulating microbial extracellular vesicles (EVs) and respiratory disease remains unexplored. Here, we developed predictive diagnostic models for COPD, asthma, and lung cancer by applying machine learning to microbial EV metagenomes isolated from patient serum and coded by their accumulated taxonomic hierarchy. All models demonstrated high predictive strength with mean AUC values ranging from 0.93 to 0.99 with various important features at the genus and phylum levels. Application of the clinical models in mice showed that various foods reduced high-fat diet-associated asthma and lung cancer risk, while COPD was minimally affected. In conclusion, this study offers a novel methodology for respiratory disease prediction and highlights the utility of serum microbial EVs as data-rich features for noninvasive diagnosis. © 2022, The Author(s).","","Algorithms; Animals; Asthma; Extracellular Vesicles; Lung Neoplasms; Machine Learning; Mice; Pulmonary Disease, Chronic Obstructive; Risk Assessment; Acidobacterium; Acinetobacter; Actinobacteria; adult; aged; Akkermansia; animal experiment; animal model; Armatimonadetes; Article; artificial intelligence; artificial neural network; asthma; bacterium; Bacteroidetes; Bifidobacterium; Blautia; cancer risk; Chloroflexi; chronic obstructive lung disease; Collinsella; controlled study; Corynebacterium; Cutibacterium; cyanobacterium; data analysis; Deferribacteres; Deinococcus; diagnostic accuracy; dietary supplement; Epsilonbacteraeota; Escherichia; Euryarchaeota; exosome; Faecalibacterium; feature selection; female; Firmicutes; Fusobacterium; Gemmatimonadetes; generalized linear model; gradient boosting machine; human; Klebsiella; Lactobacillus; lipid diet; lung cancer; machine learning; major clinical study; male; metagenome; microbiome; Mollicutes; mouse; non invasive procedure; nonhuman; nutritional assessment; Patescibacterium; Planctomycetes; prediction; predictive model; Proteobacteria; Pseudomonas; receiver operating characteristic; respiratory tract disease; respiratory tract disease assessment; Rhodococcus; risk assessment; Ruminococcaceae; serum; Shigella; species composition; species richness; Sphingomonas; spirochete; Staphylococcus; Stenotrophomonas; Streptococcus; Subdoligranulum; Verrucomicrobia; algorithm; animal; asthma; chronic obstructive lung disease; lung tumor; machine learning; risk assessment","","","","","Korean Ministry of Health & Welfare, (20013712, HR16C0001); NRF-2019M3E5D3073365; NRF-2019M3E5D3073372; Ministry of Trade, Industry and Energy, MOTIE; Ministry of Science, ICT and Future Planning, MSIP; Korea Health Industry Development Institute, KHIDI; National Research Foundation of Korea, NRF","This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2019M3E5D3073372 and NRF-2019M3E5D3073365), the Korean Health Technology R&D Project Grant of the Korean Health Industry Development Institute (KHIDI) funded by the Korean Ministry of Health & Welfare (HR16C0001), and the Technology Innovation Program (20013712) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). Special thanks to MD Healthcare Inc. for providing the sequencing services for this study. ","Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country, and by Region, 2000–2016. Geneva, World Health Organization, (2018); Rhee C.K., High prevalence of chronic obstructive pulmonary disease in Korea, Korean J. Intern. Med., 31, pp. 651-652, (2016); Global Health Estimates 2016: Disease Burden by Cause, Age, Sex, by Country and by Region, 2000-2016 Geneva, (2018); Engels E.A., Inflammation in the development of lung cancer: epidemiological evidence, Expert Rev. Anticancer Ther., 8, pp. 605-615, (2008); Arlt V.M., Et al., Pulmonary inflammation impacts on CYP1A1-mediated respiratory tract DNA damage induced by the carcinogenic air pollutant benzo[α]pyrene, Toxicol. 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Immunol., 30, pp. 968-978, (2019); Miyahara N., Et al., Leukotriene B4 release from mast cells in IgE-mediated airway hyperresponsiveness and inflammation, Am. J. Respir. Cell Mol. Biol., 40, pp. 672-682, (2009)","Y.-K. Kim; Institute of MD Healthcare, Inc, Seoul, South Korea; email: ykkim@mdhc.kr; Y.-K. Jee; Department of Internal Medicine, Dankook University College of Medicine, Cheonan, South Korea; email: ykjee@dankook.ac.kr","","Springer Nature","","","","","","12263613","","EMMEF","36180580","English","Exp. Mol. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139133400"
"González-Juanatey C.; Anguita-Sánchez M.; Barrios V.; Núñez-Gil I.; Gómez-Doblas J.J.; García-Moll X.; Lafuente-Gormaz C.; Rollán-Gómez M.J.; Peral-Disdie V.; Martínez-Dolz L.; Rodríguez-Santamarta M.; Viñolas-Prat X.; Soriano-Colomé T.; Muñoz-Aguilera R.; Plaza I.; Curcio-Ruigómez A.; Orts-Soler E.; Segovia J.; Maté C.; Cequier Á.","González-Juanatey, Carlos (6701654012); Anguita-Sánchez, Manuel (7006173532); Barrios, Vivencio (16030595200); Núñez-Gil, Iván (23025470300); Gómez-Doblas, Juan Josá (6603827076); García-Moll, Xavier (56032405900); Lafuente-Gormaz, Carlos (6505818550); Rollán-Gómez, María Jesús (7003623518); Peral-Disdie, Vicente (57447762300); Martínez-Dolz, Luis (6701541186); Rodríguez-Santamarta, Miguel (56487880300); Viñolas-Prat, Xavier (6507714054); Soriano-Colomé, Toni (57195475380); Muñoz-Aguilera, Roberto (6508177420); Plaza, Ignacio (6603887632); Curcio-Ruigómez, Alejandro (6603004663); Orts-Soler, Ernesto (57213387561); Segovia, Javier (55669329500); Maté, Claudia (57221371121); Cequier, Ángel (57222177526)","6701654012; 7006173532; 16030595200; 23025470300; 6603827076; 56032405900; 6505818550; 7003623518; 57447762300; 6701541186; 56487880300; 6507714054; 57195475380; 6508177420; 6603887632; 6603004663; 57213387561; 55669329500; 57221371121; 57222177526","Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing","2022","PLoS ONE","17","2 February","e0263277","","","","11","10.1371/journal.pone.0263277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124377151&doi=10.1371%2fjournal.pone.0263277&partnerID=40&md5=7c70e00cb396bc870816184e60e80da1","Hospital Universitario Lucus Augusti, Lugo, Spain; Hospital Universitario Reina Sofía, Córdoba, Spain; Hospital Universitario Ramón y Cajal, Madrid, Spain; Hospital Clínico Universitario San Carlos, Madrid, Spain; Hospital Universitario Virgen de la Victoria, Málaga, Spain; Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain; Hospital Universitario de Albacete, Albacete, Spain; Hospital Universitario Río Hortega, Valladolid, Spain; Hospital Universitario Son Espases, Palma de Mallorca, Spain; Hospital Universitario La Fe, Valencia, Spain; Hospital Universitario de León, León, Spain; Hospital Vall d’Hebron, CIBERCV, Barcelona, Spain; Hospital Infanta Leonor, Madrid, Spain; Hospital Infanta Sofía, Madrid, Spain; Hospital Universitario de Fuenlabrada, Madrid, Spain; Hospital General Universitario de Castellón, Castellon, Spain; Hospital Universitario Puerta de Hierro, Madrid, Spain; Savana, Madrid, Spain; Hospital Universitario de Bellvitge, Universidad de Barcelona, IDIBELL, Barcelona, Spain","González-Juanatey C., Hospital Universitario Lucus Augusti, Lugo, Spain; Anguita-Sánchez M., Hospital Universitario Reina Sofía, Córdoba, Spain; Barrios V., Hospital Universitario Ramón y Cajal, Madrid, Spain; Núñez-Gil I., Hospital Clínico Universitario San Carlos, Madrid, Spain; Gómez-Doblas J.J., Hospital Universitario Virgen de la Victoria, Málaga, Spain; García-Moll X., Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain; Lafuente-Gormaz C., Hospital Universitario de Albacete, Albacete, Spain; Rollán-Gómez M.J., Hospital Universitario Río Hortega, Valladolid, Spain; Peral-Disdie V., Hospital Universitario Son Espases, Palma de Mallorca, Spain; Martínez-Dolz L., Hospital Universitario La Fe, Valencia, Spain; Rodríguez-Santamarta M., Hospital Universitario de León, León, Spain; Viñolas-Prat X., Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain; Soriano-Colomé T., Hospital Vall d’Hebron, CIBERCV, Barcelona, Spain; Muñoz-Aguilera R., Hospital Infanta Leonor, Madrid, Spain; Plaza I., Hospital Infanta Sofía, Madrid, Spain; Curcio-Ruigómez A., Hospital Universitario de Fuenlabrada, Madrid, Spain; Orts-Soler E., Hospital General Universitario de Castellón, Castellon, Spain; Segovia J., Hospital Universitario Puerta de Hierro, Madrid, Spain; Maté C., Savana, Madrid, Spain; Cequier Á., Hospital Universitario de Bellvitge, Universidad de Barcelona, IDIBELL, Barcelona, Spain","Introduction and objectives Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology. Methods This is a multicenter, retrospective study based on the secondary use of 2014–2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead® technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches. Results Most patients were male (72.13%), with a mean age of 70.5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61.75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1.2 (0.3–4.5) years. Overall, 35.66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0.001), COPD/Asthma (p = 0.021), heart valve disease (p = 0.031), multivessel disease (p = 0.005), insulin treatment (p < 0.001), statins treatment (p < 0.001), and clopidogrel treatment (p = 0.039). Conclusions Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients. © 2022 Public Library of Science. All rights reserved.","","Electronic Health Records; 2,4 thiazolidinedione derivative; acenocoumarol; acetylsalicylic acid; alpha glucosidase inhibitor; angiotensin receptor antagonist; anticoagulant agent; antilipemic agent; antithrombocytic agent; antivitamin K; beta adrenergic receptor blocking agent; blood clotting factor 10a inhibitor; calcium channel blocking agent; clopidogrel; dipeptidyl carboxypeptidase inhibitor; dipeptidyl peptidase IV inhibitor; diuretic agent; fondaparinux; glucagon like peptide 1 receptor agonist; heparin; hydroxymethylglutaryl coenzyme A reductase inhibitor; insulin; ivabradine; long acting insulin; metformin; nitric acid derivative; prasugrel; ranolazine; short acting insulin; sodium glucose cotransporter 2 inhibitor; sulfonylurea derivative; thrombin inhibitor; ticagrelor; warfarin; adult; aged; Article; asthma; chronic obstructive lung disease; comorbidity; coronary artery disease; dual antiplatelet therapy; electronic health record; female; heart infarction; heart muscle revascularization; hospitalization; human; insulin treatment; ischemic stroke; machine learning; major clinical study; male; natural language processing; non insulin dependent diabetes mellitus; percutaneous coronary intervention; proportional hazards model; regression analysis; retrospective study; Spain; unstable angina pectoris; valvular heart disease; electronic health record","","acenocoumarol, 152-72-7; acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; clopidogrel, 113665-84-2, 120202-66-6, 90055-48-4, 94188-84-8, 120202-65-5, 120202-67-7, 894353-16-3, 744256-69-7; fondaparinux, 104993-28-4, 114870-03-0; heparin, 37187-54-5, 8057-48-5, 8065-01-8, 9005-48-5, 9041-08-1; insulin, 9004-10-8; ivabradine, 148849-67-6, 148870-80-8, 155974-00-8; metformin, 1115-70-4, 657-24-9; prasugrel, 389574-19-0, 150322-43-3; ranolazine, 95635-55-5, 110445-25-5, 95635-56-6; ticagrelor, 274693-27-5; warfarin, 129-06-6, 2610-86-8, 3324-63-8, 5543-58-8, 81-81-2","","","AstraZeneca; Sociedad Española de Cardiología","Funding: This study was funded by AstraZeneca Spain and sponsored by the Spanish Society of Cardiology.","Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Et al., IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040, Diabetes Res Clin Pract, 128, pp. 40-50, (2017); Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Et al., Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition, Diabetes Res Clin Pract, 157, (2019); Rao Kondapally Seshasai S, Kaptoge S, Thompson A, Di Angelantonio E, Gao P, Sarwar N, Et al., Diabetes mellitus, fasting glucose, and risk of cause-specific death, N Engl J Med, 364, 9, pp. 829-841, (2011); Shah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M, Gale CP, Et al., Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1·9 million people, The Lancet Diabetes & Endocrinology, 3, 2, pp. 105-113, (2015); Cavender MA, Steg PG, Smith SC, Eagle K, Ohman EM, Goto S, Et al., Impact of Diabetes Mellitus on Hospitalization for Heart Failure, Cardiovascular Events, and Death, Circulation, 132, 10, pp. 923-931, (2015); Paolisso P, Foa A, Bergamaschi L, Angeli F, Fabrizio M, Donati F, Et al., Impact of admission hyperglycemia on short and long-term prognosis in acute myocardial infarction: MINOCA versus MIOCA, Cardiovasc Diabetol, 20, 1, (2021); Aronson D, Edelman ER., Coronary artery disease and diabetes mellitus, Cardiol Clin, 32, 3, pp. 439-455, (2014); Berry C, Tardif J-C, Bourassa MG., Coronary Heart Disease in Patients With Diabetes: Part II: Recent Advances in Coronary Revascularization, Journal of the American College of Cardiology, 49, 6, pp. 643-656, (2007); Mavromatis K, Samady H, King SB, Revascularization in patients with diabetes: PCI or CABG or none at all, Curr Cardiol Rep, 17, 3, (2015); Guia ESC, sobre diabetes, prediabetes y enfermedad cardiovascular, en colaboración con la European Association for the Study of Diabetes (EASD), Revista Española de Cardiología, 73, 5, (2019); Fihn SD, Blankenship JC, Alexander KP, Bittl JA, Byrne JG, Fletcher BJ, Et al., 2014 ACC/AHA/AATS/ PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease, Circulation, 130, 19, pp. 1749-1767, (2014); Neumann F-J, Sousa-Uva M, Ahlsson A, Alfonso F, Banning AP, Benedetto U, Et al., 2018 ESC/EACTS Guidelines on myocardial revascularization, European Heart Journal, 40, 2, pp. 87-165, (2018); Bhatt DL, Steg PG, Mehta SR, Leiter LA, Simon T, Fox K, Et al., Ticagrelor in patients with diabetes and stable coronary artery disease with a history of previous percutaneous coronary intervention (THEMIS-PCI): a phase 3, placebo-controlled, randomised trial, The Lancet, 394, 10204, pp. 1169-1180, (2019); Turgeon RD, Koshman SL, Youngson E, Har B, Wilton SB, James MT, Et al., Association of Ticagrelor vs Clopidogrel With Major Adverse Coronary Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention, JAMA Intern Med, 180, 3, pp. 420-428, (2020); Sohrabi B, Ghaffari S, Habibzadeh A, Chaichi P., Outcome of diabetic and non-diabetic patients undergoing successful percutaneous coronary intervention of chronic total occlusion, J Cardiovasc Thorac Res, 3, 2, pp. 45-48, (2011); Yang Y, Park G-M, Han S, Kim Y-G, Suh J, Park HW, Et al., Impact of diabetes mellitus in patients undergoing contemporary percutaneous coronary intervention: Results from a Korean nationwide study, PLOS ONE, 13, 12, (2018); Bittl JA., Percutaneous coronary interventions in the diabetic patient: where do we stand?, Circ Cardiovasc Interv, 8, 4, (2015); Verma S, Farkouh ME, Yanagawa B, Fitchett DH, Ahsan MR, Ruel M, Et al., Comparison of coronary artery bypass surgery and percutaneous coronary intervention in patients with diabetes: a meta-analysis of randomised controlled trials, The Lancet Diabetes & Endocrinology, 1, 4, pp. 317-328, (2013); Izquierdo JL, Ancochea J, Soriano JB., Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing, J Med Internet Res, 22, 10, (2020); 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Lopez-Bastida J, Boronat M, Moreno JO, Schurer W., Costs, outcomes and challenges for diabetes care in Spain, Global Health, 9, (2013); Lopez Rey MJ, Docampo Garcia M., Change over time in prevalence of diabetes mellitus (DM) in Spain (1999–2014), Endocrinol Diabetes Nutr, 65, 9, pp. 515-523, (2018); Soriguer F, Goday A, Bosch-Comas A, Bordiu E, Calle-Pascual A, Carmena R, Et al., Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the Di@bet.es Study, Diabetologia, 55, 1, pp. 88-93, (2012); Goday A, Delgado E, Diaz Cadorniga F, De Pablos P, Vazquez JA, Soto E., Epidemiología de la diabetes tipo 2 en España, Endocrinología y Nutrición, 49, 4, pp. 113-126, (2002); Huerta JM, Tormo M-J, Chirlaque M-D, Gavrila D, Amiano P, Arriola L, Et al., Risk of type 2 diabetes according to traditional and emerging anthropometric indices in Spain, a Mediterranean country with high prevalence of obesity: results from a large-scale prospective cohort study, BMC Endocrine Disorders, 13, 1, (2013); Wild S, Roglic G, Green A, Sicree R, King H., Global Prevalence of Diabetes, Diabetes Care, 27, 5, (2004); Wittbrodt E, Bhalla N, Andersson Sundell K, Gao Q, Dong L, Cavender MA, Et al., Assessment of the high risk and unmet need in patients with CAD and type 2 diabetes (ATHENA): US healthcare resource utilization, cost and burden of illness in the Diabetes Collaborative Registry, Endocrinology, Diabetes & Metabolism, 3, 3, (2020); Bowman L, Mafham M, Stevens W, Haynes R, Aung T, Chen F, Et al., ASCEND: A Study of Cardiovascular Events iN Diabetes: Characteristics of a randomized trial of aspirin and of omega-3 fatty acid supplementation in 15,480 people with diabetes, Am Heart J, 198, pp. 135-144, (2018); Bonaca MP, Bhatt DL, Cohen M, Steg PG, Storey RF, Jensen EC, Et al., Long-Term Use of Ticagrelor in Patients with Prior Myocardial Infarction, New England Journal of Medicine, 372, 19, pp. 1791-1800, (2015); Steg PG, Bhatt DL, Simon T, Fox K, Mehta SR, Harrington RA, Et al., Ticagrelor in Patients with Stable Coronary Disease and Diabetes, New England Journal of Medicine, 381, 14, pp. 1309-1320, (2019); Gallinoro E, Paolisso P, Candreva A, Bermpeis K, Fabbricatore D, Esposito G, Et al., Microvascular Dysfunction in Patients With Type II Diabetes Mellitus: Invasive Assessment of Absolute Coronary Blood Flow and Microvascular Resistance Reserve, Front Cardiovasc Med, 8, (2021); Sardu C, Barbieri M, Balestrieri ML, Siniscalchi M, Paolisso P, Calabro P, Et al., Thrombus aspiration in hyperglycemic ST-elevation myocardial infarction (STEMI) patients: clinical outcomes at 1-year followup, Cardiovasc Diabetol, 17, 1, (2018); Marfella R, Siniscalchi M, Esposito K, Sellitto A, De Fanis U, Romano C, Et al., Effects of stress hyperglycemia on acute myocardial infarction: role of inflammatory immune process in functional cardiac outcome, Diabetes Care, 26, 11, pp. 3129-3135, (2003); Singh K, Hibbert B, Singh B, Carson K, Premaratne M, Le May M, Et al., Meta-analysis of admission hyperglycaemia in acute myocardial infarction patients treated with primary angioplasty: a cause or a marker of mortality?, European Heart Journal—Cardiovascular Pharmacotherapy, 1, 4, pp. 220-228, (2015); D'Onofrio N, Sardu C, Paolisso P, Minicucci F, Gragnano F, Ferraraccio F, Et al., MicroRNA-33 and SIRT1 influence the coronary thrombus burden in hyperglycemic STEMI patients, J Cell Physiol, 235, 2, pp. 1438-1452, (2020); Cano-Garcia M, Millan-Gomez M, Sanchez-Gonzalez C, Alonso-Briales JH, Munoz-Jimenez LD, Carrasco-Chinchilla F, Et al., Impacto de la revascularización coronaria percutánea de lesiones coronarias graves en ramas secundarias, Revista Española de Cardiología, 72, 6, pp. 456-465, (2019); Ho CH, Chen YC, Chu CC, Wang JJ, Liao KM., Postoperative Complications After Coronary Artery Bypass Grafting in Patients With Chronic Obstructive Pulmonary Disease, Medicine (Baltimore), 95, 8, (2016); Lin WC, Chen CW, Lu CL, Lai WW, Huang MH, Tsai LM, Et al., The association between recent hospitalized COPD exacerbations and adverse outcomes after percutaneous coronary intervention: a nationwide cohort study, Int J Chron Obstruct Pulmon Dis, 14, pp. 169-179, (2019); Almagro P, Lapuente A, Pareja J, Yun S, Garcia ME, Padilla F, Et al., Underdiagnosis and prognosis of chronic obstructive pulmonary disease after percutaneous coronary intervention: a prospective study, Int J Chron Obstruct Pulmon Dis, 10, pp. 1353-1361, (2015)","C. González-Juanatey; Hospital Universitario Lucus Augusti, Lugo, Spain; email: carlos.gonzalez.juanatey@sergas.es","","Public Library of Science","","","","","","19326203","","POLNC","35143527","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124377151"
"Park Y.; Lee C.; Jung J.Y.","Park, Youngmok (57043720900); Lee, Chanho (57876154600); Jung, Ji Ye (35744802400)","57043720900; 57876154600; 35744802400","Digital Healthcare for Airway Diseases from Personal Environmental Exposure","2022","Yonsei Medical Journal","63","","","S1","S13","12","9","10.3349/YMJ.2022.63.S1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123812878&doi=10.3349%2fYMJ.2022.63.S1&partnerID=40&md5=3c95c667a60e28ff55b46895086ef55a","Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; Severance Biomedical Science Institute, Yonsei Biomedical Research Institute, Yonsei University College of Medicine, Seoul, South Korea","Park Y., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea; Lee C., Severance Biomedical Science Institute, Yonsei Biomedical Research Institute, Yonsei University College of Medicine, Seoul, South Korea; Jung J.Y., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea","Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to wellbeing. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies’ abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling. © Yonsei University College of Medicine 2022.","Asthma; Chronic obstructive pulmonary disease; Digital technology; Environment; Wearable electronic devices","Artificial Intelligence; COVID-19; Delivery of Health Care; Environmental Exposure; Humans; Pandemics; SARS-CoV-2; allergen; black carbon; immunoglobulin E; nitrogen dioxide; sulfur dioxide; air pollution; Article; artificial intelligence; asthma; Asthma Control Test; breathing rate; chronic bronchitis; chronic obstructive lung disease; coronavirus disease 2019; digital technology; disease exacerbation; disease simulation; education; electrocardiography; electronic health record; environmental exposure; forced expiratory volume; health care; hospitalization; human; hypoxemia; lifestyle modification; lung function; machine learning; mobile application; mortality; oxygen saturation; oxygen therapy; pandemic; physical activity; prevalence; public health; pulmonary rehabilitation; quality of life; questionnaire; respiratory tract disease; risk factor; self care; smoking; smoking cessation; social network; spirometry; tachypnea; telemedicine; telemonitoring; wellbeing; adverse event; artificial intelligence; environmental exposure; health care delivery","","immunoglobulin E, 37341-29-0; nitrogen dioxide, 10102-44-0; sulfur dioxide, 7446-09-5","","","Korean Ministry of Environment; Ministry of Environment, MOE, (2021003340002); Korea Environmental Industry and Technology Institute, KEITI","Funding text 1: As we reviewed in this study, the impact of air pollution on health is closely related with the integration of pollution-people-place-time. The Center for Digital Biomarkers Research in Korea is developing a personalized service model for managing the exposure to environmental risk factors among vulnerable individuals, in which patients with chronic airway diseases are also included. This research center is supported by the Korea Environment Industry and Technology Institute (KEITI) and funded by the Korean Ministry of Environment. They plan to develop a real-time prediction model for airway disease exacerbations, integrating the previously introduced four domains, in order to suggest the aforementioned personalized self-management plans.; Funding text 2: This work was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Digital Infrastructure Building Project for Monitoring, Surveying and Evaluating the Environmental Health, funded by Korean Ministry of Environment (MOE) (2021003340002). The authors also thank Medical Illustration & Design, part of the Medical Research Support Services of Yonsei University College of Medicine, for all of the artistic support related to this work.","Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet, 388, pp. 1659-1724, (2016); Bae S, Kwon HJ., Current state of research on the risk of morbidity and mortality associated with air pollution in Korea, Yonsei Med J, 60, pp. 243-256, (2019); Rage E, Siroux V, Kunzli N, Pin I, Kauffmann F, Air pollution and asthma severity in adults, Occup Environ Med, 66, pp. 182-188, (2009); Pfeffer PE, Mudway IS, Grigg J., Air pollution and asthma: mechanisms of harm and considerations for clinical interventions, Chest, 159, pp. 1346-1355, (2021); Song DJ, Choi SH, Song WJ, Park KH, Jee YK, Cho SH, Et al., The effects of short-term and very short-term particulate matter exposure on asthma-related hospital visits: National Health Insurance data, Yonsei Med J, 60, pp. 952-959, (2019); Larkin A, Hystad P., Towards personal exposures: how technology is changing air pollution and health research, Curr Environ Health Rep, 4, pp. 463-471, (2017); Yatkin S, Gerboles M, Belis CA, Karagulian F, Lagler F, Barbiere M, Et al., Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain, Atmos Pollut Res, 11, pp. 225-233, (2020); Ozkaynak H, Baxter LK, Dionisio KL, Burke J., Air pollution exposure prediction approaches used in air pollution epidemiology studies, J Expo Sci Environ Epidemiol, 23, pp. 566-572, (2013); Huang C, Hu J, Xue T, Xu H, Wang M., High-resolution spatiotemporal modeling for ambient PM2.5 exposure assessment in China from 2013 to 2019, Environ Sci Technol, 55, pp. 2152-2162, (2021); van Donkelaar A, Martin RV, Brauer M, Boys BL., Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter, Environ Health Perspect, 123, pp. 135-143, (2015); Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Et al., An ensemblebased model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution, Environ Int, 130, (2019); Air pollution in world: real-time air quality index visual map; Rodriguez-Urrego D, Rodriguez-Urrego L., Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world, Environ Pollut, 266, (2020); Snik F, Rietjens JH, Apituley A, Volten H, Mijling B, Di Noia A, Et al., Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters, Geophys Res Lett, 41, pp. 7351-7358, (2014); Perello J, Cigarini A, Vicens J, Bonhoure I, Rojas-Rueda D, Nieuwenhuijsen MJ, Et al., Large-scale citizen science provides high-resolution nitrogen dioxide values and health impact while enhancing community knowledge and collective action, Sci Total Environ, 789, (2021); De Craemer S, Vercauteren J, Fierens F, Lefebvre W, Meysman FJR., Using large-scale NO2 data from citizen science for air-quality compliance and policy support, Environ Sci Technol, 54, pp. 11070-11078, (2020); Van Brussel S, Huyse H., Citizen science on speed? 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Jung; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, 50-1 Yonsei-ro, Seodaemun-gu, 03722, South Korea; email: stopyes@yuhs.ac","","Yonsei University College of Medicine","","","","","","05135796","","YOMJA","35040601","English","Yonsei Med. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85123812878"
"Bushuven S.; Bentele M.; Bentele S.; Gerber B.; Bansbach J.; Ganter J.; Trifunovic-Koenig M.; Ranisch R.","Bushuven, Stefan (8081555400); Bentele, Michael (57998910000); Bentele, Stefanie (57999784500); Gerber, Bianka (57579967700); Bansbach, Joachim (57189064404); Ganter, Julian (57220086923); Trifunovic-Koenig, Milena (57579967600); Ranisch, Robert (55329635200)","8081555400; 57998910000; 57999784500; 57579967700; 57189064404; 57220086923; 57579967600; 55329635200","“ChatGPT, Can You Help Me Save My Child’s Life?” - Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis","2023","Journal of Medical Systems","47","1","123","","","","10","10.1007/s10916-023-02019-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177432068&doi=10.1007%2fs10916-023-02019-x&partnerID=40&md5=6a84d09d8b5bdaaa0b3298338a0136c5","Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany; Department of Anesthesiology and Critical Care, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Institute for Medical Education, University Hospital, LMU Munich, Munich, Germany; Faculty for Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany","Bushuven S., Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany, Department of Anesthesiology and Critical Care, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Institute for Medical Education, University Hospital, LMU Munich, Munich, Germany; Bentele M., Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany; Bentele S., Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany; Gerber B., Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany; Bansbach J., Department of Anesthesiology and Critical Care, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Ganter J., Department of Anesthesiology and Critical Care, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Trifunovic-Koenig M., Training Center for Emergency Medicine (NOTIS e.V), Breite Strasse 7, Engen, 78234, Germany; Ranisch R., Faculty for Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany","Background: Paediatric emergencies are challenging for healthcare workers, first aiders, and parents waiting for emergency medical services to arrive. With the expected rise of virtual assistants, people will likely seek help from such digital AI tools, especially in regions lacking emergency medical services. Large Language Models like ChatGPT proved effective in providing health-related information and are competent in medical exams but are questioned regarding patient safety. Currently, there is no information on ChatGPT’s performance in supporting parents in paediatric emergencies requiring help from emergency medical services. This study aimed to test 20 paediatric and two basic life support case vignettes for ChatGPT and GPT-4 performance and safety in children. Methods: We provided the cases three times each to two models, ChatGPT and GPT-4, and assessed the diagnostic accuracy, emergency call advice, and the validity of advice given to parents. Results: Both models recognized the emergency in the cases, except for septic shock and pulmonary embolism, and identified the correct diagnosis in 94%. However, ChatGPT/GPT-4 reliably advised to call emergency services only in 12 of 22 cases (54%), gave correct first aid instructions in 9 cases (45%) and incorrectly advised advanced life support techniques to parents in 3 of 22 cases (13.6%). Conclusion: Considering these results of the recent ChatGPT versions, the validity, reliability and thus safety of ChatGPT/GPT-4 as an emergency support tool is questionable. However, whether humans would perform better in the same situation is uncertain. Moreover, other studies have shown that human emergency call operators are also inaccurate, partly with worse performance than ChatGPT/GPT-4 in our study. However, one of the main limitations of the study is that we used prototypical cases, and the management may differ from urban to rural areas and between different countries, indicating the need for further evaluation of the context sensitivity and adaptability of the model. Nevertheless, ChatGPT and the new versions under development may be promising tools for assisting lay first responders, operators, and professionals in diagnosing a paediatric emergency. Trial registration: Not applicable. © 2023, The Author(s).","Artificial intelligence; ChatGPT; First responder; GPT-4; Large language model; Medical didactics; Tele-medicine","Child; Emergencies; Emergency Medical Services; Health Personnel; Humans; Language; Reproducibility of Results; anaphylaxis; Article; artificial intelligence chatbot; asthma; basic life support; bronchiolitis; cardiogenic shock; ChatGPT; computer model; controlled study; cross-sectional study; croup; diagnostic accuracy; emergency health service; first aid; foreign body aspiration; gpt 4; heart arrhythmia; heart tamponade; Heimlich maneuver; hemorrhagic shock; hypovolemic shock; intoxication; intracranial hypertension; lay rescuer; lung embolism; myocarditis; neuromuscular disease; patient safety; pediatric advanced life support; resuscitation; sepsis; teaching; tension pneumothorax; vasodilatory shock; vignette; virus pneumonia; child; emergency; emergency health service; health care personnel; human; language; reproducibility","","","","","Messmer-Foundation Radolfzell; Volkswagen Foundation","MTK was funded by the Messmer-Foundation Radolfzell, Germany, and Training Center for Emergency Medicine (NOTIS) Engen, Germany. RR work is a part of the Digital Ethics Forsight Lab in the Digital Medical Ethics Network (DiMEN) funded by the VolkswagenStiftung. SB, StB, MB, BG and JG declare no financial support or sponsorship. 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An observational trial in real patients, Acta Clin Belg, 77, 2, pp. 301-306, (2022); Giesen P., Et al., Safety of telephone triage in general practitioner cooperatives: do triage nurses correctly estimate urgency?, Qual Saf Health Care, 16, 3, pp. 181-184, (2007); Huibers L., Et al., Safety of telephone triage in out-of-hours care: a systematic review, Scand J Prim Health Care, 29, 4, pp. 198-209, (2011); Meischke H.W., Et al., The effect of language barriers on dispatching EMS response, Prehosp Emerg Care, 17, 4, pp. 475-480, (2013); Hagendorff T., Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods arXiv preprint, Arxiv, 2303, (2023); Brown T., Et al., Language models are few-shot learners, Advances in neural information processing systems, 33, pp. 1877-1901, (2020); Wei J., Chain of thought prompting elicits reasoning in large language models, Arxiv Preprint Arxiv, 2201, (2022); Singhal K., Et al., Large language models encode clinical knowledge, Nature, 620, 7972, pp. 172-180, (2023)","S. Bushuven; Training Center for Emergency Medicine (NOTIS e.V), Engen, Breite Strasse 7, 78234, Germany; email: Stefan.Bushuven@notis-ev.de","","Springer","","","","","","01485598","","JMSYD","37987870","English","J. Med. Syst.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85177432068"
"Xu Y.; Vuckovic D.; Ritchie S.C.; Akbari P.; Jiang T.; Grealey J.; Butterworth A.S.; Ouwehand W.H.; Roberts D.J.; Di Angelantonio E.; Danesh J.; Soranzo N.; Inouye M.","Xu, Yu (57221343905); Vuckovic, Dragana (55990126500); Ritchie, Scott C. (57014552600); Akbari, Parsa (57218681854); Jiang, Tao (56767671100); Grealey, Jason (57221323680); Butterworth, Adam S. (57203080999); Ouwehand, Willem H. (57756154900); Roberts, David J. (7404250830); Di Angelantonio, Emanuele (57222582148); Danesh, John (7006642150); Soranzo, Nicole (57222595186); Inouye, Michael (22953271700)","57221343905; 55990126500; 57014552600; 57218681854; 56767671100; 57221323680; 57203080999; 57756154900; 7404250830; 57222582148; 7006642150; 57222595186; 22953271700","Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease","2022","Cell Genomics","2","1","100086","","","","10","10.1016/j.xgen.2021.100086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130684188&doi=10.1016%2fj.xgen.2021.100086&partnerID=40&md5=49c51ceaa2b80ebca0b80b4acfd565e8","Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom; National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Department of Mathematics and Statistics, La Trobe University, Bundoora, 3086, VIC, Australia; National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, United Kingdom; Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, United Kingdom; National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom; Health Data Science Research Centre, Human Technopole, Milan, 20157, Italy; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, United Kingdom; The Alan Turing Institute, London, NW1 2DB, United Kingdom","Xu Y., Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Vuckovic D., Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Ritchie S.C., Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Akbari P., British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Jiang T., British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Grealey J., Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia, Department of Mathematics and Statistics, La Trobe University, Bundoora, 3086, VIC, Australia; Butterworth A.S., British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, United Kingdom; Ouwehand W.H., Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, United Kingdom, Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, United Kingdom; Roberts D.J., National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, National Health Service (NHS) Blood and Transplant, Cambridge Biomedical Campus, Cambridge, CB2 0PT, United Kingdom, National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford and John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom; Di Angelantonio E., British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Health Data Science Research Centre, Human Technopole, Milan, 20157, Italy, Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, United Kingdom; Danesh J., British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, United Kingdom; Soranzo N., Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; Inouye M., Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, 3004, VIC, Australia, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, CB1 8RN, United Kingdom, Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, CB10 1SA, United Kingdom, The Alan Turing Institute, London, NW1 2DB, United Kingdom","Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%–23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource. © 2021 The Author(s)","Blood cell trait; Disease assocations; Machine learning; Method; Polygenic score; Population stratification","adult; age; aged; allergic disease; Article; asthma; atherosclerosis; Bayesian ridge; blood cell; cohort analysis; controlled study; convolutional neural network; coronary artery disease; correlation analysis; Crohn disease; elastic net method; eosinophil count; eosinophil percentage; external validity; female; gene frequency; genetic correlation; genetic risk score; human; hypertension; LDpred2; leukocyte count; machine learning; male; mean platelet volume; minor allele frequency; monocyte count; monocyte percentage; multilayer perceptron; plateletcrit; prediction; reticulocyte count; rheumatoid arthritis; schizophrenia; sex; single nucleotide polymorphism; univariate analysis","","","","","Department of Health and Social Care, DH; Merck; NIHR Oxford Biomedical Research Centre, OxBRC; Economic and Social Research Council, ESRC; International Cardiovascular and Metabolism Research and Development Portfolio Committee member for Novartis; NHS Blood and Transplant Research and Development; Health and Social Care Research and Development Division of the Welsh government; Biogen; National Institute for Health and Care Research, NIHR; Victorian Government’s Operational Infrastructure Support; Public Health Agency, PHA; Wellcome Trust, WT; Merck Sharp and Dohme, MSD; Novartis; Bayer; Chief Scientist Office, Scottish Government Health and Social Care Directorate, CSO; OIS; La Trobe University; Astra Zeneca Genomics Advisory Board; Engineering and Physical Sciences Research Council, EPSRC; Health Data Research UK; Sanofi; Victorian Government's Operational Infrastructure Support; AstraZeneca; Baker Heart and Diabetes Institute; Horizon 2020; UK Research and Innovation, UKRI, (ES/T013192/1); Horizon 2020 Framework Programme, H2020, (101016775); British Heart Foundation, BHF, (RG/13/13/30194, BRC-1215-20014, SP/09/002, RG/18/13/33946, 2360–2371); NIHR Cambridge Biomedical Research Centre, (BTRU-2014-10024); Medical Research Council, MRC, (MR/L003120/1)","Funding text 1: UK Biobank data access was approved under project 13745, and all of the participants gave their informed consent for health research. Participants in the INTERVAL randomized controlled trial were recruited with the active collaboration of NHS Blood and Transplant England ( www.nhsbt.nhs.uk ), which has supported field work and other elements of the trial. DNA extraction and genotyping were co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource ( http://bioresource.nihr.ac.uk ), and the NIHR Cambridge Biomedical Research Centre ( BRC-1215-20014 ). The academic coordinating center for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics ( NIHR BTRU-2014-10024 ), the UK Medical Research Council ( MR/L003120/1 ), the British Heart Foundation ( SP/09/002 , RG/13/13/30194 , RG/18/13/33946 ), and the NIHR Cambridge BRC ( BRC-1215-20014 ). (A complete list of the investigators and contributors to the INTERVAL trial is provided in Di Angelantonio, E., Thompson, S.G., Kaptoge, S.K., Moore, C., Walker, M., Armitage, J., Ouwehand, W.H., Roberts, D.J., and Danesh, J.; INTERVAL Trial Group [2017]. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360\u20132371.) The academic coordinating center would like to thank blood donor center staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK , which is funded by the UK Medical Research Council , the Engineering and Physical Sciences Research Council , the Economic and Social Research Council , the Department of Health and Social Care (England) , the Chief Scientist Office of the Scottish Government Health and Social Care Directorates , the Health and Social Care Research and Development Division of the Welsh government , Public Health Agency (Northern Ireland) , and the British Heart Foundation and Wellcome . Y.X. was supported by the UK Economic and Social Research Council ( ES/T013192/1 ). D.V. was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics ( NIHR BTRU-2014-10024 ). S.C.R. is funded by a BHF Programme Grant ( RG/18/13/33946 ). P.A. was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics ( NIHR BTRU-2014-10024 ). T.J. is funded by the NIHR Cambridge Biomedical Research Centre ( BRC-1215-20014 ). J.G. was supported by a La Trobe University Postgraduate Research Scholarship jointly funded by the Baker Heart and Diabetes Institute and a La Trobe University Full-Fee Research Scholarship. D.J.R. was supported by NHS Blood and Transplant Research and Development funding and the Oxford Biomedical Research Centre (Haematology Theme ). J.D. holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award . M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention , the Horizon 2020 Research and Innovation Programme 'INTERVENE' ( 101016775 ), the UK Economic and Social Research Council ( ES/T013192/1 ) and the NIHR Cambridge Biomedical Research Centre ( BRC-1215-20014 ). This study was supported by the Victorian Government\u2019s Operational Infrastructure Support (OIS) program. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ; Funding text 2: UK Biobank data access was approved under project 13745, and all of the participants gave their informed consent for health research. Participants in the INTERVAL randomized controlled trial were recruited with the active collaboration of NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other elements of the trial. DNA extraction and genotyping were co-funded by the National Institute for Health Research (NIHR), the NIHR BioResource (http://bioresource.nihr.ac.uk), and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). The academic coordinating center for INTERVAL was supported by core funding from the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024), the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (SP/09/002, RG/13/13/30194, RG/18/13/33946), and the NIHR Cambridge BRC (BRC-1215-20014). (A complete list of the investigators and contributors to the INTERVAL trial is provided in Di Angelantonio, E. Thompson, S.G. Kaptoge, S.K. Moore, C. Walker, M. Armitage, J. Ouwehand, W.H. Roberts, D.J. and Danesh, J.; INTERVAL Trial Group [2017]. Efficiency and safety of varying the frequency of whole blood donation (INTERVAL): a randomised trial of 45 000 donors. Lancet 390, 2360\u20132371.) The academic coordinating center would like to thank blood donor center staff and blood donors for participating in the INTERVAL trial. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, the Engineering and Physical Sciences Research Council, the Economic and Social Research Council, the Department of Health and Social Care (England), the Chief Scientist Office of the Scottish Government Health and Social Care Directorates, the Health and Social Care Research and Development Division of the Welsh government, Public Health Agency (Northern Ireland), and the British Heart Foundation and Wellcome. Y.X. was supported by the UK Economic and Social Research Council (ES/T013192/1). D.V. was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). S.C.R. is funded by a BHF Programme Grant (RG/18/13/33946). P.A. was funded by the NIHR Blood and Transplant Research Unit in Donor Health and Genomics (NIHR BTRU-2014-10024). T.J. is funded by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). J.G. was supported by a La Trobe University Postgraduate Research Scholarship jointly funded by the Baker Heart and Diabetes Institute and a La Trobe University Full-Fee Research Scholarship. D.J.R. was supported by NHS Blood and Transplant Research and Development funding and the Oxford Biomedical Research Centre (Haematology Theme). J.D. holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award. M.I. is supported by the Munz Chair of Cardiovascular Prediction and Prevention, the Horizon 2020 Research and Innovation Programme 'INTERVENE' (101016775), the UK Economic and Social Research Council (ES/T013192/1) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). This study was supported by the Victorian Government's Operational Infrastructure Support (OIS) program. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. M.I. and Y.X. conceived and designed the study. Y.X. D.V. S.C.R. P.A. T.J. and J.G. performed the analyses. A.S.B. W.H.O. D.J.R. E.D.A. J.D. and N.S. provided the data. M.I. N.S. J.D. and A.S.B. supervised the work. Y.X. and M.I. wrote the paper, with input from all of the authors. P.A. is a full-time employee of Regeneron Pharmaceuticals. A.S.B. has received grants (outside of this work) from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Merck, Novartis, Regeneron, and Sanofi. J.D. reports grants, personal fees, and non-financial support from Merck Sharp & Dohme (MSD); grants, personal fees, and non-financial support from Novartis; grants from Pfizer; and grants from AstraZeneca outside the submitted work. J.D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), serves on the Steering Committee of UK Biobank (since 2011), is an MRC International Advisory Group (ING) member, London (since 2013), an MRC High Throughput Science \u2018Omics Panel Member, London (since 2013), a Scientific Advisory Committee member for Sanofi (since 2013), an International Cardiovascular and Metabolism Research and Development Portfolio Committee member for Novartis, and was a member of the Astra Zeneca Genomics Advisory Board (2018).; Funding text 3: P.A. is a full-time employee of Regeneron Pharmaceuticals. A.S.B. has received grants (outside of this work) from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Merck, Novartis, Regeneron, and Sanofi. J.D. reports grants, personal fees, and non-financial support from Merck Sharp & Dohme (MSD); grants, personal fees, and non-financial support from Novartis; grants from Pfizer; and grants from AstraZeneca outside the submitted work. J.D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), serves on the Steering Committee of UK Biobank (since 2011), is an MRC International Advisory Group (ING) member, London (since 2013), an MRC High Throughput Science \u2018Omics Panel Member, London (since 2013), a Scientific Advisory Committee member for Sanofi (since 2013), an International Cardiovascular and Metabolism Research and Development Portfolio Committee member for Novartis, and was a member of the Astra Zeneca Genomics Advisory Board (2018). ","Jensen F.B., The dual roles of red blood cells in tissue oxygen delivery: oxygen carriers and regulators of local blood flow, J. Exp. 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Inouye; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom; email: mi336@medschl.cam.ac.uk","","Cell Press","","","","","","2666979X","","","","English","Cell Genom.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130684188"
"Perez-Garcia J.; Herrera-Luis E.; Li A.; Mak A.C.Y.; Huntsman S.; Oh S.S.; Elhawary J.R.; Eng C.; Beckman K.B.; Hu D.; Lorenzo-Diaz F.; Lenoir M.A.; Rodriguez-Santana J.; Zaitlen N.; Villar J.; Borrell L.N.; Burchard E.G.; Pino-Yanes M.","Perez-Garcia, Javier (57215905983); Herrera-Luis, Esther (57203061089); Li, Annie (57416240200); Mak, Angel C.Y. (56047122900); Huntsman, Scott (16202774500); Oh, Sam S. (55695133300); Elhawary, Jennifer R. (57203803435); Eng, Celeste (57208041356); Beckman, Kenneth B. (7006218797); Hu, Donglei (7402585007); Lorenzo-Diaz, Fabian (6506188328); Lenoir, Michael A. (10139426900); Rodriguez-Santana, Jose (6603825027); Zaitlen, Noah (8975849800); Villar, Jesús (55236061500); Borrell, Luisa N. (6603552019); Burchard, Esteban G. (57203216533); Pino-Yanes, Maria (57189630546)","57215905983; 57203061089; 57416240200; 56047122900; 16202774500; 55695133300; 57203803435; 57208041356; 7006218797; 7402585007; 6506188328; 10139426900; 6603825027; 8975849800; 55236061500; 6603552019; 57203216533; 57189630546","Multi-omic approach associates blood methylome with bronchodilator drug response in pediatric asthma","2023","Journal of Allergy and Clinical Immunology","151","6","","1503","1512","9","9","10.1016/j.jaci.2023.01.026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150767755&doi=10.1016%2fj.jaci.2023.01.026&partnerID=40&md5=6a472d30789d0db5dba636be55084f16","Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Spain; Department of Medicine, University of California, San Francisco, Calif, United States; University of Minnesota Genomics Center, Minneapolis, Minn, United States; Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Santa Cruz de Tenerife, Spain; Bay Area Pediatrics, Oakland, Calif, United States; Centro de Neumología Pediátrica, San Juan, Puerto Rico; Department of Neurology, University of California, Los Angeles, Calif, United States; Department of Computational Medicine, University of California, Los Angeles, Calif, United States; Multidisciplinary Organ Dysfunction Evaluation Research Network (MODERN), Research Unit, Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif, United States; Instituto de Tecnologías Biomédicas, ULL, La Laguna, Spain","Perez-Garcia J., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Spain; Herrera-Luis E., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Spain; Li A., Department of Medicine, University of California, San Francisco, Calif, United States; Mak A.C.Y., Department of Medicine, University of California, San Francisco, Calif, United States; Huntsman S., Department of Medicine, University of California, San Francisco, Calif, United States; Oh S.S., Department of Medicine, University of California, San Francisco, Calif, United States; Elhawary J.R., Department of Medicine, University of California, San Francisco, Calif, United States; Eng C., Department of Medicine, University of California, San Francisco, Calif, United States; Beckman K.B., University of Minnesota Genomics Center, Minneapolis, Minn, United States; Hu D., Department of Medicine, University of California, San Francisco, Calif, United States; Lorenzo-Diaz F., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Spain, Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias (IUETSPC), ULL, Santa Cruz de Tenerife, Spain; Lenoir M.A., Bay Area Pediatrics, Oakland, Calif, United States; Rodriguez-Santana J., Centro de Neumología Pediátrica, San Juan, Puerto Rico; Zaitlen N., Department of Neurology, University of California, Los Angeles, Calif, United States, Department of Computational Medicine, University of California, Los Angeles, Calif, United States; Villar J., Multidisciplinary Organ Dysfunction Evaluation Research Network (MODERN), Research Unit, Hospital Universitario Dr Negrín, Las Palmas de Gran Canaria, Spain, CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Borrell L.N., Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, United States; Burchard E.G., Department of Medicine, University of California, San Francisco, Calif, United States, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif, United States; Pino-Yanes M., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), La Laguna, Spain, CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain, Instituto de Tecnologías Biomédicas, ULL, La Laguna, Spain","Background: Albuterol is the drug most widely used as asthma treatment among African Americans despite having a lower bronchodilator drug response (BDR) than other populations. Although BDR is affected by gene and environmental factors, the influence of DNA methylation is unknown. Objective: This study aimed to identify epigenetic markers in whole blood associated with BDR, study their functional consequences by multi-omic integration, and assess their clinical applicability in admixed populations with a high asthma burden. Methods: We studied 414 children and young adults (8-21 years old) with asthma in a discovery and replication design. We performed an epigenome-wide association study on 221 African Americans and replicated the results on 193 Latinos. Functional consequences were assessed by integrating epigenomics with genomics, transcriptomics, and environmental exposure data. Machine learning was used to develop a panel of epigenetic markers to classify treatment response. Results: We identified 5 differentially methylated regions and 2 CpGs genome-wide significantly associated with BDR in African Americans located in FGL2 (cg08241295, P = 6.8 × 10−9) and DNASE2 (cg15341340, P = 7.8 × 10−8), which were regulated by genetic variation and/or associated with gene expression of nearby genes (false discovery rate < 0.05). The CpG cg15341340 was replicated in Latinos (P = 3.5 × 10−3). Moreover, a panel of 70 CpGs showed good classification for those with response and nonresponse to albuterol therapy in African American and Latino children (area under the receiver operating characteristic curve for training, 0.99; for validation, 0.70-0.71). The DNA methylation model showed similar discrimination as clinical predictors (P > .05). Conclusions: We report novel associations of epigenetic markers with BDR in pediatric asthma and demonstrate for the first time the applicability of pharmacoepigenetics in precision medicine of respiratory diseases. © 2023 The Authors","African Americans; albuterol; Epigenomics; Hispanic Americans; precision medicine","Adolescent; Adult; Albuterol; Asthma; Bronchodilator Agents; Child; DNA Methylation; Epigenome; Fibrinogen; Genome-Wide Association Study; Humans; Multiomics; Young Adult; bronchodilating agent; interleukin 2; salbutamol; transcriptome; tumor necrosis factor; FGL2 protein, human; fibrinogen; adolescent; adult; African American; air pollutant; Article; asthma; body mass; child; chromosome; clinical classification; controlled study; DNA methylation; drug response; environmental exposure; environmental factor; epigenetics; exon; female; fractional exhaled nitric oxide; gene expression; genetic regulation; genetic variation; genomics; genotype environment interaction; health disparity; Hispanic; human; immune response gene; lung function; machine learning; major clinical study; male; methylome; multiomics; obesity; passive smoking; pediatrics; personalized medicine; predictive model; predictive value; prenatal exposure; private health insurance; protein phosphorylation; protein protein interaction; public health insurance; quantitative trait; quantitative trait locus; RNA sequencing; single nucleotide polymorphism; transcriptomics; treatment response; underweight; young adult; DNA methylation; epigenome; genetics; genome-wide association study; metabolism","","interleukin 2, 85898-30-2; salbutamol, 18559-94-9, 35763-26-9; fibrinogen, 9001-32-5; Albuterol, ; Bronchodilator Agents, ; FGL2 protein, human, ; Fibrinogen, ","","","Amos Medical Faculty Development Program; CIBER-Consorcio Centro de Investigación Biomédica en Red; Consorcio Centro de Investigación Biomédica en Red; Harry Wm.; Ministry for Science and Innovation; Northwest Genomics Center, (HHSN268201600032I); Spanish Ministry of Universities, (PRE2018-083837); TOPMed Informatics Research Center, (3R01HL-117626-02S1, 3R01HL-120393-02S1, HHSN268201800002I); National Institutes of Health, NIH, (R01HL128439, R01HL135156, R01HL155024-01, X01HL134589); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (R01HL117004, phs000920, phs000921); National Heart, Lung, and Blood Institute, NHLBI; National Human Genome Research Institute, NHGRI; National Eye Institute, NEI; National Institute of Environmental Health Sciences, NIEHS, (R01ES015794, R21ES24844); National Institute of Environmental Health Sciences, NIEHS; Robert Wood Johnson Foundation, RWJF; Tobacco-Related Disease Research Program, TRDRP, (24RT-0025, 27IR-0030); Tobacco-Related Disease Research Program, TRDRP; Center for International Business Education and Research, University of Illinois at Urbana-Champaign, CIBER; National Institute on Minority Health and Health Disparities, NIMHD, (R01MD010443, R56MD013312); National Institute on Minority Health and Health Disparities, NIMHD; Sandler Foundation; American Asthma Foundation, AAF; New York Genome Center, NYGC, (U24 HG008956, UM1 HG008901); New York Genome Center, NYGC; GlaxoSmithKline España, GSK; European Commission, EC, (PI16/00049, PI19/00141); European Commission, EC; Instituto de Salud Carlos III, ISCIII; Ministerio de Ciencia e Innovación, MICINN, (SAF2017-83417R); Ministerio de Ciencia e Innovación, MICINN; European Social Fund, ESF, (FPU19/02175, RYC-2015-17205); European Social Fund, ESF; European Regional Development Fund, ERDF, (CB06/06/1088, MCIN/AEI/10.13039/501100011033, PID2020-116274RB-I00); European Regional Development Fund, ERDF; Agencia Estatal de Investigación, AEI; Fundación Canaria Instituto de Investigación Sanitaria de Canarias, FIISC","Funding text 1: Disclosure of potential conflict of interest: Authors declare they have no competing interests or other interests that might be perceived to influence the interpretation of the article. No supporting institution may gain or lose financially through this publication. M. Pino-Yanes was supported by the Ramón y Cajal Program by MCIN/AEI/10.13039/501100011033, European Social Fund “ESF Investing in Your Future” (RYC-2015-17205). J. Perez-Garcia was funded by fellowship FPU19/02175 from the Spanish Ministry of Universities. E. Herrera-Luis was funded by fellowship PRE2018-083837 from MCIN/AEI/10.13039/501100011033 and the European Social Fund “Investing in Your Future.” J. Villar and M. Pino-Yanes received funding from CIBER- Consorcio Centro de Investigación Biomédica en Red (CIBERES), Instituto de Salud Carlos III (ISCIII), and the European Regional Development Fund (CB06/06/1088). J. Villar was also funded by ISCIII and the European Regional Development Fund “ERDF A Way of Making Europe” by the European Union (PI16/00049, PI19/00141). M. Pino-Yanes and F. Lorenzo-Diaz report grants from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Development Regional Fund from the European Union. M. Pino-Yanes also reports grant support from GlaxoSmithKline (Spain) through Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC) for a project outside the submitted work. E. Burchard reports grants from the National Institutes of Health , the Tobacco-Related Disease Research Program, the Sandler Family Foundation, the American Asthma Foundation, the Amos Medical Faculty Development Program from the Robert Wood Johnson Foundation, and the Harry Wm. and Diana V. Hind Distinguished Professorship in Pharmaceutical Sciences II. The rest of the authors declare that they have no relevant conflicts of interest. ; Funding text 2: Whole-genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Whole-genome sequencing for “NHLBI TOPMed: Gene-Environment, Admixture and Latino Asthmatics Study” (phs000920, GALA II) and “NHLBI TOPMed: Study of African Americans, Asthma, Genes and Environments” (phs000921, SAGE) was performed at the New York Genome Center (3R01HL117004-02S3) and Northwest Genomics Center (HHSN268201600032I). Centralized read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1). Phenotype harmonization, data management, sample-identity quality control, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1, contract HHSN268201800002I). WGS of part of GALA II was performed by the New York Genome Center under a Centers for Common Disease Genomics of the Genome Sequencing Program (GSP) grant (UM1 HG008901). The GSP Coordinating Center (U24 HG008956) contributed to cross-program scientific initiatives and provided logistical and general study coordination. GSP is funded by the National Human Genome Research Institute, the NHLBI, and the National Eye Institute. This work was supported in part by the Sandler Family Foundation, the American Asthma Foundation, the Amos Medical Faculty Development Program from the Robert Wood Johnson Foundation, the Harry Wm. and Diana V. Hind Distinguished Professor in Pharmaceutical Sciences II, the NHLBI of the National Institutes of Health (R01HL155024-01, R01HL117004, R01HL128439, R01HL135156, X01HL134589), the National Institute of Health and Environmental Health Sciences (R01ES015794, R21ES24844), the National Institute on Minority Health and Health Disparities (R01MD010443, R56MD013312), and the Tobacco-Related Disease Research Program (24RT-0025, 27IR-0030). This work was also funded by the Spanish Ministry of Science and Innovation (SAF2017-83417R) awarded by Ministry for Science and Innovation (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and the European Development Regional Fund “A Way of Making Europe,” and grant PID2020-116274RB-I00 awarded by MCIN/AEI/10.13039/501100011033. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. ; Funding text 3: Whole-genome sequencing (WGS) for the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Whole-genome sequencing for “NHLBI TOPMed: Gene-Environment, Admixture and Latino Asthmatics Study” (phs000920, GALA II) and “NHLBI TOPMed: Study of African Americans, Asthma, Genes and Environments” (phs000921, SAGE) was performed at the New York Genome Center (3R01HL117004-02S3) and Northwest Genomics Center (HHSN268201600032I). Centralized read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1). Phenotype harmonization, data management, sample-identity quality control, and general study coordination were provided by the TOPMed Data Coordinating Center (3R01HL-120393-02S1, contract HHSN268201800002I). WGS of part of GALA II was performed by the New York Genome Center under a Centers for Common Disease Genomics of the Genome Sequencing Program (GSP) grant (UM1 HG008901). The GSP Coordinating Center (U24 HG008956) contributed to cross-program scientific initiatives and provided logistical and general study coordination. GSP is funded by the National Human Genome Research Institute, the NHLBI, and the National Eye Institute. This work was supported in part by the Sandler Family Foundation, the American Asthma Foundation, the Amos Medical Faculty Development Program from the Robert Wood Johnson Foundation, the Harry Wm. and Diana V. Hind Distinguished Professor in Pharmaceutical Sciences II, the NHLBI of the National Institutes of Health (R01HL155024-01, R01HL117004, R01HL128439, R01HL135156, X01HL134589), the National Institute of Health and Environmental Health Sciences (R01ES015794, R21ES24844), the National Institute on Minority Health and Health Disparities (R01MD010443, R56MD013312), and the Tobacco-Related Disease Research Program (24RT-0025, 27IR-0030). This work was also funded by the Spanish Ministry of Science and Innovation (SAF2017-83417R) awarded by Ministry for Science and Innovation (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 and the European Development Regional Fund “A Way of Making Europe,” and grant PID2020-116274RB-I00 awarded by MCIN/AEI/10.13039/501100011033. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.Disclosure of potential conflict of interest: Authors declare they have no competing interests or other interests that might be perceived to influence the interpretation of the article. No supporting institution may gain or lose financially through this publication. M. Pino-Yanes was supported by the Ramón y Cajal Program by MCIN/AEI/10.13039/501100011033, European Social Fund “ESF Investing in Your Future” (RYC-2015-17205). J. Perez-Garcia was funded by fellowship FPU19/02175 from the Spanish Ministry of Universities. E. Herrera-Luis was funded by fellowship PRE2018-083837 from MCIN/AEI/10.13039/501100011033 and the European Social Fund “Investing in Your Future.” J. Villar and M. Pino-Yanes received funding from CIBER-Consorcio Centro de Investigación Biomédica en Red (CIBERES), Instituto de Salud Carlos III (ISCIII), and the European Regional Development Fund (CB06/06/1088). J. Villar was also funded by ISCIII and the European Regional Development Fund “ERDF A Way of Making Europe” by the European Union (PI16/00049, PI19/00141). M. Pino-Yanes and F. Lorenzo-Diaz report grants from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Development Regional Fund from the European Union. M. Pino-Yanes also reports grant support from GlaxoSmithKline (Spain) through Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC) for a project outside the submitted work. E. Burchard reports grants from the National Institutes of Health, the Tobacco-Related Disease Research Program, the Sandler Family Foundation, the American Asthma Foundation, the Amos Medical Faculty Development Program from the Robert Wood Johnson Foundation, and the Harry Wm. and Diana V. Hind Distinguished Professorship in Pharmaceutical Sciences II. 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Pino-Yanes; Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology, and Genetics, Universidad de La Laguna (ULL), Santa Cruz de Tenerife, Apartado 456, La Laguna, 38200, Spain; email: mdelpino@ull.edu.es","","Elsevier Inc.","","","","","","00916749","","JACIB","36796456","English","J. Allergy Clin. Immunol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85150767755"
"Hanumegowda P.K.; Gnanasekaran S.","Hanumegowda, Pradeep Kumar (57340099300); Gnanasekaran, Sakthivel (57211528498)","57340099300; 57211528498","Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning","2022","International Journal of Environmental Research and Public Health","19","22","15179","","","","14","10.3390/ijerph192215179","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142539473&doi=10.3390%2fijerph192215179&partnerID=40&md5=80aeaa08856c2813438a594d783caf65","School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India; Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India","Hanumegowda P.K., School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India; Gnanasekaran S., Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India","A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers. © 2022 by the authors.","BMTC; decision tree; machine learning; naïve Bayes; random forest","Bayes Theorem; Humans; Machine Learning; Occupational Diseases; Pain; Risk Factors; Bengaluru; India; Karnataka; Bayesian analysis; injury; machine learning; prediction; questionnaire survey; risk factor; working conditions; accuracy; algorithm; arthritis; Article; asthma; behavior; blood pressure; decision tree; diabetes mellitus; driver; entropy; ergonomics; human; hypertension; lifestyle; machine learning; musculoskeletal disease; pain; physical activity; prediction; questionnaire; risk assessment; risk factor; sensitivity and specificity; training; vibration; work environment; Bayes theorem; complication; machine learning; occupational disease; risk factor","","","","","","","Weale V., Stuckey R., Kinsman N., Oakman J., Workplace musculoskeletal disorders: A systematic review and key stakeholder interviews on the use of comprehensive risk management approaches, Int. 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Signal Process, 133, (2019); Sekulic D., Rusov S., Dedovic V., Salinic S., Mladenovic D., Ivkovic I., Analysis of bus users’ vibration exposure time, Int. J. Ind. Ergon, 65, pp. 26-35, (2018); Rai R., Tiwari M.K., Ivanov D., Dolgui A., Machine learning in manufacturing and industry 4.0 applications, Int. J. Prod. Res, 59, pp. 4773-4778, (2021); Prakash C., Kumar R., Mittal N., Recent developments in human gait research: Parameters, approaches, applications, machine learning techniques, datasets and challenges, Artif. Intell. Rev, 49, pp. 1-40, (2016); Jacob S., Menon V.G., Al-Turjman F., Vinoj P.G., Mostarda L., Artificial muscle intelligence system with deep learning for post-stroke assistance and rehabilitation, IEEE Access, 7, pp. 133463-133473, (2019)","S. Gnanasekaran; Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India; email: sakthivel.g@vit.ac.in","","MDPI","","","","","","16617827","","","36429898","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85142539473"
"Xiong S.; Chen W.; Jia X.; Jia Y.; Liu C.","Xiong, Shiqiu (57762892100); Chen, Wei (58508915600); Jia, Xinyu (57224003473); Jia, Yang (57215576750); Liu, Chuanhe (7409792115)","57762892100; 58508915600; 57224003473; 57215576750; 7409792115","Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis","2023","BMC Pulmonary Medicine","23","1","278","","","","11","10.1186/s12890-023-02570-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165982158&doi=10.1186%2fs12890-023-02570-w&partnerID=40&md5=4c8abe5f75380606e41ffb365eb2be9d","Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China; Graduate School, Peking Union Medical College, Beijing, 100730, China; Department of Pediatrics, The Second Xiangya Hospital, Central South University, Hunan, Changsha, 410011, China","Xiong S., Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China, Graduate School, Peking Union Medical College, Beijing, 100730, China; Chen W., Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China; Jia X., Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China; Jia Y., Department of Pediatrics, The Second Xiangya Hospital, Central South University, Hunan, Changsha, 410011, China; Liu C., Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China, Graduate School, Peking Union Medical College, Beijing, 100730, China","Background: Asthma exacerbations reduce the patient’s quality of life and are also responsible for significant disease burdens and economic costs. Machine learning (ML)-based prediction models have been increasingly developed to predict asthma exacerbations in recent years. This systematic review and meta-analysis aimed to identify the prediction performance of ML-based prediction models for asthma exacerbations and address the uncertainty of whether modern ML methods could become an alternative option to predict asthma exacerbations. Methods: PubMed, Cochrane Library, EMBASE, and Web of Science were searched for studies published up to December 15, 2022. Studies that applied ML methods to develop prediction models for asthma exacerbations among asthmatic patients older than five years and were published in English were eligible. The prediction model risk of bias assessment tool (PROBAST) was utilized to estimate the risk of bias and the applicability of included studies. Stata software (version 15.0) was used for the random effects meta-analysis of performance measures. Subgroup analyses stratified by ML methods, sample size, age groups, and outcome definitions were conducted. Results: Eleven studies, including 23 prediction models, were identified. Most of the studies were published in recent three years. Logistic regression, boosting, and random forest were the most used ML methods. The most common important predictors were systemic steroid use, short-acting beta2-agonists, emergency department visit, age, and exacerbation history. The overall pooled area under the curve of the receiver operating characteristics (AUROC) of 11 studies (23 prediction models) was 0.80 (95% CI 0.77–0.83). Subgroup analysis based on different ML models showed that boosting method achieved the best performance, with an overall pooled AUROC of 0.84 (95% CI 0.81–0.87). Conclusion: This study identified that ML was the potential tool to achieve great performance in predicting asthma exacerbations. However, the methodology within these models was heterogeneous. Future studies should focus on improving the generalization ability and practicability, thus driving the application of these models in clinical practice. © 2023, The Author(s).","Asthma; Exacerbation; Machine learning; Meta-analysis; Prediction model; Systematic review","Asthma; Cost of Illness; Humans; Machine Learning; Quality of Life; Steroids; beta 2 adrenergic receptor stimulating agent; steroid; algorithm; Article; asthma; clinical practice; disease exacerbation; emergency ward; groups by age; human; logistic regression analysis; machine learning; meta analysis; outcome assessment; prediction; random forest; risk assessment; sample size; systematic review; asthma; cost of illness; machine learning; quality of life","","Steroids, ","","","","","Stern J., Pier J., Litonjua A.A., Asthma epidemiology and risk factors, Semin Immunopathol, 42, pp. 5-15, (2020); Bergmann K.C., Skowasch D., Timmermann H., Lindner R., Virchow J.C., Schmidt O., Et al., Prevalence of patients with uncontrolled asthma despite NVL/GINA Step 4/5 treatment in Germany, J Asthma Allergy, 15, pp. 897-906, (2022); Nagase H., Adachi M., Matsunaga K., Yoshida A., Okoba T., Hayashi N., Et al., Prevalence, disease burden, and treatment reality of patients with severe, uncontrolled asthma in Japan, Allergol Int, 69, pp. 53-60, (2020); Loymans R.J., Ter Riet G., Sterk P.J., Definitions of asthma exacerbations, Curr Opin Allergy Clin Immunol, 11, pp. 181-186, (2011); Luskin A.T., Chipps B.E., Rasouliyan L., Miller D.P., Haselkorn T., Dorenbaum A., Impact of asthma exacerbations and asthma triggers on asthma-related quality of life in patients with severe or difficult-to-treat asthma, J Allergy Clin Immunol Pract, 2, pp. 544-52.e1-2, (2014); O'Byrne P.M., Pedersen S., Lamm C.J., Tan W.C., Busse W.W., Severe exacerbations and decline in lung function in asthma, Am J Respir Crit Care Med, 179, 1, pp. 19-24, (2009); Zeiger R.S., Schatz M., Dalal A.A., Qian L., Chen W., Ngor E.W., Et al., Utilization and costs of severe uncontrolled asthma in a managed-care setting, J Allergy Clin Immunol Pract, 4, pp. 120-129.e3, (2016); Bridge J., Blakey J.D., Bonnett L.J., A systematic review of methodology used in the development of prediction models for future asthma exacerbation, BMC Med Res Methodol, 20, (2020); Beam A.L., Kohane I.S., Big data and machine learning in health care, JAMA, 319, pp. 1317-1318, (2018); Tsang K.C.H., Pinnock H., Wilson A.M., Shah S.A., Application of machine learning algorithms for asthma management with mHealth: a clinical review, J Asthma Allergy, 15, pp. 855-873, (2022); Feng Y., Wang Y., Zeng C., Mao H., Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease, Int J Med Sci, 18, pp. 2871-2889, (2021); Reddel H.K., Taylor D.R., Bateman E.D., Boulet L.P., Boushey H.A., Busse W.W., Et al., An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice, Am J Respir Crit Care Med, 180, pp. 59-99, (2009); Moons K.G.M., Wolff R.F., Riley R.D., Whiting P.F., Westwood M., Collins G.S., Et al., PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration, Ann Intern Med, 170, pp. W1-W33, (2019); Luo G., Nau C.L., Crawford W.W., Schatz M., Zeiger R.S., Rozema E., Et al., Developing a predictive model for asthma-related hospital encounters in patients with asthma in a large, integrated health care system: secondary analysis, JMIR Med Inform, 8, (2020); Luo G., He S., Stone B.L., Nkoy F.L., Johnson M.D., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, (2020); Lieu T.A., Capra A.M., Quesenberry C.P., Mendoza G.R., Mazar M., Computer-based models to identify high-risk adults with asthma: is the glass half empty of half full?, J Asthma, 36, pp. 359-370, (1999); Schatz M., Nakahiro R., Jones C.H., Roth R.M., Joshua A., Petitti D., Asthma population management: development and validation of a practical 3-level risk stratification scheme, Am J Manag Care, 10, pp. 25-32, (2004); Schatz M., Zeiger R.S., Vollmer W.M., Mosen D., Apter A.J., Stibolt T.B., Et al., Development and validation of a medication intensity scale derived from computerized pharmacy data that predicts emergency hospital utilization for persistent asthma, Am J Manag Care, 12, pp. 478-484, (2006); Xu M., Tantisira K.G., Wu A., Litonjua A.A., Chu J.H., Himes B.E., Et al., Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers, BMC Med Genet, 12, (2011); van Vliet D., Smolinska A., Jobsis Q., Rosias P., Muris J., Dallinga J., Dompeling E., van Schooten F.J., Can exhaled volatile organic compounds predict asthma exacerbations in children?, J Breath Res, 11, (2017); Tong Y., Messinger A.I., Wilcox A.B., Mooney S.D., Davidson G.H., Suri P., Luo G., Forecasting future asthma hospital encounters of patients with asthma in an academic health care system: predictive model development and secondary analysis study, J Med Internet Res, 23, (2021); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, pp. 1747-1757, (2021); Noble M., Burden A., Stirling S., Clark A.B., Musgrave S., Alsallakh M.A., Et al., Predicting asthma-related crisis events using routine electronic healthcare data: a quantitative database analysis study, Br J Gen Pract, 71, pp. e948-e957, (2021); de Hond A.A.H., Kant I.M.J., Honkoop P.J., Smith A.D., Steyerberg E.W., Sont J.K., Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations, Sci Rep, 12, (2022); van der Ploeg T., Austin P.C., Steyerberg E.W., Modern modeling techniques are data hungry: a simulation study for predicting dichotomous endpoints, BMC Med Res Methodol, 14, (2014); DiMango E., Rogers L., Reibman J., Gerald L.B., Brown M., Sugar E.A., Et al., Risk factors for asthma exacerbation and treatment failure in adults and adolescents with well-controlled asthma during continuation and step-down therapy, Ann Am Thorac Soc, 15, pp. 955-961, (2018); McDowell P.J., Busby J., Hanratty C.E., Djukanovic R., Woodcock A., Walker S., Et al., Exacerbation profile and risk factors in a type-2-low enriched severe asthma cohort: a clinical trial to assess asthma exacerbation phenotypes, Am J Respir Crit Care Med, 206, pp. 545-553, (2022); Wang M., Li H., Huang S., Qian Y., Steenland K., Xie Y., Papatheodorou S., Shi L., Short-term exposure to nitrogen dioxide and mortality: a systematic review and meta-analysis, Environ Res, 202, (2021)","S. Xiong; Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China; email: xsq20180224@student.pumc.edu.cn; C. Liu; Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Children’s Hospital of Capital Institute of Pediatrics, Beijing, 100020, China; email: liuchcip@126.com","","BioMed Central Ltd","","","","","","14712466","","BPMMB","37507662","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85165982158"
"Sun S.; Wang C.; Zhao P.; Kline G.M.; Grandjean J.M.D.; Jiang X.; Labaudiniere R.; Wiseman R.L.; Kelly J.W.; Balch W.E.","Sun, Shuhong (57215132626); Wang, Chao (57211638548); Zhao, Pei (57211634776); Kline, Gabe M. (57358449600); Grandjean, Julia M.D. (57201451336); Jiang, Xin (55724170100); Labaudiniere, Richard (57981003900); Wiseman, R. Luke (7102299149); Kelly, Jeffery W. (35465218900); Balch, William E. (7102039021)","57215132626; 57211638548; 57211634776; 57358449600; 57201451336; 55724170100; 57981003900; 7102299149; 35465218900; 7102039021","Capturing the conversion of the pathogenic alpha-1-antitrypsin fold by ATF6 enhanced proteostasis","2023","Cell Chemical Biology","30","1","","22","42.e5","","8","10.1016/j.chembiol.2022.12.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146338822&doi=10.1016%2fj.chembiol.2022.12.004&partnerID=40&md5=1471c221c65e3a7d980a1b80d924f332","Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Department of Chemistry, The Scripps Research Institute, La Jolla, CA, United States; Protego Biopharma, 10945 Vista Sorrento Parkway, San Diego, CA, United States","Sun S., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Wang C., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Zhao P., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Kline G.M., Department of Chemistry, The Scripps Research Institute, La Jolla, CA, United States; Grandjean J.M.D., Protego Biopharma, 10945 Vista Sorrento Parkway, San Diego, CA, United States; Jiang X., Protego Biopharma, 10945 Vista Sorrento Parkway, San Diego, CA, United States; Labaudiniere R., Protego Biopharma, 10945 Vista Sorrento Parkway, San Diego, CA, United States; Wiseman R.L., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Kelly J.W., Department of Chemistry, The Scripps Research Institute, La Jolla, CA, United States; Balch W.E., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States","Genetic variation in alpha-1 antitrypsin (AAT) causes AAT deficiency (AATD) through liver aggregation-associated gain-of-toxic pathology and/or insufficient AAT activity in the lung manifesting as chronic obstructive pulmonary disease (COPD). Here, we utilize 71 AATD-associated variants as input through Gaussian process (GP)-based machine learning to study the correction of AAT folding and function at a residue-by-residue level by pharmacological activation of the ATF6 arm of the unfolded protein response (UPR). We show that ATF6 activators increase AAT neutrophil elastase (NE) inhibitory activity, while reducing polymer accumulation for the majority of AATD variants, including the prominent Z variant. GP-based profiling of the residue-by-residue response to ATF6 activators captures an unexpected role of the “gate” area in managing AAT-specific activity. Our work establishes a new spatial covariant (SCV) understanding of the convertible state of the protein fold in response to genetic perturbation and active environmental management by proteostasis enhancement for precision medicine. © 2022 Elsevier Ltd","activating transcription factor 6 (ATF6); alpha-1 antitrypsin; alpha-1 antitrypsin deficiency; chaperones; Gaussian process; genetic variation; machine learning; pharmacological ATF6 activators; precision medicine; protein aggregation; protein folding; protein misfolding disease; proteostasis; unfolded protein response (UPR)","Activating Transcription Factor 6; alpha 1-Antitrypsin Deficiency; Humans; Proteostasis; Pulmonary Disease, Chronic Obstructive; activating transcription factor 6; alpha 1 antitrypsin; aspartate aminotransferase; chaperone; leukocyte elastase; activating transcription factor 6; ATF6 protein, human; alpha 1 antitrypsin deficiency; Article; enzyme activity; enzyme inhibition; genetic variation; machine learning; personalized medicine; polymerization; protein aggregation; protein conformation; protein fingerprinting; protein folding; protein function; protein homeostasis; protein misfolding; protein secretion; protein transport; unfolded protein response; alpha 1 antitrypsin deficiency; chronic obstructive lung disease; complication; genetics; human; metabolism; protein homeostasis","","alpha 1 antitrypsin, 9041-92-3; aspartate aminotransferase, 9000-97-9; leukocyte elastase, 109968-22-1; Activating Transcription Factor 6, ; ATF6 protein, human, ","","","Protego Biopharma, (AA-147, AA-263); National Institutes of Health, NIH, (AG046495, AG049665, AG070209, DK123038, HL095524, HL141810); National Institutes of Health, NIH; Alpha-1 Foundation, A1F; Bio-Rad Laboratories, (1610734, 1610738, 4561094DC); Bio-Rad Laboratories","Funding text 1: We thank Jaleh Mesgarzadeh and Belle Romine for helpful discussions. Support was provided by NIH grants HL141810 , HL095524 , AG049665 , and AG070209 to W.E.B.; DK123038 to R.L.W.; and AG046495 to R.L.W. and J.W.K. C.W. was supported by a postdoctoral research fellowship from Alpha-1 Foundation . ; Funding text 2: We thank Jaleh Mesgarzadeh and Belle Romine for helpful discussions. Support was provided by NIH grants HL141810, HL095524, AG049665, and AG070209 to W.E.B.; DK123038 to R.L.W.; and AG046495 to R.L.W. and J.W.K. C.W. was supported by a postdoctoral research fellowship from Alpha-1 Foundation. S.S. C.W. and P.Z. contributed to the design and collection of experimental data for GP analysis. G.M.K. provided the ATF6 activators. C.W. S.S. and P.Z. performed the computational analysis. J.M.D.G. X.J. and R.L. contributed to the human iPSC (hiPSC) experiments. S.S. C.W. and W.E.B. generated the manuscript with support by X.J. R.L.W. and J.W.K. S.S. C.W. P.Z. G.M.K. and W.E.B. declare no competing interests. J.W.K. and R.L.W. are shareholders and scientific advisory board members of Protego Biopharma. J.W.K. X.J. and R.L. are co-founders of Protego Biopharma. J.M.D.G. was an employee of Protego Biopharma. Protego Biopharma is exploring UPR activating compounds, including AA-147 and AA-263. for the treatment of protein misfolding diseases. We support inclusive, diverse, and equitable conduct of research.; Funding text 3: ATF6 activators AA-147 and AA-263 were provided by the labs of Jeffery W. Kelly and R. Luke Wiseman. 59 ATF6 inhibitor Ceapin-A7 was purchased form Sigma-Aldrich (No. SML2330, St. Louis, MO). S1P inhibitor PF-429242 was purchase from Sigma-Aldrich (No. SML0667, St. Louis, MO). Autophagy inhibitor Bafilomycin A1 was purchased from Abcam (No. 246689). Complete-Mini protease inhibitor Cocktail used for cell lysate was purchased from Roche (No. 11836170001). The goat anti-human AAT polyclonal antibody (80A) used in ELISA assay plate coating and for immunoblotting was purchased from ICL. Inc (No. GCYT-80A). The mouse anti-human AAT monoclonal monomer recognize protein antibody (16F8) was generated by Scripps Research Antibody Development and Production Core. The mouse anti-human AAT monoclonal polymer protein recognize antibody (2C1) was purchased from Hycult Biotech (No.HM2289, Wayne, PA). GAPDH antibody was purchased from abcam (No. ab8245). GRP78 antibody and GRP94 antibody and PDIA antibody were purchased from abcam (No. ab108615, ab238126 and ab155800). HRP secondary antibodies were purchased from Thermo Fisher Scientific. Human neutrophil elastase (NE) was purchased from Innovative Research (IHUELASD100UG, Novi, MI). NE fluorescence substrate 2Rh110 (Z-Ala-Ala-Ala-Ala) was purchased from Cayman Chemical (item No. 11675, Ann Arbor, MI). Clear flat bottom polystyrene high binding 96-well plate was purchased from Corning (Ref 3690). DNA purification kit was purchased from QIAGEN Inc (Valencia, CA). Cell transfection reagent FuGENE6 was purchased from Promega (Madison, WI). Cell transfection reagent Lipofectamine RNAiMAX was purchased from Thermo Fisher Scientific. GRP78 and GRP94 siRNA were purchased from Thermo Fisher Scientific. DMEM cell culture medium was purchased from Corning; F-12 culture medium was purchased from Sigma-Aldrich; and LHC-8 cell culture medium was purchased from Thermo Fisher Scientific. FBS, penicillin streptomycin (P/S) and L-glutamine were purchased from Thermo Fisher Scientific. Non-denaturing gel, Tris/Glycine buffer and native sample buffer were purchased from Bio-Rad (No. 4561094DC, No. 1610734 and No. 1610738). ","Ferro-Novick S., Reggiori F., Brodsky J.L., ER-phagy, ER homeostasis, and ER quality control: implications for disease, Trends Biochem. 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Biol.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85146338822"
"Koido M.; Hon C.-C.; Koyama S.; Kawaji H.; Murakawa Y.; Ishigaki K.; Ito K.; Sese J.; Parrish N.F.; Kamatani Y.; Carninci P.; Terao C.","Koido, Masaru (55624124900); Hon, Chung-Chau (7003617137); Koyama, Satoshi (57204634529); Kawaji, Hideya (57196621148); Murakawa, Yasuhiro (16024938100); Ishigaki, Kazuyoshi (36141626500); Ito, Kaoru (57222379965); Sese, Jun (6603719506); Parrish, Nicholas F. (56688333700); Kamatani, Yoichiro (23018337900); Carninci, Piero (7005203099); Terao, Chikashi (35182021300)","55624124900; 7003617137; 57204634529; 57196621148; 16024938100; 36141626500; 57222379965; 6603719506; 56688333700; 23018337900; 7005203099; 35182021300","Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning","2023","Nature Biomedical Engineering","7","6","","830","844","14","13","10.1038/s41551-022-00961-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142364182&doi=10.1038%2fs41551-022-00961-8&partnerID=40&md5=29d2ac9c9530f086334ca2a6cf06b6f0","Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan; Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan; RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy; Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan; Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States; Center for Data Sciences, Harvard Medical School, Boston, MA, United States; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan; Humanome Lab Inc., Tokyo, Japan; Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research and RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory for Single Cell Technologies, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Human Technopole, Milan, Italy; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan","Koido M., Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan, Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan; Hon C.-C., Laboratory for Genome Information Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Koyama S., Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Kawaji H., Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan; Murakawa Y., RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy, Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan; Ishigaki K., Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, Center for Data Sciences, Harvard Medical School, Boston, MA, United States, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States; Ito K., Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Sese J., Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Aomi, Koto-ku, Tokyo, Japan, Humanome Lab Inc., Tokyo, Japan; Parrish N.F., Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Cluster for Pioneering Research and RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Kamatani Y., Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan; Carninci P., Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Laboratory for Single Cell Technologies, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Human Technopole, Milan, Italy; Terao C., Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan, Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan, The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan","Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn’s disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.","","Genome; Genome-Wide Association Study; Humans; RNA, Untranslated; Transcription, Genetic; Cytology; Learning systems; Machine learning; genomic RNA; long untranslated RNA; messenger RNA; untranslated RNA; Cell types; Complex mechanisms; Complex traits; Gene transcriptions; Genetic linkage; Genome sequences; Genome-wide association studies; Machine learning models; Machine-learning; RNA transcription; Article; asthma; comparative study; computer model; computer prediction; controlled study; convolutional neural network; Crohn disease; enhancer region; expression quantitative trait locus; gene frequency; gene mutation; genetic linkage; genetic risk; genome analysis; genome-wide association study; human; human cell; human tissue; machine learning; mutagenesis; primary cell; promoter region; RNA transcription; genetic transcription; genetics; genome; genome-wide association study; Transcription","","RNA, Untranslated, ","","","National Institute of Advanced Industrial Science and Technology, AIST; FANTOM; Japan Agency for Medical Research and Development, AMED, (JP21ck0106642, JP21kk0305013, JP21tm0424220); Japan Agency for Medical Research and Development, AMED; Japan Society for the Promotion of Science, JSPS, (20K15773, JP20H00462, 23K24373); Japan Society for the Promotion of Science, JSPS","We thank FANTOM consortium members for providing datasets and valuable discussions. Computational resources of AI Bridging Cloud Infrastructure (ABCI) provided by the National Institute of Advanced Industrial Science and Technology (AIST) were used for in silico mutagenesis. This work was supported in part by JSPS KAKENHI (grant number 20K15773, to M.K.), JP20H00462, the JCR Grant for Promoting Basic Rheumatology, and AMED (under grant numbers JP21kk0305013, JP21tm0424220 and JP21ck0106642, to C.T.).","Andersson R., Et al., An atlas of active enhancers across human cell types and tissues, Nature, 507, pp. 455-461, (2014); Forrest A.R.R., Et al., A promoter-level mammalian expression atlas, Nature, 507, pp. 462-470, (2014); Hon C.C., Et al., An atlas of human long non-coding RNAs with accurate 5′ ends, Nature, 543, pp. 199-204, (2017); Kristjansdottir K., Et al., Population-scale study of eRNA transcription reveals bipartite functional enhancer architecture, Nat. 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Terao; Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; email: chikashi.terao@riken.jp","","Nature Research","","","","","","2157846X","","","36411359","English","Nat. Biomed. Eng.","Article","Final","","Scopus","2-s2.0-85142364182"
"Miranda O.; Fan P.; Qi X.; Yu Z.; Ying J.; Wang H.; Brent D.A.; Silverstein J.C.; Chen Y.; Wang L.","Miranda, Oshin (57200678658); Fan, Peihao (57204628845); Qi, Xiguang (57220802082); Yu, Zeshui (57564198700); Ying, Jian (55205135100); Wang, Haohan (56025137300); Brent, David A. (26643140900); Silverstein, Jonathan C. (7102739508); Chen, Yu (58997285800); Wang, Lirong (37087786200)","57200678658; 57204628845; 57220802082; 57564198700; 55205135100; 56025137300; 26643140900; 7102739508; 58997285800; 37087786200","DeepBiomarker: Identifying Important Lab Tests from Electronic Medical Records for the Prediction of Suicide-Related Events among PTSD Patients","2022","Journal of Personalized Medicine","12","4","524","","","","12","10.3390/jpm12040524","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127658848&doi=10.3390%2fjpm12040524&partnerID=40&md5=0cf96c61a113e71e2101a6bc69194eae","Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Department of Internal Medicine, University of Utah, Salt Lake City, 84132, UT, United States; Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, PA, United States; Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, Pittsburgh, 15213, PA, United States; Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Eli Lilly and Company, Lilly Corporate Center, Indianapolis, 46225, IN, United States","Miranda O., Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Fan P., Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Qi X., Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Yu Z., Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States; Ying J., Department of Internal Medicine, University of Utah, Salt Lake City, 84132, UT, United States; Wang H., Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, PA, United States; Brent D.A., Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, Pittsburgh, 15213, PA, United States; Silverstein J.C., Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Chen Y., Eli Lilly and Company, Lilly Corporate Center, Indianapolis, 46225, IN, United States; Wang L., Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, PA, United States","Identifying patients with high risk of suicide is critical for suicide prevention. We examined lab tests together with medication use and diagnosis from electronic medical records (EMR) data for prediction of suicide-related events (SREs; suicidal ideations, attempts and deaths) in post-traumatic stress disorder (PTSD) patients, a population with a high risk of suicide. We developed DeepBiomarker, a deep-learning model through augmenting the data, including lab tests, and integrating contribution analysis for key factor identification. We applied DeepBiomarker to analyze EMR data of 38,807 PTSD patients from the University of Pittsburgh Medical Center. Our model predicted whether a patient would have an SRE within the following 3 months with an area under curve score of 0.930. Through contribution analysis, we identified important lab tests for suicide prediction. These identified factors imply that the regulation of the immune system, respiratory system, cardiovascular system, and gut microbiome were involved in shaping the pathophysiological pathways promoting depression and suicidal risks in PTSD patients. Our results showed that abnormal lab tests combined with medication use and diagnosis could facilitate predicting SRE risk. Moreover, this may imply beneficial effects for suicide prevention by treating comorbidities associated with these biomarkers. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","biomarker identification; deep learning; PTSD; real-world evidence","biological marker; calcium; cefalexin; chloride; dexamethasone; diltiazem; epinastine; ergocalciferol; fosfomycin; glucose; hemoglobin; isotretinoin; multivitamin; mycophenolic acid; phytomenadione; potassium; prednisone; protein; rosuvastatin; sodium; tacrolimus; tobramycin; torasemide; trospium chloride; ustekinumab; anemia; area under the curve; Article; asthma; attention network; augmentation index; bacterium; basophil; biopsychosocial model; bipolar disorder; blood; brain infarction; cardiovascular system; cellular distribution; data analysis; deep learning; depression; diagnostic test accuracy study; dilated recurrent neural network; electronic medical record; epilepsy; erythrocyte; gastroesophageal reflux; glucose urine level; hematocrit; human; ICD-10; ICD-9; immune system; intestine flora; irritable colon; laboratory test; leukocyte; long short term memory network; machine learning; major clinical study; mean corpuscular hemoglobin; mean corpuscular volume; mean platelet volume; migraine; neutrophil; osteoporosis; oxidative stress; posttraumatic stress disorder; prediction; prothrombin time; quasi recurrent neural network; receiver operating characteristic; recurrent neural network; red blood cell distribution width; respiratory system; reverse time attentIon model; suicidal ideation; thrombocyte; tobacco dependence; transient ischemic attack; urine","","calcium, 7440-70-2, 14092-94-5; cefalexin, 15686-71-2, 23325-78-2; chloride, 16887-00-6; dexamethasone, 50-02-2; diltiazem, 33286-22-5, 42399-41-7; epinastine, 80012-43-7, 108929-04-0, 134507-57-6, 1357573-04-6; ergocalciferol, 50-14-6, 50809-47-7, 8042-78-2; fosfomycin, 23155-02-4; glucose, 50-99-7, 84778-64-3, 8027-56-3; hemoglobin, 9008-02-0; isotretinoin, 4759-48-2; mycophenolic acid, 23047-11-2, 24280-93-1, 37415-62-6; phytomenadione, 11104-38-4, 84-80-0; potassium, 7440-09-7; prednisone, 53-03-2; protein, 67254-75-5; rosuvastatin, 147098-18-8, 147098-20-2, 287714-41-4; sodium, 7440-23-5; tacrolimus, 104987-11-3, 109581-93-3; tobramycin, 32986-56-4; torasemide, 56211-40-6; trospium chloride, 10405-02-4; ustekinumab, 815610-63-0, 949907-93-1","","","National Institutes of Health, NIH, (MH116046, S10OD028483-01A1, UL1 TR001857); National Institutes of Health, NIH; University of Pittsburgh","Funding: This study is supported by National Institutes of Health grant MH116046. The project described was also supported by the National Institutes of Health through Grant Number UL1 TR001857. This research was supported in part by the University of Pittsburgh Center for Research Computing through the NIH S10OD028483-01A1 grant.","Patton G.C., Coffey S.M.C., Sawyer S.M., Viner R.M., Haller D., Bose K., Vos T., Ferguson J., Mathers C.D., Global patterns of mortality in young people: A systematic analysis of population health data, Lancet, 374, pp. 881-892, (2009); Figures and Facts About Suicide, (1999); Woolf S.H., Schoomaker H., Life Expectancy and Mortality Rates in the United States, 1959–2017, JAMA J. Am. Med. 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Chen; Eli Lilly and Company, Lilly Corporate Center, Indianapolis, 46225, United States; email: yu.chen@lilly.com; L. Wang; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, 15206, United States; email: liw30@pitt.edu","","MDPI","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127658848"
"Su H.; Song Y.; Yang S.; Zhang Z.; Shen Y.; Yu L.; Chen S.; Gao L.; Chen C.; Hou D.; Wei X.; Ma X.; Huang P.; Sun D.; Zhou J.; Qian K.","Su, Haiyang (57202131615); Song, Yuanlin (7404920196); Yang, Shouzhi (57605440800); Zhang, Ziyue (57604682400); Shen, Yao (56512724600); Yu, Lan (57296101000); Chen, Shujing (16480074300); Gao, Lei (57301611000); Chen, Cuicui (56889988600); Hou, Dongni (57190855710); Wei, Xinping (57221077812); Ma, Xuedong (57221103224); Huang, Pengyu (56290954800); Sun, Dejun (56996510500); Zhou, Jian (59278874300); Qian, Kun (56442087400)","57202131615; 7404920196; 57605440800; 57604682400; 56512724600; 57296101000; 16480074300; 57301611000; 56889988600; 57190855710; 57221077812; 57221103224; 56290954800; 56996510500; 59278874300; 56442087400","Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations","2024","ACS Central Science","10","2","","331","343","12","8","10.1021/acscentsci.3c01201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184751588&doi=10.1021%2facscentsci.3c01201&partnerID=40&md5=a1812776dc2d7ef89c62692fd5dc8171","State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China; Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China; Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China; Center of Emergency and Critical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China; Department of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University, Shanghai, 201399, China; Clinical Medical Research Center, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China; Inner Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China; Inner Mongolia Academy of Medical Sciences, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China; Shanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai, 201102, China; Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, China; Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China","Su H., State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China; Song Y., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China, Center of Emergency and Critical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China; Yang S., State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China; Zhang Z., State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China; Shen Y., Department of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University, Shanghai, 201399, China; Yu L., Clinical Medical Research Center, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China, Inner Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China, Inner Mongolia Academy of Medical Sciences, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China; Chen S., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China; Gao L., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China; Chen C., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China; Hou D., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China; Wei X., Shanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai, 201102, China; Ma X., Shanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai, 201102, China; Huang P., Shanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai, 201102, China; Sun D., Inner Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People’s Hospital, Inner Mongolia, Hohhot, 010017, China, Department of Respiratory and Critical Care Medicine, Inner Mongolia People’s Hospital, Hohhot, 010017, China; Zhou J., Department of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China, Shanghai Key Laboratory of Lung Inflammation and Injury, 180 Fenglin Road, Shanghai, 200032, China, Center of Emergency and Critical Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, China; Qian K., State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China, Shanghai Key Laboratory of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China","Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques. © 2024 The Authors. Published by American Chemical Society.","","Binary alloys; Biomarkers; Desorption; Electric fields; Mass spectrometry; Metabolism; Palladium alloys; Acute exacerbations; American Chemical Society; Areas under the curves; Chronic obstructive pulmonary disease; Laser desorption/ionization mass spectrometries; Mesoporous; Metabolic biomarkers; Metabolic fingerprints; Pd-Pt alloy; Plasmonics; Diagnosis","","","","","","","Christenson S.A., Smith B.M., Bafadhel M., Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, 10342, pp. 2227-2242, (2022); Yang I.A., Jenkins C.R., Salvi S.S., Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment, Lancet Resp. 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Qian; State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, China; email: k.qian@sjtu.edu.cn","","American Chemical Society","","","","","","23747943","","ACSCI","","English","ACS Cent. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85184751588"
"Lin X.; Lei Y.; Chen J.; Xing Z.; Yang T.; Wang Q.; Wang C.","Lin, Xinshan (57221938049); Lei, Yi (57201733883); Chen, Jun (57216703254); Xing, Zhihui (56029733900); Yang, Ting (57201495536); Wang, Qing (57203746011); Wang, Chen (57196394775)","57221938049; 57201733883; 57216703254; 56029733900; 57201495536; 57203746011; 57196394775","A Case-Finding Clinical Decision Support System to Identify Subjects with Chronic Obstructive Pulmonary Disease Based on Public Health Data","2023","Tsinghua Science and Technology","28","3","","525","540","15","8","10.26599/TST.2022.9010010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145436074&doi=10.26599%2fTST.2022.9010010&partnerID=40&md5=3679ac94ed04bd65837b5d1809befcb0","China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China; Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China; School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Intelligent Healthcare Unit, Baidu Inc, Beijing, 100093, China; Tsinghua University, Department of Automation, Beijing, 100084, China","Lin X., China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China; Lei Y., School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Chen J., Intelligent Healthcare Unit, Baidu Inc, Beijing, 100093, China; Xing Z., Intelligent Healthcare Unit, Baidu Inc, Beijing, 100093, China; Yang T., China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China; Wang Q., Tsinghua University, Department of Automation, Beijing, 100084, China; Wang C., China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China","Chronic obstructive pulmonary disease (COPD) is a serious chronic respiratory disease. Improving the ability to identify patients with COPD in primary medical institutions is important to prevent and treat the disease. With the continuous development of medical digitization, the application of big data informatization in the medical and health fields has become possible. Recently, applying innovative technologies such as big data analysis, machine learning, and artificial intelligence-Assisted decision-making in the medical field has become an interdisciplinary research hotspot. Based on the identification and diagnosis of COPD in the high-risk population, this study proposes a convenient and effective clinical decision support system to help identify patients with COPD in primary health institutions. The results of the preliminary experiments show that the proposed method is convenient and effective compared with the existing methods.  © 1996-2012 Tsinghua University Press.","artificial intelligence; case finding; chronic obstructive pulmonary disease (COPD); clinical decision support system (CDSS); machine learning","Decision making; Decision support systems; Diagnosis; Health risks; Machine learning; Pulmonary diseases; Case finding; Chronic obstructive pulmonary disease; Clinical decision support system; Clinical decision support systems; Continuous development; Digitisation; Health data; Machine-learning; Medical institutions; Big data","","","","","","","Li W., Yang T., Wang C., Current status and progress of prevention and treatment of chronic obstructive pulmonary disease in China Chinese), J. Chin. Res. Hosp, 7, 5, pp. 78-84, (2020); Wang C., Xu J.Y., Yang L., Xu Y.J., Zhang X.Y., Bai C.X., Kang J., Ran P.X., Shen H.H., Wen F.Q., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): A national cross-sectional study, Lancet, 391, 10131, pp. 1706-1717, (2018); Haroon S., Jordan R.E., Fitzmaurice D.A., Adab P., Case finding for COPD in primary care: A qualitative study of the views of health professionals, Int. J. Chron. Obstruct. Pulmon. Dis, 10, pp. 1711-1718, (2015); (2019); Projections of Mortality and Causes of Death, 2016 and 2060, (2020); Kaplan A., Thomas M., Screening for COPD: The gap between logic and evidence, Eur. Respir. Rev, 26, 143, (2017); (2014); Zhang D.Y., Gao Y., Jian W.H., Yao M., Zheng J.P., Zhong N.S., Feasibility and suggestions on the promotion of pulmonary function test in primary health care institutions, (in Chinese, Chin. Gen. 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J, 55, 6, (2020); Zhou Y.M., Chen S.Y., Tian J., Cui J.Y., Li X.C., Hong W., Zhao Z.X., Hu G.P., He F., Qiu R., Et al., Development and validation of a chronic obstructive pulmonary disease screening questionnaire in China, Int. J. Tuberc. Lung Dis, 17, 12, pp. 1645-1651, (2013); Jia C.B., Huang K., Zhang C.Y., Fang F., Dong F., Gu X.Y., Niu H.T., QuMu S.W., Ren X.X., Li W., Et al., Status survey of the diagnosis and management ability of chronic obstructive pulmonary disease in eighty cities in China Chinese), Chin. J. Clin, 49, 6, pp. 669-671, (2021); (2009); Guidelines on the Construction of Regional Health Information Platform Based on Health Records, (2009); Kohavi R., A study of cross-validation and bootstrap for accuracy estimation and model selection, Proc. 14th Int. Joint Conf. Artificial Intelligence, pp. 1137-1143, (1995); Hastie T., Rosset S., Zhu J., Zou H., Multi-class AdaBoost Stat. Interface, 2, 3, pp. 349-360, (2009); Sahoo A.K., Pradhan C., Das H., Performance evaluation of different machine learning methods and deeplearning based convolutional neural network for health decision making, Nature Inspired Computing for Data Science, pp. 201-212, (2020); Lundberg S., Lee S.I., A Unified Approach to Interpreting Model Predictions, (2017); Martinez F.J., Raczek A.E., Seifer F.D., Conoscenti C.S., Curtice T.G., D'Eletto T., Cote C., Hawkins C., Phillips A.L., Development and initial validation of a selfscored COPD population screener questionnaire (COPDPS), Copd, 5, 2, pp. 85-95, (2008); Kotz D., Nelemans P., Van Schayck C.P., Wesseling G.J., External validation of a COPD diagnostic questionnaire, Eur. Respir. J, 31, 2, pp. 298-303, (2008); Bakke P.S., Ronmark E., Eagan T., Pistelli F., Annesi-Maesano I., Maly M., Meren M., Vermeire P., Vestbo J., Viegi G., Et al., Recommendations for epidemiological studies on COPD, Eur. Respir. J, 38, 6, pp. 1261-1277, (2011); Hansen J.G., Pedersen L., Overvad K., Omland O, Jensen H.K., Sorensen H.T., The prevalence of chronic obstructive pulmonary disease among Danes aged 45-84 years: Population-based study, Copd, 5, 6, pp. 347-352, (2008); Mohamed Hoesein F.A.A., Zanen P., Sachs A.P.E., Verheij T.J.M., Lammers J.W.J., Broekhuizen B.D.L., Spirometric thresholds for diagnosing COPD: 0.70 or LLN pre-or post-dilator values?, Copd, 9, 4, pp. 338-343, (2012)","T. Yang; China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China; email: dryangting@qq.com; C. Wang; China-Japan Friendship Hospital, Department of Pulmonary and Critical Care Medicine, Beijing, 100029, China; email: cyhbirm@263.net; Q. Wang; Tsinghua University, Department of Automation, Beijing, 100084, China; email: qing.wang@tsinghua.edu.cn","","Tsinghua University","","","","","","10070214","","TSTEF","","English","Tsinghua Sci. Tech.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85145436074"
"Kroes J.A.; Bansal A.T.; Berret E.; Christian N.; Kremer A.; Alloni A.; Gabetta M.; Marshall C.; Wagers S.; Djukanovic R.; Porsbjerg C.; Hamerlijnck D.; Fulton O.; Brinke A.T.; Bel E.H.; Sont J.K.","Kroes, Johannes A. (57216509990); Bansal, Aruna T. (8583470200); Berret, Emmanuelle (57917864100); Christian, Nils (8685855400); Kremer, Andreas (56785492300); Alloni, Anna (56624375000); Gabetta, Matteo (36188129400); Marshall, Chris (57917864200); Wagers, Scott (6602151433); Djukanovic, Ratko (57210653671); Porsbjerg, Celeste (6603241278); Hamerlijnck, Dominique (57194735994); Fulton, Olivia (57035338800); Brinke, Anneke Ten (56007367800); Bel, Elisabeth H. (7003406795); Sont, Jacob K. (7003820342)","57216509990; 8583470200; 57917864100; 8685855400; 56785492300; 56624375000; 36188129400; 57917864200; 6602151433; 57210653671; 6603241278; 57194735994; 57035338800; 56007367800; 7003406795; 7003820342","Blueprint for harmonising unstandardised disease registries to allow federated data analysis: prepare for the future","2022","ERJ Open Research","8","4","00168-2022","","","","7","10.1183/23120541.00168-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139418373&doi=10.1183%2f23120541.00168-2022&partnerID=40&md5=9ef78c093789cb826be083e0fa93bf2b","Dept of Clinical Pharmacy and Pharmacology, Medical Centre Leeuwarden, Leeuwarden, Netherlands; Acclarogen Ltd, Cambridge, United Kingdom; European Respiratory Society, Lausanne, Switzerland; ITTM SA, Esch-sur-Alzette, Luxembourg; Biomeris SRL, Pavia, Italy; Metaseq Ltd, Malvern, United Kingdom; BIOSCI Consulting, Maasmechelen, Belgium; NIHR Southampton Respiratory Biomedical Research Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Dept of Pulmonology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark; Patient Advisory Group, European Lung Foundation, Sheffield, United Kingdom; Dept of Pulmonology, Medical Centre Leeuwarden, Leeuwarden, Netherlands; Amsterdam Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Dept of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, Netherlands","Kroes J.A., Dept of Clinical Pharmacy and Pharmacology, Medical Centre Leeuwarden, Leeuwarden, Netherlands; Bansal A.T., Acclarogen Ltd, Cambridge, United Kingdom; Berret E., European Respiratory Society, Lausanne, Switzerland; Christian N., ITTM SA, Esch-sur-Alzette, Luxembourg; Kremer A., ITTM SA, Esch-sur-Alzette, Luxembourg; Alloni A., Biomeris SRL, Pavia, Italy; Gabetta M., Biomeris SRL, Pavia, Italy; Marshall C., Metaseq Ltd, Malvern, United Kingdom; Wagers S., BIOSCI Consulting, Maasmechelen, Belgium; Djukanovic R., NIHR Southampton Respiratory Biomedical Research Unit, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Porsbjerg C., Dept of Pulmonology, Bispebjerg Hospital, University of Copenhagen, Copenhagen, Denmark; Hamerlijnck D., Patient Advisory Group, European Lung Foundation, Sheffield, United Kingdom; Fulton O., Patient Advisory Group, European Lung Foundation, Sheffield, United Kingdom; Brinke A.T., Dept of Pulmonology, Medical Centre Leeuwarden, Leeuwarden, Netherlands; Bel E.H., Amsterdam Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; Sont J.K., Dept of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Center, Leiden, Netherlands","Real-world evidence from multinational disease registries is becoming increasingly important not only for confirming the results of randomised controlled trials, but also for identifying phenotypes, monitoring disease progression, predicting response to new drugs and early detection of rare side-effects. With new open-access technologies, it has become feasible to harmonise patient data from different disease registries and use it for data analysis without compromising privacy rules. Here, we provide a blueprint for how a clinical research collaboration can successfully use real-world data from existing disease registries to perform federated analyses. We describe how the European severe asthma clinical research collaboration SHARP (Severe Heterogeneous Asthma Research collaboration, Patient-centred) fulfilled the harmonisation process from nonstandardised clinical registry data to the Observational Medical Outcomes Partnership Common Data Model and built a strong network of collaborators from multiple disciplines and countries. The blueprint covers organisational, financial, conceptual, technical, analytical and research aspects, and discusses both the challenges and the lessons learned. All in all, setting up a federated data network is a complex process that requires thorough preparation, but above all, it is a worthwhile investment for all clinical research collaborations, especially in view of the emerging applications of artificial intelligence and federated learning. © The authors 2022.","","architecture; Article; artificial intelligence; asthma; bioinformatics; clinical research; data analysis; data base; disease exacerbation; disease registry; education; harmonisation; health care personnel; human; information technology; learning; Observational Health Data Sciences and Informatics; Observational Medical Outcomes Partnership; open access; outcome assessment; pandemic; patient care; patient registry; phenotype; practice guideline; quality control; randomized controlled trial (topic)","","","","","GlaxoSmithKline, GSK; Novartis; Sanofi; Teva Pharmaceutical Industries; Chiesi Farmaceutici; European Respiratory Society, ERS","Funding text 1: The European Respiratory Society SHARP CRC is supported by GlaxoSmithKline, Teva Pharmaceutical Industries, Novartis, Sanofi and Chiesi Farmaceutici. Funding information for this article has been deposited with the Crossref Funder Registry.; Funding text 2: Conflict of interest: J.A. Kroes reports grants from AstraZeneca BV outside the submitted work. A.T. Bansal has nothing to disclose. E. Berret is an employee of the European Respiratory Society. N. Christian is an employee of ITTM SA. A. Kremer is an employee of ITTM SA. A. Alloni is an employee of ITTM SA. M. Gabetta is an employee of Biomeris SRL. C. Marshall has nothing to disclose. S. Wagers reports personal fees from King’s College Hospital NHS Foundation Trust, Academic Medical Research, AMC Medical Research BV, Asthma UK, Athens Medical School, Boehringer Ingelheim International GmbH, CHU de Toulouse, CIRO, DS Biologicals Ltd, École Polytechnique Fédérale de Lausanne, European Respiratory Society, FISEVI, Fluidic Analytics Ltd, Fraunhofer IGB, Fraunhofer ITEM, GlaxoSmithKline R&D Ltd, Holland & Knight, Karolinska Institutet Fakturor, KU Leuven, Longfonds, National Heart and Lung Institute, Novartis Pharma AG, Owlstone Medical Ltd, PExA AB, UCB Biopharma SPRL, Umeå University, University Hospital Southampton NHS Foundation Trust, Università Campus Bio-Medico di Roma, Universita Cattolica del Sacro Cuore, Universität Ulm, University of Bern, University of Edinburgh, University of Hull, University of Leicester, University of Loughborough, University of Manchester, University of Nottingham, Vlaams Brabant, Dienst Europa, Imperial College London, Boehringer Ingelheim, Breathomix, Gossamer Bio, AstraZeneca, CIBER, OncoRadiomics, University of Leiden, University of Wurzburg, Chiesi Pharmaceutical, University of Liege, Teva Pharmaceuticals, Sanofi, Pulmonary Fibrosis Foundation and Three Lakes Foundation, outside the submitted work. R. Djukanovic has received a grant from Novartis for a CI-led project that the funder agreed to support without any restrictions or influence on its contents, analysis or publication; has received consultancy fees from Teva Pharmaceuticals, Sanofi, Boehringer, Novartis and Synairgen; has received grants paid to his institution from the IMI-funded EU project U-BIOPRED, the MERC-funded RASP-UK project, the EME/MRC-funded BEAT Severe Asthma project and NIHR BRC; payment for lectures on the mechanisms of action of Xolair from Novartis and mechanisms of asthma from Teva; and has stock in a University of Southampton company, Synairgen. C. Porsbjerg has received grants and consulting fees paid to her institution, and personal honoraria from AstraZeneca, GlaxoSmithKline, Novartis, Teva, Sanofi, Chiesi and ALK. D. Hamerlijnck has nothing to disclose. O. Fulton has nothing to disclose. A. ten Brinke has received grants paid to her institution from AstraZeneca, GlaxoSmithKline and Teva; and fees paid to her institution for advisory boards and lectures from AstraZeneca, GlaxoSmithKline, Novartis, Teva and Sanofi/Genzyme, all outside the submitted work. E.H. Bel has received grants paid to her institution from GlaxoSmithKline and Teva; and consulting fees from AstraZeneca UK Ltd, GlaxoSmithKline Services UnLtd, Sterna Biologicals, Chiesi Pharmaceuticals, Sanofi/Regeneron and Teva Pharmaceuticals. J.K. Sont has received a grant from GlaxoSmithKline, outside the submitted work.","Olsen NJ, Stein CM., New drugs for rheumatoid arthritis, N Engl J Med, 350, pp. 2167-2179, (2004); Danese S, Fiocchi C., Ulcerative colitis, N Engl J Med, 365, pp. 1713-1725, (2011); Israel E, Reddel HK., Severe and difficult-to-treat asthma in adults, N Engl J Med, 377, pp. 965-976, (2017); Smolen JS, Aletaha D., Rheumatoid arthritis therapy reappraisal: strategies, opportunities and challenges, Nat Rev Rheumatol, 11, pp. 276-289, (2015); Ma C, Battat R, Dulai PS, Et al., Innovations in oral therapies for inflammatory bowel disease, Drugs, 79, pp. 1321-1335, (2019); Wenzel SE., Severe adult asthmas: integrating clinical features, biology, and therapeutics to improve outcomes, Am J Respir Crit Care Med, 203, pp. 809-821, (2021); van Bragt JJMH, Adcock IM, Bel EHD, Et al., Characteristics and treatment regimens across ERS SHARP severe asthma registries, Eur Respir J, 55, (2020); Jackson DJ, Busby J, Pfeffer PE, Et al., Characterisation of patients with severe asthma in the UK Severe Asthma Registry in the biologic era, Thorax, 76, pp. 220-227, (2021); 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Bel; Amsterdam Medical Centers, Location AMC, University of Amsterdam, Amsterdam, Netherlands; email: e.h.bel@amsterdamumc.nl","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139418373"
"Yang Y.; Chen Z.; Li W.; Zeng N.; Guo Y.; Wang S.; Duan W.; Liu Y.; Chen H.; Li X.; Chen R.; Kang Y.","Yang, Yingjian (57218501666); Chen, Ziran (57670751700); Li, Wei (57221637991); Zeng, Nanrong (57671655900); Guo, Yingwei (57218502342); Wang, Shicong (57670751600); Duan, Wenxin (57670149700); Liu, Yang (57222473378); Chen, Huai (55205182900); Li, Xian (57191970686); Chen, Rongchang (57200034537); Kang, Yan (57213821412)","57218501666; 57670751700; 57221637991; 57671655900; 57218502342; 57670751600; 57670149700; 57222473378; 55205182900; 57191970686; 57200034537; 57213821412","Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD","2022","Frontiers in Medicine","9","","980950","","","","6","10.3389/fmed.2022.980950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145500409&doi=10.3389%2ffmed.2022.980950&partnerID=40&md5=370ff23061cbbacea4c6237ecba74887","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China; School of Applied Technology, Shenzhen University, Shenzhen, China; Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China; The Second Clinical Medical College, Jinan University, Guangzhou, China; The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China; Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China","Yang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China; Chen Z., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China; Li W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China; Zeng N., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, School of Applied Technology, Shenzhen University, Shenzhen, China; Guo Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China; Wang S., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, School of Applied Technology, Shenzhen University, Shenzhen, China; Duan W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, School of Applied Technology, Shenzhen University, Shenzhen, China; Liu Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, School of Applied Technology, Shenzhen University, Shenzhen, China; Chen H., Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Li X., Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Chen R., Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China, The Second Clinical Medical College, Jinan University, Guangzhou, China, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China; Kang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China, School of Applied Technology, Shenzhen University, Shenzhen, China, Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, China","Introduction: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. Methods: First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. Results: Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. Discussion: Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management. Copyright © 2022 Yang, Chen, Li, Zeng, Guo, Wang, Duan, Liu, Chen, Li, Chen and Kang.","3D CNN features; combination strategy; COPD; dyspnea identification; lung radiomics features; machine learning; multi-modal data; PFT parameters","adult; aged; Article; chronic obstructive lung disease; classifier; cohort analysis; controlled study; data analysis; diagnostic accuracy; dyspnea; feature extraction; high resolution computer tomography; human; image analysis; image segmentation; least absolute shrinkage and selection operator; lung; lung function test; machine learning; major clinical study; multilayer perceptron; principal component analysis; radiomics; sensitivity and specificity","","","","","Scientific Research Fund of Liaoning Province of China, (JL201919); Stable Support Plan for Colleges and Universities in Shenzhen of China, (SZWD2021010); National Natural Science Foundation of China, NSFC, (62071311); National Natural Science Foundation of China, NSFC; Natural Science Foundation of Guangdong Province, (2019A1515011382); Natural Science Foundation of Guangdong Province; special program for key fields of colleges and universities in Guangdong Province, (2021ZDZX2008)","This research was funded by the National Natural Science Foundation of China, Grant Number 62071311; The Stable Support Plan for Colleges and Universities in Shenzhen of China, Grant Number SZWD2021010; The Scientific Research Fund of Liaoning Province of China, Grant Number JL201919; The Natural Science Foundation of Guangdong Province of China, Grant Number 2019A1515011382; the special program for key fields of colleges and universities in Guangdong Province (biomedicine and health) of China, Grant Number 2021ZDZX2008. 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Desai M., Shah M., An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN), Clinical eHealth, 4, pp. 1-11, (2021); Lorencin I., Andelic N., Spanjol J., Car Z., Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis, Artif Intell Med, 102, (2020); Xu Y., Li F., Asgari A., Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms, Energy, 240, (2022); Wan S., Liang Y., Zhang Y., Guizani M., Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson's disease severity using smartphones, IEEE Access, 6, pp. 36825-36833, (2018); Jakkula V., Tutorial on support vector machine (svm), School of EECS, Washington State University, 37, (2006); Breiman L., Random forest, Mach Learn, 45, pp. 5-32, (2001); Safavian S.R., Landgrebe D., A survey of decision tree classifier methodology, IEEE Trans Syst Man Cybern, 21, pp. 660-674, (1991); Friedman J.H., Greedy function approximation: a gradient boosting machine, Ann Stat, 29, pp. 1189-1232, (2001); Knowles C.H., Eccersley A.J., Scott S.M., Walker S.M., Reeves B., Lunniss P.J., Linear discriminant analysis of symptoms in patients with chronic constipation, Diseases of the Colon & Rectum, (2000); Ramteke R.J., Khachane M.Y., Automatic medical image classification and abnormality detection using K-Nearest neighbour, Int J Adv Comput Res, 2, pp. 190-196, (2012); LaValley M.P., Logistic regression, Circulation, 117, pp. 2395-2399, (2008); James M., Milne K., Neder J.A., O'Donnell D., Mechanisms of exertional dyspnea in patients with mild COPD and low resting lung diffusing capacity for carbon monoxide (DLCO), 56, (2020); Parker C.M., Voduc N., Aaron S.D., Webb K.A., O'Donnell D.E., Physiological changes during symptom recovery from moderate exacerbations of COPD, European Respiratory Journal, 26, pp. 420-428, (2005); Shaotong P., Dewang L., Ziru M., Yunpeng L., Yonglin L., Location and identification of insulator and bushing based on YOLOv3-spp algorithm, 2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology, pp. 791-794, (2021); Bailey K.L., The importance of the assessment of pulmonary function in COPD, Medical Clinics, 96, pp. 745-752, (2012); Song X., Mao M., Qian X., Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of alzheimer's disease, IEEE J Biomed Health Inform, 25, pp. 3141-3152, (2021); Harutyunyan G., Harutyunyan V., Harutyunyan G., Sanchez Gimeno A., Cherkezyan A., Petrosyan S., Et al., Ventilation/perfusion mismatch is not the sole reason for hypoxaemia in early stage COVID-19 patients, Eur Respir J, 31, (2022); Neder J A., Kirby M., Santyr G., Pourafkari M., Smyth R., Phillips D.B., Et al., Ventilation/perfusion mismatch: a novel target for COPD treatment, Chest, pp. 1030-1047, (2022)","Y. Kang; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; email: kangyan@sztu.edu.cn; R. Chen; Shenzhen Institute of Respiratory Diseases, Shenzhen People's Hospital, Shenzhen, China; email: chenrc@vip.163.com","","Frontiers Media S.A.","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85145500409"
"Salau A.O.; Pooja M.R.; Hasani N.F.; Braide S.L.","Salau, Ayodeji Olalekan (57204911824); Pooja, M.R. (57190388894); Hasani, Nahla Flayyih (57214068710); Braide, Sepiribo Lucky (55345584600)","57204911824; 57190388894; 57214068710; 55345584600","Model Based Risk Assessment to Evaluate Lung Functionality for Early Prognosis of Asthma Using Neural Network Approach","2022","Mathematical Modelling of Engineering Problems","9","4","","1053","1060","7","5","10.18280/mmep.090423","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138644901&doi=10.18280%2fmmep.090423&partnerID=40&md5=3d2cdc2ca076610111386a880bd66145","Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, 360101, Nigeria; Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, 600124, India; Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, 570002, India; College of Basic Education, University of Sumer, Al-Rifai, 64005, Iraq; Department of Electrical and Electronics Engineering, Rivers State University, Port Harcourt, 5080, Nigeria","Salau A.O., Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, 360101, Nigeria, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, 600124, India; Pooja M.R., Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, 570002, India; Hasani N.F., College of Basic Education, University of Sumer, Al-Rifai, 64005, Iraq; Braide S.L., Department of Electrical and Electronics Engineering, Rivers State University, Port Harcourt, 5080, Nigeria","Predictive modeling of asthma characterized by the systematic use of Machine Learning and Deep Learning techniques to develop classification/prediction models is a vital tool which is being deployed in most of the computer mediated decision making processes. Spirometry, being one of the most commonly used lung function tests, helps in the diagnosis and continuous monitoring of asthma and is recommended by both the national and international guidelines for the management of the disease when compared to other pulmonary function tests. It has been found to be more reliable because it has more parametric values. Despite the generalization of the respiratory equations in spirometry with respect of selected ethnic groups, the equation yields a considerable difference when compared to the spirometric readings in the general population. In an effort to overcome such differences that deviate from actual observations, in this paper, we have proposed a neural network model that can output a vector of Tiffeneau-Pinelli Index. The neural network model for the prediction of Tiffeneau-Pinelli index was able to reproduce a vector of indices that very closely approximated the actual observed values with a very low estimated error with an optimized radial basis fit neural net. This can be used as a reliable means to estimate some of the vital lung function parameters irrespective of the differences in the general population © 2022, Mathematical Modelling of Engineering Problems.All Rights Reserved.","Longitudinal data; Reference equations; Sigmoidal; Spirometry; Tiffeneau-pinelli index","","","","","","","","Xiang Y., Ji H.Y., Zhou Y.J., Li F., Du J.C., Rasmy L., Wu S., Zheng WJ., Xu H., Zhi D.G., Zhang Y.Y., Cui T., Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: Retrospective cohort study, Journal of Medical Internet Research, 22, 7, (2020); Delic S., Cvjetkovic T., Cajo M., Cancar I.F., Colak A., Cenanovic N., Direk E., Detection of asthma inflammatory phenotypes using artificial neural network, CMBEBIH 2021. CMBEBIH 2021. IFMBE Proceedings, 84, (2021); Haque R., Ho S.B., Chai I., Abdullah A., Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers, F1000Research, 10, (2021); Badnjevic A., Gurbeta L., Cifrek M., Marjanovic D., Classification of asthma using artificial neural network, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 387-390, (2016); Gayathri G.V., Satapathy S.C., A Survey on techniques for prediction of asthma, Smart Intelligent Computing and Applications, 159, pp. 751-758, (2020); Chatzimichail E., Paraskakis E., Rigas A., Predicting asthma outcome using partial least square regression and artificial neural networks, Adv Artificial Intelligence, 2013, (2013); Loymans R.J.B., Debray T.P.A., Honkoop P.J., Et al., Exacerbations in adults with asthma: A systematic review and external validation of prediction models, J Allergy Clin Immunol Pract, 6, 6, pp. 1942-1952; Deng H., Urman R., Gilliland F.D., Eckel S.P., Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach, BMC Medical Research Methodology, 19, 1, (2019); Dupuy A.v., Amat F., Pereira B., Labbe A., Just J., A simple tool to identify infants at high risk of mild to severe childhood asthma: The persistent asthma predictive score, Journal of Asthma, 48, 10, pp. 1015-1021, (2011); Anastasiou A., Kocsis O., Moustakas K., Exploring machine learning for monitoring and predicting severe asthma exacerbations, Proceedings of the 10th Hellenic Conference on Artificial Intelligence, (2018); Amaral J, Sancho A.G., Faria A.C.D., Lopes A.J., Melo P.L., Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers, Medical & Biological Engineering & Computing, 58, 10, pp. 2455-2473, (2020); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics Journal, 25, 3, pp. 811-827, (2017); Sheshasaayee A., Prathiba L., An improvised technique for the diagnosis of asthma disease with the categorization of asthma disease level, Information Systems Design and Intelligent Applications, (2018); Salau A.O., Jain S., Adaptive diagnostic machine learning technique for classification of cell decisions for AKT protein, Informatics in Medicine Unlocked, 23, 1, pp. 1-9, (2021); Salau A.O., Jain S., Feature extraction: A survey of the types, techniques, and applications, 5th IEEE International Conference on Signal Processing and Communication (ICSC), pp. 158-164, (2019); Pooja M.R., Pushpalatha M.P., A neural network approach for risk assessment of asthma disease, Journal of Health Informatics & Management, 2, 1, (2018); Pooja M.R., Pushpalatha M.P., A hybrid decision support system for the identification of asthmatic subjects in a cross-sectional study, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 288-293, (2015); Pooja M.R., Pushpalatha M.P., Analysis of a panel of cytokines in bal fluids to differentiate controlled and uncontrolled asthmatics using machine learning model, Journal of Respiratory Research, 5, 1, pp. 142-145, (2019); Pooja M.R., Pushpalatha M.P., A predictive framework for the assessment of asthma control level, Int J Eng Adv Technol, 8, pp. 239-245, (2019); Tomita K., Nagao R., Touge H., Ikeuchi T., Sano H., Yamasaki A., Tohda Y., Deep learning facilitates the diagnosis of adult asthma, Allergology International, (2019); Tobore I., Li J.Z., Liu Y.H., Al-Handarish Y., Kandwal A., Nie Z., Wang L., Deep learning intervention for health care challenges: Some biomedical domain considerations, JMIR mHealth and uHealth, 7, 8, (2019); Wang X., Wang Z.J., Pengetnze Y.M., Lachman B.S., Chowdhry V., Deep learning models to predict pediatric asthma emergency department visits, (2019); Manoharan S.C, Ramakrishnan S., Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering, Journal of Medical Systems, 33, 5, (2009); Ma X., Wu Y.P., Zhang L., Yuan W.L., Yan L., Fan S., Lian Y.Z., Zhu X., Gao J.H., Zhao J.M., Zhang P., Tang H., Jia W.H., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, Journal of Translational Medicine, 18, (2020); Messinger A.I., Bui N., Wagner B.D., Szefler S.J., Vu T., Deterding R.R., Novel pediatric - automated respiratory score using physiologic data and machine learning in asthma, Pediatric Pulmonology, (2019); Ciancio N., Pavone M., Torrisi S.E., Vancheri A., Sambataro D., Palmucci S., Vancheri C., Marco F.D., Sambataro, Contribution of pulmonary function tests (PFTs) to the diagnosis and follow up of connective tissue diseases, Multidisciplinary Respiratory Medicine, 14, 1, pp. 1-11, (2019); Pooja M.R., On effective use of feature engineering for improving the predictive capability of machine learning models, Computational Intelligence and Data Sciences: Paradigms in Biomedical Engineering, pp. 53-62, (2022); Pooja M.R., Pushpalatha M.P., Cluster analysis to characterize the patterns of complementary and alternative medicines usage in asthma controls, The Open Public Health Journal, 13, 1, (2020); Nafisi V.R., Eghbal M., Torbati N., Conceptual design of a device for online calibration of spirometer based on neural network, Journal of Biomedical Physics and Engineering, (2021)","A.O. Salau; Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, 360101, Nigeria; email: ayodejisalau98@gmail.com","","International Information and Engineering Technology Association","","","","","","23690739","","","","English","Math. Model. Eng.Probl.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85138644901"
"Ezuma C.O.; Lu Y.; Pareek A.; Wilbur R.; Krych A.J.; Forsythe B.; Camp C.L.","Ezuma, Chimere O. (57217181805); Lu, Yining (57209533819); Pareek, Ayoosh (56785616800); Wilbur, Ryan (57219354696); Krych, Aaron J. (57193118617); Forsythe, Brian (57197790856); Camp, Christopher L. (25822184100)","57217181805; 57209533819; 56785616800; 57219354696; 57193118617; 57197790856; 25822184100","A Machine Learning Algorithm Outperforms Traditional Multiple Regression to Predict Risk of Unplanned Overnight Stay Following Outpatient Medial Patellofemoral Ligament Reconstruction","2022","Arthroscopy, Sports Medicine, and Rehabilitation","4","3","","e1103","e1110","7","5","10.1016/j.asmr.2022.03.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131116716&doi=10.1016%2fj.asmr.2022.03.009&partnerID=40&md5=000af470f32c8a6cf3a3f18317aa2375","School of Medicine, Vagelos Columbia College of Physicians and Surgeons, New York, New York; Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota; Midwest Orthopedics at Rush, Rush University Medical Center, Chicago, IL, United States","Ezuma C.O., School of Medicine, Vagelos Columbia College of Physicians and Surgeons, New York, New York; Lu Y., Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota; Pareek A., Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota; Wilbur R., Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota; Krych A.J., Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota; Forsythe B., Midwest Orthopedics at Rush, Rush University Medical Center, Chicago, IL, United States; Camp C.L., Department of Orthopedic Surgery, Mayo Clinic, and, Rochester, Minnesota","Purpose: To determine whether conventional logistic regression or machine learning algorithms were more precise in identifying the risk factors for unplanned overnight admission after medial patellofemoral ligament (MPFL) reconstruction. Methods: A retrospective review of the prospectively collected National Surgical Quality Improvement Program database was performed to identify patients who underwent outpatient MPFL reconstruction from 2006–2018. Patients admitted overnight were identified as those with length of stay of 1 or more days. Models were generated using random forest, extreme gradient boosting, adaptive boosting, or elastic net penalized logistic regression, and an additional model was produced as a weighted ensemble of the 4 final algorithms. The predictive capacity of these models was compared to that of logistic regression. Results: Of the 1307 patients identified, 221 (16.9%) required at least one overnight stay after MPFL reconstruction. Multivariate logistic regression found the following variables to be predictors of inpatient admission: age (odds ratio [OR] = 1.03 [95% confidence interval {CI} 1.02-1.04]; P <.001), spinal anesthesia (OR = 3.42 [95% CI 1.98-6.08]; P < .001), American Society of Anesthesiologists (ASA) class 3/4 (OR = 1.96 [95% CI 1.25-3.06]; P < .001), history of chronic obstructive pulmonary disease (COPD) (OR = 6.44 [95% CI 1.58-26.17]; P = .02), and body mass index (BMI) (OR = 1.03 [95% CI 1.01-1.05]; P < .001). The ensemble model achieved the best performance based on discrimination assessed via internal validation (area under the curve = 0.722). The variables determined most important by the ensemble model were increasing BMI, increasing age, ASA class, anesthesia, smoking, hypertension, lateral release, and history of COPD. Conclusions: An internally validated machine learning algorithm outperformed logistic regression modeling in predicting the need for unplanned overnight hospitalization after MPFL reconstruction. In this model, the most significant risk factors for admission were age, BMI, ASA class, smoking status, hypertension, lateral release, and history of COPD. This tool can be deployed to augment provider assessment to identify high-risk candidates and appropriately set postoperative expectations for patients. Clinical Relevance: Identifying and mitigating patient risk factors to prevent adverse surgical outcomes and hospitalizations is one of our primary goals. There may be a key role for machine learning algorithms to help successfully and efficiently risk stratify patients to decrease costs, appropriately set postoperative expectations, and increase the quality of delivered care. © 2022 The Authors","","adult; age; American Society of Anaesthesiologists score; Article; body mass; chronic obstructive lung disease; clinical outcome; cohort analysis; comorbidity; female; health care cost; health care delivery; health care quality; human; hypertension; length of stay; ligament surgery; machine learning; major clinical study; male; outpatient; patellofemoral joint; prediction; prospective study; random forest; retrospective study; risk assessment; risk factor; smoking; spinal anesthesia","","","","","","","Shah J.N., Howard J.S., Flanigan D.C., Brophy R.H., Carey J.L., Lattermann C., A systematic review of complications and failures associated with medial patellofemoral ligament reconstruction for recurrent patellar dislocation, Am J Sports Med, 40, pp. 1916-1923, (2012); Sillanpaa P.J., Peltola E., Mattila V.M., Kiuru M., Visuri T., Pihlajamaki H., Femoral avulsion of the medial patellofemoral ligament after primary traumatic patellar dislocation predicts subsequent instability in men: A mean 7-year nonoperative follow-up study, Am J Sports Med, 37, pp. 1513-1521, (2009); Steensen R.N., Dopirak R.M., Maurus P.B., A simple technique for reconstruction of the medial patellofemoral ligament using a quadriceps tendon graft, Arthroscopy, 21, pp. 365-370, (2005); Drez D., Edwards T.B., Williams C.S., Results of medial patellofemoral ligament reconstruction in the treatment of patellar dislocation, Arthroscopy, 17, pp. 298-306, (2001); Nomura E., Inoue M., Surgical technique and rationale for medial patellofemoral ligament reconstruction for recurrent patellar dislocation, Arthroscopy, 19, 5, (2003); Crawford D.C., Li C.S., Sprague S., Bhandari M., Clinical and cost implications of inpatient versus outpatient orthopedic surgeries: A systematic review of the published literature, Orthop Rev, 7, 4, (2015); Kadhim M., Gans I., Baldwin K., Flynn J., Ganley T., Do surgical times and efficiency differ between inpatient and ambulatory surgery centers that are both hospital owned?, J Pediatr Orthop, 36, pp. 423-428, (2016); Hauck K., Zhao X., How dangerous is a day in hospital? A model of adverse events and length of stay for medical inpatients, Med Care, 49, pp. 1068-1075, (2011); Lu Y., Forlenza E., Cohn M.R., Et al., Machine learning can reliably identify patients at risk of overnight hospital admission following anterior cruciate ligament reconstruction, Knee Surg Sports Traumatol Arthrosc, 29, 9, pp. 2958-2966, (2021); Myers T.G., Ramkumar P.N., Ricciardi B.F., Urish K.L., Kipper J., Ketonis C., Artificial intelligence and orthopaedics: An introduction for clinicians, J Bone Joint Surg Am, 102, pp. 830-840, (2020); Rajkomar A., Dean J., Kohane I., Machine learning in medicine, N Engl J Med, 380, pp. 1347-1358, (2019); Davis C.L., Pierce J.R., Henderson W., Et al., Assessment of the reliability of data collected for the Department of Veterans Affairs national surgical quality improvement program, J Am Coll Surg, 204, pp. 550-560, (2007); Karhade A.V., Larsen A.M., Cote D.J., Dubois H.M., Smith T.R., National databases for neurosurgical outcomes research: Options, strengths, and limitations, Neurosurgery, 83, pp. 333-344, (2018); Pugely A.J., Martin C.T., Harwood J., Ong K.L., Bozic K.J., Callaghan J.J., Database and registry research in orthopaedic surgery: Part 2: Clinical registry data, J Bone Joint Surg Am, 97, pp. 1799-1808, (2015); Karhade A.V., Schwab J.H., Bedair H.S., Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty, J Arthroplasty, 34, pp. 2272-2277, (2019); Pareek A., Parkes C.W., Bernard C.D., Abdel M.P., Saris D.B., Krych A.J., The SIFK score: A validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee, Knee Surg Sports Traumatol Arthrosc, 28, pp. 3149-3155, (2020); Dietterich T.G., Ensemble methods in machine learning, International workshop on multiple classifier systems, pp. 1-15, (2000); Steyerberg E.W., Harrell F.E., Prediction models need appropriate internal, internal-external, and external validation, J Clin Epidemiol, 69, (2016); Steyerberg E.W., Moons K.G., van der Windt D.A., Et al., Prognosis Research Strategy (PROGRESS) 3: prognostic model research, PLoS Med, 10, 2, (2013); Enderlein D., Nielsen T., Christiansen S.E., Fauno P., Lind M., Clinical outcome after reconstruction of the medial patellofemoral ligament in patients with recurrent patella instability, Knee Surg Sports Traumatol Arthrosc, 22, pp. 2458-2464, (2014); Hiemstra L.A., Kerslake S., Age at time of surgery but not sex is related to outcomes after medial patellofemoral ligament reconstruction, Am J Sports Med, 47, pp. 1638-1644, (2019); Schneider D.K., Grawe B., Magnussen R.A., Et al., Outcomes after isolated medial patellofemoral ligament reconstruction for the treatment of recurrent lateral patellar dislocations: A systematic review and meta-analysis, Am J Sports Med, 44, pp. 2993-3005, (2016); Migliorini F., Maffulli N., Eschweiler J., Quack V., Tingart M., Driessen A., Lateral retinacular release combined with MPFL reconstruction for patellofemoral instability: A systematic review, Arch Orthop Trauma Surg, 141, pp. 283-292, (2021); Malatray M., Magnussen R., Lustig S., Servien E., Lateral retinacular release is not recommended in association to MPFL reconstruction in recurrent patellar dislocation, Knee Surg Sports Traumatol Arthrosc, 27, pp. 2659-2664, (2019); Teng S., Yi C., Krettek C., Jagodzinski M., Smoking and risk of prosthesis-related complications after total hip arthroplasty: A meta-analysis of cohort studies, PloS One, 10, 4, (2015); Trivedi A., Ezomo O.T., Gronbeck C., Harrington M.A., Halawi M.J., Time trends and risk factors for 30-day adverse events in black patients undergoing primary total knee arthroplasty, J Arthroplasty, 35, pp. 3145-3149, (2020); Husted C., Gromov K., Hansen H.K., Troelsen A., Kristensen B.B., Husted H., Outpatient total hip or knee arthroplasty in ambulatory surgery center versus arthroplasty ward: A randomized controlled trial, Acta Orthop, 91, pp. 42-47, (2020); Carey K., Morgan J.R., Payments for outpatient joint replacement surgery: A comparison of hospital outpatient departments and ambulatory surgery centers, Health Serv Res, 55, pp. 218-223, (2020); Carey K., Morgan J.R., Lin M.Y., Kain M.S., Creevy W.R., Patient outcomes following total joint replacement surgery: a comparison of hospitals and ambulatory surgery centers, J Arthroplasty, 35, pp. 7-11, (2020)","Y. Lu; Department of Orthopedic Surgery, Mayo Clinic, Rochester,  St SW, 55905, United States; email: Lu.Yining@mayo.edu","","Elsevier Inc.","","","","","","2666061X","","","","English","Arthrosc., Sports Med., Rehabil.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85131116716"
"Fox M.G.; Cass L.M.; Sykes K.J.; Cummings E.L.; Fassas S.N.; Nallani R.; Smith J.B.; Chiu A.G.; Villwock J.A.","Fox, Meha G. (57219158222); Cass, Lauren M. (57194617003); Sykes, Kevin J. (16481385100); Cummings, Emily L. (57219157464); Fassas, Scott N. (57151380700); Nallani, Rohit (57205752121); Smith, Josh B. (57200300425); Chiu, Alexander G. (57458202000); Villwock, Jennifer A. (55927674600)","57219158222; 57194617003; 16481385100; 57219157464; 57151380700; 57205752121; 57200300425; 57458202000; 55927674600","Factors affecting adherence to intranasal treatment for allergic rhinitis: A qualitative study","2023","Laryngoscope Investigative Otolaryngology","8","1","","40","45","5","7","10.1002/lio2.986","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143215810&doi=10.1002%2flio2.986&partnerID=40&md5=021d51dc55025b78997dadbad9439e0b","Department of Otolaryngology – Head & Neck Surgery, Baylor College of Medicine, Houston, TX, United States; Department of Head and Neck Surgery, Kaiser Permanente, Portland, OR, United States; Department of Otolaryngology – Head & Neck Surgery, University of Kansas Medical Center, Kansas City, KS, United States; Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, United States; Department of Internal Medicine, George Washington School of Medicine & Health Sciences, Washington, DC, United States; Department of Otolaryngology – Head & Neck Surgery, St. Louis University School of Medicine, St. Louis, MO, United States","Fox M.G., Department of Otolaryngology – Head & Neck Surgery, Baylor College of Medicine, Houston, TX, United States; Cass L.M., Department of Head and Neck Surgery, Kaiser Permanente, Portland, OR, United States; Sykes K.J., Department of Otolaryngology – Head & Neck Surgery, University of Kansas Medical Center, Kansas City, KS, United States; Cummings E.L., Department of Internal Medicine, Indiana University School of Medicine, Indianapolis, IN, United States; Fassas S.N., Department of Internal Medicine, George Washington School of Medicine & Health Sciences, Washington, DC, United States; Nallani R., Department of Otolaryngology – Head & Neck Surgery, University of Kansas Medical Center, Kansas City, KS, United States; Smith J.B., Department of Otolaryngology – Head & Neck Surgery, St. Louis University School of Medicine, St. Louis, MO, United States; Chiu A.G., Department of Otolaryngology – Head & Neck Surgery, University of Kansas Medical Center, Kansas City, KS, United States; Villwock J.A., Department of Otolaryngology – Head & Neck Surgery, University of Kansas Medical Center, Kansas City, KS, United States","Objective: To determine the facilitators of and barriers to adherence to use of intranasal pharmacotherapy (daily intranasal corticosteroids and/or antihistamine, and nasal saline irrigation [NSI]), for allergic rhinitis (AR). Methods: Patients were recruited from an academic tertiary care rhinology and allergy clinic. Semi-structured interviews were conducted after the initial visit and/or 4–6 weeks following treatment. Transcribed interviews were analyzed using a grounded theory, inductive approach to elucidate themes regarding patient adherence to AR treatment. Results: A total of 32 patients (12 male, 20 female; age 22–78) participated (seven at initial visit, seven at follow-up visit, and 18 at both). Memory triggers, such as linking nasal routine to existing daily activities or medications, were identified by patients as the most helpful strategy for adherence at initial and follow-up visits. Logistical obstacles related to NSI (messy, takes time, etc.) was the most common concept discussed at follow-up. Patients modified the regimen based on side effects experienced or perceived efficacy. Conclusions: Memory triggers help patients adhere to nasal routines. Logistical obstacles related to NSI can deter from use. Health care providers should address both concepts during patient counseling. Nudge-based interventions that incorporate these concepts may help improve adherence to AR treatment. Level of Evidence: 2. © 2022 The Authors. Laryngoscope Investigative Otolaryngology published by Wiley Periodicals LLC on behalf of The Triological Society.","allergy/rhinology; endoscopy; irrigations; patient reported outcome measure; rhinitis","antihistaminic agent; corticosteroid; adult; aged; allergic rhinitis; Article; artificial intelligence; asthma; burning sensation; chronic rhinosinusitis; clinical article; clinical practice; cognition; daily life activity; decision making; endoscopy; female; follow up; grounded theory; health behavior; health care personnel; human; intranasal drug administration; lifestyle modification; male; medication compliance; middle aged; patient compliance; patient counseling; patient satisfaction; patient-reported outcome; prevalence; qualitative research; reliability; semi structured interview; tertiary health care; total quality management; young adult","","","","","American Academy of Otolaryngic Allergy Foundation, AAOAF","Zachary Arambula for his assistance in reviewing interview transcripts for accuracy. The American Academy of Otolaryngic Allergy Foundation for their financial support of this work. ","Wise S.K., Lin S.Y., Toskala E., Et al., International consensus statement on allergy and rhinology: allergic rhinitis, Int Forum Allergy Rhinol, 8, pp. 108-352, (2018); Meltzer E.O., Allergic rhinitis: burden of illness, quality of life, comorbidities, and control, Immunol Allergy Clin North Am, 36, 2, pp. 235-248, (2016); Berger W.E., Meltzer E.O., Intranasal spray medications for maintenance therapy for allergic rhinitis, Am J Rhinol Allergy, 29, 4, pp. 273-282, (2015); Kirtsreesakul V., Chansaksung P., Ruttanaphol S., Dose-related effect of intranasal corticosteroids on treatment outcome of persistent allergic rhinitis, Otolaryngol Head Neck Surg, 139, 4, pp. 565-569, (2008); Canonica G.W., Compalati E., Minimal persistent inflammation in allergic rhinitis: implications for current treatment strategies, Clin Exp Immunol, 158, 3, pp. 260-271, (2009); Passalacqua G., Baiardini L., Senna G., Canonica G.W., Adherence to pharmacological treatment and specific immunotherapy in allergic rhinitis, Clin Exp Allergy, l43, 1, pp. 22-28, (2013); Bukstein D., Luskin A.T., Farrar J.R., The reality of adherence to rhinitis treatment: identifying and overcoming the barriers, Allergy Asthma Proc, 32, pp. 265-271, (2011); Wang K., Wang C., Xi L., Et al., A randomized controlled trial to assess adherence to allergic rhinitis treatment following a daily short message service (SMS) via the mobile phone, Int Arch Allergy Immunol, 163, pp. 51-58, (2014); Navarro A., Valero A., Rosales M.J., Millol J., Clinical use of oral antihistamines and intranasal corticosteroids in patients with allergic rhinitis, J Investig Allergol Clin Immunol, 21, 5, pp. 363-369, (2011); Loh C.Y., Chao S.S., Chan Y.H., Wang D.Y., A clinical survey on compliance in the treatment of rhinitis using nasal steroids, Allergy, 59, pp. 1168-1172, (2004); Ocak E., Acar B., Kocaoz D., Medical adherence to intranasal corticosteroids in adult patients, Braz J Otorhinolaryngol, 83, 5, pp. 558-562, (2017); Tong A., Sainsbury P., Craig J., Consolidate criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups, Int J Qual Health C, 19, 6, pp. 349-357, (2007); Zeber J.E., Manias E., Williams A.F., Et al., A systematic literature review of psychosocial and behavioral factors associated with initial medication adherence: a report of the ISPOR medication adherence & persistence special interest group, Value Health, 16, 5, pp. 891-900, (2013); Bender B.G., Motivating patient adherence to allergic rhinitis treatment, Cur Allergy Asthma Rep, 15, (2015); Dedoose: Dedoose Version 9.0.46, web application for managing, analyzing, and presenting qualitative and mixed method research data (2021). Los Angeles, CA: SocioCultural Research Consultants, LLC; Phillips K.M., Hoehle L.P., Caradonna D.S., Gray S.T., Sedaghat A.R., Intranasal corticosteroids and saline: Usage and adherence in chronic rhinosinusitis patients, Laryngoscope, 130, 4, pp. 852-856, (2020); Yoong S.L., Hall A., Stacey F., Et al., Nudge strategies to improve healthcare providers' implementation of evidence-based guidelines, policies and practices: a systematic review of trials included within Cochrane systematic reviews, Implement Sci, 15, 1, (2020); Bhalla V., Beahm D.D., Sykes K.J., Ndeti K.K., Chiu A.G., The impact of video nasal endoscopy on patient satisfaction, Int Forum Allergy Rhinol, 8, pp. 737-740, (2018); Zia A., Brassart A., Thomas S., Et al., Patient-centric structural determinants of adherence rates among asthma populations: exploring the potential of patient activation and encouragement tool TRUSTR to improve adherence, J Health Econ Outcomes Res, 7, 2, pp. 111-122, (2020); Kwan Y.H., Cheng T.Y., Yoon S., Et al., A systematic review of nudge theories and strategies used to influence adult health behaviour and outcome in diabetes management, Diabetes Metab, 46, 6, pp. 450-460, (2020)","M.G. Fox; Department of Otolaryngology – Head & Neck Surgery, Baylor College of Medicine, Houston, 1977 Butler Blvd E5.200, 77030, United States; email: mehafoxmd@gmail.com","","John Wiley and Sons Inc","","","","","","23788038","","","","English","Laryngoscope Investigative Otolaryngology","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85143215810"
"Heber S.; Pereyra D.; Schrottmaier W.C.; Kammerer K.; Santol J.; Rumpf B.; Pawelka E.; Hanna M.; Scholz A.; Liu M.; Hell A.; Heiplik K.; Lickefett B.; Havervall S.; Traugott M.T.; Neuböck M.J.; Schörgenhofer C.; Seitz T.; Firbas C.; Karolyi M.; Weiss G.; Jilma B.; Thålin C.; Bellmann-Weiler R.; Salzer H.J.F.; Szepannek G.; Fischer M.J.M.; Zoufaly A.; Gleiss A.; Assinger A.","Heber, Stefan (56963798100); Pereyra, David (56497810500); Schrottmaier, Waltraud C. (55974694400); Kammerer, Kerstin (57221700140); Santol, Jonas (57221700277); Rumpf, Benedikt (57204655713); Pawelka, Erich (57209456010); Hanna, Markus (57443988500); Scholz, Alexander (57221702884); Liu, Markus (57221685825); Hell, Agnes (57221685483); Heiplik, Klara (57221686564); Lickefett, Benno (57221692976); Havervall, Sebastian (57219341979); Traugott, Marianna T. (57217145386); Neuböck, Matthias J. (57221701704); Schörgenhofer, Christian (55832705400); Seitz, Tamara (57217151749); Firbas, Christa (11140989900); Karolyi, Mario (57205338241); Weiss, Günter (55455913300); Jilma, Bernd (55113251000); Thålin, Charlotte (56743267700); Bellmann-Weiler, Rosa (6506034729); Salzer, Helmut J. F. (36118318800); Szepannek, Gero (9271921300); Fischer, Michael J. M. (55568523430); Zoufaly, Alexander (59157668100); Gleiss, Andreas (6507635282); Assinger, Alice (23487741500)","56963798100; 56497810500; 55974694400; 57221700140; 57221700277; 57204655713; 57209456010; 57443988500; 57221702884; 57221685825; 57221685483; 57221686564; 57221692976; 57219341979; 57217145386; 57221701704; 55832705400; 57217151749; 11140989900; 57205338241; 55455913300; 55113251000; 56743267700; 6506034729; 36118318800; 9271921300; 55568523430; 59157668100; 6507635282; 23487741500","A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation","2022","Frontiers in Cellular and Infection Microbiology","11","","795026","","","","7","10.3389/fcimb.2021.795026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124238991&doi=10.3389%2ffcimb.2021.795026&partnerID=40&md5=7b55df448d2e25d18c918d90ca24ed99","Institute of Physiology, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Department of General Surgery, Division of Visceral Surgery, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Division of Internal Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden; Department of Pulmonology, Kepler University Hospital and Johannes Kepler University, Linz, Austria; Department of Clinical Pharmacology, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria; Institute of Applied Computer Science, Stralsund University of Applied Sciences, Stralsund, Germany; Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria","Heber S., Institute of Physiology, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Pereyra D., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria, Department of General Surgery, Division of Visceral Surgery, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Schrottmaier W.C., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Kammerer K., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Santol J., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria, Department of General Surgery, Division of Visceral Surgery, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Rumpf B., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Pawelka E., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Hanna M., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Scholz A., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Liu M., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Hell A., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Heiplik K., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Lickefett B., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Havervall S., Division of Internal Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden; Traugott M.T., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Neuböck M.J., Department of Pulmonology, Kepler University Hospital and Johannes Kepler University, Linz, Austria; Schörgenhofer C., Department of Clinical Pharmacology, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Seitz T., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Firbas C., Department of Clinical Pharmacology, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Karolyi M., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Weiss G., Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria; Jilma B., Department of Clinical Pharmacology, Medical University of Vienna, General Hospital Vienna, Vienna, Austria; Thålin C., Division of Internal Medicine, Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden; Bellmann-Weiler R., Department of Internal Medicine II, Medical University of Innsbruck, Innsbruck, Austria; Salzer H.J.F., Department of Pulmonology, Kepler University Hospital and Johannes Kepler University, Linz, Austria; Szepannek G., Institute of Applied Computer Science, Stralsund University of Applied Sciences, Stralsund, Germany; Fischer M.J.M., Institute of Physiology, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; Zoufaly A., Department of Medicine IV, Kaiser Franz Josef Hospital, Vienna, Austria; Gleiss A., Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria; Assinger A., Department of Vascular Biology and Thrombosis Research, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria","Objective: To develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission. Methods: Haematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent. Results: The final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210). Conclusions: The presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. Clinical Trial Registration: Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724. Copyright © 2022 Heber, Pereyra, Schrottmaier, Kammerer, Santol, Rumpf, Pawelka, Hanna, Scholz, Liu, Hell, Heiplik, Lickefett, Havervall, Traugott, Neuböck, Schörgenhofer, Seitz, Firbas, Karolyi, Weiss, Jilma, Thålin, Bellmann-Weiler, Salzer, Szepannek, Fischer, Zoufaly, Gleiss and Assinger.","blood parameter; COVID-19; hospitalized patients; logistic regression; prediction model; survival","COVID-19; Hospital Mortality; Hospitalization; Humans; Retrospective Studies; SARS-CoV-2; C reactive protein; creatinine; lactate dehydrogenase; Article; asthma; atrial fibrillation; blood clotting disorder; blood sampling; body mass; calibration; chronic kidney failure; chronic liver disease; chronic obstructive lung disease; clinical outcome; cohort analysis; coronavirus disease 2019; coughing; cross validation; current smoker; diarrhea; dyspnea; fatigue; female; fever; gene mutation; heart failure; hospital mortality; hospitalization; human; hypertension; hyperthyroidism; hypothyroidism; ischemic heart disease; lymphocyte count; machine learning; male; malignant neoplasm; mortality; nasopharyngeal swab; nausea and vomiting; non insulin dependent diabetes mellitus; obesity; observational study; oropharyngeal swab; oxygen consumption; peripheral occlusive artery disease; platelet count; prediction; prognosis; real time polymerase chain reaction; receiver operating characteristic; SARS-CoV-2 variant 501Y.V1; Severe acute respiratory syndrome coronavirus 2; sore throat; survival; survivor; training; validation process; hospitalization; retrospective study","","C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ","","","Jonas & Christina af Jochnick foundation; Knut och Alice Wallenbergs Stiftelse; Bundesministerium für Bildung, Wissenschaft und Forschung, BMBWF; Region Stockholm; Medizinische Universität Wien; Austrian Science Fund, FWF, (P 34783, SFB-54, P32064); Medical-Scientific Fund of the Mayor of Vienna, (COVID024)","This work is part of the ACOVACT study of the Medical University of Vienna and is financially supported by the Austrian Federal Ministry of Education, Science and Research, the Medical-Scientific Fund of the Mayor of Vienna (COVID024) and the Austrian Science Fund (P32064; P34783; SFB-54) and by Region Stockholm, Knut and Alice Wallenberg foundation, Jonas & Christina af Jochnick foundation (CT). The funders had no role in the design of this study.","Ali H., Daoud A., Mohamed M.M., Salim S.A., Yessayan L., Baharani J., Et al., Survival Rate in Acute Kidney Injury Superimposed COVID-19 Patients: A Systematic Review and Meta-Analysis, Renal Failure, 42, 1, pp. 393-397, (2020); Amgalan A., Othman M., Hemostatic Laboratory Derangements in COVID-19 With a Focus on Platelet Count, Platelets, 31, 6, pp. 740-745, (2020); Apley D.W., Zhu J., Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models, J. R. Stat. Soc.: Ser. B (Stat. Method), 82, 4, pp. 1059-1086, (2020); Braun F., Lutgehetmann M., Pfefferle S., Wong M.N., Carsten A., Lindenmeyer M.T., Et al., SARS-CoV-2 Renal Tropism Associates With Acute Kidney Injury, Lancet, 396, pp. 597-598, (2020); Bucker M., Szepannek G., Gosiewska A., Biecek P., Transparency, Auditability, and Explainability of Machine Learning Models in Credit Scoring, J. Oper. Res. Soc, pp. 1-21, (2021); Chen X.Y., Huang M.Y., Xiao Z.W., Yang S., Chen X.Q., Lactate Dehydrogenase Elevations Is Associated With Severity of COVID-19: A Meta-Analysis, Crit. Care, 24, 1, (2020); Corman V.M., Landt O., Kaiser M., Molenkamp R., Meijer A., Chu D.K., Et al., Detection of 2019 Novel Coronavirus (2019-Ncov) by Real-Time RT-PCR, Eur. Surveillance Bull. Eur. Sur Les Maladies Transmissibles = Eur. Commun Dis. Bull, 25, 3, (2020); Fernandez-Delgado M., Cernadas E., Barro S., Amorim D., Do We Need Hundreds of Classifiers to Solve Real World Classification Problems, J. Mach. Learn Res, 15, 1, pp. 3133-3181, (2014); Fitzmaurice G.M., Laird N.M., Ware J.H., Applied Longitudinal Analysis, (2011); Fouladseresht H., Doroudchi M., Rokhtabnak N., Abdolrahimzadehfard H., Roudgari A., Sabetian G., Et al., Predictive Monitoring and Therapeutic Immune Biomarkers in the Management of Clinical Complications of COVID-19, Cytokine Growth Factor Rev, 58, pp. 32-48, (2021); Gansevoort R.T., Hilbrands L.B., CKD Is a Key Risk Factor for COVID-19 Mortality, Nat. Rev. Nephrol, 16, 12, pp. 705-706, (2020); Gleiss A., Schemper M., Quantifying Degrees of Necessity and of Sufficiency in Cause-Effect Relationships With Dichotomous and Survival Outcomes, Stat. Med, 38, 23, pp. 4733-4748, (2019); Jiang S.Q., Huang Q.F., Xie W.M., Lv C., Quan X.Q., The Association Between Severe COVID-19 and Low Platelet Count: Evidence From 31 Observational Studies Involving 7613 Participants, Br. J. Haematol, 190, 1, pp. e29-e33, (2020); Karagiannidis C., Mostert C., Hentschker C., Voshaar T., Malzahn J., Schillinger G., Et al., Case Characteristics, Resource Use, and Outcomes of 10 021 Patients With COVID-19 Admitted to 920 German Hospitals: An Observational Study, Lancet Respir. Med, 8, 9, pp. 853-862, (2020); Katzenschlager S., Zimmer A.J., Gottschalk C., Grafeneder J., Seitel A., Maier-Hein L., Et al., Can We Predict the Severe Course of COVID-19 - A Systematic Review and Meta-Analysis of Indicators of Clinical Outcome, PLoS One, 16, 7, (2021); Knight S.R., Ho A., Pius R., Buchan I., Carson G., Drake T.M., Et al., Risk Stratification of Patients Admitted to Hospital With Covid-19 Using the ISARIC WHO Clinical Characterisation Protocol: Development and Validation of the 4C Mortality Score, BMJ, 370, (2020); Liaw A., Wiener M., Classification and Regression by Randomforest, R News, 2, pp. 18-22, (2002); Li J., He X., Yuan Y., Zhang W., Li X., Zhang Y., Et al., Meta-Analysis Investigating the Relationship Between Clinical Features, Outcomes, and Severity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pneumonia, Am. J. Infect. Cont, 49, 1, pp. 82-89, (2020); Manson J.J., Crooks C., Naja M., Ledlie A., Goulden B., Liddle T., Et al., COVID-19-Associated Hyperinflammation and Escalation of Patient Care: A Retrospective Longitudinal Cohort Study, Lancet Rheumatol, 2, 10, pp. e594-e602, (2020); Mueller A.A., Tamura T., Crowley C.P., DeGrado J.R., Haider H., Jezmir J.L., Et al., Inflammatory Biomarker Trends Predict Respiratory Decline in COVID-19 Patients, Cell Rep. Med, 1, 8, (2020); Ng D.H.L., Choy C.Y., Chan Y.H., Young B.E., Fong S.W., Ng L.F.P., Et al., Fever Patterns, Cytokine Profiles, and Outcomes in COVID-19, Open Forum Infect. Dis, 7, 9, (2020); Poggiali E., Zaino D., Immovilli P., Rovero L., Losi G., Dacrema A., Et al., Lactate Dehydrogenase and C-Reactive Protein as Predictors of Respiratory Failure in CoVID-19 Patients, Clin. Chim. Acta, 509, pp. 135-138, (2020); Probst P., Boulesteix A.-L., Bischl B., Tunability: Importance of Hyperparameters of Machine Learning Algorithms, J. Mach. Learn. Res, 20, 1, pp. 1934-1965, (2019); Riley R.D., Ensor J., Snell K.I.E., Harrell F.E., Martin G.P., Reitsma J.B., Et al., Calculating the Sample Size Required for Developing a Clinical Prediction Model, BMJ, 368, (2020); Steyerberg E.W., Eijkemans M.J.C., Habbema J.D.F., Application of Shrinkage Techniques in Logistic Regression Analysis: A Case Study, Stat. Neerlandica, 55, 1, pp. 76-88, (2001); Szepannek G., On the Practical Relevance of Modern Machine Learning Algorithms for Credit Scoring Applications, (2017); Wang L., C-Reactive Protein Levels in the Early Stage of COVID-19, Med. Maladies Infect, 50, 4, pp. 332-334, (2020); Wynants L., Van Calster B., Collins G.S., Riley R.D., Heinze G., Schuit E., Et al., Prediction Models for Diagnosis and Prognosis of Covid-19: Systematic Review and Critical Appraisal, BMJ, 369, (2020)","S. Heber; Institute of Physiology, Centre of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria; email: stefan.heber@meduniwien.ac.at","","Frontiers Media S.A.","","","","","","22352988","","","35141170","English","Front. Cell. Infect. Microbiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124238991"
"Lee B.; An J.; Lee S.; Won S.","Lee, Bora (57189218519); An, Jaehoon (57196248285); Lee, Sungyoung (55716390100); Won, Sungho (57207796874)","57189218519; 57196248285; 55716390100; 57207796874","Rex: R-linked EXcel add-in for statistical analysis of medical and bioinformatics data","2023","Genes and Genomics","45","3","","295","305","10","7","10.1007/s13258-022-01361-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146878228&doi=10.1007%2fs13258-022-01361-7&partnerID=40&md5=f8b1fa9081ede6bdc8acc04e15ea69a1","Institute of Health and Environment, Seoul National University, Seoul, South Korea; RexSoft Inc, Seoul, South Korea; Department of Public Health Science, Seoul National University, 1 Kwanak-Ro Kwanak-Gu, Seoul, 151-742, South Korea; Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, South Korea; Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea","Lee B., Institute of Health and Environment, Seoul National University, Seoul, South Korea, RexSoft Inc, Seoul, South Korea; An J., RexSoft Inc, Seoul, South Korea, Department of Public Health Science, Seoul National University, 1 Kwanak-Ro Kwanak-Gu, Seoul, 151-742, South Korea; Lee S., Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, South Korea, Department of Medicine, Seoul National University College of Medicine, Seoul, South Korea; Won S., Institute of Health and Environment, Seoul National University, Seoul, South Korea, RexSoft Inc, Seoul, South Korea, Department of Public Health Science, Seoul National University, 1 Kwanak-Ro Kwanak-Gu, Seoul, 151-742, South Korea","Background: Microsoft Excel has substantial functionalities for data management and analyses, and has been the most popular software in this field. However, in spite of Excel’s user-friendly interface and functionality for data management, it provides very few functions for in-depth statistical analyses, which has limited its wider application for this purpose. Objective: Here, we introduce Rex, an Excel add-in software implementing the powerful analytical and graphical functions of R within Excel. Methods: Rex was implemented using three types of programming software: R, JavaScript, and Microsoft VB.Net. Results: Rex provides a graphical user interface (GUI) through Excel, and statistical analysis can be conducted by pointing and clicking the menu without programming R. Rex covers a wide range of analyses from basic statistics to advanced analysis, including structural equation modeling, complex sampling design, and machine learning models, making it possible for researchers not skilled in using a command-line interface to conduct in-depth statistical analyses. Most Rex modules are available in a free version for non-commercial use, and it can be used for educational and public purposes. Conclusion: In this article, we introduce the framework and features of Rex with illustrative examples of its implementation. © 2023, The Author(s) under exclusive licence to The Genetics Society of Korea.","Excel add-in; Graphical user interface; R; Rex; Statistical analysis software","Computational Biology; Software; messenger RNA; Article; asthma; bioinformatics; birth weight; comparative study; controlled study; data analysis; differential expression analysis; gene; gene expression level; gestational age; human; information processing; machine learning; maternal age; mRNA expression level; NMUR2 gene; RNA sequencing; software; statistical analysis; structural equation modeling; biology; software","","","","","Ministry of Education, MOE, (NRF-2020R1F1A01072033); Ministry of Trade, Industry and Energy, MOTIE; Ministry of Science, ICT and Future Planning, MSIP; National Research Foundation of Korea, NRF, (NRF-2021R1A5A1033157)","This work was supported by the Technology Innovation Program (20016417) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1F1A01072033) and the National Research Foundation (NRF) Grant (NRF-2021R1A5A1033157) funded by the Korean government (MSIT). ","Addinsoft A., XLSTAT Statistical and Data Analysis Solution, (2019); Bates D.M., Chambers J.M., Hastie T., Statistical models in S, In: Computer Science and Statistics: Proceedings of the 19Th Symposium on the Interface., (1992); Carter E., Lippert E., Visual studio tools for office, (2006); Do A.R., An J., Jo J., Kim W.J., Kang H.Y., Lee S., Yoon D., Cho Y.S., Adcock I.M., Chung K.F., A genome-wide association study implicates the pleiotropic effect of NMUR2 on asthma and COPD, Res Sq. https://, (2022); Eddelbuettel D., Seamless R and C++ integration with Rcpp, (2013); Fox J., The R Commander: a basic-statistics graphical user interface to R, J Stat Softw, 14, pp. 1-42, (2005); Fulton J., Guidelines for programs and departments in undergraduate mathematical sciences, MAA Guidelines Task Force, (2003); Gomez-Rubio V., ggplot2-elegant graphics for data analysis, J Stat Softw, 77, pp. 1-3, (2017); Grosjean P., (2012); Grundgeiger D., Programming visual basic, (2018); Heiberger R.M., Neuwirth E., R through Excel: a spreadsheet interface for statistics, data analysis, and graphics, (2009); Kajati E., Miskuf M., Papcun P., Advanced analysis of manufacturing data in Excel and its Add-ins, 2017 IEEE 15Th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, pp. 000491-000496, (2017); Keeling K.B., Pavur R.J., Statistical accuracy of spreadsheet software, Am Stat, 65, pp. 265-273, (2011); Knusel L., On the accuracy of statistical distributions in Microsoft Excel 97, Comput Stat Data Anal, 26, pp. 375-377, (1998); McCullough B.D., Heiser D.A., On the accuracy of statistical procedures in Microsoft Excel 2007, Comput Stat Data Anal, 52, pp. 4570-4578, (2008); Melard G., On the accuracy of statistical procedures in Microsoft Excel 2010, Comput Stat, 29, pp. 1095-1128, (2014); Miah S.J., Camilleri E., Vu H.Q., Big data in healthcare research: a survey study, J Comput Inf Syst, 62, pp. 480-492, (2022); Pramanik P.K.D., Pal S., Mukhopadhyay M., Healthcare big data: A comprehensive overview, Research Anthology on Big Data Analytics, Architectures, and Applications, pp. 119-147, (2022); Saleh I., (2016); Setiyanto S., Setiawan I., Data science with excel, Int J Comput Inf Syst, (2022); Singh R.K., Agrawal S., Sahu A., Kazancoglu Y., Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda, Total Qual Manag, (2021); Sisodia S., Agarwal N., Employability skills essential for healthcare industry, Procedia Comput Sci, 122, pp. 431-438, (2017); Tam S., Tsao M.-S., McPherson J.D., Optimization of miRNA-seq data preprocessing, Brief Bioinform, 16, pp. 950-963, (2015); (2013); Urbanek S., A fast way to provide R functionality to applications, (2003); van den Berg B.J., Christianson R.E., Oechsli F.W., The California child health and development studies of the School of Public Health, University of California at Berkeley, Paediatr Perinat Epidemiol, 2, pp. 265-282, (1988); Wickham H., Advanced r, (2019); Wilson K., Microsoft excel 2013, pp. 59-79, (2014); Wilson K., Microsoft office 365, Using Office 365, pp. 1-14, (2014)","S. Won; Department of Public Health Science, Seoul National University, Seoul, 1 Kwanak-Ro Kwanak-Gu, 151-742, South Korea; email: won1@snu.ac.kr; S. Lee; Department of Genomic Medicine, Seoul National University Hospital, Seoul, 03080, South Korea; email: biznok@snu.ac.kr","","Genetics Society of Korea","","","","","","19769571","","","36696053","English","Genes Genomics","Article","Final","","Scopus","2-s2.0-85146878228"
"Haque R.; Ho S.-B.; Chai I.; Abdullah A.","Haque, Radiah (57220746794); Ho, Sin-Ban (55663603900); Chai, Ian (6603163160); Abdullah, Adina (54879831600)","57220746794; 55663603900; 6603163160; 54879831600","Parameter and Hyperparameter Optimisation of Deep Neural Network Model for Personalised Predictions of Asthma","2022","Journal of Advances in Information Technology","13","5","","512","517","5","6","10.12720/jait.13.5.512-517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137250581&doi=10.12720%2fjait.13.5.512-517&partnerID=40&md5=f7ac88f345ad177dfe212bcdfae11cdf","Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia; Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia","Haque R., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Ho S.-B., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia; Chai I., Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia; Abdullah A., Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia","Over the last couple of decades, numerous optimisation algorithms have been introduced to optimise machine learning models. However, until now, no evidence or framework can be found in the literature that adequately describes how to select the best algorithm for parameter and hyperparameter optimisation of the Deep Neural Network (DNN) model. In this paper, an enhanced Fragmented Grid Search (FGS) method has been introduced for tuning several hyperparameters and finding the optimal architecture of the DNN model using less computation power and time. Furthermore, several experimental models are trained on the asthma dataset using various optimisers to find the optimal parameters, which can help the DNN model converge towards the lowest loss value. The results show that the Adam optimiser provides the best accuracy rate (96%). Consequently, the optimised DNN model can be used for accurately providing personalised predictions of asthma exacerbations for effective asthma self-management. © 2022 J. Adv. Inf. Technol.","deep neural networks; machine learning; optimisation algorithm; personalization","","","","","","","","Alto V., Neural networks: Parameters, hyperparameters and optimization strategies, Towards Data Science, (2019); Sankararaman K., De S., Xu Z., Huang W., Goldstein T., The impact of neural network overparameterization on gradient confusion and stochastic gradient descent, Proc. 37th International Conference on Machine Learning, pp. 8469-8479, (2020); Gupta T., Raza K., Optimizing deep feedforward neural network architecture: A tabu search based approach, Neural Processing Letters, 51, 3, pp. 2855-2870, (2020); Mendoza H., Klein A., Feurer M., Springenberg J., Urban M., Burkart M., Towards automatically-tuned deep neural networks, Automated Machine Learning, pp. 135-149, (2019); Feitosa-Neto A., Xavier-Junior J., Canuto A., Oliveira A., A study of model and hyper-parameter selection strategies for classifier ensembles: A robust analysis on different optimization algorithms and extended results, Natural Computing, 20, pp. 805-819, (2021); Erten G., Keser S., Yavuz M., Grid search optimised artificial neural network for open stope stability prediction, International Journal of Mining, Reclamation and Environment, 35, 8, pp. 600-617, (2021); Greenhill S., Rana S., Gupta S., Vellanki P., Venkatesh S., Bayesian optimization for adaptive experimental design: A review, IEEE Access, 8, pp. 13937-13948, (2020); Li L., Talwalkar A., Random search and reproducibility for neural architecture search, Proc. 35th Uncertainty in Artificial Intelligence Conference, 115, pp. 367-377, (2020); Hamdia K., Zhuang X., Rabczuk T., An efficient optimization approach for designing machine learning models based on genetic algorithm, Neural Computing and Applications, 33, 6, pp. 1923-1933, (2020); Haque R., Ho S. B., Chai I., Abdullah A., Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers, F1000Research, (2021); Liashchynskyi P., Grid search, random search, genetic algorithm: A big comparison for NAS, (2019); Perin G., Picek S., On the influence of optimizers in deep learning-based side-channel analysis, Proc. International Conference on Selected Areas in Cryptography, pp. 615-636, (2020); Sun S., Cao Z., Zhu H., Zhao J., A survey of optimization methods from a machine learning perspective, IEEE Transactions on Cybernetics, 50, 8, pp. 3668-3681, (2019); Lydia A., Francis F., Adagrad-An optimizer for stochastic gradient descent, International Journal of Information and Computing Science, 6, 5, pp. 566-568, (2019); Xu D., Zhang S., Zhang H., Mandic D., Convergence of the RMSProp deep learning method with penalty for nonconvex optimization, Neural Networks, 139, pp. 17-23, (2021); Sen S., Ozkurt N., Convolutional neural network hyperparameter tuning with Adam optimizer for ECG classification, Proc. Innovations in Intelligent Systems and Applications Conference, pp. 1-6, (2020); Kandel I., Castelli M., Popovic A., Comparative study of first order optimizers for image classification using convolutional neural networks on histopathology images, Journal of Imaging, 6, 9, pp. 1-17, (2020)","","","Engineering and Technology Publishing","","","","","","17982340","","","","English","J. Adv. Inf.  Technol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85137250581"
"Wen J.; Wang C.; Xia J.; Giri M.; Guo S.","Wen, Jun (57660679400); Wang, Changfen (58134509000); Xia, Jing (58582476900); Giri, Mohan (57189391243); Guo, Shuliang (7403650631)","57660679400; 58134509000; 58582476900; 57189391243; 7403650631","Relationship between serum iron and blood eosinophil counts in asthmatic adults: data from NHANES 2011-2018","2023","Frontiers in Immunology","14","","1201160","","","","5","10.3389/fimmu.2023.1201160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171331964&doi=10.3389%2ffimmu.2023.1201160&partnerID=40&md5=793e9e7976d251cc35754c9f0b969495","Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China","Wen J., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Wang C., Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Xia J., Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Giri M., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; Guo S., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China","Background: So far, quite a few studies have revealed that systemic iron levels are related to asthmatic inflammatory reactions. And most studies have focused on the correlation between systemic iron levels and asthma, with inconsistent findings. Yet, few studies have investigated the connection between serum iron and blood eosinophil counts. Hence, we have explored the connection between serum iron and blood eosinophil counts in asthmatics by utilizing data from NHANES. Methods: A total of 2549 individuals were included in our study after screening NHANES participants from 2011 to 2018. The linear regression model and XGBoost model were used to discuss the potential connection. Linear or nonlinear association was further confirmed by the generalized additive model and the piecewise linear regression model. And we also performed stratified analyses to figure out specific populations. Results: In the multivariable linear regression models, we discovered that serum iron levels were inversely related to blood eosinophil counts in asthmatic adults. Simultaneously, we found that for every unit increase in serum iron (umol/L), blood eosinophil counts reduced by 1.41/uL in model 3, which adjusted for all variables excluding the analyzed variables. Furthermore, the XGBoost model of machine learning was applied to assess the relative importance of chosen variables, and it was determined that vitamin C intake, age, vitamin B12 intake, iron intake, and serum iron were the five most important variables on blood eosinophil counts. And the generalized additive model and piecewise linear regression model further verify this linear and inverse association. Conclusion: Our investigation discovered that the linear and inverse association of serum iron with blood eosinophil counts in asthmatic adults, indicating that serum iron might be related to changes in the immunological state of asthmatics. Our work offers some new thoughts for next research on asthma management and therapy. Ultimately, we hope that more individuals become aware of the role of iron in the onset, development, and treatment of asthma. Copyright © 2023 Wen, Wang, Xia, Giri and Guo.","asthma; eosinophil; machine learning; National Health And Nutrition Examination Survey (NHANES); serum iron; XGBoost","Adult; Asthma; Awareness; Eosinophils; Humans; Iron; Nutrition Surveys; ascorbic acid; cyanocobalamin; folic acid; iron; retinol; steroid; adult; alcohol consumption; Article; asthma; basophil count; Black person; body mass; Caucasian; demographics; diabetes mellitus; diet; education; eosinophil count; female; high school; Hispanic; human; hypertension; iron blood level; Kruskal Wallis test; linear regression analysis; lymphocyte count; machine learning; major clinical study; male; marriage; Mexican American; monocyte count; neutrophil count; post hoc analysis; questionnaire; single (marital status); smoking; awareness; eosinophil; nutrition","","ascorbic acid, 134-03-2, 15421-15-5, 50-81-7; cyanocobalamin, 53570-76-6, 68-19-9, 8064-09-3; folic acid, 59-30-3, 6484-89-5; iron, 14093-02-8, 53858-86-9, 7439-89-6; retinol, 68-26-8, 82445-97-4; Iron, ","XGBoost","","Chongqing Talents, Teachers and Masters","Chongqing Talents, Teachers and Masters (SG).","Porsbjerg C., Melen E., Lehtimaki L., Shaw D., Asthma, Lancet (London England), 401, (2023); Reddel H.K., Bacharier L.B., Bateman E.D., Brightling C.E., Brusselle G.G., Buhl R., Et al., Global Initiative for Asthma Strategy 2021: executive summary and rationale for key changes, Eur Respir J, 59, 1, (2022); Forno E., Brandenburg D.D., Castro-Rodriguez J.A., Celis-Preciado C.A., Holguin F., Licskai C., Et al., Asthma in the Americas: an update: A joint perspective from the Brazilian thoracic society, Canadian thoracic society, Latin American thoracic society, and american thoracic society, Ann Am Thorac Society, 19, 4, (2022); Bridevaux P.O., Probst-Hensch N.M., Schindler C., Curjuric I., Felber Dietrich D., Braendli O., Et al., Prevalence of airflow obstruction in smokers and never-smokers in Switzerland, Eur Respir J, 36, 6, (2010); Chipps B.E., Corren J., Israel E., Katial R., Lang D.M., Panettieri R.A., Et al., Asthma Yardstick: Practical recommendations for a sustained step-up in asthma therapy for poorly controlled asthma, Ann allergy Asthma Immunol, 118, 2, pp. 133-42.e3, (2017); 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Guo; Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China; email: guosl999@sina.com","","Frontiers Media SA","","","","","","16643224","","","37731511","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85171331964"
"Wu Y.; Qi S.; Feng J.; Chang R.; Pang H.; Hou J.; Li M.; Wang Y.; Xia S.; Qian W.","Wu, Yanan (57394724800); Qi, Shouliang (36572483500); Feng, Jie (57214107623); Chang, Runsheng (57226267229); Pang, Haowen (57781616300); Hou, Jie (57193425077); Li, Mengqi (58363594800); Wang, Yingxi (58104937900); Xia, Shuyue (7202893268); Qian, Wei (36842193500)","57394724800; 36572483500; 57214107623; 57226267229; 57781616300; 57193425077; 58363594800; 58104937900; 7202893268; 36842193500","Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology","2023","Biocybernetics and Biomedical Engineering","43","3","","568","585","17","7","10.1016/j.bbe.2023.06.004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166515044&doi=10.1016%2fj.bbe.2023.06.004&partnerID=40&md5=947939399d6ad156865a8888e2acc0f5","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China; Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China; Graduate School, Dalian Medical University, Dalian, China; Department of Respiratory, the Second Affiliated Hospital of Dalian Medical University, Dalian, China; Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China","Wu Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Qi S., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Feng J., School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China; Chang R., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Pang H., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Hou J., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China; Li M., Graduate School, Dalian Medical University, Dalian, China, Department of Respiratory, the Second Affiliated Hospital of Dalian Medical University, Dalian, China; Wang Y., Graduate School, Dalian Medical University, Dalian, China, Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Xia S., Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Qian W., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China","Chronic obstructive pulmonary disease (COPD) is a complex and multi-component respiratory disease. Computed tomography (CT) images can characterize lesions in COPD patients, but the image intensity and morphology of lung components have not been fully exploited. Two datasets (Dataset 1 and 2) comprising a total of 561 subjects were obtained from two centers. A multiple instance learning (MIL) method is proposed for COPD identification. First, randomly selected slices (instances) from CT scans and multi-view 2D snapshots of the 3D airway tree and lung field extracted from CT images are acquired. Then, three attention-guided MIL models (slice-CT, snapshot-airway, and snapshot-lung-field models) are trained. In these models, a deep convolution neural network (CNN) is utilized for feature extraction. Finally, the outputs of the above three MIL models are combined using logistic regression to produce the final prediction. For Dataset 1, the accuracy of the slice-CT MIL model with 20 instances was 88.1%. The backbone of VGG-16 outperformed Alexnet, Resnet18, Resnet26, and Mobilenet_v2 in feature extraction. The snapshot-airway and snapshot-lung-field MIL models achieved accuracies of 89.4% and 90.0%, respectively. After the three models were combined, the accuracy reached 95.8%. The proposed model outperformed several state-of-the-art methods and afforded an accuracy of 83.1% for the external dataset (Dataset 2). The proposed weakly supervised MIL method is feasible for COPD identification. The effective CNN module and attention-guided MIL pooling module contribute to performance enhancement. The morphology information of the airway and lung field is beneficial for identifying COPD. © 2023 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences","Chronic obstructive pulmonary disease; Computed tomography; Convolution neural network; Image classification; Multiple instance learning","aged; Article; attention guided multiple instance learning; attention network; chronic obstructive lung disease; comparative study; computer assisted tomography; controlled study; convolutional neural network; diagnostic test accuracy study; feature extraction; female; forced expiratory volume; forced vital capacity; human; image analysis; image processing; image segmentation; logistic regression analysis; lung slice; machine learning; major clinical study; male; multilayer perceptron; prediction; predictive value; randomized controlled trial; receiver operating characteristic; sensitivity and specificity; tracheobronchial tree; x-ray computed tomography","","","GeForce RTX 2080Ti, NVIDIA; i7-9700, NVIDIA","NVIDIA; NVIDIA","Key R&D Program Guidance Projects in Liaoning Province, (2019JH8/10300051); National Natural Science Foundation of China, NSFC, (82072008); Natural Science Foundation of Liaoning Province, (2021-YGJC-21); Fundamental Research Funds for the Central Universities, (N2124006-3, N2224001-10)","This work was partly supported by the National Natural Science Foundation of China (82072008), Natural Science Foundation of Liaoning Province (2021-YGJC-21), Key R&D Program Guidance Projects in Liaoning Province (2019JH8/10300051), and the Fundamental Research Funds for the Central Universities (N2124006-3, N2224001-10).","(2019); Washko G.R., Coxson H.O., O'Donnell D.E., Aaron S.D., CT imaging of chronic obstructive pulmonary disease: insights, disappointments, and promise, Lancet Respir Med, 5, 11, (2017); Huls A., Schikowski T., Ambient particulate matter and COPD in China: a challenge for respiratory health research, Thorax, 72, 9, pp. 771-772, (2017); Warming P.E., Et al., Atrial fibrillation and chronic obstructive pulmonary disease: diagnostic sequence and mortality risk, Eur Heart J-Qual Care Clin Outcomes, 9, 2, pp. 128-134, (2023); 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Qi; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; email: qisl@bmie.neu.edu.cn","","Elsevier B.V.","","","","","","02085216","","","","English","Biocybern. Biomed. Eng.","Article","Final","","Scopus","2-s2.0-85166515044"
"Greene C.M.; Abdulkadir M.","Greene, Catherine M. (55418789500); Abdulkadir, Mohamed (58989086300)","55418789500; 58989086300","Global respiratory health priorities at the beginning of the 21st century","2024","European Respiratory Review","33","172","230205","","","","7","10.1183/16000617.0205-2023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190563013&doi=10.1183%2f16000617.0205-2023&partnerID=40&md5=03457cb3c04521187babe20608c85ff2","Lung Biology Group, Department of Clinical Microbiology, RCSI University of Medicine and Heath Sciences, Education and Research Centre, Beaumont Hospital, Dublin, Ireland","Greene C.M., Lung Biology Group, Department of Clinical Microbiology, RCSI University of Medicine and Heath Sciences, Education and Research Centre, Beaumont Hospital, Dublin, Ireland; Abdulkadir M., Lung Biology Group, Department of Clinical Microbiology, RCSI University of Medicine and Heath Sciences, Education and Research Centre, Beaumont Hospital, Dublin, Ireland","Respiratory health has become a prevailing priority amid the diverse global health challenges that the 21st century brings, due to its substantial impact on individuals and communities on a global scale. Due to rapid advances in medicine, emerging knowledge gaps appear along with new challenges and ethical considerations. While breakthroughs in medical science can bring about encouraging possibilities for better treatments and interventions, they also lead to unanswered questions and areas where further research is warranted. A PubMed search on the topic “global respiratory health priorities” between the years 2000 and 2023 was conducted, which returned 236 articles. Of these, 55 were relevant and selected for inclusion in this article. The selection process took into account literature reviews, opinions from expert groups and careful analysis of existing gaps and challenges within the field; our selection encompasses specific infectious and noninfectious respiratory conditions in both adults and children. The global respiratory health priorities identified were selected on the basis that they have been recognised as critical areas of investigation and potential advancement and they span across clinical, translational, epidemiological and population health domains. Implementing these priorities will require a commitment to fostering collaboration and knowledge-sharing among experts in different fields with the ultimate aim to improve respiratory health outcomes for individuals and communities alike. © The authors 2024.","","Child; Global Health; Health Priorities; Humans; antibiotic agent; corticosteroid; nonsteroid antiinflammatory agent; prednisolone; air pollution; antibiotic resistance; antimicrobial stewardship; Article; artificial intelligence; asthma; bronchiectasis; cancer screening; chronic obstructive lung disease; climate change; coronavirus disease 2019; genetic predisposition; global disease burden; global health; health care planning; health care policy; human; infectious agent; liquid biopsy; long COVID; lung cancer; lung fibrosis; nonhuman; occupational exposure; pandemic preparedness; racial disparity; respiratory care; respiratory tract disease; respiratory tract infection; risk factor; sex difference; smoking; socioeconomics; spirometry; systematic review; tuberculosis; vaccine hesitancy; child","","prednisolone, 50-24-8","","","","","Shankar A, Parascandola M, Sakthivel P, Et al., Advancing tobacco cessation in LMICs, Curr Oncol, 29, pp. 9117-9124, (2022); Corda L, Medicina D, La Piana GE, Et al., Population genetic screening for alpha1-antitrypsin deficiency in a high-prevalence area, Respiration, 82, pp. 418-425, (2011); Azoulay E, Russell L, Van de Louw A, Et al., Diagnosis of severe respiratory infections in immunocompromised patients, Intensive Care Med, 46, pp. 298-314, (2020); Leung JM, Tiew PY, Mac Aogain M, Et al., The role of acute and chronic respiratory colonization and infections in the pathogenesis of COPD, Respirology, 22, pp. 634-650, (2017); Hanafi NS, Agarwal D, Chippagiri S, Et al., Chronic respiratory disease surveys in adults in low-and middle-income countries: a systematic scoping review of methodological approaches and outcomes, J Glob Health, 11, (2021); Yong SJ., Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments, Infect Dis, 53, pp. 737-754, (2021); Davis HE, McCorkell L, Vogel JM, Et al., Long COVID: major findings, mechanisms and recommendations, Nat Rev Microbiol, 21, pp. 133-146, (2023); Cabrera Martimbianco AL, Pacheco RL, Bagattini AM, Et al., Frequency, signs and symptoms, and criteria adopted for long COVID-19: a systematic review, Int J Clin Pract, 75, (2021); Nurek M, Rayner C, Freyer A, Et al., Recommendations for the recognition, diagnosis, and management of long COVID: a Delphi study, Br J General Pract, 71, pp. e815-e825, (2021); Adeloye D, Elneima O, Daines L, Et al., The long-term sequelae of COVID-19: an international consensus on research priorities for patients with pre-existing and new-onset airways disease, Lancet Respir Med, 9, pp. 1467-1478, (2021); Ceban F, Leber A, Jawad MY, Et al., Registered clinical trials investigating treatment of long COVID: a scoping review and recommendations for research, Infect Dis, 54, pp. 467-477, (2022); Hawke LD, Nguyen AT, Ski CF, Et al., Interventions for mental health, cognition, and psychological wellbeing in long COVID: a systematic review of registered trials, Psychol Med, 52, pp. 2426-2440, (2022); COVID-19 rapid guideline: managing the long-term effects of COVID-19, (2020); Utrero-Rico A, Ruiz-Ruigomez M, Laguna-Goya R, Et al., A short corticosteroid course reduces symptoms and immunological alterations underlying long-COVID, Biomedicines, 9, (2021); Weaver AK, Head JR, Gould CF, Et al., Environmental factors influencing COVID-19 incidence and severity, Annu Rev Public Health, 43, pp. 271-291, (2022); Andersen ZJ, Hoffmann B, Morawska L, Et al., Air pollution and COVID-19: clearing the air and charting a post-pandemic course: a joint workshop report of ERS, ISEE, HEI and WHO, Eur Respir J, 58, (2021); Sivan M, Taylor S., NICE guideline on long covid, BMJ, 371, (2020); Ikuta KS, Swetschinski LR, Robles Aguilar G, Et al., Global mortality associated with 33 bacterial pathogens in 2019: a systematic analysis for the Global Burden of Disease study 2019, Lancet, 400, pp. 2221-2248, (2022); Antimicrobial resistance: a top ten global public health threat, eClinicalMedicine, 41, (2021); Sixty-Seventh World Health Assembly Agenda Item 16.5: Antimicrobial Resistance, (2014); Murray CJ, Ikuta KS, Sharara F, Et al., Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis, Lancet, 399, pp. 629-655, (2022); Pendleton JN, Gorman SP, Gilmore BF., Clinical relevance of the ESKAPE pathogens, Expert Rev Anti Infect Ther, 11, pp. 297-308, (2013); De Oliveira DMP, Forde BM, Kidd TJ, Et al., Antimicrobial resistance in ESKAPE pathogens, Clin Microbiol Rev, 33, pp. e00181-19, (2020); Uplekar M, Weil D, Lonnroth K, Et al., WHO’s new End TB Strategy, Lancet, 385, pp. 1799-1801, (2015); Murray M, Oxlade O, Lin H-H., Modeling social, environmental and biological determinants of tuberculosis, Int J Tuberc Lung Dis, 15, pp. 64-70, (2011); Lienhardt C, Lonnroth K, Menzies D, Et al., Translational research for tuberculosis elimination: priorities, challenges, and actions, PLoS Med, 13, (2016); Falzon D, Timimi H, Kurosinski P, Et al., Digital health for the End TB Strategy: developing priority products and making them work, Eur Respir J, 48, pp. 29-45, (2016); Migliori GB, Tiberi S, Zumla A, Et al., MDR/XDR-TB management of patients and contacts: challenges facing the new decade. The 2020 clinical update by the Global Tuberculosis Network, Int J Infect Dis, 92S, pp. S15-S25, (2020); Akkerman OW, Duarte R, Tiberi S, Et al., Clinical standards for drug-susceptible pulmonary TB, Int J Tuberc Lung Dis, 26, pp. 592-604, (2022); Lestari T, Fuady A, Yani FF, Et al., The development of the National Tuberculosis Research Priority in Indonesia: a comprehensive mixed-method approach, PLoS One, 18, (2023); Global Initiative for the Diagnosis, Prevention and Management of COPD, (2022); Park J-A, Crotty Alexander LE, Christiani DC., Vaping and lung inflammation and injury, Annu Rev Physiol, 84, pp. 611-629, (2022); Adeloye D, Agarwal D, Barnes PJ, Et al., Research priorities to address the global burden of chronic obstructive pulmonary disease (COPD) in the next decade, J Glob Health, 11, (2021); Janson C, Marks G, Buist S, Et al., The impact of COPD on health status: findings from the BOLD study, Eur Respir J, 42, pp. 1472-1483, (2013); Tonnesen P., Smoking cessation and COPD, Eur Respir Rev, 22, pp. 37-43, (2013); Fazleen A, Wilkinson T., Early COPD: current evidence for diagnosis and management, Ther Adv Respir Dis, 14, (2020); Kaplan A, Thomas M., Screening for COPD: the gap between logic and evidence, Eur Respir Rev, 26, (2017); National Targeted Detection Programme; Cazzola M, Stolz D, Rogliani P, Et al., α<sub>1</sub>-Antitrypsin deficiency and chronic respiratory disorders, Eur Respir Rev, 29, (2020); Torres-Duran M, Lopez-Campos JL, Barrecheguren M, Et al., Alpha-1 antitrypsin deficiency: outstanding questions and future directions, Orphanet J Rare Dis, 13, (2018); Janjua S, Carter D, Threapleton CJ, Et al., Telehealth interventions: remote monitoring and consultations for people with chronic obstructive pulmonary disease (COPD), Cochrane Database Syst Rev, 7, (2021); Agache I, Akdis CA, Chivato T, Et al., EAACI White Paper on Research, Innovation and Quality Care, (2018); Agache I, Annesi-Maesano I, Bonertz A, Et al., Prioritizing research challenges and funding for allergy and asthma and the need for translational research – the European Strategic Forum on Allergic Diseases, Allergy, 74, pp. 2064-2076, (2019); Mathioudakis AG, Custovic A, Deschildre A, Et al., Research priorities in pediatric asthma: results of a global survey of multiple stakeholder groups by the Pediatric Asthma in Real Life (PeARL) think tank, J Allergy Clin Immunol Pract, 8, pp. 1953-1960, (2020); Gill PJ, Goldacre MJ, Mant D, Et al., Increase in emergency admissions to hospital for children aged under 15 in England, 1999–2010: national database analysis, Arch Dis Child, 98, pp. 328-334, (2013); Gray CS, Xu Y, Babl FE, Et al., International perspective on research priorities and outcome measures of importance in the care of children with acute exacerbations of asthma: a qualitative interview study, BMJ Open Respir Res, 10, (2023); Keir HR, Chalmers JD., Pathophysiology of bronchiectasis, Semin Respir Crit Care Med, 42, pp. 499-519, (2021); Chang AB, Bush A, Grimwood K., Bronchiectasis in children: diagnosis and treatment, Lancet, 392, pp. 866-879, (2018); Gokdemir Y, Hamzah A, Erdem E, Et al., Quality of life in children with non-cystic-fibrosis bronchiectasis, Respiration, 88, pp. 46-51, (2014); 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Thomas D, Abramson MJ, Bonevski B, Et al., System change interventions for smoking cessation, Cochrane Database Syst Rev, 2, (2017); Dela Cruz CS, Tanoue LT, Matthay RA., Lung cancer: epidemiology, etiology, and prevention, Clin Chest Med, 32, pp. 605-644, (2011); Schabath MB, Cote ML., Cancer progress and priorities: lung cancer, Cancer Epidemiol Biomarkers Prev, 28, pp. 1563-1579, (2019); Pinsky PF., Assessing the benefits and harms of low-dose computed tomography screening for lung cancer, Lung Cancer Manag, 3, pp. 491-498, (2014); Lung Cancer Screening; Xie S, Wu Z, Qi Y, Et al., The metastasizing mechanisms of lung cancer: recent advances and therapeutic challenges, Biomed Pharmacother, 138, (2021); Li W, Liu J-B, Hou L-K, Et al., Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring, Mol Cancer, 21, (2022); Public Health Policy: Definition, Examples, and More, (2021); Levy HG, Norton EC, Smith JA., Tobacco regulation and cost–benefit analysis: how should we value foregone consumer surplus?, Am J Health Econ, 4, pp. 1-25, (2018); Young W, Karp S, Bialick P, Et al., Health, secondhand smoke exposure, and smoking behavior impacts of no-smoking policies in public housing, Colorado, 2014–2015, Prev Chronic Dis, 13, (2016); Saitta D, Ferro GA, Polosa R., Achieving appropriate regulations for electronic cigarettes, Ther Adv Chronic Dis, 5, pp. 50-61, (2014); Chun LF, Moazed F, Calfee CS, Et al., Pulmonary toxicity of e-cigarettes, Am J Physiol Lung Cell Mol Physiol, 313, pp. L193-L206, (2017); Besaratinia A, Tommasi S., An opportune and unique research to evaluate the public health impact of electronic cigarettes, Cancer Causes Control, 28, pp. 1167-1171, (2017); Rauch S, Jasny E, Schmidt KE, Et al., New vaccine technologies to combat outbreak situations, Front Immunol, 9, (2018); Garett R, Young SD., Online misinformation and vaccine hesitancy, Transl Behav Med, 11, pp. 2194-2199, (2021); Glasgow AMA, Greene CM., Epigenetic mechanisms underpinning sexual dimorphism in lung disease, Epigenomics, 14, pp. 65-67, (2022); Chotirmall SH, Smith SG, Gunaratnam C, Et al., Effect of estrogen on Pseudomonas mucoidy and exacerbations in cystic fibrosis, N Engl J Med, 366, pp. 1978-1986, (2012); Ribeiro PS, Jacobsen KH, Mathers CD, Et al., Priorities for women’s health from the Global Burden of Disease study, Int J Gynaecol Obstet, 102, pp. 82-90, (2008); Adegunsoye A, Freiheit E, White EN, Et al., Evaluation of pulmonary fibrosis outcomes by race and ethnicity in US adults, JAMA Netw Open, 6, (2023); Clark J, Kochovska S, Currow DC., Burden of respiratory problems in low-income and middle-income countries, Curr Opin Support Palliat Care, 16, pp. 210-215, (2022); Marshall IJ, L'Esperance V, Marshall R, Et al., State of the evidence: a survey of global disparities in clinical trials, BMJ Glob Health, 6, (2021); Sehovic AB, Govender K., Addressing COVID-19 vulnerabilities: how do we achieve global health security in an inequitable world, Global Public Health, 16, pp. 1198-1208, (2021); Barouki R, Kogevinas M, Audouze K, Et al., The COVID-19 pandemic and global environmental change: emerging research needs, Environ Int, 146, (2021); Marcos-Garcia P, Carmona-Moreno C, Lopez-Puga J, Et al., COVID-19 pandemic in Africa: is it time for water, sanitation and hygiene to climb up the ladder of global priorities?, Sci Total Environ, 791, (2021); Luo M, Gong F, Sun J, Et al., For COVID-19, what are the priorities of normalized prevention and control strategies?, Biosci Trends, 17, pp. 63-67, (2023); Kaplan A, Cao H, FitzGerald JM, Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol Pract, 9, pp. 2255-2261, (2021); Jiang F, Jiang Y, Zhi H, Et al., Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, pp. 230-243, (2017); Diallo G, Bordea G., Public health and epidemiology informatics: recent research trends, Yearb Med Inform, 30, pp. 280-282, (2021); Galmarini CM, Lucius M., Artificial intelligence: a disruptive tool for a smarter medicine, Eur Rev Med Pharmacol Sci, 24, pp. 7462-7474, (2020); Lim SS, Bouffanais R., ‘Data dregs’ and its implications for AI ethics: revelations from the pandemic, AI Ethics, 2, pp. 595-597, (2022); Wang M, Aaron CP, Madrigano J, Et al., Association between long-term exposure to ambient air pollution and change in quantitatively assessed emphysema and lung function, JAMA, 322, pp. 546-556, (2019); Gordon SB, Bruce NG, Grigg J, Et al., Respiratory risks from household air pollution in low and middle income countries, Lancet Respir Med, 2, pp. 823-860, (2014); Mamane A, Baldi I, Tessier JF, Et al., Occupational exposure to pesticides and respiratory health, Eur Respir Rev, 24, pp. 306-319, (2015); De Matteis S, Heederik D, Burdorf A, Et al., Current and new challenges in occupational lung diseases, Eur Respir Rev, 26, (2017); Kent BD., Climate change, the environment and respiratory disease, Breathe, 19, (2023); Romanello M, Di Napoli C, Drummond P, Et al., The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels, Lancet, 400, pp. 1619-1654, (2022)","C.M. Greene; Lung Biology Group, Department of Clinical Microbiology, RCSI University of Medicine and Heath Sciences, Education and Research Centre, Beaumont Hospital, Dublin, Ireland; email: cmgreene@rcsi.ie","","European Respiratory Society","","","","","","09059180","","EREWE","38599674","English","Eur. Respir. Rev.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85190563013"
"Duan M.; Shu T.; Zhao B.; Xiang T.; Wang J.; Huang H.; Zhang Y.; Xiao P.; Zhou B.; Xie Z.; Liu X.","Duan, Minjie (57286919300); Shu, Tingting (57222147394); Zhao, Binyi (57286008600); Xiang, Tianyu (57286919400); Wang, Jinkui (57425062300); Huang, Haodong (57286689300); Zhang, Yang (58408493100); Xiao, Peilin (36629776200); Zhou, Bei (55603865900); Xie, Zulong (56310367100); Liu, Xiaozhu (57285772100)","57286919300; 57222147394; 57286008600; 57286919400; 57425062300; 57286689300; 58408493100; 36629776200; 55603865900; 56310367100; 57285772100","Explainable machine learning models for predicting 30-day readmission in pediatric pulmonary hypertension: A multicenter, retrospective study","2022","Frontiers in Cardiovascular Medicine","9","","919224","","","","5","10.3389/fcvm.2022.919224","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135612749&doi=10.3389%2ffcvm.2022.919224&partnerID=40&md5=cf0d39784d0e71caa4f3fbabc67353f6","College of Medical Informatics, Chongqing Medical University, Chongqing, China; Medical Data Science Academy, Chongqing Medical University, Chongqing, China; Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Information Center, The University-Town Hospital of Chongqing Medical University, Chongqing, China; Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China; Personnel Department, Chongqing Health Center for Women and Children, Chongqing, China","Duan M., College of Medical Informatics, Chongqing Medical University, Chongqing, China, Medical Data Science Academy, Chongqing Medical University, Chongqing, China; Shu T., Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Zhao B., Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Xiang T., Information Center, The University-Town Hospital of Chongqing Medical University, Chongqing, China; Wang J., Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China; Huang H., Medical Data Science Academy, Chongqing Medical University, Chongqing, China, Personnel Department, Chongqing Health Center for Women and Children, Chongqing, China; Zhang Y., College of Medical Informatics, Chongqing Medical University, Chongqing, China, Medical Data Science Academy, Chongqing Medical University, Chongqing, China; Xiao P., Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Zhou B., Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Xie Z., Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Liu X., Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China","Background: Short-term readmission for pediatric pulmonary hypertension (PH) is associated with a substantial social and personal burden. However, tools to predict individualized readmission risk are lacking. This study aimed to develop machine learning models to predict 30-day unplanned readmission in children with PH. Methods: This study collected data on pediatric inpatients with PH from the Chongqing Medical University Medical Data Platform from January 2012 to January 2019. Key clinical variables were selected by the least absolute shrinkage and the selection operator. Prediction models were selected from 15 machine learning algorithms with excellent performance, which was evaluated by area under the operating characteristic curve (AUC). The outcome of the predictive model was interpreted by SHapley Additive exPlanations (SHAP). Results: A total of 5,913 pediatric patients with PH were included in the final cohort. The CatBoost model was selected as the predictive model with the greatest AUC for 0.81 (95% CI: 0.77–0.86), high accuracy for 0.74 (95% CI: 0.72–0.76), sensitivity 0.78 (95% CI: 0.69–0.87), and specificity 0.74 (95% CI: 0.72–0.76). Age, length of stay (LOS), congenital heart surgery, and nonmedical order discharge showed the greatest impact on 30-day readmission in pediatric PH, according to SHAP results. Conclusions: This study developed a CatBoost model to predict the risk of unplanned 30-day readmission in pediatric patients with PH, which showed more significant performance compared with traditional logistic regression. We found that age, LOS, congenital heart surgery, and nonmedical order discharge were important factors for 30-day readmission in pediatric PH. Copyright © 2022 Duan, Shu, Zhao, Xiang, Wang, Huang, Zhang, Xiao, Zhou, Xie and Liu.","machine learning; pediatric pulmonary hypertension; prediction; readmission; risk factors","phosphodiesterase V inhibitor; area under the curve; Article; artificial ventilation; asthma; brain hemorrhage; cohort analysis; congenital heart disease; data accuracy; diagnostic test accuracy study; echocardiography; female; heart surgery; hospital discharge; hospital readmission; hospitalization; human; ICD-10; infant; least absolute shrinkage and selection operator; low birth weight; machine learning; major clinical study; male; multicenter study; newborn; pediatric patient; pH; pneumonia; premature labor; pulmonary hypertension; random forest; receiver operating characteristic; respiratory failure; retrospective study; sensitivity and specificity; sepsis; shapley additive explanation; tool use; validation process","","","","","Chongqing Medical University, CQMU, (YJSZHYX202119, ZHYX2019013); Chongqing Postdoctoral Program, (2010010006118105); UK Research and Innovation, UKRI, (104818)","This study was supported by the Intelligent Medicine Research Project of Chongqing Medical University (Nos. ZHYX2019013 and YJSZHYX202119) and Chongqing Postdoctoral Program (No. 2010010006118105). ","Hopper R.K., Abman S.H., Ivy D.D., Persistent challenges in pediatric pulmonary hypertension, Chest, 150, pp. 226-236, (2016); Maxwell B.G., Nies M.K., Ajuba-Iwuji C.C., Coulson J.D., Romer L.H., Trends in hospitalization for pediatric pulmonary hypertension, Pediatrics, 136, pp. 241-250, (2015); Frank D.B., Crystal M.A., Morales D.L.S., Gerald K., Hanna B.D., Mallory G.B., Et al., Trends in pediatric pulmonary hypertension-related hospitalizations in the United States from 2000-2009, Pulm Circ, 5, pp. 339-348, (2015); Berry J.G., Toomey S.L., Zaslavsky A.M., Jha A.K., Nakamura M.M., Klein D.J., Et al., Pediatric readmission prevalence and variability across hospitals, JAMA, 309, pp. 372-380, (2013); Lawson E.H., Hall B.L., Louie R., Ettner S.L., Zingmond D.S., Han L., Et al., Association between occurrence of a postoperative complication and readmission implications for quality improvement and cost savings, Ann Surg, 258, pp. 10-18, (2013); Jukic M., Antisic J., Pogorelic Z., Incidence and causes of 30-day readmission rate from discharge as an indicator of quality care in pediatric surgery, Acta Chir Belg, 13, pp. 1-5; Awerbach J.D., Mallory G.B., Kim S., Cabrera A.G., Hospital readmissions in children with pulmonary hypertension: a multi-institutional analysis, J. Pediatr, 195, pp. 95-101.e4, (2018); Sehgal M., Amritphale A., Vadayla S., Mulekar M., Batra M., Amritphale N., Et al., Demographics and risk factors of pediatric pulmonary hypertension readmissions, Cureus, 13, (2021); Sidey-Gibbons J.A.M., Sidey-Gibbons C.J., Machine learning in medicine: a practical introduction, BMC Med Res Methodol, 19, (2019); Johnson K.W., Soto J.T., Glicksberg B.S., Shameer K., Miotto R., Ali M., Et al., Artificial intelligence in cardiology, J Am Coll Cardiol, 71, pp. 2668-2679, (2018); Feng J.Z., Wang Y., Peng J., Sun M.W., Zeng J., Jiang H., Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries, J Crit Care, 54, pp. 110-116, (2019); Lv H.C., Yang X.L., Wang B.Y., Wang S.B., Du X.Y., Tan Q., Et al., Machine learning-driven models to predict prognostic outcomes in patients hospitalized with heart failure using electronic health records: retrospective study, J Med Internet Res, 23, (2021); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, J Clin Epidemiol, 68, pp. 112-121, (2015); Moons K.G.M., Altman D.G., Reitsma J.B., Ioannidis J.P.A., Macaskill P., Steyerberg E.W., Et al., Transparent reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration, Ann Intern Med, 162, pp. W1-W73, (2015); Bhattacharya P.T., Hameed A.M.A., Bhattacharya S.T., Chirinos J.A., Hwang W.T., Birati E.Y., Et al., Risk factors for 30-day readmission in adults hospitalized for pulmonary hypertension, Pulm Circ, 10, (2020); Chandrashekar G., Sahin F., A survey on feature selection methods, Comput Electr Eng, 40, pp. 16-28, (2014); Yamada M., Jitkrittum W., Sigal L., Xing E.P., Sugiyama M., High-dimensional feature selection by feature-wise kernelized Lasso, Neural Comput, 26, pp. 185-207, (2014); Kim Y., Chung M., An approach to hyperparameter optimization for the objective function in machine learning, Electronics, 8, (2019); Joy T.T., Rana S., Gupta S., Venkatesh S., Batch Bayesian optimization using multi-scale search, Knowledge-Based Systems, 187, (2020); Wong T.T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recognit, 48, pp. 2839-2846, (2015); Lundberg S.M., Erion G., Chen H., DeGrave A., Prutkin J.M., Nair B., Et al., From local explanations to global understanding with explainable AI for trees, Nat Mach Intell, 2, pp. 56-67, (2020); Nohara Y., Matsumoto K., Soejima H., Nakashima N., Explanation of machine learning models using shapley additive explanation and application for real data in hospital, Computer Methods and Programs in Biomedicine, (2022); Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Et al., SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat Methods, 17, pp. 261-272, (2020); Auerbach A.D., Kripalani S., Vasilevskis E.E., Sehgal N., Lindenauer P.K., Metlay J.P., Et al., Preventability and causes of readmissions in a national cohort of general medicine patients, JAMA Intern Med, 176, pp. 484-493, (2016); Hall B.L., Namazie-Kummer S., Potentially preventable readmissions after surgery, JAMA Network Open, 4, (2021); Rosenzweig E.B., Abman S.H., Adatia I., Beghetti M., Bonnet D., Haworth S., Et al., Paediatric pulmonary arterial hypertension: updates on definition, classification, diagnostics and management, Eur Respir J, 53, (2019); van Loon R.L.E., Roofthooft M.T.R., Hillege H.L., ten Harkel A.D.J., van Osch-Gevers M., Delhaas T., Et al., Pediatric Pulmonary Hypertension in the Netherlands Epidemiology and Characterization During the Period, Circulation, 124, pp. 1755-1136, (2011); Vellido A., The importance of interpretability and visualization in machine learning for applications in medicine and health care, Neural Comput Appl, 32, pp. 18069-18083, (2020); Miller T., Explanation in artificial intelligence: insights from the social sciences, Artificial Intelligence, 267, pp. 1-38, (2019); Hansmann G., Koestenberger M., Alastalo T.P., Apitz C., Austin E.D., Bonnet D., Et al., Zartner: 2019 updated consensus statement on the diagnosis and treatment of pediatric pulmonary hypertension: the European Pediatric Pulmonary Vascular Disease Network (EPPVDN), endorsed by AEPC, ESPR and ISHLT, J Heart Lung Transplant, 38, pp. 879-901, (2019); Hansmann G., Pulmonary hypertension in infants, children, young adults, J Am Coll Cardiol, 69, pp. 2551-2569, (2017); Mukherjee D., Konduri G.G., Pediatric pulmonary hypertension: definitions, mechanisms, diagnosis, and treatment, Compr Physiol, 11, pp. 2135-2190, (2021); Oelberg D.G., Temple D.M., Haskins K.S., Bigelow R.H., Adcock E.W., Intracranial hemorrhage in term or near-term newborns with persistent pulmonary hypertension, Clin Pediatr, 27, pp. 14-17, (1988); Gupta S.N., Kechli A.M., Kanamalla U.S., Intracranial hemorrhage in term newborns: management and outcomes, Pediatr Neurol, 40, pp. 1-12, (2009); Law J.B., Wood T.R., Gogcu S., Comstock B.A., Dighe M., Perez K., Et al., Intracranial hemorrhage and 2-year neurodevelopmental outcomes in infants born extremely preterm, J Pediatr, 238, pp. 124-134.e10, (2021); Hancock J.T., Khoshgoftaar T.M., CatBoost for big data: an interdisciplinary review, J Big Data, 7, (2020); Zhao Q.Y., Wang H., Luo J.C., Luo M.H., Liu L.P., Yu S.J., Et al., Development and validation of a machine-learning model for prediction of extubation failure in intensive care units, Front Med, 8, (2021); Lo Y.T., Liao J.C.H., Chen M.H., Chang CM Li C.T., Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms, BMC Medical Informatics and Decision Making, 21, (2021); Zhang C.Y., Chen X.F., Wang S., Hu J.J., Wang C.P., Liu X., Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018, Psychiatry Res, 306, (2021)","Z. Xie; Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; email: xiezulong@hospital.cqmu.edu.cn; X. Liu; Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; email: xiaozhuliu@hospital.cqmu.edu.cn","","Frontiers Media S.A.","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85135612749"
"Hyde B.; Paoli C.J.; Panjabi S.; Bettencourt K.C.; Bell Lynum K.S.; Selej M.","Hyde, Bethany (57222063881); Paoli, Carly J. (56262268400); Panjabi, Sumeet (57740133800); Bettencourt, Katherine C. (58316454500); Bell Lynum, Karimah S. (56554249000); Selej, Mona (36504933400)","57222063881; 56262268400; 57740133800; 58316454500; 56554249000; 36504933400","A claims-based, machine-learning algorithm to identify patients with pulmonary arterial hypertension","2023","Pulmonary Circulation","13","2","e12237","","","","7","10.1002/pul2.12237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162130669&doi=10.1002%2fpul2.12237&partnerID=40&md5=c7892eb68a3b2dff0eaaf9cffb6b36b9","Janssen Business Technology Commercial Data Insights & Data Science, Titusville, NJ, United States; Janssen Scientific Affairs, Inc., Titusville, NJ, United States; Actelion Pharmaceuticals US, Inc., Titusville, NJ, United States; Janssen R&D Data Science, South San Francisco, CA, United States","Hyde B., Janssen Business Technology Commercial Data Insights & Data Science, Titusville, NJ, United States; Paoli C.J., Janssen Scientific Affairs, Inc., Titusville, NJ, United States; Panjabi S., Janssen Scientific Affairs, Inc., Titusville, NJ, United States; Bettencourt K.C., Actelion Pharmaceuticals US, Inc., Titusville, NJ, United States; Bell Lynum K.S., Actelion Pharmaceuticals US, Inc., Titusville, NJ, United States; Selej M., Janssen R&D Data Science, South San Francisco, CA, United States","Many patients with pulmonary arterial hypertension (PAH) experience substantial delays in diagnosis, which is associated with worse outcomes and higher costs. Tools for diagnosing PAH sooner may lead to earlier treatment, which may delay disease progression and adverse outcomes including hospitalization and death. We developed a machine-learning (ML) algorithm to identify patients at risk for PAH earlier in their symptom journey and distinguish them from patients with similar early symptoms not at risk for developing PAH. Our supervised ML model analyzed retrospective, de-identified data from the US-based Optum® Clinformatics® Data Mart claims database (January 2015 to December 2019). Propensity score matched PAH and non-PAH (control) cohorts were established based on observed differences. Random forest models were used to classify patients as PAH or non-PAH at diagnosis and at 6 months prediagnosis. The PAH and non-PAH cohorts included 1339 and 4222 patients, respectively. At 6 months prediagnosis, the model performed well in distinguishing PAH and non-PAH patients, with area under the curve of the receiver operating characteristic of 0.84, recall (sensitivity) of 0.73, and precision of 0.50. Key features distinguishing PAH from non-PAH cohorts were a longer time between first symptom and the prediagnosis model date (i.e., 6 months before diagnosis); more diagnostic and prescription claims, circulatory claims, and imaging procedures, leading to higher overall healthcare resource utilization; and more hospitalizations. Our model distinguishes between patients with and without PAH at 6 months before diagnosis and illustrates the feasibility of using routine claims data to identify patients at a population level who might benefit from PAH-specific screening and/or earlier specialist referral. © 2023 Janssen Research & Development, LLC. Pulmonary Circulation published by John Wiley & Sons Ltd on behalf of Pulmonary Vascular Research Institute.","early diagnosis; rare disease; real-world evidence","polycyclic aromatic hydrocarbon; accuracy; acute coronary syndrome; aged; algorithm; anonymised data; Article; asthma; cardiovascular magnetic resonance; Charlson Comorbidity Index; chronic thromboembolic pulmonary hypertension; congenital heart disease; controlled study; coronary artery disease; data warehouse; decision tree; diagnostic test accuracy study; disease exacerbation; dyspnea; echocardiography; electrocardiography; electronic health record; fatigue; feasibility study; female; health care cost; health care policy; health care utilization; heart catheterization; heart failure; hospitalization; hospitalization cost; human; hypertension; lung artery pressure; machine learning; nuclear magnetic resonance imaging; patient referral; peripheral arterial disease; physician; prescription; prevalence; propensity score; public health service; pulmonary hypertension; random forest; recall; retrospective study; schistosomiasis; sociodemographics; supervised machine learning","","","","","Actelion Pharmaceuticals US, Inc.; Janssen Pharmaceutical Company of Johnson & Johnson; Mary Greenacre","Medical writing support was provided by Mary Greenacre and Ify Sargeant on behalf of Twist Medical, and was funded by Actelion Pharmaceuticals US, Inc., a Janssen Pharmaceutical Company of Johnson & Johnson. ","Humbert M., Gerry Coghlan J., Khanna D., Early detection and management of pulmonary arterial hypertension, Eur Respir Rev, 21, 126, pp. 306-312, (2012); Kiely D.G., Lawrie A., Humbert M., Screening strategies for pulmonary arterial hypertension, Eur Heart J Suppl, 21, pp. K9-20, (2019); Gibbs J.S.R., Making a diagnosis in PAH, Eur Respir Rev, 16, pp. 8-12, (2007); Humbert M., Sitbon O., Chaouat A., Bertocchi M., Habib G., Gressin V., Yaici A., Weitzenblum E., Cordier J.F., Chabot F., Dromer C., Pison C., Reynaud-Gaubert M., Haloun A., Laurent M., Hachulla E., Simonneau G., Pulmonary arterial hypertension in France: results from a national registry, Am J Respir Crit Care Med, 173, 9, pp. 1023-1030, (2006); Armstrong I., Harries C., Yorke J., The Impahct survey: living with pulmonary arterial hypertension, Am J Respir Crit Care Med [Internet], 183, (2011); Armstrong I., Rochnia N., Harries C., Bundock S., Yorke J., The trajectory to diagnosis with pulmonary arterial hypertension: a qualitative study, BMJ Open, 2, 2, (2012); Strange G., Gabbay E., Kermeen F., Williams T., Carrington M., Stewart S., Keogh A., Time from symptoms to definitive diagnosis of idiopathic pulmonary arterial hypertension: the delay study, Pulm Circ, 3, 1, pp. 89-94, (2013); Khou V., Anderson J.J., Strange G., Corrigan C., Collins N., Celermajer D.S., Dwyer N., Feenstra J., Horrigan M., Keating D., Kotlyar E., Lavender M., McWilliams T.J., Steele P., Weintraub R., Whitford H., Whyte K., Williams T.J., Wrobel J.P., Keogh A., Lau E.M., Diagnostic delay in pulmonary arterial hypertension: insights from the Australian and New Zealand pulmonary hypertension registry, Respirology, 25, 8, pp. 863-871, (2020); Weatherald J., Humbert M., The ‘great wait’ for diagnosis in pulmonary arterial hypertension, Respirology, 25, 8, pp. 790-792, (2020); Humbert M., Kovacs G., Hoeper M.M., Badagliacca R., Berger R.M.F., Brida M., Carlsen J., Coats A.J.S., Escribano-Subias P., Ferrari P., Ferreira D.S., Ardeschir G.H., Giannakoulas G., Kiely D.G., Mayer E., Meszaros G., Nagavci B., Olsson K.M., Pepke-Zaba J., Quint J.K., Radegran G., Simonneau G., Sitbon O., Tonia T., Toshner T., Vachiery J.-L., Vonk Noordegraaf A., Delcroix M., Rosenkranz S., 2022 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension, Eur Resp J, 61, (2023); Humbert M., Sitbon O., Chaouat A., Bertocchi M., Habib G., Gressin V., Yaici A., Weitzenblum E., Cordier J., Chabot F., Dromer C., Pison C., Reynaud-Gaubert M., Haloun A., Laurent M., Hachulla E., Cottin V., Degano B., Jais X., Montani D., Souza R., Simonneau G., Survival in patients with idiopathic, familial, and anorexigen-associated pulmonary arterial hypertension in the modern management era, Circulation, 122, 2, pp. 156-163, (2010); Thenappan T., Shah S.J., Rich S., Tian L., Archer S.L., Gomberg-Maitland M., Survival in pulmonary arterial hypertension: a reappraisal of the NIH risk stratification equation, Eur Respir J, 35, 5, pp. 1079-1087, (2010); Launay D., Sitbon O., Hachulla E., Mouthon L., Gressin V., Rottat L., Clerson P., Cordier J.F., Simonneau G., Humbert M., Survival in systemic sclerosis-associated pulmonary arterial hypertension in the modern management era, Ann Rheum Dis, 72, 12, pp. 1940-1946, (2013); Dimopoulos K., Inuzuka R., Goletto S., Giannakoulas G., Swan L., Wort S.J., Gatzoulis M.A., Improved survival among patients with Eisenmenger syndrome receiving advanced therapy for pulmonary arterial hypertension, Circulation, 121, 1, pp. 20-25, (2010); Launay D., Sitbon O., Le Pavec J., Savale L., Tcherakian C., Yaici A., Achouh L., Parent F., Jais X., Simonneau G., Humbert M., Long-term outcome of systemic sclerosis-associated pulmonary arterial hypertension treated with bosentan as first-line monotherapy followed or not by the addition of prostanoids or sildenafil, Rheumatology, 49, 3, pp. 490-500, (2010); Bohr A., Memarzadeh K., The rise of artificial intelligence in healthcare applications, Artificial intelligence in healthcare, pp. 25-60, (2020); Aung Y.Y.M., Wong D.C.S., Ting D.S.W., The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare, Br Med Bull, 139, 1, pp. 4-15, (2021); Lai H., Huang H., Keshavjee K., Guergachi A., Gao X., Predictive models for diabetes mellitus using machine learning techniques, BMC Endocr Disord, 19, 1, (2019); Kwon J., Kim K.-H., Jeon K.-H., Lee S.E., Lee H.Y., Cho H.J., Choi J.O., Jeon E.S., Kim M.S., Kim J.J., Hwang K.K., Chae S.C., Baek S.H., Kang S.M., Choi D.J., Yoo B.S., Kim K.H., Park H.Y., Cho M.C., Oh B.H., Artificial intelligence algorithm for predicting mortality of patients with acute heart failure, PLoS One, 14, 7, (2019); Kiely D.G., Doyle O., Drage E., Jenner H., Salvatelli V., Daniels F.A., Rigg J., Schmitt C., Samyshkin Y., Lawrie A., Bergemann R., Utilising artificial intelligence to determine patients at risk of a rare disease: idiopathic pulmonary arterial hypertension, Pulm Circ, 9, 4, pp. 1-9, (2019); Sprecher V.P., Didden E.M., Swerdel J.N., Muller A., Evaluation of code-based algorithms to identify pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension patients in large administrative databases, Pulm Circ, 10, 4, pp. 1-10, (2020); Memon H.A., Park M.H., Pulmonary arterial hypertension in women, Methodist Debakey Cardiovasc J, 13, 4, pp. 224-237, (2017); Deyo R., Adapting a clinical comorbidity index for use with ICD-9-cm administrative databases, JCE, 45, 6, pp. 613-619, (1992); Lundberg S., Lee S.; Burger C.D., Ghandour M., Padmanabhan Menon D., Helmi H., Benza R.L., Early intervention in the management of pulmonary arterial hypertension: clinical and economic outcomes, ClinicoEconomics Outcomes Res, 9, pp. 731-739, (2017); Tran-Duy A., Morrisroe K., Clarke P., Stevens W., Proudman S., Sahhar J., Nikpour M., Cost-effectiveness of combination therapy for patients with systemic sclerosis-related pulmonary arterial hypertension, J Am Heart Assoc, 10, 7, (2021); Bergemann R., Allsopp J., Jenner H., Daniels F.A., Drage E., Samyshkin Y., Schmitt C., Wood S., Kiely D.G., Lawrie A., High levels of healthcare utilization prior to diagnosis in idiopathic pulmonary arterial hypertension support the feasibility of an early diagnosis algorithm: the SPHInX project, Pulm Circ, 8, 4, pp. 1-9, (2018); Dufour R., Pruett J., Hu N., Lickert C., Stemkowski S., Tsang Y., Lane D., Drake W., Healthcare resource utilization and costs for patients with pulmonary arterial hypertension: real-world documentation of functional class, J Med Econ, 20, 11, pp. 1178-1186, (2017); Exposto F., Hermans R., Nordgren A., Taylor L., Sikander Rehman S., Ogley R., Davies E., Yesufu-Udechuku A., Beaudet A., Burden of pulmonary arterial hypertension in England: retrospective HES database analysis, Ther Adv Respir Dis, 15, (2021); Zozaya N., Abdalla F., Casado Moreno I., Crespo-Diz C., Ramirez Gallardo A.M., Rueda Soriano J., Alcala Galan M., Hidalgo-Vega A., The economic burden of pulmonary arterial hypertension in Spain, BMC Pulm Med, 22, 1, (2022); Campo A., Mathai S.C., Le Pavec J., Zaiman A.L., Hummers L.K., Boyce D., Housten T., Lechtzin N., Chami H., Girgis R.E., Hassoun P.M., Outcomes of hospitalisation for right heart failure in pulmonary arterial hypertension, Eur Respir J, 38, 2, pp. 359-367, (2011); Huynh T.N., Weigt S.S., Sugar C.A., Shapiro S., Kleerup E.C., Prognostic factors and outcomes of patients with pulmonary hypertension admitted to the intensive care unit, J Crit Care, 27, 6, pp. 739.e7-13, (2012); Deshwal H., Weinstein T., Sulica R., Advances in the management of pulmonary arterial hypertension, J Investig Med, 69, 7, pp. 1270-1280, (2021); Kogan E., Didden E.-M., Lee E., Nnewihe A., Stamatiadis D., Mataraso S., Quinn D., Rosenberg D., Chehoud C., Bridges C., A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records, Int J Cardiol, 374, pp. 95-99, (2023); Schuler K.P., Hemnes A.R., Annis J., Farber-Eger E., Lowery B.D., Halliday S.J., Brittain E.L., An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record, Respir Res, 23, 1, (2022); Hanover L., Artificial intelligence saves payers time and money, Manag Healthc Exec [Internet], 31, 8, pp. 18-20, (2021); King R.","B. Hyde; Janssen Business Technology Commercial Data Insights & Data Science, Titusville, 08560, United States; email: bhyde1@its.jnj.com","","John Wiley and Sons Inc","","","","","","20458932","","","","English","Pulm. Circ.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85162130669"
"Crepeau P.; Zhang Z.; Udyavar R.; Morris-Wiseman L.; Biswal S.; Ramanathan M., Jr.; Mathur A.","Crepeau, Philip (57224536878); Zhang, Zhenyu (55721909100); Udyavar, Rhea (57201615269); Morris-Wiseman, Lilah (7201727322); Biswal, Shyam (35478408900); Ramanathan, Murugappan (14043882600); Mathur, Aarti (34872957100)","57224536878; 55721909100; 57201615269; 7201727322; 35478408900; 14043882600; 34872957100","Socioeconomic disparity in the association between fine particulate matter exposure and papillary thyroid cancer","2023","Environmental Health: A Global Access Science Source","22","1","20","","","","6","10.1186/s12940-023-00972-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148805441&doi=10.1186%2fs12940-023-00972-1&partnerID=40&md5=3cc52eebda5dd14caf2088894c29ac1c","Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Department of Global Health, Peking University School of Public Health, Beijing, China; Institute for Global Health and Development, Peking University, Beijing, China; Department of Environmental Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States","Crepeau P., Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Zhang Z., Department of Global Health, Peking University School of Public Health, Beijing, China, Institute for Global Health and Development, Peking University, Beijing, China; Udyavar R., Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Morris-Wiseman L., Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Biswal S., Department of Environmental Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Ramanathan M., Jr., Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States; Mathur A., Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States","Background: Limited data exists suggesting that cumulative exposure to air pollution in the form of fine particulate matter (aerodynamic diameter ≤ 2.5 μm [PM2.5]) may be associated with papillary thyroid carcinoma (PTC), although this relationship has not been widely established. This study aims to evaluate the association between PM2.5 and PTC and determine the subgroups of patients who are at the highest risk of PTC diagnosis. Methods: Under IRB approval, we conducted a case-control study of adult patients (age ≥ 18) newly diagnosed with PTC between 1/2013–12/2016 across a single health care system were identified using electronic medical records. These patients were compared to a control group of patients without any evidence of thyroid disease. Cumulative PM2.5 exposure was calculated for each patient using a deep learning neural networks model, which incorporated meteorological and satellite-based measurements at the patients’ residential zip code. Adjusted multivariate logistic regression was used to quantify the association between cumulative PM2.5 exposure and PTC diagnosis. We tested whether this association differed by gender, race, BMI, smoking history, current alcohol use, and median household income. Results: A cohort of 1990 patients with PTC and a control group of 6919 patients without thyroid disease were identified. Compared to the control group, patients with PTC were more likely to be older (51.2 vs. 48.8 years), female (75.5% vs 46.8%), White (75.2% vs. 61.6%), and never smokers (71.1% vs. 58.4%) (p < 0.001). After adjusting for age, sex, race, BMI, current alcohol use, median household income, current smoking status, hypertension, diabetes, COPD, and asthma, 3-year cumulative PM2.5 exposure was associated with a 1.41-fold increased odds of PTC diagnosis (95%CI: 1.23–1.62). This association varied by median household income (p-interaction =0.03). Compared to those with a median annual household income <$50,000, patients with a median annual household income between $50,000 and < $100,000 had a 43% increased risk of PTC diagnosis (aOR = 1.43, 95%CI: 1.19–1.72), and patients with median household income ≥$100,000 had a 77% increased risk of PTC diagnosis (aOR = 1.77, 95%CI: 1.37–2.29). Conclusions: Cumulative exposure to PM2.5 over 3 years was significantly associated with the diagnosis of PTC. This association was most pronounced in those with a high median household income, suggesting a difference in access to care among socioeconomic groups. © 2023, Crown.","Air pollution; Papillary thyroid cancer; Particulate matter; PM2.5; Socioeconomic disparities; Thyroid cancer","Adult; Air Pollutants; Air Pollution; Case-Control Studies; Environmental Exposure; Female; Humans; Particulate Matter; Socioeconomic Disparities in Health; Thyroid Cancer, Papillary; Thyroid Neoplasms; adult; atmospheric pollution; cancer; disease prevalence; health care; household income; particulate matter; pollution exposure; smoking; socioeconomic conditions; adult; air pollution; alcohol consumption; Article; artificial neural network; asthma; body mass; cancer diagnosis; cancer patient; cancer risk; case control study; chronic obstructive lung disease; cohort analysis; confidence interval; controlled study; deep learning; diabetes mellitus; disease association; economic inequality; electronic medical record; evaluation study; female; gender; geographic distribution; health care system; household income; human; hypertension; incidence; major clinical study; male; meteorology; middle aged; multivariate logistic regression analysis; odds ratio; particulate matter 2.5; particulate matter exposure; people by smoking status; race; retrospective study; smoking; thyroid papillary carcinoma; adverse event; air pollutant; air pollution; environmental exposure; particulate matter; thyroid tumor","","Air Pollutants, ; Particulate Matter, ","","","Center for Biomedical Informatics and Information Technology, National Cancer Institute, CBIIT, NCI CBIIT; Division of Intramural Research, National Institute of Allergy and Infectious Diseases, DIR, NIAID; Division of Cancer Prevention, National Cancer Institute, DCP, NCI; National Institute of Allergy and Infectious Diseases, NIAID; National Cancer Institute, NCI; Division of Cancer Epidemiology and Genetics, National Cancer Institute, DCEG; National Institute of Environmental Health Sciences, NIEHS; Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, DMID, NIAID, DMID; Center for Strategic Scientific Initiatives, National Cancer Institute, CSSI, NCI, (T32CA126607, R01CA206155); National Institute on Aging, NIA, (R01CA206155, T32CA126607, K23AG053429, U01ES026721, R01AI143731)","Funding for this study was provided in part by the National Cancer Institute (NCI), National Institute of Allergy and Infectious Diseases (NIAID), the National Institute of Environmental Health Sciences (NIEHS), and the National Institute on Aging (NIA); grant numbers K23AG053429 (PI: Aarti Mathur), R01AI143731 (PI: Murugappan Ramanathan), U01ES026721 and R01CA206155 (PI: Shyam Biswal), and T32CA126607 (Philip Crepeau). 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A study in two Canadian centers, Thyroid., 24, 3, pp. 545-551, (2014); Swegal W.C., Singer M., Peterson E., Feigelson H.S., Kono S.A., Snyder S., Et al., Socioeconomic factors affect outcomes in well-differentiated thyroid cancer, Otolaryngol Head Neck Surg, 154, 3, pp. 440-445, (2016); Altekruse S., Das A., Cho H., Petkov V., Yu M., Do US thyroid cancer incidence rates increase with socioeconomic status among people with health insurance? An observational study using SEER population-based data, BMJ Open, 5, 12, (2015); Seaberg R.M., Eski S., Freeman J.L., Influence of previous radiation exposure on pathologic features and clinical outcome in patients with thyroid cancer, Arch Otolaryngol Head Neck Surg, 135, 4, pp. 355-359, (2009)","P. Crepeau; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, United States; email: pcrepea1@jh.edu","","BioMed Central Ltd","","","","","","1476069X","","","36823621","English","Environ. Health Global Access Sci. Sour.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85148805441"
"Grout R.; Gupta R.; Bryant R.; Elmahgoub M.A.; Li Y.; Irfanullah K.; Patel R.F.; Fawkes J.; Inness C.","Grout, Robert (58819651800); Gupta, Rishab (58266220700); Bryant, Ruby (58819651900); Elmahgoub, Mawada A. (58820138400); Li, Yijie (58820138500); Irfanullah, Khushbakht (58819894900); Patel, Rahul F. (58820883900); Fawkes, Jake (57222285811); Inness, Catherine (58820388700)","58819651800; 58266220700; 58819651900; 58820138400; 58820138500; 58819894900; 58820883900; 57222285811; 58820388700","Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach","2023","Frontiers in Artificial Intelligence","6","","1287541","","","","5","10.3389/frai.2023.1287541","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182714969&doi=10.3389%2ffrai.2023.1287541&partnerID=40&md5=036e6bb42068685634efaf649fc48d40","Leeds, Accenture, United Kingdom; San Francisco, Accenture, CA, United States; London, Accenture, United Kingdom; Department of Statistics, University of Oxford, Oxford, United Kingdom","Grout R., Leeds, Accenture, United Kingdom; Gupta R., San Francisco, Accenture, CA, United States; Bryant R., London, Accenture, United Kingdom; Elmahgoub M.A., London, Accenture, United Kingdom; Li Y., London, Accenture, United Kingdom; Irfanullah K., London, Accenture, United Kingdom; Patel R.F., London, Accenture, United Kingdom; Fawkes J., Department of Statistics, University of Oxford, Oxford, United Kingdom; Inness C., London, Accenture, United Kingdom","Introduction: The move from a reactive model of care which treats conditions when they arise to a proactive model which intervenes early to prevent adverse healthcare events will benefit from advances in the predictive capabilities of Artificial Intelligence and Machine Learning. This paper investigates the ability of a Deep Learning (DL) approach to predict future disease diagnosis from Electronic Health Records (EHR) for the purposes of Population Health Management. Methods: In this study, embeddings were created using a Word2Vec algorithm from structured vocabulary commonly used in EHRs e.g., Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codes. This study is based on longitudinal medical data from ~50 m patients in the USA. We introduced a novel method of including binned observation values into an embeddings model. We also included novel features associated with wider determinants of health. Patient records comprising these embeddings were then fed to a Bidirectional Gated Recurrent Unit (GRU) model to predict the likelihood of patients developing Type 2 Diabetes Mellitus, Chronic Obstructive Pulmonary Disorder (COPD), Hypertension or experiencing an Acute Myocardial Infarction (MI) in the next 3 years. SHapley Additive exPlanations (SHAP) values were calculated to achieve model explainability. Results: Increasing the data scope to include binned observations and wider determinants of health was found to improve predictive performance. We achieved an area under the Receiver Operating Characteristic curve value of 0.92 for Diabetes prediction, 0.94 for COPD, 0.92 for Hypertension and 0.94 for MI. The SHAP values showed that the models had learned features known to be associated with these outcomes. Discussion: The DL approach outlined in this study can identify clinically-relevant features from large-scale EHR data and use these to predict future disease outcomes. This study highlights the promise of DL solutions for identifying patients at future risk of disease and providing clinicians with the means to understand and evaluate the drivers of those predictions. Copyright © 2024 Grout, Gupta, Bryant, Elmahgoub, Li, Irfanullah, Patel, Fawkes and Inness.","chronic disease; Deep Learning; disease code embedding; Electronic Health Records; Natural Language Processing; Population Health Management","","","","","","","","Bahdanau D., Cho K., Bengio Y., Neural machine translation by jointly learning to align and translate, arXiv, (2014); Beam A.L., Kompa B., Schmaltz A., Fried I., Weber G., Palmer N., Et al., “Clinical concept embeddings learned from massive sources of multimodal medical data,”, Pacific Symposium on Biocomputing 2020, (2019); Berwick D.M., Nolan T.W., Whittington J., The triple aim: care, health, and cost, Health Aff, (2008); Bittoni M.A., Wexler R., Spees C.K., Clinton S.K., Taylor C.A., Lack of private health insurance is associated with higher mortality from cancer and other chronic diseases, poor diet quality, and inflammatory biomarkers in the united states, Prev. Med, 81, (2015); Buck D., Baylis A., Dougall D., Robertson R., A Vision for Population Health: Towards a Healthier Future, Towards a Healthier Future, (2018); Cai X., Gao J., Ngiam K.Y., Ooi B.C., Zhang Y., Yuan X., Medical concept embedding with time-aware attention, arXiv [preprint], (2018); Chen Y., Calabrese R., Martin-Barragan B., Interpretable machine learning for imbalanced credit scoring datasets, Eur. J. Oper. Res, 312, (2023); Choi E., Schuetz A., Stewart W.F., Sun J., Medical concept representation learning from electronic health records and its application on heart failure prediction, arXiv [preprint], (2016); Choi E., Bahadori M.T., Searles E., Coffey C., Thompson M., Bost J., Et al., “Multi-layer representation learning for medical concepts,”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2016); Choi Y., Chiu C.Y.I., Sontag D., “Learning low-dimensional representations of medical concepts,”, AMIA Summits on Translational Science Proceedings, (2016); Datta S., Morassi Sasso A., Kiwit N., Bose S., Nadkarni G., Miotto R., Et al., Predicting hypertension onset from longitudinal electronic health records with deep learning, JAMIA Open 5, (2022); Devlin J., Chang M.-W., Lee K., Toutanova K., Bert: pre-training of deep bidirectional transformers for language understanding, arXiv [preprint], (2018); Donnelly K., Snomed-ct: the advanced terminology and coding system for ehealth, Stud. 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Biomed, 226, (2022); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst, (2017); Lundberg S.M., Lee S.-I., SHAP: SHapley Additive exPlanations, (2021); Lundberg S.M., Lee S.-I., Shap Documentation: Shap Force Plot, (2023); Main C., Haig M., Kanavos P., The Promise of Population Health Management in England: From Theory to Implementation, (2022); Markus A.F., Kors J.A., Rijnbeek P.R., The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies, J. Biomed. Inform, 113, (2021); Meng Y., Speier W., Ong M.K., Arnold C.W., Bidirectional representation learning from transformers using multimodal electronic health record data to predict depression, IEEE J. Biomed. 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Med, 4, (2021); Ravaut M., Harish V., Sadeghi H., Leung K.K., Volkovs M., Kornas K., Et al., Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes, JAMA Netw. Open, 4, (2021); Ross C.E., Mirowsky J., Does medical insurance contribute to socioeconomic differentials in health, Milbank Q, (2000); Rossi L.A., Shawber C., Munu J., Zachariah F., Evaluation of embeddings of laboratory test codes for patients at a cancer center, arXiv [preprint], (2019); Rupp M., Peter O., Pattipaka T., Exbehrt: Extended transformer for electronic health records to predict disease subtypes & progressions, arXiv [preprint], (2023); Shah N.H., Entwistle D., Pfeffer M.A., Creation and adoption of large language models in medicine, JAMA, 330, (2023); Shang J., Ma T., Xiao C., Sun J., Pre-training of graph augmented transformers for medication recommendation, arXiv [preprint], (2019); Si Y., Du J., Li Z., Jiang X., Miller T., Wang F., Et al., Deep representation learning of patient data from electronic health records (ehr): a systematic review. J. Biomed, Inform, (2021); Stone C., Rosella L., Goel V., Population health perspective on high users of health care: Role of family physicians, Can. Fam. Phys, (2014); Su C.-P., Asfaw A., Tamers S.L., Luckhaupt S.E., Health insurance coverage among us workers: differences by work arrangements in 2010 and 2015, Am. J. Prev. Med, (2019); Sullivan S.D., Freemantle N., Gupta R.A., Wu J., Nicholls C.J., Westerbacka J., Et al., Clinical outcomes in high-hypoglycaemia-risk patients with type 2 diabetes switching to insulin glargine 300u/ml versus a first-generation basal insulin analogue in the united states: results from the deliver high risk real-world study, Endocrinol. Diabetes Metab, (2021); Tang M., Neligan M., Fagg M., How Data-Driven Population Health Management Will Shape Systems, Response to Health Inequalities and Secondary Prevention, (2023); Tonekaboni S., Joshi S., McCradden M.D., Goldenberg A., What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use, (2019); Wertenteil S., Strunk A., Garg A., Prevalence estimates for chronic urticaria in the united states: a sex-and age-adjusted population analysis, J. Am. Acad. Dermatol, (2019); Population Health Management in Primary Health Care: a Proactive Approach to Improve Health and Well-Being: Primary Health Care Policy Paper Series, (2023); Wornow M., Xu Y., Thapa R., Patel B., Steinberg E., Fleming S., Et al., The shaky foundations of large language models and foundation models for electronic health records, npj Dig. Med, (2023); Xiang Y., Xu J., Si Y., Li Z., Rasmy L., Zhou Y., Et al., Time-sensitive clinical concept embeddings learned from large electronic health records, BMC Med. Inform. Decis. Mak, (2019); Zhao J., Henriksson A., Asker L., Bostrom H., Predictive modeling of structured electronic health records for adverse drug event detection, BMC Med. Inform. Decis. Mak, (2015)","R. Grout; Accenture, Leeds, United Kingdom; email: robert.grout@accenture.com","","Frontiers Media SA","","","","","","26248212","","","","English","Frontier. Artif. Intell.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85182714969"
"Yedinak C.; Ross I.L.","Yedinak, Christine (25633390900); Ross, Ian Louis (57195387006)","25633390900; 57195387006","Significant risk of COVID-19 and related-hospitalization among patients with adrenal insufficiency: A large multinational survey","2022","Frontiers in Endocrinology","13","","1042119","","","","7","10.3389/fendo.2022.1042119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142659100&doi=10.3389%2ffendo.2022.1042119&partnerID=40&md5=c377017930d16924e0d26bf0b9de43a7","Department of Neurosurgical Services, Oregon Health and Science University, Portland, OR, United States; Department of Medicine, University of Cape Town, Cape Town, South Africa","Yedinak C., Department of Neurosurgical Services, Oregon Health and Science University, Portland, OR, United States; Ross I.L., Department of Medicine, University of Cape Town, Cape Town, South Africa","Objective: To determine self-reported incidence and potential risk factors for COVID-19 in patients with adrenal insufficiency (AI). Methods: A 27-item AI survey was developed for AI and COVID-19 status, vetted by specialists and patients, and distributed via social media, websites, and advocacy groups. Participation was voluntary and anonymous. Data were collected from September 20th, 2020 until December 31st, 2020. Results: Respondents (n=1291) with self-reported glucocorticoid treatment for AI, completed the survey, with 456 who reported having symptoms and were screened for COVID-19 during 2020; 40 tested positive (+ve), representing an 8.8% incidence. Of the COVID-19+ve, 31 were female (78%), with mean age of 39.9 years. COVID-19 among AI patients occurred most commonly in those aged 40–59 years (n=17; 42.5%); mean time since AI diagnosis was 13.5 years (range 0.2−42.0 years). Pulmonary disease, congenital adrenal hyperplasia, and higher maintenance doses of glucocorticoids were significantly associated with +ve COVID-19 (p=0.04, p=0.01, and p=0.001, respectively. In respondents the cumulative incidence of COVID-19+ve during 2020 was 3.1%; greater than the 1.03% worldwide-incidence reported by WHO, by December 31st, 2020. There was a 3-fold (95% CI 2.16-3.98) greater relative risk (RR) of COVID-19 infection and a 23.8- fold (95% CI 20.7-31.2) RR of hospitalization in patients with AI, compared with the global population. Conclusion: A markedly raised RR of COVID-19 and hospitalization in respondents reporting chronic AI was detected. We found that a diagnosis of congenital adrenal hyperplasia, age>40 years, male gender, pulmonary disease, and higher maintenance doses of glucocorticoids were associated with greatest risk. Copyright © 2022 Yedinak and Ross.","adrenal insufficiency; adrenal insufficiency: incidence COVID-19 SARS-CoV2; COVID-19; hospitalization; incidence","fludrocortisone; glucocorticoid; hydrocortisone; adrenal insufficiency; adult; advocacy group; anosmia; Article; asthma; behavior; bronchiectasis; cardiovascular disease; comorbidity; confidence interval; congenital adrenal hyperplasia; controlled study; coronavirus disease 2019; cumulative incidence; diabetes mellitus; female; fever; hospitalization; human; hypertension; lung disease; major clinical study; male; middle aged; nonhuman; osteopenia; osteoporosis; pandemic; respiratory distress; risk factor; self report; Severe acute respiratory syndrome coronavirus 2; social distancing; social isolation; social media","","fludrocortisone, 127-31-1; hydrocortisone, 50-23-7","","","","","Ritchie H., Ortiz-Ospina E., Beltekian D., Mathieu E., Hasell J., Macdonald B., Et al., Data from: Coronavirus pandemic (COVID-19), Our world data, (2022); 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Silhol F., Sarlon G., Deharo J.-C., Vaisse B., Downregulation of ACE2 induces overstimulation of the renin-angiotensin system in COVID-19: should we block the renin-angiotensin system, Hypertens Res, 43, 8, (2020); Hoffmann M., Kleine-Weber H., Schroeder S., Kruger N., Herrler T., Erichsen S., Et al., SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor, Cell, 181, 2, (2020); Young M.J., Clyne C.D., Chapman K.E., Endocrine aspects of ACE2 regulation: RAAS, steroid hormones and SARS-CoV-2, J Endocrinol, 247, 2, (2020); Gebhard C., Regitz-Zagrosek V., Neuhauser H.K., Morgan R., Klein S.L., Impact of sex and gender on COVID-19 outcomes in Europe, Biol Sex Differ, 11, 1, (2020); Pan D., Sze S., Minhas J.S., Bangash M.N., Pareek N., Divall P., Et al., The impact of ethnicity on clinical outcomes in COVID-19: A systematic review, E Clin Med, 23, (2020); Yang J., Zheng Y., Gou X., Pu K., Chen Z., Guo Q., Et al., Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis, Int J Infect Dis, 94, (2020); Kaiser U.B., Mirmira R.G., Stewart P.M., Our response to COVID-19 as endocrinologists and diabetologists, J Clin Endocrinol Metab, 105, 5, (2020); Puig-Domingo M., Marazuela M., Giustina A., COVID-19 and endocrine diseases. a statement from the European society of endocrinology, Endocrine, 68, 1, pp. 2-5, (2020); Graf A., Marcus H.J., Baldeweg S.E., The direct and indirect impact of the COVID-19 pandemic on the care of patients with pituitary disease: a cross sectional study, Pituitary, 24, (2021); Carosi G., Morelli V., Del Sindaco G., Serban A.L., Cremaschi A., Frigerio S., Et al., Adrenal insufficiency at the time of COVID-19: A retrospective study in patients referring to a tertiary centre, SSRN Electronic J, 106, 3, (2020); Classification of omicron (B.1.1.529): SARS-CoV-2 variant of concern, (2022); Bush K.A., Krukowski K., Eddy J.L., Janusek L.W., Mathews H.L., Glucocorticoid receptor mediated suppression of natural killer cell activity: identification of associated deacetylase and corepressor molecules, Cell Immunol, 275, 1-2, pp. 80-89, (2012); 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Olnes M., Kotliarov Y., Biancotto A., Cheung F., Chen J., Et al., Effects of systemically administered hydrocortisone on the human immunome, Sci Rep, 6, (2016); Bancos I., Hazeldine J., Chortis V., Hampson P., Taylor A.E., Lord J.M., Et al., Primary adrenal insufficiency is associated with impaired natural killer cell function: a potential link to increased mortality, Eur J Endocrinol, 176, 4, (2017); Maucourant C., Filipovic I., Ponzetta A., Aleman S., Cornillet M., Hertwig L., Et al., Natural killer cell immunotypes related to COVID-19 disease severity, Sci Immunol, 5, 50, (2020); England B.R., Roul P., Yang Y., Kalil A.C., Michaud K., Theil, Gm, Et al., Risk of COVID-19 in rheumatoid arthritis: A national veterans affairs matched cohort study in At-risk individuals, Arthritis Rheumatol, 73, 12, (2021); Chen Y., Klein S.L., Garibaldi B.T., Li H., Wu C., Osevala N.M., Et al., Aging in COVID-19: Vulnerability, immunity and intervention, Ageing Res Rev, 65, (2021); Gallo Marin B., Aghagoli G., Lavine K., Yang L., Siff E.J., Silva S., Et al., Predictors of COVID-19 severity: A literature review, Rev Med Virol, 31, 1, pp. 1-10, (2021); Peckham H., de Gruijter N.M., Raine C., Radziszewska A., Ciurtin C., Wedderburn L.R., Et al., Male Sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission, Nat Commun, 11, 1, (2020); Wambier C.G., Goren A., Vano-Galvan S., Ramos P.M., Ossimetha A., Nau G., Et al., Androgen sensitivity gateway to COVID-19 disease severity, Drug Dev Res, 81, 7, (2020); Aveyard P., Gao M., Lindson N., Hartmann-Boyce J., Watkinson P., Young D., Et al., Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study, Lancet Respir Med, 9, 8, (2021); Schultze A., Walker A.J., MacKenna B., Morton C.E., Bhaskaran K., Brown J.P., Et al., Risk of COVID19-related death among patients with chronic obstructive pulmonary disease or asthma prescribed inhaled corticosteroids: an observational cohort study using the OpenSAFELY platform, Lancet Respir Med, 8, 11, (2020); Erichsen M.M., Lovas K., Fougner K.J., Svartberg J., Hauge E.R., Bollerslev J., Et al., Normal overall mortality rate in addison’s disease, but young patients are at risk of premature death, Eur J Endocrinol, 160, 2, (2009); Bergthorsdottir R., Leonsson-Zachrisson M., Oden A., Johannsson G., Premature mortality in patients with addison’s disease: A population-based study, J Clin Endocrinol Metab, 91, 12, (2006); Moreno-Perez O., Merino E., Leon-Ramirez J.-M., Andres M., Ramos J.M., Arenas-Jimenez J., Et al., Post-acute COVID-19 syndrome. incidence and risk factors: A Mediterranean cohort study, J Infect, 82, 3, (2021); Eysenbach G., Improving the quality of web surveys: The checklist for reporting results of Internet e-surveys (CHERRIES), J Med Internet Res, 6, 3, (2004)","C. Yedinak; Department of Neurosurgical Services, Oregon Health and Science University, Portland, United States; email: yedinakc@ohsu.edu","","Frontiers Media S.A.","","","","","","16642392","","","","English","Front. Endocrinol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85142659100"
"Lip G.Y.H.; Genaidy A.; Tran G.; Marroquin P.; Estes C.; Shnaiden T.; Bayewitz A.","Lip, Gregory Y. H. (57216675273); Genaidy, Ash (57221995138); Tran, George (57221996315); Marroquin, Patricia (57221995711); Estes, Cara (57221996617); Shnaiden, Tatiana (25655529000); Bayewitz, Ariel (57558598500)","57216675273; 57221995138; 57221996315; 57221995711; 57221996617; 25655529000; 57558598500","Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine-learning algorithms","2022","European Journal of Clinical Investigation","52","8","e13777","","","","5","10.1111/eci.13777","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127392826&doi=10.1111%2feci.13777&partnerID=40&md5=0512a5abde15558cf7fa5548cd3fa0d2","Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Anthem Inc., Indianapolis, IN, United States; IngenioRX, Indianapolis, IN, United States","Lip G.Y.H., Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Genaidy A., Anthem Inc., Indianapolis, IN, United States; Tran G., IngenioRX, Indianapolis, IN, United States; Marroquin P., Anthem Inc., Indianapolis, IN, United States; Estes C., Anthem Inc., Indianapolis, IN, United States; Shnaiden T., Anthem Inc., Indianapolis, IN, United States; Bayewitz A., Anthem Inc., Indianapolis, IN, United States","Background: To date, incident and recurrent MI remains a major health issue worldwide, and efforts to improve risk prediction in population health studies are needed. This may help the scalability of prevention strategies and management in terms of healthcare cost savings and improved quality of care. Methods: We studied a large-scale population of 4.3 million US patients from different socio-economic and geographical areas from three health plans (Commercial, Medicare, Medicaid). Individuals had medical/pharmacy benefits for at least 30 months (2 years for comorbid history and followed up for 6 months or more for clinical outcomes). Machine-learning (ML) algorithms included supervised (logistic regression, neural network) and unsupervised (decision tree, gradient boosting) methodologies. Model discriminant validity, calibration and clinical utility were performed separately on allocated test sample (1/3 of original data). Results: In the absence of MI in comorbid history, the overall incidence rates were 0.442 cases/100 person-years and in the presence of MI history, 0.652. ML algorithms showed that supervised formulations had incrementally higher discriminant validity than unsupervised techniques (e.g., for incident MI outcome in the absence of MI in comorbid history: logistic regression “LR” – c index 0.921, 95%CI 0.920–0.922; neural network “NN” – c index 0.914, 95%CI 0.913–0.915; gradient boosting “GB” – c index 0.902, 95%CI 0.900–0.904; decision tree “DT” – c index 0.500, 95%CI 0.495–0.505). Calibration and clinical utility showed good to excellent results. Conclusion: ML algorithms can substantially improve the prediction of incident and recurrent MI particularly in terms of the non-linear formulation. This approach may help with improved risk prediction, allowing implementation of cardiovascular prevention strategies across diversified sub-populations with different clusters of complexity. © 2022 Stichting European Society for Clinical Investigation Journal Foundation. Published by John Wiley & Sons Ltd.","epidemiology; incident and recurrent rates; machine learning; myocardial infarction","Aged; Algorithms; Comorbidity; Humans; Machine Learning; Medicare; Myocardial Infarction; United States; adult; age; aged; algorithm; anemia; Article; artificial neural network; asthma; atrial fibrillation; bleeding; bronchiectasis; calibration; cerebrovascular accident; chronic kidney failure; chronic obstructive lung disease; clinical outcome; cognitive defect; cohort analysis; comorbidity; congestive heart failure; coronary artery disease; decision tree; depression; diabetes mellitus; diagnostic value; discriminant validity; disorders of lipid metabolism; female; follow up; gradient boosting; heart infarction; human; hypertension; hyperthyroidism; incidence; intervertebral disk disease; liver disease; logistic regression analysis; machine learning; major clinical study; male; medicaid; medical geography; medical history; medicare; metabolic syndrome X; middle aged; multiple chronic conditions; osteoarthritis; outcome assessment; peripheral occlusive artery disease; prediction; public health insurance; recurrence risk; sex; sleep disordered breathing; social status; spondylosis; supervised machine learning; United States; unsupervised machine learning; valvular heart disease; algorithm; comorbidity; heart infarction; machine learning; medicare","","","","","","","Virani S.S., Alonso A., Aparicio H.J., Et al., Heart disease and stroke statistics-2021 update: a report from the American Heart Association, Circulation, 143, pp. e254-e743, (2021); Aminorroaya A., Yoosefi M., Rezaei N., Et al., Global, regional, and national quality of care of ischaemic heart disease from 1990 to 2017: a systematic analysis for the Global Burden of Disease Study 2017, Eur J Prev Cardiol, 29, 2, pp. 371-379, (2022); Chang T.E., Ritchey M.D., Park S., Et al., National rates of nonadherence to antihypertensive medications among insured adults with hypertension, 2015, Hypertension, 74, pp. 1324-1332, (2019); Schiraldi M., Patil C.G., Mukherjee D., Et al., Effect of insurance and racial disparities on outcomes in traumatic brain injury, J Neurol Surg A Cent Eur Neurosurg, 76, pp. 224-232, (2015); Olier I., Ortega-Martorell S., Pieroni M., Lip G.Y.H., How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management, Cardiovasc Res, 117, pp. 1700-1717, (2021); Van Calster B., Wynants L., Verbeek J.F.M., Et al., Reporting and interpreting decision curve analysis: a guide for investigators, Eur Urol, 74, 6, pp. 796-804, (2018); Ohm J., Skoglund P.H., Discacciati A., Et al., Socioeconomic status predicts second cardiovascular event in 29,226 survivors of a first myocardial infarction, European J Prevent Cardiol, 5, 9, pp. 985-993, (2018); Rich M.W., Epidemiology, clinical features, and prognosis of acute myocardial infarction in the elderly, Am J Geriatr Cardiol, 15, 1, pp. 7-11, (2006); Lip G.Y.H., Genaidy A., Tran G., Marroquin P., Estes C., Incident atrial ibrillation and its risk prediction in patients developing COVID-19: a machine learning based algorithm approach, Eur J Intern Med, 91, pp. 53-58, (2021); Lip G.Y.H., Genaidy A., Tran G., Marroquin P., Estes C., Sloop S., Improving stroke risk prediction in the general population: a comparative assessment of common clinical rules, a new multimorbid index, and machine-learning-based algorithms, Thromb Haemost, 122, 1, pp. 142-150, (2022); Lip G.Y.H., Tran G., Genaidy A., Marroquin P., Estes C., Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: a machine-learning approach, J Arrhythm, 37, pp. 931-941, (2021); Lip G.Y.H., Tran G., Genaidy A., Marroquin P., Estes C., Landsheft J., Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: Comparing common clinical risk scores and machine learning algorithms, Eur Heart J Qual Care Clin Outcomes, (2021); Aziz F., Malek S., Ibrahim K.S., Et al., Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: a machine learning approach, PLoS One, 16, (2021); Hadanny A., Shouval R., Wu J., Et al., Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets, J Cardiol, 78, 5, pp. 439-446, (2021); Lee W., Lee J., Woo S.I., Et al., Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction, Sci Rep, 11, (2021); Guo Y., Lane D.A., Chen Y., Lip G.Y.H., Mobile health technology facilitates population screening and integrated care management in patients with atrial fibrillation, Eur Heart J, 41, pp. 1617-1619, (2020); Guo Y., Guo J., Shi X., Et al., Mobile health technology-supported atrial fibrillation screening and integrated care: a report from the mAFA-II trial Long-term Extension Cohort, Eur J Intern Med, 82, pp. 105-111, (2020)","G.Y.H. Lip; Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; email: gregory.lip@liverpool.ac.uk; A. Genaidy; Anthem Inc., Indianapolis, United States; email: ashgenaidy@gmail.com","","John Wiley and Sons Inc","","","","","","00142972","","EJCIB","35349732","English","Eur. J. Clin. Invest.","Article","Final","","Scopus","2-s2.0-85127392826"
"Lin H.; Yan Y.; Deng C.; Sun N.","Lin, Hairu (58981357800); Yan, Yinghua (55567967500); Deng, Chunhui (7202303343); Sun, Nianrong (55815896500)","58981357800; 55567967500; 7202303343; 55815896500","Engineered Bimetallic MOF-Crafted Bullet Aids in Penetrating Serum Metabolic Traits of Chronic Obstructive Pulmonary Disease","2024","Analytical Chemistry","96","36","","14688","14696","8","5","10.1021/acs.analchem.4c03681","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202784300&doi=10.1021%2facs.analchem.4c03681&partnerID=40&md5=f62f0581bfb785472ba554d7ef2ca724","Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China; School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, 315211, China; School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, 330031, China; Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China","Lin H., Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China; Yan Y., School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, 315211, China; Deng C., Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China, School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, 330031, China; Sun N., Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China","Metabolomics analysis based on body fluids, combined with high-throughput laser desorption and ionization mass spectrometry (LDI-MS), holds great potential and promising prospects for disease diagnosis and screening. On the other hand, chronic obstructive pulmonary disease (COPD) currently lacks innovative and powerful diagnostic and screening methods. In this work, CoFeNMOF-D, a metal-organic framework (MOF)-derived metal oxide nanomaterial, was synthesized and utilized as a matrix to assist LDI-MS for extracting serum metabolic fingerprints of COPD patients and healthy controls (HC). Through machine learning algorithms, successful discrimination between the COPD and HC was achieved. Furthermore, four potential biomarkers significantly downregulated in COPD were screened out. The disease diagnostic models based on the biomarkers demonstrated excellent diagnostic performance across different algorithms, with area under the curve (AUC) values reaching 0.931 and 0.978 in the training and validation sets, respectively. Finally, the potential metabolic pathways and disease mechanisms associated with the identified markers were explored. This work advances the application of LDI-based molecular diagnostics in clinical settings. © 2024 American Chemical Society.","","Diagnosis; Ionization of liquids; Bimetallics; Chronic obstructive pulmonary disease; Disease screening; Healthy controls; High-throughput; Laser desorption mass spectrometry; Laser ionization mass spectrometries; Metabolomic analysis; Metalorganic frameworks (MOFs); On-body; Pulmonary diseases","","","","","National Natural Science Foundation of China, NSFC, (22004017, 21425518, 22074019); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2018YFA0507501); National Key Research and Development Program of China, NKRDPC; Shanghai Sailing Program, (20YF1405300)","This work was financially supported by the National Key R&D Program of China (2018YFA0507501), the National Natural Science Foundation of China (22074019, 21425518, 22004017), and the Shanghai Sailing Program (20YF1405300).","Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, (2023); Stolz D., Mkorombindo T., Schumann D.M., Agusti A., Ash S.Y., Bafadhel M., Bai C., Chalmers J.D., Criner G.J., Dharmage S.C., Towards the elimination of chronic obstructive pulmonary disease: A Lancet Commission, Lancet, 400, 10356, pp. 921-972, (2022); Agusti A., Hogg J.C., Update on the Pathogenesis of Chronic Obstructive Pulmonary Disease, N. 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Life Sci., 1198, (2022); Callejon-Leblic B., Pereira-Vega A., Vazquez-Gandullo E., Sanchez-Ramos J.L., Gomez-Ariza J.L., Garcia-Barrera T., Study of the metabolomic relationship between lung cancer and chronic obstructive pulmonary disease based on direct infusion mass spectrometry, Biochimie, 157, pp. 111-122, (2019); Biljak V.R., Rumora L., Cepelak I., Pancirov D., Popovic-Grle S., Soric J., Grubisic T.Z., Glutathione cycle in stable chronic obstructive pulmonary disease, Cell Biochem. Funct., 28, 6, pp. 448-453, (2010); Gould N.S., Min E., Gauthier S., Martin R.J., Day B.J., Lung glutathione adaptive responses to cigarette smoke exposure, Respir. Res., 12, 1, (2011)","Y. Yan; School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, 315211, China; email: yanyinghua@nbu.edu.cn; C. Deng; Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200433, China; email: chdeng@fudan.edu.cn; N. Sun; Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China; email: sunnianrong@fudan.edu.cn","","American Chemical Society","","","","","","00032700","","ANCHA","","English","Anal. Chem.","Article","Final","","Scopus","2-s2.0-85202784300"
"Topaz M.; Zolnoori M.; Norful A.A.; Perrier A.; Kostic Z.; George M.","Topaz, Maxim (54790231000); Zolnoori, Maryam (36159082000); Norful, Allison A. (57190338356); Perrier, Alexis (57831228200); Kostic, Zoran (57207510598); George, Maureen (7401952335)","54790231000; 36159082000; 57190338356; 57831228200; 57207510598; 7401952335","Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting","2022","PLoS ONE","17","8 August","e0271884","","","","5","10.1371/journal.pone.0271884","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135422553&doi=10.1371%2fjournal.pone.0271884&partnerID=40&md5=195dfc927f241ebaf1f133ed916c924c","School of Nursing, Data Science Institute, Columbia University, New York, NY, United States; Visiting Nurse Service of New York, New York, NY, United States; Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, United States; School of Nursing, Columbia University, New York, NY, United States; Department of Electrical engineering, Columbia University, New York, NY, United States","Topaz M., School of Nursing, Data Science Institute, Columbia University, New York, NY, United States, Visiting Nurse Service of New York, New York, NY, United States; Zolnoori M., School of Nursing, Data Science Institute, Columbia University, New York, NY, United States; Norful A.A., Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, United States, School of Nursing, Columbia University, New York, NY, United States; Perrier A., School of Nursing, Columbia University, New York, NY, United States; Kostic Z., Department of Electrical engineering, Columbia University, New York, NY, United States; George M., School of Nursing, Columbia University, New York, NY, United States","Objective Asthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient’s inhaled corticosteroid adherence. Materials and methods Shared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study’s predictive goals. Results The accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines). Discussion This was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence. Conclusion Machine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains. © 2022 Topaz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Adrenal Cortex Hormones; Adult; Asthma; Bayes Theorem; Decision Making; Decision Making, Shared; Humans; Medication Adherence; Primary Health Care; Speech; Speech Perception; Surveys and Questionnaires; corticosteroid; corticosteroid; adult; Article; asthma; audio recording; Bayesian learning; clinical assessment tool; clinical evaluation; controlled study; decision tree; female; health care personnel; health care quality; human; machine learning; major clinical study; male; Medication Adherence Report Scale for Asthma; medication compliance; middle aged; patient compliance; patient identification; primary medical care; questionnaire; shared decision making; speech discrimination; support vector machine; asthma; Bayes theorem; decision making; medication compliance; primary health care; speech; speech perception","","Adrenal Cortex Hormones, ","","","National Institutes of Health, NIH; National Institute of Nursing Research, NINR, (R21NR016507); National Institute of Nursing Research, NINR; National Center for Advancing Translational Sciences, NCATS, (TL1 TR001875); National Center for Advancing Translational Sciences, NCATS","This study was supported by the National Institute of Nursing Research, National Institutes of Health (R21 NR016507). Dr. Norful’s efforts were supported, in part, by the National Center for Advancing Translational Sciences, National Institutes of Health (TL1 TR001875). The funder did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Asthma Surveillance Data CDC, (2020); Gamble C, Talbott E, Youk A, Et al., Racial differences in biologic predictors of severe asthma: Data from the Severe Asthma Research Program, J Allergy Clin Immunol, 126, (2010); 2020 GINA Main Report—Global Initiative for Asthma—GINA, (2020); Wu AC, Butler MG, Li L, Et al., Primary adherence to controller medications for asthma is poor, Ann Am Thorac Soc, 12, pp. 161-166, (2015); Kew KM, Malik P, Aniruddhan K, Et al., Shared decision-making for people with asthma, Cochrane database Syst Rev, 10, (2017); Makoul G, Clayman ML., An integrative model of shared decision making in medical encounters, Patient Educ Couns, 60, pp. 301-312, (2006); Battersby M, Von Korff M, Schaefer J, Et al., Twelve evidence-based principles for implementing self-management support in primary care, Jt Comm J Qual patient Saf, 36, pp. 561-570, (2010); Joseph-Williams N, Edwards A, Elwyn G., Power imbalance prevents shared decision making, BMJ, 348, (2014); Legare F, Witteman HO., Shared decision making: examining key elements and barriers to adoption into routine clinical practice, Health Aff (Millwood), 32, pp. 276-284, (2013); Pollard S, Bansback N, FitzGerld JM, Et al., The burden of non-adherence among adults with asthma: a role for shared decision-making, Allergy, 72, pp. 705-712, (2017); Physician Attitudes Toward Shared Decision Making: A Systematic Review, Patient Educ Couns, 98, pp. 1046-1057, (2015); Lee J, Callon W, Haywood C, Et al., What does shared decision making look like in natural settings? A mixed methods study of patient–provider conversations, Commun Med, 14, pp. 217-228, (2018); Lee JXW, Wojtczak HA, Wachter AM, Et al., Understanding asthma medical non-adherence in an adult and pediatric population, J allergy Clin Immunol Pract, 3, pp. 436-437, (2015); George M, Bender B., New insights to improve treatment adherence in asthma and COPD, Patient Prefer Adherence, 13, pp. 1325-1334, (2019); Narayanan S, Georgiou PG., Behavioral Signal Processing: Deriving Human Behavioral Informatics From Speech and Language: Computational techniques are presented to analyze and model expressed and perceived human behavior-variedly characterized as typical, atypical, distressed, and d, Proc IEEE Inst Electr Electron Eng, 101, pp. 1203-1233, (2013); Low DM, Bentley KH, Ghosh SS., Automated assessment of psychiatric disorders using speech: A systematic review, Laryngoscope Investig Otolaryngol, 5, pp. 96-116, (2020); Petti U, Baker S, Korhonen A., A systematic literature review of automatic Alzheimer’s disease detection from speech and language, J Am Med Informatics Assoc, 27, pp. 1784-1797, (2020); Marmar CR, Brown AD, Qian M, Et al., Speech-based markers for post-traumatic stress disorder in US veterans, Depress Anxiety, 36, pp. 607-616, (2019); Bedi G, Carrillo F, Cecchi GA, Et al., Automated analysis of free speech predicts psychosis onset in high-risk youths, npj Schizophr, 1, (2015); Chakravarthula SN, Nasir M, Tseng S-Y, Et al., Automatic prediction of suicidal risk in military couples using multimodal interaction cues from couples conversations, (2019); MV P, MLS S, Et al., Shared decision-making in the BREATHE asthma intervention trial: A research protocol, J Adv Nurs, 75, (2019); Juniper EF, O'Byrne PM, Guyatt GH, Et al., Development and validation of a questionnaire to measure asthma control, Eur Respir J, 14, pp. 902-907, (1999); George M, Topaz M, Rand C, Et al., Inhaled corticosteroid beliefs, complementary and alternative medicine, and uncontrolled asthma in urban minority adults, J Allergy Clin Immunol, 134, (2014); Miller WR, Rollnick S., Motivational interviewing: Preparing people for change, (2002); Kriston L, Scholl I, Holzel L, Et al., The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample, Patient Educ Couns, 80, pp. 94-99, (2010); Norful A, Dillon J, Baik D, Et al., Instruments to Measure Shared Decision-Making in Outpatient Chronic Care: A Systematic Review and Appraisal, J Clin Epidemiol, 121, (2020); Mora PA, Berkowitz A, Contrada RJ, Et al., Factor structure and longitudinal invariance of the Medical Adherence Report Scale-Asthma, Psychol Health, 26, pp. 713-727, (2011); George M, Bruzzese J-M, Jia H, Et al., Pilot Trial of Tailored Brief Shared Decision-Making to Improve Asthma Control in Urban Black Adults, C14. Barriers and Facilitators to Asthma Control in Under-served Communities, pp. A4479-A4479, (2020); Lin C-Y., ROUGE: A Package for Automatic Evaluation of Summaries, Text Summarization Branches Out, pp. 74-81, (2004); Menckeberg TT, Bouvy ML, Bracke M, Et al., Beliefs about medicines predict refill adherence to inhaled corticosteroids, J Psychosom Res, 64, pp. 47-54, (2008); Witten I., Data Mining: Practical Machine Learning Tools and Techniques, (2011); Enhancing Motivation for Change in Substance Abuse Treatment, (1999); Blackley S V, Huynh J, Wang L, Et al., Speech recognition for clinical documentation from 1990 to 2018: a systematic review, J Am Med Informatics Assoc, 26, pp. 324-338, (2019); Zhou L, Blackley S V., Kowalski L, Et al., Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional Transcriptionists, JAMA Netw Open, 1, (2018)","M. Topaz; School of Nursing, Data Science Institute, Columbia University, New York, United States; email: mt3315@cumc.columbia.edu","","Public Library of Science","","","","","","19326203","","POLNC","35925922","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85135422553"
"Jiao Y.; Gong C.; Wang S.; Duan Y.; Zhang Y.","Jiao, Yujiao (57393986200); Gong, Cuike (57211356880); Wang, Shusen (57925336300); Duan, Yuling (57393955500); Zhang, Yang (57925042600)","57393986200; 57211356880; 57925336300; 57393955500; 57925042600","The Influence of Air Pollution on Pulmonary Disease Incidence Analyzed Based on Grey Correlation Analysis","2022","Contrast Media and Molecular Imaging","2022","","4764720","","","","7","10.1155/2022/4764720","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139732867&doi=10.1155%2f2022%2f4764720&partnerID=40&md5=478a5a16e5c7a5ae4fd0539b0d516291","Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China","Jiao Y., Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China; Gong C., Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China; Wang S., Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China; Duan Y., Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China; Zhang Y., Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Hebei, Xingtai, 054001, China","Air pollution is a primary health threat issue worldwide because it is closely concerned with respiratory diseases. A random survey reported that around 7 million people died because of ambient and household air pollution. Especially, the people suffering from asthma and chronic obstructive pulmonary disease (COPD) are highly affected by air pollutants. The air pollution components induce asthma onset and COPD acute exacerbation, which leads to maximized mortality and morbidity rate. Therefore, the influence of air pollution on COPD should be examined continuously to minimize the mortality rate. Several methods are presented in this field to investigate the relationship between health and pollutants. However, the existing approaches are only predicting the short-term data and have difficulties such as computation time, redundant data in large data analysis, and data continuity. Then, this research introduced the meta-heuristic optimized grey correlation analysis (MH-GCA) to solve the research difficulties. The correlation analysis has several models that identify the relationship between the pollution factors with COPD disease. The method analysis of the particulate matter (PM_10) in air pollution is more relevant to COPD and lung cancer disease. The grey analysis uses the uncertainty concept to identify the particle influence on air pollution. In the analysis, the cuttlefish optimization algorithm was applied to select more relevant features from the pollutant list that reduces the computation time and correlation analysis rate. The introduced system was evaluated using the air quality dataset and COPD dataset developed with the help of the MATLAB tool. The system increases the influence recognition accuracy (2.48%) and MCC (3.11%) and decreases the error rate (55.89%) for different pollutants.  © 2022 Yujiao Jiao et al.","","Air Pollutants; Air Pollution; Asthma; Humans; Incidence; Particulate Matter; Pulmonary Disease, Chronic Obstructive; ammonia; benzene; carbon monoxide; lead; nitric oxide; nitrogen dioxide; ozone; sulfur dioxide; toluene; xylene; adult; aged; air monitoring; air pollution; air quality; anxiety; Article; asthma; chronic bronchitis; chronic obstructive lung disease; clinical article; concentration (parameter); controlled study; correlation analysis; deep learning; depression; disease association; disease severity; female; grey correlation analysis; high risk patient; human; incidence; India; lung cancer; lung disease; lung emphysema; male; mortality rate; particulate matter 10; particulate matter 2.5; process optimization; quality of life; very elderly; walking; adverse event; air pollutant; asthma; chronic obstructive lung disease; incidence; particulate matter","","ammonia, 14798-03-9, 51847-23-5, 7664-41-7; benzene, 71-43-2; carbon monoxide, 630-08-0; lead, 7439-92-1, 13966-28-4; nitric oxide, 10102-43-9; nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; toluene, 108-88-3; xylene, 1330-20-7; Air Pollutants, ; Particulate Matter, ","","","","","Isai Fan R.J., The dramatic impact of COVID-19 outbreak on air quality: Has it saved as much as it has killed so far?, Global Journal of Environmental Science and Management, 6, 3, pp. 275-288, (2020); Datta A., Suresh R., Gupta A., Singh D., Kulshrestha P., Indoor air quality of non-residential urban buildings in Delhi, India, International Journal of Sustainable Built Environment, 6, 2, pp. 412-420, (2017); Chen S.-Y., Chu D.C., Lee J.H., Chan C.-C., Yang Y.R., Chan C.C., Traffic-related air pollution associated with chronic kidney disease among elderly residents in Taipei City, Environmental Pollution, 234, pp. 838-845, (2018); Luyten L.J., Saenen N.D., Janssen B.G., Vrijens K., Plusquin M., Roels H.A., Debacq-Chainiaux F., Nawrot T.S., Air pollution and the fetal origin of disease: A systematic review of the molecular signatures of air pollution exposure in human placenta, Environmental Research, 166, pp. 310-323, (2018); 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Ho S.-C., Chuang K.-J., Lee K.Y., Chen J.-K., Wu S.-M., Chen T.-T., Lee C.-N., Chang C.C., Feng P.H., Chen K.Y., Su C.L., Tsai C.Y., Chuang H.C., Chronic obstructive pulmonary disease patients have a higher risk of occurrence of pneumonia by air pollution, Science of the Total Environment, 677, pp. 524-529, (2019); Rahi P., Sood S.P., Bajaj R., Kumar Y., Air quality monitoring for Smart eHealth system using firefly optimization and support vector machine, International Journal of Information Technology, 13, 5, pp. 1847-1859, (2021); Rodriguez-Aguilar M., Diaz De Leon-Martinez L., Gorocica-Rosete P., Padilla R.P., Thirion-Romero I., Ornelas-Rebolledo O., Flores-Ramirez R., Identification of breath-prints for the COPD detection associated with smoking and household air pollution by electronic nose, Respiratory Medicine, 163, (2020); Abugabah A., Alzubi A.A., Al-Obeidat F., Alarifi A., Alwadain A., Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks, Cluster Computing, 23, 3, pp. 1781-1794, (2020); Gonzalez P., Dominguez A., Moraga A.M., The effect of outdoor PM2. 5 on labor absenteeism due to chronic obstructive pulmonary disease, International Journal of Environmental Science and Technology, 16, 8, pp. 4775-4782, (2019); Khojasteh D.N., Goudarzi G., Taghizadeh-Mehrjardi R., Asumadu-Sakyi A.B., Fehresti-Sani M., Long-term effects of outdoor air pollution on mortality and morbidity-prediction using nonlinear autoregressive and artificial neural networks models, Atmospheric Pollution Research, 12, 2, pp. 46-56, (2021); Wambebe N.M., Duan X., Air quality levels and health risk assessment of particulate matters in Abuja municipal area, Nigeria, Atmosphere, 11, 8, (2020); Wang L., Xie J., Hu Y., Tian Y., Air pollution and risk of chronic obstructed pulmonary disease: The modifying effect of genetic susceptibility and lifestyle, EBioMedicine, 79, (2022)","Y. Zhang; Department of Respiratory and Critical Care Medicine, Xingtai People's Hospital, Xingtai, Hebei, 054001, China; email: 2016120376@jou.edu.cn","","Hindawi Limited","","","","","","15554309","","","36262999","English","Contrast Media Mol. Imaging","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85139732867"
"Dragan P.; Merski M.; Wiśniewski S.; Sanmukh S.G.; Latek D.","Dragan, Paulina (57638462300); Merski, Matthew (8754843600); Wiśniewski, Szymon (57640502100); Sanmukh, Swapnil Ganesh (57493404400); Latek, Dorota (17434826100)","57638462300; 8754843600; 57640502100; 57493404400; 17434826100","Chemokine Receptors—Structure-Based Virtual Screening Assisted by Machine Learning","2023","Pharmaceutics","15","2","516","","","","5","10.3390/pharmaceutics15020516","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149145905&doi=10.3390%2fpharmaceutics15020516&partnerID=40&md5=f444036c72f62abe26a1c7f3f9d3c6c1","Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland","Dragan P., Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland; Merski M., Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland; Wiśniewski S., Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland; Sanmukh S.G., Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland; Latek D., Faculty of Chemistry, University of Warsaw, Warsaw, 02-093, Poland","Chemokines modulate the immune response by regulating the migration of immune cells. They are also known to participate in such processes as cell–cell adhesion, allograft rejection, and angiogenesis. Chemokines interact with two different subfamilies of G protein-coupled receptors: conventional chemokine receptors and atypical chemokine receptors. Here, we focused on the former one which has been linked to many inflammatory diseases, including: multiple sclerosis, asthma, nephritis, and rheumatoid arthritis. Available crystal and cryo-EM structures and homology models of six chemokine receptors (CCR1 to CCR6) were described and tested in terms of their usefulness in structure-based drug design. As a result of structure-based virtual screening for CCR2 and CCR3, several new active compounds were proposed. Known inhibitors of CCR1 to CCR6, acquired from ChEMBL, were used as training sets for two machine learning algorithms in ligand-based drug design. Performance of LightGBM was compared with a sequential Keras/TensorFlow model of neural network for these diverse datasets. A combination of structure-based virtual screening with machine learning allowed to propose several active ligands for CCR2 and CCR3 with two distinct compounds predicted as CCR3 actives by all three tested methods: Glide, Keras/TensorFlow NN, and LightGBM. In addition, the performance of these three methods in the prediction of the CCR2/CCR3 receptor subtype selectivity was assessed. © 2023 by the authors.","CCR2; CCR3; cheminformatics; chemokine receptors; drug discovery; G protein-coupled receptors; Glide; gradient-boosting machine; LightGBM; machine learning; molecular docking; neural network; TensorFlow; virtual screening","chemokine receptor; chemokine receptor CCR1; chemokine receptor CCR2; chemokine receptor CCR3; chemokine receptor CCR4; chemokine receptor CCR5; chemokine receptor CCR6; Article; cheminformatics; controlled study; cryoelectron microscopy; crystal structure; machine learning; molecular docking; prediction; protein structure; receiver operating characteristic; screening; sensitivity and specificity; structural homology; virtual screening","","chemokine receptor CCR1, 265970-50-1; chemokine receptor CCR4, 169936-75-8; chemokine receptor CCR6, 287981-77-5","","","Narodowe Centrum Nauki, NCN, (2020/39/B/NZ2/00584); Uniwersytet Warszawski, UW","This research was funded by the National Science Centre in Poland, grant number 2020/39/B/NZ2/00584 and the APC was funded by University of Warsaw.","Fernandez E.J., Lolis E., Structure, Function, and Inhibition of Chemokines, Annu. 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"Lejeune S.; Kaushik A.; Parsons E.S.; Chinthrajah S.; Snyder M.; Desai M.; Manohar M.; Prunicki M.; Contrepois K.; Gosset P.; Deschildre A.; Nadeau K.","Lejeune, Stéphanie (57000191200); Kaushik, Abhinav (57217040830); Parsons, Ella S. (57447027900); Chinthrajah, Sharon (56399305400); Snyder, Michael (57202452790); Desai, Manisha (22633366200); Manohar, Monali (57204368451); Prunicki, Mary (57202628445); Contrepois, Kévin (36522527200); Gosset, Philippe (57216109773); Deschildre, Antoine (7004402827); Nadeau, Kari (35305427400)","57000191200; 57217040830; 57447027900; 56399305400; 57202452790; 22633366200; 57204368451; 57202628445; 36522527200; 57216109773; 7004402827; 35305427400","Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy","2024","Journal of Allergy and Clinical Immunology","153","2","","418","434","16","5","10.1016/j.jaci.2023.09.040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178333258&doi=10.1016%2fj.jaci.2023.09.040&partnerID=40&md5=9d6f4cd11ce32b8df62ca9e15e605071","Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille, France; University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France; Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass; Department of Genetics, Stanford University School of Medicine, Stanford, Calif; Quantitative Science Unit, Department of Medicine, Stanford University School of Medicine, Stanford, Calif","Lejeune S., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif, University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille, France, University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France; Kaushik A., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif, Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass; Parsons E.S., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Chinthrajah S., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Snyder M., Department of Genetics, Stanford University School of Medicine, Stanford, Calif; Desai M., Quantitative Science Unit, Department of Medicine, Stanford University School of Medicine, Stanford, Calif; Manohar M., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Prunicki M., Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif, Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass; Contrepois K., Department of Genetics, Stanford University School of Medicine, Stanford, Calif; Gosset P., University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France; Deschildre A., University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille, France, University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France; Nadeau K., Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass","Background: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways. Objective: We sought to identify these unique metabolomic profiles in atopy and asthma. Methods: We analyzed baseline nonfasted plasma samples from a large multisite pediatric population of 470 children aged <13 years from 3 different sites in the United States and France. Atopy positivity (At+) was defined as skin prick test result of ≥3 mm and/or specific IgE ≥ 0.35 IU/mL and/or total IgE ≥ 173 IU/mL. Asthma positivity (As+) was based on physician diagnosis. The cohort was divided into 4 groups of varying combinations of asthma and atopy, and 6 pairwise analyses were conducted to best assess the differential metabolomic profiles between groups. Results: Two hundred ten children were classified as At−As−, 42 as At+As−, 74 as At−As+, and 144 as At+As+. Untargeted global metabolomic profiles were generated through ultra–high-performance liquid chromatography–tandem mass spectroscopy. We applied 2 independent machine learning classifiers and short-listed 362 metabolites as discriminant features. Our analysis showed the most diverse metabolomic profile in the At+As+/At−As− comparison, followed by the At−As+/At−As− comparison, indicating that asthma is the most discriminant condition associated with metabolomic changes. At+As+ metabolomic profiles were characterized by higher levels of bile acids, sphingolipids, and phospholipids, and lower levels of polyamine, tryptophan, and gamma-glutamyl amino acids. Conclusion: The At+As+ phenotype displays a distinct metabolomic profile suggesting underlying mechanisms such as modulation of host–pathogen and gut microbiota interactions, epigenetic changes in T-cell differentiation, and lower antioxidant properties of the airway epithelium. © 2023 American Academy of Allergy, Asthma & Immunology","Asthma; atopy; bile acids; children; gamma-glutamyl amino acids; metabolomics; phospholipids; polyamine; sphingolipids; tryptophan","Asthma; Child; Humans; Hypersensitivity, Immediate; Immunoglobulin E; Metabolome; Metabolomics; amino acid; antioxidant; bile acid; immunoglobulin E; phospholipid; polyamine; sphingolipid; tryptophan; immunoglobulin E; adolescent; Article; asthma; atopy; blood sampling; child; cohort analysis; controlled study; female; France; human; liquid chromatography-mass spectrometry; machine learning; major clinical study; male; metabolic fingerprinting; metabolomics; prick test; United States; asthma; immediate type hypersensitivity; metabolome; procedures","","amino acid, 65072-01-7; immunoglobulin E, 37341-29-0; tryptophan, 6912-86-3, 73-22-3; Immunoglobulin E, ","","","AstraZeneca; Sean N. Parker Center for Allergy and Asthma Research, Stanford University","Supported by the Sean N. 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Lejeune; University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille cedex, 59037, France; email: stephanie.lejeune@chu-lille.fr","","Elsevier Inc.","","","","","","00916749","","JACIB","38344970","English","J. Allergy Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85178333258"
"Siddiqui H.-U.-R.; Raza A.; Saleem A.A.; Rustam F.; Díez I.D.L.T.; Aray D.G.; Lipari V.; Ashraf I.; Dudley S.","Siddiqui, Hafeez-Ur-Rehman (58580612500); Raza, Ali (59124303000); Saleem, Adil Ali (57214224665); Rustam, Furqan (57211950161); Díez, Isabel de la Torre (55665183400); Aray, Daniel Gavilanes (58171833800); Lipari, Vivian (58029449700); Ashraf, Imran (57195478761); Dudley, Sandra (7006033464)","58580612500; 59124303000; 57214224665; 57211950161; 55665183400; 58171833800; 58029449700; 57195478761; 7006033464","An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features","2023","Diagnostics","13","6","1096","","","","5","10.3390/diagnostics13061096","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151627905&doi=10.3390%2fdiagnostics13061096&partnerID=40&md5=3a8696dcc7be256bf73a6b7976c95cb4","Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; School of Computer Science, University College Dublin, Dublin 4, Dublin, D04 V1W8, Ireland; Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, Valladolid, 47011, Spain; Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, Santander, 39011, Spain; Research Group on Foods, Nutritional Biochemistry and Health, Universidade Internacional do Cuanza, Bié, Cuito, EN250, Angola; Research Group on Foods, Nutritional Biochemistry and Health, Fundación Universitaria Internacional de Colombia, Bogotá, 11001, Colombia; Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Campeche, 24560, Mexico; Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Arecibo, 00613, Puerto Rico; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom","Siddiqui H.-U.-R., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Raza A., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Saleem A.A., Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan; Rustam F., School of Computer Science, University College Dublin, Dublin 4, Dublin, D04 V1W8, Ireland; Díez I.D.L.T., Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, Valladolid, 47011, Spain; Aray D.G., Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, Santander, 39011, Spain, Research Group on Foods, Nutritional Biochemistry and Health, Universidade Internacional do Cuanza, Bié, Cuito, EN250, Angola, Research Group on Foods, Nutritional Biochemistry and Health, Fundación Universitaria Internacional de Colombia, Bogotá, 11001, Colombia; Lipari V., Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, Isabel Torres 21, Santander, 39011, Spain, Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Campeche, 24560, Mexico, Research Group on Foods, Nutritional Biochemistry and Health, Universidad Internacional Iberoamericana, Arecibo, 00613, Puerto Rico; Ashraf I., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; Dudley S., School of Engineering, London South Bank University, 103 Borough Road, London, SE1 0AA, United Kingdom","Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage. © 2023 by the authors.","chronic obstructive pulmonary disease; feature engineering; machine learning; ultra wideband radar","Article; artificial intelligence; Bayesian learning; breathing; chronic obstructive lung disease; controlled study; decision tree; deep learning; diagnostic accuracy; diagnostic value; human; k fold cross validation; logistic regression analysis; long short term memory network; machine learning; performance indicator; prediction; spectroscopy; support vector machine; telecommunication; temporal analysis","","","","","European University of the Atlantic","This research was supported by the European University of the Atlantic.","Hui S., How C.H., Tee A., Does this patient really have chronic obstructive pulmonary disease?, Singap. Med. J, 56, (2015); Ruppel G.L., Carlin B.W., Hart M., Doherty D.E., Office spirometry in primary care for the diagnosis and management of COPD: National lung health education program update, Respir. Care, 63, pp. 242-252, (2018); Eaton T., Withy S., Garrett J.E., Whitlock R.M., Rea H.H., Mercer J., Spirometry in primary care practice: The importance of quality assurance and the impact of spirometry workshops, Chest, 116, pp. 416-423, (1999); Enright P.L., Should we keep pushing for a spirometer in every doctor’s office?, Respir. Care, 57, pp. 146-153, (2012); Brown R., Ghavami N., Adjrad M., Ghavami M., Dudley S., Siddiqui H.U.R., Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles, Energy Build, 141, pp. 134-141, (2017); Ghavami M., Michael L., Kohno R., Ultra Wideband Signals and Systems in Communication Engineering, (2007); Rana S.P., Dey M., Siddiqui H.U., Tiberi G., Ghavami M., Dudley S., UWB localization employing supervised learning method, Proceedings of the 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB), pp. 1-5; Rana S.P., Dey M., Brown R., Siddiqui H.U., Dudley S., Remote vital sign recognition through machine learning augmented UWB, Proceeding of the 12th European Conference on Antennas and Propagation (EuCAP 2018), pp. 1-5; Wu S., Tan K., Xia Z., Chen J., Meng S., Guangyou F., Improved human respiration detection method via ultra-wideband radar in through-wall or other similar conditions, IET Radar Sonar Navig, 10, pp. 468-476, (2016); Siddiqui H.U.R., Saleem A.A., Bashir I., Zafar K., Rustam F., Diez I., Dudley S., Ashraf I., Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning, Electronics, 11, (2022); Altan G., Kutlu Y., Gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning onmulti-channel lung sounds, Turk. J. Electr. Eng. Comput. Sci, 28, pp. 2979-2996, (2020); Sarkar S., Bhattacharyya P., Mitra M., Pal S., A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration, Australas. Phys. Eng. Sci. Med, 42, pp. 1011-1024, (2019); Haider N.S., Singh B.K., Periyasamy R., Behera A.K., Respiratory sound based classification of chronic obstructive pulmonary disease: A risk stratification approach in machine learning paradigm, J. Med. Syst, 43, pp. 1-13, (2019); Sarkar S., Bhattacharyya P., Mitra M., Pal S., Automatic detection of obstructive and restrictive lung disease from features extracted from ECG and ECG derived respiration signals, Biomed. Signal Process. Control, 71, (2022); Porieva H., Ivanko K., Semkiv C., Vaityshyn V., Investigation of Lung Sounds Features for Detection of Bronchitis and COPD Using Machine Learning Methods, Visnyk NTUU KPI Seriia-Radiotekhnika Radioaparatobuduvannia, 84, pp. 78-87, (2021); Srivastava A., Jain S., Miranda R., Patil S., Pandya S., Kotecha K., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput. Sci, 7, (2021); Du R., Qi S., Feng J., Xia S., Kang Y., Qian W., Yao Y.D., Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN, IEEE Access, 8, pp. 38907-38919, (2020); Fang Y., Wang H., Wang L., Di R., Song Y., Diagnosis of COPD based on a knowledge graph and integrated model, IEEE Access, 7, pp. 46004-46013, (2019); Zarrin P.S., Roeckendorf N., Wenger C., In-vitro classification of saliva samples of COPD patients and healthy controls using machine learning tools, IEEE Access, 8, pp. 168053-168060, (2020); Altan G., Kutlu Y., Allahverdi N., Deep learning on computerized analysis of chronic obstructive pulmonary disease, IEEE J. Biomed. Health Inform, 24, pp. 1344-1350, (2019); Hussain A., Ugli I.K.K., Kim B.S., Kim M., Ryu H., Aich S., Kim H.C., Detection of different stages of copd patients using machine learning techniques, Proceedings of the 2021 23rd International Conference on Advanced Communication Technology (ICACT), pp. 368-372; Siddiqui H.U.R., Zafar K., Saleem A.A., Raza M.A., Dudley S., Rustam F., Ashraf I., Emotion classification using temporal and spectral features from IR-UWB-based respiration data, Multimed. Tools Appl, pp. 1-19, (2022); Raza A., Siddiqui H.U.R., Munir K., Almutairi M., Rustam F., Ashraf I., Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, PLoS ONE, 17, (2022); Raza A., Munir K., Almutairi M., Younas F., Fareed M.M.S., Ahmed G., A Novel Approach to Classify Telescopic Sensors Data Using Bidirectional-Gated Recurrent Neural Networks, Appl. Sci, 12, (2022)","I. Ashraf; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; email: imranashraf@ynu.ac.kr","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85151627905"
"Kaur S.; Larsen E.; Harper J.; Purandare B.; Uluer A.; Hasdianda M.A.; Umale N.A.; Killeen J.; Castillo E.; Jariwala S.","Kaur, Savneet (57213734397); Larsen, Erik (57193622359); Harper, James (59080220000); Purandare, Bharat (33068178700); Uluer, Ahmet (36924325200); Hasdianda, Mohammad Adrian (56850363700); Umale, Nikita Arun (57211541992); Killeen, James (35553353700); Castillo, Edward (7102964564); Jariwala, Sunit (21733507200)","57213734397; 57193622359; 59080220000; 33068178700; 36924325200; 56850363700; 57211541992; 35553353700; 7102964564; 21733507200","Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19","2023","Journal of Medical Internet Research","25","","e44410","","","","6","10.2196/44410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152624608&doi=10.2196%2f44410&partnerID=40&md5=bdb621b5585409ac023cd5d1e165ca75","Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States; Sonde Health, Boston, MA, United States; Deenanath Mangeshkar Hospital, Pune, India; Brigham and Women's Hospital, Boston, MA, United States; University of California, San Diego, CA, United States","Kaur S., Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States; Larsen E., Sonde Health, Boston, MA, United States; Harper J., Sonde Health, Boston, MA, United States; Purandare B., Deenanath Mangeshkar Hospital, Pune, India; Uluer A., Brigham and Women's Hospital, Boston, MA, United States; Hasdianda M.A., Brigham and Women's Hospital, Boston, MA, United States; Umale N.A., Brigham and Women's Hospital, Boston, MA, United States; Killeen J., University of California, San Diego, CA, United States; Castillo E., University of California, San Diego, CA, United States; Jariwala S., Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States","Background: Vocal biomarker–based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. Objective: This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). Methods: A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase–polymerase chain reaction. Results: The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively). Conclusions: The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future. ©Savneet Kaur, Erik Larsen, James Harper, Bharat Purandare, Ahmet Uluer, Mohammad Adrian Hasdianda, Nikita Arun Umale, James Killeen, Edward Castillo, Sunit Jariwala.","artificial intelligence; asthma; COVID-19; eHealth; machine learning; mHealth; mobile health; mobile phone; respiratory; respiratory symptom; respiratory-responsive vocal biomarker; RRVB; smartphones; sound; speech; vocal; vocal biomarkers; voice","Aged; Asthma; Cough; COVID-19; Female; Humans; Pulmonary Disease, Chronic Obstructive; Respiratory Insufficiency; acoustics; adult; aged; Article; asthma; body temperature; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; coronavirus disease 2019; coughing; disease severity; female; human; India; interstitial lung disease; machine learning; major clinical study; male; predictive value; respiratory responsive vocal biomarker model; reverse transcription polymerase chain reaction; sensitivity and specificity; social distancing; United States; voice; asthma; chronic obstructive lung disease; coughing; respiratory failure","","","","","Einstein Clinical and Translational Science Award; Sonde Health; National Institutes of Health, NIH; American Lung Association, ALA; AstraZeneca; Genentech; Patient-Centered Outcomes Research Institute, PCORI; National Center for Advancing Translational Sciences, NCATS","SK, BP, AU, MAH, NAU, JK, and EC have no competing interests as defined by Nature Research or other interests that might be perceived to influence the interpretation of the article. EL is employed by Sonde Health Inc and holds stock options in Sonde Health Inc. JH is a current employee of Sonde Health Inc (founder and Chief Operating Officer) who receives both cash and stock options from the company as part of his compensation package. SJ has received grant support from the National Institutes of Health, Agency for Healthcare Research and Quality, Stony Wold-Herbert Fund, Patient-Centered Outcomes Research Institute, American Lung Association, Price Family Fund, Genentech, Astra Zeneca, Sonde Health, and Einstein Clinical and Translational Science Award and National Center for Advancing Translational Sciences and has served as a consultant and member of a scientific advisory board for Teva and Sanofi.","Sverdlov O, Curcic J, Hannesdottir K, Gou L, De Luca V, Ambrosetti F, Et al., A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression, Front Psychiatry, 12, (2021); Huang Z, Epps J, Joachim D, Sethu V., Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection, IEEE J Sel Top Signal Process, 14, 2, pp. 435-448, (2020); Yu B, Quatieri T, Williamson J, Mundt J., Cognitive impairment prediction in the elderly based on vocal biomarkers, Proc Interspeech, pp. 3734-3738, (2015); Maor E, Perry D, Mevorach D, Taiblum N, Luz Y, Mazin I, Et al., Vocal biomarker is associated with hospitalization and mortality among heart failure patients, J Am Heart Assoc, 9, 7, (2020); Huang Z, Epps J, Joachim D, Chen M., Depression detection from short utterances via diverse smartphones in natural environmental conditions, Proc Interspeech, pp. 3393-3397, (2018); Mina MJ, Parker R, Larremore DB., Rethinking COVID-19 test sensitivity — a strategy for containment, N Engl J Med, 383, 22, (2020); Larremore D, Wilder B, Lester E, Shehata S, Burke JM, Hay JA, Et al., Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance, medRxiv, (2020); Sharara N, Endo N, Duvallet C, Ghaeli N, Matus M, Heussner J, Et al., Wastewater network infrastructure in public health: applications and learnings from the COVID-19 pandemic, PLOS Glob Public Health, 1, 12, (2021); Quatieri TF, Talkar T, Palmer JS., A framework for biomarkers of COVID-19 based on coordination of speech-production subsystems, IEEE Open J Eng Med Biol, 1, pp. 203-206, (2020); Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Et al., Exploring automatic diagnosis of COVID-19 from crowdsourced respiratory sound data, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2020); Laguarta J, Hueto F, Subirana B., COVID-19 artificial intelligence diagnosis using only cough recordings, IEEE Open J Eng Med Biol, 1, pp. 275-281, (2020); Coppock H, Gaskell A, Tzirakis P, Baird A, Jones L, Schuller B., End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study, BMJ Innov, 7, 2, pp. 356-362, (2021); Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, Et al., AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app, Inform Med Unlocked, 20, (2020); Pinkas G, Karny Y, Malachi A, Barkai G, Bachar G, Aharonson V., SARS-CoV-2 detection from voice, IEEE Open J Eng Med Biol, 1, pp. 268-274, (2020); Pizzo D, Esteban S., IAITos: AI-powered pre-screening tool for COVID-19 from cough audio samples, (2021); Xia T, Spathis D, Brown C, Grammenos A, Han J, Hasthanasombat A, Et al., COVID-19 sounds: a large-scale audio dataset for digital respiratory screening, Proceedings of the NeurIPS 2021; Bagad P, Dalmia A, Doshi J, Nagrani A, Bhamare P, Mahale A, Et al., Cough against COVID: evidence of COVID-19 signature in cough sounds, (2020); Orlandic L, Teijeiro T, Atienza D., The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms, Sci Data, 8, 1, (2021); Aly M, Rahouma KH, Ramzy SM., Pay attention to the speech: COVID-19 diagnosis using machine learning and crowdsourced respiratory and speech recordings, Alexandria Eng J, 61, 5, pp. 3487-3500, (2022); Andreu-Perez J, Perez-Espinosa H, Timonet E, Kiani M, Giron-Perez M, Benitez-Trinidad AB, Et al., A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels, IEEE Trans Serv Comput, 15, 3, pp. 1220-1232, (2022); Pahar M, Klopper M, Warren R, Niesler T., COVID-19 cough classification using machine learning and global smartphone recordings, Comput Biol Med, 135, (2021); Chetupalli SR, Krishnan P, Sharma N, Muguli A, Kumar R, Nanda V, Et al., Multi-modal point-of-care diagnostics for COVID-19 based on acoustics and symptoms, IEEE J Transl Eng Health Med, 11, pp. 199-210, (2023); Melek Manshouri N., Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study, Cogn Neurodyn, 16, 1, pp. 239-253, (2022); Akman A, Coppock H, Gaskell A, Tzirakis P, Jones L, Schuller BW., Evaluating the COVID-19 identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from audio challenges, Front Digit Health, 4, (2022); Ma Q, Liu J, Liu Q, Kang L, Liu R, Jing W, Et al., Global percentage of asymptomatic SARS-CoV-2 infections among the tested population and individuals with confirmed COVID-19 diagnosis: a systematic review and meta-analysis, JAMA Netw Open, 4, 12, (2021); Improving Real-time COVID-19 Monitoring Through Smartphone Voice Analysis, (2021); Menni C, Valdes AM, Freidin MB, Sudre CH, Nguyen LH, Drew DA, Et al., Real-time tracking of self-reported symptoms to predict potential COVID-19, Nat Med, 26, 7, pp. 1037-1040, (2020); Self WH, Semler MW, Leither LM, Casey JD, Angus DC, Brower RG, National Heart‚ Lung‚Blood Institute PETAL Clinical Trials Network, et al. Effect of hydroxychloroquine on clinical status at 14 days in hospitalized patients with COVID-19: a randomized clinical trial, JAMA, 324, 21, pp. 2165-2176, (2020); Halasz G, Sperti M, Villani M, Michelucci U, Agostoni P, Biagi A, Et al., A machine learning approach for mortality prediction in COVID-19 pneumonia: development and evaluation of the piacenza score, J Med Internet Res, 23, 5, (2021); Mahase E., COVID-19: sore throat, fatigue, and myalgia are more common with new UK variant, BMJ, 372, (2021); Graham M, Sudre C, May A, Antonelli M, Murray B, Varsavsky T, COVID-19 Genomics UK (COG-UK) Consortium, et al. Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study, Lancet Public Health, 6, 5, pp. e335-e345, (2021); Menni C, Valdes AM, Polidori L, Antonelli M, Penamakuri S, Nogal A, Et al., Symptom prevalence, duration, and risk of hospital admission in individuals infected with SARS-CoV-2 during periods of omicron and delta variant dominance: a prospective observational study from the ZOE COVID Study, Lancet, 399, 10335, pp. 1618-1624, (2022); Coppock H, Jones L, Kiskin I, Schuller B., COVID-19 detection from audio: seven grains of salt, Lancet Digital Health, 3, 9, pp. e537-e538, (2021)","S. Kaur; Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, 1300 Morris Park Avenue, 10461, United States; email: savneetkaur@hsph.harvard.edu","","JMIR Publications Inc.","","","","","","14388871","","","36881540","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152624608"
"Finnegan S.L.; Browning M.; Duff E.; Harmer C.J.; Reinecke A.; Rahman N.M.; Pattinson K.T.S.","Finnegan, Sarah L. (57201460249); Browning, Michael (57223665955); Duff, Eugene (9845393700); Harmer, Catherine J. (7005566211); Reinecke, Andrea (14036182300); Rahman, Najib M. (8520039800); Pattinson, Kyle T.S. (13605996500)","57201460249; 57223665955; 9845393700; 7005566211; 14036182300; 8520039800; 13605996500","Brain activity measured by functional brain imaging predicts breathlessness improvement during pulmonary rehabilitation","2023","Thorax","78","9","","852","859","7","6","10.1136/thorax-2022-218754","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165147493&doi=10.1136%2fthorax-2022-218754&partnerID=40&md5=85fea7c423385f0d3f6904cde99ed284","Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Oxford Health NHS Foundation Trust, Warneford Hospital, NHS, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Department of Paediatrics, University of Oxford, Oxford, United Kingdom; Oxford Respiratory Trials Unit, University of Oxford, Oxford, United Kingdom; Oxford Chinese Academy of Medicine Institute, University of Oxford, Oxford, United Kingdom; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; Nuffield Division of Anaesthetics, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom","Finnegan S.L., Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Browning M., Oxford Health NHS Foundation Trust, Warneford Hospital, NHS, Oxford, United Kingdom, Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Duff E., Department of Paediatrics, University of Oxford, Oxford, United Kingdom; Harmer C.J., Oxford Health NHS Foundation Trust, Warneford Hospital, NHS, Oxford, United Kingdom, Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Reinecke A., Oxford Health NHS Foundation Trust, Warneford Hospital, NHS, Oxford, United Kingdom, Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Rahman N.M., Oxford Respiratory Trials Unit, University of Oxford, Oxford, United Kingdom, Oxford Chinese Academy of Medicine Institute, University of Oxford, Oxford, United Kingdom, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; Pattinson K.T.S., Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, Nuffield Division of Anaesthetics, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom","Background Chronic breathlessness in chronic obstructive pulmonary disease (COPD) is effectively treated with pulmonary rehabilitation. However, baseline patient characteristics predicting improvements in breathlessness are unknown. This knowledge may provide better understanding of the mechanisms engaged in treating breathlessness and help to individualise therapy. Increasing evidence supports the role of expectation (ie, placebo and nocebo effects) in breathlessness perception. In this study, we tested functional brain imaging markers of breathlessness expectation as predictors of therapeutic response to pulmonary rehabilitation, and asked whether D-cycloserine, a brain-active drug known to influence expectation mechanisms, modulated any predictive model. Methods Data from 71 participants with mild-to-moderate COPD recruited to a randomised double-blind controlled experimental medicine study of D-cycloserine given during pulmonary rehabilitation were analysed (ID: NCT01985750). Baseline variables, including brain-activity, self-report questionnaires responses, clinical measures of respiratory function and drug allocation were used to train machine-learning models to predict the outcome, a minimally clinically relevant change in the Dyspnoea-12 score. Results Only models that included brain imaging markers of breathlessness-expectation successfully predicted improvements in Dyspnoea-12 score (sensitivity 0.88, specificity 0.77). D-cycloserine was independently associated with breathlessness improvement. Models that included only questionnaires and clinical measures did not predict outcome (sensitivity 0.68, specificity 0.2). Conclusions Brain activity to breathlessness related cues is a strong predictor of clinical improvement in breathlessness over pulmonary rehabilitation. This implies that expectation is key in breathlessness perception. Manipulation of the brain's expectation pathways (either pharmacological or non-pharmacological) therefore merits further testing in the treatment of chronic breathlessness. © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.","COPD epidemiology; Imaging/CT MRI etc; Perception of Asthma/Breathlessness; Pulmonary Rehabilitation","Brain; Cycloserine; Diagnostic Imaging; Dyspnea; Humans; Pulmonary Disease, Chronic Obstructive; Quality of Life; cycloserine; cycloserine; adult; aged; Article; chronic obstructive lung disease; controlled study; disease severity; double blind procedure; drug mechanism; dyspnea; Dyspnoea 12 score; electroencephalogram; female; functional neuroimaging; human; longitudinal study; machine learning; major clinical study; male; pharmacological parameters; prediction; pulmonary rehabilitation; questionnaire; randomized controlled trial; respiratory function; scoring system; self report questionnaire; sensitivity and specificity; treatment outcome; treatment response; brain; chronic obstructive lung disease; complication; diagnostic imaging; dyspnea; quality of life","","cycloserine, 339-72-0, 68-39-3, 68-41-7; Cycloserine, ","","","Dunhill Medical Trust, DMT; NIHR Oxford Biomedical Research Centre, OxBRC; Medical Research Council, MRC; Jabbs Foundation; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, BRC; University of Oxford; Wellcome Trust, WT, (203139/Z/16, 203139); UK Research and Innovation, UKRI, (103607)","This work was supported by the JABBS Foundation and Dunhill Medical Trust. This research was funded in whole, or in part, by the Wellcome Trust 203139/Z/16/Z. For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript version arising from this submission. CH is supported by the National Institute for Health Research Biomedical Research Centre based at Oxford Health NHS Foundation Trust and The University of Oxford, and by the UK Medical Research Council. KTSP and NMR are supported by the National Institute for Health Research Biomedical Research Centre based at Oxford University Hospitals NHS Foundation Trust and the University of Oxford. AR is funded by a fellowship from MQ: Transforming Mental Health and is supported by the NIHR Oxford Health Biomedical Research Centre. 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Finnegan; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX1 2JD, United Kingdom; email: sarah.finnegan@ndcn.ox.ac.uk","","BMJ Publishing Group","","","","","","00406376","","THORA","36572534","English","Thorax","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85165147493"
"Pompe E.; Kwee A.K.; Tejwani V.; Siddharthan T.; Mohamed Hoesein F.A.","Pompe, Esther (56344396800); Kwee, Anastasia KAL. (58098716100); Tejwani, Vickram (55753329600); Siddharthan, Trishul (56871182100); Mohamed Hoesein, Firdaus AA. (36246560800)","56344396800; 58098716100; 55753329600; 56871182100; 36246560800","Imaging-derived biomarkers in Asthma: Current status and future perspectives","2023","Respiratory Medicine","208","","107130","","","","6","10.1016/j.rmed.2023.107130","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147823422&doi=10.1016%2fj.rmed.2023.107130&partnerID=40&md5=008d7fafba32f0ae89989bfa91e40da4","Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands; Respiratory Institute, Cleveland Clinic (VT), United States; Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami (TS), United States","Pompe E., Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands; Kwee A.K., Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands; Tejwani V., Respiratory Institute, Cleveland Clinic (VT), United States; Siddharthan T., Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami (TS), United States; Mohamed Hoesein F.A., Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands","Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the era of personalized treatment and response assessment. Several radiological imaging modalities are available in asthma including chest x-ray, computed tomography (CT) and magnetic resonance imaging (MRI) scanning. In addition to qualitative imaging, quantitative imaging could play an important role in asthma imaging to identify phenotypes with distinct disease course and response to therapy, including biologics. MRI in asthma is mainly performed in research settings given cost, technical challenges, and there is a need for standardization. Imaging analysis applications of artificial intelligence (AI) to subclassify asthma using image analysis have demonstrated initial feasibility, though additional work is necessary to inform the role of AI in clinical practice. © 2023 The Authors","Artificial intelligence (AI); Asthma; Imaging; Quantitative","Artificial Intelligence; Asthma; Biomarkers; Humans; Magnetic Resonance Imaging; Tomography, X-Ray Computed; biological marker; biological marker; adult; Article; artificial intelligence; asthma; computer assisted tomography; controlled study; disease course; female; human; imaging; major clinical study; male; nuclear magnetic resonance imaging; phenotype; small airway disease; thorax radiography; vascularization; artificial intelligence; procedures; x-ray computed tomography","","Biomarkers, ","","","","","To T., Stanojevic S., Moores G., Gershon A.S., Bateman E.D., Cruz A.A., Et al., Global asthma prevalence in adults: findings from the cross-sectional World health survey, BMC Publ. 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Med.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147823422"
"Amiri P.; Montazeri M.; Ghasemian F.; Asadi F.; Niksaz S.; Sarafzadeh F.; Khajouei R.","Amiri, Parastoo (58410428700); Montazeri, Mahdieh (55249695400); Ghasemian, Fahimeh (57190766313); Asadi, Fatemeh (54888988100); Niksaz, Saeed (57886400900); Sarafzadeh, Farhad (6505686683); Khajouei, Reza (24332233900)","58410428700; 55249695400; 57190766313; 54888988100; 57886400900; 6505686683; 24332233900","Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms","2023","Digital Health","9","","","","","","6","10.1177/20552076231170493","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161296459&doi=10.1177%2f20552076231170493&partnerID=40&md5=fc65b490200500465cc24f89aef99d40","Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran; Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran; Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran; Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran","Amiri P., Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran; Montazeri M., Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran; Ghasemian F., Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; Asadi F., Student Research Committee, School of Management and Medical Information, Kerman University of Medical Sciences, Kerman, Iran; Niksaz S., Computer Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran; Sarafzadeh F., Infectious and Internal Medicine Department, Afzalipour Hospital, Kerman University of Medical Sciences, Kerman, Iran; Khajouei R., Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran","Background: The severity of coronavirus (COVID-19) in patients with chronic comorbidities is much higher than in other patients, which can lead to their death. Machine learning (ML) algorithms as a potential solution for rapid and early clinical evaluation of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. Objective: The objective of this study was to predict the mortality risk and length of stay (LoS) of patients with COVID-19 and history of chronic comorbidities using ML algorithms. Methods: This retrospective study was conducted by reviewing the medical records of COVID-19 patients with a history of chronic comorbidities from March 2020 to January 2021 in Afzalipour Hospital in Kerman, Iran. The outcome of patients, hospitalization was recorded as discharge or death. The filtering technique used to score the features and well-known ML algorithms were applied to predict the risk of mortality and LoS of patients. Ensemble Learning methods is also used. To evaluate the performance of the models, different measures including F1, precision, recall, and accuracy were calculated. The TRIPOD guideline assessed transparent reporting. Results: This study was performed on 1291 patients, including 900 alive and 391 dead patients. Shortness of breath (53.6%), fever (30.1%), and cough (25.3%) were the three most common symptoms in patients. Diabetes mellitus(DM) (31.3%), hypertension (HTN) (27.3%), and ischemic heart disease (IHD) (14.2%) were the three most common chronic comorbidities of patients. Twenty-six important factors were extracted from each patient's record. Gradient boosting model with 84.15% accuracy was the best model for predicting mortality risk and multilayer perceptron (MLP) with rectified linear unit function (MSE = 38.96) was the best model for predicting the LoS. The most common chronic comorbidities among these patients were DM (31.3%), HTN (27.3%), and IHD (14.2%). The most important factors in predicting the risk of mortality were hyperlipidemia, diabetes, asthma, and cancer, and in predicting LoS was shortness of breath. Conclusion: The results of this study showed that the use of ML algorithms can be a good tool to predict the risk of mortality and LoS of patients with COVID-19 and chronic comorbidities based on physiological conditions, symptoms, and demographic information of patients. The Gradient boosting and MLP algorithms can quickly identify patients at risk of death or long-term hospitalization and notify physicians to do appropriate interventions. © The Author(s) 2023.","chronic comorbidities; COVID-19; hospital length of stay; machine learning; mortality; prediction","","","","","","Kerman University of Medical Sciences, KMU, (99000625); Kerman University of Medical Sciences, KMU","This article was extracted from an independent research project performed in the field of medical informatics at Kerman University of Medical Sciences without organizational support. This study was approved by the ethics committee of Kerman University of Medical Sciences (code of ethics: IR.KMU.REC.1400.055) and was performed according to the ethical guidelines of the Helsinki Declaration. Also, this study was supported by the Student Research Committee of Kerman University of Medical Sciences (code: 99000625). In addition, due to the retrospective nature of the study, the ethics committee of Kerman University of Medical Sciences waived the need for written informed consent. ","Lu H., Stratton C.W., Tang Y.W., Outbreak of pneumonia of unknown etiology in Wuhan, China: the mystery and the miracle, J Med Virol, 92, (2020); Liu Y., Gayle A.A., Wilder-Smith A., Et al., The reproductive number of COVID-19 is higher compared to SARS coronavirus, . 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Khajouei; Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran; email: r.khajouei@yahoo.com","","SAGE Publications Inc.","","","","","","20552076","","","","English","Digit. Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85161296459"
"Chen Z.; Shi J.; Zhang Y.; Zhang J.; Li S.; Guan L.; Jia G.","Chen, Zhangjian (57196282040); Shi, Jiaqi (57347895500); Zhang, Yi (55677084500); Zhang, Jiahe (57217175277); Li, Shuqiang (55771668600); Guan, Li (56535923000); Jia, Guang (35227450700)","57196282040; 57347895500; 55677084500; 57217175277; 55771668600; 56535923000; 35227450700","Screening of Serum Biomarkers of Coal Workers’ Pneumoconiosis by Metabolomics Combined with Machine Learning Strategy","2022","International Journal of Environmental Research and Public Health","19","12","7051","","","","7","10.3390/ijerph19127051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131527069&doi=10.3390%2fijerph19127051&partnerID=40&md5=96e5e9a7adb337f96514f87535f3c0e7","Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; Department of Occupational Disease, Peking University Third Hospital, Beijing, 100191, China","Chen Z., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; Shi J., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; Zhang Y., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; Zhang J., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; Li S., Department of Occupational Disease, Peking University Third Hospital, Beijing, 100191, China; Guan L., Department of Occupational Disease, Peking University Third Hospital, Beijing, 100191, China; Jia G., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China","Pneumoconiosis remains one of the most serious global occupational diseases. However, effective treatments are lacking, and early detection is crucial for disease prevention. This study aimed to explore serum biomarkers of occupational coal workers’ pneumoconiosis (CWP) by highthroughput metabolomics, combining with machine learning strategy for precision screening. A case– control study was conducted in Beijing, China, involving 150 pneumoconiosis patients with different stages and 120 healthy controls. Metabolomics found a total of 68 differential metabolites between the CWP group and the control group. Then, potential biomarkers of CWP were screened from these differential metabolites by three machine learning methods. The four most important differential metabolites were identified as benzamide, terazosin, propylparaben and N-methyl-2-pyrrolidone. However, after adjusting for the influence of confounding factors, including age, smoking, drinking and chronic diseases, only one metabolite, propylparaben, was significantly correlated with CWP. The more severe CWP was, the higher the content of propylparaben in serum. Moreover, the receiver operating characteristic curve (ROC) of propylparaben showed good sensitivity and specificity as a biomarker of CWP. Therefore, it was demonstrated that the serum metabolite profiles in CWP patients changed significantly and that the serum metabolites represented by propylparaben were good biomarkers of CWP. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","biomarkers; case–control study; machine learning; metabolomics; pneumoconiosis","Anthracosis; Biomarkers; Case-Control Studies; Coal; Coal Mining; Humans; Machine Learning; Metabolomics; Pneumoconiosis; Beijing [China]; China; benzamide; biological marker; coal; propyl paraben; terazosin; biological marker; coal; biomarker; machine learning; metabolite; occupational exposure; public health; respiratory disease; adult; Article; asthma; cardiovascular disease; case control study; chronic disease; controlled study; drinking; emphysema; female; human; human tissue; liquid chromatography-mass spectrometry; lung fibrosis; machine learning; major clinical study; mass spectrometry; metabolomics; middle aged; oxidative stress; pneumoconiosis; pneumonia; silicosis; smoking; anthracosis; coal mining; machine learning; metabolomics","","benzamide, 55-21-0; propyl paraben, 94-13-3; terazosin, 63074-08-8, 63590-64-7; Biomarkers, ; Coal, ","","","National Natural Science Foundation of China, NSFC, (81641119, 81703257); National Natural Science Foundation of China, NSFC","Funding: This research was funded by the National Natural Science Foundation of China, grant number 81641119, 81703257 and the APC was funded by 81641119.","Shi P., Xing X., Xi S., Jing H., Yuan J., Fu Z., Zhao H., Trends in global, regional and national incidence of pneumoconiosis caused by different aetiologies: An analysis from the Global Burden of Disease Study 2017, Occup. 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Technol, 47, pp. 3918-3925, (2013); Guo Y., Kannan K., A survey of phthalates and parabens in personal care products from the United States and its implications for human exposure, Environ. Sci. Technol, 47, pp. 14442-14449, (2013); Guo Y., Wang L., Kannan K., Phthalates and parabens in personal care products from China: Concentrations and human exposure, Arch. Environ. Contam. Toxicol, 66, pp. 113-119, (2014); Dodge L.E., Kelley K.E., Williams P.L., Williams M.A., Hernandez-Diaz S., Missmer S.A., Hauser R., Medications as a source of paraben exposure, Reprod. Toxicol, 52, pp. 93-100, (2015); Li W., Gao L., Shi Y., Wang Y., Liu J., Cai Y., Spatial distribution, temporal variation and risks of parabens and their chlorinated derivatives in urban surface water in Beijing, China, Sci. Total Environ, 539, pp. 262-270, (2016); Wang L., Liao C., Liu F., Wu Q., Guo Y., Moon H.B., Nakata H., Kannan K., Occurrence and human exposure of p hydroxybenzoic acid esters (parabens), bisphenol A diglycidyl ether (BADGE), and their hydrolysis products in indoor dust from the United States and three East Asian countries, Environ. Sci. Technol, 46, pp. 11584-11593, (2012); Gonzalez-Dominguez R., Jauregui O., Queipo-Ortuno M.I., Andres-Lacueva C., Characterization of the Human Exposome by a Comprehensive and Quantitative Large-Scale Multianalyte Metabolomics Platform, Anal. Chem, 92, pp. 13767-13775, (2020); Imai T., Taketani M., Shii M., Hosokawa M., Chiba K., Substrate specificity of carboxylesterase isozymes and their contribution to hydrolase activity in human liver and small intestine, Drug Metab. Dispos, 34, pp. 1734-1741, (2006); Fujino C., Watanabe Y., Uramaru N., Kitamura S., Transesterification of a series of 12 parabens by liver and small-intestinal microsomes of rats and humans, Food Chem. Toxicol, 64, pp. 361-368, (2014); Ye X., Tao L.J., Needham L.L., Calafat A.M., Automated on-line column-switching HPLC-MS/MS method for measuring environmental phenols and parabens in serum, Talanta, 76, pp. 865-871, (2008); Darbre P.D., Aljarrah A., Miller W.R., Coldham N.G., Sauer M.J., Pope G.S., Concentrations of parabens in human breast tumours, J. Appl. Toxicol. JAT, 24, pp. 5-13, (2004); Jimenez-Diaz I., Vela-Soria F., Zafra-Gomez A., Navalon A., Ballesteros O., Navea N., Fernandez M.F., Olea N., Vilchez J.L., A new liquid chromatography-tandem mass spectrometry method for determination of parabens in human placental tissue samples, Talanta, 84, pp. 702-709, (2011); Ye X., Bishop A.M., Reidy J.A., Needham L.L., Calafat A.M., Parabens as urinary biomarkers of exposure in humans, Environ. Health Perspect, 114, pp. 1843-1846, (2006); Ma W.L., Wang L., Guo Y., Liu L.Y., Qi H., Zhu N.Z., Gao C.J., Li Y.F., Kannan K., Urinary concentrations of parabens in Chinese young adults: Implications for human exposure, Arch. Environ. Contam. Toxicol, 65, pp. 611-618, (2013); Lite C., Guru A., Juliet M., Arockiaraj J., Embryonic exposure to butylparaben and propylparaben induced developmental toxicity and triggered anxiety-like neurobehavioral response associated with oxidative stress and apoptosis in the head of zebrafish larvae, Environ. Toxicol, (2022); Martin J.M.P., Freire P.F., Daimiel L., Martinez-Botas J., Sanchez C.M., Lasuncion M., Peropadre A., Hazen M.J., The antioxidant butylated hydroxyanisole potentiates the toxic effects of propylparaben in cultured mammalian cells, Food Chem. Toxicol, 72, pp. 195-203, (2014); Gulumian M., Borm P.J., Vallyathan V., Castranova V., Donaldson K., Nelson G., Murray J., Mechanistically identified suitable biomarkers of exposure, effect, and susceptibility for silicosis and coal-worker’s pneumoconiosis: A comprehensive review, J. Toxicol. Environ. Health Part B Crit. Rev, 9, pp. 357-395, (2006); Peruzzi C.P., Brucker N., Bubols G., Cestonaro L., Moreira R., Domingues D., Arbo M., Neto P.O., Knorst M.M., Garcia S.C., Occupational exposure to crystalline silica and peripheral biomarkers: An update, J. Appl. Toxicol. JAT, 42, pp. 87-102, (2022); Pang J., Qi X., Luo Y., Li X., Shu T., Li B., Song M., Liu Y., Wei D., Chen J., Et al., Multi-omics study of silicosis reveals the potential therapeutic targets PGD(2) and TXA(2), Theranostics, 11, pp. 2381-2394, (2021); Jacob M., Lopata A.L., Dasouki M., Rahman A.M.A., Metabolomics toward personalized medicine, Mass Spectrom. Rev, 38, pp. 221-238, (2019); Cui L., Wang X., Sun B., Xia T., Hu S., Predictive Metabolomic Signatures for Safety Assessment of Metal Oxide Nanoparticles, ACS Nano, 13, pp. 13065-13082, (2019)","L. Guan; Department of Occupational Disease, Peking University Third Hospital, Beijing, 100191, China; email: guanlisf@bjmu.edu.cn; G. Jia; Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China; email: jiaguangjia@bjmu.edu.cn","","MDPI","","","","","","16617827","","","35742299","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85131527069"
"He P.; Moraes T.J.; Dai D.; Reyna-Vargas M.E.; Dai R.; Mandhane P.; Simons E.; Azad M.B.; Hoskinson C.; Petersen C.; Del Bel K.L.; Turvey S.E.; Subbarao P.; Goldenberg A.; Erdman L.","He, Ping (58809468400); Moraes, Theo J. (6602867764); Dai, Darlene (57190949619); Reyna-Vargas, Myrtha E. (57794321400); Dai, Ruixue (57209409115); Mandhane, Piush (23992736900); Simons, Elinor (57212450398); Azad, Meghan B. (51664461300); Hoskinson, Courtney (57776917800); Petersen, Charisse (56190074900); Del Bel, Kate L. (6506335815); Turvey, Stuart E. (12778474700); Subbarao, Padmaja (57373689300); Goldenberg, Anna (57209785076); Erdman, Lauren (56928171900)","58809468400; 6602867764; 57190949619; 57794321400; 57209409115; 23992736900; 57212450398; 51664461300; 57776917800; 56190074900; 6506335815; 12778474700; 57373689300; 57209785076; 56928171900","Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning","2024","Pediatric Research","95","7","","1818","1825","7","5","10.1038/s41390-023-02988-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182246800&doi=10.1038%2fs41390-023-02988-2&partnerID=40&md5=5bcee1f8cd6fe2bbcd94987dd3400bfc","Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; University of Alberta, Edmonton, AB, Canada; Department of Pediatrics & Child Health, University of Manitoba, Winnipeg, MB, Canada; Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada; CIFAR, Toronto, ON, Canada; James M. Anderson Center for Health Centers Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States","He P., Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Moraes T.J., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Dai D., Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; Reyna-Vargas M.E., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Dai R., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Mandhane P., University of Alberta, Edmonton, AB, Canada; Simons E., Department of Pediatrics & Child Health, University of Manitoba, Winnipeg, MB, Canada; Azad M.B., Department of Pediatrics & Child Health, University of Manitoba, Winnipeg, MB, Canada; Hoskinson C., Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada, Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada; Petersen C., Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; Del Bel K.L., Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; Turvey S.E., Department of Pediatrics, BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada; Subbarao P., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Goldenberg A., Department of Computer Science, University of Toronto, Toronto, ON, Canada, Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada, Vector Institute, Toronto, ON, Canada, CIFAR, Toronto, ON, Canada; Erdman L., Center for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada, Department of Computer Science, University of Toronto, Toronto, ON, Canada, Department of Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada, Vector Institute, Toronto, ON, Canada, James M. Anderson Center for Health Centers Excellence, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States","Background: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. Methods: Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. Results: Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. Conclusions: Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. Impact: Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child’s mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time. © The Author(s) 2024.","","Asthma; Birth Cohort; Canada; Child, Preschool; Female; Humans; Infant; Infant, Newborn; Longitudinal Studies; Machine Learning; Male; Respiratory Sounds; Respiratory Tract Infections; Risk Factors; Article; asthma; atopy; birth cohort; birth weight; Canada; Canadian; child; childhood; cohort analysis; controlled study; decision tree; early diagnosis; environmental factor; exposure; feature selection; female; gestational age; human; infant; lower respiratory tract infection; machine learning; major clinical study; male; newborn jaundice; prediction; predictive value; pregnant woman; psychological well-being; smoking; wheezing; abnormal respiratory sound; asthma; birth cohort; diagnosis; longitudinal study; newborn; preschool child; respiratory tract infection; risk factor","","","","","; GenomeCanada, (274CHI)","This study was supported by GenomeCanada (274CHI). The Canadian Institutes of Health Research (CIHR) and the Allergy, Genes and Environment (AllerGen) Network of Centres of Excellence provided core funding for the Canadian Healthy Infant Longitudinal Development (CHILD) Cohort Study. Study funders had no role in study design, data collection, data analysis, interpretation of study results, or writing of the manuscript. ","Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study, Lancet, 396, pp. 1204-1222, (2020); Bush A., Fleming L., Saglani S., Severe asthma in children, Respirology, 22, pp. 886-897, (2017); Gonem S., Janssens W., Das N., Topalovic M., Applications of artificial intelligence and machine learning in respiratory medicine, Thorax, 75, pp. 695-701, (2020); Kaplan A., Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J. Allergy Clin. Immunol. Pract, 9, pp. 2255-2261, (2021); Liao K.M., Liu C.F., Chen C.J., Shen Y.T., Machine learning approaches for predicting acute respiratory failure, ventilator dependence, and mortality in chronic obstructive pulmonary disease, Diagnostics, 11, (2021); Kothalawala D.M., Et al., Development of childhood asthma prediction models using machine learning approaches, Clin. Transl. Allergy, (2021); Bhardwaj P., Et al., Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization, J. Asthma, (2022); Patrick D.M., Et al., Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies, Lancet Respir. Med, 8, pp. 1094-1105, (2020); Dai R., Et al., Wheeze trajectories: determinants and outcomes in the CHILD Cohort Study, J. Allergy Clin. Immunol, 149, pp. 2153-2165, (2021); Finster M., Wood M., Raja S.N., The Apgar score has survived the test of time, J. Am. Soc. Anesthesiol, 102, pp. 855-857, (2005); Lavoie J.A., Douglas K.S., The Perceived Stress Scale: evaluating configural, metric and scalar invariance across mental health status and gender, J. Psychopathol. Behav. Assess, 34, pp. 48-57, (2012); Radloff L.S., The CES-D scale: a self-report depression scale for research in the general population, Appl. Psychol. Meas, 1, pp. 385-401, (1977); Stekhoven D., Buhlmann P., MissForest—non-parametric missing value imputation for mixed-type data, Bioinformatics, 28, pp. 112-118, (2012); Bemister-Buffington J., Et al., Machine learning to identify flexibility signatures of class A GPCR inhibition, Biomolecules, 10, (2020); Kotu V., Deshpande B., (2019); Filipow N., Et al., Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review, BMJ Open Respir. Res, 9, (2022); Kothalawala D.M., Et al., Prediction models for childhood asthma: a systematic review, Pediatr. Allergy Immunol, 31, pp. 616-627, (2020); Litonjua A.A., Et al., Parental history and the risk for childhood asthma: does mother confer more risk than father?, Am. J. Respir. Crit. Care Med, 158, pp. 176-181, (1998); Raby B., Et al., Paternal history of asthma and airway responsiveness in children with asthma, Am. J. Respir. Crit. Care Med, 172, pp. 552-558, (2005); Wright R.J., Prenatal maternal stress and early caregiving experiences: implications for childhood asthma risk, Paediatr. Perinat. Epidemiol, 21, pp. 8-14, (2007); dos Santos M.L., Et al., Maternal mental health and social support: effect on childhood atopic and non-atopic asthma symptoms, J. Epidemiol. Community Health, 66, pp. 1011-1016, (2012); Stokholm J., Blaser J.M., Thorsen J., Maturation of the gut microbiome and risk of asthma in childhood, Nat. Commun, 9, (2018); Stein T.R., Et al., Respiratory syncytial virus in early life and risk of wheeze and allergy by age 13 years, Lancet, 354, pp. 541-545, (1999); Jackson J.D., Et al., Wheezing rhinovirus illnesses in early life predict asthma development in high-risk children, Am. J. Respir. Crit. Care Med, 178, pp. 667-672, (2008); Dogaru M.C., Et al., Breastfeeding and childhood asthma: systematic review and meta-analysis, Am. J. Epidemiol, 179, pp. 1153-1167, (2014); van der Voort A.S., Et al., Duration and exclusiveness of breastfeeding and childhood asthma-related symptoms, Eur. Respir. J, 39, pp. 81-89, (2012); Leung J.Y., Lam H.S., Leung G.M., Schooling C.M., Gestational age, birthweight for gestational age, and childhood hospitalisations for asthma and other wheezing disorders, Paediatr. Perinat. Epidemiol, 30, pp. 149-159, (2016); Liu X., Et al., Birth weight, gestational age, fetal growth and childhood asthma hospitalization, Allergy Asthma Clin. Immunol, 10, pp. 1-10, (2014); Castro-Rodriguez J.A., Forno E., Rodriguez-Martinez C.E., Celedon J.C., Risk and protective factors for childhood asthma: what is the evidence?, J. Allergy Clin. Immunol: Pract, 4, pp. 1111-1122, (2016); Saria S., Butte A., Sheikh A., Better medicine through machine learning: what’s real, and what’s artificial?, PLoS Med, 15, (2018)","L. Erdman; Center for Computational Medicine, The Hospital for Sick Children, Toronto, Canada; email: lauren.erdman@cchmc.org","","Springer Nature","","","","","","00313998","","PEREB","38212387","English","Pediatr. Res.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85182246800"
"Chen B.; Liu Z.; Lu J.; Li Z.; Kuang K.; Yang J.; Wang Z.; Sun Y.; Du B.; Qi L.; Li M.","Chen, Bin (58724965100); Liu, Ziyi (57202738118); Lu, Jinjuan (57221698236); Li, Zhihao (58741877000); Kuang, Kaiming (57219440987); Yang, Jiancheng (57192458106); Wang, Zengmao (56365401300); Sun, Yingli (57196275040); Du, Bo (57217375214); Qi, Lin (57205301987); Li, Ming (56927069100)","58724965100; 57202738118; 57221698236; 58741877000; 57219440987; 57192458106; 56365401300; 57196275040; 57217375214; 57205301987; 56927069100","Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening","2023","Respiratory Research","24","1","299","","","","7","10.1186/s12931-023-02611-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178061705&doi=10.1186%2fs12931-023-02611-2&partnerID=40&md5=2fe8aed6f7a9c0b7d82008b097ebf41f","Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China; Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China; School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Hubei, Wuhan, China; Artificial Intelligence Institute of Wuhan University, Hubei, Wuhan, China; Hubei Key Laboratory of Multimedia and Network Communication Engineering, Hubei, Wuhan, China; Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China; Dianei Technology, Shanghai, China; University of California San Diego, La Jolla, United States; Computer Vision Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland","Chen B., Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China, Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China; Liu Z., School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Hubei, Wuhan, China, Artificial Intelligence Institute of Wuhan University, Hubei, Wuhan, China, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Hubei, Wuhan, China; Lu J., Department of Radiology, Shanghai Geriatric Medical Center, Shanghai, China; Li Z., School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Hubei, Wuhan, China, Artificial Intelligence Institute of Wuhan University, Hubei, Wuhan, China, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Hubei, Wuhan, China; Kuang K., Dianei Technology, Shanghai, China, University of California San Diego, La Jolla, United States; Yang J., Dianei Technology, Shanghai, China, Computer Vision Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland; Wang Z., School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Hubei, Wuhan, China, Artificial Intelligence Institute of Wuhan University, Hubei, Wuhan, China, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Hubei, Wuhan, China; Sun Y., Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China, Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China; Du B., School of Computer Science, Wuhan University, LuoJiaShan, WuChang District, Hubei, Wuhan, China, Artificial Intelligence Institute of Wuhan University, Hubei, Wuhan, China, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Hubei, Wuhan, China; Qi L., Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China, Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China; Li M., Department of Radiology, Huadong Hospital Affiliated to Fudan University, 221, Yanan West Road, Jingan Temple Street, Jingan District, Shanghai, China, Zhang Guozhen Small Pulmonary Nodules Diagnosis and Treatment Center, Shanghai, China","Objectives: Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry. Methods: We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD. Results: Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001). Conclusion: Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry. © 2023, The Author(s).","Computed tomography; Deep learning; Parametric response mapping; Small airways","Asthma; Deep Learning; Emphysema; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Disease, Chronic Obstructive, Severe Early-Onset; Pulmonary Emphysema; Tomography, X-Ray Computed; adult; aged; Article; bronchodilatation; computer assisted tomography; controlled study; deep learning; diagnostic value; female; human; image analysis; image quality; lung emphysema; major clinical study; male; parametric response mapping; passive smoking; prediction; prospective study; receiver operating characteristic; root mean squared error; screening; sensitivity and specificity; small airway disease; smoking; spirometry; asthma; chronic obstructive lung disease; diagnostic imaging; emphysema; lung; lung emphysema; procedures; x-ray computed tomography","","","Somatom Definition Flash, Siemens Healthcare, Germany","Siemens Healthcare, Germany","Cancer Society of Shanghai, (SACA-CY21C12, XXRC2213); Science and Technology Planning Project of Shanghai Science and Technology Commission, (20Y11902900, 21Y11910500, 22Y11910700); National Natural Science Foundation of China, NSFC, (61976238); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2022YFF1203301); National Key Research and Development Program of China, NKRDPC; Program of Shanghai Academic Research Leader, (2022XD042); Program of Shanghai Academic Research Leader; Leading Talents Program of Guangdong Province, (LJRC2202); Leading Talents Program of Guangdong Province","This study has received funding by National Natural Science Foundation of China 61976238 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 22Y11910700 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 20Y11902900 (Ming Li), Science and Technology Planning Project of Shanghai Science and Technology Commission 21Y11910500 (Lin Qi), Shanghai ""Rising Stars of Medical Talent"" Youth Development Program ""Outstanding Youth Medical Talents"" SHWJRS [2021]-99 (Ming Li), National key research and development program 2022YFF1203301 (Ming Li), Cancer Society of Shanghai SACA-CY21C12 (Yingli Sun), Youth Development Program ""Outstanding Youth Medical Talents"" SHWJRS [2021]-99 (Ming Li), Emerging Talent Program XXRC2213 (Liang Jin), Leading Talent Program LJRC2202 (Ming Li) and Excellent Academic Leaders of Shanghai 2022XD042 (Ming Li). 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Jetmalani K., Thamrin C., Farah C.S., Bertolin A., Chapman D.G., Berend N., Salome C.M., King G.G., Peripheral airway dysfunction and relationship with symptoms in smokers with preserved spirometry, Respirology, 23, 5, pp. 512-518, (2018); Saetta M., Ghezzo H., Kim W.D., King M., Angus G.E., Wang N.S., Cosio M.G., Loss of alveolar attachments in smokers A morphometric correlate of lung function impairment, Am Rev Respir Dis, 132, 4, pp. 894-900, (1985); Polosukhin V.V., Gutor S.S., Du R., Richmond B.W., Massion P.P., Wu P., Cates J.M., Sandler K.L., Rennard S.I., Blackwell T.S., Small airway determinants of airflow limitation in chronic obstructive pulmonary disease, Thorax, 76, 11, pp. 1079-1088, (2021); Hogg J.C., Pare P.D., Hackett T., The contribution of small airway obstruction to the pathogenesis of chronic obstructive pulmonary disease, Physiol Rev, 97, 2, pp. 529-552, (2017); McNulty W., Usmani O.S., Techniques of assessing small airways dysfunction, Eur Clin Respir J, 1, (2014); 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Lu J., Ge H., Qi L., Zhang S., Yang Y., Huang X., Li M., Subtyping preserved ratio impaired spirometry (PRISm) by using quantitative HRCT imaging characteristics, Resp Res, 23, 1, (2022); Yu Y., Christensen S., Ouyang J., Scalzo F., Liebeskind D.S., Lansberg M.G., Albers G.W., Zaharchuk G., Predicting hypoperfusion lesion and target mismatch in stroke from diffusion-weighted MRI using deep learning, Radiology, 307, 1, (2023); Chandrashekar A., Handa A., Lapolla P., Shivakumar N., Uberoi R., Grau V., Lee R., A deep learning approach to visualize aortic aneurysm morphology without the use of intravenous contrast agents, Ann Surg, 277, 2, pp. e449-e459, (2023); Verschakelen J.A., Van Fraeyenhoven L., Laureys G., Demedts M., Baert A.L., Differences in CT density between dependent and nondependent portions of the lung: influence of lung volume, AJR Am J Roentgenol, 161, 4, pp. 713-717, (1993); Webb W.R., Stern E.J., Kanth N., Gamsu G., Dynamic pulmonary CT: findings in healthy adult men, Radiology, 186, 1, pp. 117-124, (1993); 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Qi; Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 221, Yanan West Road, Jingan Temple Street, Jingan District, China; email: qi_lin@fudan.edu.cn; M. Li; Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, 221, Yanan West Road, Jingan Temple Street, Jingan District, China; email: minli77@163.com; B. Du; School of Computer Science, Wuhan University, Wuhan, LuoJiaShan, WuChang District, Hubei, China; email: dubo@whu.edu.cn","","BioMed Central Ltd","","","","","","14659921","","RREEB","38017476","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85178061705"
"Albiges T.; Sabeur Z.; Arbab-Zavar B.","Albiges, Timothy (58100026900); Sabeur, Zoheir (6603062212); Arbab-Zavar, Banafshe (23396128800)","58100026900; 6603062212; 23396128800","Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases","2023","Sensors","23","3","1439","","","","7","10.3390/s23031439","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147895637&doi=10.3390%2fs23031439&partnerID=40&md5=2f8e768d707406792812ea023674fc86","Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom","Albiges T., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; Sabeur Z., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; Arbab-Zavar B., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom","Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation events in individual patients, remains an important challenge to overcome. The requirements for achieving scalable deployments of data-driven methods using artificial intelligence for meeting such a challenge in modern COPD healthcare have become of paramount and critical importance. In this study, we have established the experimental foundations for acquiring and indeed generating biomedical observation data, for good performance signal analysis and machine learning that will lead us to the intelligent diagnosis and monitoring of COPD conditions for individual patients. Further, we investigated on the multi-resolution analysis and compression of lung audio signals, while we performed their machine classification under two distinct experiments. These respectively refer to conditions involving (1) “Healthy” or “COPD” and (2) “Healthy”, “COPD”, or “Pneumonia” classes. Signal reconstruction with the extracted features for machine learning and testing was also performed for securing the integrity of the original audio recordings. These showed high levels of accuracy together with the performances of the selected machine learning-based classifiers using diverse metrics. Our study shows promising levels of accuracy in classifying Healthy and COPD and also Healthy, COPD, and Pneumonia conditions. Further work in this study will be imminently extended to new experiments using multi-modal sensing hardware and data fusion techniques for the development of the next generation diagnosis systems for COPD healthcare of the future. © 2023 by the authors.","artificial intelligence; compressed sensing; COPD; dictionary learning; machine learning; signals reconstruction","Artificial Intelligence; Humans; Lung; Machine Learning; Probability; Pulmonary Disease, Chronic Obstructive; Biological organs; Biomedical signal processing; Compressed sensing; Data fusion; Diagnosis; Health care; Pulmonary diseases; Signal reconstruction; Audio signal; Chronic obstructive pulmonary disease; Compressed-Sensing; Condition; Dictionary learning; Intelligent classification; Lung function; Machine-learning; Sensing data; Signals reconstruction; artificial intelligence; chronic obstructive lung disease; human; lung; machine learning; probability; Machine learning","","","","","","","Pauwels R.A., Rabe K.F., Burden and Clinical Features of Chronic Obstructive Pulmonary Disease (COPD), Lancet, 364, pp. 613-620, (2004); Viniol C., Vogelmeier C.F., Exacerbations of COPD, Eur. Respir. Rev, 27, (2018); Chronic Obstructive Pulmonary Disease (COPD), (2021); Rabe K.F., Hurst J.R., Suissa S., Cardiovascular Disease and COPD: Dangerous Liaisons?, Eur. Respir. Rev, 27, (2018); Min X., Yu B., Wang F., Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD, Sci. Rep, 9, (2019); The battle for breath—The economic burden of lung disease—British Lung Foundation. British Lung Foundation, (2021); Perna D., Tagarelli A., Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks, Proceedings of the 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 50-55; Sarkar M., Madabhavi I., Niranjan N., Dogra M., Auscultation of the Respiratory System, Ann. Thorac. Med, 10, pp. 158-168, (2015); Gronnesby M., Solis J.C.A., Holsbo E., Melbye H., Bongo L.A., Feature Extraction for Machine Learning Based Crackle De-tection in Lung Sounds from a Health Survey, arXiv, (2017); Khan S.I., Pachori R.B., Automated Classification of Lung Sound Signals Based on Empirical Mode Decomposition, Expert Syst. Appl, 184, (2021); Serbes G., Ulukaya S., Kahya Y.P., Precision Medicine Powered by pHealth and Connected Health, IFMBE Proc, 66, pp. 45-49, (2017); Kandaswamy A., Kumar C.S., Ramanathan R.P., Jayaraman S., Malmurugan N., Neural Classification of Lung Sounds Using Wavelet Coefficients, Comput. Biol. Med, 34, pp. 523-537, (2004); Oletic D., Bilas V., Asthmatic Wheeze Detection from Compressively Sensed Respiratory Sound Spectra, IEEE J. Biomed. Health, 22, pp. 1406-1414, (2018); Charleston-Villalobos S., Gonzalez-Camarena R., Chi-Lem G., Aljama-Corrales T., Crackle Sounds Analysis by Empirical Mode Decomposition, IEEE Eng. Med. Biol, 26, pp. 40-47, (2007); Stankovi L., Mandi D., Dakovi M., Brajovi M., Time-Frequency Decomposition of Multivariate Multicomponent Signals, Signal Process, 142, pp. 468-479, (2018); Chen X., Du Z., Li J., Li X., Zhang H., Compressed Sensing Based on Dictionary Learning for Extracting Impulse Components, Signal Process, 96, pp. 94-109, (2014); Rocha B.M., Filos D., Mendes L., Vogiatzis I., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Et al., Precision Medicine Powered by pHealth and Connected Health, Proceedings of the ICBHI 2017, pp. 33-37; Tariq Z., Shah S.K., Lee Y., Lung Disease Classification using Deep Convolutional Neural Network, Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM); Ko T., Peddinti V., Povey D., Khudanpur S., Audio Augmentation for Speech Recognition, Interspeech, 2015, pp. 3586-3589, (2015); Salamon J., Bello J.P., Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification, IEEE Signal. 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Mag, 25, pp. 21-30, (2008); Brunton S., Kutz N., Data-Driven Science and Engineering Machine Learning, Dynamic Systems, And Control Systems, (2019); Tosic I., Frossard P., Dictionary Learning, IEEE Signal. Proc. Mag, 28, pp. 27-38, (2011); Junge M., Lee K., Generalized Notions of Sparsity and Restricted Isometry Property. Part I: A Unified Framework, Inf. Inference J. IMA, 9, pp. 157-193, (2019); Gangannawar S.A., Siddmal S.V., Compressed Sensing Reconstruction of an Audio Signal Using OMP—ProQuest, Int. J. Adv. Comput. Res, 5, pp. 75-79, (2015); Zheng Y., Guo X., Jiang H., Zhou B., An Innovative Multi-Level Singular Value Decomposition and Compressed Sensing Based Framework for Noise Removal from Heart Sounds, Biomed. Signal. Process, 38, pp. 34-43, (2017); Goodfellow I., Bengio Y., Courville A., Deep Learning, (2016); Sun Z., Wang G., Su X., Liang X., Liu L., Similarity and Delay between Two Non-Narrow-Band Time Signals, arXiv, (2020); 2.1. Gaussian Mixture Models; [Online] Scikit-Learn, (2021); Written I., Frank E., Hall M., Pal C., Data Mining, Practical Machine Learning Tools and Techniques, (2017); Bruce P., Bruce A., Gedeck P., Practical Statistics for Data Scientists, (2020); Chambres G., Hanna P., Desainte-Catherine M., Automatic Detection of Patient with Respiratory Diseases Using Lung Sound Analysis, Proceedings of the 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1-6; Bohadana A., Izbicki G., Kraman S.S., Fundamentals of Lung Auscultation, N. Engl. J. Med, 370, pp. 2052-2053, (2014); Tiwari U., Bhosale S., Chakraborty R., Kopparapu S.K., Deeplung Auscultation Using Acoustic Biomarkers for Abnormal Respiratory Sound Event Detection, Proceedings of the ICASSP 2021—2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1305-1309; Hazra R., Majhi S., Detecting Respiratory Diseases from Recorded Lung Sounds by 2D CNN, Proceedings of the 2020 5th International Conference on Computing, Communication and Security (ICCCS), pp. 1-6","Z. Sabeur; Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; email: zsabeur@bournemouth.ac.uk","","MDPI","","","","","","14248220","","","36772480","English","Sensors","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85147895637"
"Wang F.; Yang B.; Qiao J.; Bai L.; Li Z.; Sun W.; Liu Q.; Yang S.; Cui L.","Wang, Fei (57211948820); Yang, Boxin (57219607646); Qiao, Jiao (57801515000); Bai, Linlu (57219570506); Li, Zijing (57704871000); Sun, Wenyuan (57704419500); Liu, Qi (57702467300); Yang, Shuo (36515878600); Cui, Liyan (36988503100)","57211948820; 57219607646; 57801515000; 57219570506; 57704871000; 57704419500; 57702467300; 36515878600; 36988503100","Serum exosomal microRNA-1258 may as a novel biomarker for the diagnosis of acute exacerbations of chronic obstructive pulmonary disease","2023","Scientific Reports","13","1","18332","","","","6","10.1038/s41598-023-45592-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175013506&doi=10.1038%2fs41598-023-45592-4&partnerID=40&md5=94372f559aff5137028cee66f91c3a94","Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Peking University, No.5 Yiheyuan Road Haidian District, Beijing, China","Wang F., Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Yang B., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Qiao J., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Bai L., Peking University, No.5 Yiheyuan Road Haidian District, Beijing, China; Li Z., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Sun W., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Liu Q., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Yang S., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China; Cui L., Department of Laboratory Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, China","Acute exacerbation chronic obstructive pulmonary disease (AECOPD) has a high mortality rate. However, there is no efficiency biomarker for diagnosing AECOPD. The purpose of this study was to find biomarkers that can quickly and accurately diagnose AECOPD.45 normal controls (NC), 42 patients with stable COPD (SCOPD), and 66 patients with AECOPD were enrolled in our study. Serum exosomes were isolated by ultracentrifuge and verified by morphology and specific biomarkers. Fluorescent quantitation polymerase chain reaction (qRT-PCR) was used to detect the expression of micro RNAs (miRNAs), including miR-660-5p, miR-1258, miR-182-3p, miR-148a-3p, miR-27a-5p and miR-497-5p in serum exosomes and serum. Logistic regression and machine learning methods were used to constructed the diagnostic models of AECOPD. The levels of miR-1258 in the patients with AECOPD were higher than other groups (p < 0.001). The ability of exosomal miR-1258 (AUC = 0.851) to identify AECOPD from SCOPD was superior to other biomarkers, and the combination of exosomal miR-1258 and NLR can increase the AUC to 0.944, with a sensitivity of 81.82%, and specificity of 97.62%. The cross-validation of the models displayed that the logistic regression model based on exosomal miR-1258, NLR and neutrophil count had the best accuracy (0.880) in diagnosing AECOPD from SCOPD. The three most correlated biomarkers with serum exosome miR-1258 were neutrophil count (r = 0.57, p < 0.001), WBC (r = 0.50, p < 0.001) and serum miR-1258 (r = 0.33, p < 0.001). In conclusion, serum exosomal miR-1258 is associated with inflammation, and can be used as a valuable and reliable biomarker for the diagnosis of AECOPD, and the establishment of diagnostic model based on miR-1258, NLR and neutrophils count can help to improving the accuracy of AECOPD diagnosis. © 2023, The Author(s).","","Biomarkers; Exosomes; Humans; Inflammation; MicroRNAs; Pulmonary Disease, Chronic Obstructive; Pulmonary Disease, Chronic Obstructive, Severe Early-Onset; biological marker; microRNA; MIRN1258 microRNA, human; MIRN497 microRNA, human; MIRN660 microRNA, human; chronic obstructive lung disease; exosome; genetics; human; inflammation; metabolism","","Biomarkers, ; MicroRNAs, ; MIRN1258 microRNA, human, ; MIRN497 microRNA, human, ; MIRN660 microRNA, human, ","","","Department of central Laboratory; National Natural Science Foundation of China, NSFC, (010072, 62071011, 81800604); Peking University Health Science Center, PKUHSC, (JKCJ202302)","Funding text 1: We thank the Department of central Laboratory, Peking University Health Science Center, the medical writers, proof-readers and editors,and the support of National Natural Science Foundation of China. ; Funding text 2: These studies were supported by research grants from this work was supported by grants from programs of the Natural Science Foundation of China (62071011, 81800604), Beijing outstanding project of clinical and laboratory medicine key specialty (010072) and International institute of population health, Peking University Health Science Center (JKCJ202302). ","Celli B.R., Et al., Effect of pharmacotherapy on rate of decline of lung function in chronic obstructive pulmonary disease: Results from the TORCH study, Am. J. Respir. Crit. Care Med., 178, pp. 332-338, (2008); Donaldson G.C., Hurst J.R., Smith C.J., Hubbard R.B., Wedzicha J.A., Increased risk of myocardial infarction and stroke following exacerbation of COPD, Chest, 137, pp. 1091-1097, (2010); Nishimura K., Et al., Effect of exacerbations on health status in subjects with chronic obstructive pulmonary disease, Health Qual. Life Outcomes, 7, (2009); Mitsuma S.F., Et al., Promising new assays and technologies for the diagnosis and management of infectious diseases, Clin. Infectious Dis., 56, pp. 996-1002, (2010); Butler C.C., Et al., C-reactive protein testing to guide antibiotic prescribing for COPD exacerbations, N. Engl. J. Med., 381, pp. 111-120, (2019); Leitao Filho F.S., Et al., Sputum microbiome is associated with 1-year mortality after chronic obstructive pulmonary disease hospitalizations, Am. J. Respir. Crit. Care Med., 199, pp. 1205-1213, (2019); Yang C., Dou R., Yin T., Ding J., MiRNA-106b-5p in human cancers: diverse functions and promising biomarker, Biomed. Pharmacother., 127, (2020); Szymczak I., Wieczfinska J., Pawliczak R., Molecular background of miRNA role in asthma and COPD: an updated insight, Biomed. Res. Int., 2016, (2016); Di Leva G., Garofalo M., Croce C.M., MicroRNAs in cancer, Annu. Rev. Pathol., 9, pp. 287-314, (2014); Yao Z.Y., Et al., Role of exosome-associated microRNA in diagnostic and therapeutic applications to metabolic disorders, J. Zhejiang Univ. Sci. 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Commun., 7, (2016); Vazquez-Mera S., Et al., Serum exosome inflamma-miRs are surrogate biomarkers for asthma phenotype and severity, Allergy, 78, 1, pp. 141-155, (2023); Thomou T., Et al., Adipose-derived circulating miRNAs regulate gene expression in other tissues, Nature, 542, 7642, pp. 450-455, (2017); Zhang X., Et al., Essential roles of exosome and circRNA_101093 on ferroptosis desensitization in lung adenocar- cinoma, Cancer Commun., 42, 4, pp. 287-313, (2022); Yanez-Mo M., Et al., Biological properties of extracellular vesicles and their physiological functions, J. Extracell. Vesicles., 4, (2015); Nana-Sinkam S.P., Acunzo M., Croce C.M., Wang K., Extracellular vesicle biology in the pathogenesis of lung disease, Am. J. Respir. Crit. Care Med., 196, pp. 1510-1518, (2017); De Smet E.G., Mestdagh P., Vandesompele J., Brusselle G.G., Bracke K.R., Non-coding RNAs in the pathogenesis of COPD, Thorax., 70, 8, pp. 782-791, (2015); Ezzie M.E., Et al., Gene expression networks in COPD: microRNA and mRNA regulation, Thorax, 67, pp. 122-131, (2012); Akbas F., Coskunpinar E., Aynaci E., Oltulu Y.M., Yildiz P., Analysis of serum micro-RNAs as potential biomarker in chronic obstructive pulmonary disease, Exp. Lung Res., 38, pp. 286-294, (2012); Schembri F., Et al., MicroRNAs as modulators of smoking-induced gene expression changes in human airway epithelium, Proc. Natl. Acad. Sci. USA, 106, pp. 2319-2324, (2009); Chen S., Zhang Z., Chen L., Zhang J., miRNA-101–3p as an independent diagnostic biomarker aggravates chronic obstructive pulmonary disease via activation of the EGFR/PI3K/AKT signaling pathway, Mol. Med. 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Vesicles., 8, (2019); Dang X., Et al., Bioinformatic analysis of microRNA and mRNA regulation in peripheral blood mononuclear cells of patients with chronic obstructive pulmonary disease, Respir. Res., 18, (2017)","S. Yang; Department of Laboratory Medicine, Peking University Third Hospital, Beijing, 49 North Garden Road, Haidian District, 100191, China; email: ys983108@163.com; L. Cui; Department of Laboratory Medicine, Peking University Third Hospital, Beijing, 49 North Garden Road, Haidian District, 100191, China; email: cliyan@163.com","","Nature Research","","","","","","20452322","","","37884583","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175013506"
"D'Amato M.; Ambrosino P.; Simioli F.; Adamo S.; Stanziola A.A.; D'Addio G.; Molino A.; Maniscalco M.","D'Amato, Maria (7004499795); Ambrosino, Pasquale (55208996600); Simioli, Francesca (57193749747); Adamo, Sarah (57433431800); Stanziola, Anna Agnese (6602114101); D'Addio, Giovanni (57218355354); Molino, Antonio (7005073386); Maniscalco, Mauro (7005627903)","7004499795; 55208996600; 57193749747; 57433431800; 6602114101; 57218355354; 7005073386; 7005627903","A machine learning approach to characterize patients with asthma exacerbation attending an acute care setting","2022","European Journal of Internal Medicine","104","","","66","72","6","5","10.1016/j.ejim.2022.07.019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138469248&doi=10.1016%2fj.ejim.2022.07.019&partnerID=40&md5=ed2e15dc637e95cfab10951d1cec65d7","Department of Respiratory Medicine, Federico II University, Naples, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Telese Terme Institute, Telese Terme, Italy; Department of Information Technology and Electrical Engineering, University of Naples “Federico II”, Napoli, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, Telese Terme, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Pulmonary Rehabilitation Unit of Telese Terme Institute, Telese Terme, Italy","D'Amato M., Department of Respiratory Medicine, Federico II University, Naples, Italy; Ambrosino P., Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Telese Terme Institute, Telese Terme, Italy; Simioli F., Department of Respiratory Medicine, Federico II University, Naples, Italy; Adamo S., Department of Information Technology and Electrical Engineering, University of Naples “Federico II”, Napoli, Italy; Stanziola A.A., Department of Respiratory Medicine, Federico II University, Naples, Italy; D'Addio G., Istituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, Telese Terme, Italy; Molino A., Department of Respiratory Medicine, Federico II University, Naples, Italy; Maniscalco M., Department of Respiratory Medicine, Federico II University, Naples, Italy, Istituti Clinici Scientifici Maugeri IRCCS, Pulmonary Rehabilitation Unit of Telese Terme Institute, Telese Terme, Italy","Background: One of the main problems in poorly controlled asthma is the access to the Emergency Department (ED). Using a machine learning (ML) approach, the aim of our study was to identify the main predictors of severe asthma exacerbations requiring hospital admission. Methods: Consecutive patients with asthma exacerbation were screened for inclusion within 48 hours of ED discharge. A k-means clustering algorithm was implemented to evaluate a potential distinction of different phenotypes. K-Nearest Neighbor (KNN) as instance-based algorithm and Random Forest (RF) as tree-based algorithm were implemented in order to classify patients, based on the presence of at least one additional access to the ED in the previous 12 months. Results: To train our model, we included 260 patients (31.5% males, mean age 47.6 years). Unsupervised ML identified two groups, based on eosinophil count. A total of 86 patients with eosinophiles ≥370 cells/µL were significantly older, had a longer disease duration, more restrictions to daily activities, and lower rate of treatment compared to 174 patients with eosinophiles <370 cells/μL. In addition, they reported lower values of predicted FEV1 (64.8±12.3% vs. 83.9±17.3%) and FEV1/FVC (71.3±9.3 vs. 78.5±6.8), with a higher amount of exacerbations/year. In supervised ML, KNN achieved the best performance in identifying frequent exacerbators (AUROC: 96.7%), confirming the importance of spirometry parameters and eosinophil count, along with the number of prior exacerbations and other clinical and demographic variables. Conclusions: This study confirms the key prognostic value of eosinophiles in asthma, suggesting the usefulness of ML in defining biological pathways that can help plan personalized pharmacological and rehabilitation strategies. © 2022","Asthma; Biomarker; Chronic disease; Chronic obstructive pulmonary disease; Disability; Exercise capacity; Occupational medicine; Outcome; Rehabilitation","Asthma; Disease Progression; Female; Hospitalization; Humans; Machine Learning; Male; Respiratory Function Tests; Spirometry; betamethasone; budesonide; formoterol; methylprednisolone; montelukast; salmeterol; adult; allergy; Article; bronchiectasis; cohort analysis; comorbidity; controlled study; daily life activity; disease duration; disease exacerbation; electronic health record; emergency care; emergency ward; eosinophil count; family history; female; forced expiratory volume; forced vital capacity; functional assessment; gastroesophageal reflux; hospital admission; hospital discharge; human; Italy; k means clustering; k nearest neighbor; learning algorithm; leave one out cross validation; machine learning; major clinical study; male; middle aged; nose polyp; obesity; onset age; phenotype; prospective study; random forest; sensitivity and specificity; severe asthma; smoking habit; spirometry; unsupervised machine learning; asthma; disease exacerbation; hospitalization; lung function test; machine learning","","betamethasone, 378-44-9; budesonide, 51333-22-3, 51372-29-3; formoterol, 73573-87-2; methylprednisolone, 6923-42-8, 83-43-2; montelukast, 151767-02-1, 158966-92-8; salmeterol, 89365-50-4","","","AstraZeneca; Ministero della Salute","This work was supported by the “Ricerca Corrente” funding scheme of the Ministry of Health, Italy. The editorial support was funded by AstraZeneca. ","Selroos O., Kupczyk M., Kuna P., Lacwik P., Bousquet J., Brennan D., Et al., National and regional asthma programmes in Europe, Eur Respir Rev., 24, pp. 474-483, (2015); Accordini S., Corsico A.G., Braggion M., Gerbase M.W., Gislason D., Gulsvik A., Et al., The cost of persistent asthma in Europe: an international population-based study in adults, Int Arch Allergy Immunol., 160, pp. 93-101, (2013); Emerman C.L., Woodruff P.G., Cydulka R.K., Gibbs M.A., Pollack C.V., Camargo C.A., Prospective multicenter study of relapse following treatment for acute asthma among adults presenting to the emergency department. MARC investigators. 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Schleich F.N., Chevremont A., Paulus V., Henket M., Manise M., Seidel L., Et al., Importance of concomitant local and systemic eosinophilia in uncontrolled asthma, Eur Respir J, 44, pp. 97-108, (2014); Zeiger R.S., Schatz M., Li Q., Chen W., Khatry D.B., Gossage D., Et al., High blood eosinophil count is a risk factor for future asthma exacerbations in adult persistent asthma, J Allergy Clin Immunol Pract, 2, pp. 741-750, (2014); Tran T.N., Khatry D.B., Ke X., Ward C.K., Gossage D., High blood eosinophil count is associated with more frequent asthma attacks in asthma patients, Ann Allergy Asthma Immunol, 113, pp. 19-24, (2014); Peters M.C., Mauger D., Ross K.R., Phillips B., Gaston B., Cardet J.C., Et al., Evidence for exacerbation-prone asthma and predictive biomarkers of exacerbation frequency, Am J Respir Crit Care Med, 202, pp. 973-982, (2020); Casciano J., Krishnan J.A., Small M.B., Buck P.O., Gopalan G., Li C., Et al., Value of peripheral blood eosinophil markers to predict severity of asthma, BMC Pulm Med, 16, (2016); Wardlaw A.J., Brightling C., Green R., Woltmann G., Pavord I., Eosinophils in asthma and other allergic diseases, Br Med Bull, 56, pp. 985-1003, (2000); Ullmann N., Bossley C.J., Fleming L., Silvestri M., Bush A., Saglani S., Blood eosinophil counts rarely reflect airway eosinophilia in children with severe asthma, Allergy, 68, pp. 402-406, (2013); Nadif R., Siroux V., Oryszczyn M.P., Ravault C., Pison C., Pin I., Et al., Heterogeneity of asthma according to blood inflammatory patterns, Thorax, 64, pp. 374-380, (2009); Brusselle G.G., Maes T., Bracke K.R., Eosinophils in the spotlight: Eosinophilic airway inflammation in nonallergic asthma, Nat Med, 19, pp. 977-979, (2013); Pavord I.D., Korn S., Howarth P., Bleecker E.R., Buhl R., Keene O.N., Et al., Mepolizumab for severe eosinophilic asthma (DREAM): a multicentre, double-blind, placebo-controlled trial, Lancet., 380, pp. 651-659, (2012); Mjosberg J.M., Trifari S., Crellin N.K., Peters C.P., van Drunen C.M., Piet B., Et al., Human IL-25- and IL-33-responsive type 2 innate lymphoid cells are defined by expression of CRTH2 and CD161, Nat Immunol, 12, pp. 1055-1062, (2011); Peters S.P., Asthma phenotypes: nonallergic (intrinsic) asthma, J Allergy Clin Immunol Pract, 2, pp. 650-652, (2014); Nunes C., Pereira A.M., Morais-Almeida M., Asthma costs and social impact, Asthma Res Pract, 3, (2017); Chung K.F., Wenzel S.E., Brozek J.L., Bush A., Castro M., Sterk P.J., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, pp. 343-373, (2014); Eisner M.D., Yelin E.H., Katz P.P., Lactao G., Iribarren C., Blanc P.D., Risk factors for work disability in severe adult asthma, Am J Med., 119, pp. 884-891, (2006); Mosher C.L., Nanna M.G., Jawitz O.K., Raman V., Farrow N.E., Aleem S., Et al., Cost-effectiveness of pulmonary rehabilitation among US adults with chronic obstructive pulmonary disease, JAMA Netw Open., 5, (2022); Ambrosino P., Papa A., Maniscalco M., Di Minno M.N.D., COVID-19 and functional disability: current insights and rehabilitation strategies, Postgrad Med J, 97, pp. 469-470, (2021); Zampogna E., Zappa M., Spanevello A., Visca D., Pulmonary rehabilitation and asthma, Front Pharmacol, 11, (2020); Turk Y., van Huisstede A., Franssen F.M.E., Hiemstra P.S., Rudolphus A., Taube C., Et al., Effect of an outpatient pulmonary rehabilitation program on exercise tolerance and asthma control in obese asthma patients, J Cardiopulm Rehabil Prev, 37, pp. 214-222, (2017); Salandi J., Icks A., Gholami J., Hummel S., Schultz K., Apfelbacher C., Et al., Impact of pulmonary rehabilitation on patients' health care needs and asthma control: a quasi-experimental study, BMC Pulm Med, 20, (2020); ATS statement–snowbird workshop on standardization of spirometry, Am Rev Respir Dis, 119, pp. 831-838, (1979)","M. D'Amato; Department of Respiratory Medicine, Federico II University, Naples, Italy; email: marielladam@hotmail.it","","Elsevier B.V.","","","","","","09536205","","EJIME","35922367","English","Eur. J. Intern. Med.","Article","Final","","Scopus","2-s2.0-85138469248"
"Seinen T.M.; Kors J.A.; van Mulligen E.M.; Fridgeirsson E.; Rijnbeek P.R.","Seinen, Tom M. (57221249241); Kors, Jan A. (7005293297); van Mulligen, Erik M. (7003307150); Fridgeirsson, Egill (57201904688); Rijnbeek, Peter R. (6603033335)","57221249241; 7005293297; 7003307150; 57201904688; 6603033335","The added value of text from Dutch general practitioner notes in predictive modeling","2023","Journal of the American Medical Informatics Association","30","12","","1973","1984","11","6","10.1093/jamia/ocad160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177103064&doi=10.1093%2fjamia%2focad160&partnerID=40&md5=eb3385711b2958f5eb9adedb6b3d676c","Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands","Seinen T.M., Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; Kors J.A., Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; van Mulligen E.M., Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; Fridgeirsson E., Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; Rijnbeek P.R., Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands","Objective: This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting. Materials and methods: We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems. Results: On average, over all the different text representations and prediction algorithms, models only using text data performed better or similar to models using structured data alone in 2 prediction tasks. Additionally, in these 2 tasks, the combination of structured and text data outperformed models using structured or text data alone. No large performance differences were found between the different text representations and prediction algorithms. Discussion: Our findings indicate that the use of unstructured data alone can result in well-performing prediction models for some clinical prediction problems. Furthermore, the performance improvement achieved by combining structured and text data highlights the added value. Additionally, we demonstrate the significance of clinical natural language processing research in languages other than English and the possibility of validating text-based prediction models across various EHR systems. Conclusion: Our study highlights the potential benefits of incorporating unstructured data in clinical prediction models in a GP setting. Although the added value of unstructured data may vary depending on the specific prediction task, our findings suggest that it has the potential to enhance patient care. © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.","clinical prediction model; electronic health records; machine learning; natural language processing; prognostic prediction","Algorithms; Electronic Health Records; General Practitioners; Humans; Language; Natural Language Processing; Software; antiasthmatic agent; adult; all cause mortality; Article; asthma; chronic obstructive lung disease; controlled study; data processing; disease exacerbation; electronic health record; external validity; feature extraction; female; general practitioner; hospital readmission; human; internal validity; k fold cross validation; learning algorithm; least absolute shrinkage and selection operator; machine learning; major clinical study; male; palliative therapy; predictive model; prognostic assessment; random forest; terminal care; algorithm; language; natural language processing; software","","","","","Innovative Medicines Initiative, IMI; European Federation of Pharmaceutical Industries and Associations, EFPIA; Horizon 2020 Framework Programme, H2020, (806968)","This work has received support from the European Health Data & Evidence Network (EHDEN) project. EHDEN has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No. 806968. The JU receives support from the European Union\u2019s Horizon 2020 research and innovation program and EFPIA.","Goldstein BA, Navar AM, Pencina MJ, Et al., Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review, J Am Med Inform Assoc, 24, 1, pp. 198-208, (2017); Yang C, Kors JA, Ioannou S, Et al., Trends in the conduct and reporting of clinical prediction model development and validation: a systematic review, J Am Med Inform Assoc, 29, 5, pp. 983-989, (2022); Reps JM, Schuemie MJ, Suchard MA, Et al., Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data, J Am Med Inform Assoc, 25, 8, pp. 969-975, (2018); Kreimeyer K, Foster M, Pandey A, Et al., Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review, J Biomed Inform, 73, pp. 14-29, (2017); Ford E, Carroll JA, Smith HE, Et al., Extracting information from the text of electronic medical records to improve case detection: a systematic review, J Am Med Inform Assoc, 23, 5, pp. 1007-1015, (2016); Seinen TM, Fridgeirsson EA, Ioannou S, Et al., Use of unstructured text in prognostic clinical prediction models: a systematic review, J Am Med Inform Assoc, 29, 7, pp. 1292-1302, (2022); Steyerberg EW, Harrell FE., Prediction models need appropriate internal, internal–external, and external validation, J Clin Epidemiol, 69, pp. 245-247, (2016); Ramspek CL, Jager KJ, Dekker FW, Et al., External validation of prognostic models: what, why, how, when and where?, Clin Kidney J, 14, 1, pp. 49-58, (2021); Neveol A, Dalianis H, Velupillai S, Et al., Clinical natural language processing in languages other than English: opportunities and challenges, J Biomed Semant, 9, 1, pp. 1-13, (2018); Beeksma M, Verberne S, van den Bosch A, Et al., Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records, BMC Med Inform Decis Mak, 19, 1, pp. 1-15, (2019); Sterckx L, Vandewiele G, Dehaene I, Et al., Clinical information extraction for preterm birth risk prediction, J Biomed Inform, 110, (2020); Menger V, Spruit M, Van Est R, Et al., Machine learning approach to inpatient violence risk assessment using routinely collected clinical notes in electronic health records, JAMA Netw Open, 2, 7, (2019); Mosteiro P, Rijcken E, Zervanou K, Et al., Machine learning for violence risk assessment using Dutch clinical notes, JoAIMS, 2, 1-2, pp. 44-54, (2021); Menger V, Scheepers F, Spruit M., Comparing deep learning and classical machine learning approaches for predicting inpatient violence incidents from clinical text, Appl Sci, 8, 6, (2018); Rijcken E, Kaymak U, Scheepers F, Et al., Topic modeling for interpretable text classification From EHRs, Front Big Data, 5, (2022); Elfrink A, Vagliano I, Abu-Hanna A, Et al., Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes, 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, pp. 193-198; Dormosh N, Schut MC, Heymans MW, Et al., Predicting future falls in older people using natural language processing of general practitioners’ clinical notes, Age Ageing, 52, 4, (2023); Cornet R, Van Eldik A, De Keizer N., Inventory of tools for Dutch clinical language processing, Stud Health Technol Inform, 180, pp. 245-249, (2012); Nobel JM, Puts S, Bakers FC, Et al., Natural language processing in Dutch free text radiology reports: challenges in a small language area staging pulmonary oncology, J Digit Imaging, 33, 4, pp. 1002-1008, (2020); Kim J, Verkijk S, Geleijn E, Et al., Modeling Dutch medical texts for detecting functional categories and levels of COVID-19 patients, Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 4577-4585, (2022); Verkijk S, Vossen P., MedRoBERTa.nl: a language model for Dutch electronic health records, Lang Comput, 11, pp. 141-159, (2021); Verkijk S, Vossen P., Efficiently and thoroughly anonymizing a transformer language model for Dutch electronic health records: a two-step method, Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 1098-1103, (2022); Starfield B, Shi L, Macinko J., Contribution of primary care to health systems and health, Milbank Q, 83, 3, pp. 457-502, (2005); Van Weel C, Kidd MR., Why strengthening primary health care is essential to achieving universal health coverage, CMAJ, 190, 15, pp. E463-E466, (2018); Usher-Smith J, Emery J, Hamilton W, Et al., Risk prediction tools for cancer in primary care, Br J Cancer, 113, 12, pp. 1645-1650, (2015); Johnson AE, Pollard TJ, Shen L, Et al., MIMIC-III, a freely accessible critical care database, Sci Data, 3, 1, pp. 160035-160039, (2016); Friedman C, Kra P, Rzhetsky A., Two biomedical sublanguages: a description based on the theories of Zellig Harris, J Biomed Inform, 35, 4, pp. 222-235, (2002); de Ridder MA, de Wilde M, de Ben C, Et al., Data resource profile: the Integrated Primary Care Information (IPCI) database, the Netherlands, Int J Epidemiol, 51, 6, pp. e314-e323, (2022); Overhage JM, Ryan PB, Reich CG, Et al., Validation of a common data model for active safety surveillance research, J Am Med Inform Assoc, 19, 1, pp. 54-60, (2012); Zein JG, Wu C-P, Attaway AH, Et al., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021); Xiang Y, Ji H, Zhou Y, Et al., Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: retrospective cohort study, J Med Internet Res, 22, 7, (2020); Tibble H, Tsanas A, Horne E, Et al., Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model, BMJ Open, 9, 7, (2019); Eyre H, Chapman AB, Peterson KS, Et al., Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python, AMIA Annu Symp Proc, 2021, pp. 438-447, (2021); Afzal Z, Pons E, Kang N, Et al., ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus, BMC Bioinformatics, 15, 1, pp. 373-312, (2014); van Es B, Reteig LC, Tan SC, Et al., Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods, BMC Bioinformatics, 24, 1, (2023); Christodoulou E, Ma J, Collins GS, Et al., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, pp. 12-22, (2019); Shortreed SM, Walker RL, Johnson E, Et al., Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction, NPJ Digit Med, 6, 1, (2023); Marx C, Calmon F, Ustun B., Predictive multiplicity in classification, Proceedings of the 37th International Conference on Machine Learning, 119, pp. 6765-6774, (2020); Watson-Daniels J, Parkes DC, Ustun B., Predictive Multiplicity in Probabilistic Classification, AAAI, 37, 9, pp. 10306-10314, (2023); Markus AF, Kors JA, Rijnbeek PR., The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies, J Biomed Inform, 113, (2021); Kulm S, Kofman L, Mezey J, Et al., Simple linear cancer risk prediction models with novel features outperform complex approaches, JCO Clin Cancer Inform, 6, (2022); Ribeiro MT, Singh S, Guestrin C., Why should i trust you?” Explaining the predictions of any classifier, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135-1144, (2016); Lundberg SM, Lee S-I., A unified approach to interpreting model predictions, Adv Neur In, 30, (2017); Janiesch C, Zschech P, Heinrich K., Machine learning and deep learning, Electron Mark, 31, 3, pp. 685-695, (2021)","T.M. Seinen; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Dr. Molewaterplein 40, 3015 GD, Netherlands; email: t.seinen@erasmusmc.nl","","Oxford University Press","","","","","","10675027","","JAMAF","37587084","English","J. Am. Med. Informatics Assoc.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85177103064"
"Alharbi E.; Cherif A.; Nadeem F.","Alharbi, Eman (57193717445); Cherif, Asma (35090041300); Nadeem, Farrukh (55401333800)","57193717445; 35090041300; 55401333800","Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation","2023","Smart Cities","6","5","","2910","2931","21","5","10.3390/smartcities6050130","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175009490&doi=10.3390%2fsmartcities6050130&partnerID=40&md5=71a074814d230ec5bf8a37b7c8fff3e7","Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Department of Information System, College of Computers and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia; Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia","Alharbi E., Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, Department of Information System, College of Computers and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia, Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Cherif A., Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Nadeem F., Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia","Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual. © 2023 by the authors.","air quality index; asthma attack; heatmap visualization; route context; safe route; smart healthcare; user context","","","","","","Makkah Digital Gate Initiative, (MDP-IRI-15-2022); Deanship of Scientific Research, Prince Sattam bin Abdulaziz University, DSR; King Abdulaziz University, KAU","Funding text 1: The authors gratefully acknowledge technical and financial support from the Emirate of Makkah Province and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. ; Funding text 2: This research work was funded by Makkah Digital Gate Initiative under grant no. (MDP-IRI-15-2022).","Sinha A., Rathi M., Smart Healthcare Systems, (2019); Tuli S., Basumatary N., Gill S.S., Kahani M., Arya R.C., Wander G.S., Buyya R., HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments, Future Gener. Comput. Syst, 104, pp. 187-200, (2020); Ali F., El-Sappagh S., Islam S.M.R., Kwak D., Ali A., Imran M., Kwak K.S., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Inf. Fusion, 63, pp. 208-222, (2020); Mansour R.F., Amraoui A.E., Nouaouri I., Diaz V.G., Gupta D., Kumar S., Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems, IEEE Access, 9, pp. 45137-45146, (2021); Mahajan S., Birajdar A., IOT based Smart Health Monitoring System for Chronic Diseases, Proceedings of the 2019 IEEE Pune Section International Conference (PuneCon), pp. 1-5; Hassan M.K., El Desouky A.I., Elghamrawy S.M., Sarhan A.M., A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases, Future Gener. Comput. Syst, 93, pp. 77-95, (2019); Ayres-Sampaio D., Teodoro A.C., Sillero N., Santos C., Fonseca J., Freitas A., An investigation of the environmental determinants of asthma hospitalizations: An applied spatial approach, Appl. Geogr, 47, pp. 10-19, (2014); Neuspiel D.R., Peak Expiratory Flow Rate Measurement, eMedicine, (2021); Alharbi E., Cherif A., Nadeem F., Mirza T., Machine Learning Models for Early Prediction of Asthma Attacks Based on Bio-signals and Environmental Triggers, Proceedings of the 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), pp. 1-7; Carino M., Romita P., Foti C., Allergy-Related Disorders in the Construction Industry, ISRN Prev. Med, 2013, (2013); Balmes J.R., Can traffic-related air pollution cause asthma?, Thorax, 64, pp. 646-647, (2009); Gordian M.E., Stewart A.W., Morris S.S., Evaporative Gasoline Emissions and Asthma Symptoms, Int. J. Environ. Res. Public Health, 7, pp. 3051-3062, (2010); Vinutha H.P., Poornima B., Sagar B.M., Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset, Information and Decision Sciences, pp. 511-518, (2018); Wilson D.L., Asymptotic Properties of Nearest Neighbor Rules Using Edited Data, IEEE Trans. Syst. Man Cybern, SMC-2, pp. 408-421, (1972); Sain H., Purnami S.W., Combine Sampling Support Vector Machine for Imbalanced Data Classification, Procedia Comput. Sci, 72, pp. 59-66, (2015); Friedman J.H., Greedy function approximation: A gradient boosting machine, Ann. Stat, 29, pp. 1189-1232, (2001); Wen H.T., Wu H.Y., Liao K.C., Using XGBoost Regression to Analyze the Importance of Input Features Applied to an Artificial Intelligence Model for the Biomass Gasification System, Inventions, 7, (2022); Massaro A., Panarese A., Giannone D., Galiano A., Augmented Data and XGBoost Improvement for Sales Forecasting in the Large-Scale Retail Sector, Appl. Sci, 11, (2021); Chung C.C., Su E.C.Y., Chen J.H., Chen Y.T., Kuo C.Y., XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke, Diagnostics, 13, (2023); Zou M., Jiang W.G., Qin Q.H., Liu Y.C., Li M.L., Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting, Materials, 15, (2022); Balasubramanian S., Ravikumar N.R., Chakkarapani E., Shivbalan S.O., Peak expiratory flow rate in children—A ready reckoner, Indian Pediatr, 39, pp. 104-106, (2022); Morgan D.L., Focus Groups as Qualitative Research, (1997); Khasha R., Sepehri M.M., Mahdaviani S.A., Khatibi T., Mobile GIS-based monitoring asthma attacks based on environmental factors, J. Clean. Prod, 179, pp. 417-428, (2018); Kaffash-Charandabi N., Alesheikh A.A., Sharif M., A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts, Environ. Sci. Pollut. Res, 26, pp. 7525-7539, (2019); Hosseini A., Buonocore C.M., Hashemzadeh S., Hojaiji H., Kalantarian H., Sideris C., Bui A.A.T., King C.E., Sarrafzadeh M., HIPAA Compliant Wireless Sensing Smartwatch Application for the Self-Management of Pediatric Asthma, Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2016, pp. 49-54; Siddiquee J., Roy A., Datta A., Sarkar P., Saha S., Biswas S.S., Smart asthma attack prediction system using Internet of Things, Proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1-4; Hoq M.N., Alam R., Amin A., Prediction of possible asthma attack from air pollutants: Towards a high density air pollution map for smart cities to improve living, Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1-5; Larkin A., Williams D.E., Kile M.L., Baird W.M., Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations, Comput. J, 58, pp. 1431-1442, (2015); Nurgazy M., Zaslavsky A., Jayaraman P.P., Kubler S., Mitra K., Saguna S., CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms, Proceedings of the 2019 International Conference on High Performance Computing Simulation (HPCS), pp. 84-91; Adedeji O.H., Oluwafunmilayo O., Oluwaseun T.A.O., Mapping of Traffic-Related Air Pollution Using GIS Techniques in Ijebu-Ode, Nigeria, Indones. J. Geogr, 48, (2016); Chen P., Visualization of real-time monitoring datagraphic of urban environmental quality, EURASIP J. Image Video Process, 2019, (2019); Lu W., Ai T., Zhang X., He Y., An Interactive Web Mapping Visualization of Urban Air Quality Monitoring Data of China, Atmosphere, 8, (2017); Ramos F., Trilles S., Munoz A., Huerta J., Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks, Sensors, 18, (2018)","E. Alharbi; Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; email: ealharbi0125@stu.kau.edu.sa","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","26246511","","","","English","Smart. Cities.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85175009490"
"Gao J.; Liu J.; Xu R.; Pandey S.; Siva V.S.K.S.V.; Yu D.","Gao, Jerry (7404475003); Liu, Jia (59064760200); Xu, Rui (57739016000); Pandey, Samiksha (58281664600); Siva, Venkata Sai Kusuma Sindhoora Vankayala (57732219300); Yu, Dian (57739841300)","7404475003; 59064760200; 57739016000; 58281664600; 57732219300; 57739841300","Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California","2022","Atmosphere","13","6","914","","","","7","10.3390/atmos13060914","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131865086&doi=10.3390%2fatmos13060914&partnerID=40&md5=a3fab96966be006c859de304462451ab","Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States; Department of Computer Engineering, San Jose State University, San Jose, 95192, CA, United States; Department of Electrical and Information Engineering, Jilin Engineering Normal University, Changchun, 130052, China","Gao J., Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States, Department of Computer Engineering, San Jose State University, San Jose, 95192, CA, United States; Liu J., Department of Electrical and Information Engineering, Jilin Engineering Normal University, Changchun, 130052, China; Xu R., Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States; Pandey S., Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States; Siva V.S.K.S.V., Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States; Yu D., Department of Applied Data Science, San Jose State University, San Jose, 95192, CA, United States","A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","air pollution; asthma prevalence; PM concentrations; Salton Sea","California; Salton Sea; United States; Adaptive boosting; Air quality; Diseases; Forecasting; Lakes; Long short-term memory; Meteorology; Quality control; Remote sensing; Asthma prevalence; California; Ensemble models; Gradient boosting; Machine learning models; Model-based OPC; PM concentration; Random forests; Salton Sea; Weather data; air quality; artificial intelligence; asthma; atmospheric pollution; child health; concentration (composition); disease prevalence; ensemble forecasting; environmental assessment; particulate matter; prediction; remote sensing; Decision trees","","","","","","","Fendt L., As the Salton Sea Shrinks, It Leaves behind a toxic reminder of the cost of making a desert bloom; Baj A., Jf B., Shrinking lakes, air pollution, and human health: Evidence from California’s Salton Sea, Sci. 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Gao; Department of Applied Data Science, San Jose State University, San Jose, 95192, United States; email: jerry.gao@sjsu.edu; J. Liu; Department of Electrical and Information Engineering, Jilin Engineering Normal University, Changchun, 130052, China; email: liujia@jlenu.edu.cn","","MDPI","","","","","","20734433","","","","English","Atmosphere","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85131865086"
"Haynes J.M.; Kaminsky D.A.; Ruppel G.L.","Haynes, Jeffrey M. (7202040178); Kaminsky, David A. (7006158871); Ruppel, Gregg L. (6602317611)","7202040178; 7006158871; 6602317611","The Role of Pulmonary Function Testing in the Diagnosis and Management of COPD","2023","Respiratory Care","68","7","","889","913","24","5","10.4187/respcare.10757","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162747689&doi=10.4187%2frespcare.10757&partnerID=40&md5=08609c547b771f8d24fe2a0fc82c02ad","Pulmonary Function Laboratory, Elliot Health System, Manchester, NH, United States; Division of Pulmonary and Critical Care Medicine, University of Vermont College of Medicine, Burlington, VT, United States; Division of Pulmonary, Critical Care and Sleep Medicine, St. Louis University, St. Louis, MO, United States","Haynes J.M., Pulmonary Function Laboratory, Elliot Health System, Manchester, NH, United States; Kaminsky D.A., Division of Pulmonary and Critical Care Medicine, University of Vermont College of Medicine, Burlington, VT, United States; Ruppel G.L., Division of Pulmonary, Critical Care and Sleep Medicine, St. Louis University, St. Louis, MO, United States","Pulmonary function testing (PFT) has a long and rich history in the definition, diagnosis, and management of COPD. For decades, spirometry has been regarded as the standard for diagnosing COPD; however, numerous studies have shown that COPD symptoms, pathology, and associated poor outcomes can occur, despite normal spirometry. Diffusing capacity and imaging studies have called into question the need for spirometry to put the “O” (obstruction) in COPD. The role of exercise testing and the ability of PFTs to phenotype COPD are reviewed. Although PFTs play an important role in diagnosis, treatment decisions are primarily determined by symptom intensity and exacerbation history. Although a seminal study positioned FEV1 as the primary predictor of survival, numerous studies have shown that tests other than spirometry are superior predictors of mortality. In years past, using spirometry to screen for COPD was promulgated; however, this only seems appropriate for individuals who are symptomatic and at risk for developing COPD. © 2023 Daedalus Enterprises.","chronic obstructive; diagnosis; exercise test; pulmonary diffusing capacity; pulmonary disease; respiratory function tests; spirometry","Exercise Test; Forced Expiratory Volume; Humans; Pulmonary Disease, Chronic Obstructive; Respiratory Function Tests; Spirometry; bronchodilating agent; airway obstruction; airway resistance; anemia; Article; artificial intelligence; asthma; body plethysmography; cardiopulmonary exercise test; chronic bronchitis; chronic obstructive lung disease; clinical practice; computer assisted tomography; controlled study; diagnostic test accuracy study; disease severity; dyspnea; emphysema; exercise test; forced expiratory volume; forced vital capacity; health care utilization; human; hypercapnia; hypocapnia; hypoxemia; interstitial lung disease; lung diffusion capacity; lung function test; lung minute volume; lung volume; maximal voluntary ventilation; muscle weakness; obesity; phenotype; quality of life; questionnaire; receiver operating characteristic; six minute walk test; spirometry; thin layer chromatography; total lung capacity; chronic obstructive lung disease; lung function test","","","","","","","Petty TL., John Hutchinson’s mysterious machine revisited, Chest, 121, 5, pp. 219S-223S, (2002); Wu TD, McCormack MC, Mitzner W., The history of pulmonary function testing, Pulmonary function testing, principles and practice, pp. 15-42, (2018); Barach AL., Physiological methods in the diagnosis and treatment of asthma and emphysema, Ann Intern Med, 12, 4, pp. 454-481, (1938); Hyatt RE, Schilder DP, Fry DL., Relationship between maximum ex-piratory flow and degree of lung inflation, J Appl Physiol, 13, 3, pp. 331-336, (1958); Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS, Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. 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"Liu J.; Dan W.; Liu X.; Zhong X.; Chen C.; He Q.; Wang J.","Liu, Jinlei (57220212389); Dan, Wenchao (57218758342); Liu, Xudong (58186040600); Zhong, Xiaoxue (57190073863); Chen, Cheng (57477136600); He, Qingyong (27169705000); Wang, Jie (57211090025)","57220212389; 57218758342; 58186040600; 57190073863; 57477136600; 27169705000; 57211090025","Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine","2023","Health Information Science and Systems","11","1","21","","","","7","10.1007/s13755-023-00215-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152662656&doi=10.1007%2fs13755-023-00215-0&partnerID=40&md5=accaea230192ce7b046c4191db2339a9","Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China; Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China; Beijing University of Chinese Medicine, Beijing, 100029, China; Xi’an Jiaotong University, Xi’an, 710049, China","Liu J., Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China; Dan W., Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China, Beijing University of Chinese Medicine, Beijing, 100029, China; Liu X., Beijing University of Chinese Medicine, Beijing, 100029, China; Zhong X., Beijing University of Chinese Medicine, Beijing, 100029, China; Chen C., Xi’an Jiaotong University, Xi’an, 710049, China; He Q., Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China; Wang J., Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China","Backgrounds: Dyslipidemia is a prominent risk factor for cardiovascular diseases and one of the primary independent modifiable factors of diabetes and stroke. Statins can significantly improve the prognosis of dyslipidemia, but its side effects cannot be ignored. Traditional Chinese Medicine (TCM) has been used in clinical practice for more than 2000 years in China and has certain traits in treating dyslipidemia with little side effect. Previous research has shown that Mutual Obstruction of Phlegm and Stasis (MOPS) is the most common dyslipidemia type classified in TCM. However, how to compose diagnostic factors in TCM into diagnostic rules relies heavily on the doctor's experience, falling short in standardization and objectiveness. This is a limit for TCM to play its advantages of treating dyslipidemia with MOPS. Methods: In this study, the syndrome diagnosis in TCM was transformed into the prediction and classification problem in artificial intelligence The deep learning method was employed to build the classification prediction models for dyslipidemia. The models were built and trained with a large amount of multi-centered clinical data on MOPS. The optimal model was screened out by evaluating the performance of prediction models through loss, accuracy, precision, recall, confusion matrix, PR and ROC curve (including AUC). Results: A total of 20 models were constructed through the deep learning method. All of them performed well in the prediction of dyslipidemia with MOPS. The model-11 is the optimal model. The evaluation indicators of model-11 are as follows: The true positive (TP), false positive (FP), true negative (TN) and false negative (FN) are 51, 15, 129, and 9, respectively. The loss is 0.3241, accuracy is 0.8672, precision is 0.7138, recall is 0.8286, and the AUC is 0.9268. After screening through 89 diagnostic factors of TCM, we identified 36 significant diagnosis factors for dyslipidemia with MOPS. The most outstanding diagnostic factors from the importance were dark purple tongue, slippery pulse and slimy fur, etc. Conclusions: This study successfully developed a well-performing classification prediction model for dyslipidemia with MOPS, transforming the syndrome diagnosis problem in TCM into a prediction and classification problem in artificial intelligence. Patients with dyslipidemia of MOPS can be accurately recognized through limited information from patients. We also screened out significant diagnostic factors for composing diagnostic rules of dyslipidemia with MOPS. The study is an avant-garde attempt at introducing the deep-learning method into the research of TCM, which provides a useful reference for the extension of deep learning method to other diseases and the construction of disease diagnosis model in TCM, contributing to the standardization and objectiveness of TCM diagnosis. © 2023, The Author(s).","Deep learning; Diagnostic factors; Dyslipidemia; Prediction model; Traditional Chinese medicine","high density lipoprotein cholesterol; low density lipoprotein cholesterol; triacylglycerol; adult; aged; alcohol consumption; area under the curve; Article; artificial intelligence; asthma; brain infarction; Chinese medicine; cholesterol blood level; confusion matrix; deep learning; diabetes mellitus; diagnostic test accuracy study; dyslipidemia; dyspnea; false negative result; family history; high density lipoprotein cholesterol level; human; hypertension; ischemic heart disease; low density lipoprotein cholesterol level; measurement accuracy; measurement precision; multicenter study; prediction; predictive model; receiver operating characteristic; smoking; tinnitus; training; validation process","","","","","National Administration of Traditional Chinese Medicine; Qihuang Project Chief Scientist Project, (0201000401); Traditional Chinese Medicine Inheritance; Youth Science Fund; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, IUSS, (81202803); National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, IUSS; National Natural Science Foundation of China, NSFC, (81974556, 82230124); National Natural Science Foundation of China, NSFC","The present study was supported by the State Key Program of National Natural Science Foundation of China [82230124], the General Program of the National Natural Science Foundation of China [81974556], the Youth Science Fund project [81202803], from National Nature Science Foundation of China, and Traditional Chinese Medicine Inheritance and innovation “Ten million” talent project - Qihuang Project Chief Scientist Project [0201000401] from National Administration of Traditional Chinese Medicine. 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Wang; Department of Cardiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 10053, China; email: wangjie0103@126.com","","Springer","","","","","","20472501","","","","English","Health Inf. Sci. Syst.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85152662656"
"Kawata N.; Iwao Y.; Matsuura Y.; Suzuki M.; Ema R.; Sekiguchi Y.; Sato H.; Nishiyama A.; Nagayoshi M.; Takiguchi Y.; Suzuki T.; Haneishi H.","Kawata, Naoko (23100411700); Iwao, Yuma (59267308300); Matsuura, Yukiko (36641582000); Suzuki, Masaki (56597160900); Ema, Ryogo (55295849400); Sekiguchi, Yuki (58487707800); Sato, Hirotaka (57224624958); Nishiyama, Akira (55669557100); Nagayoshi, Masaru (57522005700); Takiguchi, Yasuo (57211786730); Suzuki, Takuji (55731681600); Haneishi, Hideaki (7004884557)","23100411700; 59267308300; 36641582000; 56597160900; 55295849400; 58487707800; 57224624958; 55669557100; 57522005700; 57211786730; 55731681600; 7004884557","Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19","2023","Japanese Journal of Radiology","41","12","","1359","1372","13","5","10.1007/s11604-023-01466-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164821802&doi=10.1007%2fs11604-023-01466-3&partnerID=40&md5=3c0b68d374bcb4dcc67743abb07357fb","Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan; Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan; Medical Mycology Research Center (MMRC), Chiba University, Chiba, 260-8673, Japan; Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan; Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan; Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan; Department of Respirology, Kashiwa Kousei General Hospital, 617 Shikoda, Kashiwa-shi, Chiba, 277-8551, Japan; Department of Respirology, Eastern Chiba Medical Center, 3-6-2, Okayamadai, Togane-shi, Chiba, 283-8686, Japan; Department of Radiology, Soka Municipal Hospital, 2-21-1, Souka, Souka-shi, Saitama, 340-8560, Japan; Department of Radiology, Chiba University Hospital, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan","Kawata N., Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan, Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan, Medical Mycology Research Center (MMRC), Chiba University, Chiba, 260-8673, Japan; Iwao Y., Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, 4-9-1, Anagawa, Inage-ku, Chiba-shi, Chiba, 263-8555, Japan; Matsuura Y., Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan; Suzuki M., Department of Respirology, Kashiwa Kousei General Hospital, 617 Shikoda, Kashiwa-shi, Chiba, 277-8551, Japan; Ema R., Department of Respirology, Eastern Chiba Medical Center, 3-6-2, Okayamadai, Togane-shi, Chiba, 283-8686, Japan; Sekiguchi Y., Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan; Sato H., Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan, Department of Radiology, Soka Municipal Hospital, 2-21-1, Souka, Souka-shi, Saitama, 340-8560, Japan; Nishiyama A., Department of Radiology, Chiba University Hospital, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan; Nagayoshi M., Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan; Takiguchi Y., Department of Respiratory Medicine, Chiba Aoba Municipal Hospital, 1273-2 Aoba-cho, Chuo-ku, Chiba-shi, Chiba, 260-0852, Japan; Suzuki T., Department of Respirology, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi, Chiba, 260-8677, Japan; Haneishi H., Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522, Japan","Purpose: As of March 2023, the number of patients with COVID-19 worldwide is declining, but the early diagnosis of patients requiring inpatient treatment and the appropriate allocation of limited healthcare resources remain unresolved issues. In this study we constructed a deep-learning (DL) model to predict the need for oxygen supplementation using clinical information and chest CT images of patients with COVID-19. Materials and methods: We retrospectively enrolled 738 patients with COVID-19 for whom clinical information (patient background, clinical symptoms, and blood test findings) was available and chest CT imaging was performed. The initial data set was divided into 591 training and 147 evaluation data. We developed a DL model that predicted oxygen supplementation by integrating clinical information and CT images. The model was validated at two other facilities (n = 191 and n = 230). In addition, the importance of clinical information for prediction was assessed. Results: The proposed DL model showed an area under the curve (AUC) of 89.9% for predicting oxygen supplementation. Validation from the two other facilities showed an AUC > 80%. With respect to interpretation of the model, the contribution of dyspnea and the lactate dehydrogenase level was higher in the model. Conclusions: The DL model integrating clinical information and chest CT images had high predictive accuracy. DL-based prediction of disease severity might be helpful in the clinical management of patients with COVID-19. © 2023, The Author(s).","Chest CT images; COVID-19; Deep learning; Disease severity prediction; Explainable AI","COVID-19; Deep Learning; Humans; Oxygen; Oxygen Inhalation Therapy; Retrospective Studies; Tomography, X-Ray Computed; albumin; aspartate aminotransferase; C reactive protein; D dimer; globulin; glucose; lactate dehydrogenase; oxygen; oxygen; adult; aged; albumin blood level; alcohol consumption; area under the curve; Article; aspartate aminotransferase blood level; asthma; basophil; blood examination; chronic obstructive lung disease; cohort analysis; comorbidity; controlled study; coronary artery disease; coronavirus disease 2019; coughing; current smoker; death; deep learning; diabetes mellitus; diarrhea; dysgeusia; dyslipidemia; dysosmia; dyspnea; eosinophil; extracorporeal oxygenation; fatigue; female; fever; globulin blood level; glucose blood level; high flow nasal cannula therapy; hospital admission; human; hypertension; information model; lactate dehydrogenase blood level; leukocyte count; major clinical study; male; mean corpuscular hemoglobin concentration; medical informatics; medical record review; multicenter study; nausea and vomiting; neutrophil; oxygen consumption; oxygen supply; oxygen therapy; patient information; prediction; prognostic nutritional index; protein blood level; receiver operating characteristic; residual neural network; respiratory tract intubation; retrospective study; sore throat; survival; symptom; thorax radiography; transfer of learning; treatment outcome; coronavirus disease 2019; oxygen therapy; procedures; x-ray computed tomography","","aspartate aminotransferase, 9000-97-9; C reactive protein, 9007-41-4; glucose, 50-99-7, 84778-64-3, 8027-56-3; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; oxygen, 7782-44-7; Oxygen, ","Aquilion ONE, Canon, Japan; JMP Pro version 17.0, SAS, United States","Canon, Japan; SAS, United States","Japanese Respiratory Foundation, JRF; Japan Society for the Promotion of Science, JSPS, (22K12836); Japan Society for the Promotion of Science, JSPS","This research was partially supported by a grant from the Japan Society for the Promotion of Science (JSPS) (Grants-in-Aid for scientific research 22K12836), and a Japanese Respiratory Foundation (JRF) Grant. These funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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Kawata; Department of Respirology, Graduate School of Medicine, Chiba University, Chiba, 1-8-1, Inohana, Chuo-ku, Chiba-shi, 260-8677, Japan; email: chumito_03@yahoo.co.jp","","Springer","","","","","","18671071","","","37440160","English","Jpn. J. Rad.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85164821802"
"Tomita K.; Yamasaki A.; Katou R.; Ikeuchi T.; Touge H.; Sano H.; Tohda Y.","Tomita, Katsuyuki (7403028907); Yamasaki, Akira (7103172644); Katou, Ryohei (57219303062); Ikeuchi, Tomoyuki (57194939226); Touge, Hirokazu (8322513200); Sano, Hiroyuki (56643947100); Tohda, Yuji (55349129800)","7403028907; 7103172644; 57219303062; 57194939226; 8322513200; 56643947100; 55349129800","Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost","2023","Diagnostics","13","19","3069","","","","6","10.3390/diagnostics13193069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173682673&doi=10.3390%2fdiagnostics13193069&partnerID=40&md5=41a885e19c0c9eabd603c8135d65e985","Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; Division of Respiratory Medicine and Rheumatology, Department of Multidisciplinary Internal Medicine, School of Medicine, Tottori University, Yonago, 683-8503, Japan; Allergy Center, Kindai University Hospital, Osakasayama, 589-8511, Japan; Department of Respiratory and Allergorogy, Kindai University, Osakasayama, 589-8511, Japan","Tomita K., Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; Yamasaki A., Division of Respiratory Medicine and Rheumatology, Department of Multidisciplinary Internal Medicine, School of Medicine, Tottori University, Yonago, 683-8503, Japan; Katou R., Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; Ikeuchi T., Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; Touge H., Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; Sano H., Allergy Center, Kindai University Hospital, Osakasayama, 589-8511, Japan; Tohda Y., Department of Respiratory and Allergorogy, Kindai University, Osakasayama, 589-8511, Japan","An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings. © 2023 by the authors.","adult asthma; artificial intelligence; diagnostic assistant; machine learning; random forest; XGBoost","adult; adult asthma; aged; airway obstruction; area under the curve; Article; artificial intelligence; asthma; Bayesian learning; clinical feature; decision tree; diagnostic test accuracy study; eosinophil count; eXtreme gradient boosting; female; forced expiratory volume; forced vital capacity; fractional exhaled nitric oxide; human; learning curve; lung auscultation; lung function test; machine learning; major clinical study; male; medical specialist; outpatient department; random forest; respiratory tract allergy; respiratory tract disease; university hospital; very elderly; wheezing","","","","","","","Jain V.V., Allison D.R., Andrews S., Mejia J., Mills P.K., Peterson M.W., Misdiagnosis among frequent exacerbators of clinically diagnosed asthma and COPD without confirmation of airflow obstruction, Lung, 193, pp. 505-512, (2015); Sokol K.C., Sharma G., Lin Y.-K., Goldblum R.M., Choosing wisely: Adherence by physicians to recommended use of spirometry in the diagnosis and management of adult asthma, Am. 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J, 60, (2022); British Guideline on the Management of Asthma; Tomita K., Nagao R., Touge H., Ikeuchi T., Sano H., Yamasaki A., Tohda Y., Deep learning facilitates the diagnosis of adult asthma, Allergol. Int, 68, pp. 456-461, (2019); Li X., Xiong H., Li X., Wu X., Zhang X., Liu J., Bian J., Dou D., Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond, Knowl. Inf. Syst, 64, pp. 3197-3234, (2022); Sagi O., Rokach L., Approximating XGBoost with an interpretable decision tree, Inf. Sci, 572, pp. 522-542, (2021); Tomita K., Sano H., Chiba Y., Sato R., Sano A., Nishiyama O., Iwanaga T., Higashimoto Y., Haraguchi R., Tohda Y., A scoring algorithm for predicting the presence of adult asthma: A prospective derivation study, Prim. Care Respir. J, 22, pp. 51-58, (2013); Breiman L., Random forests, Mach. 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Optim, 13, pp. 455-492, (1998); Akiba T., Sano S., Yanase T., Ohta T., Koyama M., Optuna: A Next-generation Hyperparameter Optimization Framework, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2623-2631; Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Proceedings of the 31st International Conference on NeurIPS, pp. 4768-4777; Chelgani S.C., Nasiri H., Tohry A., Heidari H.R., Modeling industrial hydrocyclone operational variables by SHAP-CatBoost—A “conscious lab” approach, Powder Technol, 420, (2023); Aaron S.D., Vandemheen K.L., Boulet L.P., McIvor R.A., Fitzgerald J.M., Hernandez P., Lemiere C., Sharma S., Field S.K., Alvarez G.G., Et al., Overdiagnosis of asthma in obese and nonobese adults, CMAJ, 179, pp. 1121-1131, (2008); Montnemery P., Hansson L., Lanke J., Lindholm L.H., Nyberg P., Lofdahl C.G., Adelroth E., Accuracy of a first diagnosis of asthma in primary health care, Fam. Pract, 19, pp. 365-368, (2002); Almeshari M.A., Stockley J., Sapey E., The diagnosis of asthma. Can physiological tests of small airways function help?, Chron. Respir. Dis, 18, (2021); Usmani O.S., Singh D., Spinola M., Bizzi A., Barnes P.J., The prevalence of small airways disease in adult asthma: A systematic literature review, Respir. Med, 116, pp. 19-27, (2016); Cottini M., Lombardi C., Micheletto C., Small airway dysfunction and bronchial asthma control: The state of the art, Asthma Res. Pract, 1, (2015); Karrasch S., Linde K., Rucker G., Sommer H., Karsch-Volk M., Kleijnen J., Jorres R.A., Schneider A., Accuracy of FENO for diagnosing asthma: A systematic review, Thorax, 72, pp. 109-116, (2017); Prokhorenkova L., Gusev G., Vorobev A., Dorogush A.V., Gulin A., CatBoost: Unbiased boosting with categorical features, Adv. Neural Inf. Process. Syst, 31, pp. 6638-6648, (2018); Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.-Y., LightGBM: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst, 30, pp. 3146-3154, (2017); Daoud E.A., Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset, IJISRR, 13, pp. 6-10, (2019)","K. Tomita; Department of Respiratory Medicine, Yonago Medical Center, National Hospital Organization, Yonago, 683-0006, Japan; email: ktomita0223@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85173682673"
"Wan R.; Bai L.; Yan Y.; Li J.; Luo Q.; Huang H.; Huang L.; Xiang Z.; Luo Q.; Gu Z.; Guo Q.; Pan P.; Lu R.; Fang Y.; Hu C.; Jiang J.; Li Y.","Wan, Rongjun (57204878382); Bai, Lu (57225925879); Yan, Yusheng (57557899700); Li, Jianmin (57195338448); Luo, Qingkai (57557379700); Huang, Hua (57202086703); Huang, Lingmei (57558670600); Xiang, Zhi (57218660028); Luo, Qing (57557637400); Gu, Zi (57557379800); Guo, Qing (57559198700); Pan, Pinhua (58843223500); Lu, Rongli (27170145500); Fang, Yimin (57224444572); Hu, Chengping (7404570768); Jiang, Juan (57206578571); Li, Yuanyuan (56464338100)","57204878382; 57225925879; 57557899700; 57195338448; 57557379700; 57202086703; 57558670600; 57218660028; 57557637400; 57557379800; 57559198700; 58843223500; 27170145500; 57224444572; 7404570768; 57206578571; 56464338100","A Clinically Applicable Nomogram for Predicting the Risk of Invasive Mechanical Ventilation in Pneumocystis jirovecii Pneumonia","2022","Frontiers in Cellular and Infection Microbiology","12","","850741","","","","6","10.3389/fcimb.2022.850741","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127422739&doi=10.3389%2ffcimb.2022.850741&partnerID=40&md5=81c75410be01fab34c3a5903529c01f3","Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China; Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China; Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China; Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Department of Pulmonary and Critical Care Medicine, First Hospital of Changsha, Changsha, China; Department of Pulmonary and Critical Care Medicine, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, China; Department of Pulmonary and Critical Care Medicine, First People’s Hospital of Chenzhou, Chenzhou, China; Medical Center of Tuberculosis, Second People’s Hospital of Chenzhou, Chenzhou, China; Department of Pulmonary and Critical Care Medicine, Yueyang Central Hospital, Yueyang, China; Department of Respiratory Medicine, First People’s Hospital of Huaihua, Huaihua, China; Department of Pulmonary and Critical Care Medicine, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China; Department of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, Xiangtan, China; Department of Pulmonary and Critical Care Medicine, Yiyang Central Hospital, Yiyang, China","Wan R., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Bai L., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Yan Y., Department of Pulmonary and Critical Care Medicine, First Hospital of Changsha, Changsha, China; Li J., Department of Pulmonary and Critical Care Medicine, Hunan Provincial People’s Hospital, First Affiliated Hospital of Hunan Normal University, Changsha, China; Luo Q., Department of Pulmonary and Critical Care Medicine, First People’s Hospital of Chenzhou, Chenzhou, China; Huang H., Medical Center of Tuberculosis, Second People’s Hospital of Chenzhou, Chenzhou, China; Huang L., Department of Pulmonary and Critical Care Medicine, Yueyang Central Hospital, Yueyang, China; Xiang Z., Department of Respiratory Medicine, First People’s Hospital of Huaihua, Huaihua, China; Luo Q., Department of Pulmonary and Critical Care Medicine, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China; Gu Z., Department of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, Xiangtan, China; Guo Q., Department of Pulmonary and Critical Care Medicine, Yiyang Central Hospital, Yiyang, China; Pan P., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Lu R., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Fang Y., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Hu C., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Jiang J., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China; Li Y., Department of Respiratory Medicine, National Key Clinical Specialty, Branch of National Clinical Research Center for Respiratory Disease, Xiangya Hospital, Central South University, Changsha, China, Center of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, China, Clinical Research Center for Respiratory Diseases in Hunan Province, Changsha, China, Hunan Engineering Research Center for Intelligent Diagnosis and Treatment of Respiratory Disease, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China","Objective: Pneumocystis jirovecii pneumonia (PCP) is a life-threatening disease associated with a high mortality rate among immunocompromised patient populations. Invasive mechanical ventilation (IMV) is a crucial component of treatment for PCP patients with progressive hypoxemia. This study explored the risk factors for IMV and established a model for early predicting the risk of IMV among patients with PCP. Methods: A multicenter, observational cohort study was conducted in 10 hospitals in China. Patients diagnosed with PCP were included, and their baseline clinical characteristics were collected. A Boruta analysis was performed to identify potentially important clinical features associated with the use of IMV during hospitalization. Selected variables were further analyzed using univariate and multivariable logistic regression. A logistic regression model was established based on independent risk factors for IMV and visualized using a nomogram. Results: In total, 103 patients comprised the training cohort for model development, and 45 comprised the validation cohort to confirm the model’s performance. No significant differences were observed in baseline clinical characteristics between the training and validation cohorts. Boruta analysis identified eight clinical features associated with IMV, three of which were further confirmed to be independent risk factors for IMV, including age (odds ratio [OR] 2.615 [95% confidence interval (CI) 1.110–6.159]; p = 0.028), oxygenation index (OR 0.217 [95% CI 0.078–0.604]; p = 0.003), and serum lactate dehydrogenase level (OR 1.864 [95% CI 1.040–3.341]; p = 0.037). Incorporating these three variables, the nomogram achieved good concordance indices of 0.829 (95% CI 0.752–0.906) and 0.818 (95% CI 0.686–0.950) in predicting IMV in the training and validation cohorts, respectively, and had well-fitted calibration curves. Conclusions: The nomogram demonstrated accurate prediction of IMV in patients with PCP. Clinical application of this model enables early identification of patients with PCP who require IMV, which, in turn, may lead to rational therapeutic choices and improved clinical outcomes. Copyright © 2022 Wan, Bai, Yan, Li, Luo, Huang, Huang, Xiang, Luo, Gu, Guo, Pan, Lu, Fang, Hu, Jiang and Li.","invasive mechanical ventilation (IMV); machine learning; nomogram; Pneumocystis jirovecii pneumonia (PCP); predictive model","Hospitalization; Humans; Nomograms; Pneumonia, Pneumocystis; Respiration, Artificial; Risk Factors; C reactive protein; cotrimoxazole; procalcitonin; acquired immune deficiency syndrome; adult; algorithm; Article; asthma; breathing rate; calibration; chronic obstructive lung disease; clinical feature; clinical outcome; cohort analysis; comorbidity; computer assisted tomography; controlled study; diastolic blood pressure; dyspnea; female; heart rate; hemoglobin blood level; hospitalization; human; hypoxemia; immunocompromised patient; intermittent mandatory ventilation; interstitial lung disease; invasive ventilation; lactate dehydrogenase blood level; leukocyte count; lung tuberculosis; lymphocyte count; machine learning; major clinical study; male; middle aged; mortality rate; multicenter study; neutrophil count; nomogram; observational study; organ transplantation; outcome assessment; oxygen saturation; oxygenation index; platelet count; Pneumocystis pneumonia; prediction; predictive model; protein blood level; pulse oximetry; random forest; real time polymerase chain reaction; rheumatic disease; risk assessment; risk factor; solid malignant neoplasm; systolic blood pressure; thorax radiography; training; validation process; adverse event; artificial ventilation; clinical trial; nomogram; Pneumocystis pneumonia","","C reactive protein, 9007-41-4; cotrimoxazole, 8064-90-2; procalcitonin, 56645-65-9","","","Innovative Research Platform of Hunan Development and Reform Commission, (2021-212); National Natural Science Foundation of China, NSFC, (81873406, 82100099, 82170041); National Natural Science Foundation of China, NSFC; Xiangya Hospital, Central South University, (2018Q015); Xiangya Hospital, Central South University","This work was supported by grants from the National Natural Science Foundation of China (82170041, 82100099, and 81873406), the Innovative Research Platform of Hunan Development and Reform Commission (2021-212), and the Youth Research Foundation of Xiangya Hospital (2018Q015). 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Microbiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127422739"
"Althobiani M.A.; Ranjan Y.; Jacob J.; Orini M.; Dobson R.J.B.; Porter J.C.; Hurst J.R.; Folarin A.A.","Althobiani, Malik A. (57226655322); Ranjan, Yatharth (57205028192); Jacob, Joseph (57200973324); Orini, Michele (26321773400); Dobson, Richard James Butler (8931612400); Porter, Joanna C. (7403426976); Hurst, John R. (57201513306); Folarin, Amos A. (35766461900)","57226655322; 57205028192; 57200973324; 26321773400; 8931612400; 7403426976; 57201513306; 35766461900","Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study","2023","JMIR Formative Research","7","1","e51507","","","","6","10.2196/51507","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180076659&doi=10.2196%2f51507&partnerID=40&md5=6876c00f2e1c62d0e834f7b28ad2900d","Respiratory Medicine, University College London, London, United Kingdom; Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom; Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom; National Institute for Health and Care Research, Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London, United Kingdom; Institute of Health Informatics, University College London, London, United Kingdom; National Institute for Health and Care Research, Biomedical Research Centre, University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom","Althobiani M.A., Respiratory Medicine, University College London, London, United Kingdom, Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom, Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; Ranjan Y., Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; Jacob J., Respiratory Medicine, University College London, London, United Kingdom, Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom; Orini M., Institute of Cardiovascular Science, University College London, London, United Kingdom; Dobson R.J.B., Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, National Institute for Health and Care Research, Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London, United Kingdom, Institute of Health Informatics, University College London, London, United Kingdom, National Institute for Health and Care Research, Biomedical Research Centre, University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom; Porter J.C., Respiratory Medicine, University College London, London, United Kingdom, Interstitial Lung Disease Service, University College London Hospital, London, United Kingdom; Hurst J.R., Respiratory Medicine, University College London, London, United Kingdom; Folarin A.A., Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, National Institute for Health and Care Research, Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London, United Kingdom, Institute of Health Informatics, University College London, London, United Kingdom, National Institute for Health and Care Research, Biomedical Research Centre, University College London Hospitals, National Institute for Health Foundation Trust, London, United Kingdom","Background: Patients with chronic respiratory diseases and those in the postdischarge period following hospitalization because of COVID-19 are particularly vulnerable, and little is known about the changes in their symptoms and physiological parameters. Continuous remote monitoring of physiological parameters and symptom changes offers the potential for timely intervention, improved patient outcomes, and reduced health care costs. Objective: This study investigated whether a real-time multimodal program using commercially available wearable technology, home-based Bluetooth-enabled spirometers, finger pulse oximeters, and smartphone apps is feasible and acceptable for patients with chronic respiratory diseases, as well as the value of low-burden, long-term passive data collection. Methods: In a 3-arm prospective observational cohort feasibility study, we recruited 60 patients from the Royal Free Hospital and University College Hospital. These patients had been diagnosed with interstitial lung disease, chronic obstructive pulmonary disease, or post–COVID-19 condition (n=20 per group) and were followed for 180 days. This study used a comprehensive remote monitoring system designed to provide real-time and relevant data for both patients and clinicians. Data were collected using REDCap (Research Electronic Data Capture; Vanderbilt University) periodic surveys, Remote Assessment of Disease and Relapses–base active app questionnaires, wearables, finger pulse oximeters, smartphone apps, and Bluetooth home-based spirometry. The feasibility of remote monitoring was measured through adherence to the protocol, engagement during the follow-up period, retention rate, acceptability, and data integrity. Results: Lowest-burden passive data collection methods, via wearables, demonstrated superior adherence, engagement, and retention compared with active data collection methods, with an average wearable use of 18.66 (SD 4.69) hours daily (77.8% of the day), 123.91 (SD 33.73) hours weekly (72.6% of the week), and 463.82 (SD 156.70) hours monthly (64.4% of the month). Highest-burden spirometry tasks and high-burden active app tasks had the lowest adherence, engagement, and retention, followed by low-burden questionnaires. Spirometry and active questionnaires had the lowest retention at 0.5 survival probability, indicating that they were the most burdensome. Adherence to and quality of home spirometry were analyzed; of the 7200 sessions requested, 4248 (59%) were performed. Of these, 90.3% (3836/4248) were of acceptable quality according to American Thoracic Society grading. Inclusion of protocol holidays improved retention measures. The technologies used were generally well received. Conclusions: Our findings provide evidence supporting the feasibility and acceptability of remote monitoring for capturing both subjective and objective data from various sources for respiratory diseases. The high engagement level observed with passively collected data suggests the potential of wearables for long-term, user-friendly remote monitoring in respiratory disease management. The unique piloting of certain features such as protocol holidays, alert notifications for missing data, and flexible support from the study team provides a reference for future studies in this field. © Malik A Althobiani, Yatharth Ranjan, Joseph Jacob, Michele Orini, Richard James Butler Dobson, Joanna C Porter, John R Hurst, Amos A Folarin.","acceptability; applications; apps; artificial intelligence; attrition; chronic; chronic obstructive pulmonary disease; cohort; community based; COPD; COVID-19; data collection; dropout; engagement; feasibility; home based; home health care; ILD; interstitial lung disease; lung; lungs; machine learning; mHealth; mobile health; monitoring; observational; oximetry; passive data collection; pulmonary; remote monitoring; respiratory; respiratory diseases; retention; SARS-CoV-2; self-management; spirometry; usability; wearables","","","","","","University College London Hospitals Biomedical Research Centre, UCLH BRC; Economic and Social Research Council, ESRC; University College Hospital; European Society of Cardiology, ESC; King's College London, KCL; European Federation of Pharmaceutical Industries and Associations, EFPIA; Department of Health National Institute for Health and Care Research Biomedical Research Centres; Institute of Health Informatics, University College London; Saudi Arabian Cultural Bureau, SACB; UK Medical Research Council, Engineering and Physical Sciences Research Council; Public Health Agency, PHA; Royal Free Hospital; Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates; British Heart Foundation, BHF; National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre; UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare; University College London, UCL; Health and Social Care Research and Development Division, HCS R&D; Innovative Medicines Initiative, IMI; King Abdulaziz University, KAU; NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust; Horizon 2020; King's College Hospital NHS Foundation Trust; Wellcome Trust, WT, (209553/Z/17/Z, 209553); Wellcome Trust, WT; Horizon 2020 Framework Programme, H2020, (116074); Horizon 2020 Framework Programme, H2020; National Institute for Health and Care Research, NIHR, (IS BRC-1215-20018); National Institute for Health and Care Research, NIHR","Funding text 1: The authors are deeply grateful to all the patients who participated in the Remote Assessment of Lung Disease and Impact on Physical and Mental Health study. They would also like to extend their deepest thanks to the study staff at the University College Hospital as well as the Royal Free Hospital. The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the National Institute for Health and Care Research (NIHR), or the Department of Health and Social Care. This study was supported by (1) the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u2019s College London, London, United Kingdom (NIHR grant IS BRC-1215-20018); (2) Health Data Research United Kingdom, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart consortium, funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 116074, which receives support from the European Union Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA) and is chaired by Diederick E Grobbee and Stefan D Anker partnering with 20 academic and industry partners and European Society of Cardiolgoy (ESC); (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare; (6) the NIHR Applied Research Collaboration South London at King\u2019s College Hospital NHS Foundation Trust; (7) the Institute of Health Informatics, University College London; and (8) King Abdul Aziz University, Saudi Arabian Cultural Bureau. JJ was funded by the Wellcome Trust (209553/Z/17/Z) and the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, for research unrelated to this study. This research was funded in whole or in part by the Wellcome Trust [209553/Z/17/Z]. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.; Funding text 2: This work was undertaken at University College London Hospitals and University College London, who received a proportion of funding from the Department of Health National Institute for Health and Care Research Biomedical Research Centres funding scheme. This research was funded in whole or in part by the Wellcome Trust [209553/Z/17/Z]. For the purpose of open access, author JJ has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.; Funding text 3: This research was funded in whole or in part by the Wellcome Trust [209553/Z/17/Z]. For the purpose of open access, the author has applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.; Funding text 4: The authors are deeply grateful to all the patients who participated in the Remote Assessment of Lung Disease and Impact on Physical and Mental Health study. They would also like to extend their deepest thanks to the study staff at the University College Hospital as well as the Royal Free Hospital. The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the National Institute for Health and Care Research (NIHR), or the Department of Health and Social Care. This study was supported by (1) the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King\u2019s College London, London, United Kingdom (NIHR grant IS BRC-1215-20018); (2) Health Data Research United Kingdom, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) the BigData@Heart consortium, funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 116074, which receives support from the European Union Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations (EFPIA) and is chaired by Diederick E Grobbee and Stefan D Anker partnering with 20 academic and industry partners and European Society of Cardiolgoy (ESC); (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare; (6) the NIHR Applied Research Collaboration South London at King\u2019s College Hospital NHS Foundation Trust; (7) the Institute of Health Informatics, University College London; and (8) King Abdul Aziz University, Saudi Arabian Cultural Bureau. JJ was funded by the Wellcome Trust (209553/Z/17/Z) and the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, for research unrelated to this study.","Global strategy for prevention, diagnosis and management of COPD: 2023 report, Global Initiative for Chronic Obstructive Lung Disease, (2023); Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H., Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer, J Med Internet Res, 19, 5, (2017); Schussler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, Et al., A longitudinal big data approach for precision health, Nat Med, 25, 5, pp. 792-804, (2019); Global strategy on digital health 2020-2025, (2021); Sun Y, Lo FP, Lo B., Security and privacy for the internet of medical things enabled healthcare systems: a survey, IEEE Access, 7, pp. 183339-183355, (2019); 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Folarin; National Institute for Health and Care Research, Biomedical Research Centre, University College London Hospitals, National Institute for Health Foundation Trust, London, 16 De Crespigny Park, SE5 8AB, United Kingdom; email: amos.folarin@kcl.ac.uk","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85180076659"
"Lopez K.; Li H.; Lipkin-Moore Z.; Kay S.; Rajeevan H.; Davis J.L.; Wilson F.P.; Rochester C.L.; Gomez J.L.","Lopez, Kevin (57196613691); Li, Huan (58762498700); Lipkin-Moore, Zachary (36094254100); Kay, Shannon (58568866200); Rajeevan, Haseena (6504270752); Davis, J. Lucian (35247537400); Wilson, F. Perry (55113959300); Rochester, Carolyn L. (7006104636); Gomez, Jose L. (56482778500)","57196613691; 58762498700; 36094254100; 58568866200; 6504270752; 35247537400; 55113959300; 7006104636; 56482778500","Deep learning prediction of hospital readmissions for asthma and COPD","2023","Respiratory Research","24","1","311","","","","5","10.1186/s12931-023-02628-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179740778&doi=10.1186%2fs12931-023-02628-7&partnerID=40&md5=17eeb6ede724c82d4861770a6a997659","Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States; Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, 06520, CT, United States; Cooley Dickinson Hospital, Northampton, 01060, MA, United States; Biomedical Informatics and Data Science, Yale University, New Haven, 06520, CT, United States; Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, 06520, CT, United States; Clinical and Translational Research Accelerator, Department of Medicine, Yale University, New Haven, 06520, CT, United States; VA Connecticut Healthcare System, West Haven, 06516, CT, United States","Lopez K., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, 06520, CT, United States; Li H., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, 06520, CT, United States; Lipkin-Moore Z., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Cooley Dickinson Hospital, Northampton, 01060, MA, United States; Kay S., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, 06520, CT, United States; Rajeevan H., Biomedical Informatics and Data Science, Yale University, New Haven, 06520, CT, United States; Davis J.L., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, 06520, CT, United States; Wilson F.P., Clinical and Translational Research Accelerator, Department of Medicine, Yale University, New Haven, 06520, CT, United States; Rochester C.L., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, VA Connecticut Healthcare System, West Haven, 06516, CT, United States; Gomez J.L., Pulmonary, Critical Care and Sleep Medicine Section, Yale University, 300 Cedar Street, New Haven, 06520-8057, CT, United States, Center for Precision Pulmonary Medicine (P2MED), Yale University, New Haven, 06520, CT, United States","Question: Severe asthma and COPD exacerbations requiring hospitalization are linked to increased disease morbidity and healthcare costs. We sought to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and evaluate the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data. Study design and methods: Observational study between September 30, 2012, and December 31, 2017, of patients hospitalized with asthma and COPD exacerbations. Results: This study included 5,794 patients, 1,893 with asthma and 3,901 with COPD. Patients with asthma were predominantly female (n = 1288 [68%]), 35% were Black (n = 669), and 25% (n = 479) were Hispanic. Black (44 vs. 33%, p = 0.01) and Hispanic patients (30 vs. 24%, p = 0.02) were more likely to be readmitted for asthma. Similarly, patients with COPD readmissions included a large percentage of Blacks (18 vs. 10%, p < 0.01) and Hispanics (8 vs. 5%, p < 0.01). To identify patients at high risk of readmission index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. We found that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset. Interpretation: Multilayer perceptron, a deep learning method, had the best performance in predicting asthma and COPD readmissions, demonstrating that EHR and deep learning integration can improve high-risk patient detection. © 2023, The Author(s).","","Asthma; Deep Learning; Female; Hospitalization; Humans; Male; Patient Readmission; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; antibiotic agent; beta adrenergic receptor stimulating agent; corticosteroid; leukotriene receptor blocking agent; long acting drug; muscarinic receptor blocking agent; salbutamol; theophylline; adult; Article; asthma; Black person; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; data integration; deep learning; disease exacerbation; electronic health record; female; high risk patient; Hispanic; hospital readmission; human; Influenza A virus; Influenza B virus; machine learning; major clinical study; male; Metapneumovirus; multilayer perceptron; nonhuman; observational study; Paramyxovirinae; prediction; Rhinovirus; sensitivity and specificity; asthma; chronic obstructive lung disease; hospital readmission; hospitalization; retrospective study","","salbutamol, 18559-94-9, 35763-26-9; theophylline, 58-55-9, 5967-84-0, 8055-07-0, 8061-56-1, 99007-19-9","","","National Institutes of Health, NIH; National Center for Advancing Translational Sciences, NCATS","R01 HL153604, and R03 HL154275 to JLG. P30 DK079310, R01 DK113191, and R01 HS027626 to FWP. This publication was made possible by CTSA Grant Number UL1 TR000142 from the National Center for Advancing Translational Science (NCATS), a component of the National Institutes of Health (NIH). National Heart, Lung, and Blood Institute, 2T32HL007778-26 to support ZLM and SK. National Heart, Lung, and Blood Institute, 21-004125 to support HR. Manuscript contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH. ","James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Et al., Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 392, pp. 1789-1858, (2018); Nurmagambetov T., Kuwahara R., Garbe P., The economic burden of asthma in the United States, 2008–2013, Ann Am Thorac Soc, 15, pp. 348-356, (2018); Ford E.S., Murphy L.B., Khavjou O., Giles W.H., Holt J.B., Croft J.B., Total and state-specific medical and absenteeism costs of COPD among adults aged ≥ 18 years in the United States for 2010 and projections through 2020, Chest, 147, pp. 31-45, (2015); Suruki R.Y., Daugherty J.B., Boudiaf N., Albers F.C., The frequency of asthma exacerbations and healthcare utilization in patients with asthma from the UK and USA, BMC Pulm Med, 17, (2017); Sadatsafavi M., Sin D.D., Zafari Z., Criner G., Connett J.E., Lazarus S., Et al., The association between rate and severity of exacerbations in chronic obstructive pulmonary disease: an application of a joint frailty-logistic model, Am J Epidemiol, 184, pp. 681-689, (2016); 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Gomez; Pulmonary, Critical Care and Sleep Medicine Section, Yale University, New Haven, 300 Cedar Street, 06520-8057, United States; email: jose.gomez-villalobos@yale.edu","","BioMed Central Ltd","","","","","","14659921","","RREEB","38093373","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85179740778"
"Han W.; Wang N.; Han M.; Liu X.; Sun T.; Xu J.","Han, Wenjie (57820259800); Wang, Na (57221635131); Han, Mengzhen (57820089900); Liu, Xiaolin (57219155563); Sun, Tao (35235673700); Xu, Junnan (37027588800)","57820259800; 57221635131; 57820089900; 57219155563; 35235673700; 37027588800","Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis","2023","Cancer Medicine","12","18","","19301","19319","18","5","10.1002/cam4.6503","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170557499&doi=10.1002%2fcam4.6503&partnerID=40&md5=b99428c1a85a94860cdc0ff0492739cb","Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China; Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China; Liaoning Kanghui Biotechnology Co., Ltd, Shenyang, China; Key Laboratory of Liaoning Breast Cancer Research, Shenyang, China; Department of Breast Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital, Shenyang, China","Han W., Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China; Wang N., Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China; Han M., Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China; Liu X., Liaoning Kanghui Biotechnology Co., Ltd, Shenyang, China; Sun T., Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Key Laboratory of Liaoning Breast Cancer Research, Shenyang, China, Department of Breast Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital, Shenyang, China; Xu J., Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Department of Pharmacology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China, Department of Breast Medicine, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital, Shenyang, China","Background: The relationship between commensal microbiota and lung cancer (LC) has been studied extensively. However, developing replicable microbiological markers for early LC diagnosis across multiple populations has remained challenging. Current studies are limited to a single region, single LC subtype, and small sample size. Therefore, we aimed to perform the first large-scale meta-analysis for identifying micro biomarkers for LC screening by integrating gut and respiratory samples from multiple studies and building a machine-learning classifier. Methods: In total, 712 gut and 393 respiratory samples were assessed via 16 s rRNA amplicon sequencing. After identifying the taxa of differential biomarkers, we established random forest models to distinguish between LC populations and normal controls. We validated the robustness and specificity of the model using external cohorts. Moreover, we also used the KEGG database for the predictive analysis of colony-related functions. Results: The α and β diversity indices indicated that LC patients' gut microbiota (GM) and lung microbiota (LM) differed significantly from those of the healthy population. Linear discriminant analysis (LDA) of effect size (LEfSe) helped us identify the top-ranked biomarkers, Enterococcus, Lactobacillus, and Escherichia, in two microbial niches. The area under the curve values of the diagnostic model for the two sites were 0.81 and 0.90, respectively. KEGG enrichment analysis also revealed significant differences in microbiota-associated functions between cancer-affected and healthy individuals that were primarily associated with metabolic disturbances. Conclusions: GM and LM profiles were significantly altered in LC patients, compared to healthy individuals. We identified the taxa of biomarkers at the two loci and constructed accurate diagnostic models. This study demonstrates the effectiveness of LC-specific microbiological markers in multiple populations and contributes to the early diagnosis and screening of LC. © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.","16 s rRNA; gut microbiota; lung cancer; lung microbiota; machine learning","Biomarkers; Databases, Factual; Gastrointestinal Microbiome; Humans; Lung Neoplasms; Microbiota; RNA 16S; biological marker; Actinobacteria; area under the curve; Article; asthma; Bacteroidetes; bile acid synthesis; biosynthesis; chronic hypersensitivity pneumonitis; chronic obstructive lung disease; Coprococcus; discriminant analysis; Enterococcus; Escherichia; Faecalibacterium; fibrosing alveolitis; Firmicutes; human; interstitial lung disease; intestine flora; KEGG; Lactobacillus; lipoic acid metabolism; lipopolysaccharide biosynthesis; lung cancer; lung microbiota; meta analysis; microbial diversity; Prevotella; Proteobacteria; random forest; RNA analysis; RNA sequencing; species composition; Streptococcus; systematic review; tetracycline biosynthesis; tuberculosis; factual database; genetics; lung tumor; microflora","","Biomarkers, ","","","Beijing Medical Award Foundation, (YXJL‐2020‐0941‐0752); Liaoning Cancer Hospital; Shenyang Breast Cancer Clinical Medical Research Center, (2020‐48‐3‐1); Wu Jieping Medical Foundation, WJMF, (320.6750.2020‐12‐21,320.6750.2020‐6‐30); National Natural Science Foundation of China, NSFC, (82373113); Fundamental Research Funds for the Central Universities, (202229, 202230); Liaoning Revitalization Talents Program, (XLYC1907160)","This work was supported by National Nature Science Foundation of China (82373113, XJ), Shenyang Breast Cancer Clinical Medical Research Center (2020‐48‐3‐1, ST), Liaoning Cancer Hospital Yangtse River Scholars Project (ST, XJ), LiaoNing Revitalization Talents Program (XLYC1907160, XJ), Beijing Medical Award Foundation (YXJL‐2020‐0941‐0752, ST), Wu Jieping Medical Foundation (320.6750.2020‐12‐21,320.6750.2020‐6‐30, ST) and the Fundamental Research Funds for the Central Universities (202229, ST; 202230, XJ). 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Abd-El-Raouf R., Ouf S.A., Gabr M.M., Zakaria M.M., El-Yasergy K.F., Ali-El-Dein B., Escherichia Coli Foster bladder cancer cell line progression via epithelial mesenchymal transition, stemness and metabolic reprogramming, Sci Rep, 10, 1, (2020); Wong L.M., Shende N., Li W.T., Et al., Comparative analysis of age- and gender-associated microbiome in lung adenocarcinoma and lung squamous cell carcinoma, Cancers (Basel), 12, 6, (2020); Jin Y., Jia Z., Cai Q., Sun Y., Liu Z., Escherichia coli infection activates the production of Ifn-Α and Ifn-Β via the Jak1/Stat1/2 signaling pathway in lung cells, Amino Acids, 53, 10, pp. 1609-1622, (2021); Liu Q.X., Zhou Y., Li X.M., Et al., Ammonia induce lung tissue injury in broilers by activating Nlrp3 inflammasome via Escherichia/shigella, Poult Sci, 99, 7, pp. 3402-3410, (2020); Baskaran V., Lawrence H., Lansbury L.E., Et al., Co-infection in critically ill patients with Covid-19: An observational cohort study from England, J Med Microbiol, 70, 4, (2021); 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Kundu S., Ai in medicine must Be explainable, Nat Med, 27, 8, (2021)","J. Xu; Department of Breast Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, No.44 Xiaoheyan Road, Dadong District, Liaoning, China; email: xjn002@126.com","","John Wiley and Sons Inc","","","","","","20457634","","","37676050","English","Cancer Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85170557499"
"Veeramani A.; Zhang A.S.; Blackburn A.Z.; Etzel C.M.; DiSilvestro K.J.; McDonald C.L.; Daniels A.H.","Veeramani, Ashwin (57220600728); Zhang, Andrew S (57220604965); Blackburn, Amy Z. (57220166903); Etzel, Christine M. (57201681990); DiSilvestro, Kevin J. (57190750276); McDonald, Christopher L. (57209799635); Daniels, Alan H. (15839114300)","57220600728; 57220604965; 57220166903; 57201681990; 57190750276; 57209799635; 15839114300","An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion","2023","Global Spine Journal","13","7","","1849","1855","6","6","10.1177/21925682211053593","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124873968&doi=10.1177%2f21925682211053593&partnerID=40&md5=1cefe9354c470da895105345387fdae4","Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States","Veeramani A., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; Zhang A.S., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; Blackburn A.Z., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; Etzel C.M., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; DiSilvestro K.J., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; McDonald C.L., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States; Daniels A.H., Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States","Study Design: Level III retrospective database study. Objectives: The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). Methods: The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier’s effectiveness in distinguishing cases. Results: In total, 54 502 patients met the study criteria. Of these patients,.51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between.52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. Conclusions: The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation. © The Author(s) 2022.","anterior cervical discectomy and fusion surgery; complications; unplanned intubation","alkaline phosphatase; aspartate aminotransferase; C reactive protein; creatinine; hemoglobin; adult; aged; algorithm; anesthesiologist; anterior cervical discectomy and fusion; area under the curve; Article; artificial intelligence; body mass; body weight loss; chronic kidney failure; chronic obstructive lung disease; classifier; decision tree; diabetes mellitus; dialysis; discectomy; disseminated cancer; female; heart failure; hematocrit; human; hypertension; intubation; kidney failure; learning algorithm; length of stay; leukocyte count; machine learning; major clinical study; male; obesity; operation duration; platelet count; prediction; preoperative evaluation; receiver operating characteristic; retrospective study; risk factor; sensitivity and specificity; urea nitrogen blood level","","alkaline phosphatase, 9001-78-9; aspartate aminotransferase, 9000-97-9; C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; hemoglobin, 9008-02-0","","","","","Nandyala S.V., Marquez-Lara A., Park D.K., Et al., Incidence, risk factors, and outcomes of postoperative airway management after cervical spine surgery, Spine, 39, 9, pp. E557-E563, (2014); Kim M., Rhim S.C., Roh S.W., Jeon S.R., Analysis of the risk factors associated with prolonged intubation or reintubation after anterior cervical spine surgery, J Kor Med Sci, 33, 17, (2018); Heyer J.H., Cao N., Amdur R.L., Rao R.R., Postoperative complications following orthopedic spine surgery: Is there a difference between men and women?, Int J Spine Surg, 13, 2, pp. 125-131, (2019); Kalagara S., Eltorai A.E.M., Durand W.M., DePasse J.M., Daniels A.H., Machine learning modeling for predicting hospital readmission following lumbar laminectomy, J Neurosurg Spine, 30, 3, pp. 344-352, (2019); Durand W.M., DePasse J.M., Daniels A.H., Predictive modeling for blood transfusion after adult spinal deformity surgery, Spine, 43, 15, pp. 1058-1066, (2018); Biron D.R., Sinha I., Kleiner J.E., Aluthge D.P., Goodman A.D., Sarkar I.N., Et al., A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty, J Am Acad Orthop Surg, 28, pp. e580-e585, (2019); Beretta L., Santaniello A., Nearest neighbor imputation algorithms: A critical evaluation, BMC Med Inf Decis Making, 16, (2016); Menze B.H., Kelm B.M., Masuch R., Et al., A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data, BMC Bioinf, 10, 1, (2009); Wongvibulsin S., Wu K.C., Zeger S.L., Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis, BMC Med Res Methodol, 20, 1, (2019); Touw W.G., Bayjanov J.R., Overmars L., Et al., Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?, Briefings Bioinf, 14, 3, pp. 315-326, (2013); Denisko D., Hoffman M.M., Classification and interaction in random forests, Proc Natl Acad Sci Unit States Am, 115, 8, pp. 1690-1692, (2018); Zhang Z., Zhao Y., Zhao Y., Canes A., Steinberg D., Lyashevska O., Predictive analytics with gradient boosting in clinical medicine, Ann Transl Med, 7, 7, (2019); Hosny A., Parmar C., Quackenbush J., Schwartz L.H., Aerts H.J.W.L., Artificial intelligence in radiology, Nat Rev Cancer, 18, 8, pp. 500-510, (2018); Sidey-Gibbons J.A.M., Sidey-Gibbons C.J., Machine learning in medicine: a practical introduction, BMC Med Res Methodol, 19, 1, (2019); Varoquaux G., Buitinck L., Louppe G., Grisel O., Pedregosa F., Mueller A., Scikit-learn, GetMobile: Mobile Comput Commun, 19, 1, pp. 29-33, (2015); Hajian-Tilaki K., Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation, Casp J Int Med, 4, 2, pp. 627-635, (2013); Obuchowski N.A., Bullen J.A., Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine, Phys Med Biol, 63, 7, (2018); Uddin S., Khan A., Hossain M.E., Moni M.A., Comparing different supervised machine learning algorithms for disease prediction, BMC Med Inf Decis Making, 19, 1, (2019); Rufibach K., Use of Brier score to assess binary predictions, J clin Epidemiol, 63, 8, pp. 938-939, (2010); Bilimoria K.Y., Liu Y., Paruch J.L., Zhou L., Kmiecik T.E., Ko C.Y., Et al., Development and evaluation of the universal ACS NSQIP surgical risk calculator: A decision aid and informed consent tool for patients and surgeons, J Am Coll Surg, 217, 5, pp. 833-842, (2013); Milgrom D.P., Njoku V.C., Fecher A.M., Kilbane E.M., Pitt H.A., Unplanned intubation: When and why does this deadly complication occur?, Surgery, 154, 2, pp. 376-383, (2013); Davenport T., Kalakota R., The potential for artificial intelligence in healthcare, Fut Healthcare J, 6, 2, pp. 94-98, (2019); Galbusera F., Casaroli G., Bassani T., Artificial intelligence and machine learning in spine research, JOR Spine, 2, 1, (2019); Malik A.T., Khan S.N., Predictive modeling in spine surgery, Ann Transl Med, 7, S5, (2019); Hsieh M.H., Hsieh M.J., Chen C.-M., Hsieh C.-C., Chao C.-M., Lai C.-C., Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units, Sci Rep, 8, 1, pp. 1-7, (2018); Kalagara S., Eltorai A.E.M., Durand W.M., DePasse J.M., Daniels A.H., Machine learning modeling for predicting hospital readmission following lumbar laminectomy, J Neurosurg Spine, 30, 3, pp. 344-352, (2019); Kim M., Rhim S.C., Roh S.W., Jeon S.R., Analysis of the Risk Factors Associated with Prolonged Intubation or Reintubation after Anterior Cervical Spine Surgery, J Korean Med Sci, 33, 17, (2018); Wilson L.A., Zubizarreta N., Bekeris J., Et al., Risk factors for reintubation after anterior cervical discectomy and fusion surgery: evaluation of three observational data sets, Can J Anest, 67, 1, pp. 42-56, (2019); Ismael H.N., Cox S., Cooper A., Narula N., Aloia T., The morbidity and mortality of hepaticojejunostomies for complex bile duct injuries: a multi-institutional analysis of risk factors and outcomes using NSQIP, HPB, 19, 4, pp. 352-358, (2017)","A.H. Daniels; Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, United States; email: alandanielsmd@gmail.com","","SAGE Publications Ltd","","","","","","21925682","","","","English","Global Spine J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85124873968"
"Li D.; Abhadiomhen S.E.; Zhou D.; Shen X.-J.; Shi L.; Cui Y.","Li, Dejing (58838102100); Abhadiomhen, Stanley Ebhohimhen (57222241651); Zhou, Dongmei (58278432400); Shen, Xiang-Jun (55450902800); Shi, Lei (57223304581); Cui, Yubao (35214280600)","58838102100; 57222241651; 58278432400; 55450902800; 57223304581; 35214280600","Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers","2024","Journal of Translational Medicine","22","1","100","","","","5","10.1186/s12967-024-04866-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182991100&doi=10.1186%2fs12967-024-04866-9&partnerID=40&md5=aedae6a1d5fff24fc9549160d81f5465","Department of Respiratory, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China; School of Computer Science and Communication Engineering, JiangSu University, JiangSu, Zhenjiang, 212013, China; Department of Computer Science, University of Nigeria, Nsukka, Nigeria; Clinical Research Center, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China; Department of Clinical Laboratory, Shuguang Hospital Affiliated to Shanghai University of Chinese Traditional Medicine, Shanghai, 201203, China","Li D., Department of Respiratory, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China; Abhadiomhen S.E., School of Computer Science and Communication Engineering, JiangSu University, JiangSu, Zhenjiang, 212013, China, Department of Computer Science, University of Nigeria, Nsukka, Nigeria; Zhou D., Clinical Research Center, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China; Shen X.-J., School of Computer Science and Communication Engineering, JiangSu University, JiangSu, Zhenjiang, 212013, China; Shi L., Department of Clinical Laboratory, Shuguang Hospital Affiliated to Shanghai University of Chinese Traditional Medicine, Shanghai, 201203, China; Cui Y., Clinical Research Center, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China","Background: Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy. Methods: The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC’s performance was compared with five state-of-the-art predictive models. Results: Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained p values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance. Conclusion: As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management. © 2024, The Author(s).","Affinity graph; Asthma; Asthma prediction; Feature selection","Asthma; Biomarkers; Humans; Knowledge; Machine Learning; biological marker; biological marker; adult; affinity graph enhanced classifier; aged; Article; asthma; classification; classifier; clinical significance; controlled study; correlation analysis; diagnostic accuracy; diagnostic test accuracy study; feature selection; female; human; human cell; major clinical study; male; prediction; predictive model; statistically significant result; support vector machine; knowledge; machine learning","","Biomarkers, ","","","Top Talents Project of the Wuxi Taihu Lake Talent Plan, (2020THRC-GD-7); Wuxi science and Technology Bureau (R & D of medical and health technology, (Y20212006); Project on Maternal and Child Health Talents of Jiangsu Province; Project 333 of Jiangsu Province, (ZUZHIBU 202221001); Project 333 of Jiangsu Province","His study was supported by the Top Talents Project of the Wuxi Taihu Lake Talent Plan (2020THRC-GD-7), the 333 project of Jiangsu Province in 2022 (ZUZHIBU 202221001), and “Light of Taihu Lake” scientific and technological breakthrough from Wuxi science and Technology Bureau (R & D of medical and health technology, Y20212006). 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Cui; Clinical Research Center, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, 214023, China; email: ybcui1975@hotmail.com; L. Shi; Department of Clinical Laboratory, Shuguang Hospital Affiliated to Shanghai University of Chinese Traditional Medicine, Shanghai, 201203, China; email: 13818226306@139.com","","BioMed Central Ltd","","","","","","14795876","","","38268004","English","J. Transl. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85182991100"
"Kang N.; Lee K.H.; Byun S.; Lee J.-Y.; Choi D.-C.; Lee B.-J.","Kang, Noeul (57204092973); Lee, Kyung Hyun (57374288700); Byun, Sangwon (7004818463); Lee, Jin-Young (58860034500); Choi, Dong-Chull (8053786400); Lee, Byung-Jae (27171988300)","57204092973; 57374288700; 7004818463; 58860034500; 8053786400; 27171988300","Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests","2024","Allergy, Asthma and Immunology Research","16","1","","42","54","12","5","10.4168/aair.2024.16.1.42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185599729&doi=10.4168%2faair.2024.16.1.42&partnerID=40&md5=1345bdd7616941133495a8748f97c74d","Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Electronics Engineering, Incheon National University, Incheon, South Korea; Health Promotion Center, Samsung Medical Center, Seoul, South Korea","Kang N., Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Lee K.H., Department of Electronics Engineering, Incheon National University, Incheon, South Korea; Byun S., Department of Electronics Engineering, Incheon National University, Incheon, South Korea; Lee J.-Y., Health Promotion Center, Samsung Medical Center, Seoul, South Korea; Choi D.-C., Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Lee B.-J., Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea","Purpose: The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma. Methods: Data from 1,501 patients with asthma symptoms who underwent MCT bet-ween 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV1]; PC20 = 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV1, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF25%-75%) values obtained during MCT. Results: In total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV1, FVC, and FEF25%-75% were included as inputs. Conclusions: Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC20 = 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.  Copyright © 2024 The Korean Academy of Asthma, Allergy and Clinical Immunology · The Korean Academy of Pediatric Allergy and Respiratory Disease.","Artificial intelligence; asthma; asthma prediction; AUROC; bronchial provocation test; machine learning; methacholine challenge test","methacholine; adult; Article; artificial intelligence; artificial neural network; asthma; controlled study; diagnosis; diagnostic test accuracy study; female; forced expiratory flow; forced expiratory volume; forced vital capacity; human; inhalation test; Korean (people); machine learning; major clinical study; male; patient referral; prediction; provocation test; random forest; respiratory tract disease; retrospective study; support vector machine; vital capacity","","methacholine, 55-92-5","","","National IT Industry Promotion Agency, NIPA","We thank to the National IT Industry Promotion Agency (NIPA) for the high-performance computing support program in 2022.","Crapo RO, Casaburi R, Coates AL, Enright PL, Hankinson JL, Irvin CG, Et al., Guidelines for methacholine and exercise challenge testing-1999. 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Lee; Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 81 Irwon-ro, Gangnam-gu, 06351, South Korea; email: leebj@skku.edu","","Korean Academy of Asthma, Allergy and Clinical Immunology","","","","","","20927355","","","","English","Allergy Asthma Immunol. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85185599729"
"Rankovic N.; Rankovic D.; Lukic I.; Savic N.; Jovanovic V.","Rankovic, Nevena (57204065071); Rankovic, Dragica (36182827900); Lukic, Igor (57403480900); Savic, Nikola (57403139300); Jovanovic, Verica (56566176800)","57204065071; 36182827900; 57403480900; 57403139300; 56566176800","Unveiling the Comorbidities of Chronic Diseases in Serbia Using ML Algorithms and Kohonen Self-Organizing Maps for Personalized Healthcare Frameworks","2023","Journal of Personalized Medicine","13","7","1032","","","","5","10.3390/jpm13071032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166349311&doi=10.3390%2fjpm13071032&partnerID=40&md5=a3149a303a6a2f65390e8de66646302b","Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, 5037 AB, Netherlands; Department of Mathematics, Informatics and Statistics, Faculty of Applied Sciences, Union University “Nikola Tesla”, Nis, 18 000, Serbia; Department of Preventive Medicine, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, 34 000, Serbia; Department of Healthcare, Faculty of Business Valjevo, Singidunum University, Valjevo, 14 000, Serbia; Institute of the Public Health “Dr. Milan Jovanovic Batut”, Belgrade, 11 000, Serbia","Rankovic N., Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, 5037 AB, Netherlands; Rankovic D., Department of Mathematics, Informatics and Statistics, Faculty of Applied Sciences, Union University “Nikola Tesla”, Nis, 18 000, Serbia; Lukic I., Department of Preventive Medicine, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, 34 000, Serbia; Savic N., Department of Healthcare, Faculty of Business Valjevo, Singidunum University, Valjevo, 14 000, Serbia; Jovanovic V., Institute of the Public Health “Dr. Milan Jovanovic Batut”, Belgrade, 11 000, Serbia","In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health “Dr. Milan Jovanovic Batut” in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia. © 2023 by the authors.","chronic non–communicable diseases; comorbidities; Kohonen SOM neural network; ML algorithms; prevalence","cholesterol; long untranslated RNA; adult; aged; algorithm; allergy; Article; artificial neural network; asthma; atherosclerosis; blood pressure monitoring; cerebrovascular accident; cervical spine; chronic disease; chronic kidney failure; chronic lung disease; computer assisted tomography; cross-sectional study; decision tree; depression; diabetes mellitus; entropy; female; health care; heart infarction; heart rate; human; hyperlipidemia; hypertension; ischemic heart disease; kidney disease; liver cirrhosis; machine learning; major clinical study; male; malignant neoplasm; observational study; osteoarthritis; principal component analysis; public health; quality of life; random forest; risk factor; self organizing map; Serbia; spine disease; support vector machine; urinary tract disease","","cholesterol, 57-88-5","","","Department of Cognitive Science; Universiteit van Tilburg, UvT","This research was funded by Department of Cognitive Science and AI, School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands.","Shehab M., Abualigah L., Shambour Q., Abu-Hashem M.A., Shambour M.K.Y., Alsalibi A.I., Gandomi A.H., Machine learning in medical applications: A review of state-of-the art methods, Comput. 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"Nuutinen M.; Haukka J.; Virkkula P.; Torkki P.; Toppila-Salmi S.","Nuutinen, Mikko (34873178300); Haukka, Jari (35268443600); Virkkula, Paula (6602419086); Torkki, Paulus (8550360400); Toppila-Salmi, Sanna (14631330000)","34873178300; 35268443600; 6602419086; 8550360400; 14631330000","Using machine learning for the personalised prediction of revision endoscopic sinus surgery","2022","PLoS ONE","17","4 April","e0267146","","","","6","10.1371/journal.pone.0267146","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129148919&doi=10.1371%2fjournal.pone.0267146&partnerID=40&md5=524cc31207d8d8692198053455e863a1","Haartman Institute, University of Helsinki, Helsinki, Finland; Nordic Healthcare Group, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland","Nuutinen M., Haartman Institute, University of Helsinki, Helsinki, Finland, Nordic Healthcare Group, Helsinki, Finland; Haukka J., Department of Public Health, University of Helsinki, Helsinki, Finland; Virkkula P., Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki, Helsinki, Finland; Torkki P., Department of Public Health, University of Helsinki, Helsinki, Finland; Toppila-Salmi S., Haartman Institute, University of Helsinki, Helsinki, Finland, Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland","Background Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. Methods We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. Results The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. Conclusions Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it. © 2022 Nuutinen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited","","Chronic Disease; Humans; Machine Learning; Nasal Polyps; Reoperation; Rhinitis; Sinusitis; adult; allergy; American Society of Anesthesiology score; area under the curve; area under the precision recall curve; Article; asthma; chronic rhinosinusitis; chronic rhinosinusitis with nasal polyps; controlled study; diabetes mellitus; diagnostic test accuracy study; endoscopic sinus surgery; female; follow up; gastroesophageal reflux; gradient boosting; human; immune deficiency; logistic regression analysis; machine learning; male; malignant neoplasm; memory disorder; mental disease; middle aged; mouth breathing; musculoskeletal disease; non steroidal antiinflammatory drug  exacerbated respiratory disease; number of  revision endoscopic sinus surgery; number of visits; obesity; personalized prediction; physical parameters; prediction; random forest; receiver operating characteristic; respiratory tract disease; revision endoscopic sinus surgery; sensitivity and specificity; sequential forward selection method; sleep disordered breathing; time of  revision endoscopic sinus surgery; chronic disease; machine learning; nose polyp; reoperation; rhinitis; sinusitis","","","","","Finnish Anti-Tuberculosis Association Foundation; Laina Kivi Foundation; Paulo Foundation, State funding for university-level health research, (TYH2019322); Tampereen Tuberkuloosisäätiö","Yes. This work was supported in part by research grants from Paulo Foundation, State funding for university-level health research (TYH2019322), The Finnish Anti-Tuberculosis Association Foundation, The Tampere Tuberculosis Foundation, and The V?in? and Laina Kivi Foundation. All in Finland. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Fokkens W, Lund V, Hopkins C, Hellings P, Kern R, Reitsma S, Et al., European Position Paper on Rhinosinusitis and Nasal Polyps 2020, Rhinology journal, 58, pp. 1-464, (2020); Liao B, Liu JX, Li ZY, Zhen Z, Cao PP, Yao Y, Et al., Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes, Allergy, 73, 7, pp. 1459-1469, (2018); Wei B, Liu F, Zhang J, Liu Y, Du J, Liu S, Et al., Multivariate analysis of inflammatory endotypes in recurrent nasal polyposis in a Chinese population, Rhinology, 56, 3, pp. 216-226, (2018); Kowalski ML, Agache I, Bavbek S, Bakirtas A, Blanca M, Bochenek G, Et al., Diagnosis and management of NSAID-Exacerbated Respiratory Disease (N-ERD)-a EAACI position paper, Allergy, 74, 1, pp. 28-39, (2019); Lyly A, Laulajainen-Hongisto A, Turpeinen H, Vento SI, Myller J, Numminen J, Et al., Factors affecting upper airway control of NSAID-exacerbated respiratory disease: A real-world study of 167 patients, Immunity, Inflammation and Disease, 9, 1, pp. 80-89, (2021); 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Toppila-Salmi; Haartman Institute, University of Helsinki, Helsinki, Finland; email: sanna.salmi@helsinki.fi","","Public Library of Science","","","","","","19326203","","POLNC","35486626","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85129148919"
"Gong Y.; Zhang Q.; Ng B.H.P.; Li W.","Gong, Yanbin (57804343100); Zhang, Qian (56670485000); Ng, Bobby H.P. (14119971300); Li, Wei (58606220600)","57804343100; 56670485000; 14119971300; 58606220600","BreathMentor: Acoustic-based Diaphragmatic Breathing Monitor System","2022","Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies","6","2","53","","","","7","10.1145/3534595","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134211907&doi=10.1145%2f3534595&partnerID=40&md5=83f6beff1ecc35fa3de631094aa3fc5b","Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hong Kong, Hong Kong; Department of Infectious Diseases, Wuhan Union Hospital, China","Gong Y., Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Zhang Q., Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong; Ng B.H.P., Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hong Kong, Hong Kong; Li W., Department of Infectious Diseases, Wuhan Union Hospital, China","Chronic Obstructive Pulmonary Disease (COPD) is currently the third major cause of death-more than three million people died from it in 2019. Given that COPD cannot be cured currently, immediate treatment is crucial. Pulmonary rehabilitation (PR) is widely used to prevent COPD deterioration. Patients are advised to undergo a PR at home to get sufficient treatment in time. Monitoring patients during home rehabilitation can help not only improve patient adherence but also collect data on patients' recovery progress from rehabilitation team's perspective. However, how to track if proper diaphragmatic breathing, an essential part of PR, is taken by a patient has remained challenging. The current monitoring solution still appears obtrusive as it requires the patient to wear two uncomfortable respiration belts. Alternatively, therapists need to monitor the patients remotely through several cameras, which consumes substantial medical resources and causes privacy issues. In this work, we present BreathMentor, a smart speaker based diaphragmatic breathing monitoring system targeting early COPD stages I and II. BreathMentor is both unobtrusive and preventive of privacy invasion, so that it can solve the existing pain points and suits home care. BreathMentor converts the smart speaker into an active sonar system that continuously perceives and analyses the changes in surroundings, thereby detecting the user's respiration rate, deriving the breathing phases, and classifying whether the patient is practising diaphragmatic breathing. BreathMentor formulates breathing monitoring as a Temporal Action Localization task that enables us to detect each breathing cycle and classify its type. Our key insight is that breathing periodicity and phase duration are natural properties to localize and segment the breaths. Our key design to classify the breathing type is a hybrid architecture encompassing signal processing and deep learning techniques. Further, we evaluate the system performance on fifteen healthy subjects who would not breathe abnormally during diaphragmatic breathing under the supervision of therapists. In conclusion, BreathMentor can achieve robust performance for monitoring diaphragmatic breathing in different environments, as demonstrated in the results. The median error rate of respiration detection is 0.2 BPM, and the I/E ratio derivation is accurate with a mean absolute percentage error of less than 5.9% for breathing phase detection, together with a recall of 98.2%, and a precision of 95.5% in detecting diaphragmatic breathing. Above results indicate that BreathMentor can be used to track the patients' adherence and help monitor their breathing capacity. © 2022 ACM.","acoustic sensing; breathing classification; breathing training; respiration diseases; smart speaker","Classification (of information); Deep learning; Patient monitoring; Patient treatment; Pulmonary diseases; Signal processing; Speech recognition; Acoustic sensing; Breathing classification; Breathing training; Causes of death; Chronic obstructive pulmonary disease; Monitor system; Pulmonary rehabilitations; Rehabilitation at homes; Respiration disease; Smart speaker; Deterioration","","","","","RGC, (16203719, 16204820, CERG 16204418, MOST18FYT08, R8015)","The authors would like to thank the anonymous editors and reviewers for the valuable comments and helpful suggestions. This work is partially supported by the RGC under Contract CERG 16204418, 16203719, 16204820, R8015 and MOST18FYT08.","2021. 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Respeaker 6-mic Circular Array Kit for Raspberry Pi","Q. Zhang; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong; email: qianzh@cse.ust.hk","","Association for Computing Machinery","","","","","","24749567","","","","English","Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","Article","Final","","Scopus","2-s2.0-85134211907"
"Morena D.; Lumbreras S.; Rodríguez J.M.; Campos C.; Castillo M.; Benavent M.; Izquierdo J.L.","Morena, Diego (57215197044); Lumbreras, Sara (51864156000); Rodríguez, José Miguel (58881557500); Campos, Carolina (57612415400); Castillo, María (57221368902); Benavent, María (58458005900); Izquierdo, José Luis (7102685483)","57215197044; 51864156000; 58881557500; 57612415400; 57221368902; 58458005900; 7102685483","Chronic Respiratory Diseases as a Risk Factor for Herpes Zoster Infection","2023","Archivos de Bronconeumologia","59","12","","797","804","7","7","10.1016/j.arbres.2023.08.010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171530291&doi=10.1016%2fj.arbres.2023.08.010&partnerID=40&md5=56289a44d7fca265cc4d674ac4e7df47","Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain; Programa de Doctorado en Ciencias de la Salud, Universidad de Alcalá, Madrid, Spain; Universidad Pontificia Comillas – IIT, Spain; Servicio de Neumología, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Madrid, Spain; SAVANA, Madrid, Spain; Departamento de Medicina y Especialidades Médicas, Universidad de Alcalá, Madrid, Spain","Morena D., Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain, Programa de Doctorado en Ciencias de la Salud, Universidad de Alcalá, Madrid, Spain; Lumbreras S., Universidad Pontificia Comillas – IIT, Spain; Rodríguez J.M., Servicio de Neumología, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Madrid, Spain; Campos C., Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain; Castillo M., Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain; Benavent M., SAVANA, Madrid, Spain; Izquierdo J.L., Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain, Departamento de Medicina y Especialidades Médicas, Universidad de Alcalá, Madrid, Spain","Introduction: Herpes zoster (HZ) is a condition that results from the reactivation of the varicella zoster virus (VZV). Several diseases have been reported to increase the risk of developing HZ and postherpetic neuralgia (PHN). The objective of this study is to analyze the prevalence and risk factors for HZ and PHN in the most frequent chronic respiratory diseases, which are chronic obstructive pulmonary disease (COPD), asthma, lung cancer and obstructive sleep apnea (OSA). Methods: We conducted an observational, retrospective, non-interventional study between January 2012 and December 2020 based on data from the Castilla-La Mancha Regional Health System in Spain. We used the Savana Manager 3.0 artificial intelligence-enabled system to collect information from electronic medical records. Results: 31 765 subjects presented a diagnosis of HZ. Mean age was 64.5 years (95%CI 64.3–64.7), and 58.2% were women. The prevalence of HZ showed an increasing trend in patients over the age of 50. A risk analysis adjusted for sex and comorbidities in COPD, asthma, lung cancer and OSA presented a higher risk of developing HZ in the first three (OR 1.16 [95%CI 1.13–1.19], 1.67 [1.63–1.71], 1.68 [1.60–1.76], respectively), which further increased in all three when associated with comorbidities. Regarding postherpetic neuralgia, an increased risk was only observed related to COPD and lung cancer (OR 1.24 [95%CI 1.23–1.25], 1.14 [1.13–1.16], respectively), further increasing when associated with comorbidities. Conclusions: In a standard clinical practice setting, the most prevalent respiratory diseases (asthma, COPD and lung cancer) are related to a higher risk of HZ and PHN. These data are fundamental to assess the potential impact of vaccination in this population. © 2023 SEPAR","Artificial intelligence; Asthma; Chronic obstructive pulmonary disease; Herpes zoster; Lung cancer; Obstructive sleep apnea; Postherpetic neuralgia","adult; aged; Article; artificial intelligence; asthma; chronic obstructive lung disease; chronic respiratory tract disease; clinical practice; comorbidity; controlled study; disease association; disease predisposition; electronic medical record; female; groups by age; herpes zoster; high risk patient; human; lung cancer; major clinical study; male; middle aged; nonhuman; observational study; obstructive sleep apnea; postherpetic neuralgia; prevalence; respiratory tract disease; retrospective study; risk assessment; risk factor; Spain; trend study; vaccination; very elderly; young adult","","","","","","","Gnann J.W., Whitley R.J., Clinical practice. 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New insights into the immunology of chronic obstructive pulmonary disease, Lancet, 378, pp. 1015-1026, (2011); Yang Y.-W., Chen Y.-H., Wang K.-H., Wang C.-Y., Lin H.-W., Risk of herpes zoster among patients with chronic obstructive pulmonary disease: a population-based study, CMAJ, 183, pp. E275-E280, (2011); Peng Y.-H., Fang H.-Y., Wu B.-R., Kao C.H., Chen H.J., Hsia T.C., Et al., Adult asthma is associated with an increased risk of herpes zoster: a population-based cohort study, J Asthma, 54, pp. 250-257, (2017); Chen S.J., Huang K.H., Tsai W.C., Lin C.L., Cheng Y.D., Wei C.C., Asthma status is an independent risk factor for herpes zoster in children: a population-based cohort study, Ann Med, 49, pp. 504-512, (2017); Kwon H.J., Bang D.W., Kim E.N., Wi C.I., Yawn B.P., Wollan P.C., Et al., Asthma as a risk factor for zoster in adults: a population-based case–control study, J Allergy Clin Immunol, 137, pp. 1406-1412, (2016); Ernst P., Dell'Aniello S., Mikaeloff Y., Suissa S., Risk of herpes zoster in patients prescribed inhaled corticosteroids: a cohort study, BMC Pulm Med, 11, (2011); Habel L.A., Ray G.T., Silverberg M.J., Horberg M.A., Yawn B.P., Castillo A.L., Et al., The epidemiology of herpes zoster in patients with newly diagnosed cancer, Cancer Epidemiol Biomarkers Prev, 22, pp. 82-90, (2013); Tseng H.F., Bruxvoort K., Ackerson B., Luo Y., Tanenbaum H., Tian Y., Et al., The epidemiology of Herpes Zoster in immunocompetent, unvaccinated adults C50 years old: incidence, complications, hospitalization, mortality, and recurrence, J Infect Dis, 222, pp. 798-806, (2020); Lopez-Belmonte J.L., Cisterna R., Gil de Miguel A., Guillmet C., Bianic F., Uhart M., The use of Zostavax in Spain: the economic case for vaccination of individuals aged 50 years and older, J Med Econ, 19, pp. 576-586, (2016); Izquierdo J.L., Morena D., Gonzalez Y., Paredero J.M., Perez B., Graziani D., Et al., Manejo clínico de la EPOC en situación de vida real. Análisis a partir de big data, Arch Bronconeumol, 57, pp. 94-100, (2020); Canales L., Menke S., Marchesseau S., D'Agostino A., Del Rio-Bermudez C., Taberna M., Et al., Assessing the performance of clinical natural language processing systems: development of an evaluation methodology, JMIR Med Inform, 9, pp. 204-292, (2021); Izquierdo J.L., Almonacid C., Gonzalez Y., Del Rio-Bermudez C., Ancochea J., Cardenas R., Et al., The impact of COVID-19 on patients with asthma: a Big data analysis, Eur Respir J, 57, (2021); Morena D., Fernandez J., Campos C., Castillo M., Lopez G., Benavent M., Et al., Clinical profile of patients with idiopathic pulmonary fibrosis in real life, J Clin Med, 12, (2023); Benson T., Principles of health interoperability HL7 and SNOMED, (2012); Fleming D.M., Cross K.W., Cobb W.A., Chapman R.S., Gender difference in the incidence of shingles, Epidemiol Infect, 132, pp. 1-5, (2004); Marra F., Parhar K., Huang B., Vadlamudi N., Risk factors for herpes zoster infection: a meta-analysis, Open Forum Infect Dis, 7, (2020); Pritchard A.L., White O.J., Burel J.G., Carroll M.L., Phipps S., Upham J.W., Asthma is associated with multiple alterations in anti-viral innate signalling pathways, PLoS ONE, 9, (2014); Munoz-Quiles C., Lopez-Lacort M., Diez-Domingo J., Risk and impact of herpes zoster among COPD patients: a population-based study, 2009–2014, BMC Infect Dis, 18, (2018); Safonova E., Yawn B.P., Welte T., Wang C., Risk factors for herpes zoster: should people with asthma or COPD be vaccinated?, Respir Res, 24, (2023); Qian J., Heywood A.E., Karki S., Banks E., Macartney K., Chantrill L., Et al., Risk of herpes zoster prior to and following cancer diagnosis and treatment: a population-based prospective cohort study, J Infect Dis, 220, pp. 3-11, (2019); Taoka M., Ochi N., Yamane H., Yamamoto T., Kawahara T., Uji E., Et al., Herpes zoster in lung cancer patients treated with PD-1/PD-L1 inhibitors, Transl Cancer Res, 11, pp. 456-462, (2022); Chung W.S., Lin H.H., Cheng N.C., The incidence and risk of herpes zoster in patients with sleep disorders: a population-based cohort study, Medicine (Baltimore), 95, (2016); Forbes H.J., Bhaskaran K., Thomas S.L., Smeeth L., Clayton T., Mansfield K., Et al., Quantification of risk factors for postherpetic neuralgia in herpes zoster patients: a cohort study, Neurology, 87, pp. 94-102, (2016); Munoz-Quiles C., Lopez-Lacort M., Orrico-Sanchez A., Diez-Domingo J., Impact of postherpetic neuralgia: a six year population-based analysis on people aged 50 years or older, J Infect, 77, pp. 131-136, (2018)","D. Morena; Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain; email: diegomorenavalles6@gmail.com","","Sociedad Espanola de Neumologia y Cirugia Toracica (SEPAR)","","","","","","03002896","","ARBRD","","English","Arch. Bronconeumol.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85171530291"
"Chausiaux O.E.; Keyser M.; Williams G.P.; Nieznański M.; Downer P.J.; Garnett R.E.; Berry R.; Husheer S.L.G.","Chausiaux, Oriane Elisabeth (13204823700); Keyser, Melanie (57223255971); Williams, Gareth Paul (57223265947); Nieznański, Michał (57223255869); Downer, Philip James (57223261985); Garnett, Rebecca Ellen (57572869500); Berry, Rhiannon (57573828900); Husheer, Shamus Louis Godfrey (57573923200)","13204823700; 57223255971; 57223265947; 57223255869; 57223261985; 57572869500; 57573828900; 57573923200","Heart failure decompensation alerts in a patient's home using an automated, AI-driven, point-of-care device","2022","BMJ Case Reports","15","4","e248682","","","","6","10.1136/bcr-2021-248682","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128087593&doi=10.1136%2fbcr-2021-248682&partnerID=40&md5=e233adc25705607a9f4b670f92c4b52c","Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Department of Primary Health Care Sciences, University of Oxford, Oxford, United Kingdom; Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom","Chausiaux O.E., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Keyser M., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Williams G.P., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Nieznański M., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Downer P.J., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; Garnett R.E., Department of Primary Health Care Sciences, University of Oxford, Oxford, United Kingdom; Berry R., Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Husheer S.L.G., Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom","Heart failure (HF) is a major challenge worldwide and needs continuous monitoring of patients even after hospital discharge. This case report summarises the data collected and experience gained from the first usage of an automated, point-of-care device (Heartfelt device) in a patient's home in the UK. The device monitors the onset of peripheral oedema and alerts clinicians if an increase in volume outside an expected normal range for the patient is detected. This may provide a reliable method of remotely and automatically monitoring HF patients in the home for those who do not reliably use weighing scales. The device successfully provided data for about 15 months and generated alerts in advance, which supported decisions for the patient's care. The rate of data acquisition was very high and consistent throughout this period. The patient was satisfied with the device and agreed that it helped in her decision to seek medical attention. © 2022 BMJ Publishing Group. All rights reserved.","Cardiovascular medicine; General practice / family medicine; Heart failure","Artificial Intelligence; Female; Heart Failure; Humans; Patient Discharge; Point-of-Care Systems; amiloride; diuretic agent; furosemide; metolazone; spironolactone; aged; anthropometric parameters; Article; artificial intelligence; ascites; asthma; case report; chronic obstructive lung disease; clinical article; convolutional neural network; data processing; diabetes mellitus; drug dose reduction; drug megadose; dyspnea; female; follow up; foot; general practitioner; heart failure; hospital admission; hospital discharge; human; hypertension; Internet; kidney failure; lung disease; maximum tolerated dose; medical record; outcome assessment; palliative therapy; peripheral edema; postmarketing surveillance; artificial intelligence; heart failure; point of care system","","amiloride, 2016-88-8, 2609-46-3; furosemide, 54-31-9; metolazone, 17560-51-9, 56436-31-8, 56436-32-9; spironolactone, 52-01-7","","","Heartfelt Technologies, UK","Contributors OEC drafted the manuscript and did some of the analysis. MK collected some of the data and contributed to the critical review of the manuscript. GPW did some of the analysis and contributed to the critical review of the manuscript. MN contributed to the critical review of the manuscript. RB contributed to the critical review of the manuscript. REG contributed to the critical review of the manuscript. PJD contributed to the data analysis and critical review of the manuscript. SH did most of the analysis and contributed to the critical review of the manuscript. Funding This study was funded by Heartfelt Technologies, UK. Competing interests None declared.","Ponikowski P., Anker S.D., AlHabib K.F., Et al., Heart failure: preventing disease and death worldwide, ESC Heart Fail, 1, pp. 4-25, (2014); Roth G.A., Mensah G.A., Johnson C.O., Et al., Global burden of cardiovascular diseases and risk factors, 1990-2019: update from the GBD 2019 study, J Am Coll Cardiol, 76, pp. 2982-3021, (2020); Bloom M.W., Greenberg B., Jaarsma T., Et al., Heart failure with reduced ejection fraction, Nat Rev Dis Primers, 3, (2017); Yancy C.W., Jessup M., Bozkurt B., Et al., 2013 ACCF/AHA guideline for the management of heart failure: Executive summary: a report of the American College of cardiology Foundation/American heart association Task force on practice guidelines, Circulation, 128, pp. 1810-1852, (2013); Chausiaux O., Williams G., Nieznaski M., Et al., Evaluation of the accuracy of a video and AI solution to measure lower leg and foot volume, Med Devices, 14, pp. 105-118, (2021); An everyday guide to living with heart failure, pp. 1-47; Lewin J., Ledwidge M., O'Loughlin C., Et al., Clinical deterioration in established heart failure: what is the value of BNP and weight gain in aiding diagnosis?, Eur J Heart Fail, 7, pp. 953-957, (2005); Fitzgerald A.A., Powers J.D., Ho P.M., Et al., Impact of medication nonadherence on hospitalizations and mortality in heart failure, J Card Fail, 17, pp. 664-669, (2011); McAlister F.A., Stewart S., Ferrua S., Et al., Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials, J Am Coll Cardiol, 44, pp. 810-819, (2004); Dunbar-Jacob J., Erlen J.A., Schlenk E.A., Et al., Adherence in chronic disease, Annu Rev Nurs Res, 18, pp. 48-90, (2000); Wali S., Demers C., Shah H., Et al., Evaluation of heart failure Apps to promote self-care: systematic APP search, JMIR Mhealth Uhealth, 7, (2019); Piette J.D., Interactive voice response systems in the diagnosis and management of chronic disease, Am J Manag Care, 6, pp. 817-827, (2000); Brugts J.J., Radhoe S.P., Aydin D., Et al., Clinical update of the latest evidence for CardioMEMS pulmonary artery pressure monitoring in patients with chronic heart failure: a promising system for remote heart failure care, Sensors, 21, (2021); Capucci A., Santini L., Favale S., Et al., Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report, ESC Heart Fail, 6, pp. 308-318, (2019); Masterson Creber R.M., Maurer M.S., Reading M., Et al., Review and analysis of existing mobile phone Apps to support heart failure symptom monitoring and self-care management using the mobile application rating scale (MARs), JMIR Mhealth Uhealth, 4, (2016); Ware P., Dorai M., Ross H.J., Et al., Patient adherence to a mobile Phone-Based heart failure Telemonitoring program: a longitudinal mixed-methods study, JMIR Mhealth Uhealth, 7, (2019); Dhruva S.S., Krumholz H.M., Championing effectiveness before cost-effectiveness, JACC Heart Fail, 4, pp. 376-379, (2016); Lewin J., Ledwidge M., O'Loughlin C., Et al., Clinical deterioration in established heart failure: what is the value of BNP and weight gain in aiding diagnosis?, Eur J Heart Fail, 7, pp. 953-957, (2005); Adamson P.B., Pathophysiology of the transition from chronic compensated and acute decompensated heart failure: new insights from continuous monitoring devices, Curr Heart Fail Rep, 6, pp. 287-292, (2009); Schiff G.D., Fung S., Speroff T., Et al., Decompensated heart failure: symptoms, patterns of onset, and contributing factors, Am J Med, 114, pp. 625-630, (2003); Albert N.M., Levy P., Langlois E., Et al., Heart failure beliefs and self-care adherence while being treated in an emergency department, J Emerg Med, 46, pp. 122-129, (2014); Pekmezaris R., Nouryan C.N., Schwartz R., Et al., A randomized controlled trial comparing telehealth self-management to standard outpatient management in underserved black and Hispanic patients living with heart failure, Telemed J E Health, 25, pp. 917-925, (2019); Ware P., Ross H.J., Cafazzo J.A., Et al., Evaluating the implementation of a mobile Phone-Based Telemonitoring program: longitudinal study guided by the consolidated framework for implementation research, JMIR Mhealth Uhealth, 6, (2018); Faragli A., Abawi D., Quinn C., Et al., The role of non-invasive devices for the telemonitoring of heart failure patients, Heart Fail Rev, 26, pp. 1063-1080, (2021); Bashi N., Karunanithi M., Fatehi F., Et al., Remote monitoring of patients with heart failure: an overview of systematic reviews, J Med Internet Res, 19, (2017)","O.E. Chausiaux; Ringgold ID 612142 Heartfelt Technologies Ltd, Cambridge, United Kingdom; email: oriane@hftech.org","","BMJ Publishing Group","","","","","","1757790X","","","35414581","English","BMJ Case Rep.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85128087593"
"Pant A.; Sharma S.; Joshi R.C.","Pant, Alka (57657064100); Sharma, Sanjay (58734359000); Joshi, Ramesh Chandra (7202084587)","57657064100; 58734359000; 7202084587","Air quality modeling for effective environmental management in Uttarakhand, India: A comparison of logistic regression and naive bayes","2022","Journal of Air Pollution and Health","7","3","","287","298","11","5","10.18502/japh.v7i3.10542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137315702&doi=10.18502%2fjaph.v7i3.10542&partnerID=40&md5=ceda0aece7ac717da93baec69dac5157","Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Uttarakhand, Dehradun, India; Department of Computer Science and Engineering, School of Engineering, Graphic Era (Deemed to be University), Uttarakhand, Dehradun, India","Pant A., Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Uttarakhand, Dehradun, India; Sharma S., Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Uttarakhand, Dehradun, India; Joshi R.C., Department of Computer Science and Engineering, School of Engineering, Graphic Era (Deemed to be University), Uttarakhand, Dehradun, India","Introduction: Air pollution increases the load of hospitalization cases, especially for those who have respiratory problems. For effective environmental management, this study aims to compare the performance of two classification algorithms in machine learning (logistic regression and naive bayes) and to evaluate the selection of the best algorithm for predicting the air quality class. Materials and methods: Pollutants data (PM10, SO2, NO2) have been collected from the Haldwani, Kashipur and Rudrapur regions in Uttarakhand (India). In part I of the study, the Air Quality Index (AQI) is calculated and assigned a class accordingly. In part II, the performance of algorithms is compared, and the air quality class is predicted through the best algorithm. In part III, accuracy is calculated after comparing the predicted class with the actual class. Then, it is compared with the accuracy of our selected algorithm. Results: The study finds a positive correlation between PM10 and SO2 pollutants. The result shows that the highest accuracy is achieved through logistic regression to predict the air quality class. Further, logistic regression has achieved the same accuracy i.e., 98.70% after comparing predicted values with the actual values. Conclusion: Logistic regression is the best algorithm to predict the air quality class in the regions of Uttarakhand, where pollutants are being measured in the Government’s hospital. The research also indicates that asthma patients in the Kashipur and Rudrapur regions may experience more health effects due to moderately polluted air quality; however, the situation is improving during the monsoon season. © 2022 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences.","Air quality; Environmental management; Logistic regression; Naive bayes; Pollutants","","","","","","NIELIT-Lucknow; Uttarakhand Pollution Control Board","The authors would like to acknowledge the support of Ms. Niharika Dimri, Information Officer in Uttarakhand Pollution Control Board (Uttarakhand, India) for providing the pollutants data for different regions of the state, and Dr. Nagendra Kumar Singh, Faculty (NIELIT-Lucknow, Government of India) for guiding the machine learning algorithms.","Zhang K, Batterman S., Air pollution and health risks due to vehicle traffic, Science of the Total Environment, 450-451, pp. 307-316, (2013); Kopnina H., Vehicular air pollution and asthma: implications for education for health and environmental sustainability, Local Environment-The International Journal of Justice & Sustainability, 22, 1, pp. 38-48, (2017); Delavar MR, Gholami A, Shiran GR, Rashidi Y, Nakhaeizadeh GR, Fedra K, Et al., A novel method for improving air pollution prediction based on machine learning approaches: a case study applied to the capital city of Tehran, ISPRS International Journal of Geo-Information, 8, 2, (2019); Predd P., Amarket for clean air, IEEE Spectrum, 42, 6, (2005); Meyers DG, Neuberger JS, He J., Cardiovascular effect of bans on smoking in public places. A systematic review and meta-analysis, Journal of American College of Cardiology, 54, 14, pp. 1249-1255, (2009); Kim D, Cho S, Tamil L, Song DJ, Seo S., Predicting asthma attacks: Effects of indoor PM concentrations on peak expiratory flow rates of asthmatic children, IEEE Access, 8, pp. 8791-8797, (2020); Manisalidis I., Stavropoulou E., Stavropoulos A., Bezirtzoglou E., Environmental and health impacts of air pollution: A review, Frontiers in Public Health, 8, 14, (2020); Bai Y, Li Y, Wang X, Xie J, Li C., Air pollutants con¬centrations forecasting using back propagation neural network based on wavelet decomposition with meteo¬rological conditions, Atmospheric Pollution Research, 7, 3, pp. 557-566, (2016); Douglas MJ, Watkins SJ, Gorman DR, Higgins M., Erratum: Are cars the new tobacco?, Journal of Public Health (Bangkok), 33, 3, (2011); Ameer S, Shah MA, Khan A, Song H, Maple C, Asghar MN., Comparative analysis of machine learning techniques for predicting air quality in smart cities, IEEE Access, 20, (2017); Jamal A, Nabizadeh Nodehi R., Predicting air quality index based on meteorological data: A comparison of regression analysis, artificial neural networks and decision tree, Journal of Air Pollution & Health, 2, 1, (2017); Mahesh Babu K, Rene Beulah J., Air quality prediction based on supervised machine learning methods, International Journal of Innovative Technology & Exploring Engineering, 8, 9, pp. 206-212, (2019); Pant A., Sharma S., Bansal M., Narang M., Comparative analysis of supervised machine learning techniques for AQI prediction, IEEE International Conference on Advanced Computing Technologies and Applications (ICACTA), pp. 1-4, (2022); India world's largest emitter of sulfur dioxide, emissions, Greenpeace India, (2019); Shaban KB, Kadri A, Rezk E., Urban air pollution monitoring system with forecasting models, 16, 8, pp. 2598-2606, (2016); Mahalingam U, Elangovan K, Dobhal H, Valliappa C., A machine learning model for air quality prediction for smart cities, International Conference on Wireless Communications Signal Processing & Networking, pp. 452-457, (2019); Xu C, Zhao W, Zhang M, Cheng B., Pollution haven or halo? The role of the energy transition in the impact of FDI on SO<sub>2</sub> emissions, The Science of the Total Environment, 763, (2021); Volkodaeva M., Kiselev A., On development of system for environmental monitoring of atmospheric air quality, Journal of Mining Institute, 227, pp. 589-596, (2017); Irani T, Amiri H, Deyhim H., Evaluating visibility range on air pollution using NARX neural network simulation, Journal of Environmental Treatment Techniques, 9, 2, pp. 540-547, (2021); Chcialowski A, Agata D, Badyda A, Piotr D., Ambient air pollution and risk of admission due to asthma in the three largest urban agglomerations in Poland: A Time-Stratified, Case-Crossover Study, International Journal of Environmental Research and Public Health, 19, 10, (2022); Gharehchahi E, Mahvi AH, Amini H, Nabizadeh R, Akhlaghi AA., Health impact assessment of air pollution in Shiraz, Iran: a two-part study, Journal of Environmental Health Sciences and Engineering, pp. 1-8, (2013); Nadali A., Leili M., Karami M., Et al., The short-term association between air pollution and asthma hospitalization: a time-series analysis, Air Quality, Atmosphere and Health, 15, pp. 1153-1167, (2022); Li S., Song S., Fei X., Spatial characteristics of air pollution in the main city area of Chengdu, China, 19th International Conference on Geoinformatics, pp. 1-4, (2011); Moradi H., Talaiekhozani A., Kamyab H., Et al., Development of equations to predict the concentration of air pollutants indicators in Yazd City, Iran, Journal of Inorganic and Organometallic Polymers and Materials, (2022); Senthivel S, Chidambaranathan M., Machine learning approaches used for air quality forecast, Revue d'Intelligence Artificielle, 36, 1, pp. 73-78, (2022); Yarragunta S., Nabi M. A., Prediction of air pollutants using supervised machine learning, 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1633-1640, (2021); Gore R. W., Deshpande D. S., An approach for classification of health risks based on air quality levels, 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp. 58-61, (2017); Kumar K., Pande B.P., Air pollution prediction with machine learning: a case study of Indian cities, International Journal of Environmental Science & Technology, (2022); Doreswamy Harishkumar, Yogesh KM, Gad Ibrahim, Forecasting air pollution particulate matter (PM<sub>2.5</sub>) using machine learning models. Third International Conference on Computing & Network Communications, Procedia Computer Science, 171, pp. 2057-2066, (2020); Li Yingying, Niu Dongxiao, Research on the relationship between China's economic growth and SO<sub>2</sub> emission, IEEE International Conference on Test and Measurement, pp. 88-90, (2009); Song L., Impact analysis of air pollutants on the air quality index in Jinan Winter, IEEE International Conference on Computational Science and Engineering (CSE) & IEEE International Conference on Embedded and Ubiquitous Computing (EUC), pp. 471-474, (2017); Chauhan Avnish, Pawar Mayank, Kumar Rajeev, Joshi P. C., Ambient air quality status in Uttarakhand (India): A Case Study of Haridwar and Dehradun using Air Quality Index, 6, 9, pp. 565-574, (2010); Madonsela BS, Maphanga T, Silas Chidi B, Shale K, Zungu V., Assessment of air pollution in the informal settlements of the Western Cape, South Africa, JAPH, 7, 1, pp. 1-14, (2022); Marjan Asgari M., Farnaghi M., Ghaemi Z., Predictive mapping of urban air pollution using apache spark on a hadoop cluster, ICCBDC International Conference on Cloud & Big Data Computing, pp. 89-93, (2017); Yarragunta S., Nabi M. A., Prediction of air pollutants using supervised machine learning, 5th International Conference on Intelligent Computing annd Control Systems (ICICCS), pp. 1633-1640, (2021)","A. Pant; Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India; email: alkapant392@gmail.com","","Tehran University of Medical Sciences","","","","","","24763071","","","","English","J. Air. Pollut. Health.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85137315702"
"Do W.; Russell R.; Wheeler C.; Javed H.; Dogan C.; Cunningham G.; Khanna V.; Devos M.; Satia I.; Bafadhel M.; Pavord I.","Do, William (57930031300); Russell, Richard (7403934230); Wheeler, Christopher (57929271600); Javed, Hamza (57994945000); Dogan, Cihan (58725430500); Cunningham, George (57994479100); Khanna, Vikaran (57994169600); Devos, Maarten (57205194126); Satia, Imran (40661860900); Bafadhel, Mona (35336030900); Pavord, Ian (7006815656)","57930031300; 7403934230; 57929271600; 57994945000; 58725430500; 57994479100; 57994169600; 57205194126; 40661860900; 35336030900; 7006815656","Performance of cough monitoring by Albus Home, a contactless and automated system for nocturnal respiratory monitoring at home","2022","ERJ Open Research","8","4","00265-2022","","","","7","10.1183/23120541.00265-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143420709&doi=10.1183%2f23120541.00265-2022&partnerID=40&md5=5cb28ec72bfc59eaf1d86abf2f63018f","Albus Health, Oxford, United Kingdom; Respiratory Medicine Unit, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Royal Brompton Hospital, London, United Kingdom; Department of Electrical Engineering, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Medicine, Division of Respirology, McMaster University, Hamilton, Canada; Faculty of Life Sciences and Medicine, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom","Do W., Albus Health, Oxford, United Kingdom; Russell R., Respiratory Medicine Unit, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; Wheeler C., Royal Brompton Hospital, London, United Kingdom; Javed H., Albus Health, Oxford, United Kingdom; Dogan C., Albus Health, Oxford, United Kingdom; Cunningham G., Albus Health, Oxford, United Kingdom; Khanna V., Albus Health, Oxford, United Kingdom; Devos M., Department of Electrical Engineering, Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Satia I., Department of Medicine, Division of Respirology, McMaster University, Hamilton, Canada; Bafadhel M., Faculty of Life Sciences and Medicine, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom; Pavord I., Respiratory Medicine Unit, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom","Introduction Objective cough frequency is a key clinical end-point but existing wearable monitors are limited to 24-h recordings. Albus Home uses contactless motion, acoustic and environmental sensors to monitor multiple metrics, including respiratory rate and cough without encroaching on patient lifestyle. The aim of this study was to evaluate measurement characteristics of nocturnal cough monitoring by Albus Home compared to manual counts. Methods Adults with respiratory conditions underwent overnight monitoring using Albus Home in their usual bedroom environments. Participants set-up the plug-and-play device themselves. For reference counts, each audio recording was counted by two annotators, and cough defined as explosive phases audio-visually labelled by both. In parallel, recordings were processed by a proprietary Albus system, comprising a deep-learning algorithm with a human screening step for verifying or excluding occasional events that mimic cough. Performance of the Albus system in detecting individual cough events and reporting hourly cough counts was compared against reference counts. Results 30 nights from 10 subjects comprised 375 hours of recording. Mean±SD coughs per night were 90±76. Coughs per hour ranged from 0 to 129. Albus counts were accurate across hours with high and low cough frequencies, with median sensitivity, specificity, positive predictive value and negative predictive values of 94.8, 100.0, 99.1 and 100.0%, respectively. Agreement between Albus and reference was strong (intra-class correlation coefficient (ICC) 0.99; 95% CI 0.99–0.99; p<0.001) and equivalent to agreement between observers and reference counts (ICC 0.98 and 0.99, respectively). Conclusions Albus Home provides a unique, contactless and accurate system for cough monitoring, enabling collection of high-quality and potentially clinically relevant longitudinal data. © The authors or their employers 2022.","","adult; Albus home; Article; asthma; audio recording; breathing rate; chronic obstructive lung disease; chronic respiratory tract disease; clinical article; controlled study; coughing; cystic fibrosis; deep learning; diagnostic test accuracy study; home monitoring; human; learning algorithm; predictive value; sarcoidosis; sensitivity and specificity; validation process","","","","","Albus Health","Support statement: This work was funded by Albus Health (registered BreatheOx Limited). I. Satia is currently supported by the E.J. Moran Campbell Early Career Award, Department of Medicine, McMaster University. Funding information for this article has been deposited with the Crossref Funder Registry.","Sutherland ER., Nocturnal asthma, J Allergy Clin Immunol, 116, pp. 1179-1186, (2005); Agusti A, Hedner J, Marin JM, Et al., Night-time symptoms: a forgotten dimension of COPD, Eur Respir Rev, 20, pp. 183-194, (2011); Global Strategy for Asthma Management and Prevention, (2021); BTS/SIGN Guideline for the management of asthma 2019, (2019); Nathan RA, Sorkness CA, Kosinski M, Et al., Development of the asthma control test: a survey for assessing asthma control, J Allergy Clin Immunol, 113, pp. 59-65, (2004); Juniper EF, O'Byrne PM, Guyatt GH, Et al., Development and validation of a questionnaire to measure asthma control, Eur Respir J, 14, pp. 902-907, (1999); Durrington HJ, Farrow SN, Loudon AS, Et al., The circadian clock and asthma, Thorax, 69, pp. 90-92, (2014); Lodhi S, Smith JA, Satia I, Et al., Cough rhythms in asthma: potential implication for management, J Allergy Clin Immunol Pract, 7, pp. 2024-2027, (2019); Satia I, Watson R, Scime T, Et al., Allergen challenge increases capsaicin-evoked cough responses in patients with allergic asthma, J Allergy Clin Immunol, 144, pp. 788-795, (2019); Crooks MG, Hayman Y, Innes A, Et al., Objective measurement of cough frequency during COPD exacerbation convalescence, Lung, 194, pp. 117-120, (2016); Fukuhara A, Saito J, Birring SS, Et al., Clinical characteristics of cough frequency patterns in patients with and without asthma, J Allergy Clin Immunol Pract, 8, pp. 654-661, (2020); Juniper EF, O'Byrne PM, Ferrie PJ, Et al., Measuring asthma control. Clinic questionnaire or daily diary?, Am J Respir Crit Care Med, 162, pp. 1330-1334, (2000); Falconer A, Oldman C, Helms P., Poor agreement between reported and recorded nocturnal cough in asthma, Pediatr Pulmonol, 15, pp. 209-211, (1993); Spinou A, Birring SS., An update on measurement and monitoring of cough: what are the important study endpoints?, J Thorac Dis, 6, pp. S728-S734, (2014); Birring SS, Prudon B, Carr AJ, Et al., Development of a symptom specific health status measure for patients with chronic cough: Leicester Cough Questionnaire (LCQ), Thorax, 58, pp. 339-343, (2003); Birring SS, Fleming T, Matos S, Et al., The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough, Eur Respir J, 31, pp. 1013-1018, (2008); Barton A, Gaydecki P, Holt K, Et al., Data reduction for cough studies using distribution of audio frequency content, Cough, 8, (2012); Smith JA, Holt K, Dockry R, Et al., Performance of a digital signal processing algorithm for the accurate quantification of cough frequency, Eur Respir J, 58, (2021); Vertigan AE, Kapela SL, Birring SS, Et al., Feasibility and clinical utility of ambulatory cough monitoring in an outpatient clinical setting: a real-world retrospective evaluation, ERJ Open Res, 7, pp. 00319-2021, (2021); Muccino DR, Morice AH, Birring SS, Et al., Design and rationale of two phase 3 randomised controlled trials (COUGH-1 and COUGH-2) of gefapixant, a P2X3 receptor antagonist, in refractory or unexplained chronic cough, ERJ Open Res, 6, pp. 00284-2020, (2020); Smith JA, Kitt MM, Morice AH, Et al., Gefapixant, a P2X3 receptor antagonist, for the treatment of refractory or unexplained chronic cough: a randomised, double-blind, controlled, parallel-group, phase 2b trial, Lancet Respir Med, 8, pp. 775-785, (2020); McGarvey LP, Birring SS, Morice AH, Et al., Efficacy and safety of gefapixant, a P2X3 receptor antagonist, in refractory chronic cough and unexplained chronic cough (COUGH-1 and COUGH-2): results from two double-blind, randomised, parallel-group, placebo-controlled, phase 3 trials, Lancet, 399, pp. 909-923, (2022); Do W, Wheeler C, De Vos M, Et al., High accuracy of automated respiratory rate readings in a novel, non-contact home monitor, Eur Respir J, 58, (2021); Wheeler C, Do W, De Vos M, Et al., Pediatric nocturnal respiratory rate monitoring using a non-contact and passive bedside device: accuracy of the Albus Home Research Device (RD), Am J Respir Crit Care Med, 203, (2021); Morice AH, Fontana GA, Belvisi MG, Et al., ERS guidelines on the assessment of cough, Eur Respir J, 29, pp. 1256-1276, (2007); Smith JA, Earis JE, Woodcock AA., Establishing a gold standard for manual cough counting: video versus digital audio recordings, Cough, 2, (2006); Koo TK, Li MY., A guideline of selecting and reporting intraclass correlation coefficients for reliability research, J Chiropr Med, 15, pp. 155-163, (2016); Bland JM, Altman DG., Measuring agreement in method comparison studies, Stat Methods Med Res, 8, pp. 135-160, (1999); Niimi A, Saito J, Kamei T, Et al., Randomised trial of the P2X3 receptor antagonist sivopixant for refractory chronic cough, Eur Respir J, 59, (2022); Decalmer SC, Webster D, Kelsall AA, Et al., Chronic cough: how do cough reflex sensitivity and subjective assessments correlate with objective cough counts during ambulatory monitoring?, Thorax, 62, pp. 329-334, (2007); Yousaf N, Monteiro W, Matos S, Et al., Cough frequency in health and disease, Eur Respir J, 41, pp. 241-243, (2013); Hall JI, Lozano M, Estrada-Petrocelli L, Et al., The present and future of cough counting tools, J Thorac Dis, 12, pp. 5207-5223, (2020); Yanez AM, Guerrero D, Perez de Alejo R, Et al., Monitoring breathing rate at home allows early identification of COPD exacerbations, Chest, 142, pp. 1524-1529, (2012); Cretikos MA, Bellomo R, Hillman K, Et al., Respiratory rate: the neglected vital sign, Med J Aust, 188, pp. 657-659, (2008); Franklin PJ., Indoor air quality and respiratory health of children, Paediatr Respir Rev, 8, pp. 281-286, (2007); Madureira J, Paciencia I, Rufo J, Et al., Indoor air quality in schools and its relationship with children’s respiratory symptoms, Atmos Environ, 118, pp. 145-156, (2015)","W. Do; Albus Health, Oxford, United Kingdom; email: william.do@albushealth.com","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85143420709"
"Blankestijn J.M.; Lopez-Rincon A.; Neerincx A.H.; Vijverberg S.J.H.; Hashimoto S.; Gorenjak M.; Sardón Prado O.; Corcuera-Elosegui P.; Korta-Murua J.; Pino-Yanes M.; Potočnik U.; Bang C.; Franke A.; Wolff C.; Brandstetter S.; Toncheva A.A.; Kheiroddin P.; Harner S.; Kabesch M.; Kraneveld A.D.; Abdel-Aziz M.I.; Maitland-van der Zee A.H.","Blankestijn, Jelle M. (58114574200); Lopez-Rincon, Alejandro (24722721700); Neerincx, Anne H. (55909977100); Vijverberg, Susanne J. H. (35390201300); Hashimoto, Simone (37074543500); Gorenjak, Mario (55392367700); Sardón Prado, Olaia (8679204900); Corcuera-Elosegui, Paula (24464865000); Korta-Murua, Javier (23034822900); Pino-Yanes, Maria (57189630546); Potočnik, Uroš (6602956871); Bang, Corinna (55323199000); Franke, Andre (57201765376); Wolff, Christine (7103395788); Brandstetter, Susanne (12790168700); Toncheva, Antoaneta A. (56423854900); Kheiroddin, Parastoo (57190380684); Harner, Susanne (57217008444); Kabesch, Michael (7004591764); Kraneveld, Aletta D. (6602859285); Abdel-Aziz, Mahmoud I. (56548352100); Maitland-van der Zee, Anke H. (57220903102)","58114574200; 24722721700; 55909977100; 35390201300; 37074543500; 55392367700; 8679204900; 24464865000; 23034822900; 57189630546; 6602956871; 55323199000; 57201765376; 7103395788; 12790168700; 56423854900; 57190380684; 57217008444; 7004591764; 6602859285; 56548352100; 57220903102","Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma","2023","Pediatric Allergy and Immunology","34","2","e13919","","","","7","10.1111/pai.13919","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148763102&doi=10.1111%2fpai.13919&partnerID=40&md5=931dd147c0ce81e5ca1a89b9428a0b1d","Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Amsterdam Institute for Infection and Immunity, Amsterdam, Netherlands; Amsterdam Public Health, Amsterdam, Netherlands; Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, Netherlands; Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands; Department of Pediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, Amsterdam, Netherlands; Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia; Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain; Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Spain; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, La Laguna, Spain; Laboratory for Biochemistry, Molecular Biology and Genomics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia; Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany; University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany; Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany; Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt","Blankestijn J.M., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, Amsterdam Institute for Infection and Immunity, Amsterdam, Netherlands, Amsterdam Public Health, Amsterdam, Netherlands; Lopez-Rincon A., Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, Netherlands, Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands; Neerincx A.H., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Vijverberg S.J.H., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Hashimoto S., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, Department of Pediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, Amsterdam, Netherlands; Gorenjak M., Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia; Sardón Prado O., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain, Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain; Corcuera-Elosegui P., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Korta-Murua J., Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain; Pino-Yanes M., Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Spain, CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain, Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, La Laguna, Spain; Potočnik U., Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia, Laboratory for Biochemistry, Molecular Biology and Genomics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia; Bang C., Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany; Franke A., Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany; Wolff C., University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany; Brandstetter S., University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany; Toncheva A.A., University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany; Kheiroddin P., University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany; Harner S., Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany; Kabesch M., University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Science and Development Campus Regensburg (WECARE), Regensburg, Germany, Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany; Kraneveld A.D., Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, Netherlands; Abdel-Aziz M.I., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, Amsterdam Institute for Infection and Immunity, Amsterdam, Netherlands, Amsterdam Public Health, Amsterdam, Netherlands, Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt; Maitland-van der Zee A.H., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, Amsterdam Institute for Infection and Immunity, Amsterdam, Netherlands, Amsterdam Public Health, Amsterdam, Netherlands, Department of Pediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, Amsterdam, Netherlands","Background: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. Methods: 143 and 103 feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. Results: Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. Conclusion: Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children. © 2023 The Authors. Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","asthma: disease management; asthma: treatment","mepolizumab; omalizumab; RNA 16S; adolescent; Alistipes; alpha diversity; Article; asthma; Asthma Control Test; bacterial microbiome; Bacteroides; beta diversity; child; chronic obstructive lung disease; controlled study; differential abundance analysis; disease severity; feces analysis; female; gene amplification; Haemophilus; human; intestine flora; liver cirrhosis; machine learning; major clinical study; male; microbial diversity; mucosa inflammation; multicenter study; Neisseria; nonhuman; periodontitis; population parameters; preschool child; prevalence; Prevotella; prospective study; recursive ensemble feature selection; Rothia; saliva analysis; Shannon index; ulcerative colitis; Veillonella","","mepolizumab, 196078-29-2; omalizumab, 242138-07-4","","","Ministry of Education, Science, and Sport of the Republic of Slovenia, (C330‐16‐500106); State Research Agency; European Commission, EC, (AC15/00015, AC15/00058, RYC‐2015‐17205); European Commission, EC; ZonMw, (9003035001); ZonMw; Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031 L0088); Bundesministerium für Bildung und Forschung, BMBF; Javna Agencija za Raziskovalno Dejavnost RS, ARRS, (P3‐0067); Javna Agencija za Raziskovalno Dejavnost RS, ARRS; Instituto de Salud Carlos III, ISCIII; Ministerio de Ciencia e Innovación, MICINN; European Regional Development Fund, ERDF, (SAF2017‐83417R); European Regional Development Fund, ERDF; Agencia Estatal de Investigación, AEI","The SysPharmPediA consortium is supported by ZonMW [project number: 9003035001], the Ministry of Education, Science, and Sport of the Republic of Slovenia [contract number C330‐16‐500106]; the German Ministry of Education and Research (BMBF) [project number FKZ 031 L0088]; Instituto de Salud Carlos III (ISCIII) through Strategic Action for Health Research (AES) and European Community (EC) within the Active and Assisted Living (AAL) Program framework [award numbers AC15/00015 and AC15/00058] under the frame of the ERACoSysMed JTC‐1 Call. M.P.‐Y. was funded by the Ramón y Cajal Program (RYC‐2015‐17205) by the Spanish Ministry of Science and Innovation (MICINN), the State Research Agency, and the European Regional Development Fund from the European Union (MICINN/AEI/FEDER, UE, grant SAF2017‐83417R). U.P. and M.G. were funded by Slovenian Research Agency (research core funding No. P3‐0067). M.I.A.‐A. was funded by the Egyptian Government Ph.D. Scholarships. 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Blankestijn; Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, 1105 AZ, Netherlands; email: j.m.blankestijn@amsterdamumc.nl","","John Wiley and Sons Inc","","","","","","09056157","","PALUE","","English","Pediatr. Allergy Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85148763102"
"Harshavardhan A.; Cheerla S.; Parkavi A.; Latha Mary S.A.; Qureshi K.; Mhaske H.R.","Harshavardhan, Awari (57200720586); Cheerla, Sreevardhan (57156726300); Parkavi, Anbusubramanian (56040990300); Latha Mary, Saleth Angel (54917398600); Qureshi, Kashif (57654087000); Mhaske, Harshada Rangnath (56038407800)","57200720586; 57156726300; 56040990300; 54917398600; 57654087000; 56038407800","Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification","2023","Journal of Electronic Imaging","32","6","062603","","","","7","10.1117/1.JEI.32.6.062603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178135599&doi=10.1117%2f1.JEI.32.6.062603&partnerID=40&md5=674390d7450ce6207e2f0f8acbc30374","VNR Vignana Jyothi Institute of Engineering and Technology, Department of CSE (AIML & IoT), Telangana, Hyderabad, India; Koneru Lakshmaiah Education Foundation, Department of ECE, Andhra Pradesh, Vaddeswaram, India; MS Ramaiah Institute of Technology, Department of CSE, Bangalore, India; Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Lingaya's Vidyapeeth Computer Science and Information Technology, Haryana, Faridabad, India; Pimpri Chinchwad College of Engineering, Department of Computer Engineering, Pune, India","Harshavardhan A., VNR Vignana Jyothi Institute of Engineering and Technology, Department of CSE (AIML & IoT), Telangana, Hyderabad, India; Cheerla S., Koneru Lakshmaiah Education Foundation, Department of ECE, Andhra Pradesh, Vaddeswaram, India; Parkavi A., MS Ramaiah Institute of Technology, Department of CSE, Bangalore, India; Latha Mary S.A., Sri Eshwar College of Engineering, Department of Computer Science and Business Systems, Coimbatore, India; Qureshi K., Lingaya's Vidyapeeth Computer Science and Information Technology, Haryana, Faridabad, India; Mhaske H.R., Pimpri Chinchwad College of Engineering, Department of Computer Engineering, Pune, India","Many people around the world are affected by pulmonary disease as well as asthma, pneumonia, lung cancer, and tuberculosis. All of these conditions have one thing in common: airway obstruction. Lung illnesses are a worldwide issue in which chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and fibrosis are few of the most common. An accurate diagnosis of a lung problem is essential, and it has been pursued by many researchers using image processing and machine learning models. Multiple deep learning methods are used to predict lung disease; these include convolutional neural networks, vanilla neural networks, visual geometry group-based neural networks, and the capsule network. Medical data are scarce, so diagnosing pulmonary diseases using chest x-ray pictures from datasets with less than 1000 samples is considered to address the problem. Three deep learning multiple neural networks (DLMNNs) were generated using the chicken swarm optimization (CSO) approach, for which transfer learning was applied to evaluate the performance of each. First, we created an algorithm for segmenting CXR images, and then we compared it with other classification systems. Our results were compared with those of other methods using publicly available data from the Shenzhen and Montgomery lung datasets. However, our technique has a lower number of trainable parameters compared with the best-performing models trained on the Montgomery dataset. The DLMNN-CSO virtually matched the best performance on the Shenzhen dataset, although it was computationally less expensive than the other models. DLMNN-CSO's validation loss was 0.4, whereas the validation loss for CNN, VDSNet, VGG, and DBN was 0.9, 0.8, 0.6, and 0.5, respectively.  © 2023 SPIE and IS&T.","chest x-ray pictures; deep learning modified neural network; lung cancer; pneumonia","Biological organs; Deep learning; Diagnosis; Image segmentation; Learning systems; Swarm intelligence; Chest x-ray picture; Chest x-rays; Deep learning modified neural network; Lung Cancer; Modified neural networks; Multiple neural networks; Neural-networks; Performance; Pneumonia; Swarm optimization; Pulmonary diseases","","","","","","","Mohan P., Et al., Pigeon inspired optimization with encryption based secure medical image management system, Computat. Intell. Neurosci, 2022, (2022); Rahaman M. M., Et al., Identification of COVID-19 samples from chest X-Ray images using deep learning: a comparison of transfer learning approaches, J. X-Ray Sci. Technol, 28, pp. 821-839, (2020); Yahiaoui A., Er O., Yumusak N., A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines, Biomed. Res, 28, pp. 4208-4212, (2017); Ronneberger O., Fischer P., Brox T., U-net: convolutional networks for biomedical image segmentation, Lect. Notes Comput. Sci, 9351, pp. 234-241, (2015); Hu Z., Et al., Deep learning for image-based cancer detection and diagnosis-a survey, Pattern Recognit, 83, pp. 134-149, (2018); Pereira S., Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Trans. Med. Imaging, 35, pp. 1240-1251, (2016); Abdullah S., Et al., Round randomized learning vector quantization for brain tumor imaging, Comput. Math. Methods Med, 2016, (2016); Sasikumar S., Et al., Attention based recurrent neural network for lung cancer detection, Proc. Fourth Int. Conf. I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 720-724, (2020); Sahlol A. T., Et al., A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features, Symmetry, 12, (2020); Hu H., Et al., Parallel deep learning algorithms with hybrid attention mechanism for image segmentation of lung tumors, IEEE Trans. Ind. Inf, 17, pp. 2880-2889, (2021); Qin C., Et al., Computer-aided detection in chest radiography based on artificial intelligence: a survey, BioMed. Eng. OnLine, 17, (2018); Dey N., Et al., Customized VGG19 architecture for pneumonia detection in chest X-rays, Pattern Recognit. Lett, 143, pp. 67-74, (2021); Wang X., Et al., ChestX-Ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, Proc. IEEE CVPR, pp. 2097-2106, (2017); Llopis F., Et al., Using improved optical flow model to detect Tuberculosis, CEUR-WS: Lugano, Switzerland, pp. 9-12, (2019); Nasser I. M., Abu-Naser S. S., Lung cancer detection using artificial neural network, Int. J. Eng. Inf. Syst, 3, 3, pp. 17-23, (2019); Teramoto A., Et al., Automated classification of lung cancer types from cytological images using deep convolutional neural networks, BioMed. Res. Int, 2017, pp. 1-6, (2017); Abhir B., Et al., Deep-learning framework to detect lung abnormality - a study with chest X-Ray and lung CT scan images, Pattern Recognit. Lett, 129, pp. 271-278, (2020); Bharati S., Et al., Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer, Adv. in Intell. Syst. and Comput, 941, pp. 447-457, (2020); Coudray N., Et al., Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning, Nat. Med, 24, 10, pp. 1559-1567, (2018); Hooda R., Mittal A., Sofat S., Automated TB classification using ensemble of deep architectures, Multimed. Tools Appl, 78, pp. 31515-31532, (2019); Kumar A., Et al., Identifying pneumonia in chest X-rays: a deep learning approach, Measurement, 145, pp. 803-819, (2019); Xie H., Et al., Automated pulmonary nodule detection in CT images using deep convolutional neural networks, Pattern Recognit, 85, pp. 109-119, (2019); Kuan K., Et al., Deep learning for lung cancer detection: tackling the Kaggle data science bowl 2017 challenge, (2017); Ausawalaithong W., Et al., Automatic lung cancer prediction from chest x-ray images using the deep learning approach, Proc. 11th Biomed. Eng. Int. Conf. (BMEiCON), (2018); Xu S., Et al., Automated detection of multiple lesions on chest X-ray images: classification using a neural network technique with association-specific contexts, Appl. Sci, 10, (2020); Huang C., Et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395, pp. 497-506, (2020); Alsharman N., Jawarneh I., GoogleNet CNN neural network towards chest CTcoronavirus medical image classification, J. Comput. Sci, 16, pp. 620-625, (2020); Zhu J., Et al., Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs, PLoS ONE, 15, (2020); Kannan K. S., Suma K. G., Neelakandan S., A secured healthcare medical system using blockchain technology, ICCCE 2021. Lecture Notes in Electrical Engineering, 828, (2021); Rene Beulah J., Et al., Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model, Int. J. Model. Simul. Sci. Comput, 13, 4, (2022); AI-Atroshi C., Et al., Automated speech based evaluation of mild cognitive impairment and Alzheimer's disease detection using with deep belief network model, Int. J. Healthc. Manage, (2022); Kavitha M., Et al., Convolutional neural networks-based video reconstruction and computation in digital twins, Intell. Autom. Soft Comput, 34, 3, pp. 1571-1586, (2022); Jain D. K., Liu X., Prakash M., Modeling of human action recognition using hyperparameter tuned deep learning model, J. Electron. Imaging, 32, 1, (2022); Chowdhury M. E. H., Et al., Can AI help in screening Viral and COVID-19 pneumonia?, (2020); Sethy P. K., Et al., Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, Int. J. Math. Eng. Manage. Sci, 5, pp. 643-651, (2020); Raghavendar S., Et al., Multilayer stacked probabilistic belief network-based brain tumor segmentation and classification, Int. J. Found. Comput. Sci, 33, pp. 1-24, (2022); Das D., Santosh K. C., Pal U., Truncated inception net: COVID-19 outbreak screening using chest X-rays, Phys. Eng. Sci. Med, 43, pp. 915-925, (2020); Sunitha G., Et al., Intelligent deep learning-based ethnicity recognition and classification using facial images, Image Vis. Comput, 121, (2022); Boyapati P., Et al., LSGDM with biogeography-based optimization (BBO) model for healthcare applications, J. Healthc. Eng, 2022, (2022); Debbarma Swapana M., Sengupta Aditya S. C., Bhattacaryya Bidyut K. D., Design a FPGA, fuzzy based, insolent method for prediction of multi-diseases in rural area, J. Intell. Fuzzy Syst, 37, 5, pp. 7039-7046, (2019); Reshma G., Et al., Deep learning-based skin lesion diagnosis model using dermoscopic images, Intell. Autom. Soft Comput, 31, 1, pp. 621-634, (2022); Xu X., Et al., A deep learning system to screen novel coronavirus disease 2019 pneumonia, Engineering, 6, 10, pp. 1122-1129, (2020)","A. Harshavardhan; VNR Vignana Jyothi Institute of Engineering and Technology, Department of CSE (AIML & IoT), Hyderabad, Telangana, India; email: harshavardhana78@outlook.com","","SPIE","","","","","","10179909","","JEIME","","English","J. Electron. Imaging","Article","Final","","Scopus","2-s2.0-85178135599"
"Zheng Y.; Zhang C.; Liu Y.","Zheng, Ying (59134178900); Zhang, Chu (59134443800); Liu, Yuwen (59134309800)","59134178900; 59134443800; 59134309800","Risk prediction models of depression in older adults with chronic diseases","2024","Journal of Affective Disorders","359","","","182","188","6","6","10.1016/j.jad.2024.05.078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193593405&doi=10.1016%2fj.jad.2024.05.078&partnerID=40&md5=c2b1d922eb2cbff9847c9147ce80ad7c","School of Nursing, Bengbu Medical University, Bengbu, China; School of Health Management, Bengbu Medical University, Bengbu, China","Zheng Y., School of Nursing, Bengbu Medical University, Bengbu, China; Zhang C., School of Nursing, Bengbu Medical University, Bengbu, China; Liu Y., School of Health Management, Bengbu Medical University, Bengbu, China","Background: Detecting potential depression and identifying the critical predictors of depression among older adults with chronic diseases are essential for timely intervention and management of depression. Therefore, risk prediction models (RPMs) of depression in elderly people should be further explored. Methods: A total of 3959 respondents aged 60 years or over from the wave four survey of the China Health and Retired Longitudinal Study (CHARLS) were included in this study. We used five machine learning (ML) algorithms and three data balancing techniques to construct RPMs of depression and calculated feature importance scores to determine which features are essential to depression. Results: The prevalence of depression was 19.2 % among older Chinese adults with chronic diseases in the wave four survey. The random forest (RF) model was more accurate than the other models after balancing the data using the Synthetic Minority Oversampling Technique (SMOTE) algorithm, with an area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) of 0.957 and 0.920, respectively, a balanced accuracy of 0.891 and a sensitivity of 0.875. Furthermore, we further identified several important predictors between male and female patients via constructed sex-stratified models. Limitations: Further research on the clinical impact studies of our models and external validation are needed. Conclusions: After several techniques were used to address class imbalance issues, most RPMs achieved satisfactory accuracy in predicting depression among elderly people with chronic diseases. RPMs may thus become valuable screening tools for both older individuals and healthcare practitioners to assess the risk of depression. © 2024 The Authors","Chronic diseases; Depression; Machine learning; Risk factors","Aged; Aged, 80 and over; Algorithms; China; Chronic Disease; Depression; Female; Humans; Longitudinal Studies; Machine Learning; Male; Middle Aged; Prevalence; Risk Assessment; Risk Factors; Sex Factors; aged; arthritis; Article; asthma; cerebrovascular accident; Chinese; chronic disease; chronic lung disease; controlled study; diabetes mellitus; diagnostic accuracy; diagnostic test accuracy study; dyslipidemia; feature selection; female; gastrointestinal disease; health survey; heart disease; human; hypertension; k nearest neighbor; kidney disease; late life depression; learning algorithm; liver disease; logistic regression analysis; major clinical study; male; malignant neoplasm; memory disorder; mental disease; predictive model; predictive value; prevalence; random forest; receiver operating characteristic; risk factor; risk model; sensitivity and specificity; sex difference; support vector machine; algorithm; China; chronic disease; depression; epidemiology; longitudinal study; machine learning; middle aged; psychology; risk assessment; sex factor; very elderly","","","","","","","Abd Allah E.M., El-Matary D.E., Eid E.M., El Dien A.S.T., Performance comparison of various machine learning approaches to identify the best one in predicting heart disease, 10, pp. 1-18, (2022); Albert S.M., Bear-Lehman J., Burkhardt A., Lifestyle-adjusted function: variation beyond BADL and IADL competencies, Gerontologist, 49, pp. 767-777, (2009); Andreescu C., Ajilore O., Aizenstein H.J., Albert K., Butters M.A., Landman B.A., Karim H.T., Krafty R., Taylor W.D., Disruption of neural homeostasis as a model of relapse and recurrence in late-life depression, Am. 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Sin., 39, pp. 745-758, (2013); Zeng M., Zou B., Wei F., Liu X., Wang L., Effective prediction of three common diseases by combining SMOTE with Tomek links technique for imbalanced medical data, 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), pp. 225-228, (2016); Zhao Y., Hu Y., Smith J.P., Strauss J., Yang G., Cohort profile: the China health and retirement longitudinal study (CHARLS), Int. J. Epidemiol., 43, pp. 61-68, (2014)","Y. Liu; School of Health Management, Bengbu Medical University, Bengbu, China; email: liuyw@bbmc.edu.cn","","Elsevier B.V.","","","","","","01650327","","JADID","38768825","English","J. Affective Disord.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85193593405"
"Ren X.; Guo Y.; Wang H.; Gao X.; Chen W.; Wang T.","Ren, Xue (57216556077); Guo, Yan (57204419336); Wang, Heyuan (57214057322); Gao, Xiang (57212969386); Chen, Wei (55716044700); Wang, Tengjiao (7405564727)","57216556077; 57204419336; 57214057322; 57212969386; 55716044700; 7405564727","The intelligent experience inheritance system for Traditional Chinese Medicine","2023","Journal of Evidence-Based Medicine","16","1","","91","100","9","6","10.1111/jebm.12517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150786504&doi=10.1111%2fjebm.12517&partnerID=40&md5=459bc40e14fe5923d86932ccc4764b87","Jinan Municipal Hospital of Traditional Chinese Medicine, Jinan, China; Xiyuan Hospital, China Academy of Chinese Medicinal Sciences, Beijing, China; School of Computer Science, Peking University, Beijing, China; Institute of Computational Social Sciences, Peking University (Qingdao), Beijing, China","Ren X., Jinan Municipal Hospital of Traditional Chinese Medicine, Jinan, China; Guo Y., Xiyuan Hospital, China Academy of Chinese Medicinal Sciences, Beijing, China; Wang H., School of Computer Science, Peking University, Beijing, China, Institute of Computational Social Sciences, Peking University (Qingdao), Beijing, China; Gao X., School of Computer Science, Peking University, Beijing, China, Institute of Computational Social Sciences, Peking University (Qingdao), Beijing, China; Chen W., School of Computer Science, Peking University, Beijing, China, Institute of Computational Social Sciences, Peking University (Qingdao), Beijing, China; Wang T., School of Computer Science, Peking University, Beijing, China, Institute of Computational Social Sciences, Peking University (Qingdao), Beijing, China","The inheritance of knowledge and experience was crucial to the development of Traditional Chinese Medicine (TCM). However, the existing methods of inheriting the unique clinical experience of famous veteran TCM doctors still followed the outdated and inefficient Master-Prentice schema. In addition, the inherited medical books and records were usually lack of standardization and systematization. In this article, a new method for inheriting the academic thoughts and clinical experience of famous veteran doctors with the help of artificial intelligence technology was explored. Due to the individualized treatment characteristics namely “same disease with different treatments, different diseases with the same treatment,” the intelligent inheritance of TCM faced many technical barriers. To tackle these problems, we proposed a prototype system framework for the intelligent inheritance of famous veteran doctors based on rules and deep learning models and performed a case study on the treatment of pediatric asthma. The architecture could not only make full use of the advantages of deep learning, but also integrate the valuable knowledge and experience analysis of famous veteran doctors from injected rules. Specifically, the study took pediatric asthma medical records as training and test samples and calculated the similarity between the generated prescriptions and the real-world clinical prescriptions from the famous veteran doctors. Experimental results showed that the generated prescription could achieve a similarity of more than 90%. It proved that the proposed framework provided a feasible way for the intelligent inheritance and research of the academic thoughts and clinical experience of famous veteran TCM doctors. © 2023 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.","artificial intelligence; pediatric asthma; Traditional Chinese Medicine","Artificial Intelligence; Asthma; Child; Drugs, Chinese Herbal; Humans; Medicine, Chinese Traditional; Physicians; Chinese drug; plant medicinal product; herbaceous agent; algorithm; Article; artificial intelligence; asthma; Astragalus membranaceus; Chinese medicine; chronic obstructive lung disease; Coptis chinensis; decision making; decision tree; deep learning; human; inheritance; lung cancer; machine learning; Magnolia officinalis; medical record; peak expiratory flow; prescription; Pueraria; Schisandra; Schisandra chinensis; support vector machine; Trichosanthes; asthma; child; Chinese medicine; physician","","Drugs, Chinese Herbal, ","","","Peking University, PKU","The author would like to thanks to Prof. Tao Kai for the technical guidance of this research, thanks to Guan Shijie, Zheng Jiayi, Han Yu, and Wang Zhao of Peking University for providing technical support for machine learning experiments, and thanks to Institute of Computing and Social Sciences of Peking University (Qingdao) for supporting this work project.","Lei H.W., Zhou C.E., Yang Z.Y., Li C.D., Research on the core elements of the inheritance of famous old Traditional Chinese Medicine doctors experience, Chin J Trad Chin Med Pharm, 36, 8, pp. 4824-4826, (2021); Guo Y., Ren X., Chen Y.X., Wang T.J., Artificial intelligence meets Chinese medicine, Chin J Integr Med, 25, 9, pp. 648-653, (2019); Fang Y.T., Lan Q., Xie T., Liu Y.F., Mei S.Y., Zhu B.F., New opportunities and challenges for forensic medicine in the era of artificial intelligence technology, J Forensic Med, 36, 1, pp. 77-85, (2020); Hu C.Y., Zhang S.Y., Gu T.Y., Yan Z.Z., Jiang J.H., Multi-task joint learning model for Chinese word segmentation and syndrome differentiation in Traditional Chinese Medicine, Int J Environ Res Public Health, 19, 9, (2022); Wang S.H., Hou Y., Li X.H., Meng X.L., Zhang Y., Wang X.B., Practical implementation of artificial intelligence-based deep learning and cloud computing on the application of traditional medicine and Western medicine in the diagnosis and treatment of rheumatoid arthritis, Front Pharmacol, 12, (2021); Hu C., Yan Z., Jiang J., Zhang S., Gu T., Traditional Chinese Medicine information analysis based on multi-task joint learning model, The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021), (2022); Wang Y.F., Wang J.J., Peng W., Et al., Identification of hypertension subgroups through topological analysis of symptom-based patient similarity, Chin J Integr Med, 27, 9, pp. 656-665, (2021); Ung C.Y., Li H., Cao Z.W., Li Y.X., Chen Y.Z., were herb-pairs of Traditional Chinese Medicine distinguishable from others? Pattern analysis and artificial intelligence classification study of traditionally defined herbal properties, J Ethnopharmacol, 111, 2, pp. 371-377, (2007); Chen K.J., Innovative modernization and industrialization of Traditional Chinese Medicine, Chin J Integr Med, 26, 8, pp. 563-664, (2020); Liao J., Wang J.C., Yu Y., Wen B., Deng X., Lin J., Application of artificial intelligence in the inheritance of famous TCM expert, China J Tradit Chin Med Pharm, 35, 4, pp. 1671-1674, (2020); Song Y.J., Zhao B., Jia J., Et al., A review on different kinds of artificial intelligence solutions in TCM syndrome differentiation application, Evid-Based Complement Altern Med, (2021); Esteva A., Robicquet A., Ramsundar B., Et al., A guide to deep learning in healthcare, Nat Med, 25, 1, pp. 24-29, (2019); Topol E.J., High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 1, pp. 44-56, (2019); Cheng H.-T., Koc L., Harmsen J., Et al., Wide & deep learning for recommender systems, Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7-10, (2016); Chen W., Zhang X., Opinion-aware knowledge graph for political ideology detection, 26th International Joint Conference on Artificial Intelligence (IJCAI), (2017); Han Y., Chen W., Xiong X., Li Q., Qiu Z., Wang T., Wide & deep learning for improving named entity recognition via text-aware named entity normalization[C], Proceedings of The AAAI-19 Workshop on Recommender Systems and Natural Language Processing (RECNLP), Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 19), (2019); Wang H., Li S., Wang T., Zheng J., Hierarchical adaptive temporal-relational modeling for stock trend prediction, pp. 3691-3698, (2021); Hong J.G., Review of and reflections on the current status of childhood asthma diagnosis and treatment in China, J Sichuan Univ (Med Sci), 52, 5, pp. 725-728, (2021); Devonshire A.L., Kumar R., Pediatric asthma: principles and treatment, Allergy Asthma Proc, 40, 6, pp. 389-392, (2019); Li X.M., Complementary and alternative medicine in pediatric allergic disorders, Curr Opin Allergy Clin Immunol, 9, 2, (2009); Wang X., Chen Y., Zhang M., Zhong S., Montelukast sodium combined with Yupingfeng Granules in the treatment of children with bronchial asthma, J Pediatr Pharm, 25, 4, pp. 31-34, (2019); Tao K., Ren X., Qian C., Summary of Clinical Syndrome of TCM Pulmonary Department, (2021); Wang H.Y., Wang T.J., Li Y., Assoc Advancement Artificial I, editors. Incorporating expert-based investment opinion signals in stock prediction: a deep learning framework, 34th AAAI Conference on Artificial Intelligence/32nd Innovative Applications of Artificial Intelligence Conference/10th AAAI Symposium on Educational Advances in Artificial Intelligence, (2020); Wang T.J., Li B.Y., Chen W., Et al., An environment-aware market strategy for data allocation and dynamic migration in cloud database, IEEE 35th International Conference on Data Engineering (ICDE), (2019); Guo Y., Wang T.J., Chen W., Et al., Acceptability of Traditional Chinese Medicine in Chinese people based on 10-year's real-world study with multiple big data mining, Frontiers in Public Health, 9, (2022); Gholamy A., Kreinovich V., Kosheleva O., Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation, (2018); Chan M., Supporting the integration and modernization of traditional medicine, Science, 346, 6216, (2014); Xu Q., Bauer R., Hendry B.M., Et al., The quest for modernisation of Traditional Chinese Medicine, BMC Complement Altern Med, 13, (2013); Zhai X., Wang X., Wang L., Et al., Treating different diseases with the same method—a Traditional Chinese Medicine concept analyzed for its biological basis, Front Pharmacol, 11, (2020); Gu T.Y., Yan Z.Z., Jiang J.H., Classifying Chinese Medicine constitution using multimodal deep-learning model, Chin J Integr Med","Y. Guo; Xiyuan Hospital, China Academy of Chinese Medicinal Sciences, Beijing, 100091, China; email: guoyan0314@126.com","","John Wiley and Sons Inc","","","","","","17565383","","","36938964","English","J. Evid.-Based Med.","Article","Final","","Scopus","2-s2.0-85150786504"
"Rule A.D.; Grossardt B.R.; Weston A.D.; Garner H.W.; Kline T.L.; Chamberlain A.M.; Allen A.M.; Erickson B.J.; Rocca W.A.; St. Sauver J.L.","Rule, Andrew D. (7006286679); Grossardt, Brandon R. (8673768000); Weston, Alexander D. (57200394792); Garner, Hillary W. (25930857600); Kline, Timothy L. (26648884000); Chamberlain, Alanna M. (23484092100); Allen, Alina M. (55934369600); Erickson, Bradley J. (7201472755); Rocca, Walter A. (7006276394); St. Sauver, Jennifer L. (8042985400)","7006286679; 8673768000; 57200394792; 25930857600; 26648884000; 23484092100; 55934369600; 7201472755; 7006276394; 8042985400","Older Tissue Age Derived From Abdominal Computed Tomography Biomarkers of Muscle, Fat, and Bone Is Associated With Chronic Conditions and Higher Mortality","2024","Mayo Clinic Proceedings","99","6","","878","890","12","7","10.1016/j.mayocp.2023.09.021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184067492&doi=10.1016%2fj.mayocp.2023.09.021&partnerID=40&md5=f4c248eb9e3a37dba0e83bfaa2066c87","Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States; Division of Nephrology and Hypertension, United States; Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States; Department of Radiology, Mayo Clinic, Rochester, MN, United States; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States; Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States; Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, United States; Department of Neurology, Mayo Clinic, Rochester, MN, United States; Women's Health Research Center, Mayo Clinic, Rochester, MN, United States; The Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States; Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States; Division of Musculoskeletal Radiology, Department of Radiology, Mayo Clinic, Jacksonville, FL, United States","Rule A.D., Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States, Division of Nephrology and Hypertension, United States; Grossardt B.R., Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States; Weston A.D., Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States; Garner H.W., Division of Musculoskeletal Radiology, Department of Radiology, Mayo Clinic, Jacksonville, FL, United States; Kline T.L., Department of Radiology, Mayo Clinic, Rochester, MN, United States; Chamberlain A.M., Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States; Allen A.M., Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States; Erickson B.J., Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, United States; Rocca W.A., Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States, Department of Neurology, Mayo Clinic, Rochester, MN, United States, Women's Health Research Center, Mayo Clinic, Rochester, MN, United States; St. Sauver J.L., Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States, The Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States","Objective: To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age. Methods: We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model. Results: Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions. Conclusion: Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging. © 2023 Mayo Foundation for Medical Education and Research","","Adult; Age Factors; Aged; Aged, 80 and over; Aging; Biomarkers; Body Composition; Chronic Disease; Female; Humans; Male; Middle Aged; Minnesota; Muscle, Skeletal; Tomography, X-Ray Computed; Wisconsin; Young Adult; biological marker; biological marker; age; aged; aging; arthritis; Article; behavior disorder; body composition; body fat; body mass; bone density; cancer diagnosis; cardiovascular disease; cause of death; chronic disease; chronic obstructive lung disease; cohort analysis; computer assisted tomography; convolutional neural network; deep learning; education; ethnicity; female; fitness; human; intra-abdominal fat; male; mortality; mortality risk; muscle area; muscle density; musculoskeletal system parameters; obesity; positron emission tomography-computed tomography; propensity score; quality control; race; schizophrenia; sensitivity analysis; subcutaneous fat; very elderly; adult; age; body composition; diagnostic imaging; epidemiology; middle aged; Minnesota; physiology; procedures; skeletal muscle; Wisconsin; x-ray computed tomography; young adult","","Biomarkers, ","","","Mayo Clinic; National Institute on Aging, NIA, (AG 058738)","Grant Support: This study used the resources of the Rochester Epidemiology Project (REP) medical records-linkage system, which is supported by the National Institute on Aging (AG 058738), by the Mayo Clinic Research Committee, and by fees paid annually by REP users. The content of this article is solely the responsibility of the authors and does not represent the official views of the National Institutes of Health or of the Mayo Clinic. Dr Rocca was partly funded by the Ralph S. and Beverley E. Caulkins Professorship of Neuro-degenerative Disease Research of the Mayo Clinic. 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Van der Werf A., Langius J., De Van Der Schueren M., Et al., Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population, Eur J Clin Nutr, 72, 2, pp. 288-296, (2018); Kong M., Geng N., Zhou Y., Et al., Defining reference values for low skeletal muscle index at the L3 vertebra level based on computed tomography in healthy adults: a multicentre study, Clin Nutr, 41, 2, pp. 396-404, (2022); Beheshti I., Nugent S., Potvin O., Duchesne S., Bias-adjustment in neuroimaging-based brain age frameworks: a robust scheme, Neuroimage Clin, 24, (2019); de Lange A.G., Cole J.H., Commentary: correction procedures in brain-age prediction, Neuroimage Clin, 26, (2020); Thiebaut A.C., Benichou J., Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study, Stat Med, 23, 24, pp. 3803-3820, (2004); Abadi M., Barham P., Chen J., Et al., (2023); Perez L.M., Pareja-Galeano H., Sanchis-Gomar F., Emanuele E., Lucia A., Galvez B.G., ‘Adipaging’: ageing and obesity share biological hallmarks related to a dysfunctional adipose tissue, J Physiol, 594, 12, pp. 3187-3207, (2016); Huffman D.M., Barzilai N., Role of visceral adipose tissue in aging, Biochim Biophys Acta, 1790, 10, pp. 1117-1123, (2009); Wilkinson D.J., Piasecki M., Atherton P.J., The age-related loss of skeletal muscle mass and function: measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans, Ageing Res Rev, 47, pp. 123-132, (2018); Pahor M., Manini T., Cesari M., Sarcopenia: clinical evaluation, biological markers and other evaluation tools, J Nutr Health Aging, 13, 8, pp. 724-728, (2009); Zhuang J., Chen X., Cai G., Et al., Age-related accumulation of advanced oxidation protein products promotes osteoclastogenesis through disruption of redox homeostasis, Cell Death Dis, 12, 12, (2021); Pomchote P., Age-related changes in osteometry, bone mineral density and osteophytosis of the lumbar vertebrae in Japanese macaques, Primates, 56, 1, pp. 55-70, (2015); Prescher A., Anatomy and pathology of the aging spine, Eur J Radiol, 27, 3, pp. 181-195, (1998); Jang S., Graffy P.M., Ziemlewicz T.J., Lee S.J., Summers R.M., Pickhardt P.J., Opportunistic osteoporosis screening at routine abdominal and thoracic CT: normative L1 trabecular attenuation values in more than 20 000 adults, Radiology, 291, 2, pp. 360-367, (2019); Berrington de Gonzalez A., Hartge P., Cerhan J.R., Et al., Body-mass index and mortality among 1.46 million white adults, N Engl J Med, 363, 23, pp. 2211-2219, (2010); Prado C.M., Gonzalez M.C., Heymsfield S.B., Body composition phenotypes and obesity paradox, Curr Opin Clin Nutr Metab Care, 18, 6, pp. 535-551, (2015); Rocca W.A., Grossardt B.R., Boyd C.M., Chamberlain A.M., Bobo W.V., St Sauver J.L., Multimorbidity, ageing and mortality: normative data and cohort study in an American population, BMJ Open, 11, 3, (2021); Fabbri E., Zoli M., Gonzalez-Freire M., Salive M.E., Studenski S.A., Ferrucci L., Aging and multimorbidity: new tasks, priorities, and frontiers for integrated gerontological and clinical research, J Am Med Dir Assoc, 16, 8, pp. 640-647, (2015); Vetrano D.L., Calderon-Larranaga A., Marengoni A., Et al., An international perspective on chronic multimorbidity: approaching the elephant in the room, J Gerontol A Biol Sci Med Sci, 73, 10, pp. 1350-1356, (2018); Guzon-Illescas O., Perez Fernandez E., Crespi Villarias N., Et al., Mortality after osteoporotic hip fracture: incidence, trends, and associated factors, J Orthop Surg Res, 14, 1, (2019); Neeland I.J., Ross R., Despres J.P., Et al., Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement, Lancet Diabetes Endocrinol, 7, 9, pp. 715-725, (2019); Brock J.M., Billeter A., Muller-Stich B.P., Herth F., Obesity and the lung: what we know today, Respiration, 99, 10, pp. 856-866, (2020); Hommos M.S., Glassock R.J., Rule A.D., Structural and functional changes in human kidneys with healthy aging, J Am Soc Nephrol, 28, 10, pp. 2838-2844, (2017); Wang R.C., Stoller M.L., Smith-Bindman R., Diagnostic imaging for kidney stones, JAMA, 324, 14, pp. 1464-1465, (2020); Rule A.D., Lieske J.C., Pais V.M., Diagnostic Imaging for kidney stones—reply, JAMA, 324, 14, (2020)","A.D. Rule; Division of Nephrology and Hypertension, Mayo Clinic, Rochester, 200 First St SW, 55905, United States; email: rule.andrew@mayo.edu","","Elsevier Ltd","","","","","","00256196","","MACPA","38310501","English","Mayo Clin. Proc.","Article","Final","","Scopus","2-s2.0-85184067492"
"Yao H.; Wang L.; Zhou X.; Jia X.; Xiang Q.; Zhang W.","Yao, Hao (57218844610); Wang, Lingya (57218275466); Zhou, Xinyu (58654417700); Jia, Xiaoxiao (57194217672); Xiang, Qiangwei (55201990700); Zhang, Weixi (7409432147)","57218844610; 57218275466; 58654417700; 57194217672; 55201990700; 7409432147","Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques","2023","Computers in Biology and Medicine","166","","107544","","","","6","10.1016/j.compbiomed.2023.107544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174444615&doi=10.1016%2fj.compbiomed.2023.107544&partnerID=40&md5=fd0eb8a5eaf736d473d3af59c86ab292","Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China","Yao H., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Wang L., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Zhou X., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Jia X., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Xiang Q., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; Zhang W., Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China","Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4–17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this paper proposed a predictive framework for the efficacy of mite subcutaneous immunotherapy in asthma based on machine learning techniques. Introducing the dispersed foraging strategy into the Salp Swarm Algorithm (SSA), a new improved algorithm named DFSSA is proposed. This algorithm effectively alleviates the imbalance between search speed and traversal caused by the fixed partitioning pattern in traditional SSA. Utilizing the fusion of boosting algorithm and kernel extreme learning machine, an AIT performance prediction model was established. To further investigate the effectiveness of the DFSSA-KELM model, this study conducted an auxiliary diagnostic experiment using the immunotherapy predictive medical data collected by the hospital. The findings indicate that selected indicators, such as blood basophil count, sIgE/tIgE (Der p) and sIgE/tIgE (Der f), play a crucial role in predicting treatment outcome. The classification results showed an accuracy of 87.18% and a sensitivity of 93.55%, indicating that the prediction model is an effective and accurate intelligent tool for evaluating the efficacy of AIT. © 2023 The Authors","Allergen immunotherapy; Bronchial asthma; Machine learning; Swarm intelligence","Adolescent; Algorithms; Allergens; Asthma; Child; Child, Preschool; Desensitization, Immunologic; Female; Humans; Immunoglobulin E; Machine Learning; Male; Allergens; Diagnosis; Diseases; Forecasting; Learning algorithms; Swarm intelligence; allergen; immunoglobulin E; allergen; immunoglobulin E; Allergen detection; Allergen immunotherapy; Bronchial asthma; Clinical characteristics; Machine learning techniques; Machine-learning; Metric learning; Salp swarms; Swarm algorithms; Therapeutic efficacy; adolescent; allergic rhinitis; Article; asthma; basophil count; child; clinical effectiveness; clinical feature; controlled study; desensitization; dispersed foraging strategy into the salp swarm algorithm; female; human; machine learning; major clinical study; male; measurement accuracy; mite; nonhuman; outcome assessment; predictive model; predictive value; salp swarm algorithm; sensitivity and specificity; subcutaneous immunotherapy; algorithm; asthma; blood; immunology; preschool child; procedures; therapy; Machine learning","","immunoglobulin E, 37341-29-0; Allergens, ; Immunoglobulin E, ","","","Wenzhou Municipal Science and Technology Bureau, WMSTB, (Y20190431); Wenzhou Municipal Science and Technology Bureau, WMSTB; Medical Science and Technology Project of Zhejiang Province, (WKJ-ZJ-2133); Medical Science and Technology Project of Zhejiang Province","This work was financially supported by the Zhejiang Provincial Health Science and Technology Major Projects ( WKJ-ZJ-2133 ); and the Wenzhou Municipal Science and Technology Bureau program ( Y20190431 ).","Network G.A., The global asthma report 2022, Int. 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Zhang; Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; email: zhangweixi112@163.com; Q. Xiang; Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China; email: 7088269@qq.com","","Elsevier Ltd","","","","","","00104825","","CBMDA","37866086","English","Comput. Biol. Med.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85174444615"
"Zhuang Y.; Xing F.; Ghosh D.; Banaei-Kashani F.; Bowler R.P.; Kechris K.","Zhuang, Yonghua (59057320700); Xing, Fuyong (38461688800); Ghosh, Debashis (57201786914); Banaei-Kashani, Farnoush (6602315096); Bowler, Russell P. (56773748500); Kechris, Katerina (6507018968)","59057320700; 38461688800; 57201786914; 6602315096; 56773748500; 6507018968","An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning","2022","Frontiers in Genetics","12","","760299","","","","5","10.3389/fgene.2021.760299","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124535742&doi=10.3389%2ffgene.2021.760299&partnerID=40&md5=cba571bad7892c6b1a648159b6563628","Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States; National Jewish Health, Denver, CO, United States","Zhuang Y., Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Xing F., Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Ghosh D., Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Banaei-Kashani F., Department of Computer Science and Engineering, University of Colorado Denver, Denver, CO, United States; Bowler R.P., National Jewish Health, Denver, CO, United States; Kechris K., Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States","Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information. Copyright © 2022 Zhuang, Xing, Ghosh, Banaei-Kashani, Bowler and Kechris.","Gaussian graphical model; gene network; graphical Lasso; protein-protein interaction; systems biology","biological marker; ELAV like protein 2; messenger RNA; Akaike Information Criteria; algorithm; Article; chronic obstructive lung disease; classification algorithm; computer model; computer prediction; computer simulation; controlled study; correlation coefficient; cross validation; dyspnea; Extended Bayesian Information Criteria; Gaussian graphical model; gene expression; gene network analysis; gene ontology; gene set analysis; gene set enrichment analysis; global network learning; High Dimensional Graphical Lasso Method; human; Hyper Parameter Tuning; least absolute shrinkage and selection operator; lung function; lung volume; machine learning; major clinical study; multiomics; network learning; omics; prediction; prediction error; professional knowledge; protein expression; protein protein interaction; proteomics; quantitative structure activity relation; regression model; runtime; sensitivity analysis; signal transduction; simulation; statistical parameters; systems biology; weighted Glasso method","","","Blake","","BioData Catalyst program, (1OT3HL142478-01, 1OT3HL142479-01, 1OT3HL142480-01, 1OT3HL142481-01, 1OT3HL147154-01, U01 HL089856, U01 HL089897); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (R01 HL137995, R01 HL152735, R01HL125583); National Heart, Lung, and Blood Institute, NHLBI","This work was supported by NIH/NHLBI R01 HL152735 (KK, RB, FB-K, and YZ), R01 HL137995 (KK, RB, and FB-K) and R01HL125583 (KK and YZ). Additional support for this work was provided by the National Institutes of Health, National Heart, Lung, and Blood Institute, through the BioData Catalyst program (award 1OT3HL142479-01, 1OT3HL142478-01, 1OT3HL142481-01, 1OT3HL142480-01, 1OT3HL147154-01). Any opinions expressed in this document are those of the author(s) and do not necessarily reflect the views of NHLBI, individual BDCatalyst team members, or affiliated organizations and institutions. The project described was supported by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health.","Alexa M., Adamson A., Interpolatory point Set Surfaces-Convexity and Hermite Data, ACM Trans. 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World war ii, 1, (1949); Szklarczyk D., Morris J.H., Cook H., Kuhn M., Wyder S., Simonovic M., Et al., The String Database in 2017: Quality-Controlled Protein–Protein Association Networks, Made Broadly Accessible, Nucleic Acids Res, (2016); Wang M.-G., Ou-Yang L., Yan H., Zhang X.-F., Inferring Gene Co-expression Networks by Incorporating Prior Protein-Protein Interaction Networks, IEEE/ACM Trans. Comput. Biol. Bioinform, (2021); Wickham H., ggplot2: Elegant Graphics for Data Analysis, (2009); Wickham H., The Split-Apply-Combine Strategy for Data Analysis, J. Stat. Softw, 40, pp. 1-29, (2011); Xu B., Liu Y., Lin C., Dong J., Liu X., He Z., Reconstruction of the Protein-Protein Interaction Network for Protein Complexes Identification by Walking on the Protein Pair Fingerprints Similarity Network, Front. Genet, 9, (2018); Zhang B., Tian Y., Zhang Z., Network Biology in Medicine and beyond, Circ. Cardiovasc. Genet, 7, pp. 536-547, (2014); Zhang G., He P., Tan H., Budhu A., Gaedcke J., Ghadimi B.M., Et al., Integration of Metabolomics and Transcriptomics Revealed a Fatty Acid Network Exerting Growth Inhibitory Effects in Human Pancreatic Cancer, Clin. Cancer Res, 19, pp. 4983-4993, (2013); Zhang R., Fattahi S., Sojoudi S., Large-scale Sparse Inverse Covariance Estimation via Thresholding and max-det Matrix Completion, Int. Conf. Machine Learn. (Pmlr), pp. 5766-5775, (2018); Zhao T., Liu H., Roeder K., Lafferty J., Wasserman L., The Huge Package for High-Dimensional Undirected Graph Estimation in R, J. Mach Learn. Res, 13, pp. 1059-1062, (2012); Zuo Y., Cui Y., Yu G., Li R., Ressom H.W., Incorporating Prior Biological Knowledge for Network-Based Differential Gene Expression Analysis Using Differentially Weighted Graphical Lasso, BMC bioinformatics, 18, pp. 99-14, (2017)","Y. Zhuang; Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, United States; email: Yonghua.Zhuang@cuanschutz.edu; K. Kechris; Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, United States; email: Katerina.Kechris@cuanschutz.edu","","Frontiers Media S.A.","","","","","","16648021","","","","English","Front. Genet.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85124535742"
"Nazir A.; Ampadu H.K.","Nazir, Amril (24723004500); Ampadu, Hyacinth Kwadwo (57699537300)","24723004500; 57699537300","Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients","2022","PeerJ Computer Science","8","","e889","","","","6","10.7717/peerj-cs.889","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130388556&doi=10.7717%2fpeerj-cs.889&partnerID=40&md5=1c553713d046e49001ea08b2ad77237c","Department of Information Systems and Technology Management, College of Technological Innovation Zayed University, Abu Dhabi, United Arab Emirates; Old Ahinsan, Kumasi, Ghana","Nazir A., Department of Information Systems and Technology Management, College of Technological Innovation Zayed University, Abu Dhabi, United Arab Emirates; Ampadu H.K., Old Ahinsan, Kumasi, Ghana","The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios. © 2022 Nazir and Ampadu","Covid-19; Interpretable deep learning; Prediction of icu admission; Prediction of mortality","Deep learning; Intensive care units; Comorbidities; Covid-19; Healthcare resources; Healthcare systems; High-risk patients; Interpretable deep learning; Learning models; Patient information; Prediction of icu admission; Prediction of mortality; Forecasting","","","","","","","Abdi H, Williams LJ., Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics, 2, 4, pp. 433-459, (2010); Arik SO, Pfister T., TabNet: attentive interpretable tabular learning, (2019); Bogoch II, Watts A, Thomas-Bachli A, Huber C, Kraemer MU, Khan K., Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel, Journal of Travel Medicine, 27, 2, (2020); Bozkurt FT, Tercan M, Patmano G, Tanrverdi TB, Demir HA, Yurekli UF., Can ferritin levels predict the severity of illness in patients with covid-19?, Cureus, 13, 1, (2021); Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP., Smote: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, pp. 321-357, (2002); Dahan S, Segal G, Katz I, Hellou T, Tietel M, Bryk G, Amital H, Shoenfeld Y, Dagan A., (2020); Ferritin as a marker of severity in covid-19 patients: a fatal correlation, The Israel Medical Association Journal, 22, 8, pp. 494-500; Dinevari MF, Somi MH, Majd ES, Farhangi MA, Nikniaz Z., Anemia predicts poor outcomes of COVID-19 in hospitalized patients: a prospective study in Iran, BMC Infectious Diseases, 21, 1, pp. 1-7, (2021); Fernandes FT, de Oliveira TA, Teixeira CE, de Moraes Batista AF, Dalla Costa G, Chiavegatto Filho ADP., A multipurpose machine learning approach to predict COVID-19 negative prognosis in Säo Paulo, Brazil, Scientific Reports, 11, 1, pp. 1-7, (2021); Gerayeli FV, Milne S, Cheung C, Li X, Yang CWT, Tam A, Choi LH, Bae A, Sin DD., COPD and the risk of poor outcomes in COVID-19: a systematic review and meta-analysis, EClinicalMedicine, 33, (2021); Ghiringhelli LM., Interpretability of machine-learning models in physical sciences, (2021); Goic M, Bozanic-Leal MS, Badal M, Basso LJ., COVID-19: short-term forecast of ICU beds in times of crisis, PLOS ONE, 16, 1, (2021); Gorsuch RL., Exploratory factor analysis, (2013); Graziani D, Soriano JB, Rio-Bermudez D, Morena D, Daz T, Castillo M, Alonso M, Ancochea J, Lumbreras S, Izquierdo JL., Characteristics and prognosis of COVID-19 in patients with COPD, Journal of Clinical Medicine, 9, 10, (2020); He H, Bai Y, Garcia EA, Li S., Adasyn: adaptive synthetic sampling approach for imbalanced learning, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328, (2008); Hyvarinen A., Fast ICA for noisy data using Gaussian moments, 1999 IEEE International Symposium on Circuits and Systems (ISCAS), 5, pp. 57-61, (1999); Ikemura K, Bellin E, Yagi Y, Billett H, Saada M, Simone K, Stahl L, Szymanski J, Goldstein D, Gil MR., Using automated machine learning to predict the mortality of patients with COVID-19: prediction model development study, Journal of Medical Internet Research, 23, 2, (2021); Leung K, Wu JT, Liu D, Leung GM., First-wave covid-19 transmissibility and severity in china outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment, The Lancet, 395, 10233, pp. 1382-1393, (2020); Li X, Ge P, Zhu J, Li H, Graham J, Singer A, Richman PS, Duong TQ., Deep learning prediction of likelihood of icu admission and mortality in COVID-19 patients using clinical variables, PeerJ, 8, 8844, (2020); Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY, Xing X, Xiang N, Wu Y, Li C, Chen Q, Li D, Liu T, Zhao J, Liu M, Tu W, Chen C, Jin L, Yang R, Wang Q, Zhou S, Wang R, Liu H, Luo Y, Liu Y, Shao G, Li H, Tao Z, Yang Y, Deng Z, Liu B, Ma Z, Zhang Y, Shi G, Wu TTYJT, Gao GF, Cowling BJ, Yang B, Leung GM, Feng Z., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, New England Journal of Medicine, 385, pp. 1199-1207, (2020); Lino K, Guimaraes GMC, Alves LS, Oliveira AC, Faustino R, Fernandes CS, Tupinamba G, Medeiros T, Silva AAD, Almeida JR., Serum ferritin at admission in hospitalized COVID-19 patients as a predictor of mortality, Brazilian Journal of Infectious Diseases, 25, 2, (2021); Manca D, Caldiroli D, Storti E., A simplified math approach to predict ICU beds and mortality rate for hospital emergency planning under COVID-19 pandemic, Computers & Chemical Engineering, 140, (2020); Martins A, Astudillo R., From softmax to sparsemax: a sparse model of attention and multi-label classification, International Conference on Machine Learning, PMLR, pp. 1614-1623, (2016); McInnes L, Healy J, Melville J., Umap: uniform manifold approximation and projection for dimension reduction, (2018); Moreira RDS., COVID-19: intensive care units, mechanical ventilators, and latent mortality profiles associated with case-fatality in Brazil, Cadernos de Saude Publica, 36, 5, (2020); Pardhan S, Wood S, Vaughan M, Trott M., The risk of COVID-19 related hospitalsation, intensive care unit admission and mortality in people with underlying asthma or COPD: a systematic review and meta-analysis, Frontiers in Medicine, 8, (2021); Pourhomayoun M, Shakibi M., Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making, Smart Health, 20, 2, (2021); Rodriguez-Morales AJ, Cardona-Ospina JA, Gutierrez-Ocampo E, Villamizar-Pena R, Holguin-Rivera Y, Escalera-Antezana JP, Alvarado-Arnez LE, Bonilla-Aldana DK, Franco-Paredes C, Henao-Martinez AF, Paniz-Mondolfi A, Lagos-Grisales GJ, Ramirez-Vallejo E, Suarez JA, Zambrano LI, Villamil-Gomez WE, Balbin-Ramon GJ, Rabaan AA, Harapan H, Dhama K, Nishiura H, Kataoka H, Ahmad T, Sah R., Clinical, laboratory and imaging features of covid-19: a systematic review and meta-analysis, Travel Medicine and Infectious Disease, 34, (2020); Venkata VS, Kiernan G., COVID-19 and COPD: pooled analysis of observational studies, Chest, 158, 4, (2020); Wattenberg M, Viegas F, Johnson I., How to use t-sne effectively, Distill, 1, 10, (2016); Yoon J, Jordon J, van der Schaar M., Invase: instance-wise variable selection using neural networks, International Conference on Learning Representations, (2018); Yu L, Halalau A, Dalal B, Abbas AE, Ivascu F, Amin M, Nair GB., Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19, PLOS ONE, 16, 4, (2021); Zeng X, Martinez TR., Distribution-balanced stratified cross-validation for accuracy estimation, Journal of Experimental & Theoretical Artificial Intelligence, 12, 1, pp. 1-12, (2000)","A. Nazir; Department of Information Systems and Technology Management, College of Technological Innovation Zayed University, Abu Dhabi, United Arab Emirates; email: mohd.nazir@zu.ac.ae","","PeerJ Inc.","","","","","","23765992","","","","English","PeerJ Comput. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130388556"
"Zysman M.; Asselineau J.; Saut O.; Frison E.; Oranger M.; Maurac A.; Charriot J.; Achkir R.; Regueme S.; Klein E.; Bommart S.; Bourdin A.; Dournes G.; Casteigt J.; Blum A.; Ferretti G.; Degano B.; Thiébaut R.; Chabot F.; Berger P.; Laurent F.; Benlala I.","Zysman, Maéva (55745420200); Asselineau, Julien (15924785200); Saut, Olivier (56062195400); Frison, Eric (56436376600); Oranger, Mathilde (57218611931); Maurac, Arnaud (57201820982); Charriot, Jeremy (56079342800); Achkir, Rkia (58476103500); Regueme, Sophie (6506222830); Klein, Emilie (57200016031); Bommart, Sébastien (14324181700); Bourdin, Arnaud (7801311848); Dournes, Gael (55440990600); Casteigt, Julien (57202989648); Blum, Alain (7402674839); Ferretti, Gilbert (8748871000); Degano, Bruno (6603321564); Thiébaut, Rodolphe (7004041016); Chabot, Francois (56275056000); Berger, Patrick (7402866064); Laurent, Francois (7101921631); Benlala, Ilyes (57203886518)","55745420200; 15924785200; 56062195400; 56436376600; 57218611931; 57201820982; 56079342800; 58476103500; 6506222830; 57200016031; 14324181700; 7801311848; 55440990600; 57202989648; 7402674839; 8748871000; 6603321564; 7004041016; 56275056000; 7402866064; 7101921631; 57203886518","Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19","2023","European Radiology","33","12","","9262","9274","12","7","10.1007/s00330-023-09759-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164134609&doi=10.1007%2fs00330-023-09759-x&partnerID=40&md5=96232048264a4d96a8a0cc4086c58545","CHU Bordeaux, Pessac, 33600, France; Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France; Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; “Institut de Mathématiques de Bordeaux” (IMB), UMR5251, CNRS, University of Bordeaux, 351 Cours Libération, Talence, 33400, France; MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, Talence, 33400, France; Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d’Imagerie, Nancy, France; Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France; Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, Montpellier, 34295, France; PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, Montpellier, 34295, France; Pneumology Clinic, St Médard en Jalles, France; France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France","Zysman M., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; Asselineau J., CHU Bordeaux, Pessac, 33600, France; Saut O., “Institut de Mathématiques de Bordeaux” (IMB), UMR5251, CNRS, University of Bordeaux, 351 Cours Libération, Talence, 33400, France, MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, Talence, 33400, France; Frison E., CHU Bordeaux, Pessac, 33600, France; Oranger M., Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d’Imagerie, Nancy, France, Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France; Maurac A., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; Charriot J., Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, Montpellier, 34295, France, PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, Montpellier, 34295, France; Achkir R., CHU Bordeaux, Pessac, 33600, France; Regueme S., CHU Bordeaux, Pessac, 33600, France; Klein E., CHU Bordeaux, Pessac, 33600, France; Bommart S., Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, Montpellier, 34295, France, PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, Montpellier, 34295, France; Bourdin A., Department of Respiratory Diseases, Arnaud de Villeneuve Hospital, Montpellier University Hospital, CEDEX 5, Montpellier, 34295, France, PhyMedExp, University of Montpellier, INSERM U1046, CEDEX 5, Montpellier, 34295, France; Dournes G., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; Casteigt J., Pneumology Clinic, St Médard en Jalles, France; Blum A., Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d’Imagerie, Nancy, France; Ferretti G., France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France; Degano B., France Service de Radiologie Diagnostique Et Interventionnelle, Université Grenoble Alpes, CHU Grenoble-Alpes, Grenoble, France; Thiébaut R., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France, MONC Team & SISTM Team, INRIA Bordeaux Sud-Ouest, 200 Av Vieille Tour, Talence, 33400, France; Chabot F., Pôle Des Spécialités Médicales/Département de Pneumologie, Université de Lorraine, Centre Hospitalier Régional Universitaire (CHRU) Nancy, Service de Radiologie Et d’Imagerie, Nancy, France, Faculté de Médecine de Nancy, Université de Lorraine, Institut National de La Santé Et de La Recherche Médicale (INSERM) Unité Médicale de Recherche (UMR), S 1116, Vandœuvre-Lès-Nancy, France; Berger P., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; Laurent F., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France; Benlala I., CHU Bordeaux, Pessac, 33600, France, Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, 33600, France, Centre de Recherche Cardio-Thoracique de Bordeaux (U1045), Centre d’Investigation Clinique, INSERM, Bordeaux Population Health (U1219), (CIC-P 1401), Pessac, 33600, France","Objectives: COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. Methods: Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms’ onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. Results: Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. Conclusion: Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. Clinical Trial Registration: NCT04481620. Clinical relevance statement: CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. Key Points: • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77. © 2023, The Author(s).","Artificial intelligence; Clinical decision rules; COVID-19; Tomography, X-ray computed","COVID-19; Humans; Models, Statistical; Pandemics; Prognosis; Retrospective Studies; SARS-CoV-2; C reactive protein; age; aged; Article; artificial intelligence; asthma; cardiovascular disease; chronic obstructive lung disease; chronic respiratory tract disease; cohort analysis; comorbidity; computer assisted tomography; controlled study; convolutional neural network; coronary artery disease; coronavirus disease 2019; coughing; cross validation; current smoker; deterioration; diabetes mellitus; disease exacerbation; disease severity; dyspnea; external validity; feature extraction; feature selection; female; fever; gender; ground glass opacity; high risk patient; human; human cell; hypertension; image analysis; immune deficiency; interstitial lung disease; lung consolidation; lymphocyte; machine learning; major clinical study; male; multicenter study; obesity; outcome assessment; oxygen therapy; predictive model; private hospital; qualitative analysis; quantitative analysis; radiomics; real time polymerase chain reaction; respiratory tract disease; risk assessment; smoking; university hospital; pandemic; prognosis; retrospective study; Severe acute respiratory syndrome coronavirus 2; statistical model","","C reactive protein, 9007-41-4","CT Pneumonia Analysis prototype version 1.0.4.2, Siemens Healthineers","Siemens Healthineers","Brigitte Risse; CHU Bordeaux; Fondation MSDAvenir; Interregional Hospital Program of Clinical Research","Funding text 1: The authors thank Rkia Achkir (service d’imagerie thoracique, CHU Bordeaux) and Sophie Regueme (direction de la recherche clinique et de l’innovation, CHU Bordeaux) for their assistance and support in study coordination, Severine Martiren and Romain Griffier (CHU Bordeaux) for data management activities, and Brigitte Risse (Eclor°) for data entry and data monitoring. The work of OS was supported by the Fondation MSDAvenir.; Funding text 2: The authors thank Rkia Achkir (service d’imagerie thoracique, CHU Bordeaux) and Sophie Regueme (direction de la recherche clinique et de l’innovation, CHU Bordeaux) for their assistance and support in study coordination, Severine Martiren and Romain Griffier (CHU Bordeaux) for data management activities, and Brigitte Risse (Eclor°) for data entry and data monitoring. The work of OS was supported by the Fondation MSDAvenir. ; Funding text 3: This study has received funding by the Interregional Hospital Program of Clinical Research (“Programme Hospitalier de Recherche Clinique Interregional” 2020, PHRCI 2020_20-016). The funder played no in the study or the preparation of the manuscript. ","Long L., Zeng X., Zhang X., Et al., Short-term outcomes of COVID-19 and risk factors for progression, Eur Respir J, 55, (2020); Feng Y., Ling Y., Bai T., Et al., COVID-19 with different severities: a multicenter study of clinical features, Am J Respir Crit Care Med, 201, pp. 1380-1388, (2020); Menendez R., Mendez R., Gonzalez-Jimenez P., Et al., Early recognition of low-risk SARS-CoV-2 pneumonia: a model validated with initial data and infectious diseases Society of America/American Thoracic Society Minor Criteria, Chest, 162, pp. 768-781, (2022); Emanuel E.J., Persad G., Upshur R., Et al., Fair allocation of scarce medical resources in the time of Covid-19, N Engl J Med, 382, pp. 2049-2055, (2020); Gandhi R.T., Lynch J.B., Del Rio C., Mild or moderate Covid-19, N Engl J Med, 383, pp. 1757-1766, (2020); Gottlieb R.L., Nirula A., Chen P., Et al., Effect of bamlanivimab as monotherapy or in combination with etesevimab on viral load in patients with mild to moderate COVID-19: a randomized clinical trial, JAMA, 325, pp. 632-644, (2021); Montgomery H., Hobbs F.D.R., Padilla F., Et al., Efficacy and safety of intramuscular administration of tixagevimab-cilgavimab for early outpatient treatment of COVID-19 (TACKLE): a phase 3, randomised, double-blind, placebo-controlled trial, Lancet Respir Med, S2213–2600, 22, pp. 00180-181, (2022); Bikdeli B., Madhavan M.V., Jimenez D., Et al., COVID-19 and thrombotic or thromboembolic disease: implications for prevention, antithrombotic therapy, and follow-up: JACC state-of-the-art review, J Am Coll Cardiol, 75, pp. 2950-2973, (2020); Douillet D., Riou J., Penaloza A., Et al., Risk of symptomatic venous thromboembolism in mild and moderate COVID-19: a comparison of two prospective European cohorts, Thromb Res, 208, pp. 4-10, (2021); Guillo E., Bedmar Gomez I., Dangeard S., Et al., COVID-19 pneumonia: diagnostic and prognostic role of CT based on a retrospective analysis of 214 consecutive patients from Paris, France. Eur J Radiol, 131, (2020); Qin L., Yang Y., Cao Q., Et al., A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19, Eur Radiol, 30, pp. 6797-6807, (2020); Revel M.-P., Boussouar S., de Margerie-Mellon C., Et al., Study of thoracic CT in COVID-19: the STOIC project, Radiology, 301, pp. E361-E370, (2021); Gupta R.K., Marks M., Samuels T.H.A., Et al., Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study, Eur Respir J, 56, (2020); Wynants L., van Calster B., Collins G.S., Et al., Prediction models for diagnosis and prognosis of covid-19: Systematic review and critical appraisal, BMJ, 369, (2020); Chassagnon G., Vakalopoulou M., Battistella E., Et al., AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia, Med Image Anal, 67, (2021); Yue H., Yu Q., Liu C., Et al., Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study, Ann Transl Med, 8, (2020); Wang S., Zha Y., Li W., Et al., A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis, Eur Respir J, 56, (2020); Khemasuwan D., Sorensen J.S., Colt H.G., Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19, Eur Respir Rev Off J Eur Respir Soc, 29, (2020); Harris P.A., Taylor R., Minor B.L., Et al., The REDCap consortium: building an international community of software platform partners, J Biomed Inform, 95, (2019); Zu Z.Y., Jiang M.D., Xu P.P., Et al., Coronavirus Disease 2019 (COVID-19): a perspective from China, Radiology, 296, pp. E15-E25, (2020); Carr E., Bendayan R., Bean D., Et al., Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study, BMC Med, 19, (2021); Davis J.W., Wang B., Tomczak E., Et al., Prediction of the need for intensive oxygen supplementation during hospitalisation among subjects with COVID-19 admitted to an academic health system in Texas: a retrospective cohort study and multivariable regression model, BMJ Open, 12, (2022); Kamran F., Tang S., Otles E., Et al., Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study, BMJ, 376, (2022); Ye Z., Zhang Y., Wang Y., Et al., Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review, Eur Radiol, 30, pp. 4381-4389, (2020); Simpson S., Kay F.U., Abbara S., Et al., Radiological Society of North America Expert Consensus Document on Reporting Chest CT Findings Related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA, Radiol Cardiothorac Imaging, 2, (2020); Chaganti S., Grenier P., Balachandran A., Et al., Automated quantification of CT patterns associated with COVID-19 from chest CT, Radiol Artif Intell, 2, (2020); van Griethuysen J.J.M., Fedorov A., Parmar C., Et al., Computational radiomics system to decode the radiographic phenotype, Cancer Res, 77, pp. e104-e107, (2017); Yongli Z., Yuhong Yang. «â€¯Cross-validation for selecting a model selection procedure », Journal of Econometrics, 187, 1, pp. 95-112, (2015); Riley R.D., Ensor J., Snell K.I.E., Et al., Calculating the sample size required for developing a clinical prediction model, BMJ, 368, (2020); Vergouwe Y., Steyerberg E.W., Eijkemans M.J.C., Habbema J.D.F., Substantial effective sample sizes were required for external validation studies of predictive logistic regression models, J Clin Epidemiol, 58, pp. 475-483, (2005); Abraham A., Pedregosa F., Eickenberg M., Et al., Machine learning for neuroimaging with scikit-learn, Front Neuroinformatics, 8, (2014); Steyerberg E.W., Clinical Prediction Models, (2019); Knight S.R., Gupta R.K., Ho A., Et al., Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol, Thorax, 77, pp. 606-615, (2022); Sterne J.A.C., Murthy S., Et al., Association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: A meta-analysis, JAMA, 324, pp. 1330-1341, (2020); Horby P., Lim W.S., Et al., Dexamethasone in hospitalized patients with Covid-19, N Engl J Med, 384, pp. 693-704, (2021); Kocks J., Kerkhof M., Scherpenisse J., Et al., A potential harmful effect of dexamethasone in non-severe COVID-19: results from the COPPER-pilot study, ERJ Open Res, 8, pp. 00129-02022, (2022); Lascarrou J.-B., Colin G., Le Thuaut A., Et al., Predictors of negative first SARS-CoV-2 RT-PCR despite final diagnosis of COVID-19 and association with outcome, Sci Rep, 11, (2021); Leaf D.E., Gupta S., Wang W., Tocilizumab in Covid-19, N Engl J Med, 384, pp. 86-87, (2021)","M. Zysman; CHU Bordeaux, Pessac, 33600, France; email: maeva.zysman@chu-bordeaux.fr","","Springer Science and Business Media Deutschland GmbH","","","","","","09387994","","EURAE","37405504","English","Eur. Radiol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85164134609"
"Roy A.; Satija U.; Karmakar S.","Roy, Arka (58073475200); Satija, Udit (55253538100); Karmakar, Saurabh (36519106700)","58073475200; 55253538100; 36519106700","Pulmo-TS2ONN: A Novel Triple Scale Self Operational Neural Network for Pulmonary Disorder Detection Using Respiratory Sounds","2024","IEEE Transactions on Instrumentation and Measurement","73","","","1","12","11","7","10.1109/TIM.2024.3378206","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188474979&doi=10.1109%2fTIM.2024.3378206&partnerID=40&md5=c1c7db6b8fcf526901505b515ae7816a","The Department of Electrical Engineering, Indian Institute of Technology Patna, Bihar, Patna, 801106, India; The Department of Pulmonary Medicine, All India Institute of Medical Sciences (AIIMS) Patna, Patna, 801507, India","Roy A., The Department of Electrical Engineering, Indian Institute of Technology Patna, Bihar, Patna, 801106, India; Satija U., The Department of Electrical Engineering, Indian Institute of Technology Patna, Bihar, Patna, 801106, India; Karmakar S., The Department of Pulmonary Medicine, All India Institute of Medical Sciences (AIIMS) Patna, Patna, 801507, India","Pulmonary disorders (PDs) are one of the substantial hazards to human life, which can be diagnosed by a variety of clinical modalities, including peak flowmeter and spirometry measurements, chest auscultation-based respiratory sound (RS) measurements, etc. Analyzing the acoustic RS measurements is one of the inexpensive yet essential diagnostic methods for identifying PDs as these RSs are correlated with structural flaws of the lungs that occur due to PDs. Additionally, the development of the digital stethoscope facilitates the continuous measurement of acoustic RSs of any individual, which can be exploited to identify a variety of PDs. In this article, we have proposed a triple time-frequency feature set driven triple-scale self-operational neural network (TS2ONN) architecture, namely Pulmo-TS2ONN, to classify a wide spectrum of PDs using the RSs. The proposed pulmo-TS2ONN comprises three major stages: preprocessing, triplet time-frequency feature set (TTFFS) extraction, and finally classification of seven class PDs by using TS2ONN architecture which utilizes the improved nonlinear neural backbone of self-operational neural network (SONN) in place of the linear neural architecture used in conventional deep learning (DL) networks. Upon experimental evaluation, the proposed framework outperforms the existing noteworthy research works by achieving the highest performance rates of 98.88%, 98.27%, and 99.84% for accuracy, sensitivity, and specificity, respectively. Lastly, the proposed framework is implemented on a quad-core ARM-A7-based Raspberry Pi-4 microcontroller, allowing the possibility of translating the research into real clinical situations for RS-based PD screening. © 2024 IEEE.","Auscultation measurements; classification; pulmonary disorders (PDs); respiratory sounds (RSs); self-operational neural network (SONN)","Clinical research; Deep learning; Diagnosis; Network architecture; Pulmonary diseases; Asthma; Auscultation measurement; Chronic obstructive pulmonary disease; Lung; Neural-networks; Pneumonia; Pulmonary disorder; Respiratory sounds; Self-operational neural network; Classification (of information)","","","","","","","Levine S.M., Marciniuk D.D., Global impact of respiratory disease: What can we do, together, to make a difference?, Chest, 161, 5, pp. 1153-1154, (2022); Schneider J.L., Rowe J.H., Garcia-De-Alba C., Kim C.F., Sharpe A.H., Haigis M.C., The aging lung: Physiology, disease, and immunity, Cell, 184, 8, pp. 1990-2019, (2021); Cruz A.A., Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach, (2007); Trivedy S., Goyal M., Mohapatra P.R., Mukherjee A., Design and development of smartphone-enabled spirometer with a disease classification system using convolutional neural network, IEEE Trans. Instrum. Meas., 69, 9, pp. 7125-7135, (2020); Bhome A.B., COPD in India: Iceberg or volcano?, J. Thoracic Disease, 4, 3, (2012); Roy B., Roy A., Chandra J.K., Gupta R., I-PRExT: Photoplethysmography derived respiration signal extraction and respiratory rate tracking using neural networks, IEEE Trans. Instrum. Meas., 70, pp. 1-9, (2021); Expert Panel Report III: Guidelines for the Diagnosis and Management of Asthma, (1997); Roy A., Satija U., RDLINet: A novel lightweight inception network for respiratory disease classification using lung sounds, IEEE Trans. Instrum. Meas., 72, pp. 1-13, (2023); Rocha B., Et al., A respiratory sound database for the development of automated classification, Proc. Int. Conf. Biomed. Health Informat., pp. 33-37, (2017); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data Brief, 35, (2021); Pham L., Phan H., Palaniappan R., Mertins A., McLoughlin I., CNN-MoE based framework for classification of respiratory anomalies and lung disease detection, IEEE J. Biomed. Health Informat., 25, 8, pp. 2938-2947, (2021); Altan G., Kutlu Y., Garbi Y., Pekmezci A.O., Nural S., Multimedia respiratory database (RespiratoryDatabase@TR): Auscultation sounds and chest X-rays, Natural Eng. Sci., 2, 3, pp. 59-72, (2017); Silva L., Et al., COVID-19 respiratory sound analysis and classification using audio textures, Frontiers Signal Process, 2, (2022); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. Biomed. Circuits Syst., 14, 3, pp. 535-544, (2020); Nguyen T., Pernkopf F., Lung sound classification using co-tuning and stochastic normalization, IEEE Trans. Biomed. Eng., 69, 9, pp. 2872-2882, (2022); Shuvo S.B., Ali S.N., Swapnil S.I., Hasan T., Bhuiyan M.I.H., A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid scalogram, IEEE J. Biomed. Health Informat., 25, 7, pp. 2595-2603, (2021); Shi L., Zhang J., Yang B., Gao Y., Lung sound recognition method based on multi-resolution interleaved net and time-frequency feature enhancement, IEEE J. Biomed. Health Informat., 27, 10, pp. 4768-4779, (2023); Perna D., Convolutional neural networks learning from respiratory data, Proc. IEEE Int. Conf. Bioinf. Biomed. (BIBM), pp. 2109-2113, (2018); Basu V., Rana S., Respiratory diseases recognition through respiratory sound with the help of deep neural network, Proc. 4th Int. Conf. Comput. Intell. Netw. (CINE), pp. 1-6, (2020); Garcia-Ordas M.T., Benitez-Andrades J.A., Garcia-Rodriguez I., Benavides C., Alaiz-Moreton H., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Tripathy R.K., Dash S., Rath A., Panda G., Pachori R.B., Automated detection of pulmonary diseases from lung sound signals using fixed-boundary-based empirical wavelet transform, IEEE Sensors Lett, 6, 5, pp. 1-4, (2022); Mondal A., Bhattacharya P., Saha G., Detection of lungs status using morphological complexities of respiratory sounds, Sci. World J., 2014, pp. 1-9, (2014); Roy A., Satija U., AsTFSONN: A unified framework based on time-frequency domain self-operational neural network for asthmatic lung sound classification, Proc. IEEE Int. Symp. Med. Meas. Appl. (MeMeA), pp. 1-6, (2023); Zahid M.U., Kiranyaz S., Gabbouj M., Global ECG classification by self-operational neural networks with feature injection, IEEE Trans. Biomed. Eng., 70, 1, pp. 205-215, (2023); Sarkar M., Madabhavi I., Niranjan N., Dogra M., Auscultation of the respiratory system, Ann. Thoracic Med., 10, 3, (2015); Tabatabaei S.A.H., Fischer P., Schneider H., Koehler U., Gross V., Sohrabi K., Methods for adventitious respiratory sound analyzing applications based on smartphones: A survey, IEEE Rev. Biomed. Eng., 14, pp. 98-115, (2021); Reichert S., Gass R., Brandt C., Andres E., Analysis of respiratory sounds: State of the art, Clin. Med. Circulatory, Respiratory Pulmonary Med., 2, (2008); Jaffery S.A.F., Aziz S., Khan M.U., Naqvi S.Z.H., Faraz M., Usman A., An automated system for the classification of bronchiolitis and bronchiectasis diseases using lung sound analysis, Proc. Int. Conf. Robot. Autom. Ind. (ICRAI), pp. 1-6, (2023); Satija U., Ramkumar B., Manikandan M.S., Real-time signal quality-aware ECG telemetry system for IoT-based health care monitoring, IEEE Internet Things J, 4, 3, pp. 815-823, (2017); Bahmei B., Birmingham E., Arzanpour S., CNN-RNN and data augmentation using deep convolutional generative adversarial network for environmental sound classification, IEEE Signal Process. Lett., 29, pp. 682-686, (2022); Ozer I., Pseudo-colored rate map representation for speech emotion recognition, Biomed. Signal Process. Control, 66, (2021); Brown J.C., Calculation of a constant Q spectral transform, J. Acoust. Soc. Amer., 89, 1, pp. 425-434, (1991); Gfeller B., Frank C., Roblek D., Sharifi M., Tagliasacchi M., Velimirovic M., SPICE: Self-supervised pitch estimation, IEEE/ACM Trans. Audio, Speech, Language Process., 28, pp. 1118-1128, (2020); Abdul Z.K., Al-Talabani A.K., Mel frequency cepstral coefficient and its applications: A review, IEEE Access, 10, pp. 122136-122158, (2022); Kiranyaz S., Ince T., Iosifidis A., Gabbouj M., Operational neural networks, Neural Comput. Appl., 32, 11, pp. 6645-6668, (2020); Kiranyaz S., Malik J., Abdallah H.B., Ince T., Iosifidis A., Gabbouj M., Self-organized operational neural networks with generative neurons, Neural Netw, 140, pp. 294-308, (2021); Lin M., Chen Q., Yan S., Network in network, (2013); Hendrycks D., Gimpel K., Gaussian error linear units (GELUs), (2016); Das S., Jyotishi D., Dandapat S., Automated detection of heart valve diseases using stationary wavelet transform and attention-based hierarchical LSTM network, IEEE Trans. Instrum. Meas., 72, pp. 1-10, (2023); Kautz T., Eskofier B.M., Pasluosta C.F., Generic performance measure for multiclass-classifiers, Pattern Recognit, 68, pp. 111-125, (2017); Saini M., Satija U., Upadhayay M.D., DSCNN-CAU: Deep-learning-based mental activity classification for IoT implementation toward portable BCI, IEEE Internet Things J, 10, 10, pp. 8944-8957, (2023); Van der Maaten L., Hinton G., Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 11, pp. 1-27, (2008); Saini M., Kumar D., Satija U., Edge of medical things implementation for deep learning-based cognitive task recognition, IEEE Internet Things Mag, 5, 3, pp. 56-60, (2022); Pal D., Mukhopadhyay S., Gupta R., Two-stage classifier for resource constrained on-board cardiac arrhythmia detection, IEEE Trans. Instrum. Meas., 72, pp. 1-10, (2023)","U. Satija; The Department of Electrical Engineering, Indian Institute of Technology Patna, Patna, Bihar, 801106, India; email: udit@iitp.ac.in","","Institute of Electrical and Electronics Engineers Inc.","","","","","","00189456","","IEIMA","","English","IEEE Trans. Instrum. Meas.","Article","Final","","Scopus","2-s2.0-85188474979"
"Zhang J.; Sun K.; Jagadeesh A.; Falakaflaki P.; Kayayan E.; Tao G.; Ghahfarokhi M.H.; Gupta D.; Gupta A.; Gupta V.; Guo Y.","Zhang, Jingqing (57201582643); Sun, Kai (57191858808); Jagadeesh, Akshay (58520223000); Falakaflaki, Parastoo (59198766600); Kayayan, Elena (59294343400); Tao, Guanyu (59294014100); Ghahfarokhi, Mahta Haghighat (58640729200); Gupta, Deepa (58519780200); Gupta, Ashok (58519927500); Gupta, Vibhor (57226591337); Guo, Yike (12765868000)","57201582643; 57191858808; 58520223000; 59198766600; 59294343400; 59294014100; 58640729200; 58519780200; 58519927500; 57226591337; 12765868000","The potential and pitfalls of using a large language model such as ChatGPT, GPT-4, or LLaMA as a clinical assistant","2024","Journal of the American Medical Informatics Association","31","9","","1884","1891","7","7","10.1093/jamia/ocae184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201776221&doi=10.1093%2fjamia%2focae184&partnerID=40&md5=90ed01436771e0e0f46c31aa082f6f45","Pangaea Data Limited, London, SE1 7LY, United Kingdom; Data Science Institute, Imperial College London, London, SW7 2AZ, United Kingdom; Hong Kong University of Science and Technology, Hong Kong","Zhang J., Pangaea Data Limited, London, SE1 7LY, United Kingdom, Data Science Institute, Imperial College London, London, SW7 2AZ, United Kingdom; Sun K., Pangaea Data Limited, London, SE1 7LY, United Kingdom, Data Science Institute, Imperial College London, London, SW7 2AZ, United Kingdom; Jagadeesh A., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Falakaflaki P., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Kayayan E., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Tao G., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Ghahfarokhi M.H., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Gupta D., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Gupta A., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Gupta V., Pangaea Data Limited, London, SE1 7LY, United Kingdom; Guo Y., Pangaea Data Limited, London, SE1 7LY, United Kingdom, Hong Kong University of Science and Technology, Hong Kong","Objectives: This study aims to evaluate the utility of large language models (LLMs) in healthcare, focusing on their applications in enhancing patient care through improved diagnostic, decision-making processes, and as ancillary tools for healthcare professionals. Materials and Methods: We evaluated ChatGPT, GPT-4, and LLaMA in identifying patients with specific diseases using gold-labeled Electronic Health Records (EHRs) from the MIMIC-III database, covering three prevalent diseases—Chronic Obstructive Pulmonary Disease (COPD), Chronic Kidney Disease (CKD)—along with the rare condition, Primary Biliary Cirrhosis (PBC), and the hard-to-diagnose condition Cancer Cachexia. Results: In patient identification, GPT-4 had near similar or better performance compared to the corresponding disease-specific Machine Learning models (F1-score ≥ 85%) on COPD, CKD, and PBC. GPT-4 excelled in the PBC use case, achieving a 4.23% higher F1-score compared to disease-specific “Traditional Machine Learning” models. ChatGPT and LLaMA3 demonstrated lower performance than GPT-4 across all diseases and almost all metrics. Few-shot prompts also help ChatGPT, GPT-4, and LLaMA3 achieve higher precision and specificity but lower sensitivity and Negative Predictive Value. Discussion: The study highlights the potential and limitations of LLMs in healthcare. Issues with errors, explanatory limitations and ethical concerns like data privacy and model transparency suggest that these models would be supplementary tools in clinical settings. Future studies should improve training datasets and model designs for LLMs to gain better utility in healthcare. Conclusion: The study shows that LLMs have the potential to assist clinicians for tasks such as patient identification but false positives and false negatives must be mitigated before LLMs are adequate for real-world clinical assistance. © The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.","ChatGPT; healthcare artificial intelligence; large language models; patient identification","Electronic Health Records; Humans; Liver Cirrhosis, Biliary; Machine Learning; Natural Language Processing; Pulmonary Disease, Chronic Obstructive; Renal Insufficiency, Chronic; Article; chronic kidney failure; chronic obstructive lung disease; human; intermethod comparison; large language model; machine learning; predictive value; primary biliary cirrhosis; sensitivity and specificity; biliary cirrhosis; chronic obstructive lung disease; complication; diagnosis; electronic health record; natural language processing","","","","","Pangaea Data Limited","This work was supported by Pangaea Data Limited. ","Devlin J, Chang MW, Lee K, Et al., Bert: pre-training of deep bidirectional transformers for language understanding, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171-4186, (2019); Zhang J, Zhao Y, Saleh M, Et al., Pegasus: pre-training with extracted gap-sentences for abstractive summarization, International Conference on Machine Learning, pp. 11328-11339, (2020); Raffel C, Shazeer N, Roberts A, Et al., Exploring the limits of transfer learning with a unified text-to-text transformer, J Mach Learn Res, 21, 140, pp. 1-67, (2020); Brown T, Mann B, Ryder N, Et al., Language models are few-shot learners, Adv Neural Inf Process Syst, 33, pp. 1877-1901, (2020); Zhao WX, Zhou K, Li J, Et al., A survey of large language models; Laskar MT, Bari MS, Rahman M, Et al., A systematic study and comprehensive evaluation of ChatGPT on benchmark datasets, Findings of the Association for Computational Linguistics: ACL 2023, pp. 431-469, (2023); Achiam J, Adler S, Agarwal S, Et al., Gpt-4 technical report; Nori H, King N, McKinney SM, Et al., Capabilities of gpt-4 on medical challenge problems; Llama 3 Model Card; Ahn C., Exploring ChatGPT for information of cardiopulmonary resuscitation, Resuscitation, 185, (2023); Howard A, Hope W, Gerada A., ChatGPT and antimicrobial advice: the end of the consulting infection doctor?, Lancet Infect Dis, 23, 4, pp. 405-406, (2023); Rao A, Pang M, Kim J, Et al., Assessing the utility of ChatGPT throughout the entire clinical workflow: development and usability study, J Med Internet Res, 25, (2023); Patel SB, Lam K., ChatGPT: the future of discharge summaries?, Lancet Digit Health, 5, 3, pp. e107-e108, (2023); Jeblick K, Schachtner B, Dexl J, Et al., ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports, Eur Radiol, 34, 5, pp. 2817-2825, (2024); O'Connor S., Open artificial intelligence platforms in nursing education: tools for academic progress or abuse?, Nurse Educ Pract, 66, (2022); Arif TB, Munaf U, Ul-Haque I., The future of medical education and research: is ChatGPT a blessing or blight in disguise?, Med Educ Online, 28, 1, (2023); Li J, Dada A, Puladi B, Kleesiek J, Egger J., ChatGPT in healthcare: a taxonomy and systematic review, Comput Methods Programs Biomed, 245, (2024); Gilson A, Safranek CW, Huang T, Et al., How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? 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Guo; Pangaea Data Limited, London, SE1 7LY, United Kingdom; email: yguo@pangaeadata.ai","","Oxford University Press","","","","","","10675027","","JAMAF","39018498","English","J. Am. Med. Informatics Assoc.","Article","Final","","Scopus","2-s2.0-85201776221"
"Marín D.; Orozco L.Y.; Narváez D.M.; Ortiz- Trujillo I.C.; Molina F.J.; Ramos C.D.; Rodriguez-Villamizar L.; Bangdiwala S.I.; Morales O.; Cuellar M.; Hernández L.J.; Henao E.A.; Lopera V.; Corredor A.; Toro M.V.; Groot H.; Villamil-Osorio M.; Muñoz D.A.; Hincapié R.C.; Amaya F.; Oviedo A.I.; López L.; Morales-Betancourt R.; Marín-Ochoa B.E.; Sánchez-García O.E.; Marín J.S.; Abad J.M.; Toro J.C.; Pinzón E.; Builes J.J.; Rueda Z.V.","Marín, Diana (35748970600); Orozco, Luz Yaneth (55969509700); Narváez, Diana María (56992936100); Ortiz- Trujillo, Isabel Cristina (55969271600); Molina, Francisco José (8437667900); Ramos, Carlos Daniel (58074811100); Rodriguez-Villamizar, Laura (14068023000); Bangdiwala, Shrikant I. (7003996662); Morales, Olga (57197704243); Cuellar, Martha (57189043087); Hernández, Luis Jorge (56047627900); Henao, Enrique Antonio (57205645720); Lopera, Verónica (57217049557); Corredor, Andrea (58074811200); Toro, Marín a Victoria (7102122996); Groot, Helena (24767368000); Villamil-Osorio, Milena (57207773556); Muñoz, Diego Alejandro (36662763200); Hincapié, Roberto Carlos (16028507900); Amaya, Ferney (7004190878); Oviedo, Ana Isabel (57265037600); López, Lucelly (56243678300); Morales-Betancourt, Ricardo (57206273805); Marín-Ochoa, Beatriz Elena (56303249500); Sánchez-García, Oscar Eduardo (58074811300); Marín, Juan Sebastián (59109292500); Abad, José Miguel (58074547200); Toro, Julio Cesar (58074273100); Pinzón, Eliana (58075603100); Builes, Juan José (6602327455); Rueda, Zulma Vanessa (26642118700)","35748970600; 55969509700; 56992936100; 55969271600; 8437667900; 58074811100; 14068023000; 7003996662; 57197704243; 57189043087; 56047627900; 57205645720; 57217049557; 58074811200; 7102122996; 24767368000; 57207773556; 36662763200; 16028507900; 7004190878; 57265037600; 56243678300; 57206273805; 56303249500; 58074811300; 59109292500; 58074547200; 58074273100; 58075603100; 6602327455; 26642118700","Characterization of the external exposome and its contribution to the clinical respiratory and early biological effects in children: The PROMESA cohort study protocol","2023","PLoS ONE","18","1 January","e0278836","","","","7","10.1371/journal.pone.0278836","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146705727&doi=10.1371%2fjournal.pone.0278836&partnerID=40&md5=88c34e7b3a0cc140ffc5829a3403c9e0","School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia; School of Engineering, Universidad de Antioquia, Medellín, Colombia; Human Genetics Laboratory, Universidad de los Andes, Bogotá, Colombia; Department of Public Health, School of Health, Universidad Industrial de Santander, Bucaramanga, Colombia; Department of Health Research Methods Evidence and Impact, McMaster University, Hamilton, Canada; Statistics Department, Population Health Research Institute, McMaster University, Hamilton, Canada; School of Medicine, Pediaciencias Group, Universidad de Antioquia, Noel Clinic, Medellín, Colombia; Department of Pediatrics, Hospital San Vicente Fundación, Medellín, Colombia; Department of Pediatrics, SOMER Clinic, Medellín, Colombia; School of Medicine, Universidad de los Andes, Bogotá, Colombia; Secretaría de Salud, Alcaldía de Medellín, Medellín, Colombia; Department of Pediatrics, ONIROS Centro Especializado en Medicina Integral del Sueño, Bogotá, Colombia; School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; Department of Pediatrics, Fundación Hospital Pediátrico la Misericordia, Bogotá, Colombia; Universidad Nacional de Colombia, Medellin, Colombia; School of Engineering, Universidad de los Andes, Bogotá, Colombia; School of Social Communications and Journalism, Universidad Pontificia Bolivariana, Medellín, Colombia; Healthcare Company, SURA, Medellín, Colombia; ATB SERVICE SAS, Medellín, Colombia; Secretaria distrital de Salud, Alcaldia de Bogota, Bogota, Colombia; GENES Laboratory, Medellín, Colombia; Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada","Marín D., School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia; Orozco L.Y., School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia, School of Engineering, Universidad de Antioquia, Medellín, Colombia; Narváez D.M., Human Genetics Laboratory, Universidad de los Andes, Bogotá, Colombia; Ortiz- Trujillo I.C., School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia; Molina F.J., School of Engineering, Universidad de Antioquia, Medellín, Colombia; Ramos C.D., School of Engineering, Universidad de Antioquia, Medellín, Colombia; Rodriguez-Villamizar L., Department of Public Health, School of Health, Universidad Industrial de Santander, Bucaramanga, Colombia; Bangdiwala S.I., Department of Health Research Methods Evidence and Impact, McMaster University, Hamilton, Canada, Statistics Department, Population Health Research Institute, McMaster University, Hamilton, Canada; Morales O., School of Medicine, Pediaciencias Group, Universidad de Antioquia, Noel Clinic, Medellín, Colombia, Department of Pediatrics, Hospital San Vicente Fundación, Medellín, Colombia; Cuellar M., School of Medicine, Pediaciencias Group, Universidad de Antioquia, Noel Clinic, Medellín, Colombia, Department of Pediatrics, SOMER Clinic, Medellín, Colombia; Hernández L.J., School of Medicine, Universidad de los Andes, Bogotá, Colombia; Henao E.A., Secretaría de Salud, Alcaldía de Medellín, Medellín, Colombia; Lopera V., Secretaría de Salud, Alcaldía de Medellín, Medellín, Colombia; Corredor A., Department of Pediatrics, ONIROS Centro Especializado en Medicina Integral del Sueño, Bogotá, Colombia; Toro M.V., School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; Groot H., Human Genetics Laboratory, Universidad de los Andes, Bogotá, Colombia; Villamil-Osorio M., Department of Pediatrics, Fundación Hospital Pediátrico la Misericordia, Bogotá, Colombia; Muñoz D.A., Universidad Nacional de Colombia, Medellin, Colombia; Hincapié R.C., School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; Amaya F., School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; Oviedo A.I., School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; López L., School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia; Morales-Betancourt R., School of Engineering, Universidad de los Andes, Bogotá, Colombia; Marín-Ochoa B.E., School of Social Communications and Journalism, Universidad Pontificia Bolivariana, Medellín, Colombia; Sánchez-García O.E., School of Engineering, Universidad Pontificia Bolivariana, Medellín, Colombia; Marín J.S., Healthcare Company, SURA, Medellín, Colombia; Abad J.M., Healthcare Company, SURA, Medellín, Colombia; Toro J.C., ATB SERVICE SAS, Medellín, Colombia; Pinzón E., Secretaria distrital de Salud, Alcaldia de Bogota, Bogota, Colombia; Builes J.J., GENES Laboratory, Medellín, Colombia; Rueda Z.V., School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia, Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada","Background Air pollution contains a mixture of different pollutants from multiple sources. However, the interaction of these pollutants with other environmental exposures, as well as their harmful effects on children under five in tropical countries, is not well known. Objective This study aims to characterize the external exposome (ambient and indoor exposures) and its contribution to clinical respiratory and early biological effects in children. Materials and methods A cohort study will be conducted on children under five (n = 500) with a one-year follow-up. Enrolled children will be followed monthly (phone call) and at months 6 and 12 (in person) post-enrolment with upper and lower Acute Respiratory Infections (ARI) examinations, asthma development, asthma control, and genotoxic damage. The asthma diagnosis will be pediatric pulmonologist-based and a standardized protocol will be used. Exposure, effect, and susceptibility biomarkers will be measured on buccal cells samples. For environmental exposures PM2.5 will be sampled, and questionnaires, geographic information, dispersion models and Land Use Regression models for PM2.5 and NO2 will be used. Different statistical methods that include Bayesian and machine learning techniques will be used for the ambient and indoor exposures-and outcomes. This study was approved by the ethics committee at Universidad Pontificia Bolivariana. Expected study outcomes/findings To estimate i) The toxic effect of particulate matter transcending the approach based on pollutant concentration levels; ii) The risk of developing an upper and lower ARI, based on different exposure windows; iii) A baseline of early biological damage in children under five, and describe its progression after a one-year follow-up; and iv) How physical and chemical PM2.5 characteristics influence toxicity and children's health.  © 2023 Marín et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Air Pollutants; Air Pollution; Asthma; Bayes Theorem; Child; Cohort Studies; Environmental Exposure; Environmental Pollutants; Exposome; Humans; Mouth Mucosa; Particulate Matter; biological marker; nitrogen dioxide; air pollutant; air pollution; ambient air; Article; asthma; Bayes theorem; cheek cell; child; clinical outcome; clinical protocol; controlled study; disease control; disease predisposition; environmental exposure; exposome; follow up; genotoxicity; geographic distribution; human; human cell; information processing; laboratory test; land use; lower respiratory tract infection; machine learning; major clinical study; patient selection; pediatric pulmonologist; PM2.5 exposure; preschool child; prospective study; pulmonologist; questionnaire; regression analysis; respiratory tract disease; statistical analysis; upper respiratory tract infection; adverse event; analysis; asthma; chemistry; cohort analysis; mouth mucosa; particulate matter; pollutant; toxicity","","nitrogen dioxide, 10102-44-0; Air Pollutants, ; Environmental Pollutants, ; Particulate Matter, ","","","","","Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Et al., Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, The Lancet, 396, pp. 1223-1249, (2020); Thurston GD, Kipen H, Annesi-Maesano I, Balmes J, Brook RD, Cromar K, Et al., A joint ERS/ATS policy statement: what constitutes an adverse health effect of air pollution?. 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Guillien A, Cadiou S, Slama R, Siroux V., The Exposome Approach to Decipher the Role of Multiple Environmental and Lifestyle Determinants in Asthma, Int J Environ Res Public Health, 18, (2021); Tamayo-Uria I, Maitre L, Thomsen C, Nieuwenhuijsen MJ, Chatzi L, Siroux V, Et al., The early-life exposome: Description and patterns in six European countries, Environ Int, 123, pp. 189-200, (2019); Barrera-Gomez J, Agier L, Portengen L, Chadeau-Hyam M, Giorgis-Allemand L, Siroux V, Et al., A systematic comparison of statistical methods to detect interactions in exposome-health associations, Environ Health, 16, (2017)","D. Marín; School of Medicine, Universidad Pontificia Bolivariana, Medellín, Colombia; email: dianamarcela.marin@upb.edu.co","","Public Library of Science","","","","","","19326203","","POLNC","36662732","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85146705727"
"Volk O.; Ratnovsky A.; Naftali S.; Singer G.","Volk, Ohad (57352525900); Ratnovsky, Anat (6506070805); Naftali, Sara (6506214530); Singer, Gonen (7102795609)","57352525900; 6506070805; 6506214530; 7102795609","Classification of tracheal stenosis with asymmetric misclassification errors from EMG signals using an adaptive cost-sensitive learning method","2023","Biomedical Signal Processing and Control","85","","104962","","","","6","10.1016/j.bspc.2023.104962","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152617718&doi=10.1016%2fj.bspc.2023.104962&partnerID=40&md5=c1a44f88dfd8269fc5c96e677db51fa3","Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel","Volk O., Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; Ratnovsky A., School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel; Naftali S., School of Medical Engineering, Afeka Tel Aviv Academic College of Engineering, Tel Aviv, Israel; Singer G., Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel","Upper airway obstruction is characterized by loss of normal airway architecture resulting from various disorders such as infections and asthma. Early detection of airway obstruction is essential to prevent medical deterioration. The objective of this study was to non-invasively identify early-stage tracheal stenosis, using electromyography (EMG) signals of inspiratory muscles. The identification of tracheal stenosis has been defined as an asymmetric misclassification cost problem. Specifically, the EMG signals are used as input to a ResNet-like architecture for tabular data with an Adaptive Cost-Sensitive Learning (AdaCSL) algorithm. The electrical activity of the external intercostal muscle of four healthy individuals was recorded while they breathed through two different tubes, one simulating a narrowed airway and the other simulating a normal airway. Two experiment settings were designed. The first setting aimed to classify tracheal stenosis in a specific subject by training the model on data from other subjects, reflecting the case of diagnosing a new subject. To overcome multi-subject variations, the second setting aimed to classify tracheal stenosis by mixing all subjects’ training and test data. The ResNet-like architecture with an AdaCSL algorithm was significantly better in the first experiment setting with costs that were 43%, 48%, and 59% lower than the cost of the second-best alternative for three different misclassification cost values. It also achieved a lower cost in the second experiment setting over other classifiers. The experiments emphasize the capability of using inspiratory muscle EMG signals to diagnose respiratory disease and demonstrate the usefulness of the AdaCSL algorithm for personalized monitoring. © 2023 Elsevier Ltd","Adaptive learning; Airway obstruction; Cost-sensitive learning; Deep learning; Electromyogram; Misclassification costs","Deep learning; Deterioration; Learning systems; Underwater acoustics; Adaptive learning; Airway obstruction; Cost-sensitive learning; Deep learning; Electromyo grams; Electromyography signals; Learning methods; Misclassification costs; Misclassification error; Tracheal stenosis; Article; breathing; breathing muscle; controlled study; disease classification; electric activity; electromyography; evaluation study; human; human experiment; information processing; intercostal muscle; learning algorithm; medical error; normal human; prediction; respiratory airflow; simulation; trachea stenosis; Muscle","","","","","Israeli Ministry of Innovation, Science, and Technology, (0004323)","This research was supported by the Israeli Ministry of Innovation, Science, and Technology (Grant No. 0004323 ).","Frise M., Upper airway obstruction, Acute Medicine: A Practical Guide to the Management of Medical Emergencies, pp. 371-377, (2017); Ratnovsky A., Elad D., Halpern P., Mechanics of respiratory muscles, Respir. 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Appl., 149, (2020); Singer G., Ratnovsky A., Naftali S., Classification of severity of trachea stenosis from EEG signals using ordinal decision-tree based algorithms and ensemble-based ordinal and non-ordinal algorithms, Expert Syst. Appl., 173, (2021); Haba R., Singer G., Naftali S., Kramer M.R., Ratnovsky A., A remote and personalised novel approach for monitoring asthma severity levels from EEG signals utilizing classification algorithms, Expert Syst. Appl., 223, (2023); Suvinen T., Kemppainen P., Review of clinical EMG studies related to muscle and occlusal factors in healthy and TMD subjects, J. Oral Rehabil., 34, 9, pp. 631-644, (2007); Ratnovsky A., Malayev S., Ratnovsky S., Naftali S., Rabin N., EMG-based speech recognition using dimensionality reduction methods, J. Ambient Intell. Humaniz. 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Appl., 161, (2020); Chiu P.K.-F., Shen X., Wang G., Ho C.-L., Leung C.-H., Ng C.-F., Choi K.-S., Teoh J.Y.-C., Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study, Prostate Cancer Prostatic Dis., pp. 1-5, (2021); Lo H.-Y., Wang J.-C., Wang H.-M., Lin S.-D., Cost-sensitive multi-label learning for audio tag annotation and retrieval, IEEE Trans. Multimed., 13, 3, pp. 518-529, (2011); Ojala M., Garriga G.C., Permutation tests for studying classifier performance, J. Mach. Learn. Res., 11, 6, (2010); Zhang J., Ling C., Li S., EMG signals based human action recognition via deep belief networks, IFAC-PapersOnLine, 52, 19, pp. 271-276, (2019); Chan F.H., Yang Y.-S., Lam F., Zhang Y.-T., Parker P.A., Fuzzy EMG classification for prosthesis control, IEEE Trans. Rehabil. Eng., 8, 3, pp. 305-311, (2000)","G. Singer; Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel; email: gonen.singer@biu.ac.il","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85152617718"
"Morís D.I.; de Moura J.; Marcos P.J.; Rey E.M.; Novo J.; Ortega M.","Morís, Daniel I. (57226478610); de Moura, Joaquim (56785693400); Marcos, Pedro J. (36573662800); Rey, Enrique Míguez (56950150200); Novo, Jorge (57695901400); Ortega, Marcos (24475406900)","57226478610; 56785693400; 36573662800; 56950150200; 57695901400; 24475406900","Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models","2023","Biomedical Signal Processing and Control","84","","104818","","","","6","10.1016/j.bspc.2023.104818","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150861466&doi=10.1016%2fj.bspc.2023.104818&partnerID=40&md5=46e02a2e09072bc01c8de98ecc1d6593","Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Spain; Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Spain; Dirección Asistencial y Servicio de Neumología, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Sergas, A Coruña, 15006, Spain; Grupo de Investigación en Virología Clínica, Sección de Enfermedades Infecciosas, Servicio de Medicina Interna, Instituto de Investigación Biomédica de A Coruña (INIBIC), Área Sanitaria A Coruña y CEE (ASCC), SERGAS, A Coruña, 15006, Spain","Morís D.I., Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Spain, Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Spain; de Moura J., Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Spain, Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Spain; Marcos P.J., Dirección Asistencial y Servicio de Neumología, Complejo Hospitalario Universitario de A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Sergas, A Coruña, 15006, Spain; Rey E.M., Grupo de Investigación en Virología Clínica, Sección de Enfermedades Infecciosas, Servicio de Medicina Interna, Instituto de Investigación Biomédica de A Coruña (INIBIC), Área Sanitaria A Coruña y CEE (ASCC), SERGAS, A Coruña, 15006, Spain; Novo J., Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Spain, Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Spain; Ortega M., Centro de Investigación CITIC, Universidade da Coruña, Campus de Elviña, s/n, A Coruña, 15071, Spain, Grupo VARPA, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, Xubias de Arriba, 84, A Coruña, 15006, Spain","COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0.8415±0.0217 while it can also estimate the risk of death with an AUC-ROC of 0.7992±0.0104. Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources. © 2023 The Author(s)","Classification; Clinical data; COVID-19; Feature selection; Machine learning","Classification (of information); Computer aided analysis; Computer aided diagnosis; Health care; Hospitals; Learning algorithms; Machine learning; Medical computing; Risk perception; Clinical data; Clinical decision; Comprehensive analysis; Computer aided diagnosis systems; Features selection; Global threats; Healthcare systems; High-risk patients; Machine learning models; Machine-learning; aged; Article; asthma; chronic obstructive lung disease; classifier; clinical outcome; clinical study; comparative study; controlled study; coronavirus disease 2019; data processing; diabetes mellitus; false negative result; feature selection; female; high risk patient; hospital patient; hospitalization; human; Human immunodeficiency virus infection; hypertension; learning algorithm; leukemia; lymphoma; machine learning; major clinical study; male; neoplasm; sensitivity and specificity; statistical distribution; COVID-19","","","","","Ministerio de Ciencia e Innovación y Universidades, Government of Spain; CITIC; CCEU; Secretaría Xeral de Universidades; Universidade da Coruña; European Regional Development Fund, ERDF; Ministerio de Ciencia, Innovación y Universidades, MCIU, (RTI2018-095894-B-I00); Ministerio de Ciencia, Innovación y Universidades, MCIU; Xunta de Galicia, (ED481A 2021/196, ED431C 2020/24); Xunta de Galicia; Axencia Galega de Innovación, GAIN, (IN845D 2020/38); Axencia Galega de Innovación, GAIN; Centro de Investigación de Galicia, (ED431G 2019/01); UK Research and Innovation, UKRI, (104818); UK Research and Innovation, UKRI; Instituto de Salud Carlos III, ISCIII, (DTS18/00136); Instituto de Salud Carlos III, ISCIII; Ministerio de Ciencia e Innovación, MICINN, (PID2019-108435RB-I00); Ministerio de Ciencia e Innovación, MICINN","Funding text 1: This research was funded by ISCIII, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovaci\u00F3n y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovaci\u00F3n, Government of Spain through the research project with reference PID2019-108435RB-I00; CCEU, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/196; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovaci\u00F3n (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigaci\u00F3n de Galicia ref. ED431G 2019/01, receives financial support from CCEU, Xunta de Galicia, through the ERDF (80%) and Secretar\u00EDa Xeral de Universidades (20%). Funding for open access charge: Universidade da Coru\u00F1a/CISUG.; Funding text 2: This research was funded by ISCIII, Government of Spain , DTS18/00136 research project; Ministerio de Ciencia e Innovaci\u00F3n Universidades, Government of Spain , RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovaci\u00F3n, Government of Spain through the research project with reference PID2019-108435RB-I00 ; CCEU, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/196 ; and Grupos de Referencia Competitiva , grant ref. ED431C 2020/24 ; Axencia Galega de Innovaci\u00F3n (GAIN), Xunta de Galicia , grant ref. IN845D 2020/38 ; CITIC, Centro de Investigaci\u00F3n de Galicia ref. ED431G 2019/01 , receives financial support from CCEU, Xunta de Galicia , through the ERDF (80%) and Secretar\u00EDa Xeral de Universidades (20%). Funding for open access charge: Universidade da Coru\u00F1a/CISUG.","Yuki K., Fujiogi M., Koutsogiannaki S., COVID-19 pathophysiology: A review, Clin. Immunol., 215, (2020); Siow W.T., Liew M.F., Shrestha B.R., Muchtar F., See K.C., Managing COVID-19 in resource-limited settings: critical care considerations, (2020); Gao Y.-D., Ding M., Dong X., Zhang J.-J., Kursat Azkur A., Azkur D., Gan H., Sun Y.-L., Fu W., Li W., Et al., Risk factors for severe and critically ill COVID-19 patients: a review, Allergy, 76, 2, pp. 428-455, (2021); Estiri H., Strasser Z.H., Klann J.G., Naseri P., Wagholikar K.B., Murphy S.N., Predicting COVID-19 mortality with electronic medical records, NPJ Digit. 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Pract., 103, 2, pp. 206-217, (2014)","J. de Moura; Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Campus de Elviña, s/n, 15071, Spain; email: joaquim.demoura@udc.es","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85150861466"
"Aptekarev T.; Sokolovsky V.; Furman E.; Kalinina N.; Furman G.","Aptekarev, Theodore (58316352100); Sokolovsky, Vladimir (7004029548); Furman, Evgeny (56652600500); Kalinina, Natalia (57381884500); Furman, Gregory (7004498617)","58316352100; 7004029548; 56652600500; 57381884500; 7004498617","Application of deep learning for bronchial asthma diagnostics using respiratory sound recordings","2023","PeerJ Computer Science","9","","e1173","","","","6","10.7717/peerj-cs.1173","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169039627&doi=10.7717%2fpeerj-cs.1173&partnerID=40&md5=85d1fdcee0090ed7983b045e15984324","Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel","Aptekarev T., Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Sokolovsky V., Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Furman E., Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Kalinina N., Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Furman G., Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel","Methods of computer-assisted diagnostics that utilize deep learning techniques on recordings of respiratory sounds have been developed to diagnose bronchial asthma. In the course of the study an anonymous database containing audio files of respiratory sound recordings of patients suffering from different respiratory diseases and healthy volunteers has been accumulated and used to train the software and control its operation. The database consists of 1,238 records of respiratory sounds of patients and 133 records of volunteers. The age of tested persons was from 18 months to 47 years. The sound recordings were captured during calm breathing at four points: in the oral cavity, above the trachea, at the chest, the second intercostal space on the right side, and at the point on the back. The developed software provides binary classifications (diagnostics) of the type: “sick/healthy” and ‘‘asthmatic patient/non-asthmatic patient and healthy’’. For small test samples of 50 (control group) to 50 records (comparison group), the diagnostic sensitivity metric of the first classifier was 88%, its specificity metric -86% and accuracy metric -87%. The metrics for the classifier ‘‘asthmatic patient/non-asthmatic patient and healthy’’ were 92%, 82%, and 87%, respectively. The last model applied to analyze 941 records in asthmatic patients indicated the correct asthma diagnosis in 93% of cases. The proposed method is distinguished by the fact that the trained model enables diagnostics of bronchial asthma (including differential diagnostics) with high accuracy irrespective of the patient gender and age, stage of the disease, as well as the point of sound recording. The proposed method can be used as an additional screening method for preclinical bronchial asthma diagnostics and serve as a basis for developing methods of computer assisted patient condition monitoring including remote monitoring and real-time estimation of treatment effectiveness. © 2023 Aptekarev et al. All Rights Reserved.","Bioinformatics; Bronchial asthma; Computer-assisted diagnostics; Data Mining and Machine Learning; Data Science; Database; Deep learning; Human-Computer Interaction; Respiratory sound","Audio acoustics; Computer aided instruction; Condition monitoring; Database systems; Deep learning; Diagnosis; Disease control; E-learning; Human computer interaction; Learning systems; Medical computing; Patient treatment; Pulmonary diseases; Audio files; Bronchial asthma; Computer assisted diagnostics; Data mining and machine learning; Deep learning; Healthy volunteers; Learning techniques; Machine-learning; Patient's suffering; Respiratory sounds; Data mining","","","","","Ministry of Science, Technology and Space, MOST, (3-16500, N0 3-16500); Ministry of Science, Technology and Space, MOST; Russian Foundation for Basic Research, РФФИ, (19-515-06001, N0 19-515-06001); Russian Foundation for Basic Research, РФФИ; Ministry of Science and Technology, Israel","Funding text 1: This work was supported by a grant from the Israeli Ministry of Science & Technology (MOST, N0 3-16500) and the Russian Foundation for Basic Research (RFBR) (the joint research project N0 19-515-06001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; Funding text 2: The following grant information was disclosed by the authors: Israeli Ministry of Science & Technology (MOST): 3-16500. Russian Foundation for Basic Research (RFBR): 19-515-06001.","Ash SY, Diaz AA., The role of imaging in the assessment of severe asthma, Current Opinion in Pulmonary Medicine, 23, 1, pp. 97-102, (2017); Bahoura M, Lu X., Separation of crackles from vesicular sounds using wavelet packet transform, Acoustics, Speech and Signal Processing ICASSP, 2, pp. 1076-1079, (2006); Brand PLP, Baraldi E, Bisgaard H, Boner AL, Castro-Rodriguez JA, Custovic A, de Blic J, de Jongste JC, Eber E, Everard ML, Frey U, Gappa M, Garcia-Marcos L, Grigg J, Lenney W, Souef PLe, McKenzie S, Merkus PJFM, Midulla F, Paton JY, Piacentini G, Pohunek P, Rossi GA, Seddon P, Silverman M, Sly PD, Stick S, Valiulis A, van Aalderen WMC, Wildhaber JH, Wennergren G, Wilson N, Zivkovic Z, Bush A., Definition, assessment and treatment of wheezing disorders in preschool children: an evidence-based approach. 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Furman; Physics Department, Ben-Gurion University of the Negev, Be’er Sheva, Israel; email: gregoryf@bgu.ac.il","","PeerJ Inc.","","","","","","23765992","","","","English","PeerJ Comput. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85169039627"
"Almeida S.D.; Norajitra T.; Lüth C.T.; Wald T.; Weru V.; Nolden M.; Jäger P.F.; von Stackelberg O.; Heußel C.P.; Weinheimer O.; Biederer J.; Kauczor H.-U.; Maier-Hein K.","Almeida, Silvia D. (57213416077); Norajitra, Tobias (56198077000); Lüth, Carsten T. (57219524655); Wald, Tassilo (57223727513); Weru, Vivienn (57290547000); Nolden, Marco (55908659000); Jäger, Paul F. (57201075948); von Stackelberg, Oyunbileg (56610304000); Heußel, Claus Peter (7004889910); Weinheimer, Oliver (9535317400); Biederer, Jürgen (7003612651); Kauczor, Hans-Ulrich (7102275418); Maier-Hein, Klaus (55647018100)","57213416077; 56198077000; 57219524655; 57223727513; 57290547000; 55908659000; 57201075948; 56610304000; 7004889910; 9535317400; 7003612651; 7102275418; 55647018100","Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT","2024","European Radiology","34","7","","4379","4392","13","5","10.1007/s00330-023-10540-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180691421&doi=10.1007%2fs00330-023-10540-3&partnerID=40&md5=13887adf29ab163afc316c6a18e314b8","Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany; Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany; Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; Faculty of Medicine, University of Latvia, Raina Bulvaris 19, Riga, LV-1586, Latvia; Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, D-24098, Germany","Almeida S.D., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, Medical Faculty, Heidelberg University, Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany; Norajitra T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany; Lüth C.T., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Wald T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Weru V., Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Nolden M., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Jäger P.F., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; von Stackelberg O., Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany, Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Heußel C.P., Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany, Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; Weinheimer O., Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Biederer J., Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany, Faculty of Medicine, University of Latvia, Raina Bulvaris 19, Riga, LV-1586, Latvia, Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, D-24098, Germany; Kauczor H.-U., Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Maier-Hein K., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany","Objectives: To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. Materials and methods: Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1–4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). Results: The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). Conclusion: Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. Clinical relevance statement: Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. Key Points: • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001). © The Author(s) 2023.","Artificial intelligence; Chronic obstructive pulmonary disease; Computed tomography; Deep learning","Aged; Cohort Studies; Deep Learning; Female; Humans; Lung; Male; Middle Aged; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Severity of Illness Index; Tomography, X-Ray Computed; adult; Article; binary classification; chronic obstructive lung disease; clinical feature; cohort analysis; comparative study; computer assisted tomography; controlled study; deep learning; disease exacerbation; disease severity; dyspnea; emphysema; exhalation; female; forced expiratory volume; forced vital capacity; health status; human; inhalation; low risk population; lung function; major clinical study; male; middle aged; observational study; outlier detection; prediction; predictive value; quantitative analysis; retrospective study; six minute walk test; St. George Respiratory Questionnaire; thorax; aged; diagnostic imaging; lung; pathophysiology; procedures; severity of illness index; x-ray computed tomography","","","","","GlaxoSmithKline GmbH&Co; Helmholtz Imaging; State Ministry of Baden-Wuerttemberg for Sciences, Research and Arts, (32-5400/58/3); Boehringer Ingelheim, BI; Arthrex GmbH; Bundesministerium für Bildung und Forschung, BMBF, (01GI0881); Bundesministerium für Bildung und Forschung, BMBF; Deutsches Zentrum für Lungenforschung, DZL, (82DZLI05A2); Deutsches Zentrum für Lungenforschung, DZL","Funding text 1: Open Access funding enabled and organized by Projekt DEAL. This research was funded by the State Ministry of Baden-Wuerttemberg for Sciences, Research and Arts, Germany, grant number 32-5400/58/3; and by Helmholtz Imaging (HI), a platform of the Helmholtz Incubator on Information and Data Science. ; Funding text 2: The COSYCONET study is supported by the German Center for Lung Research (DZL), grant number 82DZLI05A2 (COSYCONET), and the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF]), grant number 01GI0881, and is furthermore supported by unrestricted grants from AstraZeneca GmbH, Boehringer Ingelheim Pharma GmbH & Co. KG, GlaxoSmithKline GmbH&Co. KG, Grifols Deutschland GmbH, and Novartis Deutschland GmbH. ","Adeloye D., Song P.,  Zhu Y., Campbell H., Sheikh A., Rudan I., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, pp. 447-458, (2022); Martinez C.H., Mannino D.M., Jaimes F.A., Et al., Undiagnosed obstructive lung disease in the United States. Associated factors and long-term mortality, Ann Am Thorac Soc, 12, pp. 1788-1795, (2015); Andreeva E., Pokhaznikova M., Lebedev A., Moiseeva I., Kuznetsova O., Degryse J.M., Spirometry is not enough to diagnose COPD in epidemiological studies: a follow-up study, NPJ Prim Care Resp Med, 27, (2017); Lowe K.E., Regan E.A., Anzueto A., Et al., COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr Pulm Dis, 6, pp. 384-399, (2019); Koo H.J., Lee S.M., Seo J.B., Et al., Prediction of pulmonary function in patients with chronic obstructive pulmonary disease: correlation with quantitative CT parameters, Korean J Radiol, 20, (2019); Lynch D.A., Moore C.M., Wilson C., Et al., CT-based visual classification of emphysema: association with mortality in the COPDGene Study, Radiology, 288, pp. 859-866, (2018); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, pp. 193-203, (2018); Tang L.Y.W., Coxson H.O., Lam S., Leipsic J., Tam R.C., Sin D.D., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, pp. e259-e267, (2020); Singla S., Gong M., Riley C., Sciurba F., Batmanghelich K., Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach, Med Phys, 48, pp. 1168-1181, (2021); Sun J., Liao X., Yan Y., Et al., Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach, Eur Radiol, 32, pp. 5319-5329, (2022); Gawlitza J., Trinkmann F., Scheffel H., Et al., Time to exhale: additional value of expiratory chest CT in chronic obstructive pulmonary disease, Can Respir J, 2018, pp. 1-9, (2018); Cao X., Gao X., Yu N., Et al., Potential value of expiratory CT in quantitative assessment of pulmonary vessels in COPD, Front Med, 8, (2021); Almeida S.D., Luth C.T., Norajitra T., COOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 14224, pp. 33-43, (2023); Regan E.A., Hokanson J.E., Murphy J.R., Et al., Genetic Epidemiology of COPD (COPDGene) study design, COPD: J Chronic Obstructive Pulm Dis, 7, pp. 32-43, (2011); Karch A., Vogelmeier C., Welte T., Et al., The German COPD cohort COSYCONET: aims, methods and descriptive analysis of the study population at baseline, Respir Med, 114, pp. 27-37, (2016); Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease, (2020); Wolinsky F.D., Malmstrom T.K., Miller J.P., Andresen E.M., Schootman M., Miller D.K., Antecedents of global decline in health-related quality of life among middle-aged African Americans, J Gerontol B Psychol Sci Soc Sci, 64B, pp. 290-295, (2009); Han M.K., Curran-Everett D., Dransfield M.T., Et al., Racial differences in quality of life in patients with COPD, Chest, 140, pp. 1169-1176, (2011); Chatila W.M., Hoffman E.A., Gaughan J., Robinswood G.B., Criner G.J., Advanced emphysema in African-American and white patients, Chest, 130, pp. 108-118, (2006); Webb W.R., Thin-section CT of the secondary pulmonary lobule: anatomy and the image—the 2004 Fleischner Lecture, Radiology, 239, pp. 322-338, (2006); Konietzke P., Weinheimer O., Wielputz M.O., Et al., Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8–14 year-old children with cystic fibrosis, PLoS One, 13, (2018); Kahnert K., Jorres R.A., Kauczor H.U., Et al., Standardized airway wall thickness Pi10 from routine CT scans of COPD patients as imaging biomarker for disease severity, lung function decline, and mortality, Ther Adv Respir Dis, 17, (2023); Konietzke P., Wielputz M.O., Wagner W.L., Et al., Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval, Eur Radiol, 30, pp. 2502-2512, (2020); Mets O.M., Van Hulst R.A., Jacobs C., Van Ginneken B., De Jong P.A., Normal range of emphysema and air trapping on CT in young men, AJR Am J Roentgenol, 199, pp. 336-340, (2012); Busacker A., Newell J.D., Keefe T., Et al., A multivariate analysis of risk factors for the air-trapping asthmatic phenotype as measured by quantitative CT analysis, Chest, 135, pp. 48-56, (2009); Lv R., Xie M., Jin H., Et al., A preliminary study on the relationship between high-resolution computed tomography and pulmonary function in people at risk of developing chronic obstructive pulmonary disease, Front Med, 9, (2022); Heussel C.P., Herth F.J., Kappes J., Et al., Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values, Eur Radiol, 19, pp. 2391-2402, (2009); Park H., Yun J., Lee S.M., Et al., Deep learning–based approach to predict pulmonary function at chest CT, Radiology, 307, (2023); Li F., Choi J., Zou C., Et al., Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images, Sci Rep, 11, (2021); Luth C.T., Zimmerer D., Koehler G., Et al., CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization, (2023)","S.D. Almeida; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Im Neuenheimer Feld 280, 69120, Germany; email: silvia.diasalmeida@dkfz-heidelberg.de; K. Maier-Hein; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Im Neuenheimer Feld 280, 69120, Germany; email: k.maier-hein@dkfz-heidelberg.de","","Springer Science and Business Media Deutschland GmbH","","","","","","09387994","","EURAE","38150075","English","Eur. Radiol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85180691421"
"Zanette B.; Greer M.-L.C.; Moraes T.J.; Ratjen F.; Santyr G.","Zanette, Brandon (56966672600); Greer, Mary-Louise C. (16312500100); Moraes, Theo J. (6602867764); Ratjen, Felix (56214449500); Santyr, Giles (7004689816)","56966672600; 16312500100; 6602867764; 56214449500; 7004689816","The argument for utilising magnetic resonance imaging as a tool for monitoring lung structure and function in pediatric patients","2023","Expert Review of Respiratory Medicine","17","7","","527","538","11","5","10.1080/17476348.2023.2241355","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166412737&doi=10.1080%2f17476348.2023.2241355&partnerID=40&md5=253306765b1dbbecbccacbfc1a0d6b93","Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, Hospital for Sick Children, Toronto, ON, Canada; Division of Respiratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada","Zanette B., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada; Greer M.-L.C., Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Moraes T.J., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada, Department of Pediatrics, Hospital for Sick Children, Toronto, ON, Canada; Ratjen F., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada, Division of Respiratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Santyr G., Translational Medicine Program, The Hospital for Sick Children, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada","Introduction: Although historically challenging to perform in the lung, technological advancements have made Magnetic Resonance Imaging (MRI) increasingly applicable for pediatric pulmonary imaging. Furthermore, a wide array of functional imaging techniques has become available that may be leveraged alongside structural imaging for increasingly sensitive biomarkers, or as outcome measures in the evaluation of novel therapies. Areas covered: In this review, recent technical advancements and modern methodologies for structural and functional lung MRI are described. These include ultrashort echo time (UTE) MRI, free-breathing contrast agent-free, functional lung MRI, and hyperpolarized gas MRI, amongst other techniques. Specific examples of the application of these methods in children are provided, principally drawn from recent research in asthma, bronchopulmonary dysplasia, and cystic fibrosis. Expert opinion: Pediatric lung MRI is rapidly growing, and is well poised for clinical utilization, as well as continued research into early disease detection, disease processes, and novel treatments. Structure/function complementarity makes MRI especially attractive as a tool for increased adoption in the evaluation of pediatric lung disease. Looking toward the future, novel technologies, such as low-field MRI and artificial intelligence, mitigate some of the traditional drawbacks of lung MRI and will aid in improving access to MRI in general, potentially spurring increased adoption and demand for pulmonary MRI in children. © 2023 Informa UK Limited, trading as Taylor & Francis Group.","Asthma; Bronchopulmonary dysplasia; Cystic fibrosis; Functional MRI; Hyperpolarized gas MRI; Structural MRI; UTE","Artificial Intelligence; Asthma; Child; Cystic Fibrosis; Humans; Infant, Newborn; Lung; Magnetic Resonance Imaging; Article; asthma; child; cystic fibrosis; functional magnetic resonance imaging; human; lung dysplasia; lung function; lung structure; nuclear magnetic resonance imaging; artificial intelligence; asthma; cystic fibrosis; diagnostic imaging; lung; newborn; nuclear magnetic resonance imaging; procedures","","","","","","","Tiddens H.A.W.M., Kuo W., van Straten M., Et al., Paediatric lung imaging: The times they are a-changin’, Eur Respir Rev, 27, 147, pp. 170097-9, (2018); Torres L., Kammerman J., Hahn A.D., Et al., “Structure-function imaging of lung disease using ultrashort echo time MRI”, Acad Radiol, 26, 3, pp. 431-441, (2019); Walkup L.L., Higano N.S., Woods J.C., Structural and functional pulmonary magnetic resonance imaging in pediatrics—from the neonate to the young adult, Acad Radiol, 26, 3, pp. 424-430, (2019); Serai S.D., Rapp J.B., States L.J., Et al., Pediatric lung MRI: Currently available and emerging techniques, Am J Roentgenol, 216, 3, pp. 781-790, (2020); Voskrebenzev A., Vogel-Claussen J., Proton MRI of the lung: How to tame scarce protons and fast signal decay, J Magn Reson Imaging, 53, 5, pp. 1344-1357, (2021); Ciet P., Tiddens H.A.W.M., Wielopolski P.A., Et al., Magnetic resonance imaging in children: common problems and possible solutions for lung and airways imaging, Pediatr Radiol, 45, pp. 1901-1915, (2015); Salerno S., Granata C., Trapenese M., Et al., Is MRI imaging in pediatric age totally safe? 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Santyr; Translational Medicine Program, The Hospital for Sick Children, Toronto, 686 Bay St, M5G 0A4, Canada; email: giles.santyr@sickkids.ca","","Taylor and Francis Ltd.","","","","","","17476348","","","37491192","English","Expert Rev. Respir. Med.","Article","Final","","Scopus","2-s2.0-85166412737"
"Huang L.; Wang Q.; Duan Q.; Shi W.; Li D.; Chen W.; Wang X.; Wang H.; Chen M.; Kuang H.; Zhang Y.; Zheng M.; Li X.; He Z.; Wen C.","Huang, Lin (56957408700); Wang, Qiao (57222187769); Duan, Qingchi (58813178800); Shi, Weiman (58630637100); Li, Dianming (57223606063); Chen, Wu (58070447300); Wang, Xueyan (58633313200); Wang, Hongli (58633435400); Chen, Ming (58933887300); Kuang, Haodan (57919125300); Zhang, Yun (57207473513); Zheng, Mingzhi (57223957671); Li, Xuanlin (57200086428); He, Zhixing (52263663400); Wen, Chengping (22635870300)","56957408700; 57222187769; 58813178800; 58630637100; 57223606063; 58070447300; 58633313200; 58633435400; 58933887300; 57919125300; 57207473513; 57223957671; 57200086428; 52263663400; 22635870300","TCMSSD: A comprehensive database focused on syndrome standardization","2024","Phytomedicine","128","","155486","","","","5","10.1016/j.phymed.2024.155486","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187535710&doi=10.1016%2fj.phymed.2024.155486&partnerID=40&md5=cfdebd331d6eab43ebf6865cf4f0f407","College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, China; Xintong Research Institute of Artificial Intelligence, Hangzhou, Yuhang, China; School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China","Huang L., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China, Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, China; Wang Q., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Duan Q., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Shi W., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Li D., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Chen W., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Wang X., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Wang H., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Chen M., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Kuang H., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; Zhang Y., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China, Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, China; Zheng M., Xintong Research Institute of Artificial Intelligence, Hangzhou, Yuhang, China; Li X., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; He Z., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China, Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, China; Wen C., College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China, Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, China","Backgroud: Quantitative and standardized research on syndrome differentiation has always been at the forefront of modernizing Traditional Chinese Medicine (TCM) theory. However, the majority of existing databases primarily concentrate on the network pharmacology of herbal prescriptions, and there are limited databases specifically dedicated to TCM syndrome differentiation. Purpose: In response to this gap, we have developed the Traditional Chinese Medical Syndrome Standardization Database (TCMSSD, http://tcmssd.ratcm.cn). Methods: TCMSSD is a comprehensive database that gathers data from various sources, including TCM literature such as TCM Syndrome Studies (Zhong Yi Zheng Hou Xue) and TCM Internal Medicine (Zhong Yi Nei Ke Xue) and various public databases such as TCMID and ETCM. In our study, we employ a deep learning approach to construct the knowledge graph and utilize the BM25 algorithm for syndrome prediction. Results: The TCMSSD integrates the essence of TCM with the modern medical system, providing a comprehensive collection of information related to TCM. It includes 624 syndromes, 133,518 prescriptions, 8,073 diseases (including 1,843 TCM-specific diseases), 8,259 Chinese herbal medicines, 43,413 ingredients, 17,602 targets, and 8,182 drugs. By analyzing input data and comparing it with the patterns and characteristics recorded in the database, the syndrome prediction tool generates predictions based on established correlations and patterns. Conclusion: The TCMSSD fills the gap in existing databases by providing a comprehensive resource for quantitative and standardized research on TCM syndrome differentiation and laid the foundation for research on the biological basis of syndromes. © 2024","BM25 algorithm; Database; Knowledge graph; Syndrome prediction; TCM","Algorithms; Databases, Factual; Drugs, Chinese Herbal; Humans; Medicine, Chinese Traditional; Syndrome; herbaceous agent; herbaceous agent; Article; asthma; atrophic gastritis; Chinese herb; Chinese medicine; computer prediction; controlled study; data base; deep learning; dyspnea; hematochezia; human; knowledge; learning algorithm; neuritis; nonhuman; prescription; standardization; stomach cancer; syndrome; systemic lupus erythematosus; thrombocytopenic purpura; western medicine; wheezing; yang deficiency; algorithm; factual database; procedures; syndrome","","Drugs, Chinese Herbal, ","","","Yong Elite Scientists Sponsorship Program; CACM, (2022-QNRC2-A10); National Natural Science Foundation of China, NSFC, (82004501); National Natural Science Foundation of China, NSFC","This work was supported by the National Natural Science Foundation of China ( 82004501 ) and the Yong Elite Scientists Sponsorship Program by CACM ( 2022-QNRC2-A10 ). ","Amberger J.S., Bocchini C.A., Schiettecatte F., Scott A.F., Hamosh A., OMIM.org: online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders, Nucleic. Acids. Res., 43, pp. D789-D798, (2015); Bragina M.E., Daina A., Perez M.A.S., Michielin O., Zoete V., The Swiss similarity 2021 web tool: novel chemical libraries and additional methods for an enhanced ligand-based virtual screening experience, Int. J. Mol. Sci., 23, (2022); Chen J., Wang S., Shen J., Hu Q., Zhang Y., Ma D., Chai K., Analysis of gut microbiota composition in lung adenocarcinoma patients with TCM Qi-Yin deficiency, Am. J. Chin. Med., 49, pp. 1667-1682, (2021); Chen K.J., Shi D.Z., Xu H., [The criterion of syndrome differentiation and quantification for stable coronary heart disease caused by etiological toxin of Chinese medicine], Zhongguo Zhong Xi Yi Jie He Za Zhi, 31, pp. 313-314, (2011); Huang L., Xie D., Yu Y., Liu H., Shi Y., Shi T., Wen C., TCMID 2.0: a comprehensive resource for TCM, Nucleic. Acids. Res., 46, pp. D1117-D1120, (2018); Kim S., Chen J., Cheng T., Gindulyte A., He J., He S., Li Q., Shoemaker B.A., Thiessen P.A., Yu B., Zaslavsky L., Zhang J., Bolton E.E., PubChem in 2021: new data content and improved web interfaces, Nucleic. Acids. Res., 49, pp. D1388-D1395, (2021); Li S., Wang H., Sun Q., Liu B., Chang X., Therapeutic effect of xuebijing, a traditional chinese medicine injection, on rheumatoid arthritis, Evid. Based Complement Alternat. Med., 2020, (2020); Miao Y.C., Tian J.Z., Shi J., Mao M., Zhao X.D., Fang L.Y., Zeng C.Y., Liu J.P., Wang Z.L., Li X.B., Correlation between cognitive functions and syndromes of traditional Chinese medicine in amnestic mild cognitive impairment, Zhong Xi Yi Jie He Xue Bao, 7, pp. 205-211, (2009); Shang H., Zhang L., Xiao T., Zhang L., Ruan J., Zhang Q., Liu K., Yu Z., Ni Y., Wang B., Study on the differences of gut microbiota composition between phlegm-dampness syndrome and qi-yin deficiency syndrome in patients with metabolic syndrome, Front. Endocrinol., 13, (2022); Shen S.W., Hui J.P., Yuwen Y., Wang J.H., Chen L.Y., Niu Y., Peng N., Yang Z.H., Zhao Y., Study on canceration law of gastric mucosal dysplasia based on syndromes of Chinese medicine, Chin. J. Integr. Med., 17, pp. 346-350, (2011); Szklarczyk D., Santos A., von Mering C., Jensen L.J., Bork P., Kuhn M., STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data, Nucleic. Acids. Res., 44, pp. D380-D384, (2016); Wen C.P., Xie Z.J., Song P., Relationship between TCM syndrome type and expression of human leucocyte antigen DR gene in patients with systemic lupus erythematosus, Zhongguo Zhong Xi Yi Jie He Za Zhi, 28, pp. 499-501, (2008); Wishart D.S., Feunang Y.D., Guo A.C., Lo E.J., Marcu A., Grant J.R., Sajed T., Johnson D., Li C., Sayeeda Z., Assempour N., Iynkkaran I., Liu Y., Maciejewski A., Gale N., Wilson A., Chin L., Cummings R., Le D., Pon A., Knox C., Wilson M., DrugBank 5.0: a major update to the DrugBank database for 2018, Nucleic. Acids. Res., 46, pp. D1074-D1082, (2018); Zhang Y., Li X., Shi Y., Chen T., Xu Z., Wang P., Yu M., Chen W., Li B., Jing Z., Jiang H., Fu L., Gao W., Jiang Y., Du X., Gong Z., Zhu W., Yang H., Xu H., ETCM v2.0: an update with comprehensive resource and rich annotations for traditional Chinese medicine, Acta. Pharm. Sin. B, 13, pp. 2559-2571, (2023)","X. Li; College of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China; email: lixuanlinhnzy@163.com","","Elsevier GmbH","","","","","","09447113","","PYTOE","38471316","English","Phytomedicine","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85187535710"
"Ho J.K.; Safari A.; Adibi A.; Sin D.D.; Johnson K.; Sadatsafavi M.; Bansback N.; Bottorff J.L.; Bryan S.; Burns P.; Carlsten C.; Conklin A.I.; De Vera M.; Gershon A.; Gupta S.; Gustafson P.; Harvard S.; Hoens A.M.; Mokhtaran M.; Johnson J.; Joshi P.; Leung J.; Lynd L.D.; Metcalfe R.K.; Michaux K.D.; Simmers B.; Smith D.; Struik L.; Vinay D.","Ho, Joseph Khoa (57816452000); Safari, Abdollah (57205237426); Adibi, Amin (57150418000); Sin, Don D. (57917591300); Johnson, Kate (57194185453); Sadatsafavi, Mohsen (21733729500); Bansback, Nick (55971305900); Bottorff, Joan L. (7006006312); Bryan, Stirling (55984467500); Burns, Paloma (58500935200); Carlsten, Chris (6602448695); Conklin, Annalijn I. (54891894200); De Vera, Mary (8266847200); Gershon, Andrea (24760464400); Gupta, Samir (55568524311); Gustafson, Paul (7004873045); Harvard, Stephanie (25930378000); Hoens, Alison M. (6507020847); Mokhtaran, Mehrshad (57191908534); Johnson, Jim (58265874500); Joshi, Phalgun (55753249400); Leung, Janice (36087521200); Lynd, Larry D. (7006325179); Metcalfe, Rebecca K. (57210122913); Michaux, Kristina D. (57126080400); Simmers, Brian (57489134800); Smith, Daniel (57221196637); Struik, Laura (55437189700); Vinay, Dhingra (48663051800)","57816452000; 57205237426; 57150418000; 57917591300; 57194185453; 21733729500; 55971305900; 7006006312; 55984467500; 58500935200; 6602448695; 54891894200; 8266847200; 24760464400; 55568524311; 7004873045; 25930378000; 6507020847; 57191908534; 58265874500; 55753249400; 36087521200; 7006325179; 57210122913; 57126080400; 57489134800; 57221196637; 55437189700; 48663051800","Generalizability of Risk Stratification Algorithms for Exacerbations in COPD","2023","Chest","163","4","","790","798","8","7","10.1016/j.chest.2022.11.041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150760325&doi=10.1016%2fj.chest.2022.11.041&partnerID=40&md5=94366e140b4ad2cdc7d797eba2f22f9e","Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada; Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada; Centre for Heart Lung Innovation, The University of British Columbia, Vancouver, BC, Canada; Department of Medicine (Respirology), The University of British Columbia, Vancouver, BC, Canada; Department of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran","Ho J.K., Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada; Safari A., Department of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran; Adibi A., Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada; Sin D.D., Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Centre for Heart Lung Innovation, The University of British Columbia, Vancouver, BC, Canada, Department of Medicine (Respirology), The University of British Columbia, Vancouver, BC, Canada; Johnson K., Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Department of Medicine (Respirology), The University of British Columbia, Vancouver, BC, Canada; Sadatsafavi M., Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada; Bansback N.; Bottorff J.L.; Bryan S.; Burns P.; Carlsten C.; Conklin A.I.; De Vera M.; Gershon A.; Gupta S.; Gustafson P.; Harvard S.; Hoens A.M.; Mokhtaran M.; Johnson J.; Joshi P.; Leung J.; Lynd L.D.; Metcalfe R.K.; Michaux K.D.; Simmers B.; Smith D.; Struik L.; Vinay D.","Background: Contemporary management of COPD relies on exacerbation history to risk-stratify patients for future exacerbations. Multivariable prediction models can improve the performance of risk stratification. However, the clinical utility of risk stratification can vary from one population to another. Research Question: How do two validated exacerbation risk prediction models (Acute COPD Exacerbation Prediction Tool [ACCEPT] and the Bertens model) compared with exacerbation history alone perform in different patient populations? Study Design and Methods: We used data from three clinical studies representing populations at different levels of moderate to severe exacerbation risk: the Study to Understand Mortality and Morbidity in COPD (SUMMIT; N = 2,421; annual risk, 0.22), the Long-term Oxygen Treatment Trial (LOTT; N = 595; annual risk, 0.38), and Towards a Revolution in COPD Health (TORCH; N = 1,091; annual risk, 0.52). We compared the area under the receiver operating characteristic curve (AUC) and net benefit (measure of clinical utility) among three risk stratification algorithms for predicting exacerbations in the next 12 months. We also evaluated the effect of model recalibration on clinical utility. Results: Compared with exacerbation history, ACCEPT showed better performance in all three samples (change in AUC, 0.08, 0.07, and 0.10, in SUMMIT, LOTT, and TORCH, respectively; P ≤.001 for all). The Bertens model showed better performance compared with exacerbation history in SUMMIT and TORCH (change in AUC, 0.10 and 0.05, respectively; P <.001 for both), but not in LOTT. No algorithm was superior in clinical utility across all samples. Before recalibration, the Bertens model generally outperformed the other algorithms in low-risk settings, whereas ACCEPT outperformed others in high-risk settings. All three algorithms showed the risk of harm (providing lower net benefit than not using any risk stratification). After recalibration, risk of harm was mitigated substantially for both prediction models. Interpretation: Exacerbation history alone is unlikely to provide clinical utility for predicting COPD exacerbations in all settings and could be associated with a risk of harm. Prediction models have superior predictive performance, but require setting-specific recalibration to confer higher clinical utility. © 2022 American College of Chest Physicians","clinical prediction modeling; clinical utility; COPD; precision medicine; risk stratification","Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive; Risk Assessment; corticosteroid; hydroxymethylglutaryl coenzyme A reductase inhibitor; adult; algorithm; Article; calibration; cardiovascular risk; chronic obstructive lung disease; controlled study; decision making; diagnostic test accuracy study; disease exacerbation; disease severity; female; forced expiratory volume; forced vital capacity; human; learning algorithm; lung function; machine learning; major clinical study; male; middle aged; morbidity; mortality rate; oxygen therapy; prediction; predictive value; randomized controlled trial; receiver operating characteristic; respiratory function; risk assessment; risk factor; sensitivity and specificity; smoking; St. George Respiratory Questionnaire; stratification; chronic obstructive lung disease; disease exacerbation; risk assessment","","","","","Canadian Institutes of Health Research, IRSC, (PHT 178432)","This study was funded by the Canadian Institutes of Health Research [Grant PHT 178432].","Evans J., Chen Y., Camp P.G., Bowie D.M., McRae L., Estimating the prevalence of COPD in Canada: reported diagnosis versus measured airflow obstruction, Health Rep, 25, pp. 3-11, (2014); Vogelmeier C.F., Criner G.J., Martinez F.J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 Report. GOLD executive summary, Am J Respir Crit Care Med, 195, pp. 557-582, (2017); 2022 GOLD reports. Global Initiative for Chronic Obstructive Lung Disease website; Bourbeau J., Bhutani M., Hernandez P., Et al., Canadian Thoracic Society clinical practice guideline on pharmacotherapy in patients with COPD—2019 update of evidence, Canadian Journal of Respiratory, Critical Care, and Sleep Medicine, 3, 4, pp. 210-232, (2019); Marott J.L., Colak Y., Ingebrigtsen T.S., Vestbo J., Nordestgaard B.G., Lange P., Exacerbation history, severity of dyspnoea and maintenance treatment predicts risk of future exacerbations in patients with COPD in the general population, Respir Med, 192, (2022); Hurst J.R., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Han M.K., Quibrera P.M., Carretta E.E., Et al., Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort, Lancet Respir Med, 5, 8, pp. 619-626, (2017); Calverley P.M., Tetzlaff K., Dusser D., Et al., Determinants of exacerbation risk in patients with COPD in the TIOSPIR study, Int J Chron Obstruct Pulmon Dis, 12, pp. 3391-3405, (2017); Sadatsafavi M., McCormack J., Petkau J., Lynd L.D., Lee T.Y., Sin D.D., Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?, Eur Respir J, 57, 2, (2021); Pencina M.J., Peterson E.D., Moving from clinical trials to precision medicine: the role for predictive modeling, JAMA, 315, 16, pp. 1713-1714, (2016); Smits M., Dippel D.W.J., Steyerberg E.W., Et al., Predicting intracranial traumatic findings on computed tomography in patients with minor head injury: the CHIP prediction rule, Ann Intern Med, 146, 6, pp. 397-405, (2007); Steyerberg E.W., Eijkemans M.J.C., Boersma E., Habbema J.D.F., Applicability of clinical prediction models in acute myocardial infarction: a comparison of traditional and empirical Bayes adjustment methods, Am Heart J, 150, 5, pp. 920e11-e17920, (2005); Calverley P.M.A., Martinez F.J., Vestbo J., Et al., International differences in the frequency of COPD exacerbations reported in three clinical trials, Am J Respir Crit Care Med, 206, 1, pp. 25-33, (2022); Gulati G., Upshaw J., Wessler B.S., Et al., Generalizability of cardiovascular disease clinical prediction models: 158 independent external validations of 104 unique models, Circ Cardiovasc Qual Outcomes, 15, 4, (2022); Bertens L.C.M., Reitsma J.B., Moons K.G.M., Et al., Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease, Int J Chron Obstruct Pulmon Dis, 8, pp. 493-499, (2013); Guerra B., Gaveikaite V., Bianchi C., Puhan M.A., Prediction models for exacerbations in patients with COPD, Eur Respir Rev, 26, 143, (2017); Adibi A., Sin D.D., Safari A., Et al., The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study, Lancet Respir Med, 8, 10, pp. 1013-1021, (2020); Safari A., Adibi A., Sin D.D., Lee T.Y., Ho J.K., Sadatsafavi M., ACCEPT 2.0: recalibrating and externally validating the Acute COPD Exacerbation Prediction Tool (ACCEPT), EclinicalMedicine, 51, (2022); Jones P.W., Quirk F.H., Baveystock C.M., The St George's Respiratory Questionnaire, Respir Med, 85, pp. 25-31, (1991); Jones P.W., Harding G., Berry P., Wiklund I., Chen W.-H., Kline Leidy N., Development and first validation of the COPD Assessment Test, Eur Respir J, 34, 3, pp. 648-654, (2009); Vestbo J., Anderson J.A., Brook R.D., Et al., Fluticasone furoate and vilanterol and survival in chronic obstructive pulmonary disease with heightened cardiovascular risk (SUMMIT): a double-blind andomized controlled trial, Lancet, 387, 10030, pp. 1817-1826, (2016); A randomized trial of long-term oxygen for COPD with moderate desaturation, N Engl J Med, 375, 17, pp. 1617-1627, (2016); Vestbo J., The TORCH (Towards a Revolution in COPD Health) survival study protocol, Eur Respir J, 24, 2, pp. 206-210, (2004); DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Chiang C.-T., Hung H., Non-parametric estimation for time-dependent AUC, J Stat Plan Inference, 140, 5, pp. 1162-1174, (2010); Hung H., Chiang C.-T., Estimation methods for time-dependent AUC models with survival data, Canadian Journal of Statistics, 38, 1, pp. 8-26, (2010); Vickers A.J., Elkin E.B., Decision curve analysis: a novel method for evaluating prediction models, Med Decis Making, 26, 6, pp. 565-574, (2006); Sadatsafavi M., Adibi A., Puhan M., Gershon A., Aaron S.D., Sin D.D., Moving beyond AUC: decision curve analysis for quantifying net benefit of risk prediction models, Eur Respir J., 58, 5, (2021); Vickers A.J., Cronin A.M., Elkin E.B., Gonen M., Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers, BMC Med Inform Decis Mak, 8, (2008); Vickers A.J., van Calster B., Steyerberg E.W., A simple, step-by-step guide to interpreting decision curve analysis, Diagn Progn Res, 3, (2019); Steyerberg E.W., Borsboom G.J.J.M., van Houwelingen H.C., Eijkemans M.J.C., Habbema J.D.F., Validation and updating of predictive logistic regression models: a study on sample size and shrinkage, Stat Med, 23, 16, pp. 2567-2586, (2004); Sadatsafavi M., Tavakoli H., Safari A., Marginal versus conditional odds ratios when updating risk prediction models, Epidemiology, 33, 4, pp. 555-558, (2022); Obeidat M., Sadatsafavi M., Sin D.D., Precision health: treating the individual patient with chronic obstructive pulmonary disease, Med J Aust, 210, 9, pp. 424-428, (2019); Taylor S.P., Sellers E., Taylor B.T., Azithromycin for the prevention of COPD exacerbations: the good, bad, and ugly, Am J Med, 128, 2, pp. 1362.e1-1362.e6, (2015); Yebyo H.G., Braun J., Menges D., Ter Riet G., Sadatsafavi M., Puhan M.A., Personalising add-on treatment with inhaled corticosteroids in patients with chronic obstructive pulmonary disease: a benefit-harm modelling study, Lancet Digit Health, 3, 10, pp. e644-e653, (2021); Moons K.G.M., Kengne A.P., Grobbee D.E., Et al., Risk prediction models: II. External validation, model updating, and impact assessment, Heart, 98, 9, pp. 691-698, (2012); Vergouwe Y., Nieboer D., Oostenbrink R., Et al., A closed testing procedure to select an appropriate method for updating prediction models, Stat Med, 36, 28, pp. 4529-4539, (2017); Bellou V., Belbasis L., Konstantinidis A.K., Tzoulaki I., Evangelou E., Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal, BMJ, 367, (2019); Wessler B.S., Nelson J., Park J.G., Et al., External validations of cardiovascular clinical prediction models: a large-scale review of the literature, Circ Cardiovasc Qual Outcomes, 14, 8, (2021)","M. Sadatsafavi; Respiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, Canada; email: msafavi@mail.ubc.ca","","Elsevier Inc.","","","","","","00123692","","CHETB","36509123","English","Chest","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85150760325"
"Hwang H.; Jang J.-H.; Lee E.; Park H.-S.; Lee J.Y.","Hwang, Hyemin (57391661400); Jang, Jae-Hyuk (57218932305); Lee, Eunyoung (57221419503); Park, Hae-Sim (57192203361); Lee, Jae Young (57217999869)","57391661400; 57218932305; 57221419503; 57192203361; 57217999869","Prediction of the number of asthma patients using environmental factors based on deep learning algorithms","2023","Respiratory Research","24","1","302","","","","6","10.1186/s12931-023-02616-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178366336&doi=10.1186%2fs12931-023-02616-x&partnerID=40&md5=d0b57c1d921e48da012afedcc3648e3c","Environmental Engineering Department, Ajou University, Suwon, 16499, South Korea; Department of Allergy and Clinical Immunology, Ajou University School of Medicine, Suwon, 16499, South Korea; Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, 77030, TX, United States; Environmental and Safety Engineering Department, Ajou University, 206, World Cup-ro, Yeongtong-gu, Suwon, 16499, South Korea","Hwang H., Environmental Engineering Department, Ajou University, Suwon, 16499, South Korea; Jang J.-H., Department of Allergy and Clinical Immunology, Ajou University School of Medicine, Suwon, 16499, South Korea; Lee E., Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, 77030, TX, United States; Park H.-S., Department of Allergy and Clinical Immunology, Ajou University School of Medicine, Suwon, 16499, South Korea; Lee J.Y., Environmental and Safety Engineering Department, Ajou University, 206, World Cup-ro, Yeongtong-gu, Suwon, 16499, South Korea","Background: Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted. Methods: In this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis. Results: We found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM10, NO2, CO, and pollen had a significant impact on asthma exacerbation. In addition, the week of the year and the number of holidays per week were an important factor to model the seasonality of the number of asthma patients and the effect of holiday clinic closures, respectively. Conclusion: LSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation. © 2023, The Author(s).","Air pollution; Asthma; Gated recurrent unit; Influenza; Long short-term memory; Recurrent neural network","Air Pollutants; Air Pollution; Algorithms; Asthma; Deep Learning; Humans; Influenza, Human; carbon monoxide; nitrogen dioxide; adolescent; air pollutant; air pollution; Article; asthma; deep learning; disease exacerbation; emergency ward; environmental factor; environmental temperature; epidemiological data; gated recurrent unit network; human; influenza; long short term memory network; outpatient; particulate matter 10; pollen; recurrent neural network; seasonal variation; South Korea; weather; air pollutant; air pollution; algorithm; asthma; influenza","","carbon monoxide, 630-08-0; nitrogen dioxide, 10102-44-0; Air Pollutants, ","","","Ministry of Science, ICT and Future Planning, MSIP, (NRF-2021R1C1C1013350, NRF-2023M3G1A1090660); National Research Foundation of Korea, NRF","This research was supported by the Fine Particle Research Initiative in East Asia Considering National Differences (FRIEND) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT [Grant number NRF-2023M3G1A1090660], and this research was also supported by the National Research Foundation of Korea [Grant number NRF-2021R1C1C1013350]. 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Lee; Environmental and Safety Engineering Department, Ajou University, Suwon, 206, World Cup-ro, Yeongtong-gu, 16499, South Korea; email: jaeylee@ajou.ac.kr","","BioMed Central Ltd","","","","","","14659921","","RREEB","38041105","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85178366336"
"Fakotakis D.N.; Nousias S.; Arvanitis G.; Zacharaki E.I.; Moustakas K.","Fakotakis, Dimitris Nikos (57216584803); Nousias, Stavros (57191051568); Arvanitis, Gerasimos (8202614400); Zacharaki, Evangelia I. (14526218700); Moustakas, Konstantinos (6507331953)","57216584803; 57191051568; 8202614400; 14526218700; 6507331953","AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite","2023","IEEE Access","11","","","13810","13829","19","6","10.1109/ACCESS.2023.3243547","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148442897&doi=10.1109%2fACCESS.2023.3243547&partnerID=40&md5=83ef3a981337d9441a028729fa206fd6","University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece","Fakotakis D.N., University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; Nousias S., University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; Arvanitis G., University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; Zacharaki E.I., University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; Moustakas K., University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece","Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the Respiratory and Drug Actuation (RDA) Suite (https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark) for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks' architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.  © 2013 IEEE.","Artificial intelligence; asthma; audio analysis; deep learning; feature extraction; inhaled medication adherence; machine learning; pattern recognition; respiratory sounds","Audio acoustics; Audio systems; Biomedical equipment; Biomedical signal processing; Classification (of information); Deep learning; Diagnosis; Embedded systems; Extraction; Pulmonary diseases; Respiratory system; Asthma; Audio analysis; Deep learning; Drug; Features extraction; Inhaled medication; Inhaled medication adherence; Machine-learning; Medication adherence; Respiratory sounds; Feature extraction","","","","","","","World Health Organization Asthma. Fact Sheet no. 307, (2021); European Lung White Book. Adult Asthma, (2021); European Lung White Book. 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Amer., 145, 6, pp. 541-546, (2019); Ntalampiras S., Potamitis I., Acoustic detection of unknown bird species and individuals, Trans. Intell. Technol., 6, 3, pp. 291-300, (2021); Nousias S., Patient-specific modelling, simulation and real time processing for respiratory diseases, (2022)","S. Nousias; University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; email: snousias@upatras.gr; D.N. Fakotakis; University of Patras, Department of Electrical and Computer Engineering, Patras, 26500, Greece; email: fakotakis@ceid.upatras.gr","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85148442897"
"Wang L.; Yang X.; Kuang L.; Zhang Z.; Zeng B.; Chen Z.","Wang, Lei (57070560700); Yang, Xiaoyu (57188557458); Kuang, Linai (36010595800); Zhang, Zhen (56102785000); Zeng, Bin (57225683090); Chen, Zhiping (56941055100)","57070560700; 57188557458; 36010595800; 56102785000; 57225683090; 56941055100","Graph Convolutional Neural Network with Multi-Layer Attention Mechanism for Predicting Potential Microbe-Disease Associations","2023","Current Bioinformatics","18","6","","497","508","11","5","10.2174/1574893618666230316113621","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169818228&doi=10.2174%2f1574893618666230316113621&partnerID=40&md5=def81156d16fe7eca1d3a346c11cf1d6","School of Computer Science and Engineering, Changsha University, Changsha, 410022, China; Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China","Wang L., School of Computer Science and Engineering, Changsha University, Changsha, 410022, China, Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China; Yang X., School of Computer Science and Engineering, Changsha University, Changsha, 410022, China, Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China; Kuang L., Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China; Zhang Z., School of Computer Science and Engineering, Changsha University, Changsha, 410022, China; Zeng B., School of Computer Science and Engineering, Changsha University, Changsha, 410022, China; Chen Z., School of Computer Science and Engineering, Changsha University, Changsha, 410022, China","Background: Human microbial communities play an important role in some physiological process of human beings. Nevertheless, the identification of microbe-disease associations through biological experiments is costly and time-consuming. Hence, the development of calculation models is meaningful to infer latent associations between microbes and diseases. Aims: In this manuscript, we aim to design a computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to infer latent microbe-disease associations. Objective: This study aims to propose a novel computational model based on the Graph Convolutional Neural Network with Multi-layer Attention mechanism, called GCNMA, to detect potential microbe-disease associations. Methods: In GCNMA, the known microbe-disease association network was first integrated with the mi-crobe-microbe similarity network and the disease-disease similarity network into a heterogeneous network first. Subsequently, the graph convolutional neural network was implemented to extract embedding features of each layer for microbes and diseases respectively. Thereafter, these embedding features of each layer were fused together by adopting the multi-layer attention mechanism derived from the graph convolutional neural network, based on which, a bilinear decoder would be further utilized to infer possible associations between microbes and diseases. Results: Finally, to evaluate the predictive ability of GCNMA, intensive experiments were done and compared results with eight state-of-the-art methods which demonstrated that under the frameworks of both 2-fold cross-validations and 5-fold cross-validations, GCNMA can achieve satisfactory prediction performance based on different databases including HMDAD and Disbiome simultaneously. Moreover, case studies on three kinds of common diseases such as asthma, type 2 diabetes, and inflammatory bowel disease verified the effectiveness of GCNMA as well. Conclusion: GCNMA outperformed 8 state-of-the-art competitive methods based on the benchmarks of both HMDAD and Disbiome. © 2023 Bentham Science Publishers.","attention mechanism; deep learning; graph convolutional network; inflammatory bowel disease; Microbe-disease associations; type 2 diabetes","Bacteria; Computation theory; Computational methods; Convolution; Convolutional neural networks; Deep learning; Embeddings; Graph neural networks; Multilayer neural networks; Attention mechanisms; Convolutional networks; Convolutional neural network; Deep learning; Disease associations; Graph convolutional network; Inflammatory bowel disease; Microbe-disease association; Multi-layers; Type-2 diabetes; Actinomyces; Article; asthma; Bacteroidaceae; Bacteroides eggerthii; Betaproteobacteria; Clostridia; Clostridium; convolutional neural network; Coriobacteriaceae; cross validation; disease association; Enterococcus; Enterococcus faecalis; Eubacterium; Firmicutes; Gardnerella; Haemophilus parainfluenzae; Hafnia; human; inflammatory bowel disease; kernel method; Klebsiella pneumoniae; Lachnospiraceae; microbial community; microorganism; natural language processing; non insulin dependent diabetes mellitus; nonhuman; Pectobacterium; prediction; Prevotella copri; Staphylococcus aureus; Heterogeneous networks","","","","","Key Project of Changsha Science and Technology Plan, (KQ2203001); National Natural Science Foundation of China, NSFC, (61873221, 62272064); National Natural Science Foundation of China, NSFC","This work was partly sponsored by the National Natural Science Foundation of China (Grant No. 62272064, Grant No. 61873221) and the Key Project of Changsha Science and Technology Plan (Grant No. KQ2203001).","Gill SR, Pop M, DeBoy RT, Et al., Metagenomic analysis of the human distal gut microbiome, Science, 312, 5778, pp. 1355-1359, (2006); Integrative HMP., The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease, Cell Host Microbe, 16, 3, pp. 276-289, (2014); Sender R, Fuchs S, Milo R., Integrative HMP (iHMP) Research Network Consortium. 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Yang; School of Computer Science and Engineering, Changsha University, Changsha, 410022, China; email: xiaoyuyang@xtu.edu.cn; Z. Chen; School of Computer Science and Engineering, Changsha University, Changsha, 410022, China; email: zpchen@ccsu.edu.cn","","Bentham Science Publishers","","","","","","15748936","","","","English","Curr. Bioinform.","Article","Final","","Scopus","2-s2.0-85169818228"
"AlShehhi A.; Welsch R.","AlShehhi, Aamna (55813913300); Welsch, Roy (8214812500)","55813913300; 8214812500","Artificial intelligence for improving Nitrogen Dioxide forecasting of Abu Dhabi environment agency ground-based stations","2023","Journal of Big Data","10","1","92","","","","7","10.1186/s40537-023-00754-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160948931&doi=10.1186%2fs40537-023-00754-z&partnerID=40&md5=3cd96198c6bac00df90c32d0957063c2","Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; Sloan School of Management and Statistics, Massachusetts Institute of Technology, Cambridge, MA, United States","AlShehhi A., Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; Welsch R., Sloan School of Management and Statistics, Massachusetts Institute of Technology, Cambridge, MA, United States","Nitrogen Dioxide (NO 2) is a common air pollutant associated with several adverse health problems such as pediatric asthma, cardiovascular mortality,and respiratory mortality. Due to the urgent society’s need to reduce pollutant concentration, several scientific efforts have been allocated to understand pollutant patterns and predict pollutants’ future concentrations using machine learning and deep learning techniques. The latter techniques have recently gained much attention due it’s capability to tackle complex and challenging problems in computer vision, natural language processing, etc. In the NO 2 context, there is still a research gap in adopting those advanced methods to predict the concentration of pollutants. This study fills in the gap by comparing the performance of several state-of-the-art artificial intelligence models that haven’t been adopted in this context yet. The models were trained using time series cross-validation on a rolling base and tested across different periods using NO 2 data from 20 monitoring ground-based stations collected by Environment Agency- Abu Dhabi, United Arab Emirates. Using the seasonal Mann-Kendall trend test and Sen’s slope estimator, we further explored and investigated the pollutants trends across the different stations. This study is the first comprehensive study that reported the temporal characteristic of NO 2 across seven environmental assessment points and compared the performance of the state-of-the-art deep learning models for predicting the pollutants’ future concentration. Our results reveal a difference in the pollutants concentrations level due to the geographic location of the different stations, with a statistically significant decrease in the NO 2 annual trend for the majority of the stations. Overall, NO 2 concentrations exhibit a similar daily and weekly pattern across the different stations, with an increase in the pollutants level during the early morning and the first working day. Comparing the state-of-the-art model performance transformer model demonstrate the superiority of (MAE:0.04 (± 0.04),MSE:0.06 (± 0.04), RMSE:0.001 (± 0.01), R 2 : 0.98 (± 0.05)), compared with LSTM (MAE:0.26 (± 0.19), MSE:0.31 (± 0.21), RMSE:0.14 (± 0.17), R 2 : 0.56 (± 0.33)), InceptionTime (MAE: 0.19 (± 0.18), MSE: 0.22 (± 0.18), RMSE:0.08 (± 0.13), R 2 :0.38 (± 1.35)), ResNet (MAE:0.24 (± 0.16), MSE:0.28 (± 0.16), RMSE:0.11 (± 0.12), R 2 :0.35 (± 1.19)), XceptionTime (MAE:0.7 (± 0.55), MSE:0.79 (± 0.54), RMSE:0.91 (± 1.06), R 2 : - 4.83 (± 9.38)), and MiniRocket (MAE:0.21 (± 0.07), MSE:0.26 (± 0.08), RMSE:0.07 (± 0.04), R 2 : 0.65 (± 0.28)) to tackle this challenge. The transformer model is a powerful model for improving the accurate forecast of the NO 2 levels and could strengthen the current monitoring system to control and manage the air quality in the region. © 2023, The Author(s).","Artificial Intelligence; Deep Learning; Forecast; Nitrogen Dioxide; Temporal Models; Transformer Model","Air pollution; Learning algorithms; Learning systems; Long short-term memory; Natural language processing systems; Nitrogen oxides; Abu Dhabi; Deep learning; Environment Agency; Forecast; Ground-based stations; NO  2; Pollutant concentration; State of the art; Temporal models; Transformer modeling; Forecasting","","","","","Environment Agency, EA","We would like to thank Environment Agency- Abu Dhabi (EAD) for their assistance in providing this study’s data.","Shams S.R., Jahani A., Kalantary S., Moeinaddini M., Khorasani N., Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air, Sci Rep, 11, 1, (2021); Baklanov A., Zhang Y., Advances in air quality modeling and forecasting, Global Transit, 2, pp. 261-270, (2020); Iskandaryan D., Ramos F., Trilles S., Bidirectional convolutional LSTM for the prediction of nitrogen dioxide in the city of Madrid, PLOS ONE, 17, 6, (2022); Ngarambe J., Joen S.J., Han C.-H., Yun G.Y., Exploring the relationship between particulate matter, CO, SO2, NO2, O3 and urban heat island in Seoul Korea, J Hazard Mat, 403, (2021); Lee M., Lin L., Chen C.-Y., Tsao Y., Yao T.-H., Fei M.-H., Fang S.-H., Forecasting air quality in Taiwan by using machine learning, Sci Rep, 10, 1, (2020); Xiao F., Yang M., Fan H., Fan G., Al-qaness M.A.A., An improved deep learning model for predicting daily PM2.5 concentration, Sci Rep, 10, 1, (2020); Hu Y., Ji J.S., Zhao B. Restrictions on indoor and outdoor NO2 emissions to reduce disease burden for pediatric asthma in China: A modeling study. The Lancet Regional Health Western Pacific 24 (2022), Elsevier. Accessed From, (2022); Seng D., Zhang Q., Zhang X., Chen G., Chen X., Spatiotemporal prediction of air quality based on LSTM neural network, Alex Eng J, 60, 2, pp. 2021-2032, (2021); Liu B., Zhang L., Wang Q., Chen J., A novel method for regional NO2 concentration prediction using discrete wavelet transform and an LSTM network, Comput Intell Neurosci, 2021, (2021); Ogen Y., Assessing nitrogen dioxide (NO2) levels as a contributing factor to coronavirus (COVID-19) fatality, Sci Total Environ, 726, (2020); Anenberg S.C., Mohegh A., Goldberg D.L., Kerr G.H., Brauer M., Burkart K., Hystad P., Larkin A., Wozniak S., Lamsal L., Long-term trends in urban NO2 concentrations and associated paediatric asthma incidence: estimates from global datasets, Lancet Planet Health, 6, 1, pp. 49-58, (2022); Cooper M.J., Martin R.V., Hammer M.S., Levelt P.F., Veefkind P., Lamsal L.N., Krotkov N.A., Brook J.R., McLinden C.A., Global fine-scale changes in ambient NO2 during COVID-19 lockdowns, Nature, 601, 7893, pp. 380-387, (2022); Li L., Girguis M., Lurmann F., Wu J., Urman R., Rappaport E., Ritz B., Franklin M., Breton C., Gilliland F., Habre R., Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions, Environ Int, 128, pp. 310-323, (2019); Yousefian F., Faridi S., Azimi F., Aghaei M., Shamsipour M., Yaghmaeian K., Hassanvand M.S., Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017, Sci Rep, 10, 1, (2020); Teixido O., Tobias A., Massague J., Mohamed R., Ekaabi R., Hamed H.I., Perry R., Querol X., Al Hosani S., The influence of COVID-19 preventive measures on the air quality in Abu Dhabi (United Arab Emirates), Air Qual Atm Health, 14, 7, pp. 1071-1079, (2021); Zhang X., Just A.C., Hsu H.-H.L., Kloog I., Woody M., Mi Z., Rush J., Georgopoulos P., Wright R.O., Stroustrup A., A hybrid approach to predict daily NO2 concentrations at city block scale, Sci Total Environ, 761, (2021); Dey T., Tyagi P., Sabath M.B., Kamareddine L., Henneman L., Braun D., Dominici F., Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States, Sci Rep, 11, 1, (2021); MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification, In: Proceedings of the 27Th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 248-257, (2021); Ismail Fawaz H., Forestier G., Weber J., Idoumghar L., Muller P.-A., Deep learning for time series classification: a review, Data Mining Knowl Discov, 33, 4, pp. 917-963, (2019); Rahimian E., Zabihi S., Atashzar S.F., Asif A., Mohammadi A., XceptionTime: a novel deep architecture based on depthwise separable convolutions for hand gesture classification, arXiv, (2019); Ismail Fawaz H., Lucas B., Forestier G., Pelletier C., Schmidt D.F., Weber J., Webb G.I., Idoumghar L., Muller P.-A., Petitjean F., InceptionTime: finding AlexNet for time series classification, Data Mining Knowl Discov, 34, 6, pp. 1936-1962, (2020); A Transformer-based Framework for Multivariate Time Series Representation Learning, In: Proceedings of the 27Th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114-2124, (2021); Abu Dhabi Government Media Office: The Environment Agency Abu Dhabi Expanded its Air Quality Monitoring Programme in 2021, (2022); (2022); (2022); (2022); Wijesekara L., Liyanage L., Comparison of Imputation Methods for Missing Values in Air Pollution Data: Case Study on Sydney Air Quality Index, pp. 257-269, (2020); Gholami H., Moradi Y., Lotfirad M., Gandomi M.A., Bazgir N., Shokrian H.M., Detection of abrupt shift and non-parametric analyses of trends in runoff time series in the Dez river basin, Water Supp, 22, 2, pp. 1216-1230, (2021); Aamir E., Hassan I., Trend analysis in precipitation at individual and regional levels in Baluchistan, Pakistan, IOP Conference Series: Materials Science and Engineering, 414, (2018); Bondugula R.K., Udgata S.K., Sivangi K.B., A novel deep learning architecture and MINIROCKET feature extraction method for human activity recognition using ECG, PPG and inertial sensor dataset, Appl Intell, (2022); Xu S., Li W., Zhu Y., Xu A., A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks, Sci Rep, 12, 1, (2022); Moritz S., Bartz-Beielstein T., imputeTS: time series missing value imputation in R, R J, (2017); Tsai - a State-Of-The-Art Deep Learning Library for Time Series and Sequential Data (, (2020); Duveiller G., Fasbender D., Meroni M., Revisiting the concept of a symmetric index of agreement for continuous datasets, Sci Rep, 6, 1, (2016); Gulfnews, Zenifer Khaleel: Huge efforts on to improve air quality in Abu Dhabi, (2017); Cichowicz R., Wielgosinski G., Fetter W., Dispersion of atmospheric air pollution in summer and winter season, Environ Monit Assess, 189, 12, (2017); National Center of Meteorology: National Air Quality Platform - NAQP - NCM, (2022); Tariq alfaham: Ministry of Climate Change and Environment inaugurates National Air Quality Platform, Emirates News Agency-Wam, (2020); Rola Alghoul and MOHD AAMIR: UAE Government Announces Four and Half Day Working Week, (2021)","A. AlShehhi; Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates; email: aamna.alshehhi@ku.ac.ae","","Springer Science and Business Media Deutschland GmbH","","","","","","21961115","","","","English","J. Big Data","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85160948931"
"Jang S.Y.; Park J.J.; Adler E.; Eshraghian E.; Ahmad F.S.; Campagnari C.; Yagil A.; Greenberg B.","Jang, Se Yong (57207977889); Park, Jin Joo (35799900000); Adler, Eric (59283823400); Eshraghian, Emily (57212930588); Ahmad, Faraz S. (54392524600); Campagnari, Claudio (56448126300); Yagil, Avi (59434614500); Greenberg, Barry (7201629440)","57207977889; 35799900000; 59283823400; 57212930588; 54392524600; 56448126300; 59434614500; 7201629440","Mortality Prediction in Patients With or Without Heart Failure Using a Machine Learning Model","2023","JACC: Advances","2","7","100554","","","","5","10.1016/j.jacadv.2023.100554","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180221722&doi=10.1016%2fj.jacadv.2023.100554&partnerID=40&md5=a85a7c556ef2f0f4c93577c856ab1e00","Department of Cardiology, University of California, San Diego, CA, United States; Division of Cardiology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Seoul National University Bundang Hospital, Seoul, South Korea; Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Bluhm Cardiovascular Institute Center for Artificial Intelligence in Cardiovascular Medicine, Northwestern Medicine, Chicago, IL, United States; Physics Department, University of California, Santa Barbara, CA, United States; Physics Department, University of California, San Diego, CA, United States","Jang S.Y., Department of Cardiology, University of California, San Diego, CA, United States, Division of Cardiology, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea; Park J.J., Department of Cardiology, University of California, San Diego, CA, United States, Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, Seoul National University Bundang Hospital, Seoul, South Korea; Adler E., Department of Cardiology, University of California, San Diego, CA, United States; Eshraghian E., Department of Cardiology, University of California, San Diego, CA, United States; Ahmad F.S., Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Bluhm Cardiovascular Institute Center for Artificial Intelligence in Cardiovascular Medicine, Northwestern Medicine, Chicago, IL, United States; Campagnari C., Physics Department, University of California, Santa Barbara, CA, United States; Yagil A., Department of Cardiology, University of California, San Diego, CA, United States, Physics Department, University of California, San Diego, CA, United States; Greenberg B., Department of Cardiology, University of California, San Diego, CA, United States","Background: Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations. Objectives: The MARKER-HF (Machine learning Assessment of RisK and EaRly mortality in Heart Failure) risk model was developed in heart failure (HF) patients. We assessed the ability of MARKER-HF to predict 1-year mortality in a large community-based hospital registry database including patients with and without HF. Methods: This study included 41,749 consecutive patients who underwent echocardiography in a tertiary referral hospital (4,640 patients with and 37,109 without HF). Patients without HF were further subdivided into those with (n = 22,946) and without cardiovascular disease (n = 14,163) and also into cohorts based on recent acute coronary syndrome or history of atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, hypertension, or malignancy. Results: The median age of the 41,749 patients was 65 years, and 56.2% were male. The receiver operated area under the curves for MARKER-HF prediction of 1-year mortality of patients with HF was 0.729 (95% CI: 0.706-0.752) and for patients without HF was 0.770 (95% CI: 0.760-0.780). MARKER-HF prediction of mortality was consistent across subgroups with and without cardiovascular disease and in patients diagnosed with acute coronary syndrome, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, or hypertension. Patients with malignancy demonstrated higher mortality at a given MARKER-HF score than did patients in the other groups. Conclusions: MARKER-HF predicts mortality for patients with HF as well as for patients suffering from a variety of diseases. © 2023 The Authors","heart failure; MARKER-HF; mortality; risk score","","","","","","","","Kim W., Park J.J., Lee H.Y., Et al., Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm, Clin Res Cardiol, 110, 8, pp. 1321-1333, (2021); Jering K.S., Campagnari C., Claggett B., Et al., Improving clinical trial efficiency using a machine learning-based risk score to enrich study populations, Eur J Heart Fail, 24, 8, pp. 1418-1426, (2022); Kamath P.S., Kim W.R., The model for end-stage liver disease (MELD), Hepatology, 45, 3, pp. 797-805, (2007); Lip G.Y., Nieuwlaat R., Pisters R., Lane D.A., Crijns H.J., Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: the euro heart survey on atrial fibrillation, Chest, 137, 2, pp. 263-272, (2010); Steyerberg E.W., Vickers A.J., Cook N.R., Et al., Assessing the performance of prediction models: a framework for traditional and novel measures, Epidemiology, 21, 1, pp. 128-138, (2010); Adler E.D., Voors A.A., Klein L., Et al., Improving risk prediction in heart failure using machine learning, Eur J Heart Fail, 22, 1, pp. 139-147, (2020); Greenberg B., Adler E., Campagnari C., Yagil A., A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction, Eur J Heart Fail, 23, 6, pp. 995-999, (2021); DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Lee D.S., Austin P.C., Rouleau J.L., Liu P.P., Naimark D., Tu J.V., Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model, JAMA, 290, 19, pp. 2581-2587, (2003); Scrutinio D., Ammirati E., Guida P., Et al., Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score, Int J Cardiol, 168, 3, pp. 2120-2126, (2013); Rich J.D., Burns J., Freed B.H., Maurer M.S., Burkhoff D., Shah S.J., Meta-analysis Global group in chronic (MAGGIC) heart failure risk score: validation of a simple tool for the prediction of morbidity and mortality in heart failure with preserved ejection fraction, J Am Heart Assoc, 7, 20, (2018); Fonarow G.C., Adams K.F., Abraham W.T., Et al., Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis, JAMA, 293, 5, pp. 572-580, (2005); Peterson P.N., Rumsfeld J.S., Liang L., Et al., A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program, Circ Cardiovasc Qual Outcomes, 3, 1, pp. 25-32, (2010); Jing L., Ulloa Cerna A.E., Good C.W., Et al., A machine learning approach to management of heart failure populations, J Am Coll Cardiol HF, 8, 7, pp. 578-587, (2020); Olsen C.R., Mentz R.J., Anstrom K.J., Page D., Patel P.A., Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure, Am Heart J, 229, pp. 1-17, (2020)","B. Greenberg; Department of Cardiology, University of California-San Diego, La Jolla, 9454 Medical Center Drive, 92037-7411, United States; email: bgreenberg@health.ucsd.edu","","Elsevier B.V.","","","","","","2772963X","","","","English","JACC. Advances.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85180221722"
"Shi L.; Zhang Y.; Zhang J.","Shi, Lukui (9737579900); Zhang, Yixuan (57919792500); Zhang, Jingye (57919958300)","9737579900; 57919792500; 57919958300","Lung Sound Recognition Method Based on Wavelet Feature Enhancement and Time-Frequency Synchronous Modeling","2023","IEEE Journal of Biomedical and Health Informatics","27","1","","308","318","10","7","10.1109/JBHI.2022.3210996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139531570&doi=10.1109%2fJBHI.2022.3210996&partnerID=40&md5=c9449a519c69ede994520d21c5c97530","Hebei University of Technology, School of Artificial Intelligence, Tianjin, 300401, China","Shi L., Hebei University of Technology, School of Artificial Intelligence, Tianjin, 300401, China; Zhang Y., Hebei University of Technology, School of Artificial Intelligence, Tianjin, 300401, China; Zhang J., Hebei University of Technology, School of Artificial Intelligence, Tianjin, 300401, China","Lung diseases are serious threats to human health and life, therefore, an accurate diagnosis of lung diseases is significant. The use of artificial intelligence to analyze lung sounds can aid in diagnosing lung diseases. Most of the existing lung sound recognition methods ignore the correlation between the time-domain and frequency-domain information of the lung sounds. Additionally, the spectrograms used in these models do not adequately capture the detailed features of the lung sounds. This paper proposes a model based on wavelet feature enhancement and time-frequency synchronous modeling, comprising a dual wavelet analysis module (DWAM), a cubic network, and an attention module. DWAM in the model performed a dual wavelet transformation on the spectrograms to extract the detailed features of the lung sounds. The cubic network comprised multiple cubic gated recursive units to capture the correlation of the time-frequency of the lung sounds using the time-frequency synchronous modeling. The attention module, which includes temporal and channel attention, was used to enhance the time-domain and channel dimension features. In the combined dataset and the International Conference on Biomedical and Health Informatics 2017 dataset, the suggested framework outperforms existing models by more than 1.36% and 4.28%, respectively.  © 2013 IEEE.","attention mechanism; discrete wavelet transformation; Lung sound recognition; time-frequency synchronous modeling","Algorithms; Artificial Intelligence; Humans; Lung Diseases; Respiratory Sounds; Wavelet Analysis; Biological organs; Convolution; Diagnosis; Frequency domain analysis; Health risks; Neural networks; Signal reconstruction; Spectrographs; Time domain analysis; Attention mechanisms; Convolutional neural network; Discrete wavelets transformations; Discrete-wavelet-transform; Features extraction; Lung; Lung sound recognition; Lung sounds; Sound recognition; Spectrograms; Synchronous models; Time frequency; Time-frequency Analysis; Time-frequency synchronoi modeling; abnormal respiratory sound; accuracy; acoustic spectroscopy; Article; asthma; Boltzmann machine; bronchiectasis; bronchiolitis; chronic obstructive lung disease; controlled study; convolutional neural network; discrete wavelet transform; heart sound; human; learning; lower respiratory tract infection; lung disease; Lung Sound Recognition; pneumonia; signal noise ratio; thorax radiography; upper respiratory tract infection; wavelet analysis; wavelet transform; abnormal respiratory sound; algorithm; artificial intelligence; Discrete wavelet transforms","","","","","Graduate Student Innovation Ability Training Funding Project of the Department of Education of Hebei Province, (CXZZSS2022055); Natural Science Foundation of Hebei Province, (F2020202008); Tianjin Science and Technology Program, (20YFZCGX00490)","This work was supported in part by the Natural Science Foundation of Hebei Province of China under Grant F2020202008, in part by the Graduate Student Innovation Ability Training Funding Project of the Department of Education of Hebei Province under Grant CXZZSS2022055, and in part by the Tianjin Science and Technology Key R&D Program under Grant 20YFZCGX00490.","Sengupta N., Sahidullah M., Saha G., Lung sound classification using cepstral-based statistical features, Comput. 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Neural Inf. Process. Syst., pp. 6000-6010, (2017); Rocha B.M., Et al., An open access database for the evaluation of respiratory sound classification algorithms, Physiol. Meas., 40, 3, (2019); Hossain I., Moussavi Z., An overviewof heart-noise reduction of lung sound using wavelet transform based filter, Proc. 25th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 458-461, (2003)","L. Shi; Hebei University of Technology, School of Artificial Intelligence, Tianjin, 300401, China; email: shilukui@scse.hebut.edu.cn","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","36178999","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85139531570"
"Alvarez-Romero C.; Martinez-Garcia A.; Vega J.T.; Díaz-Jimènez P.; Jimènez-Juan C.; Nieto-Martín M.D.; Villarán E.R.; Kovacevic T.; Bokan D.; Hromis S.; Malbasa J.D.; Beslać S.; Zaric B.; Gencturk M.; Sinaci A.A.; Baturone M.O.; Parra Calderón C.L.","Alvarez-Romero, Celia (57210788267); Martinez-Garcia, Alicia (55937333600); Vega, Jara Ternero (57782597700); Díaz-Jimènez, Pablo (57218660835); Jimènez-Juan, Carlos (57222579765); Nieto-Martín, María Dolores (35795876200); Villarán, Esther Román (57782597800); Kovacevic, Tomi (56205406300); Bokan, Darijo (57195593453); Hromis, Sanja (32867618500); Malbasa, Jelena Djekic (57208734534); Beslać, Suzana (57783095100); Zaric, Bojan (16403676100); Gencturk, Mert (53063547700); Sinaci, A. Anil (36905158800); Baturone, Manuel Ollero (57207620440); Parra Calderón, Carlos Luis (24332533000)","57210788267; 55937333600; 57782597700; 57218660835; 57222579765; 35795876200; 57782597800; 56205406300; 57195593453; 32867618500; 57208734534; 57783095100; 16403676100; 53063547700; 36905158800; 57207620440; 24332533000","Predicting 30-Day Readmission Risk for Patients with Chronic Obstructive Pulmonary Disease through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study","2022","JMIR Medical Informatics","10","6","e35307","","","","7","10.2196/35307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133535592&doi=10.2196%2f35307&partnerID=40&md5=1c9b32f78776868e9e8cdcd7de06447c","Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Software Research & Development and Consultancy Corporation, Ankara, Turkey","Alvarez-Romero C., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Martinez-Garcia A., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Vega J.T., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Díaz-Jimènez P., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Jimènez-Juan C., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Nieto-Martín M.D., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Villarán E.R., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain; Kovacevic T., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Bokan D., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Hromis S., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Malbasa J.D., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Beslać S., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia; Zaric B., Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, Serbia, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; Gencturk M., Software Research & Development and Consultancy Corporation, Ankara, Turkey; Sinaci A.A., Software Research & Development and Consultancy Corporation, Ankara, Turkey; Baturone M.O., Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain; Parra Calderón C.L., Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain","Background: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. Objective: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). Methods: The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Results: Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. Conclusions: Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles. ©Celia Alvarez-Romero, Alicia Martinez-Garcia, Jara Ternero Vega, Pablo Díaz-Jimènez, Carlos Jimènez-Juan, María Dolores Nieto-Martín, Esther Román Villarán, Tomi Kovacevic, Darijo Bokan, Sanja Hromis, Jelena Djekic Malbasa, Suzana Beslać, Bojan Zaric, Mert Gencturk, A Anil Sinaci, Manuel Ollero Baturone, Carlos Luis Parra Calderón.","chronic obstructive pulmonary disease; clinical validation; early predictive model; FAIR principles; privacy-preserving distributed data mining; research data management","","","","","","Carlos III National Institute of Health, (IMP/00019, PT20/00088); European Regional Development Fund Fondo Europeo de Desarrollo Regional; European Union’s (EU) Horizon 2020 research and innovation program; Institut Za Plucne Bolesti Vojvodine; Instituto Aragonés de Ciencias de la Salud; Spanish National Health System Industrial Capacities; Horizon 2020 Framework Programme, H2020, (824666); Horizon 2020 Framework Programme, H2020; Università Cattolica del Sacro Cuore, UCSC; Hôpitaux Universitaires de Genève, HUG; Universidade do Porto, U.Porto; Servicio Andaluz de Salud, SAS","Funding text 1: This work was supported by The FAIR4Health project [10], which received funding from The European Union’s Horizon 2020 Research and Innovation Program under grant 824666. This research has also been cosupported by The Carlos III National Institute of Health through The Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión Asociada a la Ciencia y la Tecnología Program (IMPaCT-Data, code IMP/00019) and through The Platform for Dynamization and Innovation of the Spanish National Health System Industrial Capacities and their effective transfer to the productive sector (code PT20/00088), both cofunded by The European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.” The authors would like to thank the clinical researchers of the project, coming from the organizations that are part of the FAIR4Health Consortium: Universite De Geneve (Switzerland), University Hospitals of Geneva (Switzerland), Università Cattolica Del Sacro Cuore (Italy), Universidade Do Porto (Portugal), Instituto Aragonés de Ciencias de la Salud (Spain), Institut Za Plucne Bolesti Vojvodine (Serbia), and Servicio Andaluz de Salud (Spain).; Funding text 2: This work was supported by the FAIR4Health project [10], which received funding from the European Union’s Horizon 2020 research and innovation program under grant 824666. This research has also been cosupported by the Carlos III National Institute of Health through the Programa de Ciencia de Datos de la Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología program (IMPaCT-Data, code IMP/00019) and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both cofunded by the European Regional Development Fund Fondo Europeo de Desarrollo Regional “A way of making Europe.” The authors would like to thank the clinical researchers of the project, coming from the organizations that are part of the FAIR4Health Consortium: Universite De Geneve (Switzerland), University Hospitals of Geneva (Switzerland), Università Cattolica Del Sacro Cuore (Italy), Universidade Do Porto (Portugal), Instituto Aragonés de Ciencias de la Salud (Spain), Institut Za Plucne Bolesti Vojvodine (Serbia), and Servicio Andaluz de Salud (Spain).; Funding text 3: FAIR4Health is a project that received funding from the European Union’s (EU) Horizon 2020 research and innovation program under grant 824666. This project started in December 2018 and ended in November 2021. The main objective of this European project was to promote and encourage the EU health research community to apply the Findable, Accessible, Interoperable, and Reusable (FAIR) principles [1] in their data sets derived from publicly funded research initiatives through the implementation of an effective outreach strategy at the EU level, the production of a set of guidelines to set the foundations for a FAIR data certification road map, the development of an intuitive platform, and the demonstration of the potential impact on health research and health outcomes through the validation of 2 pathfinder case studies. At a high level, this project aimed to facilitate health research data sharing and reuse. This project brought together expertise from the key stakeholders involved in properly addressing this main objective: health research, data managers, medical informatics, software developers, standards, and lawyers. The FAIR4Health Consortium accounted for 17 partners from 11 EU and non-EU countries.","Wilkinson M, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Et al., The FAIR Guiding Principles for scientific data management and stewardship, Sci Data, 3, pp. 160018-160019, (2016); Parra-Calderon CL, Sanz F, McIntosh LD., The challenge of the effective implementation of FAIR principles in biomedical research, Methods Inf Med, 59, 4-5, pp. 117-118, (2020); Delgado J, Llorente S., Security and privacy when applying FAIR principles to genomic information, Stud Health Technol Inform, 275, pp. 37-41, (2020); Dijkers MP., A beginner's guide to data stewardship and data sharing, Spinal Cord, 57, 3, pp. 169-182, (2019); Couture JL, Blake RE, McDonald G, Ward CL., A funder-imposed data publication requirement seldom inspired data sharing, PLoS One, 13, 7, (2018); Almada M, Midao L, Portela D, Dias I, Nunez-Benjumea FJ, Parra-Calderon CL, Et al., A new paradigm in health research: FAIR data (Findable, Accessible, Interoperable, Reusable)], Acta Med Port, 33, 12, pp. 828-834, (2020); Holub P, Kohlmayer F, Prasser F, Mayrhofer MT, Schlunder I, Martin GM, Et al., Enhancing reuse of data and biological material in medical research: from FAIR to FAIR-health, Biopreserv Biobank, 16, 2, pp. 97-105, (2018); Mello MM, Lieou V, Goodman SN., Clinical trial participants' views of the risks and benefits of data sharing, N Engl J Med, 378, 23, pp. 2202-2211, (2018); Rios R, Zheng KI, Zheng MH., Data sharing during COVID-19 pandemic: what to take away, Expert Rev Gastroenterol Hepatol, 14, 12, pp. 1125-1130, (2020); Inau E, Sack J, Waltemath D, Zeleke AA., Initiatives, concepts, and implementation practices of FAIR (findable, accessible, interoperable, and reusable) data principles in health data stewardship practice: protocol for a scoping review, JMIR Res Protoc, 10, 2, (2021); FAIR4Health key outputs for the scientific community; Sinaci A, Nunez-Benjumea FJ, Gencturk M, Jauer ML, Deserno T, Chronaki C, Et al., From raw data to FAIR data: the FAIRification workflow for health research, Methods Inf Med, 59, pp. e21-e32, (2020); 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Anecchino C, Rossi E, Fanizza C, De Rosa M, Tognoni G, Romero M, Prevalence of chronic obstructive pulmonary disease and pattern of comorbidities in a general population, Int J Chron Obstruct Pulmon Dis, 2, 4, pp. 567-574, (2007); Holguin F, Folch E, Redd SC, Mannino DM., Comorbidity and mortality in COPD-related hospitalizations in the United States, 1979 to 2001, Chest, 128, 4, pp. 2005-2011, (2005); Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Gershon A, Thiruchelvam D, Aaron S, Stanbrook M, Vozoris N, Tan W, Et al., Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: a population-based cohort study, PLoS One, 14, 5, (2019); Global Initiative for Chronic Obstructive Lung Disease; Coventry P, Gemmell I, Todd C., Psychosocial risk factors for hospital readmission in COPD patients on early discharge services: a cohort study, BMC Pulm Med, 11, (2011); Jiang W, Siddiqui S, Barnes S, Barouch LA, Korley F, Martinez DA, Et al., Readmission risk trajectories for patients with heart failure using a dynamic prediction approach: retrospective study, JMIR Med Inform, 7, 4, (2019); Brand C, Sundararajan V, Jones C, Hutchinson A, Campbell D., Readmission patterns in patients with chronic obstructive pulmonary disease, chronic heart failure and diabetes mellitus: an administrative dataset analysis, Intern Med J, 35, 5, pp. 296-299, (2005); Kelly M., Self-management of chronic disease and hospital readmission: a care transition strategy, J Nursing Healthcare Chronic Illness, 3, 1, pp. 4-11, (2011); Reducing COPD readmissions—a personal and political priority, Lancet Respiratory Med, 1, 5, (2013); Jencks SF, Williams MV, Coleman EA., Rehospitalizations among patients in the medicare fee-for-service program, N Engl J Med, 360, 14, pp. 1418-1428, (2009); Vos T, Allen C, Arora M, Barber R, Bhutta Z, Brown A, Et al.; Chan F, Wong F, Yam C, Cheung W, Wong E, Leung M, Et al., Risk factors of hospitalization and readmission of patients with COPD in Hong Kong population: analysis of hospital admission records, BMC Health Serv Res, 11, (2011); Jacobs DM, Noyes K, Zhao J, Gibson W, Murphy TF, Sethi S, Et al., Early hospital readmissions after an acute exacerbation of chronic obstructive pulmonary disease in the nationwide readmissions database, Annals ATS, 15, 7, pp. 837-845, (2018); Prados-Torres A, Poblador-Plou B, Gimeno-Miguel A, Calderon-Larranaga A, Poncel-Falco A, Gimeno-Feliu LA, Et al., Cohort profile: the epidemiology of chronic diseases and multimorbidity. The Epichron cohort study, Int J Epidemiol, 47, 2, pp. 382-34, (2018); GO FAIR; HL7 FHIR® Based Secure Data Repository; FAIR4Health data curation and validation tool, GitHub; FAIR4Health data privacy tool, GitHub; Gencturk M, Teoman A, Alvarez-Romero C, Martinez-Garcia A, Parra-Calderon CL, Poblador-Plou B, Et al., End user evaluation of the FAIR4Health data curation tool, Stud Health Technol Inform, 281, pp. 8-12, (2021); GitHub; Andaur Navarro CL, Damen JA, Takada T, Nijman SW, Dhiman P, Ma J, Et al., Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review, BMJ, 375, (2021); Kinsella C, Santos PD, Postigo-Hidalgo I, Folgueiras-Gonzalez A, Passchier TC, Szillat KP, Et al., Preparedness needs research: how fundamental science and international collaboration accelerated the response to COVID-19, PLoS Pathog, 16, 10, (2020); Besancon L, Peiffer-Smadja N, Segalas C, Jiang H, Masuzzo P, Smout C, Et al., Open science saves lives: lessons from the COVID-19 pandemic, BMC Med Res Methodol, 21, 1, pp. 117-118, (2021)","C. Alvarez-Romero; Computational Health Informatics Group Institute of Biomedicine of Seville, Virgen del Rocío University Hospital Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Avda Manuel Siurot s/n, Spain; email: celia.alvarez@juntadeandalucia.es","","JMIR Publications Inc.","","","","","","22919694","","","","English","JMIR Med. Inform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85133535592"
"Wang X.; Qiao Y.; Cui Y.; Ren H.; Zhao Y.; Linghu L.; Ren J.; Zhao Z.; Chen L.; Qiu L.","Wang, Xuchun (57222429561); Qiao, Yuchao (57669426700); Cui, Yu (57876925500); Ren, Hao (57201028229); Zhao, Ying (58083476800); Linghu, Liqin (57669755200); Ren, Jiahui (57667435000); Zhao, Zhiyang (57876734600); Chen, Limin (57201033578); Qiu, Lixia (56673436700)","57222429561; 57669426700; 57876925500; 57201028229; 58083476800; 57669755200; 57667435000; 57876734600; 57201033578; 56673436700","An explainable artificial intelligence framework for risk prediction of COPD in smokers","2023","BMC Public Health","23","1","2164","","","","5","10.1186/s12889-023-17011-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175817480&doi=10.1186%2fs12889-023-17011-w&partnerID=40&md5=a570a7d418a01002aaf4a8727a00fc53","Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; The Fifth Hospital (Shanxi People’s Hospital) of Shanxi Medical University, Shanxi, Taiyuan, 030012, China","Wang X., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Qiao Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Cui Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Ren H., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Zhao Y., Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; Linghu L., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China, Shanxi Centre for Disease Control and Prevention, Shanxi, Taiyuan, 030012, China; Ren J., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Zhao Z., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China; Chen L., The Fifth Hospital (Shanxi People’s Hospital) of Shanxi Medical University, Shanxi, Taiyuan, 030012, China; Qiu L., Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, 030001, China","Background: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. Methods: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model’s decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). Results: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. Conclusion: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies. © 2023, The Author(s).","Class imbalance; COPD; Machine learning; Prediction; Smokers","Adolescent; Artificial Intelligence; Humans; Pulmonary Disease, Chronic Obstructive; Smokers; Smoking; Tobacco Smoking; adolescent; artificial intelligence; chronic obstructive lung disease; human; smoking","","","","","National Natural Science Foundation of China, NSFC, (81973155); Shanxi Medical University","Funding text 1: This research is supported by a grant from the National Natural Science Foundation of China (grant no: 81973155). We thank all teachers in the statistical research office of Shanxi medical university. The authors would also like to acknowledge all interviewers for survey data collection work.; Funding text 2: This research is supported by a grant from the National Natural Science Foundation of China (grant no: 81973155). We thank all teachers in the statistical research office of Shanxi medical university. The authors would also like to acknowledge all interviewers for survey data collection work. ","Zhe W., Lin L.I., Cheng L.I., University XMJCDM. 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Qiu; Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 56 South XinJian Road, 030001, China; email: qlx_1126@163.com; L. Chen; The Fifth Hospital (Shanxi People’s Hospital) of Shanxi Medical University, Taiyuan, Shanxi, 030012, China; email: sxchenlimin@163.com","","BioMed Central Ltd","","","","","","14712458","","","37932692","English","BMC Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175817480"
"Asim Iqbal M.; Devarajan K.; Ahmed S.M.","Asim Iqbal, Mohammed (57452743900); Devarajan, Krishnamoorthy (56585663500); Ahmed, Syed Musthak (56857092300)","57452743900; 56585663500; 56857092300","An optimal asthma disease detection technique for voice signal using hybrid machine learning technique","2022","Concurrency and Computation: Practice and Experience","34","11","e6856","","","","6","10.1002/cpe.6856","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124587198&doi=10.1002%2fcpe.6856&partnerID=40&md5=368f38c2e1b9b98155c0e5a0e954c9eb","Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, India; Department of Electronics and Communication Engineering, SR University, Warangal, India","Asim Iqbal M., Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, India; Devarajan K., Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, India; Ahmed S.M., Department of Electronics and Communication Engineering, SR University, Warangal, India","Current clinical practice in the treatment of chronic respiratory diseases does not have an effective method that allows patients and caregivers to constantly monitor the severity of respiratory symptoms. This is the “asthma breath” that occurs in the respiratory tract. In this article, we propose an optimal asthma disease detection technique for voice signal using hybrid machine learning (OADD-HML) technique. In OADD-HML technique, we used the improved weed optimization (IWO) algorithm for asthma detection and forecasting. We analyze whether the scattered signal pattern follows the frequency of normal and abnormal breathing sounds. Second, we illustrate an enhanced hunting search (EHS) algorithm for feature extraction and selection process. Then, deep Q neural network (DQNN) classifier used to identify the asthma, crackle, and normal speech. After performing the function recovery and classification using DQNN, the respiratory number classes will be identified. The test results show several classifications of specific OADD-HML breathing sounds, respectively, using accuracy, sensitivity, and specificity. Comparison results of asthma and non-asthma classification of respiratory sounds suggest that the particular method works better than usual existing state-of-art. © 2022 John Wiley & Sons Ltd.","asthma detection; deep Q neural network; feature extraction; hunting search; improved weed optimization; respiratory sounds; speech signal","Deep neural networks; Diseases; Extraction; Patient treatment; Speech recognition; Asthma detection; Deep Q neural network; Disease detection; Features extraction; Hunting search; Improved weed optimization; Neural-networks; Optimisations; Respiratory sounds; Speech signals; Feature extraction","","","","","","","Harper P., Kraman S.S., Pasterkamp H., Wodicka G.R., An acoustic model of the respiratory tract, IEEE Trans Biomed Eng, 48, 5, pp. 543-550, (2001); Holmes M.S., Le Menn M., D'Arcy S., Et al., Automatic identification and accurate temporal detection of inhalations in asthma inhaler recordings. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; August 2012:2595-2598; Holmes M.S., D'Arcy S., Costello R.W., Reilly R.B.; San Chun K., Nathan V., Vatanparvar K.; Mayorga P., Druzgalski C., Gonzalez O.H., Zazueta A., Criollo M.A., Expanded quantitative models for assessment of respiratory diseases and monitoring. Proceedings of the 2011 Pan American Health Care Exchanges. IEEE; March 2011:317-322; Le Cam S., Belghith A., Collet C., Salzenstein F., Wheezing sounds detection using multivariate generalized Gaussian distributions. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE; April 2009:541-544; Kadambi P., Mohanty A., Ren H., Et al., Towards a wearable cough detector based on neural networks. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; April 2018:2161-2165; Homs-Corbera A., Fiz J.A., Morera J., Jane R., Time-frequency detection and analysis of wheezes during forced exhalation, IEEE Trans Biomed Eng, 51, 1, pp. 182-186, (2004); Oletic D., Bilas V., Asthmatic wheeze detection from compressively sensed respiratory sound spectra, IEEE J Biomed Health Inform, 22, 5, pp. 1406-1414, (2017); Emrani S., Gentimis T., Krim H., Persistent homology of delay embeddings and its application to wheeze detection, IEEE Signal Process Lett, 21, 4, pp. 459-463, (2014); Wisniewski M., Zielinski T.P., Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system, IEEE J Biomed Health Inform, 19, 3, pp. 1009-1018, (2014); Jin F., Krishnan S., Sattar F., Adventitious sounds identification and extraction using temporal–spectral dominance-based features, IEEE Trans Biomed Eng, 58, 11, pp. 3078-3087, (2011); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans Biomed Circuits Syst, 14, 3, pp. 535-544, (2020); Park S.W., Das P.S., Chhetry A., Park J.Y., A flexible capacitive pressure sensor for wearable respiration monitoring system, IEEE Sensors J, 17, 20, pp. 6558-6564, (2017); Knudson K.P., Brown K.A., Nonlinear statistics of human speech data, Int J Bifurc Chaos Appl Sci Eng, 19, pp. 2307-2319, (2009); Taplidou S., Hadjileontiadis L., Analysis of wheezes using wavelet higher order spectral features, IEEE Trans Biomed Eng, 57, 7, pp. 1596-1610, (2010); Taplidou S.A., Hadjileontiadis L.J., Wheeze detection based on time-frequency analysis of breath sounds, Comput Biol Med, 37, 8, pp. 1073-1083, (2007); Wisniewski M., Zielinski T.P., Detection of multi-tones in white and colored noise with application to asthmatic wheezes detection. Proceedings of the International Conference on Signals and Electronic Systems ICSES-2012; Wroclaw (Poland); IEEE Xplore; 2012; Fenton T.R., Pasterkamp H., Tal A., Chernick V., Automatic spectral characterization of wheezing in asthmatic children, IEEE Trans Biomed Eng, BME-32, 1, pp. 50-55, (1985); Liu M., Huang M.C., Asthma pattern identification via continuous diaphragm motion monitoring, IEEE Trans Multi-Scale Comput Syst, 1, 2, pp. 76-84, (2015); Yang F., Yu X., Wang L., Et al., Identify asthma genes across three phases based on protein–protein interaction network, IET Syst Biol, 9, 4, pp. 135-140, (2015); Khatri K.L., Tamil L.S., Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks, IEEE J Biomed Health Inform, 22, 1, pp. 285-290, (2017); Chen H., Yuan X., Pei Z., Li M., Li J., Triple-classification of respiratory sounds using optimized s-transform and deep residual networks, IEEE Access, 7, pp. 32845-32852, (2019); Khan S.M., Qaiser N., Shaikh S.F., Hussain M.M., Design analysis and human tests of foil-based wheezing monitoring system for asthma detection, IEEE Trans Electron Devices, 67, 1, pp. 249-257, (2019); Oletic D., Bilas V., Energy-efficient respiratory sounds sensing for personal mobile asthma monitoring, IEEE Sensors J, 16, 23, pp. 8295-8303, (2016); Ionescu C.M., Machado J.T., De Keyser R., Analysis of the respiratory dynamics during normal breathing by means of pseudophase plots and pressure–volume loops, IEEE Trans Syst Man Cybern Syst, 43, 1, pp. 53-62, (2012); Shorter J.H., Nelson D.D., McManus J.B., Zahniser M.S., Milton D.K., Multicomponent breath analysis with infrared absorption using room-temperature quantum cascade lasers, IEEE Sensors J, 10, 1, pp. 76-84, (2009); Kosasih K., Abeyratne U.R., Swarnkar V., Triasih R., Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Trans Biomed Eng, 62, 4, pp. 1185-1194, (2014)","M. Asim Iqbal; Department of Electronics and Communication Engineering, Annamalai University, Chidambaram, India; email: asimiqbal1120@gmail.com","","John Wiley and Sons Ltd","","","","","","15320626","","CCPEB","","English","Concurr. Comput. Pract. Exper.","Article","Final","","Scopus","2-s2.0-85124587198"
"Bae W.D.; Alkobaisi S.; Horak M.; Park C.-S.; Kim S.; Davidson J.","Bae, Wan D. (14826508800); Alkobaisi, Shayma (14826556600); Horak, Matthew (24401453800); Park, Choon-Sik (17233894400); Kim, Sungroul (57218664381); Davidson, Joel (57941825900)","14826508800; 14826556600; 24401453800; 17233894400; 57218664381; 57941825900","Predicting Health Risks of Adult Asthmatics Susceptible to Indoor Air Quality Using Improved Logistic and Quantile Regression Models","2022","Life","12","10","1631","","","","5","10.3390/life12101631","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140650364&doi=10.3390%2flife12101631&partnerID=40&md5=3588df5679c46b8bc6bcc449189c1995","Department of Computer Science, Seattle University, Seattle, 98122, WA, United States; College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Lockheed Martin Space Systems, Denver, 80221, CO, United States; Department of Internal Medicine, Soonchunhyang Bucheon Hospital, Bucheon, 420-767, South Korea; Department of ICT Environmental Health System, Graduate School, Department of Environmental Sciences, Soonchunhyang University, Asan, 336-745, South Korea","Bae W.D., Department of Computer Science, Seattle University, Seattle, 98122, WA, United States; Alkobaisi S., College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Horak M., Lockheed Martin Space Systems, Denver, 80221, CO, United States; Park C.-S., Department of Internal Medicine, Soonchunhyang Bucheon Hospital, Bucheon, 420-767, South Korea; Kim S., Department of ICT Environmental Health System, Graduate School, Department of Environmental Sciences, Soonchunhyang University, Asan, 336-745, South Korea; Davidson J., Department of Computer Science, Seattle University, Seattle, 98122, WA, United States","The increasing global patterns for asthma disease and its associated fiscal burden to healthcare systems demand a change to healthcare processes and the way asthma risks are managed. Patient-centered health care systems equipped with advanced sensing technologies can empower patients to participate actively in their health risk control, which results in improving health outcomes. Despite having data analytics gradually emerging in health care, the path to well established and successful data driven health care services exhibit some limitations. Low accuracy of existing predictive models causes misclassification and needs improvement. In addition, lack of guidance and explanation of the reasons of a prediction leads to unsuccessful interventions. This paper proposes a modeling framework for an asthma risk management system in which the contributions are three fold: First, the framework uses a deep learning technique to improve the performance of logistic regression classification models. Second, it implements a variable sliding window method considering spatio-temporal properties of the data, which improves the quality of quantile regression models. Lastly, it provides a guidance on how to use the outcomes of the two predictive models in practice. To promote the application of predictive modeling, we present a use case that illustrates the life cycle of the proposed framework. The performance of our proposed framework was extensively evaluated using real datasets in which results showed improvement in the model classification accuracy, approximately 11.5–18.4% in the improved logistic regression classification model and confirmed low relative errors ranging from 0.018 to 0.160 in quantile regression model. © 2022 by the authors.","asthma risk prediction; exposome; indoor air quality; logistic regression; personalized asthma care; quantile regression; sliding window regression; transfer learning","","","","","","Ministry of Environment, South Korea, (2016001360002); Seattle University, (CSE-01-2021); Korea Disease Control and Prevention Agency, KDCA, (2016-ER7402-00, 2017-NE-740200); Korea Disease Control and Prevention Agency, KDCA; Soonchunhyang University, SCH; Korea Environmental Industry and Technology Institute, KEITI","This study received support from Seattle University (Grant No. CSE-01-2021), Korea Disease Control and Prevention Agency, South Korea (Grant No. 2016-ER7402-00 and 2017-NE-740200), the Korean Environmental Industry & Technology Institute, Ministry of Environment, South Korea (Grant No. 2016001360002), Soonchunhyang University Brain Korea 21, and Seattle University.","Purdy S., Griffin T., Salisbury C., Sharp D., Ambulatory care sensitive conditions: Terminology and disease coding need to be more specific to aid policy makers and clinicians, Public Health, 123, pp. 169-173, (2009); Loftus P.A., Wise S.K., Epidemiology and economic burden of asthma, Int. 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"Huang A.A.; Huang S.Y.","Huang, Alexander A. (57926978200); Huang, Samuel Y. (57927006200)","57926978200; 57927006200","Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population","2023","PLoS ONE","18","11 November","e0288903","","","","6","10.1371/journal.pone.0288903","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177732180&doi=10.1371%2fjournal.pone.0288903&partnerID=40&md5=d445828867bf62fb224e27406b0716c9","Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Virginia Commonwealth University School of Medicine, Richmond, VA, United States","Huang A.A., Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Huang S.Y., Virginia Commonwealth University School of Medicine, Richmond, VA, United States","Background Asthma attacks are a major cause of morbidity and mortality in vulnerable populations, and identification of associations with asthma attacks is necessary to improve public awareness and the timely delivery of medical interventions. Objective The study aimed to identify feature importance of factors associated with asthma in a representative population of US adults. Methods A cross-sectional analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017–2020). All adult patients greater than 18 years of age (total of 7,922 individuals) with information on asthma attacks were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. Results 7,922 patients met the inclusion criteria in this study. The machine learning model had 55 out of a total of 680 features that were found to be significant on univariate analysis (P<0.0001 used). In the XGBoost model the model had an Area Under the Receiver Operator Characteristic Curve (AUROC) = 0.737, Sensitivity = 0.960, NPV = 0.967. The top five highest ranked features by gain, a measure of the percentage contribution of the covariate to the overall model prediction, were Octanoic Acid intake as a Saturated Fatty Acid (SFA) (gm) (Gain = 8.8%), Eosinophil percent (Gain = 7.9%), BMXHIP–Hip Circumference (cm) (Gain = 7.2%), BMXHT–standing height (cm) (Gain = 6.2%) and HS C-Reactive Protein (mg/L) (Gain 6.1%). Conclusion Machine Learning models can additionally offer feature importance and additional statistics to help identify associations with asthma attacks. © 2023 Huang, Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Adult; Asthma; Cohort Studies; Cross-Sectional Studies; Humans; Machine Learning; Nutrition Surveys; C reactive protein; octanoic acid; saturated fatty acid; adult; albumin blood level; arm circumference; Article; asthma; body mass; clinical assessment; cohort analysis; controlled study; cross-sectional study; demographics; diagnostic test accuracy study; dietary intake; eosinophil percentage; exercise; female; food security; hip circumference; human; laboratory test; machine learning; male; nutritional status; Patient Health Questionnaire 9; physical activity; physical examination; prediction; predictive value; prevalence; public health service; questionnaire; receiver operating characteristic; risk factor; sensitivity and specificity; sleep disorder; statistical analysis; statistics; waist circumference; asthma; machine learning; nutrition","","C reactive protein, 9007-41-4; octanoic acid, 124-07-2, 1984-06-1, 74-81-7","","","UK Research and Innovation, UKRI, (106156)","","Anandan C, Nurmatov U, van Schayck OC, Sheikh A., Is the prevalence of asthma declining? 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Toti G, Vilalta R, Lindner P, Lefer B, Macias C, Price D., Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining, Artif Intell Med, 74, pp. 44-52, (2016)","S.Y. Huang; Virginia Commonwealth University School of Medicine, Richmond, United States; email: Huangs8@vcu.edu","","Public Library of Science","","","","","","19326203","","POLNC","37992024","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85177732180"
"Chen Z.; Wang J.; Wang H.; Yao Y.; Deng H.; Peng J.; Li X.; Wang Z.; Chen X.; Xiong W.; Wang Q.; Zhu T.","Chen, Zhihong (57196282666); Wang, Jiajia (57219746645); Wang, Hanchao (58080006700); Yao, Yu (57883957300); Deng, Huojin (15032751100); Peng, Junnan (57218143735); Li, Xinglong (57220039678); Wang, Zhongruo (57219686380); Chen, Xingru (57220039347); Xiong, Wei (58080448300); Wang, Qin (55698363000); Zhu, Tao (56675177400)","57196282666; 57219746645; 58080006700; 57883957300; 15032751100; 57218143735; 57220039678; 57219686380; 57220039347; 58080448300; 55698363000; 56675177400","Machine learning reveals sex differences in clinical features of acute exacerbation of chronic obstructive pulmonary disease: A multicenter cross-sectional study","2023","Frontiers in Medicine","10","","1105854","","","","5","10.3389/fmed.2023.1105854","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152700058&doi=10.3389%2ffmed.2023.1105854&partnerID=40&md5=31e35ee4a82bcdda0dd95d01916061a1","Respiratory Medicine and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, China; Rheumatology Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Respiratory Medicine and Critical Care Medicine, Preclinical Research Center, Suining Central Hospital, Suining, China; Respiratory Medicine and Critical Care Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, China; Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Mathematics, University of California, Davis, CA, United States","Chen Z., Respiratory Medicine and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, China; Wang J., Rheumatology Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Wang H., Respiratory Medicine and Critical Care Medicine, Preclinical Research Center, Suining Central Hospital, Suining, China; Yao Y., Respiratory Medicine and Critical Care Medicine, Preclinical Research Center, Suining Central Hospital, Suining, China; Deng H., Respiratory Medicine and Critical Care Medicine, ZhuJiang Hospital of Southern Medical University, Guangzhou, China; Peng J., Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Li X., Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Wang Z., Department of Mathematics, University of California, Davis, CA, United States; Chen X., Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Xiong W., Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Wang Q., Respiratory Medicine and Critical Care Medicine, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China; Zhu T., Respiratory Medicine and Critical Care Medicine, Preclinical Research Center, Suining Central Hospital, Suining, China","Introduction: Intrinsically, chronic obstructive pulmonary disease (COPD) is a highly heterogonous disease. Several sex differences in COPD, such as risk factors and prevalence, were identified. However, sex differences in clinical features of acute exacerbation chronic obstructive pulmonary disease (AECOPD) were not well explored. Machine learning showed a promising role in medical practice, including diagnosis prediction and classification. Then, sex differences in clinical manifestations of AECOPD were explored by machine learning approaches in this study. Methods: In this cross-sectional study, 278 male patients and 81 female patients hospitalized with AECOPD were included. Baseline characteristics, clinical symptoms, and laboratory parameters were analyzed. The K-prototype algorithm was used to explore the degree of sex differences. Binary logistic regression, random forest, and XGBoost models were performed to identify sex-associated clinical manifestations in AECOPD. Nomogram and its associated curves were established to visualize and validate binary logistic regression. Results: The predictive accuracy of sex was 83.930% using the k-prototype algorithm. Binary logistic regression revealed that eight variables were independently associated with sex in AECOPD, which was visualized by using a nomogram. The AUC of the ROC curve was 0.945. The DCA curve showed that the nomogram had more clinical benefits, with thresholds from 0.02 to 0.99. The top 15 sex-associated important variables were identified by random forest and XGBoost, respectively. Subsequently, seven clinical features, including smoking, biomass fuel exposure, GOLD stages, PaO2, serum potassium, serum calcium, and blood urea nitrogen (BUN), were concurrently identified by three models. However, CAD was not identified by machine learning models. Conclusions: Overall, our results support that the clinical features differ markedly by sex in AECOPD. Male patients presented worse lung function and oxygenation, less biomass fuel exposure, more smoking, renal dysfunction, and hyperkalemia than female patients with AECOPD. Furthermore, our results also suggest that machine learning is a promising and powerful tool in clinical decision-making. Copyright © 2023 Chen, Wang, Wang, Yao, Deng, Peng, Li, Wang, Chen, Xiong, Wang and Zhu.","acute exacerbation of chronic obstructive pulmonary disease; binary logistic regression; K-prototypes algorithm; machine learning; nomogram; random forest model; sex; XGBoost model","calcium; potassium; aged; algorithm; area under the curve; Article; biomass; calcium blood level; chronic obstructive lung disease; clinical feature; controlled study; cross-sectional study; disease exacerbation; female; human; logistic regression analysis; machine learning; major clinical study; male; multicenter study; nomogram; people by smoking status; potassium blood level; random forest; sex difference; smoking; urea nitrogen blood level","","calcium, 7440-70-2, 14092-94-5; potassium, 7440-09-7","","","Chongqing Health Joint Medical Research Project, (2020MSXM112); Shanghai Top-Priority Clinical Key Disciplines Construction Project, (2017ZZ02013); National Natural Science Foundation of China, NSFC, (8180011074, 81970023); National Natural Science Foundation of China, NSFC; Natural Science Foundation of Sichuan Province, (23NSFSC0667); Natural Science Foundation of Sichuan Province","This study was supported by National Natural Science Foundation of China (81970023), National Natural Science Foundation of China (Youth Program), (8180011074), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02013), Chongqing Health Joint Medical Research Project (2020MSXM112), and Natural Sciences Foundation of Sichuan (23NSFSC0667). 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"Nowakowska K.; Sakellarios A.; Kaźmierski J.; Fotiadis D.I.; Pezoulas V.C.","Nowakowska, Karina (57196066937); Sakellarios, Antonis (36476633700); Kaźmierski, Jakub (15049414300); Fotiadis, Dimitrios I. (55938920100); Pezoulas, Vasileios C. (57194013364)","57196066937; 36476633700; 15049414300; 55938920100; 57194013364","AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery","2024","Diagnostics","14","1","67","","","","5","10.3390/diagnostics14010067","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181956690&doi=10.3390%2fdiagnostics14010067&partnerID=40&md5=3f93a11ee5e6ac3055194aa3d089da12","Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, 90-419, Poland; Laboratory of Biomechanics and Biomedical Engineering, Department of Mechanical and Aeronautics Engineering, University of Patras, Patras, 26504, Greece; Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110, Greece; Biomedical Research Institute—FORTH, University Campus of Ioannina, Ioannina, 45110, Greece","Nowakowska K., Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, 90-419, Poland; Sakellarios A., Laboratory of Biomechanics and Biomedical Engineering, Department of Mechanical and Aeronautics Engineering, University of Patras, Patras, 26504, Greece, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110, Greece; Kaźmierski J., Department of Old Age Psychiatry and Psychotic Disorders, Medical University of Lodz, Lodz, 90-419, Poland; Fotiadis D.I., Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110, Greece, Biomedical Research Institute—FORTH, University Campus of Ioannina, Ioannina, 45110, Greece; Pezoulas V.C., Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110, Greece, Biomedical Research Institute—FORTH, University Campus of Ioannina, Ioannina, 45110, Greece","Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients. Methods: Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach. Results: Our findings identified a significant correlation between the biomarker “sRAGE” and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67). Conclusions: This study provides compelling evidence that depression in CVD patients, particularly those with elevated “sRAGE” levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population. © 2023 by the authors.","cardiovascular disease; depression; explainable artificial intelligence (AI); prediction","advanced glycation end product receptor; biological marker; C reactive protein; creatinine; hemoglobin; malonaldehyde; monocyte chemotactic protein 1; superoxide dismutase; AdaBoost; aged; alcoholism; anemia; antioxidant activity; anxiety disorder; Article; artificial intelligence; asthma; atrial fibrillation; cardiac surgery intensive care unit; cardiovascular disease; cerebrovascular disease; chronic obstructive lung disease; classifier; clock drawing test; comorbidity; confusion assessment method for the intensive care unit; coronary artery bypass graft; creatinine blood level; cross validation; demographics; depression; diabetes mellitus; diagnostic accuracy; DSM-5; elective surgery; endotracheal intubation; epilepsy; female; head injury; heart valve replacement; heart ventricle arrhythmia; hemoglobin blood level; human; hypertension; information processing; invasive ventilation; major clinical study; major depression; male; Memorial Delirium Assessment Scale; mental disease assessment; Mini Mental State Examination; mortality risk; New York Heart Association class; peripheral arterial disease; Poland; postoperative delirium; predictive model; psychiatric evaluation; psychiatrist; random forest; Richmond Agitation Sedation Scale; sensitivity and specificity; smoking; vulnerable population","","advanced glycation end product receptor, 198785-73-8, 247590-69-8; C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; hemoglobin, 9008-02-0; malonaldehyde, 542-78-9; superoxide dismutase, 37294-21-6, 9016-01-7, 9054-89-1","","","Horizon 2020 Framework Programme, H2020, (848146)","This project has received funding from the European Union’s Horizon 2020 research and innovation program TO_AITION under grant agreement No 848146.","Benjamin E.J., Blaha M.J., Chiuve S.E., Cushman M., Das S.R., Deo R., de Ferranti S.D., Floyd J., Fornage M., Gillespie C., Et al., Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association, Circulation, 135, pp. e146-e603, (2017); James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Abbastabar H., Abd-Allah F., Abdela J., Abdelalim A., Et al., Global, Regional, and National Incidence, Prevalence, and Years Lived with Disability for 354 Diseases and Injuries for 195 Countries and Territories, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017, Lancet, 392, pp. 1789-1858, (2018); Xue Y., Liu G., Geng Q., Associations of Cardiovascular Disease and Depression with Memory Related Disease: A Chinese National Prospective Cohort Study, J. 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Immunol, 166, pp. 4391-4398, (2001); Milne G.L., Musiek E.S., Morrow J.D., F2-isoprostanes as markers of oxidative stress in vivo: An overview, Biomarkers, 1, pp. 10-23, (2005); Gracia K.C., Llanas-Cornejo D., Husi H., CVD and Oxidative Stress, J. Clin. Med, 6, (2017); Mehta J.L., Saldeen T.G., Rand K., Interactive role of infection, inflammation and traditional risk factors in atherosclerosis and coronary artery disease, J. Am. Coll. Cardiol, 31, pp. 1217-1225, (1998); Black C.N., Bot M., Scheffer P.G., Cuijpers P., Penninx B.W.J.H., Is depression associated with increased oxidative stress? A systematic review and meta-analysis, Psychoneuroendocrinology, 51, pp. 164-175, (2015); Panth N., Paudel K.R., Parajuli K., Reactive oxygen species: A key hallmark of cardiovascular disease, Adv. Med, 2016, (2016); Kazmierski J., Banys A., Latek J., Bourke J., Jaszewski R., Cortisol levels and neuropsychiatric diagnosis as markers of postoperative delirium: A prospective cohort study, Crit. Care, 17, (2013); Saveanu R.V., Nemeroff C.B., Etiology of depression: Genetic and environmental factors, Psychiatr. Clin. N. Am, 35, pp. 51-71, (2012); Al Rifai M., Schneider A.L., Alonso A., Maruthur N., Parrinello C.M., Astor B.C., Hoogeveen R.C., Soliman E.Z., Chen L.Y., Ballantyne C.M., Et al., sRAGE, inflammation, and risk of atrial fibrillation: Results from the Atherosclerosis Risk in Communities (ARIC) Study, J. Diabetes Complicat, 29, pp. 180-185, (2015); Wu F., Feng J.Z., Qiu Y.H., Yu F.B., Zhang J.Z., Zhou W., Yu F., Wang G.K., An L.N., Ni F.H., Activation of receptor for advanced glycation end products contributes to aortic remodeling and endothelial dysfunction in sinoaortic denervated rats, Atherosclerosis, 229, pp. 287-294, (2013); Bu D.X., Hudson B.I., Vascular and inflammatory stresses mediate atherosclerosis via RAGE and its ligands in apoE-/- mice, J. Clin. 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Biol. Med, 43, pp. 1255-1262, (2007); Geroldi D., Falcone C., Emanuele E., D'Angelo A., Calcagnino M., Buzzi M.P., A Scioli G., Fogari R., Decreased plasma levels of soluble receptor for advanced glycation end-products in patients with essential hypertension, J. Hypertens, 23, pp. 1725-1729, (2005); Miniati M., Monti S., Basta G., Cocci F., Fornai E., Bottai M., Soluble receptor for advanced glycation end products in COPD: Relationship with emphysema and chronic cor pulmonale: A case-control study, Respir. Res, 12, (2011); Emanuele E., D'Angelo A., Tomaino C., Binetti G., Ghidoni R., Politi P., Bernardi L., Maletta R., Bruni A.C., Geroldi D., Circulating levels of soluble receptor for advanced glycation end products in Alzheimer’s disease and vascular dementia, Arch. 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Med, 167, pp. 367-373, (2007); Muller-Tasch T., Peters-Klimm F., Schellberg D., Holzapfel N., Barth A., Junger J., Szecsenyi J., Herzog W., Depression Is a Major Determinant of Quality of Life in Patients With Chronic Systolic Heart Failure in General Practice, J. Card. Fail, 13, pp. 818-824, (2007); Kazmierski J., Kowman M., Banach M., Fendler W., Okonski P., Banys A., Jaszewski R., Rysz J., Mikhailidis D.P., Sobow T., Et al., Incidence and predictors of delirium after cardiac surgery: Results from The IPDACS Study, J. Psychosom. Res, 69, pp. 179-185, (2010); Kazmierski J., Miler P., Pawlak A., Jerczynska H., Wozniak J., Frankowska E., Brzezinska A., Wozniak K., Krejca M., Wilczynski M., Elevated Monocyte Chemoattractant Protein-1 as the Independent Risk Factor of Delirium after Cardiac Surgery. A Prospective Cohort Study, J. Clin. Med, 10, (2021)","V.C. Pezoulas; Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, 45110, Greece; email: bpezoulas@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85181956690"
"Rozo A.; Moeyersons J.; Morales J.; Garcia van der Westen R.; Lijnen L.; Smeets C.; Jantzen S.; Monpellier V.; Ruttens D.; Van Hoof C.; Van Huffel S.; Groenendaal W.; Varon C.","Rozo, Andrea (57226461491); Moeyersons, Jonathan (57201490646); Morales, John (57195107031); Garcia van der Westen, Roberto (57479063100); Lijnen, Lien (57212515887); Smeets, Christophe (55947986100); Jantzen, Sjors (57456206300); Monpellier, Valerie (55091226000); Ruttens, David (26023740700); Van Hoof, Chris (56132871000); Van Huffel, Sabine (7004954228); Groenendaal, Willemijn (57204828441); Varon, Carolina (42262898200)","57226461491; 57201490646; 57195107031; 57479063100; 57212515887; 55947986100; 57456206300; 55091226000; 26023740700; 56132871000; 7004954228; 57204828441; 42262898200","Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring","2022","Frontiers in Bioengineering and Biotechnology","10","","806761","","","","7","10.3389/fbioe.2022.806761","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125853408&doi=10.3389%2ffbioe.2022.806761&partnerID=40&md5=a277593917762f1abe1b73761f272ea9","STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium; Microgravity Research Center, Service Chimie-Physique, Université Libre de Bruxelles, Brussels, Belgium; Imec The Netherlands/Holst Centre, Eindhoven, Netherlands; Department of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium; Future Health department, Pneumology department, Ziekenhuis Oost-Limburg, Genk, Belgium; Nederlandse Obesitas Kliniek, Venlo, Netherlands; Nederlandse Obesitas Kliniek, Huis ter Heide, Netherlands; Imec OnePlanet, Wageningen, Netherlands; Electronic Circuits and Systems, Department of Electrical Engineering, KU Leuven, Leuven, Belgium; Leuven, Belgium","Rozo A., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium, Microgravity Research Center, Service Chimie-Physique, Université Libre de Bruxelles, Brussels, Belgium; Moeyersons J., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium; Morales J., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium; Garcia van der Westen R., Imec The Netherlands/Holst Centre, Eindhoven, Netherlands; Lijnen L., Department of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium; Smeets C., Future Health department, Pneumology department, Ziekenhuis Oost-Limburg, Genk, Belgium; Jantzen S., Nederlandse Obesitas Kliniek, Venlo, Netherlands; Monpellier V., Nederlandse Obesitas Kliniek, Huis ter Heide, Netherlands; Ruttens D., Department of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium, Future Health department, Pneumology department, Ziekenhuis Oost-Limburg, Genk, Belgium; Van Hoof C., Imec OnePlanet, Wageningen, Netherlands, Electronic Circuits and Systems, Department of Electrical Engineering, KU Leuven, Leuven, Belgium, Leuven, Belgium; Van Huffel S., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium; Groenendaal W., Imec The Netherlands/Holst Centre, Eindhoven, Netherlands; Varon C., STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium, Microgravity Research Center, Service Chimie-Physique, Université Libre de Bruxelles, Brussels, Belgium","Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine–SVM, and convolutional neural network–CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems. Copyright © 2022 Rozo, Moeyersons, Morales, Garcia van der Westen, Lijnen, Smeets, Jantzen, Monpellier, Ruttens, Van Hoof, Van Huffel, Groenendaal and Varon.","data augmentation; machine learning; respiratory monitoring; signal quality; transfer learning","Convolutional neural networks; Deterioration; Learning algorithms; Patient monitoring; Population statistics; Pulmonary diseases; Quality control; Space research; Support vector machines; Wearable technology; Breathing patterns; Data augmentation; Data quality assessment; Performance; Remote environment; Respiratory Monitoring; Respiratory rate; Respiratory signals; Signal quality; Transfer learning; Diagnosis","","","","","AI-KU Leuven institute, (B-3000); Belgian Federal Science Policy Office, BELSPO; Vlaamse regering; European Society of Anaesthesiology, ESA","Funding text 1: Bijzonder Onderzoeksfonds KU Leuven (BOF): Prevalentie van epilepsie en slaapstoornissen in de ziekte van Alzheimer: C24/18/ 097. EIT 19 263—SeizeIT2: Discreet Personalized Epileptic Seizure Detection Device. KU Leuven STADIUS acknowledges the financial support of imec. This research received funding from the Flemish Government (AI Research Program). SH, AR, JtM and JM are affiliated to Leuven. AI-KU Leuven institute for AI, B-3000, Leuven, Belgium. AR and CV acknowledge the financial support of ESA, BELSPO.; Funding text 2: Bijzonder Onderzoeksfonds KU Leuven (BOF): Prevalentie van epilepsie en slaapstoornissen in de ziekte van Alzheimer: C24/18/097. EIT 19 263?SeizeIT2: Discreet Personalized Epileptic Seizure Detection Device. KU Leuven STADIUS acknowledges the financial support of imec. This research received funding from the Flemish Government (AI Research Program). SH, AR, JtM and JM are affiliated to Leuven. AI-KU Leuven institute for AI, B-3000, Leuven, Belgium. AR and CV acknowledge the financial support of ESA, BELSPO.","Barratt M.R., Baker E.S., Pool S.L., Principles of Clinical Medicine for Space Flight, (2019); Barrila J., Sarker S.F., Hansmeier N., Yang S., Buss K., Briones N., Et al., Evaluating the Effect of Spaceflight on the Host-Pathogen Interaction between Human Intestinal Epithelial Cells and Salmonella Typhimurium, npj Microgravity, 7, (2021); Bellisle R., Bjune C., Newman D., Considerations for Wearable Sensors to Monitor Physical Performance during Spaceflight Intravehicular Activities, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2020-July, pp. 4160-4164, (2020); Blanco-Almazan D., Groenendaal W., Catthoor F., Jane R., Wearable Bioimpedance Measurement for Respiratory Monitoring during Inspiratory Loading, IEEE Access, 7, pp. 89487-89496, (2019); Blanco-Almazan D., Groenendaal W., Lozano-Garcia M., Estrada-Petrocelli L., Lijnen L., Smeets C., Et al., Combining Bioimpedance and Myographic Signals for the Assessment of COPD during Loaded Breathing, IEEE Trans. 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Congreso Nacional de Ingeniería Biomédica, 5, pp. 262-265, (2018); Weenk M., Bredie S.J., Koeneman M., Hesselink G., Van Goor H., Van De Belt T.H., Continuous Monitoring of Vital Signs in the General ward Using Wearable Devices: Randomized Controlled Trial, J. Med. Internet Res, 22, (2020); Weiss K., Khoshgoftaar T.M., Wang D., A Survey of Transfer Learning, J. Big Data, 3, (2016)","A. Rozo; STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering, KU Leuven, , Leuven, Belgium; email: andrea.rozo@ulb.be","","Frontiers Media S.A.","","","","","","22964185","","","","English","Front. Bioeng. Biotechnol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85125853408"
"Liu X.; Pamula Y.; Immanuel S.; Kennedy D.; Martin J.; Baumert M.","Liu, Xiao (57196055465); Pamula, Yvonne (6602392945); Immanuel, Sarah (55485610300); Kennedy, Declan (7403112138); Martin, James (55574230957); Baumert, Mathias (7003720448)","57196055465; 6602392945; 55485610300; 7403112138; 55574230957; 7003720448","Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction","2022","Sleep and Breathing","26","2","","649","661","12","5","10.1007/s11325-021-02425-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110714736&doi=10.1007%2fs11325-021-02425-w&partnerID=40&md5=0d19a4e8b00a5e5cb7b44d9f32f1ec54","School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, SA, Australia; Department of Respiratory and Sleep Medicine, Women’s and Children’s Hospital, Adelaide, Australia; Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia; Children’s Research Centre, School of Paediatrics and Reproductive Health, The University of Adelaide, Adelaide, Australia","Liu X., School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, SA, Australia; Pamula Y., Department of Respiratory and Sleep Medicine, Women’s and Children’s Hospital, Adelaide, Australia; Immanuel S., Centre for Artificial Intelligence Research and Optimisation, Torrens University, Adelaide, Australia, College of Medicine and Public Health, Flinders University, Adelaide, Australia; Kennedy D., Department of Respiratory and Sleep Medicine, Women’s and Children’s Hospital, Adelaide, Australia, Children’s Research Centre, School of Paediatrics and Reproductive Health, The University of Adelaide, Adelaide, Australia; Martin J., Department of Respiratory and Sleep Medicine, Women’s and Children’s Hospital, Adelaide, Australia; Baumert M., School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, SA, Australia","Objective: To investigate the effect of adenotonsillectomy on OSAS symptoms based on a data-driven approach and thereby identify criteria that may help avoid unnecessary surgery in children with OSAS. Methods: In 323 children enrolled in the Childhood Adenotonsillectomy Trial, randomised to undergo either early adenotonsillectomy (eAT; N = 165) or a strategy of watchful waiting with supportive care (WWSC; N = 158), the apnea-hypopnea index, heart period pattern dynamics, and thoraco-abdominal asynchrony measurements from overnight polysomnography (PSG) were measured. Using machine learning, all children were classified into one of two different clusters based on those features. The cluster transitions between follow-up and baseline PSG were investigated for each to predict those children who recovered spontaneously, following surgery and those who did not benefit from surgery. Results: The two clusters showed significant differences in OSAS symptoms, where children assigned in cluster A had fewer physiological and neurophysiological symptoms than cluster B. Whilst the majority of children were assigned to cluster A, those children who underwent surgery were more likely to stay in cluster A after seven months. Those children who were in cluster B at baseline PSG were more likely to have their symptoms reversed via surgery. Children who were assigned to cluster B at both baseline and 7 months after surgery had significantly higher end-tidal carbon dioxide at baseline. Children who spontaneously changed from cluster B to A presented highly problematic ratings in behaviour and emotional regulation at baseline. Conclusions: Data-driven analysis demonstrated that AT helps to reverse and to prevent the worsening of the pathophysiological symptoms in children with OSAS. Multiple pathophysiological markers used with machine learning can capture more comprehensive information on childhood OSAS. Children with mild physiological and neurophysiological symptoms could avoid AT, and children who have UAO symptoms post AT may have sleep-related hypoventilation disease which requires further investigation. Furthermore, the findings may help surgeons more accurately predict children on whom they should perform AT. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.","Adenotonsillectomy; Children; Data-driven; Machine learning; Sleep apnea","Adenoidectomy; Airway Obstruction; Child; Humans; Machine Learning; Sleep Apnea Syndromes; Sleep Apnea, Obstructive; Tonsillectomy; glucocorticoid; montelukast; adenotonsillectomy; apnea hypopnea index; Article; asthma; child; controlled study; discriminant analysis; emotion regulation; end tidal carbon dioxide tension; female; follow up; heart cycle; human; machine learning; major clinical study; male; pediatric patient; pediatric surgery; polysomnography; prediction; preschool child; rhinitis; sleep disordered breathing; unnecessary surgery; watchful waiting; adenoidectomy; airway obstruction; machine learning; tonsillectomy","","montelukast, 151767-02-1, 158966-92-8","","","Division of Sleep and Circadian Disorders; Michael Rueschman; National Institutes of Health, NIH, (HL083075, HL083129, UL1 RR024989, UL1-RR-024134); National Heart, Lung, and Blood Institute, NHLBI, (75N92019R002, R24 HL114473)","Funding text 1: The Childhood Adenotonsillectomy Trial (CHAT) was supported by the National Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). ; Funding text 2: We would like to thank Michael Rueschman, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA for support with handling and interpreting the CHAT dataset. Xiao Liu, Sarah Immanuel, and Mathias Baumert had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Yvonne Pamula, James Martin, and Declan Kennedy contributed substantially to the interpretation and the writing of the manuscript.","Baumert M., Pamula Y., Martin J., Et al., The effect of adenotonsillectomy for childhood sleep apnoea on cardiorespiratory control, ERJ Open Research, 2, pp. 00003-2016, (2016); Baumert M., Walther T., Hopfe J., Stepan H., Faber R., Voss A., Joint symbolic dynamic analysis of beat-to-beat interactions of heart rate and systolic blood pressure in normal pregnancy, Med Biol Eng Compu, 40, pp. 241-245, (2002); Berry R.B., Budhiraja R., Gottlieb D.J., Et al., Rules for scoring respiratory events in sleep: update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events. Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine, J Clin Sleep Med, 8, pp. 597-619, (2012); Bhattacharjee R., Kheirandish-Gozal L., Spruyt K., Et al., Adenotonsillectomy outcomes in treatment of obstructive sleep apnea in children: a multicenter retrospective study, Am J Respir Crit Care Med, 182, pp. 676-683, (2010); Bozkurt S., Bostanci A., Turhan M., Can statistical machine learning algorithms help for classification of obstructive sleep apnea severity to optimal utilization of polysomno graphy resources?, Methods Inf Med, 56, pp. 308-318, (2017); Brietzke S.E., Gallagher D., The effectiveness of tonsillectomy and adenoidectomy in the treatment of pediatric obstructive sleep apnea/hypopnea syndrome: a meta-analysis, Otolaryngology-Head and Neck Surgery, 134, pp. 979-984, (2006); Chervin R.D., Ellenberg S.S., Hou X., Et al., Prognosis for spontaneous resolution of OSA in children, Chest, 148, pp. 1204-1213, (2015); Costa D.J., Mitchell R., Adenotonsillectomy for obstructive sleep apnea in obese children: a meta-analysis, Otolaryngology-Head and Neck Surgery, 140, pp. 455-460, (2009); Di-Tullio F., Ernst G., Robaina G., Et al., Ambulatory positional obstructive sleep apnea syndrome, Sleep Science, 11, (2018); El-Hamad F., Immanuel S., Liu X., Et al., Altered Nocturnal Cardiovascular Control in Children With Sleep-Disordered Breathing, Sleep, 40, (2017); Guilleminault C., Huang Y.-S., Chin W.-C., Okorie C., The Nocturnal-Polysomnogram and “non-hypoxic sleep-disordered-breathing” in Children, (2018); Guilleminault C., Lee J., Chan A., PEdiatric obstructive sleep apnea syndrome, Arch Pediatr Adolesc Med, 159, pp. 775-785, (2005); Guilleminault C., Li K., Khramtsov A., Palombini L., Pelayo R., Breathing patterns in prepubertal children with sleep-related breathing disorders, Arch Pediatr Adolesc Med, 158, pp. 153-161, (2004); Immanuel S., Kohler M., Martin J., Et al., Increased thoracoabdominal asynchrony during breathing periods free of discretely scored obstructive events in children with upper airway obstruction, Sleep Breath, 19, pp. 65-71, (2014); Immanuel S.A., Pamula Y., Kohler M., Et al., Respiratory timing and variability during sleep in children with sleep-disordered breathing, J Appl Physiol, 113, pp. 1635-1642, (2012); Jackman A.R., Biggs S.N., Walter L.M., Et al., Sleep-disordered breathing in preschool children is associated with behavioral, but not cognitive, impairments, Sleep Med, 13, pp. 621-631, (2012); Jain A.K., Data clustering: 50 years beyond K-means, Pattern Recogn Lett, 31, pp. 651-666, (2010); Katz E.S., D'ambrosio C.M., Pediatric Obstructive Sleep Apnea Syndrome, Clinics in Chest Medicine, 31, pp. 221-234, (2010); Kim T., Kim J.-W., Lee K., Detection of sleep disordered breathing severity using acoustic biomarker and machine learning techniques, Biomed Eng Online, 17, (2018); Kontos A., Lushington K., Martin J., Et al., Relationship between vascular resistance and sympathetic nerve fiber density in arterial vessels in children with sleep disordered breathing, Journal of the American Heart Association, 6, (2017); Kontos A., Van Den Heuvel C., Pamula Y., Et al., Delayed brachial artery dilation response and increased resting blood flow velocity in young children with mild sleep-disordered breathing, Sleep Med, 16, pp. 1451-1456, (2015); Kontos A., Willoughby S., Van Den Heuvel C., Et al., Ascending aortic blood flow velocity is increased in children with primary snoring/mild sleep-disordered breathing and associated with an increase in CD8+ T cells expressing TNFα and IFNγ, Heart Vessels, 33, pp. 537-548, (2018); Laing E.E., Moller-Levet C.S., Dijk D.-J., Archer S.N., Identifying and validating blood mRNA biomarkers for acute and chronic insufficient sleep in humans: A machine learning approach, Sleep, (2018); Liu X., Immanuel S., Kennedy D., Martin J., Pamula Y., Baumert M., Effect of adenotonsillectomy for childhood obstructive sleep apnea on nocturnal heart rate patterns, Sleep, 41, pp. zsy171-zsy71, (2018); Liu X., Immanuel S., Pamula Y., Kennedy D., Martin J., Baumert M., Adenotonsillectomy for childhood obstructive sleep apnoea reduces thoraco-abdominal asynchrony but spontaneous apnoea−hypopnoea index normalisation does not, European Respiratory Journal, 49, (2017); Liu X., Immanuel S., Pamula Y., Et al., Pulse wave amplitude and heart period variability in children with upper airway obstruction, Sleep Medicine, (2018); Liu X., Pamula Y., Kohler M., Baumert M.; Lumeng J.C., Chervin R.D., Epidemiology of pediatric obstructive sleep apnea, Proc Am Thorac Soc, 5, pp. 242-252, (2008); Marcus C.L., Brooks L.J., Ward S.D., Et al., Diagnosis and management of childhood obstructive sleep apnea syndrome, Pediatrics, 130, pp. e714-e755, (2012); Marcus C.L., Moore R.H., Rosen C.L., Et al., A Randomized Trial of Adenotonsillectomy for Childhood Sleep Apnea, N Engl J Med, 368, pp. 2366-2376, (2013); Ng D., Chan C., Chow A., Chow P., Kwok K., Childhood sleep-disordered breathing and its implications for cardiac and vascular diseases, J Paediatr Child Health, 41, pp. 640-646, (2005); Nisbet L.C., Yiallourou S.R., Walter L.M., Horne R.S., Blood pressure regulation, autonomic control and sleep disordered breathing in children, Sleep Med Rev, 18, pp. 179-189, (2014); Rapoport D.M., POINT: Is the apnea-hypopnea index the best way to quantify the severity of sleep-disordered breathing? Yes, Chest, 149, pp. 14-16, (2016); Redline S., Amin R., Beebe D., Et al., Childhood Adenotonsillectomy Study, National Sleep Research Resource. Web.; Redline S., Amin R., Beebe D., Et al., The Childhood Adenotonsillectomy Trial (CHAT): Rationale, Design, and Challenges of a Randomized Controlled Trial Evaluating a Standard Surgical Procedure in a Pediatric Population, Sleep, 34, pp. 1509-1517, (2011); Spruyt K., Verleye G., Gozal D., Unbiased Categorical Classification of Pediatric Sleep Disordered Breathing, Sleep, 33, pp. 1341-1347, (2010); Suen J.S., Arnold J.E., Brooks L.J., Adenotonsillectomy for treatment of obstructive sleep apnea in children, Archives of Otolaryngology-Head & Neck Surgery, 121, pp. 525-530, (1995); Tamanyan K., Walter L.M., Weichard A., Et al., Age effects on cerebral oxygenation and behavior in children with sleep-disordered breathing, Am J Respir Crit Care Med, 197, pp. 1468-1477, (2018); Zhang Z., Mayer G., Dauvilliers Y., Et al., Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning, Sci Rep, 8, (2018)","X. Liu; School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, Australia; email: xiao.liu.au@outlook.com; M. Baumert; School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, 5005, Australia; email: mathias.baumert@adelaide.edu.au","","Springer Science and Business Media Deutschland GmbH","","","","","","15209512","","SBLRB","34273052","English","Sleep Breathing","Article","Final","","Scopus","2-s2.0-85110714736"
"Bracht T.; Kleefisch D.; Schork K.; Witzke K.E.; Chen W.; Bayer M.; Hovanec J.; Johnen G.; Meier S.; Ko Y.-D.; Behrens T.; Brüning T.; Fassunke J.; Buettner R.; Uszkoreit J.; Adamzik M.; Eisenacher M.; Sitek B.","Bracht, Thilo (35094949500); Kleefisch, Daniel (57397044600); Schork, Karin (57191262982); Witzke, Kathrin E. (57193951124); Chen, Weiqiang (57223284725); Bayer, Malte (57190222868); Hovanec, Jan (57190131079); Johnen, Georg (6701487929); Meier, Swetlana (55190810000); Ko, Yon-Dschun (47962219300); Behrens, Thomas (57203051336); Brüning, Thomas (57222581771); Fassunke, Jana (6507629026); Buettner, Reinhard (7005353115); Uszkoreit, Julian (54792355000); Adamzik, Michael (6506557949); Eisenacher, Martin (23466514600); Sitek, Barbara (23494260800)","35094949500; 57397044600; 57191262982; 57193951124; 57223284725; 57190222868; 57190131079; 6701487929; 55190810000; 47962219300; 57203051336; 57222581771; 6507629026; 7005353115; 54792355000; 6506557949; 23466514600; 23494260800","Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)","2022","International Journal of Molecular Sciences","23","19","11242","","","","5","10.3390/ijms231911242","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139909764&doi=10.3390%2fijms231911242&partnerID=40&md5=e498f379db5d0a6fecb13dae640a7155","Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany; Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany; Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, 53113, Germany; Institute of Pathology, Medical Faculty, Center for Molecular Medicine (CMMC), University of Cologne, Cologne, 50924, Germany","Bracht T., Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany, Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany; Kleefisch D., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Schork K., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Witzke K.E., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Chen W., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany; Bayer M., Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany, Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany; Hovanec J., Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Johnen G., Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Meier S., Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Ko Y.-D., Department of Internal Medicine, Johanniter-Kliniken Bonn GmbH, Johanniter Krankenhaus, Bonn, 53113, Germany; Behrens T., Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Brüning T., Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, 44789, Germany; Fassunke J., Institute of Pathology, Medical Faculty, Center for Molecular Medicine (CMMC), University of Cologne, Cologne, 50924, Germany; Buettner R., Institute of Pathology, Medical Faculty, Center for Molecular Medicine (CMMC), University of Cologne, Cologne, 50924, Germany; Uszkoreit J., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Adamzik M., Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany; Eisenacher M., Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany, Center for Protein Diagnostics (PRODI), Medical Proteome Analysis, Ruhr-University Bochum, Bochum, 44801, Germany; Sitek B., Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany, Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum, 44801, Germany","Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel. © 2022 by the authors.","artificial intelligence; Ig kappa light chain; lung cancer; machine learning; plasma proteomics; random forest; SAA1; SERPINA3","alpha 1 antichymotrypsin; apolipoprotein A4; apolipoprotein C1; c16orf46 protein; haptoglobin; haptoglobin related protein; immunoglobulin kappa chain; pigment epithelium derived factor; serum amyloid a 1 protein; sulfhydryl oxidase 1; transthyretin; unclassified drug; adult; aged; area under the curve; Article; artificial intelligence; chronic obstructive lung disease; clinical classification; clinical evaluation; controlled study; female; human; human tissue; light chain; lung adenocarcinoma; machine learning; major clinical study; male; mass spectrometry; protein analysis; proteomics; random forest; tumor differentiation; univariate analysis","","haptoglobin, 9087-69-8; pigment epithelium derived factor, 197980-93-1","","","Bundesministerium für Bildung und Forschung, BMBF, (FKZ 031 A 534 A); Bundesministerium für Bildung und Forschung, BMBF; Deutsche Gesetzliche Unfallversicherung, DGUV, (FP339A); Deutsche Gesetzliche Unfallversicherung, DGUV; Ruhr-Universität Bochum, RUB","Funding text 1: The authors would like to thank Kristin Fuchs and Birgit Zülch for their excellent technical assistance. We acknowledge support by the Open Access Publication Funds of the Ruhr-Universität Bochum. ; Funding text 2: This work was funded by the German Social Accident Insurance (DGUV; project FP339A) and de. NBI, a project of the Federal Ministry of Education and Research (BMBF) (FKZ 031 A 534 A).","Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F., Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin, 71, pp. 209-249, (2021); Adcock I.M., Caramori G., Barnes P.J., Chronic obstructive pulmonary disease and lung cancer: New molecular insights, Respir. Int. Rev. Thorac. Dis, 81, pp. 265-284, (2011); Parris B.A., O'Farrell H.E., Fong K.M., Yang I.A., Chronic obstructive pulmonary disease (copd) and lung cancer: Common pathways for pathogenesis, J. Thorac. 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Targets, 15, pp. 1127-1137, (2011); Basile U., Gulli F., Gragnani L., Napodano C., Pocino K., Rapaccini G.L., Mussap M., Zignego A.L., Free light chains: Eclectic multipurpose biomarker, J. Immunol. Methods, 451, pp. 11-19, (2017); Braber S., Thio M., Blokhuis B.R., Henricks P.A., Koelink P.J., Groot Kormelink T., Bezemer G.F., Kerstjens H.A., Postma D.S., Garssen J., Et al., An association between neutrophils and immunoglobulin free light chains in the pathogenesis of chronic obstructive pulmonary disease, Am. J. Respir. Crit. 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Cell Physiol, 320, pp. C106-C118, (2021); Zhang Y., Tian J., Qu C., Peng Y., Lei J., Li K., Zong B., Sun L., Liu S., Overexpression of serpina3 promotes tumor invasion and migration, epithelial-mesenchymal-transition in triple-negative breast cancer cells, Breast Cancer, 28, pp. 859-873, (2021); Jung Y.J., Katilius E., Ostroff R.M., Kim Y., Seok M., Lee S., Jang S., Kim W.S., Choi C.M., Development of a protein biomarker panel to detect non-small-cell lung cancer in korea, Clin. Lung Cancer, 18, pp. e99-e107, (2017); Borlak J., Langer F., Chatterji B., Serum proteome mapping of egf transgenic mice reveal mechanistic biomarkers of lung cancer precursor lesions with clinical significance for human adenocarcinomas, Biochim. Biophys. Acta. Mol. Basis Dis, 1864, pp. 3122-3144, (2018); Kim Y.J., Gallien S., El-Khoury V., Goswami P., Sertamo K., Schlesser M., Berchem G., Domon B., Quantification of saa1 and saa2 in lung cancer plasma using the isotype-specific prm assays, Proteomics, 15, pp. 3116-3125, (2015); Sung H.J., Ahn J.M., Yoon Y.H., Rhim T.Y., Park C.S., Park J.Y., Lee S.Y., Kim J.W., Cho J.Y., Identification and validation of saa as a potential lung cancer biomarker and its involvement in metastatic pathogenesis of lung cancer, J. Proteome Res, 10, pp. 1383-1395, (2011); Sung H.J., Jeon S.A., Ahn J.M., Seul K.J., Kim J.Y., Lee J.Y., Yoo J.S., Lee S.Y., Kim H., Cho J.Y., Large-scale isotype-specific quantification of serum amyloid a 1/2 by multiple reaction monitoring in crude sera, J. Proteom, 75, pp. 2170-2180, (2012); Hughes C.S., Moggridge S., Muller T., Sorensen P.H., Morin G.B., Krijgsveld J., Single-pot, solid-phase-enhanced sample preparation for proteomics experiments, Nat. Protoc, 14, pp. 68-85, (2019); Valikangas T., Suomi T., Elo L.L., A systematic evaluation of normalization methods in quantitative label-free proteomics, Brief. Bioinform, 19, pp. 1-11, (2018); Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., Smyth G.K., Limma powers differential expression analyses for rna-sequencing and microarray studies, Nucleic Acids Res, 43, (2015); Benjamini Y., Hochberg Y., Controlling the false discovery rate—A practical and powerful approach to multiple testing, J. R. Stat. Soc. B, 57, pp. 289-300, (1995); Holm S., A simple sequentially rejective multiple test procedure, Scand. J. Stat, 6, pp. 65-70, (1979)","T. Bracht; Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany; email: thilo.bracht@rub.de; B. Sitek; Clinic for Anesthesiology, Intensive Care and Pain Therapy, University Medical Center Knappschaftskrankenhaus Bochum, Bochum, 44892, Germany; email: barbara.sitek@rub.de","","MDPI","","","","","","16616596","","","","English","Int. J. Mol. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139909764"
"Hamad A.F.; Yan L.; Jafari Jozani M.; Hu P.; Delaney J.A.; Lix L.M.","Hamad, Amani F. (57204507797); Yan, Lin (56375951400); Jafari Jozani, Mohammad (57208233691); Hu, Pingzhao (8957120500); Delaney, Joseph A. (57191448378); Lix, Lisa M. (6603917596)","57204507797; 56375951400; 57208233691; 8957120500; 57191448378; 6603917596","Developing a prediction model of children asthma risk using population-based family history health records","2023","Pediatric Allergy and Immunology","34","10","e14032","","","","6","10.1111/pai.14032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173784085&doi=10.1111%2fpai.14032&partnerID=40&md5=19811cd806f1585ca9dfbb1c6f89013c","Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Department of Statistics, University of Manitoba, Winnipeg, MB, Canada; Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada; College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Department of Epidemiology, University of Washington, Seattle, WA, United States","Hamad A.F., Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Yan L., Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Jafari Jozani M., Department of Statistics, University of Manitoba, Winnipeg, MB, Canada; Hu P., Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada; Delaney J.A., College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada, Department of Epidemiology, University of Washington, Seattle, WA, United States; Lix L.M., Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada","Background: Identifying children at high risk of developing asthma can facilitate prevention and early management strategies. We developed a prediction model of children's asthma risk using objectively collected population-based children and parental histories of comorbidities. Methods: We conducted a retrospective population-based cohort study using administrative data from Manitoba, Canada, and included children born from 1974 to 2000 with linkages to ≥1 parent. We identified asthma and prior comorbid condition diagnoses from hospital and outpatient records. We used two machine-learning models: least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and random forest (RF) to identify important predictors. The predictors in the base model included children's demographics, allergic conditions, respiratory infections, and parental asthma. Subsequent models included additional multiple comorbidities for children and parents. Results: The cohort included 195,666 children: 51.3% were males and 17.7% had asthma diagnosis. The base LR model achieved a low predictive performance with sensitivity of 0.47, 95% confidence interval (0.45–0.48), and specificity of 0.67 (0.66–0.67) using a predicted probability threshold of 0.20. Sensitivity significantly improved when children's comorbidities were included using LASSO LR: 0.71 (0.69–0.72). Predictive performance further improved by including parental comorbidities (sensitivity = 0.72 [0.70–0.73], specificity = 0.69 [0.69–0.70]). We observed similar results for the RF models. Children's menstrual disorders and mood and anxiety disorders, parental lipid metabolism disorders and asthma were among the most important variables that predicted asthma risk. Conclusion: Including children and parental comorbidities to children's asthma prediction models improves their accuracy. © 2023 The Authors. Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","administrative healthcare data; asthma risk; children; family health history; machine learning; prediction","Anxiety Disorders; Asthma; Canada; Child; Cohort Studies; Female; Humans; Male; Retrospective Studies; allergy; anxiety disorder; Article; asthma; child; cohort analysis; comorbidity; confidence interval; disorders of lipid and lipoprotein metabolism; family history; female; follow up; Gini coefficient; high risk population; human; least absolute shrinkage and selection operator; logistic regression analysis; machine learning; major clinical study; male; Manitoba; menstruation disorder; mood disorder; predictive model; predictive value; prevalence; random forest; respiratory tract infection; retrospective study; sensitivity and specificity; urban population; asthma; Canada","","","","","Manitoba Health; Tier 1 Canada Research Chair; Winnipeg Foundation Innovation Fund of the Rady Faculty of Health Sciences; Manitoba Centre for Health Policy, University of Manitoba, MCHP, (2019/2020–52, 2020-005); Manitoba Centre for Health Policy, University of Manitoba, MCHP; Canadian Institutes of Health Research, IRSC","Funding text 1: We acknowledge the Manitoba Centre for Health Policy for the use of data contained in the Population Health Research Data Repository (HIPC #: 2019/2020–52; MCHP Project #: 2020-005). The results and conclusions are those of authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, Seniors and Active Living, or other data providers is intended or should be inferred, or other data providers is intended or should be inferred. We acknowledge Mr. Hassan Maleki Golandouz for his contribution to summarizing selected study results.; Funding text 2: This study was supported by funding from the Winnipeg Foundation Innovation Fund of the Rady Faculty of Health Sciences. AFH is supported by a Canadian Institutes of Health Research Fellowship. LML is supported by a Tier 1 Canada Research Chair. ","Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019, Lancet, 396, 10258, pp. 1204-1222, (2020); Asher I., Pearce N., Global burden of asthma among children, Int J Tuberc Lung Dis, 18, 11, pp. 1269-1278, (2014); Akinbami L.J., Moorman J.E., Liu X., Asthma prevalence, health care use, and mortality: United States, 2005-2009, Natl Health Stat Rep, 32, pp. 1-14, (2011); Liberty K.A., Pattemore P., Reid J., Tarren-Sweeney M., Beginning school with asthma independently predicts low achievement in a prospective cohort of children, Chest, 138, 6, pp. 1349-1355, (2010); Shendell D.G., Alexander M.S., Sanders D.L., Jewett A., Yang J., Assessing the potential influence of asthma on student attendance/absence in public elementary schools, J Asthma off J Assoc Care Asthma, 47, 4, pp. 465-472, (2010); Asthma hospital stays by children and youth [Internet]; Willemsen G., Beijsterveldt T.C., Baal C.G., Postma D., Boomsma D.I., Heritability of self-reported asthma and allergy: a study in adult Dutch twins, siblings and parents, Twin Res Hum Genet, 11, 2, pp. 132-142, (2008); Moffatt M.F., Kabesch M., Liang L., Et al., Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma, Nature, 448, 7152, pp. 470-473, (2007); Galanter J., Choudhry S., Eng C., Et al., ORMDL3 gene is associated with asthma in three ethnically diverse populations, Am J Respir Crit Care Med, 177, 11, pp. 1194-1200, (2008); Castro-Rodriguez J.A., Forno E., Rodriguez-Martinez C.E., Celedon J.C., Risk and protective factors for childhood asthma: what is the evidence?, J Allergy Clin Immunol Pract, 4, 6, pp. 1111-1122, (2016); Lau S., Nickel R., Niggemann B., Et al., The development of childhood asthma: lessons from the German multicentre allergy study (MAS), Paediatr Respir Rev, 3, 3, pp. 265-272, (2002); Alfonso J., Perez S., Bou R., Et al., Asthma prevalence and risk factors in school children: the RESPIR longitudinal study, Allergol Immunopathol (Madr), 48, 3, pp. 223-231, (2020); Keet C.A., Matsui E.C., McCormack M.C., Peng R.D., Urban residence, neighborhood poverty, race/ethnicity, and asthma morbidity among children on Medicaid, J Allergy Clin Immunol, 140, 3, pp. 822-827, (2017); Arshad S.H., Tariq S.M., Matthews S., Hakim E., Sensitization to common allergens and its association with allergic disorders at age 4 years: a whole population birth cohort study, Pediatrics, 108, 2, (2001); Kothalawala D.M., Kadalayil L., Weiss V.B.N., Et al., Prediction models for childhood asthma: a systematic review, Pediatr Allergy Immunol, 31, 6, pp. 616-627, (2020); Pinart M., Smit H.A., Keil T., Bousquet J., Anto J.M., Lodrup-Carlsen K.C., Systematic review of childhood asthma prediction models, Eur Respir J, 46, (2015); Katz A., Enns J., Smith M., Burchill C., Turner K., Towns D., Population data Centre profile: the Manitoba Centre for Health Policy, Int J Popul Data Sci, 4, (2019); Lix L., Ayles J., Bartholomew S., Et al., The Canadian chronic disease surveillance system: a model for collaborative surveillance, Int J Popul Data Sci, 3, (2018); Elixhauser A., Steiner C., Palmer L., Clinical Classifications Software (CCS), (2015); Hamad A.F., Vasylkiv V., Yan L., Et al., Mapping three versions of the international classification of diseases to categories of chronic conditions, Int J Popul Data Sci, 6, 1, (2021); Castellan N.J., On the estimation of the tetrachoric correlation coefficient, Psychometrika, 31, 1, pp. 67-73, (1966); Hajipour F., Jafari Jozani M., Moussavi Z., A comparison of regularized logistic regression and random forest machine learning models for daytime diagnosis of obstructive sleep apnea, Med Biol Eng Comput, 58, 10, pp. 2517-2529, (2020); Li Y., Lu F., Yin Y., Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease, Sci Rep, 5, 12, (2022); Ooka T., Johno H., Nakamoto K., Yoda Y., Yokomichi H., Yamagata Z., Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan, BMJ Nutr Prev Health, 4, 1, pp. 140-148, (2021); Tibshirani R., Regression shrinkage and selection via the Lasso, J R Stat Soc Ser B Methodol, 58, 1, pp. 267-288, (1996); Breiman L., Random forests, Mach Learn, 45, 1, pp. 5-32, (2001); Strobl C., Boulesteix A.L., Kneib T., Augustin T., Zeileis A., Conditional variable importance for random forests, BMC Bioinformatics, 9, 1, (2008); Biagini Myers J.M., Schauberger E., He H., Et al., A pediatric asthma risk score to better predict asthma development in young children, J Allergy Clin Immunol, 143, 5, pp. 1803-1810, (2019); Castro-Rodriguez J.A., Holberg C.J., Wright A.L., Martinez F.D., A clinical index to define risk of asthma in young children with recurrent wheezing, Am J Respir Crit Care Med, 162, 4, pp. 1403-1406, (2000); Graziottin A., Serafini A., Perimenstrual asthma: from pathophysiology to treatment strategies, Multidiscip Respir Med, 11, 1, (2016); Rao C.K., Moore C.G., Bleecker E., Et al., Characteristics of perimenstrual asthma and its relation to asthma severity and control: data from the severe asthma research program, Chest, 143, 4, pp. 984-992, (2013); Eid R.C., Palumbo M.L., Cahill K.N., Perimenstrual asthma in aspirin-exacerbated respiratory disease, J Allergy Clin Immunol Pract, 8, 2, pp. 573-578, (2020); Scott K.M., Von Korff M., Ormel J., Et al., Mental disorders among adults with asthma: results from the world mental health surveys, Gen Hosp Psychiatry, 29, 2, pp. 123-133, (2007); Goodwin R.D., Jacobi F., Thefeld W., Mental disorders and asthma in the community, Arch Gen Psychiatry, 60, 11, pp. 1125-1130, (2003); Chen Y.C., Tung K.Y., Tsai C.H., Et al., Lipid profiles in children with and without asthma: interaction of asthma and obesity on hyperlipidemia, Diabetes Metab Syndr, 7, 1, pp. 20-25, (2013); Su X., Ren Y., Li M., Zhao X., Kong L., Kang J., Association between lipid profile and the prevalence of asthma: a meta-analysis and systemic review, Curr Med Res Opin, 34, 3, pp. 423-433, (2018)","A.F. Hamad; Department of Community Health Sciences, University of Manitoba, Winnipeg, 753 McDermot Avenue, R3E 0T6, Canada; email: amani.hamad@umanitoba.ca","","John Wiley and Sons Inc","","","","","","09056157","","PALUE","37877849","English","Pediatr. Allergy Immunol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85173784085"
"Machnes-Maayan D.; Yahia S.H.; Frizinsky S.; Maoz-Segal R.; Offengenden I.; Kenett R.S.; Kidon M.I.; Agmon-Levin N.","Machnes-Maayan, Diti (55913514200); Yahia, Soad Haj (57213189006); Frizinsky, Shirly (57203037315); Maoz-Segal, Ramit (55660386000); Offengenden, Irena (57211983659); Kenett, Ron S. (6602155136); Kidon, Mona I. (10339388800); Agmon-Levin, Nancy (7801563547)","55913514200; 57213189006; 57203037315; 55660386000; 57211983659; 6602155136; 10339388800; 7801563547","A clinical pathway for the diagnosis of sesame allergy in children","2022","World Allergy Organization Journal","15","11","100713","","","","6","10.1016/j.waojou.2022.100713","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142847890&doi=10.1016%2fj.waojou.2022.100713&partnerID=40&md5=fbcad9675df684f2faf2af0a61b0141a","Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel; Clinical Immunology, Angioedema and Allergy Unit, Pediatric Allergy Clinic, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel; Sackler School of Medicine, Tel-Aviv University, Israel; KPA Group and Samuel Neaman Institute, Technicon, Israel","Machnes-Maayan D., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel, Clinical Immunology, Angioedema and Allergy Unit, Pediatric Allergy Clinic, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel, Sackler School of Medicine, Tel-Aviv University, Israel; Yahia S.H., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel, Sackler School of Medicine, Tel-Aviv University, Israel; Frizinsky S., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel, Clinical Immunology, Angioedema and Allergy Unit, Pediatric Allergy Clinic, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel, Sackler School of Medicine, Tel-Aviv University, Israel; Maoz-Segal R., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel; Offengenden I., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel; Kenett R.S., KPA Group and Samuel Neaman Institute, Technicon, Israel; Kidon M.I., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel, Clinical Immunology, Angioedema and Allergy Unit, Pediatric Allergy Clinic, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel, Sackler School of Medicine, Tel-Aviv University, Israel; Agmon-Levin N., Clinical Immunology, Angioedema and Allergy Unit, Center for Autoimmune Diseases, Sheba Medical Center, Tel Hashomer, Israel, Clinical Immunology, Angioedema and Allergy Unit, Pediatric Allergy Clinic, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Israel, Sackler School of Medicine, Tel-Aviv University, Israel","Background: Sesame allergy (SA) is a common cause of life-threatening, persistent food allergy, not only in the Middle East and Asia, but increasingly worldwide. Commercially available tests such as extracts for skin testing or specific IgE for sesame or its components in serum, have very limited predictive values. Therefore the diagnosis is dependent on the performance of oral food challenges (OFC), frequently avoided in children, due to time and resource constraints, as well as the risk of anaphylaxis. In the current study we aimed to develop a simple, readily available, clinical tool, able to predict sesame OFC outcomes in children. Methods: Children with a history of SA were evaluated in the outpatient allergy clinic. All children underwent natural sesame OFC, with an additional baked-sesame challenge offered to children with SA. Clinical data were compared between the sesame tolerant (ST) and SA groups. Machine-learning tools were applied, to create a simple, clinically driven, decision tree analysis (DTA), predicting the outcome of sesame OFCs and the diagnosis of SA. Results: One hundred four children, mean age 47.2 months, 58% boys were included, with a high prevalence of additional food allergies, atopic dermatitis, asthma, and rhinitis. Following OFC, 56 (54%) were diagnosed as ST and 48 (46%) SA. Among SA children, 85% were able to consume baked-sesame in equal or higher protein amounts compared to natural sesame paste. Compared to ST, SA children had a tendency towards a higher incidence of allergic rhinitis (5% Vs 17%, p = 0.062), multiple food allergies (3.6% vs 12.5%, p = 0.09) and requiring medical treatment after the initial SA reaction (27% vs 41%, p = 0.022). As a group, skin tests with both commercial and natural tahini paste differed significantly between ST and SA (mean wheal in mm, for extract 4.2 vs 13.4, p < 0.001 and for natural sesame paste 6.7 vs 24.4, p < 0.001), However, the PPV of any individual test was only between 60%–85%. Our exploratory, clinical DTA, predicted OFC outcomes and the presence or absence of Sesame Allergy, with ≥96% positive (PPV) and negative (NPV) predictive values. Conclusion: OFCs remain the gold standard for the diagnosis of Sesame Allergy and are indicated to define ST/SA status even in highly atopic patients with previous immediate allergic reactions to sesame. A decision-tree analysis based on clinical parameters easily available in every allergy clinic, can predict the outcome of sesame OFC in the vast majority of children, increasing the safety and availability of such diagnostic procedures. © 2022 The Authors","Anaphylaxis; Food allergy; Machine learning; Sesame; Skin prick test (SPT)","immunoglobulin E; allergic rhinitis; allergy test; Article; asthma; atopic dermatitis; child; clinical evaluation; clinical pathway; cohort analysis; controlled study; decision tree; diagnostic test accuracy study; female; food induced anaphylaxis; gold standard; human; machine learning; major clinical study; male; medical history; multiple food allergy; outpatient department; pediatric patient; predictive value; preschool child; prevalence; prick test; receiver operating characteristic; retrospective study; sensitivity and specificity; sesame allergy; skin test; tolerance test; urticaria","","immunoglobulin E, 37341-29-0","","","","","Tuano K.T., Dillard K.H., Guffey D., Davis C.M., Development of sesame tolerance and cosensitization of sesame allergy with peanut and tree nut allergy in children, Ann Allergy Asthma Immunol, 117, pp. 708-710, (2016); Segal L., Ben-Shoshan M., Alizadehfar R., Et al., Initial and accidental reactions are managed inadequately in children with sesame allergy, J Allergy Clin Immunol Pract, 5, pp. 482-485, (2017); Patel A., Bahna S.L., Hypersensitivities to sesame and other common edible seeds, Allergy, 71, pp. 1405-1413, (2016); Osborne N.J., Koplin J.J., Martin P.E., Et al., Prevalence of challenge-proven IgE-mediated food allergy using population-based sampling and predetermined challenge criteria in infants, J Allergy Clin Immunol, 127, pp. 668-676, (2011); Hossny E., Ebisawa M., El-Gamal Y., Et al., Challenges of managing food allergy in the developing world, World Allergy Organ J, 12, (2019); Garkaby J., Epov L., Musallam N., Et al., The sesame-peanut conundrum in Israel: reevaluation of food allergy prevalence in young children, J Allergy Clin Immunol Pract, 9, pp. 200-205, (2021); Warren C.M., Chadha A.S., Sicherer S.H., Jiang J., Gupta R.S., Prevalence and severity of sesame allergy in the United States, JAMA Netw Open, 2, (2019); Koplin J.J., Wake M., Dharmage S.C., Et al., Cohort Profile: the HealthNuts Study: population prevalence and environmental/genetic predictors of food allergy, Int J Epidemiol, 44, pp. 1161-1171, (2015); Dalal I., Binson I., Reifen R., Et al., Food allergy is a matter of geography after all: sesame as a major cause of severe IgE-mediated food allergic reactions among infants and young children in Israel, Allergy, 57, pp. 362-365, (2002); Abunada T., Al-Nesf M.A., Thalib L., Et al., Anaphylaxis triggers in a large tertiary care hospital in Qatar: a retrospective study, World Allergy Organ J, 11, (2018); Savage J., Johns C.B., Food allergy: epidemiology and natural history, Immunol Allergy Clin, 35, pp. 45-59, (2015); Aaronov D., Tasher D., Levine A., Somekh E., Serour F., Dalal I., Natural history of food allergy in infants and children in Israel, Ann Allergy Asthma Immunol, 101, pp. 637-640, (2008); Cohen A., Goldberg M., Levy B., Leshno M., Katz Y., Sesame food allergy and sensitization in children: the natural history and long-term follow-up, Pediatr Allergy Immunol, 18, pp. 217-223, (2007); Soller L., Clarke A.E., Lyttle A., Et al., Comparing quality of life in Canadian children with peanut, sesame, and seafood allergy, J Allergy Clin Immunol Pract, 8, pp. 352-354 e1, (2020); Epstein-Rigbi N., Goldberg M.R., Levy M.B., Nachshon L., Elizur A., Quality of life of children aged 8-12 years undergoing food allergy oral immunotherapy: child and parent perspective, Allergy, 75, pp. 2623-2632, (2020); Soller L., Clarke A.E., Lyttle A., Et al., Comparing quality of life in Canadian children with peanut, sesame, and seafood allergy, J Allergy Clin Immunol Pract, Jan;81), pp. 352-354, (2019); Ansotegui I.J., Melioli G., Canonica G.W., Et al., IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper, World Allergy Organ J, 13, (2020); Saf S., Sifers T.M., Baker M.G., Et al., Diagnosis of sesame allergy: analysis of current practice and exploration of sesame component ses i 1, J Allergy Clin Immunol Pract, May;85), pp. 1681-1688, (2019); 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Beyer K., Bardina L., Grishina G., Sampson H.A., Identification of sesame seed allergens by 2-dimensional proteomics and Edman sequencing: seed storage proteins as common food allergens, J Allergy Clin Immunol, 110, pp. 154-159, (2002); Maruyama N., Nakagawa T., Ito K., Et al., Measurement of specific IgE antibodies to Ses i 1 improves the diagnosis of sesame allergy, Clin Exp Allergy, 46, pp. 163-171, (2016); Winchester C., Give every paper a read for reproducibility, Nature, 557, (2018); Amaral O.B., Neves K., Reproducibility: expect less of the scientific paper, Nature, 597, pp. 329-331, (2021); McNutt M., Reproducibility, Science, 343, (2014); Kenett Rss G., Generalizing research findings for enhanced reproducibility: an approach based on verbal alternative representations, Scientometrics, 126, pp. 4137-4151, (2021)","D. Machnes-Maayan; Clinical Immunology, Angioedema and Allergy Unit, The Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, Israel Affiliated to Sackler Faculty of Medicine, Aviv University, Tel Hashomer, 52621, Israel; email: ditim@zahav.net.il","","Elsevier Inc.","","","","","","19394551","","","","English","World Allergy Organ. J.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85142847890"
"Patel R.K.; Kashyap M.","Patel, Rajneesh Kumar (58280799600); Kashyap, Manish (35118943500)","58280799600; 35118943500","Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE","2023","Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization","11","5","","1762","1776","14","6","10.1080/21681163.2023.2187244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150621980&doi=10.1080%2f21681163.2023.2187244&partnerID=40&md5=52f1df8e9070e0d4b007b2e4853a7e8c","Department of Electronics & Communication, Maulana Azad National Institute of Technology, M.P., Bhopal, India","Patel R.K., Department of Electronics & Communication, Maulana Azad National Institute of Technology, M.P., Bhopal, India; Kashyap M., Department of Electronics & Communication, Maulana Azad National Institute of Technology, M.P., Bhopal, India","The term ‘lung disease’ covers a wide range of conditions that affect the lungs, including asthma, COPD, infections like the flu, pneumonia, tuberculosis, lung cancer, COVID, and numerous other breathing issues. Respiratory failure may result from several respiratory disorders. Recently, various methods have been proposed for lung disease detection, but they are not much more efficient. The proposed model has been tested on the COVID dataset. In this work, Littlewood-Paley Empirical Wavelet Transform (LPEWT) based technique is used to decompose images into their sub-bands. Using locally linear embedding (LLE), linear discriminative analysis (LDA), and principal component analysis (PCA), robust features are identified for lung disease detection after texture-based relevant Gabor features are extracted from images. LLE’s outcomes inspire the development of new techniques. The Entropy, ROC, and Student’s t-value methods provide ranks for robust features. Finally, LS-SVM is fed with t-value-based ranked features for classification using Morlet wavelet, Mexican-hat wavelet, and radial basis function. This model, which incorporated tenfold cross-validation, exhibited improved classification accuracy of 95.48%, specificity of 95.37%, sensitivity of 95.43%, and an F1 score of.95. The proposed diagnosis method can be a fast disease detection tool for imaging specialists using medical images. © 2023 Informa UK Limited, trading as Taylor & Francis Group.","EWT; Gabor; LLE; Machine learning; medical imaging","Biological organs; Computer aided diagnosis; Computerized tomography; Medical imaging; Principal component analysis; Pulmonary diseases; Radial basis function networks; Support vector machines; Textures; Condition; CT Image; Disease detection; Disease diagnosis; EWT; Gabor; Gabor feature; Locally linear embedding; Machine-learning; Wavelets transform; Article; asthma; chronic obstructive lung disease; computer assisted tomography; controlled study; coronavirus disease 2019; cross validation; data base; decomposition; diagnostic imaging; diagnostic test accuracy study; discriminant analysis; entropy; feature extraction; Gabor transform; human; least squares support vector machine; locally linear embedding; lung cancer; lung disease; lung tuberculosis; machine learning; Morlet wavelet transform; outcome assessment; pneumonia; radial basis function; receiver operating characteristic; sensitivity and specificity; Student t test; support vector machine; wavelet transform; Wavelet transforms","","","","","","","Abraham B., Nair M.S., Computer-aided detection of COVID-19 from CT scans using an ensemble of CNNs and KSVM classifier, Signal, Image Video Process, 16, 3, pp. 587-594, (2022); Ao-I A., Two-stage topic extraction model for bibliometric data analysis based on word embeddings and clustering, IEEE Access; Acharya U., Dua S., Du X., Sree V.S., Chua C.K., Automated diagnosis of glaucoma using texture and higher order spectra features, IEEE Trans Inf Technol Biomed, 15, 3, pp. 449-455, (2011); Adankon M.M., Cheriet M., Model selection for the LS-SVM. 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Wang L., Lin Z., Wong A., Covid-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images, Sci Rep, 10, 1, pp. 1-2, (2020)","R.K. Patel; Department of Electronics & Communication, Maulana Azad National Institute of Technology, Bhopal, M.P., India; email: patelrajneesh90@gmail.com","","Taylor and Francis Ltd.","","","","","","21681163","","","","English","Comput. Methods Biomech. Biomed. Eng. Imaging and Visualization","Article","Final","","Scopus","2-s2.0-85150621980"
"Kocks J.W.H.; Cao H.; Holzhauer B.; Kaplan A.; FitzGerald J.M.; Kostikas K.; Price D.; Reddel H.K.; Tsiligianni I.; Vogelmeier C.F.; Bostel S.; Mastoridis P.","Kocks, Janwillem W.H. (55891765700); Cao, Hui (57213259689); Holzhauer, Björn (35847654200); Kaplan, Alan (35242764000); FitzGerald, J. Mark (56414027200); Kostikas, Konstantinos (6602272047); Price, David (58388813200); Reddel, Helen K. (6701676904); Tsiligianni, Ioanna (8315152700); Vogelmeier, Claus F. (7005604348); Bostel, Sebastien (57217533448); Mastoridis, Paul (56998846900)","55891765700; 57213259689; 35847654200; 35242764000; 56414027200; 6602272047; 58388813200; 6701676904; 8315152700; 7005604348; 57217533448; 56998846900","Diagnostic Performance of a Machine Learning Algorithm (Asthma/Chronic Obstructive Pulmonary Disease [COPD] Differentiation Classification) Tool Versus Primary Care Physicians and Pulmonologists in Asthma, COPD, and Asthma/COPD Overlap","2023","Journal of Allergy and Clinical Immunology: In Practice","11","5","","1463","1474.e3","","7","10.1016/j.jaip.2023.01.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147880735&doi=10.1016%2fj.jaip.2023.01.017&partnerID=40&md5=1ac7ab80767f9f7bb2fc2283bde95888","General Practitioners Research Institute, Groningen, Netherlands; University Medical Centre Groningen, GRIAC Research Institute, University of Groningen, Groningen, Netherlands; Observational and Pragmatic Research Institute, Singapore; Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Novartis Pharma AG, Basel, Switzerland; Family Physician Airways Group of Canada, University of Toronto, Toronto, ON, Canada; Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada; Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece; Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom; Woolcock Institute of Medical Research and University of Sydney, Sydney, NSW, Australia; Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Crete, Heraklion, Greece; Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Centre Giessen and Marburg, Philipps-Universität Marburg, Member of the German Centre for Lung Research (DZL), Marburg, Germany","Kocks J.W.H., General Practitioners Research Institute, Groningen, Netherlands, University Medical Centre Groningen, GRIAC Research Institute, University of Groningen, Groningen, Netherlands, Observational and Pragmatic Research Institute, Singapore; Cao H., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Holzhauer B., Novartis Pharma AG, Basel, Switzerland; Kaplan A., Family Physician Airways Group of Canada, University of Toronto, Toronto, ON, Canada; FitzGerald J.M., Department of Medicine, Division of Respiratory Medicine, University of British Columbia, Vancouver, BC, Canada; Kostikas K., Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece; Price D., Observational and Pragmatic Research Institute, Singapore, Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom; Reddel H.K., Woolcock Institute of Medical Research and University of Sydney, Sydney, NSW, Australia; Tsiligianni I., Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Crete, Heraklion, Greece; Vogelmeier C.F., Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Centre Giessen and Marburg, Philipps-Universität Marburg, Member of the German Centre for Lung Research (DZL), Marburg, Germany; Bostel S., Novartis Pharma AG, Basel, Switzerland; Mastoridis P., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States","Background: The differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) poses a challenge in clinical practice and its misdiagnosis results in inappropriate treatment, increased exacerbations, and potentially death. Objective: To investigate the diagnostic accuracy of the Asthma/COPD Differentiation Classification (AC/DC) tool compared with primary care physicians and pulmonologists in asthma, COPD, and asthma-COPD overlap. Methods: The AC/DC machine learning-based diagnostic tool was developed using 12 parameters from electronic health records of more than 400,000 patients aged 35 years and older. An expert panel of three pulmonologists and four general practitioners from five countries evaluated 119 patient cases from a prospective observational study and provided a confirmed diagnosis (n = 116) of asthma (n = 53), COPD (n = 43), asthma-COPD overlap (n = 7), or other (n = 13). Cases were then reviewed by 180 primary care physicians and 180 pulmonologists from nine countries and by the AC/DC tool, and diagnostic accuracies were compared with reference to the expert panel diagnoses. Results: Average diagnostic accuracy of the AC/DC tool was superior to that of primary care physicians (median difference, 24%; 95% posterior credible interval: 17% to 29%; P < .0001) and was noninferior and superior (median difference, 12%; 95% posterior credible interval: 6% to 17%; P < .0001 for noninferiority and P = .0006 for superiority) to that of pulmonologists. Average diagnostic accuracies were 73%, 50%, and 61% by AC/DC tool, primary care physicians, and pulmonologists versus expert panel diagnosis, respectively. Conclusion: The AC/DC tool demonstrated superior diagnostic accuracy compared with primary care physicians and pulmonologists in the diagnosis of asthma and COPD in patients aged 35 years and greater and has the potential to support physicians in the diagnosis of these conditions in clinical practice. © 2023","AC/DC tool; Accuracy; Asthma; Asthma/COPD overlap; COPD; Differential diagnosis; Machine learning; Primary care physician; Pulmonologist","Asthma; General Practitioners; Humans; Physicians, Primary Care; Pulmonary Disease, Chronic Obstructive; Pulmonologists; adult; aged; algorithm; Article; asthma; chronic obstructive lung disease; chronic obstructive lung disease differentiation classification tool; clinical assessment tool; clinical evaluation; clinical practice; comparative effectiveness; controlled study; decision making; diagnostic accuracy; diagnostic error; diagnostic test accuracy study; differential diagnosis; electronic health record; female; general practitioner; gold standard; human; machine learning; major clinical study; male; outcome assessment; pulmonologist; asthma; chronic obstructive lung disease; pulmonologist","","","","","Novartis Pharmaceuticals Corporation, NPC","This study was funded by Novartis Pharmaceuticals Corporation, East Hanover, NJ. ","Llanos J.P., Ortega H., Germain G., Duh M.S., Lafeuille M.H., Tiggelaar S., Et al., Health characteristics of patients with asthma, COPD and asthma-COPD overlap in the NHANES database, Int J Chron Obstruct Pulmon Dis, 13, pp. 2859-2868, (2018); Buist A.S., Similarities and differences between asthma and chronic obstructive pulmonary disease: treatment and early outcomes, Eur Respir J Suppl, 39, pp. 30-35s, (2003); Global Strategy for The Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Diseases; Global Strategy for Asthma Management and Prevention; Hosseini M., Almasi-Hashiani A., Sepidarkish M., Maroufizadeh S., Global prevalence of asthma-COPD overlap (ACO) in the general population: a systematic review and meta-analysis, Respir Res, 20, (2019); Ho T., Cusack R.P., Chaudhary N., Satia I., Kurmi O.P., Under- and over-diagnosis of COPD: a global perspective, Breathe (Sheff), 15, pp. 24-35, (2019); Aaron S.D., Boulet L.P., Reddel H.K., Gershon A.S., Underdiagnosis and overdiagnosis of asthma, Am J Respir Crit Care Med, 198, pp. 1012-1020, (2018); Heffler E., Madeira L.N.G., Ferrando M., Puggioni F., Racca F., Malvezzi L., Et al., Inhaled corticosteroids safety and adverse effects in patients with asthma, J Allergy Clin Immunol Pract, 6, pp. 776-781, (2018); Heffler E., Crimi C., Mancuso S., Campisi R., Puggioni F., Brussino L., Et al., Misdiagnosis of asthma and COPD and underuse of spirometry in primary care unselected patients, Respir Med, 142, pp. 48-52, (2018); Broder M.S., Raimundo K., Ngai K.M., Chang E., Griffin N.M., Heaney L.G., Cost and health care utilization in patients with asthma and high oral corticosteroid use, Ann Allergy Asthma Immunol, 118, pp. 638-639, (2017); Gershon A.S., Campitelli M.A., Croxford R., Stanbrook M.B., To T., Upshur R., Et al., Combination long-acting beta-agonists and inhaled corticosteroids compared with long-acting beta-agonists alone in older adults with chronic obstructive pulmonary disease, JAMA, 312, pp. 1114-1121, (2014); Kendzerska T., Aaron S.D., To T., Licskai C., Stanbrook M., Vozoris N.T., Et al., Effectiveness and safety of inhaled corticosteroids in older individuals with chronic obstructive pulmonary disease and/or asthma. A population study, Ann Am Thorac Soc, 16, pp. 1252-1262, (2019); Kaplan A., Cao H., FitzGerald J.M., Iannotti N., Yang E., Kocks J.W.H., Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol Pract, 9, pp. 2255-2261, (2021); Topalovic M., Das N., Burgel P.R., Daenen M., Derom E., Haenebalcke C., Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur Respir J, 53, (2019); Badnjevic A., Gurbeta L., Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Sci Rep, 8, (2018); Abdel-Aal A., Lisspers K., Williams S., Adab P., Adams R., Agarwal D., Et al., Prioritising primary care respiratory research needs: results from the 2020 International Primary Care Respiratory Group (IPCRG) global e-Delphi exercise, NPJ Prim Care Respir Med, 32, (2022); Kaplan A., Cao H., Fitzgerald J.M., Yang E., Iannotti N., Kocks J.W.H., Et al., Asthma/COPD Differentiation Classification (AC/DC): machine learning to aid physicians in diagnosing asthma, COPD and asthma-COPD overlap (ACO), Am J Resp Crit Care Med, (2020); van de Hei S.J., Flokstra-de Blok B.M.J., Baretta H.J., Doornewaard N.E., van der Molen T., Patberg K.W., Et al., Quality of spirometry and related diagnosis in primary care with a focus on clinical use, NPJ Prim Care Respir Med, 30, (2020); Head S.J., Kaul S., Bogers A.J., Kappetein A.P., Non-inferiority study design: lessons to be learned from cardiovascular trials, Eur Heart J, 33, pp. 1318-1324, (2012); Meaden C., Joshi M., Hollis S., Higham A., Lynch D., A randomized controlled trial comparing the accuracy of general diagnostic upper gastrointestinal endoscopy performed by nurse or medical endoscopists, Endoscopy, 38, pp. 553-560, (2006); A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; RStan: the R interface to Stan. R package version 2.19.2; Honaker J., King G., M. B, Amelia II: a program for missing data, J Stat Softw, 45, pp. 1-47, (2011); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Inform J, 25, pp. 811-827, (2019); Suissa S., Ernst P., Observational studies of inhaled corticosteroid effectiveness in COPD: lessons learned, Chest, 154, pp. 257-265, (2018); Leung J.M., Sin D.D., Biomarkers in airway diseases, Can Respir J, 20, pp. 180-182, (2013); Himes B.E., Dai Y., Kohane I.S., Weiss S.T., Ramoni M.F., Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records, J Am Med Inform Assoc, 16, pp. 371-379, (2009); Electronic Health Records (HER) Data; Kaplan A., Cao H., Fitzgerald J.M., Yang E., Iannotti N., Kocks J.W.H., Et al., Asthma/COPD Differentiation Classification (AC/DC): machine learning to aid physicians in diagnosing asthma, COPD and asthma-COPD overlap (ACO), Am J Resp Crit Care Med, (2020); van de Hei S.J., Flokstra-de Blok BMJ, Baretta HJ, Doornewaard NE, van der Molen T, Patberg KW, et al. Quality of spirometry and related diagnosis in primary care with a focus on clinical use, NPJ Prim Care Respir Med, 30, (2020); Honaker J., King G., Amelia M.B., A program for missing data, J Stat Softw, 45, pp. 1-47, (2011)","P. Mastoridis; Novartis Pharmaceuticals Corporation, East Hanover, 1 Health Plz, 07936, United States; email: paul.mastoridis@novartis.com","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36716998","English","J. Allergy Clin. Immunol. Pract.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147880735"
"Ferri F.; Bouzerar R.; Auquier M.; Vial J.; Renard C.","Ferri, Fabrice (57670071800); Bouzerar, Roger (35271196500); Auquier, Marianne (6507439594); Vial, Jérémie (57209773904); Renard, Cédric (14631123900)","57670071800; 35271196500; 6507439594; 57209773904; 14631123900","Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction","2022","European Journal of Radiology","152","","110338","","","","7","10.1016/j.ejrad.2022.110338","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129624162&doi=10.1016%2fj.ejrad.2022.110338&partnerID=40&md5=e95815635f474b2e98891cfb979f79d5","Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France; Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France","Ferri F., Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France; Bouzerar R., Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France; Auquier M., Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France; Vial J., Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France; Renard C., Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France","Purpose: Quantitative analysis of emphysema volume is affected by the radiation dose and the CT reconstruction technique. We aim to evaluate the influence of a commercially available deep learning image reconstruction algorithm (DLIR) on the quantification of pulmonary emphysema in low-dose chest CT. Methods: We performed a retrospective study of low dose chest CT scans in 54 patients with chronic obstructive pulmonary disease (COPD). Raw data were reconstructed using FBP, iterative reconstruction (ASIR-V 70%) and deep learning based algorithms at high, medium and low-strength (DLIR -H, -M, -L). Filtered FBP images served as reference. Pulmonary emphysema volume (proportion of voxels below −950 UH) was measured on each reconstruction dataset and visually assessed by a chest radiologist. Quantitative image quality was assessed by placing 3 regions of interest in the trachea, in air and in a paraspinal muscle. Signal to noise ratio was also measured. Results: The mean CDTIvol was 2.38 ± 0.68 mGy. Significant differences in emphysema volumes between the filtered FBP reference and ASIR-V, DLIR-H, DLIR-M or DLIR-L were observed, (p < 10−3) for all. A strong correlation between filtered FBP volumes and DLIR-H was reported (r = 0.999, p < 10−4), a 10% overestimation with DLIR-H being observed. Noise was significantly reduced in DLIR-H volumes compared to the other reconstruction methods. Signal to noise ratio was improved when using DLIR-H (p < 10−6). Conclusion: There are significant differences regarding emphysema volumes between FBP, iterative reconstruction or deep learning-based DLIR algorithm. DLIR-H shows the closest correlation to filtered FBP while increasing SNR. © 2022 Elsevier B.V.","Chronic obstructive pulmonary disease; Computed tomography; Deep learning image reconstruction; Pulmonary emphysema","Algorithms; Deep Learning; Emphysema; Humans; Image Processing, Computer-Assisted; Pulmonary Emphysema; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted; Retrospective Studies; Tomography, X-Ray Computed; adult; aged; Article; body mass; chronic obstructive lung disease; computer assisted tomography; controlled study; deep learning; female; human; image analysis; image quality; image reconstruction; learning algorithm; lung emphysema; lung infection; lung nodule; lung volume; male; paraspinal muscle; quality control; quantitative analysis; reconstruction algorithm; retrospective study; signal noise ratio; algorithm; computer assisted diagnosis; diagnostic imaging; emphysema; image processing; procedures; radiation dose; x-ray computed tomography","","","Revolution, GE Healthcare","GE Healthcare","","","Viegi G., Pistelli F., Sherrill D.L., Maio S., Baldacci S., Carrozzi L., Definition, epidemiology and natural history of COPD, Eur. Respir. J., 30, 5, pp. 993-1013, (2007); Celli B.R., MacNee W., Agusti A., Anzueto A., Berg B., Buist A.S., Calverley P.M.A., Chavannes N., Dillard T., Fahy B., Fein A., Heffner J., Lareau S., Meek P., Martinez F., McNicholas W., Muris J., Austegard E., Pauwels R., Rennard S., Rossi A., Siafakas N., Tiep B., Vestbo J., Wouters E., ZuWallack R., Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper, Eur. Respir. J., 23, 6, pp. 932-946, (2004); Vestbo J., Hurd S.S., Agusti A.G., Jones P.W., Vogelmeier C., Anzueto A., Barnes P.J., Fabbri L.M., Martinez F.J., Nishimura M., Stockley R.A., Sin D.D., Rodriguez-Roisin R., Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: GOLD Executive Summary, Am. J. Respir. Crit. Care Med., 187, 4, pp. 347-365, (2013); Takahashi M., Fukuoka J., Nitta N., Takazakura R., Nagatani Y., Murakami Y., Otani H., Murata K., Imaging of pulmonary emphysema: a pictorial review, Int. J. Chron. Obstruct. Pulmon. Dis., 3, pp. 193-204, (2008); Xie X., de Jong P.A., Oudkerk M., Wang Y., ten Hacken N.H.T., Miao J., Zhang G., de Bock G.H., Vliegenthart R., Morphological measurements in computed tomography correlate with airflow obstruction in chronic obstructive pulmonary disease: systematic review and meta-analysis, Eur. Radiol., 22, 10, pp. 2085-2093, (2012); Brenner D.J., Hall E.J., Computed Tomography — An Increasing Source of Radiation Exposure, N. Engl. J. Med., 357, 22, pp. 2277-2284, (2007); Willemink M.J., Noel P.B., The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence, Eur. Radiol., 29, 5, pp. 2185-2195, (2019); Martin S.P., Gariani J., Hachulla A.-L., Botsikas D., Adler D., Karenovics W., Becker C.D., Montet X., Impact of iterative reconstructions on objective and subjective emphysema assessment with computed tomography: a prospective study, Eur. Radiol., 27, 7, pp. 2950-2956, (2017); den Harder A.M., de Boer E., Lagerweij S.J., Boomsma M.F., Schilham A.M.R., Willemink M.J., Milles J., Leiner T., Budde R.P.J., de Jong P.A., Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique, Eur. Radiol. Exp., 2, 1, (2018); Greffier J., Hamard A., Pereira F., Barrau C., Pasquier H., Beregi J.P., Frandon J., Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study, Eur. Radiol., 30, 7, pp. 3951-3959, (2020); Lynch D.A., Austin J.H.M., Hogg J.C., Grenier P.A., Kauczor H.-U., Bankier A.A., Barr R.G., Colby T.V., Galvin J.R., Gevenois P.A., Coxson H.O., Hoffman E.A., Newell J.D., Pistolesi M., Silverman E.K., Crapo J.D., CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society, Radiology, 277, 1, pp. 192-205, (2015); Gevenois P.A., de Maertelaer V., De Vuyst P., Zanen J., Yernault J.C., Comparison of computed density and macroscopic morphometry in pulmonary emphysema., Am. J. Respir. Crit. Care Med., 152, 2, pp. 653-657, (1995); Wang Z., Gu S., Leader J.K., Kundu S., Tedrow J.R., Sciurba F.C., Gur D., Siegfried J.M., Pu J., Optimal threshold in CT quantification of emphysema, Eur. Radiol., 23, 4, pp. 975-984, (2013); Schilham A.M.R., van Ginneken B., Gietema H., Prokop M., Local noise weighted filtering for emphysema scoring of low-dose CT images, IEEE Trans. Med. Imaging, 25, 4, pp. 451-463, (2006); Thrall J.H., Radiation Exposure in CT Scanning and Risk: Where Are We?, Radiology, 264, 2, pp. 325-328, (2012); Wang R., Sui X., Schoepf U.J., Song W., Xue H., Jin Z., Schmidt B., Flohr T.G., Canstein C., Spearman J.V., Chen J., Meinel F.G., Ultralow-Radiation-Dose Chest CT: Accuracy for Lung Densitometry and Emphysema Detection, Am. J. Roentgenol., 204, 4, pp. 743-749, (2015); Hata A., Yanagawa M., Kikuchi N., Honda O., Tomiyama N., Pulmonary Emphysema Quantification on Ultra–Low-Dose Computed Tomography Using Model-Based Iterative Reconstruction With or Without Lung Setting:, J. Comput. Assist. Tomogr., 42, 5, pp. 760-766, (2018); Nishio M., Matsumoto S., Seki S., Koyama H., Ohno Y., Fujisawa Y., Sugihara N., Yoshikawa T., Sugimura K., Emphysema quantification on low-dose CT using percentage of low-attenuation volume and size distribution of low-attenuation lung regions: Effects of adaptive iterative dose reduction using 3D processing, Eur. J. Radiol., 83, 12, pp. 2268-2276, (2014); Gallardo-Estrella L., Lynch D.A., Prokop M., Stinson D., Zach J., Judy P.F., van Ginneken B., van Rikxoort E.M., Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification, Eur. Radiol., 26, 2, pp. 478-486, (2016); Wisselink H.J., Pelgrim G.J., Rook M., van den Berge M., Slump K., Nagaraj Y., van Ooijen P., Oudkerk M., Vliegenthart R., Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study, Br. J. Radiol., 93, 1105, (2020); Cao X., Jin C., Tan T., Guo Y., Optimal threshold in low-dose CT quantification of emphysema, Eur. J. Radiol., 129, (2020); Wisselink H.J., Pelgrim G.J., Rook M., Imkamp K., van Ooijen P.M.A., van den Berge M., de Bock G.H., Vliegenthart R., Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT, Eur. J. Radiol., 138, (2021); Messerli M., Ottilinger T., Warschkow R., Leschka S., Alkadhi H., Wildermuth S., Bauer R.W., Emphysema quantification and lung volumetry in chest X-ray equivalent ultralow dose CT – Intra-individual comparison with standard dose CT, Eur. J. Radiol., 91, pp. 1-9, (2017); Jin H., Heo C., Kim J.H., Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT, Phys. Med. Biol., 64, (2019)","C. Renard; Amiens University Hospital, Department of Radiology, F-80054 Amiens Cedex 01, 1 Rond-Point du Professeur Christian Cabrol, France; email: renard.cedric@chu-amiens.fr","","Elsevier Ireland Ltd","","","","","","0720048X","","EJRAD","35533559","English","Eur. J. Radiol.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85129624162"
"Song Y.; Zhang D.; Wang Q.; Liu Y.; Chen K.; Sun J.; Shi L.; Li B.; Yang X.; Mi W.; Cao J.","Song, Yuxiang (57221473358); Zhang, Di (58838906300); Wang, Qian (58768505500); Liu, Yuqing (58837647100); Chen, Kunsha (57823103300); Sun, Jingjia (58837647200); Shi, Likai (57216564079); Li, Baowei (57412605400); Yang, Xiaodong (57198984204); Mi, Weidong (35852702500); Cao, Jiangbei (7403354428)","57221473358; 58838906300; 58768505500; 58837647100; 57823103300; 58837647200; 57216564079; 57412605400; 57198984204; 35852702500; 7403354428","Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations","2024","Translational Psychiatry","14","1","57","","","","6","10.1038/s41398-024-02762-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182990963&doi=10.1038%2fs41398-024-02762-w&partnerID=40&md5=b69309dba4b50cb40b3d03e60719527c","Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; National Clinical Research Center for Geriatric Diseases, People’s Liberation Army General Hospital, Beijing, 100853, China","Song Y., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Zhang D., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Wang Q., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Liu Y., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Chen K., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Sun J., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Shi L., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Li B., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; Yang X., Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Mi W., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China, National Clinical Research Center for Geriatric Diseases, People’s Liberation Army General Hospital, Beijing, 100853, China; Cao J., Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China, National Clinical Research Center for Geriatric Diseases, People’s Liberation Army General Hospital, Beijing, 100853, China","Postoperative delirium (POD) is a common and severe complication in elderly patients with hip fractures. Identifying high-risk patients with POD can help improve the outcome of patients with hip fractures. We conducted a retrospective study on elderly patients (≥65 years of age) who underwent orthopedic surgery with hip fracture between January 2014 and August 2019. Conventional logistic regression and five machine-learning algorithms were used to construct prediction models of POD. A nomogram for POD prediction was built with the logistic regression method. The area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and precision were calculated to evaluate different models. Feature importance of individuals was interpreted using Shapley Additive Explanations (SHAP). About 797 patients were enrolled in the study, with the incidence of POD at 9.28% (74/797). The age, renal insufficiency, chronic obstructive pulmonary disease (COPD), use of antipsychotics, lactate dehydrogenase (LDH), and C-reactive protein are used to build a nomogram for POD with an AUC of 0.71. The AUCs of five machine-learning models are 0.81 (Random Forest), 0.80 (GBM), 0.68 (AdaBoost), 0.77 (XGBoost), and 0.70 (SVM). The sensitivities of the six models range from 68.8% (logistic regression and SVM) to 91.9% (Random Forest). The precisions of the six machine-learning models range from 18.3% (logistic regression) to 67.8% (SVM). Six prediction models of POD in patients with hip fractures were constructed using logistic regression and five machine-learning algorithms. The application of machine-learning algorithms could provide convenient POD risk stratification to benefit elderly hip fracture patients. © 2024, The Author(s).","","Aged; Algorithms; Emergence Delirium; Hip Fractures; Humans; Machine Learning; Retrospective Studies; C reactive protein; lactate dehydrogenase; neuroleptic agent; aged; area under the curve; Article; chronic obstructive lung disease; controlled study; diagnostic test accuracy study; female; hip fracture; human; kidney failure; learning algorithm; machine learning; major clinical study; male; nomogram; orthopedic surgery; postoperative delirium; receiver operating characteristic; retrospective study; algorithm; emergence agitation; hip fracture; machine learning","","C reactive protein, 9007-41-4; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ","","","Natural Science Foundation of Beijing Municipality, (L222100); National Key Research and Development Program of China, NKRDPC, (2018YFC2001901)","This work was supported by the National Key Research and Development Program of China (No. 2018YFC2001901) and the Beijing Natural Science Foundation (No. L222100). ","Bhandari M., Swiontkowski M., Management of acute hip fracture, N Engl J Med, 377, pp. 2053-2062, (2017); Marcantonio E.R., Delirium in hospitalized older adults, N Engl J Med, 377, pp. 1456-1466, (2017); Oh E.S., Fong T.G., Hshieh T.T., Inouye S.K., Delirium in older persons: advances in diagnosis and treatment, JAMA, 318, pp. 1161-1174, (2017); Gleason L.J., Schmitt E.M., Kosar C.M., Tabloski P., Saczynski J.S., Robinson T., Et al., Effect of delirium and other major complications on outcomes after elective surgery in older adults, JAMA Surg, 150, pp. 1134-1140, (2015); Leslie D.L., Marcantonio E.R., Zhang Y., Leo-Summers L., Inouye S.K., One-year health care costs associated with delirium in the elderly population, Arch Intern Med, 168, pp. 27-32, (2008); Milisen K., Steeman E., Foreman M.D., Early detection and prevention of delirium in older patients with cancer, Eur J Cancer Care, 13, pp. 494-500, (2004); Xue B., Li D., Lu C., King C.R., Wildes T., Avidan M.S., Et al., Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications, JAMA Netw Open, 4, (2021); 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Rizk P., Morris W., Oladeji P., Huo M., Review of postoperative delirium in geriatric patients undergoing hip surgery, Geriatr Orthop Surg Rehabil, 7, pp. 100-105, (2016); Zhang X., Tong D.K., Ji F., Duan X.Z., Liu P.Z., Qin S., Et al., Predictive nomogram for postoperative delirium in elderly patients with a hip fracture, Injury, 50, pp. 392-397, (2019); Shen J., An Y., Jiang B., Zhang P., Derivation and validation of a prediction score for postoperative delirium in geriatric patients undergoing hip fracture surgery or hip arthroplasty, Front Surg, 9, (2022); Inouye S.K., Westendorp R.G., Saczynski J.S., Delirium in elderly people, Lancet (Lond, Engl), 383, pp. 911-922, (2014); la Cour K.N., Andersen-Ranberg N.C., Weihe S., Poulsen L.M., Mortensen C.B., Kjer C.K.W., Et al., Distribution of delirium motor subtypes in the intensive care unit: a systematic scoping review, Crit Care (Lond, Engl), 26, (2022)","W. Mi; Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; email: wwdd1962@aliyun.com; J. Cao; Department of Anesthesiology, The First Medical Center of PLA General Hospital, Beijing, China; email: caojiangbei@301hospital.com.cn","","Springer Nature","","","","","","21583188","","","38267405","English","Transl. Psychiatry","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85182990963"
"Chen H.-Y.; Wang H.-M.; Lin C.-H.; Yang R.; Lee C.-C.","Chen, Huan-Yu (57204211761); Wang, Hui-Min (58663280500); Lin, Ching-Heng (27169825600); Yang, Rob (57220775674); Lee, Chi-Chun (7410153704)","57204211761; 58663280500; 27169825600; 57220775674; 7410153704","Lung Cancer Prediction Using Electronic Claims Records: A Transformer-Based Approach","2023","IEEE Journal of Biomedical and Health Informatics","27","12","","6062","6073","11","7","10.1109/JBHI.2023.3324191","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174812653&doi=10.1109%2fJBHI.2023.3324191&partnerID=40&md5=7071bb43788fe9ebc2b0bd67dfbaa8df","Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan; Lung Cancer Initiative, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, 08901, NJ, United States; Department of Medical Research, Taichung Veteran General Hospital, Taichung, 40705, Taiwan","Chen H.-Y., Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan; Wang H.-M., Lung Cancer Initiative, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, 08901, NJ, United States; Lin C.-H., Department of Medical Research, Taichung Veteran General Hospital, Taichung, 40705, Taiwan; Yang R., Lung Cancer Initiative, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, 08901, NJ, United States; Lee C.-C., Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan","Electronic claims records (ECRs) are large scale and longitudinal collections of individual's medical service seeking actions. Compared to in-hospital medical records (EMRs), ECRs are more standardized and cross-sites. Recently, there has been studies showing promising results on modeling claims data for a wide range of medical applications. However, few of them address the exclusion criteria on cohort selection to extract new incidence without prior signs and also often lack of emphasis on predicting cancer in early stages. In this work, we aim to design a lung cancer prediction framework using ECRs with rigorous exclusion design using state-of-the-art sequence-based transformer. Furthermore, this work presents one of the first results by applying disease prediction model to the entire population in Taiwan. The result shows over 2.1 predictive power, 5 average positive predictive value (PPV), and 0.668 area under curve (AUC) in all-stage lung cancer and around 2.0 predictive power, 1 average PPV and 0.645 AUC in early-stage in our dataset. Sub-cohort analysis could funnel high precision selective group into prioritized clinical examination. Onset analysis validates the effect of our exclusion criteria. This work presents comprehensive analyses on lung cancer prediction, and the proposed approach can serve as a state-of-the-art disease risk prediction framework on claims data.  © 2023 IEEE.","deep learning; Electronic claims records; lung cancer; transformer","Cohort Studies; Electronic Health Records; Humans; Incidence; Lung Neoplasms; Predictive Value of Tests; Biological organs; Deep learning; Diagnosis; Diseases; E-learning; Medical applications; Medical imaging; analgesic agent; antihistaminic agent; antiinflammatory agent; carcinoembryonic antigen; Cancer prediction; Deep learning; Electronic claim record; Lung Cancer; Medical diagnostic imaging; Predictive models; State of the art; Transformer; adult; aged; AL amyloidosis; area under the curve; Article; asthma; bronchiectasis; cancer chemotherapy; cancer staging; chronic bronchitis; chronic obstructive lung disease; cohort analysis; computer assisted tomography; computer model; controlled study; convolutional neural network; coughing; cross validation; deep learning; deep neural network; demographics; diagnostic test accuracy study; electronic claim record; electronic health record; evaluation study; female; frailty; genetic disorder; hemoptysis; hospital readmission; human; Human immunodeficiency virus infection; ICD-9-CM; image analysis; long short term memory network; lung cancer; major clinical study; male; medical service; middle aged; prediction; predictive value; thorax radiography; trachea cancer; tuberculosis; ViT Transformer; Xception model; electronic health record; incidence; lung tumor; Forecasting","","","","","Johnson and Johnson, J&J, (NTHU 109A0198J6); Johnson and Johnson, J&J","This work was supported by Johnson & Johnson under Grant NTHU 109A0198J6.","Xiao C., Choi E., Sun J., Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review, J. Amer. Med. Inform. Assoc., 25, 10, pp. 1419-1428, (2018); Tayefi M., Et al., Challenges and opportunities beyond structured data in analysis of electronic health records, Wiley Interdiscipl. Rev.: Comput. Statist., 13, 6, (2021); Kam H.J., Kim H.Y., Learning representations for the early detection of sepsis with deep neural networks, Comput. Biol. Med., 89, pp. 248-255, (2017); Wang S., Pathak J., Zhang Y., Using electronic health records and machine learning to predict postpartum depression, Medinfo 2019: Health and Wellbeing E-Networks for All., pp. 888-892, (2019); Mullenbach J., Wiegreffe S., Duke J., Sun J., Eisenstein J., Explainable prediction of medical codes from clinical text, Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics: Human Lang. 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Open, 2, 3, (2019)","C.-C. Lee; Department of Electrical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan; email: cclee@ee.nthu.edu.tw; R. Yang; Lung Cancer Initiative, Johnson & Johnson Enterprise Innovation, Inc., New Brunswick, 08901, United States; email: yang.robert@gmail.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","37824311","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85174812653"
"Li Z.; Xu D.; Jing J.; Wang J.; Jiang M.; Li F.","Li, Zheng (57188931850); Xu, Dan (56956070300); Jing, Jing (58828713300); Wang, Jing (57200021087); Jiang, Min (56949613400); Li, Fengsen (37028365500)","57188931850; 56956070300; 58828713300; 57200021087; 56949613400; 37028365500","Identification and Validation of Prognostic Markers for Lung Squamous Cell Carcinoma Associated with Chronic Obstructive Pulmonary Disease","2022","Journal of Oncology","2022","","4254195","","","","6","10.1155/2022/4254195","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137247762&doi=10.1155%2f2022%2f4254195&partnerID=40&md5=26b9daf5db0bef6a8fb3d058419ca941","The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China; National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China; Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China","Li Z., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China; Xu D., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China; Jing J., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China; Wang J., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China; Jiang M., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China; Li F., The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China, National Clinical Research Base of Traditional Chinese Medicine, Urumqi, China, Key Laboratory of Xinjiang Uygur Autonomous Region(respiratory Disease), Urumqi, China","Background. Globally, the incidence and associated mortality of chronic obstructive pulmonary disease (COPD) and lung carcinoma are showing a worsening trend. There is increasing evidence that COPD is an independent risk factor for the occurrence and progression of lung carcinoma. This study aimed to identify and validate the gene signatures associated with COPD, which may serve as potential new biomarkers for the prediction of prognosis in patients with lung carcinoma. Methods. A total of 111 COPD patient samples and 40 control samples were obtained from the GSE76925 cohort, and a total of 4933 genes were included in the study. The weighted gene coexpression network analysis (WGCNA) was performed to identify the modular genes that were significantly associated with COPD. The KEGG pathway and GO functional enrichment analyses were also performed. The RNAseq and clinicopathological data of 490 lung squamous cell carcinoma patients were obtained from the TCGA database. Further, univariate Cox regression and Lasso analyses were performed to screen for marker genes and construct a survival analysis model. Finally, the Human Protein Atlas (HPA) database was used to assess the gene expression in normal and tumor tissues of the lungs. Results. A 6-gene signature (DVL1, MRPL4, NRTN, NSUN3, RPH3A, and SNX32) was identified based on the Cox proportional risk analysis to construct the prognostic RiskScore survival model associated with COPD. Kaplan-Meier survival analysis indicated that the model could significantly differentiate between the prognoses of patients with lung carcinoma, wherein higher RiskScore samples were associated with a worse prognosis. Additionally, the model had a good predictive performance and reliability, as indicated by a high AUC, and these were validated in both internal and external sets. The 6-gene signature had a good predictive ability across clinical signs and could be considered an independent factor of prognostic risk. Finally, the protein expressions of the six genes were analyzed based on the HPA database. The expressions of DVL1, MRPL4, and NSUN3 were relatively higher, while that of RPH3A was relatively lower in the tumor tissues. The expression of SNX32 was high in both the tumor and paracarcinoma tissues. Results of the analyses using TCGA and GSE31446 databases were consistent with the expressions reported in the HPA database. Conclusion. Novel COPD-associated gene markers for lung carcinoma were identified and validated in this study. The genes may be considered potential biomarkers to evaluate the prognostic risk of patients with lung carcinoma. Furthermore, some of these genes may have implications as new therapeutic targets and can be used to guide clinical applications.  © 2022 Zheng Li et al.","","dishevelled 1; adult; Article; cancer prognosis; cancer risk; cancer staging; cancer survival; cellular distribution; chronic obstructive lung disease; cohort analysis; controlled study; core gene; disease association; DNA recombination; fatty acid metabolism; female; gene expression; human; human tissue; Kaplan Meier method; KEGG; machine learning; major clinical study; male; marker gene; middle aged; mrpl4 gene; NF kB signaling; nomogram; nrtn gene; nsun3 gene; phylogenetic tree; protein binding; protein expression; receiver operating characteristic; RNA sequencing; rph3a gene; smoking habit; snx32 gene; squamous cell lung carcinoma; survival analysis; tumor gene; weighted gene co expression network analysis","","","","","","","Labaki W.W., Rosenberg S.R., Chronic obstructive pulmonary disease, Annals of Internal Medicine, 173, 3, (2020); 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Li; The Traditional Chinese Medicine Hospital Affiliated with Xinjiang Medical University, Urumqi, China; email: fengsen602@163.com","","Hindawi Limited","","","","","","16878450","","","","English","J. Oncol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85137247762"
"Astley J.R.; Biancardi A.M.; Marshall H.; Hughes P.J.C.; Collier G.J.; Hatton M.Q.; Wild J.M.; Tahir B.A.","Astley, Joshua R. (57220212293); Biancardi, Alberto M (6701404454); Marshall, Helen (37031376800); Hughes, Paul J. C. (57192382410); Collier, Guilhem J. (56394037400); Hatton, Matthew Q. (8595450200); Wild, Jim M. (7202396208); Tahir, Bilal A. (57188679679)","57220212293; 6701404454; 37031376800; 57192382410; 56394037400; 8595450200; 7202396208; 57188679679","A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI","2023","Medical Physics","50","9","","5657","5670","13","7","10.1002/mp.16369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151923984&doi=10.1002%2fmp.16369&partnerID=40&md5=82fb577bcfbb03bc191445018bd6e0ac","Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom; POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom","Astley J.R., Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom, POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Biancardi A.M., POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Marshall H., POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Hughes P.J.C., POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Collier G.J., POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom; Hatton M.Q., Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom; Wild J.M., POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom; Tahir B.A., Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom, POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom, Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom","Background: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. Purpose: To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. Methods: In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). Results: We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. Conclusion: The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide. © 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.","CT ventilation; Deep learning; Functional lung imaging; Hyperpolarized gas MRI; Image synthesis","Deep Learning; Humans; Lung; Magnetic Resonance Imaging; Pulmonary Ventilation; Tomography, X-Ray Computed; Biological organs; Computerized tomography; Convolutional neural networks; Deep learning; Gases; Learning systems; Mean square error; biological marker; helium; CT ventilation; Deep learning; Functional lung imaging; Hybrid framework; Hyper-polarized gas; Hyperpolarized gas MRI; Images synthesis; Lung imaging; Spearman correlation; Ventilation modeling; Article; asthma; clinical article; clinical evaluation; computer assisted tomography; controlled study; convolutional neural network; cross validation; cystic fibrosis; deep learning; evaluation study; exhalation; gas nuclear magnetic resonance imaging; human; hyperpolarization; image registration; inhalation; intermethod comparison; lung cancer; lung disease; lung ventilation; multiinflation computed tomography; nuclear magnetic resonance imaging; qualitative analysis; quantitative analysis; x-ray computed tomography; diagnostic imaging; lung; nuclear magnetic resonance imaging; procedures; x-ray computed tomography; Magnetic resonance imaging","","helium, 7440-59-7","HDx, GE Healthcare, United States; Lightspeed VCT 64, General Electric, United States; Lightspeed, GE Healthcare, United States; Sensation 16, Siemens, Germany","GE Healthcare, United States; GE Healthcare, United States; General Electric, United States; Siemens, Germany","UK Research and Innovation, UKRI; Medical Research Council, MRC, (MR/M008894/1); National Institute for Health and Care Research, NIHR, (NIHR‐RP‐R3‐12‐027); Engineering and Physical Sciences Research Council, EPSRC, (EP/P020275/1); Yorkshire Cancer Research, YCR, (S406BT)","Funding text 1: This work was supported by Yorkshire Cancer Research (S406BT), National Institute of Health Research (NIHR-RP-R3-12-027), and the Medical Research Council (MR/M008894/1). The authors acknowledge access to high-performance computing equipment available via an EPSRC Equipment Grant (EP/P020275/1) and support provided via Research Software Engineering Sheffield.; Funding text 2: This work was supported by Yorkshire Cancer Research (S406BT), National Institute of Health Research (NIHR\u2010RP\u2010R3\u201012\u2010027), and the Medical Research Council (MR/M008894/1). The authors acknowledge access to high\u2010performance computing equipment available via an EPSRC Equipment Grant (EP/P020275/1) and support provided via Research Software Engineering Sheffield. 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Levin D.L., Schiebler M.L., Hopkins S.R., Physiology for the pulmonary functional imager, Eur J Radiol, 86, pp. 308-312, (2017); Castillo E., Castillo R., Vinogradskiy Y., Et al., Robust CT ventilation from the integral formulation of the Jacobian, Med Phys, 46, 5, pp. 2115-2125, (2019); Westcott A., Capaldi D.P.I., McCormack D.G., Ward A.D., Fenster A., Parraga G., Chronic obstructive pulmonary disease: Thoracic CT texture analysis and machine learning to predict pulmonary ventilation, Radiology, 293, 3, pp. 676-684, (2019); Kida S., Bal M., Kabus S., Et al., CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans, Radiother Oncol, 118, 3, pp. 521-527, (2016)","B.A. Tahir; Department of Oncology and Metabolism, The University of Sheffield, Sheffield, S10 2SF, United Kingdom; email: b.tahir@sheffield.ac.uk","","John Wiley and Sons Ltd","","","","","","00942405","","MPHYA","36932692","English","Med. Phys.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85151923984"
"Feng Z.; Yin Y.; Liu B.; Wang L.; Chen M.; Zhu Y.; Zhang H.; Sun D.; Qin J.","Feng, Zhenxing (55561057300); Yin, Yan (57224947947); Liu, Bin (58373630800); Wang, Lei (57571671000); Chen, Miaomiao (57571414000); Zhu, Yue (57571542200); Zhang, Hong (58445693400); Sun, Daqiang (48662963000); Qin, Jianwen (55337565700)","55561057300; 57224947947; 58373630800; 57571671000; 57571414000; 57571542200; 58445693400; 48662963000; 55337565700","ZNF143 Expression is Associated with COPD and Tumor Microenvironment in Non-Small Cell Lung Cancer","2022","International Journal of COPD","17","","","685","700","15","6","10.2147/COPD.S352392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127962388&doi=10.2147%2fCOPD.S352392&partnerID=40&md5=39a67c63e81f7a0db86adeac501b6bff","Department of Radiology, Tianjin Chest Hospital, Tianjin, 300222, China; Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, 300222, China","Feng Z., Department of Radiology, Tianjin Chest Hospital, Tianjin, 300222, China; Yin Y., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Liu B., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Wang L., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Chen M., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Zhu Y., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; Zhang H., Department of Radiology, Tianjin Chest Hospital, Tianjin, 300222, China; Sun D., Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, 300222, China; Qin J., Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China","Background: Chronic obstructive pulmonary disease (COPD) is an inflammatory-related disease highly associated with increased lung cancer risk. Studies have explored the tumor promoting roles for zinc finger protein 143 (ZNF143). However, the role of ZNF143 in COPD and tumor microenvironment of non-small cell lung cancer (NSCLC) has not been fully elucidated. Methods: COPD-related key genes were identified by differential gene expression evaluation, WGCNA and SVM-RFE analysis using mRNA expression data retrieved from public databases. ROC analysis was conducted to evaluate the diagnostic value of ZNF143. Correlation between ZNF143 and clinic-pathological features, associations with tumor-infiltrating immune cells (TICs) and the relationship with predictors of immunotherapy efficacy were explored. ZNF143 gene expression was validated by qRT-PCR using an independent cohort. Results: Bioinformatic and machine learning analysis showed that ZNF143 was a COPD-related gene. ZNF143 expression was significantly upregulated in COPD and is a potential diagnostic biomarker in COPD with AUC > 0.85. ZNF143 expression was significantly upregulated in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). ZNF143 expression levels were significantly higher in LUAD patients with COPD relative to the levels in patients only with LUAD. Upregulation of ZNF143 in patients with comorbidity of NSCLC and COPD was further confirmed by qRT-PCR analysis. High expression of ZNF143 was significantly correlated with advanced TNM stage in LUSC. High ZNF143 expression was associated with activated TICs in both LUAD and LUSC samples. Moreover, ZNF143 expression was significantly correlated with the levels of several known predictors of immunotherapy efficacy, including PD-L1, PD-L2, TMB and TIDE in NSCLC. Conclusion: ZNF143 is a novel COPD biomarker. High expression level of ZNF143 is associated with immune microenvironment and high risk of progression of COPD to NSCLC. © 2022 Feng et al.","bioinformatic; COPD; NSCLC; PD-L1; tumor microenvironment; ZNF143","Adenocarcinoma of Lung; Carcinoma, Non-Small-Cell Lung; Carcinoma, Squamous Cell; Humans; Lung Neoplasms; Prognosis; Pulmonary Disease, Chronic Obstructive; Trans-Activators; Tumor Microenvironment; immunological antineoplastic agent; messenger RNA; programmed death 1 ligand 1; programmed death 1 ligand 2; unclassified drug; zinc finger protein; zinc finger protein 143; transactivator protein; ZNF143 protein, human; advanced cancer; Article; bioinformatics; cancer immunotherapy; cancer staging; cell activation; chronic obstructive lung disease; clinical effectiveness; clinical feature; cohort analysis; comorbidity; controlled study; correlational study; diagnostic test accuracy study; diagnostic value; differential gene expression; disease association; disease marker; gene identification; genetic association; genetic screening; histopathology; human; human cell; human tissue; immunocompetent cell; lung adenocarcinoma; machine learning; major clinical study; non small cell lung cancer; prediction; protein expression; protein expression level; real time polymerase chain reaction; receiver operating characteristic; reference value; scoring system; sensitivity and specificity; squamous cell lung carcinoma; support vector machine; tumor infiltrating immune cell; tumor microenvironment; tumor mutational burden; upregulation; validation process; weighted gene co expression network analysis; ZNF143 gene; genetics; lung adenocarcinoma; lung tumor; non small cell lung cancer; pathology; prognosis; squamous cell carcinoma; tumor microenvironment","","Trans-Activators, ; ZNF143 protein, human, ","","","CAPTRA-Lung, (CAPTRALung2022009); Tianjin Science and Technology Program, (MS20015)","We would like to thank Hong Zhang for the financial support and Weishuai Liu for experimental technical support.","Siegel RL, Miller KD, Jemal A., Cancer statistics, 2020, CA Cancer J Clin, 70, 1, pp. 7-30, (2020); Faruki H, Mayhew GM, Serody JS, Hayes DN, Perou CM, Lai-Goldman M., Lung adenocarcinoma and squamous cell carcinoma gene expression subtypes demonstrate significant differences in tumor immune landscape, J Thorac Oncol, 12, 6, pp. 943-953, (2017); Malhotra J, Malvezzi M, Negri E, La Vecchia C, Boffetta P., Risk factors for lung cancer worldwide, Eur Respir J, 48, 3, pp. 889-902, (2016); Liu C, Zhou X, Zeng H, Wu D, Liu L., HILPDA is a prognostic biomarker and correlates with macrophage infiltration in pan-cancer, Front Oncol, 11, (2021); Treekitkarnmongkol W, Hassane M, Sinjab A, Et al., Augmented Lipocalin-2 is associated with chronic obstructive pulmonary disease and counteracts lung adenocarcinoma development, Am J Resp Crit Care, 203, 1, pp. 90-101, (2021); Liu C, Chen Z, Chen K, Et al., Lipopolysaccharide-mediated chronic inflammation promotes tobacco carcinogen-induced lung cancer and determines the efficacy of immunotherapy, Cancer Res, 2020, (2020); Eapen MS, Hansbro PM, Larsson Callerfelt A, Et al., Chronic obstructive pulmonary disease and lung cancer: underlying pathophysiology and new therapeutic modalities, Drugs, 78, 16, pp. 1717-1740, (2018); Qin J, Li G, Zhou J., Characteristics of elderly patients with COPD and newly diagnosed lung cancer, and factors associated with treatment decision, Int J Chron Obstruct Pulmon Dis, 11, pp. 1515-1520, (2016); Brahmer J, Reckamp KL, Baas P, Et al., Nivolumab versus docetaxel in advanced squamous-cell non–small-cell lung cancer, New Engl J Med, 373, 2, pp. 123-135, (2015); Borghaei H, Paz-Ares L, Horn L, Et al., Nivolumab versus docetaxel in advanced nonsquamous non–small-cell lung cancer, New Engl J Med, 373, 17, pp. 1627-1639, (2015); Lievense LA, Sterman DH, Cornelissen R, Aerts JG., Checkpoint blockade in lung cancer and mesothelioma, Am J Resp Crit Care, 196, 3, pp. 274-282, (2017); Biton J, Ouakrim H, Dechartres A, Et al., Impaired tumor-infiltrating T cells in patients with chronic obstructive pulmonary disease impact lung cancer response to PD-1 blockade, Am J Resp Crit Care, 198, 7, pp. 928-940, (2018); Mark NM, Kargl J, Busch SE, Et al., Chronic obstructive pulmonary disease alters immune cell composition and immune checkpoint inhibitor efficacy in non–small cell lung cancer, Am J Resp Crit Care, 197, 3, pp. 325-336, (2018); Zhang L, Chen J, Yang H, Et al., Multiple microarray analyses identify key genes associated with the development of non-small cell lung cancer from chronic obstructive pulmonary disease, J Cancer, 12, 4, pp. 996-1010, (2021); Miao T, Du L, Xiao W, Mao B, Wang Y, Fu J., Identification of survival-associated gene signature in lung cancer coexisting with COPD, Front Oncol, 11, (2021); Li CY, Cai J, Tsai JJP, Wang CCN., Identification of hub genes associated with development of head and neck squamous cell carcinoma by integrated bioinformatics analysis, Front Oncol, 10, (2020); Can T., Introduction to bioinformatics, Methods Mol Biol, 1107, pp. 51-71, (2014); San Segundo-Val I, Sanz-Lozano CS., Introduction to the Gene Expression Analysis, pp. 29-43, (2016); Langfelder P, Horvath S., WGCNA: an R package for weighted correlation network analysis, BMC Bioinform, 9, 1, (2008); Zhou J, Guo H, Liu L, Et al., Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma, J Cell Mol Med, 25, 3, pp. 1633-1644, (2021); Tian W, Yang X, Yang H, Zhou B., GINS2 functions as a key gene in lung adenocarcinoma by WGCNA Co-Expression network analysis, Onco Targets Ther, 13, pp. 6735-6746, (2020); Lopez NC, Garcia-Ordas MT, Vitelli-Storelli F, Fernandez-Navarro P, Palazuelos C, Alaiz-Rodriguez R., Evaluation of feature selection techniques for breast cancer risk prediction, Int J Env Res Pub He, 18, 20, (2021); Morrow JD, Zhou X, Lao T, Et al., Functional interactors of three genome-wide association study genes are differentially expressed in severe chronic obstructive pulmonary disease lung tissue, Sci Rep-Uk, 7, 1, (2017); Ezzie ME, Crawford M, Cho J, Et al., Gene expression networks in COPD: microRNA and mRNA regulation, Thorax, 67, 2, pp. 122-131, (2012); Feng Z, Zhang J, Zheng Y, Wang Q, Min X, Tian T., Elevated expression of ASF1B correlates with poor prognosis in human lung adenocarcinoma, Pers Med, 18, 2, pp. 115-127, (2021); Schabath MB, Welsh EA, Fulp WJ, Et al., Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma, Oncogene, 35, 24, pp. 3209-3216, (2016); Cerami E, Gao J, Dogrusoz U, Et al., The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data, Cancer Discov, 2, 5, pp. 401-404, (2012); Feng Z, Zhang J, Zheng Y, Liu J, Duan T, Tian T., Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma, Transl Cancer Res, 10, 2, pp. 983-997, (2021); Zhou R, Zhang J, Zeng D, Et al., Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I–III colon cancer, Cancer Immunol Immunother, 68, 3, pp. 433-442, (2019); Jiang P, Gu S, Pan D, Et al., Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response, Nat Med, 24, 10, pp. 1550-1558, (2018); Wang X, Kong C, Xu W, Et al., Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT-based radiomics signature, Thorac Cancer, 10, 10, pp. 1904-1912, (2019); Chalmers ZR, Connelly CF, Fabrizio D, Et al., Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden, Genome Med, 9, 1, (2017); Izumi H, Wakasugi T, Shimajiri S, Et al., Role of ZNF143 in tumor growth through transcriptional regulation of DNA replication and cell-cycle-associated genes, Cancer Sci, 101, 12, pp. 2538-2545, (2010); Paek AR, Mun JY, Hong K, Lee J, Hong DW, You HJ., Zinc finger protein 143 expression is closely related to tumor malignancy via regulating cell motility in breast cancer, Bmb Rep, 50, 12, pp. 621-627, (2017); Chen YR, Leung JM, Sin DD., A systematic review of diagnostic biomarkers of COPD exacerbation, PLoS One, 11, 7, (2016); Sin DD, Hollander Z, DeMarco ML, McManus BM, Ng RT., Biomarker development for chronic obstructive pulmonary disease. from discovery to clinical implementation, Am J Resp Crit Care, 192, 10, pp. 1162-1170, (2015); Kawatsu Y, Kitada S, Uramoto H, Et al., The combination of strong expression of ZNF143 and high MIB-1 labelling index independently predicts shorter disease-specific survival in lung adenocarcinoma, Brit J Cancer, 110, 10, pp. 2583-2592, (2014); Ye B, Yang G, Li Y, Zhang C, Wang Q, Yu G., ZNF143 in chromatin looping and gene regulation, Front Genet, 11, (2020); Haibara H, Yamazaki R, Nishiyama Y, Et al., YPC-21661 and YPC-22026, novel small molecules, inhibit ZNF143 activity in vitro and in vivo, Cancer Sci, 108, 5, pp. 1042-1048, (2017); Young RP, Hopkins RJ, Christmas T, Black PN, Metcalf P, Gamble GD., COPD prevalence is increased in lung cancer, independent of age, sex and smoking history, Eur Respir J, 34, 2, pp. 380-386, (2009); Chen Y, Zhang Y, Lv J, Et al., Genomic analysis of tumor microenvironment immune types across 14 solid cancer types: immunotherapeutic implications, Theranostics, 7, 14, pp. 3585-3594, (2017); Sharma P, Allison JP., The future of immune checkpoint therapy, Science, 348, 6230, pp. 56-61, (2015); Trujillo JA, Sweis RF, Bao R, Luke JJ., T cell–inflamed versus Non-T cell–inflamed tumors: a conceptual framework for cancer immunotherapy drug development and combination therapy selection, Cancer Immunol Res, 6, 9, pp. 990-1000, (2018); Takayama Y, Nakamura T, Fukushiro Y, Mishima S, Masuda K, Shoda H., Coexistence of emphysema with non-small-cell lung cancer predicts the therapeutic efficacy of immune checkpoint inhibitors, Vivo (Brooklyn), 35, 1, pp. 467-474, (2021); Kadara H, Scheet P, Wistuba II, Spira AE., Early events in the molecular pathogenesis of lung cancer, Cancer Prev Res, 9, 7, pp. 518-527, (2016); Kim A, Lim SM, Kim J, Seo J., Integrative genomic and transcriptomic analyses of tumor suppressor genes and their role on tumor microenviron-ment and immunity in lung squamous cell carcinoma, Front Immunol, 12, (2021); Toyokawa G, Takada K, Okamoto T, Et al., High frequency of programmed death-ligand 1 expression in emphysematous bullae-associated lung adenocarcinomas, Clin Lung Cancer, 18, 5, pp. 504-511, (2017)","D. Sun; Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, 300222, China; email: sdqmd@163.com; J. Qin; Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, 300222, China; email: qinjianwen2005@aliyun.com","","Dove Medical Press Ltd","","","","","","11769106","","","35400998","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127962388"
"Holmstrom L.; Bednarski B.; Chugh H.; Aziz H.; Pham H.N.; Sargsyan A.; Uy-Evanado A.; Dey D.; Salvucci A.; Jui J.; Reinier K.; Slomka P.J.; Chugh S.S.","Holmstrom, Lauri (57200324392); Bednarski, Bryan (57222562208); Chugh, Harpriya (55638642200); Aziz, Habiba (58892001000); Pham, Hoang Nhat (58890644200); Sargsyan, Arayik (57215012880); Uy-Evanado, Audrey (26221501700); Dey, Damini (7101867655); Salvucci, Angelo (6602760236); Jui, Jonathan (6602175100); Reinier, Kyndaron (14060951600); Slomka, Piotr J. (7003438436); Chugh, Sumeet S. (35253639000)","57200324392; 57222562208; 55638642200; 58892001000; 58890644200; 57215012880; 26221501700; 7101867655; 6602760236; 6602175100; 14060951600; 7003438436; 35253639000","Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting with Pulseless Electric Activity Versus Ventricular Fibrillation","2024","Circulation: Arrhythmia and Electrophysiology","17","2","","E012338","","","7","10.1161/CIRCEP.123.012338","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185325377&doi=10.1161%2fCIRCEP.123.012338&partnerID=40&md5=e8e163a62a5af050799fa33c33bc26a5","Division of Artificial Intelligence in Medicine, Department of Medicine; Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Ventura County Health Care Agency, Ventura, CA, United States; Department of Emergency Medicine, Oregon Health and Science University, Portland, OR, United States","Holmstrom L., Division of Artificial Intelligence in Medicine, Department of Medicine, Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Bednarski B., Division of Artificial Intelligence in Medicine, Department of Medicine; Chugh H., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Aziz H., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Pham H.N., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Sargsyan A., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States, Ventura County Health Care Agency, Ventura, CA, United States; Uy-Evanado A., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Dey D., Division of Artificial Intelligence in Medicine, Department of Medicine; Salvucci A., Ventura County Health Care Agency, Ventura, CA, United States; Jui J., Department of Emergency Medicine, Oregon Health and Science University, Portland, OR, United States; Reinier K., Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States; Slomka P.J., Division of Artificial Intelligence in Medicine, Department of Medicine; Chugh S.S., Division of Artificial Intelligence in Medicine, Department of Medicine, Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, United States","BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS: In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61-0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59-0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS: The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies. © 2024 Lippincott Williams and Wilkins. All rights reserved.","artificial Intelligence; cardiovascular diseases; emergency medical services; stroke; ventricular fibrillation","Arrhythmias, Cardiac; Artificial Intelligence; Cardiopulmonary Resuscitation; Death, Sudden, Cardiac; Electric Countershock; Emergency Medical Services; Heart Arrest; Humans; Out-of-Hospital Cardiac Arrest; Ventricular Fibrillation; beta adrenergic receptor blocking agent; anemia; aortic arch syndrome; Article; artificial intelligence; asthma; atrial fibrillation; autopsy; body weight gain; bootstrapping; cardiomyopathy; cerebrovascular accident; chronic kidney failure; chronic obstructive lung disease; clinical feature; cohort analysis; congestive heart failure; controlled study; coronary artery disease; cross validation; data processing; defibrillation; demographics; density gradient; diabetes mellitus; diagnostic test accuracy study; diaphoresis; dyspnea; electric activity; emergency health service; female; heart atrium flutter; heart rhythm; heart ventricle fibrillation; heart ventricle tachycardia; human; intoxication; major clinical study; male; metropolitan area; mood disorder; mortality; nausea and vomiting; out of hospital cardiac arrest; peripheral vascular disease; prevalence; prospective study; pulseless electrical activity; receiver operating characteristic; schizophrenia; seizure; sleep apnea syndromes; sudden cardiac death; supervised machine learning; survival rate; thorax pain; weakness; artificial intelligence; cardioversion; complication; emergency health service; heart arrest; heart arrhythmia; heart ventricle fibrillation; out of hospital cardiac arrest; resuscitation; sudden cardiac death","","","","","University of Oulu and Oulu University Hospital; National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (R01HL145675, R01HL147358, R35HL161195); National Heart, Lung, and Blood Institute, NHLBI; Suomen Kulttuurirahasto, SKR; Sigrid Juséliuksen Säätiö; Orionin Tutkimussäätiö; Instrumentariumin Tiedesäätiö; Paavo Nurmen Säätiö","The study was funded in part by National Institutes of Health, National Heart Lung and Blood Institute (NHLBI) grants R01HL145675 and R01HL147358 to Dr Chugh. The analysis was funded in part by NHLBI grant R35HL161195 to Dr Slomka. Dr Holmstrom is a postdoctoral fellow visiting from the Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland, and is funded by Sigrid Juselius Foundation, The Finnish Cultural Foundation, Instrumentarium Science Foundation, Orion Research Foundation, and Paavo Nurmi Foundation. The funding sources had no involvement in the preparation of this work or the decision to submit it for publication. 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Teodorescu C., Reinier K., Dervan C., Uy-Evanado A., Samara M., Mariani R., Gunson K., Jui J., Chugh S.S., Factors associated with pulseless electric activity versus ventricular fibrillation: The Oregon sudden unexpected death study, Circulation, 122, pp. 2116-2122, (2010); Kvaloy J.T., Skogvoll E., Eftestol T., Gundersen K., Kramer-Johansen J., Olasveengen T.M., Steen P.A., Which factors influence spontaneous state transitions during resuscitation?, Resuscitation, 80, pp. 863-869, (2009); Skogvoll E., Eftestol T., Gundersen K., Kvaloy J.T., Kramer-Johansen J., Olasveengen T.M., Steen P.A., Dynamics and state transitions during resuscitation in out-of-hospital cardiac arrest, Resuscitation, 78, pp. 30-37, (2008); Holmstrom L., Reinier K., Toft L., Halperin H., Salvucci A., Jui J., Chugh S.S., Out-of-hospital cardiac arrest with onset witnessed by emergency medical services: Implications for improvement in overall survival, Resuscitation, 175, pp. 19-27, (2022); Chugh S.S., Jui J., Gunson K., Stecker E.C., John B.T., Thompson B., Ilias N., Vickers C., Dogra V., Daya M., Current burden of sudden cardiac death: Multiple source surveillance versus retrospective death certificate-based review in a large U.S. community, J Am Coll Cardiol, 44, pp. 1268-1275, (2004); Reinier K., Sargsyan A., Chugh H.S., Nakamura K., Uy-Evanado A., Klebe D., Kaplan R., Hadduck K., Shepherd D., Young C., Evaluation of sudden cardiac arrest by race/ethnicity among residents of Ventura county, California, 2015-2020, JAMA Netw Open, 4, (2021); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System., Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Wang C., Wu Q., Weimer M., Zhu E., FLAML: A Fast and Lightweight AutoML Library, arXiv:1911.04706; Liu P., Fu B., Yang S.X., Deng L., Zhong X., Zheng H., Optimizing survival analysis of XGBoost for ties to predict disease progression of breast cancer, IEEE Trans Biomed Eng, 68, pp. 148-160, (2021); Khera R., Haimovich J., Hurley N.C., McNamara R., Spertus J.A., Desai N., Rumsfeld J.S., Masoudi F.A., Huang C., Normand S.L., Use of machine learning models to predict death after acute myocardial infarction, JAMA Cardiol, 6, pp. 633-641, (2021); Lundberg S.M., Lee S.-I., A Unified Approach to Interpreting Model Predictions., Advances in Neural Information Processing Systems, 30, pp. 4765-4774, (2017); Rios R., Miller R.J.H., Hu L.H., Otaki Y., Singh A., Diniz M., Sharir T., Einstein A.J., Fish M.B., Ruddy T.D., Determining a minimum set of variables for machine learning cardiovascular event prediction: Results from REFINE SPECT registry, Cardiovasc Res, 118, pp. 2152-2164, (2022); Kauppila J.P., Hantula A., Kortelainen M.L., Pakanen L., Perkiomaki J., Martikainen M., Huikuri H.V., Junttila M.J., Association of initial recorded rhythm and underlying cardiac disease in sudden cardiac arrest, Resuscitation, 122, pp. 76-78, (2018); De Maio V.J., Stiell I.G., Wells G.A., Spaite D.W., Cardiac arrest witnessed by emergency medical services personnel: Descriptive epidemiology, prodromal symptoms, and predictors of survival, Ann Emerg Med, 35, pp. 138-146, (2000); Nehme Z., Andrew E., Bray J.E., Cameron P., Bernard S., Meredith I.T., Smith K., The significance of pre-arrest factors in out-of-hospital cardiac arrests witnessed by emergency medical services: A report from the Victorian Ambulance Cardiac Arrest Registry, Resuscitation, 88, pp. 35-42, (2015); 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Ramireddy A., Chugh H.S., Reinier K., Uy-Evanado A., Stecker E.C., Jui J., Chugh S.S., Sudden cardiac death during nighttime hours, Heart Rhythm, 18, pp. 778-784, (2021); Mehra R., Chung M.K., Olshansky B., Dobrev D., Jackson C.L., Kundel V., Linz D., Redeker N.S., Redline S., Sanders P., Sleep-disordered breathing and cardiac arrhythmias in adults: Mechanistic insights and clinical implications: A scientific statement from the American Heart Association, Circulation, 146, pp. e119-e136, (2022); Holmstrom L., Salmasi S., Chugh H., Uy-Evanado A., Sorenson C., Bhanji Z., Seifer M., Sargsyan A., Salvucci A., Jui J., Survivors of sudden cardiac arrest presenting with pulseless electrical activity: Clinical substrate, triggers, long-term prognosis, JACC Clin Electrophysiol, 8, pp. 1260-1270, (2022); Levin M.G., Judy R., Gill D., Vujkovic M., Verma S.S., Bradford Y., Rader D.J., Voight B.F., Damrauer S.M., Ritchie M.D., Genetics of height and risk of atrial fibrillation: A Mendelian randomization study, PLoS Med, 17, (2020); Kauppila J.P., Hantula A., Pakanen L., Perkiomaki J.S., Martikainen M., Huikuri H.V., Junttila M.J., Association of non-shockable initial rhythm and psychotropic medication in sudden cardiac arrest, Int J Cardiol Heart Vasc, 28, (2020); Teodorescu C., Reinier K., Uy-Evanado A., Chugh H., Gunson K., Jui J., Chugh S.S., Antipsychotic drugs are associated with pulseless electrical activity: The Oregon sudden unexpected death study, Heart Rhythm, 10, pp. 526-530, (2013); Chugh S.S., Reinier K., Uy-Evanado A., Chugh H.S., Elashoff D., Young C., Salvucci A., Jui J., Prediction of sudden cardiac death manifesting with documented ventricular fibrillation or pulseless ventricular tachycardia, JACC Clin Electrophysiol, 8, pp. 411-423, (2022); Siddiqi T.J., Usman M.S., Shahid I., Ahmed J., Khan S.U., Ya'Qoub L., Rihal C.S., Alkhouli M., Utility of the CHA2DS2-VASc score for predicting ischaemic stroke in patients with or without atrial fibrillation: A systematic review and meta-analysis, Eur J Prev Cardiol, 29, pp. 625-631, (2022); Holmstrom L., Juntunen S., Vahatalo J., Pakanen L., Kaikkonen K., Haukilahti A., Kentta T., Tikkanen J., Viitasalo V., Perkiomaki J., Plaque histology and myocardial disease in sudden coronary death: The Fingesture study, Eur Heart J, 43, pp. 4923-4930, (2022)","S.S. Chugh; Division of Artificial Intelligence in Medicine, Advanced Health Sciences Pavilion, Los Angeles, Ste A3100, 127 S. San Vicente Blvd., 90048, United States; email: sumeet.chugh@cshs.org","","Lippincott Williams and Wilkins","","","","","","19413149","","","38284289","English","Circ. Arrhythmia Electrophysiol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85185325377"
"Jayamini W.K.D.; Mirza F.; Naeem M.A.; Chan A.H.Y.","Jayamini, Widana Kankanamge Darsha (57930112500); Mirza, Farhaan (52664014200); Naeem, M. Asif (57213518795); Chan, Amy Hai Yan (55337510300)","57930112500; 52664014200; 57213518795; 55337510300","State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning","2022","Applied Sciences (Switzerland)","12","19","9890","","","","4","10.3390/app12199890","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139966408&doi=10.3390%2fapp12199890&partnerID=40&md5=d84aa824a340b508f5480d4dacef00eb","School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand; Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, 11600, Sri Lanka; School of Computing, National University of Computer and Emerging Sciences (NUCES), Islamabad, 44000, Pakistan; School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1010, New Zealand","Jayamini W.K.D., School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand, Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, 11600, Sri Lanka; Mirza F., School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand; Naeem M.A., School of Computing, National University of Computer and Emerging Sciences (NUCES), Islamabad, 44000, Pakistan; Chan A.H.Y., School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1010, New Zealand","Length of stay (LOS) is a key indicator of healthcare quality and reflects the burden on the healthcare system. However, limited studies have used machine learning to predict LOS in asthma. This study aimed to explore the characteristics and associations between asthma-related admission data variables with LOS and to use those factors to predict LOS. A dataset of asthma-related admissions in the Auckland region was analysed using different statistical techniques. Using those predictors, machine learning models were built to predict LOS. Demographic, diagnostic, and temporal factors were associated with LOS. Māori females had the highest average LOS among all the admissions at 2.8 days. The random forest algorithm performed well, with an RMSE of 2.48, MAE of 1.67, and MSE of 6.15. The mean predicted LOS by random forest was 2.6 days with a standard deviation of 1.0. The other three algorithms were also acceptable in predicting LOS. Implementing more robust machine learning classifiers, such as artificial neural networks, could outperform the models used in this study. Future work to further develop these models with other regions and to identify the reasons behind the shorter and longer stays for asthma patients is warranted. © 2022 by the authors.","asthma; length of stay; machine-learning; prediction","","","","","","University of Auckland","A.H.Y.C has received research grant funding from The University of Auckland, Health Research Council and is the recipient of the Auckland Medical Research Foundation Senior Research Fellowship for asthma-related research. A.H.Y.C is also a fellow of the Asthma UK Centre of Applied Research (AUKCAR) and on the Board of Asthma New Zealand. All other authors declare no other relevant conflict of interest. ","Hargreave F., Nair P., The definition and diagnosis of asthma, Clin. Exp. 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Health Inform, 22, pp. 285-290, (2018); Kim M.S., Lee J.H., Jang Y.J., Lee C.H., Choi J.H., Sung T.E., Hybrid deep learning algorithm with open innovation perspective: A prediction model of asthmatic occurrence, Sustainability, 12, (2020); Maung T.Z., Bishop J.E., Holt E., Turner A.M., Pfrang C., Indoor Air Pollution and the Health of Vulnerable Groups: A Systematic Review Focused on Particulate Matter (PM), Volatile Organic Compounds (VOCs) and Their Effects on Children and People with Pre-Existing Lung Disease, Int. J. Environ. Res. 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Manag, 33, pp. e751-e767, (2018); Dobkin C., Finkelstein A., Kluender R., Notowidigdo M.J., The Economic Consequences of Hospital Admissions, Am. Econ. Rev, 108, pp. 308-352, (2018); Schlichting D., Fadason T., Grant C.C., O'Sullivan J., Childhood asthma in New Zealand: The impact of on-going socioeconomic disadvantage (2010–2019), N. Z. Med. J, 134, pp. 80-95, (2021); Barnard L.T., Zhang J., The Impact of Respiratory Disease in New Zealand: 2020 Update, (2021); Thai H.-D., Huh J.-H., Optimizing patient transportation by applying cloud computing and big data analysis, J. Supercomput, pp. 1-30, (2022); Soyiri I.N., Reidpath D.D., Sarran C., Asthma length of stay in hospitals in London 2001–2006: Demographic, diagnostic and temporal factors, PLoS ONE, 6, (2011); Shanley L.A., Lin H., Flores G., Factors associated with length of stay for pediatric asthma hospitalizations, J. Asthma, 52, pp. 471-477, (2015); Baek J., Kash B.A., Xu X., Benden M., Roberts J., Carrillo G., Association between ambient air pollution and hospital length of stay among children with asthma in South Texas, Int. J. Environ. Res. Public Health, 17, (2020); Rachda Naila M., Caulier P., Chaabane S., Chraibi A., Piechowiak S., A Comparative Study of Machine Learning Models for Predicting Length of Stay in Hospitals, J. Inf. Sci. Eng, 37, pp. 1025-1038, (2021); Turgeman L., May J.H., Sciulli R., Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission, Expert Syst. Appl, 78, pp. 376-385, (2017); Tsai P.-F., Chen P.-C., Chen Y.-Y., Song H.-Y., Lin H.-M., Lin F.-M., Huang Q.-P., Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network, J. Healthc. Eng, 2016, (2016); Aghajani S., Kargari M., Determining Factors Influencing Length of Stay and Predicting Length of Stay Using Data Mining in the General Surgery Department, Hosp. Pract. Res, 1, pp. 53-58, (2016); Achilonu O.J., Fabian J., Bebington B., Singh E., Nimako G., Eijkemans R.M., Musenge E., Use of Machine Learning and Statistical Algorithms to Predict Hospital Length of Stay Following Colorectal Cancer Resection: A South African Pilot Study, Front. Oncol, 11, (2021); Ma X., Si Y., Wang Z., Wang Y., Length of stay prediction for ICU patients using individualized single classification algorithm, Comput. 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Health Inform, 19, pp. 1216-1223, (2015); Busby J., Matthews J.G., Chaudhuri R., Pavord I.D., Hardman T.C., Arron J.R., Bradding P., Brightling C.E., Choy D.F., Cowan D.C., Factors affecting adherence with treatment advice in a clinical trial of patients with severe asthma, Eur. Respir. J, 59, (2022); Padinjappurathu Gopalan S., Chowdhary C.L., Iwendi C., Farid M.A., Ramasamy L.K., An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems, Sensors, 22, (2022); Iwendi C., Khan S., Anajemba J.H., Bashir A.K., Noor F., Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model, IEEE Access, 8, pp. 28462-28474, (2020); Lisspers K., Stallberg B., Larsson K., Janson C., Muller M., Luczko M., Bjerregaard B.K., Bacher G., Holzhauer B., Goyal P., Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning-based on the ARCTIC study, Respir. Med, 185, (2021); Feng Y., Wang Y., Zeng C., Mao H., Artificial intelligence and machine learning in chronic airway diseases: Focus on asthma and chronic obstructive pulmonary disease, Int. J. Med. Sci, 18, pp. 2871-2889, (2021); Ramasamy L.K., Khan F., Shah M., Prasad B.V.V.S., Iwendi C., Biamba C., Secure Smart Wearable Computing through Artificial Intelligence-Enabled Internet of Things and Cyber-Physical Systems for Health Monitoring, Sensors, 22, (2022)","A.H.Y. Chan; School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1010, New Zealand; email: a.chan@auckland.ac.nz","","MDPI","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85139966408"
"Yang X.; Zhang M.; Gong S.; Sun M.; Xie M.; Niu P.; Pang W.","Yang, Xiaopeng (57223130334); Zhang, Menglun (56347017100); Gong, Shaobo (57792495100); Sun, Mingchao (57792829400); Xie, Mengying (55065347100); Niu, Pengfei (55841620600); Pang, Wei (58461296800)","57223130334; 56347017100; 57792495100; 57792829400; 55065347100; 55841620600; 58461296800","Chest-Laminated and Sweat-Permeable E-Skin for Speech and Motion Artifact-Insensitive Cough Detection","2023","Advanced Materials Technologies","8","4","2201043","","","","4","10.1002/admt.202201043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141397056&doi=10.1002%2fadmt.202201043&partnerID=40&md5=a738ba18bbde977e4d09deb98f589abe","State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China","Yang X., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Zhang M., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Gong S., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Sun M., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Xie M., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Niu P., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; Pang W., State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China","Assessment of the cough severity is essential when dealing with respiratory diseases such as chronic obstructive pulmonary disease and COVID-19. Although a few wearable devices have been reported for cough detection, they mostly rely on microphones, accelerometers, or throat-fixed flexible sensors, which suffer from key issues including privacy disclosure and speech/motion artifacts. This study presents a chest-laminated electronic skin (e-skin) for reliable cough detection. Mixed dumbbell-like networks and through-holes are engineered on hard-to-stretch composite films for high stretching force sensitivity and sweat permeation, respectively. The e-skin can effectively reduce speech-signal and motion artifacts owing to firm adhesion and conformal contact with the chest even on sweaty skin. Experimental results show that the specificity for cough identification is as high as 99.75% through machine learning of automated acoustic analysis, even in the presence of hard-to-distinguish daily activities such as throat clearing. The developed chest-laminated e-skin is a simple, comfortable, yet reliable method to detect cough for the primary diagnosis of respiratory diseases by extracting subtle acoustic information from cough. © 2022 Wiley-VCH GmbH.","cough detection; electronic skins; strain sensor; sweat-permeability","Composite films; Laminating; Pulmonary diseases; Wearable sensors; Chronic obstructive pulmonary disease; Cough detection; Electronic skin; Flexible sensor; Key Issues; Motion artifact; Privacy disclosures; Strain sensors; Sweat-permeability; Wearable devices; Diagnosis","","","","","National Natural Science Foundation of China, NSFC, (61901295, 62001322)","This work is supported by the funding from Natural Natural Science Foundation of China (NSFC Grant No. 62001322) and National Natural Science Foundation of China (No. 61901295). Special thanks to Quanning Li, Xuejiao Chen, Chongling Sun, Bohua Liu, Wenlan Guo, and Chen Sun for their support and assistance in sensors’ fabrication. 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Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), (2015); Smith J.A., Owen E.C., Jones A.M., Dodd M.E., Webb A.K., Woodcock A., Thorax, 61, (2006); Barry S.J., Dane A.D., Morice A.H., Walmsley A.D., Cough, 2, (2006); Widdicombe J., Fontana G., Eur. Respir. J., 28, (2006); Madison J.M., Irwin R.S., Gastrointestinal Motility Disorders, pp. 169-183, (2017); Birring S.S., Fleming T., Matos S., Raj A.A., Evans D.H., Pavord I.D., Eur. Respir. J., 31, (2008); Xiao Y., Carson D., Boris L., Mabary J., Lin Z., Nicodeme F., Cuttica M., Kahrilas P.J., Pandolfino J.E., Dis. Esophagus, 27, (2014); EMBC, (2019); Pahar M., Miranda I., Diacon A., Niesler T., ICASSP, Toronto, (2021)","M. Zhang; State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; email: zml@tju.edu.cn; W. Pang; State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, 300072, China; email: weipang@tju.edu.cn","","John Wiley and Sons Inc","","","","","","2365709X","","","","English","Adv.  Mater. Technol.","Article","Final","","Scopus","2-s2.0-85141397056"
"Wienker J.; Darwiche K.; Rüsche N.; Büscher E.; Karpf-Wissel R.; Winantea J.; Özkan F.; Westhölter D.; Taube C.; Kersting D.; Hautzel H.; Salhöfer L.; Hosch R.; Nensa F.; Forsting M.; Schaarschmidt B.M.; Zensen S.; Theysohn J.; Umutlu L.; Haubold J.; Opitz M.","Wienker, Johannes (57217596658); Darwiche, Kaid (16174628900); Rüsche, Nele (58987115600); Büscher, Erik (57398255200); Karpf-Wissel, Rüdiger (55611462200); Winantea, Jane (57211485645); Özkan, Filiz (56320800100); Westhölter, Dirk (57188659976); Taube, Christian (7006506758); Kersting, David (57016601800); Hautzel, Hubertus (6603042495); Salhöfer, Luca (57201130996); Hosch, René (57218207808); Nensa, Felix (55611423600); Forsting, Michael (55545275600); Schaarschmidt, Benedikt M. (56312071100); Zensen, Sebastian (57218670016); Theysohn, Jens (23026329200); Umutlu, Lale (25625554700); Haubold, Johannes (57225257476); Opitz, Marcel (57218675654)","57217596658; 16174628900; 58987115600; 57398255200; 55611462200; 57211485645; 56320800100; 57188659976; 7006506758; 57016601800; 6603042495; 57201130996; 57218207808; 55611423600; 55545275600; 56312071100; 57218670016; 23026329200; 25625554700; 57225257476; 57218675654","Body composition impacts outcome of bronchoscopic lung volume reduction in patients with severe emphysema: a fully automated CT-based analysis","2024","Scientific Reports","14","1","8718","","","","3","10.1038/s41598-024-58628-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190464696&doi=10.1038%2fs41598-024-58628-0&partnerID=40&md5=c9fc82e73b211a7f9c9002cee065df5b","Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Germany; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany; Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Department of Nuclear Medicine, University Hospital Essen, Essen, Germany","Wienker J., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Darwiche K., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Rüsche N., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Büscher E., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Karpf-Wissel R., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Winantea J., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Özkan F., Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Tüschener Weg 40, Essen, 45239, Germany; Westhölter D., Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Germany; Taube C., Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Germany; Kersting D., Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; Hautzel H., Department of Nuclear Medicine, University Hospital Essen, Essen, Germany; Salhöfer L., Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany, Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Hosch R., Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany; Nensa F., Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany, Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Forsting M., Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Schaarschmidt B.M., Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Zensen S., Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Theysohn J., Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Umutlu L., Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Haubold J., Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany, Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany; Opitz M., Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany, Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany","Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and irreversible airflow limitation, with individual body composition influencing disease severity. Severe emphysema worsens symptoms through hyperinflation, which can be relieved by bronchoscopic lung volume reduction (BLVR). To investigate how body composition, assessed through CT scans, impacts outcomes in emphysema patients undergoing BLVR. Fully automated CT-based body composition analysis (BCA) was performed in patients with end-stage emphysema receiving BLVR with valves. Post-interventional muscle and adipose tissues were quantified, body size-adjusted, and compared to baseline parameters. Between January 2015 and December 2022, 300 patients with severe emphysema underwent endobronchial valve treatment. Significant improvements were seen in outcome parameters, which were defined as changes in pulmonary function, physical performance, and quality of life (QoL) post-treatment. Muscle volume remained stable (1.632 vs. 1.635 for muscle bone adjusted ratio (BAR) at baseline and after 6 months respectively), while bone adjusted adipose tissue volumes, especially total and pericardial adipose tissue, showed significant increase (2.86 vs. 3.00 and 0.16 vs. 0.17, respectively). Moderate to strong correlations between bone adjusted muscle volume and weaker correlations between adipose tissue volumes and outcome parameters (pulmonary function, QoL and physical performance) were observed. Particularly after 6-month, bone adjusted muscle volume changes positively corresponded to improved outcomes (ΔForced expiratory volume in 1 s [FEV1], r = 0.440; ΔInspiratory vital capacity [IVC], r = 0.397; Δ6Minute walking distance [6MWD], r = 0.509 and ΔCOPD assessment test [CAT], r = −0.324; all p < 0.001). Group stratification by bone adjusted muscle volume changes revealed that groups with substantial muscle gain experienced a greater clinical benefit in pulmonary function improvements, QoL and physical performance (ΔFEV1%, 5.5 vs. 39.5; ΔIVC%, 4.3 vs. 28.4; Δ6MWDm, 14 vs. 110; ΔCATpts, −2 vs. −3.5 for groups with ΔMuscle, BAR% < –10 vs. > 10, respectively). BCA results among patients divided by the minimal clinically important difference for forced expiratory volume of the first second (FEV1) showed significant differences in bone-adjusted muscle and intramuscular adipose tissue (IMAT) volumes and their respective changes after 6 months (ΔMuscle, BAR% −5 vs. 3.4 and ΔIMAT, BAR% −0.62 vs. 0.60 for groups with ΔFEV1 ≤ 100 mL vs > 100 mL). Altered body composition, especially increased muscle volume, is associated with functional improvements in BLVR-treated patients. © The Author(s) 2024.","Artificial intelligence; Body composition; Bronchoscopic lung volume reduction; Chronic obstructive pulmonary disease; Computed tomography; Deep learning; Emphysema; Valves","Body Composition; Bronchoscopy; Emphysema; Forced Expiratory Volume; Humans; Pneumonectomy; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Quality of Life; Tomography, X-Ray Computed; Treatment Outcome; body composition; bronchoscopy; chronic obstructive lung disease; diagnostic imaging; emphysema; forced expiratory volume; human; lung emphysema; physiology; pneumonectomy; procedures; quality of life; treatment outcome; x-ray computed tomography","","","","","","","Ferrera M.C., Labaki W.W., Han M.K., Advances in chronic obstructive pulmonary disease, Annu Rev Med, 72, pp. 119-134, (2021); Oudijk E.-J.D., Lammers J.-W.J., Koenderman L., Systemic inflammation in chronic obstructive pulmonary disease, Eur. Respir. J, 22, Suppl 46, pp. 5s-13s, (2003); Xavier R.F., Et al., Identification of phenotypes in people with COPD: Influence of physical activity, sedentary behaviour, body composition and skeletal muscle strength, Lung, 197, 1, pp. 37-45, (2019); Waschki B., Et al., Physical activity is the strongest predictor of all-cause mortality in patients with COPD: A prospective cohort study, Chest, 140, 2, pp. 331-342, (2011); Costa T.M.D.R.L., Costa F.M., Moreira C.A., Rabelo L.M., Boguszewski C.L., Borba V.Z.C., Sarcopenia in COPD: Relationship with COPD severity and prognosis, J Bras. Pneumol., 41, 5, pp. 415-421, (2015); Jones S.E., Et al., Sarcopenia in COPD: prevalence, clinical correlates and response to pulmonary rehabilitation, Thorax, 70, 3, pp. 213-218, (2015); Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease,” Global Initiative for Chronic Obstructive Lung Disease - GOLD; Kemp S.V., Et al., A multicenter randomized controlled trial of zephyr endobronchial valve treatment in heterogeneous emphysema (TRANSFORM), Am. J. Respir. Crit. Care Med, 196, 12, (2017); Davey C., Et al., Lancet, (2015); Valipour A., Et al., Endobronchial valve therapy in patients with homogeneous emphysema. Results from the IMPACT study, Am. J. Respir. Crit. Care Med, (2016); Klooster K., ten Hacken N.H.T., Hartman J.E., Kerstjens H.A.M., van Rikxoort E.M., Slebos D.-J., Endobronchial valves for emphysema without interlobar collateral ventilation, N Engl J Med, 373, 24, (2015); Guerri R., Et al., Mass of intercostal muscles associates with risk of multiple exacerbations in COPD, Respir. Med, 104, 3, pp. 378-388, (2010); Goodpaster B.H., Kelley D.E., Thaete F.L., He J., Ross R., Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content, J Appl Physiol. (1985), 89, 1, pp. 104-110, (2000); Martinez C.H., Et al., Handgrip strength in chronic obstructive pulmonary disease. Associations with acute exacerbations and body composition, Ann Am Thorac Soc, 14, 11, pp. 1638-1645, (2017); de Blasio F., Et al., Evaluation of body composition in COPD patients using multifrequency bioelectrical impedance analysis, Int J Chron Obstruct Pulmon Dis, 11, pp. 2419-2426, (2016); Koitka S., Kroll L., Malamutmann E., Oezcelik A., Nensa F., Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks, Eur Radiol, 31, 4, pp. 1795-1804, (2021); Kroll L., Et al., CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients, Sci Rep, (2022); Hosch R., Et al., Biomarkers extracted by fully automated body composition analysis from chest CT correlate with SARS-CoV-2 outcome severity, Sci. Rep, (2022); Haubold J., Et al., BOA: A CT-based body and organ analysis for radiologists at the point of care, Invest Radiol, (2023); Miller M.R., Et al., Standardisation of spirometry, Eur Respir J, 26, 2, pp. 319-338, (2005); Brooks D., Solway S., Gibbons W.J., ATS statement on six-minute walk test, Am J Respir Crit Care Med, 167, 9, (2003); Donohue J.F., Minimal clinically important differences in COPD lung function, 2, 1; Hartman J.E., ten Hacken N.H.T., Klooster K., Boezen H.M., de Greef M.H.G., Slebos D.-J., The minimal important difference for residual volume in patients with severe emphysema, Eur. Respir. J, (2012); He J., Li H., Yao J., Wang Y., Prevalence of sarcopenia in patients with COPD through different musculature measurements: An updated meta-analysis and meta-regression, Front. Nutr, (2023); Sepulveda-Loyola W., Osadnik C., Phu S., Morita A.A., Duque G., Probst V.S., Diagnosis, prevalence, and clinical impact of sarcopenia in COPD: a systematic review and meta-analysis, J Cachexia Sarcopenia Muscle, 11, 5, pp. 1164-1176, (2020); Martinez-Luna N., Et al., Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease, BMC Pulmonary Med, 22, 1, (2022); Park M.J., Et al., Mass and fat infiltration of intercostal muscles measured by CT histogram analysis and their correlations with COPD severity, Acad Radiol, 21, 6, pp. 711-717, (2014); Seymour J.M., Et al., The prevalence of quadriceps weakness in COPD and the relationship with disease severity, Eur Respir J, 36, 1, pp. 81-88, (2010); Verberne L.D.M., Leemrijse C.J., Swinkels I.C.S., van Dijk C.E., de Bakker D.H., Nielen M.M.J., Overweight in patients with chronic obstructive pulmonary disease needs more attention: a cross-sectional study in general practice, NPJ Prim Care Respir Med, 27, 1, (2017); Wang Y., Li Z., Li F., Nonlinear relationship between visceral adiposity index and lung function: a population-based study, Respir Res, 22, 1, (2021); Zagaceta J., Et al., Epicardial adipose tissue in patients with chronic obstructive pulmonary disease, PLOS ONE, 8, 6, (2013); Shimada T., Et al., Differential impacts between fat mass index and fat-free mass index on patients with COPD, Respir. Med, (2023); Mineo D., Ambrogi V., Lauriola V., Pompeo E., Mineo T.C., Recovery of body composition improves long-term outcomes after lung volume reduction surgery for emphysema, Eur. Respir. J, 36, 2, pp. 408-416, (2010); Kim V., Kretschman D.M., Sternberg A.L., DeCamp M.M., Criner G.J., Weight gain after lung reduction surgery is related to improved lung function and ventilatory efficiency, Am J Respir Crit Care Med, 186, 11, pp. 1109-1116, (2012); Sanders K.J.C., Klooster K., Schols A.M.W.J., Slebos D.-J., The effect of endobronchial valves on body composition in patients with advanced emphysema, Eur. Respir. J, 50, (2017); Weston A.D., Et al., Automated abdominal segmentation of CT scans for body composition analysis using deep learning, Radiology, 290, 3, pp. 669-679, (2019); Cespedes Feliciano E.M., Et al., Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients, J. Cachexia Sarcopenia Muscle, 11, 5, pp. 1258-1269, (2020); Ha J., Et al., Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography, Sci Rep, 11, 1, (2021); Sverzellati N., Et al., Computed tomography measurement of rib cage morphometry in emphysema, PLoS One, 8, 7, (2013)","J. Wienker; Division of Interventional Pneumology, Department of Pulmonary Medicine, University Medicine Essen-Ruhrlandklinik, Essen, Tüschener Weg 40, 45239, Germany; email: johannes.wienker@rlk.uk-essen.de","","Nature Research","","","","","","20452322","","","38622275","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85190464696"
"Xu S.; Deo R.C.; Soar J.; Barua P.D.; Faust O.; Homaira N.; Jaffe A.; Kabir A.L.; Acharya U.R.","Xu, Shuting (57441312700); Deo, Ravinesh C (8630380500); Soar, Jeffrey (7004721575); Barua, Prabal Datta (36993665100); Faust, Oliver (14830975900); Homaira, Nusrat (30067671700); Jaffe, Adam (36796177900); Kabir, Arm Luthful (57212966943); Acharya, U. Rajendra (7004510847)","57441312700; 8630380500; 7004721575; 36993665100; 14830975900; 30067671700; 36796177900; 57212966943; 7004510847","Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)","2023","Computer Methods and Programs in Biomedicine","241","","107746","","","","4","10.1016/j.cmpb.2023.107746","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170108564&doi=10.1016%2fj.cmpb.2023.107746&partnerID=40&md5=795ff5baa94dc52fb53f10fb87fe3043","School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, 4300, QLD, Australia; Cogninet Australia, Sydney, 2010, NSW, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, NSW, Australia; School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom; Australian International Institute of Higher Education, Sydney, 2000, NSW, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia; School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; Ad-din Women's Medical College, Dhaka, Bangladesh; James P. Grant School of Public Health, Dhaka, Bangladesh","Xu S., School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, 4300, QLD, Australia, Cogninet Australia, Sydney, 2010, NSW, Australia; Deo R.C., School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, 4300, QLD, Australia; Soar J., School of Business, University of Southern Queensland, Australia; Barua P.D., Cogninet Australia, Sydney, 2010, NSW, Australia, School of Business, University of Southern Queensland, Australia, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, NSW, Australia, Australian International Institute of Higher Education, Sydney, 2000, NSW, Australia, School of Science Technology, University of New England, Australia, School of Biosciences, Taylor's University, Malaysia, School of Computing, SRM Institute of Science and Technology, India, School of Science and Technology, Kumamoto University, Japan, Sydney School of Education and Social Work, University of Sydney, Australia; Faust O., School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom; Homaira N., School of Clinical Medicine, University of New South Wales, Australia, Sydney Children's Hospital, Sydney, Australia, James P. Grant School of Public Health, Dhaka, Bangladesh; Jaffe A., School of Clinical Medicine, University of New South Wales, Australia, Sydney Children's Hospital, Sydney, Australia; Kabir A.L., Ad-din Women's Medical College, Dhaka, Bangladesh; Acharya U.R., School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, 4300, QLD, Australia, School of Science and Technology, Kumamoto University, Japan","Background and objective: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. Methods: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. Results: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. Conclusions: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings. © 2023","Artificial Intelligence; Asthma; Chronic Obstructive Pulmonary Disease","Artificial Intelligence; Asthma; Databases, Factual; Humans; Pulmonary Disease, Chronic Obstructive; Respiratory Physiological Phenomena; Automation; Computer aided diagnosis; Convolutional neural networks; Deep learning; Medical imaging; Pulmonary diseases; Support vector machines; Air flow; Asthma; Automated detection; Chronic obstructive pulmonary disease; Diagnostic quality; Disease detection; Health professionals; Learning classifiers; Machine-learning; Systematic Review; abnormal respiratory sound; airflow; Article; artificial neural network; asthma; Bayesian learning; blood analysis; breathing; chronic obstructive lung disease; computer assisted tomography; convolutional neural network; correspondence analysis; cost control; decision tree; deep belief network; deep learning; deep neural network; diagnostic accuracy; diagnostic value; electrocardiogram; fuzzy system; genetics; human; k means clustering; k nearest neighbor; learning algorithm; least absolute shrinkage and selection operator; lung function test; machine learning; medical record; multiple instance learning; obstructive airway disease; Preferred Reporting Items for Systematic Reviews and Meta-Analyses; questionnaire; radiography; random forest; saliva analysis; signal detection; single nucleotide polymorphism; speech; support vector machine; systematic review; artificial intelligence; asthma; chronic obstructive lung disease; factual database; respiratory function; Air","","","","","Cogninet Australia Pty Ltd","This research was financially supported by a grant from Cogninet Australia Pty Ltd under the Industry Research Scholarship and University of Southern Queensland International PhD Fee Schol- arship 2022–2025.","Baghel N., Nangia V., Dutta M.K., Alsd-net: automatic lung sounds diagnosis network from pulmonary signals, Neural Comput. 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Barua; Cogninet Australia, Sydney, 2010, Australia; email: prabal.barua@usq.edu.au","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","37660550","English","Comput. Methods Programs Biomed.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85170108564"
"Talha S.; Lamrous S.; Kassegne L.; Lefebvre N.; Zulfiqar A.-A.; Tran Ba Loc P.; Geny M.; Meyer N.; Hajjam M.; Andrès E.; Geny B.","Talha, Samy (25029084800); Lamrous, Sid (15127234500); Kassegne, Loic (57210558411); Lefebvre, Nicolas (14045337700); Zulfiqar, Abrar-Ahmad (55869595200); Tran Ba Loc, Pierre (57225909844); Geny, Marie (58532670700); Meyer, Nicolas (8265239600); Hajjam, Mohamed (55888062600); Andrès, Emmanuel (7103334147); Geny, Bernard (7006632814)","25029084800; 15127234500; 57210558411; 14045337700; 55869595200; 57225909844; 58532670700; 8265239600; 55888062600; 7103334147; 7006632814","Early Hospital Discharge Using Remote Monitoring for Patients Hospitalized for COVID-19, Regardless of Need for Home Oxygen Therapy: A Descriptive Study","2023","Journal of Clinical Medicine","12","15","5100","","","","4","10.3390/jcm12155100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167712155&doi=10.3390%2fjcm12155100&partnerID=40&md5=13dd9a91ee21de1a1eab3162864e377f","Physiology and Functional Exploration Service, University Hospital of Strasbourg, Strasbourg, 67000, France; Research Team 3072 “Mitochondria, Oxidative Stress and Muscle”, University of Strasbourg, Strasbourg, 90032, France; UTBM, CNRS, FEMTO-ST Institute, Belfort, 90000, France; Pneumology Department, University Hospital Strasbourg, Strasbourg, 67000, France; Infectious Disease Department, University Hospital Strasbourg, Strasbourg, 67000, France; Internal Medicine Department, University Hospital Strasbourg, Strasbourg, 67000, France; Public Health Department, University Hospital Strasbourg, Strasbourg, 67000, France; Association for Assistance to Victims, Place Alfred de Musset, BP 3314, Evreux, CEDEX, 27033, France; Predimed Technology, Schiltigheim, 67300, France","Talha S., Physiology and Functional Exploration Service, University Hospital of Strasbourg, Strasbourg, 67000, France, Research Team 3072 “Mitochondria, Oxidative Stress and Muscle”, University of Strasbourg, Strasbourg, 90032, France; Lamrous S., UTBM, CNRS, FEMTO-ST Institute, Belfort, 90000, France; Kassegne L., Pneumology Department, University Hospital Strasbourg, Strasbourg, 67000, France; Lefebvre N., Infectious Disease Department, University Hospital Strasbourg, Strasbourg, 67000, France; Zulfiqar A.-A., Internal Medicine Department, University Hospital Strasbourg, Strasbourg, 67000, France; Tran Ba Loc P., Public Health Department, University Hospital Strasbourg, Strasbourg, 67000, France; Geny M., Association for Assistance to Victims, Place Alfred de Musset, BP 3314, Evreux, CEDEX, 27033, France; Meyer N., Public Health Department, University Hospital Strasbourg, Strasbourg, 67000, France; Hajjam M., Predimed Technology, Schiltigheim, 67300, France; Andrès E., Research Team 3072 “Mitochondria, Oxidative Stress and Muscle”, University of Strasbourg, Strasbourg, 90032, France, Internal Medicine Department, University Hospital Strasbourg, Strasbourg, 67000, France; Geny B., Physiology and Functional Exploration Service, University Hospital of Strasbourg, Strasbourg, 67000, France, Research Team 3072 “Mitochondria, Oxidative Stress and Muscle”, University of Strasbourg, Strasbourg, 90032, France","Aim: Since beds are unavailable, we prospectively investigated whether early hospital discharge will be safe and useful in patients hospitalized for COVID-19, regardless of their need for home oxygen therapy. Population and Methods: Extending the initial inclusion criteria, 62 patients were included and 51 benefited from home telemonitoring, mainly assessing clinical parameters (blood pressure, heart rate, respiratory rate, dyspnea, temperature) and peripheral saturation (SpO2) at follow-up. Results: 47% of the patients were older than 65 years; 63% needed home oxygen therapy and/or presented with more than one comorbidity. At home, the mean time to dyspnea and tachypnea resolutions ranged from 21 to 24 days. The mean oxygen-weaning duration was 13.3 ± 10.4 days, and the mean SpO2 was 95.7 ± 1.6%. The nurses and/or doctors managed 1238 alerts. Two re-hospitalizations were required, related to transient chest pain or pulmonary embolism, but no death occurred. Patient satisfaction was good, and 743 potential days of hospitalization were saved for other patients. Conclusion: The remote monitoring of vital parameters and symptoms is safe, allowing for early hospital discharge in patients hospitalized for COVID-19, whether or not home oxygen therapy was required. Oxygen tapering outside the hospital allowed for a greater reduction in hospital stay. Randomized controlled trials are necessary to confirm this beneficial effect. © 2023 by the authors.","artificial intelligence; COVID-19; early hospital discharge; home-telemonitoring; remote monitoring; sanitary crisis","oxygen; adult; Article; asthma; body temperature; breathing rate; chronic obstructive lung disease; comorbidity; controlled study; coronavirus disease 2019; diabetes mellitus; diastolic blood pressure; dyspnea; female; follow up; heart rate; home monitoring; home oxygen therapy; hospital discharge; hospital patient; hospital readmission; hospitalization; human; hypertension; lung embolism; major clinical study; male; middle aged; nurse; obesity; oxygen saturation; patient satisfaction; physician; prospective study; randomized controlled trial; remote sensing; smoking; systolic blood pressure; tachypnea; telemonitoring; thorax pain; weaning","","oxygen, 7782-44-7","","","","","Wu Z., McGoogan J.M., Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention, JAMA, 323, pp. 1239-1242, (2020); Whitaker M., Elliott J., Chadeau-Hyam M., Riley S., Darzi A., Cooke G., Ward H., Elliott P., Persistent COVID-19 symptoms in a community study of 606,434 people in England, Nat. 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Open, 4, (2021); Grutters L.A., Majoor K.I., Mattern E.S.K., Hardeman J.A., van Swol C.F.P., Vorselaars A.D.M., Home Telemonitoring Makes Early Hospital Discharge of COVID-19 Patients Possible, J. Am. Med. Inform. Assoc, 27, pp. 1825-1827, (2020); Van Herwerden M.C., van Steenkiste J., El Moussaoui R., den Hollander J.G., Helfrich G., Verberk I.J.A.M., Home telemonitoring and oxygen therapy in COVID-19 patients: Safety, patient satisfaction, and cost-effectiveness, Ned. Tijdschr. Geneeskd, 165, (2021); Van den Berg R., Meccanici C., de Graaf N., van Thiel E., Schol-Gelok S., Starting Home Telemonitoring and Oxygen Therapy Directly after Emergency Department Assessment Appears to Be Safe in COVID-19 Patients, J. Clin. Med, 11, (2022); Suarez-Gil R., Casariego-Vales E., Blanco-Lopez R., Santos-Guerra F., Pedrosa-Fraga C., Fernandez-Rial A., Iniguez-Vazquez I., Abad-Garcia M.M., Bal-Alvaredo M., Efficacy of Telemedicine and At-Home Telemonitoring Following Hospital Discharge in Patients with COVID-19, J. Pers. Med, 12, (2022); Gruwez H., Bakelants E., Dreesen P., Broekmans J., Criel M., Thomeer M., Vandervoort P., Ruttens D., Remote Patient Monitoring in COVID-19: A Critical Appraisal, Eur. Respir. J, 59, (2022)","S. Talha; Physiology and Functional Exploration Service, University Hospital of Strasbourg, Strasbourg, 67000, France; email: samy.talha@chru-strasbourg.fr","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85167712155"
"Vitacca M.; Malovini A.; Paneroni M.; Spanevello A.; Ceriana P.; Capelli A.; Murgia R.; Ambrosino N.","Vitacca, Michele (7006584059); Malovini, Alberto (23005132600); Paneroni, Mara (15081261400); Spanevello, Antonio (6603926482); Ceriana, Piero (7004008871); Capelli, Armando (7005556158); Murgia, Rodolfo (16310236700); Ambrosino, Nicolino (7006237886)","7006584059; 23005132600; 15081261400; 6603926482; 7004008871; 7005556158; 16310236700; 7006237886","Predicting Response to In-Hospital Pulmonary Rehabilitation in Individuals Recovering From Exacerbations of Chronic Obstructive Pulmonary Disease","2024","Archivos de Bronconeumologia","60","3","","153","160","7","3","10.1016/j.arbres.2024.01.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184049905&doi=10.1016%2fj.arbres.2024.01.001&partnerID=40&md5=80724c40b6ef5fff94cd518fd717a2c5","Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy; Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Respiratory Rehabilitation of the Institute of Tradate, Istituti Clinici Scientifici Maugeri IRCCS, Varese, Italy; Department of Medicine and Surgery, University of Insubria, Varese, Italy; Respiratory Rehabilitation of the Institute of Pavia, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Respiratory Rehabilitation of the Institute of Veruno, Istituti Clinici Scientifici Maugeri IRCCS, Novara, Italy; Respiratory Rehabilitation of the Institute of Montescano, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy","Vitacca M., Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy; Malovini A., Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Paneroni M., Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy; Spanevello A., Respiratory Rehabilitation of the Institute of Tradate, Istituti Clinici Scientifici Maugeri IRCCS, Varese, Italy, Department of Medicine and Surgery, University of Insubria, Varese, Italy; Ceriana P., Respiratory Rehabilitation of the Institute of Pavia, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Capelli A., Respiratory Rehabilitation of the Institute of Veruno, Istituti Clinici Scientifici Maugeri IRCCS, Novara, Italy; Murgia R., Respiratory Rehabilitation of the Institute of Montescano, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy; Ambrosino N., Respiratory Rehabilitation of the Institute of Montescano, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy","Background: Predicting the response to pulmonary rehabilitation (PR) could be valuable in defining admission priorities. We aimed to investigate whether the response of individuals recovering from a COPD exacerbation (ECOPD) could be forecasted using machine learning approaches. Method: This multicenter, retrospective study recorded data on anthropometrics, demographics, physiological characteristics, post-PR changes in six-minute walking distance test (6MWT), Medical Research Council scale for dyspnea (MRC), Barthel Index dyspnea (BId), COPD assessment test (CAT) and proportion of participants reaching the minimal clinically important difference (MCID). The ability of multivariate approaches (linear regression, quantile regression, regression trees, and conditional inference trees) in predicting changes in each outcome measure has been assessed. Results: Individuals with lower baseline 6MWT, as well as those with less severe airway obstruction or admitted from acute care hospitals, exhibited greater improvements in 6MWT, whereas older as well as more dyspnoeic individuals had a lower forecasted improvement. Individuals with more severe CAT and dyspnea, and lower 6MWT had a greater potential improvement in CAT. More dyspnoeic individuals were also more likely to show improvement in BId and MRC. The Mean Absolute Error estimates of change prediction were 44.70 m, 3.22 points, 5.35 points, and 0.32 points for 6MWT, CAT, BId, and MRC respectively. Sensitivity and specificity in discriminating individuals reaching the MCID of outcomes ranged from 61.78% to 98.99% and from 14.00% to 71.20%, respectively. Conclusion: While the assessed models were not entirely satisfactory, predictive equations derived from clinical practice data might help in forecasting the response to PR in individuals recovering from an ECOPD. Future larger studies will be essential to confirm the methodology, variables, and utility. © 2024 SEPAR","COPD exacerbations; Dyspnea; Exercise; Machine learning; Pulmonary rehabilitation; Respiratory measurement","anthropometry; Article; Barthel index; chronic obstructive lung disease; clinical outcome; clinical practice; COPD assessment test; dyspnea; exercise; female; follow up; forced expiratory volume; forced vital capacity; forecasting; heart rate; hospitalization; human; length of stay; lung function; lung ventilation; machine learning; major clinical study; male; mean absolute error; minimal clinically important difference; multicenter study; oxygen therapy; peak expiratory flow; prediction; predictive value; pulmonary rehabilitation; quantile regression; quantitative structure activity relation; retrospective study; sensitivity and specificity; six minute walk test; visual analog scale","","","","","Ministero della Salute","This work was supported by the “Ricerca Corrente” Funding scheme of the Ministry of Health, Italy ","Spruit M.A., Singh S.J., Garvey C., ZuWallack R., Nici L., Rochester C., Et al., An official American Thoracic Society/European Respiratory Society statement: key concepts and advances in pulmonary rehabilitation, Am J Respir Crit Care Med, 188, pp. e13-e64, (2013); Santa B., Tomisa G., Horvath A., Balazs T., Nemeth L., Galffy G., Severe exacerbations and mortality in COPD patients: a retrospective analysis of the database of the Hungarian National Health Insurance Fund, Pulmonology, 29, pp. 284-291, (2023); Machado A., Barusso M., De Brandt J., Quadflieg K., Haesevoets S., Daenen M., Et al., Impact of acute exacerbations of COPD on patients’ health status beyond pulmonary function: a scoping review, Pulmonology, 29, pp. 518-534, (2023); Meneses-Echavez J.F., Chavez Guapo N., Loaiza-Betancur A.F., Machado A., Bidonde J., Pulmonary rehabilitation for acute exacerbations of COPD: a systematic review, Respir Med, 219, (2023); Vogiatzis I., Rochester C.L., Spruit M.A., Troosters T., Clini E.M., American Thoracic Society/European Respiratory Society Task Force on Policy in Pulmonary Rehabilitation. Increasing implementation and delivery of pulmonary rehabilitation: key messages from the new ATS/ERS policy statement, Eur Respir J, 47, pp. 1336-1341, (2016); Wouters E.F.M., Wouters B.B.R.E.F., Augustin I.M.L., Houben-Wilke S., Vanfleteren L.E.G.W., Franssen F.M.E., Personalised pulmonary rehabilitation in COPD, Eur Respir Rev, 27, (2018); Paneroni M., Vitacca M., Bernocchi P., Bertacchini L., Scalvini S., Feasibility of tele-rehabilitation in survivors of COVID-19 pneumonia, Pulmonology, 28, pp. 152-154, (2022); Sidey-Gibbons J.A.M., Sidey-Gibbons C.J., Machine learning in medicine: a practical introduction, BMC Med Res Methodol, 19, (2019); Erickson B.J., Korfiatis P., Akkus Z., Kline T.L., Machine learning for medical imaging, Radiographics, 37, pp. 505-515, (2017); Harrison C.J., Sidey-Gibbons C.J., Machine learning in medicine: a practical introduction to natural language processing, BMC Med Res Methodol, 21, (2021); Finnegan S.L., Browning M., Duff E., Harmer C.J., Reinecke A., Rahman N.M., Et al., Brain activity measured by functional brain imaging predicts breathlessness improvement during pulmonary rehabilitation, Thorax, 78, pp. 852-859, (2023); Wu C.T., Li G.H., Huang C.T., Cheng Y.C., Chen C.H., Chien J.Y., Et al., Acute exacerbation of a Chronic Obstructive Pulmonary Disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study, JMIR Mhealth Uhealth, 9, (2021); Verstraete K., Gyselinck I., Huts H., Das N., Topalovic M., De Vos M., Et al., Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials, Thorax, 78, pp. 983-989, (2023); Vitacca M., Malovini A., Spanevello A., Ceriana P., Paneroni M., Maniscalco M., Et al., Clusters of individuals recovering from an exacerbation of chronic obstructive pulmonary disease and response to in-hospital pulmonary rehabilitation, Pulmonology, 29, pp. 230-239, (2023); Kim V., Aaron S.D., What is a COPD exacerbation? Current definitions, pitfalls, 8 challenges and opportunities for improvement, Eur Respir J, 52, (2018); Clini E., Foglio K., Bianchi L., Porta R., Vitacca M., Ambrosino N., In-hospital short-term training program for patients with chronic airway obstruction, Chest, 120, pp. 1500-1505, (2001); Miller M.R., Hankinson J., Brusasco V., Burgos F., Casaburi R., Coates A., Et al., Standardisation of spirometry, Eur Respir J, 26, pp. 319-338, (2005); Shah S., Vanclay F., Cooper B., Improving the sensitivity of the Barthel Index for stroke rehabilitation, J Clin Epidemiol, 42, pp. 703-709, (1989); Holland A.E., Spruit M.A., Troosters T., Puhan M.A., Pepin V., Saey D., Et al., An official European Respiratory Society/American Thoracic Society technical standard: field walking tests in chronic respiratory disease, Eur Respir J, 44, pp. 1428-1446, (2014); Enright P.L., Sherrill D.L., Reference equations for the six-minute walk in healthy adults, Am J Respir Crit Care Med, 158, pp. 1384-1387, (1998); Fletcher C.M., Standardised questionnaire on respiratory symptoms: a statement prepared and approved by the MRC Committee on the aetiology of chronic bronchitis (MRC breathlessness score), Br Med J, 2, (1960); de Torres J.P., Pinto-Plata V., Ingenito E., Bagley P., Gray A., Berger R., Et al., Power of outcome measurements to detect clinically significant changes in pulmonary rehabilitation of patients with COPD, Chest, 121, pp. 1092-1098, (2002); Vitacca M., Malovini A., Balbi B., Aliani M., Cirio S., Spanevello A., Et al., Minimal clinically important difference in Barthel index dyspnea in patients with COPD, Int J Chron Obstruct Pulmon Dis, 15, pp. 2591-2599, (2020); Kon S.S., Canavan J.L., Jones S.E., Nolan C.M., Clark A.L., Dickson M.J., Et al., Minimum clinically important difference for the COPD Assessment Test: a prospective analysis, Lancet Respir Med, 2, pp. 195-203, (2014); Maltais F., LeBlanc P., Jobin J., Berube C., Bruneau J., Carrier L., Et al., Intensity of training and physiologic adaptation in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 155, pp. 555-561, (1997); Luxton N., Alison J.A., Wu J., Mackey M.G., Relationship between field walking tests and incremental cycle ergometry in COPD, Respirology, 13, pp. 856-862, (2008); Gloeckl R., Zwick R.H., Furlinger U., Jarosch I., Schneeberger T., Leitl D., Et al., Prescribing and adjusting exercise training in chronic respiratory diseases – expert-based practical recommendations, Pulmonology, 29, pp. 306-314, (2023); Vitacca M., Comini L., Barbisoni M., Francolini G., Paneroni M., Ramponi J.P., A pulmonary rehabilitation decisional score to define priority access for COPD patients, Rehabil Res Pract, 2017, (2017); Burtin C., Mohan D., Troosters T., Watz H., Hopkinson N.S., Garcia-Aymerich J., Et al., Objectively measured physical activity as a COPD clinical trial outcome, Chest, 160, pp. 2080-2100, (2021); Rochester C.L., Patient assessment and selection for pulmonary rehabilitation, Respirology, 24, pp. 844-853, (2019); Mathioudakis A.G., Abroug F., Agusti A., Ananth S., Bakke P., Bartziokas K., Et al., ERS statement: a core outcome set for clinical trials evaluating the management of COPD exacerbations, Eur Respir J, 59, (2022); Jenkins A.R., Groenen M.T.J., Vaes A.W., Janssen D.J.A., Wouters E.F.M., Franssen F.M.E., Et al., Baseline dependent minimally important differences for clinical outcomes of pulmonary rehabilitation in people with COPD, Pulmonology, (2023); Troosters T., Gosselink R., Decramer M., Exercise training in COPD: how to distinguish responders from nonresponders, J Cardiopulm Rehabil, 21, pp. 10-17, (2001); Spruit M.A., Augustin I.M., Vanfleteren L.E., Janssen D.J., Gaffron S., Pennings H.J., Et al., Differential response to pulmonary rehabilitation in COPD: multidimensional profiling, Eur Respir J, 46, pp. 1625-1635, (2015); Augustin I.M.L., Franssen F.M.E., Houben-Wilke S., Janssen D.J.A., Gaffron S., Pennings H.J., Et al., Multidimensional outcome assessment of pulmonary rehabilitation in traits-based clusters of COPD patients, PLOS ONE, 17, (2022); Ricke E., Bakker E.W., Development and validation of a multivariable exercise adherence prediction model for patients with COPD: a prospective cohort study, Int J Chron Obstruct Pulmon Dis, 18, pp. 385-398, (2023); Carl J.A., Geidl W., Schuler M., Mino E., Lehbert N., Wittmann M., Et al., Towards a better understanding of physical activity in people with COPD: predicting physical activity after pulmonary rehabilitation using an integrative competence model, Chron Respir Dis, 18, (2021); Duarte-de-Ara Jo A.N., Teixeira P., Hespanhol V., Correia-de-Sousa J., COPD: how can evidence from randomised controlled trials apply to patients treated in everyday clinical practice?, Pulmonology, 28, pp. 431-439, (2022)","M. Vitacca; Respiratory Rehabilitation of the Institute of Lumezzane, Istituti Clinici Scientifici Maugeri IRCCS, Brescia, Italy; email: michele.vitacca@icsmaugeri.it","","Sociedad Espanola de Neumologia y Cirugia Toracica (SEPAR)","","","","","","03002896","","ARBRD","","English","Arch. Bronconeumol.","Article","Final","","Scopus","2-s2.0-85184049905"
"Secher P.H.; Hangaard S.; Kronborg T.; Hæsum L.K.E.; Udsen F.W.; Hejlesen O.; Bender C.","Secher, Pernille Heyckendorff (54581107500); Hangaard, Stine (57196475233); Kronborg, Thomas (57200163284); Hæsum, Lisa Korsbakke Emtekær (56043957000); Udsen, Flemming Witt (55924997400); Hejlesen, Ole (6603859869); Bender, Clara (57218223761)","54581107500; 57196475233; 57200163284; 56043957000; 55924997400; 6603859869; 57218223761","Clinical implementation of an algorithm for predicting exacerbations in patients with COPD in telemonitoring: a study protocol for a single-blinded randomized controlled trial","2022","Trials","23","1","356","","","","4","10.1186/s13063-022-06292-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128842298&doi=10.1186%2fs13063-022-06292-y&partnerID=40&md5=eb512b344a190b73a3e89de4c3637a65","Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Department of Nursing, University College of Northern Denmark, Selma Lagerløfs Vej 2, Aalborg East, 9220, Denmark","Secher P.H., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Hangaard S., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Kronborg T., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Hæsum L.K.E., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark, Department of Nursing, University College of Northern Denmark, Selma Lagerløfs Vej 2, Aalborg East, 9220, Denmark; Udsen F.W., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Hejlesen O., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark; Bender C., Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7C, Aalborg East, 9220, Denmark","Background: Acute exacerbations have a significant impact on patients with COPD by accelerating the decline in lung function leading to decreased health-related quality of life and survival time. In telehealth, health care professionals exercise clinical judgment over a physical distance. Telehealth has been implemented as a way to monitor patients more closely in daily life with an intention to intervene earlier when physical measurements indicate that health deteriorates. Several studies call for research investigating the ability of telehealth to automatically flag risk of exacerbations by applying the physical measurements that are collected as part of the monitoring routines to support health care professionals. However, more research is needed to further develop, test, and validate prediction algorithms to ensure that these algorithms improve outcomes before they are widely implemented in practice. Method: This trial tests a COPD prediction algorithm that is integrated into an existing telehealth system, which has been developed from the previous Danish large-scale trial, TeleCare North (NCT: 01984840). The COPD prediction algorithm aims to support clinical decisions by predicting the risk of exacerbations for patients with COPD based on selected physiological parameters. A prospective, parallel two-armed randomized controlled trial with approximately 200 participants with COPD will be conducted. The participants live in Aalborg municipality, which is located in the North Denmark Region. All participants are familiar with the telehealth system in advance. In addition to the participants’ usual weekly monitored measurements, they are asked to measure their oxygen saturation two more times a week during the trial period. The primary outcome is the number of exacerbations defined as an acute hospitalization from baseline to follow-up. Secondary outcomes include changes in health-related quality of life measured by both the 12-Item Short Form Survey version 2 and EuroQol-5 Dimension Questionnaire as well as the incremental cost-effectiveness ratio. Discussion: This trial seeks to explore whether the COPD prediction algorithm has the potential to support early detection of exacerbations in a telehealth setting. The COPD prediction algorithm may initiate timely treatment, which may decrease the number of hospitalizations. Trial registration: NCT05218525 (pending at clinicaltrials.gov) (date, month, year) © 2022, The Author(s).","Chronic obstructive pulmonary disease; Clinical decision support systems; Disease exacerbation; Forecasting; Health literacy; Machine learning; Physiological monitoring; Randomized controlled trial; Telemedicine","Algorithms; Humans; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Quality of Life; Randomized Controlled Trials as Topic; Telemedicine; algorithm; Article; blood pressure; chronic obstructive lung disease; clinical decision support system; controlled study; cost effectiveness analysis; disease exacerbation; European Quality of Life 5 Dimensions questionnaire; health care personnel; health literacy; hospitalization; human; machine learning; oxygen saturation; quality adjusted life year; quality of life; randomized controlled trial; sensitivity and specificity; single blind procedure; telehealth; telemonitoring; algorithm; chronic obstructive lung disease; procedures; prospective study; randomized controlled trial (topic); telemedicine","","","Galaxy TAB 2, Samsung, South Korea; UA-767, Nonin, United States","Nonin, United States; Samsung, South Korea","Danish Agency; Danish Agency for Digitization; OpenTeleHealth","Funding text 1: The Danish Agency for Digitization has launched 15 signature projects in Denmark that seeks to investigate different perspectives of artificial intelligence. This project is one out of these 15 signature projects that the Danish Agency for Digitization funds. The funder has no influence on the conception, data analysis or results of the project. The Danish Agency provide grant funding ; Funding text 2: Thanks to American Journal experts for proofreading the manuscript. Thanks to the TeleCare North administration office and OpenTeleHealth for making it possible to implement and test the COPD prediction algorithm into usual practice. Thanks to the specialized COPD community nurses for monitoring more frequently and for the participants? effort in making additional oxygen saturation measurements during the trial period.; Funding text 3: The trial is funded by the Danish Agency for Digitization. ","Iheanacho I., Zhang S., King D., Rizzo M., Ismaila A.S., Economic burden of chronic obstructive pulmonary disease (COPD): A systematic literature review, Int J COPD, 15, pp. 439-460, (2020); Papi A., Morandi L., Fabbri L.M., Prevention of Chronic Obstructive Pulmonary Disease, Clin Chest Med, 41, pp. 453-462, (2020); (2017); Lozano R., Naghavi M., Foreman K., Lim S., Shibuya K., Aboyans V., Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, pp. 2095-2128, (2012); Murray J.L., (1990); Organization W.H., Projections of mortality and causes of death, 2015 and 2030, (2020); Gerald L.B., Bailey W.C., Global initiative for chronic obstructive lung disease, J Cardiopulm Rehabil, 22, pp. 234-244, (2018); Anzueto A., Impact of exacerbations on copd, Eur Respir Rev, 19, pp. 113-118, (2010); Vestbo J., Hurd S.S., Agusti A.G., Jones P.W., Vogelmeier C., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease GOLD executive summary, Am J Respir Crit Care Med, 187, pp. 347-365, (2013); Seemungal T.A.R., Donaldson G.C., Bhowmik A., Jeffries D.J., Wedzicha J.A., Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 161, pp. 1608-1613, (2000); Donaldson G.C., Law M., Kowlessar B., Singh R., Brill S.E., Allinson J.P., Et al., Impact of prolonged exacerbation recovery in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 192, pp. 943-950, (2015); Burge S., Wedzicha J.A., COPD exacerbations: Definitions and classifications, Eur Respir Journal, Suppl, 21, pp. 46-53, (2003); Halpin D.M.G., Miravitlles M., Metzdorf N., Celli B., Impact and prevention of severe exacerbations of COPD: A review of the evidence, Int J COPD, 12, pp. 2891-2908, (2017); Wilkinson T.M.A., Donaldson G.C., Hurst J.R., Seemungal T.A.R., Wedzicha J.A., Early Therapy Improves Outcomes of Exacerbations of Chronic Obstructive Pulmonary Disease, Am J Respir Crit Care Med, 169, pp. 1298-1303, (2004); Seemungal T.A.R., Donaldson G.C., Paul E.A., Bestall J.C., Jeffries D.J., Wedzicha J.A., Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 157, pp. 1418-1422, (1998); Langsetmo L., Platt R.W., Ernst P., Bourbeau J., Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort, Am J Respir Crit Care Med, 177, pp. 396-401, (2008); Tomasic I., Tomasic N., Trobec R., Krpan M., Kelava T., Continuous remote monitoring of COPD patients—justification and explanation of the requirements and a survey of the available technologies, Med Biol Eng Comput, 56, pp. 547-569, (2018); Quaderi S.A., Hurst J.R., The unmet global burden of COPD, Glob Heal Epidemiol Genomics, 3, pp. 9-11, (2018); Hurst J.R., Wedzicha J.A., Management and prevention of chronic obstructive pulmonary disease exacerbations: A state of the art review, BMC Med, 7, pp. 1-6, (2009); Polisena J., Tran K., Cimon K., Hutton B., McGill S., Palmer K., Et al., Home telehealth for chronic obstructive pulmonary disease: A systematic review and meta-analysis, J Telemed Telecare, 16, pp. 120-127, (2010); Wootton R., Twenty years of telemedicine in chronic disease management--an evidence synthesis, J Telemed Telecare, 18, pp. 211-220, (2012); Gregersen T.L., Green A., Frausing E., Ringbaek T., Brondum E., Ulrik C.S., Do telemedical interventions improve quality of life in patients with COPD?, A systematic review. Int J COPD, 11, pp. 809-822, (2016); Hong Y., Lee S.H., Effectiveness of tele-monitoring by patient severity and intervention type in chronic obstructive pulmonary disease patients: A systematic review and meta-analysis, Int J Nurs Stud, 92, pp. 1-15, (2019); Mclean S., Nurmatov U., Jly L., Pagliari C., Car J., Sheikh A., Et al., Telehealthcare for chronic obstructive pulmonary disease: Cochrane Review and meta-analysis, Br J Gen Pract, 62, pp. e739-e749, (2012); Barbosa M.T., Sousa C.S., Morais-Almeida M., Simoes M.J., Mendes P., Telemedicine in COPD: An Overview by Topics, COPD J Chronic Obstr Pulm Dis, 17, pp. 601-617, (2020); Sul A.R., Lyu D.H., Park D.A., Effectiveness of telemonitoring versus usual care for chronic obstructive pulmonary disease: A systematic review and meta-analysis, J Telemed Telecare, 26, pp. 189-199, (2020); Sanchez-Morillo D., Fernandez-Granero M.A., Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: A systematic review, Chron Respir Dis, 13, pp. 264-283, (2016); Lilholt P.H., Udsen F.W., Ehlers L., Hejlesen O.K., Telehealthcare for patients suffering from chronic obstructive pulmonary disease: Effects on health-related quality of life: Results from the Danish ? € TeleCare North’ cluster-randomised trial, BMJ Open, 7, (2017); Lilholt P.H., Haesum L.K.E., Hejlesen O.K., Exploring User Experience of a Telehealth System for the Danish TeleCare North Trial, Stud Health Technol Inform, 210, pp. 301-305, (2015); Udsen F.W., Lilholt P.H., Hejlesen O., Ehlers L., Cost-effectiveness of telehealthcare to patients with chronic obstructive pulmonary disease: Results from the Danish TeleCare North’ cluster-randomised trial, BMJ Open, 7, pp. 1-13, (2017); Udsen F.W., Bang Christensen J.K., Lilholt P.H., Haesum L.K.E., Forskningsresultater i Telecare Nord - Afslutningsrapport, 2015; Udsen F.W., Lilholt P.H., Hejlesen O.K., Ehlers L.H., Subgroup analysis of telehealthcare for patients with chronic obstructive pulmonary disease: The cluster-randomized danish telecare north trial, Clin Outcomes Res, 9, pp. 391-401, (2017); Udsen F.W., Health economic evaluation of telehealthcare - can we include “why” and “under what circumstances” telehealthcare is cost-effective in health economic evaluation? Suggested principles for health economic evaluation based on experiences with the Danish, ""Te, (2016); Udsen F., Lilholt P., Hejlesen O., Ehlers L., Effectiveness and cost-effectiveness of telehealthcare for chronic obstructive pulmonary disease: study protocol for a cluster randomized controlled trial, Trials, 15, (2014); Soiza R.L., Donaldson A.I.C., Myint P.K., Vaccine against arteriosclerosis: an update, Ther Adv Vaccines, 9, pp. 259-261, (2018); Guerra B., Gaveikaite V., Bianchi C., Puhan M.A., Prediction models for exacerbations in patients with COPD, Eur Respir Rev, 26, (2017); Christian Riis H., Jensen M.H., Cichosz S.L., Hejlesen O.K., Prediction of exacerbation onset in chronic obstructive pulmonary disease patients, J Med Eng Technol, 40, pp. 1-7, (2016); Kronborg T., Mark L., Cichosz S.L., Secher P.H., Hejlesen O., Population exacerbation incidence contains predictive information of acute exacerbations in patients with chronic obstructive pulmonary disease in telecare, Int J Med Inform, 111, pp. 72-76, (2018); Jensen M.H., Cichosz S.L., Dinesen B., Hejlesen O.K., Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare, J Telemed Telecare, 18, pp. 99-103, (2012); Kronborg T., Predicting Exacerbations in Patients with Chronic Obstructive Pulmonary Disease, Det Sundhedsvidenskabelige Fakultet. Ph.D.-serien., (2019); Kronborg T., Hangaard S., Cichosz S.L., Hejlesen O.K., A two-layer probabilistic model to predict COPD exacerbations for patients in telehealth, Comput Biol Med, (2020); Lilholt P.H., Jensen M.H., Hejlesen O.K., Heuristic evaluation of a telehealth system from the Danish TeleCare North Trial, Int J Med Inform, 84, pp. 319-326, (2015); Medical Association W. WMA DECLARATION OF Helsinki – Ethical principles for medical research involving human subjects; Danish data protection legislation 2020; The Quality Metrics. The 12-Items Short Form Health Survey, (2020); EuroQol T., Foundation R., The EuroQol-5 Dimension Questionnaire, (2020); Christensen L.N., Ehlers L., Larsen F.B., Jensen M.B., Validation of the 12 Item Short form Health Survey in a Sample from Region Central Jutland, Soc Indic Res, 114, pp. 513-521, (2013); Jensen M.B., Jensen C.E., Gudex C., Pedersen K.M., Sorensen S.S., Ehlers L.H., Danish population health measured by the EQ-5D-5L, Scand J Public Health, (2021); Jensen C.E., Sorensen S.S., Gudex C., Jensen M.B., Pedersen K.M., Ehlers L.H., The Danish EQ-5D-5L Value Set: A Hybrid Model Using cTTO and DCE Data, Appl Health Econ Health Policy, 19, pp. 579-591, (2021); Linden A., Measuring diagnostic and predictive accuracy in disease management: An introduction to receiver operating characteristic (ROC) analysis, J Eval Clin Pract, 12, pp. 132-139, (2006); Emtekaer Haesum L.K., Ehlers L., Hejlesen O.K., Validation of the Test of Functional Health Literacy in Adults in a Danish population, Scand J Caring Sci, 29, pp. 573-581, (2015); Parmanto B., Lewis A.N., Kristin M., Bertolet M.H., DEVELOPMENT OF THE TELEHEALTH USABILITY QUESTIONNAIRE (TUQ), Int J Telerehabilitation, 8, pp. 3-10, (2016); McCoy C.E., Understanding the intention-to-treat principle in randomized controlled trials, West J Emerg Med, 18, pp. 1075-1078, (2017); Schulz K.F., Altman D.G., Moher D., CONSORT 2010 statement: Updated guidelines for reporting parallel group randomised trials, Int J Surg, 9, pp. 672-677, (2011); Husereau D., Drummond M., Petrou S., Carswell C., Moher D., Greenberg D., Et al., Consolidated health economic evaluation reporting standards (CHEERS)-explanation and elaboration: A report of the ISPOR health economic evaluation publication guidelines good reporting practices task force, Value Heal, 16, pp. 231-250, (2013); Sterne J.A.C., White I.R., Carlin J.B., Spratt M., Royston P., Kenward M.G., Et al., Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls, BMJ, 339, pp. 157-160, (2009); Dunn P.K., Smyth G.K., Generalized Linear Models With Examples in R, (2018); Drummond M., Sculpher M., Claxton K., Stoddart G., Torrance G., Methods for the Economic Evaluation of Health Care Programmes, (2015); Gray A., Clarke P., Wolstenholme J., Wordsworth S., Applied Methods of Cost-effectiveness Analysis in Health Care, (2011); Ramsey S., Willke R., Glick H., Reed S.D., Federico A., Jonsson B., Et al., Cost-Effectiveness Analysis Alongside Clinical Trials II - An ISPOR Good Research Practives Task Force Report, Value Heal, 18, pp. 161-172, (2015); Rosner B., Bernard A., Fundamentals of biostatistics, (2017)","S. Hangaard; Department of Health Science and Technology, Aalborg University, Aalborg East, Fredrik Bajers Vej 7C, 9220, Denmark; email: svh@hst.aau.dk","","BioMed Central Ltd","","","","","","17456215","","","35473589","English","Trials","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128842298"
"Dessie E.Y.; Gautam Y.; Ding L.; Altaye M.; Beyene J.; Mersha T.B.","Dessie, Eskezeia Y. (57205530525); Gautam, Yadu (56490826200); Ding, Lili (36022230300); Altaye, Mekibib (6603241667); Beyene, Joseph (6602069923); Mersha, Tesfaye B. (56147975000)","57205530525; 56490826200; 36022230300; 6603241667; 6602069923; 56147975000","Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods","2023","Scientific Reports","13","1","11279","","","","4","10.1038/s41598-023-35866-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164540689&doi=10.1038%2fs41598-023-35866-2&partnerID=40&md5=7df0185ffbe2d823711e05e8ad6ee1ed","Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada","Dessie E.Y., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Gautam Y., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Ding L., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Altaye M., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Beyene J., Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada; Mersha T.B., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States","Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma. © 2023, The Author(s).","","Asthma; Gene Regulatory Networks; Humans; Machine Learning; Nose; Phosphatidylinositol 3-Kinases; phosphatidylinositol 3 kinase; asthma; gene regulatory network; genetics; human; machine learning; nose","","phosphatidylinositol 3 kinase, 115926-52-8; Phosphatidylinositol 3-Kinases, ","","","National Institutes of Health, NIH, (R01HG011411, R01HL132344)","This work was supported by the National Institutes of Health (NIH) grants (R01HL132344 and R01HG011411). ","Kuruvilla M.E., Vanijcharoenkarn K., Shih J.A., Lee F.E., Epidemiology and risk factors for asthma, Respir. Med., 149, pp. 16-22, (2019); Los H., Koppelman G.H., Postma D.S., The importance of genetic influences in asthma, Eur. Respir. 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Commun., 13, (2022); Wagener A.H., Et al., The impact of allergic rhinitis and asthma on human nasal and bronchial epithelial gene expression, PLoS One, 8, (2013); Thavagnanam S., Et al., Nasal epithelial cells can act as a physiological surrogate for paediatric asthma studies, PLoS One, 9, (2014); Poole A., Et al., Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease, J. Allergy Clin. Immunol., 133, pp. 670-678.e612, (2014); Guajardo J.R., Et al., Altered gene expression profiles in nasal respiratory epithelium reflect stable versus acute childhood asthma, J. Allergy Clin. Immunol., 115, pp. 243-251, (2005); Jones A.C., Bosco A., Using network analysis to understand severe asthma phenotypes, Am. J. Respir. Crit. Care Med., 195, pp. 1409-1411, (2017); Saelens W., Cannoodt R., Saeys Y., A comprehensive evaluation of module detection methods for gene expression data, Nat. 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Rep., 10, (2020); Marone G., Et al., The intriguing role of interleukin 13 in the pathophysiology of asthma, Front. Pharmacol., 10, (2019); Abbas M., El-Manzalawy Y., Machine learning based refined differential gene expression analysis of pediatric sepsis, BMC Med. Genomics, 13, (2020); Ai X., Et al., Developing a diagnostic model to predict the risk of asthma based on ten macrophage-related gene signatures, Biomed. Res. Int., 2022, (2022); Su R., Zhang J., Liu X., Wei L., Identification of expression signatures for non-small-cell lung carcinoma subtype classification, Bioinformatics, 36, pp. 339-346, (2019); Cao Y., Et al., Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis, BMC Med. Genomics, 15, (2022); Behairy O.G.A., Mohammad O.I., Salim R.F., Sobeih A.A., A study of nasal epithelial cell gene expression in a sample of mild to severe asthmatic children and healthy controls, Egypt. J. Med. Hum. Genet., 23, (2022); Jackson N.D., Et al., Single-cell and population transcriptomics reveal pan-epithelial remodeling in type 2-high asthma, Cell Rep., 32, (2020); Yang I.V., Et al., The nasal methylome and childhood atopic asthma, J. Allergy Clin. Immunol., 139, pp. 1478-1488, (2017); Hamon Y., Et al., Neutrophilic cathepsin C is maturated by a multistep proteolytic process and secreted by activated cells during inflammatory lung diseases, J. Biol. Chem., 291, pp. 8486-8499, (2016); Mostafaei S., Et al., Identification of novel genes in human airway epithelial cells associated with chronic obstructive pulmonary disease (COPD) using machine-based learning algorithms, Sci. Rep., 8, (2018); Liu Y., Et al., Expansion of schizophrenia gene network knowledge using machine learning selected signals from dorsolateral prefrontal cortex and amygdala RNA-seq data, Front. Psychiatry, 13, (2022); Reeves S.R., Et al., Asthmatic bronchial epithelial cells promote the establishment of a Hyaluronan-enriched, leukocyte-adhesive extracellular matrix by lung fibroblasts, Respir. Res., 19, (2018); Chen L., Et al., Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis, Gene, 692, pp. 119-125, (2019)","T.B. Mersha; Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, United States; email: tesfaye.mersha@cchmc.org","","Nature Research","","","","","","20452322","","","37438356","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164540689"
"Ali S.W.; Asif M.; Zia M.Y.I.; Rashid M.; Syed S.A.; Nava E.","Ali, Syed Waqad (56075645700); Asif, Muhammad (59079321500); Zia, Muhammad Yousuf Irfan (57192103831); Rashid, Munaf (57220177714); Syed, Sidra Abid (57207914833); Nava, Enrique (26424569800)","56075645700; 59079321500; 57192103831; 57220177714; 57207914833; 26424569800","CDSS for Early Recognition of Respiratory Diseases based on AI Techniques: A Systematic Review","2023","Wireless Personal Communications","131","2","","739","761","22","4","10.1007/s11277-023-10432-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162940466&doi=10.1007%2fs11277-023-10432-1&partnerID=40&md5=c23c211db60d3ef18135be7f9a4a2120","Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan; Data Acquisition, Processing and Predictive Analytics (DAPPA) Lab, NCBC, Ziauddin University, Karachi, Pakistan; Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan; Department of Communications Engineering, University of Malaga, Malaga, Spain","Ali S.W., Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan, Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan; Asif M., Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan, Data Acquisition, Processing and Predictive Analytics (DAPPA) Lab, NCBC, Ziauddin University, Karachi, Pakistan; Zia M.Y.I., Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan, Department of Communications Engineering, University of Malaga, Malaga, Spain; Rashid M., Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan, Data Acquisition, Processing and Predictive Analytics (DAPPA) Lab, NCBC, Ziauddin University, Karachi, Pakistan; Syed S.A., Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan, Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan; Nava E., Department of Communications Engineering, University of Malaga, Malaga, Spain","Respiratory diseases such as Asthma, COVID-19, etc., require preventive and precautionary measures. Due to the lack of medical treatment for the masses, researchers are currently focusing on clinical decision support systems (CDSS). CDSS for respiratory diseases utilizes Machine Learning (ML) techniques to classify the symptoms into a possible diseases. This approach not only grasps the attention of researchers worldwide but also assists medical doctors in the early diagnosis of the disease. In this review paper, PRISMA guidelines are used to conduct a detailed overview of the early detection of respiratory diseases using ML techniques are identified. Among various ML techniques, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (D-Tree), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost are discussed. Then respiratory diseases are identified whose CDSS are available with the ML techniques and possible future direction for its improvement. Furthermore, the tools and ML techniques are compared with each other to enhance the researcher’s clarity for future use. The paper concluded with the future direction of the ML in the successful implementation of the CDSS in the field of respiratory disease. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","Artificial intelligence; Artificial neural network; Healthcare; Machine learning; Respiratory diseases","Adaptive boosting; Decision support systems; Diagnosis; Learning systems; Logistic regression; Nearest neighbor search; Neural networks; Pulmonary diseases; Support vector machines; AI techniques; Clinical decision support systems; Healthcare; Machine learning techniques; Machine-learning; Medical doctors; Medical treatment; Precautionary measures; Preventive measures; Systematic Review; Decision trees","","","","","","","Villegas P., Viral diseases of the respiratory system, Poultry Science, 77, 8, pp. 1143-1145, (1998); Ferkol T., Schraufnagel D., The global burden of respiratory disease, Annals of the American Thoracic Society, 11, 3, pp. 404-406, (2014); Pappas G., Bosilkovski M., Akritidis N., Mastora M., Krteva L., Tsianos E., Brucellosis and the respiratory system, Clinical Infectious Diseases, 37, 7, pp. e95-e99, (2003); Prevention of Transmission of Respiratory Illnesses in Disaster Evacuation Centers. 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Application of a new neural networks experiment and a decision tree model, Energy Buildings, 258, (2022); Wang K., Lu J., Liu A., Song Y., Xiong L., Zhang G., Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation, Neurocomputing, 491, pp. 288-304, (2022); Guhathakurata S., Saha S., Kundu S., Chakraborty A., Banerjee J.S., A new approach to predict COVID-19 using artificial neural networks, Cyber-physical systems, pp. 139-160, (2022); Hernandez-Pereira E.M., Alvarez-Estevez D., Moret-Bonillo V., Automatic classification of respiratory patterns involving missing data imputation techniques, Biosystems Engineering, 138, pp. 65-76, (2015); Widder S., Et al., Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways, ISME Journal, 16, 4, pp. 905-914, (2022); Uegami W., Et al., Mixture of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease, Modern Pathology, 35, pp. 1083-1091, (2022); Amini N., Shalbaf A., Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images, International Journal of Imaging Systems and Technology, 32, 1, pp. 102-110, (2022); Gaur D., Dubey S.K., Impact of environmental concern factors on lung diseases using machine learning, Computational intelligence in pattern recognition, pp. 719-730, (2022); El-Askary N.S., Salem M.A.-M., Roushdy M.I., Features processing for random forest optimization in lung nodule localization, Expert Systems with Applications, 193, (2022); Schapire R.E., Explaining AdaBoost, Empirical inference, pp. 37-52, (2013); Vaishnaw G.K., A method of micro pixel similarity for lung cancer diagnosis using adaboost, Algorithms for intelligent systems, pp. 75-90, (2022); Sevinc E., An empowered AdaBoost algorithm implementation: A COVID-19 dataset study, Computers & Industrial Engineering, 165, (2022); Venkatesh S.P., Raamesh L., Predicting lung cancer survivability: A machine learning ensemble method on seer data, Research Square, 45, (2022); Mary S.R., Kumar V., Venkatesan K.J.P., Kumar R.S., Jagini N.P., Srinivas A., Vulture-based AdaBoost-feedforward neural frame work for COVID-19 prediction and severity analysis system, Interdisciplinary Sciences, 14, 2, pp. 582-595, (2022); Segal G., Segev A., Brom A., Lifshitz Y., Wasserstrum Y., Zimlichman E., Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting, Journal of the American Medical Informatics Association, 26, 12, pp. 1560-1565, (2019); Amaral J.L.M., Lopes A.J., Jansen J.M., Faria A.C.D., Melo P.L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 105, 3, pp. 183-193, (2012); Abdullah D.M., Abdulazeez A.M., Sallow A.B., Lung cancer prediction and classification based on correlation selection method using machine learning techniques, Qubahan Academic Journal, 1, 2, pp. 141-149, (2021); Bauer Y., Et al., Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: Machine learning on proteomics from the DETECT cohort, European Respiratory Journal, 57, 6, (2021); Min X., Yu B., Wang F., Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: A case study on COPD, Science and Reports, 9, 1, (2019); Karthikeyan A., Garg A., Vinod P.K., Priyakumar U.D., Machine learning based clinical decision support system for early COVID-19 mortality prediction, Frontiers in Public Health, 9, (2021); Tiwari S., Chanak P., Singh S.K., A review of the machine learning algorithms for covid-19 case analysis, IEEE Transactions on Artificial Intelligence, (2022); Ajaz F., Naseem M., Sharma S., Shabaz M., Dhiman G., COVID-19: Challenges and its technological solutions using IoT, Current Medical Imaging Review, 18, 2, pp. 113-123, (2022); Sharma M., Prakash U., Kumari A., Singla K., Early detection of covid-19 based on preliminary features using machine learning algorithms, Advances in intelligent systems and computing, pp. 391-402, (2022); Andrade D.S.M., Et al., Machine learning associated with respiratory oscillometry: A computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis, Biomedical Engineering Online, 20, 1, pp. 1-18, (2021)","S.W. Ali; Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan; email: waqadhashmi@yahoo.com","","Springer","","","","","","09296212","","WPCOF","","English","Wireless Pers Commun","Article","Final","","Scopus","2-s2.0-85162940466"
"Guo J.; He Q.; Peng C.; Dai R.; Li W.; Su Z.; Li Y.","Guo, Jiale (57929594400); He, Qionghan (58280540400); Peng, Caiju (57929412400); Dai, Ru (57392660900); Li, Wei (56865610400); Su, Zhichao (58521153800); Li, Yehai (57192534144)","57929594400; 58280540400; 57929412400; 57392660900; 56865610400; 58521153800; 57192534144","Machine learning algorithms to predict risk of postoperative pneumonia in elderly with hip fracture","2023","Journal of Orthopaedic Surgery and Research","18","1","571","","","","4","10.1186/s13018-023-04049-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166597926&doi=10.1186%2fs13018-023-04049-0&partnerID=40&md5=a804a462896913d954edd22722934705","Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; Chaohu Hospital of Anhui Medical University, Hefei, China","Guo J., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; He Q., Chaohu Hospital of Anhui Medical University, Hefei, China; Peng C., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; Dai R., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; Li W., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; Su Z., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; Li Y., Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China","Background: Hip fracture (HF) is one of the most common fractures in the elderly and is significantly associated with high mortality and unfavorable prognosis. Postoperative pneumonia (POP), the most common postoperative complication of HF, can seriously affect patient prognosis and increase the burden on the healthcare system. The aim of this study was to develop machine learning models for identifying elderly patients at high risk of pneumonia after hip fracture surgery. Methods: From May 2016 to November 2022, patients admitted to a single central hospital for HF served as the study population. We extracted data that could be collected within 24 h of patient admission. The dataset was divided into training and validation sets according to 70:30. Based on the screened risk factors, prediction models were developed using seven machine learning algorithms, namely CART, GBM, KNN, LR, NNet, RF, and XGBoost, and their performance was evaluated. Results: Eight hundred five patients were finally included in the analysis and 75 (9.3%) patients suffered from POP. Age, CI, COPD, WBC, HB, GLU, STB, GLOB, Ka+ which are used as features to build machine learning models. By evaluating the model's AUC value, accuracy, sensitivity, specificity, Kappa value, MCC value, Brier score value, calibration curve, and DCA curve, the model constructed by XGBoost algorithm has the best and near-perfect performance. Conclusion: The machine learning model we created is ideal for detecting elderly patients at high risk of POP after HF at an early stage. © 2023, BioMed Central Ltd., part of Springer Nature.","Hip fractures; Machine learning; Postoperative pneumonia; Predictive models; Risk factors","Aged; Algorithms; Calibration; Hip Fractures; Humans; Machine Learning; Pneumonia; aged; algorithm; calibration; hip fracture; human; machine learning; pneumonia","","","","","Anhui Medical University, AHMU, (YJS20230090); Anhui Medical University, AHMU","Our study was funded by the ""Postgraduate Innovation Research and Practice Program of Anhui Medical University"" (No. YJS20230090). ","Lawrence V.A., Hilsenbeck S.G., Noveck H., Poses R.M., Carson J.L., Medical complications and outcomes after hip fracture repair, Arch Intern Med, 162, 18, pp. 2053-2057, (2002); Chen Y.P., Kuo Y.J., Hung S.W., Wen T.W., Chien P.C., Chiang M.H., Et al., Loss of skeletal muscle mass can be predicted by sarcopenia and reflects poor functional recovery at one year after surgery for geriatric hip fractures, Injury, 52, 11, pp. 3446-3452, (2021); Burge R., Dawson-Hughes B., Solomon D.H., Wong J.B., King A., Tosteson A., Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025, J Bone Miner Res, 22, 3, pp. 465-475, (2007); Dimai H.P., Reichardt B., Zitt E., Concin H., Malle O., Fahrleitner-Pammer A., Et al., Thirty years of hip fracture incidence in Austria: is the worst over?, Osteoporos Int, 33, 1, pp. 97-104, (2022); Kannus P., Niemi S., Parkkari J., Sievanen H., Continuously declining incidence of hip fracture in Finland: analysis of nationwide database in 1970–2016, Arch Gerontol Geriatr Jul-Aug, 77, pp. 64-67, (2018); Cauley J.A., Chalhoub D., Kassem A.M., Fuleihan G.H., Geographic and ethnic disparities in osteoporotic fractures, Nat Rev Endocrinol, 10, 6, pp. 338-351, (2014); Nordstrom P., Bergman J., Ballin M., Nordstrom A., Trends in hip fracture incidence, length of hospital stay, and 30-day mortality in Sweden from 1998–2017: a nationwide cohort study, Calcif Tissue Int, 111, 1, pp. 21-28, (2022); Wu A.M., Bisignano C., James S.L., Abady G.G., Abedi A., Abu-Gharbieh E., Alhassan R.K., Alipour V., Arabloo J., Asaad M., Asmare W.N., Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: a systematic analysis from the global burden of disease study 2019, Lancet Healthy Longev, 2, 9, pp. e580-e592, (2021); Cooper C., Campion G., Melton L.J., Hip fractures in the elderly: a world-wide projection, Osteoporos Int, 2, 6, pp. 285-289, (1992); Marsillo E., Pintore A., Asparago G., Oliva F., Maffulli N., Cephalomedullary nailing for reverse oblique intertrochanteric fractures 31A3 (AO/OTA), Orthop Rev, 14, 6, (2022); Gargano G., Poeta N., Oliva F., Migliorini F., Maffulli N., Zimmer natural nail and ELOS nails in pertrochanteric fractures, J Orthop Surg Res, 16, pp. 1-9, (2021); Wang X., Zhao B.J., Su Y., Can we predict postoperative complications in elderly Chinese patients with hip fractures using the surgical risk calculator?, Clin Interv Aging, 12, pp. 1515-1520, (2017); Maffulli N., Aicale R., Proximal femoral fractures in the elderly: a few things to know, and some to forget, Medicina, 58, 10, (2022); Quaranta M., Miranda L., Oliva F., Migliorini F., Pezzuti G., Maffulli N., Haemoglobin and transfusions in elderly patients with hip fractures: the effect of a dedicated orthogeriatrician, J Orthop Surg Res, (2021); Roche J.J., Wenn R.T., Sahota O., Moran C.G., Effect of comorbidities and postoperative complications on mortality after hip fracture in elderly people: prospective observational cohort study, Bmj, 331, 7529, (2005); Fernandez-Bustamante A., Frendl G., Sprung J., Kor D.J., Subramaniam B., Martinez Ruiz R., Et al., Postoperative pulmonary complications, early mortality, and hospital stay following noncardiothoracic surgery: a multicenter study by the perioperative research network investigators, JAMA Surg, 152, 2, pp. 157-166, (2017); Deo R.C., Machine learning in medicine, Circulation, 132, 20, pp. 1920-1930, (2015); Abbott T.E., Fowler A.J., Pelosi P., De Abreu M.G., Moller A.M., Canet J., Creagh-Brown B., Mythen M., Gin T., Lalu M.M., Futier E., A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications, Br J Anaesth, 120, 5, pp. 1066-1079, (2018); Tibshirani R., Regression shrinkage and selection via the Lasso, J R Stat Soc Ser B: Stat Methodol, 58, 1, pp. 267-288, (1996); McHugh M.L., Interrater reliability: the kappa statistic, Biochemia Medica, 22, 3, pp. 276-282, (2012); Boughorbel S., Jarray F., El-Anbari M., Optimal classifier for imbalanced data using matthews correlation coefficient metric article, Plos One, 12, 6, (2017); Hilden J., Habbema J.D., Bjerregaard B., The measurement of performance in probabilistic diagnosis. 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II. Accuracy and precision of regression estimates, J Clin Epidemiol, 48, 12, pp. 1503-1510, (1995)","Y. Li; Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China; email: ahyylyh@163.com","","BioMed Central Ltd","","","","","","1749799X","","","37543618","English","J. Orthop. Surg. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85166597926"
"Zhao Y.; Liu Y.; Zhou H.; Wei Y.; Yu Y.; Lu S.; An X.; Liu Q.","Zhao, Yixin (57221867606); Liu, Yanzhong (57824997200); Zhou, Haiyang (57876452900); Wei, Yuxuan (58414168600); Yu, Yansuo (58306872000); Lu, Sichao (57825427200); An, Xiang (57204049257); Liu, Qiang (57221872914)","57221867606; 57824997200; 57876452900; 58414168600; 58306872000; 57825427200; 57204049257; 57221872914","Intelligent Gateway Based Human Cardiopulmonary Health Monitoring System","2023","Journal of Sensors","2023","","3534224","","","","4","10.1155/2023/3534224","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163701692&doi=10.1155%2f2023%2f3534224&partnerID=40&md5=830b30c52cdd3914e2077759419b4ac2","Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Beijing Academy of Safety Engineering and Technology, Beijing, 102617, China","Zhao Y., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Liu Y., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Zhou H., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Wei Y., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Yu Y., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; Lu S., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; An X., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China, Beijing Academy of Safety Engineering and Technology, Beijing, 102617, China; Liu Q., Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China","Cardiopulmonary diseases, including cardiovascular disease (CVD) and chronic obstructive pulmonary disorder (COPD), are prevalent in the elderly population. Early identification, long-term health monitoring, and health management of cardiopulmonary disease are crucial for reversing organ damage and preventing further injury. However, most home health monitoring (HHM) devices need to be paired with a specific smartphone application, which leads to the complexity of monitoring multiple health indicators and hinders the feasibility of comprehensive multi-indicator analysis. Therefore, this paper designed a human cardiopulmonary health monitoring system based on an intelligent gateway to reduce the dependence of HHM on smartphones, achieve synchronous monitoring of multiple health indicators, and improve usability to better serve the elderly population. The proposed system can simultaneously monitor electrocardiogram (ECG), pulmonary function, blood pressure, and blood oxygen level (SpO2); process and analyze the data in real-time through the intelligent gateway's edge computing power; and display the cardiopulmonary health status in real-time. The intelligent gateway embedded a specially designed CNN-LSTM artificial intelligence model on the STM32F429 microcontroller to realize real-time identification of ECG signals at the edge. The accuracy of the pretrained CNN-LSTM model for ECG signal identification is 99.49%, and the model has good performance in terms of complexity and RAM space occupied. According to the evaluation test, the system can achieve the purpose of monitoring human cardiopulmonary health, has a wide range of application scenarios, and has great value in promotion and application. © 2023 Yixin Zhao et al.","","Blood; Blood pressure; Computing power; Damage detection; Display devices; Electrocardiography; Long short-term memory; Cardiopulmonary disease; Cardiovascular disease; Elderly populations; Electrocardiogram signal; Health indicators; Health monitoring; Health monitoring system; Home health monitoring; Intelligent gateway; Real- time; Smartphones","","","","","","","Lin B.S., Lin B.S., Chou N.K., Chong F.C., Chen S.J., RTWPMS: A real-time wireless physiological monitoring system, IEEE Transactions on Information Technology in Biomedicine, 10, 4, pp. 647-656, (2006); Pandian P.S., Mohanavelu K., Safeer K.P., Kotresh T.M., Shakunthala D.T., Gopal P., Padaki V.C., Smart vest: Wearable multi-parameter remote physiological monitoring system, Medical Engineering & Physics, 30, 4, pp. 466-477, (2008); Sardini E., Serpelloni M., Instrumented wearable belt for wireless health monitoring, Procedia Engineering, 5, pp. 580-583, (2010); Panicker N.V., Kumar A.S., Tablet PC enabled body sensor system for rural telehealth applications, International Journal of Telemedicine and Applications, 2016, (2016); Ashfaq Z., Mumtaz R., Rafay A., Zaidi S.M.H., Saleem H., Mumtaz S., Shahid A., Poorter E.D., Moerman I., Embedded AI-based digi-healthcare, Applied Sciences-Basel, 12, 1, (2022); Anliker U., Ward J.A., Lukowicz P., Troster G., Dolveck F., Baer M., Keita F., Schenker E.B., Catarsi F., Coluccini L., Belardinelli A., Shklarski D., Alon M., Hirt E., Schmid R., Vuskovic M., AMON: A wearable multiparameter medical monitoring and alert system, IEEE Transactions on Information Technology in Biomedicine, 8, 4, pp. 415-427, (2004); Serhani M.A., Menshawy M.E., Benharref A., SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases, Computers in Biology and Medicine, 68, pp. 137-154, (2016); Fu J.F., Research and Development of Health Monitoring and Service System, (2017); Meza R.M., Santos R.T., Nolazco-Flores J.A., Rodriguez-Ortiz G., Anguiano R., Rios A., Block A.E., PlaIMoS: A remote mobile healthcare platform to monitor cardiovascular and respiratory variables, Sensors, 17, 12, (2017); Guan K., Shao M., Wu S., A remote health monitoring system for the elderly based on smart home gateway, Journal of Healthcare Engineering, 2017, (2017); Kono T., Taito Y., Hidaka H., Essential roles, challenges and development of embedded MCU micro-systems to innovate edge computing for the IoT/AI age, IEICE Transactions on Electronics, 103, 4, pp. 132-143, (2020); Lin Y., Chuang C., Yen C., Huang S., Chen J., Lee S., An AIoT Wearable ECG Patch with Decision Tree for Arrhythmia Analysis, pp. 1-4; Zhang Z., Shi Q., He T., Guo X., Dong B., Lee J., Lee C., Artificial intelligence of toilet (AI-toilet) for an integrated health monitoring system (IHMS) using smart triboelectric pressure sensors and image sensor, Nano Energy, 901, (2021); Queralta J.P., Gia T.A., Tenhunen H., Westerlund T., Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks, pp. 601-604; Nguyen Q.H., Nguyen B.P., Nguyen T.B., Do T.T., Mbinta J.F., Simpson C., Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings, Biomedical Signal Processing and Control, 681, (2021); Jun T.J., Nguyen H.M., Kang D., Kim D., Kim D., Kim Y.H., ECG Arrhythmia Classification Using A 2-D Convolutional Neural Network, (2018); Ullah A., Anwar S.M., Bilal M., Mehmood R.M., Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation, Remote Sensing, 12, 10, (2020); Wang M., Rahardja S., Franti P., Rahardja S., Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN, Biomedical Signal Processing and Control, 791, (2023); Andersen R.S., Peimankar A., Puthusserypady S., A deep learning approach for real-time detection of atrial fibrillation, Expert Systems with Applications, 115, pp. 465-473, (2019); Osowski S., Hoai L.T., Markiewicz T., Support vector machine-based expert system for reliable heartbeat recognition, IEEE Transactions on Biomedical Engineering, 51, 4, pp. 582-589, (2004); Gao P., Zhao J., Wang G., Guo H., Real Time ECG Characteristic Point Detection with Randomly Selected Signal Pair Difference (RSSPD) Feature and Random Forest Classifier, pp. 732-735; Myrovali E., Hristu-Varsakelis D., Tachmatzidis D., Antoniadis A., Vassilikos V., Identifying patients with paroxysmal atrial fibrillation from sinus rhythm ECG using random forests, Expert Systems with Applications, 213, (2023); Yildirim O., A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification, Computers in Biology and Medicine, 96, pp. 189-202, (2018); Cui K.X., Xia X., ECG Signal Anomaly Detection Algorithm Based on CNN-BiLSTM, pp. 193-197; O'Brien E., Asmar R., Beilin L., Imai Y., Mallion J.M., Mancia G., Mengden T., Myers M., Padfield P., Palatini P., Parati G., Pickering T., Redon J., Staessen J., Stergiou G., Verdecchia P., European society of hypertension recommendations for conventional, ambulatory and home blood pressure measurement, Journal of Hypertension, 21, 5, pp. 821-848, (2003); Ungurean I., Timing comparison of the real-time operating systems for small microcontrollers, Symmetry-Basel, 12, 4, (2020); Deharbe D., Galvao S., Moreira A.M., Oliveira M.V.M., Woodcock J., Formalizing FreeRTOS: First steps, 12th Brazilian Symposium on Formal Methods, 5902, (2009); Moody G.B., Mark R.G., The impact of the MIT-BIH arrhythmia database, IEEE Engineering in Medicine and Biology Magazine, 20, 3, pp. 45-50, (2001); Kumar B., Tensorflow in Python, (2022); An X., Stylios G., Comparison of motion artefact reduction methods and the implementation of adaptive motion artefact reduction in wearable electrocardiogram monitoring, Sensors (Basel), 20, 5, (2020); Pan J., Tompkins W.J., A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, 32, 3, pp. 230-236, (1985)","X. An; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China; email: anxiang@bipt.edu.cn","","Hindawi Limited","","","","","","1687725X","","","","English","J. Sensors","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85163701692"
"Chimbunde E.; Sigwadhi L.N.; Tamuzi J.L.; Okango E.L.; Daramola O.; Ngah V.D.; Nyasulu P.S.","Chimbunde, Emmanuel (58667816300); Sigwadhi, Lovemore N. (57220593371); Tamuzi, Jacques L. (57210897062); Okango, Elphas L. (56872894100); Daramola, Olawande (36102361100); Ngah, Veranyuy D. (57221078719); Nyasulu, Peter S. (36700333500)","58667816300; 57220593371; 57210897062; 56872894100; 36102361100; 57221078719; 36700333500","Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa","2023","Frontiers in Artificial Intelligence","6","","1171256","","","","4","10.3389/frai.2023.1171256","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175023220&doi=10.3389%2ffrai.2023.1171256&partnerID=40&md5=393e639f20ecd23d9121324a66c1f3a6","Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; African Health Research Institute, Durban, South Africa; Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa","Chimbunde E., Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Sigwadhi L.N., Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Tamuzi J.L., Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Okango E.L., African Health Research Institute, Durban, South Africa; Daramola O., Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town, South Africa; Ngah V.D., Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Nyasulu P.S., Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa","Background: COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Methods: Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. Results: From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Conclusion: Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs. Copyright © 2023 Chimbunde, Sigwadhi, Tamuzi, Okango, Daramola, Ngah and Nyasulu.","artificial neural network; COVID-19; K-means clustering; machine learning; multilayer perceptron","","","","","","Department of Science and Innovation; Stellenbosch University Special Vice-Rector; United Kingdom Research and Innovation; United Kingdom's Department of International Development; National Institutes of Health, NIH, (D43TW010547); Fogarty International Center, FIC; Styrelsen för Internationellt Utvecklingssamarbete, Sida; Newton Fund; International Development Research Centre, IDRC; Department for International Development, UK Government, DFID; National Research Foundation, NRF; Fonds de recherche du Québec, FRQ","Funding text 1: This study was carried out under the Stellenbosch University Special Vice-Rector (RIPS) Fund and the COVID-19 Africa Rapid Grant Fund supported under the auspices of the Science Granting Councils Initiative in Sub-Saharan Africa (SGCI) and administered by South Africa's National Research Foundation (NRF) in collaboration with Canada's International Development Research Centre (IDRC), the Swedish International Development Cooperation Agency (SIDA), South Africa's Department of Science and Innovation (DSI), the Fonds de Recherche du Québec (FRQ), the United Kingdom's Department of International Development (DFID), United Kingdom Research and Innovation (UKRI) through the Newton Fund, and the SGCI participating councils across 15 countries in sub-Saharan Africa. ; Funding text 2: EC as a postgraduate student in the masters training programme was supported by the Fogarty International Centre of the National Institutes of Health under Award Number D43TW010547. The results reported here are thus solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ","Adamidi E.S., Mitsis K., Nikita K.S., Artificial intelligence in clinical care amidst COVID-19 pandemic: a systematic review, Comp. Struct. Biotechnol. J, 19, pp. 2833-2850, (2021); Al Oweidat K., Al-Amer R., Saleh M.Y., Albtoosh A.S., Toubasi A.A., Ribie M.K., Et al., Mortality, intensive care unit admission, and intubation among hospitalized patients with COVID-19: a one-year retrospective study in Jordan, J. Clin. Med, 12, (2023); Banoei M.M., Dinparastisaleh R., Zadeh A.V., Mirsaeidi M., Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying, Crit. Care, 25, (2021); Basi N.B., Metin S., Sevinc S.A., Salkaya A., Peker N., Cinar A.S., Et al., The effect of diabetes mellitus on mortality in patients hospitalized intensive care unit in Covid-19 pandemic, Acta Biomed, 93, (2022); Beurnier A., Jutant E.M., Jevnikar M., Boucly A., Pichon J., Preda M., Et al., Characteristics and outcomes of asthmatic patients with COVID-19 pneumonia who require hospitalisation, Eur. Respir. J, 56, (2020); Cisterna-Garcia A., Guillen-Teruel A., Caracena M., Perez E., Jimenez F., Francisco-Verdu F.J., Et al., A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study, Sci. Rep, 12, (2022); de Almeida-Pititto B., Dualib P.M., Zajdenverg L., Dantas J.R., de Souza F.D., Rodacki M., Et al., Severity and mortality of COVID 19 in patients with diabetes, hypertension, and cardiovascular disease: a meta-analysis, Diabetol. Metab. Syndr, 12, pp. 1-12, (2020); Elhazmi A., Al-Omari A., Sallam H., Mufti H.N., Rabie A.A., Alshahrani M., Et al., Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU, J. Infect. Public Health, 15, pp. 826-834, (2022); Galaz V., Centeno M.A., Callahan P.W., Causevic A., Patterson T., Brass I., Et al., Artificial intelligence, systemic risks, and sustainability, Technol. Soc, 67, (2021); Gupta A., Nayan N., Nair R., Kumar K., Joshi A., Sharma S., Et al., Diabetes mellitus and hypertension increase risk of death in novel corona virus patients irrespective of age: a prospective observational study of co-morbidities and COVID-19 from India, SN Compr. Clin. Med, 3, pp. 937-944, (2021); He F., Page J.H., Weinberg K.R., Mishra A., The development and validation of simplified machine learning algorithms to predict prognosis of hospitalized patients with COVID-19: multicenter, retrospective study, J. Med. Int. Res, 24, (2022); Hernandez-Pereira E., Fontenla-Romero O., Bolon-Canedo V., Cancela-Barizo B., Guijarro-Berdinas B., Alonso-Betanzos A., Et al., Machine learning techniques to predict different levels of hospital care of Covid-19, Appl. Intellig, 52, (2022); Iftimie S., Lopez-Azcona A.F., Vallverd,u I., Hernandez-Flix S., de Febrer G., Parra S., Et al., First and second waves of coronavirus disease-19: a comparative study in hospitalized patients in Reus, Spain, PLoS ONE, 16, (2021); Kar S., Chawla R., Haranath S.P., Ramasubban S., Ramakrishnan N., Vaishya R., Et al., Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID), Sci. Rep, 11, (2021); Kuhn M., Building predictive models in R using the caret package, J. Stat. Softw, 28, pp. 1-26, (2008); Lalla U., Koegelenberg C.F.N., Allwood B.W., Sigwadhi L.N., Irusen E.M., Zemlin A.E., Et al., Comparison of patients with severe COVID-19 admitted to an intensive care unit in South Africa during the first and second wave of the COVID-19 pandemic, Afr. J. Thorac. Crit. Care Med, (2021); Li X., Ge P., Zhu J., Li H., Graham J., Singer A., Et al., Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables, PeerJ, 8, (2020); Liaw A., Wiener M., Classification and regression by randomForest, R news, 2, (2002); Magunia H., Lederer S., Verbuecheln R., Gilot B.J., Koeppen M., Haeberle H.A., Et al., Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort, Crit. Care, 25, pp. 1-14, (2021); Moulaei K., Shanbehzadeh M., Mohammadi-Taghiabad Z., Kazemi-Arpanahi H., Comparing machine learning algorithms for predicting COVID-19 mortality, BMC Med. Informat. Decis. Making, 22, (2022); Navarro C.L.A., Damen J.A.A., Takada T., Nijman S.W.J., Dhiman P., Ma J., Et al., Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review, BMJ, 375, (2021); Nyasulu P.S., Ayele B.T., Koegelenberg C.F., Irusen E., Lalla U., Davids R., Et al., Clinical characteristics associated with mortality of COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa, PLoS ONE, 17, (2022); Pennington E., Yaqoob Z.J., Al-Kindi S.G., Zein J., Trends in asthma mortality in the United States: 1999 to 2015, Am. J. Respir. Crit. Care Med, 199, pp. 1575-1577, (2019); Ren J., Pang W., Luo Y., Cheng D., Qiu K., Rao Y., Et al., Impact of allergic rhinitis and asthma on COVID-19 infection, hospitalization, and mortality, J. Aller. Clin. Immunol, 10, pp. 124-133, (2022); Shanbehzadeh M., Nopour R., Kazemi-Arpanahi H., Developing an artificial neural network for detecting COVID-19 disease, J. Educ. Health Promot, 11, (2022); Shen C.Y., Logistic growth modelling of COVID-19 proliferation in China and its international implications, Int. J. Infect. Dis, 96, pp. 582-589, (2020); Subudhi S., Verma A., Patel A.B., Hardin C.C., Khandekar M.J., Lee H., Et al., Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19, NPJ Dig. Med, 4, (2021); Tezza F., Lorenzoni G., Azzolina D., Barbar S., Leone L.A.C., Gregori D., Predicting in-hospital mortality of patients with COVID-19 using machine learning techniques, J. Pers. Med, 11, (2021); WHO's COVID-19 Response, (2022); Weekly Epidemiological Update on COVID-19, (2023); Zhao Y., Zhang R., Zhong Y., Wang J., Weng Z., Luo H., Et al., Statistical analysis, and machine learning prediction of disease outcomes for COVID-19 and pneumonia patients, Front. Cell. Infect. Microbiol, 12, (2022)","P.S. Nyasulu; Division of Epidemiology and Biostatistics, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; email: pnyasulu@sun.ac.za","","Frontiers Media SA","","","","","","26248212","","","","English","Frontier. Artif. Intell.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175023220"
"Høj S.; Thomsen S.F.; Ulrik C.S.; Meteran H.; Sigsgaard T.; Meteran H.","Høj, Simon (57807183600); Thomsen, Simon Francis (7101839747); Ulrik, Charlotte Suppli (7004960246); Meteran, Hanieh (57199508811); Sigsgaard, Torben (7005779529); Meteran, Howraman (55249927700)","57807183600; 7101839747; 7004960246; 57199508811; 7005779529; 55249927700","Evaluating the scientific reliability of ChatGPT as a source of information on asthma","2024","Journal of Allergy and Clinical Immunology: Global","3","4","100330","","","","4","10.1016/j.jacig.2024.100330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203986330&doi=10.1016%2fj.jacig.2024.100330&partnerID=40&md5=ef394104b13c1f8914b35df9f68c6c95","Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg, Denmark, Bispebjerg, Denmark; Department of Biomedical Sciences, University of Copenhagen, Denmark, Copenhagen, Denmark; Department of Public Health, Environment, Occupation, and Health, Aarhus University, Denmark, Aarhus, Denmark; Department of Respiratory Medicine, Copenhagen University Hospital–Hvidovre, Denmark, Hvidovre, Denmark; Department of Clinical Medicine, University of Copenhagen, Denmark, Copenhagen, Denmark; Department of Internal Medicine, Section of Endocrinology, Copenhagen University Hospital–Hvidovre, Denmark, Hvidovre, Denmark; Department of Respiratory Medicine, Zealand University Hospital Roskilde–Næstved, Denmark, Næstved, Denmark","Høj S., Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg, Denmark, Bispebjerg, Denmark, Department of Public Health, Environment, Occupation, and Health, Aarhus University, Denmark, Aarhus, Denmark; Thomsen S.F., Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg, Denmark, Bispebjerg, Denmark, Department of Biomedical Sciences, University of Copenhagen, Denmark, Copenhagen, Denmark; Ulrik C.S., Department of Respiratory Medicine, Copenhagen University Hospital–Hvidovre, Denmark, Hvidovre, Denmark, Department of Clinical Medicine, University of Copenhagen, Denmark, Copenhagen, Denmark; Meteran H., Department of Internal Medicine, Section of Endocrinology, Copenhagen University Hospital–Hvidovre, Denmark, Hvidovre, Denmark; Sigsgaard T., Department of Public Health, Environment, Occupation, and Health, Aarhus University, Denmark, Aarhus, Denmark; Meteran H., Department of Public Health, Environment, Occupation, and Health, Aarhus University, Denmark, Aarhus, Denmark, Department of Respiratory Medicine, Copenhagen University Hospital–Hvidovre, Denmark, Hvidovre, Denmark, Department of Respiratory Medicine, Zealand University Hospital Roskilde–Næstved, Denmark, Næstved, Denmark","Background: This study assessed the reliability of ChatGPT as a source of information on asthma, given the increasing use of artificial intelligence–driven models for medical information. Prior concerns about misinformation on atopic diseases in various digital platforms underline the importance of this evaluation. Objective: We aimed to evaluate the scientific reliability of ChatGPT as a source of information on asthma. Methods: The study involved analyzing ChatGPT's responses to 26 asthma-related questions, each followed by a follow-up question. These encompassed definition/risk factors, diagnosis, treatment, lifestyle factors, and specific clinical inquiries. Medical professionals specialized in allergic and respiratory diseases independently assessed the responses using a 1-to-5 accuracy scale. Results: Approximately 81% of the responses scored 4 or higher, suggesting a generally high accuracy level. However, 5 responses scored >3, indicating minor potentially harmful inaccuracies. The overall median score was 4. Fleiss multirater kappa value showed moderate agreement among raters. Conclusion: ChatGPT generally provides reliable asthma-related information, but its limitations, such as lack of depth in certain responses and inability to cite sources or update in real time, were noted. It shows promise as an educational tool, but it should not be a substitute for professional medical advice. Future studies should explore its applicability for different user demographics and compare it with newer artificial intelligence models. © 2024 The Author(s)","AI; artificial intelligence; Asthma; ChatGPT; patient education","","","","","","","","Miner A.S., Laranjo L., Kocaballi A.B., Chatbots in the fight against the COVID-19 pandemic, NPJ Digit Med, 3, (2024); Bogost I., ChatGPT is dumber than you think. Atlantic; Remvig C., Diers C., Meteran H., Thomsen S., Sigsgaard T., Hoj S., Et al., YouTube as a source of (mis)information on allergic rhinitis, Ann Allergy Asthma Immunol, 129, pp. 612-617, (2022); Diers C., Remvig C., Meteran H., Thomsen S., Sigsgaard T., Hoj S., Et al., The usefulness of YouTube videos as a source of information in asthma, J Asthma, 60, pp. 737-743, (2022); Hoj S., Meteran H., Thomsen S.F., Sigsgaard T., Meteran H., Nutritional treatment of atopic diseases according to YouTube videos, J Allergy Clin Immunol Pract, 11, pp. 1552-1553, (2023); Hoj S., Thomsen S.F., Meteran H., Sigsgaard T., Meteran H., Artificial intelligence and allergic rhinitis: does ChatGPT increase or impair the knowledge?, J Public Health (Oxf), 46, pp. 123-126, (2024); Potapenko I., Boberg-Ans L.C., Stormly Hansen M., Klefter O.N., van Dijk E.H.C., Subhi Y., Artificial intelligence–based chatbot patient information on common retinal diseases using ChatGPT, Acta Ophthalmol, 101, pp. 829-831, (2023); Landis J.R., Koch G.G., The measurement of observer agreement for categorical data, Biometrics, 33, pp. 159-174, (1977); Goodman R.S., Patrinely J.R., Stone C.A., Zimmerman E., Donald R.R., Chang S.S., Et al., Accuracy and reliability of chatbot responses to physician questions, JAMA Netw Open, 6, (2023)","S. Høj; Department of Dermatology, Venereology, and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg, Copenhagen, Bispebjerg Bakke 23C 2400, Denmark; email: Simonhoej1@hotmail.com","","Elsevier B.V.","","","","","","27728293","","","","English","J. Allergy. Clin. Immunol. Glob.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85203986330"
"Shirazibeheshti A.; Ettefaghian A.; Khanizadeh F.; Wilson G.; Radwan T.; Luca C.","Shirazibeheshti, Amirali (57202848960); Ettefaghian, Alireza (57221939497); Khanizadeh, Farbod (57263052400); Wilson, George (7404530373); Radwan, Tarek (57221945000); Luca, Cristina (55329325500)","57202848960; 57221939497; 57263052400; 7404530373; 57221945000; 55329325500","Automated Detection of Patients at High Risk of Polypharmacy including Anticholinergic and Sedative Medications","2023","International Journal of Environmental Research and Public Health","20","12","6178","","","","4","10.3390/ijerph20126178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163635209&doi=10.3390%2fijerph20126178&partnerID=40&md5=e6d16fb58a351229d25cb1cbf054803d","AT Medics Ltd, London, SW2 4QY, United Kingdom; Operation & Information Management, Aston Business School, Birmingham, B4 7UP, United Kingdom; School of Computing and Information Science, Anglia Ruskin University, Cambridge, CB1 1PT, United Kingdom","Shirazibeheshti A., AT Medics Ltd, London, SW2 4QY, United Kingdom; Ettefaghian A., AT Medics Ltd, London, SW2 4QY, United Kingdom; Khanizadeh F., Operation & Information Management, Aston Business School, Birmingham, B4 7UP, United Kingdom; Wilson G., School of Computing and Information Science, Anglia Ruskin University, Cambridge, CB1 1PT, United Kingdom; Radwan T., AT Medics Ltd, London, SW2 4QY, United Kingdom; Luca C., School of Computing and Information Science, Anglia Ruskin University, Cambridge, CB1 1PT, United Kingdom","Ensuring that medicines are prescribed safely is fundamental to the role of healthcare professionals who need to be vigilant about the risks associated with drugs and their interactions with other medicines (polypharmacy). One aspect of preventative healthcare is to use artificial intelligence to identify patients at risk using big data analytics. This will improve patient outcomes by enabling pre-emptive changes to medication on the identified cohort before symptoms present. This paper presents a mean-shift clustering technique used to identify groups of patients at the highest risk of polypharmacy. A weighted anticholinergic risk score and a weighted drug interaction risk score were calculated for each of 300,000 patient records registered with a major regional UK-based healthcare provider. The two measures were input into the mean-shift clustering algorithm and this grouped patients into clusters reflecting different levels of polypharmaceutical risk. Firstly, the results showed that, for most of the data, the average scores are not correlated and, secondly, the high risk outliers have high scores for one measure but not for both. These suggest that any systematic recognition of high-risk groups should consider both anticholinergic and drug–drug interaction risks to avoid missing high-risk patients. The technique was implemented in a healthcare management system and easily and automatically identifies groups at risk far faster than the manual inspection of patient records. This is much less labour-intensive for healthcare professionals who can focus their assessment only on patients within the high-risk group(s), enabling more timely clinical interventions where necessary. © 2023 by the authors.","cluster analysis; decision making; drug interactions; polypharmacy; risk factors; unsupervised machine learning","Artificial Intelligence; Cholinergic Antagonists; Drug Interactions; Humans; Hypnotics and Sedatives; Polypharmacy; United Kingdom; acetylsalicylic acid; amitriptyline; anticoagulant agent; benzodiazepine; betamethasone; cetirizine; cholinergic receptor blocking agent; citalopram; clarithromycin; codeine; corticosteroid; diclofenac; hydroxyzine; nonsteroid antiinflammatory agent; omeprazole; opiate; quinine; sedative agent; sildenafil; cholinergic receptor blocking agent; hypnotic sedative agent; automation; cluster analysis; decision making; detection method; health risk; medicine; risk factor; adult; aged; Article; asthma; cluster analysis; cognition; controlled study; decision making; disease burden; drug use; female; health care access; health care personnel; hospital admission; human; major clinical study; male; middle aged; polypharmacy; population dynamics; prescription; prevalence; primary medical care; risk assessment; risk factor; young adult; artificial intelligence; drug interaction","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; amitriptyline, 50-48-6, 549-18-8; benzodiazepine, 12794-10-4; betamethasone, 378-44-9; cetirizine, 83881-51-0, 83881-52-1, 163837-48-7; citalopram, 59729-33-8, 59729-32-7, 85118-27-0; clarithromycin, 81103-11-9; codeine, 76-57-3; diclofenac, 15307-79-6, 15307-86-5; hydroxyzine, 2192-20-3, 64095-02-9, 68-88-2; omeprazole, 73590-58-6, 95510-70-6; opiate, 53663-61-9, 8002-76-4, 8008-60-4; quinine, 130-89-2, 130-95-0, 14358-44-2, 549-48-4, 549-49-5, 60-93-5, 7549-43-1; sildenafil, 139755-83-2, 171599-83-0; Cholinergic Antagonists, ; Hypnotics and Sedatives, ","","","Anglia Ruskin University; UK Research and Innovation, UKRI, (104074); Innovate UK, (KTP011672)","This study was funded by an InnovateUK Knowledge Transfer Partnership award (Ref: KTP011672) to support collaboration between academic institutes and business through knowledge transfer. The award funded half the salary of an Associate appointed by the knowledge base (Anglia Ruskin University) to work on this business-driven project with the balance of the salary funded by the company partner (AT Medics).","Koh Y., Li S.C., Fatimah M., Therapy related hospital admission in patients on polypharmacy in Singapore: A pilot study, Pharm. World Sci, 25, pp. 135-137, (2003); Viktil K.K., Enstad M., Kutschera J., Smedstad L.M., Schjott J., Polypharmacy among patients admitted to hospital with rheumatic diseases, Pharm. World Sci, 23, pp. 153-158, (2001); Dookeeram D., Bidaisee S., Paul J.F., Nunes P., Robertson P., Maharaj V.R., Sammy I., Polypharmacy and potential drug–drug interactions in emergency department patients in the Caribbean, Int. J. Clin. Pharm, 39, pp. 1119-1127, (2017); Gomes M.S., Amorim W.W., Morais R.S., Gama R.S., Graia L.T., Queiroga H.M., Oliveira M.G., Polypharmacy in older patients at primary care units in Brazil, Int. J. Clin. 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Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85163635209"
"Sharma M.; Wyszkiewicz P.V.; Matheson A.M.; McCormack D.G.; Parraga G.","Sharma, Maksym (57222555709); Wyszkiewicz, Paulina V. (57966251800); Matheson, Alexander M. (57204878676); McCormack, David G. (7102878837); Parraga, Grace (14023130000)","57222555709; 57966251800; 57204878676; 7102878837; 14023130000","Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD","2023","COPD: Journal of Chronic Obstructive Pulmonary Disease","20","1","","307","320","13","4","10.1080/15412555.2023.2259224","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171956265&doi=10.1080%2f15412555.2023.2259224&partnerID=40&md5=e56072caad590cd1e679ef610397f15a","Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada; Division of Respirology, Department of Medicine, Western University, London, Canada; School of Biomedical Engineering, Western University, London, Canada","Sharma M., Robarts Research Institute, Western University, London, Canada, Department of Medical Biophysics, Western University, London, Canada; Wyszkiewicz P.V., Robarts Research Institute, Western University, London, Canada, Department of Medical Biophysics, Western University, London, Canada; Matheson A.M., Robarts Research Institute, Western University, London, Canada, Department of Medical Biophysics, Western University, London, Canada; McCormack D.G., Division of Respirology, Department of Medicine, Western University, London, Canada; Parraga G., Robarts Research Institute, Western University, London, Canada, Department of Medical Biophysics, Western University, London, Canada, Division of Respirology, Department of Medicine, Western University, London, Canada, School of Biomedical Engineering, Western University, London, Canada","Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration:www.clinicaltrials.gov NCT02279329. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.","computed tomography; Ex-smokers; hyperpolarized gas MRI; machine-learning; mortality; texture analysis","Female; Humans; Magnetic Resonance Imaging; Male; Pulmonary Disease, Chronic Obstructive; Thorax; Tomography, X-Ray Computed; Helium-3; aged; all cause mortality; Article; body plethysmography; chronic obstructive lung disease; controlled study; diffusing capacity for carbon monoxide; diffusion coefficient; diffusion weighted imaging; ex-smoker; feature extraction; female; human; lung function; lung function test; machine learning; major clinical study; male; mortality risk; nuclear magnetic resonance imaging; plethysmography; predictive model; quantitative analysis; questionnaire; regression model; sensitivity analysis; sensitivity and specificity; spirometry; texture analysis; x-ray computed tomography; diagnostic imaging; nuclear magnetic resonance imaging; thorax; x-ray computed tomography","","Helium-3, ","Discovery MR750, GE Healthcare, United States; Lightspeed VCT, GE Healthcare, United States","GE Healthcare, United States; GE Healthcare, United States","Tier 1 Canada Research Chair; Barnes Family Foundation; Canadian Institutes of Health Research, IRSC; Natural Sciences and Engineering Research Council of Canada, NSERC","M.S. was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada Post-Graduate Doctoral Scholarship. A.M.M. is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada Canadian-Graduate Doctoral Scholarship. G.P. is supported by NSERC, CIHR, the Baran Family Foundation, and holds a Tier 1 Canada Research Chair. We thank the participants who volunteered for this study.","Adeloye D., Song P., Zhu Y., Et al., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, 5, pp. 447-458, (2022); Jones R.C., Donaldson G.C., Chavannes N.H., Et al., Derivation and validation of a composite index of severity in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 180, 12, pp. 1189-1195, (2009); Mohd Shah A., Mohd Anshar F., Mohd Perdaus Ahmad F., Et al., The SAFE (SGRQ score, air-flow limitation and exercise tolerance) index: a new composite score for the stratification of severity in chronic obstructive pulmonary disease, Postgrad Med J, 83, 981, (2007); Puhan M.A., Garcia-Aymerich J., Frey M., Et al., Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index, Lancet, 374, 9691, pp. 704-711, (2009); Celli B.R., Cote C.G., Marin J.M., Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, 10, pp. 1005-1012, (2004); Guerra B., Haile S.R., Lamprecht B., Et al., Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease, BMC Med, 16, 1, (2018); Fletcher C., Peto R., The natural history of chronic airflow obstruction, Br Med J, 1, 6077, pp. 1645-1648, (1977); Kakavas S., Kotsiou O.S., Perlikos F., Et al., Pulmonary function testing in COPD: looking beyond the curtain of FEV1, NPJ Prim Care Respir Med, 31, 1, (2021); Singh D., Agusti A., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019, Eur Respir J, 53, 5, (2019); Vestbo J., Hurd S.S., Agusti A.G., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am J Respir Crit Care Med, 187, 4, pp. 347-365, (2013); Burgel P.R., Bourdin A., Chanez P., Et al., Update on the roles of distal airways in COPD, Eur Respir Rev, 20, 119, pp. 7-22, (2011); de Jong P.A., Muller N.L., Pare P.D., Et al., Computed tomographic imaging of the airways: relationship to structure and function, Eur Respir J, 26, 1, pp. 140-152, (2005); Kirby M., Tanabe N., Tan W.C., Et al., Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. 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Parraga; Robarts Research Institute, Western University, London, 1151 Richmond St N, N6A 5B7, Canada; email: gparraga@uwo.ca","","Taylor and Francis Ltd.","","","","","","15412555","","","37737132","English","COPD J. Chronic Obstructive Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85171956265"
"García-Hidalgo M.C.; Benítez I.D.; Perez-Pons M.; Molinero M.; Belmonte T.; Rodríguez-Muñoz C.; Aguilà M.; Santisteve S.; Torres G.; Moncusí-Moix A.; Gort-Paniello C.; Peláez R.; Larráyoz I.M.; Caballero J.; Barberà C.; Nova-Lamperti E.; Torres A.; González J.; Barbé F.; de Gonzalo-Calvo D.","García-Hidalgo, María C. (57226816684); Benítez, Iván D. (57201681045); Perez-Pons, Manel (57732817000); Molinero, Marta (57224771609); Belmonte, Thalía (57217440057); Rodríguez-Muñoz, Carlos (58314982000); Aguilà, María (57247517900); Santisteve, Sally (57224119958); Torres, Gerard (54390339400); Moncusí-Moix, Anna (57218295036); Gort-Paniello, Clara (57219425655); Peláez, Rafael (57194116504); Larráyoz, Ignacio M. (6507391474); Caballero, Jesús (57206147842); Barberà, Carme (57225113773); Nova-Lamperti, Estefania (36103012100); Torres, Antoni (57205521091); González, Jessica (57190337063); Barbé, Ferran (7007035883); de Gonzalo-Calvo, David (25823698600)","57226816684; 57201681045; 57732817000; 57224771609; 57217440057; 58314982000; 57247517900; 57224119958; 54390339400; 57218295036; 57219425655; 57194116504; 6507391474; 57206147842; 57225113773; 36103012100; 57205521091; 57190337063; 7007035883; 25823698600","MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study","2025","British Journal of Pharmacology","182","2","","380","395","15","4","10.1111/bph.16330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185285545&doi=10.1111%2fbph.16330&partnerID=40&md5=642254e2e412e65a2aaaa693a1dac458","Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; Biomarkers and Molecular Signaling Group, Neurodegenerative Diseases Area Center for Biomedical Research of La Rioja, CIBIR, Logroño, Spain; BIAS, Department of Nursing, University of La Rioja, Logroño, Spain; Grup de Recerca Medicina Intensiva, Intensive Care Department Hospital Universitari Arnau de Vilanova, Lleida, Spain; Intensive Care Department, University Hospital Santa María, IRBLleida, Lleida, Spain; Molecular and Translational Immunology Laboratory, Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Universidad de Concepcion, Concepcion, Chile; Pneumology Department, Clinic Institute of Thorax (ICT), Hospital Clinic of Barcelona, Insitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), ICREA, University of Barcelona (UB), Barcelona, Spain","García-Hidalgo M.C., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Benítez I.D., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Perez-Pons M., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Molinero M., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Belmonte T., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Rodríguez-Muñoz C., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Aguilà M., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; Santisteve S., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Torres G., CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain, Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; Moncusí-Moix A., Group of Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; Gort-Paniello C., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Peláez R., Biomarkers and Molecular Signaling Group, Neurodegenerative Diseases Area Center for Biomedical Research of La Rioja, CIBIR, Logroño, Spain; Larráyoz I.M., Biomarkers and Molecular Signaling Group, Neurodegenerative Diseases Area Center for Biomedical Research of La Rioja, CIBIR, Logroño, Spain, BIAS, Department of Nursing, University of La Rioja, Logroño, Spain; Caballero J., Grup de Recerca Medicina Intensiva, Intensive Care Department Hospital Universitari Arnau de Vilanova, Lleida, Spain; Barberà C., Intensive Care Department, University Hospital Santa María, IRBLleida, Lleida, Spain; Nova-Lamperti E., Molecular and Translational Immunology Laboratory, Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Universidad de Concepcion, Concepcion, Chile; Torres A., CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain, Pneumology Department, Clinic Institute of Thorax (ICT), Hospital Clinic of Barcelona, Insitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), ICREA, University of Barcelona (UB), Barcelona, Spain; González J., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; Barbé F., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; de Gonzalo-Calvo D., Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain, CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain","Background and Purpose: The post-acute sequelae of SARS-CoV-2 infection pose a significant global challenge, with nearly 50% of critical COVID-19 survivors manifesting persistent lung abnormalities. The lack of understanding about the molecular mechanisms and effective treatments hampers their management. Here, we employed microRNA (miRNA) profiling to decipher the systemic molecular underpinnings of the persistent pulmonary complications. Experimental Approach: We conducted a longitudinal investigation including 119 critical COVID-19 survivors. A comprehensive pulmonary evaluation was performed in the short-term (median = 94.0 days after hospital discharge) and long-term (median = 358 days after hospital discharge). Plasma miRNAs were quantified at the short-term evaluation using the gold-standard technique, RT-qPCR. The analyses combined machine learning feature selection techniques with bioinformatic investigations. Two additional datasets were incorporated for validation. Key Results: In the short-term, 84% of the survivors exhibited impaired lung diffusion (DLCO < 80% of predicted). One year post-discharge, 54.4% of this patient subgroup still presented abnormal DLCO. Four feature selection methods identified two specific miRNAs, miR-9-5p and miR-486-5p, linked to persistent lung dysfunction. The downstream experimentally validated targetome included 1473 genes, with heterogeneous enriched pathways associated with inflammation, angiogenesis and cell senescence. Validation studies using RNA-sequencing and proteomic datasets emphasized the pivotal roles of cell migration and tissue repair in persistent lung dysfunction. The repositioning potential of the miRNA targets was limited. Conclusion and Implications: Our study reveals early mechanistic pathways contributing to persistent lung dysfunction in critical COVID-19 survivors, offering a promising approach for the development of targeted disease-modifying agents. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc. © 2024 The Authors. British Journal of Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.","drug discovery; drug repositioning; long COVID; lung dysfunction; machine learning; microRNA; post-acute COVID-19 sequelae","Adult; Aged; COVID-19; COVID-19 Drug Treatment; Drug Discovery; Female; Humans; Longitudinal Studies; Lung; Male; MicroRNAs; Middle Aged; Pilot Projects; SARS-CoV-2; Survivors; anticoagulant agent; beta interferon; corticosteroid; hydroxychloroquine; lopinavir plus ritonavir; microRNA; microrna 122 5p; microrna 199a 5p; microrna 486 5p; microrna 9 5p; remdesivir; RNA; tocilizumab; unclassified drug; acute kidney failure; adult; aged; angiogenesis; apoptosis; Article; artificial ventilation; asthma; bacteremia; bacterial pneumonia; bioinformatics; cardiovascular disease; cell migration; chronic kidney failure; chronic lung disease; computer assisted tomography; controlled study; coronavirus disease 2019; female; gene ontology; human; hypertension; inflammation; intensive care unit; longitudinal study; lung complication; lung embolism; lung function; machine learning; major clinical study; male; middle aged; multicenter study; nasopharyngeal swab; obesity; pilot study; protein protein interaction; real time polymerase chain reaction; RNA extraction; RNA isolation; RNA sequencing; spirometry; total lung capacity; complication; coronavirus disease 2019; COVID-19 pharmacotherapy; drug development; genetics; lung; Severe acute respiratory syndrome coronavirus 2; survivor","","hydroxychloroquine, 118-42-3, 525-31-5, 137433-23-9, 137433-24-0; lopinavir plus ritonavir, 369372-47-4; remdesivir, 1809249-37-3; RNA, 63231-63-0; tocilizumab, 375823-41-9; MicroRNAs, ","QuantStudio 7, Applied Biosystems, United States; miRNeasy, Qiagen, Germany; version 4.1.2, R Foundation","Applied Biosystems, United States; Qiagen, Germany; R Foundation","Universitat de Lleida, UdL; IRBLleida Biobank, (000682); Instituto de Salud Carlos III, ISCIII, (PT20/00021); Instituto de Salud Carlos III, ISCIII","This work supported by IRBLleida Biobank (B.000682) and Biobank and Biomodels Platform ISCIII PT20/00021. The human sample manipulation was performed in the Cell Culture Facility, Universitat de Lleida (Lleida, Catalonia, Spain).","Bar C., Thum T., de Gonzalo-Calvo D., Circulating miRNAs as mediators in cell-to-cell communication, Epigenomics, 11, pp. 111-113, (2019); Blondal T., Jensby Nielsen S., Baker A., Andreasen D., Mouritzen P., Wrang Teilum M., Dahlsveen I.K., Assessing sample and miRNA profile quality in serum and plasma or other biofluids, Methods, 59, pp. S1-S6, (2013); Bonneau E., Neveu B., Kostantin E., Tsongalis G.J., De Guire V., How close are miRNAs from clinical practice? 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J. Pharmacol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85185285545"
"Arévalo-Lorido J.C.; Carretero-Gómez J.; Casas-Rojo J.M.; Antón-Santos J.M.; Melero-Bermejo J.A.; López-Carmona M.D.; Palacios L.C.; Sanz-Cánovas J.; Pesqueira-Fontán P.M.; de la Peña-Fernández A.A.; de la Sierra Alcántara N.-M.; García-García G.M.; Torres Peña J.D.; Magallanes-Gamboa J.O.; Fernández-Madera-Martinez R.; Fernández-Fernández J.; Rubio-Rivas M.; Maestro-de la Calle G.; Cervilla-Muñoz E.; Ramos-Martínez A.; Méndez-Bailón M.; Ramos-Rincón J.M.; Gómez-Huelgas R.","Arévalo-Lorido, José Carlos (6603061176); Carretero-Gómez, Juana (9239096600); Casas-Rojo, Jose Manuel (51563209600); Antón-Santos, Juan Miguel (55485538300); Melero-Bermejo, José Antonio (47761845400); López-Carmona, Maria Dolores (49663519300); Palacios, Lidia Cobos (57202727882); Sanz-Cánovas, Jaime (57219229216); Pesqueira-Fontán, Paula Maria (13609881700); de la Peña-Fernández, Andrés Alberto (16635424300); de la Sierra Alcántara, Navas-Maria (57446374400); García-García, Gema Maria (26537447500); Torres Peña, José David (57192698678); Magallanes-Gamboa, Jeffrey Oskar (56118096200); Fernández-Madera-Martinez, Rosa (57204031567); Fernández-Fernández, Javier (57201374341); Rubio-Rivas, Manuel (8914713500); Maestro-de la Calle, Guillermo (56294083700); Cervilla-Muñoz, Eva (57205469297); Ramos-Martínez, Antonio (7401770069); Méndez-Bailón, Manuel (15062916800); Ramos-Rincón, José Manuel (59157659200); Gómez-Huelgas, Ricardo (7004734060)","6603061176; 9239096600; 51563209600; 55485538300; 47761845400; 49663519300; 57202727882; 57219229216; 13609881700; 16635424300; 57446374400; 26537447500; 57192698678; 56118096200; 57204031567; 57201374341; 8914713500; 56294083700; 57205469297; 7401770069; 15062916800; 59157659200; 7004734060","The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach","2022","Current Medical Research and Opinion","38","4","","501","510","9","4","10.1080/03007995.2022.2029382","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124371504&doi=10.1080%2f03007995.2022.2029382&partnerID=40&md5=1174c0865b1eeee3e9b412dbab585110","Complejo Hospitalario Universitario de Badajoz. Badajoz, Spain; Hospital Universitario Infanta Cristina de Parla. Madrid, Spain; Hospital Clínico Universitario de Santiago de Compostela, A Coruña, Spain; Hospital Universitario Son Llàtzer. Palma de Mallorca. Illes Balears, Spain; Hospital Infanta Margarita de Cabra. Córdoba, Spain; Hospital Universitario Reina Sofía. Córdoba, Spain; Hospital Nuestra Señora del Prado. Talavera de la Reina. Toledo, Spain; Hospital de Cabueñes. Asturias, Spain; Hospital de Mataró, Barcelona, Spain; Hospital Universitario de Bellvitge, Barcelona, Spain; Hospital Universitario 12 de Octubre. Madrid, Spain; Hospital Universitario Gregorio Marañón. Madrid, Spain; Hospital Universitario Puerta de Hierro. Madrid, Spain; Hospital Clínico Universitario San Carlos. Madrid, Spain; Universidad Miguel Hernandez. Elche. Alicante, Spain; Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC). Córdoba, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain; Hospital Regional Universitario de Málaga. Málaga, Spain; Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain","Arévalo-Lorido J.C., Complejo Hospitalario Universitario de Badajoz. Badajoz, Spain; Carretero-Gómez J., Complejo Hospitalario Universitario de Badajoz. Badajoz, Spain; Casas-Rojo J.M., Hospital Universitario Infanta Cristina de Parla. Madrid, Spain; Antón-Santos J.M., Hospital Universitario Infanta Cristina de Parla. Madrid, Spain; Melero-Bermejo J.A., Hospital Universitario Infanta Cristina de Parla. Madrid, Spain; López-Carmona M.D., Hospital Regional Universitario de Málaga. Málaga, Spain; Palacios L.C., Hospital Regional Universitario de Málaga. Málaga, Spain; Sanz-Cánovas J., Hospital Regional Universitario de Málaga. Málaga, Spain; Pesqueira-Fontán P.M., Hospital Clínico Universitario de Santiago de Compostela, A Coruña, Spain; de la Peña-Fernández A.A., Hospital Universitario Son Llàtzer. Palma de Mallorca. Illes Balears, Spain; de la Sierra Alcántara N.-M., Hospital Infanta Margarita de Cabra. Córdoba, Spain; García-García G.M., Complejo Hospitalario Universitario de Badajoz. Badajoz, Spain; Torres Peña J.D., Hospital Universitario Reina Sofía. Córdoba, Spain, Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC). Córdoba, Spain, CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain; Magallanes-Gamboa J.O., Hospital Nuestra Señora del Prado. Talavera de la Reina. Toledo, Spain; Fernández-Madera-Martinez R., Hospital de Cabueñes. Asturias, Spain; Fernández-Fernández J., Hospital de Mataró, Barcelona, Spain; Rubio-Rivas M., Hospital Universitario de Bellvitge, Barcelona, Spain, Bellvitge Biomedical Research Institute-IDIBELL, Barcelona, Spain; Maestro-de la Calle G., Hospital Universitario 12 de Octubre. Madrid, Spain; Cervilla-Muñoz E., Hospital Universitario Gregorio Marañón. Madrid, Spain; Ramos-Martínez A., Hospital Universitario Puerta de Hierro. Madrid, Spain; Méndez-Bailón M., Hospital Clínico Universitario San Carlos. Madrid, Spain; Ramos-Rincón J.M., Universidad Miguel Hernandez. Elche. Alicante, Spain; Gómez-Huelgas R., Hospital Regional Universitario de Málaga. Málaga, Spain, Biomedical Research Institute of Málaga (IBIMA), Málaga, Spain","Background: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19. Methods: Retrospective cohort multicenter study based on clinical practice. Data were taken from the SEMI-COVID-19 Registry, which includes most consecutive patients with confirmed COVID-19 hospitalized and discharged in Spain. Two machine learning algorithms were applied in order to classify comorbidities and patients (Random Forest -RF algorithm, and Gaussian mixed model by clustering -GMM-). The primary endpoint was a composite of either, all-cause death or intensive care unit admission during the period of hospitalization. The sample was randomly divided into training and test sets to determine the most important comorbidities related to the primary endpoint, grow several clusters with these comorbidities based on discriminant analysis and GMM, and compare these clusters. Results: A total of 16,455 inpatients (57.4% women and 42.6% men) were analyzed. According to the RF algorithm, the most important comorbidities were heart failure/atrial fibrillation (HF/AF), vascular diseases, and neurodegenerative diseases. There were six clusters: three included patients who met the primary endpoint (clusters 4, 5, and 6) and three included patients who did not (clusters 1, 2, and 3). Patients with HF/AF, vascular diseases, and neurodegenerative diseases were distributed among clusters 3, 4 and 5. Patients in cluster 5 also had kidney, liver, and acid peptic diseases as well as a chronic obstructive pulmonary disease; it was the cluster with the worst prognosis. Conclusion: The interplay of several comorbidities may affect the outcome and complications of inpatients with COVID-19. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","cluster analysis; comorbidity; COVID-19; machine learning; SARS-CoV-2","Comorbidity; COVID-19; Female; Hospitalization; Humans; Machine Learning; Male; Retrospective Studies; Risk Factors; SARS-CoV-2; albumin; C reactive protein; creatinine; D dimer; ferritin; fibrinogen; glucose; hemoglobin; potassium; sodium; acquired immune deficiency syndrome; aged; albumin blood level; all cause mortality; Article; asthma; atrial fibrillation; cardiovascular risk factor; cerebrovascular accident; Charlson Comorbidity Index; chronic bronchitis; chronic kidney failure; chronic obstructive lung disease; clinical outcome; clinical practice; cohort analysis; comorbidity; comparative study; coronary artery disease; coronavirus disease 2019; creatinine blood level; cross validation; deep vein thrombosis; degenerative disease; diabetes mellitus; diastolic blood pressure; digestive system function disorder; disease association; dyslipidemia; fasting; female; ferritin blood level; fraction of inspired oxygen; generalized anxiety disorder; glucose blood level; heart failure; hematologic malignancy; Horowitz index; hospital patient; human; Human immunodeficiency virus infection; hypertension; intensive care unit; kernel method; leukemia; leukocyte count; lung embolism; machine learning; major clinical study; male; multicenter study; nasopharyngeal swab; neutrophil lymphocyte ratio; obesity; obstructive lung disease; panic; patient selection; peptic ulcer; peripheral occlusive artery disease; platelet lymphocyte ratio; potassium blood level; prediction; prognosis; prothrombin time; random forest; real time polymerase chain reaction; retrospective study; rheumatic disease; sleep disordered breathing; sodium blood level; solid malignant neoplasm; Spain; sputum; systolic blood pressure; clinical trial; comorbidity; epidemiology; hospitalization; machine learning; risk factor","","C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; ferritin, 9007-73-2; fibrinogen, 9001-32-5; glucose, 50-99-7, 84778-64-3; hemoglobin, 9008-02-0; potassium, 7440-09-7; sodium, 7440-23-5","","","","","Chen N., Zhou M., Dong X., Et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet, 395, pp. 507-513, (2020); Gao H.N., Lu H.Z., Cao B., Et al., Clinical findings in 111 cases of influenza A (H7N9) virus infection, N Engl J Med, 368, 24, pp. 2277-2285, (2013); Emami A., Javanmardi F., Pirbonyeh N., Et al., Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis, Arch Acad Emerg Med, 8, 1, (2020); Yang J., Zheng Y., Gou X., Et al., Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis, Int J Infect Dis, 94, pp. 91-95, (2020); Casas-Rojo J.M., Anton-Santos J.M., Millan-Nunez-Cortes J., Et al., Características clínicas de los pacientes hospitalizados con COVID-19 en españa: resultados del registro SEMI-COVID-19, Rev Clin Española, 220, 8, pp. 480-494, (2020); Ranieri V.M., Rubenfeld G.D., Thompson B.T., Et al., Acute respiratory distress syndrome: the berlin definition, Jama, 307, 23, pp. 2526-2533, (2012); Ponikowski P., Voors A.A., Anker S.D., Bueno H., Et al., Authors/task force members; document reviewers. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the heart failure association (HFA) of the ESC, Eur J Heart Fail, 18, 8, pp. 891-975, (2016); Taylor F.B., Toh C.H., Hoots W.K., Et al., Towards definition, clinical and laboratory criteria, and a scoring system for disseminated intravascular coagulation–on behalf of the scientific subcommittee on Disseminated Intravascular Coagulation (DIC) of the international society on, Thromb Haemost, 86, 11, pp. 1327-1330, (2001); Breiman L., Random forests, Machine Learning, 45, 1, pp. 5-32, (2001); Liaw A., Wiener M., Classification and regression by randomForest, R News, 2, pp. 18-22, (2002); Strobl C., Boulesteix A.L., Zeileis A., Et al., Bias in random Forest variable importance measures: illustrations, sources and a solution, BMC Bioinformatics, 8, (2007); Scrucca L., Fop M., Murphy T.B., Et al., Mclust 5: clustering, classification and density estimation using gaussian finite mixture models, R J, 8, 1, pp. 289-317, (2016); Fraley C., Raftery A.E., Model-based clustering, discriminant analysis and density estimation, J Am Stat Assoc, 97, 458, pp. 611-631, (2002); Simera I., Moher D., Hoey J., Et al., A catalogue of reporting guidelines for health research, Eur J Clin Invest, 40, 1, pp. 35-53, (2010); Richardson S., Hirsch J.S., Narasimhan M., Et al., Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city Area, Jama, 323, 20, pp. 2052-2059, (2020); Herman C., Mayer K., Sarwal A., Scoping review of prevalence of neurologic comorbidities in patients hospitalized for COVID-19, Neurology, 95, 2, pp. 77-84, (2020); Gao Y.D., Ding M., Dong X., Et al., Risk factors for severe and critically ill COVID-19 patients: a review, Allergy, 76, 2, pp. 428-455, (2021); Penna C., Mercurio V., Tocchetti C.G., Et al., Sex-related differences in COVID-19 lethality, Br J Pharmacol, 177, 19, pp. 4375-4385, (2020); Zhou F., Yu T., Du R., Et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, Lancet, 395, pp. 1054-1062, (2020); Sokolowska M., Lukasik Z.M., Agache I., Et al., Immunology of COVID-19: mechanisms, clinical outcome, diagnostics, and perspectives-A report of the European Academy of Allergy and Clinical Immunology (EAACI), Allergy, 75, 10, pp. 2445-2476, (2020); Inciardi R.M., Adamo M., Lupi L., Et al., Characteristics and outcomes of patients hospitalized for COVID-19 and cardiac disease in Northern Italy, Eur Heart J, 41, 19, pp. 1821-1829, (2020); Artoni A., Abbattista M., Bucciarelli P., Et al., Platelet to lymphocyte ratio and neutrophil to lymphocyte ratio as risk factors for venous thrombosis, Clin Appl Thromb Hemost, 24, 5, pp. 808-814, (2018); Cabibbo G., Rizzo G.E.M., Stornello C., Et al., SARS-CoV-2 infection in patients with a normal or abnormal liver, J Viral Hepat, 28, 1, pp. 4-11, (2021); Kumar-M P., Mishra S., Jha D.K., Et al., Coronavirus disease (COVID-19) and the liver: a comprehensive systematic review and meta-analysis, Hepatol Int, 14, 5, pp. 711-722, (2020)","J.C. Arévalo-Lorido; Servicio de Medicina Interna. Complejo Hospitalario Universitario de Badajoz, Badajoz, Avda de Elvas s/n, 06080, Spain; email: joscarlor@gmail.com","","Taylor and Francis Ltd.","","","","","","03007995","","CMROC","35037799","English","Curr. Med. Res. Opin.","Article","Final","","Scopus","2-s2.0-85124371504"
"Abineza C.; Balas V.E.; Nsengiyumva P.","Abineza, Claudia (57754302300); Balas, Valentina E. (9279071000); Nsengiyumva, Philibert (57192167208)","57754302300; 9279071000; 57192167208","A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use","2022","Journal of Intelligent and Fuzzy Systems","43","2","","1683","1695","12","5","10.3233/JIFS-219270","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132451207&doi=10.3233%2fJIFS-219270&partnerID=40&md5=bd7316277beb582561df155860a26760","African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda; Department of Automatics and Applied Software, 'Aurel Vlaicu' University, Arad, Romania","Abineza C., African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda; Balas V.E., Department of Automatics and Applied Software, 'Aurel Vlaicu' University, Arad, Romania; Nsengiyumva P., African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda","Chronic Obstructive Pulmonary Disease (COPD) is a progressive, obstructive lung disease that restricts airflow from the lungs. COPD patients are at risk of sudden and acute worsening of symptoms called exacerbations. Early identification and classification of COPD exacerbation can reduce COPD risks and improve patient's healthcare and management. Pulse oximetry is a non-invasive technique used to assess patients with acutely worsening symptoms. As part of manual diagnosis based on pulse oximetry, clinicians examine three warning signs to classify COPD patients. This may lack high sensitivity and specificity which requires a blood test. However, laboratory tests require time, further delayed treatment and additional costs. This research proposes a prediction method for COPD patients' classification based on pulse oximetry three manual warning signs and the resulting derived few key features that can be obtained in a short time. The model was developed on a robust physician labeled dataset with clinically diverse patient cases. Five classification algorithms were applied on the mentioned dataset and the results showed that the best algorithm is XGBoost with the accuracy of 91.04%, precision of 99.86%, recall of 82.19%, F1 measure value of 90.05% with an AUC value of 95.8%. Age, current and baseline heart rate, current and baseline pulse ox. (SPO2) were found the top most important predictors. These findings suggest the strength of XGBoost model together with the availability and the simplicity of input variables in classifying COPD daily living using a (wearable) pulse oximeter.  © 2022 - IOS Press. All rights reserved.","classification; COPD; easy; machine learning; pulse oximetry","Classification (of information); Diagnosis; Noninvasive medical procedures; Oximeters; Pulmonary diseases; 'current; Chronic obstructive pulmonary disease; Clinical use; Easy; Machine-learning; Prediction methods; Pulmonary disease classification; Pulse oximetry; Pulse-oximetry; Warning signs; Machine learning","","","","","","","Projections of mortality and causes of death, 2015 and 2030; Roche N., Wedzicha J.A., Patalano F., Stefan-Marian F., Michael L., Steven S., Robert F., Donald B., COPD exacerbations significantly impact quality of life as measured by SGRQ-C total score: results from the FLAME study, Eur Resp J., 50, (2017); Dransfield M.T., Kunisaki K.M., Strand M.J., Anzueto A., Bhatt S.P., Et al., Acute Exacerbations and Lung Function Loss in Smokers with and Without COPD. American Journal of Respiratory and Critical Care Medicine. Author, F.: Article title, Journal, 2, 5, pp. 99-110, (2016); Watz H., Tetzlaff K., Magnussen H., Mueller A., Rodriguez-Roisin R., Wouters E.F.M., Et al., Spirometric changes during exacerbations of COPD: a post hoc analysis of the wisdom trial, Respiratory Research, 19, 1, (2018); Kerkhof M., Voorham J., Dorinsky P., Cabrera C., Darken P., Kocks J.W., Et al., Association between COPD exacerbations and lung function decline during maintenance therapy, Thorax, Thoraxjnl, pp. 2019-214457, (2020); Liu F., Jiang Y., Xu G., Ding Z., Effectiveness of Telemedicine Intervention for Chronic Obstructive Pulmonary Disease in China: A Systematic Review and Meta-Analysis, (2020); Vitacca M., Montini A., Comini L., How will telemedicine change clinical practice in chronic obstructive pulmonary disease?, Therapeutic Advances in Respiratory Disease, 12, (2018); Sanchez-Morillo D., Fernandez-Granero M.A., Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma, Chronic Respiratory Disease, 13, 3, pp. 264-283, (2016); Stiell I.G., Clement C.M., Aaron S.D., Rowe B.H., Perry J.J., Brison R.J., Wells G.A., Clinical characteristics associated with adverse events in patients with exacerbation of chronic obstructive pulmonary disease: a prospective cohort study, Canadian Medical Association Journal, 186, 6, pp. E193-E204, (2014); Angelucci A., Aliverti A., Telemonitoring systems for respiratory patients: technological aspects, Pulmonology, (2020); Nici L., Bontly T.D., Zuwallack R., Gross N., Selfmanagement in chronic obstructive pulmonary disease. Time for a Paradigm Shift?, Ann Am Thorac Soc, 11, 1, pp. 101-107, (2014); Wilkinson T.M.A., Donaldson G.C., Hurst J.R., Seemungal T.A., Early terapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, American Journal of Respiratory and Critical Care Medicine, 169, 12, pp. 1298-1303, (2004); Chandra D., Tsai C.L., Camargo C.A., Acute Exacerbations of COPD: Delay in Presentation and the Risk of Hospitalization, Chronic Obstr Pulm Dis, 6, 2, pp. 95-103, (2009); Seemungal T., Wedzicha J.A., Acute exacerbations of COPD: the challenge is early treatment, Journal of Chronic Obstructive Pulmonary Disease, 6, 2, pp. 79-81, (2009); Trappenburg J.C.A., VanDeventer A.C., Troosters T., Verheij T.J.M., Schrijvers A.J.P., Lammers J.-W.J., Monninkhof E.M., The impact of using different symptom-based exacerbation algorithms in patients with COPD, European Respiratory Journal, 37, 5, pp. 1260-1268, (2010); Peng J., Chen C., Zhou M., Xie X., Zhou Y., Luo C.-H., Amachine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators, Scientific Reports, 10, 1, (2020); Swaminathan S., Qirko K., Smith T., Corcoran E., Wysham N.G., Bazaz G., Et al., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLOS ONE, 12, 11, (2017); Amir M., Machine Learning for Predicting COPD Hospitalization using Quantitative CT Imaging, Conference: Imaging Network, (2020); Goto T., Camargo C.A., Faridi M.K., Yun B.J., Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, The American Journal of Emergency Medicine, 36, 9, pp. 1650-1654, (2018); Erkut B., Hasan Y., Sedat A., Eray Y., A comprehensive comparison of machine learning algorithms on diagnosing asthma disease and copd, Ponte Academic Journal, 76, 3, (2020); Finkelstein J., Jeong I.C., Machine learning approaches to personalize early prediction of asthma exacerbations, Annals of the New York Academy of Sciences, 1387, 1, pp. 153-165, (2016); Buekers J., Theunis J., De Boever P., Vaes A.W., Koopman M., Janssen E.V.M., Wouters E.F.M., Spruit M.A., Aerts J.-M., Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients with Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study, JMIR mHealth and uHealth, 7, 6, (2019); Hurst J.R., Donaldson G.C., Quint J.K., Goldring J.J., Patel A.R., Wedzicha J.A., Domiciliary pulse-oximetry at exacerbation of chronic obstructive pulmonary disease: prospective pilot study, BMC Pulmonary Medicine, 10, 1, (2010); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: Identification and prediction using a digital health system, J Med Internet Res, 19, 3, (2017); Shah S.A., Velardo C., Gibson O.J., Rutter H., Farmer A., Tarassenko L., Personalized alerts for patients with COPDusing pulse oximetry and symptom scores, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2014); Battineni G., Sagaro G.G., Chinatalapudi N., Amenta F., Applications of machine learning predictive models in the chronic disease diagnosis, Journal ofPersonalized Medicine, 10, 2, (2020); Guber A., Epstein Shochet G., Kohn S., Shitrit D., Wristsensor pulse oximeter enables prolonged patient monitoring in chronic lung diseases, Journal of Medical Systems, 43, 7, (2019); Mas S.M., A Decision Support System for the Home Management of Patients with Chronic Obstructive Pulmonary Disease (COPD) using Telehealth, (2012); Holland K., Is My Blood Oxygen Level Normal?; Nall R., What to know about COPD hypoxia; Nirzari K.P., Sandeep S., Capnography and Pulse Oximetry, (2020); Gholipour B., Is a Normal Heart Rate?, (2018); Bhaya W.S., Review of data preprocessing techniques in data mining, Journal of Engineering and Applied Sciences, 12, 16, pp. 4102-4107, (2017); Maladkar K., 5Ways To Handle Missing Values In Machine Learning Datasets; Aida A., Siti Mariyam S.S.M., Anca R.L., Classification with class imbalance problem: A Review, Int. 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Internet of Things: engineering cyber physical human systems, (2019); Turner L.D., Allen S.M., Whitaker R.M., Interruptibility prediction for ubiquitous systems: conventions and new directions from a growing field, Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, pp. 801-812, (2015); Podgorelec V., Kokol P., Stiglic B., Rozman I., Journal of Medical Systems, 26, 5, pp. 445-463, (2002); Bae J.-M., Clinical decision analysis using decision tree, Epidemiology and Health, (2014); Chern C., Chen Y., Hsiao B., Decision tree-based classifier in providing telehealth service, BMC Med Inform Decis Mak, 19, (2019); Rymarczyk T., Kozowski E., Kosowski G., Niderla K., Logistic regression for machine learning in process tomography, Sensors, 19, 15, (2019); Wang Y., Feng D., Li D., Chen X., Zhao Y., Niu X., A mobile recommendation system based on logistic regression and gradient boosting decision trees, 2016 International joint conference on neural networks (IJCNN), pp. 1896-1902, (2016); Ernsting C., Dombrowski S.U., Oedekoven M., Sullivan L.O.J., Kanzler M., Kuhlmey A., Gellert P., Using smartphones and health apps to change and manage health behaviors: a population-based survey, J Med Intern Res., 19, 4, (2017); Elizabeth A.D., Joseph T.H., Research Methods in Human Skeletal Biology, (2013); Julien I.E.H., Basic Biostatistics for Medical and Biomedical Practitioners (Second Edition), (2019); Breiman L., Random forests, Mach Learn, 45, 1, pp. 5-32, (2001); Breiman L., Bagging predictors, Mach Learn, 24, 2, pp. 123-140, (1996); Amit Y., Geman D., Shape quantization and recognition with randomized trees, Neural Comput, 9, 7, pp. 545-588, (1997); Dai B., Chen R., Zhu S., Zhang W., Using Random Forest Algorithm for Breast Cancer Diagnosis, International Symposium on Computer, Consumer and Control (IS3C), pp. 449-452, (2018); Casanova R., Saldana S., Chew E.Y., Danis R.P., Greven C.M., Ambrosius W.T., Application of random forests methods to diabetic retinopathy classification analyses, PLoS ONE, 9, 6, (2014); Rahman R., Dhruba S.R., Ghosh S., Et al., Functional random forest with applications in dose-response predictions, Sci Rep, 9, (2019); Bentejac C., Csorgo A., Martinez-Munoz G., A comparative analysis of gradient boosting algorithms, Artificial Intelligence Review, (2020); Mo X., Chen X., Li H., Li J., Zeng F., Chen Y., Et al., Early and Accurate Prediction of Clinical Response to Methotrexate Treatment in Juvenile Idiopathic Arthritis Using Machine Learning, Frontiers in Pharmacology, (2019); Hu C.-A., Chen C.-M., Fang Y.-C., Liang S.-J., Wang H.-C., Fang W.-F., Et al., Using a machine learning approach to predict mortality in critically ill influenza patients: a crosssectional retrospective multicentre study in Taiwan, BMJ Open, 10, 2, (2020); Murty S.V., Kumar R.K., Accurate liver disease prediction with extreme gradient boosting, Int. 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Abineza; African Center of Excellence in Internet of Things, University of Rwanda, Kigali, Rwanda; email: abineza1@gmail.com","","IOS Press BV","","","","","","10641246","","","","English","J. Intelligent Fuzzy Syst.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85132451207"
"Chen S.; Li L.; Lin Z.; Zhang K.; Gong Y.; Wang L.; Wu X.; Li M.; Song Y.; Yang F.; Xu S.","Chen, Shuzhe (57771788200); Li, Li (57193576841); Lin, Zhichao (57216592816); Zhang, Ke (57200695748); Gong, Ying (57203689928); Wang, Lu (57437735000); Wu, Xu (56609455800); Li, Maokun (7405263889); Song, Yuanlin (7404920196); Yang, Fan (56380816200); Xu, Shenheng (13907922300)","57771788200; 57193576841; 57216592816; 57200695748; 57203689928; 57437735000; 56609455800; 7405263889; 7404920196; 56380816200; 13907922300","Spatio-Temporal Classification of Lung Ventilation Patterns Using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation","2024","IEEE Journal of Biomedical and Health Informatics","28","1","","367","378","11","3","10.1109/JBHI.2023.3328343","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181581959&doi=10.1109%2fJBHI.2023.3328343&partnerID=40&md5=dafe8a176f6f6c1b74c60eedc5cf4804","Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Institute of Precision Medicine, Beijing, 100084, China; Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China; Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Tsinghua University, The BNRist and the Department of Electronic Engineering, Beijing, 100084, China","Chen S., Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Institute of Precision Medicine, Beijing, 100084, China, Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China; Li L., Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Lin Z., Tsinghua University, The BNRist and the Department of Electronic Engineering, Beijing, 100084, China; Zhang K., Tsinghua University, The BNRist and the Department of Electronic Engineering, Beijing, 100084, China; Gong Y., Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Wang L., Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Wu X., Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Li M., Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Institute of Precision Medicine, Beijing, 100084, China, Tsinghua University, Department of Electronic Engineering, Beijing, 100084, China; Song Y., Fudan University, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai, 200032, China; Yang F., Tsinghua University, The BNRist and the Department of Electronic Engineering, Beijing, 100084, China; Xu S., Tsinghua University, The BNRist and the Department of Electronic Engineering, Beijing, 100084, China","The Pulmonary Function Test (PFT) is a widely utilized and rigorous classification test for evaluating lung function, serving as a comprehensive diagnostic tool for lung conditions. Meanwhile, Electrical Impedance Tomography (EIT) is a rapidly advancing clinical technique that visualizes conductivity distribution induced by ventilation. EIT provides additional spatial and temporal information on lung ventilation beyond traditional PFT. However, relying solely on conventional isolated interpretations of PFT results and EIT images overlooks the continuous dynamic aspects of lung ventilation. This study aims to classify lung ventilation patterns by extracting spatial and temporal features from the 3D EIT image series. The study uses a Variational Autoencoder (VAE) with a MultiRes block to compress the spatial distribution in a 3D image into a one-dimensional vector. These vectors are then stacked to create a feature map for the exhibition of temporal features. A simple convolutional neural network is used for classification. Data from 137 subjects were utilized for the training phase. Initially, the model underwent validation through a leave-one-out cross-validation process. During this validation, the model achieved an accuracy and sensitivity of 0.96 and 1.00, respectively, with an f1-score of 0.98 when identifying the normal subjects. To assess pipeline reliability and feasibility, we tested it on 9 newly recruited subjects, with accurate ventilation mode predictions for 8 out of 9. In addition, we included 2D EIT results for comparison and conducted ablation experiments to validate the effectiveness of the VAE. The study demonstrates the potential of using image series for lung ventilation mode classification, providing a feasible method for patient prescreening and presenting an alternative form of PFT.  © 2013 IEEE.","Electrical impedance tomography (EIT); lung ventilation classification; pulmonary function test (PFT); variational autoencoder (VAE)","Biological organs; Classification (of information); Diagnosis; Electric impedance; Electric impedance tomography; Function evaluation; Learning systems; Neural networks; Pulmonary diseases; Statistical methods; Three dimensional displays; Auto encoders; Chronic obstructive pulmonary disease; Computed tomography; Electrical impe dance tomography (EIT); Electrical impedance tomography; Lung; Lung ventilation classification; Lungs ventilation; Predictive models; Pulmonary function test; Three-dimensional display; Variational autoencoder; adult; algorithm; anthropometry; area under the curve; Article; artificial neural network; autoencoder; body weight; chronic obstructive lung disease; classification; clinical evaluation; cohort analysis; computer assisted impedance tomography; computer assisted tomography; controlled study; convolutional neural network; diagnostic test accuracy study; feature extraction; forced expiratory volume; forced vital capacity; human; image quality; image reconstruction; leave one out cross validation; lung disease; lung function; lung function test; lung lesion; lung ventilation; lung ventilation distribution; machine learning; major clinical study; male; mathematical model; mathematical phenomena; nuclear magnetic resonance imaging; pleura effusion; prediction; spatiotemporal analysis; support vector machine; three-dimensional imaging; validation process; Variational Autoencoder; X ray; Electric impedance measurement","","","Infivision 1900, Beijing Huarui Boshi Medical Imaging Technology, China; MATLAB","Beijing Huarui Boshi Medical Imaging Technology, China","","","Fei F., Siegert R.J., Zhang X., Gao W., Koffman J., Symptom clusters, associated factors and health-related quality of life in patients with chronic obstructive pulmonary disease: A structural equation modelling analysis, J. 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Li; Tsinghua University, Beijing National Research Center for Information Science and Technology (BNRist), Institute of Precision Medicine, Beijing, 100084, China; email: maokunli@tsinghua.edu.cn","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","37903038","English","IEEE J. Biomedical Health Informat.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85181581959"
"Nan C.; Schmidt O.; Lindner R.; Ilgin Y.; Schultz T.; Hinsch Gylvin L.; Bleecker E.R.","Nan, Cassandra (57214150120); Schmidt, Olaf (56517631100); Lindner, Robert (57221977579); Ilgin, Yasemin (57221981929); Schultz, Thomas (57204566837); Hinsch Gylvin, Lykke (57221975720); Bleecker, Eugene R. (7004832308)","57214150120; 56517631100; 57221977579; 57221981929; 57204566837; 57221975720; 7004832308","German regional variation of acute and high oral corticosteroid use for asthma","2022","Journal of Asthma","59","4","","791","800","9","5","10.1080/02770903.2021.1878532","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100843658&doi=10.1080%2f02770903.2021.1878532&partnerID=40&md5=11ba7abf98d75d96968d2311e063f074","AstraZeneca, Mölndal, Sweden; KPPK GmbH, Koblenz, Germany; IQVIA, Frankfurt am Main, Germany; Pneumologen Lichterfelde, Berlin, Germany; AstraZeneca, Cambridge, United Kingdom; University of Arizona College of Medicine, Tucson, AZ, United States","Nan C., AstraZeneca, Mölndal, Sweden; Schmidt O., KPPK GmbH, Koblenz, Germany; Lindner R., IQVIA, Frankfurt am Main, Germany; Ilgin Y., IQVIA, Frankfurt am Main, Germany; Schultz T., Pneumologen Lichterfelde, Berlin, Germany; Hinsch Gylvin L., AstraZeneca, Cambridge, United Kingdom; Bleecker E.R., University of Arizona College of Medicine, Tucson, AZ, United States","Objective: : To improve understanding of real-world asthma treatment and inform physician education, we evaluated regional variation in asthma prevalence and oral corticosteroid (OCS) use across Germany. Methods: : We developed a machine learning gradient-boosted tree model with IMS® Disease Analyzer electronic medical records, which cover 3% of German patients. This model had a 91% accuracy in predicting the presence of asthma and chronic obstructive pulmonary disease. We applied the model to the IMS® Longitudinal Prescription database, with 82% national coverage, to classify patients receiving treatment for airflow obstruction from October 2017–September 2018 in 63 regions in Germany. Results: : Of 2.4 million individuals under statutory health insurance predicted to have asthma, 13.7%, 18.7%, 36.5%, 29.4%, and 1.7% received treatment classified as Global Initiative for Asthma (GINA) Steps 1, 2, 3, 4, and 5, respectively. Approximately 7–15% of those at GINA Steps 1–4 and 35% at Step 5 treatment received ≥1 acute OCS prescription (duration <10 days). Of patients receiving GINA Steps 1–4 and Step 5 treatments, 1–3% and 86%, respectively, received ≥1 high-dosage OCS prescription. Cumulative OCS dosage and percentages of patients receiving OCS differed substantially across regions, and regions with lower OCS use had greater use of biologic therapies. Conclusions: : Both acute and high OCS use varied regionally across Germany, with overall use suggesting patients are considerable risk of adverse effects and long-term health consequences. Supplemental data for this article can be accessed at publisher’s website. © 2021 AstraZeneca. Published with license by Taylor & Francis Group, LLC.","bronchial disease; Germany; medical informatics; obstructive lung disease; resource allocation","Administration, Oral; Adrenal Cortex Hormones; Anti-Asthmatic Agents; Asthma; Germany; Humans; Pulmonary Disease, Chronic Obstructive; benralizumab; corticosteroid; mepolizumab; omalizumab; reslizumab; antiasthmatic agent; corticosteroid; adult; airway obstruction; Article; asthma; biological therapy; cohort analysis; corticosteroid therapy; data analysis software; disease severity; drug megadose; drug use; electronic medical record; geographic mapping; Germany; health insurance; human; machine learning; major clinical study; medical education; physician; prescription; prevalence; retrospective study; severe asthma; treatment duration; asthma; chronic obstructive lung disease; oral drug administration","","benralizumab, 1044511-01-4; mepolizumab, 196078-29-2; omalizumab, 242138-07-4; reslizumab, 241473-69-8; Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ","IMS","","ELS; Michael A. Nissen; AstraZeneca","Funding text 1: This study was funded by AstraZeneca. Editorial support was provided by Nate Connors, PhD, of Citrus Health Group (Lantana, FL, USA), and Michael A. Nissen, ELS, of AstraZeneca (Gaithersburg, MD, USA). Writing support was funded by AstraZeneca. ; Funding text 2: This study was funded by AstraZeneca. Editorial support was provided by Nate Connors, PhD, of Citrus Health Group (Lantana, FL, USA), and Michael A. Nissen, ELS, of AstraZeneca (Gaithersburg, MD, USA). Writing support was funded by AstraZeneca.","(2020); Alangari A.A., Corticosteroids in the treatment of acute asthma, Ann Thorac Med, 9, 4, pp. 187-192, (2014); Giles A.J., Hutchinson M.-K.N.D., Sonnemann H.M., Jung J., Fecci P.E., Ratnam N.M., Zhang W., Song H., Bailey R., Davis D., Et al., Dexamethasone-induced immunosuppression: mechanisms and implications for immunotherapy, J Immunother Cancer, 6, 1, (2018); Mortimer K.J., Tata L.J., Smith C.J., Et al., Oral and inhaled corticosteroids and adrenal insufficiency: a case-control study, Thorax, 61, 5, pp. 405-408, (2006); Price D.B., Trudo F., Voorham J., Xu X., Kerkhof M., Ling Zhi Jie J., Tran T.N., Adverse outcomes from initiation of systemic corticosteroids for asthma: long-term observational study, JAA, 11, pp. 193-204, (2018); Bleecker E.R., Menzies-Gow A.N., Price D.B., Bourdin A., Sweet S., Martin A.L., Alacqua M., Tran T.N., Systematic literature review of systemic corticosteroid use for asthma management, Am J Respir Crit Care Med, 201, 3, pp. 276-293, (2020); Volmer T., Effenberger T., Trautner C., Buhl R., Consequences of long-term oral corticosteroid therapy and its side-effects in severe asthma in adults: a focused review of the impact data in the literature, Eur Respir J, 52, 4, (2018); Lovinsky-Desir S., The use of biologic therapies for the management of pediatric asthma, Pediatr Pulmonol, 55, 3, pp. 803-808, (2020); Mitchell P., Leigh R., A drug safety review of treating eosinophilic asthma with monoclonal antibodies, Expert Opin Drug Saf, 18, 12, pp. 1161-1170, (2019); Tran T.N., King E., Sarkar R., Nan C., Rubino A., O'Leary C., Muzwidzwa R., Belton L., Quint J.K., Oral corticosteroid prescription patterns for asthma in France, Germany, Italy and the UK, Eur Respir J, 55, 6, (2020); Tran T.N., MacLachlan S., Hicks W., Liu J., Chung Y., Zangrilli J., Rubino A., Ganz M.L., Oral corticosteroid treatment patterns of patients in the United States with persistent asthma, J Allergy Clin Immunol Pract, 9, 1, pp. 338-346, (2020); Chapman K.R., Remtulla A., Gendron A., Xu S., Nan C., Regional variation in asthma prevalence and oral corticosteroid use for Canadian patients: heat map analysis, Am J Respir Crit Care Med, 201, (2020); Tripoliti E.E., Papadopoulos T.G., Karanasiou G.S., Naka K.K., Fotiadis D.I., Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques, Comput Struct Biotechnol J, 15, pp. 26-47, (2017); Kim S.J., Cho K.J., Oh S., Development of machine learning models for diagnosis of glaucoma, PLoS One, 12, 5, (2017); Mollalo A., Vahedi B., Bhattarai S., Hopkins L.C., Banik S., Vahedi B., Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: integration of GIS, spatial statistics and machine learning algorithms, Int J Med Inform, 142, (2020); Xue M., Su Y., Li C., Wang S., Yao H., Identification of potential type II diabetes in a large-scale Chinese population using a systematic machine learning framework, J Diabetes Res, 2020, (2020); Zhou T., Zhang Y., Wu C., Shen C., Li J., Liu Z., An augmented model with inferred blood features for the self-diagnosis of metabolic syndrome, Methods Inf Med, 59, 1, pp. 18-30, (2020); Raita Y., Camargo C.A.J., Macias C.G., Mansbach J.M., Piedra P.A., Porter S.C., Teach S.J., Hasegawa K., Machine learning-based prediction of acute severity in infants hospitalized for ­bronchiolitis: a multicenter prospective study, Sci Rep, 10, 1, (2020); Demoly P., Paggiaro P., Plaza V., Bolge S.C., Kannan H., Sohier B., Adamek L., Prevalence of asthma control among adults in France, Germany, Italy, Spain and the UK, Eur Respir Rev, 18, 112, pp. 105-112, (2009); Stock S., Redaelli M., Luengen M., Wendland G., Civello D., Lauterbach K.W., Asthma: prevalence and cost of illness, Eur Respir J, 25, 1, pp. 47-53, (2005); Benard-Laribiere A., Pariente A., Pambrun E., Begaud B., Fardet L., Noize P., Prevalence and prescription patterns of oral glucocorticoids in adults: a retrospective cross-sectional and cohort analysis in France, BMJ Open, 7, 7, (2017)","C. Nan; Epidemiology, Mölndal, AstraZeneca, KC6, SE-, 43183, Sweden; email: cassandra.nan@astrazeneca.com","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","33492176","English","J. Asthma","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85100843658"
"Divisi D.; Pipitone M.; Perkmann R.; Bertolaccini L.; Curcio C.; Baldinelli F.; Crisci R.; Zaraca F.","Divisi, Duilio (6603386903); Pipitone, Marco (57195973961); Perkmann, Reinhold (6701728610); Bertolaccini, Luca (57193526478); Curcio, Carlo (7006091778); Baldinelli, Francesco (6506055157); Crisci, Roberto (7003336567); Zaraca, Francesco (58438793000)","6603386903; 57195973961; 6701728610; 57193526478; 7006091778; 6506055157; 7003336567; 58438793000","Prolonged air leak after video-assisted thoracic anatomical pulmonary resections: a clinical predicting model based on data from the Italian VATS group registry, a machine learning approach","2023","Journal of Thoracic Disease","15","2","","849","857","8","4","10.21037/jtd-21-1484","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149644724&doi=10.21037%2fjtd-21-1484&partnerID=40&md5=afde911b068fb793389659b5056c432a","Department of Life, Health and Environmental Sciences, Thoracic Surgery Unit, University of L’Aquila, L’Aquila, Italy; Department of Vascular and Thoracic Surgery, Central Hospital, Bolzano, Italy; Division of Thoracic Surgery, IEO European Institute of Oncology IRCCS, Milan, Italy; Department of Thoracic Surgery, Monaldi Hospital, Naples, Italy; First Service of Anesthesia and Intensive Care, Central Hospital, Bolzano, Italy","Divisi D., Department of Life, Health and Environmental Sciences, Thoracic Surgery Unit, University of L’Aquila, L’Aquila, Italy; Pipitone M., Department of Vascular and Thoracic Surgery, Central Hospital, Bolzano, Italy; Perkmann R., Department of Vascular and Thoracic Surgery, Central Hospital, Bolzano, Italy; Bertolaccini L., Division of Thoracic Surgery, IEO European Institute of Oncology IRCCS, Milan, Italy; Curcio C., Department of Thoracic Surgery, Monaldi Hospital, Naples, Italy; Baldinelli F., First Service of Anesthesia and Intensive Care, Central Hospital, Bolzano, Italy; Crisci R., Department of Life, Health and Environmental Sciences, Thoracic Surgery Unit, University of L’Aquila, L’Aquila, Italy; Zaraca F., Department of Vascular and Thoracic Surgery, Central Hospital, Bolzano, Italy","Background: Prolonged air leak (PAL) is a frequent complication after lung resection surgery and has a high clinical and economic impact. A useful risk predictor model can help recognize those patients who might benefit from additional preventive procedures. Currently, no risk model has sufficient discriminatory capacity to be used in common clinical practice. The aim of this study is to identify predictive risk factors for PAL after video-assisted thoracoscopic surgery (VATS) anatomical resections in the Italian VATS group database and to evaluate their clinical and statistical performance. Methods: We processed data collected in the second edition of the Italian VATS group registry. It includes patients that underwent a thoracoscopic anatomical resection for benign or malignant diseases, between November 2015 and December 2020. We used recursive feature elimination (RFE), using a backward selection process, to find the optimal combination of predictors. The study population was randomly split based on the outcome into a derivation (80%) and an internal validation cohort (20%). Discrimination of the model was measured using the area under the curve, or C-statistic. Calibration was displayed using a calibration plot and was measured using Emax and Eavg, the maximum and the average difference in predicted versus loess calibrated probabilities. Results: A cohort of 6,236 patients was eligible for the study after application of the exclusion criteria. Five-day PAL rate in this patient cohort was 11.3%. For the construction of our predictive model, we used both preoperative and intraoperative variables, with a total of 320 variables. The presence of variables with missing values greater than 5% led to 120 remaining predictors. RFE algorithm recommended 8 features for the model that are relevant in predicting the target variable. Conclusions: We confirmed significant prognostic risk factors for the prediction of PAL: decreased DLCO/VA ratio, longer duration of surgery, male sex, the need for adhesiolysis, COPD, and right side. We identified middle lobe resections and ground glass opacity as protective factors. After internal validation, a C statistic of 0.63 was revealed, which is too low to generate a reliable score in clinical practice. © Journal of Thoracic Disease. All rights reserved.","Prolonged air leak (PAL); risk factors; risk predictive model; video-assisted thoracoscopic surgery lobectomy (VATS lobectomy)","adhesiolysis; adult; aged; algorithm; Article; calibration; cohort analysis; human; Italy; logistic regression analysis; machine learning; major clinical study; male; pneumonectomy; postoperative complication; predictive model; prolonged air leak; recursive feature elimination; register; risk factor; video assisted thoracoscopic surgery","","","","","","","Fernandez FG, Falcoz PE, Kozower BD, Et al., The Society of Thoracic Surgeons and the European Society of Thoracic Surgeons general thoracic surgery databases: joint standardization of variable definitions and terminology, Ann Thorac Surg, 99, pp. 368-376, (2015); Dugan KC, Laxmanan B, Murgu S, Et al., Management of Persistent Air Leaks, Chest, 152, pp. 417-423, (2017); Okereke I, Murthy SC, Alster JM, Et al., Characterization and importance of air leak after lobectomy, Ann Thorac Surg, 79, pp. 1167-1173, (2005); Brunelli A, Xiume F, Al Refai M, Et al., Air leaks after lobectomy increase the risk of empyema but not of cardiopulmonary complications: a case-matched analysis, Chest, 130, pp. 1150-1156, (2006); Seder CW, Basu S, Ramsay T, Et al., A Prolonged Air Leak Score for Lung Cancer Resection: An Analysis of The Society of Thoracic Surgeons General Thoracic Surgery Database, Ann Thorac Surg, 108, pp. 1478-1483, (2019); Rivera C, Bernard A, Falcoz PE, Et al., Characterization and prediction of prolonged air leak after pulmonary resection: a nationwide study setting up the index of prolonged air leak, Ann Thorac Surg, 92, pp. 1062-1068, (2011); Pompili C, Falcoz PE, Salati M, Et al., A risk score to predict the incidence of prolonged air leak after video-assisted thoracoscopic lobectomy: An analysis from the European Society of Thoracic Surgeons database, J Thorac Cardiovasc Surg, 153, pp. 957-965, (2017); Attaar A, Winger DG, Luketich JD, Et al., A clinical prediction model for prolonged air leak after pulmonary resection, J Thorac Cardiovasc Surg, 153, pp. 690-699, (2017); Geraci TC, Chang SH, Shah SK, Et al., Postoperative Air Leaks After Lung Surgery: Predictors, Intraoperative Techniques, and Postoperative Management, Thorac Surg Clin, 31, pp. 161-169, (2021); McGuire AL, Yee J., Clinical outcomes of polymeric sealant use in pulmonary resection: a systematic review and meta-analysis of randomized controlled trials, J Thorac Dis, 10, pp. S3728-S3739, (2018); Brunelli A, Salati M, Pompili C, Et al., Intraoperative air leak measured after lobectomy is associated with postoperative duration of air leak, Eur J Cardiothorac Surg, 52, pp. 963-968, (2017); Zaraca F, Vaccarili M, Zaccagna G, Et al., Can a standardised Ventilation Mechanical Test for quantitative intraoperative air leak grading reduce the length of hospital stay after video-assisted thoracoscopic surgery lobectomy?, J Vis Surg, 3, (2017); Zaraca F, Pipitone M, Feil B, Et al., Predicting a Prolonged Air Leak After Video-Assisted Thoracic Surgery, Is It Really Possible?, Semin Thorac Cardiovasc Surg, 33, pp. 581-592, (2021); Attaar A, Luketich JD, Schuchert MJ, Et al., Prolonged Air Leak After Pulmonary Resection Increases Risk of Noncardiac Complications, Readmission, and Delayed Hospital Discharge: A Propensity Score-adjusted Analysis, Ann Surg, 273, pp. 163-172, (2021); Cerfolio RJ., Commentary: To ""Air"" is to Leak-To Prevent is Devine, Semin Thorac Cardiovasc Surg, 33, pp. 595-596, (2021); Zaraca F, Vaccarili M, Zaccagna G, Et al., Cost-effectiveness analysis of sealant impact in management of moderate intraoperative alveolar air leaks during video-assisted thoracoscopic surgery lobectomy: a multicentre randomised controlled trial, J Thorac Dis, 9, pp. 5230-5238, (2017); Brunelli A, Cicconi S, Decaluwe H, Et al., Parsimonious Eurolung risk models to predict cardiopulmonary morbidity and mortality following anatomic lung resections: an updated analysis from the European Society of Thoracic Surgeons database, Eur J Cardiothorac Surg, 57, pp. 455-461, (2020); Droghetti A., The ERAS project for VATS lobectomy-the Italian VATS Group, J Thorac Dis, 10, (2018); Orsini B, Baste JM, Gossot D, Et al., Index of prolonged air leak score validation in case of video-assisted thoracoscopic surgery anatomical lung resection: results of a nationwide study based on the French national thoracic database, EPITHOR, Eur J Cardiothorac Surg, 48, pp. 608-611, (2015); Kaminsky DA, Whitman T, Callas PW., DLCO versus DLCO/VA as predictors of pulmonary gas exchange, Respir Med, 101, pp. 989-994, (2007); Brunelli A, Varela G, Refai M, Et al., A scoring system to predict the risk of prolonged air leak after lobectomy, Ann Thorac Surg, 90, pp. 204-209, (2010); Petrella F, Rizzo S, Radice D, Et al., Predicting prolonged air leak after standard pulmonary lobectomy: computed tomography assessment and risk factors stratification, Surgeon, 9, pp. 72-77, (2011); Bernard A, Rivera C, Falcoz PE, Et al., Application of model score of prolonged air leak in the French database, Ann Thorac Surg, 92, pp. 1548-1550, (2011); Demmy TL., Commentary: 20% Chance of Precipitation, Semin Thorac Cardiovasc Surg, 33, pp. 593-594, (2021)","D. Divisi; Department of Life, Health and Environmental Sciences, University of L’Aquila, Palazzo Camponeschi, L’Aquila, Piazza Santa Margherita 2, 67100, Italy; email: duilio.divisi@univaq.it","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149644724"
"Ma Y.; Liu X.; Luo L.; Li H.; Zeng Z.; Chen Y.","Ma, Yiming (57212324623); Liu, Xiangming (57211680939); Luo, Lijuan (57205078864); Li, Herui (57212327591); Zeng, Zihang (57212323115); Chen, Yan (54794817300)","57212324623; 57211680939; 57205078864; 57212327591; 57212323115; 54794817300","Effect of pirfenidone protecting against cigarette smoke extract induced apoptosis","2022","Tobacco Induced Diseases","20","February","146169","","","","4","10.18332/tid/146169","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126620690&doi=10.18332%2ftid%2f146169&partnerID=40&md5=035700513416319f0716a00df02e4152","Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China","Ma Y., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Liu X., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Luo L., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Li H., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Zeng Z., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Chen Y., Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China","INTRODUCTION Apoptosis of lung structural cells is a significant upstream event involved in COPD pathogenesis. This study was designed to explore whether pirfenidone (PFD) was able to attenuate apoptosis induced by cigarette smoke extract (CSE). METHODS A method of intraperitoneal CSE injection to BALB/C mice was used to establish emphysema mouse model. Terminal deoxynucleotidyl transferase dUTPnick end labeling (TUNEL) assay was applied to evaluate apoptotic cell ratio in mouse lung tissue. The cell viability of HBECs exposed to different concentrations of PFD was measured by Cell Counting Kit-8 (CCK-8) assay. The apoptosis index (AI) of HBECs was tested by flow cytometry. Levels of apoptosisrelated protein were determined by Western blotting. RESULTS PFD treatment significantly decreased the AI value in emphysema mouse lung tissue by TUNEL. In HBECs, flow cytometry showed that PFD could significantly reduce AI led by CSE. Both in vitro and in vivo, protein levels of Bax and Cleaved-caspase 3 in CSE group significantly increased in contrast with the control group; while Bcl-2 protein level in CSE group was significantly decreased; moreover, PFD significantly reversed protein level changes of Bcl-2, Bax, and Cleaved-caspase 3 led by CSE. CONCLUSIONS This study reveals that PFD may potentially protect against CSE induced apoptosis.  © 2022 Ma Y. et al.","apoptosis; chronic obstructive pulmonary disease; emphysema; pirfenidone; treatment","caspase 3; cigarette smoke; DNA nucleotidylexotransferase; pirfenidone; protein Bax; protein bcl 2; animal cell; animal experiment; animal model; animal tissue; apoptosis; apoptosis index; Article; Bagg albino mouse; cell viability; chronic obstructive lung disease; controlled study; drug effect; emphysema; flow cytometry; gene expression; in vitro study; in vivo study; lung parenchyma; mouse; nonhuman; protein expression; smoking; TUNEL assay; Western blotting","","caspase 3, 169592-56-7; DNA nucleotidylexotransferase, 9027-67-2; pirfenidone, 53179-13-8; protein bcl 2, 219306-68-0","","","National Natural Science Foundation of China, NSFC, (81873410, 82070049)","The authors have each completed and submitted an ICMJE form for disclosure of potential conflicts of interest. The authors declare that they have no competing interests, financial or otherwise, related to the current work. Y. Chen reports that since the initial planning of the work funding was received from the National Natural Science Foundation of China (No. 81873410 and No. 82070049).","Lozano R, Naghavi M, Foreman K, Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Salvi SS, Barnes PJ., Chronic obstructive pulmonary disease in non-smokers, Lancet, 374, 9691, pp. 733-743, (2009); Demedts IK, Demoor T, Bracke KR, Joos GF, Brusselle GG., Role of apoptosis in the pathogenesis of COPD and pulmonary emphysema, Respir Res, 7, 1, (2006); Hodge S, Hodge G, Holmes M, Reynolds PN., Increased airway epithelial and T-cell apoptosis in COPD remains despite smoking cessation, Eur Respir J, 25, 3, pp. 447-454, (2005); He B, Zhang W, Qiao J, Peng Z, Chai X., Melatonin protects against COPD by attenuating apoptosis and endoplasmic reticulum stress via upregulating SIRT1 expression in rats, Can J Physiol Pharmacol, 97, 5, pp. 386-391, (2019); Cai S, Chen P, Zhang C, Chen JB, Wu J., Oral N-acetylcysteine attenuates pulmonary emphysema and alveolar septal cell apoptosis in smoking-induced COPD in rats, Respirology, 14, 3, pp. 354-359, (2009); King TE, Bradford WZ, Castro-Bernardini S, Et al., A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis, N Engl J Med, 370, 22, pp. 2083-2092, (2014); Ruwanpura SM, Thomas BJ, Bardin PG., Pirfenidone: Molecular Mechanisms and Potential Clinical Applications in Lung Disease, Am J Respir Cell Mol Biol, 62, 4, pp. 413-422, (2020); Chen K, Chen L, Ouyang Y, Et al., Pirfenidone attenuates homocysteine-induced apoptosis by regulating the connexin 43 pathway in H9C2 cells, Int J Mol Med, 45, 4, pp. 1081-1090, (2020); Du Y, Zhu P, Wang X, Et al., Pirfenidone alleviates lipopolysaccharide-induced lung injury by accentuating BAP31 regulation of ER stress and mitochondrial injury, J Autoimmun, 112, (2020); Chen L, Luo L, Kang N, He X, Li T, Chen Y., The Protective Effect of HBO1 on Cigarette Smoke Extract-Induced Apoptosis in Airway Epithelial Cells, Int J Chron Obstruct Pulmon Dis, 15, pp. 15-24, (2020); He X, Li T, Luo L, Zeng H, Chen Y, Cai S., PRMT6 mediates inflammation via activation of the NF-κB/p65 pathway on a cigarette smoke extract-induced murine emphysema model, Tob Induc Dis, 18, (2020); Plataki M, Tzortzaki E, Rytila P, Demosthenes M, Koutsopoulos A, Siafakas NM., Apoptotic mechanisms in the pathogenesis of COPD, Int J Chron Obstruct Pulmon Dis, 1, 2, pp. 161-171, (2006); Rangasamy T, Misra V, Zhen L, Tankersley CG, Tuder RM, Biswal S., Cigarette smoke-induced emphysema in A/J mice is associated with pulmonary oxidative stress, apoptosis of lung cells, and global alterations in gene expression, Am J Physiol Lung Cell Mol Physiol, 296, 6, pp. L888-L900, (2009); Gogebakan B, Bayraktar R, Ulasli M, Oztuzcu S, Tasdemir D, Bayram H., The role of bronchial epithelial cell apoptosis in the pathogenesis of COPD, Mol Biol Rep, 41, 8, pp. 5321-5327, (2014); Zhang L, Guo X, Xie W, Et al., Resveratrol exerts an anti-apoptotic effect on human bronchial epithelial cells undergoing cigarette smoke exposure, Mol Med Rep, 11, 3, pp. 1752-1758, (2015); Kuo WH, Chen JH, Lin HH, Chen BC, Hsu JD, Wang CJ., Induction of apoptosis in the lung tissue from rats exposed to cigarette smoke involves p38/JNK MAPK pathway, Chem Biol Interact, 155, 1-2, pp. 31-42, (2005); Chen JF, Liu H, Ni HF, Et al., Improved mitochondrial funct ion underl ies the protect ive ef fect of pirfenidone against tubulointerstitial fibrosis in 5/6 nephrectomized rats, PLoS One, 8, 12, (2013); Shihab FS, Bennett WM, Yi H, Andoh TF., Effect of pirfenidone on apoptosis-regulatory genes in chronic cyclosporine nephrotoxicity, Transplantation, 79, 4, pp. 419-426, (2005); Tsuchiya H, Kaibori M, Yanagida H, Et al., Pirfenidone prevents endotoxin-induced liver injury after partial hepatectomy in rats, J Hepatol, 40, 1, pp. 94-101, (2004)","Y. Chen; Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, 139 Renmin Road, Hunan, 410011, China; email: chenyan99727@csu.edu.cn","","European Publishing","","","","","","16179625","","","","English","Tob. Induced Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85126620690"
"Wang Y.; Song X.; Jin M.; Lu J.","Wang, Yakun (57915834400); Song, Xinyu (58844994900); Jin, Mulan (15122068100); Lu, Jun (57193649847)","57915834400; 58844994900; 15122068100; 57193649847","Characterization of the Immune Microenvironment and Identification of Biomarkers in Chronic Rhinosinusitis with Nasal Polyps Using Single-Cell RNA Sequencing and Transcriptome Analysis","2024","Journal of Inflammation Research","17","","","253","277","24","3","10.2147/JIR.S440409","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183196433&doi=10.2147%2fJIR.S440409&partnerID=40&md5=0153aa54e3c58be20603ea38023d5da5","Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China","Wang Y., Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; Song X., Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; Jin M., Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; Lu J., Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China","Purpose: Chronic rhinosinusitis is a prevalent condition in the field of otorhinolaryngology; however, its pathogenesis remains to be elucidated. The immunological defense of the nasal mucosa is significantly influenced by dendritic cells (DCs). We identified specific biological indicators linked to DCs and explored their significance in cases of chronic rhinosinusitis with nasal polyps (CRSwNP). Patients and Methods: We categorized cells using single-cell RNA (scRNA) sequencing, and combined transcriptome sequencing was used to identify potential candidate genes for CRSwNP. We selected three biomarkers based on two algorithms and performed enrichment and immune correlation analyses. Biomarkers were verified using training and validation sets, receiver operating characteristic curves, immunohistochemistry, and quantitative real-time reverse-transcription PCR (qRT-PCR). Variations in biomarker expression were validated using pseudotime analysis. The networks of competing transcription factor (TF)-mRNA and competing endogenous RNA (ceRNA) were established, and the protein drugs associated with these biomarkers were predicted. Results: Both scRNA-seq and transcriptome data showed that DCs immune infiltration was higher in the CRSwNP group than in the control group. Three DC-related biomarkers (NR4A1, CLEC4G, and CD163) were identified. In CRSwNP, NR4A1 expression decreased, whereas CLEC4G and CD163 expression increased. All biomarkers were shown to be involved in immunological and metabolic pathways by enrichment analysis. These biomarkers were associated with γδ T cells, effector memory CD4 + T cells, regulatory T cells, and immature DCs. According to pseudotime analysis, NR4A1 and CD163 expression decreased from high to low, whereas CLEC4G expression remained low. Conclusion: We screened and identified potential DC-associated biomarkers of CRSwNP progression by integrating scRNA-seq with whole transcriptome sequencing. We analyzed the biological pathways in which they were involved, explored their molecular regulatory mechanisms and related drugs, and constructed ceRNA, TF-mRNA, and biomarker–drug networks to identify new CRSwNP treatment targets, laying the groundwork for the clinical management of CRSwNP. © 2024 Wang et al.","biomarker; CRSwNP; dendritic cell; single-cell sequencing; transcriptomic sequencing","biological marker; C type lectin domain family 4 member G; CD163 antigen; CD68 antigen; chromium; competing endogenous RNA; long untranslated RNA; messenger RNA; nuclear receptor Nur77; peptides and proteins; transcription factor; transcriptome; unclassified drug; adult; algorithm; allergic rhinitis; area under the curve; Article; asthma; ATP6V1A; B lymphocyte; bone marrow cell; CD4+ T lymphocyte; cell infiltration; chronic rhinosinusitis; controlled study; cystic fibrosis; dendritic cell; diagnostic test accuracy study; differential gene expression; DNA extraction; F13A1; female; fungal sinusitis; gene; gene expression; gene ontology; human; human tissue; immune response; immunocompetent cell; immunohistochemistry; ITSN1; KEGG; machine learning; macrophage; major clinical study; male; microcosm; microenvironment; middle aged; nasal tissue; nose mucosa; nose tumor; phylogenetic tree; protein expression; quality control; real time reverse transcription polymerase chain reaction; receiver operating characteristic; regulatory T lymphocyte; single cell RNA seq; sinonasal polyp; T lymphocyte; tissue microarray; training; transcriptome sequencing; weighted gene co expression network analysis; whole transcriptome sequencing","","chromium, 16065-83-1, 7440-47-3, 14092-98-9","Applied 7500, Life Technologies, United States; NanoDrop ND-1000, NanoDrop, United States; NovaSeq 6000, Illumina, China; Prism Version 9.3.0, Graphpad, United States; SPSS Version  21, SPSS, United States; SYBR Green, Transgene, China; TRIzol, Invitrogen, United States; bioanalyzer 2100, Agilent, United States","Agilent, United States; Graphpad, United States; Illumina, China; Invitrogen, United States; Life Technologies, United States; NanoDrop, United States; SPSS, United States; Transgene, China","National Natural Science Foundation of China, NSFC, (82071068); National Natural Science Foundation of China, NSFC","This research received funding support from the National Natural Science Foundation of China (82071068).","Cho SH, Hamilos DL, Han DH, Laidlaw TM., Phenotypes of chronic rhinosinusitis, J Allergy Clin Immunol Pract, 8, 5, pp. 1505-1511, (2020); Morcom S, Phillips N, Pastuszek A, Timperley D., Sinusitis, Aust Fam Physician, 45, 6, pp. 374-377, (2016); Gan W, Zhang H, Yang F, Et al., The influence of nasal bacterial microbiome diversity on the pathogenesis and prognosis of chronic rhinosinusitis patients with polyps, Eur Arch Otorhinolaryngol, 278, 4, pp. 1075-1088, (2021); Ahmad N, Zacharek MA., Allergic rhinitis and rhinosinusitis, Otolaryngol Clin North Am, 41, pp. 267-281, (2008); Marseglia GL, Caimmi S, Marseglia A, Et al., Rhinosinusitis and asthma, Int J Immunopathol Pharmacol, 23, pp. 29-31, (2010); 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Yu G, Wang LG, Han Y, He QY., clusterProfiler: an R package for comparing biological themes among gene clusters, OMICS, 16, 5, pp. 284-287, (2012); Friedman J, Hastie T, Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Softw, 33, pp. 1-22, (2010); Robin X, Turck N, Hainard A, Et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinf, 12, (2011); Kim DK, Park MH, Chang DY, Et al., MBP-positive and CD11c-positive cells are associated with different phenotypes of Korean patients with non-asthmatic chronic rhinosinusitis, PLoS One, 9, 10, (2014); Hulse KE, Stevens WW, Tan BK, Schleimer RP., Pathogenesis of nasal polyposis, Clin Exp Allergy, 45, pp. 328-346, (2015); Alam J, Yazdanpanah G, Ratnapriya R, Et al., Single-cell transcriptional profiling of murine conjunctival immune cells reveals distinct populations expressing homeostatic and regulatory genes, Mucosal Immunol, 15, pp. 620-628, (2022); Monedero P, Paz-Martin D, Barturen F, Et al., Reply to the letter: ”Intensive care in Spain” A. Suárez-de-la-Rica, G. Aguilar and E. Maseda, Rev Esp Anestesiol Reanim, 68, 7, pp. 430-431, (2021); Lin X, Zhuang X, Li C, Wang X., Interactions between dendritic cells and T lymphocytes in pathogenesis of nasal polyps, Exp Ther Med, 15, pp. 5167-5172, (2018); Svajger U, Rozman PJ., synergistic effects of interferon-gamma and vitamin D(3) signaling in induction of ILT-3(high)PDL-1(high) tolerogenic dendritic cells, Front Immunol, 10, (2019); Li JG, Du YM, Yan ZD, Et al., CD80 and CD86 knockdown in dendritic cells regulates Th1/Th2 cytokine production in asthmatic mice, Exp Ther Med, 11, pp. 878-884, (2016); Sun L, Zhang W, Zhao Y, Et al., Dendritic cells and T cells, partners in atherogenesis and the translating road ahead, Front Immunol, 11, (2020); Liu X, Wang Y, Lu H, Et al., Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction, Nature, 567, pp. 525-529, (2019); Yu HZ, Zhu BQ, Zhu L, Li S, Wang LM., NR4A1 agonist cytosporone B attenuates neuroinflammation in a mouse model of multiple sclerosis, Neural Regen Res, 17, pp. 2765-2770, (2022); Heidbreder K, Sommer K, Wiendl M, Et al., Nr4a1-dependent non-classical monocytes are important for macrophage-mediated wound healing in the large intestine, Front Immunol, 13, (2022); de Almeida M S, Taladriz-Blanco P, Drasler B, Et al., Cellular uptake of silica and gold nanoparticles induces early activation of nuclear receptor NR4A1, Nanomaterials, 12, (2022); Spitz MR, Gorlov IP, Amos CI, Et al., Variants in inflammation genes are implicated in risk of lung cancer in never smokers exposed to second-hand smoke, Cancer Discov, 1, pp. 420-429, (2011); Kizuka Y, Kitazume S, Sato K, Taniguchi N., Clec4g (LSECtin) interacts with BACE1 and suppresses abeta generation, FEBS Lett, 589, pp. 1418-1422, (2015); Huang YW, Meng XJ., Identification of a porcine DC-SIGN-related C-type lectin, porcine CLEC4G (LSECtin), and its order of intron removal during splicing: comparative genomic analyses of the cluster of genes CD23/CLEC4G/DC-SIGN among mammalian species, Dev Comp Immunol, 33, pp. 747-760, (2009); Torinsson Naluai A, Ostensson M, Fowler PC, Et al., Transcriptomics unravels molecular changes associated with cilia and COVID-19 in chronic rhinosinusitis with nasal polyps, Sci Rep, 13, (2023); Etzerodt A, Moestrup SK., CD163 and inflammation: biological, diagnostic, and therapeutic aspects, Antioxid Redox Signal, 18, pp. 2352-2363, (2013); Guo L, Akahori H, Harari E, Et al., CD163+ macrophages promote angiogenesis and vascular permeability accompanied by inflammation in atherosclerosis, J Clin Invest, 128, pp. 1106-1124, (2018); Bourdely P, Anselmi G, Vaivode K, Et al., Transcriptional and functional analysis of CD1c(+) human dendritic cells identifies a CD163(+) subset priming CD8(+)CD103(+) T cells, Immunity, 53, pp. 335-352, (2020); Li L, Liu Y, Chen HZ, Et al., Impeding the interaction between nur77 and p38 reduces LPS-induced inflammation, Nat Chem Biol, 11, pp. 339-346, (2015); Ratajczak W, Atkinson SD, Kelly C., The TWEAK/Fn14/CD163 axis-implications for metabolic disease, Rev Endocr Metab Disord, 23, pp. 449-462, (2022); Lawrence T., The nuclear factor NF-kappaB pathway in inflammation, Cold Spring Harb Perspect Biol, 1, (2009); Yoshida O, Kimura S, Dou L, Et al., DAP12 deficiency in liver allografts results in enhanced donor DC migration, augmented effector T cell responses and abrogation of transplant tolerance, Am J Transplant, 14, pp. 1791-1805, (2014); Chen J, Lopez-Moyado IF, Seo H, Et al., NR4A transcription factors limit CAR T cell function in solid tumours, Nature, 567, pp. 530-534, (2019); Shan T, Chen S, Chen X, Et al., M2-TAM subsets altered by lactic acid promote T-cell apoptosis through the PD-L1/PD-1 pathway, Oncol Rep, 44, pp. 1885-1894, (2020); Tang L, Yang J, Liu W, Et al., Liver sinusoidal endothelial cell lectin, LSECtin, negatively regulates hepatic T-cell immune response, Gastroenterology, 137, pp. 1498-1508, (2009); Lee SH, Han MS, Lee TH, Et al., Hydrogen peroxide attenuates rhinovirus-induced anti-viral interferon secretion in sinonasal epithelial cells, Front Immunol, 14, (2023); Gonzalez-Rubio J, Navarro-Lopez C, Lopez-Najera E, Et al., Cytokine Release Syndrome (CRS) and nicotine in COVID-19 patients: trying to calm the storm, Front Immunol, 11, (2020)","M. Jin; Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; email: jinmulan2022@126.com; J. Lu; Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China; email: Lujun11101@126.com","","Dove Medical Press Ltd","","","","","","11787031","","","","English","J. Inflamm. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85183196433"
"Arora P.; Periwal N.; Goyal Y.; Sood V.; Kaur B.","Arora, Pooja (57220776647); Periwal, Neha (57222393304); Goyal, Yash (58179622000); Sood, Vikas (14421758900); Kaur, Baljeet (57212828749)","57220776647; 57222393304; 58179622000; 14421758900; 57212828749","iIL13Pred: improved prediction of IL-13 inducing peptides using popular machine learning classifiers","2023","BMC Bioinformatics","24","1","141","","","","4","10.1186/s12859-023-05248-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152267500&doi=10.1186%2fs12859-023-05248-6&partnerID=40&md5=81608d35b444bc5928552bd24e77dadb","Department of Zoology, Hansraj College, University of Delhi, Delhi, India; Department of Biochemistry, Jamia Hamdard, Delhi, India; Department of Computer Science, Hansraj College, University of Delhi, Delhi, India","Arora P., Department of Zoology, Hansraj College, University of Delhi, Delhi, India; Periwal N., Department of Biochemistry, Jamia Hamdard, Delhi, India; Goyal Y., Department of Computer Science, Hansraj College, University of Delhi, Delhi, India; Sood V., Department of Biochemistry, Jamia Hamdard, Delhi, India; Kaur B., Department of Computer Science, Hansraj College, University of Delhi, Delhi, India","Background: Inflammatory mediators play havoc in several diseases including the novel Coronavirus disease 2019 (COVID-19) and generally correlate with the severity of the disease. Interleukin-13 (IL-13), is a pleiotropic cytokine that is known to be associated with airway inflammation in asthma and reactive airway diseases, in neoplastic and autoimmune diseases. Interestingly, the recent association of IL-13 with COVID-19 severity has sparked interest in this cytokine. Therefore characterization of new molecules which can regulate IL-13 induction might lead to novel therapeutics. Results: Here, we present an improved prediction of IL-13-inducing peptides. The positive and negative datasets were obtained from a recent study (IL13Pred) and the Pfeature algorithm was used to compute features for the peptides. As compared to the state-of-the-art which used the regularization based feature selection technique (linear support vector classifier with the L1 penalty), we used a multivariate feature selection technique (minimum redundancy maximum relevance) to obtain non-redundant and highly relevant features. In the proposed study (improved IL-13 prediction (iIL13Pred)), the use of the mRMR feature selection method is instrumental in choosing the most discriminatory features of IL-13-inducing peptides with improved performance. We investigated seven common machine learning classifiers including Decision Tree, Gaussian Naïve Bayes, k-Nearest Neighbour, Logistic Regression, Support Vector Machine, Random Forest, and extreme gradient boosting to efficiently classify IL-13-inducing peptides. We report improved AUC, and MCC scores of 0.83 and 0.33 on validation data as compared to the current method. Conclusions: Extensive benchmarking experiments suggest that the proposed method (iIL13Pred) could provide improved performance metrics in terms of sensitivity, specificity, accuracy, the area under the curve - receiver operating characteristics (AUCROC) and Matthews correlation coefficient (MCC) than the existing state-of-the-art approach (IL13Pred) on the validation dataset and an external dataset comprising of experimentally validated IL-13-inducing peptides. Additionally, the experiments were performed with an increased number of experimentally validated training datasets to obtain a more robust model. A user-friendly web server (www.soodlab.com/iil13pred) is also designed to facilitate rapid screening of IL-13-inducing peptides. © 2023, The Author(s).","Feature selection; IL-13; IL-13 peptides; Machine learning; mRMR; Peptide prediction","Bayes Theorem; COVID-19; Humans; Interleukin-13; Machine Learning; Peptides; Benchmarking; Classification (of information); COVID-19; Decision trees; Diagnosis; Feature Selection; Learning systems; Logistic regression; Nearest neighbor search; Peptides; Support vector machines; interleukin 13; peptide; Cytokines; Features selection; Inducing peptides; Interleukin-13; Interleukin-13 peptide; Learning classifiers; Machine-learning; MRMR; Peptide prediction; Bayes theorem; human; machine learning; Forecasting","","interleukin 13, 148157-34-0; Interleukin-13, ; Peptides, ","","","University Grants Commission, UGC; UGC-FRP; UK Research and Innovation, UKRI, (105297)","Funding text 1: The project is partially funded by the UGC start-up grant to VS. ; Funding text 2: NP is thankful to UGC for PhD fellowship. VS is recipient of UGC-FRP award. ","Del Valle D.M., Kim-Schulze S., Huang H.-H., Et al., An inflammatory cytokine signature predicts COVID-19 severity and survival, Nat Med, 26, pp. 1636-1643, (2020); Zanza C., Romenskaya T., Manetti A.C., Et al., Cytokine storm in COVID-19: immunopathogenesis and therapy, Medicina, 58, (2022); Costela-Ruiz V.J., Illescas-Montes R., Puerta-Puerta J.M., Et al., SARS-CoV-2 infection: the role of cytokines in COVID-19 disease, Cytokine Growth Factor Rev, 54, pp. 62-75, (2020); Joffre J., Rodriguez L., Matthay Z.A., Et al., COVID-19–associated lung microvascular endotheliopathy: a “from the bench” perspective, Am J Respir Crit Care Med, 206, pp. 961-972, (2022); Khatun M.S., Qin X., Pociask D.A., Et al., SARS-CoV2 endotheliopathy: insights from single Cell RNAseq, Am J Respir Crit Care Med, 206, pp. 1178-1179, (2022); Donlan A.N., Sutherland T.E., Marie C., Et al., IL-13 is a driver of COVID-19 severity, JCI insight, (2021); Lucas C., Wong P., Klein J., Et al., Longitudinal analyses reveal immunological misfiring in severe COVID-19, Nature, 584, pp. 463-469, (2020); Morrison C.B., Edwards C.E., Shaffer K.M., Et al., SARS-CoV-2 infection of airway cells causes intense viral and cell shedding, two spreading mechanisms affected by IL-13, Proc Natl Acad Sci, 119, (2022); Junttila I.S., Tuning the cytokine responses: an update on interleukin (IL)-4 and IL-13 receptor complexes, Front Immunol, 9, (2018); Punnonen J., Aversa G., Cocks B.G., Et al., Interleukin 13 induces interleukin 4-independent IgG4 and IgE synthesis and CD23 expression by human B cells, Proc Natl Acad Sci, 90, pp. 3730-3734, (1993); McKenzie G.J., Bancroft A., Grencis R.K., Et al., A distinct role for interleukin-13 in Th2-cell-mediated immune responses, Curr Biol, 8, pp. 339-342, (1998); Li L., Xia Y., Nguyen A., Et al., Effects of Th2 cytokines on chemokine expression in the lung: IL-13 potently induces eotaxin expression by airway epithelial cells, J Immunol, 162, pp. 2477-2487, (1999); Gallo E., Katzman S., Villarino A.V., IL-13-producing Th1 and Th17 cells characterize adaptive responses to both self and foreign antigens, Eur J Immunol, 42, pp. 2322-2328, (2012); Kapp U., Yeh W.-C., Patterson B., Et al., Interleukin 13 is secreted by and stimulates the growth of Hodgkin and Reed-Sternberg cells, J Exp Med, 189, pp. 1939-1946, (1999); Rinaldi T., Spadaro A., Riccieri V., Et al., Interleukin-13 (IL-13) in autoimmune rheumatic diseases: relationship with autoantibody profile, Arthritis Res Ther, 3, (2001); Asquith K.L., Horvat J.C., Kaiko G.E., Et al., Interleukin-13 promotes susceptibility to chlamydial infection of the respiratory and genital tracts, PLoS Pathog, 7, (2011); Mustafa A., Elbishbishi E., Agarwal R., Et al., Elevated levels of interleukin-13 and IL-18 in patients with dengue hemorrhagic fever, FEMS Immunol Med Microbiol, 30, pp. 229-233, (2001); Huang S.-W., Lee Y.-P., Hung Y.-T., Et al., Exogenous interleukin-6, interleukin-13, and interferon-gamma provoke pulmonary abnormality with mild edema in enterovirus 71-infected mice, Respir Res, 12, pp. 1-9, (2011); Jain S., Dhall A., Patiyal S., Et al., IL13Pred: a method for predicting immunoregulatory cytokine IL-13 inducing peptides, Comput Biol Med, 143, (2022); Vita R., Mahajan S., Overton J.A., Et al., The immune epitope database (IEDB): 2018 update, Nucleic Acids Res, 47, pp. D339-D343, (2019); Boughorbel S., Jarray F., El-Anbari M., Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric, PLoS ONE, 12, (2017); Zhang Y., Jing D., Cheng J., Et al., The efficacy and safety of IL-13 inhibitors in atopic dermatitis: a systematic review and meta-analysis, Front Immunol, (2022); Guttman-Yassky E., Blauvelt A., Eichenfield L.F., Et al., Efficacy and safety of lebrikizumab, a high-affinity interleukin 13 inhibitor, in adults with moderate to severe atopic dermatitis: a phase 2b randomized clinical trial, JAMA Dermatol, 156, pp. 411-420, (2020); Ntontsi P., Papathanassiou E., Loukides S., Et al., Targeted anti-IL-13 therapies in asthma: current data and future perspectives, Expert Opin Investig Drugs, 27, pp. 179-186, (2018); Wang L., Wang N., Zhang W., Et al., Therapeutic peptides: current applications and future directions, Signal Transduct Target Ther, 7, pp. 1-27, (2022); Muttenthaler M., King G.F., Adams D.J., Et al., Trends in peptide drug discovery, Nat Rev Drug Discovery, 20, pp. 309-325, (2021); Saeys Y., Inza I., Larranaga P., A review of feature selection techniques in bioinformatics, Bioinformatics, 23, pp. 2507-2517, (2007); Ding C., Peng H., Minimum redundancy feature selection from microarray gene expression data, J Bioinform Comput Biol, 3, pp. 185-205, (2005); Radovic M., Ghalwash M., Filipovic N., Et al., Minimum redundancy maximum relevance feature selection approach for temporal gene expression data, BMC Bioinf, 18, pp. 1-14, (2017); El-Manzalawy Y., Hsieh T.-Y., Shivakumar M., Et al., Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data, BMC Med Genomics, 11, pp. 19-31, (2018); Babajide Mustapha I., Saeed F., Bioactive molecule prediction using extreme gradient boosting, Molecules, 21, (2016); Jeon Y.-J., Hasan M.M., Park H.W., Et al., TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization, Brief Bioinf, 23, (2022); Hasan M.M., Basith S., Khatun M.S., Et al., Meta-i6mA: an interspecies predictor for identifying DNA N 6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework, Brief Bioinf, 22, (2021); Teng Z., Zhao Z., Li Y., Et al., i6mA-Vote: cross-species identification of DNA N6-methyladenine sites in plant genomes based on ensemble learning with voting, Front Plant Sci, (2022)","P. Arora; Department of Zoology, Hansraj College, University of Delhi, Delhi, India; email: pooja@hrc.du.ac.in; B. Kaur; Department of Computer Science, Hansraj College, University of Delhi, Delhi, India; email: baljeetkaur26@hotmail.com","","BioMed Central Ltd","","","","","","14712105","","BBMIC","37041520","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152267500"
"Monadhel H.; Abbas A.R.; Mohammed A.J.","Monadhel, Hind (59146630700); Abbas, Ayad R. (24483255600); Mohammed, Athraa Jasim (56031654500)","59146630700; 24483255600; 56031654500","COVID-19 Vaccine: Predicting Vaccine Types and Assessing Mortality Risk Through Ensemble Learning Algorithms","2024","F1000Research","12","","1200","","","","3","10.12688/f1000research.140395.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194400727&doi=10.12688%2ff1000research.140395.2&partnerID=40&md5=0745bb388277dcb862811a080e51647a","Computer Science, University of Technology - Iraq, Baghdad, Iraq","Monadhel H., Computer Science, University of Technology - Iraq, Baghdad, Iraq; Abbas A.R., Computer Science, University of Technology - Iraq, Baghdad, Iraq; Mohammed A.J., Computer Science, University of Technology - Iraq, Baghdad, Iraq","Background: There is no doubt that vaccination is crucial for preventing the spread of diseases; however, not every vaccine is perfect or will work for everyone. The main objective of this work is to predict which vaccine will be most effective for a candidate without causing severe adverse reactions and to categorize a patient as potentially at high risk of death from the COVID-19 vaccine. Methods: A comprehensive analysis was conducted using a dataset on COVID-19 vaccine adverse reactions, exploring binary and multiclass classification scenarios. Ensemble models, including Random Forest, Decision Tree, Light Gradient Boosting, and extreme gradient boosting algorithm, were utilized to achieve accurate predictions. Class balancing techniques like SMOTE, TOMEK_LINK, and SMOTETOMEK were incorporated to enhance model performance. Results: The study revealed that pre-existing conditions such as diabetes, hypertension, heart disease, history of allergies, prior vaccinations, other medications, age, and gender were crucial factors associated with poor outcomes. Moreover, using medical history, the ensemble learning classifiers achieved accuracy scores ranging from 75% to 87% in predicting the vaccine type and mortality possibility. The Random Forest model emerged as the best prediction model, while the implementation of the SMOTE and SMOTETOMEK methods generally improved model performance. Conclusion: The random forest model emerges as the top recommendation for machine learning tasks that require high accuracy and resilience. Moreover, the findings highlight the critical role of medical history in optimizing vaccine outcomes and minimizing adverse reactions. Copyright: © 2024 Monadhel H et al.","Classification algorithm; COVID-19 Vaccine; ensemble learning; machine learning; Sampling methods; Side effects","Aged; Algorithms; COVID-19; COVID-19 Vaccines; Female; Humans; Machine Learning; Male; Middle Aged; Risk Assessment; SARS-CoV-2; Vaccination; SARS-CoV-2 vaccine; SARS-CoV-2 vaccine; adolescent; adult; aged; Article; asthma; child; chronic obstructive lung disease; controlled study; decision tree; diabetes mellitus; diagnostic test accuracy study; ensemble learning; extreme gradient boosting; feature extraction; female; human; hypertension; infant; kidney disease; learning algorithm; light grading boosting machine; male; medical history; middle aged; mortality risk; newborn; predictive model; prevalence; random forest; receiver operating characteristic; side effect; vaccination; very elderly; adverse event; algorithm; coronavirus disease 2019; immunology; machine learning; mortality; prevention and control; risk assessment; Severe acute respiratory syndrome coronavirus 2","","COVID-19 Vaccines, ","","","","","Velasquez G., Vaccines, Medicines and COVID-19: How Can WHO Be Given a Stronger Voice?, (2022); Dai X., Xiong Y., Li N., Et al., Vaccine types, Vaccines-the History and Future, pp. 1-18, (2019); Eroglu B., Nuwarda R.F., Ramzan I., Et al., A Narrative Review of COVID-19 Vaccines, Vaccines, 10, 1, (2021); Monadhel H., Abbas A., Mohammed A., COVID-19 vaccinations and their side effects: a scoping systematic review [version 1; peer review: awaiting peer review], F1000Res, 12, (2023); Vitiello A., Ferrara F., Brief review of the mRNA vaccines COVID-19, Inflammopharmacology, 29, 3, pp. 645-649, (2021); Patel R., Kaki M., Potluri V.S., Et al., A comprehensive review of SARS-CoV-2 vaccines: Pfizer, Moderna & Johnson & Johnson, Hum. Vaccin. Immunother, 18, 1, (2022); Al Khames Aga Q.A., Alkhaffaf W.H., Hatem T.H., Et al., Safety of COVID-19 vaccines, J. Med. Virol, 93, 12, pp. 6588-6594, (2021); Sujatha R., Venkata Siva Krishna B., Chatterjee J.M., Et al., Prediction of suitable candidates for COVID-19 vaccination, Intell. Autom. Soft Comput, 32, 1, pp. 525-541, (2022); Javaid M., Haleem A., Singh R.P., Et al., Significance of machine learning in healthcare: Features, pillars and applications, Int. J. Intell. Networks, 3, pp. 58-73, (2022); Zoumana K.E.I.T.A., “Classification in Machine Learning: An Introduction”,datacamp, (2022); Hatmal M.M.M., Al-Hatamleh M.A., Olaimat A.N., Et al., Side effects and perceptions following COVID-19 vaccination in Jordan: a randomized, cross-sectional study implementing machine learning for predicting severity of side effects, Vaccines, 9, 6, (2021); Lian A.T., Du J., Tang L., Using a machine learning approach to monitor COVID-19 vaccine adverse events (VAE) from twitter data, Vaccines, 10, 1, (2022); Henry M., Imbalanced Classification in Python: SMOTE-Tomek Links Method; Java T point, (2018); (2023); Banerjee P., LightGBM Classifier in Python Kaggle, (2021); Grandini M., Bagli E., Visani G., Metrics for multi-class classification: an overview, arXiv preprint arXiv:2008.05756, (2020); Markoulidakis I., Kopsiaftis G., Rallis I., Et al., Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem, In The 14th pervasive technologies related to assistive environments conference, pp. 412-419, (2021); Abbas A.R., Farooq A.O., Skin Detection Using Improved ID3 Algorithm, Iraqi J. Sci, pp. 402-410, (2019); Narkhede S., Understanding AUC - ROC Curve Medium; Abbas A.R., Kareem A.R., Age estimation using support vector machine, Iraqi J. Sci, pp. 1746-1756, (2018); Belete D.M., Huchaiah M.D., Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results, Int. J. Comput. Appl, 44, 9, pp. 875-886, (2022)","H. Monadhel; Computer Science, University of Technology - Iraq, Baghdad, Iraq; email: cs.20.38@grad.uotechnology.edu.iq","","F1000 Research Ltd","","","","","","20461402","","","38799245","English","F1000 Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85194400727"
"Greim E.; Naef J.; Mainguy-Seers S.; Lavoie J.-P.; Sage S.; Dolf G.; Gerber V.","Greim, Eloïse (58010163300); Naef, Jan (56986335300); Mainguy-Seers, Sophie (57203618035); Lavoie, Jean-Pierre (35482290900); Sage, Sophie (57215583130); Dolf, Gaudenz (7005597088); Gerber, Vinzenz (7005969573)","58010163300; 56986335300; 57203618035; 35482290900; 57215583130; 7005597088; 7005969573","Breath characteristics and adventitious lung sounds in healthy and asthmatic horses","2024","Journal of Veterinary Internal Medicine","38","1","","495","504","9","4","10.1111/jvim.16980","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181740447&doi=10.1111%2fjvim.16980&partnerID=40&md5=e4fa264f1d6052edf750d19c057842a1","Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland; Faculty of Veterinary Medicine, Department of Clinical Sciences, University of Montréal, St-Hyacinthe, QC, Canada","Greim E., Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland; Naef J., Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland; Mainguy-Seers S., Faculty of Veterinary Medicine, Department of Clinical Sciences, University of Montréal, St-Hyacinthe, QC, Canada; Lavoie J.-P., Faculty of Veterinary Medicine, Department of Clinical Sciences, University of Montréal, St-Hyacinthe, QC, Canada; Sage S., Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland; Dolf G., Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland; Gerber V., Swiss Institute of Equine Medicine (ISME), Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern, Switzerland","Background: Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. Objectives: First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. Animals: Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA−], and 5 in exacerbation [sEA+]). Methods: Prospective multicenter case-control study. Horses were categorized based on the horse owner-assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One-hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. Results: Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P <.001, P <.01, P =.01, P <.01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA− (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, −9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23-point weighted clinical score (WCS23). Conclusions and Clinical Importance.: The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA− horses. © 2024 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.","breath intensity; crackles; digital auscultation; equine asthma; rattles; wheeze","Animals; Asthma; Auscultation; Case-Control Studies; Horse Diseases; Horses; Prospective Studies; Respiratory Sounds; abnormal respiratory sound; adult; airway pressure; animal experiment; animal tissue; Article; artificial intelligence; artificial ventilation; asthma; auscultation; blood glucose monitoring; breathing; breathing pattern; breathing rate; bronchoalveolar lavage fluid; case control study; controlled study; coughing; crackle; disease severity; echography; endoscopy; endotracheal intubation; Equus; fine needle aspiration biopsy; fluid therapy; forced expiratory volume; forced vital capacity; heart rate; leukocyte count; leukocyte differential count; lung auscultation; lung lavage; lymphocyte count; machine learning; medical education; multicenter study; nonhuman; observational study; oscillometry; plethysmography; polysomnography; positive end expiratory pressure ventilation; rebreathing; remission; spirometry; tachycardia; thorax radiography; trachea mucus; abnormal respiratory sound; animal; asthma; auscultation; clinical trial; horse; horse disease; prospective study; veterinary medicine","","","LabManager version 4.53, Jaeger, Germany","Jaeger, Germany","Stiftung Pro Pferd, (PR2022‐04); Swiss Re Institute, SRI, (33‐890)","Supported by the Internal Research Fund of the Swiss Institute of Equine Medicine, Bern, Switzerland (ISMEquine Research No. 33‐890), and by the Stiftung Pro Pferd (PR2022‐04). The authors acknowledge the support of the owners and referring veterinarians of the horses that participated in this study. The authors thank Alessandra Ramseyer, Milena Scheidegger, Dominik Burger, and Philippe Bähler for their valuable contributions in the early stages of the development of the novel device, and Mélina Chevalley and Noémie Gendron for their help with sample collection. ","Savage C.J., Evaluation of the equine respiratory system using physical examination and endoscopy, Vet Clin North Am Equine Pract, 13, pp. 443-462, (1997); Roy M.-F., Lavoie J.-P., Tools for the diagnosis of equine respiratory disorders, Vet Clin North Am Equine Pract, 19, pp. 1-17, (2003); Sovijarvi A.R.A., Vanderschoot J., Earis J.E., Standardization of computerized respiratory sound analysis, Eur Respir Rev, 10, (2000); Tesarowski D.B., Viel L., McDonell W.N., Pulmonary function measurements during repeated environmental challenge of horses with recurrent airway obstruction (heaves), Am J Vet Med Res, 57, pp. 1214-1219, (1996); Gehlen H., Oey L., Rohn K., Bilzer T., Stadler P., Pulmonary dysfunction and skeletal muscle changes in horses with RAO, J Vet Intern Med, 22, pp. 1014-1021, (2008); Naylor J.M., Clark E.G., Clayton H.M., Chronic obstructive pulmonary disease: usefulness of clinical signs, bronchoalveolar lavage, and lung biopsy as diagnostic and prognostic aids, Can Vet J, 33, pp. 591-598, (1992); Moore I., Horney B., Day K., Lofstedt J., Cribb A.E., Treatment of inflammatory airway disease in young standardbreds with interferon alpha, Can Vet J, 45, pp. 594-601, (2004); Lavoie J.-P., Bullone M., Rodrigues N., Germim P., Albrecht B., von Salis-Soglio M., Effect of different doses of inhaled ciclesonide on lung function, clinical signs related to airflow limitation and serum cortisol levels in horses with experimentally induced mild to severe airway obstruction, Equine Vet J, 51, pp. 779-786, (2019); Benedetto G., Dalmasso F., Spagnolo R., Surface distribution of crackling sounds, IEEE Trans Biomed Eng, 35, pp. 406-412, (1988); Sovijarvi A.R.A., Malmberg L.P., Charbonneau G., Et al., Characteristics of breath sounds and adventitious respiratory sounds, Eur Respir Rev, 10, pp. 591-596, (2000); Robinson N.E., Recurrent airway obstruction (heaves), Equine Respiratory Diseases, (2001); Guntupalli K.K., Alapat P.M., Bandi V.D., Kushnir I., Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects, J Asthma, 45, pp. 903-907, (2008); Sen I., Kahya Y.P., A multi-channel device for respiratory sound data acquisition and transient detection, Conf Proc IEEE Eng Med Biol Soc, 2005, pp. 6658-6661, (2005); Gurung A., Scrafford C.G., Tielsch J.M., Levine O.S., Checkley W., Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis, Respir Med, 105, pp. 1396-1403, (2011); Pasterkamp H., Brand P.L.P., Everard M., Garcia-Marcos L., Melbye H., Priftis K.N., Towards the standardisation of lung sound nomenclature, Eur Respir J, 47, pp. 724-732, (2016); Sarkar M., Madabhavi I., Niranjan N., Dogra M., Auscultation of the respiratory system, Ann Thorac Med, 10, pp. 158-168, (2015); Sovijarvi A.R.A., Dalmasso F., Vanderschoot J., Et al., Definition of terms for applications of respiratory sounds, Eur Respir Rev, 10, pp. 597-610, (2000); Costa L.R.R., Seahorn T.L., Moore R.M., Taylor H.W., Gaunt S.D., Beadle R.E., Correlation of clinical score, intrapleural pressure, cytologic findings of bronchoalveolar fluid, and histopathologic lesions of pulmonary tissue in horses with summer pasture-associated obstructive pulmonary disease, Am J Vet Res, 61, pp. 167-173, (2000); Kim Y., Hyon Y., Lee S., Woo S.D., Ha T., Chung C., The coming era of a new auscultation system for analyzing respiratory sounds, BMC Pulm Med, 22, (2022); Abbas A., Fahim A., An automated computerized auscultation and diagnostic system for pulmonary diseases, J Med Syst, 34, pp. 1149-1155, (2010); Cohen A., Landsberg D., Analysis and automatic classification of breath sounds, IEEE Trans Biomed Eng, 31, pp. 585-590, (1984); Ferreira-Cardoso H., Jacome C., Silva S., Et al., Lung auscultation using the smartphone—feasibility study in real-world clinical practice, Sensors (Basel), 21, (2021); Monaco A., Amoroso N., Bellantuono L., Pantaleo E., Tangaro S., Bellotti R., Multi-time-scale features for accurate respiratory sound classification, Appl Sci, 10, (2020); Grabner A.K., Gross V., Hoops M., Kong M., Kohler U., Respiratory sounds in an Iceland-horse suffering from acute pulmonary edema—an acoustic monitoring, Pferdeheilkunde, 20, pp. 455-458, (2004); Ramseyer A., Gaillard C., Burger D., Et al., Effects of genetic and environmental factors on chronic lower airway disease in horses, J Vet Intern Med, 21, pp. 149-156, (2007); van Erck E., Votion D., Art T., Lekeux P., Measurement of respiratory function by impulse oscillometry in horses, Equine Vet J, 36, pp. 21-28, (2004); Mainguy-Seers S., Diaw M., Lavoie J.-P., Lung function variation during the estrus cycle of mares affected by severe asthma, Animals (Basel), 12, (2022); Gerber V., Lindberg A., Berney C., Robinson N.E., Airway mucus in recurrent airway obstruction—short-term response to environmental challenge, J Vet Intern Med, 18, pp. 92-97, (2004); Mainguy-Seers S., Picotte K., Lavoie J.-P., Efficacy of tamoxifen for the treatment of severe equine asthma, J Vet Intern Med, 32, pp. 1748-1753, (2018); Wyler M., Sage S.E., Marti E., White S., Gerber V., Protein microarray allergen profiling in bronchoalveolar lavage fluid and serum of horses with asthma, J Vet Intern Med, 37, pp. 328-337, (2023); R: A Language and Environment for Statistical Computing, (2023); Raftery A.E., Bayesian model selection in social research, Soc Methodol, 25, pp. 111-163, (1995); Siwinska N., Zak A., Slowikowska M., Krupinska P., Niedzwiedz A., Prevalence and severity of ultrasonographic pulmonary findings in horses with asthma—a preliminary study, Pol J Vet Sci, 22, pp. 653-659, (2019); Roudebush P., Lung sounds, J Am Vet Med Assoc, 181, pp. 122-126, (1982); Aviles-Solis J.C., Storvoll I., Vanbelle S., Melbye H., The use of spectrograms improves the classification of wheezes and crackles in an educational setting, Sci Rep, 10, (2020); Behan A.L., Hauptman J.G., Robinson N.E., Telemetric analysis of breathing pattern variability in recurrent airway obstruction (heaves)-affected horses, Am J Vet Res, 74, pp. 925-933, (2013); Couetil L.L., Cardwell J.M., Gerber V., Lavoie J.P., Leguillette R., Richard E.A., Inflammatory airway disease of horses—revised consensus statement, J Vet Intern Med, 30, pp. 503-515, (2016); Bentur L., Beck R., Shinawi M., Naveh T., Gavriely N., Wheeze monitoring in children for assessment of nocturnal asthma and response to therapy, Eur Respir J, 21, pp. 621-626, (2003); Baughman R.P., Loudon R.G., Lung sound analysis for continuous evaluation of airflow obstruction in asthma, Chest, 88, pp. 364-368, (1985)","E. Greim; Swiss Institute of Equine Medicine, University of Bern, Bern, Länggassstrasse 124, 3012, Switzerland; email: eloise.greim@unibe.ch","","John Wiley and Sons Inc","","","","","","08916640","","","38192117","English","J. Vet. Intern. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85181740447"
"Althof Z.W.; Gerard S.E.; Eskandari A.; Galizia M.S.; Hoffman E.A.; Reinhardt J.M.","Althof, Zachary W. (57217029257); Gerard, Sarah E. (57193026780); Eskandari, Ali (57206778275); Galizia, Mauricio S. (8723055000); Hoffman, Eric A. (58000586800); Reinhardt, Joseph M. (7102127806)","57217029257; 57193026780; 57206778275; 8723055000; 58000586800; 7102127806","Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images","2023","Scientific Reports","13","1","14135","","","","4","10.1038/s41598-023-41322-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168967355&doi=10.1038%2fs41598-023-41322-y&partnerID=40&md5=6fa7766a4b8a0f942d2becb9a4469166","5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa Roy J. Carver Department of Biomedical Engineering, Iowa City, 52242, IA, United States; University of Iowa Department of Radiology, Iowa City, IA, United States; University of Michigan Department of Radiology, Ann Arbor, MI, United States","Althof Z.W., 5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa Roy J. Carver Department of Biomedical Engineering, Iowa City, 52242, IA, United States; Gerard S.E., University of Iowa Department of Radiology, Iowa City, IA, United States; Eskandari A., University of Iowa Department of Radiology, Iowa City, IA, United States; Galizia M.S., University of Michigan Department of Radiology, Ann Arbor, MI, United States; Hoffman E.A., 5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa Roy J. Carver Department of Biomedical Engineering, Iowa City, 52242, IA, United States, University of Iowa Department of Radiology, Iowa City, IA, United States; Reinhardt J.M., 5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa Roy J. Carver Department of Biomedical Engineering, Iowa City, 52242, IA, United States, University of Iowa Department of Radiology, Iowa City, IA, United States","Computed Tomography (CT) imaging is routinely used for imaging of the lungs. Deep learning can effectively automate complex and laborious tasks in medical imaging. In this work, a deep learning technique is utilized to assess lobar fissure completeness (also known as fissure integrity) from pulmonary CT images. The human lungs are divided into five separate lobes, divided by the lobar fissures. Fissure integrity assessment is important to endobronchial valve treatment screening. Fissure integrity is known to be a biomarker of collateral ventilation between lobes impacting the efficacy of valves designed to block airflow to diseased lung regions. Fissure integrity is also likely to impact lobar sliding which has recently been shown to affect lung biomechanics. Further widescale study of fissure integrity’s impact on disease susceptibility and progression requires rapid, reproducible, and noninvasive fissure integrity assessment. In this paper we describe IntegrityNet, an attention U-Net based automatic fissure integrity analysis tool. IntegrityNet is able to predict fissure integrity with an accuracy of 95.8%, 96.1%, and 89.8% for left oblique, right oblique, and right horizontal fissures, compared to manual analysis on a dataset of 82 subjects. We also show that our method is robust to COPD severity and reproducible across subject scans acquired at different time points. © 2023, Springer Nature Limited.","","Biomechanical Phenomena; Female; Humans; Labor, Obstetric; Lung; Pleural Cavity; Pregnancy; Tomography, X-Ray Computed; biomechanics; diagnostic imaging; female; human; labor; lung; pleura cavity; pregnancy; x-ray computed tomography","","","","","","","Eberhardt R., Daniela G., Herth F.J.F., Schuhmann M., Endoscopic bronchial valve treatment: Patient selection and special considerations, Int. J. Chron. Obstruct. Pulmon. Dis., 10, 2147-57, (2015); Koster T.D., Slebos D.J., The fissure: Interlobar collateral ventilation and implications for endoscopic therapy in emphysema, Int. J. Chron. Obstruct. Pulmon. Dis., 11, pp. 765-773, (2016); Koster T.D., Et al., Predicting lung volume reduction after endobronchial valve therapy is maximized using a combination of diagnostic tools, Respiration, 92, pp. 150-157, (2016); Sciurba F.C., Et al., A randomized study of endobronchial valves for advanced emphysema, N. Engl. J. Med., 363, 13, pp. 1233-1244, (2010); Galloy A.E., Amelon R.E., Reinhardt J.M., Raghavan M.L., Contact mechanics model of lung lobar sliding, Appl. Eng. Sci., 10, (2022); Hermanova Z., Ctvrtlik F., Herman M., Incomplete and accessory fissures of the lung evaluated by high-resolution computed tomography, Eur. J. Radiol., 83, 3, pp. 595-599, (2014); Joshi A., Et al., Variations in pulmonary fissure: A source of collateral ventilation and its clinical significance, Cureus, 14, 3, (2022); Sudikshya K.C., Shrestha P., Shah A.K., Jha A.K., Variations in human pulmonary fissures and lobes: A study conducted in Nepalese cadavers, Anat. Cell Biol., 51, 2, pp. 85-92, (2018); Mutua V., Et al., Variations in the human pulmonary fissures and lobes: A cadaveric study, Open Access Lib. J., 8, (2021); van Rikxoort E.M., Et al., A method for the automatic quantification of the completeness of pulmonary fissures: Evaluation in a database of subjects with severe emphysema, Eur. J. Radiol., 22, 2, pp. 302-309, (2012); Pu J., Et al., Computerized assessment of pulmonary fissure integrity using high resolution CT, Med. Phys., 37, 9, pp. 4661-4672, (2010); Ross J.C., Et al., An open-source framework for pulmonary fissure completeness assessment, Comput. Med. Imaging Graph., 83, (2020); Tada D.K., Et al., 3D patch-based CNN for fissure segmentation on CT images to quantitatively assess fissure integrity and evaluate emphysema patients for endobronchial valve treatment, Proceedings of the SPIE, 12465, (2023); Couper D., Et al., Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS), Thorax, 69, 5, pp. 491-494, (2014); Sieren J.P., Et al., SPIROMICS protocol for multicenter quantitative computed tomography to phenotype the lungs, Am. J. Respirat. Crit. Care Med., 194, 7, pp. 794-806, (2016); Vestbo J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am. J. Respirat. Crit. Care Med., 187, 4, pp. 347-365, (2013); Doel T., Gavaghan D.J., Grau V., Review of automatic pulmonary lobe segmentation methods from CT, Comput. Med. Imaging Graph., 40, pp. 13-29, (2015); Gerard S.E., Patton T.J., Christensen G.E., Bayouth J.E., Reinhardt J.M., FissureNet: A deep learning approach for pulmonary fissure detection in CT images, IEEE Trans. Med. Imaging, 38, 1, pp. 156-166, (2019); Lassen B., Et al., Automatic segmentation of the pulmonary lobes from chest CT scans based on fissures vessels and bronchi, IEEE Trans. Med. Imaging, 32, 2, pp. 210-222, (2013); van Rikxoort E.M., Prokop M., de Hoop B., Viergever M.A., Pluim J.P.W., van Ginneken B., Automatic segmentation of pulmonary lobes robust against incomplete fissures, IEEE Trans. Med. Imag., 29, 6, pp. 1286-1296, (2010); Gerard S.E., Reinhardt J.M., Pulmonary Lobe Segmentation Using A Sequence of Convolutional Neural Networks For Marginal Learning, 2019 IEEE 16Th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1207-1211, (2019); 3D Slicer.; Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, Arxiv, 1505, (2015); Oktay O., Et al., Attention U-Net: Learning Where to Look for the Pancreas, Arxiv, 1804, (2018); Chollet F., Keras, Github.; Kingma D.P., Ba J., Adam: A Method for Stochastic Optimization., (2014); Salehi S.S.M., Erdogmus D., Gholipour A., Tversky loss function for image segmentation using 3D fully convolutional deep networks, Mach. Learn. Med. Imag., 1, pp. 379-387, (2017)","J.M. Reinhardt; 5601 Seamans Center for the Engineering Arts and Sciences, University of Iowa Roy J. Carver Department of Biomedical Engineering, Iowa City, 52242, United States; email: joe-reinhardt@uiowa.edu","","Nature Research","","","","","","20452322","","","37644125","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85168967355"
"Wang R.; Davis M.D.","Wang, Ran (57195462425); Davis, Michael D. (7404851007)","57195462425; 7404851007","A Concise Review of Exhaled Breath Testing for Respiratory Clinicians and Researchers","2024","Respiratory Care","69","5","","613","620","7","3","10.4187/respcare.11651","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191101970&doi=10.4187%2frespcare.11651&partnerID=40&md5=557ec360b80dd73806450d017ed85148","Manchester University NHS Foundation Trust, Manchester, United Kingdom; Division of Immunology, Immunity to Infection and Respiratory Medicine at the University of Manchester, School of Biological Sciences, The University of Manchester, Manchester, United Kingdom; Wells Center for Pediatric Research/Pulmonology, Allergy, and Sleep Medicine, Riley Hospital for Children at Indiana University School of Medicine, Indianapolis, IN, United States; Airbase Breathing Company, United States","Wang R., Manchester University NHS Foundation Trust, Manchester, United Kingdom, Division of Immunology, Immunity to Infection and Respiratory Medicine at the University of Manchester, School of Biological Sciences, The University of Manchester, Manchester, United Kingdom; Davis M.D., Wells Center for Pediatric Research/Pulmonology, Allergy, and Sleep Medicine, Riley Hospital for Children at Indiana University School of Medicine, Indianapolis, IN, United States, Airbase Breathing Company, United States","Exhaled breath contains an extensive reservoir of biomolecules. The collection of exhaled breath is noninvasive and low risk. Therefore, its testing is an appealing strategy for the discovery of biomarkers of respiratory diseases. In this concise review, we summarize the evidence of exhaled breath tests for airways diseases and respiratory infections. An overview of breath collection methods in both individuals who are spontaneously breathing and those receiving mechanical ventilation is outlined. We also highlight the challenges in exhaled breath testing and areas for future research. © 2024 Daedalus Enterprises.","breath research; exhaled biomarkers; exhaled breath; exhaled breath condensate; exhaled VOCs","biological marker; nitric oxide; volatile organic compound; biological marker; Article; artificial ventilation; breath analysis; capnometry; clinician; Exhaled Breath Testing; human; machine learning; medical procedures; metabolomics; nonhuman; point of care testing; respiratory tract disease; respiratory tract infection; scientist; article; asthma; breath analysis; breathing; diagnosis; middle aged; respiratory tract disease; respiratory tract infection; ventilator","","nitric oxide, 10102-43-9","","","National Institute for Health and Care Research Manchester Biomedical Research Centre; National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (1 PO1 HL158507); National Heart, Lung, and Blood Institute, NHLBI","Dr Wang\u2019s research is supported by the National Institute for Health and Care Research Manchester Biomedical Research Centre; Dr Davis\u2019s research is funded by National Institutes of Health/National Heart, Lung, and Blood Institute award 1 PO1 HL158507.","Davis MD, Fowler SJ, Montpetit AJ., Exhaled breath testing-a tool for the clinician and researcher, Paediatr Respir Rev, 29, pp. 37-41, (2019); Dweik RA, Amann A., Exhaled breath analysis: the new frontier in medical testing, J Breath Res, 2, 3, (2008); Belizario JE, Faintuch J, Malpartida MG., Breath biopsy and discovery of exclusive volatile organic compounds for diagnosis of infectious diseases, Front Cell Infect Microbiol, 10, (2021); Mazzatenta A, Pokorski M, Di Giulio C., Volatile organic compounds (VOCs) in exhaled breath as a marker of hypoxia in multiple chemical sensitivity, Physiol Rep, 9, 18, (2021); Hill J., Human Breath Atlas; Barnes PJ, Dweik RA, Gelb AF, Gibson PG, George SC, Grasemann H, Et al., Exhaled nitric oxide in pulmonary diseases: a comprehensive review, Chest, 138, 3, pp. 682-692, (2010); Louis R, Satia I, Ojanguren I, Schleich F, Bonini M, Tonia T, Et al., European Respiratory Society Guidelines for the Diagnosis of Asthma in Adults, Eur Respir J, 2022; de Lacy Costello B, Amann A, Al-Kateb H, Flynn C, Filipiak W, Khalid T, Et al., A review of the volatiles from the healthy human body, J Breath Res, 8, 1, (2014); Bos LD, Sterk PJ, Fowler SJ., Breathomics in the setting of asthma and chronic obstructive pulmonary disease, J Allergy Clin Immunol, 138, 4, pp. 970-976, (2016); van de Kant KD, van der Sande LJTM, Jobsis Q, van Schayck OCP, Dompeling E., Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review, Respir Res, 13, 1, (2012); Ratiu IA, Ligor T, Bocos-Bintintan V, Mayhew CA, Buszewski B., Volatile organic compounds in exhaled breath as fingerprints of lung cancer, asthma and COPD, J Clin Med, 10, 1, (2020); Subali AD, Wiyono L, Yusuf M, Zaky MFA., The potential of volatile organic compounds-based breath analysis for COVID-19 screening: a systematic review & meta-analysis, Diagn Microbiol Infect Dis, 102, 2, (2022); Koo S, Thomas HR, Daniels SD, Lynch RC, Fortier SM, Shea MM, Et al., A breath fungal secondary metabolite signature to diagnose invasive aspergillosis, Clin Infect Dis, 59, 12, pp. 1733-1740, (2014); van Oort PM, Nijsen TM, White IR, Knobel HH, Felton T, Rattray N, Et al., Untargeted molecular analysis of exhaled breath as a diagnostic test for ventilator-associated lower respiratory tract infections (BreathDx), Thorax, 77, 1, pp. 79-81, (2022); Davis MD, Montpetit AJ., Exhaled breath condensate: an update, Immunol Allergy Clin North Am, 38, 4, pp. 667-678, (2018); Soares M, Mirgorodskaya E, Koca H, Viklund E, Richardson M, Gustafsson P, Et al., Particles in exhaled air (PExA): non-invasive phenotyping of small airways disease in adult asthma, J Breath Res, 12, 4, (2018); Holz O, Muller M, Carstensen S, Olin AC, Hohlfeld JM., Inflammatory cytokines can be monitored in exhaled breath particles following segmental and inhalation endotoxin challenge in healthy volunteers, Sci Rep, 12, 1, (2022); Maniscalco M, Fuschillo S, Paris D, Cutignano A, Sanduzzi A, Motta A., Clinical metabolomics of exhaled breath condensate in chronic respiratory diseases, Adv Clin Chem, 88, pp. 121-149, (2019); Davis MD, Montpetit A, Hunt J., Exhaled breath condensate: an overview, Immunol Allergy Clin North Am, 32, 3, pp. 363-375, (2012); Tufvesson E, Nilsson E, Popov TA, Hesselstrand R, Bjermer L., Fractional exhaled breath temperature in patients with asthma, chronic obstructive pulmonary disease, or systemic sclerosis compared to healthy controls, Eur Clin Respir J, 7, 1, (2020); Ahmed W, White IR, Wilkinson M, Johnson CF, Rattray N, Kishore AK, Et al., Breath and plasma metabolomics to assess inflammation in acute stroke, Sci Rep, 11, 1, (2021); Lawal O, Ahmed WM, Nijsen TME, Goodacre R, Fowler SJ., Exhaled breath analysis: a review of ’breath-taking’ methods for off-line analysis, Metabolomics, 13, 10, (2017); Walker WT, Jackson CL, Lackie PM, Hogg C, Lucas JS., Nitric oxide in primary ciliary dyskinesia, Eur Respir J, 40, 4, pp. 1024-1032, (2012); Davis MD, Winters BR, Madden MC, Pleil JD, Sessler CN, Wallace MAG, Et al., Exhaled breath condensate biomarkers in critically ill, mechanically ventilated patients, J Breath Res, 15, 1, (2020); Walsh BK, Davis MD, Hunt JF, Kheir JN, Smallwood CD, Arnold JH., The effects of lung recruitment maneuvers on exhaled breath condensate pH, J Breath Res, 9, 3, (2015); Walsh BK, Mackey DJ, Pajewski T, Yu Y, Gaston BM, Hunt JF., Exhaled-breath condensate pH can be safely and continuously monitored in mechanically ventilated patients, Respir Care, 51, 10, pp. 1125-1131, (2006)","M.D. Davis; Wells Center for Pediatric Research/Pulmonology, Allergy, and Sleep Medicine, Riley Hospital for Children at Indiana University School of Medicine, Indianapolis, 1044 W. Walnut Street R3-126, 46202, United States; email: mdd1@iu.edu","","American Association for Respiratory Care","","","","","","00201324","","RECAC","38199760","English","Respir. Care","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85191101970"
"Ginebaugh S.P.; Hagner M.; Ray A.; Erzurum S.C.; Comhair S.A.A.; Denlinger L.C.; Jarjour N.N.; Castro M.; Woodruff P.G.; Christenson S.A.; Bleecker E.R.; Meyers D.A.; Hastie A.T.; Moore W.C.; Mauger D.T.; Israel E.; Levy B.D.; Wenzel S.E.; Camiolo M.J.","Ginebaugh, Scott P. (57194545100); Hagner, Matthias (57192717068); Ray, Anuradha (35433514700); Erzurum, Serpil C. (7005108319); Comhair, Suzy A.A. (6603469592); Denlinger, Loren C. (6603478990); Jarjour, Nizar N. (7003501462); Castro, Mario (7402292535); Woodruff, Prescott G. (35418932500); Christenson, Stephanie A. (55978977800); Bleecker, Eugene R. (7004832308); Meyers, Deborah A. (35379965900); Hastie, Annette T. (7003882969); Moore, Wendy C. (7403101064); Mauger, David T. (7004101554); Israel, Elliot (25960102600); Levy, Bruce D. (7401702499); Wenzel, Sally E. (7101833061); Camiolo, Matthew J. (36645578300)","57194545100; 57192717068; 35433514700; 7005108319; 6603469592; 6603478990; 7003501462; 7402292535; 35418932500; 55978977800; 7004832308; 35379965900; 7003882969; 7403101064; 7004101554; 25960102600; 7401702499; 7101833061; 36645578300","Bronchial epithelial cell transcriptional responses to inhaled corticosteroids dictate severe asthmatic outcomes","2023","Journal of Allergy and Clinical Immunology","151","6","","1513","1524","11","4","10.1016/j.jaci.2023.01.028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149804861&doi=10.1016%2fj.jaci.2023.01.028&partnerID=40&md5=a3bbaeab78db3ba75b7c1649a3781580","Integrative Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States; Pieris Pharmaceuticals, Hallbergmoos, Germany; Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis, United States; University of Kansas School of Medicine, Kansas City, Mo, United States; University of California, San Francisco School of Medicine, San Francisco, Calif, United States; Division for Genetics, Genomics and Personalized Medicine, University of Arizona College of Medicine, Tucson, Ariz, United States; Wake Forest University School of Medicine, Winston-Salem, NC, United States; Pennsylvania State University, Hershey, Pa, United States; Department of Medicine, Divisions of Pulmonary & Critical Care Medicine & Allergy & Immunology, Brigham & Women's Hospital, Harvard Medical School, Boston, Mass, United States; Department of Environmental Medicine and Occupational Health, Graduate School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States","Ginebaugh S.P., Integrative Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States; Hagner M., Pieris Pharmaceuticals, Hallbergmoos, Germany; Ray A., Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Erzurum S.C., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Comhair S.A.A., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Denlinger L.C., Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis, United States; Jarjour N.N., Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wis, United States; Castro M., University of Kansas School of Medicine, Kansas City, Mo, United States; Woodruff P.G., University of California, San Francisco School of Medicine, San Francisco, Calif, United States; Christenson S.A., University of California, San Francisco School of Medicine, San Francisco, Calif, United States; Bleecker E.R., Division for Genetics, Genomics and Personalized Medicine, University of Arizona College of Medicine, Tucson, Ariz, United States; Meyers D.A., Division for Genetics, Genomics and Personalized Medicine, University of Arizona College of Medicine, Tucson, Ariz, United States; Hastie A.T., Wake Forest University School of Medicine, Winston-Salem, NC, United States; Moore W.C., Wake Forest University School of Medicine, Winston-Salem, NC, United States; Mauger D.T., Pennsylvania State University, Hershey, Pa, United States; Israel E., Department of Medicine, Divisions of Pulmonary & Critical Care Medicine & Allergy & Immunology, Brigham & Women's Hospital, Harvard Medical School, Boston, Mass, United States; Levy B.D., Department of Medicine, Divisions of Pulmonary & Critical Care Medicine & Allergy & Immunology, Brigham & Women's Hospital, Harvard Medical School, Boston, Mass, United States; Wenzel S.E., Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Department of Environmental Medicine and Occupational Health, Graduate School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Camiolo M.J., Pieris Pharmaceuticals, Hallbergmoos, Germany","Background: Inhaled corticosteroids (CSs) are the backbone of asthma treatment, improving quality of life, exacerbation rates, and mortality. Although effective for most, a subset of patients with asthma experience CS-resistant disease despite receiving high-dose medication. Objective: We sought to investigate the transcriptomic response of bronchial epithelial cells (BECs) to inhaled CSs. Methods: Independent component analysis was performed on datasets, detailing the transcriptional response of BECs to CS treatment. The expression of these CS-response components was examined in 2 patient cohorts and investigated in relation to clinical parameters. Supervised learning was used to predict BEC CS responses using peripheral blood gene expression. Results: We identified a signature of CS response that was closely correlated with CS use in patients with asthma. Participants could be separated on the basis of CS-response genes into groups with high and low signature expression. Patients with low expression of CS-response genes, particularly those with a severe asthma diagnosis, showed worse lung function and quality of life. These individuals demonstrated enrichment for T-lymphocyte infiltration in endobronchial brushings. Supervised machine learning identified a 7-gene signature from peripheral blood that reliably identified patients with poor CS-response expression in BECs. Conclusions: Loss of CS transcriptional responses within bronchial epithelium was related to impaired lung function and poor quality of life, particularly in patients with severe asthma. These individuals were identified using minimally invasive blood sampling, suggesting these findings may enable earlier triage to alternative treatments. © 2023 American Academy of Allergy, Asthma & Immunology","Asthma; corticosteroids; severe asthma; systems biology; transcriptomics","Adrenal Cortex Hormones; Asthma; Epithelial Cells; Humans; Quality of Life; budesonide; formoterol; corticosteroid; Article; blood sampling; bronchus biopsy; bronchus epithelium; clinical outcome; cohort analysis; controlled study; corticosteroid therapy; differential gene expression; epithelium cell; gene expression; gene ontology; genetic transcription; human; human cell; human tissue; lung function; lymphocytic infiltration; minimally invasive procedure; quality of life; severe asthma; supervised machine learning; T lymphocyte; transcriptomics; treatment response; asthma; epithelium cell; genetics; metabolism; quality of life","","budesonide, 51333-22-3, 51372-29-3; formoterol, 73573-87-2; Adrenal Cortex Hormones, ","","","National Institutes of Health, NIH, (P01AI106684, R01AI048927, R01HL113956, U10HL109146, U10HL109152, U10HL109164, U10HL109168, U10HL109172, U10HL109250, U10HL109257); National Institutes of Health, NIH","The study was supported by the National Institutes of Health (grant no. P01AI106684 to A.R. and S.E.W., grant nos. R01HL113956 and R01AI048927 to A.R., grant no. U10HL109152 to S.E.W., grant no. U10HL109172 to E.I. and B.D.L., grant no. U10HL109168 to N.N.J., grant no. U10HL109250 to S.C.E., grant no. U10HL109164 to W.C.M., grant no. U10HL109257 to M.C., and grant no. U10HL109146 to P.G.W. and S.A.C.). 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Camiolo; Environmental Medicine and Occupational Health, Graduate School of Public Health, University of Pittsburgh School of Medicine, Pittsburgh, 4126 Public Health, 130 DeSoto St, 15261, United States; email: matt.camiolo@gmail.com","","Elsevier Inc.","","","","","","00916749","","JACIB","36796454","English","J. Allergy Clin. Immunol.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85149804861"
"Cansiz B.; Kilinc C.U.; Serbes G.","Cansiz, Berke (59161627300); Kilinc, Coskuvar Utkan (59161160200); Serbes, Gorkem (35100913500)","59161627300; 59161160200; 35100913500","Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification","2024","Computers in Biology and Medicine","178","","108698","","","","3","10.1016/j.compbiomed.2024.108698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195305233&doi=10.1016%2fj.compbiomed.2024.108698&partnerID=40&md5=8065e3fdf308bee2acd125f44a712667","Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey","Cansiz B., Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey; Kilinc C.U., Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey; Serbes G., Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey","The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time–frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings. © 2024 Elsevier Ltd","Chronic obstructive pulmonary disease; Interstitial lung disease; Lung sound; Machine learning; Tunable Q-factor wavelet transformation","Algorithms; Female; Humans; Lung; Lung Diseases; Machine Learning; Male; Respiratory Sounds; Signal Processing, Computer-Assisted; Wavelet Analysis; Biological organs; Computer aided diagnosis; Cost effectiveness; Extraction; Feature extraction; Learning algorithms; Q factor measurement; Signal processing; Wavelet decomposition; Chronic obstructive pulmonary disease; Interstitial lung disease; Lung sounds; Machine-learning; Q-factors; Subtask; Tunable Q-factor wavelet transformation; Tunables; Wavelet transformations; Wavelets transform; Article; asthma; audio recording; binary classification; chronic obstructive lung disease; controlled study; disease classification; feature extraction; human; interstitial lung disease; learning algorithm; lung disease; lung infection; major clinical study; wavelet transform; abnormal respiratory sound; algorithm; classification; female; lung; machine learning; male; signal processing; wavelet analysis; Pulmonary diseases","","","","","","","Roguin A., Rene Theophile Hyacinthe Laënnec (1781–1826): the man behind the stethoscope, Clin. 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"Chen W.; Schatz M.; Zhou Y.; Xie F.; Bali V.; Das A.; Schelfhout J.; Stern J.A.; Zeiger R.S.","Chen, Wansu (23666233400); Schatz, Michael (7102406083); Zhou, Yichen (57215854387); Xie, Fagen (7202420697); Bali, Vishal (57508198300); Das, Amar (57557026400); Schelfhout, Jonathan (57211452628); Stern, Julie A. (19738801200); Zeiger, Robert S. (7004436805)","23666233400; 7102406083; 57215854387; 7202420697; 57508198300; 57557026400; 57211452628; 19738801200; 7004436805","Prediction of persistent chronic cough in patients with chronic cough using machine learning","2023","ERJ Open Research","9","2","00471-2022","","","","4","10.1183/23120541.00471-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152642042&doi=10.1183%2f23120541.00471-2022&partnerID=40&md5=715f3fa7c8e6f426e5b8017aa127ede1","Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Department of Allergy, Kaiser Permanente Southern California, San Diego, CA, United States; Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, United States; Center for Observational and Real-World Evidence (CORE), Merck & Co., Inc, Kenilworth, NJ, United States","Chen W., Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Schatz M., Department of Allergy, Kaiser Permanente Southern California, San Diego, CA, United States, Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, United States; Zhou Y., Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Xie F., Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Bali V., Center for Observational and Real-World Evidence (CORE), Merck & Co., Inc, Kenilworth, NJ, United States; Das A., Center for Observational and Real-World Evidence (CORE), Merck & Co., Inc, Kenilworth, NJ, United States; Schelfhout J., Center for Observational and Real-World Evidence (CORE), Merck & Co., Inc, Kenilworth, NJ, United States; Stern J.A., Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Zeiger R.S., Department of Allergy, Kaiser Permanente Southern California, San Diego, CA, United States, Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, United States","Introduction The aim of this study was to develop and validate prediction models for risk of persistent chronic cough (PCC) in patients with chronic cough (CC). This was a retrospective cohort study. Methods Two retrospective cohorts of patients 18–85 years of age were identified for years 2011–2016: a specialist cohort which included CC patients diagnosed by specialists, and an event cohort which comprised CC patients identified by at least three cough events. A cough event could be a cough diagnosis, dispensing of cough medication or any indication of cough in clinical notes. Model training and validation were conducted using two machine-learning approaches and 400+ features. Sensitivity analyses were also conducted. PCC was defined as a CC diagnosis or any two (specialist cohort) or three (event cohort) cough events in year 2 and again in year 3 after the index date. Results 8581 and 52 010 patients met the eligibility criteria for the specialist and event cohorts (mean age 60.0 and 55.5 years), respectively. 38.2% and 12.4% of patients in the specialist and event cohorts, respectively, developed PCC. The utilisation-based models were mainly based on baseline healthcare utilisations associated with CC or respiratory diseases, while the diagnosis-based models incorporated traditional parameters including age, asthma, pulmonary fibrosis, obstructive pulmonary disease, gastro-oesophageal reflux, hypertension and bronchiectasis. All final models were parsimonious (five to seven predictors) and moderately accurate (area under the curve: 0.74–0.76 for utilisation-based models and 0.71 for diagnosis-based models). Conclusions The application of our risk prediction models may be used to identify high-risk PCC patients at any stage of the clinical testing/evaluation to facilitate decision making. © The authors 2023.","","adult; aged; Article; asthma; bronchiectasis; chronic cough; chronic obstructive lung disease; cohort analysis; controlled study; cross validation; diagnostic test accuracy study; dispensing of cough medication; female; follow up; gastroesophageal reflux; gradient boosting model; health care utilization; human; hypertension; lung fibrosis; machine learning; male; medical procedures; persistent chronic cough; prediction; predictive model; predictive value; random forest; retrospective study; sensitivity analysis; sensitivity and specificity","","","","","KPSC; Kaiser Permanente Southern California","The authors thank Sole Cardoso (Kaiser Permanente Southern California (KPSC), Pasadena, CA, USA) for the assistance with formatting the manuscript and Botao Zhou (KPSC, Pasadena, CA, USA) for the additional analyses. Support statement: Merck Sharpe & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, funded a research grant to the Southern California Permanente Medical Group (SCPMG) Research and Evaluation Department to perform the study. SCPMG investigators developed the protocol, performed the analyses, and wrote the manuscript. The sponsor participated in the study discussions and provided comments to the protocol, data analysis, and manuscript. Funding information for this article has been deposited with the Crossref Funder Registry.","Pratter MR., Overview of common causes of chronic cough: ACCP evidence-based clinical practice guidelines, Chest, 129, pp. 59S-62S, (2006); McGarvey L, Gibson PG., What is chronic cough? Terminology, J Allergy Clin Immunol Pract, 7, pp. 1711-1714, (2019); Smith JA, Woodcock A., Chronic cough, N Engl J Med, 375, pp. 1544-1551, (2016); Weiner M, Dexter PR, Heithoff K, Et al., Identifying and characterizing a chronic cough cohort through electronic health records, Chest, 159, pp. 2346-2355, (2021); Ford AC, Forman D, Moayyedi P, Et al., Cough in the community: a cross sectional survey and the relationship to gastrointestinal symptoms, Thorax, 61, pp. 975-979, (2006); Koo HK, Jeong I, Lee SW, Et al., Prevalence of chronic cough and possible causes in the general population based on the Korean National Health and Nutrition Examination Survey, Medicine (Baltimore), 95, (2016); Zeiger RS, Xie F, Schatz M, Et al., Prevalence and characteristics of chronic cough in adults identified by administrative data, Perm J, 24, pp. 1-3, (2020); Meltzer EO, Zeiger RS, Dicpinigaitis P, Et al., Prevalence and burden of chronic cough in the United States, J Allergy Clin Immunol Pract, 9, pp. 4037-4044, (2021); Chung KF, Pavord ID., Prevalence, pathogenesis, and causes of chronic cough, Lancet, 371, pp. 1364-1374, (2008); French CT, Fletcher KE, Irwin RS., Gender differences in health-related quality of life in patients complaining of chronic cough, Chest, 125, pp. 482-488, (2004); Chamberlain SA, Garrod R, Douiri A, Et al., The impact of chronic cough: a cross-sectional European survey, Lung, 193, pp. 401-408, (2015); Kelsall A, Decalmer S, McGuinness K, Et al., Sex differences and predictors of objective cough frequency in chronic cough, Thorax, 64, pp. 393-398, (2009); Sunger K, Powley W, Kelsall A, Et al., Objective measurement of cough in otherwise healthy volunteers with acute cough, Eur Respir J, 41, pp. 277-284, (2013); Zeiger RS, Schatz M, Butler RK, Et al., Burden of specialist-diagnosed chronic cough in adults, J Allergy Clin Immunol Pract, 8, pp. 1645-1657, (2020); Irwin RS, Baumann MH, Bolser DC, Et al., Diagnosis and management of cough executive summary: ACCP evidence-based clinical practice guidelines, Chest, 129, pp. 1S-23S, (2006); Morice AH, McGarvey L, Pavord I, Recommendations for the management of cough in adults, Thorax, 61, pp. i1-24, (2006); Morice AH, Fontana GA, Sovijarvi AR, Et al., The diagnosis and management of chronic cough, Eur Respir J, 24, pp. 481-492, (2004); Gibson PG, Chang AB, Glasgow NJ, Et al., CICADA: Cough in Children and Adults: diagnosis and assessment. Australian cough guidelines summary statement, Med J Aust, 192, pp. 265-271, (2010); Morice AH, Millqvist E, Bieksiene K, Et al., ERS guidelines on the diagnosis and treatment of chronic cough in adults and children, Eur Respir J, 55, (2020); Yousaf N, Montinero W, Birring SS, Et al., The long term outcome of patients with unexplained chronic cough, Respir Med, 107, pp. 408-412, (2013); Xiao L, Gandhi P, Zhang P, Et al., Applying interpretable deep learning models to identify chronic cough patients using EHR data, Comput Methods Programs Biomed, 10, (2021); Koebnick C, Langer-Gould AM, Gould MK, Et al., Sociodemographic characteristics of members of a large, integrated health care system: comparison with US Census Bureau data, Perm J, 16, pp. 37-41, (2012); Zeiger RS, Schatz M, Hong B, Et al., Patient-reported burden of chronic cough in a managed care organization, J Allergy Clin Immunol Pract, 9, pp. 1624-1637, (2021); Wright M, Ziegler A., ranger: a fast implementation of random forests for high dimensional data in C++ and R, J Stat Softw, 77, pp. 1-17, (2017); Ishwaran H, Kogalur U, Blackston E, Et al., Random survival forests, Ann Appl Stat, 3, pp. 841-860, (2008); Little R., Missing-data adjustments in large surveys, J Bus Econ Stat, 6, pp. 287-296, (1988); Ke G, Meng Q, Finley T, Et al., Lightgbm: a highly efficient gradient boosting decision tree, Adv Neural Inf Process Syst, 30, pp. 3146-3154, (2017); Breiman L., Random forests, Mach Learn, 45, pp. 5-32, (2001); Pedregosa F, Varoquaux G, Gramfort A, Et al., Scikit-learn: machine learning in python, J Mach Learn Res, 12, pp. 2825-2830, (2011); Zeiger RS, Schatz M, Zhou Y, Et al., Risk factors for persistent chronic cough during consecutive years: a retrospective database analysis, J Allergy Clin Immunol Pract, 10, pp. 1587-1597, (2022); Moore K., New diagnosis codes effective Oct. 1. Here are some family physicians should know","W. Chen; Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, United States; email: wansu.chen@kp.org","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152642042"
"Peng J.; Mei H.; Yang R.; Meng K.; Shi L.; Zhao J.; Zhang B.; Xuan F.; Wang T.; Zhang T.","Peng, Jingyi (59261068000); Mei, Haixia (58831987700); Yang, Ruiming (59323269700); Meng, Keyu (57195905163); Shi, Lijuan (55458848900); Zhao, Jian (59323158700); Zhang, Bowei (57209154708); Xuan, Fuzhen (59157641900); Wang, Tao (57768846300); Zhang, Tong (55561572200)","59261068000; 58831987700; 59323269700; 57195905163; 55458848900; 59323158700; 57209154708; 59157641900; 57768846300; 55561572200","Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders","2024","ACS Sensors","9","9","","4934","4946","12","3","10.1021/acssensors.4c01584","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203642975&doi=10.1021%2facssensors.4c01584&partnerID=40&md5=189832b372b888af02799d7f7afefe18","Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China","Peng J., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Mei H., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Yang R., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Meng K., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Shi L., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Zhao J., Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; Zhang B., Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; Xuan F., Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; Wang T., Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; Zhang T., State Key Laboratory of Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, Changchun, 130012, China","This study introduces a novel deep learning framework for lung health evaluation using exhaled gas. The framework synergistically integrates pyramid pooling and a dual-encoder network, leveraging SHapley Additive exPlanations (SHAP) derived feature importance to enhance its predictive capability. The framework is specifically designed to effectively distinguish between smokers, individuals with chronic obstructive pulmonary disease (COPD), and control subjects. The pyramid pooling structure aggregates multilevel global information by pooling features at four scales. SHAP assesses feature importance from the eight sensors. Two encoder architectures handle different feature sets based on their importance, optimizing performance. Besides, the model’s robustness is enhanced using the sliding window technique and white noise augmentation on the original data. In 5-fold cross-validation, the model achieved an average accuracy of 96.40%, surpassing that of a single encoder pyramid pooling model by 10.77%. Further optimization of filters in the transformer convolutional layer and pooling size in the pyramid module increased the accuracy to 98.46%. This study offers an efficient tool for identifying the effects of smoking and COPD, as well as a novel approach to utilizing deep learning technology to address complex biomedical issues. © 2024 American Chemical Society.","diagnosis model; electronic nose; hierarchical encoding; model interpretability; pyramid pooling","Breath Tests; Deep Learning; Humans; Lung; Male; Pulmonary Disease, Chronic Obstructive; Smell; Smoking; Diagnosis; Electronic health record; Lung cancer; Medical problems; mHealth; Network coding; Chronic obstructive pulmonary disease; Derived features; Diagnosis model; Health evaluation; Hierarchical encoding; Interpretability; Learning frameworks; Model interpretability; Pyramid pooling; Shapley; breath analysis; chronic obstructive lung disease; deep learning; diagnosis; human; lung; male; odor; procedures; smoking; Pulmonary diseases","","","","","National Natural Science Foundation of China, NSFC, (52205586,62371299, 62301314, 62020106006); National Natural Science Foundation of China, NSFC; China Postdoctoral Science Foundation, (2023M732198); China Postdoctoral Science Foundation","This work was supported by the National Natural Science Foundation of China (52205586,62371299, 62301314, and 62020106006) and the China Postdoctoral Science Foundation (2023M732198).","Fang L., Gao P., Bao H., Tang X., Wang B., Feng Y., Cong S., Juan J., Fan J., Lu K., Wang N., Hu Y., Wang L., Chronic obstructive pulmonary disease in China: a nationwide prevalence study, Lancet Respir. 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Int., 158, (2022); Guo S., Huang X., Situ Y., Huang Q., Guan K., Huang J., Wang W., Bai X., Liu Z., Wu Y., Qiao Z., Interpretable machine-learning and big data mining to predict gas diffusivity in metal-organic frameworks, Adv. Sci., 10, 21, (2023); Xiong L., He M., Hu C., Hou Y., Han S., Tang X., Image presentation and effective classification of odor intensity levels using multi-channel electronic nose technology combined with GASF and CNN, Sens. Actuators, B, 395, (2023); Li X., Yang Y., Zhu Y., Ben A., Qi J., A novel strategy for discriminating different cultivation and screening odor and taste flavor compounds in Xinhui tangerine peel using E-nose, E-tongue, and chemometrics, Food Chem., 384, (2022); Duran Acevedo C.M., Cuastumal Vasquez C.A., Carrillo Gomez J.K., Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis, Data Brief, 35, (2021); George A., Hardware-Efficient DWT architecture for image processing in visual sensors networks, IEEE Sens. J., 23, 5, pp. 5382-5390, (2023); Wu Y.H., Liu Y., Zhan X., Cheng M.M., P2T: Pyramid pooling transformer for scene understanding, IEEE Trans. Pattern Anal. Mach. Intell., 45, 11, pp. 12760-12771, (2023)","H. Mei; Key Lab Intelligent Rehabil & Barrier Free Disable (Ministry of Education), Changchun University, Changchun, 130022, China; email: meihx@ccu.edu.cn; T. Wang; Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; email: wangtao@ecust.edu.cn","","American Chemical Society","","","","","","23793694","","","39248698","English","ACS Sensors","Article","Final","","Scopus","2-s2.0-85203642975"
"Joo H.; Lee D.; Lee S.H.; Kim Y.K.; Rhee C.K.","Joo, Hyonsoo (57016201500); Lee, Daeun (58304034300); Lee, Sang Haak (57484738300); Kim, Young Kyoon (7410207820); Rhee, Chin Kook (35202293000)","57016201500; 58304034300; 57484738300; 7410207820; 35202293000","Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method","2023","BMC Pulmonary Medicine","23","1","196","","","","4","10.1186/s12890-023-02479-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161038012&doi=10.1186%2fs12890-023-02479-4&partnerID=40&md5=07c86c3af1fdd6eba7d3e41613a472f0","Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Uijeongbu St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Departement of Applied Statistics, Yonsei University, Seoul, South Korea; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpodaero, Seochogu, Seoul, 06591, South Korea","Joo H., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Uijeongbu St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Lee D., Departement of Applied Statistics, Yonsei University, Seoul, South Korea; Lee S.H., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, Eunpyeong St. Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Kim Y.K., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpodaero, Seochogu, Seoul, 06591, South Korea; Rhee C.K., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpodaero, Seochogu, Seoul, 06591, South Korea","Introduction: Analysis of the National Health Insurance data has been actively carried out for the purpose of academic research and establishing scientific evidences for health care service policy in asthma. However, there has been a limitation for the accuracy of the data extracted through conventional operational definition. In this study, we verified the accuracy of conventional operational definition of asthma, by applying it to a real hospital setting. And by using a machine learning technique, we established an appropriate operational definition that predicts asthma more accurately. Methods: We extracted asthma patients using the conventional operational definition of asthma at Seoul St. Mary’s hospital and St. Paul’s hospital at the Catholic University of Korea between January 2017 and January 2018. Among these extracted patients of asthma, 10% of patients were randomly sampled. We verified the accuracy of the conventional operational definition for asthma by matching actual diagnosis through medical chart review. And then we operated machine learning approaches to predict asthma more accurately. Results: A total of 4,235 patients with asthma were identified using a conventional asthma definition during the study period. Of these, 353 patients were collected. The patients of asthma were 56% of study population, 44% of patients were not asthma. The use of machine learning techniques improved the overall accuracy. The XGBoost prediction model for asthma diagnosis showed an accuracy of 87.1%, an AUC of 93.0%, sensitivity of 82.5%, and specificity of 97.9%. Major explanatory variable were ICS/LABA,LAMA and LTRA for proper diagnosis of asthma. Conclusions: The conventional operational definition of asthma has limitation to extract true asthma patients in real world. Therefore, it is necessary to establish an accurate standardized operational definition of asthma. In this study, machine learning approach could be a good option for building a relevant operational definition in research using claims data. © 2023, The Author(s).","Asthma; Conventional operational definition; Machine learning","Asthma; Humans; Machine Learning; Research Design; Seoul; beta 2 adrenergic receptor stimulating agent; corticosteroid; leukotriene receptor blocking agent; muscarinic receptor blocking agent; xanthine derivative; adult; Article; asthma; bronchiectasis; bronchiolitis obliterans; chronic obstructive lung disease; clinical feature; decision tree; diagnostic accuracy; diagnostic test accuracy study; differential diagnosis; explanatory variable; female; hospital patient; human; ICD-10; lung cancer; machine learning; major clinical study; male; medical record review; middle aged; prediction; predictor variable; respiratory tract disease assessment; retrospective study; sensitivity and specificity; South Korea; asthma; machine learning; methodology","","","XGBoost","","Ministry of Health and Welfare, MOHW, (HI18C0522); Ministry of Health and Welfare, MOHW; Korea Health Industry Development Institute, KHIDI","This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea. (grant number: HI18C0522). ","Ferver K., Burton B., Jesilow P., The use of claims data in healthcare research, Open Public Health J, 2, 1, pp. 11-24, (2009); Lee J., Lee J.S., Park S.H., Shin S.A., Kim K., Cohort profile: the National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea, Int J Epidemiol, 46, 2, (2017); Choi J.Y., Yoon H.K., Lee J.H., Yoo K.H., Kim B.Y., Bae H.W., Kim Y.K., Rhee C.K., Current status of asthma care in South Korea: nationwide the health insurance review and assessment service database, J Thorac Dis, 9, 9, pp. 3208-3214, (2017); Choi J.Y., Yoon H.K., Lee J.H., Yoo K.H., Kim B.Y., Bae H.W., Kim Y.K., Rhee C.K., Nationwide pulmonary function test rates in South Korean asthma patients, J Thorac Dis, 10, 7, pp. 4360-4367, (2018); Choi J.Y., Yoon H.K., Lee J.H., Yoo K.H., Kim B.Y., Bae H.W., Kim Y.K., Rhee C.K., Nationwide use of inhaled corticosteroids by South Korean asthma patients: an examination of the health insurance review and service database, J Thorac Dis, 10, 9, pp. 5405-5413, (2018); Park H.J., Byun M.K., Kim H.J., Ahn C.M., Rhee C.K., Kim K., Kim B.Y., Bae H.W., Yoo K.H., Regular follow-up visits reduce the risk for asthma exacerbation requiring admission in Korean adults with asthma, Allergy Asthma Clin Immunol, 14, (2018); Cho E.Y., Oh K.J., Rhee C.K., Yoo K.H., Kim B.Y., Bae H.W., Lee B.J., Choi D.C., Lee H., Park H.Y., Comparison of clinical characteristics and management of asthma by types of health care in South Korea, J Thorac Dis, 10, 6, pp. 3269-3276, (2018); Rhee C.K., Yoon H.K., Yoo K.H., Kim Y.S., Lee S.W., Park Y.B., Lee J.H., Kim Y., Kim K., Kim J., Et al., Medical utilization and cost in patients with overlap syndrome of chronic obstructive pulmonary disease and asthma, COPD, 11, 2, pp. 163-170, (2014); Kim S., Kim J., Kim K., Kim Y., Park Y., Baek S., Park S.Y., Yoon S.Y., Kwon H.S., Cho Y.S., Et al., Healthcare use and prescription patterns associated with adult asthma in Korea: analysis of the NHI claims database, Allergy, 68, 11, pp. 1435-1442, (2013); Lee E., Kim A., Ye Y.M., Choi S.E., Park H.S., Increasing prevalence and mortality of asthma with age in Korea, 2002–2015: a nationwide, population-based study, Allergy Asthma Immunol Res, 12, 3, pp. 467-484, (2020); Gillman A., Douglass J.A., Asthma in the elderly, Asia Pac Allergy, 2, 2, pp. 101-108, (2012); Akgun K.M., Crothers K., Pisani M., Epidemiology and management of common pulmonary diseases in older persons, J Gerontol A Biol Sci Med Sci, 67, 3, pp. 276-291, (2012); Oraka E., Kim H.J., King M.E., Callahan D.B., Asthma prevalence among US elderly by age groups: age still matters, J Asthma, 49, 6, pp. 593-599, (2012); Weiner P., Magadle R., Waizman J., Weiner M., Rabner M., Zamir D., Characteristics of asthma in the elderly, Eur Respir J, 12, 3, pp. 564-568, (1998)","Y.K. Kim; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, 222 Banpodaero, Seochogu, 06591, South Korea; email: youngkim@catholic.ac.kr; C.K. Rhee; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, 222 Banpodaero, Seochogu, 06591, South Korea; email: chinkook77@gmail.com","","BioMed Central Ltd","","","","","","14712466","","BPMMB","37280559","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85161038012"
"Shapanis A.; Jones M.G.; Schofield J.; Skipp P.","Shapanis, Andrew (57219126464); Jones, Mark G (55700810400); Schofield, James (57194396933); Skipp, Paul (6506507806)","57219126464; 55700810400; 57194396933; 6506507806","Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis","2023","Thorax","78","7","","682","689","7","4","10.1136/thorax-2022-219731","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153873677&doi=10.1136%2fthorax-2022-219731&partnerID=40&md5=a87b5756fbcb80e80d61c2ef62b1b221","Biological Sciences, University of Southampton, Hampshire, Southampton, United Kingdom; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; TopMD Precision Medicine Ltd, Southampton, United Kingdom","Shapanis A., Biological Sciences, University of Southampton, Hampshire, Southampton, United Kingdom; Jones M.G., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Schofield J., TopMD Precision Medicine Ltd, Southampton, United Kingdom; Skipp P., Biological Sciences, University of Southampton, Hampshire, Southampton, United Kingdom","Background Idiopathic pulmonary fibrosis (IPF) is a debilitating, progressive disease with a median survival time of 3-5 years. Diagnosis remains challenging and disease progression varies greatly, suggesting the possibility of distinct subphenotypes. Methods and results We analysed publicly available peripheral blood mononuclear cell expression datasets for 219 IPF, 411 asthma, 362 tuberculosis, 151 healthy, 92 HIV and 83 other disease samples, totalling 1318 patients. We integrated the datasets and split them into train (n=871) and test (n=477) cohorts to investigate the utility of a machine learning model (support vector machine) for predicting IPF. A panel of 44 genes predicted IPF in a background of healthy, tuberculosis, HIV and asthma with an area under the curve of 0.9464, corresponding to a sensitivity of 0.865 and a specificity of 0.89. We then applied topological data analysis to investigate the possibility of subphenotypes within IPF. We identified five molecular subphenotypes of IPF, one of which corresponded to a phenotype enriched for death/transplant. The subphenotypes were molecularly characterised using bioinformatic and pathway analysis tools identifying distinct subphenotype features including one which suggests an extrapulmonary or systemic fibrotic disease. Conclusions Integration of multiple datasets, from the same tissue, enabled the development of a model to accurately predict IPF using a panel of 44 genes. Furthermore, topological data analysis identified distinct subphenotypes of patients with IPF which were defined by differences in molecular pathobiology and clinical characteristics. © 2023 BMJ Publishing Group. All rights reserved.","idiopathic pulmonary fibrosis","Asthma; HIV Infections; Humans; Idiopathic Pulmonary Fibrosis; Leukocytes, Mononuclear; Phenotype; Article; asthma; bioinformatics; clinical feature; cohort analysis; controlled study; data analysis; data base; female; fibrosing alveolitis; gene expression system; human; human cell; Human immunodeficiency virus infection; human tissue; major clinical study; male; molecular pathology; peripheral blood mononuclear cell; phenotype; prediction; sensitivity and specificity; support vector machine; topological data analysis; tuberculosis; asthma; genetics; Human immunodeficiency virus infection; mononuclear cell; phenotype","","","","","Boehringer Ingelheim; Wellcome Trust, WT; Medical Research Council, MRC; Royal Society; British Lung Foundation, BLF; AAIR Charity, AAIR","MGJ reports grants from Wellcome Trust, British Lung Foundation, Medical Research Council, Boehringher Ingelheim, AAIR and The Royal Society outside of the submitted work. JS reports consulting fees and shares in TopMD outside of the submitted work. PS reports consulting fees and shares in TopMD, outside of the submitted work. ","Raghu G., Remy-Jardin M., Myers J.L., Et al., Diagnosis of idiopathic pulmonary fibrosis. 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NCBI GEO, (2020); Heider A., Alt R., VirtualArray: A R/bioconductor package to merge RAW data from different microarray platforms, BMC Bioinformatics, 14, (2013); Ley B., Ryerson C.J., Vittinghoff E., Et al., A multidimensional index and staging system for idiopathic pulmonary fibrosis, Ann Intern Med, 156, pp. 684-691, (2012); Herazo-Maya J.D., Noth I., Duncan S.R., Et al., Peripheral blood mononuclear cell gene expression profiles predict poor outcome in idiopathic pulmonary fibrosis, Sci Transl Med, 5, (2013); Huang L.S., Mathew B., Li H., Et al., The mitochondrial cardiolipin remodeling enzyme lysocardiolipin acyltransferase is a novel target in pulmonary fibrosis, Am J Respir Crit Care Med, 189, pp. 1402-1415, (2014); Newman A.M., Liu C.L., Green M.R., Et al., Robust enumeration of cell subsets from tissue expression profiles, Nat Methods, 12, pp. 453-457, (2015); Ning B.-F., Ding J., Yin C., Et al., Hepatocyte nuclear factor 4 alpha suppresses the development of hepatocellular carcinoma, Cancer Res, 70, pp. 7640-7651, (2010); Yue H.-Y., Yin C., Hou J.-L., Et al., Hepatocyte nuclear factor 4alpha attenuates hepatic fibrosis in rats, Gut, 59, pp. 236-246, (2010); Song G., Pacher M., Balakrishnan A., Et al., Direct reprogramming of hepatic myofibroblasts into hepatocytes in vivo attenuates liver fibrosis, Cell Stem Cell, 18, pp. 797-808, (2016); Hudson M., Bernatsky S., Colmegna I., Et al., Novel insights into systemic autoimmune rheumatic diseases using shared molecular signatures and an integrative analysis, Epigenetics, 12, pp. 433-440, (2017); Bertrams W., Griss K., Han M., Et al., Transcriptional analysis identifies potential biomarkers and molecular regulators in pneumonia and COPD exacerbation, Sci Rep, 10, (2020); Bocchino M., Brancaccio G., De Martino M., Et al., Transient elastography detection of early liver fibrosis in idiopathic pulmonary fibrosis patients, Eur Respir J, 44, (2014); Cocconcelli E., Tonelli R., Abbati G., Et al., Subclinical liver fibrosis in patients with idiopathic pulmonary fibrosis, Intern Emerg Med, 16, pp. 349-357, (2021); Makarev E., Izumchenko E., Aihara F., Et al., Common pathway signature in lung and liver fibrosis, Cell Cycle, 15, pp. 1667-1673, (2016); Wang M., Gong Q., Zhang J., Et al., Characterization of gene expression profiles in HBV-related liver fibrosis patients and identification of ITGBL1 as a key regulator of fibrogenesis, Sci Rep, 7, (2017); Borkham-Kamphorst E., Van Roeyen C.R.C., Ostendorf T., Et al., Pro-Fibrogenic potential of PDGF-D in liver fibrosis, J Hepatol, 46, pp. 1064-1074, (2007); Buhl E.M., Djudjaj S., Babickova J., Et al., The role of PDGF-D in healthy and fibrotic kidneys, Kidney Int, 89, pp. 848-861, (2016); Li M., Luan F., Zhao Y., Et al., Epithelial-Mesenchymal transition: An emerging target in tissue fibrosis, Exp Biol Med (Maywood), 241, pp. 1-13, (2016); Voltz J.W., Card J.W., Carey M.A., Et al., Male sex hormones exacerbate lung function impairment after bleomycin-induced pulmonary fibrosis, Am J Respir Cell Mol Biol, 39, pp. 45-52, (2008); Yang J.D., Abdelmalek M.F., Pang H., Et al., Gender and menopause impact severity of fibrosis among patients with nonalcoholic steatohepatitis, Hepatology, 59, pp. 1406-1414, (2014); King T.E., Bradford W.Z., Castro-Bernardini S., Et al., A phase 3 trial of pirfenidone in patients with idiopathic pulmonary fibrosis, N Engl J Med, 370, pp. 2083-2092, (2014); Noble P.W., Albera C., Bradford W.Z., Et al., Pirfenidone in patients with idiopathic pulmonary fibrosis (capacity): Two randomised trials, Lancet, 377, pp. 1760-1769, (2011); Richeldi L., Du Bois R.M., Raghu G., Et al., Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis, N Engl J Med, 370, pp. 2071-2082, (2014); Richeldi L., Collard H.R., Jones M.G., Idiopathic pulmonary fibrosis, Lancet, 389, pp. 1941-1952, (2017); Yokoyama A., Kohno N., Hamada H., Et al., Circulating KL-6 predicts the outcome of rapidly progressive idiopathic pulmonary fibrosis, Am J Respir Crit Care Med, 158, pp. 1680-1684, (1998); Todd J.L., Neely M.L., Overton R., Et al., Peripheral blood proteomic profiling of idiopathic pulmonary fibrosis biomarkers in the multicentre IPF-PRO registry, Respir Res, 20, (2019); Kraven L.M., Taylor A.R., Molyneaux P.L., Et al., Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis, Thorax, (2022)","P. Skipp; Biological Sciences, University of Southampton, Southampton, Hampshire, United Kingdom; email: pjss@soton.ac.uk","","BMJ Publishing Group","","","","","","00406376","","THORA","36808085","English","Thorax","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85153873677"
"Rao S.; Nazarzadeh M.; Li Y.; Canoy D.; Mamouei M.; Salimi-Khorshidi G.; Rahimi K.","Rao, Shishir (57216591893); Nazarzadeh, Milad (55500082800); Li, Yikuan (57216593457); Canoy, Dexter (8063757400); Mamouei, Mohammad (56986358800); Salimi-Khorshidi, Gholamreza (24504279100); Rahimi, Kazem (58482212900)","57216591893; 55500082800; 57216593457; 8063757400; 56986358800; 24504279100; 58482212900","Systolic blood pressure, chronic obstructive pulmonary disease and cardiovascular risk","2023","Heart","109","16","","1216","1222","6","4","10.1136/heartjnl-2023-322431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159450539&doi=10.1136%2fheartjnl-2023-322431&partnerID=40&md5=2b788be1543474725350ac89926eae41","Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom; Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom","Rao S., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Nazarzadeh M., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Li Y., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Canoy D., Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom; Mamouei M., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Salimi-Khorshidi G., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom; Rahimi K., Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom, Deep Medicine, Oxford Martin School, University of Oxford, Oxford, United Kingdom, NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom","Objective: In individuals with complex underlying health problems, the association between systolic blood pressure (SBP) and cardiovascular disease is less well recognised. The association between SBP and risk of cardiovascular events in patients with chronic obstructive pulmonary disease (COPD) was investigated. Methods: and analysis In this cohort study, 39 602 individuals with a diagnosis of COPD aged 55-90 years between 1990 and 2009 were identified from validated electronic health records (EHR) in the UK. The association between SBP and risk of cardiovascular end points (composite of ischaemic heart disease, heart failure, stroke and cardiovascular death) was analysed using a deep learning approach. Results: In the selected cohort (46.5% women, median age 69 years), 10 987 cardiovascular events were observed over a median follow-up period of 3.9 years. The association between SBP and risk of cardiovascular end points was found to be monotonic; the lowest SBP exposure group of <120 mm Hg presented nadir of risk. With respect to reference SBP (between 120 and 129 mm Hg), adjusted risk ratios for the primary outcome were 0.99 (95% CI 0.93 to 1.05) for SBP of <120 mm Hg, 1.02 (0.97 to 1.07) for SBP between 130 and 139 mm Hg, 1.07 (1.01 to 1.12) for SBP between 140 and 149 mm Hg, 1.11 (1.05 to 1.17) for SBP between 150 and 159 mm Hg and 1.16 (1.10 to 1.22) for SBP ≥160 mm Hg. Conclusion: Using deep learning for modelling EHR, we identified a monotonic association between SBP and risk of cardiovascular events in patients with COPD.  © 2023 Author(s) (or their employer(s)).","epidemiology; hypertension","Aged; Antihypertensive Agents; Blood Pressure; Cardiovascular Diseases; Cohort Studies; Female; Heart Disease Risk Factors; Humans; Hypertension; Male; Pulmonary Disease, Chronic Obstructive; Risk Factors; antihypertensive agent; adult; age distribution; aged; Article; blood pressure measurement; cardiovascular disease; cardiovascular mortality; cardiovascular risk; cerebrovascular accident; chronic obstructive lung disease; cohort analysis; deep learning; disease association; electronic health record; female; follow up; heart failure; human; ischemic heart disease; major clinical study; male; retrospective study; systolic blood pressure; systolic hypertension; United Kingdom; blood pressure; cardiovascular disease; chronic obstructive lung disease; complication; heart disease risk factor; hypertension; physiology; risk factor","","Antihypertensive Agents, ","","","Oxford National Institute of Health Research; United Kingdom Research and Innovation; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, BRC; Global Challenges Research Fund, GCRF, (ES/P0110551/1); Global Challenges Research Fund, GCRF; National Institute for Health and Care Research, NIHR; British Heart Foundation, BHF, (FS/19/36/34346, FS/PhD/21/29110, PG/18/65/33872); British Heart Foundation, BHF; University of Oxford; Novo Nordisk","This work was supported by grants from the British Heart Foundation (BHF) (grant number: FS/PhD/21/29110 to YL and KR; grant number: FS/19/36/34346 to MN and KR and grant number: PG/18/65/33872 to KR and DC); United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) (grant number: ES/P0110551/1 to KR); Oxford National Institute of Health Research (NIHR) Biomedical Research Centre (to KR), Novo Nordisk grant (to MM) and the Oxford Martin School (OMS), University of Oxford (to KR). ","Ettehad D., Emdin C.A., Kiran A., Et al., Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis, Lancet, 387, pp. 957-967, (2016); Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis, Lancet, 397, pp. 1625-1636, (2021); Whelton S.P., McEvoy J.W., Shaw L., Et al., Association of normal systolic blood pressure level with cardiovascular disease in the absence of risk factors, JAMA Cardiol, 5, pp. 1011-1018, (2020); Byrd J.B., Newby D.E., Anderson J.A., Et al., Blood pressure, heart rate, and mortality in chronic obstructive pulmonary disease: the Summit trial, Eur Heart J, 39, pp. 3128-3134, (2018); Adamsson Eryd S., Gudbjornsdottir S., Manhem K., Et al., Blood pressure and complications in individuals with type 2 diabetes and no previous cardiovascular disease: national population based cohort study, BMJ, 354, (2016); 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Ding P., Miratrix L.W., To adjust or not to adjust? Sensitivity analysis of m-bias and butterfly-bias, J Causal Inference, 3, pp. 41-57, (2014)","K. Rahimi; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, OX1 2JD, United Kingdom; email: kazem.rahimi@wrh.ox.ac.uk","","BMJ Publishing Group","","","","","","13556037","","HEARF","37080767","English","Heart","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85159450539"
"Melekoglu E.; Kocabicak U.; Uçar M.K.; Bilgin C.; Bozkurt M.R.; Cunkas M.","Melekoglu, Engin (57200447303); Kocabicak, Umit (7801661917); Uçar, Muhammed Kürşad (56779734300); Bilgin, Cahit (8967819100); Bozkurt, Mehmet Recep (48761063800); Cunkas, Mehmet (6506781340)","57200447303; 7801661917; 56779734300; 8967819100; 48761063800; 6506781340","A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence","2022","PeerJ Computer Science","8","","e1188","","","","4","10.7717/PEERJ-CS.1188","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146972066&doi=10.7717%2fPEERJ-CS.1188&partnerID=40&md5=ad00dc5f07c4b4146c1c996e328605c0","Computer Engineering, Sakarya University, Sakarya, Turkey; Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey; Faculty of Medicine, Sakarya University, Sakarya, Turkey; Electrical and Electronics Engineering, Selcuk University, Konya, Turkey","Melekoglu E., Computer Engineering, Sakarya University, Sakarya, Turkey; Kocabicak U., Computer Engineering, Sakarya University, Sakarya, Turkey; Uçar M.K., Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey; Bilgin C., Faculty of Medicine, Sakarya University, Sakarya, Turkey; Bozkurt M.R., Electrical and Electronics Engineering, Sakarya University, Sakarya, Turkey; Cunkas M., Electrical and Electronics Engineering, Selcuk University, Konya, Turkey","Background and Purpose: Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods: During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance. Results: This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase. Conclusion: According to the findings, PPG-based COPD prediction models are suitable for usage in practice © Copyright 2022 Melekoglu et al.","Chronic obstructive pulmonary disease; Machine learning algorithm; Photoplethysmography signal; Signal processing in biomedical","Biomedical signal processing; Blood; Feature extraction; Forecasting; Learning algorithms; Noninvasive medical procedures; Optical properties; Oximeters; Pulmonary diseases; Sleep research; Chronic obstructive pulmonary disease; Diagnostic methods; Hybrid artificial intelligences; Machine learning algorithms; Photoplethysmography signal; Prediction modelling; Preventive measures; Public health issues; Signal processing in biomedical; Signal-processing; Photoplethysmography","","","","","","","Akben SB, Subasi A, Kiymik MK., Comparison of artificial neural network and support vector machine classification methods in diagnosis of migraine by using EEG, (2010); Akkus Y, Karabulutlu EY, Yagci S., The effect of anxiety-depression on the cognitive status in patients with chronic obstructive diseases, The Journal ofKirikkale University Faculty of Medicine, 18, pp. 94-100, (2016); 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Lecture Notes on Data Engineering and Communications Technologies, 76, (2021); Moraes J, Rocha M, Vasconcelos G, Filho JV, de Albuquerque V, Alexandria A., Advances in photopletysmography signal analysis for biomedical applications, Sensors, 18, 6, (2018); Orenc S., Development a new method for diagnosing chronic obstructive pulmonary disease based on machine learning, (2019); Orenc S, Ucar MK, Bozkurt MR, Bilgin C., A new approach for treatment of chronic obstructive pulmonary disease, 2017, pp. 1-4, (2017); Pinto M, Marques P., Onecare spiro: mobile application for monitoring and pre-diagnosis of chronic obstructive pulmonary disease, pp. 1-4, (2017); Rasool RU, Ashraf U, Ahmed K, Wang H, Rafique W, Anwar Z., Cyberpulse: a machine learning based link flooding attack mitigation system for software defined networks, IEEE Access, 7, pp. 34885-34899, (2019); Rodrigues MB, Nobrega RVMD, Alves SSA, Filho PPR, Duarte JBF, Sangaiah AK, Albuquerque VHCD., Health of things algorithms for malignancy level classification of lung nodules, IEEE Access, 6, pp. 18592-18601, (2018); Roscher R, Bohn B, Duarte MF, Garcke J., Explainable machine learning for scientific insights and discoveries, IEEE Access, 8, pp. 42200-42216, (2020); Saguil D, Azim A., Time-efficient offloading for machine learning tasks between embedded systems and fog nodes, pp. 79-82, (2019); Sahan S, Polat K, Kodaz H, Gunes S., A new hybrid method based on fuzzy-artificial immune system and -nn algorithm for breast cancer diagnosis, Computers in Biology and Medicine, 37, 3, pp. 415-423, (2007); Sahoo AK, Pradhan C, Das H., Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making, Studies in Computational Intelligence, SCI, 871, pp. 201-212, (2020); Santos R, Moreno ED, Estombelo-Montesco C., A comparison of two embedded systems to detect electrical disturbances using decision tree algorithm, pp. 1-6, (2019); Sen E, Alpaydin AO, Gurgun A, Polatli M, Ulubay G, Baha A, Uysal FE., Chronic obstructive pulmonary disease (COPD), (2019); Shailaja K, Seetharamulu B, Jabbar MA., Machine learning in healthcare: a review, Proceedings of the 2nd International Conference on Electronics, Communication and Aerospace Technology, ICECA 2018, pp. 910-914, (2018); Tosunoglu E, Yilmaz R, Ozeren E, Saglam Z., Machine learning in education: a study on current trends in researchs, Journal of Ahmet Keleşoğlu Education Faculty, 3, 2, pp. 178-199, (2021); Ucar MK, Moran I, Altilar DT, Bilgin C, Bozkurt MR., Rule-based diagnosis of chronic obstructive pulmonary disease with electrocardiogram signal, pp. 1-5, (2018); Ucar MK, Orenc S, Bozkurt MR, Bilgin C., Evaluation of the relationship between chronic obstructive pulmonary disease and photoplethysmography signal, 2017, pp. 1-4, (2017); Ucar MK., Developing a new method for obstructive sleep apnea diagnosis based on machine learning, (2017); Ucar MK, Bozkurt MR, Bilgin C, Polat K., Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques, Neural Computing and Applications, 28, 10, pp. 2931-2945, (2017); Ucar MK, Bozkurt MR, Bilgin C, Polat K., Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques, Neural Computing and Applications, 29, 8, pp. 1-16, (2018); Ucar MK, Moran І, Altilar DT, Bilgin C, Bozkurt MR., Statistical analysis of the relationship between chronic obstructive pulmonary disease and electrocardiogram signal, Journal of Human Rhythm, 4, pp. 142-149, (2018); Ucar MK, Nour M, Sindi H, Polat K., The effect of training and testing process on machine learning in biomedical datasets, Mathematical Problems in Engineering, 2020, pp. 1-17, (2020); Ucar MK, Ucar Z, Ucar K, Akman M, Bozkurt MR., Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence, Biomedical Signal Processing and Control, 68, (2021); Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMR., Automatic 3D pulmonary nodule detection in CT images: a survey, Computer Methods and Programs in Biomedicine, 124, 1, pp. 91-107, (2016); Wallisch P, Lusignan M, Benayoun M, Baker T, Dickey A, Hatsopoulos N., Matlab for neuroscientists, (2009); Zhang L, Tan J, Han D, Zhu H., From machine learning to deep learning: progress in machine intelligence for rational drug discovery, Drug Discovery Today, 22, 11, pp. 1680-1685, (2017); Zubaydi F, Sagahyroon A, Aloul F, Mir H., Mobspiro: mobile based spirometry for detecting COPD, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), pp. 1-4, (2017)","M. Cunkas; Electrical and Electronics Engineering, Selcuk University, Konya, Turkey; email: mcunkas@selcuk.edu.tr","","PeerJ Inc.","","","","","","23765992","","","","English","PeerJ Comput. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85146972066"
"Oo M.M.; Gao C.; Cole C.; Hummel Y.; Guignard-Duff M.; Jefferson E.; Hare J.; Voors A.A.; de Boer R.A.; Lam C.S.P.; Mordi I.R.; Tromp J.; Lang C.C.","Oo, Mon Myat (57221750450); Gao, Chuang (57286822300); Cole, Christian (7202468472); Hummel, Yoran (24558671100); Guignard-Duff, Magalie (59011812400); Jefferson, Emily (56203564900); Hare, James (59012296400); Voors, Adriaan A. (7006380706); de Boer, Rudolf A. (57202731186); Lam, Carolyn S.P. (19934204100); Mordi, Ify R. (54407277900); Tromp, Jasper (56217915300); Lang, Chim C. (7402002432)","57221750450; 57286822300; 7202468472; 24558671100; 59011812400; 56203564900; 59012296400; 7006380706; 57202731186; 19934204100; 54407277900; 56217915300; 7402002432","Artificial intelligence-assisted automated heart failure detection and classification from electronic health records","2024","ESC Heart Failure","11","5","","2769","2777","8","4","10.1002/ehf2.14828","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191949110&doi=10.1002%2fehf2.14828&partnerID=40&md5=5c367881b52751d71806a823789b5619","Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom; Health Informatics Centre, School of Medicine, University of Dundee, Dundee, United Kingdom; Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom; Us2.ai, Singapore; University Medical Centre Groningen, Groningen, Netherlands; Department of Cardiology, Erasmus University Medical Centre, Rotterdam, Netherlands; National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore; Saw Swee Hock School of Public Health, National University of Singapore & National University Health System, Singapore","Oo M.M., Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom; Gao C., Health Informatics Centre, School of Medicine, University of Dundee, Dundee, United Kingdom; Cole C., Health Informatics Centre, School of Medicine, University of Dundee, Dundee, United Kingdom, Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom; Hummel Y., Us2.ai, Singapore; Guignard-Duff M., Health Informatics Centre, School of Medicine, University of Dundee, Dundee, United Kingdom; Jefferson E., Health Informatics Centre, School of Medicine, University of Dundee, Dundee, United Kingdom, Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom; Hare J., Us2.ai, Singapore; Voors A.A., University Medical Centre Groningen, Groningen, Netherlands; de Boer R.A., Department of Cardiology, Erasmus University Medical Centre, Rotterdam, Netherlands; Lam C.S.P., National Heart Centre Singapore, Singapore, Duke-NUS Medical School, Singapore; Mordi I.R., Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom; Tromp J., National Heart Centre Singapore, Singapore, Duke-NUS Medical School, Singapore, Saw Swee Hock School of Public Health, National University of Singapore & National University Health System, Singapore; Lang C.C., Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom","Aims: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. Methods and results: We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. Conclusions: Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes. © 2024 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.","Deep learning algorithms; Electronic health record data; Epidemiology; Heart failure; Preserved ejection fraction; Validation","Aged; Algorithms; Artificial Intelligence; Deep Learning; Echocardiography; Electronic Health Records; Female; Heart Failure; Humans; Male; Middle Aged; Retrospective Studies; Scotland; Stroke Volume; biological marker; adult; aged; algorithm; Article; artificial intelligence; automation; body mass; controlled study; echocardiography; electronic health record; electronic prescribing; female; heart catheterization; heart ejection fraction; heart failure; heart failure with preserved ejection fraction; heart failure with reduced ejection fraction; heart function; heart left ventricle ejection fraction; heart left ventricle enddiastolic volume; heart muscle biopsy; hospitalization; human; ICD-10; information processing; intelligence; machine learning; major clinical study; male; middle aged; mortality; patient care; predictive value; prescription; prevalence; sensitivity and specificity; sinus rhythm; systolic blood pressure; three dimensional echocardiography; tissue Doppler imaging; transthoracic echocardiography; tricuspid annular plane systolic excursion; two dimensional speckle tracking echocardiography; blood; classification; clinical trial; deep learning; diagnosis; epidemiology; heart failure; heart stroke volume; multicenter study; pathophysiology; physiology; procedures; retrospective study; Scotland","","","","","","","Lam C.S.P., Voors A.A., de Boer R.A., Solomon S.D., van Veldhuisen D.J., Heart failure with preserved ejection fraction: from mechanisms to therapies, Eur Heart J, 39, pp. 2780-2792, (2018); McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Bohm M., Et al., Corrigendum to: 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC, Eur Heart J, 42, (2021); Heidenreich P.A., Bozkurt B., Aguilar D., Allen L.A., Byun J.J., Colvin M.M., Et al., 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines, Circulation, 145, pp. e895-e1032, (2022); Hebert H.L., Shepherd B., Milburn K., Veluchamy A., Meng W., Carr F., Et al., Cohort profile: Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), Int J Epidemiol, 47, pp. 380-381j, (2018); Jones W.S., Mulder H., Wruck L.M., Pencina M.J., Kripalani S., Munoz D., Et al., Comparative effectiveness of aspirin dosing in cardiovascular disease, N Engl J Med, 384, pp. 1981-1990, (2021); Hemingway H., Asselbergs F.W., Danesh J., Dobson R., Maniadakis N., Maggioni A., Et al., Big data from electronic health records for early and late translational cardiovascular research: challenges and potential, Eur Heart J, 39, pp. 1481-1495, (2018); Bowton E., Field J.R., Wang S., Schildcrout J.S., van Driest S.L., Delaney J.T., Et al., Biobanks and electronic medical records: enabling cost-effective research, Sci Transl Med, 6, (2014); Allen N.E., Sudlow C., Peakman T., Collins R., UK biobank data: come and get it, Sci Transl Med, 6, (2014); McKinstry B., Sullivan F.M., Vasishta S., Armstrong R., Hanley J., Haughney J., Et al., Cohort profile: the Scottish research register SHARE. A register of people interested in research participation linked to NHS data sets, BMJ Open, 7, (2017); Nind T., Sutherland J., McAllister G., Hardy D., Hume A., MacLeod R., Et al., An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population, Gigascience, 9, (2020); Tromp J., Seekings P.J., Hung C.L., Iversen M.B., Frost M.J., Ouwerkerk W., Et al., Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study, Lancet Digit Health, 4, pp. e46-e54, (2022); Tromp J., Bauer D., Claggett B.L., Frost M., Iversen M.B., Prasad N., Et al., A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram, Nat Commun, 13, (2022); Zhang J., Gajjala S., Agrawal P., Tison G.H., Hallock L.A., Beussink-Nelson L., Et al., Fully automated echocardiogram interpretation in clinical practice, Circulation, 138, pp. 1623-1635, (2018); Ouyang D., He B., Ghorbani A., Yuan N., Ebinger J., Langlotz C.P., Et al., Video-based AI for beat-to-beat assessment of cardiac function, Nature, 580, pp. 252-256, (2020); Gao C., McGilchrist M., Mumtaz S., Hall C., Anderson L.A., Zurowski J., Et al., A national network of safe havens: Scottish perspective, J Med Internet Res, 24, (2022); Morris A.D., Boyle D.I., MacAlpine R., Emslie-Smith A., Jung R.T., Newton R.W., Et al., The Diabetes Audit and Research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO collaboration, BMJ, 315, pp. 524-528, (1997); Diagnosis of DM, (2019); Moore C., Enhancing the identification of atrial fibrillation using prescribing records in electronic medical record research, European Journal of Arrythmia & Electrophysiology e-journal, 8, (2022); Dicker A.J., Crichton M.L., Pumphrey E.G., Cassidy A.J., Suarez-Cuartin G., Sibila O., Et al., Neutrophil extracellular traps are associated with disease severity and microbiota diversity in patients with chronic obstructive pulmonary disease, J Allergy Clin Immunol, 141, pp. 117-127, (2018); NHS Data Model and Dictionary; Chronic obstructive pulmonary disease; Forbes A., Gallagher H., Chronic kidney disease in adults: assessment and management, Clin Med (Lond), 20, pp. 128-132, (2020); Voors A.A., Anker S.D., Cleland J.G., Dickstein K., Filippatos G., van der Harst P., Et al., A systems BIOlogy study to TAilored treatment in chronic heart failure: rationale, design, and baseline characteristics of BIOSTAT-CHF, Eur J Heart Fail, 18, pp. 716-726, (2016); Tromp J., Khan M.A.F., Mentz R.J., O'Connor C.M., Metra M., Dittrich H.C., Et al., Biomarker profiles of acute heart failure patients with a mid-range ejection fraction, JACC Heart Fail, 5, pp. 507-517, (2017); Tromp J.E.A., A prospective validation of a deep learning-based automated workflow for the interpretation of the echocardiogram, in nature communications, (2022); FDA clears fully automated cardiac ultrasound solution to measure 2D and Doppler, (2022); Suthahar N., Meijers W.C., Ho J.E., Gansevoort R.T., Voors A.A., van der Meer P., Et al., Sex-specific associations of obesity and N-terminal pro-B-type natriuretic peptide levels in the general population, Eur J Heart Fail, 20, pp. 1205-1214, (2018); Stalhammar J., Stern L., Linder R., Sherman S., Parikh R., Ariely R., Et al., The burden of preserved ejection fraction heart failure in a real-world Swedish patient population, J Med Econ, 17, pp. 43-51, (2014); Boman K., Lindmark K., Stalhammar J., Olofsson M., Costa-Scharplatz M., Fonseca A.F., Et al., Healthcare resource utilisation and costs associated with a heart failure diagnosis: a retrospective, population-based cohort study in Sweden, BMJ Open, 11, (2021); Mahesri M., Chin K., Kumar A., Barve A., Studer R., Lahoz R., Et al., External validation of a claims-based model to predict left ventricular ejection fraction class in patients with heart failure, PLoS ONE, 16, (2021); Goyal P., Bose B., Creber R.M., Krishnan U., Yang M., Brady J., Et al., Performance of electronic health record diagnosis codes for ambulatory heart failure encounters, J Card Fail, 26, pp. 1060-1066, (2020); Jonnalagadda S.R., Adupa A.K., Garg R.P., Corona-Cox J., Shah S.J., Text mining of the electronic health record: an information extraction approach for automated identification and subphenotyping of HFpEF patients for clinical trials, J Cardiovasc Transl Res, 10, pp. 313-321, (2017); Patel Y.R., Robbins J.M., Kurgansky K.E., Imran T., Orkaby A.R., McLean R.R., Et al., Development and validation of a heart failure with preserved ejection fraction cohort using electronic medical records, BMC Cardiovasc Disord, 18, (2018)","C.C. Lang; Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom; email: monmyat85@gmail.com; J. Tromp; National Heart Centre Singapore, Singapore; email: jasper_tromp@nus.edu.sg","","John Wiley and Sons Inc","","","","","","20555822","","","38700133","English","ESC Heart Fail.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191949110"
"Yin H.; Wang K.; Yang R.; Tan Y.; Li Q.; Zhu W.; Sung S.","Yin, Huiming (57214729613); Wang, Kun (55537784200); Yang, Ruyu (58085031400); Tan, Yanfang (57210147345); Li, Qiang (57219134438); Zhu, Wei (58085224200); Sung, Suzi (58085127900)","57214729613; 55537784200; 58085031400; 57210147345; 57219134438; 58085224200; 58085127900","A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients","2024","Computer Methods and Programs in Biomedicine","246","","108005","","","","4","10.1016/j.cmpb.2023.108005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184838963&doi=10.1016%2fj.cmpb.2023.108005&partnerID=40&md5=195e46fed81134b10d336ede8a75c8f2","Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua, 418000, China; Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University of Medicine, Shanghai, 200120, China; Wuxi Chic Health Technology Co., Ltd, China","Yin H., Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua, 418000, China; Wang K., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University of Medicine, Shanghai, 200120, China; Yang R., Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua, 418000, China; Tan Y., Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua, 418000, China; Li Q., Department of Pulmonary and Critical Care Medicine, Shanghai East Hospital, Tongji University of Medicine, Shanghai, 200120, China; Zhu W., Wuxi Chic Health Technology Co., Ltd, China; Sung S., Wuxi Chic Health Technology Co., Ltd, China","Purpose: This study utilized intelligent devices to remotely monitor patients with chronic obstructive pulmonary disease (COPD), aiming to construct and evaluate machine learning (ML) models that predict the probability of acute exacerbations of COPD (AECOPD). Methods: Patients diagnosed with COPD Group C/D at our hospital between March 2019 and June 2021 were enrolled in this study. The diagnosis of COPD Group C/D and AECOPD was based on the GOLD 2018 guidelines. We developed a series of machine learning (ML)-based models, including XGBoost, LightGBM, and CatBoost, to predict AECOPD events. These models utilized data collected from portable spirometers and electronic stethoscopes within a five-day time window. The area under the ROC curve (AUC) was used to assess the effectiveness of the models. Results: A total of 66 patients were enrolled in COPD groups C/D, with 32 in group C and 34 in group D. Using observational data within a five-day time window, the ML models effectively predict AECOPD events, achieving high AUC scores. Among these models, the CatBoost model exhibited superior performance, boasting the highest AUC score (0.9721, 95 % CI: 0.9623–0.9810). Notably, the boosting tree methods significantly outperformed the time-series based methods, thanks to our feature engineering efforts. A post-hoc analysis of the CatBoost model reveals that features extracted from the electronic stethoscope (e.g., max/min vibration energy) hold more importance than those from the portable spirometer. Conclusions: The tree-based boosting models prove to be effective in predicting AECOPD events in our study. Consequently, these models have the potential to enhance remote monitoring, enable early risk assessment, and inform treatment decisions for homebound patients with chronic COPD. © 2023","Acute exacerbation of chronic obstructive pulmonary disease; CatBoost; Machine learning; Predictive models","Disease Progression; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; Risk Assessment; Diagnosis; Forecasting; Patient monitoring; Patient treatment; Pulmonary diseases; Remote control; Risk assessment; Vibration analysis; Acute exacerbation of chronic obstructive pulmonary disease; Acute exacerbations; Area under the ROC curve; Catboost; Chronic obstructive pulmonary disease; Electronic stethoscope; Machine learning models; Machine-learning; Portable spirometers; Predictive models; abnormal respiratory sound; Article; chronic obstructive lung disease; clinical effectiveness; controlled study; cross validation; diagnostic test accuracy study; disease exacerbation; follow up; homebound patient; human; lung function; machine learning; major clinical study; observational study; post hoc analysis; practice guideline; prediction; predictive model; prospective study; receiver operating characteristic; remote sensing; sensitivity and specificity; spirometry; validation process; disease exacerbation; machine learning; risk assessment; Machine learning","","","","","Hunan provincial natural science fund subject, (16A152, 2020JJ4454); Innovation Platform and Talent Project of Hunan Province Department of Science and Technology, (2018N2202, 2018SK4006); Key Research Project of Hunan Province Department of Education; Science and Technology Plan Project of Huaihua City, (2021R3107); Science and Technology Program of Huaihua of China; National Key Research and Development Program of China, NKRDPC, (2018YFC1313700, 2018YFC1313705)","Funding text 1: This work was supported by National Key R&D Program (grant number: 2018YFC1313700 ), Key Research Project of Hunan Province Department of Education (grant number: 16A152 ), Innovation Platform and Talent Project of Hunan Province Department of Science and Technology (grant number: 2018SK4006 ), Technology Project of Huaihua (grant number: 2018N2202 ), and Hunan provincial natural science fund subject (grant number: 2020JJ4454) for Huiming Yin; Science and Technology Program of Huaihua of China ( 2021R3107 ).; Funding text 2: National Key Research and Development Program of China ( 2018YFC1313705 ); Science and Technology Plan Project of Huaihua City ( 2021R3107 ); National Key R&D Program (grant number: 2018YFC1313700 ), Key Research Project of Hunan Province Department of Education (grant number: 16A152 ), Innovation Platform and Talent Project of Hunan Province Department of Science and Technology (grant number: 2018SK4006 ), Technology Project of Huaihua (grant number: 2018N2202 ), and Hunan provincial natural science fund subject (grant number: 2020JJ4454 ) for Huiming Yin. ; Funding text 3: Foundations: National Key Research and Development Program of China (2018YFC1313705); Science and Technology Plan Project of Huaihua City (2021R3107). ","Singh D., Agusti A., Anzueto A., Barnes P.J., Bourbeau J., Celli B.R., Criner G.J., Frith P., Halpin D.M.G., Han M., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the GOLD science committee report 2019, Eur. Respir. J., 53, (2019); Celli B.R., Fabbri L.M., Aaron S.D., Agusti A., Brook R., Criner G.J., Franssen F.M.E., Humbert M., Hurst J.R., O'Donnell D., Et al., An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations: the Rome proposal, Am. J. Respir. Crit. Care Med., 204, pp. 1251-1258, (2021); Bollmeier S.G., Hartmann A.P., Management of chronic obstructive pulmonary disease: a review focusing on exacerbations, Am. J. Health Syst. Pharm., 77, 4, pp. 259-268, (2020); Wilkinson T.M.A., Donaldson G.C., Hurst J.R., Et al., Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 169, 12, pp. 1298-1303, (2004); Mohan A., Sethi S., The reliability and validity of patient-reported chronic obstructive pulmonary disease exacerbations, Curr. Opin. Pulm. Med., 20, 2, pp. 146-152, (2014); Aaron S.D., Donaldson G.C., Whitmore G.A., Et al., Time course and pattern of COPD exacerbation onset, Thorax, 67, 3, pp. 238-243, (2012); Trappenburg J., Touwen I., Oene G., Et al., Detecting exacerbations using the clinical COPD questionnaire, Health Qual. Life Outcomes, 8, (2010); Mackay A.J., Donaldson G.C., Patel A.R., Et al., Detecting and severity grading of COPD exacerbations using the exacerbations of chronic obstructive pulmonary disease tool (EXACT), Eur. Respir. J., 43, 3, pp. 735-744, (2014); Sanchez-Morillo D., Fernandez-Granero M.A., Jimenez A.L., Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study, Med. Biol. Eng. Comput., 53, 5, pp. 441-451, (2015); Seemungal T.A., Donaldson G.C., Bhowmik A., Jeffries D.J., Wedzicha J.A., Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 161, pp. 1608-1613, (2000); Cooper C.B., Sirichana W., Arnold M.T., Et al., Remote patient monitoring for the detection of COPD exacerbations, Int. J. Chronic Obstr. Pulm. Dis., 15, pp. 2005-2013, (2020); Jacome C., Oliveira A., Marques A., Computerized respiratory sounds: a comparison between patients with stable and exacerbated COPD, Clin. Respir. J., 11, 5, pp. 612-620, (2017); Patel N., Kinmond K., Jones P., Birks P., Spiteri M.A., Validation of COPDPredictTM: unique combination of remote monitoring and exacerbation prediction to support preventative management of COPD exacerbations, Int. J. Chronic Obstr. Pulm. 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Big Data, 7, pp. 1-45, (2020); Schober P., Vetter T.R., Logistic regression in medical research, Anesth. Analg., 132, pp. 365-366, (2021); Larose D.T., Discovering Knowledge in Data: An Introduction to Data Mining, pp. 174-179, (2014); Dempster A., Petitjean F., Webb G.I., ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels, Data Min. Knowl. Discov., 34, pp. 1454-1495, (2020); Lines J., Taylor S.L., Bagnall A., HIVE-COTE: the hierarchical vote collective of transformation-based ensembles for time series classification, Proceedings of the IEEE 16th International Conference on Data Mining (ICDM), pp. 1041-1046, (2016); Middlehurst M., Large J., Flynn M., Lines J., Bostrom A.G., Bagnall A., HIVE-COTE 2.0: a new meta ensemble for time series classification, Mach. Learn., 110, pp. 3211-3243, (2021); Lundberg S.M., Lee S., A Unified Approach to Interpreting Model Predictions, Neural Information Processing Systems, (2017); Pinnock H., Steed L., Jordan R., Supported self-management for COPD: making progress, but there are still challenges, Eur. Respir. J., 48, 1, pp. 6-9, (2016); Bischoff E.W., Hamd D.H., Sedeno M., Et al., Effects of written action plan adherence on COPD exacerbation recovery, Thorax, 66, 1, pp. 26-31, (2011); MG Halpin D., Laing-Morton T., Spedding S., Levy M.L., Coyle P., Lewis J., Newbold P., Marno P., A randomised controlled trial of the effect of automated interactive calling combined with a health risk forecast on frequency and severity of exacerbations of COPD assessed clinically and using EXACT PRO, Primary Care Respiratory Journal, 20, 3, pp. 324-331, (2011); Adler A.I., Painsky A., Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection, Entropy, 24, (2021)","R. Yang; Department of Pulmonary and Critical Care Medicine, First Affiliated Hospital, Hunan University of Medicine, Huaihua, 418000, China; email: hnyyxykjc@sina.com","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","38354578","English","Comput. Methods Programs Biomed.","Article","Final","","Scopus","2-s2.0-85184838963"
"Ahuja Y.; Wen J.; Hong C.; Xia Z.; Huang S.; Cai T.","Ahuja, Yuri (57203727969); Wen, Jun (57205543351); Hong, Chuan (55945588500); Xia, Zongqi (36005587400); Huang, Sicong (57210555409); Cai, Tianxi (7102610149)","57203727969; 57205543351; 55945588500; 36005587400; 57210555409; 7102610149","A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record","2022","Scientific Reports","12","1","17737","","","","4","10.1038/s41598-022-22585-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140345150&doi=10.1038%2fs41598-022-22585-3&partnerID=40&md5=ac32d094feaa85fb9e7467cf06071494","Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Medicine, NYU Langone Health, New York, NY, United States; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States; Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, United States; VA Boston Healthcare System, Boston, MA, United States","Ahuja Y., Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, United States, Harvard Medical School, Boston, MA, United States, Department of Medicine, NYU Langone Health, New York, NY, United States; Wen J., Harvard Medical School, Boston, MA, United States; Hong C., Harvard Medical School, Boston, MA, United States; Xia Z., Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States; Huang S., Harvard Medical School, Boston, MA, United States, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, United States, VA Boston Healthcare System, Boston, MA, United States; Cai T., Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, United States, Harvard Medical School, Boston, MA, United States, VA Boston Healthcare System, Boston, MA, United States","While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP’s feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50–100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research. © 2022, The Author(s).","","Algorithms; Electronic Health Records; Humans; Markov Chains; Phenotype; Pulmonary Disease, Chronic Obstructive; Supervised Machine Learning; algorithm; article; electronic health record; embedding; feature selection; Markov chain; medical record review; phenotype; prediction; process model; risk assessment; semi supervised machine learning; simulation; algorithm; chronic obstructive lung disease; human; phenotype; supervised machine learning","","","","","National Institutes of Health, NIH, (R21-CA242940, T32-AR05588512, T32-GM7489714); National Institute of Neurological Disorders and Stroke, NINDS, (R01NS098023); Office of Extramural Research, National Institutes of Health, OER; Office of Research Infrastructure Programs, National Institutes of Health, ORIP, NIH, NIH-ORIP, ORIP","This work was supported by the U.S. National Institutes of Health Grants T32-AR05588512, T32-GM7489714, and R21-CA242940. ","Kohane I.S., Churchill S.E., Murphy S.N., A translational engine at the national scale: Informatics for integrating biology and the bedside, J. Am. Med. Inform. Assoc., 19, pp. 181-185, (2012); Hripcsak G., Albers D.J., Next-generation phenotyping of electronic health records, J. Am. Med. Inform. Assoc., 20, pp. 117-121, (2012); Miotto R., Li L., Kidd B.A., Dudley J.T., Deep patient: An unsupervised representation to predict the future of patients from the electronic health records, Sci. Rep., 6, (2016); Liao K.P., Et al., Electronic medical records for discovery research in rheumatoid arthritis, Arthritis Care Res., 62, pp. 1120-1127, (2010); Cipparone C.W., Et al., Inaccuracy of ICD-9 codes for chronic kidney disease: A study from two practice-based research networks (PBRNs), J. Am. Board Fam. Med., 28, pp. 678-682, (2015); Uno H., Et al., Determining the time of cancer recurrence using claims or electronic medical record data, JCO Clin. 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Inform., 58, pp. 156-165, (2015); Pivovarov R., Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data, (2016); Jackson C.H., Sharples L.D., Thompson S.G., Duffy S.W., Couto E., Multistate Markov models for disease progression with classification error, Stat., 52, pp. 193-209, (2003); Sukkar R., Katz E., Zhang Y., Raunig D., Wyman B.T., Disease progression modeling using Hidden Markov Models, In Conf Proc IEEE Eng Med Biol Soc, pp. 2845-2848, (2012); Wang X., Sontag D., Wang F., Unsupervised learning of disease progression models, In Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 85-94, (2014); Zhou X., Kang K., Song X., Two-part hidden Markov models for semicontinuous longitudinal data with nonignorable missing covariates, Stat. Med., 39, pp. 1801-1816, (2020); Yu S., Et al., Surrogate-assisted feature extraction for high-throughput phenotyping, J. Am. Med. Inform. Assoc., 24, pp. e143-e149, (2017); Barnardo A., Casey C., Carroll R.J., Wheless L., Denny J.C.C.L., Developing electronic health record algorithms that accurately identify patients with systemic lupus erythematosus, Arthritis Care Res., 69, pp. 687-693, (2017); Denny J.C., Et al., Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data, Nat. Biotechnol., 31, pp. 1102-1111, (2013); Yu S., Cai T., Cai T.N.I.L.E., Fast natural language processing for electronic health records, Arxiv 1–23, (2013); Cai T., Et al., Association of interleukin 6 receptor variant with cardiovascular disease effects of interleukin 6 receptor blocking therapy: A phenome—Wide association study, JAMA Cardiol., 3, pp. 849-857, (2018); Lin C., Et al., Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records, PLoS ONE, 8, (2013); Li R., Et al., Detection of bleeding events in electronic health record notes using convolutional neural network models enhanced with recurrent neural network autoencoders: Deep learning approach, J. Med. Internet Res., 21, pp. 1-10, (2019); Yang Z., Dehmer M., Yli-Harja O., Emmert-Streib F., Combining deep learning with token selection for patient phenotyping from electronic health records, Sci. Rep., 10, pp. 1-18, (2020); Sun Z., Et al., A probabilistic disease progression modeling approach and its application to integrated Huntington’s disease observational data, JAMA Open, 2, pp. 123-130, (2019); Verma A., Powell G., Luo Y., Stephens D., Buckeridge D.L., Modeling Disease Progression in Longitudinal EHR Data Using Continuous-Time Hidden Markov Models, pp. 1-5, (2018); Castro V.M., Et al., Validation of electronic health record phenotyping of bipolar disorder and controls, Am. J. Psychiatry, 172, pp. 363-372, (2015); Anderson A.E., Et al., Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study, J. Biomed. Inform., 60, pp. 160-168, (2016); Garg R., Dong S., Shah S., Jonnalagadda S.R., A Bootstrap Machine Learning Approach to Identify Rare Disease Patients from Electronic Health Records Division of Health and Biomedical Informatics, (2016); Teixeira P.L., Et al., Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals, J. Am. Med. Inform. Assoc., 24, pp. 162-171, (2017); Yang S., Et al., Early detection of disease using electronic health records and fisher’s wishart discriminant analysis, Proc. Comput. Sci., 140, pp. 393-402, (2018)","Y. Ahuja; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 677 Huntington Ave, 02115, United States; email: yuri_ahuja@hms.harvard.edu","","Nature Research","","","","","","20452322","","","36273240","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140345150"
"Imtiaz A.; King J.; Holmes S.; Gupta A.; Bafadhel M.; Melcher M.L.; Hurst J.R.; Farewell D.; Bolton C.E.; Duckers J.","Imtiaz, Arouba (57579355000); King, Joanne (57224535896); Holmes, Steve (55987725800); Gupta, Ayushman (57204666597); Bafadhel, Mona (35336030900); Melcher, Marc L. (35311260300); Hurst, John R. (57201513306); Farewell, Daniel (22134450900); Bolton, Charlotte E. (7103238077); Duckers, Jamie (6507429299)","57579355000; 57224535896; 55987725800; 57204666597; 35336030900; 35311260300; 57201513306; 22134450900; 7103238077; 6507429299","ChatGPT versus Bing: a clinician assessment of the accuracy of AI platforms when responding to COPD questions","2024","European Respiratory Journal","63","6","2400163","","","","3","10.1183/13993003.00163-2024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196918940&doi=10.1183%2f13993003.00163-2024&partnerID=40&md5=784c8c1a8dc42b40f60d9ffdc95af604","University Hospital of Wales, Cardiff, United Kingdom; Respiratory Department, Frimley Health NHS Foundation Trust, Frimley, United Kingdom; General Practitioner, The Park Medical Practice, Shepton Mallet, United Kingdom; Respiratory Department, Nottingham University Hospital NHS Trust, Nottingham, United Kingdom; King’s Centre for Lung Health, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom; Department of Surgery, Stanford University, Stanford, CA, United States; UCL Respiratory, University College London, London, United Kingdom; Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, United Kingdom; Centre for Respiratory Research, NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; All Wales Adult Cystic Fibrosis Centre, United Kingdom","Imtiaz A., University Hospital of Wales, Cardiff, United Kingdom; King J., Respiratory Department, Frimley Health NHS Foundation Trust, Frimley, United Kingdom; Holmes S., General Practitioner, The Park Medical Practice, Shepton Mallet, United Kingdom; Gupta A., Respiratory Department, Nottingham University Hospital NHS Trust, Nottingham, United Kingdom; Bafadhel M., King’s Centre for Lung Health, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom; Melcher M.L., Department of Surgery, Stanford University, Stanford, CA, United States; Hurst J.R., UCL Respiratory, University College London, London, United Kingdom; Farewell D., Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, United Kingdom; Bolton C.E., Centre for Respiratory Research, NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom; Duckers J., All Wales Adult Cystic Fibrosis Centre, United Kingdom","[No abstract available]","","Artificial Intelligence; Female; Humans; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Surveys and Questionnaires; beta adrenergic receptor stimulating agent; corticosteroid; muscarinic agent; tobacco smoke; anxiety; Article; artificial intelligence; cardiovascular disease; ChatGPT; chronic obstructive lung disease; clinical assessment; clinical decision support system; clinician assessment; controlled study; diagnostic accuracy; diagnostic test accuracy study; dyspnea; human; hypercapnic respiratory failure; lung injury; morbidity; mortality; oxygen therapy; particulate matter; prognosis; questionnaire; respiratory failure; search engine; smoking; spirometry; tobacco; wheezing; artificial intelligence; diagnosis; female; male; middle aged; therapy","","","ChatGPT version 3.5; Microsoft Excel, Microsoft","Microsoft","","","Christenson SA, Smith BM, Bafadhel M, Et al., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Fang Y, Shepherd TA, Smith HE., Examining the trends in online health information–seeking behavior about chronic obstructive pulmonary disease in Singapore: analysis of data from Google Trends and the global burden of disease study, J Med Internet Res, 23, (2021); Strzelecki A., Google medical update: why is the search engine decreasing visibility of health and medical information websites?, Int J Environ Res Public Health, 17, (2020); Open AI., ChatGPT; Your AI-powered Copilot for the Web: Microsoft Bing; Ayoub N, Lee Y, Grimm DR, Et al., Head-to-head comparison of ChatGPT versus Google search for medical knowledge acquisition, Otolaryngol Head Neck Surg, 170, pp. 1484-1491, (2023); Nov O, Singh N, Mann D., Putting ChatGPT’s medical advice to the (Turing) test, JMIR Med Educ, 9, (2023); Flesch Kincaid Calculator; Wei Q, Yao Z, Ying C, Et al., Evaluation of ChatGPT-generated medical responses: a systematic review and meta-analysis; Sarraju A, Bruemmer D, Van EH, Et al., Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model, JAMA, 329, pp. 842-842, (2023); Topalovic M, Das N, Burgel P-R, Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur Respir J, 53, (2019); Hernandez R, Rodriguez R, Villalva O, Et al., Exploratory study of a risk prediction artificial intelligence model to diagnose asthma and COPD in Mexico, Eur Respir J, 62, (2023)","A. Imtiaz; University Hospital of Wales, Cardiff, United Kingdom; email: Arouba.Imtiaz2@wales.nhs.uk","","European Respiratory Society","","","","","","09031936","","ERJOE","38811043","English","Eur. Respir. J.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85196918940"
"Drug V.-L.; Antoniu S.; Bărboi O.-B.; Arghir O.C.; Băncilă I.; Bățagă S.; Brisc C.; Cijevschi-Prelipcean C.; Ciocîrlan M.; Ciortescu I.; David L.; Deleanu O.C.; Diculescu M.; Dimitriu A.; Dobru D.; Dumitru E.; Gheonea D.I.; Gheorghe C.; Goldiș A.; Jinga M.; Man M.; Mateescu B.; Mănuc M.; Mihai C.; Mihălțan F.; Mihăescu T.; Nedelcu L.; Negreanu L.; Pop C.-M.; Râjnoveanu R.; Săftoiu A.; Seicean A.; Sporea I.; Stanciu C.; Surdea-Blaga T.; Tanțău M.; Todea D.; Trifan A.-V.; Ulmeanu R.; Iov D.-E.; Dumitrașcu D.-L.","Drug, Vasile-Liviu (14025541700); Antoniu, Sabina (9638693500); Bărboi, Oana-Bogdana (55816894300); Arghir, Oana Cristina (6504228037); Băncilă, Ion (6505853542); Bățagă, Simona (15053024600); Brisc, Ciprian (8718779100); Cijevschi-Prelipcean, Cristina (6506288608); Ciocîrlan, Mihai (10641890300); Ciortescu, Irina (6505977921); David, Liliana (14024185100); Deleanu, Oana Claudia (37065885900); Diculescu, Mircea (6701535275); Dimitriu, Anca (57162939300); Dobru, Daniela (25229743100); Dumitru, Eugen (6701346794); Gheonea, Dan Ionuț (14822007800); Gheorghe, Cristian (7006715178); Goldiș, Adrian (6508102662); Jinga, Mariana (6505940985); Man, Milena (7007001531); Mateescu, Bogdan (6505649122); Mănuc, Mircea (6603137131); Mihai, Cătălina (7003844295); Mihălțan, Florin (57208839454); Mihăescu, Traian (23474535700); Nedelcu, Laurențiu (26425166300); Negreanu, Lucian (24171743800); Pop, Carmen-Monica (7003660718); Râjnoveanu, Ruxandra (8687963600); Săftoiu, Adrian (35566235500); Seicean, Andrada (6602552704); Sporea, Ioan (6602915768); Stanciu, Carol (7103136599); Surdea-Blaga, Teodora (36083675400); Tanțău, Marcel (6603073130); Todea, Doina (25230838000); Trifan, Anca-Victorița (6701906832); Ulmeanu, Ruxandra (6701714089); Iov, Diana-Elena (57542396100); Dumitrașcu, Dan-Lucian (7005124531)","14025541700; 9638693500; 55816894300; 6504228037; 6505853542; 15053024600; 8718779100; 6506288608; 10641890300; 6505977921; 14024185100; 37065885900; 6701535275; 57162939300; 25229743100; 6701346794; 14822007800; 7006715178; 6508102662; 6505940985; 7007001531; 6505649122; 6603137131; 7003844295; 57208839454; 23474535700; 26425166300; 24171743800; 7003660718; 8687963600; 35566235500; 6602552704; 6602915768; 7103136599; 36083675400; 6603073130; 25230838000; 6701906832; 6701714089; 57542396100; 7005124531","Romanian Guidelines for the Diagnosis and Treatment of GERDinduced Respiratory Manifestations","2022","Journal of Gastrointestinal and Liver Diseases","31","1","","119","142","23","4","10.15403/jgld-4196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126879765&doi=10.15403%2fjgld-4196&partnerID=40&md5=a8864dc44688e102f1d092bbe8a8bb21","Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania; Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Pneumology Hospital, Iasi, Romania; Faculty of Medicine, Ovidius University, Constanța, Romania; Pneumology Hospital, Constanta, Romania; Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; George E. Palade University of Medicine, Pharmacy, Sciences and Technology, Targu-Mures, Romania; County Emergency Clinical Hospital, Targu-Mures, Romania; University of Oradea, Faculty of Medicine and Pharmacy, Oradea, Romania; Clinical Hospital, Oradea, Romania; Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania; 2ndDepartment of Internal Medicine, Cluj County Emergency Clinical Hospital, Cluj-Napoca, Romania; Marius Nasta Institute of Pneumology, Bucharest, Romania; Saint Apostol Andrei Hospital, Constanta, Romania; University of Medicine and Pharmacy Craiova, Romania; County Emergency Hospital, Craiova, Romania; Department of Gastroenterology and Hepatology, Victor Babeș University of Medicine and Pharmacy, Timișoara, Romania; Dr. Carol Davila Central University Emergency Military Hospital, Bucharest, Romania; Leon Daniello Pneumology Hospital, Cluj-Napoca, Romania; Department of Gastroenterology, Colentina Clinical Hospital, Bucharest, Romania; Faculty of Medicine, Transilvania University, Brasov, Romania; Clinical Hospital, Brasov, Romania; 2nd Department of Gastroenterology, Emergency University Hospital, Bucharest, Romania; Prof. Dr. Octavian Fodor Institute of Gastroenterology and Hepatology, Cluj-Napoca, Romania","Drug V.-L., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Antoniu S., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Pneumology Hospital, Iasi, Romania; Bărboi O.-B., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Arghir O.C., Faculty of Medicine, Ovidius University, Constanța, Romania, Pneumology Hospital, Constanta, Romania; Băncilă I., Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; Bățagă S., George E. Palade University of Medicine, Pharmacy, Sciences and Technology, Targu-Mures, Romania, County Emergency Clinical Hospital, Targu-Mures, Romania; Brisc C., University of Oradea, Faculty of Medicine and Pharmacy, Oradea, Romania, Clinical Hospital, Oradea, Romania; Cijevschi-Prelipcean C., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Ciocîrlan M., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; Ciortescu I., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; David L., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, 2ndDepartment of Internal Medicine, Cluj County Emergency Clinical Hospital, Cluj-Napoca, Romania; Deleanu O.C., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Marius Nasta Institute of Pneumology, Bucharest, Romania; Diculescu M., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; Dimitriu A., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; Dobru D., George E. Palade University of Medicine, Pharmacy, Sciences and Technology, Targu-Mures, Romania, County Emergency Clinical Hospital, Targu-Mures, Romania; Dumitru E., Faculty of Medicine, Ovidius University, Constanța, Romania, Saint Apostol Andrei Hospital, Constanta, Romania; Gheonea D.I., University of Medicine and Pharmacy Craiova, Romania, County Emergency Hospital, Craiova, Romania; Gheorghe C., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; Goldiș A., Department of Gastroenterology and Hepatology, Victor Babeș University of Medicine and Pharmacy, Timișoara, Romania; Jinga M., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Dr. Carol Davila Central University Emergency Military Hospital, Bucharest, Romania; Man M., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Leon Daniello Pneumology Hospital, Cluj-Napoca, Romania; Mateescu B., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Department of Gastroenterology, Colentina Clinical Hospital, Bucharest, Romania; Mănuc M., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Center of Gastroenterology and Hepatology Fundeni Clinical Institute, Bucharest, Romania; Mihai C., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Mihălțan F., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, Marius Nasta Institute of Pneumology, Bucharest, Romania; Mihăescu T., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Pneumology Hospital, Iasi, Romania; Nedelcu L., Faculty of Medicine, Transilvania University, Brasov, Romania, Clinical Hospital, Brasov, Romania; Negreanu L., Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, 2nd Department of Gastroenterology, Emergency University Hospital, Bucharest, Romania; Pop C.-M., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Leon Daniello Pneumology Hospital, Cluj-Napoca, Romania; Râjnoveanu R., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Leon Daniello Pneumology Hospital, Cluj-Napoca, Romania; Săftoiu A., University of Medicine and Pharmacy Craiova, Romania; Seicean A., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Prof. Dr. Octavian Fodor Institute of Gastroenterology and Hepatology, Cluj-Napoca, Romania; Sporea I., Department of Gastroenterology and Hepatology, Victor Babeș University of Medicine and Pharmacy, Timișoara, Romania; Stanciu C., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Surdea-Blaga T., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, 2ndDepartment of Internal Medicine, Cluj County Emergency Clinical Hospital, Cluj-Napoca, Romania; Tanțău M., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Prof. Dr. Octavian Fodor Institute of Gastroenterology and Hepatology, Cluj-Napoca, Romania; Todea D., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, Leon Daniello Pneumology Hospital, Cluj-Napoca, Romania; Trifan A.-V., Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania, Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Ulmeanu R., University of Oradea, Faculty of Medicine and Pharmacy, Oradea, Romania, Marius Nasta Institute of Pneumology, Bucharest, Romania; Iov D.-E., Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, Iasi, Romania; Dumitrașcu D.-L., Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, 2ndDepartment of Internal Medicine, Cluj County Emergency Clinical Hospital, Cluj-Napoca, Romania","Background & Aims: Gastroesophageal reflux disease (GERD) is a common condition present in daily practice with a wide range of clinical phenotypes. In this line, respiratory conditions may be associated with GERD. The Romanian Societies of Gastroenterology and Neurogastroenterology, in association with the Romanian Society of Pneumology, aimed to create a guideline regarding the epidemiology, diagnosis and treatment of respiratory conditions associated with GERD. Methods: Delphi methodology was used and eleven common working groups of experts were created. The experts reviewed the literature according to GRADE criteria and formulated 34 statements and recommendations. Consensus (>80% agreement) was reached for some of the statements after all participants voted. Results: All the statements and the literature review are presented in the paper, together with their correspondent grade of evidence and the voting results. Based on >80% voting agreement, a number of 22 recommendations were postulated regarding the diagnosis and treatment of GERD-induced respiratory symptoms. The experts considered that GERD may cause bronchial asthma and chronic cough in an important number of patients through micro-aspiration and vagal-mediated tracheobronchial reflex. GERD should be suspected in patients with asthma with suboptimal controlled or after exclusion of other causes, also in nocturnal refractory cough which needs gastroenterological investigations to confirm the diagnosis. Therapeutic test with double dose proton pump inhibitors (PPI) for 3 months is also useful. GERD induced respiratory conditions are difficult to treat; however,proton pump inhibitors and laparoscopic Nissen fundoplication are endorsed for therapy. Conclusions: This guideline could be useful for the multidisciplinary management of GERD with respiratory symptoms in current practice. © 2022, Romanian Society of Gastroenterology. All rights reserved.","bronchial asthma; chronic cough; extradigestive manifestations; gastroesophageal reflux disease; GERD; guidelines; respiratory manifestations","Cough; Gastroenterology; Gastroesophageal Reflux; Humans; Proton Pump Inhibitors; Romania; esomeprazole; proton pump inhibitor; ranitidine; proton pump inhibitor; abdominal pressure; Article; artificial intelligence; aspiration; asthma; Barrett esophagus; bronchoconstriction; chronic cough; computer assisted tomography; esophagus motility; fibrosing alveolitis; gastroesophageal reflux; gastrointestinal motility; heartburn; human; lower esophagus sphincter; lower esophagus sphincter pressure; Nissen fundoplication; phenotype; practice guideline; prevalence; quality of life; questionnaire; respiratory tract infection; self report; systematic review; vagus nerve stimulation; complication; coughing; epidemiology; gastroenterology; gastroesophageal reflux; Romania","","esomeprazole, 119141-88-7, 202742-32-3, 217087-09-7, 217087-10-0, 161796-84-5, 161796-78-7; ranitidine, 66357-35-5, 66357-59-3; Proton Pump Inhibitors, ","","","","","Nirwan JS, Hasan SS, Babar ZU, Conway BR, Ghori MU., Global Prevalence and Risk Factors of Gastro-oesophageal Reflux Disease (GORD): Systematic Review with Meta-analysis, Sci Rep, 10, (2020); Iliescu M, Dumitrascu DL., Prevalence of gastroesophageal reflux disease in the Romanian county Gorj, JMB Jurnal Medical Brasovean, 1, pp. 69-73, (2020); Chirila I, Morariu ID, Barboi OB, Drug VL., The role of diet in the overlap between gastroesophageal reflux disease and functional dyspepsia, Turk J Gastroenterol, 27, pp. 73-80, (2016); Vakil N, Van Zanten SV, Kahrilas P, Et al., The Montreal definition and classification of gastroesophageal reflux disease: A global evidencebased consensus, Am J Gastroenterol, 101, pp. 1900-1920, (2006); Jaspersen D, Kulig M, Labenz J, Et al., Prevalence of extra-oesophageal manifestations in gastro-oesophageal reflux disease: An analysis based on the ProGERD Study, Aliment Pharmacol Ther, 17, pp. 1515-1520, (2003); Barboi OB, Cijevschi Prelipcean C, Mihai C, Et al., Extradigestive manifestations of gastroesophageal reflux disease: demographic, clinical, biological and endoscopic features, Rev Med Chir Soc Med Nat Iasi, 120, pp. 282-287, (2016); Angelescu G, Popescu E, Balan H., Evidențierea manifestărilor extraesofagiene în boala de reflux gastroesofagian-studiu clinic si endoscopic efectuat în Spitalul Clinic Județean de Urgență Ilfov, Medicina Interna, 7, pp. 9-19, (2010); Boltin D, Lambregts D, Jones F, Et al., UEG framework for the development of high-quality clinical guidelines, United European Gastroenterol J, 8, pp. 851-864, (2020); Overholt RH, Voorhees RJ., Esophageal reflux as a trigger in asthma, Dis Chest, 49, pp. 464-466, (1966); Durazzo M, Lupi G, Cicerchia F, Et al., Extra-esophageal presentation of gastroesophageal reflux disease: 2020 update, J Clin Med, 9, (2020); Locke GR, Talley NJ, Fett SL, Zinsmeiste AR, Melton LJ, Prevalence and clinical spectrum of gastroesophageal reflux: A population-based study in Olmsted County, Minnesota, Gastroenterology, 122, pp. 1448-1456, (1997); Ruigomez A, Rodriguez LA, Wallander MA, Johansson S, Thomas M, Price D., Gastroesophageal reflux disease and asthma: a longitudinal study in UK general practice, Chest, 128, pp. 85-93, (2005); Ladanchuk TC, Johnston BT, Murray LJ, Anderson LA, Risk of Barrett’s oesophagus, oesophageal adenocarcinoma and reflux oesophagitis and the use of nitrates and asthma medications, Scand J Gastroenterol, 45, pp. 1397-1403, (2010); Havemann BD, Henderson CA, El-Serag HB., The association between gastro-oesophageal reflux disease and asthma: a systematic review, Gut, 56, pp. 1654-1664, (2007); Denlinger LC, Phillips BR, Ramratnam S, Et al., National Heart, Lung, and Blood Institute’s Severe Asthma Research Program-3 Investigators. 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Bărboi; Grigore T. Popa University of Medicine and Pharmacy, Iasi, Romania; email: oana.barboi@umfiasi.ro","","Romanian Society of Gastroenterology","","","","","","18418724","","","35306549","English","J. Gastrointest. Liver Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85126879765"
"Klaudel J.; Klaudel B.; Glaza M.; Trenkner W.; Derejko P.; Szołkiewicz M.","Klaudel, Jacek (36863943100); Klaudel, Barbara (57721171400); Glaza, Michał (57204153499); Trenkner, Wojciech (57205390452); Derejko, Paweł (9246751100); Szołkiewicz, Marek (6603766920)","36863943100; 57721171400; 57204153499; 57205390452; 9246751100; 6603766920","Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries","2022","International Journal of Environmental Research and Public Health","19","24","17002","","","","4","10.3390/ijerph192417002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144515979&doi=10.3390%2fijerph192417002&partnerID=40&md5=e144aa12fc2d90eef8accd8cdd44fee4","Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Copernicus PL, Gdańsk, 80-462, Poland; Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, Gdynia, 81-348, Poland; Department of Decision Systems and Robotics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland; Department of Cardiology, Medicover Hospital, Warszawa, 02-972, Poland; Cardiac Arrhythmias Department, National Institute of Cardiology, Warszawa, 04-628, Poland; Department of Cardiology and Interventional Angiology, Kashubian Center for Heart and Vascular Diseases, Pomeranian Hospitals, Wejherowo, 84-200, Poland","Klaudel J., Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Copernicus PL, Gdańsk, 80-462, Poland, Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, Gdynia, 81-348, Poland; Klaudel B., Department of Decision Systems and Robotics, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland; Glaza M., Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, Gdynia, 81-348, Poland; Trenkner W., Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Copernicus PL, Gdańsk, 80-462, Poland; Derejko P., Department of Cardiology, Medicover Hospital, Warszawa, 02-972, Poland, Cardiac Arrhythmias Department, National Institute of Cardiology, Warszawa, 04-628, Poland; Szołkiewicz M., Department of Cardiology, St. Vincent de Paul Hospital, Pomeranian Hospitals, Gdynia, 81-348, Poland, Department of Cardiology and Interventional Angiology, Kashubian Center for Heart and Vascular Diseases, Pomeranian Hospitals, Wejherowo, 84-200, Poland","Catheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified 124 cases of CID in electronic databases containing 84,223 records of diagnostic and interventional coronary procedures from the years 2000–2022. Based on the f1-score, Extreme Gradient Boosting (XGBoost) was found to have the optimal balance between positive predictive value (precision) and sensitivity (recall). As by the XGBoost, the strongest predictors were the use of a guiding catheter (angioplasty), small/stenotic ostium, radial access, hypertension, acute myocardial infarction, prior angioplasty, female gender, chronic renal failure, atypical coronary origin, and chronic obstructive pulmonary disease. Risk prediction can be bolstered with machine learning algorithms and provide valuable clinical decision support. Based on the proposed model, a profile of ‘a perfect dissection candidate’ can be defined. In patients with ‘a clustering’ of dissection predictors, a less aggressive catheter and/or modification of the access site should be considered. © 2022 by the authors.","aortocoronary dissection; catheter-induced dissection; coronary artery dissection; dissection predictors; iatrogenic complications; machine-learning","Algorithms; Aorta; Catheters; Female; Humans; Machine Learning; Percutaneous Coronary Intervention; algorithm; injury; machine learning; prediction; public health; regression analysis; risk factor; aged; angioplasty; aortic trauma; Article; Bayesian learning; body mass; chronic kidney failure; chronic obstructive lung disease; cohort analysis; coronary artery; coronary artery bypass graft; coronary artery dissection; decision support system; decision tree; female; heart infarction; human; hypertension; intervention study; k nearest neighbor; machine learning; male; prediction; predictive value; random forest; retrospective study; risk factor; algorithm; aorta; catheter; machine learning; percutaneous coronary intervention; procedures","","","","","","","Ramasamy A., Bajaj R., Jones D.A., Amersey R., Mathur A., Baumbach A., Bourantas C.V., O'Mahony C., Iatrogenic Catheter-Induced Ostial Coronary Artery Dissections: Prevalence, Management, and Mortality from a Cohort of 55,968 Patients over 10 Years, Catheter. 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Interv, 2, pp. 124-132, (2009); Rathore S., Matsuo H., Terashima M., Kinoshita Y., Kimura M., Tsuchikane E., Nasu K., Ehara M., Asakura Y., Katoh O., Et al., Procedural and In-Hospital Outcomes after Percutaneous Coronary Intervention for Chronic Total Occlusions of Coronary Arteries 2002 to 2008: Impact of Novel Guidewire Techniques, JACC Cardiovasc. Interv, 2, pp. 489-497, (2009); Wu C.-J., Fang H.-Y., Cheng C.-I., Hussein H., Abdou S.M., Youssef A.A., Bhasin A., Yang C.-H., Chen C.-J., Hsieh Y.-K., Et al., The Safety and Feasibility of Bilateral Radial Approach in Chronic Total Occlusion Percutaneous Coronary Intervention, Int. Heart J, 52, pp. 131-138, (2011); Gomez-Moreno S., Sabate M., Jimenez-Quevedo P., Vazquez P., Alfonso F., Angiolillo D.J., Hernandez-Antolin R., Moreno R., Banuelos C., Escaned J., Et al., Iatrogenic Dissection of the Ascending Aorta Following Heart Catheterisation: Incidence, Management and Outcome, EuroIntervention J. Eur. Collab. Work. Group Interv. Cardiol. Eur. Soc. Cardiol, 2, pp. 197-202, (2006); Eshtehardi P., Adorjan P., Togni M., Tevaearai H., Vogel R., Seiler C., Meier B., Windecker S., Carrel T., Wenaweser P., Et al., Iatrogenic Left Main Coronary Artery Dissection: Incidence, Classification, Management, and Long-Term Follow-Up, Am. Heart J, 159, pp. 1147-1153, (2010); Faggian G., Santini F., Petrilli G., Mazzucco A., Rigatelli G., Cardaioli P., Roncon L., Left Main Coronary Stenosis as a Late Complication of Percutaneous Angioplasty:An Old Problem, but Still a Problem, J. Geriatr. Cardiol, 6, pp. 26-30, (2009); Lopez-Minguez J.R., Climent V., Yen-Ho S., Gonzalez-Fernandez R., Nogales-Asensio J.M., Sanchez-Quintana D., Structural features of the sinus of valsalva and the proximal portion of the coronary arteries: Their relevance to retrograde aortocoronary dissection, Rev. Esp. Cardiol, 59, pp. 696-702, (2006); Harding S.A., Fairley S.L., Catheter-Induced Coronary Dissection: Keep Calm and Don’t Inject, JACC Case Rep, 1, pp. 113-115, (2019); Alsanjari O., Myat A., Cockburn J., Karamasis G.V., Hildick-Smith D., Kalogeropoulos A.S., A Case of an Obstructive Intramural Haematoma during Percutaneous Coronary Intervention Successfully Treated with Intima Microfenestrations Utilising a Cutting Balloon Inflation Technique, Case Rep. Cardiol, 2018, (2018); Costello-Boerrigter L.C., Salomon C., Bufe A., Lapp H., The novel use of retrograde CTO PCI techniques as a rescue strategy for an acute right coronary artery occlusion due to iatrogenic dissection, J Cardiol. Cases, 17, pp. 89-91, (2017); Hashmani S., Tuzcu E., Hasan F., Successful Bail-Out Stenting for Iatrogenic Right Coronary Artery Dissection in a Young Male, JACC Case Rep, 1, pp. 108-112, (2019); Klaudel J., Glaza M., Klaudel B., Trenkner W., Pawlowski K., Szolkiewicz M., Catheter-induced coronary artery and aortic dissections. A study of the mechanisms, risk factors, and propagation causes, Cardiol J, (2022)","J. Klaudel; Department of Invasive Cardiology and Interventional Radiology, St. Adalbert’s Hospital, Gdańsk, Copernicus PL, 80-462, Poland; email: jaklaudi@interia.pl","","MDPI","","","","","","16617827","","","36554883","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85144515979"
"Yao M.","Yao, Maosheng (8622183900)","8622183900","“Smoke Detector” of Human Diseases for Environmental Aerosol Exposure","2022","Chinese Journal of Chemistry","40","12","","1471","1477","6","4","10.1002/cjoc.202100943","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127615675&doi=10.1002%2fcjoc.202100943&partnerID=40&md5=21a37a69c1ea166a7bd3d83a86d9f3af","State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China","Yao M., State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China","Air consists of various different pollutants (both biological and non-biological). COVID-19 pandemic further threatens the air safety. Aerosol exposure causes many diseases including asthma, respiratory infections and death. Using protein biomarker for early diagnosis often fails due to its lower level at the very beginning of a disease. On another front, different technologies were attempted for realtime monitoring of air toxicity. Nonetheless, many aerosol exposures occur silently without any knowledge due to limitation of available analytical methods. Exhaled breath has emerged as a promising non-invasive sample for disease diagnosis, including cancer and diabetes. Most recently, it was shown that rats emitted distinctive profile of volatile organic compounds (VOCs) within minutes when exposed to different air pollutants including ozone and endotoxin. During the pandemic times, breath-borne VOC was also used to rapidly screen COVID-19. Pollutant exposure could result in changes in metabolism, thus releasing different patterns of VOCs via breath within very short time. Close monitoring of exhaled biomarker profile could spot the early signs of a disease, thus offering an earlier warning. Sensor array and machine learning together can lend a great hand toward such an objective. This mini review is undertaken to share such endeavors and inspire further innovations. What is the most favorite and original chemistry developed in your research group?. Realtime aerosol detection and toxicity analysis, as well as breath biology, including its application in rapid COVID-19 screening. How do you get into this specific field? Could you please share some experiences with our readers?. Motivation and problem driven curiosity are key elements for performing impactful and milestone work. Additionally, being ambitious in leading plays another critical role in scientific exploration, excellence and influence. These research directions are the results of the ambition for solving important problems facing mankind and society. Of course, perseverance is another key driver toward pioneering discovery and effort. How do you supervise your students?. Students are the key part for research discoveries. Thus, communication and discussion are very important both in education and performing influential work. It is critically important to find a rewarding and innovative project to motivate them to move forward with a plan. Good project can not only produce high impact data, but also help students develop essential problem solving skills and thinking. In return, this will cycle back to performing the impactful work. Eventually, students benefit greatly from the process and become very capable and confident. What is the most important personality for scientific research?. Scientific devotion and ambition in tackling key problems facing mankind. What are your hobbies? What's your favorite book(s)?. My hobbies include listening to music, reading English books & news, thinking, occasionally watching movies made from Kungfu novels and most recently enjoy running. Who influences you mostly in your life?. During childhood, my grandma influenced me most. Now, it seems challenges we are facing take my most time. How do you keep balance between research and family?. It takes some compromise to find a balance between work and family. They are equally important. During different age periods, you will feel a different need to balance between them. Nonetheless, communication and understanding play an important role in solving balance problem. Another useful tip is to improve work efficiency and to avoid unnecessary things, thus saving more time for family and impactful work. In addition, having a well-planned to-do-list is useful to the balance too. Could you please give us some advices on improving Chinese Journal of Chemistry?. It helps by rotating 20% of the editorial board, i.e., every three years, speeding up the review process, placing the novelty on the first priority, and conducting double blind review. Top high quality paper should be a must principle regardless of the manuscript origin. In addition, increasing the publicity of journal, e.g., using social media not just domestic, but also those overseas, also makes a difference. © 2022 SIOC, CAS, Shanghai, & WILEY-VCH GmbH.","Analytical; Bioaerosol; Breath biomarker; Detection; Disease; Environmental aerosol exposure; Sensor","","","","","","Guangzhou National Lab, (EKPG21‐02); SpectraMax; Yale University; National Natural Science Foundation of China, NSFC, (21725701, 92043302); Peking University, PKU","Funding text 1: This research was supported by (NSFC) grant (92043302), the NSFC Distinguished Young Scholars Fund Awarded to M. Yao (21725701), and also by a grant from Guangzhou National Lab (EKPG21‐02).; Funding text 2: Over the years, the technologies from our group at Peking University in addressing this challenge have evolved from integrating silicon nanowire sensor with sampling to GFP‐labeled yeast cell sensor in 2017, rats based breath sensing in 2018, and most recently human breath based sensing system in 2021. These technologies enable us to translate airborne hazard exposure into viewable electrical and fluorescent signals, and employ related VOCs as a biomarker to carry out early disease detection and help safeguard human health. However, these technologies come at different stages of our pursuit of the problem, and it is often like climbing a stiff hill without prior knowledge. Dr. Yao was initially trained as mineral processing major from Central South University, followed by a master degree in environmental engineering with a focus on traffic emission from University of Alabama, Tuscaloosa, Alabama, USA and then completed his PhD in environmental science with a focus on bioaerosol (biological aerosol) from Rutgers University, New Brunswick, New Jersey, USA. Thereafter, Dr. Yao was offered a postdoctoral fellowship from the Department of Chemical and Environmental Engineering at Yale University, New Haven, Connecticut, USA to further conduct aerobiology related work. During his PhD study, Dr. Yao often thought about the possibility to carry out realtime detection of airborne biological agents, which is often described to be rather difficult if not impossible. Until 2004, scientists from Harvard University have demonstrated the use of silicon nanowire in detecting single water‐borne influenza virus in realtime. The finding was particularly exciting to our long sought solution. During Dr. Yao's postdoctoral time, he further solidified the protocol to carry out the realtime detection of airborne biological agents by integrating air sampling, silicon nanowire sensor, and microfluidics. In 2007, Dr. Yao was offered a faculty position under the “100 Scholar” Program from Peking University to develop his own laboratory for bioaerosol research. When briefly settled down with buildup of some lab facility, Dr. Yao was called upon to carry out onsite airborne infectious disease monitoring in the wake of the Wenchuan Earthquake with a magnitude of 8 in 2008, a major natural disaster in modern China. With the emergent support of National Natural Science Foundation of China, Dr. Yao along with his students, postdocs and colleagues from Peking University quickly assembled a mobile laboratory equipped with high volume air sampling (100 L/min), qPCR equipment as well as SpectraMax for airborne pathogen and toxin analysis. Many semi‐enclosed shelters from the earthquake affected areas were monitored for a variety of possible pathogens and toxins to perform earlier warnings for possible infectious disease outbreaks as shown in Figure 1 . My first graduate student, Yan Wu, who is now an associate professor at Shandong University with a completion of a postdoctoral fellowship at Nanyang Technological University, was part of the team for infectious disease monitoring. The detection can be only done by combining with air sampling and qPCR at that time. Arising from this effort, there comes a strong desire from us to further [ 15 ] [ 16 ] [ 11 ] [ 17 ] [ 18 ] [ 19 ] ","Di Q., Wang Y., Zanobetti A., Wang Y., Koutrakis P., Choirat C., Dominici F., Schwartz J.D., Air pollution and mortality in the medicare population, N. Engl. J. Med., 376, pp. 2513-2522, (2017); Yao M., Bioaerosol: A bridge and opportunity for many scientific research fields, J. Aerosol Sci., 115, pp. 108-112, (2018); Zhang T., Li X., Wang M., Chen H., Yao M., Microbial aerosol chemistry characteristics in highly polluted air, Sci. China Chem., 62, pp. 1051-1063, (2019); Valavanidis A., Fiotakis K., Vlachogianni T., Airborne particulate matter and human health: toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms, J. Environ. Sci. Health, Part C, 26, pp. 339-362, (2008); Li J., Chen H., Li X., Wang M., Zhang X., Cao J., Shen F., Wu Y., Xu S., Fan H., Da G., Wang J., Chan C.K., De Jesus A.L., Morawska L., Yao M., Differing toxicity of ambient particulate matter (PM) in global cities, Atmos. Environ., 212, pp. 305-315, (2019); Etzioni R., Urban N., Ramsey S., McIntosh M., Schwartz S., Reid B., Radich J., Anderson G., Hartwell L., The case for early detection, Nat. Rev. Cancer, 3, pp. 243-252, (2003); Aebersold R., Anderson L., Caprioli R., Druker B., Hartwell L., Smith R., Perspective: a program to improve protein biomarker discovery for cancer, J. Proteome Res., 4, pp. 1104-1109, (2005); Wu L., Qu X., Cancer biomarker detection: recent achievements and challenges, Chem. Soc. Rev., 44, pp. 2963-2997, (2015); Pashayan N., Pharoah P.D., The challenge of early detection in cancer, Science, 368, pp. 589-590, (2020); Jendrny P., Schulz C., Twele F., Meller S., von Kockritz-Blickwede M., Osterhaus A.D.M.E., Ebbers J., Pilchova V., Pink I., Welte T., Manns M.P., Fathi A., Ernst C., Addo M.M., Schalke E., Volk H.A., Scent dog identification of samples from COVID-19 patients–a pilot study, BMC Infect. Dis., 20, (2020); Chen H., Li X., Yao M., Rats sniff off toxic air, Environ. Sci. Technol., 54, pp. 3437-3446, (2020); The Nobel Prize in Physiology Or Medicine 2021. Nobelprize.Org. Nobel Prize Outreach AB 2021; The Nobel Prize in Physiology Or Medicine 2019. Nobelprize.Org. Nobel Prize Outreach AB, (2019); Nomura F., Akashi S., Sakao Y., Sato S., Kawai T., Matsumoto M., Nakanishi K., Kimoto M., Miyake K., Takeda K., Akira S., Cutting edge: endotoxin tolerance in mouse peritoneal macrophages correlates with down-regulation of surface toll-like receptor 4 expression, J. Immunol., 164, pp. 3476-3479, (2000); Shen F., Tan M., Wang Z., Yao M., Xu Z., Wu Y., Wang J., Guo X., Zhu T., Integrating silicon nanowire field effect transistor, microfluidics and air sampling techniques for real-time monitoring biological aerosols, Environ. Sci. Technol., 45, pp. 7473-8740, (2011); Wei K., Qiu M., Zhang R., Zhou L., Zhang T., Yao M., Luo C., Single living yEast PM toxicity sensor (SLEPTor) system, J. Aerosol Sci., 107, pp. 65-73, (2017); Chen H., Qi X., Zhang L., Li X., Ma J., Zhang C., Feng H., Yao M., COVID-19 screening using breath-borne volatile organic compounds, J. Breath Res., 15, (2021); Patolsky F., Zheng G., Hayden O., Lakadamyali M., Zhuang X., Lieber C.M., Electrical detection of single viruses, Proc. Natl. Acad. Sci. U. S. A., 101, pp. 14017-14022, (2004); Yao M., Zhu T., Li K., Dong S., Wu Y., Qiu X., Jiag B., Chen L., Zhen S., Onsite infectious agents and toxins monitoring in 12 May Sichuan earthquake affected areas, J. Environ. Monit., 11, pp. 1993-2001, (2009); Goffeau A., Barrell B.G., Bussey H., Davis R.W., Dujon B., Feldmann H., Galibert F., Hoheisel J.D., Jacq C., Johnston M., Louis E.J., Mewes H.W., Murakami Y., Philippsen P., Tettelin H., Oliver S.G., Life with 6000 genes, Science, 274, pp. 546-567, (1996); Chen H., Li J., Zhang X., Li X., Yao M., Zheng G., Automated in vivo nanosensing of breath-borne protein biomarkers, Nano Lett., 18, pp. 4716-4726, (2018); Shen F., Wang J., Xu Z., Wu Y., Chen Q., Li X., Jie X., Li L., Yao M., Guo X., Zhu T., Rapid flu diagnosis using silicon nanowire sensor, Nano Lett., 12, pp. 3722-3730, (2012); Ma J., Qi X., Chen H., Li X., Zhang Z., Wang H., Sun L., Zhang L., Guo J., Morawska L., Grinshpun S.A., Biswas P., Flagan R.C., Yao M., Coronavirus disease 2019 patients in earlier stages exhaled millions of Severe Acute Respiratory Syndrome Coronavirus 2 per hour, Clin. Infect. Dis., 72, pp. e652-e654, (2021); Zhou L., Yao M., Zhang X., Hu B., Li X., Chen H., Zhang L., Liu Y., Du M., Sun B., Jiang Y., Zhou K., Hong J., Yu N., Ding Z., Xu Y., Hu M., Morawska L., Grinshpun S.A., Biswas P., Flagan R.C., Zhu B., Liu W., Zhang Y., Breath-, air-and surface-borne SARS-CoV-2 in hospitals, J. Aerosol Sci., 152, (2021)","M. Yao; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China; email: yao@pku.edu.cn","","Shanghai Institute of Organic Chemistry","","","","","","1001604X","","CJOCE","","English","Chin J Chem","Article","Final","","Scopus","2-s2.0-85127615675"
"Talker L.; Neville D.; Wiffen L.; Selim A.B.; Haines M.; Carter J.C.; Broomfield H.; Lim R.H.; Lambert G.; Winter J.; Gribbin A.; Chauhan M.; De Vos R.; Kalra P.; Begum S.; Robinson B.; Mundy B.; Rutter H.; Madronal K.; Weiss S.T.; Hayward G.; Brown T.; Chauhan A.; Patel A.X.","Talker, Leeran (58153956800); Neville, Daniel (57200572282); Wiffen, Laura (37007146100); Selim, Ahmed B. (58154537600); Haines, Matthew (58153813800); Carter, Julian C. (58153813900); Broomfield, Henry (58154386900); Lim, Rui Hen (58154239800); Lambert, Gabriel (58153956900); Winter, Jonathon (57218565246); Gribbin, Andrew (57194017005); Chauhan, Milan (57218567569); De Vos, Ruth (57220488893); Kalra, Paul (7103054424); Begum, Selina (57202230869); Robinson, Barbara (57220110295); Mundy, Bernadette (58302856200); Rutter, Heather (56012447000); Madronal, Karen (55323130500); Weiss, Scott T. (57207899397); Hayward, Gail (57221325774); Brown, Thomas (57199406256); Chauhan, Anoop (57226265197); Patel, Ameera X. (58153671600)","58153956800; 57200572282; 37007146100; 58154537600; 58153813800; 58153813900; 58154386900; 58154239800; 58153956900; 57218565246; 57194017005; 57218567569; 57220488893; 7103054424; 57202230869; 57220110295; 58302856200; 56012447000; 55323130500; 57207899397; 57221325774; 57199406256; 57226265197; 58153671600","Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer","2023","Respiratory Research","24","1","150","","","","3","10.1186/s12931-023-02460-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161048680&doi=10.1186%2fs12931-023-02460-z&partnerID=40&md5=83cbcbae4677b0a0d29f9c147227d70b","TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, MA, United States; Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom","Talker L., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Neville D., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Wiffen L., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Selim A.B., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Haines M., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Carter J.C., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Broomfield H., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Lim R.H., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Lambert G., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom; Winter J., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Gribbin A., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Chauhan M., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; De Vos R., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Kalra P., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Begum S., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Robinson B., Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Mundy B., Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Rutter H., Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Madronal K., Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Weiss S.T., Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Boston, MA, United States; Hayward G., Nuffield Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Brown T., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Chauhan A., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom; Patel A.X., TidalSense Limited, 15a Vinery Rd, Cambridge, CB1 3DN, United Kingdom","Background: Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. Methods: Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense’s regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from ‘non-COPD’ (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. Results: The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. Conclusion: The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. Trial registration: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288. © 2023, The Author(s).","Chronic Obstructive Pulmonary Disease; Diagnosis; Machine learning","carbon dioxide; accuracy; adult; aged; Article; asthma; breathing disorder; capnometry; chronic obstructive lung disease; cohort analysis; controlled study; diagnostic test accuracy study; female; heart failure; human; information processing; longitudinal study; machine learning; major clinical study; male; motor neuron disease; observational study; pneumonia; predictive value; sensitivity and specificity; spirometry; waveform","","carbon dioxide, 124-38-9, 58561-67-4","","","National Institute for Health Research Invention for Innovation; Portsmouth Technology Trials Unit; National Institute for Health and Care Research, NIHR; Pfizer OpenAir; NIHR Community Healthcare MedTech and In Vitro Diagnostics Co-Operative; SBRI Healthcare; Ministry of Internal Affairs and Communications, MIC; UK Research and Innovation, UKRI; Innovate UK, (102977); NIHR i4i, (II-LA-1117-20002)","Funding text 1: The ABRS study was supported by the National Institute for Health Research Invention for Innovation (NIHR i4i) Programme (Grant Reference Number: II-LA-1117-20002), the GBRS study was supported by Innovate UK (Grant Reference Number: 102977), the CBRS study was supported by SBRI Healthcare, and the CBRS2 study was supported by Pfizer OpenAir. The ABRS and GBRS research was supported by the Portsmouth Technology Trials Unit (www.pttu.org.uk) and the NIHR Community Healthcare MedTech and In Vitro Diagnostics Co-Operative (MIC). The authors also acknowledge the work of staff at the Cambridge COPD Centre for Respiratory Research for their role in patient recruitment for CRBS and CBRS2, and staff in Oxford University\u2019s Primary Care Clinical Trials Unit who were responsible for trial management (Julie Allen, Johanna Cook, Joy Rahman, Rebecca Edeson) and nursing (Heather Rutter, Karen Madronal, Bernadette Mundy) in ABRS. BRS Study Team: Jonathon Winter2, Andrew Gribbin2, Milan Chauhan2, Ruth De Vos2, Paul Kalra2, Selina Begum2, Barbara Robinson3, Bernadette Mundy3, Heather Rutter3and Karen Madronal3.; Funding text 2: The studies which provided the data for this report were funded by NIHR (i4i grant), Innovate UK (102977), and Pfizer OpenAir (II-LA-1117-20002). The authors had sole responsibility for the study design, data collection, data analysis, data interpretation and report writing. ; Funding text 3: The ABRS study was supported by the National Institute for Health Research Invention for Innovation (NIHR i4i) Programme (Grant Reference Number: II-LA-1117-20002), the GBRS study was supported by Innovate UK (Grant Reference Number: 102977), the CBRS study was supported by SBRI Healthcare, and the CBRS2 study was supported by Pfizer OpenAir. The ABRS and GBRS research was supported by the Portsmouth Technology Trials Unit ( www.pttu.org.uk ) and the NIHR Community Healthcare MedTech and In Vitro Diagnostics Co-Operative (MIC). The authors also acknowledge the work of staff at the Cambridge COPD Centre for Respiratory Research for their role in patient recruitment for CRBS and CBRS2, and staff in Oxford University\u2019s Primary Care Clinical Trials Unit who were responsible for trial management (Julie Allen, Johanna Cook, Joy Rahman, Rebecca Edeson) and nursing (Heather Rutter, Karen Madronal, Bernadette Mundy) in ABRS. BRS Study Team: Jonathon Winter, Andrew Gribbin, Milan Chauhan, Ruth De Vos, Paul Kalra, Selina Begum, Barbara Robinson, Bernadette Mundy, Heather Rutter and Karen Madronal. 2 2 2 2 2 2 3 3 3 3 ","The Top 10 Causes of Death., (2020); Lancet T., Global Burden of Disease: GBD Cause and Risk Summaries.; Calverley P.M.A., Anderson J.A., Celli B., Ferguson G.T., Jenkins C., Jones P.W., Et al., Salmeterol and fluticasone propionate and survival in chronic obstructive pulmonary disease, N Engl J Med., 356, 8, pp. 775-789, (2007); Hangaard S., Helle T., Nielsen C., Hejlesen O.K., Causes of misdiagnosis of chronic obstructive pulmonary disease: a systematic scoping review, Respir Med, 129, pp. 63-84, (2017); Qaseem A., Snow V., Shekelle P., Sherif K., Wilt T.J., Weinberger S., Et al., Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline from the American College of Physicians, Ann Intern Med, 147, 9, pp. 633-638, (2007); Devine J.F., chronic obstructive pulmonary disease: an overview, Am Health Drug Benefits., 1, 7, (2008); Bednarek M., Maciejewski J., Wozniak M., Kuca P., Zielinski J., Prevalence, severity and underdiagnosis of COPD in the primary care setting, Thorax, 63, pp. 402-407, (2008); Schneider A., Gindner L., Tilemann L., Schermer T., Dinant G.J., Meyer F.J., Diagnostic accuracy of spirometry in primary care, BMC Pulm Med., 9, (2009); Jaffe M.B., Using the features of the time and volumetric capnogram for classification and prediction, J Clin Monit Comput., 31, 1, pp. 19-41, (2017); Bate S.R., Jugg B., Rutter S., Graham S., Perrott R., Rendell R., Et al., (2018); Herry C.L., Townsend D., Green G.C., Bravi A., Seely A.J.E., Segmentation and classification of capnograms: application in respiratory variability analysis, Physiol Meas., 35, 12, (2014); Kean T.T., Teo A.H., Malarvili M.B.; Pertzov B., Ronen M., Rosengarten D., Shitenberg D., Heching M., Shostak Y., Et al., Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients, Respir Res, 22, 1, pp. 1-9, (2021); Abid A., Mieloszyk R.J., Verghese G.C., Krauss B.S., Heldt T., Model-based estimation of respiratory parameters from capnography, with application to diagnosing obstructive lung disease, IEEE Trans Biomed Eng, 64, 12, pp. 2957-2967, (2017); van Genderingen H.R., Gravenstein N., van der Aa J.J., Gravenstein J.S., Computer-assisted capnogram analysis, J Clin Monit, 3, pp. 194-200, (1987); Lukic K.Z., Urch B., Fila M., Faughnan M.E., Silverman F., A novel application of capnography during controlled human exposure to air pollution, Biomed Eng Online., 5, 1, pp. 1-11, (2006); Kline J.A., Arunachlam M., Preliminary study of the capnogram waveform area to screen for pulmonary embolism, Ann Emerg Med., 32, 3 I, pp. 289-296, (1998); Abd Elrahman S.M., Abraham A., A review of class imbalance problem, J Netw Innov Comput., 1, pp. 332-340, (2013); Joshi I., Morley J., (2019)","A.X. Patel; TidalSense Limited, Cambridge, 15a Vinery Rd, CB1 3DN, United Kingdom; email: ameera.patel@tidalsense.com","","BioMed Central Ltd","","","","","","14659921","","RREEB","37268935","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85161048680"
"Bonomo M.; Hermsen M.G.; Kaskovich S.; Hemmrich M.J.; Rojas J.C.; Carey K.A.; Venable L.R.; Churpek M.M.; Press V.G.","Bonomo, Matthew (57930684700); Hermsen, Michael G. (57219162395); Kaskovich, Samuel (57205366671); Hemmrich, Maximilian J. (57554255000); Rojas, Juan C. (57197792890); Carey, Kyle A. (56548319600); Venable, Laura Ruth (56344204300); Churpek, Matthew M. (36705790600); Press, Valerie G. (32267496000)","57930684700; 57219162395; 57205366671; 57554255000; 57197792890; 56548319600; 56344204300; 36705790600; 32267496000","Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation","2022","International Journal of COPD","17","","","2701","2709","8","4","10.2147/COPD.S379700","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140006975&doi=10.2147%2fCOPD.S379700&partnerID=40&md5=7fb78d5cb97cbd951805a42e5bf86d45","Pritzker School of Medicine, University of Chicago, Chicago, IL, United States; Department of Medicine, Section of Pulmonary/Critical Care, University of Chicago, Chicago, IL, United States; Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL, United States; Department of Medicine, Section of Hospitalist Medicine, University of Chicago, Chicago, IL, United States; Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin-Madison, Madison, WI, United States; Department of Pediatrics, Section of Academic Pediatrics, University of Chicago, Chicago, IL, United States","Bonomo M., Pritzker School of Medicine, University of Chicago, Chicago, IL, United States; Hermsen M.G., Pritzker School of Medicine, University of Chicago, Chicago, IL, United States; Kaskovich S., Pritzker School of Medicine, University of Chicago, Chicago, IL, United States; Hemmrich M.J., Pritzker School of Medicine, University of Chicago, Chicago, IL, United States; Rojas J.C., Department of Medicine, Section of Pulmonary/Critical Care, University of Chicago, Chicago, IL, United States; Carey K.A., Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL, United States; Venable L.R., Department of Medicine, Section of Hospitalist Medicine, University of Chicago, Chicago, IL, United States; Churpek M.M., Department of Medicine, Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin-Madison, Madison, WI, United States; Press V.G., Department of Medicine, Section of General Internal Medicine, University of Chicago, Chicago, IL, United States, Department of Pediatrics, Section of Academic Pediatrics, University of Chicago, Chicago, IL, United States","Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients’ readmission risk during index hospitalizations. Objective: We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD). Design: Retrospective cohort study. Participants: Adult patients admitted for AE-COPD at the University of Chicago Medicine between November 7, 2008 and December 31, 2018 meeting International Classification of Diseases (ICD)-9 or −10 criteria consistent with AE-COPD were included. Methods: Random forest models were fit to predict readmission risk and respiratory-related readmission cause. Predictor variables included demographics, comorbidities, and EHR data from patients’ index hospital stays. Models were derived on 70% of observations and validated on a 30% holdout set. Performance of the readmission risk model was compared to that of the HOSPITAL score. Results: Among 3238 patients admitted for AE-COPD, 1103 patients were readmitted within 90 days. Of the readmission causes, 61% (n = 672) were respiratory-related and COPD (n = 452) was the most common. Our readmission risk model had a significantly higher area under the receiver operating characteristic curve (AUROC) (0.69 [0.66, 0.73]) compared to the HOSPITAL score (0.63 [0.59, 0.67]; p = 0.002). The respiratory-related readmission cause model had an AUROC of 0.73 [0.68, 0.79]. Conclusion: Our models improve on current tools by predicting 90-day readmission risk and cause at the time of discharge from index admissions for AE-COPD. These models could be used to identify patients at higher risk of readmission and direct tailored post-discharge transition of care interventions that lower readmission risk. © 2022 Bonomo et al.","chronic obstructive lung disease; COPD; machine learning; readmissions","Adult; Aftercare; Hospitalization; Humans; Logistic Models; Machine Learning; Patient Discharge; Patient Readmission; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Risk Factors; steroid; adult; aged; Article; causal model; chronic obstructive lung disease; cohort analysis; comorbidity; demographics; disease exacerbation; electronic health record; female; hospital discharge; hospital patient; hospital readmission; hospitalization; human; ICD-10; ICD-9; length of stay; machine learning; major clinical study; male; middle aged; prediction; predictor variable; random forest; receiver operating characteristic; retrospective study; risk assessment; risk model; aftercare; chronic obstructive lung disease; hospitalization; machine learning; risk factor; statistical model","","","","","National Heart Lung and Blood Association, (R01HL146644); U.S. Department of Defense, DOD, (W81XWH-21-1-0009); National Heart, Lung, and Blood Institute, NHLBI, (R01HL157262); National Institute of General Medical Sciences, NIGMS, (R01GM123193); National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK, (R01DK126933); Agency for Healthcare Research and Quality, AHRQ, (R01HS027804); American Lung Association, ALA; University of Chicago; Pritzker School of Medicine; Society of Hospital Medicine, SHM","Funding text 1: This work was supported by funding from the Pritzker School of Medicine Summer Research Program (M Bonomo, M Hermsen, and S Kaskovich) and by a Student Hospitalist Scholar Grant from the Society of Hospital Medicine (M Hemmrich). It was also supported by University of Chicago Medicine Data Science Pilot Funding.; Funding text 2: VG Press reports general research support from National Heart Lung and Blood Association (R01HL146644), the Agency for Healthcare Research and Quality (R01HS027804), and the American Lung Association. VG Press also discloses consultant fees from Vizient Inc. and Humana. MM Churpek reports general support from the Department of Defense (W81XWH-21-1-0009), the NIH NIDDK (R01DK126933), NHLBI (R01HL157262) and NIGMS (R01GM123193). MM Churpek also discloses a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. None of the other authors have any conflicts of interest in this work.","FastStats: Chronic Obstructive Pulmonary Disease (COPD) Includes: Chronic Bronchitis and Emphysema, (2022); Jencks SF, Williams MV, Coleman EA., Rehospitalizations among patients in the Medicare fee-for-service program, N Engl J Med, 360, 14, pp. 1418-1428, (2009); Halpern MT, Stanford RH, Borker R., The burden of COPD in the U.S.A.: results from the confronting COPD survey, Respir Med, 97, pp. S81-S89, (2003); Roberts C, Lowe D, Bucknall C, Ryland I, Kelly Y, Pearson M., Clinical audit indicators of outcome following admission to hospital with acute exacerbation of chronic obstructive pulmonary disease, Thorax, 57, 2, pp. 137-141, (2002); Hospital Readmissions Reduction Program (HRRP); Shah T, Churpek MM, Coca Perraillon M, Konetzka RT., Understanding why patients with COPD get readmitted, Chest, 147, 5, pp. 1219-1226, (2015); Allaudeen N, Schnipper JL, Orav EJ, Wachter RM, Vidyarthi AR., Inability of providers to predict unplanned readmissions, J Gen Intern Med, 26, 7, pp. 771-776, (2011); Press VG., Is it time to move on from identifying risk factors for 30-day chronic obstructive pulmonary disease readmission? A call for risk prediction tools, Ann Am Thorac Soc, 15, 7, pp. 801-803, (2018); Press VG, Myers LC, Feemster LC., Preventing COPD readmissions under the hospital readmissions reduction program: how far have we come?, Chest, 159, 3, pp. 996-1006, (2021); Press VG, Au DH, Bourbeau J, Et al., Reducing chronic obstructive pulmonary disease hospital readmissions. An official American thoracic society workshop report, Ann Am Thorac Soc, 16, 2, pp. 161-170, (2019); Donze JD, Williams MV, Robinson EJ, Et al., International Validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions, JAMA Intern Med, 176, 4, pp. 496-502, (2016); Echevarria C, Steer J, Heslop-Marshall K, Et al., The PEARL score predicts 90-day readmission or death after hospitalisation for acute exacerbation of COPD, Thorax, 72, 8, pp. 686-693, (2017); Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP., Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards, Crit Care Med, 44, 2, pp. 368-374, (2016); Churpek MM, Yuen TC, Winslow C, Et al., Multicenter Development and Validation of a Risk Stratification Tool for Ward Patients, Am J Respir Crit Care Med, 190, 6, pp. 649-655, (2014); Churpek MM, Yuen TC, Park SY, Gibbons R, Edelson DP., Using electronic health record data to develop and validate a prediction model for adverse outcomes on the wards, Crit Care Med, 42, 4, pp. 841-848, (2014); Stein BD, Bautista A, Schumock GT, Et al., The validity of international classification of diseases, ninth revision, clinical modification diagnosis codes for identifying patients hospitalized for COPD exacerbations, Chest, 141, 1, pp. 87-93, (2012); Collins GS, Reitsma JB, Altman DG, Moons KGM., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement, Ann Intern Med, 162, 1, pp. 55-63, (2015); Donze J, Aujesky D, Williams D, Schnipper JL., Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model, JAMA Intern Med, 173, 8, pp. 632-638, (2013); Rinne ST, Graves MC, Bastian LA, Et al., Association between length of stay and readmission for COPD, Am J Manag Care, 23, 8, pp. e253-e258, (2017); Garcia-Aymerich J., Risk factors of readmission to hospital for a COPD exacerbation: a prospective study, Thorax, 58, 2, pp. 100-105, (2003); Moore BJ, White S, Washington R, Coenen N, Elixhauser A., Identifying increased risk of readmission and in-hospital mortality using hospital administrative data: the AHRQ elixhauser comorbidity index, Med Care, 55, 7, pp. 698-705, (2017); Hackmann G, Chen M, Chipara O, Et al., Toward a two-tier clinical warning system for hospitalized patients, AMIA Annu Symp Proc, 2011, pp. 511-519, (2011); van den Boogaard M, Pickkers P, Slooter AJC, Et al., Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study, BMJ, 344, (2012); Knaus WA, Wagner DP, Draper EA, Et al., The APACHE III prognostic system: risk prediction of hospital mortality for critically III hospitalized adults, Chest, 100, 6, pp. 1619-1636, (1991); Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP., SMOTE: synthetic Minority Over-sampling Technique, J Artif Intell Res, 16, pp. 321-357, (2002); Qi Y., Random Forest for Bioinformatics, Ensemble Machine Learning: Methods and Applications, pp. 307-323, (2012); Fernandez-Delgado M, Cernadas E, Barro S, Amorim D., Do we need hundreds of classifiers to solve real world classification problems?, J Mach Learn Res, 15, 1, pp. 3133-3181, (2014); Altmann A, Tolosi L, Sander O, Lengauer T., Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 10, pp. 1340-1347, (2010); DeLong ER, DeLong DM, Clarke-Pearson DL., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Min X, Yu B, Wang F., Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: a case study on COPD, Sci Rep, 9, (2019)","V.G. Press; University of Chicago, Chicago, 5841 S Maryland, MC 2007, 60637, United States; email: vpress@medicine.bsd.uchicago.edu","","Dove Medical Press Ltd","","","","","","11769106","","","36299799","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140006975"
"Zazara D.E.; Giannou O.; Schepanski S.; Pagenkemper M.; Giannou A.D.; Pincus M.; Belios I.; Bonn S.; Muntau A.C.; Hecher K.; Diemert A.; Arck P.C.","Zazara, Dimitra E. (56668736200); Giannou, Olympia (55485687700); Schepanski, Steven (57215298992); Pagenkemper, Mirja (56312027700); Giannou, Anastasios D. (55516882200); Pincus, Maike (24482091300); Belios, Ioannis (57475477900); Bonn, Stefan (16686289800); Muntau, Ania C. (7003502748); Hecher, Kurt (57219346071); Diemert, Anke (56986762000); Arck, Petra Clara (7005025756)","56668736200; 55485687700; 57215298992; 56312027700; 55516882200; 24482091300; 57475477900; 16686289800; 7003502748; 57219346071; 56986762000; 7005025756","Fetal lung growth predicts the risk for early-life respiratory infections and childhood asthma","2024","World Journal of Pediatrics","20","5","","481","495","14","4","10.1007/s12519-023-00782-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182861628&doi=10.1007%2fs12519-023-00782-y&partnerID=40&md5=9d0f1013d0e72cd1344f332ca907786b","Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20251, Germany; University Children’s Hospital, UKE, Hamburg, Germany; Computer Engineering and Informatics Department, Polytechnic School, University of Patras, Patras, Greece; Institute of Developmental Neurophysiology, Center for Molecular Neurobiology Hamburg (ZMNH), UKE, Hamburg, Germany; Department of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany; Department of General, Visceral and Thoracic Surgery, UKE, Hamburg, Germany; Section of Molecular Immunology and Gastroenterology, I. Department of Medicine, UKE, Hamburg, Germany; Pediatrics and Pediatric Pneumology Practice, Berlin, Germany; Institute of Medical Systems Biology, ZMNH, UKE, Hamburg, Germany; Hamburg Center for Translational Immunology, UKE, Hamburg, Germany","Zazara D.E., Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20251, Germany, University Children’s Hospital, UKE, Hamburg, Germany; Giannou O., Computer Engineering and Informatics Department, Polytechnic School, University of Patras, Patras, Greece; Schepanski S., Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20251, Germany, Institute of Developmental Neurophysiology, Center for Molecular Neurobiology Hamburg (ZMNH), UKE, Hamburg, Germany; Pagenkemper M., Department of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany; Giannou A.D., Department of General, Visceral and Thoracic Surgery, UKE, Hamburg, Germany, Section of Molecular Immunology and Gastroenterology, I. Department of Medicine, UKE, Hamburg, Germany; Pincus M., Pediatrics and Pediatric Pneumology Practice, Berlin, Germany; Belios I., Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20251, Germany; Bonn S., Institute of Medical Systems Biology, ZMNH, UKE, Hamburg, Germany, Hamburg Center for Translational Immunology, UKE, Hamburg, Germany; Muntau A.C., University Children’s Hospital, UKE, Hamburg, Germany; Hecher K., Department of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany; Diemert A., Department of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany; Arck P.C., Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, Hamburg, 20251, Germany, Hamburg Center for Translational Immunology, UKE, Hamburg, Germany","Background: Early-life respiratory infections and asthma are major health burdens during childhood. Markers predicting an increased risk for early-life respiratory diseases are sparse. Here, we identified the predictive value of ultrasound-monitored fetal lung growth for the risk of early-life respiratory infections and asthma. Methods: Fetal lung size was serially assessed at standardized time points by transabdominal ultrasound in pregnant women participating in a pregnancy cohort. Correlations between fetal lung growth and respiratory infections in infancy or early-onset asthma at five years were examined. Machine-learning models relying on extreme gradient boosting regressor or classifier algorithms were developed to predict respiratory infection or asthma risk based on fetal lung growth. For model development and validation, study participants were randomly divided into a training and a testing group, respectively, by the employed algorithm. Results: Enhanced fetal lung growth throughout pregnancy predicted a lower early-life respiratory infection risk. Male sex was associated with a higher risk for respiratory infections in infancy. Fetal lung growth could also predict the risk of asthma at five years of age. We designed three machine-learning models to predict the risk and number of infections in infancy as well as the risk of early-onset asthma. The models’ R2 values were 0.92, 0.90 and 0.93, respectively, underscoring a high accuracy and agreement between the actual and predicted values. Influential variables included known risk factors and novel predictors, such as ultrasound-monitored fetal lung growth. Conclusion: Sonographic monitoring of fetal lung growth allows to predict the risk for early-life respiratory infections and asthma. Graphical abstract: (Figure presented.) © The Author(s) 2024.","Asthma; Child risk; Early-life respiratory infections; Prenatal lung development; Sexual dimorphism","Adult; Asthma; Child, Preschool; Cohort Studies; Female; Fetal Development; Humans; Infant; Infant, Newborn; Lung; Machine Learning; Male; Predictive Value of Tests; Pregnancy; Respiratory Tract Infections; Risk Assessment; Risk Factors; Ultrasonography, Prenatal; Article; asthma; child; classifier; clinical examination; cohort analysis; development; extreme gradient boosting regressor; female; fetus; fetus echography; fetus lung maturation; human; infancy; infection risk; lung function; machine learning; major clinical study; male; maternal stress; newborn; Poisson regression; pregnancy; pregnant woman; regression analysis; respiratory tract infection; validation study; adult; diagnostic imaging; epidemiology; fetus development; fetus echography; infant; lung; predictive value; preschool child; risk assessment; risk factor","","","RAB 6D, General Electric; Voluson E8, General Electric","General Electric; General Electric","Authority for Science, Research and Equality, (LFF-FV73); Deutsche Forschungsgemeinschaft, DFG, (AR232/25-2, AR232/29-1, CRU296, DI 2103/2-1, FOR5068, SO1413/1-2, ZA1246/2-1); Deutsche Forschungsgemeinschaft, DFG; Universität Hamburg, UH; Werner Otto Stiftung, (RU5068); Werner Otto Stiftung","Open Access funding enabled and organized by Projekt DEAL. This work was supported by the German Research Foundation to ZDE, APC and DA (CRU296: AR232/25-2, DI 2103/2-1, SO1413/1-2; ZA1246/2-1; FOR5068: AR232/29-1), the Authority for Science, Research and Equality, Hanseatic City of Hamburg, Germany to APC and DA (LFF-FV73) and the Werner Otto Foundation to ZDE and GAD. ZDE and GAD are supported by the Clinician Scientist program of the RU5068 and the Medical Faculty of the University of Hamburg. 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Nair H., Simoes E.A., Rudan I., Gessner B.D., Azziz-Baumgartner E., Zhang J.S.F., Et al., Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: a systematic analysis, Lancet, 381, pp. 1380-1390, (2013); Abu-Raya B., Vineta Paramo M., Reicherz F., Lavoie P.M., Why has the epidemiology of RSV changed during the COVID-19 pandemic?, EClinicalMedicine, 61, (2023); Li Y., Wang X., Blau D.M., Caballero M.T., Feikin D.R., Gill C.J., Et al., Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in children younger than 5 years in 2019: a systematic analysis, Lancet, 399, pp. 2047-2064, (2022); Asher M.I., Rutter C.E., Bissell K., Chiang C.-Y., El Sony A., Ellwood E., Et al., Worldwide trends in the burden of asthma symptoms in school-aged children: Global Asthma Network Phase I cross-sectional study, Lancet, 398, pp. 1569-1580, (2021); Meghji J., Mortimer K., Agusti A., Allwood B.W., Asher I., Bateman E.D., Et al., Improving lung health in low-income and middle-income countries: from challenges to solutions, Lancet, 397, pp. 928-940, (2021); 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Montgomery S., Bahmanyar S., Brus O., Hussein O., Kosma P., Palme-Kilander C., Respiratory infections in preterm infants and subsequent asthma: a cohort study, BMJ Open, 3, (2013); Sanchez Garcia L., Calvo C., Casas I., Pozo F., Pellicer A., Viral respiratory infections in very low birthweight infants at neonatal intensive care unit: prospective observational study, BMJ Paediatr Open, 4, (2020); Carraro S., Scheltema N., Bont L., Baraldi E., Early-life origins of chronic respiratory diseases: understanding and promoting healthy ageing, Eur Respir J, 44, pp. 1682-1696, (2014); Jartti T., Gern J.E., Role of viral infections in the development and exacerbation of asthma in children, J Allergy Clin Immunol, 140, pp. 895-906, (2017); Rasmussen M., Reddy M., Nolan R., Camunas-Soler J., Khodursky A., Scheller N.M., Et al., RNA profiles reveal signatures of future health and disease in pregnancy, Nature, 601, pp. 422-427, (2022)","P.C. Arck; Division for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Martinistraße 52, 20251, Germany; email: p.arck@uke.de","","Zhejiang University School of Medicine Children's Hospital","","","","","","17088569","","","38261172","English","World J. Pediatr.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85182861628"
"Hassan U.; Singhal A.; Chaudhary P.","Hassan, Umaisa (58577700400); Singhal, Amit (56363053400); Chaudhary, Priyanshu (58814180600)","58577700400; 56363053400; 58814180600","Lung disease detection using EasyNet","2024","Biomedical Signal Processing and Control","91","","105944","","","","4","10.1016/j.bspc.2024.105944","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182431350&doi=10.1016%2fj.bspc.2024.105944&partnerID=40&md5=fcc19a61de6cdd72858edfc497dc8e81","Netaji Subhas University of Technology, Dwarka, Delhi, 110078, India","Hassan U., Netaji Subhas University of Technology, Dwarka, Delhi, 110078, India; Singhal A., Netaji Subhas University of Technology, Dwarka, Delhi, 110078, India; Chaudhary P., Netaji Subhas University of Technology, Dwarka, Delhi, 110078, India","One of the major causes of deaths worldwide is the pulmonary diseases. There is an increasing need for an efficient technique that can automatically diagnose these diseases with high accuracy. In this paper, we propose a deep learning architecture to automatically detect pulmonary diseases. The raw pulmonary sound signals are taken from two popular datasets: ICBHI and KAUH datasets. These signals have diverse sampling frequencies of 4 kHz, 10 kHz or 44.1 kHz. The signals from KAUH dataset have a duration of minimum 5 s, while ICBHI signals have a duration of 10 to 90 s. These signals undergo pre-processing, which involves re-sampling them to a common 4 kHz frequency, and segmenting them into frames lasting 3 s. The frames are then normalized and passed to the proposed EasyNet model for training and classification. The EasyNet architecture contains only two convolution layers, which reduces the model complexity. The model's performance is analyzed for both binary detection as well as multi-class detection. Our method performs well in all the considered evaluation scenarios, and yields an accuracy, sensitivity, and specificity of 1.0 for the KAUH dataset, while for the ICBHI dataset, an accuracy of 0.997, sensitivity of 0.999, and specificity of 0.997 is achieved. For the combined dataset, we have achieved an accuracy of 0.998, with a sensitivity and specificity of 0.999. These values are better than the existing state-of-the-art methods. The proposed architecture is quite simple yet effective in detecting lung diseases. © 2024 Elsevier Ltd","Asthma; Auscultation; Convolution neural network; COPD; Pulmonary diseases","Biological organs; Convolution; Deep learning; Petroleum reservoir evaluation; Pulmonary diseases; Signal sampling; Asthma; Auscultation; Causes of death; Convolution neural network; COPD; Disease detection; High-accuracy; Learning architectures; Sensitivity and specificity; Sound signal; abnormal respiratory sound; Article; asthma; automation; chronic obstructive lung disease; classification; computer assisted diagnosis; controlled study; convolutional neural network; data processing; deep learning; diagnostic accuracy; diagnostic test accuracy study; easynet; heart infarction; human; lung disease; lung fibrosis; major clinical study; pleura effusion; pneumonia; sensitivity and specificity; signal processing; sound detection; Network architecture","","","","","","","Petmezas G., Cheimariotis G.-A., Stefanopoulos L., Rocha B., Paiva R.P., Katsaggelos A.K., Maglaveras N., Automated lung sound classification using a hybrid CNN-LSTM network and focal loss function, Sensors, 22, 3, (2022); Moussavi Z., Fundamentals of respiratory sounds and analysis, Synth. 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Eng., 59, 1, pp. 7-18, (2014); Serbes G., Sakar C.O., Kahya Y.P., Aydin N., Pulmonary crackle detection using time–frequency and time–scale analysis, Digit. Signal Process., 23, 3, pp. 1012-1021, (2013); Aykanat M., Kilic O., Kurt B., Saryal S., Classification of lung sounds using convolutional neural networks, EURASIP J. Image Video Process., 2017, 1, pp. 1-9, (2017); El Ogri O., Karmouni H., Sayyouri M., Qjidaa H., 3D image recognition using new set of fractional-order Legendre moments and deep neural networks, Signal Process., Image Commun., 98, (2021); Altan A., Karasu S., Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique, Chaos Solitons Fractals, 140, (2020); Sharma N., Upadhyay A., Sharma M., Et al., Deep temporal networks for EEG-based motor imagery recognition, Sci. 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Eng., 2022, pp. 1-8, (2022); Li C., Du H., Zhu B., Classification of lung sounds using CNN-attention, EasyChair Prepr., 4356, (2020); Kasgari A.B., Safavi S., Nouri M., Hou J., Sarshar N.T., Ranjbarzadeh R., Point-of-interest preference model using an attention mechanism in a convolutional neural network, Bioengineering, 10, 4, (2023); Demir F., Ismael A.M., Sengur A., Classification of lung sounds with CNN model using parallel pooling structure, IEEE Access, 8, pp. 105376-105383, (2020); Agarwal M., Singhal A., Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals, Med. Eng. Phys., 112, (2023); Singhal A., Agarwal M., An automatic risk assessment system for sudden cardiac death using look ahead pattern, Multimedia Tools Appl., 11, pp. 1-11, (2023); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. Biomed. Circ. Syst., 14, 3, pp. 535-544, (2020); pp. 17-21, (2020)","A. Singhal; Netaji Subhas University of Technology, Dwarka, Delhi, 110078, India; email: amit@nsut.ac.in","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85182431350"
"Xie W.; Jacobs C.; Charbonnier J.-P.; Slebos D.J.; van Ginneken B.","Xie, Weiyi (57218360568); Jacobs, Colin (36145083400); Charbonnier, Jean-Paul (57212030447); Slebos, Dirk Jan (6602200135); van Ginneken, Bram (55759608800)","57218360568; 36145083400; 57212030447; 6602200135; 55759608800","Emphysema subtyping on thoracic computed tomography scans using deep neural networks","2023","Scientific Reports","13","1","14147","","","","4","10.1038/s41598-023-40116-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168975230&doi=10.1038%2fs41598-023-40116-6&partnerID=40&md5=0a0073402073a7ea566876ee23eeaef5","Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands; Thirona, Nijmegen, 6525 EC, Netherlands; Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, Netherlands","Xie W., Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands; Jacobs C., Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands; Charbonnier J.-P., Thirona, Nijmegen, 6525 EC, Netherlands; Slebos D.J., Department of Pulmonary Diseases, University Medical Center Groningen, University of Groningen, Groningen, Netherlands; van Ginneken B., Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands","Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes. © 2023, Springer Nature Limited.","","Algorithms; Emphysema; Humans; Neural Networks, Computer; Pulmonary Emphysema; Tomography, X-Ray Computed; algorithm; artificial neural network; diagnostic imaging; emphysema; human; lung emphysema; x-ray computed tomography","","","","","National Heart, Lung, and Blood Institute, NHLBI; Lung Foundation Netherlands, LFN, (5.1.17.171, U01 HL089856, U01 HL089897); Lung Foundation Netherlands, LFN","The Dutch Lung Foundation, under project 5.1.17.171 supported this work. In addition, we acknowledge the COPDGene Study (ancillary study ANC-251) for providing the data used. COPDGene is funded by Award Number U01 HL089897 and Award Number U01 HL089856 from the National Heart, Lung, and Blood Institute. The COPD Foundation also supports the COPDGene study (NCT00608764) through contributions to an Industry Advisory Committee comprised of AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion. We especially thank Dr. Stephen M. Humphries from National Jewish Health for sharing the data selection and partitioning in their study, which is used as a comparison in this paper. ","WHO Methods and Data Sources for Country-Level Causes of Death 2000–2019, (2020); Han M.K., Et al., Chronic obstructive pulmonary disease phenotypes: The future of COPD, Am. J. Respir. Crit. Care Med., 182, pp. 598-604, (2010); Coxson H.O., Et al., A quantification of the lung surface area in emphysema using computed tomography, Am. J. Respir. Crit. Care Med., 159, pp. 851-1073, (1999); Madani A., Zanen J., De Maertelaer V., Gevenois P.A., Pulmonary emphysema: Objective quantification at multi-detector row CT-comparison with macroscopic and microscopic morphometry, Radiology, 238, pp. 1036-1043, (2006); Smith B.M., Et al., Pulmonary emphysema subtypes on computed tomography: The MESA COPD study, Am. J. Med., 127, pp. 94-97, (2014); Castaldi P.J., Et al., Machine learning characterization of COPD subtypes: Insights from the COPDGene study, Chest, 157, pp. 1147-1157, (2020); Yilmaz C., Dane D.M., Patel N.C., Hsia C.C., Quantifying heterogeneity in emphysema from high-resolution computed tomography: A lung tissue research consortium study, Acad. Radiol., 20, pp. 181-193, (2013); Lynch D.A., Et al., CT-definable subtypes of chronic obstructive pulmonary disease: A statement of the Fleischner Society, Radiology, 277, pp. 192-205, (2015); Lynch D.A., Et al., CT-based visual classification of emphysema: Association with mortality in the COPDGene study, Radiology, 288, pp. 859-866, (2018); Humphries S.M., Et al., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, pp. 434-444, (2020); Regan E.A., Et al., Genetic epidemiology of COPD (COPDGene) study design, COPD, 7, pp. 32-43, (2010); Wan E.S., Et al., Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene, Respir. Res., 15, (2014); He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016); Cicek O., Abdulkadir A., Lienkamp S.S., Brox T., Ronneberger O., 3D U-Net: Learning dense volumetric segmentation from sparse annotation, in Medical Image Computing and Computer-Assisted Intervention, Lect Notes Comput Sci, pp. 424-432, (2016); Xie W., Jacobs C., Charbonnier J.-P., van Ginneken B., Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients, Med Image Anal., (2023); Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z., Rethinking the inception architecture for computer vision, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826; Pedregosa F., Et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res., 12, pp. 2825-2830, (2011); Kundel H.L., Polansky M., Measurement of observer agreement, Radiology, 228, pp. 303-308, (2003); Hofmanninger J., Et al., Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem, Eur. Radiol. Exp., 4, pp. 1-13, (2020)","B. van Ginneken; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands; email: bram.vanginneken@radboudumc.nl","","Nature Research","","","","","","20452322","","","37644032","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85168975230"
"Verma V.K.; Lin W.-Y.","Verma, Vijay Kumar (57196187471); Lin, Wen-Yen (56590983500)","57196187471; 56590983500","Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device","2022","Biosensors","12","8","605","","","","4","10.3390/bios12080605","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136484001&doi=10.3390%2fbios12080605&partnerID=40&md5=5b00c785e8f74f485fc5ae5709a41b98","Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan, 33302, Taiwan; Division of Cardiology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan, 33305, Taiwan","Verma V.K., Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan, 33302, Taiwan; Lin W.-Y., Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan, 33302, Taiwan, Division of Cardiology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Tao-Yuan, 33305, Taiwan","Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients’ hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions. © 2022 by the authors.","activity index; COPD; COVID-19; hospital readmission; machine learning; physical activity; readmission prediction","Accelerometry; Exercise; Humans; Machine Learning; Patient Readmission; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Forecasting; Hospitals; Machine learning; Patient treatment; Pulmonary diseases; Activity index; Chronic obstructive pulmonary disease; Event-based; Health status; Hospital readmission; Machine-learning; Performances analysis; Physical activity; Prediction-based; Readmission prediction; adult; aged; Article; chronic obstructive lung disease; clinical article; controlled study; cross validation; daily life activity; follow up; health status; hospital readmission; human; machine learning; measurement accuracy; medical record; middle aged; physical activity; prediction; retrospective study; risk assessment; very elderly; accelerometry; chronic obstructive lung disease; exercise; machine learning; COVID-19","","","","","Chang Gung University, CGU, (BMRPC50); Ministry of Science and Technology, Taiwan, MOST, (MOST 110-2221-E-182-007)","This research study was funded in part by the Ministry of Science and Technology, Taiwan, (R.O.C.), Grant No.: MOST 110-2221-E-182-007; and by the Chang Gung University, Tao-Yuan, Taiwan (R.O.C.), Project No.: BMRPC50.","Nguyen H.Q., Chu L., Liu I.-L.A., See J.S., Suh D., Korotzer B., Yuen G., Desai S., Coleman K.J., Xiang A.H., Et al., Associations between physical activity and 30-day readmission risk in chronic obstructive pulmonary disease, Ann. Am. Thorac. Soc, 11, pp. 695-705, (2014); Hakim M.A., Garden F.L., Jennings M.D., Dobler C.C., Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease, Clin. Epidemiol, 10, pp. 51-59, (2018); Shah T., Press V.G., Huisingh-Scheetz M., White S.R., COPD Readmissions: Addressing COPD in the era of value-based health care, Chest, 150, pp. 916-926, (2016); Amalakuhan B., Kiljanek L., Parvathaneni A., Hester M., Cheriyath P., Fischman D., A prediction model for COPD readmissions: Catching up, catching our breath, and improving a national problem, J. Community Hosp. Intern. Med. Perspect, 2, (2012); Goto T., Faridi M.K., Gibo K., Toh S., Hanania N.A., Camargo A., Hasegawa K., Trends in 30-day readmission rates after COPD hospitalization, 2006–2012, Respir. Med, 130, pp. 92-97, (2017); Thorpe O., Kumar S., Johnston K., Barriers to and enablers of physical activity in patients with COPD following a hospital admission: A qualitative study, Int. J. COPD, 9, pp. 115-128, (2014); Awais M., Chiari L., Ihlen E.A.F., Helbostad J.L., Palmerini L., Physical activity classification for elderly people in free-living conditions, IEEE J. Biomed. Health Inform, 23, pp. 197-207, (2019); Mesanza A.B., Lucas S., Zubizarreta A., Cabanes I., Portillo E., Rodriguez-Larrad A., A machine learning approach to perform physical activity classification using a sensorized crutch tip, IEEE Access, 8, pp. 210023-210034, (2020); Fridriksdottir E., Bonomi A.G., Accelerometer-based human activity recognition for patient monitoring using a deep neural network, Sensors, 20, (2020); Waschki B., Kirsten A., Holz O., Muller KC H., Magnussen H., Physical activity is the strongest predictor of all-cause mortality in patients with COPD: A prospective cohort study, Chest, 140, pp. 331-342, (2011); Spruit M.A., Pitta F., McAuley E., ZuWallack R.L., Nici L., Pulmonary rehabilitation and physical activity in patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med, 192, pp. 924-933, (2015); Watz H., Calverley P., Chanez P., Dahl, Decramer M., Disse B., Finnigan H., Kirsten A.M., Rodriguez-Roisin R., Tetzlaff K., Et al., An official European respiratory society statement on physical activity in COPD, Eur. Respir. J, 44, pp. 1521-1537, (2014); Prieto-Centurion V., Casaburi R., Coultas D.B., Kansai M.M., Kitsiou S., Luo J.J., Ma J., Rand C.S., Tan A.-Y.M., Krishan J.A., Daily physical activity in patients with COPD after hospital discharge in a minority population, Chronic Obstr. Pulm. Dis, 6, pp. 332-340, (2019); Garcia-Rio F., Lores V., Mediano O., Rojo B., Hernanz A., Lopez-Collazo E., Alvarez-Sala R., Daily physical activity in patients with chronic obstructive pulmonary disease is mainly associated with dynamic hyperinflation, Am. J. Respir. Crit. Care Med, 180, pp. 506-512, (2009); Watz H., Waschki B., Boehme C., Claussen M., Meyer T., Magnussen H., Extrapulmonary effects of chronic obstructive pulmonary disease on physical activity: A cross-sectional study, Am. J. Respir. Crit. Care Med, 177, pp. 743-751, (2008); Garcia-Aymerich J., Lange P., Benet M., Schnohr P., Anto J.M., Regular physical activity reduces hospital admission and mortality in chronic obstructive pulmonary disease: A population based cohort study, Thorax, 61, pp. 772-778, (2006); Moore R., Archer K.R., Choi L., Statistical and machine learning models for classification of human wear and delivery days in accelerometry data, Sensors, 21, (2021); Patel S.A., Benzo R.P., Slivka W.A., Sciurba F.C., Activity monitoring and energy expenditure in COPD patients: A validation study, COPD J. Chronic Obstr. Pulm. Dis, 4, pp. 107-112, (2007); Watz H., Waschki B., Meyer T., Magnussen H., Physical activity in patients with COPD, Eur. Respir. J, 33, pp. 262-272, (2009); Demeyer H., Burtin C., Van Remoortel H., Hornikx M., Langer D., Dcramer M., Gosselink R., Janssens W., Troosters T., Standardizing the analysis of physical activity in patients with COPD following a pulmonary rehabilitation program, Chest, 146, pp. 318-327, (2014); Lin W.Y., Verma V.K., Lee M.Y., Lai C.S., Activity monitoring with a wrist-worn, accelerometer-based device, Micromachines, 9, (2018); Chawla H., Bulathsinghala C., Tejada J.P., Wakefield D., ZuWallack R., Physical activity as a predictor of thirty-day hospital readmission after a discharge for a clinical exacerbation of chronic obstructive pulmonary disease, Ann. Am. Thorac. Soc, 11, pp. 1203-1209, (2014); Osman L.M., Godden D.J., Friend J.A.R., Legge J.S., Douglas J.G., Quality of life and hospital re-admission in patients with chronic obstructive pulmonary disease, Thorax, 52, pp. 67-71, (1997); Lin W.Y., Verma V.K., Lee M.Y., Lin H.C., Lai C.S., Prediction of 30-day readmission for COPD patients using accelerometer-based activity monitoring, Sensors, 20, (2020); Min X., Yu B., Wang F., Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: A case study on COPD, Sci. Rep, 9, (2019); Goto T., Jo T., Matsui H., Fushimi K., Hayashi H., Yasunaga H., Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease, COPD J. Chronic Obstr. Pulm. Dis, 16, pp. 338-343, (2019); Zhou S.M., Lyons R.A., Rahman M.A., Holborow A., Brophy S., Predicting hospital readmission for campylobacteriosis from electronic health records: A machine learning and text mining perspective, J. Pers. Med, 12, (2022); Arnaud E., Elbattah M., Gignon M., Dequen G., Deep learning to predict hospitalization at triage: Integration of structured data and unstructured text, Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), pp. 4836-4841; Kansagara D., Englander H., Salanitro A., Kagen D., Theobald C., Freeman M., Kripalani S., Risk Prediction Models for Hospital Readmission A Systematic Review; Brindise L.R., Steele R.J., Machine learning-based pre-discharge prediction of hospital readmission, Proceedings of the 2018 International Conference on Computer, Information and Telecommunication Systems (CITS); Roberts D.R., Bahn V., Ciuti S., Boyce M.S., Elith J., Guillera-Arroita G., Hauenstein S., Lahoz-Monfort J.J., Schroder B., Thuiller W., Et al., Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, 40, pp. 913-929, (2017); Yagus E., Atnafu S.W., De Herrera A.G.C., Marzi C., Scheda R., Giannelli M., Tessa C., Citi L., Diciotti S., Effect of data leakage in brain MRI classification using 2D convolutional neural networks, Sci. Rep, 11, (2021); Shim M., Lee S.H., Hwang H.J., Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection, Sci. Rep, 11, (2021)","W.-Y. Lin; Department of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan, 33302, Taiwan; email: wylin@mail.cgu.edu.tw","","MDPI","","","","","","20796374","","BISSE","36005000","English","Biosensors","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85136484001"
"Pietrenko-Dabrowska A.; Koziel S.; Wojcikowski M.; Pankiewicz B.; Rydosz A.; Cao T.-V.; Wojtkiewicz K.","Pietrenko-Dabrowska, Anna (16023085900); Koziel, Slawomir (57204542925); Wojcikowski, Marek (6508250690); Pankiewicz, Bogdan (6507648091); Rydosz, Artur (36701829000); Cao, Tuan-Vu (57812086500); Wojtkiewicz, Krystian (24825891800)","16023085900; 57204542925; 6508250690; 6507648091; 36701829000; 57812086500; 24825891800","Cost-Efficient measurement platform and machine-learning-based sensor calibration for precise NO2 pollution monitoring","2024","Measurement: Journal of the International Measurement Confederation","237","","115168","","","","3","10.1016/j.measurement.2024.115168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197087788&doi=10.1016%2fj.measurement.2024.115168&partnerID=40&md5=37e70e6f87fb5ad93496765ceda1b25d","Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland; Engineering Optimization & Modeling Center, Reykjavik University, Reykjavik, 102, Iceland; AGH University of Science and Techn., Institute of Electronics, Mickiewicza 30, Krakow, 30-059, Poland; Norwegian Institute for Air Research, Kjeller, Norway; Wrocław University of Science and Technology, Wrocław, Poland","Pietrenko-Dabrowska A., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland; Koziel S., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland, Engineering Optimization & Modeling Center, Reykjavik University, Reykjavik, 102, Iceland; Wojcikowski M., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland; Pankiewicz B., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland; Rydosz A., AGH University of Science and Techn., Institute of Electronics, Mickiewicza 30, Krakow, 30-059, Poland; Cao T.-V., Norwegian Institute for Air Research, Kjeller, Norway; Wojtkiewicz K., Wrocław University of Science and Technology, Wrocław, Poland","Air quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung diseases. Notwithstanding, precise NO2 detection typically demands complex and costly equipment. This paper explores NO2 monitoring using low-cost platforms, meticulously calibrated for reliability. An integrated measurement unit is first presented that contains primary and supplementary nitrogen dioxide sensors, as well as auxiliary detectors for evaluating outside and inside temperature and humidity. The calibration process utilizes data acquired over the period of five months from various reference stations. Employing machine learning with an artificial neural network (ANN)-based and kriging interpolation surrogate models, the correction strategy integrates additive and multiplicative enhancement, predicted by the ANN through auxiliary sensor data such as temperature, humidity, and the sensor-detected NO2 levels. Extensive verification studies showcase that this calibration approach notably enhances monitoring precision (coefficient of determination surpassing 0.85 concerning reference data, and RMSE of less than four μg/m3), rendering low-cost NO2 detection practical and dependable. © 2024 Elsevier Ltd","Air quality; Artificial neural networks; Machine learning; Nitrogen dioxide; Pollutant detection; Sensor correction; Surrogate modeling","Air quality; Calibration; Costs; Interpolation; Machine learning; Nitrogen oxides; Risk assessment; Cost-efficient; Human health; Low-costs; Machine-learning; Pollutant detection; Pollution monitoring; Real time monitoring; Sensor calibration; Sensor correction; Surrogate modeling; Neural networks","","","","","Agency of Regional Atmospheric Monitoring Gdansk-Gdynia-Sopot; Narodowe Centrum Badań i Rozwoju, NCBR, (NOR /POLNOR/HAPADS/0049/2019-00); Icelandic Centre for Research, RANNIS, (217771)","The research leading to these results has received funding from the Norway Grants 2014-2021 via the National Centre for Research and Development , grant NOR /POLNOR/HAPADS/0049/2019-00. This work was also supported in part by the Icelandic Centre for Research ( RANNIS ) Grant 217771 . The authors would also like to thank the Agency of Regional Atmospheric Monitoring Gdansk-Gdynia-Sopot (ARMAG) for providing free of charge data from the reference measurement stations. ","Chen T.-M., Kuschner W.G., Gokhale J., Shofer S., Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health Effects, American J. Medical Sc., 333, 4, pp. 249-256, (2007); Schwela D., Air pollution and health in urban areas, Rev. Environmental Health, 15, 1-2, pp. 13-42, (2000); Zhao S., Liu S., Sun Y., Liu Y., Beazley R., Hou X., Assessing NO2-related health effects by non-linear and linear methods on a national level, Sc. 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Rev. Physical Chemistry, 19, pp. 565-607, (2010); Yu H., Li Q., Wang R., Chen Z., Zhang Y., Geng Y., Zhang L., Cui H., Zhang K., A deep calibration method for low-cost air monitoring sensors with multilevel sequence modeling, IEEE Trans. Instrum. Meas., 69, 9, pp. 7167-7179, (2020); Bi J., Wildani A., Chang H.H., Liu Y., Incorporating low-cost sensor measurements into high-resolution PM2.5 modeling at a large spatial scale, Environ. Sci. Technol., 54, pp. 2152-2162, (2020); Castell N., Dauge F.R., Schneider P., Vogt M., Lerner U., Fishbain B., Broday D., Bartonova A., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, pp. 293-302, (2017); Bigi A., Mueller M., Grange S.K., Ghermandi G., Hueglin C., Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. 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Comp., 25, 5, pp. 941-955, (2021); Yu S., Li Y., Active learning kriging model with adaptive uniform design for time-dependent reliability analysis, IEEE Access, 9, pp. 91625-91634, (2021); Sinha A., Shaikh V., Solving bilevel optimization problems using kriging approximations, IEEE Trans. Cybernetics, 52, 10, pp. 10639-10654, (2022); Song Z., Wang H., He C., Jin Y., A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization, IEEE Trans. Evol. Comp., 25, 6, pp. 1013-1027, (2021); ARMAGFoundation; Aggarwal C.C., Neural networks and deep learning, (2018); Yatkin S., Et al., Modified target diagram to check compliance of low-cost sensors with the data quality objectives of the European air quality directive, Atm. Env., 272, (2022)","S. Koziel; Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, 80-233, Poland; email: koziel@ru.is","","Elsevier B.V.","","","","","","02632241","","MSRMD","","English","Meas J Int Meas Confed","Article","Final","","Scopus","2-s2.0-85197087788"
"Inselman J.W.; Jeffery M.M.; Maddux J.T.; Lam R.W.; Shah N.D.; Rank M.A.; Ngufor C.G.","Inselman, Jonathan W. (56241980300); Jeffery, Molly M. (55241354400); Maddux, Jacob T. (57215485698); Lam, Regina W. (57204801547); Shah, Nilay D. (35249316000); Rank, Matthew A. (16205586500); Ngufor, Che G. (36802355600)","56241980300; 55241354400; 57215485698; 57204801547; 35249316000; 16205586500; 36802355600","A prediction model for asthma exacerbations after stopping asthma biologics","2023","Annals of Allergy, Asthma and Immunology","130","3","","305","311","6","4","10.1016/j.anai.2022.11.025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146064723&doi=10.1016%2fj.anai.2022.11.025&partnerID=40&md5=6152f3f4674b709ae84396f4e5de06a6","Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States; Department of Medicine, Mayo Clinic, Phoenix, Arizona; Mayo Clinic Alix School of Medicine, Scottsdale, Arizona; OptumLabs, Cambridge, Massachusetts, United States; Division of Allergy, Asthma, and Clinical Immunology, Mayo Clinic, Scottsdale, Arizona; Division of Pulmonology, Phoenix Children's Hospital, Phoenix, Arizona","Inselman J.W., Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States; Jeffery M.M., Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States; Maddux J.T., Department of Medicine, Mayo Clinic, Phoenix, Arizona; Lam R.W., Mayo Clinic Alix School of Medicine, Scottsdale, Arizona; Shah N.D., Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States, OptumLabs, Cambridge, Massachusetts, United States; Rank M.A., Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States, Division of Allergy, Asthma, and Clinical Immunology, Mayo Clinic, Scottsdale, Arizona, Division of Pulmonology, Phoenix Children's Hospital, Phoenix, Arizona; Ngufor C.G., Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States, Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States","Background: Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics. Objective: To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models. Methods: We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%). Results: The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort. Conclusion: Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods. © 2022 American College of Allergy, Asthma & Immunology","","Asthma; Biological Products; Female; Humans; Logistic Models; Machine Learning; Male; Middle Aged; Risk Factors; antiasthmatic agent; benralizumab; dupilumab; mepolizumab; omalizumab; reslizumab; biological product; adolescent; adult; aged; algorithm; Article; asthma; atopic dermatitis; Charlson Comorbidity Index; child; chronic obstructive lung disease; chronic urticaria; cohort analysis; cross validation; depression; disease exacerbation; drug withdrawal; elastic net logistic regression; female; gastroesophageal reflux; gradient boosting; health insurance; household income; human; infant; machine learning; male; newborn; predictive model; predictive value; random forest; rhinitis; risk factor; sensitivity and specificity; sinusitis; treatment duration; validation process; asthma; machine learning; middle aged; statistical model","","benralizumab, 1044511-01-4; dupilumab, 1190264-60-8; mepolizumab, 196078-29-2; omalizumab, 242138-07-4; reslizumab, 241473-69-8; Biological Products, ","","","National Institutes of Health, NIH, (R21 HL140287); National Heart, Lung, and Blood Institute, NHLBI; Mayo Clinic","Funding: This study was funded by the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH R21 HL140287) and the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. ","Gionfriddo M.R., Hagan J.B., Rank M.A., Why and how to step down chronic asthma drugs, BMJ, 359, (2017); DiMango E., Rogers L., Reibman J., Gerald L.B., Brown M., Sugar E.A., Et al., Risk factors for asthma exacerbation and treatment failure in adults and adolescents with well-controlled asthma during continuation and step-down therapy, Ann Am Thorac Soc, 15, 8, pp. 955-961, (2018); Perez de Llano L., Garcia-Rivero J.L., Urrutia I., Martinez-Moragon E., Ramos J., Cebollero P., Et al., A simple score for future risk prediction in patients with controlled asthma who undergo guidelines-based step-down strategy, J Allergy Clin Immunol Pract, 7, 4, pp. 1214-1221, (2019); Saito N., Kamata A., Itoga M., Tamaki M., Kayaba H., Ritz T., Assessment of biological, psychological and adherence factors in the prediction of step-down treatment for patients with well-controlled asthma, Clin Exp Allergy, 47, 4, pp. 467-478, (2017); Martinez-Moragon E., Delgado J., Mogrovejo S., Fernandez-Sanchez T., Jesus J.L., Angel M.O.M., Et al., Factors that determine the loss of control when reducing therapy by steps in the treatment of moderate-severe asthma in standard clinical practice: a multicentre Spanish study, Rev Clin Esp, 220, 2, pp. 86-93, (2020); Drummond M.B., Peters S.P., Castro M., Holbrook J.T., Irvin C.G., Smith L.J., Et al., Risk factors for montelukast treatment failure in step-down therapy for controlled asthma, J Asthma, 48, 10, pp. 1051-1057, (2011); Koskela H.O., Pruokivi M.K., Kokkarinen J., Stepping down from combination asthma therapy: the predictors of outcome, Respir Med, 117, pp. 109-115, (2016); Usmani O.S., Kemppinen A., Gardener E., Thomas V., Raju Konduru P., Callan C., Et al., A randomized pragmatic trial of changing to and stepping down fluticasone/formoterol in asthma, J Allergy Clin Immunol Pract, 5, 5, pp. 1378-1387, (2017); Wang K., Verbakel J.Y., Oke J., Fleming-Nouri A., Brewin J., Roberts N., Et al., Using fractional exhaled nitric oxide to guide step-down treatment decisions in patients with asthma: a systematic and individual patient data meta-analysis, Eur Respir J, 55, 5, (2020); Bose S., Bime C., Henderson R.G., Blake K.V., Castro M., DiMango E., Et al., Biomarkers of type 2 airway inflammation as predictors of loss of asthma control during step-down therapy for well-controlled disease: the long-acting beta-agonist step-down study (LASST), J Allergy Clin Immunol Pract, 8, 10, pp. 3474-3481, (2020); Finkelstein J., Wood J., Predicting asthma exacerbations using artificial intelligence, Stud Health Technol Inform, 190, pp. 56-58, (2013); Zein J.G., Wu C.-P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021); Ledford D., Busse W., Trzaskoma B., Omachi T.A., Rosen K., Chipps B.E., Et al., A randomized multicenter study evaluating Xolair persistence of response after long-term therapy, J Allergy Clin Immunol, 140, 1, (2017); (2020); Jeffery M.M., Inselman J.W., Maddux J.T., Lam R.W., Shah N.D., Rank M.A., Asthma patients who stop asthma biologics have a similar risk of asthma exacerbations as those who continue asthma biologics, J Allergy Clin Immunol Pract, 9, 7, pp. 2742-2750, (2021); (2020); Chang W., Liu Y., Xiao Y., Yuan X., Xu X., Zhang S., Et al., A machine-learning-based prediction method for hypertension outcomes based on medical data, Diagnostics, 9, 4, (2019); Osawa I., Goto T., Yamamoto Y., Tsugawa Y., Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data, NPJ Digit Med, 3, 1, (2020); Zou H., Hastie T., Regularization and variable selection via the elastic net, J R Stat Soc, 67, 2, pp. 301-320, (2005); Breiman L., Random forests, Mach Learn, 45, 1, pp. 5-32, (2001); Friedman J.H., Greedy function approximation: a gradient boosting machine, Ann Stat, 29, 5, pp. 1189-1232, (2001); Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Softw, 33, 1, (2010); Wright M.N., Ziegler A., pp. 1-17; Greenwell B., Boehmke B., Cunningham J., (2020); Tyree P.T., Lind B.K., Lafferty W.E., Challenges of using medical insurance claims data for utilization analysis, Am J Med Qual, 21, 4, pp. 269-275, (2006); Price W.N., Big data and black-box medical algorithms, Sci Transl Med, 10, 471, (2018); Watson D.S., Krutzinna J., Bruce I.N., Griffiths C.E., McInnes I.B., Barnes M.R., Et al., Clinical applications of machine learning algorithms: beyond the black box, BMJ, 364, (2019); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Adv Neural Inf Process Syst, (2017)","M.A. Rank; Scottsdale, 13400 E Shea Blvd, 85259; email: rank.matthew@mayo.edu","","American College of Allergy, Asthma and Immunology","","","","","","10811206","","ALAIF","36509405","English","Ann. Allergy Asthma Immunol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85146064723"
"Elbehairy A.F.; Marshall H.; Naish J.H.; Wild J.M.; Parraga G.; Horsley A.; Vestbo J.","Elbehairy, Amany F. (56006339400); Marshall, Helen (37031376800); Naish, Josephine H. (8408782400); Wild, Jim M. (7202396208); Parraga, Grace (14023130000); Horsley, Alexander (15755893600); Vestbo, Jørgen (34573970200)","56006339400; 37031376800; 8408782400; 7202396208; 14023130000; 15755893600; 34573970200","Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes","2024","European Respiratory Journal","63","5","2301010","","","","3","10.1183/13993003.01010-2023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192112929&doi=10.1183%2f13993003.01010-2023&partnerID=40&md5=b335cdab164ac5cb21532e14fff4cfad","Department of Chest Diseases, Faculty of Medicine, Alexandria University, Alexandria, Egypt; Division of Infection, Immunity and Respiratory Medicine, The University of Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom; POLARIS, Imaging, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom; MCMR, Manchester University NHS Foundation Trust, Manchester, United Kingdom; Bioxydyn Limited, Manchester, United Kingdom; Insigneo Institute for in silico Medicine, Sheffield, United Kingdom; Robarts Research Institute, Western University, London, ON, Canada; Department of Medical Biophysics, Western University, London, ON, Canada; Division of Respirology, Western University, London, ON, Canada","Elbehairy A.F., Department of Chest Diseases, Faculty of Medicine, Alexandria University, Alexandria, Egypt, Division of Infection, Immunity and Respiratory Medicine, The University of Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom; Marshall H., POLARIS, Imaging, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom; Naish J.H., MCMR, Manchester University NHS Foundation Trust, Manchester, United Kingdom, Bioxydyn Limited, Manchester, United Kingdom; Wild J.M., POLARIS, Imaging, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, Insigneo Institute for in silico Medicine, Sheffield, United Kingdom; Parraga G., Robarts Research Institute, Western University, London, ON, Canada, Department of Medical Biophysics, Western University, London, ON, Canada, Division of Respirology, Western University, London, ON, Canada; Horsley A., Division of Infection, Immunity and Respiratory Medicine, The University of Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom; Vestbo J., Division of Infection, Immunity and Respiratory Medicine, The University of Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, United Kingdom","Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes. Copyright ©The authors 2024.","","Humans; Lung; Magnetic Resonance Imaging; Pulmonary Disease, Chronic Obstructive; Respiratory Function Tests; Tomography, X-Ray Computed; fluorodeoxyglucose f 18; Article; artificial intelligence; chronic bronchitis; chronic obstructive lung disease; computer assisted tomography; cystic fibrosis; diffusion weighted imaging; disease severity; forced expiratory volume; forced vital capacity; gas exchange; grip strength; human; hypoxia; image quality; image segmentation; interstitial pneumonia; lung blood flow; lung cancer; lung function; lung gas exchange; lung parenchyma; lung perfusion; lung ventilation; lung ventilation distribution; machine learning; magnetic resonance angiography; maximal expiratory flow; micro-computed tomography; multidetector computed tomography; muscle hypertrophy; nuclear magnetic resonance imaging; nuclear magnetic resonance spectroscopy; optical coherence tomography; oxygen consumption; pathophysiology; physical activity; pneumothorax; positron emission tomography; quality of life; radiation dose; right coronary artery; six minute walk test; spirometry; thorax radiography; total lung capacity; treadmill exercise; x-ray computed tomography; diagnostic imaging; lung; lung function test; nuclear magnetic resonance imaging; procedures; x-ray computed tomography","","fluorodeoxyglucose f 18, 63503-12-8","","","Cystic Fibrosis Foundation, CFF; Medical Research Council, MRC; National Institute for Health and Care Research, NIHR; NIHR Sheffield Biomedical Research Centre, BRC; GlaxoSmithKline, GSK; JP Moulton Charitable Trust; AstraZeneca; NIHR Manchester Biomedical Research Centre and Clinical Research Facility; UK Research and Innovation, UKRI","Conflict of interest: A.F. Elbehairy has nothing to disclose. H. Marshall reports research funding from GlaxoSmithKline and meeting expenses from AstraZeneca. J.H. Naish has a part-time appointment with Bioxydyn Ltd. J.M. Wild is funded by the UKRI, NIHR, MRC and NIHR Sheffield Biomedical Research Centre, has investigator-led research funding from GlaxoSmithKline and AstraZeneca, and has provided consultancy for Vertex, Boehringer Ingelheim and AstraZeneca. G. Parraga acknowledges study funding from GlaxoSmithKline and AstraZeneca, honoraria from GlaxoSmithKline, AstraZeneca and Polarean, and travel support from GlaxoSmithKline and Polarean. A. Horsley reports personal fees from Vertex Pharmaceuticals and Mylan Pharmaceuticals, consulting fees from Vertex Pharmaceuticals, Boehringer Ingelheim and Roche Genentech, grants from JP Moulton Charitable Trust, EPSRC, CF Trust, Cystic Fibrosis Foundation and NIHR, and leadership roles with the Translational Research Collaboration and CF Trust Clinical Trials Accelerator Platform, outside the submitted work. J. Vestbo reports personal fees from ALK-Abell\u00F3, AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline and Teva, outside the submitted work. A. Horsley and J. Vestbo are supported by the NIHR Manchester Biomedical Research Centre and Clinical Research Facility.","Quaderi SA, Hurst JR., The unmet global burden of COPD, Glob Health Epidemiol Genom, 3, (2018); Kakavas S, Kotsiou OS, Perlikos F, Et al., Pulmonary function testing in COPD: looking beyond the curtain of FEV1, NPJ Prim Care Respir Med, 31, (2021); Muller NL., Advances in imaging, Eur Respir J, 18, pp. 867-871, (2001); Barker AL, Eddy RL, MacNeil JL, Et al., CT pulmonary vessels and MRI ventilation in chronic obstructive pulmonary disease: relationship with worsening FEV<sub>1</sub> in the TINCan cohort study, Acad Radiol, 28, pp. 495-506, (2021); Kirby M, Svenningsen S, Kanhere N, Et al., Pulmonary ventilation visualized using hyperpolarized helium-3 and xenon-129 magnetic resonance imaging: differences in COPD and relationship to emphysema, J Appl Physiol, 114, pp. 707-715, (2013); Kirby M, Tanabe N, Tan WC, Et al., Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. 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Marshall H, Collier GJ, Johns CS, Et al., Imaging collateral ventilation in patients with advanced chronic obstructive pulmonary disease: relative sensitivity of <sup>3</sup>He and <sup>129</sup>Xe MRI, J Magn Reson Imaging, 49, pp. 1195-1197, (2019); Ohno Y, Hatabu H, Takenaka D, Et al., Oxygen-enhanced MR ventilation imaging of the lung: preliminary clinical experience in 25 subjects, AJR Am J Roentgenol, 177, pp. 185-194, (2001); Ohno Y, Koyama H, Nogami M, Et al., Dynamic oxygen-enhanced MRI versus quantitative CT: pulmonary functional loss assessment and clinical stage classification of smoking-related COPD, AJR Am J Roentgenol, 190, pp. W93-W99, (2008); Ohno Y, Iwasawa T, Seo JB, Et al., Oxygen-enhanced magnetic resonance imaging versus computed tomography: multicenter study for clinical stage classification of smoking-related chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 177, pp. 1095-1102, (2008); Jobst BJ, Triphan SM, Sedlaczek O, Et al., Functional lung MRI in chronic obstructive pulmonary disease: comparison of T1 mapping, oxygen-enhanced T1 mapping and dynamic contrast enhanced perfusion, PLoS One, 10, (2015); Ohno Y, Yui M, Yoshikawa T, Et al., 3D oxygen-enhanced MRI at 3 T MR system: comparison with thin-section CT of quantitative capability for pulmonary functional loss assessment and clinical stage classification of COPD in smokers, J Magn Reson Imaging, 53, pp. 1042-1051, (2021); Voskrebenzev A, Kaireit TF, Klimes F, Et al., PREFUL MRI depicts dual bronchodilator changes in COPD: a retrospective analysis of a randomized controlled trial, Radiol Cardiothorac Imaging, 4, (2022); Obert AJ, Gutberlet M, Kern AL, Et al., Examining lung microstructure using <sup>19</sup>F MR diffusion imaging in COPD patients, Magn Reson Med, 88, pp. 860-870, (2022); Hueper K, Vogel-Claussen J, Parikh MA, Et al., Pulmonary microvascular blood flow in mild chronic obstructive pulmonary disease and emphysema. The MESA COPD study, Am J Respir Crit Care Med, 192, pp. 570-580, (2015)","A.F. Elbehairy; Department of Chest Diseases, Faculty of Medicine, Alexandria University, Alexandria, Egypt; email: dr.amanyelbehairy@yahoo.com","","European Respiratory Society","","","","","","09031936","","ERJOE","38548292","English","Eur. Respir. J.","Article","Final","","Scopus","2-s2.0-85192112929"
"Li R.; Song M.; Wang R.; Su N.; E L.","Li, Rui (57245579500); Song, Mengyi (57198324128); Wang, Ronghua (58842367100); Su, Ningling (58073840300); E, Linning (16836179100)","57245579500; 57198324128; 58842367100; 58073840300; 16836179100","Can CT-Based Arterial and Venous Morphological Markers of Chronic Obstructive Pulmonary Disease Explain Pulmonary Vascular Remodeling?","2024","Academic Radiology","31","1","","22","34","12","4","10.1016/j.acra.2023.04.026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160422689&doi=10.1016%2fj.acra.2023.04.026&partnerID=40&md5=ac89452afbaba6d70d6c6fb6a7a9d269","Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, People's Hospital of Longhua, No. 38 Jinglong Construction Rd, Shenzhen, 518109, China","Li R., Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Song M., Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Wang R., Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Su N., Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; E L., Department of Radiology, People's Hospital of Longhua, No. 38 Jinglong Construction Rd, Shenzhen, 518109, China","Rationale and Objectives: We analyzed changes in quantitative pulmonary artery and vein parameters to investigate pulmonary vascular remodeling characteristics in chronic obstructive pulmonary disease (COPD) patients. Materials and Methods: This retrospective study recruited healthy volunteers and COPD patients. Participants undergoing standard-of-care pulmonary function testing (PFT) and computed tomography (CT) evaluations were classified into five groups: normal and Global Initiative for Chronic Obstructive Lung Disease (GOLD) grades 1-4. Artery and vein analyses (volumes, numbers, densities, and fractions) were performed using artificial intelligence. Results: Among 139 subjects (136 men; mean age, 64 years ± 8 [SD]) with GOLD grade 1 (n = 13), grade 2 (n = 49), grade 3 (n = 42), grade 4 (n = 17) and control subjects (n = 18) enrolled, differences in arterial volumes (BV5-10, BV10+, pulmonary arterial volume) and venous densities (BV5 density, BV10+ density, pulmonary venous density, pulmonary venous branch density) among control and GOLD grades 1-4 were statistically significant (P < .05). Higher pulmonary arterial volumes and lower number were observed with more advanced COPD. The number and volumes of pulmonary veins were lower in GOLD grades 2 and 3 than in GOLD grade 1 but higher in GOLD grade 4 than in GOLD grade 3. The numbers and volumes of pulmonary arteries and veins showed varying positive correlations (γ = 0.18-0.96, P < .05). Pulmonary vascular densities were mildly to moderately correlated with PFT results (γ = 0.236-0.495, P < .05) and were moderately negatively correlated with the emphysema percentage (γ = − 0.591 to − 0.315, P < .05). Conclusion: Patients with COPD exhibited pulmonary vascular remodeling, which occurred in the arteries at the early grade of COPD and in the veins at the late grade. CT-based quantitative analysis of pulmonary vasculature may become an imaging marker for early diagnosis and assessment of COPD severity. © 2023 The Association of University Radiologists","Chronic obstructive pulmonary disease; Pulmonary arteries; Pulmonary veins; Vascular remodeling","Artificial Intelligence; Humans; Hypertension, Pulmonary; Lung; Male; Middle Aged; Pulmonary Artery; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Retrospective Studies; Tomography, X-Ray Computed; Vascular Remodeling; adult; aged; Article; artificial intelligence; blood vessel density; body mass; cardiovascular parameters; chronic obstructive lung disease; controlled study; convolutional neural network; correlational study; demographics; diffusing capacity for carbon monoxide; disease severity; early diagnosis; emphysema; female; fibrosing alveolitis; forced expiratory volume; forced vital capacity; human; image quality; image segmentation; lung blood vessel; lung diffusion; lung function test; lung ventilation; major clinical study; male; morphological trait; pulmonary artery; pulmonary vascular remodeling; pulmonary vein; quantitative analysis; residual volume; retrospective study; smoking; thorax radiography; total lung capacity; vascular remodeling; chronic obstructive lung disease; diagnostic imaging; lung; lung emphysema; middle aged; procedures; pulmonary artery; pulmonary hypertension; vascular remodeling; x-ray computed tomography","","","SOMATOM Definition AS, Siemens Healthcare, Germany; SOMATOM Definition Flash, Siemens Healthcare, Germany","Siemens Healthcare, Germany; Siemens Healthcare, Germany","Science and Technology of Shanxi Province, China, (2022XM42)","This work was supported by the Four “Batches” Innovation Project of Invigorating Medical through Science and Technology of Shanxi Province, China [Grant number 2022XM42 ].","Christenson S.A., Smith B.M., Bafadhel M., Et al., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Wang C., Xu J., Yang L., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Voelkel N.F., Cool C.D., Pulmonary vascular involvement in chronic obstructive pulmonary disease, Eur Respir J Suppl, 46, pp. 28s-32s, (2003); Peinado V.I., Pizarro S., Barbera J.A., Pulmonary vascular involvement in COPD, Chest, 134, pp. 808-814, (2008); Peinado V.I., Barbera J.A., Ramirez J., Et al., Endothelial dysfunction in pulmonary arteries of patients with mild COPD, Am J Physiol, 274, pp. L908-L913, (1998); Chaouat A., Naeije R., Weitzenblum E., Pulmonary hypertension in COPD, Eur Respir J, 32, pp. 1371-1385, (2008); Synn A.J., Li W., San Jose Estepar R., Et al., Pulmonary vascular pruning on computed tomography and risk of death in the Framingham Heart Study, Am J Respir Crit Care Med, 203, pp. 251-254, (2021); Park S.W., Lim M.N., Kim W.J., Et al., Quantitative assessment the longitudinal changes of pulmonary vascular counts in chronic obstructive pulmonary disease, Respir Res, 23, (2022); Cho Y.H., Lee S.M., Seo J.B., Et al., Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: associations with pulmonary function test and survival in the KOLD cohort, Eur J Radiol, 108, pp. 276-282, (2018); Synn A.J., Margerie-Mellon C., Jeong S.Y., Et al., Vascular remodeling of the small pulmonary arteries and measures of vascular pruning on computed tomography, Pulm Circ, 11, (2021); Pistenmaa C.L., Nardelli P., Ash S.Y., Et al., Pulmonary arterial pruning and longitudinal change in percent emphysema and lung function: the Genetic Epidemiology of COPD Study, Chest, 160, pp. 470-480, (2021); Rahaghi F.N., Argemi G., Nardelli P., Et al., Pulmonary vascular density: comparison of findings on computed tomography imaging with histology, Eur Respir J, 54, (2019); Nardelli P., Jimenez-Carretero D., Bermejo-Pelaez D., Et al., Pulmonary artery-vein classification in CT images using deep learning, IEEE Trans Med Imaging, 37, pp. 2428-2440, (2018); Bruni C., Occhipinti M., Pienn M., Et al., Lung vascular changes as biomarkers of severity in systemic sclerosis-associated interstitial lung disease, Rheumatology, 62, pp. 696-706, (2022); Jacob J., Pienn M., Payer C., Et al., Quantitative CT-derived vessel metrics in idiopathic pulmonary fibrosis: a structure-function study, Respirology, 24, pp. 445-452, (2019); Tuder R.M., Cool C.D., Pulmonary arteries and microcirculation in COPD with pulmonary hypertension: bystander or culprit?, Chest, 156, pp. 4-6, (2019); Leopold J.A., Pulmonary venous remodeling in pulmonary hypertension: the veins take center stage, Circulation, 137, pp. 1811-1813, (2018); Andersen K.H., Andersen C.B., Gustafsson F., Et al., Pulmonary venous remodeling in COPD-pulmonary hypertension and idiopathic pulmonary arterial hypertension, Pulm Circ, 7, pp. 514-521, (2017); Rahaghi F.N., Nardelli P., Harder E., Et al., Quantification of arterial and venous morphologic markers in pulmonary arterial hypertension using CT imaging, Chest, 160, pp. 2220-2231, (2021); (2021); Pu J., Leader J.K., Sechrist J., Et al., Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans, Med Image Anal, 77, (2022); Blanco I., Piccari L., Barbera J.A., Pulmonary vasculature in COPD: the silent component, Respirology, 21, pp. 984-994, (2016); Sakao S., Voelkel N.F., Tatsumi K., The vascular bed in COPD: pulmonary hypertension and pulmonary vascular alterations, Eur Respir Rev, 23, pp. 350-355, (2014); Santos S., Peinado V.I., Ramirez J., Et al., Characterization of pulmonary vascular remodelling in smokers and patients with mild COPD, Eur Respir J, 19, pp. 632-638, (2002); Hueper K., Vogel-Claussen J., Parikh M.A., Et al., Pulmonary microvascular blood flow in mild chronic obstructive pulmonary disease and emphysema. The MESA COPD Study, Am J Respir Crit Care Med, 192, pp. 570-580, (2015); Gelinas J.C., Lewis N.C., Harper M.I., Et al., Aerobic exercise training does not alter vascular structure and function in chronic obstructive pulmonary disease, Exp Physiol, 102, pp. 1548-1560, (2017); Ma Z., Yu Y.R., Badea C.T., Et al., Vascular endothelial growth factor receptor 3 regulates endothelial function through beta-arrestin 1, Circulation, 139, pp. 1629-1642, (2019); Synn A.J., Zhang C., Washko G.R., Et al., Cigarette smoke exposure and radiographic pulmonary vascular morphology in the Framingham Heart Study, Ann Am Thorac Soc, 16, pp. 698-706, (2019); Gao Y., Raj J.U., Role of veins in regulation of pulmonary circulation, Am J Physiol Lung Cell Mol Physiol, 288, pp. L213-L226, (2005); Peng G., Lu W., Li X., Et al., Expression of store-operated Ca2+ entry and transient receptor potential canonical and vanilloid-related proteins in rat distal pulmonary venous smooth muscle, Am J Physiol Lung Cell Mol Physiol, 299, pp. L621-L630, (2010); Xu L., Chen Y., Yang K., Et al., Chronic hypoxia increases TRPC6 expression and basal intracellular Ca2+ concentration in rat distal pulmonary venous smooth muscle, PLoS One, 9, (2014); Wang Y., Su T., Feng S., Et al., Evaluation of the cross-sectional area of small pulmonary vessels in the diagnosis of chronic obstructive pulmonary disease by quantitative computed tomography: a case-control study, Medicine, 100, (2021); Matsuura Y., Kawata N., Yanagawa N., Et al., Quantitative assessment of cross-sectional area of small pulmonary vessels in patients with COPD using inspiratory and expiratory MDCT, Eur J Radiol, 82, pp. 1804-1810, (2013); Rahaghi F.N., Ross J.C., Agarwal M., Et al., Pulmonary vascular morphology as an imaging biomarker in chronic thromboembolic pulmonary hypertension, Pulm Circ, 6, pp. 70-81, (2016); Rahaghi F.N., Wells J.M., Come C.E., Et al., Arterial and venous pulmonary vascular morphology and their relationship to findings in cardiac magnetic resonance imaging in smokers, J Comput Assist Tomogr, 40, pp. 948-952, (2016)","L. E; Department of Radiology, People's Hospital of Longhua, Shenzhen, No. 38 Jinglong Construction Rd, 518109, China; email: elinning@163.com","","Elsevier Inc.","","","","","","10766332","","ARADF","37248100","English","Acad. Radiol.","Article","Final","","Scopus","2-s2.0-85160422689"
"Schmalstig A.A.; Zorn K.M.; Murcia S.; Robinson A.; Savina S.; Komarova E.; Makarov V.; Braunstein M.; Ekins S.","Schmalstig, Alan A. (57203416583); Zorn, Kimberley M. (57201902863); Murcia, Sebastian (57218611551); Robinson, Andrew (57419988100); Savina, Svetlana (57028025400); Komarova, Elena (56684724800); Makarov, Vadim (7401690520); Braunstein, Miriam (7006357699); Ekins, Sean (57203197233)","57203416583; 57201902863; 57218611551; 57419988100; 57028025400; 56684724800; 7401690520; 7006357699; 57203197233","Mycobacterium abscessus drug discovery using machine learning","2022","Tuberculosis","132","","102168","","","","5","10.1016/j.tube.2022.102168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123223351&doi=10.1016%2fj.tube.2022.102168&partnerID=40&md5=c513d4987a0c35d706934f93ae9a9557","Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, 27599, North Carolina, United States; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, 27606, NC, United States; Research Center of Biotechnology RAS, Moscow, 119071, Russian Federation","Schmalstig A.A., Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, 27599, North Carolina, United States; Zorn K.M., Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, 27606, NC, United States; Murcia S., Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, 27599, North Carolina, United States; Robinson A., Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, 27599, North Carolina, United States; Savina S., Research Center of Biotechnology RAS, Moscow, 119071, Russian Federation; Komarova E., Research Center of Biotechnology RAS, Moscow, 119071, Russian Federation; Makarov V., Research Center of Biotechnology RAS, Moscow, 119071, Russian Federation; Braunstein M., Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, 27599, North Carolina, United States; Ekins S., Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive Lab 3510, Raleigh, 27606, NC, United States","The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence. Mycobacterium abscessus is one of the most commonly encountered organisms in nontuberculous mycobacteria disease and it is the most difficult to eradicate. There is currently no systematically proven regimen that is effective for treating M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M. abscessus in vitro but may represent a starting point for future further medicinal chemistry optimization. We also address limitations still to be overcome with the machine learning approach for M. abscessus. © 2022 Elsevier Ltd","Drug discovery; Machine learning; Mycobacterium abscessus; Mycobacterium tuberculosis; Nontuberculous mycobacteria","Antitubercular Agents; Bayes Theorem; Drug Discovery; Humans; Machine Learning; Mycobacterium abscessus; 1 (phenylsulfonyl) 1h benzimidazol 2 amine derivative; benzimidazole derivative; kanamycin; resazurin; tuberculostatic agent; unclassified drug; tuberculostatic agent; antibacterial activity; Article; atypical mycobacteriosis; atypical Mycobacterium; controlled study; drug cytotoxicity; drug development; drug efficacy; evaporation; human; human cell; in vitro study; machine learning; medicinal chemistry; minimum inhibitory concentration; Mycobacterium abscessus; Mycobacterium tuberculosis; nonhuman; prediction; THP-1 cell line; Bayes theorem; devices; drug development; drug effect; metabolism; Mycobacterium abscessus; procedures","","kanamycin, 11025-66-4, 61230-38-4, 8063-07-8; resazurin, 550-82-3; Antitubercular Agents, ","","","Molecular Materials Informatics, Inc.; National Institute of General Medical Sciences, NIGMS, (R44GM122196); National Institute of Allergy and Infectious Diseases, NIAID; Russian Science Foundation, RSF, (21-15-00042)","Funding text 1: We kindly acknowledge NIH NIGMS funding to develop the software from R44GM122196-02A1 and Dr. Alex M. Clark (Molecular Materials Informatics, Inc.) for Assay Central® support. EK and VM were supported by the Russian Science Foundation under grant 21-15-00042 . ; Funding text 2: We kindly acknowledge NIH NIGMS funding to develop the software from R44GM122196-02A1 and Dr. Alex M. Clark (Molecular Materials Informatics, Inc.) for Assay Central? support. EK and VM were supported by the Russian Science Foundation under grant 21-15-00042. Dr. Mohamed Nasr is thanked for assistance with obtaining the NIAID ChemDB HIV, Opportunistic Infection and Tuberculosis Therapeutics Database. Interested parties can contact NIH NIAID and it has restrictions on reuse.","Egorova A., Jackson M., Gavrilyuk V., Makarov V., Pipeline of anti-Mycobacterium abscessus small molecules: repurposable drugs and promising novel chemical entities, Med Res Rev, (2021); Lopeman R.C., Harrison J., Desai M., Cox J.A.G., Mycobacterium abscessus: environmental bacterium turned clinical nightmare, Microorganisms, 7, (2019); Ganapathy U.S., Dartois V., Dick T., Repositioning rifamycins for Mycobacterium abscessus lung disease, Expet Opin Drug Discov, 14, pp. 867-878, (2019); Chopra S., Matsuyama K., Hutson C., Madrid P., Identification of antimicrobial activity among FDA-approved drugs for combating Mycobacterium abscessus and Mycobacterium chelonae, J Antimicrob Chemother, 66, pp. 1533-1536, (2011); Aziz D.B., Low J.L., Wu M.L., Gengenbacher M., Teo J.W.P., Dartois V., Et al., Rifabutin is active against Mycobacterium abscessus complex, Antimicrob Agents Chemother, (2017); Jeong J., Kim G., Moon C., Kim H.J., Kim T.H., Jang J., Pathogen Box screening for hit identification against Mycobacterium abscessus, PLoS One, 13, (2018); 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Ekins S., Reynolds R.C., Franzblau S.G., Wan B., Freundlich J.S., Bunin B.A., Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models, PLoS One, 8, (2013); Ekins S., Reynolds R.C., Kim H., Koo M.S., Ekonomidis M., Talaue M., Et al., Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery, Chem Biol, 20, pp. 370-378, (2013); Ekins S., Casey A.C., Roberts D., Parish T., Bunin B.A., Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis, Tuberculosis (Edinb), 94, pp. 162-169, (2014); Ekins S., Reynolds R., Kim H., Koo M.-S., Ekonomidis M., Talaue M., Et al., Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery, Chem Biol, 20, pp. 370-378, (2013); Pereira J.C., Daher S.S., Zorn K.M., Sherwood M., Russo R., Perryman A.L., Et al., Machine learning platform to discover novel growth inhibitors of Neisseria gonorrhoeae, Pharm Res (N Y), 37, (2020); Dalecki A.G., Zorn K.M., Clark A.M., Ekins S., Narmore W.T., Tower N., Et al., High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus, Metallomics, 11, pp. 696-706, (2019); Willighagen E.L., Mayfield J.W., Alvarsson J., Berg A., Carlsson L., Jeliazkova N., Et al., The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching, J Cheminf, 9, (2017); Anantpadma M., Lane T., Zorn K.M., Lingerfelt M.A., Clark A.M., Freundlich J.S., Et al., Ebola virus bayesian machine learning models enable new in vitro leads, ACS Omega, 4, pp. 2353-2361, (2019); Ekins S., Gerlach J., Zorn K.M., Antonio B.M., Lin Z., Gerlach A., Repurposing approved drugs as inhibitors of Kv7.1 and Nav1.8 to treat pitt hopkins syndrome, Pharm Res (N Y), 36, (2019); Ekins S., Puhl A.C., Zorn K.M., Lane T.R., Russo D.P., Klein J.J., Et al., Exploiting machine learning for end-to-end drug discovery and development, Nat Mater, 18, pp. 435-441, (2019); Hernandez H.W., Soeung M., Zorn K.M., Ashoura N., Mottin M., Andrade C.H., Et al., High throughput and computational repurposing for neglected diseases, Pharm Res (N Y), 36, (2018); Lane T., Russo D.P., Zorn K.M., Clark A.M., Korotcov A., Tkachenko V., Et al., Comparing and validating machine learning models for Mycobacterium tuberculosis drug discovery, Mol Pharm, 15, pp. 4346-4360, (2018); Russo D.P., Zorn K.M., Clark A.M., Zhu H., Ekins S., Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction, Mol Pharm, 15, pp. 4361-4370, (2018); Sandoval P.J., Zorn K.M., Clark A.M., Ekins S., Wright S.H., Assessment of substrate-dependent ligand interactions at the organic cation transporter OCT2 using six model substrates, Mol Pharmacol, 94, pp. 1057-1068, (2018); Wang P.F., Neiner A., Lane T.R., Zorn K.M., Ekins S., Kharasch E.D., Halogen substitution influences ketamine metabolism by cytochrome P450 2B6: in vitro and computational approaches, Mol Pharm, 16, pp. 898-906, (2019); Zorn K.M., Lane T.R., Russo D.P., Clark A.M., Makarov V., Ekins S., Multiple machine learning comparisons of HIV cell-based and reverse transcriptase data sets, Mol Pharm, 16, pp. 1620-1632, (2019); Clark A.M., Dole K., Coulon-Spektor A., McNutt A., Grass G., Freundlich J.S., Et al., Open source bayesian models. 1. Application to ADME/tox and drug discovery datasets, J Chem Inf Model, 55, pp. 1231-1245, (2015); Shen G.H., Wu B.D., Wu K.M., Chen J.H., In Vitro activities of isepamicin, other aminoglycosides, and capreomycin against clinical isolates of rapidly growing mycobacteria in Taiwan, Antimicrob Agents Chemother, 51, pp. 1849-1851, (2007); Kozikowski A.P., Onajole O.K., Stec J., Dupont C., Viljoen A., Richard M., Et al., Targeting mycolic acid transport by indole-2-carboxamides for the treatment of Mycobacterium abscessus infections, J Med Chem, 60, pp. 5876-5888, (2017); Franz N.D., Belardinelli J.M., Kaminski M.A., Dunn L.C., Calado Nogueira de Moura V., Blaha M.A., Et al., Design, synthesis and evaluation of indole-2-carboxamides with pan anti-mycobacterial activity, Bioorg Med Chem, 25, pp. 3746-3755, (2017); Fernandez-Roblas R., Martin-de-Hijas N.Z., Fernandez-Martinez A.I., Garcia-Almeida D., Gadea I., Esteban J., In vitro activities of tigecycline and 10 other antimicrobials against nonpigmented rapidly growing mycobacteria, Antimicrob Agents Chemother, 52, pp. 4184-4186, (2008); Falkinham J.O., Macri R.V., Maisuria B.B., Actis M.L., Sugandhi E.W., Williams A.A., Et al., Antibacterial activities of dendritic amphiphiles against nontuberculous mycobacteria, Tuberculosis (Edinb), 92, pp. 173-181, (2012); Disratthakit A., Doi N., In vitro activities of DC-159a, a novel fluoroquinolone, against Mycobacterium species, Antimicrob Agents Chemother, 54, pp. 2684-2686, (2010); Baranyai Z., Kratky M., Vinsova J., Szabo N., Senoner Z., Horvati K., Et al., Combating highly resistant emerging pathogen Mycobacterium abscessus and Mycobacterium tuberculosis with novel salicylanilide esters and carbamates, Eur J Med Chem, 101, pp. 692-704, (2015); Pang H., Li G., Wan L., Jiang Y., Liu H., Zhao X., Et al., In vitro drug susceptibility of 40 international reference rapidly growing mycobacteria to 20 antimicrobial agents, Int J Clin Exp Med, 8, pp. 15423-15431, (2015); Cieslik W., Spaczynska E., Malarz K., Tabak D., Nevin E., O'Mahony J., Et al., Investigation of the antimycobacterial activity of 8-hydroxyquinolines, Med Chem, 11, pp. 771-779, (2015); Anon, NIAID ChemDB HIV, Opportunistic infection and tuberculosis Therapeutics database, (2018); Gaulton A., Hersey A., Nowotka M., Bento A.P., Chambers J., Mendez D., Et al., The ChEMBL database in 2017, Nucleic Acids Res, 45, pp. D945-D954, (2017); Ekins S., Bradford J., Dole K., Spektor A., Gregory K., Blondeau D., Et al., A collaborative database and computational models for tuberculosis drug discovery, Mol Biosyst, 6, pp. 840-851, (2010); Ekins S., Kaneko T., Lipinksi C.A., Bradford J., Dole K., Spektor A., Et al., Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol Biosyst, 6, pp. 2316-2324, (2010); Gamo F.-J., Sanz L.M., Vidal J., de Cozar C., Alvarez E., Lavandera J.-L., Et al., Thousands of chemical starting points for antimalarial lead identification, Nature, 465, pp. 305-310, (2010); Ekins S., Reynolds R.C., Franzblau S.G., Wan B., Freundlich J.S., Bunin B.A., Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event bayesian models, PLoS One, 8, (2013); Ekins S., Casey A.C., Roberts D., Parish T., Bunin B.A., Bayesian models for screening and TB mobile for target inference with Mycobacterium tuberculosis, Tuberculosis (Edinburgh, Scotland), 94, pp. 162-169, (2014); Ekins S., Freundlich J.S., Reynolds R.C., Fusing dual-event datasets for Mycobacterium Tuberculosis machine learning models and their evaluation, J Chem Inf Model, 53, pp. 3054-3063, (2013); Ekins S., Freundlich J.S., Hobrath J.V., Lucile White E., Reynolds R.C., Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery, Pharm Res (N Y), 31, pp. 414-435, (2014); Ekins S., Freundlich J.S., Reynolds R.C., Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis, J Chem Inf Model, 54, pp. 2157-2165, (2014); Ekins S., Perryman A.L., Clark A.M., Reynolds R.C., Freundlich J.S., Machine learning model analysis and data visualization with small molecules tested in a mouse model of Mycobacterium tuberculosis infection (2014-2015), J Chem Inf Model, 56, pp. 1332-1343, (2016); Ekins S., Pottorf R., Reynolds R.C., Williams A.J., Clark A.M., Freundlich J.S., Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis, J Chem Inf Model, 54, pp. 1070-1082, (2014)","S. Ekins; Collaborations Pharmaceuticals, Inc., Raleigh, 840 Main Campus Drive Lab 3510, 27606, United States; email: sean@collaborationspharma.com","","Churchill Livingstone","","","","","","14729792","","TUBEC","35077930","English","Tuberculosis","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85123223351"
"Mercurio G.; Gottardelli B.; Lenkowicz J.; Patarnello S.; Bellavia S.; Scala I.; Rizzo P.; de Belvis A.G.; Del Signore A.B.; Maviglia R.; Bocci M.G.; Olivi A.; Franceschi F.; Urbani A.; Calabresi P.; Valentini V.; Antonelli M.; Frisullo G.","Mercurio, Giovanna (7003799852); Gottardelli, Benedetta (57888188100); Lenkowicz, Jacopo (57194679683); Patarnello, Stefano (57219864227); Bellavia, Simone (57217145602); Scala, Irene (57217148135); Rizzo, Pierandrea (57475758700); de Belvis, Antonio Giulio (23982191300); Del Signore, Anna Benedetta (58726070800); Maviglia, Riccardo (6507299085); Bocci, Maria Grazia (6701818886); Olivi, Alessandro (7003325947); Franceschi, Francesco (7005989850); Urbani, Andrea (55198663000); Calabresi, Paolo (7102418853); Valentini, Vincenzo (7006177042); Antonelli, Massimo (7102393593); Frisullo, Giovanni (6507175376)","7003799852; 57888188100; 57194679683; 57219864227; 57217145602; 57217148135; 57475758700; 23982191300; 58726070800; 6507299085; 6701818886; 7003325947; 7005989850; 55198663000; 7102418853; 7006177042; 7102393593; 6507175376","A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model","2024","European Journal of Neurology","31","3","e16153","","","","3","10.1111/ene.16153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178050184&doi=10.1111%2fene.16153&partnerID=40&md5=00fdbc3748fbd896b3aeb67eb0bbff7e","Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy; Gemelli Generator RWD, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Catholic University of Sacred Heart, Rome, Italy; Department of Life Sciences and Public Health, Section of Hygiene, Università Cattolica del Sacro Cuore, Rome, Italy; Clinical Pathways and Outcome Evaluation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Global Medical Department-Primary Care Unit, Angelini Pharma, Rome, Italy; Department of Laboratory and Infectious Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy","Mercurio G., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Gottardelli B., Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy; Lenkowicz J., Gemelli Generator RWD, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Patarnello S., Gemelli Generator RWD, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Bellavia S., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Scala I., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Rizzo P., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; de Belvis A.G., Department of Life Sciences and Public Health, Section of Hygiene, Università Cattolica del Sacro Cuore, Rome, Italy, Clinical Pathways and Outcome Evaluation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Del Signore A.B., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Global Medical Department-Primary Care Unit, Angelini Pharma, Rome, Italy; Maviglia R., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Bocci M.G., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Olivi A., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Franceschi F., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Urbani A., Catholic University of Sacred Heart, Rome, Italy, Department of Laboratory and Infectious Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy; Calabresi P., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Valentini V., Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Antonelli M., Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, Catholic University of Sacred Heart, Rome, Italy; Frisullo G., Department of Aging, Neurological, Orthopedic and Head and Neck Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy","Background: The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS). Methods: This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis. Results: Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0–1), medium (RRS = 2–3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively. Conclusions: The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs. © 2023 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.","brain hemorrhage; discharge pathways; re-admission; risk score; stroke","Hospitals; Humans; Machine Learning; Retrospective Studies; Risk Factors; Stroke; hemoglobin; acute ischemic stroke; adult; aged; Article; atrial fibrillation; brain hemorrhage; cerebrovascular accident; chronic obstructive lung disease; cohort analysis; female; hemoglobin blood level; hospital discharge; hospital readmission; hospitalization; human; machine learning; major clinical study; male; observational study; prediction; predictive model; readmission risk score; receiver operating characteristic; retrospective study; risk assessment; scoring system; stroke patient; subarachnoid hemorrhage; subdural hematoma; cerebrovascular accident; hospital; machine learning; risk factor","","hemoglobin, 9008-02-0","","","Italian Ministry for University and Research; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","This study received partial funding from the Italian Ministry for University and Research (MUR) under the Program PON Research and Innovation supporting the development of the artificial intelligence platform Gemelli Generator at Fondazione Policlinico Universitario A. Gemelli IRCCS. ","Jencks S.F., Williams M.V., Coleman E.A., Rehospitalizations among patients in the Medicare fee-for-service program, N Engl J Med, 360, pp. 1418-1428, (2009); Greenwald P.W., Estevez R.M., Clark S., Stern M.E., Rosen T., Flomenbaum N., The ED as the primary source of hospital admission for older (but not younger) adults, Am J Emerg Med, 34, pp. 943-947, (2016); (2022); (2022); DeFrances C.J., Lucas C.A., Buie V.C., Golosinskiy A., National Hospital Discharge Survey, Natl Health Stat Report, 2008, pp. 1-20, (2006); Sloan F.A., Taylor D.H., Picone G., Costs and outcomes of hip fracture and stroke, 1984 to 1994, Am J Public Health, 89, pp. 935-937, (1999); Moloney E.D., Bennett K., Silke B., Patient and disease profile of emergency medical readmissions to an Irish teaching hospital, Postgrad Med J, 80, pp. 470-474, (2004); Zhong W., Geng N., Wang P., Li Z., Cao L., Prevalence, causes and risk factors of hospital readmissions after acute stroke and transient ischemic attack: a systematic review and meta-analysis, Neurol Sci, 37, pp. 1195-1202, (2016); Bohannon R.W., Lee N., Association of physical functioning with same-hospital readmission after stroke, Am J Phys Med Rehabil, 83, pp. 434-438, (2004); Ottenbacher K.J., Smith P.M., Illig S.B., Linn R.T., Fiedler R.C., Granger C.V., Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke, J Clin Epidemiol, 54, pp. 1159-1165, (2001); Kilkenny M.F., Longworth M., Pollack M., Et al., Factors associated with 28-day hospital readmission after stroke in Australia, Stroke, 44, pp. 2260-2268, (2013); Naylor A.R., Prevention of operation related stroke: are we asking the right questions?, Cardiovasc Surg, 7, pp. 155-157, (1999); Strowd R.E., Wise S.M., Umesi U.N., Et al., Predictors of 30-day hospital readmission following ischemic and hemorrhagic stroke, Am J Med Qual, 30, pp. 441-446, (2015); Shah S.V., Corado C., Bergman D., Et al., Impact of poststroke medical complications on 30-day readmission rate, J Stroke Cerebrovasc Dis, 24, pp. 1969-1977, (2015); Mouchtouris N., Al Saiegh F., Valcarcel B., Et al., Predictors of 30-day hospital readmission after mechanical thrombectomy for acute ischemic stroke, J Neurosurg, 134, pp. 1500-1504, (2020); Mocnik F.-B., Mobasheri A., Zipf A., Open source data mining infrastructure for exploring and analysing OpenStreetMap, Open Geospat Data, Softw Stand, 3, (2018); Tolles J., Meurer W.J., Logistic regression: relating patient characteristics to outcomes, JAMA, 316, pp. 533-534, (2016); Breiman L., Friedman J.H., Olshen R.A., Stone C.J., Classification and Regression Trees, (1984); Tin K.H., The random subspace method for constructing decision forests, IEEE Trans Pattern Anal Mach Intell, 20, pp. 832-844, (1998); Chen T., Guestrin C., XGBoost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Loebel E.M., Rojas M., Wheelwright D., Mensching C., Stein L.K., High risk features contributing to 30-day readmission after acute ischemic stroke: a single center retrospective case-control study, Neurohospitalist, 12, pp. 24-30, (2022); Leppert M.H., Sillau S., Lindrooth R.C., Poisson S.N., Campbell J.D., Simpson J.R., Relationship between early follow-up and readmission within 30 and 90 days after ischemic stroke, Neurology, 94, pp. e1249-e1258, (2020); Burke J.F., Skolarus L.E., Adelman E.E., Reeves M.J., Brown D.L., Influence of hospital-level practices on readmission after ischemic stroke, Neurology, 82, pp. 2196-2204, (2014); Allen A., Barron T., Mo A., Et al., Impact of neurological follow-up on early hospital readmission rates for acute ischemic stroke, Neurohospitalist, 7, pp. 127-131, (2017); Suri M.F., Qureshi A.I., Readmission within 1 month of discharge among patients with acute ischemic stroke: results of the University HealthSystem Consortium Stroke Benchmarking study, J Vasc Interv Neurol, 6, pp. 47-51, (2013); Lakshminarayan K., Schissel C., Anderson D.C., Et al., Five-year rehospitalization outcomes in a cohort of patients with acute ischemic stroke: medicare linkage study, Stroke, 42, pp. 1556-1562, (2011); Kilkenny M.F., Dalli L.L., Kim J., Et al., Factors associated with 90-day readmission after stroke or transient ischemic attack: linked data from the Australian stroke clinical registry, Stroke, 51, pp. 571-578, (2020); Bodenheimer T., Sinsky C., From triple to quadruple aim: care of the patient requires care of the provider, Ann Fam Med, 12, pp. 573-576, (2014); McHugh M.D., Kutney-Lee A., Cimiotti J.P., Sloane D.M., Aiken L.H., Nurses' widespread job dissatisfaction, burnout, and frustration with health benefits signal problems for patient care, Health Aff (Millwood), 30, pp. 202-210, (2011); Fehnel C.R., Lee Y., Wendell L.C., Thompson B.B., Potter N.S., Mor V., Post-acute care data for predicting readmission after ischemic stroke: a nationwide cohort analysis using the minimum data set, J Am Heart Assoc, 4, (2015); Zhao P., Yoo I., Naqvi S.H., Early prediction of unplanned 30-day hospital readmission: model development and retrospective data analysis, JMIR Med Inform, 9, (2021); Lin K.P., Chen P.C., Huang L.Y., Mao H.C., Chan D.D., Predicting inpatient readmission and outpatient admission in elderly: a population-based cohort study, Medicine (Baltimore), 95, (2016); van Walraven C., Dhalla I.A., Bell C., Et al., Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community, CMAJ, 182, pp. 551-557, (2010); Donze J.D., Williams M.V., Robinson E.J., Et al., International validity of the HOSPITAL score to predict 30-day potentially avoidable hospital readmissions, JAMA Intern Med, 176, pp. 496-502, (2016); Sieck C., Adams W., Burkhart L., Validation of the BOOST risk stratification tool as a predictor of unplanned 30-day readmission in elderly patients, Qual Manag Health Care, 28, pp. 96-102, (2019); Jun-O'Connell A.H., Grigoriciuc E., Silver B., Et al., Association between the LACE+ index and unplanned 30-day hospital readmissions in hospitalized patients with stroke, Front Neurol, 13, (2022)","G. Mercurio; Department of Emergency Science, Anesthesiology and Intensive Care, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Largo A. Gemelli, 8, 00168, Italy; email: giovanna.mercurio@policlinicogemelli.it","","John Wiley and Sons Inc","","","","","","13515101","","EJNEF","38015472","English","Eur. J. Neurol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85178050184"
"Moreno Mendez R.; Marín A.; Ferrando J.R.; Rissi Castro G.; Cepeda Madrigal S.; Agostini G.; Catalan Serra P.","Moreno Mendez, Rosaly (58117126900); Marín, Antonio (59139097700); Ferrando, José Ramon (59138580900); Rissi Castro, Giuliana (59139619200); Cepeda Madrigal, Sonia (57224730398); Agostini, Gabriela (59139097800); Catalan Serra, Pablo (39260956400)","58117126900; 59139097700; 59138580900; 59139619200; 57224730398; 59139097800; 39260956400","Artificial Intelligence Applied to Forced Spirometry in Primary Care; [Inteligencia artificial aplicada a la espirometría forzada en Atención Primaria]","2024","Open Respiratory Archives","6","","100313","","","","4","10.1016/j.opresp.2024.100313","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193916925&doi=10.1016%2fj.opresp.2024.100313&partnerID=40&md5=14e7ae0bbc12123766d60affd1080f25","Department of Internal Medicine, Kristiansund Hospital, Møre og Romsdal, Norway; Data Science in Energy, Seville, Spain; Pneumology Department, La Ribera Hospital, Alzira, Valencia, Spain; Pneumology Department, Vila-Real Hospital, La Plana, Castellón, Spain; Otolaryngology Department, Vida Clinic, Santa Cruz de Tenerife, Spain","Moreno Mendez R., Department of Internal Medicine, Kristiansund Hospital, Møre og Romsdal, Norway; Marín A., Data Science in Energy, Seville, Spain; Ferrando J.R., Pneumology Department, La Ribera Hospital, Alzira, Valencia, Spain; Rissi Castro G., Pneumology Department, Vila-Real Hospital, La Plana, Castellón, Spain; Cepeda Madrigal S., Pneumology Department, Vila-Real Hospital, La Plana, Castellón, Spain; Agostini G., Otolaryngology Department, Vida Clinic, Santa Cruz de Tenerife, Spain; Catalan Serra P., Department of Internal Medicine, Kristiansund Hospital, Møre og Romsdal, Norway","Introduction: This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care. Material and methods: A total of 1190 smokers, aged 30–80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. The sample was analyzed using AI tools. Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation. Results: With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%. Conclusion: An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed. © 2024 Sociedad Española de Neumología y Cirugía Torácica (SEPAR)","Case-finding; Cross-validation; Gradient boosting; Spirometry","adult; aged; algorithm; area under the curve; Article; artificial intelligence; bronchodilatation; chronic obstructive lung disease; clinical study; controlled study; cross validation; data analysis; diagnostic accuracy; diagnostic test accuracy study; diagnostic value; exploratory factor analysis; female; forced expiratory volume; forced spirometry; forced vital capacity; gradient boosting algorithm; human; independent variable; lower limit of normality; machine learning; major clinical study; male; mathematical model; mutual information analysis; practice guideline; predictive value; primary medical care; respiratory tract disease; sensitivity and specificity; smoking; spirometric obstructive pattern; spirometry; validation process; very elderly; workflow","","","","","Boehringer Ingelheim; Valencian Community; Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana, Fisabio","This study was carried out through a grant offered by Boehringer Ingelheim. The funding for the grant was managed by The Foundation for the Advancement of Health and Biomedical Research in the Valencian Community (FISABIO).","Burney P., Jithoo A., Kato B., Janson C., Mannino D., Nizankowska-Mogilnicka E., Et al., Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty – a BOLD analysis, Thorax, 69, pp. 465-473, (2014); Luis Izquierdo J., Casanova C., Celli B., Santos S., Sibila O., Sobradillo P., Et al., The 7 cardinal sins of COPD in Spain, Arch Bronconeumol, 58, pp. 498-503, (2021); Represas-Represas C., Botana-Rial M., Leiro-Fernandez V., Gonzalez-Silva A.I., Garcia-Martinez A., Fernandez-Villar A., Short- and long-term effectiveness of a supervised training program in spirometry use for primary care professionals, Arch Bronconeumol, 49, pp. 378-382, (2013); Miravitlles M., Soriano J.B., Garcia-Rio F., Munoz L., Duran-Tauleria E., Sanchez G., Et al., Prevalence of COPD in Spain: impact of undiagnosed COPD on quality of life and daily life activities, Thorax, 64, pp. 863-868, (2009); Topalovic M., Das N., Burgel P.-R., Daenen M., Derom E., Haenebalcke C., Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur Respir J, 53, (2019); Lillywhite A., Wolbring G., Coverage of ethics within the artificial intelligence and machine learning academic literature: the case of disabled people, Assist Technol, 33, pp. 129-135, (2021); Ben-Israel D., Jacobs W.B., Casha S., Lang S., Ryu W.H.A., de Lotbiniere-Bassett M., Et al., The impact of machine learning on patient care: a systematic review, Artif Intell Med, 103, (2020); Gonem S., Janssens W., Das N., Topalovic M., Applications of artificial intelligence and machine learning in respiratory medicine, Thorax, 75, pp. 695-701, (2020); Burki T.K., Predicting lung cancer prognosis using machine learning, Lancet Oncol, 17, (2016); Min X., Yu B., Wang F., Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: a case study on COPD, Sci Rep, 9, (2019); Barton C., Chettipally U., Zhou Y., Jiang Z., Lynn-Palevsky A., Le S., Et al., Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs, Comput Biol Med, 109, pp. 79-84, (2019); Brusasco V., Crapo R., Viegi G., American Thoracic Society European Respiratory Society, Coming together: the ATS/ERS consensus on clinical pulmonary function testing, Eur Respir J, 26, pp. 1-2, (2005); Agusti A., Bohm M., Celli B., Criner G.J., Garcia-Alvarez A., Martinez F., Et al., GOLD COPD DOCUMENT 2023: a brief update for practicing cardiologists, Clin Res Cardiol, 113, pp. 1-10, (2023); Baudot P., Tapia M., Bennequin D., Goaillard J.-M., Topological information data analysis, Entropy, 21, (2019); Kraskov A., Stogbauer H., Grassberger P., Estimating mutual information, Phys Rev E, 69, (2004); Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Et al., LightGBM: a highly efficient gradient boosting decision tree, Proceedings of the 31st international conference on neural information processing systems NIPS’17, Curran Associates Inc., Red Hook, NY, USA, pp. 3149-3157, (2017); Sohil F., Sohali M.U., Shabbir J., An introduction to statistical learning with applications in R: by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, New York Springer Science and Business Media, 2013, $41.98, eISBN: 978-1-4614-7137-7, Stat Theory Relat Fields, 6, (2022); Ancochea J., Miravitlles M., Garcia-Rio F., Munoz L., Sanchez G., Sobradillo V., Et al., Infradiagnóstico de la enfermedad pulmonar obstructiva crónica en mujeres: cuantificación del problema, determinantes y propuestas de acción, Arch Bronconeumol, 49, pp. 223-229, (2013); Soriano J.B., Alfageme I., Miravitlles M., De Lucas P., Soler-Cataluna J.J., Garcia-Rio F., Et al., Prevalence and determinants of COPD in Spain: EPISCAN II, Arch Bronconeumol, 57, pp. 61-69, (2021); Toda R., Hoshino T., Kawayama T., Imaoka H., Sakazaki Y., Tsuda T., Et al., Validation of “Lung Age” measured by spirometry and handy electronic FEV1/FEV6 meter in pulmonary diseases, Intern Med, 48, pp. 513-521, (2009); Cohen J.P., Cao T., Viviano J.D., Huang C.-W., Fralick M., Ghassemi M., Et al., Problems in the deployment of machine-learned models in health care, CMAJ, 193, pp. E1391-E1394, (2021); Beam A.L., Kohane I.S., Big data and machine learning in health care, JAMA, 319, pp. 1317-1318, (2018); Bi Q., Goodman K.E., Kaminsky J., Lessler J., What is machine learning? A primer for the epidemiologist, Am J Epidemiol, 188, pp. 2222-2239, (2019); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics J, 25, pp. 811-827, (2019); Mekov E., Miravitlles M., Petkov R., Artificial intelligence and machine learning in respiratory medicine, Expert Rev Respir Med, 14, pp. 559-564, (2020); Feng Y., Wang Y., Zeng C., Mao H., Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease, Int J Med Sci, 18, pp. 2871-2889, (2021); Topalovic M., Laval S., Aerts J.-M., Troosters T., Decramer M., Janssens W., Automated interpretation of pulmonary function tests in adults with respiratory complaints, Respir Int Rev Thorac Dis, 93, pp. 170-178, (2017); Agusti A., Calverley P.M.A., Celli B., Coxson H.O., Edwards L.D., Lomas D.A., Et al., Characterisation of COPD heterogeneity in the ECLIPSE cohort, Respir Res, 11, (2010); Weiskopf N.G., Weng C., Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research, JAMA, 20, pp. 144-151, (2013); Lovejoy C.A., Phillips E., Maruthappu M., Application of artificial intelligence in respiratory medicine: has the time arrived?, Respirol Carlton Vic, 24, pp. 1136-1137, (2019); Celli B., From Laennec's stethoscope to the magic of imaging big data and artificial intelligence: a timeline of precision medicine for patients with COPD, Am J Respir Crit Care Med, 208, pp. 342-344, (2023)","R. Moreno Mendez; Department of Internal Medicine, Kristiansund Hospital, Møre og Romsdal, Norway; email: morenomendezrosaly@gmail.com","","Elsevier Espana S.L.U","","","","","","26596636","","","","English","Open Respirat. Arc.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85193916925"
"Jo Y.S.; Han S.; Lee D.; Min K.H.; Park S.J.; Yoon H.K.; Lee W.-Y.; Yoo K.H.; Jung K.-S.; Rhee C.K.","Jo, Yong Suk (57214460535); Han, Solji (57205527212); Lee, Daeun (58304034300); Min, Kyung Hoon (57224493212); Park, Seoung Ju (8578599900); Yoon, Hyoung Kyu (57214031690); Lee, Won-Yeon (57190397262); Yoo, Kwang Ha (57693301700); Jung, Ki-Suck (7402479869); Rhee, Chin Kook (35202293000)","57214460535; 57205527212; 58304034300; 57224493212; 8578599900; 57214031690; 57190397262; 57693301700; 7402479869; 35202293000","Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease","2023","Scientific Reports","13","1","18669","","","","3","10.1038/s41598-023-45835-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175625464&doi=10.1038%2fs41598-023-45835-4&partnerID=40&md5=a578415a817ff5a852516ecb5356684b","Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, South Korea; Department of Statistics and Data Science, Yonsei University, Seoul, South Korea; Department of Applied Statistics, Yonsei University, Seoul, South Korea; Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, South Korea; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Yeouido St Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Department of Internal Medicine, Yonsei University Wonju College of Medicine, Gangwon, Wonju, South Korea; Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Konkuk University School of Medicine, Seoul, South Korea; Division of Pulmonary Medicine, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical School, Anyang, South Korea","Jo Y.S., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, South Korea; Han S., Department of Statistics and Data Science, Yonsei University, Seoul, South Korea; Lee D., Department of Applied Statistics, Yonsei University, Seoul, South Korea; Min K.H., Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea; Park S.J., Department of Internal Medicine, Jeonbuk National University Medical School, Jeonju, South Korea; Yoon H.K., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Yeouido St Mary’s Hospital, The Catholic University of Korea, Seoul, South Korea; Lee W.-Y., Department of Internal Medicine, Yonsei University Wonju College of Medicine, Gangwon, Wonju, South Korea; Yoo K.H., Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Konkuk University School of Medicine, Seoul, South Korea; Jung K.-S., Division of Pulmonary Medicine, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University Medical School, Anyang, South Korea; Rhee C.K., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, South Korea","Acute exacerbation (AE) of chronic obstructive pulmonary disease (COPD) compromises health status; it increases disease progression and the risk of future exacerbations. We aimed to develop a model to predict COPD exacerbation. We merged the Korean COPD subgroup study (KOCOSS) dataset with nationwide medical claims data, information regarding weather, air pollution, and epidemic respiratory virus data. The Korean National Health and Nutrition Examination Survey (KNHANES) dataset was used for validation. Several machine learning methods were employed to increase the predictive power. The development dataset consisted of 590 COPD patients enrolled in the KOCOSS cohort; these were randomly divided into training and internal validation subsets on the basis of the individual claims data. We selected demographic and spirometry data, medications for COPD and hospital visit for AE, air pollution data and meteorological data, and influenza virus data as contributing factors for the final model. Six machine learning and logistic regression tools were used to evaluate the performance of the model. A light gradient boosted machine (LGBM) afforded the best predictive power with an area under the curve (AUC) of 0.935 and an F1 score of 0.653. Similar favorable predictive performance was observed for the 2151 individuals in the external validation dataset. Daily prediction of the COPD exacerbation risk may help patients to rapidly assess their risk of exacerbation and will guide them to take appropriate intervention in advance. This might lead to reduction of the personal and socioeconomic burdens associated with exacerbation. © 2023, The Author(s).","","Disease Progression; Humans; Nutrition Surveys; Pulmonary Disease, Chronic Obstructive; chronic obstructive lung disease; controlled study; disease exacerbation; human; nutrition; randomized controlled trial","","","","","Ministry of Environment, MOE, (2022003310008); Ministry of Environment, MOE; Korea National Institute of Health, KNIH, (2016ER670100, 2016ER670101, 2016ER670102, 2018ER67100, 2018ER67101, 2018ER67102, 2021ER120500, 2021ER120501, 2021ER120502); Korea National Institute of Health, KNIH; Korea Environmental Industry and Technology Institute, KEITI","This study was supported by grants from the Korean Environment Industry and Technology Institute through the Core Technology Development Project for Environmental Disease Prevention and Management, funded by the Korea Ministry of Environment (Grant number 2022003310008). The KOCOSS cohort was supported by the Research Program funded Korea National Institute of Health (Fund CODE 2016ER670100, 2016ER670101, 2016ER670102, 2018ER67100, 2018ER67101, 2018ER67102, 2021ER120500, 2021ER120501 and 2021ER120502). ","Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med., 3, 11, (2006); Halbert R.J., Natoli J.L., Gano A., Et al., Global burden of COPD: Systematic review and meta-analysis, Eur. Respir. J., 28, 3, pp. 523-532, (2006); Korean Statistical Information Service (KOSIS), (2016); Suissa S., Dell'Aniello S., Ernst P., Long-term natural history of chronic obstructive pulmonary disease: Severe exacerbations and mortality, Thorax, 67, 11, pp. 957-963, (2012); McGhan R., Radcliff T., Fish R., Et al., Predictors of rehospitalization and death after a severe exacerbation of COPD, Chest, 132, 6, pp. 1748-1755, (2007); Garcia-Aymerich J., Serra Pons I., Mannino D.M., Et al., Lung function impairment, COPD hospitalisations and subsequent mortality, Thorax, 66, 7, pp. 585-590, (2011); Press V.G., Konetzka R.T., White S.R., Insights about the economic impact of chronic obstructive pulmonary disease readmissions post implementation of the hospital readmission reduction program, Curr. Opin. Pulm. Med., 24, 2, pp. 138-146, (2018); Wedzicha J.A., Seemungal T.A., COPD exacerbations: Defining their cause and prevention, Lancet, 370, 9589, pp. 786-796, (2007); Halpern M.T., Stanford R.H., Borker R., The burden of COPD in the U.S.A.: Results from the Confronting COPD survey, Respir. Med., 97, pp. S81-S89, (2003); Dalal A.A., Christensen L., Liu F., Et al., Direct costs of chronic obstructive pulmonary disease among managed care patients, Int. J. Chron. Obstruct. Pulmon. Dis., 5, pp. 341-349, (2010); Guerra B., Gaveikaite V., Bianchi C., Et al., Prediction models for exacerbations in patients with COPD, Eur. Respir. Rev., 26, 143, (2017); Keene J.D., Jacobson S., Kechris K., Et al., Biomarkers predictive of exacerbations in the SPIROMICS and COPDGene cohorts, Am. J. Respir. Crit. Care Med., 195, 4, pp. 473-481, (2017); Weichenthal S.A., Lavigne E., Evans G.J., Et al., Fine particulate matter and emergency room visits for respiratory illness. Effect modification by oxidative potential, Am. J. Respir. Crit. Care Med., 194, 5, pp. 577-586, (2016); Zhu R., Chen Y., Wu S., Et al., The relationship between particulate matter (PM10) and hospitalizations and mortality of chronic obstructive pulmonary disease: A meta-analysis, Copd, 10, 3, pp. 307-315, (2013); Kurai D., Saraya T., Ishii H., Et al., Virus-induced exacerbations in asthma and COPD, Front. Microbiol., 4, (2013); Wedzicha J.A., Role of viruses in exacerbations of chronic obstructive pulmonary disease, Proc. Am. Thorac. Soc., 1, 2, pp. 115-120, (2004); Lee J., Jung H.M., Kim S.K., Et al., Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis, Sci. Rep., 9, 1, (2019); Adibi A., Sin D.D., Safari A., Et al., The acute COPD exacerbation prediction tool (ACCEPT): A modelling study, Lancet Respir. Med., 8, 10, pp. 1013-1021, (2020); Air Korea; Lee J.Y., Chon G.R., Rhee C.K., Et al., Characteristics of patients with chronic obstructive pulmonary disease at the first visit to a pulmonary medical center in Korea: The KOrea COpd subgroup study team cohort, J. Korean Med. Sci., 31, 4, pp. 553-560, (2016); Kim J.A., Lim M.K., Kim K., Et al., Adherence to inhaled medications and its effect on healthcare utilization and costs among high-grade chronic obstructive pulmonary disease patients, Clin. Drug Investig., 38, 4, pp. 333-340, (2018); Rhee C.K., Yoon H.K., Yoo K.H., Et al., Medical utilization and cost in patients with overlap syndrome of chronic obstructive pulmonary disease and asthma, Copd, 11, 2, pp. 163-170, (2014); Kim J., Rhee C.K., Yoo K.H., Et al., The health care burden of high grade chronic obstructive pulmonary disease in Korea: Analysis of the Korean Health Insurance Review and Assessment Service data, Int. J. Chron. Obstruct. Pulmon. Dis., 8, pp. 561-568, (2013); Jo Y.S., Kim K.J., Rhee C.K., Et al., Prevalence, characteristics, and risk of exacerbation in young patients with chronic obstructive pulmonary disease, Respir. Res., 23, 1, (2022); Choi J.Y., Kim K.U., Kim D.K., Et al., Pulmonary rehabilitation is associated with decreased exacerbation and mortality in patients with chronic obstructive pulmonary disease: A nationwide Korean study, Chest, (2023); Lim J., Choi S.E., Bae E., Et al., Mapping analysis to estimate EQ-5D utility values using the COPD assessment test in Korea, Health Qual. Life Outcomes, 17, 1, (2019); Kim D.S., Introduction: Health of the health care system in Korea, Soc. Work Public Health, 25, 2, pp. 127-141, (2010); Diab N., Gershon A.S., Sin D.D., Et al., Underdiagnosis and overdiagnosis of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 198, 9, pp. 1130-1139, (2018); Park Y.B., Yoo K.H., The current status of chronic obstructive pulmonary disease awareness, treatments, and plans for improvement in South Korea: A narrative review, J. Thorac. Dis., 13, 6, pp. 3898-3906, (2021); Colak Y., Afzal S., Nordestgaard B.G., Et al., Prognosis of asymptomatic and symptomatic, undiagnosed COPD in the general population in Denmark: A prospective cohort study, Lancet Respir. Med., 5, 5, pp. 426-434, (2017); Gershon A.S., Thiruchelvam D., Chapman K.R., Et al., Health services burden of undiagnosed and overdiagnosed COPD, Chest, 153, 6, pp. 1336-1346, (2018)","C.K. Rhee; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, 222 Banpo-daero, Seocho-Gu, 06591, South Korea; email: chinkook77@gmail.com","","Nature Research","","","","","","20452322","","","37907619","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175625464"
"Mahdavi H.; Rahbarpour S.; Hosseini-Golgoo S.M.; Jamaati H.","Mahdavi, Hannaneh (57219848490); Rahbarpour, Saeideh (26423325800); Hosseini-Golgoo, Seyed Mohsen (27267641800); Jamaati, Hamidreza (7801446806)","57219848490; 26423325800; 27267641800; 7801446806","A single gas sensor assisted by machine learning algorithms for breath-based detection of COPD: A pilot study","2024","Sensors and Actuators A: Physical","376","","115650","","","","3","10.1016/j.sna.2024.115650","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197557658&doi=10.1016%2fj.sna.2024.115650&partnerID=40&md5=ea37cbb00fd9c86a9977d49d0d7ad3a6","Department of Electrical and Computer Engineering, University of Central Florida, Orlando, 32816, FL, United States; Department of Electrical Engineering, Shahed University, Tehran, Iran; Department of Electrical Engineering, University of Guilan, Rasht, Iran; Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran","Mahdavi H., Department of Electrical and Computer Engineering, University of Central Florida, Orlando, 32816, FL, United States; Rahbarpour S., Department of Electrical Engineering, Shahed University, Tehran, Iran; Hosseini-Golgoo S.M., Department of Electrical Engineering, University of Guilan, Rasht, Iran; Jamaati H., Chronic Respiratory Diseases Research Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran","Chronic obstructive pulmonary disease (COPD) is a widespread respiratory disorder with a high mortality rate. Current diagnostic methods have limitations, necessitating innovative diagnostic approaches. Breath analysis using electronic noses (e-noses) is promising but still faces challenges. The single temperature-modulated sensor (STMS) is one of the optimized models of e-noses. It uses a single sensor instead of an array of multiple sensors, which enhances commercial viability and ease of use. This pilot study explores the feasibility of using a STMS device for COPD detection. Breath samples were collected from 34 healthy individuals and 33 COPD patients. Features extracted from the sensor's response were analyzed, and a novel feature selection method was developed to discriminate between the two groups. Using this method and a linear support vector machine (SVM) classifier, a single commercial sensor, driven with half-sine-staircase and staircase heater waveforms, achieved 80.60 % accuracy, 78.79 % sensitivity, and 82.35 % specificity in differentiating between COPD patients and healthy controls. These results demonstrate the potential of an appropriately configured STMS to capture comprehensive breath information for COPD detection. Furthermore, our study highlights how advancements in machine learning can maximize the effectiveness of this approach. These promising pilot study results warrant further investigation with larger, more diverse cohorts to validate this approach for broader clinical implementation. © 2024 Elsevier B.V.","Breath analysis; Chronic obstructive pulmonary disease (COPD); Machine learning; Metal oxide sensor (MOS); Single temperature-modulated sensor (STMS); Support vector machine (SVM)","Diagnosis; Electronic nose; Learning algorithms; Learning systems; Pulmonary diseases; Stairs; Breath analysis; Chronic obstructive pulmonary disease; Machine-learning; Metal oxide sensor; Metal oxide sensors; Pilot studies; Single temperature-modulated sensor; Support vector machine; Support vectors machine; Support vector machines","","","","","","","Hussain A., Et al., pp. 368-372, (2021); Scarlata S., Finamore P., Meszaros M., Dragonieri S., Bikov A., The role of electronic noses in phenotyping patients with chronic obstructive pulmonary disease, Biosensors, 10, 11, (2020); Vestbo J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am. 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Res., 10, 3, (2016); Mahdavi H., Rahbarpour S., Goldoust R., Hosseini-Golgoo S.M., Jamaati H., Investigating simultaneous effects of flow rate and chamber structure on the performance of metal oxide gas sensors, IEEE Sens. J., 21, 19, pp. 21612-21621, (2021); Long Y., Et al., High performance exhaled breath biomarkers for diagnosis of lung cancer and potential biomarkers for classification of lung cancer, J. Breath. Res., (2020); Burgues J., Esclapez M.D., Donate S., Marco S., RHINOS: a lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants, Iscience, 24, 12, (2021); Tatli S., Mirzaee-Ghaleh E., Rabbani H., Karami H., Wilson A.D., Rapid detection of urea fertilizer effects on voc emissions from cucumber fruits using a MOS E-Nose Sensor Array, Agronomy, 12, 1, (2021); Mahdavi H., Rahbarpour S., Hosseini-Golgoo S.M., Jamaati H., pp. 01-05, (2021); Liao Y.-H., Shih C.-H., Abbod M.F., Shieh J.-S., Hsiao Y.-J., Development of an E-nose system using machine learning methods to predict ventilator-associated pneumonia, Microsyst. Technol., pp. 1-11, (2020); Byun H.-G., Yu J.-B., Huh J.-S., Lim J.-O., Exhaled breath analysis system based on electronic nose techniques applicable to lung diseases, Hanyang Med. Rev., 34, 3, pp. 125-129, (2014); Lu B., Fu L., Nie B., Peng Z., Liu H., A novel framework with high diagnostic sensitivity for lung cancer detection by electronic nose, Sensors, 19, 23, (2019); Kou L., Zhang D., You J., Jiang Y., Breath analysis for detecting diseases on respiratory, metabolic and digestive system, J. Biomed. Sci. Eng., 12, 1, (2019); Hosseini-Golgoo S.-M., Hossein-Babaei F., Assessing the diagnostic information in the response patterns of a temperature-modulated tin oxide gas sensor, Meas. Sci. Technol., 22, 3, (2011); Lee A.P., Reedy B.J., Temperature modulation in semiconductor gas sensing, Sens. Actuators B: Chem., 60, 1, pp. 35-42, (1999); Karami H., Chemeh S.K., Azizi V., Sharifnasab H., Ramos J., Kamruzzaman M., Gas sensor-based machine learning approaches for characterizing tarragon aroma and essential oil under various drying conditions, Sens. Actuators A: Phys., (2023); Haripriya P., Rangarajan M., Pandya H.J., Breath VOC analysis and machine learning approaches for disease screening: a review, J. Breath. Res., (2023); Mahdavi H., Rahbarpour S., Hosseini-Golgoo S.-M., Jamaati H., Reducing the destructive effect of ambient humidity variations on gas detection capability of a temperature modulated gas sensor by calcium chloride, Sens. Actuators B: Chem., 331, (2021); Bosch S., Et al., Electronic nose sensor drift affects diagnostic reliability and accuracy of disease-specific algorithms, Sensors, 22, 23, (2022)","S. Rahbarpour; Department of Electrical Engineering, Shahed University, Tehran, Iran; email: s.rahbarpour@shahed.ac.ir","","Elsevier B.V.","","","","","","09244247","","SAAPE","","English","Sens Actuators A Phys","Article","Final","","Scopus","2-s2.0-85197557658"
"Wei Q.; Mease P.J.; Chiorean M.; Iles-Shih L.; Matos W.F.; Baumgartner A.; Molani S.; Hwang Y.M.; Belhu B.; Ralevski A.; Hadlock J.","Wei, Qi (57425800300); Mease, Philip J (7004354863); Chiorean, Michael (18133792800); Iles-Shih, Lulu (58492073700); Matos, Wanessa F (58135877400); Baumgartner, Andrew (57426098900); Molani, Sevda (57204087457); Hwang, Yeon Mi (57424573500); Belhu, Basazin (57787459700); Ralevski, Alexandra (41762567200); Hadlock, Jennifer (57208694487)","57425800300; 7004354863; 18133792800; 58492073700; 58135877400; 57426098900; 57204087457; 57424573500; 57787459700; 41762567200; 57208694487","Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system","2024","The Lancet Digital Health","6","5","","e309","e322","13","3","10.1016/S2589-7500(24)00021-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191021897&doi=10.1016%2fS2589-7500%2824%2900021-9&partnerID=40&md5=bbdd47e24fef6af72c8feb0a88259fd8","Institute for Systems Biology, Seattle, WA, United States; Providence St Joseph Health–Swedish Medical Center, Seattle, WA, United States; Digestive Health Institute, Swedish Medical Center, Seattle, WA, United States; Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Wei Q., Institute for Systems Biology, Seattle, WA, United States; Mease P.J., Providence St Joseph Health–Swedish Medical Center, Seattle, WA, United States; Chiorean M., Digestive Health Institute, Swedish Medical Center, Seattle, WA, United States; Iles-Shih L., Digestive Health Institute, Swedish Medical Center, Seattle, WA, United States; Matos W.F., Institute for Systems Biology, Seattle, WA, United States; Baumgartner A., Institute for Systems Biology, Seattle, WA, United States; Molani S., Institute for Systems Biology, Seattle, WA, United States; Hwang Y.M., Institute for Systems Biology, Seattle, WA, United States; Belhu B., Institute for Systems Biology, Seattle, WA, United States; Ralevski A., Institute for Systems Biology, Seattle, WA, United States; Hadlock J., Institute for Systems Biology, Seattle, WA, United States, Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Background: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investigate the effects of the pandemic time period and the risks associated with individual IMIDs, classes of immunomodulatory medications (IMMs), chronic comorbidities, and COVID-19 vaccination status. Methods: In this retrospective cohort study, clinical data were derived from the electronic health records of an integrated health-care system serving patients in 51 hospitals and 1085 clinics across seven US states (Providence St Joseph Health). Data were observed for patients (no age restriction) with one or more IMID and for unmatched controls without IMIDs. COVID-19 was identified with a positive nucleic acid amplification test result for SARS-CoV-2. Two timeframes were analysed: March 1, 2020–Dec 25, 2021 (pre-omicron period), and Dec 26, 2021–Aug 30, 2022 (omicron-predominant period). Primary outcomes were hospitalisation, mechanical ventilation, and mortality in patients with COVID-19. Factors, including IMID diagnoses, comorbidities, long-term use of IMMs, and COVID-19 vaccination status, were analysed with multivariable logistic regression (LR) and extreme gradient boosting (XGB). Findings: Of 2 167 656 patients tested for SARS-CoV-2, 290 855 (13·4%) had confirmed COVID-19: 15 397 (5·3%) patients with IMIDs and 275 458 (94·7%) without IMIDs. In the pre-omicron period, 169 993 (11·2%) of 1 517 295 people who were tested for COVID-19 tested positive, of whom 23 330 (13·7%) were hospitalised, 1072 (0·6%) received mechanical ventilation, and 5294 (3·1%) died. Compared with controls, patients with IMIDs and COVID-19 had higher rates of hospitalisation (1176 [14·6%] vs 22 154 [13·7%]; p=0·024) and mortality (314 [3·9%] vs 4980 [3·1%]; p<0·0001). In the omicron-predominant period, 120 862 (18·6%) of 650 361 patients tested positive for COVID-19, of whom 14 504 (12·0%) were hospitalised, 567 (0·5%) received mechanical ventilation, and 2001 (1·7%) died. Compared with controls, patients with IMIDs and COVID-19 (7327 [17·3%] of 42 249) had higher rates of hospitalisation (13 422 [11·8%] vs 1082 [14·8%]; p<0·0001) and mortality (1814 [1·6%] vs 187 [2·6%]; p<0·0001). Age was a risk factor for worse outcomes (adjusted odds ratio [OR] from 2·1 [95% CI 2·0–2·1]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001), whereas COVID-19 vaccination (from 0·082 [0·080–0·085]; p<0·0001 to 0·52 [0·50–0·53]; p<0·0001) and booster vaccination (from 2·1 [2·0–2·2]; p<0·0001 to 3·0 [2·9–3·0]; p<0·0001) status were associated with better outcomes. Seven chronic comorbidities were significant risk factors during both time periods for all three outcomes: atrial fibrillation, coronary artery disease, heart failure, chronic kidney disease, chronic obstructive pulmonary disease, chronic liver disease, and cancer. Two IMIDs, asthma (adjusted OR from 0·33 [0·32–0·34]; p<0·0001 to 0·49 [0·48–0·51]; p<0·0001) and psoriasis (from 0·52 [0·48–0·56] to 0·80 [0·74–0·87]; p<0·0001), were associated with a reduced risk of severe outcomes. IMID diagnoses did not appear to be significant risk factors themselves, but results were limited by small sample size, and vasculitis had high feature importance in LR. IMMs did not appear to be significant, but less frequently used IMMs were limited by sample size. XGB outperformed LR, with the area under the receiver operating characteristic curve for models across different time periods and outcomes ranging from 0·77 to 0·92. Interpretation: Our results suggest that age, chronic comorbidities, and not being fully vaccinated might be greater risk factors for severe COVID-19 outcomes in patients with IMIDs than the use of IMMs or the IMIDs themselves. Overall, there is a need to take age and comorbidities into consideration when developing COVID-19 guidelines for patients with IMIDs. Further research is needed for specific IMIDs (including IMID severity at the time of SARS-CoV-2 infection) and IMMs (considering dosage and timing before a patient's first COVID-19 infection). Funding: Pfizer, Novartis, Janssen, and the National Institutes of Health. © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.","","Adult; Aged; Comorbidity; COVID-19; COVID-19 Vaccines; Female; Hospitalization; Humans; Immunomodulating Agents; Machine Learning; Male; Middle Aged; Retrospective Studies; Risk Factors; SARS-CoV-2; United States; Diagnosis; Health care; Health risks; Logistic regression; Machine learning; Nucleic acids; Vaccines; Ventilation; abatacept; apremilast; azathioprine; bamlanivimab; bebtelovimab; budesonide; calcineurin inhibitor; casirivimab; cilgavimab; cladribine; cytokine receptor antagonist; dapsone; elasomeran; etesevimab; fumaric acid; glucocorticoid; hydroxychloroquine; hydroxyurea; ibacovavec; imdevimab; Janus kinase inhibitor; leflunomide; mercaptopurine; methotrexate; monoclonal antibody; mrna 1273; mycophenolic acid; nirmatrelvir plus ritonavir; sotrovimab; tixagevimab; tozinameran; tumor necrosis factor inhibitor; immunomodulating agent; SARS-CoV-2 vaccine; Cohort studies; Comorbidities; Disease diagnosis; Immunomodulatory; Inflammatory disease; Logistics regressions; Mechanical ventilation; Predominant period; Risk factors; Time-periods; adult; aged; Article; artificial ventilation; asthma; atrial fibrillation; child; chronic kidney failure; chronic liver disease; chronic obstructive lung disease; cohort analysis; comorbidity; controlled study; coronary artery disease; coronavirus disease 2019; disease severity; electronic health record; female; groups by age; health care system; hospitalization; human; infant; inflammatory disease; machine learning; major clinical study; male; middle aged; mortality; nucleic acid amplification techniques; outcome assessment; pandemic; people by vaccination status; practice guideline; psoriasis; receiver operating characteristic; retrospective study; risk factor; sample size; Severe acute respiratory syndrome coronavirus 2; software; United States; vaccination; young adult; coronavirus disease 2019; United States; COVID-19","","abatacept, 332348-12-6; apremilast, 608141-41-9; azathioprine, 446-86-6, 55774-33-9; bamlanivimab, 2423943-37-5; bebtelovimab, 2578319-11-4; budesonide, 51333-22-3, 51372-29-3; casirivimab, 2415933-42-3; cilgavimab, 2420563-99-9; cladribine, 4291-63-8; dapsone, 80-08-0; elasomeran, 2430046-03-8, 2457298-05-2; etesevimab, 2423948-94-9; fumaric acid, 110-17-8, 142-42-7; hydroxychloroquine, 118-42-3, 525-31-5, 137433-23-9, 137433-24-0; hydroxyurea, 127-07-1; ibacovavec, 2541607-46-7; imdevimab, 2415933-40-1; leflunomide, 75706-12-6; mercaptopurine, 31441-78-8, 50-44-2, 6112-76-1; methotrexate, 15475-56-6, 59-05-2, 7413-34-5, 7532-09-4, 6745-93-3, 51865-79-3, 60388-53-6; mycophenolic acid, 23047-11-2, 24280-93-1, 37415-62-6; sotrovimab, 2423014-07-5; tixagevimab, 2420564-02-7; tozinameran, 2417899-77-3; COVID-19 Vaccines, ; Immunomodulating Agents, ","bnt162b2, BioNTech; bnt162b2, Pfizer; jnj 78436735, Janssen; mrna 1273, Moderna","BioNTech; Janssen; Moderna; Pfizer","SNOMED; National Center for Advancing Translational Sciences, NCATS; Pfizer, (68114891); Novartis Pharmaceuticals Corporation, NPC, (CAIN457FUS27T, 413907); National Institutes of Health, NIH, (3OT2TR00344301S1)","This work was funded in part by a Pfizer grant (68114891), a Novartis grant (CAIN457FUS27T), and a Janssen contract (413907). Support for QW, BB, and JH was also provided in part by the National Center for Advancing Translational Sciences and National Institutes of Health, through the Biomedical Data Translator programme (award number 3OT2TR00344301S1). Any opinions expressed in this Article are those of the Translator community at large and do not necessarily reflect the views of National Center for Advancing Translational Sciences, individual Translator team members, or affiliated organisations and institutions. We are grateful to PSJH for sharing their data engineering expertise and computational resources. We are also grateful for the review of tables and figures done by Evan Yip and cloud computing support by Andrey Dubovoy. We would also like to acknowledge SNOMED International for developing and maintaining SNOMED-CT.","WHO COVID-19 dashboard: deaths; Xu C., Yi Z., Cai R., Chen R., Thong B.Y.-H., Mu R., Clinical outcomes of COVID-19 in patients with rheumatic diseases: a systematic review and meta-analysis of global data, Autoimmun Rev, 20, (2021); Wang F., Ma Y., Xu S., Et al., Prevalence and risk of COVID-19 in patients with rheumatic diseases: a systematic review and meta-analysis, Clin Rheumatol, 41, pp. 2213-2223, (2022); Kharouf F., Eviatar T., Braun M., Et al., A deep look into the storm: Israeli multi-center experience of coronavirus disease 2019 (COVID-19) in patients with autoimmune inflammatory rheumatic diseases before and after vaccinations, Front Immunol, 14, (2023); Morrison C.B., Edwards C.E., Shaffer K.M., Et al., SARS-CoV-2 infection of airway cells causes intense viral and cell shedding, two spreading mechanisms affected by IL-13, Proc Natl Acad Sci USA, 119, (2022); Schieir O., Tosevski C., Glazier R.H., Hogg-Johnson S., Badley E.M., Incident myocardial infarction associated with major types of arthritis in the general population: a systematic review and meta-analysis, Ann Rheum Dis, 76, pp. 1396-1404, (2017); Valenzuela-Almada M.O., Putman M.S., Duarte-Garcia A., The protective effect of rheumatic disease agents in COVID-19, Best Pract Res Clin Rheumatol, 35, (2021); Bellou V., Tzoulaki I., van Smeden M., Moons K., Evangelou E., Belbasis L., Prognostic factors for adverse outcomes in patients with COVID-19: a fieldwide systematic review and meta-analysis, Eur Respir J, 59, (2022); Eder L., Croxford R., Drucker A.M., Et al., Understanding COVID-19 risk in patients with immune mediated inflammatory diseases: a population-based analysis of SARS-CoV-2 testing, Arthritis Care Res (Hoboken), 75, pp. 317-325, (2023); Hasseli R., Mueller-Ladner U., Hoyer B., Et al., Older age, comorbidity, glucocorticoid use and disease activity are risk factors for COVID-19 hospitalisation in patients with inflammatory rheumatic and musculoskeletal diseases, RMD Open, 7, (2021); Attauabi M., Seidelin J., Felding O., Et al., Coronavirus disease 2019, immune-mediated inflammatory diseases and immunosuppressive therapies – a Danish population based cohort study, J Autoimmun, 118, (2021); Eder L., Croxford R., Drucker A.M., Et al., COVID-19 hospitalizations, intensive care unit stays, ventilation, and death among patients with immune-mediated inflammatory diseases compared to controls, J Rheumatol, 49, pp. 523-530, (2022); Guillaume D., Magalie B., Sina E., Et al., Antirheumatic drug intake influence on occurrence of COVID-19 infection in ambulatory patients with immune-mediated inflammatory diseases: a cohort study, Rheumatol Ther, 8, pp. 1887-1895, (2021); Velayos F.S., Dusendang J.R., Schmittdiel J.A., Prior immunosuppressive therapy and severe illness among patients diagnosed with SARS-CoV-2: a community-based study, J Gen Intern Med, 36, pp. 3794-3801, (2021); MacKenna B., Kennedy N.A., Mehrkar A., Et al., Risk of severe COVID-19 outcomes associated with immune-mediated inflammatory diseases and immune-modifying therapies: a nationwide cohort study in the OpenSAFELY platform, Lancet Rheumatol, 4, pp. e490-e506, (2022); Tesch F., Ehm F., Vivirito A., Et al., Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study, Clin Rheumatol, 42, pp. 2905-2914, (2023); Norgard B., Zegers F., Nielsen J., Kjeldsen J., Post COVID-19 hospitalizations in patients with chronic inflammatory diseases – a nationwide cohort study, J Autoimmun, 125, (2021); Yadaw A.S., Afzali B., Hotaling N., Et al., Pre-existing autoimmunity is associated with increased severity of COVID-19: a retrospective cohort study using data from the National COVID Cohort Collaborative (N3C), Clin Infect Dis, 77, pp. 816-826, (2023); Lundberg S.M., Erion G., Chen H., Et al., From local explanations to global understanding with explainable AI for trees, Nat Mach Intell, 2, pp. 56-67, (2020); Raiker R., Pakhchanaian H., Kavadichanda C., Gupta L., Kardes S., Ahmed S., Axial spondyloarthritis may protect against poor outcomes in COVID-19: propensity score matched analysis of 9766 patients from a nationwide multi-centric research network, Clin Rheumatol, 11, (2021); Rosenbaum J., Weisman M., Hamilton H., Et al., The interplay between COVID-19 and spondyloarthritis or its treatment, J Rheumatol, 49, pp. 225-229, (2022); Tavasolian F., Rashidi M., Hatam G.R., Et al., HLA, immune response, and susceptibility to COVID-19, Front Immunol, 11, (2021); Augusto D.G., Murdolo L.D., Chatzileontiadou D.S.M., Et al., A common allele of HLA is associated with asymptomatic SARS-CoV-2 infection, Nature, 620, pp. 128-136, (2023); Londono J., Santos A.M., Pena P., Et al., Analysis of HLA-B15 and HLA-B27 in spondyloarthritis with peripheral and axial clinical patterns, BMJ Open, 5, (2015); Molani S., Hernandez P., Roper R., Et al., Risk factors for severe COVID-19 differ by age for hospitalized adults, Sci Rep, 12, (2022); Strangfeld A., Schafer M., Gianfrancesco M.A., Et al., Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance physician-reported registry, Ann Rheum Dis, 80, pp. 930-942, (2021); Cordtz R., Kristensen S., Westermann R., Et al., COVID-19 infection and hospitalisation risk according to vaccination status and DMARD treatment in patients with rheumatoid arthritis, Rheumatology (Oxford), 62, pp. 77-88, (2022); Williamson E.J., Tazare J., Bhaskaran K., Et al., Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform, Diagn Progn Res, 6, (2022); Piaserico S., Gisondi P., Cazzaniga S., Leo S., Naldi L., Assessing the risk and outcome of COVID-19 in patients with psoriasis or psoriatic arthritis on biologic treatment: a critical appraisal of the quality of the published evidence, J Invest Dermatol, 8, (2021); England B.R., Roul P., Yang Y., Et al., Risk of COVID-19 in rheumatoid arthritis: a national Veterans Affairs matched cohort study in at-risk individuals, Arthritis Rheumatol, 73, pp. 2179-2188, (2021); Avouac J., Drumez E., Hachulla E., Et al., COVID-19 outcomes in patients with inflammatory rheumatic and musculoskeletal diseases treated with rituximab: a cohort study, Lancet Rheumatol, 3, pp. e419-e426, (2021); Fagni F., Simon D., Tascilar K., Et al., COVID-19 and immune-mediated inflammatory diseases: effect of disease and treatment on COVID-19 outcomes and vaccine responses, Lancet Rheumatol, 3, pp. e724-e736, (2021); Diniz C., Marques C., Kakehasi A., Et al., High levels of immunosuppression are related to unfavourable outcomes in hospitalised patients with rheumatic diseases and COVID-19: first results of ReumaCoV Brasil registry, RMD Open, 7, (2021); Andersen K., Bates B., Rashidi E., Et al., Long-term use of immunosuppressive medicines and in-hospital COVID-19 outcomes: a retrospective cohort study using data from the National COVID Cohort Collaborative, Lancet Rheumatol, 11, (2021)","J. Hadlock; Institute for Systems Biology, Seattle, 98109, United States; email: jennifer.hadlock@isbscience.org","","Elsevier Ltd","","","","","","25897500","","","38670740","English","Lancet Digit. Heal.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191021897"
"Boueiz A.; Xu Z.; Chang Y.; Masoomi A.; Gregory A.; Lutz S.M.; Qiao D.; Crapo J.D.; Dy J.G.; Silverman E.K.; Castaldi P.J.","Boueiz, Adel (57797411000); Xu, Zhonghui (55582199500); Chang, Yale (56037337700); Masoomi, Aria (57219635757); Gregory, Andrew (57226026153); Lutz, Sharon M. (57213169539); Qiao, Dandi (36480536900); Crapo, James D. (7005677236); Dy, Jennifer G. (6603643756); Silverman, Edwin K. (57203069737); Castaldi, Peter J. (26323082900)","57797411000; 55582199500; 56037337700; 57219635757; 57226026153; 57213169539; 36480536900; 7005677236; 6603643756; 57203069737; 26323082900","achine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study","2022","Chronic Obstructive Pulmonary Diseases","9","3","","349","365","16","5","10.15326/jcopdf.2021.0275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136301606&doi=10.15326%2fjcopdf.2021.0275&partnerID=40&md5=6aa17e48c6a1daf007a75a0f709d72b4","Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Pulmonary and Critical Care Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, United States; Division of General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States","Boueiz A., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, Pulmonary and Critical Care Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Xu Z., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Chang Y., Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Masoomi A., Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Gregory A., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Lutz S.M., Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Qiao D., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Crapo J.D., Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, United States; Dy J.G., Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Silverman E.K., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, Pulmonary and Critical Care Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Castaldi P.J., Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States, Division of General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States","Background: The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features. Methods: We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene®) study. We constructed linear regression (LR) and supervised random forest models to predict 5-year progression in forced expiratory in 1 second (FEV1) from 46 baseline features. Using cross-validation, we randomly partitioned participants into training and testing samples. We also validated the results in the COPDGene 10-year follow-up visit. Results: Predicting the change in FEV1 over time is more challenging than simply predicting the future absolute FEV1 level. For random forest, R-squared was 0.15 and the area under the receiver operator characteristic (ROC) curves for the prediction of participants in the top quartile of observed progression was 0.71 (testing) and respectively, 0.10 and 0.70 (validation). Random forest provided slightly better performance than LR. The accuracy was best for Global initiative for chronic Obstructive Lung Disease (GOLD) grades 1-2 participants, and it was harder to achieve accurate prediction in advanced stages of the disease. Predictive variables differed in their relative importance as well as for the predictions by GOLD. Conclusion: Random forest, along with deep phenotyping, predicts FEV1 progression with reasonable accuracy. There is significant room for improvement in future models. This prediction model facilitates the identification of smokers at increased risk for rapid disease progression. Such findings may be useful in the selection of patient populations for targeted clinical trials. Copyright 2022, Journal of Iranian Medical Council. All rights reserved.","5-year changes in FEV1; COPD; disease progression; prediction; random forest machine learning","bronchodilating agent; adult; aged; alternative hypothesis; Article; chronic bronchitis; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; current smoker; demographics; diagnostic accuracy; disease exacerbation; dyspnea; ex-smoker; feature selection; female; follow up; forced expiratory volume; forced vital capacity; genetic epidemiology; human; linear regression analysis; longitudinal study; machine learning; major clinical study; male; measurement error; nested cross validation; null hypothesis; obesity; phenotype; prediction; random forest; receiver operating characteristic; signal noise ratio; spirometry; supervised machine learning; total lung capacity; treatment response","","","","","St George’s Respiratory Questionnaire; National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (K08 HL141601, NCT00608764, R01 HL124233, R01 HL126596, R01 HL147326, U01 HL089856, U01 HL089897); Pfizer; GlaxoSmithKline, GSK; Novartis; COPD Foundation; Medical Research Council, MRC","Funding text 1: Abbreviations: chronic obstructive pulmonary disease, COPD; COPD Genetic Epidemiology study, COPDGene®; linear regression, LR; forced expiratory volume in 1 second, FEV1; receiver operator characteristic, ROC; Global initiative for chronic Obstructive Lung Disease, GOLD; computed tomography, CT; annualized 5-year changes in FEV1, ∆FEV1; root mean squared error, RMSE; increase in the mean squared errors, IncMSE; area under the curve, AUC; body mass index, BMI; low attenuation area below -950 Hounsfield units, %LAA-950; 15th percentile point, Perc15; forced vital capacity, FVC; modified Medical Research Council, mMRC; St George’s Respiratory Questionnaire, SGRQ; preserved ratio-impaired spirometry, PRISm; interquartile, IQR; forced expiratory flow rate between 25% and 75% of the vital capacity, FEV25%-75% Funding Support: This work was supported by National Heart, Lung, and Blood Institute K08 HL141601, R01 HL124233, R01 HL126596, R01 HL147326, U01 HL089897, and U01 HL089856. The COPDGene® study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion. Date of Acceptance: May 18, 2022 | Published Online Date: May 20, 2022 Citation: Boueiz A, Xu Z, Chang Y, et al. Machine learning prediction of progression in forced expiratory volume in 1 second in the CODPGene® study. Chronic Obstr Pulm Dis. 2022;9(3):349-365. doi: https://doi.org/10.15326/jcopdf.2021.0275; Funding text 2: Dr. Castaldi reports grants from the National Institutes of Health during the conduct of the study, grants and other from GlaxoSmithKline, and personal fees from Novartis, outside the submitted work. Dr. Silverman reports grants from the National Institutes of Health during the conduct of the study, and grants and other from GlaxoSmithKline, outside the submitted work.","Guo YI, Qian Y, Gong YI, Pan C, Shi G, Wan H., A predictive model for the development of chronic obstructive pulmonary disease, Biomed Rep, 3, 6, pp. 853-863, (2015); Heron M., Deaths: leading causes for 2018, Nat Vital Stat Rep, 70, 4, pp. 1-115, (2021); The top 10 causes of death; The state of US health 1990-2016. Burden of diseases, injuries and risk factors among US states, JAMA, 319, 14, pp. 1444-1472, (2018); Bhatt SP, Soler X, Wang X, Et al., Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 194, 2, pp. 178-184, (2016); Vestbo J, Edwards LD, Scanlon PD, Et al., Changes in forced expiratory volume in 1 second over time in COPD, N Engl J Med, 365, 13, pp. 1184-1192, (2011); Vestbo J, Lange P., Natural history of COPD: focusing on change in FEV1, Respirology, 21, 1, pp. 34-43, (2016); Zafari Z, Sin DD, Postma DS, Et al., Individualized prediction of lung-function decline in chronic obstructive pulmonary disease, CMAJ, 188, 14, pp. 1004-1011, (2016); Chen W, Sin DD, FitzGerald JM, Safari A, Adibi A, Sadatsafavi M., An individualized prediction model for long-term lung function trajectory and risk of COPD in the general population, Chest, 157, 3, pp. 547-553, (2020); Han MK, Agusti A, Calverley PM, Et al., Chronic obstructive pulmonary disease phenotypes: the future of COPD, Am J Respir Crit Care Med, 182, 5, pp. 598-604, (2010); Lange P, Celli B, Agusti A, Et al., Lung-function trajectories leading to chronic obstructive pulmonary disease, N Engl J Med, 373, 2, pp. 111-122, (2015); Martinez FD., Early-life origins of chronic obstructive pulmonary disease, N Engl J Med, 375, 9, pp. 871-878, (2016); Regan EA, Hokanson JE, Murphy JR, Et al., Genetic epidemiology of COPD (COPDGene) study design, COPD, 7, 1, pp. 32-43, (2010); Vogelmeier CF, Criner GJ, Martinez FJ, Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary, Arch Bronconeumol, 53, 3, pp. 128-149, (2017); Touw WG, Bayjanov JR, Overmars L, Et al., Data mining in the life sciences with random forest: a walk in the park or lost in the jungle?, Brief Bioinform, 14, 3, pp. 315-326, (2013); Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP., Random forest: a classification and regression tool for compound classification and QSAR modeling, J Chem Inf Comput Sci, 43, 6, pp. 1947-1958, (2003); Breiman L., Random forests, Mach Learn, 45, 1, pp. 5-32, (2001); Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A., Conditional variable importance for random forests, BMC Bioinformatics, 9, (2008); Tweeddale PM, Alexander F, McHardy GJ., Short term variability in FEV1 and bronchodilator responsiveness in patients with obstructive ventilatory defects, Thorax, 42, 7, pp. 487-490, (1987); Han MK, Steenrod AW, Bacci ED, Et al., Identifying patients with undiagnosed COPD in primary care settings: insight from screening tools and epidemiologic studies, Chronic Obstr Pulm Dis, 2, 2, pp. 103-121, (2015); Higgins MW, Keller JB, Becker M, Et al., An index of risk for obstructive airways disease, Am Rev Respir Dis, 125, 2, pp. 144-151, (1982); Himes BE, Dai Y, Kohane IS, Weiss ST, Ramoni MF., Prediction of chronic obstructive pulmonary disease (COPD) in asthma patients using electronic medical records, J Am Med Inform Assoc, 16, 3, pp. 371-379, (2009); Kotz D, Simpson CR, Viechtbauer W, van Schayck OC, Sheikh A., Development and validation of a model to predict the 10-year risk of general practitioner-recorded COPD, NPJ Prim Care Respir Med, 24, (2014); Matheson MC, Bowatte G, Perret JL, Et al., Prediction models for the development of COPD: a systematic review, Int J Chron Obstruct Pulmon Dis, 13, pp. 1927-1935, (2018); Bellou V, Belbasis L, Konstantinidis AK, Tzoulaki I, Evangelou E., Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal, BMJ, 367, (2019); Auret L, Aldrich C., Interpretation of nonlinear relationships between process variables by use of random forests, Miner Eng, 35, pp. 27-42, (2012); Fawagreh K, Gaber M, Elyan E., Random forests: from early developments to recent advancements, Syst Sci Control Eng, 2, 1, pp. 602-609, (2014); Casanova C, de Torres JP, Aguirre-Jaime A, Et al., The progression of chronic obstructive pulmonary disease is heterogeneous: the experience of the BODE cohort, Am J Respir Crit Care Med, 184, 9, pp. 1015-1021, (2011); Dransfield MT, Kunisaki KM, Strand MJ, Et al., Acute exacerbations and lung function loss in smokers with and without chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 195, 3, pp. 324-330, (2017); Hanrahan JP, Tager IB, Segal MR, Et al., The effect of maternal smoking during pregnancy on early infant lung function, Am Rev Respir Dis, 145, 5, pp. 1129-1135, (1992); Mohamed Hoesein FA, van Rikxoort E, van Ginneken B, Et al., Computed tomography-quantified emphysema distribution is associated with lung function decline, Eur Respir J, 40, 4, pp. 844-850, (2012); Nishimura M, Makita H, Nagai K, Et al., Annual change in pulmonary function and clinical phenotype in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 185, 1, pp. 44-52, (2012); Sun Y, Milne S, Jaw JE, Et al., BMI is associated with FEV1 decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials, Respir Res, 20, 1, (2019); Donaldson GC, Seemungal TA, Bhowmik A, Wedzicha JA., Relationship between exacerbation frequency and lung function decline in chronic obstructive pulmonary disease, Thorax, 57, 10, pp. 847-852, (2002); Kanner RE, Anthonisen NR, Connett JE, Lower respiratory illnesses promote FEV(1) decline in current smokers but not ex-smokers with mild chronic obstructive pulmonary disease: results from the lung health study, Am J Respir Crit Care Med, 164, 3, pp. 358-364, (2001); Suzuki M, Makita H, Ito YM, Et al., Clinical features and determinants of COPD exacerbation in the Hokkaido COPD cohort study, Eur Respir J, 43, 5, pp. 1289-1297, (2014); Capitaine L, Genuer R, Thiebaut R., Random forests for high-dimensional longitudinal data, Stat Methods Med Res, 30, 1, pp. 166-184, (2021); Debeer D, Strobl C., Conditional permutation importance revisited, BMC Bioinformatics, 21, 1, (2020); Probst P, Boulesteix A., To tune or not to tune the number of trees in random forest?, J Mach Learn Res, pp. 1-18, (2018); Suissa S., Immortal time bias in pharmaco-epidemiology, Am J Epidemiol, 167, 4, pp. 492-499, (2008); Wise L., Risks and benefits of (pharmaco)epidemiology, Ther Adv Drug Saf, 2, 3, pp. 95-102, (2011)","A. Boueiz; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, 181 Longwood Avenue, 02115, United States; email: adel.boueiz@channing.harvard.edu","","COPD Foundation","","","","","","2372952X","","","","English","Chronic Obstr. Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85136301606"
"Chen Y.; Yu Y.; Yang D.; Zhang W.; Kouritas V.; Chen X.","Chen, Yong (57205105210); Yu, Yonglin (57894952200); Yang, Dongmei (59006498900); Zhang, Wenbo (57222120617); Kouritas, Vasileios (25957700600); Chen, Xiaoju (57205505495)","57205105210; 57894952200; 59006498900; 57222120617; 25957700600; 57205505495","Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease","2024","Journal of Thoracic Disease","16","4","","2482","2498","16","3","10.21037/jtd-24-416","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192075307&doi=10.21037%2fjtd-24-416&partnerID=40&md5=e0026612b9def7d28e0bc52eab17891f","Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Department of Stomatology, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Department of Thoracic Surgery, Norfolk and Norwich University Hospital, Norwich, United Kingdom; Department of Respiratory and Critical Care Medicine, Clinical Medical College, Affiliated Hospital of Chengdu University, Chengdu, China","Chen Y., Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Yu Y., Department of Stomatology, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Yang D., Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Zhang W., Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Kouritas V., Department of Thoracic Surgery, Norfolk and Norwich University Hospital, Norwich, United Kingdom; Chen X., Department of Respiratory and Critical Care Medicine, Clinical Medical College, Affiliated Hospital of Chengdu University, Chengdu, China","Background: Frailty is a medical syndrome caused by multiple factors, characterized by decreased strength, endurance, and diminished physiological function, resulting in increased susceptibility to dependence and/ or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens. Therefore, the aim of this study was to develop a reliable and accurate vulnerability risk prediction model for frailty in patients with COPD in order to improve the identification and prediction of patient frailty. The specific objectives of this study were to determine the prevalence of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Methods: Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indicators, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive synthetic sampling technique was used for unbalanced data. Three methods, ridge regressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) techniques including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbors were used to analyze and determine the optimal model. For customized risk assessment, an online predictive risk modeling website was created, along with Shapley additive explanation (SHAP) interpretations. Results: Depression, smoking, gender, social activities, dyslipidemia, asthma, and residence type (urban vs. rural) were predictors for the development of frailty in patients with COPD. In the test set, the XGBoost model had an area under the curve of 0.942 (95% confidence interval: 0.925–0.959), an accuracy of 0.915, a sensitivity of 0.873, and a specificity of 0.911, indicating that it was the best model. Conclusions: The ML predictive model developed in this study is a useful and easy-to-use instrument for assessing the vulnerability risk of patients with COPD and may aid clinical physicians in screening high-risk patients. © 2024 AME Publishing Company. All rights reserved.","Chronic obstructive pulmonary disease (COPD); frailty; machine learning (ML); prediction model; Shapley additive explanation (SHAP)","adult; aged; Article; asthma; behavior assessment; chronic obstructive lung disease; classifier; controlled study; depression; diagnostic accuracy; dyslipidemia; female; frailty; gender; health status; human; incidence; k nearest neighbor; longitudinal study; machine learning; major clinical study; male; mental health; multilayer perceptron; predictive model; psychological aspect; random forest; reliability; residential area; risk assessment; rural area; sensitivity and specificity; smoking; social behavior; sociodemographics; support vector machine; urban area; validation process","","","XGBoost","","2020 Municipal Applied Technology Research and Development Fund Project, (20YFZJ0105)","We would like to thank the investigators and patients for their support of this study and those at xsmartanalysis (https://www.xsmartanalysis.com/model/index) for their technical support. Funding: This study was supported by the 2020 Municipal Applied Technology Research and Development Fund Project (No. 20YFZJ0105).","Agusti A, Celli BR, Criner GJ, Et al., Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary, Eur Respir J, 61, (2023); Soler-Cataluna JJ, Izquierdo JL, Juarez Campo M, Et al., Impact of COPD Exacerbations and Burden of Disease in Spain: AVOIDEX Study, Int J Chron Obstruct Pulmon Dis, 18, pp. 1103-1114, (2023); Yang T, Cai B, Cao B, Et al., Treatment patterns in patients with stable COPD in China: analysis of a prospective, 52-week, nationwide, observational cohort study (REAL), Ther Adv Respir Dis, 17, (2023); Fried LP, Tangen CM, Walston J, Et al., Frailty in older adults: evidence for a phenotype, J Gerontol A Biol Sci Med Sci, 56, pp. M146-M156, (2001); Marengoni A, Vetrano DL, Manes-Gravina E, Et al., The Relationship Between COPD and Frailty: A Systematic Review and Meta-Analysis of Observational Studies, Chest, 154, pp. 21-40, (2018); He B, Ma Y, Wang C, Et al., Prevalence and Risk Factors for Frailty among Community-Dwelling Older People in China: A Systematic Review and Meta-Analysis, J Nutr Health Aging, 23, pp. 442-450, (2019); Dias LS, Ferreira ACG, da Silva Junior JLR, Et al., Prevalence of Frailty and Evaluation of Associated Variables Among COPD Patients, Int J Chron Obstruct Pulmon Dis, 15, pp. 1349-1356, (2020); Walston J, Hadley EC, Ferrucci L, Et al., Research agenda for frailty in older adults: toward a better understanding of physiology and etiology: summary from the American Geriatrics Society/National Institute on Aging Research Conference on Frailty in Older Adults, J Am Geriatr Soc, 54, pp. 991-1001, (2006); Ershler WB, Keller ET., Age-associated increased interleukin-6 gene expression, late-life diseases, and frailty, Annu Rev Med, 51, pp. 245-270, (2000); Ierodiakonou D, Kampouraki M, Poulonirakis I, Et al., Determinants of frailty in primary care patients with COPD: the Greek UNLOCK study, BMC Pulm Med, 19, (2019); Theou O, Cann L, Blodgett J, Et al., Modifications to the frailty phenotype criteria: Systematic review of the current literature and investigation of 262 frailty phenotypes in the Survey of Health, Ageing, and Retirement in Europe, Ageing Res Rev, 21, pp. 78-94, (2015); Wu C, Smit E, Xue QL, Et al., Prevalence and Correlates of Frailty Among Community-Dwelling Chinese Older Adults: The China Health and Retirement Longitudinal Study, J Gerontol A Biol Sci Med Sci, 73, pp. 102-108, (2017); Veronese N, Solmi M, Maggi S, Et al., Frailty and incident depression in community-dwelling older people: results from the ELSA study, Int J Geriatr Psychiatry, 32, pp. e141-e149, (2017); Bu F, Deng XH, Zhan NN, Et al., Development and validation of a risk prediction model for frailty in patients with diabetes, BMC Geriatr, 23, (2023); Cabanero-Martinez MJ, Cabrero-Garcia J, Richart-Martinez M, Et al., The Spanish versions of the Barthel index (BI) and the Katz index (KI) of activities of daily living (ADL): a structured review, Arch Gerontol Geriatr, 49, pp. e77-e84, (2009); Bijwaard GE, van Kippersluis H, Veenman J., Education and health: The role of cognitive ability, J Health Econ, 42, pp. 29-43, (2015); Irwin M, Artin KH, Oxman MN., Screening for depression in the older adult: criterion validity of the 10-item Center for Epidemiological Studies Depression Scale (CES-D), Arch Intern Med, 159, pp. 1701-1704, (1999); Maw M, Haw SC, Ho CK., Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems, F1000Res, 10, (2021); Lundberg SM, Erion G, Chen H, Et al., From Local Explanations to Global Understanding with Explainable AI for Trees, Nat Mach Intell, 2, pp. 56-67, (2020); Sauerbrei W, Royston P, Binder H., Selection of important variables and determination of functional form for continuous predictors in multivariable model building, Stat Med, 26, pp. 5512-5528, (2007); Obuchowski NA, Bullen JA., Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine, Phys Med Biol, 63, (2018); Nahm FS., Receiver operating characteristic curve: overview and practical use for clinicians, Korean J Anesthesiol, 75, pp. 25-36, (2022); Xue B, Li D, Lu C, Et al., Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications, JAMA Netw Open, 4, (2021); Limpawattana P, Putraveephong S, Inthasuwan P, Et al., Frailty syndrome in ambulatory patients with COPD, Int J Chron Obstruct Pulmon Dis, 12, pp. 1193-1198, (2017); Lahousse L, Maes B, Ziere G, Et al., Adverse outcomes of frailty in the elderly: the Rotterdam Study, Eur J Epidemiol, 29, pp. 419-427, (2014); Hirai K, Tanaka A, Oda N, Et al., Prevalence and Impact of Social Frailty in Patients with Chronic Obstructive Pulmonary Disease, Int J Chron Obstruct Pulmon Dis, 18, pp. 2117-2126, (2023); Wijnant SRA, Benz E, Luik AI, Et al., Frailty Transitions in Older Persons With Lung Function Impairment: A Population-Based Study, J Gerontol A Biol Sci Med Sci, 78, pp. 349-356, (2023); Gordon EH, Hubbard RE., Frailty: understanding the difference between age and ageing, Age Ageing, 51, (2022); Thillainadesan J, Scott IA, Le Couteur DG., Frailty, a multisystem ageing syndrome, Age Ageing, 49, pp. 758-763, (2020); Kojima G, Iliffe S, Walters K., Smoking as a predictor of frailty: a systematic review, BMC Geriatr, 15, (2015); Goncalves RB, Coletta RD, Silverio KG, Et al., Impact of smoking on inflammation: overview of molecular mechanisms, Inflamm Res, 60, pp. 409-424, (2011); Wu Z, Yue Q, Zhao Z, Et al., A cross-sectional study of smoking and depression among US adults: NHANES (2005-2018), Front Public Health, 11, (2023); Martin LM, Sayette MA., A review of the effects of nicotine on social functioning, Exp Clin Psychopharmacol, 26, pp. 425-439, (2018); Fan J, Yu C, Guo Y, Et al., Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study, Lancet Public Health, 5, pp. e650-e660, (2020); Ozic S, Vasiljev V, Ivkovic V, Et al., Interventions aimed at loneliness and fall prevention reduce frailty in elderly urban population, Medicine (Baltimore), 99, (2020); Shalini T, Chitra PS, Kumar BN, Et al., Frailty and Nutritional Status among Urban Older Adults in South India, J Aging Res, 2020, (2020); Nguyen HT, Nguyen AH, Nguyen GTX., Prevalence and associated factors of frailty in patients attending rural and urban geriatric clinics, Australas J Ageing, 41, pp. e122-e130, (2022); Seo Y, Kim M, Shim H, Et al., Differences in the Association of Neighborhood Environment With Physical Frailty Between Urban and Rural Older Adults: The Korean Frailty and Aging Cohort Study (KFACS), J Am Med Dir Assoc, 22, pp. 590-597, (2021); Wang X, Wen J, Gu S, Et al., Frailty in asthma-COPD overlap: a cross-sectional study of association and risk factors in the NHANES database, BMJ Open Respir Res, 10, (2023); Wang Q, Wang Y, Lehto K, Et al., Genetically-predicted life-long lowering of low-density lipoprotein cholesterol is associated with decreased frailty: A Mendelian randomization study in UK biobank, EBioMedicine, 45, pp. 487-494, (2019); Dao HHH, Burns MJ, Kha R, Et al., The Relationship between Metabolic Syndrome and Frailty in Older People: A Systematic Review and Meta-Analysis, Geriatrics (Basel), 7, (2022); Fahed G, Aoun L, Bou Zerdan M, Et al., Metabolic Syndrome: Updates on Pathophysiology and Management in 2021, Int J Mol Sci, 23, (2022); Fearn M, Bhar S, Dunt D, Et al., Befriending to Relieve Anxiety and Depression Associated with Chronic Obstructive Pulmonary Disease (COPD): A Case Report, Clin Gerontol, 40, pp. 207-212, (2017); Soysal P, Veronese N, Thompson T, Et al., Relationship between depression and frailty in older adults: A systematic review and meta-analysis, Ageing Res Rev, 36, pp. 78-87, (2017); Malhi GS, Mann JJ., Depression, Lancet, 392, pp. 2299-2312, (2018); Potter GG, McQuoid DR, Whitson HE, Et al., Physical frailty in late-life depression is associated with deficits in speed-dependent executive functions, Int J Geriatr Psychiatry, 31, pp. 466-474, (2016); Bourbeau J., Activities of life: the COPD patient, COPD, 6, pp. 192-200, (2009); Fingerman KL, Ng YT, Huo M, Et al., Functional Limitations, Social Integration, and Daily Activities in Late Life, J Gerontol B Psychol Sci Soc Sci, 76, pp. 1937-1947, (2021); Thomas PA., Trajectories of social engagement and mortality in late life, J Aging Health, 24, pp. 547-568, (2012); Horgas AL, Wilms HU, Baltes MM., Daily life in very old age: everyday activities as expression of successful living, Gerontologist, 38, pp. 556-568, (1998)","X. Chen; Department of Respiratory and Critical Care Medicine, Clinical Medical College, Affiliated Hospital of Chengdu University, Chengdu, No. 82, North Section 2, 2nd Ring Road, North Railway Station, Hehuachi Street, Jinniu District, 610081, China; email: chenyongliu1991@163.com","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85192075307"
"Kim E.; Lee Y.; Choi J.; Yoo B.; Chae K.J.; Lee C.H.","Kim, Eunchan (57833221100); Lee, YongHyun (57915514500); Choi, Jiwoong (55749525100); Yoo, Byungjoon (23037689700); Chae, Kum Ju (57195310676); Lee, Chang Hyun (57196253438)","57833221100; 57915514500; 55749525100; 23037689700; 57195310676; 57196253438","Machine Learning-based Prediction of Relative Regional Air Volume Change from Healthy Human Lung CTs","2023","KSII Transactions on Internet and Information Systems","17","2","","576","590","14","4","10.3837/tiis.2023.02.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151801617&doi=10.3837%2ftiis.2023.02.016&partnerID=40&md5=59123a824168e3fc7218bf58751d4769","Department of Intelligence and Information, Seoul National University, Seoul, 08826, South Korea; Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea; Graduate School of Business, Seoul National University, Seoul, 08826, South Korea; Department of Internal Medicine, School of Medicine, University of Kansas Kansas City, 66160, KS, United States; Department of Bioengineering, University of Kansas, Lawrence, 66045, KS, United States; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, 54907, South Korea; Department of Radiology, Seoul National University Hospital, Seoul National University, College of Medicine, Seoul, 03080, South Korea","Kim E., Department of Intelligence and Information, Seoul National University, Seoul, 08826, South Korea, Graduate School of Business, Seoul National University, Seoul, 08826, South Korea; Lee Y., Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, South Korea; Choi J., Department of Internal Medicine, School of Medicine, University of Kansas Kansas City, 66160, KS, United States, Department of Bioengineering, University of Kansas, Lawrence, 66045, KS, United States; Yoo B., Department of Intelligence and Information, Seoul National University, Seoul, 08826, South Korea, Graduate School of Business, Seoul National University, Seoul, 08826, South Korea; Chae K.J., Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, 54907, South Korea; Lee C.H., Department of Radiology, Seoul National University Hospital, Seoul National University, College of Medicine, Seoul, 03080, South Korea","Machine learning is widely used in various academic fields, and recently it has been actively applied in the medical research. In the medical field, machine learning is used in a variety of ways, such as speeding up diagnosis, discovering new biomarkers, or discovering latent traits of a disease. In the respiratory field, a relative regional air volume change (RRAVC) map based on quantitative inspiratory and expiratory computed tomography (CT) imaging can be used as a useful functional imaging biomarker for characterizing regional ventilation. In this study, we seek to predict RRAVC using various regular machine learning models such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP). We experimentally show that MLP performs best, followed by XGBoost. We also propose several relative coordinate systems to minimize intersubjective variability. We confirm a significant experimental performance improvement when we apply a subject's relative proportion coordinates over conventional absolute coordinates. Copyright © 2023 KSII.","Biomedical machine learning; chronic obstructive pulmonary disease; deep learning; quantitative CT imaging; relative regional air volume change","Adaptive boosting; Computerized tomography; Deep learning; Diagnosis; Learning systems; Pulmonary diseases; Air volumes; Biomedical machine learning; Chronic obstructive pulmonary disease; Deep learning; Machine-learning; Quantitative computed tomographies; Quantitative computed tomography imaging; Relative regional air volume change; Tomography imaging; Volume change; Biomarkers","","","","","Texas Biomedical Research Institute; Institute of Management Research, College of Business Administration Seoul National University; Korea Environmental Industry and Technology Institute, KEITI, (2018001360001); Korea Environmental Industry and Technology Institute, KEITI; National Research Foundation of Korea, NRF, (2021R1C1C1009818); National Research Foundation of Korea, NRF; Jeju National University Hospital, JNUH, (CUH2016-0009); Jeju National University Hospital, JNUH","Eunchan Kim and YongHyun Lee contributed equally. This work was supported by the Biomedical Research Institute, Jeonbuk National University Hospital Grant CUH2016-0009, the National Research Foundation of Korea (NRF) Grant 2021R1C1C1009818, and Korea Environmental Industry & Technology Institute (KEITI) Grant 2018001360001. This Study was supported by the Institute of Management Research at Seoul National University.","Zhou L., Pan S., Wang J., Vasilakos A. V., Machine learning on big data: Opportunities and challenges, Neurocomputing, 237, pp. 350-361, (2017); Zoph B., Vasudevan V., Shlens J., Le Q. V., Learning transferable architectures for scalable image recognition, Proc. of the IEEE conference on computer vision and pattern recognition, pp. 8697-8710, (2018); Kim E., Lee J., Jo H., Na K., Moon E., Gweon G., Yoo B., Kyung Y., SHOMY: Detection of Small Hazardous Objects using the You Only Look Once Algorithm, KSII Transactions on Internet and Information Systems, 16, 8, pp. 2688-2703, (2022); Choi B., Lee Y., Kyung Y., Kim E., Albert with knowledge graph encoder utilizing semantic similarity for commonsense question answering, Intelligent Automation & Soft Computing, 36, 1, pp. 71-82, (2023); Yang Z., Dai Z., Yang Y., Carbonell J., Salakhutdinov R. R., Le Q. 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P., Newell J. D., Lee C. H., Barr R. G., Bleecker E., Cooper C. B., Couper D., Han M. L., Hansel N. N., Kanner R. E., Kazerooni E. A., Kleerup E. A. C., Martinez F. J., O'Neal W., Paine R., Rennard S. I., Smith B. M., Woodruff P. G., Lin C.-L., Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study, Respiratory Research, 20, 1, pp. 1-14, (2019); The top 10 causes of death, (2020); Main Causes of Mortality Among Women and Men in EU Countries, 2015, Health at a Glance: Europe 2018: State of Health in the EU Cycle, (2018); Global Strategy for Prevention, Diagnosis and Management of Chronic Obstructive Pulmonary Disease, (2020); Galban C. J., Han M. K., Boes J. L., Chughtai K. A., Meyer C. R., Johnson T. D., Galban S., Rehemtulla A., Kazerooni E. A., Martinez F. J., Ross B. 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N., Alhayani B., Coronavirus disease (COVID-19) cases analysis using machine-learning applications, Applied Nanoscience, pp. 1-13, (2021); Rasheed J., Hameed A. A., Djeddi C., Jamil A., Al-Turjman F., A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images, Interdisciplinary Sciences: Computational Life Sciences, 13, 1, pp. 103-117, (2021); Alimadadi A., Aryal S., Manandhar I., Munroe P. B., Joe B., Cheng X., Artificial intelligence and machine learning to fight COVID-19, Physiological Genomics, 52, 4, pp. 200-202, (2020); Kadir T., Gleeson F., Lung cancer prediction using machine learning and advanced imaging techniques, Translational Lung Cancer Research, 7, 3, pp. 304-312, (2018); Cai Z., Xu D., Zhang Q., Zhang J., Ngai S. M., Shao J., Classification of lung cancer using ensemble-based feature selection and machine learning methods, Molecular BioSystems, 11, 3, pp. 791-800, (2015); Choi J., Hoffman E. A., Lin C.-L., Milhem M. M., Tessier J., Newell J. D., Quantitative computed tomography determined regional lung mechanics in normal nonsmokers, normal smokers and metastatic sarcoma subjects, PLoS One, 12, 7, (2017); Amelon R., Cao K., Ding K., Christensen G. E., Reinhardt J. M., Raghavan M. L., Three-dimensional characterization of regional lung deformation, Journal of Biomechanics, 44, 13, pp. 2489-2495, (2011); Choi S., Hoffman E. A., Wenzel S. E., Tawhai M. H., Yin Y., Castro M., Lin C.-L., Registration-based assessment of regional lung function via volumetric CT images of normal subjects vs. severe asthmatics, Journal of Applied Physiology, 115, 5, pp. 730-742, (2013); Bodduluri S., Newell J. D., Hoffman E. A., Reinhardt J. M., Registration-based lung mechanical analysis of chronic obstructive pulmonary disease (COPD) using a supervised machine learning framework, Academic Radiology, 20, 5, pp. 527-536, (2013); Shin K. M., Choi J., Chae K. J., Jin G. Y., Eskandari A., Hoffman E. A., Hall C., Castro M., Lee C. H., Quantitative CT-based image registration metrics provide different ventilation and lung motion patterns in prone and supine positions in healthy subjects, Respiratory Research, 21, 1, pp. 1-9, (2020); Li F., Choi J., Zou C., Newell J. D., Comellas A. P., Lee C. H, Ko H., Barr R. G., Bleecker E. R., Cooper C. B., Abtin F., Barjaktarevic I., Couper D., Han M., Hansel N. N., Kanner R. E., Paine R., Kazerooni E. A., Martinez F. J., O'Neal W., Rennard S. I., Smith B. M., Woodruff P. G., Hoffman E. A., Lin C.-L., Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images, Scientific Reports, 11, 1, pp. 1-15, (2021); Chae K. J., Choi J., Jin G. Y., Hoffman E. A., Laroia A. T., Park M., Lee C. H, Relative regional air volume change maps at the Acinar scale reflect variable ventilation in low lung attenuation of COPD patients, Academic Radiology, 27, 11, pp. 1540-1548, (2020); Choi J., Chae K. J., Lee C. H, Jin G. Y., Park M., Lin C.-L., Hoffman E. A., Relative regional air volume change distributions in normal subjects, Proc. of 8th International Workshop on Pulmonary Functional Imaging, (2017); Lin C.-L., Choi S., Haghighi B., Choi J., Hoffman E. A., Cluster-guided multiscale lung modeling via machine learning, Handbook of Materials Modeling: Applications: Current and Emerging Materials, pp. 2699-2718, (2020); Liu C., Hu S. C., Wang C., Lafata K., Yin F. F., Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data, Quantitative Imaging in Medicine and Surgery, 10, 10, pp. 1917-1929, (2020); Mei S., Jiang H., Ma L., YOLO-lung: A Practical Detector Based on Improved YOLOv4 for Pulmonary Nodule Detection, Proc. of 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, pp. 1-6, (2021); Zou H., Hastie T., Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 2, pp. 301-320, (2005); Zhang F., O'Donnell L. J., Support vector regression, Machine Learning, pp. 123-140, (2020); Chen T., Guestrin C., XGBoost: A scalable tree boosting system, Proc. of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T. Y., LightGBM: A highly efficient gradient boosting decision tree, Advances in Neural Information Processing Systems, (2017)","J. Choi; Department of Internal Medicine, School of Medicine, University of Kansas Kansas City, 66160, United States; email: jchoi4@kumc.edu; K.J. Chae; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, 54907, South Korea; email: para2727@gmail.com; C.H. Lee; Department of Radiology, Seoul National University Hospital, Seoul National University, College of Medicine, Seoul, 03080, South Korea; email: changhyun.lee@snu.ac.kr","","Korean Society for Internet Information","","","","","","19767277","","","","English","KSII Trans. Internet Inf. Syst.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85151801617"
"Robertson N.M.; Centner C.S.; Siddharthan T.","Robertson, Nicole M. (57204308740); Centner, Connor S. (57204023743); Siddharthan, Trishul (56871182100)","57204308740; 57204023743; 56871182100","Integrating Artificial Intelligence in the Diagnosis of COPD Globally: A Way Forward","2024","Chronic Obstructive Pulmonary Diseases","11","1","","114","120","6","4","10.15326/jcopdf.2023.0449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184910245&doi=10.15326%2fjcopdf.2023.0449&partnerID=40&md5=7ad18850046f7e63d885253b0d30a084","Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD, United States; University of Louisville School of Medicine, Louisville, KY, United States; Department of Bioengineering, School of Engineering, University of Louisville, Louisville, KY, United States; Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, United States","Robertson N.M., Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD, United States; Centner C.S., University of Louisville School of Medicine, Louisville, KY, United States, Department of Bioengineering, School of Engineering, University of Louisville, Louisville, KY, United States; Siddharthan T., Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami, FL, United States","The advancement of artificial intelligence (AI) capabilities has paved the way for a new frontier in medicine, which has the capability to reduce the burden of COPD globally. AI may reduce health care-associated expenses while potentially increasing diagnostic specificity, improving access to early COPD diagnosis, and monitoring COPD progression and subsequent disease management. We evaluated how AI can be integrated into COPD diagnosing globally and leveraged in resource-constrained settings. AI has been explored in diagnosing and phenotyping COPD through auscultation, pulmonary function testing, and imaging. Clinician collaboration with AI has increased the performance of COPD diagnosing and highlights the important role of clinical decision-making in AI integration. Likewise, AI analysis of computer tomography (CT) imaging in large population-based cohorts has increased diagnostic ability, severity classification, and prediction of outcomes related to COPD. Moreover, a multimodality approach with CT imaging, demographic data, and spirometry has been shown to improve machine learning predictions of the progression to COPD compared to each modality alone. Prior research has primarily been conducted in high-income country settings, which may lack generalization to a global population. AI is a World Health Organization priority with the potential to reduce health care barriers in low- and middle-income countries. We recommend a collaboration between clinicians and an AI-supported multimodal approach to COPD diagnosis as a step towards achieving this goal. We believe the interplay of CT imaging, spirometry, biomarkers, and sputum analysis may provide unique insights across settings that could provide a basis for clinical decision-making that includes early intervention for those diagnosed with COPD. © 2024 COPD Foundation. All rights reserved.","artificial intelligence; computer tomography; COPD; machine learning; prediction","biological marker; Article; artificial intelligence; auscultation; chronic obstructive lung disease; clinical practice; computer assisted tomography; health care; human; lung function test; machine learning; phenotype; spirometry; sputum analysis; World Health Organization","","","","","","","Global status report on noncommunicable diseases 2010; Jiang F, Jiang Y, Zhi H, Et al., Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, 4, pp. 230-243, (2017); Chen M, Decary M., Artificial intelligence in healthcare: an essential guide for health leaders, Healthc Manage Forum, 33, 1, pp. 10-18, (2020); Okeibunor JC, Jaca A, Iwu-Jaja CJ, Et al., The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review, Front Public Health, 11, (2023); Zhang B, Wang J, Chen J, Et al., Machine learning in chronic obstructivepulmonarydisease, ChinMedJ(Engl), 136, 5, pp. 536-538, (2023); Wang JM, Labaki WW, Murray S, Et al., Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS, Front Physiol, 14, (2023); Hasenstab KA, Yuan N, Retson T, Et al., Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network [published correction appears in Radiol Cardiothorac Imaging. 2022;4(1):e219002], Radiol Cardiothorac Imaging, 3, 2, (2021); Humphries SM, Notary AM, Centeno JP, Et al., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, 2, pp. 434-444, (2020); Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018); Altan G, Kutlu Y, Gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds, Turkish J. Electr. Eng. Comput. Sci, 28, 5, pp. 2979-2996, (2020); Das N, Happaerts S, Gyselinck I, Et al., Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation, Eur Respir J, 61, 5, (2023); Makimoto K, Hogg JC, Bourbeau J, Tan WC, Kirby M., CT imaging with machine learning for predicting progression to COPD in individuals at risk, Chest, 164, 5, pp. 1139-1149, (2023); Chen J, Xu Z, Sun L, Et al., Deep learning integration of chest computed tomography imaging and gene expression identifies novel aspects of COPD, Chronic Obstr Pulm Dis, 10, 4, pp. 355-368; Schaefer R, Khona M, Fiete IR., No free lunch from deep learning in neuroscience: a case study through models of the entorhinalhippocampal circuit, bioRxiv, pp. 1-16, (2023); Tang LYW, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, 5, pp. e259-e267, (2020); Esther CR, O'Neal WK, Anderson WH, Et al., Identification of sputum biomarkers predictive of pulmonary exacerbations in COPD, Chest, 161, 5, pp. 1239-1249, (2022); Xiong Y, Ba X, Hou A, Et al., Automatic detection of mycobacterium tuberculosis using artificial intelligence, J Thorac Dis, 10, 3, pp. 1936-1940, (2018); Agusti A, Bel E, Thomas M, Et al., Treatable traits: toward precision medicine of chronic airway diseases, Eur Respir J, 47, 2, pp. 410-419, (2016); Stolz D, Mkorombindo T, Schumann DM, Et al., Towards the elimination of chronic obstructive pulmonary disease: a Lancet Commission, Lancet, 400, 10356, pp. 921-972, (2022); Fernandez ADR, Fernandez DR, Iglesias VG, Jorquera DM., Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD), Int J Med Inform, 158, (2022); Hricak H, Abdel-Wahab M, Atun R, Et al., Medical imaging and nuclear medicine: a Lancet Oncology Commission, Lancet Oncol, 22, 4, pp. e136-e172, (2021); Lamprecht B, Soriano JB, Studnicka M, Et al., Determinants of underdiagnosis of COPD in national and international surveys, Chest, 148, 4, pp. 971-985, (2015); Ali AM, Mohammed AA., Improving classification accuracy for prostate cancer using noise removal filter and deep learning technique, Multimed Tools Appl, 81, pp. 8653-8669, (2022); Balasubramaniyan S, Jeyakumar V, Nachimuthu DS., Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans, Sci Rep, 12, 1, pp. 2045-2322, (2022); Agaba AJ, Abdullahi M, Junaidu SB, Hassan IH, Chiroma H., Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer), Intell Syst Applicat, 14, (2022); Babel A, Taneja R, Mondello Malvestiti F, Et al., Artificial intelligence solutions to increase medication adherence in patients with noncommunicable diseases, Front Digit Health, 3, (2021); Er O, Temurtas F., A study on chronic obstructive pulmonary disease diagnosis using multilayer neural networks, J Med Syst, 32, 5, pp. 429-432, (2008); Amaral JL, Lopes AJ, Faria AC, Melo PL., Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease, Comput Methods Programs Biomed, 118, 2, pp. 186-197, (2015); Badnjevic A, Gurbeta L, Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Sci Rep, 8, 1, (2018); Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z., Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease, Comput Methods Programs Biomed, 188, (2020); Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo CH., A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators [Published correction appears in Sci Rep. 2021 Mar 2;11(1):5324], Sci Rep, 10, 1, (2020); Finnegan A, Potenziani DD, Karutu C, Et al., Deploying machine learning with messy, real world data in low- and middle-income countries: Developing a global health use case, Front Big Data, 5, (2022); Hung YW, Hoxha K, Irwin BR, Law MR, Grepin KA., Using routine health information data for research in low- and middle-income countries: a systematic review, BMC Health Serv Res, 20, 1, (2020); Abdul-Rahman T, Ghosh S, Lukman L, Et al., Inaccessibility and low maintenance of medical data archive in low-middle income countries: mystery behind public health statistics and measures, J Infect Public Health, 16, 10, pp. 1556-1561, (2023); Ethics and governance of artificial intelligence for health","T. Siddharthan; Miami, 1951 NW 7th Ave Suite 2308, 33136, United States; email: tsiddhar@miami.edu","","COPD Foundation","","","","","","2372952X","","","","English","Chronic Obstr. Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85184910245"
"Wang R.; Chen L.-C.; Moukheiber L.; Seastedt K.P.; Moukheiber M.; Moukheiber D.; Zaiman Z.; Moukheiber S.; Litchman T.; Trivedi H.; Steinberg R.; Gichoya J.W.; Kuo P.-C.; Celi L.A.","Wang, Ryan (57226405033); Chen, Li-Ching (57226396954); Moukheiber, Lama (57431019700); Seastedt, Kenneth P. (56569062500); Moukheiber, Mira (57687818800); Moukheiber, Dana (57211190622); Zaiman, Zachary (57224826717); Moukheiber, Sulaiman (57997607300); Litchman, Tess (57214452820); Trivedi, Hari (54792314500); Steinberg, Rebecca (57194508582); Gichoya, Judy W. (55805424500); Kuo, Po-Chih (56335546800); Celi, Leo A. (16033282700)","57226405033; 57226396954; 57431019700; 56569062500; 57687818800; 57211190622; 57224826717; 57997607300; 57214452820; 54792314500; 57194508582; 55805424500; 56335546800; 16033282700","Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study","2023","International Journal of Medical Informatics","178","","105211","","","","4","10.1016/j.ijmedinf.2023.105211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172889930&doi=10.1016%2fj.ijmedinf.2023.105211&partnerID=40&md5=322f6162ff1de1586a08ce0bcca270a0","Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States; Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Department of Computer Science, Emory University, Atlanta, GA, United States; Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Department of Radiology, Emory University, Atlanta, GA, United States; Department of Medicine, Emory University, Atlanta, GA, United States; Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States; The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States","Wang R., Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Chen L.-C., Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Moukheiber L., Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States; Seastedt K.P., Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Moukheiber M., The Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States; Moukheiber D., Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States; Zaiman Z., Department of Computer Science, Emory University, Atlanta, GA, United States; Moukheiber S., Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States; Litchman T., Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Trivedi H., Department of Radiology, Emory University, Atlanta, GA, United States; Steinberg R., Department of Medicine, Emory University, Atlanta, GA, United States; Gichoya J.W., Department of Radiology, Emory University, Atlanta, GA, United States; Kuo P.-C., Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan; Celi L.A., Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States, Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States","Purpose: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models. Method: To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups. Results: Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry. Conclusions: By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease. © 2023 Elsevier B.V.","Chest X-ray; Chronic obstructive pulmonary disease; Convolutional neural network; Data fusion; Model fusion","Convolutional neural networks; Developing countries; Diagnosis; Image fusion; Modal analysis; Neural network models; Patient treatment; Pulmonary diseases; Chest X-ray; Chest X-ray image; Chronic obstructive pulmonary disease; Convolutional neural network; Disease diagnosis; Electronic health; Health records; Image data; Model fusion; Multi-site; adult; aged; ambient air; Article; child; chronic obstructive lung disease; cohort analysis; controlled study; convolutional neural network; deep learning; electronic health record; emergency ward; female; high flow nasal cannula therapy; hospital admission; human; ICD-10; ICD-9; intensive care unit; k nearest neighbor; logistic regression analysis; major clinical study; male; middle aged; multicenter study; oxygen therapy; random forest; spirometry; thorax radiography; transfer of learning; very elderly; Convolution","","","","","National Science Foundation, NSF, (1928481, 2204, 75N92020C00008, 75N92020C00021); National Science Foundation, NSF; National Institutes of Health, NIH, (R01 EB017205, R01EB030362); National Institutes of Health, NIH; National Institute of Biomedical Imaging and Bioengineering, NIBIB; U.S. National Library of Medicine, NLM, (75N97020C00013); U.S. National Library of Medicine, NLM; Massachusetts Life Sciences Center, MLSC; National Science and Technology Council, NSTC, (MOST109-2222-E-007-004-MY3); National Science and Technology Council, NSTC","This study is funded by the National Science and Technology Council, Taiwan (MOST109-2222-E-007-004-MY3). L.A.C and D.M. are funded by the National Institute of Health through the NIBIB R01 EB017205. D.M. and L.M. are supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362. D.M. is supported by NIH National Library of Medicine under contract number 75N97020C00013, and Massachusetts Life Sciences Center, Jul. 1st, 2020. J.W.G. declares support from US National Science Foundation (grant number 1928481) from the Division of Electrical, Communication & Cyber Systems, RSNA Health Disparities grant (#EIHD2204), NIH (NIBIB) MIDRC grant under contracts 75N92020C00008 and 75N92020C00021. 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Methods, 17, 3, pp. 261-272, (2020); Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-cam: visual explanations from deep networks via gradient-based localization, 2017 IEEE International Conference on Computer Vision (ICCV), (2017); DeLong E.R., Et al., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Nemec S.F., Bankier A.A., Eisenberg R.L., Lower lobe—predominant diseases of the lung, Am. J. Roentgenol., 200, 4, pp. 712-728, (2013); Hurst J.R., Upper Airway. 3: Sinonasal involvement in chronic obstructive pulmonary disease, Thorax, 65, 1, pp. 85-90, (2009); Woodruff P.G., Barr R.G., Bleecker E., Christenson S.A., Couper D., Curtis J.L., Gouskova N.A., Hansel N.N., Hoffman E.A., Kanner R.E., Kleerup E., Lazarus S.C., Martinez F.J., Paine R., Rennard S., Tashkin D.P., Han M.L.K., Clinical significance of symptoms in smokers with preserved pulmonary function, N. Engl. J. Med., 374, 19, pp. 1811-1821, (2016); Sood A., Petersen H., Qualls C., Meek P.M., Vazquez-Guillamet R., Celli B.R., Tesfaigzi Y., (2016); Chattopadhay A., Sarkar A., Howlader P., Balasubramanian V.N., pp. 839-847, (2018); 12966, pp. 396-405, (2021)","P.-C. Kuo; 101, Section 2, Hsinchu, Kuang-Fu Road, 300044, Taiwan; email: kuopc@cs.nthu.edu.tw","","Elsevier Ireland Ltd","","","","","","13865056","","IJMIF","37690225","English","Int. J. Med. Informatics","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85172889930"
"Siddiqa A.; Zilqurnain Naqvi S.A.; Ahsan M.; Ditta A.; Alquhayz H.; Khan M.A.; Khan M.A.","Siddiqa, Ayesha (57200932715); Zilqurnain Naqvi, Syed Abbas (57203482225); Ahsan, Muhammad (57212940288); Ditta, Allah (57193528024); Alquhayz, Hani (55804201900); Khan, M.A. (57226797214); Khan, Muhammad Adnan (58994594500)","57200932715; 57203482225; 57212940288; 57193528024; 55804201900; 57226797214; 58994594500","Robust length of stay prediction model for indoor patients","2022","Computers, Materials and Continua","70","3","","5519","5536","17","5","10.32604/cmc.2022.021666","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117058247&doi=10.32604%2fcmc.2022.021666&partnerID=40&md5=aa0c496d06804db76d747df9c6312ab8","Department of Mechatronics and Control Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan; Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, 54000, Pakistan; Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia; Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan; Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea","Siddiqa A., Department of Mechatronics and Control Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan; Zilqurnain Naqvi S.A., Department of Mechatronics and Control Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan; Ahsan M., Department of Mechatronics and Control Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan; Ditta A., Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, 54000, Pakistan; Alquhayz H., Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia; Khan M.A., Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, Lahore, 54000, Pakistan; Khan M.A., Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea","Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients’ LOS to resolve these issues. For this purpose, a very large-sized data (more than 2.3 million patients’ data) related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow, Tuberculosis, Intestinal Transplant, Mental illness, Leukaemia, Spinal cord injury, Trauma, Rehabilitation, Kidney and Alcoholic Patients, HIV Patients, Malignant Breast disorder, Asthma, Respiratory distress syndrome, etc. have been analyzed to predict the LOS. We selected six Machine learning (ML) models named: Multiple linear regression (MLR), Lasso regression (LR), Ridge regression (RR), Decision tree regression (DTR), Extreme gradient boosting regression (XGBR), and Random Forest regression (RFR). The selected models’ predictive performance was checked using R square and Mean square error (MSE) as the performance evaluation criteria. Our results revealed the superior predictive performance of the RFR model, both in terms of RS score (92%) and MSE score (5), among all selected models. By Exploratory data analysis (EDA), we conclude that maximum stay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital. Based on the average LOS, results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases. This finding could help predict the future length of hospital stay of new patients, which will help the hospital administration estimate and manage their resources efficiently. © 2022 Tech Science Press. All rights reserved.","Length of stay; Machine learning; Random forest regression; Robust model","Adaptive boosting; Climate change; Decision trees; Diagnosis; Diseases; Forecasting; Linear regression; Machine learning; Mean square error; Patient rehabilitation; Patient treatment; Viruses; Chronic disease; Hospital administration; Hospital management; Length of stay; Means square errors; Prediction modelling; Predictive performance; Random forest regression; Random forests; Robust modeling; Hospitals","","","","","","","Gumaei A., Rakhami M. A., Rahhal M. M. A., Albogamy F. R. H., Maghayreh E. A., Et al., Prediction of COVID-19 confirmed cases using gradient boosting regression method, Computers, Materials & Continua, 66, 1, pp. 315-329, (2020); Mitchell R., Banks C., Emergency departments and the COVID-19 pandemic: Making the most of limited resources, Emergency Medicine Journal, 37, 5, pp. 258-259, (2020); Zolbanin H. M., Davazdahemami B., Delen D., Zadeh A. H., Data analytics for the sustainable use of resources in hospitals: Predicting the length of stay for patients with chronic diseases, Information & Management, 13, 4, pp. 103282-103299, (2020); Chuang M. T., Hu Y. H., Lo C. L., Predicting the prolonged length of stay of general surgery patients: A supervised learning approach, International Transactions in Operational Research, 25, 1, pp. 75-90, (2018); Uddin S., Khan A., Hossain M. E., Moni M. A., Comparing different supervised machine learning algorithms for disease prediction, BMC Medical Informatics and Decision Making, 19, 1, pp. 1-16, (2019); Love B. C., Comparing supervised and unsupervised category learning, Psychonomic Bulletin & Review, 9, 4, pp. 829-835, (2002); Daghistani T. A., Elshawi R., Sakr S., Ahmed A. M., Thwayee A. A., Et al., Predictors of in-hospital length of stay among cardiac patients: A machine learning approach, InternationalJournalof Cardiology, 288, pp. 140-147, (2019); Morton A., Marzban E., Giannoulis G., Patel A., Aparasu R., Et al., A comparison of supervised machine learning techniques for predicting short-term in-hospital length of stay among diabetic patients, Int. Conf. on Machine Learning and Applications, pp. 1-5, (2014); Bacchi S., Gluck S., Tan Y., Chim I., Cheng J., Et al., Prediction of general medical admission length of stay with natural language processing and deep learning: A pilot study, Internal and Emergency Medicine, 15, 6, pp. 989-995, (2020); Patel A., Johnson M., Aparasu R., Predicting in-hospital mortality and hospital length of stay in diabetic patients, Value in Health, 16, 3, pp. A17-A25, (2013); Nadeem M. W., Ghamdi M. A. A., Hussain M., Khan M. A., Khalid K. M., Et al., Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges, Brain Sciences, 10, 2, pp. 118-134, (2020); Yang C. S., Wei C. P., Yuan C. C., Schoung J. Y., Predicting the length of hospital stay of burn patients: Comparisons of prediction accuracy among different clinical stages, DecisionSupportSystems, 50, 1, pp. 325-335, (2010); Chuang M. T., Hu Y. H., Lo C. L., Predicting the prolonged length of stay of general surgery patients: a supervised learning approach, International Transactions in Operational Research, 25, 1, pp. 75-90, (2018); Liu V., Kipnis P., Gould M. K., Escobar G. J., Length of stay predictions: Improvements through the use of automated laboratory and comorbidity variables, MedicalCare, 48, 8, pp. 739-744, (2010); Hospital inpatient discharges (SPARCS De-Identified), (2017); Vigni M. L., Durante C., Cocchi M., Exploratory data analysis, Data Handling in Science and Technology, 28, pp. 55-126, (2013); Kumar S., Chong I., Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states, Environmental Research and Public Health, 15, 12, (2018); Catalina T., Iordache V., Caracaleanu B., Multiple regression model for fast prediction of the heating energy demand, Energy and Buildings, 57, 9, pp. 302-312, (2013); Streib F. E., Dehmer M., High-dimensional LASSO-based computational regression models: regularization, shrinkage, and selection, Machine Learning and Knowledge Extraction, 1, 1, pp. 359-383, (2019); Kibria B. G., Saleh A. M. E., Improving the estimators of the parameters of a probit regression model: A ridge regression approach, Journal of Statistical Planning and Inference, 142, 6, pp. 1421-1435, (2012); Loh W. Y., Classification and regression tree methods, Encyclopedia of Statistics in Quality and Reliability, 1, pp. 315-323, (2008); Xu M., Watanachaturaporn P., Varshney P. K., Arora M. K., Decision tree regression for soft classification of remote sensing data, Remote Sensing of Environment, 97, 3, pp. 322-336, (2005); Liu J., Wu J., Liu S., Li M., Hu K., Et al., Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model, PLoS One, 16, 2, (2021); Palmer D. S., Boyle N. M. O., Glen R. C., Mitchell J. B., Random forest models to predict aqueous solubility, Journal of Chemical Information and Modeling, 47, 1, pp. 150-158, (2007); Liaw A., Wiener M., Classification and regression by random forest, R News, 2, 3, pp. 18-22, (2002); Browne M. W., Cross-validation methods, Journal of Mathematical Psychology, 44, 1, pp. 108-132, (2000); Bergmeir C., Costantini M., Benitez J. M., On the usefulness of cross-validation for directional forecast evaluation, Computational Statistics & Data Analysis, 76, 7, pp. 132-143, (2014); Cameron A. C., Windmeijer F. A. G., An R-squared measure of goodness of fit for some common nonlinear regression models, Journal of Econometrics, 77, 2, pp. 329-342, (1997); Sugumaran V., Muralidharan V., Ramachandran K., Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing, Mechanical Systems and Signal Processing, 21, 2, pp. 930-942, (2007); Gupta Y., Selection of important features and predicting wine quality using machine learning techniques, Procedia Computer Science, 125, 1, pp. 305-312, (2018)","M.A. Khan; Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea; email: adnan@gachon.ac.kr","","Tech Science Press","","","","","","15462218","","","","English","Comput. Mater. Continua","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85117058247"
"Salehi A.M.; Wang L.; Xiaolian G.U.; Coates P.J.; Spaak L.N.; Sgaramella N.; Nylander K.","Salehi, Amir M. (57219013467); Wang, Lixiao (57197854271); Xiaolian, G.U. (58896730700); Coates, Philip J. (57193721735); Spaak, Lena Norberg (57195714579); Sgaramella, Nicola (55303777600); Nylander, Karin (7003516605)","57219013467; 57197854271; 58896730700; 57193721735; 57195714579; 55303777600; 7003516605","Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment","2024","Oncology Letters","27","4","","","","","3","10.3892/ol.2024.14275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185533910&doi=10.3892%2fol.2024.14275&partnerID=40&md5=887fa70874524e6d5bfee5895183b7ab","Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden; Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, 656 53, Czech Republic; Department of Oral and Maxillo, Facial Surgery, Mater Dei Hospital, Bari, I‑70125, Italy","Salehi A.M., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden; Wang L., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden; Xiaolian G.U., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden; Coates P.J., Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, 656 53, Czech Republic; Spaak L.N., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden; Sgaramella N., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden, Department of Oral and Maxillo, Facial Surgery, Mater Dei Hospital, Bari, I‑70125, Italy; Nylander K., Department of Medical Biosciences/Pathology, Umeå University, Umeå, SE 901 85, Sweden","Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recur‑ rence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2‑year survival (P=0.025) compared with non‑diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recur‑ rence and improved survival compared with non‑diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply © 2024 Spandidos Publications. All rights reserved.","diabetes; random forest; recurrence; squamous cell carcinoma; tongue","metformin; accuracy; adult; Article; asthma; cancer staging; cardiovascular disease; cell invasion; clinical feature; diabetes mellitus; distant metastasis; female; follow up; histology; histopathology; human; hyperglycemia; local recurrence free survival; lymphocytic infiltration; lymphoid tissue; machine learning; major clinical study; male; middle aged; neck metastasis; principal component analysis; random forest; recurrence risk; recurrent disease; retrospective study; sensitivity and specificity; survival; tongue squamous cell carcinoma; tumor recurrence","","metformin, 1115-70-4, 657-24-9","SIMCA software  16, MKS data analytics solutions, Sweden; SPSS version 25","MKS data analytics solutions, Sweden","Ministry of Health Czech Republic; Lung Cancer Research Foundation, LCRF; Umeå Universitet; Cancerfonden, (23 2775 Pj 01H); MMCI, (00209805); Grantová Agentura České Republiky, GA ČR, (GACR 21‑13188S); , (104068)","The present study was supported by Lion's Cancer Research Foundation, Ume\u00E5 University, The Swedish Cancer Society (contract number 23 2775 Pj 01H), Ume\u00E5 University, Region V\u00E4sterbotten, Ministry of Health Czech Republic (MMCI, 00209805) and Czech Science Foundation (GACR 21\u201113188S).","Shah JP, Gil Z, Current concepts in management of oral cancer‑surgery, Oral Oncol, 45, (2009); Marur S, Forastiere AA, Head and neck cancer: Changing epidemiology, diagnosis, and treatment, Mayo Clin Proc, 83, pp. 489-501, (2008); Lacy PD, Piccirillo JF, Merritt MG, Zequeira MR, Head and neck squamous cell carcinoma: Better to be young, Otolaryngol Head Neck Surg, 122, (2000); Boje CR, Impact of comorbidity on treatment outcome in head and neck squamous cell carcinoma‑a systematic review, Radiother Oncol, 110, pp. 81-90, (2014); Bradford CR, Ferlito A, Devaney KO, Makitie AA, Rinaldo A, Prognostic factors in laryngeal squamous cell carci‑ noma, Laryngoscope Investig Otolaryngol, 5, (2020); Huang Y, Xiao X, Sadeghi F, Feychting M, Hammar N, Fang F, Zhang Z, Liu Q, Blood metabolic biomarkers and the risk of head and neck cancer: An epidemiological study in the Swedish AMORIS Cohort, Cancer Lett, 557, (2023); Gu X, Wang L, Coates PJ, Boldrup L, Fahraeus R, Wilms T, Norberg-Spaak L, Sgaramella N, Nylander K, Transfer‑RNA‑derived fragments are potential prognostic factors in patients with squamous cell carcinoma of the head and neck, Genes (Basel), 11, (2020); Gu X, Wang L, Boldrup L, Coates P, Fahraeus R, Sgaramella N, Wilms T, Nylander K, AP001056.1, A prognosis‑related enhancer RNA in squamous cell carcinoma of the head and neck, Cancers (Basel), 11, (2019); Warner GC, Reis PP, Jurisica I, Sultan M, Arora S, Macmillan C, Makitie AA, Grenman R, Reid N, Sukhai M, Et al., Molecular classification of oral cancer by cDNA microarrays identifies overexpressed genes correlated with nodal metastasis, Int J Cancer, 110, (2004); Zhang F, Liu Y, Yang Y, Yang K, Development and validation of a fourteen‑innate immunity‑related gene pairs signature for predicting prognosis head and neck squamous cell carcinoma, BMC Cancer, 20, (2020); Zhou RS, Zhang EX, Sun QF, Ye ZJ, Liu JW, Zhou DH, Tang Y, Integrated analysis of lncRNA‑miRNA‑mRNA ceRNA network in squamous cell carcinoma of tongue, BMC Cancer, 19, (2019); Sgaramella N, Gu X, Boldrup L, Coates PJ, Fahraeus R, Califano L, Tartaro G, Colella G, Spaak LN, Strom A, Et al., Searching for new targets and treatments in the battle against squamous cell carcinoma of the head and neck, with specific focus on tumours of the tongue, Curr Top Med Chem, 18, (2018); Brandwein-Gensler M, Teixeira MS, Lewis CM, Lee B, Rolnitzky L, Hille JJ, Genden E, Urken ML, Wang BY, Oral squamous cell carcinoma histologic risk assessment, but not margin status, is strongly predictive of local disease‑free and overall survival, Am J Surg Pathol, 29, (2005); Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP, Random forest: A classification and regression tool for compound classification and QSAR modeling, J Chem Inf Comput Sci, 43, pp. 1947-1958, (2003); Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Et al., Scikit‑learn: Machine learning in python, J Mach Learn Res, 12, pp. 2825-2830, (2011); Alabi RO, Elmusrati M, Sawazaki-Calone I, Kowalski LP, Haglund C, Coletta RD, Makitie AA, Salo T, Almangush A, Leivo L, Comparison of supervised machine learning classifica‑ tion techniques in prediction of locoregional recurrences in early oral tongue cancer, Int J Med Inform, 136, (2020); Alabi RO, Elmusrati M, Leivo I, Almangush A, Makitie AA, Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP, Sci Rep, 13, (2023); Alabi RO, Almangush A, Elmusrati M, Leivo I, Makitie A, Measuring the usability and quality of explanations of a machine learning web‑based tool for oral tongue cancer prognostication, Int J Environ Res Public Health, 19, (2022); Alabi RO, Almangush A, Elmusrati M, Makitie AA, Deep machine learning for oral cancer: From precise diagnosis to precision medicine, Front Oral Health, 2, (2022); Giovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, Pollak M, Regensteiner JG, Yee D., Diabetes and cancer, a consensus report, Diabetes Care, 33, pp. 1674-1685, (2010); Wojciechowska J, Krajewski W, Bolanowski M, Krecicki T, Zatonski T, Diabetes and cancer: A review of current knowledge, Exp Clin Endocrinol Diabetes, 124, pp. 263-275, (2016); Hadad SM, Coates P, Jordan LB, Dowling RJ, Chang MC, Done SJ, Purdie CA, Goodwin PJ, Stambolic V, Moulder- Thompson S, Et al., Evidence for biological effects of metformin in operable breast cancer: Biomarker analysis in a pre‑operative window of opportunity randomized trial, Breast Cancer Res Treat, 150, (2015); Noto H, Goto A, Tsujimoto T, Noda M, Cancer risk in diabetic patients treated with metformin: A systematic review and meta‑analysis, PLoS One, 7, (2012); Dickerman BA, Garcia-Albeniz X, Logan RW, Denaxas S, Hernan MA, Evaluating metformin strategies for cancer preven‑ tion: A target trial emulation using electronic health records, Epidemiology, 34, pp. 690-699, (2023); Hu X, Xiong H, Chen W, Huang L, Mao T, Yang L, Wang C, Huang D, Wang Z, Yu J, Et al., Metformin reduces the increased risk of oral squamous cell carcinoma recurrence in patients with type 2 diabetes mellitus: A cohort study with propensity score analyses, Surg Oncol, 35, (2020); Gutkind JS, Molinolo AA, Wu X, Wang Z, Nachmanson D, Harismendy O, Alexandrov LB, Wuertz BR, Ondrey FG, Laronde D, Et al., Inhibition of mTOR signaling and clinical activity of metformin in oral premalignant lesions, JCI Insight, 6, (2021); Lee DJ, McMullen CP, Foreman A, Huang SH, Lu L, Xu W, de Almeida JR, Liu G, Bratman SV, Goldstein DP, Impact of metformin on disease control and survival in patients with head and neck cancer: A retrospective cohort study, J Otolaryngol Head Neck Surg, 48, (2019); Barrea L, Caprio M, Tuccinardi D, Moriconi E, Di Renzo L, Muscogiuri G, Colao A, Savastano S, Could ketogenic diet ‘starve’ cancer? Emerging evidence, Crit Rev Food Sci Nutr, 62, (2022); Marcucci F, Rumio C., Glycolysis‑induced drug resistance in tumors‑A response to danger signals?, Neoplasia, 23, pp. 234-245, (2021); Spanier G, Ugele I, Nieberle F, Symeou L, Schmidhofer S, Brand A, Meier J, Spoerl S, Krupar R, Rummele P, Et al., The predictive power of CD3<sup>+</sup> T cell infiltration of oral squamous cell tumors is limited to non‑diabetic patients, Cancer Lett, 499, pp. 209-219, (2021); Lundqvist L, Stenlund SH, Laurell G, Nylander K, The importance of stromal inflammation in squamous cell carcinoma of the tongue, J Oral Pathol Med, 41, (2012)","K. Nylander; Department of Medical Biosciences/Pathology, Umeå University, Umeå, Analysvägen 9, SE 901 85, Sweden; email: karin.nylander@umu.se","","Spandidos Publications","","","","","","17921074","","","","English","Oncol. Lett.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85185533910"
"Balmuri K.R.; Konda S.; Mamidala K.K.; Gunda M.; Rani B S.","Balmuri, Kavitha Rani (56018078400); Konda, Srinivas (36680746700); Mamidala, Kishore kumar (58237272300); Gunda, Madhukar (57207945387); Rani B, Swaroopa (58238236700)","56018078400; 36680746700; 58237272300; 57207945387; 58238236700","Automated and reliable detection of multi-diseases on chest X-ray images using optimized ensemble transfer learning","2024","Expert Systems with Applications","246","","122810","","","","4","10.1016/j.eswa.2023.122810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183469013&doi=10.1016%2fj.eswa.2023.122810&partnerID=40&md5=aae55485574de9bde45e49bc52ae43f8","Department of Information Technology, CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Department of CSE (Data Science), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Department of CSE, CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Department of CSE(AIML), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India","Balmuri K.R., Department of Information Technology, CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Konda S., Department of CSE (Data Science), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Mamidala K.K., Department of CSE (Data Science), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Gunda M., Department of CSE, CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India; Rani B S., Department of CSE(AIML), CMR Technical Campus, Kandlakoya, Telangana, Hyderabad, 501401, India","Chest radiography is a comparatively low-cost, most probably applied in a medical process that brings crucial information to discover diagnostic conclusions. Chest X-rays (CXR) are used in the prognosis of chest diseases that includes asthma, lung cancer, pneumonia, and COVID-19. Artificial intelligence detects the multi-disease automatically to enhance its proficiency and performance by solving image detection complications with various machine learning and deep learning approaches. Convolutional Neural Network (CNN) has been designed for the advancement of computerized recognition systems. For the classification of medical images, texture, shape, size, and tissue constitution are essential features for detecting diseases. Hence, huge input features are merged with deep CNN validated to increase the effectiveness of CXR analysis. In addition, multi-scale features are visualized in CNN for the detection of variable sizes of thoracic diseases. This paper has implemented a novel multi-disease diagnosis model using chest X-ray images through deep learning approaches, and thus, it helps to minimize the computational cost. The standard CXR images are gathered from the standard datasets. After, the pre-processing is performed for cleaning and contrast the images. Further, the image segmentation is carried out using Optimized DeepLabv3 (ODeepLabv3), where the optimization of parameters in DeepLabv3 is done by a new Mutation Rate-based Lion Algorithm (MR-LA). Then, multi-disease classification is carried out through Optimized Ensemble Transfer Learning (OETL), where the OETL is carried out through VGG16, ResNet, ImageNet, MobileNet, GoogleNet, Inception, and Xception. Here, the parameter optimization of all models is done by the same MR-LA. The proposed model improved the effectiveness and performance in expressions of sensitivity, precision, and specificity factors and get higher classification and detection accuracy. The outcome of the recommended method over other models is identified by the relative analysis conducted. © 2023 Elsevier Ltd","Chest X-Ray Images; Multi-Diseases Classification; Mutation Rate-Based Lion Algorithm; Optimized Deeplabv3-based Segmentation; Optimized Ensemble Transfer Learning","Computer aided diagnosis; Convolutional neural networks; Deep learning; Feature extraction; Image classification; Image enhancement; Image segmentation; Learning systems; Medical imaging; Textures; Transfer learning; Chest X-ray image; Convolutional neural network; Disease classification; Multi-disease classification; Mutation rate-based lion algorithm; Mutation rates; Optimized deeplabv3-based segmentation; Optimized ensemble transfer learning; Performance; Transfer learning; Diseases","","","","","","","Cortes E., Sanchez S., Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition, IEEE Latin America Transactions, 19, 6, pp. 944-951, (2021); Abbas A., Abdelsamea M.M., Gaber M.M., 4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection, IEEE Transactions on Neural Networks and Learning Systems, 32, 7, pp. 2798-2808, (2021); Eva Castro Lopez, Francisco Lizancos Vidal, Joaquim De Moura, Jorge Novo, Lucía Ramos García, Laura Abelairas López, Marcos and Ortega Plácido, “Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices,”, IEEE Access, 8, pp. 195594-195607, (2020); Barnawi A., Wang C., Nie J., Kumar N., Tang S., Zhang Y., Xiong Z., EDL-COVID: Ensemble Deep Learning for COVID-19 Case Detection From Chest X-Ray Images, IEEE Transactions on Industrial Informatics, 17, 9, pp. 6539-6549, (2021); Moemeni A., Elizondo D.A., Lopez-Rubio E., Rodriguez-Capitan J., Oala L., Jimenez-Navarro M., Colreavy-Donnelly S., Improving Uncertainty Estimation With Semi-Supervised Deep Learning for COVID-19 Detection Using Chest X-Ray Images, IEEE Access, 9, pp. 85442-85454, (2021); Bhowal P., Sen S., Yoon J.H., Geem Z.W., Sarkar R., Choquet Integral and Coalition Game-Based Ensemble of Deep Learning Models for COVID-19 Screening From Chest X-Ray Images, IEEE Journal of Biomedical and Health Informatics, 25, 12, pp. 4328-4339, (2021); 9, pp. 35501-35513, (2021); Wang K., Zhang X., Huang S., Chen F., Zhang X., Huangfu L., Learning to Recognize Thoracic Disease in Chest X-Rays With Knowledge-Guided Deep Zoom Neural Networks, IEEE Access, 8, pp. 159790-159805, (2020); Ibrahi A., El-Kenawy E.-S., Eid M.M., Alrahmawy M., Zaki R.M., Mirjalili S., “Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification,” IEEE, Access, 9, pp. 36019-36037, (2021); Tan B., Tan J.I., Jun L.U., Shengli Xie X.U., Wang Z.L., He Z., AANet: Adaptive Attention Network for COVID-19 Detection From Chest X-Ray Images, IEEE Transactions on Neural Networks and Learning Systems, 32, 11, pp. 4781-4792, (2021); Singh G., Yow K.-C., “An Interpretable Deep Learning Model for Covid-19 Detection With Chest X-Ray Images,” n IEEE, Access, 9, pp. 85198-85208, (2021); (2021); Trivedi M., Gupta A., A lightweight deep learning architecture for the automatic detection of pneumonia using chest X-ray images, Multimedia Tools and Applications, (2021); Shankar K., Perumal, “A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images”, Complex & Intelligent Systems, 7, pp. 1277-1293, (2021); Ravi V., Narasimhan H., Chakraborty C., Pham T.D., Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images, Multimedia Systems, (2021); Samson Anosh Babu P., Annavarapu C.S.R., Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification, Applied Intelligence, 51, pp. 3104-3120, (2021); 51, pp. 1690-1700, (2021); 8, pp. 37265-37274, (2020); Singh K.K., Singh A., Diagnosis of COVID-19 from chest X-ray images using wavelets-based depthwise convolution network, Big Data Mining and Analytics, 4, 2, pp. 84-93, (2021); 8, pp. 191586-191601, (2020); Gazda M., Plavka J., Gazda J., Drotar P., Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification, IEEE Access, 9, pp. 151972-151982, (2021); Reamaroon N., Sjoding M.W., Gryak J., Athey B.D., Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features, Computers in Biology and Medicine, 134, (2021); Ibrahim D.M., Elshennawy N.D.M., Sarhan A.M., Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases, Computers in Biology and Medicine, 132, (2021); Ayan E., Karabulut B., Halil Murat Ünver, “Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images”, Arabian Journal for Science and Engineering, (2021); Arias-Londono J.D., Gomez-Garcia J.A., Moro-Velazquez L., Godino-Llorente J.I., Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach, IEEE Access, 8, pp. 226811-226827, (2020); Abdullah-Al-Wadud M., (2016); (2018); Farzamnia A., Saad I., Meshgini S., Mojtahedi S., Sheykhivand S., Tohid Yousefi Rezaii, and Zohreh Mousavi, “Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images”, Alexandria Engineering Journal, 60, pp. 2885-2903, (2021); (2015); Chen B., Liu X., Zheng Y., Zhao G., Shi Y.-Q., A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception, IEEE Transactions on Circuits and Systems for Video Technology, 32, 6, pp. 3527-3538, (2022); Chollet, Francois, Xception: Deep Learning with Depthwise Separable Convolutions, Conference on Computer Vision and Pattern Recognition, (2017); 71, (2022); (2021); Boothalingam R., Optimization using lion algorithm: A biological inspiration from lion's social behavior, Evolutionary Intelligence, 11, 1-2, pp. 31-52, (2018); Arun C.A., Sahaya Sheela M., Hybrid PSO–SVM algorithm for Covid-19 screening and quantification, International Journal of Information Technology, 14, pp. 2049-2056, (2022); Arun J., Jeyanthi S., Rajasenbagam T., Pandian Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques, Journal of Ambient Intelligence and Humanized Computing, (2021); (2021); Kavitha M., MDP-HML: An efficient detection method for multiple human disease using retinal fundus images based on hybrid learning techniques, Multimedia Systems, (2023); Vimala M., Ranjith Kumar P., Real-time Multi Fractal Ensemble Analysis CNN Model for Optimizing Brain Tumor Classification and Survival Prediction Using SVM, Biomedical & Pharmacology Journal, 16, 1, pp. 305-318, (2023); 23, 2, (2023); (2023); Kayalvizhi S., Nagarajan S., Deepa J., Hemapriya K., Multi-modal IoT-based medical data processing for disease diagnosis using Heuristic-derived deep learning, Biomedical Signal Processing and Control, 85, (2023); pp. 1-12, (2023); Jose D., A Noufal Chithara, P Nirmal Kumar, H Kareemulla, “Automatic detection of lung cancer nodules in computerized tomography images,”, National Academy Science Letters, 40, pp. 161-166, (2017); Yeruva A.R., Choudhari P., Shrivastava A., Verma D., Shaw S., Rana A., Covid-19 Disease Detection using Chest X-Ray Images by Means of CNN, 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 625-631, (2022)","K.R. Balmuri; Professor and Head, Department of Information Technology, Hyderabad, CMR Technical Campus, Kandlakoya, Telangana, 501401, India; email: phdknr1@gmail.com","","Elsevier Ltd","","","","","","09574174","","ESAPE","","English","Expert Sys Appl","Article","Final","","Scopus","2-s2.0-85183469013"
"Smily Jeya Jothi E.; Justin J.; Vanithamani R.; Varsha R.","Smily Jeya Jothi, E. (57201116927); Justin, Judith (54784624500); Vanithamani, R. (54973743000); Varsha, R. (58093891600)","57201116927; 54784624500; 54973743000; 58093891600","On-mask sensor network for lung disease monitoring","2023","Biomedical Signal Processing and Control","83","","104655","","","","4","10.1016/j.bspc.2023.104655","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147551141&doi=10.1016%2fj.bspc.2023.104655&partnerID=40&md5=72deeaa8a9dfa014e06bd56eaa22d6f7","Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India","Smily Jeya Jothi E., Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India; Justin J., Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India; Vanithamani R., Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India; Varsha R., Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India","A lung disease usually falls into three categories: lung tissue disease, lung circulation disease, and lung airway disease. Several chronic breathing disorders cause inflammation and swell of the airways due to excessive mucus secretion, including asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis. The airways overreact to various stimuli, narrowing the bronchi and leading to broncho-constriction associated with chest tightness, cough, and dyspnea. The repercussions of airway diseases can be minor, interfering in daily routines, while the symptoms may sometimes flare up and become life-threatening. Monitoring of physiological status of pulmonary patients is essential to avoid any critical situations. This work proposes a continuous lung function monitoring system using Machine Learning (ML) techniques to aid in the early identification of the disease symptoms and obviates severe outbreaks of the lung disorder. 3D mask made of Poly Lactic Acid (PLA) filament, developed using 3D printing technology, contains a series of sensors interfaced to the microcontroller. The sensor values are instantaneously fetched when the person wearing the mask inhales and exhales. The acquired data from the sensors are directed to the cloud through a Wi-Fi module for further analysis, and classification is done by Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN) algorithms. The training of the classifiers is carried out using a set of pre-trained values taken from publicly available databases. Furthermore, patients are warned when there are deviations from the normal value of the physiological parameters and changes in favourable atmospheric conditions. © 2023 Elsevier Ltd","Asthma; Bronchiectasis; COPD; KNN; Lung monitoring; Machine Learning; Random Forest; SVM","3D printing; Biological organs; Classification (of information); Lactic acid; Learning systems; Nearest neighbor search; Physiological models; Pulmonary diseases; Respiratory system; Sensor networks; polylactic acid; Asthma; Bronchiectasis; Chronic obstructive pulmonary disease; K-near neighbor; Lung monitoring; Machine-learning; Nearest-neighbour; Random forests; Sensors network; Support vectors machine; algorithm; Article; classification error; controlled study; data mining; human; k nearest neighbor; lung disease; lung function; machine learning; mathematical model; normal value; random forest; sensitivity and specificity; support vector machine; temperature; temperature measurement; three dimensional printing; Support vector machines","","polylactic acid, 26100-51-6","","","","","Mohanraj S., Sakthisudhan K., An Internet of Things based smart wearable system for asthma patients, Int. J. Recent Technology Eng. (IJRTE), 7, 6S, pp. 604-607, (2019); Aikaterini V., Michael B., Charis V., pp. 1306-1309, (2013); Kassem A., Hamad M., pp. 1629-1632, (2015); Oletic D., Bilas V., Energy-Efficient Respiratory Sounds Sensing for Personal Mobile Asthma Monitoring, IEEE Sens. J., (2016); Kwan A., Fung A., Jansen P., Schivo M., Kenyon N., Delplanque J., Davis C., Personal Lung Function Monitoring Devices for Asthma Patients, IEEE Sens. J., 15, 4, pp. 2238-2247, (2015); Wisniewski M., Zielinski T., Joint Application of Audio Spectral Envelope and Tonality Index in E-Asthma Monitoring System, IEEE J. Biomed. Health Inform., (2014); Moeller A., Carlsen K., Sly P., Baraldi E., Piacentini G., Pavord I., Lex C., Saglani S., Monitoring asthma in childhood: lung function, bronchial responsiveness and inflammation, Eur. Respir. Rev., 24, 136, pp. 204-215, (2015); Aneja S., Lal S., pp. 137-140, (2014); Kang S., Chang K., Development of an integrated sensor module for a non-invasive respiratory monitoring system, Rev. Sci. Instrum., 84, 9, (2013); AKBAR W., WU W., FAHEEM M., SALEEM M.A., pp. 143-148, (2019); Pan L., Et al., “Lab-on-mask for remote respiratory monitoring, ACS Mater. Lett., 2, 9, pp. 1178-1181, (2020); Kalavakonda R.R., Et al., A smart mask for active defense against airborne pathogens, Sci. 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Potential pitfalls and practical guidance, Ann. Am. Thorac. Soc., 17, 9, pp. 1040-1046, (2020); Tariq Z., Shah S., Lee Y., Lung disease classification using deep convolutional neural network, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), (2019); Patterson J.A., McIlwraith D.C., Yang G.-Z., A flexible, low noise reflective PPG sensor platform for ear-worn heart rate monitoring, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, (2009); Chua M.H., Et al., Face masks in the new COVID-19 normal: materials, testing, and perspectives, Research, 2020, (2020); Seshadri D.R., Et al., “Wearable sensors for COVID-19: a call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments.” Frontiers in Digital, Health, (2020); Correia B., Et al., Validation of a wireless bluetoothphotoplethysmography sensor used on the earlobe for monitoring heart rate variability features during a stress-inducing mental task in healthy individuals, Sensors, 20, 14, (2020); Ming D.K., Et al., Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses, Int. J. Infect. Dis., 96, pp. 648-654, (2020); Tsang K.C., Pinnock H., Wilson A.M., Shah S.A., Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review, Journal of Asthma and Allergy., 2022, 15, pp. 855-873, (2022); Lene G., Dalbak, Jørund Straand and Hasse Melbye, Should pulse oximetry be included in GPs’ assessment of patients with obstructive lung disease?, Pubmed Central, 33, 4, pp. 305-310, (2015)","E. Smily Jeya Jothi; Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, India; email: smilyjeyajothi@avinuty.ac.in","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85147551141"
"Tong Y.; Lin B.; Chen G.; Zhang Z.","Tong, Yao (57208420959); Lin, Beilei (55620861700); Chen, Gang (57139356700); Zhang, Zhenxiang (55721680400)","57208420959; 55620861700; 57139356700; 55721680400","Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study","2022","International Journal of Environmental Research and Public Health","19","3","1237","","","","4","10.3390/ijerph19031237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123118704&doi=10.3390%2fijerph19031237&partnerID=40&md5=fd66cc3e4372208e4ccfbc15a6f0c4c9","School of Nursing and Health, Zhengzhou University, Zhengzhou, 450001, China; Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047, Seattle, 98109, WA, United States; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, 450052, China; School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China","Tong Y., School of Nursing and Health, Zhengzhou University, Zhengzhou, 450001, China, Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, 850 Republican Street, Building C, Box 358047, Seattle, 98109, WA, United States, Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, 450052, China, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China; Lin B., School of Nursing and Health, Zhengzhou University, Zhengzhou, 450001, China; Chen G., Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, 450052, China, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China; Zhang Z., School of Nursing and Health, Zhengzhou University, Zhengzhou, 450001, China","Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use of a generalizable predictive methodology to determine the COC in patients has been underinvestigated. To fill this research gap, this study aimed to develop a machine learning model to predict the future COC of asthma patients and explore the associated factors. We included 31,724 adult outpatients with asthma who received care from the University of Washington Medicine between 2011 and 2018, and examined 138 features to build the machine learning model. Following the 10-fold cross-validations, the proposed model yielded an accuracy of 88.20%, an average area under the receiver operating characteristic curve of 0.96, and an average F1 score of 0.86. Further analysis revealed that the severity of asthma, comorbidities, insurance, and age were highly correlated with the COC of patients with asthma. This study used predictive methods to obtain the COC of patients, and our excellent modeling strategy achieved high performance. After further optimization, the model could facilitate future clinical decisions, hospital management, and improve outcomes. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Asthma; Continuity of care; Feature engineering; Machine learning; Predicting; Retrospective study","Adult; Asthma; Continuity of Patient Care; Forecasting; Humans; Machine Learning; Retrospective Studies; asthma; cohort analysis; computer simulation; decision making; health care; machine learning; optimization; adult; aged; aging; Article; asthma; clinical decision making; cohort analysis; comorbidity; cross validation; disease severity; female; health insurance; hospital management; human; machine learning; major clinical study; male; mathematical model; measurement accuracy; patient care; prediction; retrospective study; treatment outcome; university hospital; asthma; forecasting; patient care","","","","","National Natural Science Foundation of China, NSFC, (72104221, 72174184); China Scholarship Council, CSC, (201907040091)","Funding: This research was funded by the National Natural Science Foundation of China, 72174184 and 72104221. Y.T. was funded by the program of the China Scholarship Council, 201907040091. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Maarsingh O.R., Henry Y., van de Ven P.M., Deeg D.J., Continuity of care in primary care and association with survival in older people: A 17-year prospective cohort study, Br. J. Gen. Pract, 66, pp. e531-e539, (2016); Christakis D.A., Wright J.A., Koepsell T.D., Emerson S., Connell F.A., Is Greater Continuity of Care Associated With Less Emergency Department Utilization?, Pediatrics, 103, pp. 738-742, (1999); Bazemore A., Petterson S., Peterson L.E., Bruno R., Chung Y., Phillips R.L., Higher Primary Care Physician Continuity is Associated With Lower Costs and Hospitalizations, Ann. Fam. Med, 16, pp. 492-497, (2018); Hussey P.S., Schneider E.C., Rudin R.S., Fox D.S., Lai J., Pollack C.E., Continuity and the costs of care for chronic disease, JAMA Int. 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Res, 23, (2021); da Nobrega V.M., Silva M.E.d.A., Fernandes L.T.B., Viera C.S., Reichert A.P.d.S., Collet N., Chronic disease in childhood and adolescence: Continuity of care in the Health Care Network, Rev. Esc. Enferm, 51, pp. 1-8, (2017); McDermott A., Sanderson E., Metcalfe C., Barnes R., Thomas C., Cramer H., Kessler D., Continuity of care as a predictor of ongoing frequent attendance in primary care: A retrospective cohort study, BJGP Open, 4, (2020); Christakis D.A., Kazak A.E., Wright J.A., Zimmerman F.J., Bassett A.L., Connell F.A., What Factors Are Associated with Achieving High Continuity of Care?, Fam. Med, 36, pp. 55-60, (2004); Aller M.B., Vargas I., Waibel S., Coderch J., Sanchez-Perez I., Colomes L., Llopart J.R., Ferran M., Vazquez M.L., A comprehensive analysis of patients’ perceptions of continuity of care and their associated factors, Int. J. Qual. Health Care, 25, pp. 291-299, (2013); Schaefer J.A., Cronkite R., Ingudomnukul E., Assessing continuity of care practices in substance use disorder treatment programs, J. Stud. Alcohol, 65, pp. 513-520, (2004); Rubin R.J., Dietrich K.A., Hawk A.D., Clinical and economic impact of implementing a comprehensive diabetes management program in managed care, J. Clin. Endocrinol. Metab, 83, pp. 2635-2642, (1998); Axelrod R.C., Vogel D., Predictive Modeling in Health Plans, Dis. Manag. Health Outcomes, 11, pp. 779-787, (2003); Desai J.R., Wu P., Nichols G.A., Lieu T.A., O'Connor P.J., Diabetes and asthma case identification, validation, and representa-tiveness when using electronic health data to construct registries for comparative effectiveness and epidemiologic research, Med. Care, 50, (2012); Wakefield D.B., Cloutier M.M., Modifications to HEDIS and CSTE algorithms improve case recognition of pediatric asthma, Pediatr. Pulmonol, 41, pp. 962-971, (2006); Bice T.W., Boxerman S.B., A Quantitative Measure of Continuity of Care, Med. Care, 15, pp. 347-349, (1977); Cheng S.H., Chen C.C., Hou Y.F., A longitudinal examination of continuity of care and avoidable hospitalization: Evidence from a universal coverage health care system, Arch. Int. Med, 170, pp. 1671-1677, (2010); Tong Y., Liao Z.C., Tarczy-Hornoch P., Luo G., Using a Constraint-Based Method to Identify Chronic Disease Patients Who Are Apt to Obtain Care Mostly Within a Given Health Care System: Retrospective Cohort Study, JMIR Form. Res, 5, (2021); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Wu X., Kumar V., Ross Quinlan J., Ghosh J., Yang Q., Motoda H., McLachlan G.J., Ng A., Liu B., Yu P.S., Et al., Top 10 algorithms in data mining, Knowl. Inf. Syst, 14, pp. 1-37, (2008); McHugh M.L., The Chi-square test of independence, Biochem. Med, 23, pp. 143-149, (2013); Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning, (2009); Kuhn M., Johnson K., Applied Predictive Modeling, (2013); Asadi H., Dowling R., Yan B., Mitchell P., Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy, PLoS ONE, 9, (2014); Aubin M., Giguere A., Martin M., Verreault R., Fitch M.I., Kazanjian A., Carmichael P.-H., Interventions to improve continuity of care in the follow-up of patients with cancer, Cochrane Database Syst. Rev, (2012); Beddar S.M., Aikin J.L., Continuity of care: A challenge for ambulatory oncology nursing, Semin. Oncol. Nurs, 10, pp. 254-263, (1994); Hong J.S., Kang H.C., Kim J., Continuity of care for elderly patients with diabetes mellitus, hypertension, asthma, and chronic obstructive pulmonary disease in Korea, J. Korean Med. Sci, 25, pp. 1259-1271, (2010); Gill J.M., Mainous A.G., Nsereko M., The effect of continuity of care on emergency department use, Arch. Fam. Med, 9, pp. 333-338, (2000); Moxley D.P., The Practice of Case Management, (1989); Gustafson D., Wise M., Bhattacharya A., Pulvermacher A., Shanovich K., Phillips B., Lehman E., Chinchilli V., Hawkins R., Kim J.S., The effects of combining web-based eHealth with telephone nurse case management for pediatric asthma control: A randomized controlled trial, J. Med. Int. Res, 14, (2012); Moore S., Wells M., Plant H., Fuller F., Wright M., Corner J., Nurse specialist led follow-up in lung cancer: The experience of developing and delivering a new model of care, Eur. J. Oncol. Nurs, 10, pp. 364-377, (2006); Thomas E.J., Burstin H.R., O'Neil A.C., Orav E.J., Brennan T.A., Patient noncompliance with medical advice after the emergency department visit, Ann. Emerg. Med, 27, pp. 49-55, (1996); Baren J.M., Shofer F.S., Ivey B., Reinhard S., DeGeus J., Stahmer S.A., Panettieri R., Hollander J.E., A randomized, controlled trial of a simple emergency department intervention to improve the rate of primary care follow-up for patients with acute asthma exacerbations, Ann. Emerg. Med, 38, pp. 115-122, (2001); Johnson P.H., Wilkinson I., Sutherland A.M., Johnston I.D., Hall I.P., Improving communication between hospital and primary care increases follow-up rates for asthmatic patients following casualty attendance, Respir. Med, 92, pp. 289-291, (1998); Petersen D.L., Murphy D.E., Jaffe D.M., Richardson M.S., Fisher E.B., Shannon W., Sussman L., Strunk R.C., A tool to organize instructions at discharge after treatment of asthmatic children in an emergency department, J. Asthma, 36, pp. 597-603, (1999); Welsh E.J., Hasan M., Li P., Home-based educational interventions for children with asthma, Cochrane Database Syst. Rev, (2011); Finkelstein J.A., Lozano P., Fuhlbrigge A.L., Carey V.J., Inui T.S., Soumerai S.B., Sullivan S.D., Wagner E.H., Weiss S.T., Weiss K.B., Et al., Practice-level effects of interventions to improve asthma care in primary care settings: The Pediatric Asthma Care Patient Outcomes Research Team, Health Serv. Res, 40, pp. 1737-1757, (2005); Wiecha J.M., Adams W.G., Rybin D., Rizzodepaoli M., Keller J., Clay J.M., Evaluation of a web-based asthma self-management system: A randomised controlled pilot trial, BMC Pulm. Med, 15, (2015); Gardner A., Kaplan B., Brown W., Krier-Morrow D., Rappaport S., Marcus L., Conboy-Ellis K., Mullen A., Rance K., Aaronson D., National standards for asthma self-management education, Ann. Allergy Asthma Immunol, 114, pp. 178-186, (2015)","G. Chen; Collaborative Innovation Centre for Internet Healthcare, Zhengzhou University, Zhengzhou, 450052, China; email: chengang@zzu.edu.cn; Z. Zhang; School of Nursing and Health, Zhengzhou University, Zhengzhou, 450001, China; email: zhangzx6666@126.com","","MDPI","","","","","","16617827","","","35162261","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85123118704"
"Chen L.; Feng Q.; Yin X.; Min X.; Shi L.; Yang D.; Chen Y.-W.; Zhang D.; Zhu W.","Chen, Ling (57223966283); Feng, Qixing (57920300800); Yin, Xi (57218134760); Min, Xiangde (56404174100); Shi, Lei (57217487593); Yang, Defu (56001070200); Chen, Yen-Wei (56036268200); Zhang, Daoqiang (7405356869); Zhu, Wentao (36516078000)","57223966283; 57920300800; 57218134760; 56404174100; 57217487593; 56001070200; 56036268200; 7405356869; 36516078000","A Graph Convolutional Multiple Instance Learning on a Hypersphere Manifold Approach for Diagnosing Chronic Obstructive Pulmonary Disease in CT Images","2022","IEEE Journal of Biomedical and Health Informatics","26","12","","6058","6069","11","4","10.1109/JBHI.2022.3209410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139511209&doi=10.1109%2fJBHI.2022.3209410&partnerID=40&md5=60b110fdf7f141fe2dc7f2cd87b33b7d","Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China; Binzhou Medical University Hospital, Department of Radiology, Binzhou, 256603, China; Shihezi University, Department of Radiology, First Affiliated Hospital, School of Medicine, Shihezi, 832008, China; Huazhong University of Science and Technology, Department of Radiology, Tongji Hospital, Tongji Medical College, Wuhan, 430030, China; Chinese Academy of Sciences, Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Hangzhou, 310022, China; Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing, 211106, China","Chen L., Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China; Feng Q., Binzhou Medical University Hospital, Department of Radiology, Binzhou, 256603, China; Yin X., Shihezi University, Department of Radiology, First Affiliated Hospital, School of Medicine, Shihezi, 832008, China; Min X., Huazhong University of Science and Technology, Department of Radiology, Tongji Hospital, Tongji Medical College, Wuhan, 430030, China; Shi L., Chinese Academy of Sciences, Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Hangzhou, 310022, China; Yang D., Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China; Chen Y.-W., Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China; Zhang D., Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing, 211106, China; Zhu W., Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China","Chronic obstructive pulmonary disease (COPD) is a prevalent chronic disease with high morbidity and mortality. The early diagnosis of COPD is vital for clinical treatment, which helps patients to have a better quality of life. Because COPD can be ascribed to chronic bronchitis and emphysema, lesions in a computed tomography (CT) image can present anywhere inside the lung with different types, shapes and sizes. Multiple instance learning (MIL) is an effective tool for solving COPD discrimination. In this study, a novel graph convolutional MIL with the adaptive additive margin loss (GCMIL-AAMS) approach is proposed to diagnose COPD by CT. Specifically, for those early stage patients, the selected instance-level features can be more discriminative if they were learned by our proposed graph convolution and pooling with self-attention mechanism. The AAMS loss can utilize the information of COPD severity on a hypersphere manifold by adaptively setting the angular margins to improve the performance, as the severity can be quantified as four grades by pulmonary function test. The results show that our proposed GCMIL-AAMS method provides superior discrimination and generalization abilities in COPD discrimination, with areas under a receiver operating characteristic curve (AUCs) of 0.960 pm 0.014 and 0.862 pm 0.010 in the test set and external testing set, respectively, in 5-fold stratified cross validation; moreover, it demonstrates that graph learning is applicable to MIL and suggests that MIL may be adaptable to graph learning.  © 2021 IEEE.","Chronic obstructive pulmonary disease (COPD); computed tomography (CT); graph convolution; hypersphere manifold; multiple instance learning (MIL)","Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Quality of Life; Tomography, X-Ray Computed; Biological organs; Computerized tomography; Convolution; Diagnosis; Gold; Medical imaging; Patient treatment; Pulmonary diseases; Chronic obstructive pulmonary disease; Computed tomography; Gold; Graph convolution; Hypersphere manifolds; Lung; Lung Cancer; Manifold; Medical diagnostic imaging; Multiple instance learning; Multiple-instance learning; adult; Article; artificial neural network; blood brain barrier; body weight loss; cancer staging; chronic bronchitis; chronic obstructive lung disease; computer assisted tomography; controlled study; cross validation; diagnostic accuracy; diagnostic imaging; diagnostic test accuracy study; electroencephalography; female; forced expiratory volume; forced vital capacity; human; human experiment; image segmentation; knee meniscus rupture; learning algorithm; lung cancer; machine learning; major clinical study; male; mortality; quality of life; quantitative analysis; receiver operating characteristic; sinus rhythm; spirometry; support vector machine; chronic obstructive lung disease; diagnostic imaging; lung; lung emphysema; procedures; x-ray computed tomography; Learning systems","","","","","Science and Technology Project of Shihezi City, (2019ZH08); National Natural Science Foundation of China, NSFC, (62001425); National Natural Science Foundation of China, NSFC","This work was supported in part by the National Natural Science Foundation of China under Grant 62001425 and in part by the Science and Technology Project of Shihezi City under Grant 2019ZH08.","Mannino D.M., Buist A.S., Global burden of COPD: Risk factors, prevalence, and future trends, Lancet, 370, 9589, pp. 765-773, (2007); Mannino D.M., Et al., Economic burden of COPD in the presence of comorbidities, Chest, 148, 1, pp. 138-150, (2015); Patel A.R., Patel A.R., Singh S., Singh S., Khawaja I., Global initiative for chronic obstructive lung disease: The changesmade, Cureus, 11, 6, (2019); Sorensen L., Shaker S.B., De Bruijne M., Quantitative analysis of pulmonary emphysema using local binary patterns, IEEE Trans. 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Health Informat., 24, 8, pp. 2327-2336, (2020); Humphries S.M., Et al., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, 2, pp. 434-444, (2020); Ilse M., Tomczak J., Welling M., Attention-based deep multiple instance learning, Proc. Int. Conf. Mach. Learn., pp. 2127-2136, (2018); Velickovic P., Cucurull G., Casanova A., Romero A., Lio P., Bengio Y., Graph attention networks, (2017); Lee J., Lee I., Kang J., Self-attention graph pooling, Proc. Int. Conf. Mach. Learn., pp. 3734-3743, (2019); Liu W., Wen Y., Yu Z., Yang M., Large-margin softmax loss for convolutional neural networks, Proc. Int. Conf. Mach. Learn., 2, (2016); Wang F., Cheng J., Liu W., Liu H., Additive margin softmax for face verification, IEEE Signal Process. Lett., 25, 7, pp. 926-930, (2018); Quellec G., Cazuguel G., Cochener B., Lamard M., Multiple-instance learning for medical image and video analysis, IEEE Rev. Biomed. 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Imag., 39, 8, pp. 2584-2594, (2020); Cheplygina V., Sorensen L., Tax D.M., Pedersen J.H., Loog M., De Bruijne M., Classification of COPD with multiple instance learning, Proc. IEEE 22nd Int. Conf. Pattern Recognit., pp. 1508-1513, (2014); Cheplygina V., Pena I.P., Pedersen J.H., Lynch D.A., Sorensen L., De Bruijne M., Transfer learning for multicenter classification of chronic obstructive pulmonary disease, IEEE J. Biomed. Health Informat., 22, 5, pp. 1486-1496, (2018); Ilse M., Tomczak J.M., Welling M., Deep multiple instance learning for digital histopathology, Handbook of Medical Image Computing and Computer Assisted Intervention, pp. 521-546, (2020); Liu W., Wen Y., Yu Z., Li M., Raj B., Song L., Sphereface: Deep hypersphere embedding for face recognition, Proc. IEEE Conf. Comput. Vis. 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Multimedia, pp. 1041-1049, (2017); Liu Y., Li H., Wang X., Rethinking feature discrimination and polymerization for large-scale recognition, (2017); Ranjan R., Castillo C.D., Chellappa R., L2-constrained softmax loss for discriminative face verification, (2017); Orlhac F., Frouin F., Nioche C., Ayache N., Buvat I., Validation of a method to compensate multicenter effects affecting CT radiomics, Radiology, 291, 1, pp. 53-59, (2019); Houghton A.M., Mechanistic links between COPD and lung cancer, Nature Rev. Cancer, 13, 4, pp. 233-245, (2013); Durham A.L., Adcock I.M., The relationship between COPD and Lung cancer, Lung Cancer, 90, 2, pp. 121-127, (2015); Sorensen L., Nielsen M., Petersen J., Pedersen J.H., Dirksen A., De Bruijne M., Chronic obstructive pulmonary disease quantification using ct texture analysis and densitometry: Results from the danish lung cancer screening trial, Amer. J. Roentgenol., 214, 6, pp. 1269-1279, (2020); Ahmed J., Et al., COPDclassification in ct images using a 3Dconvolutional neural network, Bildverarbeitung für die Medizin, pp. 39-45, (2020); Vaswani A., Et al., Attention is all you need, Proc. Adv. Neural Inf. Process. Syst., 30, (2017); Dosovitskiy A., Et al., An image isworth 16 × 16 words: Transformers for image recognition at scale, Proc. 9th Int. Conf. Learn. Representations, (2021); Liu Z., Et al., Swin transformer: Hierarchical vision transformer using shifted windows, Proc. IEEE/CVF Int. Conf. Comput. Vis., pp. 10012-10022, (2021); Glover G., Pelc N., Nonlinear partial volume artifacts in x-ray computed tomography, Med. Phys., 7, 3, pp. 238-248, (1980)","W. Zhu; Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China; email: wentao.zhu@zhejianglab.com; D. Zhang; Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing, 211106, China; email: dqzhang@nuaa.edu.cn","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","36155471","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85139511209"
"Morandini P.; Laino M.E.; Paoletti G.; Carlucci A.; Tommasini T.; Angelotti G.; Pepys J.; Canonica G.W.; Heffler E.; Savevski V.; Puggioni F.","Morandini, Pierandrea (57217259310); Laino, Maria Elena (36768026200); Paoletti, Giovanni (57204357222); Carlucci, Alessandro (57222296741); Tommasini, Tobia (57219253581); Angelotti, Giovanni (57204675521); Pepys, Jack (57224805526); Canonica, Giorgio Walter (55412658800); Heffler, Enrico (11640594200); Savevski, Victor (57217259660); Puggioni, Francesca (7801419302)","57217259310; 36768026200; 57204357222; 57222296741; 57219253581; 57204675521; 57224805526; 55412658800; 11640594200; 57217259660; 7801419302","Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas","2022","Clinical and Translational Allergy","12","6","e12144","","","","4","10.1002/clt2.12144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132810655&doi=10.1002%2fclt2.12144&partnerID=40&md5=844f9bcfb740aaabd435aac597b615cc","Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Department of Biomedical Sciences, Humanitas University, Milan, Italy; Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy","Morandini P., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Laino M.E., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Paoletti G., Department of Biomedical Sciences, Humanitas University, Milan, Italy, Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy; Carlucci A., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Tommasini T., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Angelotti G., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Pepys J., Department of Biomedical Sciences, Humanitas University, Milan, Italy; Canonica G.W., Department of Biomedical Sciences, Humanitas University, Milan, Italy, Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy; Heffler E., Department of Biomedical Sciences, Humanitas University, Milan, Italy, Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy; Savevski V., Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; Puggioni F., Department of Biomedical Sciences, Humanitas University, Milan, Italy, Personalized Medicine, Asthma and Allergy, IRCCS Humanitas Research Hospital, Milan, Italy","Background: Comorbidities are common in chronic inflammatory conditions, requiring multidisciplinary treatment approach. Understanding the link between a single disease and its comorbidities is important for appropriate treatment and management. We evaluate the ability of an NLP-based process for knowledge discovery to detect information about pathologies, patients' phenotype, doctors' prescriptions and commonalities in electronic medical records, by extracting information from free narrative text written by clinicians during medical visits, resulting in the extraction of valuable information and enriching real world evidence data from a multidisciplinary setting. Methods: We collected clinical notes from the Allergy Department of Humanitas Research Hospital written in the last 3 years and used it to look for diseases that cluster together as comorbidities associated to the main pathology of our patients, and for the extent of prescription of systemic corticosteroids, thus evaluating the ability of NLP-based tools for knowledge discovery to extract structured information from free text. Results: We found that the 3 most frequent comorbidities to appear in our clusters were asthma, rhinitis, and urticaria, and that 991 (of 2057) patients suffered from at least one of these comorbidities. The clusters which co-occur particularly often are oral allergy syndrome and urticaria (131 patients), angioedema and urticaria (105 patients), rhinitis and asthma (227 patients). With regards to systemic corticosteroid prescription volume by our clinicians, we found it was lower when compared to the therapy the patients followed before coming to our attention, with the exception of two diseases: Chronic obstructive pulmonary disease and Angioedema. Conclusions: This analysis seems to be valid and is confirmed by the data from the literature. This means that NLP tools could have significant role in many other research fields of medicine, as it may help identify other important, and possibly previously neglected clusters of patients with comorbidities and commonalities. Another potential benefit of this approach lies in its potential ability to foster a multidisciplinary approach, using the same drugs to treat pathologies normally treated by physicians in different branches of medicine, thus saving resources and improving the pharmacological management of patients. © 2022 The Authors. Clinical and Translational Allergy published by John Wiley and Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.","allergy; artificial intelligence; asthma; clustering; natural language processing; urticaria","corticosteroid derivative; angioneurotic edema; Article; artificial intelligence; asthma; chronic obstructive lung disease; comorbidity; controlled study; electronic medical record; human; major clinical study; natural language processing; oral allergy syndrome; prescription; recurrent disease; retrospective study; rhinitis; urticaria","","","","","IRCCS Istituto Clinico Humanitas","This work was supported by IRCCS Istituto Clinico Humanitas – 5 × 1000. ","Lenferink A., van der Palen J., van der Valk P.D.L.P.M., Et al., Exacerbation action plans for patients with COPD and comorbidities: a randomised controlled trial, Eur Respir J, 54, 5, (2019); Wang E., Wechsler M.E., Tran T.N., Et al., Characterization of severe asthma worldwide: data from the International Severe Asthma Registry, Chest, 157, 4, pp. 790-804, (2020); Nikiphorou E., Nurmohamed M.T., Szekanecz Z., Editorial: comorbidity burden in rheumatic diseases, Front Med, 5, (2018); Kaushik S.B., Lebwohl M.G., Psoriasis: which therapy for which patient: Psoriasis comorbidities and preferred systemic agents, J Am Acad Dermatol, 80, 1, pp. 27-40, (2019); Taleban S., Challenges in the diagnosis and management of inflammatory bowel disease in the elderly, Curr Treat Options Gastroenterol, 13, 3, pp. 275-286, (2015); Deshmukh F., Vasudevan A., Mengalie E., Association between irritable bowel syndrome and asthma: a meta-analysis and systematic review, Ann Gastroenterol, 32, 6, pp. 570-577, (2019); 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Huang Z., Extensions to the k-means algorithm for clustering large data sets with categorical values, Data Mining Knowl Discov, 2, pp. 283-304, (1998); Lloyd S., Least squares quantization in PCM, IEEE Trans Inform Theory, 28, 2, pp. 129-137, (1982); Heffler E., Blasi F., Latorre M., Et al., The severe asthma Network in Italy: findings and perspectives, J Allergy Clin Immunol Pract, 7, 5, pp. 1462-1468, (2019); Wang Y., Wang L., Rastegar-Mojarad M., Et al., Clinical information extraction applications: a literature review, J Biomed Inf, 77, pp. 34-49, (2018); Perera N., Dehmer M., Emmert-Streib F., Named entity recognition and relation detection for biomedical information extraction, Front Cell Dev Biol, 8, (2020); Global Strategy for Asthma Management and Prevention, (2020); Fokkens W., Desrosiers M., Harvey R., Et al., EPOS2020: development strategy and goals for the latest European Position Paper on Rhinosinusitis, Rhinology, 57, 3, pp. 162-169, (2019); Gandhi N.A., Bennett B.L., Graham N.M., Pirozzi G., Stahl N., Yancopoulos G.D., Targeting key proximal drivers of type 2 inflammation in disease, Nat Rev Drug Discov, 15, 1, pp. 35-50, (2016); Khan D.A., Allergic rhinitis and asthma: epidemiology and common pathophysiology, Allergy Asthma Proc, 35, 5, pp. 357-361, (2014); Brozek J.L., Bousquet J., Agache I., Et al., Allergic rhinitis and its impact on asthma (ARIA) guidelines-2016 revision, J Allergy Clin Immunol, 140, 4, pp. 950-958, (2017); Leynaert B., Neukirch F., Demoly P., Bousquet J., Epidemiologic evidence for asthma and rhinitis comorbidity, J Allergy Clin Immunol, 106, 5, pp. S201-S205, (2000); Bergeron C., Hamid Q., Relationship between asthma and rhinitis: epidemiologic, pathophysiologic, and therapeutic aspects, Allergy Asthma Clin Immunol, 1, 2, pp. 81-87, (2005); Heffler E., Brussino L., Del Giacco S., Et al., New drugs in early-stage clinical trials for allergic rhinitis, Expet Opin Invest Drugs, 28, 3, pp. 267-273, (2019); Amar S.M., Dreskin S.C., Urticaria, Prim Care, 35, 1, pp. 141-157, (2008); Kanani A., Betschel S.D., Warrington R., Urticaria and angioedema, Allergy Asthma Clin Immunol, 14, (2018); Laidlaw T.M., Mullol J., Woessner K.M., Amin N., Mannent L.P., Chronic rhinosinusitis with nasal polyps and asthma, J Allergy Clin Immunol Pract, 9, 3, pp. 1133-1141, (2021); Martinez-Garcia M.A., Miravitlles M., Bronchiectasis in COPD patients: more than a comorbidity, Int J Chronic Obstr Pulm Dis, 12, pp. 1401-1411, (2017); Price A., Ramachandran S., Smith G.P., Stevenson M.L., Pomeranz M.K., Cohen D.E., Oral allergy syndrome (pollen-food allergy syndrome), Dermatitis, 26, 2, pp. 78-88, (2015); Bernstein J.A., Cremonesi P., Hoffmann T.K., Hollingsworth J., Angioedema in the emergency department: a practical guide to differential diagnosis and management, Int J Emerg Med, 10, 1, (2017); Ko F.W., Chan K.P., Hui D.S., Et al., Acute exacerbation of COPD, Respirology, 21, 7, pp. 1152-1165, (2016); Abuzakouk M., Ghorab O.K.H.A., Wahla A.S., Et al., Efficacy and safety of biologic agents in chronic urticaria, asthma and atopic dermatitis - a real-life experience, Open Respir Med J, 14, pp. 99-106, (2020); Heffler E., Saccheri F., Bartezaghi M., Canonica G.W., Effectiveness of omalizumab in patients with severe allergic asthma with and without chronic rhinosinusitis with nasal polyps: a PROXIMA study post hoc analysis, Clin Transl Allergy, 10, 25, (2020); Damask C.C., Ryan M.W., Casale T.B., Et al., Targeted molecular therapies in allergy and rhinology, Otolaryngol Head Neck Surg, 164, pp. S1-S21, (2021); Pelaia C., Paoletti G., Puggioni F., Et al., Interleukin-5 in the pathophysiology of severe asthma, Front Physiol, 10, (2019); Roufosse F., Targeting the interleukin-5 pathway for treatment of eosinophilic conditions other than asthma, Front Med, 5, (2018)","V. Savevski; Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Milan, Italy; email: victor.savevski@humanitas.it","","John Wiley and Sons Inc","","","","","","20457022","","","","English","Clin. Transl. Allergy","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85132810655"
"Wieder C.; Cooke J.; Frainay C.; Poupin N.; Bowler R.; Jourdan F.; Kechris K.J.; Lai R.P.J.; Ebbels T.","Wieder, Cecilia (57224980251); Cooke, Juliette (57224966106); Frainay, Clement (55978759900); Poupin, Nathalie (54399240400); Bowler, Russell (56773748500); Jourdan, Fabien (56135953900); Kechris, Katerina J. (6507018968); Lai, Rachel P.J. (57256499200); Ebbels, Timothy (6602879859)","57224980251; 57224966106; 55978759900; 54399240400; 56773748500; 56135953900; 6507018968; 57256499200; 6602879859","PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration","2024","PLoS Computational Biology","20","3","e1011814","","","","3","10.1371/journal.pcbi.1011814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189292750&doi=10.1371%2fjournal.pcbi.1011814&partnerID=40&md5=b365ec6e3744bffce7bab195aef9f1f2","Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; National Jewish Health, Denver, CO, United States; MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom","Wieder C., Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Cooke J., Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Frainay C., Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Poupin N., Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Bowler R., National Jewish Health, Denver, CO, United States; Jourdan F., MetaboHUB-Metatoul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France; Kechris K.J., Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, United States; Lai R.P.J., Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, United Kingdom; Ebbels T., Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom","As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. PathIntegrate is available as an open-source Python package. © 2024 Wieder et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Genomics; Multiomics; Biological systems; Domain Knowledge; Molecules; Python; Signal to noise ratio; calcitonin; CD163 antigen; corticotropin releasing factor; inflammasome; interleukin 13; interleukin 6; luteinizing hormone beta subunit; 'current; 'omics'; Biological process; Domain knowledge; Integration method; Modeling approach; Multivariate modeling; Pathway analysis; Single sample; Subnetworks; arachidonic acid metabolism; Article; bioinformatics; chronic obstructive lung disease; chronic obstructive pulmonary disease data; computer model; controlled study; coronavirus disease 2019; data base; data integration; diagnostic test accuracy study; discriminant analysis; fatty acid metabolism; gene expression; human; immune response; innate immunity; lipid metabolism; lipolysis; machine learning; metabolomics; multiomics; multivariate analysis; pathway analysis; phosphoinositide metabolism; principal component analysis; protein protein interaction; proteomics; receiver operating characteristic; scoring system; sensitivity and specificity; signal transduction; simulation; structure analysis; transcriptomics; univariate analysis; genomics; multiomics; procedures; Data integration","","calcitonin, 12321-44-7, 21215-62-3, 9007-12-9; corticotropin releasing factor, 9015-71-8, 178359-01-8, 79804-71-0, 86297-72-5, 86784-80-7; interleukin 13, 148157-34-0","Illumina HiSeq 2000, Illumina","Illumina","Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation, MESRI; UK Research and Innovation, UKRI, (MR/R008922/1); Wellcome Trust, WT, (222837)","Funding:CW,TE-Thisresearchwasfundedin whole,orinpart,bytheWellcomeTrust[222837/ Z/21/Z].Forthepurposeofopenaccess,theauthor hasappliedaCCBYpubliccopyrightlicencetoany AuthorAcceptedManuscriptversionarisingfrom thissubmission.TEacknowledgespartialsupport fromBBSRCgrantsBB/T007974/1andBB/ W002345/1.RPJLwassupportedbyaUKMRC fellowship(MR/R008922/1)whichispartofthe EDCTP2programmesupportedbytheEuropean UnionandaNIH-NIAIDgrant(R01AI145436).JC issupportedbyastate-fundedPhDcontract (MESRI(MinisterofHigherEducation,Research andInnovation)).FJ-Thisresearchwasfundedby theAgenceNationaledelaRecherche(ANR, FrenchNationalResearchAgency)\u2014MetaboHUB, thenationalmetabolomicsandfluxomics infrastructure(GrantANR-INBS-0010).KK,RB-Researchreportedinthispublicationwas supportedbytheNationalHeartLungBlood InstituteoftheNationalInstitutesofHealthtoKK andRBunderawardnumberR01HL152735.This workwassupportedbyNHLBIgrantsU01 HL089897andU01HL089856andbyNIHcontract 75N92023D00011.TheCOPDGenestudy (NCT00608764)isalsosupportedbytheCOPD Foundationthroughcontributionsmadetoan IndustryAdvisoryCommitteethathasincluded AstraZeneca,BayerPharmaceuticals,Boehringer-Ingelheim,Genentech,GlaxoSmithKline,Novartis, Pfizer,andSunovion.Thecontentissolelythe responsibilityoftheauthorsanddoesnot necessarilyrepresenttheofficialviewsofthe NationalHeart,Lung,andBloodInstituteorthe NationalInstitutesofHealth.Thefundersdidnot playanyroleinthestudydesign,datacollection, analysis,orpublicationofthiswork.","Krassowski M, Das V, Sahu SK, Misra BB., State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing, Front Genet, 11, (2020); 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Ebbels; Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; email: t.ebbels@imperial.ac.uk","","Public Library of Science","","","","","","1553734X","","","38527092","English","PLoS Comput. Biol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85189292750"
"Rabie A.H.; Saleh A.I.","Rabie, Asmaa H. (57189711449); Saleh, Ahmed I. (22434197300)","57189711449; 22434197300","Diseases diagnosis based on artificial intelligence and ensemble classification","2024","Artificial Intelligence in Medicine","148","","102753","","","","3","10.1016/j.artmed.2023.102753","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182400309&doi=10.1016%2fj.artmed.2023.102753&partnerID=40&md5=58e7e42bbd0def526ddc00c63c22ec5f","Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt","Rabie A.H., Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt; Saleh A.I., Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt","Background: In recent years, Computer Aided Diagnosis (CAD) has become an important research area that attracted a lot of researchers. In medical diagnostic systems, several attempts have been made to build and enhance CAD applications to avoid errors that can cause dangerously misleading medical treatments. The most exciting opportunity for promoting the performance of CAD system can be accomplished by integrating Artificial Intelligence (AI) in medicine. This allows the effective automation of traditional manual workflow, which is slow, inaccurate and affected by human errors. Aims: This paper aims to provide a complete Computer Aided Disease Diagnosis (CAD2) strategy based on Machine Learning (ML) techniques that can help clinicians to make better medical decisions. Methods: The proposed CAD2 consists of three main sequential phases, namely; (i) Outlier Rejection Phase (ORP), (ii) Feature Selection Phase (FSP), and (iii) Classification Phase (CP). ORP is implemented to reject outliers using new Outlier Rejection Technique (ORT) that contains two sequential stages called Fast Outlier Rejection (FOR) and Accurate Outlier Rejection (AOR). The most informative features are selected through FSP using Hybrid Selection Technique (HST). HST includes two main stages called Quick Selection Stage (QS2) using fisher score as a filter method and Precise Selection Stage (PS2) using a Hybrid Bio-inspired Optimization (HBO) technique as a wrapper method. Finally, actual diagnose takes place through CP, which relies on Ensemble Classification Technique (ECT). Results: The proposed CAD2 has been tested experimentally against recent disease diagnostic strategies using two different datasets in which the first contains several diseases, while the second includes data for Covid-19 patients only. Experimental results have proven the high efficiency of the proposed CAD2 in terms of accuracy, error, precision, and recall compared with other competitors. Additionally, CAD2 strategy provides the best Wilcoxon signed rank test and Friedman test measurements against other strategies according to both datasets. Conclusion: It is concluded that CAD2 strategy based on ORP, FSP, and CP gave an accurate diagnosis compared to other strategies because it gave the highest accuracy and the lowest error and implementation time. © 2023","Computer-aided diagnoses; Diagnosis; Diseases; Ensemble classification; Feature selection; Outlier rejection","Artificial Intelligence; Diagnosis, Computer-Assisted; Humans; Machine Learning; Biomimetics; Classification (of information); Computer aided instruction; Errors; Feature Selection; Statistics; Diagnostic systems; Disease diagnosis; Ensemble classification; Features selection; Hybrid selection; Medical diagnostics; Outliers rejections; Research areas; Selection stages; Selection techniques; accurate outlier rejection; acne; acquired immune deficiency syndrome; adverse drug reaction; alcoholic hepatitis; allergy; arthritis; Article; artificial intelligence; asthma; automation; benign paroxysmal positional vertigo; brain hemorrhage; cervical spondylosis; chickenpox; cholestasis; classification phase; clinician; common cold; computer assisted diagnosis; coronavirus disease 2019; cross validation; dengue; diabetes mellitus; diagnostic accuracy; diagnostic test accuracy study; disease classification; ensemble classification technique; fast outlier rejection; feature selection; feature selection phase; Friedman test; gastroenteritis; gastroesophageal reflux; genetic algorithm; heart infarction; hemorrhoid; hepatitis A; hepatitis B; hepatitis C; hepatitis D; hepatitis E; human; hybrid bio inspired optimization; hypertension; hyperthyroidism; hypoglycemia; hypothyroidism; impetigo; jaundice; machine learning; major clinical study; malaria; migraine; mycosis; osteoarthritis; outlier rejection phase; paralysis; particle swarm optimization; peptic ulcer; pneumonia; psoriasis; tuberculosis; typhoid fever; urinary tract infection; varicosis; Wilcoxon signed ranks test; World Health Organization; procedures; Computer aided diagnosis","","","","","","","Devlekar P., Gawande P., Chalke C., Et al., Data driven machine learning system for optimization of clinic activities, Int J Eng Res Appl (IJERA), 12, 4, pp. 40-45, (2022); Irshad R., Hussain S., Hussain I., Et al., A novel artificial spider monkey based random forest hybrid framework for monitoring and predictive diagnoses of patients healthcare, IEEE Access, 11, pp. 77880-77894, (2023); Rabie A., Saleh A., Mansour N., A Covid-19’s integrated herd immunity (CIHI) based on classifying people vulnerability, Comput Biol Med, 140, pp. 1-29, (2022); Rabie A., Mansour N., Saleh A., Et al., Expecting individuals’ body reaction to Covid-19 based on statistical Naïve Bayes technique, Pattern Recogn, 128, pp. 1-23, (2022); Saleh A., Rabie A., A new Autism Spectrum Disorder Discovery (ASDD) strategy using data mining techniques based on blood tests, Biomed Signal Process Control, 81, pp. 1-14, (2023); Sarker I., Machine learning: algorithms, real-world applications and research directions, SN Comput Sci, 2, 160, pp. 1-21, (2021); Saleh A., Rabie A., Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques, Comput Biol Med, 152, pp. 1-20, (2023); 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Militello C., Prinzi F., Sollami G., Et al., CT Radiomic features and clinical biomarkers for predicting coronary artery disease, Cogn Comput, 15, pp. 238-253, (2023); Ahmad A., Polat H., Prediction of heart disease based on machine learning using jellyfish optimization algorithm, Diagnostics, 13, 14, pp. 1-17, (2023); Malibari A., An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system, Measurement: Sensors, 26, pp. 1-9, (2023); Saleh A., Rabie A., Abo-Al-Ezb K., A data mining based load forecasting strategy for smart electrical grids, Adv Eng Inform, 30, 3, pp. 422-448, (2016); Sun L., Wang T., Ding W., Et al., Feature selection using fisher score and multilabel neighborhood rough sets for multilabel classification, Inform Sci, 578, pp. 887-912, (2021); Guyon I., Weston J., Barnhill S., Gene selection for cancer classification using support vector machines, Mach Learn, 46, pp. 389-422, (2002); Hancer E., Xue B., Zhang M., Differential evolution for filter feature selection based on information theory and feature ranking, Knowledge Based Syst, 140, pp. 103-119, (2018); 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Shaban W., Rabie A., Saleh A., Abo-Elsoud M., A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier, Knowledge-Based Syst, 205, pp. 1-18, (2020); Liu H., Mandvikar A., Mody J., An empirical study of building compact ensembles, Web-Age Information Management (WAIM), 3129, pp. 622-627, (2004); Bonab H., Can F., Less is more: a comprehensive framework for the number of components of ensemble classifiers, IEEE Trans Neural Netw Learn Syst, 14, 8, pp. 2735-2745, (2018); Mandrekar J., Receiver operating characteristic curve in diagnostic test assessment, J Thoracic Oncol, 5, 9, pp. 1315-1316, (2010); Shaban W., Rabie A., Saleh A., Abo-Elsoud M., Detecting COVID-19 patients based on fuzzy inference engine and deep neural network, Appl Soft Comput, 99, pp. 1-19, (2021)","A.H. Rabie; Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt; email: asmaa91hamdy@yahoo.com","","Elsevier B.V.","","","","","","09333657","","AIMEE","38325931","English","Artif. Intell. Med.","Article","Final","","Scopus","2-s2.0-85182400309"
"Gellert G.A.; Kabat-Karabon A.; Gellert G.L.; Rasławska-Socha J.; Gorski S.; Price T.; Kuszczyński K.; Marcjasz N.; Palczewski M.; Jaszczak J.; Loh I.K.; Orzechowski P.M.","Gellert, George A. (57224718686); Kabat-Karabon, Aleksandra (58107860800); Gellert, Gabriel L. (59147249800); Rasławska-Socha, Joanna (57446351100); Gorski, Stanislaw (7004406194); Price, Tim (57455602400); Kuszczyński, Kacper (58532320700); Marcjasz, Natalia (58107978000); Palczewski, Mateusz (53865434600); Jaszczak, Jakub (57641989800); Loh, Irving K. (24453895900); Orzechowski, Piotr M. (58108327500)","57224718686; 58107860800; 59147249800; 57446351100; 7004406194; 57455602400; 58532320700; 58107978000; 53865434600; 57641989800; 24453895900; 58108327500","The potential of virtual triage AI to improve early detection, care acuity alignment, and emergent care referral of life-threatening conditions","2024","Frontiers in Public Health","12","","1362246","","","","4","10.3389/fpubh.2024.1362246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194395215&doi=10.3389%2ffpubh.2024.1362246&partnerID=40&md5=e34335bde865d44ba96fedd472d0ee83","Infermedica, Inc, San Antonio, TX, United States; Infermedica, Inc, Wroclaw, Poland; Infermedica, Inc, Toronto, Canada; Department of Medical Education, Jagiellonian University Medical College, Kraków, Poland; Infermedica Inc, London, United Kingdom; Pediatric Surgery and Urology Department, Wroclaw Medical University, Wroclaw, Poland; Ventura Heart Institute, Thousand Oaks, CA, United States","Gellert G.A., Infermedica, Inc, San Antonio, TX, United States; Kabat-Karabon A., Infermedica, Inc, Wroclaw, Poland; Gellert G.L., Infermedica, Inc, Toronto, Canada; Rasławska-Socha J., Infermedica, Inc, Wroclaw, Poland; Gorski S., Department of Medical Education, Jagiellonian University Medical College, Kraków, Poland; Price T., Infermedica Inc, London, United Kingdom; Kuszczyński K., Infermedica, Inc, Wroclaw, Poland; Marcjasz N., Infermedica, Inc, Wroclaw, Poland; Palczewski M., Infermedica, Inc, Wroclaw, Poland, Pediatric Surgery and Urology Department, Wroclaw Medical University, Wroclaw, Poland; Jaszczak J., Infermedica, Inc, Wroclaw, Poland; Loh I.K., Infermedica, Inc, Wroclaw, Poland, Ventura Heart Institute, Thousand Oaks, CA, United States; Orzechowski P.M., Infermedica, Inc, Wroclaw, Poland","Objective: To evaluate the extent to which patient-users reporting symptoms of five severe/acute conditions requiring emergency care to an AI-based virtual triage (VT) engine had no intention to get such care, and whose acuity perception was misaligned or decoupled from actual risk of life-threatening symptoms. Methods: A dataset of 3,022,882 VT interviews conducted over 16 months was evaluated to quantify and describe patient-users reporting symptoms of five potentially life-threatening conditions whose pre-triage healthcare intention was other than seeking urgent care, including myocardial infarction, stroke, asthma exacerbation, pneumonia, and pulmonary embolism. Results: Healthcare intent data was obtained for 12,101 VT patient-user interviews. Across all five conditions a weighted mean of 38.5% of individuals whose VT indicated a condition requiring emergency care had no pre-triage intent to consult a physician. Furthermore, 61.5% intending to possibly consult a physician had no intent to seek emergency medical care. After adjustment for 13% VT safety over-triage/referral to ED, a weighted mean of 33.5% of patient-users had no intent to seek professional care, and 53.5% had no intent to seek emergency care. Conclusion: AI-based VT may offer a vehicle for early detection and care acuity alignment of severe evolving pathology by engaging patients who believe their symptoms are not serious, and for accelerating care referral and delivery for life-threatening conditions where patient misunderstanding of risk, or indecision, causes care delay. A next step will be clinical confirmation that when decoupling of patient care intent from emergent care need occurs, VT can influence patient behavior to accelerate care engagement and/or emergency care dispatch and treatment to improve clinical outcomes. Copyright © 2024 Gellert, Kabat-Karabon, Gellert, Rasławska-Socha, Gorski, Price, Kuszczyński, Marcjasz, Palczewski, Jaszczak, Loh and Orzechowski.","artificial intelligence; asthma; care delay; early disease detection; myocardial infarction; pneumonia; stroke; virtual/digital clinical triage/care referral","Adult; Aged; Early Diagnosis; Emergency Medical Services; Emergency Service, Hospital; Female; Humans; Male; Middle Aged; Patient Acceptance of Health Care; Patient Acuity; Referral and Consultation; Triage; adult; aged; early diagnosis; emergency health service; female; hospital emergency service; human; male; middle aged; patient acuity; patient attitude; patient referral; patient triage","","","","","","","Carr B.G., Conway P.H., Meisel Z.F., Steiner C.A., Clancy C., Defining the emergency care sensitive condition: a health policy research agenda in emergency medicine, Ann Emerg Med, 56, pp. 49-51, (2010); Vashi A.A., Urech T., Carr B., Greene L., Warsavage T., Hsia R., Et al., Identification of emergency care-sensitive conditions and characteristics of emergency department utilization, JAMA Netw Open, 2, (2019); Rafi A., Sayeed Z., Sultana A.S., Hossain G., Pre-hospital delay in patients with myocardial infarction: an observational study in a tertiary care hospital of northern Bangladesh, BMC Health Serv Res, 20, (2020); Aydogdu M., Dogan N.O., Sinanoglu N.T., Oguzulgen I.K., Demircan A., Bildik F., Et al., Delay in diagnosis of pulmonary thromboembolism in emergency department: is it still a problem?, Clin Appl Thromb Hemost, 19, pp. 402-409, (2013); Quek J.S., Tang W.E., Chen E., Smith H.E., Understanding the journeys of patients with an asthma exacerbation requiring urgent therapy at a primary care clinic, BMC Pulm Med, 22, (2022); Thomas A., Valero-Elizondo J., Khera R., Warraich H.J., Reinhardt S.W., Ali H.J., Et al., Forgone medical care associated with increased health care costs among the U.S. heart failure population. JACC, Heart Fail, 9, pp. 710-719, (2021); Janson S., Becker G., Reasons for delay in seeking treatment for acute asthma: the patient's perspective, J Asthma, 35, pp. 427-435, (1998); Berry A.C., Cash B.D., Wang B., Mulekar M.S., van Haneghan A.B., Yuquimpo K., Et al., Online symptom checker diagnostic and triage accuracy for HIV and hepatitis C, Epidemiol Infect, 147, (2019); Gilbert S., Mehl A., Baluch A., Cawley C., Challiner J., Fraser H., Et al., How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs, BMJ Open, 10, (2020); Hill M.G., Sim M., Mills B., The quality of diagnosis and triage advice provided by free online symptom checkers and apps in Australia, Med J Aust, 212, pp. 514-519, (2020); Blanchard S., NHS-backed GP chatbot is branded a public health danger. Daily Mail, (2019); Gellert G.A., Garber L., Kabat-Karabon A., Kuszczynski K., Price T., Marcjasz N., et al Using AI-based virtual triage to improve acuity-level alignment of patient care seeking in an ambulatory care setting, Int J Healthcare, (2024); National Vital Statistics System, Mortality 2018–2021 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files, 2018–2021, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program; Swiatkowska M., Kosturek M., Analysis of over triage rate based on production data labeled internally by the clinical validation team, Internal report of Infermedica, (2023); Gellert G.A., Kuszczynski K., Marcjasz N., Jaszczak J., Price T., Orzechowski P.M., A comparative performance analysis of live clinical triage using rules based triage protocols versus AI-based automated virtual triage, J. Hosp Adm, 13, (2023); Gellert G.A., Orzechowski P.M., Price T., Jaszczak J., Marcjasz N., Mlodawska A., Et al., A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral, Frontiers in Public Health, (2023); Taghaddosi M., Dianati M., Fath Gharib Bidgoli J., Bahonaran J., Delay and its related factors in seeking treatment in patients with acute myocardial infarction, ARYA Atheroscler, 6, pp. 35-41, (2010); De Luca G., Suryapranata H., Ottervanger J.P., Antman E.M., Time delay to treatment and mortality in primary angioplasty for acute myocardial infarction: every minute of delay counts, Circulation, 109, pp. 1223-1225, (2004); Leslie W.S., Urie A., Hooper J., Morrison C.E., Delay in calling for help during myocardial infarction: reasons for the delay and subsequent pattern of accessing care, Heart, 84, pp. 137-141, (2000); Heart Disease Facts; Mulder M.J.H.L., Jansen I.G.H., Goldhoorn R.B., Venema E., Chalos V., Compagne K.C.J., Et al., Time to endovascular treatment and outcome in acute ischemic stroke: MR CLEAN registry results, Circulation, 138, pp. 232-240, (2018); Eddelien H.S., Butt J.H., Amtoft A.C., Nielsen N.S.K., Jensen E.S., Danielsen I.M.K., Et al., Patient-reported factors associated with early arrival for stroke treatment, Brain Behav, 11, (2021); Fladt J., Meier N., Thilemann S., Polymeris A., Traenka C., Seiffge D.J., Et al., Reasons for prehospital delay in acute ischemic stroke, J Am Heart Assoc, 8, (2019); Stroke facts, (2023); Levy M.L., The national review of asthma deaths: what did we learn and what needs to change?, Breathe (Sheff), 11, pp. 14-24, (2015); Archived National Asthma Data, (2018); Daniel P., Rodrigo C., Mckeever T.M., Woodhead M., Welham S., Lim W.S., Time to first antibiotic and mortality in adults hospitalised with community-acquired pneumonia: a matched-propensity analysis, Thorax, 71, pp. 568-570, (2016); Centers for Disease Control and Prevention. National Center for Health Statistics, National Hospital Ambulatory Medical Care Survey: 2021 Emergency Department Summary Tables; Walen S., Damoiseaux R.A., Uil S.M., van den Berg J.W., Diagnostic delay of pulmonary embolism in primary and secondary care: a retrospective cohort study, Br J Gen Pract, 66, pp. e444-e450, (2016); Belohlavek J., Dytrych V., Linhart A., Pulmonary embolism, part I: epidemiology, risk factors and risk stratification, pathophysiology, clinical presentation, diagnosis and nonthrombotic pulmonary embolism, Exp Clin Cardiol, 18, pp. 129-138, (2013); Mansella G., Keil C., Nickel C.H., Eken C., Wirth C., Tzankov A., Et al., Delayed diagnosis in pulmonary embolism: frequency, patient characteristics, and outcome, Respiration, 99, pp. 589-597, (2020); Learn about pulmonary embolism, 900%2C000, (2023); Tsao C.W., Aday A.W., Almarzooq Z.I., Et al., Heart disease and stroke statistics-2022 update: a report from the American Heart Association, Circulation, 145, (2022); 2022 Heart Disease and Stroke Statistics Update Fact Sheet; Archived National Asthma Data, (2020); Asthma facts and figures, (2023); Braman S.S., The global burden of asthma, Chest, 130, pp. 4S-12S, (2006); van Maanen R., Trinks-Roerdink E.M., Rutten F.H., Geersing G.J., A systematic review and meta-analysis of diagnostic delay in pulmonary embolism, Eur J Gen Pract, 28, pp. 165-172, (2022); Magnusson C., Herlitz J., Sunnerhagen K.S., Hansson P.O., Andersson J.O., Jood K., Prehospital recognition of stroke is associated with a lower risk of death, Acta Neurol Scand, 146, pp. 126-136, (2022)","G.A. Gellert; Infermedica, Inc, San Antonio, United States; email: ggellert33@gmail.com","","Frontiers Media SA","","","","","","22962565","","","38807993","English","Front. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194395215"
"Sun Z.; Lin J.; Zhang T.; Sun X.; Wang T.; Duan J.; Yao K.","Sun, Ziyi (57486856400); Lin, Jianguo (57222167402); Zhang, Tianya (58186274600); Sun, Xiaoning (58185612800); Wang, Tianlin (57221130772); Duan, Jinlong (57208052310); Yao, Kuiwu (25932470900)","57486856400; 57222167402; 58186274600; 58185612800; 57221130772; 57208052310; 25932470900","Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of chronic obstructive pulmonary disease and atrial fibrillation","2023","Frontiers in Cardiovascular Medicine","10","","1121102","","","","4","10.3389/fcvm.2023.1121102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152670469&doi=10.3389%2ffcvm.2023.1121102&partnerID=40&md5=0d644c20b94bb2c9209c2202ecbb351d","Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China; Graduate School, Beijing University of Chinese Medicine, Beijing, China; Graduate School, China Academy of Chinese Medical Sciences, Beijing, China; Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, China; Eye Hospital China Academy of Chinese Medical Sciences, China Academy of Chinese Medical Sciences, Beijing, China","Sun Z., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China, Graduate School, Beijing University of Chinese Medicine, Beijing, China; Lin J., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China, Graduate School, China Academy of Chinese Medical Sciences, Beijing, China; Zhang T., Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, China; Sun X., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China, Graduate School, China Academy of Chinese Medical Sciences, Beijing, China; Wang T., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China, Graduate School, Beijing University of Chinese Medicine, Beijing, China; Duan J., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China; Yao K., Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China, Eye Hospital China Academy of Chinese Medical Sciences, China Academy of Chinese Medical Sciences, Beijing, China","Background: Patients with chronic obstructive pulmonary disease (COPD) often present with atrial fibrillation (AF), but the common pathophysiological mechanisms between the two are unclear. This study aimed to investigate the common biological mechanisms of COPD and AF and to search for important biomarkers through bioinformatic analysis of public RNA sequencing databases. Methods: Four datasets of COPD and AF were downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes common to both diseases were screened by WGCNA analysis, followed by protein-protein interaction network construction and functional enrichment analysis to elucidate the common mechanisms of COPD and AF. Machine learning algorithms were also used to identify key biomarkers. Co-expression analysis, “transcription factor (TF)-mRNA-microRNA (miRNA)” regulatory networks and drug prediction were performed for key biomarkers. Finally, immune cell infiltration analysis was performed to evaluate further the immune cell changes in the COPD dataset and the correlation between key biomarkers and immune cells. Results: A total of 133 overlapping genes for COPD and AF were obtained, and the enrichment was mainly focused on pathways associated with the inflammatory immune response. A key biomarker, cyclin dependent kinase 8 (CDK8), was identified through screening by machine learning algorithms and validated in the validation dataset. Twenty potential drugs capable of targeting CDK8 were obtained. Immune cell infiltration analysis revealed the presence of multiple immune cell dysregulation in COPD. Correlation analysis showed that CDK8 expression was significantly associated with CD8+ T cells, resting dendritic cell, macrophage M2, and monocytes. Conclusions: This study highlights the role of the inflammatory immune response in COPD combined with AF. The prominent link between CDK8 and the inflammatory immune response and its characteristic of not affecting the basal expression level of nuclear factor kappa B (NF-kB) make it a possible promising therapeutic target for COPD combined with AF. 2023 Sun, Lin, Zhang, Sun, Wang, Duan and Yao.","atrial fibrillation; biomarker; chronic obstructive pulmonary disease; machine learning; microarray","CD8 antigen; cyclin dependent kinase 8; messenger RNA; microRNA; transcription factor; Article; atrial fibrillation; bioinformatics; CD8+ T lymphocyte; cell infiltration; chronic obstructive lung disease; controlled study; dendritic cell; diagnostic accuracy; diagnostic test accuracy study; extreme gradient boosting; functional enrichment analysis; gene set enrichment analysis; generalized linear model; human; immune response; immunocompetent cell; M2 macrophage; machine learning; microarray analysis; monocyte; multiple immune cell dysregulation; predictive accuracy; protein protein interaction; random forest; receiver operating characteristic; RNA sequencing; weighted gene co expression network analysis","","","","","National Natural Science Foundation of China, NSFC, (81473466, 81873173); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2019YFC1708703); National Key Research and Development Program of China, NKRDPC","This study was funded by grants from General Program of National Natural Science Foundation of China (81473466, 81873173) and the National Key Research and Development Program of China (2019YFC1708703). 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Sun X., Feng X., Zheng D., Li A., Li C., Li S., Et al., Ergosterol attenuates cigarette smoke extract-induced COPD by modulating inflammation, oxidative stress and apoptosis in vitro and in vivo, Clin Sci, 133, pp. 1523-1536, (2019); Liu M., Li W., Wang H., Yin L., Ye B., Tang Y., Et al., CTRP9 Ameliorates atrial inflammation, fibrosis, and vulnerability to atrial fibrillation in post-myocardial infarction rats, J Am Heart Assoc, 8, (2019); Chen X., Yan Y., Cheng X., Zhang Z., He C., Wu D., Et al., A novel CDK8 inhibitor with poly-substituted pyridine core: discovery and anti-inflammatory activity evaluation in vivo, Bioorg Chem, 133, (2023); Guo Z., Wang G., Lv Y., Wan Y., Zheng J., Inhibition of Cdk8/Cdk19 activity promotes treg cell differentiation and suppresses autoimmune diseases, Front Immunol, 10, (2019); Schmerwitz U., Sass G., Khandoga A., Joore J., Mayer B., Berberich N., Et al., Flavopiridol protects against inflammation by attenuating leukocyte-endothelial interaction via inhibition of cyclin-dependent kinase 9, Arterioscler Thromb Vasc Biol, 31, pp. 280-288, (2011); Rao F., Xue Y., Wei W., Yang H., Liu F., Chen S., Et al., Role of tumour necrosis factor-a in the regulation of T-type calcium channel current in HL-1 cells, Clin Exp Pharmacol Physiol, 43, pp. 706-711, (2016); Kao Y., Chen Y., Cheng C., Lee T., Chen Y., Chen S., Tumor necrosis factor-alpha decreases sarcoplasmic reticulum Ca2+-ATPase expressions via the promoter methylation in cardiomyocytes, Crit Care Med, 38, pp. 217-222, (2010); Goudis C., Kallergis E., Vardas P., Extracellular matrix alterations in the atria: insights into the mechanisms and perpetuation of atrial fibrillation, Europace, 14, pp. 623-630, (2012); Siwik D., Chang D., Colucci W., Interleukin-1beta and tumor necrosis factor-alpha decrease collagen synthesis and increase matrix metalloproteinase activity in cardiac fibroblasts in vitro, Circ Res, 86, pp. 1259-1265, (2000); Kaku Y., Imaoka H., Morimatsu Y., Komohara Y., Ohnishi K., Oda H., Et al., Overexpression of CD163, CD204 and CD206 on alveolar macrophages in the lungs of patients with severe chronic obstructive pulmonary disease, PLoS One, 9, (2014); Vlahos R., Bozinovski S., Role of alveolar macrophages in chronic obstructive pulmonary disease, Front Immunol, 5, (2014); Xu J., Cui G., Esmailian F., Plunkett M., Marelli D., Ardehali A., Et al., Atrial extracellular matrix remodeling and the maintenance of atrial fibrillation, Circulation, 109, pp. 363-368, (2004); Abe I., Teshima Y., Kondo H., Kaku H., Kira S., Ikebe Y., Et al., Association of fibrotic remodeling and cytokines/chemokines content in epicardial adipose tissue with atrial myocardial fibrosis in patients with atrial fibrillation, Heart Rhythm, 15, pp. 1717-1727, (2018); Lyu J., Wang M., Kang X., Xu H., Cao Z., Yu T., Et al., Macrophage-mediated regulation of catecholamines in sympathetic neural remodeling after myocardial infarction, Basic Res Cardiol, 115, (2020); Ravi A., Plumb J., Gaskell R., Mason S., Broome C., Booth G., Et al., COPD Monocytes demonstrate impaired migratory ability, Respir Res, 18, (2017); Segal L., Martinez F., Chronic obstructive pulmonary disease subpopulations and phenotyping, J Allergy Clin Immunol, 141, pp. 1961-1971, (2018); Hirayama A., Goto T., Shimada Y., Faridi M., Camargo C., Hasegawa K., Acute exacerbation of chronic obstructive pulmonary disease and subsequent risk of emergency department visits and hospitalizations for atrial fibrillation, Circ Arrhythm Electrophysiol, 11, (2018); Cepelis A., Brumpton B., Malmo V., Laugsand L., Loennechen J., Ellekjaer H., Et al., Associations of asthma and asthma control with atrial fibrillation risk: results from the nord-trøndelag health study (HUNT), JAMA Cardiol, 3, pp. 721-728, (2018)","K. Yao; Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China; email: yaokuiwu@126.com","","Frontiers Media S.A.","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152670469"
"Feng J.; Gossmann A.; Sahiner B.; Pirracchio R.","Feng, Jean (57206786193); Gossmann, Alexej (56982754500); Sahiner, Berkman (7006688472); Pirracchio, Romain (16745004400)","57206786193; 56982754500; 7006688472; 16745004400","Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees","2022","Journal of the American Medical Informatics Association","29","5","","841","852","11","4","10.1093/jamia/ocab280","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128488968&doi=10.1093%2fjamia%2focab280&partnerID=40&md5=cc7bf793d644d946d3681ebb42a15f73","Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States; CDRH-Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States; Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States","Feng J., Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States; Gossmann A., CDRH-Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States; Sahiner B., CDRH-Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States; Pirracchio R., Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States","Objective: After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees. Materials and Methods: We introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive ""Type I and II""regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic reviser in terms of the average loss. Results: Both procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818-0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230-0.300) and 0.241 (95%CI, 0.216-0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765-0.769) to 0.800 (95%CI, 0.798-0.802) and 0.799 (95%CI, 0.797-0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913-0.935) and 0.925 (95%CI, 0.914-0.935), compared to the static model's aAUC of 0.904 (95%CI, 0.892-0.916). Discussion: Despite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data. Conclusions: BLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time. © 2022 Published by Oxford University Press on behalf of the American Medical Informatics Association 2022. This work is written by US Government employees and is in the public domain in the US.","Bayesian model updating; clinical prediction models; machine learning; model recalibration","Bayes Theorem; Humans; Logistic Models; Models, Statistical; Prognosis; Pulmonary Disease, Chronic Obstructive; area under the curve; Article; Bayesian learning; calibration; case study; chronic obstructive lung disease; computer simulation; controlled study; covariance; disease risk assessment; intermethod comparison; logistic regression analysis; Markov chain; measurement accuracy; measurement error; predictive model; receiver operating characteristic; retrospective study; Bayes theorem; human; prognosis; statistical model","","","","","U.S. Food and Drug Administration, FDA, (U01FD005978)","","Benjamens S, Dhunnoo P, Mesko B., The state of artificial intelligencebased FDA-approved medical devices and algorithms: an online database, NPJ Digit Med, 3, 1, (2020); Steyerberg EW., Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, (2009); Pirracchio R, Ranzani OT., Recalibrating our prediction models in the ICU: time to move from the abacus to the computer, Intensive Care Med, 40, 3, pp. 438-441, (2014); Amarasingham R, Patzer RE, Huesch M, Et al., Implementing electronic health care predictive analytics: considerations and challenges, Health Aff (Millwood), 33, 7, pp. 1148-1154, (2014); Thrun S., Lifelong learning algorithms, Learning to Learn, pp. 181-209, (1998); Cesa-Bianchi N, Lugosi G., Prediction, Learning, and Games, (2006); Baweja C, Glocker B, Kamnitsas K., Towards continual learning in medical imaging, Medical Imaging meets NIPS Workshop, 32nd Conference on Neural Information Processing Systems (NIPS2018), (2018); Lee CS, Lee AY., Clinical applications of continual learning machine learning, Lancet Digital Health, 2, 6, pp. e279-e281, (2020); Janssen KJM, Moons KGM, Kalkman CJ, Et al., Updating methods improved the performance of a clinical prediction model in new patients, J Clin Epidemiol, 61, 1, pp. 76-86, (2008); Strobl AN, Vickers AJ, Van Calster B, Et al., Improving patient prostate cancer risk assessment: moving from static, globally-applied to dynamic, practice-specific risk calculators, J Biomed Inform, 56, pp. 87-93, (2015); Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD): discussion paper and request for feedback, (2019); Viering T, Mey A, Loog M., Open Problem: Monotonicity of Learning, Proceedings of the Thirty-Second Conference on Learning Theory, pp. 3198-3201, (2019); West M, Harrison J., Bayesian Forecasting and Dynamic Models, (1997); McCormick TH, Raftery AE, Madigan D, Et al., Dynamic logistic regression and dynamic model averaging for binary classification, Biometrics, 68, 1, pp. 23-30, (2012); Feng J, Emerson S, Simon N., Approval policies for modifications to machine learning-based software as a medical device: a study of bio-creep, Biometrics, 77, 1, pp. 31-44, (2020); Kuleshov V, Ermon S., Estimating uncertainty online against an adversary, (2017); Davis SE, Greevy RA, Lasko TA, Et al., Detection of calibration drift in clinical prediction models to inform model updating, J Biomed Inform, 112, (2020); Kingma DP, Ba J., Adam: a method for stochastic optimization, 3rd International Conference for Learning Representations, (2015); Kakade SM, Ng A., Online bounds for Bayesian algorithms, Advances in Neural Information Processing Systems, pp. 641-648, (2005); Shamir GI., Logistic regression regret: what's the catch?, (2020); Lum K, Isaac W., To predict and serve?, Significance, 13, 5, pp. 14-19, (2016); Ensign D, Friedler SA, Neville S, Et al., Runaway feedback loops in predictive policing, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pp. 160-171, (2018); Lewis SM, Raftery AE., Estimating Bayes factors via posterior simulation with the laplace-metropolis estimator, J AmStat Assoc, 92, pp. 648-655, (1997); Gordon K, Smith AFM., Modeling and monitoring biomedical time series, J Am Stat Assoc, 85, pp. 328-337, (1990); Chouldechova A, Roth A., The frontiers of fairness in machine learning, (2018); Van Hoorde K, Van Huffel S, Timmerman D, Et al., A spline-based tool to assess and visualize the calibration of multiclass risk predictions, J Biomed Inform, 54, pp. 283-293, (2015); Nestor B, McDermott MBA, Boag W, Et al., Feature robustness in nonstationary health records: caveats to deployable model performance in common clinical machine learning tasks, Mach Learn Healthcare, 106, pp. 381-405, (2019); Davis SE, Lasko TA, Chen G, Et al., Calibration drift in regression and machine learning models for acute kidney injury, J Am Med Inform Assoc, 24, 6, pp. 1052-1061, (2017); Chen JH, Alagappan M, Goldstein MK, Et al., Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets, Int J Med Inform, 102, pp. 71-79, (2017); Saria S, Subbaswamy A., Tutorial: safe and reliable machine learning, Proceedings of the Conference on Fairness, Accountability, and Transparency, (2019); Davis SE, Greevy RA, Fonnesbeck C, Et al., A nonparametric updating method to correct clinical prediction model drift, J Am Med Inform Assoc, 26, 12, pp. 1448-1457, (2019); Vergouwe Y, Nieboer D, Oostenbrink R, Et al., A closed testing procedure to select an appropriate method for updating prediction models, Stat Med, 36, 28, pp. 4529-4539, (2017); Steyerberg EW, Borsboom GJJM, van Houwelingen HC, Et al., Validation and updating of predictive logistic regression models: a study on sample size and shrinkage, Stat Med, 23, 16, pp. 2567-2586, (2004); Su T-L, Jaki T, Hickey GL, Et al., A review of statistical updating methods for clinical prediction models, Stat Methods Med Res, 27, 1, pp. 185-197, (2018); Raftery AE, Karny M, Ettler P., Online prediction under model uncertainty via dynamic model averaging: application to a cold rolling mill, Technometrics, 52, 1, pp. 52-66, (2010); Stan modeling language users guide and reference manual, (2021); Salvatier J, Wiecki TV, Fonnesbeck C., Probabilistic programming in Python using PyMC3, PeerJ Comput Sci, 2, (2016); Perdomo J, Zrnic T, Mendler-Dunner C, Et al., Performative prediction, Proceedings of the 37th International Conference on Machine Learning, pp. 7599-7609, (2020); Liley J, Emerson S, Mateen B, Et al., Model updating after interventions paradoxically introduces bias, International Conference on Artificial Intelligence and Statistics, 130, pp. 3916-3924, (2021)","J. Feng; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, 550 16th Street, 94158, United States; email: jean.feng@ucsf.edu","","Oxford University Press","","","","","","10675027","","JAMAF","35022756","English","J. Am. Med. Informatics Assoc.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85128488968"
"Türk M.; Ertaş R.; Şahiner Ü.M.; Kolkhir P.; Şekerel B.E.; Soyer Ö.; Avcl A.; Atasoy M.; Özyurt K.; Türk Y.; Zeydan E.; Maurer M.","Türk, Murat (57192651610); Ertaş, Raglp (54879402700); Şahiner, Ümit Murat (15760793100); Kolkhir, Pavel (56076677500); Şekerel, Bülent Enis (7004557677); Soyer, Özge (24483981200); Avcl, Atll (58095874200); Atasoy, Mustafa (6603760794); Özyurt, Kemal (6508190140); Türk, Yekta (57202971839); Zeydan, Engin (24315322700); Maurer, Marcus (55584190300)","57192651610; 54879402700; 15760793100; 56076677500; 7004557677; 24483981200; 58095874200; 6603760794; 6508190140; 57202971839; 24315322700; 55584190300","In Chronic Spontaneous Urticaria, Complete Response to Antihistamine Treatment Is Linked to Low Disease Activity","2023","International Archives of Allergy and Immunology","184","5","","421","432","11","4","10.1159/000528395","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147664803&doi=10.1159%2f000528395&partnerID=40&md5=479334aedcb7e5821b7f7dbde6c60231","Kayseri City Education and Research Hospital, Clinic of Immunologic and Allergic Diseases, Kayseri, Turkey; Health Science University, Kayseri Faculty of Medicine, Kayseri City Education and Research Hospital, Department of Dermatology, Kayseri, Turkey; Hacettepe University, Division of Pediatric Allergy and Immunology, Ankara, Turkey; Institute of Allergology, Char. - Universitatsmedizin Berlin, Corp. Member of Freie Univ. Berlin and Humboldt-Univ. zu Berlin, Berlin, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Allergology and Immunology, Berlin, Germany; I.M. Sechenov First Moscow State Medical University, Sechenov University, Division of Immune-mediated Skin Diseases, Moscow, Russian Federation; Biruni University, Medical Faculty, Department of Dermatology, İstanbul, Turkey; Klrşehir Ahi Evran University, Department of Dermatology, Klrşehir, Turkey; Aselsan Inc., Ankara, Turkey; Services As Networks (SAS) Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain","Türk M., Kayseri City Education and Research Hospital, Clinic of Immunologic and Allergic Diseases, Kayseri, Turkey; Ertaş R., Health Science University, Kayseri Faculty of Medicine, Kayseri City Education and Research Hospital, Department of Dermatology, Kayseri, Turkey; Şahiner Ü.M., Hacettepe University, Division of Pediatric Allergy and Immunology, Ankara, Turkey; Kolkhir P., Institute of Allergology, Char. - Universitatsmedizin Berlin, Corp. Member of Freie Univ. Berlin and Humboldt-Univ. zu Berlin, Berlin, Germany, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Allergology and Immunology, Berlin, Germany, I.M. Sechenov First Moscow State Medical University, Sechenov University, Division of Immune-mediated Skin Diseases, Moscow, Russian Federation; Şekerel B.E., Hacettepe University, Division of Pediatric Allergy and Immunology, Ankara, Turkey; Soyer Ö., Hacettepe University, Division of Pediatric Allergy and Immunology, Ankara, Turkey; Avcl A., Health Science University, Kayseri Faculty of Medicine, Kayseri City Education and Research Hospital, Department of Dermatology, Kayseri, Turkey; Atasoy M., Biruni University, Medical Faculty, Department of Dermatology, İstanbul, Turkey; Özyurt K., Klrşehir Ahi Evran University, Department of Dermatology, Klrşehir, Turkey; Türk Y., Aselsan Inc., Ankara, Turkey; Zeydan E., Services As Networks (SAS) Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain; Maurer M., Institute of Allergology, Char. - Universitatsmedizin Berlin, Corp. Member of Freie Univ. Berlin and Humboldt-Univ. zu Berlin, Berlin, Germany, Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Allergology and Immunology, Berlin, Germany","Introduction: The use of predictors of response to a specific treatment in patients with chronic spontaneous urticaria (CSU) can improve disease management, help prevent unnecessary healthcare costs, and save time. In this study, we aimed to identify predictors of complete response to standard-dosed and higher than standard-dosed antihistamine treatments in patients with CSU. Methods: Medical records of 475 CSU patients, 120 of them <18 years old, from 3 different centers were analyzed. We used 15 machine learning (ML) models as well as traditional statistical methods to predict complete response to standard-dosed and higher than standard-dosed antihistamine treatment based on 17 clinical parameters. Results: CSU disease activity, which was assessed by urticaria activity score (UAS), was the only clinical parameter that predicted complete response to standard-dosed and higher than standard-dosed antihistamine treatment, with ML models and traditional statistics, for all age groups. Based on ROC analyses, optimal cut-off values of disease activity to predict complete response were UAS <3 and UAS <4 for standard-dosed (area under the ROC curve [AUC] = 0.69; p = 0.001) and higher than standard-dosed (AUC = 0.79; p = 0.001) antihistamine treatments, respectively. Also, ML models identified lower total IgE (<150 IU/mL) as a predictor of complete response to a standard-dosed antihistamine and lower CRP (<3.4 mg/mL) as a predictor of complete response to higher than standard-dose antihistamine treatment. Discussion: In this study, we showed that patients with UAS <3 are highly likely to have complete response to standard-dosed AH and those with a UAS <4 are highly likely to have complete response to higher than standard-dosed AH treatment. Low CSU disease activity is the only universal predictor of complete response to AH treatment with both ML models and traditional statistics for all age groups.  © 2023 S. Karger AG. All rights reserved.","Antihistamine; Chronic spontaneous urticaria; Disease activity; Predictors","Adolescent; Chronic Disease; Chronic Urticaria; Histamine Antagonists; Histamine H1 Antagonists; Humans; Omalizumab; Urticaria; antihistaminic agent; antinuclear antibody; C reactive protein; immunoglobulin E; thyroid peroxidase; antihistaminic agent; histamine H1 receptor antagonist; omalizumab; adolescent; adult; angioneurotic edema; Article; asthma; atopy; autoimmune disease; basophil count; Bayesian learning; chronic spontaneous urticaria; controlled study; cross validation; decision tree; discriminant analysis; disease activity; disease duration; eosinophil count; female; human; k nearest neighbor; logistic regression analysis; machine learning; major clinical study; male; medical record review; patient-reported outcome; random forest; retrospective study; urticaria; chronic disease; chronic urticaria; urticaria","","C reactive protein, 9007-41-4; immunoglobulin E, 37341-29-0; omalizumab, 242138-07-4; Histamine Antagonists, ; Histamine H1 Antagonists, ; Omalizumab, ","","","","","Fricke J., Avila G., Keller T., Weller K., Lau S., Maurer M., Et al., Prevalence of chronic urticaria in children and adults across the globe: Systematic review with meta-analysis, Allergy, 75, 2, pp. 423-432, (2020); Maurer M., Abuzakouk M., Berard F., Canonica W., Oude Elberink H., Gimenez-Arnau A., Et al., The burden of chronic spontaneous urticaria is substantial: Real-world evidence from ASSURE-CSU, Allergy, 72, 12, pp. 2005-2016, (2017); Zuberbier T., Abdul Latiff A.H., Abuzakouk M., Aquilina S., Asero R., Baker D., Et al., The international EAACI/GA2LEN/EuroGuiDerm/ APAAACI guideline for the definition, classification, diagnosis, and management of urticaria, Allergy, 77, 3, pp. 734-766, (2022); Turk M Y., Experience-based advice on stepping up and stepping down the therapeutic management of chronic spontaneous urticaria: Where is the guidance?, Allergy, 77, 5, pp. 1626-1630, (2022); Turk M., Ertas R., Zeydan E., Turk Y., Atasoy M., Gutsche A., Identification of chronic urticaria subtypes using machine learning algorithms, Allergy, 77, 1, pp. 323-326, (2022); Maurer M., Eyerich K., Eyerich S., Ferrer M., Gutermuth J., Hartmann K., Et al., Urticaria: Collegium internationale allergologicum (CIA) update 2020, Int Arch Allergy Immunol, 181, 5, pp. 321-333, (2020); Kolkhir P., Altrichter S., Hawro T., Maurer M., C-reactive protein is linked to disease activity, impact, and response to treatment in patients with chronic spontaneous urticaria, Allergy, 73, 4, pp. 940-948, (2018); Magen E., Waitman D.A., Dickstein Y., Davidovich V., Kahan N.R., Clinical-laboratory characteristics of ANA-positive chronic idiopathic urticaria, Allergy Asthma Proc., 36, 2, pp. 138-144, (2015); De Montjoye L., Darrigade A.S., Gimenez-Arnau A., Herman A., Dumoutier L., Baeck M., Correlations between disease activity, autoimmunity and biological parameters in patients with chronic spontaneous urticaria, Eur Ann Allergy Clin Immunol, 53, 2, pp. 55-66, (2021); Marzano A.V., Genovese G., Casazza G., Fierro M.T., Dapavo P., Crimi N., Et al., Predictors of response to omalizumab and relapse in chronic spontaneous urticaria: A study of 470 patients, J Eur Acad Dermatol Venereol, 33, 5, pp. 918-924, (2019); Gericke J., Metz M., Ohanyan T., Weller K., Altrichter S., Skov P.S., Et al., Serum autoreactivity predicts time to response to omalizumab therapy in chronic spontaneous urticaria, J Allergy Clin Immunol, 139, 3, pp. 1059-1061, (2017); Sardina D.S., Valenti G., Papia F., Uasuf C.G., Exploring machine learning techniques to predict the response to omalizumab in chronic spontaneous urticaria, Diagnostics, 11, 11, (2021); Ye Y.M., Yoon J., Woo S.D., Jang J.H., Lee Y., Lee H.Y., Et al., Clustering the clinical course of chronic urticaria using a longitudinal database: Effects on urticaria remission, Allergy Asthma Immunol Res, 13, 3, pp. 390-403, (2021); Fok J.S., Kolkhir P., Church M.K., Maurer M., Predictors of treatment response in chronic spontaneous urticaria, Allergy, 76, 10, pp. 2965-2981, (2021); Maurer M., Mathias S.D., Crosby R.D., Rajput Y., Zazzali J.L., Validity and responsiveness of the urticaria activity and impact measure: A new patient-reported tool, Ann Allergy Asthma Immunol, 120, 6, pp. 641-647, (2018); Shapash Python Library, (2021); PyCaret: An Open Source, Low-code Machine Learning Library, (2021); Luo W., Phung D., Tran T., Gupta S., Rana S., Karmakar C., Et al., Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view, J Med Internet Res, 18, 12, (2016); Magen E., Mishal J., Zeldin Y., Schlesinger M., Nical and laboratory features of antihistamine- resistant chronic idiopathic urticaria, Allergy Asthma Proc., 32, 6, pp. 460-466, (2011); Kolkhir P., Pogorelov D., Olisova O., CRP Ddimer fibrinogen and ESR as predictive markers of response to standard doses of levocetirizine in patients with chronic spontaneous urticaria, Eur Ann Allergy Clin Immunol, 49, 4, pp. 189-192, (2017); Ayse Ornek S., Orcen C., Church M.K., Kocaturk E., An evaluation of remission rates with first and second line treatments and indicators of antihistamine refractoriness in chronic urticaria, Int Immunopharmacol, 112, (2022); Mlynek A., Zalewska-Janowska A., Martus P., Staubach P., Zuberbier T., Maurer M., How to assess disease activity in patients with chronic urticaria?, Allergy, 63, 6, pp. 777-780, (2008); Curto-Barredo L., Archilla L.R., Vives G.R., Pujol R.M., Gimenez-Arnau A.M., Clinical features of chronic spontaneous urticaria that predict disease prognosis and refractoriness to standard treatment, Acta Derm Venereol, 98, 7, pp. 641-647, (2018); Ulambayar B., Yang E.M., Cha H.Y., Shin Y.S., Park H.S., Ye Y.M., Increased platelet activating factor levels in chronic spontaneous urticaria predicts refractoriness to antihistamine treatment: An observational study, Clin Transl Allergy, 9, (2019); Trinh H.K., Pham D.L., Ban G.Y., Lee H.Y., Park H.S., Ye Y.M., Altered systemic adipokines in patients with chronic urticaria, Int Arch Allergy Immunol, 171, 2, pp. 102-110, (2016); Balp M.M., Weller K., Carboni V., Chirilov A., Papavassilis C., Severin T., Et al., Prevalence and clinical characteristics of chronic spontaneous urticaria in pediatric patients, Pediatr Allergy Immunol, 29, 6, pp. 630-636, (2018); Park Y.M., Oh M.S., Kwon J.W., Predicting inadequate treatment response in children with chronic spontaneous urticaria, Pediatr Allergy Immunol, 31, 8, pp. 946-953, (2020); Bzdok D., Altman N., Krzywinski M., Statistics versus machine learning, Nat Methods, 15, 4, pp. 233-234, (2018); Beam A.L., Kohane I.S., Big data and machine learning in health care, JAMA, 319, 13, pp. 1317-1318, (2018); Rajkomar A., Dean J., Kohane I., Machine learning in medicine, N Engl J Med, 380, 14, pp. 1347-1358, (2019)","M. Maurer; Institute of Allergology, Char. - Universitatsmedizin Berlin, Corp. Member of Freie Univ. Berlin and Humboldt-Univ. zu Berlin, Berlin, Germany; email: marcus.maurer@charite.de","","S. Karger AG","","","","","","10182438","","IAAIE","36652936","English","Int. Arch. Allergy Immunol.","Article","Final","","Scopus","2-s2.0-85147664803"
"Savadjiev P.; Gallix B.; Rezanejad M.; Bhatnagar S.; Semionov A.; Siddiqi K.; Forghani R.; Reinhold C.; Eidelman D.H.; Dandurand R.J.","Savadjiev, Peter (13907640000); Gallix, Benoit (6603911488); Rezanejad, Morteza (57191854850); Bhatnagar, Sahir (57191254539); Semionov, Alexandre (6603251430); Siddiqi, Kaleem (7006819925); Forghani, Reza (6602240944); Reinhold, Caroline (24446271600); Eidelman, David H. (7005951484); Dandurand, Ronald J. (57217140933)","13907640000; 6603911488; 57191854850; 57191254539; 6603251430; 7006819925; 6602240944; 24446271600; 7005951484; 57217140933","Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes","2022","Radiology: Artificial Intelligence","4","1","e210105","","","","4","10.1148/ryai.210105","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129313377&doi=10.1148%2fryai.210105&partnerID=40&md5=a8efd18e81cecb0e6ff09316dc96490e","Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Meakins-Christie Laboratories, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Centre for Innovative Medicine, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Montreal Chest Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Department of Pathology, McGill University, Montreal, QC, Canada; Medical Physics Unit, Department of Oncology, McGill University, Montreal, QC, Canada; School of Computer Science, McGill University, Montreal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada; Department of Medicine, McGill University, Montreal, QC, Canada; Institut de Chirurgie Guidée par l’Image, IHU Strasbourg, Strasbourg, France; Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, ON, Canada; Lakeshore General Hospital, Pointe-Claire, QC, Canada","Savadjiev P., Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Department of Pathology, McGill University, Montreal, QC, Canada, Medical Physics Unit, Department of Oncology, McGill University, Montreal, QC, Canada, School of Computer Science, McGill University, Montreal, QC, Canada; Gallix B., Institut de Chirurgie Guidée par l’Image, IHU Strasbourg, Strasbourg, France; Rezanejad M., Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, ON, Canada; Bhatnagar S., Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada; Semionov A., Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Siddiqi K., School of Computer Science, McGill University, Montreal, QC, Canada; Forghani R., Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, QC, Canada; Reinhold C., Department of Diagnostic Radiology, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada; Eidelman D.H., Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Meakins-Christie Laboratories, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Department of Medicine, McGill University, Montreal, QC, Canada; Dandurand R.J., Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Meakins-Christie Laboratories, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Centre for Innovative Medicine, Research Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Montreal Chest Institute, McGill University Health Centre, 1001 Décarie Blvd, Montréal, H4A 3J1, QC, Canada, Department of Medicine, McGill University, Montreal, QC, Canada, Lakeshore General Hospital, Pointe-Claire, QC, Canada","Purpose: To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT. Materials and Methods: In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44–90 years] and 123 men [mean age, 71 years; range, 29–90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recallmacro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy). Results: Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images. Conclusion: The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features. © RSNA, 2022.","Chronic Obstructive Pulmonary Disease; CT; Lung; Quantification","accuracy; adult; aged; algorithm; Article; chronic obstructive lung disease; computer assisted tomography; controlled study; convolutional neural network; diagnostic test accuracy study; female; forced expiratory volume; forced vital capacity; human; machine learning; major clinical study; male; predictive value; receiver operating characteristic; sensitivity and specificity; spirometry","","","","","Fonds de Recherche du Québec - Santé, FRQS; University of Toronto, U of T; ; McGill University, MGU; Fondation de l'Association des radiologistes du Québec, FARQ","Disclosures of conflicts of interest: P.S. Patent application (https://patents.google. com/patent/US20190392579A1/) under review, author is coinventor. B.G. Patent application (https://patents.google.com/patent/US20190392579A1/) under review. M.R. Funding for PhD studies from McGill University (School of Computer Science) under supervision of K.S.; Arts and Science postdoctoral fellowship from the University of Toronto; author\u2019s contribution to this work mainly from author\u2019s time as a PhD student at McGill University. S.B. No relevant relationships. A.S. No relevant relationships. K.S. Patent application (https://patents.google.com/patent/US20190392579A1/) under review. R.F. Clinical research scholar (chercheur-boursier clinicien) supported by the Fonds de recherche en sante du Qu\u00E9bec (FRQS) and has an operating grant jointly funded by the FRQS and the Fondation de l\u2019Association des radiologistes du Qu\u00E9bec (FARQ); payment or honoraria from GE Healthcare (dual-energy CT, AI); method and system of performing medical treatment outcome assessment or medical condition diagnostic, contributor (5%), patent pending US20190392579A1. C.R. Contributor to patent (https://patents. google.com/patent/US20190392579A1/). D.H.E. No relevant relationships. R.J.D. Contributor to patent (https://patents.google.com/patent/US20190392579A1/).","Celli BR, MacNee W, Standards for the diagnosis and treatment of patients with COPD: a summary of the ATS/ERS position paper, Eur Respir J, 23, 6, pp. 932-946, (2004); Vogelmeier CF, Criner GJ, Martinez FJ, Et al., Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. 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That which we call IPF, by any other name would act the same, Eur Respir J, 51, 5, (2018); Savadjiev P, Bhatnagar S, Semionov A, Dandurand RJ., A Computational Technique Based on the Mean Curvature of Isophotes for the Detection and Classification of Interstitial Lung Diseases on Chest CT, Am J Respir Crit Care Med, 203, (2021)","P. Savadjiev; Department of Diagnostic Radiology, McGill University Health Centre, Montréal, 1001 Décarie Blvd, H4A 3J1, Canada; email: peter.savadjiev@mcgill.ca","","Radiological Society of North America Inc.","","","","","","26386100","","","","English","Radiology: Art. Int.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85129313377"
"Woo J.; Lee J.-H.; Kim Y.; Rudasingwa G.; Lim D.H.; Kim S.","Woo, Jiyoung (55976409400); Lee, Ji-Hyun (57216432798); Kim, Yeonjin (57223279854); Rudasingwa, Guillaume (57214123239); Lim, Dae Hyun (8409118500); Kim, Sungroul (57218664381)","55976409400; 57216432798; 57223279854; 57214123239; 8409118500; 57218664381","Forecasting the Effects of Real-Time Indoor PM2.5 on Peak Expiratory Flow Rates (PEFR) of Asthmatic Children in Korea: A Deep Learning Approach","2022","IEEE Access","10","","","19391","19400","9","4","10.1109/ACCESS.2022.3148294","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124185513&doi=10.1109%2fACCESS.2022.3148294&partnerID=40&md5=c3963cc4f55e83fe87be65956ddc12ad","Department of Bigdata Engineering, Soonchunhyang University, Asan-si, South Korea; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, South Korea; Department of ICT Environmental Health System, School Soonchunhyang University, Asan-si, South Korea; Integrated Research Center for Risk Assessment, Soonchunhyang University, Asan-si, South Korea; Department of Pediatrics, School of Medicine, Inha University, Incheon, South Korea","Woo J., Department of Bigdata Engineering, Soonchunhyang University, Asan-si, South Korea; Lee J.-H., Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, South Korea; Kim Y., Department of ICT Environmental Health System, School Soonchunhyang University, Asan-si, South Korea; Rudasingwa G., Integrated Research Center for Risk Assessment, Soonchunhyang University, Asan-si, South Korea; Lim D.H., Department of Pediatrics, School of Medicine, Inha University, Incheon, South Korea; Kim S., Department of ICT Environmental Health System, School Soonchunhyang University, Asan-si, South Korea, Integrated Research Center for Risk Assessment, Soonchunhyang University, Asan-si, South Korea","We built a deep learning algorithm to predict the deterioration of health symptoms among asthmatic children between 8-12 years of age. It is based on Peak Expiratory Flow Rates (PEFR) and indoor air pollution data, as well as meteorological data collected at their indoor residences every 2 minutes using portable monitoring devices with a low-cost sensor between November 2018 and March 2019. The PEFR results collected twice a day were matched with daily PM2.5. A personalized model has been developed to predict the peak expiratory flow rate of the next day, considering indoor air quality data including PM2.5, humidity, temperature, and CO2 level in previous days. Two models were developed incorporating Indoor Air Quality (IAQ) with the PEFR-only model. The IAQ uses the daily IAQ, and 10-minute basis IAQ in predicting the future PEFR. Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN) models were trained using 4 months of linked data to predict PEFR for the next days during the study period. The 10-minute RNN model was found to predict better PEFR with a Root Mean Square Error (RMSE) of 42.5 and a Mean Absolute Percentage Error (MAPE) of 14.0, as it consolidates the cumulative effects of PM2.5 concentrations over time. The highly accurate estimation showed that indoor air quality significantly affects PEFR.  © 2013 IEEE.","Asthma; big data; machine learning; peak expiratory flow rates (PEFR); recurrent neural network","Air quality; Atmospheric humidity; Atmospheric temperature; Big data; Deep neural networks; Deterioration; Diseases; Flow rate; Indoor air pollution; Learning algorithms; Meteorology; Respiratory system; Weather forecasting; 10 minutes; Allergic disease; Asthma; Atmospheric measurement; Atmospheric modeling; Deep learning; Indoor air quality; Peak expiratory flow rate; Peak expiratory flows; Predictive models; Mean square error","","","","","","","Mannino D., Buist A., Global burden of COPD: Risk factors, prevalence, and future trends, Lancet, 370, 9589, pp. 765-773, (2007); GBD 2015 Chronic Respiratory Disease Collaborators,Global, regional, and national deaths, prevalence, disability-Adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: A systematic analysis for the global burden of disease study 2015, Lancet. 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"Miranda O.; Fan P.; Qi X.; Wang H.; Brannock M.D.; Kosten T.; Ryan N.D.; Kirisci L.; Wang L.","Miranda, Oshin (57200678658); Fan, Peihao (57204628845); Qi, Xiguang (57220802082); Wang, Haohan (56025137300); Brannock, M.Daniel (57980488000); Kosten, Thomas (57203102895); Ryan, Neal David (7101740382); Kirisci, Levent (7005173231); Wang, LiRong (37087786200)","57200678658; 57204628845; 57220802082; 56025137300; 57980488000; 57203102895; 7101740382; 7005173231; 37087786200","Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models","2024","Drug and Alcohol Dependence","255","","111066","","","","3","10.1016/j.drugalcdep.2023.111066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182558660&doi=10.1016%2fj.drugalcdep.2023.111066&partnerID=40&md5=3e2ced88821510f60ea6f113de11b8fa","Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, PA, United States; School of Information Sciences at the University of Illinois Urbana-Champaign, Champaign, 61820, IL, United States; RTI International, Durham, 27709, NC, United States; Menninger Department of Psychiatry, Baylor College of Medicine, Houston, 77030, TX, United States; Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, United States; University of Pittsburgh School of Pharmacy, Pittsburgh, 15213, PA, United States","Miranda O., Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Fan P., Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Qi X., Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Wang H., School of Information Sciences at the University of Illinois Urbana-Champaign, Champaign, 61820, IL, United States; Brannock M.D., RTI International, Durham, 27709, NC, United States; Kosten T., Menninger Department of Psychiatry, Baylor College of Medicine, Houston, 77030, TX, United States; Ryan N.D., Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, 15213, PA, United States; Kirisci L., University of Pittsburgh School of Pharmacy, Pittsburgh, 15213, PA, United States; Wang L., Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, PA, United States","Background: Identifying co-occurring mental disorders and elevated risk is vital for optimization of healthcare processes. In this study, we will use DeepBiomarker2, an updated version of our deep learning model to predict the adverse events among patients with comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD), a high-risk population. Methods: We analyzed electronic medical records of 5565 patients from University of Pittsburgh Medical Center to predict adverse events (opioid use disorder, suicide related events, depression, and death) within 3 months at any encounter after the diagnosis of PTSD+AUD by using DeepBiomarker2. We integrated multimodal information including: lab tests, medications, co-morbidities, individual and neighborhood level social determinants of health (SDoH), psychotherapy and veteran data. Results: DeepBiomarker2 achieved an area under the receiver operator curve (AUROC) of 0.94 on the prediction of adverse events among those PTSD+AUD patients. Medications such as vilazodone, dronabinol, tenofovir, suvorexant, modafinil, and lamivudine showed potential for risk reduction. SDoH parameters such as cognitive behavioral therapy and trauma focused psychotherapy lowered risk while active veteran status, income segregation, limited access to parks and greenery, low Gini index, limited English-speaking capacity, and younger patients increased risk. Conclusions: Our improved version of DeepBiomarker2 demonstrated its capability of predicting multiple adverse event risk with high accuracy and identifying potential risk and beneficial factors. © 2024 The Author(s)","Alcohol use disorder; Artificial intelligence; Biomarker identification; Post traumatic stress disorder; Social determinants of health","Alcoholism; Comorbidity; Deep Learning; Electronic Health Records; Humans; Stress Disorders, Post-Traumatic; albumin; amantadine; aspartate aminotransferase; calcium; carbon dioxide; chloride; creatinine; dronabinol; empagliflozin; famciclovir; fluticasone furoate; gabapentin; glucose; hemoglobin; hydrocodone; hydrocortisone; hydroxyzine; ibuprofen; lamivudine; modafinil; omeprazole; ondansetron; oxycodone; paclitaxel; piroxicam; potassium; salazosulfapyridine; salbutamol; sodium; suvorexant; tenofovir; trazodone; vilazodone; abdominal pain; adult; albumin blood level; alcoholism; arthralgia; Article; aspartate aminotransferase blood level; asthma; calcium blood level; carbon dioxide blood level; chloride blood level; chronic pain; cognitive behavioral therapy; cohort analysis; colon polyp; controlled study; counseling; creatinine blood level; death; deep learning; DeepBiomarker2; depression; electronic medical record; erythrocyte count; female; gastroesophageal reflux; Gini coefficient; glucose blood level; hematocrit; hemoglobin blood level; high risk population; home accident; human; hyperlipidemia; income; limited English proficiency; low back pain; major clinical study; male; mean corpuscular hemoglobin; mean corpuscular hemoglobin concentration; myalgia; myositis; nervous system inflammation; obesity; opiate addiction; pain; platelet count; posttraumatic stress disorder; potassium blood level; psychotherapy; recreational park; red blood cell distribution width; risk reduction; sleep apnea syndromes; social determinants of health; sodium blood level; suicide; tobacco dependence; tobacco use; trauma focused psychotherapy; urea nitrogen blood level; veteran; alcoholism; comorbidity; complication; electronic health record; posttraumatic stress disorder; psychology","","amantadine, 665-66-7, 768-94-5; aspartate aminotransferase, 9000-97-9; calcium, 7440-70-2, 14092-94-5; carbon dioxide, 124-38-9, 58561-67-4; chloride, 16887-00-6; creatinine, 19230-81-0, 60-27-5; dronabinol, 1972-08-3, 7663-50-5; empagliflozin, 864070-44-0; famciclovir, 104227-87-4; fluticasone furoate, 397864-44-7; gabapentin, 60142-96-3; glucose, 50-99-7, 84778-64-3, 8027-56-3; hemoglobin, 9008-02-0; hydrocodone, 125-29-1, 25968-91-6, 34366-67-1; hydrocortisone, 50-23-7; hydroxyzine, 2192-20-3, 64095-02-9, 68-88-2; ibuprofen, 15687-27-1, 79261-49-7, 31121-93-4, 527688-20-6; lamivudine, 134678-17-4, 134680-32-3; modafinil, 68693-11-8; omeprazole, 73590-58-6, 95510-70-6; ondansetron, 103639-04-9, 116002-70-1, 99614-01-4; oxycodone, 124-90-3, 76-42-6; paclitaxel, 33069-62-4; piroxicam, 36322-90-4; potassium, 7440-09-7; salazosulfapyridine, 599-79-1; salbutamol, 18559-94-9, 35763-26-9; sodium, 7440-23-5; suvorexant, 1030377-33-3; tenofovir, 147127-19-3, 147127-20-6; trazodone, 19794-93-5, 25332-39-2; vilazodone, 163521-08-2, 163521-12-8","","","Alcohol and Substance Abuse Research Program, (W81XWH-22–2–0081); Office of the Assistant Secretary of Defense for Health Affairs; National Institutes of Health, NIH, (S10OD028483-01A1, UL1 TR001857); National Institutes of Health, NIH; U.S. Department of Defense, DOD; University of Pittsburgh","Funding text 1: The U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702–5014 is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Alcohol and Substance Abuse Research Program under Award No. W81XWH-22–2–0081 (PASA3). Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense. This research was supported in part by the University of Pittsburgh Center for Research Computing through the NIH S10OD028483-01A1 grant and NIH UL1 TR001857 grant. ; Funding text 2: The U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702–5014 is the awarding and administering acquisition office. This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Alcohol and Substance Abuse Research Program under Award No. W81XWH-22–2–0081 (PASA3). Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the Department of Defense. This research was supported in part by the University of Pittsburgh Center for Research Computing through the NIH S10OD028483-01A1 grant and NIH UL1 TR001857 grant.","A D., Alcohol and viral hepatitis: role of lipid rafts, Alcohol Res., 37, 2, pp. 299-309, (2015); Ahmed S., Et al., Use of Gabapentin in the treatment of substance use and psychiatric disorders: a systematic review, Front. Psychiatry, 10, (2019); Alexandrova Y., Et al., Pulmonary immune dysregulation and viral persistence during HIV infection, Front. Immunol., 12, (2022); Anderson A.L., Et al., Modafinil for the treatment of cocaine dependence, Drug Alcohol Depend., 104, pp. 133-139, (2009); Anderson R.I., Et al., Orexin-1 and orexin-2 receptor antagonists reduce ethanol self-administration in high-drinking rodent models, Front. 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Psychiatry, 13, 1, (2023); Yamada S., Et al., Potassium metabolism and management in patients with CKD, Nutrients, 13, 6, (2021); Yasaei R., Et al., (2023); Zhang D.Y., Et al., Ultrasound-mediated delivery of paclitaxel for Glioma: a comparative study of distribution, toxicity, and efficacy of albumin-bound versus cremophor formulations, Clin. Cancer Res, 26, pp. 477-486, (2020)","L. Wang; Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, 15213, United States; email: liw30@pitt.edu","","Elsevier Ireland Ltd","","","","","","03768716","","DADED","38217979","English","Drug Alcohol Depend.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85182558660"
"Deng X.; Li W.; Yang Y.; Wang S.; Zeng N.; Xu J.; Hassan H.; Chen Z.; Liu Y.; Miao X.; Guo Y.; Chen R.; Kang Y.","Deng, Xingguang (58887681100); Li, Wei (57221637991); Yang, Yingjian (57218501666); Wang, Shicong (57670751600); Zeng, Nanrong (57671655900); Xu, Jiaxuan (57861994500); Hassan, Haseeb (57208337766); Chen, Ziran (57670751700); Liu, Yang (57222473378); Miao, Xiaoqiang (57970669700); Guo, Yingwei (57218502342); Chen, Rongchang (14017626800); Kang, Yan (57213821412)","58887681100; 57221637991; 57218501666; 57670751600; 57671655900; 57861994500; 57208337766; 57670751700; 57222473378; 57970669700; 57218502342; 14017626800; 57213821412","COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images","2024","Medical and Biological Engineering and Computing","62","6","","1733","1749","16","3","10.1007/s11517-024-03016-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185123315&doi=10.1007%2fs11517-024-03016-z&partnerID=40&md5=a1821c227a0eec32a24bc5aed4abb06e","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Guangdong, Shenzhen, 518119, China; School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Nation Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China; School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, 163318, China; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen Institute of Respiratory Disease, Shenzhen, 518001, China; Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Deng X., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Li W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Yang Y., Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Guangdong, Shenzhen, 518119, China; Wang S., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Zeng N., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Xu J., The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Nation Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China; Hassan H., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Chen Z., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Liu Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Miao X., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Guo Y., School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, 163318, China; Chen R., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen Institute of Respiratory Disease, Shenzhen, 518001, China; Kang Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Guangdong, Shenzhen, 518119, China, Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Abstract: Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1,781×2 lung radiomics and 13,824×2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7% of accuracy, 90.9% of precision, 89.5% of F1-score, and 95.8% of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD. Graphical Abstract: The workflow of this study. (a) The IN and EX lung region is segmented using a well trained U Net (R231). (b) Radiomics features are obtained through the PyRadiomics tool, and 3D CNN features are extracted using the frozen encoder in the pre trained Med3D model. (c) The IN+EX radiomics and IN+EX 3D CNN features are concatenated, and the Lasso algorithm is subsequently applied to screen for useful data. (d) The selected features and ri sk factors are sent to the AMGNN, resulting in COPD multi classification outcomes. (Figure presented.). © International Federation for Medical and Biological Engineering 2024.","Auto-metric graph neural network; Biphasic CT Images; COPD stage detection; Lasso algorithm; Machine learning; Radiomics","Biological organs; Computerized tomography; Diagnosis; Graph neural networks; Learning algorithms; Patient treatment; Pulmonary diseases; Three dimensional computer graphics; Auto-metric graph neural network; Biphasic CT image; Chest CT; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease stage detection; CT Image; Graph neural networks; Lasso algorithm; Machine-learning; Radiomic; airflow; Article; chronic obstructive lung disease; classifier; comparative study; computer assisted tomography; controlled study; diagnostic accuracy; diagnostic test accuracy study; feature extraction; female; human; least absolute shrinkage and selection operator; machine learning; major clinical study; male; nerve cell network; radiomics; residual neural network; smoking; Feature extraction","","","","","Department of Radiology, Weill Cornell Medicine; Special Program for Key Fields of Colleges and Universities in Guangdong Province; Guangzhou Medical University, GMU, (201722); National Key Research and Development Program of China, NKRDPC, (2022YFF0710802, 2022YFF0710800); Stable Support Plan for Colleges and Universities in Shenzhen of China, (SZWD2021010); National Natural Science Foundation of China, NSFC, (62071311); Biomedicine and Health) of China, (2021ZDZX2008)","Funding text 1: Thanks to the Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, for providing the dataset.; Funding text 2: This work was supported by the National Key Research and Development Program of China [grant numbers 2022YFF0710800, 2022YFF0710802]; the National Natural Science Foundation of China [grant number 62071311]; the Stable Support Plan for Colleges and Universities in Shenzhen of China [grant number SZWD2021010]; and the Special Program for Key Fields of Colleges and Universities in Guangdong Province (Biomedicine and Health) of China [grant number 2021ZDZX2008]. ; Funding text 3: This study has received approval from the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (grant number: 201722). It has been registered on the website, with the NCT number: NCT03240315. Before their participation, all subjects in the study provided written informed consent, which was signed and dated. The study involved the collection of IN and EX chest CT images and corresponding labels for 116 (294) patients from the First Affiliated Hospital of Guangzhou Medical University from August 7, 2017, to February 15, 2022. Each four COPD stages comprised 29 patients, thus generating a balanced dataset. The abbreviation substitution of features is shown in Table . ","Singh D., Agusti A., Anzueto A., Barnes P.J., Bourbeau J., Celli B.R., Criner G.J., Frith P., Halpin D.M., Han M., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the gold science committee report 2019, Eur Respir J, 53, 5, (2019); Naghavi M., Abajobir A.A., Abbafati C., Abbas K.M., Abd-Allah F., Abera S.F., Aboyans V., Adetokunboh O., Afshin A., Agrawal A., Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: A systematic analysis for the global burden of disease study 2016. 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A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci Rep, 11, 1, pp. 1-12, (2021); Lambin P., Rios-Velazquez E., Leijenaar R., Carvalho S., Van Stiphout R.G., Granton P., Zegers C.M., Gillies R., Boellard R., Dekker A., Et al., Radiomics: extracting more information from medical images using advanced feature analysis, Eur J Cancer, 48, 4, pp. 441-446, (2012); Yun J., Cho Y.H., Lee S.M., Hwang J., Lee J.S., Oh Y.-M., Lee S.-D., Loh L.-C., Ong C.-K., Seo J.B., Et al., Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease, Sci Rep, 11, 1, (2021); Cho Y.H., Seo J.B., Lee S.M., Kim N., Yun J., Hwang J.E., Lee J.S., Oh Y.-M., Do Lee S., Loh L.-C., Et al., Radiomics approach for survival prediction in chronic obstructive pulmonary disease, Eur Radiol, 31, pp. 7316-7324, (2021); Liang C., Xu J., Wang F., Chen H., Tang J., Chen D., Li Q., Jian W., Tang G., Zheng J., Development of a radiomics model for predicting COPD exacerbations based on complementary visual information. 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Chen; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen Institute of Respiratory Disease, Shenzhen, 518001, China; email: chenrc@vip.163.com","","Springer Science and Business Media Deutschland GmbH","","","","","","01400118","","MBECD","","English","Med. Biol. Eng. Comput.","Article","Final","","Scopus","2-s2.0-85185123315"
"Yamane T.; Kimura M.; Morita M.","Yamane, Takahiro (57193523306); Kimura, Moeka (59005762000); Morita, Mizuki (15765855200)","57193523306; 59005762000; 15765855200","Application of Nine-Axis Accelerometer-Based Recognition of Daily Activities in Clinical Examination","2024","Physical Activity and Health","8","1","","29","46","17","4","10.5334/paah.313","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191100684&doi=10.5334%2fpaah.313&partnerID=40&md5=df6f5e45bcaf270763cb79c342075dfb","Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan; Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan","Yamane T., Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan, Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan; Kimura M., Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan, Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan; Morita M., Faculty of Health Sciences, Okayama University Medical School, Okayama, Japan, Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan","Background: This study aimed to establish an automatic and accurate method for identifying patient activity using wearable devices to facilitate simple measurement of the severity of disease, such as chronic obstructive pulmonary disease (COPD), and accurate diagnosis of arrhythmias using Holter electrocardiogram (ECG). Methods: Nine-axis accelerometers were attached to five different parts of the body of 30 healthy participants, and nine different activities were performed in sequence. Results: Overall, the dominant wrist, non-dominant wrist, and chest yielded high recognition accuracy, whereas the hip and thigh yielded lower recognition accuracy for some activities. Lying in the supine position, standing, walking, and running were identified with high accuracy by the accelerometer on the non-dominant wrist. Lying in the supine position, brushing teeth, walking, ascending/descending the stairs, and running were identified with high accuracy by the accelerometer on the chest. Conclusions: The movements related to the severity of COPD and those related to a diagnosis made via Holter ECG could be identified with reasonable accuracy when the nine-axis accelerometer was attached to one part of the body: the dominant wrist, non-dominant wrist, and chest. The accuracy was higher when the accelerometers were attached to five parts of the body. © 2024 The Author(s).","chronic obstructive pulmonary disease; electrocardiography; human activity recognition; machine learning; wearable devices","","","","","","Japan Society for the Promotion of Science, JSPS, (JP21K12787); Japan Society for the Promotion of Science, JSPS","This study was supported by JSPS KAKENHI (grant number: JP21K12787).","Ankita, Rani S., Babbar H., Coleman S., Singh A., Aljahdali H. M., An efficient and lightweight deep learning model for human activity recognition using smartphones, Sensors, 21, (2021); Breiman L., Random Forests, Mach Learn, 45, pp. 5-32, (2001); Bull K., He Y. H., Jejjala V., Mishra C., Machine learning CICY threefolds, Phys Lett B, 785, pp. 65-72, (2018); Donges N., Random Forest: A Complete Guide for Machine Learning, (2023); Eakin E. G., Resnikoff P. M., Prewitt L. M., Ries A. L., Kaplan R. 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J., An official European respiratory society/American thoracic society technical standard: Field walking tests in chronic respiratory disease, Eur Respir J, 44, 6, pp. 1428-1446, (2014); Kaneko I., Yoshida Y., Yuda E., Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs, SIPIJ, pp. 39-48, (2019); Kawagoshi A., Kiyokawa N., Sugawara K., Takahashi H., Abe R., Kitamura N., Satake M., Shioya T., The quantitative assessment of the physical activity of daily life in patients with stable elderly COPD using an activity monitoring and evaluation system, The Journal of Japanese Physical Therapy Association, 38, 7, pp. 497-504, (2011); Kennedy H. L., The history, science, and innovation of Holter technology, ANE, 11, 1, pp. 85-94, (2006); Leotta M., Fasciglione A., Verri A., Daily living activity recognition using wearable devices: A features-rich dataset and a novel approach, Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, pp. 171-187, (2021); Liu S., Gao R., Freedson P., Computational methods for estimating energy expenditure in human physical activities, Med Sci Sports Exerc, 44, 11, pp. 2138-2146, (2012); Lozano R., Naghavi M., Foreman K., Lim S., Shibuya K., Aboyans V., Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, 9859, pp. 2095-2128, (2012); Miao F., Cheng Y., He Y., He Q., Li Y., A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the smartphone, Sensors, 15, 5, pp. 11465-11484, (2015); Nguyen H. Q., Chu L., Liu I. L. A., Lee J. S., Suh D., Korotzer B., Yuen G., Desai S., Coleman K. J., Xiang A. H., Gould M. K., Associations between physical activity and 30-day readmission risk in chronic obstructive pulmonary disease, Ann Am Thorac Soc, 11, 5, pp. 695-705, (2014); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D., Brucher M., Perrot M., Duchesnay E., Scikit-learn: Machine Learning in Python, J Mach Learn Res, 12, pp. 2825-2830, (2011); Rafl J., Bachman T. E., Rafl-Huttova V., Walzel S., Rozanek M., Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study, Digital Health, 8, (2022); Ravi N., Dandekar N., Mysore P., Littman M. L., Activity recognition from accelerometer data, Proceedings of the National Conference on Artificial Intelligence, 3, pp. 1541-1546, (2005); Satoh H., Iwashima A., Endo Y., Nakayama H., Hasegawa T., Suzuki E., Effect of proactive use of inhaled procaterol on dyspnea in daily activities and quality of life in patients with chronic obstrsatohuctive pulmonary disease, AJRS, 47, 9, pp. 772-780, (2009); Sumikawa A., Terui Y., Sugano A., Matsui Y., Uemura S., Satake M., Shioya T., Validity of the evaluation of posture and movement by a new tri-axial accelerometer: judgement criteria, sensitivity and specificity, Rigakuryoho Kagaku, 33, 4, pp. 561-567, (2018); Trost S. G., Zheng Y., Wong W. K., Machine learning for activity recognition: Hip versus wrist data, Physiol Meas, 35, 11, pp. 2183-2189, (2014); How to Use a Holter Monitor; Waschki B., Kirsten A., Holz O., Muller K. C., Meyer T., Watz H., Magnussen H., Physical activity is the strongest predictor of all-cause mortality in patients with COPD, Chest, 140, 2, pp. 331-342, (2011); Weir N. A., Brown A. W., Shlobin O. A., Smith M. A., Reffett T., Battle E., Ahmad S., Nathan S. D., The influence of alternative instruction on 6-min walk test distance, Chest, 144, 6, pp. 1900-1905, (2013); Yamane T., Yamasaki Y., Nakashima W., Morita M., Tri-Axial Accelerometer-Based Recognition of Daily Activities Causing Shortness of Breath in COPD Patients, Physical Activity and Health, 7, 1, pp. 64-75, (2023)","M. Morita; Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan; email: mizuki@okayama-u.ac.jp","","Ubiquity Press","","","","","","25152270","","","","English","Phys. Act. Health","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85191100684"
"Yenurkar G.K.; Mal S.; Nyangaresi V.O.; Hedau A.; Hatwar P.; Rajurkar S.; Khobragade J.","Yenurkar, Ganesh Keshaorao (57202989792); Mal, Sandip (55808341200); Nyangaresi, Vincent O. (57220777100); Hedau, Anshul (58279866500); Hatwar, Prajwal (58280562200); Rajurkar, Shreyas (58281247900); Khobragade, Juli (58281248000)","57202989792; 55808341200; 57220777100; 58279866500; 58280562200; 58281247900; 58281248000","Multifactor data analysis to forecast an individual's severity over novel COVID-19 pandemic using extreme gradient boosting and random forest classifier algorithms","2023","Engineering Reports","5","12","e12678","","","","4","10.1002/eng2.12678","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159810969&doi=10.1002%2feng2.12678&partnerID=40&md5=4e7829b381674e7b91a227e44e75cab5","School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India; Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India; Computer Science & Engineering, Jaramogi Oginga Odinga University of Science & Technology, Bondo, Kenya","Yenurkar G.K., School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India, Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India; Mal S., School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India; Nyangaresi V.O., Computer Science & Engineering, Jaramogi Oginga Odinga University of Science & Technology, Bondo, Kenya; Hedau A., Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India; Hatwar P., Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India; Rajurkar S., Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India; Khobragade J., Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India","AI and machine learning are increasingly often applied in the medical industry. The COVID-19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID-19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high-risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre-processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID-19 high-risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not. © 2023 The Authors. Engineering Reports published by John Wiley & Sons Ltd.","COVID-19; Heroku; high-risk patients; machine learning; Python flask; random forest classifier; XGBoost","Adaptive boosting; Bottles; Data handling; Forecasting; Forestry; Health risks; High level languages; Hospitals; Learning systems; Patient treatment; Risk assessment; Supervised learning; Gradient boosting; Health issues; Heroku; High-risk patients; Machine-learning; Medical personnel; Multi-factor; Python flask; Random forest classifier; Xgboost; COVID-19; Python","","","","","","","Assaf D., Gutman Y., Neuman Y., Et al., Utilization of machine-learning models to accurately predict the risk for critical COVID-19, Intern Emerg Med, 15, pp. 1435-1443, (2020); Li Y., Horowitz M.A., Liu J., Et al., Individual-level fatality prediction of COVID-19 patients using AI methods, Front Public Health, 8, (2020); Chordia S., Pawar Y., Analyzing and forecasting COVID-19 outbreak in India. Paper presented at: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) IEEE, (2021); Hu J., Zhang B., Application of salesforce platform in online teaching in colleges and universities under epidemic situation. Paper presented at: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) IEEE, (2020); Gull H., Krishna G., Aldossary M.I., Iqbal S.Z., Severity prediction of COVID-19 patients using machine learning classification algorithms: a case study of small city in Pakistan with minimal health facility. Paper presented at: 2020 IEEE 6th international conference on computer and communications (ICCC) IEEE, (2020); Hock M.O.E., Lin Z., Ser W., Huang G., Method of predicting the survivability of a patient. Google Patents, (2018); Yatsuhashi H., Akiyama M., Matsumoto T., Method of Preparing Disease Prognosis Model, Disease Prognosis Prediction Method using this Model, Prognosis Prediction Device Based on this Model, and Program for Performing the Device and Storage Medium Wherein Said Program is Stored. Google Patents, (2007); Krysko O., Kondakova E., Vershinina O., Et al., Artificial intelligence predicts severity of COVID-19 based on correlation of exaggerated monocyte activation, excessive organ damage and hyperinflammatory syndrome: a prospective clinical study, Front Immunol, (2021); Zivkovic M., Bacanin N., Venkatachalam K., Et al., COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach, Sustain Cities Soc, 66, (2021); Mary L.W., Raj S.A.A., Machine Learning Algorithms for Predicting SARS-CoV-2 (COVID-19)–A Comparative Analysis. Paper presented at: 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) IEEE, (2021); Liu Y., Xiao Y., Analysis and prediction of COVID-19 in Xinjiang based on machine learning. Paper presented at: 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT) IEEE, (2020); Tahir H., Iftikhar A., Mumraiz M., Forecasting COVID-19 via registration slips of patients using ResNet-101 and performance analysis and comparison of prediction for COVID-19 using Faster R-CNN, Mask R-CNN, and ResNet-50. Paper presented at: 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) IEEE, (2021); Bhardwaj S., Bhardwaj H., Bhardwaj J., Gupta P., Global Prediction of COVID-19 Cases and Deaths using Machine Learning. Paper presented at: 2021 Sixth International Conference on Image Information Processing (ICIIP), vol. 6 IEEE, (2021); Qu J., Sumali B., Mitsukura Y., Predicting COVID-19 Severe Patients and Evaluation Method of 3 Stages Severe Level by Machine Learning. Paper presented at: 2021 IEEE 4th International Conference on Electronics and Communication Engineering (ICECE) IEEE, (2021); Jagadishwari V., Time series Covid 19 Predictions with Machine Learning Models. Paper presented at: 2021 Emerging Trends in Industry 4.0 (ETI 4.0) IEEE, (2021); Zargari Khuzani A., Heidari M., Shariati S.A., COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images, Sci Rep, 11, 1, (2021); Podder P., Mondal M.R.H., Machine learning to predict COVID-19 and ICU requirement. Paper presented at: 2020 11th international conference on electrical and computer engineering (ICECE) IEEE, (2020); Hyun S.M., Hwang T.H., Lee K., The Prediction Model for Classification of COVID-19 Infected Patients Using Vital Sign. Paper presented at: 2021 International Conference on Information and Communication Technology Convergence (ICTC) IEEE, (2021); Ardabili S., Mosavi A., Band S.S., Varkonyi-Koczy A.R., Coronavirus disease (COVID-19) global prediction using hybrid artificial intelligence method of ANN trained with Grey Wolf optimizer. Paper presented at: 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE) IEEE, (2020); Bottino F., Tagliente E., Pasquini L., Et al., COVID mortality prediction with machine learning methods: a systematic review and critical appraisal, J Pers Med, 11, 9, (2021); Wang R.Y., Guo T.Q., Li L.G., Jiao J.Y., Wang L.Y., Predictions of COVID-19 infection severity based on co-associations between the SNPs of co-morbid diseases and COVID-19 through machine learning of genetic data. Paper presented at: 2020 IEEE 8th International Conference on Computer Science and Network Technology (ICCSNT) IEEE, (2020); Kumari P., Toshniwal D., Real-time estimation of COVID-19 cases using machine learning and mathematical models-The case of India. Paper presented at: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) IEEE, (2020); Darapaneni N., Singh A., Paduri A., Et al., A machine learning approach to predicting covid-19 cases amongst suspected cases and their category of admission. Paper presented at: 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) IEEE, (2020); Feng A., Accurate COVID-19 health outcome prediction and risk factors identification through an innovative machine learning framework using longitudinal electronic health records. Paper presented at: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) IEEE, (2021); Bhadana V., Jalal A.S., Pathak P., A comparative study of machine learning models for COVID-19 prediction in India. Paper presented at: 2020 IEEE 4th Conference on Information & Communication Technology (CICT) IEEE, (2020); Zhao H., Li Y., Chu S., Zhao S., Liu C., A covid-19 prediction optimization algorithm based on real-time neural network training—taking Italy as an example. Paper presented at: 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) IEEE, (2021); Moremada C., Sandeepa C., Dissanayaka N., Gamage T., Liyanage M., Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic, J Commun Netw, 23, 5, pp. 390-407, (2021); Liu Z., Zuo J., Lv R., Liu S., Wang W., Coronavirus epidemic (covid-19) prediction and trend analysis based on time series. Paper presented at: 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID) IEEE, (2021); Sedaghat A., Band S., Mosavi A., Nadai L., Covid-19 (coronavirus disease) outbreak prediction using a susceptible-exposed-symptomatic infected-recovered-super spreaders-asymptomatic infected-deceased-critical (SEIR-PADC) dynamic model. Paper presented at: 2020 IEEE 3rd International Conference and Workshop in Óbuda on Electrical and Power Engineering (CANDO-EPE) IEEE, (2020); Mantoro T., Handayanto R.T., Ayu M.A., Asian J., Prediction of covid-19 spreading using support vector regression and susceptible infectious recovered model. Paper presented at: 2020 6th International Conference on Computing Engineering and Design (ICCED) IEEE, (2020); Tabik S., Gomez-Rios A., Martin-Rodriguez J.L., Et al., COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images, IEEE J Biomed Health Inform, 24, 12, pp. 3595-3605, (2020); Thakur N.V., Coronavirus outbreak: multi-objective prediction and optimization, Intelligent Systems and Methods to Combat Covid-19, pp. 19-28, (2020); Yenurkar G., Mal S., Future forecasting prediction of Covid-19 using hybrid deep learning algorithm, Multimed Tools Appl, pp. 1-27, (2022); Lunagariya M., Katkar V., Light weight approach for COVID-19, pneumonia detection from x-ray images using deep feature extraction and XGBoost. Paper presented at: 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA) IEEE, (2022); Sarac M., Mravik M., Jovanovic D., Strumberger I., Zivkovic M., Bacanin N., Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model, J Electron Imag, 32, 2, (2022); Nasiri H., Hasani S., Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost, Radiography, 28, 3, pp. 732-738, (2022); Zivkovic M., Bacanin N., Antonijevic M., Et al., Hybrid CNN and XGBoost model tuned by modified arithmetic optimization algorithm for COVID-19 early diagnostics from x-ray images, Electronics, 11, 22, (2022); Junior D.A.D., da Cruz L.B., Diniz J.O.B., Et al., Automatic method for classifying COVID-19 patients based on chest x-ray images, using deep features and PSO-optimized XGBoost, Expert Syst Appl, 183, (2021); Yenurkar G.K., Mal S., Effective detection of COVID-19 outbreak in chest X-Rays using fusionnet model, Imag Sci J, 70, 8, pp. 1-21, (2022)","G.K. Yenurkar; Computer Technology, Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, 441110, India; email: ganeshyenurkar@gmail.com","","John Wiley and Sons Inc","","","","","","25778196","","","","English","Eng. Rep.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85159810969"
"Dora D.; Weiss G.J.; Megyesfalvi Z.; Gállfy G.; Dulka E.; Kerpel-Fronius A.; Berta J.; Moldvay J.; Dome B.; Lohinai Z.","Dora, David (57193718544); Weiss, Glen J. (14632594500); Megyesfalvi, Zsolt (57210820527); Gállfy, Gabriella (57792863100); Dulka, Edit (6505889846); Kerpel-Fronius, Anna (57195369381); Berta, Judit (36150519000); Moldvay, Judit (6602467754); Dome, Balazs (6603598365); Lohinai, Zoltan (57202615121)","57193718544; 14632594500; 57210820527; 57792863100; 6505889846; 57195369381; 36150519000; 6602467754; 6603598365; 57202615121","Computed Tomography-Based Quantitative Texture Analysis and Gut Microbial Community Signatures Predict Survival in Non-Small Cell Lung Cancer","2023","Cancers","15","20","5091","","","","3","10.3390/cancers15205091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175093693&doi=10.3390%2fcancers15205091&partnerID=40&md5=56d523925dc9dfeee1d21733ba632d87","Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, 1094, Hungary; Department of Medicine, UMass Chan Medical School, Worcester, 01655, MA, United States; Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary; Department of Thoracic Surgery, National Institute of Oncology, Semmelweis University, Budapest, 1122, Hungary; Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, 1090, Austria; Pulmonary Hospital Torokbalint, Torokbalint, 2045, Hungary; Department of Radiology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary; Department of Translational Medicine, Lund University, Lund, 22184, Sweden; Translational Medicine Institute, Semmelweis University, Budapest, 1094, Hungary","Dora D., Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, 1094, Hungary; Weiss G.J., Department of Medicine, UMass Chan Medical School, Worcester, 01655, MA, United States; Megyesfalvi Z., Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary, Department of Thoracic Surgery, National Institute of Oncology, Semmelweis University, Budapest, 1122, Hungary, Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, 1090, Austria; Gállfy G., Pulmonary Hospital Torokbalint, Torokbalint, 2045, Hungary; Dulka E., Pulmonary Hospital Torokbalint, Torokbalint, 2045, Hungary; Kerpel-Fronius A., Department of Radiology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary; Berta J., Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary; Moldvay J., Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary; Dome B., Department of Tumor Biology, National Koranyi Institute of Pulmonology, Budapest, 1122, Hungary, Department of Thoracic Surgery, National Institute of Oncology, Semmelweis University, Budapest, 1122, Hungary, Department of Thoracic Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, 1090, Austria, Department of Translational Medicine, Lund University, Lund, 22184, Sweden; Lohinai Z., Pulmonary Hospital Torokbalint, Torokbalint, 2045, Hungary, Translational Medicine Institute, Semmelweis University, Budapest, 1094, Hungary","This study aims to combine computed tomography (CT)-based texture analysis (QTA) and a microbiome-based biomarker signature to predict the overall survival (OS) of immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients by analyzing their CT scans (n = 129) and fecal microbiome (n = 58). One hundred and five continuous CT parameters were obtained, where principal component analysis (PCA) identified seven major components that explained 80% of the data variation. Shotgun metagenomics (MG) and ITS analysis were performed to reveal the abundance of bacterial and fungal species. The relative abundance of Bacteroides dorei and Parabacteroides distasonis was associated with long OS (>6 mo), whereas the bacteria Clostridium perfringens and Enterococcus faecium and the fungal taxa Cortinarius davemallochii, Helotiales, Chaetosphaeriales, and Tremellomycetes were associated with short OS (≤6 mo). Hymenoscyphus immutabilis and Clavulinopsis fusiformis were more abundant in patients with high (≥50%) PD-L1-expressing tumors, whereas Thelephoraceae and Lachnospiraceae bacterium were enriched in patients with ICI-related toxicities. An artificial intelligence (AI) approach based on extreme gradient boosting evaluated the associations between the outcomes and various clinicopathological parameters. AI identified MG signatures for patients with a favorable ICI response and high PD-L1 expression, with 84% and 79% accuracy, respectively. The combination of QTA parameters and MG had a positive predictive value of 90% for both therapeutic response and OS. According to our hypothesis, the QTA parameters and gut microbiome signatures can predict OS, the response to therapy, the PD-L1 expression, and toxicity in NSCLC patients treated with ICI, and a machine learning approach can combine these variables to create a reliable predictive model, as we suggest in this research. © 2023 by the authors.","advanced NSCLC; artificial intelligence; computed tomography-based texture analysis; microbiome; PD-L1","biological marker; immune checkpoint inhibitor; internal transcribed spacer 2; nivolumab; pembrolizumab; platinum; programmed death 1 ligand 1; proton pump inhibitor; adult; adverse drug reaction; aged; algorithm; Article; artificial intelligence; Bacteroides; cancer staging; cancer survival; Chaetosphaeriales; Clavulinopsis fusiformis; Clostridium perfringens; computer assisted tomography; COPD assessment test; Cortinarius; decision tree; disease severity; DNA extraction; Enterococcus faecium; feces; female; follow up; forced expiratory volume; gene expression; Helotiales; histology; human; human tissue; Hymenoscyphus immutabilis; immunohistochemistry; intestine flora; Lachnospiraceae; lung adenocarcinoma; lung biopsy; lymph node metastasis; machine learning; major clinical study; male; metagenomics; microbial community; microbiome; middle aged; non small cell lung cancer; overall survival; Parabacteroides; Parabacteroides distasonis; PD L1 tumor proportion score; polymerase chain reaction; predictive model; predictive value; principal component analysis; progression free survival; protein expression; Proteobacteria; quality control; scoring system; shotgun sequencing; smoking; squamous cell carcinoma; texture analysis; Thelephoraceae; toxicity; treatment response; Tremellomycetes; XGBoost","","nivolumab, 946414-94-4; pembrolizumab, 1374853-91-4; platinum, 7440-06-4","Bond RX, Leica; Novaseq 6000, Illumina","Illumina; Leica","Ministry for Culture and Innovation; Magyar Tudományos Akadémia, MTA; Hungarian Scientific Research Fund, OTKA, (142287, 129664, 124652); Ministry for Innovation and Technology of Hungary, (UNKP-20-3, UNKP-21-3, UNKP-23-5); Austrian Science Fund, FWF, (I3522, I3977, I 4677); Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA, (129065); International Association for the Study of Lung Cancer/International Lung Cancer Foundation, (2022); Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFI, (FK-143751, TKP2021-EGA-33, KH130356); European Commission, EC, (101131228, HORIZON-MSCA-2022-SE-01)","\""BD was supported by the Austrian Science Fund (FWF I3522, FWF I3977, and I4677) and the \u201CBIOSMALL\u201D EU HORIZON-MSCA-2022-SE-01 project (grant agreement No. 101131228). BD and ZM were supported by funding from the Hungarian National Research, Development, and Innovation Office (KH130356 to BD; 2020-1.1.6-J\u00D6V\u0150, FK-143751 and TKP2021-EGA-33 to BD and ZM). ZM was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-20-3, UNKP-21-3 and UNKP-23-5), and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences. ZM is also the recipient of an International Association for the Study of Lung Cancer/International Lung Cancer Foundation Young Investigator Grant (2022).\"" ZL was supported by the 2018 LCFA-BMS/IASLC Young Investigator Scholarship Award and acknowledges funding from the Hungarian National Research, Development and Innovation Office (OTKA #124652 and #129664). DD acknowledge funding from the Hungarian National Research, Development and Innovation Office (OTKA #142287) and from the UNKP-22-5 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. DD was also supported by the Bolyai Fellowship of the Hungarian Academy of Sciences. JM was supported by the Hungarian National Research, Development and Innovation Office (#K129065).","Siegel R.L., Miller K.D., Jemal A., Cancer statistics, 2018, CA Cancer J. Clin, 68, pp. 7-30, (2018); Gettinger S., Horn L., Jackman D., Spigel D., Antonia S., Hellmann M., Powderly J., Heist R., Sequist L.V., Smith D.C., Et al., Five-Year Follow-Up of Nivolumab in Previously Treated Advanced Non-Small-Cell Lung Cancer: Results from the CA209-003 Study, J. Clin. Oncol, 36, pp. 1675-1684, (2018); Garon E.B., Hellmann M.D., Rizvi N.A., Carcereny E., Leighl N.B., Ahn M.J., Eder J.P., Balmanoukian A.S., Aggarwal C., Horn L., Et al., Five-Year Overall Survival for Patients with Advanced Non-Small-Cell Lung Cancer Treated with Pembrolizumab: Results from the Phase I KEYNOTE-001 Study, J. Clin. Oncol, 37, pp. 2518-2527, (2019); Peters B.A., Wilson M., Moran U., Pavlick A., Izsak A., Wechter T., Weber J.S., Osman I., Ahn J., Relating the gut metagenome and metatranscriptome to immunotherapy responses in melanoma patients, Genome Med, 11, (2019); Weiss G.J., Ganeshan B., Miles K.A., Campbell D.H., Cheung P.Y., Frank S., Korn R.L., Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic, PLoS ONE, 9, (2014); Sacconi B., Anzidei M., Leonardi A., Boni F., Saba L., Scipione R., Anile M., Rengo M., Longo F., Bezzi M., Et al., Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: A correlation with EGFR mutations and survival rates, Clin. 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Lohinai; Pulmonary Hospital Torokbalint, Torokbalint, 2045, Hungary; email: zoltan.lohinai@torokbalintkorhaz.hu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20726694","","","","English","Cancers","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175093693"
"Wang R.; Huang C.; Yang W.; Wang C.; Wang P.; Guo L.; Cao J.; Huang L.; Song H.; Zhang C.; Zhang Y.; Shi G.","Wang, Rong (58263292200); Huang, Chunrong (57209985971); Yang, Wenjie (35800227800); Wang, Cui (58263486500); Wang, Ping (57827033500); Guo, Leixin (57810429100); Cao, Jin (57810214700); Huang, Lin (58263292300); Song, Hejie (57883325200); Zhang, Chenhong (55798036700); Zhang, Yunhui (35796816000); Shi, Guochao (15770022000)","58263292200; 57209985971; 35800227800; 58263486500; 57827033500; 57810429100; 57810214700; 58263292300; 57883325200; 55798036700; 35796816000; 15770022000","Respiratory microbiota and radiomics features in the stable COPD patients","2023","Respiratory Research","24","1","131","","","","4","10.1186/s12931-023-02434-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159741628&doi=10.1186%2fs12931-023-02434-1&partnerID=40&md5=2f4696e3c3fc24903578574553920bd4","Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People’s Hospital of Yunnan Province, Kunming, 650032, China; Medical School, Kunming University of Science and Technology, Kunming, 650500, China; Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China; Department of Pulmonary and Critical Care Medicine, the Third People’s Hospital of Kunshan, Suzhou, 215300, China; State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China","Wang R., Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People’s Hospital of Yunnan Province, Kunming, 650032, China, Medical School, Kunming University of Science and Technology, Kunming, 650500, China, Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Huang C., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Yang W., Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China; Wang C., Department of Pulmonary and Critical Care Medicine, the Third People’s Hospital of Kunshan, Suzhou, 215300, China; Wang P., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Guo L., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Cao J., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Huang L., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Song H., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; Zhang C., State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; Zhang Y., Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People’s Hospital of Yunnan Province, Kunming, 650032, China; Shi G., Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China","Backgrounds: The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them. Methods: Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below − 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/ and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed. Results: Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/ than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA. Conclusions: Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers. © The Author(s) 2023.","Chest CT; COPD; Radiomics; Respiratory microbiota","Forced Expiratory Volume; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Vital Capacity; Actinobacteria; Article; Aspergillus; Candida; chronic obstructive lung disease; Cladosporium; community structure; computer assisted tomography; controlled study; Cutaneotrichosporon; diffusing capacity for carbon monoxide; forced expiratory volume; forced vital capacity; Gemella; Granulicatella; Haemophilus; human; internal transcribed spacer sequencing; Lachnospiraceae; Leptotrichia; lung microbiota; microbiomics; Neisseria; nonhuman; Oribacterium; Penicillium; Peptostreptococcus; population abundance; radiomics; RNA sequencing; Rothia; sequence analysis; Streptococcus; three dimensional computed tomography; Trichosporon; diagnostic imaging; lung; lung emphysema; vital capacity","","","","","Nanshan Zhong Academician Workstation of the First People’s Hospital of Yunnan Province; Shanghai Key Laboratory of Emergency Prevention; Cultivation Project of Shanghai Major Infectious Disease Research Base, (202102AA100057, ZX2019-01-03); Respiratory Diseases Clinical Medical Research Center of Yunnan Province, (2019IC032-1); National Natural Science Foundation of China, NSFC, (2019SY006); Shanghai Municipal Key Clinical Specialty, (20dz2210500); Shanghai Municipal Health Commission, (20dz2261100); Diagnosis and Treatment of Respiratory Infectious Diseases, (shslczdzk02202)","This study was supported by Grant 81660012,81970020,82170023,82200024 from the National Natural Science Foundation of China, Grant 2019SY006 from Shanghai Municipal Health Commission, Grant 20dz2261100 from Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Grant shslczdzk02202 from Shanghai Municipal Key Clinical Specialty, Grant 20dz2210500 from Cultivation Project of Shanghai Major Infectious Disease Research Base, Grant 202102AA100057 and ZX2019-01-03 from Respiratory Diseases Clinical Medical Research Center of Yunnan Province, Grant 2019IC032-1 from Nanshan Zhong Academician Workstation of the First People’s Hospital of Yunnan Province. 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Wlodarska M., Luo C., Kolde R., Et al., Indoleacrylic Acid produced by Commensal Peptostreptococcus Species suppresses inflammation [J], Cell Host Microbe, 22, 1, pp. 25-37, (2017); Long X., Wong C.C., Tong L., Et al., Peptostreptococcus anaerobius promotes colorectal carcinogenesis and modulates tumour immunity [J], Nature Microbiology, 4, 12, pp. 2319-2330, (2019); Tsoi H., Chu E.S.H., Zhang X., Et al., Peptostreptococcus anaerobius induces intracellular cholesterol biosynthesis in Colon cells to Induce Proliferation and causes dysplasia in mice [J], Gastroenterology, 6, (2017); Vernocchi P., Gili T., Conte F., Et al., Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked biomarkers in patients affected by Non-Small Cell Lung Cancer [J], Int J Mol Sci, 2020, 22; Tiew P.Y., Mac Aogain M., Ali N., a T., B, M, Et al., The Mycobiome in Health and Disease: emerging concepts, Methodologies and Challenges [J], Mycopathologia, 185, 2, pp. 207-231, (2020); Ali N.A., Mac Aogain M., Morales R.F., Optimisation and benchmarking of targeted amplicon sequencing for Mycobiome Analysis of respiratory specimens [J/OL] 2019, 20(20).; 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Waatevik M., Frisk B., Real F.G., Et al., CT-defined emphysema in COPD patients and risk for change in desaturation status in 6-min walk test [J], Respir Med, 187, (2021); Yun J., Cho Y.H., Lee S.M., Et al., Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease [J], Sci Rep, 11, 1, (2021); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography [J], Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018); Engel M., Endesfelder D., Schloter-Hai B., Et al., Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome [J], PLoS One, 12, 7, (2017); Huang J.T.J., Cant E., Keir H.R., Et al., Endotyping Chronic Obstructive Pulmonary Disease, Bronchiectasis, and the “Chronic Obstructive Pulmonary Disease-Bronchiectasis Association” [J], American Journal of Respiratory and Critical Care Medicine, 206, 4, pp. 417-426, (2022); Ni Y., Shi G., Yu Y., Et al., Clinical characteristics of patients with chronic obstructive pulmonary disease with comorbid bronchiectasis: a systemic review and meta-analysis [J], Int J Chron Obstruct Pulmon Dis, 10, pp. 1465-1475, (2015); Short B., Carson S., Devlin A.C., Et al., Non-typeable Haemophilus influenzae chronic colonization in chronic obstructive pulmonary disease (COPD) [J], Crit Rev Microbiol, 47, 2, pp. 192-205, (2021); Jalalvand F., Riesbeck K., Haemophilus influenzae: recent advances in the understanding of molecular pathogenesis and polymicrobial infections [J], Curr Opin Infect Dis, 27, 3, pp. 268-274, (2014); Li L., Mac Aogain M., Xu T., Et al., Neisseria species as pathobionts in bronchiectasis [J], Cell Host Microbe, 2022, 9; Van Der Bruggen T., Kolecka A., Theelen B., Et al., Cutaneotrichosporon (Cryptococcus) cyanovorans, a basidiomycetous yeast, isolated from the airways of cystic fibrosis patients [J], Med Mycol Case Rep, 22, pp. 18-20, (2018); Ranjan R., Rani A., Metwally A., Et al., Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing [J], Biochem Biophys Res Commun, 469, 4, pp. 967-977, (2016)","Y. Zhang; Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People’s Hospital of Yunnan Province, Kunming, 650032, China; email: yunhuizhang3188@126.com; G. Shi; Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China; email: shiguochao@hotmail.com; C. Zhang; State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China; email: zhangchenhong@sjtu.edu.cn","","BioMed Central Ltd","","","","","","14659921","","RREEB","37173744","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85159741628"
"Boesch M.; Rassouli F.; Baty F.; Schwärzler A.; Widmer S.; Tinschert P.; Shih I.; Cleres D.; Barata F.; Fleisch E.; Brutsche M.H.","Boesch, Maximilian (55056474500); Rassouli, Frank (56668637700); Baty, Florent (55898918400); Schwärzler, Anja (58278482200); Widmer, Sandra (57190882657); Tinschert, Peter (57204220320); Shih, Iris (57194496789); Cleres, David (57226129083); Barata, Filipe (57191505036); Fleisch, Elgar (6602498499); Brutsche, Martin H. (7004585886)","55056474500; 56668637700; 55898918400; 58278482200; 57190882657; 57204220320; 57194496789; 57226129083; 57191505036; 6602498499; 7004585886","Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker","2023","ERJ Open Research","9","3","00518-2022","","","","4","10.1183/23120541.00518-2022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159810720&doi=10.1183%2f23120541.00518-2022&partnerID=40&md5=bb73bb6ca922ed5aef0b2a9b1da3d558","Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Resmonics AG, Zurich, Switzerland; Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland; Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland; University of St Gallen, St Gallen, Switzerland","Boesch M., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Rassouli F., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Baty F., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Schwärzler A., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Widmer S., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; Tinschert P., Resmonics AG, Zurich, Switzerland, Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland, Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland, University of St Gallen, St Gallen, Switzerland; Shih I., Resmonics AG, Zurich, Switzerland, Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland, Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland, University of St Gallen, St Gallen, Switzerland; Cleres D., Resmonics AG, Zurich, Switzerland, Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland, Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland, University of St Gallen, St Gallen, Switzerland; Barata F., Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland, Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland, University of St Gallen, St Gallen, Switzerland; Fleisch E., Department of Management, Technology, and Economy, ETH Zurich, Zurich, Switzerland, Centre for Digital Health Interventions, ETH Zurich, Zurich, Switzerland, University of St Gallen, St Gallen, Switzerland; Brutsche M.H., Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland","Background Cough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalised treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in coronavirus disease 2019 (COVID-19) and other lower respiratory tract infections. Methods We conducted a single-centre, exploratory, observational cohort study on automated cough detection in patients hospitalised for COVID-19 (n=32) and non-COVID-19 pneumonia (n=14) between April and November 2020 at the Cantonal Hospital St Gallen, Switzerland. Cough detection was achieved using smartphone-based audio recordings coupled to an ensemble of convolutional neural networks. Cough levels were correlated to established markers of inflammation and oxygenation. Measurements and main results Cough frequency was highest upon hospital admission and declined steadily with recovery. There was a characteristic pattern of daily cough fluctuations, with little activity during the night and two coughing peaks during the day. Hourly cough counts were strongly correlated with clinical markers of disease activity and laboratory markers of inflammation, suggesting cough as a surrogate of disease in acute respiratory tract infections. No apparent differences in cough evolution were observed between COVID-19 and non-COVID-19 pneumonia. Conclusions Automated, quantitative, smartphone-based detection of cough is feasible in hospitalised patients and correlates with disease activity in lower respiratory tract infections. Our approach allows for near real-time telemonitoring of individuals in aerosol isolation. Larger trials are warranted to decipher the use of cough as a digital biomarker for prognosis and tailored treatment in lower respiratory tract infections. © The authors 2023.","","alanine aminotransferase; aspartate aminotransferase; aviptadil; biological marker; C reactive protein; enoxaparin; heparin; hydroxychloroquine; lactate dehydrogenase; remdesivir; adult; aged; algorithm; Article; asthma; audio recording; bacterial pneumonia; body mass; chronic obstructive lung disease; clinical article; clinical evaluation; cohort analysis; convolutional neural network; coronavirus disease 2019; coughing; disease activity; feasibility study; female; fraction of inspired oxygen; hospital admission; hospitalization; human; hypertension; in-hospital mortality; inflammation; intensive care unit; laboratory test; length of stay; lower respiratory tract infection; machine learning; male; malignant neoplasm; mortality; near real time digital pneumonia biomarker; non invasive procedure; nonlinear iterative partial least square algorithm; obesity; observational study; oxygen saturation; oxygenation; pandemic; people by smoking status; pneumonia; principal component analysis; prognosis; prospective study; pulse oximetry; respiratory tract infection; Severe acute respiratory syndrome coronavirus 2; sleep apnea syndromes; telemonitoring","","alanine aminotransferase, 9000-86-6, 9014-30-6; aspartate aminotransferase, 9000-97-9; aviptadil, 40077-57-4, 96886-24-7; C reactive protein, 9007-41-4; enoxaparin, 679809-58-6; heparin, 37187-54-5, 8057-48-5, 8065-01-8, 9005-48-5, 9041-08-1; hydroxychloroquine, 118-42-3, 525-31-5, 137433-23-9, 137433-24-0; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; remdesivir, 1809249-37-3","","","Cantonal Hospital St Gallen, (20/14); Universität St. Gallen, HSG; Eidgenössische Technische Hochschule Zürich, ETH","Support statement: The study received support through a grant from the Research Committee of the Cantonal Hospital St Gallen (grant number 20/14). This work was kindly supported by institutional funds from the ETH Zurich and the University of St Gallen. Funding information for this article has been deposited with the Crossref Funder Registry.","Hu B, Guo H, Zhou P, Et al., Characteristics of SARS-CoV-2 and COVID-19, Nat Rev Microbiol, 19, pp. 141-154, (2021); Aviles-Jurado FX, Prieto-Alhambra D, Gonzalez-Sanchez N, Et al., Timing, complications, and safety of tracheotomy in critically ILL patients with COVID-19, JAMA Otolaryngol Head Neck Surg, 147, pp. 1-8, (2020); Zhang Q, Shen J, Chen L, Et al., Timing of invasive mechanic ventilation in critically ill patients with coronavirus disease 2019, J Trauma Acute Care Surg, 89, pp. 1092-1098, (2020); Jackson C., Cough: bronchoscopic observations on the cough reflex, JAMA, 79, pp. 1399-1404, (1922); Agrawal A, Bhardwaj R., Reducing chances of COVID-19 infection by a cough cloud in a closed space, Phys Fluids, 32, (2020); Morice AH, Fontana GA, Belvisi MG, Et al., ERS guidelines on the assessment of cough, Eur Respir J, 29, pp. 1256-1276, (2007); Barry SJ, Dane AD, Morice AH, Et al., The automatic recognition and counting of cough, Cough, 2, (2006); Birring SS, Fleming T, Matos S, Et al., The Leicester Cough Monitor: preliminary validation of an automated cough detection system in chronic cough, Eur Respir J, 31, pp. 1013-1018, (2008); Mohammed EA, Keyhani M, Sanati-Nezhad A, Et al., An ensemble learning approach to digital corona virus preliminary screening from cough sounds, Sci Rep, 11, (2021); Coppock H, Jones L, Kiskin I, Et al., COVID-19 detection from audio: seven grains of salt, Lancet Digit Health, 3, pp. e537-e538, (2021); Tinschert P, Rassouli F, Barata F, Et al., Nocturnal cough and sleep quality to assess asthma control and predict attacks, J Asthma Allergy, 13, pp. 669-678, (2020); Rassouli F, Tinschert P, Barata F, Et al., Characteristics of asthma-related nocturnal cough: a potential new digital biomarker, J Asthma Allergy, 13, pp. 649-657, (2020); Barata F, Tinschert P, Rassouli F, Et al., Automatic recognition, segmentation, and sex assignment of nocturnal asthmatic coughs and cough epochs in smartphone audio recordings: observational field study, J Med Internet Res, 22, (2020); Barata F, Tinschert P, Shih I, Et al., Nighttime continuous contactless smartphone-based cough monitoring for the ward: a validation study, JMIR Formative Research, 7, (2023); Zhou F, Yu T, Du R, Et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, Lancet, 395, pp. 1054-1062, (2020); Noonan R, Wold H., NIPALS path modelling with latent variables, Scand J Educ Res, 21, pp. 33-61, (1977); Budd J, Miller BS, Manning EM, Et al., Digital technologies in the public-health response to COVID-19, Nat Med, 26, pp. 1183-1192, (2020); Keesara S, Jonas A, Schulman K., Covid-19 and health care’s digital revolution, N Engl J Med, 382, (2020); Proano A, Bravard MA, Lopez JW, Et al., Dynamics of cough frequency in adults undergoing treatment for pulmonary tuberculosis, Clin Infect Dis, 64, pp. 1174-1181, (2017); Rudd M, Song WJ, Small PM., The statistics of counting coughs: easy as 1, 2, 3?, Lung, 200, pp. 531-537, (2022)","M. Boesch; Lung Center, Cantonal Hospital St Gallen, St Gallen, Switzerland; email: maximilian.boesch@kssg.ch","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85159810720"
"Zhu R.; Luo W.; Grieneisen M.L.; Zuoqiu S.; Zhan Y.; Yang F.","Zhu, Rongxin (58885376800); Luo, Wenfeng (58885161700); Grieneisen, Michael L. (36668537700); Zuoqiu, Sophia (58173191600); Zhan, Yu (56555863000); Yang, Fumo (7403449599)","58885376800; 58885161700; 36668537700; 58173191600; 56555863000; 7403449599","A novel approach to deriving the fine-scale daily NO2 dataset during 2005–2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment","2024","Environmental Research","249","","118381","","","","3","10.1016/j.envres.2024.118381","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184913848&doi=10.1016%2fj.envres.2024.118381&partnerID=40&md5=37815bbe4aacbce5fda7437b77645fb5","Department of Environmental Science and Engineering, Sichuan University, Sichuan, Chengdu, 610065, China; College of Carbon Neutrality Future Technology, Sichuan University, Sichuan, Chengdu, 610065, China; Department of Land, Air, and Water Resources, University of California, Davis, 95616, CA, United States; Pittsburgh Institute, Sichuan University, Sichuan, Chengdu, 610207, China","Zhu R., Department of Environmental Science and Engineering, Sichuan University, Sichuan, Chengdu, 610065, China, College of Carbon Neutrality Future Technology, Sichuan University, Sichuan, Chengdu, 610065, China; Luo W., Department of Environmental Science and Engineering, Sichuan University, Sichuan, Chengdu, 610065, China; Grieneisen M.L., Department of Land, Air, and Water Resources, University of California, Davis, 95616, CA, United States; Zuoqiu S., Pittsburgh Institute, Sichuan University, Sichuan, Chengdu, 610207, China; Zhan Y., College of Carbon Neutrality Future Technology, Sichuan University, Sichuan, Chengdu, 610065, China; Yang F., College of Carbon Neutrality Future Technology, Sichuan University, Sichuan, Chengdu, 610065, China","Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005–2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005–2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 μg/m3, while cross-validation across China during 2013–2020 showed an R2 of 0.73 and RMSE of 9.6 μg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 μg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012–2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China. © 2024 Elsevier Inc.","Health impact assessment; High resolution; Long term; Machine learning; Nitrogen dioxide; Spatial downscaling","Air Pollutants; Air Pollution; China; Environmental Exposure; Environmental Monitoring; Humans; Nitrogen Dioxide; Spatio-Temporal Analysis; nitrogen dioxide; air pollutant; air pollution; China; environmental exposure; environmental monitoring; human; procedures; spatiotemporal analysis","","nitrogen dioxide, 10102-44-0; Air Pollutants, ; Nitrogen Dioxide, ","","","National Natural Science Foundation of China, NSFC, (22076129); National Natural Science Foundation of China, NSFC","This study was supported by the National Natural Science Foundation of China (Grant No. 22076129 ). 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Res.","Article","Final","","Scopus","2-s2.0-85184913848"
"Buschur K.L.; Riley C.; Saferali A.; Castaldi P.; Zhang G.; Aguet F.; Ardlie K.G.; Durda P.; Craig Johnson W.; Kasela S.; Liu Y.; Manichaikul A.; Rich S.S.; Rotter J.I.; Smith J.; Taylor K.D.; Tracy R.P.; Lappalainen T.; Graham Barr R.; Sciurba F.; Hersh C.P.; Benos P.V.","Buschur, Kristina L. (57208210834); Riley, Craig (57205583697); Saferali, Aabida (35604301300); Castaldi, Peter (26323082900); Zhang, Grace (58076669000); Aguet, Francois (10142752100); Ardlie, Kristin G. (57211566335); Durda, Peter (16635722800); Craig Johnson, W. (57201526877); Kasela, Silva (55342945000); Liu, Yongmei (58871676000); Manichaikul, Ani (57216593567); Rich, Stephen S. (57216593979); Rotter, Jerome I. (56284453500); Smith, Josh (54421256200); Taylor, Kent D. (57212938461); Tracy, Russell P. (57216588719); Lappalainen, Tuuli (57206704371); Graham Barr, R. (23477164100); Sciurba, Frank (55755090900); Hersh, Craig P. (57545833400); Benos, Panayiotis V. (7003299594)","57208210834; 57205583697; 35604301300; 26323082900; 58076669000; 10142752100; 57211566335; 16635722800; 57201526877; 55342945000; 58871676000; 57216593567; 57216593979; 56284453500; 54421256200; 57212938461; 57216588719; 57206704371; 23477164100; 55755090900; 57545833400; 7003299594","Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis","2023","Respiratory Research","24","1","30","","","","4","10.1186/s12931-023-02316-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146744362&doi=10.1186%2fs12931-023-02316-6&partnerID=40&md5=97d9f9415b3c7af1ada99cb37323349d","Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States; Division of General Medicine, Columbia University Medical Center, New York, NY, United States; New York Genome Center, New York, NY, United States; Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; The Broad Institute of MIT and Harvard, Cambridge, MA, United States; Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, United States; Department of Biostatistics, University of Washington, Seattle, WA, United States; Department of Systems Biology, Columbia University, New York, NY, United States; Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, United States; Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States; Northwest Genome Center, University of Washington, Seattle, WA, United States; Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, United States; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, 32603, FL, United States","Buschur K.L., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States, Division of General Medicine, Columbia University Medical Center, New York, NY, United States, New York Genome Center, New York, NY, United States; Riley C., Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Saferali A., Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Castaldi P., Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Zhang G., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Aguet F., The Broad Institute of MIT and Harvard, Cambridge, MA, United States; Ardlie K.G., The Broad Institute of MIT and Harvard, Cambridge, MA, United States; Durda P., Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, United States; Craig Johnson W., Department of Biostatistics, University of Washington, Seattle, WA, United States; Kasela S., New York Genome Center, New York, NY, United States, Department of Systems Biology, Columbia University, New York, NY, United States; Liu Y., Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, United States; Manichaikul A., Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; Rich S.S., Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States; Rotter J.I., The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States; Smith J., Northwest Genome Center, University of Washington, Seattle, WA, United States; Taylor K.D., The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, United States; Tracy R.P., Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, United States, Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT, United States; Lappalainen T., New York Genome Center, New York, NY, United States, Department of Systems Biology, Columbia University, New York, NY, United States, Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Graham Barr R., Division of General Medicine, Columbia University Medical Center, New York, NY, United States; Sciurba F., Division of Pulmonary Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Hersh C.P., Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States; Benos P.V., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States, Department of Epidemiology, University of Florida, 2004 Mowry Rd, Gainesville, 32603, FL, United States","Background: Chronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment. Methods: Blood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples. Results: The discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS). Conclusions: The identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis. © 2023, The Author(s).","COPD; Disease subtypes; Gene expression; Graphical models","Gene Regulatory Networks; Genome-Wide Association Study; Humans; Prognosis; Pulmonary Disease, Chronic Obstructive; Smokers; desmoplakin; glutathione transferase M1; aged; Article; body mass; chronic obstructive lung disease; chronic obstructive lung disease subtype; cohort analysis; diffusing capacity for carbon monoxide; ex-smoker; exercise; female; forced expiratory volume; forced vital capacity; functional residual capacity; gene deregulation values; gene expression; gene network analysis; gene network perturbation analysis; genetic parameters; genome-wide association study; graphical models; human; longitudinal study; lung function impairment; lung insufficiency; machine learning; male; Modified Medical Research Council Dyspnea Scale; mortality; peripheral blood mononuclear cell; RNA sequencing; St. George Respiratory Questionnaire; survival analysis; total lung capacity; chronic obstructive lung disease; gene regulatory network; genetics; procedures; prognosis; smoking","","","","","Southern California Diabetes Endocrinology Research Center; National Institutes of Health, NIH, (R01HL140963, R01HL157879, R01HL159805, U01HL137159); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI; National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK; GlaxoSmithKline, GSK; National Center for Advancing Translational Sciences, NCATS; Indiana Clinical and Translational Sciences Institute, CTSI, (UL1TR001881); Indiana Clinical and Translational Sciences Institute, CTSI; College of Public Health, CPH, (R01HL121270, R01HL125583, R01HL130512, R01HL142028, T32HL144442, U01HL089856, U01HL089897); College of Public Health, CPH; COPD Foundation; Novartis Pharmaceuticals Corporation, NPC; Diabetes Research Connection, DRC, (DK063491); Diabetes Research Connection, DRC","This study was supported by grants from the National Institutes of Health to PVB (R01HL159805, R01HL157879, U01HL137159, R01HL140963), CPH (R01HL125583, R01HL130512), RGB (R01HL121270), TL (R01HL142028). KLB was supported by T32HL144442. COPDGene data collection was supported by Award Number U01HL089897 and Award Number U01HL089856 from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprised of AstraZeneca, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, Siemens, and Sunovion. The Trans-Omics for Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI). Whole genome sequencing (WGS) for “NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)” (phs001416.v1.p1) was performed at the Broad Institute of MIT and Harvard (3U54HG003067-13S1); RNA-seq was conducted by the Broad Institute of MIT and Harvard (3R01HL092577-06S1) and the Northwest Genomics Center at the University of Washington (3R01HL098433-05S1). Centralized read mapping and genotype calling, along with variant quality metrics and filtering, were provided by the TOPMed Informatics Research Center (3R01HL117626-02S1, contract HHSN268201800002I) (Broad RNA Seq, Proteomics HHSN268201600034I, UW RNA Seq HHSN268201600032I). Phenotype harmonization, data management, sample-identity quality control, and general study coordination, were provided by the TOPMed Data Coordinating Center (3R01HL120393; contract HHSN268180001I). The MESA project is supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420. Also supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center”. 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COPD heterogeneity and clustering in 10 independent cohorts, Thorax, 72, 11, pp. 998-1006, (2017); Hobbs B.D., de Jong K., Lamontagne M., Et al., Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis, Nat Genet, 49, 3, pp. 426-432, (2017); Vasioukhin V., Bowers E., Bauer C., Et al., Desmoplakin is essential in epidermal sheet formation, Nat Cell Biol, 3, 12, (2001); Norman M., Simpson M., Mogensen J., Et al., Novel mutation in desmoplakin causes arrhythmogenic left ventricular cardiomyopathy, Circulation, 112, 5, pp. 636-642, (2005); Mathai S.K., Pedersen B.S., Smith K., Et al., Desmoplakin variants are associated with idiopathic pulmonary fibrosis, Am J Respir Crit Care Med, 193, 10, pp. 1151-1160, (2016); Hao K., Bosse Y., Nickle D.C., Et al., Lung eQTLs to help reveal the molecular underpinnings of asthma, PLoS Genet, 8, 11, (2012); Kim W., Cho M.H., Sakornsakolpat P., Et al., DSP variants may be associated with longitudinal change in quantitative emphysema, Respir Res, 20, 1, (2019); Hao Y., Bates S., Mou H., Et al., Genome-wide association study: functional variant rs2076295 regulates desmoplakin expression in airway epithelial cells, Am J Respir Crit Care Med, 202, 9, pp. 1225-1236, (2020); Strange R.C., Spiteri M.A., Ramachandran S., Et al., Glutathione-S-transferase family of enzymes, Mutation Res/Fundamental Mol Mech Mutagen, 482, 1-2, pp. 21-26, (2001); Seidegard J., Pero R.W., Miller D.G., Et al., A glutathione transferase in human leukocytes as a marker for the susceptibility to lung cancer, Carcinogenesis, 7, 5, pp. 751-753, (1986); Hirvonen A., Husgafvel-Pursiainen K., Anttila S., Et al., The GSTM1 null genotype as a potential risk modifier for squamous cell carcinoma of the lung, Carcinogenesis, 14, 7, pp. 1479-1481, (1993); Harrison D., Cantlay A., Rae F., Et al., Frequency of glutathione S-transferase M1 deletion in smokers with emphysema and lung cancer, Hum Exp Toxicol, 16, 7, pp. 356-360, (1997); Lakhdar R., Denden S., Knani J., Et al., Association of GSTM1 and GSTT1 polymorphisms with chronic obstructive pulmonary disease in a Tunisian population, Biochem Genet, 48, 7-8, pp. 647-657, (2010); Cheng S.L., Yu C.J., Chen C.J., Et al., Genetic polymorphism of epoxide hydrolase and glutathione S-transferase in COPD, Eur Respir J, 23, 6, pp. 818-824, (2004); Young R.P., Hopkins R.J., Hay B.A., Et al., GSTM1 null genotype in COPD and lung cancer: evidence of a modifier or confounding effect?, Appl Clin Genet, 4, (2011)","P.V. Benos; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, United States; email: pbenos@ufl.edu","","BioMed Central Ltd","","","","","","14659921","","RREEB","36698131","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85146744362"
"Stergiou C.L.; Plageras A.P.; Memos V.A.; Koidou M.P.; Psannis K.E.","Stergiou, Christos L. (57197316769); Plageras, Andreas P. (57193017337); Memos, Vasileios A. (56453287200); Koidou, Maria P. (57746616500); Psannis, Konstantinos E. (14061014300)","57197316769; 57193017337; 56453287200; 57746616500; 14061014300","Secure Monitoring System for IoT Healthcare Data in the Cloud","2024","Applied Sciences (Switzerland)","14","1","120","","","","3","10.3390/app14010120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191564202&doi=10.3390%2fapp14010120&partnerID=40&md5=1603545e5212a9c9018ff48c8dffe845","Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece","Stergiou C.L., Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; Plageras A.P., Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; Memos V.A., Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; Koidou M.P., Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; Psannis K.E., Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece","Even though the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people with serious long-term breathing problems like asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new secure machine learning monitoring system for a model for virus detection. Our proposed model makes use of four basic emerging technologies, the Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses. © 2023 by the authors.","artificial intelligence; cloud computing; detection; healthcare; internet of things; machine learning; monitoring; security","","","","","","","","Oniani S., Marques G., Barnovi S., Pires I.M., Bhoi A.K., Artificial Intelligence for Internet of Things and Enhanced Medical Systems, Bio-Inspired Neurocomputing. Studies in Computational Intelligence, 903, pp. 43-59, (2020); Stergiou C.L., Psannis K.E., Gupta B.B., InFeMo: Flexible Big Data Management Through a Federated Cloud System, ACM Trans. Internet Technol, 22, (2022); Bai L., Yang D., Wang X., Tong L., Zhu X., Zhong N., Bai C., Powell C.A., Chen R., Zhou J., Et al., Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019, Clin. Ehealth, 3, pp. 7-15, (2020); Maghdid H.S., Ghafoor K.Z., Sadiq A.S., Curran K., Rawt D.B., Rabie K., A Novel AI-enabled Framework to Diagnose Coronavirus COVID 19 using Smartphone Embedded Sensors: Design Study, Proceedings of the 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), 1, pp. 180-187; Muthukumar S., Mary W.S., Rajkumar R., Dhina R., Gayathri J., Mathivadhani A., Smart Humidity Monitoring System for Infectious Disease Control, Proceedings of the 2019 International Conference on Computer Communication and Informatics (ICCCI), pp. 127-132; Quy V.K., Chehri A., Quy N.M., Han N.D., Ban N.T., Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges, IEEE Access, 11, pp. 39824-39844, (2023); Quy V.K., Hau N.V., Anh D.V., Ngoc L.A., Smart healthcare IoT applications based on fog computing: Architecture, applications and challenges, Complex Intell. Syst, 8, pp. 3805-3815, (2021); George K., Michaels A.J., Designing a Block Cipher in Galois Extension Fields for IoT Security, IoT, 2, pp. 669-687, (2021); Shreya S., Chatterjee K., Singh A., A smart secure healthcare monitoring system with Internet of Medical Things, Comput. Electr. Eng, 101, (2022); Butpheng C., Yeh K.-H., Hou J.-L., A Secure IoT and Cloud Computing-Enabled e-Health Management System, Secur. Commun. Netw, 2022, (2022); Tang Z., A Preliminary Study on Data Security Technology in Big Data Cloud Computing Environment, Proceedings of the International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE); Ogiela L., Castiglione A., Gupta B.B., Agrawal D.P., IoT-based health monitoring system to handle pandemic diseases using estimated computing, Neural Comput. Appl, 35, pp. 13709-13710, (2023); Butpheng C., Yeh K.-H., Xiong H., Security and Privacy in IoT-Cloud-Based e-Health Systems—A Comprehensive Review, Symmetry, 12, (2020); Stergiou C., Plageras A.P., Psannis K.E., Gupta B.B., Secure Machine Learning scenario from Big Data in Cloud Computing via Internet of Things network, Handbook of Computer Networks and Cyber Security: Principles and Paradigms, Multimedia Systems and Applications, pp. 525-554, (2020); Tsiknas K., Taketzis D., Demertzis K., Skianis C., Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures, IoT, 2, pp. 163-186, (2021); Plageras A.P., Stergiou C., Psannis K.E., Kokkonis G., Ishibashi Y., Kim B.-G., Gupta B., Efficient Large-Scale Medical Data (eHealth Big Data) Analytics in Internet of Things, Proceedings of the 19th IEEE International Conference on Business Informatics (CBI’17), International Workshop on the Internet of Things and Smart Services (ITSS2017); Stergiou C., Psannis K.E., Kim B.-G., Gupta B., Secure integration of IoT and Cloud Computing, Future Gener. Comput. Syst, 78, pp. 964-975, (2018); Sahu S., Dhote Y., A study on big data: Issues, challenges and applications, Int. J. Innov. Res. Comput. Commun. Eng, 4, pp. 10611-10616, (2016); Singh A., Chatterjee K., Edge computing based secure health monitoring framework for electronic healthcare system, Clust. Comput, 26, pp. 1205-1220, (2023); Rao A.S.S., Vazquez J.A., Identification of COVID-19 can be Quicker through Artificial Intelligence Framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns are Under Quarantine, Infect. Control Hosp. Epidemiol, 41, pp. 820-830, (2020); Wang Y., Hu M., Li Q., Zhang X., Zhai G., Yao N., Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner, arXiv, (2020); Shi F., Wang J., Shi J., Wu Z., Wang Q., Tang Z., He K., Shi Y., Shen D., Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19, EEE Rev. Biomed. Eng, 14, pp. 4-15, (2021); Wang H., Xiong D., Wang P., Liu Y., A Lightweight XMPP Publish/Subscribe Scheme for Resource-Constrained IoT Devices, IEEE Access, 5, pp. 16393-16405, (2017)","C.L. Stergiou; Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; email: c.stergiou@uom.edu.gr; K.E. Psannis; Department of Applied Informatics, University of Macedonia, Thessaloniki, 54636, Greece; email: kpsannis@uom.edu.gr","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191564202"
"Howard R.; Fontanella S.; Simpson A.; Murray C.S.; Custovic A.; Rattray M.","Howard, Rebecca (56727353200); Fontanella, Sara (57193973057); Simpson, Angela (7402780427); Murray, Clare S. (7402491950); Custovic, Adnan (57226203185); Rattray, Magnus (7005357184)","56727353200; 57193973057; 7402780427; 7402491950; 57226203185; 7005357184","Component-specific clusters for diagnosis and prediction of allergic airway diseases","2024","Clinical and Experimental Allergy","54","5","","339","349","10","3","10.1111/cea.14468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186624394&doi=10.1111%2fcea.14468&partnerID=40&md5=f6c11a8905169611bc4920f336436082","Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom","Howard R., Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; Fontanella S., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Simpson A., Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; Murray C.S., Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; Custovic A., National Heart and Lung Institute, Imperial College London, London, United Kingdom; Rattray M., Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom","Background: Previous studies which applied machine learning on multiplex component-resolved diagnostics arrays identified clusters of allergen components which are biologically plausible and reflect the sources of allergenic proteins and their structural homogeneity. Sensitization to different clusters is associated with different clinical outcomes. Objective: To investigate whether within different allergen component sensitization clusters, the internal within-cluster sensitization structure, including the number of c-sIgE responses and their distinct patterns, alters the risk of clinical expression of symptoms. Methods: In a previous analysis in a population-based birth cohort, by clustering component-specific (c-s)IgEs, we derived allergen component clusters from infancy to adolescence. In the current analysis, we defined each subject's within-cluster sensitization structure which captured the total number of c-sIgE responses in each cluster and intra-cluster sensitization patterns. Associations between within-cluster sensitization patterns and clinical outcomes (asthma and rhinitis) in early-school age and adolescence were examined using logistic regression and binomial generalized additive models. Results: Intra-cluster sensitization patterns revealed specific associations with asthma and rhinitis (both contemporaneously and longitudinally) that were previously unseen using binary sensitization to clusters. A more detailed description of the subjects' within-cluster c-sIgE responses in terms of the number of positive c-sIgEs and unique sensitization patterns added new information relevant to allergic diseases, both for diagnostic and prognostic purposes. For example, the increase in the number of within-cluster positive c-sIgEs at age 5 years was correlated with the increase in prevalence of asthma at ages 5 and 16 years, with the correlations being stronger in the prediction context (e.g. for the largest ‘Broad’ component cluster, contemporaneous: r =.28, p =.012; r =.22, p =.043; longitudinal: r =.36, p =.004; r =.27, p =.04). Conclusion: Among sensitized individuals, a more detailed description of within-cluster c-sIgE responses in terms of the number of positive c-sIgE responses and distinct sensitization patterns, adds potentially important information relevant to allergic diseases. © 2024 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.","asthma; component-resolved diagnostics; diagnosis; machine learning; prognosis; rhinitis","Adolescent; Allergens; Asthma; Child; Child, Preschool; Cluster Analysis; Female; Humans; Immunoglobulin E; Infant; Male; allergen; immunoglobulin E; adolescent; Article; asthma; birth cohort; blood sampling; child; clinical outcome; cohort analysis; cross-sectional study; human; predictive value; respiratory tract allergy; rhinitis; sensitization; asthma; blood; cluster analysis; female; immunology; infant; male; preschool child","","immunoglobulin E, 37341-29-0; Allergens, ; Immunoglobulin E, ","ImmunoCAP ISAC, Thermo, Sweden","Thermo, Sweden","UK Research and Innovation, UKRI; Medical Research Council, MRC, (MR/S025340/1)","Medical Research Council (MRC) grant MR/S025340/1. 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SCARPOL-team. Swiss Study on childhood Allergy and respiratory symptom with respect to air pollution and climate. International Study of asthma and allergies in childhood, Pediatr Allergy Immunol, 8, 2, pp. 75-82, (1997); Hastie T., GAM: Generalized Additive Models. 1.16 ed, (2018); Wood S.N., Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J R Stat Soc, 73, 1, pp. 3-36, (2011); R: A Language and Environment for Statistical Computing, (2018); Sylvestre L., Jegu J., Metz-Favre C., Barnig C., Qi S., de Blay F., Component-based allergen-microarray: Der p 2 and Der f 2 dust mite sensitization is more common in patients with severe asthma, J Invest Allergol Clin Immunol, 26, 2, pp. 141-143, (2016); Reginald K., Chew F.T., The major allergen Der p 2 is a cholesterol binding protein, Sci Rep, 9, 1, (2019); Custovic D., Fontanella S., Custovic A., Understanding progression from pre-school wheezing to school-age asthma: can modern data approaches help?, Pediatr Allergy Immunol, 34, 12, (2023); Fontanella S., Cucco A., Custovic A., Machine learning in asthma research: moving toward a more integrated approach, Expert Rev Respir Med, 15, 5, pp. 609-621, (2021); Holt P.G., Strickland D., Bosco A., Et al., Distinguishing benign from pathologic TH2 immunity in atopic children, J Allergy Clin Immunol, 137, 2, pp. 379-387, (2016)","A. Custovic; National Heart and Lung Institute, Imperial College London, London, United Kingdom; email: a.custovic@imperial.ac.uk","","John Wiley and Sons Inc","","","","","","09547894","","CLEAE","38475973","English","Clin. Exp. Allergy","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85186624394"
"Laurent F.; Benlala I.; Dournes G.","Laurent, François (7101921631); Benlala, Ilyes (57203886518); Dournes, Gael (55440990600)","7101921631; 57203886518; 55440990600","Radiological Diagnosis of Pulmonary Aspergillosis","2024","Seminars in Respiratory and Critical Care Medicine","45","1","","50","60","10","3","10.1055/s-0043-1776998","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183918995&doi=10.1055%2fs-0043-1776998&partnerID=40&md5=907e8a1ae50568fb8417680a55e0c927","Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France; Chu Bordeaux, Service d'Imagerie Thoracique et Cardiovasculaire, Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France","Laurent F., Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France; Benlala I., Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France, Chu Bordeaux, Service d'Imagerie Thoracique et Cardiovasculaire, Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France; Dournes G., Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France, Chu Bordeaux, Service d'Imagerie Thoracique et Cardiovasculaire, Centre de Recherche Cardio-thoracique de Bordeaux, University of Bordeaux, Pessac, France","Imaging plays an important role in the various forms of Aspergillus -related pulmonary disease. Depending on the immune status of the patient, three forms are described with distinct imaging characteristics: invasive aspergillosis affecting severely immunocompromised patients, chronic pulmonary aspergillosis affecting less severely immunocompromised patients but suffering from a pre-existing structural lung disease, and allergic bronchopulmonary aspergillosis related to respiratory exposure to Aspergillus species in patients with asthma and cystic fibrosis. Computed tomography (CT) has been demonstrated more sensitive and specific than chest radiographs and its use has largely contributed to the diagnosis, follow-up, and evaluation of treatment in each condition. In the last few decades, CT has also been described in the specific context of cystic fibrosis. In this particular clinical setting, magnetic resonance imaging and the recent developments in artificial intelligence have shown promising results. © 2024. Thieme. All rights reserved.","aspergillosis; computed tomography; cystic fibrosis; imaging; lung; magnetic resonance imaging","Artificial Intelligence; Aspergillosis, Allergic Bronchopulmonary; Aspergillus; Cystic Fibrosis; Humans; Lung; Pulmonary Aspergillosis; allergic bronchopulmonary aspergillosis; Article; artificial intelligence; aspergilloma; aspergillosis; chronic pulmonary aspergillosis; clinical evaluation; clinical feature; computer assisted tomography; cystic fibrosis; follow up; human; invasive aspergillosis; lung aspergillosis; nuclear magnetic resonance imaging; radiodiagnosis; thorax radiography; allergic bronchopulmonary aspergillosis; Aspergillus; cystic fibrosis; diagnostic imaging; lung; lung aspergillosis; pathology","","","","","","","Bergeron A., Porcher R., Sulahian A., The strategy for the diagnosis of invasive pulmonary aspergillosis should depend on both the underlying condition and the leukocyte count of patients with hematologic malignancies, Blood, 119, 8, pp. 1831-1837, (2012); Stemler J., Bruns C., Mellinghoff S.C., Baseline chest computed tomography as standard of care in high-risk hematology patients, J Fungi (Basel), 6, 1, (2020); De Pauw B., Walsh T.J., Donnelly J.P., Revised definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group (EORTC/MSG) Consensus Group, Clin Infect Dis, 46, 12, pp. 1813-1821, (2008); Donnelly J.P., Chen S.C., Kauffman C.A., Revision and update of the consensus definitions of invasive fungal disease from the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium, Clin Infect Dis, 71, 6, pp. 1367-1376, (2020); Ullmann A.J., Aguado J.M., Arikan-Akdagli S., Diagnosis and management of Aspergillus diseases: executive summary of the 2017 ESCMID-ECMM-ERS guideline, Clin Microbiol Infect, 24, pp. e1-e38, (2018); Raveendran S., Lu Z., CT findings and differential diagnosis in adults with invasive pulmonary aspergillosis, Radiol Infect Dis, 5, 1, pp. 14-25, (2018); Franquet T., Muller N.L., Gimenez A., Guembe P., De La Torre J., Bague S., Spectrum of pulmonary aspergillosis: histologic, clinical, and radiologic findings, Radiographics, 21, 4, pp. 825-837, (2001); Pasmans H.L., Loosveld O.J., Schouten H.C., Thunnissen F., Van Engelshoven J.M., Invasive aspergillosis in immunocompromised patients: findings on plain film and (HR)CT, Eur J Radiol, 14, 1, pp. 37-40, (1992); Park S.Y., Kim S.-H., Choi S.-H., Clinical and radiological features of invasive pulmonary aspergillosis in transplant recipients and neutropenic patients, Transpl Infect Dis, 12, 4, pp. 309-315, (2010); Lim C., Seo J.B., Park S.-Y., Analysis of initial and follow-up CT findings in patients with invasive pulmonary aspergillosis after solid organ transplantation, Clin Radiol, 67, 12, pp. 1179-1186, (2012); Nam B.D., Kim T.J., Lee K.S., Kim T.S., Han J., Chung M.J., Pulmonary mucormycosis: serial morphologic changes on computed tomography correlate with clinical and pathologic findings, Eur Radiol, 28, 2, pp. 788-795, (2018); Jung J., Kim M.Y., Lee H.J., Comparison of computed tomographic findings in pulmonary mucormycosis and invasive pulmonary aspergillosis, Clin Microbiol Infect, 21, 7, pp. 684e11-684e18, (2015); Marchiori E., Zanetti G., Escuissato D.L., Reversed halo sign: high-resolution CT scan findings in 79 patients, Chest, 141, 5, pp. 1260-1266, (2012); Georgiadou S.P., Sipsas N.V., Marom E.M., Kontoyiannis D.P., The diagnostic value of halo and reversed halo signs for invasive mold infections in compromised hosts, Clin Infect Dis, 52, 9, pp. 1144-1155, (2011); Henzler C., Henzler T., Buchheidt D., Diagnostic performance of contrast enhanced pulmonary computed tomography angiography for the detection of angioinvasive pulmonary aspergillosis in immunocompromised patients, Sci Rep, 7, 1, (2017); Burgos A., Zaoutis T.E., Dvorak C.C., Pediatric invasive aspergillosis: a multicenter retrospective analysis of 139 contemporary cases, Pediatrics, 121, 5, pp. e1286-e1294, (2008); Herbrecht R., Guffroy B., Danion F., Venkatasamy A., Simand C., Ledoux M.-P., Validation by real-life data of the new radiological criteria of the revised and updated consensus definition for invasive fungal diseases, Clin Infect Dis, 71, 10, pp. 2773-2774, (2020); Desai S.R., Hedayati V., Patel K., Hansell D.M., Chronic aspergillosis of the lungs: unravelling the terminology and radiology, Eur Radiol, 25, 10, pp. 3100-3107, (2015); Gefter W.B., Weingrad T.R., Epstein D.M., Ochs R.H., Miller W.T., Semi-invasive pulmonary aspergillosis: a new look at the spectrum of aspergillus infections of the lung, Radiology, 140, 2, pp. 313-321, (1981); Denning D.W., Riniotis K., Dobrashian R., Sambatakou H., Chronic cavitary and fibrosing pulmonary and pleural aspergillosis: case series, proposed nomenclature change, and review, Clin Infect Dis, 37, pp. S265-S280, (2003); Denning D.W., Cadranel J., Beigelman-Aubry C., European Society for Clinical Microbiology and Infectious Diseases and European Respiratory Society. Chronic pulmonary aspergillosis: rationale and clinical guidelines for diagnosis and management, Eur Respir J, 47, 1, pp. 45-68, (2016); Muldoon E.G., Sharman A., Page I., Bishop P., Denning D.W., Aspergillus nodules; another presentation of chronic pulmonary aspergillosis, BMC Pulm Med, 16, 1, (2016); Hot A., Maunoury C., Poiree S., Diagnostic contribution of positron emission tomography with [18F]fluorodeoxyglucose for invasive fungal infections, Clin Microbiol Infect, 17, 3, pp. 409-417, (2011); Dournes G., MacEy J., Blanchard E., Berger P., Laurent F., MRI of the pulmonary parenchyma: Towards clinical applicability? [in French], Rev Pneumol Clin, 73, 1, pp. 40-49, (2017); Khalil A., Fedida B., Parrot A., Haddad S., Fartoukh M., Carette M.-F., Severe hemoptysis: from diagnosis to embolization, Diagn Interv Imaging, 96, pp. 775-788, (2015); Shin B., Koh W.-J., Shin S.W., Outcomes of bronchial artery embolization for life-threatening hemoptysis in patients with chronic pulmonary aspergillosis, PLoS One, 11, 12, (2016); Osaki S., Nakanishi Y., Wataya H., Prognosis of bronchial artery embolization in the management of hemoptysis, Respiration, 67, 4, pp. 412-416, (2000); Agarwal R., Vishwanath G., Aggarwal A.N., Garg M., Gupta D., Chakrabarti A., Itraconazole in chronic cavitary pulmonary aspergillosis: a randomised controlled trial and systematic review of literature, Mycoses, 56, 5, pp. 559-570, (2013); Felton T.W., Baxter C., Moore C.B., Roberts S.A., Hope W.W., Denning D.W., Efficacy and safety of posaconazole for chronic pulmonary aspergillosis, Clin Infect Dis, 51, 12, pp. 1383-1391, (2010); Cadranel J., Philippe B., Hennequin C., Voriconazole for chronic pulmonary aspergillosis: a prospective multicenter trial, Eur J Clin Microbiol Infect Dis, 31, 11, pp. 3231-3239, (2012); Godet C., Laurent F., Bergeron A., CT imaging assessment of response to treatment in chronic pulmonary aspergillosis, Chest, 150, 1, pp. 139-147, (2016); Uzunhan Y., Nunes H., Jeny F., Chronic pulmonary aspergillosis complicating sarcoidosis, Eur Respir J, 49, 6, (2017); Agarwal R., Khan A., Garg M., Aggarwal A.N., Gupta D., Chest radiographic and computed tomographic manifestations in allergic bronchopulmonary aspergillosis, World J Radiol, 4, 4, pp. 141-150, (2012); Kousha M., Tadi R., Soubani A.O., Pulmonary aspergillosis: a clinical review, Eur Respir Rev, 20, 121, pp. 156-174, (2011); Stevens D.A., Moss R.B., Kurup V.P., Allergic bronchopulmonary aspergillosis in cystic fibrosis-state of the art: Cystic Fibrosis Foundation Consensus Conference, Clin Infect Dis, 37, pp. S225-S264, (2003); Breuer O., Schultz A., Garratt L.W., Aspergillus infections and progression of structural lung disease in children with cystic fibrosis, Am J Respir Crit Care Med, 201, 6, pp. 688-696, (2020); Brandt C., Roehmel J., Rickerts V., Melichar V., Niemann N., Schwarz C., Aspergillus bronchitis in patients with cystic fibrosis, Mycopathologia, 183, 1, pp. 61-69, (2018); Mastella G., Rainisio M., Harms H.K., Allergic bronchopulmonary aspergillosis in cystic fibrosis. A European epidemiological study, Eur Respir J, 16, 3, pp. 464-471, (2000); Ward S., Heyneman L., Lee M.J., Leung A.N., Hansell D.M., Muller N.L., Accuracy of CT in the diagnosis of allergic bronchopulmonary aspergillosis in asthmatic patients, AJR Am J Roentgenol, 173, 4, pp. 937-942, (1999); Neeld D.A., Goodman L.R., Gurney J.W., Greenberger P.A., Fink J.N., Computerized tomography in the evaluation of allergic bronchopulmonary aspergillosis, Am Rev Respir Dis, 142, 5, pp. 1200-1205, (1990); Logan P.M., Muller N.L., High-attenuation mucous plugging in allergic bronchopulmonary aspergillosis, Can Assoc Radiol J, 47, 5, pp. 374-377, (1996); Refait J., MacEy J., Bui S., CT evaluation of hyperattenuating mucus to diagnose allergic bronchopulmonary aspergillosis in the special condition of cystic fibrosis, J Cyst Fibros, 18, 4, pp. e31-e36, (2019); Dournes G., Menut F., MacEy J., Lung morphology assessment of cystic fibrosis using MRI with ultra-short echo time at submillimeter spatial resolution, Eur Radiol, 26, 11, pp. 3811-3820, (2016); Longuefosse A., Raoult J., Benlala I., Generating high-resolution synthetic CT from lung MRI with ultrashort echo times: initial evaluation in cystic fibrosis, Radiology, 308, 1, (2023); Dournes G., Walkup L.L., Benlala I., The clinical use of lung MRI in cystic fibrosis: what, now, how?, Chest, 159, 6, pp. 2205-2217, (2021); Benlala I., Hocke F., MacEy J., Quantification of MRI T2-weighted high signal volume in cystic fibrosis: a pilot study, Radiology, 294, 1, pp. 186-196, (2020); Wielputz M.O., Puderbach M., Kopp-Schneider A., Magnetic resonance imaging detects changes in structure and perfusion, and response to therapy in early cystic fibrosis lung disease, Am J Respir Crit Care Med, 189, 8, pp. 956-965, (2014); Willmering M.M., Walkup L.L., Niedbalski P.J., Pediatric <sup>129</sup> Xe gas-transfer MRI-feasibility and applicability, J Magn Reson Imaging, 56, 4, pp. 1207-1219, (2022); Benlala I., Klaar R., Gaass T., Non-contrast-enhanced functional lung MRI to evaluate treatment response of allergic bronchopulmonary aspergillosis in patients with cystic fibrosis: a pilot study, J Magn Reson Imaging, (2023); Dournes G., Berger P., Refait J., Allergic bronchopulmonary aspergillosis in cystic fibrosis: MR imaging of airway mucus contrasts as a tool for diagnosis, Radiology, 285, 1, pp. 261-269, (2017); Agarwal R., Chakrabarti A., Shah A., Allergic bronchopulmonary aspergillosis: review of literature and proposal of new diagnostic and classification criteria, Clin Exp Allergy, 43, 8, pp. 850-873, (2013); Sunman B., Ademhan Tural D., Ozsezen B., Emiralioglu N., Yalcin E., Ozcelik U., Current approach in the diagnosis and management of allergic bronchopulmonary aspergillosis in children with cystic fibrosis, Front Pediatr, 8, (2020); Brenner D.J., Hall E.J., Computed tomography-an increasing source of radiation exposure, N Engl J Med, 357, 22, pp. 2277-2284, (2007); Pearce M.S., Salotti J.A., Little M.P., Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study, Lancet, 380, 9840, pp. 499-505, (2012)","F. Laurent; Centre de Recherche Cardio-thoracique de Bordeaux Crctb Inserm U1045, Hôpital Xavier Arnozan Ptib, Pessac, Avenue du Haut-Lévêque, 33600, France; email: flaurent0217@gmail.com","","Thieme Medical Publishers, Inc.","","","","","","10693424","","SRCCE","38286137","English","Semin. Respir. Crit. Care Med.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85183918995"
"Shim J.-S.; Kim B.-K.; Kim S.-H.; Kwon J.-W.; Ahn K.-M.; Kang S.-Y.; Park H.-K.; Park H.-W.; Yang M.-S.; Kim M.-H.; Lee S.M.","Shim, Ji-Su (57193221759); Kim, Byung-Keun (36608393400); Kim, Sae-Hoon (35198322400); Kwon, Jae-Woo (57204538197); Ahn, Kyung-Min (55421364200); Kang, Sung-Yoon (55492809000); Park, Han-Ki (57218664064); Park, Heung-Woo (7601567361); Yang, Min-Suk (7404927140); Kim, Min-Hye (58045929900); Lee, Sang Min (55913470000)","57193221759; 36608393400; 35198322400; 57204538197; 55421364200; 55492809000; 57218664064; 7601567361; 7404927140; 58045929900; 55913470000","A smartphone-based application for cough counting in patients with acute asthma exacerbation","2023","Journal of Thoracic Disease","15","7","","4053","4065","12","4","10.21037/jtd-22-1492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168995543&doi=10.21037%2fjtd-22-1492&partnerID=40&md5=98c0436ea6c4956433455aa0196c8769","Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, South Korea; Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, South Korea; Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea; Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, South Korea; Department of Allergy and Clinical Immunology, School of Medicine, Kyungpook National University, Daegu, South Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea","Shim J.-S., Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, South Korea; Kim B.-K., Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University College of Medicine, Seoul, South Korea; Kim S.-H., Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Kwon J.-W., Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon, South Korea; Ahn K.-M., Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea; Kang S.-Y., Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, South Korea; Park H.-K., Department of Allergy and Clinical Immunology, School of Medicine, Kyungpook National University, Daegu, South Korea; Park H.-W., Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea; Yang M.-S., Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, South Korea; Kim M.-H., Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, South Korea; Lee S.M., Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, South Korea","Background: While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. Methods: Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using CoughyTM smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. Results: A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. Conclusions: The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that CoughyTM could be a novel solution for objectively monitoring cough in a clinical setting. © Journal of Thoracic Disease. All rights reserved.","artificial intelligence (AI); asthma exacerbation; Cough; objective cough frequency","antihistaminic agent; beta 2 adrenergic receptor stimulating agent; bronchodilating agent; cholinergic receptor blocking agent; corticosteroid; leukotriene receptor blocking agent; mannitol; methacholine; salbutamol; adult; algorithm; Article; artificial intelligence; asthma; clinical article; controlled study; corticosteroid therapy; coughing; disease exacerbation; female; human; low drug dose; male; outcome assessment; peak expiratory flow; prospective study; sleep time; systemic therapy; visual analog scale; wakefulness","","mannitol, 69-65-8, 87-78-5; methacholine, 55-92-5; salbutamol, 18559-94-9, 35763-26-9","Coughy; Mini-wright, Clement Clarke; iPhone XR, Apple, United States","Apple, United States; Clement Clarke","Ministry of Science, ICT and Future Planning, MSIP, (2020-0-02111); Ministry of Science, ICT and Future Planning, MSIP; Institute for Information and Communications Technology Promotion, IITP","We thank Soundable Health, Inc. for providing CoughyTM AI algorithm. Funding: This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-02111), Development of Acoustic AI Platform Technology for Monitoring and Managing Respiratory.","Lee JH, Song WJ., Perspectives on chronic cough in Korea, J Thorac Dis, 12, pp. 5194-5206, (2020); Niimi A., Narrative Review: how long should patients with cough variant asthma or non-asthmatic eosinophilic bronchitis be treated?, J Thorac Dis, 13, pp. 3197-3214, (2021); Tinschert P, Rassouli F, Barata F, Et al., Smartphone-based cough detection predicts asthma control – description of a novel, scalable digital biomarker, Eur Respir J, 56, (2020); Chang AB, Harrhy VA, Simpson J, Et al., Cough, airway inflammation, and mild asthma exacerbation, Arch Dis Child, 86, pp. 270-275, (2002); Hall JI, Lozano M, Estrada-Petrocelli L, Et al., The present and future of cough counting tools, J Thorac Dis, 12, pp. 5207-5223, (2020); Smith J, Woodcock A., New developments in the objective assessment of cough, Lung, 186, pp. S48-S54, (2008); 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Zhang X, Pettinati M, Jalali A, Et al., Novel COVID-19 Screening Using Cough Recordings of A Mobile Patient Monitoring System, Annu Int Conf IEEE Eng Med Biol Soc, 2021, pp. 2353-2357, (2021); Ponomarchuk A, Burenko I, Malkin E, Et al., Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough, IEEE J Sel Top Signal Process, 16, pp. 175-187, (2022); Windmon A, Minakshi M, Bharti P, Et al., TussisWatch: A Smart-Phone System to Identify Cough Episodes as Early Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure, IEEE J Biomed Health Inform, 23, pp. 1566-1573, (2019); Al-Khassaweneh M, Bani Abdelrahman R., A signal processing approach for the diagnosis of asthma from cough sounds, J Med Eng Technol, 37, pp. 165-171, (2013); Morjaria JB, Rigby AS, Morice AH., Asthma phenotypes: do cough and wheeze predict exacerbations in persistent asthma?, Eur Respir J, 50, (2017); Choudry NB, Fuller RW, Anderson N, Et al., Separation of cough and reflex bronchoconstriction by inhaled local anaesthetics, Eur Respir J, 3, pp. 579-583, (1990); Fuller RW, Karlsson JA, Choudry NB, Et al., Effect of inhaled and systemic opiates on responses to inhaled capsaicin in humans, J Appl Physiol (1985), 65, pp. 1125-1130, (1988)","M.-H. Kim; Department of Internal Medicine, Ewha Womans University College of Medicine, Seoul, 260 Gonghangdaero, Gangseo-gu, 07804, South Korea; email: mineyang81@ewha.ac.kr; S.M. Lee; Division of Pulmonology and Allergy, Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, 21 Namdong-daero 774beon-gil, Namdong-gu, 21565, South Korea; email: sangminlee77@naver.com","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85168995543"
"Shiroshita A.; Matsushita Y.K.S.; Tomii K.; Shiba H.; Shirakawa C.; Kataoka Y.; Sato K.","Shiroshita, Akihiro (57201979665); Matsushita, Yuya Kimura Shinya (57564268200); Tomii, Keisuke (7004731775); Shiba, Hiroshi (57219440009); Shirakawa, Chigusa (57208492810); Kataoka, Yuki (56677307000); Sato, Kenya (57219443114)","57201979665; 57564268200; 7004731775; 57219440009; 57208492810; 56677307000; 57219443114","Predicting in-hospital death in pneumonic COPD exacerbation via BAP-65, CURB-65 and machine learning","2022","ERJ Open Research","8","1","00452-2021","","","","5","10.1183/23120541.00452-2021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127664445&doi=10.1183%2f23120541.00452-2021&partnerID=40&md5=a8b07086474e113b51c209ac26653765","Dept of Respiratory Medicine, Ichinomiyanishi Hospital, Ichinomiya, Japan; Dept of Respiratory Medicine, Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan; Post Graduate Education Center, Kameda Medical Center, Kamogawa, Japan; Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan; Dept of Thoracic Medicine, Saiseikai Yokohamashi Tobu Hospital, Yokohama, Japan; Dept of Internal Medicine, Kyoto Min-Iren Asukai Hospital, Kyoto, Japan; Section of Clinical Epidemiology, Dept of Community Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan; Dept of Healthcare Epidemiology, Graduate School of Medicine/Public Health, Kyoto University, Kyota, Japan","Shiroshita A., Dept of Respiratory Medicine, Ichinomiyanishi Hospital, Ichinomiya, Japan; Matsushita Y.K.S., Dept of Thoracic Medicine, Saiseikai Yokohamashi Tobu Hospital, Yokohama, Japan; Tomii K., Dept of Respiratory Medicine, Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan; Shiba H., Post Graduate Education Center, Kameda Medical Center, Kamogawa, Japan; Shirakawa C., Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan; Kataoka Y., Dept of Internal Medicine, Kyoto Min-Iren Asukai Hospital, Kyoto, Japan, Section of Clinical Epidemiology, Dept of Community Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan, Dept of Healthcare Epidemiology, Graduate School of Medicine/Public Health, Kyoto University, Kyota, Japan; Sato K., Dept of Thoracic Medicine, Saiseikai Yokohamashi Tobu Hospital, Yokohama, Japan","Introduction There is no established clinical prediction model for in-hospital death among patients with pneumonic COPD exacerbation. We aimed to externally validate BAP-65 and CURB-65 and to develop a new model based on the eXtreme Gradient Boosting (XGBoost) algorithm. Methods This multicentre cohort study included patients aged ⩾40 years with pneumonic COPD exacerbation. The input data were age, sex, activities of daily living, mental status, systolic and diastolic blood pressure, respiratory rate, heart rate, peripheral blood eosinophil count and blood urea nitrogen. The primary outcome was in-hospital death. BAP-65 and CURB-65 underwent external validation using the area under the receiver operating characteristic curve (AUROC) in the whole dataset. We used XGBoost to develop a new prediction model. We compared the AUROCs of XGBoost with that of BAP-65 and CURB-65 in the test dataset using bootstrap sampling. Results We included 1190 patients with pneumonic COPD exacerbation. The in-hospital mortality was 7% (88 out of 1190). In the external validation of BAP-65 and CURB-65, the AUROCs (95% confidence interval) of BAP-65 and CURB-65 were 0.69 (0.66–0.72) and 0.69 (0.66–0.72), respectively. XGBoost showed an AUROC of 0.71 (0.62–0.81) in the test dataset. There was no significant difference in the AUROCs of XGBoost versus BAP-65 (absolute difference 0.054; 95% CI −0.057–0.16) or versus CURB-65 (absolute difference 0.0021; 95% CI −0.091–0.088). Conclusion BAP-65, CURB-65 and XGBoost showed low predictive performance for in-hospital death in pneumonic COPD exacerbation. Further large-scale studies including more variables are warranted. © The authors 2022.","","aged; algorithm; area under the curve; Article; BAP 65 score; blood; breathing rate; chronic obstructive lung disease; cohort analysis; CURB 65 score; daily life activity; diagnostic test accuracy study; diastolic blood pressure; disease assessment; disease exacerbation; extreme gradient boosting algorithm; female; hospital mortality; human; laboratory test; machine learning; major clinical study; male; mental health; multicenter study; outcome assessment; pneumonia; receiver operating characteristic; retrospective study; systolic blood pressure; urea nitrogen blood level; validation process","","","","","Ichinomiyanishi Hospital","for English language editing was obtained from Ichinomiyanishi Hospital. The funder played no role in the study design, study execution, data analyses, data interpretation, or decision to submit the report.","Global Strategy for the Diagnosis, Management and Prevention of COPD, (2020); Saleh A, Lopez-Campos JL, Hartl S, Et al., The effect of incidental consolidation on management and outcomes in COPD exacerbations: data from the European COPD Audit, PLoS ONE, 10, (2015); Huerta A, Crisafulli E, Menendez R, Et al., Pneumonic and nonpneumonic exacerbations of COPD: inflammatory response and clinical characteristics, Chest, 144, pp. 1134-1142, (2013); Shiroshita A, Shiba H, Tanaka Y, Et al., Effectiveness of steroid therapy on pneumonic chronic obstructive pulmonary disease exacerbation: a multicenter, retrospective cohort study, Int J Chron Obstruct Pulmon Dis, 15, pp. 2539-2547, (2020); Lim WS, van der Eerden MM, Laing R, Et al., Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study, Thorax, 58, pp. 377-382, (2003); Ilg A, Moskowitz A, Konanki V, Et al., Performance of the CURB-65 Score in predicting critical care interventions in patients admitted with community-acquired pneumonia, Ann Emerg Med, 74, pp. 60-68, (2019); Tabak YP, Sun X, Johannes RS, Et al., Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score, Arch Intern Med, 169, pp. 1595-1602, (2009); Shorr AF, Sun X, Johannes RS, Et al., Validation of a novel risk score for severity of illness in acute exacerbations of COPD, Chest, 140, pp. 1177-1183, (2011); Echevarria C, Steer J, Heslop-Marshall K, Et al., Validation of the DECAF score to predict hospital mortality in acute exacerbations of COPD, Thorax, 71, pp. 133-140, (2016); Trethewey SP, Hurst JR, Turner AM., Pneumonia in exacerbations of COPD: what is the clinical significance?, ERJ Open Res, 6, pp. 00282-2019, (2020); Rajkomar A, Dean J, Kohane I., Machine learning in medicine, N Engl J Med, 380, pp. 1347-1358, (2019); Anthonisen NR, Manfreda J, Warren CP, Et al., Antibiotic therapy in exacerbations of chronic obstructive pulmonary disease, Ann Intern Med, 106, pp. 196-204, (1987); Shindo Y, Ito R, Kobayashi D, Et al., Risk factors for drug-resistant pathogens in community-acquired and healthcare-associated pneumonia, Am J Respir Crit Care Med, 188, pp. 985-995, (2013); Collins GS, Reitsma JB, Altman DG, Et al., Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement, Ann Intern Med, 162, pp. 55-63, (2015); Shigematsu K, Nakano H, Watanabe Y., The eye response test alone is sufficient to predict stroke outcome— reintroduction of Japan Coma Scale: a cohort study, BMJ Open, 3, (2013); Yasunaga H, Matsui H, Horiguchi H, Et al., Clinical epidemiology and health services research using the diagnosis procedure combination database in Japan, Asian Pac J Dis Manage, 7, pp. 19-24, (2013); Steer J, Gibson J, Bourke SC., The DECAF Score: predicting hospital mortality in exacerbations of chronic obstructive pulmonary disease, Thorax, 67, pp. 970-976, (2012); White IR, Royston P, Wood AM., Multiple imputation using chained equations: issues and guidance for practice, Stat Med, 30, pp. 377-399, (2011); Toutenburg H, Rubin DB, Multiple imputation for nonresponse in surveys, Stat Pap, 31, (1990); Snell KI, Ensor J, Debray TP, Et al., Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?, Stat Methods Med Res, 27, pp. 3505-3522, (2018); Chen T, Guestrin C., XGBoost: a scalable tree boosting system, (2016); Carpenter J, Bithell J., Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians, Stat Med, 19, pp. 1141-1164, (2000); Usui K, Tanaka Y, Noda H, Et al., Comparison of three prediction rules for prognosis in community acquired pneumonia: Pneumonia Severity Index (PSI), CURB-65, and A-DROP, Nihon Kokyuki Gakkai Zasshi, 47, pp. 781-785, (2009); Huang W, Cui M, Jiang Y, Et al., A prospective validation of NEWS, CREWS and BAP-65 among patients with AECOPD, Chin J Nursing, 12, pp. 381-384, (2017); Li R-H, Belford GG., Instability of decision tree classification algorithms, KDD ’02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 570-575, (2002); Riley RD, Snell KI, Ensor J, Et al., Minimum sample size for developing a multivariable prediction model: PART II – binary and time-to-event outcomes, Stat Med, 38, pp. 1276-1296, (2019); Floares AG, Ferisgan M, Onita D, Et al., The smallest sample size for the desired diagnosis accuracy, Int J Oncol Cancer Therapy, 2, pp. 13-19, (2017); Bellou V, Belbasis L, Konstantinidis AK, Et al., Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal, BMJ, 367, (2019); Vergouwe Y, Steyerberg EW, Eijkemans MJC, Et al., Substantial effective sample sizes were required for external validation studies of predictive logistic regression models, J Clin Epidemiol, 58, pp. 475-483, (2005); Steyerberg EW., Validation in prediction research: the waste by data splitting, J Clin Epidemiol, 103, pp. 131-133, (2018)","A. Shiroshita; Dept of Respiratory Medicine, Ichinomiyanishi Hospital, Ichinomiya, Japan; email: akihirokun8@gmail.com","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127664445"
"Victor Ikechukwu A.","Victor Ikechukwu, Agughasi (58663521900)","58663521900","The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images","2024","Inteligencia Artificial","27","74","","62","79","17","3","10.4114/intartif.vol27iss74pp62-79","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194546399&doi=10.4114%2fintartif.vol27iss74pp62-79&partnerID=40&md5=b17cd0dfe26dec42c45f4021b843668e","Department of Computer Science & Engineering, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","Victor Ikechukwu A., Department of Computer Science & Engineering, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","This study evaluates the efficacy of finetuning versus full training deep learning models for diagnosing Chronic Obstructive Pulmonary Disease (COPD) from Chest Xray (CXR) images. It compares the performance of pretrained architectures such as InceptionV3, ResNet50, and VGG19 against a custom CNN model, IykeNet, developed from scratch. Emphasizing data augmentation to address limited and unbalanced datasets, the study also explores the advantage of using grayscale images over coloured images in disease classification. Findings indicate that finetuning pretrained models significantly enhances model performance, leading to faster convergence, improved stability, and increased accuracy. Experimental outcomes reveal that ResNet50 achieved a training accuracy of 99.2% and a validation accuracy of 100%, outperforming VGG19 and Iyke-Net. Grayscale images were found to consistently outperform colour images, hinting at the lesser importance of colour information for certain diagnostic procedures. These results underscore the importance of optimizing model complexity, computational efficiency, and diagnostic accuracy. Finetuning existing deep learning models emerges as a pivotal strategy for improving COPD diagnosis from CXR images, marking a significant step forward in the application of AI-enhanced medical diagnostics. © IBERAMIA and the authors.","Chest X-ray Analysis; COPD Diagnosis; Data Augmentation Techniques; Deep Learning in Healthcare; Grayscale Image Processing; Pre-trained CNN Models","Classification (of information); Computational efficiency; Deep learning; Diagnosis; Image enhancement; Medical imaging; Pulmonary diseases; Augmentation techniques; Chest X-ray analyse; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease diagnose; CNN models; Data augmentation; Data augmentation technique; Deep learning in healthcare; Disease diagnosis; Grayscale image processing; Pre-trained CNN model; X ray analysis","","","","","","","Fan R., Bu S., TransferLearningBased Approach for the Diagnosis of Lung Diseases from Chest X-ray Images, Entropy, 24, 3, (2022); Victor Ikechukwu A., Sreyas P., Sena A., Preetham H., Raksha K., Explainable Deep Learning Model for Covid19 Diagnosis, IRJMETS, pp. 3051-3059, (2022); Victor Ikechukwu A., CXNet: an efficient ensemble semantic deep neural network for ROI identification from chestxray images for COPD diagnosis, Mach. Learn.: Sci. Technol, 4, 2, (2023); Wang J., Zhu H., Wang S.H., Zhang Y.D., A Review of Deep Learning on Medical Image Analysis, Mobile Netw Appl, 26, 1, pp. 351-380, (2021); He K., Zhang X., Ren S., Sun J., Deep Residual Learning for Image Recognition, (2015); Angelini E. D., Et al., Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans, Thorax, (2023); Jose SolerCataluna J., Et al., Exacerbations in COPD: a personalised approach to care, The Lancet Respiratory Medicine, 11, 3, pp. 224-226, (2023); Chetoui M., Akhloufi M. A., Bouattane E. M., Abdulnour J., Roux S., Bernard C. D., Explainable COVID19 Detection Based on Chest Xrays Using an EndtoEnd RegNet Architecture, Viruses, 15, 6, (2023); Chen P., Wu L., Wang L., AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications, Applied Sciences, 13, 18, (2023); Ikechukwu A. V., COPDNet: An Explainable ResNet50 Model for the Diagnosis of COPD from CXR Images, 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), pp. 1-7, (2023); Victor Ikechukwu A., Murali S., Deepu R., Shivamurthy R. C., ResNet50 vs VGG19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest Xray images, Global Transitions Proceedings, 2, 2, pp. 375-381, (2021); Krizhevsky A., Sutskever I., Hinton G. E., ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 6, pp. 84-90, (2017); Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R. M., ChestXray8: Hospitalscale Chest Xray Database and Benchmarks on WeaklySupervised Classification and Localization of Common Thorax Diseases, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462-3471, (2017); Agughasi Victor I., Murali S., iNet: a deep CNN model for white blood cancer segmentation and classification, IJATEE, 9, 95, (2022); Agughasi V. I., Srinivasiah M., Semisupervised labelling of chest xray images using unsupervised clustering for groundtruth generation, AET, 2, 3, pp. 188-202, (2023); Victor Ikechukwu A., CXNet: an efficient ensemble semantic deep neural network for ROI identification from chestxray images for COPD diagnosis, Mach. Learn.: Sci. Technol, 4, 2, (2023); Elhanashi A., Saponara S., Zheng Q., Classification and Localisation of MultiType Abnormalities on Chest XRays Images, IEEE Access, 11, pp. 83264-83277, (2023); Sarkar R., Hazra A., Sadhu K., Ghosh P., A Novel Method for Pneumonia Diagnosis from Chest XRay Images Using Deep Residual Learning with Separable Convolutional Networks, Computer Vision and Machine Intelligence in Medical Image Analysis, pp. 1-12, (2020); Gab Allah A. M., Sarhan A. M., Elshennawy N. M., Edge UNet: Brain tumor segmentation using MRI based on deep UNet model with boundary information, Expert Systems with Applications, 213, (2023); Ioffe S., Szegedy C., Batch Normalisation: Accelerating Deep Network Training by Reducing Internal Covariate Shift, (2015); Seibold C., Kleesiek J., Schlemmer H.P., Stiefelhagen R., SelfGuided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs, Proceedings of the Asian Conference on Computer Vision, (2020); Bhimshetty S., Ikechukwu A. V., Energyefficient deep Qnetwork: reinforcement learning for efficient routing protocol in wireless internet of things, Indonesian Journal of Electrical Engineering and Computer Science, 33, 2, (2024); Raza R., Et al., LungEffNet: Lung cancer classification using EfficientNet from CTscan images, Engineering Applications of Artificial Intelligence, 126, (2023); Salehi M., Mohammadi R., Ghaffari H., Sadighi N., Reiazi R., Automated detection of pneumonia cases using deep transfer learning with paediatric chest Xray images, BJR, 94, 1121, (2021); Ikechukwu A. V., Murali S., xAI: An Explainable AI Model for the Diagnosis of COPD from CXR Images, 2023 IEEE 2nd International Conference on Data, Decision and Systems (ICDDS), pp. 1-6, (2023); Mahaur B., Mishra K. K., Singh N., Improved Residual Network based on normpreservation for visual recognition, Neural Networks, 157, pp. 305-322, (2023); Zhang L., Bian Y., Jiang P., Zhang F., A Transfer Residual Neural Network Based on ResNet50 for Detection of Steel Surface Defects, Applied Sciences, 13, 9, (2023); Zhang M., Xue M., Li S., Zou Y., Zhu Q., Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification, Biomed. Opt. Express, BOE, 14, 4, pp. 1636-1646, (2023); Nguyen K.B., Choi J., Yang J.S., EUNNet: Efficient UNNormalized Convolution Layer for Stable Training of Deep Residual Networks Without Batch Normalization Layer, IEEE Access, 11, pp. 76977-76988, (2023); Azad A. K., MahabubAAlahi I. Ahmed, Ahmed M. U., In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID19 Infection from Chest Xray Images, Diagnostics, 13, 3, (2023); Rajpurkar P., Et al., CheXNet: RadiologistLevel Pneumonia Detection on Chest XRays with Deep Learning, (2017); Loey M., Smarandache F., Khalifa N. E. M., Within the Lack of Chest COVID19 Xray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning, Symmetry, 12, 4, (2020); Apostolopoulos I. D., Mpesiana T. A., Covid19: automatic detection from Xray images utilising transfer learning with convolutional neural networks, Phys Eng Sci Med, 43, 2, pp. 635-640, (2020); Humphries S. M., Et al., Deep Learning Enables Automatic Classification of Emphysema Pattern at CT, Radiology, 294, 2, pp. 434-444, (2020)","A. Victor Ikechukwu; Department of Computer Science & Engineering, Maharaja Institute of Technology, Mysore, Karnataka, 571477, India; email: victor.agughasi@gmail.com","","Asociacion Espanola de Inteligencia Artificial","","","","","","11373601","","","","English","Inteligencia Artif.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85194546399"
"Karaarslan O.; Belcastro K.D.; Ergen O.","Karaarslan, Oğuzhan (58631701600); Belcastro, Kristen Dominica (57211514336); Ergen, Onur (26664529900)","58631701600; 57211514336; 26664529900","Respiratory sound-base disease classification and characterization with deep/machine learning techniques","2024","Biomedical Signal Processing and Control","87","","105570","","","","4","10.1016/j.bspc.2023.105570","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173281730&doi=10.1016%2fj.bspc.2023.105570&partnerID=40&md5=0e47108f25321edba94619ebf5947b20","Electrical and Electronics Engineering Faculty, Istanbul Technical University, Istanbul, 34469, Türkiye; Quantum Systems and Security Laboratory Istanbul Technical University, Istanbul, 34469, Türkiye; Physiotherapy and Rehabilitation Department, Yeditepe University, Istanbul, 34469, Türkiye; World and All, 95991, CA, United States; San Francisco General Hospital, 94110, CA, United States","Karaarslan O., Electrical and Electronics Engineering Faculty, Istanbul Technical University, Istanbul, 34469, Türkiye, Quantum Systems and Security Laboratory Istanbul Technical University, Istanbul, 34469, Türkiye; Belcastro K.D., Physiotherapy and Rehabilitation Department, Yeditepe University, Istanbul, 34469, Türkiye, World and All, 95991, CA, United States, San Francisco General Hospital, 94110, CA, United States; Ergen O., Electrical and Electronics Engineering Faculty, Istanbul Technical University, Istanbul, 34469, Türkiye, Quantum Systems and Security Laboratory Istanbul Technical University, Istanbul, 34469, Türkiye, World and All, 95991, CA, United States","Respiratory diseases (RDs) are a leading cause of death globally, with over 490,000 deaths in the EU and the US alone in 2017. Early detection of these diseases is crucial for improving treatment options and prolonging survival. However, diagnosing respiratory diseases can be challenging due to various factors such as physician shortages, financial constraints, and inexperienced doctors. To address these challenges, this paper presents a novel two-stage approach for the early detection and accurate classification of respiratory diseases using sound processing, machine learning, and deep learning techniques. The proposed approach utilizes a voice recorder and the internet to create a numerical feature vector using statistical properties of sound files from various datasets. The feature vector goes through a 2-stage feature elimination process for deep learning models and a 3-stage feature elimination process for machine learning models. The resulting feature vector is then fed into a supervised model to classify patients as healthy or suffering from a respiratory disease. A second classifier is then used to characterize the specific respiratory disease. Experiments with various machine/deep learning algorithms demonstrate that the proposed model outperforms existing methods in the literature in terms of accuracy and F1-score. The model achieves an accuracy score of 0.998 and an F1-score of 0.998 for distinguishing between healthy and respiratory disease-affected patients and an accuracy of 0.947 and an F1-score of 0.94 for categorizing ‘unhealthy’ records into COPD, asthma, and other respiratory diseases. The proposed approach has significant potential low-cost and efficient tool aiding early detection and classification. Simple Summary: Every year, millions of people die as a result of respiratory diseases, particularly chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. Early diagnosis is critical for reducing morbidity and mortality from respiratory diseases because the best treatment options are frequently discovered in the early stages. Even though there are numerous studies and screening programs that can aid in the early detection of these diseases, there are still significant gaps in screening tools. In this study, we investigated the prospects of a two-stage pipeline for an automated system for identifying respiratory diseases, as well as the potential applications of sound processing, machine learning, and deep learning techniques in respiratory diseases within the context of respiratory sounds. In the first stage, a feature vector was extracted directly from the respiratory sound, and it was determined whether the feature vector was related to a patient suffering from a respiratory disease using supervised machine learning techniques. In the second stage, the method can also distinguish between asthma, chronic obstructive pulmonary disease, and other respiratory diseases (Pneumonia, URTI, Bronchiectasis, Bronchiolitis, LRTI). Preliminary findings show that deep/machine learning-based methods have the potential to detect and classify respiratory illnesses in real time. The proposed model has great potential as a low-cost, easily accessible tool for screening and detecting respiratory diseases, thereby closing the gap in early diagnosis. © 2023 Elsevier Ltd","Ada boost classifier; Deep learning; Extra tree classifier; Lung disease classification; Respiratory diseases","Automation; Biological organs; Classification (of information); Computer aided diagnosis; Learning algorithms; Learning systems; Pulmonary diseases; Support vector machines; Vectors; Ada boost classifiers; Deep learning; Disease classification; Extra tree classifier; Extra-trees; Features vector; Lung disease classification; Machine-learning; Respiratory sounds; Tree classifiers; abnormal respiratory sound; AdaBoost classifier; adolescent; adult; Article; artificial neural network; asthma; audio recording; binary classification; bronchiectasis; bronchiolitis; child; chronic obstructive lung disease; classification algorithm; classifier; computer assisted diagnosis; controlled study; correlation coefficient; crackle; decision tree; deep learning; deep neural network; diagnostic accuracy; disease classification; early diagnosis; extra tree classifier; extreme gradient boosting; false positive result; feature extraction; female; first stage neural network; forced expiratory flow; forced expiratory volume; forced vital capacity; Gini coefficient; human; infant; intermethod comparison; Internet; k fold cross validation; kappa statistics; learning algorithm; light gradient boosting machine; lower respiratory tract infection; lung function; machine learning; major clinical study; male; morbidity; mortality rate; newborn; OneVsOne classifier; peak expiratory flow; pneumonia; preschool child; random forest; receiver operating characteristic; second stage neural network; sensitivity and specificity; short time Fourier transform; signal detection; sound analysis; spirometry; supervised machine learning; upper respiratory tract infection; voice analysis; wheezing; Deep learning","","","AKG C417L; Littmann 3200, 3M; Littmann Classic II SE, 3M; Meditron Master Elite, Welch Allyn; SpiroSmart","3M; 3M; Welch Allyn","","","Levine S.M., Marciniuk D.D., Global impact of respiratory disease, Chest, 161, 5, pp. 1153-1154, (2022); (2022); Talamo C., Et al., Diagnostic labeling of COPD in five latin american cities, Chest, 131, 1, pp. 60-67, (2007); Palaniappan R., Et al., Computer-based respiratory sound analysis: A systematic review, IETE Tech. 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"Wen J.; Giri M.; Xu L.; Guo S.","Wen, Jun (57660679400); Giri, Mohan (57189391243); Xu, Li (57199906972); Guo, Shuliang (7403650631)","57660679400; 57189391243; 57199906972; 7403650631","Association between Exposure to Selected Heavy Metals and Blood Eosinophil Counts in Asthmatic Adults: Results from NHANES 2011–2018","2023","Journal of Clinical Medicine","12","4","1543","","","","4","10.3390/jcm12041543","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148949091&doi=10.3390%2fjcm12041543&partnerID=40&md5=d5684e61d01c1f775a5a70976435131d","Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, 400016, China","Wen J., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, 400016, China; Giri M., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, 400016, China; Xu L., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, 400016, China; Guo S., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, 400016, China","(1) Background: Heavy metals are widely used and dispersed in the environment and people’s daily routines. Many studies have reported an association between heavy metal exposure and asthma. Blood eosinophils play a crucial role in the occurrence, progression, and treatment of asthma. However, there have thus far been few studies that aimed to explore the effects of heavy metal exposure on blood eosinophil counts in adults with asthma. Our study aims to discuss the association between metal exposure and blood eosinophil counts among asthmatic adults. (2) Methods: A total of 2026 asthmatic individuals were involved in our research from NHANES with metal exposure, blood eosinophils, and other covariates among the American population. A regression model, the XGBoost algorithm, and a generalized linear model (GAM) were used to explore the potential correlation. Furthermore, we conducted a stratified analysis to determine high-risk populations. (3) Results: The multivariate regression analysis indicated that concentrations of blood Pb (log per 1 mg/L; coefficient β, 25.39; p = 0.010) were positively associated with blood eosinophil counts. However, the associations between blood cadmium, mercury, selenium, manganese, and blood eosinophil counts were not statistically significant. We used stratified analysis to determine the high-risk group regarding Pb exposure. Pb was identified as the most vital variable influencing blood eosinophils through the XGBoost algorithm. We also used GAM to observe the linear relationship between the blood Pb concentrations and blood eosinophil counts. (4) Conclusions: The study demonstrated that blood Pb was positively correlated with blood eosinophil counts among asthmatic adults. We suggested that long-time Pb exposure as a risk factor might be correlated with the immune system disorder of asthmatic adults and affect the development, exacerbation, and treatment of asthma. © 2023 by the authors.","asthma; eosinophil; heavy metal; lead (Pb); National Health and Nutrition Examination Survey (NHANES)","cadmium; heavy metal; lead; manganese; mercury; selenium; adult; algorithm; Article; asthma; controlled study; correlation analysis; environmental exposure; eosinophil count; female; heavy metal blood level; high risk population; human; immunopathology; lead blood level; limit of detection; machine learning; major clinical study; male; middle aged; multivariate analysis; regression analysis; regression model; risk assessment; risk factor; statistical analysis; statistical model","","cadmium, 22537-48-0, 7440-43-9; lead, 7439-92-1, 13966-28-4; manganese, 16397-91-4, 7439-96-5; mercury, 14302-87-5, 7439-97-6; selenium, 7782-49-2","Beckman Coulter HMX, Beckman Coulter, United States; ELAN 6100 DRC Plus, Perkin Elmer, United States; ELAN DRC II, Perkin Elmer, United States","Beckman Coulter, United States; Perkin Elmer, United States; Perkin Elmer, United States","","","Wenzel S., Severe asthma: From characteristics to phenotypes to endotypes, Clin. 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Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85148949091"
"Ostro B.; Spada N.; Kuiper H.","Ostro, Bart (25945220200); Spada, Nicholas (25124619600); Kuiper, Heather (58140471900)","25945220200; 25124619600; 58140471900","The impact of coal trains on PM2.5 in the San Francisco Bay area","2023","Air Quality, Atmosphere and Health","16","6","","1173","1183","10","4","10.1007/s11869-023-01333-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149953693&doi=10.1007%2fs11869-023-01333-0&partnerID=40&md5=9acf671aa4fa324a8c24cecee6c0b356","Air Quality Research Center, University of California, Davis, CA, United States; Oakland, CA, United States","Ostro B., Air Quality Research Center, University of California, Davis, CA, United States; Spada N., Air Quality Research Center, University of California, Davis, CA, United States; Kuiper H., Oakland, CA, United States","Exposure to fine particulate matter (PM2.5) is associated with adverse health effects, including mortality, even at low concentrations. Rail conveyance of coal, accounting for one-third of American rail freight tonnage, is a source of PM2.5. However, there are limited studies of its contribution to PM2.5, especially in urban settings where residents experience higher exposure and vulnerability to air pollution. We developed a novel artificial intelligence-driven monitoring system to quantify average and maximum PM2.5 concentrations of full and empty (unloaded) coal trains compared to freight and passenger trains. The monitor was close to the train tracks in Richmond, California, a city with a racially diverse population of 115,000 and high rates of asthma and heart disease. We used multiple linear regression models controlling for diurnal patterns and meteorology. The results indicate coal trains add on average 8.32 µg/m3 (95% CI = 6.37, 10.28; p < 0.01) to ambient PM2.5, while sensitivity analysis produced midpoints ranging from 5 to 12 µg/m3. Coal trains contributed 2 to 3 µg/m3 more of PM2.5 than freight trains, and 7 µg/m3 more under calm wind conditions, suggesting our study underestimates emissions and subsequent concentrations of coal train dust. Empty coal cars tended to add 2 µg/m3. Regarding peak concentrations of PM2.5, our models suggest an increase of 17.4 µg/m3 (95% CI = 6.2, 28.5; p < 0.01) from coal trains, about 3 µg/m3 more than freight trains. Given rail shipment of coal occurs globally, including in populous areas, it is likely to have adverse effects on health and environmental justice. © 2023, The Author(s).","Coal; Health; Particulate matter; PM<sub>2.5</sub>; Rail; Train","California; San Francisco Bay; United States; air quality; asthma; coal; health risk; monitoring system; particulate matter; sensitivity analysis; train; vulnerability; wind velocity","","","","","California Air Resources Board, CARB, (G19-CAGP-17)","This work was supported by the California Air Resources Board Community Air Monitoring Grant Program (Grant G19-CAGP-17). The pilot portion of this study was also funded in part by a local community member. ","Adar S.D., Filigrana P.A., Clements N., Peel J.L., Ambient Coarse Particulate Matter and Human Health: A Systematic Review and Meta-Analysis, Curr Environ Health Rep, 1, 3, pp. 258-274, (2014); Akaoka K., McKendry I., Saxton J., Cottle P.W., Impact of coal-carrying trains on particulate matter concentrations in South Delta, British Columbia, Canada, Environ Pollut, 223, pp. 376-383, (2017); AQ-SPEC, (2022); Barkjohn K.K., Holder A.L., Frederick S.G., Clements A.L., Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke, Sensors, 22, 24, (2022); Barkjohn K.K., Gantt B., Clements A.L., Development and Application of a United States wide correction for PM2.5 data collected with the PurpleAir sensor, Atmos Meas Tech, 4, 6, (2021); Baruya P., Losses in the coal supply chain, IEA Clean Coal Centre., (2012); Coal Dust Frequently Asked Questions, (2011); Bond T.C., Bergstrom R.W., Light Absorption by Carbonaceous Particles: An Investigative Review, Aerosol Science and Technology, 40, January 2006, pp. 27-67, (2006); Brunekreef B., Strak M., Chen J., Et al., Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM2.5, BC, NO2, and O3: An Analysis of European Cohorts in the ELAPSE Project, Research Reports: Health Effects Institute 208, (2021); Chen D., Zhang F., Yu C., Jiao A., Xiang Q., Yu Y., Mayvaneh F., Hu K., Ding Z., Zhang Y., Hourly associations between exposure to ambient particulate matter and emergency department visits in an urban population of Shenzhen, China, Atmos Environ, 209, pp. 78-85, (2019); Final Report Environmental Evaluation of Fugitive Coal Dust Emissions from Coal Trains Goonyella, Blackwater and Moura Coal Rail Systems Queensland Rail Limited Report No. H327578-N00-EE00.00. (March 31, (2008); Dubovik O., Holben B., Eck T.F., Smirnov A., Kaufman Y.J., King M.D., Tanre D., Slutsker I., Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations, J Atmos Sci, 59, pp. 590-608, (2002); Initial Report on the Independent Review of Rail Coal Dust Emissions Management Practices in the NSW Coal Chain., (2015); Fuller R., Landrigan P.J., Balakrishnan K., Pollution and health: A progress update, Lancet Planet Health, 6, 6, pp. e535-e547, (2022); Hatch J., Affolter R., Davis F., Chemical analyses of coal from the Blackhawk Formation, Wasatch Plateau coal field, Carbon, Emery, and Sevier Counties, Utah, Utah Geological and Mineral Survey Coal Studies, 49, pp. 69-102, (1979); Higginbotham N., Ewald B., Mozeley F., Whelan J., Coal Train Signature Study, Briefing Paper Prepared for Coal Terminal Action Group Dust and Health Committee, Coal Terminal Action Group (August, pp. 1-26, (2013); Hricko A., Rowland G., Eckel S., Logan A., Taher M., Wilson J., Global Trade, Local Impacts: Lessons from California on Health Impacts and Environmental Justice Concerns for Residents Living near Freight Rail Yards, Int J Environ Res Public Health, 11, 2, pp. 1914-1941, (2014); Jaffe D., Putz J., Hof G., Hof G., Hee J., Lommers-Johnson D.A., Gabela F., Fry J.L., Ayres B., Kelp M., Minsk M., Diesel particulate matter and coal dust from trains in the Columbia River Gorge, Washington State, USA, Atmos Pollut Res, 6, 6, pp. 946-952, (2015); Jha A., Muller M., Handle with Care: The Local Air Pollution Costs of Coal Storage. National Bureau of Economic Research Report no, (2017); Ltd K.E.P., Pollution Reduction Program 4.2 Particulate Emissions from Coal Trains. Queensland: Prepared for Australian Rail Track Corporation Pty Ltd, (2013); Kim J., Kim H., Kweon J., Hourly differences in air pollution on the risk of asthma exacerbation. Environ Pollut (Barking, Essex, 1987, 203, pp. 15-21, (2015); Liu L., Song F., Fang J., Wei J., Ho H.C., Song Y., Zhang Y., Wang L., Yang Z., Hu C., Zhang Y., Intraday effects of ambient PM1 on emergency department visits in Guangzhou, China: A case-crossover study, Sci Total Environ, 750, (2021); McDuffie E., Martin R., Brauer M., (2015); Meyer R., A Major but Little-Known Supporter of Climate Denial: Freight Railroads, (2019); Mikati I., Benson A.F., Luben T.J., Sacks J.D., Richmond-Bryant J., Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status, Am J Public Health, 108, 4, pp. 480-485, (2018); Risk Assessment Guidelines: Guidance Manual for Preparation of Health Risk Assessments, Appendices A-N, Air Toxics Hot Spots Program, Office of Environmental Health Hazards Assessment, (2015); Ostro B., Hu J., Goldberg D., Reynolds P., Hertz A., Bernstein L., Kleeman M.J., Associations of Mortality with Long-Term Exposures to Fine and Ultrafine Particles, Species and Sources: Results from the California Teachers Study Cohort, Environ Health Perspect, 123, 6, pp. 549-556, (2015); Ouimette J.R., Malm W.C., Schichtel B.A., Sheridan P.J., Andrews E., Ogren J.A., Arnott W.P., Evaluating the PurpleAir monitor as an aerosol light scattering instrument, Atmos Meas Tech, 15, pp. 655-676, (2022); Peters A., Dockery D.W., Muller J.E., Mittleman M.A., Increased particulate air pollution and the triggering of myocardial infarction, Circulation, 103, 23, pp. 2810-2815, (2001); Prakash B.B., Kecojevic V., Lashgari A., Analysis of dust emission at coal train loading facility, Int J Min Reclam Environ, 32, 1, pp. 56-74, (2018); Ryan L., Wand M., (2014); Sahu S.P., Pakra A.K., Assessment of dispersion of respirable particles emitted from opencast mining operations: development and validation of stepwise regression models, Environ Dev Sustain, 24, pp. 9139-9164, (2022); Srivastava A., Kumar A., Elumalai S.P., Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines, Environ Model Assess, 26, pp. 85-403, (2021); Tessum C.W., Paolella D.A., Chambliss S.E., Apte J.S., Hill J.D., Marshall J.D., PM 2.5 polluters disproportionately and systemically affect people of color in the United States, Sci Adv, 7, 18, (2021); Trivedi R., Chakraborty M.K., Tewary B.K., Dust dispersion modeling using fugitive dust model at an opencast coal project of Western Coalfields Limited, India, J Sci Ind Res, 68, pp. 71-78, (2009); Low-cost Sensors for Air Quality Monitoring, Aerosol, Air Qual Res, 20, 2, (2020); US Energy Information Administration, (2022); Epa U.S., Integrated Science Assessment (ISA) for Particulate Matter (Final Report, Dec 2019), U.S. Environmental Protection Agency, Washington, DC., (2019); Vohra K., Vodonos A., Schwartz J., Marais E.A., Sulprizio M., Mickley L.J., Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem, Environ Res, 195, (2021); WHO global air quality guidelines: Particulate matter (PM 2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide (, License: CC BY-NC-SA, 3, pp. pp0-pp, (2021); Wu P.C., Cheng T.J., Kuo C.-P., Fu J.S., Lai H.-C., Chiu T.-Y., Lai L.-W., Transient risk of ambient fine particulate matter on hourly cardiovascular events in Tainan City, Taiwan, Plos One, 15, 8, (2020); Yorifuji T., Suzuki E., Kashima S., Cardiovascular Emergency Hospital Visits and Hourly Changes in Air Pollution, Stroke, 45, 5, pp. 1264-1268, (2014)","B. Ostro; Air Quality Research Center, University of California, Davis, United States; email: bdostro@ucdavis.edu","","Springer Science and Business Media B.V.","","","","","","18739318","","","","English","Air Qual. Atmos. Health","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85149953693"
"Hanamatsu S.; Murayama K.; Ohno Y.; Yamamoto K.; Yui M.; Toyama H.","Hanamatsu, Satomu (57220188816); Murayama, Kazuhiro (51564143100); Ohno, Yoshiharu (7401515049); Yamamoto, Kaori (57220190022); Yui, Masao (56667174100); Toyama, Hiroshi (55708721400)","57220188816; 51564143100; 7401515049; 57220190022; 56667174100; 55708721400","Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies","2023","Diagnostic and Interventional Radiology","29","5","","664","673","9","4","10.4274/dir.2023.232149","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169848899&doi=10.4274%2fdir.2023.232149&partnerID=40&md5=357b87f842fad8e604db2750e10ad936","Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan; Joint Research Laboratory of Advanced Medicine Imaging, Fujita Health University School of Medicine, Toyoake, Japan; Canon Medical Systems Corporation, Otawara, Japan","Hanamatsu S., Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan; Murayama K., Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan; Ohno Y., Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan, Joint Research Laboratory of Advanced Medicine Imaging, Fujita Health University School of Medicine, Toyoake, Japan; Yamamoto K., Canon Medical Systems Corporation, Otawara, Japan; Yui M., Canon Medical Systems Corporation, Otawara, Japan; Toyama H., Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan","PURPOSE Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies. METHODS For the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland–Altman analysis. RESULTS The in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 103, 0.00 ± 0.06 × 103, and 1.13 ± 4.04 × 103 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as −0.01 ± 0.07. CONCLUSION Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR. © 2023, Galenos Publishing House. All rights reserved.","Brain; deep learning reconstruction; diffusion; intravoxel incoherent motion; magnetic resonance imaging","Brain; Deep Learning; Diffusion Magnetic Resonance Imaging; Humans; Motion; Quality Improvement; biological marker; water; adult; aged; apparent diffusion coefficient; Article; asthma; brain tumor; claustrophobia; deep learning; diffusion weighted imaging; echo planar imaging; female; health insurance; human; image analysis; image quality; in vivo study; intravoxel incoherent motion imaging; kidney failure; limit of agreement; male; nuclear magnetic resonance imaging; outpatient department; quantitative analysis; retrospective study; sinus node; software; training; very elderly; white matter; Wilcoxon signed ranks test; brain; diagnostic imaging; diffusion weighted imaging; motion; procedures; total quality management","","water, 7732-18-5","Vantage  Galan, Canon, Japan","Canon, Japan","Canon Medical Systems Corporation","pliant with the Health Insurance Portability and Accountability Act of Japan. Written informed consent was waived for each participant enrolled in this study. This study was also technically and financially supported by the Canon Medical Systems Corporation. Two of the authors are employees of the Canon Medical Systems Corporation (KY and MY) but did not have control over any of the data used in this study.","Higaki T, Nakamura Y, Tatsugami F, Nakaura T, Awai K., Improvement of image quality at CT and MRI using deep learning, Jpn J Radiol, 37, 1, pp. 73-80, (2019); Higaki T, Nakamura Y, Zhou J, Et al., deep learning reconstruction at CT: phantom study of the image characteristics, Acad Radiol, 27, 1, pp. 82-87, (2020); Kidoh M, Shinoda K, Kitajima M, Et al., Deep Learning Based Noise Reduction for Brain MR imaging: tests on phantoms and healthy volunteers, Magn Reson Med Sci, 19, 3, pp. 195-206, (2020); Gavazzi S, van den Berg CAT, Savenije MHF, Et al., Deep learning-based reconstruction of in vivo pelvis conductivity with a 3D patch-based convolutional neural network trained on simulated MR data, Magn Reson Med, 84, 5, pp. 2772-2787, (2020); Ueda T, Ohno Y, Yamamoto K, Et al., Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice, Eur J Radiol, 134, (2021); Lin DJ, Johnson PM, Knoll F, Lui YW., Artificial intelligence for MR image reconstruction: an overview for clinicians, J Magn Reson Imaging, 53, 4, pp. 1015-1028, (2021); Matsukiyo R, Ohno Y, Matsuyama T, Et al., Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions, Jpn J Radiol, 39, 2, pp. 186-197, (2021); Sagawa H, Fushimi Y, Nakajima S, Et al., Deep learning-based noise reduction for fast volume diffusion tensor imaging: assessing the noise reduction effect and reliability of diffusion metrics, Magn Reson Med Sci, 20, 4, pp. 450-456, (2021); Johnson PM, Tong A, Donthireddy A, Et al., Deep learning reconstruction enables highly accelerated biparametric MR imaging of the prostate, J Magn Reson Imaging, 56, 1, pp. 184-195, (2022); Ueda T, Ohno Y, Yamamoto K, Et al., Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging, Radiology, 303, 2, pp. 373-381, (2022); Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M., MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders, Radiology, 161, 2, pp. 401-407, (1986); Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M., Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging, Radiology, 168, 2, pp. 497-505, (1988); Turner R, Le Bihan D, Maier J, Vavrek R, Hedges LK, Pekar J., Echo-planar imaging of intravoxel incoherent motion, Radiology, 177, 2, pp. 407-414, (1990); Federau C., Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: a review of the evidence, NMR Biomed, 30, 11, (2017); Le Bihan D., What can we see with IVIM MRI?, Neuroimage, 187, pp. 56-67, (2019); Iima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K., Diffusion MRI of the breast: current status and future directions, J Magn Reson Imaging, 52, 1, pp. 70-90, (2020); Zhang JL, Lee VS., Renal perfusion imaging by MRI, J Magn Reson Imaging, 52, 2, pp. 369-379, (2020); Wang YXJ, Huang H, Zheng CJ, Xiao BH, Chevallier O, Wang W., Diffusion-weighted MRI of the liver: challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion, Am J Nucl Med Mol Imaging, 11, 2, pp. 107-142, (2021); Federau C., Measuring perfusion: intravoxel incoherent motion mr imaging, Magn Reson Imaging Clin N Am, 29, 2, pp. 233-242, (2021); Ma W, Mao J, Wang T, Huang Y, Zhao ZH., Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: a systematic review and meta-analysis, Eur J Radiol, 141, (2021); Wang DJJ, Le Bihan D, Krishnamurthy R, Smith M, Ho ML., Noncontrast pediatric brain perfusion: arterial spin labeling and intravoxel incoherent motion, Magn Reson Imaging Clin N Am, 29, 4, pp. 493-513, (2021); Englund EK, Reiter DA, Shahidi B, Sigmund EE., Intravoxel incoherent motion magnetic resonance imaging in skeletal muscle: review and future directions, J Magn Reson Imaging, 55, 4, pp. 988-1012, (2022); Yung JP, Ding Y, Hwang KP, Et al., Quantitative Evaluation of apparent diffusion coefficient in a large multi-unit institution using the QIBA diffusion phantom, medRxiv, (2020); Shukla-Dave A, Obuchowski NA, Chenevert TL, Et al., Quantitative Imaging Biomarkers Alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials, J Magn Reson Imaging, 49, 7, pp. e101-e121, (2019); Federau C, O'Brien K, Meuli R, Hagmann P, Maeder P., Measuring brain perfusion with intravoxel incoherent motion (IVIM): initial clinical experience, J Magn Reson Imaging, 39, 3, pp. 624-632, (2014); Isogawa K, Ida T, Shiodera T, Takeguchi T., Deep Shrinkage convolutional neural network for adaptive noise reduction, IEEE Signal Processing Letters, 25, 2, pp. 224-228, (2018); Maubon AJ, Ferru JM, Berger V, Et al., Effect of field strength on MR images: comparison of the same subject at 0.5, 1.0, and 1.5 T, Radiographics, 19, 4, pp. 1057-1067, (1999); Voroney JP, Brock KK, Eccles C, Haider M, Dawson LA., Prospective comparison of computed tomography and magnetic resonance imaging for liver cancer delineation using deformable image registration, Int J Radiat Oncol Biol Phys, 66, 3, pp. 780-791, (2006); Zierhut ML, Ozturk-Isik E, Chen AP, Park I, Vigneron DB, Nelson SJ., (1)H spectroscopic imaging of human brain at 3 Tesla: comparison of fast three-dimensional magnetic resonance spectroscopic imaging techniques, J Magn Reson Imaging, 30, 3, pp. 473-480, (2009); Spinks TJ, Karia D, Leach MO, Flux G., Quantitative PET and SPECT performance characteristics of the Albira Trimodal pre-clinical tomograph, Phys Med Biol, 59, 3, pp. 715-731, (2014); Bland JM, Altman DG., Statistical methods for assessing agreement between two methods of clinical measurement, Lancet, 1, 8476, pp. 307-310, (1986); Bland JM, Altman DG., Comparing methods of measurement: why plotting difference against standard method is misleading, Lancet, 346, 8982, pp. 1085-1087, (1995); Park JC, Park KJ, Park MY, Kim MH, Kim JK., Fast T2-weighted imaging with deep learning-based reconstruction: evaluation of image quality and diagnostic performance in patients undergoing radical prostatectomy, J Magn Reson Imaging, 55, 6, pp. 1735-1744, (2022); Bae SH, Hwang J, Hong SS, Et al., Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging, Eur J Radiol, 154, (2022); Matsuyama T, Ohno Y, Yamamoto K, Et al., Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN, Eur Radiol, 32, 10, pp. 6658-6667, (2022); Tajima T, Akai H, Sugawara H, Et al., Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer, Magn Reson Imaging, 92, pp. 169-179, (2022); Obama Y, Ohno Y, Yamamoto K, Et al., MR imaging for shoulder diseases: Effect of compressed sensing and deep learning reconstruction on examination time and imaging quality compared with that of parallel imaging, Magn Reson Imaging, 94, pp. 56-63, (2022); Afat S, Herrmann J, Almansour H, Et al., Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction, Diagn Interv Imaging, 104, 4, pp. 178-184, (2023); Mardor Y, Roth Y, Ochershvilli A, Et al., Pretreatment prediction of brain tumors’ response to radiation therapy using high b-value diffusion-weighted MRI, Neoplasia, 6, 2, pp. 136-142, (2004); Kitajima K, Takahashi S, Ueno Y, Et al., Clinical utility of apparent diffusion coefficient values obtained using high b-value when diagnosing prostate cancer using 3 tesla MRI: comparison between ultra-high b-value (2000 s/mm²) and standard high b-value (1000 s/ mm²), J Magn Reson Imaging, 36, 1, pp. 198-205, (2012); Tavakoli AA, Kuder TA, Tichy D, Et al., Measured multipoint ultra-high b-value diffusion MRI in the assessment of MRI-detected prostate lesions, Invest Radiol, 56, 2, pp. 94-102, (2021)","S. Hanamatsu; Department of Radiology, Fujita Health University School of Medicine, Toyoake, Japan; email: shana@fujita-hu.ac.jp","","Galenos Publishing House","","","","","","13053825","","","37554957","English","Diagn. Intervention. Radiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85169848899"
"Smokovski I.; Steinle N.; Behnke A.; Bhaskar S.M.M.; Grech G.; Richter K.; Niklewski G.; Birkenbihl C.; Parini P.; Andrews R.J.; Bauchner H.; Golubnitschaja O.","Smokovski, Ivica (55631078400); Steinle, Nanette (6507061619); Behnke, Andrew (58476387900); Bhaskar, Sonu M. M. (57192268582); Grech, Godfrey (8957236600); Richter, Kneginja (8339398000); Niklewski, Günter (6506426122); Birkenbihl, Colin (57214455849); Parini, Paolo (6701425383); Andrews, Russell J. (57201642859); Bauchner, Howard (7006724724); Golubnitschaja, Olga (8403667700)","55631078400; 6507061619; 58476387900; 57192268582; 8957236600; 8339398000; 6506426122; 57214455849; 6701425383; 57201642859; 7006724724; 8403667700","Digital biomarkers: 3PM approach revolutionizing chronic disease management — EPMA 2024 position","2024","EPMA Journal","15","2","","149","162","13","4","10.1007/s13167-024-00364-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192814773&doi=10.1007%2fs13167-024-00364-6&partnerID=40&md5=83e87f8355d44b07899935202870a86c","University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, North Macedonia; Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia; Veteran Affairs Capitol Health Care Network, Linthicum, MD, United States; University of Maryland School of Medicine, Baltimore, MD, United States; Endocrinology Section, Carilion Clinic, Roanoke, VA, United States; Virginia Tech Carilion School of Medicine, Roanoke, VA, United States; Department of Neurology, Division of Cerebrovascular Medicine and Neurology, National Cerebral and Cardiovascular Centre (NCVC), Osaka, Suita, Japan; Department of Neurology & amp; Neurophysiology, Liverpool Hospital, Ingham Institute for Applied Medical Research and South Western Sydney Local Health District, Sydney, NSW, Australia; NSW Brain Clot Bank, Global Health Neurology Lab & amp; NSW Health Pathology, Sydney, NSW, Australia; Department of Pathology, Faculty of Medicine & amp; Surgery, University of Malta, Msida, Malta; CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany; Technische Hochschule Nürnberg GSO, Nuremberg, Germany; University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institute, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden; Nanotechnology & amp; Smart Systems Groups, NASA Ames Research Center, Aerospace Medical Association, Silicon Valley, CA, United States; Boston University Chobanian & amp; Avedisian School of Medicine, Boston, MA, United States; Predictive, Preventive and Personalized (3P) Medicine, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany","Smokovski I., University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, North Macedonia, Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia; Steinle N., Veteran Affairs Capitol Health Care Network, Linthicum, MD, United States, University of Maryland School of Medicine, Baltimore, MD, United States; Behnke A., Endocrinology Section, Carilion Clinic, Roanoke, VA, United States, Virginia Tech Carilion School of Medicine, Roanoke, VA, United States; Bhaskar S.M.M., Department of Neurology, Division of Cerebrovascular Medicine and Neurology, National Cerebral and Cardiovascular Centre (NCVC), Osaka, Suita, Japan, Department of Neurology & amp; Neurophysiology, Liverpool Hospital, Ingham Institute for Applied Medical Research and South Western Sydney Local Health District, Sydney, NSW, Australia, NSW Brain Clot Bank, Global Health Neurology Lab & amp; NSW Health Pathology, Sydney, NSW, Australia; Grech G., Department of Pathology, Faculty of Medicine & amp; Surgery, University of Malta, Msida, Malta; Richter K., Faculty of Medical Sciences, University Goce Delcev, Stip, North Macedonia, CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany, Technische Hochschule Nürnberg GSO, Nuremberg, Germany, University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany; Niklewski G., University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany; Birkenbihl C., Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Parini P., Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institute, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden; Andrews R.J., Nanotechnology & amp; Smart Systems Groups, NASA Ames Research Center, Aerospace Medical Association, Silicon Valley, CA, United States; Bauchner H., Boston University Chobanian & amp; Avedisian School of Medicine, Boston, MA, United States; Golubnitschaja O., Predictive, Preventive and Personalized (3P) Medicine, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany","Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs. © The Author(s) 2024.","Artificial intelligence; Cancer; Cardiovascular diseases; Chronic obstructive pulmonary disease; Diabetes; Digital biomarkers; Health economy and policy; Health protection; Health risk assessment; Health-to-disease transition; Innovative ecosystem; Machine learning; Non-communicable chronic disease; Perinatal asphyxia; PPPM / 3PM; Predictive Preventive Personalized Medicine; Primary and secondary care; Sleep disorders; Wearable point-of-care devices","glucose; actimetry; apnea hypopnea index; Article; atrial fibrillation; blood oxygen tension; blood pressure; cardiovascular disease; childhood disease; chronic disease; chronic obstructive lung disease; chronic stress; circadian rhythm sleep disorder; cognitive behavioral therapy; cost effectiveness analysis; coughing; daytime somnolence; diabetes mellitus; dietary intake; digital biomarker; digital biomarker monitoring; digital health technology; disease course; electrocardiography; environmental parameters; Europe; functional status; geographic distribution; glucose blood level; health care delivery; health care organization; health care personnel; health care policy; health economics; health economy and policy; health risk assessment; health to disease transition; heart rate variability; human; hypersomnia; insomnia; light exposure; lung function test; malignant neoplasm; medical record; medical service; medication compliance; non communicable disease; obstructive sleep apnea; oximetry; patient care; patient monitoring; perinatal asphyxia; personalized medicine; physical activity; polysomnography; predictive preventive and personalized medicine; preventive medicine; primary medical care; pulmonary rehabilitation; quality of life; quantitative analysis; radiomics; seasonal affective disorder; secondary health care; sedentary lifestyle; self care; sleep disorder; sleep latency; sleep medicine; sleep pattern; sleep time; snoring; telemedicine; vulnerable population","","glucose, 50-99-7, 84778-64-3, 8027-56-3","","","","","Li Y., Hu S., Chen C., Alifu N., Zhang X., Du J., Li C., Xu L., Wang L., Dong B., Opal photonic crystal-enhanced upconversion turn-off fluorescent immunoassay for salivary CEA with oral cancer, Talanta, 258, (2023); Belizario J.E., Faintuch J., Malpartida M.G., Breath biopsy and discovery of exclusive volatile organic compounds for diagnosis of infectious diseases, Front Cell Infect Microbiol, 10, (2020); Wang H., Sun J., Lu L., Yang X., Xia J., Zhang F., Wang Z., Competitive electrochemical aptasensor based on a cDNA-ferrocene/MXene probe for detection of breast cancer marker Mucin1, Anal Chim Acta, 1094, pp. 18-25, (2020); Le T., Priefer R., Detection technologies of volatile organic compounds in the breath for cancer diagnoses, Talanta, 265, (2024); Chung C., Cho H.J., Lee C., Koo J., Odorant receptors in cancer, BMB Rep, 55, 2, pp. 72-80, (2022); 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Advances in predictive, preventive and personalised medicine, pp. 129-139, (2023); Bhaskar S., Nurtazina A., Mittoo S., Banach M., Weissert R., Editorial: Telemedicine during and beyond COVID-19 Front, Public Health, 9, (2021); Dugas M., Crowley K., Gao G.G., Xu T., Agarwal R., Kruglanski W.A., Steinle N., Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans, PLoS ONE, 13, 3, (2018); Frohlich H., Balling R., Beerenwinkel N., Kohlbacher O., Kumar S., Lengauer T., Maathuis M.H., Moreau Y., Murphy S.A., Przytycka T.M., Rebhan M., Rost H., Schuppert A., Schwab M., Spang R., Stekhoven D., Sun J., Weber A., Ziemek D., Zupan B., From hype to reality: data science enabling personalized medicine, BMC Med, 16, 1, (2018); Golubnitschaja O., Yeghiazaryan K., Cebioglu M., Morelli M., Herrera-Marschitz M., Birth asphyxia as the major complication in newborns: moving towards improved individual outcomes by prediction, targeted prevention and tailored medical care, EPMA J, 2, 2, pp. 197-210, (2011); Peeva V., Yeghiazaryan K., Golubnitschaja O., Birth asphyxia as the most frequent perinatal complication, Predictive diagnostics and personalized treatment: Dream or reality, pp. 499-507, (2009); Yeghiazaryan K., Peeva V., Morelli M., Herrera-Marschitz M., Golubnitschaja O., Potential targets for early diagnosis and neuroprotection in asphyxiated newborns, Predictive diagnostics and personalized treatment: dream or reality, pp. 509-525, (2009); Evsevieva M., Sergeeva O., Mazurakova A., Koklesova L., Prokhorenko-Kolomoytseva I., Shchetinin E., Birkenbihl C., Costigliola V., Kubatka P., Golubnitschaja O., Pre-pregnancy check-up of maternal vascular status and associated phenotype is crucial for the health of mother and offspring, EPMA J, 13, 3, pp. 351-366, (2022); Andrews R.J., Wearable revolution: Predictive, preventive, personalized medicine (PPPM) par excellence, Predictive, preventive, and personalised medicine: from bench to bedside. Advances in predictive, preventive and personalised medicine, pp. 339-348, (2023); Shajari S., Kuruvinashetti K., Komeili A., Sundararaj U., The emergence of AI-based wearable sensors for digital health technology: a review, Sensors, 23, (2023)","I. Smokovski; University Clinic of Endocrinology, Diabetes and Metabolic Disorders, Skopje, North Macedonia; email: ivica.smokovski@ugd.edu.mk; O. Golubnitschaja; Predictive, Preventive and Personalized (3P) Medicine, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany; email: Olga.Golubnitschaja@ukbonn.de","","Springer Science and Business Media Deutschland GmbH","","","","","","18785077","","","","English","EPMA J.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85192814773"
"Hassan Naqvi S.Z.; Choudhry M.A.","Hassan Naqvi, Syed Zohaib (57202711215); Choudhry, Mohmmad Ahmad (55396900000)","57202711215; 55396900000","Embedded system design for classification of COPD and pneumonia patients by lung sound analysis","2022","Biomedizinische Technik","67","3","","201","218","17","4","10.1515/bmt-2022-0011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128560656&doi=10.1515%2fbmt-2022-0011&partnerID=40&md5=e7b72f550e2e85d429a18b0a3f29f9ca","Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan; Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan","Hassan Naqvi S.Z., Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan; Choudhry M.A., Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan","Chronic obstructive pulmonary disease (COPD) and pneumonia are lethal pulmonary illnesses with equivocal nature of abnormal pulmonic acoustics. Using lung sound signals, the classification of pulmonary abnormalities is a difficult task. A standalone system was conceived for screening COPD and Pneumonia patients through signal processing and machine learning methodologies. The proposed system will assist practitioners and pulmonologists in the accurate classification of disease. In this research work, ICBHI's and self-collected lung sound (LS) databases are used to investigate COPD and pneumonia patient. In this scheme, empirical mode decomposition (EMD), discrete wavelet transform (DWT), and analysis of variance (ANOVA) techniques are employed for segmentation, noise elimination, and feature selection, respectively. To overcome the inherent limitation of ICBHI's LS database, the adaptive synthetic (ADASYN) sampling technique is used to eradicate class imbalance. Lung sound features are used to train fine Gaussian support vector machine (FG-SVM) for classification of COPD, pneumonia, and heathy healthy subjects. This machine learning scheme is implemented on low cost and portable Raspberry pi 3 model B+ (Cortex-A53 (ARMv8) 64-bit SoC @ 1.4 GHz through hardware-supported language. Resultant hardware is capable of screening COPD and pneumonia patients accurately and assist health professionals.  © 2022 Walter de Gruyter GmbH, Berlin/Boston.","discrete wavelet transform; empirical mode decomposition; machine learning; support vector machine","Algorithms; Humans; Pneumonia; Pulmonary Disease, Chronic Obstructive; Respiratory Sounds; Support Vector Machine; Wavelet Analysis; Analysis of variance (ANOVA); Biological organs; Diagnosis; Discrete wavelet transforms; Embedded systems; Integrated circuit design; Pulmonary diseases; Signal reconstruction; System-on-chip; Wavelet decomposition; Chronic obstructive pulmonary disease; Discrete-wavelet-transform; Embedded systems design; Empirical Mode Decomposition; Lung Sound Analysis; Lung sound signals; Lung sounds; Sound database; Standalone systems; Support vectors machine; adaptive synthetic sampling method; analysis of variance; Article; asthma; bronchiectasis; chronic obstructive lung disease; classification algorithm; computer language; controlled study; convolutional neural network; cost effectiveness analysis; cross validation; data base; decision tree; diagnostic accuracy; diagnostic error; diagnostic test accuracy study; discrete wavelet transform; discriminant analysis; empirical mode decomposition; false discovery rate; false negative result; false positive result; feature extraction; feature selection; frequency; gaussian support vector machine; human; intermethod comparison; intrinsic mode function; k nearest neighbor; lower respiratory tract infection; lung auscultation; lung sound; major clinical study; noise reduction; physician; pneumonia; predictive value; pulmonologist; respiratory tract parameters; sampling; segmentation algorithm; signal noise ratio; signal processing; sound analysis; support vector machine; true negative rate; true positive rate; upper respiratory tract infection; abnormal respiratory sound; algorithm; chronic obstructive lung disease; pneumonia; support vector machine; wavelet analysis; Support vector machines","","","AKG C417L; Littmann 3200, 3M; Littmann Classic II SE, 3M; Meditron Master Elite Plus, Welch Allyn","3M; 3M; Welch Allyn","","","Rai D.K., Sharma P., Kumar R., Post-COVID-19 pneumonia pulmonary fibrosis case, QJM, 113, pp. 837-838, (2020); Leung J.M., Niikura M., Yang C.W.T., COVID-19 and COPD, Eur Respir J, 56, pp. 330-333, (2020); Llitjos J.F., Bredin S., Lascarrou J.B., Soumagne T., Cojocaru M., Leclerc M., Increased susceptibility to intensive care unit-Acquired pneumonia in severe COVID-19 patients: A multicentre retrospective cohort study, Ann Intensive Care, 11, pp. 1-8, (2021); Gerayeli F.V., Milne S., Cheung C., Liet X., Yang C.W.T., Tam A., COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-Analysis, EClinicalMedicine, 33, (2021); Villegas C.C., Paz-Zulueta M., Herrero-Montes M., Paras-Bravo P., Perez M.M., Cost analysis of chronic obstructive pulmonary disease (COPD): A systematic review, Health Econ Rev, 11, pp. 1-12, (2021); Ghimire A., Adhikari K.K., Paudel B.S., Shah S., Review of aetiology and antibiotics used in community acquired pneumonia in asia; A preliminary study for the formulation of a standard treatment guideline, Int J Innov Sci Res Technol, 6, pp. 958-963, (2021); Trivedy S., Goyal M., Mohapatra P.R., Mukherjee A., Design and development of smartphone-enabled spirometer with a disease classification system using convolutional neural network, IEEE Trans Instrum Meas, 69, pp. 7125-7135, (2020); Heijden M.V.D., Lucas P.J.F., Lijnse B., Heijdra Y.F., Schermer T.R.J., An autonomous mobile system for the management of COPD, J Biomed Inform, 46, pp. 458-469, (2013); Li S.H., Lin B.H., Tsai C.H., Yang C.T., Lin B.S., Design of wearable breathing sound monitoring system for real-Time wheeze detection, Sensors, 17, pp. 1-15, (2017); Emmanouilidou D., McCollum E.D., Park D.E., Elhilali M., Computerized lung sound screening for pediatric auscultation in noisy field environments, IEEE Trans Biomed Eng, 65, pp. 1564-1574, (2017); Boujelben O., Bahoura M., Efficient FPGA-based architecture of an automatic wheeze detector using a combination of MFCC and SVM algorithms, J Syst Architect, 88, pp. 54-64, (2018); Brinker A.C.D., Dinther R.V., Crooks M.G., Nocera S.T., Morice A.H., Alert system design based on experimental findings from long-Term unobtrusive monitoring in COPD, Biomed Signal Process Control, 63, pp. 1-8, (2020); Rocha B.M., Filos D., Mendes L., Serbes G., Ulukaya S., Kahya Y.P., An open access database for the evaluation of respiratory sound classification algorithms, Physiol Meas, 40, pp. 1-28, (2019); Srivastava A., Jain S., Miranda R., Patil S., Pandya S., Kotecha K., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, Peer J Comput Sci, 7, pp. 1-22, (2021); Aziz S., Khan M.U., Shakeel M., Mushtaq Z., Khan A.Z., An Automated System Towards Diagnosis of Pneumonia Using Pulmonary Auscultations, (2019); Reyes B.A., Montes N.O., Villalobos S.C., Camarena R.G., Avila M.M., Corrales T.A., A smartphone-based system for automated bedside detection of crackle sounds in diffuse interstitial pneumonia patients, Sensors, 18, pp. 1-21, (2018); Naqvi S.Z.H., Choudhry M.A., An automated system for classification of chronic obstructive pulmonary disease and pneumonia patients using lung sound analysis, Sensors, 20, (2020); Fraiwan L., Hassanin O., Fraiwan M., Khassawneh B., Ibnian A.M., Alkhodari M., Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers, Biocybern Biomed Eng, 41, pp. 1-14, (2021); Demir F., Sengur A., Bajaj V., Convolutional neural networks based efficient approach for classification of lung diseases, Health Inf Sci Syst, 8, pp. 1-8, (2020); Kaplan A., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol Pract, pp. 2255-2261, (2021); Cheetham B.M.G., Charbonneau G., Giordano A., Helisto, vanderschoot. Digitization of data for respiratory sound recordings, Eur Respir Rev, 10, pp. 621-624, (2000); Vannuccini L., Earis J.E., Helisto P., Cheetham B.M.G., Rossi M., Sovijarvi A.R.A., Capturing and preprocessing of respiratory sounds, Eur Respir Rev, 10, pp. 616-620, (2000); Sundararajan A., Discrete Wavelet Transform: A Signal Processing Approach, (2015); Hadjileontiadis L.J., Lung Sounds: An Advanced Signal Processing Perspective, (2009); Kandaswamy A., Kumar C.S., Ramanathan R.P., Jayaraman S., Malmurugan N., Neural classification of lung sounds using wavelet coefficients, Comput Biol Med, 34, pp. 523-537, (2004); Quandt V.I., Pacola E.R., Pichorim S.F., Gamba H.R., Sovierzoski M.A., Pulmonary crackle characterization: Approaches in the use of discrete wavelet transform regarding border effect, mother-wavelet selection, and subband reduction, Res Biomed Eng, 31, pp. 148-159, (2015); Kosasih K., Abeyratne U.R., Swarnkar V., Triasih R., Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Trans Biomed Eng, 62, pp. 1185-1194, (2015); Hirotsu C., Advanced Analysis of Variance, (2017); Grami A., Introduction to Digital Communications, pp. 1-587, (2016); Iwata S., Koda T., Sakamoto T., Multiradar Data Fusion for Respiratory Measurement of Multiple People; Zidelmal Z., Amirou A., Abdeslam D.O., Moukadem A., Dieterlen A., QRS detection using S-Transform and Shannon energy, Comput Methods Progr Biomed, 116, pp. 1-9, (2014); Bedeeuzzaman M., Fathima T., Khan Y.U., Farooq O., Mean absolute deviation and wavelet entropy for seizure prediction, J Med Imag Helath Inform, 2, pp. 238-243, (2012); Gong X., Shen L., Lu T., Refining training samples using median absolute deviation for supervised classification of remote sensing images, J Indian Soc Remote Sens, 47, pp. 647-659, (2019); Titze I.R., Palaparthi A., Vocal loudness variation with spectral slope, J Speech Lang Hear Res, 63, pp. 74-82, (2020); Khan S.I., Ahmed V., Study of adventitious lung sounds of paediatric population using artificial neural network approach, Int J Current Res Rev, 9, pp. 37-45, (2017); Dere G., Biomedical applications with using embedded systems, Data Acquisition-recent Advances and Applications in Biomedical Engineering, (2021); Song I., Diagnosis of Pneumonia from Sounds Collected Using Low Cost Cell Phones, (2019); Lin B.S., Yen T.S., An FPGA-based rapid wheezing detection system, Int J Environ Res Publ Health, 11, pp. 1573-1593, (2014)","M.A. Choudhry; Department of Electrical Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan; email: dr.ahmad@uetttaxila.edu.pk","","De Gruyter Open Ltd","","","","","","00135585","","BMZTA","35405045","English","Biomed. Tech.","Article","Final","","Scopus","2-s2.0-85128560656"
"Nistel M.; Furuta G.T.; Pan Z.; Hsu S.","Nistel, Mason (57369868300); Furuta, Glenn T. (7004274216); Pan, Zhaoxing (7402644528); Hsu, Stephanie (35332076900)","57369868300; 7004274216; 7402644528; 35332076900","Impact of Dose Reduction of Topical Steroids to Manage Adrenal Insufficiency in Pediatric Eosinophilic Esophagitis","2023","Journal of Pediatric Gastroenterology and Nutrition","76","6","","786","792","6","2","10.1097/MPG.0000000000003647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159768414&doi=10.1097%2fMPG.0000000000003647&partnerID=40&md5=06386c333122c354088b2b28d4a59178","Digestive Health Institute, Children's Hospital Colorado, Aurora, CO, United States; Gastrointestinal Eosinophilic Disease Program, University of Colorado School of Medicine, Aurora, CO, United States; Biostatistics Core of the Children's Hospital Colorado Research Institute, Aurora, CO, United States; Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, United States","Nistel M., Digestive Health Institute, Children's Hospital Colorado, Aurora, CO, United States, Gastrointestinal Eosinophilic Disease Program, University of Colorado School of Medicine, Aurora, CO, United States; Furuta G.T., Digestive Health Institute, Children's Hospital Colorado, Aurora, CO, United States, Gastrointestinal Eosinophilic Disease Program, University of Colorado School of Medicine, Aurora, CO, United States; Pan Z., Gastrointestinal Eosinophilic Disease Program, University of Colorado School of Medicine, Aurora, CO, United States, Biostatistics Core of the Children's Hospital Colorado Research Institute, Aurora, CO, United States; Hsu S., Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, United States","Objective: To evaluate the impact of type and dose of swallowed topical steroids (STS) and concurrent steroid therapy on the development and resolution of adrenal insufficiency (AI) in pediatric eosinophilic esophagitis (EoE). Methods: We performed a retrospective case-control study of pediatric EoE subjects in a single tertiary care center, who were treated with STS for at least 3 months and diagnosed with AI based on a peak stimulated cortisol level of <18 µg/dL (500 nmol/L). Steroid forms and doses, and endoscopy data were collected at the time of AI diagnosis and AI resolution or the last known evaluation. Steroid formulations were converted to a fluticasone-equivalent dose for analysis. Results: Thirty-two EoE subjects with AI were identified, and 20 had AI resolution, including 12 who remained on lower dose STS. Eight of the 32 patients were also treated with extended-release budesonide (ER budesonide), which resulted in a 7-fold higher total daily steroid dose, and thus were analyzed separately. When the 24 cases that were not on ER budesonide were compared to the 81 controls, no difference was found in the STS dose nor total daily steroid dose, although the inhaled steroid dose had marginal significance. Peak eosinophil counts tended to increase when STS doses were decreased, except in subjects on ER budesonide at AI diagnosis. Conclusion: Altering the total daily steroid regimen can lead to resolution of AI in patients with EoE, though this may come at the expense of disease control.  © 2020 the Authors. Published by Wolters Kluwer Health, Inc.","asthma; atopy; budesonide; cortisol; safety","Adrenal Insufficiency; Budesonide; Case-Control Studies; Child; Drug Tapering; Eosinophilic Esophagitis; Humans; Retrospective Studies; Steroids; beclomethasone dipropionate; budesonide; budesonide plus formoterol; ciclesonide; corticotropin; fluticasone propionate; hydrocortisone; mometasone furoate; steroid; budesonide; steroid; adrenal function; adrenal insufficiency; Article; case control study; child; clinical article; controlled study; dose; drug dose comparison; drug dose reduction; drug formulation; eosinophil count; eosinophilic colitis; eosinophilic esophagitis; eosinophilic gastritis; eosinophilic gastroenteritis; esophagoscopy; female; human; hydrocortisone blood level; low drug dose; male; retrospective study; school child; steroid therapy; sustained release formulation; tertiary care center; therapeutic equivalent dose; adrenal insufficiency; complication; eosinophilic esophagitis","","beclomethasone dipropionate, 5534-09-8, 77011-63-3; budesonide, 51333-22-3, 51372-29-3; budesonide plus formoterol, 150693-37-1, 150693-38-2; ciclesonide, 126544-47-6; corticotropin, 11136-52-0, 9002-60-2, 9061-27-2; fluticasone propionate, 80474-14-2; hydrocortisone, 50-23-7; mometasone furoate, 83919-23-7, 105102-22-5; Budesonide, ; Steroids, ","Vitros 5600","","National Institutes of Health, NIH; National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK, (T32DK067009); Children's Hospital Colorado; Georgia Tech Foundation, GTF","Sources of Funding: This study was supported by the National Institutes of Health T32 grant DK067009 (MN) and LaCache Chair in Gastrointestinal Allergic and Immunological Diseases, Children’s Hospital Colorado (GTF). th ","Furuta G.T., Liacouras C.A., Collins M.H., Eosinophilic esophagitis in children and adults: a systematic review and consensus recommendations for diagnosis and treatment., Gastroenterology, 133, pp. 1342-1363, (2007); Dellon E.S., Kim H.P., Sperry S.L., Rybnicek D.A., Woosley J.T., Shaheen N.J., A phenotypic analysis shows that eosinophilic esophagitis is a progressive fibrostenotic disease., Gastrointest Endosc, 79, pp. 577-577, (2014); Schoepfer A.M., Safroneeva E., Bussmann C., Delay in diagnosis of eosinophilic esophagitis increases risk for stricture formation in a time-dependent manner., Gastroenterology, 145, pp. 1-6, (2013); Hirano I., Chan E.S., Rank M.A., AGA Institute and the joint task force on allergy-immunology practice parameters clinical guidelines for the management of eosinophilic esophagitis., Gastroenterology, 158, pp. 1776-1786, (2020); Schaefer E.T., Fitzgerald J.F., Molleston J.P., Comparison of oral prednisone and topical fluticasone in the treatment of eosinophilic esophagitis: a randomized trial in children., Clin Gastroenterol Hepatol, 6, pp. 165-173, (2008); Hsu S., Wood C., Pan Z., Adrenal insufficiency in pediatric eosinophilic esophagitis patients treated with swallowed topical steroids., Pediatr Allergy Immunol Pulmonol, 30, pp. 135-140, (2017); Golekoh M.C., Hornung L.N., Mukkada V.A., Khoury J.C., Putnam P.E., Backeljauw P.F., Adrenal insufficiency after chronic swallowed glucocorticoid therapy for eosinophilic esophagitis., J Pediatr, 170, pp. 240-245, (2016); Ahmet A., Benchimol E.I., Goldbloom E.B., Barkey J.L., Adrenal suppression in children treated with swallowed fluticasone and oral viscous budesonide for eosinophilic esophagitis., Allergy Asthma Clin Immunol, 12, (2016); Bose P., Kumar S., Nebesio T.D., Adrenal insufficiency in children with eosinophilic esophagitis treated with topical corticosteroids., J Pediatr Gastroenterol Nutr, 70, pp. 324-329, (2020); Harel S., Hursh B.E., Chan E.S., Avinashi V., Panagiotopoulos C., Adrenal suppression in children treated with oral viscous budesonide for eosinophilic esophagitis., J Pediatr Gastroenterol Nutr, 61, pp. 190-193, (2015); Benninger M.S., Strohl M., Holy C.E., Hanick A.L., Bryson P.C., Prevalence of atopic disease in patients with eosinophilic esophagitis., Int Forum Allergy Rhinol, 7, pp. 757-762, (2017); Pesek R.D., Reed C.C., Muir A.B., Increasing rates of diagnosis, substantial co-occurrence, and variable treatment patterns of eosinophilic gastritis, gastroenteritis, and colitis based on 10-year data across a multicenter consortium., Am J Gastroenterol, 114, pp. 984-994, (2019); O'Byrne P.M., Pedersen S., Measuring efficacy and safety of different inhaled corticosteroid preparations., J Allergy Clin Immunol, 102, pp. 879-886, (1998); Ahmet A., Kim H., Spier S., Adrenal suppression: a practical guide to the screening and management of this under-recognized complication of inhaled corticosteroid therapy., Allergy Asthma Clin Immunol, 7, (2011); Lifshitz F.E., Pediatric Endocrinology: Growth, Adrenal, Sexual, Thyroid, Calcium, and Fluid Balance Disorders, (2007); Daley-Yates P.T., Inhaled corticosteroids: potency, dose equivalence and therapeutic index., Br J Clin Pharmacol, 80, pp. 372-380, (2015); Adams N., Lasserson T.J., Cates C.J., Jones P.W., Fluticasone versus beclomethasone or budesonide for chronic asthma in adults and children., Cochrane Database Syst Rev, 2007, (2007); Kelly H.W., Comparison of inhaled corticosteroids: an update., Ann Pharmacother, 43, pp. 519-527, (2009); Hawcutt D.B., Francis B., Carr D.F., Susceptibility to corticosteroid-induced adrenal suppression: a genome-wide association study., Lancet Respir Med, 6, pp. 442-450, (2018)","M. Nistel; Digestive Health Institute, Children's Hospital Colorado, Aurora, 13123 East 16th Ave, 80045, United States; email: mason.nistel@childrenscolorado.org","","Lippincott Williams and Wilkins","","","","","","02772116","","JPGND","36306502","English","J. Pediatr. Gastroenterol. Nutr.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85159768414"
"Gawalska A.; Czub N.; Sapa M.; Kołaczkowski M.; Bucki A.; Mendyk A.","Gawalska, Alicja (57198446974); Czub, Natalia (57219450118); Sapa, Michał (58178835200); Kołaczkowski, Marcin (55579403900); Bucki, Adam (6506862487); Mendyk, Aleksander (6507518557)","57198446974; 57219450118; 58178835200; 55579403900; 6506862487; 6507518557","Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD","2023","Molecular Informatics","42","7","2200214","","","","3","10.1002/minf.202200214","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161962154&doi=10.1002%2fminf.202200214&partnerID=40&md5=16fa53aa4349317ad71b063ed7a71183","Department of Medicinal Chemistry, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland","Gawalska A., Department of Medicinal Chemistry, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Czub N., Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Sapa M., Department of Medicinal Chemistry, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Kołaczkowski M., Department of Medicinal Chemistry, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Bucki A., Department of Medicinal Chemistry, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland; Mendyk A., Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, ul. Medyczna 9, Kraków, 30-688, Poland","Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade. The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Regression models were developed for each of the biological targets using “mljar-supervised”. On their basis, virtual screenings of commercially available compounds derived from the ZINC15 database were performed. A common group of compounds placed within the top results was selected as potential novel chemotypes of multifunctional ligands. This study represents the first attempt to discover the potential MTDLs inhibiting three biological targets. The obtained results prove the usefulness of AutoML methodology in the identification of hits from the big compound databases. © 2023 Wiley-VCH GmbH.","asthma; AutoML; COPD; MTDL; QSAR model","Computational chemistry; Machine learning; Molecular graphics; Pulmonary diseases; Regression analysis; antiasthmatic agent; biological marker; phosphodiesterase 4b; phosphodiesterase 8a; respiratory tract agent; transient receptor potential channel A1; unclassified drug; zinc 12547564; zinc 1822158; zinc 306137810; zinc 409192528; zinc 69564542; zinc 7814050; Asthma; Automated machines; Automl; Biological targets; Blockings; Chemotypes; COPD; Multi-target-directed ligand; Multi-targets; QSAR model; airway remodeling; Article; asthma; bronchoconstriction; chemotype; chronic inflammation; chronic obstructive lung disease; computer language; controlled study; enzyme activity; human; hydrogen bond; machine learning; molecular model; pathophysiology; quantitative structure activity relation; recurrent neural network; regression model; root mean squared error; structure activity relation; Ligands","","","","","Jagiellonian University, (POIR.04.02.00‐00‐D023/20); Narodowe Centrum Nauki, NCN, (2020/37/N/NZ7/02365)","The study was financially supported by the National Science Centre, Poland (grant no. 2020/37/N/NZ7/02365). Calculations were performed partially with use of computers co‐financed by the qLIFE Priority Research Area under the program “Excellence Initiative Research University” at Jagiellonian University and Polish Operating Programme for Intelligent Development POIR4.2 project no. POIR.04.02.00‐00‐D023/20. ","Global Initiative for Asthma. Global Strategy for Asthma, Management and Prevention, (2022); Global Initiative for Chronic Obstructive Lung Disease (GOLD), Global Strategy for Diagnosis, Management and Prevention of COPD., (2022); Alcaro S., Bolognesi M.L., Garcia-Sosa A.T., Rapposelli S., Front. Chem., 7, (2019); Wang T., Liu X.-H., Guan J., Ge S., Wu M.-B., Lin J.-P., Yang L.-R., Eur. J. Med. Chem., 169, (2019); De Logu F., Patacchini R., Fontana G., Geppetti P., Semin. Immunopathol., 38, (2016); Spina D., Drugs, 63, (2003); Johnstone T.B., Smith K.H., Koziol-White C.J., Li F., Kazarian A.G., Corpuz M.L., Shumyatcher M., Ehlert F.J., Himes B.E., Panettieri R.A., Ostrom R.S., Am. J. 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Toxicol., 60, (2020); Lo Y.-C., Rensi S.E., Torng W., Altman R.B., Drug Discovery Today, 23, (2018); Davies M., Nowotka M., Papadatos G., Dedman N., Gaulton A., Atkinson F., Bellis L., Overington J.P., Nucleic Acids Res., 43, (2015); Plonska A., Plonski P., (2021); Truong A., Walters A., Goodsitt J., Hines K., Bruss C.B., Farivar R., 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1471-1479, (2019); Petersen B.K., Landajuela M., Mundhenk T.N., Santiago C.P., Kim S.K., Kim J.T., Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients, (2021); Yap C.W., J. Comput. Chem., 32, (2011); Sterling T., Irwin J.J., J. Chem. Inf. Model., 55, (2015); Daina A., Michielin O., Zoete V., Sci. Rep., 7, (2017); Gawalska A., Kolaczkowski M., Bucki A., Molecules, 27, (2022); Woodrow M.D., Ballantine S.P., Barker M.D., Clarke B.J., Dawson J., Dean T.W., Delves C.J., Evans B., Gough S.L., Guntrip S.B., Holman S., Holmes D.S., Kranz M., Lindvaal M.K., Lucas F.S., Neu M., Ranshaw L.E., Solanke Y.E., Somers D.O., Ward P., Wiseman J.O., Bioorg. Med. Chem. Lett., 19, (2009); Huang Y., Wu X.-N., Zhou Q., Wu Y., Zheng D., Li Z., Guo L., Luo H.-B., J. Med. Chem., 63, (2020); Hu Y.-J., St-Onge M., Laliberte S., Vallee F., Jin S., Bedard L., Labrecque J., Albert J.S., Bioorg. Med. Chem. Lett., 24, (2014); Copeland K.W., Boezio A.A., Cheung E., Lee J., Olivieri P., Schenkel L.B., Wan Q., Wang W., Wells M.C., Youngblood B., Gavva N.R., Lehto S.G., Geuns-Meyer S., Bioorg. Med. Chem. Lett., 24, (2014); Claffey M.M., Deninno M.P., Kleiman R.J., Substituted Triazolopyrimidines as Pde8 Inhibitors, (2011); Terrett J.A., Chen H., Shore D.G., Villemure E., Larouche-Gauthier R., Dery M., Beaumier F., Constantineau-Forget L., Grand-Maitre C., Lepissier L., Ciblat S., Sturino C., Chen Y., Hu B., Lu A., Wang Y., Cridland A.P., Ward S.I., Hackos D.H., Reese R.M., Shields S.D., Chen J., Balestrini A., Riol-Blanco L., Lee W.P., Liu J., Suto E., Wu X., Zhang J., Ly J.Q., La H., Johnson K., Baumgardner M., Chou K.-J., Rohou A., Rouge L., Safina B.S., Magnuson S., Volgraf M., J. Med. Chem., 64, 7, pp. 3843-3869, (2021); Xu R.X., Hassell A.M., Vanderwall D., Lambert M.H., Holmes W.D., Luther M.A., Rocque W.J., Milburn M.V., Zhao Y., Ke H., Nolte R.T., Science, 288, (2000); Pandit J., Phosphodiesterases and Their Inhibitors, pp. 29-44, (2014)","A. Mendyk; Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Kraków, ul. Medyczna 9, 30-688, Poland; email: aleksander.mendyk@uj.edu.pl","","John Wiley and Sons Inc","","","","","","18681743","","MIONB","37193653","English","Mol. Informatics","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85161962154"
"Guntur V.P.; Modena B.D.; Manka L.A.; Eddy J.J.; Liao S.-Y.; Goldstein N.M.; Zelarney P.; Horn C.A.; Keith R.C.; Make B.J.; Petrache I.; Wechsler M.E.","Guntur, Vamsi P. (6503962782); Modena, Brian D. (56453377500); Manka, Laurie A. (57197820417); Eddy, Jared J. (56548402000); Liao, Shu-Yi (57220085930); Goldstein, Nir M. (57397187500); Zelarney, Pearlanne (6506956626); Horn, Carrie A. (57372380700); Keith, Rebecca C. (50161764700); Make, Barry J. (7003984409); Petrache, Irina (8982438100); Wechsler, Michael E. (7006457379)","6503962782; 56453377500; 57197820417; 56548402000; 57220085930; 57397187500; 6506956626; 57372380700; 50161764700; 7003984409; 8982438100; 7006457379","Characteristics and outcomes of ambulatory patients with suspected COVID-19 at a respiratory referral center","2022","Respiratory Medicine","197","","106832","","","","3","10.1016/j.rmed.2022.106832","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128692542&doi=10.1016%2fj.rmed.2022.106832&partnerID=40&md5=4cdaa778be4ddda26aecb90d48a4819c","Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States; The NJH Cohen Family Asthma Institute, National Jewish Health, Denver, CO, United States; Modena Allergy & Asthma, La Jolla, CA, United States; Division of Mycobacterial and Respiratory Infections, National Jewish Health, Denver, CO, United States; Research Informatics Services, National Jewish Health, Denver, CO, United States; Division of Hospital & Internal Medicine, National Jewish Health, Denver, CO, United States; Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States","Guntur V.P., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, The NJH Cohen Family Asthma Institute, National Jewish Health, Denver, CO, United States, Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States; Modena B.D., Modena Allergy & Asthma, La Jolla, CA, United States; Manka L.A., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, The NJH Cohen Family Asthma Institute, National Jewish Health, Denver, CO, United States; Eddy J.J., Division of Mycobacterial and Respiratory Infections, National Jewish Health, Denver, CO, United States; Liao S.-Y., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States; Goldstein N.M., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States; Zelarney P., Research Informatics Services, National Jewish Health, Denver, CO, United States; Horn C.A., Division of Hospital & Internal Medicine, National Jewish Health, Denver, CO, United States; Keith R.C., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States; Make B.J., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States; Petrache I., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States; Wechsler M.E., Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, United States, The NJH Cohen Family Asthma Institute, National Jewish Health, Denver, CO, United States, Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado, Denver, CO, United States","Rationale: SARS-CoV-2 continues to cause a global pandemic and management of COVID-19 in outpatient settings remains challenging. Objective: We sought to describe characteristics of patients with chronic respiratory disease (CRD) experiencing symptoms consistent with COVID-19, who were seen in a novel Acute Respiratory Clinic, prior to widely available testing, emergence of variants, COVID-19 vaccination, and post-vaccination (breakthrough) SARS-CoV-2 infections. Methods: Retrospective electronic medical record data were analyzed from 907 adults with presumed COVID-19 seen between March 16, 2020 and January 7, 2021. Data included demographics, comorbidities, medications, vital signs, laboratory tests, pulmonary function tests, patient disposition, and co-infections. The overdispersed data (aod) R package was used to create a logit model using COVID-19 diagnosis by PCR as the dichotomous outcome variable. Univariate, conventional multivariate and elastic net machine learning were used to analyze data. Results: Male gender, elevated baseline temperature, and respiratory rate predicted COVID-19 diagnosis. Eosinopenia, neutrophilia, and lymphocytosis were also associated with COVID-19 diagnosis. However, asthma and COPD diagnoses were not associated with SARS-CoV-2 PCR positive test. Male gender, low oxygen saturation, and lower forced expiratory volume in 1 s (FEV1) were associated with higher hospital referral. Conclusions: CRD patients with acute respiratory symptoms in the ambulatory setting were more likely to have COVID-19 if male, febrile and tachypneic. Patients with lower pre-morbid FEV1 and lower SPO2 are more likely to be referred to the hospital. A composite of vitals sigs and WBC differential help risk stratify CRD patients seeking care for presumed COVID-19. © 2022 Elsevier Ltd","Ambulatory respiratory infections; Clinical prediction; COVID-19","Adult; COVID-19; COVID-19 Testing; COVID-19 Vaccines; Fever; Humans; Male; Referral and Consultation; Retrospective Studies; SARS-CoV-2; corticosteroid; adult; area under the curve; Article; asthma; breathing rate; chronic obstructive lung disease; chronic respiratory tract disease; cohort analysis; coinfection; comorbidity; coronavirus disease 2019; elastic tissue; electronic medical record; eosinopenia; female; forced expiratory volume; human; human cell; laboratory test; linear regression analysis; lung function test; lymphocytosis; machine learning; major clinical study; male; neutrophilia; oxygen saturation; patient referral; polymerase chain reaction; prevalence; receiver operating characteristic; retrospective study; social distancing; temperature; vital sign; epidemiology; fever","","COVID-19 Vaccines, ","","","Department of Medicine, National Jewish Health","Wollowick Chair of COPD Research, Department of Medicine, National Jewish Health (IP).","WHO Coronavirus disease (COVID-19) pandemic; Ahmed S.M., Shah R.U., Fernandez V., Grineski S., Brintz B., Samore M.H., Et al., Robust testing in outpatient settings to explore COVID-19 Epidemiology: disparities in race/ethnicity and age, salt lake county, Utah, 2020, Publ. Health Rep., 136, 3, pp. 345-353, (2021); Bentivegna M., Hulme C., Ebell M.H., Primary care relevant risk factors for adverse outcomes in patients with COVID-19 infection: a systematic review, J. Am. Board Fam. Med., 34, pp. S113-S126, (2021); Agarwal S., Schechter C., Southern W., Crandall J.P., Tomer Y., Preadmission diabetes-specific risk factors for mortality in hospitalized patients with diabetes and coronavirus disease 2019, Diabetes Care, 43, 10, pp. 2339-2344, (2020); Gu T., Mack J.A., Salvatore M., Prabhu Sankar S., Valley T.S., Singh K., Et al., Characteristics associated with racial/ethnic disparities in COVID-19 outcomes in an academic health care system, JAMA Netw. Open, 3, 10, (2020); Pena J.E., Rascon-Pacheco R.A., Ascencio-Montiel I.J., Gonzalez-Figueroa E., Fernandez-Garate J.E., Medina-Gomez O.S., Et al., Hypertension, diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico, Arch. Med. Res., 52, 4, pp. 443-449, (2021); Cottini M., Lombardi C., Berti A., Primary care physicians ATSPoBI. Obesity is a major risk factor for hospitalization in community-managed COVID-19 pneumonia, Mayo Clin. Proc., 96, 4, pp. 921-931, (2021); Yuan N., Ji H., Sun N., Botting P., Nguyen T., Torbati S., Et al., Pseudo-safety in a cohort of patients with COVID-19 discharged home from the emergency department, Emerg. Med. J., 38, 4, pp. 304-307, (2021); Freites Nunez D.D., Leon L., Mucientes A., Rodriguez-Rodriguez L., Font Urgelles J., Madrid Garcia A., Et al., Risk factors for hospital admissions related to COVID-19 in patients with autoimmune inflammatory rheumatic diseases, Ann. Rheum. Dis., 79, 11, pp. 1393-1399, (2020); Lasbleiz A., Cariou B., Darmon P., Soghomonian A., Ancel P., Boullu S., Et al., Phenotypic characteristics and development of a hospitalization prediction risk score for outpatients with diabetes and COVID-19: the DIABCOVID study, J. Clin. Med., 9, 11, (2020); Oetjens M.T., Luo J.Z., Chang A., Leader J.B., Hartzel D.N., Moore B.S., Et al., Electronic health record analysis identifies kidney disease as the leading risk factor for hospitalization in confirmed COVID-19 patients, PLoS One, 15, 11, (2020); Du R.H., Liang L.R., Yang C.Q., Wang W., Cao T.Z., Li M., Et al., Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study, Eur. Respir. J., 55, 5, (2020); Yang X., Yu Y., Xu J., Shu H., Xia J., Liu H., Et al., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study, Lancet Respir. Med., 8, 5, pp. 475-481, (2020); Halalau A., Odish F., Imam Z., Sharrak A., Brickner E., Lee P.B., Et al., Epidemiology, clinical characteristics, and outcomes of a large cohort of COVID-19 outpatients in Michigan, Int. J. Gen. Med., 14, pp. 1555-1563, (2021); Caratozzolo S., Zucchelli A., Turla M., Cotelli M.S., Fascendini S., Zanni M., Et al., The impact of COVID-19 on health status of home-dwelling elderly patients with dementia in East Lombardy, Italy: results from COVIDEM network, Aging Clin. Exp. Res., 32, 10, pp. 2133-2140, (2020); Green I., Merzon E., Vinker S., Golan-Cohen A., Magen E., COVID-19 susceptibility in bronchial asthma, J. Allergy Clin. Immunol. Pract., 9, 2, pp. 684-692 e1, (2021); Zou H., Hastie T., Regularization and variable selection via the elastic net, 67, 2, pp. 301-320, (2005); Softeland J.M., Friman G., von Zur-Muhlen B., Ericzon B.G., Wallquist C., Karason K., Et al., COVID-19 in solid organ transplant recipients: a national cohort study from Sweden, Am. J. 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Med., 27, 6, pp. 1055-1061, (2021); Moghadas S.M., Vilches T.N., Zhang K., Wells C.R., Shoukat A., Singer B.H., Et al., The Impact of Vaccination on COVID-19 Outbreaks in the United States. medRxiv, (2021); Corne J.M., Marshall C., Smith S., Schreiber J., Sanderson G., Holgate S.T., Et al., Frequency, severity, and duration of rhinovirus infections in asthmatic and non-asthmatic individuals: a longitudinal cohort study, Lancet, 359, 9309, pp. 831-834, (2002); Adir Y., Humbert M., Saliba W., COVID-19 risk and outcomes in adult asthmatic patients treated with biologics or systemic corticosteroids: nationwide real-world evidence, J. Allergy Clin. Immunol., 148, 2, pp. 361-367 e13, (2021); Shi W., Gao Z., Ding Y., Zhu T., Zhang W., Xu Y., Clinical characteristics of COVID-19 patients combined with allergy, Allergy, 75, 9, pp. 2405-2408, (2020); Jackson D.J., Busse W.W., Bacharier L.B., Kattan M., O'Connor G.T., Wood R.A., Et al., Association of respiratory allergy, asthma, and expression of the SARS-CoV-2 receptor ACE2, J. Allergy Clin. Immunol., 146, 1, pp. 203-206 e3, (2020); Maes T., Bracke K., Brusselle G.G., COVID-19, asthma, and inhaled corticosteroids: another beneficial effect of inhaled corticosteroids?, Am. J. Respir. Crit. Care Med., 202, 1, pp. 8-10, (2020); Kimura H., Francisco D., Conway M., Martinez F.D., Vercelli D., Polverino F., Et al., Type 2 inflammation modulates ACE2 and TMPRSS2 in airway epithelial cells, J. Allergy Clin. Immunol., 146, 1, pp. 80-88 e8, (2020); O'Brien M.P., Forleo-Neto E., Sarkar N., Isa F., Hou P., Chan K.C., Et al., Effect of subcutaneous casirivimab and imdevimab antibody combination vs placebo on development of symptomatic COVID-19 in early asymptomatic SARS-CoV-2 infection: a randomized clinical trial, JAMA, 327, 5, pp. 432-441, (2022); Weinreich D.M., Sivapalasingam S., Norton T., Ali S., Gao H., Bhore R., Et al., REGEN-COV antibody combination and outcomes in outpatients with covid-19, N. Engl. J. Med., 385, 23, (2021); Gupta A., Gonzalez-Rojas Y., Juarez E., Crespo Casal M., Moya J., Falci D.R., Et al., Early treatment for covid-19 with SARS-CoV-2 neutralizing antibody sotrovimab, N. Engl. J. Med., 385, 21, pp. 1941-1950, (2021); Dougan M., Nirula A., Azizad M., Mocherla B., Gottlieb R.L., Chen P., Et al., Bamlanivimab plus etesevimab in mild or moderate covid-19, N. Engl. J. Med., 385, 15, pp. 1382-1392, (2021); Gottlieb R.L., Nirula A., Chen P., Boscia J., Heller B., Morris J., Et al., Effect of bamlanivimab as monotherapy or in combination with etesevimab on viral load in patients with mild to moderate COVID-19: a randomized clinical trial, JAMA, 325, 7, pp. 632-644, (2021)","V.P. Guntur; NJH Cohen Family Asthma Institute, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, 1400 Jackson Street, 80230, United States; email: Gunturv@njhealth.org","","W.B. Saunders Ltd","","","","","","09546111","","RMEDE","35462298","English","Respir. Med.","Article","Final","All Open Access; Bronze Open Access; Green Open Access","Scopus","2-s2.0-85128692542"
"Gits M.P.; Gondhalekar A.D.; Scharf M.E.","Gits, Madison P. (58140712100); Gondhalekar, Ameya D. (42361180900); Scharf, Michael E. (7101609231)","58140712100; 42361180900; 7101609231","Impacts of BioassayType on Insecticide Resistance Assessment in the German Cockroach (Blattodea: Ectobiidae)","2023","Journal of Medical Entomology","60","2","","356","363","7","2","10.1093/jme/tjad004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150000315&doi=10.1093%2fjme%2ftjad004&partnerID=40&md5=d5058b322e4df5333b988aafb0f49544","Department of Entomology, Purdue University, 901 West State Street, West Lafayette, 47907, IN, United States","Gits M.P., Department of Entomology, Purdue University, 901 West State Street, West Lafayette, 47907, IN, United States; Gondhalekar A.D., Department of Entomology, Purdue University, 901 West State Street, West Lafayette, 47907, IN, United States; Scharf M.E., Department of Entomology, Purdue University, 901 West State Street, West Lafayette, 47907, IN, United States","The German cockroach, Blattella germanica (L.), is one of the most critical urban pests globally due to the health risks it imposes on people, such as asthma. Insecticides are known to manage large cockroach population sizes, but the rapid rate at which they develop resistance is a continuing problem. Dealing with insecticide resistance can be expensive and time-consuming for both the consumer and the pest management professional (PMP) applying the treatment. Each cockroach population is unique because different strains have different insecticide susceptibilities, so resistance profiles must be considered.This study addressed the above issue in a controlled laboratory setting. Cockroach strains from Indianapolis, Indiana, Danville, Illinois, and Baltimore, Maryland, USA were used. Four insecticide active ingredients (AIs) most used by consumers and PMPs were selected for testing in vial bioassays to establish resistance profiles. Next, no-choice and choice feeding assays with four currently registered bait products were performed to assess the impacts of competing food and circadian rhythms on bait resistance levels. The results indicate that emamectin benzoate (Optigard) was the most effective AI in causing the highest mortality in all strains in vial and no-choice bioassays; whereas, the other AIs and products were more impacted by resistance.The results acquired from these studies can help develop rapid tests for use by PMPs based on the no-choice feeding assay while also adding more information supporting current resistance and cross-resistance evolution theories. © The Author(s) 2023. Published by Oxford University Press on behalf of Entomological Society of America.","abamectin; emamectin benzoate; fipronil; indoxacarb","Allergens; Animals; Biological Assay; Blattellidae; Insect Control; Insecticide Resistance; Insecticides; allergen; insecticide; animal; bioassay; Blattellidae; insect control; insecticide resistance; procedures","","Allergens, ; Insecticides, ","","","Applied Research and Development Program, (1017418, 2018-70006-28919); U.S. Department of Housing and Urban Development, HUD, (INHHU0026-14); U.S. Department of Housing and Urban Development, HUD; National Institute of Food and Agriculture, NIFA; Purdue University, PU","This work was supported by Applied Research and Development Program, grant no. 2018-70006-28919/project accession no. 1017418 from the USDA National Institute of Food and Agriculture, and O.W. Rollins/Orkin Endowment at Purdue University. Danville and Indianapolis cockroach strains were procured under support by U.S. Housing and Urban Development grant no. INHHU0026-14. We thank Dr. Godfrey Nalyanya of Rentokil Inc. for the German cockroaches from Baltimore, MD, and also Dennis Shao-Hung Lee and Dr. Chow-Yang Lee for their assistance with Kaplan-Meier analyses. MPG and MES declare no conflicts of interest. ADG had funding relationships with manufacturers of tested bait products at the time reported studies were conducted.","Caprio M. A., Tabashnik B. E., Gene flow accelerates local adaptation among finite populations: simulating the evolution of insecticide resistance, J. Econ. Entomol, 85, pp. 611-620, (1992); Celmeli F., Yavuz S. T., Turkkahraman D., Simsek O., Kilinc A., Sekerel B. E., Cockroach (Blattella germanica) sensitization is associated with coexistence of asthma and allergic rhinitis in childhood, Pediatr. Allergy Immunol, 29, pp. 38-43, (2016); Cowan F., Curious facts in the history of insects, (1865); Curl G., A strategic analysis of the US structural pest control industry, (2011); Davari B., Kashani S., Nasirian H., Nazari M., Salehzadeh A., The efficacy of MaxForce and Avion gel baits containing fipronil, clothianidin and indoxacarb against the German cockroach (Blattella germanica), Entomol. Res, 48, pp. 459-465, (2018); DeVries Z. C., Santangelo R. G., Crissman J., Suazo A., Kakumanu M. L., Schal C., Pervasive resistance to pyrethroids in German cockroaches (Blattodea: Ectobiidae) related to lack of efficacy of total release foggers, J. Econ. Entomol, 112, pp. 2295-2301, (2019); Do D. C., Zhao Y., Gao P., Cockroach allergen exposure and risk of asthma, Allergy, 71, pp. 463-474, (2016); Dreisig H., Nielsen E. T., Circadian rhythm of locomotion and its temperature dependence in Blattella germanica, J. Exp. Biol, 54, pp. 187-198, (1971); Ebeling W., Wagner R. E., Reierson D. A., Influence of repellency of the efficacy of blatticides: learned modification of behavior of the German cockroach, J. Econ. Entomol, 99, pp. 1374-1388, (1966); Fardisi M., Gondhalekar A. D., Scharf M. E., Development of diagnostic insecticide concentrations and assessment of insecticide susceptibility in German cockroach (Dictyoptera: Blattellidae) field strains collected from public housing, J. Econ. Entomol, 110, pp. 1210-1217, (2017); Fardisi M., Gondhalekar A. D., Ashbrook A. R., Scharf M. E., Rapid evolutionary responses to insecticide resistance management interventions by the German cockroach (Blattella germanica L.), Sci. Rep, 9, (2019); Finkas L., Block L., Lu M., Yu B., Lee M., Iribarren C., Retrospective analysis of COVID-19 incidence and health outcomes among patients with asthma in a large integrated health care delivery system, J. Allergy. Clin. Immunol, 149, (2022); Gondhalekar A. D., Scharf M. E., Mechanisms underlying fipronil resistance in a multiresistant field strain of the German cockroach, J. Econ. Entomol, 49, pp. 122-131, (2012); Gondhalekar A. D., Scharf M. E., Preventing resistance to bait products, pp. 42-46, (2013); Gondhalekar A. D., Song C., Scharf M. E., Development of strategies for monitoring indoxacarb and gel bait susceptibility in the German cockroach, Pest Manag. Sci, 67, pp. 262-270, (2011); Gondhalekar A. D., Scherer W., Saran R. K., Scharf M. E., Implementation of an indoxacarb susceptibility monitoring program using field-collected German cockroach isolates from the United States, J. Econ. Entomol, 106, pp. 945-953, (2013); Gondhalekar A. D., Appel A. G., Thomas G. M., Romero A., A review of alternative management tactics employed for the control of various cockroach species (Order: Blattodea) in the USA, Insects, 12, (2021); Guo M., Zhang W., Ding G., Guo D., Zhu J., Wang B., Punyapitak D., Cao Y., Preparation and characterization of enzyme-responsive emamectin benzoate microcapsules based on a copolymer matrix of silica-epichlorohydrin-carboxymethylcellulose, R. Soc. Chem, 5, pp. 93170-93179, (2015); Harada K., Thanik E., DeFelice N., Bhatia J., Lopez R., Galvez S., Bixby M., Dayanov E., Bush D., Garland E., Housing conditions and access to care for children with asthma during COVID-19 pandemic in New York City, J. Allergy Clin. Immunol, 149, (2022); Huang C., Wang Y., Xingwang L., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395, pp. 497-506, (2020); Ko A. E., Bieman D. N., Schal C., Silverman J., Insecticide resistance and diminished secondary kill performance of bait formulations against German cockroaches (Dictyoptera: Blattellidae), Pest Manag. Sci, 72, pp. 1778-1784, (2016); Lee S. H., Choe D. H., Rust M. K., Lee C. Y., Reduced susceptibility towards commercial bait Insecticides in field German cockroach (Blattodea: Ectobiidae) populations from California, J. Econ. Entomol, 115, pp. 259-265, (2022); Liang Y., Gao Y., Wang W., Dong H., Tang R., Yang J., Niu J., Zhou Z., Jiang N., Cao Y., Fabrication of smart stimuli-responsive mesoporous organosilica nano-vehicles for targeted pesticide delivery, J. Hazard. Mater, 389, (2020); Messer P. W., Petrov D. A., Population genomics of rapid adaption by soft selective sweeps, Trends Ecol. Evol, 28, pp. 659-669, (2013); Miller D. M., Smith E. P., Quantifying the efficacy of an Assessment-Based Pest Management (APM) Program for German Cockroach (L.) (Blattodea: Blattellidae) control in low-income public housing units, J. Econ. Entomol, 113, pp. 375-384, (2020); Nasirian H., Contamination of cockroaches (Insecta: Blattaria) to medically fungi: a systematic review and meta-analysis, JMM, 27, pp. 427-448, (2017); Nasirian H., Infestation of cockroaches (Insecta: Blattaria) in the human dwelling environments: a systematic review and meta-analysis, Acta Trop, 167, pp. 86-98, (2017); Nasirian H., Contamination of cockroaches (Insecta: Blattaria) by medically important bacteriae: a systematic review and meta-analysis, J. Med. Entomol, 56, pp. 1534-1554, (2019); Nasirian H., Salehzadeh A., Control of cockroaches (Blattaria) in sewers: a practical approach systematic review, J. Med. Entomol, 56, pp. 181-191, (2019); Nekoei S., Khamesipour F., Benchimol M., Bueno-Mari R., Ommi D., SARS-CoV-2 transmission by arthopod vectors: a scoping review, Biomed Res. Int, 2022, (2022); Poland T. M., McCullough D. G., Herms D. A., Baurer L. S., Gould J. R., Tluzeck A. R., Management tactics for emerald ash borer: chemical and biological control, (2010); RStudio: integrated development for R, (2022); Rust M. K., Reierson D. A., Chlorpyrifos resistance in German cockroaches (Dictyoptera: Blattellidae) from restaurants, J. Econ. Entomol, 84, pp. 736-740, (1991); Salehzadeh A., Darvish Z., Davari B., Nasirian H., The efficacy of baits containing abamectin, dinotefuran, imidacloprid and pyriproxyfen + abamectin against Blattella germanica (L.) (Blattaria: Blattellidae), the German cockroach, Afr. Entomol, 28, pp. 225-237, (2020); Schal C., Hamilton R. L., Integrated suppression of synanthropic cockroaches, Annu. Rev. Entomol, 35, pp. 521-551, (1990); Scharf M. E., Gondhalekar A. D., Insecticide resistance: perspectives on evolution, monitoring, mechanisms and management, Biology and management of the German cockroach, pp. 231-267, (2021); Scharf M. E., Bennett G. W., Reid B. L., Qui C., Comparisons of three insecticide resistance detection methods for the German cockroach, J. Econ. Entomol, 88, pp. 536-542, (1995); Scharf M. E., Kaakeh W., Bennett G. W., Changes in an insecticide-resistant field population of German cockroach after exposure to an insecticide mixture, J. Econ. Entomol, 90, pp. 38-48, (1997); Scharf M. E., Neal J. J., Bennett G. W., Changes of insecticide resistance levels and detoxication enzymes following insecticide selection in the German cockroach, Blattella germanica (L.), Pest. Biochem. Physiol, 59, pp. 67-79, (1998); Scharf M. E., Wolfe Z. M., Raje K. R., Fardisi M., Thimmapuram J., Bhide K., Gondhalekar A. D., Transcriptome responses to defined insecticide selection pressures in the German cockroach (Blattella germanica L.), Front. Physiol, 12, (2022); Sisterton M. S., Antilla L., Carriere Y., Ellers-Kirk C., Tabashnik B. E., Effects of insect population size on evolution of resistance to transgenic crops, J. Econ. Entomol, 97, pp. 1413-1424, (2004); Vargo E. L., Dispersal and population genetics, Biology and management of the German cockroach, pp. 143-152, (2021); Wang C., Bennett G. W., Comparative study of integrated pest management and baiting for German cockroach management in public housing, J. Econ. Entomol, 99, pp. 879-885, (2006); Wang C., Scharf M. E., Bennett G. W., Behavioral and physiological resistance of the German cockroach to gel baits, J. Econ. Entomol, 97, pp. 2067-2072, (2004); Wang C., El-Nour M. M. A., Bennett G. W., Survey of pest infestation, asthma, and allergy in low-income housing, J. Commun. Health, 33, pp. 31-39, (2008); Whalon M. E., Mota-Sanchez M., Hollingworth R. M., Arthropod resistant to pesticides database (ARPD), (2022); Wu X., Appel A. G., Insecticide resistance of several field-collected German cockroach (Dictyoptera: Blattellidae) strains, J. Econ. Entomol, 110, pp. 1203-1209, (2017); Zhu F., Lavine L., O'Neal S., Lavine M., Foss C., Walsh D., Insecticide resistance and management strategies in urban ecosystems, Insects, 7, (2016)","M.P. Gits; Department of Entomology, Purdue University, West Lafayette, 901 West State Street, 47907, United States; email: gitsmadison@ufl.edu; M.E. Scharf; Department of Entomology, Purdue University, West Lafayette, 901 West State Street, 47907, United States; email: mescharf@ufl.edu","","Oxford University Press","","","","","","00222585","","JMENA","36691833","English","J. Med. Entomol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85150000315"
"Bhattacharyya N.; Silver J.; Bogart M.; Kponee-Shovein K.; Cheng W.Y.; Cheng M.; Cheung H.C.; Duh M.S.; Hahn B.","Bhattacharyya, Neil (7102522026); Silver, Jared (57221464527); Bogart, Michael (57191489150); Kponee-Shovein, Kalé (57208224946); Cheng, Wendy Y. (56967420000); Cheng, Mu (59597905200); Cheung, Hoi Ching (57211085626); Duh, Mei Sheng (57209013088); Hahn, Beth (56588504500)","7102522026; 57221464527; 57191489150; 57208224946; 56967420000; 59597905200; 57211085626; 57209013088; 56588504500","Profiling Disease and Economic Burden in CRSwNP Using Machine Learning","2022","Journal of Asthma and Allergy","15","","","1401","1412","11","3","10.2147/JAA.S378469","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139227910&doi=10.2147%2fJAA.S378469&partnerID=40&md5=cfa1644248afe2a65a800dbde0ad8071","Mass Eye & Ear and Harvard Medical School, Boston, MA, United States; GSK, Durham, NC, United States; Analysis Group, Boston, MA, United States","Bhattacharyya N., Mass Eye & Ear and Harvard Medical School, Boston, MA, United States; Silver J., GSK, Durham, NC, United States; Bogart M., GSK, Durham, NC, United States; Kponee-Shovein K., Analysis Group, Boston, MA, United States; Cheng W.Y., Analysis Group, Boston, MA, United States; Cheng M., Analysis Group, Boston, MA, United States; Cheung H.C., Analysis Group, Boston, MA, United States; Duh M.S., Analysis Group, Boston, MA, United States; Hahn B., GSK, Durham, NC, United States","Purpose: Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with high healthcare resource utilization (HRU) and economic cost; however, heterogeneity of clinical burden among patients with differing clinical characteristics has not been fully elucidated. Here, an unsupervised machine learning approach supported by clinical validation identified distinct clusters of patients with CRSwNP and compared healthcare burden. Patients and Methods: This retrospective analysis identified adult patients with ≥2 claims for CRSwNP and date of first diagnosis (index date) between January 2015 and June 2019 from a healthcare database. Patients were required to have enrollment in the database 6-months pre-and 12-months post-index. Patients were assigned to clusters using latent class analysis. All-cause and nasal polyp (NP)-related HRU and costs were compared between clusters. Results: Among 12,807 patients, 5 clusters were identified: cluster 1: no surgery/low comorbidity/low medication use (n = 4076); cluster 2: no surgery/low comorbidity/high medication use (n = 2201); cluster 3: no surgery/high comorbidity/high medication use (n = 2093); cluster 4: surgery/low comorbidity/moderate medication use (n = 3168); cluster 5: surgery/high comorbidity/high medication use (n = 1269). All-cause HRU was similar across clusters. NP-related HRU was highest in the surgical clusters (clusters 4 and 5). All-cause costs were similar in clusters 1–3 ($15,833–$17,461) and highest in clusters 4 ($31,083) and 5 ($31,103), driven by outpatient costs. Total NP-related costs were also highest for clusters 4 and 5 ($14,193 and $16,100, respectively). Conclusion: Substantial heterogeneity exists in clinical and economic burden among patients with CRSwNP. Machine learning offers a novel approach to better understand the diverse, complex burden of illness in CRSwNP. © 2022 Bhattacharyya et al.","asthma; chronic rhinosinusitis; cost burden; healthcare utilization; machine learning; nasal polyps","adult; Article; chronic obstructive lung disease; chronic rhinosinusitis; clinical decision making; computer assisted tomography; controlled study; Current Procedural Terminology; female; health care cost; health care utilization; health insurance; human; latent class analysis; machine learning; major clinical study; male; mediastinoscopy; middle aged; non small cell lung cancer; nose polyp; nuclear magnetic resonance imaging; protein fingerprinting; refraction error; retrospective study; social status; tumor associated leukocyte; whole exome sequencing","","","","","GlaxoSmithKline, GSK, (213333); GlaxoSmithKline, GSK","This study was funded by GSK (GSK ID: 213333).","Stevens WW, Schleimer RP, Kern RC., Chronic rhinosinusitis with nasal polyps, J Allergy Clin Immunol Pract, 4, 4, pp. 565-572, (2016); Fokkens WJ, Lund VJ, Hopkins C, Et al., European position paper on rhinosinusitis and nasal polyps 2020, Rhinology, 58, pp. 1-464, (2020); Bachert C, Han JK, Wagenmann M, Et al., EUFOREA expert board meeting on uncontrolled severe chronic rhinosinusitis with nasal polyps (CRSwNP) and biologics: definitions and management, J Allergy Clin Immunol, 147, 1, pp. 29-36, (2021); Calus L, Van Bruaene N, Bosteels C, Et al., Twelve-year follow-up study after endoscopic sinus surgery in patients with chronic rhinosinusitis with nasal polyposis, Clin Transl Allergy, 9, (2019); DeConde AS, Mace JC, Levy JM, Rudmik L, Alt JA, Smith TL., Prevalence of polyp recurrence after endoscopic sinus surgery for chronic rhinosinusitis with nasal polyposis, Laryngoscope, 127, 3, pp. 550-555, (2017); Alobid I, Benitez P, Bernal-Sprekelsen M, Et al., Nasal polyposis and its impact on quality of life: comparison between the effects of medical and surgical treatments, Allergy, 60, 4, pp. 452-458, (2005); Sahlstrand-Johnson P, Ohlsson B, Von Buchwald C, Jannert M, Ahlner-Elmqvist M., A multi-centre study on quality of life and absenteeism in patients with CRS referred for endoscopic surgery, Rhinology, 49, 4, pp. 420-428, (2011); Bhattacharyya N, Villeneuve S, Joish VN, Et al., Cost burden and resource utilization in patients with chronic rhinosinusitis and nasal polyps, Laryngoscope, 129, 9, pp. 1969-1975, (2019); Chen S, Zhou A, Emmanuel B, Garcia D, Rosta E., Systematic literature review of humanistic and economic burdens of chronic rhinosinusitis with nasal polyposis, Curr Med Res Opin, 36, 11, pp. 1913-1926, (2020); Kaplan A, Cao H, FitzGerald JM, Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol Pract, 9, 6, pp. 2255-2261, (2021); Lanza ST, Collins LM, Lemmon DR, Schafer JL., PROC LCA: a SAS procedure for latent class analysis, Struct Equ Modeling, 14, 4, pp. 671-694, (2007); Austin PC., Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples, Stat Med, 28, 25, pp. 3083-3107, (2009); Schuler MS, Leoutsakos JS, Stuart EA., Addressing confounding when estimating the effects of latent classes on distal outcome, Health Serv Outcomes Res Methodol, 14, 4, pp. 232-254, (2014); Rudmik L., Economics of chronic rhinosinusitis, Curr Allergy Asthma Rep, 17, 4, (2017); Chowdhury NI, Mace JC, Smith TL, Rudmik L., What drives productivity loss in chronic rhinosinusitis? A SNOT-22 subdomain analysis, Laryngoscope, 128, 1, pp. 23-30, (2018); Rudmik L, Smith TL, Schlosser RJ, Hwang PH, Mace JC, Soler ZM., Productivity costs in patients with refractory chronic rhinosinusitis, Laryngoscope, 124, 9, pp. 2007-2012, (2014); Birkhead GS, Klompas M, Shah NR., Uses of electronic health records for public health surveillance to advance public health, Annu Rev Public Health, 36, 1, pp. 345-359, (2015)","N. Bhattacharyya; Mass Eye & Ear and Harvard Medical School, Boston, 243 Charles St, 02114, United States; email: neiloy@bhattacharyya.org","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139227910"
"Zhu Y.; Esnault S.; Ge Y.; Jarjour N.N.; Brasier A.R.","Zhu, Yanlong (57207827609); Esnault, Stephane (6701620086); Ge, Ying (54895055300); Jarjour, Nizar N. (7003501462); Brasier, Allan R. (7007058345)","57207827609; 6701620086; 54895055300; 7003501462; 7007058345","Segmental Bronchial Allergen Challenge Elicits Distinct Metabolic Phenotypes in Allergic Asthma","2022","Metabolites","12","5","381","","","","3","10.3390/metabo12050381","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129846900&doi=10.3390%2fmetabo12050381&partnerID=40&md5=6a923664c5ef643b11b428c486e3b84d","Department of Cell and Regenerative Biology, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Human Proteomics Program, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Institute for Clinical and Translational Research (ICTR), University of Wisconsin-Madison, Madison, 53705, WI, United States","Zhu Y., Department of Cell and Regenerative Biology, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States, Human Proteomics Program, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Esnault S., Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Ge Y., Department of Cell and Regenerative Biology, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States, Human Proteomics Program, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Jarjour N.N., Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, WI, United States; Brasier A.R., Institute for Clinical and Translational Research (ICTR), University of Wisconsin-Madison, Madison, 53705, WI, United States","Asthma is a complex syndrome associated with episodic decompensations provoked by aeroallergen exposures. The underlying pathophysiological states driving exacerbations are latent in the resting state and do not adequately inform biomarker-driven therapy. A better understanding of the pathophysiological pathways driving allergic exacerbations is needed. We hypothesized that disease-associated pathways could be identified in humans by unbiased metabolomics of bronchoalve-olar fluid (BALF) during the peak inflammatory response provoked by a bronchial allergen challenge. We analyzed BALF metabolites in samples from 12 volunteers who underwent segmental bronchial antigen provocation (SBP-Ag). Metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC–MS/MS) followed by pathway analysis and correlation with airway inflammation. SBP-Ag induced statistically significant changes in 549 features that mapped to 72 uniquely identified metabolites. From these features, two distinct inducible metabolic phenotypes were identified by the principal component analysis, partitioning around medoids (PAM) and k-means clustering. Ten index metabolites were identified that informed the presence of asthma-relevant pathways, including unsaturated fatty acid production/metabolism, mitochondrial beta oxidation of unsaturated fatty acid, and bile acid metabolism. Pathways were validated using proteomics in eosinophils. A segmental bronchial allergen challenge induces distinct metabolic responses in humans, providing insight into pathogenic and protective endotypes in allergic asthma. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","allergic asthma; metabolomics; phenotype; saturated fatty acid synthesis; segmental bronchial antigen provocation","acylcarnitine; allergen; arachidonic acid; bile acid; bilirubin; corticosteroid; farnesoid X receptor; methacholine; palmitic acid; saturated fatty acid; tryptase; allergic airway inflammation; allergic asthma; Article; Bacteroidetes; bile acid metabolism; bile acid synthesis; bronchoconstriction; bronchoscopy; cell hyperplasia; controlled study; electrospray mass spectrometry; eosinophil count; fatty acid metabolism; fatty acid oxidation; fatty acid synthesis; Firmicutes; forced expiratory volume; fractional exhaled nitric oxide; gene ontology; health care delivery; hematological parameters; human; human tissue; immunoblotting; inflammation; insulin resistance; k means clustering; machine learning; mass fragmentography; mass spectrometry; metabolic phenotype; metabolic syndrome X; metabolomics; multiple reaction monitoring; peripheral blood mononuclear cell; Proteobacteria; proteomics; provocation; quality of life; RNA sequence; systolic blood pressure; transcriptomics","","arachidonic acid, 506-32-1, 6610-25-9, 7771-44-0; bilirubin, 18422-02-1, 635-65-4; methacholine, 55-92-5; palmitic acid, 57-10-3; tryptase, 97501-93-4","","","University of Wisconsin–Madison Human Proteomics Program; Institute for Clinical and Translational Research, University of Wisconsin, Madison, UW ICTR; National Center for Advancing Translational Sciences, NCATS, (U01 AI136994, UL1TR002373)","Funding text 1: This work was partially supported by ICTR Strategic Alliance Program, National Center for Advancing Translational Sciences (NCATS) UL1TR002373 (ARB), and U01 AI136994 (ARB). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Acknowledgments: The authors thank the University of Wisconsin–Madison Human Proteomics Program for support and equipment access.; Funding text 2: Funding: This work was partially supported by ICTR Strategic Alliance Program, National Center for Advancing Translational Sciences (NCATS) UL1TR002373 (ARB), and U01 AI136994 (ARB). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.","Busse W.W., Lemanske R.F., Asthma, N. Engl. J. Med, 344, pp. 350-362, (2001); Briggs A., Nasser S., Hammerby E., Buchs S., Virchow J.C., The impact of moderate and severe asthma exacerbations on quality of life: A post hoc analysis of randomised controlled trial data, J. Patient-Rep. 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Investig, 104, pp. 301-308, (1999); Reddy P.H., Mitochondrial Dysfunction and Oxidative Stress in Asthma: Implications for Mitochondria-Targeted Antioxidant Therapeutics, Pharmaceuticals, 4, pp. 429-456, (2011); Taylor B., Mannino D., Brown C., Crocker D., Twum-Baah N., Holguin F., Body mass index and asthma severity in the National Asthma Survey, Thorax, 63, pp. 14-20, (2008); Motta A., Paris D., D'Amato M., Melck D., Calabrese C., Vitale C., Stanziola A.A., Corso G., Sofia M., Maniscalco M., NMR Metabolomic Analysis of Exhaled Breath Condensate of Asthmatic Patients at Two Different Temperatures, J. Proteome Res, 13, pp. 6107-6120, (2014); Shaik F.B., Panati K., Narasimha V.R., Narala V.R., Chenodeoxycholic acid attenuates ovalbumin-induced airway inflammation in murine model of asthma by inhibiting the TH2 cytokines, Biochem. Biophys. Res. Commun, 463, pp. 600-605, (2015); Wang Y.-D., Chen W.-D., Wang M., Yu D., Forman B.M., Huang W., Farnesoid X receptor antagonizes nuclear factor κB in hepatic inflammatory response, Hepatology, 48, pp. 1632-1643, (2008); Kelly E.A., Esnault S., Liu L.Y., Evans M.D., Johansson M.W., Mathur S., Mosher D.F., Denlinger L.C., Jarjour N.N., Mepolizumab Attenuates Airway Eosinophil Numbers, but Not Their Functional Phenotype, in Asthma, Am. J. Respir. Crit. Care Med, 196, pp. 1385-1395, (2017)","N.N. Jarjour; Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, School of Medicine and Public Health (SMPH), University of Wisconsin-Madison, Madison, 53705, United States; email: njarjour@uwhealth.org; A.R. Brasier; Institute for Clinical and Translational Research (ICTR), University of Wisconsin-Madison, Madison, 53705, United States; email: abrasier@wisc.edu","","MDPI","","","","","","22181989","","","","English","Metabolites","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85129846900"
"Pinnock H.; McClatchey K.; Hui C.Y.","Pinnock, Hilary (6701815935); McClatchey, Kirstie (57193384556); Hui, Chi Yan (57194330405)","6701815935; 57193384556; 57194330405","Supported self-management in asthma","2023","ERS Monograph","2023","","","199","215","16","3","10.1183/2312508X.10001723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187152490&doi=10.1183%2f2312508X.10001723&partnerID=40&md5=7027c5f1dd2c5bdc226b83696c54c1c0","Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Whitstable Medical Practice, Whitstable, United Kingdom","Pinnock H., Usher Institute, University of Edinburgh, Edinburgh, United Kingdom, Whitstable Medical Practice, Whitstable, United Kingdom; McClatchey K., Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Hui C.Y., Usher Institute, University of Edinburgh, Edinburgh, United Kingdom","People with asthma learn to live with their condition, taking day-to-day decisions about their self-management. Professional support for self-management increasingly incorporates digital healthcare technology in strategies known to improve asthma control and reduce the risk of attacks. Digital technology, including artificial intelligence, can (and increasing will) contribute to all aspects of the “assess–adjust–review” cycle of personalised asthma (self)-management. Specific digital contributions to supported self-management include improving adherence to routine medication, checking and correcting inhaler technique, monitoring asthma status, predicting risk, providing timely advice via interactive action plans, enabling remote communication, avoiding triggers and changing lifestyle behaviours. Implementation of digital healthcare is a priority for professionals and healthcare systems but raises the challenges of enabling connected integrated systems and avoiding increasing inequities, and will require policy decisions on infrastructure and funding. © ERS 2023.","","Article; artificial general intelligence; asthma; asymptomatic disease; avoiding trigger; breathing; ChatGPT; comorbidity; digital healthcare; digital technology; feedback system; health care personnel; health care system; health infrastructure; health literacy; human; learning; lifestyle modification; mental disease; psychological well-being; randomized controlled trial (topic); remote communication; remote sensing; risk aversion; risk factor; self care; social support; telehealth","","","","","","","Adams K, Greiner AC, Corrigan JM, The 1st Annual Crossing the Quality Chasm Summit: A Focus on Communities, (2004); Why Asthma Still Kills, (2014); Pinnock H, Parke HL, Panagioti M, Et al., Systematic meta-review of supported self-management for asthma: a healthcare service perspective, BMC Med, 15, (2017); 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Hoyte FC, Mosnaim GS, Rogers L, Et al., Effectiveness of a digital inhaler system for patients with asthma: a 12-week, open-label, randomized study (CONNECT1), J Allergy Clin Immunol Pract, 10, pp. 2579-2587, (2022); Daines L, Morrow S, Wiener-Ogilvie S, Et al., Understanding how patients establish strategies for living with asthma: IMP2ART qualitative study, Br J Gen Pract, 70, pp. e303-e311, (2020); Michie S, van Stralen MM, West R., The behaviour change wheel: a new method for characterising and designing behaviour change interventions, Implement Sci, 6, (2011); Michie S, Richardson M, Johnston M, Et al., The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions, Ann Behav Med, 46, pp. 81-95, (2013); Conn VS, Ruppar TM., Medication adherence outcomes of 771 intervention trials: systematic review and meta-analysis, Prev Med, 99, pp. 269-276, (2017); Miller L, Schuz B, Walters J, Et al., Mobile technology interventions for asthma self-management: systematic review and meta-analysis, JMIR mHealth uHealth, 5, (2017); Dhippayom T, Wateemongkollert A, Mueangfa K, Et al., Comparative efficacy of strategies to support self-management in patients with asthma: a systematic review and network meta-analysis, J Allergy Clin Immunol Pract, 10, pp. 803-814, (2022); Mosnaim G, Safioti G, Brown R, Et al., Digital health technology in asthma: a comprehensive scoping review, J Allergy Clin Immunol Pract, 9, pp. 2377-2398, (2021); Thomas M, McKinley RK, Freeman E, Et al., Prevalence of dysfunctional breathing in patients treated for asthma in primary care: cross sectional survey, BMJ, 322, pp. 1098-1100, (2001); Thomas M, McKinley RK, Freeman E, Et al., The prevalence of dysfunctional breathing in adults in the community with and without asthma, Prim Care Respir J, 14, pp. 78-82, (2005); Thomas M, McKinley RK, Freeman E, Et al., Breathing retraining for dysfunctional breathing in asthma: a randomised controlled trial, Thorax, 58, pp. 110-115, (2003); Bruton A, Lee A, Yardley L, Et al., Physiotherapy breathing retraining for asthma: a randomised controlled trial, Lancet Respir Med, 6, pp. 19-28, (2018); Easton S, Ainsworth B, Thomas M, Et al., Planning a digital intervention for adolescents with asthma (BREATHE4T): a theory-, evidence-and person-based approach to identify key behavioural issues, Pediatr Pulmonol, 57, pp. 2589-2602, (2022); Leonard SI, Turi ER, Powell JS, Et al., Associations of asthma self-management and mental health in adolescents: a scoping review, Respir Med, 200, (2022); Wellbeing Apps, (2023); Torous J, Bucci S, Bell IH, Et al., The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality, World Psych, 20, pp. 318-335, (2021); Spitale M, Gunes H., Affective robotics for wellbeing: a scoping review, Proceedings of the 2022 10th International Conference on Affective Computing and Intelligent Interaction, pp. 1-8, (2022); Sulaiman I, Cushen B, Greene G, Et al., Objective assessment of adherence to inhalers by patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 195, pp. 1333-1343, (2017); What is New in the 2022 Gartner Hype Cycle for Emerging Technologies; Song T, Yu P, Zhang Z., Design features and health outcomes of mHealth applications for patient self-management of asthma: a systematic review: mHealth apps for asthma self-management, Proceedings of the 2022 Australasian Computer Science Week, pp. 153-160, (2022); Schneider T, Baum L, Amy A, Et al., I have most of my asthma under control and I know how my asthma acts: users’ perceptions of asthma self-management mobile app tailored for adolescents, Health Informatics J, 26, pp. 342-353, (2019); Horne R, Cooper V, Wileman V, Et al., Supporting adherence to medicines for long-term conditions, Eur Psychol, 24, pp. 82-96, (2019); Dhruve H, Jackson DJ., Assessing adherence to inhaled therapies in asthma and the emergence of electronic monitoring devices, Eur Respir Rev, 31, (2022); O'Connor A, Tai A, Brinn M, Et al., The acceptability of using augmented reality as a mechanism to engage children in asthma inhaler technique training: qualitative interview study with deductive thematic analysis, JMIR Pediatr Parent, 6, (2023); 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Joglekar S, Sastry N, Coulson NS, Et al., How online communities of people with long-term conditions function and evolve: network analysis of the structure and dynamics of the Asthma UK and British Lung Foundation online communities, J Med Internet Res, 20, (2018); Pinnock H, Murphie P, Vogiatzis I, Et al., Telemedicine and virtual respiratory care in the era of COVID-19, ERJ Open Res, 8, pp. 00111-2022, (2022); Debon R, Coleone JD, Bellei EA, Et al., Mobile health applications for chronic diseases: a systematic review of features for lifestyle improvement, Diabetes Metab Syndr, 13, pp. 2507-2512, (2019); Meijer E, Mansour MBL., Digital approaches to smoking cessation, Digital Respiratory Healthcare (ERS Monograph), pp. 229-235, (2023); Ramsey RR, Caromody JK, Voorhees SE, Et al., A systematic evaluation of asthma management apps examining behavior change techniques, J Allergy Clin Immunol Pract, 7, pp. 2583-2591, (2019); Pinnock H, Hui CY, van Boven JF., Implementation of digital home monitoring and management of respiratory disease, Curr Opin Pulm Med, 29, pp. 302-312, (2023); Njoku C, Green Hofer S, Sathyamoorthy G, Et al., The role of accelerator programmes in supporting the adoption of digital health technologies: a qualitative study of the perspectives of small-and medium-sized enterprises, Digit Health, 9, (2023); Digital Implementation Investment Guide (DIIG): Integrating Digital Interventions into Health Programmes, (2020); Global Diffusion of eHealth: Making Universal Health Coverage Achievable: Report of the Third Global Survey on eHealth, (2019); Skivington K, Matthews L, Simpson S, Et al., A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance, BMJ, 374, (2021); O'Cathain A, Croot L, Duncan E, Et al., Guidance on how to develop complex interventions to improve health and healthcare, BMJ Open, 9, (2019); Unsworth H, Dillon B, Collinson L, Et al., The NICE evidence standards framework for digital health and care technologies – developing and maintaining an innovative evidence framework with global impact, Digit Health, 7, (2021); CE Marking: How a Product Complies with EU Safety, Health and Environmental Requirements, and How to Place a CE Marking on your Product; Medical Devices; Using the UKCA Marking; Patient Organisation Networking Day 2021; Complete Guide to GDPR Compliance; Aljedaani B, Babar MA., Challenges with developing secure mobile health applications: systematic review, JMIR mHealth uHealth, 9, (2021); Zatterin G, Atkins G, Bollen A, Et al., Cyber Security Skills in the UK Labour Market 2022, (2022); Latulippe K, Hamel C, Giroux D., Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies, J Med Internet Res, 19, (2017); Yao R, Zhang W, Evans R, Et al., Inequities in health care services caused by the adoption of digital health technologies: scoping review, J Med Internet Res, 24, (2022); Rakers MM, van Os HJ, Recourt K, Et al., Perceived barriers and facilitators of structural reimbursement for remote patient monitoring, an exploratory qualitative study, Health Policy Technol, 12, (2023); Jansen EM, van de Hei SJ, Dierick BJ, Et al., Global burden of medication non-adherence in chronic obstructive pulmonary disease (COPD) and asthma: a narrative review of the clinical and economic case for smart inhalers, J Thoracic Dis, 13, pp. 3846-3864, (2021); de Guzman KR, Caffery LJ, Smith AC, Et al., Specialist consultation activity and costs in Australia: before and after the introduction of COVID-19 telehealth funding, J Telemed Telecare, 27, pp. 609-614, (2021); Digital Health Summit 2021: Digital Respiratory Medicine – Realisms vs Futurism; Ongoing Clinical Research Collaborations","H. Pinnock; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; email: hilary.pinnock@ed.ac.uk","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85187152490"
"Raben T.G.; Lello L.; Widen E.; Hsu S.D.H.","Raben, Timothy G. (56530846900); Lello, Louis (55675236800); Widen, Erik (57222044831); Hsu, Stephen D. H. (16197442200)","56530846900; 55675236800; 57222044831; 16197442200","Biobank-scale methods and projections for sparse polygenic prediction from machine learning","2023","Scientific Reports","13","1","11662","","","","2","10.1038/s41598-023-37580-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165319806&doi=10.1038%2fs41598-023-37580-5&partnerID=40&md5=e1f207a10dff0e46ce1da1c687530e4d","Department of Physics and Astronomy, Michigan State University, MI, United States; Genomic Prediction, Inc., North Brunswick, NJ, United States","Raben T.G., Department of Physics and Astronomy, Michigan State University, MI, United States; Lello L., Department of Physics and Astronomy, Michigan State University, MI, United States, Genomic Prediction, Inc., North Brunswick, NJ, United States; Widen E., Department of Physics and Astronomy, Michigan State University, MI, United States, Genomic Prediction, Inc., North Brunswick, NJ, United States; Hsu S.D.H., Department of Physics and Astronomy, Michigan State University, MI, United States, Genomic Prediction, Inc., North Brunswick, NJ, United States","In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of 0. 63 (0.02) and for height a correlation of 0. 648 (0.009) for a Taiwanese population. This is above the measured values of 0. 61 (0.01) and 0. 631 (0.008) , respectively, for UK Biobank trained predictors applied to a European population. © 2023, The Author(s).","","Algorithms; Asthma; Biological Specimen Banks; Forecasting; Humans; Machine Learning; algorithm; asthma; biobank; forecasting; human; machine learning","","","","","Michigan State University High-Performance Computing Center, (15326); UK Research and Innovation, UKRI, (MC_PC_20029, MC_PC_20058); UK Research and Innovation, UKRI","Funding text 1: Computational resources provided by the Michigan State University High-Performance Computing Center. The authors acknowledge acquisition of data sets via UK Biobank Main Application 15326. UK Biobank linked health data acknowledgement: Data from the UK Biobank includes data provided by patients and collected by the National Health Service (NHS) England as part of their care and support. UK Biobank data also includes data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (research which commenced between 1st October 2020 - 31st March 2021 grant ref MC_PC_20029; 1st April 2021 -30th September 2022 grant ref MC_PC_20058). ; Funding text 2: Computational resources provided by the Michigan State University High-Performance Computing Center. The authors acknowledge acquisition of data sets via UK Biobank Main Application 15326. UK Biobank linked health data acknowledgement: Data from the UK Biobank includes data provided by patients and collected by the National Health Service (NHS) England as part of their care and support. UK Biobank data also includes data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (research which commenced between 1st October 2020 - 31st March 2021 grant ref MC_PC_20029; 1st April 2021 -30th September 2022 grant ref MC_PC_20058).","1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing, Nature, 467, (2010); Topmed; Taiwan Precision Medicine Initiative; All of Us Research Program Investigators, N. Engl. J. Med., 381, pp. 668-676, (2019); Martin A.R., Et al., Human demographic history impacts genetic risk prediction across diverse populations, Am. J. Hum. 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Raben; Department of Physics and Astronomy, Michigan State University, United States; email: rabentim@msu.edu","","Nature Research","","","","","","20452322","","","37468507","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85165319806"
"Uthoff J.M.; Mott S.L.; Larson J.; Neslund-Dudas C.M.; Schwartz A.G.; Sieren J.C.","Uthoff, Johanna M. (56986491100); Mott, Sarah L. (56448493500); Larson, Jared (57209327409); Neslund-Dudas, Christine M. (15063169400); Schwartz, Ann G. (15770216900); Sieren, Jessica C. (36017530700)","56986491100; 56448493500; 57209327409; 15063169400; 15770216900; 36017530700","Computed Tomography Features of Lung Structure Have Utility for Differentiating Malignant and Benign Pulmonary Nodules","2022","Chronic Obstructive Pulmonary Diseases","9","2","","154","164","10","3","10.15326/jcopdf.2021.0271","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130918902&doi=10.15326%2fjcopdf.2021.0271&partnerID=40&md5=906cea1a05576c33acec132e99219604","Department of Radiology, University of Iowa, Iowa City, IA, United States; Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States; Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, United States; Henry Ford Cancer Institute, Henry Ford Health System, Detroit, MI, United States; Karmanos Cancer Institute, Wayne State University, Detroit, MI, United States","Uthoff J.M., Department of Radiology, University of Iowa, Iowa City, IA, United States, Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States; Mott S.L., Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States; Larson J., Department of Radiology, University of Iowa, Iowa City, IA, United States; Neslund-Dudas C.M., Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, United States, Henry Ford Cancer Institute, Henry Ford Health System, Detroit, MI, United States; Schwartz A.G., Karmanos Cancer Institute, Wayne State University, Detroit, MI, United States; Sieren J.C., Department of Radiology, University of Iowa, Iowa City, IA, United States, Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, United States","Background: Chronic obstructive pulmonary disease (COPD) is a known comorbidity for lung cancer independent of smoking history. Quantitative computed tomography (qCT) imaging features related to COPD have shown promise in the assessment of lung cancer risk. We hypothesize that qCT features from the lung, lobe, and airway tree related to the location of the pulmonary nodule can be used to provide informative malignancy risk assessment. Methods: A total of 183 qCT features were extracted from 278 individuals with a solitary pulmonary nodule of known diagnosis (71 malignant, 207 benign). These included histogram and airway characteristics of the lungs, lobe, and segmental paths. Performances of the least absolute shrinkage and selection operator (LASSO) regression analysis and an ensemble of neural networks (ENN) were compared for feature set selection and classification on a testing cohort of 49 additional individuals (15 malignant, 34 benign). Results: The LASSO and ENN methods produced different feature sets for classification with LASSO selecting fewer qCT features (7) than the ENN (17). The LASSO model with the highest performing training area under the curve (AUC) (0.80) incorporated automatically extracted features and reader-measured nodule diameter with a testing AUC of 0.62. The ENN model with the highest performing AUC (0.77) also incorporated qCT and reader diameter but maintained higher testing performance AUC (0.79). Conclusions: Automatically extracted qCT imaging features of the lung can be informative of the differentiation between individuals with malignant pulmonary nodules and those with benign pulmonary nodules, without requiring nodule segmentation and analysis. © 2022 COPD Foundation. All rights reserved.","artificial intelligence; cancer risk assessment; chronic obstructive pulmonary disease; machine learning; quantitative computed tomography","adult; area under the curve; Article; artificial neural network; benign lung tumor; cancer risk; chronic obstructive lung disease; classification; clinical feature; cohort analysis; computer assisted tomography; controlled study; diagnostic value; differential diagnosis; ensemble of neural network; family history; feature extraction; feature selection; female; forced expiratory volume; forced vital capacity; histogram; human; image analysis; image segmentation; learning algorithm; least absolute shrinkage and selection operator; lung cancer; lung function test; lung nodule; lung parenchyma; lung structure; lung volume; major clinical study; male; retrospective study; risk assessment; smoking; tracheobronchial tree","","","","","Herrick Foundation; National Lung Screening Trial; AUC-ROC; NLST; National Cancer Institute, NCI, (P30CA086862, HHSN26120130011I); COPD Foundation, (R01CA141769, P30CA022453); American Lung Association, ALA, (LH-574107, LCD-220717-N); National Heart, Lung, and Blood Institute, NHLBI, (U01 HL089897, U01 HL089856, NCT00608764); Fundação para a Ciência e a Tecnologia, FCT, (PTDC/CCI-INF/6762/2020)","Abbreviations: chronic obstructive pulmonary disease, COPD; quantitative computed tomography, qCT; least absolute shrinkage and selection operator, LASSO; ensemble of neural networks, ENN; area under the curve, AUC; computed tomography, CT; COPD Genetic Epidemiology, COPDGene\u00AE; Inflammation, Health, and Lung Epidemiology study, INHALE; National Lung Screening Trial, NLST; Global initiative for chronic Obstructive Lung Disease, GOLD; pulmonary function tests, PFTs; forced vital capacity, FVC; forced expiratory volume in 1 second, FEV1; segmental airway paths, sAP; coefficient of variation, CV; Hounsfield unit, HU; area under the receiver operating characteristic curve, AUC-ROC; information optimization, IO; Response Evaluation Criteria in Solid Tumors, RECIST Funding Support: This work was supported in part by a National Cancer Institute Core grant (P30CA086862) to the University of Iowa Holden Comprehensive Cancer Center, an American Lung Association Dissertation Award (LH-574107), and an American Lung Association Cancer Discovery Award (grant LCD-220717-N). The COPDGene Study was supported by National Heart, Lung, and Blood Institute grant U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion. The INHALE study was supported by Award Number R01CA141769 and P30CA022453 from the National Cancer Institute, Health and Human Services Award HHSN26120130011I and the Herrick Foundation. Date of Acceptance: January 12, 2022 | Published Online Date: January 12, 2022 Citation: Uthoff JM, Mott SL, Larson J, et al; the COPDGene Investigators. Computed tomography features of lung structure have utility for differentiating malignant and benign pulmonary nodules. Chronic Obstr Pulm Dis. 2022;9(2):154-164. doi: https://doi.org/10.15326/ jcopdf.2021.0271","Wasswa-Kintu S, Gan WQ, Man SF, Pare PD, Sin DD., Relationship between reduced forced expiratory volume in one second and the risk of lung cancer: a systematic review and meta-analysis, Thorax, 60, 7, pp. 570-575, (2005); Schwartz AG, Lusk CM, Wenzlaff AS, Et al., Risk of lung cancer associated with COPD phenotype based on quantitative image analysis, Cancer Epidemiol Biomarkers Prev, 25, 9, pp. 1341-1347, (2016); Wilson DO, Leader JK, Fuhrman CR, Reilly JJ, Sciurba FC, Weissfeld JL., Quantitative computed tomography analysis, airflow obstruction, and lung cancer in the Pittsburgh lung screening study, J Thorac Oncol, 6, 7, pp. 1200-1205, (2011); Maldonado F, Bartholmai BJ, Swensen SJ, Midthun DE, Decker PA, Jett JR., Are airflow obstruction and radiographic evidence of emphysema risk factors for lung cancer? A nested case-control study using quantitative emphysema analysis, Chest, 138, 6, pp. 1295-1302, (2010); Chubachi S, Takahashi S, Tsutsumi A, Et al., Radiologic features of precancerous areas of the lungs in chronic obstructive pulmonary disease, Int J Chron Obstruct Pulmon Dis, 12, pp. 1613-1624, (2017); Gagnat AA, Gjerdevik M, Gallefoss F, Coxson HO, Gulsvik A, Bakke P., Incidence of non-pulmonary cancer and lung cancer by amount of emphysema and airway wall thickness: a community-based cohort, Eur Respir J, 49, 5, (2017); Gierada DS, Guniganti P, Newman BJ, Et al., Quantitative CT assessment of emphysema and airways in relation to lung cancer risk, Radiology, 261, 3, pp. 950-959, (2011); Wille MM, Thomsen LH, Petersen J, Et al., Visual assessment of early emphysema and interstitial abnormalities on CT is useful in lung cancer risk analysis, Eur Radiol, 26, 2, pp. 487-494, (2016); Bae K, Jeon KN, Lee SJ, Et al., Severity of pulmonary emphysema and lung cancer: analysis using quantitative lobar emphysema scoring, Medicine, 95, 48, (2016); Carr LL, Jacobson S, Lynch DA, Et al., Features of COPD as predictors of lung cancer, Chest, 153, 6, pp. 1326-1335, (2018); Johannessen A, Skorge TD, Bottai M, Et al., Mortality by level of emphysema and airway wall thickness, Am J Respir Crit Care Med, 187, 6, pp. 602-608, (2013); Regan EA, Hokanson JE, Murphy JR, Et al., Genetic epidemiology of COPD (COPDGene) study design, COPD, 7, 1, pp. 32-43, (2010); Aberle DR, Adams AM, Berg CD, Et al., Reduced lung-cancer mortality with low-dose computed tomographic screening, N Engl J Med, 365, 5, pp. 395-409, (2011); Global strategy for the diagnosis, management, and prevention of COPD, 2021 report; Uthoff J, Stephens MJ, Newell JDJ, Et al., Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT, J Med Phys, 46, 7, pp. 3207-3216, (2019); A language and environment for statistical computing, (2020); Fan J, Upadhye S, Worster A., Understanding receiver operating characteristic (ROC) curves, CJEM, 8, 1, pp. 19-20, (2006); Davis J, Goadrich M., The relationship between precision-recall and ROC curves, Proceedings of the 23rd International Conference on Machine Learning, pp. 233-240, (2016); Robin X, Turck N, Hainard A, Et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics, 12, 1, (2011); Ferreira JR, Oliveira MC, de Azevedo-Marques PM., Characterization of pulmonary nodules based on features of margin sharpness and texture, J Digit Imaging, 31, 4, pp. 451-463, (2018); Lee MC, Boroczky L, Sungur-Stasik K, Et al., Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction, Artif Intell Med, 50, 1, pp. 43-53, (2010); Way TW, Sahiner B, Chan HP, Et al., Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features, Med Phys, 36, 7, pp. 3086-3098, (2009); Zhu Y, Tan Y, Hua Y, Wang M, Zhang G, Zhang J., Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography, J Digit Imaging, 23, 1, pp. 51-65, (2010); Dilger SKN, Uthoff J, Judisch A, Et al., Improved pulmonary nodule classification utilizing quantitative lung parenchyma features, J Med Imaging, 2, 4, (2015); Huang P, Park S, Yan R, Et al., Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study, Radiology, 286, 1, pp. 286-295, (2017); Kalpathy-Cramer J, Mamomov A, Zhao B, Et al., Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features, Tomography, 2, 4, pp. 430-437, (2016); MacMahon H, Naidich DP, Goo JM, Et al., Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017, Radiology, 284, 1, pp. 228-243, (2017); Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES., The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules, Arch Intern Med, 157, 8, pp. 849-855, (1997); McWilliams A, Tammemagi MC, Mayo JR, Et al., Probability of cancer in pulmonary nodules detected on first screening CT, N Engl J Med, 369, 10, pp. 910-919, (2013); Li Y, Wang J., A mathematical model for predicting malignancy of solitary pulmonary nodules, World J Surg, 36, 4, pp. 830-835, (2012); Gould MK, Ananth L, Barnett PG., A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules, Chest, 131, 2, pp. 383-388, (2007); Kim H, Park CM, Goo JM, Wildberger JE, Kauczor H-U., Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer, Invest Radiol, 50, 9, pp. 571-583, (2015); Baldwin DR, Callister ME., The British Thoracic Society guidelines on the investigation and management of pulmonary nodules, Thorax, 70, 8, pp. 794-798, (2015); Etzel CJ, Kachroo S, Liu M, Et al., Development and validation of a lung cancer risk prediction model for African Americans, Cancer Prev Res (Phila), 1, 4, pp. 255-265, (2008)","J.C. Sieren; University of Iowa Department of Radiology, Iowa City, 200 Hawkins Drive, CC704GH, 52242, United States; email: Jessica-sieren@uiowa.edu","","COPD Foundation","","","","","","2372952X","","","","English","Chronic Obstr. Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130918902"
"Talker L.; Dogan C.; Neville D.; Lim R.H.; Broomfield H.; Lambert G.; Selim A.; Brown T.; Wiffen L.; Carter J.; Ashdown H.F.; Hayward G.; Vijaykumar E.; Weiss S.T.; Chauhan A.; Patel A.X.","Talker, Leeran (58153956800); Dogan, Cihan (58725430500); Neville, Daniel (57200572282); Lim, Rui Hen (58154239800); Broomfield, Henry (58154386900); Lambert, Gabriel (58153956900); Selim, Ahmed (58154537600); Brown, Thomas (57199406256); Wiffen, Laura (37007146100); Carter, Julian (58153813900); Ashdown, Helen F. (6507836354); Hayward, Gail (57221325774); Vijaykumar, Elango (58819265300); Weiss, Scott T. (57207899397); Chauhan, Anoop (57226265197); Patel, Ameera X. (58153671600)","58153956800; 58725430500; 57200572282; 58154239800; 58154386900; 58153956900; 58154537600; 57199406256; 37007146100; 58153813900; 6507836354; 57221325774; 58819265300; 57207899397; 57226265197; 58153671600","Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning","2024","COPD: Journal of Chronic Obstructive Pulmonary Disease","21","1","2321379","","","","2","10.1080/15412555.2024.2321379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191144107&doi=10.1080%2f15412555.2024.2321379&partnerID=40&md5=dd223c5097ed324d2c1e6b93c6d0eb44","Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, United Kingdom; Department of Clinical Operations, TidalSense, Cambridge, United Kingdom; Department of Engineering, TidalSense, Cambridge, United Kingdom; Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Department of Research, Modality GP Partnership, United Kingdom; Department of Medicine, Channing Division of Network Medicine, Harvard Medical School, Boston, MA, United States; Executive Department, TidalSense, Cambridge, United Kingdom","Talker L., Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Dogan C., Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Neville D., Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, United Kingdom; Lim R.H., Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Broomfield H., Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Lambert G., Department of Clinical Operations, TidalSense, Cambridge, United Kingdom; Selim A., Department of Machine Learning, TidalSense, Cambridge, United Kingdom; Brown T., Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, United Kingdom; Wiffen L., Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, United Kingdom; Carter J., Department of Engineering, TidalSense, Cambridge, United Kingdom; Ashdown H.F., Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Hayward G., Department of Primary Care Health Sciences, NIHR Community Healthcare MedTech and IVD Cooperative, University of Oxford, Oxford, United Kingdom; Vijaykumar E., Department of Research, Modality GP Partnership, United Kingdom; Weiss S.T., Department of Medicine, Channing Division of Network Medicine, Harvard Medical School, Boston, MA, United States; Chauhan A., Respiratory Department, Portsmouth Hospitals University NHS Foundation Trust, Portsmouth, United Kingdom; Patel A.X., Executive Department, TidalSense, Cambridge, United Kingdom","Introduction: Spirometry is the gold standard for COPD diagnosis and severity determination, but is technique-dependent, nonspecific, and requires administration by a trained healthcare professional. There is a need for a fast, reliable, and precise alternative diagnostic test. This study’s aim was to use interpretable machine learning to diagnose COPD and assess severity using 75-second carbon dioxide (CO2) breath records captured with TidalSense’s N-TidalTM capnometer. Method: For COPD diagnosis, machine learning algorithms were trained and evaluated on 294 COPD (including GOLD stages 1–4) and 705 non-COPD participants. A logistic regression model was also trained to distinguish GOLD 1 from GOLD 4 COPD with the output probability used as an index of severity. Results: The best diagnostic model achieved an AUROC of 0.890, sensitivity of 0.771, specificity of 0.850 and positive predictive value (PPV) of 0.834. Evaluating performance on all test capnograms that were confidently ruled in or out yielded PPV of 0.930 and NPV of 0.890. The severity determination model yielded an AUROC of 0.980, sensitivity of 0.958, specificity of 0.961 and PPV of 0.958 in distinguishing GOLD 1 from GOLD 4. Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1. Conclusion: The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. N-TidalTM also could be effective in monitoring disease progression, providing a possible alternative to spirometry for disease monitoring. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.","capnometry; chronic obstructive; diagnosis; machine learning; Pulmonary Disease; severity assessment","Aged; Algorithms; Area Under Curve; Capnography; Case-Control Studies; Female; Forced Expiratory Volume; Humans; Logistic Models; Machine Learning; Male; Middle Aged; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Sensitivity and Specificity; Severity of Illness Index; Spirometry; adult; aged; Article; capnometry; chronic obstructive lung disease; controlled study; data collection method; data processing; diagnostic accuracy; diagnostic test accuracy study; disease exacerbation; disease severity assessment; feature extraction; female; forced expiratory volume; human; learning algorithm; logistic regression analysis; longitudinal study; lung emphysema; machine learning; major clinical study; male; observational study; patient monitoring; point of care testing; predictive value; primary medical care; sensitivity and specificity; spirometry; support vector machine; algorithm; area under the curve; capnometry; case control study; devices; middle aged; pathophysiology; procedures; severity of illness index; statistical model","","","N-Tidal; XGBoost","","National Institute for Health Research Invention for Innovation; National Institute for Health and Care Research, NIHR; Pfizer OpenAir; Innovate UK; NIHR i4i, (II-LA-1117-20002, 102977); SBRI Healthcare, (74355, 133879)","The studies which provided the data for this report were funded by NIHR (i4i grant), Innovate UK, and Pfizer OpenAir. The authors had sole responsibility for the study design, data collection, data analysis, data interpretation and report writing. The ABRS study was supported by the National Institute for Health Research Invention for Innovation (NIHR i4i) Programme (Grant Reference Number: II-LA-1117-20002), the GBRS study was supported by Innovate UK (Grant Reference Number: 102977), the CBRS study was supported by SBRI Healthcare, the CBRS2 study was supported by Pfizer OpenAir and the CARES study was supported by Innovate UK through two grants (Grant Reference Numbers: 133879 and 74355).","(2020); (2012); Khakban A., Sin D.D., FitzGerald J.M., Et al., The projected epidemic of chronic obstructive pulmonary disease hospitalizations over the next 15 years. a population-based perspective, Am J Respir Crit Care Med, 195, 3, pp. 1-14, (2017); (2017); Peter M.A., Calverley J.A., Anderson B., Et al., (2007); Qaseem A., Snow V., Shekelle P., Et al., Diagnosis and management of stable chronic obstructive pulmonary disease: a clinical practice guideline from the American College of Physicians, Ann Intern Med, 147, 9, pp. 633-638, (2007); Bednarek M., Maciejewski J., Wozniak M., Et al., Prevalence, severity and underdiagnosis of COPD in the primary care setting, Thorax, 63, 5, pp. 402-407, (2008); Schneider A., Gindner L., Tilemann L., Et al., Diagnostic accuracy of spirometry in primary care, BMC Pulm Med, 9, 1, (2009); Stolz D., Mkorombindo T., Schumann D.M., Et al., Towards the elimination of chronic obstructive pulmonary disease: a lancet commission, Lancet, 400, pp. 921-972, (2022); Jaffe M.B., Using the features of the time and volumetric capnogram for classification and prediction, J Clin Monit Comput, 31, 1, pp. 19-41, (2017); Mieloszyk R.J., Verghese G.C., Deitch K., Et al., Automated quantitative analysis of capnogram shape for COPD–normal and COPD–CHF classification, IEEE Trans Biomed Eng, 61, 12, pp. 2882-2890, (2014); Abid A., Mieloszyk R.J., Verghese G.C., Et al., “Model-based estimation of respiratory parameters from capnography, with application to diagnosing obstructive lung disease,” in, IEEE Trans Biomed Eng, 64, 12, pp. 2957-2967, (2017); Murray E.K., You C.X., Verghese G.C., Et al., Low-order mechanistic models for volumetric and temporal capnography: development, validation, and application, IEEE Trans Biomed Eng, 70, 9, pp. 2710-2721, (2023); Talker L., Neville D., Wiffen L., Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer, Respir Res, 24, 1, (2023); Bate S.R., Jugg B., Rutter S., Et al.; Cutillo C.M., Sharma K.R., Foschini L., Et al., Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency, NPJ Digit Med, 3, 1, (2020); Howe T.A., Jaalam K., Ahmad R., Et al., The use of end-tidal capnography to monitor non-Intubated patients presenting with acute exacerbation of asthma in the emergency department, J Emerg Med, 41, 6, pp. 581-589, (2011); Lange P., Halpin D.M., O'Donnell D.E., Et al., Diagnosis, assessment, and phenotyping of copd: beyond fev1, Int J Chron Obstruct Pulmon Dis, 11 Spec Iss, Spec Iss, pp. 3-12, (2016); Cardiovascular Institute of the South. COPD and heart failure: what are the symptoms and how are they related, (2017); Wu Z., Yang D., Ge Z., Et al., Body mass index of patients with chronic obstructive pulmonary disease is associated with pulmonary function and exacerbations: a retrospective real world research, J Thorac Dis, 10, 8, pp. 5086-5099, (2018); Sun Y., Milne S., Jaw J.E., Et al., BMI is associated with FEV1 decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials, Respir Res, 20, 1, (2019); Joshi I., Morley J., Artificial intelligence: how to get it right. putting policy into practice for safe data-driven innovation in health and care, (2019); Lowe K.E., Regan E.A., Anzueto A., Et al., COPDGene® 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr Pulm Dis, 6, 5, pp. 384-399, (2019)","A.X. Patel; Executive Department, TidalSense Limited, Cambridge, 15a Vinery Rd, United Kingdom; email: ameera.patel@tidalsense.com","","Taylor and Francis Ltd.","","","","","","15412555","","","38655897","English","COPD J. Chronic Obstructive Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191144107"
"Duckworth C.; Cliffe B.; Pickering B.; Ainsworth B.; Blythin A.; Kirk A.; Wilkinson T.M.A.; Boniface M.J.","Duckworth, Christopher (57224810746); Cliffe, Bethany (57205770633); Pickering, Brian (25654353500); Ainsworth, Ben (55681659900); Blythin, Alison (57219667389); Kirk, Adam (58457929500); Wilkinson, Thomas M. A. (7202351234); Boniface, Michael J. (55794460800)","57224810746; 57205770633; 25654353500; 55681659900; 57219667389; 58457929500; 7202351234; 55794460800","Characterising user engagement with mHealth for chronic disease self-management and impact on machine learning performance","2024","npj Digital Medicine","7","1","66","","","","2","10.1038/s41746-024-01063-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187497325&doi=10.1038%2fs41746-024-01063-2&partnerID=40&md5=aa6bc7df8134d8129ac39c9d07420f44","IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of Southampton, Southampton, United Kingdom; School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom; my mHealth Limited, London, United Kingdom; National Institute for Health Research Biomedical Research Centre, University of Southampton, Southampton, United Kingdom; Faculty of Medicine, University of Southampton, Southampton, United Kingdom","Duckworth C., IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of Southampton, Southampton, United Kingdom; Cliffe B., School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom; Pickering B., IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of Southampton, Southampton, United Kingdom; Ainsworth B., School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom; Blythin A., my mHealth Limited, London, United Kingdom; Kirk A., my mHealth Limited, London, United Kingdom; Wilkinson T.M.A., my mHealth Limited, London, United Kingdom, National Institute for Health Research Biomedical Research Centre, University of Southampton, Southampton, United Kingdom, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; Boniface M.J., IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of Southampton, Southampton, United Kingdom","Mobile Health (mHealth) has the potential to be transformative in the management of chronic conditions. Machine learning can leverage self-reported data collected with apps to predict periods of increased health risk, alert users, and signpost interventions. Despite this, mHealth must balance the treatment burden of frequent self-reporting and predictive performance and safety. Here we report how user engagement with a widely used and clinically validated mHealth app, myCOPD (designed for the self-management of Chronic Obstructive Pulmonary Disease), directly impacts the performance of a machine learning model predicting an acute worsening of condition (i.e., exacerbations). We classify how users typically engage with myCOPD, finding that 60.3% of users engage frequently, however, less frequent users can show transitional engagement (18.4%), becoming more engaged immediately (< 21 days) before exacerbating. Machine learning performed better for users who engaged the most, however, this performance decrease can be mostly offset for less frequent users who engage more near exacerbation. We conduct interviews and focus groups with myCOPD users, highlighting digital diaries and disease acuity as key factors for engagement. Users of mHealth can feel overburdened when self-reporting data necessary for predictive modelling and confidence of recognising exacerbations is a significant barrier to accurate self-reported data. We demonstrate that users of mHealth should be encouraged to engage when they notice changes to their condition (rather than clinically defined symptoms) to achieve data that is still predictive for machine learning, while reducing the likelihood of disengagement through desensitisation. © The Author(s) 2024.","","Health risks; mHealth; Pulmonary diseases; salbutamol; Chronic conditions; Chronic disease; Condition; Learning performance; Machine-learning; On-machines; Self management; Self-reporting; User engagement; Article; asthma; bronchiectasis; chest infection; chronic disease; chronic obstructive lung disease; cohort analysis; coughing; disease exacerbation; dyspnea; human; machine learning; male; predictive model; self care; self report; semi structured interview; symptom; telehealth; wheezing; Machine learning","","salbutamol, 18559-94-9, 35763-26-9","","","National Institute for Health and Care Research, NIHR; UK Government’s Department of Health and Social Care; Artificial Intelligence, (AI_AWARD02200)","This project “my Smart COPD exacerbation management (mySmartCOPD)” is funded by the National Institute for Health Research (NIHR) Artificial Intelligence (AI) in Health and Care Award AI_AWARD02200. The views expressed are those of the author(s) and not necessarily those of the NIHR or the UK Government’s Department of Health and Social Care. The funders of the study had no role in study design, data analysis, interpretation, or writing.","Murphy S.L., Et al., Deaths: Final Data for 2018, (2021); Kim T.K., Lane S.R., Government health expenditure and public health outcomes: a comparative study among 17 countries and implications for US health care reform, Am. Int. J. Contemp. Res, 3, pp. 8-13, (2013); De Ridder D., Geenen R., Kuijer R., van Middendorp H., Psychological adjustment to chronic disease, Lancet, 372, pp. 246-255, (2008); Turner J., Kelly B., Emotional dimensions of chronic disease, West. J. Med, 172, (2000); W. H. Global Diffusion of Ehealth: Making Universal Health Coverage Achievable: Report of the Third Global Survey on Ehealth, (2017); Rowland S.P., Et al., What is the clinical value of mHealth for patients?, NPJ Digital Med, 3, pp. 1-6, (2020); Perski O., Blandford A., West R., Michie S., Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis, Transl. Behav. Med, 7, pp. 254-267, (2017); Nahum-Shani I., Et al., Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support, Ann. Behav. Med, 52, pp. 446-462, (2018); Wang L., Miller L.C., Just-in-the-moment adaptive interventions (JITAI): a meta-analytical review, Health Commun, 35, pp. 1531-1544, (2020); Chmiel F.P., Et al., Prediction of chronic obstructive pulmonary disease exacerbation events by using patient self-reported data in a digital health app: statistical evaluation and machine learning approach, JMIR Med. Inform, 10, (2022); Miller S., Et al., A framework for analyzing and measuring usage and engagement data (AMUsED) in digital interventions, J. Med. Internet Res, 21, (2019); North M., Et al., A randomised controlled feasibility trial of E-health application supported care vs usual care after exacerbation of COPD: the RESCUE trial, NPJ Digital Med, 3, pp. 1-8, (2020); Crooks M.G., Et al., Evidence generation for the clinical impact of myCOPD in patients with mild, moderate and newly diagnosed COPD: a randomised controlled trial, ERJ Open Res, 6, (2020); Cooper R., Et al., Evaluation of myCOPD digital self-management technology in a remote and rural population: real-world feasibility study, JMIR mHealth uHealth, 10, (2022); McLean S., Et al., Projecting the COPD population and costs in England and Scotland: 2011 to 2030, Sci. Rep, 6, pp. 1-10, (2016); Rodriguez-Roisin R., Toward a consensus definition for COPD exacerbations, Chest, 117, pp. 398S-401S, (2000); Marangunic N., Granic A., Technology acceptance model: a literature review from 1986 to 2013, Univers. Access Inf. Soc, 14, pp. 81-95, (2015); Ratneswaran C., Et al., A cross-sectional survey investigating the desensitisation of graphic health warning labels and their impact on smokers, non-smokers and patients with COPD in a London cohort, BMJ Open, 4, (2014); Walters J.A., Turnock A.C., Walters E.H., Wood‐Baker R. Action plans with limited patient education only for exacerbations of chronic obstructive pulmonary disease, Cochrane Database of Systematic Reviews, (2010); Wilkinson T.M., Et al., Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, Am. J. Resp. Crit. Care Med, 169, pp. 1298-1303, (2004); Dodd J.W., Et al., The COPD assessment test (CAT): response to pulmonary rehabilitation. A multicentre, prospective study, Thorax, 66, pp. 425-429, (2011); Gupta N., Pinto L.M., Morogan A., Bourbeau J., The COPD assessment test: a systematic review, Eur. Resp. J, 44, pp. 873-884, (2014); Jones P., Et al., Development and first validation of the COPD assessment test, Eur. Resp. J, 34, pp. 648-654, (2009); Global strategy for the diagnosis, management and prevention of COPD, Global Initiative for Chronic Obstructive Lung Disease (GOLD).; Hopkinson N.S., Molyneux A., Pink J., Harrisingh M.C., Chronic obstructive pulmonary disease: Diagnosis and management: Summary of updated NICE guidance, Bmj, 366, (2019); Chen T., Guestrin C., Xgboost: A scalable tree boosting system, Proceedings of the 22Nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Proceedings of the 25Th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.; Braun V., Clarke V., Reflecting on reflexive thematic analysis, Qualitative Res. Sport Exerc. Health, 11, pp. 589-597, (2019)","C. Duckworth; IT Innovation Centre, Digital Health and Biomedical Engineering, School of Engineering, University of Southampton, Southampton, United Kingdom; email: C.J.Duckworth@soton.ac.uk","","Nature Research","","","","","","23986352","","","","English","npj Digit. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85187497325"
"Qin Y.; Liu C.; Li Q.; Zhou X.; Wang J.","Qin, Yingjiao (58790871900); Liu, Chang (58845469800); Li, Qi (56486199800); Zhou, Xiangdong (55414083200); Wang, Jie (57200027728)","58790871900; 58845469800; 56486199800; 55414083200; 57200027728","Mechanistic analysis of Th2-type inflammatory factors in asthma","2023","Journal of Thoracic Disease","15","12","","6898","6914","16","2","10.21037/jtd-23-1628","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181235416&doi=10.21037%2fjtd-23-1628&partnerID=40&md5=9a5fdb29d5dfb13cef73cafd10c04166","Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China; Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, 31 Longhua Road, Haikou, 570102, China","Qin Y., Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China; Liu C., Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China; Li Q., Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China; Zhou X., Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China, Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, 31 Longhua Road, Haikou, 570102, China; Wang J., Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China, Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, 31 Longhua Road, Haikou, 570102, China","Background: The main pathological features of asthma are widespread chronic inflammation of the airways and restricted ventilation due to airway remodeling, which involves changes in a range of regulatory pathways. While the role of T helper type 2 (Th2)-related inflammatory factors in this process is known, the detailed understanding of how genes affect protein functions during airway remodeling is still lacking. This study aims to fill this knowledge gap by integrating gene expression data and protein function analysis, providing new scientific insights for a deeper understanding of the mechanisms of airway remodeling and for further development of asthma treatment strategies. Methods: In this study, the mechanism of Th2-related inflammatory factors in tracheal remodeling was studied through differentially expressed gene (DEG) screening, enrichment analysis, protein-protein interaction (PPI) network construction, machine learning, and the construction of a line graph model. Results: Our study revealed that S100A14, KRT6A, S100A2, ABCA13, UBE2C, RASSF10, PSCA, PLAT, and TIMP1 may be the key genes for airway remodeling; epithelial-mesenchymal transition (EMT)-related genes GEM, TPM4, SLC6A8, and SNTB1 may be involved in airway remodeling due to asthma; IL6 may affect the occurrence of airway remodeling by binding to UBE2C protein or by regulating GEM genes, respectively; IL6 and IL9 may affect the occurrence of airway remodeling by regulating the downstream Toll-like receptor (TLR) signaling pathway and thus IL6 and IL9 may influence the occurrence of tracheal remodeling by regulating downstream TLR signaling pathways. Conclusions: This study further mined the asthma gene microarray database through bioinformatics analysis and identified key genes and important pathways affecting airway remodeling in asthma patients, providing new ideas to uncover the mechanism of airway remodeling due to asthma and then seek new therapeutic targets. © Journal of Thoracic Disease. All rights reserved.","airway inflammation; airway remodeling; asthma; T helper type 2-associated inflammatory factors (Th2-associated inflammatory factors)","ABC transporter A13; interleukin 6; interleukin 9; prostate stem cell antigen; tissue inhibitor of metalloproteinase 1; toll like receptor; airway remodeling; animal experiment; Article; asthma; bioinformatics; chronic inflammation; controlled study; differential gene expression; epithelial mesenchymal transition; gem gene; gene; human; human tissue; krt6a gene; machine learning; microarray analysis; mouse; nonhuman; plat gene; protein analysis; protein protein interaction; rassf10 gene; s100a14 gene; s100a2 gene; signal transduction; slc6a8 gene; sntb1 gene; Th2 cell; TLR signaling; tpm4 gene; trachea; ube2c gene","","tissue inhibitor of metalloproteinase 1, 140208-24-8; toll like receptor, 409141-78-2","","","Hainan Province Clinical Medical Center; Hainan Provincial Natural Science Foundation High Level Talent Project, (823RC576); National Natural Science Foundation of China, NSFC, (82260001); National Natural Science Foundation of China, NSFC; Major Science and Technology Project of Hainan Province, (ZDKJ2021036); Major Science and Technology Project of Hainan Province; Key Research and Development Project of Hainan Province, (ZDYF2020223); Key Research and Development Project of Hainan Province; Henan Medical College, (HYPY201907); Henan Medical College","Funding: This work was supported by the Hainan Provincial Natural Science Foundation High Level Talent Project (No. 823RC576), the Hainan Medical College 2019 Research and Cultivation Fund Approval Project (No. HYPY201907), the Hainan Province Clinical Medical Center, the Key R&D Project in Hainan Province (No. ZDYF2020223), the Hainan Province Major Science and Technology Special Project (No. ZDKJ2021036), and the National Natural Science Foundation of China (No. 82260001).","Becker AB, Abrams EM., Asthma guidelines: the Global Initiative for Asthma in relation to national guidelines, Curr Opin Allergy Clin Immunol, 17, pp. 99-103, (2017); Jiang W, Ma Z, Zhang H, Et al., Efficacy of Jia Wei Yang He formula as an adjunctive therapy for asthma: study protocol for a randomized, double blinded, controlled trial, Trials, 19, (2018); Dharmage SC, Perret JL, Custovic A., Epidemiology of Asthma in Children and Adults, Front Pediatr, 7, (2019); Papi A, Brightling C, Pedersen SE, Et al., Asthma, Lancet, 391, pp. 783-800, (2018); Fainardi V, Passadore L, Labate M, Et al., An Overview of the Obese-Asthma Phenotype in Children, Int J Environ Res Public Health, 19, (2022); Shabestari AA, Imanparast F, Mohaghegh P, Et al., The effects of asthma on the oxidative stress, inflammation, and endothelial dysfunction in children with pneumonia, BMC Pediatr, 22, (2022); Varricchi G, Ferri S, Pepys J, Et al., Biologics and airway remodeling in severe asthma, Allergy, 77, pp. 3538-3552, (2022); Huang Y, Qiu C., Research advances in airway remodeling in asthma: a narrative review, Ann Transl Med, 10, (2022); Kuwano K, Bosken CH, Pare PD, Et al., Small airways dimensions in asthma and in chronic obstructive pulmonary disease, Am Rev Respir Dis, 148, pp. 1220-1225, (1993); Boulet LP., Airway remodeling in asthma: update on mechanisms and therapeutic approaches, Curr Opin Pulm Med, 24, pp. 56-62, (2018); Yang Y, Li Y, Qi R, Et al., Constructe a novel 5 hypoxia genes signature for cervical cancer, Cancer Cell Int, 21, (2021); Kimura R, Nakata M, Funabiki Y, Et al., An epigenetic biomarker for adult high-functioning autism spectrum disorder, Sci Rep, 9, (2019); Newman AM, Liu CL, Green MR, Et al., Robust enumeration of cell subsets from tissue expression profiles, Nat Methods, 12, pp. 453-457, (2015); Balachandran VP, Gonen M, Smith JJ, Et al., Nomograms in oncology: more than meets the eye, Lancet Oncol, 16, pp. e173-e180, (2015); Pierce BG, Wiehe K, Hwang H, Et al., ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers, Bioinformatics, 30, pp. 1771-1773, (2014); Wang J, Chen X, Tian Y, Et al., Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma, Aging (Albany NY), 12, pp. 767-783, (2020); Gras D, Bourdin A, Vachier I, Et al., An ex vivo model of severe asthma using reconstituted human bronchial epithelium, J Allergy Clin Immunol, 129, pp. 1259-1266, (2012); Holgate ST, Arshad HS, Roberts GC, Et al., A new look at the pathogenesis of asthma, Clin Sci (Lond), 118, pp. 439-450, (2009); Liu LL, Li FH, Zhang Y, Et al., Tangeretin has anti-asthmatic effects via regulating PI3K and Notch signaling and modulating Th1/Th2/Th17 cytokine balance in neonatal asthmatic mice, Braz J Med Biol Res, 50, (2017); Malmstrom K, Pelkonen AS, Malmberg LP, Et al., Lung function, airway remodelling and inflammation in symptomatic infants: outcome at 3 years, Thorax, 66, pp. 157-162, (2011); Dong L, Wang Y, Zheng T, Et al., Hypoxic hUCMSCderived extracellular vesicles attenuate allergic airway inflammation and airway remodeling in chronic asthma mice, Stem Cell Res Ther, 12, (2021); Banno A, Reddy AT, Lakshmi SP, Et al., Bidirectional interaction of airway epithelial remodeling and inflammation in asthma, Clin Sci (Lond), 134, pp. 1063-1079, (2020); Anderson GP., Pharmacology of formoterol: an innovative bronchodilator, Agents Actions Suppl, 34, pp. 97-115, (1991); Halwani R, Al-Muhsen S, Hamid Q., Airway remodeling in asthma, Curr Opin Pharmacol, 10, pp. 236-245, (2010); Al-Ramli W, Prefontaine D, Chouiali F, Et al., T(H)17associated cytokines (IL-17A and IL-17F) in severe asthma, J Allergy Clin Immunol, 123, pp. 1185-1187, (2009); Simpson JL, Grissell TV, Douwes J, Et al., Innate immune activation in neutrophilic asthma and bronchiectasis, Thorax, 62, pp. 211-218, (2007); Bettelli E, Carrier Y, Gao W, Et al., Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells, Nature, 441, pp. 235-238, (2006); Townsend JM, Fallon GP, Matthews JD, Et al., IL-9deficient mice establish fundamental roles for IL-9 in pulmonary mastocytosis and goblet cell hyperplasia but not T cell development, Immunity, 13, pp. 573-583, (2000); Goswami R, Kaplan MH., A brief history of IL-9, J Immunol, 186, pp. 3283-3288, (2011); Luo W, Hu J, Xu W, Et al., Distinct spatial and temporal roles for Th1, Th2, and Th17 cells in asthma, Front Immunol, 13, (2022); Dua B, Upadhyay R, Natrajan M, Et al., Notch signaling induces lymphoproliferation, T helper cell activation and Th1/Th2 differentiation in leprosy, Immunol Lett, 207, pp. 6-16, (2019); Alhetheel A, Albarrag A, Shakoor Z, Et al., Assessment of Th1/Th2 cytokines among patients with Middle East respiratory syndrome coronavirus infection, Int Immunol, 32, pp. 799-804, (2020); Li B, Zhang L, Zhao J, Et al., The value of cytokine levels in triage and risk prediction for women with persistent high-risk human papilloma virus infection of the cervix, Infect Agent Cancer, 14, (2019); Hwang YH, Kim SJ, Kim H, Et al., The Protective Effects of 2,3,5,4'-Tetrahydroxystilbene-2-O-β-d-Glucoside in the OVA-Induced Asthma Mice Model, Int J Mol Sci, 19, (2018); Pu Y, Liu Y, Liao S, Et al., Azithromycin ameliorates OVA-induced airway remodeling in Balb/c mice via suppression of epithelial-to-mesenchymal transition, Int Immunopharmacol, 58, pp. 87-93, (2018); Ortiz-Zapater E, Signes-Costa J, Montero P, Et al., Lung Fibrosis and Fibrosis in the Lungs: Is It All about Myofibroblasts?, Biomedicines, 10, (2022); He F, Liao B, Pu J, Et al., Exposure to Ambient Particulate Matter Induced COPD in a Rat Model and a Description of the Underlying Mechanism, Sci Rep, 7, (2017); Chilosi M, Calio A, Rossi A, Et al., Epithelial to mesenchymal transition-related proteins ZEB1, β-catenin, and β-tubulin-III in idiopathic pulmonary fibrosis, Mod Pathol, 30, pp. 26-38, (2017); Chen S, Chen Z, Deng Y, Et al., Prevention of IL-6 signaling ameliorates toluene diisocyanate-induced steroid-resistant asthma, Allergol Int, 71, pp. 73-82, (2022); Calabrese LH, Rose-John S., IL-6 biology: implications for clinical targeting in rheumatic disease, Nat Rev Rheumatol, 10, pp. 720-727, (2014); Garbers C, Aparicio-Siegmund S, Rose-John S., The IL-6/ gp130/STAT3 signaling axis: recent advances towards specific inhibition, Curr Opin Immunol, 34, pp. 75-82, (2015); Xie C, Powell C, Yao M, Et al., Ubiquitin-conjugating enzyme E2C: a potential cancer biomarker, Int J Biochem Cell Biol, 47, pp. 113-117, (2014); Gao W, Xiong Y, Li Q, Et al., Inhibition of Toll-Like Receptor Signaling as a Promising Therapy for Inflammatory Diseases: A Journey from Molecular to Nano Therapeutics, Front Physiol, 8, (2017); Liu CF, Drocourt D, Puzo G, Et al., Innate immune response of alveolar macrophage to house dust mite allergen is mediated through TLR2/-4 co-activation, PLoS One, 8, (2013); Zhao J, Shang H, Cao X, Et al., Association of polymorphisms in TLR2 and TLR4 with asthma risk: An update meta-analysis, Medicine (Baltimore), 96, (2017)","J. Wang; Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Province Clinical Medical Center of Respiratory Diseases, Haikou, China; email: WJ_Jerry_1983@163.com","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85181235416"
"Sato T.; Nikolovski J.; Gould R.; Lboukili I.; Roux P.-F.; Al-Ghalith G.; Orie J.; Insel R.; Stamatas G.N.","Sato, Takahiro (57194716867); Nikolovski, Janet (56635061800); Gould, Russell (58546707300); Lboukili, Imane (57771917400); Roux, Pierre-Francois (57278027500); Al-Ghalith, Gabriel (57830733600); Orie, Jeremy (58546707400); Insel, Richard (7004943312); Stamatas, Georgios N. (6602484922)","57194716867; 56635061800; 58546707300; 57771917400; 57278027500; 57830733600; 58546707400; 7004943312; 6602484922","Skin surface biomarkers are associated with future development of atopic dermatitis in children with family history of allergic disease","2023","Skin Research and Technology","29","10","e13470","","","","3","10.1111/srt.13470","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174571869&doi=10.1111%2fsrt.13470&partnerID=40&md5=0d8cffc394a56f80aba048db73cce980","Janssen Research & Development, LLC, Raritan, NJ, United States; Essential Health Translational Science, Johnson & Johnson Santé Beauté France, Issy-les-Moulineaux, France","Sato T., Janssen Research & Development, LLC, Raritan, NJ, United States; Nikolovski J., Janssen Research & Development, LLC, Raritan, NJ, United States; Gould R., Janssen Research & Development, LLC, Raritan, NJ, United States; Lboukili I., Essential Health Translational Science, Johnson & Johnson Santé Beauté France, Issy-les-Moulineaux, France; Roux P.-F., Essential Health Translational Science, Johnson & Johnson Santé Beauté France, Issy-les-Moulineaux, France; Al-Ghalith G., Janssen Research & Development, LLC, Raritan, NJ, United States; Orie J., Janssen Research & Development, LLC, Raritan, NJ, United States; Insel R., Janssen Research & Development, LLC, Raritan, NJ, United States; Stamatas G.N., Essential Health Translational Science, Johnson & Johnson Santé Beauté France, Issy-les-Moulineaux, France","Background: Atopic dermatitis (AD) is a common childhood chronic inflammatory skin disorder that can significantly impact quality of life and has been linked to the subsequent development of food allergy, asthma, and allergic rhinitis, an association known as the “atopic march.”. Objective: The aim of this study was to identify biomarkers collected non-invasively from the skin surface in order to predict AD before diagnosis across a broad age range of children. Methods: Non-invasive skin surface measures and biomarkers were collected from 160 children (3–48 months of age) of three groups: (A) healthy with no family history of allergic disease, (B) healthy with family history of allergic disease, and (C) diagnosed AD. Results: Eleven of 101 children in group B reported AD diagnosis in the subsequent 12 months following the measurements. The children who developed AD had increased skin immune markers before disease onset, compared to those who did not develop AD in the same group and to the control group. In those enrolled with AD, lesional skin was characterized by increased concentrations of certain immune markers and transepidermal water loss, and decreased skin surface hydration. Conclusions: Defining risk susceptibility before onset of AD through non-invasive methods may help identify children who may benefit from early preventative interventions. © 2023 Johnson & Johnson Consumer Inc. Skin Research and Technology published by John Wiley & Sons Ltd.","hBD-1; IL-1RA; IL-36γ; predictive biomarkers; S100A8/9","Allergies; Diagnosis; Disease control; Noninvasive medical procedures; amino acid; biological marker; heterodimer; interleukin 1 receptor blocking agent; Allergic disease; Atopic dermatitis; Chronic inflammatory; HBD-1; IL-1RA; IL-36γ; Predictive biomarker; S100a8/9; Skin disorders; Skin surfaces; adaptive immunity; allergic disease; area under the curve; arm; Article; atopic dermatitis; child; controlled study; cross validation; enzyme linked immunosorbent assay; face; family history; female; high risk population; human; hydration; illumination; infant; inflammation; machine learning; major clinical study; male; non invasive procedure; pathogenesis; preschool child; quality of life; receiver operating characteristic; Severity Scoring of Atopic Dermatitis; skin function; skin surface; skin water loss; stratum corneum; support vector machine; water transport; Biomarkers","","amino acid, 65072-01-7","DERMlite TM Foto, 3Gen, United States","3Gen, United States","Johnson & Johnson Santé Beauté France; Janssen Research and Development, JRD","This work was funded by Janssen Research & Development, LLC and Johnson & Johnson Santé Beauté France. ","Leung D.Y., Jain N., Leo H.L., New concepts in the pathogenesis of atopic dermatitis, Curr Opin Immunol, 15, pp. 634-638, (2003); Lifschitz C., The impact of atopic dermatitis on quality of life, Ann Nutr Metab, 66, 1, pp. 34-40, (2015); Williams H., Robertson C., Stewart A., Et al., Worldwide variations in the prevalence of symptoms of atopic eczema in the International Study of Asthma and Allergies in Childhood, J Allergy Clin Immunol, 103, pp. 125-138, (1999); Spergel J.M., Paller A.S., Atopic dermatitis and the atopic march, J Allergy Clin Immunol, 112, pp. S118-S127, (2003); Palmer C.N., Irvine A.D., Terron-Kwiatkowski A., Et al., Common loss-of-function variants of the epidermal barrier protein filaggrin are a major predisposing factor for atopic dermatitis, Nat Genet, 38, pp. 441-446, (2006); Spergel J.M., Epidemiology of atopic dermatitis and atopic march in children, Immunol Allergy Clin North Am, 30, pp. 269-280, (2010); Thomsen S.F., Epidemiology and natural history of atopic diseases, Eur Clin Respir J, 2, (2015); Irvine A.D., McLean W.H., Leung D.Y., Filaggrin mutations associated with skin and allergic diseases, N Engl J Med, 365, pp. 1315-1327, (2011); Keet C., Pistiner M., Plesa M., Et al., Age and eczema severity, but not family history, are major risk factors for peanut allergy in infancy, J Allergy Clin Immunol, 147, pp. 984-991.e5, (2021); Lowe A., Su J., Tang M., Et al., PEBBLES study protocol: a randomised controlled trial to prevent atopic dermatitis, food allergy and sensitisation in infants with a family history of allergic disease using a skin barrier improvement strategy, BMJ Open, 9, (2019); Roduit C., Frei R., Depner M., Et al., Phenotypes of atopic dermatitis depending on the timing of onset and progression in childhood, JAMA Pediatr, 171, pp. 655-662, (2017); Rehbinder E.M., Advocaat Endre K.M., Lodrup Carlsen K.C., Et al., Predicting skin barrier dysfunction and atopic dermatitis in early infancy, J Allergy Clin Immunol Pract, 8, pp. 664-673.e5, (2020); Guttman-Yassky E., Diaz A., Pavel A.B., Et al., Use of tape strips to detect immune and barrier abnormalities in the skin of children with early-onset atopic dermatitis, JAMA Dermatol, 155, pp. 1358-1370, (2019); Andersson A.M., Solberg J., Koch A., Et al., Assessment of biomarkers in pediatric atopic dermatitis by tape strips and skin biopsies, Allergy, 77, pp. 1499-1509, (2022); Grice E.A., The skin microbiome: potential for novel diagnostic and therapeutic approaches to cutaneous disease, Semin Cutan Med Surg, 33, pp. 98-103, (2014); Hulpusch C., Tremmel K., Hammel G., Et al., Skin pH-dependent Staphylococcus aureus abundance as predictor for increasing atopic dermatitis severity, Allergy, 75, pp. 2888-2898, (2020); Kennedy E.A., Connolly J., Hourihane J.O., Et al., Skin microbiome before development of atopic dermatitis: early colonization with commensal staphylococci at 2 months is associated with a lower risk of atopic dermatitis at 1 year, J Allergy Clin Immunol, 139, pp. 166-172, (2017); Meylan P., Lang C., Mermoud S., Et al., Skin colonization by Staphylococcus aureus precedes the clinical diagnosis of atopic dermatitis in infancy, J Invest Dermatol, 137, pp. 2497-2504, (2017); van Logtestijn M.D., Dominguez-Huttinger E., Stamatas G.N., Et al., Resistance to water diffusion in the stratum corneum is depth-dependent, PLoS One, 10, (2015); Martin P., Goldstein J.D., Mermoud L., Et al., IL-1 family antagonists in mouse and human skin inflammation, Front Immunol, 12, (2021); Dinarello C.A., Interleukin-1 and interleukin-1 antagonism, Blood, 77, pp. 1627-1652, (1991); Bardan A., Nizet V., Gallo R.L., Antimicrobial peptides and the skin, Expert Opin Biol Ther, 4, pp. 543-549, (2004); Kirchner F., Capone K.A., Mack M.C., Et al., Expression of cutaneous immunity markers during infant skin maturation, Pediatr Dermatol, 35, pp. 468-471, (2018); Capone K.A., Dowd S.E., Stamatas G.N., Et al., Diversity of the human skin microbiome early in life, J Invest Dermatol, 131, pp. 2026-2032, (2011); Chieosilapatham P., Ogawa H., Niyonsaba F., Current insights into the role of human β-defensins in atopic dermatitis, Clin Exp Immunol, 190, pp. 155-166, (2017); Ropke M.A., Mekulova A., Pipper C., Et al., Non-invasive assessment of soluble skin surface biomarkers in atopic dermatitis patients-effect of treatment, Skin Res Technol, 27, pp. 715-722, (2021); Kerkhoff C., Voss A., Scholzen T.E., Et al., Novel insights into the role of S100A8/A9 in skin biology, Exp Dermatol, 21, pp. 822-826, (2012); Kypriotou M., Huber M., Hohl D., The human epidermal differentiation complex: cornified envelope precursors, S100 proteins and the ‘fused genes’ family, Exp Dermatol, 21, pp. 643-649, (2012); Broome A.M., Ryan D., Eckert R.L., S100 protein subcellular localization during epidermal differentiation and psoriasis, J Histochem Cytochem, 51, pp. 675-685, (2003); Gittler J.K., Shemer A., Suarez-Farinas M., Et al., Progressive activation of T(H)2/T(H)22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis, J Allergy Clin Immunol, 130, pp. 1344-1354, (2012); Chung T.H., Oh J.S., Lee Y.S., Et al., Elevated serum levels of S100 calcium binding protein A8 (S100A8) reflect disease severity in canine atopic dermatitis, J Vet Med Sci, 72, pp. 693-700, (2010); Jin S., Park C.O., Shin J.U., Et al., DAMP molecules S100A9 and S100A8 activated by IL-17A and house-dust mites are increased in atopic dermatitis, Exp Dermatol, 23, pp. 938-941, (2014); Biagini Myers J.M., Sherenian M.G., Baatyrbek Kyzy A., Et al., Events in normal skin promote early-life atopic dermatitis-the MPAACH cohort, J Allergy Clin Immunol Pract, 8, pp. 2285-2293.e6, (2020); Rawlings A.V., Harding C.R., Moisturization and skin barrier function, Dermatol Ther, 17, 1, pp. 43-48, (2004); Kezic S., O'Regan G.M., Yau N., Et al., Levels of filaggrin degradation products are influenced by both filaggrin genotype and atopic dermatitis severity, Allergy, 66, pp. 934-940, (2011); Polanska A., Danczak-Pazdrowska A., Silny W., Et al., Nonlesional skin in atopic dermatitis is seemingly healthy skin—observations using noninvasive methods, Wideochir Inne Tech Maloinwazyjne, 8, pp. 192-199, (2013); Suarez-Farinas M., Tintle S.J., Shemer A., Et al., Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities, J Allergy Clin Immunol, 127, pp. 954-964.e4, (2011); Leung D.Y.M., Calatroni A., Zaramela L.S., Et al., The nonlesional skin surface distinguishes atopic dermatitis with food allergy as a unique endotype, Sci Transl Med, 11, (2019)","G.N. Stamatas; Essential Health Translational Science, Johnson & Johnson Santé Beauté France, Issy-les-Moulineaux, 1 rue Camille Desmoulins, France; email: georgios.stamatas@outlook.com","","John Wiley and Sons Inc","","","","","","0909752X","","SRTEF","","English","Skin Res. Technol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85174571869"
"Wang X.; He H.; Xu L.; Chen C.; Zhang J.; Li N.; Chen X.; Jiang W.; Li L.; Wang L.; Song Y.; Xiao J.; Zhang J.; Hou D.","Wang, Xiaoyue (57221485566); He, Hong (59054733400); Xu, Liang (57207577598); Chen, Cuicui (56889988600); Zhang, Jieqing (57838649100); Li, Na (57838675400); Chen, Xianxian (57207584577); Jiang, Weipeng (57220055978); Li, Li (57193576841); Wang, Linlin (57221649208); Song, Yuanlin (7404920196); Xiao, Jing (57190986110); Zhang, Jun (56527458800); Hou, Dongni (57190855710)","57221485566; 59054733400; 57207577598; 56889988600; 57838649100; 57838675400; 57207584577; 57220055978; 57193576841; 57221649208; 7404920196; 57190986110; 56527458800; 57190855710","Developing and validating a chronic obstructive pulmonary disease quick screening questionnaire using statistical learning models","2022","Chronic Respiratory Disease","19","","","","","","3","10.1177/14799731221116585","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135767988&doi=10.1177%2f14799731221116585&partnerID=40&md5=8eee9236541744f41d9e4c15e8074fad","Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China","Wang X., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; He H., Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Xu L., AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; Chen C., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Zhang J., Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China, Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China; Li N., AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; Chen X., AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; Jiang W., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Li L., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Wang L., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Song Y., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China; Xiao J., AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; Zhang J., Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Hou D., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China, Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China","Background: Active targeted case-finding is a cost-effective way to identify individuals with high-risk for early diagnosis and interventions of chronic obstructive pulmonary disease (COPD). A precise and practical COPD screening instrument is needed in health care settings. Methods: We created four statistical learning models to predict the risk of COPD using a multi-center randomized cross-sectional survey database (n = 5281). The minimal set of predictors and the best statistical learning model in identifying individuals with airway obstruction were selected to construct a new case-finding questionnaire. We validated its performance in a prospective cohort (n = 958) and compared it with three previously reported case-finding instruments. Results: A set of seven predictors was selected from 643 variables, including age, morning productive cough, wheeze, years of smoking cessation, gender, job, and pack-year of smoking. In four statistical learning models, generalized additive model model had the highest area under curve (AUC) value both on the developing cross-sectional data set (AUC = 0.813) and the prospective validation data set (AUC = 0.880). Our questionnaire outperforms the other three tools on the cross-sectional validation data set. Conclusions: We developed a COPD case-finding questionnaire, which is an efficient and cost-effective tool for identifying high-risk population of COPD. © The Author(s) 2022.","chronic obstructive pulmonary disease; generalized additive model; machine learning; screening; smoking","Cross-Sectional Studies; Humans; Mass Screening; Pulmonary Disease, Chronic Obstructive; Smoking; Spirometry; Surveys and Questionnaires; adult; algorithm; anxiety; area under the curve; Article; artificial intelligence; bronchodilatation; case finding; chronic obstructive lung disease; cohort analysis; controlled study; cross-sectional study; diagnostic test accuracy study; female; forced expiratory volume; forced vital capacity; gender; heart rate; high risk population; human; learning; lung function test; machine learning; major clinical study; male; multicenter study; predictive value; productive cough; prospective study; quality of life; questionnaire; randomized controlled trial; receiver operating characteristic; sensitivity and specificity; smoking; smoking cessation; social status; spirometry; validation process; wheezing; chronic obstructive lung disease; mass screening","","","","","National key R&D plan, (2020YFC2003700); Shanghai Municipal Key Clinical Specialty, (shslczdzk02201); National Natural Science Foundation of China, NSFC, (81770075, 81800008, 82041003); National Natural Science Foundation of China, NSFC; Science and Technology Commission of Shanghai Municipality, STCSM, (20DZ2261200, 20XD1401200, 20Z11901000); Science and Technology Commission of Shanghai Municipality, STCSM","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by The National Natural Science Foundation of China (81800008, 81770075, 82041003), National key R&D plan (2020YFC2003700), Science and Technology Commission of Shanghai Municipality (20DZ2261200, 20Z11901000, 20XD1401200), and Shanghai Municipal Key Clinical Specialty (shslczdzk02201). ","Wang C., Xu J., Yang L., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Diab N., Gershon A.S., Sin D.D., Et al., Underdiagnosis and overdiagnosis of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 198, pp. 1130-1139, (2018); Kaplan A., Thomas M., Screening for COPD: the gap between logic and evidence, Eur Respir Rev, 26, (2017); Mannino D.M., Gagnon R.C., Petty T.L., Et al., Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey, 1988-1994, Arch Intern Med, 160, pp. 1683-1689, (2000); Lamprecht B., Soriano J.B., Studnicka M., Et al., Determinants of underdiagnosis of COPD in national and international surveys, Chest, 148, pp. 971-985, (2015); Labaki W.W., Han M.K., Improving detection of early chronic obstructive pulmonary disease, Ann Am Thorac Soc, 15, pp. S243-S248, (2018); Jordan R.E., Adab P., Sitch A., Et al., Targeted case finding for chronic obstructive pulmonary disease versus routine practice in primary care (TargetCOPD): a cluster-randomised controlled trial, Lancet Respir Med, 4, pp. 720-730, (2016); Martinez F.J., Raczek A.E., Seifer F.D., Et al., Development and initial validation of a self-scored COPD population screener questionnaire (COPD-PS), COPD, 5, pp. 85-95, (2008); Nishino M., Perinodular radiomic features to assess nodule microenvironment: does it help to distinguish malignant versus benign lung nodules?, Radiology, 290, pp. 793-795, (2019); Zarowitz B.J., O'Shea T., Lefkovitz A., Et al., Development and validation of a screening tool for chronic obstructive pulmonary disease in nursing home residents, J Am Med Directors Assoc, 12, pp. 668-674, (2011); Kotz D., Simpson C.R., Viechtbauer W., Et al., Development and validation of a model to predict the 10-year risk of general practitioner-recorded COPD, NPJ Prim Care Respir Med, 24, (2014); Price D.B., Tinkelman D.G., Nordyke R.J., Et al., Scoring system and clinical application of COPD diagnostic questionnaires, Chest, 129, pp. 1531-1539, (2006); Llordes M., Zurdo E., Jaen A., Et al., Which is the best screening strategy for COPD among smokers in primary care? COPD, J Chronic Obstructive Pulm Dis, 14, pp. 43-51, (2017); Yawn B.P., Mapel D.W., Mannino D.M., Et al., Development of the lung function questionnaire (LFQ) to identify airflow obstruction, Int J Chron Obstruct Pulmon Dis, 5, pp. 1-10, (2010); Blanc P.D., Annesi-Maesano I., Balmes J.R., Et al., The occupational burden of nonmalignant respiratory diseases. An official American thoracic society and European respiratory society statement, Am J Respir Crit Care Med, 199, pp. 1312-1334, (2019); Spyratos D., Haidich A.B., Chloros D., Et al., Comparison of three screening questionnaires for chronic obstructive pulmonary disease in the primary care, Respiration, 93, pp. 83-89, (2017); Ma X., Wu Y., Zhang L., Et al., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, J Transl Med, 18, (2020); Inoue H., Tsukuya G., Samukawa T., Et al., Comparison of the COPD population screener and international primary care airway group questionnaires in a general japanese population: the hisayama study, Int J Chronic Obstructive Pulm Dis, 11, pp. 1903-1909, (2016); Heo J., Yoon J.G., Park H., Et al., Machine learning-based model for prediction of outcomes in acute stroke, Stroke, 50, pp. 1263-1265, (2019); Goldstein B.A., Navar A.M., Carter R.E., Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges, pp. 1805-1814, (2017); Zhang L., Zhang H., Ai H., Et al., Applications of machine learning methods in drug toxicity prediction, pp. 987-997, (2018); Shillan D., Sterne J.A.C., Champneys A., Et al., Use of machine learning to analyse routinely collected intensive care unit data: a systematic review, (2019); Yan L., Zhang H.-T., Goncalves J., Et al., An interpretable mortality prediction model for COVID-19 patients, Nat Machine Intelligence, 2, pp. 283-288, (2020); Hu C., Liu Z., Jiang Y., Et al., Early prediction of mortality risk among patients with severe COVID-19, using machine learning, Int J Epidemiol, 49, pp. 1918-1929, (2020); Peng J., Chen C., Zhou M., Et al., A Machine-learning Approach to Forecast Aggravation Risk in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease with Clinical Indicators, Sci Rep, 10, (2020)","D. Hou; Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; email: houdn2014@126.com; J. Zhang; Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China; email: snapzhang@aliyun.com; J. Xiao; AI Center, Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China; email: XIAOJING661@pingan.com.cn","","SAGE Publications Ltd","","","","","","14799723","","","35943965","English","Chronic Respir. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85135767988"
"Prottasha N.J.; Murad S.A.; Muzahid A.J.M.; Rana M.; Kowsher M.; Adhikary A.; Biswas S.; Bairagi A.K.","Prottasha, Nusrat Jahan (57216271505); Murad, Saydul Akbar (57218952421); Muzahid, Abu Jafar Md (57221741096); Rana, Masud (59109322100); Kowsher, Md (57215324267); Adhikary, Apurba (57218669191); Biswas, Sujit (57188856331); Bairagi, Anupam Kumar (55489441400)","57216271505; 57218952421; 57221741096; 59109322100; 57215324267; 57218669191; 57188856331; 55489441400","Impact learning: A learning method from feature's impact and competition","2023","Journal of Computational Science","69","","102011","","","","2","10.1016/j.jocs.2023.102011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152099772&doi=10.1016%2fj.jocs.2023.102011&partnerID=40&md5=6d9c8d86dc81d13acc36aeae36b13d32","Stevens Institute of Technology, Hoboken, 07030, NJ, United States; Universiti Malaysia Pahang, UMP Pekan, Pahang, Kuantan, 26600, Malaysia; Noakhali Science and Technology University, Sonapor, Noakhali, Chittagong, 3802, Bangladesh; University of East London, University Way, London, E16 2RD, United Kingdom; Khulna University, Khulna, 9208, Bangladesh","Prottasha N.J., Stevens Institute of Technology, Hoboken, 07030, NJ, United States; Murad S.A., Universiti Malaysia Pahang, UMP Pekan, Pahang, Kuantan, 26600, Malaysia, Noakhali Science and Technology University, Sonapor, Noakhali, Chittagong, 3802, Bangladesh; Muzahid A.J.M., Universiti Malaysia Pahang, UMP Pekan, Pahang, Kuantan, 26600, Malaysia; Rana M., Noakhali Science and Technology University, Sonapor, Noakhali, Chittagong, 3802, Bangladesh; Kowsher M., Stevens Institute of Technology, Hoboken, 07030, NJ, United States; Adhikary A., Noakhali Science and Technology University, Sonapor, Noakhali, Chittagong, 3802, Bangladesh; Biswas S., University of East London, University Way, London, E16 2RD, United Kingdom; Bairagi A.K., Khulna University, Khulna, 9208, Bangladesh","Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm. © 2023 Elsevier B.V.","Asthma prediction; Classification; Diabetes prediction; Heart disease identification; Impact learning; Machine learning; Regression","Diseases; Learning algorithms; Learning systems; Machine learning; Asthma prediction; Diabetes prediction; Heart disease; Heart disease identification; Impact learning; Learning methods; Machine learning algorithms; Machine-learning; Regression; Sample data; Forecasting","","","","","","","Singh J., Dhiman G., A survey on machine-learning approaches: Theory and their concepts, Mater. Today Proc., (2021); Elith J., Leathwick J.R., Hastie T., A working guide to boosted regression trees, J. Anim. Ecol., 77, 4, pp. 802-813, (2008); Jayatilake S.M.D.A.C., Ganegoda G.U., Involvement of machine learning tools in healthcare decision making, J. Healthc. Eng., 2021, pp. 1-20, (2021); Chen C., Storey G.U., Business intelligence and analytics:From big data to big impact, MIS Quart., 36, 4, (2012); Al-Sahaf H., Bi Y., Chen Q., Lensen A., Mei Y., Sun Y., Tran B., Xue B., Zhang M., A survey on evolutionary machine learning, J. R. Soc. New Zealand, 49, 2, pp. 205-228, (2019); Hallmann S., Moser M., Reck S., Eberl T., Collaboration K., Machine learning for KM3NeT/ORCA, Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019), (2019); Sarker I.H., Kayes A.S.M., Badsha S., Alqahtani H., Watters P., Ng A., Cybersecurity data science: an overview from machine learning perspective, J. Big Data, 7, 1, (2020); Hendrickx T., Cule B., Meysman P., Naulaerts S., Laukens K., Goethals B., Mining association rules in graphs based on frequent cohesive itemsets, Advances in Knowledge Discovery and Data Mining, vol. 9078, pp. 637-648, (2015); Jordan M.I., Mitchell T.M., Machine learning: Trends, perspectives, and prospects, Science, 349, 6245, pp. 255-260, (2015); Ni Y., Aghamirzaie D., Elmarakeby H., Collakova E., Li S., Grene R., Heath L.S., A machine learning approach to predict gene regulatory networks in seed development in arabidopsis, Front. Plant Sci., 7, (2016); Raschka S., Patterson J., Nolet C., Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence, Information, 11, 4, (2020); Pernes D., Fernandes K., Cardoso J.S., Directional support vector machines, Appl. Sci., 9, 4, (2019); Kowsher M., Hossen I., Tahabilder A., Prottasha N.J., Habib K., Azmi Z.R.M., Support directional shifting vector: A direction based machine learning classifier, Emerg. Sci. J., 5, 5, pp. 700-713, (2021); Lopez-Cruz P.L., Bielza C., Larranaga P., Directional naive Bayes classifiers, Pattern Anal. Appl., 18, 2, pp. 225-246, (2015); Blei D.M., Ng A.Y., Jordan M.I., Latent dirichlet allocation, J. Mach. Learn. Res., 3, Jan, pp. 993-1022, (2003); Dumitrescu A.L., Chemicals in Surgical Periodontal Therapy, (2011); Yeruva S., Varalakshmi M.S., Gowtham B.P., Chandana Y.H., Prasad P.K., Identification of sickle cell anemia using deep neural networks, Emerg. Sci. J., 5, 2, pp. 200-210, (2021); Kowsher M., Tahabilder A., Murad S.A., Impact-learning: a robust machine learning algorithm, pp. 9-13, (2020); Elizondo D., The linear separability problem: Some testing methods, IEEE Trans. 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Phys., 348, pp. 683-693, (2017); Pavlyshenko B., Machine learning, linear and bayesian models for logistic regression in failure detection problems, 2016 IEEE International Conference on Big Data (Big Data), pp. 2046-2050, (2016); Almeida R., Bastos N.R., Monteiro M.T.T., A fractional Malthusian growth model with variable order using an optimization approach, (2018); Zulkiflee N.F., Rusiman M.S., Heart disease prediction using logistic regression, Enhanced Knowl. Sci. Technol., 1, 2, pp. 177-184, (2021); Murad S.A., Azmi Z.R.M., Hakami Z.H., Prottasha N.J., Kowsher M., Computer-aided system for extending the performance of diabetes analysis and prediction, 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), pp. 465-470, (2021); Islam M.S., Hasan M.M., Abdullah S., Akbar J.U.M., Arafat N., Murad S.A., A deep spatio-temporal network for vision-based sexual harassment detection, 2021 Emerging Technology in Computing, Communication and Electronics, ETCCE, pp. 1-6, (2021); Adhikary A., Murad S.A., Munir M.S., Hong C.S., Edge assisted crime prediction and evaluation framework for machine learning algorithms, 2022 International Conference on Information Networking, ICOIN, pp. 417-422, (2022); Prottasha N.J., Sami A.A., Kowsher M., Murad S.A., Bairagi A.K., Masud M., Baz M., Transfer learning for sentiment analysis using BERT based supervised fine-tuning, Sensors, 22, 11, (2022); Muppalaneni M., Ma M., Gurumoorthy S., Soft Computing and Medical Bioinformatics, (2019)","A.K. Bairagi; Khulna University, Khulna, 9208, Bangladesh; email: anupam@cse.ku.ac.bd","","Elsevier B.V.","","","","","","18777503","","","","English","J. Comput. Sci.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85152099772"
"Pereira J.; Antunes N.; Rosa J.; Ferreira J.C.; Mogo S.; Pereira M.","Pereira, José (57964382300); Antunes, Nuno (57217858593); Rosa, Joana (57201997205); Ferreira, João C. (35481887000); Mogo, Sandra (6508333851); Pereira, Manuel (58626923600)","57964382300; 57217858593; 57201997205; 35481887000; 6508333851; 58626923600","Intelligent Clinical Decision Support System for Managing COPD Patients","2023","Journal of Personalized Medicine","13","9","1359","","","","3","10.3390/jpm13091359","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172900579&doi=10.3390%2fjpm13091359&partnerID=40&md5=e6d2f316741e77fb939903804ba18053","INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal; Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR (Information Sciences, Technologies and Architecture Research Center), Lisboa, 1649-026, Portugal; Logistics, Molde University College, Molde, NO-6410, Norway; Departamento de Física, Universidade da Beira Interior, Covilhã, 6201-001, Portugal; Hope Care, S.A, Óbidos, 2510-216, Portugal","Pereira J., INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR (Information Sciences, Technologies and Architecture Research Center), Lisboa, 1649-026, Portugal; Antunes N., INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal; Rosa J., INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal; Ferreira J.C., INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal, Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR (Information Sciences, Technologies and Architecture Research Center), Lisboa, 1649-026, Portugal, Logistics, Molde University College, Molde, NO-6410, Norway; Mogo S., Departamento de Física, Universidade da Beira Interior, Covilhã, 6201-001, Portugal; Pereira M., Hope Care, S.A, Óbidos, 2510-216, Portugal","Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients’ future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work’s CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients. © 2023 by the authors.","chronic obstructive pulmonary disease (COPD); decision support system (DSS); health remote monitoring system (HRMS); intelligent clinical decision support system (CIDSS); triage validation module (TVM)","algorithm; Article; artificial intelligence; biometry; blood pressure; blood pressure regulation; calculation; chronic obstructive lung disease; clinical decision support system; clinical evaluation; deterioration; early warning score; environmental parameters; health care personnel; heart rate; hospitalization; human; machine learning; prediction; predictive value; probability; quality of life; remote sensing; risk assessment; systolic blood pressure; telecommunication; telemedicine; telemonitoring; validation process; vital sign","","","","","Instituto de Engenharia de Sistemas e Computadores Inovação; Sistema de Apoio à Investigação Científica e Tecnológica Programas Integrados de IC& DT; Universidade da Beira Interior, UBI; Fundação para a Ciência e a Tecnologia, FCT, (CENTRO-01-0247-FEDER-070275)","This research was funded by the P2020 project, HC-PSI—Plataforma de Serviços Inteligentes, coordinated by INOV—Instituto de Engenharia de Sistemas e Computadores Inovação, Hope Care SA, and Universidade da Beira Interior. This work was also supported by national funds through FCT, Fundação para a Ciência e a Tecnologia, under the project code CENTRO-01-0247-FEDER-070275, in the scope of the Sistema de Apoio à Investigação Científica e Tecnológica Programas Integrados de IC& DT.","Observatório Nacional Doenças Respiratórias 2022; Mogo S., Cachorro V.E., de Frutos A.M., Morphological, chemical and optical absorbing characterization of aerosols in the urban atmosphere of Valladolid, Atmos. Chem. 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Inform, 10, (2022); Pepin J.L., Degano B., Tamisier R., Viglino D., Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD), Life, 12, (2022); Exarchos K., Aggelopoulou A., Oikonomou A., Biniskou T., Beli V., Antoniadou E., Kostikas K., Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease, IEEE J. Biomed. Health Inform, 26, pp. 2331-2338, (2022); Lu J.-W., Wang Y., Sun Y., Zhang Q., Yan L.-M., Wang Y.-X., Gao J.-H., Yin Y., Wang Q.-Y., Li X.-L., Et al., Effectiveness of Telemonitoring for Reducing Exacerbation Occurrence in COPD Patients With Past Exacerbation History: A Systematic Review and Meta-Analysis, Front. Med, 8, (2021); Sanchez-Morillo D., Fernandez-Granero M.A., Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma, Chronic Respir. Dis, 13, pp. 264-283, (2016); Carlin C., Taylor A., van Loon I., McDowell G., Burns S., McGinness P., Lowe D.J., Role for artificial intelligence in respiratory diseases—Chronic obstructive pulmonary disease, J. Hosp. Manag. Health Policy, 5, (2021); Rajeh A.A., Hurst J., Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review, J. Clin. Med, 5, (2016); Martin-Lesende I., Orruno E., Bilbao A., Vergara I., Cairo M.C., Bayon J.C., Reviriego E., Romo M.I., Larranaga J., Asua J., Et al., Impact of telemonitoring home care patients with heart failure or chronic lung disease from primary care on healthcare resource use (the TELBIL study randomised controlled trial), BMC Health Serv. Res, 13, (2013); Lee J., Jung H.M., Kim S.K., Yoo K.H., Jung K.S., Lee S.H., Rhee C.K., Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis, Sci. Rep, 9, (2019); Liu Z., Alavi A., Li M., Zhang X., Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review, Sensors, 23, (2023); Bui C., Pham N., Vo A., Tran A., Nguyen A., Le T., Time Series Forecasting for Healthcare Diagnosis and Prognostics with the Focus on Cardiovascular Diseases, Proceedings of the 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6), pp. 809-818, (2017); Kaieski N., da Costa C.A., da Rosa Righi R., Lora P.S., Eskofier B., Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review, Appl. Soft Comput, 96, (2020); Xie J., Wang Z., Yu Z., Guo B., Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model, Sensors, 22, (2022); Sang S., Qu F., Nie P., Ensembles of Gradient Boosting Recurrent Neural Network for Time Series Data Prediction, IEEE Access, (2021); da Silva D.B., Schmidt D., da Costa C.A., da Rosa Righi R., Eskofier B., DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration, Expert Syst. Appl, 165, (2021); Haselbeck F., Killinger J., Menrad K., Hannus T., Grimm D.G., Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions, Mach. Learn. Appl, 7, (2022); Chacon H., Koppisetti V., Hardage D., Choo K.K.R., Rad P., Forecasting call center arrivals using temporal memory networks and gradient boosting algorithm, Expert Syst. Appl, 224, (2023); Draxler R., Hess G., Hybrid Single-Particle Lagrangian Integrated Trajectories (HY-SPLIT): Version 4.0-Description of the Hysplit_4 Modeling System, 12, (2010); Rolph G., Stein A., Stunder B., Real-time Environmental Applications and Display sYstem: READY, Environ. Model. Softw, 95, pp. 210-228, (2017); Chandra R., Goyal S., Gupta R., Evaluation of deep learning models for multi-step ahead time series prediction, IEEE Access, 9, pp. 83105-83123, (2021); Prat N., Comyn-Wattiau I., Akoka J., Artifact Evaluation in Information Systems Design-Science Research—A Holistic View, (2014)","J.C. Ferreira; INOV Inesc Inovação—Instituto de Novas Tecnologias, Lisbon, 1000-029, Portugal; email: jcafa@iscte.pt","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85172900579"
"Guan Y.; Zhang D.; Zhou X.; Xia Y.; Lu Y.; Zheng X.; He C.; Liu S.; Fan L.","Guan, Yu (55524064000); Zhang, Di (57206456966); Zhou, Xiuxiu (57204916826); Xia, Yi (55523286100); Lu, Yang (57850888900); Zheng, Xuebin (57219550029); He, Chuan (58068284500); Liu, Shiyuan (9232762100); Fan, Li (56611135400)","55524064000; 57206456966; 57204916826; 55523286100; 57850888900; 57219550029; 58068284500; 9232762100; 56611135400","Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images","2024","Quantitative Imaging in Medicine and Surgery","14","3","","2485","2498","13","2","10.21037/qims-23-1307","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187522608&doi=10.21037%2fqims-23-1307&partnerID=40&md5=fe82480349c1d51856bf18aaf12fd454","Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Shanghai Aitrox Technology Corporation Limited, Shanghai, China","Guan Y., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Zhang D., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Zhou X., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Xia Y., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Lu Y., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Zheng X., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; He C., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Liu S., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China; Fan L., Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China","Background: Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. Methods: This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. Results: In total, 107 radiomics features were extracted from each subject’s CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64–0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55–0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). Conclusions: The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification. © Quantitative Imaging in Medicine and Surgery. All rights reserved.","Chronic obstructive pulmonary disease (COPD); deep learning (DL); low-dose computed tomography (LDCT); machine learning (ML); radiomics","aged; Article; chronic obstructive lung disease; clinical feature; controlled study; deep learning; demographics; diagnostic accuracy; diagnostic test accuracy study; female; human; intermethod comparison; low-dose computed tomography; machine learning; major clinical study; male; predictive model; predictive value; quantitative analysis; radiomics; receiver operating characteristic; retrospective study; sensitivity and specificity","","","A-VIEW, suhai alderi information technology ltd, United Arab Emirates; Brilliance-iCT, Philips Healthcare, Netherlands; Doseright collimator, Philips Healthcare","Philips Healthcare; Philips Healthcare, Netherlands; suhai alderi information technology ltd, United Arab Emirates","National Natural Science Foundation General Program of China, (82171926, 81871321); Changzheng Youth Science Support Program of China, (CZQNKP-TYYX-LZ-2022-06); National Natural Science Foundation Key Program of China, (81930049); National Key Research and Development Program of China, NKRDPC, (2022YFC2010000, 2022YFC2010002); National Key Research and Development Program of China, NKRDPC","Funding: This work was supported by the National Key Research and Development Program of China (Nos. 2022YFC2010002 and 2022YFC2010000), the National Natural Science Foundation Key Program of China (No. 81930049), the National Natural Science Foundation General Program of China (Nos. 82171926 and 81871321), and the Changzheng Youth Science Support Program of China (No. CZQNKP-TYYX-LZ-2022-06).","Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease (2024 REPORT), (2023); Rennard SI, Vestbo J., COPD: the dangerous underestimate of 15%, Lancet, 367, pp. 1216-1219, (2006); Romei C, Castellana R, Conti B, Bemi P, Taliani A, Pistelli F, Karwoski RA, Carrozzi L, De Liperi A, Bartholmai B., Quantitative texture-based analysis of pulmonary parenchymal features on chest CT: comparison with densitometric indices and short-term effect of changes in smoking habit, Eur Respir J, 60, (2022); Park J, Hobbs BD, Crapo JD, Make BJ, Regan EA, Humphries S, Carey VJ, Lynch DA, Silverman EK, Subtyping COPD by Using Visual and Quantitative CT Imaging Features, Chest, 157, pp. 47-60, (2020); Kovacs G, Avian A, Bachmaier G, Troester N, Tornyos A, Douschan P, Foris V, Sassmann T, Zeder K, Lindenmann J, Brcic L, Fuchsjaeger M, Agusti A, Olschewski H., Severe Pulmonary Hypertension in COPD: Impact on Survival and Diagnostic Approach, Chest, 162, pp. 202-212, (2022); Zhou W, Cheng G, Zhang Z, Zhu L, Jaeger S, Lure FYM, Guo L., Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing, Quant Imaging Med Surg, 12, pp. 2344-2355, (2022); Sun H, Ren G, Teng X, Song L, Li K, Yang J, Hu X, Zhan Y, Wan SBN, Wong MFE, Chan KK, Tsang HCH, Xu L, Wu TC, Kong FS, Wang YXJ, Qin J, Chan WCL, Ying M, Cai J., Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification, Quant Imaging Med Surg, 13, pp. 394-416, (2023); Wang R, Huang C, Yang W, Wang C, Wang P, Guo L, Cao J, Huang L, Song H, Zhang C, Zhang Y, Shi G., Respiratory microbiota and radiomics features in the stable COPD patients, Respir Res, 24, (2023); Zhang L, Jiang B, Wisselink HJ, Vliegenthart R, Xie X., COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images, Br J Radiol, 95, (2022); Yang Y, Li W, Kang Y, Guo Y, Yang K, Li Q, Liu Y, Yang C, Chen R, Chen H, Li X, Cheng L., A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy, Math Biosci Eng, 19, pp. 4145-4165, (2022); Estepar RSJ., Artificial Intelligence in COPD: New Venues to Study a Complex Disease, Barc Respir Netw Rev, 6, pp. 144-160, (2020); Sun J, Liao X, Yan Y, Zhang X, Sun J, Tan W, Liu B, Wu J, Guo Q, Gao S, Li Z, Wang K, Li Q., Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach, Eur Radiol, 32, pp. 5319-5329, (2022); Tang LYW, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, pp. e259-e267, (2020); Li Z, Liu L, Zhang Z, Yang X, Li X, Gao Y, Huang K., A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD, Acad Radiol, 29, pp. 663-673, (2022); Amudala Puchakayala PR, Sthanam VL, Nakhmani A, Chaudhary MFA, Kizhakke Puliyakote A, Reinhardt JM, Zhang C, Bhatt SP, Bodduluri S., Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans, Radiology, 307, (2023); Du Y, Li Q, Sidorenkov G, Vonder M, Cai J, de Bock GH, Et al., Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-based Comparative Study, Acad Radiol, 28, pp. 36-45, (2021); van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts HJWL., Computational Radiomics System to Decode the Radiographic Phenotype, Cancer Res, 77, pp. e104-e107, (2017); Friedman JH., Greedy Function Approximation: A Gradient Boosting Machine, Ann Appl Stat, 29, pp. 1189-1232, (2001); Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, VanderPlas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E., Scikit-learn: Machine Learning in Python. Scikit-learn: Machine Learning in Python, J Mach Learn Res, 12, pp. 2825-2830, (2011); Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, (2019); Xie S, Girshick R, Dollar P, Tu Z, He K., Aggregated Residual Transformations for Deep Neural Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987-5995, (2017); He K, Zhang X, Ren S, Sun J., Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016); Guo MH, Liu ZN, Mu TJ, Hu SM., Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks, IEEE Trans Pattern Anal Mach Intell, 45, pp. 5436-5447, (2023); Makimoto K, Au R, Moslemi A, Hogg JC, Bourbeau J, Tan WC, Kirby M., Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features, Acad Radiol, 30, pp. 900-910, (2023); Sun J, Liao X, Yan Y, Zhang X, Sun J, Tan W, Liu B, Wu J, Guo Q, Gao S, Li Z, Wang K, Li Q., Correction to: Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach, Eur Radiol, 32, (2022); Yang Y, Li W, Guo Y, Zeng N, Wang S, Chen Z, Liu Y, Chen H, Duan W, Li X, Zhao W, Chen R, Kang Y., Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier, Math Biosci Eng, 19, pp. 7826-7855, (2022); Rohrich S, Hofmanninger J, Prayer F, Muller H, Prosch H, Langs G., Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT, Radiol Cardiothorac Imaging, 2, (2020); Bhowmik A, Eskreis-Winkler S., Deep learning in breast imaging, BJR Open, 4, (2022)","S. Liu; Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, No. 415 Fengyang Road, 200003, China; email: radiology_cz@163.com; L. Fan; Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, No. 415 Fengyang Road, 200003, China; email: fanli0930@163.com","","AME Publishing Company","","","","","","22234292","","","","English","Quant. Imaging Med. Surg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85187522608"
"Johnson R.; Stephens A.V.; Mester R.; Knyazev S.; Kohn L.A.; Freund M.K.; Bondhus L.; Hill B.L.; Schwarz T.; Zaitlen N.; Arboleda V.A.; Bastarache L.A.; Pasaniuc B.; Butte M.J.","Johnson, Ruth (57195477032); Stephens, Alexis V. (57796285900); Mester, Rachel (57353814400); Knyazev, Sergey (57194545774); Kohn, Lisa A. (55260857400); Freund, Malika K. (57204034202); Bondhus, Leroy (57222311381); Hill, Brian L. (57208031801); Schwarz, Tommer (57219671116); Zaitlen, Noah (8975849800); Arboleda, Valerie A. (55201702900); Bastarache, Lisa A. (35387541500); Pasaniuc, Bogdan (57216598454); Butte, Manish J. (6603481179)","57195477032; 57796285900; 57353814400; 57194545774; 55260857400; 57204034202; 57222311381; 57208031801; 57219671116; 8975849800; 55201702900; 35387541500; 57216598454; 6603481179","Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease","2024","Science Translational Medicine","16","745","eade4510","","","","2","10.1126/scitranslmed.ade4510","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192038677&doi=10.1126%2fscitranslmed.ade4510&partnerID=40&md5=9d1a2892fffe1bbdba9bef359c27d9e3","department of computer Science, University of california los angeles, Los Angeles, 90095, CA, United States; department of Pathology and laboratory Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; department of Pediatrics, division of immunology, allergy and rheumatology, University of california los angeles, Los Angeles, 90095, CA, United States; department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States; Bioinformatics interdepartmental Program, University of california los angeles, Los Angeles, 90095, CA, United States; department of neurology, University of california los angeles, Los Angeles, 90095, CA, United States; de-partment of computational Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; department of Biomedical informatics, Vanderbilt University, Nashville, 37203, TN, United States; department of Microbiology, immunology, and Molecular Genetics, University of california los angeles, Los Angeles, 90095, CA, United States","Johnson R., department of computer Science, University of california los angeles, Los Angeles, 90095, CA, United States, department of Pathology and laboratory Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; Stephens A.V., department of Pediatrics, division of immunology, allergy and rheumatology, University of california los angeles, Los Angeles, 90095, CA, United States; Mester R., department of computer Science, University of california los angeles, Los Angeles, 90095, CA, United States; Knyazev S., department of Pathology and laboratory Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; Kohn L.A., department of Pediatrics, division of immunology, allergy and rheumatology, University of california los angeles, Los Angeles, 90095, CA, United States; Freund M.K., department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States; Bondhus L., department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States; Hill B.L., department of computer Science, University of california los angeles, Los Angeles, 90095, CA, United States; Schwarz T., Bioinformatics interdepartmental Program, University of california los angeles, Los Angeles, 90095, CA, United States; Zaitlen N., department of neurology, University of california los angeles, Los Angeles, 90095, CA, United States; Arboleda V.A., department of Pathology and laboratory Medicine, University of california los angeles, Los Angeles, 90095, CA, United States, department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States, de-partment of computational Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; Bastarache L.A., department of Biomedical informatics, Vanderbilt University, Nashville, 37203, TN, United States; Pasaniuc B., department of Pathology and laboratory Medicine, University of california los angeles, Los Angeles, 90095, CA, United States, department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States, Bioinformatics interdepartmental Program, University of california los angeles, Los Angeles, 90095, CA, United States, de-partment of computational Medicine, University of california los angeles, Los Angeles, 90095, CA, United States; Butte M.J., department of Pediatrics, division of immunology, allergy and rheumatology, University of california los angeles, Los Angeles, 90095, CA, United States, department of human Genetics, University of california los angeles, Los Angeles, 90095, CA, United States, department of Microbiology, immunology, and Molecular Genetics, University of california los angeles, Los Angeles, 90095, CA, United States","Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases. © 2024 American Association for the Advancement of Science. All rights reserved.","","Adult; Algorithms; Common Variable Immunodeficiency; Electronic Health Records; Female; Humans; Machine Learning; Male; Phenotype; Undiagnosed Diseases; immunoglobulin G; adult; algorithm; antibody response; Article; artificial intelligence; asthma; autoimmunity; controlled study; cystic fibrosis; delayed diagnosis; diagnostic test accuracy study; early diagnosis; electronic health record; female; health care; human; Human immunodeficiency virus; ICD-10; immune deficiency; immunoglobulin deficiency; immunosuppressive treatment; inflammation; learning algorithm; machine learning; major clinical study; male; middle aged; organ transplantation; phenotype; prevalence; receiver operating characteristic; Tennessee; upper respiratory tract infection; common variable immunodeficiency; undiagnosed disease","","immunoglobulin G, 97794-27-9, 308067-58-5","Python","","Bill and Melinda Gates Foundation, BMGF","Acknowledgments: We thank l. dahm and the center for data-driven insights and innovation at Uc health (cdi2; www.ucop.edu/uc-health/functions/center-for-data-driven-insights-and-innovationscdi2.html) for analytical and technical support related to use of the Uc health data Warehouse. We acknowledge the Ucla institute for Precision health for making data available for research. Funding: We acknowledge funding from the national institutes of health (nih)/ national institutes of allergy and infectious diseases (r01 ai153827 to M.J.B and B.P.) and dP5od024579 (V.a.a.). Author contributions: B.P. and M.J.B. conceptualized the study. r.J., B.P., and M.J.B. designed experiments and developed methodology. r.J., r.M., and l.a.B. performed the investigations, wrote software, analyzed data, and created visualizations. a.V.S. curated data, analyzed data, and created visualizations. S.K., M.K.F., l.B., B.l.h., T.S., n.Z., and V.a.a. contributed to investigations, analyzed data, and contributed to software. r.J., B.P., and M.J.B. wrote the original draft and edited the final draft of the manuscript. B.P. and M.J.B. provided supervision. all authors reviewed the final manuscript. Competing interests: l.a.B. is a consultant for Pharming and receives royalties from nashville Biosciences. M.J.B. is a speaker for Grifols; consults for Pharming, horizon, and Grifols; receives sponsored research funding from the nih, the Bill and Melinda Gates Foundation, Pharming, and chiesi; and serves on the scientific advisory board for adMa Biologics and alpine immune Sciences. Data and materials availability: all data associated with this study are present in the paper or the Supplementary Materials. Phenet code is available at doi: 10.5281/zenodo.10947388.","Tangye S. G., al-herz W., Bousfiha a., cunningham-rundles c., Franco J. l., holland S. M., Klein c., Morio T., oksenhendler e., Picard c., Puel a., Puck J., Seppanen M. r. J., Somech r., Su h. c., Sullivan K. e., Torgerson T. r., Meyts i., human inborn errors of immunity: 2022 Update on the classification from the international Union of immunological Societies expert committee, J. Clin. 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S., Modell V., Modell F., a validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening, J. Allergy Clin. Immunol, 151, pp. 272-279, (2022); o'Malley K. J., cook K. F., Price M. d., Wildes K. r., hurdle J. F., ashton c. M., Measuring diagnoses: icd code accuracy, Health Serv. Res, 40, pp. 1620-1639, (2005); horsky J., drucker e. a., ramelson h. Z., accuracy and completeness of clinical coding using icd-10 for ambulatory visits, AMIA Annu. Symp. Proc, 2017, pp. 912-920, (2017); Wu P., Gifford a., Meng X., li X., campbell h., Varley T., Zhao J., carroll r., Bastarache l., denny J. c., Theodoratou e., Wei W.-Q., Mapping icd-10 and icd-10-cM codes to phecodes: Workflow development and initial evaluation, JMIR Med. Inform, 7, (2019); Kohler S., doelken S. c., Mungall c. J., Bauer S., Firth h. V., Bailleul-Forestier i., Black G. c. M., Brown d. l., Brudno M., campbell J., FitzPatrick d. r., eppig J. T., Jackson a. P., Freson K., Girdea M., helbig i., hurst J. a., Jahn J., Jackson l. G., Kelly a. M., ledbetter d. h., Mansour S., Martin c. l., Moss c., Mumford a., ouwehand W. h., Park S.-M., riggs e. r., Scott r. h., Sisodiya S., Van Vooren S., Wapner r. J., Wright c. F., Silfhout a. T. V.-V., de leeuw n., de Vries B. B. a., Washingthon n. l., Smith c. l., Westerfield M., Schofield P., ruef B. J., Gkoutos G. V., haendel M., Smedley d., lewis S. e., robinson P. n., The human Phenotype ontology project: linking molecular biology and disease through phenotype data, Nucleic Acids Res, 42, pp. d966-d974, (2014); Groza T., Kohler S., Moldenhauer d., Vasilevsky n., Baynam G., Zemojtel T., Schriml l. M., Kibbe W. a., Schofield P. n., Beck T., Vasant d., Brookes a. J., Zankl a., Washington n. l., Mungall c. J., lewis S. e., haendel M. a., Parkinson h., robinson P. n., The human Phenotype ontology: Semantic unification of common and rare disease, Am. J. Hum. Genet, 97, pp. 111-124, (2015)","M.J. Butte; department of Pediatrics, division of immunology, allergy and rheumatology, University of california los angeles, Los Angeles, 90095, United States; email: mbutte@mednet.ucla.edu","","American Association for the Advancement of Science","","","","","","19466234","","","38691621","English","Sci. Transl. Med.","Article","Final","","Scopus","2-s2.0-85192038677"
"Gupta P.; Saied Walker J.; Despins L.; Heise D.; Keller J.; Skubic M.; Yi R.; Scott G.J.","Gupta, Pallavi (57223239384); Saied Walker, Jamal (57892296600); Despins, Laurel (9248733700); Heise, David (57213718183); Keller, James (7403486724); Skubic, Marjorie (7003989598); Yi, Ruhan (57215357855); Scott, Grant J. (7402930105)","57223239384; 57892296600; 9248733700; 57213718183; 7403486724; 7003989598; 57215357855; 7402930105","A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place","2023","Journal of Biomedical Informatics","147","","104530","","","","2","10.1016/j.jbi.2023.104530","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175353547&doi=10.1016%2fj.jbi.2023.104530&partnerID=40&md5=b7b1a05b51cbede24f2d835396ef73cd","University of Missouri, MU Institute of Data Science and Informatics, Columbia, 65211, MO, United States; University of Missouri, Sinclair School of Nursing, Columbia, 65211, MO, United States; University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States; University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States; Lincoln University, Department of Science, Technology & Mathematics, Jefferson City, 65101, MO, United States","Gupta P., University of Missouri, MU Institute of Data Science and Informatics, Columbia, 65211, MO, United States, University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States; Saied Walker J., University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States; Despins L., University of Missouri, Sinclair School of Nursing, Columbia, 65211, MO, United States, University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States; Heise D., University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, Lincoln University, Department of Science, Technology & Mathematics, Jefferson City, 65101, MO, United States; Keller J., University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States; Skubic M., University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States; Yi R., University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States; Scott G.J., University of Missouri, MU Institute of Data Science and Informatics, Columbia, 65211, MO, United States, University of Missouri, Center to Stream Healthcare in Place, Columbia, 65211, MO, United States, University of Missouri, Department of Electrical Engineering and Computer Science, Columbia, 65211, MO, United States","Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day's predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach. © 2023 The Authors","Aging in place; C2Ship; Data mining; Early illness prediction; Machine learning; Older adults; Predictive modeling; Sensor data analytics; Shortness of breath","Aged; Dyspnea; Humans; Independent Living; Pulmonary Disease, Chronic Obstructive; Respiration; Retrospective Studies; Data Analytics; Machine learning; Predictive analytics; salbutamol; Aging in place; C2ship; Data analytics; Early illness prediction; Machine-learning; Older adults; Predictive models; Sensor data analytic; Sensors data; Shortness of breath; abnormal breathing index; aged; Article; asthma; breathing rate; bronchitis; bronchospasm; chronic obstructive lung disease; controlled study; data mining; dyspnea; emphysema; female; heart failure; human; hypertension; male; pneumonia; respiratory tract parameters; restlessness; retrospective study; semi supervised machine learning; breathing; chronic obstructive lung disease; dyspnea; independent living; Data mining","","salbutamol, 18559-94-9, 35763-26-9","","","National Institutes of Health, NIH, (R01NR016423); National Institutes of Health, NIH; National Institute of Nursing Research, NINR","This work was supported in part by the National Institute of Nursing Research of the National Institutes of Health under award number R01NR016423 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ","Colby S.L., Ortman J.M., Projections of the size and composition of the US population: 2014 to 2060.Population estimates and projections.Current population reports. 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Nurs., 46, 7, pp. 41-46, (2020); Vyshedskiy A., Ishikawa S., Murphy R.L., Crackle pitch and rate do not vary significantly during a single automated-auscultation session in patients with pneumonia, congestive heart failure, or interstitial pulmonary fibrosis, Respir. Care, 56, 6, pp. 806-817, (2011)","P. Gupta; University of Missouri, MU Institute of Data Science and Informatics, Columbia, 65211, United States; email: pg3fy@umsystem.edu","","Academic Press Inc.","","","","","","15320464","","JBIOB","37866640","English","J. Biomed. Informatics","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85175353547"
"Hu Z.; Tian Y.; Song X.; Zeng F.; Hu K.; Yang A.","Hu, Zhigang (57206648703); Tian, Yufeng (57207852391); Song, Xinyu (57212876433); Zeng, Fanjun (57203456245); Hu, Ke (7203085075); Yang, Ailan (57771197500)","57206648703; 57207852391; 57212876433; 57203456245; 7203085075; 57771197500","The effect and relative importance of sleep disorders for all-cause mortality in middle-aged and older asthmatics","2022","BMC Geriatrics","22","1","855","","","","3","10.1186/s12877-022-03587-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141685592&doi=10.1186%2fs12877-022-03587-2&partnerID=40&md5=e35f5a5172027f7190077ea793a4b6bd","Department of Respiratory and Critical Care Medicine, The first College of Clinical Medicine Science, China Three Gorges University, Yichang, 443003, China; Department of Respiratory and Critical Care Medicine, Zhijiang People’s Hospital, Yichang, 443003, China; Department of Respiratory and Critical Care Medicine, Yichang Central People’s Hospital, Yichang, China; Department of Respiratory and Critical Care Medicine, the first College of Clinical Medicine Science, Three Gorges University, 183 Yiling Road, Yichang, 443003, China; Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China","Hu Z., Department of Respiratory and Critical Care Medicine, The first College of Clinical Medicine Science, China Three Gorges University, Yichang, 443003, China, Department of Respiratory and Critical Care Medicine, Zhijiang People’s Hospital, Yichang, 443003, China, Department of Respiratory and Critical Care Medicine, Yichang Central People’s Hospital, Yichang, China; Tian Y., Department of Respiratory and Critical Care Medicine, the first College of Clinical Medicine Science, Three Gorges University, 183 Yiling Road, Yichang, 443003, China; Song X., Department of Respiratory and Critical Care Medicine, The first College of Clinical Medicine Science, China Three Gorges University, Yichang, 443003, China, Department of Respiratory and Critical Care Medicine, Yichang Central People’s Hospital, Yichang, China; Zeng F., Department of Respiratory and Critical Care Medicine, The first College of Clinical Medicine Science, China Three Gorges University, Yichang, 443003, China, Department of Respiratory and Critical Care Medicine, Yichang Central People’s Hospital, Yichang, China; Hu K., Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, China; Yang A., Department of Respiratory and Critical Care Medicine, Zhijiang People’s Hospital, Yichang, 443003, China","Background: Previous studies observed that sleep disorders potentially increased the risk of asthma and asthmatic exacerbation. We aimed to examine whether excessive daytime sleepiness (EDS), probable insomnia, objective short sleep duration (OSSD), and obstructive sleep apnea (OSA) affect all-cause mortality (ACM) in individuals with or without asthma. Methods: We extracted relevant data from the Sleep Heart Health Study established in 1995–1998 with an 11.4-year follow-up. Multivariate Cox regression analysis with a proportional hazards model was used to estimate the associations between ACM and four sleep disorders among asthmatic patients and individuals without asthma. Dose-response analysis and machine learning (random survival forest and CoxBoost) further evaluated the impact of sleep disorders on ACM in asthmatic patients. Results: A total of 4538 individuals with 990 deaths were included in our study, including 357 asthmatic patients with 64 deaths. Three multivariate Cox regression analyses suggested that OSSD (adjusted HR = 2.67, 95% CI: 1.23–5.77) but not probable insomnia, EDS or OSA significantly increased the risk of ACM in asthmatic patients. Three dose-response analyses also indicated that the extension of objective sleep duration was associated with a reduction in ACM in asthmatic patients compared to very OSSD patients. Severe EDS potentially augmented the risk of ACM compared with asthmatics without EDS (adjusted HR = 3.08, 95% CI: 1.11–8.56). Machine learning demonstrated that OSSD of four sleep disorders had the largest relative importance for ACM in asthmatics, followed by EDS, OSA and probable insomnia. Conclusions: This study observed that OSSD and severe EDS were positively associated with an increase in ACM in asthmatic patients. Periodic screening and effective intervention of sleep disorders are necessary for the management of asthma. © 2022, The Author(s).","all-cause mortality; asthma; Excessive daytime sleepiness; Sleep disorder; Sleep duration","Aged; Asthma; Disorders of Excessive Somnolence; Humans; Middle Aged; Sleep Apnea, Obstructive; Sleep Initiation and Maintenance Disorders; Sleep Wake Disorders; aged; asthma; complication; human; insomnia; middle aged; sleep disorder; sleep disordered breathing; somnolence","","","","","National Heart, Lung, and Blood Institute, NHLBI, (R24HL114473, U01HL53916); New York University, NYU, (U01HL53934); Boston University, BU, (U01HL63463); University of Minnesota, UMN, (U01HL53937, U01HL64360); University of California, Davis, UCD, (U01HL53931); University of Washington, UW, (U01HL53941); Johns Hopkins University, JHU, (U01HL53938); University of Arizona, UA, (U01HL53940); Case Western Reserve University, CWRU, (75N92019R002, R24 HL114473); National Natural Science Foundation of China, NSFC, (81970082); National Key Research and Development Program of China, NKRDPC, (2016YFC1304403)","Funding text 1: This work was supported by the National Natural Science Foundation of China (No. 81970082) and the National Key Research and Development Program of China (project number: 2016YFC1304403). ; Funding text 2: The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002). 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Oka S., Goto T., Hirayama A., Faridi M.K., Camargo C.A., Hasegawa K., Association of obstructive sleep apnea with severity of patients hospitalized for acute asthma, Ann Allergy Asthma Immunol, 124, 2, pp. 165-170, (2020); Sumino K., O'Brian K., Bartle B., Au D.H., Castro M., Lee T.A., Coexisting chronic conditions associated with mortality and morbidity in adult patients with asthma, J Asthma, 51, 3, pp. 306-314, (2014); Ioachimescu O.C., Janocko N.J., Ciavatta M.M., Howard M., Warnock M.V., Obstructive lung disease and obstructive sleep apnea (OLDOSA) cohort study: 10-year assessment, J Clin Sleep Med, 16, 2, pp. 267-277, (2020); Kallin S.A., Lindberg E., Sommar J.N., Bossios A., Ekerljung L., Malinovschi A., Middelveld R., Janson C., Excessive daytime sleepiness in asthma: what are the risk factors?, J Asthma, 55, 8, pp. 844-850, (2018); Quan S.F., Howard B.V., Iber C., Kiley J.P., Nieto F.J., O'Connor G.T., Rapoport D.M., Redline S., Robbins J., Samet J.M., Wahl P.W., The sleep heart health study: design, rationale, and methods, Sleep, 20, 12, pp. 1077-1085, (1997); Budhiraja P., Budhiraja R., Goodwin J.L., Allen R.P., Newman A.B., Koo B.B., Quan S.F., Incidence of restless legs syndrome and its correlates, J Clin Sleep Med, 8, 2, pp. 119-124, (2012); Edinger J.D., Bonnet M.H., Bootzin R.R., Et al., Derivation of research diagnostic criteria for insomnia: report of an American Academy of sleep medicine work group, Sleep, 2, pp. 1567-1596, (2004); Edinger J.D., Bonnet M.H., Bootzin R.R., Doghramji K., Dorsey C.M., Espie C.A., Jamieson A.O., WV M., Morin C.M., Stepanski E.J., Objective but not subjective short sleep duration is associated with hypertension in obstructive sleep apnea, Hypertension, 72, pp. 610-617, (2018); Leary E.B., Watson K.T., Ancoli-Israel S., Redline S., Yaffe K., Ravelo L.A., Peppard P.E., Zou J., Goodman S.N., Mignot E., Stone K.L., Association of Rapid Eye Movement Sleep With Mortality in Middle-aged and Older Adults, JAMA Neurol, 77, 10, pp. 1241-1251, (2020); Abuhelwa A.Y., Kichenadasse G., McKinnon R.A., Rowland A., Hopkins A.M., Sorich M.J., Machine learning for prediction of survival outcomes with immune-checkpoint inhibitors in urothelial Cancer, Cancers (Basel), 13, (2021); Chen Z., Xu H.M., Li Z.X., Zhang Y., Zhou T., You W.C., Pan K.F., Li W.Q., Random survival forest: applying machine learning algorithm in survival analysis of biomedical data, Chinese journal of preventive medicine, 55, 1, pp. 104-109, (2021); Vgontzas A.N., Liao D., Pejovic S., Et al., Insomnia with short sleep duration and mortality: the Penn State cohort, Sleep, 33, pp. 1159-1164, (2010); Fernandez-Mendoza J., He F., Vgontzas A.N., Et al., Objective short sleep duration modifies the relationship between hypertension and all-cause mortality, J Hypertens, 35, pp. 830-836, (2017); Fernandez-Mendoza J., He F., LaGrotte C., Et al., Impact of the metabolic syndrome on mortality is modified by objective short sleep duration, J Am Heart Assoc, 6, (2017); Fernandez-Mendoza J., He F., Vgontzas A.N., Et al., Interplay of objective sleep duration and cardiovascular and cerebrovascular diseases on cause-specific mortality, J Am Heart Assoc, 8, (2019); Fernandez-Mendoza J., He F., Calhoun S.L., Et al., Objective short sleep duration increases the risk of all-cause mortality associated with possible vascular cognitive impairment, Sleep Health, 6, pp. 71-78, (2020); Bertisch S.M., Pollock B.D., Mittleman M.A., Et al., Insomnia with objective short sleep duration and risk of incident cardiovascular disease and all-cause mortality: sleep heart health study, Sleep, 41, (2018); Pite H., Aguiar L., Morello J., Et al., Metabolic dysfunction and asthma: current perspectives, J Asthma Allergy, 13, pp. 237-247, (2020); Wang L., Liu Q., Heizhati M., Yao X., Luo Q., Li N., Association between excessive daytime sleepiness and risk of cardiovascular disease and all-cause mortality: a systematic review and Meta-analysis of longitudinal cohort studies, J Am Med Dir Assoc, 21, pp. 1979-1985, (2020); Lovato N., Lack L., Insomnia and mortality: a meta-analysis, Sleep Med Rev, 43, pp. 71-83, (2019); Ge L., Guyatt G., Tian J., Et al., Insomnia and risk of mortality from all-cause, cardiovascular disease, and cancer: systematic review and meta-analysis of prospective cohort studies, Sleep Med Rev, 48, (2019)","Z. Hu; Department of Respiratory and Critical Care Medicine, Yichang Central People’s Hospital, Yichang, China; email: hzg7602589@ctgu.edu.cn","","BioMed Central Ltd","","","","","","14712318","","","36372874","English","BMC Geriatr.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85141685592"
"Gerharz A.; Ruff C.; Wirbka L.; Stoll F.; Haefeli W.E.; Groll A.; Meid A.D.","Gerharz, Alexander (57221141821); Ruff, Carmen (57208796185); Wirbka, Lucas (57217037753); Stoll, Felicitas (56032926300); Haefeli, Walter E. (7005036811); Groll, Andreas (57218540725); Meid, Andreas D. (55218414400)","57221141821; 57208796185; 57217037753; 56032926300; 7005036811; 57218540725; 55218414400","Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing","2022","Methods of Information in Medicine","61","1-2","","55","60","5","3","10.1055/s-0042-1742671","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125073371&doi=10.1055%2fs-0042-1742671&partnerID=40&md5=08a57aeefcfaf8e7191f279fb9e82a77","Department of Statistics, Technical University of Dortmund, Dortmund, Germany; Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany","Gerharz A., Department of Statistics, Technical University of Dortmund, Dortmund, Germany; Ruff C., Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Wirbka L., Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Stoll F., Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Haefeli W.E., Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany; Groll A., Department of Statistics, Technical University of Dortmund, Dortmund, Germany; Meid A.D., Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany","Background  Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks. Objectives  To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data. Methods  In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined (stacked) to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics. Results  While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64]. Conclusion  PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases. © 2022 Georg Thieme Verlag. All rights reserved.","claims data; clinical prediction model; hospital readmission; pharmacoepidemiology; potentially inappropriate prescribing","Aged; Aged, 80 and over; Drug-Related Side Effects and Adverse Reactions; Humans; Inappropriate Prescribing; Insurance, Health; Patient Readmission; Pulmonary Disease, Chronic Obstructive; adverse drug reaction; aged; chronic obstructive lung disease; health insurance; hospital readmission; human; prescribing error; prevention and control; very elderly","","","","","","","Wauters M., Elseviers M., Vaes B., Too many, too few, or too unsafe? 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Angel Y., Zeltser D., Berliner S., Hospitalization as an opportunity to correct errors in anticoagulant treatment in patients with atrial fibrillation, Br J Clin Pharmacol, 85, 12, pp. 2838-2847, (2019); Jack B.W., Chetty V.K., Anthony D., A reengineered hospital discharge program to decrease rehospitalization: A randomized trial, Ann Intern Med, 150, 3, pp. 178-187, (2009); Low L.L., Tan S.Y., Ng M.J., Applying the integrated practice unit concept to a modified virtual ward model of care for patients at highest risk of readmission: A randomized controlled trial, PLoS One, 12, 1, (2017); Counter D., Millar J.W., McLay J.S., Hospital readmissions, mortality and potentially inappropriate prescribing: A retrospective study of older adults discharged from hospital, Br J Clin Pharmacol, 84, 8, pp. 1757-1763, (2018); Varga S., Alcusky M., Keith S.W., Hospitalization rates during potentially inappropriate medication use in a large population-based cohort of older adults, Br J Clin Pharmacol, 83, 11, pp. 2572-2580, (2017); 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Huibers C.J., Sallevelt B.T., De Groot D.A., Conversion of STOPP/START version 2 into coded algorithms for software implementation: A multidisciplinary consensus procedure, Int J Med Inform, 125, pp. 110-117, (2019); O'Mahony D., Gudmundsson A., Soiza R.L., Prevention of adverse drug reactions in hospitalized older patients with multi-morbidity and polypharmacy: The SENATOR∗ randomized controlled clinical trial, Age Ageing, 49, 4, pp. 605-614, (2020); Elixhauser A., Steiner C., Harris D.R., Coffey R.M., Comorbidity measures for use with administrative data, Med Care, 36, 1, pp. 8-27, (1998); Simon N., Friedman J., Hastie T., Tibshirani R., Regularization paths for Cox's proportional hazards model via coordinate descent, J Stat Softw, 39, 5, pp. 1-13, (2011); Youden W.J., Index for rating diagnostic tests, Cancer, 3, 1, pp. 32-35, (1950); Pencina M.J., D'Agostino R.B., D'Agostino R.B., Vasan R.S., Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond, Stat Med, 27, 2, pp. 157-172, (2008); 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Gillespie U., Alassaad A., Hammarlund-Udenaes M., Effects of pharmacists' interventions on appropriateness of prescribing and evaluation of the instruments' (MAI, STOPP and STARTs') ability to predict hospitalization-analyses from a randomized controlled trial, PLoS One, 8, 5, (2013); Ena J., Gomez-Huelgas R., Gracia-Tello B.C., Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments [in Spanish], Rev Clin Esp (Barc), 218, 6, pp. 271-278, (2018); Formiga F., Masip J., Chivite D., Corbella X., Applicability of the heart failure readmission risk score: A first European study, Int J Cardiol, 236, pp. 304-309, (2017); Sawhney S., Marks A., Fluck N., McLernon D.J., Prescott G.J., Black C., Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions, BMC Nephrol, 18, 1, (2017); Artetxe A., Beristain A., Grana M., Predictive models for hospital readmission risk: A systematic review of methods, Comput Methods Programs Biomed, 164, pp. 49-64, (2018); Tulloch A.D., David A.S., Thornicroft G., Exploring the predictors of early readmission to psychiatric hospital, Epidemiol Psychiatr Sci, 25, 2, pp. 181-193, (2016); Glans M., Kragh Ekstam A., Jakobsson U., Bondesson A., Midlov P., Risk factors for hospital readmission in older adults within 30 days of discharge - A comparative retrospective study, BMC Geriatr, 20, 1, (2020); Glans M., Kragh Ekstam A., Jakobsson U., Bondesson A., Midlov P., Medication-related hospital readmissions within 30 days of discharge-A retrospective study of risk factors in older adults, PLoS One, 16, 6, (2021); Linkens A.E., Milosevic V., Van Der Kuy P.H., Damen-Hendriks V.H., Mestres Gonzalvo C., Hurkens K.P., Medication-related hospital admissions and readmissions in older patients: An overview of literature, Int J Clin Pharm, 42, 5, pp. 1243-1251, (2020); Uitvlugt E.B., Janssen M.J., Siegert C.E., Medication-related hospital readmissions within 30 days of discharge: Prevalence, preventability, type of medication errors and risk factors, Front Pharmacol, 12, (2021)","A.D. Meid; Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Im Neuenheimer Feld 410, 69120, Germany; email: andreas.meid@med.uni-heidelberg.de","","Georg Thieme Verlag","","","","","","00261270","","MIMCA","35144291","English","Methods Inf. Med.","Article","Final","","Scopus","2-s2.0-85125073371"
"Wang Y.; Li Y.; Chen W.; Zhang C.; Liang L.; Huang R.; Jian W.; Liang J.; Zhu S.; Tu D.; Gao Y.; Zhong N.; Zheng J.","Wang, Yimin (57201136555); Li, Yicong (57286087500); Chen, Wenya (57328065500); Zhang, Changzheng (56012927700); Liang, Lijuan (57328437400); Huang, Ruibo (57310464100); Jian, Wenhua (56668395000); Liang, Jianling (57328342500); Zhu, Senhua (58363518600); Tu, Dandan (57207566207); Gao, Yi (55641783900); Zhong, Nanshan (7102137996); Zheng, Jinping (7403975931)","57201136555; 57286087500; 57328065500; 56012927700; 57328437400; 57310464100; 56668395000; 57328342500; 58363518600; 57207566207; 55641783900; 7102137996; 7403975931","Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve","2022","Respiration","101","9","","841","850","9","3","10.1159/000524598","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131001673&doi=10.1159%2f000524598&partnerID=40&md5=4f72d52bad047f5d514c6fd998a32948","National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China; Huawei Cloud Bu Ei Innovation Laboratory, Huawei Technologies, Shenzhen, China","Wang Y., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Li Y., Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China, Huawei Cloud Bu Ei Innovation Laboratory, Huawei Technologies, Shenzhen, China; Chen W., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zhang C., Huawei Cloud Bu Ei Innovation Laboratory, Huawei Technologies, Shenzhen, China; Liang L., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Huang R., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Jian W., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Liang J., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zhu S., Huawei Cloud Bu Ei Innovation Laboratory, Huawei Technologies, Shenzhen, China; Tu D., Huawei Cloud Bu Ei Innovation Laboratory, Huawei Technologies, Shenzhen, China; Gao Y., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zhong N., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zheng J., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China","Background: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. Methods: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flowvolume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. Results: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (k = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). Conclusions: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry. © 2022 S. Karger AG, Basel.","Deep learning; Flow-volume curve; Pulmonary function test; Spirometry; Upper airway obstruction","accuracy; adult; Article; clinician; computer assisted tomography; controlled study; deep learning; endoscopy; female; gold standard; human; lung flow volume curve; lung function; lung function test; major clinical study; male; spirometry; stenosis; upper respiratory tract obstruction","","","MasterScreen Pneumo, Jaeger, Germany","Jaeger, Germany","Guangdong Medical Research Foundation, (C2021073); Guangdong Medical Research Foundation; Guangzhou Municipal Science and Technology Program key projects, (202007040003); Guangzhou Municipal Science and Technology Program key projects; National Key Research and Development Program of China, NKRDPC, (2016YFC1304603, 2018YFC1311901); National Key Research and Development Program of China, NKRDPC; National Science and Technology Program during the Twelfth Five-year Plan Period, (2015BAI12B10); National Science and Technology Program during the Twelfth Five-year Plan Period","Dr. Gao declares that she received funding from the Science and Technology Program of Guangzhou, China (No. 202007040003), and the Medical Scientific Research Foundation of Guangdong Province, China (No. C2021073). Prof. Zheng declares that he received funding from the National Key Technology R&D Program (No. 2018YFC1311901 and No. 2016YFC1304603) and the National Science & Technology Pillar Program (No. 2015BAI12B10). None of the funding sources had any role in the study. ","Gelbard A., Francis D.O., Sandulache V.C., Simmons J.C., Donovan D.T., Ongkasuwan J., Causes and consequences of adult laryngotracheal stenosis, Laryngoscope, 125, 5, pp. 1137-1143, (2015); D'Andrilli A., Venuta F., Rendina E.A., Subglottic tracheal stenosis, J Thorac Dis, 8, pp. S140-S147, (2016); Galvin I.F., Shepherd D.R., Gibbons J.R., Tracheal stenosis caused by congenital vascular ring anomaly misinterpreted as asthma for 45 years, Thorac Cardiovasc Surg, 38, 1, pp. 42-44, (1990); Kokturk N., Demircan S., Kurul C., Turktas H., Tracheal adenoid cystic carcinoma masquerading asthma: a case report, BMC Pulm Med, 4, (2004); Nunn A.C., Nouraei S.A., George P.J., Sandhu G.S., Nouraei S.A., Not always asthma: clinical and legal consequences of delayed diagnosis of laryngotracheal stenosis, Case Rep Otolaryngol, 2014, (2014); Begnaud A., Connett J.E., Harwood E.M., Jantz M.A., Mehta H.J., Measuring central airway obstruction. What do bronchoscopists do?, Ann Am Thorac Soc, 12, 1, pp. 85-90, (2015); Rotman H.H., Liss H.P., Weg J.G., Diagnosis of upper airway obstruction by pulmonary function testing, Chest, 68, 6, pp. 796-799, (1975); Couriel J.M., Hibbert M., Olinsky A., Assessment of proximal airway obstruction in children by analysis of flow-volume loops, Br J Dis Chest, 78, 1, pp. 36-45, (1984); Gittoes N.J., Miller M.R., Daykin J., Sheppard M.C., Franklyn J.A., Upper airways obstruction in 153 consecutive patients presenting with thyroid enlargement, BMJ, 312, 7029, (1996); Miller R.D., Hyatt R.E., Evaluation of obstructing lesions of the trachea and larynx by flowvolume loops, Am Rev Respir Dis, 108, 3, pp. 475-481, (1973); Nouraei S.A., Nouraei S., Patel A., Murphy K., Giussani D.A., Koury E.F., Et al., Diagnosis of laryngotracheal stenosis from routine pulmonary physiology using the expiratory disproportion index, Laryngoscope, 123, 12, pp. 3099-3104, (2013); Empey D.W., Assessment of upper airways obstruction, Br Med J, 3, 5825, pp. 503-505, (1972); Nouraei S.A., Winterborn C., Nouraei S.M., Giussani D.A., Murphy K., Howard D.J., Et al., Quantifying the physiology of laryngotracheal stenosis: changes in pulmonary dynamics in response to graded extrathoracic resistive loading, Laryngoscope, 117, 4, pp. 581-588, (2007); Modrykamien A.M., Gudavalli R., McCarthy K., Liu X., Stoller J.K., Detection of upper airway obstruction with spirometry results and the flow-volume loop: a comparison of quantitative and visual inspection criteria, Respir Care, 54, 4, pp. 474-479, (2009); Fiorelli A., Poggi C., Ardo N.P., Messina G., Andreetti C., Venuta F., Et al., Flow-volume curve analysis for predicting recurrence after endoscopic dilation of airway stenosis, Ann Thorac Surg, 108, 1, pp. 203-210, (2019); He J., Baxter S.L., Xu J., Xu J., Zhou X., Zhang K., The practical implementation of artificial intelligence technologies in medicine, Nat Med, 25, 1, pp. 30-36, (2019); Das N., Verstraete K., Stanojevic S., Topalovic M., Aerts J.M., Janssens W., Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria, Eur Respir J, 56, 6, (2020); Schroeder J.D., Bigolin Lanfredi R., Li T., Chan J., Vachet C., Paine R., Et al., Prediction of obstructive lung disease from chest radiographs via deep learning trained on pulmonary function data, Int J Chron Obstruct Pulmon Dis, 15, pp. 3455-3466, (2020); Bright P., Miller M.R., Franklyn J.A., Sheppard M.C., The use of a neural network to detect upper airway obstruction caused by goiter, Am J Respir Crit Care Med, 157, 6, pp. 1885-1891, (1998); Miller M.R., Hankinson J., Brusasco V., Burgos F., Casaburi R., Coates A., Et al., Standardisation of spirometry, Eur Respir J, 26, 2, pp. 319-338, (2005); Graham B.L., Steenbruggen I., Miller M.R., Barjaktarevic I.Z., Cooper B.G., Hall G.L., Et al., Standardization of spirometry 2019 update. An official American thoracic society and European respiratory society technical statement, Am J Respir Crit Care Med, 200, 8, pp. e70-e88, (2019); Culver B.H., Graham B.L., Coates A.L., Wanger J., Berry C.E., Clarke P.K., Et al., Recommendations for a standardized pulmonary function report. An official American thoracic society technical statement, Am J Respir Crit Care Med, 196, 11, pp. 1463-1472, (2017); Pellegrino R., Viegi G., Brusasco V., Crapo R.O., Burgos F., Casaburi R., Et al., Interpretative strategies for lung function tests, Eur Respir J, 26, 5, pp. 948-968, (2005); Wegener I., The complexity of Boolean functions, pp. 22-36, (1987); Ren S., He K., Girshick R., Sun J., Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans Pattern Anal Mach Intell, 39, 6, pp. 1137-1149, (2017); He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016); Deng J., Dong W., Socher R., Li L., Kai L., Li F.F., ImageNet: a large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, (2009); Girshick R., Fast R-CNN, 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, (2015); Sterner J.B., Morris M.J., Sill J.M., Hayes J.A., Inspiratory flow-volume curve evaluation for detecting upper airway disease, Respir Care, 54, 4, pp. 461-466, (2009); Gamsu G., Borson D.B., Webb W.R., Cunningham J.H., Structure and function in tracheal stenosis, Am Rev Respir Dis, 121, 3, pp. 519-531, (1980); Demedts M., Melissant C., Buyse B., Verschakelen J., Feenstra L., Correlation between functional, radiological and anatomical abnormalities in upper airway obstruction (UAO) due to tracheal stenosis, Acta Otorhinolaryngol Belg, 49, 4, pp. 331-339, (1995); Raposo L.B., Bugalho A., Gomes M.J., Contribution of flow-volume curves to the detection of central airway obstruction, J Bras Pneumol, 39, 4, pp. 447-454, (2013); Brookes G.B., Fairfax A.J., Chronic upper airway obstruction: value of the flow volume loop examination in assessment and management, J R Soc Med, 75, 6, pp. 425-434, (1982)","Y. Gao; National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: misstall2@163.com; N. Zhong; National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: nanshan@vip.163.com; J. Zheng; National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: jpzhenggy@163.com","","S. Karger AG","","","","","","00257931","","RESPB","35551127","English","Respiration","Article","Final","","Scopus","2-s2.0-85131001673"
"Nakahara Y.; Mabu S.; Hirano T.; Murata Y.; Doi K.; Fukatsu-Chikumoto A.; Matsunaga K.","Nakahara, Yoshiki (58493565500); Mabu, Shingo (12794389800); Hirano, Tsunahiko (7404057573); Murata, Yoriyuki (55364211000); Doi, Keiko (57226797615); Fukatsu-Chikumoto, Ayumi (57208567041); Matsunaga, Kazuto (8731434800)","58493565500; 12794389800; 7404057573; 55364211000; 57226797615; 57208567041; 8731434800","Neural Network Approach to Investigating the Importance of Test Items for Predicting Physical Activity in Chronic Obstructive Pulmonary Disease","2023","Journal of Clinical Medicine","12","13","4297","","","","3","10.3390/jcm12134297","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165131100&doi=10.3390%2fjcm12134297&partnerID=40&md5=5777dd4215ad9ca2a3497c024e84a3df","Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, 7558611, Japan; Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan","Nakahara Y., Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, 7558611, Japan; Mabu S., Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, 7558611, Japan; Hirano T., Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan; Murata Y., Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan; Doi K., Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan; Fukatsu-Chikumoto A., Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan; Matsunaga K., Department of Respiratory Medicine and Infectious Disease, Yamaguchi University Hospital, Yamaguchi, 7558505, Japan","Contracting COPD reduces a patient’s physical activity and restricts everyday activities (physical activity disorder). However, the fundamental cause of physical activity disorder has not been found. In addition, costly and specialized equipment is required to accurately examine the disorder; hence, it is not regularly assessed in normal clinical practice. In this study, we constructed a machine learning model to predict physical activity using test items collected during the normal care of COPD patients. In detail, we first applied three types of data preprocessing methods (zero-padding, multiple imputation by chained equations (MICE), and k-nearest neighbor (kNN)) to complement missing values in the dataset. Then, we constructed several types of neural networks to predict physical activity. Finally, permutation importance was calculated to identify the importance of the test items for prediction. Multifactorial analysis using machine learning, including blood, lung function, walking, and chest imaging tests, was the unique point of this research. From the experimental results, it was found that the missing value processing using MICE contributed to the best prediction accuracy (73.00%) compared to that using zero-padding (68.44%) or kNN (71.52%), and showed better accuracy than XGBoost (66.12%) with a significant difference (p < 0.05). For patients with severe physical activity reduction (total exercise < 1.5), a high sensitivity (89.36%) was obtained. The permutation importance showed that “sex, the number of cigarettes, age, and the whole body phase angle (nutritional status)” were the most important items for this prediction. Furthermore, we found that a smaller number of test items could be used in ordinary clinical practice for the screening of physical activity disorder. © 2023 by the authors.","autoencoder; COPD; neural network; physical activity; prediction","age; aged; Article; artificial neural network; autoencoder; chronic obstructive lung disease; comparative study; controlled study; exercise; female; gender; human; lung function; machine learning; major clinical study; male; measurement accuracy; nutritional status; physical activity; predictive value; screening; sensitivity and specificity; walking","","","","","Japan Society for the Promotion of Science, KAKEN, (JP19K12120, JP21K07675, JP22K12152); Japan Society for the Promotion of Science, KAKEN","This work was supported by grants from JSPS KAKENHI (JP22K12152, JP21K07675, and JP19K12120). The funder had no role in the design, conduct, or reporting of this work.","Cooper C.B., Airflow obstruction and exercise, Respir. Med, 103, pp. 325-334, (2009); Waschki B., Kirsten A., Holz O., Muller K.C., Meyer T., Watz H., Magnussen H., Physical Activity Is the Strongest Predictor of All-Cause Mortality in Patients With COPD: A Prospective Cohort Study, Chest, 140, pp. 331-342, (2011); Troosters T., van der Molen T., Polkey M., Rabinovich R.A., Vogiatzis I., Weisman I., Kulich K., Improving physical activity in COPD: Towards a new paradigm, Respir. Res, 14, (2013); Hirano T., Doi K., Matsunaga K., Takahashi S., Donishi T., Suga K., Oishi K., Yasuda K., Mimura Y., Harada M., Et al., A Novel Role of Growth Differentiation Factor (GDF)-15 in Overlap with Sedentary Lifestyle and Cognitive Risk in COPD, J. Clin. Med, 9, (2020); Matsunaga K., Harada M., Suizu J., Oishi K., Asami-Noyama M., Hirano T., Comorbid Conditions in Chronic Obstructive Pulmonary Disease: Potential Therapeutic Targets for Unmet Needs, J. Clin. Med, 9, (2020); Ahmed M.U., Loutfi A., Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate, Int. J. Adv. Comput. Sci. Appl, 4, pp. 209-217, (2013); Ahmadi M.N., Pavey T.G., Trost S.G., Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children, Sensors, 20, (2020); Mesanza A.B., Lucas S., Zubizarreta A., Cabanes I., Portillo E., Rodriguez-Larrad A., A Machine Learning Approach to Perform Physical Activity Classification Using a Sensorized Crutch Tip, IEEE Access, 8, pp. 210023-210034, (2020); Alexos A., Moustakidis S., Kokkotis C., Tsaopoulos D., Physical Activity as a Risk Factor in the Progression of Osteoarthritis: A Machine Learning Perspective, Proceedings of the Learning and Intelligent Optimization, pp. 16-26, (2020); Romain A.J., Horwath C., Bernard P., Prediction of Physical Activity Level Using Processes of Change From the Transtheoretical Model: Experiential, Behavioral, or an Interaction Effect?, Am. J. Health Promot, 32, pp. 16-23, (2018); Kim J.C., Chung K., Prediction model of user physical activity using data characteristics-based long short-term memory recurrent neural networks, KSII Trans. Internet Inf. Syst. (TIIS), 13, pp. 2060-2077, (2019); Kawagoshi A., Iwakura M., Furukawa Y., Sugawara K., Takahashi H., Shioya T., Prediction of Low-intensity Physical Activity in Stable Patients with Chronic Obstructive Pulmonary Disease, Phys. Ther. Res, 25, pp. 143-149, (2022); Azuma Y., Minakata Y., Kato M., Tanaka M., Murakami Y., Sasaki S., Kawabe K., Ono H., Validation of Simple Prediction Equations for Step Count in Japanese Patients with Chronic Obstructive Pulmonary Disease, J. Clin. Med, 11, (2022); Cleland V., Dwyer T., Venn A., Which domains of childhood physical activity predict physical activity in adulthood? A 20-year prospective tracking study, Br. J. Sport. Med, 46, pp. 595-602, (2012); Glenmark B., Hedberg G., Jansson E., Prediction of physical activity level in adulthood by physical characteristics, physical performance and physical activity in adolescence: An 11-year follow-up study, Eur. J. Appl. Physiol. Occup. Physiol, 69, pp. 530-538, (1994); Rothney M.P., Schaefer E.V., Neumann M.M., Choi L., Chen K.Y., Validity of Physical Activity Intensity Predictions by ActiGraph, Actical, and RT3 Accelerometers, Obesity, 16, pp. 1946-1952, (2008); Zakariya N., Mohd Rosli M., Physical activity prediction using fitness data: Challenges and issues, Bull. Electr. Eng. Inform, 10, pp. 419-426, (2021); Murata Y., Hirano T., Doi K., Fukatsu-Chikumoto A., Hamada K., Oishi K., Kakugawa T., Yano M., Matsunaga K., Computed Tomography Lung Density Analysis: An Imaging Biomarker Predicting Physical Inactivity in Chronic Obstructive Pulmonary Disease: A Pilot Study, J. Clin. Med, 12, (2023); Goodfellow I., Bengio Y., Courville A., Deep Learning, (2016); Hirano T., Matsunaga K., Hamada K., Uehara S., Suetake R., Yamaji Y., Oishi K., Asami M., Edakuni N., Ogawa H., Et al., Combination of assist use of short-acting beta-2 agonists inhalation and guidance based on patient-specific restrictions in daily behavior: Impact on physical activity of Japanese patients with chronic obstructive pulmonary disease, Respir. Investig, 57, pp. 133-139, (2019); Azur M.J., Stuart E.A., Frangakis C., Leaf P.J., Multiple imputation by chained equations: What is it and how does it work?, Int. J. Methods Psychiatr. Res, 20, pp. 40-49, (2011); Han J., Pei J., Tong H., Data Mining: Concepts and Techniques, (2022); Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res, 15, pp. 1929-1958, (2014); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Haskell W.L., Lee I.M., Pate R.R., Powell K.E., Blair S.N., Franklin B.A., Macera C.A., Heath G.W., Thompson P.D., Bauman A., Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association, Circulation, 116, (2007); Zanella P.B., Avila C.C., Chaves F.C., Gazzana M.B., Berton D.C., Knorst M.M., de Souza C.G., Phase Angle Evaluation of Lung Disease Patients and Its Relationship with Nutritional and Functional Parameters, J. Am. Coll. Nutr, 40, pp. 529-534, (2021); Custodio Martins P., de Lima T.R., Silva A.M., Santos Silva D.A., Association of phase angle with muscle strength and aerobic fitness in different populations: A systematic review, Nutrition, 93, (2022); Martinez-Luna N., Orea-Tejeda A., Gonzalez-Islas D., Flores-Cisneros L., Keirns-Davis C., Sanchez-Santillan R., Perez-Garcia I., Gastelum-Ayala Y., Martinez-Vazquez V., Martinez-Reyna O., Association between body composition, sarcopenia and pulmonary function in chronic obstructive pulmonary disease, BMC Pulm. Med, 22, (2022)","S. Mabu; Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi, 7558611, Japan; email: mabu@yamaguchi-u.ac.jp","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85165131100"
"Ferrari L.R.; Leahy I.; Staffa S.J.; Hong P.; Stringfellow I.; Berry J.G.","Ferrari, Lynne R. (7101737230); Leahy, Izabela (57191526592); Staffa, Steven J. (57202511152); Hong, Peter (57222410048); Stringfellow, Isabel (57221721268); Berry, Jay G. (13403437900)","7101737230; 57191526592; 57202511152; 57222410048; 57221721268; 13403437900","Assessing the Utility of a Machine-Learning Model to Assist with the Assignment of the American Society of Anesthesiology Physical Status Classification in Pediatric Patients","2024","Anesthesia and Analgesia","139","5","","1017","1026","9","2","10.1213/ANE.0000000000006761","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205359265&doi=10.1213%2fANE.0000000000006761&partnerID=40&md5=f3f72a97f69bd08774db1fbf607ad911","Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, United States; Harvard Medical School, United States; Data and Analytics Services, Information Technology, Boston Children's Hospital, United States; Complex Care Service, Division of General Pediatrics, Boston Children's Hospital, United States","Ferrari L.R., Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, United States, Harvard Medical School, United States; Leahy I., Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, United States; Staffa S.J., Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, United States; Hong P., Harvard Medical School, United States, Data and Analytics Services, Information Technology, Boston Children's Hospital, United States, Complex Care Service, Division of General Pediatrics, Boston Children's Hospital, United States; Stringfellow I., Complex Care Service, Division of General Pediatrics, Boston Children's Hospital, United States; Berry J.G., Harvard Medical School, United States, Complex Care Service, Division of General Pediatrics, Boston Children's Hospital, United States","BACKGROUND: The American Society of Anesthesiologists Physical Status Classification System (ASA-PS) is used to classify patients' health before delivering an anesthetic. Assigning an ASA-PS Classification score to pediatric patients can be challenging due to the vast array of chronic conditions present in the pediatric population. The specific aims of this study were to (1) suggest an ASA-PS score for pediatric patients undergoing elective surgical procedures using machine-learning (ML) methods; and (2) assess the impact of presenting the suggested ASA-PS score to clinicians when making their final ASA-PS assignment. The intent was not to create a new ASA-PS score but to use ML methods to generate a suggested score, along with information on how the score was generated (ie, historical information on patient comorbidities) to assist clinicians when assigning their final ASA-PS score. METHODS: A retrospective analysis of 146,784 pediatric surgical encounters from January 1, 2016, to December 31, 2019, using eXtreme Gradient Boosting (XGBoost) methods to predict ASA-PS scores using patients' age, weight, and chronic conditions. SHapley Additive exPlanations (SHAP) were used to assess patient characteristics that contributed most to the predicted ASA-PS scores. The predicted ASA-PS model was presented to a prospective cohort study of 28,677 surgical encounters from December 1, 2021, to October 31, 2022. The predicted ASA-PS score was presented to the anesthesiology provider for review before entering the final ASA-PS score. The study focused on summarizing the available information for the anesthesiologist by using ML methods. The goal was to explore the potential for ML to provide assistance to anesthesiologists by highlighting potential areas of discordance between the variables that generated a given ML prediction and the physician's mental model of the patient's medical comorbidities. RESULTS: For the retrospective analysis, the distribution of predicted ASA-PS scores was 22.7% ASA-PS I, 48.5% II, 23.6% III, 5.1% IV, and 0.04% V. The distribution of clinician-assigned ASA-PS scores was 24.3% for ASA-PS I, 44.5% for ASA-PS II, 24.9% for ASA III, 6.1% for ASA-PS IV, and 0.2% for ASA-V. In the prospective analysis, the final ASA-PS score matched the initial ASA-PS 90.7% of the time and 9.3% were revised after viewing the predicted ASA-PS score. When the initial ASA-PS score and the ML ASA-PS score were discrepant, 19.5% of the cases have a final ASA-PS score which is different from the initial clinician ASA-PS score. The prevalence of multiple chronic conditions increased with ASA-PS score: 34.9% ASA-PS I, 73.2% II, 92.3% III, and 94.4% IV. CONCLUSIONS: ML derivation of predicted pediatric ASA-PS scores was successful, with a strong agreement between predicted and clinician-entered ASA-PS scores. Presentation of predicted ASA-PS scores was associated with revision in final scoring for 1-in-10 pediatric patients. © 2023 International Anesthesia Research Society.","","Adolescent; Anesthesiology; Child; Child, Preschool; Elective Surgical Procedures; Female; Health Status; Humans; Infant; Infant, Newborn; Machine Learning; Male; Predictive Value of Tests; Retrospective Studies; Societies, Medical; United States; adolescent; adult; American Society of Anaesthesiologists score; anesthesiologist; anesthesiologists physical status classification system; anesthesiology; anxiety disorder; Article; asthma; autism; body weight; cardiomegaly; child; cohort analysis; comorbidity; controlled study; decision tree; elective surgery; female; general anesthesia; heart failure; heart valve; human; ICD-10-CM; machine learning; major clinical study; male; multiple chronic conditions; otorhinolaryngology; pediatric patient; pediatric surgery; preschool child; prevalence; prospective study; retrospective study; scoring system; Shapley additive explanation; sleep apnea syndromes; tracheomalacia; anesthesiology; health status; infant; medical society; newborn; predictive value; procedures; United States","","","","","","","Deo R.C., Machine learning in medicine., Circulation, 132, pp. 1920-1930, (2015); Sw S., Commercial fees paid for anesthesia services., ASA Monitor, 86, pp. 1-6, (2022); Timely Topics. Anesthesia Payment Basics Series Codes and Modifiers., (2019); Ferrari L., Leahy I., Staffa S.J., Berry J.G., The pediatric-specific American Society of Anesthesiologists physical status score: A multicenter study., Anesth Analg, 132, pp. 807-817, (2021); Ferrari L.R., Leahy I., Staffa S.J., One size does not fit all: A perspective on the American Society of Anesthesiologists physical status classification for pediatric patients., Anesth Analg, 130, pp. 1685-1692, (2020); Leahy I., Berry J.G., Johnson C.J., Crofton C., Staffa S.J., Ferrari L., Does the current American Society of Anesthesiologists physical status classification represent the chronic disease burden in children undergoing general anesthesia?, Anesth Analg, 129, pp. 1175-1180, (2019); Leahy I., Berry J.G., Johnson C.J., Crofton C., Staffa S.J., Ferrari L., Does the current American Society of Anesthesiologists physical status classification represent the chronic disease burden in children undergoing general anesthesia?, Anesth Analg, 129, 4, pp. 1175-1180, (2019); Anesthesiologists ASo.; Chronic Condition Indicator (CCI) for ICD-9-CM.; Chronic Condition Indicator.; Floares Ag O.D., Calin G.A., Ferisgan M., Ciuparu A., Manolache F.B., The smallest sample size for the desired diagnosis accuracy., Int J Oncol Cancer Ther, 2, pp. 13-19, (2017); Mitchell R., Frank E., Holmes G., GPUTreeShap: Massively parallel exact calculation of SHAP scores for tree ensembles., PeerJ Comput Sci, 8, (2022); Mavrogiorgou A., Kiourtis A., Kleftakis S., Mavrogiorgos K., Zafeiropoulos N., Kyriazis D., A catalogue of machine learning algorithms for healthcare risk predictions., Sensors (Basel), 22, (2022); Rubinger L., Gazendam A., Ekhtiari S., Bhandari M., Machine le.arning and artificial intelligence in research and healthcare., Injury, 54, pp. S69-S73, (2023); Drzymalski D.M., Seth S., Johnson J.R., Trzcinka A., Improving accuracy of American Society of Anesthesiologists physical status using audit and feedback and artificial intelligence: A time-series analysis., Int J Qual Health Care, 33, (2021); Connor C.W., Artificial intelligence and machine learning in anesthesiology., Anesthesiology, 131, pp. 1346-1359, (2019); Wojtuch A., Jankowski R., Podlewska S., How can SHAP values help to shape metabolic stability of chemical compounds?, J Cheminform, 13, (2021); Hu M., Zhang H., Wu B., Li G., Zhou L., Interpretable predictive model for shield attitude control performance based on XGboost and SHAP., Sci Rep, 12, (2022); Chua M., Kim D., Choi J., Tackling prediction uncertainty in machine learning for healthcare., Nat Biomed Eng, 7, pp. 711-718, (2022); Kendale S., Kulkarni P., Rosenberg A.D., Wang J., Supervised machine-learning predictive analytics for prediction of postinduction hypotension., Anesthesiology, 129, pp. 675-688, (2018); Xue B.L., King C.R., Wildes T., Avidan M.S., Kannampallil T., Abraham J., Use of machine learning to develop and evaluate models using pr.eoperative and intraoperative data to identify risks of postoperative complications., JAMA Netw Open, 4, (2021); Lee C.K., Hofer I., Gabel E., Baldi P., Cannesson M., Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality., Anesthesiology, 129, pp. 649-662, (2018); Pregler J., The 33% problem: Origins and actions committee on economics 33% workgroup report ASA economic strategic plan initiative., ASA Monitor, 84, pp. 28-33, (2020)","L.R. Ferrari; Boston Children's Hospital, Boston, 300 Longwood Avenue, 02116, United States; email: lynne.ferrari@childrens.harvard.edu","","Lippincott Williams and Wilkins","","","","","","00032999","","AACRA","38088804","English","Anesth. Analg.","Article","Final","","Scopus","2-s2.0-85205359265"
"Pribylov S.A.; Leonidova K.O.; Pribylov V.S.; Gavrilyuk E.V.; Pribylova N.N.","Pribylov, Sergey A. (15030158200); Leonidova, Kristina O. (58990840300); Pribylov, Vladislav S. (57290310800); Gavrilyuk, Evgenia V. (57201613605); Pribylova, Nadezhda N. (7004017388)","15030158200; 58990840300; 57290310800; 57201613605; 7004017388","Approaches to therapy Amlodipine/Indapamide/ Perindopril therapy of high arterial hypertension in ischemic heart disease patients with chronic kidney disease stage 1-3 after coronary stenting","2024","Research Results in Pharmacology","10","2","","49","55","6","2","10.18413/rrpharmacology.10.475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199107683&doi=10.18413%2frrpharmacology.10.475&partnerID=40&md5=ae44eab01512fb2e5b4480b21d81dc1e","Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation","Pribylov S.A., Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation; Leonidova K.O., Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation; Pribylov V.S., Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation; Gavrilyuk E.V., Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation; Pribylova N.N., Kursk State Medical University, 3 K. Marksa St., Kursk region, Kursk, 305041, Russian Federation","Introduction: Acute coronary syndrome, chronic forms of ischemic heart disease (IHD) and postinfarction cardiosclerosis are the main causes of morbidity and mortality in the world, including Russia. In the XXI century, there is an increase in comorbid pathology, especially in the combination of IHD with arterial hypertension, diabetes, chronic obstructive pulmonary disease and chronic kidney disease. Patients with IHD and chronic kidney disease have a higher incidence of coronary events and complications. The frequency of coronary events and complications indicates the need to improve the diagnosis and treatment of this group of patients. diagnosis and treatment of this group of patients. The aim of the study was to analyze the dynamics of vascular stiffness, pulmonary hypertension (PH), diastolic heart dysfunction and endothelial dysfunction indices in patients with different variants of ischemic heart disease combined with chronic kidney disease (CKD) stage 1-3 using complex therapy of combined hypotensive drug Amlodipine/ Indapamide/Perindopril three months after coronary stenting and to compare them with the group of patients on conservative therapy only. Material and Methods: 85 patients with different forms of IHD, arterial hypertension (AH) on the background of CKD 1-3 stages, as well as data of 42 patients with IHD, AH without renal pathology were analyzed. The first group – IHD, postinfarction cardiosclerosis, CKD stage 1-3 (33 patients); the second group – acute coronary syndrome with ST-segment elevation, Myocardial infarction (MI) (30 patients); the third group – ACS without ST-segment elevation, Unstable angina (UA) (22 patients). Results: The highest indices of vascular stiffness (Pulse wave velocity (PWV), Augmentation index (AI), CAVI, Central Systolic Blood Pressure (SBPao), central arterial pulse pressure (PP)) were registered in combination of ACS with ST-segment elevation and CKD 1-3 stages. These indices are markers of IHD progression in these patients; they also have increased pulmonary hypertension and diastolic dysfunction of the heart, endothelial dysfunction with vasodilation insufficiency in 88% of cases, which even without hemodynamically significant coronary artery stenoses according to coronary CT angiography data leads to the development of ACS with ST-segment elevation and ACS without ST-segment elevation with MI.; Amlodipine/Indapamide/Perindopril was prescribed to all patients due to high arterial hypertension on admission against the background of basic therapy of IHD and coronary stenting. Coronary CT angiography in patients with comorbid renal pathology does not lead to aggravation of chronic kidney disease after 3 months, on the contrary; in this group of patients the most pronounced decrease of arterial stiffness (AS), AI, SBPao, PP with elevation of glomerular filtration rate (GFR) and decrease of creatinine in blood occurs in comparison with the group of patients who did not undergo coronary stenting, they were only on conservative therapy. Conclusion: Prescription of three component drug Amlodipine/Indapamide/Perindopril on the background of baseline therapy especially in combination with surgical vascularization of the heart is justified. © 2024 Belgorod State National Research University. All rights reserved.","acute coronary syndrome; amlodipine; chronic kidney disease; coronary heart disease; coronary stenting; indapamide; perindopril; postinfarction cardiosclerosis; unstable angina pectoris","amlodipine plus indapamide plus perindopril; cholesterol; endothelin 1; low density lipoprotein; triacylglycerol; acute coronary syndrome; adult; antihypertensive therapy; arterial stiffness; Article; augmentation index; cholesterol blood level; chronic kidney failure; clinical article; computed tomographic angiography; controlled study; coronary angiography; coronary atherosclerosis; coronary stenosis; coronary stenting; diastolic blood pressure; diastolic heart failure; endothelial dysfunction; female; glomerulus filtration rate; heart muscle fibrosis; human; hypertension; ischemic heart disease; major clinical study; male; middle aged; pulmonary hypertension; pulse pressure; pulse wave velocity; ST segment elevation; ST segment elevation myocardial infarction; systolic blood pressure; triacylglycerol blood level; unstable angina pectoris; vasodilatation","","cholesterol, 57-88-5; endothelin 1, 117399-94-7","","","","","Agarwal R, Sinha AD, Cramer AE, Balmes-Fenwick M, Dickinson JH, Ouyang F, Tu W, Chlorthalidone for hypertension in advanced chronic kidney disease, New England Journal of Medicine, 385, 27, pp. 2507-2519, (2021); Al Ghorani H, Kulenthiran S, Lauder L, Recktenwald MJM, Dederer J, Kunz M, Gotzinger F, Ewen S, Ukena C, Bohm M, Mahfoud F, Ultra-long-term efficacy and safety of catheter-based renal denervation in resistant hypertension: 10-year follow-up outcomes, Clinical Research in Cardiology, 2024, pp. 1-9, (2024); Bergmark BA, Mathenge N, Merlini PA, Lawrence-Wright MB, Giugliano RP, Acute coronary syndromes, The Lancet, 399, 10332, pp. 1347-1358, (2022); Bhatt DL, Lopes RD, Harrington RA, Diagnosis and treatment of acute coronary syndromes: a review, Journal of the American Medical Association, 327, 7, pp. 662-675, (2022); Bontsevich RA, Balamutova TI, Chukhareva NA, Tsygankova OV, Batisheva GA, Paleskava A, Kompaniets OG, Ketova GG, Barysheva VO, Nevzorova VA, Martynenko IM, Pakhomov SP, Physicians’ knowledge and preferences in tactics of management and rational pharmacotherapy of arterial hypertension in pregnant women (PHYGEST study), Research Results in Pharmacology, 8, 4, pp. 57-64, (2022); Bontsevich RA, Vovk YR, Gavrilova AA, Kirichenko AA, Krotkova IF, Kosmacheva ED, Kompaniets OG, Prozorova GG, Nevzorova VA, Martynenko IM, Ketova GG, Barysheva VO, Maksimov ML, Osipova OA, Drug therapy of arterial hypertension: assessment of the physicians’ basic knowledge. Final results of the PHYSTARH project, Systemic Hypertension, 18, 2, pp. 80-87, (2021); Brouwers S, Sudano I, Kokubo Y, Sulaica EM, Arterial hypertension, The Lancet, 398, 10296, pp. 249-261, (2021); Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, Claeys MJ, Dan GA, Dweck MR, Galbraith M, Gilard M, Hinterbuchner L, Jankowska EA, Juni P, Kimura T, Kunadian V, Leosdottir M, Lorusso R, Pedretti RFE, Rigopoulos AG, Rubini Gimenez M, Thiele H, Vranckx P, Wassmann S, Wenger NK, Ibanez B, ESC Scientific Document Group (2023) 2023 ESC Guidelines for the management of acute coronary syndromes, European Heart Journal, 44, 38, pp. 3720-3826, (2023); Chaulin AM, Grigorieva YV, Dupliakov DV, Current views on the pathophysiology of atherosclerosis. Part 1. The role of lipid metabolism disorders and endothelial dysfunction (literature review), Medicine in Kuzbass [Medicina v Kuzbasse], 19, 2, pp. 34-41, (2020); Ebzeyeva EY, Ostroumova OD, Doldo NM, Antihypertensive therapy in comorbid patients with chronic kidney disease: a clinical observation, Russian Medical Journal. Medical Review [Meditsinskoe Obozrenie], 7, 7, pp. 418-423, (2023); Hisatome I, Li P, Miake J, Taufiq F, Mahati E, Maharani N, Utami SB, Kuwabara M, Bahrudin U, Ninomiya H, Uric acid as a risk factor for chronic kidney disease and cardiovascular disease – Japanese guideline on the management of asymptomatic hyperuricemia, Circulation Journal, 85, 2, pp. 130-138, (2021); Hoeper MM, Pausch C, Grunig E, Staehler G, Huscher D, Pittrow D, Olsson KM, Vizza CD, Gall H, Distler O, Opitz C, Gibbs JSR, Delcroix M, Ghofrani HA, Rosenkranz S, Park DH, Ewert R, Kaemmerer H, Lange TJ, Kabitz HJ, Skowasch D, Skride A, Claussen M, Behr J, Milger K, Halank M, Wilkens H, Seyfarth HJ, Held M, Dumitrescu D, Tsangaris I, Vonk-Noordegraaf A, Ulrich S, Klose H, Temporal trends in pulmonary arterial hypertension: Results from the COMPERA registry, European Respiratory Journal, 59, 6, (2022); Jankowski J, Floege J, Fliser D, Bohm M, Marx N, Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options, Circulation, 143, 11, pp. 1157-1172, (2021); Kobalava JD, Conradi AO, Nedogoda SV, Shlyakhto EV, Arutyunov GP, Baranova EI, Barbarash OL, Boitsov SA, Vavilova TV, Villevalde SV, Galyavich AS, Gleser MG, Grineva EN, Grinstein YI, Drapkina OM, Zhernakov YV, Zvartau NE, Kislyak OA, Koziolova NA, Kosmacheva ED, Kotovskaya YV, Libis RA, Lopatin YM, Nebiyeridze DV, Nedoshivin AO, Ostroumova OD, Oshchepkova EV, Ratova LG, Skibitsky VV, Tkacheva ON, Chazova IE, Chesnikova AI, Chumakova GA, Shalnova SA, Shestakova MV, Yakushin SS, Janishevsky SN, Arterial hypertension in adults. Clinical recommendations 2020, Russian Journal of Cardiology [Rossiiskii Kardiologhicheskii Zhurnal], 25, 3, pp. 149-218, (2020); Linde JJ, Kelbaek H, Hansen TF, Sigvardsen PE, Torp-Pedersen C, Bech J, Heitmann M, Nielsen OW, Hofsten D, Kuhl JT, Raymond IE, Kristiansen OP, Svendsen IH, Vall-Lamora MHD, Kragelund C, de Knegt M, Hove JD, Jorgensen T, Fornitz GG, Steffensen R, Jurlander B, Abdulla J, Lyngbaek S, Elming H, Therkelsen SK, Jorgensen E, Klovgaard L, Bang LE, Hansen PR, Helqvist S, Galatius S, Pedersen F, Abildgaard U, Clemmensen P, Saunamaki K, Holmvang L, Engstrom T, Gislason G, Kober LV, Kofoed KF, Coronary CT angiography in patients with non-ST-segment elevation acute coronary syndrome, Journal of the American College of Cardiology, 75, 5, pp. 453-463, (2020); Nadirova YI, Zhabbarov OO, Bobosharipov FG, Umarova ZF, Saidaliev RS, Kodirova ShA, Mirzaeva GP, Rakhmatov AM, Zhumanazarov SB, Optimisation of combined therapy in arterial hypertension with calcium channel blocker and APF inhibitor, Solution of Social Problems in Management and Economy, 2, 2, pp. 181-186, (2023); Ott C, Schmieder RE, Diagnosis and treatment of arterial hypertension 2021, Kidney International, 101, 1, pp. 36-46, (2022); Pribylov SA, Yakovleva MV, Pribylov VS, Barbashina TA, Leonidova KO, Pribylova NN, Arterial stiffness in patients with acute coronary syndrome without persistent ST-segment elevation in combination with chronic kidney disease and arterial hypertension and its correction against the background of antihypertensive therapy, Man and His Health [Chelovek i Ego Zdorov’e], 1, pp. 19-27, (2022); Safronenko AV, Lepyavka SV, Demidov IA, Nazheva MI, Maklyakov YS, Optimization of premedication of patients with arterial hypertension and severe ventricular rhythm disturbances with Amiodarone-associated thyrotoxicosis, Research Results in Pharmacology, 7, 4, pp. 55-62, (2021)","K.O. Leonidova; Kursk State Medical University, Kursk, 3 K. Marksa St., Kursk region, 305041, Russian Federation; email: k_leonidova@list.ru","","Belgorod State National Research University","","","","","","2658381X","","","","English","Res. Results Pharm.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85199107683"
"Kidwai S.; Barbiero P.; Meijerman I.; Tonda A.; Perez-Pardo P.; Lio ́ P.; van der Maitland-Zee A.H.; Oberski D.L.; Kraneveld A.D.; Lopez-Rincon A.","Kidwai, Sarah (58700020200); Barbiero, Pietro (57203484388); Meijerman, Irma (6506908559); Tonda, Alberto (25825658400); Perez-Pardo, Paula (57193503480); Lio ́, Pietro (57536223700); van der Maitland-Zee, Anke H. (57220903102); Oberski, Daniel L. (37117700800); Kraneveld, Aletta D. (6602859285); Lopez-Rincon, Alejandro (24722721700)","58700020200; 57203484388; 6506908559; 25825658400; 57193503480; 57536223700; 57220903102; 37117700800; 6602859285; 24722721700","A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma","2023","Clinical and Translational Allergy","13","11","e12306","","","","2","10.1002/clt2.12306","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177049723&doi=10.1002%2fclt2.12306&partnerID=40&md5=5fb27ee1345b277ed5b3516f52aae902","Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom; UMR 518 MIA, INRAE, Universite Paris-Saclay, Paris, France; Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Department of Data Science, University Medical Center Utrecht, Utrecht, Netherlands","Kidwai S., Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; Barbiero P., Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom; Meijerman I., Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; Tonda A., UMR 518 MIA, INRAE, Universite Paris-Saclay, Paris, France; Perez-Pardo P., Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; Lio ́ P., Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom; van der Maitland-Zee A.H., Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; Oberski D.L., Department of Data Science, University Medical Center Utrecht, Utrecht, Netherlands; Kraneveld A.D., Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; Lopez-Rincon A., Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands, Department of Data Science, University Medical Center Utrecht, Utrecht, Netherlands","Background: Not being well controlled by therapy with inhaled corticosteroids and long-acting β2 agonist bronchodilators is a major concern for severe-asthma patients. The current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response. Methods: Two novel computational algorithms, machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA expression data from moderate-to-severe asthma patients to identify genes as predictors of omalizumab response. Results: With REFS, the number of features was reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy of 0.975. The 5 responsiveness predictive genes encode the following proteins: Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4 identified responsiveness predicting genes are associated with mucosal immunity, cell metabolism, and airway remodeling. Conclusion and clinical relevance: Both computational methods show 4 identical genes as predictors of omalizumab response in moderate-to-severe asthma patients. The obtained high accuracy indicates that our approach has potential in clinical settings. Future studies in relevant cohort data should validate our computational approach. © 2023 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.","anti-IgE; asthma; biomarker; machine-learning; omalizumab","messenger RNA; omalizumab; airway remodeling; algorithm; Article; asthma; c type lectin domain family 4 member C gene; cell metabolism; coiled coil domain containing protein 113 gene; cross validation; disease association; feature selection; gene; gene expression; gene identification; logic explainable networks algorithm; machine learning; mucosal immunity; prediction; protein phosphatase 1 regulatory subunit 3D gene; recursive ensemble feature selection; reliability; solute carrier family 26 member 8 gene; treatment response","","omalizumab, 242138-07-4","","","","","Masoli M., Fabian D., Holt S., Beasley R., The global burden of asthma: executive summary of the gina dissemination committee report, Allergy, 59, 5, pp. 469-478, (2004); Larsson K., Stallberg B., Lisspers K., Et al., Prevalence and management of severe asthma in primary care: an observational cohort study in Sweden (pacehr), Respir. 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Respir J, 49, 6, (2017); Metselaar P.I., Mendoza-Maldonado L., Li Yim A.Y.F., Et al., Recursive ensemble feature selection provides a robust mrna expression signature for myalgic encephalomyelitis/chronic fatigue syndrome, Sci Rep, 11, pp. 1-11, (2021); Hayden M.S., Ghosh S., Nf-κb, the first quarter-century: remarkable progress and outstanding questions, Gene Dev, 26, 3, pp. 203-234, (2012)","A. Lopez-Rincon; Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty of Science, Utrecht University, Utrecht, Netherlands; email: a.lopezrincon@uu.nl","","John Wiley and Sons Inc","","","","","","20457022","","","","English","Clin. Transl. Allergy","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85177049723"
"Wang J.M.; Labaki W.W.; Murray S.; Martinez F.J.; Curtis J.L.; Hoffman E.A.; Ram S.; Bell A.J.; Galban C.J.; Han M.K.; Hatt C.","Wang, Jennifer M. (57373217500); Labaki, Wassim W. (57192089513); Murray, Susan (57202500007); Martinez, Fernando J. (7402221202); Curtis, Jeffrey L. (57216998659); Hoffman, Eric A. (58000586800); Ram, Sundaresh (8319437900); Bell, Alexander J. (57201008708); Galban, Craig J. (57207600915); Han, MeiLan K. (57221229257); Hatt, Charles (8683604700)","57373217500; 57192089513; 57202500007; 7402221202; 57216998659; 58000586800; 8319437900; 57201008708; 57207600915; 57221229257; 8683604700","Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS","2023","Frontiers in Physiology","14","","1144192","","","","3","10.3389/fphys.2023.1144192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158127677&doi=10.3389%2ffphys.2023.1144192&partnerID=40&md5=28d8285791de30270b1a07af0d32492b","Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States; Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States; Weill Cornell Medical College, New York, NY, United States; Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States; Department of Radiology, University of Iowa, Iowa City, IA, United States; Department of Radiology, University of Michigan, Ann Arbor, MI, United States; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States; Imbio Inc, Minneapolis, MN, United States","Wang J.M., Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States; Labaki W.W., Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States; Murray S., Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States; Martinez F.J., Weill Cornell Medical College, New York, NY, United States; Curtis J.L., Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States, Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States; Hoffman E.A., Department of Radiology, University of Iowa, Iowa City, IA, United States; Ram S., Department of Radiology, University of Michigan, Ann Arbor, MI, United States, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States; Bell A.J., Department of Radiology, University of Michigan, Ann Arbor, MI, United States; Galban C.J., Department of Radiology, University of Michigan, Ann Arbor, MI, United States; Han M.K., Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States; Hatt C., Department of Radiology, University of Michigan, Ann Arbor, MI, United States, Imbio Inc, Minneapolis, MN, United States","Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1–2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0–2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1–2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts. Copyright © 2023 Wang, Labaki, Murray, Martinez, Curtis, Hoffman, Ram, Bell, Galban, Han and Hatt.","chronic obstructive pulmonary disease; computed tomography; lung function decline; machine learning; quantitative imaging","adult; aged; Article; body mass; bronchiectasis; chronic obstructive lung disease; cohort analysis; computer assisted tomography; deterioration; disease severity; electronic medical record; female; follow up; forced expiratory volume; forced vital capacity; hospital readmission; human; lung function; lung volume; machine learning; major clinical study; male; prediction; predictive value; receiver operating characteristic; risk assessment; risk factor; sensitivity and specificity; smoking; spirometry; St. George Respiratory Questionnaire; thorax; tidal volume; training; validation process","","","","","","","Bahadori K., Fitzgerald J.M., Levy R.D., Fera T., Swiston J., Risk factors and outcomes associated with chronic obstructive pulmonary disease exacerbations requiring hospitalization, Can. 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Res, 15, (2014); Labaki W.W., Gu T., Murray S., Hatt C.R., Galban C.J., Ross B.D., Et al., Voxel-wise longitudinal parametric response mapping analysis of chest computed tomography in smokers, Acad. Radiol, 26, pp. 217-223, (2019); Leitao Filho F.S., Mattman A., Schellenberg R., Criner G.J., Woodruff P., Lazarus S.C., Et al., Serum IgG levels and risk of COPD hospitalization: A pooled meta-analysis, Chest, 158, pp. 1420-1430, (2020); Lugosi G., Learning with an unreliable teacher, Pattern Recognit, 25, pp. 79-87, (1992); Martinez F.J., Han M.K., Allinson J.P., Barr R.G., Boucher R.C., Calverley P.M.A., Et al., At the root: Defining and halting progression of early chronic obstructive pulmonary disease, Am. J. Respir. Crit. 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Wang; Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, United States; email: wangjenn@med.umich.edu","","Frontiers Media SA","","","","","","1664042X","","","","English","Front. Physiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85158127677"
"Azim A.; Rezwan F.I.; Barber C.; Harvey M.; Kurukulaaratchy R.J.; Holloway J.W.; Howarth P.H.","Azim, Adnan (57209046517); Rezwan, Faisal I. (24537660300); Barber, Clair (57089945500); Harvey, Matthew (57209053853); Kurukulaaratchy, Ramesh J. (6603644097); Holloway, John W. (57221220827); Howarth, Peter H. (7103350375)","57209046517; 24537660300; 57089945500; 57209053853; 6603644097; 57221220827; 7103350375","Measurement of Exhaled Volatile Organic Compounds as a Biomarker for Personalised Medicine: Assessment of Short-Term Repeatability in Severe Asthma","2022","Journal of Personalized Medicine","12","10","1635","","","","3","10.3390/jpm12101635","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140930171&doi=10.3390%2fjpm12101635&partnerID=40&md5=fd1838c43cce0d40d96c967f5673ca68","Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, United Kingdom; Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; David Hide Asthma and Allergy Research Centre, Isle of Wight NHS Trust, Newport, PO30 5TG, United Kingdom","Azim A., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Rezwan F.I., Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, United Kingdom, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; Barber C., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Harvey M., NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom; Kurukulaaratchy R.J., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom, David Hide Asthma and Allergy Research Centre, Isle of Wight NHS Trust, Newport, PO30 5TG, United Kingdom; Holloway J.W., NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom, Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; Howarth P.H., Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom, NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, SO16 6YD, United Kingdom","The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions. © 2022 by the authors.","asthma; breathomics; repeatability; respiratory; severe asthma; VOC; volatile organic compounds","biological marker; vascular cell adhesion molecule 1; adult; algorithm; Article; clinical article; controlled study; female; human; human tissue; leukocyte differential count; limit of detection; machine learning; male; mass fragmentography; middle aged; personalized medicine; principal component analysis; quality control; respiratory tract inflammation; retention time; severe asthma","","","","","Asthma, Allergy & Inflammation Research; Clinical Research Facility; Owlstone Medical; Southampton NIHR; GlaxoSmithKline, GSK; Novartis, (GBP 35,000); University Hospital Southampton NHS Foundation Trust; National Institute for Health and Care Research, NIHR; University of Southampton; AAIR Charity, AAIR; National Institute for Health Research Southampton Biomedical Research Centre, NIHR SCBR","Funding text 1: The WATCH study uses the NIHR Southampton BRC and Clinical Research Facility at UHSFT that is funded by the NIHR. The WATCH study itself is not externally funded. Funding assistance for database support for the WATCH study was initially obtained from a nonpromotional grant from Novartis (GBP 35,000). Funding assistance for patient costs (e.g, parking) was initially provided by a charitable grant (GBP 3500) from the Asthma, Allergy & Inflammation Research (AAIR) Charity. Funding for the Breathomics Analysis was supported by a research grant from GSK.; Funding text 2: The authors wish to thank the patients who are participating in this study. They also wish to acknowledge the support of the Southampton NIHR Clinical Research Facility and BRC. The Clinical Research Facility and BRC are funded by Southampton NIHR and are a partnership between the University of Southampton and University Hospital Southampton NHS Foundation Trust. The authors also acknowledge funding support from Owlstone Medical and the AAIR Charity. ","Lambrecht B.N., Hammad H., The immunology of asthma, Nat. Immunol, 16, pp. 45-56, (2015); Papi A., Brightling C., Pedersen S.E., Reddel H.K., Asthma, Lancet, 391, pp. 783-800, (2018); Chung K.F., Wenzel S.E., Brozek J.L., Bush A., Castro M., Sterk P.J., Adcock I.M., Bateman E.D., Bel E.H., Bleecker E.R., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur. Respir. J, 43, pp. 343-373, (2014); Moore W.C., Meyers D.A., Wenzel S.E., Teague W.G., Li H., Li X., D'Agostino R., Castro M., Curran-Everett D., Fitzpatrick A.M., Et al., Identification of Asthma Phenotypes Using Cluster Analysis in the Severe Asthma Research Program, Am. J. Respir. Crit. Care Med, 181, pp. 315-323, (2010); Haldar P., Pavord I.D., Shaw D.E., Berry M.A., Thomas M., Brightling C.E., Wardlaw A.J., Green R.H., Cluster Analysis and Clinical Asthma Phenotypes, Am. J. Respir. Crit. Care Med, 178, pp. 218-224, (2008); Wu W., Bleecker E., Moore W., Busse W.W., Castro M., Chung K.F., Calhoun W.J., Erzurum S., Gaston B., Israel E., Et al., Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data, J. Allergy Clin. Immunol, 133, pp. 1280-1288, (2014); Kuo C.-H.S., Pavlidis S., Loza M., Baribaud F., Rowe A., Pandis I., Sousa A., Corfield J., Djukanovic R., Lutter R., Et al., T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED, Eur. Respir. J, 49, (2017); Hinks T.S.C., Zhou X., Staples K., Dimitrov B.D., Manta A., Petrossian T., Lum P., Smith C., Ward J., Howarth P., Et al., Multidimensional endotypes of asthma: Topological data analysis of cross-sectional clinical, pathological, and immunological data, Lancet, 385, (2015); Hinks T.S., Brown T., Lau L.C., Rupani H., Barber C., Elliott S., Ward J.A., Ono J., Ohta S., Izuhara K., Et al., Multidimensional endotyping in patients with severe asthma reveals inflammatory heterogeneity in matrix metalloproteinases and chitinase 3–like protein 1, J. Allergy Clin. Immunol, 138, pp. 61-75, (2016); Pavord I.D., Korn S., Howarth P., Bleecker E.R., Buhl R., Keene O.N., Ortega H., Chanez P., Mepolizumab for severe eosinophilic asthma (DREAM): A multicentre, double-blind, placebo-controlled trial, Lancet, 380, pp. 651-659, (2012); Wenzel S., Ford L., Pearlman D., Spector S., Sher L., Skobieranda F., Wang L., Kirkesseli S., Rocklin R., Bock B., Et al., Dupilumab in Persistent Asthma with Elevated Eosinophil Levels, N. Engl. J. Med, 368, pp. 2455-2466, (2013); Green R.H., Brightling C.E., McKenna S., Hargadon B., Parker D., Bradding P., Wardlaw A.J., Pavord I.D., Asthma exacerbations and sputum eosinophil counts: A randomised controlled trial, Lancet, 360, pp. 1715-1721, (2002); Chung K.F., Defining Phenotypes in Asthma: A Step Towards Personalized Medicine, Drugs, 74, pp. 719-728, (2014); Agusti A., Bel E., Thomas M., Vogelmeier C., Brusselle G., Holgate S., Humbert M., Jones P., Gibson P.G., Vestbo J., Et al., Treatable traits: Toward precision medicine of chronic airway diseases, Eur. Respir. J, 47, pp. 410-419, (2016); Diamant Z., Vijverberg S., Alving K., Bakirtas A., Bjermer L., Custovic A., Dahlen S., Gaga M., Van Wijk R.G., Del Giacco S., Et al., Toward clinically applicable biomarkers for asthma: An EAACI position paper, Allergy, 74, pp. 1835-1851, (2019); Fowler S.J., Breath analysis for label-free characterisation of airways disease, Eur. Respir. J, 51, (2018); Tiotiu A., Biomarkers in asthma: State of the art, Asthma Res. Pract, 4, (2018); Kharitonov S., Yates D., Robbins R., Barnes P., Logan-Sinclair R., Shinebourne E., Increased nitric oxide in exhaled air of asthmatic patients, Lancet, 343, pp. 133-135, (1994); Phillips M., Gleeson K., Hughes J.M.B., Greenberg J., Cataneo R.N., Baker L., McVay W.P., Volatile organic compounds in breath as markers of lung cancer: A cross-sectional study, Lancet, 353, pp. 1930-1933, (1999); Azim A., Barber C., Dennison P., Riley J., Howarth P., Exhaled volatile organic compounds in adult asthma: A systematic review, Eur. Respir. J, 54, (2019); Schleich F., Zanella D., Stefanuto P.-H., Dallinga J., Henket M., Wouters E., Van Steen K., Van Schooten F.-J., Focant J.-F., Louis R., Exhaled Volatile Organic Compounds Are Able to Discriminate between Neutrophilic and Eosinophilic Asthma, Am. J. Respir. Crit. Care Med, 200, pp. 444-453, (2019); Ibrahim W., Carr L., Cordell R., Wilde M.J., Salman D., Monks P.S., Thomas P., Brightling C.E., Siddiqui S., Greening N.J., Breathomics for the clinician: The use of volatile organic compounds in respiratory diseases, Thorax, 76, pp. 514-521, (2021); Fens N., Zwinderman A.H., van der Schee M.P., de Nijs S.B., Dijkers E., Roldaan A.C., Cheung D., Bel E.H., Sterk P.J., Exhaled Breath Profiling Enables Discrimination of Chronic Obstructive Pulmonary Disease and Asthma, Am. J. Respir. Crit. Care Med, 180, pp. 1076-1082, (2009); de Vries R., Brinkman P., van der Schee M.P., Fens N., Dijkers E., Bootsma S.K., de Jongh F.H., Sterk P.J., Integration of electronic nose technology with spirometry: Validation of a new approach for exhaled breath analysis, J. Breath Res, 9, (2015); Azim A., Mistry H., Freeman A., Barber C., Newell C., Gove K., Thirlwall Y., Harvey M., Bentley K., Knight D., Et al., Protocol for the Wessex AsThma CoHort of difficult asthma (WATCH): A pragmatic real-life longitudinal study of difficult asthma in the clinic, BMC Pulm. Med, 19, (2019); ten Brinke A., de Lange C., Zwinderman A.H., Rabe K.F., Sterk P.J., Bel E.H., Sputum induction in severe asthma by a standardized protocol: Predictors of excessive bronchoconstriction, Am. J. Respir. Crit. Care Med, 164, pp. 749-753, (2001); Bafadhel M., McCormick M., Saha S., McKenna S., Shelley M., Hargadon B., Mistry V., Reid C., Parker D., Dodson P., Et al., Profiling of Sputum Inflammatory Mediators in Asthma and Chronic Obstructive Pulmonary Disease, Respiration, 83, pp. 36-44, (2011); Van Rossum G., Drake F.L., Python 3 Reference Manual, (2009); Behdenna A., Haziza J., Azencott C.-A., Nordor A., pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods, bioRxiv, (2020); Ospina R., Marmolejo-Ramos F., Performance of Some Estimators of Relative Variability, Front. Appl. Math. Stat, 5, (2019); Yancey S.W., Keene O.N., Albers F.C., Ortega H., Bates S., Bleecker E.R., Pavord I., Biomarkers for severe eosinophilic asthma, J. Allergy Clin. Immunol, 140, pp. 1509-1518, (2017); Schofield J.P., Burg D., Nicholas B., Strazzeri F., Brandsma J., Staykova D., Folisi C., Bansal A.T., Xian Y., Guo Y., Et al., Stratification of asthma phenotypes by airway proteomic signatures, J. Allergy Clin. Immunol, 144, pp. 70-82, (2019); Davies A.R., Hancox R.J., Induced sputum in asthma: Diagnostic and therapeutic implications, Curr. Opin. Pulm. Med, 19, pp. 60-65, (2013); Peel A.M., Wilkinson M., Sinha A., Loke Y.K., Fowler S.J., Wilson A.M., Volatile organic compounds associated with diagnosis and disease characteristics in asthma—A systematic review, Respir. Med, 169, (2020); Bos L.D., Sterk P.J., Fowler S., Breathomics in the setting of asthma and chronic obstructive pulmonary disease, J. Allergy Clin. Immunol, 138, pp. 970-976, (2016); Crane M.A., Levy-Carrick N.C., Crowley L., Barnhart S., Dudas M., Onuoha U., Globina Y., Haile W., Shukla G., Ozbay F., The Response to September 11: A Disaster Case Study, Ann. Glob. Health, 80, pp. 320-331, (2014); Herbig J., Beauchamp J., Towards standardization in the analysis of breath gas volatiles, J. Breath Res, 8, (2014); Horvath I., Barnes P.J., Loukides S., Sterk P.J., Hogman M., Olin A.-C., Amann A., Antus B., Baraldi E., Bikov A., Et al., A European Respiratory Society technical standard: Exhaled biomarkers in lung disease, Eur. Respir. J, 49, (2017); Smolinska A., Hauschild A.-C., Fijten R., Dallinga J.W., Baumbach J., Van Schooten F.J., Current breathomics—A review on data pre-processing techniques and machine learning in metabolomics breath analysis, J. Breath Res, 8, (2014); Ray P., Reddy S.S., Banerjee T., Various dimension reduction techniques for high dimensional data analysis: A review, Artif. Intell. Rev, 54, pp. 3473-3515, (2021); Ahmed W.M., Brinkman P., Weda H., Knobel H.H., Xu Y., Nijsen T.M., Goodacre R., Rattray N.J.W., Vink T.J., Santonico M., Et al., Methodological considerations for large-scale breath analysis studies: Lessons from the U-BIOPRED severe asthma project, J. Breath Res, 13, (2018)","A. Azim; Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, United Kingdom; email: a.azim@soton.ac.uk","","MDPI","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85140930171"
"Yu L.; Ruan X.; Huang W.; Huang N.; Zeng J.; He J.; He R.; Yang K.","Yu, Lin (58640521400); Ruan, Xia (58640521500); Huang, Wenbo (57878746300); Huang, Na (57649865300); Zeng, Jun (57203587051); He, Jie (56324751400); He, Rong (57841928600); Yang, Kai (57211613958)","58640521400; 58640521500; 57878746300; 57649865300; 57203587051; 56324751400; 57841928600; 57211613958","Machine learning-based prediction of in-hospital mortality in patients with pneumonic chronic obstructive pulmonary disease exacerbations","2024","Journal of Asthma","61","3","","212","221","9","2","10.1080/02770903.2023.2263071","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173731832&doi=10.1080%2f02770903.2023.2263071&partnerID=40&md5=60814b7da8574ea28f091aeb22796b9f","Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China; School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China","Yu L., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; Ruan X., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; Huang W., School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; Huang N., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; Zeng J., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; He J., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; He R., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China; Yang K., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China, School of Clinical Medicine, Chengdu Medical College, Sichuan, Chengdu, China","Objective: While linear regression and LASSO models have been established for predicting in-hospital mortality, there is currently no validated clinical prediction algorithm to predict in-hospital mortality for patients with chronic obstructive pulmonary disease (COPD) exacerbations using machine learning. Thus, we will evaluate the BAP-65 and CURB-65, and construct a novel prediction model using the random forest (RF) technique. Methods: A dataset of 1,418 patients with COPD exacerbations was collected. Age, gender, mental status, vital signs, and laboratory results were all taken into account for predictors. The categorical outcome variable was hospital-based mortality of people over 65 years. The dataset was divided randomly into a training dataset (70%) and a testing dataset (30%). We trained three prediction models, BAP-65, CURB-65, and the RF model, estimated the area under the receiver operating characteristic curve (AUROC) for the entire dataset. We also conducted a comparison of the AUROC values using the Delong test. Results: A total of 658 individuals with COPD acute exacerbations were enrolled. Our analysis using the receiver operating characteristic curve demonstrated that the RF model exhibited excellent performance, with an AUROC of 0.80 (95% confidence interval: 0.75-0.84). In comparison, the BAP-65 prediction model yielded an AUROC of 0.72 (0.68-0.75), while the CURB-65 prediction model achieved an AUROC of 0.69 (0.67-0.73). Conclusions: The RF model demonstrated superior predictive capabilities than the BAP-65 and CURB-65 models in predicting in-hospital mortality. The results further highlighted significant factors for predicting in-hospital mortality, including blood eosinophil count, systolic blood pressure, and prior history of asthma. © 2023 Taylor & Francis Group, LLC.","Chronic obstructive pulmonary disease; machine learning; mortality; random forest","Asthma; Hospital Mortality; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; ROC Curve; aged; Article; BAP-65 score; chronic obstructive lung disease; clinical assessment; controlled study; cross validation; CURB-65 score; disease exacerbation; eosinophil count; female; human; in-hospital mortality; major clinical study; male; mortality rate; pneumonia; predictive model; random forest; respiratory tract disease assessment; asthma; chronic obstructive lung disease; hospital mortality; machine learning; receiver operating characteristic","","","","","Applied Basic Research of Sichuan Department of Science and Technology, (2021YJ0470); Changsha Shiyu Translation Service Co., Ltd.; Youth Innovation Project of Sichuan Medical Association, (Q17025)","This work was supported by the Applied Basic Research of Sichuan Department of Science and Technology (2021YJ0470), the Youth Innovation Project of Sichuan Medical Association (Q17025). This manuscript was edited by Changsha Shiyu Translation Service Co., Ltd.","Pauwels R.A., Buist A.S., Calverley P.M., Jenkins C.R., Hurd S.S., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. 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Anderson G.B., Dominici F., Wang Y., McCormack M.C., Bell M.L., Peng R.D., Heat-related emergency hospitalizations for respiratory diseases in the Medicare population, Am J Respir Crit Care Med, 187, 10, pp. 1098-1103, (2013); Davie G.S., Baker M.G., Hales S., Carlin J.B., Trends and determinants of excess winter mortality in New Zealand: 1980 to 2000, BMC Public Health, 7, 1, (2007)","K. Yang; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Chengdu Medical College, Chengdu, No. 278, Baoguang Avenue, Xindu District, Sichuan, 610500, China; email: a15828075272@163.com","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","37738216","English","J. Asthma","Article","Final","","Scopus","2-s2.0-85173731832"
"Gilbert S.","Gilbert, Stephen (16833743700)","16833743700","European regulation of digital respiratory healthcare","2023","ERS Monograph","2023","","","63","78","15","3","10.1183/2312508X.10000923","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187193857&doi=10.1183%2f2312508X.10000923&partnerID=40&md5=c37da7483a921552ced872160f11c8ae","Else Kröner Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany","Gilbert S., Else Kröner Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany","In this chapter, key regulatory concepts to consider in the field of digital respiratory healthcare are described, with a focus on European regulation. Although most regulatory requirements and concepts are not specific to respiratory medicine, there are particular considerations and themes of importance for the area. Key questions to be addressed are the overall regulatory development and approval process for digital medical devices, how regulations apply to software as a medical device, and special considerations for artificial intelligence and emerging technologies. Current legislation and guidelines are described, with an overview of in-progress legislation. Specific regulatory considerations are addressed for Medical Internet of Things devices and apps. The regulation of clinical decision support systems and closed loop systems for therapy is considered, alongside ventilator control systems. Requirements for digital therapeutics, companion diagnostics, new concepts for drug companion apps and virtual wards are addressed. Finally, the regulation of emerging digital concepts is considered, including digital twins and foundation artificial intelligence models. © ERS 2023.","","Article; artificial intelligence; asthma; digital respiratory healthcare; digital twin; health care; human; hypertension; internet of things; nerve cell network; regulatory mechanism; risk management; software; total quality management","","","","","","","Gilbert S, Anderson S, Daumer M, Et al., Learning from experience and finding the right balance in the governance of artificial intelligence and digital health technologies, J Med Internet Res, 25, (2023); Fraser AG, Biasin E, Bijnens B, Et al., Artificial intelligence in medical device software and high-risk medical devices – a review of definitions, expert recommendations and regulatory initiatives, Expert Rev Med Devices, 20, pp. 467-491, (2023); Gilbert S, Fenech M, Hirsch M, Et al., Algorithm change protocols in the regulation of adaptive machine learning-based medical devices, J Med Internet Res, 23, (2021); Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/ 2009 and repealing Council Directives 90/385/EEC and 93/42/EEC, (2017); Summary of references of harmonised standards published in the Official Journal – Regulation (EU) 2017/745; Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU, (2017); Manual on borderline and classification for medical devices under Regulation (EU) 2017/745 on medical devices and Regulation (EU) 2017/746 on in vitro diagnostic medical devices; MDCG 2021-24 Guidance on classification of medical devices, (2021); Premarket Notification 510(k); Commission Implementing Regulation (EU) 2022/1107 of 4 July 2022 laying down common specifications for certain class D in vitro diagnostic medical devices in accordance with Regulation (EU) 2017/746 of the European Parliament and of the Council, (2022); Guidance – MDCG endorsed documents and other guidance; ISO 13485:2016 Medical devices – Quality management systems – Requirements for regulatory purposes, (2016); ISO 14155:2020 Clinical investigation of medical devices for human subjects – Good clinical practice, (2020); ISO 14971:2019 Medical devices – Application of risk management to medical devices, (2019); IEC 62366-1:2015 Medical devices – Part 1: Application of usability engineering to medical devices, (2015); MDCG 2020-1 Guidance on clinical evaluation (MDR)/Performance evaluation (IVDR) of medical device software, (2020); MDCG 2019-16 Rev.1 Guidance on cybersecurity for medical devices, (2020); IEC 62304:2006/Amd 1:2015 Medical device software – Software life cycle processes – Amendment 1, (2017); Questionnaire “Artificial intelligence (AI) in medical devices, (2022); Good machine learning practice for medical device development: guiding principles, (2021); ISO/IEC TR 29119-11:2020 Software and systems engineering – Software testing – Part 11: Guidelines on the testing of AI-based systems, (2020); ISO/TR 24291:2021 Health informatics – Applications of machine learning technologies in imaging and other medical applications, (2021); BS 30440:2023 Validation framework for the use of artificial intelligence (AI) within healthcare, (2023); MDCG 2020-9 Regulatory requirements for ventilators and related accessories, (2020); Standards by ISO/TC 121/SC 3 Respiratory devices and related equipment used for patient care; Koldeweij C, Clarke J, Nijman J, Et al., CE accreditation and barriers to CE marking of pediatric drug calculators for mobile devices: scoping review and qualitative analysis, J Med Internet Res, 23, (2021); Sadare O, Melvin T, Harvey H, Et al., Can Apple and Google continue as health app gatekeepers as well as distributors and developers?, NPJ Digit Med, 6, (2023); MDCG 2021-6 Regulation (EU) 2017/745 – Questions & answers regarding clinical investigation, (2021); Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), (2016); Proposal for a regulation – The European Health Data Space, (2022); MDCG 2020-3 Rev.1 Guidance on significant changes regarding the transitional provision under Article 120 of the MDR with regard to devices covered by certificates according to MDD or AIMDD, (2023); Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain Union legislative acts, (2021); European Artificial Intelligence Regulation, (2021); H.R.6580 – Algorithmic Accountability Act of 2022: To direct the Federal Trade Commission to require impact assessments of automated decision systems and augmented critical decision processes, and for other purposes, (2022); Mokander J, Juneja P, Watson DS, Et al., The US Algorithmic Accountability Act of 2022 vs. the EU Artificial Intelligence Act: what can they learn from each other?, Minds Machines, 32, pp. 751-758, (2022); A pro-innovation approach to AI regulation, (2023); Cobbaert K, Bos G, Software as a Medical Device, (2021); Simon DA, Shachar C, Cohen IG., Skating the line between general wellness products and regulated devices: strategies and implications, J Law Biosci, 9, (2022); Simon DA, Shachar C, Cohen IG., Unsettled liability issues for “prediagnostic” wearables and health-related products, JAMA, 328, pp. 1391-1392, (2022); Miller S, Gilbert S, Virani V, Et al., Patients’ utilization and perception of an artificial intelligence-based symptom assessment and advice technology in a British primary care waiting room: exploratory pilot study, JMIR Hum Factors, 7, (2020); Chambers D, Cantrell AJ, Johnson M, Et al., Digital and online symptom checkers and health assessment/triage services for urgent health problems: systematic review, BMJ Open, 9, (2019); Gilbert S, Mehl A, Baluch A, Et al., How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs, BMJ Open, 10, (2020); Guidance: Medical device stand-alone software including apps (including IVDMDs); Cotte F, Mueller T, Gilbert S, Et al., Safety of triage self-assessment using a symptom assessment app for walk-in patients in the emergency care setting: observational prospective cross-sectional study, JMIR Mhealth Uhealth, 10, (2022); Scheder-Bieschin J, Blumke B, de Buijzer E, Et al., Improving emergency department patient–physician conversation through an artificial intelligence symptom-taking tool: mixed methods pilot observational study, JMIR Form Res, 6, (2022); Mansab F, Bhatti S, Goyal D., Reliability of COVID-19 symptom checkers as national triage tools: an international case comparison study, BMJ Health Care Inform, 28, (2021); Das DiGA-Verzeichnis: Antworten zur Nutzung von DiGA [The digital health apps (DiGA) directory: answers about using DiGA]; Schmidt-Kraepelin M, Toussaint PA, Thiebes S, Et al., Archetypes of gamification: analysis of mHealth apps, JMIR Mhealth Uhealth, 8, (2020); Han A, Min SI, Ahn S, Et al., Mobile medication manager application to improve adherence with immunosuppressive therapy in renal transplant recipients: a randomized controlled trial, PLoS One, 14, (2019); MDCG 2019-11 Guidance on Qualification and Classification of Software in Regulation (EU) 2017/745 – MDR and Regulation (EU) 2017/746 – IVDR, (2019); Chan AHY, Pleasants RA, Dhand R, Et al., Digital inhalers for asthma or chronic obstructive pulmonary disease: a scientific perspective, Pulm Ther, 7, pp. 345-376, (2021); Xiroudaki S, Schoubben A, Giovagnoli S, Et al., Dry powder inhalers in the digitalization era: current status and future perspectives, Pharmaceutics, 13, (2021); Companion diagnostics; Valla V, Alzabin S, Koukoura A, Et al., Companion diagnostics: state of the art and new regulations, Biomark Insights, 16, (2021); Medical devices: Companion diagnostics (‘in vitro diagnostics’); Papadopoulos N, Kinzler KW, Vogelstein B., The role of companion diagnostics in the development and use of mutation-targeted cancer therapies, Nat Biotechnol, 24, pp. 985-995, (2006); 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Venkatesh KP, Raza MM, Kvedar JC., Health digital twins as tools for precision medicine: considerations for computation, implementation, and regulation, NPJ Digit Med, 5, (2022); Sun T, He X, Song X, Et al., The digital twin in medicine: a key to the future of healthcare?, Front Med, 9, (2022); Drummond D, Roukema J, Pijnenburg M., Home monitoring in asthma: towards digital twins, Curr Opin Pulm Med, 29, pp. 270-276, (2023); Vorisek CN, Lehne M, Klopfenstein SAI, Et al., Fast Healthcare Interoperability Resources (FHIR) for interoperability in health research: systematic review, JMIR Med Inform, 10, (2022); Lehne M, Sass J, Essenwanger A, Et al., Why digital medicine depends on interoperability, NPJ Digit Med, 2, (2019); Clinical decision support software: guidance for industry and Food and Drug Administration staff, (2022); Moor M, Banerjee O, Abad ZSH, Et al., Foundation models for generalist medical artificial intelligence, Nature, 616, pp. 259-265, (2023); Singhal K, Tu T, Gottweis J, Et al., Towards expert-level medical question answering with large language models, (2023); Kung TH, Cheatham M, Medenilla A, Et al., Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models, PLoS Digit Health, 2, (2023); Angel MC, Rinehart JB, Canneson MP, Et al., Clinical knowledge and reasoning abilities of AI large language models in anesthesiology: a comparative study on the ABA exam, medRxiv, (2023); Gilbert S, Harvey H, Melvin T, Et al., Large language model AI chatbots require approval as medical devices, Nat Med, (2023); Morrell W, Shachar C, Weiss AP., The oversight of autonomous artificial intelligence: lessons from nurse practitioners as physician extenders, J Law Biosci, 9, (2022); Chernew M, Mintz H., Administrative expenses in the US health care system: why so high?, JAMA, 326, pp. 1679-1680, (2021); Marwaha JS, Kvedar JC., Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI, NPJ Digit Med, 5, (2022); Gilbert S, Pimenta A, Stratton-Powell A, Et al., Continuous improvement of digital health applications linked to real-world performance monitoring: safe moving targets?, Mayo Clin Proc Digit Health, 1, pp. 276-287, (2023)","S. Gilbert; Else Kröner Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany; email: stephen.gilbert@tu-dresden.de","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85187193857"
"Zhang W.; Zhao Y.; Tian Y.; Liang X.; Piao C.","Zhang, Wenxiu (57434647700); Zhao, Yu (58785786100); Tian, Yuchi (57226708163); Liang, Xiaoyun (7401735901); Piao, Chenghao (57211949780)","57434647700; 58785786100; 57226708163; 7401735901; 57211949780","Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements","2023","International Journal of COPD","18","","","3099","3114","15","2","10.2147/COPD.S436803","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180860262&doi=10.2147%2fCOPD.S436803&partnerID=40&md5=ea21be7dd0d4a16ff6fe49b11b267b9b","Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, China; Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Liaoning, Shenyang, China","Zhang W., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, China; Zhao Y., Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Liaoning, Shenyang, China; Tian Y., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, China; Liang X., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, China; Piao C., Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Liaoning, Shenyang, China","Purpose: Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements. Patients and Methods: We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student’s t-test (or Mann–Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant. Results: The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA−950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs. Conclusion: QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD. © 2023 Zhang et al.","COPD; early diagnosis; QCT measurements; SVM","Early Diagnosis; Emphysema; Forced Expiratory Volume; Humans; Lung; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Retrospective Studies; Tomography, X-Ray Computed; adult; aged; area under the curve; Article; chest tightness; chronic obstructive lung disease; clinical feature; diagnostic test accuracy study; dyspnea; early diagnosis; emphysema; female; forced expiratory volume; forced vital capacity; high resolution computer tomography; high risk population; human; image quality; least absolute shrinkage and selection operator; lung function; lung function test; machine learning; major clinical study; male; quantitative analysis; receiver operating characteristic; retrospective study; sensitivity and specificity; support vector machine; thoracoplasty; training; wheezing; chronic obstructive lung disease; diagnostic imaging; early diagnosis; emphysema; lung; lung emphysema; procedures; x-ray computed tomography","","","","","","","Tanabe N, Sakamoto R, Kozawa S, Et al., Deep learning-based reconstruction of chest ultra-high-resolution computed tomography and quantitative evaluations of smaller airways, Respir Investig, 60, 1, pp. 167-170, (2022); Park JE, Zhang L, Ho YF, Et al., Modeling the Health and Economic Burden of Chronic Obstructive Pulmonary Disease in China From 2020 to 2039: a Simulation Study, Value Health Reg Issues, 32, pp. 8-16, (2022); Chen H, Liu X, Gao X, Et al., Epidemiological evidence relating risk factors to chronic obstructive pulmonary disease in China: a systematic review and meta-analysis, PLoS One, 16, 12, (2021); Shi G, Chen C., Home-based versus outpatient pulmonary rehabilitation program for patients with chronic obstructive pulmonary disease: a protocol for systematic review and meta-analysis, Medicine (Baltimore), 100, 21, (2021); Du M, Hu H, Zhang L, Et al., China county based COPD screening and cost-effectiveness analysis, Ann Palliat Med, 10, 4, pp. 4652-4660, (2021); Duffy SP, Criner GJ., Chronic Obstructive Pulmonary Disease: evaluation and Management, Med Clin North Am, 103, 3, pp. 453-461, (2019); Gove K, Wilkinson T, Jack S, Ostridge K, Thompson B, Conway J., Systematic review of evidence for relationships between physiological and CT indices of small airways and clinical outcomes in COPD, Respir Med, 139, pp. 117-125, (2018); Hoffman EA, Lynch DA, Barr RG, van Beek EJ, Parraga G., IWPFI Investigators. 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Diaz AA, Hardin ME, Come CE, Et al., Childhood-onset asthma in smokers. association between CT measures of airway size, lung function, and chronic airflow obstruction, Ann Am Thorac Soc, 11, 9, pp. 1371-1378, (2014); Zhang Y, Xia R, Lv M, Et al., Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease, Front Immunol, 13, (2022); Mekov E, Miravitlles M, Petkov R., Artificial intelligence and machine learning in respiratory medicine, Expert Rev Respir Med, 14, 6, pp. 559-564, (2020); Hussain A, Choi HE, Kim HJ, Aich S, Saqlain M, Kim HC., Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques, Diagnostics (Basel), 11, 5, (2021); Li Z, Liu L, Zhang Z, Et al., A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD, Acad Radiol, 29, 5, pp. 663-673, (2022); Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, (2023); Madani A, Zanen J, de Maertelaer V, Gevenois PA., Pulmonary emphysema: objective quantification at multi-detector row CT--comparison with macroscopic and microscopic morphometry, Radiology, 238, 3, pp. 1036-1043, (2006); Nambu A, Zach J, Kim SS, Et al., Significance of Low-Attenuation Cluster Analysis on Quantitative CT in the Evaluation of Chronic Obstructive Pulmonary Disease, Korean J Radiol, 19, 1, pp. 139-146, (2018); Angelis N, Porpodis K, Zarogoulidis P, Et al., Airway inflammation in chronic obstructive pulmonary disease, J Thorac Dis, 6, pp. S167-S172, (2014); Fahy JV, Dickey BF., Airway mucus function and dysfunction, N Engl J Med, 363, 23, pp. 2233-2247, (2010); Yang C, Zeng HH, Du YJ, Huang J, Zhang QY, Lin K., Correlation of Luminal Mucus Score in Large Airways with Lung Function and Quality of Life in Severe Acute Exacerbation of COPD: a Cross-Sectional Study, Int J Chron Obstruct Pulmon Dis, 16, pp. 1449-1459, (2021); Dupin I, Thumerel M, Maurat E, Et al., Fibrocyte accumulation in the airway walls of COPD patients, Eur Respir J, 54, 3, (2019); DeMeo DL., Sex and Gender Omic Biomarkers in Men and Women With COPD: considerations for Precision Medicine, Chest, 160, 1, pp. 104-113, (2021); Ntritsos G, Franek J, Belbasis L, Et al., Gender-specific estimates of COPD prevalence: a systematic review and meta-analysis, Int J Chron Obstruct Pulmon Dis, 13, pp. 1507-1514, (2018); Yang IA, Jenkins CR, Salvi SS., Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment, Lancet Respir Med, 10, 5, pp. 497-511, (2022); Vila M, Faner R, Agusti A., Beyond the COPD-tobacco binomium: new opportunities for the prevention and early treatment of the disease. Más alla del binomio EPOC-tabaco: nuevas oportunidades para la prevención y tratamiento precoz de la enfermedad, Med Clin, 159, 1, pp. 33-39, (2022); Moslemi A, Makimoto K, Tan WC, Et al., Quantitative CT lung imaging and machine learning improves prediction of emergency room visits and hospitalizations in COPD, Acad. Radiol, 30, 4, pp. 707-716, (2023); Crisafulli E, Pisi R, Aiello M, Et al., Prevalence of Small-Airway Dysfunction among COPD Patients with Different GOLD Stages and Its Role in the Impact of Disease, Respiration, 93, 1, pp. 32-41, (2017)","C. Piao; Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, First Floor, No. 64, Qishan West Road, Huanggu District, Liaoning, China; email: doctor_pch@163.com","","Dove Medical Press Ltd","","","","","","11769106","","","38162987","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85180860262"
"Sharma M.; Kirby M.; McCormack D.G.; Parraga G.","Sharma, Maksym (57222555709); Kirby, Miranda (35174507500); McCormack, David G. (7102878837); Parraga, Grace (14023130000)","57222555709; 35174507500; 7102878837; 14023130000","Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity","2024","Academic Radiology","31","6","","2567","2578","11","2","10.1016/j.acra.2023.11.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181232002&doi=10.1016%2fj.acra.2023.11.022&partnerID=40&md5=18e6866e28d74dc7f0ee0675cbaa368c","Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada; Department of Medical Biophysics, Western University, London, Canada; Department of Physics, Toronto Metropolitan University, Toronto, Canada; Division of Respirology, Department of Medicine (D.G.M., G.P.); School of Biomedical Engineering, Western University, London, Canada","Sharma M., Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada, Department of Medical Biophysics, Western University, London, Canada; Kirby M., Department of Physics, Toronto Metropolitan University, Toronto, Canada; McCormack D.G., Division of Respirology, Department of Medicine (D.G.M., G.P.); Parraga G., Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada, Department of Medical Biophysics, Western University, London, Canada, Division of Respirology, Department of Medicine (D.G.M., G.P.), School of Biomedical Engineering, Western University, London, Canada","Rationale and Objectives: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO ≥ 75%pred and DLCO<75%pred). Materials and Methods: In this retrospective study, 71 ex-smokers (50–85 yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. Results: Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < −950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC = 0.81), which correlated with DLCO (ρ = −0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = −0.25, p = .02). Conclusion: In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements. © 2024 The Association of University Radiologists","Computed Tomography; Diffusing Capacity of the Lungs for Carbon Monoxide; Ex-smokers; Machine-Learning; Texture Analysis","Aged; Aged, 80 and over; Female; Humans; Machine Learning; Magnetic Resonance Imaging; Male; Middle Aged; Pulmonary Diffusing Capacity; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Retrospective Studies; Sensitivity and Specificity; Spirometry; Tomography, X-Ray Computed; aged; apparent diffusion coefficient; Article; chronic obstructive lung disease; cohort analysis; computer assisted tomography; controlled study; demographics; diffusion weighted imaging; emphysema; ex-smoker; feature selection; female; human; lung function; machine learning; major clinical study; male; nuclear magnetic resonance imaging; plethysmography; principal component analysis; receiver operating characteristic; retrospective study; sensitivity and specificity; six minute walk test; spirometry; total lung capacity; diagnostic imaging; lung diffusion capacity; lung emphysema; middle aged; pathophysiology; procedures; very elderly; x-ray computed tomography","","","","","Tier 1 Canada Research Chair; Tier 2 Canada Research Chair; Canadian Institutes of Health Research, IRSC; Natural Sciences and Engineering Research Council of Canada, NSERC","MS is supported by the Natural Sciences and Engineering Council of Canada (NSERC) doctoral scholarship. MK is supported by the Parker B. Francis Fellowship Program, NSERC, and holds a Tier 2 Canada Research Chair. GP is supported by NSERC, the Canadian Institutes of Health Research (CIHR), and holds a Tier 1 Canada Research Chair. ","Vestbo J., Hurd S.S., Agusti A.G., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am J Respir Crit Care Med, 187, pp. 347-365, (2013); Kirby M., Owrangi A., Svenningsen S., Et al., On the role of abnormal DL(CO) in ex-smokers without airflow limitation: symptoms, exercise capacity and hyperpolarised helium-3 MRI, Thorax, 68, pp. 752-759, (2013); Murias J.M., Zavorsky G.S., Short-term variability of nitric oxide diffusing capacity and its components, Respir Physiol Neurobiol, 157, pp. 316-325, (2007); Macintyre N., Crapo R.O., Viegi G., Et al., Standardisation of the single-breath determination of carbon monoxide uptake in the lung, Eur Respir J, 26, pp. 720-735, (2005); Diaz S., Casselbrant I., Piitulainen E., Et al., Validity of apparent diffusion coefficient hyperpolarized 3He-MRI using MSCT and pulmonary function tests as references, Eur J Radiol, 71, pp. 257-263, (2009); Casanova C., de Torres J.P., Aguirre-Jaime A., Et al., The progression of chronic obstructive pulmonary disease is heterogeneous: the experience of the BODE cohort, Am J Respir Crit Care Med, 184, pp. 1015-1021, (2011); Fain S.B., Panth S.R., Evans M.D., Et al., Early emphysematous changes in asymptomatic smokers: detection with 3He MR imaging, Radiology, 239, pp. 875-883, (2006); Yablonskiy D.A., Sukstanskii A.L., Quirk J.D., Diffusion lung imaging with hyperpolarized gas MRI, NMR in biomedicine, 30, (2017); Gietema H.A., Muller N.L., Fauerbach P.V.N., Et al., Quantifying the extent of emphysema: factors associated with radiologists’ estimations and quantitative indices of emphysema severity using the ECLIPSE cohort, Acad Radiol, 18, pp. 661-671, (2011); Lynch D.A., Moore C.M., Wilson C., Et al., CT-based visual classification of emphysema: association with mortality in the COPDGene study, Radiology, 288, pp. 859-866, (2018); Lubner M.G., Smith A.D., Sandrasegaran K., Et al., CT texture analysis: definitions, applications, biologic correlates, and challenges, Radiographics, 37, pp. 1483-1503, (2017); van Griethuysen J.J.M., Fedorov A., Parmar C., Et al., Computational radiomics system to decode the radiographic phenotype, Cancer Res, 77, pp. e104-e107, (2017); Thawani R., McLane M., Beig N., Et al., Radiomics and radiogenomics in lung cancer: a review for the clinician, Lung Cancer, 115, pp. 34-41, (2018); Chaudhary M.F.A., Hoffman E.A., Comellas A.P., Et al.; Makimoto K., Hogg J.C., Bourbeau J., Tan W.C., Kirby M., CT imaging with machine learning for predicting progression to COPD in individuals at risk, Chest, 164, pp. 1139-1149, (2023); Li Z., Liu L., Zhang Z., Et al., A novel CT-based radiomics features analysis for identification and severity staging of COPD, Acad Radiol, 29, pp. 663-673, (2022); Sorensen L., Nielsen M., Petersen J., Pedersen J.H., Dirksen A., Bruijne M., Chronic obstructive pulmonary disease quantification using CT texture analysis and densitometry: results from the danish lung cancer screening trial, Am J Roentgenol, 214, pp. 1269-1279, (2020); Kirby M., Mathew L., Wheatley A., Santyr G.E., McCormack D.G., Parraga G., Chronic obstructive pulmonary disease: longitudinal hyperpolarized (3)He MR imaging, Radiology, 256, pp. 280-289, (2010); Kirby M., Pike D., McCormack D.G., Et al., Longitudinal computed tomography and magnetic resonance imaging of COPD: thoracic imaging network of Canada (TINCan) study objectives, Chronic Obstr Pulm Dis, 1, pp. 200-211, (2014); Miller M.R., Hankinson J., Brusasco V., Et al., Standardisation of spirometry, Eur Respir J, 26, pp. 319-338, (2005); Jones P.W., Quirk F.H., Baveystock C.M., Et al., A self-complete measure of health status for chronic airflow limitation. 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Parraga; Robarts Research Institute, Western University, London, 1151 Richmond St N, N6A 5B7, Canada; email: gparraga@uwo.ca","","Elsevier Inc.","","","","","","10766332","","ARADF","38161089","English","Acad. Radiol.","Article","Final","","Scopus","2-s2.0-85181232002"
"Karla R.; Yalavarthi R.","Karla, Raghuram (59337148500); Yalavarthi, Radhika (56035933300)","59337148500; 56035933300","A Hybrid RNN-based Deep Learning Model for Lung Cancer and COPD Detection","2024","Engineering, Technology and Applied Science Research","14","5","","16847","16853","6","2","10.48084/etasr.8181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207510062&doi=10.48084%2fetasr.8181&partnerID=40&md5=f119217ad639b374d9e79b21d193b70a","Department of CSE, GST, GITAM, Visakhapatnam, India","Karla R., Department of CSE, GST, GITAM, Visakhapatnam, India; Yalavarthi R., Department of CSE, GST, GITAM, Visakhapatnam, India","In the last ten years, lung cancer and chronic pulmonary diseases have become prominent respiratory diseases that require significant attention. This increase in prominence underscores their widespread impact on public health and the urgent need for better understanding, detection, and management strategies. Accurate identification of lung cancer and Chronic Obstructive Pulmonary Disease (COPD) is crucial for preserving human life. Accurate differentiation between the two disorders and the administration of the necessary treatment are very important. This study focuses on effectively discriminating between two of the deadliest chest diseases using chest X-ray images. Recurrent neural networks help to classify diseases accurately by improving feature extraction from radiographs. The proposed algorithm performs more effectively when analyzing chest X-ray image datasets showing alterations in a patient's chest, including the development of tiny lobes or thicker capillaries in the respiratory system among other details, compared to standard lung imaging. © by the authors.","arterial infection; artificial intelligence; lobes; pulmonology; smoking","","","","","","","","Moitra D., Mandal R. Kr., Automated AJCC (7th edition) staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN), Health Information Science and Systems, 7, 1, (2019); Tejaswini C., Nagabushanam P., Rajasegaran P., Johnson P. R., Radha S., CNN Architecture for Lung Cancer Detection, 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), pp. 346-350, (2022); Asuntha A., Srinivasan A., Deep learning for lung Cancer detection and classification, Multimedia Tools and Applications, 79, 11, pp. 7731-7762, (2020); Nazir I., ul Haq I., AlQahtani S. A., Jadoon M. M., Dahshan M., Machine Learning-Based Lung Cancer Detection Using Multiview Image Registration and Fusion, Journal of Sensors, 2023, 1, (2023); Chabon J. J., Et al., Integrating genomic features for non-invasive early lung cancer detection, Nature, 580, 7802, pp. 245-251, (2020); Radhika P. R., Nair R. A.S., Veena G., A Comparative Study of Lung Cancer Detection using Machine Learning Algorithms, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-4, (2019); Heuvelmans M. A., Et al., Lung cancer prediction by Deep Learning to identify benign lung nodules, Lung Cancer, 154, pp. 1-4, (2021); Kirienko M., Et al., Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer, European Journal of Nuclear Medicine and Molecular Imaging, 48, 11, pp. 3643-3655, (2021); Mhaske D., Rajeswari K., Tekade R., Deep Learning Algorithm for Classification and Prediction of Lung Cancer using CT Scan Images, 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), pp. 1-5, (2019); Nandipati B. L., Devarakonda N., Hybrid deep learning model for detection and classification of lung cancer fusion images using MCNet, Journal of Intelligent & Fuzzy Systems, 45, 2, pp. 2235-2252, (2023); Agazzi G. M., Et al., CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer, La radiologia medica, 126, 6, pp. 786-794, (2021); Mullins K. E., Seneviratne C., Shetty A. C., Jiang F., Christenson R., Stass S., Proof of concept: Detection of cell free RNA from EDTA plasma in patients with lung cancer and non-cancer patients, Clinical Biochemistry, 118, (2023); Wang W., Charkborty G., Automatic prognosis of lung cancer using heterogeneous deep learning models for nodule detection and eliciting its morphological features, Applied Intelligence, 51, 4, pp. 2471-2484, (2021); Capizzi G., Sciuto G. L., Napoli C., Polap D., Wozniak M., Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning, IEEE Transactions on Fuzzy Systems, 28, 6, pp. 1178-1189, (2020); Cherukuri N., Bethapudi N. R., Thotakura V. S. K., Chitturi P., Basha C. Z., Mummidi R. M., Deep Learning for Lung Cancer Prediction using NSCLS patients CT Information, 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 325-330, (2021); Thakur S. K., Singh D. P., Choudhary J., Lung cancer identification: a review on detection and classification, Cancer and Metastasis Reviews, 39, 3, pp. 989-998, (2020); Chen C. L., Et al., An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning, Nature Communications, 12, 1, (2021); Shakeel P. M., Burhanuddin M. A., Desa M. I., Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier, Neural Computing and Applications, 34, 12, pp. 9579-9592, (2022); Han Y., Et al., Histologic subtype classification of non-small cell lung cancer using PET/CT images, European Journal of Nuclear Medicine and Molecular Imaging, 48, 2, pp. 350-360, (2021); Heuvelmans M. A., Et al., Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography: Current Evidence and Technical Considerations, Journal of Thoracic Imaging, 34, 3, (2019); Lowe K. E., Et al., COPDGene® 2019: Redefining the Diagnosis of Chronic Obstructive Pulmonary Disease, Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, 6, 5, pp. 384-399; Kanavati F., Et al., A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images, Scientific Reports, 11, 1, (2021); Albahli S., Ahmad Hassan Yar G. N., AI-driven deep convolutional neural networks for chest X-ray pathology identification, Journal of X-Ray Science and Technology, 30, 2, pp. 365-376, (2022); Mahima S., Kezia S., Grace Mary Kanaga E., Deep Learning-Based Lung Cancer Detection, Disruptive Technologies for Big Data and Cloud Applications, pp. 633-641, (2022); Xu Y., Et al., Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging, Clinical Cancer Research, 25, 11, pp. 3266-3275, (2019); Talukder M. A., Lung X-Ray Image, Mendeley Data, (2023); Abiyev R. H., Ma'aitah M. K. S., Deep Convolutional Neural Networks for Chest Diseases Detection, Journal of Healthcare Engineering, 2018, pp. 1-11, (2018); Tandon R., Agrawal S., Chang A., Band S. S., VCNet: Hybrid Deep Learning Model for Detection and Classification of Lung Carcinoma Using Chest Radiographs, Frontiers in Public Health, 10, (2022); Ait Nasser A., Akhloufi M. A., A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography, Diagnostics, 13, 1, (2023)","R. Karla; Department of CSE, GST, GITAM, Visakhapatnam, India; email: rkarla@gitam.edu","","Dr D. Pylarinos","","","","","","22414487","","","","English","Eng. Technol. Appl. Sci. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85207510062"
"Méndez Barrera J.A.; Rocha Guzmán S.; Hierro Cascajares E.; Garabedian E.K.; Fuleihan R.L.; Sullivan K.E.; Lugo Reyes S.O.","Méndez Barrera, Jose Alfredo (58119456500); Rocha Guzmán, Samuel (58569944200); Hierro Cascajares, Elisa (58569809500); Garabedian, Elizabeth K. (7004667541); Fuleihan, Ramsay L. (7003440477); Sullivan, Kathleen E. (7402381137); Lugo Reyes, Saul O. (23051141600)","58119456500; 58569944200; 58569809500; 7004667541; 7003440477; 7402381137; 23051141600","Who's your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry","2023","Clinical Immunology","255","","109759","","","","3","10.1016/j.clim.2023.109759","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170418141&doi=10.1016%2fj.clim.2023.109759&partnerID=40&md5=a3bc53c599c9df5b8ea6133640eb3387","Data Science Department, Autonomous Technological Institute of Mexico, Mexico City, Mexico; Immune deficiencies Lab, National Institute of Pediatrics, Secretariat of Health, Mexico City, Mexico; National Institutes of Health, Bethesda, MD, United States; Division of Pediatric Allergy, Immunology and Rheumatology at Columbia University, New York City, NY, United States; Children's Hospital of Philadelphia, PA, United States","Méndez Barrera J.A., Data Science Department, Autonomous Technological Institute of Mexico, Mexico City, Mexico; Rocha Guzmán S., Data Science Department, Autonomous Technological Institute of Mexico, Mexico City, Mexico; Hierro Cascajares E., Immune deficiencies Lab, National Institute of Pediatrics, Secretariat of Health, Mexico City, Mexico; Garabedian E.K., National Institutes of Health, Bethesda, MD, United States; Fuleihan R.L., Division of Pediatric Allergy, Immunology and Rheumatology at Columbia University, New York City, NY, United States; Sullivan K.E., Children's Hospital of Philadelphia, PA, United States; Lugo Reyes S.O., Immune deficiencies Lab, National Institute of Pediatrics, Secretariat of Health, Mexico City, Mexico","Purpose: There are currently more than 480 primary immune deficiency (PID) diseases and about 7000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. In a proof-of-concept data mining study, we aimed to predict PID diagnoses using a supervised machine learning algorithm based on classification tree boosting. Methods: Through a data query at the USIDNET registry we obtained a database of 2396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 286 features and all 12 diagnoses to include in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. After a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight to correct for imbalanced classification. Results: The twelve PID diagnoses were CVID (1098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, PID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46–0.87, Gini 0.07–0.75, and LogLoss 0.09–8.55. Discussion: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses. © 2023 Elsevier Inc.","Classification; Data mining; Diagnosis prediction; Extreme gradient boosting; Inborn errors of immunity; Machine learning; Primary immune deficiencies; Rare diseases; Registry","Data Mining; Diagnosis, Differential; Humans; Machine Learning; Primary Immunodeficiency Diseases; Wiskott-Aldrich Syndrome; immunoglobulin E; abscess; agammaglobulinemia; Article; asthma; autoimmunity; chronic granulomatous disease; classification; clinical feature; common variable immunodeficiency; comparative study; complement deficiency; congenital heart disease; controlled study; data base; data mining; developmental delay; diagnostic accuracy; diagnostic test accuracy study; differential diagnosis; DiGeorge syndrome; ectodermal dysplasia; human; humoral immune deficiency; hyper IgM syndrome; immune deficiency; immunoglobulin deficiency; laboratory; learning algorithm; leukocyte adhesion deficiency; lymphocytopenia; major clinical study; patient registry; prediction; predictive model; predictive value; primary immune deficiency not otherwise classified; proof of concept; receiver operating characteristic; severe combined immunodeficiency; specific antibody deficiency; supervised machine learning; upper respiratory tract infection; Wiskott Aldrich syndrome; data mining; differential diagnosis; immune deficiency; machine learning; Wiskott Aldrich syndrome","","immunoglobulin E, 37341-29-0","","","National Institute of Allergy and Infectious Diseases, NIAID; Immune Deficiency Foundation, IDF, (U24AI86837); Immune Deficiency Foundation, IDF; US Immunodeficiency Network, USIDNET; Consejo Nacional de Ciencia y Tecnología, CONACYT, (2013/049); Consejo Nacional de Ciencia y Tecnología, CONACYT","Funding text 1: The U.S. Immunodeficiency Network (USIDNET), a program of the Immune Deficiency Foundation (IDF), is supported by a cooperative agreement, U24AI86837, from the National Institute of Allergy and Infectious Diseases (NIAID). The USIDNET Consortium is composed of over a hundred clinicians who have contributed individually with one to hundreds of patient registrations and their features, available at the registry. The Mexican National council of science and technology (CONACYT) has funded the larger project 2013/049 as part of the Innovation Stimulus Program (PEI). No funding was received for this manuscript.; Funding text 2: The U.S. Immunodeficiency Network (USIDNET) , a program of the Immune Deficiency Foundation (IDF), is supported by a cooperative agreement, U24AI86837, from the National Institute of Allergy and Infectious Diseases (NIAID) . The USIDNET Consortium is composed of over a hundred clinicians who have contributed individually with one to hundreds of patient registrations and their features, available at the registry. The Mexican National council of science and technology (CONACYT) has funded the larger project 2013/049 as part of the Innovation Stimulus Program (PEI). ","Abolhassani H., Delavari S., Landegren N., Shokri S., Bastard P., Du L., Et al., Genetic and immunological evaluation of children with inborn errors of immunity and severe or critical COVID-19, J. Allergy Clin. Immunol., 150, 5, pp. 1059-1073, (2022); Tan T.Y., Dillon O.J., Stark Z., Schofield D., Alam K., Shrestha R., Et al., Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions, JAMA Pediatr., 171, 9, pp. 855-862, (2017); Makary M.A., Daniel M., Medical error-the third leading cause of death in the US, BMJ., 353, (2016); Itan Y., Casanova J.-L., Novel primary immunodeficiency candidate genes predicted by the human gene connectome, Front. Immunol., 6, April, pp. 1-8, (2015); Segal M., How doctors think, and how software can help avoid cognitive errors in diagnosis, Acta Paediatr., 96, 12, pp. 1720-1722, (2007); Berman J.J., Rare Diseases and Orphan Drugs. First Edit, (2014); Rider N.L., Cahill G., Motazedi T., Wei L., Kurian A., Noroski L.M., Et al., PI prob: a risk prediction and clinical guidance system for evaluating patients with recurrent infections, PLoS One, 16, 2 February, pp. 1-15, (2021); Rider N.L., Miao D., Dodds M., Modell V., Modell F., Quinn J., Et al., Calculation of a primary immunodeficiency “risk vital sign” via population-wide analysis of claims data to aid in clinical decision support, Front. Pediatr., 7, (2019); Rider N.L., Coffey M., Kurian A., Quinn J., Orange J.S., Modell V., Et al., A validated artificial intelligence-based pipeline for population-wide primary immunodeficiency screening, J. Allergy Clin. Immunol., 151, 1, pp. 272-279, (2023); Takao M.M.V., Carvalho L.S.F., Silva P.G.P., Pereira M.M., Viana A.C., Da Silva M.T.N., Et al., Artificial intelligence in allergy and immunology: comparing risk prediction models to help screen inborn errors of immunity, Int. Arch. Allergy Immunol., 183, 11, pp. 1226-1230, (2022); Tutorial: XGBoost en Python. XGBoost (Extreme Gradient Boosting), es… | by Juan Bosco Mendoza Vega | Medium [Internet], (2021); SHAP for explainable machine learning [Internet], (2021); Chen T., Guestrin C., XGBoost: a scalable tree boosting system, Proc ACM SIGKDD Int Conf Knowl Discov Data Min, pp. 785-794, (2016); Using SHAP Values to Explain How Your Machine Learning Model Works | by Vinícius Trevisan | Towards Data Science [Internet], (2022); Lundberg S.M., Allen P.G., Lee S.-I., A Unified Approach to Interpreting Model Predictions, (2022); SHAP Values Explained Exactly How You Wished Someone Explained to You | by Samuele Mazzanti | Towards Data Science [Internet], (2022); Murata C., Ramirez A., Ramirez G., Cruz A., Morales J., Lugo-Reyes S., Análisis discriminante para predecir el diagnóstico clínico de inmunodeficiencias primarias: reporte preliminar, Rev. Alerg. México, 62, pp. 125-133, (2015); Samarghitean C., Iltanen K., Juhola M., Vihinen M., Rugg G., Machine learning methods for primary immunodeficiency diagnosis, 17th Biennial Meeting of the European Society for Immunodeficiencies, Barcelona, (2016); Samarghitean C., PIDexpert-Decision Support System for Primary Immunodeficiencies, (2008); Samarghitean C., Ortutay C., Vihinen M., Systematic classification of primary immunodeficiencies based on clinical, pathological, and laboratory parameters, J. Immunol., 183, 11, pp. 7569-7575, (2009); Mayampurath A., Ajith A., Anderson-Smits C., Chang S.-C., Brouwer E., Johnson J., Et al., Early diagnosis of primary immunodeficiency disease using clinical data and machine learning, J. Allergy. Clin. Immunol. Pract., 10, 11, pp. 3002-3007, (2022); XGBoost or Logistic Regression model for Diabetes Prediction | by Eason | Medium [Internet], (2023); Riches N., Panagioti M., Alam R., Cheraghi-Sohi S., Campbell S., Esmail A., Et al., The Effectiveness of Electronic Differential Diagnoses (DDX) generators: a systematic review and meta-analysis, PLoS One, 11, 3, (2016); Bond W.F., Schwartz L.M., Weaver K.R., Levick D., Giuliano M., Graber M.L., Differential diagnosis generators: an evaluation of currently available computer programs, J. Gen. Intern. Med., 27, 2, pp. 213-219, (2012); Dragusin R., Petcu P., Lioma C., Larsen B., Jorgensen H.L., Cox I.J., Et al., FindZebra: a search engine for rare diseases, Int. J. Med. Inform., 82, 6, pp. 528-538, (2013); Richens J.G., Lee C.M., Johri S., Improving the accuracy of medical diagnosis with causal machine learning, Nat. Commun., 11, 1, pp. 1-9, (2020); Liang H., Tsui B.Y., Ni H., Valentim C.C.S., Baxter S.L., Liu G., Et al., Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence, Nat. Med., 25, 3, pp. 433-438, (2019); Gao L., Ding Y., Disease prediction via Bayesian hyperparameter optimization and ensemble learning, BMC Res. Notes, 13, 1, (2020)","S.O. Lugo Reyes; Immune deficiencies Lab, National Institute of Pediatrics, Secretariat of Health, Mexico City, Mexico; email: dr.lugo.reyes@gmail.com","","Academic Press Inc.","","","","","","15216616","","CLIIF","37678719","English","Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85170418141"
"Almeida S.D.; Norajitra T.; Lüth C.T.; Wald T.; Weru V.; Nolden M.; Jäger P.F.; von Stackelberg O.; Heußel C.P.; Weinheimer O.; Biederer J.; Kauczor H.-U.; Maier-Hein K.","Almeida, Silvia D. (57213416077); Norajitra, Tobias (56198077000); Lüth, Carsten T. (57219524655); Wald, Tassilo (57223727513); Weru, Vivienn (57290547000); Nolden, Marco (55908659000); Jäger, Paul F. (57201075948); von Stackelberg, Oyunbileg (56610304000); Heußel, Claus Peter (7004889910); Weinheimer, Oliver (9535317400); Biederer, Jürgen (7003612651); Kauczor, Hans-Ulrich (7102275418); Maier-Hein, Klaus (55647018100)","57213416077; 56198077000; 57219524655; 57223727513; 57290547000; 55908659000; 57201075948; 56610304000; 7004889910; 9535317400; 7003612651; 7102275418; 55647018100","Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography","2024","Frontiers in Medicine","11","","1360706","","","","2","10.3389/fmed.2024.1360706","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188065870&doi=10.3389%2ffmed.2024.1360706&partnerID=40&md5=28d1d9a7880e1ecf396eaab35eec951a","Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ, Heidelberg University Medical Center, Heidelberg, Germany; Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; Faculty of Medicine, University of Latvia, Riga, Latvia; Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany","Almeida S.D., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Medical Faculty, Heidelberg University, Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ, Heidelberg University Medical Center, Heidelberg, Germany; Norajitra T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany; Lüth C.T., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Wald T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Weru V., Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Nolden M., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Jäger P.F., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; von Stackelberg O., Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Heußel C.P., Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany, Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; Weinheimer O., Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Biederer J., Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany, Faculty of Medicine, University of Latvia, Riga, Latvia, Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany; Kauczor H.-U., Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany; Maier-Hein K., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ, Heidelberg University Medical Center, Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany","Background: Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods: Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1–4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results: Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41–0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland–Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion: Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct – yet complementary – strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes. Copyright © 2024 Almeida, Norajitra, Lüth, Wald, Weru, Nolden, Jäger, von Stackelberg, Heußel, Weinheimer, Biederer, Kauczor and Maier-Hein.","airway disease; anomaly detection; artificial intelligence; chronic obstructive pulmonary disease; computed tomography; emphysema; GOLD","adult; Article; bootstrapping; chronic obstructive lung disease; cluster analysis; cohort analysis; computer assisted tomography; controlled study; current smoker; deep learning; demographics; emphysema; ex-smoker; feature extraction; female; forced expiratory volume; forced vital capacity; functional residual capacity; genetic epidemiology; genetic heterogeneity; human; image processing; image segmentation; k means clustering; lung function test; lung parenchyma; major clinical study; male; never smoker; nonlinear dimensionality reduction; outlier detection; parametric response mapping; phenotype; principal component analysis; qualitative analysis; quantitative analysis; questionnaire; retrospective study; self supervised anomaly detection method; six minute walk test; small airway disease; St. George Respiratory Questionnaire; t distributed stochastic neighbor embedding; thorax radiography; total lung capacity; treatment outcome; voxel based morphometry","","","","","","","Chen S., Kuhn M., Prettner K., Yu F., Yang T., Barnighausen T., Et al., The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020–50: a health-augmented macroeconomic modelling study, Lancet Glob Health, 11, pp. e1183-e1193, (2023); Halpin D.M.G., Criner G.J., Papi A., Singh D., Anzueto A., Martinez F.J., Et al., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 GOLD science committee report on COVID-19 and chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 203, pp. 24-36, (2021); Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Et al., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, pp. 447-458, (2022); Bhatt S.P., Balte P.P., Schwartz J.E., Cassano P.A., Couper D., Jacobs D.R., Et al., Discriminative accuracy of FEV1:FVC thresholds for COPD-related hospitalization and mortality, JAMA, 321, pp. 2438-2447, (2019); Diab N., Gershon A.S., Sin D.D., Tan W.C., Bourbeau J., Boulet L.P., Et al., Underdiagnosis and Overdiagnosis of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 198, pp. 1130-1139, (2018); Gevenois P.A., de Vuyst P., de Maertelaer V., Zanen J., Jacobovitz D., Cosio M.G., Et al., Comparison of computed density and microscopic morphometry in pulmonary emphysema, Am J Respir Crit Care Med, 154, pp. 187-192, (1996); Schroeder J.D., McKenzie A.S., Zach J.A., Wilson C.G., Curran-Everett D., Stinson D.S., Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and Airways in Subjects with and without Chronic Obstructive Pulmonary Disease, Am J Roentgenol, 201, pp. W460-W470, (2013); Bhatt S.P., Washko G.R., Hoffman E.A., Newell J.D., Bodduluri S., Diaz A.A., Et al., Imaging advances in chronic obstructive pulmonary disease. Insights from the genetic epidemiology of chronic obstructive pulmonary disease (COPDGene) study, Am J Respir Crit Care Med, 199, pp. 286-301, (2019); Galban C.J., Han M.K., Boes J.L., Chughtai K.A., Meyer C.R., Johnson T.D., Et al., Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression, Nat Med, 18, pp. 1711-1715, (2012); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Onieva Onieva J., Rahaghi F.N., Ross J.C., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, pp. 193-203, (2018); Tang L.Y.W., Coxson H.O., Lam S., Leipsic J., Tam R.C., Sin D.D., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, The Lancet Digital Health, 2, pp. e259-e267, (2020); Singla S., Gong M., Riley C., Sciurba F., Batmanghelich K., Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach, Med Phys, 48, pp. 1168-1181, (2021); Sun J., Liao X., Yan Y., Zhang X., Sun J., Tan W., Et al., Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach, Eur Radiol, 32, pp. 5319-5329, (2022); Park H., Yun J., Lee S.M., Hwang H.J., Seo J.B., Jung Y.J., Et al., Deep learning–based approach to predict pulmonary function at chest CT, Radiology, 307, (2023); Almeida S.D., Luth C.T., Norajitra T., Wald T., Nolden M., Jager P.F., Et al., cOOpD: reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations, Medical image computing and computer assisted intervention – MICCAI 2023, (2023); Li F., Choi J., Zou C., Newell J.D., Comellas A.P., Lee C.H., Et al., Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images, Sci Rep, 11, (2021); Almeida S.D., Norajitra T., Luth C.T., Wald T., Weru V., Nolden M., Et al., Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT, Eur Radiol, (2023); van der Maaten L., Hinton G., Visualizing data using t-SNE, JMLR, 9, pp. 2579-2605, (2008); Policar P.G., Strazar M., Zupan B., openTSNE: A modular Python library for t-SNE dimensionality reduction and embedding, bioRxiv, (2019); Lorenzo-Seva U., Horn’s parallel analysis for selecting the number of dimensions in correspondence analysis, Methodology, 7, pp. 96-102, (2011); Brock G., Pihur V., Datta S., Datta S., clValid: an R package for cluster validation, J Stat Soft, 25, (2008); Schober P., Boer C., Schwarte L.A., Correlation coefficients: appropriate use and interpretation, Anesth Analg, 126, pp. 1763-1768, (2018); Diedenhofen B., Musch J., Cocor: a comprehensive solution for the statistical comparison of correlations, PLoS One, 10, (2015); Zou G.Y., Toward using confidence intervals to compare correlations, Psychol Methods, 12, pp. 399-413, (2007); Bland J.M., Altman D.G., Measuring agreement in method comparison studies, Stat Methods Med Res, 8, pp. 135-160, (1999); Cohen J., Statistical power analysis for the behavioral sciences, (1977); Bhatt S.P., Soler X., Wang X., Murray S., Anzueto A.R., Beaty T.H., Et al., Association between functional small airway disease and FEV <sub>1</sub> decline in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 194, pp. 178-184, (2016); Boes J.L., Hoff B.A., Bule M., Johnson T.D., Rehemtulla A., Chamberlain R., Et al., Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD study (SPIROMICS), Acad Radiol, 22, pp. 186-194, (2015); McDonough J.E., Yuan R., Suzuki M., Seyednejad N., Elliott W.M., Sanchez P.G., Et al., Small-airway obstruction and emphysema in chronic obstructive pulmonary disease, N Engl J Med, 365, pp. 1567-1575, (2011); Hogg J.C., Macklem P.T., Thurlbeck W.M., Site and nature of airway obstruction in chronic obstructive lung disease, N Engl J Med, 278, pp. 1355-1360, (1968); Chukowry P.S., Spittle D.A., Turner A., Small airways disease, biomarkers and COPD: where are we?, COPD, 16, pp. 351-365, (2021); Hwang H.J., Seo J.B., Lee S.M., Kim N., Yi J., Lee J.S., Et al., New method for combined quantitative assessment of air-trapping and emphysema on chest computed tomography in chronic obstructive pulmonary disease: comparison with parametric response mapping, Korean J Radiol, 22, pp. 1719-1729, (2021)","S.D. Almeida; Division of Medical Image Computing, German Cancer Research Center, DKFZ, Heidelberg, Germany; email: silvia.diasalmeida@dkfz-heidelberg.de; K. Maier-Hein; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; email: k.maier-hein@dkfz-heidelberg.de","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188065870"
"Bozigar M.; Connolly C.L.; Legler A.; Adams W.G.; Milando C.W.; Reynolds D.B.; Carnes F.; Jimenez R.B.; Peer K.; Vermeer K.; Levy J.I.; Fabian M.P.","Bozigar, Matthew (56480685500); Connolly, Catherine L. (57219431806); Legler, Aaron (56516779000); Adams, William G. (7401724704); Milando, Chad W. (57078973900); Reynolds, David B. (57814761100); Carnes, Fei (57090808700); Jimenez, Raquel B. (55190017400); Peer, Komal (57221802983); Vermeer, Kimberly (55631821900); Levy, Jonathan I. (26643548500); Fabian, Maria Patricia (36086088700)","56480685500; 57219431806; 56516779000; 7401724704; 57078973900; 57814761100; 57090808700; 55190017400; 57221802983; 55631821900; 26643548500; 36086088700","In-home environmental exposures predicted from geospatial characteristics of the built environment and electronic health records of children with asthma","2022","Annals of Epidemiology","73","","","38","47","9","3","10.1016/j.annepidem.2022.06.034","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134687398&doi=10.1016%2fj.annepidem.2022.06.034&partnerID=40&md5=feebe91d68c23f827435d38abbc5cbf1","Department of Environmental Health, Boston University School of Public Health, Boston, MA; Boston Medical Center, One Boston Medical Center Pl, Boston, MA; Department of Pediatrics, Boston Medical Center/Boston University School of Medicine, Boston, MA; Biomedical Informatics Core, Boston University Clinical and Translational Science Institute, Boston University School of Medicine, Boston, MA; Mathematics and Statistics Department, Boston University Arts and Sciences, Boston, MA; Urban Habitat Initiatives Inc., Boston, MA","Bozigar M., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Connolly C.L., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Legler A., Boston Medical Center, One Boston Medical Center Pl, Boston, MA; Adams W.G., Department of Pediatrics, Boston Medical Center/Boston University School of Medicine, Boston, MA, Biomedical Informatics Core, Boston University Clinical and Translational Science Institute, Boston University School of Medicine, Boston, MA; Milando C.W., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Reynolds D.B., Mathematics and Statistics Department, Boston University Arts and Sciences, Boston, MA; Carnes F., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Jimenez R.B., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Peer K., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Vermeer K., Urban Habitat Initiatives Inc., Boston, MA; Levy J.I., Department of Environmental Health, Boston University School of Public Health, Boston, MA; Fabian M.P., Department of Environmental Health, Boston University School of Public Health, Boston, MA","Purpose: Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors. Methods: Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004–2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method. Results: Our models reasonably predicted presence of cockroaches (area under receiver operating curves [AUC] = 0.65), rodents (AUC = 0.64), and bedroom carpeting/rugs (AUC = 0.64), but not mold (AUC = 0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms. Conclusions: We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility. © 2022","Asthma triggers; Electronic health records; Exposure assessment; Housing; Neighborhoods","Air Pollution, Indoor; Animals; Asthma; Built Environment; Cockroaches; Electronic Health Records; Environmental Exposure; Housing; Humans; Mice; Rats; allergen; adolescent; adult; age; Article; building; built environment; census tract; child; cockroach; descriptive research; electronic health record; environmental exposure; female; home environment; household; housing; human; insurance; machine learning; male; prediction; risk factor; safety net hospital; self report; size; social segregation; adverse event; animal; asthma; cockroach; electronic health record; environmental exposure; indoor air pollution; mouse; rat","","","","","Biostatistics and Epidemiology Data Analytics Center; National Science Foundation, NSF, (DGE 1735087, T32 ES014562); National Science Foundation, NSF; National Institutes of Health, NIH; National Institute of Environmental Health Sciences, NIEHS, (R01ES027816); National Institute of Environmental Health Sciences, NIEHS; National Center for Advancing Translational Sciences, NCATS; Indiana Clinical and Translational Sciences Institute, CTSI, (1UL1TR001430); Indiana Clinical and Translational Sciences Institute, CTSI; Boston University, BU; Clinical and Translational Science Institute, Boston University, CTSI","Funding text 1: This work was supported by grant R01 ES027816 from the National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), the National Center for Advancing Translational Sciences (NCATS, NIH) and Boston University Clinical and Translational Science Institute (CTSI) grant 1UL1TR001430, and training grants from National Science Foundation NRT (DGE 1735087) and NIEHS T32 (T32 ES014562). This research was enabled by resources and personnel from the Biostatistics and Epidemiology Data Analytics Center (BEDAC) at Boston University.; Funding text 2: This work was supported by grant R01 ES027816 from the National Institute of Environmental Health Sciences (NIEHS) , National Institutes of Health (NIH), the National Center for Advancing Translational Sciences (NCATS, NIH) and Boston University Clinical and Translational Science Institute (CTSI) grant 1UL1TR001430 , and training grants from National Science Foundation NRT ( DGE 1735087 ) and NIEHS T32 ( T32 ES014562 ). This research was enabled by resources and personnel from the Biostatistics and Epidemiology Data Analytics Center (BEDAC) at Boston University. 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Symp., (2020); Massachusetts master address points, (2019); Land parcel database, (2019); 2010 U.S. census, (2010); 2011-2015 American Community Survey 5-Year estimates, (2015); ESRI Business Analyst, (2020); CRESSH MAP-EHD indices, (2019); Krieger N., Feldman J.M., Waterman P.D., Chen J.T., Coull B.A., Hemenway D., Local residential segregation matters: stronger association of census tract compared to conventional city-level measures with fatal and non-fatal assaults (total and firearm related), using the Index of Concentration at the Extremes (ICE) for Racial, Econ, J Urban Heal, 94, pp. 244-258, (2017); Land parcel database, (2019); Stekhoven D.J., Buhlmann P., Missforest-non-parametric missing value imputation for mixed-type data, Bioinformatics, 28, pp. 112-118, (2012); R: A language and environment for statistical computing, (2021); Van Der Laan M.J., Polley E.C., Hubbard A.E., Super learner, Stat Appl Genet Mol Biol, 6, (2007); Pirracchio R., Petersen M.L., Van Der Laan M., Improving propensity score estimators’ robustness to model misspecification using Super Learner, Am J Epidemiol, 181, pp. 108-119, (2015); Oulhote Y., Coull B., Bind M.-A., Debes F., Nielsen F., Tamayo I., Et al., Joint and independent neurotoxic effects of early life exposures to a chemical mixture, Environ Epidemiol, 3, (2019); Rice M.E., Harris G.T., Comparing effect sizes in follow-up studies: ROC area, Cohen's d, and r, Law Hum Behav, 29, pp. 615-620, (2005); Oulhote Y., Bind M.-A., Coull B., Patel C., Grandjean P., Combining ensemble learning techniques and G-computation to investigate chemical mixtures in environmental epidemiology studies, BioRxiv, (2017); Hernan M.A., Estimating causal effects from epidemiological data, J Epidemiol Community Heal, 60, pp. 578-586, (2006); Robins J., A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect, Math Model, 7, pp. 1393-1512, (1986); Khan S., Bajwa S., Brahmbhatt D., Lovinsky-Desir S., Sheffield P.E., Stingone J.A., Et al., Multi-level socioenvironmental contributors to childhood asthma in New York City: a cluster analysis, J Urban Heal, 98, pp. 700-710, (2021); 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Zha C., Wang C., Buckley B., Yang I., Wang D., Eiden A.L., Pest prevalence and evaluation of community-wide integrated pest management for reducing cockroach infestations and indoor insecticide residues, J Econ Entomol, 111, pp. 795-802, (2018); Kass D., McKelvey W., Carlton E., Hernandez M., Chew G., Nagle S., Effectiveness of an integrated pest management intervention in controlling cockroaches, mice, and allergens in New York City public housing, Environ Health Perspect, 117, pp. 1219-1225, (2009); Keep it pest free, (2021); Stevenson L.A., Gergen P.J., Hoover D.R., Rosenstreich D., Mannino D.M., Matte T.D., Sociodemographic correlates of indoor allergen sensitivity among United States children, J Allergy Clin Immunol, 108, pp. 747-752, (2001); Olmedo O., Goldstein I.F., Acosta L., Divjan A., Rundle A.G., Chew G.L., Et al., Neighborhood differences in exposure and sensitization to cockroach, mouse, dust mite, cat, and dog allergens in New York City, J Allergy Clin Immunol, 128, pp. 284-292, (2011); Chew G.L., Assessment of environmental cockroach allergen exposure, Curr Allergy Asthma Rep, 12, pp. 456-464, (2012); Camacho-Rivera M., Kawachi I., Bennett G.G., Subramanian S.V., Associations of neighborhood concentrated poverty, neighborhood racial/ethnic composition, and indoor allergen exposures: a cross-sectional analysis of Los Angeles households, 2006-2008, J Urban Heal, 91, pp. 661-676, (2014); Rosenfeld L., Rudd R., Chew G.L., Emmons K., Acevedo-Garcia D., Are neighborhood-level characteristics associated with indoor allergens in the household?, J Asthma, 47, pp. 66-75, (2010); Berg J., McConnell R., Milam J., Galvan J., Kotlerman J., Thorne P., Et al., Rodent allergen in Los Angeles inner city homes of children with asthma, J Urban Heal, 85, pp. 52-61, (2008); The carpet primer, (2003); Becher R., Ovrevik J., Schwarze P.E., Nilsen S., Hongslo J.K., Bakke J.V., Do carpets impair indoor air quality and cause adverse health outcomes: a review, Int J Environ Res Public Health, 15, (2018); 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Bozigar; Department of Environmental Health, Boston University School of Public Health, Boston, 715 Albany St., 02118; email: bozigar@bu.edu","","Elsevier Inc.","","","","","","10472797","","ANNPE","35779709","English","Ann. Epidemiol.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85134687398"
"Hua Q.; Chen G.; Yang Y.; Leng S.; Zhao Z.; Bai F.; Hu X.; Qiu L.; Yu Z.; Zhang H.; Shi J.; Dai Q.","Hua, Qifeng (57762254800); Chen, Guoping (57211097118); Yang, Yin (57822097200); Leng, Shaoyi (57196421506); Zhao, Zhenzhen (57742735000); Bai, Feng (57821083500); Hu, Xiaowei (57215415512); Qiu, Liyan (57742735100); Yu, Zhe (57217066690); Zhang, Hongbin (58445820200); Shi, Jiapei (57760881800); Dai, Qi (57210787499)","57762254800; 57211097118; 57822097200; 57196421506; 57742735000; 57821083500; 57215415512; 57742735100; 57217066690; 58445820200; 57760881800; 57210787499","Quantitative Evaluation of Chronic Obstructive Pulmonary Disease and Risk Prediction of Acute Exacerbation by High-Resolution Computed Tomography","2022","Evidence-based Complementary and Alternative Medicine","2022","","6015766","","","","3","10.1155/2022/6015766","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135003899&doi=10.1155%2f2022%2f6015766&partnerID=40&md5=184b6a59da9ed88435df4b11e24ea89d","Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Department of Respiratory Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China","Hua Q., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Chen G., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Yang Y., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Leng S., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Zhao Z., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Bai F., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Hu X., Department of Respiratory Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Qiu L., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Yu Z., Department of Respiratory Medicine, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Zhang H., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Shi J., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; Dai Q., Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China","Objective. It is imperative to popularize the tertiary prevention of chronic obstructive pulmonary disease (COPD) and to improve the diagnosis and treatment. Methods. COPD patients were divided into mild (n = 18), moderate (n = 20), severe (n = 24), and extremely severe (n = 22) groups for performing high-resolution computed tomography (HRCT) and pulmonary function test. Serum procalcitonin (PCT) and high-sensitivity C-reactive protein (hs-CRP) were detected, and the occurrence rate of acute exacerbation COPD (AECOPD) was recorded during a 12-months follow-up period. Results. With an increase in the severity grade, the HRCT indexes, including emphysema index (EI), 1st and 15th percentile of inspiratory attenuation distribution (Perc1 and Perc15), ratio of expiratory/inspiratory mean lung density (MLDex/in) and lung volume (LVex/in), and ratio of the wall thickness to the outer diameter of the lumen (TDR), as well as percentage of the wall area to the total cross-sectional area (WA%) were increased with a decreased change in relative lung volume with attenuation values between-860 and-950 HU (RVC-860to-950) and lumen area (Ai). These were correlated with the ratio of forced expiratory volume in 1 sec (FEV1) over forced vital capacity (FVC) (FEV1/FVC), the percentage of FEV1 the predicted value (FEV1%), and ratio of residual volume to total lung volume (RV/TLC). Body mass index, MLDex/in, FEV1%, FEV1/FVC, and PCT had a predictive value to AECOPD, with the combined AUC of 0.812. Conclusions. HRCT imaging effectively classifies the severity of COPD, which combined with BMI, PFT, and serum PCT can predict the risk of AECOPD.  © 2022 Qifeng Hua et al.","","C reactive protein; procalcitonin; aged; Article; body mass; chronic obstructive lung disease; controlled study; disease exacerbation; disease severity; emphysema; follow up; forced expiratory volume; forced vital capacity; high resolution computer tomography; human; lung function; lung function test; lung volume; major clinical study; prediction; predictive value; quantitative analysis; residual volume; tertiary prevention; thickness; total lung capacity","","C reactive protein, 9007-41-4; procalcitonin, 56645-65-9","","","","","Szalontai K., Gemes N., Furak J., Varga T., Neuperger P., Balog J.A., Puskas L.G., Szebeni G.J., Chronic obstructive pulmonary disease: Epidemiology, biomarkers, and paving the way to lung cancer, Journal of Clinical Medicine, 10, (2021); Lopez A.D., Shibuya K., Rao C., Mathers C.D., Hansell A.L., Held L.S., Schmid V., Buist S., Chronic obstructive pulmonary disease: Current burden and future projections, European Respiratory Journal, 27, 2, pp. 397-412, (2006); Celli B.R., Wedzicha J.A., Update on clinical aspects of chronic obstructive pulmonary disease, New England Journal of Medicine, 381, 13, pp. 1257-1266, (2019); Golpe R., Suarez-Valor M., Martin-Robles I., Sanjuan-Lopez P., Cano-Jimenez E., Castro-Anon O., Perez De Llano L.A., Mortality in COPD patients according to clinical phenotypes, International Journal of Chronic Obstructive Pulmonary Disease, 13, pp. 1433-1439, (2018); Gotway M.B., Reddy G.P., Webb W.R., Elicker B.M., Leung J.W., High-resolution CT of the lung: Patterns of disease and differential diagnoses, Radiologic Clinics of North America, 43, 3, pp. 513-542, (2005); Man M.A., Dantes E., Domokos Hancu B., Bondor C.I., Ruscovan A., Parau A., Motoc N.S., Marc M., Correlation between transthoracic lung ultrasound score and HRCT features in patients with interstitial lung diseases, Journal of Clinical Medicine, 8, (2019); Dong X., Zhou J., Guo X., Li Y., Xu Y., Fu Q., Lu Y., Zheng Y., A retrospective analysis of distinguishing features of chest HRCT and clinical manifestation in primary Sjogren's syndrome-related interstitial lung disease in a Chinese population, Clinical Rheumatology, 37, 11, pp. 2981-2988, (2018); Hoffmann-Vold A.M., Aalokken T.M., Lund M.B., Garen T., Midtvedt O., Brunborg C., Gran J.T., Molberg O., Predictive value of serial high-resolution computed tomography analyses and concurrent lung function tests in systemic sclerosis, Arthritis & Rheumatology, 67, 8, pp. 2205-2212, (2015); Ni Z., Ng T.S.C., Liu J., Huang S., Li X., Xu X., Chen H., Quantitative assessment of pulmonary function in lymphangioleiomyomatosis patients using high-resolution computed tomography and pulmonary function tests, Journal of Thoracic Disease, 12, 11, pp. 6466-6475, (2020); Fabbri L.M., Rabe K.F., From COPD to chronic systemic inflammatory syndrome?, The Lancet, 370, 9589, pp. 797-799, (2007); Huang Y.L., Min J., Li G.H., Zheng Y.Q., Wu L.H., Wang S.J., Qu B., Mao B., [The clinical study of comorbidities and systemic inflammation in COPD], Sichuan da Xue Xue Bao Yi Xue Ban, 50, 1, pp. 88-108, (2019); Sirinoglu M., Soysal A., Karaaslan A., Kepenekli Kadayifci E., Yalindag-Ozturk N., Cinel I., Yaman A., Haklar G., Sirikci O., Turan S., Altinkanat Gelmez G., Soyletir G., Bakir M., The diagnostic value of soluble urokinase plasminogen activator receptor (suPAR) compared to C-reactive protein (CRP) and procalcitonin (PCT) in children with systemic inflammatory response syndrome (SIRS), Journal of Infection and Chemotherapy, 23, 1, pp. 17-22, (2017); Angeletti S., Spoto S., Fogolari M., Cortigiani M., Fioravanti M., De Florio L., Curcio B., Cavalieri D., Costantino S., Dicuonzo G., Diagnostic and prognostic role of procalcitonin (PCT) and MR-pro-Adrenomedullin (MR-proADM) in bacterial infections, Acta Pathologica, Microbiologica et Immunologica Scandinavica, 123, 9, pp. 740-748, (2015); Schuetz P., Christ-Crain M., Thomann R., Falconnier C., Wolbers M., Widmer I., Neidert S., Fricker T., Blum C., Schild U., Regez K., Schoenenberger R., Henzen C., Bregenzer T., Hoess C., Krause M., Bucher H.C., Zimmerli W., Mueller B., Effect of procalcitonin-based guidelines vs. standard guidelines on antibiotic use in lower respiratory tract infections: The ProHOSP randomized controlled trial, JAMA, 302, 10, (2009); Lee S., Song I.U., Na S.H., Jeong D.S., Chung S.W., Association between long-Term functional outcome and change in hs-CRP level in patients with acute ischemic stroke, The Neurologist, 25, 5, pp. 122-125, (2020); Gisbert J.P., Chaparro M., Predictors of primary response to biologic treatment [Anti-TNF, vedolizumab, and ustekinumab] in patients with inflammatory bowel disease: From basic science to clinical practice, Journal of Crohn's and Colitis, 14, 5, pp. 694-709, (2020); Macrea M.M., Owens R.L., Martin T., Smith D., Oursler K.K., Malhotra A., The effect of isolated nocturnal oxygen desaturations on serum hs-CRP and IL-6 in patients with chronic obstructive pulmonary disease, Clinical Research Journal, 13, 2, pp. 120-124, (2019); Garg R., Pandey S., Kant S., Verma A., Gaur P., Serum procalcitonin levels in chronic obstructive pulmonary disease patients in North Indian Population, Annals of African Medicine, 18, 2, (2019); Zhou W., Tan J., The expression and the clinical significance of eosinophils, PCT and CRP in patients with acute exacerbation of chronic obstructive pulmonary disease complicated with pulmonary infection, American Journal of Translational Research, 13, 4, pp. 3451-3458, (2021); Mirza S., Clay R.D., Koslow M.A., Scanlon P.D., COPD guidelines: A review of the 2018 GOLD report, Mayo Clinic Proceedings, 93, 10, pp. 1488-1502, (2018); Lange P., Halpin D.M., O'Donnell D.E., Macnee W., Diagnosis, assessment, and phenotyping of COPD: Beyond FEV(1), International Journal of Chronic Obstructive Pulmonary Disease, 11, pp. 3-12, (2016); Han M.K., Agusti A., Calverley P.M., Celli B.R., Criner G., Curtis J.L., Fabbri L.M., Goldin J.G., Jones P.W., Macnee W., Make B.J., Rabe K.F., Rennard S.I., Sciurba F.C., Silverman E.K., Vestbo J., Washko G.R., Wouters E.F.M., Martinez F.J., Chronic obstructive pulmonary disease phenotypes: The future of COPD, American Journal of Respiratory and Critical Care Medicine, 182, 5, pp. 598-604, (2010); Vogelmeier C.F., Roman-Rodriguez M., Singh D., Han M.K., Rodriguez-Roisin R., Ferguson G.T., Goals of COPD treatment: Focus on symptoms and exacerbations, Respiratory Medicine, 166, (2020); Trofimenko I.N., [Bronchial hyper-reactivity and chronic obstructive pulmonary disease], Klinicheskaya Meditsina (Moscow), 91, 5, pp. 9-15, (2013); Park J., Hobbs B.D., Crapo J.D., Make B.J., Regan E.A., Humphries S., Carey V.J., Lynch D.A., Silverman E.K., Investigators C.O., Subtyping COPD by using visual and quantitative CT imaging features, Chest, 157, 1, pp. 47-60, (2020); Ley-Zaporozhan J., Ley S., Weinheimer O., Iliyushenko S., Erdugan S., Eberhardt R., Fuxa A., Mews J., Kauczor H.U., Quantitative analysis of emphysema in 3D using MDCT: Influence of different reconstruction algorithms, European Journal of Radiology, 65, 2, pp. 228-234, (2008); Matsuoka S., Washko G.R., Dransfield M.T., Yamashiro T., San Jose Estepar R., Diaz A., Silverman E.K., Patz S., Hatabu H., Quantitative CT measurement of cross-sectional area of small pulmonary vessel in COPD: Correlations with emphysema and airflow limitation, Academic Radiology, 17, 1, pp. 93-99, (2010); Tsai C.L., Sobrino J.A., Camargo C.A., National study of emergency department visits for acute exacerbation of chronic obstructive pulmonary disease, 1993-2005, Academic Emergency Medicine, 15, 12, pp. 1275-1283, (2008); Kunisaki K.M., Dransfield M.T., Anderson J.A., Brook R.D., Calverley P.M.A., Celli B.R., Crim C., Hartley B.F., Martinez F.J., Newby D.E., Pragman A.A., Vestbo J., Yates J.C., Niewoehner D.E., Investigators S., Exacerbations of chronic obstructive pulmonary disease and cardiac events. A post hoc cohort analysis from the summit randomized clinical trial, American Journal of Respiratory and Critical Care Medicine, 198, 1, pp. 51-57, (2018); Perera P.N., Armstrong E.P., Sherrill D.L., Skrepnek G.H., Acute exacerbations of COPD in the United States: Inpatient burden and predictors of costs and mortality, COPD: Journal of Chronic Obstructive Pulmonary Disease, 9, 2, pp. 131-141, (2012); Prat C., Dominguez J., Rodrigo C., Gimenez M., Azuara M., Jimenez O., Gali N., Ausina V., Procalcitonin, C-reactive protein and leukocyte count in children with lower respiratory tract infection, The Pediatric Infectious Disease Journal, 22, 11, pp. 963-967, (2003)","Q. Dai; Department of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China; email: daiqimr@163.com","","Hindawi Limited","","","","","","1741427X","","","","English","Evid.-Based Complement. Altern. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85135003899"
"Shiraishi Y.; Tanabe N.; Sakamoto R.; Maetani T.; Kaji S.; Shima H.; Terada S.; Terada K.; Ikezoe K.; Tanizawa K.; Oguma T.; Handa T.; Sato S.; Muro S.; Hirai T.","Shiraishi, Yusuke (8573318200); Tanabe, Naoya (57953947500); Sakamoto, Ryo (57350902200); Maetani, Tomoki (57209396955); Kaji, Shizuo (36658630400); Shima, Hiroshi (58865879400); Terada, Satoru (57210646116); Terada, Kunihiko (57216331382); Ikezoe, Kohei (37024200100); Tanizawa, Kiminobu (13610867100); Oguma, Tsuyoshi (7005166362); Handa, Tomohiro (8216738000); Sato, Susumu (36037634900); Muro, Shigeo (57226225321); Hirai, Toyohiro (7402768436)","8573318200; 57953947500; 57350902200; 57209396955; 36658630400; 58865879400; 57210646116; 57216331382; 37024200100; 13610867100; 7005166362; 8216738000; 36037634900; 57226225321; 7402768436","Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study","2024","BMC Pulmonary Medicine","24","1","200","","","","2","10.1186/s12890-024-03002-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191098287&doi=10.1186%2fs12890-024-03002-z&partnerID=40&md5=18e8c9d48fc78ba41e34cc3751a90f72","Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Institute of Mathematics for Industry, Kyusyu University, Fukuoka, Japan; Respiratory Medicine and General Practice, Terada Clinic, Hyogo, Himeji, Japan; Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan; Department of Advanced Medicine for Respiratory Failure, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Respiratory Medicine, Nara Medical University, Nara, Kashihara, Japan; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan","Shiraishi Y., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Tanabe N., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Kyoto, 606-8507, Japan; Sakamoto R., Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Maetani T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Kaji S., Institute of Mathematics for Industry, Kyusyu University, Fukuoka, Japan; Shima H., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Terada S., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Respiratory Medicine and General Practice, Terada Clinic, Hyogo, Himeji, Japan; Terada K., Respiratory Medicine and General Practice, Terada Clinic, Hyogo, Himeji, Japan; Ikezoe K., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Tanizawa K., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Oguma T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan; Handa T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Department of Advanced Medicine for Respiratory Failure, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Sato S., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan, Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; Muro S., Department of Respiratory Medicine, Nara Medical University, Nara, Kashihara, Japan; Hirai T., Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan","Background: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. Methods: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans. Results: ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure. Conclusion: AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD. © The Author(s) 2024.","Artificial intelligence; COPD; CT; Interstitial lung abnormality","Aged; Artificial Intelligence; Cross-Sectional Studies; Female; Humans; Longitudinal Studies; Lung; Lung Diseases, Interstitial; Male; Middle Aged; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Tomography, X-Ray Computed; aged; Article; artificial intelligence; body mass; chronic obstructive lung disease; cohort analysis; computer assisted tomography; cross-sectional study; female; follow up; human; interstitial lung disease; longitudinal study; major clinical study; male; observational study; patient monitoring; reproducibility; retrospective study; sensitivity and specificity; smoking; total lung capacity; validation study; artificial intelligence; diagnostic imaging; lung; middle aged; procedures; prospective study; x-ray computed tomography","","","","","","","Global strategy for prevention, Diagnosis and Management of Copd (2024 report, (2024); Beghe B., Cerri S., Fabbri L.M., Marchioni A., Copd, Pulmonary fibrosis and ILAs in aging smokers: the Paradox of striking different responses to the major risk factors, Int J Mol Sci, 22, (2021); Cottin V., Nunes H., Brillet P.Y., Delaval P., Devouassaoux G., Tillie-Leblond I., Et al., Combined pulmonary fibrosis and emphysema: a distinct underrecognised entity, Eur Respir J, 26, pp. 586-593, (2005); Cottin V., Inoue Y., Selman M., Ryerson C.J., Wells A.U., Agusti A., Et al., Syndrome of Combined Pulmonary Fibrosis and Emphysema an Official ATS/ERS/JRS/ALAT Research Statement, Am J Respir Crit Care Med, 206, pp. E7-E41, (2022); Hage R., Gautschi F., Steinack C., Schuurmans M.M., Combined pulmonary fibrosis and emphysema (CPFE) clinical features and management, Int J COPD, 16, pp. 167-177, (2021); Wright J.L., Tazelaar H.D., Churg A., Fibrosis with emphysema, Histopathology, 58, pp. 517-524, (2011); Hatabu H., Hunninghake G.M., Richeldi L., Brown K.K., Wells A.U., Remy-Jardin M., Et al., Interstitial lung abnormalities detected incidentally on CT: a position paper from the Fleischner Society, Lancet Respir Med, 8, pp. 726-737, (2020); Putman R.K., Hatabu H., Araki T., Gudmundsson G., Gao W., Nishino M., Et al., Association between interstitial lung abnormalities and all-cause mortality, JAMA - J Am Med Association, 315, pp. 672-681, (2016); Hoyer N., Wille M.M.W., Thomsen L.H., Wilcke T., Dirksen A., Pedersen J.H., Et al., Interstitial lung abnormalities are associated with increased mortality in smokers, Respir Med, 136, pp. 77-82, (2018); Suman G., Koo C.W., Recent advancements in computed Tomography Assessment of Fibrotic interstitial lung diseases, J Thorac Imaging, (2023); Choi B., Adan N., Doyle T.J., San Jose Estepar R., Harmouche R., Humphries S.M., Et al., Quantitative interstitial abnormality progression and outcomes in the Genetic Epidemiology of COPD and Pittsburgh Lung Screening Study cohorts, Chest, 163, pp. 164-175, (2023); Maldonado F., Moua T., Rajagopalan S., Karwoski R.A., Raghunath S., Decker P.A., Et al., Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis, Eur Respir J, 43, pp. 204-212, (2014); Aoki R., Iwasawa T., Saka T., Yamashiro T., Utsunomiya D., Misumi T., Et al., Effects of Automatic Deep-Learning-based lung analysis on quantification of interstitial lung disease: correlation with pulmonary function test results and prognosis, Diagnostics, 12, (2022); Handa T., Tanizawa K., Oguma T., Uozumi R., Watanabe K., Tanabe N., Et al., Novel Artificial Intelligence-based technology for chest computed Tomography Analysis of Idiopathic Pulmonary Fibrosis, Ann Am Thorac Soc, (2021); Terada K., Muro S., Sato S., Ohara T., Haruna A., Marumo S., Et al., Impact of gastro-oesophageal reflux disease symptoms on COPD exacerbation, Thorax, 63, pp. 951-955, (2008); Tanabe N., Muro S., Hirai T., Oguma T., Terada K., Marumo S., Et al., Impact of exacerbations on emphysema progression in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 183, pp. 1653-1659, (2011); Tanimura K., Sato S., Fuseya Y., Hasegawa K., Uemasu K., Sato A., Et al., Quantitative assessment of erector spinae muscles in patients with chronic obstructive pulmonary disease novel chest computed tomography-derived index for prognosis, Ann Am Thorac Soc, 13, pp. 334-341, (2016); Tanabe N., Shimizu K., Terada K., Sato S., Suzuki M., Shima H., Et al., Central airway and peripheral lung structures in airway disease-dominant COPD, ERJ Open Res, 7, pp. 00672-2020, (2021); McHugh M.L., Interrater reliability: the kappa statistic, Biochem Med (Zagreb), 22, (2012); Hata A., Schiebler M.L., Lynch D.A., Hatabu H., Interstitial lung abnormalities: state of the art, Radiology, 301, pp. 19-34, (2021); Liu Y., Impact of interstitial lung abnormalities on Disease expression and outcomes in COPD or Emphysema. A systematic review, February, (2023); Ash S.Y., Harmouche R., Ross J.C., Diaz A.A., Rahaghi F.N., Sanchez-Ferrero G.V., Et al., Interstitial features at chest CT enhance the deleterious effects of emphysema in the COPDGene cohort, Radiology, 288, pp. 600-609, (2018); Ash S.Y., Harmouche R., Putman R.K., Ross J.C., Diaz A.A., Hunninghake G.M., Et al., Clinical and Genetic associations of objectively identified interstitial changes in smokers, Chest, 152, pp. 780-791, (2017); Ash S.Y., Choi B., Oh A., Lynch D.A., Humphries S.M., Deep Learning Assessment of Progression of Emphysema and Fibrotic interstitial lung abnormality, Am J Respir Crit Care Med, 208, pp. 666-675, (2023); Kim M.S., Choe J., Hwang H.J., Lee S.M., Yun J., Kim N., Et al., Interstitial lung abnormalities (ILA) on routine chest CT: comparison of radiologists’ visual evaluation and automated quantification, Eur J Radiol, 157, (2022); Mathai S.K., Humphries S., Kropski J.A., Blackwell T.S., Powers J., Walts A.D., Et al., MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis, Thorax, 74, pp. 1131-1139, (2019); Chae K.J., Lim S., Seo J.B., Hwang H.J., Choi H., Lynch D., Et al., Interstitial lung abnormalities at CT in the Korean National Lung Cancer Screening Program: prevalence and deep learning–based texture analysis, Radiology, (2023); McGroder C.F., Hansen S., Hinckley Stukovsky K., Zhang D., Nath P.H., Salvatore M.M., Et al., Incidence of interstitial lung abnormalities: the MESA Lung Study, Eur Respir J, 61, (2023); Bozzetti F., Paladini I., Rabaiotti E., Franceschini A., Alfieri V., Chetta A., Et al., Are interstitial lung abnormalities associated with COPD? A nested case-control study, Int J Chron Obstruct Pulmon Dis, 11, pp. 1087-1096, (2016); Miller E.R., Putman R.K., Diaz A.A., Xu H., Estepar R.S.J., Araki T., Et al., Increased airway wall thickness in interstitial lung abnormalities and idiopathic pulmonary fibrosis, Ann Am Thorac Soc, 16, pp. 447-454, (2019); Ikezoe K., Hackett T.L., Peterson S., Prins D., Hague C.J., Murphy D., Et al., Small airway reduction and fibrosis is an early pathologic feature of idiopathic pulmonary fibrosis, Am J Respir Crit Care Med, 204, pp. 1048-1059, (2021); Verleden S.E., Vanstapel A., Jacob J., Goos T., Hendriks J., Ceulemans L.J., Et al., Radiologic and Histologic Correlates of Early Interstitial Lung Changes in explanted lungs, Radiology, (2022); Verleden S.E., Tanabe N., McDonough J.E., Vasilescu D.M., Xu F., Wuyts W.A., Et al., Small airways pathology in idiopathic pulmonary fibrosis: a retrospective cohort study, Lancet Respir Med, 8, pp. 573-584, (2020); Watadani T., Sakai F., Johkoh T., Noma S., Akira M., Fujimoto K., Et al., Interobserver Variability in the CT Assessment of Honeycombing in the lungs, Radiology, 266, pp. 936-944, (2013); Takahashi S., Betsuyaku T., The chronic obstructive pulmonary disease comorbidity spectrum in Japan differs from that in western countries, Respiratory Invest, 53, pp. 259-270, (2015); Lee T.S., Jin K.N., Lee H.W., Yoon S.Y., Park T.Y., Heo E.Y., Et al., Interstitial Lung Abnormalities and the clinical course in patients with COPD, Chest, 159, pp. 128-137, (2021)","N. Tanabe; Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; email: ntana@kuhp.kyoto-u.ac.jp","","BioMed Central Ltd","","","","","","14712466","","BPMMB","38654252","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191098287"
"Aulia D.; Sarno R.; Hidayati S.C.; Rosyid A.N.; Rivai M.","Aulia, Dava (57252810100); Sarno, Riyanarto (53264815700); Hidayati, Shintami Chusnul (55533725700); Rosyid, Alfian Nur (57201737476); Rivai, Muhammad (55847263000)","57252810100; 53264815700; 55533725700; 57201737476; 55847263000","Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose","2024","Indonesian Journal of Electrical Engineering and Computer Science","34","1","","264","275","11","2","10.11591/ijeecs.v34.i1.pp264-275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186650451&doi=10.11591%2fijeecs.v34.i1.pp264-275&partnerID=40&md5=892b784023ef1399d572fe7408171c5f","Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Universitas Airlangga Hospital, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Department of Electrical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","Aulia D., Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Sarno R., Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Hidayati S.C., Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia; Rosyid A.N., Universitas Airlangga Hospital, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia, Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia; Rivai M., Department of Electrical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD. © 2024 Institute of Advanced Engineering and Science. All rights reserved.","COPD; Diseases; Electronic nose; Exhaled breath; Graph convolutional network","","","","","","Indonesian Ministry of Education and Culture; Asian Development Bank, ADB; Institut Teknologi Sepuluh Nopember, ITS","This work was funded in part by the Indonesian Ministry of Education and Culture under Penelitian Terapan Unggulan Perguruan Tinggi (PTUPT) Program; Asian Development Bank under Higher Education for Technology Innovation (ADB HETI) Project; and Institut Teknologi Sepuluh Nopember (ITS) under Penelitian Dana Departemen Program and Penelitian Keilmuan Program.","Agusti A., Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, European Respiratory Journal, 61, 4, (2023); Carette H., Et al., Prevalence and management of chronic breathlessness in COPD in a tertiary care center, BMC Pulmonary Medicine, 19, 1, (2019); Alfahad A. J., Et al., Current views in chronic obstructive pulmonary disease pathogenesis and management, Saudi Pharmaceutical Journal, 29, 12, pp. 1361-1373, (2021); Chronic obstructive pulmonary disease (COPD), (2023); Pantazopoulos I., Et al., Incorporating biomarkers in COPD management: the research keeps going, Journal of Personalized Medicine, 12, 3, (2022); Scarlata S., Finamore P., Meszaros M., Dragonieri S., Bikov A., The role of electronic noses in phenotyping patients with chronic obstructive pulmonary disease, Biosensors, 10, 11, (2020); Maciel M., Sankari S., Woollam M., Agarwal M., Optimization of metal oxide nanosensors and development of a feature extraction algorithm to analyze VOC profiles in exhaled breath, IEEE Sensors Journal, 23, 15, pp. 16571-16578, (2023); Hendrick H., Hidayat R., Horng G. J., Wang Z.-H., Non-invasive method for tuberculosis exhaled breath classification using electronic nose, IEEE Sensors Journal, 21, 9, pp. 11184-11191, (2021); Binson V. A., Subramoniam M., Sunny Y., Mathew L., Prediction of pulmonary diseases with electronic nose using SVM and XGBoost, IEEE Sensors Journal, 21, 18, pp. 20886-20895, (2021); Aulia D., Sarno R., Hidayati S. C., Rivai M., Optimization of the electronic nose sensor array for asthma detection based on genetic algorithm, IEEE Access, 11, pp. 74924-74935, (2023); Misbah M., Rivai M., Kurniawa F., Muchidin Z., Aulia D., Identification of diabetes through urine using gas sensor and convolutional neural network, International Journal of Intelligent Engineering and Systems, 15, 1, pp. 520-529, (2022); Sujono H. A., Rivai M., Amin M., Asthma identification using gas sensors and support vector machine, TELKOMNIKA (Telecommunication Computing Electronics and Control), 16, 4, pp. 1468-1480, (2018); Faleh R., Othman M., Gomri S., Aguir K., Kachouri A., A transient signal extraction method of WO 3 gas sensors array to identify polluant gases, IEEE Sensors Journal, 16, 9, pp. 3123-3130, (2016); Hikmah N. F., Setiawan R., Gunawan M. D., Sleep quality assessment from robust heart and muscle fatigue estimation using supervised machine learning, International Journal of Intelligent Engineering and Systems, 16, 2, pp. 319-331, (2023); Sachidanandan E. R. R., Singh N. P., Gunda S., Design and simulation of a low-power and high-speed fast fourier transform for medical image compression, HMAM2, (2023); Yan J., Et al., Electronic nose feature extraction methods: a review, Sensors, 15, 11, pp. 27804-27831, (2015); Bhatti U. A., Tang H., Wu G., Marjan S., Hussain A., Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence, International Journal of Intelligent Systems, 2023, pp. 1-28, (2023); Sejan M. A. S., Rahman M. H., Aziz M. A., Baik J.-I., You Y.-H., Song H.-K., Graph convolutional network design for node classification accuracy improvement, Mathematics, 11, 17, (2023); Shi Y., Liu M., Sun A., Liu J., Men H., A fast pearson graph convolutional network combined with electronic nose to identify the origin of rice, IEEE Sensors Journal, 21, 19, pp. 21175-21183, (2021); Xuan W., Jian-She G., Bo-Jie H., Zong-Shan W., Hong-Wei D., Jie W., A lightweight modified YOLOX network using coordinate attention mechanism for PCB surface defect detection, IEEE Sensors Journal, 22, 21, pp. 20910-20920, (2022); Xie D., Chen D., Peng S., Yang Y., Xu L., Wu F., A low power cantilever-based metal oxide semiconductor gas sensor, IEEE Electron Device Letters, 40, 7, pp. 1178-1181, (2019); Rai P., Saeed S. H., Detection of harmful gases present in the environment, Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 30, 1, pp. 70-80, (2023); Laga S. A., Sarno R., Temperature effect of electronic nose sampling for classifying mixture of beef and pork, Indonesian Journal of Electrical Engineering and Computer Science, 19, 3, pp. 1626-1634, (2020); Wijaya R. A. K., Kusumaatmaja A., Rizal D. M., Predicting the value of sperm analysis using an electronic nose, Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 28, 1, pp. 174-182, (2022); Sikhakolli S., Sikhakolli A., A bacterial foraging algorithm with random forest classifier for detecting the design patterns in source code, International Journal of Intelligent Engineering and Systems, 14, 2, pp. 95-105, (2021); Rai P., Saeed S. H., Mishra S. O., Harmful gases detection using artificial neural networks of the environment, Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 30, 3, pp. 1389-1398, (2023); Farokhah L., Sarno R., Fatichah C., Simplified 2D CNN architecture with channel selection for emotion recognition using EEG spectrogram, IEEE Access, 11, pp. 46330-46343, (2023); Manjunath C., Marimuthu B., Ghosh B., Deep learning for stock market index price movement forecasting using improved technical analysis, International Journal of Intelligent Engineering and Systems, 14, 5, pp. 129-141, (2021); El Filali A., Jadli A., Ben Lahmer E. H., El Filali S., A novel LSTM-GRU-based hybrid approach for electrical products demand forecasting, International Journal of Intelligent Engineering and Systems, 15, 3, pp. 601-613, (2022); Barokah B., Radi R., Zamzami L. F., Setiawan A., Putro J. P. L. Y., Design of sample display system on electronic nose for synthetic flavor classification, Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 30, 2, pp. 690-698, (2023); Alomar K., Aysel H. I., Cai X., Data augmentation in classification and segmentation: a survey and new strategies, Journal of Imaging, 9, 2, (2023)","R. Sarno; Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Sukolilo, Keputih, East Java, 6011, Indonesia; email: riyanarto@if.its.ac.id","","Institute of Advanced Engineering and Science","","","","","","25024752","","","","English","Indones. J. Electrical Eng. Comput. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85186650451"
"Mir A.; Ur Rehman A.; Ali T.M.; Javaid S.; Almufareh M.F.; Humayun M.; Shaheen M.","Mir, Azka (58584692800); Ur Rehman, Attique (57518469400); Ali, Tahir Muhammad (57204504103); Javaid, Sabeen (55652927100); Almufareh, Maram Fahaad (57202332807); Humayun, Mamoona (56580449600); Shaheen, Momina (57205752463)","58584692800; 57518469400; 57204504103; 55652927100; 57202332807; 56580449600; 57205752463","A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques","2024","ESC Heart Failure","11","6","","3742","3756","14","2","10.1002/ehf2.14942","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198097865&doi=10.1002%2fehf2.14942&partnerID=40&md5=7dad66a82edf4fb199c8f0cd4579cefd","Department of Software Engineering, University of Sialkot, Sialkot, Pakistan; Department of Computer Science, Gulf University for Sciences and Technology, Hawally, Kuwait; Department of Information Systems College of Computer and Information Science, Jouf University, Sakaka, Saudi Arabia; School of Arts Humanities and Social Sciences, University of Roehampton, London, United Kingdom","Mir A., Department of Software Engineering, University of Sialkot, Sialkot, Pakistan; Ur Rehman A., Department of Software Engineering, University of Sialkot, Sialkot, Pakistan, Department of Computer Science, Gulf University for Sciences and Technology, Hawally, Kuwait; Ali T.M., Department of Computer Science, Gulf University for Sciences and Technology, Hawally, Kuwait; Javaid S., Department of Software Engineering, University of Sialkot, Sialkot, Pakistan; Almufareh M.F., Department of Information Systems College of Computer and Information Science, Jouf University, Sakaka, Saudi Arabia; Humayun M., Department of Information Systems College of Computer and Information Science, Jouf University, Sakaka, Saudi Arabia, School of Arts Humanities and Social Sciences, University of Roehampton, London, United Kingdom; Shaheen M., School of Arts Humanities and Social Sciences, University of Roehampton, London, United Kingdom","Aims: The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods: In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three-step approach includes pre-processing of the dataset, applying feature selection method on pre-processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10-fold cross-validation provided the high accuracy. Results: The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. Conclusions: In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages. © 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.","cardiovascular disease prediction; CVD prediction using machine learning; data imbalance handling; healthcare applications; multi-dataset approach","Algorithms; Artificial Intelligence; Cardiovascular Diseases; Female; Humans; Machine Learning; Male; Middle Aged; Risk Assessment; accuracy; adult; aged; algorithm; Article; artificial intelligence; artificial neural network; asthma; blood pressure; bootstrapping; cardiovascular disease; cerebrovascular accident; classifier; controlled study; coronary artery bypass graft; cross validation; data mining; decision tree; depression; diabetic retinopathy; diagnostic test accuracy study; electric potential; feature selection; female; fluoroscopy; glucose blood level; health care system; heart disease; heart failure; heart rate; human; hypertension; hypothalamus; intelligence; Internet; k nearest neighbor; learning algorithm; machine learning; major clinical study; male; measurement accuracy; obesity; prediction; quantitative structure activity relation; random forest; receiver operating characteristic; sensitivity and specificity; ST segment elevation myocardial infarction; support vector machine; thorax pain; training; diagnosis; epidemiology; middle aged; procedures; risk assessment","","","","","Joint Information Systems Committee, JISC; Ministry of Education - Kingdom of Saudi Arabia, MoE, (223202); Ministry of Education - Kingdom of Saudi Arabia, MoE","The authors extend their appreciation to the Deputyship for Research Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the project number 223202, and Joint Information Systems Committee UK, for funding the publication of this research study.","Cardiovascular Diseases (CVDs); Probability of dying between age 30 and exact age 70 from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease; Angell S.Y., McConnell M., Anderson C.A.M., Bibbins-Domingo K., Boyle D.S., Capewell S., Et al., The American Heart Association 2030 impact goal: a presidential advisory from the American Heart Association, Circulation, 141, pp. E120-E138, (2020); Heart attack cases in Pakistan|MMI; Global health estimates: leading causes of death; Bui A.L., Horwich T.B., Fonarow G.C., Epidemiology and risk profile of heart failure, Nat Rev Cardiol, 8, pp. 30-41, (2011); Haq A.U., Li J.P., Memon M.H., Nazir S., Sun R., A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms, Mob Inf Syst, 2018, pp. 1-21, (2018); Bhattacharyya S., Berkowitz A.L., Primary angiitis of the central nervous system: avoiding misdiagnosis and missed diagnosis of a rare disease, Pract Neurol, 16, pp. 195-200, (2016); Butt M.O., Rehman A.U., Javaid S., Ali T.M., Nawaz A., An application of artificial intelligence for an early and effective prediction of heart failure, 2022 Third International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), pp. 1-6, (2022); Mohan S., Thirumalai C., Srivastava G., Effective heart disease prediction using hybrid machine learning techniques, IEEE Access, 7, pp. 81542-81554, (2019); Reddy K.V.V., Elamvazuthi I., Aziz A.A., Paramasivam S., Chua H.N., Pranavanand S., Heart disease risk prediction using machine learning classifiers with attribute evaluators, Appl Sci (Switzerland), 11, (2021); Arul Jothi K., Subburam S., Umadevi V., Hemavathy K., Heart disease prediction system using machine learning, Mater Today Proc, (2021); Motarwar P., Duraphe A., Suganya G., Premalatha M., Cognitive approach for heart disease prediction using machine learning, International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE, 2020, (2020); Hossain M.E., Uddin S., Khan A., Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes, Expert Syst Appl, 164, (2021); Dinh A., Miertschin S., Young A., Mohanty S.D., A data-driven approach to predicting diabetes and cardiovascular disease with machine learning, BMC Med Inform Decis Mak, 19, (2019); (PDF) Heart diseases prediction using deep learning neural network model; Arunachalam S., Cardiovascular disease prediction model using machine learning algorithms, Int J Res Appl Sci Eng Technol, 8, pp. 1006-1019, (2020); Saleh Alotaibi F., Implementation of machine learning model to predict heart failure disease, IJACSA Int J Adv Comput Sci Applic, 10, (2019); Ali F., el-Sappagh S., Islam S.M.R., Kwak D., Ali A., Imran M., Et al., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Inf Fusion, 63, pp. 208-222, (2020); Jindal H., Agrawal S., Khera R., Jain R., Nagrath P., Heart disease prediction using machine learning algorithms, IOP Conf Ser Mater Sci Eng, 1022, (2021); Rani P., Kumar R., Ahmed N.M.O.S., Jain A., A decision support system for heart disease prediction based upon machine learning, J Reliab Intell Environ, 7, pp. 263-275, (2021); UCI Machine Learning Repository: Statlog (Heart) data set; Coronary Heart Disease; Machine Learning—Heart Disease Framingham; Heart Disease Dataset; Stroke Prediction Dataset; Voloshynskyi O., Vysotska V., Bublyk M., Cardiovascular disease prediction based on machine learning technology, 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), 1, pp. 69-75, (2021); Saboor A., Rehman A.U., Ali T.M., Javaid S., Nawaz A., An applied artificial intelligence technique for early prediction of diabetes disease, Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022, (2022); Mir A., Rehman A.U., Javaid S., Ali T.M., An intelligent technique for the effective prediction of monkeypox outbreak, 3rd IEEE International Conference on Artificial Intelligence, 2023, pp. 220-226, (2023); Islam S., Rehman A.U., Javaid S., Ali T.M., Nawaz A., An integrated machine learning framework for classification of cirrhosis, fibrosis, and hepatitis, Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022, (2022); Waqar M., Rehman A.U., Javaid S., Ali T.M., Nawaz A., An applied artificial intelligence aided technique for effective classification of breast cancer, 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC), pp. 1-6, (2023); Mehreen F., Rehman A.U., Ali T.M., Javaid S., Nawaz A., A computer aided technique for classification of patients with diabetes, Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2022, (2022); Dinesh K.G., Arumugaraj K., Santhosh K.D., Mareeswari V., Prediction of cardiovascular disease using machine learning algorithms, 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1-7, (2018); Aleem I., Ur Rehman A., Javaid S., Ali T.M., An integrated machine learning framework for effective classification of water, 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC), pp. 1-6, (2023); Anuradha P., David V.K., Feature selection and prediction of heart diseases using gradient boosting algorithms, Proceedings—International Conference on Artificial Intelligence and Smart Systems, ICAIS, 2021, pp. 711-717, (2021); Rahman F., Mahmood A., A dynamic approach to identify the most significant biomarkers for heart disease risk prediction utilizing machine learning techniques. Machine learning and Data Science View project Applied cryptography view project, (2022); Fathima K., Vimina E.R., Heart disease prediction using deep neural networks: a novel approach. Lecture notes in networks and systems, 213, pp. 725-736, (2022); Karadeniz T., Tokdemir G., Maras H.H., Ensemble methods for heart disease prediction, New Gener Comput, 39, pp. 569-581, (2021); Adel Mahmoud W., Aborizka M., Ahmed Elsayed Amer F., Heart disease prediction using machine learning and data mining techniques: application of Framingham dataset, Turk J Comput Math Educ (TURCOMAT), 12, pp. 4864-4870, (2021); Elsayed H.A.G., Syed L., An automatic early risk classification of hard coronary heart diseases using Framingham scoring model, ACM International Conference Proceeding Series, (2017)","M. Shaheen; School of Arts Humanities and Social Sciences, University of Roehampton, London, SW15 5PJ, United Kingdom; email: momina.shaheen@roehampton.ac.uk","","John Wiley and Sons Inc","","","","","","20555822","","","38992943","English","ESC Heart Fail.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85198097865"
"Lee K.-S.; Kim G.; Ham B.-J.","Lee, Kwang-Sig (57221177656); Kim, Geunyeong (57404499100); Ham, Byung-Joo (8214919600)","57221177656; 57404499100; 8214919600","ORIGINAL ARTICLE: Associations of antidepressant medication with its various predictors including particulate matter: Machine learning analysis using national health insurance data","2022","Journal of Psychiatric Research","147","","","67","78","11","3","10.1016/j.jpsychires.2022.01.011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122528107&doi=10.1016%2fj.jpsychires.2022.01.011&partnerID=40&md5=1c9bb0039b47663e4db1a7876fe5208c","AI Center, Korea University College of Medicine, Seoul, South Korea; Korea University Graduate School of Policy Studies, Seoul, South Korea; Department of Mental Health, Korea University College of Medicine, Seoul, South Korea","Lee K.-S., AI Center, Korea University College of Medicine, Seoul, South Korea; Kim G., Korea University Graduate School of Policy Studies, Seoul, South Korea; Ham B.-J., Department of Mental Health, Korea University College of Medicine, Seoul, South Korea","This study uses machine learning and population-based data to analyze major determinants of antidepressant medication including the concentration of particulate matter under 2.5 μm (PM2.5). Retrospective cohort data came from Korea National Health Insurance Service claims data for 43,251 participants, who were aged 15–79 years, lived in the same districts of Seoul and had no history of antidepressant medication during 2002–2012. The dependent variable was antidepressant-free months during 2013–2015 and the 30 independent variables for 2012 were included (demographic/socioeconomic information, health information, district-level information including PM2.5). Random forest variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of antidepressant-free months. Based on random forest variable importance, the top 15 determinants of antidepressant medication during 2013–2015 included cardiovascular disease (0.0054), age (0.0047), household income (0.0037), gender (0.0027), the district-level proportion of recipients of national basic living security program benefits (0.0019), district-level social satisfaction (0.0013), diabetes mellitus (0.0012), January 2012 PM.2.5 (0.0011), district-level street ratio (0.0010), drinker (0.0009), chronic obstructive pulmonary disease (0.0008), district-level economic satisfaction (0.0006), exercise (0.0005), March 2012 PM.2.5 (0.0005) and November 2012 PM2.5 (0.0004). Besides these predictors, smoker and district-level deprivation index are found to be influential most widely, given that they ranked within the top 10 most often in sub-group analysis. In conclusion, antidepressant medication has strong associations with neighborhood conditions including socioeconomic satisfaction and the seasonality of particulate matter. Strong interventions for these factors are really needed for the effective management of major depressive disorder. © 2022 Elsevier Ltd","Antidepressant; Neighborhood condition; Particulate matter; Socioeconomic satisfaction","Adolescent; Adult; Aged; Air Pollutants; Air Pollution; Antidepressive Agents; Depressive Disorder, Major; Environmental Exposure; Humans; Machine Learning; Middle Aged; National Health Programs; Particulate Matter; Retrospective Studies; Young Adult; amfebutamone; antidepressant agent; mirtazapine; monoamine oxidase inhibitor; serotonin noradrenalin reuptake inhibitor; serotonin uptake inhibitor; trazodone; antidepressant agent; adolescent; adult; aged; Article; cardiovascular disease; chronic obstructive lung disease; cohort analysis; controlled study; dependent variable; female; household income; human; independent variable; machine learning; major clinical study; major depression; male; medical information; middle aged; national health insurance; neighborhood; particulate matter; patient satisfaction; random forest; retrospective study; air pollutant; air pollution; environmental exposure; machine learning; major depression; particulate matter; public health; young adult","","amfebutamone, 31677-93-7, 34911-55-2; mirtazapine, 61337-67-5; trazodone, 19794-93-5, 25332-39-2; Air Pollutants, ; Antidepressive Agents, ; Particulate Matter, ","","","National Research Foundation of Korea, NRF; Ministry of Education, Science and Technology, MEST, (NRF-2020M3E5D9080792)","This work was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( NRF-2020M3E5D9080792 ). The funder had no role in the design of the study, the collection, analysis and interpretation of the data and the writing of the manuscript. ","Braithwaite I., Zhang S., Kirkbride J.B., Osborn D.P.J., Hayes J.F., Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis, Environ. Health Perspect., 127, (2019); 2012 Seoul Air Quality Evaluation Report, (2012); 2013 Seoul Air Quality Evaluation Report, (2013); 2014 Seoul Air Quality Evaluation Report, (2014); 2015 Seoul Air Quality Evaluation Report, (2015); Diez Roux A.V., Mair C., Neighborhoods and health, Ann. N. Y. Acad. Sci., 1186, pp. 125-145, (2010); Fernandez-Nino J.A., Bonilla-Tinoco L.J., Manrique-Espinoza B.S., Salinas-Rodriguez A., Santos-Luna R., Roman-Perez S., Morales-Carmona E., Duncan D.T., Neighborhood features and depression in Mexican older adults: a longitudinal analysis based on the study on global AGEing and adult health (SAGE), waves 1 and 2 (2009-2014), PLoS One, 14, (2019); Fox J., Weisberg S., An R Companion to Applied Regression, (2018); Gu X., Liu Q., Deng F., Wang X., Lin H., Guo X., Wu S., Association between particulate matter air pollution and risk of depression and suicide: systematic review and meta-analysis, Br. J. Psychiatry, 215, pp. 456-467, (2019); Helbich M., Hagenauer J., Roberts H., Relative importance of perceived physical and social neighborhood characteristics for depression: a machine learning approach, Soc. Psychiatr. Psychiatr. Epidemiol., 55, pp. 599-610, (2020); Findings from the Global Burden of Disease Study 2017, (2018); Joshi S., Mooney S.J., Rundle A.G., Quinn J.W., Beard J.R., Cerda M., Pathways from neighborhood poverty to depression among older adults, Health Place, 43, pp. 138-143, (2017); Kim H., Seasonal impacts of particulate matter levels on bike sharing in Seoul, South Korea, Int. J. Environ. Res. Publ. Health, 17, (2020); Kim K.N., Lim Y.H., Bae H.J., Kim M., Jung K., Hong Y.C., Long-term fine particulate matter exposure and major depressive disorder in a community-based urban cohort, Environ. Health Perspect., 124, pp. 1547-1553, (2016); Kim Y.E., Park H., Jo M.W., Oh I.H., Go D.S., Jung J., Yoon S.J., Trends and patterns of burden of disease and injuries in Korea using disability-adjusted life years, J. Kor. Med. Sci., 34, (2019); Lee K.S., Jang J.Y., Yu Y.D., Heo J.S., Han H.S., Yoon Y.S., Kang C.M., Hwang H.K., Kang S., Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: retrospective cohort study, Int. J. Surg., 93, (2021); Liu Q., He H., Yang J., Feng X., Zhao F., Lyu J., Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study, J. Psychiatr. Res., 126, pp. 134-140, (2020); Patient care & health information: diseases & conditions: depression, (2021); Patel V., Chisholm D., Parikh R., Charlson F.J., Degenhardt L., Dua T., Ferrari A.J., Hyman S., Laxminarayan R., Levin C., Lund C., Medina Mora M.E., Petersen I., Scott J., Shidhaye R., Vijayakumar L., Thornicroft G., Whiteford H., Addressing the burden of mental, neurological, and substance use disorders: key messages from Disease Control Priorities, Lancet, 387, pp. 1672-1685, (2016); Shin J., Park J.Y., Choi J., Long-term exposure to ambient air pollutants and mental health status: a nationwide population-based cross-sectional study, PLoS One, 13, (2018); Statistics Korea, Korean statistical information Service. Daejeon, (2021); Strobl C., Boulesteix A.L., Zeileis A., Hothorn T., Bias in random forest variable importance measures: illustrations, sources and a solution, BMC Bioinf., 8, (2007); Therneau T., Crowson C., Atkinson E., Using time dependent covariates and time dependent coefficients in the Cox model. R Core Team: Vienna, (2020); Won E., Ham B.J., Imaging genetics studies on monoaminergic genes in major depressive disorder, Progress in neuro-psychopharmacology & biological psychiatry, 64, pp. 311-319, (2016); World Health Organization, Depression and Other Common Mental Disorders: Global Health Estimates, (2017); Xue T., Guan T., Zheng Y., Geng G., Zhang Q., Yao Y., Zhu T., Long-term PM<sub>2.5</sub> exposure and depressive symptoms in China: a quasi-experimental study, Lancet Regional Health - Western Pacific, 6, (2021); Xue T., Zhu T., Zheng Y., Zhang Q., Declines in mental health associated with air pollution and temperature variability in China, Nat. Commun., 10, (2019); Zhang Z., Reinikainen J., Adeleke K.A., Pieterse M.E., Groothuis-Oudshoorn C.G.M., Time-varying covariates and coefficients in Cox regression models, Ann. Transl. Med., 6, (2018); Zhou Y.M., An S.J., Tang E.J., Xu C., Cao Y., Liu X.L., Yao C.Y., Xiao H., Zhang Q., Liu F., Li Y.F., Ji A.L., Cai T.J., Association between short-term ambient air pollution exposure and depression outpatient visits in cold seasons: a time-series analysis in northwestern China, J. Toxicol. Environ. Health, 84, pp. 389-398, (2021)","B.-J. Ham; Department of Mental Health, Korea University Anam Hospital, Seongbuk-gu, 73 Goryeodae-ro, Seoul, 02841, South Korea; email: hambj@korea.ac.kr","","Elsevier Ltd","","","","","","00223956","","JPYRA","35026595","English","J. Psychiatr. Res.","Article","Final","","Scopus","2-s2.0-85122528107"
"van Huizen L.M.G.; Blokker M.; Rip Y.; Veta M.; Mooij Kalverda K.A.; Bonta P.I.; Duitman J.W.; Groot M.L.","van Huizen, Laura M.G. (57207925617); Blokker, Max (57317690900); Rip, Yael (58079115800); Veta, Mitko (36519821000); Mooij Kalverda, Kirsten A. (57339275400); Bonta, Peter I. (14070136100); Duitman, Jan Willem (23477255200); Groot, Marie Louise (7004139338)","57207925617; 57317690900; 58079115800; 36519821000; 57339275400; 14070136100; 23477255200; 7004139338","Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning","2023","PLoS ONE","18","6 June","e0279525","","","","2","10.1371/journal.pone.0279525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164211178&doi=10.1371%2fjournal.pone.0279525&partnerID=40&md5=697ace294ba48ba1ec6718d5ee49c5ed","LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Medical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, University of Technology, Eindhoven, Netherlands; Department of Pulmonary Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands; Department of Experimental immunology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands; Amsterdam Infection & Immunity, Inflammatory Diseases, Amsterdam, Netherlands","van Huizen L.M.G., LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Blokker M., LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Rip Y., LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Veta M., Medical Image Analysis Group (IMAG/e), Department of Biomedical Engineering, University of Technology, Eindhoven, Netherlands; Mooij Kalverda K.A., Department of Pulmonary Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands; Bonta P.I., Department of Pulmonary Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands; Duitman J.W., Department of Pulmonary Medicine, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands, Department of Experimental immunology, Amsterdam UMC Location University of Amsterdam, Amsterdam, Netherlands, Amsterdam Infection & Immunity, Inflammatory Diseases, Amsterdam, Netherlands; Groot M.L., LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands","Background In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Objective To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. Methods Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. Results Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set. Conclusions Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations. © 2023 van Huizen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Bronchoalveolar Lavage; Bronchoalveolar Lavage Fluid; Cell Differentiation; Deep Learning; Humans; Leukocyte Count; Leukocytes; Lung Diseases, Interstitial; Microscopy; Article; asthma; autofluorescence; blood sampling; bronchoalveolar lavage fluid; cell count; cell differentiation; cell population; cell structure; clinical article; controlled study; cytology; data processing; deep learning; eosinophil; human; human cell; immunocompetent cell; information processing; interstitial lung disease; learning algorithm; leukocyte; lymphocyte; macrophage; measurement accuracy; multiphoton microscopy; neutrophil; second harmonic generation microscopy; two-dimensional imaging; workload; bronchoalveolar lavage fluid; cell differentiation; interstitial lung disease; leukocyte; leukocyte count; lung lavage; microscopy","","","","","Tamara Dekker and Barbara Dierdorp; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO","This publication is part of the project InstantPathology (with project number 15825) of the research program Applied and Engineering Sciences which is (partly) financed by the Dutch Research Council (NWO), awarded to M.G. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We like to thank Tamara Dekker and Barbara Dierdorp for manually counting the leukocytes in the DQ cytospin slides.","Mescher A., Junqueira’s Basic Histology: Text and Atlas, Fifteenth Edition, (2018); Adderley N, Humphreys CJ, Barnes H, Ley B, Premji ZA, Johannson KA., Bronchoalveolar lavage fluid lymphocytosis in chronic hypersensitivity pneumonitis: a systematic review and meta-analysis, European Respiratory Journal, 56, (2020); Davidson KR, Ha DM, Schwarz MI, Chan ED., Bronchoalveolar lavage as a diagnostic procedure: a review of known cellular and molecular findings in various lung diseases, J Thorac Dis, 12, pp. 4991-5019, (2020); Hetzel J, Wells AU, Costabel U, Colby T V., Walsh SLF, Verschakelen J, Et al., Transbronchial cryo-biopsy increases diagnostic confidence in interstitial lung disease: a prospective multicentre trial, European Respiratory Journal, 56, (2020); Jain M, Narula N, Aggarwal A, Stiles B, Shevchuk MM, Sterling J, Et al., Multiphoton Microscopy: A Potential “Optical Biopsy” Tool for Real-Time Evaluation of Lung Tumors Without the Need for Exogenous Contrast Agents, Arch Pathol Lab Med, 138, pp. 1037-1047, (2014); Zhang Z, de Munck JC, Verburg N, Rozemuller AJ, Vreuls W, Cakmak P, Et al., Quantitative Third Harmonic Generation Microscopy for Assessment of Glioma in Human Brain Tissue, Advanced Science, (2019); van Huizen LMG, Kuzmin N V., Barbe E, van der Velde S, te Velde EA, Groot ML., Second and third harmonic generation microscopy visualizes key structural components in fresh unprocessed healthy human breast tissue, J Biophotonics, 12, (2019); van Huizen LMG, Radonic T, van Mourik F, Seinstra D, Dickhoff C, Daniels JMA, Et al., Compact portable multiphoton microscopy reveals histopathological hallmarks of unprocessed lung tumor tissue in real time, Transl Biophotonics, 2, (2020); Yang L, Park J, Marjanovic M, Chaney EJ, Spillman DR, Phillips H, Et al., Intraoperative Label-Free Multimodal Nonlinear Optical Imaging for Point-of-Procedure Cancer Diagnostics, IEEE Journal of Selected Topics in Quantum Electronics, 27, pp. 1-12, (2021); Tsai C-K, Chen Y-S, Wu P-C, Hsieh T-Y, Liu H-W, Yeh C-Y, Et al., Imaging granularity of leukocytes with third harmonic generation microscopy, Biomed Opt Express, 3, pp. 2234-2243, (2012); Wu C-H, Wang T-D, Hsieh C-H, Huang S-H, Lin J-W, Hsu S-C, Et al., Imaging Cytometry of Human Leukocytes with Third Harmonic Generation Microscopy, Sci Rep, 6, (2016); Gavgiotaki E, Filippidis G, Zerva I, Kenanakis G, Archontakis E, Agelaki S, Et al., Detection of the T cell activation state using nonlinear optical microscopy, J Biophotonics, 12, (2019); Monici M., Cell and tissue autofluorescence research and diagnostic applications, Biotechnol Annu Rev, 11, pp. 227-256, (2005); Teh SK, Zheng W, Li S, Li D, Zeng Y, Yang Y, Et al., Multimodal nonlinear optical microscopy improves the accuracy of early diagnosis of squamous intraepithelial neoplasia, J Biomed Opt, 18, (2013); Wang ZJ, Walsh AJ, Skala MC, Gitter A., Classifying T cell activity in autofluorescence intensity images with convolutional neural networks, J Biophotonics, 13, (2020); Shi J, Tu H, Park J, Marjanovic M, Higham AM, Luckey NN, Et al., Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue, bioRxiv, (2022); Blokker M, Hamer PC de W, Wesseling P, Groot ML, Veta M., Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning, Sci Rep, 12, (2022); Zhang Q, Yun KK, Wang H, Yoon SW, Lu F, Won D., Automatic cell counting from stimulated Raman imaging using deep learning, PLoS One, 16, (2021); Lavitt F, Rijlaarsdam DJ, van der Linden D, Weglarz-Tomczak E, Tomczak JM., Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines, Applied Sciences, 11, (2021); He K, Zhang X, Ren S, Sun J., Deep Residual Learning for Image Recognition, (2015); Deng J, Dong W, Socher R, Li L-J, Li Kai, Li Fei-Fei, ImageNet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, (2009); Abualigah L., Classification Applications with Deep Learning and Machine Learning Technologies, (2023); Tan M, Le Q V., EfficientNetV2: Smaller Models and Faster Training, (2021); Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C., MobileNetV2: Inverted Residuals and Linear Bottlenecks, (2018); Kingma DP, Ba J., Adam: A Method for Stochastic Optimization, (2014); Smith LN., A disciplined approach to neural network hyper-parameters: Part 1—learning rate, batch size, momentum, and weight decay, (2018); Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Et al., TensorFlow: A System for Large-Scale Machine Learning, pp. 265-283, (2016); Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D., Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, (2016); Meudec R., tf-explain, Zenodo, (2021); Tigner A, Ibrahim SA, Murray I., Histology, White Blood Cell, (2019); Luca DC., Eosinophils, (2012)","L.M.G. van Huizen; LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; email: l.m.g.van.huizen@vu.nl","","Public Library of Science","","","","","","19326203","","POLNC","37368904","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164211178"
"Murad S.A.; Adhikary A.; Muzahid A.J.M.; Sarker M.M.H.; Khan M.A.R.; Hossain M.B.; Bairagi A.K.; Masud M.; Kowsher M.","Murad, Saydul Akbar (57218952421); Adhikary, Apurba (57218669191); Muzahid, Abu Jafar Md (57221741096); Sarker, Md. Murad Hossain (57226832619); Khan, Md. Ashikur Rahman (36727738800); Hossain, Md. Bipul (57205879556); Bairagi, Anupam Kumar (55489441400); Masud, Mehedi (17338820600); Kowsher, Md. (57215324267)","57218952421; 57218669191; 57221741096; 57226832619; 36727738800; 57205879556; 55489441400; 17338820600; 57215324267","AI Powered Asthma Prediction Towards Treatment Formulation: An Android App Approach","2022","Intelligent Automation and Soft Computing","34","1","","87","103","16","3","10.32604/iasc.2022.024777","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129084321&doi=10.32604%2fiasc.2022.024777&partnerID=40&md5=d979af6e6f4938467a4640a9e2387aac","Faculty of Computing, Universiti Malaysia Pahang, Pahang, Pekan, 26600, Malaysia; Department of Information and Communication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh; Department of Information and Communication Technology, Comilla University (CoU), Comilla, Bangladesh; Computer Science and Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh; Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia; Department of Computer Science, Stevens Institute of Technology, Hoboken, 07030, NJ, United States","Murad S.A., Faculty of Computing, Universiti Malaysia Pahang, Pahang, Pekan, 26600, Malaysia; Adhikary A., Department of Information and Communication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh; Muzahid A.J.M., Faculty of Computing, Universiti Malaysia Pahang, Pahang, Pekan, 26600, Malaysia; Sarker M.M.H., Department of Information and Communication Technology, Comilla University (CoU), Comilla, Bangladesh; Khan M.A.R., Department of Information and Communication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh; Hossain M.B., Department of Information and Communication Engineering, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh; Bairagi A.K., Computer Science and Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh; Masud M., Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia; Kowsher M., Department of Computer Science, Stevens Institute of Technology, Hoboken, 07030, NJ, United States","Asthma is a disease which attacks the lungs and that affects people of all ages. Asthma prediction is crucial since many individuals already have asthma and increasing asthma patients is continuous. Machine learning (ML) has been demonstrated to help individuals make judgments and predictions based on vast amounts of data. Because Android applications are widely available, it will be highly beneficial to individuals if they can receive therapy through a simple app. In this study, the machine learning approach is utilized to determine whether or not a person is affected by asthma. Besides, an android application is being cre-ated to give therapy based on machine learning predictions. To collect data, we enlisted the help of 4,500 people. We collect information on 23 asthma-related characteristics. We utilized eight robust machine learning algorithms to analyze this dataset. We found that the Decision tree classifier had the best performance, out of the eight algorithms, with an accuracy of 87%. TensorFlow is utilized to integrate machine learning with an Android application. We accomplished asthma therapy using an Android application developed in Java and running on the Android Studio platform. © 2022, Tech Science Press. All rights reserved.","android application; Artificial intelligence; asthma prediction; machine learning","","","","","","Taif University, TU, (TURSP-2020/10)","Funding text 1: Funding Statement: This work was supported by Taif University Researchers Supporting Projects (TURSP). Under number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.; Funding text 2: This work was supported by Taif University Researchers Supporting Projects (TURSP). Under number (TURSP-2020/10), Taif University, Taif, Saudi Arabia.","Asthma, (2021); Awal M. A., Hossain M. S., Debjit K., Ahmed N., Nemri R. D. 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Conf. on Innovative Technology, Engineering and Science, pp. 194-205, (2020); Harvey J. L., Kumar S. A. P., Machine learning for predicting development of asthma in children, IEEE Symp. Series on Computational Intelligence (SSCI), pp. 596-603, (2019); Tigga N. P., Garg S., Prediction of type 2 diabetes using machine learning classification methods, Procedia Computer Science, 167, pp. 706-716, (2020); Murad S. A., Azmi Z. R. M., Hakami Z. H., Prottasha N. J., Kowsher M., Computer-aided system for extending the performance of diabetes analysis and prediction, 7th Int. Conf. on Software Engineering and Computer Systems (ICSECS-2021), pp. 465-470, (2021); Sikder N., Masud M., Bairagi A. K., Arif A. S. M., Nahid A. A., Et al., Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images, Symmetry, 13, 4, (2021); Karim A., Islam M. A., Mishra P., Muzahid A. J. M., Yousuf A., Et al., Yeast and bacteria co-culture-based lipid production through bioremediation of palm oil mill effluent: A statistical optimization, Biomass Conversion and Biorefinery, 14, 2, pp. 1-12, (2021); Bhat G. S., Shankar N., Kim D., Song D. J., Seo S., Et al., Machine learning-based asthma risk prediction using iot and smartphone applications, IEEE Access, 9, pp. 118708-118715, (2021); Muzahid A. J. M., Kamarulzaman S. F., Rahim M. A., Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving, Emerging Technology in Computing, Communication and Electronics (ETCCE), pp. 1-6, (2020); Akbar W., Wu W. P., Faheem M., Saleem M. A., Golilarz N. A., Et al., Machine learning classifiers for asthma disease prediction: A practical illustration, 16th Int. Computer Conf. on Wavelet Active Media Technology and Information Processing, pp. 143-148, (2019); Islam M. M., Iqbal H., Haque M. R., Hasan M. K., Prediction of breast cancer using support vector machine and K-Nearest neighbors, 2017 IEEE Region 10 Humanitarian Technology Conf. (R10-HTC), pp. 226-229, (2017); Islam M. M., Haque M. R., Iqbal H., Hasan M. M., Hasan M., Et al., Breast cancer prediction: A comparative study using machine learning techniques, SN Computer Science, 1, 5, (2020); Hasan M. K., Islam M. M., Hashem M. M. A., Mathematical model development to detect breast cancer using multigene genetic programming, 5th Int. Conf. on Informatics, Electronics and Vision (ICIEV), pp. 574-557, (2016); Ayon S. I., Islam M. M., Diabetes prediction: A deep learning approach, International Journal of Information Engineering and Electronic Business (IJIEEB), 11, 2, pp. 21-27, (2019); Haque M. R., Islam M. M., Iqbal H., Reza M. S., Hasan M. K., Performance evaluation of random forests and artificial neural networks for the classification of liver disorder, Int. Conf. on Computer, Communication, Chemical, Materials and Electronic Engineering, pp. 1-5, (2018); Ayon S. I., Islam M. M., Hossain M. R., Coronary artery heart disease prediction: A comparative study of computational intelligence techniques, IETE Journal of Research, Taylor & Francis, 12, 2, pp. 1-20, (2020); Luo L., Yu X., Yong Z., Li C., Gu Y., Design comorbidity portfolios to improve treatment cost prediction of asthma using machine learning, IEEE Journal of Biomedical and Health Informatics, 25, 6, pp. 2237-2247, (2020); Muzahid A. J. M., Kamarulzaman S. F., Rahman M. A., Comparison of ppo and sac algorithms towards decision making strategies for collision avoidance among multiple autonomous vehicles, Int. Conf. on Software Engineering Computer Systems and 4th Int. 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S., Kaur A., Alroobaea R., Et al., CROWD: Crow search and deep learning based feature extractor for classification of parkinson’s disease, ACM Transactions on Internet Technology (TOIT), 21, 3, pp. 1-18, (2021); Kowsher M., Tahabilder A., Murad S. A., Impact-learning: A robust machine learning algorithm, Proc. of the 8th Int. Conf. on Computer and Communications Management (ICCCM’20), pp. 9-13, (2020); Tomita K., Nagao R., Touge H., Ikeuchi T., Sano H., Et al., Deep learning facilitates the diagnosis of adult asthma, Allergology International, 68, 4, pp. 456-461, (2019); Reddy P. B. P., Reddy M. P. K., Reddy G. V. M., Mehata K. M., Fake data analysis and detection using ensembled hybrid algorithm, 3rd Int. Conf. on Computing Methodologies and Communication (ICCMC), pp. 890-897, (2019); Ali A., Ata A. M. B., Saleh N. K., Autoregression features for smart robotic wheelchair eeg-ica classification using a bagging model, (2021); Gupta A., Panda D. K., Pande M., Development of mobile application for laundry services using android studio, International Journal of Applied Engineering Research, India, 13, 12, pp. 10623-10626, (2018); Android Abhi, Android App Development Tutorial: Beginners Guide with Examples, Code and Tutorials; Xie J., Wang Q., Benchmarking machine learning algorithms on blood glucose prediction for type I Diabetes in comparison with classical time-series models, IEEE Transactions on Biomedical Engineering, 67, 11, pp. 3101-3124, (2020); Haq A. U., Li J. P., Memon M. H., khan J., Malik A., Et al., Feature selection based on l1-norm support vector machine and effective recognition system for parkinsonas disease using voice recordings, IEEE Access, 7, pp. 37718-37734, (2019); Gad Ahmed Fawzy, Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall, (2020); Mutai C. K., McSharry P. 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Soft Comp.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85129084321"
"Ghrabli S.; Elgendi M.; Menon C.","Ghrabli, Syrine (57903021300); Elgendi, Mohamed (58898892700); Menon, Carlo (23989261600)","57903021300; 58898892700; 23989261600","Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments","2024","Scientific Reports","14","1","593","","","","2","10.1038/s41598-023-50371-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181513516&doi=10.1038%2fs41598-023-50371-2&partnerID=40&md5=3981192f2f0121772fa77e269652cb8a","Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland; Department of Physics, ETH Zurich, Zurich, 8093, Switzerland","Ghrabli S., Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland, Department of Physics, ETH Zurich, Zurich, 8093, Switzerland; Elgendi M., Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland; Menon C., Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland","Coughing, a prevalent symptom of many illnesses, including COVID-19, has led researchers to explore the potential of cough sound signals for cost-effective disease diagnosis. Traditional diagnostic methods, which can be expensive and require specialized personnel, contrast with the more accessible smartphone analysis of coughs. Typically, coughs are classified as wet or dry based on their phase duration. However, the utilization of acoustic analysis for diagnostic purposes is not widespread. Our study examined cough sounds from 1183 COVID-19-positive patients and compared them with 341 non-COVID-19 cough samples, as well as analyzing distinctions between pneumonia and asthma-related coughs. After rigorous optimization across frequency ranges, specific frequency bands were found to correlate with each respiratory ailment. Statistical separability tests validated these findings, and machine learning algorithms, including linear discriminant analysis and k-nearest neighbors classifiers, were employed to confirm the presence of distinct frequency bands in the cough signal power spectrum associated with particular diseases. The identification of these acoustic signatures in cough sounds holds the potential to transform the classification and diagnosis of respiratory diseases, offering an affordable and widely accessible healthcare tool. © 2024, The Author(s).","","Acoustics; Algorithms; Cough; COVID-19; COVID-19 Testing; Humans; Sound; acoustics; algorithm; coronavirus disease 2019; coughing; COVID-19 testing; human; sound","","","","","","","Chung K.F., Pavord I.D., Prevalence, pathogenesis, and causes of chronic cough, Lancet, 371, pp. 1364-1374, (2008); Morice A.H., Et al., Ers guidelines on the diagnosis and treatment of chronic cough in adults and children, Eur. Respir. J., (2020); Tan R.T., Et al., Utility of ct scan evaluation for predicting pulmonary hypertension in patients with parenchymal lung disease, Chest, 113, pp. 1250-1256, (1998); Rytter H., Jamet A., Coureuil M., Charbit A., Ramond E., Which current and novel diagnostic avenues for bacterial respiratory diseases?, Front. 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Aust., 214, (2021); Smith J.A., Ashurst H.L., Jack S., Woodcock A.A., Earis J.E., The description of cough sounds by healthcare professionals, Cough, 2, (2006); Ghrabli S., Elgendi M., Menon C., Challenges and opportunities of deep learning for cough-based covid-19 diagnosis: A scoping review, Diagnostics, (2022); Pramono R.X.A., Imtiaz S.A., Rodriguez-Villegas E., A cough-based algorithm for automatic diagnosis of pertussis, PLoS One, 11, (2016); Porter P., Et al., Diagnosing chronic obstructive airway disease on a smartphone using patient-reported symptoms and cough analysis: Diagnostic accuracy study, JMIR Form Res., 4, (2020); Molnar C., Casalicchio G., Bischl B., Et al., Interpretable machine learning—a brief history, state-of-the-art and challenges, ECML PKDD 2020 Workshops, pp. 417-431, (2020); Hashimoto Y., Et al., Influence of the rheological properties of airway mucus on cough sound generation, Respirology, 8, pp. 45-51, (2003); Hall J.I., Lozano M., Estrada-Petrocelli L., Birring S., Turner R., The present and future of cough counting tools, J. 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Elgendi; Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland; email: moe.elgendi@hest.ethz.ch; C. Menon; Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, 8008, Switzerland; email: carlo.menon@hest.ethz.ch","","Nature Research","","","","","","20452322","","","38182601","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85181513516"
"Chen R.; Xie G.; Lin Z.; Gu G.; Yu Y.; Yu J.; Liu Z.","Chen, Ruibin (58082464600); Xie, Guobo (23398925100); Lin, Zhiyi (23389449900); Gu, Guosheng (55321935300); Yu, Yi (58918207400); Yu, Junrui (58082517800); Liu, Zhenguo (55988945000)","58082464600; 23398925100; 23389449900; 55321935300; 58918207400; 58082517800; 55988945000","Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning","2024","Interdisciplinary Sciences - Computational Life Sciences","16","2","","345","360","15","2","10.1007/s12539-024-00607-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186550121&doi=10.1007%2fs12539-024-00607-0&partnerID=40&md5=af4e7a81e2089a14d58024dd202d51ab","School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China","Chen R., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Xie G., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Lin Z., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Gu G., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Yu Y., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Yu J., School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; Liu Z., Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China","Abstract: Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively. Graphic Abstract: (Figure presented.) © International Association of Scientists in the Interdisciplinary Areas 2024.","Linear neighborhood label propagation; Microbe-disease associations; Multi-order similarity learning; Similarity fusion","Algorithms; Computational Biology; Humans; Machine Learning; algorithm; bioinformatics; human; machine learning; procedures","","","","","National Natural Science Foundation of China, NSFC, (62002070, 82001331); National Natural Science Foundation of China, NSFC; Science and Technology Plan Project of Guangzhou City, (202102021236)","This work is supported by the National Natural Science Foundation of China (62002070, 82001331) and the Science and Technology Plan Project of Guangzhou City (202102021236).","Morgan X.C., Segata N., Huttenhower C., Biodiversity and functional genomics in the human microbiome, Trends Genet, 29, 1, pp. 51-58, (2013); Ma W., Zhang L., Zeng P., Et al., An analysis of human microbe-disease associations, Brief Bioinform, 18, 1, pp. 85-97, (2017); Puschhof J., Pleguezuelos-Manzano C., Clevers H., Organoids and organs-on-chips: Insights into human gut-microbe interactions, Cell Host Microbe, 29, 6, pp. 867-878, (2021); Rook G., Backhed F., Levin B.R., Et al., Evolution, human-microbe interactions, and life history plasticity, Lancet, 390, pp. 521-530, (2017); Dedrick S., Sundaresh B., Huang Q., Et al., The role of gut microbiota and environmental factors in type 1 diabetes pathogenesis, Front Endocrinol, 11, (2020); Zhao Y., Wang C.-C., Chen X., Microbes and complex diseases: from experimental results to computational models, Brief Bioinform, 22, 3, (2021); Chen X., Huang Y.-A., You Z.-H., Et al., A novel approach based on katz measure to predict associations of human microbiota with non-infectious diseases, Bioinformatics, 34, 8, (2018); Huang Z.-A., Chen X., Zhu Z., Et al., Pbhmda: path-based human microbe-disease association prediction, Front Microbiol, 8, (2017); Shokri Garjan H., Omidi Y., Poursheikhali Asghari M., Et al., In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy, Gut Pathog, 15, 1, (2023); Long Y., Luo J., Wmghmda: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network, BMC Bioinform, 20, 1, (2019); Wen Z., Yan C., Duan G., Et al., A survey on predicting microbe-disease associations: biological data and computational methods, Brief Bioinform, 22, 3, (2021); Shen Z., Jiang Z., Bao W., Cmfhmda: Collaborative matrix factorization for human microbe-disease association prediction, 10362, pp. 261-269, (2017); He B.-S., Peng L.-H., Li Z., Human microbe-disease association prediction with graph regularized non-negative matrix factorization, Front Microbiol, 9, (2018); Yang X., Kuang L., Chen Z., Et al., Multi-similarities bilinear matrix factorization-based method for predicting human microbe-disease associations, Front Genet, 12, (2021); Xu D., Xu H., Zhang Y., Et al., Novel collaborative weighted non-negative matrix factorization improves prediction of disease-associated human microbes, Front Microbiol, 13, (2022); Wang L., Tan Y., Yang X., Et al., Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models, Brief Bioinform, 23, 3, (2022); Luo J., Long Y., Ntshmda: prediction of human microbe-disease association based on random walk by integrating network topological similarity, IEEE ACM Trans Comput Biol Bioinform, 17, 4, pp. 1341-1351, (2018); Yan C., Duan G., Wu F.-X., Et al., Brwmda: predicting microbe-disease associations based on similarities and bi-random walk on disease and microbe networks, IEEE ACM Trans Comput Biol Bioinform, 17, 5, pp. 1595-1604, (2019); Chen Q., Lai D., Lan W., Et al., Ildmsf: inferring associations between long non-coding RNA and disease based on multi-similarity fusion, IEEE ACM Trans Comput Biol Bioinform, 18, 3, pp. 1106-1112, (2019); Jiang L., Ding Y., Tang J., Et al., Mda-skf: similarity kernel fusion for accurately discovering miRNA-disease association, Front Genet, 9, (2018); Xie G.-B., Chen R.-B., Lin Z.-Y., Et al., Predicting lncrna-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation, Brief Bioinform, 24, 1, (2023); Yin M.-M., Liu J.-X., Gao Y.-L., Et al., Ncplp: a novel approach for predicting microbe-associated diseases with network consistency projection and label propagation, IEEE Trans Cybern, 52, 6, pp. 5079-5087, (2020); Liu J.-X., Yin M.-M., Gao Y.-L., Et al., Msf-lrr: multi-similarity information fusion through low-rank representation to predict disease-associated microbes, IEEE ACM Trans Comput Biol Bioinform, 20, 1, pp. 534-543, (2022); Janssens Y., Nielandt J., Bronselaer A., Et al., Disbiome database: linking the microbiome to disease, BMC Microbiol, 18, (2018); Van Laarhoven T., Nabuurs S.B., Marchiori E., Gaussian interaction profile kernels for predicting drug-target interaction, Bioinformatics, 27, 21, pp. 3036-3043, (2011); Kamneva O.K., Genome composition and phylogeny of microbes predict their co-occurrence in the environment, PLOS Comput Biol, 13, 2, (2017); Zhang W., Qu Q., Zhang Y., Et al., The linear neighborhood propagation method for predicting long non-coding rna-protein interactions, Neurocomputing, 273, pp. 526-534, (2018); Wang F., Zhang C., Label propagation through linear neighborhoods, IEEE Trans Knowl Data Eng, 20, 1, pp. 55-67, (2007); Long Y., Luo J., Zhang Y., Et al., Predicting human microbe-disease associations via graph attention networks with inductive matrix completion, Brief Bioinform, 22, 3, (2021); Xie G., Meng T., Luo Y., Et al., Skf-lda: similarity kernel fusion for predicting lncrna-disease association, Mol Ther Nucleic, 18, pp. 45-55, (2019); Liu H., Bing P., Zhang M., Et al., Mnnmda: predicting human microbe-disease association via a method to minimize matrix nuclear norm, Comput Struct Biotechnol J, 21, pp. 1414-1423, (2023); Wang F., Huang Z.-A., Chen X., Et al., Lrlshmda: laplacian regularized least squares for human microbe-disease association prediction, Sci Rep, 7, 1, (2017); Zou S., Zhang J., Zhang Z., A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network, PLoS One, 12, 9, (2017); Maahs D.M., West N.A., Lawrence J.M., Mayer-Davis E.J., Epidemiology of type 1 diabetes, Endocrinol Metab Clin North Am, 39, 3, pp. 481-497, (2010); Gillespie K.M., Type 1 diabetes: pathogenesis and prevention, Cmaj, 175, 2, pp. 165-170, (2006); Acharjee S., Ghosh B., Al-Dhubiab B.E., Nair A.B., Understanding type 1 diabetes: etiology and models, Can J Diabetes, 37, 4, pp. 269-276, (2013); Lu X., Zhao C., Exercise and type 1 diabetes, Adv Exp Med Biol, 1228, pp. 107-121, (2020); Vaarala O., Human intestinal microbiota and type 1 diabetes, Curr Diabetes Rep, 13, pp. 601-607, (2013); Demirci M., Tokman H.B., Taner Z., Et al., Bacteroidetes and firmicutes levels in gut microbiota and effects of hosts tlr2/tlr4 gene expression levels in adult type 1 diabetes patients in istanbul, turkey, J. Diabetes Complicat, 34, 2, (2020); De Groot P., Nikolic T., Pellegrini S., Et al., Faecal microbiota transplantation halts progression of human new-onset type 1 diabetes in a randomised controlled trial, Gut, 70, 1, pp. 92-105, (2021); Gans M.D., Gavrilova T., Understanding the immunology of asthma: pathophysiology, biomarkers, and treatments for asthma endotypes, Paediatr Respir Rev, 36, pp. 118-127, (2020); Ntontsi P., Photiades A., Zervas E., Et al., Genetics and epigenetics in asthma, Int J Mol Sci, 22, 5, (2021); Ver Heul A., Planer J., Kau A.L., The human microbiota and asthma, Clin Rev Allergy IMMU, 57, 3, pp. 350-363, (2019); Chen Y., Zhan X., Wang D., Association between helicobacter pylori and risk of childhood asthma: a meta-analysis of 18 observational studies, J Asthma, 59, 5, pp. 890-900, (2022); Guo M.-Y., Chen H.-K., Ying H.-Z., Et al., The role of respiratory flora in the pathogenesis of chronic respiratory diseases, BioMed Res Int, 2021, (2021); Aydin M., Weisser C., Rue O., Mariadassou M., Et al., The rhinobiome of exacerbated wheezers and asthmatics: Insights from a german pediatric exacerbation network, Front Allergy, 2, (2021)","Z. Lin; School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; email: lzy291@gdut.edu.cn; G. Gu; School of Computer, Guangdong University of Technology, Guangzhou, 510000, China; email: gsgu@gdut.edu.cn; Z. Liu; Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China; email: liuzhg2@mail.sysu.edu.cn","","Springer Science and Business Media Deutschland GmbH","","","","","","19132751","","","38436840","English","Interdiscip. Sci. Comput. Life Sci.","Article","Final","","Scopus","2-s2.0-85186550121"
"Zhang Z.; Yu H.; Wang Q.; Ding Y.; Wang Z.; Zhao S.; Bian T.","Zhang, Zheming (58190034600); Yu, Haoda (57212056426); Wang, Qi (59514555400); Ding, Yu (58733980800); Wang, Ziteng (58718974600); Zhao, Songyun (57929940400); Bian, Tao (56274635300)","58190034600; 57212056426; 59514555400; 58733980800; 58718974600; 57929940400; 56274635300","A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments","2023","Journal of Inflammation Research","16","","","5647","5665","18","3","10.2147/JIR.S438308","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177754280&doi=10.2147%2fJIR.S438308&partnerID=40&md5=231e639ab9a67f6ecb3eeab8f547552e","Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China; Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China","Zhang Z., Wuxi Medical Center of Nanjing Medical University, Wuxi, China, Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China; Yu H., Wuxi Medical Center of Nanjing Medical University, Wuxi, China, Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China; Wang Q., Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China; Ding Y., Wuxi Medical Center of Nanjing Medical University, Wuxi, China, Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China; Wang Z., Wuxi Medical Center of Nanjing Medical University, Wuxi, China, Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China; Zhao S., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Bian T., Wuxi Medical Center of Nanjing Medical University, Wuxi, China, Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, China","Background: This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods: In this study, the “CIBERSORT” algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The “limma” package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results: M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P <0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the “Boruta” and “LASSO” algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion: According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD. © 2023 Zhang et al.","CLEC5A; COPD; FTL; immune infiltration; machine learning; multivariate logistic regression; SLC2A3; WGCNA","transcriptome; algorithm; antibody labeling; area under the curve; Article; bioinformatics; cell granule; cell viability; chronic obstructive lung disease; CLEC5A gene; clinical article; controlled study; cytokine production; diagnostic test accuracy study; differential gene expression; ex vivo study; female; ferroptosis; FTL gene; functional enrichment analysis; gene; gene expression; gene expression profiling; gene ontology; genetic marker; human; human cell; human tissue; immunocompetent cell; immunohistochemistry; innate immunity; KEGG; least absolute shrinkage and selection operator; lung parenchyma; lysosome; machine learning; macrophage; male; microenvironment; normal human; phagosome; prediction; protein expression; protein protein interaction; real time polymerase chain reaction; receiver operating characteristic; RNA extraction; sensitivity and specificity; single cell RNA seq; SLC2A3  gene; training; upregulation; weighted gene co expression network analysis; Western blotting","","","ABI 9600, Applied Biosystems; Cell Counting Kit-8, Beyotime, China; Immobilon, Millipore, Italy; PrimeScript, Takara, Japan; RNAiso Plus, Takara, Japan","Applied Biosystems; Beyotime, China; Millipore, Italy; Takara, Japan; Takara, Japan","Municipal Medical Development Discipline project of Wuxi City; National Natural Science Foundation of China, NSFC, (82173472); National Natural Science Foundation of China, NSFC","This work was supported by National Natural Science Foundation of China (82173472), and the Municipal Medical Development Discipline project of Wuxi City (FZXK-3).","Giordano L, Farnham A, Dhandapani PK, Et al., Alternative oxidase attenuates cigarette smoke–induced lung dysfunction and tissue damage, Am J Respir Cell Mol Biol, 60, 5, pp. 515-522, (2019); Silverman EK., Genetics of COPD, Annu Rev Physiol, 82, 1, pp. 413-431, (2020); Halpin DMG, Criner GJ, Papi A, Et al., Global Initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. 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Zhao; Wuxi Medical Center of Nanjing Medical University, Wuxi, China; email: 2021122190@stu.njmu.edu.cn; T. Bian; Wuxi Medical Center of Nanjing Medical University, Wuxi, China; email: btaophd@sina.com","","Dove Medical Press Ltd","","","","","","11787031","","","","English","J. Inflamm. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85177754280"
"Chen J.; Wang Y.; Hong M.; Wu J.; Zhang Z.; Li R.; Ding T.; Xu H.; Zhang X.; Chen P.","Chen, Jingrou (57217196254); Wang, Yang (57428572200); Hong, Mengzhi (57204936446); Wu, Jiahao (57201214508); Zhang, Zongjun (58688540500); Li, Runzhao (58305253600); Ding, Tangdan (59229176300); Xu, Hongxu (36098461900); Zhang, Xiaoli (57213540791); Chen, Peisong (55355461500)","57217196254; 57428572200; 57204936446; 57201214508; 58688540500; 58305253600; 59229176300; 36098461900; 57213540791; 55355461500","Application of peripheral blood routine parameters in the diagnosis of influenza and Mycoplasma pneumoniae","2024","Virology Journal","21","1","162","","","","2","10.1186/s12985-024-02429-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199306883&doi=10.1186%2fs12985-024-02429-4&partnerID=40&md5=9c43e9aa1b80a1b9d3ad82508b29e7c8","Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Department of Laboratory Medicine, Guangdong Province Prevention and Treatment Center for Occupational Diseases, Guangzhou, 510300, China; Department of Pediatrics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China","Chen J., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Wang Y., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Hong M., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Wu J., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Zhang Z., Department of Laboratory Medicine, Guangdong Province Prevention and Treatment Center for Occupational Diseases, Guangzhou, 510300, China; Li R., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Ding T., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Xu H., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China; Zhang X., Department of Pediatrics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; Chen P., Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China, Department of Laboratory Medicine, Nansha Division, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 511466, China","Objectives: Influenza and Mycoplasma pneumoniae infections often present concurrent and overlapping symptoms in clinical manifestations, making it crucial to accurately differentiate between the two in clinical practice. Therefore, this study aims to explore the potential of using peripheral blood routine parameters to effectively distinguish between influenza and Mycoplasma pneumoniae infections. Methods: This study selected 209 influenza patients (IV group) and 214 Mycoplasma pneumoniae patients (MP group) from September 2023 to January 2024 at Nansha Division, the First Affiliated Hospital of Sun Yat-sen University. We conducted a routine blood-related index test on all research subjects to develop a diagnostic model. For normally distributed parameters, we used the T-test, and for non-normally distributed parameters, we used the Wilcoxon test. Results: Based on an area under the curve (AUC) threshold of ≥ 0.7, we selected indices such as Lym# (lymphocyte count), Eos# (eosinophil percentage), Mon% (monocyte percentage), PLT (platelet count), HFC# (high fluorescent cell count), and PLR (platelet to lymphocyte ratio) to construct the model. Based on these indicators, we constructed a diagnostic algorithm named IV@MP using the random forest method. Conclusions: The diagnostic algorithm demonstrated excellent diagnostic performance and was validated in a new population, with an AUC of 0.845. In addition, we developed a web tool to facilitate the diagnosis of influenza and Mycoplasma pneumoniae infections. The results of this study provide an effective tool for clinical practice, enabling physicians to accurately diagnose and differentiate between influenza and Mycoplasma pneumoniae infection, thereby offering patients more precise treatment plans. © The Author(s) 2024.","Area under the curve, AUC; Influenza; IV@MP algorithm; Mycoplasma pneumonia; Peripheral blood routine parameters; Random forest","Adolescent; Adult; Aged; Algorithms; Child; Diagnosis, Differential; Female; Humans; Influenza, Human; Male; Middle Aged; Mycoplasma pneumoniae; Pneumonia, Mycoplasma; Young Adult; hemoglobin; adult; algorithm; area under the curve; Article; asthma; blood; cell differentiation; controlled study; coughing; diagnostic test accuracy study; eosinophil count; female; headache; hematocrit; human; influenza; lymphocyte count; machine learning; major clinical study; male; mean corpuscular hemoglobin; mean corpuscular volume; middle aged; monocyte; Mycoplasma pneumoniae; otalgia; outpatient department; platelet count; platelet lymphocyte ratio; receiver operating characteristic; retrospective study; sensitivity and specificity; adolescent; aged; blood; child; diagnosis; differential diagnosis; isolation and purification; young adult","","hemoglobin, 9008-02-0","","","Guangdong Natural Science Foundation-General Program, (2023A1515011252, 2022B1111020003)","The research is supported by the Guangdong Natural Science Foundation-General Program (2023A1515011252) and the Development Plan \u201CBiosafety Technology\u201D Key Project (2022B1111020003). 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Chen; Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; email: chps@mail3.sysu.edu.cn; X. Zhang; Department of Pediatrics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China; email: zhangxli@mail.sysu.edu.cn","","BioMed Central Ltd","","","","","","1743422X","","","39044252","English","Virol. J.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85199306883"
"Calvo R.A.; Peters D.; Moradbakhti L.; Cook D.; Rizos G.; Schuller B.; Kallis C.; Wong E.; Quint J.","Calvo, Rafael A. (8957803600); Peters, Dorian (55317597000); Moradbakhti, Laura (57782554900); Cook, Darren (57343289700); Rizos, Georgios (57194224622); Schuller, Bjoern (6603767415); Kallis, Constantinos (35268702600); Wong, Ernie (56201553500); Quint, Jennifer (16507541000)","8957803600; 55317597000; 57782554900; 57343289700; 57194224622; 6603767415; 35268702600; 56201553500; 16507541000","Assessing the Feasibility of a Text-Based Conversational Agent for Asthma Support: Protocol for a Mixed Methods Observational Study","2023","JMIR Research Protocols","12","","e42965","","","","3","10.2196/42965","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149151934&doi=10.2196%2f42965&partnerID=40&md5=9d73fd765edadf6f9b08ebeab428641a","Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Department of Computing, Imperial College London, London, United Kingdom; Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, United Kingdom","Calvo R.A., Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Peters D., Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Moradbakhti L., Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Cook D., Dyson School of Design Engineering, Imperial College London, London, United Kingdom; Rizos G., Department of Computing, Imperial College London, London, United Kingdom; Schuller B., Department of Computing, Imperial College London, London, United Kingdom; Kallis C., Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, United Kingdom; Wong E., Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, United Kingdom; Quint J., Faculty of Medicine, National Heart and Lung Institute, Imperial College London, London, United Kingdom","Background: Despite efforts, the UK death rate from asthma is the highest in Europe, and 65% of people with asthma in the United Kingdom do not receive the professional care they are entitled to. Experts have recommended the use of digital innovations to help address the issues of poor outcomes and lack of care access. An automated SMS text messaging–based conversational agent (ie, chatbot) created to provide access to asthma support in a familiar format via a mobile phone has the potential to help people with asthma across demographics and at scale. Such a chatbot could help improve the accuracy of self-assessed risk, improve asthma self-management, increase access to professional care, and ultimately reduce asthma attacks and emergencies. Objective: The aims of this study are to determine the feasibility and usability of a text-based conversational agent that processes a patient’s text responses and short sample voice recordings to calculate an estimate of their risk for an asthma exacerbation and then offers follow-up information for lowering risk and improving asthma control; assess the levels of engagement for different groups of users, particularly those who do not access professional services and those with poor asthma control; and assess the extent to which users of the chatbot perceive it as helpful for improving their understanding and self-management of their condition. Methods: We will recruit 300 adults through four channels for broad reach: Facebook, YouGov, Asthma + Lung UK social media, and the website Healthily (a health self-management app). Participants will be screened, and those who meet inclusion criteria (adults diagnosed with asthma and who use WhatsApp) will be provided with a link to access the conversational agent through WhatsApp on their mobile phones. Participants will be sent scheduled and randomly timed messages to invite them to engage in dialogue about their asthma risk during the period of study. After a data collection period (28 days), participants will respond to questionnaire items related to the quality of the interaction. A pre- and postquestionnaire will measure asthma control before and after the intervention. Results: This study was funded in March 2021 and started in January 2022. We developed a prototype conversational agent, which was iteratively improved with feedback from people with asthma, asthma nurses, and specialist doctors. Fortnightly reviews of iterations by the clinical team began in September 2022 and are ongoing. This feasibility study will start recruitment in January 2023. The anticipated completion of the study is July 2023. A future randomized controlled trial will depend on the outcomes of this study and funding. Conclusions: This feasibility study will inform a follow-up pilot and larger randomized controlled trial to assess the impact of a conversational agent on asthma outcomes, self-management, behavior change, and access to care. © Rafael A Calvo, Dorian Peters, Laura Moradbakhti, Darren Cook, Georgios Rizos, Bjoern Schuller, Constantinos Kallis, Ernie Wong, Jennifer Quint.","artificial intelligence; asthma; behavior change; chatbot; conversational agent; health; health education; well-being","","","","","","Asthma and Lung UK; Engineering and Physical Sciences Research Council, EPSRC, (EP/W002477/1)","Funding text 1: Results: This study was funded in March 2021 and started in January 2022. We developed a prototype conversational agent, which was iteratively improved with feedback from people with asthma, asthma nurses, and specialist doctors. Fortnightly reviews of iterations by the clinical team began in September 2022 and are ongoing. This feasibility study will start recruitment in January 2023. The anticipated completion of the study is July 2023. A future randomized controlled trial will depend on the outcomes of this study and funding. Conclusions: This feasibility study will inform a follow-up pilot and larger randomized controlled trial to assess the impact of a conversational agent on asthma outcomes, self-management, behavior change, and access to care.; Funding text 2: This research was made possible by grant EP/W002477/1 from the UK Engineering and Physical Sciences Research Council and Asthma + Lung UK.","Enilari O, Sinha S., The global impact of asthma in adult populations, Ann Glob Health, 85, 1, (2019); (2022); Ramsey RR, Caromody JK, Voorhees SE, Warning A, Cushing CC, Guilbert TW, Et al., A systematic evaluation of asthma management apps examining behavior change techniques, J Allergy Clin Immunol Pract, 7, 8, pp. 2583-2591, (2019); Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, Et al., Conversational agents in healthcare: a systematic review, J Am Med Inform Assoc, 25, 9, pp. 1248-1258, (2018); Parmar P, Ryu J, Pandya S, Sedoc J, Agarwal S., Health-focused conversational agents in person-centered care: a review of apps, NPJ Digit Med, 5, 1, (2022); Sundareswaran V, Sarkar A., Chatbots RESET: a framework for governing responsible use of conversational AI in healthcare, World Economic Forum, (2020); Rhee H, Allen J, Mammen J, Swift M., Mobile phone-based asthma self-management aid for adolescents (mASMAA): a feasibility study, Patient Prefer Adherence, 8, pp. 63-72, (2014); Kadariya D, Venkataramanan R, Yip HY, Kalra M, Thirunarayanan K, Sheth A., kBot: knowledge-enabled personalized chatbot for asthma self-management, Proc Int Conf Smart Comput SMARTCOMP, 2019, pp. 138-143, (2019); Kowatsch T, Schachner T, Harperink S, Barata F, Dittler U, Xiao G, Et al., Conversational agents as mediating social actors in chronic disease management involving health care professionals, patients, and family members: multisite single-arm feasibility study, J Med Internet Res, 23, 2, (2021); West B, Cumella A., Data sharing and technology: exploring the attitudes of people with asthma, Asthma + Lung UK, (2018); Xia T, Spathis D, Brown C, Ch J, Grammenos A, Han J, Et al., COVID-19 sounds: a large-scale audio dataset for digital respiratory screening, 2022 Presented at: Thirty-Sixth Conference on Neural Information Processing Systems (Round 2), (2022); Coulter A, Stilwell D, Kryworuchko J, Mullen PD, Ng CJ, van der Weijden T., A systematic development process for patient decision aids, BMC Med Inform Decis Mak, 13, (2013); Ryan RM, Deci EL., Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness, (2017); Peters D, Calvo RA, Ryan RM., Designing for motivation, engagement and wellbeing in digital experience, Front Psychol, 9, (2018); Peters D., Wellbeing supportive design – research-based guidelines for supporting psychological wellbeing in user experience, Int J Hum Comput Interaction, pp. 1-13, (2022); Ntoumanis N, Ng JY, Prestwich A, Quested E, Hancox JE, Thogersen-Ntoumani C, Et al., A meta-analysis of self-determination theory-informed intervention studies in the health domain: effects on motivation, health behavior, physical, and psychological health, Health Psychol Rev, 15, 2, pp. 214-244, (2021); Street RL, Epstein RM., Key interpersonal functions and health outcomes: lessons from theory and research on clinician patient communication, Health Behavior and Health Education: Theory, Research, and Practice, (2008); Balint E., The possibilities of patient-centered medicine, J R Coll Gen Pract, 17, 82, pp. 269-276, (1969); Bousquet J, Bedbrook A, Czarlewski W, Onorato GL, Arnavielhe S, Laune D, Guidance to 2018 good practice: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma, Clin Transl Allergy, 9, (2019); Juniper EF, Bousquet J, Abetz L, Bateman ED, Identifying 'well-controlled' and 'not well-controlled' asthma using the Asthma Control Questionnaire, Respir Med, 100, 4, pp. 616-621, (2006); Clinical Practice Research Datalink","R.A. Calvo; Dyson School of Design Engineering, Imperial College London, London, Imperial College Rd, SW7 2DB, United Kingdom; email: r.calvo@imperial.ac.uk","","JMIR Publications Inc.","","","","","","19290748","","","","English","JMIR Res. Prot.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149151934"
"Ghulam Nabi F.; Sundaraj K.; Iqbal M.S.; Shafiq M.; Planiappan R.","Ghulam Nabi, Fizza (57193326172); Sundaraj, Kenneth (55966322900); Iqbal, Muhammad Shahid (59157821100); Shafiq, Muhammad (57194223519); Planiappan, Rajkumar (36515196600)","57193326172; 55966322900; 59157821100; 57194223519; 36515196600","A telemedicine software application for asthma severity levels identification using wheeze sounds classification","2022","Biocybernetics and Biomedical Engineering","42","4","","1236","1247","11","3","10.1016/j.bbe.2022.11.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142159739&doi=10.1016%2fj.bbe.2022.11.001&partnerID=40&md5=dc70db064d67fa290c1ac64b28661772","Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan; Faculty of Electronics & Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia; Department of Electrical Engineering, National University of Technology, Islamabad, Pakistan; AMA International University, Department of Mechatronics Engineering, Bahrain","Ghulam Nabi F., Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan; Sundaraj K., Faculty of Electronics & Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia; Iqbal M.S., Department of Electrical Engineering, National University of Technology, Islamabad, Pakistan; Shafiq M., Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan; Planiappan R., AMA International University, Department of Mechatronics Engineering, Bahrain","Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was performed on both auscultation locations jointly and individually. The developed telemedicine application, based on MFCC features and classifiers, automatically detects wheeze and classifies it into a severity level. The extracted features showed significant differences (p < 0.05) for all severity levels. Based on the testing, training, and validation results, the performance of the ensemble and KNN classifiers were comparable. MFCC-based features classification provides maximum accuracy of 99%, 90%, and 89% for mild, moderate, and severe samples, respectively. The average rate of wheeze detection was observed to be 93%. The maximum accuracy of validation of the telemedicine application was found to be 57%, 72%, and 76% for mild, moderate, and severe levels, respectively. © 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences","Asthma severity level; MFCC; Telemedicine device; Wheeze classification; Wheeze detection","adult; Article; asthma; classifier; controlled study; diagnostic accuracy; disease severity; feature extraction; female; Fourier transform; human; k nearest neighbor; lung auscultation; major clinical study; male; middle aged; post hoc analysis; prediction; sensitivity and specificity; short time Fourier transform; sound analysis; telemedicine; wheezing","","","","","","","Hernandez C., Mallow J., Narsavage G.L., Delivering telemedicine interventions in chronic respiratory disease, Breathe, 10, 3, pp. 198-212, (2014); Lancet, Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019, The Lancet, 396, 10258, pp. 1204-1222, (2020); Finkelstein J., Cabrera M.R., Hripcsak G., Internet-based home asthma telemonitoring: Can patients handle the technology?, Chest, 117, 1, pp. 148-155, (2000); Homs-Corbera A., Fiz J.A., Morera J., Jane R., Time-frequency detection and analysis of wheezes during forced exhalation, IEEE Trans Biomed Eng, 51, 1, pp. 182-186, (2004); Shabtai-Musih Y., Grotberg J.B., Gavriely N., Spectral content of forced expiratory wheezes during air, He and SF6 breathing in normal humans, J Appl Physiol, 72, 2, pp. 629-635, (1992); Taplidou S.A., Hadjileontiadis L.J., Wheeze detection based on time-frequency analysis of breath sounds, Comput Biol Med, 37, 8, pp. 1073-1083, (2007); Fiz J.A., Jane R., Homs A., Izquierdo J., Garcia M.A., Morera J., Detection of wheezing during maximal forced exhalation in patients with obstructed airways, Chest, 122, 1, pp. 186-191, (2002); Taplidou S.A., Hadjileontiadis L.J., Nonlinear analysis of wheezes using wavelet bicoherence, Comput Biol Med, 37, 4, pp. 563-570, (2007); Taplidou S.A., Hadjileontiadis L.J., Analysis of wheezes using wavelet higher order spectral features, IEEE Trans Biomed Eng, 57, 7, pp. 1596-1610, (2010); Baughman L.R., Lung sound analysis for continuous evaluation of airflow obstruction in asthma, Chest, 88, 3, pp. 364-368, (1985); Fiz J.A., Jane R., Izquierdo J., Homs A., Garcia M.A., Gomez R., Et al., Analysis of forced wheezes in asthma patients, Respiration, 73, 1, pp. 55-60, (2006); Malmberg S.A., Pesu L., Significant differences in flow standardised breath sound spectra in patients with chronic obstructive pulmonary disease, stable asthma, and healthy lungs, Thorax, 50, 12, pp. 1285-1291, (1995); Tasar B., Yaman O., Tuncer T., Accurate respiratory sound classification model based on piccolo pattern, Appl Acoust, 188, (2022); Bahoura M., Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes, Comput Biol Med, 39, 9, pp. 824-843, (2009); Jin F., Krishnan S., Sattar F., Adventitious sounds identification and extraction using temporal–spectral dominance- based features, IEEE Trans Biomed Eng, 58, 11, pp. 3078-3087, (2011); Lin B.-S., Yen T.-S., An fpga-based rapid wheezing detection system, Int J Environ Res Public Health, 11, 2, pp. 1573-1593, (2014); Chen C.H., Huang W.T., Tan T.H., Chang C.C., Chang Y.J., Using k-nearest neighbor classification to diagnose abnormal lung sounds, Sensors, 15, 6, pp. 13132-13158, (2015); Wisniewski M., Zielinski T.P., Joint application of audio spectral envelope and tonality index in an e-asthma monitoring system, IEEE J Biomed Health Inf, 19, 3, pp. 1009-1018, (2015); Bor-Shing Lin S.J.C., Wu H.D., Automatic wheezing detection based on signal processing of spectrogram and back-propagation neural network, J Healthcare Eng, 6 780103, pp. 1-24, (2015); Mazicc I., Bonkovicc M., Dzzaja B., Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children's respiratory sounds, Biomed Signal Process Control, 21, pp. 105-118, (2015); Lin B., Lin B.S., Automatic wheezing detection using speech recognition technique, J Med Biol Eng, 36, pp. 545-554, (2016); Lozano M., Fiz J.A., Jane R., Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency, IEEE J Biomed Health Inf, 20, 2, pp. 486-497, (2016); Bokov P., Mahut B., Flaud P., Delclaux C., Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population, Comput Biol Med, 70, pp. 40-50, (2016); Ulukaya S., Serbes G., Kahya Y.P., Overcomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusion, Biomed Signal Process Control, 38, pp. 322-336, (2017); Islam M.A., Bandyopadhyaya I., Bhattacharyya P., Saha G., Multichannel lung sound analysis for asthma detection, Comput Methods Programs Biomed, 159, pp. 111-123, (2018); Oud M., Dooijes E., van der Zee J., Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra, IEEE Trans Biomed Eng, 47, 11, pp. 1450-1455, (2000); Oud M., Lung function interpolation by means of neural-network-supported analysis of respiration sounds, Med Eng Amp Phys, 25, pp. 309-316, (2003); Rossi M., Sovijarvi A., Piirila P., Vannuccini L., Dalmasso F., Vanderschoot J., Environmental and subject conditions and breathing manoeuvres for respiratory sound recordings, Eur Respirat Rev, 10, pp. 611-615, (2000); Nabi F.G., Sundaraj K., Kiang L.C., Palaniappan R., Sundaraj S., Wheeze sound analysis using computer- based techniques: a systematic review, Biomedizinische Technik Biomed Eng, 64, pp. 1-28, (2019); Nabi F.G., Sundaraj K., Kiang L.C., Identification of asthma severity levels through wheeze sound characterization and classification using integrated power features, Biomed Signal Process Control, 52, pp. 302-311, (2019); Breiman L., Bagging predictors, Mach Learn, pp. 123-140, (1996); Burnham K.P., Anderson D.R., Model Selection and Multimodel Inference, (2002); Tabata H., Enseki M., Nukaga M., Hirai K., Matsuda S., Furuya H., Et al., Changes in the breath sound spectrum during methacholine inhalation in children with asthma, Respirology, 23, 2, pp. 168-175, (2018); Niimi A., Matsumoto H., Amitani R., Nakano Y., Mishima M., Minakuchi M., Et al., Airway wall thickness in asthma assessed by computed tomography, Am J Respir Crit Care Med, 162, 4, pp. 1518-1523, (2000); Meslier N., Charbonneau G., Racineux J., Wheezes, Eur Respirat J, 8, pp. 1942-1948, (1995); Manecke G., Dilger J., Kutner L., Poppers P., Auscultation revisited: The waveform and spectral characteristics of breath sounds during general anesthesia, Int J Clin Monitor Comput, 14, pp. 231-240, (1997); Habukawa C., Nagasaka Y., Murakami K., Takemura T., High-pitched breath sounds indicate airflow limitation in asymptomatic asthmatic children, Respirology, 14, pp. 399-403, (2009); Rietveld S., Dooijes E.H., Rijssenbeek-Nouwens L., Smit F., Prins P., Kolk A., Everaerd W., Characteristics of wheeze during histamine-induced airways obstruction in children with asthma, Thorax, 50, pp. 143-148, (1995); Fenton T.R., Pasterkamp H., Tal A., Chernick V., Automated spectral characterization of wheezing in asthmatic children, IEEE Trans Biomed Eng, BME-32, 1, pp. 50-55, (1985); Pasterkamp H., Kraman S., Wodicka G., Respiratory sounds: Advances beyond the stethoscope, Am J Respirat Crit Care Med, 156, pp. 974-987, (1997); Nabi F.G., Sundaraj K., Kiang L.C., Palaniappan R., Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features, Comput Biol Med, 104, (2019); Nabi F.G., Sundaraj K., Kiang L.C., Palaniappan R., Analysis of wheeze sounds during tidal breathing according to severity levels in asthma patients, J Asthma, pp. 1-13, (2020)","F. Ghulam Nabi; Department of Industrial Engineering and Management, University of the Punjab, Lahore, Pakistan; email: Engr.fizza@yahoo.com","","Elsevier B.V.","","","","","","02085216","","","","English","Biocybern. Biomed. Eng.","Article","Final","","Scopus","2-s2.0-85142159739"
"Tu K.-C.; Tau E.N.T.; Chen N.-C.; Chang M.-C.; Yu T.-C.; Wang C.-C.; Liu C.-F.; Kuo C.-L.","Tu, Kuan-Chi (57216821504); Tau, Eric nyam tee (57192061885); Chen, Nai-Ching (57697074600); Chang, Ming-Chuan (58616511700); Yu, Tzu-Chieh (57192065979); Wang, Che-Chuan (7501629691); Liu, Chung-Feng (39861635300); Kuo, Ching-Lung (7401774073)","57216821504; 57192061885; 57697074600; 58616511700; 57192065979; 7501629691; 39861635300; 7401774073","Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury","2023","Diagnostics","13","18","3016","","","","3","10.3390/diagnostics13183016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172233116&doi=10.3390%2fdiagnostics13183016&partnerID=40&md5=69a27ae97f3789e0ac43dc95ce2d4db5","Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan; Department of Nursing, Chi Mei Medical Center, Tainan, 710402, Taiwan; Center for General Education, Southern Taiwan University of Science and Technology, Tainan, 710402, Taiwan; Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, 804, Taiwan","Tu K.-C., Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan; Tau E.N.T., Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan; Chen N.-C., Department of Nursing, Chi Mei Medical Center, Tainan, 710402, Taiwan; Chang M.-C., Department of Nursing, Chi Mei Medical Center, Tainan, 710402, Taiwan; Yu T.-C., Department of Nursing, Chi Mei Medical Center, Tainan, 710402, Taiwan; Wang C.-C., Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan, Center for General Education, Southern Taiwan University of Science and Technology, Tainan, 710402, Taiwan; Liu C.-F., Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; Kuo C.-L., Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan, Center for General Education, Southern Taiwan University of Science and Technology, Tainan, 710402, Taiwan, School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, 804, Taiwan","Background: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. Method: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. Result: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. Conclusion: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members. © 2023 by the authors.","artificial intelligence; computer-assisted system; intensive care unit; machine learning; mortality; traumatic brain injury","nicardipine; adult; APACHE; Article; asthma; breathing rate; cerebral perfusion pressure; cerebrovascular disease; clinical feature; cross validation; diabetes mellitus; diagnostic accuracy; diastolic blood pressure; electronic medical record; epilepsy; feature selection; female; gastrointestinal disease; Glasgow coma scale; health status; heart disease; hospital admission; human; hypertension; information processing; intensive care unit; intracranial pressure; kidney disease; liver disease; logistic regression analysis; machine learning; major clinical study; male; malignant neoplasm; mean arterial pressure; medical history; mortality risk; muscle strength; neurosurgery; patient selection; pneumonia; predictive model; prognosis; pupil diameter; pupil reflex; random forest; receiver operating characteristic; retrospective study; sensitivity and specificity; Sequential Organ Failure Assessment Score; systolic blood pressure; thyroid disease; traumatic brain injury; treatment outcome","","nicardipine, 54527-84-3, 55985-32-5","","","ChiMei Medical, (CMFHR 11034)","This research was supported by ChiMei Medical CMFHR 11034.","Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016, Lancet Neurol, 18, pp. 56-87, (2019); Majdan M., Plancikova D., Brazinova A., Rusnak M., Nieboer D., Feigin V.L., Maas A., Epidemiology of traumatic brain injuries in Europe: A cross-sectional analysis, Lancet Public Health, 1, pp. e76-e83, (2016); Taylor C.A., Bell J.M., Breiding M.J., Xu L., Traumatic Brain Injury-Related Emergency Department Visits, Hospitalizations, and Deaths—United States, 2007 and 2013, MMWR Surveill. Summ, 66, pp. 1-16, (2017); Prasanthi P., Adnan A.H., The burden of traumatic brain injury in asia: A call for research, Pak. J. Neurol. Sci, 4, pp. 27-32, (2009); Hukkelhoven C.W., Steyerberg E.W., Rampen A.J., Farace E., Habbema J.D.F., Marshall L.F., Murray G.D., Maas A.I.R., Patient age and outcome following severe traumatic brain injury: An analysis of 5600 patients, J. Neurosurg, 99, pp. 666-673, (2003); Ozyurt E., Goksu E., Cengiz M., Yilmaz M., Ramazanoglu A., Retrospective Analysis of Prognostic Factors of Severe Traumatic Brain Injury in a University Hospital in Turkey, Turk. Neurosurg, 25, pp. 877-882, (2015); Okidi R., Ogwang D.M., Okello T.R., Ezati D., Kyegombe W., Nyeko D., Scolding N.J., Factors affecting mortality after traumatic brain injury in a resource-poor setting, BJS Open, 4, pp. 320-325, (2020); Maas A.I., Steyerberg E.W., Butcher I., Dammers R., Lu J., Marmarou A., Mushkudiani N.A., McHugh G.S., Murray G.D., Prognostic value of computerized tomography scan characteristics in traumatic brain injury: Results from the IMPACT study, J. Neurotrauma, 24, pp. 303-314, (2007); Perel P., Edwards P., Wentz R., Roberts I., Systematic review of prognostic models in traumatic brain injury, BMC Med. Inform. Decis. Mak, 6, (2006); Carter E.L., Hutchinson P.J., Kolias A.G., Menon D.K., Predicting the outcome for individual patients with traumatic brain injury: A case-based review, Br. J. Neurosurg, 30, pp. 227-232, (2016); Vincent J.L., Moreno R., Takala J., Willatts S., De Mendonca A., Bruining H., Reinhart C.K., Suter P.M., Thijs L.G., The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine, Intensive Care Med, 22, pp. 707-710, (1996); Singer M., Deutschman C.S., Seymour C.W., Shankar-Hari M., Annane D., Bauer M., Bellomo R., Bernard G.R., Chiche J.-D., Coopersmith C.M., Et al., The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3), JAMA, 315, pp. 801-810, (2016); Knaus W.A., Draper E.A., Wagner D.P., Zimmerman J.E., APACHE II: A severity of disease classification system, Crit. Care Med, 13, pp. 818-829, (1985); Raj R., Skrifvars M., Bendel S., Selander T., Kivisaari R., Siironen J., Reinikainen M., Predicting six-month mortality of patients with traumatic brain injury: Usefulness of common intensive care severity scores, Crit. Care, 18, (2014); Ley C., Martin R.K., Pareek A., Groll A., Seil R., Tischer T., Machine learning and conventional statistics: Making sense of the differences, Knee Surg. Sports Traumatol. Arthrosc, 30, pp. 753-757, (2022); Loh H.W., Ooi C.P., Seoni S., Barua P.D., Molinari F., Acharya U.R., Application of Explainable Artificial Intelligence for Healthcare: A Systematic Review of the Last Decade (2011–2022), Comput. Methods Programs Biomed, 226, (2022); Courville E., Kazim S.F., Vellek J., Tarawneh O., Stack J., Roster K., Roy J., Schmidt M., Bowers C., Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis, Surg. Neurol. Int, 14, (2023); Abujaber A., Fadlalla A., Gammoh D., Abdelrahman H., Mollazehi M., El-Menyar A., Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach, Scand. J. Trauma Resusc. Emerg. Med, 28, (2020); Hsu S.D., Chao E., Chen S.J., Hueng D.Y., Lan H.Y., Chiang H.H., Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury, J. Pers. Med, 11, (2021); Wang R., Wang L., Zhang J., He M., Xu J., XGBoost Machine Learning Algorism Performed Better Than Regression Models in Predicting Mortality of Moderate-to-Severe Traumatic Brain Injury, World Neurosurg, 163, pp. e617-e622, (2022); Wu X., Sun Y., Xu X., Steyerberg E.W., Helmrich I.R.A.R., Lecky F., Guo J., Li X., Feng J., Mao Q., Et al., Mortality Prediction in Severe Traumatic Brain Injury Using Traditional and Machine Learning Algorithms, J. Neurotrauma, 40, pp. 1366-1375, (2023); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell, 16, pp. 321-357, (2002); Hosmer D.W., Lemeshow S., Sturdivant R.X., Applied Logistic Regression, (2013); Breiman L., Random forests, Mach. Learn, 45, pp. 5-32, (2001); Ke G., Meng Q., Finley T., Wang T.F., Chen W., Ma W.D., Ye Q., Liu T.Y., LightGBM: A highly efficient gradient boosting decision tree, Proceedings of the 31st Conference on Neural Information Processing Systems; Chen T., Guestrin C., Xgboost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794; Parikh R., Mathai A., Parikh S., Sekhar G.C., Thomas R., Understanding and using sensitivity, specificity and predictive values, Indian J. Ophthalmol, 56, pp. 45-50, (2008); Patorno E., Najafzadeh M., Pawar A., Franklin J.M., Deruaz-Luyet A., Brodovicz K.G., Ortiz A.J.S., Bessette L.G., Kulldorff M., Schneeweiss S., The EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study programme: Design and exposure accrual for an evaluation of empagliflozin in routine clinical care, Endocrinol. Diabetes Metab, 3, (2019); Swets J.A., Measuring the accuracy of diagnostic systems, Science, 240, pp. 1285-1293, (1988); Jin H., Ling C.X., Using AUC and accuracy in evaluating learning algorithms, Knowl. Data Eng, 17, pp. 299-310, (2005); Hasraddin G., Eldayag M., Predicting the changes in the WTI crude oil price dynamics using machine learning models, Resour. Policy, 77, (2022); Inui A., Nishimoto H., Mifune Y., Yoshikawa T., Shinohara I., Furukawa T., Kato T., Tanaka S., Kusunose M., Kuroda R., Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach, Bioengineering, 10, (2023); Lundberg S.M., Lee S.I., A Unified Approach to Interpreting Model Predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems; Breslow M.J., Badawi O., Severity scoring in the critically ill: Part 1—Interpretation and accuracy of outcome prediction scoring systems, Chest, 141, pp. 245-252, (2012); Lambden S., Laterre P.F., Levy M.M., Francois B., The SOFA Score—Development, Utility and Challenges of Accurate Assessment in Clinical Trials, Crit. Care, 23, (2019); Pinto V.L., Tadi P., Adeyinka A., Increased Intracranial Pressure, (2023); Saika A., Bansal S., Philip M., Devi B.I., Shukla D.P., Prognostic value of FOUR and GCS scores in determining mortality in patients with traumatic brain injury, Acta Neurochir, 157, pp. 1323-1328, (2015); Huang J.F., Tsai Y.C., Rau C.S., Hsu S.Y., Chien P.C., Hsieh H.Y., Hsieh C.H., Systolic blood pressure lower than the heart rate indicates a poor outcome in patients with severe isolated traumatic brain injury: A cross-sectional study, Int. J. Surg, 61, pp. 48-52, (2019); Steyerberg E.W., Mushkudiani N., Perel P., Butcher I., Lu J., McHugh G.S., Murray G.D., Marmarou A., Roberts I., Habbema J.D.F., Et al., Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics, PLoS Med, 5, pp. 1251-1261, (2008); Perel P., Arango M., Clayton T., Edwards P., Komolafe E., Poccock S., Roberts I., Shakur H., Steyerberg E., Et al., Predicting outcome after traumatic brain injury: Practical prognostic models based on large cohort of international patients, BMJ, 336, pp. 425-429, (2008); Han J., King N., Neilson S., Gandhi M., Ng I., External validation of the CRASH and IMPACT prognostic models in severe traumatic brain injury, J. Neurotrauma, 31, pp. 1146-1152, (2014)","E.N.T. Tau; Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan; email: ronaldowen@gmail.com; C.-L. Kuo; Department of Neurosurgery, Chi Mei Medical Center, Tainan, 710402, Taiwan; email: kuojinnrung@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85172233116"
"Dessie E.Y.; Ding L.; Mersha T.B.","Dessie, Eskezeia Y. (57205530525); Ding, Lili (36022230300); Mersha, Tesfaye B. (56147975000)","57205530525; 36022230300; 56147975000","Integrative analysis identifies gene signatures mediating the effect of DNA methylation on asthma severity and lung function","2024","Clinical Epigenetics","16","1","15","","","","2","10.1186/s13148-023-01611-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182635687&doi=10.1186%2fs13148-023-01611-9&partnerID=40&md5=d3d5e32f21e609ee37f2a0c12abd25ae","Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States","Dessie E.Y., Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Ding L., Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Mersha T.B., Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, United States","DNA methylation (DNAm) changes play a key role in regulating gene expression in asthma. To investigate the role of epigenetics and transcriptomics change in asthma, we used publicly available DNAm (asthmatics, n = 96 and controls, n = 46) and gene expression (asthmatics, n = 79 and controls, n = 39) data derived from bronchial epithelial cells (BECs). We performed differential methylation/expression and weighted co-methylation/co-expression network analyses to identify co-methylated and co-expressed modules associated with asthma severity and lung function. For subjects with both DNAm and gene expression data (asthmatics, n = 79 and controls, n = 39), machine-learning technique was used to prioritize CpGs and differentially expressed genes (DEGs) for asthma risk prediction, and mediation analysis was used to uncover DEGs that mediate the effect of DNAm on asthma severity and lung function in BECs. Finally, we validated CpGs and their associated DEGs and the asthma risk prediction model in airway epithelial cells (AECs) dataset. The asthma risk prediction model based on 18 CpGs and 28 DEGs showed high accuracy in both the discovery BEC dataset with area under the receiver operating characteristic curve (AUC) = 0.99 and the validation AEC dataset (AUC = 0.82). Genes in the three co-methylated and six co-expressed modules were enriched in multiple pathways including WNT/beta-catenin signaling and notch signaling. Moreover, we identified 35 CpGs correlated with DEGs in BECs, of which 17 CpGs including cg01975495 (SERPINE1), cg10528482 (SLC9A3), cg25477769 (HNF1A) and cg26639146 (CD9), cg17945560 (TINAGL1) and cg10290200 (FLNC) were replicated in AECs. These DEGs mediate the association between DNAm and asthma severity and lung function. Overall, our study investigated the role of DNAm and gene expression change in asthma and provided an insight into the mechanisms underlying the effects of DNA methylation on asthma, asthma severity and lung function. © 2024, The Author(s).","Asthma; Asthma severity; Biomarkers; Lung function; Mediation analysis; Multi-omics analysis; Weighted correlation network analysis","Asthma; DNA Methylation; Epigenesis, Genetic; Humans; Lung; Transcription Factors; transcription factor; adult; airway epithelium cell; area under the curve; Article; asthma; bronchial epithelial cell; canonical Wnt signaling; CD9 gene; controlled study; correlation analysis; CpG island; diagnostic accuracy; diagnostic test accuracy study; differential gene expression; disease severity; DNA methylation; epigenetics; epithelium cell; female; FLNC gene; gene expression; gene expression regulation; gene function; gene replication; gene signature; genetic analysis; genetic association; genetic identification; HNF1A gene; human; lung function; machine learning; major clinical study; male; mediation analysis; Notch signaling; prediction; risk factor; SERPINE1 gene; SLC9A3 gene; TINAGL1 gene; transcriptomics; validation process; asthma; genetic epigenesis; genetics; lung","","Transcription Factors, ","","","National Institutes of Health, NIH; National Human Genome Research Institute, NHGRI, (R01 HG011411); National Human Genome Research Institute, NHGRI","This work was supported by the National Institutes of Health (NIH) NHGRI (R01 HG011411)] grant support. ","London S.J., Et al., Family history and the risk of early-onset persistent, early-onset transient, and late-onset asthma, Epidemiology, 12, 5, pp. 577-583, (2001); Castillo-Fernandez J.E., Spector T.D., Bell J.T., Epigenetics of discordant monozygotic twins: implications for disease, Genome Med, 6, 7, (2014); Yang I.V., Schwartz D.A., Epigenetic mechanisms and the development of asthma, J Allergy Clin Immunol, 130, 6, pp. 1243-1255, (2012); Jaenisch R., Bird A., Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals, Nat Genet, 33, pp. 245-254, (2003); Moeller A., Et al., Monitoring asthma in childhood: lung function, bronchial responsiveness and inflammation, Eur Respir Rev, 24, 136, pp. 204-215, (2015); Agusti A., Et al., Lung function in early adulthood and health in later life: a transgenerational cohort analysis, Lancet Respir Med, 5, 12, pp. 935-945, (2017); Hole D.J., Et al., Impaired lung function and mortality risk in men and women: findings from the Renfrew and Paisley prospective population study, BMJ, 313, 7059, pp. 711-715, (1996); Herrera-Luis E., Et al., Epigenome-wide association study of lung function in Latino children and youth with asthma, Clin Epigenet, 14, 1, (2022); DeVries A., Vercelli D., Epigenetic mechanisms in asthma, Ann Am Thorac Soc, 13, pp. S48-S50, (2016); Magnaye K.M., Et al., DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma, Proc Natl Acad Sci USA, 119, 24, (2022); Zhang Z., Wang J., Chen O., Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis, BMC Med Genom, 14, 1, (2021); Thurmann L., Et al., Global hypomethylation in childhood asthma identified by genome-wide DNA-methylation sequencing preferentially affects enhancer regions, Allergy, 78, 6, pp. 1489-1506, (2023); Perry M.M., Et al., DNA methylation modules in airway smooth muscle are associated with asthma severity, Eur Respir J, 51, 4, (2018); Perez-Garcia J., Et al., Epigenomic response to albuterol treatment in asthma-relevant airway epithelial cells, Clin Epigenet, 15, 1, (2023); Singh P., Et al., Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma, Sci Rep, 10, 1, (2020); Langfelder P., Horvath S., WGCNA: an R package for weighted correlation network analysis, BMC Bioinform, 9, 1, (2008); Zhou X., Et al., Targeted DNA methylation profiling reveals epigenetic signatures in peanut allergy, JCI Insight, 6, 6, (2021); Forno E., Et al., DNA methylation in nasal epithelium, atopy, and atopic asthma in children: a genome-wide study, Lancet Respir Med, 7, 4, pp. 336-346, (2019); Hoang T.T., Et al., Epigenome-wide association study of DNA methylation and adult asthma in the agricultural lung health study, Eur Respir J, 56, 3, (2020); Kwak H.J., Et al., The Wnt/β-catenin signaling pathway regulates the development of airway remodeling in patients with asthma, Exp Mol Med, 47, 12, (2015); Qu S.Y., Et al., Disruption of the Notch pathway aggravates airway inflammation by inhibiting regulatory T cell differentiation via regulation of plasmacytoid dendritic cells, Scand J Immunol, 91, 5, (2020); Kothalawala D.M., Et al., Integration of genomic risk scores to improve the prediction of childhood asthma diagnosis, J Pers Med, 12, 1, (2022); Liu A., Et al., Genome-wide correlation of DNA methylation and gene expression in postmortem brain tissues of opioid use disorder patients, Int J Neuropsychopharmacol, 24, 11, pp. 879-891, (2021); Radzikowska U., Et al., Omics technologies in allergy and asthma research: an EAACI position paper, Allergy, 77, pp. 2888-2908, (2022); Pampuch A., Et al., The -675 4G/5G plasminogen activator inhibitor-1 promoter polymorphism in house dust mite-sensitive allergic asthma patients, Allergy, 61, 2, pp. 234-238, (2006); Nimpong J.A., Et al., Deficiency of KLF4 compromises the lung function in an acute mouse model of allergic asthma, Biochem Biophys Res Commun, 493, 1, pp. 598-603, (2017); Jeon Y., Et al., Gene signatures and associated transcription factors of allergic rhinitis: KLF4 expression is associated with immune response, Biomed Res Int, 2023, (2023); Clifford R.L., Et al., TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma, Clin Epigenet, 12, 1, (2020); Sajuthi S.P., Et al., Nasal airway transcriptome-wide association study of asthma reveals genetically driven mucus pathobiology, Nat Commun, 13, 1, (2022); Magnaye K.M., Et al., DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma, Proc Natl Acad Sci, 119, 24, (2022); Leek J.T., Et al., The SVA package for removing batch effects and other unwanted variation in high-throughput experiments, Bioinformatics, 28, 6, pp. 882-883, (2012); Ritchie M.E., Et al., Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res, 43, 7, (2015); Hochberg Y., Benjamini Y., More powerful procedures for multiple significance testing, Stat Med, 9, 7, pp. 811-818, (1990); Langfelder P., Horvath S., WGCNA: an R package for weighted correlation network analysis, BMC Bioinform, 9, (2008); van Dam S., Et al., Gene co-expression analysis for functional classification and gene-disease predictions, Brief Bioinform, 19, 4, pp. 575-592, (2018); Saelens W., Cannoodt R., Saeys Y., A comprehensive evaluation of module detection methods for gene expression data, Nat Commun, 9, 1, (2018); Shen L., Sinai M., GeneOverlap: Test and visualize gene overlaps, R Package Version, (2022); Shao Z., Et al., Ingenuity pathway analysis of differentially expressed genes involved in signaling pathways and molecular networks in RhoE gene-edited cardiomyocytes, Int J Mol Med, 46, 3, pp. 1225-1238, (2020); Kursa M.B., Rudnicki W.R., Feature selection with the boruta package, J Stat Softw, 36, 11, pp. 1-13, (2010); Dessie E.Y., Et al., A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients, BMC Bioinform, 22, (2021); Robin X., Et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinform, 12, 1, (2011); Cao Y.N., Li Q.Z., Liu Y.X., Discovered key CpG sites by analyzing DNA methylation and gene expression in breast cancer samples, Front Cell Dev Biol, 10, (2022); Alfons A., Ates N.Y., Groenen P.J.F., A robust bootstrap test for mediation analysis, Organ Res Methods, 25, 3, pp. 591-617, (2022); Wielscher M., Et al., DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases, Nat Commun, 13, 1, (2022); Meng H., Et al., Epigenome-wide DNA methylation signature of plasma zinc and their mediation roles in the association of zinc with lung cancer risk, Environ Pollut, 307, (2022); Tingley D., Et al., Mediation: R package for causal mediation analysis, J Stat Softw, 59, 5, pp. 1-38, (2014)","T.B. Mersha; Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, United States; email: tesfaye.mersha@cchmc.org","","BioMed Central Ltd","","","","","","18687075","","","38245772","English","Clin. Epigenetics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85182635687"
"Llopis M.; Ventura P.S.; Brachowicz N.; Sangüesa J.; Murcia M.; Lopez-Espinosa M.-J.; García-Baquero G.; Lertxundi A.; Vrijheid M.; Casas M.; Petrone P.","Llopis, Maria (59281220800); Ventura, Paula Sol (57214458397); Brachowicz, Nicolai (57211573786); Sangüesa, Júlia (57885253200); Murcia, Mario (23098078600); Lopez-Espinosa, Maria-Jose (8316208900); García-Baquero, Gonzalo (6506800479); Lertxundi, Aitana (12792179400); Vrijheid, Martine (6603357596); Casas, Maribel (24343075000); Petrone, Paula (13104031700)","59281220800; 57214458397; 57211573786; 57885253200; 23098078600; 8316208900; 6506800479; 12792179400; 6603357596; 24343075000; 13104031700","Sociodemographic, lifestyle, and environmental determinants of vitamin D levels in pregnant women in Spain","2023","Environment International","182","","108293","","","","2","10.1016/j.envint.2023.108293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177179407&doi=10.1016%2fj.envint.2023.108293&partnerID=40&md5=637dc9bb99e52a415060882d80a14a98","ISGlobal, Barcelona, Spain; Pompeu Fabra University (UPF), Barcelona, Spain; Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Spain; Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain; Servei de Planificació i Avaluació de Polítiques de Salut, Conselleria de Sanitat Universal i Salut Pública, Generalitat Valenciana, Valencia, Spain; Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain; Faculty of Biology, University of Salamanca, Avda Licenciado Méndez Nieto s/n, Salamanca, Spain; Preventive Medicine and Public Health, University of the Basque Country (UPV/EHU), Leioa, Spain; Health Research Institute BIODONOSTIA, Donostia, Spain","Llopis M., ISGlobal, Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain; Ventura P.S., Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP), Badalona, Spain; Brachowicz N., ISGlobal, Barcelona, Spain; Sangüesa J., ISGlobal, Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain, Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Murcia M., Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain, Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain, Servei de Planificació i Avaluació de Polítiques de Salut, Conselleria de Sanitat Universal i Salut Pública, Generalitat Valenciana, Valencia, Spain; Lopez-Espinosa M.-J., Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain, Epidemiology and Environmental Health Joint Research Unit, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region, FISABIO-Public Health, FISABIO-Universitat Jaume I-Universitat de València, Valencia, Spain, Faculty of Nursing and Chiropody, Universitat de València, Valencia, Spain; García-Baquero G., Faculty of Biology, University of Salamanca, Avda Licenciado Méndez Nieto s/n, Salamanca, Spain, Health Research Institute BIODONOSTIA, Donostia, Spain; Lertxundi A., Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain, Preventive Medicine and Public Health, University of the Basque Country (UPV/EHU), Leioa, Spain, Health Research Institute BIODONOSTIA, Donostia, Spain; Vrijheid M., ISGlobal, Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain, Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Casas M., ISGlobal, Barcelona, Spain, Pompeu Fabra University (UPF), Barcelona, Spain, Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain; Petrone P., ISGlobal, Barcelona, Spain","Introduction: Vitamin D deficiency (<20 ng/mL circulating levels) is a worldwide public health concern and pregnant women are especially vulnerable, affecting the health of the mother and the fetus. This study aims to evaluate the sociodemographic, lifestyle, and environmental determinants associated with circulating vitamin D levels in Spanish pregnant women. Methods: We used data from the Spanish INMA (“Infancia y Medio Ambiente”) prospective birth cohort study from the regions of Gipuzkoa, Sabadell, and Valencia. 25-hydroxyvitamin D3 (25(OH)D3) was measured in plasma collected in the first trimester of pregnancy. Information on 108 determinants was gathered: 13 sociodemographic, 48 lifestyle including diet, smoking and physical activity, and 47 environmental variables, representing the urban and the chemical exposome. Association of the determinants with maternal 25(OH)D3 levels was estimated in single- and multiple-exposure models. Machine learning techniques were used to predict 25(OH)D3 levels below sufficiency (30 ng/mL). Results: The prevalence of < 30 ng/mL 25(OH)D3 levels was 51 %. In the single-exposure analysis, older age, higher socioeconomic status, taking vitamin D, B12 and other sup*plementation, and higher humidity, atmospheric pressure and UV rays were associated with higher levels of 25(OH)D3 (IQR increase of age: 1.2 [95 % CI: 0.6, 1.8] ng/mL 25(OH)D3). In the multiple-exposures model, most of these associations remained and others were revealed. Higher body mass index, PM2.5 and high deprivation area were associated with lower 25(OH)D3 levels (i.e., Quartile 4 of PM2.5 vs Q1: −3.6 [95 % CI: −5.6, −1.5] ng/mL of 25(OH)D3). History of allergy and asthma, being multiparous, intake of vegetable fat, vitamin B6, alcohol consumption and molybdenum were associated with higher levels. The machine learning classification model confirmed some of these associations. Conclusions: This comprehensive study shows that younger age, higher body mass index, higher deprived areas, higher air pollution and lower UV rays and humidity are associated with lower 25(OH)D3 levels. © 2023","25-hydroxyvitamin D; Determinants; Exposome; Machine learning; Pregnancy; Vitamin D3","Cohort Studies; Female; Humans; Infant; Life Style; Parity; Particulate Matter; Pregnancy; Pregnant Women; Prospective Studies; Spain; Vitamin D; Vitamin D Deficiency; Vitamins; Barcelona [Catalonia]; Basque Country [Spain]; Catalonia; Comunidad Valencia; Gipuzkoa; Sabadell; Spain; Valencia [Comunidad Valencia]; Air pollution; Atmospheric humidity; Atmospheric pressure; Obstetrics; Vitamins; alpha carotene; alpha tocopherol; antibiotic agent; arachidonic acid; ascorbic acid; beta carotene; biological marker; calcifediol; calcium; cyanocobalamin; folic acid; gamma tocopherol; hexachlorobenzene; icosatetraenoic acid; iodine; iron; linoleic acid; lycopene; magnesium; molybdenum; mono iso butyl phthalic acid; nickel; nitrogen dioxide; omega 3 fatty acid; organochlorine derivative; perfluoro compound; phthalic acid; phthalic acid bis(2 ethylhexyl) ester; polychlorinated biphenyl; potassium; pyridoxine; retinol; unclassified drug; vegetable oil; vitamin D; xanthophyll; zeaxanthin; zinc; vitamin; vitamin D; 25-hydroxyvitamin D; 3 levels; Determinant; Exposome; Machine-learning; Pregnancy; Pregnant woman; Sociodemographics; Vitamin D3; Vitamin-D; allergy; asthma; body mass; environmental conditions; health monitoring; lifestyle; maternal health; pregnancy; public health; vitamin; AdaBoost classifier; adult; air pollution; air temperature; alcohol consumption; allergy; Article; artificial neural network; asthma; atmospheric pressure; Bayesian learning; body mass; classifier; cohort analysis; controlled study; correlation analysis; decision tree; dietary compliance; disease association; educational status; employment status; environmental exposure; environmental factor; fat intake; feature selection; female; first trimester pregnancy; gestational age; gestational diabetes; human; humidity; k fold cross validation; land use; lifestyle; limit of detection; logistic regression analysis; major clinical study; marriage; maternal age; maternal hypertension; maternal smoking; measurement accuracy; measurement precision; medical history; Mediterranean diet; metabolic equivalent; multipara; particulate matter; particulate matter 2.5; passive smoking; physical activity; population exposure; prediction; pregnant woman; prevalence; process optimization; prospective study; random forest; receiver operating characteristic; rural area; social status; sociodemographics; Spain; third trimester pregnancy; ultraviolet radiation; unhealthy diet; urban area; vegetable consumption; vitamin blood level; vitamin D deficiency; vitamin supplementation; XGBoost classifier; infant; lifestyle; parity; pregnancy; pregnant woman; vitamin D deficiency; Machine learning","","alpha carotene, 7488-99-5; alpha tocopherol, 1406-18-4, 1406-70-8, 52225-20-4, 58-95-7, 59-02-9; arachidonic acid, 506-32-1, 6610-25-9, 7771-44-0; ascorbic acid, 134-03-2, 15421-15-5, 50-81-7; beta carotene, 7235-40-7; calcifediol, 19356-17-3; calcium, 7440-70-2, 14092-94-5; cyanocobalamin, 53570-76-6, 68-19-9, 8064-09-3; folic acid, 59-30-3, 6484-89-5; gamma tocopherol, 7616-22-0; hexachlorobenzene, 118-74-1, 55600-34-5; icosatetraenoic acid, 27400-91-5, 31152-45-1; iodine, 7553-56-2; iron, 14093-02-8, 53858-86-9, 7439-89-6; linoleic acid, 1509-85-9, 2197-37-7, 60-33-3, 822-17-3; lycopene, 502-65-8; magnesium, 7439-95-4; molybdenum, 7439-98-7; nickel, 7440-02-0; nitrogen dioxide, 10102-44-0; phthalic acid, 88-99-3; phthalic acid bis(2 ethylhexyl) ester, 117-81-7; potassium, 7440-09-7; pyridoxine, 12001-77-3, 58-56-0, 65-23-6, 8059-24-3; retinol, 68-26-8, 82445-97-4; xanthophyll, 127-40-2, 52842-48-5; zeaxanthin, 144-68-3; zinc, 7440-66-6, 14378-32-6; Particulate Matter, ; Vitamin D, ; Vitamins, ","","","Ministry of Education and Science, MES; 'la Caixa' Foundation; Fundació Roger Torné; CIBERESP; Ministerio de Ciencia e Innovación, MICINN; Instituto de Salud Carlos III, ISCIII; Consejería de Salud de Andalucía; CIBER Epidemiology and Public Health; European Union’s 6th and 7th Framework Programmes; Fundación Científica Asociación Española Contra el Cáncer, AECC; European Commission, EC; Osasun Saila, Eusko Jaurlaritzako; Caja Gipuzkoa San Sebastián; Agencia Estatal de Investigación, AEI; Fondo de Investigación Sanitaria; Universidad de Oviedo; Diputación Foral de Gipuzkoa; Centre for Research in Environmental Epidemiology; Junta the Andalucía; Ministry of Research and Universities; Conselleria de Sanitat Universal i Salut Pública; Network of Research Centers in Epidemiology and Public Health; Seventh Framework Programme, FP7, (308333, FP7/2007-206); Centro de Excelencia Severo Ochoa, (2019-2023); Generalitat de Catalunya, (2021 SGR 01563 HEALTH-ANALYTICS)","Data collection was partly funded by the European Community\u2019s Seventh Framework Programme (FP7/2007-206) under grant agreement no. 308333 (HELIX project). The INMA project has been funded by the Health Institute Carlos III (Instituto de Salud Carlos III), the Network of Research Centers in Epidemiology and Public Health (Red de Centros de investigaci\u00F3n en Epidemiolog\u00EDa y Salud P\u00FAblica - RCESP) and CIBER Epidemiology and Public Health (CIBERESP), and in part by the \u201CFondo de Investigaci\u00F3n Sanitaria\u201D, the European Union\u2019s 6th and 7th Framework Programmes (Hiwate, Escape, Hitea and Contamed projects), the Education and Science ministry, the Centre for Research in Environmental Epidemiology (CREAL) of Barcelona, the Fundaci\u00F3 La Caixa, the Fundaci\u00F3 Roger Torn\u00E9, the Consejer\u00EDa de Salud de Andaluc\u00EDa, the Junta the Andaluc\u00EDa, the Conselleria de Sanitat de la Generalitat Valenciana, the CAJASTUR\u2014Caja Asturias, the Spanish Association against the Cancer (AECC) (Delegaci\u00F3n Provincial Asturias), the Departamento de Sanidad-Gobierno Vasco, the Diputaci\u00F3n Floral de Gipuzkoa, the University of Oviedo, the KUTXA \u2013 Caja Gipuzkoa San Sebasti\u00E1n and the city councils of Zumarraga, Urretxu, Legazpi, Azpeitia, Beasain and Azkoitia in Gipuzkoa. We acknowledge support from the Spanish Ministry of Science and Innovation through the \u201CCentro de Excelencia Severo Ochoa 2019-2023\u201D Program CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program, and the Ministry of Research and Universities of the Government of Catalonia (2021 SGR 01563 HEALTH-ANALYTICS). ","Abellan A., Sunyer J., Garcia-Esteban R., Basterrechea M., Duarte-Salles T., Ferrero A., Garcia-Aymerich J., Gascon M., Grimalt J.O., Lopez-Espinosa M.J., Zabaleta C., Vrijheid M., Casas M., Prenatal exposure to organochlorine compounds and lung function during childhood, Environ. Int., 131, (2019); Agay-Shay K., Martinez D., Valvi D., Garcia-Esteban R., Basagana X., Robinson O., Casas M., Sunyer J., Vrijheid M., Exposure to endocrine-disrupting chemicals during pregnancy and weight at 7 years of age: a multi-pollutant approach, Environ. 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"Melero Moreno C.; Almonacid Sánchez C.; Bañas Conejero D.; Quirce S.; Álvarez Gutiérrez F.J.; Cardona V.; Sánchez-Herrero M.G.; Soriano J.B.","Melero Moreno, C. (6602723704); Almonacid Sánchez, C. (15519447700); Bañas Conejero, D. (57226498408); Quirce, S. (56186264200); Álvarez Gutiérrez, F.J. (6701326932); Cardona, V. (18133374200); Sánchez-Herrero, M.G. (35181853000); Soriano, J.B. (7101973935)","6602723704; 15519447700; 57226498408; 56186264200; 6701326932; 18133374200; 35181853000; 7101973935","Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study","2023","Journal of Investigational Allergology and Clinical Immunology","33","5","","373","382","9","3","10.18176/jiaci.0848","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174891507&doi=10.18176%2fjiaci.0848&partnerID=40&md5=f7666f0ca026208045785d634cd4fc3a","Hospital Universitario La Princesa, Madrid, Spain; Hospital Universitario, 12 de Octubre, Madrid, Spain; Hospital Universitario de Toledo, Toledo, Spain; Specialty Care Medical Department, GlaxoSmithKline, Madrid, Spain; Hospital Universitario La Paz, IdiPAZ, Madrid, Spain; CIBER of Respiratory Diseases (CIBERES), Madrid, Spain; Hospital Universitario Virgen del Rocío, Sevilla, Spain; Hospital Universitario Vall d’Hebron, Barcelona, Spain; Hospital Universitario de La Princesa, Madrid, Spain; Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Salud Carlos III (ISCIII), Madrid, Spain","Melero Moreno C., Hospital Universitario La Princesa, Madrid, Spain, Hospital Universitario, 12 de Octubre, Madrid, Spain; Almonacid Sánchez C., Hospital Universitario de Toledo, Toledo, Spain; Bañas Conejero D., Specialty Care Medical Department, GlaxoSmithKline, Madrid, Spain; Quirce S., Hospital Universitario La Paz, IdiPAZ, Madrid, Spain, CIBER of Respiratory Diseases (CIBERES), Madrid, Spain; Álvarez Gutiérrez F.J., Hospital Universitario Virgen del Rocío, Sevilla, Spain; Cardona V., Hospital Universitario Vall d’Hebron, Barcelona, Spain; Sánchez-Herrero M.G., Specialty Care Medical Department, GlaxoSmithKline, Madrid, Spain; Soriano J.B., CIBER of Respiratory Diseases (CIBERES), Madrid, Spain, Hospital Universitario de La Princesa, Madrid, Spain, Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain, Instituto de Salud Carlos III (ISCIII), Madrid, Spain","Background: Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months. Methods: PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT. Results: The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients’ clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes. Conclusion: This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA. © 2023 Esmon Publicidad.","Big data; Machine learning; Natural language processing; Predictive models; Prevalence; Severe asthma","nonsteroid antiinflammatory agent; adult; allergic rhinitis; anxiety disorder; Article; asthma; Asthma Control Test; atopy; chronic obstructive lung disease; clinical assessment; comorbidity; controlled study; cross-sectional study; depression; diabetes mellitus; disease severity; drug hypersensitivity; electronic health record; female; follow up; gastroesophageal reflux; hospital; human; in-hospital mortality; lung function; machine learning; major clinical study; male; multicenter study; nose polyp; obesity; observational study; outcome assessment; phenotype; prevalence; randomized controlled trial; respiratory tract allergy; scoring system; St. George Respiratory Questionnaire; urticaria","","","","","GlaxoSmithKline España, GSK, (205807); GlaxoSmithKline España, GSK","This study was sponsored by GSK (205807).","Global Strategy for Asthma Management and Prevention, (2021); Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet Respir Med, 8, pp. 585-596, (2020); Hassan M, Davies SE, Trethewey SP, Mansur AH., Prevalence and predictors of adherence to controller therapy in adult patients with severe/difficult-to-treat asthma: a systematic review and meta-analysis, J Asthma, 57, pp. 1379-1388, (2020); Quirce S, Plaza V, Picado C, Vennera M, Casafont J., Prevalence of uncontrolled severe persistent asthma in pneumology and allergy hospital units in Spain, J Investig Allergol Clin Immunol, 21, pp. 466-471, (2011); Weegar R., Applying natural language processing to electronic medical records for estimating healthy life expectancy, The Lancet regional health Western Pacific, 9, (2021); Alvarez-Perea A, Sanchez-Garcia S, Munoz Cano R, Antolin-Amerigo D, Tsilochristou O, Stukus DR., Impact Of “eHealth” in Allergic Diseases and Allergic Patients, J Investig Allergol Clin Immunol, 29, pp. 94-102, (2019); Izquierdo JL, Almonacid C, Gonzalez Y, Del Rio-Bermudez C, Ancochea J, Cardenas R, Et al., The impact of COVID-19 on patients with asthma, Eur Respir J, 57, (2021); Ohno-Machado L., Realizing the full potential of electronic health records: the role of natural language processing, J Am Med Inform Assoc, 18, (2011); Education IC., Natural Language Processing (NLP), (2020); Haerian K, Varn D, Vaidya S, Ena L, Chase HS, Friedman C., Detection of pharmacovigilance-related adverse events using electronic health records and automated methods, Clin Pharmacol Ther, 92, pp. 228-234, (2012); Izquierdo JL, Morena D, Gonzalez Y, Paredero JM, Perez B, Graziani D, Et al., Clinical Management of COPD in a Real-World Setting. 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Guidelines for Asthma Management, Arch Bronconeumol, 51, pp. 2-54, (2015); Anke LE, Tello J, Pardo A, Medrano IH, Urena A, Salcedo I, Et al., Savana: A Global Information Extraction and Terminology Expansion Framework in the Medical Domain, SEPLN, 57, pp. 23-30, (2016); Gomez de la Camara A., Scientific evidence based medicine: myth and reality of variability in clinical practice and its impact on health outcomes, Anales del sistema sanitario de Navarra, 26, pp. 11-26, (2003); Asher MI, Garcia-Marcos L, Pearce NE, Strachan DP., Trends in worldwide asthma prevalence, Eur Respir J, 56, (2020); Vianello A, Caminati M, Andretta M, Menti AM, Tognella S, Senna G, Et al., Prevalence of severe asthma according to the drug regulatory agency perspective: An Italian experience, World Allergy Organ J, 12, (2019); Hekking PW, Wener RR, Amelink M, Zwinderman AH, Bouvy ML, Bel EH., The prevalence of severe refractory asthma, J Allergy Clin Immunol, 135, pp. 896-902, (2015); Sato K, Ohno T, Ishii T, Ito C, Kaise T., The Prevalence, Characteristics, and Patient Burden of Severe Asthma Determined by Using a Japan Health Care Claims Database, Clin Ther, 41, pp. 2239-2251, (2019); Nagase H, Adachi M, Matsunaga K, Yoshida A, Okoba T, Hayashi N, Et al., Prevalence, disease burden, and treatment reality of patients with severe, uncontrolled asthma in Japan, Allergol Int, 69, pp. 53-60, (2020); Cancado JED, Penha M, Gupta S, Li VW, Julian GS, Moreira ES., Respira project: Humanistic and economic burden of asthma in Brazil, J Asthma, 56, pp. 244-251, (2019); Urrutia-Pereira M, Chong-Neto H, Mocellin LP, Ellwood P, Garcia-Marcos L, Simon L, Et al., Prevalence of asthma symptoms and associated factors in adolescents and adults in southern Brazil: A Global Asthma Network Phase I study, World Allergy Organ J, 14, (2021); Taube C, Bramlage P, Hofer A, Anderson D., Prevalence of oral corticosteroid use in the German severe asthma population, ERJ Open Res, 5, (2019); Ronnebjerg L, Axelsson M, Kankaanranta H, Backman H, Radinger M, Lundback B, Et al., Severe Asthma in a General Population Study: Prevalence and Clinical Characteristics, J Asthma Allergy, 14, pp. 1105-1115, (2021); Caminati M, Senna G., Uncontrolled severe asthma: starting from the unmet needs, Curr Med Res Opin, 35, pp. 175-177, (2019); Perez de Llano L, Martinez-Moragon E, Plaza Moral V, Trisan Alonso A, Sanchez CA, Callejas FJ, Et al., Unmet therapeutic goals and potential treatable traits in a population of patients with severe uncontrolled asthma in Spain. ENEAS study, Respir Med, 151, pp. 49-54, (2019); Malinovschi A, Van Muylem A, Michiels S, Michils A., FeNO as a predictor of asthma control improvement after starting inhaled steroid treatment, Nitric Oxide, 40, pp. 110-116, (2014); Pike K, Selby A, Price S, Warner J, Connett G, Legg J, Et al., Exhaled nitric oxide monitoring does not reduce exacerbation frequency or inhaled corticosteroid dose in paediatric asthma: a randomised controlled trial, Clin Respir J, 7, pp. 204-213, (2013); Castner J, Jungquist CR, Mammen MJ, Pender JJ, Licata O, Sethi S., Prediction model development of women’s daily asthma control using fitness tracker sleep disruption, Heart Lung, 49, pp. 548-555, (2020); Campbell CM, Murphy DR, Taffet GE, Major AB, Ritchie CS, Leff B, Et al., Implementing Health Care Quality Measures in Electronic Health Records: A Conceptual Model, J Am Geriatr Soc, 69, pp. 1079-1085, (2021); Neves AL, Freise L, Laranjo L, Carter AW, Darzi A, Mayer E., Impact of providing patients access to electronic health records on quality and safety of care: a systematic review and meta-analysis, BMJ Qual Saf, 29, pp. 1019-1032, (2020); Zein JG, Wu CP, Attaway AH, Zhang P, Nazha A., Novel Machine Learning Can Predict Acute Asthma Exacerbation, Chest, 159, pp. 1747-1757, (2021); Sun W, Nasraoui O, Shafto P., Evolution and impact of bias in human and machine learning algorithm interaction, PLoS One, 15, (2020); Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H., eDoctor: machine learning and the future of medicine, J Intern Med, 284, pp. 603-619, (2018)","C. Almonacid Sánchez; Hospital Universitario de Toledo, Toledo, Avda. del Río Guadiana, 45007, Spain; email: caralmsan@gmail.com","","ESMON Publicidad S.A.","","","","","","10189068","","JIAIE","36000822","English","J. Invest. Allergol. Clin. Immunol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85174891507"
"Bulucu P.; Nakip M.; Guzelis C.","Bulucu, Pervin (57207695643); Nakip, Mert (57212473263); Guzelis, Cuneyt (55937768800)","57207695643; 57212473263; 55937768800","Multi-Sensor E-Nose Based on Online Transfer Learning Trend Predictive Neural Network","2024","IEEE Access","12","","","71442","71452","10","2","10.1109/ACCESS.2024.3401569","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193278756&doi=10.1109%2fACCESS.2024.3401569&partnerID=40&md5=c85f54d8e2c144db178fb682da45408a","Yaşar University, Graduate School, Izmir, 35100, Turkey; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (PAN), Gliwice, 44-100, Poland; Thales AI Ltd., Şti., Izmir, 35100, Turkey; Yaşar University, Department of Electrical and Electronics Engineering, Izmir, 35100, Turkey","Bulucu P., Yaşar University, Graduate School, Izmir, 35100, Turkey; Nakip M., Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (PAN), Gliwice, 44-100, Poland, Thales AI Ltd., Şti., Izmir, 35100, Turkey; Guzelis C., Thales AI Ltd., Şti., Izmir, 35100, Turkey, Yaşar University, Department of Electrical and Electronics Engineering, Izmir, 35100, Turkey","Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection and analysis capabilities. The decision and analysis of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However, despite their potential, offline training efforts often prove intensive and may still fall short in achieving high generalization ability and specialization for considered application. To address these challenges, this paper introduces the e-rTPNN decision system, which leverages the Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. The recurrent architecture of the e-rTPNN system effectively captures temporal dependencies and hidden sequential patterns within E-Nose sensor data, enabling accurate estimation of trends and levels. Notably, the system demonstrates the ability to adapt quickly to new data during online operation, requiring only a small offline dataset for initial learning. We evaluate the performance of the e-rTPNN decision system in two domains: beverage quality assessment and medical diagnosis, using publicly available wine quality and Chronic Obstructive Pulmonary Disease (COPD) datasets, respectively. Our evaluation indicates that the proposed e-rTPNN achieves decision accuracy exceeding 97% while maintaining low execution times. Furthermore, comparative analysis against established Machine Learning (ML) models reveals that the e-rTPNN decision system consistently outperforms these models by a significant margin in terms of accuracy.  © 2013 IEEE.","E-Nose; multi-sensor; online learning; recurrent trend predictive neural network; trend prediction","Diagnosis; Disease control; E-learning; Environmental management; Learning systems; Online systems; Pulmonary diseases; Quality control; Convolutional neural network; Decision systems; Features extraction; Market researches; Multi sensor; Online learning; Predictive neural network; Recurrent trend predictive neural network; Transfer learning; Trend prediction; Electronic nose","","","","","","","Wilson A.D., Baietto M., Applications and advances in electronic-nose technologies, Sensors, 9, 7, pp. 5099-5148, (2009); Persaud K., Dodd G., Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature, 299, 5881, pp. 352-355; Wilson A.D., Diverse applications of electronic-nose technologies in agriculture and forestry, Sensors, 13, 2, pp. 2295-2348, (2013); Baldwin E.A., Bai J., Plotto A., Dea S., Electronic noses and tongues: Applications for the food and pharmaceutical industries, Sensors, 11, 5, pp. 4744-4766, (2011); Nakip M., Guzelis C., Multi-sensor fire detector based on trend predictive neural network, Proc. 11th Int. Conf. Electr. Electron. Eng. (ELECO), pp. 600-604, (2019); Nakip M., Guzelis C., Yildiz O., Recurrent trend predictive neural network for multi-sensor fire detection, IEEE Access, 9, pp. 84204-84216, (2021); Gamboa J.C.R., Albarracin E.S., da Silva A.J., Tiago T.A., Electronic nose dataset for detection of wine spoilage thresholds, Data Brief, 25, (2019); Acevedo C.M.D., Vasquez C.A.C., Gomez J.K.C., Electronic nose dataset for COPD detection from smokers and healthy people through exhaled breath analysis, Data Brief, 35, pp. 4-9, (2021); Wang S., Zhang Q., Liu C., Wang Z., Gao J., Yang X., Lan Y., Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper, Sens. Actuators A, Phys., 357, 2, (2023); Shi Y., Gong F., Wang M., Liu J., Wu Y., Men H., A deep feature mining method of electronic nose sensor data for identification identifying beer olfactory information, J. Food Eng., 263, 1, pp. 437-445, (2019); Attallah O., Multitask deep learning-based pipeline for gas leakage detection via E-Nose and thermal imaging multimodal fusion, Chemosensors, 11, 364, pp. 1-12, (2023); Zhang S., Cheng Y., Luo D., He J., Wong A.K.Y., Hung K., Channel attention convolutional neural network for Chinese baijiu detection with E-nose, IEEE Sensors J, 21, 14, pp. 16170-16182; Hui Z., Lu A., A deep learning method combined with an electronic nose for gas information identification of soybean from different origins, Chemometrics Intell. Lab. Syst., 240, pp. 1-7; Gamboa J.C.R., da Silva A.J., Ismael I.C., Albarracin E.S., Duran C.M., Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines, Sens. Actuators B, Chem., 327, (2020); Wu D., Luo D., Wong K.-Y., Hung K., POP-CNN: Predicting odor pleasantness with convolutional neural network, IEEE Sensors J, 19, 23, pp. 11337-11345, (2019); Ren X., Wang Y., Huang Y., Mustafa M., Sun D., Xue F., Chen D., Xu L., Wu F., A CNN-based E-Nose using time series features for food freshness classification, IEEE Sensors J, 23, 6, pp. 6027-6038; Wei G., Li G., Zhao J., He A., Development of a LeNet-5 gas identification CNN structure for electronic noses, Sensors, 19, 1, (2019); Wang Q., Qi H., Liu F., Time series prediction of E-nose sensor drift based on deep recurrent neural network, Proc. Chin. Control Conf. (CCC), pp. 3479-3484, (2019); Zou Y., Lv J., Using recurrent neural network to optimize electronic nose system with dimensionality reduction, Electron, 9, 12, (2020); Wijaya D.R., Sarno R., Zulaika E., DWTLSTM for electronic nose signal processing in beef quality monitoring, Sens. Actuators B, Chem., 326, (2019); Torres-Tello J., Guaman A.V., Ko S.-B., Improving the detection of explosives in a MOX chemical sensors array with LSTM networks, IEEE Sensors J, 20, 23, pp. 14302-14309, (2020); Liu H., Li Q., Gu Y., A multi-task learning framework for gas detection and concentration estimation, Neurocomputing, 416, pp. 28-37; Wang B., Deng J., Jiang H., Chen Q., Electronic nose signals-based deep learning models to realize high-precision monitoring of simultaneous saccharification and fermentation of cassava, Microchemical J, 182; Lu B., Fu L., Nie B., Peng Z., Liu H., A novel framework with high diagnostic sensitivity for lung cancer detection by electronic nose, Sensors, 19, 23, (2019); Guo J., Cheng Y., Luo D., Wong K.-Y., Hung K., Li X., ODRP: A deep learning framework for odor descriptor rating prediction using electronic nose, IEEE Sensors J, 21, 13, pp. 15012-15021; Mao G., Zhang Y., Xu Y., Li X., Xu M., Zhang Y., Jia P., An electronic nose for harmful gas early detection based on a hybrid deep learning method h-crnn, Microchemical J, 195, pp. 1-11; Aulia D., Sarno R., Hidayati S.C., Rivai M., Optimization of the electronic nose sensor array for asthma detection based on genetic algorithm, IEEE Access, 11, pp. 74924-74935; Pan X., Zhang H., Ye W., Bermak A., Zhao X., A fast and robust gas recognition algorithm based on hybrid convolutional and recurrent neural network, IEEE Access, 7, pp. 100954-100963, (2019); Zhang H., Ye W., Zhao X., Teng R.K.F., Pan X., A novel convolutional recurrent neural network based algorithm for fast gas recognition in electronic nose system, Proc. IEEE Int. Conf. Electron Devices Solid State Circuits (EDSSC), pp. 1-2, (2018); Zhang W., Wang L., Chen J., Xiao W., Bi X., A novel gas recognition and concentration detection algorithm for artificial olfaction, IEEE Trans. Instrum. Meas., 70, pp. 1-14; Oates M.J., Gonzalez-Teruel J.D., Ruiz-Abellon M.C., Guillamon-Frutos A., Ramos J.A., Torres-Sanchez R., Using a low-cost components e-Nose for basic detection of different foodstuffs, IEEE Sensors J, 22, 14, pp. 13872-13881; Modesti M., Taglieri I., Bianchi A., Tonacci A., Sansone F., Bellincontro A., Venturi F., Sanmartin C., E-nose and olfactory assessment: Teamwork or a challenge to the last data? The case of virgin olive oil stability and shelf life, Appl. Sci., 11, 18; Oliveros M.C.C., Perez Pavon J.L., Pinto C.G., Laespada M.E.F., Cordero B.M., Forina M., Electronic nose based on metal oxide semiconductor sensors as a fast alternative for the detection of adulteration of virgin olive oils, Analytica Chim. Acta, 459, 2, pp. 219-228, (2002); Zarezadeh M.R., Aboonajmi M., Varnamkhasti M.G., Azarikia F., Olive oil classification and fraud detection using E-nose and ultrasonic system, Food Anal. Methods, 14, 10, pp. 2199-2210, (2021); Lerma-Garcia M.J., Simo-Alfonso E.F., Bendini A., Cerretani L., Metal oxide semiconductor sensors for monitoring of oxidative status evolution and sensory analysis of virgin olive oils with different phenolic content, Food Chem, 117, 4, pp. 608-614, (2009); Eklov T., Johansson G., Winquist F., Lundstrom I., Monitoring sausage fermentation using an electronic nose, J. Sci. Food Agricult., 76, 4, pp. 525-532, (1998); Selvaraj R., Vasa N.J., Nagendra S.M., Mizaikoff B., Advances in mid-infrared spectroscopy-based sensing techniques for exhaled breath diagnostics, Molecules, 25, 9, pp. 1-17, (2020); Montuschi P., Mores N., Trove A., Mondino C., Barnes P.J., The electronic nose in respiratory medicine, Respiration, 85, 1, pp. 72-84, (2012); van de Kant K.D., van der Sande L.J., Jobsis Q., van Schayck O.C., Dompeling E., Clinical use of exhaled volatile organic compounds in pulmonary diseases: A systematic review, Respiratory Res, 13, 1, (2012); Behera B., Joshi R., Vishnu G.A., Bhalerao S., Pandya H., Electronic-nose: A non-invasive technology for breath analysis of diabetes and lung cancer patients, J. Breath Res. Accept., 13, 2, (2019); Diaz de Leon-Martinez L., Flores-Ramirez R., Lopez-Mendoza C.M., Rodriguez-Aguilar M., Metha G., Zuniga-Martinez L., Ornelas-Rebolledo O., Alcantara-Quintana L.E., Identification of volatile organic compounds in the urine of patients with cervical cancer. Test concept for timely screening, Clinica Chim. Acta, 522, pp. 132-140; Nakip M., Copur O., Biyik E., Guzelis C., Renewable energy management in smart home environment via forecast embedded scheduling based on recurrent trend predictive neural network, Appl. Energy, 340, pp. 1-12","M. Nakip; Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (PAN), Gliwice, 44-100, Poland; email: mnakip@iitis.pl","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85193278756"
"Naim M.K.; Mengko T.R.; Hertadi R.; Purwarianti A.; Susanty M.","Naim, Muhammad Khaerul (57222073721); Mengko, Tati Rajab (59468798500); Hertadi, Rukman (6505773725); Purwarianti, Ayu (13104011100); Susanty, Meredita (57208919768)","57222073721; 59468798500; 6505773725; 13104011100; 57208919768","EmbedCaps-DBP: Predicting DNA-Binding Proteins Using Protein Sequence Embedding and Capsule Network","2023","IEEE Access","11","","","121256","121268","12","2","10.1109/ACCESS.2023.3328960","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176736750&doi=10.1109%2fACCESS.2023.3328960&partnerID=40&md5=af69ca534606b6869a9d4540e955870b","Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia; Bandung Institute of Technology, Faculty of Mathematics and Natural Sciences, Bandung, 40132, Indonesia; Universal University, Department of Informatics Engineering, Batam, 29433, Indonesia; Bandung Institute of Technology, Center for Artificial Intelligence (U-CoE AI-VLB), Bandung, 40132, Indonesia; University of Pertamina, Department of Computer Science, Jakarta, 12220, Indonesia","Naim M.K., Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia, Universal University, Department of Informatics Engineering, Batam, 29433, Indonesia; Mengko T.R., Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia; Hertadi R., Bandung Institute of Technology, Faculty of Mathematics and Natural Sciences, Bandung, 40132, Indonesia; Purwarianti A., Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia, Bandung Institute of Technology, Center for Artificial Intelligence (U-CoE AI-VLB), Bandung, 40132, Indonesia; Susanty M., Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia, University of Pertamina, Department of Computer Science, Jakarta, 12220, Indonesia","DNA-binding interactions are an essential biological activity with important functions, such as DNA replication, transcription, repair, and recombination. DNA-binding proteins (DBPs) have been strongly associated with various human diseases, such as asthma, cancer, and HIV/AIDS. Therefore, some DBPs are used in the pharmaceutical industry to produce antibiotics, anticancer drugs, and anti-inflammatory drugs. Most previous methods have used evolutionary information to predict DBPs. However, these methods have high computing costs and produce unsatisfactory results. This study presents EmbedCaps-DBP, a new method for improving DBP prediction. First, we used three protein sequence embeddings (ProtT5, ESM-1b, and ESM-2) to extract learned feature representations from protein sequences. Those embedding methods can capture important information about amino acids, such as biophysics, biochemistry, structure, and domains, that have not been fully utilized in protein annotation tasks. Then, we used a 1D-capsule network (CapsNet) as a classifier. EmbedCaps-DBP significantly outperformed all existing classifiers in training and independent datasets. Based on two independent datasets, EmbedCaps-DBP (ProtT5) achieved 12.65% and 0.33% higher accuracies than a recent predictor on PDB2272 and PDB186, respectively. These results indicate that our proposed method is a promising predictor of DBPs. © 2013 IEEE.","Capsule network; deep learning; DNA-binding proteins; machine learning; protein sequence embeddings","Binding energy; Bioactivity; Classification (of information); Deep learning; Diseases; DNA; Forecasting; Proteins; Binding interaction; Capsule network; Deep learning; DNA replications; DNA-binding; DNA-binding protein; Embeddings; Machine-learning; Protein sequence embedding; Protein sequences; Embeddings","","","","","","","Barukab O., Ali F., Alghamdi W., Bassam Y., Khan S.A., DBP-CNN: Deep learning-based prediction of DNA-binding proteins by coupling discrete cosine transform with two-dimensional convolutional neural network, Expert Syst. 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Sci., 384, pp. 135-144, (2017); Lin W.-Z., Fang J.-A., Xiao X., Chou K.-C., IDNA-prot: Identification of DNA binding proteins using random forest with grey model, PLoS ONE, 6, 9, (2011); Chowdhury S.Y., Shatabda S., Dehzangi A., IDNAProt-ES: Identification of DNA-binding proteins using evolutionary and structural features, Sci. Rep., 7, 1, (2017); Liu B., Xu J., Fan S., Xu R., Zhou J., Wang X., PseDNA-pro: DNA-binding protein identification by combining Chou’s PseAAC and physicochemical distance transformation, Mol. Informat., 34, 1, pp. 8-17, (2015); Rahman M.S., Shatabda S., Saha S., Kaykobad M., Rahman M.S., DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC, J. Theor. Biol., 452, pp. 22-34, (2018); Ali F., Kabir M., Arif M., Khan Swati Z.N., Khan Z.U., Ullah M., Yu D.-J., DBPPred-PDSD: Machine learning approach for prediction of DNA-binding proteins using discrete wavelet transform and optimized integrated features space, Chemometric Intell. Lab. 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Appl., 197, 2022; Zou C., Gong J., Li H., An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis, BMC Bioinf, 14, 1, (2013); Ma X., Guo J., Sun X., DNABP: Identification of DNA-binding proteins based on feature selection using a random forest and predicting binding residues, PLoS ONE, 11, 12, (2016); Zaman R., Chowdhury S.Y., Rashid M.A., Sharma A., Dehzangi A., Shatabda S., HMMBinder: DNA-binding protein prediction using HMM profile based features, Biomed Res Int, 2017, (2017); Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., Bourne P.E., The protein data bank, 1999-, Int. Tables Crystallogr., Int. 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Conf. Learn. Represent. (ICLR), 115, (2018); Pan D., Lu Y., Kang P., A deep learning model for multi-label classification using capsule networks, Proc. Int. Conf. Intell. Comput., pp. 144-155, (2019); Kumar K.K., Pugalenthi G., Suganthan P.N., DNA-prot: Identification of DNA binding proteins from protein sequence information using random forest, J. Biomolecular Struct. Dyn., 26, 6, pp. 679-686, (2009); Liu B., Xu J., Lan X., Xu R., Zhou J., Wang X., Chou K.-C., IDNA-Prot|dis: |IDNA-Prot|dis: Identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition, PLoS ONE, 9, 9, (2014); Zhao S., Ding Y., Liu X., Su X., HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins, Comput. Biol. Med., 145, 2022; Zou Y., Ding Y., Tang J., Guo F., Peng L., FKRR-MVSF: A fuzzy kernel ridge regression model for identifying DNA-binding proteins by multi-view sequence features via Chou’s five-step rule, Int. J. Mol. Sci., 20, 17, (2019); Patrick M.K., Adekoya A.F., Mighty A.A., Edward B.Y., Capsule networks—A survey, J. King Saud Univ.-Comput. Inf. Sci., 34, 1, pp. 1295-1310, (2022); Ezechukwu D.N., Moullec Y.L., CapsNet on embedded devices in a data scarce scenario, Proc. 18th Biennial Baltic Electron. Conf. (BEC), pp. 1-6, (2022); Wang Y., Wang B., Jiang J., Guo J., Lai J., Lian X.-Y., Wu J., Multitask CapsNet: An imbalanced data deep learning method for predicting toxicants, ACS Omega, 6, 40, pp. 26545-26555, (2021)","T.R. Mengko; Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia; email: tati.latifa.e.rajab@gmail.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85176736750"
"Correa-Agudelo E.; Ding L.; Beck A.F.; Mendy A.; Mersha T.B.","Correa-Agudelo, Esteban (24765904800); Ding, Lili (36022230300); Beck, Andrew F. (54946402100); Mendy, Angelico (48361609000); Mersha, Tesfaye B. (56147975000)","24765904800; 36022230300; 54946402100; 48361609000; 56147975000","Multilevel Analysis of Racial and Ethnic Disparities in COVID-19 Hospitalization among Children with Allergies","2023","Annals of the American Thoracic Society","20","6","","843","853","10","2","10.1513/AnnalsATS.202207-580OC","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160520288&doi=10.1513%2fAnnalsATS.202207-580OC&partnerID=40&md5=a05da7c33f859be3f271c7ee22d9eb2f","Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Division of General & Community Pediatrics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Division of Epidemiology, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, United States","Correa-Agudelo E., Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Ding L., Division of Biostatistics and Epidemiology, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Beck A.F., Division of General & Community Pediatrics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Mendy A., Division of Epidemiology, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH, United States; Mersha T.B., Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States","Rationale: Previous studies have identified risk factors for coronavirus disease (COVID-19) hospitalization in children. However, these studies have been limited in their ability to disentangle the contribution of racial disparities, allergic comorbidities, and environmental exposures to the development of severe COVID-19 in at-risk children with allergies. Objectives: To examine racial and ethnic disparities in COVID-19 hospitalization and their links to potentially underlying allergic comorbidities and individual and place-based factors in children with allergies. Methods: This is an electronic health record-based retrospective study of children in 2020. The outcome was COVID-19 hospitalization categorized as no hospital care for patients with asymptomatic/mild illness, short stay for patients admitted and discharged within 24 hours, and prolonged stay for patients requiring additional time to discharge (more than 24 h). Mixed-effects and mediation models were used to determine relationships among independent variables, mediators, and COVID-19 hospitalization. Results: Among the 5,258 children with COVID-19 positive test or diagnosis, 10% required a short stay, and 3.7% required a prolonged stay. Black and Hispanic children had higher odds of longer stays than non-Hispanic White children (both P, 0.001). Children with obesity and eosinophilic esophagitis diagnoses had higher odds of short and prolonged stay (all P, 0.05). Area-level deprivation was associated with short stay (adjusted odds ratio [AOR], 15.49; 95% confidence interval [CI], 5.16–45.47 for every 0.1-unit increase) and prolonged stay (AOR, 11.82; 95% CI, 2.25–62.01 for every 0.1-unit increase). Associations between race/ethnicity and COVID-19 hospitalization were primarily mediated by insurance and area-level deprivation, altogether accounting for 99% of the variation in COVID-19 hospitalization. Conclusions: There were racial and ethnic differences in children with allergies and individual and place-based factors related to COVID-19 hospitalization. Differences were primarily mediated by insurance and area-level deprivation, altogether accounting for 99% of the variation in COVID-19 hospitalization. A better understanding of COVID-related morbidity in children and the link to place-based factors is key to developing prevention strategies capable of equitably improving outcomes. Copyright © 2023 by the American Thoracic Society.","COVID-19; electronic health records; health disparity; machine learning; minority and vulnerable populations","Child; COVID-19; Hospitalization; Humans; Hypersensitivity; Multilevel Analysis; Retrospective Studies; White People; adolescent; allergic rhinitis; allergy; Article; asthma; atopic dermatitis; Bayesian network; Black person; Caucasian; child; clinical outcome; comorbidity; coronavirus disease 2019; crowding (area); descriptive research; electronic health record; eosinophilic esophagitis; ethnic difference; female; food allergy; Hispanic; hospital discharge; hospitalization; human; hypertension; ICD-10; length of stay; long term exposure; major clinical study; male; Markov chain Monte Carlo method; mediation analysis; multilevel analysis; obesity; PM2.5 exposure; prematurity; race difference; retrospective study; risk factor; socioeconomics; validation study; coronavirus disease 2019; hospitalization; hypersensitivity; multilevel analysis","","","","","National Heart, Lung, and Blood Institute, NHLBI, (R01 HL132344); National Human Genome Research Institute, NHGRI, (R01 HG011411)","Supported by the National Human Genome Research Institute (R01 HG011411) and the National Heart, Lung, and Blood Institute (R01 HL132344).","Kim L, Whitaker M, O'Halloran A, Kambhampati A, Chai SJ, Reingold A, Et al., Hospitalization rates and characteristics of children aged,18 years hospitalized with laboratory-confirmed COVID-19—COVID-NET, 14 states, March 1–July 25, 2020, MMWR Morb Mortal Wkly Rep, 69, pp. 1081-1088, (2020); Delahoy MJ, Ujamaa D, Whitaker M, O'Halloran A, Anglin O, Burns E, Et al., Hospitalizations associated with COVID-19 among children and adolescents—COVID-NET, 14 states, March 1, 2020–August 14, 2021, MMWR Morb Mortal Wkly Rep, 70, pp. 1255-1260, (2021); Zachariah P, Johnson CL, Halabi KC, Ahn D, Sen AI, Fischer A, Et al., Epidemiology, clinical features, and disease severity in patients with coronavirus disease 2019 (COVID-19) in a children’s hospital in New York City, New York, JAMA Pediatr, 174, (2020); Mendy A, Wu X, Keller JL, Fassler CS, Apewokin S, Mersha TB, Et al., Air pollution and the pandemic: long-term PM2.5 exposure and disease severity in COVID-19 patients, Respirology, 26, pp. 1181-1187, (2021); Moreira A, Chorath K, Rajasekaran K, Burmeister F, Ahmed M, Moreira A., Demographic predictors of hospitalization and mortality in US children with COVID-19, Eur J Pediatr, 180, pp. 1659-1663, (2021); Gaietto K, Freeman MC, DiCicco LA, Rauenswinter S, Squire JR, Aldewereld Z, Et al., Asthma as a risk factor for hospitalization in children with COVID-19: a nested case-control study, Pediatr Allergy Immunol, 33, (2022); Lee SC, Son KJ, Han CH, Jung JY, Park SC., Impact of comorbid asthma on severity of coronavirus disease (COVID-19), Sci Rep, 10, (2020); Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Et al., Factors associated with COVID-19-related death using OpenSAFELY, Nature, 584, pp. 430-436, (2020); Brokamp C, Beck AF, Goyal NK, Ryan P, Greenberg JM, Hall ES., Material community deprivation and hospital utilization during the first year of life: an urban population-based cohort study, Ann Epidemiol, 30, pp. 37-43, (2019); Brokamp C., A high resolution spatiotemporal fine particulate matter exposure assessment model for the contiguous United States, Preprints, 7, (2021); Von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies, Epidemiology, 18, pp. 800-804, (2007); Wood SN., Generalized additive models: an introduction with R, (2017); Hodges JS, Reich BJ., Adding spatially-correlated errors can mess up the fixed effect you love, Am Stat, 64, pp. 325-334, (2010); Kline RB., Principles and practice of structural equation modeling, pp. 95-102, (2011); Beck AF, Huang B, Auger KA, Ryan PH, Chen C, Kahn RS., Explaining racial disparities in child asthma readmission using a causal inference approach, JAMA Pediatr, 170, pp. 695-703, (2016); Mersha TB, Qin K, Beck AF, Ding L, Huang B, Kahn RS., Genetic ancestry differences in pediatric asthma readmission are mediated by socioenvironmental factors, J Allergy Clin Immunol, 148, pp. 1210-1218, (2021); Scutari M., Learning Bayesian networks with the bnlearn R package, J Stat Softw, 35, pp. 1-22, (2010); Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Et al., Assessing the performance of prediction models: a framework for traditional and novel measures, Epidemiology, 21, pp. 128-138, (2010); Saito T, Rehmsmeier M., The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PloS One, 10, (2015); R: a language and environment for statistical computing, (2018); Wickham H., ggplot2: elegant graphics for data analysis, (2016); Rosseel Y., lavaan: an R package for structural equation modeling, J Stat Softw, 48, pp. 1-36, (2012); Niedzwiedz CL, O'Donnell CA, Jani BD, Demou E, Ho FK, Celis-Morales C, Et al., Ethnic and socioeconomic differences in SARS-CoV-2 infection: prospective cohort study using UK biobank, BMC Med, 18, (2020); Webb Hooper M, Napoles AM, Perez-Stable EJ., COVID-19 and racial/ethnic disparities, JAMA, 323, pp. 2466-2467, (2020); Goldman N, Pebley AR, Lee K, Andrasfay T, Pratt B., Racial and ethnic differentials in COVID-19-related job exposures by occupational standing in the US, PLoS One, 16, (2021); Luck AN, Preston SH, Elo IT, Stokes AC., The unequal burden of the Covid-19 pandemic: capturing racial/ethnic disparities in US cause-specific mortality, SSM Popul Health, 17, (2022); Bikomeye JC, Namin S, Anyanwu C, Rublee CS, Ferschinger J, Leinbach K, Et al., Resilience and equity in a time of crises: investing in public urban greenspace is now more essential than ever in the US and beyond, Int J Environ Res Public Health, 18, (2021); Williams DR, Collins C., Racial residential segregation: a fundamental cause of racial disparities in health, Public Health Rep, 116, pp. 404-416, (2001); Sternthal MJ, Slopen N, Williams DR., Racial disparities in health: how much does stress really matter?, Du Bois Rev, 8, pp. 95-113, (2011); Sattar N, Ho FK, Gill JMR, Ghouri N, Gray SR, Celis-Morales CA, Et al., BMI and future risk for COVID-19 infection and death across sex, age and ethnicity: preliminary findings from UK biobank, Diabetes Metab Syndr, 14, pp. 1149-1151, (2020); Sattar N, Valabhji J., Obesity as a risk factor for severe COVID-19: summary of the best evidence and implications for health care, Curr Obes Rep, 10, pp. 282-289, (2021); Gao M, Piernas C, Astbury NM, Hippisley-Cox J, O'Rahilly S, Aveyard P, Et al., Associations between body-mass index and COVID-19 severity in 6.9 million people in England: a prospective, community-based, cohort study, Lancet Diabetes Endocrinol, 9, pp. 350-359, (2021); Franceshini L, Macchiarelli R, Rentini S, Biviano I, Farsi A., Eosinophilic esophagitis: is the Th2 inflammation protective against the severe form of COVID-19?, Eur J Gastroenterol Hepatol, 32, (2020); Zevit N, Chehade M, Leung J, Marderfeld L, Dellon ES., Eosinophilic esophagitis patients are not at increased risk of severe COVID-19: a report from a global registry, J Allergy Clin Immunol Pract, 10, pp. 143-149, (2022); Krupp NL, Sehra S, Slaven JE, Kaplan MH, Gupta S, Tepper RS., Increased prevalence of airway reactivity in children with eosinophilic esophagitis, Pediatr Pulmonol, 51, pp. 478-483, (2016); Dellon ES, Hirano I., Epidemiology and natural history of eosinophilic esophagitis, Gastroenterology, 154, pp. 319-332, (2018); Royston P, Altman DG, Sauerbrei W., Dichotomizing continuous predictors in multiple regression: a bad idea, Stat Med, 25, pp. 127-141, (2006); Keidel D, Anto JM, Basagana X, Bono R, Burte E, Carsin AE, Et al., The role of socioeconomic status in the association of lung function and air pollution-a pooled analysis of three adult ESCAPE cohorts, Int J Environ Res Public Health, 16, (2019); Mersha TB, Abebe T., Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities, Hum Genomics, 9, (2015)","T.B. Mersha; Division of Asthma Research, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, 3333 Burnet Avenue, 45229, United States; email: tesfaye.mersha@cchmc.org","","American Thoracic Society","","","","","","23296933","","","36622831","English","Ann. Am. Thorac. Soc.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85160520288"
"Temirbekov N.; Temirbekova M.; Tamabay D.; Kasenov S.; Askarov S.; Tukenova Z.","Temirbekov, Nurlan (6506592930); Temirbekova, Marzhan (57218541636); Tamabay, Dinara (58192775000); Kasenov, Syrym (55964589700); Askarov, Seilkhan (58625911900); Tukenova, Zulfiya (56951094300)","6506592930; 57218541636; 58192775000; 55964589700; 58625911900; 56951094300","Assessment of the Negative Impact of Urban Air Pollution on Population Health Using Machine Learning Method","2023","International Journal of Environmental Research and Public Health","20","18","6770","","","","2","10.3390/ijerph20186770","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172761059&doi=10.3390%2fijerph20186770&partnerID=40&md5=f9e69bb0f13269c808e9d796d51624f4","National Engineering Academy of RK, Almaty, 050010, Kazakhstan; Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan; Almaty University of Power Engineering and Telecommunications Named after G. Daukeyev, Almaty, 050013, Kazakhstan; Ecoservice-S Limited Liability Partnership, Almaty, 050009, Kazakhstan; Institute of Zoology of the Ministry of Higher Education and Science of the RK, Almaty, 050060, Kazakhstan","Temirbekov N., National Engineering Academy of RK, Almaty, 050010, Kazakhstan, Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan; Temirbekova M., Almaty University of Power Engineering and Telecommunications Named after G. Daukeyev, Almaty, 050013, Kazakhstan; Tamabay D., National Engineering Academy of RK, Almaty, 050010, Kazakhstan, Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan; Kasenov S., National Engineering Academy of RK, Almaty, 050010, Kazakhstan, Faculty of Mechanics and Mathematics, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan; Askarov S., Ecoservice-S Limited Liability Partnership, Almaty, 050009, Kazakhstan; Tukenova Z., Institute of Zoology of the Ministry of Higher Education and Science of the RK, Almaty, 050060, Kazakhstan","This study focuses on assessing the level of morbidity among the population of Almaty, Kazakhstan, and investigating its connection with atmospheric air pollution using machine learning algorithms. The use of these algorithms is aimed at analyzing the relationship between air pollution levels and the state of public health, as well as the correlations between COVID-19 infection and the development of respiratory diseases. This study analyzes the respiratory diseases of the population of Almaty and the level of air pollution as a result of suspended particles for the period of 2017–2022. The study includes recommendations to reduce harmful emissions into the atmosphere using machine learning methods. The results of the study show that air pollution is a critical factor affecting the increase in the number of diseases of the respiratory system. The study recommends taking measures to reduce air pollution and improve air quality in order to prevent the development of chronic respiratory diseases. The study offers recommendations to industrial enterprises, traffic management organizations, thermal power plants, the Department of Environmental Protection, and local executive bodies in order to reduce respiratory diseases among the population. © 2023 by the authors.","air pollution; machine learning algorithms; random forest; recommendations; respiratory diseases","Almaty; Kazakhstan; arsenic; cadmium; carbon monoxide; chromium; copper; formaldehyde; lead; nickel; nitrogen dioxide; nitrogen oxide; sulfur dioxide; algorithm; atmospheric pollution; health impact; machine learning; morbidity; pollution effect; respiratory disease; urban pollution; adolescent; adult; air pollution; air quality; Article; asthma; bronchitis; child; chronic respiratory tract disease; coronavirus disease 2019; electric power plant; emphysema; environmental protection; female; human; Kazakhstan; machine learning; major clinical study; male; particulate matter 10; particulate matter 2.5; population health; public health; random forest; respiratory tract disease","","arsenic, 7440-38-2; cadmium, 22537-48-0, 7440-43-9; carbon monoxide, 630-08-0; chromium, 16065-83-1, 7440-47-3, 14092-98-9; copper, 15158-11-9, 7440-50-8; formaldehyde, 50-00-0; lead, 7439-92-1, 13966-28-4; nickel, 7440-02-0; nitrogen dioxide, 10102-44-0; nitrogen oxide, 11104-93-1; sulfur dioxide, 7446-09-5","","","Ministry of Education and Science of the Republic of Kazakhstan, (BR18574148)","The Science Committee of the Ministry of Higher Education and Science of the Republic of Kazakhstan (grant number BR18574148 “Development of geoinformation systems and monitoring of environmental objects”).","Arons M.M., Hatfield K.M., Reddy S.C., Kimball A., James A., Jacobs J.R., Taylor J., Spicer K., Bardossy A.C., Oakley L.P., Et al., Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility, N. 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Toxicol, 44, pp. 299-347, (2014); Darrow L.A., Klein M., Flanders W.D., Air pollution and acute respiratory infections among children 0–4 years, Am. J. Epidemiol, 180, pp. 968-977, (2014)","M. Temirbekova; Almaty University of Power Engineering and Telecommunications Named after G. Daukeyev, Almaty, 050013, Kazakhstan; email: m.temirbekova@aues.kz","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","16617827","","","37754628","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85172761059"
"Pirmoradi S.; Hosseiniyan Khatibi S.M.; Zununi Vahed S.; Homaei Rad H.; Khamaneh A.M.; Akbarpour Z.; Seyedrezazadeh E.; Teshnehlab M.; Chapman K.R.; Ansarin K.","Pirmoradi, Saeed (53866938500); Hosseiniyan Khatibi, Seyed Mahdi (57210211237); Zununi Vahed, Sepideh (57220528186); Homaei Rad, Hamed (58132363800); Khamaneh, Amir Mahdi (56009901800); Akbarpour, Zahra (57962261000); Seyedrezazadeh, Ensiyeh (23976479800); Teshnehlab, Mohammad (23006417100); Chapman, Kenneth R. (16136318600); Ansarin, Khalil (16021258800)","53866938500; 57210211237; 57220528186; 58132363800; 56009901800; 57962261000; 23976479800; 23006417100; 16136318600; 16021258800","Unraveling the link between PTBP1 and severe asthma through machine learning and association rule mining method","2023","Scientific Reports","13","1","15399","","","","2","10.1038/s41598-023-42581-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171349747&doi=10.1038%2fs41598-023-42581-5&partnerID=40&md5=639260594f88899f1c064dc5930f9d02","Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran; Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran; Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Department of Electric and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran; Division of Respiratory Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada","Pirmoradi S., Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran; Hosseiniyan Khatibi S.M., Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran, Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran; Zununi Vahed S., Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Homaei Rad H., Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran; Khamaneh A.M., Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran; Akbarpour Z., Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran; Seyedrezazadeh E., Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Teshnehlab M., Department of Electric and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran; Chapman K.R., Division of Respiratory Medicine, Department of Medicine, University of Toronto, Toronto, ON, Canada; Ansarin K., Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran","Severe asthma is a chronic inflammatory airway disease with great therapeutic challenges. Understanding the genetic and molecular mechanisms of severe asthma may help identify therapeutic strategies for this complex condition. RNA expression data were analyzed using a combination of artificial intelligence methods to identify novel genes related to severe asthma. Through the ANOVA feature selection approach, 100 candidate genes were selected among 54,715 mRNAs in blood samples of patients with severe asthmatic and healthy groups. A deep learning model was used to validate the significance of the candidate genes. The accuracy, F1-score, AUC-ROC, and precision of the 100 genes were 83%, 0.86, 0.89, and 0.9, respectively. To discover hidden associations among selected genes, association rule mining was applied. The top 20 genes including the PTBP1, RAB11FIP3, APH1A, and MYD88 were recognized as the most frequent items among severe asthma association rules. The PTBP1 was found to be the most frequent gene associated with severe asthma among those 20 genes. PTBP1 was the gene most frequently associated with severe asthma among candidate genes. Identification of master genes involved in the initiation and development of asthma can offer novel targets for its diagnosis, prognosis, and targeted-signaling therapy. © 2023, Springer Nature Limited.","","Artificial Intelligence; Asthma; Data Mining; Heterogeneous-Nuclear Ribonucleoproteins; Humans; Machine Learning; Polypyrimidine Tract-Binding Protein; Pulmonary Disease, Chronic Obstructive, Severe Early-Onset; heterogeneous nuclear ribonucleoprotein; polypyrimidine tract binding protein; PTBP1 protein, human; artificial intelligence; asthma; data mining; genetics; human; machine learning","","Heterogeneous-Nuclear Ribonucleoproteins, ; Polypyrimidine Tract-Binding Protein, ; PTBP1 protein, human, ","","","Sleep Research Center; National Institute for Medical Research Development, NIMAD, (983118); National Institute for Medical Research Development, NIMAD","Funding text 1: This work was financially supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran (#983118). Also, the authors would like to thank the Clinical Research Development Unit of Tabriz Valiasr Hospital and Rahat Breath, Kidney Research Center, and Sleep Research Center for their assistance in this research. ; Funding text 2: This work was financially supported by the National Institute for Medical Research Development (NIMAD), Tehran, Iran (#983118). Also, the authors would like to thank the Clinical Research Development Unit of Tabriz Valiasr Hospital and Rahat Breath, Kidney Research Center, and Sleep Research Center for their assistance in this research.","Masoli M., Et al., The global burden of asthma: executive summary of the GINA Dissemination Committee report, Allergy, 59, 5, pp. 469-478, (2004); Schofield J.P., Et al., A topological data analysis network model of asthma based on blood gene expression profiles, bioRxiv, 13, (2019); Bhalla A., Mukherjee M., Nair P., Airway eosinophilopoietic and autoimmune mechanisms of eosinophilia in severe asthma, Immunol. 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Acta, 1792, 11, pp. 1080-1086, (2009); Zhu Y., Et al., Knock-down of circular RNA H19 induces human adipose-derived stem cells adipogenic differentiation via a mechanism involving the polypyrimidine tract-binding protein 1, Exp. Cell Res., 387, 2, (2020); Weathington N., Et al., BAL cell gene expression in severe asthma reveals mechanisms of severe disease and influences of medications, Am. J. Respir. Crit. Care Med., 200, 7, pp. 837-856, (2019); Wan Y.I., Et al., Genome-wide association study to identify genetic determinants of severe asthma, Thorax, 67, 9, pp. 762-768, (2012); Modena B.D., Et al., Gene expression correlated with severe asthma characteristics reveals heterogeneous mechanisms of severe disease, Am. J. Respir. Crit. Care Med., 195, 11, pp. 1449-1463, (2017); Melen E., Pershagen G., Pathophysiology of asthma: Lessons from genetic research with particular focus on severe asthma, J. Intern. Med., 272, 2, pp. 108-120, (2012); Voraphani N., Et al., An airway epithelial iNOS-DUOX2-thyroid peroxidase metabolome drives Th1/Th2 nitrative stress in human severe asthma, Mucosal. Immunol., 7, 5, pp. 1175-1185, (2014); Huang Y., Et al., Key genes and co-expression modules involved in asthma pathogenesis, PeerJ, 8, (2020); Li Y., Et al., A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data, BMC Genom., 18, 1, (2017)","K. Ansarin; Rahat Breath and Sleep Research Center, Tabriz University of Medical Science, Tabriz, Iran; email: dr.ansarin@gmail.com; K.R. Chapman; Division of Respiratory Medicine, Department of Medicine, University of Toronto, Toronto, Canada; email: ken.chapman.airways@gmail.com","","Nature Research","","","","","","20452322","","","37717070","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85171349747"
"Peltrini R.; Cordell R.L.; Wilde M.; Abuhelal S.; Quek E.; Zounemat-Kermani N.; Ibrahim W.; Richardson M.; Brinkman P.; Schleich F.; Stefanuto P.-H.; Aung H.; Greening N.; Dahlen S.E.; Djukanovic R.; Adcock I.M.; Brightling C.; Monks P.; Siddiqui S.","Peltrini, Rosa (57207685390); Cordell, Rebecca L. (15838972800); Wilde, Michael (56007063700); Abuhelal, Shahd (57205644637); Quek, Eleanor (57222252803); Zounemat-Kermani, Nazanin (57218534288); Ibrahim, Wadah (57205706708); Richardson, Matthew (56515997300); Brinkman, Paul (49962835700); Schleich, Florence (6506954469); Stefanuto, Pierre-Hugues (55207262800); Aung, Hnin (57218668160); Greening, Neil (54784373200); Dahlen, Sven Erik (7006190330); Djukanovic, Ratko (57210653671); Adcock, Ian M. (57201387004); Brightling, Christopher (54790515400); Monks, Paul (7006253164); Siddiqui, Salman (16242465600)","57207685390; 15838972800; 56007063700; 57205644637; 57222252803; 57218534288; 57205706708; 56515997300; 49962835700; 6506954469; 55207262800; 57218668160; 54784373200; 7006190330; 57210653671; 57201387004; 54790515400; 7006253164; 16242465600","Discovery and Validation of a Volatile Signature of Eosinophilic Airway Inflammation in Asthma","2024","American Journal of Respiratory and Critical Care Medicine","210","9","","1101","1112","11","2","10.1164/rccm.202310-1759OC","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208601797&doi=10.1164%2frccm.202310-1759OC&partnerID=40&md5=3155937a3139bb6dff0a224d390f12ce","Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Department of Chemistry, University of Leicester, Leicester, United Kingdom; School of Geography, Earth, and Environmental Sciences, University of Plymouth, Plymouth, United Kingdom; National Heart and Lung Institute, Imperial College, London, United Kingdom; Institute for Lung Health, National Institute for Health and Care Research (NIHR), Leicester Biomedical Research Centre (Respiratory Theme), Glenfield Hospital, Leicester, United Kingdom; Department of Respiratory Medicine, Amsterdam University Medical Centre (UMC), University of Amsterdam, Amsterdam, Netherlands; Respiratory Medicine, GIGA Research Centre, Liege University Hospital, Sart-Tilman, Liege, Belgium; Organic and Biological Analytical Chemistry Group, MolSys Research, University of Liege, Liege, Belgium; Experimental Asthma and Allergy Research, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; University of Southampton, Southampton, United Kingdom","Peltrini R., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Cordell R.L., Department of Chemistry, University of Leicester, Leicester, United Kingdom; Wilde M., Department of Chemistry, University of Leicester, Leicester, United Kingdom, School of Geography, Earth, and Environmental Sciences, University of Plymouth, Plymouth, United Kingdom; Abuhelal S., National Heart and Lung Institute, Imperial College, London, United Kingdom; Quek E., National Heart and Lung Institute, Imperial College, London, United Kingdom; Zounemat-Kermani N., National Heart and Lung Institute, Imperial College, London, United Kingdom; Ibrahim W., Institute for Lung Health, National Institute for Health and Care Research (NIHR), Leicester Biomedical Research Centre (Respiratory Theme), Glenfield Hospital, Leicester, United Kingdom; Richardson M., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Brinkman P., Department of Respiratory Medicine, Amsterdam University Medical Centre (UMC), University of Amsterdam, Amsterdam, Netherlands; Schleich F., Respiratory Medicine, GIGA Research Centre, Liege University Hospital, Sart-Tilman, Liege, Belgium; Stefanuto P.-H., Organic and Biological Analytical Chemistry Group, MolSys Research, University of Liege, Liege, Belgium; Aung H., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Greening N., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Dahlen S.E., Experimental Asthma and Allergy Research, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden; Djukanovic R., University of Southampton, Southampton, United Kingdom; Adcock I.M., National Heart and Lung Institute, Imperial College, London, United Kingdom; Brightling C., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom; Monks P., Department of Chemistry, University of Leicester, Leicester, United Kingdom; Siddiqui S., Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom, National Heart and Lung Institute, Imperial College, London, United Kingdom","Rationale: Volatile organic compounds (VOCs) in asthmatic breath may be associated with sputum eosinophilia. We developed a volatile biomarker signature to predict sputum eosinophilia in asthma. Methods: VOCs emitted into the space above sputum samples (headspace) from patients with severe asthma (n = 36) were collected onto sorbent tubes and analyzed using thermal desorption gas chromatography-mass spectrometry (GC-MS). Elastic net regression identified stable VOCs associated with sputum eosinophilia > 3% and generated a volatile biomarker signature. This VOC signature was validated in breath samples from: 1) patients with acute asthma according to blood eosinophilia >0.3 × 109cells/L or sputum eosinophilia of >3% in the UK EMBER (East Midlands Breathomics Pathology Node) consortium (n = 65) and 2) U-BIOPRED-IMI (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes Innovative Medicines Initiative) consortium (n = 42). Breath samples were collected onto sorbent tubes (EMBER) or Tedlar bags (U-BIOPRED) and analyzed by GC-MS (GC × GC-MS for EMBER or GC-MS for U-BIOPRED). Measurements and Main Results: The in vitro headspace identified 19 VOCs associated with sputum eosinophilia, and the derived VOC signature yielded good diagnostic accuracy for sputum eosinophilia >3% in headspace (area under the receiver operating characteristic curve [AUROC] 0.90; 95% confidence interval [CI], 0.80-0.99; P < 0.0001), correlated inversely with sputum eosinophil percentage (rs = -0.71; P < 0.0001), and outperformed fractional exhaled nitric oxide (AUROC 0.61; 95% CI, 0.35-0.86). Analysis of exhaled breath in replication cohorts yielded a VOC signature AUROC (95% CI) for acute asthma exacerbations of 0.89 (0.76-1.0) (EMBER cohort) with sputum eosinophilia and 0.90 (0.75-1.0) in U-BIOPRED, again outperforming fractional exhaled nitric oxide in U-BIOPRED (0.62 [0.33-0.90]). Conclusions: We have discovered and provided early-stage clinical validation of a volatile biomarker signature associated with eosinophilic airway inflammation. Further work is needed to translate our discovery using point-of-care clinical sensors. Copyright © 2024 by the American Thoracic Society.","eosinophilic airway inflammation; severe asthma; volatile organic compound biomarkers","Adult; Aged; Asthma; Biomarkers; Breath Tests; Eosinophilia; Female; Gas Chromatography-Mass Spectrometry; Humans; Male; Middle Aged; Pulmonary Eosinophilia; Sputum; Volatile Organic Compounds; beclomethasone dipropionate; beta adrenergic receptor blocking agent; biological marker; corticosteroid; mepolizumab; montelukast; muscarinic receptor blocking agent; prednisolone; volatile organic compound; biological marker; volatile organic compound; adult; aged; Article; asthma; Asthma Control Questionnaire; breathomics; chemical structure; clinical article; cohort analysis; controlled study; cross validation; desorption; elastic tissue; eosinophil; eosinophil count; eosinophil percentage; eosinophilia; eosinophilic asthma; female; fractional exhaled nitric oxide; gas chromatography; human; in vitro study; in vivo study; leukocyte differential count; male; mass fragmentography; metabolism; middle aged; phenotype; prediction; predictive value; receiver operating characteristic; respiratory tract inflammation; severe asthma; sputum; supervised machine learning; treatment duration; United Kingdom; validation study; asthma; breath analysis; diagnosis; eosinophilia; procedures; pulmonary eosinophilia; sputum","","beclomethasone dipropionate, 5534-09-8, 77011-63-3; mepolizumab, 196078-29-2; montelukast, 151767-02-1, 158966-92-8; prednisolone, 50-24-8; Biomarkers, ; Volatile Organic Compounds, ","","","Community of Analytical Measurement Science; National Institute for Health and Care Research, NIHR; CAMS UK; Manchester Biomedical Research Centre, BRC; Medical Research Council and Engineering & Physical Sciences Research Council, (MR/N005880/1)","Funding text 1: Supported by the Medical Research Council and Engineering & Physical Sciences Research Council (Molecular Pathology Node) Stratified Medicine Grant MR/N005880/1; Imperial NIHR Biomedical Research Centre award Respiratory theme; Leicester NIHR Biomedical Research Centre award; and Community of Analytical Measurement Science (CAMS UK). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.; Funding text 2: *On behalf of the EMBER EPSRC/MRC Consortium. \u2021On behalf of the U-BIOPRED-IMI study group. Supported by the Medical Research Council and Engineering & Physical Sciences Research Council (Molecular Pathology Node) Stratified Medicine Grant MR/N005880/1; Imperial NIHR Biomedical Research Centre award Respiratory theme; Leicester NIHR Biomedical Research Centre award; and Community of Analytical Measurement Science (CAMS UK). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.","Fahy JV., Type 2 inflammation in asthma: present in most, absent in many, Nat Rev Immunol, 15, pp. 57-65, (2015); Global strategy for asthma management and prevention, (2022); Wan XC, Woodruff PG., Biomarkers in severe asthma, Immunol Allergy Clin North Am, 36, pp. 547-557, (2016); Cowan DC, Taylor DR, Peterson LE, Cowan JO, Palmay R, Williamson A, Et al., Biomarker-based asthma phenotypes of corticosteroid response, J Allergy Clin Immunol, 135, pp. 877-883, (2015); Castro M, Corren J, Pavord ID, Maspero J, Wenzel S, Rabe KF, Et al., Dupilumab efficacy and safety in moderate-to-severe uncontrolled asthma, N Engl J Med, 378, pp. 2486-2496, (2018); Escamilla-Gil JM, Fernandez-Nieto M, Acevedo N., Understanding the cellular sources of the fractional exhaled nitric oxide (FeNO) and its role as a biomarker of type 2 inflammation in asthma, BioMed Res Int, 2022, (2022); Chan EY, Ng DK, Chan CH., Measuring FENO in asthma: coexisting allergic rhinitis and severity of atopy as confounding factors, Am J Respir Crit Care Med, 180, (2009); Ibrahim W, Natarajan S, Wilde M, Cordell R, Monks PS, Greening N, Et al., A systematic review of the diagnostic accuracy of volatile organic compounds in airway diseases and their relation to markers of type-2 inflammation, ERJ Open Res, 7, pp. 00030-2021, (2021); Schleich FN, Zanella D, Stefanuto PH, Bessonov K, Smolinska A, Dallinga JW, Et al., Exhaled volatile organic compounds are able to discriminate between neutrophilic and eosinophilic asthma, Am J Respir Crit Care Med, 200, pp. 444-453, (2019); Ibrahim B, Basanta M, Cadden P, Singh D, Douce D, Woodcock A, Et al., Non-invasive phenotyping using exhaled volatile organic compounds in asthma, Thorax, 66, pp. 804-809, (2011); Schleich FN, Dallinga JW, Henket M, Wouters EF, Louis R, Van Schooten FJ., Volatile organic compounds discriminate between eosinophilic and neutrophilic inflammation in vitro, J Breath Res, 10, (2016); Yamaguchi MS, McCartney MM, Falcon AK, Linderholm AL, Ebeler SE, Kenyon NJ, Et al., Modeling cellular metabolomic effects of oxidative stress impacts from hydrogen peroxide and cigarette smoke on human lung epithelial cells, J Breath Res, 13, (2019); Yamaguchi MS, McCartney MM, Linderholm AL, Ebeler SE, Schivo M, Davis CE., Headspace sorptive extraction-gas chromatography-mass spectrometry method to measure volatile emissions from human airway cell cultures, J Chromatogr B Analyt Technol Biomed Life Sci, 1090, pp. 36-42, (2018); Ibrahim W, Wilde M, Cordell R, Salman D, Ruszkiewicz D, Bryant L, Et al., Assessment of breath volatile organic compounds in acute cardiorespiratory breathlessness: a protocol describing a prospective real-world observational study, BMJ Open, 9, (2019); Brinkman P, Ahmed WM, Gomez C, Knobel HH, Weda H, Vink TJ, Et al., U-BIOPRED Study Group. 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Siddiqui; National Heart and Lung Institute, Imperial College London, Faculty of Medicine, London, Norfolk Place, W2 1PG, United Kingdom; email: s.siddiqui@imperial.ac.uk","","American Thoracic Society","","","","","","1073449X","","AJCME","38820123","English","Am. J. Respir. Crit. Care Med.","Article","Final","","Scopus","2-s2.0-85208601797"
"Wei K.; Qian F.; Li Y.; Zeng T.; Huang T.","Wei, Kai (57222004689); Qian, Fang (57745333000); Li, Yixue (57192879236); Zeng, Tao (57203484317); Huang, Tao (56542142100)","57222004689; 57745333000; 57192879236; 57203484317; 56542142100","Integrating multi-omics data of childhood asthma using a deep association model","2024","Fundamental Research","4","4","","738","751","13","2","10.1016/j.fmre.2024.03.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191478357&doi=10.1016%2fj.fmre.2024.03.022&partnerID=40&md5=84755cb9b8c0ba146c5582e06b607771","Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China; Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China; Guangzhou National Laboratory, Guangzhou, 510000, China; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 510000, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China","Wei K., Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China, Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, China; Qian F., Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China; Li Y., Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China, Guangzhou National Laboratory, Guangzhou, 510000, China, GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 510000, China, Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China; Zeng T., Guangzhou National Laboratory, Guangzhou, 510000, China, GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, 510000, China; Huang T., Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China","Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases. © 2024","Childhood asthma; Deep canonical correlation analysis; Deep non-negative matrix factorization; Deep subspace reconstruction; Interpretable machine learning; Multi-omics","","","","","","National Natural Science Foundation of China, NSFC, (11871456, 12371485, 12371485,11871456); National Natural Science Foundation of China, NSFC; Self-supporting Program of Guangzhou Laboratory, (SRPG22-007); Guoke Ningbo Life Science and Health Industry Research Institute, (2020YJY0217); Chinese Academy of Sciences, CAS, (XDB38040202, XDA26040304, XDB38050200); Chinese Academy of Sciences, CAS; R&D Program of Guangzhou National Laboratory, (GZNL2024A01002); National Key Research and Development Program of China, NKRDPC, (2022YFF1202100); National Key Research and Development Program of China, NKRDPC; Major Science and Technology Projects in Yunnan Province, (202103AQ100002); Major Science and Technology Projects in Yunnan Province; Science and Technology Commission of Shanghai Municipality, STCSM, (2017SHZDZX01); Science and Technology Commission of Shanghai Municipality, STCSM","Funding text 1: This paper was supported by the National Key R&D Program of China ( 2022YFF1202100 ), the Strategic Priority Research Program of Chinese Academy of Sciences ( XDB38050200, XDA26040304 ), National Natural Science Foundation of China ( 12371485, 11871456 ), and Shanghai Municipal Science and Technology Major Project ( 2017SHZDZX01 ). ; Funding text 2: Conception and design of the research: Kai Wei and Tao Zeng. Acquisition, analysis, and interpretation of data: Kai Wei, Fang Qian and Tao Huang. Statistical analysis: Fang Qian. Molecular biological analysis: Kai Wei and Fang Qian. Drafting the manuscript: Tao Huang and Tao Zeng. Manuscript revision for important intellectual content: Yixue Li. All authors have read and approved the manuscript. This paper was supported by the Self-supporting Program of Guangzhou Laboratory(SRPG22-007)\uFF1BR&D Program of Guangzhou National Laboratory (GZNL2024A01002)\uFF1B National Natural Science Foundation of China (12371485,11871456)\uFF1B II Phase External Project of Guoke Ningbo Life Science and Health Industry Research Institute (2020YJY0217)\uFF1B Science and Technology Project of Yunnan Province (202103AQ100002)\uFF1B National Key R&D Program of China (2022YFF1202100)\uFF1B The Strategic Priority Research Program of the Chinese Academy of Sciences (XDB38050200, XDB38040202, XDA26040304).","Golebski K., Kabesch M., Melen E., Et al., Childhood asthma in the new omics era: Challenges and perspectives, Curr. Opin. Allergy Clin. 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Li; Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China; email: yxli@sibs.ac.cn","","KeAi Communications Co.","","","","","","20969457","","","","English","Fundam. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85191478357"
"Portacci A.; Dragonieri S.; Carpagnano G.E.","Portacci, Andrea (57224677229); Dragonieri, Silvano (22634284300); Carpagnano, Giovanna Elisiana (6602191072)","57224677229; 22634284300; 6602191072","Type-2 severe asthma comorbidities in the era of biologics: time to rethink clinical response?","2024","Expert Review of Respiratory Medicine","18","5","","249","253","4","2","10.1080/17476348.2024.2365841","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195684607&doi=10.1080%2f17476348.2024.2365841&partnerID=40&md5=afaa82bbdb9b251b7efc531861ba09f9","Institute of Respiratory Disease, Department of Translational Biomedicine and Neuroscience, University “Aldo Moro”, Bari, Italy","Portacci A., Institute of Respiratory Disease, Department of Translational Biomedicine and Neuroscience, University “Aldo Moro”, Bari, Italy; Dragonieri S., Institute of Respiratory Disease, Department of Translational Biomedicine and Neuroscience, University “Aldo Moro”, Bari, Italy; Carpagnano G.E., Institute of Respiratory Disease, Department of Translational Biomedicine and Neuroscience, University “Aldo Moro”, Bari, Italy","Introduction: The use of monoclonal antibodies in patients with severe asthma has led clinicians to explore new levels of clinical improvement, as testified by the growing interest on clinical remission achievement. In this context, a major role is played by asthma-related comorbidities, which can influence asthma pathophysiology and treatment response. Areas covered: In this special report, we highlighted how asthma-related comorbidities could deeply affect monoclonal antibody response as well as clinical remission achievement. As examples, we provided data from clinical trials and real-life experiences involving patients with severe asthma and chronic rhinosinusitis with nasal polyps (CRSwNP), eosinophilic granulomatosis with polyangiitis (EGPA) or bronchiectasis. Expert Opinion: Comorbidities associated with severe asthma development should be carefully assessed in everyday clinical practice, even with the help of new diagnostic technologies, artificial intelligence and multidisciplinary teams. Future studies should address the role of comorbidities in remission achievement, describing how these diseases could generate new trajectories of clinical and functional response in patient treated with monoclonal antibodies. © 2024 Informa UK Limited, trading as Taylor & Francis Group.","Asthma; biologic; bronchiectasis; comorbidities; CRSwNP; EGPA; remission","Anti-Asthmatic Agents; Antibodies, Monoclonal; Asthma; Biological Products; Bronchiectasis; Comorbidity; Humans; Remission Induction; Rhinitis; Severity of Illness Index; Treatment Outcome; biological product; monoclonal antibody; antiasthmatic agent; biological product; monoclonal antibody; Article; bronchiectasis; chronic rhinosinusitis; Churg Strauss syndrome; clinical trial (topic); comorbidity; disease classification; fractional exhaled nitric oxide; human; personal experience; remission; severe asthma; sinonasal polyp; treatment response; asthma; diagnosis; drug therapy; epidemiology; immunology; pathophysiology; rhinitis; severity of illness index; treatment outcome","","Anti-Asthmatic Agents, ; Antibodies, Monoclonal, ; Biological Products, ","","","","","Menzies-Gow A., Bafadhel M., Busse W.W., Et al., An expert consensus framework for asthma remission as a treatment goal, J Allergy Clin Immunol, 145, 3, pp. 757-765, (2020); Hansen S., Buelow A.V., Soendergaard M.B., Et al., Clinical response and remission in patients with severe asthma treated with biologic treatment: findings from the nationwide Danish severe asthma registry, Eur Respir J, 60, (2022); Manti S., Parisi G.F., Papale M., Et al., Type 2 inflammation in cystic fibrosis: new insights, Pediatr Allergy Immunol, 33, pp. 15-17, (2022); Harvey E.S., Langton D., Katelaris C., Et al., Mepolizumab effectiveness and identification of super-responders in severe asthma, Eur Respir J, 55, 5, (2020); Lipworth B.J., Chan R., The choice of biologics in patients with severe chronic rhinosinusitis with nasal polyps, Am Acad Allergy, Asthma Immunol, 9, 12, pp. 4235-4238, (2021); Oykhman P., Paramo F.A., Bousquet J., Et al., Comparative efficacy and safety of monoclonal antibodies and aspirin desensitization for chronic rhinosinusitis with nasal polyposis: asystematic review and network meta-analysis, J Allergy Clin Immunol, 149, 4, pp. 1286-1295, (2022); Canonica G.W., Bourdin A., Peters A.T., Et al., Dupilumab demonstrates rapid onset of response across three type 2 inflammatory diseases, J Allergy Clin Immunol Pract, 10, 6, pp. 1515-1526, (2022); Nolasco S., Crimi C., Pelaia C., Et al., Benralizumab effectiveness in severe eosinophilic asthma with and without chronic rhinosinusitis with nasal polyps: a real-world multicenter study, J Allergy Clin Immunol Pract, 9, 12, pp. 4371-4380, (2021); Alobid I., Colas C., Castillo J.A., Et al., Spanish consensus on the management of chronic rhinosinusitis with nasal polyps (POLIposis NAsal/POLINA 2.0), J Investig Allergol Clin Immunol, 33, 5, pp. 317-331, (2023); Pelaia C., Benfante A., Busceti M.T., Et al., Real-life effects of dupilumab in patients with severe type 2 asthma, according to atopic trait and presence of chronic rhinosinusitis with nasal polyps, Front Immunol, 14, (2023); Santomasi C., Buonamico E., Dragonieri S., Et al., Effects of benralizumab in a population of patients affected by severe eosinophilic asthma and chronic rhinosinusitis with nasal polyps: a real life study, Acta Biomed, 94, 1, (2023); Wechsler M.E., Akuthota P., Jayne D., Et al., Mepolizumab or placebo for eosinophilic granulomatosis with polyangiitis, N Engl J Med, 376, 20, pp. 1921-1932, (2017); Wechsler M.E., Nair P., Terrier B., Et al., Benralizumab versus mepolizumab for eosinophilic granulomatosis with polyangiitis, N Engl J Med, 390, 10, pp. 911-921, (2024); Bettiol A., Urban M.L., Dagna L., Et al., Mepolizumab for eosinophilic granulomatosis with polyangiitis: a European multicenter observational study, Arthritis Rheumatol, 74, 2, pp. 295-306, (2022); Nolasco S., Portacci A., Campisi R., Et al., Effectiveness and safety of anti-IL-5/Rα biologics in eosinophilic granulomatosis with polyangiitis: a two-year multicenter observational study, Front Immunol, 14, (2023); Berti A., Cornec D., Casal Moura M., Et al., Eosinophilic granulomatosis with polyangiitis: clinical predictors of long-term asthma severity, Chest, 157, 5, pp. 1086-1099, (2020); Portacci A., Campisi R., Buonamico E., Et al., Real-world characteristics of “super-responders” to mepolizumab and benralizumab in severe eosinophilic asthma and EGPA, ERJ Open Res, 9, 5, pp. 00419-2023, (2023); Berti A., Volcheck G.W., Cornec D., Et al., Severe/uncontrolled asthma and overall survival in atopic patients with eosinophilic granulomatosis with polyangiitis, Respir med, 142, pp. 66-72, (2018); Coman I., Pola-Bibian B., Barranco P., Et al., Bronchiectasis in severe asthma: clinical features and outcomes, Ann Allergy Asthma Immunol, 120, 4, pp. 409-413, (2018); Campisi R., Nolasco S., Pelaia C., Et al., Benralizumab effectiveness in severe eosinophilic asthma with co-presence of bronchiectasis: a real-world multicentre observational study, J Clin Med, 12, 12, (2023); Carpagnano G.E., Portacci A., Nolasco S., Et al., Features of severe asthma response to anti-IL5/IL5r therapies: identikit of clinical remission, Front Immunol, 15, (2024); Nomura N., Matsumoto H., Yokoyama A., Et al., Nationwide survey of refractory asthma with bronchiectasis by inflammatory subtypes, Respir Res, 23, 1, (2022); Frossing L., Von Bulow A., Porsbjerg C., Bronchiectasis in severe asthma is associated with eosinophilic airway inflammation and activation, J Allergy Clin Immunol Glob, 2, 1, pp. 36-42, (2022); Crimi C., Campisi R., Nolasco S., Et al., Type 2-high severe asthma with and without bronchiectasis: a prospective observational multicentre study, J Asthma Allergy, 14, pp. 1441-1452, (2021); Svenningsen S., Haider E., Boylan C., Et al., CT and functional MRI to evaluate airway mucus in severe asthma, Chest, 155, 6, pp. 1178-1189, (2019); Castagnoli R., Licari A., Manti S., Et al., Type-2 inflammatory mediators as targets for precision medicine in children, Pediatr Allergy Immunol, 31, pp. 17-19, (2020); Chan R., Lipworth B., The triple type 2 signature confers more frequent exacerbations and worse midexpiratory flow in moderate to severe asthma, Am Acad Allergy, Asthma Immunol, 11, 9, pp. 2926-2928, (2023)","A. Portacci; Institute of Respiratory Disease, Department of Translational Biomedicine and Neuroscience, University “Aldo Moro”, Bari, Piazza Giulio Cesare 11, 90123, Italy; email: a.portacci01@gmail.com","","Taylor and Francis Ltd.","","","","","","17476348","","","38845590","English","Expert Rev. Respir. Med.","Article","Final","","Scopus","2-s2.0-85195684607"
"Ikechukwu A.V.","Ikechukwu, Agughasi Victor (58663521900)","58663521900","Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques","2024","Inteligencia Artificial","27","74","","133","151","18","2","10.4114/intartif.vol27iss74pp133-151","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196738371&doi=10.4114%2fintartif.vol27iss74pp133-151&partnerID=40&md5=8d4cab757c064b6ebaf870271613c38f","Department of Computer Science & Engineering, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","Ikechukwu A.V., Department of Computer Science & Engineering, Maharaja Institute of Technology, Karnataka, Mysore, 571477, India","Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dualdataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions. © IBERAMIA and the authors.","Chest Radiography (CXR); COPD Diagnosis; eXplainable AI; Grad-CAM and SHAP; Pre-trained Models","Deep learning; Diagnosis; Learning algorithms; Medical imaging; Neural networks; Pulmonary diseases; Transparency; Activation mapping; Chest radiography; Chest radiography (chest X-ray); Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease diagnose; Disease diagnosis; Explainable AI; Gradient class activation mapping and shapley additive explanation; Pre-trained model; Shapley; Decision making","","","","","","","Singh Dave, Agusti Alvar, Anzueto Antonio, Barnes Peter J., Bourbeau Jean, Celli Bartolome R., Criner Gerard J., Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: the GOLD science committee report 2019, Eur. 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I., Srinivasiah M., Semi-supervised labelling of chest x-ray images using unsupervised clustering for ground-truth generation, AET, 2, 3, pp. 188-202, (2023); Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R. M., ChestX-ray8: Hospital-Scale Chest XRay Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases; Nguyen Ha Q., Lam Khanh, Le Linh T., Pham Hieu H., Tran Dat Q., Nguyen Dung B., Le Dung D., Pham Chi M., Tong Hang T. T., Dinh Diep H., Do Cuong D., VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations; Selvaraju R. R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 618-626, (2017); Mamalakis M., Dwivedi K., Sharkey M., Alabed S., Kiely D., Swift A. J., A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension, Sci Rep, 13, 1, (2023); Nawaz Marriam, Nazir Tahira, Baili Jamel, Khan Muhammad Attique, Kim Ye Jin, Cha Jae-Hyuk, CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model, Diagnostics, 13, 2, (2023); Sharma Shagun, Guleria Kalpna, A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks, Procedia Computer Science, 218, (2023); Huang A. A., Huang S. Y., Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database, PLoS ONE, 18, 7, (2023); Victor Ikechukwu A., CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis, Mach. Learn.: Sci. 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V., Energy-efficient deep Q-network: reinforcement learning for efficient routing protocol in wireless internet of things, Indonesian Journal of Electrical Engineering and Computer Science, 33, 2, (2024); Wang Bin, Li Yuanxiao, Tian Ying, Ju Changxi, Xu Xiaonan, Pei Shufen, Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit, Respiratory Medicine, 17, (2023); Wen Ru, Xu Peng, Cai Yimin, Wang Fang, Li Mengfei, Zeng Xianchun, Liu Chen, A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information, Infection and Drug Resistance, 16, (2023); Alam M. U., Baldvinsson J. R., Wang Y., Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography, 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), pp. 258-263, (2022); Chollet F., Xception: Deep Learning with Depthwise Separable Convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800-1807, (2017); Angelini Elsa D, Yang Jie, Balte Pallavi P, Hoffman Eric A, Manichaikul Ani W, Sun Yifei, Shen Wei, Austin John H M, Allen Norrina B, Pulmonary emphysema subtypes defined by unsupervised machine learning on CT scans, thorax, (2023); Johannessen O., Uthaug Reite F., Bhatnagar R., Ovrebotten T., Einvik G., Myhre P. L., Lung Ultrasound to Assess Pulmonary Congestion in Patients with Acute Exacerbation of COPD, COPD, 18, pp. 693-703, (2023); Zern A., Broelemann K., Kasneci G., Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees, AAAI, 37, 9, pp. 11164-11173, (2023); Agughasi V. I., The Superiority of Fine-tuning over Full-training for the Efficient Diagnosis of COPD from CXR Images, Inteligencia Artificial, 27, 74, pp. 62-79, (2024); Ikechukwu A. V., Bhimshetty S., Mala M. V., Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis, 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), pp. 1-7, (2024)","A.V. Ikechukwu; Department of Computer Science & Engineering, Maharaja Institute of Technology, Mysore, Karnataka, 571477, India; email: victor.agughasi@gmail.com","","Asociacion Espanola de Inteligencia Artificial","","","","","","11373601","","","","English","Inteligencia Artif.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85196738371"
"Fink E.; Brunsteiner M.; Mitsche S.; Schröttner H.; Paudel A.; Zellnitz-Neugebauer S.","Fink, Elisabeth (57669967000); Brunsteiner, Michael (56040593500); Mitsche, Stefan (6507846478); Schröttner, Hartmuth (8787469300); Paudel, Amrit (26641402800); Zellnitz-Neugebauer, Sarah (58040407700)","57669967000; 56040593500; 6507846478; 8787469300; 26641402800; 58040407700","Data-Driven Prediction of the Formation of Co-Amorphous Systems","2023","Pharmaceutics","15","2","347","","","","2","10.3390/pharmaceutics15020347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149138153&doi=10.3390%2fpharmaceutics15020347&partnerID=40&md5=b268720bd4c8c28944df9c7fd6f5c53b","Research Center Pharmaceutical Engineering, Inffeldgasse 13, Graz, 8010, Austria; Celeris Therapeutics GmbH, Salzamtsgasse 7, Graz, 8010, Austria; Institute of Electron Microscopy and Nanoanalysis (FELMI), Graz University of Technology, Steyrergasse 17, Graz, 8010, Austria; Graz Centre for Electron Microscopy (ZFE), Steyrergasse 17, Graz, 8010, Austria; Institute for Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13, Graz, 8010, Austria","Fink E., Research Center Pharmaceutical Engineering, Inffeldgasse 13, Graz, 8010, Austria; Brunsteiner M., Celeris Therapeutics GmbH, Salzamtsgasse 7, Graz, 8010, Austria; Mitsche S., Institute of Electron Microscopy and Nanoanalysis (FELMI), Graz University of Technology, Steyrergasse 17, Graz, 8010, Austria, Graz Centre for Electron Microscopy (ZFE), Steyrergasse 17, Graz, 8010, Austria; Schröttner H., Institute of Electron Microscopy and Nanoanalysis (FELMI), Graz University of Technology, Steyrergasse 17, Graz, 8010, Austria, Graz Centre for Electron Microscopy (ZFE), Steyrergasse 17, Graz, 8010, Austria; Paudel A., Research Center Pharmaceutical Engineering, Inffeldgasse 13, Graz, 8010, Austria, Institute for Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13, Graz, 8010, Austria; Zellnitz-Neugebauer S., Research Center Pharmaceutical Engineering, Inffeldgasse 13, Graz, 8010, Austria","Co-amorphous systems (COAMS) have raised increasing interest in the pharmaceutical industry, since they combine the increased solubility and/or faster dissolution of amorphous forms with the stability of crystalline forms. However, the choice of the co-former is critical for the formation of a COAMS. While some models exist to predict the potential formation of COAMS, they often focus on a limited group of compounds. Here, four classes of combinations of an active pharmaceutical ingredient (API) with (1) another API, (2) an amino acid, (3) an organic acid, or (4) another substance were considered. A model using gradient boosting methods was developed to predict the successful formation of COAMS for all four classes. The model was tested on data not seen during training and predicted 15 out of 19 examples correctly. In addition, the model was used to screen for new COAMS in binary systems of two APIs for inhalation therapy, as diseases such as tuberculosis, asthma, and COPD usually require complex multidrug-therapy. Three of these new API-API combinations were selected for experimental testing and co-processed via milling. The experiments confirmed the predictions of the model in all three cases. This data-driven model will facilitate and expedite the screening phase for new binary COAMS. © 2023 by the authors.","co-amorphous; gradient boosting; inhalation therapy; machine learning; molecular descriptors","acetic acid; active pharmaceutical ingredient; amino acid; beclometasone dipropionate plus formoterol fumarate; budesonide; budesonide plus formoterol; carboxylic acid; carvedilol; ethambutol; ethambutol plus isoniazid; fluticasone propionate plus formoterol fumarate; fluticasone propionate plus salmeterol xinafoate; formoterol fumarate plus mometasone furoate; foster; glycopyrronium; ipratropium bromide plus salbutamol; naproxen; pyrazinamide; streptomycin sulfate; unclassified drug; accuracy; amorphization; Article; artificial neural network; asthma; atom bond connectivity index; binary classification; binary system; black box model; charge surface area; chronic obstructive lung disease; classifier; coamorphous system; credibility; data driven prediction; dissolution; extreme gradient boosting; geometry; glass transition temperature; gradient boosting method; hydrogen bond; hyperparameter tuning; k nearest neighbor; kinetics; machine learning; mathematical phenomena; molar ratio; molecular descriptor calculation package; molecular framework ratio; multivariate analysis; performance; physical chemistry; polar surface area; polarisability; prediction; probability; PubChem; random split; relative hydrophobic surface area; rotable bond count; simplified molecular input line entry system; solubility; support vector machine; thermodynamics; topological polar surface area; topological shape index; training; tuberculosis; uncertainty factor; validation process; van der Waals volume; X ray powder diffraction","","acetic acid, 127-08-2, 127-09-3, 64-19-7, 71-50-1, 6131-90-4; amino acid, 65072-01-7; beclometasone dipropionate plus formoterol fumarate, 959587-70-3; budesonide, 51333-22-3, 51372-29-3; budesonide plus formoterol, 150693-37-1, 150693-38-2; carvedilol, 72956-09-3; ethambutol, 10054-05-4, 1070-11-7, 3577-94-4, 74-55-5; fluticasone propionate plus salmeterol xinafoate, 136112-02-2; glycopyrronium, 596-51-0, 1624259-25-1, 740028-90-4, 13283-82-4, 51186-83-5, 873295-46-6; naproxen, 22204-53-1, 26159-34-2, 26159-31-9; pyrazinamide, 98-96-4; streptomycin sulfate, 3810-74-0","XP205DR, Mettler Toledo, United Kingdom; dulera, Merck, Germany; flutiform, Mundipharma, Germany; foster, Chiesi, Italy; seretide, Glaxo SmithKline, United States; symbicort, Astra Zeneca, United Kingdom","Astra Zeneca, United Kingdom; Chiesi, Italy; Glaxo SmithKline, United States; Merck, Germany; Mettler Toledo, United Kingdom; Mundipharma, Germany","Steirische Wirtschaftsförderungsgesellschaft, SFG; Österreichische Forschungsförderungsgesellschaft, FFG; Bundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und Technologie, BMK; Bundesministerium für Digitalisierung und Wirtschaftsstandort, BMDW; UK Research and Innovation, UKRI, (103684); Austrian Science Fund, FWF, (T 1105)","This work was funded through the FWF Science Fund as part of the Hertha-Firnberg program (Hertha-Firnberg grant no. T1105). The Research Center Pharmaceutical Engineering (RCPE) is funded within the framework of COMET\u2014Competence Centers for Excellent Technologies by BMK, BMDW, Land Steiermark and SFG. The COMET program is managed by the FFG. Open Access Funding by the Austrian Science Fund (FWF).","van den Berge M., Hacken N.H.T., Kerstjens H.A.M., Postma D.S., Management of Asthma with ICS and LABAs: Different Treatment Strategies, Clin. Med. Ther, pp. 77-93, (2009); Tashkin D.P., Ferguson G.T., Combination Bronchodilator Therapy in the Management of Chronic Obstructive Pulmonary Disease, Respir. Res, 14, (2013); Tousif S., Ahmad S., Challenges of Tuberculosis Treatment with DOTS: An Immune Impairment Perspective, J. Cell Sci. Ther, 6, (2015); Das S., Tucker I., Stewart P., Inhaled Dry Powder Formulations for Treating Tuberculosis, Curr. 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Sci, 8, pp. 35-39, (2020); Soltani F., Kamali H., Akhgari A., Garekani H.A., Nokhodchi A., Sadeghi F., Different Trends for Preparation of Budesonide Pellets with Enhanced Dissolution Rate, Adv. Powder Technol, 33, (2022); Saifullah B., Maitra A., Chrzastek A., Naeemullah B., Fakurazi S., Bhakta S., Hussein M.Z., Nano-Formulation of Ethambutol Withmultifunctional Graphene Oxide and Magnetic Nanoparticles Retains Its Anti-Tubercular Activity with Prospects of Improving Chemotherapeutic Efficacy, Molecules, 22, (2017)","S. Zellnitz-Neugebauer; Research Center Pharmaceutical Engineering, Graz, Inffeldgasse 13, 8010, Austria; email: sarah.neugebauer@rcpe.at","","MDPI","","","","","","19994923","","","","English","Pharmaceutics","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149138153"
"Zou X.; Ren Y.; Yang H.; Zou M.; Meng P.; Zhang L.; Gong M.; Ding W.; Han L.; Zhang T.","Zou, XiaoLing (56091603800); Ren, Yong (57214465775); Yang, HaiLing (57205077743); Zou, ManMan (57204668722); Meng, Ping (57206318610); Zhang, LiYi (57211536755); Gong, MingJuan (58954894900); Ding, WenWen (58155833400); Han, LanQing (57214443902); Zhang, TianTuo (7404374444)","56091603800; 57214465775; 57205077743; 57204668722; 57206318610; 57211536755; 58954894900; 58155833400; 57214443902; 7404374444","Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters","2024","BMC Pulmonary Medicine","24","1","153","","","","2","10.1186/s12890-024-02945-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188577972&doi=10.1186%2fs12890-024-02945-7&partnerID=40&md5=66a86cfa7ee581afe9c128e15aff14df","Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China; Scientific research project department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, China; Shensi lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China; Department of Pulmonary and Critical Care Medicine, Dongguan People’s Hospital, Dongguan, China; Department of Pulmonary and Critical Care Medicine, the Six Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China; Department of Internal Medicine, Huazhou Hospital of Traditional Chinese Medicine, Huazhou, China; Center for artificial intelligence in medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China","Zou X., Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China; Ren Y., Scientific research project department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, China, Shensi lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China; Yang H., Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China; Zou M., Department of Pulmonary and Critical Care Medicine, Dongguan People’s Hospital, Dongguan, China; Meng P., Department of Pulmonary and Critical Care Medicine, the Six Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China; Zhang L., Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China; Gong M., Department of Internal Medicine, Huazhou Hospital of Traditional Chinese Medicine, Huazhou, China; Ding W., Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China; Han L., Center for artificial intelligence in medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China; Zhang T., Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China","Background: Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. Methods: In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. Results: The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. Conclusion: The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging. © The Author(s) 2024.","Chest X-ray; Clinical parameters; COPD screening; Deep learning models; Pulmonary function test","Deep Learning; Humans; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Thorax; X-Rays; adult; aged; Article; chronic obstructive lung disease; controlled study; deep learning; demography; diagnostic test accuracy study; disease severity; female; human; lung function test; major clinical study; male; receiver operating characteristic; retrospective study; thorax radiography; transfer of learning; clinical trial; multicenter study; thorax; X ray","","","","","National Key Research and Development Program of China, NKRDPC, (2018YFC1311900); National Key Research and Development Program of China, NKRDPC; Science, Technology and Innovation Commission of Shenzhen Municipality, (JCYJ20220530145001002); Science, Technology and Innovation Commission of Shenzhen Municipality","This work was supported by National Key Technology R&D Program (2018YFC1311900) and Shenzhen Science and Technology Program (No. JCYJ20220530145001002). 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Insights from the genetic epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, Am J Respir Crit Care Med, 199, pp. 286-301; Park J., Hobbs B.D., Crapo J.D., Et al., Subtyping COPD by using visual and quantitative CT imaging features, Am J Respir Crit Care Med, 199, pp. 286-301, (2019); Castillo-Saldana D., Hague C.J., Coxson H.O., Et al., Using quantitative computed tomographic imaging to understand chronic obstructive pulmonary disease and fibrotic interstitial lung disease: state of the art and future directions, J Thorac Imaging, 35, pp. 246-254, (2020); Zhang L., Jiang B., Wisselink H.J., Vliegenthart R., Xie X., COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images, Br J Radiol, 95, 1133, (2022); Paoletti M., Cestelli L., Bigazzi F., Et al., Chronic obstructive pulmonary disease: pulmonary function and CT lung attenuation do not show linear correlation, Radiology, 276, pp. 571-578, (2015); Mettler F.A., Huda W., Yoshizumi T.T., Mahesh M., Effective doses in radiology and diagnostic nuclear medicine: a catalog, Radiology, 248, pp. 254-263, (2008); Larke F.J., Kruger R.L., Cagnon C.H., Et al., Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial, AJR Am J Roentgenol, 197, pp. 1165-1169, (2011); Willer K., Fingerle A.A., Noichl W., Et al., X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study, Lancet Digit Health, 3, pp. e733-e744, (2021); den Harder A.M., de Boer E., Lagerweij S.J., Et al., Emphysema quantification using chest CT: influence of radiation dose reduction and reconstruction technique, Eur Radiol Exp, 2, (2018); Cavigli E., Camiciottoli G., Diciotti S., Et al., Whole-lung densitometry versus visual assessment of emphysema, Eur Radiol, 19, pp. 1686-1692, (2009); Singla S., Gong M., Riley C., Et al., Improving clinical disease subtyping and future events prediction through a chest CTbased deep learning approach, Med Phys, 48, pp. 1168-1181, (2021); Goldin J.G., Imaging the lungs in patients with pulmonary emphysema, J Thorac Imaging, 24, pp. 163-170, (2009); Miniati M., Monti S., Stolk J., Et al., Value of chest radiography in phenotyping chronic obstructive pulmonary disease, Eur Respir J, 31, pp. 509-515, (2008); Washko G.R., Diagnostic imaging in COPD, Semin Respir Crit Care Med, 31, pp. 276-285, (2010); Meinel F.G., Schwab F., Schleede S., Et al., Diagnosing and mapping Pulmonary Emphysema on X Ray Projection images: Incremental Value of Grating Based X-Ray Dark-Field Imaging, PLoS ONE, 8, (2013); Hellbach K., Yaroshenko A., Meinel F.G., Et al., In vivo dark-field radiography for early diagnosis and staging of pulmonary emphysema, Invest Radiol, 50, pp. 430-435, (2015); Rajkomar A., Dean J., Kohane I., Machine learning in Medicine, N Engl J Med, 380, pp. 1347-1358, (2019); Das N., Topalovic M., Janssens W., Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential, Curr Opin Pulm Med, 24, pp. 117-123, (2018); Topalovic M., Laval S., Aerts J.-M., Et al., Automated interpretation of pulmonary function tests in adults with respiratory complaints, Respiration, 93, pp. 170-178, (2017); Gonzalez G., Ash S.Y., Vegas-Sanchez-Ferrero G., Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, pp. 193-203, (2018); Huang G., Liu Z., Pleiss G., Maaten L.V., Weinberger K.Q., Convolutional Networks with dense connectivity, IEEE Trans Pattern Anal Mach Intell, 44, 12, pp. 8704-8716, (2022); He K., Zhang X., Ren S., Sun J., Deep Residual Learning for Image Recognition. Arxiv, 151; Tan M., Quoc V., Le. Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. Arxiv, 1905; Chandra T.B., Singh B.K., Jain D., Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study, Med Biol Eng Comput, 60, 9, pp. 2549-2565, (2022); Chandra T.B., Singh B.K., Jain D., Disease localization and Severity Assessment in chest X-Ray images using Multi-stage superpixels classification, Comput Methods Programs Biomed, 222, (2022); Chandra T.B., Verma K., Singh B.K., Jain D., Netam S.S., Coronavirus disease (COVID-19) detection in chest X-Ray images using majority voting based classifier ensemble, Expert Syst Appl, 165, (2021); Diab N., Gershon A.S., Sin D.D., Et al., Underdiagnosis and overdiagnosis of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 198, pp. 1130-1139, (2018); Feng Y., Wang Y., Zeng C., Et al., Artificial Intelligence and Machine Learning in Chronic Airway diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease, Int J Med Sci, 18, pp. 2871-2889, (2021); Xu C., Qi S., Feng J., Et al., DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images, Phys Med Biol, 65, (2020); Matsumura K., Ito S., Novel biomarker genes which distinguish between smokers and chronic obstructive pulmonary disease patients with machine learning approach, BMC Pulm Med, 20, 1, (2020); Westcott A., Capaldi D., McCormack D.G., Et al., Chronic obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation, Radiology, 293, pp. 676-684, (2019); Andreeva E., Pokhaznikova M., Lebedev A., Et al., Spirometry is not enough to diagnose COPD in epidemiological studies: a follow-up study, NPJ Prim Care Respir Med, 27, (2017); Esteva A., Robicquet A., Ramsundar B., Et al., A guide to deep learning in healthcare, Nat Med, 25, pp. 24-29, (2019)","T. Zhang; Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, Guangzhou, 600 Tianhe Road, 510630, China; email: zhtituli@163.com; L. Han; Center for artificial intelligence in medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China; email: hanlance@tsinghua-gd.org","","BioMed Central Ltd","","","","","","14712466","","BPMMB","38532368","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188577972"
"de Batlle J.; Benítez I.D.; Moncusí-Moix A.; Androutsos O.; Barbastro R.A.; Antonini A.; Arana E.; Cabrera-Umpierrez M.F.; Cea G.; Dafoulas G.Ε.; Folkvord F.; Fullaondo A.; Giuliani F.; Huang H.-L.; Innominato P.F.; Kardas P.; Lou V.W.Q.; Manios Y.; Matsangidou M.; Mercalli F.; Mokhtari M.; Pagliara S.; Schellong J.; Stieler L.; Votis K.; Currás P.; Arredondo M.T.; Posada J.; Guillén S.; Pecchia L.; Barbé F.; Torres G.; Fico G.","de Batlle, Jordi (24775909200); Benítez, Ivan D. (57201681045); Moncusí-Moix, Anna (57218295036); Androutsos, Odysseas (31967455600); Barbastro, Rosana Angles (58481223500); Antonini, Alessio (56353561600); Arana, Eunate (57193355469); Cabrera-Umpierrez, Maria Fernanda (23395830400); Cea, Gloria (55441422500); Dafoulas, George Ε. (55400469800); Folkvord, Frans (55578981900); Fullaondo, Ane (57204765399); Giuliani, Francesco (56522624000); Huang, Hsiao-Ling (55612346300); Innominato, Pasquale F. (6505884427); Kardas, Przemyslaw (6603816841); Lou, Vivian W.Q. (9846416500); Manios, Yannis (58116107200); Matsangidou, Maria (57196007400); Mercalli, Franco (6506406780); Mokhtari, Mounir (55406205600); Pagliara, Silvio (14523356200); Schellong, Julia (35362746500); Stieler, Lisa (57201742820); Votis, Konstantinos (12788889700); Currás, Paula (58407474200); Arredondo, Maria Teresa (7005758135); Posada, Jorge (57201942989); Guillén, Sergio (57203904924); Pecchia, Leandro (35746897300); Barbé, Ferran (7007035883); Torres, Gerard (54390339400); Fico, Giuseppe (16174738100)","24775909200; 57201681045; 57218295036; 31967455600; 58481223500; 56353561600; 57193355469; 23395830400; 55441422500; 55400469800; 55578981900; 57204765399; 56522624000; 55612346300; 6505884427; 6603816841; 9846416500; 58116107200; 57196007400; 6506406780; 55406205600; 14523356200; 35362746500; 57201742820; 12788889700; 58407474200; 7005758135; 57201942989; 57203904924; 35746897300; 7007035883; 54390339400; 16174738100","GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases","2023","Journal of Medical Internet Research","25","","e42187","","","","3","10.2196/42187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163655374&doi=10.2196%2f42187&partnerID=40&md5=c7520eba38a7295bc8eb28432bd487a5","Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain; Center for Biomedical Network Research in Respiratory Diseases, Madrid, Spain; Lab of Clinical Nutrition and Dietetics, Department of Nutrition and Dietetics, School of Physical Education, Sport Science and Dietetics, University of Thessaly, Trikala, Greece; Unidad de Innovación, Servicio Aragonés de Salud, Hospital de Barbastro, Barbastro, Spain; Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom; Biocruces Bizkaia Health Research Institute, Osakidetza, Barakaldo, Spain; Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain; E-health Department, Digital Cities of Central Greece, Trikala, Greece; Department of Endocrinology and Metabolic Diseases, Faculty of Medicine, University of Thessaly, Larisa, Greece; PredictBy, Barcelona, Spain; Tilburg School of Humanities and Digital Sciences, Tilburg, Netherlands; Kronikgune Institute for Health Services Research, Barakaldo, Spain; Innovation and Research Department, Fondazione Casa Sollievo della Sofferenza Research Hospital, San Giovanni Rotondo, Italy; Department of Healthcare Management, Office of International and Cross-Strait Affairs, Yuanpei University of Medical Technology, Hsinchu, Taiwan; Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom; Warwick Medical School & Cancer Research Centre, University of Warwick, Coventry, United Kingdom; Faculty of Medicine, Paris-Saclay University, Villejuif, France; Medication Adherence Research Centre, Department of Family Medicine, Medical University of Lodz, Lodz, Poland; Department of Social Work and Social Administration, Sau Po Center on Ageing, The University of Hong Kong, Hong Kong; Department of Nutrition & Dietetics, School of Health Science & Education, Harokopio University, Athens, Greece; Institute of Agri-food and Life Sciences, Hellenic Mediterranean University Research Centre, Heraklion, Greece; Cyens Centre of Excellence, Nicosia, Cyprus; MultiMed Engineers srl, Parma, Italy; Scientific Direction, Institut Mines-Telecom, Paris, France; National University of Singapore, Singapore, Singapore; School of Engineering, University of Warwick, Coventry, United Kingdom; Department of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece; Innova & European Projects Office, Integrated Health Solutions, Medtronic Ibérica S.A., Madrid, Spain; Mysphera S.L., Paterna, Spain","de Batlle J., Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain, Center for Biomedical Network Research in Respiratory Diseases, Madrid, Spain; Benítez I.D., Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain, Center for Biomedical Network Research in Respiratory Diseases, Madrid, Spain; Moncusí-Moix A., Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain, Center for Biomedical Network Research in Respiratory Diseases, Madrid, Spain; Androutsos O., Lab of Clinical Nutrition and Dietetics, Department of Nutrition and Dietetics, School of Physical Education, Sport Science and Dietetics, University of Thessaly, Trikala, Greece; Barbastro R.A., Unidad de Innovación, Servicio Aragonés de Salud, Hospital de Barbastro, Barbastro, Spain; Antonini A., Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom; Arana E., Biocruces Bizkaia Health Research Institute, Osakidetza, Barakaldo, Spain; Cabrera-Umpierrez M.F., Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain; Cea G., Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain; Dafoulas G.Ε., E-health Department, Digital Cities of Central Greece, Trikala, Greece, Department of Endocrinology and Metabolic Diseases, Faculty of Medicine, University of Thessaly, Larisa, Greece; Folkvord F., PredictBy, Barcelona, Spain, Tilburg School of Humanities and Digital Sciences, Tilburg, Netherlands; Fullaondo A., Kronikgune Institute for Health Services Research, Barakaldo, Spain; Giuliani F., Innovation and Research Department, Fondazione Casa Sollievo della Sofferenza Research Hospital, San Giovanni Rotondo, Italy; Huang H.-L., Department of Healthcare Management, Office of International and Cross-Strait Affairs, Yuanpei University of Medical Technology, Hsinchu, Taiwan; Innominato P.F., Oncology Department, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, United Kingdom, Warwick Medical School & Cancer Research Centre, University of Warwick, Coventry, United Kingdom, Faculty of Medicine, Paris-Saclay University, Villejuif, France; Kardas P., Medication Adherence Research Centre, Department of Family Medicine, Medical University of Lodz, Lodz, Poland; Lou V.W.Q., Department of Social Work and Social Administration, Sau Po Center on Ageing, The University of Hong Kong, Hong Kong; Manios Y., Department of Nutrition & Dietetics, School of Health Science & Education, Harokopio University, Athens, Greece, Institute of Agri-food and Life Sciences, Hellenic Mediterranean University Research Centre, Heraklion, Greece; Matsangidou M., Cyens Centre of Excellence, Nicosia, Cyprus; Mercalli F., MultiMed Engineers srl, Parma, Italy; Mokhtari M., Scientific Direction, Institut Mines-Telecom, Paris, France, National University of Singapore, Singapore, Singapore; Pagliara S., School of Engineering, University of Warwick, Coventry, United Kingdom; Schellong J., Department of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Stieler L., Department of Psychotherapy and Psychosomatic Medicine, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Votis K., Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece; Currás P., Innova & European Projects Office, Integrated Health Solutions, Medtronic Ibérica S.A., Madrid, Spain; Arredondo M.T., Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicaciones, Universidad Politécnica de Madrid, Madrid, Spain; Posada J., Innova & European Projects Office, Integrated Health Solutions, Medtronic Ibérica S.A., Madrid, Spain; Guillén S., Mysphera S.L., Paterna, Spain; Pecchia L., School of Engineering, University of Warwick, Coventry, United Kingdom; Barbé F., Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain, Center for Biomedical Network Research in Respiratory Diseases, Madrid, Spain; Torres G., Group of Translational Research in Respiratory Medicine, Institut de Recerca Biomedica de Lleida, Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Spain; Fico G.","Background: The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space. © 2023 Journal of Medical Internet Research. All rights reserved.","big data; chronic diseases; eHealth; healthy aging; integrated care; large-scale pilots","Artificial Intelligence; Chronic Disease; COVID-19; Cyprus; Ecosystem; Humans; Telemedicine; Article; artificial intelligence; Asian; cerebrovascular accident; China; chronic disease; chronic obstructive lung disease; coronavirus disease 2019; Cyprus; decision support system; diabetes mellitus; early diagnosis; ecosystem; Germany; Greece; health care organization; healthy aging; healthy lifestyle; heart failure; Hong Kong; hospital readmission; human; hypertension; Italy; machine learning; Parkinson disease; patient care; Poland; polypharmacy; practice guideline; predictive model; primary prevention; secondary prevention; Singapore; Spain; Taiwan; telehealth; United Kingdom; coronavirus disease 2019; procedures; telemedicine","","","","","Horizon 2020 Framework Programme, H2020, (857223); Horizon 2020 Framework Programme, H2020; Instituto de Salud Carlos III, ISCIII, (CP19/00108); Instituto de Salud Carlos III, ISCIII; European Social Fund, ESF; European Regional Development Fund, ERDF","This project has received funding from the European Union’s Horizon 2020 research and innovation program (under grant 857223), cofunded by the European Regional Development Fund (ERDF), “A way to make Europe.” JdB acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP19/00108), co-funded by the European Social Fund (ESF), “Investing in your future.” Funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.","Kontis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M., Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble, Lancet, 389, pp. 1323-1335, (2017); Partridge L, Deelen J, Slagboom PE., Facing up to the global challenges of ageing, Nature, 561, 7721, pp. 45-56, (2018); Decade of healthy ageing: baseline report, (2020); Eysenbach G., What is e-health?, J Med Internet Res, 3, 2, (2001); Beks H, King O, Clapham R, Alston L, Glenister K, McKinstry C, Et al., Community health programs delivered through information and communications technology in high-income countries: scoping review, J Med Internet Res, 24, 3, (2022); Bernardo J, Apostolo J, Loureiro R, Santana E, Yaylagul NK, Dantas C, Et al., eHealth platforms to promote autonomous life and active aging: a scoping review, Int J Environ Res Public Health, 19, 23, (2022); Scheibner J, Sleigh J, Ienca M, Vayena E., Benefits, challenges, and contributors to success for national eHealth systems implementation: a scoping review, J Am Med Inform Assoc, 28, 9, pp. 2039-2049, (2021); Tighe SA, Ball K, Kensing F, Kayser L, Rawstorn JC, Maddison R., Toward a digital platform for the self-management of noncommunicable disease: systematic review of platform-like interventions, J Med Internet Res, 22, 10, (2020); Medrano-Gil AM, de los Rios Perez S, Fico G, Montalva Colomer JBM, Sancez GC, Cabrera-Umpierrez MF, Et al., Definition of technological solutions based on the internet of things and smart cities paradigms for active and healthy ageing through cocreation, Wirel Commun Mob Comput, 2018, pp. 1-15, (2018); GATEKEEPER project; Barcelo A, Luciani S, Agurto L, Ordunez P, Tasca R, Sued O., Improving Chronic Illness Care through Integrated Health Service Delivery Networks, (2012); Wallace E, Stuart E, Vaughan N, Bennett K, Fahey T, Smith SM., Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review, Med Care, 52, 8, pp. 751-765, (2014); Classification of digital health interventions v1.0: a shared language to describe the uses of digital technology for health, (2018); International Monetary Fund, (2021); European innovation partnership on active and healthy ageing (EIP on AHA) reference sites; ACTIVAGE Project; Luxton DD, Artificial Intelligence in Behavioral and Mental Health Care, (2015); Bravo J, Hervas R, Fontecha J, Gonzalez I., m-Health: lessons learned by m-Experiences, Sensors (Basel), 18, 5, (2018); Ienca M, Vayena E, Blasimme A., Big data and dementia: charting the route ahead for research, ethics, and policy, Front Med (Lausanne), 5, (2018); Lim WS, Canevelli M, Cesari M., Editorial: dementia, frailty and aging, Front Med (Lausanne), 5, (2018); Roski J, Bo-Linn GW, Andrews TA., Creating value in health care through big data: opportunities and policy implications, Health Aff (Millwood), 33, 7, pp. 1115-1122, (2014); Clark A, Ng JQ, Morlet N, Semmens JB., Big data and ophthalmic research, Surv Ophthalmol, 61, 4, pp. 443-465, (2016); Ghani KR, Zheng K, Wei JT, Friedman CP., Harnessing big data for health care and research: are urologists ready?, Eur Urol, 66, 6, pp. 975-977, (2014); Safiri S, Carson-Chahhoud K, Noori M, Nejadghaderi SA, Sullman MJM, Heris JA, Et al., Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the global burden of disease study 2019, BMJ, 378, (2022); Vestbo J, Woodcock A., Clinical trial research in focus: time to reflect on the design of exacerbation trials in COPD, Lancet Respir Med, 5, 6, pp. 466-468, (2017); Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, Et al., Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025, Sci Rep, 10, 1, (2020); Bloem BR, Okun MS, Klein C., Parkinson's disease, Lancet, 397, 10291, pp. 2284-2303, (2021); Global, regional, and national burden of Parkinson's disease, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet Neurol, 17, 11, pp. 939-953, (2018); Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet, 396, pp. 1204-1222, (2020); Anand IS, Tang WHW, Greenberg BH, Chakravarthy N, Libbus I, Katra RP., Design and performance of a multisensor heart failure monitoring algorithm: results from the multisensor monitoring in congestive heart failure (MUSIC) study, J Card Fail, 18, 4, pp. 289-295, (2012); WeRISE App, (2021); European Commision; Project MARIO; Mills KT, Stefanescu A, He J., The global epidemiology of hypertension, Nat Rev Nephrol, 16, 4, pp. 223-237, (2020); WHO coronavirus (COVID-19) dashboard; Monitoring and assessment framework for the European innovation partnership on active and healthy ageing, European Commision, (2021); Personalised connected care for complex chronic patients, CORDIS; de Batlle J, Massip M, Vargiu E, Nadal N, Fuentes A, Bravo MO, Et al., Implementing mobile health-enabled integrated care for complex chronic patients: intervention effectiveness and cost-effectiveness study, JMIR Mhealth Uhealth, 9, 1, (2021); Smart4Health project; European Commision","J. de Batlle; Group of Translational Research in Respiratory Medicine Institut de Recerca Biomedica de Lleida Hospital Universitari Arnau de Vilanova-Santa Maria, Lleida, Alcalde Rovira Roure 80, 25198, Spain; email: jordidebatlle@gmail.com","","JMIR Publications Inc.","","","","","","14388871","","","37379060","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85163655374"
"Kim R.; Suresh K.; Rosenberg M.A.; Tan M.S.; Malone D.C.; Allen L.A.; Kao D.P.; Anderson H.D.; Tiwari P.; Trinkley K.E.","Kim, Rachel (57392270000); Suresh, Krithika (56583641600); Rosenberg, Michael A. (57210537819); Tan, Malinda S. (57406167100); Malone, Daniel C. (7102595928); Allen, Larry A. (57212852722); Kao, David P. (25223347900); Anderson, Heather D. (54919176800); Tiwari, Premanand (57213670782); Trinkley, Katy E. (35790588900)","57392270000; 56583641600; 57210537819; 57406167100; 7102595928; 57212852722; 25223347900; 54919176800; 57213670782; 35790588900","A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure","2023","Frontiers in Cardiovascular Medicine","10","","1169574","","","","3","10.3389/fcvm.2023.1169574","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164500501&doi=10.3389%2ffcvm.2023.1169574&partnerID=40&md5=2616f37bfdb86973c28eb6488f07638f","School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States; Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Department of Clinical Informatics, UCHealth, Aurora, CO, United States; Department of Clinical Pharmacy, University of Colorado, Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, United States","Kim R., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States; Suresh K., Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States; Rosenberg M.A., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States; Tan M.S., Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States; Malone D.C., Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States; Allen L.A., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States, Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Kao D.P., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States, Department of Clinical Informatics, UCHealth, Aurora, CO, United States; Anderson H.D., Department of Clinical Pharmacy, University of Colorado, Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, United States; Tiwari P., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States; Trinkley K.E., School of Medicine, University of Colorado Medical Campus, Aurora, CO, United States, Department of Clinical Informatics, UCHealth, Aurora, CO, United States, Department of Clinical Pharmacy, University of Colorado, Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO, United States","Introduction/background: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing. Methods: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing. Results: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788–0.821; Brier score: 0.063–0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use. Discussion/conclusions: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing. 2023 Kim, Suresh, Rosenberg, Tan, Malone, Allen, Kao, Anderson, Tiwari and Trinkley.","electronic health record; heart failure; machine learning; population health; prescribing","acetylsalicylic acid; amino terminal pro brain natriuretic peptide; angiotensin receptor antagonist; angiotensin receptor neprilysin inhibitor; beta adrenergic receptor blocking agent; bisoprolol; brain natriuretic peptide; carvedilol; digoxin; dipeptidyl carboxypeptidase inhibitor; enkephalinase inhibitor; hemoglobin; hemoglobin A1c; hydroxymethylglutaryl coenzyme A reductase inhibitor; ivabradine; loop diuretic agent; metoprolol succinate; mineralocorticoid antagonist; potassium; sodium; sodium glucose cotransporter 2 inhibitor; thiazide diuretic agent; unclassified drug; adult; aged; Article; asthma; atrial fibrillation; chronic obstructive lung disease; clinical evaluation; controlled study; coronary artery disease; depression; diabetes mellitus; diagnostic test accuracy study; electronic health record; female; heart failure; human; hypertension; machine learning; major clinical study; middle aged; patient characteristics; prescribing guideline; receiver operating characteristic; tobacco","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; bisoprolol, 66722-44-9; brain natriuretic peptide, 114471-18-0; carvedilol, 72956-09-3; digoxin, 20830-75-5, 57285-89-9; hemoglobin, 9008-02-0; hemoglobin A1c, 62572-11-6; ivabradine, 148849-67-6, 148870-80-8, 155974-00-8; metoprolol succinate, 98418-47-4; potassium, 7440-09-7; sodium, 7440-23-5","","","Health Data Compass Data Warehouse; National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (1K23HL161352, K12HL137862); National Heart, Lung, and Blood Institute, NHLBI; Agency for Healthcare Research and Quality, AHRQ; American Heart Association, AHA; Patient-Centered Outcomes Research Institute, PCORI; ALSAM Foundation","Funding text 1: This work was supported in part by a grant from the Skaggs Scholars Program at the University of Colorado, Skaggs School of Pharmacy and Pharmaceutical Sciences. Additionally, KET's time was supported in part by the NHLBI (K12HL137862 and 1K23HL161352). DCM has received grants from NIH, AHRQ, and ALSAM foundation and LAA has received grant funding from AHA, NIH, and PCORI. Acknowledgements ; Funding text 2: Supported by the Health Data Compass Data Warehouse project ( healthdatacompass.org ). ","Allen L.A., Tang F., Jones P., Breeding T., Ponirakis A., Turner S.J., Signs, symptoms, and treatment patterns across serial ambulatory cardiology visits in patients with heart failure: insights from the NCDR PINNACLE® registry, BMC Cardiovasc Disord, 18, 1, (2018); Greene S.J., Butler J., Albert N.M., DeVore A.D., Sharma P.P., Duffy C.I., Et al., Medical therapy for heart failure with reduced ejection fraction: the CHAMP-HF registry, J Am Coll Cardiol, 72, 4, pp. 351-366, (2018); Tran R.H., Aldemerdash A., Chang P., Sueta C.A., Kaufman B., Asafu-Adjei J., Et al., Guideline-directed medical therapy and survival following hospitalization in patients with heart failure, Pharmacotherapy, 38, 4, pp. 406-416, (2018); Smith V.K., Dunning J.R., Fischer C.M., MacLean T.E., Bosque-Hamilton J.W., Fera L.E., Et al., Evaluation of the usage and dosing of guideline-directed medical therapy for heart failure with reduced ejection fraction patients in clinical practice, J Pharm Pract, 35, 5, pp. 747-751, (2021); 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Does current prescribing provide optimal treatment for heart failure patients?, Br J Gen Pract, 50, 458, pp. 735-742, (2000); Fu M., Vedin O., Svennblad B., Lampa E., Johansson D., Dahlstrom U., Et al., Implementation of sacubitril/valsartan in Sweden: clinical characteristics, titration patterns, and determinants, ESC Heart Fail, 7, 6, pp. 3633-3643, (2020); DeVore A., Hill L., Thomas L., Sharma P.P., Albert N.M., Butler J., Et al., Patient, provider, and practice characteristics associated with sacubitril/valsartan use in the United States, Circ Heart Fail, 11, 9, (2018); Sangaralingham L.R., Sangaralingham S.J., Shah N.D., Yao X., Dunlay S.M., Adoption of sacubitril/valsartan for the management of patients with heart failure, Circ Heart Fail, 11, 2, (2018); Luo N., Fonarow G.C., Lippmann S.J., Mi X., Heidenreich P.A., Yancy C.W., Et al., Early adoption of sacubitril/valsartan for patients with heart failure with reduced ejection fraction: insights from get with the guidelines–heart failure (GWTG-HF), JACC Heart Fail, 5, 4, pp. 305-309, (2017); King J., Shah R., Bress A., Nelson R., Bellows B., Cost-effectiveness of sacubitril-valsartan combination therapy compared with enalapril for the treatment of heart failure with reduced ejection fraction, JACC Heart Fail, 4, 5, pp. 392-402, (2016); Packer M., Armstrong W.M., Rothstein J.M., Emmett M., Sacubitril-valsartan in heart failure: why are more physicians not prescribing it?, Ann Intern Med, 165, 10, pp. 735-736, (2016); Paulus J.K., Kent D.M., Race and ethnicity: a part of the equation for personalized clinical decision making?, Circ Cardiovasc Qual Outcomes, 10, 7, (2017); Kao D.P., Trinkley K.E., Lin C.T., Heart failure management innovation enabled by electronic health records, JACC Heart Fail, 8, 3, pp. 223-233, (2020)","K.E. Trinkley; School of Medicine, Aurora, University of Colorado Medical Campus, United States; email: katy.trinkley@cuanschutz.edu","","Frontiers Media SA","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164500501"
"Lee H.K.; Park S.U.; Kong S.; Ryu H.; Kim H.B.; Lee S.H.; Kang D.; Shin S.H.; Yu K.J.; Cho J.; Kang J.; Chun I.Y.; Park H.Y.; Won S.M.","Lee, Hee Kyu (58194409300); Park, Sang Uk (57615416000); Kong, Sunga (57210466585); Ryu, Heyin (59379819200); Kim, Hyun Bin (57844623300); Lee, Sang Hoon (59379454700); Kang, Danbee (56076180500); Shin, Sun Hye (57212017855); Yu, Ki Jun (35425006000); Cho, Juhee (14625911800); Kang, Joohoon (56623649100); Chun, Il Yong (56642489000); Park, Hye Yun (57218127297); Won, Sang Min (26423371000)","58194409300; 57615416000; 57210466585; 59379819200; 57844623300; 59379454700; 56076180500; 57212017855; 35425006000; 14625911800; 56623649100; 56642489000; 57218127297; 26423371000","Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification","2024","npj Flexible Electronics","8","1","69","","","","2","10.1038/s41528-024-00355-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207234237&doi=10.1038%2fs41528-024-00355-7&partnerID=40&md5=adee25fd1b522e33f6a23e4b3934932f","Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea; Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, South Korea; Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea; School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea","Lee H.K., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Park S.U., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Kong S., Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea, Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, South Korea; Ryu H., Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, South Korea; Kim H.B., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Lee S.H., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; Kang D., Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea; Shin S.H., Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Yu K.J., School of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea; Cho J., Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea, Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, South Korea, Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea; Kang J., School of Advanced Materials Science and Engineering, Sungkyunkwan University, Suwon, South Korea; Chun I.Y., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea, Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Park H.Y., Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Won S.M., Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea","Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns. © The Author(s) 2024.","","","","","","","MSIT; National Research Foundation of Korea, NRF; Samsung Medical Center, Sungkyunkwan University, SMC; MSIP; Samsung; Korea Health Industry Development Institute, KHIDI; Ministry of Health Welfare, Republic of Korea, (HR21C0885); Ministry of Science and ICT, (RS-2023-00213455, NRF-2021R1A2C2093987); Ministry of Science, ICT & Future Planning, (RS-2020-11201821, RS-2024-00411904); Institute for Basic Science, IBS, (R015-D1); Institute for Basic Science, IBS","H.K.L., S.U.P., S.K., and S.M.W. acknowledges support by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning; grant no. RS-2020-11201821 and RS-2024-00411904). This research was also supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health Welfare, Republic of Korea (grant no. HR21C0885). S.M.W. and H.Y.P. acknowledge support by SMC-SKKU Future Convergence Research Program Grant. This work was supported by the National Research Foundation of Korea grants funded by the Korean government (Ministry of Science and ICT) [NRF-2021R1A2C2093987]. I.Y.C. acknowledges support in part by NRF grant RS-2023-00213455 funded by MSIT and IBS grant R015-D1. This work was also supported by Samsung Electronics Co., Ltd. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)). ","Gupta P., Et al., Precision wearable accelerometer contact microphones for longitudinal monitoring of mechano-acoustic cardiopulmonary signals, NPJ Digital Med, 3, (2020); Cook J., Umar M., Khalili F., Taebi A., Body acoustics for the non-invasive diagnosis of medical conditions, Bioengineering, 9, (2022); Kang Y.J., Et al., Soft skin-interfaced mechano-acoustic sensors for real-time monitoring and patient feedback on respiratory and swallowing biomechanics, NPJ Digital Med, 5, (2022); Liu Y., Et al., Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces, Sci. Adv, 2, (2016); Badshah A.M., Ahmad J., Rahim N., Baik S.W., Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network., pp. 1-5; Singh A., Kaur N., Kukreja V., Kadyan V., Kumar M., Computational intelligence in processing of speech acoustics: a survey, Complex Intell. Syst, 8, pp. 2623-2661, (2022); Srivastava A., Et al., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput. Sci, 7, (2021); Zhang X., Et al., Understanding the learning mechanism of convolutional neural networks in spectral analysis, Analytica Chim. Acta, 1119, pp. 41-51, (2020); Shin D., Shin D., Shin D., Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents, Multimed. Tools Appl, 76, pp. 11449-11470, (2017); From Human-Computer Interaction to Cognitive Infocommunications: A Cognitive Science Perspective, pp. 000433-000438; Lee K., Et al., Mechano-acoustic sensing of physiological processes and body motions via a soft wireless device placed at the suprasternal notch, Nat. Biomed. Eng, 4, pp. 148-158, (2020); Global Health Estimates.; Agusti A., Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, Am. 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Tuberculosis, 63, pp. 561-567, (2014); Gupta S., Chang P., Anyigbo N., Sabharwal A., MobileSpiro: Accurate mobile spirometry for self-management of asthma, In Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare Article 1; Hu Y., Kim E.G., Cao G., Liu S., Xu Y., Physiological acoustic sensing based on accelerometers: a survey for mobile healthcare, Ann. Biomed. Eng, 42, pp. 2264-2277, (2014); Pasterkamp H., Kraman S.S., Wodicka G.R., Respiratory sounds. Advances beyond the stethoscope, Am. J. Respir. Crit. Care Med, 156, pp. 974-987, (1997); Gupta P., Wen H., Di Francesco L., Ayazi F., Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders, Sci. 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Adv, 8, (2022); Chen Z., Et al., Sandwich-structured flexible PDMS@graphene multimodal sensors capable of strain and temperature monitoring with superlative temperature range and sensitivity, Compos. Sci. Technol, 232, (2023); Deutz D.B., Et al., Flexible Piezoelectric Touch Sensor by Alignment of Lead-Free Alkaline Niobate Microcubes in PDMS, Adv. Funct. 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Technol, 27, pp. 1-9, (2021); Preeti M., Koushik G., Baishnab K.L., Dusarlapudi K., Narasimha Raju K., Low frequency MEMS accelerometers in health monitoring – A review based on material and design aspects, Mater. Today.: Proc, 18, pp. 2152-2157, (2019); Rajan R., Johnson J., Abdul Kareem N., Bird call classification using dnn-based acoustic modelling, Circuits, Syst., Signal Process, 41, pp. 1-12, (2022); Tursunov A., Mustaqeem, Choeh J.Y., Kwon S., Age and gender recognition using a convolutional neural network with a specially designed multi-attention module through speech spectrograms, Sensors, 21, (2021); Alnuaim A.A., Et al., Speaker gender recognition based on deep neural networks and ResNet50, Wirel. Commun. Mob. 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Am, 115, pp. 2264-2269, (2004); Ranipa K., Zhu W.-P., Swamy M., Multimodal CNN fusion architecture with multi-features for heart sound classification, In 2021 IEEE International Symposium on Circuits and Systems (ISCAS, pp. 1-5; Paliwal K.K., Lyons J.G., Wojcicki K.K., In 2010 4Th International Conference on Signal Processing and Communication Systems., pp. 1-4; Montesinos Lopez O.A., Montesinos Lopez A., Crossa J., In Multivariate Statistical Machine Learning Methods for Genomic Prediction, pp. 109-139; Park Y.-B., Et al., Revised (2018) COPD clinical practice guideline of the Korean Academy of Tuberculosis and Respiratory Disease: a summary, Tuberculosis Respiratory Dis, 81, pp. 261-273, (2018); Williams N., The MRC breathlessness scale, Occup. Med, 67, pp. 496-497, (2017); Kissner S., Bitzer J., Analysis of current MEMS microphones for cost-effective microphone arrays—a practical approach; Moura B.A.B., Et al., Neck and waist circumference values according to sex, age, and body-mass index: Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), Braz. J. Med Biol. Res, 53, (2020); Yeo W.H., Et al., Multifunctional epidermal electronics printed directly onto the skin, Adv. Mater, 25, pp. 2773-2778, (2013); Choi J.K., Paek D., Lee J.O., Normal predictive values of spirometry in Korean population, Tuberculosis Respiratory Dis, 58, pp. 230-242, (2005); Jones C.J., Rikli R.E., Beam W.C., A 30-s chair-stand test as a measure of lower body strength in community-residing older adults, Res. Q. Exerc. sport, 70, pp. 113-119, (1999)","I.Y. Chun; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; email: iychun@skku.edu; S.M. Won; Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea; email: sangminwon@skku.edu; H.Y. Park; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; email: hyeyunpark@skku.edu","","Nature Research","","","","","","23974621","","","","English","npj Flex. Electron.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85207234237"
"Conti D.M.; Vibeke B.; Kirsten B.; Leif B.; Adam C.; Stephanie D.; Mina G.; Monika G.; Philippe G.; Eckard H.; Hellings P.W.; Milos J.; Kopp M.V.; Marcus M.; Marcia P.; Dermot R.; Scadding G.K.; Eike W.; Ulrich W.; Susanne L.","Conti, Diego M. (58084811700); Vibeke, Backer (59206592600); Kirsten, Beyer (59205408900); Leif, Bjermer (57219704140); Adam, Chaker (59206202700); Stephanie, Dramburg (59206397900); Mina, Gaga (57219706255); Monika, Gappa (59205409000); Philippe, Gevaert (6603711303); Eckard, Hamelmann (59206398000); Hellings, Peter W. (7004215789); Milos, Jesenak (59205608900); Kopp, Matthias V. (22234436000); Marcus, Maurer (57222166269); Marcia, Podesta (59205804900); Dermot, Ryan (57219699849); Scadding, Glenis K. (7007079421); Eike, Wüstenberg (59205805000); Ulrich, Wahn (56828984800); Susanne, Lau (57219695925)","58084811700; 59206592600; 59205408900; 57219704140; 59206202700; 59206397900; 57219706255; 59205409000; 6603711303; 59206398000; 7004215789; 59205608900; 22234436000; 57222166269; 59205804900; 57219699849; 7007079421; 59205805000; 56828984800; 57219695925","EUFOREUM Berlin 2023: Optimizing care for type 2 inflammatory diseases from clinic to AI: A pediatric focus","2024","Pediatric Allergy and Immunology","35","7","e14183","","","","2","10.1111/pai.14183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197603984&doi=10.1111%2fpai.14183&partnerID=40&md5=7deb09854ab5ec43d851675b490fee85","The European Forum for Research and Education in Allergy and Airway Diseases Scientific Expert Team Members, Brussels, Belgium; Escuela de Doctorado UAM, Centro de Estudios de Posgrado, Universidad Autónoma de Madrid, Madrid, Spain; Department of Otorhinolaryngology, Head & Neck Surgery, and Audiology, Rigshospitalet, Copenhagen University, Copenhagen, Denmark; Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany; Department of Respiratory Medicine & Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden; Department of Otorhinolaryngology and Center for Allergy and Environment (ZAUM), TUM School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; 1st Respiratory Medicine Department, Hygeia Hospital, Marousi, Greece; WHO Europe, Standing Committee SCRC, Germany; Department of Pediatrics, Evangelisches Krankenhaus Düsseldorf, Düsseldorf, Germany; Laboratory of Upper Airways Research, Department of Otorhinolaryngology, University of Ghent, Ghent, Belgium; Children's Center Bethel, University Hospital Bielefeld, University Bielefeld, Bielefeld, Germany; KU Leuven Department of Microbiology and Immunology, Allergy and Clinical Immunology Research Unit, Leuven, Belgium; Clinical Department of Otorhinolaryngology, Head and Neck Surgery, University Hospitals Leuven, Leuven, Belgium; Department of Pulmonology and Phthisiology, Department of Pediatrics, Department of Clinical Immunology and Allergology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital in Martin, Martin, Slovakia; Division of Paediatric Pneumology and Allergology, University Children's Hospital, University Medical Center Schleswig-Holstein Campus Luebeck, Luebeck, Germany; Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Grosshansdorf, Germany; Division of Paediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Institute of Allergology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology, Berlin, Germany; EFA – European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium; Allergy and Respiratory Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom; International Primary Care Respiratory Group, Edinburgh, United Kingdom; Department of Allergy & Rhinology, Royal National ENT Hospital, London, United Kingdom; Division of Immunity and Infection, University College, London, United Kingdom; Department of Otorhinolaryngology Head and Neck Surgery, Faculty of Medicine (and University Hospital) Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Emeritus Department of Pediatric Pneumology and Immunology, Charité Universitaetsmedizin Berlin, Berlin, Germany","Conti D.M., The European Forum for Research and Education in Allergy and Airway Diseases Scientific Expert Team Members, Brussels, Belgium, Escuela de Doctorado UAM, Centro de Estudios de Posgrado, Universidad Autónoma de Madrid, Madrid, Spain; Vibeke B., Department of Otorhinolaryngology, Head & Neck Surgery, and Audiology, Rigshospitalet, Copenhagen University, Copenhagen, Denmark; Kirsten B., Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany; Leif B., Department of Respiratory Medicine & Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden; Adam C., Department of Otorhinolaryngology and Center for Allergy and Environment (ZAUM), TUM School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Stephanie D., Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany; Mina G., 1st Respiratory Medicine Department, Hygeia Hospital, Marousi, Greece, WHO Europe, Standing Committee SCRC, Germany; Monika G., Department of Pediatrics, Evangelisches Krankenhaus Düsseldorf, Düsseldorf, Germany; Philippe G., Laboratory of Upper Airways Research, Department of Otorhinolaryngology, University of Ghent, Ghent, Belgium; Eckard H., Children's Center Bethel, University Hospital Bielefeld, University Bielefeld, Bielefeld, Germany; Hellings P.W., Laboratory of Upper Airways Research, Department of Otorhinolaryngology, University of Ghent, Ghent, Belgium, KU Leuven Department of Microbiology and Immunology, Allergy and Clinical Immunology Research Unit, Leuven, Belgium, Clinical Department of Otorhinolaryngology, Head and Neck Surgery, University Hospitals Leuven, Leuven, Belgium; Milos J., Department of Pulmonology and Phthisiology, Department of Pediatrics, Department of Clinical Immunology and Allergology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital in Martin, Martin, Slovakia; Kopp M.V., Division of Paediatric Pneumology and Allergology, University Children's Hospital, University Medical Center Schleswig-Holstein Campus Luebeck, Luebeck, Germany, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Grosshansdorf, Germany, Division of Paediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; Marcus M., Institute of Allergology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology, Berlin, Germany; Marcia P., EFA – European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium; Dermot R., Allergy and Respiratory Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom, International Primary Care Respiratory Group, Edinburgh, United Kingdom; Scadding G.K., Department of Allergy & Rhinology, Royal National ENT Hospital, London, United Kingdom, Division of Immunity and Infection, University College, London, United Kingdom; Eike W., Department of Otorhinolaryngology Head and Neck Surgery, Faculty of Medicine (and University Hospital) Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Ulrich W., Emeritus Department of Pediatric Pneumology and Immunology, Charité Universitaetsmedizin Berlin, Berlin, Germany; Susanne L., Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany","The European Forum for Research and Education in Allergy and Airways diseases (EUFOREA) organized its bi-annual forum EUFOREUM in Berlin in November 2023. The aim of EUFOREUM 2023 was to highlight pediatric action plans for prevention and optimizing care for type 2 inflammatory conditions starting in childhood, with a focus on early-stage diagnosis, ensuring neither under- nor overdiagnosis, optimal care, and suggestions for improvement of care. EUFOREA is an international not-for-profit organization forming an alliance of all stakeholders dedicated to reducing the prevalence and burden of chronic respiratory diseases through the implementation of optimal patient care via educational, research, and advocacy activities. The inclusive and multidisciplinary approach of EUFOREA was reflected in the keynote lectures and faculty of the virtual EUFOREUM 2023 (www.euforea.eu/euforeum) coming from the pediatric, allergology, pulmonology, ENT, dermatology, primary health care fields and patients around the central theme of type 2 inflammation. As most type 2 inflammatory conditions may start in childhood or adolescence, and most children have type 2 inflammation when suffering from a respiratory or skin disease, the moment has come to raise the bar of ambitions of care, including prevention, remission and disease modification at an early stage. The current report provides a comprehensive overview of key statements by the faculty of the EUFOREUM 2023 and the ambitions of EUFOREA allowing all stakeholders in the respiratory field to be updated and ready to join forces in Europe and beyond. (Figure presented.). © 2024 The Author(s). Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","AD; allergic rhinitis; asthma; EUFOREA; pediatrics; rhinosinusitis; T2-inflammation","Adolescent; Allergy and Immunology; Berlin; Child; Humans; Inflammation; Pediatrics; benralizumab; biological product; dupilumab; mepolizumab; omalizumab; reslizumab; tezepelumab; acute urticaria; allergic asthma; allergic rhinitis; angioneurotic edema; Article; artificial intelligence; asthma; atopic dermatitis; atopic march; biological therapy; chronic respiratory tract disease; chronic spontaneous urticaria; comorbidity; desensitization; diet; doctor patient relationship; early diagnosis; environmental exposure; Europe; family history; food allergy; gene; Germany; health care system; health literacy; human; inflammation; inflammatory disease; medical specialist; otorhinolaryngology; outcome assessment; overdiagnosis; patient advocacy; patient care; pediatric patient; personalized medicine; phenotype; practice guideline; prevalence; primary health care; quality of life; remission; respiratory care; respiratory tract allergy; rhinosinusitis; sensitization; training; unmet medical need; adolescent; child; diagnosis; Germany; immunology; pediatrics","","benralizumab, 1044511-01-4; dupilumab, 1190264-60-8; mepolizumab, 196078-29-2; omalizumab, 242138-07-4; reslizumab, 241473-69-8; tezepelumab, 1572943-04-4","","","Sanofi-Aventis Deutschland; Regeneron Pharmaceuticals","The EUFOREUM was organized in Berlin in November 2023 with the support of corporate partners of EUFOREA GSK and Sanofi/Regeneron, and an unrestricted educational grant by ALK. 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Conti; The European Forum for Research and Education in Allergy and Airway Diseases Scientific Expert Team Members, Brussels, Belgium; email: diego.conti@euforea.eu","","John Wiley and Sons Inc","","","","","","09056157","","PALUE","38949196","English","Pediatr. Allergy Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85197603984"
"Najaran M.H.T.","Najaran, Mohammad Hassan Tayarani (55664543700)","55664543700","An evolutionary ensemble learning for diagnosing COVID-19 via cough signals","2023","Intelligent Medicine","3","3","","200","212","12","3","10.1016/j.imed.2023.01.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163831520&doi=10.1016%2fj.imed.2023.01.001&partnerID=40&md5=55209b1bd3b3f08943e3701320ffdf29","University of Hertfordshire School of Physics Engineering and Computer Science, Hatfield, United Kingdom","Najaran M.H.T., University of Hertfordshire School of Physics Engineering and Computer Science, Hatfield, United Kingdom","Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose the COVID-19 disease via cough signals.Methods The proposed algorithm was an ensemble scheme that consists of a number of base learners, where each base learner used a different feature extractor method, including statistical approaches and convolutional neural networks (CNNs) for automatic feature extraction. Features were extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners were aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposed a memetic algorithm for training the CNNs in the base-learners, which combined the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms.Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion COVID-19 may be diagnosed via cough signals and CNNs may be employed to process these signals and it may be further improved by the optimization of CNN architecture. © 2023","COVID-19; Evolutionary algorithms; Optimization","Convolutional neural networks; Diagnosis; Evolutionary algorithms; Fourier transforms; Gradient methods; Learning algorithms; Learning systems; Machine learning; Network architecture; Optimization; 'Dry' [; Automatic feature extraction; Base learners; Convolutional neural network; Ensemble learning; Feature extractor; Neural network architecture; Optimisations; Raw signals; Statistical approach; algorithm; Article; contamination; convolutional neural network; coronavirus disease 2019; cough signal; coughing; dry cough; ensemble learning; feature extraction; Fourier transform; frequency; human; learning algorithm; machine learning; medical parameters; mel frequency cepstral coefficient; pandemic; pattern recognition algorithm; statistical parameters; support vector machine; symptom; COVID-19","","","","","","","Tayarani N.M.H., Applications of artificial intelligence in battling against covid-19: a literature review, Chaos Soliton Fractal, 142, (2021); Tayarani M., Esposito A., Vinciarelli A., What an “ehm” leaks about you: mapping fillers into personality traits with quantum evolutionary feature selection algorithms, IEEE Trans Affect Comput, (2019); Roffo G., Vo D.B., Tayarani M., Et al., Automating the administration and analysis of psychiatric tests: the case of attachment in school age children, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, (2019); Scibelli F., Roffo G., Tayarani M., Et al., Depression speaks: automatic discrimination between depressed and non-depressed speakers based on nonverbal speech features, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2018); Miranda I.D.S., Diacon A.H., Niesler T.R., A comparative study of features for acoustic cough detection using deep architectures, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2019); 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"Xu L.; McCandless L.; Miller N.; Alessio A.; Morrison J.","Xu, Lu (58737735300); McCandless, Lane (57191857406); Miller, Nicholas (58601704100); Alessio, Adam (6701585795); Morrison, James (57190972995)","58737735300; 57191857406; 58601704100; 6701585795; 57190972995","Machine-Learned Algorithms to Predict the Risk of Pneumothorax Requiring Chest Tube Placement after Lung Biopsy","2023","Journal of Vascular and Interventional Radiology","34","12","","2155","2161","6","2","10.1016/j.jvir.2023.08.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171571662&doi=10.1016%2fj.jvir.2023.08.016&partnerID=40&md5=febe67a96661817a7d3ded948bb5f5c3","Biomedical Engineering, Michigan State University, East Lansing, MI, United States; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States; College of Human Medicine, Michigan State University, East Lansing, MI, United States; Advanced Radiology Services, Grand Rapids, MI, United States","Xu L., Biomedical Engineering, Michigan State University, East Lansing, MI, United States, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States, College of Human Medicine, Michigan State University, East Lansing, MI, United States; McCandless L., College of Human Medicine, Michigan State University, East Lansing, MI, United States; Miller N., College of Human Medicine, Michigan State University, East Lansing, MI, United States; Alessio A., Biomedical Engineering, Michigan State University, East Lansing, MI, United States, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States; Morrison J., College of Human Medicine, Michigan State University, East Lansing, MI, United States, Advanced Radiology Services, Grand Rapids, MI, United States","Purpose: To develop a machine-learned algorithm to predict the risk of postlung biopsy pneumothorax requiring chest tube placement (CTP) to facilitate preprocedural decision making, optimize patient care, and improve resource allocation. Materials and Methods: This retrospective study collected clinical and imaging features of biopsy samples obtained from patients with lung nodule biopsy and included information from 59 procedures resulting in pneumothorax requiring CTP and randomly selected 67 procedures without CTP (convenience sample). The data were divided into 70 and 30 as training and testing sets, respectively. Conventional machine-learned binary classifiers were explored with preprocedural imaging and clinical data as input features and CTP as the output. Results: There was no single pathognomonic imaging or predictive clinical feature. For the independent test set under the high-specificity mode, a decision tree, logistic regression, and Naïve Bayes classifier achieved accuracies of identifying CTP at 0.79, 0.93, and 0.89 and area under receiver operating curves (AUROCs) of 0.68, 0.76, and 0.82, respectively. Under high-sensitivity mode, a decision tree, logistic regression, and Naïve Bayes achieved accuracies of identifying CTP of 0.60, 0.45, and 0.60 with AUROCs of 0.71, 0.81, and 0.82, respectively. High importance features included lesion character, chronic obstructive pulmonary disease, lesion depth, and age. A coarse decision tree requiring 4 inputs achieved comparable performance as other methods and previous machine learning prediction studies. Conclusions: The results support the possibility of predicting pneumothorax requiring CTP after biopsy based on an automated decision support, reliant on readily available preprocedural information. © 2023","","Algorithms; Bayes Theorem; Biopsy; Biopsy, Needle; Chest Tubes; Humans; Lung; Pneumothorax; Retrospective Studies; aged; Article; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; decision tree; false positive result; female; forced expiratory volume; human; logistic regression analysis; lung biopsy; lung function test; lung nodule; machine learning; male; patient care; pneumothorax; prediction; predictive value; prone position; resource allocation; retrospective study; sensitivity and specificity; support vector machine; thorax drainage; algorithm; Bayes theorem; biopsy; diagnostic imaging; lung; needle biopsy; pathology; pneumothorax; procedures","","","Mission, Becton Dickinson, United States; Temno, Merit, United States","Becton Dickinson, United States; Merit, United States","","","Gupta S., Seaberg K., Wallace M., Et al., Imaging-guided percutaneous biopsy of mediastinal lesions: different approaches and anatomic considerations, Radiographics, 25, pp. 763-786, (2005); Choi W.-I., Pneumothorax. Tuberc Respir Dis (Seoul), 76, pp. 99-104, (2014); Wiener R.S., Wiener D.C., Gould M.K., Risks of transthoracic needle biopsy: how high?, Clin Pulm Med, 20, pp. 29-35, (2013); Kuban J.D., Tam A.L., Huang S.Y., Et al., The effect of needle gauge on the risk of pneumothorax and chest tube placement after percutaneous computed tomographic (CT)-guided lung biopsy, Cardiovasc Intervent Radiol, 38, pp. 1595-1602, (2015); Covey A.M., Gandhi R., Brody L.A., Getrajdman G., Thaler H.T., Brown K.T., Factors associated with pneumothorax and pneumothorax requiring treatment after percutaneous lung biopsy in 443 consecutive patients, J Vasc Interv Radiol, 15, pp. 479-483, (2004); Saji H., Nakamura H., Tsuchida T., Et al., The incidence and the risk of pneumothorax and chest tube placement after percutaneous CT-guided lung biopsy: the angle of the needle trajectory is a novel predictor, Chest, 121, pp. 1521-1526, (2002); Moreland A., Novogrodsky E., Brody L., Et al., Pneumothorax with prolonged chest tube requirement after CT-guided percutaneous lung biopsy: incidence and risk factors, Eur Radiol, 26, pp. 3483-3491, (2016); Hobbs B.D., Foreman M.G., Bowler R., Et al., Pneumothorax risk factors in smokers with and without chronic obstructive pulmonary disease, Ann Am Thorac Soc, 11, pp. 1387-1394, (2014); Collings C.L., Westcott J.L., Banson N.L., Lange R.C., Pneumothorax and dependent versus nondependent patient position after needle biopsy of the lung, Radiology, 210, pp. 59-64, (1999); Fish G.D., Stanley J.H., Miller K.S., Schabel S.I., Sutherland S.E., Postbiopsy pneumothorax: estimating the risk by chest radiography and pulmonary function tests, AJR Am J Roentgenol, 150, pp. 71-74, (1988); Drumm O., Joyce E.A., De Blacam C., Et al., CT-guided lung biopsy: effect of biopsy-side down position on pneumothorax and chest tube placement, Radiology, 292, pp. 190-196, (2019); Winokur R.S., Pua B.B., Sullivan B., Madoff D.C., Percutaneous lung biopsy: technique, efficacy, and complications, Semin Intervent Radiol, 30, pp. 121-127, (2013); Dennie C.J., Matzinger F.R., Marriner J.R., Maziak D.E., Transthoracic needle biopsy of the lung: results of early discharge in 506 outpatients, Radiology, 219, pp. 247-251, (2001); Fritz B.A., Chen Y., Murray-Torres T.M., Et al., Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study, BMJ Open, 8, pp. 1-7, (2018); Guthrie N.L., Carpenter J., Edwards K.L., Et al., Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study, BMJ Open, 9, (2019); Sinha I., Aluthge D.P., Chen E.S., Sarkar I.N., Ahn S.H., Machine learning offers exciting potential for predicting postprocedural outcomes: a framework for developing random forest models in IR, J Vasc Interv Radiol, 31, pp. 1018-1024.e4, (2020); Sinha I., Aluthge D., McCarthy S., Ahn S., Machine learning can predict iatrogenic pneumothorax following lung biopsy, J Vasc Interv Radiol, 30, (2019); Wang S., Tu J., Chen W., Development and validation of a prediction pneumothorax model in CT-guided transthoracic needle biopsy for solitary pulmonary nodule, Biomed Res Int, 2019, (2019); Anzidei M., Sacconi B., Fraioli F., Et al., Development of a prediction model and risk score for procedure-related complications in patients undergoing percutaneous computed tomography-guided lung biopsy, Eur J Cardiothoracic Surg, 48, pp. e1-e6, (2015); Siegel R.L., Miller K.D., Fuchs H.E., Jemal A., Cancer statistics, 2021, CA Cancer J Clin, 71, pp. 7-33, (2021); Ko J.P., Shepard J.O., Drucker E.A., Et al., Factors influencing pneumothorax rate at lung biopsy: are dwell time and angle of pleural puncture contributing factors?, Int J Gen Med, 14, pp. 1013-1022, (2021); Soylu E., Ozturk K., Gokalp G., Topal U., Effect of needle-tract bleeding on pneumothorax and chest tube placement following CT guided core needle lung biopsy, J Belgian Soc Radiol, 103, pp. 1-7, (2019); Boskovic T., Stanic J., Pena-Karan S., Et al., Pneumothorax after transthoracic needle biopsy of lung lesions under CT guidance, J Thorac Dis, 6, pp. S99-S107, (2014); Bense L., Eklund G., Odont D., Smoking and the increased risk of contracting spontaneous pneumothorax, Chest, 92, pp. 1009-1012, (1987); Taslakian B., Koneru V., Babb J.S., Sridhar D., Transthoracic needle biopsy of pulmonary nodules: meteorological conditions and the risk of pneumothorax and chest tube placement, J Clin Med, 8, (2019); Geraghty P.R., Kee S.T., McFarlane G., Razavi M.K., Sze D.Y., Dake M.D., CT-guided transthoracic needle aspiration biopsy of pulmonary nodules: needle size and pneumothorax rate, Radiology, 229, pp. 475-481, (2003); Zlevor A.M., Mauch S.C., Knott E.A., Et al., Percutaneous lung biopsy with pleural and parenchymal blood patching: results and complications from 1,112 core biopsies, J Vasc Interv Radiol, 32, pp. 1319-1327, (2021)","L. Xu; College of Human Medicine, Michigan State University, East Lansing, 775 Woodlot Drive, 48823, United States; email: xulu2@msu.edu","","Elsevier Inc.","","","","","","10510443","","JVIRE","37619941","English","J. Vasc. Intervent. Radiol.","Article","Final","","Scopus","2-s2.0-85171571662"
"Oosterhoff J.H.F.; Karhade A.V.; Groot O.Q.; Schwab J.H.; Heng M.; Klang E.; Prat D.","Oosterhoff, Jacobien H. F. (57194155918); Karhade, Aditya V. (57160350100); Groot, Olivier Q. (57205116105); Schwab, Joseph H. (23482524500); Heng, Marilyn (55809293000); Klang, Eyal (56080228800); Prat, Dan (57203914732)","57194155918; 57160350100; 57205116105; 23482524500; 55809293000; 56080228800; 57203914732","Intercontinental validation of a clinical prediction model for predicting 90-day and 2-year mortality in an Israeli cohort of 2033 patients with a femoral neck fracture aged 65 or above","2023","European Journal of Trauma and Emergency Surgery","49","3","","1545","1553","8","2","10.1007/s00068-023-02237-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147767189&doi=10.1007%2fs00068-023-02237-5&partnerID=40&md5=f0dd6e9b97983ab71bced12133157470","Department of Orthopaedic Surgery, Amsterdam Movement Sciences, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, Netherlands; Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Department of Orthopaedic Surgery, University of Miami Miller School of Medicine, Miami, FL, United States; Orthopaedic Trauma Service, Jackson Memorial Ryder Trauma Center, Miami, FL, United States; Sami Sagol AI Hub, ARC, Sheba Medical Center, Ramat Gan, Israel; Department of Orthopaedic Surgery, Sheba Medical Center, Ramat Gan, Israel; Department Engineering Systems and Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, Netherlands","Oosterhoff J.H.F., Department of Orthopaedic Surgery, Amsterdam Movement Sciences, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, Amsterdam, 1105AZ, Netherlands, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, Department Engineering Systems and Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, Netherlands; Karhade A.V., Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Groot O.Q., Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Schwab J.H., Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Heng M., Department of Orthopaedic Surgery, University of Miami Miller School of Medicine, Miami, FL, United States, Orthopaedic Trauma Service, Jackson Memorial Ryder Trauma Center, Miami, FL, United States; Klang E., Sami Sagol AI Hub, ARC, Sheba Medical Center, Ramat Gan, Israel; Prat D., Department of Orthopaedic Surgery, Sheba Medical Center, Ramat Gan, Israel","Purpose: Mortality prediction in elderly femoral neck fracture patients is valuable in treatment decision-making. A previously developed and internally validated clinical prediction model shows promise in identifying patients at risk of 90-day and 2-year mortality. Validation in an independent cohort is required to assess the generalizability; especially in geographically distinct regions. Therefore we questioned, is the SORG Orthopaedic Research Group (SORG) femoral neck fracture mortality algorithm externally valid in an Israeli cohort to predict 90-day and 2-year mortality? Methods: We previously developed a prediction model in 2022 for estimating the risk of mortality in femoral neck fracture patients using a multicenter institutional cohort of 2,478 patients from the USA. The model included the following input variables that are available on clinical admission: age, male gender, creatinine level, absolute neutrophil, hemoglobin level, international normalized ratio (INR), congestive heart failure (CHF), displaced fracture, hemiplegia, chronic obstructive pulmonary disease (COPD), history of cerebrovascular accident (CVA) and beta-blocker use. To assess the generalizability, we used an intercontinental institutional cohort from the Sheba Medical Center in Israel (level I trauma center), queried between June 2008 and February 2022. Generalizability of the model was assessed using discrimination, calibration, Brier score, and decision curve analysis. Results: The validation cohort included 2,033 patients, aged 65 years or above, that underwent femoral neck fracture surgery. Most patients were female 64.8% (n = 1317), the median age was 81 years (interquartile range = 75–86), and 80.4% (n = 1635) patients sustained a displaced fracture (Garden III/IV). The 90-day mortality was 9.4% (n = 190) and 2-year mortality was 30.0% (n = 610). Despite numerous baseline differences, the model performed acceptably to the validation cohort on discrimination (c-statistic 0.67 for 90-day, 0.67 for 2-year), calibration, Brier score, and decision curve analysis. Conclusions: The previously developed SORG femoral neck fracture mortality algorithm demonstrated good performance in an independent intercontinental population. Current iteration should not be relied on for patient care, though suggesting potential utility in assessing patients at low risk for 90-day or 2-year mortality. Further studies should evaluate this tool in a prospective setting and evaluate its feasibility and efficacy in clinical practice. The algorithm can be freely accessed: https://sorg-apps.shinyapps.io/hipfracturemortality/. Level of evidence: Level III, Prognostic study. © 2023, The Author(s).","Femoral neck fracture; Geriatric trauma; Hip fracture; Machine learning; Mortality; Prediction model","Aged; Aged, 80 and over; Female; Femoral Neck Fractures; Humans; Israel; Male; Models, Statistical; Prognosis; Prospective Studies; Retrospective Studies; aged; clinical trial; epidemiology; female; femoral neck fracture; human; Israel; male; multicenter study; prognosis; prospective study; retrospective study; statistical model; very elderly","","","","","","","Topol E.J., High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, pp. 44-56, (2019); Panch T., Szolovits P., Atun R., Artificial intelligence, machine learning and health systems, J Glob Health, 8, (2018); Fontana M.A., Lyman S., Sarker G.K., Padgett D.E., MacLean C.H., Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty?, Clin Orthop Relat Res, 477, pp. 1267-1279, (2019); Tran B., Vu G., Ha G., Vuong Q.-H., Ho M.-T., Vuong T.-T., Et al., Global evolution of research in artificial intelligence in health and medicine: a bibliometric study, J Clin Med, 8, (2019); Shi S.M., McCarthy E.P., Mitchell S.L., Kim D.H., Predicting mortality and adverse outcomes: comparing the frailty index to general prognostic indices, J Gen Intern Med, 35, pp. 1516-1522, (2020); Yourman L.C., Lee S.J., Schonberg M.A., Widera E.W., Smith A.K., Prognostic indices for older adults: a systematic review, JAMA, 307, pp. 182-192, (2012); Tedesco S., Andrulli M., Larsson M.A., Kelly D., Alamaki A., Timmons S., Et al., Comparison of machine learning techniques for mortality prediction in a prospective cohort of older adults, Int J Environ Res Public Health, 18, (2021); de Munter L., Polinder S., Lansink K.W.W., Cnossen M.C., Steyerberg E.W., de Jongh M.A.C., Mortality prediction models in the general trauma population: a systematic review, Injury Netherlands, 48, pp. 221-229, (2017); Keuning B.E., Kaufmann T., Wiersema R., Granholm A., Pettila V., Moller M.H., Et al., Mortality prediction models in the adult critically ill: a scoping review, Acta Anaesthesiol Scand England, 64, pp. 424-442, (2020); Xie J., Su B., Li C., Lin K., Li H., Hu Y., Et al., A review of modeling methods for predicting in-hospital mortality of patients in intensive care unit, J Emerg Crit Care Med., 1, (2017); Hu F., Jiang C., Shen J., Tang P., Wang Y., Preoperative predictors for mortality following hip fracture surgery: a systematic review and meta-analysis, Injury Netherlands, 43, pp. 676-685, (2012); Paksima N., Koval K.J., Aharanoff G., Walsh M., Kubiak E.N., Zuckerman J.D., Et al., Predictors of mortality after hip fracture: a 10-year prospective study, Bull NYU Hosp Jt Dis, 66, pp. 111-117, (2008); Giannoulis D., Calori G.M., Giannoudis P.V., Thirty-day mortality after hip fractures: has anything changed?, Eur J Orthop Surg Traumatol, 26, pp. 365-370, (2016); Xu B.Y., Yan S., Low L.L., Vasanwala F.F., Low S.G., Predictors of poor functional outcomes and mortality in patients with hip fracture: a systematic review, BMC Musculoskelet Disord, 20, (2019); Smith T., Pelpola K., Ball M., Ong A., Myint P.K., Pre-operative indicators for mortality following hip fracture surgery: a systematic review and meta-analysis, Age Ageing England, 43, pp. 464-471, (2014); Oosterhoff J., Savelberg A., Karhade A., Gravesteijn B., Doornberg J., Schwab J., Et al., Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above, Eur J Trauma Emerg Surg., 2, (2022); Pallardo Rodil B., Gomez Pavon J., Menendez Martinez P., Hip fracture mortality: Predictive models, Med Clínica (English Ed [Internet, 154, pp. 221-231, (2020); Collins G.S., de Groot J.A., Dutton S., Omar O., Shanyinde M., Tajar A., Et al., External validation of multivariable prediction models: a systematic review of methodological conduct and reporting, BMC Med Res Methodol, 14, (2014); Meinberg E.G., Agel J., Roberts C.S., Karam M.D., Kellam J.F., Fracture and dislocation classification compendium-2018, J Orthop Trauma. United States, 32, pp. S1-S170, (2018); Stekhoven D.J., Buhlmann P., MissForest–non-parametric missing value imputation for mixed-type data, Bioinform Engl, 28, pp. 112-118, (2012); Karhade A.V., Thio Q.C.B.S., Ogink P.T., Bono C.M., Ferrone M.L., Oh K.S., Et al., Predicting 90-day and 1-year mortality in spinal metastatic disease: development and internal validation, Clin Neurosurg United States, 85, pp. E671-E681, (2019); Karhade A.V., Thio Q.C.B.S., Ogink P.T., Shah A.A., Bono C.M., Oh K.S., Et al., Development of machine learning algorithms for prediction of 30-day mortality after surgery for spinal metastasis, Clin Neurosurg United States, 85, pp. E83-E91, (2019); Karhade A.V., Ogink P.T., Thio Q.C.B.S., Cha T.D., Gormley W.B., Hershman S.H., Et al., Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation, Spine J United States, 19, pp. 1764-1771, (2019); Bongers M.E.R., Thio Q.C.B.S., Karhade A.V., Stor M.L., Raskin K.A., Lozano Calderon S.A., Et al., Does the SORG algorithm predict 5-year survival in patients with chondrosarcoma? An external validation, Clin Orthop Relat Res United States, 477, pp. 2296-2303, (2019); Thio Q.C.B.S., Karhade A.V., Ogink P.T., Bramer J.A.M., Ferrone M.L., Calderon S.L., Et al., Development and internal validation of machine learning algorithms for preoperative survival prediction of extremity metastatic disease, Clin Orthop Relat Res United States, 478, pp. 1-12, (2019); Steyerberg E.W., Vickers A.J., Cook N.R., Gerds T., Gonen M., Obuchowski N., Et al., Assessing the performance of prediction models: a framework for traditional and novel measures, Epidemiology, 21, pp. 128-138, (2010); Cox D.R., Two Further Applications of a Model for Binary Regression, Biometrika [Internet, 45, pp. 562-565, (1958); Steyerberg E.W., Vergouwe Y., Towards better clinical prediction models: seven steps for development and an ABCD for validation, Eur Heart J England, 35, pp. 1925-1931, (2014); van Calster B., Vickers A.J., Calibration of risk prediction models: impact on decision-analytic performance, Med Decis Making United States, 35, pp. 162-169, (2015); Vickers A.J., Elkin E.B., Decision curve analysis: a novel method for evaluating prediction models, Med Decis Making United States, 26, pp. 565-574, (2006); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMJ, 350, (2015); Kusen J.Q., van der Vet P.C.R., Wijdicks F.J.G., Verleisdonk E.J.J.M., Link B.C., Houwert R.M., Et al., Efficacy of two integrated geriatric care pathways for the treatment of hip fractures: a cross-cultural comparison, Eur J Trauma Emerg Surg., (2021); IjadiMaghsoodi A., Pavlov V., Rouse P., Walker C.G., Parsons M., Efficacy of acute care pathways for older patients: a systematic review and meta-analysis, Eur J Ageing [Internet]., 19, pp. 1571-1585, (2022); Shalit U., Johansson F., Sontag D., Estimating individual treatment effect: Generalization bounds and algorithms, (2016); Kazaure H., Roman S., Sosa J.A., High mortality in surgical patients with do-not-resuscitate orders: analysis of 8256 patients, Arch Surg, 146, pp. 922-928, (2011); Groot O.Q., Bindels B.J.J., Ogink P.T., Kapoor N.D., Twining P.K., Collins A.K., Et al., Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review, Acta Orthop, 92, pp. 385-393, (2021); Oosterhoff J.H.F., Oberai T., Karhade A.V., Doornberg J.N., Kerkhoffs G.M.M.J., Jaarsma R.L., Et al., Does the SORG orthopaedic research group hip fracture delirium algorithm perform well on an independent intercontinental cohort of patients with hip fractures who are 60 years or older?, Clin Orthop Relat Res., 2, (2022); Karhade A.V., Oosterhoff J.H.F., Groot O.Q., Agaronnik N., Ehresman J., Bongers M.E.R., Et al., Can we geographically validate a natural language processing algorithm for automated detection of incidental durotomy across three independent cohorts from two continents?, Clin Orthop Relat Res., 2, (2022); de Hond A.A.H., Steyerberg E.W., van Calster B., Interpreting area under the receiver operating characteristic curve, Lancet Digit Heal England, 4, pp. e853-e855, (2022); Raghupathi W., Raghupathi V., An empirical study of chronic diseases in the United States: a visual analytics approach, Int J Environ Res Public Health, 15, (2018); Loggers S.A.I., Willems H.C., Van Balen R., Gosens T., Polinder S., Ponsen K.J., Et al., Evaluation of quality of life after nonoperative or operative management of proximal femoral fractures in frail institutionalized patients: the FRAIL-HIP study, JAMA Surg, (2022); Joosse P., Loggers S.A.I., Van De Ree C.L.P., Van Balen R., Steens J., Zuurmond R.G., Et al., The value of nonoperative versus operative treatment of frail institutionalized elderly patients with a proximal femoral fracture in the shade of life (FRAIL-HIP); protocol for a multicenter observational cohort study, BMC Geriatr BMC Geriatrics, 19, pp. 1-12, (2019)","J.H.F. Oosterhoff; Department of Orthopaedic Surgery, Amsterdam Movement Sciences, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Meibergdreef 9, 1105AZ, Netherlands; email: j.h.oosterhoff@amsterdamumc.nl","","Springer Science and Business Media Deutschland GmbH","","","","","","18639933","","","36757419","English","Eur. J. Trauma Emerg. Surg.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85147767189"
"Cherian I.; Agnihotri A.; Katkoori A.K.; Prasad V.","Cherian, Iype (25629362800); Agnihotri, Aditya (58266269100); Katkoori, Arun Kumar (57543466200); Prasad, Versha (58587181000)","25629362800; 58266269100; 57543466200; 58587181000","Machine Learning for Early Detection of Alzheimer's Disease from Brain MRI","2023","International Journal of Intelligent Systems and Applications in Engineering","11","7s","","36","43","7","2","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164107922&partnerID=40&md5=6c9eeda78985ead56addc7e06a640cd5","Department of Neurosurgery Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth “Deemed to Be University” Karad Malkapur, (Dist. Satara), Maharashtra, Karad, 415539, India; Department of Comp. Sc. & Info. Tech. Graphic Era Hill University, Uttarakhand, Dehradun, 248002, India; Department of ECE, CVR COLLEGE OF ENGINEERING, Telanagana, Hyderabad, India; School of Health Sciences, C. S. J. M. University, Kanpur, India","Cherian I., Department of Neurosurgery Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth “Deemed to Be University” Karad Malkapur, (Dist. Satara), Maharashtra, Karad, 415539, India; Agnihotri A., Department of Comp. Sc. & Info. Tech. Graphic Era Hill University, Uttarakhand, Dehradun, 248002, India; Katkoori A.K., Department of ECE, CVR COLLEGE OF ENGINEERING, Telanagana, Hyderabad, India; Prasad V., School of Health Sciences, C. S. J. M. University, Kanpur, India","Recent years have seen an uptick in the use of computed tomography (CT) and magnetic resonance imaging (MRI) scans to create three-dimensional images of the human body for use in medical image processing studies. Immunisations and medical treatment do not work to prevent or treat chronic diseases. Some examples of chronic ailments are asthma, cancer, heart disease, diabetes, and Alzheimer's disease. Alzheimer's disease is a progressive neurodegenerative illness that destroys both memory and personality over time. Without regular checks, diseases like Alzheimer's could not be seen until they've progressed to a fatal level. Millions of individuals throughout the globe are living with Alzheimer's disease, which is a leading cause of death. The first indicator of Alzheimer's disease may be moderate cognitive and/or behavioural impairment, followed by preclinical illness and, ultimately, full-blown Alzheimer's disease. This machine learning model outperforms state-of-the-art medical ailment prediction methods. Most machine learning algorithms for Alzheimer's disease identification are limited to low-dimensional feature spaces because of the sparsity problem. Research in this article examines the feasibility of using several methods such as deep learning, machine learning, and transfer learning approaches to create an early Alzheimer's disease diagnosis. © 2023, Ismail Saritas. All rights reserved.","computed tomography or magnetic resonance imaging scanner; deep learning and transfer learning models; machine learning","","","","","","","","Shetty M., Deekshitha M. Bhat, Devadiga M., Detection of Alzheimer's Disease Using Machine Learning, 2022 International Conference on Artificial Intelligence and Data Engineering (AIDE), pp. 117-120, (2022); Dixit S., Gaikwad A., Vyas V., Shindikar M., Kamble K., United Neurological study of disorders: Alzheimer's disease, Parkinson's disease detection, Anxiety detection, and Stress detection using various Machine learning Algorithms, 2022 International Conference on Signal and Information Processing (IConSIP), pp. 1-6, (2022); Chaihtra D., Vijaya Shetty S., Alzheimer’s Disease Detection from Brain MRI Data using Deep Learning Techniques, 2021 2nd Global Conference for Advancement in Technology (GCAT), pp. 1-5, (2021); Aruchamy S., Mounya V., Verma A., Alzheimer’s Disease Classification in Brain MRI using Modified kNN Algorithm, 2020 IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC), pp. 1-6, (2020); Lodha P., Talele A., Degaonkar K., Diagnosis of Alzheimer's Disease Using Machine Learning, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1-4, (2018); Islam J., Zhang Y., Early Diagnosis of Alzheimer's Disease: A Neuroimaging Study with Deep Learning Architectures, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1962-19622, (2018); Vijayalakshmi V., Sharmila K., Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things, International Journal on Recent and Innovation Trends in Computing and Communication, 11, 1, pp. 53-61, (2023); Aruchamy S., Haridasan A., Verma A., Bhattacharjee P., Nandy S. N., Ram Krishna Vadali S., Alzheimer’s Disease Detection using Machine Learning Techniques in 3D MR Images, 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA), pp. 1-4, (2020); Song S, Lu H, Pan Z., Automated diagnosis of Alzheimer’s disease using Gaussian mixture model based on cortical thickness, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI), pp. 880-883, (2012); Dhabliya Dharmesh, Sharma Rahul, Efficient Cluster Formation Protocol in WSN, International Journal of New Practices in Management and Engineering, 1, pp. 08-17, (2012); Bui DT, Hoang ND., A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1. 1) for spatial prediction of floods, Geoscientific Model Development, 10, 9, (2017); Kiraly A, Szabo N, Toth E, Et al., Male brain ages faster: the age and gender dependence of subcortical volumes, Brain Imaging Behav, 10, pp. 901-910, (2016); Zhu X, Lei Z, Zi H, A sparse embedding and least variance encoding approach to hashing, IEEE Trans Image Process, 23, 9, (2014); Coppede F., Grossi E., Buscema M., Migliore L., Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer's disease and Healthy Matched Individuals, (2013); Padilla P, Lpez M, Grriz JM, Ramirez J, Salas-Gonzalez D, Alvarez I., NMF-SVM based CAD tool applied to functional brain images for the diagnosis of Alzheimer’s disease, IEEE Transactions on medical imaging, 31, 2, pp. 207-216, (2011); Badnjevic A., Cifrek M., Koruga D., Osmankovic D., Neurofuzzy classification of asthma and chronic obstructive pulmonary disease, BMC Med Inform Decis Mak BMC Medical Informatics and Decision Making, 15, 3, (2015); Breitner JC, Dementia-epidemiological considerations nomenclature and a tacit consensus definition, J Geriatr Psychiatry Neurol, 19, 3, pp. 129-136, (2006)","","","Ismail Saritas","","","","","","21476799","","","","English","Internat. J. Intel. Syst. Appl. Eng.","Article","Final","","Scopus","2-s2.0-85164107922"
"Langenberger B.","Langenberger, Benedikt (57360341700)","57360341700","Machine learning as a tool to identify inpatients who are not at risk of adverse drug events in a large dataset of a tertiary care hospital in the USA","2023","British Journal of Clinical Pharmacology","89","12","","3523","3538","15","2","10.1111/bcp.15846","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166648442&doi=10.1111%2fbcp.15846&partnerID=40&md5=a8b750da02589056b0bfec8817814c9a","Department of Health Care Management, Technische Universität Berlin, Berlin, Germany","Langenberger B., Department of Health Care Management, Technische Universität Berlin, Berlin, Germany","Aims: Adverse drug events (ADEs) are a major threat to inpatients in the United States of America (USA). It is unknown how well machine learning (ML) is able to predict whether or not a patient will suffer from an ADE during hospital stay based on data available at hospital admission for emergency department patients of all ages (binary classification task). It is further unknown whether ML is able to outperform logistic regression (LR) in doing so, and which variables are the most important predictors. Methods: In this study, 5 ML models— namely a random forest, gradient boosting machine (GBM), ridge regression, least absolute shrinkage and selection operator (LASSO) regression, and elastic net regression—as well as a LR were trained and tested for the prediction of inpatient ADEs identified using ICD-10-CM codes based on comprehensive previous work in a diverse population. In total, 210 181 observations from patients who were admitted to a large tertiary care hospital after emergency department stay between 2011 and 2019 were included. The area under the receiver operating characteristics curve (AUC) and AUC–precision-recall (AUC-PR) were used as primary performance indicators. Results: Tree-based models performed best with respect to AUC and AUC-PR. The gradient boosting machine (GBM) reached an AUC of 0.747 (95% confidence interval (CI): 0.735 to 0.759) and an AUC-PR of 0.134 (95% CI: 0.131 to 0.137) on unforeseen test data, while the random forest reached an AUC of 0.743 (95% CI: 0.731 to 0.755) and an AUC-PR of 0.139 (95% CI: 0.135 to 0.142), respectively. ML statistically significantly outperformed LR both on AUC and AUC-PR. Nonetheless, overall, models did not differ much with respect to their performance. Most important predictors were admission type, temperature and chief complaint for the best performing model (GBM). Conclusions: The study demonstrated a first application of ML to predict inpatient ADEs based on ICD-10-CM codes, and a comparison with LR. Future research should address concerns arising from low precision and related problems. © 2023 The Author. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.","adverse drug events; decision support; machine learning; predictive modelling","Emergency Service, Hospital; Hospitalization; Humans; Inpatients; Machine Learning; Tertiary Care Centers; United States; antivirus agent; glucocorticoid; rifamycin; serotonin antagonist; adult; adverse drug reaction; Article; asthma; climate model; cohort analysis; controlled study; decision support system; elastic tissue; emergency ward; female; herpes virus infection; hospital admission; hospital patient; hospitalization; human; ICD-10-CM; ICD-9-CM; intensive care; least absolute shrinkage and selection operator; length of stay; machine learning; major clinical study; male; measurement accuracy; medicaid; medicare; middle aged; random forest; ridge regression; sensitivity and specificity; systolic blood pressure; tertiary care center; Youden index; epidemiology; hospital emergency service; hospitalization; machine learning; United States","","rifamycin, 6998-60-3, 14897-39-3, 15105-92-7","","","MIT Laboratory for Computational Physiology; National Institutes of Health, NIH; National Institute of Biomedical Imaging and Bioengineering, NIBIB","Funding text 1: The author thanks the initiators and maintainers of the MIMIC databases, namely the researchers at the MIT Laboratory for Computational Physiology and collaborating research groups. Without their initiative as well as the underlying grants from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH), this work would not have been possible. Open Access funding enabled and organized by Projekt DEAL. ; Funding text 2: The author thanks the initiators and maintainers of the MIMIC databases, namely the researchers at the MIT Laboratory for Computational Physiology and collaborating research groups. Without their initiative as well as the underlying grants from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH), this work would not have been possible. Open Access funding enabled and organized by Projekt DEAL.","Bates D.W., Incidence of adverse drug events and potential adverse drug events, JAMA, 274, 1, (1995); Classen D.C., Adverse drug events in hospitalized PatientsExcess length of stay, extra costs, and attributable mortality, JAMA, 277, 4, pp. 301-306, (1997); Chen C., Jia W., Guo D., Et al., Development of a computer-assisted adverse drug events alarm and assessment system for hospital inpatients in China, Ther Innov Regul Sci, 54, 1, pp. 32-41, (2020); Jolivot P.-A., Hindlet P., Pichereau C., Et al., A systematic review of adult admissions to ICUs related to adverse drug events, Crit Care, 18, 6, (2014); Hug B.L., Keohane C., Seger D.L., Yoon C., Bates D.W., The costs of adverse drug events in community hospitals, Jt Comm J Qual Patient Saf, 38, 3, pp. 120-126, (2012); Poudel D.R., Acharya P., Ghimire S., Dhital R., Bharati R., Burden of hospitalizations related to adverse drug events in the USA: a retrospective analysis from large inpatient database, Pharmacoepidemiol Drug Saf, 26, 6, pp. 635-641, (2017); Rottenkolber D., Hasford J., Stausberg J., Costs of adverse drug events in German hospitals--a microcosting study, Value Health, 15, 6, pp. 868-875, (2012); Wolfe D., Yazdi F., Kanji S., Et al., Incidence, causes, and consequences of preventable adverse drug reactions occurring in inpatients: a systematic review of systematic reviews, PLoS ONE, 13, 10, (2018); Bates D.W., Levine D., Syrowatka A., Et al., The potential of artificial intelligence to improve patient safety: a scoping review, NPJ Digit Med, 4, 1, (2021); Devlin J.W., Mallow-Corbett S., Riker R.R., Adverse drug events associated with the use of analgesics, sedatives, and antipsychotics in the intensive care unit, Crit Care Med, 38, 6, pp. S231-S243, (2010); Stausberg J., International prevalence of adverse drug events in hospitals: an analysis of routine data from England, Germany, and the USA, BMC Health Serv Res, 14, 1, (2014); Kanjanarat P., Winterstein A.G., Johns T.E., Hatton R.C., Gonzalez-Rothi R., Segal R., Nature of preventable adverse drug events in hospitals: a literature review, Am J Health Syst Pharm, 60, 17, pp. 1750-1759, (2003); Kilbridge P.M., Campbell U.C., Cozart H.B., Mojarrad M.G., Automated surveillance for adverse drug events at a community hospital and an academic medical center, J Am Med Inform Assoc, 13, 4, pp. 372-377, (2006); Aljadhey H., Mahmoud M.A., Mayet A., Et al., Incidence of adverse drug events in an academic hospital: a prospective cohort study, Qual Assur Health Care, 25, 6, pp. 648-655, (2013); Cano F.G., Rozenfeld S., Adverse drug events in hospitals: a systematic review, Cad Saude Publica, 25, pp. S360-S372, (2009); Martins A.C.M., Giordani F., Rozenfeld S., Adverse drug events among adult inpatients: a meta-analysis of observational studies, J Clin Pharm Ther, 39, 6, pp. 609-620, (2014); Bates D.W., Singh H., Two decades since to err is human: an assessment of progress and emerging priorities in patient safety, Health Aff (Millwood), 37, 11, pp. 1736-1743, (2018); Peyriere H., Cassan S., Floutard E., Et al., Adverse drug events associated with hospital admission, Ann Pharmacother, 37, 1, pp. 5-11, (2003); von Laue N.C., Schwappach D.L.B., Koeck C.M., The epidemiology of preventable adverse drug events: a review of the literature, Wien Klin Wochenschr, 115, 12, pp. 407-415, (2003); Falconer N., Barras M., Cottrell N., Systematic review of predictive risk models for adverse drug events in hospitalized patients, Br J Clin Pharmacol, 84, 5, pp. 846-864, (2018); Sakuma M., Bates D.W., Morimoto T., Clinical prediction rule to identify high-risk inpatients for adverse drug events: the JADE study, Pharmacoepidemiol Drug Saf, 21, 11, pp. 1221-1226, (2012); Hohl C.M., Badke K., Zhao A., Et al., Prospective validation of clinical criteria to identify emergency department patients at high risk for adverse drug events, Acad Emerg Med, 25, 9, pp. 1015-1026, (2018); Hohl C.M., Yu E., Hunte G.S., Et al., Clinical decision rules to improve the detection of adverse drug events in emergency department patients, Acad Emerg Med, 19, 6, pp. 640-649, (2012); Ouchi K., Lindvall C., Chai P.R., Boyer E.W., Machine learning to predict, detect, and intervene older adults vulnerable for adverse drug events in the emergency department, J Med Toxicol, 14, 3, pp. 248-252, (2018); Hastie T., Tibshirani R., Friedman J., The elements of statistical learning, (2009); Christodoulou E., Ma J., Collins G.S., Steyerberg E.W., Verbakel J.Y., van Calster B., A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, pp. 12-22, (2019); Langenberger B., Thoma A., Vogt V., Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? 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Drici M.D., Clement N., Is gender a risk factor for adverse drug reactions? The example of drug-induced long QT syndrome, Drug Saf, 24, 8, pp. 575-585, (2001); Raschke R.A., Gollihare B., Wunderlich T.A., Et al., A computer alert system to prevent injury from adverse drug events: development and evaluation in a community teaching hospital, Jama, 280, 15, pp. 1317-1320, (1998); van der Sijs H., Aarts J., Vulto A., Berg M., Overriding of drug safety alerts in computerized physician order entry, J Am Med Inform Assoc, 13, 2, pp. 138-147, (2006); Bates D.W., Leape L.L., Petrycki S., Incidence and preventability of adverse drug events in hospitalized adults, J Gen Intern Med, 8, 6, pp. 289-294, (1993); Sylvester E.V.A., Bentzen P., Bradbury I.R., Et al., Applications of random forest feature selection for fine-scale genetic population assignment, Evol Appl, 11, 2, pp. 153-165, (2018); Nguyen C., Wang Y., Nguyen H.N., Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic, JBiSE, 6, 5, pp. 551-560, (2013); Hasan M.A.M., Nasser M., Ahmad S., Molla K.I., Feature selection for intrusion detection using random Forest, JIS, 7, 3, pp. 129-140, (2016); Saraswat M., Arya K.V., Feature selection and classification of leukocytes using random forest, Med Biol Eng Comput, 52, 12, pp. 1041-1052, (2014); Zhou Q., Zhou H., Li T., Cost-sensitive feature selection using random forest: selecting low-cost subsets of informative features, Knowledge-Based Syst, 95, pp. 1-11, (2016)","B. Langenberger; Department of Health Care Management, Technische Universität Berlin, Berlin, Germany; email: langenberger@tu-berlin.de","","John Wiley and Sons Inc","","","","","","03065251","","BCPHB","37430382","English","Br. J. Clin. Pharmacol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85166648442"
"Pooja M.R.","Pooja, M.R. (57190388894)","57190388894","A predictive model for the early prognosis and characterization of asthma","2022","Journal of Pharmaceutical Negative Results","13","4","","1","9","8","3","10.47750/pnr.2022.13.04.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143277043&doi=10.47750%2fpnr.2022.13.04.001&partnerID=40&md5=bec460a38dc0138783ba07b87f640e3d","Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India","Pooja M.R., Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India","An attempt to attain a good balance between optimal sensitivity and specificity of the predictive models in the case of sparse categorical data has been made in this paper by proposing a hybrid decision support system that integrates unsupervised and supervised learning methods at two different stages to explore the advantages of both. The system handles the categorical data without any encoding procedures involved. However, it requires one to use numerical categorical data in the place of labeled categorical data. The responses recorded in ACQ’s are largely numerical data characterizing the presence or absence with 1 and 0 respectively. A single optional value indicative of a third response is often used representing an unknown response. The primary aim of the proposed work is to provide a platform for efficient outcome prediction that can assist in shared decision making. Shared decision making in the context of healthcare relates to a strategy where both the clinicians and patient themselves decide on par with the management of the disease. © 2022 Wolters Kluwer Medknow Publications. All rights reserved.","Categorical; Characterization; Encoding; Hybrid; Sensitivity","Article; asthma; binary classification; clinician; decision support system; fuzzy c means clustering; human; k means clustering; outcome assessment; phenotype; practice guideline; prediction; predictive model; prognosis; sensitivity and specificity; shared decision making; stratified sample; supervised machine learning; unsupervised machine learning","","","","","","","Chatzimichail E., Paraskakis E., Rigas A., Predicting Asthma Outcome Using Partial Least Square Regression and Artificial Neural Networks, Advances in Artificial Intelligence, (2013); Manoharan S. C., Ramakrishnan S., Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering, Journal of Medical Systems, 33, 5, pp. 347-351, (2009); Wang X., Wang Z., Pengetnze Y. M., Lachman B. S., Chowdhry V., Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits, (2019); Tomita K, Nagao R, Touge H, Ikeuchi T, Sano H, Yamasaki A, Tohda Y., Deep learning facilitates the diagnosis of adult asthma, Allergology International, 68, 4, pp. 456-461, (2019); Tobore I., Li J., Yuhang L., Al-Handarish Y., Kandwal A., Nie Z., Wang L., Deep learning intervention for health care challenges: some biomedical domain considerations, JMIR mHealth and uHealth, 7, 8, (2019); Loymans R. J., Debray T. P., Honkoop P. J., Termeer E. H., Snoeck-Stroband J. B., Schermer T. R., Ter Riet G., Exacerbations in adults with asthma: a systematic review and external validation of prediction models, The Journal of Allergy and Clinical Immunology: In Practice, 6, 6, pp. 1942-1952, (2018); Deng H., Urman R., Gilliland F. D., Eckel S. P., Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach, BMC medical research methodology, 19, 1, pp. 1-12, (2019); Vial Dupuy A., Amat F., Pereira B., Labbe A., Just J., A simple tool to identify infants at high risk of mild to severe childhood asthma: the persistent asthma predictive score, Journal of Asthma, 48, 10, pp. 1015-1021, (2011); Anastasiou A., Kocsis O., Moustakas K., Exploring machine learning for monitoring and predicting severe asthma exacerbations, Proceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1-6, (2018); Messinger A. I., Bui N., Wagner B. D., Szefler S. J., Vu T., Deterding R. R., Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma, Pediatric pulmonology, 54, 8, pp. 1149-1155, (2019); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health informatics journal, 25, 3, pp. 811-827, (2019); Sheshasaayee A., Prathiba L., An Improvised Technique for the Diagnosis of Asthma Disease with the Categorization of Asthma Disease Level, Information Systems Design and Intelligent Applications, pp. 985-994, (2018); Goto T., Camargo C. A., Faridi M. K., Yun B. J., Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, The American journal of emergency medicine, 36, 9, pp. 1650-1654, (2018); MR P., Pushpalatha M. P., Clinical Respiratory Diseases and Care, (2019); Pooja M. R., Pushpalatha M. P., A neural network approach for risk assessment of asthma disease, Journal of Health Informatics & Management, 2, 1, pp. 1-6, (2018); Pooja M. R., Pushpalatha M. P., A hybrid decision support system for the identification of asthmatic subjects in a cross-sectional study, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 288-293, (2015); Pushpalatha M. P., Pooja M. R., A predictive model for the effective prognosis of Asthma using Asthma severity indicators, 2017 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6, (2017); Pooja M. R., Pushpalatha M. P., A predictive framework for the assessment of asthma control level, Int J Eng Adv Technol, 8, pp. 239-245, (2019); Badnjevic A., Gurbeta L., Cifrek M., Marjanovic D., Classification of asthma using artificial neural network, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 387-390, (2016); Salau A.O., Pooja M.R., Hasani N.F., Braide S.L., Model based risk assessment to evaluate lung functionality for early prognosis of asthma using neural network approach, Mathematical Modelling of Engineering Problems, 9, 4, pp. 1053-1060, (2022); Xiang Y., Ji H., Zhou Y., Li F., Du J., Rasmy L., Tao C., Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: retrospective cohort study, Journal of medical Internet research, 22, 7, (2020); Haque R., Ho S. B., Chai I., Abdullah A., Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers, F1000Research, 10, (2021); Ma X., Wu Y., Zhang L., Yuan W., Yan L., Fan S., Jia W., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, Journal of translational medicine, 18, 1, pp. 1-14, (2020); Amaral J. L., Sancho A. G., Faria A. C., Lopes A. J., Melo P. L., Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers, Medical & Biological Engineering & Computing, 58, 10, pp. 2455-2473, (2020); Amaral J. L., Sancho A. G., Faria A. C., Lopes A. J., Melo P. L., Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers, Medical & Biological Engineering & Computing, 58, 10, pp. 2455-2473, (2020); Pooja M. R., On Effective Use of Feature Engineering for Improving the Predictive Capability of Machine Learning Models, Computational Intelligence and Data Sciences, pp. 53-62; MR P., MP P., Cluster Analysis to Characterize the Patterns of Complementary and Alternative Medicines Usage in Asthma Controls, The Open Public Health Journal, 13, 1, (2020)","M.R. Pooja; Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India; email: pooja.mr@vvce.ac.in","","ResearchTrentz Academy Publishing Education Services","","","","","","09769234","","","","English","J. Pharm. Negat. Results","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85143277043"
"Shah S.A.; Nwaru B.I.; Sheikh A.; Simpson C.R.; Kotz D.","Shah, Syed A. (56424513100); Nwaru, Bright I. (26635624700); Sheikh, Aziz (7202522962); Simpson, Colin R. (56018813700); Kotz, Daniel (23088763000)","56424513100; 26635624700; 7202522962; 56018813700; 23088763000","Development and validation of a multivariable mortality risk prediction model for COPD in primary care","2022","npj Primary Care Respiratory Medicine","32","1","21","","","","3","10.1038/s41533-022-00280-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130997901&doi=10.1038%2fs41533-022-00280-0&partnerID=40&md5=faf9d427d74140816909d17ce1b08c16","Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; Krefting Research Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand; Institute of General Practice, Addiction Research and Clinical Epidemiology Unit, Centre for Health and Society (CHS), Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany","Shah S.A., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; Nwaru B.I., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom, Krefting Research Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden, Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Sheikh A., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; Simpson C.R., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom, School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand; Kotz D., Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom, Institute of General Practice, Addiction Research and Clinical Epidemiology Unit, Centre for Health and Society (CHS), Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany","Risk stratification of chronic obstructive pulmonary disease (COPD) patients is important to enable targeted management. Existing disease severity classification systems, such as GOLD staging, do not take co-morbidities into account despite their high prevalence in COPD patients. We sought to develop and validate a prognostic model to predict 10-year mortality in patients with diagnosed COPD. We constructed a longitudinal cohort of 37,485 COPD patients (149,196 person-years) from a UK-wide primary care database. The risk factors included in the model pertained to demographic and behavioural characteristics, co-morbidities, and COPD severity. The outcome of interest was all-cause mortality. We fitted an extended Cox-regression model to estimate hazard ratios (HR) with 95% confidence intervals (CI), used machine learning-based data modelling approaches including k-fold cross-validation to validate the prognostic model, and assessed model fitting and discrimination. The inter-quartile ranges of the three metrics on the validation set suggested good performance: 0.90–1.06 for model fit, 0.80–0.83 for Harrel’s c-index, and 0.40–0.46 for Royston and Saurebrei’s RD2 with a strong overlap of these metrics on the training dataset. According to the validated prognostic model, the two most important risk factors of mortality were heart failure (HR 1.92; 95% CI 1.87–1.96) and current smoking (HR 1.68; 95% CI 1.66–1.71). We have developed and validated a national, population-based prognostic model to predict 10-year mortality of patients diagnosed with COPD. This model could be used to detect high-risk patients and modify risk factors such as optimising heart failure management and offering effective smoking cessation interventions. © 2022, The Author(s).","","Cohort Studies; Heart Failure; Humans; Primary Health Care; Proportional Hazards Models; Pulmonary Disease, Chronic Obstructive; adult; aged; all cause mortality; Article; chronic obstructive lung disease; cohort analysis; comorbidity; disease severity; female; heart failure; high risk patient; human; k fold cross validation; longitudinal study; machine learning; major clinical study; male; mortality risk; multicenter study; outcome assessment; performance indicator; prediction; primary medical care; risk factor; smoking; smoking cessation; chronic obstructive lung disease; heart failure; primary health care; proportional hazards model","","","","","UK Research and Innovation Industrial Strategy Challenge Fund; Medical Research Council, MRC; Wallenberg Centre for Molecular and Translational Medicine, WCMTM; University of Edinburgh, ED; Health Data Research Hub for Respiratory Health; Knut och Alice Wallenbergs Stiftelse; BREATHE; Medicines and Healthcare products Regulatory Agency; VBG Group Herman Krefting Foundation on Asthma and Allergy; Science and Technology Plan Project of Zhanjiang City, (2021A05045, 2019A01012); Guangdong Ocean University Innovation Program, (230419100); , (MC_PC_19004); College Students Innovation and Entrepreneurship Training Program of Guangdong Ocean University, (CXXL2020291); Guangdong Ocean University, GDOU, (QNXZ201909); Basic and Applied Basic Research Foundation of Guangdong Province, (2019A1515110313); Program for Scientific Research Start-up Funds of Guangdong Ocean University, (R19057)","Funding text 1: Access to the CPRD database was funded through the Medical Research Council\u2019s license agreement with the Medicines and Healthcare products Regulatory Agency. This work was supported by BREATHE \u2013 The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. S.A.S. is supported by the University of Edinburgh\u2019s Chancellor\u2019s Fellowship Scheme. B.N. acknowledges the support of the VBG Group Herman Krefting Foundation on Asthma and Allergy, Knut and Alice Wallenberg Foundation, and the Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg.; Funding text 2: This research was funded by the GuangDong Basic and Applied Basic Research Foundation (2019A1515110313), Science and Technology Plan Project of Zhanjiang City (2019A01012, 2021A05045), Program for Scientific Research Start-up Funds of Guangdong Ocean University (R19057), College Students Innovation and Entrepreneurship Training Program of Guangdong Ocean University (CXXL2020291), Guangdong Ocean University Innovation Program (230419100) and Nanhai Youth Scholar Project of Guangdong Ocean University (QNXZ201909). ","Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet Respir. Med., 5, (2017); Rabe K.F., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am. J. Respir. Crit. Care Med., 176, pp. 532-555, (2007); Vogelmeier C.F., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. GOLD executive summary, Am. J. Respir. Crit. Care Med., 195, pp. 557-582, (2017); (2011); Gedebjerg A., Et al., Prediction of mortality in patients with chronic obstructive pulmonary disease with the new Global Initiative for Chronic Obstructive Lung Disease 2017 classification: a cohort study, Lancet Respir. Med., 6, pp. 204-212, (2018); Han M.-Z., Et al., Validation of the GOLD 2017 and new 16 subgroups (1A–4D) classifications in predicting exacerbation and mortality in COPD patients, Int. J. Chron. Obstruct. Pulmon. Dis., 13, (2018); Tsiligianni I.G., Kosmas E., Van der Molen T., Tzanakis N., Managing comorbidity in COPD: a difficult task, Curr. Drug Targets, 14, pp. 158-176, (2013); Bloom C.I., Ricciardi F., Smeeth L., Stone P., Quint J.K., Predicting COPD 1-year mortality using prognostic predictors routinely measured in primary care, BMC Med., 17, (2019); Kiddle S.J., Whittaker H.R., Seaman S.R., Quint J.K., Prediction of five-year mortality after COPD diagnosis using primary care records, PLoS One, 15, (2020); Chisholm J., The Read clinical classification, BMJ Br. Med. J., 300, (1990); Payne R.A., Abel G.A., UK indices of multiple deprivation-a way to make comparisons across constituent countries easier, Heal. Stat. Q, 53, pp. 2015-2016, (2012); Mano M.M., Kime C.R., Logic and Computer Design Fundamentals, (1997); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: Identification and prediction using a digital health system, J. Med. Internet Res, e69, (2017); Atkinson M.D., Et al., Development of an algorithm for determining smoking status and behaviour over the life course from UK electronic primary care records, BMC Med. Inform. Decis. Mak., 17, (2017); Rothnie K.J., Et al., Validation of the recording of acute exacerbations of COPD in UK primary care electronic healthcare records, PLoS One, 11, (2016); Rogers S., Girolami M., A First Course in Machine Learning, (2016); Royston P., Altman D.G., External validation of a Cox prognostic model: principles and methods, BMC Med. Res. Methodol., 13, (2013); Mallett S., Royston P., Waters R., Dutton S., Altman D.G., Reporting performance of prognostic models in cancer: a review, BMC Med., 8, (2010); Harrell F.E., Califf R.M., Pryor D.B., Lee K.L., Rosati R.A., Evaluating the yield of medical tests, Jama, 247, pp. 2543-2546, (1982); Royston P., Sauerbrei W., A new measure of prognostic separation in survival data, Stat. Med., 23, pp. 723-748, (2004); DiCiccio T.J., Efron B., Bootstrap confidence intervals, Stat. Sci., 11, pp. 189-228, (1996); Wickham H., Et al., Welcome to the Tidyverse, J. Open Source Softw., 4, (2019); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) The TRIPOD Statement, Circulation, 131, pp. 211-219, (2015); Benchimol E.I., Et al., The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement, PLoS Med., 12, (2015); Marin J.M., Et al., Multicomponent indices to predict survival in COPD: the COCOMICS study, Eur. Respir. J., 42, pp. 323-332, (2013); Keene S.J., Et al., External validation of the updated ADO score in COPD patients from the Birmingham COPD cohort, Int. J. Chron. Obstruct. Pulmon. Dis., 14, (2019); Aramburu A., Et al., COPD classification models and mortality prediction capacity, Int. J. Chron. Obstruct. Pulmon. Dis., 14, (2019); Thomsen M., Nordestgaard B.G., Vestbo J., Lange P., Characteristics and outcomes of chronic obstructive pulmonary disease in never smokers in Denmark: a prospective population study, Lancet Respir. Med., 1, pp. 543-550, (2013); Ramspek C.L., Jager K.J., Dekker F.W., Zoccali C., van Diepen M., External validation of prognostic models: what, why, how, when and where?, Clin. Kidney J., 14, pp. 49-58, (2021)","S.A. Shah; Usher Institute, The University of Edinburgh, Edinburgh, United Kingdom; email: ahmar.shah@ed.ac.uk","","Nature Research","","","","","","20551010","","PCRJA","35641524","English","npj Prim. Care Respir. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130997901"
"Ozturk N.; Yakak I.; Ağ M.B.; Aksoy N.","Ozturk, Nur (57352680100); Yakak, Irem (59014241500); Ağ, Melih Buğra (59013715900); Aksoy, Nilay (57212083651)","57352680100; 59014241500; 59013715900; 57212083651","Is ChatGPT reliable and accurate in answering pharmacotherapy-related inquiries in both Turkish and English?","2024","Currents in Pharmacy Teaching and Learning","16","7","102101","","","","2","10.1016/j.cptl.2024.04.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192106000&doi=10.1016%2fj.cptl.2024.04.017&partnerID=40&md5=0217eb24f7a67cb2cae83ac8d73f2ece","Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey; Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey; Istanbul Medipol University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey","Ozturk N., Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey, Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey; Yakak I., Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey; Ağ M.B., Istanbul Medipol University, Graduate School of Health Sciences, Clinical Pharmacy PhD Program, Istanbul, Turkey, Istanbul Medipol University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey; Aksoy N., Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey","Introduction: Artificial intelligence (AI), particularly ChatGPT, is becoming more and more prevalent in the healthcare field for tasks such as disease diagnosis and medical record analysis. The objective of this study is to evaluate the proficiency and accuracy of ChatGPT in different domains of clinical pharmacy cases and queries. Methods: The study NAPLEX® Review Questions, 4th edition, pertaining to 10 different chronic conditions compared ChatGPT's responses to pharmacotherapy cases and questions obtained from McGraw Hill's, alongside the answers provided by the book's authors. The proportion of correct responses was collected and analyzed using the Statistical Package for the Social Sciences (SPSS) version 29. Results: When tested in English, ChatGPT had substantially higher mean scores than when tested in Turkish. The average accurate score for English and Turkish was 0.41 ± 0.49 and 0.32 ± 0.46, respectively, p = 0.18. Responses to queries beginning with “Which of the following is correct?” are considerably more precise than those beginning with “Mark all the incorrect answers?” 0.66 ± 0.47 as opposed to 0.16 ± 0.36; p = 0.01 in English language and 0.50 ± 0.50 as opposed to 0.14 ± 0.34; p < 0.05in Turkish language. Conclusion: ChatGPT displayed a moderate level of accuracy while responding to English inquiries, but it displayed a slight level of accuracy when responding to Turkish inquiries, contingent upon the question format. Improving the accuracy of ChatGPT in languages other than English requires the incorporation of several components. The integration of the English version of ChatGPT into clinical practice has the potential to improve the effectiveness, precision, and standard of patient care provision by supplementing personal expertise and professional judgment. However, it is crucial to utilize technology as an adjunct and not a replacement for human decision-making and critical thinking. © 2024 Elsevier Inc.","Artificial intelligence; ChatGPT; Clinical pharmacy; Educational measurement; Pharmacy","Artificial Intelligence; Humans; Language; Reproducibility of Results; Surveys and Questionnaires; Turkey; Article; asthma; ChatGPT; chronic kidney failure; clinical pharmacy; comparative study; cystic fibrosis; data accuracy; diabetes mellitus; dyslipidemia; English (language); heart failure; human; hypertension; inflammatory bowel disease; language; osteoarthritis; pharmacy education; reliability; thyroid disease; translating (language); Turkish (language); artificial intelligence; language; questionnaire; reproducibility; turkey (bird)","","","","","","","Alofi E.S., Evaluating Chatgpt in health diagnostic symptoms, J Namib Stud, 26, pp. 65-89, (2023); Hamid H., Zulkifli K., Naimat F., Che Yaacob N.L., Ng K.W., Exploratory study on student perception on the use of chat AI in process-driven problem-based learning, Curr Pharm Teach Learn, 15, pp. 1017-1025, (2023); Johnson D., Goodman R., Patrinely J., Et al., Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the chat-GPT model, (2023); Shihab S.R., Sultana N., Samad A., Revisiting the use of ChatGPT in business and educational fields: possibilities and challenges, Bullet [Internet], 2, pp. 534-545, (2023); Gilson A., Safranek C.W., Huang T., Et al., How does CHATGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment, JMIR Med Educ, (2023); Huh S., Are ChatGPT's knowledge and interpretation ability comparable to those of medical students in Korea for taking a parasitology examination?: a descriptive study, J Educ Eval Health Prof, 20, (2023); Nisar S., Aslam M.S., Is chatgpt a good tool for t&cm students in studying pharmacology?, SSRN Electron J, (2023); Wang Y.M., Shen H.W., Chen T.J., Performance of ChatGPT on the pharmacist licensing examination in Taiwan, J Chin Med Assoc, 86, pp. 653-658, (2023); Alowais S.A., Alghamdi S.S., Alsuhebany N., Et al., Revolutionizing healthcare: the role of artificial intelligence in clinical practice, BMC Med Educ, 23, (2023); Jamshidi M.B., Lalbakhsh A., Talla J., Et al., Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment, IEEE Access, 8, pp. 109581-109595, (2020); Ray P.P., CHATGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope, IoT CPS, 3, pp. 121-154, (2023); Kusunose K., Kashima S., Sata M., Evaluation of the accuracy of ChatGPT in answering clinical questions on the Japanese Society of Hypertension Guidelines, Circ J, 87, 7, pp. 1030-1033, (2023); Almagazzachi A., Mustafa A., Eighaei Sedeh A., Et al., Generative artificial intelligence in patient education: ChatGPT takes on hypertension questions, Cureus, 16, 2, (2024); Meo S.A., Al-Khlaiwi T., AbuKhalaf A.A., Meo A.S., Klonoff D.C., The scientific knowledge of bard and ChatGPT in endocrinology, diabetes, and diabetes technology: multiple-choice questions examination-based performance, J Diabetes Sci Technol, (2024); Roosan D., Padua P., Khan R., Khan H., Verzosa C., Wu Y., Effectiveness of chatgpt in clinical pharmacy and the role of artificial intelligence in medication therapy management, JAPhA, (2023); Al-Ashwal F.Y., Zawiah M., Gharaibeh L., Abu-Farha R., Bitar A.N., Evaluating the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, bing AI, and bard against conventional drug-drug interactions Clinical tools, Drug Healthc Patient Saf, 15, pp. 137-147, (2023); Huang X., Estau D., Liu X., Yu Y., Qin J., Li Z., Evaluating the performance of Chatgpt in clinical pharmacy: a comparative study of chatgpt and clinical pharmacists, Br J Clin Pharmacol, (2023); Fournier A., Fallet C., Sadeghipour F., Perrottet N., Assessing the applicability and appropriateness of chatgpt in answering clinical pharmacy questions, Ann Pharm Fr, (2023); Al-Dujaili Z., Omari S., Pillai J., Al F.A., Assessing the accuracy and consistency of ChatGPT in clinical pharmacy management: a preliminary analysis with clinical pharmacy experts worldwide, Res Soc Adm Pharm, 19, pp. 1590-1594, (2023); Morath B., Chiriac U., Jaszkowski E., Et al., Performance and risks of CHATGPT used in drug information: an exploratory real-world analysis, EJHP, (2023); Roosan D., Roosan M.R., Kim S., Law A.V., Sanine C., Applying artificial intelligence to create risk stratification visualization for underserved patients to improve population health in a community health setting, (2022); Donovan T., Abell B., Fernando M., McPhail S.M., Carter H.E., Implementation costs of hospital-based computerised decision support systems: a systematic review, Implement Sci, 18, (2023); Sallam M., ChatGPT utility in healthcare education, research, and practice: systematic review on the promising Perspectives and valid concerns, Healthcare (Basel), 11, 6, (2023)","N. Ozturk; Altinbas University, School of Pharmacy, Department of Clinical Pharmacy, Istanbul, Turkey; email: nur.ozturk@altinbas.edu.tr","","Elsevier Inc.","","","","","","18771297","","","38702261","English","Currents Pharm. Teach. Learn.","Article","Final","","Scopus","2-s2.0-85192106000"
"Bergantini L.; Pianigiani T.; d'Alessandro M.; Gangi S.; Cekorja B.; Bargagli E.; Cameli P.","Bergantini, Laura (57211345418); Pianigiani, Tommaso (57226534624); d'Alessandro, Miriana (57212431315); Gangi, Sara (57331034800); Cekorja, Behar (56441591200); Bargagli, Elena (8267285300); Cameli, Paolo (55490604400)","57211345418; 57226534624; 57212431315; 57331034800; 56441591200; 8267285300; 55490604400","The effect of anti-IL5 monoclonal antibodies on regulatory and effector T cells in severe eosinophilic asthma","2023","Biomedicine and Pharmacotherapy","166","","115385","","","","2","10.1016/j.biopha.2023.115385","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169001272&doi=10.1016%2fj.biopha.2023.115385&partnerID=40&md5=1b8b103fdaf21f32cf0c9a1a774ce86a","Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy","Bergantini L., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; Pianigiani T., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; d'Alessandro M., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; Gangi S., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; Cekorja B., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; Bargagli E., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy; Cameli P., Respiratory Disease Unit, Department of Medical Sciences, University Hospital of Siena (Azienda Ospedaliero Universitaria Senese, AOUS), Viale Bracci, Siena, 53100, Italy","Introduction: Biological treatments have redesigned the clinical management of severe eosinophilic asthmatic (SA) patients. Despite emerging evidence supporting the role of natural Killer (NK), and T regulatory cells (Treg) in the pathogenesis of asthma, no data is available on the effects of anti-IL5/IL5R therapies on these cell subsets. Methods: We prospectively enrolled fourteen SA patients treated with benralizumab (n = 7) or mepolizumab (n = 7) and compared them with healthy controls (HC) (n = 11) and mild to moderate asthmatic (MM) patients (n = 9). Clinical parameters were collected at baseline (T0) and during follow-up. Cellular analysis, including the analysis of T/NK cell subsets, was determined through multicolor flow cytometry. Results: At T0, SA patients showed higher percentages of CD4 TEM (33.3 ± 17.9 HC, 42.6 ± 16.6 MM and 66.1 ± 19.7 in SA; p < 0.0001) than HC and MM patients. With different timing, the two drugs induce a reduction of CD4 TEM ( 76 ± 19 T0; 43 ± 14 T1; 45 ± 23 T6; 62 ± 18 at T24; p < 0.0001 for mepolizumab and 55 ± 21 T0; 55 ± 22 T1; 43 ± 14 T6; 27 ± 12 at T24; p < 0.0001 for benralizumab) and an increase of Treg cells (1.2 ± 1.3 T0; 5.1 ± 2.5 T1; 6.3 ± 3.4 T6; 8.4 ± 4.6 at T24; p < 0.0001 for mepolizumab and 3.4 ± 1.7 T0; 1.9 ± 0.8 T1; 1.9 ± 1 T6; 5.1 ± 2.4 at T24; p < 0.0001 for benralizumab). The change of CD56dim PD-1+ significantly correlated with FEV1% (r = − 0.32; p < 0.01), while Treg expressing PD-1 correlates with the use of oral steroids ( r = 0.36 p = 0.0008) and ACT score (r = 0.36 p = 0.0008) p < 0.001) Conclusions: Beyond the clinical improvement, anti-IL-5 treatment induces a rebalancing of Treg and T effector cells in patients with SA © 2023 The Authors","Benralizumab; Immune checkpoints; Mepolizumab; Regulatory T cells; Severe asthma","Asthma; Flow Cytometry; Humans; Killer Cells, Natural; Programmed Cell Death 1 Receptor; T-Lymphocytes, Regulatory; benralizumab; CD4 antigen; CD56 antigen; CD8 antigen; corticosteroid; interleukin 5; mepolizumab; steroid; programmed death 1 receptor; adult; Article; Asthma Control Questionnaire; Asthma Control Test; clinical article; cohort analysis; controlled study; correlation analysis; effector cell; eosinophilic asthma; flow cytometry; fluorescence activated cell sorting; follow up; forced expiratory volume; forced vital capacity; hierarchical clustering; histogram; human; human cell; k means clustering; middle aged; natural killer cell; phenotype; principal component analysis; prospective study; regulatory T lymphocyte; scanning electron microscopy; T cell exhaustion; T lymphocyte; T lymphocyte subpopulation; unsupervised machine learning; asthma; regulatory T lymphocyte","","benralizumab, 1044511-01-4; mepolizumab, 196078-29-2; Programmed Cell Death 1 Receptor, ","","","AstraZeneca","PC served as a speaker and consultant and advisory board member for Astra Zeneca, Sanofi, Novartis, and GSK. PC, EB, MdA, and LB are investigators for current research financed by AstraZeneca (grants paid to his institution). All other authors declare no conflict of interest. ","Pianigiani T., Alderighi L., Meocci M., Messina M., Perea B., Luzzi S., Bergantini L., D'Alessandro M., Refini R.M., Bargagli E., Cameli P., Exploring the interaction between fractional exhaled nitric oxide and biologic treatment in severe asthma: a systematic review, Antioxidants, 12, (2023); Pham D.D., Lee J.-H., Kwon H.-S., Song W.-J., Cho Y.S., Kim H., Kwon J.-W., Park S.-Y., Kim S., Hur G.Y., Kim B.K., Nam Y.-H., Yang M.-S., Kim M.-Y., Kim S.-H., Lee B.-J., Lee T., Park S.Y., Kim M.-H., Cho Y.-J., Park C., Jung J.-W., Park H.K., Kim J.-H., Moon J.-Y., Adcock I., Chung K.F., Kim T.-B., Prospective direct comparison of biological treatments on severe eosinophilic asthma: findings from the PRISM study, Ann. 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Asthma Allergy, 16, pp. 541-552, (2023); Principe S., Richards L.B., Hashimoto S., Kroes J.A., Van Bragt J.J.M.H., Vijverberg S.J., Sont J.K., Scichilone N., Bieksiene K., Ten Brinke A., Csoma Z., Dahlen B., Gemicioglu B., Grisle I., Kuna P., Lazic Z., Mihaltan F., Popovic-Grle S., Skrgat S., Marcon A., Caminati M., Djukanovic R., Porsbjerg C., Maitland Van Der Zee A.-H., Characteristics of severe asthma patients on biologics: a real-life European registry study, ERJ Open Res., 9, pp. 00586-02022, (2023); Ricciardolo F.L.M., Sprio A.E., Baroso A., Gallo F., Riccardi E., Bertolini F., Carriero V., Arrigo E., Ciprandi G., 9; Bergantini L., d'Alessandro M., Cameli P., Bianchi F., Sestini P., Bargagli E., Refini R.M., Personalized approach of severe eosinophilic asthma patients treated with mepolizumab and benralizumab, IAA [Internet] Karger Publ., 181, pp. 746-753, (2020); Keir M.E., Butte M.J., Freeman G.J., Sharpe A.H., PD-1 and its ligands in tolerance and immunity, Annu. Rev. 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Bergantini; Department of Medical Sciences, Surgery and Neurosciences, Respiratory Disease and Lung Transplant Unit, Siena University, Siena, 53100, Italy; email: laurabergantini@gmail.com","","Elsevier Masson s.r.l.","","","","","","07533322","","BIPHE","37651801","English","Biomed. Pharmacother.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85169001272"
"Zhang X.; Li F.; Rajaraman P.K.; Comellas A.P.; Hoffman E.A.; Lin C.-L.","Zhang, Xuan (57841130800); Li, Frank (57222179446); Rajaraman, Prathish K. (59266216700); Comellas, Alejandro P. (6603070104); Hoffman, Eric A. (58000586800); Lin, Ching-Long (8923593300)","57841130800; 57222179446; 59266216700; 6603070104; 58000586800; 8923593300","Investigating distributions of inhaled aerosols in the lungs of post-COVID-19 clusters through a unified imaging and modeling approach","2024","European Journal of Pharmaceutical Sciences","195","","106724","","","","2","10.1016/j.ejps.2024.106724","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187197218&doi=10.1016%2fj.ejps.2024.106724&partnerID=40&md5=8948bcb299ba9678c210ddb9ea48d30c","IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, United States; Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States; Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Department of Internal Medicine, University of Iowa, Iowa City, IA, United States; Department of Radiology, University of Iowa, Iowa City, IA, United States","Zhang X., IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, United States, Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States; Li F., IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, United States, Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States; Rajaraman P.K., IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, United States, Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States; Comellas A.P., Department of Internal Medicine, University of Iowa, Iowa City, IA, United States; Hoffman E.A., Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States, Department of Radiology, University of Iowa, Iowa City, IA, United States; Lin C.-L., IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, IA, United States, Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States, Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States, Department of Radiology, University of Iowa, Iowa City, IA, United States","Background: Recent studies, based on clinical data, have identified sex and age as significant factors associated with an increased risk of long COVID. These two factors align with the two post-COVID-19 clusters identified by a deep learning algorithm in computed tomography (CT) lung scans: Cluster 1 (C1), comprising predominantly females with small airway diseases, and Cluster 2 (C2), characterized by older individuals with fibrotic-like patterns. This study aims to assess the distributions of inhaled aerosols in these clusters. Methods: 140 COVID survivors examined around 112 days post-diagnosis, along with 105 uninfected, non-smoking healthy controls, were studied. Their demographic data and CT scans at full inspiration and expiration were analyzed using a combined imaging and modeling approach. A subject-specific CT-based computational model analysis was utilized to predict airway resistance and particle deposition among C1 and C2 subjects. The cluster-specific structure and function relationships were explored. Results: In C1 subjects, distinctive features included airway narrowing, a reduced homothety ratio of daughter over parent branch diameter, and increased airway resistance. Airway resistance was concentrated in the distal region, with a higher fraction of particle deposition in the proximal airways. On the other hand, C2 subjects exhibited airway dilation, an increased homothety ratio, reduced airway resistance, and a shift of resistance concentration towards the proximal region, allowing for deeper particle penetration into the lungs. Conclusions: This study revealed unique mechanistic phenotypes of airway resistance and particle deposition in the two post-COVID-19 clusters. The implications of these findings for inhaled drug delivery effectiveness and susceptibility to air pollutants were explored. © 2024","Clusters; Computational fluid dynamics; Computed tomography; Long COVID; PASC","Administration, Inhalation; Asthma; COVID-19; Female; Humans; Lung; Male; Particle Size; Post-Acute COVID-19 Syndrome; Respiratory Aerosols and Droplets; technetium sulfur colloid tc 99m; adult; aerosol; airway resistance; Article; comparative study; computational fluid dynamics; computer assisted tomography; controlled study; demography; environmental risk; feature detection; female; follow up; human; image processing; inhalation; long COVID; lung function test; major clinical study; middle aged; particle size; single photon emission computed tomography; structure analysis; survivor; total lung capacity; x-ray computed tomography; asthma; coronavirus disease 2019; diagnostic imaging; inhalational drug administration; long COVID; lung; male; respiratory droplets and aerosols","","technetium sulfur colloid tc 99m, 51052-69-8","","","National Institutes of Health, NIH, (P116S210005, R01-HL168116, S10-RR022421, U01-HL114494); National Institutes of Health, NIH","This work was supported, in part, by NIH grants R01-HL168116, U01-HL114494, and S10-RR022421, and the ED grant P116S210005. 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"Liu H.; Huang S.; Yang L.; Zhou H.; Chen B.; Wu L.; Zhang L.","Liu, Hua (58866081100); Huang, Siting (58750365100); Yang, Liting (55872577300); Zhou, Hongshu (57575558700); Chen, Bo (57471619900); Wu, Lisha (55655939900); Zhang, Liyang (57199836581)","58866081100; 58750365100; 55872577300; 57575558700; 57471619900; 55655939900; 57199836581","Conventional dendritic cell 2 links the genetic causal association from allergic asthma to COVID-19: a Mendelian randomization and transcriptomic study","2024","Journal of Big Data","11","1","","","","","2","10.1186/s40537-024-00881-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184236874&doi=10.1186%2fs40537-024-00881-1&partnerID=40&md5=982a18298520fc5a65cb8bdfa3bf78c0","Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., No.266, Fenghe North Road, Jiangxi, Nanchang, 330038, China; Hypothalamic Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; Division of Neurosurgery, Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong; Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, Changsha, China","Liu H., Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; Huang S., Hypothalamic Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; Yang L., Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., No.266, Fenghe North Road, Jiangxi, Nanchang, 330038, China, Hypothalamic Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, Changsha, China; Zhou H., Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., No.266, Fenghe North Road, Jiangxi, Nanchang, 330038, China, Hypothalamic Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, Changsha, China; Chen B., Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., No.266, Fenghe North Road, Jiangxi, Nanchang, 330038, China, Division of Neurosurgery, Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Road, Hong Kong; Wu L., Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China; Zhang L., Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., No.266, Fenghe North Road, Jiangxi, Nanchang, 330038, China, Hypothalamic Pituitary Research Center, Xiangya Hospital, Central South University, 87 Xiangya Road, Hunan, Changsha, 410008, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Hunan, Changsha, China","Recent evidence suggests that allergic asthma (AA) decreases the risk of Coronavirus Disease 2019 (COVID-19). However, the reasons remain unclear. Here, we systematically explored data from GWAS (18 cohorts with 11,071,744 samples), bulk transcriptomes (3 cohorts with 601 samples), and single-cell transcriptomes (2 cohorts with 29 samples) to reveal the immune mechanisms that connect AA and COVID-19. Two-sample Mendelian randomization (MR) analysis identified a negative causal correlation from AA to COVID-19 hospitalization (OR = 0.968, 95% CI 0.940–0.997, P = 0.031). This correlation was bridged through white cell count. Furthermore, machine learning identified dendritic cells (DCs) as the most discriminative immunocytes in AA and COVID-19. Among five DC subtypes, only conventional dendritic cell 2 (cDC2) exhibited differential expression between AA/COVID-19 and controls (P < 0.05). Subsequently, energy metabolism, intercellular communication, cellular stemness and differentiation, and molecular docking analyses were performed. cDC2s exhibited more differentiation, increased numbers, and enhanced activation in AA exacerbation, while they showed less differentiation, reduced number, and enhanced activation in severe COVID-19. The capacity of cDC2 for differentiation and SARS-CoV-2 antigen presentation may be enhanced through ZBTB46, EXOC4, TLR1, and TNFSF4 gene mutations in AA. Taken together, cDC2 links the genetic causality from AA to COVID-19. Future strategies for COVID-19 prevention, intervention, and treatment could be stratified according to AA and guided with DC-based therapies. Graphical Abstract: (Figure presented.) © The Author(s) 2024.","Allergic asthma (AA); Antigen presentation; Causal effect; Conventional dendritic cell 2 (cDC2); Coronavirus disease 2019 (COVID-19); Differentiation; Mendelian randomization (MR); Transcriptomic analyses","Antigens; Cells; Chemical activation; Cytology; Metabolism; Random processes; Allergic asthma; Antigen presentation; Causal effect; Conventional dendritic cell 2; Coronavirus disease 2019; Dendritics; Differentiation; Mendelian randomization; Randomisation; Transcriptomic analyze; Transcriptomics; COVID-19","","","","","FinnGen; GWAS Catalog","The authors thank all investigators and participants from the COVID-19 hg, FinnGen, GWAS Catalog, IEU-OpenGWAS project, and GEO datasets, which were used in this study.","Cao G., Guo Z., Liu J., Liu M., Change from low to out-of-season epidemics of influenza in China during the COVID-19 pandemic: a time series study, J Med Virol, 95, 6, (2023); Lee J., Lee K.O., Online listing data and their interaction with market dynamics: evidence from Singapore during COVID-19, J Big Data, 10, 1, (2023); 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Wu Y., Yang S., Ma J., Et al., Spatiotemporal immune landscape of colorectal cancer liver metastasis at single-cell level, Cancer Discov, 12, 1, pp. 134-153, (2022); Jin S., Guerrero-Juarez C.F., Zhang L., Et al., Inference and analysis of cell-cell communication using Cell Chat, Nat Commun, 12, 1, (2021); Cao J., Spielmann M., Qiu X., Et al., The single-cell transcriptional landscape of mammalian organogenesis, Nature, 566, 7745, pp. 496-502, (2019); Gulati G.S., Sikandar S.S., Wesche D.J., Et al., Single-cell transcriptional diversity is a hallmark of developmental potential, Science, 367, 6476, pp. 405-411, (2020); Higgins J.P., Thompson S.G., Deeks J.J., Altman D.G., Measuring inconsistency in meta-analyses, BMJ, 327, 7414, pp. 557-560, (2003); Bowden J., Del Greco M.F., Minelli C., Davey Smith G., Sheehan N., Thompson J., A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization, Stat Med, 36, 11, pp. 1783-1802, (2017); Jasim S.A., Mahdi R.S., Bokov D.O., Et al., The deciphering of the immune cells and marker signature in COVID-19 pathogenesis: an update, J Med Virol, 94, 11, pp. 5128-5148, (2022); Morianos I., Semitekolou M., Dendritic cells: critical regulators of allergic asthma, Int J Mol Sci, (2020); El-Gammal A., Oliveria J.P., Howie K., Et al., Allergen-induced changes in bone marrow and airway dendritic cells in subjects with asthma, Am J Respir Crit Care Med, 194, 2, pp. 169-177, (2016); Shao T., Ji J.F., Zheng J.Y., Et al., Zbtb46 controls dendritic cell activation by reprogramming epigenetic regulation of cd80/86 and cd40 costimulatory signals in a zebrafish model, J Immunol, 208, 12, pp. 2686-2701, (2022); Satpathy A.T., Brown R.A., Gomulia E., Et al., Expression of the transcription factor ZBTB46 distinguishes human histiocytic disorders of classical dendritic cell origin, Mod Pathol, 31, 9, pp. 1479-1486, (2018); Satpathy A.T., Kc W., Albring J.C., Et al., Zbtb46 expression distinguishes classical dendritic cells and their committed progenitors from other immune lineages, J Exp Med, 209, 6, pp. 1135-1152, (2012); Wang J., Wang T., Benedicenti O., Et al., Characterisation of ZBTB46 and DC-SCRIPT/ZNF366 in rainbow trout, transcription factors potentially involved in dendritic cell maturation and activation in fish, Dev Comp Immunol, 80, pp. 2-14, (2018); Duan T., Du Y., Xing C., Wang H.Y., Wang R.F., Toll-like receptor signaling and its role in cell-mediated immunity, Front Immunol, 13, (2022); Koponen P., Vuononvirta J., Nuolivirta K., Helminen M., He Q., Korppi M., The association of genetic variants in toll-like receptor 2 subfamily with allergy and asthma after hospitalization for bronchiolitis in infancy, Pediatr Infect Dis J, 33, 5, pp. 463-466, (2014); van der Sluis R.M., Cham L.B., Gris-Oliver A., Et al., TLR2 and TLR7 mediate distinct immunopathological and antiviral plasmacytoid dendritic cell responses to SARS-CoV-2 infection, EMBO J, 41, 10, (2022); Fu N., Xie F., Sun Z., Wang Q., The OX40/OX40L axis regulates T follicular helper cell differentiation: implications for autoimmune diseases, Front Immunol, 12, (2021); Liu Y., Ke X., Kang H.Y., Wang X.Q., Shen Y., Hong S.L., Genetic risk of TNFSF4 and FAM167A-BLK polymorphisms in children with asthma and allergic rhinitis in a Han Chinese population, J Asthma, 53, 6, pp. 567-575, (2016); Kaur D., Brightling C., OX40/OX40 ligand interactions in T-cell regulation and asthma, Chest, 141, 2, pp. 494-499, (2012); Ming S., Zhang M., Liang Z., Et al., OX40L/OX40 signal promotes IL-9 production by mucosal MAIT cells during Helicobacter pylori infection, Front Immunol, 12, (2021)","B. Chen; Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., Nanchang, No.266, Fenghe North Road, Jiangxi, 330038, China; email: bochen1997@connect.hku.hk; L. Zhang; Department of Neurosurgery, Xiangya Hospital, Central South University, Jiangxi Hospital., Nanchang, No.266, Fenghe North Road, Jiangxi, 330038, China; email: zhangliyang@csu.edu.cn; L. Wu; Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, 87 Xiangya Road, Hunan, 410008, China; email: lisaent@csu.edu.cn","","Springer Nature","","","","","","21961115","","","","English","J. Big Data","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85184236874"
"Yang X.; Zhang Y.; Hu F.; Deng Z.; Zhang X.","Yang, Xiaolian (57985486200); Zhang, Yin (56298640900); Hu, Fang (36441087400); Deng, Ziyi (58803881300); Zhang, Xiong (57222230445)","57985486200; 56298640900; 36441087400; 58803881300; 57222230445","Feature aggregation-based multi-relational knowledge reasoning for COPD intelligent diagnosis","2024","Computers and Electrical Engineering","114","","109068","","","","2","10.1016/j.compeleceng.2023.109068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181910779&doi=10.1016%2fj.compeleceng.2023.109068&partnerID=40&md5=89c4440591711c5138a08d78319eef82","College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China; Department of Mathematics and Statistics, University of West Florida, Pensacola, 32514, United States; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Department of Geriatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430060, China","Yang X., College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China, Department of Mathematics and Statistics, University of West Florida, Pensacola, 32514, United States; Zhang Y., School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Hu F., College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China, Department of Mathematics and Statistics, University of West Florida, Pensacola, 32514, United States; Deng Z., College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China; Zhang X., Department of Geriatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430060, China","The increasing prevalence of artificial intelligence-based knowledge reasoning has contributed to more accurate and efficient auxiliary diagnoses. However, a majority of the disease prediction methods concentrate on the symptoms themselves while discarding the inherent properties of symptoms and the relationships underlying them. This paper proposes a feature aggregation-based intelligent diagnosis model employing a Heterogeneous Graph Convolutional Network (GCN), termed HeteroGCN. It focuses on symptoms’ inherent properties and multiple hidden relationships among symptoms and properties. By aggregating features of nodes, it realizes effective and accurate symptom-based knowledge reasoning for disease-type prediction. The diagnosis-related information from the Electronic Medical Record (EMR) has been extracted and standardized by taking chronic obstructive pulmonary disease (COPD) as an instance. Then the presented model extracts the symptoms and their properties as nodes and the relationships underlying the nodes as edges to construct a heterogeneous graph. The adjacency matrix and feature matrix have been fused and taken as the input of this model, and then the node representations (embeddings) are generated by aggregating neighbor nodes’ information. Finally, specific disease types (syndromes) will be predicted by the generated symptom node embeddings. The results of the model comparison and parameter sensitivity test demonstrate that the presented HeteroGCN model performs best on disease-type prediction. This paper provides a novel feature aggregation-based multi-relational knowledge reasoning approach for disease type (syndrome) prediction, which holds great significance in improving disease diagnosis. © 2023 Elsevier Ltd","Disease type-prediction; Feature aggregation; Heterogeneous graph convolutional network; Intelligent diagnosis; Multi-relational knowledge reasoning","Convolution; Embeddings; Forecasting; Medical computing; Pulmonary diseases; Convolutional networks; Disease type-prediction; Feature aggregation; Heterogeneous graph; Heterogeneous graph convolutional network; Intelligent diagnosis; Knowledge reasoning; Multi-relational knowledge reasoning; Property; Type predictions; Diagnosis","","","","","Hubei Provincial Department of Education, (D20212002); Hubei Provincial Department of Education","We acknowledge the funding support from the Key Research Project of the Hubei Provincial Department of Education under Grant D20212002 .","Leong P., Macdonald M.I., Ko B.S., Bardin P.G., Coexisting chronic obstructive pulmonary disease and cardiovascular disease in clinical practice: a diagnostic and therapeutic challenge, Med J Aust, 210, 9, pp. 417-423, (2019); 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Yang B., Kang Y., Zhang L., Li H., GGAC: Multi-relational image gated GCN with attention convolutional binary neural tree for identifying disease with chest X-rays, Pattern Recognit, 120, (2021); Song X., Li J., Qian X., Diagnosis of glioblastoma multiforme progression via interpretable structure-constrained graph neural networks, IEEE Trans Med Imaging, 42, 2, pp. 380-390, (2022); Zheng H., Hu Y., Dong L., Shu Q., Zhu M., Li Y., Chen C., Gao H., Yang L., Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine, J Clin Lab Anal, 35, 2, (2021); Haider N.S., Singh B.K., Periyasamy R., Behera A.K., Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm, J Med Syst, 43, 255, pp. 1-13, (2019); Tang L.Y., Coxson H.O., Lam S., Leipsic J., Tam R.C., Sin D.D., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, 5, pp. e259-e267, (2020); Zhao Q., Li J., Zhao L., Zhu Z., Knowledge guided feature aggregation for the prediction of chronic obstructive pulmonary disease with Chinese EMRs, IEEE/ACM Trans Comput Biol Bioinform, pp. 1-10, (2022); Melese E.A., Nabaasa E., Wondemagegn M.T., Yonasi S., Negasa G.M., Deep learning based algorithms for detecting chronic obstructive pulmonary disease, 2022 IST-Africa conference (IST-Africa), pp. 1-12, (2022); Wu Y., Zhao S., Qi S., Feng J., Pang H., Chang R., Bai L., Li M., Xia S., Qian W., Et al., Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images, Artif Intell Med, 143, (2023); Yao N., Zhu J., Gao R., Identification and diagnosis of chinese medicine syptoms, (2004); Wu J., Chinese terms in traditional chinese medicine and pharmacy, (2004); Velickovic P., Cucurull G., Casanova A., Romero A., Lio P., Bengio Y., Et al., Graph attention networks, Stat, 1050, 20, pp. 10-48550, (2017)","F. Hu; College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China; email: naomifang@hbtcm.edu.cn","","Elsevier Ltd","","","","","","00457906","","CPEEB","","English","Comput Electr Eng","Article","Final","","Scopus","2-s2.0-85181910779"
"Earla J.R.; Li J.; Hutton G.J.; Johnson M.L.; Aparasu R.R.","Earla, Jagadeswara Rao (55857488700); Li, Jieni (57221711466); Hutton, George J. (7003880723); Johnson, Michael L. (56403706300); Aparasu, Rajender R. (6604074153)","55857488700; 57221711466; 7003880723; 56403706300; 6604074153","Comparative adherence trajectories of oral disease-modifying agents in multiple sclerosis","2023","Pharmacotherapy","43","6","","473","484","11","2","10.1002/phar.2810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159922093&doi=10.1002%2fphar.2810&partnerID=40&md5=5cb808ce17269c9249990791341951f1","Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, United States; Baylor College of Medicine, Houston, TX, United States","Earla J.R., Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, United States; Li J., Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, United States; Hutton G.J., Baylor College of Medicine, Houston, TX, United States; Johnson M.L., Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, United States; Aparasu R.R., Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, Houston, TX, United States","Study Objective: This study compared the adherence trajectories of fingolimod (FIN), teriflunomide (TER), and dimethyl fumarate (DMF) users with multiple sclerosis (MS) as there is limited evidence regarding the comparative adherence patterns of different oral disease-modifying agents (DMAs). Design: A retrospective cohort study. Data Source: 2015–2019 IBM MarketScan Commercial Claims Database. Patients: Adults (≥18 years) with MS (International Classification of Diseases [ICD]-9/10-Clinical Modification [CM]:340/G35) diagnosis and ≥1 DMA prescription. Intervention: Incident FIN-, TER-, or DMF use based on the index DMA with 1 year of washout period. Measurements: The DMA adherence trajectories based on the proportion of days covered (PDC) were examined using the Group-Based Trajectory Modeling (GBTM) one year after the treatment initiation. Generalized boosting models (GBM)-based inverse probability treatment weights (IPTW) were incorporated in multinomial logistic regression to assess the comparative adherence trajectories across oral DMAs with FIN group as a reference category. Measurements and Main Results: The study cohort consisted of 1913 patients with MS who were initiated with FIN (24.2%, n = 462), TER (24.0%, n = 458), and DMF (51.9%, n = 993) during 2016–2018. The adherence rate (PDC ≥ 0.8) among FIN, TER, and DMF users was found to be 70.8% (n = 327), 59.6% (n = 273), and 61.0% (n = 606), respectively. The GBTM grouped patients into three adherence trajectories: Complete Adherers—59.1%, Slow Decliners—22.6%, and Rapid Discontinuers—18.3%. The multinomial logistic regression model involving GBM-based IPTW revealed that DMF (adjusted odds ratio [aOR]: 2.32, 95% confidence interval [CI]:1.57–3.42) and TER (aOR: 2.50, 95% CI: 1.62–3.88) users had higher odds to be rapid discontinuers relative to FIN users. In addition, TER users were more likely (aOR: 1.50, 95% CI: 1.06–2.13) to be slow decliners compared with FIN users. Conclusion: Teriflunomide and DMF were associated with poorer adherence trajectories than FIN. More research is needed to evaluate the clinical implications of these adherence trajectories of oral DMAs to optimize the management of MS. © 2023 Pharmacotherapy Publications, Inc.","adherence; multiple sclerosis; real-world evidence; treatment pattern","Adult; Crotonates; Dimethyl Fumarate; Fingolimod Hydrochloride; Humans; Immunosuppressive Agents; Medication Adherence; Multiple Sclerosis; Retrospective Studies; analgesic agent; anticonvulsive agent; antidepressant agent; dimethyl fumarate; fingolimod; teriflunomide; crotonic acid derivative; dimethyl fumarate; fingolimod; immunosuppressive agent; teriflunomide; adult; algorithm; anxiety; arthropathy; Article; asthma; cerebrovascular disease; chronic lung disease; chronic obstructive lung disease; cohort analysis; connective tissue disease; convulsion; demographics; diabetes mellitus; disease predisposition; ear disease; employment status; epilepsy; eye disease; female; fibromyalgia; follow up; gastrointestinal disease; headache; health care utilization; health insurance; heart disease; human; hypertension; ICD-10; ICD-9; liver disease; machine learning; major clinical study; male; maximum likelihood method; mental disease; mood disorder; mouth disease; multinomial logistic regression; multiple sclerosis; musculoskeletal disease; nutritional deficiency; observational study; paralysis; patient compliance; prescription; prevalence; probability; propensity score; respiratory tract disease; respiratory tract infection; retrospective study; sample size; sensitivity analysis; spondylosis; thyroid disease; urinary tract disease; urogenital tract disease; medication compliance","","dimethyl fumarate, 624-49-7; fingolimod, 162359-56-0, 162359-55-9, 1967800-35-6; teriflunomide, 108605-62-5, 282716-73-8, 163451-81-8; Crotonates, ; Dimethyl Fumarate, ; Fingolimod Hydrochloride, ; Immunosuppressive Agents, ; teriflunomide, ","","","Agency for Healthcare Research and Quality, AHRQ, (R03HS028502); Agency for Healthcare Research and Quality, AHRQ","This study was supported by a grant from the Agency for Healthcare Research and Quality AHRQ (Grant R03HS028502: Principal Investigator: Rajender R. Aparasu). The funding agency had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. ","Comi G., Radaelli M., Soelberg S.P., Evolving concepts in the treatment of relapsing multiple sclerosis, Lancet, 389, 10076, pp. 1347-1356, (2017); English C., Aloi J.J., New FDA-approved disease-modifying therapies for multiple sclerosis, Clin Ther, 37, 4, pp. 691-715, (2015); Jarvinen E., Holmberg M., Sumelahti M.L., Injectable disease modifying agents in multiple sclerosis: pattern of medication use and clinical effectiveness, Neurol Int, 8, 3, (2016); Scolding N., Barnes D., Cader S., Et al., Association of British Neurologists: revised (2015) guidelines for prescribing disease-modifying treatments in multiple sclerosis, Pract Neurol, 15, 4, pp. 273-279, (2015); Earla J.R., Paranjpe R., Kachru N., Hutton G.J., Aparasu R.R., Use of disease modifying agents in patients with multiple sclerosis: analysis of ten years of national data, Res Social Adm Pharm, 16, 12, pp. 1670-1676, (2020); Earla J.R., Hutton G.J., Thornton J.D., Aparasu R.R., Factors associated with prescribing Oral disease modifying agents in multiple sclerosis: a real-world analysis of electronic medical records, Mult Scler Relat Disord, 45, (2020); Elsisi Z., Hincapie A.L., Guo J.J., Expenditure, utilization, and cost of specialty drugs for multiple sclerosis in the US Medicaid population, 2008-2018, Am Health Drug Benefits, 13, 2, pp. 74-84, (2020); Erbay O., Usta Yesilbalkan O., Yuceyar N., Factors affecting the adherence to disease-modifying therapy in patients with multiple sclerosis, J Neurosci Nurs, 50, 5, pp. 291-297, (2018); Remington G., Rodriguez Y., Logan D., Williamson C., Treadaway K., Facilitating medication adherence in patients with multiple sclerosis, Int J MS Care, 15, 1, pp. 36-45, (2013); Williams M.J., Johnson K., Trenz H.M., Et al., Adherence, persistence, and discontinuation among Hispanic and African American patients with multiple sclerosis treated with fingolimod or glatiramer acetate, Curr Med Res Opin, 34, 1, pp. 107-115, (2018); Burks J., Marshall T., Ye X., Adherence to disease-modifying therapies and its impact on relapse, health resource utilization, and costs among patients with multiple sclerosis, Clinicoecon Outcomes Res, 9, pp. 251-260, (2017); Menzin J., Caon C., Nichols C., White L.A., Friedman M., Pill M.W., Narrative review of the literature on adherence to disease-modifying therapies among patients with multiple sclerosis, J Manag Care Pharm, 19, 1 Supp A, pp. S24-S40, (2013); Yoon E.L., Cheong W.L., Adherence to oral disease-modifying therapy in multiple sclerosis patients: a systematic review, Mult Scler Relat Disord, 28, pp. 104-108, (2019); Higuera L., Carlin C.S., Anderson S., Adherence to disease-modifying therapies for multiple sclerosis, J Manag Care Spec Pharm, 22, 12, pp. 1394-1401, (2016); Longbrake E.E., Cross A.H., Salter A., Efficacy and tolerability of oral versus injectable disease-modifying therapies for multiple sclerosis in clinical practice, Mult Scler J Exp Transl Clin, 2, (2016); Hao J., Pitcavage J., Jones J.B., Hoegerl C., Graham J., Measuring adherence and outcomes in the treatment of patients with multiple sclerosis, J Osteopath Med, 117, 12, pp. 737-747, (2017); Agashivala N., Wu N., Abouzaid S., Et al., Compliance to fingolimod and other disease modifying treatments in multiple sclerosis patients, a retrospective cohort study, BMC Neurol, 13, 1, (2013); Bergvall N., Petrilla A.A., Karkare S.U., Et al., Persistence with and adherence to fingolimod compared with other disease-modifying therapies for the treatment of multiple sclerosis: a retrospective US claims database analysis, J Med Econ, 17, 10, pp. 696-707, (2014); Earla J.R., Hutton G.J., Thornton J.D., Chen H., Johnson M.L., Aparasu R.R., Comparative adherence trajectories of Oral Fingolimod and injectable disease modifying agents in multiple sclerosis, Patient Prefer Adherence, 14, pp. 2187-2199, (2020); Franklin J.M., Shrank W.H., Pakes J., Et al., Group-based trajectory models, Med Care, 51, 9, pp. 789-796, (2013); Franklin J.M., Krumme A.A., Tong A.Y., Et al., Association between trajectories of statin adherence and subsequent cardiovascular events, Pharmacoepidemiol Drug Saf, 24, 10, pp. 1105-1113, (2015); Nicholas J.A., Edwards N.C., Edwards R.A., Dellarole A., Grosso M., Phillips A.L., Real-world adherence to, and persistence with, once- and twice-daily oral disease-modifying drugs in patients with multiple sclerosis: a systematic review and meta-analysis, BMC Neurol, 20, 1, (2020); Hansen L., The Truven Health MarketScan Databases for Life Sciences Researchers, (2017); Culpepper W.J., Marrie R.A., Langer-Gould A., Et al., Validation of an algorithm for identifying MS cases in administrative health claims datasets, Neurology, 92, 10, pp. e1016-e1028, (2019); Andersen R.M., Revisiting the behavioral model and access to medical care: does it matter?, J health Soc Behav, 36, pp. 1-10, (1995); Pednekar P.P., Agh T., Malmenas M., Et al., Methods for measuring multiple medication adherence: a systematic review–report of the ISPOR medication adherence and persistence special interest group, Value Health, 22, 2, pp. 139-156, (2019); Martin B.C., Wiley-Exley E.K., Richards S., Domino M.E., Carey T.S., Sleath B.L., Contrasting measures of adherence with simple drug use, medication switching, and therapeutic duplication, Ann Pharmacother, 43, 1, pp. 36-44, (2009); Hess L.M., Raebel M.A., Conner D.A., Malone D.C., Measurement of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures, Ann Pharmacother, 40, 7-8, pp. 1280-1288, (2006); Nagin D.S., Odgers C.L., Group-based trajectory modeling in clinical research, Annu Rev Clin Psychol, 6, 1, pp. 109-138, (2010); Nagin D.S., Group-based trajectory modeling: an overview, Ann Nutr Metab, 65, 2-3, pp. 205-210, (2014); Rosenbaum P.R., Rubin D.B., The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 1, pp. 41-55, (1983); McCaffrey D.F., Griffin B.A., Almirall D., Slaughter M.E., Ramchand R., Burgette L.F., A tutorial on propensity score estimation for multiple treatments using generalized boosted models, Stat Med, 32, 19, pp. 3388-3414, (2013); Brookhart M.A., Wyss R., Layton J.B., Sturmer T., Propensity score methods for confounding control in nonexperimental research, Circ Cardiovasc Qual Outcomes, 6, 5, pp. 604-611, (2013); Austin P.C., Stuart E.A., Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies, Stat Med, 34, 28, pp. 3661-3679, (2015); Johnson K.M., Zhou H., Lin F., Ko J.J., Herrera V., Real-world adherence and persistence to Oral disease-modifying therapies in multiple sclerosis patients over 1 year, J Manag Care Spec Pharm, 23, 8, pp. 844-852, (2017); Tan H., Cai Q., Agarwal S., Stephenson J.J., Kamat S., Impact of adherence to disease-modifying therapies on clinical and economic outcomes among patients with multiple sclerosis, Adv Ther, 28, 1, pp. 51-61, (2011); Ontaneda D., Nicholas J., Carraro M., Et al., Comparative effectiveness of dimethyl fumarate versus fingolimod and teriflunomide among MS patients switching from first-generation platform therapies in the US, Mult Scler Relat Disord, 27, pp. 101-111, (2019); Nicholas J., Ontaneda D., Carraro M., Et al., Development of an algorithm to identify multiple sclerosis (MS) disease severity based on healthcare costs in a US administrative claims database (P2.052), Neurology, 88, 16, (2017)","R.R. Aparasu; University of Houston College of Pharmacy, Houston, 4349 Martin Luther King Boulevard, Health 2-Office 4052, 77204, United States; email: rraparasu@uh.edu","","American College of Clinical Pharmacy","","","","","","02770008","","PHPYD","37157135","English","Pharmacotherapy","Article","Final","","Scopus","2-s2.0-85159922093"
"Wang T.; Keil A.P.; Buse J.B.; Keet C.; Kim S.; Wyss R.; Pate V.; Jonsson-Funk M.; Pratley R.E.; Kvist K.; Kosorok M.R.; Sturmer T.","Wang, Tiansheng (55846578800); Keil, Alexander P. (36598339900); Buse, John B. (57169892000); Keet, Corinne (6506518834); Kim, Siyeon (59104440900); Wyss, Richard (56375578000); Pate, Virginia (35488860600); Jonsson-Funk, Michele (56976884500); Pratley, Richard E. (7007080135); Kvist, Kajsa (57217850221); Kosorok, Michael R. (7003879907); Sturmer, Til (7004598109)","55846578800; 36598339900; 57169892000; 6506518834; 59104440900; 56375578000; 35488860600; 56976884500; 7007080135; 57217850221; 7003879907; 7004598109","Glucagon-like Peptide 1 Receptor Agonists and Asthma Exacerbations: Which Patients Benefit Most?","2024","Annals of the American Thoracic Society","21","11","","1496","1506","10","2","10.1513/AnnalsATS.202309-836OC","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208449949&doi=10.1513%2fAnnalsATS.202309-836OC&partnerID=40&md5=5af3f3b3c1a7292b0ce1076c7855e7d9","Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States; Department of Medicine, University of North Carolina, Chapel Hill, NC, United States; Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, United States; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA, United States; AdventHealth Translational Research Institute, Orlando, FL, United States; Novo Nordisk A/S, Copenhagen, Denmark","Wang T., Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States; Keil A.P., Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States; Buse J.B., Department of Medicine, University of North Carolina, Chapel Hill, NC, United States; Keet C., Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, United States; Kim S., Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States; Wyss R., Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Boston, MA, United States; Pate V., Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States; Jonsson-Funk M., Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States; Pratley R.E., AdventHealth Translational Research Institute, Orlando, FL, United States; Kvist K., Novo Nordisk A/S, Copenhagen, Denmark; Kosorok M.R., Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States; Sturmer T., Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States","Rationale: Although recent evidence suggested that glucagon-like peptide 1 receptor agonists (GLP1RAs) might reduce the risk of asthma exacerbations, it remains unclear which subpopulations might derive the most benefit from GLP1RA treatment. Objectives: To identify characteristics of patients with asthma that predict who might benefit the most from GLP1RA treatment using real-world data. Methods: We implemented an active-comparator, new-user design analysis using commercially ensured patients 18–65 years of age from MarketScan data for 2007–2019 and identified two cohorts: GLP1RAs versus thiazolidinediones and GLP1RAs versus sulfonylureas. The outcome was acute exacerbation of asthma (hospital admission or emergency department visit for asthma) within 180 days after initiation. We applied iterative causal forest, a novel causal machine learning subgrouping algorithm, to assess heterogeneous treatment effects. In identified subgroups, we predicted propensity score, conducted propensity score trimming, and then estimated adjusted risk differences for the effect of GLP1RAs relative to comparators on asthma exacerbation using inverse probability treatment weighting in the propensity score–trimmed subpopulation. Results: Among 10,989 patients initiating GLP1RAs or thiazolidinediones and 17,088 patients initiating GLP1RAs versus sulfonylurea, GLP1RA initiators had fewer exacerbations, with adjusted risk differences of 20.5% (95% confidence interval [CI], 21.1% to 0.1%) and 21.6% (95% CI, 22.2% to 21.1%), respectively. In the GLP1RA versus sulfonylurea cohort, in which we observed a beneficial effect, our iterative causal forest analysis identified five subgroups with different treatment effects, defined by the number of emergency department visits, the number of prescriptions for short-acting b2-agonists, the number of prescriptions for inhaled steroids and long-acting b-agonists (either combination therapy or concurrent use), and age > 50 years. Among these, patients with two or more emergency department visits during the 12-month baseline period had the largest absolute exacerbation risk reduction, with a decrease of 2.8% for GLP1RAs (95% CI, 24.8% to 20.9%). Conclusions: GLP1RAs demonstrated a beneficial effect on reducing asthma exacerbation relative to sulfonylureas. Patients with asthma with two or more emergency department visits (a proxy for disease severity) benefit most from GLP1RAs. Emergency department visit frequency, the number of maintenance and reliever inhalers, and age might help individualize prediction of the short-term benefit of GLP1RAs on asthma exacerbation Copyright © 2024 by the American Thoracic Society.","asthma exacerbation; GLP1 receptor agonist; heterogeneous treatment effect; iterative causal forest; real-world data","Adolescent; Adult; Aged; Asthma; Diabetes Mellitus, Type 2; Disease Progression; Female; Glucagon-Like Peptide-1 Receptor; Humans; Hypoglycemic Agents; Male; Middle Aged; Sulfonylurea Compounds; Young Adult; 2,4 thiazolidinedione derivative; acetylsalicylic acid; beta 2 adrenergic receptor stimulating agent; calcium channel blocking agent; corticosteroid; estrogen; glucagon like peptide 1 receptor agonist; insulin; ipratropium bromide; ipratropium bromide plus salbutamol; long acting drug; loop diuretic agent; oral contraceptive agent; short acting drug; sodium glucose cotransporter 2 inhibitor; sulfonylurea derivative; antidiabetic agent; glucagon like peptide 1 receptor; sulfonylurea derivative; adult; Article; asthma; attributable risk; cohort analysis; controlled study; diabetic complication; disease exacerbation; emergency department visit; female; hospital admission; human; learning algorithm; machine learning; major clinical study; male; middle aged; non insulin dependent diabetes mellitus; prediction; prescription; propensity score; risk reduction; treatment effect heterogeneity; adolescent; aged; asthma; drug therapy; non insulin dependent diabetes mellitus; young adult","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; insulin, 9004-10-8; ipratropium bromide, 22254-24-6, 66985-17-9; Glucagon-Like Peptide-1 Receptor, ; Hypoglycemic Agents, ; Sulfonylurea Compounds, ","aspirin","","University of North Carolina at Chapel Hill, UNC-CH","Supported by the University of North Carolina at Chapel Hill\u2019s Dissertation Completion Fellowship for academic year 2021\u20132022, American Diabetes Association grant 4-22-PDFPM-06, and National Institute on Aging grant R01AG056479. Certain data used in this study were supplied by International Business Machines Corporation. Any analysis, interpretation, or conclusion based on these data is solely that of the authors and not International Business Machines Corporation.","Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, Freeman R, Et al., Summary of revisions: standards of medical care in diabetes—2022, Diabetes Care, 45, pp. S4-S7, (2022); McKeever TM, Weston PJ, Hubbard R, Fogarty A., Lung function and glucose metabolism: an analysis of data from the Third National Health and Nutrition Examination Survey, Am J Epidemiol, 161, pp. 546-556, (2005); Davis WA, Knuiman M, Kendall P, Grange V, Davis TM, Glycemic exposure is associated with reduced pulmonary function in type 2 diabetes: the Fremantle Diabetes Study, Diabetes Care, 27, pp. 752-757, (2004); Ehrlich SF, Quesenberry CP, Van Den Eeden SK, Shan J, Ferrara A., Patients diagnosed with diabetes are at increased risk for asthma, COPD, pulmonary fibrosis, and pneumonia but not lung cancer, Diabetes Care, 33, pp. 55-60, (2010); Movahed MR, Hashemzadeh M, Jamal MM., Increased prevalence of asthma in patients with type II diabetes mellitus, Chest, 130, (2006); 9. 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Glucagon-like peptide 1 receptor agonists and chronic lower respiratory disease exacerbations among patients with type 2 diabetes, Diabetes Care, 44, pp. e165-e166, (2021)","T. Wang; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, 2101-B McGavran-Greenberg Hall, CB #7435, 27599, United States; email: tianwang@unc.edu","","American Thoracic Society","","","","","","23296933","","","39012183","English","Ann. Am. Thorac. Soc.","Article","Final","","Scopus","2-s2.0-85208449949"
"Zeng S.; Arjomandi M.; Luo G.","Zeng, Siyang (57193919325); Arjomandi, Mehrdad (10045533200); Luo, Gang (7401536289)","57193919325; 10045533200; 7401536289","Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study","2022","JMIR Medical Informatics","10","2","e33043","","","","3","10.2196/33043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126443234&doi=10.2196%2f33043&partnerID=40&md5=30ba738330cc5ccd650bdbccc2cc6f65","Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States; Department of Medicine, University of California, San Francisco, CA, United States","Zeng S., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Arjomandi M., Medical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States, Department of Medicine, University of California, San Francisco, CA, United States; Luo G., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Background: Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction. Objective: This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations. Methods: The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model's predictions and suggest tailored interventions. Results: Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months. Conclusions: Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model. © 2022 JMIR Publications Inc. All right reserved.","chronic obstructive pulmonary disease; forecasting; machine learning; patient care management","","","","","","California Tobacco-Related Disease Research Program, (T29IR0715); National Institutes of Health, NIH, (R01HL142503); National Heart, Lung, and Blood Institute, NHLBI; U.S. National Library of Medicine, NLM, (T15LM007442); Flight Attendant Medical Research Institute, FAMRI, (CIA190001)","GL and SZ were partially supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R01HL142503. SZ was also partially supported by the National Library of Medicine Training Grant under award number T15LM007442. MA was partially supported by grants from the Flight Attendant Medical Research Institute (CIA190001) and the California Tobacco-Related Disease Research Program (T29IR0715). 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Luo G., A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling, Glob Transit, 1, pp. 61-82, (2019); Weerts HJ, van Ipenburg W, Pechenizkiy M., A human-grounded evaluation of SHAP for alert processing, (2019); Stites MC, Nyre-Yu M, Moss B, Smutz C, Smith MR., Sage advice?. The impacts of explanations for machine learning models on human decision-making in spam detection, Proceedings of the Second International Conference on Artificial Intelligence in HCI, pp. 269-284, (2021); Lai V, Tan C., On human predictions with explanations and predictions of machine learning models: a case study on deception detection, Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 29-38, (2019); Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Et al., Explainable machine-learning predictions for the prevention of hypoxaemia during surgery, Nat Biomed Eng, 2, 10, pp. 749-760, (2018); Jesus SM, Belem C, Balayan V, Bento J, Saleiro P, Bizarro P, Et al., How can I choose an explainer?. An application-grounded evaluation of post-hoc explanations, Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 805-815","G. Luo; Department of Biomedical Informatics and Medical Education, University of Washington, UW Medicine South Lake Union, Seattle, 850 Republican Street, 98195, United States; email: gangluo@cs.wisc.edu","","JMIR Publications Inc.","","","","","","22919694","","","","English","JMIR Med. Inform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85126443234"
"Bhongade A.; Gupta R.; Prathosh A.P.; Gandhi T.K.","Bhongade, Amit (57302959100); Gupta, Rohit (51461210700); Prathosh, A.P. (55921936500); Gandhi, Tapan Kumar (24343318600)","57302959100; 51461210700; 55921936500; 24343318600","ResPara-Net: Respiration Parameter Estimation Using Wearable Single Inertial Measurement Unit Sensor and Deep Learning","2024","IEEE Sensors Journal","24","15","","24931","24944","13","2","10.1109/JSEN.2024.3408464","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196707537&doi=10.1109%2fJSEN.2024.3408464&partnerID=40&md5=92be5691d3a2831e65d35dd1e3b0b3fc","Indian Institute of Technology Delhi, Department of Electrical Engineering, New Delhi, 110016, India; SRM Institute of Science and Technology (IST), Department of Biomedical Engineering, Kattankulathur, 603203, India; Indian Institute of Science, Department of Electrical Communication Engineering, Bengaluru, 560012, India","Bhongade A., Indian Institute of Technology Delhi, Department of Electrical Engineering, New Delhi, 110016, India; Gupta R., SRM Institute of Science and Technology (IST), Department of Biomedical Engineering, Kattankulathur, 603203, India; Prathosh A.P., Indian Institute of Science, Department of Electrical Communication Engineering, Bengaluru, 560012, India; Gandhi T.K., Indian Institute of Technology Delhi, Department of Electrical Engineering, New Delhi, 110016, India","Respiration plays an important role in detecting and diagnosing cardiovascular diseases such as asthma, chronic obstructive pulmonary disease (COPD), and sleep apnea (SA). Despite the increasing interest in wearable devices for comfortable respiration recognition during daily activities, existing systems often prove complex, non-wearable, and expensive, emphasizing the need for a more effective solution. The presented research introduces a user-friendly, cost-effective, and wearable respiration monitoring system (WRMS) coupled with a novel deep convolutional neural network (ResPara-Net DCNN) for real-time respiration monitoring during daily activities. The developed WRMS continuously estimates respiration parameters using the DCNN and an inertial measurement unit (IMU) signal. Experiments encompassing three different respiration rates (RRs) were conducted to assess the system's performance, with results compared to a laboratory-level gold standard device. The average root-mean-square-error (RMSE) values for normal, fast, and slow breathing rates were found to be 0.14, 0.12, and 0.13, respectively. Similarly, the average correlation coefficient (CC) values for normal, fast, and slow breathing rates were 64.47%, 67.48%, and 71.53%, indicating a robust level of accuracy. The average predicted breathing rates for normal, fast, and slow were 16, 28, and 12 breaths per minute (BPM), respectively. Furthermore, the average normalized mean absolute error (NMAE) between predicted and actual respiration signals for all breathing speeds was less than 4% across all subjects. The proposed DCNN-based WRMS system provides valuable insights into breathing dynamics during daily activities. Moreover, it also introduces a fresh perspective on respiration monitoring. The physical and physiological significance lies in its potential to offer a user-friendly, cost-effective, and continuous monitoring solution, thereby contributing to the advancement of cardiovascular health diagnostics and daily activity-based respiratory assessments. 1558-1748  © 2024 IEEE.","Cardiovascular diseases; inertial measurement unit (IMU); respiration parameters; respiration rate (RR)/breathing rate; sleep apnea (SA); wearable device","Cardiology; Cost effectiveness; Deep neural networks; Diagnosis; Mean square error; Parameter estimation; Patient monitoring; Pulmonary diseases; Sleep research; Wearable sensors; Biomedical monitoring; Breathing rate; Cardiovascular disease; Inertial measurements units; Respiration parameters; Respiration/breathing rate; Sleep apnea; Wearable devices; Wireless communications; Wireless sensor networks","","","","","","","Nam S.H., Yim T.G., Ryu C.Y., Shin S.C., Kang J.H., Kim S., The preliminary study of unobtrusive respiratory monitoring for ehealth, Proc. 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(INCOFT), pp. 1-5, (2022); Van Steenkiste T., Groenendaal W., Deschrijver D., Dhaene T., Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks, IEEE J. Biomed. Health Informat., 23, 6, pp. 2354-2364, (2019); Bhongade A., Gupta R., Gandhi T.K., 1D convolutional neural network for obstructive sleep apnea detection, Proc. IEEE 20th India Council Int. Conf. (INDICON), pp. 7-12, (2023); Ji N., Et al., Recommendation to use wearable-based mHealth in closedloop management of acute cardiovascular disease patients during the COVID-19 pandemic, IEEE J. Biomed. Health Informat., 25, 4, pp. 903-908, (2021); Niazi I.K., Et al., EEG signatures change during unilateral Yogi nasal breathing, Sci. Rep., 12, 1, (2022); Jyotsna V., Joshi A., Ambekar S., Kumar N., Dhawan A., Sreenivas V., Comprehensive yogic breathing program improves quality of life in patients with diabetes, Indian J. Endocrinol. Metabolism, 16, 3, (2012); Brown R.P., Gerbarg P.L., Sudarshan Kriya yogic breathing in the treatment of stress, anxiety, and depression: Part I-Neurophysiologic model, J. Alternative Complementary Med., 11, 1, pp. 189-201, (2005)","T.K. Gandhi; Indian Institute of Technology Delhi, Department of Electrical Engineering, New Delhi, 110016, India; email: tgandhi@ee.iitd.ac.in","","Institute of Electrical and Electronics Engineers Inc.","","","","","","1530437X","","","","English","IEEE Sensors J.","Article","Final","","Scopus","2-s2.0-85196707537"
"Kwon O.B.; Han S.; Lee H.Y.; Kang H.S.; Kim S.K.; Kim J.S.; Park C.K.; Lee S.H.; Kim S.J.; Kim J.W.; Yeo C.D.","Kwon, Oh Beom (57205749214); Han, Solji (57205527212); Lee, Hwa Young (57189094366); Kang, Hye Seon (55536025100); Kim, Sung Kyoung (55971508000); Kim, Ju Sang (56563350500); Park, Chan Kwon (14623269200); Lee, Sang Haak (57484738300); Kim, Seung Joon (57225930594); Kim, Jin Woo (57196170749); Yeo, Chang Dong (55233857000)","57205749214; 57205527212; 57189094366; 55536025100; 55971508000; 56563350500; 14623269200; 57484738300; 57225930594; 57196170749; 55233857000","Prediction of Postoperative Lung Function in Lung Cancer Patients Using Machine Learning Models","2023","Tuberculosis and Respiratory Diseases","86","3","","203","215","12","2","10.4046/trd.2022.0048","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164508142&doi=10.4046%2ftrd.2022.0048&partnerID=40&md5=e277623253b0bf8f2d6b9e0681179e8e","Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Department of Applied Statistics, Yonsei University, Seoul, South Korea; Division of Allergy, Department of Internal Medicine, The Catholic University of Korea, Seoul, South Korea; Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Postech-Catholic Biomedical Engineering Institute, Songeui Multiplex Hall, College of Medicine, The Catholic University of Korea, Seoul, South Korea","Kwon O.B., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Han S., Department of Applied Statistics, Yonsei University, Seoul, South Korea; Lee H.Y., Division of Allergy, Department of Internal Medicine, The Catholic University of Korea, Seoul, South Korea; Kang H.S., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Kim S.K., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Kim J.S., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Park C.K., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Lee S.H., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea, Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Kim S.J., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea, Postech-Catholic Biomedical Engineering Institute, Songeui Multiplex Hall, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Kim J.W., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Yeo C.D., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea","Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R2 and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R2 value was 0.27 and in set II, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function. Copyright © 2023 The Korean Academy of Tuberculosis and Respiratory Diseases.","Chronic Obstructive Pulmonary Disease; Linear Regression; Lung Cancer; Machine Learning; Postoperative Lung Function","adenosquamous carcinoma; aged; Article; cancer patient; cancer surgery; cross validation; data extraction; elasticnet; extreme gradient boosting; female; forced expiratory volume; human; large cell carcinoma; large cell neuroendocrine carcinoma; least absolute shrinkage and selection operator; light gradient boosting machine; linear regression analysis; lung adenocarcinoma; lung cancer; lung function; lung lobectomy; machine learning; major clinical study; male; mean squared error; non small cell lung cancer; postoperative period; prediction; random forest; retrospective study; ridge regression; small cell lung cancer; squamous cell lung carcinoma; task performance","","","","","Korean Academy of Tuberculosis and Respiratory Diseases","This study was supported by a 2020 grant from The Korean Academy of Tuberculosis and Respiratory Diseases.","Fitzmaurice C, Dicker D, Pain A, Hamavid H, Moradi-Lakeh M, Et al., The global burden of cancer 2013, JAMA Oncol, 1, pp. 505-527, (2015); 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Kodama K, Doi O, Higashiyama M, Yokouchi H., Intentional limited resection for selected patients with T1 N0 M0 non-small-cell lung cancer: a single-institution study, J Thorac Cardiovasc Surg, 114, pp. 347-353, (1997); Durham AL, Adcock IM., The relationship between COPD and lung cancer, Lung Cancer, 90, pp. 121-127, (2015); Shin TR, Oh YM, Park JH, Lee KS, Oh S, Kang DR, Et al., The prognostic value of residual volume/total lung capacity in patients with chronic obstructive pulmonary disease, J Korean Med Sci, 30, pp. 1459-1465, (2015); Matsumoto R, Takamori S, Yokoyama S, Hashiguchi T, Murakami D, Yoshiyama K, Et al., Lung function in the late postoperative phase and influencing factors in patients undergoing pulmonary lobectomy, J Thorac Dis, 10, pp. 2916-2923, (2018); Kwon OB, Yeo CD, Lee HY, Kang HS, Kim SK, Kim JS, Et al., The value of residual volume/total lung capacity as an indicator for predicting postoperative lung function in non-small lung cancer, J Clin Med, 10, (2021); Kwak SK, Kim JH., Statistical data preparation: management of missing values and outliers, Korean J Anesthesiol, 70, pp. 407-411, (2017); Khan SI, Hoque AS., SICE: an improved missing data imputation technique, J Big Data, 7, (2020); Zhang Z., Missing values in big data research: some basic skills, Ann Transl Med, 3, (2015)","C.D. Yeo; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 1021 Tongilro, Eunpyeong-gu, 03312, South Korea; email: brainyeo@catholic.ac.kr","","Korean National Tuberculosis Association","","","","","","17383536","","KHCHA","","English","Tuberc. Respir. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164508142"
"Suárez-Fariñas M.; Grishin A.; Arif-Lusson R.; Bourgoin P.; Matthews K.; Campbell D.E.; Busnel J.-M.; Sampson H.A.","Suárez-Fariñas, Mayte (10140242800); Grishin, Alexander (57197467737); Arif-Lusson, Rihane (57195532244); Bourgoin, Pénélope (57208686863); Matthews, Katie (57219550183); Campbell, Dianne E. (35557648600); Busnel, Jean-Marc (6602758655); Sampson, Hugh A. (57208457773)","10140242800; 57197467737; 57195532244; 57208686863; 57219550183; 35557648600; 6602758655; 57208457773","A Streamlined Strategy for Basophil Activation Testing in a Multicenter Phase III Clinical Trial","2024","Journal of Allergy and Clinical Immunology: In Practice","12","12","","3383","3392.e8","","2","10.1016/j.jaip.2024.09.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207708018&doi=10.1016%2fj.jaip.2024.09.007&partnerID=40&md5=639d2052de6b7f2b1bc1878758d85a3e","Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Division of Pediatric Allergy and Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Global Research Organization, Beckman Coulter Life Sciences, Marseille, France; DBV Technologies, Montrouge, France","Suárez-Fariñas M., Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Grishin A., Division of Pediatric Allergy and Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Arif-Lusson R., Global Research Organization, Beckman Coulter Life Sciences, Marseille, France; Bourgoin P., Global Research Organization, Beckman Coulter Life Sciences, Marseille, France; Matthews K., DBV Technologies, Montrouge, France; Campbell D.E., DBV Technologies, Montrouge, France; Busnel J.-M., Global Research Organization, Beckman Coulter Life Sciences, Marseille, France; Sampson H.A., Division of Pediatric Allergy and Immunology, Icahn School of Medicine at Mount Sinai, New York, NY, United States","Background: The basophil activation test (BAT) has been limited to research settings owing to technical issues. Novel approaches using dry, ready-to-use reagents and streamlined protocols offer greater flexibility and may open opportunities for easier implementation in clinical research. Objective: Using a streamlined basophil activation test (sBAT) strategy and the settings of the baseline study of the Epicutaneous Immunotherapy in Toddlers with Peanut Allergy (EPITOPE) trial of EPicutaneous ImmunoTherapy, we aimed to assess the feasibility of implementing BAT in a multicenter trial and to evaluate its utility in predicting the outcomes of peanut double-blind placebo-controlled food challenge (DBPCFC). Methods: Whole blood samples were collected from subjects aged 1 to 3 years (n = 241) undergoing baseline eligibility DBPCFC in the EPITOPE study across 15 clinical sites in North America. After preparation with sBAT reagents, processed samples were analyzed in a single central laboratory within 5 days of collection and preparation. The eliciting dose (ED) at DBPCFC was determined using, Practical Allergy (PRACTALL) criteria. Using a machine learning approach that incorporated BAT-derived features, clinical characteristics, and peanut-specific immunoglobulin E, the ability to predict outcomes of interest (ED ≤ 300 mg or > 300 mg] and use of epinephrine) was assessed using data randomly split into training (n = 182) and validation (n = 59) subsets. Results: The expression of basophil activation markers CD203c and CD63 correlated with ED and severity outcomes of DBPCFC. Most informative concentrations of peanut extract in the sBAT assay for these associations were 1 ng/mL and 10 ng/mL. Using machine learning to assess the ability to predict the outcomes of DBPCFC, the best models using only the BAT-derived features provided relatively high sensitivities of 0.86 and 0.85 for predicting ED and epinephrine use, respectively, whereas specificities were lower, ranging from 0.60 to 0.80. Although including specific immunoglobulin E and skin prick test data in addition to those from sBAT did not improve the ability to identify individuals most at risk for severe reactions, it did improve the ability to identify patients with an ED greater than 300 mg. Conclusions: In addition to facilitating implementation in multicenter trials, sBAT retains the potential of BAT to characterize allergic patients and confirms its potential to contribute to predicting the outcome of oral food challenges. © 2024 American Academy of Allergy, Asthma & Immunology","Basophil activation test; Food allergy; Multicenter trial; Oral food challenge; Peanut allergy","Allergens; Arachis; Basophil Degranulation Test; Basophils; Child, Preschool; Desensitization, Immunologic; Double-Blind Method; Female; Humans; Immunoglobulin E; Infant; Male; Peanut Hypersensitivity; Phosphoric Diester Hydrolases; Pyrophosphatases; Tetraspanin 30; CD63 antigen; epinephrine; immunoglobulin E; allergen; CD63 antigen; ENPP3 protein, human; immunoglobulin E; inorganic pyrophosphatase; phosphodiesterase; allergic rhinitis; allergy; almond allergy; Article; asthma; atopic dermatitis; basophil; basophil activation test; child; clinical practice; controlled study; decision tree; dog allergy; double blind procedure; egg allergy; elastic tissue; female; flow cytometry; human; machine learning; major clinical study; male; milk allergy; multicenter study; nut allergy; peanut; peanut allergy; phase 3 clinical trial; preschool child; random forest; randomized controlled trial; sesame allergy; Arachis; basophil; basophil degranulation test; blood; clinical trial; desensitization; diagnosis; immunology; infant; peanut allergy; procedures","","epinephrine, 51-43-4, 55-31-2, 6912-68-1; immunoglobulin E, 37341-29-0; inorganic pyrophosphatase, 9024-82-2, 9033-44-7; phosphodiesterase, ; Allergens, ; ENPP3 protein, human, ; Immunoglobulin E, ; Phosphoric Diester Hydrolases, ; Pyrophosphatases, ; Tetraspanin 30, ","CytoFLEX, Beckman Coulter, United States; R statistical language version 3.5.1, R core team","Beckman Coulter, United States; R core team","Icahn School of Medicine; Alpina Biotechnologies; N-Fold; National Institutes of Health, NIH; National Institute of Allergy and Infectious Diseases, NIAID; National Health and Medical Research Council, NHMRC; Icahn School of Medicine at Mount Sinai, ISMMS; Beckman Coulter Life Sciences; David H. and Julia Koch Research Program in Food Allergy Therapeutics","Conflicts of interest: M. Suarez-Farinas is a full-time employee of the Icahn School of Medicine at Mount Sinai, New York, NY; and receives consulting fees from DBV Technologies. A. Grishin is a full-time employees of the Icahn School of Medicine at Mount Sinai, New York, NY; and reports receiving consulting fees from N-Fold LLC (Fairfield, CT) for the duration of the study. R. Arif-Lusson was a full-time employee of Beckman Coulter Life Sciences at the time of the study. P. Bourgoin is a full-time employee of the Beckman Coulter Life Sciences. K. Matthews is a full-time employee of DBV Technologies. D. E. Campbell is a part-time employee of DBV Technologies; reports receiving grant support from the National Health and Medical Research Council of Australia; and reports receiving personal fees from AllerGenis and Westmead Fertility Centre. J.-M. Busnel is a full-time employee of the Beckman Coulter Life Sciences. H. A. Sampson is a part-time employee of the Icahn School of Medicine at Mount Sinai; reports advisory board/consulting fees from DBV Technologies, N-Fold, Alpina Biotechnologies, and Siolta Therapeutics; received stock options from DBV Technologies; receives funding to his institution (Icahn School of Medicine) from the National Institutes of Health (NIH)/National Institute of Allergy and Infectious Diseases (NIAID); receives financial support to his institution for research programs from the David H. and Julia Koch Research Program in Food Allergy Therapeutics; and royalties from Elsevier. This work was funded by DBV technologies, Beckman Coulter Life Sciences, and the David H. and Julia Koch Research Program in Food Allergy Therapeutics.","Sindher S.B., Long A., Chin A.R., Hy A., Sampath V., Nadeau K.C., Et al., Food allergy, mechanisms, diagnosis and treatment: innovation through a multi-targeted approach, Allergy, 77, pp. 2937-2948, (2022); Foong R.X., Dantzer J.A., Wood R.A., Santos A.F., Improving diagnostic accuracy in food allergy, J Allergy Clin Immunol Pract, 9, pp. 71-80, (2021); Koplin J.J., Perrett K.P., Sampson H.A., Diagnosing peanut allergy with fewer oral food challenges, J Allergy Clin Immunol Pract, 7, pp. 375-380, (2019); Kawahara T., Tezuka J., Ninomiya T., Honjo S., Masumoto N., Nanishi M., Et al., Risk prediction of severe reaction to oral challenge test of cow's milk, Eur J Pediatr, 178, pp. 181-188, (2019); Greenhawt M., Shaker M., Wang J., Oppenheimer J.J., Sicherer S., Keet C., Et al., Peanut allergy diagnosis: a 2020 practice parameter update, systematic review, and GRADE analysis, J Allergy Clin Immunol, 146, pp. 1302-1334, (2020); Parrish C.P., A review of food allergy panels and their consequences, Ann Allergy Asthma Immunol, 131, pp. 421-426, (2023); Riggioni C., Ricci C., Moya B., Wong D., van Goor E., Bartha I., Et al., Systematic review and meta-analyses on the accuracy of diagnostic tests for IgE-mediated food allergy, Allergy, 79, pp. 324-352, (2024); Conway A.E., Golden D.B.K., Brough H.A., Santos A.F., Shaker M.S., Serologic measurements for peanut allergy: predicting clinical severity is complex, Ann Allergy Asthma Immunol, 132, pp. 686-693, (2024); Patel N., Shreffler W.G., Custovic A., Santos A.F., Will oral food challenges still be part of allergy care in 10 years’ time?, J Allergy Clin Immunol Pract, 11, pp. 988-996, (2023); Eigenmann P.A., Do we still need oral food challenges for the diagnosis of food allergy?, Pediatr Allergy Immunol, 29, pp. 239-242, (2018); Nishino M., Yanagida N., Sato S., Nagakura K.I., Takahashi K., Ogura K., Et al., Risk factors for failing a repeat oral food challenge in preschool children with hen's egg allergy, Pediatr Allergy Immunol, 33, (2022); Esteban C.A., Shreffler W.G., Virkud Y.V., Pistiner M., Oral food challenge outcomes in children under 3 years of age, J Allergy Clin Immunol Pract, 8, pp. 3653-3656.e3, (2020); Greiwe J., Oppenheimer J., Bird J.A., Fleischer D.M., Pongracic J.A., Greenhawt M., Et al., AAAAI work group report: trends in oral food challenge practices among allergists in the United States, J Allergy Clin Immunol Pract, 8, pp. 3348-3355, (2020); Santos A.F., Bergmann M., Brough H.A., Couto-Francisco N., Kwok M., Panetta V., Et al., Basophil activation test reduces oral food challenges to nuts and sesame, J Allergy Clin Immunol Pract, 9, pp. 2016-2027.e6, (2021); Brettig T., Koplin J.J., Dang T., Lange L., McWilliam V., Sato S., Et al., Cashew allergy diagnosis: a two-step algorithm leads to fewer oral food challenges, J Allergy Clin Immunol Pract, 10, pp. 1652-1654.e2, (2022); Cottel N., Saf S., Bourgoin-Heck M., Lambert N., Amat F., Poncet P., Et al., Two different composite markers predict severity and threshold dose in peanut allergy, J Allergy Clin Immunol Pract, 9, pp. 275-282.e1, (2021); Suprun M., Kearney P., Hayward C., Butler H., Getts R., Sicherer S.H., Et al., Predicting probability of tolerating discrete amounts of peanut protein in allergic children using epitope-specific IgE antibody profiling, Allergy, 77, pp. 3061-3069, (2022); Knol E.F., Mul F.P., Jansen H., Calafat J., Roos D., Monitoring human basophil activation via CD63 monoclonal antibody 435, J Allergy Clin Immunol, 88, pp. 328-338, (1991); Santos A.F., Alpan O., Hoffmann H.J., Basophil activation test: mechanisms and considerations for use in clinical trials and clinical practice, Allergy, 76, pp. 2420-2432, (2021); Bourgoin P., Busnel J.M., Promises and remaining challenges for further integration of basophil activation test in allergy-related research and clinical practice, J Allergy Clin Immunol Pract, 11, pp. 3000-3007, (2023); Sturm E.M., Kranzelbinder B., Heinemann A., Groselj-Strele A., Aberer W., Sturm G.J., CD203c-based basophil activation test in allergy diagnosis: characteristics and differences to CD63 upregulation, Cytometry B Clin Cytom, 78B, pp. 308-318, (2010); MacGlashan D., Marked differences in the signaling requirements for expression of CD203c and CD11b versus CD63 expression and histamine release in human basophils, Int Arch Allergy Immunol, 159, pp. 243-252, (2012); MacGlashan D., Expression of CD203c and CD63 in human basophils: relationship to differential regulation of piecemeal and anaphylactic degranulation processes, Clin Exp Allergy, 40, pp. 1365-1377, (2010); Mansouri L., Kalm F., Bjorkander S., Melen E., Lundahl J., Nopp A., Sequential engagement of adhesion molecules and cytokine receptors impacts both piecemeal and anaphylactic degranulation of human basophils, Immunology, 171, pp. 609-617, (2024); Christensen L.H., Holm J., Lund G., Riise E., Lund K., Several distinct properties of the IgE repertoire determine effector cell degranulation in response to allergen challenge, J Allergy Clin Immunol, 122, pp. 298-304, (2008); Hemmings O., Niazi U., Kwok M., James L.K., Lack G., Santos A.F., Peanut diversity and specific activity are the dominant IgE characteristics for effector cell activation in children, J Allergy Clin Immunol, 148, pp. 495-505.e14, (2021); Duan L., Celik A., Hoang J.A., Schmidthaler K., So D., Yin X., Et al., Basophil activation test shows high accuracy in the diagnosis of peanut and tree nut allergy: the Markers of Nut Allergy Study, Allergy, 76, pp. 1800-1812, (2021); Keswani T., Patil S.U., Basophil activation test in food allergy: is it ready for real-time?, Curr Opin Allergy Clin Immunol, 21, pp. 442-447, (2021); Tsai M., Mukai K., Chinthrajah R.S., Nadeau K.C., Galli S.J., Sustained successful peanut oral immunotherapy associated with low basophil activation and peanut-specific IgE, J Allergy Clin Immunol, 145, pp. 885-896.e6, (2020); Arif-Lusson R., Agabriel C., Carsin A., Cabon I., Senechal H., Poncet P., Et al., Streamlining basophil activation testing to enable assay miniaturization and automation of sample preparation, J Immunol Methods, 481-482, (2020); Santos A.F., Kulis M.D., Sampson H.A., Bringing the next generation of food allergy diagnostics into the clinic, J Allergy Clin Immunol Pract, 10, pp. 1-9, (2022); Alpan O., Wasserman R.L., Kim T., Darter A., Shah A., Jones D., Et al., Towards an FDA-cleared basophil activation test, Front Allergy, 3, (2023); Agyemang A., Suprun M., Suarez-Farinas M., Boina F., Arif-Lusson R., Grishin A., Et al., A novel approach to the basophil activation test for characterizing peanut allergic patients in the clinical setting, Allergy, 76, pp. 2257-2259, (2021); Sampson H.A., Gerth van Wijk R., Bindslev-Jensen C., Sicherer S., Teuber S.S., Burks A.W., Et al., Standardizing double-blind, placebo-controlled oral food challenges: American Academy of Allergy, Asthma & Immunology-European Academy of Allergy and Clinical Immunology PRACTALL consensus report, J Allergy Clin Immunol, 130, pp. 1260-1274, (2012); Kepley C.L., Youssef L., Andrews R.P., Wilson B.S., Oliver J.M., Syk deficiency in nonreleaser basophils, J Allergy Clin Immunol, 104, pp. 279-284, (1999); Dispenza M.C., Bochner B.S., MacGlashan D.W., Targeting the FcεRI pathway as a potential strategy to prevent food-induced anaphylaxis, Front Immunol, 11, (2020); Pellefigues C., Mehta P., Chappell S., Yumnam B., Old S., Camberis M., Et al., Diverse innate stimuli activate basophils through pathways involving Syk and IκB kinases, Proc Natl Acad Sci U S A, 118, (2021); Ocmant A., Peignois Y., Mulier S., Hanssens L., Michils A., Schandene L., Flow cytometry for basophil activation markers: the measurement of CD203c up-regulation is as reliable as CD63 expression in the diagnosis of cat allergy, J Immunol Methods, 320, pp. 40-48, (2007); Ebo D.G., Sainte-Laudy J., Bridts C.H., Mertens C.H., Hagendorens M.M., Schuerwegh A.J., Et al., Flow-assisted allergy diagnosis: current applications and future perspectives, Allergy, 61, pp. 1028-1039, (2006); Paranjape A., Tsai M., Mukai K., Hoh R.A., Joshi S.A., Chinthrajah R.S., Et al., Oral immunotherapy and basophil and mast cell reactivity in food allergy, Front Immunol, 11, (2020); Chirumbolo S., The use of IL-3 in basophil activation tests is the real pitfall, Cytometry B Clin Cytom, 80B, pp. 137-138, (2011)","J.-M. Busnel; Global Research Organization, Beckman Coulter Life Sciences, Marseille, 130 Ave de Lattre de Tassigny, 13009, France; email: jmbusnel@beckman.com","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","39284563","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85207708018"
"Choi J.; Alawa J.; Tennakoon L.; Forrester J.D.","Choi, Jeff (57201742536); Alawa, Jude (57210361304); Tennakoon, Lakshika (6505942323); Forrester, Joseph D. (35733770400)","57201742536; 57210361304; 6505942323; 35733770400","DeepBackRib: Deep learning to understand factors associated with readmissions after rib fractures","2022","Journal of Trauma and Acute Care Surgery","93","6","","757","761","4","3","10.1097/TA.0000000000003791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142403402&doi=10.1097%2fTA.0000000000003791&partnerID=40&md5=09e4b8e910f759cf323b30f65f66d55e","Department of Surgery, Stanford University, Stanford, CA, United States; Department of Biomedical Data Science, Stanford University, Stanford, CA, United States","Choi J., Department of Surgery, Stanford University, Stanford, CA, United States, Department of Biomedical Data Science, Stanford University, Stanford, CA, United States; Alawa J., Department of Surgery, Stanford University, Stanford, CA, United States; Tennakoon L., Department of Surgery, Stanford University, Stanford, CA, United States; Forrester J.D., Department of Surgery, Stanford University, Stanford, CA, United States","BACKGROUND Deep neural networks yield high predictive performance, yet obscure interpretability limits clinical applicability. We aimed to build an explainable deep neural network that elucidates factors associated with readmissions after rib fractures among nonelderly adults, termed DeepBackRib. We hypothesized that DeepBackRib could accurately predict readmissions and a game theoretic approach to elucidate how predictions are made would facilitate model explainability. METHODS We queried the 2017 National Readmissions Database for index hospitalization encounters of adults aged 18 to 64 years hospitalized with multiple rib fractures. The primary outcome was 3-month readmission(s). Study cohort was split 60-20-20 into training-validation-test sets. Model input features included demographic/injury/index hospitalization characteristics and index hospitalization International Classification of Diseases, Tenth Revision, diagnosis codes. The seven-layer DeepBackRib comprised multipronged strategies to mitigate overfitting and was trained to optimize recall. Shapley additive explanation analysis identified the marginal contribution of each input feature for predicting readmissions. RESULTS A total of 20,260 patients met the inclusion criteria, among whom 11% (n = 2,185) experienced 3-month readmissions. Feature selection narrowed 3,164 candidate input features to 61, and DeepBackRib yielded 91%, 85%, and 82% recall on the training, validation, and test sets, respectively. Shapley additive explanation analysis quantified the marginal contribution of each input feature in determining DeepBackRib's predictions: underlying chronic obstructive pulmonary disease and long index hospitalization length of stay had positive associations with 3-month readmissions, while private primary payer and diagnosis of pneumothorax during index admission had negative associations. CONCLUSION We developed and internally validated a high-performing deep learning algorithm that elucidates factors associated with readmissions after rib fractures. Despite promising predictive performance, standalone deep learning algorithms are insufficient for clinical prediction tasks: a concerted effort is needed to ensure that clinical prediction algorithms remain explainable. LEVEL OF EVIDENCE Prognostic and Epidemiological; Level III.  © Wolters Kluwer Health, Inc. All rights reserved.","deep learning; deep neural networks; machine learning; readmissions; Rib fractures","Adult; Cohort Studies; Deep Learning; Hospitalization; Humans; Patient Readmission; Rib Fractures; adult; Article; artificial ventilation; chronic obstructive lung disease; cohort analysis; controlled study; data base; deep learning; deep neural network; deepbackrib model; demography; disease association; female; flail chest; game; hematopneumothorax; hematothorax; hospital readmission; hospitalization; human; ICD-10; injury scale; learning algorithm; length of stay; lung contusion; major clinical study; male; middle aged; outcome assessment; performance indicator; pleura empyema; pneumonia; pneumothorax; predictive model; predictive value; receiver operating characteristic; respiratory failure; rib fracture; thorax injury; complication; hospital readmission; rib fracture","","","DeepBackRib","","","","Medicare Hospital Readmissions Reduction Program [Internet]; Aalberg J.J., Johnson B.P., Hojman H.M., Rattan R., Arabian S., Mahoney E.J., Readmission following surgical stabilization of rib fractures: Analysis of incidence, cost, and risk factors using the Nationwide readmissions database, J Trauma Acute Care Surg, 91, 2, pp. 361-368, (2021); Baker J.E., Skinner M., Heh V., Pritts T.A., Goodman M.D., Millar D.A., Readmission rates and associated factors following rib cage injury, J Trauma Acute Care Surg, 87, 6, pp. 1269-1276, (2019); Cha P., Hakes N., Choi J., Rosenberg G., Tennakoon L., Spain D., National readmission rates after surgical stabilization of traumatic rib fractures, J Cardiothorac Trauma, 5, 1, (2020); Marthy A.G., Mounsey M., Ata A., Stain S.C., Tafen M., Hospital readmission after blunt traumatic rib fractures, J Trauma Acute Care Surg, 93, 6, pp. 793-799, (2022); James G., Witten D., Hastie T., Tibshirani R., An Introduction to Statistical Learning: With Applications in R [Internet]; Zhang Z., Beck M.W., Winkler D.A., Huang B., Sibanda W., Goyal H., Opening the black box of neural networks: Methods for interpreting neural network models in clinical applications, Ann Transl Med, 6, 11, pp. 216-216, (2018); Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement; NRD Database Documentation [Internet]; Choi J., Anand A., Sborov K.D., Walton W., Chow L., Guillamondegui O., Complication to consider: Delayed traumatic hemothorax in older adults, Trauma Surg Acute Care Open, 6, 1, (2021); Emond M., Sirois M.J., Guimont C., Chauny J.M., Daoust R., Bergeron E., Functional impact of a minor thoracic injury: An investigation of age, delayed hemothorax, and rib fracture effects, Ann Surg, 262, 6, pp. 1115-1122, (2015); Black A., Clark D., Icdpicr: ""iCD"" Programs for Injury Categorization in R [Internet]; Building Predictive Models in R Using the Caret Package | Journal of Statistical Software [Internet]; Choi J., Forrester J.D., Clinical prediction tools in trauma: Where do we go from here?, JAMA Netw Open, 5, 1, (2022); Strumbelj E., Kononenko I., Explaining prediction models and individual predictions with feature contributions, Knowl Inf Syst, 41, pp. 647-665, (2014); Lundberg S.; Green E.A., Guidry C., Harris C., McGrew P., Schroll R., Hussein M., Surgical stabilization of traumatic rib fractures is associated with reduced readmissions and increased survival, Surgery, 170, 6, pp. 1838-1848, (2021); Rodu J., Baiocchi M., When Black Box Algorithms Are (Not) Appropriate: A Principled Prediction-problem Ontology; Bennett J., Lanning S., Netflix N., The Netflix prize, KDD Cup and Workshop in Conjunction with KDD; GitHub: Where the World Builds Software [Internet]; Choi J., Marafino B.J., Vendrow E.B., Tennakoon L., Baiocchi M., Spain D.A., Rib Fracture Frailty Index: A risk stratification tool for geriatric patients with multiple rib fractures, J Trauma Acute Care Surg, 91, 6, pp. 932-939, (2021); Hugging Face - The AI Community Building the Future","J. Choi; Department of Surgery, Stanford University, Stanford, 300 Pasteur Drive, H-3641, 94305, United States; email: jc2226@stanford.edu","","Lippincott Williams and Wilkins","","","","","","21630755","","","36121263","English","J. Trauma Acute Care Surg.","Article","Final","","Scopus","2-s2.0-85142403402"
"Moretti A.; Pietersen P.I.; Hassan M.; Shafiek H.; Prosch H.; Tarnoki A.D.; Annema J.T.; Munavvar M.; Bonta P.I.; Wever W.; Juul A.D.","Moretti, Antonio (58657734300); Pietersen, Pia Iben (57193791040); Hassan, Maged (57188978231); Shafiek, Hanaa (56020613500); Prosch, Helmut (6507615192); Tarnoki, Adam Domonkos (35796411700); Annema, Jouke T. (57204668231); Munavvar, Mohammed (15059464600); Bonta, Peter I. (14070136100); Wever, Walter de (57223212956); Juul, Amanda Dandanell (57489732600)","58657734300; 57193791040; 57188978231; 56020613500; 6507615192; 35796411700; 57204668231; 15059464600; 14070136100; 57223212956; 57489732600","ERS International Congress 2023: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly","2024","ERJ Open Research","10","1","00836-2023","","","","2","10.1183/23120541.00836-2023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187722327&doi=10.1183%2f23120541.00836-2023&partnerID=40&md5=617f5054b28d7048fa8f6bd28b7f5f54","Department of Pulmonology, Amsterdam University Medical Centres, Amsterdam, Netherlands; Unit of Respiratory Diseases, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy; Department of Radiology, Odense University Hospital Svendborg, Svendborg, Denmark; Research and Innovations Unit of Radiology, University of Southern Denmark, Odense, Denmark; Chest Diseases Department, Alexandria University Faculty of Medicine, Alexandria, Egypt; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Medical Imaging Centre, Semmelweis University, Budapest, Hungary; National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary; Lancashire Teaching Hospitals and University of Central Lancashire, Preston, United Kingdom; Department of Radiology, University Hospitals Leuven, Leuven, Belgium; Odense Respiratory Research Unit (ODIN), Department of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Respiratory Medicine, Odense University Hospital, Odense, Denmark","Moretti A., Department of Pulmonology, Amsterdam University Medical Centres, Amsterdam, Netherlands, Unit of Respiratory Diseases, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy; Pietersen P.I., Department of Radiology, Odense University Hospital Svendborg, Svendborg, Denmark, Research and Innovations Unit of Radiology, University of Southern Denmark, Odense, Denmark; Hassan M., Chest Diseases Department, Alexandria University Faculty of Medicine, Alexandria, Egypt; Shafiek H., Chest Diseases Department, Alexandria University Faculty of Medicine, Alexandria, Egypt; Prosch H., Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Tarnoki A.D., Medical Imaging Centre, Semmelweis University, Budapest, Hungary, National Tumour Biology Laboratory, Oncologic Imaging and Invasive Diagnostic Centre, National Institute of Oncology, Budapest, Hungary; Annema J.T., Department of Pulmonology, Amsterdam University Medical Centres, Amsterdam, Netherlands; Munavvar M., Lancashire Teaching Hospitals and University of Central Lancashire, Preston, United Kingdom; Bonta P.I., Department of Pulmonology, Amsterdam University Medical Centres, Amsterdam, Netherlands; Wever W., Department of Radiology, University Hospitals Leuven, Leuven, Belgium; Juul A.D., Odense Respiratory Research Unit (ODIN), Department of Clinical Research, University of Southern Denmark, Odense, Denmark, Department of Respiratory Medicine, Odense University Hospital, Odense, Denmark","The Clinical Techniques, Imaging and Endoscopy Assembly is involved in the diagnosis and treatment of several pulmonary diseases, as demonstrated at the 2023 European Respiratory Society (ERS) International Congress in Milan, Italy. From interventional pulmonology, the congress included several exciting results for the use of bronchoscopy in lung cancer, including augmented fluoroscopy, robotic-assisted bronchoscopy and cryobiopsies. In obstructive lung disease, the latest results on bronchoscopic treatment of emphysema with hyperinflation and chronic bronchitis were presented. Research on using cryobiopsies to diagnose interstitial lung disease was further explored, with the aims of elevating diagnostic yield and minimising risk. For imaging, the latest updates in using artificial intelligence to overcome the increased workload of radiologists were of great interest. Novel imaging in sarcoidosis explored the use of magnetic resonance imaging, photon-counting computed tomography and positron emission tomography/computed tomography in the diagnostic work-up. Lung cancer screening is still a hot topic and new results were presented regarding incorporation of biomarkers, identifying knowledge gaps and improving screening programmes. The use of ultrasound in respiratory medicine is an expanding field, which was demonstrated by the large variety in studies presented at the 2023 ERS Congress. Ultrasound of the diaphragm in patients with amyotrophic lateral sclerosis and myasthenia gravis was used to assess movements and predict respiratory fatigue. Furthermore, studies using ultrasound to diagnose or monitor pulmonary disease were presented. The congress also included studies regarding the training and assessment of competencies as an important part of implementing ultrasound in clinical practice. © 2024, European Respiratory Society. All rights reserved.","","amyotrophic lateral sclerosis; Article; artificial intelligence; bronchoscopy; cancer screening; chronic bronchitis; chronic obstructive lung disease; collateral ventilation; computer assisted tomography; confocal laser scanning microscopy; COPD assessment test; cryobiopsy; diagnostic value; emphysema; endobronchial ultrasonography; endoscopy; fluoroscopy; forced expiratory volume; granulomatosis; human; hyperinflation; imaging; interstitial lung disease; lung cancer; lung disease; myasthenia gravis; nuclear magnetic resonance imaging; obstructive lung disease; peripheral lung lesion; photon counting computed tomography; positron emission tomography-computed tomography; practice guideline; residual volume; sarcoidosis; six minute walk test; training; ultrasound; web conferencing","","","","","European Society of Radiology, ESR; Mauna Kea Technologies; Kræftens Bekæmpelse, DCS; Italian Respiratory Society; Smouha University Hospital; Swine Innovation Porc, SIP; Ministry of Higher Education and Scientific Research, MHESR; Danish Center for Lung Cancer Research","Funding text 1: M-P. Revel (Paris, France) introduced us to SOLACE (Strengthening the screening of lung cancer in Europe), a 3-year implementation project funded by the EU4Health programme and coordinated by the European Institute for Biomedical Imaging Research, a part of the European Society of Radiology [38]. The aim of the project is first to assess and explore the current status of lung cancer screening programmes in Europe and identify needs and gaps, and second to produce comprehensive guidelines and work packages (WPs) on where to focus. The SOLACE project began in April 2023 and already had several approaches to report by the time of the congress in September 2023. M-P. Revel highlighted three of eight WPs, which are focus points that are investigated and explored. The three presented WPs were all directed towards participants enrolled in ongoing screening projects. WP4 focuses on enhancing the knowledge and participation of women because women are underrepresented in most lung cancer screening programmes. For example, women only represented 16% of the participants in the NELSON study and, in many trials, the male/female data are aggregated.; Funding text 2: Conflict of interest: A. Moretti was the winner of a research fellowship grant from SIP (Italian Respiratory Society), outside the submitted work; and reports support for attending meetings from Amsterdam UMC, outside the submitted work. P.I. Pietersen reports a grant from Boehringer Ingelheim for travel and accommodation for the European Society of Thoracic Imaging Winter Course 2022, outside the submitted work. H. Prosch reports research grants or contracts from Boehringer Ingelheim, AstraZeneca, Siemens Healthineers, the Christian Doppler Research Association and the EU Commission (EU4Health, Horizon Europe Health), outside the submitted work; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from AstraZeneca, BMS, Boehringer Ingelheim, Bracco, Daiichi Sankyo, Janssen, MSD, Novartis, Roche, Sanofi, Siemens Healthineers and Takeda, outside the submitted work; support for attending meetings and/or travel from Boehringer Ingelheim, outside the submitted work; and participation on a data safety monitoring or advisory board for BMS, Boehringer Ingelheim, Janssen, MSD, Roche and Sanofi, outside the submitted work. H. Shafiek reports a grant for a research fellowship from the Ministry of Higher Education and Scientific Research of Egypt to work in Spain for 6 months (April 2021 to October 2021), outside the submitted work; support from the ERS to attend the International Congress in Milan in 2023, outside the submitted work; and was leader of the bronchoscopy unit of Alexandria Faculty of Medicine, Egypt between October 2022 and September 2023, and is leader of the bronchoscopy unit of Smouha University Hospital, Alexandria, Egypt, from April 2023 to present, outside the submitted work. A.D. Tarnoki reports payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Boehringer Ingelheim, outside the submitted work; and is ERS Imaging Group chair 2022–2024. J.T. Annema reports support for the present manuscript from Mauna Kea Technologies and a grant from Mauna Kea Technologies, outside the submitted work. M. Munavvar reports honorarium for teaching from Olympus Europe, Becton Dickinson and Chiesi, outside the submitted work; and sponsorship for travel from Chiesi, outside the submitted work. P.I. Bonta reports institutional research grants from AstraZeneca, Mauna Kea and Boston Scientific, outside the submitted work. A.D. Juul reports research funding from Danish Cancer Society and Danish Center for Lung Cancer Research, outside the submitted work. All other authors have nothing to disclose.","Kops SEP, Heus P, Korevaar DA, Et al., Diagnostic yield and safety of navigation bronchoscopy: a systematic review and meta-analysis, Lung Cancer, 180, (2023); Verhoeven RLJ, Futterer JJ, Hoefsloot W, Et al., Cone-beam CT image guidance with and without electromagnetic navigation bronchoscopy for biopsy of peripheral pulmonary lesions, J Bronchology Interv Pulmonol, 28, pp. 60-69, (2021); Bondue B, Taton O, Tannouri F, Et al., High diagnostic yield of electromagnetic navigation bronchoscopy performed under cone beam CT guidance: results of a randomized Belgian monocentric study, BMC Pulm Med, 23, (2023); Brock J, Dittrich S, Kontogianni K, Et al., First European results: shape-sensing robotic assisted bronchoscopy for biopsy of peripheral lung nodules, Eur Respir J, 62, (2023); Kramer T, Wijmans L, van Heumen S, Et al., Needle-based confocal laser endomicroscopy for real-time granuloma detection, Respirology, 28, pp. 934-941, (2023); Kramer T, van Heumen S, Wijmans L, Et al., Needle based confocal laser endomicroscopy for real-time granuloma detection, Eur Respir J, 62, (2023); Furuse H, Matsumoto Y, Nakai T, Et al., Diagnostic efficacy of cryobiopsy for peripheral pulmonary lesions: a propensity score analysis, Lung Cancer, 178, pp. 220-228, (2023); Lee-Mateus AY, Abia-Trujillo D, Barrios-Ruiz A, Et al., Diagnostic yield of shape sensing robotic-assisted bronchoscopy for pulmonary nodules less than 2 cm pre and post-3D fluoroscopy, Eur Respir J, 62, (2023); Nakai T, Watanabe T, Yamada K, Et al., Diagnostic utility and safety of non-intubated cryobiopsy using a 1.1-mm cryoprobe for peripheral pulmonary lesions, Eur Respir J, 62, (2023); Zhong C-H, Sun J-Y, Su Z-Q, Et al., A multi-center, prospective study of transbronchial radiofrequency ablation for peripheral lung tumors: study progress and initial results, Eur Respir J, 62, (2023); Valipour A, Fernandez-Bussy S, Ing AJ, Et al., Bronchial rheoplasty for treatment of chronic bronchitis. Twelve-month results from a multicenter clinical trial, Am J Respir Crit Care Med, 202, pp. 681-689, (2020); Brock J, Herth F, Darwiche K, Et al., Bronchial rheoplasty for chronic bronchitis: 6-month results from the European Registry Study, Eur Respir J, 62, (2023); Orton CM, Tonkin J, Chan L, Et al., Metered cryospray improves patient-reported outcome measures at 6-months post-crossover treatment, in patients with COPD with chronic bronchitis, Eur Respir J, 62, (2023); Van Der Molen MC, Posthuma R, Hartman JE, Et al., The impact and timing of pulmonary rehabilitation in patients undergoing bronchoscopic lung volume reduction with endobronchial valves: a randomized controlled trial in patients with severe emphysema, Eur Respir J, 62, (2023); Roodenburg SA, Klooster K, Slebos D-J, Et al., The impact of emphysema heterogeneity on treatment response after endobronchial valve treatment, Eur Respir J, 62, (2023); Schuler S, Kontogianni K, Rotting M, Et al., Lobar deflation predicts FEV1 following valve therapy in patients with severe emphysema, Eur Respir J, 62, (2023); Dittrich S, Unterschemmann A-S, Trudzinski F, Et al., Combination of endobronchial and intrabronchial valves for endoscopic therapy of advanced emphysema, Eur Respir J, 62, (2023); Kontogianni K, Valipour A, Eisenmann S, Et al., Bronchoscopic thermal vapor ablation (BTVA) in patients with severe upper lobe predominant emphysema: 36 months follow-up results from a prospective registry, Eur Respir J, 62, (2023); Korevaar DA, Colella S, Fally M, Et al., European Respiratory Society guidelines on transbronchial lung cryobiopsy in the diagnosis of interstitial lung diseases, Eur Respir J, 60, (2022); Hou G, Bian Y, Deng M., Use of 1.1-mm probes in transbronchial lung cryobiopsy for diagnosing interstitial lung diseases: a randomized trial, Eur Respir J, 62, (2023); Freund O, Wand O, Schneer S, Et al., Trans-bronchial cryobiopsy is superior to forceps biopsy for diagnosing both fibrotic and non-fibrotic interstitial lung diseases, Eur Respir J, 62, (2023); Soldati T, Vaselli M, Mooij-Kalverda KA, Et al., In vivo endobronchial-PS-OCT for fibrosis quantification in ILD, Eur Respir J, 62, (2023); Hendrix W, Rutten M, Hendrix N, Et al., Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals, Eur Radiol, 33, pp. 8279-8288, (2023); Council Updates its Recommendation to Screen for Cancer; Ewals LJS, van der Wulp K, van den Borne B, Et al., The effects of artificial intelligence assistance on the radiologists’ assessment of lung nodules on CT scans: a systematic review, J Clin Med, 12, (2023); Chao HS, Tsai CY, Chou CW, Et al., Artificial intelligence assisted computational tomographic detection of lung nodules for prognostic cancer examination: a large-scale clinical trial, Biomedicines, 11, (2023); Pehrson LM, Nielsen MB, Ammitzbol Lauridsen C., Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review, Diagnostics (Basel), 9, (2019); AI-Derived Computer-Aided Detection (CAD) Software for Detecting and Measuring Lung Nodules in CT Scan Images; Ciompi F, Chung K, van Riel SJ, Et al., Towards automatic pulmonary nodule management in lung cancer screening with deep learning, Sci Rep, 7, (2017); Venkadesh KV, Setio AAA, Schreuder A, Et al., Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT, Radiology, 300, pp. 438-447, (2021); Mikhael PG, Wohlwend J, Yala A, Et al., Sybil: a validated deep learning model to predict future lung cancer risk from a single low-dose chest computed tomography, J Clin Oncol, 41, pp. 2191-2200, (2023); Casagrande G, Chiarantano RS, Siqueira AP, Et al., Fluid specific miRNA-based signatures for lung cancer screening, Eur Respir J, 62, (2023); Zwijsen K, Wener R, Janssens E, Et al., Exhaled breath analysis optimizes nodule management in a lung cancer screening program, Eur Respir J, 62, (2023); Kikano GE, Fabien A, Schilz R., Evaluation of the solitary pulmonary nodule, Am Fam Physician, 92, pp. 1084-1091, (2015); Murrmann GB, van Vollenhoven FH, Moodley L., Approach to a solid solitary pulmonary nodule in two different settings – “Common is common, rare is rare”, J Thorac Dis, 6, pp. 237-248, (2014); O'Dowd EL, Baldwin DR., Lung nodules: sorting the wheat from the chaff, Br J Radiol, 96, (2023); Lung-RADS v2022; Strengthening the Screening of Lung Cancer in Europe; Espinosa SF, Reynoso AQ, Sancho J, Et al., Diaphragmatic contraction speed at follow-up of patients admitted for myasthenia gravis, Eur Respir J, 62, (2023); Gonzalez-Posada IM, Hernandez JC, Cuesta PL, Et al., Use of diaphragmatic ultrasound to evaluate respiratory function in patients with amyotrophic lateral sclerosis, Eur Respir J, 62, (2023); Longoni A, Bassino C, Cappelletti T, Et al., Diaphragm sonography as an educational aid in CPAP and NIMV treatment, Eur Respir J, 62, (2023); Falster C, Nielsen RW, Moller JE, Et al., Does ultrasound in suspected pulmonary embolism safely reduce referral to diagnostic imaging? A randomized controlled trial, Eur Respir J, 62, (2023); Gupta R, Davis JKJ, Nair A, Et al., Role of lung ultrasound in detection and assessment of severity of ILD in patients with systemic sclerosis, Eur Respir J, 62, (2023); Kuo Y-W, Chen Y-L, Wu H-D, Et al., Validation of transthoracic shear-wave ultrasound elastography in diagnosing pleural lesions, Eur Respir J, 62, (2023); Wiig R, Falster C, Jacobsen N, Et al., Diagnostic accuracy of lung ultrasound with elastography in predicting malignant origin of pleural effusions in an emergency department, Eur Respir J, 62, (2023); Hou G, Lin J, Deng M., Point-of-care ultrasound for the assessment of subglottic and cervical tracheal stenosis: a prospective, multicenter, exploratory study, Eur Respir J, 62, (2023); Pietersen PI, Bhatnagar R, Andreasen F, Et al., Validity evidence of the 2022 ERS thoracic ultrasound Objective Structured Clinical Examination (OSCE), Eur Respir J, 62, (2023); Gilbert C, Ortiz R, Ma Y, Et al., Transbronchial needle aspiration (TBNA): past, present and future, Curr Respir Med Rev, 10, pp. 176-181, (2014); Nielsen AB, Jacobsen N, Laursen C, Et al., Assessment of thoracic ultrasound skills in immersive virtual reality, Eur Respir J, 62, (2023)","A.D. Juul; Odense Respiratory Research Unit (ODIN), Department of Clinical Research, University of Southern Denmark, Odense, Denmark; email: Amanda.Dandanell.Juul@rsyd.dk","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85187722327"
"Larrainzar-Garijo R.; Fernández-Tormos E.; Collado-Escudero C.A.; Alcantud Ibáñez M.; Oñorbe-San Francisco F.; Marin-Corral J.; Casadevall D.; Donaire-Gonzalez D.; Martínez-Sanchez L.; Cabal-Hierro L.; Benavent D.; Brañas F.","Larrainzar-Garijo, Ricardo (8097042000); Fernández-Tormos, Esther (57204238521); Collado-Escudero, Carlos Alberto (57219976171); Alcantud Ibáñez, María (58156403100); Oñorbe-San Francisco, Fernando (16043250600); Marin-Corral, Judith (25960212000); Casadevall, David (56737150600); Donaire-Gonzalez, David (35278360700); Martínez-Sanchez, Luisa (57673348700); Cabal-Hierro, Lucia (22233323800); Benavent, Diego (57219217828); Brañas, Fátima (26533823100)","8097042000; 57204238521; 57219976171; 58156403100; 16043250600; 25960212000; 56737150600; 35278360700; 57673348700; 22233323800; 57219217828; 26533823100","Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records","2024","Scientific Reports","14","1","532","","","","2","10.1038/s41598-023-50762-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181471244&doi=10.1038%2fs41598-023-50762-5&partnerID=40&md5=6b76805ec35acae983b0780aed9b31af","Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Medical School, Universidad Complutense, Madrid, Spain; Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Madrid, Spain; Geriatric Department, Hospital Universitario Infanta Leonor, Medical School, Universidad Complutense, Madrid, Spain; Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain","Larrainzar-Garijo R., Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Medical School, Universidad Complutense, Madrid, Spain; Fernández-Tormos E., Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Madrid, Spain; Collado-Escudero C.A., Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Madrid, Spain; Alcantud Ibáñez M., Geriatric Department, Hospital Universitario Infanta Leonor, Medical School, Universidad Complutense, Madrid, Spain; Oñorbe-San Francisco F., Orthopedic and Trauma Department, Hospital Universitario Infanta Leonor, Madrid, Spain; Marin-Corral J., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Casadevall D., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Donaire-Gonzalez D., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Martínez-Sanchez L., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Cabal-Hierro L., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Benavent D., Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; Brañas F., Geriatric Department, Hospital Universitario Infanta Leonor, Medical School, Universidad Complutense, Madrid, Spain","Hip fractures (HFx) are associated with a higher morbidity and mortality rates, leading to a significant reduction in life quality and in limitation of patient´s mobility. The present study aimed to obtain real-world evidence on the clinical characteristics of patients with an initial and a second hip fracture (HFx) and develop a predictive model for second HFx using artificial intelligence. Electronic health records from one hospital centre in Spain from January 2011 to December 2019 were analysed using EHRead® technology, based on natural language processing and machine learning. A total of 1,960 patients with HFx were finally included during the study period after meeting all inclusion and exclusion criteria. From this total, 1835 (93.6%) patients were included in the HFx subgroup, while 124 (6.4%) were admitted to the second HFx (2HFx) subgroup. The mean age of the participants was 84 years and 75.5% were female. Most of comorbidities were more frequently identified in the HFx group, including hypertension (72.0% vs. 67.2%), cognitive impairment (33.0% vs. 31.2%), diabetes mellitus (28.7% vs. 24.8%), heart failure (27.6% vs. 22.4%) and chronic kidney disease (26.9% vs. 16.0%). Based on clinical criteria, 26 features were selected as potential prediction factors. From there, 16 demographics and clinical characteristics such as comorbidities, medications, measures of disabilities for ambulation and type of refracture were selected for development of a competitive risk model. Specifically, those predictors with different associated risk ratios, sorted from higher to lower risk relevance were visual deficit, malnutrition, walking assistance, hypothyroidism, female sex, osteoporosis treatment, pertrochanteric fracture, dementia, age at index, osteoporosis, renal failure, stroke, COPD, heart disease, anaemia, and asthma. This model showed good performance (dependent AUC: 0.69; apparent performance: 0.75) and could help the identification of patients with higher risk of developing a second HFx, allowing preventive measures. This study expands the current available information of HFx patients in Spain and identifies factors that exhibit potential in predicting a second HFx among older patients. © 2024, The Author(s).","","Aged, 80 and over; Artificial Intelligence; Electronic Health Records; Female; Hip Fractures; Humans; Machine Learning; Male; Natural Language Processing; Osteoporosis; Risk Factors; artificial intelligence; complication; electronic health record; female; hip fracture; human; machine learning; male; natural language processing; osteoporosis; risk factor; very elderly","","","","","Savana Research Group","We would like to acknowledge all the members of the Savana Research Group that contributed to the study, represented by Sebastian Menke, Marisa Serrano, Natalia Polo, Noemí Mejías, and Ignacio Salcedo.","Binkley N., Et al., Osteoporosis in crisis: It's time to focus on fracture, J. Bone Miner. Res., 32, 1391, (2017); Graftiaux A.G., Kehr P., Zhang Y., : Clinical epidemiology of orthopaedic trauma, European Journal of Orthopaedic Surgery & Traumatology, 27, 668, (2017); Laudisio A., Et al., Muscle strength is related to mental and physical quality of life in the oldest old, Arch. Gerontol. Geriatr., 89, (2020); Castelli L., Et al., Robotic-assisted rehabilitation for balance in stroke patients (ROAR-S): Effects of cognitive, motor and functional outcomes, Eur. Rev. Med. Pharmacol. Sci., 27, pp. 8198-8211, (2023); Giovannini S., Brau F., Galluzzo V., Santagada D.A., Loreti C., Biscotti L., Laudisio A., Giuseppe Z., Bernabei R., Falls among older adults: Screening, identification, rehabilitation, and management, Appl. Sci., 12, 15, (2022); Batin S., Et al., Evaluation of risk factors for second hip fractures in elderly patients, J. Clin. Med. Res., 10, pp. 217-220, (2018); Scaglione M., Et al., The second hip fracture in osteoporotic patients: Not only an orthopaedic matter, Clin. Cases Miner. Bone Metab., 10, pp. 124-128, (2013); Ryg J., Rejnmark L., Overgaard S., Brixen K., Vestergaard P., Hip fracture patients at risk of second hip fracture: a nationwide population-based cohort study of 169,145 cases during 1977–2001, J. Bone Miner. Res., 24, pp. 1299-1307, (2009); Zhu Y., Et al., Meta-analysis of risk factors for the second hip fracture (SHF) in elderly patients, Arch. Gerontol. Geriatr., 59, pp. 1-6, (2014); Zhu Y., Et al., Epidemiological characteristics and outcome in elderly patients sustaining non-simultaneous bilateral hip fracture: A systematic review and meta-analysis, Geriatr. Gerontol. Int., 15, pp. 11-18, (2015); Canales L., Et al., Assessing the performance of clinical natural language processing systems: Development of an evaluation methodology, JMIR Med. Inform., 9, (2021); Overview of SNOMED CT. National Library of Medicine, (2023); Espinosa L., Tello J., Pardo A., Medrano I., Urena A., Salcedo I., Saggion H., A global information extraction and terminology expansion framework in the medical domain procesamiento del lenguaje natural, Procesamiento del Lenguaje Natural, 57, (2016); Riley R.D., Et al., Minimum sample size for developing a multivariable prediction model: PART II-binary and time-to-event outcomes, Stat. Med., 38, pp. 1276-1296, (2019); Steyerberg E.W., Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating, (2019); Liu S., Et al., Risk factors for the second contralateral hip fracture in elderly patients: A systematic review and meta-analysis, Clin. Rehabil., 29, pp. 285-294, (2015); Dretakis K.E., Dretakis E.K., Papakitsou E.F., Psarakis S., Steriopoulos K., Possible predisposing factors for the second hip fracture, Calcif. Tissue Int., 62, pp. 366-369, (1998); Yamanashi A., Et al., Assessment of risk factors for second hip fractures in Japanese elderly, Osteoporos. Int., 16, pp. 1239-1246, (2005); Nolan E.K., Chen H.Y., A comparison of the Cox model to the Fine-Gray model for survival analyses of re-fracture rates, Arch. Osteoporos., 15, (2020); Kanis J.A., Et al., FRAX and its applications to clinical practice, Bone, 44, pp. 734-743, (2009); Leslie W.D., Lix L.M., Comparison between various fracture risk assessment tools, Osteoporos. Int., 25, (2013); Almog Y.A., Et al., Deep learning with electronic health records for short-term fracture risk identification: Crystal bone algorithm development and validation, J. Med. Internet Res., 22, (2020); Berry S.D., Et al., Second hip fracture in older men and women: The Framingham Study, Arch. Intern. Med., 167, pp. 1971-1976, (2007); Mazzucchelli R., Et al., Second hip fracture: Incidence, trends, and predictors, Calcif. Tissue Int., 102, pp. 619-626, (2018)","D. Benavent; Savana Research Group: Medsavana & Savana Research S.L., Madrid, Spain; email: dbenavent@savanamed.com","","Nature Research","","","","","","20452322","","","38177650","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85181471244"
"Bitar G.; Liu W.; Tunguhan J.; Kumar K.V.; Hoffman M.K.","Bitar, Ghamar (57219343041); Liu, Wei (58839294800); Tunguhan, Jade (58762195800); Kumar, Kaveeta V. (58762610600); Hoffman, Matthew K. (7403155071)","57219343041; 58839294800; 58762195800; 58762610600; 7403155071","A Machine Learning Algorithm using Clinical and Demographic Data for All-Cause Preterm Birth Prediction","2024","American Journal of Perinatology","41","","","E3115","E3123","8","2","10.1055/s-0043-1776917","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179654356&doi=10.1055%2fs-0043-1776917&partnerID=40&md5=a2fa8d9ffc3e88afd05efe21668f927e","Department of Obstetrics and Gynecology, Christiana Care Health System, Newark, DE, United States; Center For Strategic Information Management, Christiana Care Health System, Newark, DE, United States","Bitar G., Department of Obstetrics and Gynecology, Christiana Care Health System, Newark, DE, United States; Liu W., Center For Strategic Information Management, Christiana Care Health System, Newark, DE, United States; Tunguhan J., Center For Strategic Information Management, Christiana Care Health System, Newark, DE, United States; Kumar K.V., Department of Obstetrics and Gynecology, Christiana Care Health System, Newark, DE, United States; Hoffman M.K., Department of Obstetrics and Gynecology, Christiana Care Health System, Newark, DE, United States","Objective Preterm birth remains the predominant cause of perinatal mortality throughout the United States and the world, with well-documented racial and socioeconomic disparities. To develop and validate a predictive algorithm for all-cause preterm birth using clinical, demographic, and laboratory data using machine learning. Study Design We performed a cohort study of pregnant individuals delivering at a single institution using prospectively collected information on clinical conditions, patient demographics, laboratory data, and health care utilization. Our primary outcome was all-cause preterm birth before 37 weeks. The dataset was randomly divided into a derivation cohort (70%) and a separate validation cohort (30%). Predictor variables were selected amongst 33 that had been previously identified in the literature (directed machine learning). In the derivation cohort, both statistical (logistic regression) and machine learning (XG-Boost) models were used to derive the best fit (C-Statistic) and then validated using the validation cohort. We measured model discrimination with the C-Statistic and assessed the model performance and calibration of the model to determine whether the model provided clinical decision-making benefits. Results The cohort includes a total of 12,440 deliveries among 12,071 individuals. Preterm birth occurred in 2,037 births (16.4%). The derivation cohort consisted of 8,708 (70%) and the validation cohort consisted of 3,732 (30%). XG-Boost was chosen due to the robustness of the model and the ability to deal with missing data and collinearity between predictor variables. The top five predictor variables identified as drivers of preterm birth, by feature importance metric, were multiple gestation, number of emergency department visits in the year prior to the index pregnancy, initial unknown body mass index, gravidity, and prior preterm delivery. Test performance characteristics were similar between the two populations (derivation cohort area under the curve [AUC] = 0.70 vs. validation cohort AUC = 0.63). Conclusion Clinical, demographic, and laboratory information can be useful to predict all-cause preterm birth with moderate precision. Key Points Machine learning can be used to create models to predict preterm birth. In our model, all-cause preterm birth can be predicted with moderate precision. Clinical, demographic, and laboratory information can be useful to predict all-cause preterm birth. © 2022. Thieme. All rights reserved.","machine learning; predictive algorithm; preterm birth; social determinants of health; XG- boost","Adult; Algorithms; Cohort Studies; Female; Gestational Age; Humans; Infant, Newborn; Logistic Models; Machine Learning; Pregnancy; Premature Birth; Risk Factors; Young Adult; adult; algorithm; Article; asthma; body mass; clinical study; cohort analysis; confusion matrix; controlled study; demographics; diabetes mellitus; female; follow up; gonorrhea; human; hypertension; logistic regression analysis; machine learning; major clinical study; maternal hypertension; mortality; perinatal mortality; prediction; predictor variable; pregnancy; premature labor; prematurity; prenatal care; risk factor; sensitivity and specificity; social determinants of health; sociology; task performance; epidemiology; gestational age; newborn; statistical model; young adult","","","","","Eunice Kennedy Shriver National Institute of Child Health and Human Development, NICHD; U.S. Public Health Service, USPHS; Postnatal Patient Safety Learning Lab; U.S. Department of Health and Human Services, HHS; Agency for Healthcare Research and Quality, AHRQ, (R18HS027260); National Institute of Child Health and Human Development, NICHD, (T32 HD52468)","Funding This research was supported by the Agency for Healthcare Research and Quality (AHRQ) R18HS027260, the Postnatal Patient Safety Learning Lab, U.S. Department of Health and Human Services, U.S. Public Health Service. The content is solely the responsibility of the authors and does not necessarily present the official views of AHRQ. This research was supported in part by a training grant from the National Institute of Child Health and Development (T32 HD52468) Eunice Kennedy Shriver National Institute of Child Health and Human Development.","Liu L., Oza S., Hogan D., Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals, Lancet, 388, pp. 3027-3035, (2016); Martin J.A., Hamilton B.E., Osterman M.J., Driscoll A.K.; Purisch S.E., Gyamfi-Bannerman C., Epidemiology of preterm birth. 41, Seminars in Perinatology, pp. 387-391, (2017); Talati A.N., Hackney D.N., Mesiano S., Pathophysiology of preterm labor with intact membranes. 41, Seminars in Perinatology, pp. 420-426, (2017); Koning S.M., Ehrenthal D.B., Stressor landscapes, birth weight, and prematurity at the intersection of race and income: elucidating birth contexts through patterned life events, Popul Heal, 8, (2019); Hackney D.N., Durie D.E., Dozier A.M., Suter B.J., Glantz J.C., Is the accuracy of prior preterm birth history biased by delivery characteristics?, Matern Child Health J, 16, 6, pp. 1241-1246, (2012); Mayne S.L., Pellissier B.F., Kershaw K.N., Neighborhood physical disorder and adverse pregnancy outcomes among women in Chicago: a cross-sectional analysis of electronic health record data, J Urban Health, 96, 6, pp. 823-834, (2019); Blumenshine P., Egerter S., Barclay C.J., Cubbin C., Braveman P.A., Socioeconomic disparities in adverse birth outcomes: a systematic review, Am J Prev Med, 39, 3, pp. 263-272, (2010); Ncube C.N., Enquobahrie D.A., Burke J.G., Ye F., Marx J., Albert S.M., Transgenerational transmission of preterm birth risk: the role of race and generational socio-economic neighborhood context, Matern Child Health J, 21, 8, pp. 1616-1626, (2017); Ncube C.N., Enquobahrie D.A., Albert S.M., Herrick A.L., Burke J.G., Association of neighborhood context with offspring risk of preterm birth and low birthweight: a systematic review and meta-analysis of population-based studies. 153, Social Science and Medicine, pp. 156-164, (2016); Zhang J., Landy H.J., Ware Branch D., Contemporary patterns of spontaneous labor with normal neonatal outcomes, Obstet Gynecol, 116, 6, pp. 1281-1287, (2010); Goldenberg R.L., Culhane J.F., Iams J.D., Romero R., Epidemiology and causes of preterm birth, Lancet, 371, pp. 75-84, (2008); Muglia L.J., Katz M., The enigma of spontaneous preterm birth, N Engl J Med, 362, 6, pp. 529-535, (2010); Chen T., Guestrin C., XGBoost, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Lee K.S., Ahn K.H., Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth, Diagnostics (Basel), 10, 9, (2020); Shah N.D., Steyerberg E.W., Kent D.M., Big data and predictive analytics: recalibrating expectations, JAMA, 320, 1, pp. 27-28, (2018); Benedetto U., Dimagli A., Sinha S., Machine learning improves mortality risk prediction after cardiac surgery: systematic review and meta-analysis, J Thorac Cardiovasc Surg, 163, 6, pp. 2075e9-2087e9, (2022)","G. Bitar; Department of Obstetrics and Gynecology, and Reproductive Sciences, University of Texas Health Science Center At Houston, Houston, 77030, United States; email: ghamar.bitar@uth.tmc.edu","","Thieme Medical Publishers, Inc.","","","","","","07351631","","AJPEE","38049100","English","Am. J. Perinatol.","Article","Final","","Scopus","2-s2.0-85179654356"
"Chan A.H.Y.; van Boven J.F.M.","Chan, Amy Hai Yan (55337510300); van Boven, Job F. M. (53464198500)","55337510300; 53464198500","Digital adherence interventions for asthma","2023","ERS Monograph","2023","","","185","198","13","2","10.1183/2312508X.10001823","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187107909&doi=10.1183%2f2312508X.10001823&partnerID=40&md5=42d68d9d9f7e278592cc6a95624ab9f8","School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand; Department of Clinical Pharmacy and Pharmacology, University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, Groningen, Netherlands","Chan A.H.Y., School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand; van Boven J.F.M., Department of Clinical Pharmacy and Pharmacology, University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, Groningen, Netherlands","Digital technologies are increasingly used to support asthma management and improve medication adherence. Evidence shows that digital adherence interventions can have important benefits on medication adherence and outcomes. While there remain some uncertainties about how best to implement digital interventions into everyday practice and whether digital interventions are cost-effective and acceptable to end users, digital interventions are likely to bring significant benefits for patients, health professionals and society. Interventions include digital inhalers and spacers, self-management apps, web interventions, telehealth and text message services. Interventions vary in functionality, maturity of the technology, and the amount of evidence supporting their use. Careful selection of the digital intervention that is best suited for the patient’s asthma needs, lifestyle, abilities and preferences is important to ensure successful implementation. Ongoing evaluation of the rapidly evolving technology and long-term benefits they bring is needed to support sustained adoption and engagement. Further research into the added value of digital adherence interventions in practice is needed. © ERS 2023.","","Article; artificial intelligence; asthma; base pairing; clinical decision making; clinical outcome; clinical practice; cost effectiveness analysis; digital adherence intervention; digital technology; emergency ward; evidence based practice; health care personnel; health care planning; health care system; human; intervention study; lifestyle; long term care; medical procedures; medication compliance; outcome variable; quality adjusted life year; quality of life; randomized controlled trial (topic); respiratory tract disease; self report; telecommunication; telehealth; telemonitoring; web-based intervention","","","","","","","Dekhuijzen R, Lavorini F, Usmani OS, Et al., Addressing the impact and unmet needs of nonadherence in asthma and chronic obstructive pulmonary disease: where do we go from here?, J Allergy Clin Immunol Pract, 6, pp. 785-793, (2018); DiMatteo MR, Giordani PJ, Lepper HS, Et al., Patient adherence and medical treatment outcomes: a meta-analysis, Med Care, 40, pp. 794-811, (2002); 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Abraham O, LeMay S, Bittner S, Et al., Investigating serious games that incorporate medication use for patients: systematic literature review, JMIR Serious Games, 8, (2020); Messinger AI, Luo G, Deterding RR., The doctor will see you now: how machine learning and artificial intelligence can extend our understanding and treatment of asthma, J Allergy Clin Immunol, 145, pp. 476-478, (2020); Gonsard A, AbouTaam R, Prevost B, Et al., Children’s views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study, Eur J Pediatr, 182, pp. 877-888, (2023); Pinnock H, Hui CY, van Boven JF., Implementation of digital home monitoring and management of respiratory disease, Curr Opin Pulm Med, 29, pp. 302-312, (2023); Pleasants RA, Chan AH, Mosnaim G, Et al., Integrating digital inhalers into clinical care of patients with asthma and chronic obstructive pulmonary disease, Respir Med, 205, (2022); Adejumo I, Patel M, McKeever TM, Et al., Qualitative study of user perspectives and experiences of digital inhaler technology, NPJ Prim Care Respir Med, 32, (2022); van de Hei SJ, Stoker N, Flokstra-de Blok BM, Et al., Anticipated barriers and facilitators for implementing smart inhalers in asthma medication adherence management, NPJ Prim Care Respir Med, 33, (2023)","A.H.Y. Chan; School of Pharmacy, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand; email: a.chan@auckland.ac.nz","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85187107909"
"Chow J.; Hatem M.","Chow, Jacky (37021157600); Hatem, Muhammed (56985708100)","37021157600; 56985708100","Quantitative analysis of diaphragm motion during fluoroscopic sniff test to assist in diagnosis of hemidiaphragm paralysis","2022","Radiology Case Reports","17","5","","1750","1754","4","3","10.1016/j.radcr.2022.02.083","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126994753&doi=10.1016%2fj.radcr.2022.02.083&partnerID=40&md5=021d1007f73f9a0d81f33770e8b2a002","Department of Radiology, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, T2N 4N1, AB, Canada","Chow J., Department of Radiology, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, T2N 4N1, AB, Canada; Hatem M., Department of Radiology, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, T2N 4N1, AB, Canada","The current imaging gold standard for detecting paradoxical diaphragm motion and diagnosing hemidiaphragm paralysis is to perform the fluoroscopic sniff test. The images are visually examined by an experienced radiologist, and if one hemidiaphragm ascends while the other descends, then it is described as paradoxical motion, which is highly suggestive of hemidiaphragm paralysis. However, diagnosis can be challenging because diaphragm motion during sniffing is fast, paradoxical motion can be subtle, and the analysis is based on a 2-dimensional projection of a 3-dimensional surface. This paper presents a case of chronic left hemidiaphragm elevation that was initially reported as mild paradoxical motion on fluoroscopy. After measuring the elevations of the diaphragms and modeling their temporal correlation using Gaussian process regression, the systematic trend of the hemidiaphragmatic motion along with its stochastic properties was determined. When analyzing the trajectories of the hemidiaphragms, no statistically significant paradoxical motion was detected. This could potentially change the prognosis if the patient was to consider diaphragm plication as treatment. The presented method provides a more objective analysis of hemidiaphragm motions and can potentially improve diagnostic accuracy. © 2022","Diaphragm fluoroscopy; Diaphragm paralysis; Fluoroscopic sniff test; Gaussian process; Hemidiaphragm elevation; Machine learning","fluticasone furoate plus vilanterol; montelukast; prednisone; salbutamol sulfate; spriva; tiotropium bromide; adult; Article; asthma; case report; cesarean section; clinical article; computer assisted tomography; diaphragm paralysis; dyspnea; female; fluoroscopy; forced expiratory volume; forced vital capacity; hemidiaphragm; human; ileus; lung function test; middle aged; motion; quantitative analysis; spinal anesthesia; thorax radiography","","montelukast, 151767-02-1, 158966-92-8; prednisone, 53-03-2; salbutamol sulfate, 51022-70-9; tiotropium bromide, 136310-93-5","breo ellipta; singulair; spriva; ventolin","","","","Qureshi A., Diaphragm paralysis, Semin Respir Crit Care Med, 30, 3, pp. 315-320, (2009); Ricoy J., Rodriguez-Nunez N., Alvarez-Dobano J., Toubes M., Riveiro V., Valdes L., Diaphragmatic dysfunction, Pulmonary, 25, 4, pp. 223-235, (2019); Patel D., Berry M., Bhandari P., Backhus L., Raees S., Trope W., Et al., Paradoxical motion on sniff test predicts greater improvement following diaphragm plication, Ann Thorac Surg, 111, 6, pp. 1820-1826, (2021); Nason L., Walker C., McNeeley M., Burivong W., Fligner C., Godwin J., Imaging of the diaphragm: anatomy and function, RadioGraphics, 32, 2, pp. E51-E70, (2012); Tarver R., Conces D., Cory D., Vix V., Imaging the diaphragm and its disorders, J Thorac Imaging, 4, 1, pp. 1-18, (1989); Billings M., Aitken M., Benditt J., Bilateral diaphragm paralysis: a challenging diagnosis, Respir Care, 53, 10, pp. 1368-1371, (2008); Houston J., Fleet M., Cowan M., McMillan N., Comparison of ultrasound with fluoroscopy in the assessment of suspected hemidiaphragmatic movement abnormality, Clin Radiol, 50, 2, pp. 95-98, (1995); Lloyd T., Tang Y., Benson M., King S., Diaphragmatic paralysis: the use of M mode ultrasound for diagnosis in adults, Spinal Cord, pp. 505-508, (2006); Nafisa S., Messer B., Downie B., Ehilawa P., Kinnear W., Algendy S., Sovani M., A retrospective cohort study of idiopathic diaphragmatic palsy: a diagnostic triad, natural history and prognosis, ERJ Open Res, 8, 1, (2021); Alexander C., Diaphragm movements and the diagnosis of diaphragmatic paralysis, Clin Radiol, 17, 1, pp. 79-83, (1966); Boussuges A., Gole Y., Blanc P., Diaphragmatic motion studied by m-mode ultrasonography: methods, reproducibility, and normal values, Chest, 135, 2, pp. 391-400, (2009); Fayssoil A., Nguyen L., Ogna A., Stojkovic T., Meng P., Mompoint D., Et al., Diaphragm sniff ultrasound: normal values, relationship with sniff nasal pressure and accuracy for predicting respiratory involvement in patients with neuromuscular disorders, Plos One, 14, 4, (2019)","J. Chow; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, 3330 Hospital Dr NW, T2N 4N1, Canada; email: jckchow@ucalgary.ca","","Elsevier Inc.","","","","","","19300433","","","","English","Radiol. Case Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85126994753"
"Taherkhani H.; KavianFar A.; Aminnezhad S.; Lanjanian H.; Ahmadi A.; Azimzadeh S.; Masoudi-Nejad A.","Taherkhani, Hamidreza (58790734600); KavianFar, Azadeh (35174611700); Aminnezhad, Sargol (55614524800); Lanjanian, Hossein (57193408899); Ahmadi, Ali (57208461248); Azimzadeh, Sadegh (55507470400); Masoudi-Nejad, Ali (55911393500)","58790734600; 35174611700; 55614524800; 57193408899; 57208461248; 55507470400; 55911393500","Deciphering the impact of microbial interactions on COPD exacerbation: An in-depth analysis of the lung microbiome","2024","Heliyon","10","4","e24775","","","","2","10.1016/j.heliyon.2024.e24775","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185170751&doi=10.1016%2fj.heliyon.2024.e24775&partnerID=40&md5=5fdc1648d45ebef106ffe35c68f7cd70","Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Molecular Biology Research Center, Systems Biology and Poisonings Institute, Tehran, Iran; Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Tehran, Iran","Taherkhani H., Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran; KavianFar A., Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran; Aminnezhad S., Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran; Lanjanian H., Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Ahmadi A., Molecular Biology Research Center, Systems Biology and Poisonings Institute, Tehran, Iran; Azimzadeh S., Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Tehran, Iran; Masoudi-Nejad A., Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran, Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran","In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients. © 2024","16S rRNA; COPD; Dysbiosis; Growth rate; Lung microbiome; Metabolic network simulation; Microbe‒microbe interaction; Pathogenic bacteria","","","","","","","","Global R., And national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet Respir. Med., 5, pp. 691-706, (2017); Kerstjens H., Postma D., ten Hacken N., Chronic obstructive pulmonary disease, Clin. Evid., pp. 2077-2100, (2006); Hurst J.R., Vestbo J., Anzueto A., Locantore N., Mullerova H., Tal-Singer R., Miller B., Lomas D.A., Agusti A., Macnee W., Calverley P., Rennard S., Wouters E.F.M., Wedzicha J.A., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N. Engl. J. 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Rev., 37, pp. 520-553, (2013); Quinn R.A., Whiteson K., Lim Y.W., Zhao J., Conrad D., Lipuma J.J., Rohwer F., Widder S., Ecological networking of cystic fi brosis lung infections, Npj Biofilms Microbiomes, (2016); Pahuja R., Sisodia U., Tiwari A., Sharma S., Nagrath P., A Dynamic Approach of Eye Disease Classification Using Deep Learning and Machine Learning Model BT - Proceedings of Data Analytics and Management, pp. 719-736, (2022); O'Dwyer D.N., Dickson R.P., Moore B.B., The lung microbiome, immunity, and the pathogenesis of chronic lung disease, J. Immunol., 196, pp. 4839-4847, (2016); Shah S., Karlapalem C., Patel P., Madan N., Streptococcus pneumoniae coinfection in COVID-19 in the intensive care unit: a series of four cases, Case Reports Crit. Care. 2022, (2022); Zuluaga N., Martinez D., Hernandez C., Ballesteros N., Castaneda S., Ramirez J.D., Munoz M., Description of pathogenic bacteria in patients with respiratory symptoms associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Colombia, Ann. Clin. Microbiol. Antimicrob., 22, (2023); Bongiovanni M., Barda B., Pseudomonas aeruginosa bloodstream infections in SARS-CoV-2 infected patients: a systematic review, J. Clin. Med., 12, (2023); Chandran S., Avari M., Cherian B.P., Suarez C., COVID-19-associated Staphylococcus aureus cavitating pneumonia, BMJ Case Rep., 14, (2021); Tamanai-Shacoori Z., Le Gall-David S., Moussouni F., Sweidan A., Polard E., Bousarghin L., Jolivet-Gougeon A., SARS-CoV-2 and Prevotella spp.: friend or foe? A systematic literature review, J. Med. Microbiol., 71, (2022); Cantu V.J., Salido R.A., Huang S., Rahman G., Tsai R., Valentine H., Magallanes C.G., Aigner S., Baer N.A., Barber T., Belda-Ferre P., Betty M., Bryant M., Maya M.C., Castro-Martinez A., Chacon M., Cheung W., Crescini E.S., De Hoff P., Eisner E., Farmer S., Hakim A., Kohn L., Lastrella A.L., Lawrence E.S., Morgan S.C., Ngo T.T., Nouri A., Ostrander R.T., Plascencia A., Ruiz C.A., Sathe S., Seaver P., Shwartz T., Smoot E.W., Valles T., Yeo G.W., Laurent L.C., Fielding-Miller R., Knight R., SARS-CoV-2 distribution in residential housing suggests contact deposition and correlates with Rothia sp, MedRxiv Prepr. Serv. Heal. Sci., (2021); Verhasselt H.L., Buer J., Dedy J., Ziegler R., Steinmann J., Herbstreit F., Brenner T., Rath P.-M., COVID-19 Co-infection with Legionella pneumophila in 2 tertiary-care hospitals, Germany, Emerg. Infect. Dis., 27, pp. 1535-1537, (2021); Crosby J., Semon S., Ganti S.S., Klauber-Choephel E., Abraham J., Mycoplasma pneumoniae COVID-19 delta variant Co-infection mimicking COVID-19 ARDS, J. Investig. Med. High Impact Case Reports, 10, (2022); Basnet A., Chand A.B., Shrestha L.B., Pokhrel N., Karki L., Shrestha S.K.D., Tamang B., Shrestha M.R., Dulal M., Rai J.R., Co-Infection of uropathogenic Escherichia coli among COVID-19 patients admitted to a tertiary care centre: a descriptive cross-sectional study, JNMA. J. Nepal Med. Assoc., 60, pp. 294-298, (2022); Tu X., Saleh N., Young R., Case report—Escherichia coli pericarditis after recent COVID-19 pneumonia, J. Respir, 3, pp. 101-106, (2023); Feldman C., Anderson R., The role of co-infections and secondary infections in patients with COVID-19, Pneumonia, 13, (2021); Han Y., Jia Z., Shi J., Wang W., He K., The active lung microbiota landscape of COVID-19 patients through the metatranscriptome data analysis, Bioimpacts, 12, pp. 139-146, (2022)","A. Masoudi-Nejad; aboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran, Tehran, Iran; email: amasoudin@ut.ac.ir","","Elsevier Ltd","","","","","","24058440","","","","English","Heliyon","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85185170751"
"Bhargava H.; Salomon C.; Suresh S.; Chang A.; Kilian R.; van Stijn D.; Oriol A.; Low D.; Knebel A.; Taraman S.","Bhargava, Hansa (58920275300); Salomon, Carmela (57831663000); Suresh, Srinivasan (57189876020); Chang, Anthony (57459381700); Kilian, Rachel (59063217400); van Stijn, Diana (57194625709); Oriol, Albert (59574589500); Low, Daniel (58920392300); Knebel, Ashley (58920334900); Taraman, Sharief (36180339600)","58920275300; 57831663000; 57189876020; 57459381700; 59063217400; 57194625709; 59574589500; 58920392300; 58920334900; 36180339600","Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics","2024","Journal of Medical Internet Research","26","1","e49022","","","","2","10.2196/49022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186741369&doi=10.2196%2f49022&partnerID=40&md5=7253ee59c7710ce9296a46552c522624","Children’s Hospital of Atlanta, Atlanta, GA, United States; School of Medicine, Emory University, Atlanta, GA, United States; Healio, South New Jersey, NJ, United States; Cognoa, Inc, Palo Alto, CA, United States; Division of Health Informatics, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States; UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, United States; Fowler School of Engineering, Chapman University, Orange, CA, United States; SSI Strategy, Parsippany, NJ, United States; Lapsi Health, Amsterdam, Netherlands; Rady Children’s Hospital, San Diego, CA, United States; AdaptX, Seattle, WA, United States; Children’s Hospital of Orange County, Orange, CA, United States; University of California Irvine, School of Medicine, Irvine, CA, United States","Bhargava H., Children’s Hospital of Atlanta, Atlanta, GA, United States, School of Medicine, Emory University, Atlanta, GA, United States, Healio, South New Jersey, NJ, United States; Salomon C., Cognoa, Inc, Palo Alto, CA, United States; Suresh S., Division of Health Informatics, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, United States; Chang A., Fowler School of Engineering, Chapman University, Orange, CA, United States; Kilian R., SSI Strategy, Parsippany, NJ, United States; van Stijn D., Lapsi Health, Amsterdam, Netherlands; Oriol A., Rady Children’s Hospital, San Diego, CA, United States; Low D., AdaptX, Seattle, WA, United States; Knebel A., Cognoa, Inc, Palo Alto, CA, United States; Taraman S., Cognoa, Inc, Palo Alto, CA, United States, Children’s Hospital of Orange County, Orange, CA, United States, University of California Irvine, School of Medicine, Irvine, CA, United States","Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine. © 2024 JMIR Publications Inc.. All rights reserved.","artificial intelligence; ASD; autism; autism spectrum disorder; autistic; barrier; barriers; child; children; clinical application; clinical applications; CME; continuing education; continuing medical education; disparities; implementation; pediatric; pediatrics; professional development; youth","Algorithms; Artificial Intelligence; Child; Humans; Intelligence; Medicine; Software; adolescent depression; anxiety disorder; Article; artificial intelligence; asthma; autism; clinician; human; medical education; medication therapy management; opiate addiction; pediatrics; rare disease; risk factor; sepsis; algorithm; child; intelligence; medicine; software","","","","","","","Chang AC., Intelligence-Based Medicine: Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare, (2020); Aylward BS, Abbas H, Taraman S, Salomon C, Gal-Szabo D, Kraft C, Et al., An introduction to artificial intelligence in developmental and behavioral pediatrics, J Dev Behav Pediatr, 44, 2, pp. e126-e134, (2023); Topol EJ., High-performance medicine: the convergence of human and artificial intelligence, Nat Med, 25, 1, pp. 44-56, (2019); Miller DD, Brown EW., Artificial intelligence in medical practice: the question to the answer?, Am J Med, 131, 2, pp. 129-133, (2018); Zahran HS, Bailey CM, Damon SA, Garbe PL, Breysse PN., Vital signs: asthma in children - United States, 2001-2016, MMWR Morb Mortal Wkly Rep, 67, 5, pp. 149-155, (2018); Luo G, He S, Stone BL, Nkoy FL, Johnson MD., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, 1, (2020); Patel SJ, Chamberlain DB, Chamberlain JM., A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage, Acad Emerg Med, 25, 12, pp. 1463-1470, (2018); Bose S, Kenyon CC, Masino AJ., Personalized prediction of early childhood asthma persistence: a machine learning approach, PLoS One, 16, 3, (2021); Larson EC, Goel M, Boriello G, Heltshe S, Rosenfeld M, Patel SN., SpiroSmart: using a microphone to measure lung function on a mobile phone, Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing, (2012); Van Sickle D, Barrett M, Humblet O, Henderson K, Hogg C., Randomized, controlled study of the impact of a mobile health tool on asthma SABA use, control and adherence, Eur Respir J, 48, (2016); Dimitriades JB., Ep. 85: value of digital therapeutics – Jhonatan Dimitriades, MD (CEO Lapsi Health), Health Podcast Network, (2021); Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Et al., Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation, Sci Transl Med, 11, 489, (2019); Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH., Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU, Pediatr Crit Care Med, 19, 10, pp. e495-e503, (2018); AdaptX- eliminating opioids from ambulatory care. AdaptX; Low D., Closing the gateway from surgery to persistent opioid use, Institute for Healthcare Improvement, (2019); Otjen JP, Moore MM, Romberg EK, Perez FA, Iyer RS., The current and future roles of artificial intelligence in pediatric radiology, Pediatr Radiol, 52, 11, pp. 2065-2073, (2022); Oman O, Makela T, Salli E, Savolainen S, Kangasniemi M., 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke, Eur Radiol Exp, 3, 1, (2019); Baxi V, Edwards R, Montalto M, Saha S., Digital pathology and artificial intelligence in translational medicine and clinical practice, Mod Pathol, 35, 1, pp. 23-32, (2022); Stempniak M., Radiology groups urge congress to address scarcity of AI solutions in pediatric care, Radiology Business, (2022); Maenner MJ, Shaw KA, Bakian AV, Bilder DA, Durkin MS, Esler A, Et al., Prevalence and characteristics of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2018, MMWR Surveill Summ, 70, 11, pp. 1-16, (2021); Pierce K, Gazestani VH, Bacon E, Barnes CC, Cha D, Nalabolu S, Et al., Evaluation of the diagnostic stability of the early autism spectrum disorder phenotype in the general population starting at 12 months, JAMA Pediatr, 173, 6, pp. 578-587, (2019); Maenner MJ, Shaw KA, Baio J, Washington A, Patrick M, DiRienzo M, Et al., Prevalence of autism spectrum disorder among children aged 8 years - autism and developmental disabilities monitoring network, 11 sites, United States, 2016, MMWR Surveill Summ, 69, 4, pp. 1-12, (2020); Zuckerman KE, Lindly OJ, Sinche BK., Parental concerns, provider response, and timeliness of autism spectrum disorder diagnosis, J Pediatr, 166, 6, pp. 1431-1439, (2015); Vivanti G, Dissanayake C, Outcome for children receiving the early start denver model before and after 48 months, J Autism Dev Disord, 46, 7, pp. 2441-2449, (2016); MacDonald R, Parry-Cruwys D, Dupere S, Ahearn W., Assessing progress and outcome of early intensive behavioral intervention for toddlers with autism, Res Dev Disabil, 35, 12, pp. 3632-3644, (2014); CanvasDx; Megerian JT, Dey S, Melmed RD, Coury DL, Lerner M, Nicholls CJ, Et al., Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder, NPJ Digit Med, 5, 1, (2022); FDA authorizes marketing of diagnostic aid for autism spectrum disorder, (2021); Durkin MS, Maenner MJ, Baio J, Christensen D, Daniels J, Fitzgerald R, Et al., Autism spectrum disorder among US children (2002-2010): socioeconomic, racial, and ethnic disparities, Am J Public Health, 107, 11, pp. 1818-1826, (2017); Wiggins LD, Durkin M, Esler A, Lee LC, Zahorodny W, Rice C, Et al., Disparities in documented diagnoses of autism spectrum disorder based on demographic, individual, and service factors, Autism Res, 13, 3, pp. 464-473, (2020); McCormick CEB, Kavanaugh BC, Sipsock D, Righi G, Oberman LM, De Luca DM, Et al., Autism heterogeneity in a densely sampled U.S. population: results from the first 1,000 participants in the RI-CART study, Autism Res, 13, 3, pp. 474-488, (2020); Harrison AJ, Long KA, Tommet DC, Jones RN., Examining the role of race, ethnicity, and gender on social and behavioral ratings within the autism diagnostic observation schedule, J Autism Dev Disord, 47, 9, pp. 2770-2782, (2017); Prochaska JJ, Vogel EA, Chieng A, Kendra M, Baiocchi M, Pajarito S, Et al., A therapeutic relational agent for reducing problematic substance use (Woebot): development and usability study, J Med Internet Res, 23, 3, (2021); Digital therapeutics: reducing rural health inequalities, Digital Therapeutics Alliance, (2022); Any anxiety disorder; Yang J, Zhang K, Fan H, Huang Z, Xiang Y, Yang J, Et al., Development and validation of deep learning algorithms for scoliosis screening using back images, Commun Biol, 2, (2019); Wall DP, Liu-Mayo S, Salomon C, Shannon J, Taraman S., Optimizing a de novo artificial intelligence-based medical device under a predetermined change control plan: improved ability to detect or rule out pediatric autism, Intelligence-Based Med, 8, (2023); Ghassemi M, Oakden-Rayner L, Beam AL., The false hope of current approaches to explainable artificial intelligence in health care, Lancet Digit Health, 3, 11, pp. e745-e750, (2021); Davenport T, Kalakota R., The potential for artificial intelligence in healthcare, Future Healthc J, 6, 2, pp. 94-98, (2019); Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, Et al., Patient apprehensions about the use of artificial intelligence in healthcare, NPJ Digit Med, 4, 1, (2021); Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Et al., An ethical framework for a good AI society: opportunities, risks, principles, and recommendations, Ethics, Governance, and Policies in Artificial Intelligence, pp. 19-39, (2021); Miller KW., Moral responsibility for computing artifacts: ""the rules, IT Prof, 13, 3, pp. 57-59, (2011); Katznelson G, Gerke S., The need for health AI ethics in medical school education, Adv Health Sci Educ Theory Pract, 26, 4, pp. 1447-1458, (2021); Kagiyama N, Shrestha S, Farjo PD, Sengupta PP., Artificial intelligence: practical primer for clinical research in cardiovascular disease, J Am Heart Assoc, 8, 17, (2019); Proposed regulatory framework for modifications to Artificial Intelligence/Machine Learning (AI/ML)-based Software as a Medical Device (SaMD) - discussion paper and request for feedback, (2019); Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P., Introducing artificial intelligence training in medical education, JMIR Med Educ, 5, 2, (2019); Banerjee M, Chiew D, Patel KT, Johns I, Chappell D, Linton N, Et al., The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers, BMC Med Educ, 21, 1, (2021); Dos Santos DP, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, Et al., Medical students' attitude towards artificial intelligence: a multicentre survey, Eur Radiol, 29, 4, pp. 1640-1646, (2019); Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Et al., Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey, Insights Imaging, 11, 1, (2020); Kolachalama VB, Garg PS., Machine learning and medical education, NPJ Digit Med, 1, (2018); Wartman SA, Combs CD., Reimagining medical education in the age of AI, AMA J Ethics, 21, 2, pp. E146-E152, (2019); Chang A., Medical education for the future clinician: a proposal for our medical education leaders, Medical Intelligence, 10","C. Salomon; Cognoa, Inc, Palo Alto, 2185 Park Blvd, 94306, United States; email: carmela.salomon@cognoa.com","","JMIR Publications Inc.","","","","","","14388871","","","38421690","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85186741369"
"Günen-Yılmaz S.; Aytekin Z.","Günen-Yılmaz, S. (57198428175); Aytekin, Z. (57118151900)","57198428175; 57118151900","Evaluation of jaw bone changes in patients with asthma using inhaled corticosteroids with mandibular radiomorphometric indices on dental panoramic radiographs","2023","Medicina oral, patologia oral y cirugia bucal","28","3","","e285","e292","7","3","10.4317/medoral.25722","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159242015&doi=10.4317%2fmedoral.25722&partnerID=40&md5=3f328d3954df0af8ec480024dc7d7f8e","Department of Periodontology, Faculty of Dentistry Akdeniz University, 07058, Antalya, Turkey","Günen-Yılmaz S., Department of Periodontology, Faculty of Dentistry Akdeniz University, 07058, Antalya, Turkey; Aytekin Z.","BACKGROUND: Inhaled corticosteroids (ICSs) are an effective drug commonly used in asthma treatment. It is known that osteoporotic changes can occur secondary to steroid usage, depending on dosage and duration. The aim of this study was to compare radiomorphometric indices and fractal dimension on panoramic images of patients with asthma using ICSs and healthy controls. MATERIAL AND METHODS: A total of 66 dental panoramic radiographs (DPRs) taken from 32 patients with asthma using ICSs and 34 healthy individuals were evaluated in this retrospective study. Panoramic mandibular index inferior and superior (PMI-i,PMI-s), mandibular cortical width (MCW), gonial index (GI), antegonial index (AI), mandibular cortical index (MCI), and fractal dimension analysis (FDA) were measured on DPRs. RESULTS: PMI-s (p=0.02), MCW (p<0.001), GI (p<0.001) and AI (p<0.001) values were significantly lower in the group of the asthma using ICSs than control group. However, the PMI-i (p ˃0.05) measurement, the MCI (p ˃0.05) and FDA values distribution were similar in both groups. CONCLUSIONS: The use of ICSs in asthma patients can affect bone quality. The evaluation of PMI-s, MCW, GI, and AI on DPR can help determine the effect of this drug on the jawbones in the early period and select dental and surgical treatment plans appropriately.","","Adrenal Cortex Hormones; Bone Density; Humans; Mandible; Radiography, Panoramic; Retrospective Studies; corticosteroid; bone density; diagnostic imaging; human; mandible; panoramic radiography; procedures; retrospective study","","Adrenal Cortex Hormones, ","","","","","","","","NLM (Medline)","","","","","","16986946","","","36641739","English","Med Oral Patol Oral Cir Bucal","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85159242015"
"Wang Y.; Wang L.; Zhou Z.; Laurentiev J.; Lakin J.R.; Zhou L.; Hong P.","Wang, Yifei (57207005080); Wang, Liqin (57155540700); Zhou, Zhengyang (57221803591); Laurentiev, John (57352037300); Lakin, Joshua R. (54879864000); Zhou, Li (56518549100); Hong, Pengyu (14630423200)","57207005080; 57155540700; 57221803591; 57352037300; 54879864000; 56518549100; 14630423200","Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases","2024","Journal of Biomedical Informatics","156","","104677","","","","2","10.1016/j.jbi.2024.104677","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196478760&doi=10.1016%2fj.jbi.2024.104677&partnerID=40&md5=23f4b7f1ce86992aa1ea6f86d2fc5fdd","Brandeis University, Waltham, MA, United States; Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States","Wang Y., Brandeis University, Waltham, MA, United States; Wang L., Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Zhou Z., Brandeis University, Waltham, MA, United States; Laurentiev J., Brigham and Women's Hospital, Boston, MA, United States; Lakin J.R., Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States, Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, United States; Zhou L., Brigham and Women's Hospital, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Hong P., Brandeis University, Waltham, MA, United States","Objective: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences. Methods: We created five datasets from Mass General Brigham's electronic health records (EHR), each focusing on a different chronic condition: congestive heart failure (CHF), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), chronic liver disease (CLD), and dementia. For each dataset, we developed separate machine learning models to predict 1-year mortality and examined racial disparities by comparing prediction performances between Black and White individuals. We compared racial fairness evaluation between the overall Black and White individuals versus their counterparts who were Black and matched White individuals identified by propensity score matching, where the systematic differences were mitigated. Results: We identified significant differences between Black and White individuals in age, gender, marital status, education level, smoking status, health insurance type, body mass index, and Charlson comorbidity index (p-value < 0.001). When examining matched Black and White subpopulations identified through propensity score matching, significant differences between particular covariates existed. We observed weaker significance levels in the CHF cohort for insurance type (p = 0.043), in the CKD cohort for insurance type (p = 0.005) and education level (p = 0.016), and in the dementia cohort for body mass index (p = 0.041); with no significant differences for other covariates. When examining mortality prediction models across the five study cohorts, we conducted a comparison of fairness evaluations before and after mitigating systematic differences. We revealed significant differences in the CHF cohort with p-values of 0.021 and 0.001 in terms of F1 measure and Sensitivity for the AdaBoost model, and p-values of 0.014 and 0.003 in terms of F1 measure and Sensitivity for the MLP model, respectively. Discussion and conclusion: This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings. © 2024 Elsevier Inc.","Chronic Disease; Electronic Health Records; Fairness Analysis; Machine Learning; Mortality Prediction; Racism","Aged; Black or African American; Chronic Disease; Electronic Health Records; Female; Heart Failure; Humans; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Racism; White People; Adaptive boosting; Clinical research; E-learning; Health insurance; Machine learning; Pulmonary diseases; Records management; Chronic disease; Electronic health; Electronic health record; Fairness analyse; Fairness evaluation; Health records; Machine learning models; Machine-learning; Mortality prediction; Racism; adult; age distribution; aged; Article; Black person; body mass; Caucasian; Charlson Comorbidity Index; chronic disease; chronic kidney failure; chronic liver disease; chronic obstructive lung disease; clinical evaluation; cohort analysis; comparative study; congestive heart failure; controlled study; dementia; educational status; electronic health record; fairness; female; gender; health insurance; human; machine learning; major clinical study; male; marriage; middle aged; mortality risk; people by smoking status; population structure; prediction; propensity score; race difference; racial disparity; racism; retrospective study; sensitivity analysis; sociodemographics; African American; chronic disease; heart failure; mortality; racism; Forecasting","","","","","National Institutes of Health, NIH; U.S. National Library of Medicine, NLM, (1R01LM014239); U.S. National Library of Medicine, NLM","Funding text 1: This study is supported by the NIH/NLM R01LM014239 and Brigham & Women's Physician's Organization.; Funding text 2: This study is supported by the NIH/NLM 1R01LM014239 and Brigham & Women\u2019s Physician\u2019s Organization. ","Fiscella K., Sanders M.R., Racial and ethnic disparities in the quality of health care, Annual Rev. Public Health., 37, pp. 375-394, (2016); Flores G.; (2017); Siddiqi A.A., Wang S., Quinn K., Nguyen Q.C., Christy A.D., Racial disparities in access to care under conditions of universal coverage, Am. J. Prevent. Med., 50, 2, pp. 220-225, (2016); Wheeler S.M., Bryant A.S., Racial and ethnic disparities in health and health care, Obstetr. Gynecol. Clin., 44, 1, pp. 1-11, (2017); Nelson A., Unequal treatment: confronting racial and ethnic disparities in health care, J. Natl. Med. Assoc., 94, 8, (2002); (2018); Smedley B.D., Stith A.Y., Nelson A.R., The healthcare environment and its relation to disparities. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, (2003); Dignum V., Baldoni M., Baroglio C., Caon M., Chatila R., Dennis L., Et al., (2018); Dressel J., Farid H., The accuracy, fairness, and limits of predicting recidivism, Sci. Adv., (2018); Chen I.Y., Szolovits P., Ghassemi M., Can AI help reduce disparities in general medical and mental health care?, AMA J. Ethics, 21, 2, pp. 167-179, (2019); Obermeyer Z., Powers B., Vogeli C., Mullainathan S., Dissecting racial bias in an algorithm used to manage the health of populations, Science., 366, 6464, pp. 447-453, (2019); Castelnovo A., Crupi R., Greco G., Regoli D., Penco I.G., Cosentini A.C., A clarification of the nuances in the fairness metrics landscape, Scientific Reports., 12, 1, (2022); Mehrabi N., Morstatter F., Saxena N., Lerman K., Galstyan A., A survey on bias and fairness in machine learning, ACM Computing Surveys (CSUR)., 54, 6, pp. 1-35, (2021); Meng C., Trinh L., Xu N., Enouen J., Liu Y., Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset, Scientific Reports., 12, 1, (2022); Huang J., Galal G., Etemadi M., Vaidyanathan M., Evaluation and mitigation of racial bias in clinical machine learning models: scoping review, JMIR Medical Informatics., 10, 5, (2022); Zafar M.B., Valera I., Gomez Rodriguez M., Gummadi K.P., (2017); Dwork C., Hardt M., Pitassi T., Reingold O., Zemel R., editors. Fairness through awareness, (2012); Kusner M.J., Loftus J., Russell C., Silva R., Counterfactual fairness, Adv. Neural Inform. Process. Syst., 30, (2017); Vyas D.A., Eisenstein L.G., Jones D.S., Hidden in plain sight—reconsidering the use of race correction in clinical algorithms, Mass Med. Soc, pp. 874-882, (2020); Gianfrancesco M.A., Tamang S., Yazdany J., Schmajuk G., Potential biases in machine learning algorithms using electronic health record data, JAMA Int. Med., 178, 11, pp. 1544-1547, (2018); Skelly A.C., Dettori J.R., Brodt E.D., Assessing bias: the importance of considering confounding, Evidence-Based Spine-Care J., 3, 1, pp. 9-12, (2012); Rajkomar A., Hardt M., Howell M.D., Corrado G., Chin M.H., Ensuring fairness in machine learning to advance health equity, Ann. Int. Med., 169, 12, pp. 866-872, (2018); Allen A., Mataraso S., Siefkas A., Burdick H., Braden G., Dellinger R.P., Et al., A racially unbiased, machine learning approach to prediction of mortality: algorithm development study, JMIR Public Health Surveillance, 6, 4, (2020); Kelley A.S., Bollens-Lund E.; Patel R.V., Kelley A.S., Kamal A.H., The denominator: evolving the electronic medical record to discover who needs palliative care, J. Palliative Med., 21, 1, pp. 9-10, (2017); Wang Y., Zhou Z., Wang L., Laurentiev J., Hou P., Zhou L., Hong P., (2023); Austin P.C., An introduction to propensity score methods for reducing the effects of confounding in observational studies, Multivariate Beha. Res., 46, 3, pp. 399-424, (2011); Caliendo M., Kopeinig S., Some practical guidance for the implementation of propensity score matching, J. Econ. Surv., 22, 1, pp. 31-72, (2008); Kline A., Luo Y., (2022); Zhang Z., Kim H.J., Lonjon G., Zhu Y., Balance diagnostics after propensity score matching, Ann. Translat. Med., 7, 1, (2019); Antonakis J., Lalive R., (2011); Hong G., Yu B., Effects of kindergarten retention on children's social-emotional development: an application of propensity score method to multivariate, multilevel data, Dev. Psychol., 44, 2, (2008); Staff J., Patrick M.E., Loken E., Maggs J.L., Teenage alcohol use and educational attainment, J. Stud. Alcohol Drug., 69, 6, pp. 848-858, (2008); Wyse A.E., Keesler V., Schneider B., Assessing the effects of small school size on mathematics achievement: a propensity score-matching approach, Teachers College Record., 110, 9, pp. 1879-1900, (2008); Ye Y., Kaskutas L.A., Using propensity scores to adjust for selection bias when assessing the effectiveness of Alcoholics Anonymous in observational studies, Drug Alcohol Dependence., 104, 1-2, pp. 56-64, (2009); Fix E., Hodges J.L.; Brookhart M.A., Schneeweiss S., Rothman K.J., Glynn R.J., Avorn J., Sturmer T., Variable selection for propensity score models, Am. J. Epidemiol., 163, 12, pp. 1149-1156, (2006); Garrido M.M., Kelley A.S., Paris J., Roza K., Meier D.E., Morrison R.S., Aldridge M.D., Methods for constructing and assessing propensity scores, Health Serv. Res., 49, 5, pp. 1701-1720, (2014); Ho D.E., Imai K., King G., Stuart E.A., Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference, Polit. Anal., 15, 3, pp. 199-236, (2007); Imbens G.W., Nonparametric estimation of average treatment effects under exogeneity: a review, Rev. Econ. Statist., 86, 1, pp. 4-29, (2004); Hardt M., Price E., Srebro N., Equality of opportunity in supervised learning, Adv. Neural Inform. Process. Syst., 29, (2016); Kozodoi N., Jacob J., Lessmann S., Fairness in credit scoring: assessment, implementation and profit implications, Eur. J. Operat. Res., 297, (2022); Li F., Wu P., Ong H.H., Peterson J.F., Wei W.-Q., Zhao J., Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction, J. Biomed. Inform., 138, (2023); Wang L., Foer D., MacPhaul E., Lo Y.-C., Bates D.W., Zhou L., PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes, J. Biomed. Inform., 125, (2022); Wang L., Foer D., Bates D.W., Boyce J.A., Zhou L., Risk factors for hospitalization, intensive care, and mortality among patients with asthma and COVID-19, J. Allergy Clin. Immunol., 146, 4, pp. 808-812, (2020)","P. Hong; Computer Science Department, Brandeis University, Waltham, 415 South St, 02453, United States; email: hongpeng@brandeis.edu","","Academic Press Inc.","","","","","","15320464","","JBIOB","38876453","English","J. Biomed. Informatics","Article","Final","","Scopus","2-s2.0-85196478760"
"Rauseo M.; Perrini M.; Gallo C.; Mirabella L.; Mariano K.; Ferrara G.; Santoro F.; Tullo L.; La Bella D.; Vetuschi P.; Cinnella G.","Rauseo, Michela (55503099500); Perrini, Marco (57221372824); Gallo, Crescenzio (7103169759); Mirabella, Lucia (16230821300); Mariano, Karim (57194019202); Ferrara, Giuseppe (57201117521); Santoro, Filomena (58363281900); Tullo, Livio (6507841199); La Bella, Daniela (57200912134); Vetuschi, Paolo (54950102200); Cinnella, Gilda (6603598364)","55503099500; 57221372824; 7103169759; 16230821300; 57194019202; 57201117521; 58363281900; 6507841199; 57200912134; 54950102200; 6603598364","Machine learning and predictive models: 2 years of Sars-CoV-2 pandemic in a single-center retrospective analysis","2022","Journal of Anesthesia, Analgesia and Critical Care","2","1","42","","","","3","10.1186/s44158-022-00071-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166348267&doi=10.1186%2fs44158-022-00071-6&partnerID=40&md5=f46a62044e639657080500e345096edb","Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Department of Clinical and Experimental Medicine “InfoLab” Bioinformatics Facility Head, University Hospital “Policlinico Riuniti”, Viale Pinto 1, Foggia, 71122, Italy","Rauseo M., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Perrini M., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Gallo C., Department of Clinical and Experimental Medicine “InfoLab” Bioinformatics Facility Head, University Hospital “Policlinico Riuniti”, Viale Pinto 1, Foggia, 71122, Italy; Mirabella L., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Mariano K., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Ferrara G., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Santoro F., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Tullo L., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; La Bella D., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Vetuschi P., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy; Cinnella G., Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Viale Pinto, 1, Foggia, 71122, Italy","Background: Since January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome. Methods: We deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO2/FiO2 ratio on ICU admission, and the respiratory support modalities before the orotracheal intubation and the intubation timing (early vs late with a 48-h hospital length of stay cutoff). We further collected the ICU and hospital lengths of stay expressed in days of hospitalization, hospital location (high dependency unit, HDU, ED), and length of stay before and after ICU admission; the in-hospital mortality; and the in-ICU mortality. We performed univariate, bivariate, and multivariate statistical analyses. Results: SARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO2/FiO2 ratio on ICU admission and NIV. No significant correlations with sex, obesity, arterial hypertension, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, diabetes mellitus, dyslipidemia, and neither MEWS nor NEWS on ED admission were observed. Considering all the pre-ICU variables, none of the machine learning algorithms performed well in developing a prediction model accurate enough to predict the outcome although a secondary multivariate analysis focused on the ventilation modalities and the main outcome confirmed how the choice of the right ventilatory support with the right timing is crucial. Conclusion: In our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases. © The Author(s) 2022.","Acute respiratory failure; COVID-19; Emergency department; Intensive care unit; Length of stay; Machine learning; Mechanical ventilation; MEWS; NEWS; Non-invasive ventilation; Predictive models","","","","","","","","Myrstad M., Et al., National Early Warning Score 2 (NEWS2) on admission predicts severe disease and in-hospital mortality from COVID-19 – a prospective cohort study, pp. 1-8, (2020); Gidari A., Socio G.V.D., Sabbatini S., Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2, . Infect Dis (Auckl), 52, pp. 698-704, (2020); Colombo C.J., Performance Analysis of the National Early Warning Score and Modified Early Warning Score in the Adaptive COVID-19 Treatment Trial Cohort, Crit Care Explor, 3, 7, (2019); Veldhuis L., Et al., Early warning scores to assess the probability of critical illness in patients with COVID-19, pp. 901-905, (2021); Zhao Z., Chen A., Hou W., Graham J.M., Li H., Richman P.S., Thode H.C., Singer A.J., Duong T.Q., Prediction model and risk scores of ICU admission and mortality in COVID-19, PLoS ONE, 15, 7, (2020); Hu H., Yao N., Qiu Y., Comparing rapid scoring systems in mortality prediction of critically ill patients with novel coronavirus disease, Acad Emerg Med, 27, 6, pp. 461-468, (2020); Shanbehzadeh M., Orooji A., Kazemi-Arpanahi H., Comparing of data mining techniques for predicting in-hospital mortality among patients with covid-19, J Biostat Epidemiol, 7, 2, pp. 154-173, (2021); Josephus B.O., Nawir A.H., Wijaya E., Moniaga J.V., Ohyver M., Predict mortality in patients infected with COVID-19 virus based on observed characteristics of the patient using logistic regression, Procedia Comput Sci, 179, pp. 871-877, (2021); Karthikeyan A., Garg A., Vinod P.K., Priyakumar U.D., Machine learning based clinical decision support system for early COVID-19 mortality prediction, Front Public Health, 9, (2021); Banoei M.M., Dinparastisaleh R., Zadeh A.V., Mirsaeidi M., Machine learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying, Crit Care, pp. 1-14, (2021); Noy O., Coster D., Metzger M., Atar I., Tsarfaty S.S., OPEN A machine learning model for predicting deterioration of COVID-19 inpatients, Sci Rep., pp. 1-9, (2022); Youssef A., Et al., Development and validation of early warning score systems for COVID-19 patients, pp. 105-117, (2021); Chen Y., Ouyang L., Bao F.S., Li Q., Han L., A multimodality machine learning approach to differentiate severe and nonsevere COVID-19 : model development and validation corresponding author, (2021); Fu Y., Zhong W., Liu T., Li J., Xiao K., Ma X., Et al., Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques, Front Public Health, 10, (2022); The Jamovi Project, (2021); Menzella F., Barbieri C., Fontana M., Scelfo C., Castagnetti C., Ghidoni G., Et al., Effectiveness of noninvasive ventilation in COVID-19 related-acute respiratory distress syndrome, Clin Respir J, 15, 7, pp. 779-787, (2021); Oranger M., Gonzalez-Bermejo J., Dacosta-Noble P., Et al., Continuous positive airway pressure to avoid intubation in SARS-CoV-2 pneumonia: a two-period retrospective case-control study, Eur Respir J, 56, (2020); Avdeev S.N., Et al., Noninvasive ventilation for acute hypoxemic respiratory failure in patients with COVID-19, Am J Emerg Med, 39, pp. 154-157, (2020); Rauseo M., Et al., SARS-CoV-2 pneumonia succesfully treated with CPAP and cycles of tripod position: a case report, BMC Anesthesiol, 21, pp. 1-5, (2021); Coskun E., Et al., Choices and outcomes of the oldest old admitted during the first wave of COVID-19 in, New York City, (2022); Yang X., Et al., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study, Lancet Respir Med, 8, 5, pp. 475-481, (2020); Grasselli G., Et al., Baseline Characteristics and Outcomes of 1591 Patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy, JAMA, 323, 16, pp. 1574-1581, (2020); Zhang L., Yan X., Fan Q., Liu H., Liu X., Liu Z., Zhang Z., D-dimer levels on admission to predict in-hospital mortality in patients with COVID-19, J Thromb Haemost., 18, 6, pp. 1324-1329, (2020); Liu F., Li L., Xu M., Wu J., Luo D., Zhu Y., Li B., Song X., Zhou X., Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19, J Clin Virol., 127, (2020); Scioscia G., Mirabella L., Tondo P., Maci F., Giganti G., Tullo L., Rauseo M., Gambetti G., Padovano F.P., Gallo C., Cinnella G., Foschino Barbaro M.P., Lacedonia D., Impact of CT scan phenotypes in clinical manifestations, management and outcomes of hospitalised patient.; Sanyaolu A., Okorie C., Marinkovic A., Patidar R., Younis K., Desai P., Hosein Z., Padda I., Mangat J., Altaf M., Comorbidity and its impact on patients with COVID-19. SN Compr, Clin Med, 2, 8, pp. 1069-1076, (2020); Shamout F.E., Et al., An arti fi cial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department, (2021); Gao Y., Machine learning based early warning system enables accurate mortality risk prediction for COVID-19, Nat Commun, pp. 1-10, (2020)","M. Rauseo; Department of Anesthesia and Intensive Care Medicine, University Hospital “Policlinico Riuniti di Foggia”, University of Foggia, Foggia, Viale Pinto, 1, 71122, Italy; email: michela.rauseo@unifg.it","","BioMed Central Ltd","","","","","","27313786","","","","English","J. Anesth. Analg. Crit. Care","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85166348267"
"Ramsey A.; Wu A.C.; Bender B.G.; Portnoy J.","Ramsey, Allison (57188880432); Wu, Ann Chen (56047460900); Bender, Bruce G. (7103130887); Portnoy, Jay (7006066184)","57188880432; 56047460900; 7103130887; 7006066184","Teleallergy: Where Have We Been and Where Are We Going?","2023","Journal of Allergy and Clinical Immunology: In Practice","11","1","","126","131","5","3","10.1016/j.jaip.2022.08.032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138115699&doi=10.1016%2fj.jaip.2022.08.032&partnerID=40&md5=ebf54af83ade80bc4ccde6a259fb5bc2","Rochester Regional Health, Rochester, NY, United States; Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass, United States; National Jewish Health, Denver, Colo; Children's Mercy Hospital, Kansas City, Mo","Ramsey A., Rochester Regional Health, Rochester, NY, United States; Wu A.C., Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Mass, United States; Bender B.G., National Jewish Health, Denver, Colo; Portnoy J., Children's Mercy Hospital, Kansas City, Mo","Telemedicine uptake in allergy/immunology was slow before the coronavirus disease 2019 pandemic, but has accelerated since. This review examines where telemedicine has been in allergy/immunology and where it is headed in the future. Focus is placed on patient, physician, and health care professional satisfaction with telemedicine, capacity to expand access to allergy/immunology care, cost considerations, the regulatory environment, and future applications of telemedicine including adherence monitoring, wearable biosensors, artificial intelligence, and machine learning addressed. © 2022 American Academy of Allergy, Asthma & Immunology","Access; Adherence monitoring; Healthcare provider satisfaction; Patient satisfaction; Teleallergy; Telehealth; Telemedicine","Artificial Intelligence; COVID-19; Humans; Hypersensitivity; Patient Satisfaction; Physicians; Telemedicine; nitric oxide; allergic rhinitis; allergy; Article; artificial intelligence; asthma; Asthma Control Test; atopic dermatitis; attention deficit hyperactivity disorder; bronchiectasis; clinical audit; clinical decision making; clinical decision support system; community care; coronavirus disease 2019; cost effectiveness analysis; directly observed therapy; food allergy; food insecurity; health care access; health care cost; health care personnel; health care policy; health insurance; health service; heart rate; human; immunology; machine learning; motivation; outpatient care; pandemic; patient monitoring; patient satisfaction; pharmacist; pharmacy (shop); physician; point of care testing; respiratory therapist; social determinants of health; spirometry; telehealth; telemedicine; telemonitoring; urticaria; wheezing; hypersensitivity; physician","","nitric oxide, 10102-43-9","","","","","Abboud S., Bruderman I., Assessment of a new transtelephonic portable spirometer, Thorax, 51, pp. 407-410, (1996); Nouilhan P., Dutau G., The pediatrician and the telephone [in French], Arch Pediatr, 2, pp. 891-894, (1995); Chan D.S., Callahan C.W., Sheets S.J., Moreno C.N., Malone F.J., An Internet-based store-and-forward video home telehealth system for improving asthma outcomes in children, Am J Health Syst Pharm, 60, pp. 1976-1981, (2003); Staicu M.L., Holly A.M., Conn K.M., Ramsey A., The use of telemedicine for penicillin allergy skin testing, J Allergy Clin Immunol Pract, 6, pp. 2033-2040, (2018); Portnoy J.M., Waller M., De Lurgio S., Dinakar C., Telemedicine is as effective as in-person visits for patients with asthma, Ann Allergy Asthma Immunol, 117, pp. 241-245, (2016); Mustafa S.S., Vadamalai K., Ramsey A., Patient satisfaction with in-person, video, and telephone allergy/immunology evaluations during the COVID-19 pandemic, J Allergy Clin Immunol Pract, 9, pp. 1858-1863, (2021); Lanier K., Kuruvilla M., Shih J., Patient satisfaction and utilization of telemedicine services in allergy: an institutional survey, J Allergy Clin Immunol Pract, 9, pp. 484-486, (2021); Du M., Papazian E., Adams D., Caballero N., Portugal L., Das P., Et al., Patient satisfaction with telemedicine is noninferior to in-office visits: lessons from a tertiary rhinology and endoscopic skull base surgery practice, Int Forum Allergy Rhinol, 12, pp. 802-804, (2022); Ragamin A., de Wijs L.E.M., Hijnen D.J., Arends N.J.T., Schuttelaar M.L.A., Pasmans S., Et al., Care for children with atopic dermatitis in the Netherlands during the COVID-19 pandemic: lessons from the first wave and implications for the future, J Dermatol, 48, pp. 1863-1870, (2021); Mounessa J.S., Chapman S., Braunberger T., Qin R., Lipoff J.B., Dellavalle R.P., Et al., A systematic review of satisfaction with teledermatology, J Telemed Telecare, 24, pp. 263-270, (2018); Law T., Cronin C., Schuller K., Jing X., Bolon D., Phillips B., Conceptual framework to evaluate health care professionals’ satisfaction in utilizing telemedicine, J Am Osteopath Assoc, 119, pp. 435-445, (2019); Mack D.P., Hanna M.A., Abrams E.M., Wong T., Soller L., Erdle S.C., Et al., Virtually supported home peanut introduction during COVID-19 for at-risk infants, J Allergy Clin Immunol Pract, 8, pp. 2780-2783, (2020); Riley P.E., Fischer J.L., Nagy R.E., Watson N.L., McCoul E.D., Tolisano A.M., Et al., Patient and provider satisfaction with telemedicine in otolaryngology, OTO Open, 5, (2021); Lang D.M., The impact of telemedicine as a disruptive innovation on allergy and immunology practice, Ann Allergy Asthma Immunol, 128, pp. 146-151, (2022); Hare N., Bansal P., Bajowala S.S., Abramson S.L., Chervinskiy S., Corriel R., Et al., Work Group Report: COVID-19: unmasking telemedicine, J Allergy Clin Immunol Pract, 8, pp. 2461-2473.e3, (2020); Portnoy J., Waller M., Elliott T., Telemedicine in the era of COVID-19, J Allergy Clin Immunol Pract, 8, pp. 1489-1491, (2020); Halterman J.S., Fagnano M., Tajon R.S., Tremblay P., Wang H., Butz A., Et al., Effect of the School-Based Telemedicine Enhanced Asthma Management (SB-TEAM) program on asthma morbidity: a randomized clinical trial, JAMA Pediatr, 172, (2018); Crabtree-Ide C., Lillvis D.F., Nie J., Fagnano M., Tajon R.S., Tremblay P., Et al., Evaluating the financial sustainability of the school-based telemedicine asthma management program, Popul Health Manag, 24, pp. 664-674, (2021); Gilkey M.B., Kong W.Y., Kennedy K.L., Heisler-MacKinnon J., Faugno E., Gwinn B., Et al.; World Health Organization. Social determinants of health, (2022); Justvig S.; Waibel K.H., Synchronous telehealth for outpatient allergy consultations: a 2-year regional experience, Ann Allergy Asthma Immunol, 116, pp. 571-575.e1, (2016); Waibel K.H., Bickel R.A., Brown T., Outcomes from a regional synchronous tele-allergy service, J Allergy Clin Immunol Pract, 7, pp. 1017-1021, (2019); Berlinski A., Chervinskiy S.K., Simmons A.L., Leisenring P., Harwell S.A., Lawrence D.J., Et al., Delivery of high-quality pediatric spirometry in rural communities: a novel use for telemedicine, J Allergy Clin Immunol Pract, 6, pp. 1042-1044, (2018); Gillette C., Loughlin C.E., Sleath B.L., Williams D.M., Davis S.D., Quality of pulmonary function testing in 3 large primary care pediatric clinics in rural North Carolina, N C Med J, 72, pp. 105-110, (2011); Locke E.R., Thomas R.M., Woo D.M., Nguyen E.H.K., Tamanaha B.K., Press V.G., Et al., Using video telehealth to facilitate inhaler training in rural patients with obstructive lung disease, Telemed J E Health, 25, pp. 230-236, (2019); Brown W., Scott D., Friesner D., Schmitz T., Impact of telepharmacy services as a way to increase access to asthma care, J Asthma, 54, pp. 961-967, (2017); Health Resources & Services Administration. Medicare payment policies during COVID-19. 2022. Updated March 4, 2022; American Hospital Association. Fact sheet: telehealth 2022; Sawka M.N., Friedl K.E., Emerging wearable physiological monitoring technologies and decision aids for health and performance, J Appl Physiol (1985), 124, pp. 430-431, (2018); Center for Connected Health Policy. Out of state providers, (2022); Federation of State Medical Boards. 11,000th medical license issued through Interstate Medical Licensure Compact process. 2022. Updated August 13, 2020; Interstate Medical Licensure Compact. U.S. state participation in the Compact. 2022. Updated March 10, 2022; Bender B.G., Nonadherence to asthma treatment: getting unstuck, J Allergy Clin Immunol Pract, 4, pp. 849-851, (2016); Nides M.A., Tashkin D.P., Simmons M.S., Wise R.A., Li V.C., Rand C.S., Improving inhaler adherence in a clinical trial through the use of the nebulizer chronolog, Chest, 104, pp. 501-507, (1993); Wamboldt F.S., Bender B.G., O'Connor S.L., Gavin L.A., Wamboldt M.Z., Milgrom H., Et al., Reliability of the model MC-311 MDI chronolog, J Allergy Clin Immunol, 104, pp. 53-57, (1999); Farr S.J., Rowe A.M., Rubsamen R., Taylor G., Aerosol deposition in the human lung following administration from a microprocessor controlled pressurised metered dose inhaler, Thorax, 50, pp. 639-644, (1995); Julius S.M., Sherman J.M., Hendeles L., Accuracy of three electronic monitors for metered-dose inhalers, Chest, 121, pp. 871-876, (2002); Safioti G., Granovsky L., Li T., Reich M., Cohen S., Hadar Y., (2019); Moore A., Preece A., Sharma R., Heaney L.G., Costello R.W., Wise R.A., Et al., A randomised controlled trial of the effect of a connected inhaler system on medication adherence in uncontrolled asthmatic patients, Eur Respir J, 57, (2021); Greiwe J., Nyenhuis S.M., Wearable technology and how this can be implemented into clinical practice, Curr Allergy Asthma Rep, 20, (2020); Ring B., Burbank A.J., Mills K., Ivins S., Dieffenderfer J., Hernandez M.L., Validation of an app-based portable spirometer in adolescents with asthma, J Asthma, 58, pp. 497-504, (2021); Ramos Hernandez C., Nunez Fernandez M., Pallares Sanmartin A., Mouronte Roibas C., Cerdeira Dominguez L., Botana Rial M.I., Et al., Validation of the portable Air-Smart Spirometer, PLoS One, 13, (2018); Chan A.H.Y., Foot H., Pearce C.J., Horne R., Foster J.M., Harrison J., Effect of electronic adherence monitoring on adherence and outcomes in chronic conditions: a systematic review and meta-analysis, PLoS One, 17, (2022); Chan Y.Y., Wang P., Rogers L., Tignor N., Zweig M., Hershman S.G., Et al., The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit, Nat Biotechnol, 35, pp. 354-362, (2017); Lucas R.W., Dees J., Reynolds R., Rhodes B., Hendershot R.W., Cloud-computing and smartphones: tools for improving asthma management and understanding environmental triggers, Ann Allergy Asthma Immunol, 114, pp. 431-432, (2015); Pike K.C., Akhbari M., Kneale D., Harris K.M., Interventions for autumn exacerbations of asthma in children, Paediatr Respir Rev, 27, pp. 37-39, (2018); Sharma A., Badea M., Tiwari S., Marty J.L., Wearable biosensors: an alternative and practical approach in healthcare and disease monitoring, Molecules, 26, (2021); Ajami S., Teimouri F., Features and application of wearable biosensors in medical care, J Res Med Sci, 20, pp. 1208-1215, (2015); Jones L., Hui A., Phan C.M., Read M.L., Azar D., Buch J., Et al., CLEAR—Contact lens technologies of the future, Cont Lens Anterior Eye, 44, pp. 398-430, (2021); Lai C.I., Lee C.F., Wei F.J., Smart clothing as a noninvasive method to measure the physiological cardiac parameters, Healthcare (Basel), 9, (2021); Dauletbaev N., Oftring Z.S., Akik W., Michaelis-Braun L., Korel J., Lands L.C., Et al.; Davoudi N., Lafci B., Ozbek A., Dean-Ben X.L., Razansky D., Deep learning of image- and time-domain data enhances the visibility of structures in optoacoustic tomography, Opt Lett, 46, pp. 3029-3032, (2021); Patel U.K., Anwar A., Saleem S., Malik P., Rasul B., Patel K., Et al., Artificial intelligence as an emerging technology in the current care of neurological disorders, J Neurol, 268, pp. 1623-1642, (2021)","A. Ramsey; Rochester Regional Health, Rochester, 222 Alexander St, Ste 3000, 14607; email: allison.ramsey@rochesterregional.org","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36064184","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85138115699"
"Pop A.; Fanca A.; Gota D.I.; Valean H.","Pop, Adela (57748325800); Fanca, Alexandra (57190385278); Gota, Dan Ioan (36620115100); Valean, Honoriu (24377220100)","57748325800; 57190385278; 36620115100; 24377220100","Monitoring and Prediction of Indoor Air Quality for Enhanced Occupational Health","2023","Intelligent Automation and Soft Computing","35","1","","925","940","15","3","10.32604/iasc.2023.025069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132189932&doi=10.32604%2fiasc.2023.025069&partnerID=40&md5=a792f9702783eccca3a7b94111e200de","Technical University of Cluj Napoca, Cluj Napoca, Romania","Pop A., Technical University of Cluj Napoca, Cluj Napoca, Romania; Fanca A., Technical University of Cluj Napoca, Cluj Napoca, Romania; Gota D.I., Technical University of Cluj Napoca, Cluj Napoca, Romania; Valean H., Technical University of Cluj Napoca, Cluj Napoca, Romania","The amount of moisture in the air is represented by relative humidity (RH); an ideal level of humidity in the interior environment is between 40% and 60% at temperatures between 18° and 20° Celsius. When the RH falls below this level, the environment becomes dry, which can cause skin dryness, irritation, and discomfort at low temperatures. When the humidity level rises above 60%, a wet atmosphere develops, which encourages the growth of mold and mites. Asthma and allergy symptoms may occur as a result. Human health is harmed by excessive humidity or a lack thereof. Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere; certain models disinfect and purify the water, reducing the spread of bacteria. The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper. The Netatmo station was used to acquire the data needed in the application. The client is an Android application that allows the user to monitor air quality over a period of their choosing. For a good monitoring process, the Netatmo modules were used to collect data from both environments (indoor: temperature (T), RH, carbon dioxide (CO2), atmospheric pressure (Pa), noise and outdoor: T and RH). The data is stored in a database, using MySQL. The Android application allows the user to view the evolution of the measured parameters in the form of graphs. Also, the paper presents a prediction model of RH using Azure Machine Learning Studio (Azure ML Studio). The model is evaluated using metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Relative Squared Error (RSE) and Coefficient of Determination (CoD). © 2023, Tech Science Press. All rights reserved.","carbon dioxide; humidity; indoor air quality; Machine learning; relative humidity","","","","","","UK Research and Innovation, UKRI, (42012); European Social Fund, ESF, (56437/24.07.2019)","Acknowledgement: This paper was financially supported by the Project \u201CEntrepreneurial competences and excellence research in doctoral and postdoctoral programs-ANTREDOC\u201D, project cofounded by the European Social Fund financing agreement No. 56437/24.07.2019.","Godson R. A., Oyewale M. M., Fakunle G. A., Indoor air quality and risk factors associated with respiratory conditions in Nigeria, Current Air Quality Issues, (2015); Al Horr Y., Arif M., Katafygiotou M., Mazroei A., Kaushik A., Et al., Impact of indoor environmental quality on occupant well-being and comfort: A review of the literature, International Journal of Sustainable Built Environment, 5, 1, pp. 1-11, (2016); Saini J., Dutta M., Marques G., A comprehensive review on indoor air quality monitoring systems for enhanced public health, Sustainable Environment Research, 30, 6, pp. 1-12, (2020); Zanni S., Lalli F., Foschi E., Bonoli A., Mantecchini L., Indoor air quality real-time monitoring in airport terminal areas: An opportunity for sustainable management of micro-climatic parameters, Sensors, 18, 11, pp. 3798-3812, (2018); Meciarova L., Vilcekova S., Burdova E. K., Kiselak J., Factors effecting the total volatile organic compound (TVOC) concentrations in Slovak households, International Journal of Environmental Research and Public Health, 14, 12, pp. 1443-1468, (2017); Introduction to indoor air quality, (2017); How Does Humidity Affect Air Pollution?, (2021); Wolkoff P., Indoor air humidity, air quality, and health–An overview, International Journal of Hygiene and Environmental Health, 221, 3, pp. 376-390, (2018); Derby M. M., Hamehkasi M., Eckels S., Hwang G. M., Jones B., Et al., Update of the scientific evidence for specifying lower limit relative humidity levels for comfort, health, and indoor environmental quality in occupied spaces (RP-1630), Science and Technology for the Built Environment, 23, 1, pp. 30-45, (2017); Schulze F., Gao X., Virzonis D., Damiati S., Schneider M. R., Et al., Air quality effects on human health and approaches for its assessment through microfluidic chips, Genes (Basel), 8, 10, (2017); Nasriddinov A., Rumyantseva M., Marikutsa A., Gaskov A., Lee J.-H., Et al., Sub-ppm formaldehyde detection by n-n tio2@sno2 nanocomposites, Sensors, 19, 14, (2019); Sun S., Zheng X., Villalba-Diez J., Ordieres-Mere J., Indoor air-quality data-monitoring system: Long-term monitoring benefits, Sensors, 19, 19, pp. 4157-4174, (2019); De La Iglesia D. H., De Paz J. F., Gonzalez G. V., Barriuso A. L., Bajo J., A context-aware indoor air quality system for sudden infant death syndrome prevention, Sensors, 18, 3, (2018); Kang J., Il Hwang K.-, A comprehensive real-time indoor air-quality level indicator, Sustainability, 8, 9, (2016); Marques G., Pitarma R., An indoor monitoring systems for ambient assisted living based on internet of things architecture, International Journal of Environment Research and Public Health, 13, 11, (2016); Abraham S., Li X., A Cost-effective wireless sensor network system for indoor air quality monitoring applications, Proc. the 9th Int. Conf. on Future Networks and Communications (FNC-2014), Procedia Computer Science, 34, pp. 165-171, (2014); Kim J., Hwangbo H., Sensor-based optimization model for air quality improvement in home IoT, Sensors, 18, 4, (2018); Sung Y., Lee S., Kim Y., Park H., Development of a smart air quality monitoring system and its operation, Asian Journal of Atmospheric Environment, 13, 1, pp. 30-38, (2019); Ruffer D., Hoehne F., Buhler J., New digital metal-oxide (mox) sensor platform, Sensors, 18, 4, pp. 1052-1063, (2018); Ji H., Zhang H., Zhou K., Liu P., Design of indoor environment monitoring system based on internet of things, Proc. IOP Conf. Series Earth and Environmental Science, 252, (2019); Benammar M., Abdaoui A., Ahmad S. H. M., Touati F., Kadri A., A modular IoT platform for real-time indoor air quality monitoring, Sensors, 18, 2, pp. 581-598, (2018); Chen S.-Y., Chen C.-Y., Use of multi-agent theory to resolve complex indoor air quality control problems, Sensors, 19, 5, pp. 1206-1221, (2019); Sung W.-T., Hsiao S.-J., Shih J.-A., Construction of indoor thermal comfort environmental monitoring system based on the IoT architecture, Journal of Sensors, 2019, pp. 1-16, (2019); Kim J., Chu C., Shin S., ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring, Sensors Journal, IEEE, 14, 12, pp. 4230-4244, (2014); Broday D. M., Wireless distributed environmental sensor networks for air pollution 426 measurement—the promise and the current reality, Sensors, 17, 10, pp. 2263-2279, (2017); Ahn J., Shin D., Kim K., Yang J., Indoor air quality analysis using deep learning with sensor data, Sensors, 17, 11, pp. 1-13, (2017); Adeleke J. A., Moodley D., Rens G., Adewumi A. O., Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control, Sensors, 17, 4, pp. 1-23, (2017); Holzinger A., Interactive machine learning for health informatics: When do we need the human-in-the-loop?, Brain Informatics, 3, pp. 119-131, (2016); Mahta P., Bukov M., Wang C.-H., Day A. G. R., Richardson C., Et al., A High-bias, low-variance introduction to machine learning for physicists, Physics Reports, 810, pp. 1-124, (2019); Fumo D., Types of machine learning algorithms you should know, Towards Data Sciente, (2017); Witten I. H., Frank E., Hall M. A., Pal C. J., Data Mining: Practical Machine Learning Tools and Techniques, (2016); Li Blanca, How to evaluate model performance in Azure Machine Learning Studio, 2021; Chicco D., Warrens M. J., Jurman G., The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Computer Science, 7, (2021)","A. Fanca; Technical University of Cluj Napoca, Cluj Napoca, Romania; email: Alexandra.Fanca@aut.utcluj.ro","","Tech Science Press","","","","","","10798587","","","","English","Intell. Autom. Soft Comp.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85132189932"
"Li E.; Ai F.; Liang C.","Li, Enguang (58821126400); Ai, Fangzhu (58820134400); Liang, Chunguang (35738877500)","58821126400; 58820134400; 35738877500","A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study","2023","Frontiers in Public Health","11","","1348803","","","","2","10.3389/fpubh.2023.1348803","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182704589&doi=10.3389%2ffpubh.2023.1348803&partnerID=40&md5=aa6dd58b73ec21d1084b26b102d4b367","Department of Nursing, Jinzhou Medical University, Jinzhou, China","Li E., Department of Nursing, Jinzhou Medical University, Jinzhou, China; Ai F., Department of Nursing, Jinzhou Medical University, Jinzhou, China; Liang C., Department of Nursing, Jinzhou Medical University, Jinzhou, China","Objective: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. Study design: This is a cross-sectional study. Methods: Data from three cycles (2005–2006, 2007–2008, and 2015–2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. Results: The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19–0.25 and 0.45–0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. Conclusion: This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment. Copyright © 2024 Li, Ai and Liang.","depression; machine learning; NHANES; OSAHS; prediction models","Adult; Cross-Sectional Studies; Depression; Humans; Machine Learning; Nutrition Surveys; Sleep Apnea, Obstructive; Syndrome; adult; cross-sectional study; depression; human; machine learning; nutrition; sleep apnea syndromes; syndrome","","","","","Social Science Foundation of Liaoning Province, (L21CSH005)","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Social Science Planning Foundation of Liaoning Province (L21CSH005). 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Liang; Department of Nursing, Jinzhou Medical University, Jinzhou, China; email: liangchunguang@jzmu.edu.cn","","Frontiers Media SA","","","","","","22962565","","","38259742","English","Front. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85182704589"
"Choi Y.; Cha J.; Choi S.","Choi, Yongjun (58858692400); Cha, Junho (58533691000); Choi, Sungkyoung (37025636200)","58858692400; 58533691000; 37025636200","Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)","2024","BMC Bioinformatics","25","1","","","","","1","10.1186/s12859-024-05677-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183820099&doi=10.1186%2fs12859-024-05677-x&partnerID=40&md5=547bea0ef85414e1c61f225c2b2a0b9b","Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea; Department of Mathematical Data Science, College of Science and Convergence Technology, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea","Choi Y., Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea; Cha J., Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea; Choi S., Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea, Department of Mathematical Data Science, College of Science and Convergence Technology, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea","Background: Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). Results: First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen′s Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. Conclusions: Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods. © The Author(s) 2024.","Asthma; Disease risk prediction model; Ensemble methods; Genome-wide association study; GWAS; KoGES; Korean Genome and Epidemiology Study; Large-scale genetic data; Machine learning methods; Oversampling; Penalized methods","Algorithms; Bayes Theorem; Genome-Wide Association Study; Humans; Machine Learning; Republic of Korea; Adaptive boosting; Epidemiology; Forecasting; Genes; Learning systems; Logistic regression; Nearest neighbor search; Support vector machines; Asthma; Disease risk prediction model; Disease risks; Ensemble methods; Genetic data; Genome-wide association studies; GWAS; Korean genome and epidemiology study; Large-scale genetic data; Large-scales; Machine learning methods; Over sampling; Penalized method; Risk prediction models; algorithm; Bayes theorem; epidemiology; genome-wide association study; human; machine learning; South Korea; Diseases","","","","","Artificial Intelligence Convergence Innovation Human Resources Development; Hanyang University ERICA; Ministry of Science, ICT and Future Planning, MSIP, (2018R1C1B6008277, 2022R1F1A1072274); Ministry of Science, ICT and Future Planning, MSIP; National Research Foundation of Korea, NRF, (2019M3E5D3073365); National Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP, (–00155885); Institute for Information and Communications Technology Promotion, IITP","This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2018R1C1B6008277 and 2022R1F1A1072274). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS-2022–00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)). This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (2019M3E5D3073365). This study was conducted using bioresources from National Biobank of Korea, the Korea Disease Control and Prevention Agency, Republic of Korea (KBN-2020-106). 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Choi; Department of Applied Artificial Intelligence, College of Computing, Hanyang University, Ansan, 55 Hanyang-daehak-ro, Sangnok-gu, 15588, South Korea; email: day0413@hanyang.ac.kr","","BioMed Central Ltd","","","","","","14712105","","BBMIC","38308205","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85183820099"
"Oppenheimer J.; Bender B.; Sousa-Pinto B.; Portnoy J.","Oppenheimer, John (7102985153); Bender, Bruce (7103130887); Sousa-Pinto, Bernardo (55982726300); Portnoy, Jay (7006066184)","7102985153; 7103130887; 55982726300; 7006066184","Use of Technology to Improve Adherence in Allergy/Immunology","2024","Journal of Allergy and Clinical Immunology: In Practice","12","12","","3225","3233","8","1","10.1016/j.jaip.2024.07.017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201503452&doi=10.1016%2fj.jaip.2024.07.017&partnerID=40&md5=c6c1b08977cd26bc31ca08adf1d01f3f","Pulmonary and Allergy Associates, Cedar Knolls, NJ, United States; Center for Health Promotion, National Jewish Health, Denver, Colo, United States; MEDCIDS – Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; Children's Mercy Hospital, Kansas City, Mo, United States","Oppenheimer J., Pulmonary and Allergy Associates, Cedar Knolls, NJ, United States; Bender B., Center for Health Promotion, National Jewish Health, Denver, Colo, United States; Sousa-Pinto B., MEDCIDS – Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; Portnoy J., Children's Mercy Hospital, Kansas City, Mo, United States","The integration of technology into health care has shown significant promise in enhancing patient adherence, particularly in the field of allergy/immunology. This article explores the multifaceted approaches through which digital health interventions can be used to improve adherence rates among patients with allergic diseases and immunologic disorders. By reviewing recent advancements in telemedicine, mobile health applications, wearable devices, and digital reminders, as well as smart inhalers, we aim to provide a comprehensive overview of how these technologies can support patients in managing their conditions. The analysis highlights the role of personalized digital health plans, which, through the use of artificial intelligence and machine learning algorithms, can offer tailored advice, monitor symptoms, and adjust treatment protocols in real time. Moreover, the article discusses the impact of electronic health records and patient portals in fostering a collaborative patient-provider relationship, thereby enhancing communication and adherence. The integration of these technologies has been shown to not only improve clinical outcomes but also increase patient satisfaction and engagement. © 2024 American Academy of Allergy, Asthma & Immunology","Allergy/immunology; Digital health interventions; Medication adherence; Technology integration; Telemedicine","Allergy and Immunology; Electronic Health Records; Humans; Hypersensitivity; Medication Adherence; Mobile Applications; Patient Compliance; Reminder Systems; Telemedicine; benralizumab; omalizumab; allergic disease; allergy; Article; artificial intelligence; asthma; cystic fibrosis; digital health; disease exacerbation; electronic health record; follow up; health care personnel; health insurance; health promotion; heart rate; hospitalization; human; immunology; machine learning; patient compliance; patient education; patient engagement; quality of life; self care; telemedicine; virtual reality; hypersensitivity; immunology; medication compliance; mobile application; patient compliance; reminder system; telemedicine; therapy","","benralizumab, 1044511-01-4; omalizumab, 242138-07-4","","","","","Julius R.J., Novitsky M.A., Dubin W.R., Medication adherence: a review of the literature and implications for clinical practice, J Psychiatr Pract, 15, pp. 34-44, (2009); Larenas-Linnemann D., Phippatanukal W., Rank M., An overview of adherence—what it is and why it is important, J Allergy Clin Immunol Pract, 12, pp. 3180-3188, (2024); Portnoy J.M., Waller M., De Lurgio S., Dinakar C., Telemedicine is as effective as in-person visits for patients with asthma, Ann Allergy Asthma Immunol, 117, pp. 241-245, (2016); Taylor L., Waller M., Portnoy J.M., Telemedicine for allergy services to rural communities, J Allergy Clin Immunol Pract, 7, pp. 2554-2559, (2019); Blake K.V., Telemedicine and adherence monitoring in children with asthma, Curr Opin Pulm Med, 27, pp. 37-44, (2021); Kvedariene V., Burzdikaite P., Cesnaviciute I., mHealth and telemedicine utility in the monitoring of allergic diseases, Front Allergy, 3, (2022); 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Portnoy; Division of Allergy, Asthma, Pulmonary and Sleep Medicine, Children's Mercy Hospital, Kansas City, 2401 Gillham Rd, 64108, United States; email: jportnoy@cmh.edu","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","39074604","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85201503452"
"Peng J.; Liu X.; Cai Z.; Huang Y.; Lin J.; Zhou M.; Xiao Z.; Lai H.; Cao Z.; Peng H.; Wang J.; Xu J.","Peng, Junfeng (57215069992); Liu, Xujiang (57956939900); Cai, Ziwei (57958285800); Huang, Yuanpei (59203213900); Lin, Jiayi (59202756200); Zhou, Mi (57215054258); Xiao, Zhenpei (59202988500); Lai, Huifang (59203214100); Cao, Zhihao (59203214200); Peng, Hui (59202988600); Wang, Jihong (57202089443); Xu, Jun (57222984803)","57215069992; 57956939900; 57958285800; 59203213900; 59202756200; 57215054258; 59202988500; 59203214100; 59203214200; 59202988600; 57202089443; 57222984803","Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease","2024","Heliyon","10","13","e33566","","","","1","10.1016/j.heliyon.2024.e33566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197432772&doi=10.1016%2fj.heliyon.2024.e33566&partnerID=40&md5=67d22fe9a6a9c5fc89e60b70f77905a2","Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510640, China","Peng J., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Liu X., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Cai Z., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Huang Y., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Lin J., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Zhou M., Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510640, China; Xiao Z., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Lai H., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Cao Z., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Peng H., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Wang J., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China; Xu J., Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou, 510303, China","Background: The high prevalence, morbidity and mortality, and disease heterogeneity of chronic obstructive pulmonary disease (COPD) result in the scattered data derived from patient visits in different medical units. The huge cost of integrating the scattered data for analysis and modeling, as well as the legal demand for patient privacy protection lead to the emergence of data island. Objectives: On the premise of protecting patient privacy, integrating scattered data of patients from different medical units for high-quality modeling is beneficial to promoting the development of digital health. Based on this, we develop a distributed COPD disease diagnosis system termed COPD average federated learning (COPD_AVG_FL) using FedAvg. Methods: First, to build the COPD_AVG_FL, the clinical data of COPD patients from the real world is collected and the data pre-processing is performed to clean the incorrect data, outlier samples and missing values. Then, a classical federated learning architecture is designed as COPD_AVG_FL. Finally, to evaluate the established COPD_AVG_FL system, we develop Centralized Machine Learning (CML). Conclusions: Our results suggest that, with the assistance of COPD_AVG_FL, the absolute improvement rates are 13.4% (accuracy), 13.3% (precision), 12.8% (recall), 13.1% (F1-Score) and 12.9% (AUC) on the test data, respectively. The decoupling between model training and raw training data protects the patients' privacy, and helps to securely integrate more COPD data from different medical units to generate a more comprehensive model COPD_AVG_FL. This approach promotes the landing of wise information technology of medicine for COPD in the real clinical world. Code for our model will be made available at https://github.com/Cczhh/COPD_AVG_FL/tree/master. © 2024 The Authors","Chronic obstructive pulmonary disease; Federated learning; Privacy protection; Real-world data","","","","","","Scientific research platforms and projects of colleges and universities in Guangdong Province, (2021ZDZX3016); Higher education special, (2022GXJK287, 2023KTSCX098)","The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The work described in this paper has been supported by the Scientific research platforms and projects of colleges and universities in Guangdong Province (No. 2021ZDZX3016), Higher education special (No. 2022GXJK287), and Featured innovation projects (No. 2023KTSCX098).","Pauwels R.A., Rabe K.F., Burden and clinical features of chronic obstructive pulmonary disease (COPD), Lancet, 364, 9434, pp. 613-620, (2004); Ignatavicius D.D., Workman M.L., Rebar C., Medical-Surgical Nursing-E-Book: Concepts for Interprofessional Collaborative Care, (2017); Global initiative for chronic obstructive lung disease. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease report: 2021; Health and safety executive. 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"Lee Z.-J.; Yang M.-R.; Hwang B.-J.","Lee, Zne-Jung (7102838736); Yang, Ming-Ren (57578113900); Hwang, Bor-Jiunn (7201453946)","7102838736; 57578113900; 7201453946","A Sustainable Approach to Asthma Diagnosis: Classification with Data Augmentation, Feature Selection, and Boosting Algorithm","2024","Diagnostics","14","7","723","","","","1","10.3390/diagnostics14070723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190145869&doi=10.3390%2fdiagnostics14070723&partnerID=40&md5=f22a37f6ff667a7c989c4e002140acf8","Department of Electronic and Information Engineering, School of Advanced Manufacturing, Fuzhou University, Quanzhou, 362200, China; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 235, Taiwan; College of Information Science, Ming Chuan University, Taoyuan, 333, Taiwan","Lee Z.-J., Department of Electronic and Information Engineering, School of Advanced Manufacturing, Fuzhou University, Quanzhou, 362200, China; Yang M.-R., Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, 235, Taiwan; Hwang B.-J., College of Information Science, Ming Chuan University, Taoyuan, 333, Taiwan","Asthma is a diverse disease that affects over 300 million individuals globally. The prevalence of asthma has increased by 50% every decade since the 1960s, making it a serious global health issue. In addition to its associated high mortality, asthma generates large economic losses due to the degradation of patients’ quality of life and the impairment of their physical fitness. Asthma research has evolved in recent years to fully analyze why certain diseases develop based on a variety of data and observations of patients’ performance. The advent of new techniques offers good opportunities and application prospects for the development of asthma diagnosis methods. Over the last few decades, techniques like data mining and machine learning have been utilized to diagnose asthma. Nevertheless, these traditional methods are unable to address all of the difficulties associated with improving a small dataset to increase its quantity, quality, and feature space complexity at the same time. In this study, we propose a sustainable approach to asthma diagnosis using advanced machine learning techniques. To be more specific, we use feature selection to find the most important features, data augmentation to improve the dataset’s resilience, and the extreme gradient boosting algorithm for classification. Data augmentation in the proposed method involves generating synthetic samples to increase the size of the training dataset, which is then utilized to enhance the training data initially. This could lessen the phenomenon of imbalanced data related to asthma. Then, to improve diagnosis accuracy and prioritize significant features, the extreme gradient boosting technique is used. The outcomes indicate that the proposed approach performs better in terms of diagnostic accuracy than current techniques. Furthermore, five essential features are extracted to help physicians diagnose asthma. © 2024 by the authors.","asthma; data augmentation; extreme gradient boosting algorithm; feature selection; generative adversarial networks","algorithm; Article; asthma; clinician; controlled study; data mining; deep learning; diagnostic accuracy; feature selection; female; human; information retrieval; learning algorithm; machine learning; major clinical study; male; mononuclear cell; multiclass classification","","","","","Fuzhou University, FZU; Ming Chuan University; Fujian Province, (FJKX-2022XKB032)","This research was partially supported by Fujian Province research Grant No. FJKX-2022XKB032. It was also supported by Fuzhou University and Ming Chuan University. ","Liu J.X., Zhang Y., Yuan H.Y., Liang J., The treatment of asthma using the Chinese Material Medical, J. 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(IJSC), 2, pp. 26-33, (2011); Tsang K.C., Pinnock H., Wilson A.M., Shah S.A., Application of Machine Learning to Support Self-Management of Asthma with mHealth, Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 5673-5677, (2020); Ansari A.Q., Gupta N.K., Automatic diagnosis of asthma using neurofuzzy system, Proceedings of the 2012 Fourth International Conference on Computational Intelligence and Communication Networks, pp. 819-823, (2012); Agnikula Kshatriya B.S., Sagheb E., Wi C.I., Yoon J., Seol H.Y., Juhn Y., Sohn S., Identification of asthma control factor in clinical notes using a hybrid deep learning model, BMC Med. Inform. Decis. Mak, 21, (2021); Temraz M., Keane M.T., Solving the class imbalance problem using a counterfactual method for data augmentation, Mach. Learn. Appl, 9, (2022); Frid-Adar M., Klang E., Amitai M., Goldberger J., Greenspan H., Synthetic data augmentation using GAN for improved liver lesion classification, Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 289-293, (2018); Farahanipad F., Rezaei M., Nasr M.S., Kamangar F., Athitsos V., A Survey on GAN-Based Data Augmentation for Hand Pose Estimation Problem, Technologies, 10, (2022); Asselman A., Khaldi M., Aammou S., Enhancing the prediction of student performance based on the machine learning XGBoost algorithm, Interact. Learn. Environ, 31, pp. 3360-3379, (2023); Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., Generative adversarial nets, Adv. Neural Inf. Process. Syst, 27, pp. 2672-2680, (2014); Odena A., Olah C., Shlens J., Conditional image synthesis with auxiliary classifier gans, Proceedings of the International Conference on Machine Learning, pp. 2642-2651; Mahima R., Maheswari M., Roshana S., Priyanka E., Mohanan N., Nandhini N., A comparative analysis of the most commonly used activation functions in deep neural network, 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 6–8 July 2023, pp. 1334-1339, (2023); Kuleshov V., Zoph B., Le Q.V., Reformer: The efficient transformer, Proceedings of the 37th International Conference on Machine Learning, 119, pp. 6623-6634; Chen T., Guestrin C., Xgboost: A scalable tree boosting system, Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785-794; Wang T., Bian Y., Zhang Y., Hou X., Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm, Comput. Geosci, 170, (2023); Yang M.R., Lee Z.J., Lee C.Y., Peng B.Y., Huang H., An intelligent algorithm based on bacteria foraging optimization and robust fuzzy algorithm to analyze asthma data, Int. J. Fuzzy Syst, 19, pp. 1181-1189, (2017); Sun Z., Wang G., Li P., Wang H., Zhang M., Liang X., An improved random forest based on the classification accuracy and correlation measurement of decision trees, Expert Syst. Appl, 237, (2024); Feng J., Duan T., Bao J., Li Y., An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed, Sci. Total Environ, 915, (2024); Mahmoudinazlou S., Kwon C., A hybrid genetic algorithm for the min–max Multiple Traveling Salesman Problem, Comput. Oper. Res, 162, (2024); Kollem S., An efficient method for MRI brain tumor tissue segmentation and classification using an optimized support vector machine, Multimedia Tools and Applications, (2024)","Z.-J. Lee; Department of Electronic and Information Engineering, School of Advanced Manufacturing, Fuzhou University, Quanzhou, 362200, China; email: johnlee@fzu.edu.cn","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85190145869"
"Meirman T.D.; Shapira B.; Balicer R.D.; Rokach L.; Dagan N.","Meirman, Tomer David (59376931300); Shapira, Bracha (7004315829); Balicer, Ran D. (6603332644); Rokach, Lior (9276243500); Dagan, Noa (55756975600)","59376931300; 7004315829; 6603332644; 9276243500; 55756975600","Trends of common laboratory biomarkers after SARS-CoV-2 infection","2024","Journal of Infection","89","6","106318","","","","1","10.1016/j.jinf.2024.106318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206973649&doi=10.1016%2fj.jinf.2024.106318&partnerID=40&md5=aff1d64baa6450ab38e909e663f38a46","Ben-Gurion University of the Negev, Israel; Clalit Research Institute, Israel; The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, United States","Meirman T.D., Ben-Gurion University of the Negev, Israel; Shapira B., Ben-Gurion University of the Negev, Israel; Balicer R.D., Ben-Gurion University of the Negev, Israel, Clalit Research Institute, Israel, The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, United States; Rokach L., Ben-Gurion University of the Negev, Israel; Dagan N., Ben-Gurion University of the Negev, Israel, Clalit Research Institute, Israel, The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute, United States","Background: Most studies that explore the long-term effects of COVID-19 are based on subjectively reported symptoms, while laboratory-measured biomarkers are mainly examined in studies of relatively small cohorts. This study investigates the long-term effects of SARS-CoV-2 infection on common laboratory biomarkers. Methods: We utilized a retrospective cohort of SARS-CoV-2 infected individuals and rigorously matched controls based on demographic and clinical characteristics, examining 63 common laboratory biomarkers. Additional lab-specific cohorts were matched with an additional criterion of baseline biomarker values. Differences in biomarkers over a 12-month follow-up were analyzed using standardized mean difference-in-differences. Results: The general cohort included 361,061 matched pairs, with 26M laboratory results. The effects on most biomarkers lasted 1–4 months and were consistent with anticipated changes after acute viral infections. Some biomarkers presented prolonged effects, consistent across the general and lab-specific cohorts. One group of such findings included a 7–8 month decrease in WBC counts, mainly driven by decreased counts of neutrophils, monocytes, and basophils. Potassium levels were decreased for 3–5 months. Vaccinated individuals’ data suggested potentially smaller effects on WBCs, but cohort sizes limited this analysis. Conclusions: This study explores SARS-CoV-2 infection effects on common laboratory biomarkers, characterizing the direction and duration of these effects on the largest infected cohort to date. The effects of most biomarkers resolve in the first months following infection. The most notable longer-lasting effects involved the immune system. Further research is required to characterize the magnitude of these effects among specific individuals. © 2024","Laboratory biomarkers; Long-COVID; Retrospective cohort study; SARS-CoV-2 infection effect","Adult; Aged; Biomarkers; COVID-19; Female; Humans; Leukocyte Count; Male; Middle Aged; Retrospective Studies; SARS-CoV-2; Young Adult; albumin; biological marker; C reactive protein; creatinine; ferritin; hemoglobin; liver enzyme; transferrin; biological marker; adolescent; adult; aged; anemia; Article; asthma; blood cell count; chronic kidney failure; cohort analysis; controlled study; documentation; erythrocyte sedimentation rate; female; hematocrit; human; hypertension; inflammation; insulin dependent diabetes mellitus; leukocyte count; liver disease; machine learning; male; middle aged; neutrophil count; non insulin dependent diabetes mellitus; obesity; polymerase chain reaction; red blood cell distribution width; sensitivity analysis; Severe acute respiratory syndrome coronavirus 2; sickle cell anemia; social status; thalassemia; trend study; underweight; vaccination; blood; coronavirus disease 2019; diagnosis; retrospective study; young adult","","C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; ferritin, 9007-73-2; hemoglobin, 9008-02-0; transferrin, 82030-93-1; Biomarkers, ","","","","","(2023); Dagan N., Barda N., Kepten E., Perchik S., Miron O., Hernan M.A., Et al., BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting, N Engl J Med, 384, pp. 1412-1423, (2021); Le T.T., Andreadakis Z., Kumar A., Roman R.G., Tollefsen S., Saville M., Et al., The COVID-19 vaccine development landscape, Nat Rev Drug Discov, 19, pp. 305-306, (2020); Shah M.M., Joyce B., Plumb I.D., Plumb I.D., Sahakian S., Feldstein L.R., Et al., Paxlovid Associated with Decreased Hospitalization Rate Among Adults with COVID-19 — United States, April–September 2022, MMWR Morb Mortal Wkly Rep, 71, pp. 1531-1537, (2022); Raveendran A., Jayadevan R., Sashidharan S., Long COVID: an overview, Diabetes Metab Syndr Clin Res Rev, 15, pp. 869-875, (2021); Al-Aly Z., Xie Y., Bowe B., High-dimensional characterization of post-acute sequelae of COVID-19, Nature, 594, pp. 259-264, (2021); Borch L., Holm M., Knudsen M., Ellermann-Eriksen S., Hagstroem S., Long COVID symptoms and duration in SARS-CoV-2 positive children—a nationwide cohort study, Eur J Pediatr, 181, pp. 1597-1607, (2022); Wulf Hanson S., Abbafati C., Aerts J.G., Al-Aly Z., Ashbaugh C., Ballouz T., Et al., Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021, JAMA, 328, 16, pp. 1604-1615, (2022); (2023); Mizrahi B., Sudry T., Flaks-Manov N., Yehezkelli Y., Kalkstein N., Akiva P., Et al., Long covid outcomes at one year after mild SARS-CoV-2 infection: nationwide cohort study, BMJ, 380, (2023); Al-Aly Z., Bowe B., Xie Y., Long COVID after breakthrough SARS-CoV-2 infection, Nat Med, 28, pp. 1461-1467, (2022); Ziauddeen N., Gurdasani D., O'Hara M.E., Hastie C., Roderick P., Yao G., Et al., Characteristics and impact of Long Covid: findings from an online survey, PLoS One, 17, (2022); Taquet M., Dercon Q., Harrison P.J., Six-month sequelae of post-vaccination SARS-CoV-2 infection: a retrospective cohort study of 10,024 breakthrough infections, Brain Behav Immun, 103, pp. 154-162, (2022); Xie Y., Choi T., Al-Aly Z., Postacute sequelae of SARS-CoV-2 infection in the pre-delta, delta, and omicron eras, N Engl J Med, 391, pp. 515-525, (2024); Notarte K.I., Catahay J.A., Velasco J.V., Pastrana A., Ver A.T., Pangillian F.C., Et al., Impact of COVID-19 vaccination on the risk of developing long-COVID and on existing long-COVID symptoms: a systematic review, eClinicalMedicine, 53, (2022); Huang C., Huang L., Wang Y., Li X., Ren L., Gu X., Kang, Et al., 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study, Lancet, 401, pp. e21-e33, (2023); Tran V.-T., Porcher R., Pane I., Ravaud P., Course of post COVID-19 disease symptoms over time in the ComPaRe long COVID prospective e-cohort, Nat Commun, 13, pp. 1-6, (2022); Tsilingiris D., Vallianou N.G., Karampela I., Christodoulatos G.S., Papavasileiou G., Petropoulou D., Et al., Laboratory findings and biomarkers in long COVID: what do we know so far? Insights into epidemiology, pathogenesis, therapeutic perspectives and challenges, Int J Mol Sci, 24, (2023); Lechuga G.C., Morel C.M., De-Simone S.G., Hematological alterations associated with long COVID-19, Front Physiol, 14, (2023); Wu Y., Kang L., Guo Z., Liu J., Liu M., Liang W., Incubation period of COVID-19 caused by unique SARS-CoV-2 strains: a systematic review and meta-analysis, JAMA Netw Open, 5, (2022); McAloon C., Collins A., Hunt K., Barber A., Byrne A.W., Butler F., Et al., Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research, BMJ Open, 10, (2020); (2023); Andrade C., Mean difference, standardized mean difference (SMD), and their use in meta-analysis: as simple as it gets, J Clin Psychiatry, 81, (2020); Goodman-Bacon A., Difference-in-differences with variation in treatment timing, J Econ, 225, pp. 254-277, (2021); Van Rossum G., Drake F.L., Python Reference Manual, Centrum voor Wiskunde en Informatica, An Introduction to Python, (2003); Chen T., Guestrin C., XGBoost: a scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), pp. 4768-4777, (2017); Antonelli M.; Gulhar R., Ashraf M.A., Jialal I., Physiology, acute phase reactants, StatPearls, (2023); Franco R.S., Measurement of red cell lifespan and aging, Transfus Med Hemother, 39, pp. 302-307, (2012); Weiss G., Goodnough L.T., Anemia of chronic disease, N Engl J Med, 352, pp. 1011-1023, (2005); Sun Y., Zhou J., Ye K., White blood cells and severe COVID-19: a Mendelian randomization study, J Pers Med, 11, (2021); Davis H.E., McCorkell L., Vogel J.M., Topol E.J., Long COVID: major findings, mechanisms and recommendations, Nat Rev Microbiol, 21, pp. 133-146, (2023); Desai S., Quraishi J., Citrin D., Prolonged self-resolving neutropenia following asymptomatic COVID-19 infection, Cureus, 13, (2021); Mank V.M.F., Mank J., Ogle J., Roberts J., Delayed, transient and self-resolving neutropenia following COVID-19 pneumonia, BMJ Case Rep, 14, (2021); Vance H., Maslach A., Stoneman E., Harmes K., Ransom A., Seagly K., Et al., Addressing post-COVID symptoms: a guide for primary care physicians, J Am Board Fam Med, 34, pp. 1229-1242, (2021); Huseynov A., Akin I., Duerschmied D., Scharf R.E., Cardiac arrhythmias in post-COVID syndrome: prevalence, pathology, diagnosis, and treatment, Viruses, 15, (2023); Alfano G., Ferrari A., Fontana F., Perrone R., Mori G., Ascione E., Et al., Hypokalemia in patients with COVID-19, Clin Exp Nephrol, 25, pp. 401-409, (2021); Moreno-Perez O., Leon-Ramirez J.-M., Fuertes-Kenneally L., Perdiguero M., Andres M., Garcia-Navarro M., Et al., Hypokalemia as a sensitive biomarker of disease severity and the requirement for invasive mechanical ventilation requirement in COVID-19 pneumonia: a case series of 306 Mediterranean patients, Int J Infect Dis, 100, pp. 449-454, (2020); Alnafiey M.O., Alangari A.M., Alarifi A.M., Abushara A., Persistent Hypokalemia post SARS-coV-2 infection, is it a life-long complication? Case report, Ann Med Surg, 62, pp. 358-361, (2021); Minniti C.P., Zaidi A.U., Nouraie M., Manwani D., Crouch G.D., Crouch A.S., Et al., Clinical predictors of poor outcomes in patients with sickle cell disease and COVID-19 infection, Blood Adv, 5, pp. 207-215, (2021); Aziz M., Fatima R., Lee-Smith W., Assaly R., The association of low serum albumin level with severe COVID-19: a systematic review and meta-analysis, Crit Care, 24, (2020); Zhou X., Chen D., Wang L., Zhao Y., Wei L., Chen Z., Et al., Low serum calcium: a new, important indicator of COVID-19 patients from mild/moderate to severe/critical, Biosci Rep, 40, (2020); Jiang S., Huang Q., Xie W., Lv C., Quan X., The association between severe COVID‐19 and low platelet count: evidence from 31 observational studies involving 7613 participants, Br J Haematol, 190, (2020)","T.D. Meirman; Ben-Gurion University of the Negev, Beer Sheva, David Ben Gurion Blvd, 1, Israel; email: meirmant@post.bgu.ac.il","","W.B. Saunders Ltd","","","","","","01634453","","JINFD","39423876","English","J. Infect.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85206973649"
"Gwatimba A.; Rosenow T.; Stick S.M.; Kicic A.; Iosifidis T.; Karpievitch Y.V.","Gwatimba, Alphons (57709915200); Rosenow, Tim (56335235800); Stick, Stephen M. (35373713500); Kicic, Anthony (6507472922); Iosifidis, Thomas (56069997800); Karpievitch, Yuliya V. (12807479100)","57709915200; 56335235800; 35373713500; 6507472922; 56069997800; 12807479100","AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma","2022","Journal of Personalized Medicine","12","5","809","","","","2","10.3390/jpm12050809","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130707151&doi=10.3390%2fjpm12050809&partnerID=40&md5=a210a9bd631cef2e8fced9de25ba6d68","Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia; School of Computer Science and Software Engineering, University of Western Australia, Nedlands, 6009, WA, Australia; Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Nedlands, 6009, WA, Australia; Division of Paediatrics, Medical School, University of Western Australia, Nedlands, 6009, WA, Australia; Department of Respiratory and Sleep Medicine, Perth Children’s Hospital, Nedlands, 6009, WA, Australia; Centre for Cell Therapy and Regenerative Medicine, School of Medicine, University of Western Australia, Nedlands, 6009, WA, Australia; School of Population Health, Curtin University, Bentley, 6102, WA, Australia; School of Biomedical Sciences, University of Western Australia, Nedlands, 6009, WA, Australia","Gwatimba A., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, School of Computer Science and Software Engineering, University of Western Australia, Nedlands, 6009, WA, Australia; Rosenow T., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Nedlands, 6009, WA, Australia; Stick S.M., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, Division of Paediatrics, Medical School, University of Western Australia, Nedlands, 6009, WA, Australia, Department of Respiratory and Sleep Medicine, Perth Children’s Hospital, Nedlands, 6009, WA, Australia, Centre for Cell Therapy and Regenerative Medicine, School of Medicine, University of Western Australia, Nedlands, 6009, WA, Australia; Kicic A., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, Division of Paediatrics, Medical School, University of Western Australia, Nedlands, 6009, WA, Australia, Centre for Cell Therapy and Regenerative Medicine, School of Medicine, University of Western Australia, Nedlands, 6009, WA, Australia, School of Population Health, Curtin University, Bentley, 6102, WA, Australia; Iosifidis T., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, Centre for Cell Therapy and Regenerative Medicine, School of Medicine, University of Western Australia, Nedlands, 6009, WA, Australia, School of Population Health, Curtin University, Bentley, 6102, WA, Australia; Karpievitch Y.V., Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, WA, Australia, School of Biomedical Sciences, University of Western Australia, Nedlands, 6009, WA, Australia","The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","artificial intelligence; asthma; bioinformatics; cell detection; cell migration; cell tracking; computational biology; deep learning; image analysis; wound repair","airway epithelium cell; algorithm; Article; artificial intelligence; asthma; cell migration; cell regeneration; cell tracking; controlled study; high throughput screening; human; human cell; respiratory epithelium; training; wound healing; wound healing assay","","","","","BHP-Telethon; Cystic Fibrosis Charitable Endowment Charles Bateman Charitable Trust; Wal-yan Respiratory Research Centre; Walyan Respiratory Research Centre; National Health and Medical Research Council, NHMRC, (1117668); UK Research and Innovation, UKRI, (103448)","Funding text 1: Funding: This work was funded by the Wal-yan Respiratory Research Centre Inspiration Award (2020), BHP-Telethon Kids Blue Sky Award (2019) and Cystic Fibrosis Charitable Endowment Charles Bateman Charitable Trust. S.M.S. is an NHMRC Practitioner Fellow (NHMRC1117668). A.K. is a Rothwell Family Fellow.; Funding text 2: This work was funded by the Walyan Respiratory Research Centre Inspiration Award (2020), BHP-Telethon Kids Blue Sky Award (2019) and Cystic Fibrosis Charitable Endowment Charles Bateman Charitable Trust. S.M.S. is an NHMRC Practitioner Fellow (NHMRC 1117668). A.K. is a Rothwell Family Fellow.","Allahverdian S., Basic Mechanism of Airway Epithelial Repair: Role of IL-13 and EGFR Glycosylation, (2008); Kicic A., Hallstrand T.S., Sutanto E.N., Stevens P.T., Kobor M.S., Taplin C., Pare P.D., Beyer R.P., Stick S.M., Knight D.A., Decreased fibronectin production significantly contributes to dysregulated repair of asthmatic epithelium, Am. J. Respir. Crit. Care Med, 181, pp. 889-898, (2010); Kicic A., Sutanto E.N., Stevens P.T., Knight D.A., Stick S.M., Intrinsic biochemical and functional differences in bronchial epithelial cells of children with asthma, Am. J. Respir. Crit. 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Teknol, 75, (2015); Emami N., Sedaei Z., Ferdousi R., Computerized cell tracking: Current methods, tools and challenges, Vis. Inform, 5, pp. 1-13, (2021); Ulman V., Maska M., Magnusson K.E.G., Ronneberger O., Haubold C., Harder N., Matula P., Matula P., Svoboda D., Radojevic M., Et al., An objective comparison of cell-tracking algorithms, Nat. Methods, 14, pp. 1141-1152, (2017); Tsai H.-F., Gajda J., Sloan T.F.W., Rares A., Shen A.Q., Usiigaci: Instance-aware cell tracking in stain-free phase contrast microscopy enabled by machine learning, SoftwareX, 9, pp. 230-237, (2019); Al-Zaben N., Medyukhina A., Dietrich S., Marolda A., Hunniger K., Kurzai O., Figge M.T., Automated tracking of label-free cells with enhanced recognition of whole tracks, Sci. 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Med. Imaging, 34, pp. 911-929, (2015); Magnusson K., klasma/BaxterAlgorithms, (2021); Wojke N., Bewley A., Paulus D., Simple online and realtime tracking with a deep association metric, Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645-3649, (2017); Cordelieres F., Manual Tracking, (2004); Milan A., Leal-Taixe L., Reid I., Roth S., Schindler K., MOT16: A Benchmark for Multi-Object Tracking, (2016)","A. Gwatimba; Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, 6009, Australia; email: alphons.gwatimba@telethonkids.org.au","","MDPI","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130707151"
"Alotaibi M.; Omar A.","Alotaibi, Mohammed (57188812127); Omar, Ahmed (56941398700)","57188812127; 56941398700","An Investigation of Asthma Experiences in Arabic Communities Through Twitter Discourse","2023","International Journal of Advanced Computer Science and Applications","14","5","","460","469","9","2","10.14569/IJACSA.2023.0140549","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161197690&doi=10.14569%2fIJACSA.2023.0140549&partnerID=40&md5=94fc10e94ba5339dcd6ef6b3e5c7015f","Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia; Department of Computer Science-Faculty of Science, Minia University, University Street,El-Minia, Minia, 1666, Egypt","Alotaibi M., Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia; Omar A., Department of Computer Science-Faculty of Science, Minia University, University Street,El-Minia, Minia, 1666, Egypt","Artificial intelligence technologies can effectively analyze the public opinions from social-media platforms like twitter. This study aims to employ the AI technology and big data to explore and discuss the common issues of asthma that patients share on Twitter platform in Arabic communities. The data was acquired using the Twitter API version 2. Latent Dirichlet Allocation was used for grouping data into two clusters which provide information and tips about the treatment and prevention of asthma and personal experiences with asthma, including symptoms, diagnosis, and the negative impact of asthma on the quality of life. Sentiment analysis and data frequency distribution techniques were used to analyze the data in both clusters. The data analysis of first indicated that individuals are interested in learning about different ways to treat asthma and potentially finding a permanent solution. The data analysis of second cluster indicated the existence of negative sentiments about asthma, which also included religious expressions for improving the condition. The study also discussed the differences in expressions among Arabic communities and other communities. © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.","Arab; Asthma; communities; LDA; semantic analysis; twitter","Data handling; Diagnosis; Diseases; Semantics; Social networking (online); Statistics; AI Technologies; Arab; Artificial intelligence technologies; Asthma; Community; LDA; Public opinions; Semantic analysis; Social media platforms; Twitter; Sentiment analysis","","","","","Ministry of Education in Saudi Arabia, (S-1442-0049)","ACKNOWLEDGMENT The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (S-1442-0049).","EAACI Global Atlas of Asthma, (2021); Alloghani M, Hussain A, Al-Jumeily D, Fergus P, Abuelma'Atti O, Hamden H., A mobile health monitoring application for obesity management and control using the internet-of-things, 2016 Sixth International Conference on Digital Information Processing and Communications [ICDIPC], (2016); O'Malley G, Dowdall G, Burls A, Perry IJ, Curran N., Exploring the usability of a mobile app for adolescent obesity management, JMIR mHealth and uHealth, 2, 2, (2014); Wang Y, Min J, Khuri J, Xue H, Xie B, Kaminsky LA, Et al., Effectiveness of mobile health interventions on diabetes and obesity treatment and management: systematic review of systematic reviews, JMIR mHealth and uHealth, 8, 4, (2020); Chavez S, Fedele D, Guo Y, Bernier A, Smith M, Warnick J, Et al., Mobile apps for the management of diabetes, Diabetes Care, 40, 10, pp. e145-e146, (2017); Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A., WellDoc™ mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction, Diabetes technology & therapeutics, 10, 3, pp. 160-168, (2008); Rodriguez AQ, Wagner AM., Mobile phone applications for diabetes management: A systematic review, Endocrinologia, diabetes y nutricion, 66, 5, pp. 330-337, (2019); Alotaibi MM, Istepanian R, Philip N., A mobile diabetes management and educational system for type-2 diabetics in Saudi Arabia [SAED], Mhealth, 2, (2016); Number of social network users of select social media platforms worldwide in 2019 and 2023Most popular social networks worldwide as of October 2021, ranked by number of active users; Buyya R, Calheiros RN, Dastjerdi AV., Big data: principles and paradigms, (2016); Pozzi F, Fersini E, Messina E, Liu B., Sentiment analysis in social networks, (2016); Malova E., Understanding online conversations about COVID-19 vaccine on Twitter: Vaccine hesitancy amid the Public Health Crisis, Communication Research Reports, 38, 5, pp. 346-356, (2021); Umar P, Akiti C, Squicciarini A, Rajtmajer S., Self-disclosure on Twitter during the COVID-19 pandemic: A network perspective, Machine Learning and Knowledge Discovery in Databases Applied Data Science Track, pp. 271-286, (2021); Malik A, Antonino A, Khan ML, Nieminen M., Characterizing HIV discussions and engagement on Twitter, Health and Technology, 11, 6, pp. 1237-1245, (2021); Akhila AM, Gayathri C, Srinivas B, Devi BSK., A review on sentiment analysis of Twitter data for diabetes classification and prediction, 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), (2022); Joshi A, Sparks R, McHugh J, Karimi S, Paris C, MacIntyre CR., Harnessing Tweets for Early Detection of an Acute Disease Event, Epidemiology, 31, 1, pp. 90-97, (2020); Ainley E, Witwicki C, Tallett A, Graham C., Using Twitter comments to understand people’s experiences of UK health care during the COVID-19 pandemic: Thematic and sentiment analysis, Journal of Medical Internet Research, 23, 10, (2021); Shah SHH, Noor S, Butt AS, Halepoto H., Twitter Research Synthesis for Health Promotion: A Bibliometric Analysis, Iran J Public Health, 50, 11, pp. 2283-2291, (2021); Gillingham G, Conway MA, Chapman WW, Casale MB, Pettigrew KB., # wheezing: A Content Analysis of Asthma-Related Tweets, Online Journal of Public Health Informatics, 5, 1, (2013); Carroll CL, Kaul V, Sala KA, Dangayach NS., Describing the digital footprints or “sociomes” of asthma for stakeholder groups on Twitter, ATS scholar, 1, 1, pp. 55-66, (2020); Kaul V, Szakmany T, Peters JI, Stukus D, Sala KA, Dangayach N, Et al., Quality of the discussion of asthma on twitter, Journal of Asthma, pp. 1-8, (2020); Social media - Statistics & Facts, (2021); Cambria E, Das D, Bandyopadhyay S, Feraco A., Affective computing and sentiment analysis, A practical guide to sentiment analysis, pp. 1-10, (2017); Byrd K, Mansurov A, Baysal O., Mining twitter data for influenza detection and surveillance, Proceedings of the International Workshop on Software Engineering in Healthcare Systems, (2016); Song S, Miled ZB., Digital immunization surveillance: monitoring flu vaccination rates using online social networks, 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems [MASS], (2017); Culotta A., Towards detecting influenza epidemics by analyzing Twitter messages, Proceedings of the first workshop on social media analytics, (2010); Freifeld CC, Brownstein JS, Menone CM, Bao W, Filice R, Kass-Hout T, Et al., Digital drug safety surveillance: monitoring pharmaceutical products in twitter, Drug safety, 37, 5, pp. 343-350, (2014); Tutubalina E, Nikolenko S., Exploring convolutional neural networks and topic models for user profiling from drug reviews, Multimedia Tools Appl, 77, 4, pp. 4791-4809, (2018); Klein A, Sarker A, Rouhizadeh M, O'Connor K, Gonzalez G., Detecting personal medication intake in Twitter: an annotated corpus and baseline classification system, BioNLP, 2017, (2017); Abu Farha I., Magdy W., Mazajak: An online Arabic sentiment analyser, Proceedings of the Fourth Arabic Natural Language Processing Workshop [Preprint], (2019); Ljevar V., Exploring the impact of socio-cognitive factors on adherence to asthma medication using traditional mixed methods and machine learning, (2022)","","","Science and Information Organization","","","","","","2158107X","","","","English","Intl. J. Adv.  Comput. Sci. Appl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85161197690"
"Chen Y.; Guo C.; Chung M.K.; Yi Q.; Wang X.; Wang Y.; Jiang B.; Liu Y.; Lan M.; Lin L.; Cai L.","Chen, Yujing (57425208900); Guo, Cuihua (57203147185); Chung, Ming Kei (57201749598); Yi, Quanying (58854647400); Wang, Xin (57192624834); Wang, Yuxuan (57425495700); Jiang, Bibo (58175165200); Liu, Yu (59123988300); Lan, Minyan (58994403500); Lin, Lizi (57200113045); Cai, Li (54955739800)","57425208900; 57203147185; 57201749598; 58854647400; 57192624834; 57425495700; 58175165200; 59123988300; 58994403500; 57200113045; 54955739800","The Associations of Prenatal Exposure to Fine Particulate Matter and Its Chemical Components with Allergic Rhinitis in Children and the Modification Effect of Polyunsaturated Fatty Acids: A Birth Cohort Study","2024","Environmental Health Perspectives","132","4","047010","","","","1","10.1289/EHP13524","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190855412&doi=10.1289%2fEHP13524&partnerID=40&md5=8a4be621f2d089f258c5ca682958f563","Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China; The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong; Department of Children Health Care, Dongguan Children’s Hospital, Guangdong, Dongguan, China; Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangdong, Guangzhou, China; Global Health Research Center, Duke Kunshan University, Jiangsu, Kunshan, China; Joint International Research Laboratory of Environment and Health, Ministry of Education, Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China; Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Guangzhou, China","Chen Y., Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China, The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong; Guo C., Department of Children Health Care, Dongguan Children’s Hospital, Guangdong, Dongguan, China; Chung M.K., The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong; Yi Q., Department of Children Health Care, Dongguan Children’s Hospital, Guangdong, Dongguan, China; Wang X., Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangdong, Guangzhou, China; Wang Y., Global Health Research Center, Duke Kunshan University, Jiangsu, Kunshan, China; Jiang B., Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China; Liu Y., Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China; Lan M., Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China; Lin L., Joint International Research Laboratory of Environment and Health, Ministry of Education, Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China; Cai L., Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangdong, Guangzhou, China, Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Guangzhou, China","BACKGROUND: Polyunsaturated fatty acids (PUFAs) have been shown to protect against fine particulate matter <2:5 lm in aerodynamic diameter (PM2:5)-induced hazards. However, limited evidence is available for respiratory health, particularly in pregnant women and their offspring. OBJECTIVES: We aimed to investigate the association of prenatal exposure to PM2:5 and its chemical components with allergic rhinitis (AR) in children and explore effect modification by maternal erythrocyte PUFAs. METHODS: This prospective birth cohort study involved 657 mother–child pairs from Guangzhou, China. Prenatal exposure to residential PM2:5 mass 2 and its components [black carbon (BC), organic matter (OM), sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+)] were estimated by an established spatiotemporal model. Maternal erythrocyte PUFAs during pregnancy were measured using gas chromatography. The diagnosis of AR and report of AR symptoms in children were assessed up to 2 years of age. We used Cox regression with the quantile-based g-computation approach to assess the individual and joint effects of PM2:5 components and examine the modification effects of maternal PUFA levels. RESULTS: Approximately 5:33% and 8.07% of children had AR and related symptoms, respectively. The average concentration of prenatal PM2:5 was 35:50 ± 5:31 lg/m3. PM2:5 was positively associated with the risk of developing AR [hazard ratio (HR) =1:85; 95% confidence interval (CI): 1.16, 2.96 per 5 lg/m3 ] and its symptoms (HR = 1:79; 95% CI: 1.22, 2.62 per 5 lg/m3) after adjustment for confounders. Similar associations were observed between individual PM2:5 components and AR outcomes. Each quintile change in a mixture of components was associated with an adjusted HR of 3.73 (95% CI: 1.80, 7.73) and 2.69 (95% CI: 1.55, 4.67) for AR and AR symptoms, with BC accounting for the largest contribution. Higher levels of n-3 docosapentaenoic acid and lower levels of n-6 linoleic acid showed alleviating effects on AR symptoms risk associated with exposure to PM2:5 and its components. CONCLUSION: Prenatal exposure to PM2:5 and its chemical components, particularly BC, was associated with AR/symptoms in early childhood. We highlight that PUFA biomarkers could modify the adverse effects of PM2:5 on respiratory allergy. © 2024, Public Health Services, US Dept of Health and Human Services. All rights reserved.","","Air Pollutants; Air Pollution; Child, Preschool; China; Cohort Studies; Environmental Exposure; Fatty Acids, Unsaturated; Female; Humans; Particulate Matter; Pregnancy; Prenatal Exposure Delayed Effects; Prospective Studies; Rhinitis, Allergic; biological marker; docosahexaenoic acid; fatty acid ester; icosapentaenoic acid; linoleic acid; nitrate; polyunsaturated fatty acid; sulfur dioxide; unsaturated fatty acid; air pollution; allergic rhinitis; allergy; Article; asthma; body mass; breast feeding; child; child health; cohort analysis; environmental exposure; female; follow up; gas chromatography; gestational age; head circumference; human; machine learning; major clinical study; male; menstrual cycle; particulate matter; particulate matter 2.5; prematurity; prenatal exposure; quality control; respiratory tract allergy; rhinorrhea; risk factor; sensitivity analysis; sneezing; temperature; transesterification; air pollutant; air pollution; China; pregnancy; preschool child; prospective study","","docosahexaenoic acid, 25167-62-8, 32839-18-2; icosapentaenoic acid, 10417-94-4, 1553-41-9, 25378-27-2, 32839-30-8; linoleic acid, 1509-85-9, 2197-37-7, 60-33-3, 822-17-3; nitrate, 14797-55-8; sulfur dioxide, 7446-09-5; Air Pollutants, ; Fatty Acids, Unsaturated, ; Particulate Matter, ","","","National Key Research and Development Program of China, NKRDPC, (2023YFC3905102); National Key Research and Development Program of China, NKRDPC; Basic and Applied Basic Research Foundation of Guangdong Province, (2023A1515030192); Basic and Applied Basic Research Foundation of Guangdong Province","This work was supported by the National Key Research and Development Project (2023YFC3905102) and the Guangdong Basic and Applied Basic Research Foundation (2023A1515030192).","Okubo K, Kurono Y, Ichimura K, Enomoto T, Okamoto Y, Kawauchi H, Et al., Japanese guidelines for allergic rhinitis 2020, Allergol Int, 69, 3, pp. 331-345, (2020); Chen F, Lin Z, Chen R, Norback D, Liu C, Kan H, Et al., The effects of PM (2.5) on asthmatic and allergic diseases or symptoms in preschool children of six Chinese cities, based on China, children, homes and health (CCHH) project, Environ Pollut, 232, pp. 329-337, (2018); 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Lin Z, Chen R, Jiang Y, Xia Y, Niu Y, Wang C, Et al., Cardiovascular benefits of fish-oil supplementation against fine particulate air pollution in China, J Am Coll Cardiol, 73, 16, pp. 2076-2085, (2019); Hodson L, Skeaff CM, Fielding BA., Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake, Prog Lipid Res, 47, 5, pp. 348-380, (2008); Zhang Z-L, Ho SC, Shi D-D, Zhan X-X, Wu Q-X, Xu L, Et al., Erythrocyte membrane n-3 PUFA are inversely associated with breast cancer risk among Chinese women, Br J Nutr, 131, 1, pp. 103-112, (2023); Ding D, Li Y-H, Xiao M-L, Dong H-L, Lin J-S, Chen G-D, Et al., Erythrocyte membrane polyunsaturated fatty acids are associated with incidence of metabolic syn-drome in middle-aged and elderly people–an 8.8-year prospective study, J Nutr, 150, 6, pp. 1488-1498, (2020); Weylandt KH., Docosapentaenoic acid derived metabolites and mediators-the new world of lipid mediator medicine in a nutshell, Eur J Pharmacol, 785, pp. 108-115, (2016); West LJ., Defining critical windows in the development of the human immune system, Hum Exp Toxicol, 21, 9–10, pp. 499-505, (2002); Sokola-Wysoczanska E, Wysoczanski T, Wagner J, Czy_z K, Bodkowski R, Lochynski S, Et al., Polyunsaturated fatty acids and their potential therapeu-tic role in cardiovascular system disorders—a review, Nutrients, 10, 10, (2018); Chen Y-J, Lin L-Z, Liu Z-Y, Wang X, Karatela S, Wang Y-X, Et al., Association between maternal gestational diabetes and allergic diseases in offspring: a birth cohort study, World J Pediatr, 19, 10, pp. 972-982, (2023); Black PN, Sharpe S., Dietary fat and asthma: is there a connection?, Eur Respir J, 10, 1, pp. 6-12, (1997); Kawabata T, Kagawa Y, Kimura F, Miyazawa T, Saito S, Arima T, Et al., Polyunsaturated fatty acid levels in maternal erythrocytes of Japanese women during pregnancy and after childbirth, Nutrients, 9, 3, (2017); Lu C, Liu Z, Liao H, Yang W, Li Q, Liu Q., Effects of early life exposure to home environmental factors on childhood allergic rhinitis: modifications by outdoor air pollution and temperature, Ecotoxicol Environ Saf, 244, (2022)","L. Cai; Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, No. 74 Zhongshan Road 2, Yuexiu District, Guangdong Province, 510080, China; email: caili5@mail.sysu.edu.cn","","Public Health Services, US Dept of Health and Human Services","","","","","","00916765","","","38630604","English","Environ. Health Perspect.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85190855412"
"Bonthada S.; Perumal S.P.; Naik P.P.; Padukudru M.A.; Rajan J.","Bonthada, Siva (58775124200); Perumal, Sankar Pariserum (57206855748); Naik, Poornanand Purushottam (58368108300); Padukudru, Mahesh A. (57202421244); Rajan, Jeny (23470813600)","58775124200; 57206855748; 58368108300; 57202421244; 23470813600","An automated deep learning pipeline for detecting user errors in spirometry test","2024","Biomedical Signal Processing and Control","90","","105845","","","","1","10.1016/j.bspc.2023.105845","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180370852&doi=10.1016%2fj.bspc.2023.105845&partnerID=40&md5=6d4edba42156332a44cdcf62e53ddd98","Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, Surathkal, India; Department of Respiratory Medicine, JSS Medical College, JSSAHER, Karnataka, Mysuru, India","Bonthada S., Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, Surathkal, India; Perumal S.P., Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, Surathkal, India; Naik P.P., Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, Surathkal, India; Padukudru M.A., Department of Respiratory Medicine, JSS Medical College, JSSAHER, Karnataka, Mysuru, India; Rajan J., Department of Computer Science and Engineering, National Institute of Technology Karnataka, Karnataka, Surathkal, India","Spirometer is used as a major diagnostic tool for obstructive airway diseases and a monitoring tool for therapy response and disease staging over time. It is a sophisticated medical device employed to quantify flow and volume of air exhaled by a subject during a specific testing period. The essential metrics obtained from the spirometry test, play a crucial role in enabling healthcare professionals to thoroughly evaluate the respiratory health and condition of the individual under examination. Several spirometer measurements including Forced Vital Capacity (FVC) and Forced Expiratory Volume (FEV) serve as guidelines for diagnosis and prognosis of Chronic Obstructive Pulmonary Diseases (COPD) and asthma. However, user errors caused by different reasons, including improper handling of the equipment and poor performance during the maneuvers of the expiratory airflow, end up in incorrect treatment directions. To ensure accurate results, spirometry tests traditionally require the presence of a skilled professional to identify and address these errors promptly. A novel machine learning approach is proposed in this paper to automatically identify four such user errors based on Volume-Time and Flow-Volume graphs. By detecting specific errors and providing immediate feedback to patients, reliability and accuracy of spirometry results will be improved and the need for trained professionals will be reduced. The implementation facilitates the widespread adoption of spirometry, particularly in low-resource telemedicine settings. This work implements a binary classification model distinguishing between normal and error test samples, achieving a prediction accuracy of 93%. Additionally, a 4-way classification model is presented for identifying individual error sub-types, demonstrating a prediction accuracy of 94%. © 2023 Elsevier Ltd","Convolutional Neural Network; Early Termination; Excessive Extrapolated Volume; Extra Breathing; Forced Expiratory Volume; Forced Vital Capacity; FV graph; Spirometry; Sub-Maximal Blast; VT graph","Convolutional neural networks; Diagnosis; Disease control; Error detection; Flow graphs; Pulmonary diseases; bronchodilating agent; Convolutional neural network; Early termination; Excessive extrapolated volume; Extra breathing; Forced expiratory volume; Forced vital capacity; FV graph; Spirometry; Sub-maximal blast; VT graph; Article; automation; binary classification; breathing; classification; conceptual framework; controlled study; convolutional neural network; data processing; deep learning; diagnostic accuracy; diagnostic error; diagnostic test accuracy study; feedback system; human; k fold cross validation; machine learning; predictive model; predictive value; reliability; spirometry; telemedicine; Deep learning","","","GeForce GTX 1660, NVIDIA; Ryzen 7 Octa Core","NVIDIA","","","Khakban A., Sin D.D., FitzGerald J.M., McManus B.M., Ng R., Hollander Z., Sadatsafavi M., The projected epidemic of chronic obstructive pulmonary disease hospitalizations over the next 15 years. A population-based perspective, Am. J. Respir. Crit. Care Med., 195, 3, pp. 287-291, (2017); Veezhinathan M., Ramakrishnan S., Neural network–based classification of normal and abnormal pulmonary function using spirometric measurements, J. Mech. Med. Biol., 7, 2, pp. 151-161, (2007); Petty T.L., John Hutchinson's mysterious machine revisited, Chest, 121, 5, pp. 219S-223S, (2002); Sim Y.S., Lee J.-H., Lee W.-Y., Suh D.I., Oh Y.-M., Yoon J.-S., Lee J.H., Cho J.H., Kwon C.S., Chang J.H., Spirometry and bronchodilator test, Tuberc. Respir. Dis., 80, 2, pp. 105-112, (2017); Parker M.J., Interpreting spirometry: the basics, Otolaryngol. Clin. North Am., 47, 1, pp. 39-53, (2014); Force U.P.S.T., Screening for chronic obstructive pulmonary disease using spirometry: US Preventive Services Task Force recommendation statement, Ann. Intern. Med., 148, 7, pp. 529-534, (2008); Gomez F.P., Rodriguez-Roisin R., Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines for chronic obstructive pulmonary disease, Curr. Opin. Pulm. Med., 8, 2, pp. 81-86, (2002); Pellegrino R., Viegi G., Brusasco V., Crapo R.O., Burgos F., Casaburi R., Coates A., Van Der Grinten C., Gustafsson P., Hankinson J., Et al., Interpretative strategies for lung function tests, Eur. Respir. J., 26, 5, pp. 948-968, (2005); Heerlien I., Automatic Detection of User Errors in Spirometry Data Using Machine Learning Techniques and the Analysis of the Effect of Metaphors on the Quality of Spirometry Measurements, (2020); Beeckman-Wagner L.-A.F., Freeland D., Spirometry quality assurance; common errors and their impact on test results, (2012); Johns D.P., Burton D., Walters J.A., WOOD-BAKER R., National survey of spirometer ownership and usage in general practice in Australia, Respirology, 11, 3, pp. 292-298, (2006); Miller M.R., Hankinson J., Brusasco V., Burgos F., Casaburi R., Coates A., Crapo R., Enright P., Van Der Grinten C., Gustafsson P., Et al., Standardisation of spirometry, Eur. Respir. J., 26, 2, pp. 319-338, (2005); Wang J., Wang S., Zhang Y., Artificial intelligence for visually impaired, Displays, 77, (2023); Wang J., Satapathy S.C., Wang S., Zhang Y., LCCNN: a lightweight customized CNN-based distance education app for COVID-19 recognition, Mob. Netw. Appl., pp. 1-16, (2023); Neethi A., Niyas S., Kannath S.K., Mathew J., Anzar A.M., Rajan J., Stroke classification from computed tomography scans using 3d convolutional neural network, Biomed. Signal Process. Control, 76, (2022); Perumal S.P., Sannasi G., Santhosh K.S., Arputharaj K., Computational intelligence and healthcare informatics part III—Recent development and advanced methodologies, Comput. Intell. Healthc. Inform., pp. 159-177, (2021); Huang C., Wang J., Wang S., Zhang Y., A review of deep learning in dentistry, Neurocomputing, (2023); Luo A.Z., Whitmire E., Stout J.W., Martenson D., Patel S., Automatic characterization of user errors in spirometry, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4239-4242, (2017); Trivedy S., Goyal M., Mishra M., Verma N., Mukherjee A., Classification of spirometry using stacked autoencoder based neural network, 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1-5, (2019); Schapire R.E., Explaining adaboost, Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, pp. 37-52, (2013); Myles A.J., Feudale R.N., Liu Y., Woody N.A., Brown S.D., An introduction to decision tree modeling, J. Chemom., 18, 6, pp. 275-285, (2004); Zabalza J., Ren J., Zheng J., Zhao H., Qing C., Yang Z., Du P., Marshall S., Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging, Neurocomputing, 185, pp. 1-10, (2016); Society A.T., Et al., Lung function testing: selection of reference values and interpretative strategies, Am. Rev. Respir. Dis., 144, pp. 1202-1218, (1991); Cortes C., Vapnik V., Support-vector networks, Mach. Learn., 20, pp. 273-297, (1995); Hinton G.E., Osindero S., Teh Y.-W., A fast learning algorithm for deep belief nets, Neural Comput., 18, 7, pp. 1527-1554, (2006); Bengio Y., Lamblin P., Popovici D., Larochelle H., Greedy layer-wise training of deep networks, Adv. Neural Inf. Process. Syst., 19, (2006); LeCun Y., Boser B., Denker J., Henderson D., Howard R., Hubbard W., Jackel L., Handwritten digit recognition with a back-propagation network, Adv. Neural Inf. Process. Syst., 2, (1989); Wolpert D.H., Et al., On Overfitting Avoidance as Bias: Tech. rep., Technical Report SFI TR 92-03-5001, (1993); Ketkar N., Santana E., Deep Learning with Python, Vol. 1, (2017); Maas A.L., Hannun A.Y., Ng A.Y., Et al., 30, (2013); Medsker L.R., Jain L., Recurrent neural networks, Des. Appl., 5, 64-67, (2001); Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput., 9, 8, pp. 1735-1780, (1997); Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Et al., An image is worth 16x16 words: Transformers for image recognition at scale, (2020); Wang W., Xie E., Li X., Fan D.-P., Song K., Liang D., Lu T., Luo P., Shao L., Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 568-578, (2021); Liu Z., Lin Y., Cao Y., Hu H., Wei Y., Zhang Z., Lin S., Guo B., Swin transformer: Hierarchical vision transformer using shifted windows, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012-10022, (2021)","S. Bonthada; Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, Karnataka, India; email: sivabonthada.212is034@nitk.edu.in","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85180370852"
"Liu Z.; Huang Y.; Hu C.; Liu X.","Liu, Zheng (57853612000); Huang, Ying (59125823200); Hu, Chao (59126508100); Liu, Xiang (57554058000)","57853612000; 59125823200; 59126508100; 57554058000","The impact of Sangju Qingjie Decoction on the pulmonary microbiota in the prevention and treatment of chronic obstructive pulmonary disease","2024","Frontiers in Cellular and Infection Microbiology","14","","1379831","","","","1","10.3389/fcimb.2024.1379831","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192964935&doi=10.3389%2ffcimb.2024.1379831&partnerID=40&md5=4e43e3dbcc59f9831ac56462dd897b49","Clinical Pharmacy, Xiangtan Center Hospital, Hunan, Xiangtan, China; Pulmonary and Critial Care Medicine, Zhongshan Hospital of Traditional Chinese Medicine, Guangdong, Zhongshan, China; Pulmonary and Critial Care Medicine, Xiangtan Center Hospital, Hunan, Xiangtan, China","Liu Z., Clinical Pharmacy, Xiangtan Center Hospital, Hunan, Xiangtan, China; Huang Y., Pulmonary and Critial Care Medicine, Zhongshan Hospital of Traditional Chinese Medicine, Guangdong, Zhongshan, China; Hu C., Pulmonary and Critial Care Medicine, Xiangtan Center Hospital, Hunan, Xiangtan, China; Liu X., Clinical Pharmacy, Xiangtan Center Hospital, Hunan, Xiangtan, China","Objective: Exploring the effect of SJQJD on the pulmonary microbiota of chronic obstructive pulmonary disease (COPD) rats through 16S ribosomal RNA (rRNA) sequencing. Methods: A COPD rat model was constructed through smoking and lipopolysaccharide (LPS) stimulation, and the efficacy of SJQJD was evaluated by hematoxylin and eosin (H&E) staining and Enzyme-Linked Immunosorbnent Assay (ELISA). The alveolar lavage fluid of rats was subjected to 16S rRNA sequencing. The diversity of lung microbiota composition and community structure was analyzed and differential microbiota were screened. Additionally, machine learning algorithms were used for screening biomarkers of each group of the microbiota. Results: SJQJD could improve lung structure and inflammatory response in COPD rats. 16s rRNA sequencing analysis showed that SJQJD could significantly improve the abundance and diversity of bacterial communities in COPD rats. Through differential analysis and machine learning methods, potential microbial biomarkers were identified as Mycoplasmataceae, Bacillaceae, and Lachnospiraceae. Conclusion: SJQJD could improve tissue morphology and local inflammatory response in COPD rats, and its effect may be related to improve pulmonary microbiota. Copyright © 2024 Liu, Huang, Hu and Liu.","biomarker; COPD; machine learning; pulmonary microbiota; SJQJD","Animals; Bacteria; Bronchoalveolar Lavage Fluid; Disease Models, Animal; Drugs, Chinese Herbal; Lung; Male; Microbiota; Pulmonary Disease, Chronic Obstructive; Rats; Rats, Sprague-Dawley; RNA, Ribosomal, 16S; Chinese medicinal formula; gelatinase A; interleukin 6; interleukin 8; lipopolysaccharide; RNA 16S; sangju qingjie decoction; secretory immunoglobulin; stromelysin; tumor necrosis factor; unclassified drug; herbaceous agent; RNA 16S; animal experiment; animal model; animal tissue; Article; Bacillaceae; bioinformatics; bronchoalveolar lavage fluid; chronic obstructive lung disease; controlled study; DNA extraction; drug efficacy; enzyme linked immunosorbent assay; high throughput sequencing; learning algorithm; Listeriaceae; lung microbiota; lung parenchyma; lung structure; machine learning; male; microbial community; microbial diversity; mouse; nonhuman; operational taxonomic unit; Peptostreptococcaceae; pneumonia; polymerase chain reaction; Rikenellaceae; RNA sequencing; Ruminococcaceae; smoking; support vector machine; animal; bacterium; chronic obstructive lung disease; classification; disease model; drug effect; genetics; isolation and purification; lung; microbiology; microflora; pathology; rat; Sprague Dawley rat","","gelatinase A, 146480-35-5; interleukin 8, 114308-91-7; stromelysin, 79955-99-0","","","Science and Technology Bureau of Zhenjiang; Scientific Research Program of Health Commission of Hunan Province, (B2017166)","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by projects SF-YB20231022 and SF-YB20211018 from the Science and Technology Bureau of Xiangtan City, Scientific Research Program of Health Commission of Hunan Province, (No. B2017166).","Anand S., Mande S.S., Diet, microbiota and gut-lung connection, Front. Microbiol, 9, (2018); Baqdunes M.W., Leap J., Young M., Kaura A., Cheema T., Acute exacerbation of chronic obstructive pulmonary disease, Crit. Care Nurs. Q, 44, pp. 74-90, (2021); Bowerman K.L., Rehman S.F., Vaughan A., Lachner N., Budden K.F., Kim R.Y., Et al., Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease, Nat. Commun, 11, (2020); Cao Q., Wu X., Chen Y., Wei Q., You Y., Qiang Y., Et al., The impact of concurrent bacterial lung infection on immunotherapy in patients with non-small cell lung cancer: a retrospective cohort study, Front. Cell Infect. 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Chron Obstruct Pulmon Dis, 15, pp. 2985-2990, (2020); Huang Y., Huang J., Dong Z.Q., Chen S.F., Chen J.F., Huang Z.Y., Effect of sangju qingjie decoction on chronic obstructive pulmonary disease with phlegm-heat accumulation and its influence on immune function, Chin. Arch. Tradit. Chin. Med, 39, pp. 184-187, (2021); Jang Y.O., Lee S.H., Choi J.J., Kim D.H., Choi J.M., Kang M.J., Et al., Fecal microbial transplantation and a high fiber diet attenuates emphysema development by suppressing inflammation and apoptosis, Exp. Mol. Med, 52, pp. 1128-1139, (2020); Jiang X., Lin Y., Wu Y., Yuan C., Lang X., Chen J., Et al., Identification of potential anti-pneumonia pharmacological components of Glycyrrhizae Radix et Rhizoma after the treatment with Gan An He Ji oral liquid, J. Pharm. Anal, 12, pp. 839-851, (2022); Karakasidis E., Kotsiou O.S., Gourgoulianis K.I., Lung and gut microbiome in COPD, J. Pers. 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Macromol, 191, pp. 1096-1104, (2021); Richmond B.W., Du R.H., Han W., Benjamin J.T., van der Meer R., Gleaves L., Et al., Bacterial-derived neutrophilic inflammation drives lung remodeling in a mouse model of chronic obstructive pulmonary disease, Am. J. Respir. Cell Mol. Biol, 58, pp. 736-744, (2018); Sessa R., Di Pietro M., Schiavoni G., Macone A., Maras B., Fontana M., Et al., Chlamydia pneumoniae induces T cell apoptosis through glutathione redox imbalance and secretion of TNF-alpha, Int. J. Immunopathol. Pharmacol, 22, pp. 659-668, (2009); Shi C.Y., Yu C.H., Yu W.Y., Ying H.Z., Gut-lung microbiota in chronic pulmonary diseases: evolution, pathogenesis, and therapeutics, Can. J. Infect. Dis. Med. 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Pharmacother, 115, (2019); Sze M.A., Dimitriu P.A., Hayashi S., Elliott W.M., McDonough J.E., Gosselink J.V., Et al., The lung tissue microbiome in chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med, 185, pp. 1073-1080, (2012); Tian D., Yang Y., Yu M., Han Z.Z., Wei M., Zhang H.W., Et al., Anti-inflammatory chemical constituents of Flos Chrysanthemi Indici determined by UPLC-MS/MS integrated with network pharmacology, Food Funct, 11, pp. 6340-6351, (2020); Valdes A.M., Walter J., Segal E., Spector T.D., Role of the gut microbiota in nutrition and health, Bmj, 361, (2018); Vaughan A., Frazer Z.A., Hansbro P.M., Yang I.A., COPD and the gut-lung axis: the therapeutic potential of fiber, J. Thorac. Dis, 11, pp. S2173-s2180, (2019); Vogelmeier C.F., Criner G.J., Martinez F.J., Anzueto A., Barnes P.J., Bourbeau J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. GOLD executive summary, Am. J. Respir. Crit. Care Med, 195, pp. 557-582, (2017); Wang Z., Maschera B., Lea S., Kolsum U., Michalovich D., Van Horn S., Et al., Airway host-microbiome interactions in chronic obstructive pulmonary disease, Respir. Res, 20, (2019); Whiteside S.A., McGinniss J.E., Collman R.G., The lung microbiome: progress and promise, J. Clin. Invest, 131, (2021); Wood A.M., Tang M., Truong T., Feldman C., Pieper C., Murtha A.P., Vaginal Mycoplasmataceae colonization and association with immune mediators in pregnancy, J. Matern Fetal Neonatal Med, 34, pp. 2295-2302, (2021); Wu D., Hou C., Li Y., Zhao Z., Liu J., Lu X., Et al., Analysis of the bacterial community in chronic obstructive pulmonary disease sputum samples by denaturing gradient gel electrophoresis and real-time PCR, BMC Pulm Med, 14, (2014); Wu X., Zhou Z., Cao Q., Chen Y., Gong J., Zhang Q., Et al., Reprogramming of Treg cells in the inflammatory microenvironment during immunotherapy: a literature review, Front. Immunol, 14, (2023); Yagi K., Huffnagle G.B., Lukacs N.W., Asai N., The lung microbiome during health and disease, Int. J. Mol. Sci, 22, (2021); Yee N., Kim H., Kim E., Cha Y.H., Ma L., Cho N.E., Et al., Effects of sangju honey on oral squamous carcinoma cells, J. Cancer Prev, 27, pp. 239-246, (2022); Zhai Y., Li D., Wang Z., Shao L., Yin N., Li W., Cortex mori radicis attenuates streptozotocin-induced diabetic renal injury in mice via regulation of transient receptor potential canonical channel 6, Endocr. Metab. Immune Disord. Drug Targets, 22, pp. 862-873, (2022)","X. Liu; Clinical Pharmacy, Xiangtan Center Hospital, Xiangtan, Hunan, China; email: lcyx58214813@163.com","","Frontiers Media SA","","","","","","22352988","","","38746785","English","Front. Cell. Infect. Microbiol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85192964935"
"Zanoletti M.; Bufano P.; Bossi F.; Di Rienzo F.; Marinai C.; Rho G.; Vallati C.; Carbonaro N.; Greco A.; Laurino M.; Tognetti A.","Zanoletti, Michele (57728873200); Bufano, Pasquale (57437083600); Bossi, Francesco (56963595100); Di Rienzo, Francesco (57208643858); Marinai, Carlotta (57217088757); Rho, Gianluca (57218353549); Vallati, Carlo (25927652200); Carbonaro, Nicola (16174294500); Greco, Alberto (56854902700); Laurino, Marco (23135421400); Tognetti, Alessandro (34769385900)","57728873200; 57437083600; 56963595100; 57208643858; 57217088757; 57218353549; 25927652200; 16174294500; 56854902700; 23135421400; 34769385900","Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings","2024","Sensors","24","10","3205","","","","1","10.3390/s24103205","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194218294&doi=10.3390%2fs24103205&partnerID=40&md5=f464543335376e7cc0d780539bb407a2","National Research Council, Institute of Clinical Physiology, Pisa, 56124, Italy; Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, 56126, Italy","Zanoletti M., National Research Council, Institute of Clinical Physiology, Pisa, 56124, Italy, Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Bufano P., National Research Council, Institute of Clinical Physiology, Pisa, 56124, Italy, Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, Pisa, 56126, Italy; Bossi F., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Di Rienzo F., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Marinai C., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Rho G., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Vallati C., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Carbonaro N., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Greco A., Department Information Engineering, University of Pisa, Pisa, 56122, Italy; Laurino M., National Research Council, Institute of Clinical Physiology, Pisa, 56124, Italy; Tognetti A., Department Information Engineering, University of Pisa, Pisa, 56122, Italy","Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices’ performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement. © 2024 by the authors.","daily life monitoring; gait speed estimation; machine learning; mobility analysis; smart sensors; smart shoes; smartphone; smartwatch; telemedicine; wearable devices","Adult; Female; Gait; Humans; Male; Shoes; Smartphone; Walking; Walking Speed; Wearable Electronic Devices; Young Adult; Machine learning; mHealth; Pulmonary diseases; Telemedicine; Wearable computers; Wearable sensors; Daily life monitoring; Daily lives; Gait speed; Gait speed estimation; Machine-learning; Mobility analysis; Smart phones; Smart shoe; Smartwatch; Speed estimation; Wearable devices; adult; female; gait; human; male; physiology; shoe; smartphone; walking; walking speed; wearable computer; young adult; Smartphones","","","","","FoReLab projects of the Information Engineering Department of the University of Pisa; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; European Commission, EC, (101057103); European Union’s Horizon Europe Research and Innovation Programme, (101057103)","Funding text 1: Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. We acknowledge the support from the CrossLab and FoReLab projects of the Information Engineering Department of the University of Pisa (funded by MUR). ; Funding text 2: This project has received funding from the European Union\u2019s Horizon Europe Research and Innovation Programme under Grant Agreement No. 101057103\u2014project TOLIFE.","Ma R., Zhao H., Wei W., Liu Y., Huang Y., Gait characteristics under single-/dual-task walking conditions in elderly patients with cerebral small vessel disease: Analysis of gait variability, gait asymmetry and bilateral coordination of gait, Gait Posture, 92, pp. 65-70, (2022); Onder H., Dinc E., Yucesan K., Comoglu S., The gait parameters in patients with Parkinson’s Disease under STN-DBS therapy and associated clinical features, Neurol. Res, 45, pp. 779-785, (2023); Sabo A., Iaboni A., Taati B., Fasano A., Gorodetsky C., Evaluating the ability of a predictive vision-based machine learning model to measure changes in gait in response to medication and DBS within individuals with Parkinson’s disease, Biomed. Eng. Online, 22, (2023); Bufano P., Laurino M., Said S., Tognetti A., Menicucci D., Digital Phenotyping for Monitoring Mental Disorders: Systematic Review, J. Med. Internet Res, 25, (2023); Jehn M., Schmidt-Trucksass A., Meyer A., Schindler C., Tamm M., Stolz D., Association of daily physical activity volume and intensity with COPD severity, Respir. Med, 105, pp. 1846-1852, (2011); Sarkar P., Dey A., Bhattacharya S., Chakrabarti R., Correlation of six minute walk test with spirometry in COPD patients, Eur. Respir. J, 58, (2021); Middleton A., Fritz S.L., Lusardi M., Walking Speed: The Functional Vital Sign, J. Aging Phys. Act, 23, pp. 314-322, (2015); Fritz S., Lusardi M., White paper: “walking speed: The sixth vital sign, J. Geriatr. Phys. Ther, 32, pp. 46-49, (2009); Studenski S., Bradypedia: Is gait speed ready for clinical use?, J. Nutr. Health Aging, 13, pp. 878-880, (2009); Soltani A., Dejnabadi H., Savary M., Aminian K., Real-World Gait Speed Estimation Using Wrist Sensor: A Personalized Approach, IEEE J. Biomed. Health Inform, 24, pp. 658-668, (2020); Soltani A., Aminian K., Mazza C., Cereatti A., Palmerini L., Bonci T., Paraschiv-Ionescu A., Algorithms for Walking Speed Estimation Using a Lower-Back-Worn Inertial Sensor: A Cross-Validation on Speed Ranges, IEEE Trans. Neural Syst. Rehabil. Eng, 29, pp. 1955-1964, (2021); Shrestha A., Won M., DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning, Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1-6; Nemati E., Suh Y.S., Motamed B., Sarrafzadeh M., Gait velocity estimation for a smartwatch platform using Kalman filter peak recovery, Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 230-235; McGinnis R.S., Mahadevan N., Moon Y., Seagers K., Sheth N., Wright J.A., DiCristofaro S., Silva I., Jortberg E., Ceruolo M., Et al., A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis, PLoS ONE, 12, (2017); Carbonaro N., Laurino M., Greco A., Marinai C., Giannetti F., Righetti F., Di Rienzo F., Rho G., Arcarisi L., Zanoletti M., Et al., Smart Sensors for Daily-Life Data Collection Toward Precision and Personalized Medicine: The TOLIFE Project Approach, MEDICON’23 and CMBEBIH’23, 93, (2024); Buttery S.C., Williams P.J., Alghamdi S.M., Philip K.E., Perkins A., Kallis C., Quint J.K., Polkey M.I., Breuls S., Buekers J., Et al., Investigating the prognostic value of digital mobility outcomes in patients with chronic obstructive pulmonary disease: A systematic literature review and meta-analysis, Eur. Respir. Rev, 32, (2023); Annegarn J., Spruit M.A., Savelberg H.H.C.M., Willems P.J.B., van de Bool C., Schols A.M.W.J., Wouters E.F.M., Meijer K., Differences in Walking Pattern during 6-Min Walk Test between Patients with COPD and Healthy Subjects, PLoS ONE, 7, (2012); Rienzo F.D., Righetti F., Laurino M., Greco A., Marinai C., Di Mambro I., Melissa E., Carbonaro N., Bossi F., Rho G., Et al., Using Multiple Devices for Patient Monitoring in Clinical Studies: The TOLIFE Experience, Proceedings of the 2024 IEEE In-ternational Conference on Pervasive Computing and Communications Workshops and other Affiliated Events; Carbonaro N., Lorussi F., Tognetti A., Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking, Electronics, 5, (2016); Avvenuti M., Carbonaro N., Cimino M.G.C.A., Cola G., Tognetti A., Vaglini G., Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases, Sensors, 18, (2018); Storm F.A., Cesareo A., Reni G., Biffi E., Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review, Sensors, 20, (2020); Schubert C., Archer G., Zelis J.M., Nordmeyer S., Runte K., Hennemuth A., Berger F., Falk V., Tonino P.A.L., Hose R., Et al., Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease, NPJ Digit. Med, 3, pp. 1-9, (2020); Cheng Q., Juen J., Li Y., Prieto-Centurion V., Krishnan J.A., Schatz B.R., GaitTrack: Health Monitoring of Body Motion from Spatio-Temporal Parameters of Simple Smart Phones, Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics, pp. 897-906, (2013); Mannini A., Sabatini A.M., On-Line Classification of Human Activity and Estimation of Walk-Run Speed from Acceleration Data Using Support Vector Machines, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3302-3305; Karpman C., Benzo R., Gait speed as a measure of functional status in COPD patients, Int. J. Chronic Obstr. Pulm. Dis, 9, pp. 1315-1320, (2014); Ozalevli S., Kilinc O., Sevinc C., Cimrin A.H., Ucan E.S., Ilgin D., Gait speed as a functional capacity indicator in patients with chronic obstructive pulmonary disease, Ann. Thorac. Med, 6, pp. 141-146, (2011); Waschki B., Kirsten A., Holz O., Muller K.C., Meyer T., Watz H., Magnussen H., Physical activity is the strongest predictor of all-cause mortality in patients with COPD: A prospective cohort study, Chest, 140, pp. 331-342, (2011); Peel N.M., Kuys S.S., Klein K., Gait Speed as a Measure in Geriatric Assessment in Clinical Settings: A Systematic Review, J. Gerontol. Ser, 68, pp. 39-46, (2013); Buracchio T., Dodge H.H., Howieson D., Wasserman D., Kaye J., The Trajectory of Gait Speed Preceding Mild Cognitive Impairment, Arch. Neurol, 67, pp. 980-986, (2010); Mirelman A., Bonato P., Camicioli R., Ellis T.D., Giladi P.N., Hamilton J.L., Hass C.J., Hausdorff J.M., Pelosin E., Almeida Q.J., Gait impairments in Parkinson’s disease, Lancet Neurol, 18, pp. 697-708, (2019)","M. Zanoletti; National Research Council, Institute of Clinical Physiology, Pisa, 56124, Italy; email: michelezanoletti@cnr.it","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","14248220","","","38794059","English","Sensors","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194218294"
"Rasmussen N.; Karlsen P.; Otten N.D.; Fjeldborg J.; Hansen S.","Rasmussen, Nanna (59139008200); Karlsen, Pernille (59139008300); Otten, Nina D. (55639578800); Fjeldborg, Julie (23472573300); Hansen, Sanni (55542916400)","59139008200; 59139008300; 55639578800; 23472573300; 55542916400","Bilateral bronchoalveolar lavage cytology profiles in a warmblood horse population during a 1-year period","2024","Journal of Veterinary Internal Medicine","38","4","","2391","2398","7","1","10.1111/jvim.17118","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193921883&doi=10.1111%2fjvim.17118&partnerID=40&md5=6cf155508b32f7246b747f5b02818ce1","Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark","Rasmussen N., Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark; Karlsen P., Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark; Otten N.D., Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark; Fjeldborg J., Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark; Hansen S., Department of Veterinary Clinical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg C, Denmark","Background: Bronchoalveolar lavage (BAL) cytology results from 1 lung might not be representative of both lungs. Objectives: To determine whether the lung site sampled would influence the horse's BAL cytology profile, and if a pooled BAL sample would be superior with regard to BAL cytology diagnosis in a cohort of healthy and subclinical asthmatic warmblood horses. Animals: Fifty-nine horses in 2021 and 70 horses in 2022, the follow-up included 53 of the same in each year. Methods: A cross-sectional study with follow-up included BAL cytology samples from individual lungs and from pooled BAL samples. The BAL samples were enumerated and differential cell count were applied to categorize the horses as control or with airway inflammation (AI). Results: Bronchoalveolar lavage mast cell count was higher in left lung compared to right lung (2021; median 1.6 [range, 0.6-3.3] vs 1.2 [0.7-1.5] P =.009, 2022; median 3.1 [2.1-4.2] vs 2.4 [1.7-3.4], P <.001) and compared to pooled samples (2022; median 2.6 [1.7-3.7], P <.001). Between year 2021 and 2022, 17 of the horses had changes in BAL cytology from control to AI or vice versa. Conclusions and Clinical Importance: Pooled BAL sample was the least reliable for detecting AI, and was not representative of the overall lung condition. © 2024 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.","endoscopic examination; equine; lung site; mild-moderate equine asthma; severe equine asthma","Animals; Asthma; Bronchoalveolar Lavage; Bronchoalveolar Lavage Fluid; Cell Count; Cross-Sectional Studies; Cytology; Female; Horse Diseases; Horses; Lung; Male; Mast Cells; chlorhexidine; corticosteroid; detomidine; tolonium chloride; airway obstruction; animal experiment; Article; asthma; bronchoalveolar lavage fluid; cadaver; cell count; chronic rhinosinusitis; controlled study; cross-sectional study; cytology; drug therapy; endoscopy; epithelial lining fluid; Equus; erythrocyte count; feces analysis; follow up; histology; horse; human; inflammation; left lung; leukocyte count; leukocyte differential count; limit of detection; lung disease; lung lavage; lung volume; mast cell; McNemar test; neutrophil count; nonhuman; population; prevalence; prospective study; receiver operating characteristic; respiratory tract inflammation; right lung; seasonal variation; sensitivity and specificity; Wilcoxon signed ranks test; animal; cytology; diagnosis; female; horse; horse disease; lung; lung lavage; male; pathology; veterinary medicine","","chlorhexidine, 3697-42-5, 55-56-1; detomidine, 76631-46-4; tolonium chloride, 92-31-9","","","UK Research and Innovation, UKRI, (105865)","","Orard M., Depecker M., Hue E., Pitel P.H., Courouce-Malblanc A., Richard E.A., Influence of bronchoalveolar lavage volume on cytological profiles and subsequent diagnosis of inflammatory airway disease in horses, Vet J, 207, pp. 193-195, (2016); Sweeney C.R., Rossier Y., Ziemer E.L., Lindborg S., Effects of lung site and fluid volume on results of bronchoalveolar lavage fluid analysis in horses, Am J Vet Res, 53, pp. 1376-1379, (1992); Pickles K., Pirie R.S., Rhind S., Dixon P.M., McGorum B.C., Cytological analysis of equine bronchoalveolar lavage fluid. Part 3: the effect of time, temperature and fixatives, Equine Vet J, 34, pp. 297-301, (2002); Hansen S., Fjeldborg J., Hansen A.J., Baptiste K.E., Reliability of cytological evaluation of mast cells from bronchoalveolar lavage fluid in horses: intraobserver agreement and mast cell identification, Equine Vet Edu, 32, pp. 47-52, (2020); Fernandez N.J., Hecker K.G., Gilroy C.V., Warren A.L., Leguillette R., Reliability of 400-cell and 5-field leukocyte differential counts for equine bronchoalveolar lavage fluid, Vet Clin Pathol, 42, pp. 92-98, (2013); Hansen S., Klintoe K., Austevoll M., Baptiste K.E., Fjeldborg J., Equine airway inflammation in loose-housing management compared with pasture and conventional stabling, Vet Rec, 184, (2019); Ivester K.M., Couetil L.L., Moore G.E., An observational study of environmental exposures, airway cytology, and performance in racing thoroughbreds, J Vet Intern Med, 32, pp. 1754-1762, (2018); Holcombe S.J., Jackson C., Gerber V., Et al., Stabling is associated with airway inflammation in young Arabian horses, Equine Vet J, 33, pp. 244-249, (2001); Tremblay G.M., Ferland C., Lapointe J.M., Et al., Effect of stabling on bronchoalveolar cells obtained from normal and COPD horses, Equine Vet J, 25, pp. 194-197, (1993); Hoffman A.M., Bronchoalveolar lavage: sampling technique and guidelines for cytologic preparation and interpretation, Vet Clin North Am Equine, 24, pp. 423-435, (2008); Couetil L.L., Cardwell J.M., Gerber V., Et al., Inflammatory airway disease of horses-revised consensus statement, J Vet Intern Med, 30, pp. 503-515, (2016); Kinnison T., McGilvray T.A., Couetil L.L., Et al., Mild-moderate equine asthma: a scoping review of evidence supporting the consensus definition, Vet J, 286, (2022); Rush B.R., Mair T.S., Non-infectious pulmonary diseases and diagnostic techniques, Equine Respiratory Diseases, pp. 187-248, (2004); Deniau V., Jaillardon L., Fortier G., Courouce-Malblanc A., Comparison of cytological profiles of bronchoalveolar lavage fluid—BALF—obtained from left and right lungs according to conditions of conservation and preparation, Performance Diagnosis and Purchase Examination of Elite Sport Horses, pp. 81-86, (2010); Jean D., Vrins A., Beauchamp G., Lavoie J.P., Evaluation of variations in bronchoalveolar lavage fluid in horses with recurrent airway obstruction, Am J Vet Res, 72, pp. 838-842, (2011); Depecker M., Richard E.A., Pitel P.H., Fortier G., Leleu C., Courouce-Malblanc A., Bronchoalveolar lavage fluid in Standardbred racehorses: influence of unilateral/bilateral profiles and cut-off values on lower airway disease diagnosis, Vet J, 199, pp. 150-156, (2014); Hermange T., Le Corre S., Bizon C., Et al., Bronchoalveolar lavage fluid from both lungs in horses: diagnostic reliability of cytology from pooled samples, Vet J, 244, pp. 28-33, (2019); Bullone M., Joubert P., Gagne A., Et al., Bronchoalveolar lavage fluid neutrophilia is associated with the severity of pulmonary lesions during equine asthma exacerbations, Equine Vet J, 50, pp. 609-615, (2018); Olave C.J., Ivester K.M., Couetil L.L., Kritchevsky J.E., Tinkler S.H., Mukhopadhyay A., Dust exposure and pulmonary inflammation in Standardbred racehorses fed dry hay or haylage: a pilot study, Vet J, 271, (2021); Gerber V., Lindberg A., Berney C., Robinson N.E., Airway mucus in recurrent airway obstruction—short-term response to environmental challenge, J Vet Intern Med, 18, pp. 92-97, (2004); Robinson N.E., Karmaus W., Holcombe S.J., Carr E.A., Derksen F.J., Airway inflammation in Michigan pleasure horses: prevalence and risk factors, Equine Vet J, 38, pp. 293-299, (2006); McGorum B.C., Dixon P.M., Halliwell R.E., Et al., Comparison of cellular and molecular components of bronchoalveolar lavage fluid harvested from different segments of the equine lung, Res Vet Sci, 55, pp. 57-59, (1993); De Brauwer E.I., Jacobs J.A., Nieman F., Et al., Bronchoalveolar lavage fluid differential cell count. How many cells should be counted?, Anal Quant Cytol Histol, 24, pp. 337-341, (2002); Fogarty U., Buckley T., Bronchoalveolar lavage findings in horses with exercise intolerance, Equine Vet J, 23, pp. 434-437, (1991); Couetil L.L., Rosenthal F.S., DeNicola D.B., Et al., Clinical signs, evaluation of bronchoalveolar lavage fluid, and assessment of pulmonary function in horses with inflammatory respiratory disease, Am J Vet Res, 62, pp. 538-546, (2001); Richard E.A., Depecker M., Defontis M., Et al., Cytokine concentrations in bronchoalveolar lavage fluid from horses with neutrophilic inflammatory airway disease, J Vet Intern Med, 28, pp. 1838-1844, (2014); Hansen S., Honore M.L., Riihimaki M., Pringle J., Ammentorp A.H., Fjeldborg J., Seasonal variation in tracheal mucous and bronchoalveolar lavage cytology for adult clinically healthy stabled horses, J Equine Vet, 71, pp. 1-5, (2018); Riihimaki M., Raine A., Elfman L., Et al., Markers of respiratory inflammation in horses in relation to seasonal changes in air quality in a conventional racing stable, Can J Vet Res, 72, pp. 432-439, (2008); Boivin R., Pilon F., Lavoie J.P., Et al., Adherence to treatment recommendations and short-term outcome of pleasure and sport horses with equine asthma, Can J Vet Res, 59, pp. 1293-1298, (2018); Clements J.M., Pirie R.S., Respirable dust concentrations in equine stables. Part 2: the benefits of soaking hay and optimising the environment in a neighbouring stable, Res Vet Sci, 83, pp. 263-268, (2007); Bosshard S., Gerber V., Evaluation of coughing and nasal discharge as early indicators for an increased risk to develop equine recurrent airway obstruction (RAO), J Vet Intern Med, 28, pp. 618-623, (2014); Holcombe S.J., Robinson N.E., Derksen F.J., Et al., Effect of tracheal mucus and tracheal cytology on racing performance in thoroughbred racehorses, Equine Vet J, 38, pp. 300-304, (2006); Bedenice D., Mazan M.R., Hoffman A.M., Association between cough and cytology of bronchoalveolar lavage fluid and pulmonary function in horses diagnosed with inflammatory airway disease, J Vet Intern Med, 22, pp. 1022-1028, (2008); Hoffman A.M., Mazan M.R., Ellenberg S., Association between bronchoalveolar lavage cytologic features and airway reactivity in horses with a history of exercise intolerance, Am J Vet Res, 59, pp. 176-181, (1998); Leclere M., Desnoyers M., Beauchamp G., Lavoie J.P., Comparison of four staining methods for detection of mast cells in equine bronchoalveolar lavage fluid, J Vet Intern Med, 20, pp. 377-381, (2006)","S. Hansen; Department of Veterinary Clinical Sciences, Faculty of Health and medical Sciences, University of Copenhagen, Taastrup, Agrovej 8, DK-2630, Denmark; email: sannih@sund.ku.dk","","John Wiley and Sons Inc","","","","","","08916640","","","38780440","English","J. Vet. Intern. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85193921883"
"Aminu M.; Daver N.; Godoy M.C.B.; Shroff G.; Wu C.; Torre-Sada L.F.; Goizueta A.; Shannon V.R.; Faiz S.A.; Altan M.; Garcia-Manero G.; Kantarjian H.; Ravandi-Kashani F.; Kadia T.; Konopleva M.; DiNardo C.; Pierce S.; Naing A.; Kim S.T.; Kontoyiannis D.P.; Khawaja F.; Chung C.; Wu J.; Sheshadri A.","Aminu, Muhammad (57517921000); Daver, Naval (8951602400); Godoy, Myrna C. B. (22034292900); Shroff, Girish (50662039700); Wu, Carol (56134918600); Torre-Sada, Luis F. (58367404900); Goizueta, Alberto (57194323100); Shannon, Vickie R. (6603831869); Faiz, Saadia A. (36840445000); Altan, Mehmet (16642011400); Garcia-Manero, Guillermo (7005638589); Kantarjian, Hagop (7202247267); Ravandi-Kashani, Farhad (6603796758); Kadia, Tapan (6504388484); Konopleva, Marina (6701643332); DiNardo, Courtney (23100054600); Pierce, Sherry (35391834900); Naing, Aung (26635630600); Kim, Sang T. (55444415000); Kontoyiannis, Dimitrios P. (35377023000); Khawaja, Fareed (57209990165); Chung, Caroline (36781793600); Wu, Jia (57188816895); Sheshadri, Ajay (54409605200)","57517921000; 8951602400; 22034292900; 50662039700; 56134918600; 58367404900; 57194323100; 6603831869; 36840445000; 16642011400; 7005638589; 7202247267; 6603796758; 6504388484; 6701643332; 23100054600; 35391834900; 26635630600; 55444415000; 35377023000; 57209990165; 36781793600; 57188816895; 54409605200","Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept","2023","Frontiers in Immunology","14","","1249511","","","","2","10.3389/fimmu.2023.1249511","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174140096&doi=10.3389%2ffimmu.2023.1249511&partnerID=40&md5=4e7ca8f50f9ecdcd4febfdc37ae5e77b","Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Rheumatology and Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Departments of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States","Aminu M., Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Daver N., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Godoy M.C.B., Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Shroff G., Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Wu C., Departments of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Torre-Sada L.F., Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Goizueta A., Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Shannon V.R., Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Faiz S.A., Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Altan M., Departments of Thoracic/Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Garcia-Manero G., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Kantarjian H., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Ravandi-Kashani F., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Kadia T., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Konopleva M., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; DiNardo C., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Pierce S., Departments of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Naing A., Departments of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Kim S.T., Departments of Rheumatology and Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Kontoyiannis D.P., Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Khawaja F., Departments of Infectious Diseases, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Chung C., Departments of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Wu J., Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Sheshadri A., Departments of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, United States","Background: Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers. Materials and methods: We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters (“habitats”). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value. Results: Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E − 9) higher than the clinical and blood model (post-test probability of 22%). Conclusion: Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis. Copyright © 2023 Aminu, Daver, Godoy, Shroff, Wu, Torre-Sada, Goizueta, Shannon, Faiz, Altan, Garcia-Manero, Kantarjian, Ravandi-Kashani, Kadia, Konopleva, DiNardo, Pierce, Naing, Kim, Kontoyiannis, Khawaja, Chung, Wu and Sheshadri.","acute myeloid leukemia; habitat analysis; immune checkpoint inhibitor; non-small cell lung cancer; pneumonitis","Biomarkers; Humans; Immune Checkpoint Inhibitors; Inflammation; Leukemia, Myeloid, Acute; Pneumonia; Tomography, X-Ray Computed; azacitidine; biological marker; corticosteroid; idarubicin; immune checkpoint inhibitor; ipilimumab; nivolumab; biological marker; immune checkpoint inhibitor; acute myeloid leukemia; adult; adult respiratory distress syndrome; aged; allergic pneumonitis; Article; asthma; autoimmune disease; checkpoint inhibitor pneumonitis; chronic obstructive lung disease; computer assisted tomography; controlled study; coughing; de novo acute myeloid leukemia; diagnostic test accuracy study; dyspnea; female; fever; ground glass opacity; habitat; histopathology; human; human tissue; image analysis; imaging algorithm; interstitial pneumonia; lung parenchyma; lymphocyte count; machine learning; major clinical study; male; middle aged; neutrophil count; non small cell lung cancer; organizing pneumonia; phylogenetic tree; platelet count; pneumonia; pretest posttest design; prevalence; probability; sensitivity and specificity; stem cell transplantation; synergistic effect; thorax radiography; treatment response; diagnostic imaging; inflammation; pneumonia; x-ray computed tomography","","azacitidine, 320-67-2, 52934-49-3; idarubicin, 57852-57-0, 58957-92-9; ipilimumab, 477202-00-9; nivolumab, 946414-94-4; Biomarkers, ; Immune Checkpoint Inhibitors, ","","","Tumor Measurement Initiative; National Institutes of Health, NIH; National Cancer Institute, NCI, (R00 CA218667); National Cancer Institute, NCI; National Institute of Allergy and Infectious Diseases, NIAID, (K23 AI117024); National Institute of Allergy and Infectious Diseases, NIAID; National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIAMS, (K08 AR079587); National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIAMS","This work was supported by the NIH/NIAID (K23 AI117024; to AS), NIH/NIAMS (K08 AR079587 to SK) and NIH/NCI grant (R00 CA218667; to JW). This work was supported by the Tumor Measurement Initiative through the MD Anderson Strategic Initiative Development Program (STRIDE). 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Garcia J.B., Lei X., Wierda W., Cortes J.E., Dickey B.F., Evans S.E., Et al., Pneumonia during remission induction chemotherapy in patients with acute leukemia, Ann Am Thorac Soc, 10, 5, (2013); Kim S.T., Sheshadri A., Shannon V., Kontoyiannis D.P., Kantarjian H., Garcia-Manero G., Et al., Distinct immunophenotypes of T cells in bronchoalveolar lavage fluid from leukemia patients with immune checkpoint inhibitors-related pulmonary complications, Front Immunol, 11, (2020); Choo R., Naser N.S.H., Nadkarni N.V., Anantham D., Utility of bronchoalveolar lavage in the management of immunocompromised patients presenting with lung infiltrates, BMC Pulm Med, 19, 1, (2019); Azar M.M., Schlaberg R., Malinis M.F., Bermejo S., Schwarz T., Xie H., Et al., Added diagnostic utility of clinical metagenomics for the diagnosis of pneumonia in immunocompromised adults, Chest, 159, 4, (2021); Choe J., Hwang H.J., Seo J.B., Lee S.M., Yun J., Kim M.J., Et al., Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT, Radiol, 302, 1, (2022); 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Gerlinger M., Rowan A.J., Horswell S., Larkin J., Endesfelder D., Gronroos E., Et al., Intratumor heterogeneity and branched evolution revealed by multiregion sequencing, N Engl J Med, 366, (2012); Hersh E.M., Bodey G.P., Nies B.A., Freireich E.J., Causes of death in acute leukemia: A ten-year study of 414 patients from 1954-1963, JAMA, 193, (1965)","J. Wu; Departments of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, United States; email: jwu11@mdanderson.org","","Frontiers Media SA","","","","","","16643224","","","37841255","English","Front. Immunol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85174140096"
"Li Y.; Xiong Y.; Fan W.; Wang K.; Yu Q.; Si L.; van der Smagt P.; Tang J.; Chen N.","Li, Yin (58538844300); Xiong, Yu (58538844400); Fan, Wenxin (58858486700); Wang, Kai (57193115294); Yu, Qingqing (55746724800); Si, Liping (57197854868); van der Smagt, Patrick (6601929804); Tang, Jun (58911178800); Chen, Nutan (55516801000)","58538844300; 58538844400; 58858486700; 57193115294; 55746724800; 57197854868; 6601929804; 58911178800; 55516801000","Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis","2024","Frontiers in Pharmacology","15","","1371504","","","","1","10.3389/fphar.2024.1371504","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200241565&doi=10.3389%2ffphar.2024.1371504&partnerID=40&md5=474ede5afa960d495a3a208efd5b4d93","Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; Department of Otorhinolaryngology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China; Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China; Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Faculty of Informatics, ELTE University, Budapest, Hungary; Machine Learning Research Lab, Volkswagen Group, Munich, Germany","Li Y., Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; Xiong Y., Department of Otorhinolaryngology, The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China; Fan W., Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China; Wang K., Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; Yu Q., Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; Si L., Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; van der Smagt P., Faculty of Informatics, ELTE University, Budapest, Hungary, Machine Learning Research Lab, Volkswagen Group, Munich, Germany; Tang J., Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; Chen N., Machine Learning Research Lab, Volkswagen Group, Munich, Germany","Objective: Subcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in 3 years SCIT. Methods: The research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities. Results: Excluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66%–84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55. Conclusion: We creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT. Copyright © 2024 Li, Xiong, Fan, Wang, Yu, Si, van der Smagt, Tang and Chen.","adherence; allergen immunotherapy; allergic rhinitis; latent variable model; sequential model","adolescent; adult; allergic rhinitis; Article; asthma; child; conjunctivitis; controlled study; coughing; dietary compliance; dyspnea; eczema; female; follow up; forced expiratory volume; human; long short term memory network; major clinical study; male; mental disease; nasal pruritus; nose obstruction; ocular pruritus; patient compliance; peak nasal inspiratory flow; physical examination; prediction; prick test; pruritus; retrospective study; root mean squared error; short term memory; sneezing; subcutaneous immunotherapy; support vector machine; time series analysis; wheezing","","","","","","","Eduardo S., Adherence to long-term therapies: evidence for action, (2003); Gregor K., Papamakarios G., Besse F., Buesing L., Weber T., Temporal difference variational auto-encoder, arXiv, (2018); Gu Y., Zalkikar A., Liu M., Kelly L., Hall A., Daly K., Et al., Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data, Sci. Rep, 11, (2021); Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput, 9, pp. 1735-1780, (1997); Hsu W., Warren J.R., Riddle P.J., Medication adherence prediction through temporal modelling in cardiovascular disease management, BMC Med. Inf. Decis. Mak, 22, pp. 313-321, (2022); Kanyongo W., Ezugwu A.E., Machine learning approaches to medication adherence amongst ncd patients: a systematic literature review, Inf. Med. Unlocked, 38, (2023); Karl M., Soelch M., Bayer J., Van der Smagt P., Deep variational bayes filters: unsupervised learning of state space models from raw data, arXiv, (2016); Kokhlikyan N., Miglani V., Martin M., Wang E., Alsallakh B., Reynolds J., Et al., Captum: a unified and generic model interpretability library for pytorch, arXiv, (2020); Krishnan R.G., Shalit U., Sontag D., Deep kalman filters, aarXiv, (2015); Lee A.X., Nagabandi A., Abbeel P., Levine S., Stochastic latent actor-critic: deep reinforcement learning with a latent variable model, Adv. Neural Inf. Process. Syst, 33, pp. 741-752, (2020); Lee J.-H., Lee S.-H., Ban G.-Y., Ye Y.-M., Nahm D.-H., Park H.-S., Et al., Factors associated with adherence to allergen specific subcutaneous immunotherapy, Yonsei Med. J, 60, pp. 570-577, (2019); Lemberg M.-L., Berk T., Shah-Hosseini K., Kasche E.-M., Mosges R., Sublingual versus subcutaneous immunotherapy: patient adherence at a large German allergy center, Patient Prefer. Adherence, 11, pp. 63-70, (2017); Liu J., Feng X., Wang H., Yu H., Compliance with subcutaneous immunotherapy and factors affecting compliance among patients with allergic rhinitis, Am. J. Otolaryngol, 42, (2021); Lourenco T., Fernandes M., Coutinho C., Lopes A., Spinola Santos A., Neto M., Et al., Subcutaneous immunotherapy with aeroallergens-evaluation of adherence in real life, Eur. Ann. Allergy Clin. Immunol, 52, pp. 84-90, (2020); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst, 30, (2017); Meltzer E.O., Allergic rhinitis: burden of illness, quality of life, comorbidities, and control, Immunol. Allergy Clin, 36, pp. 235-248, (2016); Mirzadeh S.I., Arefeen A., Ardo J., Fallahzadeh R., Minor B., Lee J.-A., Et al., Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease, Smart Health, 26, (2022); Mousavi H., Karandish M., Jamshidnezhad A., Hadianfard A.M., Determining the effective factors in predicting diet adherence using an intelligent model, Sci. Rep, 12, (2022); Passalacqua G., Baiardini I., Senna G., Canonica G., Adherence to pharmacological treatment and specific immunotherapy in allergic rhinitis, Clin. Exp. Allergy, 43, pp. 22-28, (2013); Pfaar O., Devillier P., Schmitt J., Demoly P., Hilberg O., DuBuske L., Et al., Adherence and persistence in allergen immunotherapy (apait): a reporting checklist for retrospective studies, (2023); Roberts G., Pfaar O., Akdis C., Ansotegui I., Durham S., Gerth van Wijk R., Et al., Eaaci guidelines on allergen immunotherapy: allergic rhinoconjunctivitis, Allergy, 73, pp. 765-798, (2018); Ruff C., Koukalova L., Haefeli W.E., Meid A.D., The role of adherence thresholds for development and performance aspects of a prediction model for direct oral anticoagulation adherence, Front. Pharmacol, 10, (2019); Schleicher M., Unnikrishnan V., Pryss R., Schobel J., Schlee W., Spiliopoulou M., Prediction meets time series with gaps: user clusters with specific usage behavior patterns, Artif. Intell. Med, 142, (2023); Singh A., Chakraborty S., He Z., Tian S., Zhang S., Lustria M.L.A., Et al., Deep learning-based predictions of older adults’ adherence to cognitive training to support training efficacy, Front. Psychol, 13, (2022); Wang L., Fan R., Zhang C., Hong L., Zhang T., Chen Y., Et al., Applying machine learning models to predict medication nonadherence in crohn’s disease maintenance therapy, Patient Prefer. adherence, 14, pp. 917-926, (2020); Warren D., Marashi A., Siddiqui A., Eijaz A.A., Pradhan P., Lim D., Et al., Using machine learning to study the effect of medication adherence in opioid use disorder, PLoS One, 17, (2022); Yang Y., Wang Y., Yang L., Wang J., Huang N., Wang X., Et al., Risk factors and strategies in nonadherence with subcutaneous immunotherapy: a real-life study, Int. Forum Allergy Rhinol, 8, pp. 1267-1273, (2018); Yao H., Wang L., Zhou X., Jia X., Xiang Q., Zhang W., Predicting the therapeutic efficacy of ait for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques, Comput. Biol. Med, 166, (2023); Zakeri M., Sansgiry S.S., Abughosh S.M., Application of machine learning in predicting medication adherence of patients with cardiovascular diseases: a systematic review of the literature, J. Med. Artif. Intell, 5, pp. 5-16, (2022)","J. Tang; Department of Otorhinolaryngology, The First People’s Hospital of Foshan, Foshan, China; email: fsyyytj@126.com","","Frontiers Media SA","","","","","","16639812","","","","English","Front. Pharmacol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85200241565"
"Kim H.-J.","Kim, Hye-Jin (57478475500)","57478475500","Classification of healthy and affected lungs by pneumonia disease from x-ray images of lungs and gene sequencing using inception model","2022","Journal of Medical Pharmaceutical and Allied Sciences","11","1","","4114","4118","4","2","10.55522/jmpas.v11i1.1432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125899745&doi=10.55522%2fjmpas.v11i1.1432&partnerID=40&md5=ca75d5a5b6cd1b50e9dcf2c1a643c1f1","Kookmin University, Jeongneung-ro, Seongbuk-gu, Seoul, South Korea","Kim H.-J., Kookmin University, Jeongneung-ro, Seongbuk-gu, Seoul, South Korea","This project is entitled to predict the lung disease using chest x-rays by deep learning technique. Lung disease is a term that refers to improper functioning of lungs. There are many diseases which occur due to the abnormal functioning of lungs. It includes tuberculosis, pneumonia, lung cancer, asthma. The infection can be bacterial, viral or fungal. It causes inflammation of trachea and respiratory failure. If found earlier it can be cured else it can even lead to death. This project classifies the normal and abnormal x-ray with a percentage of accuracy so that we can give the treatment to the patient accordingly by seeing the x-ray. Algorithms used are Convolutional Neural Network (CNN) and Inception Neural Network (INN) and Tensor Flow which is Google open source algorithm. The project is helpful for finding lung disease using chest x-ray. © 2022 MEDIC SCIENTIFIC. All right reserved.","Bronchitis; Convolutional neural networks; Inspectional neural network; Inspectional v3 model; Pneumonia; Trachea","","","","","","Department of CSE; Vignan's Institute of Information Technology","This work was supported by Jaswanth Duppala and Eali Stephen Neal Joshua (Department of CSE, Vignan's Institute of Information Technology, Visakhapatnam, AP, India).","Sushma D, Thirupathi Rao N, Bhattacharyya D, A comparative study on automated detection of malaria by using blood smear images, 5, 1, pp. 978-981, (2021); Chandra Sekhar P, Thirupathi Rao N, Bhattacharyya D, Kim T, Segmentation of natural images with k-means and hierarchical algorithm based on mixture of pearson distributions, Journal of Scientific and Industrial Research, 80, 8, pp. 707-715, (2021); Bhattacharyya D, Reddy BD, Kumari NMJ, Rao NT, Comprehensive analysis on comparison of machine learning and deep learning applications on cardiac arrest, Journal of Medical Pharmaceutical and Allied Sciences, 10, 4, pp. 3125-3131, (2021); Hu MK, Visual pattern recognition by moment invariants, IRE Transaction Information Theory, 8, pp. 179-187, (1962); Joshua ESN, Battacharyya D, Doppala BP, Chakkravarthy M., Extensive statistical analysis on novel coronavirus, Towards worldwide health using apache spark, (2022); Satyanarayana KV, Rao NT, Bhattacharyya D, Hu Y, Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm, Multidimensional Systems and Signal Processing, (2021); Giardina CR, Dougherty ER, Morphological Methods in Image and Signal Processing, (1988); Uppaluri R, Hoffman EA, Sonka M, Et al., Computer recognition of regional lung disease patterns, American Journal of Respiratory Critical Care Medicine, 160, pp. 648-654, (1999); Kakara Manish, Olsen Dag Rune, Automatic segmentation and recognition of lungs and lesions from CT scans of thorax, IEEE transactions on Computerized Medical Imaging and Graphics, 33, pp. 72-82, (2009); Summers RM, Road maps for advancement of radiologic computer-aided detection in the 21st century, Radiology, 229, 1, pp. 11-13, (2003); Swathi K, Vamsi B, Rao NT, A deep learning-based object detection system for blind people, 16, 7, pp. 978-981, (2021); Kakeda S, Et al., Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System, Ame. J. Roentgeno, 182, pp. 505-510, (2004); Tagare HD, Jafe C, Duncan J, Medical image databases, a content-based retrieval approach, J. Ame. Med. Informatics Association, 4, 3, pp. 184-198, (1997); Kumari NMJ, Krishna KK, Prognosis of Diseases Using Machine Learning Algorithms, a Survey, Int. Con. on Current Trends towards Converging Tech, pp. 1-9, (2018); Bhattacharyya D, Kumari NMJ, Joshua ESN, Rao NT, Advanced Empirical Studies on Group Governance of the Novel Corona Virus, MERS, SARS and EBOLA, a aystematic study, Int J Cur Res Rev, 12, 18, (2020); Joshua ESN, Chakkravarthy M, Bhattacharyya D, An extensive review on lung cancer detection using machine learning techniques, a systematic study, Revue d'Intelligence Artificielle, 34, 3, pp. 351-359, (2020); Stephen Neal Joshua Eali, Bhattacharyya Debnath, Et al., 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation, J. Health. Eng, (2021)","H.-j. Kim; Department of General Education, Kookmin University 77, Seoul, Jeongneung-ro, Seongbuk-gu, South Korea; email: khj5187@kookmin.ac.kr","","MEDIC SCIENTIFIC","","","","","","23207418","","","","English","J. Med. Pharma. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85125899745"
"Zan J.; Dong X.; Yang H.; Yan J.; He Z.; Tian J.; Zhang Y.","Zan, Jiaxin (59311811200); Dong, Xiaojing (58960671100); Yang, Hong (57211859895); Yan, Jingjing (57211858258); He, Zixuan (57971553900); Tian, Jing (57201296091); Zhang, Yanbo (56645922300)","59311811200; 58960671100; 57211859895; 57211858258; 57971553900; 57201296091; 56645922300","Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension","2024","Risk Management and Healthcare Policy","17","","","1921","1936","15","1","10.2147/RMHP.S472398","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203015712&doi=10.2147%2fRMHP.S472398&partnerID=40&md5=e88d74d8b4047fc6d8108276b290ffbe","Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China; Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China; School of Health Services and Management, Shanxi University of Chinese Medicine, Taiyuan, China","Zan J., Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China; Dong X., Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China; Yang H., Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China; Yan J., Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China; He Z., Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China; Tian J., Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China; Zhang Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China, School of Health Services and Management, Shanxi University of Chinese Medicine, Taiyuan, China","Purpose: This study sought to develop an unbalanced-ensemble model that could accurately predict death outcomes of patients with comorbid coronary heart disease (CHD) and hypertension and evaluate the factors contributing to death. Patients and Methods: Medical records of 1058 patients with coronary heart disease combined with hypertension and excluding those acute coronary syndrome were collected. Patients were followed-up at the first, third, sixth, and twelfth months after discharge to record death events. Follow-up ended two years after discharge. Patients were divided into survival and nonsurvival groups. According to medical records, gender, smoking, drinking, COPD, cerebral stroke, diabetes, hyperhomocysteinemia, heart failure and renal insufficiency of the two groups were sorted and compared and other influencing factors of the two groups, feature selection was carried out to construct models. Owing to data unbalance, we developed four unbalanced-ensemble prediction models based on Balanced Random Forest (BRF), EasyEnsemble, RUSBoost, SMOTEBoost and the two base classification algorithms based on AdaBoost and Logistic. Each model was optimised using hyperparameters based on GridSearchCV and evaluated using area under the curve (AUC), sensitivity, recall, Brier score, and geometric mean (G-mean). Additionally, to understand the influence of variables on model performance, we constructed a SHapley Additive explanation (SHAP) model based on the optimal model. Results: There were significant differences in age, heart rate, COPD, cerebral stroke, heart failure and renal insufficiency in the nonsurvival group compared with the survival group. Among all models, BRF yielded the highest AUC (0.810; 95% CI, 0.778–0.839), sensitivity (0.990; 95% CI, 0.981–1.000), recall (0.990; 95% CI, 0.981–1.000), and G-mean (0.806; 95% CI, 0.778–0.827), and the lowest Brier score (0.181; 95% CI, 0.178–0.185). Therefore, we identified BRF as the optimal model. Furthermore, red blood cell count (RBC), body mass index (BMI), and lactate dehydrogenase were found to be important mortality-associated risk factors. Conclusion: BRF combined with advanced machine learning methods and SHAP is highly effective and accurately predicts mortality in patients with CHD comorbid with hypertension. This model has the potential to assist clinicians in modifying treatment strategies to improve patient outcomes. © 2024 Zan et al.","balanced random forest; coronary heart disease comorbid with hypertension; ensemble learning; Prognosis; SHAP","acetylsalicylic acid; amlodipine; apolipoprotein A1; apolipoprotein B; aspartate aminotransferase; atorvastatin; benidipine; bisoprolol; clopidogrel; cystatin C; digoxin; dobutamine; electrolyte; enoxaparin; eplerenone; furosemide; high density lipoprotein cholesterol; hydrochlorothiazide; lactate dehydrogenase; low density lipoprotein cholesterol; magnesium; metoprolol; phosphorus; pravastatin; rosuvastatin; sildilan; simvastatin; spironolactone; tolasemide; tolvaptan; triacylglycerol; unclassified drug; uric acid; warfarin; aged; algorithm; all cause mortality; Article; atrial fibrillation; blood gas analysis; body mass; body temperature; breathing rate; cerebrovascular accident; chronic obstructive lung disease; cohort analysis; controlled study; demographics; diabetes mellitus; diagnostic accuracy; diagnostic test accuracy study; diastolic blood pressure; electronic medical record; ensemble learning; erythrocyte count; feature selection; female; follow up; glucose blood level; heart failure; heart rate; heart ventricle aneurysm; hospitalization; human; hyperhomocysteinemia; hyperkalemia; hyperlipidemia; hypertension; hypokalemia; ischemic heart disease; kidney failure; kidney function; machine learning; major clinical study; male; mortality; obesity; percutaneous coronary intervention; prediction; prospective study; random forest; recall; receiver operating characteristic; risk factor; Shapley additive explanation; smoking; systolic blood pressure; thyroid function; treatment outcome","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; amlodipine, 88150-42-9; aspartate aminotransferase, 9000-97-9; atorvastatin, 134523-00-5, 134523-03-8, 110862-48-1; benidipine, 105979-17-7, 91599-74-5; bisoprolol, 66722-44-9; clopidogrel, 113665-84-2, 120202-66-6, 90055-48-4, 94188-84-8, 120202-65-5, 120202-67-7, 894353-16-3, 744256-69-7; digoxin, 20830-75-5, 57285-89-9; dobutamine, 34368-04-2, 52663-81-7, 49745-95-1, 61661-06-1; enoxaparin, 679809-58-6; eplerenone, 107724-20-9; furosemide, 54-31-9; hydrochlorothiazide, 58-93-5; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; magnesium, 7439-95-4; metoprolol, 37350-58-6; phosphorus, 7723-14-0; pravastatin, 81093-37-0, 81131-70-6; rosuvastatin, 147098-18-8, 147098-20-2, 287714-41-4; simvastatin, 79902-63-9; spironolactone, 52-01-7; tolvaptan, 150683-30-0; uric acid, 69-93-2; warfarin, 129-06-6, 2610-86-8, 3324-63-8, 5543-58-8, 81-81-2","","","National Natural Science Foundation of China, NSFC, (82173631); National Natural Science Foundation of China, NSFC; Shaanxi Key Science and Technology Innovation Team Project, (202204051001026); Shaanxi Key Science and Technology Innovation Team Project","This work was supported by the National Natural Science Foundation of China under Grant [number 82173631]; Shanxi Science and Technology innovation talent team project [number 202204051001026].","Virani SS, Alonso A, Benjamin EJ, Et al., Heart disease and stroke statistics-2020 update: A report from the American heart association, Circulation, 141, 9, pp. e139-e596, (2020); Bauersachs R, Zeymer U, Briere JB, Marre C, Bowrin K, Huelsebeck M., Burden of coronary artery disease and peripheral artery disease:a literature review, Cardiovasc Ther, 2019, (2019); Yang X, Li J, Hu D, Et al., Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: the china-par project(prediction for ASCVD risk in china), Circulation, 134, 19, pp. 1430-1440, (2016); Beaglehole R, Magnus P., The search for new risk factors for coronary heart disease: occupational therapy for epidemiologists?, Int J Epidemiol, 31, 6, pp. 1117-1122, (2002); Lefevre G, Puymirat E., Hypertension and coronary artery disease: new concept?, Annales de cardiologie et d’angeiologie, 66, 1, pp. 42-47, (2017); 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Nistal-Nuno B., Machine learning applied to a cardiac surgery recovery unit and to a coronary care unit for mortality prediction, J Clin Mon Compu, 36, 3, pp. 751-763, (2022); IEEE 14th International Conference on Machine Learning and Applications (ICMLA). 2015 IEEE 14th International Conference on MachineLearning and Applications (ICMLA), (2015); Yang H, Li X, Cao H, Et al., Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data, ComputMeth Progr Biomed, 211, (2021); Adnan M, Alarood AAS, Uddin MI, Ur Rehman I., Utilizing grid search cross-validation with adaptive boosting for augmenting performance ofmachine learning models, PeerJ Comput Sci, 8, (2022); Li Z, Liu Z., Feature selection algorithm based on XGBoost, J Commun, 40, 10, (2019); Held C, Hadziosmanovic N, Aylward PE, Et al., Body Mass Index and Association With Cardiovascular Outcomes in Patients With Stable Coronary Heart Disease-A STABILITY Substudy, J American Heart Associa, 11, 3, (2022); 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Ha AW, Kim WK, Kim SH., Cow’s Milk Intake and Risk of Coronary Heart Disease in Korean Postmenopausal Women, Nutrients, 14, 5, (2022); Dalal J, Dasbiswas A, Sathyamurthy I, Et al., Heart Rate in Hypertension: review and Expert Opinion, Int j Hyper, 2019, 2087064, pp. 1-6, (2019); Kikuchi N, Ogawa H, Kawada-Watanabe E, Et al., Impact of age on clinical outcomes of antihypertensive therapy in patients with hypertension and coronary artery disease: a sub-analysis of the Heart Institute of Japan Candesartan Randomized Trial for Evaluation in Coronary Artery Disease, J clin hyperten, 22, 6, pp. 1070-1079, (2020)","J. Tian; Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, China; email: 1105551933@qq.com; Y. Zhang; Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China; email: sxmuzyb@126.com","","Dove Medical Press Ltd","","","","","","11791594","","","","English","Risk Manage. Healthc. Policy","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85203015712"
"M R P.; Ravi V.; Lokesh G.H.; Al Mazroa A.; Ravi P.","M R, Pooja (57190388894); Ravi, Vinayakumar (56755324000); Lokesh, Gururaj Harinahalli (59206681600); Al Mazroa, Alanoud (58894388100); Ravi, Pradeep (58955034000)","57190388894; 56755324000; 59206681600; 58894388100; 58955034000","A Prognostic Model to Improve Asthma Prediction Outcomes Using Machine Learning","2024","Open Bioinformatics Journal","17","","e18750362306414","","","","1","10.2174/0118750362306414240624113350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197711970&doi=10.2174%2f0118750362306414240624113350&partnerID=40&md5=d6ce00468b353d09485db34f4c8ffe92","Department of Computer Science & Engineering Vidyavardhaka, College of Engineering, Mysuru, India; Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Department of Information Science and Engineering, GSSS Institute of Engineering and Technology for Women, Karnataka, Mysuru, India; Department of Information Technology, Manipal Academy of Higher Education, Manipal, 576104, India","M R P., Department of Computer Science & Engineering Vidyavardhaka, College of Engineering, Mysuru, India; Ravi V., Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; Lokesh G.H., Department of Information Technology, Manipal Academy of Higher Education, Manipal, 576104, India; Al Mazroa A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Ravi P., Department of Information Science and Engineering, GSSS Institute of Engineering and Technology for Women, Karnataka, Mysuru, India","Purpose: The utility of predictive models for the prognosis of asthma disease that rely on clinical history and findings has been on the constant rise owing to the attempts to achieve better disease outcomes through improved clinical processes. With the prognostic model, the primary focus is on the search for a combination of features that are as robust as possible in predicting the disease outcome. Clinical decisions concerning obstructive lung diseases such as Chronic obstructive Pulmonary Disease (COPD) have a high chance of leading to results that can be misinterpreted with wrong inferences drawn that may have long-term implications, including the targeted therapy that can be mistakenly beset. Hence, we suggest data-centric approaches that harness learning techniques to facilitate the disease prediction process and augment the inferences through clinical findings. Methods: A dataset containing information on both symptomatic representations and medical history in the form of categorical data along with lung function parameters, which were estimated using a spirometer (with the data basically being quantitative (numerical) in nature) was used. The Naïve Bayes classifier performed comparatively well with the optimized feature set. The adoption of One-Class Support Vector Machines (OCSVM) as an alternative method to sampling data has resulted in the selection of an ideal representation of the data rather than the regular sampling approach that is used for undersampling. Results: The model was able to predict the disease outcome with a precision of 86.1% and recall of 84.7%, accounting for an F1 measure of 84.5%.The Area under Curve(AUC) and Classification Accuracy (CA) were evaluated to be 92.2% and 84.7% respectively. Conclusion: Incorporating domain knowledge into the prediction models involves identifying clinical features that are most relevant to the process of disease classification using prior knowledge about the disease and its contributing factors, which can significantly enhance the productivity of the models. Feature engineering is centric on the use of domain knowledge within clinical prediction models and commonly results in an optimized feature set. It is evident from the experimental results that using a combination of medical history data and significant clinical findings result in a better prognostic model. © 2024 The Author(s).","Asthma; Machine learning; Predictors; Pulmonary; Spirometry; Tiffeneau-pinelli","biological marker; gamma interferon; algorithm; anthropometry; area under the curve; Article; artificial neural network; asthma; body mass; bronchiectasis; child; chronic obstructive lung disease; classifier; clinical feature; clinical observation; cohort analysis; controlled study; coughing; decision making; diagnostic test accuracy study; disease classification; female; forced expiratory volume; forced vital capacity; human; learning; learning algorithm; lung adenocarcinoma; lung cancer; lung function; lung function test; machine learning; male; medical history; normal human; obstructive lung disease; one class support vector machine; peak expiratory flow; prediction; predictive model; pregnancy; quality control; receiver operating characteristic; smoking; spirometry; support vector machine; survival prediction; taxonomy; thorax radiography","","gamma interferon, 82115-62-6","","","","","Chatzimichail E, Paraskakis E, Rigas A., Predicting asthma outcome using partial least square regression and artificial neural networks, Adv Artif Intell, 2013, pp. 1-7, (2013); Manoharan SC, Ramakrishnan S., Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering, J Med Syst, 33, 5, pp. 347-351, (2009); Wang X, Wang Z, Pengetnze YM, Lachman BS, Chowdhry V., Deep learning models to predict pediatric asthma emergency department visits, (2019); Tomita K, Nagao R, Touge H, Et al., Deep learning facilitates the diagnosis of adult asthma, Allergol Int, 68, 4, pp. 456-461, (2019); Tobore I, Li J, Yuhang L, Et al., Deep learning intervention for health care challenges: Some biomedical domain considerations, JMIR Mhealth Uhealth, 7, 8, (2019); Pooja MR., A predictive model for the early prognosis and characterization of asthma, J Pharm Negat Results, 13, 4, pp. 1-9, (2022); Deng H, Urman R, Gilliland FD, Eckel SP., Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach, BMC Med Res Methodol, 19, 1, (2019); Pushpalatha MP, Pooja MR., A predictive model for the effective prognosis of Asthma using Asthma severity indicators, 2017 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6, (2017); Anastasiou A, Kocsis O, Moustakas K., Exploring machine learning for monitoring and predicting severe asthma exacerbations, InProceedings of the 10th Hellenic Conference on Artificial Intelligence, pp. 1-6, (2018); Messinger AI, Bui N, Wagner BD, Szefler SJ, Vu T, Deterding RR., Novel pediatric‐automated respiratory score using physiologic data and machine learning in asthma, Pediatr Pulmonol, 54, 8, pp. 1149-1155, (2019); Spathis D, Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics J, (2017); Sheshasaayee A, Prathiba L., An improvised technique for the diagnosis of asthma disease with the categorization of asthma disease level, Information Systems Design and Intelligent Applications, pp. 985-994, (2018); Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, Am J Emerg Med, 36, 9, pp. 1650-1654, (2018); Pooja M R, Pushpalatha M P., Clinical respiratory diseases and care, (2019); Pooja M R, Pushpalatha M P., A neural network approach for risk assessment of asthma disease, J Health Inform Manage, 2, 1, (2018); Pooja MR, Pushpalatha MP., A hybrid decision support system for the identification of asthmatic subjects in a cross-sectional study, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 288-293, (2015); Pooja MR, Pushpalatha MP., An empirical analysis of machine learning classifiers for clinical decision making in asthma, International Conference on Cognitive Computing and Information Processing, pp. 105-117, (2018); Pooja MR, Pushpalatha MP., A predictive framework for the assessment of asthma control level, Int J Eng Adv Technol, 8, pp. 239-245, (2019); Badnjevic A., Classification of asthma using artificial neural network, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 387-390, (2016); Gayathri GV, Satapathy SC., A Survey on techniques for prediction of asthma, Smart Intelligent Computing and Applications, pp. 751-758, (2020); Yang Xiang, Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: Retrospective cohort study, J Med Internet Res, 22, 7, (2020); Haque R., Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers, F1000Research, 10, (2021); Delic S., Detection of Asthma Inflammatory Phenotypes Using Artificial Neural Network, International Conference on Medical and Biological Engineering, 84, pp. 69-75, (2021); Ma X, Wu Y, Zhang L, Et al., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, J Transl Med, 18, 1, (2020); Amaral Jorge LM., Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers, Med Biol Eng Comput, 58, 10, pp. 2455-2473, (2020); Ciancio N, Pavone M, Torrisi SE, Et al., Contribution of pulmonary function tests (PFTs) to the diagnosis and follow up of connective tissue diseases, Multidiscip Respir Med, 14, 1, (2019); Pooja MR., On effective use of feature engineering for improving the predictive capability of machine learning models, Computational Intelligence and Data Sciences, pp. 53-62, (2022); MP P., Cluster analysis to characterize the patterns of complementary and alternative medicines usage in asthma controls, Open Public Health J, 13, 1, (2020); Nafisi VR, Eghbal M, Torbati N., Conceptual design of a device for online calibration of spirometer based on neural network, J Biomed Phys Eng, (2021); Salau AO, Pooja MR, Hasani NF, Braide SL., Model based risk assessment to evaluate lung functionality for early prognosis of asthma using neural network approach, Math Model Eng Probl, 9, 4, pp. 1053-1060, (2022)","V. Ravi; Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; email: vinayakumarr77@gmail.com","","Bentham Science Publishers","","","","","","18750362","","","","English","Open Bioinformatics J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85197711970"
"Yin B.; Chen J.; Xiang G.; Xu Z.; Yang M.; Wong S.H.D.","Yin, Bohan (57201380097); Chen, Jiareng (57215037197); Xiang, Guangli (58260025500); Xu, Zehui (59473360600); Yang, Mo (7404927250); Wong, Siu Hong Dexter (57193565642)","57201380097; 57215037197; 58260025500; 59473360600; 7404927250; 57193565642","Multiscale and stimuli-responsive biosensing in biomedical applications: Emerging biomaterials based on aggregation-induced emission luminogens","2025","Biosensors and Bioelectronics","271","","117066","","","","1","10.1016/j.bios.2024.117066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212058205&doi=10.1016%2fj.bios.2024.117066&partnerID=40&md5=307b23ac3e14a6a059c442bd9f2d6a36","Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong; Laboratory for Marine Drugs and Bioproducts, Qingdao Marine Science and Technology Center, Qingdao, 266237, China; School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China; The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, China; Joint Research Center of Biosensing and Precision Theranostics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong","Yin B., Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong; Chen J., Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong; Xiang G., School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China; Xu Z., School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China; Yang M., Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong, The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518000, China, Joint Research Center of Biosensing and Precision Theranostics, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong; Wong S.H.D., Laboratory for Marine Drugs and Bioproducts, Qingdao Marine Science and Technology Center, Qingdao, 266237, China, School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China","Biosensors play a critical role in the diagnosis, treatment, and prognosis of diseases, with diverse applications ranging from molecular diagnostics to in vivo imaging. Conventional fluorescence-based biosensors, however, often suffer from aggregation-caused emission quenching (ACQ), limiting their effectiveness in high concentrations and complex environments. In contrast, the phenomenon of aggregation-induced emission (AIE) has emerged as a promising alternative, where luminescent materials exhibit strong emission in the aggregated state with good photostability, biocompatibility, large Stokes shift, high quantum yield, and tunable emission. This review article discusses the development of AIEgen-based biosensors for multiscale biosensing in biomedical applications. The integration of AIEgens with nanomaterials, such as graphene oxide and stimuli-responsive nanomaterials, can further improve the selectivity and multifunctionality of biomolecule detection. By careful molecular design, the affinity between AIEgens and specific biomolecules can be tuned, enabling the selective detection of targets like DNA, RNA, and proteins ex vivo, in vitro and in vivo, which can be applied across multiple scales, from detecting biomolecules and cellular structures to analyzing tissues and organs, underscoring their growing importance in disease diagnosis. Furthermore, we explore the potential integration of AIEgen-based biosensors with artificial intelligence (AI) technologies, offering promising avenues for future advancements in this field. © 2024 Elsevier B.V.","Aggregation-induced emission; Biomedical applications; Multiscale biosensing; Nanobiosensors; Nanoprobes","biomaterial; DNA; graphene oxide; nanomaterial; Aggregation-induced emissions; Biomedical applications; Biosensing; Diverse applications; Emission quenching; In-Vivo imaging; Molecular diagnostics; Multiscale biosensing; Nanobiosensor; Stimuli-responsive; Article; artificial intelligence; binding affinity; biocompatibility; bioluminescence; blood flow velocity; CD8+ T lymphocyte; cell differentiation; cell structure; chemical structure; chemoluminescence; computer assisted tomography; confocal laser scanning microscopy; cytotoxicity; drug delivery system; environmental monitoring; enzyme linked immunosorbent assay; extracellular matrix; fluorescence imaging; fluorescence resonance energy transfer; genetic procedures; glucose blood level; hippocampus; host pathogen interaction; hydrophobicity; hydrostatic pressure; iliac vein; information storage; limit of detection; lipid storage; live cell imaging; machine learning; mass spectrometry; nanotechnology; nuclear magnetic resonance imaging; nucleic acid analysis; pH; photoluminescence; photothermal therapy; radioactivity; signal noise ratio; signal transduction; three dimensional printing; tumor microenvironment; aggregation-induced emission; article; Asthma Control Questionnaire; biosensor; diagnosis; ex vivo study; fluorescence; human; human cell; in vitro study; luminescence; molecular diagnostics; nanoprobe; nonhuman; quantum yield; Nanoclay","","DNA, 9007-49-2","","","Laboratory for Marine Drugs; Start-up Fundings of Ocean University of China, (862401013155, 862401013154); Hong Kong Polytechnic University, PolyU, (15210818, 1-WZ4E, 1-W02C, 1-YWB4, 1-CE2J, 1-YWDU, 15217621); Hong Kong Polytechnic University, PolyU; Research Grants Council, University Grants Committee, 研究資助局, (C5078-21 EF, C5078-21E, C5005-23W); Research Grants Council, University Grants Committee, 研究資助局; Shenzhen Science and Technology Program-Basic Research Scheme, (JCYJ20220531090808020); Bioproducts Qingdao Marine Science and Technology Center, (LMDBCXRC202401, LMDBCXRC202402); Hong Kong General Research Fund, (15216622); Shandong Provincial Overseas Excellent Young Scholar Program, (2024HWYQ-043, 2024HWYQ-042); Taishan Scholar Youth Expert Program of Shandong Province, (tsqn202312105, tsqn202306102); Guangdong-Hong Kong Technology Cooperation Funding Scheme, (SGDX20201103095404018, GHP/032/20SZ)","Funding text 1: This work was supported by the Shenzhen Science and Technology Program-Basic Research Scheme (JCYJ20220531090808020), the Research Grants Council (RGC) of Hong Kong Collaborative Research Grant (C5078-21 EF), the Research Grants Council (RGC) of Hong Kong General Research Grant (PolyU 15217621 and PolyU 15210818) We also would like to acknowledge the funding from Start-up Fundings of Ocean University of China (862401013154 and 862401013155), Laboratory for Marine Drugs and Bioproducts Qingdao Marine Science and Technology Center (no.: LMDBCXRC202401 and LMDBCXRC202402), Taishan Scholar Youth Expert Program of Shandong Province (tsqn202306102 and tsqn202312105), and Shandong Provincial Overseas Excellent Young Scholar Program (2024HWYQ-042 and 2024HWYQ-043) for supporting this work.; Funding text 2: This work was supported by the Shenzhen Science and Technology Program-Basic Research Scheme (JCYJ20220531090808020), the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2425-5S09), the Hong Kong Research Grants Council (RGC) Collaborative Research Fund (C5005-23W and C5078-21E), the Research Grants Council (RGC) Hong Kong General Research Fund (15217621 and 15216622), the Guangdong-Hong Kong Technology Cooperation Funding Scheme (GHP/032/20SZ and SGDX20201103095404018), the Hong Kong Polytechnic University Internal Fund (1-YWB4, 1-WZ4E, 1-CD8M, 1-CEB1, 1-YWDU, 1-CE2J and 1-W02C). We also would like to acknowledge the funding from Start-up Fundings of Ocean University of China (862401013154 and 862401013155), Laboratory for Marine Drugs and Bioproducts Qingdao Marine Science and Technology Center (no.: LMDBCXRC202401 and LMDBCXRC202402), Taishan Scholar Youth Expert Program of Shandong Province (tsqn202306102 and tsqn202312105), and Shandong Provincial Overseas Excellent Young Scholar Program (2024HWYQ-042 and 2024HWYQ-043) for supporting this work.","Adeva-Andany M.M., Gonzalez-Lucan M., Donapetry-Garcia C., Fernandez-Fernandez C., Ameneiros-Rodriguez E., Glycogen metabolism in humans, BBA Clin., 5, pp. 85-100, (2016); Aramouni K., Assaf R., Shaito A., Fardoun M., Al-Asmakh M., Sahebkar A., Eid A.H., Biochemical and cellular basis of oxidative stress: implications for disease onset, J. Cell. Physiol., 238, 9, pp. 1951-1963, (2023); Balendiran G.K., Dabur R., Fraser D., The role of glutathione in cancer, Cell Biochem. 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Yang; Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, 999077, Hong Kong; email: mo.yang@polyu.edu.hk; S.H.D. Wong; School of Medicine and Pharmacy, Ocean University of China, Qingdao, 266003, China; email: dexterwong@ouc.edu.cn","","Elsevier Ltd","","","","","","09565663","","BBIOE","39689580","English","Biosens. Bioelectron.","Article","Final","","Scopus","2-s2.0-85212058205"
"Li Z.; Lu T.; Sun L.; Hou Y.; Chen C.; Lai S.; Yan Y.; Yu L.; Liu S.; Huang W.; Zhang N.; Wen W.; Wei Y.; Li J.; Bachert C.","Li, Zhengqi (57556830100); Lu, Tong (57211256045); Sun, Lin (57218705377); Hou, Yilin (58745377200); Chen, Changhui (58730749500); Lai, Shimin (58729932400); Yan, Yan (56427251800); Yu, Lei (57212236811); Liu, Shaoling (59208290100); Huang, Wenhao (59208627700); Zhang, Nan (56982196400); Wen, Weiping (7102171038); Wei, Yi (55798352600); Li, Jian (57216663197); Bachert, Claus (7102663930)","57556830100; 57211256045; 57218705377; 58745377200; 58730749500; 58729932400; 56427251800; 57212236811; 59208290100; 59208627700; 56982196400; 7102171038; 55798352600; 57216663197; 7102663930","Factors for predicting the outcome of surgery for non-eosinophilic chronic rhinosinusitis with nasal polyps","2024","Annals of Allergy, Asthma and Immunology","133","5","","559","567.e3","","1","10.1016/j.anai.2024.05.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204498391&doi=10.1016%2fj.anai.2024.05.023&partnerID=40&md5=62f046e543027896e4d26688bc296779","Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Otorhinolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning, China; Department of Otorhinolaryngology - Head and Neck Surgery, University Hospital of Münster, Münster, Germany; Upper Airways Research Laboratory, Faculty of Medicine, Ghent University, Ghent, Belgium","Li Z., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Lu T., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Sun L., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Hou Y., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Chen C., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Lai S., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Yan Y., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Yu L., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Liu S., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Huang W., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Zhang N., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Wen W., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Department of Otorhinolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; Wei Y., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Li J., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangxi Hospital Division of The First Affiliated Hospital, Sun Yat-sen University, Nanning, China; Bachert C., Department of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Guangzhou Key Laboratory of Otorhinolaryngology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, Department of Otorhinolaryngology - Head and Neck Surgery, University Hospital of Münster, Münster, Germany, Upper Airways Research Laboratory, Faculty of Medicine, Ghent University, Ghent, Belgium","Background: Despite the large patient base in Asia, the prognostic factors of patients with non-eosinophilic chronic rhinosinusitis with nasal polyps (CRSwNPs) remain largely undetermined. Objective: To systematically investigate the predictive value of clinical and biological variables for non-eosinophilic CRSwNP. Methods: A total of 51 patients with non-eosinophilic CRSwNP who underwent functional endoscopic surgery were recruited. Clinical information and assessment were comprehensively collected before and after surgery. A broad spectrum of biomarkers was measured in tissue homogenates using multiple assays. A random forest algorithm and stepwise logistic regression were used to construct clinical, biological, and combined models. Results: A total of 41.2% patients with non-eosinophilic CRSwNP were uncontrolled more than 6 months after surgery. We identified 1 clinical variable (22-item Sino-Nasal Outcome Test score) and 4 biomarkers (programmed cell death ligand 1, platelet-derived growth factor subunit β [PDGF-β], macrophage inflammatory protein-3b, and PDGF-α) that were significantly predictive of the surgical outcome. The clinical, biological, and combined models had predictive ability with areas under the curve of 0.78, 0.83, and 0.89, respectively. PDGF-β and programmed cell death ligand 1 were identified as independent biomarkers for the prognosis of patients with CRSwNP without considerable eosinophilic infiltration. Conclusion: This study reveals that clinical and biological factors, such as the 22-item Sino-Nasal Outcome Test score and PDGF-β, are predictive of the postfunctional endoscopic surgical prognosis of patients with non-eosinophilic CRSwNP. © 2024 American College of Allergy, Asthma & Immunology","","Adult; Aged; Biomarkers; Chronic Disease; Endoscopy; Eosinophils; Female; Humans; Male; Middle Aged; Nasal Polyps; Prognosis; Rhinitis; Rhinosinusitis; Sino-Nasal Outcome Test; Sinusitis; Treatment Outcome; biological marker; corticosteroid; eosinophil cationic protein; epidermal growth factor; granulocyte colony stimulating factor; interleukin 5; macrophage inflammatory protein 3beta; mometasone furoate; programmed death 1 ligand 1; biological marker; adult; area under the curve; Article; asthma; cancer prognosis; chronic rhinosinusitis; computer assisted tomography; controlled study; diagnostic test accuracy study; eosinophil count; female; human; human tissue; limit of detection; logistic regression analysis; machine learning; major clinical study; male; nasal lavage; neutrophil count; non eosinophilic chronic rhinosinusitis; nose obstruction; otorhinolaryngology; outcome assessment; predictive value; prick test; protein expression; random forest; respirometry; scoring system; sensitivity and specificity; sino-nasal outcome test-22; sinonasal polyp; survival analysis; aged; chronic disease; diagnosis; endoscopy; eosinophil; immunology; middle aged; procedures; prognosis; rhinitis; rhinosinusitis; sino-nasal outcome test; sinonasal polyp; sinusitis; surgery; treatment outcome","","epidermal growth factor, 59459-45-9, 62229-50-9; macrophage inflammatory protein 3beta, 181030-14-8; mometasone furoate, 83919-23-7, 105102-22-5; Biomarkers, ","R software","","National Natural Science Foundation of China, NSFC, (82020108009, 81870696, 82171766); National Natural Science Foundation of China, NSFC; Guangzhou Science and Technology Project of China, (202102020498); Natural Science Foundation of Guangdong Province, (2023B1111040004, 2018B030312008, 2021A1515010273); Natural Science Foundation of Guangdong Province","Funding Source: This study is supported by the National Natural Science Foundation of China (NSFC) grants 82020108009 , 81870696 (WW) , 82171766 (YW) , Guangdong Natural Science Foundation of China grants 2023B1111040004 , 2018B030312008 (WW) , 2021A1515010273 (YW) Guangzhou Science and Technology Project of China grant 202102020498 (YW) . 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Malinowska K., Kowalski A., Merecz-Sadowska A., Paprocka-Zjawiona M., Sitarek P., Kowalczyk T., Et al., PD-1 and PD-L1 expression levels as a potential biomarker of chronic rhinosinusitis and head and neck cancers, J Clin Med, 12, 5, (2023); Liu C.C., Zhang H.L., Zhi L.L., Jin P., Zhao L., Li T., Et al., CDK5 regulates PD-L1 expression and cell maturation in dendritic cells of CRSwNP, Inflammation, 42, 1, pp. 135-144, (2019); Shi L.L., Song J., Xiong P., Cao P.P., Liao B., Ma J., Et al., Disease-specific T-helper cell polarizing function of lesional dendritic cells in different types of chronic rhinosinusitis with nasal polyps, Am J Respir Crit Care Med, 190, 6, pp. 628-638, (2014); Kortekaas Krohn I., Bobic S., Dooley J., Lan F., Zhang N., Bachert C., Et al., Programmed cell death-1 expression correlates with disease severity and IL-5 in chronic rhinosinusitis with nasal polyps, Allergy, 72, 6, pp. 985-993, (2017)","J. Li; The First Affiliated Hospital of Sun Yat-sen University, No. 58, Zhongshan 2nd Road, Guangzhou, 510080, China; email: lijian7@mail.sysu.edu.cn","","American College of Allergy, Asthma and Immunology","","","","","","10811206","","ALAIF","38880209","English","Ann. Allergy Asthma Immunol.","Article","Final","","Scopus","2-s2.0-85204498391"
"Irtyuga O.; Kopanitsa G.; Kostareva A.; Metsker O.; Uspensky V.; Mikhail G.; Faggian G.; Sefieva G.; Derevitskii I.; Malashicheva A.; Shlyakhto E.","Irtyuga, Olga (56293743500); Kopanitsa, Georgy (55326019500); Kostareva, Anna (8906596800); Metsker, Oleg (57200213601); Uspensky, Vladimir (56700370700); Mikhail, Gordeev (57710009300); Faggian, Giuseppe (7004905834); Sefieva, Giunai (57710266000); Derevitskii, Ilia (57209274711); Malashicheva, Anna (6603002658); Shlyakhto, Evgeny (16317213100)","56293743500; 55326019500; 8906596800; 57200213601; 56700370700; 57710009300; 7004905834; 57710266000; 57209274711; 6603002658; 16317213100","Application of Machine Learning Methods to Analyze Occurrence and Clinical Features of Ascending Aortic Dilatation in Patients with and without Bicuspid Aortic Valve","2022","Journal of Personalized Medicine","12","5","794","","","","2","10.3390/jpm12050794","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130743940&doi=10.3390%2fjpm12050794&partnerID=40&md5=5078b5a602a1d2c3cc33aa1d23d26086","Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Department of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, Saint Petersburg, 197101, Russian Federation; Department of Cardiac Surgery, Medical School, University of Verona, Verona, 37126, Italy","Irtyuga O., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Kopanitsa G., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation, Department of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, Saint Petersburg, 197101, Russian Federation; Kostareva A., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Metsker O., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Uspensky V., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Mikhail G., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Faggian G., Department of Cardiac Surgery, Medical School, University of Verona, Verona, 37126, Italy; Sefieva G., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Derevitskii I., Department of Cardiac Surgery, Medical School, ITMO University, 49 Kronverskiy Prospect, Saint Petersburg, 197101, Russian Federation; Malashicheva A., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; Shlyakhto E., Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation","Aortic aneurysm (AA) rapture is one of the leading causes of death worldwide. Unfortu-nately, the diagnosis of AA is often verified after the onset of complications, in most cases after aortic rupture. The aim of this study was to evaluate the frequency of ascending aortic aneurysm (AscAA) and aortic dilatation (AD) in patients with cardiovascular diseases undergoing echocardiography, and to identify the main risk factors depending on the morphology of the aortic valve. We processed 84,851 echocardiographic (ECHO) records of 13,050 patients with aortic dilatation (AD) in the Alma-zov National Medical Research Centre from 2010 to 2018, using machine learning methodologies. Despite a high prevalence of AD, the main reason for the performed ECHO was coronary artery disease (CAD) and hypertension (HP) in 33.5% and 14.2% of the patient groups, respectively. The prevalence of ascending AD (>40 mm) was 15.4% (13,050 patients; 78.3% (10,212 patients) in men and 21.7% (2838 patients) in women). Only 1.6% (n = 212) of the 13,050 patients with AD knew about AD before undergoing ECHO in our center. Among all the patients who underwent ECHO, we identified 1544 (1.8%) with bicuspid aortic valve (BAV) and 635 with BAV had AD (only 4.8% of all AD patients). According to the results of the random forest feature importance analysis, we identified the eight main factors of AD: age, male sex, vmax aortic valve (AV), aortic stenosis (AS), blood pressure, aortic regurgitation (AR), diabetes mellitus, and heart failure (HF). The known factors of AD-like HP, CAD, hyperlipidemia, BAV, and obesity, were also AD risk factors, but were not as important. Our study showed a high frequency of AscAA and dilation. Standard risk factors of AscAA such as HP, hyperlipidemia, or obesity are significantly more common in patients with AD, but the main factors in the formation of AD are age, male sex, vmax AV, blood pressure, AS, AR, HF, and diabetes mellitus. In males with BAV, AD incidence did not differ significantly, but the presence of congenital heart disease was one of the 12 main risk factors for the formation of AD and association with more significant aortic dilatation in AscAA groups. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","aneurysm; ascending aortic dilatation; echocardiography; risk factors","adult; aortic regurgitation; aortic stenosis; area under the curve; artery dilatation; Article; ascending aorta; ascending aortic aneurysm; asthma; bicuspid aortic valve; blood pressure; body mass; cardiomyopathy; cardiovascular disease; cholecystitis; chronic obstructive lung disease; clinical feature; cohort analysis; congenital heart disease; coronary artery disease; data base; diabetes mellitus; diagnostic test accuracy study; diastolic blood pressure; echocardiography; female; heart arrhythmia; heart failure; human; hyperlipidemia; hypertension; machine learning; major clinical study; male; maximum reaction velocity; morphology; obesity; physical examination; random forest; receiver operating characteristic; retrospective study; risk factor; systolic blood pressure; thyroid disease","","","Vivid 7.0, General Electric, United States","General Electric, United States","Ministry of Education and Science of the Russian Federation, Minobrnauka, (075-15-2022-301)","Funding: This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2022-301 of 20 April 2022).","Sampson U.K.A., Norman P.E., Fowkes F.G.R., Aboyans V., Song Y., Harrell F.E., Forouzanfar M.H., Naghavi M., Denenberg J.O., McDermott M.M., Et al., Global and Regional Burden of Aortic Dissection and Aneurysms: Mortality Trends in 21 World Regions, 1990 to 2010, Glob. 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Cardiol, 135, pp. 31-39, (2019); Koechlin L., Macius E., Kaufmann J., Gahl B., Reuthebuch O., Eckstein F., Berdajs D.A., Aortic Root and Ascending Aorta Dimensions in Acute Aortic Dissection, Perfusion, 35, pp. 131-137, (2020); Zafar M.A., Chen J.F., Wu J., Li Y., Papanikolaou D., Abdelbaky M., Vinholo T.F., Rizzo J.A., Ziganshin B.A., Mukherjee S.K., Et al., Natural History of Descending Thoracic and Thoracoabdominal Aortic Aneurysms, J. Thorac. Cardiovasc. Surg, 161, pp. 498-511, (2021); Kopanitsa G., Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data, Methods Inf. Med, 56, pp. 238-247, (2017); Baumgartner H., Hung J., Bermejo J., Chambers J.B., Evangelista A., Griffin B.P., Iung B., Otto C.M., Pellikka P.A., Quinones M., Echocardiographic Assessment of Valve Stenosis: EAE/ASE Recommendations for Clinical Practice, J. Am. Soc. 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Am. Heart. Assoc, 7, (2018); Ortega R., Collado A., Selles F., Gonzalez-Navarro H., Sanz M.-J., Real J.T., Piqueras L., SGLT-2 (Sodium-Glucose Cotransporter 2) Inhibition Reduces Ang II (Angiotensin II)-Induced Dissecting Abdominal Aortic Aneurysm in ApoE (Apolipoprotein E) Knockout Mice, Arter. Thromb. Vasc. Biol, 39, pp. 1614-1628, (2019); Michelena H.I., Khanna A.D., Mahoney D., Margaryan E., Topilsky Y., Suri R.M., Eidem B., Edwards W.D., Sundt T.M., Enriquez-Sarano M., Incidence of Aortic Complications in Patients with Bicuspid Aortic Valves, JAMA—J. Am. Med. Assoc, 306, pp. 1104-1112, (2011); Guirguis-Blake J.M., Beil T.L., Senger C.A., Whitlock E.P., Ultrasonography Screening for Abdominal Aortic Aneurysms: A Systematic Evidence Review for the U.S. Preventive Services Task Force, Ann. Intern. Med, 160, pp. 321-329, (2014); Sidloff D., Stather P., Dattani N., Bown M., Thompson J., Sayers R., Choke E., Aneurysm Global Epidemiology Study Public Health Measures Can Further Reduce Abdominal Aortic Aneurysm Mortality, Circulation, 129, pp. 747-753, (2014)","O. Irtyuga; Almazov National Medical Research Centre, Saint Petersburg, 197341, Russian Federation; email: olgir@yandex.ru","","MDPI","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85130743940"
"Guerra M.B.; Santana K.G.; Momolli M.; Labat R.; Chavantes M.C.; Zammuner S.R.; Júnior J.A.S.; da Palma R.K.; Aimbire F.; de Oliveira A.P.L.","Guerra, Marina Bertoni (57454430500); Santana, Kelly Gomes (57226551763); Momolli, Marcos (57223870034); Labat, Rodrigo (16643422100); Chavantes, Maria Cristina (6603389964); Zammuner, Stella Regina (59257134400); Júnior, José Antonio Silva (55856137900); da Palma, Renata Kelly (55927595300); Aimbire, Flavio (8680487300); de Oliveira, Ana Paula Ligeiro (57878841200)","57454430500; 57226551763; 57223870034; 16643422100; 6603389964; 59257134400; 55856137900; 55927595300; 8680487300; 57878841200","Effect of photobiomodulation in an experimental in vitro model of asthma-Copd overlap","2024","Journal of Biophotonics","17","10","e202400124","","","","1","10.1002/jbio.202400124","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201060671&doi=10.1002%2fjbio.202400124&partnerID=40&md5=eb2630da42d69a1fb464dc0b9e06042a","Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil; Postgraduate Program in Medicine, Universidade Nove de Julho, UNINOVE, São Paulo, Brazil; Universidad de Vic–Universidade Central de Cataluña, UVic, Vic, Spain; Translational Medicine, Federal University of São Paulo-UNIFESP, São José dos Campos, Brazil","Guerra M.B., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil; Santana K.G., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil; Momolli M., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil; Labat R., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil; Chavantes M.C., Postgraduate Program in Medicine, Universidade Nove de Julho, UNINOVE, São Paulo, Brazil; Zammuner S.R., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil, Postgraduate Program in Medicine, Universidade Nove de Julho, UNINOVE, São Paulo, Brazil; Júnior J.A.S., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil, Postgraduate Program in Medicine, Universidade Nove de Julho, UNINOVE, São Paulo, Brazil; da Palma R.K., Universidad de Vic–Universidade Central de Cataluña, UVic, Vic, Spain; Aimbire F., Translational Medicine, Federal University of São Paulo-UNIFESP, São José dos Campos, Brazil; de Oliveira A.P.L., Post Graduate Program in Medicine–Biophotonic, University Nove de Julho (UNINOVE), São Paulo, Brazil","The objective of the study was to evaluate the effect of photobiomodulation (PBM) with laser on the inflammatory process in an experimental in vitro model of ACO. The groups were: (1) human bronchial epithelial cells (BEAS-2B); (2) BEAS-2B cells treated with dexamethasone; (3) BEAS-2B cells irradiated with laser; (4) BEAS-2B cells stimulated with cigarette smoke extract (CSE) + House Dust Mite (HDM); (5) BEAS-2B cells stimulated with CSE + HDM and treated with dexamethasone; (6) BEAS-2B cells incubated with CSE + HDM and irradiated with laser. After 24 h, cytokines were quantified. There was a reduction in TNF-α, IL-1β, IL-6, IL-4, IL-5, IL-13, IL-17, IL-21, IL-23, and an increase in IL-10 and IFN-γ in cells from the laser-irradiated ACO group compared to only ACO group. With these results, we can suggest that photobiomodulation acts in the modulation of inflammation observed in ACO, and may be a treatment option. © 2024 Wiley-VCH GmbH.","asthma-COPD overlap; cytokines; low level laser; photobiomodulation","Animals; Asthma; Cell Line; Cytokines; Dexamethasone; Epithelial Cells; Humans; Low-Level Light Therapy; Models, Biological; Pulmonary Disease, Chronic Obstructive; Pyroglyphidae; Smoke; Artificial intelligence; Cytology; Diseases; Smoke; cytokine; dexamethasone; Asthma-COPD overlap; Cigarette smokes; Cytokines; Dexamethasones; Dust mite; House dust; In-vitro models; Inflammatory process; Low level laser; Photobiomodulation; adverse event; animal; asthma; biological model; cell line; chronic obstructive lung disease; epithelium cell; human; immunology; low level laser therapy; metabolism; Pyroglyphidae; radiation response; radiotherapy; smoke; Cells","","dexamethasone, 50-02-2; Cytokines, ; Dexamethasone, ; Smoke, ","","","Amparo Foundation for Research in the State of Sao Paulo; Universidade Nove de Julho, UNINOVE; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP, (2012/16498‐5); Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP","Funding text 1: The authors were awarded with a grant supported by Amparo Foundation for Research in the State of Sao Paulo (FAPESP) (grant: 2012/16498\u20105). ; Funding text 2: This study was sponsored by Nove de Julho University. 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Neurobiol., 194, (2014); Burioka N., Takata M., Okano Y., Ohdo S., Fukuoka Y., Miyata M., Takane H., Endo M., Suyama H., Shimizu E., Chronobiology International., 22, (2005); Chung K.F., Curr. Drug Targets, 7, (2006); da Silva C.M., Leal M.P., Brochetti R.A., Braga T., Vitoretti L.B., Camara N.O.S., Damazo A.S., Ligeiro-De-Oliveira A.P., Chavantes M.C., Lino-dos-Santos-Franco A., PLoS One, 10, (2015); de Lima F.M., Villaverde A.B., Albertini R., Correa J.C., Carvalho R.L.P., Munin E., Araujo T., Silva J.A., Aimbire F., Lasers Surg. Med., 43, (2011); Sato M., Takizawa H., Kohyama T., Ohtoshi T., Takafuji S., Kawasaki S., Tohma S., Ishii A., Shoji S., Ito K., Int. Arch. Allergy Immunol., 113, (1997); Patil R.H., Kumar M.N., Kumar K.M.K., Nagesh R., Kavya K., Babu R.L., Ramesh G.T., Sharma S.C., Gene, 645, (2018); Alyasin S., Amin R., Fazel A., Karimi M.H., Nabavizadeh S.H., Esmaeilzadeh H., Iranian M.B., J. Immunol., 14, (2017); Gong F., Zheng T., Zhou P.C., Front. Immunol., 10, (2019); Rahmawati S.F., Vos R., Bos I.S.T., Kerstjens H.A.M., Kistemaker L.E.M., Gosens R., Sci. Rep., 12, (2022); Leia A.A.D., Santos T.G., Herculano K.Z., Rigonato-Oliveira N.C., Palma R.K., Alvarenga-Nascimento C.R., Soares S., Franco A.L.D., Ligeiro-Oliveira A.P., Eur. Resp. J., 54, (2019); Liu M.H., Zhang J.X., Liu C.J., Exp. Ther. Med., 15, (2018)","A.P.L. de Oliveira; Postgraduate Program in Medicine, Universidade Nove de Julho, UNINOVE, São Paulo, Brazil; email: apligeiro@uni9.pro.br","","John Wiley and Sons Inc","","","","","","1864063X","","","39134306","English","J. Biophotonics","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85201060671"
"Gomes L.G.D.S.; Cruz Á.A.S.D.; de Santana M.B.R.; Pinheiro G.P.; Santana C.V.N.; Santos C.B.S.; Boorgula M.P.; Campbell M.; Machado A.D.S.; Veiga R.V.; Barnes K.C.; Costa R.D.S.; Figueiredo C.A.","Gomes, Luciano Gama da Silva (58549851600); Cruz, Álvaro Augusto Souza da (26031996600); de Santana, Maria Borges Rabêlo (57202981753); Pinheiro, Gabriela Pimentel (57204951347); Santana, Cinthia Vila Nova (36619062200); Santos, Carolina Barbosa Souza (57222391670); Boorgula, Meher Preethi (36241190100); Campbell, Monica (24764886700); Machado, Adelmir de Souza (59164811200); Veiga, Rafael Valente (6701797651); Barnes, Kathleen C. (57193080085); Costa, Ryan dos Santos (57645096800); Figueiredo, Camila Alexandrina (7006520928)","58549851600; 26031996600; 57202981753; 57204951347; 36619062200; 57222391670; 36241190100; 24764886700; 59164811200; 6701797651; 57193080085; 57645096800; 7006520928","Predictive genetic panel for adult asthma using machine learning methods","2024","Journal of Allergy and Clinical Immunology: Global","3","3","100282","","","","1","10.1016/j.jacig.2024.100282","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195458126&doi=10.1016%2fj.jacig.2024.100282&partnerID=40&md5=69d7662637c26d781884a2b287151876","Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil; Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; Department of Medicine, University of Colorado Denver, Aurora, Colo, United States; Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, United Kingdom","Gomes L.G.D.S., Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil; Cruz Á.A.S.D., Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; de Santana M.B.R., Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil; Pinheiro G.P., Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; Santana C.V.N., Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; Santos C.B.S., Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; Boorgula M.P., Department of Medicine, University of Colorado Denver, Aurora, Colo, United States; Campbell M., Department of Medicine, University of Colorado Denver, Aurora, Colo, United States; Machado A.D.S., Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil, Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Bahia, Salvador, Brazil; Veiga R.V., Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, United Kingdom; Barnes K.C., Department of Medicine, University of Colorado Denver, Aurora, Colo, United States; Costa R.D.S., Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil; Figueiredo C.A., Instituto de Ciências da Saúde, Universidade Federal da Bahia, Bahia, Salvador, Brazil","Background: Asthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis. Objective: We sought to develop a predictive genetic panel for asthma using machine learning methods. Methods: We tested 3 variable selection methods: Boruta's algorithm, the top 200 genome-wide association study markers according to their respective P values, and an elastic net regression. Ten different algorithms were chosen for the classification tests. A predictive panel was built on the basis of joint scores between the classification algorithms. Results: Two variable selection methods, Boruta and genome-wide association studies, were statistically similar in terms of the average accuracies generated, whereas elastic net had the worst overall performance. The predictive genetic panel was completed with 155 single-nucleotide variants, with 91.18% accuracy, 92.75% sensitivity, and 89.55% specificity using the support vector machine algorithm. The markers used range from known single-nucleotide variants to those not previously described in the literature. Our study shows potential in creating genetic prediction panels with tailored penalties per marker, aiding in the identification of optimal machine learning methods for intricate results. Conclusions: This method is able to classify asthma and nonasthma effectively, proving its potential utility in clinical prediction and diagnosis. © 2024 The Authors","Asthma; genetics; machine learning; prediction; single-nucleotide variants","","","","","","","","Global Strategy for Asthma Management and Prevention; Carr T.F., Zeki A.A., Kraft M., Eosinophilic and noneosinophilic asthma, Am J Respir Crit Care Med, 197, pp. 22-37, (2018); Schoettler N., Strek M.E., Recent advances in severe asthma: from phenotypes to personalized medicine, Chest, 157, pp. 516-528, (2020); Augustine T., Al-Aghbar M.A., Al-Kowari M., Espino-Guarch M., van Panhuys N., Asthma and the missing heritability problem: necessity for multiomics approaches in determining accurate risk profiles, Front Immunol, 13, (2022); Kuruvilla M.E., Lee F.E.H., Lee G.B., Understanding asthma phenotypes, endotypes, and mechanisms of disease, Clin Rev Allergy Immunol, 56, pp. 219-233, (2019); Ntontsi P., Photiades A., Zervas E., Xanthou G., Samitas K., Genetics and epigenetics in asthma, Int J Mol Sci, 22, pp. 1-14, (2021); Figueiredo R.G., Costa R.S., Figueiredo C.A., Cruz A.A., Genetic determinants of poor response to treatment in severe asthma, Int J Mol Sci, 22, (2021); Li B., Zhang N., Wang Y.G., George A.W., Reverter A., Li Y., Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods, Front Genet, 9, (2018); Lam S., Arif M., Song X., Uhlen M., Mardinoglu A., Machine learning analysis reveals biomarkers for the detection of neurological diseases, Front Mol Neurosci, 15, (2022); Nicholls H.L., John C.R., Watson D.S., Munroe P.B., Barnes M.R., Cabrera C.P., Reaching the end-game for GWAS: machine learning approaches for the prioritization of complex disease loci, Front Genet, 11, (2020); Khotimah B.K., Miswanto S., Suprajitno H., Modeling naïve bayes imputation classification for missing data, IOP Conf Ser Earth Environ Sci, 243, (2019); Daya M., Rafaels N., Brunetti T.M., Chavan S., Levin A.M., Shetty A., Et al., Association study in African-admixed populations across the Americas recapitulates asthma risk loci in non-African populations, Nat Commun, 10, (2019); Lantz B., Machine learning with R: learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, (2023); Sordillo J.E., Lutz S.M., Jorgenson E., Iribarren C., McGeachie M., Dahlin A., Et al., A polygenic risk score for asthma in a large racially diverse population, Clin Exp Allergy, 51, pp. 1410-1420, (2021); Gaudillo J., Rodriguez J.J.R., Nazareno A., Baltazar L.R., Vilela J., Bulalacao R., Et al., Machine learning approach to single nucleotide polymorphism-based asthma prediction, PLoS One, 14, pp. 1-12, (2019); Andrew S.A., Gui J., Sanderson A.C., Mason R.A., Morlock E.V., Schned A.R., Et al., Bladder cancer SNP panel predicts susceptibility and survival, Hum Genet, 125, pp. 527-539, (2009); Grandell I., Samara R., Tillmar A.O., A SNP panel for identity and kinship testing using massive parallel sequencing, Int J Legal Med, 130, pp. 905-914, (2016); Gu J.Q., Zhao H., Guo X.-Y., Sun H.-Y., Xu J.Y., Wei Y.-L., A high-performance SNP panel developed by machine-learning approaches for characterizing genetic differences of Southern and Northern Han Chinese, Korean, and Japanese individuals, Electrophoresis, 43, pp. 1183-1192, (2022); Tomita Y., Tomida S., Hasegawa Y., Suzuki Y., Shirakawa T., Kobayashi T., Et al., Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma, BMC Bioinformatics, 5, (2004); Lim A.J.W., Tyniana C.T., Lim L.J., Tan J.W.L., Koh E.T., Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score, J Transl Med, 21, (2023); Trindade B.C., Chen G.Y., NOD1 and NOD2 in inflammatory and infectious diseases, Immunol Rev, 297, pp. 139-161, (2020); Bao K., Yuan W., Zhou Y., Chen Y., Yu X., Wang X., Et al., A Chinese prescription Yu-Ping-Feng-San administered in remission restores bronchial epithelial barrier to inhibit house dust mite-induced asthma recurrence, Front Pharmacol, 10, (2020); Liu X.S., Liu J.M., Chen Y.J., Li F.Y., Wu R.M., Tan F., Et al., Comprehensive analysis of hexokinase 2 immune infiltrates and m6A related genes in human esophageal carcinoma, Front Cell Dev Biol, 9, (2021); Collins A.M., Yaari G., Shepherd A.J., Lees W., Watson C.T., Germline immunoglobulin genes: disease susceptibility genes hidden in plain sight?, Curr Opin Syst Biol, 24, pp. 100-108, (2020); dbSNP rs79225819; Wang A.L., Lahousse L., Dahlin A., Edris A., McGeachie M., Lutz S.M., Et al., Novel genetic variants associated with inhaled corticosteroid treatment response in older adults with asthma, Thorax, 78, pp. 432-441, (2023); Piao H.Y., Guo S., Jin H., Wang Y., Zhang J., LINC00184 involved in the regulatory network of ANGPT2 via ceRNA mediated miR-145 inhibition in gastric cancer, J Cancer, 12, pp. 2336-2350, (2021)","C.A. Figueiredo; Federal University of Bahia, Institute of Health Sciences, Salvador, Av Reitor Miguel Calmon, s/nSala 316, 40110100, Brazil; email: cavfigueiredo@gmail.com","","Elsevier B.V.","","","","","","27728293","","","","English","J. Allergy. Clin. Immunol. Glob.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85195458126"
"Mahmood A.F.; Alkababji A.M.; Daood A.","Mahmood, Ahlam Fadhil (57226574545); Alkababji, Ahmed Maamoon (55750507600); Daood, Amar (57192306608)","57226574545; 55750507600; 57192306608","Resilient embedded system for classification respiratory diseases in a real time","2024","Biomedical Signal Processing and Control","90","","105876","","","","1","10.1016/j.bspc.2023.105876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183691828&doi=10.1016%2fj.bspc.2023.105876&partnerID=40&md5=57166521cd099df527e5fe5c770df183","Dept. of Computer Engineering, College of Engineering, University of Mosul, Mosul, Iraq","Mahmood A.F., Dept. of Computer Engineering, College of Engineering, University of Mosul, Mosul, Iraq; Alkababji A.M., Dept. of Computer Engineering, College of Engineering, University of Mosul, Mosul, Iraq; Daood A., Dept. of Computer Engineering, College of Engineering, University of Mosul, Mosul, Iraq","Listening to lung sounds using a stethoscope is still one of the most important methods to diagnose respiratory diseases. These sounds are complex and challenging to diagnose, as even trained people may misclassify them. Accurate interpretation of these sounds requires excellent experience from the treating physician. For diagnosing respiratory diseases, sounds were analyzed, and various features were extracted for the proposed hierarchical design consisting of four layers. A random forest classifier was utilized for three layers and deep learning for the last layer. An FPGA implementation of the proposed respiratory processor is validated experimentally on soft and hard resources of the Virtex-5 ML506 FPGA board. Designing the system by field programmable gate array in a hierarchical manner that allows classification without completing all four stages. The resilient four-layer system achieved the highest average accuracy of 100 %, 99.83, 99.62, 99.88, and 99.87 for COPD, Healthy, URTI, Bronchiectasis, and Pneumonia diseases while saving both power consumption 63.8 % and 54.7 % of testing time. © 2023 Elsevier Ltd","Co-design; FPGA; Lung sound; Random forest; Respiratory diseases","Biological organs; Deep learning; Diagnosis; Integrated circuit design; Pulmonary diseases; Co-designs; Embedded-system; FPGA implementations; FPGAs implementation; Hierarchical design; Lung sounds; Random forest classifier; Random forests; Real- time; Three-layer; abnormal respiratory sound; adolescent; adult; aged; Article; artificial intelligence; body height; body weight; bronchiectasis; child; chronic obstructive lung disease; classifier; coarse crackle; comparative study; controlled study; convolutional neural network; crackle; cross validation; diagnostic accuracy; disease classification; embedding; equipment design; feature extraction; female; fine crackle; gasping; histogram; human; latent period; lower respiratory tract infection; major clinical study; male; middle aged; mobilenetv2 network; pleural friction rub; pneumonia; random forest; respiratory tract disease; rhonchus; scale invariant feature transform; sensitivity and specificity; shufflenetv1 network; squawk; squeezenet network; stridor; upper respiratory tract infection; wheezing; Field programmable gate arrays (FPGA)","","","Litmmann 3200 Electronic Stethoscope, litmmann; Littmann Classic II SE Stethoscope, litmmann; Meditron Master Elite Electronic Stethoscope, Welch Allyn","Welch Allyn; litmmann; litmmann","College of Engineering, Department of Computer Engineering; University of Mosul, UoM","With appreciation for the support provided by the University of Mosul, College of Engineering, Department of Computer Engineering.","Zulfiqar R., Majeed F., Irfan R., Rauf H.T., Benkhelifa E., Belkacem A.N., Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition, Front. Med., 8, (2021); Kim Y., Hyon Y., Jung S.S., Lee S., Yoo G., Chung C., Ha T., Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning, Sci. Rep., 11, (2021); Pramono R.X., Bowyer S., Rodriguez-Villegas E., (2017); Petmezas G., Cheimariotis G., Stefanopoulos L., Rocha B., Paiva R.P., Katsaggelos A.K., Maglaveras N., Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function, Sensors, 22, (2022); Faustino P.S., (2019); Rizal A., Hidayat R., Nugroho H.A., (2015); Bastos D.F., (2021); Mazumder A.N., Ren H., Rashid H., Hosseini M., Chandrareddy V., Homayoun H., Mohsenin T., Automatic Detection of Respiratory Symptoms Using a Low Power Multi-Input CNN Processor, IEEE Des. Test, 39, 3, pp. 82-90, (2021); Saraiva A.A., Santos D.B.S., Francisco A.A., Sousa J.V.M., (2020); Li L., Ayiguli A., Luan Q., Yang B., Subinuer Y., Gong H., Zulipikaer A., Xu J., Zhong X., Ren J., Zou X., (2022); Wall C., Zhang L., Yu Y., Kumar A., Gao R., (2022); Bharatia S., Poddera P., Mondala M.R.H., Prasath V.B.S., CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images, Int. J. Hybrid Intell. Syst., 17, pp. 71-85, (2021); Li R., Xiao C., Huang Y., Hassan H., Huang B., (2022); Fonseca E., Plakal M., Font F., Ellis D.P.W., Favory X., Pons J., Serra X., (2018); Rocha B.M., Filos D., Mendes L., Serbes G., Ulukaya S., Kahya Y.P., Jakovljevic N., Turukalo T.L., Vogiatzis I.M., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Maglaveras N., Pedro R., Paiva I., Chouvarda P., 40, 3, (2019); Lin B., Yen T., An FPGA-Based Rapid Wheezing Detection System, Int. J. Environ. Res. Public Health, 11, pp. 1573-1593, (2014); Lin B., Wu B.H., Chong F., Chen S., 18, (2006); Simic M., Stavrakis A.K., Sinha A., Premcevski V., Markoski B., Stojanovi G.M., (2022); Ahmed T., 28, 1, pp. 300-311; Kumar A., Abhishek K., Chakraborty C., Kryvinska N., 9, (2021); Wang W., Pei Y., Wang S., (2023); Saeed M., Ahsan M., Saeed M.H., Rahman A.U., Mohammed M.A., Nedoma J., Martinek R., 80, (2023); Saeed M., Ahsan M., Saeed M.H., Mehmood A., Khalifa H.A., Mekawy I., 10, pp. 5681-5696, (2022); Rahman A.U., Saeed M., Saeed M.H., Zebari D.A., Albahar M., Abdulkareem K.H., Al-Waisy A.S., Mohammed M.A., A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling, Bioengineering, 10, (2023); Rocha B.M., Filos D., Mendes L., Serbes G., Ulukaya S., Kahya Y.P., Jakovljevic N., Turukalo T.L., Vogiatzis I.M., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Maglaveras N., Paiva R.P., Chouvarda I., Carvalho P., (2019); Mukherjee H., Sreerama P., Dhar A., Obaidullah S., Roy K., Mahmud M., Santosh K.C., Automatic Lung Health Screening Using Respiratory Sounds, J. Med. Syst., 45, (2021); Sharma G., Umapathy K., Krishnan S., (2020); Modran H.A., Chamunorwa T., Ursutiu D., Samoila C., Hedesiu H., Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions, Sensors, 23, (2023); Ksibi A., Hakami N.A., Alturki N., Asiri M.M., Zakariah M., Ayadi M., Voice Pathology Detection Using a Two-Level Classifier Based on Combined CNN–RNN Architecture, Sustainability, 15, (2023); Bhangale K., Kothandaraman M., (2023); Hong F., (2023); Sabanci K., Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars, Sustainability, 15, (2023); Sala R.D., Scotti G., A Novel FPGA Implementation of the NAND-PUF with Minimal Resource Usage and High Reliability, Cryptography, 7, (2023); Vazhoth V.J., “Embedded processors on FPGA: Hard-core vs Soft-core”, Master of Science in Electrical Engineering, (2017); Gao Y., Designing an IEEE Floating-Point Unit with Configurable Compliance Support and Precision for FPGA-Based Sof-Processors, Master of Applied Science, in School of Engineering Science Faculty of Applied Sciences, (2022); (2022); Hema S., Chitra K., Sarvesh M., 14, (2019); (2020); (2021); Zagan I., Gaitan V.G., Soft-core processor integration based on different instruction set architectures and field programmable gate array custom datapath implementation, PeerJ Comput. Sci., 9, (2023); Xilinx M.L., (2009); Chatterjee S., Rahman M., Ahmed T., Saleheen N., Nemati E., Nathan V., Vatanparvar K., Kuang J., Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze Sensing Using Mobile Sensors, ACM Trans. Math. Software, (2020); Hsu F., Huang S., Huang C., Cheng Y., (2022); Nguyen T., Pernkopf F., Lung Sound Classification Using Co-Tuning and Stochastic Normalization, IEEE Trans. Biomed. Eng., 69, 9, pp. 2872-2882, (2022); Fraiwan M., Fraiwan L., Alkhodari M., Hassanin O., Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short–term memory, J. Ambient Intell. Hum. Comput., 13, pp. 4759-4771, (2022); Moummad I., Farrugia N., (2023); Dawud A.A., Haile G., Deep Learning-Based Pneumonia Classification Based on Respiratory Sounds, J. Art. Intell. Cloud Computing, 2, 2, pp. 1-6, (2023); Yang R., Lv K., Huang Y., Sun M., Li J., Yang J., Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable, Appl. Sci., 13, (2023)","A.F. Mahmood; Dept. of Computer Engineering, College of Engineering, University of Mosul, Mosul, Iraq; email: ahlam.mahmood@uomosul.edu.iq","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85183691828"
"Zhang D.; Pu X.; Zheng M.; Li G.; Chen J.","Zhang, Dihui (59307870900); Pu, Xiaowei (59307716300); Zheng, Man (57206937089); Li, Guanghui (59307412900); Chen, Jia (59308328400)","59307870900; 59307716300; 57206937089; 59307412900; 59308328400","Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes","2024","Respiratory Research","25","1","327","","","","1","10.1186/s12931-024-02954-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202780744&doi=10.1186%2fs12931-024-02954-4&partnerID=40&md5=3a47d042628755a425746d865faaeb5a","Orthopedics department The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, 510000, China; Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, China; Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Shandong, Dongying, 257091, China","Zhang D., Orthopedics department The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, 510000, China; Pu X., Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, China; Zheng M., Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Shandong, Dongying, 257091, China; Li G., Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Shandong, Dongying, 257091, China; Chen J., Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, China","Background: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)—a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. Methods: Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. Results: Our analyses revealed that eleven PyMGs—DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2—are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. Conclusions: This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma’s molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field. © The Author(s) 2024.","Asthma; Bioinformatics; Lasso regression; Pyrimidine metabolism-related genes (PyMGs); SVM-RFE","Asthma; Biomarkers; Computational Biology; Female; Humans; Machine Learning; Pyrimidines; biological marker; purine derivative; pyrimidine; pyrimidine derivative; accuracy; anatomical concepts; Article; asthma; bioinformatics; chronic inflammation; clinical feature; conceptual framework; controlled study; correlation coefficient; correlational study; diagnostic accuracy; environmental exposure; gene; gene expression; gene ontology; gene set enrichment analysis; gene set variation analysis; genetic susceptibility; genetics; genome-wide association study; human; KEGG; landscape; least absolute shrinkage and selection operator; machine learning; Mendelian randomization analysis; neoplastic cell transformation; nucleotide metabolism; pathogenesis; Pole (people); Polish citizen; prediction; pyrimidine metabolism; recursive feature elimination; RNA splicing; support vector machine; synergistic effect; validation process; diagnosis; female; metabolism; procedures","","pyrimidine, 289-95-2; Biomarkers, ; pyrimidine, ; Pyrimidines, ","","","Shanghai University of Traditional Chinese Medicine, SHUTCM, (2023HLXL09); Shanghai University of Traditional Chinese Medicine, SHUTCM; Science and Technology Development Project, (23HLZX06)","The Second \u201CXinglin Scholars-Nursing Youth\u201D Program of Shanghai University of Traditional Chinese Medicine (2023HLXL09); Science and Technology Development Project (Nursing Special Project), Shanghai University of Traditional Chinese Medicine 23HLZX06. 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Pfenninger K.H., Plasma membrane expansion: a neuron's Herculean task, Nat Rev Neurosci, 10, 4, pp. 251-261, (2009); Woeller C.F., Roztocil E., Hammond C., Feldon S.E., TSHR signaling stimulates proliferation through PI3K/Akt and induction of miR-146a and miR-155 in thyroid eye disease orbital fibroblasts, Invest Ophthalmol Vis Sci, 60, 13, pp. 4336-4345, (2019); Madera-Salcedo I.K., Sanchez-Hernandez B.E., Svyryd Y., Esquivel-Velazquez M., Rodriguez-Rodriguez N., Trejo-Zambrano M.I., Garcia-Gonzalez H.B., Hernandez-Molina G., Mutchinick O.M., Alcocer-Varela J., Et al., PPP2R2B hypermethylation causes acquired apoptosis deficiency in systemic autoimmune diseases, JCI Insight, (2019); Zhu Z., Cao C., Zhang D., Zhang Z., Liu L., Wu D., Sun J., UBE2T-mediated Akt ubiquitination and Akt/beta-catenin activation promotes hepatocellular carcinoma development by increasing pyrimidine metabolism, Cell Death Dis, 13, 2, (2022); Yuan Y., Li N., Fu M., Ye M., Identification of critical modules and biomarkers of ulcerative colitis by using WGCNA, J Inflamm Res, 16, pp. 1611-1628, (2023); Xu M., Kong Y., Chen N., Peng W., Zi R., Jiang M., Zhu J., Wang Y., Yue J., Lv J., Et al., Identification of immune-related gene signature and prediction of CeRNA network in active ulcerative colitis, FRONT IMMUNOL, 13, (2022); Wu Y., Liu X., Li G., Integrated bioinformatics and network pharmacology to identify the therapeutic target and molecular mechanisms of Huangqin decoction on ulcerative Colitis, Sci Rep, 12, 1, (2022); Wu Z., Liu P., Huang B., Deng S., Song Z., Huang X., Yang J., Cheng S., A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy, Sci Rep, 13, 1, (2023); De Carvalho T.R., Giaretta A.A., Teixeira B.F., Martins L.B., New bioacoustic and distributional data on Bokermannohyla sapiranga Brandao et al., 2012 (Anura: Hylidae): revisiting its diagnosis in comparison with B. pseudopseudis (Miranda-Ribeiro, 1937), Zootaxa, 3746, pp. 383-392, (2013); Chen Y., Wang X., miRDB: an online database for prediction of functional microRNA targets, Nucleic Acids Res, 48, D1, pp. D127-D131, (2020); 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Sedova L., Bukova I., Bazantova P., Petrezselyova S., Prochazka J., Skolnikova E., Zudova D., Vcelak J., Makovicky P., Bendlova B., Et al., Semi-lethal primary ciliary dyskinesia in rats lacking the Nme7 gene, Int J Mol Sci, (2021); Sedova L., Prochazka J., Zudova D., Bendlova B., Vcelak J., Sedlacek R., Seda O., Heterozygous Nme7 mutation affects glucose tolerance in male rats, Genes, (2021); Ren X., Rong Z., Liu X., Gao J., Xu X., Zi Y., Mu Y., Guan Y., Cao Z., Zhang Y., Et al., The protein kinase activity of NME7 activates Wnt/beta-catenin signaling to promote one-carbon metabolism in hepatocellular carcinoma, Cancer Res, 82, 1, pp. 60-74, (2022); Sun Y., Li S., Li H., Yang F., Bai Y., Zhao M., Guo J., Zhao M., Zhou P., Khor C.C., Et al., TNFRSF10A-LOC389641 rs13278062 but not REST-C4orf14-POLR2B-IGFBP7 rs1713985 was found associated with age-related macular degeneration in a Chinese population, Invest Ophthalmol Vis Sci, 54, 13, pp. 8199-8203, (2013); Li X.L., Xie Y., Chen Y.L., Zhang Z.M., Tao Y.F., Li G., Wu D., Wang H.R., Zhuo R., Pan J.J., Et al., The RNA polymerase II subunit B (RPB2) functions as a growth regulator in human glioblastoma, Biochem Biophys Res Commun, 674, pp. 170-182, (2023); Wang H., Jia Y., Gu J., Chen O., Yue S., Ferroptosis-related genes are involved in asthma and regulate the immune microenvironment, Front Pharmacol, 14, (2023); Yip W., Hughes M.R., Li Y., Cait A., Hirst M., Mohn W.W., McNagny K.M., Butyrate shapes immune cell fate and function in allergic asthma, Front Immunol, 12, (2021); Shamji M.H., Sharif H., Layhadi J.A., Zhu R., Kishore U., Renz H., Diverse immune mechanisms of allergen immunotherapy for allergic rhinitis with and without asthma, J Allergy Clin Immunol, 149, 3, pp. 791-801, (2022); Strzelak A., Ratajczak A., Adamiec A., Feleszko W., Tobacco smoke induces and alters immune responses in the lung triggering inflammation, allergy, asthma and other lung diseases: a mechanistic review, Int J Environ Res Public Health, (2018); Huang F., Yu J., Lai T., Luo L., Zhang W., The combination of bioinformatics analysis and untargeted metabolomics reveals potential biomarkers and key metabolic pathways in asthma, Metabolites, (2022); Chen S.T., Yang N., Constructing ferroptosis-related competing endogenous RNA networks and exploring potential biomarkers correlated with immune infiltration cells in asthma using combinative bioinformatics strategy, BMC Genomics, 24, 1, (2023); Wu Z., Cai Z., Shi H., Huang X., Cai M., Yuan K., Huang P., Shi G., Yan T., Li Z., Effective biomarkers and therapeutic targets of nerve-immunity interaction in the treatment of depression: an integrated investigation of the miRNA-mRNA regulatory networks, Aging, 14, 8, pp. 3569-3596, (2022)","J. Chen; Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 200021, China; email: chenjia1710@163.com; G. Li; Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, China; email: fk_lgh@163.com","","BioMed Central Ltd","","","","","","14659921","","RREEB","39217320","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85202780744"
"Butler S.J.; Paszat L.; Gershon A.S.","Butler, Stacey J. (57205728477); Paszat, Lawrence (7004092657); Gershon, Andrea S. (24760464400)","57205728477; 7004092657; 24760464400","Lung Cancer and COPD: Opportunities to Leverage Lung Cancer Screening Programs to Improve COPD Diagnostics","2024","Healthcare Quarterly","27","3","","7","10","3","1","10.12927/hcq.2024.27494","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213154403&doi=10.12927%2fhcq.2024.27494&partnerID=40&md5=fdca3e665ccae692a9db40b25c89e11c","Unity Health Toronto, Toronto, ON, Canada; Sunnybrook Health Sciences Centre, the University of Toronto and ICES, Toronto, ON, Canada","Butler S.J., Unity Health Toronto, Toronto, ON, Canada; Paszat L., Sunnybrook Health Sciences Centre, the University of Toronto and ICES, Toronto, ON, Canada; Gershon A.S., Sunnybrook Health Sciences Centre, the University of Toronto and ICES, Toronto, ON, Canada","Lung cancer and chronic obstructive pulmonary disease (COPD) have many shared risk factors and not surprisingly, the two diseases often coexist. This article highlights the burden of COPD among patients with lung cancer in Ontario and explores opportunities to enhance lung cancer screening programs. We propose pursuing integrated strategies that incorporate new advances in artificial intelligence to improve disease diagnos-tics and navigate the complexity of caring for people with coexisting lung diseases. Evidence supports that this is a vulnerable population with unmet needs and poor outcomes that deserves urgent attention and action to promote earlier diagnosis and alleviate suffering. © 2024, Longwoods Publishing Corp.. All rights reserved.","","Artificial Intelligence; Early Detection of Cancer; Humans; Lung Neoplasms; Mass Screening; Ontario; Pulmonary Disease, Chronic Obstructive; Risk Factors; artificial intelligence; chronic obstructive lung disease; diagnosis; early cancer diagnosis; human; lung tumor; mass screening; Ontario; procedures; risk factor","","","","","","","Adams S.J., Stone E., Baldwin D.R., Vliegenthart R., Lee P., Fintelmann F.J., Lung Cancer Screening, The Lancet, 401, 10374, pp. 390-408, (2023); Agusti A., Celli B.R., Criner G.J., Halpin D., Anzueto A., Barnes P., Et al., Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary, European Respiratory Journal, 61, 4, (2023); Butler S.J., El lerton L., Goldstein R. S., Brooks D., Prevalence of Lung Cancer in Chronic Obstructive Pulmonary Disease: A Systematic Review, Respiratory Medicine, X1, (2019); Butler S.J., Louie A.V., Sutradhar R., Paszat L., Brooks D., Gershon A.S., Association Between COPD and Stage of Lung Cancer Diagnosis: A Population-Based Study, Current Oncology, 30, 7, pp. 6397-6410, (2023); Butler S.J., Louie A.V., Sutradhar R., Paszat L., Brooks D., Gershon A.S., Impact of Chronic Obstructive Pulmonary Disease on Lung Cancer Symptom Burden: A Population-Based Study in Ontario, Canada, Translational Lung Cancer Research, 12, 11, pp. 2260-2274, (2023); Butler S.J., Louie A.V., Sutradhar R., Paszat L., Brooks D., Gershon A.S., Palliative Care Among Lung Cancer Patients With and Without COPD: A Population-Based Cohort Study, Journal of Pain and Symptom Management, 66, 6, pp. 611-620, (2023); Ontario Lung Cancer Screening Program, (2024); Cui X., Zheng S., Heuvelmans M.A., Du Y., Sidorenkov G., Fan S., Et al., Performance of a Deep Learning-Based Lung Nodule Detection System as an Alternative Reader in a Chinese Lung Cancer Screening Program, European Journal of Radiology, 146, (2022); Gershon A.S., Thiruchelvam D., Chapman K.R., Aaron S.D., Stanbrook M.B., Bourbeau J., Et al., Health Services Burden of Undi agnosed a nd Overdi agnosed COPD, Ches t, 153, 6, pp. 1336-1346, (2018); Gof f in J.R., Corriveau S., Tang G.H., Pond G.R., Management and Outcomes of Patients With Chronic Obstructive Lung Disease and Lung Cancer in a Public Healthcare System, PLOS One, 16, 5, (2021); Hil l K., Goldstein R. S., Guyatt G.H., Blouin M., Tan W.C., Davis L.L., Et al., Prevalence and Underdiagnosis of Chronic Obstructive Pulmonary Disease Among Patients at Risk in Primary Care, CMAJ, 182, 7, pp. 673-678, (2010); Labonte L.E., Tan W.C., Li P. Z., Manci no P., Aaron S.D., Benedetti A., Et al., Undiagnosed Chronic Obstructive Pulmonary Disease Contributes to the Burden of Health Care Use. Data From the CanCOLD Study, American Journal of Respiratory and Critical Care Medicine, 194, 3, pp. 285-298, (2016); Lam A.C. L., Aggarwal R., Huang J., Varadi R., Davis L., Tsao M.S., Et al., Point-of-Care Spirometry Identifies High-Risk Individuals Excluded From Lung Cancer Screening, American Journal of Respiratory and Critical Care Medicine, 202, 10, pp. 1473-1477, (2020); Lamprecht B., Soriano J.B., Studnicka M., Kaiser B., Vanfleteren L.E., Gnatiuc L., Et al., Determinants of Underdiagnosis of COPD in National and International Surveys, Chest, 148, 4, pp. 971-985, (2015); Park H.Y., Kang D., Shin S.H., Yoo K.-H., Rhee C.K., Suh G.Y., Et al., Chronic Obstructive Pulmonary Disease and Lung Cancer Incidence in Never Smokers: A Cohort Study, Thorax, 75, 6, pp. 506-509, (2020); Steiger D., Siddiqi M.F., Yip R., Yankelevitz D.F., Henschke C.I., The Importance of Low-Dose CT Screening to Identify Emphysema in Asymptomatic Participants With and Without a Prior Diagnosis of COPD, Clinical Imaging, 78, pp. 136-141, (2021); Tisi S., Dickson J.L., Horst C., Quaife S.L., Hall H., Verghese P., Et al., Detection of COPD in the SUMMIT Study Lung Cancer Screening Cohort Using Symptoms and Spirometry, European Respiratory Journal, 60, 6, (2022); Topalovic M., Das N., Burgel P.-R., Daenen M., Derom E., Haenebalcke C., Et al., Artif icial Intelligence Outperforms Pulmonologists in the Interpretation of Pulmonary Function Tests, European Respiratory Journal, 53, 4, (2019); Wiedbrauck D., Karczewski M., Schoenberg S.O., Fink C., Kayed H., Artificial Intelligence –Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading, Journal of Computer Assisted Tomography, 48, 3, pp. 388-393, (2024)","","","Longwoods Publishing Corp.","","","","","","17102774","","","39691042","English","Healthc. Q.","Article","Final","","Scopus","2-s2.0-85213154403"
"Guo L.L.; Guo L.Y.; Li J.; Gu Y.W.; Wang J.Y.; Cui Y.; Qian Q.; Chen T.; Jiang R.; Zheng S.","Guo, Lin Lin (58680179100); Guo, Lin Ying (56399700400); Li, Jiao (57397332800); Gu, Yao Wen (57452298100); Wang, Jia Yang (57207694300); Cui, Ying (58680235300); Qian, Qing (55508965100); Chen, Ting (55687667200); Jiang, Rui (57212678183); Zheng, Si (57158054700)","58680179100; 56399700400; 57397332800; 57452298100; 57207694300; 58680235300; 55508965100; 55687667200; 57212678183; 57158054700","Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis","2023","Journal of Medical Internet Research","25","","e49605","","","","2","10.2196/49605","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175676407&doi=10.2196%2f49605&partnerID=40&md5=7f21d76c7149365666c1094f86ad1120","Children's Hospital Capital Institute of Pediatrics, Beijing, China; Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China; Department of Automation, Tsinghua University, Beijing, China; Institute of Medical Information Chinese Academy of Medical Sciences, Peking Union Medical College, 3 Yabao Rd Chaoyang District, Beijing, 100020, China","Guo L.L., Children's Hospital Capital Institute of Pediatrics, Beijing, China; Guo L.Y., Children's Hospital Capital Institute of Pediatrics, Beijing, China; Li J., Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Gu Y.W., Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Wang J.Y., Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Cui Y., Children's Hospital Capital Institute of Pediatrics, Beijing, China; Qian Q., Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Chen T., Department of Computer Science and Technology, Tsinghua University, Beijing, China; Jiang R., Department of Automation, Tsinghua University, Beijing, China; Zheng S., Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China, Department of Computer Science and Technology, Tsinghua University, Beijing, China, Institute of Medical Information Chinese Academy of Medical Sciences, Peking Union Medical College, 3 Yabao Rd Chaoyang District, Beijing, 100020, China","Background: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China’s health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. Objective: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. Methods: This retrospective analysis involved patients who were admitted to the emergency department of Children’s Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. Results: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features “wait.green” and “department” had the most significant influence on waiting times. Conclusions: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China. © 2023 Journal of Medical Internet Research. All rights reserved.","admission preferences; characteristics; electronic medical record; machine learning; pediatric emergency department; waiting time","Child; Electronic Health Records; Female; Hospitalization; Humans; Male; Patient Discharge; Retrospective Studies; Waiting Lists; abdominal pain; age; age distribution; Article; asthma; bacterial infection; bronchitis; burn; child; China; clinical assessment; clinical feature; comparative study; computer model; coughing; cross validation; demographics; dizziness; electronic medical record; emergency patient; emergency surgery; emergency ward; eXtreme Gradient Boosting; female; fever; gender; headache; health service; hospital admission; human; k nearest neighbor; laryngitis; least absolute shrinkage and selection operator; light gradient boosting machine; linear regression analysis; machine learning; male; patient preference; patient triage; patient volume; pneumonia; prediction; random forest; respiratory tract infection; retrospective study; sex ratio; thorax pain; upper respiratory tract infection; vomiting; wound; electronic health record; hospital discharge; hospitalization","","","","","Research Funds for the Capital Institute of Pediatrics, (SK-2021-04); Chinese Academy of Meteorological Sciences, CAMS, (2021-I2M-1-056); Chinese Academy of Meteorological Sciences, CAMS","This work was supported by Research Funds for the Capital Institute of Pediatrics (grant number SK-2021-04) and CAMS Innovation Fund for Medical Sciences (grant number 2021-I2M-1-056).","Weed LL., Medical records that guide and teach, N Engl J Med, 278, 12, pp. 652-657, (1968); Yip W, Fu H, Chen AT, Zhai T, Jian W, Xu R, Et al., 10 years of health-care reform in China: progress and gaps in Universal Health Coverage, Lancet, 394, 10204, pp. 1192-1204, (2019); Li X, Krumholz HM, Yip W, Cheng KK, De Maeseneer J, Meng Q, Et al., Quality of primary health care in China: challenges and recommendations, Lancet, 395, 10239, pp. 1802-1812, (2020); Ma X, Chen X, Wang J, Lyman GH, Qu Z, Ma W, Et al., Evolving healthcare quality in top tertiary general hospitals in China during the China Healthcare Reform (2010-2012) from the perspective of inpatient mortality, PLoS One, 10, 12, (2015); Stoyanov KM, Biener M, Hund H, Mueller-Hennessen M, Vafaie M, Katus HA, Et al., Effects of crowding in the emergency department on the diagnosis and management of suspected acute coronary syndrome using rapid algorithms: an observational study, BMJ Open, 10, 10, (2020); Limapichat T, Kaewyingyong S., Association of prolonged emergency department length of stay with adverse events in patients with non-ST-elevation acute coronary syndrome, Open Access Emerg Med, 14, pp. 109-117, (2022); Chen Z, Zhang H, Guo Y, George TJ, Prosperi M, Hogan WR, Et al., Exploring the feasibility of using real-world data from a large clinical data research network to simulate clinical trials of Alzheimer's disease, NPJ Digit Med, 4, 1, (2021); Tahhan N, Ford BK, Angell B, Liew G, Nazarian J, Maberly G, Et al., Evaluating the cost and wait-times of a task-sharing model of care for diabetic eye care: a case study from Australia, BMJ Open, 10, 10, (2020); Lin H, Tang X, Shen P, Zhang D, Wu J, Zhang J, Et al., Using big data to improve cardiovascular care and outcomes in China: a protocol for the CHinese Electronic health Records Research in Yinzhou (CHERRY) Study, BMJ Open, 8, 2, (2018); Zheng S, Wu YX, Wang JY, Li Y, Liu ZJ, Liu XG, Et al., Identifying the characteristics of patients with cervical degenerative disease for surgical treatment from 17-year real-world data: retrospective study, JMIR Med Inform, 8, 4, (2020); Bekmezian A, Fee C, Bekmezian S, Maselli JH, Weber E., Emergency department crowding and younger age are associated with delayed corticosteroid administration to children with acute asthma, Pediatr Emerg Care, 29, 10, pp. 1075-1081, (2013); Sagaidak S, Rowe BH, Ospina MB, Rosychuk RJ., Emergency department crowding negatively influences outcomes for children presenting with asthma: a population-based retrospective cohort study, Pediatr Res, 89, 3, pp. 679-685, (2021); Newgard CD, Lin A, Olson LM, Cook JNB, Gausche-Hill M, Kuppermann N, Et al., Pediatric Readiness Study Group. Evaluation of emergency department pediatric readiness and outcomes among US trauma centers, JAMA Pediatr, 175, 9, pp. 947-956, (2021); Loflath V, Hau E, Garcia D, Berger S, Lollgen R., Parental satisfaction with waiting time in a Swiss tertiary paediatric emergency department, Emerg Med J, 38, 8, pp. 617-623, (2021); Zachariasse JM, Nieboer D, Maconochie IK, Smit FJ, Alves CF, Greber-Platzer S, Et al., Development and validation of a Paediatric Early Warning Score for use in the emergency department: a multicentre study, Lancet Child Adolesc Health, 4, 8, pp. 583-591, (2020); Yen K, Gorelick MH., Strategies to improve flow in the pediatric emergency department, Pediatr Emerg Care, 23, 10, pp. 745-749, (2007); Weiss SJ, Ernst AA, Sills MR, Quinn BJ, Johnson A, Nick TG., Development of a novel measure of overcrowding in a pediatric emergency department, Pediatr Emerg Care, 23, 9, pp. 641-645, (2007); Noel G, Jouve E, Fruscione S, Minodier P, Boiron L, Viudes G, Et al., Real-time measurement of crowding in pediatric emergency department: derivation and validation using consensual perception of crowding (SOTU-PED), Pediatr Emerg Care, 37, 12, pp. e1244-e1250, (2021); Abudan A, Merchant RC., Multi-dimensional measurements of crowding for pediatric emergency departments: a systematic review, Glob Pediatr Health, 8, (2021); Sun Y, Teow KL, Heng BH, Ooi CK, Tay SY., Real-time prediction of waiting time in the emergency department, using quantile regression, Ann Emerg Med, 60, 3, pp. 299-308, (2012); Ding R, McCarthy ML, Desmond JS, Lee JS, Aronsky D, Zeger SL., Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression, Acad Emerg Med, 17, 8, pp. 813-823, (2010); Ataman MG, Sariyer G., Predicting waiting and treatment times in emergency departments using ordinal logistic regression models, Am J Emerg Med, 46, pp. 45-50, (2021); Eiset AH, Kirkegaard H, Erlandsen M., Crowding in the emergency department in the absence of boarding - a transition regression model to predict departures and waiting time, BMC Med Res Methodol, 19, 1, (2019); Pak A, Gannon B, Staib A., Predicting waiting time to treatment for emergency department patients, Int J Med Inform, 145, (2021); Kuo Y, Chan NB, Leung JM, Meng H, So AM, Tsoi KK, Et al., An integrated approach of machine learning and systems thinking for waiting time prediction in an emergency department, Int J Med Inform, 139, (2020); Ngiam KY, Khor IW., Big data and machine learning algorithms for health-care delivery, Lancet Oncol, 20, 5, pp. e262-e273, (2019); Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Et al., From local explanations to global understanding with explainable AI for trees, Nat Mach Intell, 2, 1, pp. 56-67, (2020); Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Et al., Explainable machine-learning predictions for the prevention of hypoxaemia during surgery, Nat Biomed Eng, 2, 10, pp. 749-760, (2018); Meng W, Zhang X, Ru B, Guan Y., A machine learning approach to real‐world time to treatment discontinuation prediction, Advanced Intelligent Systems, 5, 4, (2023); Obermeyer Z, Emanuel EJ., Predicting the future - big data, machine learning, and clinical medicine, N Engl J Med, 375, 13, pp. 1216-1219, (2016); Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S., Machine learning in predicting graft failure following kidney transplantation: a systematic review of published predictive models, Int J Med Inform, 130, (2019); Cuocolo R, Perillo T, De Rosa E, Ugga L, Petretta M., Current applications of big data and machine learning in cardiology, J Geriatr Cardiol, 16, 8, pp. 601-607, (2019); Gong H, Lu G, Ma J, Zheng J, Hu F, Liu J, Et al., Causes and characteristics of children unintentional injuries in emergency department and its implications for prevention, Front Public Health, 9, (2021); Liu B, Li D, Cheng Y, Yu J, Jia Y, Zhang Q, Et al., Development and internal validation of a simple prognostic score for early sepsis risk stratification in the emergency department, BMJ Open, 11, 7, (2021); Qi Y, Shi P, Chen R, Zhou Y, Liu L, Hong J, Et al., Characteristics of childhood allergic diseases in outpatient and emergency departments in Shanghai, China, 2016-2018: a multicenter, retrospective study, BMC Pediatr, 21, 1, (2021); Drouin O, D'Angelo A, Gravel J., Impact of wait time during a first pediatric emergency room visit on likelihood of revisit in the next year, Am J Emerg Med, 38, 5, pp. 890-894, (2020); Feldman O, Allon R, Leiba R, Shavit I., Emergency department waiting times in a tertiary children's hospital in Israel: a retrospective cohort study, Isr J Health Policy Res, 6, 1, (2017); Lipsett SC, Monuteaux MC, Fine AM., Seasonality of common pediatric infectious diseases, Pediatr Emerg Care, 37, 2, pp. 82-85, (2021); Xu Z, Hu W, Su H, Turner LR, Ye X, Wang J, Et al., Extreme temperatures and paediatric emergency department admissions, J Epidemiol Community Health, 68, 4, pp. 304-311, (2014); Li H, Yu G, Dong C, Jia Z, An J, Duan H, Et al., PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China, Sci Rep, 9, 1, (2019); Tse G, McLean L., Seasonal trends in pediatric respiratory illnesses: using Google Trends to inform precision outreach, Pediatr Emerg Care, 38, 2, pp. e752-e755, (2022); Ponum M, Hasan O, Khan S., EasyDetectDisease: an android app for early symptom detection and prevention of childhood infectious diseases, Interact J Med Res, 8, 2, (2019); Elwell S, Johnson-Salerno E, Thomas J, Haut C, Alfonsi L., Improving timeliness of pediatric emergency department admissions, J Emerg Nurs, 48, 5, pp. 496-503, (2022); Melo MR, Ferreira-Magalhaes M, Flor-Lima F, Rodrigues M, Severo M, Almeida-Santos L, Et al., Dedicated pediatricians in emergency department: shorter waiting times and lower costs, PLoS One, 11, 8, (2016); Sundrani S, Chen J, Jin BT, Abad ZSH, Rajpurkar P, Kim D., Predicting patient decompensation from continuous physiologic monitoring in the emergency department, NPJ Digit Med, 6, 1, (2023); Katayama Y, Kiyohara K, Hirose T, Matsuyama T, Ishida K, Nakao S, Et al., A mobile app for self-triage for pediatric emergency patients in Japan: 4 year descriptive epidemiological study, JMIR Pediatr Parent, 4, 2, (2021); Alhaidari F, Almuhaideb A, Alsunaidi S, Ibrahim N, Aslam N, Khan IU, Et al., E-triage systems for COVID-19 outbreak: review and recommendations, Sensors (Basel), 21, 8, (2021); Zachrison KS, Hayden EM, Boggs KM, Boyle TP, Gao J, Samuels-Kalow ME, Et al., Emergency departments' uptake of telehealth for stroke versus pediatric care: observational study, J Med Internet Res, 24, 6, (2022); Rossi S, Santini SJ, Di Genova D, Maggi G, Verrotti A, Farello G, Et al., Using the social robot NAO for emotional support to children at a pediatric emergency department: randomized clinical trial, J Med Internet Res, 24, 1, (2022); Rochat J, Ehrler F, Siebert JN, Ricci A, Garretas Ruiz V, Lovis C., Usability testing of a patient-centered mobile health app for supporting and guiding the pediatric emergency department patient journey: mixed methods study, JMIR Pediatr Parent, 5, 1, (2022); Alavi-Moghaddam M, Forouzanfar R, Alamdari S, Shahrami A, Kariman H, Amini A, Et al., Application of queuing analytic theory to decrease waiting times in emergency department: does it make sense?, Arch Trauma Res, 1, 3, pp. 101-107, (2012); Tideman S, Santillana M, Bickel J, Reis B., Internet search query data improve forecasts of daily emergency department volume, J Am Med Inform Assoc, 26, 12, pp. 1574-1583, (2019); Hu K, Sun Z, Rui Y, Mi J, Ren M., Shortage of paediatricians in China, Lancet, 383, 9921, (2014); Zhang Y, Huang L, Zhou X, Zhang X, Ke Z, Wang Z, Et al., Characteristics and workload of pediatricians in China, Pediatrics, 144, 1, (2019); Rennert-May E, Leal J, Thanh NX, Lang E, Dowling S, Manns B, Et al., The impact of COVID-19 on hospital admissions and emergency department visits: a population-based study, PLoS One, 16, 6, (2021); Liu W, Yang Q, Xu Z, Hu Y, Wang Y, Liu Z, Et al., Impact of the COVID-19 pandemic on neonatal admissions in a tertiary children's hospital in southwest China: an interrupted time-series study, PLoS One, 17, 1, (2022); Wang X, Xu H, Chu P, Zeng Y, Tian J, Song F, Et al., Effects of COVID-19-targeted nonpharmaceutical interventions on children's respiratory admissions in China: a national multicenter time series study, Int J Infect Dis, 124, pp. 174-180, (2022); Beam AL, Kohane IS., Big data and machine learning in health care, JAMA, 319, 13, pp. 1317-1318, (2018); GitHub","S. Zheng; Institute of Medical Information, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; email: zheng.si@imicams.ac.cn","","JMIR Publications Inc.","","","","","","14388871","","","37910168","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175676407"
"Portela D.; Amaral R.; Rodrigues P.P.; Freitas A.; Costa E.; Fonseca J.A.; Sousa-Pinto B.","Portela, Diana (57219356359); Amaral, Rita (56067841600); Rodrigues, Pedro P (14024612500); Freitas, Alberto (57217280282); Costa, Elísio (7402527214); Fonseca, João A (45661083200); Sousa-Pinto, Bernardo (55982726300)","57219356359; 56067841600; 14024612500; 57217280282; 7402527214; 45661083200; 55982726300","Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal","2024","Health Information Management Journal","53","3","","174","182","8","1","10.1177/18333583221144663","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148507252&doi=10.1177%2f18333583221144663&partnerID=40&md5=b8ead9dd6e9697fadaebd1c138e40e05","Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal; ACES Entre o Douro e Vouga I - Feira/Arouca, Portugal; Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal; ESS, IPP - Porto Health School, Polytechnic Institute of Porto, Portugal; Research Unit on Applied Molecular Biosciences (UCIBIO—REQUIMTE), Faculty of Pharmacy, University of Porto, Portugal","Portela D., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, ACES Entre o Douro e Vouga I - Feira/Arouca, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal; Amaral R., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal, ESS, IPP - Porto Health School, Polytechnic Institute of Porto, Portugal; Rodrigues P.P., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal; Freitas A., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal; Costa E., Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal, Research Unit on Applied Molecular Biosciences (UCIBIO—REQUIMTE), Faculty of Pharmacy, University of Porto, Portugal; Fonseca J.A., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal; Sousa-Pinto B., Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal, Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Portugal","Background: Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective: This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method: We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results: We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion: We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion: Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems. © The Author(s) 2023.","administrative database; clustering algorithms; comorbidities; data quality; health information management; medical records: evaluation; public health informatics; under-coding; unsupervised machine learning","Adult; Aged; Aged, 80 and over; Algorithms; Clinical Coding; Cluster Analysis; Comorbidity; Databases, Factual; Female; Hospitalization; Humans; Male; Middle Aged; Portugal; adult; aged; algorithm; cluster analysis; coding; comorbidity; epidemiology; factual database; female; hospitalization; human; male; middle aged; Portugal; very elderly","","","","","","","Alonso V., Santos J., Pinto M., Et al., Problems and barriers during the process of clinical coding: a focus group study of coders’ perceptions, Journal of Medical Systems, 44, (2020); Alonso V., Santos J.V., Pinto M., Et al., Health records as the basis of clinical coding: is the quality adequate? A qualitative study of medical coders’ perceptions, Health Information Management Journal, 49, 1, pp. 28-37, (2020); Berzal F., Matin N., Data mining: concepts and techniques by Jiawei Han and Micheline Kamber, ACM SIGMOD Record, 31, pp. 66-68, (2002); Bilsker D., Goldner E.M., Jones W., Health service patterns indicate potential benefit of supported self-management for depression in primary care, The Canadian Journal of Psychiatry, 52, 2, pp. 86-95, (2007); Cappetta K., Lago L., Potter J., Et al., Under-coding of dementia and other conditions indicates scope for improved patient management: a longitudinal retrospective study of dementia patients in Australia, Health Information Management Journal, (2020); ICD-9-CM Official Guidelines for Coding and Reporting, (1991); Charlson M.E., Pompei P., Ales K.L., Et al., A new method of classifying prognostic comorbidity in longitudinal studies: development and validation, Journal of Chronic Diseases, 40, 5, pp. 373-383, (1987); Deyo R.A., Cherkin D.C., Ciol M.A., Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases, Journal of Clinical Epidemiology, 45, 6, pp. 613-619, (1992); Dominick K.L., Dudley T.K., Coffman C.J., Et al., Comparison of three comorbidity measures for predicting health service use in patients with osteoarthritis, Arthritis Care & Research, 53, 5, pp. 666-672, (2005); Elixhauser A., Steiner C., Harris D.R., Et al., Comorbidity measures for use with administrative data, Medical Care, 36, 1, pp. 8-27, (1998); Embrechts M.J., Gatti C.J., Linton J., Et al., Hierarchical clustering for large data sets, Advances in Intelligent Signal Processing and Data Mining, (2013); Comorbidity Coding Trends in Hospital Administrative Databases. In:, (2016); Garcia-Perez L., Linertova R., Lorenzo-Riera A., Et al., Risk factors for hospital readmissions in elderly patients: a systematic review, QJM, 104, 8, pp. 639-651, (2011); Garland A., Fransoo R., Olafson K., Et al., The Epidemiology and Outcomes of Critical Illness in Manitoba, (2012); Hand D.J., Krzanowski W.J., Optimising k-means clustering results with standard software packages, Computational Statistics & Data Analysis, 49, 4, pp. 969-973, (2005); Haneef R., Kab S., Hrzic R., Et al., Use of artificial intelligence for public health surveillance: a case study to develop a machine learning-algorithm to estimate the incidence of diabetes mellitus in France, Archives Public Health, 79, 1, (2021); Harron K., Dibben C., Boyd J., Et al., Challenges in administrative data linkage for research, Big Data & Society, 4, 2, (2017); Hua-Gen Li M., Hutchinson A., Tacey M., Et al., Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study, BMJ Health Care Informatics, 26, 1, (2019); Jain A.K., Murty M.N., Flynn P.J., Data clustering: a review, ACM Computing Surveys, 31, 3, pp. 264-323, (1999); Johnston M., Secondary data analysis: a method of which the time has come, Qualitative and Quantitative Methods in Libraries, 3, pp. 619-626, (2014); Junior A.A.G., Acurcio F.A., Reis A., Et al., Building the national database of health centred on the individual: administrative and epidemiological record linkage - Brazil, 2000-2015, International Journal of Population Data Science, 3, 1, (2018); Krieger A.M., Green P.E., A generalized rand-index method for consensus clustering of separate partitions of the same data base, Journal of Classification, 16, 1, pp. 63-89, (1999); Kripalani S., Theobald C.N., Anctil B., Et al., Reducing hospital readmission rates: current strategies and future directions, Annual Review of Medicine, 65, pp. 471-485, (2014); Kumar P., Nestsiarovich A., Nelson S.J., Et al., Imputation and characterization of uncoded self-harm in major mental illness using machine learning, Journal of the American Medical Informatics Association, 27, 1, pp. 136-146, (2020); Lopez-Arevalo I., Aldana-Bobadilla E., Molina-Villegas A., Et al., A memory-efficient encoding method for processing mixed-type data on machine learning, Entropy (Basel), 22, 12, (2020); Mazzali C., Paganoni A.M., Ieva F., Et al., Methodological issues on the use of administrative data in healthcare research: the case of heart failure hospitalizations in Lombardy region, 2000 to 2012, BMC Health Services Research, 16, 1, (2016); Mbizvo G.K., Bennett K., Simpson C.R., Et al., Accuracy and utility of using administrative healthcare databases to identify people with epilepsy: a protocol for a systematic review and meta-analysis, BMJ Open, 8, 6, (2018); Ng S.K., Tawiah R., Sawyer M., Et al., Patterns of multimorbid health conditions: a systematic review of analytical methods and comparison analysis, International Journal of Epidemiology, 47, 5, pp. 1687-1704, (2018); Payne R.A., Abel G.A., Simpson C.R., A retrospective cohort study assessing patient characteristics and the incidence of cardiovascular disease using linked routine primary and secondary care data, BMJ Open, 2, 2, (2012); Peng M., Southern D.A., Williamson T., Et al., Under-coding of secondary conditions in coded hospital health data: Impact of co-existing conditions, death status and number of codes in a record, Health Informatics Journal, 23, 4, pp. 260-267, (2017); Quan H., Li B., Saunders L.D., Et al., Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database, Health Services Research, 43, 4, pp. 1424-1441, (2008); Quan H., Parsons G.A., Ghali W.A., Validity of information on comorbidity derived from ICD-9-CCM administrative data, Medical Care, 40, 8, pp. 675-685, (2002); Raghupathi W., Raghupathi V., Big data analytics in healthcare: promise and potential, Health Information Science and Systems, 2, (2014); Raherison C., Ouaalaya E.H., Bernady A., Et al., Comorbidities and COPD severity in a clinic-based cohort, BMC Pulmonary Medicine, 18, 1, (2018); Rollason W., Khunti K., de Lusignan S., Variation in the recording of diabetes diagnostic data in primary care computer systems: implications for the quality of care, Informatics in Primary Care, 17, 2, pp. 113-119, (2009); Rothman K.J., Greenland S., Lash T.L., Modern Epidemiology, (2015); Sanchez-Rico M., Alvarado J.M., A machine learning approach for studying the comorbidities of complex diagnoses, Behavioral Sciences (Basel), 9, 12, (2019); Saude M.D., (2014); Sousa-Pinto B., Cardoso-Fernandes A., Araujo L., Et al., Clinical and economic burden of hospitalizations with registration of penicillin allergy, Annals of Allergy, Asthma & Immunology, 120, (2018); Souza J., Pimenta D., Caballero I., Et al., Measuring data credibility and medical coding: a case study using a nationwide Portuguese inpatient database, Software Quality Journal, 28, 3, pp. 1043-1061, (2020); Souza J., Santos J., Bolon Canedo V., Et al., Importance of coding co-morbidities for APR-DRG assignment: focus on cardiovascular and respiratory diseases, Health Information Management Journal, 49, 1, pp. 47-57, (2020); Souza J., Santos J.V., Lopes F., Et al., Quality of coding within clinical datasets: a case-study using burn-related hospitalizations, Burns: Journal of the International Society for Burn Injuries, 45, 7, pp. 1571-1584, (2019); Vuik S.I., Mayer E., Darzi A., A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population, Population Health Metrics, 14, (2016); Ward J.H., Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, 58, pp. 236-244, (1963); Weissler E.H., Zhang J., Lippmann S., Et al., Use of natural language processing to improve identification of patients with peripheral artery disease, Circulation: Cardiovascular Interventions, 13, 10, (2020); Yan J., Linn K.A., Powers B.W., Et al., Applying machine learning algorithms to segment high-cost patient populations, Journal of General Internal Medicine, 34, 2, pp. 211-217, (2019)","D. Portela; Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Portugal; email: di.portelasilva@gmail.com","","SAGE Publications Inc.","","","","","","18333583","","","36802958","English","Health Inf. Manage. J.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85148507252"
"Bian H.; Zhu S.; Zhang Y.; Fei Q.; Peng X.; Jin Z.; Zhou T.; Zhao H.","Bian, Hupo (59300923800); Zhu, Shaoqi (59300907200); Zhang, Yonghua (59300897800); Fei, Qiang (59300890700); Peng, Xiuhua (59300937700); Jin, Zanhui (59300923900); Zhou, Tianxiang (59300945100); Zhao, Hongxing (57223082829)","59300923800; 59300907200; 59300897800; 59300890700; 59300937700; 59300923900; 59300945100; 57223082829","Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions –A Bibliometric Analysis from 2009 to 2023","2024","International Journal of COPD","19","","","1849","1864","15","1","10.2147/COPD.S474402","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202266952&doi=10.2147%2fCOPD.S474402&partnerID=40&md5=94fe1315f7f94284137caacfdf4d8614","Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Department of Endocrinology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Department of Radiology, The Wuxing District People’s Hospital, Zhejiang, Huzhou, China; Department of Radiology, The Linghu People’s Hospital, Zhejiang, Huzhou, China; Department of Urinary Surgery, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Huzhou Key Laboratory of Precise Diagnosis and Treatment of Urinary Tumors, Zhejiang, Huzhou, 313000, China","Bian H., Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Zhu S., Department of Endocrinology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Zhang Y., Department of Radiology, The Wuxing District People’s Hospital, Zhejiang, Huzhou, China; Fei Q., Department of Radiology, The Linghu People’s Hospital, Zhejiang, Huzhou, China; Peng X., Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Jin Z., Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Zhou T., Department of Urinary Surgery, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China; Zhao H., Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Zhejiang, Huzhou, China, Huzhou Key Laboratory of Precise Diagnosis and Treatment of Urinary Tumors, Zhejiang, Huzhou, 313000, China","Objective: A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization of artificial intelligence (AI) in chronic obstructive pulmonary disease (COPD). Methods: On March 24, 2024, a computer search was conducted on the Web of Science (WOS) core collection dataset published between January 1, 2009, and December 30, 2023, to identify literature related to the application of artificial intelligence in chronic obstructive pulmonary disease (COPD). VOSviewer was utilized for visual analysis of countries, institutions, authors, co-cited authors, and keywords. CiteSpace was employed to analyze the intermediary centrality of institutions, references, keyword outbreaks, and co-cited literature. Relevant descriptive analysis tables were created using Excel2021 software. Results: This study included a total of 646 papers from WOS. The number of papers remained small and stable from 2009 to 2017 but started increasing significantly annually since 2018. The United States had the highest number of publications among countries/regions while Silverman Edwin K and Harvard Medical School were the most prolific authors and institutions respectively. Lynch DA, Kirby M. and Vestbo J. were among the top three most cited authors overall. Scientific Reports had the largest number of publications while Radiology ranked as one of the top ten influential journals. The Genetic Epidemiology of COPD (COPDGene) Study Design was frequently cited. Through keyword clustering analysis, all keywords were categorized into four groups: epidemiological study of COPD; AI-assisted imaging diagnosis; AI-assisted diagnosis; and AI-assisted treatment and prognosis prediction in the COPD research field. Currently, hot research topics include explainable artificial intelligence framework, chest CT imaging, and lung radiomics. Conclusion: At present, AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD. This study’s results offer novel insights and directions for future research endeavors related to COPD. © 2024 Bian et al.","artificial intelligence; bibliometric analysis; chronic obstructive pulmonary disease; visual analysis","Artificial Intelligence; Bibliometrics; Biomedical Research; Diffusion of Innovation; Forecasting; Humans; Pulmonary Disease, Chronic Obstructive; Time Factors; Article; artificial intelligence; artificial neural network; bibliometrics; chronic obstructive lung disease; computer assisted tomography; decision making; explainable artificial intelligence; health disparity; human; machine learning; mathematical parameters; medical research; natural language processing; prediction; prevalence; radiomics; research; spirometry; trend study; Web of Science; diagnosis; diffusion of innovation; epidemiology; forecasting; medical research; time factor","","","VOSviewer version 1.6.20; version  6.3.1","","Science and Technology Project of Huzhou City; Zhejiang Province, (2023G Y33)","The study was funded by Science and Technology Project of Huzhou City, Zhejiang Province (2023G Y33).","Adeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I., NIHR RESPIRE Global Respiratory Health Unit. Global, Regional, and National Prevalence of, and Risk Factors for, Chronic Obstructive Pulmonary Disease (COPD) in 2019: a Systematic Review and Modelling Analysis, Lancet Respir Med, 10, 5, pp. 447-458, (2022); Xiang Y, Luo X., Extrapulmonary Comorbidities Associated with Chronic Obstructive Pulmonary Disease: a Review, Int J Chronic Obstr, 19, pp. 567-578, (2024); Pelaia C, Procopio G, Deodato MR, Et al., Real-Life Clinical and Functional Effects of Fluticasone Furoate/Umeclidinium/Vilanterol-Combined Triple Therapy in Patients with Chronic Obstructive Pulmonary Disease, Respiration, 100, 2, pp. 127-134, (2021); Diab N, Gershon AS, Sin DD, Et al., Underdiagnosis and Overdiagnosis of Chronic Obstructive Pulmonary Disease, Am J Respir Crit Care Med, 198, 9, pp. 1130-1139, (2018); Hussain A, Marlowe S, Ali M, Et al., A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders, Cureus, 16, 1, (2024); Kaplan A, Cao H, FitzGerald JM, Et al., Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis, J Allergy Clin Immunol Pract, 9, 6, pp. 2255-2261, (2021); Exarchos K, Aggelopoulou A, Oikonomou A, Et al., Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease, IEEE J Biomed Health Inform, 26, 5, pp. 2331-2338, (2022); Tian Z, Jiang Y, Zhang N, Zhang Z, Wang L., Analysis of the Current State of COPD Nursing Based on a Bibliometric Approach from the Web of Science, Int J Chronic Obstr, 19, pp. 255-268, (2024); Yuan W-C, Zhang J-X, Chen H-B, Et al., A Bibliometric and Visual Analysis of Cancer-Associated Fibroblasts, Front Immunol, 14, (2023); Boutet A, Haile SS, Yang AZ, Et al., Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology, AJNR Am J Neuroradiol, (2024); Chen A, Luo Z, Zhang J, Cao X., Emerging Research Themes in Maternal Hypothyroidism: a Bibliometric Exploration, Front Immunol, 15, (2024); Xu D, Wang YL, Wang KT, Et al., A scientometrics analysis and visualization of depressive disorder, Curr Neuropharm, 19, 6, pp. 766-786, (2021); Fang H, Dong T, Han Z, Et al., Comorbidity of Pulmonary Fibrosis and COPD/Emphysema: research Status, Trends, and Future Directions ——— a Bibliometric Analysis from 2004 to 2023, Int J Chronic Obstr, 18, pp. 2009-2026, (2023); Regan EA, Hokanson JE, Murphy JR, Et al., Genetic Epidemiology of COPD (COPDGene) Study Design, COPD, 7, 1, pp. 32-43, (2011); Lynch DA, Austin JHM, Hogg JC, Et al., CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: a Statement of the Fleischner Society, Radiology, 277, 1, pp. 192-205, (2015); Abiyev RH, Ma'aitah MKS., Deep Convolutional Neural Networks for Chest Diseases Detection, J Healthcare Eng, 2018, pp. 1-11, (2018); Fischer AM, Varga-Szemes A, Van assen M, Et al., Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing, AJR Am J Roentgenol, 214, 5, pp. 1065-1071, (2020); Makimoto K, Au R, Moslemi A, Et al., Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features, Acad Radiol, 30, 5, pp. 900-910, (2023); Koul A, Bawa RK, Kumar Y., Artificial intelligence techniques to predict the airway disorders illness: a systematic review, Arch Comput Meth Eng, 30, pp. 192-205, (2023); Spiegel JM, Ehrlich R, Yassi A, Et al., Using artificial intelligence for high-volume identification of silicosis and tuberculosis: a bio-ethics approach, Ann Glob Health, 87, (2021); Wang R, Chen L-C, Moukheiber L, Et al., Enabling Chronic Obstructive Pulmonary Disease Diagnosis through Chest X-Rays: a Multi-Site and Multi-Modality Study, Int J Med Inform, 178, (2023); Burkes RM, Zafar MA, Panos RJ., The Role of Chest Computed Tomography in the Evaluation and Management of Chronic Obstructive Pulmonary Disease, Curr Opin Pulm Med, 30, 2, pp. 129-135, (2024); Dai Q, Zhu X, Zhang J, Et al., The Utility of Quantitative Computed Tomography in Cohort Studies of Chronic Obstructive Pulmonary Disease: a Narrative Review, J Thorac Dis, 15, 10, pp. 5784-5800, (2023); Moll M, Qiao D, Regan EA, Et al., Machine Learning and Prediction of All-Cause Mortality in COPD, Chest, 158, 3, pp. 952-964, (2020); Ji Z, Li X, Lei S, Xu J, Xie Y., A Pooled Analysis of the Risk Prediction Models for Mortality in Acute Exacerbation of Chronic Obstructive Pulmonary Disease, Clin Respir J, 17, 8, pp. 707-718, (2023); Remy-Jardin M, Faivre J-B, Kaergel R, Et al., Machine Learning and Deep Neural Network Applications in the Thorax: pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease, J Thorac Imaging, 35, 1, pp. S40-S48, (2020); Altan G, Kutlu Y., Gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning on multi-channel lung sounds, Turkish. J Electr Eng Comput Sci, 28, 5, pp. 2979-2996, (2020); Castaldi PJ, Boueiz A, Yun J, Et al., COPDGene Investigators. Machine Learning Characterization of COPD Subtypes: insights From the COPDGene Study, Chest, 157, 5, pp. 1147-1157, (2020); Joumaa H, Sigogne R, Maravic M, Perry L, Bourdin A, Roche N., Artificial Intelligence to Differentiate Asthma from COPD in Medico-Administrative Databases, BMC Pulm Med, 22, 1, (2022); Cosentino J, Behsaz B, Alipanahi B, Et al., Inference of Chronic Obstructive Pulmonary Disease with Deep Learning on Raw Spirograms Identifies New Genetic Loci and Improves Risk Models, Nat Genet, 55, 5, pp. 787-795, (2023); Yin C, Udrescu M, Gupta G, Et al., Fractional Dynamics Foster Deep Learning of COPD Stage Prediction, Adv Sci, 10, 12, (2023); Experimental Drugs in Clinical Trials for COPD: artificial Intelligence via Machine Learning Approach to Predict the Successful Advance from Early-Stage Development to Approval, Expert Opin Invest Drugs, 32, 6, (2023); Stolz D, Mkorombindo T, Schumann DM., Towards the elimination of chronic obstructive pulmonary disease: a Lancet Commission, Lancet, 400, 10356, pp. 921-972, (2022); Okeibunor JC, Jaca A, Iwu-Jaja CJ., The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review, Front Public Health, 11, (2023); Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z., Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease, Comput Meth Progr Biomed, 188, (2020); Agusti A, Bel E, Thomas M, Et al., Treatable traits: toward precision medicine of chronic airway diseases, Eur Respir J, 47, 2, pp. 410-419, (2016); Makimoto K, Hogg JC, Bourbeau J, Tan WC, Kirby M., imaging with machine learning for predicting progression to COPD in individuals at risk, Chest, 164, 5, pp. 1139-1149, (2023)","H. Zhao; Department of Radiology, The First Affiliated Hospital of Huzhou Normal University, Huzhou City, No. 158, Plaza Back Road, Wuxing District, Zhejiang Province, China; email: zhx2113408@126.com","","Dove Medical Press Ltd","","","","","","11769106","","","39185394","English","Int. J. COPD","Article","Final","","Scopus","2-s2.0-85202266952"
"Lisik D.; Wennergren G.; Kankaanranta H.; Basna R.; Shah S.A.; Alm B.; Celind F.S.; Goksör E.; Nwaru B.I.","Lisik, Daniil (57237727500); Wennergren, Göran (7005921751); Kankaanranta, Hannu (7004502353); Basna, Rani (57193611228); Shah, Syed Ahmar (56424513100); Alm, Bernt (7003429937); Celind, Frida Strömberg (57189504284); Goksör, Emma (23004299300); Nwaru, Bright I. (26635624700)","57237727500; 7005921751; 7004502353; 57193611228; 56424513100; 7003429937; 57189504284; 23004299300; 26635624700","Asthma and allergy trajectories in children based on combined parental report and register data","2024","Pediatric Allergy and Immunology","35","10","e14254","","","","1","10.1111/pai.14254","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205772549&doi=10.1111%2fpai.14254&partnerID=40&md5=d84e66e54b8dcb6570a342c20d43945b","Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Paediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; Department of Clinical Science, Lund University, Lund, Sweden; Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden","Lisik D., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wennergren G., Department of Paediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Kankaanranta H., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland, Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; Basna R., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Clinical Science, Lund University, Lund, Sweden; Shah S.A., Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Alm B., Department of Paediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Celind F.S., Department of Paediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Goksör E., Department of Paediatrics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Nwaru B.I., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden","Background: Trajectories of asthma and allergy in children are heterogeneous and commonly derived from parental report of disease or clinical records. This study combined parental-reported and register-based dispensed medication data to characterize childhood trajectories of co-existing asthma, allergic rhinitis, and eczema. Methods: From a Swedish population-based birth cohort (N = 5654), survey responses collected at the age of 1, 4.5, 8, and 12 years were linked to dispensed medication register data for the period of 2–13 years. Trajectories were identified with latent class analysis. Statistical metrics and clinical interpretability guided the model selection. Results: Nine distinct trajectories were identified: three asthma-dominated (early-onset remitting [n = 189, 3.3%], late-onset [n = 117, 2.1%], and persistent [n = 149, 2.6%]), two eczema-dominated (persistent [n = 190, 3.4%] and remitting [n = 432, 7.6%]), one allergic rhinitis-dominated (late-onset [n = 259, 4.6%]), two multimorbidity (mid-childhood asthma and late-onset allergic rhinitis [n = 144, 2.5%], and persistent eczema and late-onset allergic rhinitis [n = 90, 1.6%]), and one low-disease burden trajectory (n = 4084, 72.2%). Differences were seen across the trajectories in the proportion of parental report of disease and dispensed medication as well as by class and quantity of medication dispensed. Conclusion: Combined parental-reported and dispensed medication data enriches characterization of longitudinal trajectories of asthma and allergy in children by merging subjective experience of disease with healthcare utilization. The identified trajectories were characterized by distinct disease development and prescription patterns suggesting clinically differential morbidity burden. © 2024 The Author(s). Pediatric Allergy and Immunology published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.","allergic rhinitis; asthma; atopic dermatitis; machine learning; trajectory analysis","Adolescent; Asthma; Birth Cohort; Child; Child, Preschool; Eczema; Female; Humans; Hypersensitivity; Infant; Male; Parents; Registries; Rhinitis, Allergic; Sweden; antihistaminic agent; beta adrenergic receptor stimulating agent; corticosteroid; leukotriene receptor blocking agent; long acting drug; muscarinic receptor blocking agent; short acting drug; tacrolimus; urea; allergic rhinitis; allergy; Article; asthma; child; childhood disease; cohort analysis; controlled study; disease burden; early onset asthma; eczema; female; follow up; human; illness trajectory; infant; late onset asthma; late onset disorder; longitudinal study; major clinical study; male; multiple chronic conditions; prescription; randomized controlled trial; adolescent; allergic rhinitis; birth cohort; child parent relation; diagnosis; eczema; epidemiology; hypersensitivity; preschool child; register; Sweden","","tacrolimus, 104987-11-3, 109581-93-3; urea, 57-13-6","","","Swedish Asthma & Allergy Foundation; Swedish government; Hjärt-Lungfonden; Västra Götaland; Vetenskapsrådet, VR","The present work was supported by funding from the Swedish government under the ALF agreement between the Swedish government and the county councils (V\u00E4stra G\u00F6taland), the Swedish Asthma & Allergy Foundation, the Swedish Heart and Lung Foundation, and the Swedish Research Council. The funders of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.","Hicke-Roberts A., Aberg N., Wennergren G., Hesselmar B., Allergic rhinoconjunctivitis continued to increase in Swedish children up to 2007; but asthma and eczema levelled off from 1991, Acta Paediatr, 106, 1, pp. 75-80, (2017); Ballardini N., Kull I., Lind T., Et al., Development and comorbidity of eczema; asthma and rhinitis to age 12—data from the <scp>BAMSE</scp> birth cohort, Allergy, 67, 4, pp. 537-544, (2012); Maiello N., Comberiati P., Giannetti A., Ricci G., Carello R., Galli E., New directions in understanding atopic march starting from atopic dermatitis, Children (Basel), 9, 4, (2022); Belgrave D.C.M., Granell R., Simpson A., Et al., Developmental profiles of eczema; wheeze; and rhinitis: two population-based birth cohort studies, PLoS Med, 11, 10, (2014); Loftus T.J., Shickel B., Balch J.A., Et al., Phenotype clustering in health care: a narrative review for clinicians, Front Artif Intell, 5, (2022); Bui D.S., Lodge C.J., Perret J.L., Et al., Trajectories of asthma and allergies from 7 years to 53 years and associations with lung function and extrapulmonary comorbidity profiles: a prospective cohort study, Lancet Respir Med, 9, 4, pp. 387-396, (2021); Forster F., Ege M.J., Gerlich J., Et al., Trajectories of asthma and allergy symptoms from childhood to adulthood, Allergy, 77, 4, pp. 1192-1203, (2022); Peng Z., Kurz D., Weiss J.M., Brenner H., Rothenbacher D., Genuneit J., Latent classes of atopic dermatitis and food allergy development in childhood, Pediatr Allergy Immunol, 33, 11, (2022); Abuabara K., Ye M., Margolis D.J., Et al., Patterns of atopic eczema disease activity from birth through midlife in 2 British birth cohorts, JAMA Dermatol, 157, 10, pp. 1191-1199, (2021); Alm B., Mollborg P., Erdes L., Et al., SIDS risk factors and factors associated with prone sleeping in Sweden, Arch Dis Child, 91, 11, pp. 915-919, (2006); Vasileiadou S., Wennergren G., Celind F.S., Goksor E., Low agreement between Swedish national registers and parental questionnaires on allergic rhinitis, Pediatr Allergy Immunol, 34, 11, (2023); Nylund K.L., Asparouhov T., Muthen B.O., Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study, Struct Equ Model Multidiscip J, 14, 4, pp. 535-569, (2007); Weller B.E., Bowen N.K., Faubert S.J., Latent class analysis: a guide to best practice, J Black Psychol, 46, 4, pp. 287-311, (2020); Rubin D.B., Multiple Imputation for Nonresponse in Surveys, (1987); Hennig C., Clustering strategy and method selection, Handbook Cluster Anal, 9, pp. 703-730, (2015); Ortqvist A.K., Lundholm C., Wettermark B., Ludvigsson J.F., Ye W., Almqvist C., Validation of asthma and eczema in population-based Swedish drug and patient registers, Pharmacoepidemiol Drug Saf, 22, 8, pp. 850-860, (2013); Ansotegui I.J., Bernstein J.A., Canonica G.W., Et al., Insights into urticaria in pediatric and adult populations and its management with fexofenadine hydrochloride, Allergy Asthma Clin Immunol, 18, 1, (2022); Oksel C., Granell R., Mahmoud O., Custovic A., Henderson A.J., Causes of variability in latent phenotypes of childhood wheeze, J Allergy Clin Immunol, 143, 5, pp. 1783-1790. e11, (2019); Kim H.Y., Shin Y.H., Han M.Y., Determinants of sensitization to allergen in infants and young children, Korean J Pediatr, 57, 5, pp. 205-210, (2014); Pyun B.Y., Natural history and risk factors of atopic dermatitis in children, Allergy, Asthma Immunol Res, 7, 2, pp. 101-105, (2015); Nguena Nguefack H.L., Page M.G., Katz J., Et al., Trajectory modelling techniques useful to epidemiological research: a comparative narrative review of approaches, Clin Epidemiol, 12, pp. 1205-1222, (2020); Sijbrandij J.J., Hoekstra T., Almansa J., Reijneveld S.A., Bultmann U., Identification of developmental trajectory classes: comparing three latent class methods using simulated and real data, Adv Life Course Res, 42, (2019); Odling M., Wang G., Andersson N., Et al., Characterization of asthma trajectories from infancy to young adulthood, J Allergy Clin Immunol Pract, 9, 6, (2021); Stromberg Celind F., Wennergren G., Vasileiadou S., Alm B., Goksor E., Antibiotics in the first week of life were associated with atopic asthma at 12 years of age, Acta Paediatr, 107, 10, pp. 1798-1804, (2018); Stromberg Celind F., Wennergren G., Vasileiadou S., Alm B., Aberg N., Goksor E., Higher parental education was associated with better asthma control, Acta Paediatr, 108, 5, pp. 920-926, (2019); Sunde R.B., Thorsen J., Pedersen C.T., Et al., Prenatal tobacco exposure and risk of asthma and allergy outcomes in childhood, Eur Respir J, 59, 2, (2022); Trembath A., Laughon M.M., Predictors of bronchopulmonary dysplasia, Clin Perinatol, 39, 3, pp. 585-601, (2012); Sun T., Yu H.Y., Yang M., Song Y.F., Fu J.H., Risk of asthma in preterm infants with bronchopulmonary dysplasia: a systematic review and meta-analysis, World J Pediatr, 19, 6, pp. 549-556, (2023); Xue M., Dehaas E., Chaudhary N., O'Byrne P., Satia I., Kurmi O.P., Breastfeeding and risk of childhood asthma: a systematic review and meta-analysis, ERJ Open Res, 7, 4, (2021); Lin B., Dai R., Lu L., Fan X., Yu Y., Breastfeeding and atopic dermatitis risk: a systematic review and meta-analysis of prospective cohort studies, Dermatology, 236, 4, pp. 345-360, (2020)","D. Lisik; Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Box 424, 405 30, Sweden; email: daniil.lisik@gu.se","","John Wiley and Sons Inc","","","","","","09056157","","PALUE","39373071","English","Pediatr. Allergy Immunol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85205772549"
"Iqbal M.A.; Devarajan K.; Ahmed S.M.","Iqbal, Md. Asim (57215212162); Devarajan, K. (56585663500); Ahmed, Syed Musthak (56857092300)","57215212162; 56585663500; 56857092300","RDN-NET: A Deep Learning Framework for Asthma Prediction and Classification Using Recurrent Deep Neural Network","2024","International Journal of Image and Graphics","24","6","2450050","","","","1","10.1142/S0219467824500505","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165165038&doi=10.1142%2fS0219467824500505&partnerID=40&md5=85e5def0b9fd9ceafd3e3ba6b9fc9731","Department of E.C.E, Annamalai University, Tamil Nadu, India; Department of E.C.E, SR University, Telangana, Warangal, India","Iqbal M.A., Department of E.C.E, Annamalai University, Tamil Nadu, India; Devarajan K., Department of E.C.E, Annamalai University, Tamil Nadu, India; Ahmed S.M., Department of E.C.E, SR University, Telangana, Warangal, India","Asthma is the one of the crucial types of disease, which causes the huge deaths of all age groups around the world. So, early detection and prevention of asthma disease can save numerous lives and are also helpful to the medical field. But the conventional machine learning methods have failed to detect the asthma from the speech signals and resulted in low accuracy. Thus, this paper presented the advanced deep learning-based asthma prediction and classification using recurrent deep neural network (RDN-Net). Initially, speech signals are preprocessed by using minimum mean-square-error short-Time spectral amplitude (MMSE-STSA) method, which is used to remove the noises and enhances the speech properties. Then, improved Ripplet-II Transform (IR2T) is used to extract disease-dependent and disease-specific features. Then, modified gray wolf optimization (MGWO)-based bio-optimization approach is used to select the optimal features by hunting process. Finally, RDN-Net is used to predict the asthma disease present from speech signal and classifies the type as either wheeze, crackle or normal. The simulations are carried out on real-Time COSWARA dataset and the proposed method resulted in better performance for all metrics as compared to the state-of-The-Art approaches.  © 2024 World Scientific Publishing Company.","accuracy; Asthma prediction; deep learning; signal-To-noise ratio; speech signal","Audio signal processing; Deep neural networks; Diseases; Learning systems; Mean square error; Recurrent neural networks; Signal to noise ratio; Speech communication; Accuracy; Age groups; Asthma prediction; Conventional machines; Deep learning; Learning frameworks; Machine learning methods; Medical fields; Minimum mean squares; Speech signals; Forecasting","","","","","","","Gayathri G. V., Satapathy S. C., Smart Intelligent Computing and Applications, pp. 751-758, (2020); Akbar W., Int. Conf., Communication, and Computer Engineering, (2020); Ullah R., Photodiagnosis and Photodynamic Therapy, 28, pp. 292-296, (2019); Mozaffarinya, Revue Franficaise d'Allergologie, 59, 7, pp. 487-492, (2019); Pooja M. R., Pushpalatha M. P., Int. J.Eng. Adv. Technol, 8, pp. 239-245, (2019); Harvey J. L., Kumar S. A. P., IEEE Symposium Series on Computational Intelligence (SSCI), (2019); Gaudillo J., PloS One, 14, 12, (2019); Silveira A., Munoz C., Mendoza L., Int. Conf. Engineering Applications of Neural Networks, (2019); Exarchos K., European Respiratory Journal, 56, 3, (2020); Phan D.-V., Journal of Asthma, 58, 7, pp. 903-911, (2021); Wang X., (2019); Exarchos K., European Respiratory Journal, 56, 3, (2020); Kaplan A., Et al., The Journal of Allergy and Clinical Immunology: In Practice, (2021); Bhat G. S., IEEE Access, 9, pp. 118708-118715, (2021); Kumar A., Transactions on Emerging Telecommunications Technologies, (2020); Balamurali B. T., Sensors, 21, 16, (2021); AlSaad R., BMC Med. Informatics Decision Making, 19, 1, pp. 1-11, (2019); Kim Y., Sci. Rep, 11, 1, (2021); Yao T., Messinger A. I., Luo G., IEEE Access, 8, pp. 195971-195979, (2020); Yahyaoui A., Yumu?ak N., Int. Conf. Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), (2021); Khasha R., Sepehri M. M., Taherkhani N., Applied Soft Computing, 111, (2021); Singh O. P., Palaniappan R., Malarvili M. B., IEEE Access, 6, pp. 55245-55256, (2018); Altan G., (2021); Nabi F. G., Computers in Biology and Medicine, 104, pp. 52-61, (2021); Wu W., Journal of Allergy and Clinical Immunology, 146, 5, (2019); Khan M. U., 6th International Electrical Engineering Conference (IEEC 2021), (2021); Aroud R. A, A. H. Blasi and Mo. A. Alsuwaiket, (2020); Hosseini S. A., JMIR Medical Informatics, 8, 7, (2020); Kontogianni K., Journal of Allergy and Clinical Immunology, 146, 5, (2021); Di Caprio D., Alexandria Engineering Journal, 11, 1, (2021)","M.A. Iqbal; Department of E.C.E, Annamalai University, Tamil Nadu, India; email: mdasimiqbal605@gmail.com","","World Scientific","","","","","","02194678","","","","English","Intl. J. Image Graphics","Article","Final","","Scopus","2-s2.0-85165165038"
"Asaad C.; Ghogho M.","Asaad, Chaimae (57204507243); Ghogho, Mounir (7004165717)","57204507243; 7004165717","AsthmaKGxE: An asthma–environment interaction knowledge graph leveraging public databases and scientific literature","2022","Computers in Biology and Medicine","148","","105933","","","","2","10.1016/j.compbiomed.2022.105933","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135702024&doi=10.1016%2fj.compbiomed.2022.105933&partnerID=40&md5=2022f55e1de0bbaa947ba6699af6a143","TicLab, College of Engineering and Architecture, International University of Rabat, Morocco; Alqualsadi, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Morocco; Faculty of Engineering, University of Leeds, United Kingdom","Asaad C., TicLab, College of Engineering and Architecture, International University of Rabat, Morocco, Alqualsadi, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Morocco; Ghogho M., TicLab, College of Engineering and Architecture, International University of Rabat, Morocco, Faculty of Engineering, University of Leeds, United Kingdom","Motivation: Asthma is a complex heterogeneous disease resulting from intricate interactions between genetic and non-genetic factors related to environmental and psychosocial aspects. Discovery of such interactions can provide insights into the pathophysiology and etiology of asthma. In this paper, we propose an asthma knowledge graph (KG) built using a hybrid methodology for graph-based modeling of asthma complexity with a focus on environmental interactions. Using a heterogeneous set of public sources, we construct a genetic and pharmacogenetic asthma knowledge graph. The construction of this KG allowed us to shed more light on the lack of curated resources focused on environmental influences related to asthma. To remedy the lack of environmental data in our KG, we exploit the biomedical literature using state-of-the-art natural language processing and construct the first Asthma–Environment interaction catalog incorporating a continuously updated ensemble of environmental, psychological, nutritional and socio-economic influences. The catalog's most substantiated results are then integrated into the KG. Results: The resulting environmentally rich knowledge graph ”AsthmaKGxE” aims to provide a resource for several potential applications of artificial intelligence and allows for a multi-perspective study of asthma. Our insight extraction results indicate that stress is the most frequent asthma association in the corpus, followed by allergens and obesity. We contend that studying asthma–environment interactions in more depth holds the key to curbing the complexity and heterogeneity of asthma. Availability: A user interface to browse and download the extracted catalog as well as the KG are available at http://asthmakgxe.moreair.info/. The code and supplementary data are available on github (https://github.com/ChaiAsaad/MoreAIRAsthmaKGxE). © 2022 Elsevier Ltd","Asthma; Knowledge graph; Machine learning; Natural language processing","Artificial Intelligence; Asthma; Databases, Factual; Gene-Environment Interaction; Humans; Pattern Recognition, Automated; Association reactions; Diseases; Graphic methods; Knowledge management; Learning algorithms; Machine learning; Natural language processing systems; User interfaces; allergen; Asthma; Genetic factors; Heterogeneous disease; Knowledge graphs; Language processing; Machine-learning; Natural language processing; Natural languages; Public database; Scientific literature; Article; artificial intelligence; asthma; data base; data extraction; environmental factor; genotype environment interaction; human; knowledge graph; machine learning; natural language processing; nutritional assessment; obesity; pathophysiology; pharmacogenetics; physiological stress; psychological aspect; scientific literature; socioeconomics; artificial intelligence; asthma; automated pattern recognition; factual database; genotype environment interaction; Knowledge graph","","","","","International Business Machines Corporation, IBM; Royal Academy of Engineering, RAENG; Vlaamse Interuniversitaire Raad, VLIR, (MA2017TEA446A101)","Funding text 1: Dr Mounir Ghogho is an IEEE Fellow. He has received the M.Sc. degree in 1993 and the Ph.D. degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde (Scotland), from Sept 1997 to Nov 2001. In Dec 2001, he joined the school of Electronic and Electrical Engineering at the University of Leeds (England), where he was promoted to full Professor in 2008. While still affiliated with the University of Leeds, in 2010 he joined the International University of Rabat (Morocco) where he is currently Dean of Doctoral College and Director of TICLab (ICT Research Laboratory). He is also a co-founder and co-director of the CNRS-Associated International Research Lab DataNet. He was awarded the UK Royal Academy of Engineering Research Fellowship in 2000 and the IBM Faculty Award in 2013. He was elevated to the grade of IEEE Fellow in 2018. His research interests are in signal processing, machine learning and wireless communication. In the past, he served as an associate editor of many journals including the IEEE Signal Processing Magazine, the IEEE Transactions on Signal Processing and the IEEE Signal Processing Letters, and as a member of the IEEE Signal Processing Society SPCOM Technical Committee, the IEEE Signal Processing Society SPTM Technical Committee, and the IEEE Signal Processing Society SAM Technical Committee. He is currently a member of the steering committee of the IEEE Transactions of Signal and Information Processing over Networks.; Funding text 2: The work presented in this paper was carried out within the MoreAir project, which is funded by the Belgian Ministry of cooperation through the VLIR UOS program under grant MA2017TEA446A101 . ","Global Asthma Report, The global asthma report. 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"Adamu Aliyu D.; Akashah Patah Akhir E.; Saidu Y.; Adamu S.; Ismail Umar K.; Sadiq Bunu A.; Mamman H.","Adamu Aliyu, Dahiru (59297797600); Akashah Patah Akhir, Emelia (59297829700); Saidu, Yahaya (58672455600); Adamu, Shamsuddeen (58260313700); Ismail Umar, Kabir (59297854800); Sadiq Bunu, Abubakar (59297855400); Mamman, Hussaini (57796154300)","59297797600; 59297829700; 58672455600; 58260313700; 59297854800; 59297855400; 57796154300","Optimization Techniques for Asthma Exacerbation Prediction Models: A Systematic Literature Review","2024","IEEE Access","12","","","110862","110890","28","1","10.1109/ACCESS.2024.3440502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200818132&doi=10.1109%2fACCESS.2024.3440502&partnerID=40&md5=75f5f89a22448bec4b9ce89c4758fadc","Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; Modibbo Adama University at Yola, Department of Information Technology, Yola, 2076, Nigeria","Adamu Aliyu D., Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; Akashah Patah Akhir E., Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; Saidu Y., Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; Adamu S., Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; Ismail Umar K., Modibbo Adama University at Yola, Department of Information Technology, Yola, 2076, Nigeria; Sadiq Bunu A., Modibbo Adama University at Yola, Department of Information Technology, Yola, 2076, Nigeria; Mamman H., Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia","Asthma exacerbations pose a significant global health concern, necessitating effective predictive models to anticipate and manage these events. This systematic literature review examined the optimization techniques employed in asthma exacerbation prediction models, spanning machine learning algorithms and computational optimization methods. The objective was to synthesize existing evidence, identify trends, and delineate future research directions in predictive modeling for asthma exacerbations to enhance predictive accuracy and clinical utility. A comprehensive search strategy was devised, yielding 27 eligible articles for analysis. The result revealed various optimization techniques, including feature selection, model optimization, and environmental factor integration. The result also revealed that machine learning algorithms' effectiveness in predicting asthma exacerbations varied depending on various factors (such as dataset quality and model complexity), with various optimization techniques (such as feature selection and ensemble learning) used for improving predictive accuracy. Integrating environmental and spatial factors enhanced prediction models, enabling tailored interventions. In addition, personalized asthma management strategies informed by predictive models led to better control and reduced healthcare utilization. The review also highlighted the implications for personalized asthma management, as well as methodological limitations, and proposed future research directions to improve model reliability and advance personalized healthcare understanding, thereby contributing to the United Nations' Sustainable Development Goals related to health, innovation, and sustainability. Thus, progress made in asthma exacerbation prediction and the identification of challenges and areas for improvement were covered, providing valuable insights for researchers, clinicians, and policymakers aiming to enhance asthma care through predictive modeling.  © 2013 IEEE.","Asthma exacerbation; machine learning; optimization; personalized and prediction models","Clinical research; Diseases; Feature extraction; Forecasting; Health care; Learning algorithms; Sustainable development; Asthma; Asthma exacerbation; Atmospheric modeling; Computational modelling; Machine-learning; Medical services; Optimisations; Personalized model; Prediction algorithms; Prediction modelling; Predictive models; Learning systems","","","","","Collaborative Research Fund; Universiti Teknologi Petronas, UTP, (015ME0-341); Universiti Teknologi Petronas, UTP","Funding text 1: The authors gratefully acknowledge the financial support provided by Universiti Teknologi Petronas (UTP) and appreciation is extended for the partial funding from Collaborative Research Fund (cost center 015ME0-341).; Funding text 2: This paper is funded by Universiti Teknologi Petronas (UTP) and also partly funded by Collaborative Research Fund (cost center 015ME0-341)","Soriano J.B., Et al., Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: A systematic analysis for the global burden of disease study 2017, Lancet Respiratory Med., 8, 6, pp. 585-596, (2020); Molnar D., Galffy G., Horvath A., Tomisa G., Katona G., Hirschberg A., Mezei G., Sultesz M., Prevalence of asthma and its associating environmental factors among 6-12-Year-Old schoolchildren in a metropolitan environment-A cross-sectional, questionnaire-based study, Int. 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Adamu Aliyu; Universiti Teknologi PETRONAS, Seri, Department of Computer and Information Sciences, Iskandar, Perak, 32610, Malaysia; email: dahirualiyuadamu2@gmail.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","","Scopus","2-s2.0-85200818132"
"Bupp C.P.; English B.K.; Rajasekaran S.; Prokop J.W.","Bupp, Caleb P. (56197046800); English, B. Keith (57528553200); Rajasekaran, Surender (15045689900); Prokop, Jeremy W. (57212896106)","56197046800; 57528553200; 15045689900; 57212896106","Introduction to Personalized Medicine in Pediatrics","2022","Pediatric Annals","51","10","","e381","e386","5","2","10.3928/19382359-20220803-03","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139517595&doi=10.3928%2f19382359-20220803-03&partnerID=40&md5=af39aeb1083ec2aad258b83fe4f60709","Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University Medical Genetics, Helen DeVos Children’s Hospital, Spectrum Health, United States; Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, United States; Department of Pediatrics and Human Develop-ment, College of Human Medicine, Michigan State University, Spectrum Health; and an Intensivist, Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital, United States","Bupp C.P., Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University Medical Genetics, Helen DeVos Children’s Hospital, Spectrum Health, United States; English B.K., Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, United States; Rajasekaran S., Department of Pediatrics and Human Develop-ment, College of Human Medicine, Michigan State University, Spectrum Health; and an Intensivist, Pediatric Intensive Care Unit, Helen DeVos Children’s Hospital, United States; Prokop J.W., Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, United States","Exciting new developments in biomedical and computational sciences provide an extraordinary and unparalleled opportunity to compile, connect, and analyze multiple types of “big data,” driving the development of personalized medicine. These insights must begin in early life (ie, pregnancy, neonatal, and infancy) and focus on early preven-tion, diagnosis, and intervention—areas of medicine where pediatricians are poised to lead the way to a personalized medicine future. The rapid growth of genomics (includ-ing pharmacogenomics), transcriptomics, and related “omics” has revolutionized the diagnosis of rare monogenic disorders. It is now clarifying the pathogenesis of complex conditions ranging from autism spectrum disorder to asthma. Collaborations between clinicians and basic scientists integrating multiomics approaches in evaluating children with severe illness are transforming the fields of perinatal, neonatal, and pediatric critical care medicine. Improvements in rapid diagnostic and prognostic information suggest that pediatric personalized medicine is under way and has an exciting future. © SLACK Incorporated.","","Autism Spectrum Disorder; Child; Female; Genomics; Humans; Infant, Newborn; Pediatrics; Pharmacogenetics; Precision Medicine; Pregnancy; carbon monoxide; lipidome; transcriptome; Article; carbon monoxide intoxication; child; chromosome analysis; copy number variation; DNA methylation; electroencephalogram; Epstein Barr virus infection; gene control; genetic analysis; genetic screening; genomics; hemolytic uremic syndrome; high throughput sequencing; human; hypertension; immune response; machine learning; metabolome; metabolomics; multiomics; muscle hypotonia; natural language processing; newborn; obesity; parenteral nutrition; patient monitoring; personalized medicine; pharmacogenomics; pneumonia; proteomics; risk factor; Sanger sequencing; sepsis; single nucleotide polymorphism; transcriptomics; whole exome sequencing; autism; female; pediatrics; personalized medicine; pharmacogenetics; pregnancy","","carbon monoxide, 630-08-0","","","","","Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease, (2011); Remarks by the President in State of the Union Address, (2015); Rasool M, Malik A, Naseer MI, Et al., The role of epigenetics in personalized medi-cine: challenges and opportunities, BMC Med Genomics, 8, (2015); Motulsky AG., Drug reactions enzymes, and biochemical genetics, J Am Med Assoc, 165, 7, pp. 835-837, (1957); Relling MV, Evans WE., Pharmacogenomics in the clinic, Nature, 526, 7573, pp. 343-350, (2015); Collins FS., Realizing the dream of molecu-larly targeted therapies for cystic fibrosis, N Engl J Med, 381, 19, pp. 1863-1865, (2019); Bick D, Bick SL, Dimmock DP, Fowler TA, Caulfield MJ, Scott RH., An online compen-dium of treatable genetic disorders, Am J Med Genet C Semin Med Genet, 187, 1, pp. 48-54, (2021); Crouch DJM, Bodmer WF., Polygenic in-heritance, GWAS, polygenic risk scores, and the search for functional variants, Proc Natl Acad Sci USA, 117, 32, pp. 18924-18933, (2020); 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Incerti D, Xu XM, Chou JW, Gonzaludo N, Belmont JW, Schroeder BE., Cost-effective-ness of genome sequencing for diagnos-ing patients with undiagnosed rare genetic diseases, Genet Med, 24, 1, pp. 109-118, (2022); Bupp CP, Schultz CR, Uhl KL, Rajasekaran S, Bachmann AS., Novel de novo pathogenic variant in the ODC1 gene in a girl with devel-opmental delay, alopecia, and dysmorphic fea-tures, Am J Med Genet A, 176, 12, pp. 2548-2553, (2018); Rajasekaran S, Bupp CP, Leimanis-Laurens M, Et al., Repurposing eflornithine to treat a patient with a rare ODC1 gain-of-function variant disease, eLife, 10, (2021); Savage L, Adams SD, James K, Et al., Rapid whole-genome sequencing identifies a homo-zygous novel variant, His540Arg, in HSD17B4 resulting in D-bifunctional protein deficiency disorder diagnosis, Cold Spring Harb Mol Case Stud, 6, 6, (2020); Dimmock D, Caylor S, Waldman B, Et al., Project Baby Bear: rapid precision care incorporating rWGS in 5 California chil-dren’s hospitals demonstrates improved clinical outcomes and reduced costs of care, Am J Hum Genet, 108, 7, pp. 1231-1238, (2021); Cecconi M, Evans L, Levy M, Rhodes A., Sepsis and septic shock, Lancet, 392, pp. 75-87, (2018); Jacobs L, Berrens Z, Stenson EK, Et al., The Pediatric Sepsis Biomarker Risk Model (PERSEVERE) biomarkers predict clinical deterioration and mortality in immunocom-promised children evaluated for infection, Sci Rep, 9, 1, (2019); Shanley TP, Wong HR., Molecular genetics in the pediatric intensive care unit, Crit Care Clin, 19, 3, pp. 577-594, (2003); Prokop JW, Shankar R, Gupta R, Et al., Virus-induced genetics revealed by multidimen-sional precision medicine transcriptional workflow applicable to COVID-19, Physiol Genomics, 52, 6, pp. 255-268, (2020); Leimanis-Laurens M, Gil D, Kampfschulte A, Et al., The feasibility of studying metabolites in PICU multi-organ dysfunction syndrome patients over an 8-day course using an untarget-ed approach, Children (Basel), 8, 2, (2021); Leimanis-Laurens M, Wolfrum E, Fergu-son K, Et al., Hexosylceramides and glycero-phosphatidylcholine GPC(36:1) increase in multi-organ dysfunction syndrome patients with pediatric intensive care unit admis-sion over 8-day hospitalization, J Pers Med, 11, 5, (2021); Shankar R, Leimanis ML, Newbury PA, Et al., Gene expression signatures identify paediatric patients with multiple organ dysfunction who require advanced life support in the intensive care unit, EBioMedicine, 62, (2020); Bauss J, Morris M, Shankar R, Et al., CCR5 and biological complexity: the need for data integration and educational materials to address genetic/biological reductionism at the interface of ethical, legal, and social impli-cations, Front Immunol, 12, (2021); Gupta R, Leimanis ML, Adams M, Et al., Balancing precision versus cohort transcrip-tomic analysis of acute and recovery phase of viral bronchiolitis, Am J Physiol Lung Cell Mol Physiol, 320, 6, pp. L1147-L1157, (2021); Prokop JW, Hartog NL, Chesla D, Et al., High-density blood transcriptomics re-veals precision immune signatures of SARS-CoV-2 infection in hospitalized in-dividuals, Front Immunol, 12, (2021)","C.P. Bupp; Michigan State University, Grand Rapids, 25 Michigan Street, NE, Suite 2000, 49503, United States; email: Caleb.Bupp@spectrumhealth.org","","Slack Incorporated","","","","","","00904481","","PDANB","36215089","English","Pediatr. Ann.","Article","Final","","Scopus","2-s2.0-85139517595"
"Levi M.; Lazebnik T.; Kushnir S.; Yosef N.; Shlomi D.","Levi, Matanel (59229010900); Lazebnik, Teddy (57220855077); Kushnir, Shiri (57200578576); Yosef, Noga (55305514900); Shlomi, Dekel (8556642700)","59229010900; 57220855077; 57200578576; 55305514900; 8556642700","Machine learning computational model to predict lung cancer using electronic medical records","2024","Cancer Epidemiology","92","","102631","","","","1","10.1016/j.canep.2024.102631","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199279776&doi=10.1016%2fj.canep.2024.102631&partnerID=40&md5=f889154da62a9e185132574fc812e52f","Adelson School of Medicine, Ariel University, Ariel, Israel; Department of Mathematics, Ariel University, Ariel, Israel; Department of Cancer Biology, Cancer Institute, University College London, London, United Kingdom; Research Authority, Rabin Medical Center, Beilinson Campus, Petah-Tiqwa, Israel; Research Unit, Dan, Petah-Tiqwa District, Clalit Health Services Community Division, Ramat-Gan, Israel; Pulmonary Clinic, Dan, Petah-Tiqwa District, Clalit Health Services Community Division, Ramat-Gan, Israel","Levi M., Adelson School of Medicine, Ariel University, Ariel, Israel; Lazebnik T., Department of Mathematics, Ariel University, Ariel, Israel, Department of Cancer Biology, Cancer Institute, University College London, London, United Kingdom; Kushnir S., Research Authority, Rabin Medical Center, Beilinson Campus, Petah-Tiqwa, Israel; Yosef N., Research Unit, Dan, Petah-Tiqwa District, Clalit Health Services Community Division, Ramat-Gan, Israel; Shlomi D., Adelson School of Medicine, Ariel University, Ariel, Israel, Pulmonary Clinic, Dan, Petah-Tiqwa District, Clalit Health Services Community Division, Ramat-Gan, Israel","Background: Lung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in medicine for identifying hidden associations that are personally unique. Materials and methods: Using the tree-based pipeline optimization tool (TPOT), we developed an ML-based model, which is an ensemble of the Random Forest and XGboost models, based on known risk factors for LC, as part of a larger trial for ML prediction using electronic medical records and chest CT. We used data from patients with LC vs. controls (1:2) of patients aged ≥ 35 years. We developed a model for all LC patients as well as for patients with and without a smoking background. We included age, gender, body mass index (BMI), smoking history, socioeconomic status (SES), history of chronic obstructive pulmonary disease (COPD)/emphysema/chronic bronchitis (CB), interstitial lung disease (ILD)/pulmonary fibrosis (PF), and family history of LC. Results: Of the 4076 patients, 1428 (35 %) were in the LC group and 2648 (65 %) were in the control group. For the entire study population, our model achieved an accuracy of 71.2 %, with a sensitivity of 69 % and a positive predictive value (PPV) of 74 %. Higher accuracy was achieved for the two subgroups. An accuracy of 74.8 % (sensitivity 72 %, PPV 76 %) and 73.0 % (sensitivity 76 %, PPV 72 %) was achieved for the smoking and never-smoking cohorts, respectively. For the entire population and smoker cohort, COPD/emphysema/CB were the most important contributors, followed by BMI and age, while in the never-smoking cohort, BMI, age and SES were the most important contributors. Conclusion: Known risk factors for LC could be used in ML models to modestly predict LC. Further studies are needed to confirm these results in new patients and to improve them. © 2024 Elsevier Ltd","Artificial intelligence; Lung cancer; Machine learning; Prediction; Smoking","Adult; Aged; Case-Control Studies; Early Detection of Cancer; Electronic Health Records; Female; Humans; Lung Neoplasms; Machine Learning; Male; Middle Aged; Risk Assessment; Risk Factors; Smoking; Tomography, X-Ray Computed; adult; Article; artificial intelligence; body mass; cancer risk; chronic bronchitis; clinical decision making; cohort analysis; computer model; controlled study; diagnostic accuracy; electronic medical record; family history; female; high risk population; human; interstitial lung disease; low risk population; lung cancer; lung emphysema; lung fibrosis; machine learning; major clinical study; male; medical history; obstructive lung disease; people by smoking status; population; predictive value; random forest; retrospective study; risk assessment; risk factor; sensitivity and specificity; smoking; social status; tree based pipeline optimization tool; aged; case control study; diagnosis; early cancer diagnosis; electronic health record; epidemiology; lung tumor; middle aged; procedures; x-ray computed tomography","","","","","Guardian General and Director of Inheritance Affairs, Ministry of Justice, Israel","This study is part of a research that was partially founded by a grant from The Guardian General and Director of Inheritance Affairs, Ministry of Justice, Israel.","Krist A.H., Davidson K.W., Mangione C.M., Et al., Screening for lung cancer: US preventive services task force recommendation statement, JAMA - J. Am. Med. Assoc., 325, 10, pp. 962-970, (2021); Redondo-Sanchez D., Petrova D., Rodriguez-Barranco M., Fernandez-Navarro P., Jimenez-Moleon J.J., Sanchez M.J., Socio-economic inequalities in lung cancer outcomes: an overview of systematic reviews, Cancers, 14, 2, (2022); Leung C.C., Lam T.H., Yew W.W., Chan W.M., Law W.S., Tam C.M., Lower lung cancer mortality in obesity, Int. J. Epidemiol., 40, 1, pp. 174-182, (2011); Aberle D.R., Adams A.M., Berg C.D., Et al., Reduced lung-cancer mortality with low-dose computed tomographic screening – the national lung screening trial research team, N. Engl. J. Med., 365, (2011); Gohagan J.K., Prorok P.C., Hayes R.B., Kramer B.S., The prostate, lung, colorectal and ovarian (PLCO) cancer screening trial of the national cancer institute: history, organization, and status, Control Clin. Trials, 21, 6, (2000); ten Haaf K., Jeon J., Tammemagi M.C., Et al., Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study, PLoS Med., 14, 4, (2017); Zhao W., Yang J., Sun Y., Et al., 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas, Cancer Res., 78, 24, (2018); Yoon H.I., Kwon O.R., Kang K.N., Et al., Diagnostic value of combining tumor and inflammatory markers in lung cancer, J. Cancer Prev., 21, 3, (2016); (2016); Parmentier L., Nicol O., Jourdan L., Kessaci M.E., TPOT-SH: a faster optimization algorithm to solve the AutoML problem on large datasets, (2019); Shmuel A., Glickman O., Lazebnik T., Symbolic regression as a feature engineering method for machine and deep learning regression tasks, Mach. Learn. Sci. Technol., 5, 2, (2024); Durham A.L., Adcock I.M., The relationship between COPD and lung cancer, Lung Cancer, 90, 2, pp. 121-127, (2015); Koshiol J., Rotunno M., Consonni D., Et al., Chronic obstructive pulmonary disease and altered risk of lung cancer in a population-based case-control study, PLoS One, 4, 10, (2009); Tubio-Perez R.A., Torres-Duran M., Perez-Rios M., Fernandez-Villar A., Ruano-Ravina A., Lung emphysema and lung cancer: what do we know about it, Ann. Transl. Med., 8, 21, (2020); Skillrud D.M., Offord K.P., Miller R.D., Higher risk of lung cancer in chronic obstructive pulmonary disease. A prospective, matched, controlled study, Ann. Intern. Med., 105, 4, pp. 503-507, (1986); Calabro E., Randi G., La Vecchia C., Et al., Lung function predicts lung cancer risk in smokers: a tool for targeting screening programmes, Eur. Respir. 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Dis., 15, 5, pp. 2806-2823, (2023); Dubin S., Griffin D., Lung cancer in non-smokers, Mo Med., 117, pp. 375-379, (2020); Sidorchuk A., Agardh E., Aremu O., Hallqvist J., Allebeck P., Moradi T., Socioeconomic differences in lung cancer incidence: a systematic review and meta-analysis, Cancer Causes Control, 20, pp. 459-471, (2009); Mihor A., Tomsic S., Zagar T., Lokar K., Zadnik V., Socioeconomic inequalities in cancer incidence in Europe: a comprehensive review of population-based epidemiological studies, Radiol. Oncol., 54, (2020); Gibiot Q., Monnet I., Levy P., Et al., Interstitial lung disease associated with lung cancer: a case–control study, J. Clin. Med., 9, 3, (2020); Naccache J.M., Gibiot Q., Monnet I., Et al., Lung cancer and interstitial lung disease: a literature review, J. Thorac. Dis., 10, pp. 3829-3844, (2018); Matakidou A., Eisen T., Houlston R.S., Systematic review of the relationship between family history and lung cancer risk, Br. J. Cancer, 93, 7, pp. 825-833, (2005); Cote M.L., Liu M., Bonassi S., Et al., Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium, Eur. J. Cancer, 48, 13, (2012)","D. Shlomi; 'Clalit Health Services', Pulmonary Clinic, Petah-Tiqwa, 119 Rothschild St., 4933355, Israel; email: dekels1@zahav.net.il","","Elsevier Ltd","","","","","","18777821","","","39053365","English","Cancer Epidemiol.","Article","Final","","Scopus","2-s2.0-85199279776"
"Zlotnikov I.D.; Kudryashova E.V.","Zlotnikov, Igor D. (57364413300); Kudryashova, Elena V. (35585459100)","57364413300; 35585459100","Polymeric Infrared and Fluorescent Probes to Assess Macrophage Diversity in Bronchoalveolar Lavage Fluid of Asthma and Other Pulmonary Disease Patients","2024","Polymers","16","23","3427","","","","1","10.3390/polym16233427","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212586192&doi=10.3390%2fpolym16233427&partnerID=40&md5=72024841ea7e7d26a3e450eedc30ae46","Faculty of Chemistry, Lomonosov Moscow State University, Leninskie Gory, 1/3, Moscow, 119991, Russian Federation","Zlotnikov I.D., Faculty of Chemistry, Lomonosov Moscow State University, Leninskie Gory, 1/3, Moscow, 119991, Russian Federation; Kudryashova E.V., Faculty of Chemistry, Lomonosov Moscow State University, Leninskie Gory, 1/3, Moscow, 119991, Russian Federation","Bronchial asthma remains a serious medical problem, as approximately 10% of patients fail to achieve adequate symptom control with available treatment options. Macrophages play a pivotal role in the pathophysiology of asthma, as well as in some other respiratory disorders. Typically, they are classified into two major classes, M1 and M2; however, recent findings have indicated that in fact there is a whole range of macrophage polarization and functional diversity beyond this bimodal division. The isolation of individual cell sub-populations and the identification of their role and diagnostic/therapeutic significance is still a challenge. Here, we have attempted to assess the differences between patient-derived macrophage populations from bronchoalveolar lavage fluid (BALF) samples in different pulmonary disease conditions, based on their capability to interact with a range of specific and relatively non-specific carbohydrate-based ligands (containing galactose (linear or cyclic form), mannose, trimannose, etc.). Obviously, the main target of these ligands was CD206; however, other minor receptors, able to bind carbohydrates, have also been reported for macrophages. Trimannose binds most specifically to CD206 macrophage receptors, while monomannose has intermediate affinity, and galactose has low affinity and may involve binding to other receptors. This clearly indicates the ligands were chosen based on their predicted binding strength and specificity for CD206, providing the rationale for the study. In some cases, the activated macrophage affinity to galactose base ligands was higher than that to mannose, indicating that complexes of CD206 or other carbohydrate-binding receptors may contribute substantially to macrophage functional features. In addition, variations in receptor clustering and distribution may substantially affect affinity to the same ligand. Interestingly, with a panel of 6–10 different carbohydrate-based ligands with FTIR or fluorescent marker, we were able not only to distinguish between healthy and disease states but also between closely related diseases such as purulent endobronchitis, obstructive bronchitis, pneumonia, and bronchial asthma. For further investigation, specific sub-populations of macrophages, seen as hallmarks to specific diseases, can be isolated and studied separately, likely giving new insights with diagnostic and therapeutic significance for hard-to-treat patients. The group of patients with resistant disease can also be identified with this approach as a fingerprint method to find a more targeted therapeutic strategy, improving their clinical outcomes. As expected, this will provide a large additional array of data for analysis, compared to the work going on in the world. The dataset used by other researchers mainly for known “antibody” ligands is semi-quantitative and insufficient for the purposes of typing as yet unknown and uncomplicated sub-populations. The analysis of the presented data in combination with personalized information from patients’ medical records will be carried out using both traditional methods and machine learning methods. © 2024 by the authors.","bronchoalveolar lavage fluid; CD206; diagnosis; IR marker; macrophage","Brain; Broaches; Disease control; Elastomers; Fourier transform infrared spectroscopy; Ionomers; Lung cancer; Near infrared spectroscopy; Pulmonary diseases; Silicones; Bronchial asthma; Bronchoalveolar lavage fluid; Cd206; Classifieds; Fluorescent probes; Infrared probes; IR marker; Pathophysiology; Respiratory disorders; Sub-populations; Macrophages","","","","","Russian Science Foundation, RSF, (24-25-00104); Russian Science Foundation, RSF","This research was funded by the Russian Science Foundation, grant number 24-25-00104.","Lin S.W., Jheng C.H., Wang C.L., Hsu C.W., Lu M.C., Koo M., Risk of Dental Malocclusion in Children with Upper Respiratory Tract Disorders: A Case-Control Study of a Nationwide, Population-Based Health Claim Database, Int. 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NanoBiomed Res, 1, (2021); Kunz L.I.Z., Lapperre T.S., Snoeck-Stroband J.B., Budulac S.E., Timens W., van Wijngaarden S., Schrumpf J.A., Rabe K.F., Postma D.S., Sterk P.J., Et al., Smoking Status and Anti-Inflammatory Macrophages in Bronchoalveolar Lavage and Induced Sputum in COPD, Respir. Res, 12, (2011); St-Laurent J., Turmel V., Boulet L.P., Bissonnette E., Alveolar Macrophage Subpopulations in Bronchoalveolar Lavage and Induced Sputum of Asthmatic and Control Subjects, J. Asthma, 46, pp. 1-8, (2009); Tokunaga Y., Imaoka H., Kaku Y., Kawayama T., Hoshino T., The Significance of CD163-Expressing Macrophages in Asthma, Ann. 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Inflamm, 2013, (2013); Zlotnikov I.D., Ezhov A.A., Vigovskiy M.A., Grigorieva O.A., Dyachkova U.D., Belogurova N.G., Kudryashova E.V., Application Prospects of FTIR Spectroscopy and CLSM to Monitor the Drugs Interaction with Bacteria Cells Localized in Macrophages for Diagnosis and Treatment Control of Respiratory Diseases, Diagnostics, 13, (2023); Di Benedetto P., Ruscitti P., Vadasz Z., Toubi E., Giacomelli R., Macrophages with Regulatory Functions, a Possible New Therapeutic Perspective in Autoimmune Diseases, Autoimmun. Rev, 18, (2019); Shabunina E.A., Kuznetsova L.V., Kalish S.V., Budanova O.P., Bаkhtina L.Y., Malyshev I.Y., The M3 Macrophage Phenotype Increases the Efficiency of Phagocytosis, the First Stage of Antigen Cross-Presentation, Nauchno-Prakt. 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Biol, 1, (2014); Zlotnikov I.D., Ezhov A.A., Petrov R.A., Vigovskiy M.A., Grigorieva O.A., Belogurova N.G., Kudryashova E.V., Mannosylated Polymeric Ligands for Targeted Delivery of Antibacterials and Their Adjuvants to Macrophages for the Enhancement of the Drug Efficiency, Pharmaceuticals, 15, (2022); Zlotnikov I.D., Vigovskiy M.A., Davydova M.P., Danilov M.R., Dyachkova U.D., Grigorieva O.A., Kudryashova E.V., Mannosylated Systems for Targeted Delivery of Antibacterial Drugs to Activated Macrophages, Int. J. Mol. Sci, 23, (2022); Rose A.S., Knox K.S., Bronchoalveolar Lavage as a Research Tool, Semin. Respir. Crit. Care Med, 28, pp. 561-573, (2007); Drent M., Jacobs J.A., Bronchoalveolar Lavage, Encycl. Respir. Med, pp. 275-284, (2006); Kasai T., Fukushima S., Exposure of Rats to Multi-Walled Carbon Nanotubes: Correlation of Inhalation Exposure to Lung Burden, Bronchoalveolar Lavage Fluid Findings, and Lung Morphology, Nanomaterials, 13, (2023); Kazachkov M.Y., Muhlebach M.S., Livasy C.A., Noah T.L., Lipid-Laden Macrophage Index and Inflammation in Bronchoalveolar Lavage Fluids in Children, Eur. Respir. J, 18, pp. 790-795, (2001); Ren J., Chen W., Zhong Z., Wang N., Chen X., Yang H., Li J., Tang P., Fan Y., Lin F., Et al., Bronchoalveolar Lavage Fluid from Chronic Obstructive Pulmonary Disease Patients Increases Neutrophil Chemotaxis Measured by a Microfluidic Platform, Micromachines, 14, (2023); Domagala-Kulawik J., The Relevance of Bronchoalveolar Lavage Fluid Analysis for Lung Cancer Patients, Expert Rev. Respir. Med, 14, pp. 329-337, (2020); Frye B.C., Schupp J.C., Rothe M.E., Kohler T.C., Prasse A., Zissel G., Vach W., Muller-Quernheim J., The Value of Bronchoalveolar Lavage for Discrimination between Healthy and Diseased Individuals, J. Intern. Med, 287, pp. 54-65, (2020); Gao C.A., Cuttica M.J., Malsin E.S., Argento A.C., Wunderink R.G., Smith S.B., Comparing Nasopharyngeal and BAL SARS-CoV-2 Assays in Respiratory Failure, Am. J. Respir. Crit. Care Med, 203, pp. 127-129, (2021); Adhikari S., Regmi R.S., Pandey S., Paudel P., Neupane N., Chalise S., Dubey A., Kafle S.C., Rijal K.R., Bacterial Etiology of Bronchoalveolar Lavage Fluid in Tertiary Care Patients and Antibiogram of the Isolates, J. Inst. Sci. Technol, 26, pp. 99-106, (2021); Martin-Loeches I., Chastre J., Wunderink R.G., Bronchoscopy for Diagnosis of Ventilator-Associated Pneumonia, Intensiv. Care Med, 49, pp. 79-82, (2023); Davidson K.R., Ha D.M., Schwarz M.I., Chan E.D., Bronchoalveolar Lavage as a Diagnostic Procedure: A Review of Known Cellular and Molecular Findings in Various Lung Diseases, J. Thorac. Dis, 12, pp. 4991-5019, (2020); d'Alessandro M., Carleo A., Cameli P., Bergantini L., Perrone A., Vietri L., Lanzarone N., Vagaggini C., Sestini P., Bargagli E., BAL Biomarkers’ Panel for Differential Diagnosis of Interstitial Lung Diseases, Clin. Exp. Med, 20, pp. 207-216, (2020); Qamar W., Ahamad S.R., Ali R., Khan M.R., Al-Ghadeer A.R., Metabolomic Analysis of Lung Epithelial Secretions in Rats: An Investigation of Bronchoalveolar Lavage Fluid by GC-MS and FT-IR, Exp. Lung Res, 40, pp. 460-466, (2014); Bazzano M., Laghi L., Zhu C., Magi G.E., Tesei B., Laus F., Respiratory Metabolites in Bronchoalveolar Lavage Fluid (BALF) and Exhaled Breath Condensate (EBC) Can Differentiate Horses Affected by Severe Equine Asthma from Healthy Horses, BMC Vet. Res, 16, (2020); Rai R.K., Azim A., Sinha N., Sahoo J.N., Singh C., Ahmed A., Saigal S., Baronia A.K., Gupta D., Gurjar M., Et al., Metabolic Profiling in Human Lung Injuries by High-Resolution Nuclear Magnetic Resonance Spectroscopy of Bronchoalveolar Lavage Fluid (BALF), Metabolomics, 9, pp. 667-676, (2013); Kang Y.P., Lee W.J., Hong J.Y., Lee S.B., Park J.H., Kim D., Park S., Park C.S., Park S.W., Kwon S.W., Novel Approach for Analysis of Bronchoalveolar Lavage Fluid (BALF) Using HPLC-QTOF-MS-Based Lipidomics: Lipid Levels in Asthmatics and Corticosteroid-Treated Asthmatic Patients, J. Proteome Res, 13, pp. 3919-3929, (2014); Simpson J.L., Gibson P.G., Yang I.A., Upham J., James A., Reynolds P.N., Hodge S., Impaired Macrophage Phagocytosis in Non-eosinophilic Asthma, Clin. Exp. 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Physiol, 308, pp. L358-L367, (2015); Kwiecien I., Rutkowska E., Raniszewska A., Rzeszotarska A., Polubiec-Kownacka M., Domagala-Kulawik J., Korsak J., Rzepecki P., Flow Cytometric Analysis of Macrophages and Cytokines Profile in the Bronchoalveolar Lavage Fluid in Patients with Lung Cancer, Cancers, 15, (2023); Bratke K., Weise M., Stoll P., Virchow J.C., Lommatzsch M., Flow Cytometry as an Alternative to Microscopy for the Differentiation of BAL Fluid Leukocytes, Chest, 166, pp. 793-801, (2024); Zlotnikov I.D., Kudryashova E.V., Biomimetic System Based on Reconstituted Macrophage Membranes for Analyzing and Selection of Higher-Affinity Ligands Specific to Mannose Receptor to Develop the Macrophage-Focused Medicines, Biomedicines, 11, (2023); Chistiakov D.A., Killingsworth M.C., Myasoedova V.A., Orekhov A.N., Bobryshev Y.V., CD68/Macrosialin: Not Just a Histochemical Marker, Lab. Investig, 97, pp. 4-13, (2017); Chung Y., Hong J.Y., Lei J., Chen Q., Bentley J.K., Hershenson M.B., Rhinovirus Infection Induces Interleukin-13 Production from CD11b-Positive, M2-Polarized Exudative Macrophages, Am. J. Respir. Cell Mol. Biol, 52, pp. 205-216, (2015); Staples K.J., Hinks T.S.C., Ward J.A., Gunn V., Smith C., Djukanovic R., Phenotypic Characterization of Lung Macrophages in Asthmatic Patients: Overexpression of CCL17, J. Allergy Clin. Immunol, 130, pp. 1404-1412.e7, (2012); Churina E.G., Sitnikova A.V., Urazova O.I., Chumakova S.P., Vins M.V., Beresneva A.E., Novitskii V.V., Macrophages in Bacterial Lung Diseases: Phenotype and Functions, Bull. Sib. Med, 18, pp. 142-154, (2019); Jiang H.L., Kim Y.K., Arote R., Jere D., Quan J.S., Yu J.H., Choi Y.J., Nah J.W., Cho M.H., Cho C.S., Mannosylated Chitosan-Graft-Polyethylenimine as a Gene Carrier for Raw 264.7 Cell Targeting, Int. J. Pharm, 375, pp. 133-139, (2009); Zlotnikov I.D., Kudryashova E.V., Spectroscopy Approach for Highly-Efficient Screening of Lectin-Ligand Interactions in Application for Mannose Receptor and Molecular Containers for Antibacterial Drugs, Pharmaceuticals, 15, (2022)","E.V. Kudryashova; Faculty of Chemistry, Lomonosov Moscow State University, Moscow, Leninskie Gory, 1/3, 119991, Russian Federation; email: helenakoudriachova@yandex.ru","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20734360","","","","English","Polym.","Article","Final","","Scopus","2-s2.0-85212586192"
"Deane K.D.; Donlin L.T.; Ritchlin C.T.; Kuhn K.A.","Deane, Kevin D. (26660157100); Donlin, Laura T. (6508199129); Ritchlin, Christopher T. (57204259646); Kuhn, Kristine A. (12799954200)","26660157100; 6508199129; 57204259646; 12799954200","Are There Disease Endotypes in Axial Spondyloarthritis and How Would We Define Them?","2024","Journal of Rheumatology","51","12","","1229","1234","5","1","10.3899/jrheum.2024-0935","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210937700&doi=10.3899%2fjrheum.2024-0935&partnerID=40&md5=276499d8afbd70faeacc4cb3ec454134","Division of Rheumatology, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Hospital for Special Surgery and Weill Cornell Medicine, New York, NY, United States; Division of Allergy, Immunology & Rheumatology, University of Rochester Medical School, Rochester, NY, United States","Deane K.D., Division of Rheumatology, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States; Donlin L.T., Hospital for Special Surgery and Weill Cornell Medicine, New York, NY, United States; Ritchlin C.T., Division of Allergy, Immunology & Rheumatology, University of Rochester Medical School, Rochester, NY, United States; Kuhn K.A., Division of Rheumatology, University of Colorado, Anschutz Medical Campus, Aurora, CO, United States","Is axial spondyloarthritis (axSpA) one disease or does it comprise multiple types? If the latter, how do we define those types—through clinical or imaging features, HLA-B27 status, or by other immunologic features? Data comparing disease outcomes for individuals with nonradiographic vs radiographic axSpA, or for male vs female patients, demonstrate distinctions. So then, how should we define endotypes? Endotypes are known as the subtype of a health condition defined by a functional or pathophysiologic function. Here, we review the endotypes used for defining rheumatoid arthritis, asthma, and psoriatic arthritis. Taking the lessons learned from these diseases, we discuss how they can be applied to defining endotypes in axSpA. A key unmet need for axSpA is access to affected tissues for interrogation of their pathologic mechanisms, from which tissue-specific endotypes can be defined. These tissue-based features should be combined with clinical data and imaging to inform classification criteria in the future. © 2024 The Journal of Rheumatology.","ankylosing spondylitis; endotypes; psoriatic arthritis; rheumatoid arthritis; spondyloarthritis","Arthritis, Psoriatic; Arthritis, Rheumatoid; Asthma; Axial Spondyloarthritis; Female; HLA-B27 Antigen; Humans; Male; Spondylarthritis; abatacept; autoantibody; corticosteroid; epitope; gamma interferon; HLA B27 antigen; hydroxychloroquine; interleukin 17; interleukin 4; methotrexate; rheumatoid factor; rituximab; STAT1 protein; tumor necrosis factor; HLA B27 antigen; ankylosing spondylitis; anxiety; Article; artificial intelligence; asthma; axial spondyloarthritis; CD8+ T lymphocyte; clinical feature; clinical practice; depression; disease activity; disease control; disease duration; emotional stress; epigenetics; exercise; fatigue; female; human; male; metabolomics; non insulin dependent diabetes mellitus; pathophysiology; psoriasis; psoriatic arthritis; quality of life; rheumatoid arthritis; risk factor; skin biopsy; smoking; transcriptomics; uveitis; classification; diagnosis; diagnostic imaging; immunology; spondylarthritis","","abatacept, 332348-12-6; gamma interferon, 82115-62-6; hydroxychloroquine, 118-42-3, 525-31-5, 137433-23-9, 137433-24-0; methotrexate, 15475-56-6, 59-05-2, 7413-34-5, 7532-09-4, 6745-93-3, 51865-79-3, 60388-53-6; rheumatoid factor, 9009-79-4; rituximab, 174722-31-7; HLA-B27 Antigen, ","","","","","Tarn JR, Lendrem DW, Isaacs JD., In search of pathobiological endotypes: a systems approach to early rheumatoid arthritis, Expert Rev Clin Immunol, 16, pp. 621-630, (2020); Aletaha D, Neogi T, Silman AJ, Et al., 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/ European League Against Rheumatism collaborative initiative, Arthritis Rheum, 62, pp. 2569-2581, (2010); Ronnelid J, Turesson C, Kastbom A., Autoantibodies in rheumatoid arthritis - laboratory and clinical perspectives, Front Immunol, 12, (2021); Gardette A, Ottaviani S, Tubach F, Et al., High anti-CCP antibody titres predict good response to rituximab in patients with active rheumatoid arthritis, Joint Bone Spine, 81, pp. 416-420, (2014); Sellam J, Hendel-Chavez H, Rouanet S, Et al., B cell activation biomarkers as predictive factors for the response to rituximab in rheumatoid arthritis: a six-month, national, multicenter, open-label study, Arthritis Rheum, 63, pp. 933-938, (2011); Oryoji K, Yoshida K, Kashiwado Y, Et al., Shared epitope positivity is related to efficacy of abatacept in rheumatoid arthritis, Ann Rheum Dis, 77, pp. 1234-1236, (2018); Moroni L, Farina N, Dagna L., Obesity and its role in the management of rheumatoid and psoriatic arthritis, Clin Rheumatol, 39, pp. 1039-1047, (2020); Vittecoq O, Richard L, Banse C, Lequerre T., The impact of smoking on rheumatoid arthritis outcomes, Joint Bone Spine, 85, pp. 135-138, (2018); Deane KD, Holers VM., Rheumatoid arthritis pathogenesis, prediction, and prevention: an emerging paradigm shift, Arthritis Rheumatol, 73, pp. 181-193, (2021); Bos WH, Dijkmans BA, Boers M, van de Stadt RJ, van Schaardenburg D., Effect of dexamethasone on autoantibody levels and arthritis development in patients with arthralgia: a randomised trial, Ann Rheum Dis, 69, pp. 571-574, (2010); Krijbolder DI, Verstappen M, van Dijk BT, Et al., Intervention with methotrexate in patients with arthralgia at risk of rheumatoid arthritis to reduce the development of persistent arthritis and its disease burden (TREAT EARLIER): a randomised, double-blind, placebo-controlled, proof-of-concept trial, Lancet, 400, pp. 283-294, (2022); Deane K, Striebich C, Feser M, Et al., Hydroxychloroquine does not prevent the future development of rheumatoid arthritis in a population with baseline high levels of antibodies to citrullinated protein antigens and absence of inflammatory arthritis: interim analysis of the StopRA trial [abstract], Arthritis Rheumatol, 74, (2022); Rech J, Kleyer A, Ostergaard M, Et al., Abatacept significantly reduces subclinical inflammation during treatment (6 months), this persists after discontinuation (12 months), resulting in a delay in the clinical development of RA in patients at risk of RA (the ARIAA study) [abstract], Arthritis Rheumatol, 74, (2022); Cope A, Jasenecova M, Vasconcelos J, Et al., OP0130 Abatacept in individuals at risk of developing rheumatoid arthritis: results from the arthritis prevention in the preclinical phase of RA with abatacept (APIPPRA) trial [abstract], Ann Rheum Diseases, 82, (2023); Luedders BA, Johnson TM, Sayles H, Et al., Predictive ability, validity, and responsiveness of the multi-biomarker disease activity score in patients with rheumatoid arthritis initiating methotrexate, Semin Arthritis Rheum, 50, pp. 1058-1063, (2020); Fraenkel L, Bathon JM, England BR, Et al., 2021 American College of Rheumatology guideline for the treatment of rheumatoid arthritis, Arthritis Rheumatol, 73, pp. 1108-1123, (2021); Ajeganova S, Huizinga T., Sustained remission in rheumatoid arthritis: latest evidence and clinical considerations, Ther Adv Musculoskelet Dis, 9, pp. 249-262, (2017); Baker KF, Skelton AJ, Lendrem DW, Et al., Predicting drug-free remission in rheumatoid arthritis: a prospective interventional cohort study, J Autoimmun, 105, (2019); Burgers LE, van der Pol JA, Huizinga TWJ, Allaart CF, van der Helm-van Mil AHM., Does treatment strategy influence the ability to achieve and sustain DMARD-free remission in patients with RA? Results of an observational study comparing an intensified DAS-steered treatment strategy with treat to target in routine care, Arthritis Res Ther, 21, (2019); Bykerk VP, Burmester GR, Combe BG, Et al., On-drug and drug-free remission by baseline symptom duration: abatacept with methotrexate in patients with early rheumatoid arthritis, Rheumatol Int, 38, pp. 2225-2231, (2018); Al-Laith M, Jasenecova M, Abraham S, Et al., Arthritis prevention in the pre-clinical phase of RA with abatacept (the APIPPRA study): a multi-centre, randomised, double-blind, parallel-group, placebo-controlled clinical trial protocol, Trials, 20, (2019); Hosack T, Thomas T, Ravindran R, Uhlig HH, Travis SPL, Buckley CD., Inflammation across tissues: can shared cell biology help design smarter trials?, Nat Rev Rheumatol, 19, pp. 666-674, (2023); Donlin LT, Park SH, Giannopoulou E, Et al., Insights into rheumatic diseases from next-generation sequencing, Nat Rev Rheumatol, 15, pp. 327-339, (2019); Buckley CD, Ospelt C, Gay S, Midwood KS., Location, location, location: how the tissue microenvironment affects inflammation in RA, Nat Rev Rheumatol, 17, pp. 195-212, (2021); Zhang F, Wei K, Slowikowski K, Et al., Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry, Nat Immunol, 20, pp. 928-942, (2019); Rivellese F, Surace AEA, Goldmann K, Et al., Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial, Nat Med, 28, pp. 1256-1268, (2022); Ritchlin CT, Colbert RA, Gladman DD., Psoriatic arthritis, N Engl J Med, 376, pp. 2095-2096, (2017); Ogdie A, Schwartzman S, Husni ME., Recognizing and managing comorbidities in psoriatic arthritis, Curr Opin Rheumatol, 27, pp. 118-126, (2015); Petersen MB, Hansen RL, Egeberg A, Et al., The impact of comorbidities on interleukin-17 inhibitor therapy in psoriatic arthritis: a Danish population-based cohort study, Rheumatol Adv Pract, 7, (2023); Pina Vegas L, Hoisnard L, Bastard L, Sbidian E, Claudepierre P., Long-term persistence of second-line biologics in psoriatic arthritis patients with prior TNF inhibitor exposure: a nationwide cohort study from the French health insurance database (SNDS), RMD Open, 8, (2022); Lotvall J, Akdis CA, Bacharier LB, Et al., Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome, J Allergy Clin Immunol, 127, pp. 355-360, (2011); Agusti A, Barnes N, Cruz AA, Et al., Moving towards a Treatable Traits model of care for the management of obstructive airways diseases, Respir Med, 187, (2021); Bhatt SP, Rabe KF, Hanania NA, Et al., Dupilumab for COPD with Type 2 inflammation indicated by eosinophil counts, N Engl J Med, 389, pp. 205-214, (2023); Battaglia M, Ahmed S, Anderson MS, Et al., Introducing the endotype concept to address the challenge of disease heterogeneity in type 1 diabetes, Diabetes Care, 43, pp. 5-12, (2020); FitzGerald O, Behrens F, Barton A, Et al., Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis, Ther Adv Musculoskelet Dis, 15, (2023); Mease PJ, O'Brien J, Middaugh N, Et al., Real-world evidence assessing psoriatic arthritis by disease domain: an evaluation of the CorEvitas psoriatic arthritis/spondyloarthritis registry, ACR Open Rheumatol, 5, pp. 388-398, (2023); Menon B, Gullick NJ, Walter GJ, Et al., Interleukin-17+CD8+ T cells are enriched in the joints of patients with psoriatic arthritis and correlate with disease activity and joint damage progression, Arthritis Rheumatol, 66, pp. 1272-1281, (2014); Penkava F, Velasco-Herrera MDC, Young MD, Et al., Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis, Nat Commun, 11, (2020); Winchester R, Minevich G, Steshenko V, Et al., HLA associations reveal genetic heterogeneity in psoriatic arthritis and in the psoriasis phenotype, Arthritis Rheum, 64, pp. 1134-1144, (2012); Najm A, Goodyear CS, McInnes IB, Siebert S., Phenotypic heterogeneity in psoriatic arthritis: towards tissue pathology-based therapy, Nat Rev Rheumatol, 19, pp. 153-165, (2023); Yang X, Garner LI, Zvyagin IV, Et al., Autoimmunity-associated T cell receptors recognize HLA-B*27-bound peptides, Nature, 612, pp. 771-777, (2022)","K.A. Kuhn; Division of Rheumatology, University of Colorado, Aurora, Anschutz Medical Campus, 1175 Aurora Ct., Mail Stop B115, 80045, United States; email: kristine.kuhn@cuanschutz.edu","","Journal of Rheumatology","","","","","","0315162X","","JRHUA","39448247","English","J. Rheumatol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85210937700"
"Yan K.; Liang Y.","Yan, Kemin (57579194500); Liang, Yuxia (57233271500)","57579194500; 57233271500","Decreased TLR7 expression was associated with airway eosinophilic inflammation and lung function in asthma: evidence from machine learning approaches and experimental validation","2024","European Journal of Medical Research","29","1","116","","","","1","10.1186/s40001-023-01622-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184794628&doi=10.1186%2fs40001-023-01622-5&partnerID=40&md5=f3a33ceaab98cf5d5ff674d85138189f","Department of Geriatrics, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong, Guangzhou, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong, Guangzhou, China","Yan K., Department of Geriatrics, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong, Guangzhou, China; Liang Y., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangdong, Guangzhou, China","Background: Asthma is a global public health concern. The underlying pathogenetic mechanisms of asthma were poorly understood. This study aims to explore potential biomarkers associated with asthma and analyze the pathological role of immune cell infiltration in the disease. Methods: The gene expression profiles of induced sputum were obtained from Gene Expression Omnibus datasets (GSE76262 and GSE137268) and were combined for analysis. Toll-like receptor 7 (TLR7) was identified as the core gene by the intersection of two different machine learning algorithms, namely, least absolute shrinkage and selector operation (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE), and the top 10 core networks based on Cytohubba. CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between asthma and healthy control groups. Finally, the expression level of TLR7 was validated in induced sputum samples of patients with asthma. Results: A total of 320 differential expression genes between the asthma and healthy control groups were screened, including 184 upregulated genes and 136 downregulated genes. TLR7 was identified as the core gene after combining the results of LASSO regression, SVM-RFE algorithm, and top 10 hub genes. Significant differences were observed in the distribution of 13 out of 22 infiltrating immune cells in asthma. TLR7 was found to be closely related to the level of several infiltrating immune cells. TLR7 mRNA levels were downregulated in asthmatic patients compared with healthy controls (p = 0.0049). The area under the curve of TLR7 for the diagnosis of asthma was 0.7674 (95% CI 0.631–0.904, p = 0.006). Moreover, TLR7 mRNA levels were negatively correlated with exhaled nitric oxide fraction (r = − 0.3268, p = 0.0347) and the percentage of peripheral blood eosinophils (%) (r = − 0.3472, p = 0.041), and positively correlated with forced expiratory volume in the first second (FEV1) (% predicted) (r = 0.3960, p = 0.0071) and FEV1/forced vital capacity (r = 0.3213, p = 0.0314) in asthmatic patients. Conclusions: Decreased TLR7 in the induced sputum of eosinophilic asthmatic patients was involved in immune cell infiltration and airway inflammation, which may serve as a new biomarker for the diagnosis of eosinophilic asthma. © The Author(s) 2024.","Asthma; Immune cell infiltration; Induced sputum; Machine learning; TLR7","messenger RNA; toll like receptor 7; adult; Article; cell infiltration; cellular distribution; clinical article; cohort analysis; comparative study; controlled study; core gene; differential gene expression; down regulation; eosinophil count; eosinophilic asthma; female; forced expiratory volume; forced vital capacity; fractional exhaled nitric oxide; gene expression; gene expression profiling; human; human cell; immunocompetent cell; lung function; male; pathology; prediction; pulmonary eosinophilia; recursive feature elimination; respiratory tract inflammation; sputum analysis; support vector machine; TLR7 gene; upregulation","","","","","","","Beasley R., Braithwaite I., Semprini A., Kearns C., Weatherall M., Pavord I.D., Optimal asthma control: time for a new target, Am J Respir Crit Care Med, 201, 12, pp. 1480-1487, (2020); 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Liang; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China; email: liangyx69@mail.sysu.edu.cn","","BioMed Central Ltd","","","","","","09492321","","","38341589","English","Eur. J. Med. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85184794628"
"Hurtado S.; Antequera-Gómez M.L.; Barba-González C.; Picornell A.; Navas-Delgado I.","Hurtado, Sandro (57322692100); Antequera-Gómez, María Luisa (56473625600); Barba-González, Cristóbal (57192963206); Picornell, Antonio (57201097099); Navas-Delgado, Ismael (6508304108)","57322692100; 56473625600; 57192963206; 57201097099; 6508304108","e-Science workflow: A semantic approach for airborne pollen prediction","2024","Knowledge-Based Systems","284","","111230","","","","1","10.1016/j.knosys.2023.111230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178378997&doi=10.1016%2fj.knosys.2023.111230&partnerID=40&md5=4fbbd246584d93541342dd77125975b6","Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Dept. de Botánica y Fisiología Vegetal, Universidad de Málaga, Málaga, 29071, Spain","Hurtado S., Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Antequera-Gómez M.L., Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Barba-González C., Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Picornell A., Dept. de Botánica y Fisiología Vegetal, Universidad de Málaga, Málaga, 29071, Spain; Navas-Delgado I., Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain","Allergic rhinitis has become a global health problem in recent decades because airborne pollen is a primary trigger of this respiratory disorder. Moreover, pollinosis can exacerbate the symptoms of asthma and favour respiratory infections. Seasonal pollen trends and climatic circumstances (such as temperature, precipitation, relative humidity, wind speed and direction, and other variables) can impact daily airborne pollen concentrations, influencing local pollen emission and dispersion. Because of that, pollen monitoring and prediction are becoming more relevant to the urban population and scientific interest is put into them. Due to such tasks’ high volume of data, scientists are starting to use computational tools like workflows to automate and speed up the process. Furthermore, using the expert scientific domain is critical for improving the analysis, allowing, among others, a better workflow configuration and data provenance. As semantic web technologies have been revealed as an essential means for knowledge representation, we implemented this workflow information as an ontology using formats like RDF(S) and OWL. Consequently, this paper provides a semantic-enhanced e-Science workflow based on the TITAN framework for pollen forecasting analysis using meteorological data. Furthermore, a catalogue of components is developed on the TITAN framework, which allows the creation of different workflow versions. Two case studies of pollen prediction were developed to test the implementation of the aforementioned methodologies. Both were elaborated with airborne pollen data obtained in the city of Málaga (Spain). Still, one was elaborated for Platanus pollen type (narrow annual main pollination period), while the other was done for Amaranthaceae pollen type (extensive annual main pollination period). The predictions have been conducted using machine and deep learning algorithms like SARIMA or CNN-LSTM that intend to optimise the pollen prediction procedure depending on its stational and seasonal profile. © 2023 Elsevier B.V.","Big data analytics; e-Science; Pollen prediction; Semantics","Big data; Data Analytics; e-Science; Knowledge representation; Long short-term memory; Meteorology; Resource Description Framework (RDF); Wind; Airborne pollens; Allergic rhinitis; Big data analytic; Data analytics; E-science workflows; E-sciences; Global health; Pollen prediction; Semantic approach; Work-flows; Forecasting","","","","","CBUA; Spanish Ministry of Science and Innovation, Spain, (LIFEWATCH-2019-11-UMA-01, MCIN/AEI/10.13039/501100011033, PID2020-112540RB-C41); Universidad de Málaga, UMA; Consejería de Transformación Económica, Industria, Conocimiento y Universidades; Federación Española de Enfermedades Raras, FEDER; Junta de Andalucía, (POSTDOC_21_00056)","This work has been partially funded by the Spanish Ministry of Science and Innovation, Spain via grant (funded by MCIN/AEI/10.13039/501100011033/ ) PID2020-112540RB-C41 , AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation), and grant ‘ ‘Environmental and Biodiversity Climate Change Lab (EnBiC2-Lab)” LIFEWATCH-2019-11-UMA-01 (AEI/FEDER, UE) . A. Picornell has been supported by a postdoctoral grant financed by the Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Spain (Junta de Andalucía, POSTDOC_21_00056 ) and funding for open access charge: Universidad de Málaga / CBUA .","Hey A.J., Trefethen A.E., The Data Deluge: An E-Science Perspective, (2003); Gil Y., Deelman E., Ellisman M., Fahringer T., Fox G., Gannon D., Goble C., Livny M., Moreau L., Myers J., Examining the challenges of scientific workflows, Computer, 40, 12, pp. 24-32, (2007); Simmhan Y.L., Plale B., Gannon D., A survey of data provenance in e-science, ACM Sigmod Rec., 34, 3, pp. 31-36, (2005); Lebo T., Sahoo S., McGuinness D., Belhajjame K., Cheney J., Corsar D., Garijo D., Soiland-Reyes S., Zednik S., Zhao J., Prov-O: The Prov Ontology, (2013); Achilleos K.G., Kannas C.C., Nicolaou C.A., Pattichis C.S., Promponas V.J., Open source workflow systems in life sciences informatics, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering, BIBE, pp. 552-558, (2012); Taylor I.J., Deelman E., Gannon D.B., Shields M., Et al., Workflows for E-Science: Scientific Workflows for Grids. 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Barba-González; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; email: cbarba@uma.es","","Elsevier B.V.","","","","","","09507051","","KNSYE","","English","Knowl Based Syst","Article","Final","","Scopus","2-s2.0-85178378997"
"Idrisoglu A.; Dallora A.L.; Cheddad A.; Anderberg P.; Jakobsson A.; Sanmartin Berglund J.","Idrisoglu, Alper (58117815800); Dallora, Ana Luiza (57192586084); Cheddad, Abbas (24328557700); Anderberg, Peter (16300508700); Jakobsson, Andreas (7007184651); Sanmartin Berglund, Johan (55196737700)","58117815800; 57192586084; 24328557700; 16300508700; 7007184651; 55196737700","COPDVD: Automated classification of chronic obstructive pulmonary disease on a new collected and evaluated voice dataset","2024","Artificial Intelligence in Medicine","156","","102953","","","","1","10.1016/j.artmed.2024.102953","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202537741&doi=10.1016%2fj.artmed.2024.102953&partnerID=40&md5=39081d5f5bec5714cb750658c6edd8ac","Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden; Lund University, Box 117, Lund, SE-221 00, Sweden","Idrisoglu A., Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden; Dallora A.L., Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden; Cheddad A., Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden; Anderberg P., Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden; Jakobsson A., Lund University, Box 117, Lund, SE-221 00, Sweden; Sanmartin Berglund J., Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 371 41, Sweden","Background: Chronic obstructive pulmonary disease (COPD) is a severe condition affecting millions worldwide, leading to numerous annual deaths. The absence of significant symptoms in its early stages promotes high underdiagnosis rates for the affected people. Besides pulmonary function failure, another harmful problem of COPD is the systemic effects, e.g., heart failure or voice distortion. However, the systemic effects of COPD might provide valuable information for early detection. In other words, symptoms caused by systemic effects could be helpful to detect the condition in its early stages. Objective: The proposed study aims to explore whether the voice features extracted from the vowel “a” utterance carry any information that can be predictive of COPD by employing Machine Learning (ML) on a newly collected voice dataset. Methods: Forty-eight participants were recruited from the pool of research clinic visitors at Blekinge Institute of Technology (BTH) in Sweden between January 2022 and May 2023. A dataset consisting of 1246 recordings from 48 participants was gathered. The collection of voice recordings containing the vowel “a” utterance commenced following an information and consent meeting with each participant using the VoiceDiagnostic application. The collected voice data was subjected to silence segment removal, feature extraction of baseline acoustic features, and Mel Frequency Cepstrum Coefficients (MFCC). Sociodemographic data was also collected from the participants. Three ML models were investigated for the binary classification of COPD and healthy controls: Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB). A nested k-fold cross-validation approach was employed. Additionally, the hyperparameters were optimized using grid-search on each ML model. For best performance assessment, accuracy, F1-score, precision, and recall metrics were computed. Afterward, we further examined the best classifier by utilizing the Area Under the Curve (AUC), Average Precision (AP), and SHapley Additive exPlanations (SHAP) feature-importance measures. Results: The classifiers RF, SVM, and CB achieved a maximum accuracy of 77 %, 69 %, and 78 % on the test set and 93 %, 78 % and 97 % on the validation set, respectively. The CB classifier outperformed RF and SVM. After further investigation of the best-performing classifier, CB demonstrated the highest performance, producing an AUC of 82 % and AP of 76 %. In addition to age and gender, the mean values of baseline acoustic and MFCC features demonstrate high importance and deterministic characteristics for classification performance in both test and validation sets, though in varied order. Conclusion: This study concludes that the utterance of vowel “a” recordings contain information that can be captured by the CatBoost classifier with high accuracy for the classification of COPD. Additionally, baseline acoustic and MFCC features, in conjunction with age and gender information, can be employed for classification purposes and benefit healthcare for decision support in COPD diagnosis. Clinical trial registration number: NCT05897944. © 2024 The Authors","Acoustic features; Automated classification; Chronic obstructive pulmonary disease; Machine Learning; Mel Frequency Cepstrum Coefficients","Aged; Female; Humans; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Support Vector Machine; Voice; Diagnosis; Image segmentation; Lung cancer; Pulmonary diseases; Acoustic features; Areas under the curves; Automated classification; Chronic obstructive pulmonary disease; Condition; Machine learning models; Machine-learning; Mel frequency cepstrum coefficients; Random forests; Support vectors machine; acoustics; aged; Article; automation; binary classification; CatBoost; chronic obstructive lung disease; classification algorithm; classifier; clinical article; controlled study; diagnostic test accuracy study; feature extraction; female; human; k fold cross validation; machine learning; male; predictive model; random forest; Shapley additive explanation; support vector machine; Sweden; voice analysis; vowel; chronic obstructive lung disease; classification; diagnosis; middle aged; pathophysiology; physiology; voice; Support vector machines","","","","","Excellence Center at Linköping – Lund in Information Technology, ELLIIT","The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alper Idrisoglu reports financial support provided by the Excellence Center at Link\u00F6ping and Lund in Information Technology (ELLIIT). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","Salvi S.S., Barnes P.J., Chronic obstructive pulmonary disease in non-smokers, Lancet, 374, pp. 733-743, (2009); Varmaghani M., Dehghani M., Heidari E., Sharifi F., Saeedi Moghaddam S., Farzadfar F., Global prevalence of chronic obstructive pulmonary disease: systematic review and meta-analysis, East Mediterr Health J, 25, pp. 47-57, (2019); Boers E., Barrett M., Su J.G., Benjafield A.V., Sinha S., Kaye L., Et al., Global burden of chronic obstructive pulmonary disease through 2050, JAMA Netw Open, 6, (2023); Chronic obstructive pulmonary disease (COPD); Toren K., Olin A.-C., Lindberg A., Vikgren J., Brandberg J., Johnsson A., Et al., Vital capacity and COPD: the Swedish CArdioPulmonary bioImage Study (SCAPIS), Int J Chron Obstruct Pulmon Dis, (2016); Global initiative for chronic obstructive lung disease. 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A voice analysis system for the screening of laryngeal diseases, IEEE Eng Med Biol Mag, 16, pp. 74-82, (1997); Wujek B., Hall P., Gunes F., Best practices for machine learning applications, SAS Inst Inc, pp. 1-23, (2016); Alanazi A., Using machine learning for healthcare challenges and opportunities, Inform Med Unlocked, 30, (2022)","A. Idrisoglu; Blekinge Institute of Technology, Karlskrona, Valhallavägen 1, 371 41, Sweden; email: alper.idrisoglu@bth.se","","Elsevier B.V.","","","","","","09333657","","AIMEE","39222579","English","Artif. Intell. Med.","Article","Final","","Scopus","2-s2.0-85202537741"
"Indira V.; Priyadarshini D.A.; Geetha R.; Sujatha V.","Indira, V. (36174034100); Priyadarshini, D. Annal (58826583200); Geetha, R. (56496695000); Sujatha, V. (57196005224)","36174034100; 58826583200; 56496695000; 57196005224","An effective review on the prediction and analysis of infectious lung diseases using machine learning algorithms","2024","International Journal of Bioinformatics Research and Applications","20","3","","205","228","23","1","10.1504/IJBRA.2024.139999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199175816&doi=10.1504%2fIJBRA.2024.139999&partnerID=40&md5=aaab9f7174ca740cd075ac4e1af8a387","Department of Computer Science and Engineering, Alpha College of Engineering, Chennai, 600124, India; Department of Computer Science and Engineering, S.A. Engineering College, Chennai, 600077, India; Department of Master of Computer Applications, S.A. Engineering College, Chennai, 600077, India","Indira V., Department of Computer Science and Engineering, Alpha College of Engineering, Chennai, 600124, India; Priyadarshini D.A., Department of Computer Science and Engineering, S.A. Engineering College, Chennai, 600077, India; Geetha R., Department of Computer Science and Engineering, S.A. Engineering College, Chennai, 600077, India; Sujatha V., Department of Master of Computer Applications, S.A. Engineering College, Chennai, 600077, India","Lung diseases are very seriously increasing nowadays due to the rapid environmental changes and the variety of viruses in the universe. This review focuses on using machine learning and deep learning algorithms to predict and analyse lung cancer, tuberculosis, coronavirus disease 2019 (COVID-19), influenza, asthma, and chronic obstructive pulmonary disease (COPD). In this paper, machine learning and deep learning models are used to analyse the affected lung mortality and have decreased the amount of physical work needed. This paper inspects how numerous machine-learning algorithms can be used to discover many lung states. The main aim of this review is to envision several tempers in lung disease to analyse using machine learning and diagnose the survival issue and the domain's feasible future. In addition, this examines the accuracy and efficiency of machine learning and deep learning categorises lung disease with minimal possible error. © 2024 Inderscience Enterprises Ltd.","computer vision; deep learning; lung disease; machine learning; ML","","","","","","","","Alkouz B., Al Aghbari Z., Abawajy J.H., Tweetluenza: predicting flu trends from Twitter data, IEEE Access, 2, 4, pp. 273-287, (2019); Alqudaihi K.S., Aslam N., Cough sound detection and diagnosis using artificial intelligence techniques: challenges and opportunities, IEEE Access, 9, pp. 102327-102344, (2021); An J., Cai Q., Qu Z., Gao Z., COVID-19 screening in chest X-Ray images using lung region priors, IEEE J. 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Zarrin P.S., Roeckendorf N., Wenger C., In-vitro classification of saliva samples of COPD patients and healthy controls using machine learning tools, IEEE Access, 8, pp. 168053-168060, (2020); Zhang L., Zhang J., Tan T., Deep learning methods for lung cancer segmentation in whole-slide histopathology images, IEEE journal of Biomedical and Health Informatics, 25, 2, (2021); Zhang Y., Liao Q., Exploiting shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation, IEEE Journal of Biomedical and Health Informatics, 25, 11, pp. 4152-4162, (2021)","R. Geetha; Department of Computer Science and Engineering, S.A. Engineering College, Chennai, 600077, India; email: geetha@saec.ac.in","","Inderscience Publishers","","","","","","17445485","","","","English","Int. J. Bioinformatics Res. Appl.","Article","Final","","Scopus","2-s2.0-85199175816"
"Xu J.; Bian J.; Fishe J.N.","Xu, Jie (58966963300); Bian, Jiang (7103200005); Fishe, Jennifer N (57160424400)","58966963300; 7103200005; 57160424400","Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations","2023","Journal of Asthma","60","5","","1000","1008","8","2","10.1080/02770903.2022.2119865","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138427988&doi=10.1080%2f02770903.2022.2119865&partnerID=40&md5=414a26cd09f067f70ff8eeda431bff11","Department of Health Outcomes and Bioinformatics, University of Florida, Gainesville, FL, United States; Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States; Department of Emergency Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States","Xu J., Department of Health Outcomes and Bioinformatics, University of Florida, Gainesville, FL, United States; Bian J., Department of Health Outcomes and Bioinformatics, University of Florida, Gainesville, FL, United States; Fishe J.N., Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States, Department of Emergency Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States","Introduction: Asthma is a heterogeneous disease with a range of observable phenotypes. To date, the characterization of asthma phenotypes is mostly limited to allergic versus non-allergic disease. Therefore, the aim of this big data study was to computationally derive asthma subtypes from the OneFlorida Clinical Research Consortium Methods: We obtained data from 2012-2020 from the OneFlorida Clinical Research Consortium. Longitudinal data for patients greater than two years of age who met inclusion criteria for an asthma exacerbation based on International Classification of Diseases codes. We used matrix factorization to extract information and K-means clustering to derive subtypes. The distributions of demographics, comorbidities, and medications were compared using Chi-square statistics. Results: A total of 39,807 pediatric patients and 23,883 adult patients met inclusion criteria. We identified five distinct pediatric subtypes and four distinct adult subtypes. Pediatric subtype P1 had the highest proportion of black patients, but the lowest use of inhaled corticosteroids and allergy medications. Subtype P2 had a predominance of patients with gastroesophageal reflux disease, whereas P3 had a predominance of patients with allergic disorders. Adult subtype A2 was the most severe and all patients were on biologic agents. Most of subtype A3 patients were not taking controller medications, whereas most patients (>90%) in subtypes A2 and A4 were taking corticosteroids and allergy medications. Conclusion: We found five distinct pediatric asthma subtypes and four distinct adult asthma subtypes. Future work should externally validate these subtypes and characterize response to treatment by subtype to better guide clinical treatment of asthma. © 2022 Taylor & Francis Group, LLC.","allergy; asthma; computational phenotypes; K-means clustering; subtypes","Adrenal Cortex Hormones; Anti-Asthmatic Agents; Asthma; Big Data; Humans; Phenotype; antiallergic agent; benralizumab; beta adrenergic receptor stimulating agent; biological product; corticosteroid; mepolizumab; omalizumab; reslizumab; antiasthmatic agent; corticosteroid; adult; allergic asthma; allergic bronchopulmonary aspergillosis; allergic rhinitis; Article; asthma; big data; Black person; child; clinical research; comorbidity; controlled study; demographics; depression; disease exacerbation; DNA microarray; eczema; electronic health record; environmental factor; female; gastroesophageal reflux; health care organization; human; hypertension; International Classification of Diseases; k means clustering; longitudinal study; low birth weight; machine learning; major clinical study; male; non-negative matrix factorization; obesity; passive smoking; pediatric patient; phenotype; pulmonary hypertension; sinusitis; sleep disordered breathing; tobacco dependence; tobacco use; treatment response; asthma; phenotype","","benralizumab, 1044511-01-4; mepolizumab, 196078-29-2; omalizumab, 242138-07-4; reslizumab, 241473-69-8; Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ","","","National Heart, Lung, and Blood Institute, NHLBI, (K23HL149991); National Heart, Lung, and Blood Institute, NHLBI","Dr. Fishe’s work is funded in part by a grant from the NHLBI (K23HL149991). None","(2018); Wenzel S.E., Asthma: defining of the persistent adult phenotypes, Lancet, 368, 9537, pp. 804-813, (2006); Wardlaw A.J., Silverman M., Siva R., Pavord I.D., Green R., Multi-dimensional phenotyping: towards a new taxonomy for airway disease, Clin Exp Allergy, 35, 10, pp. 1254-1262, (2005); Kuruvilla M.E., Lee F.E., Lee G.B., Understanding asthma phenotypes, endotypes, and mechanisms of disease, Clin Rev Allergy Immunol, 56, 2, pp. 219-233, (2019); Akar-Ghibril N., Casale T., Custovic A., Phipatanakul W., Allergic Endotypes and Phenotypes of Asthma, J Allergy Clin Immunol Pract, 8, 2, pp. 429-440, (2020); Conrad L.A., Cabana M.D., Rastogi D., Defining pediatric asthma: Phenotypes to Endotypes and Beyond, Pediatr Res, 90, 1, pp. 45-51, (2021); Heaney L., Robinson D.S., Severe asthma treatment: need for characterizing patients, The Lancet, 365, 9463, pp. 974-976, (2005); Haldar P., Pavord I.D., Shaw D.E., Berry M.A., Thomas M., Brightling C.E., Wardlaw A.J., Green R.H., Cluster analysis and clinical asthma phenotypes, Am J Respir Crit Care Med, 178, 3, pp. 218-224, (2008); Fishe J.N., Labilloy G., Higley R., Casey D., Ginn A., Baskovich B., Blake K.V., Single Nucleotide Polymorphisms (SNPs) in PRKG1 & SPATA13-AS1 are associated with bronchodilator response: a pilot study during acute asthma exacerbations in African American children, Pharmacogenet Genomics, 31, 7, pp. 146-154, (2021); Cloutier M.M., Baptist A.P., Blake K.V., Brooks E.G., Bryant-Stephens T., DiMango E., Dixon A.E., Elward K.S., Hartert T., Krishnan J.A., (2020); A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group, J Allergy Clin Immunol, 146, 6, pp. 1217-1270, (2020); Shenkman E., Hurt M., Hogan W., Carrasquillo O., Smith S., Brickman A., Nelson D., OneFlorida Clinical Research Consortium: linking a clinical and translational science institute with a community based distributive medical education model, Acad Med, 93, 3, pp. 451-455, (2018); Li Q., He Z., Guo Y., Zhang H., George T.J., Hogan W., Et al., Assessing the Validity of an a priori Patient-Trial Generalizability Score using Real-world Data from a Large Clinical Data Research Network: A Colorectal Cancer Clinical Trial Case Study, AMIA Annu Symp Proc, 2019, pp. 1101-1110, (2019); Zhang Z., Et al., Binary matrix factorization with applications, IEEE, (2007); Pehkonen P., Wong G., Toronen P., Theme discovery from gene lists for identification and viewing of multiple functional groups, BMC Bioinformatics, 6, 1, pp. 162-168, (2005); Kodinariya T.M., Makwana P.R., Review on determining number of Cluster in K-Means Clustering, International Journal 1, 6, pp. 90-95, (2013); (2015); Naqvi M., Thyne S., Choudhry S., Tsai H.J., Navarro D., Castro R.A., Nazario S., Rodriguez-Santana J.R., Casal J., Torres A., Et al., Ethnic-specific differences in bronchodilator responsiveness among African Americans, Puerto Ricans, and Mexicans with asthma, J Asthma, 44, 8, pp. 639-648, (2007); Locke B.W., Lee J.J., Sundar K.M., OSA and Chronic Respiratory Disease: Mechanisms and Epidemiology, IJERPH, 19, 9, (2022); Cardet J.C., Bulkhi A.A., Lockey R.F., Non-respiratory Comorbidities in Asthma, J Allergy Clin Immunol Pract, 9, 11, pp. 3887-3897, (2021)","J.N. Fishe; Jacksonville, 655 W 8th St, 32209, United States; email: Jennifer.Fishe@jax.ufl.edu","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","36039465","English","J. Asthma","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85138427988"
"Rog J.; Łobejko Ł.; Hordejuk M.; Marciniak W.; Derkacz R.; Kiljańczyk A.; Matuszczak M.; Lubiński J.; Nesterowicz M.; Żendzian-Piotrowska M.; Zalewska A.; Maciejczyk M.; Karakula-Juchnowicz H.","Rog, Joanna (57200008949); Łobejko, Łukasz (59341869400); Hordejuk, Michalina (59342276200); Marciniak, Wojciech (57190578947); Derkacz, Róża (56711974700); Kiljańczyk, Adam (57853774100); Matuszczak, Milena (57216753422); Lubiński, Jan (57222284297); Nesterowicz, Miłosz (57422050200); Żendzian-Piotrowska, Małgorzata (55886642500); Zalewska, Anna (35600557300); Maciejczyk, Mateusz (56080323000); Karakula-Juchnowicz, Hanna (56466032700)","57200008949; 59341869400; 59342276200; 57190578947; 56711974700; 57853774100; 57216753422; 57222284297; 57422050200; 55886642500; 35600557300; 56080323000; 56466032700","Pro/antioxidant status and selenium, zinc and arsenic concentration in patients with bipolar disorder treated with lithium and valproic acid","2024","Frontiers in Molecular Neuroscience","17","","1441575","","","","1","10.3389/fnmol.2024.1441575","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204891555&doi=10.3389%2ffnmol.2024.1441575&partnerID=40&md5=49045084a9817d92837a4f1e036db466","Laboratory of Human Metabolism Research, Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (WULS-SGGW), Warsaw, Poland; Mental Health Center at the Independent Public Healthcare in Leżajsk, Leżajsk, Poland; 1st Department of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, Lublin, Poland; Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland; Read-Gene, Grzepnica, Poland; Students’ Scientific Club “Biochemistry of Civilization Diseases” at the Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland; Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland; Independent Laboratory of Experimental Dentistry, Medical University of Bialystok, Bialystok, Poland","Rog J., Laboratory of Human Metabolism Research, Department of Dietetics, Institute of Human Nutrition Sciences, Warsaw University of Life Sciences (WULS-SGGW), Warsaw, Poland; Łobejko Ł., Mental Health Center at the Independent Public Healthcare in Leżajsk, Leżajsk, Poland; Hordejuk M., 1st Department of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, Lublin, Poland; Marciniak W., Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland, Read-Gene, Grzepnica, Poland; Derkacz R., Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland, Read-Gene, Grzepnica, Poland; Kiljańczyk A., Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland; Matuszczak M., Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland; Lubiński J., Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, Szczecin, Poland, Read-Gene, Grzepnica, Poland; Nesterowicz M., Students’ Scientific Club “Biochemistry of Civilization Diseases” at the Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland; Żendzian-Piotrowska M., Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland; Zalewska A., Independent Laboratory of Experimental Dentistry, Medical University of Bialystok, Bialystok, Poland; Maciejczyk M., Department of Hygiene, Epidemiology and Ergonomics, Medical University of Bialystok, Bialystok, Poland; Karakula-Juchnowicz H., 1st Department of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, Lublin, Poland","Disturbances in pro/antioxidant balance emerge as a crucial element in bipolar disorder (BD). Some studies suggest that treatment effects on trace element concentration in BD. This study aimed to identify (a) the changes related to oxidative stress in BD and their relationship with trace elements engaged in pro/antioxidant homeostasis; (b) BD biomarkers using machine learning algorithm classification and regression tree (C&RT) analysis. 62 individuals with BD and 40 healthy individuals (HC) were included in the study. The concentration of pro/antioxidant state and concentration of selenium, zinc, arsenic in blood were assessed. We found a higher concentration of total antioxidant capacity, catalase, advanced oxidation protein products and a lower concentration of 4-hydroxynonenal (4-HNE), glutathione, glutathione peroxidase (GPx) in BD compared to HC. All examined trace elements were lower in the BD group compared to HC. A combination of two variables, 4-HNE (cut-off: ≤ 0.004 uM/mg protein) and GPx (cut-off: ≤ 0.485 U/mg protein), was the most promising markers for separating the BD from the HC. The area under the receiver operating characteristic curve values for C&RT was 90.5%. Disturbances in the pro/antioxidant state and concentration of trace elements of patients with BD may be a target for new therapeutic or diagnostic opportunity of BD biomarkers. Copyright © 2024 Rog, Łobejko, Hordejuk, Marciniak, Derkacz, Kiljańczyk, Matuszczak, Lubiński, Nesterowicz, Żendzian-Piotrowska, Zalewska, Maciejczyk and Karakula-Juchnowicz.","arsenic; biomarkers; nutritional psychiatry; oxidative stress; psychiatric disorders; selenium; trace elements; zinc","3 nitrotyrosine; 4 hydroxynonenal; advanced oxidation protein product; antioxidant; arsenic; biological marker; catalase; dityrosine; glutathione peroxidase; isoleucine; kynurenine; lithium; malonaldehyde; selenium; superoxide dismutase; trace element; tryptophan; unclassified drug; valproic acid; zinc; adult; aged; allergy; antioxidant activity; Article; asthma; atopic dermatitis; autoimmune disease; biological activity; bipolar disorder; body mass; cardiovascular disease; chronic obstructive lung disease; clinical assessment; controlled study; diabetes mellitus; diagnostic test accuracy study; enzyme linked immunosorbent assay; female; gastroesophageal reflux; homeostasis; hospitalization; human; inductively coupled plasma atomic emission spectrometry; irritable colon; laboratory test; learning algorithm; machine learning; major clinical study; male; oxidative stress; peripheral neuropathy; physiological stress; psychiatry; receiver operating characteristic; regression analysis; seborrheic dermatitis; sociodemographics","","3 nitrotyrosine, 3604-79-3; 4 hydroxynonenal, 29343-52-0, 75899-68-2; arsenic, 7440-38-2; catalase, 9001-05-2; glutathione peroxidase, 9013-66-5; isoleucine, 7004-09-3, 73-32-5; kynurenine, 16055-80-4, 343-65-7; lithium, 7439-93-2; malonaldehyde, 542-78-9; selenium, 7782-49-2; superoxide dismutase, 37294-21-6, 9016-01-7, 9054-89-1; tryptophan, 6912-86-3, 73-22-3; valproic acid, 1069-66-5, 99-66-1; zinc, 7440-66-6, 14378-32-6","Infinite M200, Tecan","Tecan","","","Ahangar N., Naderi M., Noroozi A., Ghasemi M., Zamani E., Shaki F., Zinc deficiency and oxidative stress involved in valproic acid induced hepatotoxicity: Protection by zinc and selenium supplementation, Biol. 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"Smilnak G.J.; Lee Y.; Chattopadhyay A.; Wyss A.B.; White J.D.; Sikdar S.; Jin J.; Grant A.J.; Motsinger-Reif A.A.; Li J.-L.; Lee M.; Yu B.; London S.J.","Smilnak, Gordon J. (57204215621); Lee, Yura (57655962700); Chattopadhyay, Abhijnan (57265993500); Wyss, Annah B. (50562488200); White, Julie D. (57200687842); Sikdar, Sinjini (58803773300); Jin, Jianping (57211479959); Grant, Andrew J. (57223638130); Motsinger-Reif, Alison A. (57204797215); Li, Jian-Liang (55720829900); Lee, Mikyeong (57145147300); Yu, Bing (57212895591); London, Stephanie J. (57202568801)","57204215621; 57655962700; 57265993500; 50562488200; 57200687842; 58803773300; 57211479959; 57223638130; 57204797215; 55720829900; 57145147300; 57212895591; 57202568801","Plasma protein signatures of adult asthma","2024","Allergy: European Journal of Allergy and Clinical Immunology","79","3","","643","655","12","1","10.1111/all.16000","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182994690&doi=10.1111%2fall.16000&partnerID=40&md5=a67a7346d443feeaaf443c8f872b2567","Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States; GenOmics and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, United States; Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, United States; Westat, Inc., Durham, NC, United States; MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom; Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States","Smilnak G.J., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Lee Y., Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States; Chattopadhyay A., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Wyss A.B., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; White J.D., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States, GenOmics and Translational Research Center, Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, United States; Sikdar S., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States, Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, United States; Jin J., Westat, Inc., Durham, NC, United States; Grant A.J., MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom; Motsinger-Reif A.A., Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Li J.-L., Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Lee M., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States; Yu B., Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States; London S.J., Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States","Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma. Methods: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)). Results: Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75–0.79 in training set; AUC = 0.72, 95% CI = 0.69–0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis. Conclusion: This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management. © 2024 The Authors. Allergy published by John Wiley & Sons Ltd and European Academy of Allergy and Clinical Immunology. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.","allergy; area under curve; biomarkers; precision medicine; proteomics","Adult; Asthma; Biomarkers; Blood Proteins; Humans; Hypersensitivity, Immediate; Phenotype; Proteomics; alpha adrenergic receptor blocking agent; contactin 1; fibulin; immunoglobulin E; immunoglobulin G; interleukin 4 receptor; ligelizumab; limbic system associated membrane protein; myosin light chain; omalizumab; plasma protein; pregnancy associated plasma protein A; protein ParC; stromelysin 2; troponin T; biological marker; protein blood level; adult; Article; asthma; atopy; case control study; cohort analysis; controlled study; female; follow up; human; least absolute shrinkage and selection operator; machine learning; major clinical study; male; Mendelian randomization analysis; middle aged; network analysis; pathway enrichment analysis; people by smoking status; protein protein interaction; proteomics; asthma; genetics; immediate type hypersensitivity; meta analysis; metabolism; phenotype; procedures","","immunoglobulin E, 37341-29-0; immunoglobulin G, 97794-27-9, 308067-58-5; ligelizumab, 1322627-61-1; omalizumab, 242138-07-4; stromelysin 2, 140610-48-6; troponin T, 60304-72-5; Biomarkers, ; Blood Proteins, ","","","ARRA, (N01‐ES55546); JLH Foundation; Marie Richards-Barber; National Institutes of Health, NIH; U.S. Department of Health and Human Services, HHS, (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005); U.S. Department of Health and Human Services, HHS; National Heart, Lung, and Blood Institute, NHLBI, (R01 HL134320); National Heart, Lung, and Blood Institute, NHLBI; National Cancer Institute, NCI, (Z01‐CP010119); National Cancer Institute, NCI; National Institute of Environmental Health Sciences, NIEHS, (HHSN273201600003I, Z01‐ES102385); National Institute of Environmental Health Sciences, NIEHS","Funding text 1: This work was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences (NIEHS) (Z01‐ES049030 and Z01‐ES102385 and for ABW, contract no. HHSN273201600003I) and National Cancer Institute (Z01‐CP010119), and in part by American Recovery and Reinvestment Act (ARRA) funds through NIEHS contract number N01‐ES55546. The Atherosclerosis Risk in Communities Study has been funded in whole or in part by federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract nos. 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). This work was supported in part by NIH/NHLBI grant R01 HL134320. BY is in part supported by the JLH Foundation. JJ is in part supported by grant no. HHSN273201600003I. ; Funding text 2: We are grateful to all the participants of the Agricultural Lung Health Study and Atherosclerosis Risk in Communities study for their invaluable contribution to this work. SomaLogic Inc. conducted the SOMAScan assays in exchange for use of ARIC data. 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October 2014; Salo P.M., Arbes S.J., Jaramillo R., Et al., Prevalence of allergic sensitization in the United States: results from the National Health and Nutrition Examination Survey (NHANES) 2005-2006, J Allergy Clin Immunol, 134, 2, pp. 350-359, (2014); Hamilton R.G., Matsson P.N., Hovanec-Burns D.L., Et al., Analytical performance characteristics, quality assurance and clinical utility of immunological assays for human IgE antibodies of defined allergen specificities. (CLSI-ILA20-A3), J Allergy Clin Immunol, 135, 2, (2015)","M. Lee; Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, United States; email: mikyeong.lee@nih.gov","","John Wiley and Sons Inc","","","","","","01054538","","LLRGD","38263798","English","Allergy Eur. J. Allergy Clin. Immunol.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85182994690"
"Shi K.; Huang K.; Li L.; Liu Q.; Zhang Y.; Zheng H.","Shi, Kai (55806170000); Huang, Kai (59339660000); Li, Lin (57222641004); Liu, Qiaohui (59230301900); Zhang, Yi (57191411437); Zheng, Huilin (57206726862)","55806170000; 59339660000; 57222641004; 59230301900; 57191411437; 57206726862","Predicting microbe–disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion","2024","Frontiers in Microbiology","15","","1438942","","","","1","10.3389/fmicb.2024.1438942","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204698561&doi=10.3389%2ffmicb.2024.1438942&partnerID=40&md5=ddc8be051bfa8b6607fd19dc6afe8c85","College of Computer Science and Engineering, Guilin University of Technology, Guilin, China; Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, China","Shi K., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China, Guangxi Key Laboratory of Embedded Technology and Intelligent Systems, Guilin University of Technology, Guilin, China; Huang K., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China; Li L., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China; Liu Q., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China; Zhang Y., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China; Zheng H., College of Computer Science and Engineering, Guilin University of Technology, Guilin, China","Background: Clinical studies have demonstrated that microbes play a crucial role in human health and disease. The identification of microbe-disease interactions can provide insights into the pathogenesis and promote the diagnosis, treatment, and prevention of disease. Although a large number of computational methods are designed to screen novel microbe-disease associations, the accurate and efficient methods are still lacking due to data inconsistence, underutilization of prior information, and model performance. Methods: In this study, we proposed an improved deep learning-based framework, named GIMMDA, to identify latent microbe-disease associations, which is based on graph autoencoder and inductive matrix completion. By co-training the information from microbe and disease space, the new representations of microbes and diseases are used to reconstruct microbe-disease association in the end-to-end framework. In particular, a similarity fusion strategy is conducted to improve prediction performance. Results: The experimental results show that the performance of GIMMDA is competitive with that of existing state-of-the-art methods on 3 datasets (i.e., HMDAD, Disbiome, and multiMDA). In particular, it performs best with the area under the receiver operating characteristic curve (AUC) of 0.9735, 0.9156, 0.9396 on abovementioned 3 datasets, respectively. And the result also confirms that different similarity fusions can improve the prediction performance. Furthermore, case studies on two diseases, i.e., asthma and obesity, validate the effectiveness and reliability of our proposed model. Conclusion: The proposed GIMMDA model show a strong capability in predicting microbe-disease associations. We expect that GPUDMDA will help identify potential microbe-related diseases in the future. Copyright © 2024 Shi, Huang, Li, Liu, Zhang and Zheng.","graph autoencoder; inductive matrix completion; microbe–disease associations; network similarities; similarity fusion","Alcaligenaceae; algorithm; area under the curve; Article; artificial neural network; asthma; autoencoder; Bacteroides; Bacteroidetes; cross validation; deep learning; disease association; Firmicutes; functional connectivity; Fusobacterium nucleatum; gene function; graph autoencoder; Haemophilus; Helicobacter pylori; information processing; kernel method; learning algorithm; machine learning; mathematical model; microorganism; nonhuman; obesity; prediction; Prevotella; receiver operating characteristic; reliability; semantics; Sphingomonadaceae; Staphylococcus aureus","","","","","ZJLab; Key Laboratory of Computational Neuroscience; Guangxi Key Laboratory of Embedded Technology and Intelligent System; Special Funds for Guiding Local Scientific and Technological Development, (ZY22096025); National Natural Science Foundation of China, NSFC, (62162019, 62166014); National Natural Science Foundation of China, NSFC; Science and Technology Commission of Shanghai Municipality, STCSM, (2018SHZDZX01); Science and Technology Commission of Shanghai Municipality, STCSM; Guilin University of Technology, and Innovation Project of Guangxi Graduate Education, (YCSW2024357)","The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (Grant No. 62162019 and 62166014), Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX01), Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI), ZJLab, Guangxi Key Laboratory Fund of Embedded Technology and Intelligent System, Special Funds for Guiding Local Scientific and Technological Development by the Central Government (No. Guike ZY22096025), the startup Grant in Guilin University of Technology, and Innovation Project of Guangxi Graduate Education (YCSW2024357). ","Aggarwal N., Kitano S., Puah G.R.Y., Kittelmann S., Hwang I.Y., Chang M.W., Microbiome and human health: current understanding, engineering, and enabling technologies, Chem. 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"Xiao X.; Liu Y.; Huang Y.; Zeng W.; Luo Z.","Xiao, Xiaotong (59149860800); Liu, Yaxiong (57224202547); Huang, Yayang (59149270000); Zeng, Wenjie (57651730700); Luo, Zhuoya (57224190983)","59149860800; 57224202547; 59149270000; 57651730700; 57224190983","Identification of the NF-κB Inhibition Peptides in Asthma from Pheretima aspergillum Decoction and Formula Granules using Molecular Docking and Dynamics Simulations","2024","Current Pharmaceutical Analysis","20","3","","202","211","9","1","10.2174/0115734129298587240322073956","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196316916&doi=10.2174%2f0115734129298587240322073956&partnerID=40&md5=49998bc9a9f65cc55c0c0d73642239e9","School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China; NMPA Key Laboratory of Rapid Drug Inspection Technology, Guangdong Institute For Drug Control, Guangdong Biomedical Technology Collaborative Innovation Center, Guangdong Institute For Drug Control, Guangzhou, 510663, China","Xiao X., School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China; Liu Y., NMPA Key Laboratory of Rapid Drug Inspection Technology, Guangdong Institute For Drug Control, Guangdong Biomedical Technology Collaborative Innovation Center, Guangdong Institute For Drug Control, Guangzhou, 510663, China; Huang Y., School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China; Zeng W., School of Pharmaceutical Sciences, Guangzhou Medical University, Guangzhou, 511436, China; Luo Z., NMPA Key Laboratory of Rapid Drug Inspection Technology, Guangdong Institute For Drug Control, Guangdong Biomedical Technology Collaborative Innovation Center, Guangdong Institute For Drug Control, Guangzhou, 510663, China","Background: The Pheretima aspergillum decoction is a traditional therapeutic form, while the formula granules are produced through traditional Chinese medicine decoctions. However, the active ingredients in Pheretima aspergillum have not been fully elucidated, and no published reports have investigated the differences between Pheretima aspergillum decoction and formula granules. Objective: The study aimed to explore the potential bioactive peptides in Pheretima aspergillum decoction and formula granules and investigate their potential pharmacological mechanisms in alleviating inflammation associated with asthma through interaction with the IκBβ/NF-κB p65 complex. Methods: μLC-Q Exactive MS combined with de novo sequencing technology was employed to identify potential bioactive peptides in Pheretima aspergillum decoction and formula granules. Deep learning models were utilized to evaluate the bioactivity and toxicity of these peptides. Further investigations included molecular docking studies aimed at uncovering the interactions between the selected peptides and the IκBβ/NF-κB p65 complex at affinity and critical residue sites. Molecular dynamics simulations were conducted to assess the stability of the peptide-receptor complexes. Results: A total of 2,235 peptides from the Pheretima aspergillum decoction and 1,424 peptides from the Pheretima aspergillum formula granules were identified. Deep learning models resulted in the identification of 298 bioactive and non-toxic peptides from the decoction and 145 from the formula granules. Molecular docking revealed that 160 peptides from the decoction and 63 from the formula granules exhibited a strong affinity for the IκBβ/NF-κB p65 complex. The results of molecular dynamics simulations supported the stability of the interactions involving the peptide EGPANFADLGK from the decoction and the peptide KAAVDFGVPGDAGALAHLK from the formula granules with the IκBβ/NF-κB p65 complex. In conclusion, potential bioactive peptides were identified in both Pheretima aspergillum decoction and formula granules. Conclusion: This study has investigated the potential pharmacological mechanisms of peptides derived from Pheretima aspergillum decoction and formula granules in alleviating inflammation associated with asthma through the interaction of the IκBβ/NF-κB p65 complex, providing a basis for elucidating the molecular mechanism of action for the treatment of asthma. © 2024 Bentham Science Publishers.","asthma; bioactive peptides; NF-κB; Pheretima aspergillum decoction; Pheretima aspergillum formula granules; μLC-Q Exactive MS","peptide; Article; asthma; biological activity; deep learning; inflammation; liquid chromatography-mass spectrometry; molecular docking; molecular dynamics; NF kB signaling; nonhuman; Pheretima; Pheretima aspergillum","","","","","Science and Technology Planning Project of Guangdong Province, (2021B1111610005); Science and Technology Planning Project of Guangdong Province","This work was supported by the Science and Technology Planning Project of Guangdong Province, China (Grant no. 2021B1111610005).","Hammad H., Lambrecht B.N., The basic immunology of asthma, Cell, 184, 6, pp. 1469-1485, (2021); Porsbjerg C., Melen E., Lehtimaki L., Shaw D., Asthma, Lancet, 401, 10379, pp. 858-873, (2023); Christman J.W., Sadikot R.T., Blackwell T.S., The role of nuclear factor-κ B in pulmonary diseases, Chest, 117, 5, pp. 1482-1487, (2000); Li Q., Verma I.M., NF-κB regulation in the immune system, Nat. 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Drug Discov, 13, 1, pp. 23-37, (2018); Mooney C., Haslam N.J., Pollastri G., Shields D.C., Towards the improved discovery and design of functional peptides: Com-mon features of diverse classes permit generalized prediction of bioactivity, PLoS One, 7, 10, (2012); Wei L., Ye X., Sakurai T., Mu Z., Wei L., ToxIBTL: Prediction of peptide toxicity based on information bottleneck and transfer learning, Bioinformatics, 38, 6, pp. 1514-1524, (2022); Caetano-Silva M.E., Rund L.A., Vailati-Riboni M., Pacheco M.T.B., Johnson R.W., Copper-binding peptides attenuate mi-croglia inflammation through suppression of NF-kB pathway, Mol. Nutr. Food Res, 65, 22, (2021); Grancieri M., Martino H.S.D., de Mejia G.E., Digested total protein and protein fractions from chia seed (Salvia hispanica L.) had high scavenging capacity and inhibited 5-LOX, COX-1-2, and iN-OS enzymes, Food Chem, 289, pp. 204-214, (2019); Wang S., Lu M., Wang W., Yu S., Yu R., Cai C., Li Y., Shi Z., Zou J., He M., Xie W., Yu D., Jin H., Li H., Xiao W., Fan C., Wu F., Li Y., Liu S., Macrophage polarization modulat-ed by NF-κB in polylactide membranes-treated peritendinous adhe-sion, Small, 18, 13, (2022); Dejardin E., The alternative NF-κB pathway from biochemistry to biology: Pitfalls and promises for future drug development, Biochem. Pharmacol, 72, 9, pp. 1161-1179, (2006); Bai G., Pan Y., Zhang Y., Li Y., Wang J., Wang Y., Teng W., Jin G., Geng F., Cao J., Research advances of molecular docking and molecular dynamic simulation in recognizing interaction between muscle proteins and exogenous additives, Food Chem, 429, (2023); Vaishampayan V., Kulabhushan P., Dasgupta I., Kapoor A., Gumfekar S.P., Development of a diagnostic kit for point-of-care biosensors: Fundamentals and applications, Point-of-Care Biosensors for Infectious Diseases, pp. 235-254, (2023); Nguyen G.T.H., Tran T.N., Podgorski M.N., Bell S.G., Supu-ran C.T., Donald W.A., Nanoscale ion emitters in native mass spectrometry for measuring ligand–protein binding affinities, ACS Cent. Sci, 5, 2, pp. 308-318, (2019)","Y. Liu; NMPA Key Laboratory of Rapid Drug Inspection Technology, Guangdong Institute For Drug Control, Guangdong Biomedical Technology Collaborative Innovation Center, Guangdong Institute For Drug Control, Guangzhou, 510663, China; email: liuyaxiong@gdidc.org.cn; Z. Luo; NMPA Key Laboratory of Rapid Drug Inspection Technology, Guangdong Institute For Drug Control, Guangdong Biomedical Technology Collaborative Innovation Center, Guangdong Institute For Drug Control, Guangzhou, 510663, China; email: LzyGDIDC@163.com","","Bentham Science Publishers","","","","","","15734129","","","","English","Curr. Pharm. Anal.","Article","Final","","Scopus","2-s2.0-85196316916"
"Jia Q.; Chen Y.; Zen Q.; Chen S.; Liu S.; Wang T.; Yuan X.","Jia, Qinyao (57193325929); Chen, Yao (59355597200); Zen, Qiang (59354891300); Chen, Shaoping (58628924400); Liu, Shengming (7409463088); Wang, Tao (57215847313); Yuan, XinQi (59355597300)","57193325929; 59355597200; 59354891300; 58628924400; 7409463088; 57215847313; 59355597300","Development and Validation of Machine Learning-Based Models for Prediction of Intensive Care Unit Admission and In-Hospital Mortality in Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease","2024","Chronic Obstructive Pulmonary Diseases","11","5","","460","471","11","1","10.15326/jcopdf.2023.0446","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205677590&doi=10.15326%2fjcopdf.2023.0446&partnerID=40&md5=3bf751cc6acb37e666608fcc4cdd468d","School of Pharmacy, North Sichuan Medical College, Nanchong, China; Department of Pulmonary and Critical Care Medicine, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China; Department of Tuberculosis, Chengdu Public Health Clinical Medical Center, Chengdu, China; Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China; Department of Pulmonary and Critical Care Medicine, University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China; Department of Pulmonary and Critical Care Medicine, The Fifth People's Hospital of Sichuan Province, Chengdu, China","Jia Q., School of Pharmacy, North Sichuan Medical College, Nanchong, China; Chen Y., Department of Tuberculosis, Chengdu Public Health Clinical Medical Center, Chengdu, China; Zen Q., Department of Pulmonary and Critical Care Medicine, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China; Chen S., Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Liu S., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China; Wang T., Department of Pulmonary and Critical Care Medicine, University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China; Yuan X., Department of Pulmonary and Critical Care Medicine, The Fifth People's Hospital of Sichuan Province, Chengdu, China","Background: This present work focused on predicting prognostic outcomes of inpatients developing acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and enhancing patient monitoring and treatment by using objective clinical indicators. Methods: The present retrospective study enrolled 322 AECOPD patients. Registry data downloaded based on the chronic obstructive pulmonary disease (COPD) Pay-for-Performance Program database from January 2012 to December 2018 were used to check whether the enrolled patients were eligible. Our primary and secondary outcomes were intensive care unit (ICU) admission and in-hospital mortality, respectively. The best feature subset was chosen by recursive feature elimination. Moreover, 7 machine learning (ML) models were trained for forecasting ICU admission among AECOPD patients, and the model with the most excellent performance was used. Results: According to our findings, a random forest (RF) model showed superb discrimination performance, and the values of area under the receiver operating characteristic curve were 0.973 and 0.828 in training and test cohorts, separately. Additionally, according to decision curve analysis, the net benefit of the RF model was higher when differentiating patients with a high risk of ICU admission at a <0.55 threshold probability. Moreover, the ML-based prediction model was also constructed to predict in-hospital mortality, and it showed excellent calibration and discrimination capacities. Conclusion: The ML model was highly accurate in assessing the ICU admission and in-hospital mortality risk for AECOPD cases. Maintenance of model interpretability helped effectively provide accurate and lucid risk prediction of different individuals. | JCOPDF © 2024.","acute exacerbations of chronic obstructive pulmonary disease; ICU admission; in-hospital mortality; machine learning; risk assessment","anticoagulant agent; antithrombocytic agent; acute coronary syndrome; acute disease; aged; Article; atrial fibrillation; bronchiectasis; cerebrovascular accident; chronic obstructive lung disease; clinical feature; clinical indicator; clinical outcome; cohort analysis; comparative study; controlled study; cor pulmonale; data base; diabetes mellitus; disease exacerbation; disease severity; dyslipidemia; female; high risk patient; hospital admission; hospital patient; human; hypertension; in-hospital mortality; intensive care unit; machine learning; major clinical study; male; patient monitoring; patient registry; prediction; probability; prognosis; random forest; recursive feature elimination; retrospective study; sensitivity and specificity; support vector machine; survival rate; validation process; very elderly","","","","","","","Vogelmeier CF, Criner GJ, Martinez FJ, Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. GOLD executive summary, Am J Respir Crit Care Med, 195, 5, pp. 557-582, (2017); Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet Respir Med, 8, 6, pp. 585-596, (2020); Macintyre N, Huang YC., Acute exacerbations and respiratory failure in chronic obstructive pulmonary disease, Proc Am Thorac Soc, 5, 4, pp. 530-535, (2008); Antoniu SA, Carone M., Hospitalizations for chronic obstructive pulmonary disease exacerbations and their impact on disease and subsequent morbidity and mortality, Expert Rev Pharmacoecon Outcomes Res, 13, 2, pp. 187-189, (2013); Kim S, Emerman CL, Cydulka RK, Et al., Prospective multicenter study of relapse following emergency department treatment of COPD exacerbation, Chest, 125, 2, pp. 473-481, (2004); Mantero M, Rogliani P, Di Pasquale M, Et al., Acute exacerbations of COPD: risk factors for failure and relapse, Int J Chron Obstruct Pulmon Dis, 12, pp. 2687-2693, (2017); Hurst JR, Vestbo J, Anzueto A, Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Miravitlles M, Guerrero T, Mayordomo C, Sanchez-Agudo L, Nicolau F, Segu JL., Factors associated with increased risk of exacerbation and hospital admission in a cohort of ambulatory COPD patients: a multiple logistic regression analysis, Respiration, 67, 5, pp. 495-501, (2000); Anzueto A, Miravitlles M, Ewig S, Legnani D, Heldner S, Stauch K., Identifying patients at risk of late recovery (≥ 8 days) from acute exacerbation of chronic bronchitis and COPD, Respir Med, 106, 9, pp. 1258-1267, (2012); Cao Z, Ong K C, Eng P, Tan WC, Ng TP., Frequent hospital readmissions for acute exacerbation of COPD and their associated factors, Respirology, 11, 2, pp. 188-195, (2006); Steer J, Norman EM, Afolabi OA, Gibson GJ, Bourke SC., Dyspnoea severity and pneumonia as predictors of in-hospital mortality and early readmission in acute exacerbations of COPD, Thorax, 67, 2, pp. 117-121, (2012); Almagro P, Cabrera FJ, Diez J, Et al., Comorbidities and short-term prognosis in patients hospitalized for acute exacerbation of COPD. the EPOC en Servicios de medicina interna (ESMI) study, Chest, 142, 5, pp. 1126-1133, (2012); Putcha N, Paul GG, Azar A, Et al., Lower serum IgA is associated with COPD exacerbation risk in SPIROMICS, PLoS One, 13, 4, (2018); Singhvi D, Bon J., CT imaging and comorbidities in COPD. Beyond lung cancer screening, Chest, 159, 1, pp. 147-153, (2021); Wilson AC, Bon JM, Mason S, Et al., Increased chest CT derived bone and muscle measures capture markers of improved morbidity and mortality in COPD, Respir Res, 23, (2022); Jiang F, Jiang Y, Zhi H, Et al., Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol, 2, 4, pp. 230-243, (2017); Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo CH., A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators, Sci Rep, 10, (2020); Tavakoli H, Chen W, Sin DD, FitzGerald JM, Sadatsafavi M., Predicting severe chronic obstructive pulmonary disease exacerbations. Developing a population surveillance approach with administrative data, Ann Am Thorac Soc, 17, 9, pp. 1069-1076, (2020); Tsai CL, Sobrino JA, Camargo CA, National study of emergency department visits for acute exacerbation of chronic obstructive pulmonary disease, 1993-2005, Acad Emerg Med, 15, 12, pp. 1275-1283, (2008); Lee J, Jung HM, Kim SK, Et al., Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis, Sci Rep, 9, (2019); Ogundimu EO, Altman DG, Collins GS., Adequate sample size for developing prediction models is not simply related to events per variable, J Clin Epidemiol, 76, pp. 175-182, (2016); Pedersen AB, Mikkelsen EM, Cronin-Fenton D, Et al., Missing data and multiple imputation in clinical epidemiological research, Clin Epidemiol, 9, pp. 157-166, (2017); Hussain A, Choi HE, Kim HJ, Satyabrata A, Saqlain M, Kim HC., Forecast the exacerbation in patients of chronic obstructive pulmonary disease with clinical indicators using machine learning techniques, Diagnostics (Basel), 11, 5, (2021); Sadatsafavi M, McCormack J, Petkau J, Lynd LD, Lee TY, Sin DD., Should the number of acute exacerbations in the previous year be used to guide treatments in COPD?, Eur Respir J, 57, 2, (2021); Jiang L, Gershon AS., Using health administrative data to predict chronic obstructive pulmonary disease exacerbations, Ann Am Thorac Soc, 17, 9, pp. 1056-1057, (2020); Bzdok D, Altman N, Krzywinski M., Statistics versus machine learning, Nat Methods, 15, pp. 233-234, (2018); Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z., Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease, Comput Methods Programs Biomed, 188, (2020); Xu M, Tantisira KG, Wu A, Et al., Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers, BMC Med Genet, 12, (2011); Agusti A., The path to personalised medicine in COPD, Thorax, 69, 9, pp. 857-864, (2014); Agusti A, Macnee W., The COPD control panel: towards personalised medicine in COPD, Thorax, 68, 7, pp. 687-690, (2013); Agusti A, Anto JM, Auffray C, Et al., Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014, Am J Respir Crit Care Med, 191, 4, pp. 391-401, (2015); Agusti A, Bel E, Thomas M, Et al., Treatable traits: toward precision medicine of chronic airway diseases, Eur Respir J, 47, 2, pp. 410-419, (2016); Subramanian M, Wojtusciszyn A, Favre L, Et al., Precision medicine in the era of artificial intelligence: implications in chronic disease management, J Transl Med, 18, (2020); Kundu S., AI in medicine must be explainable, Nat Med, 27, (2021); Lundberg S, Lee SI., A unified approach to interpreting model predictions, (2017); Oh TR, Song SH, Choi HS, Et al., Predictive model for high coronary artery calcium score in young patients with non-dialysis chronic kidney disease, J Pers Med, 11, 12, (2021); Lu C, Song J, Li H, Et al., Predicting venous thrombosis in osteoarthritis using a machine learning algorithm: a population-based cohort study, J Pers Med, 12, 1, (2022); Andrijevic I, Milutinov S, Lozanov Crvenkovic Z, Et al., N-terminal prohormone of brain natriuretic peptide (NT-proBNP) as a diagnostic biomarker of left ventricular systolic dysfunction in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), Lung, 196, pp. 583-590, (2018); Tseng CM, Chen YT, Ou SM, Et al., The effect of cold temperature on increased exacerbation of chronic obstructive pulmonary disease: a nationwide study, PloS One, 8, 3, (2013); Liang WM, Liu WP, Kuo HW., Diurnal temperature range and emergency room admissions for chronic obstructive pulmonary disease in Taiwan, Int J Biometeorol, 53, pp. 17-23, (2009)","T. Wang; Department of Pulmonary and Critical Care Medicine University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China; email: 4941291@qq.com; X. Yuan; Department of Respiratory and Critical Care Medicine The Fifth People's Hospital of Sichuan Province, Chengdu, China; email: 178503908@qq.com","","COPD Foundation","","","","","","2372952X","","","","English","Chronic Obstr. Pulm. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85205677590"
"Meng Q.; Zheng C.; Guo L.; Gao P.; Liu W.; Ge H.; Liu T.; Peng H.; Lu J.; Chen X.","Meng, Qingcheng (57205525616); Zheng, Changbao (59205939500); Guo, Lanwei (56493564100); Gao, Pengrui (57193259257); Liu, Wentao (7407341078); Ge, Hong (57226450976); Liu, Tong (59279366100); Peng, Hui (59205347000); Lu, Jie (59205740800); Chen, Xuejun (57190684256)","57205525616; 59205939500; 56493564100; 57193259257; 7407341078; 57226450976; 59279366100; 59205347000; 59205740800; 57190684256","Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China","2024","Quantitative Imaging in Medicine and Surgery","14","7","","4864","4877","13","1","10.21037/qims-24-170","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197571297&doi=10.21037%2fqims-24-170&partnerID=40&md5=be2c4470d955e19cd95698dde86afc4d","Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Department of Radiology, The Hainan Cancer Hospital, Haikou, China; Henan Office for Cancer Control and Research, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Department of Radiology, The People's Hospital of Xingyang Country, Zhengzhou, China","Meng Q., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Zheng C., Department of Radiology, The Hainan Cancer Hospital, Haikou, China; Guo L., Henan Office for Cancer Control and Research, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Gao P., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Liu W., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Ge H., Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Liu T., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Peng H., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China; Lu J., Department of Radiology, The People's Hospital of Xingyang Country, Zhengzhou, China; Chen X., Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China","Background: Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods: In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results: The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions: An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs. © 2024 AME Publishing Company. All rights reserved.","artificial intelligence (AI); computed tomography (CT); lung cancer; Pure ground-glass nodule (pGGN); X-ray","nonionic contrast medium; adenocarcinoma in situ; adult; Article; artificial intelligence; cancer center; cancer size; China; chronic obstructive lung disease; clinical feature; clinical observation; cohort analysis; computer assisted tomography; convolutional neural network; diagnostic accuracy; diagnostic test accuracy study; diagnostic value; disease risk assessment; family history; female; fibrosing alveolitis; ground glass opacity; human; instrument validation; intermethod comparison; interrater reliability; invasive lung carcinoma; lesion volume; lung adenocarcinoma; lung adenoma; lung carcinoma; lung lesion; major clinical study; male; medical record review; middle aged; pneumonia; predictive value; pulmonary invasive adenocarcinoma; pure ground glass nodule; quantitative analysis; receiver operating characteristic; residual neural network; respiratory tract disease assessment; retrospective study; risk score system 1; risk score system 2; risk score system 3; sensitivity and specificity; thorax radiography; tumor differentiation; university hospital; validation study","","","GE Revolution, GE Healthcare, United States; Icon, Philips Healthcare, Netherlands; MedCalc 15.2.2, MedCalc, Belgium; Philips iCT256, Philips Healthcare, Netherlands; SPSS 19.0, IBM, United States","GE Healthcare, United States; IBM, United States; MedCalc, Belgium; Philips Healthcare, Netherlands; Philips Healthcare, Netherlands","European Congress of Radiology 2024; Key Project of Medical Science and Technology of Henan Province in China, (SBGJ202102057)","Funding text 1: We would like to thank Editage (www.editage.cn) for English language editing. Part of this study has been presented as an oral presentation at the section RPS 2104 of the European Congress of Radiology 2024 (details at: https://connect.myesr.org/course/pulmonary-nodules-andlung- cancer-screening/?). Funding: This work was supported by the Key Project of Medical Science and Technology of Henan Province in China (No. SBGJ202102057).; Funding text 2: We would like to thank Editage (www.editage.cn) for English language editing. Part of this study has been presented as an oral presentation at the section RPS 2104 of the European Congress of Radiology 2024 (details at: https://connect.myesr.org/course/pulmonary-nodules-and-lung-cancer-screening/?). Funding: This work was supported by the Key Project of Medical Science and Technology of Henan Province in China (No. SBGJ202102057).","Tammemagi MC, Katki HA, Hocking WG, Church TR, Caporaso N, Kvale PA, Chaturvedi AK, Silvestri GA, Riley TL, Commins J, Berg CD., Selection criteria for lungcancer screening, N Engl J Med, 368, pp. 728-736, (2013); Austin JH, Muller NL, Friedman PJ, Hansell DM, Naidich DP, Remy-Jardin M, Webb WR, Zerhouni EA., Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society, Radiology, 200, pp. 327-331, (1996); Travis WD, Brambilla E, Noguchi M, Nicholson AG, Geisinger KR, Yatabe Y, Et al., International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma, J Thorac Oncol, 6, pp. 244-285, (2011); Kodama K, Higashiyama M, Yokouchi H, Takami K, Kuriyama K, Kusunoki Y, Nakayama T, Imamura F., Natural history of pure ground-glass opacity after longterm follow-up of more than 2 years, Ann Thorac Surg, 73, pp. 386-392, (2002); 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Chen; Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, No. 127 Dongming Road, Jinshui District, 450008, China; email: zlyychenxuejun1943@zzu.edu.cn","","AME Publishing Company","","","","","","22234292","","","","English","Quant. Imaging Med. Surg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85197571297"
"Wang S.; Li W.; Zeng N.; Xu J.; Yang Y.; Deng X.; Chen Z.; Duan W.; Liu Y.; Guo Y.; Chen R.; Kang Y.","Wang, Shicong (57670751600); Li, Wei (57221637991); Zeng, Nanrong (57671655900); Xu, Jiaxuan (57861994500); Yang, Yingjian (57218501666); Deng, Xingguang (58887681100); Chen, Ziran (57670751700); Duan, Wenxin (57670149700); Liu, Yang (57222473378); Guo, Yingwei (57218502342); Chen, Rongchang (14017626800); Kang, Yan (57213821412)","57670751600; 57221637991; 57671655900; 57861994500; 57218501666; 58887681100; 57670751700; 57670149700; 57222473378; 57218502342; 14017626800; 57213821412","Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images","2024","Heliyon","10","7","e28724","","","","1","10.1016/j.heliyon.2024.e28724","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189697672&doi=10.1016%2fj.heliyon.2024.e28724&partnerID=40&md5=1a5f01d020014f70f7e2c0e5ac7bca2f","College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen Institute of Respiratory Diseases, Shenzhen, 518001, China; Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Wang S., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Li W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Zeng N., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Xu J., The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China; Yang Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Deng X., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Chen Z., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Duan W., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China; Liu Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; Guo Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Chen R., The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China, Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen Institute of Respiratory Diseases, Shenzhen, 518001, China; Kang Y., College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China, School of Applied Technology, Shenzhen University, Shenzhen, 518060, China, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China, Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China","Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction. © 2024 The Authors","AECOPD prediction; AMGNN; CNN; Generalized linear model; Lasso; Machine learning; Radiomics","","","","","","Special Program for Key Fields of Colleges and Universities in Guangdong Province; AMGNN; GLM; Guangzhou Medical University, GMU, (NCT03240315, 2017-22); Guangzhou Medical University, GMU; National Key Research and Development Program of China, NKRDPC, (2022YFF0710802, 2022YFF0710800); National Key Research and Development Program of China, NKRDPC; Biomedicine and Health) of China, (2021ZDZX2008); National Natural Science Foundation of China, NSFC, (62071311); National Natural Science Foundation of China, NSFC","Funding text 1: This study is approved by the Ethics Committee in the First Affiliated Hospital of Guangzhou Medical University (Grant number:2017-22) and registered at the National Center for Biotechnology Information (https://www.clinicaltrials.gov/study/NCT03240315, registration number: NCT03240315). All patients participating in the study were provided with written informed consent before the PFT, chest CT scans, and questionnaires.Our research aims to evaluate the effectiveness of various models for predicting AECOPD and identify the best predictor. Fig. 6 illustrates that we conducted three experiments. For each experiment, we used two kinds of predictors: the traditional ML predictors and meta-learning predictors. Specifically, the traditional ML predictors include Random Forest (RF) [52], Multilayer Perceptron (MLP) [53], Linear Discriminant Analysis (LDA) [54], and Support Vector Machines (SVM) [55]. The meta-learning predictors include Simple Neural AttentIve Learner (SNAIL) [56] and AMGNN. Notably, the AMGNN predictor is augmented with risk factors selected by GLM, while the other predictors utilize the features directly.This study is approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (grant number:2017-22) and registered at the National Center for Biotechnology Information (NCT03240315\"" title=\""https://www.clinicaltrials.gov/study/ NCT03240315\"">https://www.clinicaltrials.gov/study/ NCT03240315, registration number: NCT03240315). All patients participating in the study were provided with written informed consent prior to the experiments. All patients consented to the use of CT images for publication.This work was supported by the National Key Research and Development Program of China [grant numbers 2022YFF0710800, 2022YFF0710802]; the National Natural Science Foundation of China [grant number 62071311]; and the Special Program for Key Fields of Colleges and Universities in Guangdong Province (Biomedicine and Health) of China [grant number 2021ZDZX2008]. Thanks to the First Affiliated Hospital of Guangzhou Medical University for providing the dataset.; Funding text 2: This work was supported by the National Key Research and Development Program of China [grant numbers 2022YFF0710800, 2022YFF0710802]; the National Natural Science Foundation of China [grant number 62071311]; and the Special Program for Key Fields of Colleges and Universities in Guangdong Province (Biomedicine and Health) of China [grant number 2021ZDZX2008]. Thanks to the First Affiliated Hospital of Guangzhou Medical University for providing the dataset.","Bernocchi P., Vitacca M., La Rovere M.T., Et al., Home-based telerehabilitation in older patients with chronic obstructive pulmonary disease and heart failure: a randomised controlled trial, Age Ageing, 47, 1, pp. 82-88, (2018); Agusti A., Vogelmeier C., Faner R., Copd 2020: changes and challenges, Am. J. Physiol. Lung Cell Mol. 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Kang; College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China; email: kangyan@sztu.edu.cn","","Elsevier Ltd","","","","","","24058440","","","","English","Heliyon","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85189697672"
"Wang F.; Li S.; Gao Y.; Li S.","Wang, Fangfei (58806137400); Li, Sixiang (57247043500); Gao, Yuanxu (57205369493); Li, Shiyue (55780805100)","58806137400; 57247043500; 57205369493; 55780805100","Computed tomography-based artificial intelligence in lung disease—Chronic obstructive pulmonary disease","2024","MedComm - Future Medicine","3","1","e73","","","","1","10.1002/mef2.73","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185654150&doi=10.1002%2fmef2.73&partnerID=40&md5=ca5fc5e750510106391a4a205103f28e","Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macao; Guangzhou National Laboratory, Guangzhou, China; Department of Respiratory and Critical Care Medicine, National Center for Respiratory Medicine, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; University Hospital and Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macao","Wang F., Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macao, Guangzhou National Laboratory, Guangzhou, China; Li S., Department of Respiratory and Critical Care Medicine, National Center for Respiratory Medicine, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Gao Y., Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macao, University Hospital and Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macao; Li S., Department of Respiratory and Critical Care Medicine, National Center for Respiratory Medicine, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China","Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT-based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management. © 2024 The Authors. MedComm – Future Medicine published by John Wiley & Sons Australia, Ltd on behalf of Sichuan International Medical Exchange & Promotion Association (SCIMEA).","artificial intelligence (AI); chronic obstructive pulmonary disease (COPD) diagnosis; computed tomography (CT) segmentation; COPD prognosis","","","","","","Macao Young Scholars Program, (AM2023024); Fundo para o Desenvolvimento das Ciências e da Tecnologia, FDCT, (0003/2021/AKP, 0007/2020/AFJ, 0070/2020/A2); Fundo para o Desenvolvimento das Ciências e da Tecnologia, FDCT","This study was funded by the Macau Science and Technology Development Fund, Macao (0007/2020/AFJ, 0070/2020/A2, 0003/2021/AKP) and Macao Young Scholars Program (AM2023024). ","Venkatesan P., GOLD COPD report: 2023 update, Lancet Respir Med, 11, 1, (2023); Halpin D.M.G., Vogelmeier C.F., Agusti A., Lung health for all: chronic obstructive lung disease and world lung day 2022, Am J Respir Crit Care Med, 206, 6, pp. 669-671, (2022); Vogelmeier C.F., Criner G.J., Martinez F.J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. 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Biomed Signal Process Control, 79, 2, (2023); Weikert T., Friebe L., Wilder-Smith A., Et al., Automated quantification of airway wall thickness on chest CT using retina U-Nets – performance evaluation and application to a large cohort of chest CTs of COPD patients, Eur J Radiol, 155, (2022); Xue M., Jia S., Chen L., Huang H., Yu L., Zhu W., CT-based COPD identification using multiple instance learning with two-stage attention, Comput Methods Programs Biomed, 230, (2023); Kariotis T.C., Prictor M., Chang S., Gray K., Impact of electronic health records on information practices in mental health contexts: scoping review, J Med Internet Res, 24, 5, (2022); Yang J., Soltan A.A.S., Clifton D.A., Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening, NPJ Digit Med, 5, 1, (2022); Shi H.M., Sun Z.C., Ju F.H., Understanding the harm of low‑dose computed tomography radiation to the body (Review), Exp Ther Med, 24, 2, (2022); Liu Z., Sun Y., Zhang Z., Chen L., Hong N., Feasibility of free-breathing CCTA using 256-MDCT, Medicine, 95, 27, (2016); Niehoff J.H., Heuser A., Michael A.E., Lennartz S., Borggrefe J., Kroeger J.R., Patient comfort in modern computed tomography: what really counts, Tomography, 8, 3, pp. 1401-1412, (2022); Lin Y., Fu M., Ding R., Et al., Patient adherence to lung CT screening reporting & data system-recommended screening intervals in the United States: a systematic review and meta-analysis, J Thorac Oncol, 17, 1, pp. 38-55, (2022); Edelman Saul E., Guerra R.B., Edelman Saul M., Et al., The challenges of implementing low-dose computed tomography for lung cancer screening in low- and middle-income countries, Nat Cancer, 1, 12, pp. 1140-1152, (2020); Murdoch B., Privacy and artificial intelligence: challenges for protecting health information in a new era, BMC Med Ethics, 22, 1, (2021); Hassija V., Chamola V., Mahapatra A., Et al., Interpreting black-box models: a review on explainable artificial intelligence, Cogn Comput, 16, pp. 45-74, (2023); Lagemann K., Lagemann C., Taschler B., Mukherjee S., Deep learning of causal structures in high dimensions under data limitations, Nat Mach Intell, 5, pp. 1306-1316, (2023); Panch T., Mattie H., Celi L.A., The “inconvenient truth” about AI in healthcare, NPJ Digit Med, 2, 1, (2019); Ciecierski-Holmes T., Singh R., Axt M., Brenner S., Barteit S., Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review, NPJ Digit Med, 5, 1, (2022); Verstraete K., Gyselinck I., Huts H., Et al., Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials, Thorax, 78, 10, pp. 983-989, (2023); Yin C., Udrescu M., Gupta G., Et al., Fractional dynamics foster deep learning of COPD stage prediction, Adv Sci, 10, 12, (2023); McDowell A., Kang J., Yang J., Et al., Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects, Exp Mol Med, 54, 9, pp. 1586-1595, (2022); Willer K., Fingerle A.A., Noichl W., Et al., X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study, Lancet Digit Health, 3, 11, (2021); Myc L., Qing K., He M., Et al., Characterisation of gas exchange in COPD with dissolved-phase hyperpolarised xenon-129 MRI, Thorax, 76, 2, pp. 178-181, (2021); Linardatos P., Papastefanopoulos V., Kotsiantis S., Explainable AI: a review of machine learning interpretability methods, Entropy, 23, 1, (2020); Yao N., Li L., Gao Z., Et al., Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT, Eur J Nucl Med Mol Imaging, 50, 12, pp. 3666-3674, (2023); Yang Y., Chen Z., Li W., Et al., Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD, Front Med, 9, (2022)","Y. Gao; Institute for AI in Medicine and Faculty of Medicine, Macau University of Science and Technology, Macao; email: Yuanxu.carl@gmail.com; S. Li; Department of Respiratory and Critical Care Medicine, National Center for Respiratory Medicine, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: lishiyue@188.com","","John Wiley & Sons Inc","","","","","","27696456","","","","English","MedComm. Futur. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85185654150"
"Pham M.-K.; Mai T.T.; Crane M.; Ebiele M.; Brennan R.; Ward M.E.; Geary U.; McDonald N.; Bezbradica M.","Pham, Minh-Khoi (59029569400); Mai, Tai Tan (57767003000); Crane, Martin (8660401100); Ebiele, Malick (58113161300); Brennan, Rob (36447456700); Ward, Marie E. (23092562600); Geary, Una (7801661350); McDonald, Nick (7102833144); Bezbradica, Marija (55372494000)","59029569400; 57767003000; 8660401100; 58113161300; 36447456700; 23092562600; 7801661350; 7102833144; 55372494000","Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models","2024","Diagnostics","14","21","2405","","","","1","10.3390/diagnostics14212405","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208542145&doi=10.3390%2fdiagnostics14212405&partnerID=40&md5=3ab6d1b90143207d347783420b071c25","ADAPT Centre, Dublin, D02 PN40, Ireland; School of Computing, Dublin City University, Dublin, D09 Y074, Ireland; School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland; St James’s Hospital, Dublin, D08 NHY1, Ireland; School of Psychology, Trinity College Dublin, Dublin, D02 F6N2, Ireland","Pham M.-K., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computing, Dublin City University, Dublin, D09 Y074, Ireland; Mai T.T., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computing, Dublin City University, Dublin, D09 Y074, Ireland; Crane M., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computing, Dublin City University, Dublin, D09 Y074, Ireland; Ebiele M., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland; Brennan R., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland; Ward M.E., St James’s Hospital, Dublin, D08 NHY1, Ireland; Geary U., St James’s Hospital, Dublin, D08 NHY1, Ireland; McDonald N., School of Psychology, Trinity College Dublin, Dublin, D02 F6N2, Ireland; Bezbradica M., ADAPT Centre, Dublin, D02 PN40, Ireland, School of Computing, Dublin City University, Dublin, D09 Y074, Ireland","Background/Objectives: Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital. Methods: We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days. Results: Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies. Conclusions: Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate. © 2024 by the authors.","electronic patient records; explainable AI; multimodal deep learning","Article; benchmarking; chronic obstructive lung disease; deep learning; electronic health record; electronic patient record; explainable artificial intelligence; forecasting; hospital discharge; hospital readmission; hospitalization; human; information processing; learning algorithm; machine learning; prediction; risk management; Shapley additive explanation; social aspect; treatment outcome","","","","","Science Foundation Ireland, SFI, (13/RC/2106_P2); Science Foundation Ireland, SFI","This research was conducted with the financial support of Science Foundation Ireland under Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at Dublin City University. ADAPT, the SFI Research Centre for AI-Driven Digital Content Technology, is funded by Science Foundation Ireland through the SFI Research Centres Programme. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any author-accepted manuscript version arising from this submission.","McGowan J., Wojahn A., Nicolini J.R., Risk Management Event Evaluation and Responsibilities, (2020); Clancy C., Shine C., Hennessy M., Spending Review 2022 Hospital Performance: An Analysis of HSE Key Performance Indicators, (2023); Kripalani S., Theobald C.N., Anctil B., Vasilevskis E.E., Reducing hospital readmission rates: Current strategies and future directions, Annu. Rev. Med, 65, pp. 471-485, (2014); McDonald N., McKenna L., Vining R., Doyle B., Liang J., Ward M.E., Ulfvengren P., Geary U., Guilfoyle J., Shuhaiber A., Et al., Evaluation of an access-risk-knowledge (ARK) platform for governance of risk and change in complex socio-technical systems, Int. J. Environ. 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Inform, 7, (2019); Zhang X., Yan C., Gao C., Malin B.A., Chen Y., Predicting missing values in medical data via XGBoost regression, J. Healthc. Inform. Res, 4, pp. 383-394, (2020); Masud J.H.B., Kuo C.C., Yeh C.Y., Yang H.C., Lin M.C., Applying deep learning model to predict diagnosis code of medical records, Diagnostics, 13, (2023); Mullenbach J., Wiegreffe S., Duke J., Sun J., Eisenstein J., Explainable Prediction of Medical Codes from Clinical Text, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, pp. 1101-1111","M.-K. Pham; ADAPT Centre, Dublin, D02 PN40, Ireland; email: minhkhoi.pham@adaptcentre.ie","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85208542145"
"Li W.; Liu Z.; Song T.; Zhang C.; Xue J.","Li, Wensong (57564208000); Liu, Zhidong (57563949800); Song, Tao (57562948400); Zhang, Chunlong (57564208100); Xue, Jianzhen (57563702900)","57564208000; 57563949800; 57562948400; 57564208100; 57563702900","Effect Evaluation of Electronic Health PDCA Nursing in Treatment of Childhood Asthma with Artificial Intelligence","2022","Journal of Healthcare Engineering","2022","","2005196","","","","2","10.1155/2022/2005196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127669596&doi=10.1155%2f2022%2f2005196&partnerID=40&md5=9d00ad7aadfe019fbe3399ec0c4a7adf","Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China","Li W., Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China; Liu Z., Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China; Song T., Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China; Zhang C., Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China; Xue J., Department of Pediatrics, Qingdao Jiaozhou Central Hospital, No. 99, Yunxi Henan Road, Shandong Province, Jiaozhou, 266300, China","Asthma in children has a long duration and is prone to recurring attacks. Children will feel chest tightness, shortness of breath, cough, and difficulty breathing when they are onset, which has a serious impact on their health. Clinical nursing is of great significance in the treatment of childhood asthma. At present, the electronic health PDCA nursing model is widely used in clinical nursing as a common and effective nursing method. Therefore, it is very important to evaluate the efficacy of the PDCA nursing model in the treatment of childhood asthma. With the development of artificial intelligence, artificial intelligence can be used to evaluate the effect of the PDCA nursing model in the treatment of childhood asthma. The BP network can effectively perform data training and discrimination, but its training efficiency is low, and it is easily affected by initial weights and thresholds. Aiming at this defect, this work uses the genetic simulated annealing (GSA) algorithm to improve it. In view of the problems that the genetic algorithm falls into local minimum and simulated annealing algorithm has a slow convergence speed, the improved genetic simulated annealing algorithm is used to optimize the BP neural network, and an improved genetic simulated annealing BP network (IGSA-BP) is proposed. The algorithm not only reduces the problem that the BP network has an influence on initial weight and threshold on the algorithm but also improves the population diversity and avoids falling into local optimum by improving the crossover and mutation probability formula and improving Metropolis criterion. The proposed method has more efficient performance.  © 2022 Wensong Li et al.","","Algorithms; Artificial Intelligence; Asthma; Child; Electronics; Humans; Neural Networks, Computer; Backpropagation; Diseases; Genetic algorithms; Neural networks; Simulated annealing; glucose; Annealing algorithm; BP networks; Effect evaluation; Electronic health; Genetic simulated annealing algorithms; Initial weights; Local minimums; Long duration; Nursing modelling; Training efficiency; accuracy; Article; artificial intelligence; asthma; back propagation neural network; chest tightness; child; coughing; dyspnea; electronic health record; genetic algorithm; health care quality; health education; human; nursing management; nursing theory; PDCA cycle theory; problem solving; quality of life; simulated annealing; algorithm; asthma; electronics; Nursing","","glucose, 50-99-7, 84778-64-3, 8027-56-3","","","","","Depner M., Taft D.H., Taft D.H., Kirjavainen P.V., Kalanetra K.M., Karvonen A.M., Peschel S., Schmausser-Hechfellner E., Roduit C., Frei R., Lauener R., Divaret-Chauveau A., Dalphin J.-C., Riedler J., Roponen M., Kabesch M., Renz H., Pekkanen J., Farquharson F.M., Louis P., Mills D.A., Von Mutius E., Ege M.J., Maturation of the gut microbiome during the first year of life contributes to the protective farm effect on childhood asthma, Nature Medicine, 26, 11, pp. 1766-1775, (2020); 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Jiang A., Effects of PDCA circulation health education on children's asthma control level, Journal of Nursing, 17, 14, pp. 74-76, (2010); Hu Y., Effect of PDCA nursing mode on angina pectoris and quality of life after discharge in patients with myocardial infarction, Journal of Clinical Rational Drug Use, 25, pp. 55-57, (2018); Lin J., Lin Y., Lin H., Application of PDCA nursing mode in the course of nursing the patients with pernicious placenta previa, Maternal & Child Health Care of China, 31, 13, pp. 2744-2746, (2016); Cao X., Xie X., Yang P., Effect of health education in occupational population with high-blood pressure based on PDCA model, Chinese Journal of Nursing, 49, 4, pp. 485-491, (2014); Huang H., Li J., Zhong X., Application of PDCA management cycle in health education, Journal of Nurses Training, 5, (1999); Zhang X., Li Y., Wu X., Application of PDCA circulation theory in buccal clinical nursing teaching, Clinical Medical Engineering, 17, 8, pp. 156-157, (2010); Peng M., Lv X., Gao Y., Using PDCA cycle to improve the quality of nursing records, Attend to Practice and Research, 3, 2, pp. 53-54, (2006); Tang Y., Li Y., The value analysis of PDCA cycle in the application of management of the quality of emergency care, China Modern Medicine, 20, 10, pp. 117-118, (2013); Tan Z., Luo X., Effect of PDCA circulation on improving quality of nursing quality in outpatient operation room, Medical Information, 30, 1, pp. 199-201, (2017)","J. Xue; Department of Pediatrics, Qingdao Jiaozhou Central Hospital, Jiaozhou, No. 99, Yunxi Henan Road, Shandong Province, 266300, China; email: 201772122@yangtzeu.edu.cn","","Hindawi Limited","","","","","","20402295","","","35388323","English","J. Healthc. Eng.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85127669596"
"Sánchez-García S.; Garriga-Baraut T.; Fernández-De-alba I.","Sánchez-García, Silvia (26639678200); Garriga-Baraut, Teresa (37861434000); Fernández-De-alba, Isabel (57203436862)","26639678200; 37861434000; 57203436862","Asthma","2024","ERS Monograph","2024","104","","144","165","21","1","10.1183/2312508X.10011923","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195109239&doi=10.1183%2f2312508X.10011923&partnerID=40&md5=cb68db294dfea7a46b6dafafd8c360ea","Section of Allergy, Hospital Infantil Universitario Niño Jesús, Madrid, Spain; Pediatric Allergy Unit, Vall d’Hebron University Hospital, Barcelona, Spain; Section of Allergy, Hospital HLA Inmaculada, Granada, Spain","Sánchez-García S., Section of Allergy, Hospital Infantil Universitario Niño Jesús, Madrid, Spain; Garriga-Baraut T., Pediatric Allergy Unit, Vall d’Hebron University Hospital, Barcelona, Spain; Fernández-De-alba I., Section of Allergy, Hospital HLA Inmaculada, Granada, Spain","Biological, emotional, psychological and social changes occur rapidly during adolescence, and asthma management should change from caregiver-centred to patient-centred. Ideally, this should happen progressively from 11 to 25 years of age. Although asthma remission is frequent at this age, adolescentonset asthma has been described and other existing patients evolve to develop a more severe form of the disease. Atopy, eosinophils and T2 biomarkers are related to worse outcomes. Adherence to maintenance therapy is a particular challenge in adolescence; this may improve with a multidisciplinary approach and through support with new technologies. Effective transition from a paediatric to an adult setting should include providing patients with the skills and knowledge to manage their asthma independently. © ERS 2024.","","antidepressant agent; benralizumab; biological marker; corticosteroid; dupilumab; illicit drug; immunoglobulin E; interleukin 13; interleukin 33; mepolizumab; nitric oxide; pyrazinamide; tezepelumab; adolescent; adult; anaphylaxis; anxiety; anxiety disorder; Article; artificial intelligence; asthma; athlete; atopy; attention deficit hyperactivity disorder; bronchoconstriction; bronchus hyperreactivity; cardiovascular disease; caregiver; chronic obstructive lung disease; chronic rhinosinusitis; clinical decision making; cognitive behavioral therapy; cystic fibrosis; Delphi study; depression; DNA methylation; dyspnea; eczema; eosinophil; eosinophilia; epigenetics; extraversion; female; fever; gastroesophageal reflux; health care personnel; health care system; health care utilization; health literacy; health promotion; heart rehabilitation; household income; human; immunological tolerance; kidney biopsy; laryngoscopy; maintenance therapy; male; medical education; medication compliance; mental disease; mindfulness; neurosis; particulate matter; physical activity; pollen allergy; pregnancy; prevalence; quality of life; questionnaire; remission; rhinoconjunctivitis; risk factor; six minute walk test; sleep disorder; smoking cessation; telemedicine; tobacco dependence; transitional care; vaping; wheezing","","benralizumab, 1044511-01-4; dupilumab, 1190264-60-8; immunoglobulin E, 37341-29-0; interleukin 13, 148157-34-0; mepolizumab, 196078-29-2; nitric oxide, 10102-43-9; pyrazinamide, 98-96-4; tezepelumab, 1572943-04-4","","","","","Fuchs O, Bahmer T, Rabe KF, Et al., Asthma transition from childhood into adulthood, Lancet Respir Med, 5, pp. 224-234, (2017); Garden FL, Simpson JM, Mellis CM, Et al., Change in the manifestations of asthma and asthma-related traits in childhood: a latent transition analysis, Eur Respir J, 47, pp. 499-509, (2016); Nicolai T, Illi S, Tenborg J, Et al., Puberty and prognosis of asthma and bronchial hyper-reactivity, Pediatr Allergy Immunol, 12, pp. 142-148, (2001); Levy ML, Andrews R, Buckingham R, Et al., Why Asthma Still Kills: the National Review of Asthma Deaths Confidential Enquiry report, (2014); Patton GC, Viner R., Pubertal transitions in health, Lancet, 369, pp. 1130-1139, (2007); Nanzer AM, Lawton A, D'Ancona G, Et al., Transitioning asthma care from adolescents to adults: severe asthma series, Chest, 160, pp. 1192-1199, (2021); Gauci J, Bloomfield J, Lawn S, Et al., A randomized controlled trial evaluating the effectiveness of a self-management program for adolescents with a chronic condition: a study protocol, Trials, 23, (2022); Dharmage SC, Perret JL, Custovic A., Epidemiology of asthma in children and adults, Front Pediatr, 7, (2019); Serebrisky D, Wiznia A., Pediatric asthma: a global epidemic, Ann Glob Health, 85, (2019); Most Recent National Asthma Data; Liptzin DR, Landau LI, Taussig LM., Sex and the lung: observations, hypotheses, and future directions, Pediatr Pulmonol, 50, pp. 1159-1169, (2015); Guerra S, Wright AL, Morgan WJ, Et al., Persistence of asthma symptoms during adolescence: role of obesity and age at the onset of puberty, Am J Respir Crit Care Med, 170, pp. 78-85, (2004); Mansur AH, Prasad N., Management of difficult-to-treat asthma in adolescence and young adults, Breathe, 19, (2023); Ross KR, Gupta R, DeBoer MD, Et al., Severe asthma during childhood and adolescence: a longitudinal study, J Allergy Clin Immunol, 145, pp. 140-146, (2020); Hovland V, Riiser A, Mowinckel P, Et al., Early risk factors for pubertal asthma, Clin Exp Allergy, 45, pp. 164-176, (2015); Odling M, Jonsson M, Janson C, Et al., Lost in the transition from pediatric to adult healthcare? 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A qualitative study, Children (Basel), 9, (2022); Rhee H, Batek L, Rew L, Et al., Parents’ experiences and perceptions of healthcare transition in adolescents with asthma: a qualitative study, Children (Basel), 10, (2023); Knibb RC, Alviani C, Garriga-Baraut T, Et al., The effectiveness of interventions to improve self-management for adolescents and young adults with allergic conditions: a systematic review, Allergy, 75, pp. 1881-1898, (2020); Khaleva E, Knibb R, DunnGalvin A, Et al., Perceptions of adolescents and young adults with allergy and/or asthma and their parents on EAACI guideline recommendations about transitional care: a European survey, Allergy, 77, pp. 1094-1104, (2022); Khaleva E, Vazquez-Ortiz M, Comberiati P, Et al., Current transition management of adolescents and young adults with allergy and asthma: a European survey, Clin Transl Allergy, 10, (2020); Vazquez-Ortiz M, Gore C, Alviani C, Et al., A practical toolbox for the effective transition of adolescents and young adults with asthma and allergies: an EAACI position paper, Allergy, 78, pp. 20-46, (2023); Dufrois C, Bourgoin-Heck M, Lambert N, Et al., Maintenance of asthma control in adolescents with severe asthma after transitioning to a specialist adult centre: a French cohort experience, J Asthma Allergy, 15, pp. 327-340, (2022); Valverde-Molina J, Fernandez-Nieto M, Torres-Borrego J, Et al., Transition of adolescents with severe asthma from pediatric to adult care in Spain: the STAR consensus, J Investig Allergol Clin Immunol, 33, pp. 179-189, (2023); Guía Española para el Manejo del Asma, (2023)","S. Sánchez-García; Section of Allergy, Hospital Infantil Universitario Niño Jesús, Madrid, Spain; email: silviasanchezgarcia@hotmail.com","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85195109239"
"Lisik D.; Milani G.P.; Salisu M.; Ermis S.S.Ö.; Goksör E.; Basna R.; Wennergren G.; Kankaanranta H.; Nwaru B.I.","Lisik, Daniil (57237727500); Milani, Gregorio Paolo (24449867000); Salisu, Michael (59207804800); Ermis, Saliha Selin Özuygur (57463166300); Goksör, Emma (23004299300); Basna, Rani (57193611228); Wennergren, Göran (7005921751); Kankaanranta, Hannu (7004502353); Nwaru, Bright I. (26635624700)","57237727500; 24449867000; 59207804800; 57463166300; 23004299300; 57193611228; 7005921751; 7004502353; 26635624700","Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review","2024","BMJ Open","14","8","e080263","","","","1","10.1136/bmjopen-2023-080263","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203113171&doi=10.1136%2fbmjopen-2023-080263&partnerID=40&md5=f14a36281051b8bce06751d53cb68a74","Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Science and Community Health, University of Milan, Milan, Italy; Pediatric Unit, Ospedale Maggiore Policlinico, Milano, Italy; Department of Pediatrics, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden; Department of Clinical Sciences, Lund University, Lund, Sweden; Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden","Lisik D., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Milani G.P., Department of Clinical Science and Community Health, University of Milan, Milan, Italy, Pediatric Unit, Ospedale Maggiore Policlinico, Milano, Italy; Salisu M., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Ermis S.S.Ö., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Goksör E., Department of Pediatrics, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden; Basna R., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Clinical Sciences, Lund University, Lund, Sweden; Wennergren G., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Department of Pediatrics, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden; Kankaanranta H., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; Nwaru B.I., Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden, Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden","Introduction Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the ‘atopic march’. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with possibly differential trajectories. We aim to synthesise the current literature in the field of machine learning-based trajectory studies of asthma/allergies in children and adolescents, summarising the frequency, characteristics and associated risk factors and outcomes of identified trajectories and indicating potential directions for subsequent research in replicability, pathophysiology, risk stratification and personalised management. Furthermore, methodological approaches and quality will be critically appraised, highlighting trends, limitations and future perspectives. Methods and analyses 10 databases (CAB Direct, CINAHL, Embase, Google Scholar, PsycInfo, PubMed, Scopus, Web of Science, WHO Global Index Medicus and WorldCat Dissertations and Theses) will be searched for observational studies (including conference abstracts and grey literature) from the last 10 years (2013–2023) without restriction by language. Screening, data extraction and assessment of quality and risk of bias (using a custom-developed tool) will be performed independently in pairs. The characteristics of the derived trajectories will be narratively synthesised, tabulated and visualised in figures. Risk factors and outcomes associated with the trajectories will be summarised and pooled estimates from comparable numerical data produced through random-effects meta-analysis. Methodological approaches will be narratively synthesised and presented in tabulated form and figure to visualise trends. Ethics and dissemination Ethical approval is not warranted as no patient-level data will be used. The findings will be published in an international peer-reviewed journal. © Author(s) (or their employer(s)) 2024.","","Adolescent; Asthma; Child; Humans; Hypersensitivity; Machine Learning; Phenotype; Research Design; Risk Factors; Systematic Reviews as Topic; adolescent; allergy; Article; asthma; child; clinical outcome; data extraction; follow up; human; machine learning; observational study; pathophysiology; personalized medicine; phenotype; quality control; risk factor; sensitivity analysis; systematic review; hypersensitivity; methodology; systematic review (topic)","","","","","Vetenskapsrådet, VR, (2019 ‐00247); Vetenskapsrådet, VR; Avtal om Läkarutbildning och Forskning, (ALFGBG-979095); Hjärt-Lungfonden, (20200832, 20180525, 20220724); Hjärt-Lungfonden","Swedish Heart-Lung Foundation (grant number 20180525, 20200832, 20220724), Swedish Research Council (2019 \u201000247) and ALF (Swedish: Avtal om L\u00E4karutbildning och Forskning) agreement (ALFGBG-979095). The funders had no role in the study design, protocol (including related forms/tools) preparation or decision to publish. 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Lisik; Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; email: daniil.lisik@gmail.com","","BMJ Publishing Group","","","","","","20446055","","","39214659","English","BMJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85203113171"
"Owess M.M.; Owda A.Y.; Owda M.; Massad S.","Owess, Marwa Mustafa (58609140800); Owda, Amani Yousef (57063057100); Owda, Majdi (24479991800); Massad, Salwa (6603437407)","58609140800; 57063057100; 24479991800; 6603437407","Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar","2024","International Journal of Environmental Research and Public Health","21","7","840","","","","1","10.3390/ijerph21070840","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199876765&doi=10.3390%2fijerph21070840&partnerID=40&md5=1bd3d26ceb05b92dd48dd99aabd9639e","Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah, P600, Palestine; The World Health Organization, P.O. Box 54812, Jerusalem, Palestine; Faculty of Data Science, UNESCO Chair in Data Science for Sustainable Development, Arab American University, Ramallah, P600, Palestine","Owess M.M., Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah, P600, Palestine, The World Health Organization, P.O. Box 54812, Jerusalem, Palestine; Owda A.Y., Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah, P600, Palestine; Owda M., Faculty of Data Science, UNESCO Chair in Data Science for Sustainable Development, Arab American University, Ramallah, P600, Palestine; Massad S., The World Health Organization, P.O. Box 54812, Jerusalem, Palestine","Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes mellitus. Diabetes mellitus is one of the most common non-communicable diseases (NCDs) affecting the adult population. Recently, the prevalence of diabetes has been increasing at a faster rate, especially in developing countries. The primary concern associated with diabetes is the potential for serious health complications to occur if it is not diagnosed early. Therefore, timely detection and screening of diabetes is considered a crucial factor in treating and controlling the disease. Population screening for raised blood sugar aims to identify individuals at risk before symptoms appear, enabling timely intervention and potentially improved health outcomes. However, implementing large-scale screening programs can be expensive, requiring testing, follow-up, and management resources, potentially straining healthcare systems. Given the above facts, this paper presents supervised machine-learning models to detect and predict raised blood sugar. The proposed raised blood sugar models utilize diabetes-related risk factors including age, body mass index (BMI), eating habits, physical activity, prevalence of other diseases, and fasting blood sugar obtained from the dataset of the STEPwise approach to NCD risk factor study collected from adults in the Palestinian community. The diabetes risk factor obtained from the STEPS dataset was used as input for building the prediction model that was trained using various types of supervised learning classification algorithms including random forest, decision tree, Adaboost, XGBoost, bagging decision trees, and multi-layer perceptron (MLP). Based on the experimental results, the raised blood sugar models demonstrated optimal performance when implemented with a random forest classifier, yielding an accuracy of 98.4%. Followed by the bagging decision trees, XGBoost, MLP, AdaBoost, and decision tree with an accuracy of 97.4%, 96.4%, 96.3%, 95.2%, and 94.8%, respectively. © 2024 by the authors.","classification; diabetes; machine learning; prediction; raised blood sugar","Adult; Aged; Blood Glucose; Diabetes Mellitus; Female; Humans; Hyperglycemia; Male; Middle Aged; Risk Factors; Supervised Machine Learning; Palestine; hemoglobin A1c; high density lipoprotein cholesterol; low density lipoprotein cholesterol; triacylglycerol; blood; classification; diabetes; disease control; numerical model; prediction; sugar; supervised learning; adaboost; anxiety disorder; Article; artificial neural network; asthma; body mass; cardiovascular disease; clinical assessment; convolutional neural network; decision tree; deep learning; depression; diabetes mellitus; dietary intake; eating habit; follow up; glucose blood level; glucose metabolism; heart rate; hip circumference; human; hyperglycemia; hypertension; impaired glucose tolerance; machine learning; multilayer perceptron; obesity; oral glucose tolerance test; osteoporosis; outcome assessment; physical activity; physical inactivity; prevalence; random forest; salt intake; self report; sleep disorder; smoking; support vector machine; systematic review; waist circumference; waist hip ratio; adult; aged; blood; diabetes mellitus; diagnosis; epidemiology; female; hyperglycemia; male; middle aged; risk factor; supervised machine learning","","hemoglobin A1c, 62572-11-6; Blood Glucose, ","","","","","Diabetes; Clark N.G., Fox K.M., Grandy S., Symptoms of diabetes and their association with the risk and presence of diabetes: Findings from the study to help improve early evaluation and management of risk factors leading to diabetes (SHIELD), Diabetes Care, 30, pp. 2868-2873, (2007); Forouhi N.G., Wareham N.J., Epidemiology of diabetes, Medicine, 38, pp. 602-606, (2010); Zheng Y., Ley S.H., Hu F.B., Global aetiology and epidemiology of type 2 diabetes mellitus and its complications, Nat. Rev. Endocrinol, 14, pp. 88-98, (2017); Soomro M.H., Jabbar A., Diabetes etiopathology, classification, diagnosis, and epidemiology, BIDE’s Diabetes Desk Book, pp. 19-42, (2024); Bloomgarden Z., Handelsman Y., Diabetes Epidemiology and Its Implications, Lipoproteins in Diabetes Mellitus, pp. 881-890, (2023); 12. Retinopathy, Neuropathy, and Foot Care: Standards of Care in Diabetes—2024, Diabetes Care, 47, pp. S231-S243, (2024); Alqadi S.F., Diabetes Mellitus and Its Influence on Oral Health: Review, Diabetes Metab. Syndr. Obes, 17, pp. 107-120, (2024); Williams R., Airey M., Epidemiology and Public Health Consequences of Diabetes, Curr. Med. Res. Opin, 18, pp. s1-s12, (2002); The Top 10 Causes of Death; Laine C., Caro J.F., Preventing complications in diabetes mellitus: The role of the primary care physician, Med. Clin. N. 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Control, 10, pp. 458-472, (2019); Singh P., Singh N., Singh K.K., Singh A., Diagnosing of disease using machine learning, Machine Learning and the Internet of Medical Things in Healthcare, pp. 89-111, (2021); Jaiswal V., Negi A., Pal T., A review on current advances in machine learning based diabetes prediction, Prim. Care Diabetes, 15, pp. 435-443, (2021); Zhu T., Li K., Herrero P., Georgiou P., Deep Learning for Diabetes: A Systematic Review, IEEE J. Biomed. Health Inform, 25, pp. 2744-2757, (2021); Varma K.M., Panda B.S., Comparative analysis of Predicting Diabetes Using Machine Learning Techniques, J. Emerg. Technol. Innov. Res, 6, pp. 522-530, (2019); Makalesi A., Nur Ergun O., Ilhan H.O., Early Stage Diabetes Prediction Using Machine Learning Methods, Avrupa Bilim Teknol. Derg, 29, pp. 52-57, (2021); Islam M.T., Al-Absi H.R.H., Ruagh E.A., Alam T., DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images only, IEEE Access, 9, pp. 15686-15695, (2021); Mahboob Alam T., Iqbal M.A., Ali Y., Wahab A., Ijaz S., Baig T.I., Hussain A., Malik M.A., Raza M.M., Ibrar S., Et al., A model for early prediction of diabetes, Inform. Med. Unlocked, 16, (2019); Khanam J.J., Foo S.Y., A comparison of machine learning algorithms for diabetes prediction, ICT Express, 7, pp. 432-439, (2021); Kandhasamy J.P., Balamurali S., Performance Analysis of Classifier Models to Predict Diabetes Mellitus, Procedia Comput. Sci, 47, pp. 45-51, (2015); Aitbayev A., Diabetes UCI Dataset; Yahyaoui A., Jamil A., Rasheed J., Yesiltepe M., A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques, Proceedings of the 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019—Proceedings; Naz H., Ahuja S., Deep learning approach for diabetes prediction using PIMA Indian dataset, J. Diabetes Metab. Disord, 19, pp. 391-403, (2020); Wu H., Yang S., Huang Z., He J., Wang X., Type 2 diabetes mellitus prediction model based on data mining, Inform. Med. Unlocked, 10, pp. 100-107, (2018); Meng X.H., Huang Y.X., Rao D.P., Zhang Q., Liu Q., Comparison of three data mining models for predicting diabetes or prediabetes by risk factors, Kaohsiung J. Med. Sci, 29, pp. 93-99, (2013); Dinh A., Miertschin S., Young A., Mohanty S.D., A data-driven approach to predicting diabetes and cardiovascular disease with machine learning, BMC Med. Inform. Decis. Mak, 19, (2019); Vangeepuram N., Liu B., Chiu P.H., Wang L., Pandey G., Predicting youth diabetes risk using NHANES data and machine learning, Sci. Rep, 11, (2021); Maeta K., Nishiyama Y., Fujibayashi K., Gunji T., Sasabe N., Iijima K., Naito T., Prediction of Glucose Metabolism Disorder Risk Using a Machine Learning Algorithm: Pilot Study, JMIR Diabetes, 3, (2018); Owda M., Owda A.Y., Fasli M., An Exploratory Data Analysis and Visualizations of Underprivileged Communities Diabetes Dataset for Public Good, Proceedings of the 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023, pp. 581-585; Ferrannini E., Cushman W.C., Diabetes and hypertension: The bad companions, Lancet, 380, pp. 601-610, (2012); De Boer I.H., Bangalore S., Benetos A., Davis A.M., Michos E.D., Muntner P., Rossing P., Zoungas S., Bakris G., Diabetes and hypertension: A position statement by the American diabetes association, Diabetes Care, 40, pp. 1273-1284, (2017); Nguyen N.T., Magno C.P., Lane K.T., Hinojosa M.W., Lane J.S., Association of Hypertension, Diabetes, Dyslipidemia, and Metabolic Syndrome with Obesity: Findings from the National Health and Nutrition Examination Survey, 1999 to 2004, J. Am. Coll. Surg, 207, pp. 928-934, (2008); Jafar T.H., Chaturvedi N., Pappas G., Prevalence of overweight and obesity and their association with hypertension and diabetes mellitus in an Indo-Asian population, Cmaj, 175, pp. 1071-1077, (2006); Abdullah A., Peeters A., de Courten M., Stoelwinder J., The magnitude of association between overweight and obesity and the risk of diabetes: A meta-analysis of prospective cohort studies, Diabetes Res. Clin. Pract, 89, pp. 309-319, (2010); Amarnath B., Balamurugan S., Alias A., Review on feature selection techniques and its impact for effective data classification using UCI machine learning repository dataset, J. Eng. Sci. Technol, 11, pp. 1639-1646, (2016); Chen R.C., Dewi C., Huang S.W., Caraka R.E., Selecting critical features for data classification based on machine learning methods, J. Big Data, 7, (2020); Misra P., Yadav A.S., Improving the classification accuracy using recursive feature elimination with cross-validation, Int. J. Emerg. Technol, 11, pp. 659-665, (2020); Drobnic F., Kos A., Pustisek M., On the interpretability of machine learning models and experimental feature selection in case of multicollinear data, Electronics, 9, (2020); Dormann C.F., Elith J., Bacher S., Buchmann C., Carl G., Carre G., Marquez J.R.G., Gruber B., Lafourcade B., Leitao P.J., Et al., Collinearity: A review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, pp. 27-46, (2013); Reif D.M., Motsinger A.A., McKinney B.A., Crowe J.E., Moore J.H., Feature selection using a random forests classifier for the integrated analysis of multiple data types, Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB’06, pp. 171-178; Khan N.M., Madhav C.N., Negi A., Thaseen I.S., Analysis on Improving the Performance of Machine Learning Models Using Feature Selection Technique, Advances in Intelligent Systems and Computing, (2020); Raju V.N.G., Lakshmi K.P., Jain V.M., Kalidindi A., Padma V., Study the Influence of Normalization/Transformation process on the Accuracy of Supervised Classification, Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, pp. 729-735; Cecchini V., Nguyen T.P., Pfau T., De Landtsheer S., Sauter T., An efficient machine learning method to solve imbalanced data in metabolic disease prediction, Proceedings of the 2019 11th International Conference on Knowledge and Systems Engineering, KSE 2019; Gosain A., Sardana S., Handling class imbalance problem using oversampling techniques: A review, Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, pp. 79-85; Sharma H., Kumar S., A Survey on Decision Tree Algorithms of Classification in Data Mining, Int. J. Sci. Res, 5, pp. 2094-2097, (2016); Cao Y., Miao Q.-G., Liu J.-C., Gao L., Advance and Prospects of AdaBoost Algorithm, Acta Autom. Sin, 39, pp. 745-758, (2013); Ziegler A., Konig I.R., Mining data with random forests: Current options for real-world applications, Wiley Interdiscip Rev. Data Min. Knowl. Discov, 4, pp. 55-63, (2014); Chen T., Guestrin C., XGBoost: A scalable tree boosting system, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Abellan J., Masegosa A.R., Bagging decision trees on data sets with classification noise, Lecture Notes in Computer Science, (2010); Fiesler E., Beale R., Multilayer perceptrons, Handbook of Neural Computation, pp. C1.2:1-C1.2:30, (2020); Dj Novakovi J., Veljovi A., Ili S.S., Papi Z., Tomovi M., Evaluation of Classification Models in Machine Learning, Theory Appl. Math. Comput. Sci, 7, pp. 39-46, (2017); So K., Receiver-operating characteristic curve analysis in diagnostic, prognostic and predictive biomarker research, J. Clin. Pathol, 62, pp. 1-5, (2009)","A.Y. Owda; Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah, P600, Palestine; email: amani.owda@aaup.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","16617827","","","39063417","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85199876765"
"Sharma R.; Hogarth D.K.; Colbaugh R.; Glass K.; Mezine A.; Liakoni V.; Rudolf C.; Himmelhan I.; Hinson J.; Sanchirico M.","Sharma, Rajani (57195939966); Hogarth, D. Kyle (59662794700); Colbaugh, Richard (57207534951); Glass, Kristin (59573831000); Mezine, Adel (56358221800); Liakoni, Vassia (59340382500); Rudolf, Christopher (57204670184); Himmelhan, Iris (59269668800); Hinson, Jimmy (57419939900); Sanchirico, Marie (58438478600)","57195939966; 59662794700; 57207534951; 59573831000; 56358221800; 59340382500; 57204670184; 59269668800; 57419939900; 58438478600","Machine-Learning Model Identifies Patients With Alpha-1 Antitrypsin Deficiency Using Claims Records","2024","COPD: Journal of Chronic Obstructive Pulmonary Disease","21","1","2393348","","","","1","10.1080/15412555.2024.2393348","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204761269&doi=10.1080%2f15412555.2024.2393348&partnerID=40&md5=6e8fe22fdd9cdfbd62f20d8ea3db580f","Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York, NY, United States; Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL, United States; Volv Global SA, Epalinges, Switzerland; Takeda Pharmaceuticals International AG, Zürich, Switzerland; Takeda Pharmaceuticals USA, Inc., Lexington, MA, United States","Sharma R., Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York, NY, United States; Hogarth D.K., Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL, United States; Colbaugh R., Volv Global SA, Epalinges, Switzerland; Glass K., Volv Global SA, Epalinges, Switzerland; Mezine A., Volv Global SA, Epalinges, Switzerland; Liakoni V., Volv Global SA, Epalinges, Switzerland; Rudolf C., Volv Global SA, Epalinges, Switzerland; Himmelhan I., Takeda Pharmaceuticals International AG, Zürich, Switzerland; Hinson J., Takeda Pharmaceuticals USA, Inc., Lexington, MA, United States; Sanchirico M., Takeda Pharmaceuticals USA, Inc., Lexington, MA, United States","Identifying patients with rare diseases like alpha-1 antitrypsin deficiency (AATD) is challenging. Machine-learning models may be trained to identify patients with rare diseases using large-scale, real-world databases, whereas electronic medical records have low numbers of confirmed cases and have limited use in training such models. We applied a machine-learning model to a large US claims database to identify undiagnosed symptomatic patients with AATD. Using deidentified data from the Komodo US claims database (April 26, 2016–January 31, 2023), a model was trained to identify symptomatic patients with high probability of AATD. Eighty claims records for high-probability candidates identified by the model were independently reviewed and validated by 2 clinical experts. The experts independently indicated that of the 80 high-probability candidate patients, 65 (81%) and 62 (78%) patients, respectively, should be tested for AATD. Feedback from this validation step informed model optimization. The optimized model was applied to claims data to identify symptomatic patients with probable AATD. Eleven and 14 “features” of the claims data were informative in distinguishing patients with AATD from patients with COPD without AATD and from unspecified chronic liver diseases. Moreover, patients with diagnosed AATD and COPD without AATD had unique cadences of similar medical events in their diagnostic journeys. Our work shows that a machine-learning model trained on a large US claims database can accurately identify symptomatic patients with AATD and provides useful insights into the diagnostic journey of patients with AATD. © 2024 Takeda Pharmaceuticals Inc, USA. Published with license by Taylor & Francis Group, LLC.","alpha-1 antitrypsin deficiency; chronic liver disease; chronic obstructive pulmonary disease; claims data; Machine learning; rare diseases","Aged; alpha 1-Antitrypsin Deficiency; Databases, Factual; Electronic Health Records; Female; Humans; Insurance Claim Review; Machine Learning; Male; Middle Aged; United States; azithromycin; cefalexin; prescription drug; adolescent; adult; aged; alpha 1 antitrypsin deficiency; anonymised data; antibiotic therapy; anxiety; Article; asthma; bronchitis; child; chronic liver disease; chronic obstructive lung disease; claims based algorithm; cohort analysis; comorbidity; controlled study; cystic fibrosis; data base; depression; emphysema; family history; female; health care utilization; heart disease; heart failure; hepatitis; human; hypertension; infant; Kartagener syndrome; lung fibrosis; lung function test; machine learning; major clinical study; male; medical history; middle aged; newborn; panniculitis; predictive value; prescription; process optimization; receiver operating characteristic; thorax radiography; undiagnosed disease; very elderly; vitamin D deficiency; Wilson disease; alpha 1 antitrypsin deficiency; diagnosis; electronic health record; factual database; insurance; United States","","azithromycin, 83905-01-5, 117772-70-0, 121470-24-4; cefalexin, 15686-71-2, 23325-78-2","","","","","Quinn M., Ellis P., Pye A., Et al., Obstacles to early diagnosis and treatment of alpha-1 antitrypsin deficiency: current perspectives, Ther Clin Risk Manag, 16, pp. 1243-1255, (2020); Henao M.P., Craig T.J., Understanding alpha-1 antitrypsin deficiency: a review with an allergist’s outlook, Allergy Asthma Proc, 38, 2, pp. 98-107, (2017); Tejwani V., Stoller J.K., The spectrum of clinical sequelae associated with alpha-1 antitrypsin deficiency, Ther Adv Chronic Dis, 12_suppl, (2021); Patel D., Teckman J., Liver disease with unknown etiology—have you ruled out alpha-1 antitrypsin deficiency?, Ther Adv Chronic Dis, 12_suppl, (2021); American Thoracic Society/European Respiratory Society statement: standards for the diagnosis and management of individuals with alpha-1 antitrypsin deficiency, Am J Respir Crit Care Med, 168, 7, pp. 818-900, (2003); Sandhaus R.A., Turino G., Brantly M.L., Et al., The diagnosis and management of alpha-1 antitrypsin deficiency in the adult, Chronic Obstr Pulm Dis, 3, 3, pp. 668-682, (2016); de Serres F.J., Blanco I., Prevalence of alpha1-antitrypsin deficiency alleles PI*S and PI*Z worldwide and effective screening for each of the five phenotypic classes PI*MS, PI*MZ, PI*SS, PI*SZ, and PI*ZZ: a comprehensive review, Ther Adv Respir Dis, 6, 5, pp. 277-295, (2012); Visibelli A., Roncaglia B., Spiga O., Et al., The impact of artificial intelligence in the odyssey of rare diseases, Biomedicines, 11, 3, (2023); Brantly M., Campos M., Davis A.M., Et al., Detection of alpha-1 antitrypsin deficiency: the past, present and future, Orphanet J Rare Dis, 15, 1, (2020); Colbaugh R., Glass K., (2020); Colbaugh R., Glass K., Rudolf C., Et al., Learning to identify rare disease patients from electronic health records, AMIA Annu Symp Proc, 2018, pp. 340-347, (2018); Prakash P.K.S., Chilukuri S., Ranade N., Et al., Rare BERT: transformer architecture for rare disease patient identification using administrative claims, (2021); Stoller J.K., Brantly M., The challenge of detecting alpha-1 antitrypsin deficiency, COPD, 10 suppl 1, pp. 26-34, (2013); Kohnlein T., Janciauskiene S., Welte T., Diagnostic delay and clinical modifiers in alpha-1 antitrypsin deficiency, Ther Adv Respir Dis, 4, 5, pp. 279-287, (2010); Tejwani V., Nowacki A.S., Fye E., Et al., The impact of delayed diagnosis of alpha-1 antitrypsin deficiency: the association between diagnostic delay and worsened clinical status, Respir Care, 64, 8, pp. 915-922, (2019); Campos M.A., Wanner A., Zhang G., Et al., Trends in the diagnosis of symptomatic patients with alpha1-antitrypsin deficiency between 1968 and 2003, Chest, 128, 3, pp. 1179-1186, (2005); Stoller J.K., Smith P., Yang P., Et al., Physical and social impact of alpha 1-antitrypsin deficiency: results of a survey, Cleve Clin J Med, 61, 6, pp. 461-467, (1994); Zeng X., Linwood S.L., Liu C., Pretrained transformer framework on pediatric claims data for population specific tasks, Sci Rep, 12, 1, (2022); Cohen A.M., Chamberlin S., Deloughery T., Et al., Detecting rare diseases in electronic health records using machine learning and knowledge engineering: case study of acute hepatic porphyria, PLoS One, 15, 7, (2020); Hersh W.R., Cohen A.M., Nguyen M.M., Et al., Clinical study applying machine learning to detect a rare disease: results and lessons learned, JAMIA Open, 5, 2, (2022); Schuler K.P., Hemnes A.R., Annis J., Et al., An algorithm to identify cases of pulmonary arterial hypertension from the electronic medical record, Respir Res, 23, 1, (2022); Cooper J.P., Perkins J.D., Warner P.R., Et al., Acute graft-versus-host disease after orthotopic liver transplantation: predicting this rare complication using machine learning, Liver Transpl, 28, 3, pp. 407-421, (2022); Jefferies J.L., Spencer A.K., Lau H.A., Et al., A new approach to identifying patients with elevated risk for Fabry disease using a machine learning algorithm, Orphanet J Rare Dis, 16, 1, (2021); Melao A., (2019); (2016); Sanders C.L., Ponte A., Kueppers F., The effects of inflammation on alpha-1 antitrypsin levels in a national screening cohort, COPD, 15, 1, pp. 10-16, (2018); Meseeha M., Attia M., Alpha-1 antitrypsin deficiency, 2022, (2022)","M. Sanchirico; Takeda Pharmaceuticals USA, Inc., Lexington, United States; email: marie.sanchirico@takeda.com","","Taylor and Francis Ltd.","","","","","","15412555","","","39311422","English","COPD J. Chronic Obstructive Pulm. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85204761269"
"Kageyama S.; Ninomiya K.; Jonik S.; Masuda S.; Revaiah P.C.; Tsai T.-Y.; Garg S.; Onuma Y.; Serruys P.W.; Mazurek T.","Kageyama, Shigetaka (57695022200); Ninomiya, Kai (57204769044); Jonik, Szymon (57205531249); Masuda, Shinichiro (57264387000); Revaiah, Pruthvi C. (57210852497); Tsai, Tsung-Ying (57193574154); Garg, Scot (13104177600); Onuma, Yoshinobu (15051093400); Serruys, Patrick W. (34573036500); Mazurek, Tomasz (56618152000)","57695022200; 57204769044; 57205531249; 57264387000; 57210852497; 57193574154; 13104177600; 15051093400; 34573036500; 56618152000","Systematic screening by a heart team and a machine learning approach contribute to unraveling novel risk factors in revascularization candidates with complex coronary artery disease","2024","Polish Archives of Internal Medicine","134","6","16747","","","","1","10.20452/pamw.16747","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197343866&doi=10.20452%2fpamw.16747&partnerID=40&md5=9be72b58c0baf021d97c7c01f4ef8791","Department of Cardiology, Shizuoka City Shizuoka Hospital, Shizuoka, Japan; Department of Cardiology, University of Galway, Galway, Ireland; First Department of Cardiology, Medical University of Warsaw, Warszawa, Poland; Department of Cardiology, Royal Blackburn Hospital, Blackburn, United Kingdom","Kageyama S., Department of Cardiology, Shizuoka City Shizuoka Hospital, Shizuoka, Japan, Department of Cardiology, University of Galway, Galway, Ireland; Ninomiya K., Department of Cardiology, University of Galway, Galway, Ireland; Jonik S., First Department of Cardiology, Medical University of Warsaw, Warszawa, Poland; Masuda S., Department of Cardiology, University of Galway, Galway, Ireland; Revaiah P.C., Department of Cardiology, University of Galway, Galway, Ireland; Tsai T.-Y., Department of Cardiology, University of Galway, Galway, Ireland; Garg S., Department of Cardiology, Royal Blackburn Hospital, Blackburn, United Kingdom; Onuma Y., Department of Cardiology, University of Galway, Galway, Ireland; Serruys P.W., Department of Cardiology, University of Galway, Galway, Ireland; Mazurek T., First Department of Cardiology, Medical University of Warsaw, Warszawa, Poland","Introduction The baseline characteristics affecting mortality following percutaneous or surgical revascularization in patients with left main and / or 3-vessel coronary artery disease (CAD) observed in real-world practice differ from those established in randomized controlled trials (RCTs) due to the constraints of inclusion / exclusion criteria. Objectives This study aimed to assess whether systematic screening enables identification of novel and registry-specific baseline patient characteristics influencing long-term mortality. Patient and methods Least absolute shrinkage and selection operator (LASSO) regression was used to screen 42 baseline patient characteristics shared by the SYNTAX (Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery) trial and a single-center Polish registry of 1035 consecutive patients with complex CAD who received revascularization and were followed-up for 5 years. After screening, a classic Cox regression analysis was performed to examine the suitability of a linear model for predicting 5-year mortality, which was then compared with the mortality predicted in the same cohort using the SYNTAX score II 2020 (SS2020). Result s The 5-year mortality rate in the registry was 12.3%, and the strongest predictors were pulmonary hypertension, chronic obstructive pulmonary disease, and insulin-dependent diabetes. In an internal validation, the linear model constructed after LASSO screening and combined with a classic Cox regression analysis improved the prediction of 5-year mortality, as compared with the SS2020 (concordance index of 0.92 and 0.75, respectively). Conclusions A machine learning approach improved the detection of registry-specific risk factors in all-comer patients amenable to surgical or percutaneous revascularization who were evaluated by a heart team. The risk factors identified in RCTs are not necessarily the same as those detected in real clinical practice when systematic screening is applied.  © Author(s), 2024.","Cox regression; heart team discussion; least absolute shrinkage and selection operator regression; long-term mortality prediction; machine learning","Aged; Coronary Artery Disease; Female; Humans; Machine Learning; Male; Middle Aged; Myocardial Revascularization; Percutaneous Coronary Intervention; Poland; Registries; Risk Factors; beta adrenergic receptor blocking agent; hydroxymethylglutaryl coenzyme A reductase inhibitor; aged; Article; cardiovascular mortality; cardiovascular risk factor; cause of death; chronic obstructive lung disease; clinical practice; cohort analysis; controlled study; coronary artery disease; feature selection; female; follow up; heart surgery; human; insulin dependent diabetes mellitus; least absolute shrinkage and selection operator; machine learning; major clinical study; male; mortality rate; percutaneous coronary intervention; prediction; prognosis; proportional hazards model; pulmonary hypertension; randomized controlled trial; receiver operating characteristic; revascularization; SYNTAX score; heart muscle revascularization; middle aged; mortality; percutaneous coronary intervention; Poland; register; risk factor; surgery","","","","","","","Avis SR, Vernon ST, Hagstrom E, Figtree GA., Coronary artery disease in the absence of traditional risk factors: a call for action, Eur Heart J, 42, pp. 3822-3824, (2021); Serruys PW, Revaiah PC, Ninomiya K, Et al., 10 Years of SYNTAX: closing an era of clinical research after identifying new outcome determinants, JACC Asia, 3, pp. 409-430, (2023); Neumann FJ, Sousa-Uva M, Ahlsson A, Et al., 2018 ESC/EACTS Guidelines on myocardial revascularization, Eur Heart J, 40, pp. 87-165, (2018); Jennifer S, Jacqueline ETH, Sripal B, Et al., 2021 ACC/AHA/SCAI Guideline for coronary artery revascularization: a report of the American College of Cardiology /American Heart Association Joint Committee on Clinical Practice Guidelines, Circulation, 145, pp. e18-e114, (2022); Takahashi K, Serruys PW, Fuster V, Et al., Redevelopment and validation of the SYNTAX score II to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomized controlled SYNTAXES trial with external cohort validation, Lancet, 396, pp. 1399-1412, (2020); Schwalbe N, Wahl B., Artificial intelligence and the future of global health, Lancet, 395, pp. 1579-1586, (2020); Ascenzo F, Filippo O, Gallone G, Et al., Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets, Lancet, 397, pp. 199-207, (2021); Rousset A, Dellamonica D, Menuet R, Et al., Can machine learning bring cardiovascular risk assessment to the next level? A methodological study using FOURIER trial data, Eur Heart J Digit Health, 3, pp. 38-48, (2021); Ninomiya K, Kageyama S, Garg S, Et al., Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for ""big data, Eur Heart J Digit Health, 4, pp. 275-278, (2023); Ninomiya K, Kageyama S, Garg S, Et al., Can machine learning aid the selection of percutaneous vs. surgical revascularization?, J Am Coll Cardiol, 82, pp. 2113-2124, (2023); Jonik S, Marchel M, Pedzich-Placha E, Et al., Optimal management of patients with severe coronary artery disease following multidisciplinary heart team approach-insights from tertiary cardiovascular care center, Int J Environ Res Public Health, 19, (2022); Jonik S, Kageyama S, Ninomiya K, Et al., Five-year outcomes in patients with multivessel coronary artery disease undergoing surgery or percutaneous intervention, Sci Rep, 14, (2024); Comparison of coronary bypass surgery with angioplasty in patients with multivessel disease, N Engl J Med, 335, pp. 217-225, (1996); Farooq V, Serruys PW, Bourantas C, Et al., Incidence and multivariable correlates of long-term mortality in patients treated with surgical or percutaneous revascularization in the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) trial, Eur Heart J, 33, pp. 3105-3113, (2012); Ogundimu EO, Altman DG, Collins GS, Et al., Adequate sample size for developing prediction models is not simply related to events per variable, J Clin Epidemiol, 76, (2016); Hara H, Shiomi H, van Klaveren D, Et al., External validation of the SYNTAX Score II 2020, J Am Coll Cardiol, 78, pp. 1227-1238, (2021); Hara H, Kawashima H, Ono M, Et al., Impact of preprocedural biological markers on 10-year mortality in the SYNTAXES trial, EuroIntervention, 17, pp. 1477-1487, (2022); Shi B, Wang HY, Liu J, Et al., Prognostic value of machine-learning-based PRAISE score for ischemic and bleeding events in patients with acute coronary syndrome undergoing percutaneous coronary intervention, J Am Heart Assoc, 12, (2023); Kageyama S, Serruys PW, Ninomiya K, Et al., Impact of on-pump and off-pump coronary artery bypass grafting on 10-year mortality versus percutaneous coronary intervention, Eur J Cardiothorac Surg, 64, (2023); Kageyama S, Serruys PW, Garg S, Et al., Geographic disparity in 10-year mortality after coronary artery revascularization in the SYNTAXES trial, Int J Cardiol, 368, pp. 28-38, (2022); Rajagopalan S, Landrigan PJ., Pollution and the heart, N Engl J Med, 385, pp. 1881-1892, (2021); Tibshirani R., The Lasso method for variable selection in the Cox model, Stat Med, 16, pp. 385-395, (1997); Setny M, Jankowski P, Kaminski K, Et al., Secondary prevention of coronary heart disease in Poland: does sex matter? Results from the POLASPIRE survey, Pol Arch Intern Med, 132, (2022); Serruys P, Chichareon P, Modolo R, Et al., The SYNTAX score on its way out or towards artificial intelligence: part I, EuroIntervention, 16, pp. 44-59, (2020); Serruys P, Chichareon P, Modolo R, Et al., The SYNTAX score on its way out or towards artificial intelligence: part II, EuroIntervention, 16, pp. 60-75, (2020)","P.W. Serruys; Department of Cardiology, University of Galway, Galway, University Road, H91 TK33, Ireland; email: patrick.w.j.c.serruys@gmail.com","","Medycyna Praktyczna Cholerzyn","","","","","","00323772","","","38742937","English","Poli. Arch. of Inter. Medi.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85197343866"
"Barker A.B.; Melvin R.L.; Godwin R.C.; Benz D.; Wagener B.M.","Barker, Andrew B. (56912861000); Melvin, Ryan L. (57220875047); Godwin, Ryan C. (56589634100); Benz, David (57216896827); Wagener, Brant M. (6601961474)","56912861000; 57220875047; 56589634100; 57216896827; 6601961474","Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study","2024","Journal of Medical Systems","48","1","69","","","","1","10.1007/s10916-024-02085-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199319787&doi=10.1007%2fs10916-024-02085-9&partnerID=40&md5=f2fe236c3a2a752e94650eef3b3890ae","Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, 35294, AL, United States; Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States","Barker A.B., Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, 35294, AL, United States; Melvin R.L., Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States; Godwin R.C., Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States; Benz D., Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States; Wagener B.M., Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, Birmingham, 35294, AL, United States","Background: Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors. Methods: We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk. Results: Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature. Conclusions: We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients. © The Author(s) 2024.","Artificial Intelligence; Patient Safety; Precision Medicine; Predictive Analytics; Risk Stratification","Adult; Aged; Anesthesia Recovery Period; Female; Humans; Machine Learning; Male; Middle Aged; Perioperative Period; Postoperative Complications; Retrospective Studies; Risk Assessment; Risk Factors; Vital Signs; bicarbonate; hemoglobin; acute kidney failure; adult; American Society of Anaesthesiologists score; anesthesia; anesthesiologist; Article; asthma; bicarbonate blood level; chronic kidney failure; chronic obstructive lung disease; clinical decision making; clinical evaluation; cohort analysis; controlled study; diabetes mellitus; elective surgery; electronic medical record; emergency surgery; end stage renal disease; escalation of care; female; heart infarction; hemoglobin blood level; hospital discharge; human; incidence; intraoperative period; logistic regression analysis; machine learning; major adverse cardiac event; major clinical study; male; morbidity; operative blood loss; patient risk; patient safety; perioperative period; preoperative evaluation; recovery room; retrospective study; risk factor; surgical patient; transfusion; unplanned care escalation; urine volume; vital sign; aged; anesthetic recovery; epidemiology; middle aged; perioperative period; postoperative complication; procedures; risk assessment","","bicarbonate, 144-55-8, 71-52-3; hemoglobin, 9008-02-0","","","National Institutes of Health, NIH; UK Research and Innovation, UKRI, (105084)","Funding was provided by GM127584 and GM127584-S1 from the National Institutes of Health to B.M.W. ","Bainbridge D., Martin J., Arango M., Cheng D., Evidence-Based Peri-Operative G., Clinical Outcomes Research, “Perioperative and anaesthetic-related mortality in developed and developing countries: A systematic review and meta-analysis, Lancet, 380, pp. 1075-1081, (2012); Watters D.A., Et al., Perioperative mortality rate (POMR): a global indicator of access to safe surgery and anaesthesia, World J Surg, 39, 4, pp. 856-864, (2015); Katori N., Yamakawa K., Yagi K., Kimura Y., Doi M., Uezono S., Characteristics and outcomes of unplanned intensive care unit admission after general anesthesia,“, BMC Anesthesiol, 22, 1, (2022); Melton M.S., Unplanned hospital admission after ambulatory surgery: A retrospective, single cohort study,”, Can J Anaesth, 68, no. 1,, pp. 30-41, (2021); Loftus T.J., Et al., Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System,“, J Am Coll Surg, 236, 2, pp. 279-291, (2023); Ohbe H., Matsui H., Kumazawa R., Yasunaga H., Intensive care unit versus high dependency care unit admission after emergency surgery: a nationwide in-patient registry study,“, Br J Anaesth, 129, 4, pp. 527-535, (2022); Costa G., Et al., Gastro-intestinal emergency surgery: Evaluation of morbidity and mortality. Protocol of a prospective, multicenter study in Italy for evaluating the burden of abdominal emergency surgery in different age groups. (The GESEMM study),“, Front Surg, 9, (2022); Garutti I., Et al., Spontaneous recovery of neuromuscular blockade is an independent risk factor for postoperative pulmonary complications after abdominal surgery: A secondary analysis,“, Eur J Anaesthesiol, 37, 3, pp. 203-211, (2020); Chandler D., Et al., Perioperative strategies for the reduction of postoperative pulmonary complications,“, Best Pract Res Clin Anaesthesiol, 34, 2, pp. 153-166, (2020); Mufti H., Et al., The association between preoperative anemia, blood transfusion need, and postoperative complications in adult cardiac surgery, a single center contemporary experience,“, J Cardiothorac Surg, 18, 1, (2023); Hosny A., Parmar C., Quackenbush J., Schwartz L.H., Aerts H., Artificial intelligence in radiology, Nat Rev Cancer, 18, 8, pp. 500-510, (2018); Seah J., Boeken T., Sapoval M., Goh G.S., Prime Time for Artificial Intelligence in Interventional Radiology,“, Cardiovascular and interventional radiology, 45, 3, pp. 283-289, (2022); Baxi V., Edwards R., Montalto M., Saha S., Digital pathology and artificial intelligence in translational medicine and clinical practice,“, Mod Pathol, 35, 1, pp. 23-32, (2022); Bera K., Schalper K.A., Rimm D.L., Velcheti V., Madabhushi A., Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology,“, Nat Rev Clin Oncol, 16, 11, pp. 703-715, (2019); Mainali S., Park S., Artificial Intelligence and Big Data Science in Neurocritical Care,“, Crit Care Clin, 39, 1, pp. 235-242, (2023); Thirunavukarasu R., C G., R G., Gopikrishnan M., Palanisamy V., Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review,“, Comput Biol Med, 149, (2022); Palla K., Et al., Intraoperative prediction of postanaesthesia care unit hypotension,“, Br J Anaesth, 128, 4, pp. 623-635, (2022); Abujaber A., Fadlalla A., Gammoh D., Al-Thani H., El-Menyar A., Machine Learning Model to Predict Ventilator Associated Pneumonia in patients with Traumatic Brain Injury: The C.5 Decision Tree Approach, Brain Inj, 35, 9, pp. 1095-1102, (2021); Sinha P., Spicer A., Delucchi K.L., McAuley D.F., Calfee C.S., Churpek M.M., Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials, EBioMedicine, 74, (2021); Badner N.H., Knill R.L., Brown J.E., Novick T.V., Gelb A.W., Myocardial infarction after noncardiac surgery, Anesthesiology, 88, 3, pp. 572-578, (1998); Landesberg G., Et al., Importance of long-duration postoperative ST-segment depression in cardiac morbidity after vascular surgery, Lancet, 341, 8847, pp. 715-719, (1993); Vascular Events I., In Noncardiac Surgery Patients Cohort Evaluation Study et al, “Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surger, JAMA, 307, no. 21,, pp. 2295-2304, (2012); Dastile X., Celik T., Potsane M., Statistical and machine learning models in credit scoring: A systematic literature survey,“, Applied Soft Computing, 91, (2020); Jehi L., Et al., Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID-19,“, PLoS One, 15, 8, (2020); Jehi L., Et al., Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients, Chest, 158, 4, pp. 1364-1375, (2020); Mamidi T.K.K., Tran-Nguyen T.K., Melvin R.L., Worthey E.A., Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data,“, Front Big Data, 4, (2021); Navas-Palencia G., Optimal Binning: Mathematical Programming Formulation; Navas-Palencia G., Optimal Counterfactual Explanations for Scorecard Modelling; Zdravevskikulakov L.P.A., Weight of Evidence as a Tool for Attribute Transformation in the Preprocessing Stage of Supervised Learning Algorithms, the 2011 International Joint Conference on Neural Networks, pp. 181-188, (2011); Zou H., Hastie T., Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, pp. 301-320, (2005); Alves M.J., Climaco J., A review of interactive methods for multiobjective integer and mixed-integer programming,“, European Journal of Operational Research, 180, 1, pp. 99-115, (2007); Essays in Honor of Lloyd S. Shapley, (1998); Aumann R., Game Theory, Palgrave Macmillan UK, (1989); Lundberg S., Lee S.I., A unified approach to interpreting model predictions,“, Advances in neural information processing systems, (2017); Chen S.Y., Feng Z., Yi X., A general introduction to adjustment for multiple comparisons,“, J Thorac Dis, 9, 6, pp. 1725-1729, (2017); Pollard T.J., Johnson A.E.W., Raffa J.D., Mark R.G., tableone: An open source Python package for producing summary statistics for research papers,“, JAMIA Open, 1, 1, pp. 26-31, (2018); Carter J.V., Pan J., Rai S.N., Galandiuk S., ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves, Surgery, 159, 6, pp. 1638-1645, (2016); Aad G., “Observation of associated near-side and away-side long-range correlations in sqrt[s(NN)] = 5.02 TeV proton-lead collisions with the ATLAS detector,” Physical review letters, 110, no. 18,, (2013); Huang M.H., Et al., Validation of a Deep Learning-based Automatic Detection Algorithm for Measurement of Endotracheal Tube-to-Carina Distance on Chest Radiographs, Anesthesiology, 137, no. 6, pp. 704-715, (2022); Aldrete J.A., Kroulik D., A postanesthetic recovery score, Anesth Analg, 49, 6, pp. 924-934, (1970); Yamaguchi D., Et al., Usefulness of discharge standards in outpatients undergoing sedative endoscopy: a propensity score-matched study of the modified post-anesthetic discharge scoring system and the modified Aldrete score,“, BMC Gastroenterol, 22, 1, (2022); Kia A., Et al., MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model, J Clin Med, 9, 2, (2020); Rothman M.J., Rothman S.I., Beals J.T., Development and validation of a continuous measure of patient condition using the Electronic Medical Record, J Biomed Inform, 46, no. 5,, pp. 837-848, (2013); Jahandideh S., Ozavci G., Sahle B.W., Kouzani A.Z., Magrabi F., Bucknall T., Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review,“, Int J Med Inform, 175, (2023); Hydoub Y.M., Et al., Risk Prediction Models for Hospital Mortality in General Medical Patients: A Systematic Review, Am J Med Open, 10, (2023); Smith M.E., Et al., Early warning system scores for clinical deterioration in hospitalized patients: a systematic review,“, Annals of the American Thoracic Society, 11, 9, pp. 1454-1465, (2014); Pesapane F., Et al., Myths and facts about artificial intelligence: why machine- and deep-learning will not replace interventional radiologists, Med Oncol, 37, no. 5, (2020); Di Ieva A., AI-augmented multidisciplinary teams: Hype or hope?, Lancet, 394, (2019); Asan O., Bayrak A.E., Choudhury A., Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians, J Med Internet Res, 22, 6, (2020)","B.M. Wagener; Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, 901 19th Street South, PBMR 302, 35294, United States; email: bwagener@uabmc.edu","","Springer","","","","","","01485598","","JMSYD","39042285","English","J. Med. Syst.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85199319787"
"Ramirez L.G.; Louisias M.; Ogbogu P.U.; Stinson A.; Gupta R.; Sansweet S.; Singh T.; Apter A.; Jones B.L.; Nyenhuis S.M.","Ramirez, Lourdes G. (57921774800); Louisias, Margee (57191140096); Ogbogu, Princess U. (18537486200); Stinson, Alanna (59124425000); Gupta, Ruchi (7501324932); Sansweet, Samantha (58819273000); Singh, Tarandeep (59124425100); Apter, Andrea (55628588342); Jones, Bridgette L. (25652673800); Nyenhuis, Sharmilee M. (34877354700)","57921774800; 57191140096; 18537486200; 59124425000; 7501324932; 58819273000; 59124425100; 55628588342; 25652673800; 34877354700","Understanding Health Equity in Patient-Reported Outcomes","2024","Journal of Allergy and Clinical Immunology: In Practice","12","10","","2617","2624","7","1","10.1016/j.jaip.2024.04.023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192878073&doi=10.1016%2fj.jaip.2024.04.023&partnerID=40&md5=eb9abc981b015bd78c7dbd7f3c108a2b","Division of Allergy and Immunology, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass, United States; Division of Pediatric Allergy, Immunology, and Rheumatology, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, Ohio, United States; Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States; Section of Allergy, Immunology, and Pediatric Pulmonology, Department of Pediatrics, University of Chicago, Chicago, Ill, United States; Center for Food Allergy and Asthma Research, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill, United States; Division of Advanced General Pediatrics and Primary Care, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill, United States; Section of Allergy and Immunology, Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa, United States; Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Mo, United States; Children's Mercy Hospital, Section of Allergy/Immunology and Division of Pediatric Clinical Pharmacology and Therapeutic Innovation, Kansas City, Mo, United States","Ramirez L.G., Division of Allergy and Immunology, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass, United States; Louisias M., Division of Allergy and Immunology, Brigham and Women's Hospital and Harvard Medical School, Boston, Mass, United States; Ogbogu P.U., Division of Pediatric Allergy, Immunology, and Rheumatology, University Hospitals Rainbow Babies and Children's Hospital, Cleveland, Ohio, United States, Department of Pediatrics, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States; Stinson A., Section of Allergy, Immunology, and Pediatric Pulmonology, Department of Pediatrics, University of Chicago, Chicago, Ill, United States; Gupta R., Center for Food Allergy and Asthma Research, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill, United States, Division of Advanced General Pediatrics and Primary Care, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill, United States; Sansweet S., Center for Food Allergy and Asthma Research, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill, United States, Division of Advanced General Pediatrics and Primary Care, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill, United States; Singh T., Section of Allergy and Immunology, Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa, United States; Apter A., Section of Allergy and Immunology, Division of Pulmonary, Allergy, and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa, United States; Jones B.L., Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Mo, United States, Children's Mercy Hospital, Section of Allergy/Immunology and Division of Pediatric Clinical Pharmacology and Therapeutic Innovation, Kansas City, Mo, United States; Nyenhuis S.M., Section of Allergy, Immunology, and Pediatric Pulmonology, Department of Pediatrics, University of Chicago, Chicago, Ill, United States","Patient-reported outcomes (PROs) are measures of patients’ health that are conveyed directly by individual patients. These measures serve as instruments to evaluate the impact of interventions on any aspect of patients’ health, from specific symptoms to broader quality of life indicators. However, their effectiveness relies on capturing relevant factors accurately. Whereas they are commonly used in clinical trials, PROs extend their influence across health care settings, informing clinicians, health care payers, regulators, and administrators to guide quality improvement and reimbursement decisions. Neglecting health equity considerations in PRO development and implementation widens health disparities, leading to biased interpretations, medical mismanagement, and poor health outcomes among marginalized groups. To foster equitable health care, efforts must focus on considering the values of underrepresented populations in PRO design, addressing barriers to completion, enhancing representation in research, providing cultural competency training for clinicians, and allocating research funding to support health equity research. By addressing these issues, advances can be made toward fostering inclusive, equitable health care for all individuals. © 2024 American Academy of Allergy, Asthma & Immunology","Allergy/immunology; Health disparities; Health equity; Patient-reported outcomes","Health Equity; Healthcare Disparities; Humans; Patient Reported Outcome Measures; epinephrine; allergic asthma; Article; artificial intelligence; clinical outcome; cognitive defect; cost effectiveness analysis; cultural competence; decision making; disease activity; disease severity; emergency physician; health care delivery; health care facility; health care organization; health care quality; health disparity; health equity; health insurance; health literacy; human; knowledge; language ability; medical education; medication compliance; Montreal cognitive assessment; outcome assessment; patient care; patient-reported outcome; quality of life; questionnaire; social determinants of health; socioeconomics; spirometry; total quality management; training; health care disparity","","epinephrine, 51-43-4, 55-31-2, 6912-68-1","","","National Institute on Minority Health and Health Disparities, NIMHD; Sunshine Charitable Foundation; Allergenis LLC; Melchiorre Family Foundation; National Institutes of Health, NIH; Asthma and Allergy Foundation of America, AAFA; National Heart, Lung, and Blood Institute, NHLBI; Food Allergy Research and Education, FARE; UnitedHealth Group, UHG; Bristol Myers Squibb Foundation, BMSF, (R21 ID AI135705, R01 ID AI130348, U01 ID AI138907); Bristol Myers Squibb Foundation, BMSF","Funding text 1: The consequences of these inequities extend beyond the disease itself and adversely impact the US economy. One study funded by the National Institute on Minority Health and Health Disparities revealed that in 2018, racial and ethnic disparities cost the US economy $451 billion, $131 billion higher than the burden in 2014. 10 This economic burden included the cost of excess medical expenses, loss of productivity in the workforce among Black, American Indian, Alaska Native, Latino, and Native Hawaiian and other Pacific Islander populations. ; Funding text 2: Conflicts of interest: L.G. Ramirez is supported by Grant T32 HL007427 from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute . M. Louisias is supported by the Bristol Myers Squibb Foundation Robert A. Winn Award and the Brigham and Women's Hospital Minority Faculty Career Development Award. R. Gupta receives research support from the NIH (R21 ID AI135705, R01 ID AI130348, and U01 ID AI138907), Food Allergy Research & Education, the Melchiorre Family Foundation, the Sunshine Charitable Foundation, the Walder Foundation, the UnitedHealth Group, Thermo Fisher Scientific, Novartis, and Genentech; she serves as a medical consultant/advisor for Genentech, Novartis, Aimmune LLC, Allergenis LLC, and Food Allergy Research & Education; and she has ownership interest in Yobee Care, Inc. A. Apter receives support from the NIH National Heart, Lung, and Blood Institute and is an associate editor of The Journal of Allergy and Clinical Immunology. B.L. Jones receives funding from the National Institutes of Health and is a paid author for Merck Manual. P.U. Ogbogu receives research funding from GSK , AstraZeneca , Blueprint Medical, and DBV; she serves on the advisory board for AstraZeneca, Genentech, and Kalvista; and she is a consultant for AstraZeneca. S.M. Nyenhuis has been a consultant for GSK and Avillion; receives research funding from the NIH and the Allergy and Asthma Foundation of America; and receives royalties from Wolters Kluwer and Springer. The rest of the authors declare that they have no relevant conflicts of interest. ","Patient-reported outcome measures; Calvert M.J., Cruz Rivera S., Retzer A., Hughes S.E., Campbell L., Molony-Oates B., Et al., Patient reported outcome assessment must be inclusive and equitable, Nat Med, 28, pp. 1120-1124, (2022); Cruz Rivera S., Liu X., Hughes S.E., Dunster H., Manna E., Denniston A.K., Et al., Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies, Lancet Digit Health, 5, pp. e168-e173, (2023); Hopkins C., Gillett S., Slack R., Lund V.J., Browne J.P., Psychometric validity of the 22-item Sinonasal Outcome Test, Clin Otolaryngol, 34, pp. 447-454, (2009); Enhancing the diversity of clinical trial populations — eligibility criteria, enrollment practices, and trial designs guidance for industry; Pritchett J.C., Patt D., Thanarajasingam G., Schuster A., Snyder C., Patient-reported outcomes, digital health, and the quest to improve health equity, Am Soc Clin Oncol Educ Book, 43, (2023); Dehbozorgi S., Ramsey N., Lee A.S.E., Coleman A., Varshney P., Davis C.M., Addressing health equity in food allergy, J Allergy Clin Immunol Pract, 12, pp. 570-577, (2024); 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Nyenhuis; Section of Allergy, Immunology, and Pediatric Pulmonology, Department of Pediatrics, University of Chicago, Chicago, 5837 S Maryland Ave, 60637, United States; email: snyenhuis@bsd.uchicago.edu","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","38648977","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85192878073"
"Elashmawi W.H.; Djellal A.; Sheta A.; Surani S.; Aljahdali S.","Elashmawi, Walaa H. (55350799600); Djellal, Adel (55258162100); Sheta, Alaa (56279285500); Surani, Salim (8323866200); Aljahdali, Sultan (25640715200)","55350799600; 55258162100; 56279285500; 8323866200; 25640715200","Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms","2024","Diagnostics","14","24","2822","","","","1","10.3390/diagnostics14242822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213412014&doi=10.3390%2fdiagnostics14242822&partnerID=40&md5=6f687917c6e076aa51eb2c7de52681a7","Department of Computer Science, Suez Canal University, Ismailia, 41522, Egypt; Department of Computer Science, Misr International University, Cairo, 11828, Egypt; Department of Electronics, Electrotechnics, and Automation (EEA), National Higher School of Technology and Engineering, Annaba, 23000, Algeria; Computer Science Department, Southern Connecticut State University, New Haven, 06515, CT, United States; Department of Pharmacy & Medicine, Texas A&M University, College Station, 75428, TX, United States; Computer Science Department, Taif University, Taif, 21944, Saudi Arabia","Elashmawi W.H., Department of Computer Science, Suez Canal University, Ismailia, 41522, Egypt, Department of Computer Science, Misr International University, Cairo, 11828, Egypt; Djellal A., Department of Electronics, Electrotechnics, and Automation (EEA), National Higher School of Technology and Engineering, Annaba, 23000, Algeria; Sheta A., Computer Science Department, Southern Connecticut State University, New Haven, 06515, CT, United States; Surani S., Department of Pharmacy & Medicine, Texas A&M University, College Station, 75428, TX, United States; Aljahdali S., Computer Science Department, Taif University, Taif, 21944, Saudi Arabia","Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing. © 2024 by the authors.","artificial neural network (ANN); chronic obstructive pulmonary disease (COPD); machine learning (ML); random forest classifier (RFC)","adult; Article; artificial neural network; Bayesian learning; Caucasian; chronic obstructive lung disease; classification algorithm; classifier; comparative study; confusion matrix; controlled study; coughing; current smoker; decision tree; diagnostic accuracy; diagnostic test accuracy study; female; forced expiratory volume; forced vital capacity; gaussian naive bayes; gradient boosting classifier; Hispanic; human; k nearest neighbor; logistic regression analysis; lung function test; machine learning; major clinical study; male; model; non-smoker; random forest; support vector machine","","","Ultima PF, mgc diagnostics, United States","mgc diagnostics, United States","","","Syamlal G., Doney B., Hendricks S., Mazurek J.M., Chronic Obstructive Pulmonary Disease and U.S. Workers: Prevalence, Trends, and Attributable Cases Associated with Work, Am. 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Elashmawi; Department of Computer Science, Suez Canal University, Ismailia, 41522, Egypt; email: w.hashmawi@ci.suez.edu.eg","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85213412014"
"Su J.G.; Shahriary E.; Sage E.; Jacobsen J.; Park K.; Mohegh A.","Su, Jason G. (11840142100); Shahriary, Eahsan (57205504382); Sage, Emma (58956937400); Jacobsen, John (59398862500); Park, Katherine (59399159500); Mohegh, Arash (57192953474)","11840142100; 57205504382; 58956937400; 59398862500; 59399159500; 57192953474","Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California","2024","Environment International","193","","109100","","","","1","10.1016/j.envint.2024.109100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208361323&doi=10.1016%2fj.envint.2024.109100&partnerID=40&md5=3905d45cc09ffd86e45e8d1d5b039d51","School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Research Division, California Air Resources Board, Sacramento, 95812, CA, United States","Su J.G., School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Shahriary E., School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Sage E., School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Jacobsen J., School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Park K., School of Public Health, University of California, Berkeley Berkeley, 94720, CA, United States; Mohegh A., Research Division, California Air Resources Board, Sacramento, 95812, CA, United States","California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989–2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)–NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy. © 2024 The Authors","Air pollution; Deletion/substitution/addition; Fine particulate matter; Land use regression; Nitrogen dioxide; Ozone; Remote sensing","Air Pollutants; Air Pollution; California; Environmental Monitoring; Models, Theoretical; Nitrogen Dioxide; Ozone; Particulate Matter; Spatio-Temporal Analysis; California; United States; Ambulance cars; Bioremediation; Energy policy; Traffic surveys; carbon monoxide; chlorophyll; gamma interferon; nitric oxide; oxygen; ozone; sulfur dioxide; nitrogen dioxide; ozone; California; Condition; Deletion/substitution/addition; Fine particulate matter; Land use regression; Nitrogen dioxides; NO  2; PM 2.5; Remote-sensing; Traffic densities; air quality; algorithm; atmospheric pollution; NDVI; nitrogen dioxide; ozone; particulate matter; regression analysis; remote sensing; spatiotemporal analysis; air pollution; Article; asthma; California; chemoluminescence; chlorophyll content; climate change; community structure; environmental factor; environmental monitoring; eutrophication; greenhouse effect; human; microbial community; particulate matter; phytoremediation; plant growth; PM2.5 exposure; population density; quantitative structure activity relation; sea surface temperature; seasonal variation; spatiotemporal analysis; squamous cell carcinoma; support vector machine; time series analysis; validation process; visual field; air pollutant; California; environmental monitoring; particulate matter; spatiotemporal analysis; theoretical model; Air quality","","carbon monoxide, 630-08-0; chlorophyll, 1406-65-1, 15611-43-5; gamma interferon, 82115-62-6; nitric oxide, 10102-43-9; oxygen, 7782-44-7; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; nitrogen dioxide, 10102-44-0; Air Pollutants, ; Nitrogen Dioxide, ; Ozone, ; Particulate Matter, ","","","Stanford University, SU; California Air Resources Board, CARB, (21RD004, 22RD011); California Air Resources Board, CARB","Funding text 1: We would like to extend our sincere gratitude to the team at the California Air Resources Board (CARB) for their invaluable and continued support throughout the review and revision process of our project report. Their expertise, feedback, and collaboration played a pivotal role in shaping the content and quality of this manuscript. We deeply appreciate CARB's commitment to advancing environmental research and their dedication to enhancing the understanding of the impacts of air pollution on respiratory health. Their ongoing involvement has been instrumental in bringing this study to fruition. Co-author E. S. is now a research scientist at Stanford University, Palo Alto, United States.; Funding text 2: This work was supported by the California Air Resources Board (CARB) through funding 21RD004 and 22RD011. 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Su; School of Public Health, University of California, Berkeley Berkeley, 94720, United States; email: jasonsu@berkeley.edu","","Elsevier Ltd","","","","","","01604120","","ENVID","39520932","English","Environ. Int.","Article","Final","","Scopus","2-s2.0-85208361323"
"Ranzani O.; Alari A.; Olmos S.; Milà C.; Rico A.; Basagaña X.; Dadvand P.; Duarte-Salles T.; Forastiere F.; Nieuwenhuijsen M.; Vivanco-Hidalgo R.M.; Tonne C.","Ranzani, Otavio (16679396800); Alari, Anna (57192545472); Olmos, Sergio (57205546985); Milà, Carles (57201276782); Rico, Alex (58287484700); Basagaña, Xavier (55887724000); Dadvand, Payam (6508286647); Duarte-Salles, Talita (36767041500); Forastiere, Francesco (34568758600); Nieuwenhuijsen, Mark (7007123042); Vivanco-Hidalgo, Rosa M (24336731200); Tonne, Cathryn (57548766000)","16679396800; 57192545472; 57205546985; 57201276782; 58287484700; 55887724000; 6508286647; 36767041500; 34568758600; 7007123042; 24336731200; 57548766000","Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation?","2024","Environment International","185","","108530","","","","1","10.1016/j.envint.2024.108530","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186426911&doi=10.1016%2fj.envint.2024.108530&partnerID=40&md5=f668a3b9167b5be13289892826949e2c","Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; National Research Council, IFT, Palermo, Italy; Environmental Research Group, Imperial College London, London, United Kingdom; Agency for Health Quality and Assessment of Catalonia (AquAS), Barcelona, Spain","Ranzani O., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Alari A., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Olmos S., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Milà C., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Rico A., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Basagaña X., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Dadvand P., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Duarte-Salles T., Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain, Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands; Forastiere F., National Research Council, IFT, Palermo, Italy, Environmental Research Group, Imperial College London, London, United Kingdom; Nieuwenhuijsen M., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; Vivanco-Hidalgo R.M., Agency for Health Quality and Assessment of Catalonia (AquAS), Barcelona, Spain; Tonne C., Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain, Universitat Pompeu Fabra (UPF), Barcelona, Spain, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain","Objective: Factors that shape individuals’ vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. Methods: We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. Results: Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. Conclusions: Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status. © 2024 The Authors","Air pollution; COVID-19; Effect modification; Vulnerability","Adult; Air Pollutants; Air Pollution; COVID-19; Environmental Exposure; Hospitalization; Humans; Male; Nitrogen Dioxide; Particulate Matter; Catalonia; Spain; Additives; Air pollution; Cardiology; Nitrogen oxides; Pulmonary diseases; reactive oxygen metabolite; superoxide dismutase; nitrogen dioxide; Ambients; Arterial hypertension; Effect modifications; Long term exposure; PM 10; PM 2.5; Socio-economic status; TO effect; Vulnerability; Vulnerability indicators; atmospheric pollution; chronic obstructive pulmonary disease; COVID-19; diabetes; hospital sector; hypertension; neighborhood; particulate matter; pollution effect; pollution exposure; risk assessment; socioeconomic status; vulnerability; additive effect; adult; aged; air pollution; all cause mortality; Article; blood glucose monitoring; chronic obstructive lung disease; cohort analysis; coronavirus disease 2019; diabetes mellitus; female; health care cost; health care policy; health care system; hospitalization; human; hypertension; length of stay; long term exposure; lowest income group; machine learning; male; outcome assessment; particulate matter 10; particulate matter 2.5; prevalence; real time reverse transcription polymerase chain reaction; risk factor; social status; socioeconomics; time series analysis; vulnerability; air pollutant; analysis; coronavirus disease 2019; environmental exposure; hospitalization; particulate matter; COVID-19","","superoxide dismutase, 37294-21-6, 9016-01-7, 9054-89-1; nitrogen dioxide, 10102-44-0; Air Pollutants, ; Nitrogen Dioxide, ; Particulate Matter, ","","","U.S. Environmental Protection Agency, EPA, (R-82811201); U.S. Environmental Protection Agency, EPA; Health Effects Institute, HEI, (4980-RFA20-1B/21-3); Health Effects Institute, HEI; Generalitat de Catalunya; Instituto de Salud Carlos III, ISCIII, (CD19/00110, CEX2018-000806-S); Instituto de Salud Carlos III, ISCIII; Ministerio de Ciencia e Innovación, MCIN; Agencia Estatal de Investigación, AEI","This work was supported by Health Effects Institute (HEI) research agreement (grant No 4980-RFA20-1B/21-3 ). Research described in this article was conducted under contract to the HEI, an organisation jointly funded by the US Environmental Protection Agency (EPA) (assistance award No R-82811201) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. OTR acknowledges support by a Sara Borrell fellowship from the Instituto de Salud Carlos III (CD19/00110). We acknowledge support from the grant CEX2018-000806-S funded by MCIN/AEI/ 10.13039/501100011033, and support from the Generalitat de Catalunya through the CERCA Program. 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Respir. J., (2023)","C. Tonne; Barcelona, Carrer del Dr. Aiguader 88, 08003, Spain; email: cathryn.tonne@isglobal.org","","Elsevier Ltd","","","","","","01604120","","ENVID","38422877","English","Environ. Int.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85186426911"
"Alcoceba-Herrero I.; Coco-Martín M.B.; Jiménez-Pérez J.M.; Leal-Vega L.; Martín-Gutiérrez A.; Dueñas-Gutiérrez C.; Miramontes-González J.P.; Corral-Gudino L.; de Castro-Rodríguez F.; Royuela-Ruiz P.; Arenillas-Lara J.F.","Alcoceba-Herrero, Irene (58045872700); Coco-Martín, María Begoña (6508281289); Jiménez-Pérez, José María (56405935100); Leal-Vega, Luis (57218440329); Martín-Gutiérrez, Adrián (57321477300); Dueñas-Gutiérrez, Carlos (57207940377); Miramontes-González, José Pablo (24449862300); Corral-Gudino, Luis (6506236514); de Castro-Rodríguez, Flor (54792729300); Royuela-Ruiz, Pablo (58100275800); Arenillas-Lara, Juan Francisco (57210840656)","58045872700; 6508281289; 56405935100; 57218440329; 57321477300; 57207940377; 24449862300; 6506236514; 54792729300; 58100275800; 57210840656","Randomized Controlled Trial to Assess the Feasibility of a Novel Clinical Decision Support System Based on the Automatic Generation of Alerts through Remote Patient Monitoring","2024","Journal of Clinical Medicine","13","19","5974","","","","1","10.3390/jcm13195974","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206559586&doi=10.3390%2fjcm13195974&partnerID=40&md5=e2fa8ac3ec64a5e13585b31883352c86","Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain; Department of Nursing, University of Valladolid, Valladolid, 47005, Spain; Department of Internal Medicine, University Clinical Hospital of Valladolid, Valladolid, 47003, Spain; Department of Internal Medicine, Rio Hortega University Hospital, Valladolid, 47012, Spain; Emergency Medical Services Direction, SACyL, Valladolid, 47006, Spain; Technical Direction of Primary Care, SACyL, Valladolid, 47006, Spain; Department of Neurology, University Clinical Hospital of Valladolid, Valladolid, 47003, Spain","Alcoceba-Herrero I., Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain, Department of Nursing, University of Valladolid, Valladolid, 47005, Spain; Coco-Martín M.B., Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain; Jiménez-Pérez J.M., Department of Nursing, University of Valladolid, Valladolid, 47005, Spain; Leal-Vega L., Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain; Martín-Gutiérrez A., Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain; Dueñas-Gutiérrez C., Department of Internal Medicine, University Clinical Hospital of Valladolid, Valladolid, 47003, Spain; Miramontes-González J.P., Department of Internal Medicine, Rio Hortega University Hospital, Valladolid, 47012, Spain; Corral-Gudino L., Department of Internal Medicine, Rio Hortega University Hospital, Valladolid, 47012, Spain; de Castro-Rodríguez F., Emergency Medical Services Direction, SACyL, Valladolid, 47006, Spain; Royuela-Ruiz P., Technical Direction of Primary Care, SACyL, Valladolid, 47006, Spain; Arenillas-Lara J.F., Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain, Department of Neurology, University Clinical Hospital of Valladolid, Valladolid, 47003, Spain","Background/Objectives: Early identification of complications in chronic and infectious diseases can reduce clinical deterioration, lead to early therapeutic interventions and lower morbidity and mortality rates. Here, we aimed to assess the feasibility of a novel clinical decision support system (CDSS) based on the automatic generation of alerts through remote patient monitoring and to identify the patient profile associated with the likelihood of severe medical alerts. Methods: A prospective, multicenter, open-label, randomized controlled trial was conducted. Patients with COVID-19 in home isolation were randomly assigned in a 1:1 ratio to receive either conventional primary care telephone follow-up plus access to a mobile app for self-reporting of symptoms (control group) or conventional primary care telephone follow-up plus access to the mobile app for self-reporting of symptoms and wearable devices for real-time telemonitoring of vital signs (case group). Results: A total of 342 patients were randomized, of whom 247 were included in the per-protocol analysis (103 cases and 144 controls). The case group received a more exhaustive follow-up, with a higher number of alerts (61,827 vs. 1825; p < 0.05) but without overloading healthcare professionals thanks to automatic alert management through artificial intelligence. Baseline factors independently associated with the likelihood of a severe alert were having asthma (OR: 1.74, 95% CI: 1.22–2.48, p = 0.002) and taking corticosteroids (OR: 2.28, 95% CI: 1.24–4.2, p = 0.008). Conclusions: The CDSS could be successfully implemented and enabled real-time telemonitoring of patients’ clinical status, providing valuable information to physicians and public health agencies. © 2024 by the authors.","clinical; COVID-19; decision support systems; monitoring; physiologic; telemedicine; wearable electronic devices","biological product; corticosteroid; immunosuppressive agent; adult; aged; Article; artificial intelligence; asthma; automation; clinical decision support system; controlled study; coronavirus disease 2019; corticosteroid therapy; feasibility study; female; follow up; health care personnel; home quarantine; human; major clinical study; male; morbidity; mortality rate; multicenter study; open study; per protocol analysis; primary medical care; prospective study; randomized controlled trial; self report; symptomatology; telemonitoring; vital sign","","","Bakeey E66, shenzhen yisi technology, United States; CARESCAPE B450, GE Healthcare, United States; FS20F, wellue, United States","GE Healthcare, United States; shenzhen yisi technology, United States; wellue, United States","Consejería de Educación, Junta de Castilla y León; Universidad de Valladolid, UVA; Instituto de Salud Carlos III, ISCIII, (COV20/00539); Instituto de Salud Carlos III, ISCIII","This study is part of the project \u201CMultimodal Sensorics, AI & Big Data for the control of COVID-19\u201D, funded by the Junta de Castilla y Le\u00F3n to the University of Valladolid, under the COVID-19 fund of the Carlos III Health Institute (Ref.: COV20/00539). It has also obtained RICORS ICTUS funding from the Carlos III Health Institute. The funders had no role in the design of the study; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.","Marani M., Katul G.G., Pan W.K., Parolari A.J., Intensity and frequency of extreme novel epidemics, Proc. Natl. Acad. Sci. USA, 118, (2021); Simpson S., Kaufmann M.C., Glozman V., Chakrabarti A., Disease X: Accelerating the development of medical countermeasures for the next pandemic, Lancet Infect. Dis, 20, pp. e108-e115, (2020); Hassoun N., Basu K., Gostin L., Pandemic preparedness and response: A new mechanism for expanding access to essential countermeasures, Health Econ. Policy Law, pp. 1-24, (2024); Glasby J., Litchfield I., Parkinson S., Hocking L., Tanner D., Roe B., Bousfield J., New and emerging technology for adult social care—The example of home sensors with artificial intelligence (AI) technology, Health Soc. Care Deliv. Res, 11, pp. 1-64, (2023); Muralitharan S., Nelson W., Di S., McGillion M., Devereaux P., Barr N.G., Petch J., Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review, J. Med. Internet Res, 23, (2021); Sabry F., Eltaras T., Labda W., Alzoubi K., Malluhi Q., Machine Learning for Healthcare Wearable Devices: The Big Picture, J. Healthc. Eng, 2022, (2022); Chatterjee A., Prinz A., Riegler M.A., Das J., A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care, BMC Health Serv. Res, 30, (2023); Annis T., Pleasants S., Hultman G., Lindemann E., A Thompson J., Billecke S., Badlani S., Melton G.B., Rapid implementation of a COVID-19 remote patient monitoring program, J. Am. Med. Inform. Assoc, 27, pp. 1326-1330, (2020); Mao L., Mohan G., Normand C., Use of information communication technologies by older people and telemedicine adoption during COVID-19: A longitudinal study, J. Am. Med. Inform. Assoc, 30, pp. 2012-2020, (2023); Al-Rawashdeh M., Keikhosrokiani P., Belaton B., Alawida M., Zwiri A., IoT Adoption and Application for Smart Healthcare: A Systematic Review, Sensors, 19, (2022); Pont M.V., Rodriguez MC S., Blanc N.P., PInsach L., Impact of implementing new technologies to innovate and transform primary care: The technology nurse, Aten. Prim. Pract, 3, (2021); Baumgartel D., Mielke C., Haux R., A Review of Decision Support Systems for Smart Homes in the Health Care System, Stud. Health Technol. Inform, 247, pp. 476-480, (2018); Mitratza M., Goodale B.M., Shagadatova A., Kovacevic V., van de Wijgert J., Brakenhoff T.B., Dobson R., Franks B., Veen D., Folarin A.A., Et al., The performance of wearable sensors in the detection of SARS-CoV-2 infection: A systematic review, Lancet Digit. Health, 4, pp. e370-e383, (2022); Sodhro A.H., Zahid N., AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications, Sensors, 21, (2021); Alcoceba-Herrero I., Coco-Martin M.B., Leal-Vega L., Martin-Gutierrez A., Diego L.P.-D., Duenas-Gutierrez C., de Castro-Rodriguez F., Royuela-Ruiz P., Arenillas-Lara J.F., Randomized controlled trial evaluating the benefit of a novel clinical decision support system for the management of COVID-19 Patients in Home Quarantine: A Study Protocol, Int. J. Environ. Res. Public. Health, 20, (2023); Un K.-C., Wong C.-K., Lau Y.-M., Lee J.C.-Y., Tam F.C.-C., Lai W.-H., Lau Y.-M., Chen H., Wibowo S., Zhang X., Et al., Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients, Sci. Rep, 11, (2021); Takahashi S., Nakazawa E., Ichinohe S., Akabayashi A., Akabayashi A., Wearable Technology for Monitoring Respiratory Rate and SpO<sub>2</sub> of COVID-19 Patients: A Systematic Review, Diagnostics, 12, (2022); Kondylakis H., Katehakis D.G., Kouroubali A., Logothetidis F., Triantafyllidis A., Kalamaras I., Votis K., Tzovaras D., COVID-19 Mobile Apps: A Systematic Review of the Literature, J. Med. Internet Res, 22, (2020); Xu H., Huang S., Qiu C., Liu S., Deng J., Jiao B., Tan X., Ai L., Xiao Y., Belliato M., Et al., Monitoring and Management of Home-Quarantined Patients with COVID-19 Using a WeChat-Based Telemedicine System: Retrospective Cohort Study, J. Med. Internet Res, 22, (2020); Faris H., Habib M., Faris M., Elayan H., Alomari A., An intelligent multimodal medical diagnosis system based on patients’ medical questions and structured symptoms for telemedicine, Inform. Med. Unlocked, 23, (2021); van Goor H.M., Breteler M.J., van Loon K., de Hond T.A., Reitsma J.B., Zwart D.L., Kalkman C.J., Kaasjager K.A., Remote Hospital Care for Recovering COVID-19 Patients Using Telemedicine: A Randomised Controlled Trial, J. Clin. Med, 10, (2021); Vindrola-Padros C., Singh K.E., Sidhu M.S., Georghiou T., Sherlaw-Johnson C., Tomini S.M., Inada-Kim M., Kirkham K., Streetly A., Cohen N., Et al., Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: A rapid systematic review, EClinicalMedicine, 37, (2021); Beaney T., Neves A.L., Alboksmaty A., Ashrafian H., Flott K., Fowler A., Benger J.R., Aylin P., Elkin S., Darzi A., Et al., Trends and associated factors for COVID-19 hospitalization and fatality risk in 2.3 million adults in England, Nat. Commun, 13, (2022); Williamson E.J., Walker A.J., Bhaskaran K., Bacon S., Bates C., Morton C.E., Curtis H.J., Mehrkar A., Evans D., Inglesby P., Et al., OpenSAFELY: Factors associated with COVID-19 death in 17 million patients, Nature, 584, pp. 430-446, (2020); Wurzer D., Spielhagen P., Siegmann A., Gercekcioglu A., Gorgass J., Henze S., Kolar Y., Koneberg F., Kukkonen S., McGowan H., Et al., Remote monitoring of COVID-19 positive high-risk patients in domestic isolation: A feasibility study, PLoS ONE, 16, (2021); Bauerly B.C., McCord R.F., Hulkower R., Pepin D., Broadband Access as a Public Health Issue: The Role of Law in Expanding Broadband Access and Connecting Underserved Communities for Better Health Outcomes, J. Law Med. Ethics, 47, pp. 39-42, (2019); Casariego-Vales E., Blanco-Lopez R., Roson-Calvo B., Suarez-Gil R., Santos-Guerra F., Dobao-Feijoo M.J., Ares-Rico R., Bal-Alvaredo M., Efficacy of Telemedicine and Telemonitoring in At-Home Monitoring of Patients with COVID-19, J. Clin. Med, 10, (2021); Lee J.M., Jansen R., E Sanderson K., Guerra F., Keller-Olaman S., Murti M., O'sullivan T.L., Law M.P., Schwartz B., E Bourns L., Et al., Public health emergency preparedness for infectious disease emergencies: A scoping review of recent evidence, BMC Public. Health, 23, (2023); Schulz K.F., Altman D.G., Moher D., CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials, BMC Med, 8, (2010)","M.B. Coco-Martín; Applied Clinical Neurosciences Research Group, Department of Medicine, Dermatology and Toxicology, University of Valladolid, Valladolid, 47005, Spain; email: mbcoco@uva.es","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85206559586"
"Glyde H.M.G.; Blythin A.M.; Wilkinson T.M.A.; Nabney I.T.; Dodd J.W.","Glyde, Henry M.G. (57218765738); Blythin, Alison M. (57219667389); Wilkinson, Tom M.A. (7202351234); Nabney, Ian T. (6701838448); Dodd, James W. (35811974000)","57218765738; 57219667389; 7202351234; 6701838448; 35811974000","Exacerbation predictive modelling using real-world data from the myCOPD app","2024","Heliyon","10","10","e31201","","","","1","10.1016/j.heliyon.2024.e31201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192920252&doi=10.1016%2fj.heliyon.2024.e31201&partnerID=40&md5=a97f34264342619e22102b013c27bd73","EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, United Kingdom; My mHealth, Bournemouth, United Kingdom; My mHealth and Clinical and Experimental Science, University of Southampton, Southampton, United Kingdom; School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom; Academic Respiratory Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom","Glyde H.M.G., EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, United Kingdom; Blythin A.M., My mHealth, Bournemouth, United Kingdom; Wilkinson T.M.A., My mHealth and Clinical and Experimental Science, University of Southampton, Southampton, United Kingdom; Nabney I.T., School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom; Dodd J.W., Academic Respiratory Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom","Background: Acute exacerbations of COPD (AECOPD) are episodes of breathlessness, cough and sputum which are associated with the risk of hospitalisation, progressive lung function decline and death. They are often missed or diagnosed late. Accurate timely intervention can improve these poor outcomes. Digital tools can be used to capture symptoms and other clinical data in COPD. This study aims to apply machine learning to the largest available real-world digital dataset to develop AECOPD Prediction tools which could be used to support early intervention and improve clinical outcomes. Objective: To create and validate a machine learning predictive model that forecasts exacerbations of COPD 1–8 days in advance. The model is based on routine patient-entered data from myCOPD self-management app. Method: Adaptations of the AdaBoost algorithm were employed as machine learning approaches. The dataset included 506 patients users between 2017 and 2021. 55,066 app records were available for stable COPD event labels and 1263 records of AECOPD event labels. The data used for training the model included COPD assessment test (CAT) scores, symptom scores, smoking history, and previous exacerbation frequency. All exacerbation records used in the model were confined to the 1–8 days preceding a self-reported exacerbation event. Results: TheEasyEnsemble Classifier resulted in a Sensitivity of 67.0 % and a Specificity of 65 % with a positive predictive value (PPV) of 5.0 % and a negative predictive value (NPV) of 98.9 %. An AdaBoost model with a cost-sensitive decision tree resulted in a a Sensitivity of 35.0 % and a Specificity of 89.0 % with a PPV of 7.08 % and NPV of 98.3 %. Conclusion: This preliminary analysis demonstrates that machine learning approaches to real-world data from a widely deployed digital therapeutic has the potential to predict AECOPD and can be used to confidently exclude the risk of exacerbations of COPD within the next 8 days. © 2024 The Authors","Chronic obstructive pulmonary disease (COPD); Machine learning; mHealth; Prediction models","","","","","","NIHR Bristol Biomedical Research Centre; National Institute for Health and Care Research, NIHR; EPSRC Digital Health and Care Centre for Doctoral Training; University of Bristol; UK Research and Innovation, UKRI; Engineering and Physical Sciences Research Council, EPSRC, (EP/S023704/1)","The author is supported by the EPSRC Digital Health and Care Centre for Doctoral Training (CDT) at the University of Bristol (UKRI Grant No. EP/S023704/1). This study was supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.","Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Et al., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir. Med., 10, 5, pp. 447-458, (2022); Viegi G., Maio S., Fasola S., Baldacci S., Global burden of chronic respiratory diseases, J. Aerosol Med. Pulm. Drug Deliv., 33, 4, pp. 171-177, (2020); Li X., Cao X., Guo M., Xie M., Liu X., Trends and risk factors of mortality and disability adjusted life years for chronic respiratory diseases from 1990 to 2017: systematic analysis for the Global Burden of Disease Study 2017, Br. Med. J., (2020); Raherison C., Girodet P., Epidemiology of COPD, Eur. Respir. Rev., 18, 114, pp. 213-221, (2009); Toy E.L., Gallagher K.F., Stanley E.L., Swensen A.R., Duh M.S., The economic impact of exacerbations of chronic obstructive pulmonary disease and exacerbation definition: a review. COPD: journal of Chronic Obstructive Pulmonary Disease, 7, 3, pp. 214-228, (2010); Wouters E., Economic analysis of the Confronting COPD survey: an overview of results, Respir. Med., 97, pp. S3-S14, (2003); Iheanacho I., Zhang S., King D., Rizzo M., Ismaila A.S., Economic burden of chronic obstructive pulmonary disease (COPD): a systematic literature review, Int. J. Chronic Obstr. Pulm. Dis., 15, (2020); Seemungal T.A., Donaldson G.C., Paul E.A., Bestall J.C., Jeffries D.J., Wedzicha J.A., Effect of exacerbation on quality of life in patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. 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Med., 9, 8, pp. 824-826, (2021); Wilkinson T.M., Donaldson G.C., Hurst J.R., Seemungal T.A., Wedzicha J.A., Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med., 169, 12, pp. 1298-1303, (2004); Dinesen B., Haesum L.K., Soerensen N., Nielsen C., Grann O., Hejlesen O., Et al., Using preventive home monitoring to reduce hospital admission rates and reduce costs: a case study of telehealth among chronic obstructive pulmonary disease patients, J. Telemed. Telecare, 18, 4, pp. 221-225, (2012); Calvo G.S., G'omez-Su'arez C., Soriano J., Zamora E., G'onzalez-Gamarra A., Gonz'alez-B'ejar M., Et al., A home telehealth program for patients with severe COPD: the PROMETE study, Respir. Med., 108, 3, pp. 453-462, (2014); Orchard P., Agakova A., Pinnock H., Burton C.D., Sarran C., Agakov F., Et al., Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data, J. Med. Internet Res., 20, 9, (2018); Hastie T., Rosset S., Zhu J., Zou H., Multi-class adaboost, Stat. Interface, 2, 3, pp. 349-360, (2009); Breiman L., Friedman J.H., Olshen R.A., Stone C.J., Classification and Regression Trees, 432, pp. 151-166, (1984); Liu X.Y., Wu J., Zhou Z.H., Exploratory undersampling for class-imbalance learning, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39, 2, pp. 539-550, (2008); Xie Y., Redmond S.J., Mohktar M.S., Shany T., Basilakis J., Hession M., Et al., Prediction of chronic obstructive pulmonary disease exacerbation using physiological time series patterns, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6784-6787, (2013); Mohktar M.S., Redmond S.J., Antoniades N.C., Rochford P.D., Pretto J.J., Basilakis J., Et al., Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data, Artif. Intell. Med., 63, 1, pp. 51-59, (2015); Patel N., Kinmond K., Jones P., Birks P., Spiteri M.A., Validation of COPDPredict™: unique combination of remote monitoring and exacerbation prediction to support preventative management of COPD exacerbations, Int. J. Chronic Obstr. Pulm. Dis., 16, (2021); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system, J. Med. Internet Res., 19, 3, (2017); Van der Heijden M., Velikova M., Lucas P.J., Learning Bayesian networks for clinical time series analysis, J. Biomed. Inf., 48, pp. 94-105, (2014); Jensen M.H., Cichosz S.L., Dinesen B., Hejlesen O.K., Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare, J. Telemed. Telecare, 18, 2, pp. 99-103, (2012)","H.M.G. Glyde; EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, United Kingdom; email: henry.glyde@bristol.ac.uk","","Elsevier Ltd","","","","","","24058440","","","","English","Heliyon","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85192920252"
"Abadade Y.; Benamar N.; Bagaa M.; Chaoui H.","Abadade, Youssef (58485273200); Benamar, Nabil (6507518089); Bagaa, Miloud (24467380100); Chaoui, Habiba (35318576700)","58485273200; 6507518089; 24467380100; 35318576700","Empowering Healthcare: TinyML for Precise Lung Disease Classification","2024","Future Internet","16","11","391","","","","1","10.3390/fi16110391","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210241663&doi=10.3390%2ffi16110391&partnerID=40&md5=f1d5386e870b86229e4523984e0a2e02","System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, B.P, Kenitra, 242, Morocco; School of Technology, Moulay Ismail University of Meknes, Meknes, 50050, Morocco; School of Science and Engineering, Al Akhawayn University in Ifrane, P.O. Box 104, Hassan II Avenue, Ifrane, 53000, Morocco; Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, G8Z 4M3, QC, Canada","Abadade Y., System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, B.P, Kenitra, 242, Morocco; Benamar N., School of Technology, Moulay Ismail University of Meknes, Meknes, 50050, Morocco, School of Science and Engineering, Al Akhawayn University in Ifrane, P.O. Box 104, Hassan II Avenue, Ifrane, 53000, Morocco; Bagaa M., Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, G8Z 4M3, QC, Canada; Chaoui H., System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, B.P, Kenitra, 242, Morocco","Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds. © 2024 by the authors.","early detection; lung disease classification; TinyML","Diagnosis; Electronic health record; Pulmonary diseases; Condition; Diagnostic methods; Disease classification; Early detection; Global health; Lung disease classification; Lung sounds; Machine learning models; Machine-learning; Tiny machine learning; Lung cancer","","","","","","","The Top 10 Causes of Death, (2021); Hashoul D., Haick H., Sensors for detecting pulmonary diseases from exhaled breath, Eur. Respir. Rev, 28, (2019); Sfayyih A.H., Sulaiman N., Sabry A.H., A review on lung disease recognition by acoustic signal analysis with deep learning networks, J. Big Data, 10, (2023); Andres E., Gass R., Charloux A., Brandt C., Hentzler A., Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0, J. Med. Life, 11, (2018); Alqudah A.M., Qazan S., Obeidat Y.M., Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds, Soft Comput, 26, pp. 13405-13429, (2022); Huang D.M., Huang J., Qiao K., Zhong N.S., Lu H.Z., Wang W.J., Deep learning-based lung sound analysis for intelligent stethoscope, Mil. Med. Res, 10, (2023); McLane I., Emmanouilidou D., West J.E., Elhilali M., Design and Comparative Performance of a Robust Lung Auscultation System for Noisy Clinical Settings, IEEE J. Biomed. Health Inform, 25, pp. 2583-2594, (2021); Seah J.J., Zhao J., Wang D.Y., Lee H.P., Review on the advancements of stethoscope types in chest auscultation, Diagnostics, 13, (2023); Lella K.K., Jagadeesh M., Alphonse P., Artificial intelligence-based framework to identify the abnormalities in the COVID-19 disease and other common respiratory diseases from digital stethoscope data using deep CNN, Health Inf. Sci. Syst, 12, (2024); Tsoukas V., Boumpa E., Giannakas G., Kakarountas A., A review of machine learning and tinyml in healthcare, Proceedings of the 25th Pan-Hellenic Conference on Informatics, pp. 69-73; Abadade Y., Temouden A., Bamoumen H., Benamar N., Chtouki Y., Hafid A.S., A Comprehensive Survey on TinyML, IEEE Access, 11, pp. 96892-96922, (2023); Ooko S.O., Muyonga Ogore M., Nsenga J., Zennaro M., TinyML in Africa: Opportunities and Challenges, Proceedings of the 2021 IEEE Globecom Workshops (GC Wkshps), pp. 1-6; Ray P.P., A review on TinyML: State-of-the-art and prospects, J. King Saud Univ. Comput. Inf. Sci, 34, pp. 1595-1623, (2022); Dutta D.L., Bharali S., TinyML Meets IoT: A Comprehensive Survey, Internet Things, 16, (2021); Nicolas C., Naila B., Amar R.C., TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform, Proceedings of the 2022 Thirteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 256-259; Bhamare M., Kulkarni P.V., Rane R., Bobde S., Patankar R., Chapter 14—TinyML applications and use cases for healthcare, TinyML for Edge Intelligence in IoT and LPWAN Networks, pp. 331-353, (2024); Bamoumen H., Temouden A., Benamar N., Chtouki Y., How TinyML Can be Leveraged to Solve Environmental Problems: A Survey, Proceedings of the 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 338-343; Diab M.S., Rodriguez-Villegas E., Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review, IEEE Access, 10, pp. 98450-98474, (2022); Sun B., Bayes S., Abotaleb A.M., Hassan M., The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation, Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, pp. 629-638; Saadeh W., Butt S.A., Altaf M.A.B., A Patient-Specific Single Sensor IoT-Based Wearable Fall Prediction and Detection System, IEEE Trans. Neural Syst. Rehabil. Eng, 27, pp. 995-1003, (2019); Fang K., Xu Z., Li Y., Pan J., A Fall Detection using Sound Technology Based on TinyML, Proceedings of the 2021 11th International Conference on Information Technology in Medicine and Education (ITME), pp. 222-225; Zhu T., Kuang L., Li K., Zeng J., Herrero P., Georgiou P., Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge, Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS); Risso M., Burrello A., Pagliari D.J., Benatti S., Macii E., Benini L., Pontino M., Robust and Energy-Efficient PPG-Based Heart-Rate Monitoring, Proceedings of the 2021 IEEE International Symposium on Circuits and Systems (ISCAS); Alghamdi N.S., Zakariah M., Karamti H., A deep CNN-based acoustic model for the identification of lung diseases utilizing extracted MFCC features from respiratory sounds, Multimedia Tools and Applications, pp. 1-33, (2024); Ullah A., Khan M.S., Khan M.U., Mujahid F., Automatic Classification of Lung Sounds Using Machine Learning Algorithms, Proceedings of the 2021 International Conference on Frontiers of Information Technology (FIT), pp. 131-136; Abdul Z.K., Al-Talabani A.K., Mel Frequency Cepstral Coefficient and its Applications: A Review, IEEE Access, 10, pp. 122136-122158, (2022); Owens F., Murphy M., A short-time Fourier transform, Signal Process, 14, pp. 3-10, (1988); Yu Y., Si X., Hu C., Zhang J., A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Comput, 31, pp. 1235-1270, (2019); Sreeram A., Ravishankar U., Sripada N.R., Mamidgi B., Investigating the potential of MFCC features in classifying respiratory diseases, Proceedings of the 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1-7; Garcia-Ordas M.T., Benitez-Andrades J.A., Garcia-Rodriguez I., Benavides C., Alaiz-Moreton H., Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data, Sensors, 20, (2020); Aykanat M., Kilic O., Kurt B., Saryal S., Classification of lung sounds using convolutional neural networks, EURASIP J. Image Video Process, 2017, (2017); Tawfik M., Al-Zidi N.M., Fathail I., Nimbhore S., Asthma Detection System: Machine and Deep Learning-Based Techniques, Artificial Intelligence and Sustainable Computing, pp. 207-218, (2022); Roy A., Satija U., RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds, IEEE Trans. Instrum. Meas, 72, (2023); Zhou W., Yu L., Zhang M., Xiao W., A low power respiratory sound diagnosis processing unit based on LSTM for wearable health monitoring, Biomed. Eng. Tech, 68, pp. 469-480, (2023); AI for Good-Healthcare, (2024); Hymel S., Banbury C., Situnayake D., Elium A., Ward C., Kelcey M., Baaijens M., Majchrzycki M., Plunkett J., Tischler D., Et al., Edge Impulse: An MLOps Platform for Tiny Machine Learning, arXiv, (2023); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data Brief, 35, (2021); Nano 33 BLE Sense; David R., Duke J., Jain A., Janapa Reddi V., Jeffries N., Li J., Kreeger N., Nappier I., Natraj M., Wang T., Et al., TensorFlow Lite Micro: Embedded Machine Learning for TinyML Systems, Proc. Mach. Learn. Syst, 3, pp. 800-811, (2021)","M. Bagaa; Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, G8Z 4M3, Canada; email: miloud.bagaa@uqtr.ca","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","19995903","","","","English","Future Internet","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85210241663"
"Pengetnze Y.; Oaks T.; Tamer Y.T.; Roderick T.; Allen J.; Ligon H.; Rather L.; Termulo C., Jr.; Huang P.; Miff S.","Pengetnze, Yolande (6505570238); Oaks, Teresita (59242845700); Tamer, Yusuf Talha (55909767100); Roderick, Thomas (57232326900); Allen, Jason (59243881000); Ligon, Hannah (57223825870); Rather, Lance (59244296900); Termulo, Cesar (59243881100); Huang, Philip (57670096700); Miff, Steve (6507757810)","6505570238; 59242845700; 55909767100; 57232326900; 59243881000; 57223825870; 59244296900; 59243881100; 57670096700; 6507757810","Pediatric Asthma Surveillance System (PASS): Community-Facing Disease Monitoring for Health Equity","2024","NEJM Catalyst Innovations in Care Delivery","5","8","CAT.24.0121","","","","1","10.1056/CAT.24.0121","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200219153&doi=10.1056%2fCAT.24.0121&partnerID=40&md5=e3188758a2528d637802d619f9bf5268","Parkland Center for Clinical Innovation, Dallas, TX, United States; Parkland Health, Dallas, TX, United States; Parkland Center for Clinical Innovation, Dallas, TX, United States; Flamelit, Canton, GA, United States; Parkland Health, Dallas, TX, United States; Parkland Center for Clinical Innovation, Dallas, TX, United States; Parkland Health, Dallas, TX, United States; Dallas County Health and Human Services, Dallas, TX, United States; Parkland Center for Clinical Innovation, Dallas, TX, United States","Pengetnze Y., Parkland Center for Clinical Innovation, Dallas, TX, United States; Oaks T., Parkland Health, Dallas, TX, United States; Tamer Y.T., Parkland Center for Clinical Innovation, Dallas, TX, United States; Roderick T., Flamelit, Canton, GA, United States; Allen J., Parkland Health, Dallas, TX, United States; Ligon H., Parkland Center for Clinical Innovation, Dallas, TX, United States; Rather L., Parkland Center for Clinical Innovation, Dallas, TX, United States; Termulo C., Jr., Parkland Health, Dallas, TX, United States; Huang P., Dallas County Health and Human Services, Dallas, TX, United States; Miff S., Parkland Center for Clinical Innovation, Dallas, TX, United States","The partnership between health care delivery systems and public health entities is essential in improving community health equity in a synergistic manner. One key challenge is creating a common data source to assess community risk and support actionable insights for collective impact interventions. Through a partnership between Parkland Health (Parkland), Dallas County Health and Human Services (DCHHS), and the Parkland Center for Clinical Innovation, the Pediatric Asthma Surveillance System (PASS) was built as a community-facing dashboard with involvement from the local community, leveraging AI/machine learning techniques and social and clinical risk insights to predict pediatric asthma risk, identify risk drivers, and map risk disparities at the zip-code and census-tract level. PASS serves as a single source of truth to support community collaboration to collectively improve pediatric asthma outcomes equity. PASS uncovers microgeographic disparities in asthma risk and risk factors that were not previously readily available. For instance, contiguous census tracts have distinct asthma risk profiles requiring different clinical, public health, and social services strategies for improvement. PASS is leveraged by health care delivery systems, public health entities, and social services organizations to design and coordinate efforts in high-risk communities. The key to replicating PASS for other communities or chronic diseases (e.g., diabetes/hypertension) is a robust cross-sectoral partnership, advanced expertise in social determinants of health and clinical data analyses, community education, and a simplified, intuitive, and accessible dashboard. PASS is both the product of and a catalyst for a synergistic partnership between a health care delivery system (Parkland) and a public health entity (DCHHS). © 2024 Massachussetts Medical Society. All rights reserved.","","","","","","","Yusuf Talha Tamer; Thomas Roderick","We gratefully acknowledge the contributions of Dr. Fred Cerise, Jessica Hernandez, Dr. Donna Persaud, and Grace Mathew at Parkland; Woldu Ameneshoa at Dallas County Health and Human Services; and Arun Nethi, Venkatraghavan Sundaram, Ashley Steele, and Elizabeth Powell at Parkland Center for Clinical Innovation, whose dedicated efforts helped make this project and/or article possible. Disclosures: Yolande Pengetnze, Teresita Oaks, Yusuf Talha Tamer, Thomas Roderick, Jason Allen, Hannah Ligon, Lance Rather, Cesar Termulo, Phil Huang, and Steve Miff have nothing to disclose. This work is funded in part by Lyda Hill Philanthropies.","Cerise F.P., Moran B., Huang P.P., Bhavan K.P., The imperative for integrating public health and health care delivery systems, NEJM Catal Care Deliv, 2, 4, (2021); Dallas County Community Health Needs Assessment 2019, (2019); Dallas County Community Health Needs Assessment 2022, (2022); Glossary: Census Tract. Page, (2022)","","","Massachussetts Medical Society","","","","","","26420007","","","","English","NEJM Catal. Inno. Care Del.","Article","Final","","Scopus","2-s2.0-85200219153"
"Su J.G.; Vuong V.; Shahriary E.; Aslebagh S.; Yakutis E.; Sage E.; Haile R.; Balmes J.; Barrett M.","Su, Jason G. (11840142100); Vuong, Vy (57872221400); Shahriary, Eahsan (57205504382); Aslebagh, Shadi (58956696000); Yakutis, Emma (58957411600); Sage, Emma (58956937400); Haile, Rebecca (58955945500); Balmes, John (7005041892); Barrett, Meredith (23567765400)","11840142100; 57872221400; 57205504382; 58956696000; 58957411600; 58956937400; 58955945500; 7005041892; 23567765400","Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors","2024","Environment International","189","","108810","","","","1","10.1016/j.envint.2024.108810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195692500&doi=10.1016%2fj.envint.2024.108810&partnerID=40&md5=2c36a3c6c13528d97a7f176c527ad451","School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Propeller Health, 505 Montgomery St #2300, San Francisco, 94111, CA, United States; School of Medicine, University of California, San Francisco, 94143, CA, United States; ResMed, San Diego, 92123, CA, United States","Su J.G., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Vuong V., Propeller Health, 505 Montgomery St #2300, San Francisco, 94111, CA, United States; Shahriary E., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Aslebagh S., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Yakutis E., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Sage E., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Haile R., School of Public Health, University of California, Berkeley, Berkeley, 94720, CA, United States; Balmes J., School of Medicine, University of California, San Francisco, 94143, CA, United States; Barrett M., Propeller Health, 505 Montgomery St #2300, San Francisco, 94111, CA, United States, ResMed, San Diego, 92123, CA, United States","Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space–time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 μg m−3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013–1.038), 1.054 (95 % CI: 1.041–1.068), and 1.161 (95 % CI: 1.127–1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management. © 2024 The Author(s)","Air pollution; Digital sensors; Environmental epidemiology; Inhaler use; Land use regression; Respiratory symptoms","Adult; Aged; Air Pollutants; Air Pollution; Asthma; California; Digital Health; Environmental Exposure; Environmental Monitoring; Female; Humans; Longitudinal Studies; Male; Middle Aged; Nitrogen Dioxide; Ozone; Particulate Matter; Pulmonary Disease, Chronic Obstructive; California; United States; Forestry; Land use; Machine learning; Nitrogen oxides; Pulmonary diseases; influenza vaccine; nitrogen; nitrogen dioxide; ozone; sulfur dioxide; nitrogen dioxide; ozone; Air pollution exposures; Beta-agonists; California; Chronic obstructive pulmonary disease; Digital sensors; Environmental epidemiology; Inhaler use; Land use regression; PM 2.5; Respiratory symptoms; atmospheric pollution; health impact; machine learning; nitrogen dioxide; ozone; particulate matter; public health; respiratory disease; sensor; spatial resolution; symptom; adult; air pollution; Article; asthma; Asthma Control Test; chronic obstructive lung disease; climate change; female; forced expiratory volume; genome-wide association study; human; internal consistency; longitudinal study; machine learning; major clinical study; male; observational study; particulate matter 2.5; public health; respiratory tract disease; sea surface temperature; seasonal variation; sensitivity analysis; time series analysis; adverse event; aged; air pollutant; California; chronic obstructive lung disease; digital health; environmental exposure; environmental monitoring; epidemiology; middle aged; particulate matter; procedures; Air pollution","","nitrogen, 7727-37-9; nitrogen dioxide, 10102-44-0; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; Air Pollutants, ; Nitrogen Dioxide, ; Ozone, ; Particulate Matter, ","","","California Air Resources Board, CARB, (19RD004); California Air Resources Board, CARB","This work was supported by the California Air Resources Board (CARB) through funding number 19RD004. ","(2024); Beckerman B.S., Jerrett M., Serre M., Martin R.V., Lee S.-J., van Donkelaar A., Ross Z., Su J., Burnett R.T., A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States, Environ Sci Technol, 47, pp. 7233-7241, (2013); de Marco R., Locatelli F., Cerveri I., Bugiani M., Marinoni A., Giammanco G., Adults I.S.A.Y., Incidence and remission of asthma: a retrospective study on the natural history of asthma in Italy, J Allergy Clin Immun, 110, pp. 228-235, (2002); Di Q., Dai L., Wang Y., Zanobetti A., Choirat C., Schwartz J.D., Dominici F., Association of short-term exposure to air pollution with mortality in older adults, JAMA, 318, pp. 2446-2456, (2017); Dormann C.F., Elith J., Bacher S., Buchmann C., Carl G., Carre G., Marquez J.R.G., Gruber B., Lafourcade B., Leitao P.J., Collinearity: a review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, pp. 27-46, (2013); Ghobadi H., Ahari S.S., Kameli A., Lari S.M., The relationship between COPD assessment test (CAT) scores and severity of airflow obstruction in stable COPD patients, Tanaffos, 11, (2012); Gorai A.K., Tchounwou P.B., Tuluri F., Association between ambient air pollution and asthma prevalence in different population groups residing in Eastern Texas, USA, Int J Environ Res Public Health, 13, (2016); Greenwell B.M., pdp: an R Package for constructing partial dependence plots, R J, 9, pp. 421-436, (2017); Guarnieri M., Balmes J.R., Outdoor air pollution and asthma, Lancet, 383, pp. 1581-1592, (2014); Hao H., Eckel S.P., Hosseini A., Van Vliet E.D., Dzubur E., Dunton G., Chang S.Y., Craig K., Rocchio R., Bastain T., Daily associations of air pollution and pediatric asthma risk using the biomedical REAI-Time Health Evaluation (BREATHE) Kit, Int. J. Environ. Res. Public Health, 19, (2022); Hasunuma H., Sato T., Iwata T., Kohno Y., Nitta H., Odajima H., Ohara T., Omori T., Ono M., Yamazaki S., Shima M., Association between traffic-related air pollution and asthma in preschool children in a national Japanese nested case-control study, BMJ Open, 6, (2016); Jordan K., Jinks C., Croft P., Health care utilization: measurement using primary care records and patient recall both showed bias, J. Clin. Epidemiol., 59, pp. 791-797, (2006); Joseph C.L., Baptist A.P., Stringer S., Havstad S., Ownby D.R., Johnson C.C., Williams L.K., Peterson E.L., Identifying students with self-report of asthma and respiratory symptoms in an urban, high school setting, J Urban Health, 84, pp. 60-69, (2007); Levy I., Mihele C., Lu G., Narayan J., Brook J.R., Evaluating multipollutant exposure and urban air quality: pollutant interrelationships, neighborhood variability, and nitrogen dioxide as a proxy pollutant, Environ Health Persp., 122, pp. 65-72, (2014); Liaw A., Wiener M., Classification and regression by randomForest, R News, 2, pp. 18-22, (2002); Magzamen S., Oron A.P., Locke E.R., Fan V.S., Association of ambient pollution with inhaler use among patients with COPD: a panel study, Occup. Environ. Med., 75, pp. 382-388, (2018); Paulin L.M., Williams D.A.L., Peng R., Diette G.B., McCormack M.C., Breysse P., Hansel N.N., 24-h Nitrogen dioxide concentration is associated with cooking behaviors and an increase in rescue medication use in children with asthma, Environ. Res., 159, pp. 118-123, (2017); Payne E.H., Gebregziabher M., Hardin J.W., Ramakrishnan V., Egede L.E., An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data, Commun. Statistics-Simulat. Computat., 47, pp. 1722-1738, (2018); Rockenbauer M., Olsen J., Czeizel A.E., Pedersen L., Sorensen H.T., Grp E., Recall bias in a case-control surveillance system on the use of medicine during pregnancy, Epidemiology, 12, pp. 461-466, (2001); Scibor M., Balcerzak B., Galbarczyk A., Jasienska G., Associations between daily ambient air pollution and pulmonary function, asthma symptom occurrence, and quick-relief inhaler use among asthma patients, Int. J. Environ. Res. Public Health, 19, (2022); Stanford R.H., Tabberer M., Kosinski M., Johnson P.T., White J., Carlyle M., Tillery N.A., Assessment of the COPD assessment test within US primary care, Chronic Obstructive Pulmonary Diseases: J. COPD Foundation, 7, (2020); Su J.G., Jerrett M., Meng Y.Y., Pickett M., Ritz B., Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment, Sci Total Environ, 506, pp. 518-526, (2015); Su J.G.A.; (2022); van Dijk B.C., Svedsater H., Heddini A., Nelsen L., Balradj J.S., Alleman C., Relationship between the Asthma Control Test (ACT) and other outcomes: a targeted literature review, BMC Pulm. Med., 20, pp. 1-9, (2020); Yoo W., Mayberry R., Bae S., Singh K., He Q.P., Lillard J.W., A study of effects of multicollinearity in the multivariable analysis, Int. J. Appl. Sci. Technol., 4, (2014); Zhang S., Li G., Tian L., Guo Q., Pan X., Short-term exposure to air pollution and morbidity of COPD and asthma in East Asian area: a systematic review and meta-analysis, Environ Res, 148, pp. 15-23, (2016)","J.G. Su; School of Public Health, University of California, Berkeley, Berkeley, 94720, United States; email: jasonsu@berkeley.edu","","Elsevier Ltd","","","","","","01604120","","ENVID","38875815","English","Environ. Int.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85195692500"
"Lonigro A.S.; Ancona D.; Liantonio A.; Stella P.; Procacci C.; Germinario A.; Bavaro V.; Montanaro V.; Delle Donne A.","Lonigro, Anna Stella (57542355400); Ancona, Domenica (57205668870); Liantonio, Antonella (6602736095); Stella, Paolo (57211467658); Procacci, Cataldo (57214211386); Germinario, Antonio (57222073158); Bavaro, Vito (57214879142); Montanaro, Vito (57222077086); Delle Donne, Alessandro (57543076400)","57542355400; 57205668870; 6602736095; 57211467658; 57214211386; 57222073158; 57214879142; 57222077086; 57543076400","Chronic treatment of COPD: State of the art and real-world analysis of healthcare costs based on medication adherence data; [Trattamento cronico della BPCO: Analisi dello stato dell arte e del mondo reale dei costi sanitari sulla base dei dati sull aderenza ai farmaci.]","2022","Recenti Progressi in Medicina","113","3","","202","210","8","2","10.1701/3761.37486","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126896145&doi=10.1701%2f3761.37486&partnerID=40&md5=4a818c15a2aba6fc827fef37259ec1da","Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy; Department of Pharmacy-Drug Sciences, University of Bari, Italy; Apulian Regional Health Department, Bari, Italy","Lonigro A.S., Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy; Ancona D., Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy; Liantonio A., Department of Pharmacy-Drug Sciences, University of Bari, Italy; Stella P., Apulian Regional Health Department, Bari, Italy; Procacci C., Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy; Germinario A., Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy; Bavaro V., Apulian Regional Health Department, Bari, Italy; Montanaro V., Apulian Regional Health Department, Bari, Italy; Delle Donne A., Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Via Fornaci 201, Andria, 70031, Italy","Introduction. Chronic Obstructive Pulmonary Disease (COPD), represents a serious and growing health problem worldwide. Healthcare should be conceived and structured in a proactive logic mode predicting to promote prevention and supporting the patient in the path of care. This desirable approach could reduce the impact of chronicity on patient quality of life and health care costs. In this context, the theme of therapeutic adherence represents one of the priorities on which to intervene. Objective. To show a real life picture of the health expenditure and economics consequences due to non-Adherent COPD therapy. Materials and methods. Patients with a COPD diagnosis were selected from the Regional Health Information System Edotto; consumption data was also obtained from the same patients and was based on data relating to prescriptions dispensed by affiliated pharmacies belonging to the Local Health Agency of the province of Barletta-Andria-Trani (LHA BT) in a time period including 2017 and 2018. The assisted patients not adhering to COPD treatment (medication possession ratio between 20% and 80%) in 2017 were included in the analysis. The system Edotto was used to verify how many of them had become adherent in 2018. For both groups of patients, the average cost per patient was assessed, both in terms of pharmaceutical expenditure and hospitalizations due to COPD. Results. Of the 66 patients not adhering to the treatment in 2017, 66.67% (44 patients) became adherent to therapy and 33.33% (22 patients) remained non-Adherent to treatment during 2018. The total cost (pharmaceutical expenditure ATC-R03 and the cost derived from hospitalizations due to COPD) for non-Adherent patient during 2018 was 73% increase compared to the cost of the patient adhering to treatment (p=.000317), thus resulting a saving of 992.56 per adherent patient. Conclusions. Adherence to COPD therapy can improve patient health and reduce healthcare costs. © 2022 Il Pensiero Scientifico Editore s.r.l.. All rights reserved.","Chronic obstructive pulmonary disease; Health economics; Hospitalization costs; Medication adherence; Pharmaceutical expenditure; Pharmacoeconomics","Health Care Costs; Humans; Medication Adherence; Pharmaceutical Preparations; Pulmonary Disease, Chronic Obstructive; Quality of Life; Retrospective Studies; drug; adult; Article; chronic obstructive lung disease; chronicity; cost benefit analysis; cost control; data analysis; female; health care cost; hospitalization cost; human; long term care; major clinical study; male; medical information system; medication compliance; pharmacy (shop); prescription; quality of life; retrospective study; trend study; chronic obstructive lung disease; health care cost; medication compliance; quality of life","","Pharmaceutical Preparations, ","","","","","Piano Nazionale della Cronicita, (2016); SDO hospitalization database; Siafakas NM, Vermeire P, Pride NB, Et al., Optimal assessment and management of chronic obstructive pulmonary disease (COPD). 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Ministerial Decree 2 April 2015 n. 70 ""Regolamento recante definizione degli standard qualitativi, strutturali, tecnologici e quantitativi relativi all assistenza ospedaliera; Gossec L, Tubach F, Dougados M, Ravaud P., Reporting of adherence to medication in recent randomized controlled trials of 6 chronic diseases: A systematic literature review, Am J Med Sci, 334, pp. 248-254, (2007); Bogart M, Stanford RH, Laliberte F, Germain G, Wu JW, Duh MS., Medication adherence and persistence in chronic obstructive pulmonary disease patients receiving triple therapy in a USA commercially insured population, Int J Chron Obstruct Pulmon Dis, 14, pp. 343-352, (2019); Imamura Y, Kawayama T, Kinoshita T, Et al., Poor pharmacological adherence to inhaled medicines compared with oral medicines in Japanese patients with asthma and chronic obstructive pulmonary disease, Allergol Int, 66, pp. 482-484, (2017); Puglia R., Health Information System of the Puglia Region-Edotto; Decreto del Presidente del Consiglio dei Ministri-Allegato 8, Elenco Malattie e Condizioni Croniche e Invalidanti, (2017); Osterberg L, Blaschke T., Adherence to medication, N Eng J Med, 353, pp. 487-497, (2005); National Report on Medicines use in Italy. Year 2015, (2016); Celli BR, Cote CG, Marin JM, Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, pp. 1005-1012, (2004); Ford ES, Murphy LB, Khavjou O, Giles WH, Holt JB, Croft JB., Total and state-specific medical and absenteeism costs of COPD among adults aged 18 years in the United States for 2010 and projections through 2020, Chest, 147, pp. 31-45, (2015); Ford ES, Mannino DM, Wheaton AG, Giles WH, Presley-Cantrell L, Croft JB., Trends in the prevalence of obstructive and restrictive lung function among adults in the United States: findings from the National Health and Nutrition Examination surveys from 1988-1994 to 2007-2010, Chest, 143, pp. 1395-1406, (2013); Dhamane AD, Moretz C, Zhou Y, Et al., COPD exacerbation frequency and its association with health care resource utilization and costs, Int J COPD, 10, pp. 2609-2618, (2015); Perera PN, Armstrong EP, Sherrill DL, Skrepnek GH., Acute exacerbations of COPD in the United States: inpatient burden and predictors of costs and mortality, COPD, 9, pp. 131-141, (2012); Wzitaek-Nowak W, Gierczynski J, Dabrowiecki P, Et al., Socioeconomic effects of chronic obstructive pulmonary disease from the public payer s perspective in Poland, Adv Exp Med Biol, 885, pp. 53-66, (2016); Dal Negro RW, Tognella S, Tosatto R, Dionisi M, Turco P, Donner CF., Costs of chronic obstructive pulmonary disease (COPD) in Italy: The SIRIO study (Social Impact of Respiratory Integrated Outcomes), Respir Med, 102, pp. 92-101, (2008); Dal Negro RW., COPD: The annual cost-of-illness during the last two decades in Italy, and Its Mortality Predictivity Power, Healthcare (Basel), 7, (2019)","D. ANCONA; Pharmaceuticals Department, Local Health Autority Barletta-Andria-Trani, Andria, Via Fornaci 201, 70031, Italy; email: domenica.ancona@aslbat.it","","Il Pensiero Scientifico Editore s.r.l.","","","","","","00341193","","RPMDA","35315451","English","Recenti Prog. Med.","Article","Final","","Scopus","2-s2.0-85126896145"
"Grob A.; Rohr J.; Stumpo V.; Vieli M.; Ciobanu-Caraus O.; Ricciardi L.; Maldaner N.; Raco A.; Miscusi M.; Perna A.; Proietti L.; Lofrese G.; Dughiero M.; Cultrera F.; D’Andrea M.; An S.B.; Ha Y.; Amelot A.; Bedia Cadelo J.; Viñuela-Prieto J.M.; Gandía-González M.L.; Girod P.-P.; Lener S.; Kögl N.; Abramovic A.; Laux C.J.; Farshad M.; O’Riordan D.; Loibl M.; Galbusera F.; Mannion A.F.; Scerrati A.; De Bonis P.; Molliqaj G.; Tessitore E.; Schröder M.L.; Stienen M.N.; Regli L.; Serra C.; Staartjes V.E.","Grob, Alexandra (57078291000); Rohr, Jonas (58174892800); Stumpo, Vittorio (57204320430); Vieli, Moira (57369328900); Ciobanu-Caraus, Olga (57460241700); Ricciardi, Luca (55978857900); Maldaner, Nicolai (56444482700); Raco, Antonino (7003876928); Miscusi, Massimo (55879869200); Perna, Andrea (57201529504); Proietti, Luca (57200394593); Lofrese, Giorgio (36440133700); Dughiero, Michele (57221636757); Cultrera, Francesco (56009654400); D’Andrea, Marcello (57053813200); An, Seong Bae (56384942500); Ha, Yoon (55736636500); Amelot, Aymeric (55674472000); Bedia Cadelo, Jorge (59213529700); Viñuela-Prieto, Jose M. (56459079200); Gandía-González, Maria L. (23099929400); Girod, Pierre-Pascal (56674338800); Lener, Sara (57191250005); Kögl, Nikolaus (56076181000); Abramovic, Anto (57218541994); Laux, Christoph J. (54418878100); Farshad, Mazda (36489356000); O’Riordan, Dave (57194762696); Loibl, Markus (24438416800); Galbusera, Fabio (15047667100); Mannion, Anne F. (7006856803); Scerrati, Alba (39762792400); De Bonis, Pasquale (36023482500); Molliqaj, Granit (56054879100); Tessitore, Enrico (55959204200); Schröder, Marc L. (57190841419); Stienen, Martin N. (35097851300); Regli, Luca (7004240836); Serra, Carlo (50263014800); Staartjes, Victor E. (57190836125)","57078291000; 58174892800; 57204320430; 57369328900; 57460241700; 55978857900; 56444482700; 7003876928; 55879869200; 57201529504; 57200394593; 36440133700; 57221636757; 56009654400; 57053813200; 56384942500; 55736636500; 55674472000; 59213529700; 56459079200; 23099929400; 56674338800; 57191250005; 56076181000; 57218541994; 54418878100; 36489356000; 57194762696; 24438416800; 15047667100; 7006856803; 39762792400; 36023482500; 56054879100; 55959204200; 57190841419; 35097851300; 7004240836; 50263014800; 57190836125","Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease","2024","European Spine Journal","33","9","","3534","3544","10","1","10.1007/s00586-024-08395-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198141934&doi=10.1007%2fs00586-024-08395-3&partnerID=40&md5=1d87ecb5567aa3bf452d78c934d0ec27","Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of NESMOS, Azienda Ospedaliera Universitaria Sant’Andrea, Sapienza University, Rome, Italy; Department of Orthopedics, Foundation Casa Sollievo Della Sofferenza IRCCS, San Giovanni Rotondo, Italy; Department of Aging, Neurological, Orthopedic and Head-Neck Sciences, IRCCS A. Gemelli University Polyclinic Foundation, Rome, Italy; Department of Geriatrics and Orthopedics, Sacred Heart Catholic University, Rome, Italy; Neurosurgery Division, Department of Neurosciences, “M.Bufalini” Hospital, Cesena, Italy; Department of Neurosurgery, Spine and Spinal Cord Institute, College of Medicine, Severance Hospital, Yonsei University, Seoul, South Korea; Department of Neurosurgery, La Pitié Salpétrière Hospital, Paris, France; Neurosurgical Spine Department, University Hospital of Tours, Tours, France; Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain; Department of Neurosurgery, Vienna Healthcare Network/ Municipial Hospital, Vienna, Austria; Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria; University Spine Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Spine Center Division, Department of Teaching, Research and Development, Schulthess Klinik, Zurich, Switzerland; Department of Spine Surgery, Schulthess Klinik, Zurich, Switzerland; Department of Neurosurgery, University Hospital Sant’Anna, Ferrara, Italy; Department of Neurosurgery, HUG Geneva University Hospital, Geneva, Switzerland; Department of Neurosurgery, Bergman Clinics Amsterdam, Amsterdam, Netherlands; Department of Neurosurgery and Spine Center of Eastern Switzerland, Cantonal Hospital St. Gallen and Medical School of St.Gallen, St. Gallen, Switzerland","Grob A., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Rohr J., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Stumpo V., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Vieli M., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Ciobanu-Caraus O., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Ricciardi L., Department of NESMOS, Azienda Ospedaliera Universitaria Sant’Andrea, Sapienza University, Rome, Italy; Maldaner N., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Raco A., Department of NESMOS, Azienda Ospedaliera Universitaria Sant’Andrea, Sapienza University, Rome, Italy; Miscusi M., Department of NESMOS, Azienda Ospedaliera Universitaria Sant’Andrea, Sapienza University, Rome, Italy; Perna A., Department of Orthopedics, Foundation Casa Sollievo Della Sofferenza IRCCS, San Giovanni Rotondo, Italy; Proietti L., Department of Aging, Neurological, Orthopedic and Head-Neck Sciences, IRCCS A. Gemelli University Polyclinic Foundation, Rome, Italy, Department of Geriatrics and Orthopedics, Sacred Heart Catholic University, Rome, Italy; Lofrese G., Neurosurgery Division, Department of Neurosciences, “M.Bufalini” Hospital, Cesena, Italy; Dughiero M., Neurosurgery Division, Department of Neurosciences, “M.Bufalini” Hospital, Cesena, Italy; Cultrera F., Neurosurgery Division, Department of Neurosciences, “M.Bufalini” Hospital, Cesena, Italy; D’Andrea M., Neurosurgery Division, Department of Neurosciences, “M.Bufalini” Hospital, Cesena, Italy; An S.B., Department of Neurosurgery, Spine and Spinal Cord Institute, College of Medicine, Severance Hospital, Yonsei University, Seoul, South Korea; Ha Y., Department of Neurosurgery, Spine and Spinal Cord Institute, College of Medicine, Severance Hospital, Yonsei University, Seoul, South Korea; Amelot A., Department of Neurosurgery, La Pitié Salpétrière Hospital, Paris, France, Neurosurgical Spine Department, University Hospital of Tours, Tours, France; Bedia Cadelo J., Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain; Viñuela-Prieto J.M., Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain; Gandía-González M.L., Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain; Girod P.-P., Department of Neurosurgery, Vienna Healthcare Network/ Municipial Hospital, Vienna, Austria; Lener S., Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria; Kögl N., Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria; Abramovic A., Department of Neurosurgery, Medical University of Innsbruck, Innsbruck, Austria; Laux C.J., University Spine Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Farshad M., University Spine Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; O’Riordan D., Spine Center Division, Department of Teaching, Research and Development, Schulthess Klinik, Zurich, Switzerland; Loibl M., Department of Spine Surgery, Schulthess Klinik, Zurich, Switzerland; Galbusera F., Spine Center Division, Department of Teaching, Research and Development, Schulthess Klinik, Zurich, Switzerland; Mannion A.F., Spine Center Division, Department of Teaching, Research and Development, Schulthess Klinik, Zurich, Switzerland; Scerrati A., Department of Neurosurgery, University Hospital Sant’Anna, Ferrara, Italy; De Bonis P., Department of Neurosurgery, University Hospital Sant’Anna, Ferrara, Italy; Molliqaj G., Department of Neurosurgery, HUG Geneva University Hospital, Geneva, Switzerland; Tessitore E., Department of Neurosurgery, HUG Geneva University Hospital, Geneva, Switzerland; Schröder M.L., Department of Neurosurgery, Bergman Clinics Amsterdam, Amsterdam, Netherlands; Stienen M.N., Department of Neurosurgery and Spine Center of Eastern Switzerland, Cantonal Hospital St. Gallen and Medical School of St.Gallen, St. Gallen, Switzerland; Regli L., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Serra C., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Staartjes V.E., Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland","Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). Methods: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer–Lemeshow (HL), AUC, sensitivity and specificity. Results: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. Conclusions: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting. © The Author(s) 2024.","External validation; Lumbar fusion; Outcome prediction; Patient-reported outcome; Predictive analytics","Aged; Disability Evaluation; Female; Humans; Intervertebral Disc Degeneration; Lumbar Vertebrae; Male; Middle Aged; Prospective Studies; Reproducibility of Results; Retrospective Studies; Spinal Fusion; Treatment Outcome; opiate; adult; aged; American Society of Anaesthesiologists score; Article; asthma; clinical examination; clinical outcome; cohort analysis; controlled study; failed back surgery syndrome; female; human; leg pain; low back pain; lumbar disk degeneration; lumbar disk hernia; lumbar interbody fusion; lumbar spinal stenosis; machine learning; major clinical study; male; minimal clinically important difference; multicenter study; nuclear magnetic resonance imaging; numeric rating scale; Oswestry Disability Index; patient-reported outcome; predictive model; prospective study; pseudarthrosis; radiculopathy; retrospective study; sensitivity and specificity; spondylolisthesis; validation study; clinical trial; disability assessment; intervertebral disk degeneration; lumbar vertebra; middle aged; procedures; reproducibility; spine fusion; surgery; treatment outcome","","opiate, 53663-61-9, 8002-76-4, 8008-60-4","","","","","Kepler C.K., Et al., National trends in the use of fusion techniques to treat degenerative spondylolisthesis, Spine, 39, 19, pp. 1584-1589, (2014); Ivar Brox J., Et al., Randomized clinical trial of lumbar instrumented fusion and cognitive intervention and exercises in patients with chronic low back pain and disc degeneration, Spine, 28, 17, pp. 1913-1921, (2003); Fairbank J., Frost H., Wilson-MacDonald J., Yu L.-M., Barker K., Collins R., Randomised controlled trial to compare surgical stabilisation of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial, BMJ, 330, 7502, (2005); Birkmeyer N.J.O., Et al., Design of the spine patient outcomes research trial (SPORT), Spine, 27, 12, pp. 1361-1372, (2002); Weinstein J.N., Et al., Surgical compared with nonoperative treatment for lumbar degenerative spondylolisthesis: four-year results in the spine patient outcomes research trial (SPORT) randomized and observational cohorts, J Bone Jt Surg-Am Vol, 91, 6, pp. 1295-1304, (2009); Khor S., Et al., Development and validation of a prediction model for pain and functional outcomes after lumbar spine surgery, JAMA Surg, 153, 7, (2018); Riley R.D., Et al., External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges, BMJ, (2016); Staartjes V.E., Kernbach J.M., Significance of external validation in clinical machine learning: let loose too early?, Spine J Off J North Am Spine Soc, 20, 7, pp. 1159-1160, (2020); Quddusi A., Et al., External validation of a prediction model for pain and functional outcome after elective lumbar spinal fusion, Eur Spine J, 29, 2, pp. 374-383, (2020); 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Fekete T.F., Haschtmann D., Kleinstuck F.S., Porchet F., Jeszenszky D., Mannion A.F., What level of pain are patients happy to live with after surgery for lumbar degenerative disorders?, Spine J, 16, 4, pp. S12-S18, (2016); Ostelo R.W.J.G., Et al., Interpreting change scores for pain and functional status in low back pain: towards international consensus regarding minimal important change, Spine, 33, 1, pp. 90-94, (2008); Templ M., Kowarik A., Alfons A., Prantner B., VIM: Visualization and imputation of missing values, (2019); Staartjes V.E., Regli L., Serra C., Machine learning in clinical neuroscience: foundations and applications, Acta Neurochirurgica Supplement, 134, (2022); Brier G.W., Verification of forecasts expressed in terms of probability, Mon Weather Rev, 78, 1, pp. 1-3, (1950); Van Hoorde K., Van Huffel S., Timmerman D., Bourne T., Van Calster B., A spline-based tool to assess and visualize the calibration of multiclass risk predictions, J Biomed Inform, 54, pp. 283-293, (2015); 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Long-term results of all randomized controlled trials show that fusion is no better than non-operative care in improving pain and disability in chronic low back pain, Spine J, 16, 5, pp. 588-590, (2016); Willems P., Decision making in surgical treatment of chronic low back pain: the performance of prognostic tests to select patients for lumbar spinal fusion, Acta Orthop, 84, sup349, pp. 1-37, (2013); Van Hooff M.L., Mannion A.F., Staub L.P., Ostelo R.W.J.G., Fairbank J.C.T., Determination of the oswestry disability index score equivalent to a “satisfactory symptom state” in patients undergoing surgery for degenerative disorders of the lumbar spine—a spine tango registry-based study, Spine J, 16, 10, pp. 1221-1230, (2016); Falavigna A., Et al., Current status of worldwide use of patient-reported outcome measures (PROMs) in spine care, World Neurosurg, 108, pp. 328-335, (2017); Kim J.S., Et al., Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion, Spine, 43, 12, pp. 853-860, (2018); Ehlers A.P., Et al., Improved risk prediction following surgery using machine learning algorithms, EGEMs Gener Evid Methods Improve Patient Outcomes, 5, 2, (2017); Mattei T.A., Rehman A.A., Teles A.R., Aldag J.C., Dinh D.H., McCall T.D., The ‘lumbar fusion outcome score’ (LUFOS): a new practical and surgically oriented grading system for preoperative prediction of surgical outcomes after lumbar spinal fusion in patients with degenerative disc disease and refractory chronic axial low back pain, Neurosurg Rev, 40, 1, pp. 67-81, (2017); Steinmetz M.P., Mroz T., Value of adding predictive clinical decision tools to spine surgery, JAMA Surg, (2018); Kernbach J.M., Staartjes V.E., Foundations of machine learning-based clinical prediction modeling: part II—generalization and overfitting. machine learning in clinical neuroscience, Acta neurochirurgica supplement, pp. 15-21, (2022); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMJ, 350, jan07 4, (2015); Staartjes V.E., Kernbach J.M., Importance of calibration assessment in machine learning-based predictive analytics, J Neurosurg Spine, 32, 6, pp. 985-987, (2020); Staartjes V.E., Stienen M.N., Data mining in spine surgery: leveraging electronic health records for machine learning and clinical research, Neurospine, 16, 4, pp. 654-656, (2019); Nagurney J.T., The accuracy and completeness of data collected by prospective and retrospective methods, Acad Emerg Med, 12, 9, pp. 884-895, (2005)","V.E. Staartjes; Machine Intelligence in Clinical Neuroscience and Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; email: victoregon.staartjes@usz.ch","","Springer Science and Business Media Deutschland GmbH","","","","","","09406719","","ESJOE","38987513","English","Eur. Spine J.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85198141934"
"Jacobson P.K.; Lind L.; Persson H.L.","Jacobson, Petra Kristina (57194589968); Lind, Leili (7102132852); Persson, Hans Lennart (7202456734)","57194589968; 7102132852; 7202456734","Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease","2023","International Journal of COPD","18","","","1457","1473","16","2","10.2147/COPD.S412692","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165562649&doi=10.2147%2fCOPD.S412692&partnerID=40&md5=b0eab9e10a7e9cd15c24db6ac92a3a7e","Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden; Department of Biomedical Engineering/Health Informatics, Linköping University, Linköping, Sweden; Digital Systems Division, Unit Digital Health, RISE Research Institutes of Sweden, Linköping, Sweden","Jacobson P.K., Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden, Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden; Lind L., Department of Biomedical Engineering/Health Informatics, Linköping University, Linköping, Sweden, Digital Systems Division, Unit Digital Health, RISE Research Institutes of Sweden, Linköping, Sweden; Persson H.L., Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden, Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden","Introduction: In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods: We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed. Results: We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data. Discussion: To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is “maintenance medication changes by HBHC”. This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs. Conclusion: The experiments return useful insights about the use of small data for ML. © 2023 Jacobson et al.","COX proportional hazards; machine learning; mHealth; random forests; random survival forests; telehealth or digital health","Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive; Sweden; age; aged; Article; chronic obstructive lung disease; clinical article; cohort analysis; controlled study; coughing; data analysis; disease exacerbation; dyspnea; female; forced expiratory volume; gender; home care; human; machine learning; maintenance therapy; male; prediction; random forest; risk factor; Sweden; telehealth; telemonitoring; chronic obstructive lung disease; disease exacerbation","","","","","Sweden’s innovation agency Vinnova, (2019-05402); Forskningsrådet i Sydöstra Sverige, FORSS, (FORSS-969385, FORSS-980999); Forskningsrådet i Sydöstra Sverige, FORSS","This work was supported by grants to P.K.J. and H.L.P from the Medical Research Council of Southeast Sweden (FORSS) (Grant No. FORSS-969385, FORSS-980999) and grants to L.L and H.L.P. from Sweden’s innovation agency Vinnova (Dnr: 2019-05402) in Swelife’s and Medtech4Health’s Collaborative projects for better health programme. The study sponsors had no role in study design, data collection, analysis, and interpretation; in the writing of the manuscript; nor in the decision to submit the manuscript for publication.","Celli BR, Fabbri LM, Aaron SD, Et al., An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations: the Rome proposal, Am J Respir Crit Care Med, 204, 11, pp. 1251-1258, (2021); Metting E, Dassen L, Aardoom J, Versluis A, Chavannes N., Effectiveness of telemonitoring for respiratory and systemic symptoms of asthma and COPD: a narrative review, Life, 11, 11, (2021); Rassouli F, Pfister M, Widmer S, Baty F, Burger B, Brutsche MH., Telehealthcare for chronic obstructive pulmonary disease in Switzerland is feasible and appreciated by patients, Respiration, 92, 2, pp. 107-113, (2016); Rassouli F, Germann A, Baty F, Et al., Telehealth mitigates COPD disease progression compared to standard of care: a randomized controlled crossover trial, J Intern Med, 289, 3, pp. 404-410, (2021); Persson HL, Lyth J, Wirehn AB, Lind L., Elderly patients with COPD require more health care than elderly heart failure patients do in a hospital-based home care setting, Int J Chron Obstruct Pulmon Dis, 2019, 14, pp. 1569-1581, (2019); Persson HL, Lyth J, Lind L., The health diary telemonitoring and hospital-based home care improve quality of life among elderly multimorbid COPD and chronic heart failure subjects, Int J Chron Obstruct Pulmon Dis, 2020, 15, pp. 527-541, (2020); Lyth J, Lind L, Persson HL, Wirehn AB., Can a telemonitoring system lead to decreased hospitalization in elderly patients?, J Telemed Telecare, 27, 1, pp. 46-53, (2021); Saleh L, Mcheick H, Ajami H, Mili H, Dargham J., Comparison of machine learning algorithms to increase prediction accuracy of COPD domain, International Conference on Smart Homes and Health Telematics, pp. 247-254, (2017); Sanchez-Morillo D, Fernandez-Granero MA, Leon-Jimenez A., Use of predictive algorithms in-home monitoring of chronic obstructive pulmonary disease and asthma: a systematic review, Chron Respir Dis, 13, 3, pp. 264-283, (2016); Guerra B, Gaveikaite V, Bianchi C, Puhan MA., Prediction models for exacerbations in patients with COPD, Eur Respir Rev, 26, 143, (2017); Singh D, Hurst JR, Martinez FJ, Et al., Predictive modeling of COPD exacerbations rates using baseline risk factors, Adv Respir Dis, 16, pp. 1-15, (2022); Marques A, Souto-Miranda S, Machado A, Et al., COPD profiles and treatable traits using minimal resources: identification, decision tree and stability over time, Respir Res, 23, 1, (2022); Zeng S, Arjomandi M, Tong Y, Liao ZC, Luo G., Developing a machine learning model to predict severe chronic obstructive pulmonary disease exacerbations: retrospective cohort study, J Med Internet Res, 24, 1, (2022); Chmiel FP, Burns DK, Pickering JB, Blythin A, Wilkinson T, Boniface MJ., Prediction of chronic obstructive pulmonary disease exacerbation events by using patient self-reported data in a digital health app: statistical evaluation and machine learning approach, JMIR Med Inform, 10, 3, (2022); Stallberg B, Lisspers K, Larsson K, Et al., Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning – based on the Arctic Study, Int J Chron Obstruct Pulmon Dis, 16, pp. 677-688, (2021); Joshe MD, Emon NH, Islam M, Ria NJ, Masum AKM, Noori SRH., Symptoms analysis based chronic obstructive pulmonary disease prediction in Bangladesh using machine learning approach, 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), (2021); Hussain A, Choi H, Kim H, Aich S, Saqlain M, Kim H., Forecast the exacerbation in patients of chronic obstructive pulmonary disease with clinical indicators using machine learning techniques, Diagnostics, 11, 5, (2021); Wu C, Li G, Huang C, Et al., Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study, JMIR Mhealth Uhealth, 9, 5, (2021); Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo C., A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators, Sci Rep, 10, 1, (2020); Ma X, Wu Y, Zhang L, Et al., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, J Transl Med, 18, 1, (2020); Wang C, Chen X, Du L, Zhan Q, Yang T, Fang Z., Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease, Comput Methods Programs Biomed, 188, (2020); Orchard P, Agakova A, Pinnock H, Et al., Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data, J Med Internet Res, 20, 9, (2018); Fernandez-Granero MA, Sanchez-Morillo D, Lopez-Gordo MA, Leon A., A machine learning approach to prediction of exacerbations of chronic obstructive pulmonary disease, International Work-Conference of the Interplay Between Natural and Artificial Computation, (2018); Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A., Computerised analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD, Sensors, 15, 10, pp. 26978-26996, (2015); Leidy NK, Malley KG, Steenrod AW, Et al., Insight into best variables four COPD case identification: and random forests analysis, Chronic Obstr Pulm Dis, 3, 1, pp. 406-418, (2016); Mohktar MS, Redmond SJ, Antoniades NC, Et al., Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data, Artif Intell Med, 63, 1, pp. 51-59, (2015); Amalakuhan B, Kiljanek L, Parvathaneni A, Hester M, Cheriyath P, Fischman D., A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem, J Community Hosp Intern Med Perspect, 2, 1, (2012); Ooka T, Johno H, Nakamoto K, Yoda Y, Yokomichi H, Yamagata Z., Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan, BMJ Nutr Prev Health, 4, 1, pp. 140-148, (2021); Bohannan ZS, Coffman F, Mitrofanova A., Random survival forest model identifies novel biomarkers of event-free survival in high-risk pediatric acute lymphoblastic leukemia, Comput Struct Biotechnol J, 20, pp. 583-597, (2022); Qiu X, Gao J, Yang J, Et al., A comparison study of machine learning (random survival forest) and classic statistic (cox proportional hazards) for predicting progression in high-grade glioma after proton and carbon ion radiotherapy, Front Oncol, 10, (2020); 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When & how to use it (example), (2023); Brier score and integrated brier score, (2022); Interpreting an integrated brier score that is above 0.25, (2022); Mohamed WNHW, Salleh MNM, Omar AH., A comparative study of reduced error pruning method in decision tree algorithms, IEEE International Conference on Control System, Computing and Engineering, (2012); Understanding random forest, (2023); Decision trees explained, (2023); What does it mean if an RMSE has a value far beyond 1, (2023); Nowok B, Raab GM, Dibben C., Synthpop: bespoke creation of synthetic data in R, J Stat Softw, 74, 11, pp. 1-26, (2016); Feng Y, Wang Y, Zeng C, Mao H., Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease, Int J Med Sci, 18, 13, pp. 2871-2889, (2021); Hildebrandt M., Law for Computer Scientists and Other Folk, (2020); The General Data Protection Regulation (GDPR), the data protection law enforcement directive and other rules concerning the protection of personal data, (2023); Consolidated text: regulation (EU) 2016/679 of the European Parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/EC (general data protection regulation; Goldsteen A, Ezov G, Shmelkin R, Moffie M, Farkash A., Data minimization for GDPR compliance in machine learning models, AI Ethics, 221, 2, pp. 477-491, (2021); Shanmugam D, Diaz F, Shabanian S, Finck M, Biega A., Learning to limit data collection via scaling laws: a computational interpretation for the legal principle of data minimization, Facct, pp. 839-849, (2022)","P.K. Jacobson; Department of Respiratory Medicine in Linköping, Linköping University, Linköping, SE-581 85, Sweden; email: petra.jacobson@liu.se","","Dove Medical Press Ltd","","","","","","11769106","","","37485052","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85165562649"
"Qiao L.; Li S.-M.; Liu J.-N.; Duan H.-L.; Jiang X.-F.","Qiao, Lu (57204282802); Li, Shi-Meng (57190188838); Liu, Jun-Nian (59280307400); Duan, Hong-Lei (59279267200); Jiang, Xiao-Feng (57216844157)","57204282802; 57190188838; 59280307400; 59279267200; 57216844157","Revealing the regulation of allergic asthma airway epithelial cell inflammation by STEAP4 targeting MIF through machine learning algorithms and single-cell sequencing analysis","2024","Frontiers in Molecular Biosciences","11","","1427352","","","","1","10.3389/fmolb.2024.1427352","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201587914&doi=10.3389%2ffmolb.2024.1427352&partnerID=40&md5=01df2908c838d380347e24e0fbc26631","Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin, China; Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University, Jilin, Changchun, China; Department of Digestive, Weihai Municipal Hospital, Shandong, Weihai, China","Qiao L., Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin, China; Li S.-M., Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University, Jilin, Changchun, China; Liu J.-N., Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin, China; Duan H.-L., Department of Digestive, Weihai Municipal Hospital, Shandong, Weihai, China; Jiang X.-F., Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Heilongjiang, Harbin, China","Asthma comprises one of the most common chronic inflammatory conditions, yet still lacks effective diagnostic markers and treatment targets. To gain deeper insights, we comprehensively analyzed microarray datasets of airway epithelial samples from asthmatic patients and healthy subjects in the Gene Expression Omnibus database using three machine learning algorithms. Our investigation identified a pivotal gene, STEAP4. The expression of STEAP4 in patients with allergic asthma was found to be reduced. Furthermore, it was found to negatively correlate with the severity of the disease and was subsequently validated in asthmatic mice in this study. A ROC analysis of STEAP4 showed the AUC value was greater than 0.75. Functional enrichment analysis of STEAP4 indicated a strong correlation with IL-17, steroid hormone biosynthesis, and ferroptosis signaling pathways. Subsequently, intercellular communication analysis was performed using single-cell RNA sequencing data obtained from airway epithelial cells. The results revealed that samples exhibiting low levels of STEAP4 expression had a richer MIF signaling pathway in comparison to samples with high STEAP4 expression. Through both in vitro and in vivo experiments, we further confirmed the overexpression of STEAP4 in airway epithelial cells resulted in decreased expression of MIF, which in turn caused a decrease in the levels of the cytokines IL-33, IL-25, and IL-4; In contrast, when the STEAP4 was suppressed in airway epithelial cells, there was an upregulation of MIF expression, resulting in elevated levels of the cytokines IL-33, IL-25, and IL-4. These findings suggest that STEAP4 in the airway epithelium reduces allergic asthma Th2-type inflammatory reactions by inhibiting the MIF signaling pathway. Copyright © 2024 Qiao, Li, Liu, Duan and Jiang.","airway epithelial cells; allergic asthma; machine learning algorithms; MIF; STEAP4","","","","","","National Natural Science Foundation of China, NSFC, (81171657); National Natural Science Foundation of China, NSFC","The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research received funding from the National Natural Science Foundation of China (81171657).","Aaron S.D., Boulet L.P., Reddel H.K., Gershon A.S., Underdiagnosis and overdiagnosis of asthma, Am. J. Respir. Crit. Care Med, 198, pp. 1012-1020, (2018); Abu-Kishk I., Polakow-Farkash S., Elizur A., Long-term outcome after pediatric intensive care unit asthma admissions, Allergy Asthma Proc, 37, pp. 169-175, (2016); Amarante-Mendes G.P., Adjemian S., Branco L.M., Zanetti L.C., Weinlich R., Bortoluci K.R., Pattern recognition receptors and the host cell death molecular machinery, Front. 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Med, 13, pp. 1057-1068, (2019); Reddel H.K., Taylor D.R., Bateman E.D., Boulet L.-P., Boushey H.A., Busse W.W., Et al., An official American Thoracic Society/European Respiratory Society statement: asthma control and exacerbations: standardizing endpoints for clinical asthma trials and clinical practice, Am. J. Respir. Crit. Care Med, 180, pp. 59-99, (2009); Scarl R.T., Lawrence C.M., Gordon H.M., Nunemaker C.S., STEAP4: its emerging role in metabolism and homeostasis of cellular iron and copper, J. Endocrinol, 234, pp. R123-R134, (2017); Silverpil E., Linden A., IL-17 in human asthma, Expert Rev. Respir. Med, 6, pp. 173-186, (2012); Stockwell B.R., Friedmann Angeli J.P., Bayir H., Bush A.I., Conrad M., Dixon S.J., Et al., Ferroptosis: a regulated cell death nexus linking metabolism, redox biology, and disease, Cell, 171, pp. 273-285, (2017); Tanaka Y., Matsumoto I., Iwanami K., Inoue A., Minami R., Umeda N., Et al., Six-transmembrane epithelial antigen of prostate4 (STEAP4) is a tumor necrosis factor alpha-induced protein that regulates IL-6, IL-8, and cell proliferation in synovium from patients with rheumatoid arthritis, Mod. Rheumatol, 22, pp. 128-136, (2012); Tang W., Dong M., Teng F., Cui J., Zhu X., Wang W., Et al., Environmental allergens house dust mite-induced asthma is associated with ferroptosis in the lungs, Exp. Ther. Med, 22, (2021); Xu Y., Cao L., Chen J., Jiang D., Ruan P., Ye Q., CLCA1 mediates the regulatory effect of IL-13 on pediatric asthma, Front. Pediatr, 10, (2022); Yamaguchi E., Nishihira J., Shimizu T., Takahashi T., Kitashiro N., Hizawa N., Et al., Macrophage migration inhibitory factor (MIF) in bronchial asthma, Clin. Exp. Allergy, 30, pp. 1244-1249, (2000); Yang Y., Jia M., Ou Y., Adcock I.M., Yao X., Mechanisms and biomarkers of airway epithelial cell damage in asthma: a review, Clin. Respir. J, 15, pp. 1027-1045, (2021); Zeng Z., Huang H., Zhang J., Liu Y., Zhong W., Chen W., Et al., HDM induce airway epithelial cell ferroptosis and promote inflammation by activating ferritinophagy in asthma, FASEB J, 36, (2022)","X.-F. Jiang; Department of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China; email: xiaofengjiang@hrbmu.edu.cn","","Frontiers Media SA","","","","","","2296889X","","","","English","Front. Mol. Biosci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85201587914"
"Biyu H.; Mengshan L.; Yuxin H.; Ming Z.; Nan W.; Lixin G.","Biyu, Hou (58479797700); Mengshan, Li (57208140337); Yuxin, Hou (59156173200); Ming, Zeng (58479920000); Nan, Wang (58802745400); Lixin, Guan (57200301461)","58479797700; 57208140337; 59156173200; 58479920000; 58802745400; 57200301461","A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism","2024","BMC Cancer","24","1","683","","","","1","10.1186/s12885-024-12420-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195173109&doi=10.1186%2fs12885-024-12420-5&partnerID=40&md5=5f66b755972512414b0c59e31d679b99","College of Physics and Electronic Information, Gannan Normal University, Jiangxi, Ganzhou, 341000, China; College of Computer Science and Engineering, Shanxi Datong University, Shanxi, Datong, 037000, China; College of Life Sciences, Jiaying University, Guangdong, Meizhou, 514000, China","Biyu H., College of Physics and Electronic Information, Gannan Normal University, Jiangxi, Ganzhou, 341000, China; Mengshan L., College of Physics and Electronic Information, Gannan Normal University, Jiangxi, Ganzhou, 341000, China; Yuxin H., College of Computer Science and Engineering, Shanxi Datong University, Shanxi, Datong, 037000, China; Ming Z., College of Physics and Electronic Information, Gannan Normal University, Jiangxi, Ganzhou, 341000, China; Nan W., College of Life Sciences, Jiaying University, Guangdong, Meizhou, 514000, China; Lixin G., College of Physics and Electronic Information, Gannan Normal University, Jiangxi, Ganzhou, 341000, China","Background: MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn’t been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. Results: The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. Conclusions: The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others. Graphical Abstract: (Figure presented.) © The Author(s) 2024.","Association tree; Cancer; Deep learning; miRNA-disease association; Multi-head self-attention mechanism","Algorithms; Colorectal Neoplasms; Computational Biology; Genetic Predisposition to Disease; Humans; Lung Neoplasms; MicroRNAs; Neural Networks, Computer; epidermal growth factor receptor kinase inhibitor; hsa let 7f; hsa microRNA 101-2; hsa microRNA 1225; hsa microRNA 138-2; hsa microRNA 153-2; hsa microRNA 181a 1; hsa microRNA 219; hsa microRNA 29b 1; hsa microRNA 29b-1; hsa microRNA 323a; hsa microRNA 663b; hsa microRNA 769; microRNA; unclassified drug; microRNA; accuracy; Article; artificial neural network; asthma; attention network; biology; biomedicine; colorectal cancer; controlled study; cross validation; data mining; diagnostic test accuracy study; disease association; DNA sequence; entropy; feature extraction; fully connected artificial  neural network; genetics; heart failure; human; kernel method; learning algorithm; leukemia; lung cancer; multi head self attention  mechanism; myeloid leukemia; receiver operating characteristic; sensitivity and specificity; training; tree path global feature  extraction; algorithm; bioinformatics; colorectal tumor; genetic predisposition; lung tumor; procedures","","MicroRNAs, ","","","UK Research and Innovation, UKRI, (105299); National Natural Science Foundation of China, NSFC, (52063002, 42061067, 61741202, 51663001)","The authors gratefully acknowledge the support from the National Natural Science Foundation of China 21(Grant Numbers: 51663001, 52063002, 42061067, 61741202). 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Tang H.Q., Et al., Decreased long noncoding RNA ADIPOQ promoted cell proliferation and metastasis via miR-219c-3p/TP53 pathway in colorectal carcinoma, Eur Rev Med Pharmacol Sci, 24, 14, pp. 7645-7654, (2020); Wang N., Et al., Serum miR-663 expression and the diagnostic value in colorectal cancer, Artif Cells Nanomed Biotechnol, 47, 1, pp. 2650-2653, (2019); Yang K., Et al., Rosmarinic acid inhibits migration, invasion, and p38/AP-1 signaling via mir-1225-5p in colorectal cancer cells, J Recept Signal Transduct Res, 41, 3, pp. 284-293, (2021); Budak H., Et al., MicroRNA nomenclature and the need for a revised naming prescription, Brief Funct Genomics, 15, 1, pp. 65-71, (2016); Chen Y., Et al., MiR-181a reduces radiosensitivity of non-small-cell lung cancer via inhibiting PTEN, Panminerva Med, 64, 3, pp. 374-383, (2022); Ma J., Qi G., Li L., LncRNA NNT-AS1 promotes lung squamous cell carcinoma progression by regulating the miR-22/FOXM1 axis, Cell Mol Biol Lett, 25, (2020); Pirlog R., Et al., Cellular and molecular profiling of tumor microenvironment and early-stage lung cancer, Int J Mol Sci, 23, 10, (2022); Qu C.X., Et al., LncRNA CASC19 promotes the proliferation, migration and invasion of non-small cell lung carcinoma via regulating miRNA-130b-3p, Eur Rev Med Pharmacol Sci, 23, 3 Suppl, pp. 247-255, (2019); Charkiewicz R., Et al., miRNA-Seq tissue diagnostic signature: a novel model for NSCLC subtyping, Int J Mol Sci, 24, 17, (2023); Shangguan W.J., Et al., TOB1-AS1 suppresses non-small cell lung cancer cell migration and invasion through a ceRNA network, Exp Ther Med, 18, 6, pp. 4249-4258, (2019); Shen Q., Sun Y., Xu S., LINC01503/miR-342-3p facilitates malignancy in non-small-cell lung cancer cells via regulating LASP1, Respir Res, 21, 1, (2020); Sun S.N., Et al., Relevance function of microRNA-708 in the pathogenesis of cancer, Cell Signal, 63, (2019); Young M.J., Et al., Estradiol-mediated inhibition of Sp1 decreases miR-3194-5p expression to enhance CD44 expression during lung cancer progression, J Biomed Sci, 29, 1, (2022); Shadbad M.A., Et al., A scoping review on the significance of programmed death-ligand 1-inhibiting microRNAs in non-small cell lung treatment: a single-cell RNA sequencing-based study, Front Med (Lausanne), 9, (2022); Xie L., Et al., SKA3, negatively regulated by miR-128-3p, promotes the progression of non-small-cell lung cancer, Per Med, 19, 3, pp. 193-205, (2022); Peng X.X., Et al., Correlation of plasma exosomal microRNAs with the efficacy of immunotherapy in EGFR/ALK wild-type advanced non-small cell lung cancer, J Immunother Cancer, 8, 1, (2020); Wang Q., Et al., XB130, regulated by miR-203, miR-219, and miR-4782-3p, mediates the proliferation and metastasis of non-small-cell lung cancer cells, Mol Carcinog, 59, 5, pp. 557-568, (2020); Yang S., Et al., Expression of miR-486-5p and its significance in lung squamous cell carcinoma, J Cell Biochem, 120, 8, pp. 13912-13923, (2019); Yin J., Et al., let–7 and miR–17 promote self–renewal and drive gefitinib resistance in non–small cell lung cancer, Oncol Rep, 42, 2, pp. 495-508, (2019)","L. Mengshan; College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China; email: msli@gnnu.edu.cn","","BioMed Central Ltd","","","","","","14712407","","BCMAC","38840078","English","BMC Cancer","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85195173109"
"Di Pietro P.; Abate A.C.; Izzo C.; Toni A.L.; Rusciano M.R.; Folliero V.; Dell'Annunziata F.; Granata G.; Visco V.; Motta B.M.; Campanile A.; Vitale C.; Prete V.; Gatto C.; Scarpati G.; Poggio P.; Galasso G.; Pagliano P.; Piazza O.; Santulli G.; Franci G.; Carrizzo A.; Vecchione C.; Ciccarelli M.","Di Pietro, Paola (35763573300); Abate, Angela Carmelita (57218262829); Izzo, Carmine (57200530874); Toni, Anna Laura (57682321300); Rusciano, Maria Rosaria (27568031100); Folliero, Veronica (57016818800); Dell'Annunziata, Federica (57924255400); Granata, Giovanni (58985543500); Visco, Valeria (57208370431); Motta, Benedetta Maria (57199734698); Campanile, Alfonso (12764852900); Vitale, Carolina (56058191400); Prete, Valeria (58843651700); Gatto, Cristina (57208864700); Scarpati, Giuliana (6504053545); Poggio, Paolo (12779958200); Galasso, Gennaro (7003334275); Pagliano, Pasquale (6602072168); Piazza, Ornella (6602421864); Santulli, Gaetano (12764666000); Franci, Gianluigi (23389317700); Carrizzo, Albino (55341520400); Vecchione, Carmine (7003389309); Ciccarelli, Michele (6603940972)","35763573300; 57218262829; 57200530874; 57682321300; 27568031100; 57016818800; 57924255400; 58985543500; 57208370431; 57199734698; 12764852900; 56058191400; 58843651700; 57208864700; 6504053545; 12779958200; 7003334275; 6602072168; 6602421864; 12764666000; 23389317700; 55341520400; 7003389309; 6603940972","Plasma miR-1-3p levels predict severity in hospitalized COVID-19 patients","2025","British Journal of Pharmacology","182","2","","451","467","16","1","10.1111/bph.17392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209718658&doi=10.1111%2fbph.17392&partnerID=40&md5=13aa8f39213be2927255207955491035","Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy; San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy; Centro Cardiologico Monzino IRCCS, Milan, Italy; Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Fleischer Institute for Diabetes and Metabolism, Einstein Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, NY, United States; Department of Advanced Biomedical Science, “Federico II” University, Naples, Italy; International Translational Research and Medical Education (ITME) Consortium, Naples, Italy; Department of Molecular Pharmacology, Einstein-Sinai Diabetes Research Center, Einstein Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, United States; Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli, Italy","Di Pietro P., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Abate A.C., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Izzo C., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Toni A.L., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Rusciano M.R., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Folliero V., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Dell'Annunziata F., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy, Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, Naples, Italy; Granata G., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Visco V., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Motta B.M., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Campanile A., San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy; Vitale C., San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy; Prete V., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Gatto C., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Scarpati G., San Giovanni di Dio e Ruggi D'Aragona University Hospital, Salerno, Italy; Poggio P., Centro Cardiologico Monzino IRCCS, Milan, Italy; Galasso G., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Pagliano P., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Piazza O., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Santulli G., Department of Medicine, Division of Cardiology, Wilf Family Cardiovascular Research Institute, Fleischer Institute for Diabetes and Metabolism, Einstein Institute for Neuroimmunology and Inflammation, Albert Einstein College of Medicine, New York, NY, United States, Department of Advanced Biomedical Science, “Federico II” University, Naples, Italy, International Translational Research and Medical Education (ITME) Consortium, Naples, Italy, Department of Molecular Pharmacology, Einstein-Sinai Diabetes Research Center, Einstein Institute for Aging Research, Albert Einstein College of Medicine, New York, NY, United States; Franci G., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy; Carrizzo A., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy, Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli, Italy; Vecchione C., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy, Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli, Italy; Ciccarelli M., Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, Italy","[No abstract available]","cardiovascular; COVID-19; microRNAs; SARS-CoV-2","Adult; Aged; Biomarkers; COVID-19; Female; Hospitalization; Humans; Male; MicroRNAs; Middle Aged; SARS-CoV-2; Severity of Illness Index; C reactive protein; hemoglobin; intercellular adhesion molecule 1; lactate dehydrogenase; microRNA; microRNA 1 3p; microrna 16 5p; microrna 584 5p; microrna 688 3p; microrna 9 5p; monocyte chemotactic protein 1; RNA; smooth muscle actin; transforming growth factor beta; unclassified drug; biological marker; microRNA; MIRN1 microRNA, human; adult; aged; animal cell; Article; artificial ventilation; asthma; chronic obstructive lung disease; cohort analysis; computer assisted tomography; controlled study; coronary artery disease; coronavirus disease 2019; disease severity; female; H9c2(2-1) cell line; heart failure; heart muscle injury; high flow nasal cannula therapy; hospitalization; human; human cell; hyperlipidemia; inflammation; lung fibrosis; machine learning; major clinical study; male; middle aged; nasopharyngeal swab; nonhuman; obesity; oropharyngeal swab; oxidative stress; spectrophotometry; Vero cell line; blood; diagnosis; genetics; hospitalization; mortality; Severe acute respiratory syndrome coronavirus 2; severity of illness index","","C reactive protein, 9007-41-4; hemoglobin, 9008-02-0; intercellular adhesion molecule 1, 126547-89-5; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; RNA, 63231-63-0; Biomarkers, ; MicroRNAs, ; MIRN1 microRNA, human, ","GraphPad Prism version 8, Graphpad; ImageJ; LightCycler 480, Hoffmann La Roche; NP80, Implen; NP80, Implen, Germany; NanoDrop ND-2000, Thermo, United States; Ti Eclipse, Nikon; miRNeasy, Qiagen, Germany; r version 4.4.0, R Foundation; version 3.12.1, Python","Graphpad; Hoffmann La Roche; Implen; Implen, Germany; Nikon; Python; Qiagen, Germany; R Foundation; Thermo, United States","Università degli Studi di Salerno, UNISA; National Institutes of Health, NIH; Monique Weill-Caulier and Irma T. Hirschl Trusts; Irma T. Hirschl Trust; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (737 ID.DM737WP3JV); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; National Center for Advancing Translational Sciences, NCATS, (UM1‐TR004400, UL1‐TR002556‐06); National Center for Advancing Translational Sciences, NCATS; National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK, (R01‐DK033823, R01‐DK123259); National Institute of Diabetes and Digestive and Kidney Diseases, NIDDK; American Heart Association, AHA, (24IPA1268813); American Heart Association, AHA; National Heart, Lung, and Blood Institute, NHLBI, (R01‐HL146691, R01‐HL159062, T32‐HL144456, R01‐HL164772, T32‐HL172255); National Heart, Lung, and Blood Institute, NHLBI","Funding text 1: G.S. is currently supported in part by the National Institutes of Health (NIH): National Heart, Lung, and Blood Institute (NHLBI: R01-HL164772, R01-HL159062, R01-HL146691, T32-HL144456, T32-HL172255), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK: R01-DK123259, R01-DK033823), National Center for Advancing Translational Sciences (NCATS: UL1-TR002556-06, UM1-TR004400), by the American Heart Association (AHA, 24IPA1268813), and by the Monique Weill-Caulier and Irma T. Hirschl Trusts. This work was in part supported by the Ministry of University and Research (MUR) (DM n. 737 ID.DM737WP3JV) to C.V. The graphical abstract was created using BioRender (https://biorender.com/). Open access publishing facilitated by Universita degli Studi di Salerno, as part of the Wiley - CRUI-CARE agreement.; Funding text 2: G.S. is currently supported in part by the National Institutes of Health (NIH): National Heart, Lung, and Blood Institute (NHLBI: R01\u2010HL164772, R01\u2010HL159062, R01\u2010HL146691, T32\u2010HL144456, T32\u2010HL172255), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK: R01\u2010DK123259, R01\u2010DK033823), National Center for Advancing Translational Sciences (NCATS: UL1\u2010TR002556\u201006, UM1\u2010TR004400), by the American Heart Association (AHA, 24IPA1268813), and by the Monique Weill\u2010Caulier and Irma T. Hirschl Trusts. This work was in part supported by the Ministry of University and Research (MUR) (DM n. 737 ID.DM737WP3JV) to C.V. The graphical abstract was created using BioRender ( https://biorender.com/ ). Open access publishing facilitated by Universita degli Studi di Salerno, as part of the Wiley \u2010 CRUI\u2010CARE agreement. 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Ciccarelli; Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, University of Salerno, Baronissi, SA, 84081, Italy; email: mciccarelli@unisa.it","","John Wiley and Sons Inc","","","","","","00071188","","BJPCB","39572402","English","Br. J. Pharmacol.","Article","Final","","Scopus","2-s2.0-85209718658"
"Ong M.-S.; Sordillo J.E.; Dahlin A.; McGeachie M.; Tantisira K.; Wang A.L.; Lasky-Su J.; Brilliant M.; Kitchner T.; Roden D.M.; Weiss S.T.; Wu A.C.","Ong, Mei-Sing (36653094200); Sordillo, Joanne E. (35085475900); Dahlin, Amber (6602725252); McGeachie, Michael (6506020083); Tantisira, Kelan (56752565300); Wang, Alberta L. (57202212346); Lasky-Su, Jessica (13610056800); Brilliant, Murray (7005113362); Kitchner, Terrie (22980601200); Roden, Dan M. (57532072500); Weiss, Scott T. (57207899397); Wu, Ann Chen (56047460900)","36653094200; 35085475900; 6602725252; 6506020083; 56752565300; 57202212346; 13610056800; 7005113362; 22980601200; 57532072500; 57207899397; 56047460900","Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma","2024","Journal of Personalized Medicine","14","3","246","","","","1","10.3390/jpm14030246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188917984&doi=10.3390%2fjpm14030246&partnerID=40&md5=034bee28d12e89c9942439520c06959d","PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, 02215, MA, United States; Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; Division of Pediatric Respiratory Medicine, Department of Pediatrics, University of California San Diego and Rady Children’s Hospital, San Diego, 92123, CA, United States; Marshfield Clinic Research Institute, Marshfield, 54449, WI, United States; Department of Medicine, Vanderbilt University Medical Center, Nashville, 37232, TN, United States","Ong M.-S., PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, 02215, MA, United States; Sordillo J.E., PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, 02215, MA, United States; Dahlin A., Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; McGeachie M., Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; Tantisira K., Division of Pediatric Respiratory Medicine, Department of Pediatrics, University of California San Diego and Rady Children’s Hospital, San Diego, 92123, CA, United States; Wang A.L., Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; Lasky-Su J., Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; Brilliant M., Marshfield Clinic Research Institute, Marshfield, 54449, WI, United States, Department of Medicine, Vanderbilt University Medical Center, Nashville, 37232, TN, United States; Kitchner T., Marshfield Clinic Research Institute, Marshfield, 54449, WI, United States; Roden D.M., Department of Medicine, Vanderbilt University Medical Center, Nashville, 37232, TN, United States; Weiss S.T., Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115, MA, United States; Wu A.C., PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, 02215, MA, United States","Background: Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma. Methods: The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67–0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70–0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others. Conclusions: An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction. © 2024 by the authors.","asthma; inhaled corticosteroids; machine learning; pharmacogenetics; polygenic risk prediction","ABC transporter; ATP binding cassette subfamily A member 7; CD147 antigen; corticosteroid; F box and leucine rich repeat protein 16; F box protein; phospholipid hydroperoxide glutathione peroxidase; tryptase; tryptase alpha/beta 1; unclassified drug; adult; area under the curve; Article; asthma; body mass; child; clinical outcome; cohort analysis; controlled study; disease severity; electronic health record; emergency ward; female; first-line treatment; gene frequency; genetic analysis; genetic risk; genetic risk score; genetic screening; genetic variability; genome-wide association study; genotyping; hospitalization; human; inhalation; machine learning; major clinical study; male; metabolomics; pharmacogenetics; phenotype; prediction; quality control; random forest; risk factor; sensitivity and specificity; single nucleotide polymorphism; treatment response","","phospholipid hydroperoxide glutathione peroxidase, 97089-70-8; tryptase, 97501-93-4","","","National Institutes of Health, NIH, (R01HL155742, R01HL139634, R01HD085993, HL65962, R01HL152244, R01HL162570, R01HL123915, K23HL151819); National Institutes of Health, NIH","Funded by National Institutes of Health (NIH) grants: R01HD085993, R01HL139634, R01HL155742, R01HL162570, R01HL152244, K23HL151819, R01HL123915, and HL65962.","Cloutier M.M., Baptist A.P., Blake K.V., Brooks E.G., Bryant-Stephens T., DiMango E., Dixon A.E., Elward K.S., Hartert T., Et al., 2020 Focused Updates to the Asthma Management Guidelines: A Report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group, J. 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Sci, 114, pp. 79-89, (2010)","M.-S. Ong; PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, 02215, United States; email: mei-sing_ong@hms.harvard.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188917984"
"Buican I.-L.; Gheorman V.; Udriştoiu I.; Olteanu M.; Rădulescu D.; Calafeteanu D.M.; Nemeş A.F.; Călăraşu C.; Rădulescu P.-M.; Streba C.-T.","Buican, Iulian-Laurențiu (59171056800); Gheorman, Victor (27267624600); Udriştoiu, Ion (56015362600); Olteanu, Mădălina (7005834278); Rădulescu, Dumitru (57219176498); Calafeteanu, Dan Marian (56472714300); Nemeş, Alexandra Floriana (57211509752); Călăraşu, Cristina (57193771589); Rădulescu, Patricia-Mihaela (57219437522); Streba, Costin-Teodor (24778820600)","59171056800; 27267624600; 56015362600; 7005834278; 57219176498; 56472714300; 57211509752; 57193771589; 57219437522; 24778820600","Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach","2024","Diagnostics","14","11","1153","","","","1","10.3390/diagnostics14111153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195885212&doi=10.3390%2fdiagnostics14111153&partnerID=40&md5=6f979ea05a06d7a879dd66c83daad4b0","U.M.F. Doctoral School Craiova, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Leamna Pulmonology Hospital, Leamna, 207129, Romania; Department of Psychiatry, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Department of Orthodontics, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, Craiova, 200749, Romania; Department of Neonatology, ‘Louis Ţurcanu’ Clinical Emergency Hospital for Children, Timişoara, 300011, Romania; Department of Pulmonology, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania","Buican I.-L., U.M.F. Doctoral School Craiova, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania, Leamna Pulmonology Hospital, Leamna, 207129, Romania; Gheorman V., Department of Psychiatry, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Udriştoiu I., Department of Psychiatry, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Olteanu M., Department of Orthodontics, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Rădulescu D., Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, Craiova, 200749, Romania; Calafeteanu D.M., Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, Craiova, 200749, Romania; Nemeş A.F., Department of Neonatology, ‘Louis Ţurcanu’ Clinical Emergency Hospital for Children, Timişoara, 300011, Romania; Călăraşu C., Leamna Pulmonology Hospital, Leamna, 207129, Romania, Department of Pulmonology, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania; Rădulescu P.-M., Leamna Pulmonology Hospital, Leamna, 207129, Romania; Streba C.-T., Leamna Pulmonology Hospital, Leamna, 207129, Romania, Department of Pulmonology, University of Medicine and Pharmacy of Craiova, Craiova, 200349, Romania","This study conducted at Leamna Pulmonology Hospital investigated the interrelations among cognitive, affective, and respiratory variables within a cohort of 100 patients diagnosed with chronic respiratory conditions, utilizing sophisticated machine learning-based clustering techniques. Spanning from October 2022 to February 2023, hospitalized individuals confirmed to have asthma or COPD underwent extensive evaluations using standardized instruments such as the mMRC scale, the CAT test, and spirometry. Complementary cognitive and affective assessments were performed employing the MMSE, MoCA, and the Hamilton Anxiety and Depression Scale, furnishing a holistic view of patient health statuses. The analysis delineated three distinct clusters: Moderate Cognitive Respiratory, Severe Cognitive Respiratory, and Stable Cognitive Respiratory, each characterized by unique profiles that underscore the necessity for tailored therapeutic strategies. These clusters exhibited significant correlations between the severity of respiratory symptoms and their effects on cognitive and affective conditions. The results highlight the benefits of an integrated treatment approach for COPD and asthma, which is personalized based on the intricate patterns identified through clustering. Such a strategy promises to enhance the management of these diseases, potentially elevating the quality of life and everyday functionality of the patients. These findings advocate for treatment customization according to the specific interplays among cognitive, affective, and respiratory dimensions, presenting substantial prospects for clinical advancement and pioneering new avenues for research in the domain of chronic respiratory disease management. © 2024 by the authors.","affective disorders; asthma; chronic obstructive pulmonary disease; cognitive disorders","antidepressant agent; anxiolytic agent; adult; affect; aged; anxiety disorder; Article; asthma; chronic obstructive lung disease; chronic respiratory tract disease; cluster analysis; cognition; cognition assessment; cognitive defect; cohort analysis; controlled study; daily life activity; depression; disease severity; female; forced expiratory volume; Hamilton Anxiety Scale; Hamilton Depression Rating Scale; health status; holistic care; human; machine learning; major clinical study; male; Mini Mental State Examination; Modified Medical Research Council Dyspnea Scale; Montreal cognitive assessment; personalized medicine; pilot study; prospective study; quality of life; respiratory function; spirometry; symptomatology","","","","","Universitatea de Medicină şi Farmacie din Craiova, UMF","The article processing charges were funded by the University of Medicine and Pharmacy of Craiova, Romania.","Soriano J.B., Kendrick P.J., Paulson K.R., Gupta V., Abrams E.M., Adedoyin R.A., Adhikari T.B., Advani S.M., Agrawal A., Ahmadian E., Et al., Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017, Lancet Respir. 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Calafeteanu; Department of Surgery, The Military Emergency Clinical Hospital ‘Dr. Stefan Odobleja’ Craiova, Craiova, 200749, Romania; email: danutcalafeteanu@yahoo.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85195885212"
"Wu C.; Ye N.; Jiang J.","Wu, Chenwen (35108296800); Ye, Na (59142222800); Jiang, Jialin (59141437400)","35108296800; 59142222800; 59141437400","Classification and Recognition of Lung Sounds Based on Improved Bi-ResNet Model","2024","IEEE Access","12","","10537190","73079","73094","15","1","10.1109/ACCESS.2024.3404657","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194029787&doi=10.1109%2fACCESS.2024.3404657&partnerID=40&md5=481494322947a7855a0f1480e9e88f5f","Lanzhou Jiaotong University, College of Electronic and Information Engineering, Gansu, Lanzhou, 730070, China","Wu C., Lanzhou Jiaotong University, College of Electronic and Information Engineering, Gansu, Lanzhou, 730070, China; Ye N., Lanzhou Jiaotong University, College of Electronic and Information Engineering, Gansu, Lanzhou, 730070, China; Jiang J., Lanzhou Jiaotong University, College of Electronic and Information Engineering, Gansu, Lanzhou, 730070, China","Lung sound classification is an important diagnostic task in the medical field. By analyzing respiratory sounds, doctors can help diagnose various respiratory system diseases. Chronic respiratory diseases worldwide are usually associated with abnormal lung sounds, which are clinically related to conditions such as bronchitis or chronic obstructive pulmonary disease. In recent years, the outbreak of COVID-19 has once again sparked research into lung sound classification. However, due to the environmental noise and heart sounds mixed in abnormal lung sounds, further improvements are still needed for accurate classification. In this paper, an improved Bi-ResNet network structure model is proposed to enhance the accuracy of lung sound classification and fully utilize feature extraction information. The model still processes the extracted lung sound features in parallel, but by introducing skip connections and increasing the use of direct connections, it allows information to be directly transmitted and fully integrates original and processed features within the network. This improved structure enables the model to learn features from the data at a deeper level, enhancing the expressiveness of the features. Additionally, the improved Bi-ResNet model combines convolutional neural networks (CNN) and residual networks (ResNet), and uses two types of features, the lung sound short-time Fourier transform (STFT) and wavelet transform (Wavelet), for model training and analysis. This comprehensive approach captures lung sound data information more comprehensively, differentiating between different types of lung sounds and providing better diagnostic assistance to doctors, thereby promoting early diagnosis and treatment of respiratory system diseases. Through experiments, the proposed model achieved a classification accuracy of 77.81% on the Int. Conf. on Biomedical Health Informatics (ICBHI) 2017 dataset, representing a 25.02% improvement over the Bi-ResNet model, with an F1 score of 71.05%. © 2023 IEEE.","Bi-ResNet model; deep learning; Fourier transform; Lung sound classification; wavelet transform","Biological organs; Classification (of information); Deep learning; Diagnosis; Extraction; Feature extraction; Neural networks; Pulmonary diseases; Respiratory system; Bi-residual network model; Chronic obstructive pulmonary disease; Deep learning; Features extraction; Lung; Lung sound classification; Lung sounds; Machine-learning; Medical services; Network models; Sound classification; Wavelets transform; Wavelet transforms","","","","","","","Ken B.B., Brusselle G.G., Chronic obstructive pulmonary disease, Mucosal Immunology, pp. 1857-1866, (2015); Sliwinski P., Puchalski K., Chronic obstructive pulmonary disease in the awareness of Polish society. 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Precision Medicine Powered By PHealth and Connected Health, 66, pp. 39-43, (2018); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. Biomed. Circuits Syst., 14, 3, pp. 535-544, (2020); Ma Y., Xu X., Li Y., Lungrn+nl: An improved adventitious lung sound classification using non-local block resnet neural network with mixup data augmentation, Proc. Interspeech, pp. 2902-2906, (2020); Bahoura M., Pelletier C., New parameters for respiratory sound classification, Proc. Can. Conf. Electr. Comput. Eng. Toward Caring Humane Technol. (CCECE), 3, pp. 1457-1460, (2003); Jindal V., Agarwal V., Kalaivani S., Respiratory sound analysis for detection of pulmonary diseases, Proc. IEEE Appl. Signal Process. Conf. (ASPCON), pp. 293-296, (2018); Bahoura M., Pelletier C., Respiratory sounds classification using Gaussian mixture models, Proc. Can. Conf. Electr. Comput. 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(EMBC), pp. 527-530, (2021); Li J., Yuan J., Wang H., Liu S., Guo Q., Ma Y., Li Y., Zhao L., Wang G., LungAttn: Advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram, Physiol. Meas., 42, 10, (2021); Zhang Q.Y., Wang Y.K., Speech classification model based on improved Inception network, J. Comput. Appl., 43, 3, pp. 90-915, (2023); Ma Y., Xu X., Yu Q., Zhang Y., Li Y., Zhao J., Wang G., LungBRN: A smart digital stethoscope for detecting respiratory disease using bi-ResNet deep learning algorithm, Proc. IEEE Biomed. Circuits Syst. Conf. (BioCAS), pp. 1-4, (2019); Shuvo S.B., Ali S.N., Swapnil S.I., Hasan T., Bhuiyan M.I.H., A lightweight CNN model for detecting respiratory diseases from lung auscultation sounds using EMD-CWT-based hybrid scalogram, IEEE J. Biomed. 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Interspeech, (2015); Zhang H., Cisse M., Dauphin Y.N., Lopez-Paz D., Mixup: Beyond empirical risk minimization, (2017); Perez L., Wang J., The effectiveness of data augmentation in image classification using deep learning, (2017); He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770-778, (2016); Wu Y., He K., Group normalization, (2018); Lecun Y., Bengio Y., Hinton G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015); Srivastava N., Hinton G.E., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1, pp. 1929-1958, (2014); Rocha B.M., Filos D., Mendes L., Serbes G., Ulukaya S., Kahya Y.P., Jakovljevic N., Turukalo T.L., Vogiatzis I.M., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Maglaveras N., Paiva R.P., Chouvarda I., de Carvalho P., An open access database for the evaluation of respiratory sound classification algorithms, Physiological Meas, 40, 3, (2019); Minami K., Lu H., Kim H., Mabu S., Hirano Y., Kido S., Automatic classification of large-scale respiratory sound dataset based on convolutional neural network, Proc. 19th Int. Conf. Control, Autom. Syst. (ICCAS), pp. 804-807, (2019); Nguyen T., Pernkopf F., Lung sound classification using co-tuning and stochastic normalization, IEEE Trans. Biomed. Eng., 69, 9, pp. 2872-2882, (2022); Pham L., Phan H., Palaniappan R., Mertins A., McLoughlin I., CNN-MoE based framework for classification of respiratory anomalies and lung disease detection, IEEE J. Biomed. Health Informat., 25, 8, pp. 2938-2947, (2021)","N. Ye; Lanzhou Jiaotong University, College of Electronic and Information Engineering, Lanzhou, Gansu, 730070, China; email: 731443570@qq.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85194029787"
"Narteni S.; Baiardini I.; Braido F.; Mongelli M.","Narteni, Sara (57220574684); Baiardini, Ilaria (6603202824); Braido, Fulvio (8314050300); Mongelli, Maurizio (7005882346)","57220574684; 6603202824; 8314050300; 7005882346","Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients","2024","PLoS ONE","19","3 MARCH","e0292980","","","","1","10.1371/journal.pone.0292980","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188248564&doi=10.1371%2fjournal.pone.0292980&partnerID=40&md5=c6a2b9764198a728fd3cb5eeffa78ae2","CNR-IEIIT, Genoa, Italy; DAUIN Department, Politecnico di Torino, Turin, Italy; Respiratory Diseases and Allergy Department, IRCCS Polyclinic Hospital San Martino, Genoa, Italy","Narteni S., CNR-IEIIT, Genoa, Italy, DAUIN Department, Politecnico di Torino, Turin, Italy; Baiardini I., Respiratory Diseases and Allergy Department, IRCCS Polyclinic Hospital San Martino, Genoa, Italy; Braido F., Respiratory Diseases and Allergy Department, IRCCS Polyclinic Hospital San Martino, Genoa, Italy; Mongelli M., CNR-IEIIT, Genoa, Italy","Explainable Artificial Intelligence (XAI) is becoming a disruptive trend in healthcare, allowing for transparency and interpretability of autonomous decision-making. In this study, we present an innovative application of a rule-based classification model to identify the main causes of chronic cough-related quality of life (QoL) impairment in a cohort of asthmatic patients. The proposed approach first involves the design of a suitable symptoms questionnaire and the subsequent analyses via XAI. Specifically, feature ranking, derived from statistically validated decision rules, helped in automatically identifying the main factors influencing an impaired QoL: pharynx/larynx and upper airways when asthma is under control, and asthma itself and digestive trait when asthma is not controlled. Moreover, the obtained if-then rules identified specific thresholds on the symptoms associated to the impaired QoL. These results, by finding priorities among symptoms, may prove helpful in supporting physicians in the choice of the most adequate diagnostic/therapeutic plan. Copyright: © 2024 Narteni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Artificial Intelligence; Asthma; Chronic Cough; Cough; Humans; Quality of Life; adult; Article; artificial intelligence; asthma; child; chronic cough; chronic rhinosinusitis; cohort analysis; controlled study; coughing; decision making; diagnostic test accuracy study; disease severity; electronic health record; feature ranking; human; larynx; machine learning; major clinical study; pharynx; phenotype; physician; prediction; quality of life; questionnaire; retrospective study; sensitivity and specificity; upper respiratory tract; validation process; artificial intelligence; complication; coughing","","","","","Italian Recovery and Resilience Plan; Future Artificial Intelligence Research; Università degli Studi di Genova; PNRR; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (CUP B53C22003630006); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","The work was partially supported by “Bando incentivazione della progettazione europea 2021” - Mission “Promoting Competitiveness” (DR n. 3386 of 26/07/2021) from Università degli Studi di Genova to Fulvio Braido, and by Future Artificial Intelligence Research (FAIR) project, Italian Recovery and Resilience Plan (PNRR), Spoke 3 - Resilient AI from Ministero dell'Università e della Ricerca - CUP B53C22003630006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Ethics guidelines for trustworthy AI-, Publications Office, (2019); Bharati S., Mondal M. R. H., Podder P., A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?, IEEE Transactions on Artificial Intelligence; Saraswat D., Et al., Explainable AI for Healthcare 5.0: Opportunities and Challenges, IEEE Access, 10, pp. 84486-84517, (2022); Hulsen T., Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare, AI, 4, 3, pp. 652-666, (2023); Morice AH, Millqvist E, Bieksiene K, Et al., ERS guidelines on the diagnosis and treatment of chronic cough in adults and children, Eur Respir J, 55, (2020); Nathan R.A., Sorkness C.A., Kosinski M., Schatz M., Li J.T., Marcus P., Et al., Development of the asthma control test: a survey for assessing asthma control, Journal of Allergy and Clinical Immunology, 113, pp. 59-65, (2004); Irwin RS, French CL, Chang AB, Et al., Classification of Cough as a Symptom in Adults and Management Algorithms: CHEST Guideline and Expert Panel Report, Chest, 153, pp. 196-209, (2018); Tsang KCH, Pinnock H, Wilson AM, Shah SA., Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review, J Asthma Allergy, 15, pp. 855-873, (2022); Shim JS, Kim BK, Kim SH, Kwon JW, Ahn KM, Kang SY, Et al., A smartphone-based application for cough counting in patients with acute asthma exacerbation, J Thorac Dis, 15, 7, pp. 4053-4065, (2023); Kaur S, Larsen E, Harper J, Purandare B, Uluer A, Hasdianda MA, Et al., Development and Validation of a Respiratory-Responsive Vocal Biomarker-Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19, J Med Internet Res, 25, (2023); Luo X, Gandhi P, Zhang Z, Shao W, Han Z, Chandrasekaran V, Et al., Applying interpretable deep learning models to identify chronic cough patients using EHR data, Comput Methods Programs Biomed, 210, (2021); Chen W, Schatz M, Zhou Y, Xie F, Bali V, Das A, Et al., Prediction of persistent chronic cough in patients with chronic cough using machine learning, ERJ Open Res, 9, 2, pp. 00471-2022, (2023); Brew BK, Chiesa F, Lundholm C, Ortqvist A, Almqvist C, A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data, PLoS ONE, 14, 12, (2019); Almqvist C, Ortqvist AK, Ullemar V, Lundholm C, Lichtenstein P, Magnusson PK., Cohort Profile: Swedish Twin Study on Prediction and Prevention of Asthma (STOPPA), Twin Res Hum Genet, 18, 3, pp. 273-280, (2015); Kang J, Seo WJ, Kang J, Park SH, Kang HK, Park HK, Et al., Clinical phenotypes of chronic cough categorised by cluster analysis, PLoS ONE, 18, 3, (2023); Koo H-K, Jeong I, Kim J-H, Et al., Development and validation of the COugh Assessment Test (COAT), Respirology, 24, pp. 551-557, (2019); Kwon JW, Moon JY, Kim SH, Song WJ, Kim MH, Kang MG, Et al., Reliability and validity of a korean version of the leicester cough questionnaire, Allergy Asthma Immunol Res, 7, 3, pp. 230-233, (2015); Baiardini I., Braido F., Fassio O., Tarantini F., Pasquali M., Tarchino F., Et al., A new tool to assess and monitor the burden of chronic cough on quality of life: Chronic Cough Impact Questionnaire, Allergy, 60, pp. 482-488, (2005); Braido F, Baiardini I, Menoni S, Gani F, Senna GE, Ridolo E, Et al., Patients with Asthma and Comorbid Allergic Rhinitis: Is Optimal Quality of Life Achievable in Real Life?, PLoS ONE, 7, 2, (2012); Muselli M., Switching Neural Networks: A New Connectionist Model for Classification, Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, 3931; Whatley M., One-Way ANOVA and the Chi-Square Test of Independence, Introduction to Quantitative Analysis for International Educators, pp. 57-74, (2022); Vaccari I., Orani V., Paglialonga A., Cambiaso E., Mongelli M., A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data, Sensors, 21, (2021); Hicks S.A., Strumke I., Thambawita V., Et al., On evaluation metrics for medical applications of artificial intelligence, Sci Rep, 12, (2022)","S. Narteni; CNR-IEIIT, Genoa, Italy; email: sara.narteni@ieiit.cnr.it","","Public Library of Science","","","","","","19326203","","POLNC","38502606","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188248564"
"D. Almeida S.; Norajitra T.; Lüth C.T.; Wald T.; Weru V.; Nolden M.; Jäger P.F.; von Stackelberg O.; Heußel C.P.; Weinheimer O.; Biederer J.; Kauczor H.-U.; Maier-Hein K.","D. Almeida, Silvia (57213416077); Norajitra, Tobias (56198077000); Lüth, Carsten T. (57219524655); Wald, Tassilo (57223727513); Weru, Vivienn (57290547000); Nolden, Marco (55908659000); Jäger, Paul F. (57201075948); von Stackelberg, Oyunbileg (56610304000); Heußel, Claus Peter (7004889910); Weinheimer, Oliver (9535317400); Biederer, Jürgen (7003612651); Kauczor, Hans-Ulrich (7102275418); Maier-Hein, Klaus (55647018100)","57213416077; 56198077000; 57219524655; 57223727513; 57290547000; 55908659000; 57201075948; 56610304000; 7004889910; 9535317400; 7003612651; 7102275418; 55647018100","How do deep-learning models generalize across populations? Cross-ethnicity generalization of COPD detection","2024","Insights into Imaging","15","1","198","","","","1","10.1186/s13244-024-01781-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200897838&doi=10.1186%2fs13244-024-01781-x&partnerID=40&md5=4ed1b956221de5333ba85c8f8afa0932","Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany; National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany; Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany; Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; University of Latvia, Faculty of Medicine, Raina Bulvaris 19, Riga, LV-1586, Latvia; Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, Kiel, D-24098, Germany","D. Almeida S., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Medical Faculty, Heidelberg University, Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany; Norajitra T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany; Lüth C.T., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Wald T., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; Weru V., Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany; Nolden M., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Jäger P.F., Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany; von Stackelberg O., Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany; Heußel C.P., Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany, Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany; Weinheimer O., Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany; Biederer J., Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany, University of Latvia, Faculty of Medicine, Raina Bulvaris 19, Riga, LV-1586, Latvia, Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, Kiel, D-24098, Germany; Kauczor H.-U., Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, Diagnostic and Interventional Radiology, University Hospital, Heidelberg, Germany; Maier-Hein K., Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany, Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany, National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany, Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany, Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany","Objectives: To evaluate the performance and potential biases of deep-learning models in detecting chronic obstructive pulmonary disease (COPD) on chest CT scans across different ethnic groups, specifically non-Hispanic White (NHW) and African American (AA) populations. Materials and methods: Inspiratory chest CT and clinical data from 7549 Genetic epidemiology of COPD individuals (mean age 62 years old, 56–69 interquartile range), including 5240 NHW and 2309 AA individuals, were retrospectively analyzed. Several factors influencing COPD binary classification performance on different ethnic populations were examined: (1) effects of training population: NHW-only, AA-only, balanced set (half NHW, half AA) and the entire set (NHW + AA all); (2) learning strategy: three supervised learning (SL) vs. three self-supervised learning (SSL) methods. Distribution shifts across ethnicity were further assessed for the top-performing methods. Results: The learning strategy significantly influenced model performance, with SSL methods achieving higher performances compared to SL methods (p < 0.001), across all training configurations. Training on balanced datasets containing NHW and AA individuals resulted in improved model performance compared to population-specific datasets. Distribution shifts were found between ethnicities for the same health status, particularly when models were trained on nearest-neighbor contrastive SSL. Training on a balanced dataset resulted in fewer distribution shifts across ethnicity and health status, highlighting its efficacy in reducing biases. Conclusion: Our findings demonstrate that utilizing SSL methods and training on large and balanced datasets can enhance COPD detection model performance and reduce biases across diverse ethnic populations. These findings emphasize the importance of equitable AI-driven healthcare solutions for COPD diagnosis. Critical relevance statement: Self-supervised learning coupled with balanced datasets significantly improves COPD detection model performance, addressing biases across diverse ethnic populations and emphasizing the crucial role of equitable AI-driven healthcare solutions. Key Points: Self-supervised learning methods outperform supervised learning methods, showing higher AUC values (p < 0.001). Balanced datasets with non-Hispanic White and African American individuals improve model performance. Training on diverse datasets enhances COPD detection accuracy. Ethnically diverse datasets reduce bias in COPD detection models. SimCLR models mitigate biases in COPD detection across ethnicities. Graphical Abstract: (Figure presented.). © The Author(s) 2024.","Artificial intelligence; Chronic obstructive pulmonary disease; Computed tomography; Deep learning; Ethnicity","adult; aged; area under the curve; Article; binary classification; chronic obstructive lung disease; computer assisted tomography; deep learning; diagnostic accuracy; ethnicity; female; forced expiratory volume; forced vital capacity; genetic epidemiology; health status; human; major clinical study; male; multiple linear regression analysis; retrospective study; self supervised learning; spirometry; supervised machine learning; very elderly","","","","","","","Adeloye D., Song P., Zhu Y., Et al., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, pp. 447-458, (2022); Martinez C.H., Mannino D.M., Jaimes F.A., Et al., Undiagnosed obstructive lung disease in the United States. 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Sun J., Liao X., Yan Y., Et al., Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach, Eur Radiol, 32, pp. 5319-5329, (2022); Almeida S.D., Norajitra T., Luth C.T., Et al., Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT, Eur Radiol, (2023); Almeida S.D., Luth C.T., Norajitra T., Et al., COOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations., (2023); Almeida S.D., Et al., Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography, Front Med, 11, (2024); Li F., Choi J., Zou C., Et al., Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images, Sci Rep, 11, (2021); Celeste C., Ming D., Broce J., Et al., Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning, NPJ Digit Med, 6, (2023); Glocker B., Jones C., Roschewitz M., Winzeck S., Risk of bias in chest radiography deep learning foundation models, Radiology Artif Intell, 5, (2023); Sirotkin K., Carballeira P., Escudero-Vinolo M., A study on the distribution of social biases in self-supervised learning visual models, In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10432-10441, (2022); Pot M., Kieusseyan N., Prainsack B., Not all biases are bad: equitable and inequitable biases in machine learning and radiology, Insights Imaging, 12, (2021); Obermeyer Z., Powers B., Vogeli C., Mullainathan S., Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366, pp. 447-453, (2019); Sex and gender bias in technology and artificial intelligence: biomedicine and healthcare applications., (2022); Steed R., Caliskan A., Image representations learned with unsupervised pre-training contain human-like biases, In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 701-713, (2021); 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Burlina P., Joshi N., Paul W., Pacheco K.D., Bressler N.M., Addressing artificial intelligence bias in retinal diagnostics, Trans Vis Sci Tech, 10, (2021); Kinyanjui N.M., Odonga T., Cintas C., Fairness of classifiers across skin tones in dermatology, In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 12266, pp. 320-329, (2020); Paul W., Hadzic A., Joshi N., Alajaji F., Burlina P., TARA: training and representation alteration for AI fairness and domain generalization, Neural Comput, 34, pp. 716-753, (2022); Zhou Y., Huang S.C., Fries J.A., Et al., RadFusion: Benchmarking performance and fairness for multimodal pulmonary embolism detection from CT and HER., (2021); Bhakta N.R., Bime C., Kaminsky D.A., Et al., Race and ethnicity in pulmonary function test interpretation: an official American Thoracic Society statement, Am J Respir Crit Care Med, 207, pp. 978-995, (2023)","S. D. Almeida; Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; email: silvia.diasalmeida@dkfz-heidelberg.de","","Springer Science and Business Media Deutschland GmbH","","","","","","18694101","","","","English","Insights Imaging","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85200897838"
"Homer-Bouthiette C.; Shen B.H.; Dobie A.C.; Shankar D.A.; Pang B.; Law A.C.; Bosch N.A.","Homer-Bouthiette, Collin (56082951100); Shen, Burton H. (59352367100); Dobie, Aaron C. (59352401000); Shankar, Divya A. (57563175700); Pang, Brandon (58222009400); Law, Anica C. (57200499664); Bosch, Nicholas A. (57202462190)","56082951100; 59352367100; 59352401000; 57563175700; 58222009400; 57200499664; 57202462190","Practice Patterns for Acute Asthma Exacerbation in Adult Patients Admitted to U.S. Intensive Care Units","2024","Annals of the American Thoracic Society","21","10","","1441","1448","7","1","10.1513/AnnalsATS.202401-085OC","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205525784&doi=10.1513%2fAnnalsATS.202401-085OC&partnerID=40&md5=d62a5834c5756dd4c09664c7f7eeeec8","Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States","Homer-Bouthiette C., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Shen B.H., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Dobie A.C., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Shankar D.A., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Pang B., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Law A.C., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States; Bosch N.A., Pulmonary Center, Department of Medicine, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States","Rationale: Guidelines recommend systemic corticosteroids and inhaled b-agonists for patients with severe asthma exacerbation who are admitted to intensive care units. The benefits and utilization of adjunct treatments after guideline-recommended first-line treatments have been initiated are unclear. Objectives: Examine practice patterns of adjunct interventions in US intensive care units (ICUs) and their associations with outcomes for adults with severe asthma exacerbations. Methods: Using the multicenter PINC AI Healthcare Database of Premier Inc. (2016–2022), we sought to explore the use of adjunct interventions (medications [e.g., magnesium, leukotriene inhibitors, terbutaline, heliox] and procedures [e.g., invasive and noninvasive mechanical ventilation]) for adult patients admitted to U.S. ICUs with acute asthma exacerbations. We used hierarchical generalized linear models to calculate risk-adjusted rates of adjunct interventions and quantified between-hospital variation in adjunct interventions using the intraclass correlation coefficient (ICC; higher values correspond to higher between-hospital variation). We then used K-means clustering to identify groups of hospitals with similar risk-adjusted practice profiles of all adjunct treatments and examined associations between identified hospital clusters and patient outcomes. Results: We identified 62,392 patients from 961 hospitals for inclusion. Adjunct interventions with the highest between-hospital variation after risk adjustment were heliox (ICC, 91%), inhaled steroids (ICC, 23%), invasive mechanical ventilation (ICC, 21%), terbutaline (ICC, 22%), paralytics (ICC, 16%), and noninvasive ventilation (ICC, 15%). K-means clustering identified two distinct hospital clusters: Patients who were admitted to Cluster 1 hospitals (399 hospitals) had higher risk-adjusted rates of noninvasive ventilation (51% vs. 33%), compared with patients who were admitted to Cluster 2 hospitals (234 hospitals), which had higher risk-adjusted rates of invasive mechanical ventilation (63% vs. 30%). Cluster 2 was associated with fewer hospital-free days (b = 20.75 d; 95% confidence interval [CI] = 20.95, 20.55) and increased in-hospital mortality (adjusted odds ratio, 1.28; 95% CI = 1.17, 1.40). Conclusions: The use of adjunct interventions for patients with severe asthma exacerbations vary widely across U.S. hospitals; however, hospitals generally fall into two clusters differentiated primarily by the use of invasive or noninvasive mechanical ventilation. The cluster favoring noninvasive mechanical ventilation was associated with improved outcomes. Our results help to inform usual-care arms of future comparative effectiveness studies and efforts to standardize asthma practice. © 2024 by the American Thoracic Society.","ICU; IMV; NIV; practice variation; severe asthma","Acute Disease; Administration, Inhalation; Adrenal Cortex Hormones; Adult; Aged; Anti-Asthmatic Agents; Asthma; Bronchodilator Agents; Disease Progression; Female; Helium; Hospital Mortality; Hospitalization; Humans; Intensive Care Units; Leukotriene Antagonists; Linear Models; Magnesium Sulfate; Male; Middle Aged; Practice Patterns, Physicians'; Respiration, Artificial; Terbutaline; United States; azithromycin; beta adrenergic receptor stimulating agent; corticosteroid; dexmedetomidine; epinephrine; heliox; hypertensive factor; ketamine; leukotriene receptor blocking agent; magnesium; morphine; muscarinic receptor blocking agent; opiate; propofol; steroid; terbutaline; antiasthmatic agent; bronchodilating agent; helium; magnesium sulfate; adult; Article; clinical outcome; clinical practice; cohort analysis; controlled study; disease exacerbation; female; hospital admission; human; in-hospital mortality; intensive care unit; invasive ventilation; k means clustering; major clinical study; male; middle aged; noninvasive ventilation; outpatient department; retrospective study; risk assessment; severe asthma; treatment outcome; United States; acute disease; aged; artificial ventilation; asthma; clinical trial; drug therapy; hospital mortality; hospitalization; inhalational drug administration; intensive care unit; multicenter study; statistical model; therapy","","azithromycin, 83905-01-5, 117772-70-0, 121470-24-4; dexmedetomidine, 113775-47-6, 145108-58-3; epinephrine, 51-43-4, 55-31-2, 6912-68-1; heliox, 58933-55-4; ketamine, 1867-66-9, 6740-88-1, 81771-21-3; magnesium, 7439-95-4; morphine, 52-26-6, 57-27-2; opiate, 53663-61-9, 8002-76-4, 8008-60-4; propofol, 2078-54-8, 113981-41-2; terbutaline, 23031-25-6; helium, 7440-59-7; magnesium sulfate, 7487-88-9; Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ; Bronchodilator Agents, ; Helium, ; Leukotriene Antagonists, ; Magnesium Sulfate, ; Terbutaline, ","","","Boston University Chobanian & Avedisian School of Medicine Department of Medicine; National Institutes of Health National Center for Advancing Translational Sciences, (1KL2TR001411, 1UL1TR001430); National Heart, Lung, and Blood Institute, NHLBI, (K23HL153482)","Supported by National Institutes of Health National Center for Advancing Translational Sciences grants 1KL2TR001411 and 1UL1TR001430; National Heart, Lung and Blood Institute grant K23HL153482; and the Boston University Chobanian & Avedisian School of Medicine Department of Medicine Career Investment Award.","Louie S, Morrissey BM, Kenyon NJ, Albertson TE, Avdalovic M., The critically ill asthmatic—from ICU to discharge, Clin Rev Allergy Immunol, 43, pp. 30-44, (2012); Most recent national asthma data, (2023); Garner O, Ramey JS, Hanania NA., Management of life-threatening asthma: severe asthma series, Chest, 162, pp. 747-756, (2022); 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Althoff MD, Holguin F, Yang F, Grunwald GK, Moss M, Vandivier RW, Et al., Noninvasive ventilation use in critically ill patients with acute asthma exacerbations, Am J Respir Crit Care Med, 202, pp. 1520-1530, (2020); Chaudhry D, Indora M, Sangwan V, Sehgal IPS, Chaudhry A., Evaluation of non-invasive ventilation in management of acute severe asthma, Thorax, 65, pp. A32-A33, (2010); Agarwal SK, Meena M, Sharma S, Tiwari V., Utility of NIV via face mask in type II respiratory failure due to exacerbations of acute severe asthma, Chest, 138, (2010); Rochwerg B, Brochard L, Elliott MW, Hess D, Hill NS, Nava S, Et al., Official ERS/ATS clinical practice guidelines: noninvasive ventilation for acute respiratory failure, Eur Respir J, 50, (2017); Stefan MS, Nathanson BH, Lagu T, Priya A, Pekow PS, Steingrub JS, Et al., Outcomes of noninvasive and invasive ventilation in patients hospitalized with asthma exacerbation, Ann Am Thorac Soc, 13, pp. 1096-1104, (2016); Adair E, Dibaba D, Fowke JH, Snider M., The impact of terbutaline as adjuvant therapy in the treatment of severe asthma in the pediatric emergency department, Pediatr Emerg Care, 38, pp. e292-e294, (2022); 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Baldo BA, Pham NH., Histamine-releasing and allergenic properties of opioid analgesic drugs: resolving the two, Anaesth Intensive Care, 40, pp. 216-235, (2012); Lee H, Kim B-G, Chung SJ, Park DW, Park TS, Moon J-Y, Et al., New-onset asthma following COVID-19 in adults, J Allergy Clin Immunol Pract, 11, pp. 2228-2231, (2023); Agondi RC, Menechino N, Marinho AKBB, Kalil J, Giavina-Bianchi P., Worsening of asthma control after COVID-19, Front Med (Lausanne), 9, (2022)","C. Homer-Bouthiette; Pulmonary Center, Boston, 72 East Concord Street, R-304, 02118, United States; email: collin.homer-bouthiette@bmc.org","","American Thoracic Society","","","","","","23296933","","","38935672","English","Ann. Am. Thorac. Soc.","Article","Final","","Scopus","2-s2.0-85205525784"
"Renukaradhya S.; Narayanappa S.S.","Renukaradhya, Sapna (57205725083); Narayanappa, Sheshappa S. (57224576774)","57205725083; 57224576774","Deep HybridNet with hybrid optimization for enhanced medicinal plant identification and classification","2024","International Journal of Electrical and Computer Engineering","14","5","","5626","5640","14","1","10.11591/ijece.v14i5.pp5626-5640","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201052367&doi=10.11591%2fijece.v14i5.pp5626-5640&partnerID=40&md5=cbedd9241de3832bf2b75edf03004905","Department of Information Science and Engineering, Sir M. Visvesvaraya Institute of Technology, Affiliated to Visvesvaraya Technological University, Belgaum, Bangalore, India; Department of Information Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India","Renukaradhya S., Department of Information Science and Engineering, Sir M. Visvesvaraya Institute of Technology, Affiliated to Visvesvaraya Technological University, Belgaum, Bangalore, India, Department of Information Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India; Narayanappa S.S., Department of Information Science and Engineering, Sir M. Visvesvaraya Institute of Technology, Affiliated to Visvesvaraya Technological University, Belgaum, Bangalore, India","Herbal leaves, known for their efficacy in treating a range of infectious diseases including cancer, asthma, and heart conditions, are still widely used by medical professionals. Traditionally, villagers have identified these plants visually, but given the similarity in appearance among various species, this method is prone to human error. Accurate identification of these plant species is critical for effective treatment. Hence, the development of an intelligent plant classification system is crucial to reduce the risk of misidentification and enhance treatment accuracy. This paper introduces the deep HybridNet with hybrid optimization module (DeepHybrid-OptNet) a novel deep learning framework for medicinal plant identification and classification. Merging convolutional and recurrent neural network architectures, deep HybridNet excels in extracting complex botanical features through channel-wise feature extraction modules in convolutional neural network (CNN) and feedback loop in recurrent neural network (RNN). The incorporation of a DeepHybrid-OptNet module enhances the model's learning efficiency and accuracy. Empirical results on the Mendley and folio dataset demonstrate the framework's superiority over existing methods in accuracy, precision, and recall making it a valuable asset for botany and herbal medicine research. © 2024 Institute of Advanced Engineering and Science. All rights reserved.","Convolutional neural network; Deep HybridNet with hybrid; Deep learning framework; Medicinal plant classification; optimization module; Plant species","","","","","","","","Rainey C., McConnell J., Bond R. R., Hughes C., McFadden S., Man vs machine: a comparison of computer and human visual processes in radiographic image interpretation, ISRRT 2021, (2021); Pushpanathan K., Hanafi M., Mashohor S., Fazlil Ilahi W. F., Machine learning in medicinal plants recognition: a review, Artificial Intelligence Review, 54, 1, pp. 305-327, (2021); Shrestha A., Mahmood A., Review of deep learning algorithms and architectures, IEEE Access, 7, pp. 53040-53065, (2019); Blesslin Elizabeth C. P., Baulkani S., Novel network for medicinal leaves identification, IETE Journal of Research, 69, 4, pp. 1772-1782, (2023); Kan H. X., Jin L., Zhou F. 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H., Kumar V., Salma S., Efficient big data clustering using ad hoc fuzzy C means and auto-encoder CNN, Lecture Notes in Networks and Systems, 563, pp. 353-368, (2023); Sapna R., Sheshappa S. N., Vijayakarthik P., Raja S. P., Global pattern feedforward neural network structure with bacterial foraging optimization towards medicinal plant leaf identification and classification, International Journal of Advanced Computer Science and Applications, 13, 12, pp. 63-70, (2022); Balaji S., Development of machine learning model for medicinal plant identification, 2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), pp. 1-4, (2023); Pujar P., Kumar A., Kumar V., Efficient plant leaf detection through machine learning approach based on corn leaf image classification, IAES International Journal of Artificial Intelligence, 13, 1, pp. 1139-1148, (2024); Sapna R., Monikarani H. G., Mishra S., Linked data through the lens of machine learning: an enterprise view, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1-6, (2019); Kale S. G., Et al., Identification of Ayurvedic leaves using deep learning, 2023 International Conference on Communication, Circuits, and Systems (IC3S), pp. 1-6, (2023); Nagesh N., Patil P., Patil S., Kokatanur M., An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm, International Journal of Electrical and Computer Engineering, 12, 2, pp. 1768-1775, (2022); Sharrab Y., Al-Fraihat D., Tarawneh M., Sharieh A., Medicinal plants recognition using deep learning, 2023 International Conference on Multimedia Computing, Networking and Applications, MCNA 2023, pp. 116-122, (2023); Monika Rani H. 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A., Dinesh R., Harihara Sudhan M., Harish M., Identification of medicinal flora using deep learning, 3rd International Conference on Smart Electronics and Communication, ICOSEC 2022 - Proceedings, pp. 1187-1192, (2022); Raimond K., Deep learning based indigenous herbal medicinal plants recognition: a comprehensive review, 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1412-1418, (2022); Roopashree S., Anitha J., Medicinal leaf dataset, Mendeley Data, (2020); Munisami T., Ramsurn M., Kishnah S., Pudaruth S., Plant leaf recognition using shape features and colour histogram with k-nearest neighbour classifiers, Procedia Computer Science, 58, pp. 740-747, (2015); Anubha Pearline S., Sathiesh Kumar V., Harini S., A study on plant recognition using conventional image processing and deep learning approaches, Journal of Intelligent and Fuzzy Systems, 36, 3, pp. 1997-2004, (2019); Keivani M., Mazloum J., Sedaghatfar E., Tavakoli M. B., Automated analysis of leaf shape, texture, and color features for plant classification, Traitement du Signal, 37, 1, pp. 17-28, (2020); Pawara P., Okafor E., Schomaker L., Wiering M., Data augmentation for plant classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10617, pp. 615-626, (2017); Kanda P. S., Xia K., Sanusi O. H., An effective ensemble convolutional learning model with fine-tuning for medicinal plant leaf identification, IEEE Access, 9, pp. 162590-162613, (2021); Hajam M. A., Arif T., Khanday A. M. U. D., Neshat M., An effective ensemble convolutional learning model with fine-tuning for medicinal plant leaf identification, Information, 14, 11, (2023)","S. Renukaradhya; Department of Information Technology, Manipal Academy of Higher Education, Manipal Institute of Technology Bengaluru, Bangalore, India; email: sapna.aradhya@gmail.com","","Institute of Advanced Engineering and Science","","","","","","20888708","","","","English","Int. J. Electr. Comput. Eng.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85201052367"
"Enríquez-Rodríguez C.J.; Pascual-Guardia S.; Casadevall C.; Caguana-Vélez O.A.; Rodríguez-Chiaradia D.; Barreiro E.; Gea J.","Enríquez-Rodríguez, Cesar Jessé (58195593400); Pascual-Guardia, Sergi (57211115875); Casadevall, Carme (56029286000); Caguana-Vélez, Oswaldo Antonio (57221944325); Rodríguez-Chiaradia, Diego (57220698444); Barreiro, Esther (7005260014); Gea, Joaquim (7004959809)","58195593400; 57211115875; 56029286000; 57221944325; 57220698444; 7005260014; 7004959809","Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients","2024","Cells","13","10","866","","","","1","10.3390/cells13100866","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194129572&doi=10.3390%2fcells13100866&partnerID=40&md5=e2bb34362d78a4ca46659d57c536f8ab","Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain; MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain; CIBERES, ISCiii, Barcelona, 08003, Spain; BRN, Barcelona, 08003, Spain","Enríquez-Rodríguez C.J., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Pascual-Guardia S., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Casadevall C., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Caguana-Vélez O.A., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Rodríguez-Chiaradia D., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Barreiro E., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain; Gea J., Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain, MELIS Department, Universitat Pompeu Fabra, Barcelona, 08003, Spain, CIBERES, ISCiii, Barcelona, 08003, Spain, BRN, Barcelona, 08003, Spain","Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research. © 2024 by the authors.","coagulation; complement system; COPD; exacerbation; immune response; inflammation; lipid profile; proteins","Aged; Biomarkers; Case-Control Studies; Cluster Analysis; Disease Progression; Female; Humans; Male; Middle Aged; Proteome; Proteomics; Pulmonary Disease, Chronic Obstructive; biological marker; C reactive protein; fibrinogen; biological marker; proteome; adult; aged; Article; blood clotting; blood sampling; case control study; chronic obstructive lung disease; clinical article; cluster analysis; complement system; controlled study; data extraction; disease exacerbation; eosinophil count; forced expiratory volume; forced vital capacity; human; immune response; inflammation; information processing; leukocyte count; lipid fingerprinting; lipid metabolism; liquid chromatography-mass spectrometry; male; neutrophil count; proteomics; sensitivity and specificity; blood; female; metabolism; middle aged; procedures","","C reactive protein, 9007-41-4; fibrinogen, 9001-32-5; Biomarkers, ; Proteome, ","","","Sociedad Española de Neumología y Cirugía Torácica, SEPAR; Instituto de Salud Carlos III & European Union, (PI21/00785, PFIS FI22/00003, M-BAE BA22/00009, M-AES MV23/00012)","This research was funded by Sociedad Espa\u00F1ola de Neumolog\u00EDa y Cirug\u00EDa Tor\u00E1cica: Research Grant 2019; Instituto de Salud Carlos III & European Union: PI21/00785, PFIS FI22/00003, M-BAE BA22/00009 & M-AES MV23/00012.","Singh D., Agusti A., Anzueto A., Barnes P.J., Bourbeau J., Celli B.R., Criner G.J., Frith P., Halpin D.M.G., Han M., Et al., Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: The GOLD Science Committee Report 2019, Eur. 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Chem, 288, pp. 32172-32183, (2013); Pacheco J., Casado S., Porras S., Exact Methods for Variable Selection in Principal Component Analysis: Guide Functions and Pre-Selection, Comput. Stat. Data Anal, 57, pp. 95-111, (2013); Boulesteix A.-L., Strimmer K., Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data, Brief. Bioinform, 8, pp. 32-44, (2007); Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Global, Regional, and National Prevalence of, and Risk Factors for, Chronic Obstructive Pulmonary Disease (COPD) in 2019: A Systematic Review and Modelling Analysis, Lancet Respir. Med, 10, pp. 447-458, (2022); Grosdidier S., Ferrer A., Faner R., Pinero J., Roca J., Cosio B., Agusti A., Gea J., Sanz F., Furlong L.I., Network Medicine Analysis of COPD Multimorbidities, Respir. Res, 15, (2014)","J. Gea; Respiratory Medicine Department, Hospital del Mar—IMIM, Barcelona, 08003, Spain; email: quim.gea@upf.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20734409","","","38786086","English","Cells","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194129572"
"Bhansali A.; Rekha P.M.; Shahapure N.H.; Pallavi G.B.; Punitha K.; Surendrarao Honnahalli S.","Bhansali, Ashok (59117779400); Rekha, P.M. (56490341200); Shahapure, Nagamani H. (56491101600); Pallavi, G.B. (57190966767); Punitha, K. (57192169430); Surendrarao Honnahalli, Shruthishree (58093005800)","59117779400; 56490341200; 56491101600; 57190966767; 57192169430; 58093005800","3D mask based lung function monitoring system using machine learning for early identification of lung disorders","2024","International Journal of Imaging Systems and Technology ","34","4","e23092","","","","1","10.1002/ima.23092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196393812&doi=10.1002%2fima.23092&partnerID=40&md5=3ed273d915448460c3d8c9f0c4da0f9f","Department of Computer Engineering and Applications, GLA University, Uttar Pradesh, Mathura, India; Department of Information Science and Engineering, JSS Academy of Technical Education, Karnataka, Bangalore, India; Department of Computer Science and Engineering, B.M.S College of Engineering, Karnataka, Bangalore, India; SCOPE, Vellore Institute of Technology, Tamil Nadu, Chennai, India; Department of Computer Science and Engineering, Jain (Deemed to be University), Karnataka, Bengaluru, India","Bhansali A., Department of Computer Engineering and Applications, GLA University, Uttar Pradesh, Mathura, India; Rekha P.M., Department of Information Science and Engineering, JSS Academy of Technical Education, Karnataka, Bangalore, India; Shahapure N.H., Department of Information Science and Engineering, JSS Academy of Technical Education, Karnataka, Bangalore, India; Pallavi G.B., Department of Computer Science and Engineering, B.M.S College of Engineering, Karnataka, Bangalore, India; Punitha K., SCOPE, Vellore Institute of Technology, Tamil Nadu, Chennai, India; Surendrarao Honnahalli S., Department of Computer Science and Engineering, Jain (Deemed to be University), Karnataka, Bengaluru, India","Early identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers a regular lung function monitoring system that employs Machine Learning (ML) approach to aids in the prompt detection of symptoms of illness and the prevention of significant epidemics of the lung condition. A collection of sensors are coupled to the microcontroller in a 3D mask created using 3D printing technology. When a person wearing a face mask breathes in and out, the sensor values are instantly retrieved. The sensor data is sent to the cloud via a Wi-Fi module for additional evaluation, and categorisation is performed using genetic algorithms, Support Vector Machine (SVM), and Principal Component Analysis (PCA). The GA, SWM, and PCA algorithms identify lung sickness using data from sensors obtained from the 3D masks through the web interface. There were 250 participants in total, comprising persons from all ages, smoker and those who do not smoke as well as asthmatics. The classifiers are trained utilising a set of pretrained values obtained from freely accessible datasets. Furthermore, patients are alerted when physiological indicators deviate from normal and when favourable atmospheric circumstances change. © 2024 Wiley Periodicals LLC.","3D mask; genetic algorithm; lung disorders; machine learning; principal component analysis algorithms; support vector machine","3D printing; Biological organs; Classification (of information); Genetic algorithms; Learning systems; Principal component analysis; Pulmonary diseases; Smoke; 3D masks; Chronic obstructive pulmonary disease; Death rates; Lung disorder; Lung function; Machine-learning; Monitoring system; Principal components analysis algorithms; Respiratory illness; Support vectors machine; Support vector machines","","","","","","","Barnes H., Humphries S.M., George P.M., Et al., Machine learning in radiology: the new frontier in interstitial lung diseases, Lancet Dig Health, 5, 1, pp. e41-e50, (2023); Avetisyan L., Chernukha M., Rusakova E., Et al., P132 microbiological monitoring of chronic lung infection with Achromobacter spp. in cystic fibrosis patients, J Cystic Fibros, 21, 1, (2022); Elidottir H., Diemer S., Eklund E., Hansen C.R., Abnormal glucose tolerance and lung function in children with cystic fibrosis. Comparing oral glucose tolerance test and continuous glucose monitoring, J Cyst Fibros, 20, 5, pp. 779-784, (2021); Katz J.B., Shah P., Trillo C.A., Alshaer M.H., Peloquin C., Lascano J., Therapeutic drug monitoring in cystic fibrosis and associations with pulmonary exacerbations and lung function, Respir Med, 212, (2023); Xue W., Chen C., Chen X., Et al., Raman spectroscopy combined with machine learning algorithms for rapid detection primary Sjögren's syndrome associated with interstitial lung disease, Photodiagnosis Photodyn Ther, 40, (2022); Burman J., Malmberg L.P., Remes S., Jartti T., Pelkonen A.S., Makela M.J., Impulse oscillometry and free-running tests for diagnosing asthma and monitoring lung function in young children, Ann Allergy Asthma Immunol, 127, 3, pp. 326-333, (2021); Tian D., Shiiya H., Takahashi M., Et al., Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based 18F-fluorodeoxyglucose positron emission tomography radiomics, J Heart Lung Transplant, 41, 6, pp. 722-731, (2022); Perrem L., Routine clinical monitoring fails to identify children at high risk of lung function decline, J Cyst Fibros, 21, 6, pp. 904-905, (2022); Shah P., Keller M., Mathew J., Kelley M., Nolley E., Agbor-Enoh S., Telemedicine with a cell-free DNA based monitoring approach maintains lung allograft function while reducing frequency of invasive bronchoscopy, J Heart Lung Transplant, 40, 4, (2021); Sheshadri A., Sacks N.C., Healey B.E., Raza S., Boerner G., Huang H.J., Lung function monitoring after lung transplantation and allogeneic hematopoietic stem cell transplantation, Clin Ther, 44, 5, pp. 755-765.e6, (2022); Kong C., Lai L., Jin X., Et al., Machine learning classifier for preoperative prediction of early recurrence after bronchial arterial chemoembolization treatment in lung cancer patients, Acad Radiol, 30, pp. 2880-2893, (2023); Boddu R.S.K., Karmakar P., Bhaumik A., Nassa V.K., Vandana Bhattacharya S., Analyzing the impact of machine learning and artificial intelligence and its effect on management of lung cancer detection in covid-19 pandemic, Mater Today Proc, 56, 4, pp. 2213-2216, (2022); Abdar M., Wojciech Ksiazek U., Acharya R., Tan R.-S., Makarenkov V., Plawiak P., A new machine learning technique for an accurate diagnosis of coronary artery disease, Comput Methods Programs Biomed, 179, (2019); Loo N.L., Chiew Y.S., Tan C.P., Arunachalam G., Ralib A.M., Mat-Nor M.-B., A machine learning model for real-time asynchronous breathing monitoring, IFAC-PapersOnLine, 51, 27, pp. 378-383, (2018); Ye Z., Sun B., Xiao Z., Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma, Gene, 749, (2020); Lang R., Ruibo L., Zhao C., Qin H., Liu G., Graph-based semi-supervised one class support vector machine for detecting abnormal lung sounds, Appl Math Comput, 364, (2020); Kaplan A., Hui Cao J., FitzGerald M., Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol In Pract, 9, 6, pp. 2255-2261, (2021); Zein J.G., Wu C.-P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021); Jothi E.S., Jeya J.J., Vanithamani R., Varsha R., On-mask sensor network for lung disease monitoring, Biomed Signal Process Control, 83, (2023); Irshad R.R., Hussain S., Sohail S.S., Et al., A novel IoT-enabled healthcare monitoring framework and improved grey wolf optimization algorithm-based deep convolution neural network model for early diagnosis of lung cancer, Sensors, 23, 6, (2023)","A. Bhansali; Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India; email: ashok.bhansali@gla.ac.in; S. Surendrarao Honnahalli; Department of Computer Science and Engineering, Jain (Deemed to be University), Bengaluru, Karnataka, India; email: sh.shruthi@jainuniversity.ac.in","","John Wiley and Sons Inc","","","","","","08999457","","IJITE","","English","Int J Imaging Syst Technol","Article","Final","","Scopus","2-s2.0-85196393812"
"Sang B.; Wen H.; Junek G.; Neveu W.; Di Francesco L.; Ayazi F.","Sang, Brian (58017602200); Wen, Haoran (57075683100); Junek, Gregory (57207958274); Neveu, Wendy (16205485900); Di Francesco, Lorenzo (7003923040); Ayazi, Farrokh (6603956971)","58017602200; 57075683100; 57207958274; 16205485900; 7003923040; 6603956971","An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning","2024","Biosensors","14","3","118","","","","1","10.3390/bios14030118","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188828136&doi=10.3390%2fbios14030118&partnerID=40&md5=c642a3c5d48c57412420d33f3f86fc47","School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, United States; StethX Microsystems Inc, Atlanta, 30308, GA, United States; Department of Medicine, Emory University School of Medicine, Atlanta, 30322, GA, United States","Sang B., School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, United States; Wen H., StethX Microsystems Inc, Atlanta, 30308, GA, United States; Junek G., StethX Microsystems Inc, Atlanta, 30308, GA, United States; Neveu W., Department of Medicine, Emory University School of Medicine, Atlanta, 30322, GA, United States; Di Francesco L., Department of Medicine, Emory University School of Medicine, Atlanta, 30322, GA, United States; Ayazi F., School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, United States, StethX Microsystems Inc, Atlanta, 30308, GA, United States","Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient’s chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time–frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set—outperforming the deterministic time–frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously. © 2024 by the authors.","accelerometer contact microphone; asthma; chronic obstructive pulmonary disease (COPD); deep learning; remote patient monitoring (RPM); wheezing","Accelerometry; Deep Learning; Humans; Respiratory Rate; Respiratory Sounds; Wearable Electronic Devices; Biological organs; Deep learning; Diagnosis; Learning systems; Patient monitoring; Pulmonary diseases; Wearable sensors; Accelerometer contact microphone; Asthma; Chronic obstructive pulmonary disease; Deep learning; Induced vibrations; Learning models; Low-profile; Remote patient monitoring; Wheezing; abnormal respiratory sound; adult; aged; Article; asthma; body mass; breathing pattern; breathing rate; chronic obstructive lung disease; convolutional neural network; data quality; deep learning; emergency ward; female; Fourier transform; human; lung auscultation; machine learning; major clinical study; male; middle aged; nerve cell network; noise pollution; patient monitoring; respiratory tract disease; retina blood vessel; sensitivity and specificity; sinus rhythm; support vector machine; telemonitoring; wheezing; abnormal respiratory sound; accelerometry; breathing rate; Accelerometers","","","","","Georgia Research Alliance, GRA; National Institutes of Health, NIH, (R03 EB029099)","This work was supported by the Georgia Research Alliance (GRA) and the National Institute of Health (NIH) R03 EB029099.","Stern J., Pier J., Litonjua A.A., Asthma epidemiology and risk factors, Semin. 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Otorhinolaryngol, 23, pp. 141-148, (2011); Gibson P.G., Simpson J.L., The overlap syndrome of asthma and COPD: What are its features and how important is it?, Thorax, 64, pp. 728-735, (2009); Korpas J., Sadlon Ova J., Vrabec M., Methods of Assessing Cough and Antitussives in Man Analysis of the Cough Sound: An Overview, Pulm. Pharmacol, 9, pp. 261-268, (1996); Morice A.H., Recommendations for the management of cough in adults, Thorax, 61, pp. i1-i24, (2006); Irwin R.S., Boulet L.P., Cloutier M.M., Fuller R., Gold P.M., Hoffstein V., Ing A.J., McCool F.D., O'Byrne P., Poe R.H., Et al., Managing Cough as a Defense Mechanism and as a Symptom: A Consensus Panel Report of the American College of Chest Physicians Summary and Recommendations, Chest, 114, pp. 113S-181S, (1998); Chang A.B., The physiology of cough, Paediatr. Respir. Rev, 7, pp. 2-8, (2006); Infante C., Chamberlain D., Fletcher R., Thorat Y., Kodgule R., Use of cough sounds for diagnosis and screening of pulmonary disease, Proceedings of the 2017 IEEE Global Humanitarian Technology Conference (GHTC); Piirila P., Sovijarvi A., Objective assessment of cough, Eur. Respir. J, 8, pp. 1949-1956, (1995); Mohammadi H., Samadani A.-A., Steele C., Chau T., Automatic discrimination between cough and non-cough accelerometry signal artefacts, Biomed. Signal Process. Control, 52, pp. 394-402, (2018)","B. Sang; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, United States; email: bsang3@gatech.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20796374","","BISSE","38534225","English","Biosensors","Article","Final","","Scopus","2-s2.0-85188828136"
"Usha Ruby A.; Chandran J.G.C.; Theerthagiri P.; Patil R.; Chaithanya B.N.; Jain T.J.S.","Usha Ruby, A. (56377734900); Chandran, J. George Chellin (57874174800); Theerthagiri, Prasannavenkatesan (56441559800); Patil, Renuka (56857192700); Chaithanya, B.N. (57217533868); Jain, T. J. Swasthika (57216862523)","56377734900; 57874174800; 56441559800; 56857192700; 57217533868; 57216862523","Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model","2024","Optical Memory and Neural Networks (Information Optics)","33","1","","86","96","10","1","10.3103/S1060992X24010107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188566155&doi=10.3103%2fS1060992X24010107&partnerID=40&md5=2ba50a0ef9f431e7a974beced10a7d9d","Department of Computer Science and Engineering, SRMIST Ramapuram Campus, Tamil Nadu, India; Director, Gullas College of Medicine, Bengaluru, Cebu, Philippines; Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India","Usha Ruby A., Department of Computer Science and Engineering, SRMIST Ramapuram Campus, Tamil Nadu, India; Chandran J.G.C., Director, Gullas College of Medicine, Bengaluru, Cebu, Philippines; Theerthagiri P., Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India; Patil R., Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India; Chaithanya B.N., Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India; Jain T.J.S., Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India","Abstract: Air pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision, allergic responses, pneumonia, asthma, cardiovascular disorders, lung cancer, and even mortality can result from prolonged exposure to the concentration of air’s small particulate matter. Air quality prediction can offer reliable information for future air pollution status to operate air pollution control effectively and make preventative plans. Tracking, predicting, and regulating emissions is crucial. Controlling PM2.5 is the key for enhancing air quality, and it can be accomplished by forecasting PM2.5 concentrations. This work develops a methodology for forecasting PM2.5 concentrations using a gradient-boosted regression tree with Convolutional Neural Network (CNN) and fuzzy K-nearest neighbour (fuzzy-KNN). The results of the proposed methodology have been comparatively analysed with multiple linear regression, stacked long short-term memory, bidirectional gated recurrent unit, and gradient-boosted regression tree. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are evaluated, and it shows that the gradient-boosted regression tree model produces a reduced error with improved accuracy in forecasting air quality. © Allerton Press, Inc. 2024. ISSN 1060-992X, Optical Memory and Neural Networks, 2024, Vol. 33, No. 1, pp. 86–96. Allerton Press, Inc., 2024.","convolutional neural network; data imputation; deep learning; fuzzy KNN; PM2.5","Air pollution control; Convolutional neural networks; Diseases; Errors; Forecasting; Fuzzy inference; Fuzzy neural networks; Linear regression; Long short-term memory; Mean square error; Quality control; Boosted regression trees; Convolutional neural network; Data imputation; Deep learning; Fuzzy KNN; Learning models; Neural network learning; Particle matter; Pm2.5; PM2.5 concentration; Air quality","","","","","","","Pope Iii C.A., Burnett R.T., Thun M.J., Calle E.E., Krewski D., Ito K., Thurston G.D., Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution, JAMA, 287, pp. 1132-1141, (2002); Baker K.R., Foley K.M., A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2. 5, Atmos. 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Phys, 408, (2020); Harishkumar K.S., Yogesh K.M., Gad I., Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models, Proc. Comput. 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IEEE, 86, pp. 2278-2324, (1998); Cleeremans A., Servan-Schreiber D., McClelland J.L., Finite state automata and simple recurrent networks, Neural Comput, 1, pp. 372-381, (1989); Tao Q., Liu F., Li Y., Sidorov D., Air pollution forecasting using a deep learning model based on 1D convnets and bidirectional GRU, IEEE access, 7, pp. 76690-76698, (2019); Hao X., Hu X., Liu T., Wang C., Wang L., Estimating urban PM2. 5 concentration: An analysis on the nonlinear effects of explanatory variables based on gradient boosted regression tree, Urban Clim, 44, (2022); Ruby A.U., Chaithanya B.N., Swasthika Jain T.J., Darandale S., Kerenalli S., Patil R., An effective feature descriptor method to classify plant leaf diseases using eXtreme Gradient Boost, J. Integr. Sci. Technol, 10, pp. 43-52, (2022); Das K., Das S., Energy-efficient cloud-integrated sensor network model based on data forecasting through ARIMA, Int. J. E-Collab. (Ijec), 18, 1, pp. 1-17, (2022)","P. Theerthagiri; Department of Computer Science and Engineering, GITAM University, Bengaluru, 561203, India; email: prasannait91@gmail.com","","Pleiades Publishing","","","","","","1060992X","","","","English","Opt. Mem. Neural Netw. (Inf. Opt.)","Article","Final","","Scopus","2-s2.0-85188566155"
"Almuhanna H.; Alenezi M.; Abualhasan M.; Alajmi S.; Alfadhli R.; Karar A.S.","Almuhanna, Hajar (59388864200); Alenezi, Manayer (59388534200); Abualhasan, Mariam (59388453300); Alajmi, Shouq (59388864300); Alfadhli, Raghad (59388942900); Karar, Abdullah S. (23970664000)","59388864200; 59388534200; 59388453300; 59388864300; 59388942900; 23970664000","AI Asthma Guard: Predictive Wearable Technology for Asthma Management in Vulnerable Populations","2024","Applied System Innovation","7","5","78","","","","1","10.3390/asi7050078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207717360&doi=10.3390%2fasi7050078&partnerID=40&md5=79ed991ffc8f7ea626b8e44a1b7d8ae9","College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait","Almuhanna H., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Alenezi M., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Abualhasan M., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Alajmi S., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Alfadhli R., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; Karar A.S., College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait","This paper presents AI Asthma Guard, a novel wearable device designed to predict and alert users of impending asthma attacks using artificial intelligence. The system integrates physiological and environmental sensors to monitor health metrics such as the heart rate, oxygen saturation, and exposure to specific air pollutants, which are crucial in managing asthma in children and individuals with mental disabilities. Utilizing machine learning models, including support vector machines and random forest, AI Asthma Guard classifies the risk levels of asthma attacks and provides timely notifications. This study details the device’s design, implementation, and preliminary testing results, underscoring its potential to improve health outcomes by enabling proactive asthma management. The implications of this technology reflect its alignment with the Sustainable Development Goals by enhancing individual health and well-being. The integration of a companion app leveraging large language models like ChatGPT facilitates user interaction, providing personalized advice and educational content about asthma management. © 2024 by the authors.","artificial intelligence; asthma management; environmental sensing; machine learning; predictive healthcare; sustainable health solutions; wearable technology","","","","","","","","Jafari E.A., Childhood asthma: A growing global concern, J. Pediatr. Health, 29, pp. 101-112, (2023); Chronic Respiratory Diseases: Asthma, (2020); Shanmugapriya E.A., IoT-based monitoring devices for pediatric asthma management, Int. J. Respir. Care, 40, pp. 234-245, (2023); Smith J., Doe J., Application of Convolutional Neural Networks in the Diagnosis of Respiratory Diseases, J. Med. Inform, 58, pp. 202-210, (2023); Johnson A., Kumar R., AI in Asthma Treatment: A Revolution in Personalized Medicine, J. Clin. Asthma Manag, 15, pp. 117-123, (2023); Lee C., Singh A., Harnessing AI for Chronic Disease Management: A Case Study in Asthma, Healthc. Technol. Lett, 10, pp. 150-157, (2023); Tsang K.C.H., Pinnock H., Wilson A.M., Salvi D., Shah S.A., Home monitoring with connected mobile devices for asthma attack prediction with machine learning, Sci. Data, 10, (2023); Lugogo N.L., DePietro M., Reich M., Merchant R., Chrystyn H., Pleasants R., Granovsky L., Li T., Hill T., Brown R.W., Et al., A Predictive Machine Learning Tool for Asthma Exacerbations: Results from a 12-Week, Open-Label Study Using an Electronic Multi-Dose Dry Powder Inhaler with Integrated Sensors, J. Asthma Allergy, 15, pp. 1623-1637, (2022); Pak J.G., Park K.H., Advanced Pulse Oximetry System for Remote Monitoring and Management, J. Biomed. Biotechnol, 2012, pp. 1-8, (2012); Chen L., Kumar A., Development of an I2C Compatible O<sub>2</sub> Sensor for Portable Health Devices, Sens. Actuators, 331, pp. 115-123, (2021); Lee C., Singh A., Evaluation of DHT22 Sensor for Ambient Humidity and Temperature Monitoring in Healthcare Applications, J. Environ. Health, 82, pp. 24-30, (2020); Patel R., Kim S., Utilizing MQ2 Sensors for Smoke Detection in Asthma Health Monitoring Systems, J. Saf. Res, 49, pp. 213-219, (2019); Garcia M., Zhao L., Application of MQ135 Sensor for Air Quality Monitoring in Medical Environments, Environ. Technol, 39, pp. 2097-2104, (2018); Kur D., Asthma Disease Prediction; Kaggle; 2023","A.S. Karar; College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait; email: abdullah.karar@aum.edu.kw","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","25715577","","","","English","Appl. Syst. Innov.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85207717360"
"Liu J.; Sun Y.; Tian C.; Qin D.; Gao L.","Liu, Jingping (57215297535); Sun, Yujia (59118096400); Tian, Chunxin (59119871700); Qin, Dong (59119277500); Gao, Lanying (59118096500)","57215297535; 59118096400; 59119871700; 59119277500; 59118096500","Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis","2024","Journal of Asthma","61","10","","1316","1327","11","1","10.1080/02770903.2024.2349596","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192713404&doi=10.1080%2f02770903.2024.2349596&partnerID=40&md5=7be5126bfa556acb536563dace029c5a","Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China","Liu J., Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China; Sun Y., Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China; Tian C., Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China; Qin D., Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China; Gao L., Nanjing Pukou Hospital of Traditional Chinese Medicine, Jiangsu, Nanjing, China","Objective: Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA. Methods: The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms. Results: Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA. Conclusion: This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.","Cuptoptosis; immune cells; machine learning; pediatric allergic asthma; single-cell RNA sequencing","Asthma; CD4-Positive T-Lymphocytes; Child; Copper; Female; Gene Expression Profiling; Humans; Male; Mast Cells; RNA-Seq; Sequence Analysis, RNA; Single-Cell Analysis; Single-Cell Gene Expression Analysis; Support Vector Machine; copper; algorithm; allergic asthma; Article; CD4+ T lymphocyte; cell infiltration; differential gene expression; disease exacerbation; gene expression; gene set enrichment analysis; human; human cell; immunocompetent cell; machine learning; macrophage; mast cell; prediction; random forest; RNA sequencing; single cell RNA seq; support vector machine; weighted gene co expression network analysis; asthma; child; female; gene expression profiling; genetics; immunology; male; metabolism; single cell analysis; single-cell gene expression analysis","","copper, 15158-11-9, 7440-50-8; Copper, ","","","","","Chetta A., Calzetta L., Bronchial asthma: an update, Minerva Med, 113, 1, pp. 1-3, (2022); 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Johansson E., Martin L.J., He H., Chen X., Weirauch M.T., Kroner J.W., Khurana Hershey G.K., Biagini J.M., Second-hand smoke and NFE2L2 genotype interaction increases paediatric asthma risk and severity, Clin Exp Allergy, 51, 6, pp. 801-810, (2021); Cordova E.J., Jimenez-Morales S., Centeno F., Martinez-Hernandez A., Martinez-Aguilar N., Del-Rio-Navarro B.E., Gomez-Vera J., Orozco L., NFE2L2 gene variants and susceptibility to childhood-onset asthma, Rev Invest Clin, 63, 4, pp. 407-411, (2011); Wang X., Wang J., Xing C.Y., Zang R., Pu Y.Y., Yin Z.X., Comparative analysis of the role of CD4(+) and CD8(+) T cells in severe asthma development, Molekuliarnaia Biologiia, 49, 3, pp. 482-490, (2015); Hammad H., Lambrecht B.N., The basic immunology of asthma, Cell, 184, 6, pp. 1469-1485, (2021); Seumois G., Ramirez-Suastegui C., Schmiedel B.J., Liang S., Peters B., Sette A., Vijayanand P., Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma, Sci Immunol, 5, 48, (2020); Luo W., Hu J., Xu W., Dong J., Distinct spatial and temporal roles for Th1, Th2, and Th17 cells in asthma, Front Immunol, 13, (2022); Tejwani V., McCormack A., Suresh K., Woo H., Xu N., Davis M.F., Brigham E., Hansel N.N., McCormack M.C., D'Alessio F.R., Dexamethasone-induced FKBP51 expression in CD4(+) T-lymphocytes is uniquely associated with worse asthma control in obese children with asthma, Front Immunol, 12, (2021); Mendez-Enriquez E., Hallgren J., Mast cells and their progenitors in allergic asthma, Front Immunol, 10, (2019); Mendez-Enriquez E., Alvarado-Vazquez P.A., Abma W., Simonson O.E., Rodin S., Feyerabend T.B., Rodewald H.R., Malinovschi A., Janson C., Adner M., Et al., Mast cell-derived serotonin enhances methacholine-induced airway hyperresponsiveness in house dust mite-induced experimental asthma, Allergy, 76, 7, pp. 2057-2069, (2021)","L. Gao; Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, No. 18 Gongyuan North Road, Pukou District, Jiangsu, 210000, China; email: glypkqzyy@163.com","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","38687912","English","J. Asthma","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85192713404"
"Barua P.D.; Keles T.; Kuluozturk M.; Kobat M.A.; Dogan S.; Baygin M.; Tuncer T.; Tan R.-S.; Acharya U.R.","Barua, Prabal Datta (36993665100); Keles, Tugce (57765143300); Kuluozturk, Mutlu (57008328900); Kobat, Mehmet Ali (55213695300); Dogan, Sengul (25653093400); Baygin, Mehmet (55293658600); Tuncer, Turker (37062172100); Tan, Ru-San (7201984906); Acharya, U. Rajendra (7004510847)","36993665100; 57765143300; 57008328900; 55213695300; 25653093400; 55293658600; 37062172100; 7201984906; 7004510847","Automated asthma detection in a 1326-subject cohort using a one-dimensional attractive-and-repulsive center-symmetric local binary pattern technique with cough sounds","2024","Neural Computing and Applications","36","27","","16857","16871","14","1","10.1007/s00521-024-09895-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194940779&doi=10.1007%2fs00521-024-09895-5&partnerID=40&md5=c005af2eb2a7bcf0d00abf6c1e2e2566","School of Business (Information System), University of Southern Queensland, Toowoomba, 4350, QLD, Australia; Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Department of Pulmonology Clinic, Firat University Hospital, Firat University, Elazig, 23119, Turkey; Department of Cardiology, Firat University Hospital, Firat University, Elazig, 23119, Turkey; Department of Computer Engineering, Faculty of Engineering, Erzurum Technical University, Erzurum, Turkey; Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore; School of Mathematics, Physics and Computing and Centre for Health Research, University of Southern Queensland, Springfield, Australia; Centre for Health Research, University of Southern Queensland, Springfield, Australia","Barua P.D., School of Business (Information System), University of Southern Queensland, Toowoomba, 4350, QLD, Australia; Keles T., Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Kuluozturk M., Department of Pulmonology Clinic, Firat University Hospital, Firat University, Elazig, 23119, Turkey; Kobat M.A., Department of Cardiology, Firat University Hospital, Firat University, Elazig, 23119, Turkey; Dogan S., Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Baygin M., Department of Computer Engineering, Faculty of Engineering, Erzurum Technical University, Erzurum, Turkey; Tuncer T., Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey; Tan R.-S., Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore, Duke-NUS Medical School, Singapore, Singapore; Acharya U.R., School of Mathematics, Physics and Computing and Centre for Health Research, University of Southern Queensland, Springfield, Australia, Centre for Health Research, University of Southern Queensland, Springfield, Australia","Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening. © The Author(s) 2024.","Asthma disease detection; Biomedical engineering; Cough sounds; NCA","Biomedical engineering; Biomedical signal processing; Diagnosis; Diseases; Extraction; Feature Selection; Local binary pattern; Q factor measurement; Wavelet decomposition; Asthma disease detection; Center-symmetric local binary patterns; Clinical diagnosis; Common disease; Cough sounds; Cross validation; Disease detection; Features extraction; NCA; One-dimensional; Support vector machines","","","","","","","Rubinfeld A., Pain M., Perception of asthma, Lancet, 307, 7965, pp. 882-884, (1976); Eder W., Ege M.J., von Mutius E., The asthma epidemic, N Engl J Med, 355, 21, pp. 2226-2235, (2006); Burgel P.-R., Bergeron A., De Blic J., Bonniaud P., Bourdin A., Chanez P., Et al., Small airways diseases, excluding asthma and COPD: an overview, Eur Respir Rev, 22, 128, pp. 131-147, (2013); Douwes J., Pearce N., Epidemiology of respiratory allergies and asthma, Handb Epidemiol, (2014); Asthma., (2022); Vos T., Lim S.S., Abbafati C., Abbas K.M., Abbasi M., Abbasifard M., Et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet, 396, pp. 1204-1222, (2020); Dodge R.R., Burrows B., The prevalence and incidence of asthma and asthma-like symptoms in a general population sample, Am Rev Respir Dis, 122, 4, pp. 567-575, (1980); Jones S.C., Iverson D., Burns P., Evers U., Caputi P., Morgan S., Asthma and ageing: an end user's perspective-the perception and problems with the management of asthma in the elderly, Clin Exp Allergy, 41, 4, pp. 471-481, (2011); Gold D., Wright R., Population disparities in asthma, Annu Rev Public Health, 26, pp. 89-113, (2005); Bousquet J., Khaltaev N., Cruz A.A., Denburg J., Fokkens W., Togias A., Et al., Allergic rhinitis and its impact on asthma (ARIA) 2008, Allergy, 63, pp. 8-160, (2008); Burke W., Fesinmeyer M., Reed K., Hampson L., Carlsten C., Family history as a predictor of asthma risk, Am J Prev Med, 24, 2, pp. 160-169, (2003); Baldacci S., Maio S., Cerrai S., Sarno G., Baiz N., Simoni M., Et al., Allergy and asthma: effects of the exposure to particulate matter and biological allergens, Respir Med, 109, 9, pp. 1089-1104, (2015); de Lange E.E., Altes T.A., Patrie J.T., Gaare J.D., Knake J.J., Mugler J.P., Et al., Evaluation of asthma with hyperpolarized helium-3 MRI: correlation with clinical severity and spirometry, Chest, 130, 4, pp. 1055-1062, (2006); Sharek P.J., Mayer M.L., Loewy L., Robinson T.N., Shames R.S., Umetsu D.T., Et al., Agreement among measures of asthma status: a prospective study of low-income children with moderate to severe asthma, Pediatrics, 110, 4, pp. 797-804, (2002); Cox L., Williams B., Sicherer S., Oppenheimer J., Sher L., Hamilton R., Et al., Pearls and pitfalls of allergy diagnostic testing: report from the American college of allergy, asthma and immunology/American academy of allergy, asthma and immunology specific IgE test task force, Ann Allergy Asthma Immunol, 101, 6, pp. 580-592, (2008); Sennhauser F.H., Braun-Fahrlander C., Wildhaber J.H., The burden of asthma in children: a European perspective, Paediatr Respir Rev, 6, 1, pp. 2-7, (2005); Haider N.S., Behera A., Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybern Biomed Eng, 42, 1, pp. 42-59, (2022); Kilic M., Barua P.D., Keles T., Yildiz A.M., Tuncer I., Dogan S., Et al., GCLP: an automated asthma detection model based on global chaotic logistic pattern using cough sounds, Eng Appl Artif Intell, 127, (2024); Asim Iqbal M., Devarajan K., Ahmed S.M., An optimal asthma disease detection technique for voice signal using hybrid machine learning technique, Concur Comput Pract Exp, 34, 11, (2022); Iqbal M.A., Devarajan K., Ahmed S.M., Real time detection and forecasting technique for asthma disease using speech signal and DENN classifier, Biomed Signal Process Control, 76, (2022); Sen I., Saraclar M., Kahya Y.P., Differential diagnosis of asthma and COPD based on multivariate pulmonary sounds analysis, IEEE Trans Biomed Eng, 68, 5, pp. 1601-1610, (2021); Khan M.U., Mobeen A., Samer S., Samer A., Embedded system design for real-time detection of asthmatic diseases using lung sounds in cepstral domain, 6Th International Electrical Engineering Conference, pp. 1-6, (2021); Yahyaoui A., Yumusak N., Deep and machine learning towards pneumonia and asthma detection. In, International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). IEEE, Pp 494–497, (2021); Topaloglu I., Barua P.D., Yildiz A.M., Keles T., Dogan S., Baygin M., Et al., Explainable attention ResNet18-based model for asthma detection using stethoscope lung sounds, Eng Appl Artif Intell, 126, (2023); Yue L., Xu W., Automatic classification of childhood asthma and pneumonia based on cough sound analysis, 2021 2Nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE). 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IEEE, pp. 172-176, (2016); Khasha R., Sepehri M.M., Taherkhani N., Detecting asthma control level using feature-based time series classification, Appl Soft Comput, 111, (2021); Singh O.P., Palaniappan R., Malarvili M., Automatic quantitative analysis of human respired carbon dioxide waveform for asthma and non-asthma classification using support vector machine, IEEE Access, 6, pp. 55245-55256, (2018)","P.D. Barua; School of Business (Information System), University of Southern Queensland, Toowoomba, 4350, Australia; email: prabal.barua@usq.edu.au","","Springer Science and Business Media Deutschland GmbH","","","","","","09410643","","","","English","Neural Comput. Appl.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85194940779"
"Mou X.; Wang P.; Sun J.; Chen X.; Du L.; Zhan Q.; Xia J.; Yang T.; Fang Z.","Mou, Xiuying (57447693900); Wang, Peng (57222164710); Sun, Jie (57221543088); Chen, Xianxiang (8722171000); Du, Lidong (24450055100); Zhan, Qingyuan (11940758400); Xia, Jingen (35340277200); Yang, Ting (57201495536); Fang, Zhen (55533717900)","57447693900; 57222164710; 57221543088; 8722171000; 24450055100; 11940758400; 35340277200; 57201495536; 55533717900","A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography","2024","Bioengineering","11","6","530","","","","1","10.3390/bioengineering11060530","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197886572&doi=10.3390%2fbioengineering11060530&partnerID=40&md5=20502eea7f27a60e9eac30d1d20c2b36","Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, 100029, China; Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, 100190, China","Mou X., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Wang P., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Sun J., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Chen X., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Du L., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China; Zhan Q., Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, 100029, China; Xia J., Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, 100029, China; Yang T., Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, 100029, China; Fang Z., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China, Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing, 100190, China","Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet’s performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography. © 2024 by the authors.","COPD; deep learning; volumetric capnography","","","","","","Chinese Academy of Meteorological Sciences, CAMS, (2019-I2M-5-019); National Natural Science Foundation of China, NSFC, (62071451, 62331025, U21A20447); Special Equipment Scientific Research Key Project, (LB2020LA060003)","This study was supported by the National Natural Science Foundation of China [grant number 62071451, 62331025, U21A20447], Special Equipment Scientific Research Key Project [grant number LB2020LA060003], and CAMS Innovation Fund for Medical Sciences [grant number 2019-I2M-5-019].","Robinson T., Scullion J., Chronic obstructive pulmonary disease (COPD), Oxford Handbook of Respiratory Nursing, pp. 235-284, (2021); Hillas G., Perlikos F., Tzanakis N., Acute exacerbation of COPD: Is it the “stroke of the lungs, Int. J. Chron. Obstruct. Pulmon. 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Comput, 34, pp. 7-16, (2020); Parazzi P., Marson F., Ribeiro M., Schivinski C., Ribeiro J., Correlation between parameters of volumetric capnography and spirometry during a submaximal exercise protocol on a treadmill in patients with cystic fibrosis and healthy controls, Pulmonology, 25, pp. 21-31, (2019); Kellerer C., Schneider A., Klutsch K., Husemann K., Sorichter S., Jorres R.A., Correspondence between capnovolumetric and conventional lung function parameters in the diagnosis of obstructive airway diseases, Respiration, 99, pp. 389-397, (2020); Zhang C., Liu S., Wang J., Liu Y., Guo Y., Liu X., Liu Q., Ji Y., Li C., The relationship between volumetric capnography and the severity of chronic obstructive pulmonary disease, Chin. J. Geriatr, 36, pp. 765-769, (2017); Pertzov B., Ronen M., Rosengarten D., Shitenberg D., Heching M., Shostak Y., Kramer M.R., Use of capnography for prediction of obstruction severity in non-intubated COPD and asthma patients, Respir. Res, 22, (2021); Talker L., Neville D., Wiffen L., Selim A.B., Haines M., Carter J.C., Broomfield H., Lim R.H., Lambert G., Weiss S.T., Et al., Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer, Respir. Res, 24, (2024); Koyama T., Kobayashi M., Ichikawa T., Wakabayashi Y., Abe H., Technology Applications of Capnography Waveform Analytics for Evaluation of Heart Failure Severity, J. Cardiovasc. Transl. Res, 13, pp. 1044-1054, (2020); Talker L., Dogan C., Neville D., Lim R.H., Broomfield H., Lambert G., Selim A., Brown T., Wiffen L., Carter J., Et al., Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning, COPD J. Chronic Obstr. Pulm. Dis, 21, (2024); Jaffe M.B., Using the features of the time and volumetric capnogram for classification and prediction, J. Clin. Monit. Comput, 31, pp. 19-41, (2017); Mieloszyk R.J., Verghese G.C., Deitch K., Cooney B., Khalid A., Mirre-Gonzalez M.A., Heldt T., Krauss B.S., Automated quantitative analysis of capnogram shape for COPD–normal and COPD–CHF classification, IEEE Trans. Biomed. Eng, 61, pp. 2882-2890, (2014); Abubaker M.B., Babayigit B., Detection of cardiovascular diseases in ECG images using Machine learning and deep learning methods, IEEE Trans. Artif. Intell, 4, pp. 373-382, (2022); Rezaee K., Khosravi M.R., Jabari M., Hesari S., Anari M.S., Aghaei F., Graph convolutional network-based deep feature learning for cardiovascular disease recognition from heart sound signals, Int. J. Intell. Syst, 37, pp. 11250-11274, (2022); Sushma V.R.S., Nikhil K., Reddy K.A., Sunitha L., Diagnosis of cardiovascular disease using deep learning, Int. J. Res. Appl. Sci. Eng. Technol, 11, pp. 371-378, (2023); Bhagawati M., Paul S., Agarwal S., Protogeron A., Sfikakis P.P., Kitas G.D., Khanna N.N., Ruzsa Z., Sharma A.M., Tomazu O., Et al., Cardiovascular disease/stroke risk stratification in deep learning framework: A review, Cardiovasc. Diagn. Ther, 13, (2023); Levine A.B., Schlosser C., Grewal J., Coope R., Jones S.J., Yip S., Rise of the machines: Advances in deep learning for cancer diagnosis, Trends Cancer, 5, pp. 157-169, (2019); Jiang X., Hu Z., Wang S., Zhang Y., Deep learning for medical image-based cancer diagnosis, Cancers, 15, (2023); Rehman M.U., Shafique A., Ghadi Y.Y., Boulila W., Jan S.U., Gadekallu T.R., Driss M., Ahmad J., A novel chaos-based privacy-preserving deep learning model for cancer diagnosis, IEEE Trans. Network Sci. Eng, 9, pp. 4322-4337, (2022); Zhou R., Wang P., Li Y., Mou X., Zhao Z., Chen X., Du L., Yang T., Zhan Q., Fang Z., Prediction of pulmonary function parameters based on a combination algorithm, Bioengineering, 9, (2022); Wang Z., Tim O., Encoding time series as images for visual inspection and classification using tiled convolutional neural networks, Proceedings of the Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence","Z. Fang; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; email: zfang@mail.ie.ac.cn; T. Yang; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China–Japan Friendship Hospital, Beijing, 100029, China; email: zryyyangting@163.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","23065354","","","","English","Bioeng.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85197886572"
"Mohamed I.","Mohamed, Israa (57192916377)","57192916377","Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms","2022","International Journal of Decision Support System Technology","14","1","93","","","","2","10.4018/IJDSST.286693","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149642158&doi=10.4018%2fIJDSST.286693&partnerID=40&md5=d9bfc8c7e364a305aed5e6203812e268","Zagazig University, Egypt","Mohamed I., Zagazig University, Egypt","Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value, and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. The results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy, and hence, it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD. Copyright © 2022, IGI Global.","Classification Algorithms; COPD; Data Mining; Healthcare Data Analytics; Machine Learning","Data Analytics; Data mining; Decision making; Disease control; Forecasting; Learning algorithms; Pulmonary diseases; Associated costs; Chronic obstructive pulmonary disease; Classification algorithm; Data analytics; Disease severity; Healthcare data analytic; Machine learning algorithms; Machine-learning; Mortality rate; Optimal predictions; Machine learning","","","","","","","Aljahdali S., Hussain S. N., Comparative prediction performance with support vector machine and random forest classification techniques, International Journal of Computers and Applications, 69, 11, (2013); Amaral J. L., Lopes A. J., Faria A. C., Melo P. L., Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 118, 2, pp. 186-197, (2015); Amaral J. L., Lopes A. J., Jansen J. M., Faria A. C., Melo P. L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 105, 3, pp. 183-193, (2012); Amin M. M., Bina B., Ebrahimi A., Yavari Z., Mohammadi F., Rahimi S., The occurrence, fate, and distribution of natural and synthetic hormones in different types of wastewater treatment plants in Iran, Chinese Journal of Chemical Engineering, 26, 5, pp. 1132-1139, (2018); Austin P. C., A comparison of regression trees, logistic regression, generalized additive models, and multivariate adaptive regression splines for predicting AMI mortality, Statistics in Medicine, 26, 15, pp. 2937-2957, (2007); Brydon H., Blignaut R., Jacobs J., A weighted bootstrap approach to logistic regression modelling in identifying risk behaviours associated with sexual activity, Journal of Social Aspects of HIV/AIDS Research Alliance, 16, 1, pp. 62-69, (2019); Cao Y., Hu Z. D., Liu X. F., Deng A. M., Hu C. J., An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters, Disease Markers, 35, (2013); Chaovalitwongse W., Jeong Y., Jeong M. K., Danish S., Wong S., Pattern recognition approaches for identifying subcortical targets during deep brain stimulation surgery, IEEE Intelligent Systems, 26, 5, pp. 54-63, (2011); Chen Y. F., Chen J. H., Hung L. W., Lin Y. J., Tai C. J., Diagnosis and prediction of patients with severe obstructive apneas using support vector machine, 2008 International Conference on Machine Learning and Cybernetics, 6, pp. 3236-3241, (2008); Coleman E. A., Min S. J., Chomiak A., Kramer A. M., Posthospital care transitions, (2004); Cui S., Wang D., Wang Y., Yu P. W., Jin Y., An improved support vector machine-based diabetic readmission prediction, Computer Methods and Programs in Biomedicine, 166, pp. 123-135, (2018); Demir E., A decision support tool for predicting patients at risk of readmission: A comparison of classification trees, logistic regression, generalized additive models, and multivariate adaptive regression splines, Decision Sciences, 45, 5, pp. 849-880, (2014); Dessai I. S. F., Intelligent heart disease prediction system using probabilistic neural network, International Journal on Advanced Computer Theory and Engineering, 2, 3, pp. 2319-2526, (2013); Dwivedi A. K., Analysis of computational intelligence techniques for diabetes mellitus prediction, Neural Computing & Applications, 30, 12, pp. 3837-3845, (2018); Feng J. Z., Wang Y., Peng J., Sun M. W., Zeng J., Jiang H., Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries, Journal of Critical Care, 54, pp. 110-116, (2019); Futoma J., Morris J., Lucas J., A comparison of models for predicting early hospital readmissions, Journal of Biomedical Informatics, 56, pp. 229-238, (2015); Garner S. R., Weka: The waikato environment for knowledge analysis, Proceedings of the New Zealand computer science research students conference, 1995, pp. 57-64, (1995); Kuncheva L. I., Combining pattern classifiers: methods and algorithms, (2014); Lombardo L., Cama M., Conoscenti C., Marker M., Rotigliano E. J. N. H., Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy), Natural Hazards, 79, 3, pp. 1621-1648, (2015); Magnin B., Mesrob L., Kinkingnehun S., Pelegrini-Issac M., Colliot O., Sarazin M., Dubois B., Lehericy S., Benali H., Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI, Neuroradiology, 51, 2, pp. 73-83, (2009); Melhem L. B., Azmi M. S., Muda A. K., Bani-Melhim N. J., Alweshah M., Text line segmentation of Al-Quran pages using binary representation, Advanced Science Letters, 23, 11, pp. 11498-11502, (2017); Nijeweme-d'Hollosy W. O., van Velsen L., Poel M., Groothuis-Oudshoorn C. G., Soer R., Hermens H., Evaluation of three machine learning models for self-referral decision support on low back pain in primary care, International Journal of Medical Informatics, 110, pp. 31-41, (2018); Ozcift A., Gulten A., Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms, Computer Methods and Programs in Biomedicine, 104, 3, pp. 443-451, (2011); Prashanth R., Roy S. D., Mandal P. K., Ghosh S., High-accuracy detection of early Parkinson’s disease through multimodal features and machine learning, International Journal of Medical Informatics, 90, pp. 13-21, (2016); Rodriguez-Roisin R., Rabe K. F., Vestbo J., Vogelmeier C., Agusti A., Global Initiative for Chronic Obstructive Lung Disease (GOLD) 20th anniversary: a brief history of time, (2017); Singh D., Agusti A., Anzueto A., Barnes P. J., Bourbeau J., Celli B. R., Criner G. J., Frith P., Halpin D. M. G., Han M., Lopez Varela M. V., Martinez F., Montes de Oca M., Papi A., Pavord I. D., Roche N., Sin D. D., Stockley R., Vestbo J., Vogelmeier C., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: The GOLD science committee report 2019, The European Respiratory Journal, 53, 5, (2019); Specht D. F., Probabilistic neural networks, Neural Networks, 3, 1, pp. 109-118, (1990); Tapak L., Shirmohammadi-Khorram N., Amini P., Alafchi B., Hamidi O., Poorolajal J., Prediction of survival and metastasis in breast cancer patients using machine learning classifiers, Clinical Epidemiology and Global Health, 7, 3, pp. 293-299, (2019); Vazquez Guillamet R., Ursu O., Iwamoto G., Moseley P. L., Oprea T., Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records, Health Informatics Journal, 24, 4, pp. 394-409, (2018); Wang C., Chen X., Du L., Zhan Q., Yang T., Fang Z., Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 188, (2020); Wu C. C., Yeh W. C., Hsu W. D., Islam M. M., Nguyen P. A. A., Poly T. N., Li Y. C. J., Et al., Prediction of fatty liver disease using machine learning algorithms, Computer Methods and Programs in Biomedicine, 170, pp. 23-29, (2019); Yu W., Liu T., Valdez R., Gwinn M., Khoury M. J., Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes, BMC Medical Informatics and Decision Making, 10, 1, pp. 1-7, (2010)","","","IGI Global","","","","","","19416296","","","","English","Int. J. Decis. Support Syst. Technol.","Article","Final","","Scopus","2-s2.0-85149642158"
"Choi B.; Kim H.; Jeon S.H.","Choi, Beomseo (58964910300); Kim, Hongjun (35205820300); Jeon, Seung Hyun (26646533800)","58964910300; 35205820300; 26646533800","LSTM-Based Time Series Forecasting of Pulmonary Function Test for COPD Early Diagnosis","2024","Journal of Korean Institute of Communications and Information Sciences","49","3","","346","355","9","1","10.7840/kics.2024.49.3.346","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188675342&doi=10.7840%2fkics.2024.49.3.346&partnerID=40&md5=252278e8eca5b33f3fa60048b60e478b","Daejeon University, Department of Computer Engineering, South Korea","Choi B., Daejeon University, Department of Computer Engineering, South Korea; Kim H., Daejeon University, Department of Computer Engineering, South Korea; Jeon S.H., Daejeon University, Department of Computer Engineering, South Korea","Chronic Obstructive Pulmonary Disease (COPD) is a serious lung disease that makes breathing difficult and cannot be easily detected. Even though early diagnosis technology for COPD using machine learning has been developed, Pulmonary Function Test (PFT) data-based time series prediction studies are still lacking. We use PFT data with insufficient measurement intervals, propose a Long Short-Term Memory (LSTM) to predict PFT values for the future 1Q from the past 2Q, and classify whether COPD occurs or not. The data were interpolated to resolve the imbalanced time period. To confirm the validity of the augmented data, Multivariate Analysis of Variance (MANOVA) was performed, and through the rigorous MANOVA, we proved that there was no significant difference between the original and interpolated data. Mean Absolute Percentage Error (MAPE), recalls, and F1 scores, which are the harmonic mean of precision and recall for classification, were measured for two test scenarios: only the original data and the augmented data. Finally, we found the interpolated data decreased MAPE by almost 7%, however, improved recall and F1 score by almost 22% and 12% for obstructive pulmonary disease, compared with the original data. Besides, we can predict COPD within 3 months, irrelevant to smokers and non-smokers. © 2024, Korean Institute of Communications and Information Sciences. All rights reserved.","Chronic Obstructive Pulmonary Disease; Early Diagnosis; Interpolation; Long Short-Term Memory; Pulmonary Function Test","","","","","","Ministry of Science, ICT and Future Planning, MSIP; Institute for Information and Communications Technology Promotion, IITP, (2022-0-00849)","Funding text 1: This work was supported by the ICT R&D program of MSIT/IITP(2022-0-00849).; Funding text 2: ※ This work was supported by the ICT R&D program of MSIT/IITP(2022-0-00849).","Sin D. D., The importance of early chronic obstructive pulmonary disease: A lecture from 2022 asian pacific society of respirology, Tuberculosis and Respiratory Diseases, 86, 2, pp. 71-81, (2023); Park H., Et al., Deep learning-based approach to predict pulmonary function at chest CT, Radiology, 307, 2, (2023); Gonem S., Et al., Applications of artificial intelligence and machine learning in respiratory medicine, Thorax, 75, 8, pp. 695-701, (2020); Beverin L., Et al., Predicting total lung capacity from spirometry: A machine learning approach, Frontiers in Med, 10, (2023); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics J, 25, 3, pp. 811-827, (2019); Nunavath V., Et al., Deep neural networks for prediction of exacerbations of patients with chronic obstructive pulmonary disease, EANN 2018, pp. 217-228, (2018); Perna D., Tagarelli A., Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks, 2019 IEEE 32nd Int. Symp. CBMS, pp. 50-55, (2019); Abayomi-Alli O. O., Et al., BiLSTM with data augmentation using interpolation methods to improve early detection of parkinson disease, Proc. FedCSIS, pp. 371-380, (2020); Fritsch F. N, Butland J., A method for constructing local monotone piecewise cubic interpolants, SIAM, 5, 2, pp. 300-304, (1984); Watz H., Et al., Spirometric changes during exacerbations of COPD: A post hoc analysis of the WISDOM trial, Respiratory Res, 19, 1, (2018); Fletcher C., Peto R., The natural history of chronic airflow obstruction, Br. Med. J, 1, 6077, pp. 1645-1648, (1977); Sim Y. S., Et al., Spirometry and bronchodilator test, Tuberculosis and Respiratory Diseases, 80, 2, pp. 105-112, (2017); Crapo R. O., Morris A. H., Gardner R. M., Reference spirometric values using techniques and equipment that meet ATS recommendations, Am. Rev. Respiratory Disease, 123, 6, pp. 659-664, (1981); Kline R. B., Principles and Practice of Structural Equation Modeling, (2005)","S.H. Jeon; Daejeon University, Department of Computer Engineering, South Korea; email: creemur@dju.kr","","Korean Institute of Communications and Information Sciences","","","","","","12264717","","","","English","J. Korean. Inst. Commun. Inf. Sci.","Article","Final","","Scopus","2-s2.0-85188675342"
"Patil C.; Chaware A.","Patil, Charushila (57221947846); Chaware, Anita (57218881779)","57221947846; 57218881779","A Novel Fuzzy Neuro Deep Neural Network Model (FNDNN) for Classification of COPD Severity Levels","2024","International Journal of Intelligent Systems and Applications in Engineering","12","12s","","296","303","7","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185699296&partnerID=40&md5=cff1d097f8e4ea38db8d90176be8ea0e","PG Department of Computer Science, SNDTWU, Santacruz(w),Maharashtra, Mumbai, India; Pratibha College, Chinchwad, Maharashtra, Pune, India","Patil C., PG Department of Computer Science, SNDTWU, Santacruz(w),Maharashtra, Mumbai, India, Pratibha College, Chinchwad, Maharashtra, Pune, India; Chaware A., PG Department of Computer Science, SNDTWU, Santacruz(w),Maharashtra, Mumbai, India","In recent years, there has been an increase in the mortality rate due to lung diseases like Chronic Obstructive Pulmonary Disease (COPD), and it is estimated that it will increase in upcoming years. The majority of deaths (80%) occurred in most of the nations having low and middle income hence, there is a need for a system that can help to reduce the mortality rate by providing proper treatment to the needy patient. Deep learning has shown outstanding performance in solving many real life problems in the healthcare domain. But it does not handle uncertain data. Its black box nature imposes restrictions on understanding its structure. In this study, a novel optimized fuzzy neuro deep-learning approach called FNDNN is proposed for classification. The main idea is the fusion of fuzzy logic and DNN model to deal with data uncertainty and rule extraction. To overcome overfitting of model different techniques like cross fold validation, changing learning rate is applied but the best result is achieved using L2 regularization. Pre-training and optimizing methods for learning parameters of the FNDNN are proposed. The proposed model for classification of COPD severity levels has better performance as compared with other classifiers as shown in results. This FNDNN model is very beneficial to the society and healthcare providers to accurately diagnose the severity levels of COPD and serve the emergency treatment to the needy one. . © 2024, Ismail Saritas. All rights reserved.","Bayesian Regularization; Chronic Obstructive Pulmonary Disease (COPD); Deep Neural Network; Defuzzification; Fuzzification; Over fitting; Spirometry","","","","","","","","Jarhyan P, Hutchinson A, Khaw D, Prabhakaran D, Mohan S., Prevalence of chronic obstructive pulmonary disease and chronic bronchitis in eight countries: a systematic review and meta-analysis, Bull World Health Organ, 100, 3, pp. 216-230, (2022); Murray CJ, Lopez AD., Alternative projections of mortality and disability by cause 1990-2020: Global Burden of Disease Study, Lancet, 349, 9064, pp. 1498-1504, (1997); Nielsen K. G., Bisgaard H, The effect of inhaled budesonide on symptoms, lung function, and cold air and methacholine responsiveness in 2-to 5-year-old asthmatic children, American Journal of Respiratory and Critical Care Medicine, 162, pp. 1500-1506, (2005); Anthonisen N. R., Wright E. C., Bronchodilator response in chronic obstructive pulmonary disease, The American review of respiratory disease, 133, 5, pp. 814-819, (1986); Song TW, Correlation between spirometry and impulse oscillometry in children with asthma, Acta Pediatr, 97, pp. 51-54, (2008); Barua M, Nazeran H, Nava P, Granda V, Diong B., Classification of Pulmonary Diseases Based on Impulse Oscillometric Measurements of Lung Function Using Neural Networks, (2004); Winkler J, Hagert-Winkler A, Wirtz H, Schauer J, Kahn T, Hoheisel G, Impulse oscillometry in the diagnosis of the severity of obstructive pulmonary disease, Pneumol, 63, pp. 266-275, (2009); Asaithambi M, Manoharan SC, Subramanian S., Classification of respiratory abnormalities using adaptive neuro fuzzy inference system, Int Inf and Database Sys Lecture Notes in Computer Science, 7198, pp. 65-73, (2012); Meraz E, Nazeran H, Goldman M, Nava P, Diong B., Impulse Oscillometric features of lung function: towards computer-aided classification of respiratory diseases in children; Hafezi N., An integrated software package for model-based neuro-fuzzy classification of small airway dysfunction, (2009); Zhou S., Chen Q., Wang X., Fuzzy deep belief networks for semi-supervised sentiment classification, Neurocomputing, 131, pp. 312-322, (2014); Deng Y., Ren Z., Kong Y., Bao F., Dai Q., A hierarchical fused fuzzy deep neural network for data classification, IEEE Trans. Fuzzy Syst, (2016); An J., Xinzhi L., Wen G., Stability analysis of delayed takagi-sugeno fuzzy systems: a new integral inequality approach, J. Nonlinear Sci. Appl, 10, 4, pp. 1941-1959, (2017); Kasabov N.K., Song Q., Denfis,”dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Trans. Fuzzy Syst, 10, 2, pp. 144-154, (2002); Kingma D.P., Ba J., Adam a method for stochastic optimization, Comput. Sci, (2014); Fu L., Rule generation from neural networks. Systems, Man and Cybernetics, IEEE Transactions on, 24, 8, pp. 1114-1124, (1994); Tsukimoto H., Extracting rules from trained neural networks, Neural Net-works, IEEE Transactions on, 11, 2, pp. 377-389, (2000); Sato M., Tsukimoto H., Rule extraction from neural networks via decision tree induction, Neural Networks, Proceedings. IJCNN’01. International Joint Conference on, 3, pp. 1870-1875, (2001); Tickle A. B., Andrews R., Golea M., Diederich J., The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks, IEEE Transactions on Neural Networks, 9, 6, pp. 1057-1068, (1998); Thrun S., Extracting rules from artificial neural networks with distributed representations, Advances in neural information processing systems, (1995); Craven M., Shavlik J. W., Using sampling and queries to extract rules from trained neural networks, ICML, pp. 37-45, (1994); Johansson U., Lofstrom T., Konig R., Sonstrod C., Niklasson L., Rule extraction from opaque models–a slightly different perspective, Machine Learning and Applications”, 2006. ICMLA’06. 5th International Conference on, pp. 22-27; Craven M., Shavlik J., Rule extraction: Where do we go from here?, University of Wisconsin Machine Learning Research Group Working Paper, pp. 99-108, (1999); Sethi K. K., Mishra D. K., Mishra B., KDRuleEx: A novel approach for enhancing user comprehensibility using rule extraction, Intelligent Systems, Modelling and Simulation (ISMS),Third International Conference, pp. 55-60, (2012); Ngiam J., Khosla A., Kim M., Nam J., Lee H., Ng A.Y., Multimodal deep learning, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689-696, (2011); Lazaro E., Armero C., Alvares D., Bayesian regularization for flexible baseline hazard functions in Cox survival models, Biometrical Journal, 63, 1, pp. 7-26, (2021); Mbuvha R., Jonsson M., Ehn N., Herman P., Bayesian neural networks for one-hour ahead wind power forecasting, 6th International Conference on Renewable Energy Research and Applications, ICRERA, pp. 591-596, (2017); Burden F., Winkler D., Bayesian Regularization of Neural Networks, pp. 23-42, (2008); Pandey J.K., Ahamad S., Veeraiah V., Adil N., Dhabliya D., Koujalagi A., Gupta A., Impact of call drop ratio over 5G network, Innovative Smart Materials Used in Wireless Communication Technology, pp. 201-224, (2023); Anand R., Ahamad S., Veeraiah V., Janardan S.K., Dhabliya D., Sindhwani N., Gupta A., Optimizing 6G wireless network security for effective communication, Innovative Smart Materials Used in Wireless Communication Technology, pp. 1-20, (2023)","","","Ismail Saritas","","","","","","21476799","","","","English","Internat. J. Intel. Syst. Appl. Eng.","Article","Final","","Scopus","2-s2.0-85185699296"
"Jeon H.-J.; Jeon H.-J.; Jeon S.H.","Jeon, Hyeon-Ju (57221705299); Jeon, Hyeon-Jin (58984981100); Jeon, Seung Ho (59174322000)","57221705299; 58984981100; 59174322000","Predicting the daily number of patients for allergic diseases using PM10 concentration based on spatiotemporal graph convolutional networks","2024","PLoS ONE","19","6","e0304106","","","","1","10.1371/journal.pone.0304106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196105076&doi=10.1371%2fjournal.pone.0304106&partnerID=40&md5=8e9a4892a6f3a6319bae6311baf78ef5","Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea; Department of Artificial Intelligence, Dongguk University, Seoul, South Korea; Department of Occupational and Environmental Medicine, Korea Industrial Health Association (KIHA), Gyeonggi-do, Seoul, South Korea","Jeon H.-J., Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea; Jeon H.-J., Department of Artificial Intelligence, Dongguk University, Seoul, South Korea; Jeon S.H., Department of Occupational and Environmental Medicine, Korea Industrial Health Association (KIHA), Gyeonggi-do, Seoul, South Korea","Air pollution causes and exacerbates allergic diseases including asthma, allergic rhinitis, and atopic dermatitis. Precise prediction of the number of patients afflicted with these diseases and analysis of the environmental conditions that contribute to disease outbreaks play crucial roles in the effective management of hospital services. Therefore, this study aims to predict the daily number of patients with these allergic diseases and determine the impact of particulate matter (PM10) on each disease. To analyze the spatiotemporal correlations between allergic diseases (asthma, atopic dermatitis, and allergic rhinitis) and PM10 concentrations, we propose a multi-variable spatiotemporal graph convolutional network (MST-GCN)-based disease prediction model. Data on the number of patients were collected from the National Health Insurance Service from January 2013 to December 2017, and the PM10 data were collected from Airkorea during the same period. As a result, the proposed disease prediction model showed higher performance (R2 0.87) than the other deep-learning baseline methods. The synergic effect of spatial and temporal analyses improved the prediction performance of the number of patients. The prediction accuracies for allergic rhinitis, asthma, and atopic dermatitis achieved R2 scores of 0.96, 0.92, and 0.86, respectively. In the ablation study of environmental factors, PM10 improved the prediction accuracy by 10.13%, based on the R2 score. © 2024 Jeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Air Pollutants; Air Pollution; Asthma; Dermatitis, Atopic; Humans; Hypersensitivity; Neural Networks, Computer; Particulate Matter; Rhinitis, Allergic; Spatio-Temporal Analysis; air pollution; allergic asthma; allergic rhinitis; allergy; Article; asthma; atopic dermatitis; dermatitis; epidemic; forced expiratory volume; gene interaction; health insurance; human; Korea; learning algorithm; lymph node dissection; machine learning; nerve cell network; particulate matter; pollution; Proteobacteria; recurrent disease; sea surface temperature; seasonal variation; spatiotemporal analysis; spatiotemporal graph convolutional network; time series analysis; adverse event; air pollutant; allergic rhinitis; artificial neural network; asthma; atopic dermatitis; epidemiology; hypersensitivity; particulate matter","","Air Pollutants, ; Particulate Matter, ","","","Korea Institute of Atmospheric Prediction System; Ministry of Environment, MOE; Inha University Hospital; Korea Meteorological Administration, KMA, (KMA2020-02211); Korea Meteorological Administration, KMA","This work was supported in part by the R&D project \u201CDevelopment of a Next-Generation Data Assimilation System by the Korea Institute of Atmospheric Prediction System (KIAPS)\u201D, funded by the Korea Meteorological Administration (KMA2020-02211). This work was supported in part by the R&D project \u201CDevelopment of a Next-Generation Data Assimilation System by the Korea Institute of Atmospheric Prediction System (KIAPS)\u201D, funded by the Korea Meteorological Administration (KMA2020-02211) (H.-J.J.) and in part by the Inha University Hospital\u2019s Environmental Health Center for Training Environmental Medicine Professionals funded by the Ministry of Environment, Republic of Korea (2022) (S. H.J.).","Asher MI, Montefort S, Bjorksten B, Lai CKW, Strachan DP, Weiland SK, Et al., Worldwide time trends in the prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and eczema in childhood: ISAAC Phases One and Three repeat multicountry cross-sectional surveys, Lancet, 368, pp. 733-743, (2006); MacIntyre EA, Gehring U, Molter A, Fuertes E, Klumper C, Kramer U, Et al., Air pollution and respiratory infections during early childhood: an analysis of 10 European birth cohorts within the ESCAPE Project, Environmental health perspectives, 122, pp. 107-113, (2014); McIntire DD, Bloom SL, Casey BM, Leveno KJ., Birth weight in relation to morbidity and mortality among newborn infants, The New England journal of medicine, 340, pp. 1234-1238, (1999); Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, Et al., Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution, Environmental science & technology, 46, 2, pp. 652-660, (2012); 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Ritz B, Hoffmann B, Peters A., The Effects of Fine Dust, Ozone, and Nitrogen Dioxide on Health, Deutsches Arzteblatt international, 51-52, pp. 881-886, (2019)","H.-J. Jeon; Data Assimilation Group, Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, South Korea; email: hjjeon@kiaps.org","","Public Library of Science","","","","","","19326203","","POLNC","38870112","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196105076"
"Saravanakumar S.M.; Revathi T.","Saravanakumar, S.M. (59344496900); Revathi, T. (56779739600)","59344496900; 56779739600","Computer aided disease detection and prediction of novel corona virus disease using machine learning","2024","Multimedia Tools and Applications","83","35","","82177","82198","21","1","10.1007/s11042-024-18317-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187460146&doi=10.1007%2fs11042-024-18317-6&partnerID=40&md5=8b82674257eafc5e43635f5c9f8659b4","Department of Computer Science, PSG College of Arts &  Science, Coimbatore, India","Saravanakumar S.M., Department of Computer Science, PSG College of Arts &  Science, Coimbatore, India; Revathi T., Department of Computer Science, PSG College of Arts &  Science, Coimbatore, India","Machine Learning is recent emerging technique in prediction of various health related issues in medical system. It is very essential to predict the COVID-19 virus before it spreads and affects an entire community. Machine Learning is being used to detect the presence of COVID-19 virus as early as possible by analyzing patient’s health condition and collecting data such as gender, age, Body Mass Index (BMI), asthma symptoms, wheezing, dyspnea, respiratory failure, cough, blood sugar level etc., with this information used eighteen machine learning algorithms such as ELM, Logistic Regression, SGD, KNN, SVM, QDA, LDA, XGBoost etc., to analyze the data and predict the presence of COVID-19 virus. Table and Charts are plotted with the help of the results acquired from the machine learning algorithm. As a result, early prediction of COVID-19 becomes possible and huge loss in terms of both health and economy can be avoided. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.","Computer Aided Disease Diagnosis; Corona Virus Disease; COVID19; Lung Disease Diagnosis; Machine Learning","Computer aided diagnosis; Computer aided instruction; Forecasting; Learning algorithms; Machine learning; Computer aided disease diagnose; Computer-aided; Corona virus disease; COVID19; Disease diagnosis; Lung disease diagnose; Machine learning algorithms; Machine-learning; Virus disease; COVID-19","","","","","","","Lei R., Dongqian M., Prediction of COVID-19 spread based on fractional SIR model [J], Adv Appl Mathematics, 10, 10, pp. 3233-3238, (2021); Ruguo F., Yibo W., Ming L., Yingqing Z., Chaoping Z., SEIR-Based Transmission Model and Inflection Point Prediction Analysis of COVID-19 [J], J University Electron Sci Technol China, 49, 3, pp. 369-374, (2020); Alenezi M.N., Al-Anzi F.S., Alabdulrazzaq H., Building a Sensible SIR Estimation Model for COVID-19 Outspread in Kuwait, Alexan-dria Eng J, 60, pp. 3161-3175, (2021); Chimmula V.K.R., Zhang L., Time Series Forecasting of COVID-19 Transmission in Canada Using LSTM Networks, Chaos, Soli-tons & Fractals, 135, (2020); Kirbas I., Sozen A., Tuncer A.D., Kazancioglu F.S., Comparative Analysis and Forecasting of COVID-19 Cases in Various Eu-ropean Countries with ARIMA, NARNN and LSTM Approaches, Chaos Solitons & Fractals, 138, (2020); Azarafza M., Azarafza M., Tanha J., COVID-19 Infection Forecasting Based on Deep Learning in Iran, MedRxiv, (2020); Arora P., Kumar H., Panigrahi B.K., Prediction and Analysis of COVID-19 Positive Cases Using Deep Learning Models: A Descriptive Case Study of India, Chaos, Soli-tons & Fractals, 139, (2020); Omran N.F., Abd-el Ghany S.F., Saleh H., Ali A.A., Gumaei A., Al-Rakhami M., Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia, Complexity, 2021, (2021); Verma H., Mandal S., Gupta A., Temporal Deep Learning Architecture for Prediction of COVID-19 Cases in India, Expert Systems App, 195, (2022); Zhang M., Chu R., Dong C., Wei J., Lu W., Xiong N., Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things, IEEE Trans Industr Inf, 17, 9, pp. 6510-6518, (2021); Binai C., Liang M., Et al., Prediction of Epidemic T ediction of Epidemic Transmission and E ansmission and Evaluation of Pr aluation of Prevention and Contr ention and Control Measures Based on, Artificial Society, 32, 12, (2020); Ruguo F., Yibo W., Ming L., Et al., SEIR-based COVID-19 Spread Model and Inflection Point Prediction Analysis [J], J University Electron Sci Technol China, 49, 3, pp. 369-374, (2020); Geng Hui X., Anding W.X., Et al., Analysis of the role of relevant intervention measures in the outbreak of novel coronavirus pneumonia based on SEIR model [J], J Jinan University (Natural Sci Med), 41, 2, pp. 175-180, (2020); Shengli C., Peihua F., Pengpeng S., Modified SEIR infectious disease dynamics model applied to prediction and assessment of coronavirus disease 2019 (COVID-19) in Hubei Province [J], J Zhejiang Univ (Med Sci), 49, 2, pp. 178-184, (2020); Yang Z.F., Zeng Z.Q., Wang K., Et al., Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions[J], J Thorac Dis, 12, 3, pp. 165-174, (2020); Zhixin W., Zhi L., Zhaojun L., Analysis and Prediction of Novel Coronavirus (COVID-19) Epidemic Based on Machine Learning [J], Biomed Eng Res, 39, 1, pp. 1-5, (2020); Zhang L., Fitness of the generalized growth to the COVID-19 data [J], J University Electron Sci Technol China, 49, 3, pp. 345-348, (2020); Guo H., Yuping Z., Huanying H., Application of Seasonal ARIMA Model in Prediction of Hand, Foot and Mouth Disease Epidemic in Jiangmen City [J], China Health Statistics, 36, 1, pp. 65-67, (2019); Hao L., Deguang D., Xueqiang T., Et al., Review of Infectious Disease Dynamics Model and Its Application in Simulation and Prediction of Novel Coronavirus Pneumonia Epidemic[J], Med Health Equip, 41, 3, pp. 7-12, (2020); Lihong H., Yongyue W., Sipeng S., Et al., Epidemic prediction methods and evaluation of common novel coronavirus pneumonia [J], China Health Statistics, 37, 3, pp. 322-326, (2020); Zhichao S., Research on simulation technologies of the epidemics transmission and control based on artificial society[D], (2012); Junxiang T., Research on AIDS transmission simulation modeling technology based on Multi-Agent and GIS Integration[D], (2009); Kun Y., Jiangrong L., Qingxiong C., Et al., Research on the Integrated Model of AIDS Spreading Agent and GIS[J], Journal of Yunnan Normal University (Philosophy and Social Science Edition), 40, 4, pp. 14-20, (2008)","S.M. Saravanakumar; Department of Computer Science, PSG College of Arts &  Science, Coimbatore, India; email: saravmath@gmail.com","","Springer","","","","","","13807501","","MTAPF","","English","Multimedia Tools Appl","Article","Final","","Scopus","2-s2.0-85187460146"
"Laforce C.; Albers F.C.; Cooper M.; Rees R.; Cappelletti C.; Danilewicz A.; Dunsire L.","Laforce, Craig (26642891900); Albers, Frank C. (56425711000); Cooper, Mark (57725470900); Rees, Robert (57725495500); Cappelletti, Christy (57207032225); Danilewicz, Anna (58290953100); Dunsire, Lynn (57195513425)","26642891900; 56425711000; 57725470900; 57725495500; 57207032225; 58290953100; 57195513425","A Fully Decentralized Randomized Controlled Study of As-Needed Albuterol–Budesonide Fixed-Dose Inhaler in Mild Asthma: The BATURA Study Design","2024","Journal of Asthma and Allergy","17","","","801","811","10","1","10.2147/JAA.S471134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202720860&doi=10.2147%2fJAA.S471134&partnerID=40&md5=484e472032dc56df98ea830b42d8c420","North Carolina Clinical Research, Chapel Hill, NC, United States; Avillion, Northbrook, IL, United States; BioPharmaceuticals Research and Development, AstraZeneca, Cambridge, United Kingdom; Avillion, London, United Kingdom; BioPharmaceuticals Research and Development, AstraZeneca, Durham, NC, United States","Laforce C., North Carolina Clinical Research, Chapel Hill, NC, United States; Albers F.C., Avillion, Northbrook, IL, United States; Cooper M., BioPharmaceuticals Research and Development, AstraZeneca, Cambridge, United Kingdom; Rees R., Avillion, London, United Kingdom; Cappelletti C., BioPharmaceuticals Research and Development, AstraZeneca, Durham, NC, United States; Danilewicz A., Avillion, London, United Kingdom; Dunsire L., BioPharmaceuticals Research and Development, AstraZeneca, Cambridge, United Kingdom","Purpose: Decentralized clinical trials, where trial-related activities occur at locations other than traditional clinical sites (eg participant homes, local healthcare facilities), have the potential to improve trial access for people for whom time and/or distance constraints may impede participation. Albuterol–budesonide 180/160 µg pressurized metered-dose inhaler (pMDI) is FDA approved for the as-needed treatment or prevention of bronchoconstriction and to reduce the risk of exacerbations in patients with asthma 18 years or older. BATURA (NCT05505734) is a fully decentralized study, investigating as-needed albuterol–budesonide in participants with mild asthma. Methods: BATURA is a fully decentralized, phase 3b, randomized, double-blind, event-driven exacerbation study conducted in the United States. Participants aged ≥12 years using as-needed short-acting β2-agonist (SABA), alone or with low-dose inhaled corticosteroid or leukotriene receptor antagonist maintenance, are randomized 1:1 to as-needed albuterol–budesonide 180/160 µg or albuterol 180 µg pMDI for up to 52 weeks (minimum 12 weeks). Participants continue their current maintenance therapy, if applicable. Participants must have used SABA for ≥2 days in the 2 weeks pre-enrollment and have an Asthma Impairment Risk Questionnaire score ≥2 at screening and randomization. All trial-related visits, including screening and consent, are conducted virtually, with study medication shipped directly to each participant’s residence. The primary objective is to evaluate the efficacy of as-needed albuterol–budesonide versus albuterol on severe asthma exacerbation risk, measured by time-to-first severe asthma exacerbation (primary endpoint). Secondary endpoints include annualized rate of severe asthma exacerbation and total systemic corticosteroid exposure. Study medication use is captured via a Hailie sensor attached to the study medication pMDI. The intended sample size is 2500 participants. Conclusion: BATURA evaluates as-needed albuterol–budesonide in participants with mild asthma. The decentralized study model enables the trial to move out of research sites into participant homes, reducing participant burden and improving access. © 2024 LaForce et al.","albuterol–budesonide; decentralized clinical trials; SABA–ICS; trial design","budesonide plus salbutamol; corticosteroid; leukotriene receptor blocking agent; adult; Article; artificial intelligence; asthma; bronchoconstriction; controlled study; double blind procedure; Food and Drug Administration; hospitalization; human; inhalation; major clinical study; patient-reported outcome; questionnaire; randomized controlled trial; social media","","","","","AstraZeneca, (GPP2022)","The authors would like to thank Lucy C. Cooper of inScience Communications, Springer Healthcare, UK, for providing medical writing support, which was funded by AstraZeneca in accordance with Good Publication Practice 2022 (GPP2022) guidelines (https://www.ismpp.org/gpp-2022).","Inan OT, Tenaerts P, Prindiville SA, Et al., Digitizing clinical trials, npj Digi Med, 3, (2020); Rogers A, De Paoli G, Subbarayan S, Et al., A systematic review of methods used to conduct decentralised clinical trials, Br J Clin Pharmacol, 88, 6, pp. 2843-2862, (2022); Anderson M., How the COVID-19 pandemic is changing clinical trial conduct and driving innovation in bioanalysis, Bioanalysis, 13, 15, pp. 1195-1203, (2021); Apostolaros M, Babaian D, Corneli A, Et al., Legal, regulatory, and practical issues to consider when adopting decentralized clinical trials: recommendations from the Clinical Trials Transformation Initiative, Ther Innov Regul Sci, 54, 4, pp. 779-787, (2020); Decentralized clinical trials for drugs, biological products, and devices, (2023); Van Norman GA., Decentralized clinical trials: the future of medical product development?, JACC Basic Transl Sci, 6, 4, pp. 384-387, (2021); Psotka MA, Abraham WT, Fiuzat M, Et al., Conduct of clinical trials in the era of COVID-19: JACC Scientific Expert Panel, J Am Coll Cardiol, 76, 20, pp. 2368-2378, (2020); The evolving role of decentralized clinical trials and digital health technologies, (2023); Petrini C, Mannelli C, Riva L, Gainotti S, Gussoni G., Decentralized clinical trials (DCTs): a few ethical considerations, Front Public Health, 10, (2022); LaPlante A, Yen RW, Isaacs T, Et al., Enrollment, retention, and strategies for including disadvantaged populations in randomized controlled trials: a systematic review protocol, Syst Rev, 10, 1, (2021); Coyle J, Rogers A, Copland R, Et al., Learning from remote decentralised clinical trial experiences: a qualitative analysis of interviews with trial personnel, patient representatives and other stakeholders, Br J Clin Pharmacol, 88, 3, pp. 1031-1042, (2022); 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Chipps BE, Israel E, Beasley R, Et al., Albuterol-budesonide pressurized metered dose inhaler in patients with mild-to-moderate asthma: results of the DENALI double-blind randomized controlled trial, Chest, 164, 3, pp. 585-595, (2023); Papi A, Chipps BE, Beasley R, Et al., Albuterol–budesonide fixed-dose combination rescue inhaler for asthma: a plain language summary of the MANDALA study, Ther Adv Respir Dis, 18, (2024); Murphy KR, Chipps B, Beuther DA, Et al., Development of the Asthma Impairment and Risk Questionnaire (AIRQ): a composite control measure, J Allergy Clin Immunol Pract, 8, 7, pp. 2263-2274, (2020); Beuther DA, Murphy KR, Zeiger RS, Et al., The Asthma Impairment and Risk Questionnaire (AIRQ) control level predicts future risk of asthma exacerbations, J Allergy Clin Immunol Pract, 10, 12, pp. 3204-3212, (2022); AIRQ, (2022); Wu TD, Diamant Z, Hanania NA., An update on patient-reported outcomes in asthma, Chest, 165, 5, pp. 1049-1057, (2024); Murphy KR, Beuther DA, Chipps BE, Et al., Impact of clinical characteristics and biomarkers on Asthma Impairment and Risk Questionnaire exacerbation prediction ability, J Allergy Clin Immunol Pract, 12, 8, pp. 2092-2101, (2024); Chipps BE, Zeiger RS, Beuther DA, Et al., Advancing assessment of asthma control with a composite tool: the Asthma Impairment and Risk Questionnaire, Ann Allergy Asthma Immunol, 133, 1, pp. 49-56, (2024); Ramsahai JM, Hansbro PM, Wark PAB., Mechanisms and management of asthma exacerbations, Am J Respir Crit Care Med, 199, 4, pp. 423-432, (2019); Frey U, Suki B., Complexity of chronic asthma and chronic obstructive pulmonary disease: implications for risk assessment, and disease progression and control, Lancet, 372, 9643, pp. 1088-1099, (2008); Larsson K, Kankaanranta H, Janson C, Et al., Bringing asthma care into the twenty-first century, npj Prim Care Resp Med, 30, 1, (2020); Stolk E, Ludwig K, Rand K, van Hout B, Ramos-Goni JM., Overview, update, and lessons learned from the international EQ-5D-5L valuation work: version 2 of the EQ-5D-5L valuation protocol, Value Health, 22, 1, pp. 23-30, (2019); Griffiths J, Fox L, Williamson PR, Quantifying the carbon footprint of clinical trials: guidance development and case studies, BMJ Open, 14, 1, (2024); Mackillop N, Shah J, Collins M, Costelloe T, Ohman D., Carbon footprint of industry-sponsored late-stage clinical trials, BMJ Open, 13, 8, (2023); VENTOLIN HFA (albuterol sulfate) inhalation aerosol: prescribing information, (2014); Drug approval package: Pulmicort turbuhaler [budesonide powder for oral inhalation], (2006); de Jong AJ, van Rijssel TI, Zuidgeest MGP, Et al., Opportunities and challenges for decentralized clinical trials: European regulators’ perspective, Clin Pharmacol Ther, 112, 2, pp. 344-352, (2022); Miyata BL, Tafuto B, Jose N., Methods and perceptions of success for patient recruitment in decentralized clinical studies, J Clin Transl Sci, 7, 1, (2023)","M. Cooper; BioPharmaceuticals Research and Development, AstraZeneca, Cambridge, United Kingdom; email: mark.cooper1@astrazeneca.com","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85202720860"
"Maldonado-Franco A.; Giraldo-Cadavid L.F.; Tuta-Quintero E.; Cagy M.; Goyes A.R.B.; Botero-Rosas D.A.","Maldonado-Franco, Adriana (57204012536); Giraldo-Cadavid, Luis F. (55520891000); Tuta-Quintero, Eduardo (57210806632); Cagy, Mauricio (6603187543); Goyes, Alirio R Bastidas (59158068500); Botero-Rosas, Daniel A. (8355353700)","57204012536; 55520891000; 57210806632; 6603187543; 59158068500; 8355353700","Curve-Modelling and Machine Learning for a Better COPD Diagnosis","2024","International Journal of COPD","19","","","1333","1343","10","1","10.2147/COPD.S456390","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196611032&doi=10.2147%2fCOPD.S456390&partnerID=40&md5=241a6d3f966443c587fd9127683ff24e","School of Engineering, Universidad de La Sabana, Chía, Colombia; School of Medicine, Universidad de La Sabana, Chía, Colombia; Interventional Pulmonology Service, Fundación Neumológica Colombiana, DC, Bogotá, Colombia; Biomedical Engineering Program, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil","Maldonado-Franco A., School of Engineering, Universidad de La Sabana, Chía, Colombia; Giraldo-Cadavid L.F., School of Medicine, Universidad de La Sabana, Chía, Colombia, Interventional Pulmonology Service, Fundación Neumológica Colombiana, DC, Bogotá, Colombia; Tuta-Quintero E., School of Medicine, Universidad de La Sabana, Chía, Colombia; Cagy M., Biomedical Engineering Program, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil; Goyes A.R.B., School of Medicine, Universidad de La Sabana, Chía, Colombia; Botero-Rosas D.A., School of Medicine, Universidad de La Sabana, Chía, Colombia","Background: Development of new tools in artificial intelligence has an outstanding performance in the recognition of multi-dimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Methods: This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. Results: A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Conclusion: Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage. © 2024 Maldonado-Franco et al.","accuracy; artificial neural networks; COPD; diagnosis; machine learning","Aged; Bronchodilator Agents; Diagnosis, Computer-Assisted; Female; Humans; Lung; Machine Learning; Male; Middle Aged; Neural Networks, Computer; Peak Expiratory Flow Rate; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Reproducibility of Results; Spirometry; Vital Capacity; bronchodilating agent; salbutamol; adult; aged; Article; artificial neural network; asthma; cigarette smoking; data base; decision making; dyspnea; female; forced expiratory volume; forced vital capacity; human; machine learning; major clinical study; male; medical care; middle aged; multinomial logistic regression; observational study; peak expiratory flow; polynomial regression analysis; predictive value; primary medical care; pulmonologist; questionnaire; respiratory therapist; risk factor; sensitivity and specificity; smoking; spirometry; support vector machine; training; artificial neural network; chronic obstructive lung disease; computer assisted diagnosis; diagnosis; lung; pathophysiology; predictive value; reproducibility; spirometry; vital capacity","","salbutamol, 18559-94-9, 35763-26-9; Bronchodilator Agents, ","vmax, CareFusion, India","CareFusion, India","Universidad de La Sabana, (MED-294-2020); Universidad de La Sabana","This work was supported by Universidad de La Sabana grant number MED-294-2020.","POCKET GUIDE TO COPD DIAGNOSIS, MANAGEMENT, AND PREVENTION A guide for Health Care Professionals, (2023); Enfermedad Pulmonar Obstructiva Crónica, (2023); Menezes AMB, Perez-Padilla R, Jardim JRB, Et al., Chronic obstructive pulmonary disease in five Latin American cities (the PLATINO study): a prevalence study, Lancet, 366, 9500, pp. 1875-1881, (2005); Zou J, Sun T, Song X, Et al., Distributions and trends of the global burden of COPD attributable to risk factors by SDI, age, and sex from 1990 to 2019: a systematic analysis of GBD 2019 data, Respir Res, 23, 1, (2022); Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease-2023 report, (2023); Celli BR., Update on the Management of COPD, Chest, 133, 6, pp. 1451-1462, (2008); Stanojevic S, Kaminsky DA, Miller MR, Et al., ERS/ATS technical standard on interpretive strategies for routine lung function tests, Eur Respir J, 60, 1, (2022); Casas Herrera A, De Oca MM, Lopez Varela MV, Aguirre C, Schiavi E, Jardim JR., COPD underdiagnosis and misdiagnosis in a high-risk primary care population in four Latin American countries. A key to enhance disease diagnosis: the PUMA Study, PLoS One, 11, 4, (2016); Das N, Topalovic M, Aerts JM, Janssens W., Area under the forced expiratory flow-volume loop in spirometry indicates severe hyperinflation in COPD patients, Int J COPD, 14, pp. 409-418, (2019); Mochizuki F, Iijima H, Watanabe A, Et al., The concavity of the maximal expiratory flow–volume curve reflects the extent of emphysema in obstructive lung diseases, Sci Rep, 9, 1, pp. 1-10, (2019); Bhatt SP, Bhakta NR, Wilson CG, Et al., New spirometry indices for detecting mild airflow obstruction, Sci Rep, 8, 1, pp. 1-8, (2018); Oh A, Morris TA, Yoshii IT, Morris TA., Flow decay: a novel spirometric index to quantify dynamic airway resistance, Respir Care, 62, 7, pp. 928-935, (2017); Li H, Liu C, Zhang Y, Xiao W., The concave shape of the forced expiratory flow-volume curve in 3 seconds is a practical surrogate of FEV1/FVC for the diagnosis of airway limitation in inadequate spirometry, Respir Care, 62, 3, pp. 363-369, (2017); Johns DP, Das A, Toelle BG, Et al., Improved spirometric detection of small airway narrowing: concavity in the expiratory flow–volume curve in people aged over 40 years, Int J Chron Obstruct Pulmon Dis, 12, (2017); Tang LYW, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD., Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, 5, pp. e259-e267, (2020); Bodduluri S, Nakhmani A, Reinhardt JM, Et al., Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease, JCI Insight, 5, 13, (2020); Ioachimescu OC, Stoller JK., An alternative spirometric measurement: area under the expiratory flow–volume curve, Ann Am Thorac Soc, 17, 5, pp. 582-588, (2020); Swaminathan S, Qirko K, Smith T, Et al., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLoS One, 12, 11, (2017); Jafari S, Arabalibeik H, Agin K., Classification of Normal and Abnormal Respiration Pattern Using Flow Volume Curve and Neural Network, 5th International Symposium on Health Informatics and Bioinformatics IEEE, pp. 110-113, (2010); Baemani J, Baemani MJ, Monadjemi A, Moallem P., Detection of respiratory abnormalities using artificial neural networks, J Comput Sci, 4, 8, pp. 663-667, (2008); Graham BL, Steenbruggen I, Barjaktarevic IZ, Et al., Standardization of spirometry 2019 update an official American Thoracic Society and European Respiratory Society technical statement, Am J Respir Crit Care Med, 200, 8, pp. E70-E88, (2019); Machin D, Campbell MJ, Tan SB, Tan SH., Sample Sizes for Clinical, Laboratory and Epidemiology Studies, (2018); Hudson DL, Cohen ME., Neural Networks and Artificial Intelligence for Biomedical Engineering, (2000); Berrar D., Cross-validation, Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, pp. 542-545, (2018); Simundic AM., Measures of diagnostic accuracy: basic definitions, EJIFCC, 19, 4, pp. 203-211, (2009); Maxim LD, Niebo R, Utell MJ., Screening tests: a review with examples, Inhalation Toxicology, 26, pp. 811-828, (2014); Moreno Giraldo AMM, Giraldo Cadavid LF, Botero Rosas D, Et al., Comparison of new spirometry measures to diagnose COPD, Respir Care, 2022, (2022)","D.A. Botero-Rosas; Universidad de La Sabana, Morphophysiology Department, Chía, Km 7, Northern Highway, Cundinamarca, 140013, Colombia; email: Daniel.botero@unisabana.edu.co","","Dove Medical Press Ltd","","","","","","11769106","","","38895045","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196611032"
"Huang Y.; Alvernaz S.; Kim S.J.; Maki P.; Dai Y.; Peñalver Bernabé B.","Huang, Yongchao (58294365400); Alvernaz, Suzanne (58540836900); Kim, Sage J. (58795805700); Maki, Pauline (7005027962); Dai, Yang (23093417500); Peñalver Bernabé, Beatriz (36981283700)","58294365400; 58540836900; 58795805700; 7005027962; 23093417500; 36981283700","Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models","2024","Biological Psychiatry Global Open Science","4","6","100376","","","","1","10.1016/j.bpsgos.2024.100376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204875321&doi=10.1016%2fj.bpsgos.2024.100376&partnerID=40&md5=f0b23d271888054ed9980dca70820245","Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, Illinois, United States; Division of Health Policy and Administration, School of Public Health, University of Illinois, Chicago, Illinois, United States; Department of Psychiatry, College of Medicine, University of Illinois, Chicago, Illinois, United States; Department of Psychology, College of Medicine, University of Illinois, Chicago, Illinois, United States; Department of Obstetrics and Gynecology, College of Medicine, University of Illinois, Chicago, Illinois, United States; Center of Bioinformatics and Quantitative Biology, University of Illinois, Chicago, Illinois, United States","Huang Y., Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, Illinois, United States; Alvernaz S., Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, Illinois, United States; Kim S.J., Division of Health Policy and Administration, School of Public Health, University of Illinois, Chicago, Illinois, United States; Maki P., Department of Psychiatry, College of Medicine, University of Illinois, Chicago, Illinois, United States, Department of Psychology, College of Medicine, University of Illinois, Chicago, Illinois, United States, Department of Obstetrics and Gynecology, College of Medicine, University of Illinois, Chicago, Illinois, United States; Dai Y., Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, Illinois, United States, Center of Bioinformatics and Quantitative Biology, University of Illinois, Chicago, Illinois, United States; Peñalver Bernabé B., Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, Illinois, United States, Center of Bioinformatics and Quantitative Biology, University of Illinois, Chicago, Illinois, United States","Background: Perinatal depression is one of the most common medical complications during pregnancy and postpartum period, affecting 10% to 20% of pregnant individuals, with higher rates among Black and Latina women who are also less likely to be diagnosed and treated. Machine learning (ML) models based on electronic medical records (EMRs) have effectively predicted postpartum depression in middle-class White women but have rarely included sufficient proportions of racial/ethnic minorities, which has contributed to biases in ML models. Our goal is to determine whether ML models could predict depression in early pregnancy in racial/ethnic minority women by leveraging EMR data. Methods: We extracted EMRs from a large U.S. urban hospital serving mostly low-income Black and Hispanic women (n = 5875). Depressive symptom severity was assessed using the Patient Health Questionnaire-9 self-report questionnaire. We investigated multiple ML classifiers using Shapley additive explanations for model interpretation and determined prediction bias with 4 metrics: disparate impact, equal opportunity difference, and equalized odds (standard deviations of true positives and false positives). Results: Although the best-performing ML model's (elastic net) performance was low (area under the receiver operating characteristic curve = 0.61), we identified known perinatal depression risk factors such as unplanned pregnancy and being single and underexplored factors such as self-reported pain, lower prenatal vitamin intake, asthma, carrying a male fetus, and lower platelet levels. Despite the sample comprising mostly low-income minority women (54% Black, 27% Latina), the model performed worse for these communities (area under the receiver operating characteristic curve: 57% Black, 59% Latina women vs. 64% White women). Conclusions: EMR-based ML models could moderately predict early pregnancy depression but exhibited biased performance against low-income minority women. © 2024","Electronic medical records; Health disparities; Machine learning; Model performance bias; Perinatal depression","adolescent; adult; aged; antenatal depression; Article; asthma; Black person; clinical assessment; controlled study; data processing; disease severity; electronic medical record; female; Hispanic; human; lowest income group; machine learning; major clinical study; middle aged; minority group; outpatient department; Patient Health Questionnaire 9; people of color; platelet volume; population research; prediction; risk factor; self report; United States; unplanned pregnancy; urban hospital; vitamin intake","","","","","National Institute on Minority Health and Health Disparities, NIMHD; National Center for Advancing Translational Sciences, NCATS; National Institute of Mental Health, NIMH, (U54MD012523, K12HD101373); National Institutes of Health, NIH, (IRB 2020-0553, UL1TR002003); Eunice Kennedy Shriver National Institute of Child Health and Human Development, NICHD, (R21HD110779)","Funding text 1: This work was funded through the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant No. R21HD110779 [to BPB and YD]). BPB has been supported by the National Institute on Minority Health and Health Disparities (Grant No. U54MD012523) and K12 BIRCWH Award (Grant No. K12HD101373). In addition, this work has been also supported by the National Center for Advancing Translational Sciences, National Institutes of Health (Grant No. UL1TR002003). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. BPB and YD conceptualized the idea; BPB, YD, and YH designed the methodology for model creation and validation; BPB, YH, and SA curated and preprocessed the data; YH performed the analysis and subsequent validation; BPB, YD, and YH interpreted the results and wrote the original draft; and all the authors critically reviewed and edited the manuscript. We thank Dr. Subhash Kumar Kolar Rajanna for his assistance in extracting the electronic medical records used in this manuscript. Data are available to the research community upon approval of the University of Illinois Chicago Institutional Review Board (IRB 2020-0553). Code is available at https://github.com/LabBea/EMR_project. The authors report no biomedical financial interests or potential conflicts of interest.; Funding text 2: This work is funded through NICHD (R21HD110779)). BPB has been supported by NIMH (U54MD012523) and K12 BIRCWH Award (K12HD101373). Additionally this work has been also supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002003. We would like to thank Dr. Subhash Kumar Kolar Rajanna for his assistance in extracting the electronic medical records used in this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.","Gavin N.I., Gaynes B.N., Lohr K.N., Meltzer-Brody S., Gartlehner G., Swinson T., Perinatal depression: A systematic review of prevalence and incidence, Obstet Gynecol, 106, pp. 1071-1083, (2005); Pino E.C., Zuo Y., Schor S.H., Zatwarnicki S., Henderson D.C., Borba C.P., Et al., Temporal trends of co-diagnosis of depression and/or anxiety among female maternal and non-maternal hospitalizations: Results from Nationwide Inpatient Sample 2004–2013, Psychiatry Res, 272, pp. 42-50, (2019); MacDorman M.F., Declercq E., Cabral H., Morton C., Recent increases in the U.S. maternal mortality rate: Disentangling trends from measurement issues, Obstet Gynecol, 128, pp. 447-455, (2016); Mukherjee S., Trepka M.J., Pierre-Victor D., Bahelah R., Avent T., Racial/ethnic disparities in antenatal depression in the United States: A systematic review, Matern Child Health J, 20, pp. 1780-1797, (2016); 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Peñalver Bernabé; Department of Biomedical Engineering, Colleges of Engineering and Medicine, University of Illinois, Chicago, United States; email: penalver@uic.edu; Y. Dai; email: yangdai@uic.edu","","Elsevier Inc.","","","","","","26671743","","","","English","Biol. Psychiatry Glob. Open Sci.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85204875321"
"Salih W.; Koyuncu H.","Salih, Waleed (57984612300); Koyuncu, Hakan (55655941400)","57984612300; 55655941400","Merging Two Models of One-Dimensional Convolutional Neural Networks to Improve the Differential Diagnosis between Acute Asthma and Bronchitis in Preschool Children","2024","Diagnostics","14","6","599","","","","1","10.3390/diagnostics14060599","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188932276&doi=10.3390%2fdiagnostics14060599&partnerID=40&md5=bd1c6ff490ec8ec97e68e7801e8d2f79","Information Technologies Department, Altinbas University, Istanbul, 34217, Turkey; Computer Engineering Department, Altinbas University, Istanbul, 34217, Turkey","Salih W., Information Technologies Department, Altinbas University, Istanbul, 34217, Turkey; Koyuncu H., Computer Engineering Department, Altinbas University, Istanbul, 34217, Turkey","(1) Background: Acute asthma and bronchitis are common infectious diseases in children that affect lower respiratory tract infections (LRTIs), especially in preschool children (below six years). These diseases can be caused by viral or bacterial infections and are considered one of the main reasons for the increase in the number of deaths among children due to the rapid spread of infection, especially in low- and middle-income countries (LMICs). People sometimes confuse acute bronchitis and asthma because there are many overlapping symptoms, such as coughing, runny nose, chills, wheezing, and shortness of breath; therefore, many junior doctors face difficulty differentiating between cases of children in the emergency departments. This study aims to find a solution to improve the differential diagnosis between acute asthma and bronchitis, reducing time, effort, and money. The dataset was generated with 512 prospective cases in Iraq by a consultant pediatrician at Fallujah Teaching Hospital for Women and Children; each case contains 12 clinical features. The data collection period for this study lasted four months, from March 2022 to June 2022. (2) Methods: A novel method is proposed for merging two one-dimensional convolutional neural networks (2-1D-CNNs) and comparing the results with merging one-dimensional neural networks with long short-term memory (1D-CNNs + LSTM). (3) Results: The merged results (2-1D-CNNs) show an accuracy of 99.72% with AUC 1.0, then we merged 1D-CNNs with LSTM models to obtain the accuracy of 99.44% with AUC 99.96%. (4) Conclusions: The merging of 2-1D-CNNs is better because the hyperparameters of both models will be combined; therefore, high accuracy results will be obtained. The 1D-CNNs is the best artificial neural network technique for textual data, especially in healthcare; this study will help enhance junior and practitioner doctors’ capabilities by the rapid detection and differentiation between acute bronchitis and asthma without referring to the consultant pediatrician in the hospitals. © 2024 by the authors.","acute asthma and bronchitis; differential diagnosis; one-dimensional convolutional neural network","air pollution; area under the curve; Article; artificial neural network; asthma; bronchitis; child; clinical feature; comparative study; convolutional neural network; data analysis; death toll; decision tree; deep learning; diagnostic error; diagnostic test accuracy study; differential diagnosis; elastic tissue; emergency ward; female; hospital management; human; Iraq; long short term memory network; low temperature; machine learning; major clinical study; male; middle income country; nerve cell network; recurrent neural network; short term memory; support vector machine; teaching hospital","","","","","Majeed Al-Ajeli in Iraq’s Fallujah Teaching Hospital for Women and Children","This paper was supported by the data collection of consultant pediatrician Majeed Al-Ajeli in Iraq’s Fallujah Teaching Hospital for Women and Children. This study is a part of Waleed Salih’s master’s thesis at Altinbas University. This study expands upon the research presented in a conference paper by the same authors, titled “A Deep Learning Approach to Differentiate Between Acute Asthma and Bronchitis in Preschool Children”. ","Patel D., Hall G.L., Broadhurst D., Smith A., Schultz A., Foong R.E., Does machine learning have a role in the prediction of asthma in children?, Paediatr. Respir. Rev, 41, pp. 51-60, (2022); Stokes K., Castaldo R., Franzese M., Salvatore M., Fico G., Pokvic L.G., Badnjevic A., Pecchia L., A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings, Biocybern. Biomed. Eng, 41, pp. 1288-1302, (2021); Tam Y., Johansson E., Mersha T.B., Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm, J. Pers. Med, 12, (2022); De Rose C., Sopo S.M., Valentini P., Morello R., Biasucci D., Buonsenso D., Potential Application of Lung Ultrasound in Children with Severe Uncontrolled Asthma: Preliminary Hypothesis Based on a Case Series, Medicines, 9, (2022); Olayemi O.C., Sunday A.O., Olasehinde O.O., Ojokoh B.A., Adetunmbi A.O., Application of Machine Learning to the Diagnosis of Lower Respiratory Tract Infections in Paediatric Patients, I-Manag. J. Pattern Recognit, 5, pp. 1-46, (2018); McCune J.P.D., Reducing Unnecessary Antibiotic Treatment for Acute Bronchitis Using Real-Time, Text-Based Primary Care, Telehealth Med. Today, 6, pp. 1-9, (2021); Kleiman R., Kuusisto F., Ross I., Peissig P.L., Stewart R., Page C.D., Weiss J., Machine Learning Assisted Discovery of Novel Predictive Lab Tests Using Electronic Health Record Data, AMIA Summits Transl. Sci. 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Signal, 39, pp. 1567-1575, (2022); Mishra S., Tripathy H.K., Mallick P.K., EAGA-MLP—An Enhanced and Adaptive Hybrid, Sensors, 20, (2020); Chicco D., Jurman G., The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genom, 21, (2020)","H. Koyuncu; Computer Engineering Department, Altinbas University, Istanbul, 34217, Turkey; email: hakan.koyuncu@altinbas.edu.tr","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188932276"
"Pascual-Saldaña H.; Masip-Bruin X.; Asensio A.; Alonso A.; Blanco I.","Pascual-Saldaña, Heribert (58109465900); Masip-Bruin, Xavi (22433466100); Asensio, Adrián (55783332100); Alonso, Albert (56228341600); Blanco, Isabel (35894869100)","58109465900; 22433466100; 55783332100; 56228341600; 35894869100","Innovative Predictive Approach towards a Personalized Oxygen Dosing System","2024","Sensors","24","3","764","","","","1","10.3390/s24030764","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184655604&doi=10.3390%2fs24030764&partnerID=40&md5=7ed605b4a055a13847b7844c216cf078","Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, Vilanova i la Geltrú, 08800, Spain; Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, 08036, Spain; Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, Barcelona, 08036, Spain","Pascual-Saldaña H., Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, Vilanova i la Geltrú, 08800, Spain; Masip-Bruin X., Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, Vilanova i la Geltrú, 08800, Spain; Asensio A., Advanced Network Architectures Lab (CRAAX), Universitat Politècnica de Catalunya, Vilanova i la Geltrú, 08800, Spain; Alonso A., Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, 08036, Spain; Blanco I., Department of Pulmonary Medicine, Hospital Clínic, University of Barcelona, Barcelona, 08036, Spain","Despite the large impact chronic obstructive pulmonary disease (COPD) that has on the population, the implementation of new technologies for diagnosis and treatment remains limited. Current practices in ambulatory oxygen therapy used in COPD rely on fixed doses overlooking the diverse activities which patients engage in. To address this challenge, we propose a software architecture aimed at delivering patient-personalized edge-based artificial intelligence (AI)-assisted models that are built upon data collected from patients’ previous experiences along with an evaluation function. The main objectives reside in proactively administering precise oxygen dosages in real time to the patient (the edge), leveraging individual patient data, previous experiences, and actual activity levels, thereby representing a substantial advancement over conventional oxygen dosing. Through a pilot test using vital sign data from a cohort of five patients, the limitations of a one-size-fits-all approach are demonstrated, thus highlighting the need for personalized treatment strategies. This study underscores the importance of adopting advanced technological approaches for ambulatory oxygen therapy. © 2024 by the authors.","artificial intelligence; blood oxygen saturation; chronic obstructive pulmonary disease COPD; edge computing; edge predictions; machine learning; personalized modeling","Diagnosis; Edge computing; Hospital data processing; Machine learning; Patient treatment; Pulmonary diseases; Blood oxygen saturation; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease chronic obstructive pulmonary disease; Current practices; Dosing systems; Edge computing; Edge prediction; Machine-learning; Personalized model; Oxygen","","","","","AGAUR Catalan Agency, (2021_SGR_00326); Ministerio de Ciencia, Innovación y Universidades, MCIU; Federación Española de Enfermedades Raras, FEDER, (PID2021-124463OB-100); Federación Española de Enfermedades Raras, FEDER","This research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100, and by the AGAUR Catalan Agency, grant number 2021_SGR_00326.","Benjafield A., Tellez D., Barrett M., Gondalia R., Nunez C., Wedzicha J., Malhotra A., An estimate of the European prevalence of COPD in 2050, Eur. 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"Sigawi T.; Israeli A.; Ilan Y.","Sigawi, Tal (58318423300); Israeli, Adir (57242378600); Ilan, Yaron (24561459600)","58318423300; 57242378600; 24561459600","Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies’ Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders","2024","ImmunoTargets and Therapy","13","","","525","539","14","1","10.2147/ITT.S477841","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207421105&doi=10.2147%2fITT.S477841&partnerID=40&md5=dd90602fb0cb80a5c5501ec6f228a501","Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel","Sigawi T., Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel; Israeli A., Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel; Ilan Y., Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel","Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined and change between and among subjects, the current methods for overcoming the loss of response are insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that change in response to internal and external perturbations. Inter and intra-subject variability characterize the immune system, making it difficult to provide a single therapeutic regimen to all patients and even the same patients over time. The dynamicity of the immune variability is also a significant challenge for personalizing immunotherapies. The CDP-based second-generation artificial intelligence system is an outcome-based dynamic platform that incorporates personalized variability signatures into the therapeutic regimen and may provide methods for improving the response and overcoming the loss of response to treatments. The signatures of immune variability may also offer a method for identifying new biomarkers for early diagnosis, monitoring immune-related disorders, and evaluating the response to treatments. © 2024 Sigawi et al.","artificial intelligence; immune system; immunotherapy; variability","B lymphocyte receptor; biological marker; disease modifying antirheumatic drug; HLA antigen; HLA DQB1 antigen; major histocompatibility antigen; T lymphocyte receptor; tumor necrosis factor; tumor necrosis factor inhibitor; ankylosing spondylitis; Article; artificial intelligence; asthma; autoimmune disease; biology; constrained disorder principle; drug tolerance; genetic polymorphism; human; immune associated disorders; immune response; immune system; immune-related gene; immunogenicity; immunoglobulin structure, function and variability; immunological tolerance; immunopathology; immunosignature; immunotherapy; inflammatory bowel disease; insulin dependent diabetes mellitus; multiple sclerosis; natural killer cell; 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Ilan; Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel; email: ilan@hadassah.org.il","","Dove Medical Press Ltd","","","","","","22531556","","","","English","ImmunoTargets Ther.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85207421105"
"Liu H.M.; Rayner A.; Mendelsohn A.R.; Shneyderman A.; Chen M.; Pun F.W.","Liu, Hei Man (58738007700); Rayner, Andre (58546803800); Mendelsohn, Andrew R. (36446355000); Shneyderman, Anastasia (57226110402); Chen, Michelle (58546860800); Pun, Frank W. (58497190700)","58738007700; 58546803800; 36446355000; 57226110402; 58546860800; 58497190700","Applying Artificial Intelligence to Identify Common Targets for Treatment of Asthma, Eczema, and Food Allergy","2024","International Archives of Allergy and Immunology","185","2","","99","110","11","1","10.1159/000534827","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178352199&doi=10.1159%2f000534827&partnerID=40&md5=bc7448f20a3c2cb082926c2393d9f5ed","Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, SAR, Hong Kong, Hong Kong; Henry M. Gunn High School, Palo Alto, CA, United States; Regenerative Sciences Institute, Sunnyvale, CA, United States","Liu H.M., Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, SAR, Hong Kong, Hong Kong; Rayner A., Henry M. Gunn High School, Palo Alto, CA, United States, Regenerative Sciences Institute, Sunnyvale, CA, United States; Mendelsohn A.R., Regenerative Sciences Institute, Sunnyvale, CA, United States; Shneyderman A., Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, SAR, Hong Kong, Hong Kong; Chen M., Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, SAR, Hong Kong, Hong Kong; Pun F.W., Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, SAR, Hong Kong, Hong Kong","Introduction: Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients. Methods: We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease. Results: Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets’ dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies. Conclusion: Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets. © 2023 S. Karger AG, Basel.","Allergy; Artificial intelligence; Drug repurposing; Novelty; Target discovery","Artificial Intelligence; Asthma; Eczema; Food Hypersensitivity; Humans; Interleukin-5; abrocitinib; crisaborole; desonide; dupilumab; formoterol fumarate; mepolizumab; mometasone furoate; montelukast; pimecrolimus; prednisone; propionic acid; reslizumab; ruxolitinib; salbutamol sulfate; salmeterol xinafoate; tacrolimus; tralokinumab; umeclidinium; upadacitinib; vilanterol; zileuton; interleukin 5; allergic disease; Article; artificial intelligence; asthma; controlled study; drug repositioning; eczema; food allergy; Food and Drug Administration; human; immune response; immunopathology; major clinical study; meta analysis; nonhuman; pathophysiology; transcriptomics; artificial intelligence; asthma; eczema; food allergy","","abrocitinib, 1622902-68-4; crisaborole, 906673-24-3; desonide, 638-94-8; dupilumab, 1190264-60-8; formoterol fumarate, 43229-80-7, 183814-30-4; mepolizumab, 196078-29-2; mometasone furoate, 83919-23-7, 105102-22-5; montelukast, 151767-02-1, 158966-92-8; pimecrolimus, 137071-32-0; prednisone, 53-03-2; propionic acid, 72-03-7, 79-09-4; reslizumab, 241473-69-8; ruxolitinib, 1092939-17-7, 941678-49-5; salbutamol sulfate, 51022-70-9; salmeterol xinafoate, 94749-08-3; tacrolimus, 104987-11-3, 109581-93-3; tralokinumab, 1044515-88-9; umeclidinium, 869113-09-7, 869185-19-3; upadacitinib, 1310726-60-3, 1607431-21-9, 2095311-41-2, 2050057-56-0; vilanterol, 503068-34-6; zileuton, 111406-87-2, 132880-11-6, 154003-29-9, 133305-01-8, 142606-21-1, 143200-94-6; Interleukin-5, ","","","","","Nadeau K., Barnett S., The end of food allergy: the first program to prevent and reverse a 21st century epidemic, (2020); Panel Ni S.E., Boyce J.A., Assa'ad A., Burks A.W., Jones S.M., Sampson H.A., Et al., Guidelines for the diagnosis and management of food allergy in the United States: report of the NIAID-sponsored expert panel, J Allergy Clin Immunol, 126, 6, pp. S1-S58, (2010); 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Pun; Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong, SAR, Hong Kong; email: frank.pun@insilico.com","","S. Karger AG","","","","","","10182438","","IAAIE","37989115","English","Int. Arch. Allergy Immunol.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85178352199"
"Fatima M.; Ahmad A.; Butt I.; Arshad S.; Kiani B.","Fatima, Munazza (57220400690); Ahmad, Adeel (57217248770); Butt, Ibtisam (57218106649); Arshad, Sana (55815607600); Kiani, Behzad (57194208671)","57220400690; 57217248770; 57218106649; 55815607600; 57194208671","Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan","2024","Environmental Monitoring and Assessment","196","10","929","","","","1","10.1007/s10661-024-13105-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204006345&doi=10.1007%2fs10661-024-13105-z&partnerID=40&md5=590963a6a52f9629971de0e68d165cf6","Department of Geography, The Islamia University of Bahawalpur, Punjab, 63100, Pakistan; Taylor Geospatial Institute, St. Louis, 63103, United States; Department of Computer Science & amp; Engineering, Washington University in St. Louis, St. Louis, 63130, United States; Institute of Geography, University of Punjab Lahore, Lahore, 54590, Pakistan; Centre for Clinical Research, The University of Queensland, Brisbane, Australia","Fatima M., Department of Geography, The Islamia University of Bahawalpur, Punjab, 63100, Pakistan; Ahmad A., Taylor Geospatial Institute, St. Louis, 63103, United States, Department of Computer Science & amp; Engineering, Washington University in St. Louis, St. Louis, 63130, United States, Institute of Geography, University of Punjab Lahore, Lahore, 54590, Pakistan; Butt I., Institute of Geography, University of Punjab Lahore, Lahore, 54590, Pakistan; Arshad S., Department of Geography, The Islamia University of Bahawalpur, Punjab, 63100, Pakistan; Kiani B., Centre for Clinical Research, The University of Queensland, Brisbane, Australia","Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran’s index = −0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran’s I = −0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.","Air pollution; COPD; Random forest regression; Remote sensing; Space-time clustering; Spatial prediction","Air Pollutants; Air Pollution; Carbon Monoxide; Environmental Monitoring; Humans; Nitrogen Dioxide; Pakistan; Pulmonary Disease, Chronic Obstructive; Spatio-Temporal Analysis; Sulfur Dioxide; Pakistan; Decision trees; Haze pollution; Health risks; Kyoto Protocol; Risk assessment; carbon monoxide; nitrogen dioxide; sulfur dioxide; carbon monoxide; nitrogen dioxide; sulfur dioxide; Air pollutants; Chronic obstructive pulmonary disease; Geospatial model; Pakistan; Random forest modeling; Random forest regression; Random forests; Remote-sensing; Space time clustering; Spatial prediction; aerosol; ambient air; atmospheric modeling; atmospheric pollution; carbon monoxide; chronic obstructive pulmonary disease; cluster analysis; health risk; nitrogen dioxide; regression analysis; spatiotemporal analysis; sulfur dioxide; aerosol; air pollutant; air pollution; ambient air; Article; artificial neural network; chronic obstructive lung disease; controlled study; diagnostic test accuracy study; environmental factor; health care facility; health hazard; human; incidence; machine learning; particulate matter; pollution; prediction; prevalence; quantitative structure activity relation; remote sensing; risk factor; support vector machine; work environment; air pollution; environmental monitoring; epidemiology; Pakistan; spatiotemporal analysis; Sulfur dioxide","","carbon monoxide, 630-08-0; nitrogen dioxide, 10102-44-0; sulfur dioxide, 7446-09-5; Air Pollutants, ; Carbon Monoxide, ; Nitrogen Dioxide, ; Sulfur Dioxide, ","","","","","Abolhassani A., Prates M., An up-to-date review of scan statistics, Statistic Surveys, 15, pp. 111-153, (2021); Adeloye D., Chua S., Lee C., Basquill C., Papana A., Theodoratou E., Rudan I., Global and regional estimates of COPD prevalence: Systematic review and meta-analysis, Journal of Global Health, 5, 2, (2015); Aghapour M., Ubags N.D., Bruder D., Hiemstra P.S., Sidhaye V., Rezaee F., Heijink I.H., Role of air pollutants in airway epithelial barrier dysfunction in asthma and COPD, European Respiratory Review, 31, 163, (2022); Al Wachami N., Louerdi M., Iderdar Y., Boumendil K., Chahboune M., Chronic obstructive pulmonary disease (COPD) and air pollution: The case of Morocco, Materials Today: Proceedings, 72, pp. 3738-3748, (2023); Alam K., Trautmann T., Blaschke T., Subhan F., Changes in aerosol optical properties due to dust storms in the Middle East and Southwest Asia, Remote Ssensing of Environment, 143, pp. 216-227, (2014); Amir Khan M., Ahmar Khan M., Walley J.D., Khan N., Imtiaz Sheikh F., Feasibility of delivering integrated COPD-asthma care at primary and secondary level public healthcare facilities in Pakistan: A process evaluation, BJGP Open, 3 (1), Bjgpopen18x101632, (2019); Anjum M.S., Ali S.M., Subhani M.A., Anwar M.N., Nizami A.-S., Ashraf U., Khokhar M.F., An emerged challenge of air pollution and ever-increasing particulate matter in Pakistan; A critical review, Journal of Hazardous Materials, 402, (2021); Annesi-Maesano I., Air pollution and chronic obstructive pulmonary disease exacerbations: When prevention becomes feasible, American Journal of Respiratory Critical Care Medicine, 199, 5, pp. 547-548, (2019); Pakistan Fact Sheet. 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"Nirmalarajah K.; Aftanas P.; Barati S.; Chien E.; Crowl G.; Faheem A.; Farooqi L.; Jamal A.J.; Khan S.; Kotwa J.D.; Li A.X.; Mozafarihashjin M.; Nasir J.A.; Shigayeva A.; Yim W.; Yip L.; Zhong X.Z.; Katz K.; Kozak R.; McArthur A.G.; Daneman N.; Maguire F.; McGeer A.J.; Duvvuri V.R.; Mubareka S.","Nirmalarajah, Kuganya (57224950900); Aftanas, Patryk (57218594357); Barati, Shiva (57220058839); Chien, Emily (57535965800); Crowl, Gloria (57220060546); Faheem, Amna (57192893915); Farooqi, Lubna (57220050430); Jamal, Alainna J. (55985637200); Khan, Saman (57220048039); Kotwa, Jonathon D. (57203034813); Li, Angel X. (59033733200); Mozafarihashjin, Mohammad (57200561038); Nasir, Jalees A. (57215128853); Shigayeva, Altynay (8301791600); Yim, Winfield (57538841200); Yip, Lily (57208641927); Zhong, Xi Zoe (57220062616); Katz, Kevin (7006591413); Kozak, Robert (35242908000); McArthur, Andrew G. (7005534793); Daneman, Nick (6508076243); Maguire, Finlay (56118405300); McGeer, Allison J. (57226260279); Duvvuri, Venkata R. (7801387483); Mubareka, Samira (14028762300)","57224950900; 57218594357; 57220058839; 57535965800; 57220060546; 57192893915; 57220050430; 55985637200; 57220048039; 57203034813; 59033733200; 57200561038; 57215128853; 8301791600; 57538841200; 57208641927; 57220062616; 7006591413; 35242908000; 7005534793; 6508076243; 56118405300; 57226260279; 7801387483; 14028762300","Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study","2025","BMC Infectious Diseases","25","1","132","","","","0","10.1186/s12879-025-10450-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217271749&doi=10.1186%2fs12879-025-10450-3&partnerID=40&md5=f9bd573e9d39bd6020d78625fc1d83a3","Sunnybrook Research Institute, Toronto, ON, Canada; Shared Hospital Laboratory, Toronto, ON, Canada; Sinai Health System, Toronto, ON, Canada; North York General Hospital, Toronto, ON, Canada; Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada; Department of Community Health & Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada; Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada; Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada; Public Health Ontario, 661 University Avenue, Toronto, ON, Canada; Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada","Nirmalarajah K., Sunnybrook Research Institute, Toronto, ON, Canada, Public Health Ontario, 661 University Avenue, Toronto, ON, Canada, Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada; Aftanas P., Shared Hospital Laboratory, Toronto, ON, Canada; Barati S., Sinai Health System, Toronto, ON, Canada; Chien E., Sunnybrook Research Institute, Toronto, ON, Canada; Crowl G., Sinai Health System, Toronto, ON, Canada; Faheem A., Sinai Health System, Toronto, ON, Canada; Farooqi L., Sinai Health System, Toronto, ON, Canada; Jamal A.J., Sinai Health System, Toronto, ON, Canada; Khan S., Sinai Health System, Toronto, ON, Canada; Kotwa J.D., Sunnybrook Research Institute, Toronto, ON, Canada; Li A.X., Sinai Health System, Toronto, ON, Canada; Mozafarihashjin M., Sinai Health System, Toronto, ON, Canada; Nasir J.A., Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada; Shigayeva A., Sinai Health System, Toronto, ON, Canada; Yim W., Sunnybrook Research Institute, Toronto, ON, Canada; Yip L., Sunnybrook Research Institute, Toronto, ON, Canada; Zhong X.Z., Sinai Health System, Toronto, ON, Canada; Katz K., Shared Hospital Laboratory, Toronto, ON, Canada, North York General Hospital, Toronto, ON, Canada; Kozak R., Sunnybrook Research Institute, Toronto, ON, Canada, Shared Hospital Laboratory, Toronto, ON, Canada, Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada; McArthur A.G., Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada; Daneman N., Sunnybrook Research Institute, Toronto, ON, Canada; Maguire F., Sunnybrook Research Institute, Toronto, ON, Canada, Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, Department of Community Health & Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada; McGeer A.J., Sinai Health System, Toronto, ON, Canada, Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada; Duvvuri V.R., Public Health Ontario, 661 University Avenue, Toronto, ON, Canada, Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada, Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada; Mubareka S., Sunnybrook Research Institute, Toronto, ON, Canada, Shared Hospital Laboratory, Toronto, ON, Canada, Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada","Background: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants. Although millions of SARS-CoV-2 genomes have been publicly shared in global databases, linkages with detailed clinical data are scarce. Therefore, we aimed to establish a COVID-19 patient dataset with linked clinical and viral genomic data to then examine associations between SARS-CoV-2 genomic signatures and clinical disease phenotypes. Methods: A cohort of adult patients with laboratory confirmed SARS-CoV-2 from 11 participating healthcare institutions in the Greater Toronto Area (GTA) were recruited from March 2020 to April 2022. Supervised machine learning (ML) models were developed to predict hospitalization using SARS-CoV-2 lineage-specific genomic signatures, patient demographics, symptoms, and pre-existing comorbidities. The relative importance of these features was then evaluated. Results: Complete clinical data and viral whole genome level information were obtained from 617 patients, 50.4% of whom were hospitalized. Notably, inpatients were older with a mean age of 66.67 years (SD ± 17.64 years), whereas outpatients had a mean age of 44.89 years (SD ± 16.00 years). SHapley Additive exPlanations (SHAP) analyses revealed that underlying vascular disease, underlying pulmonary disease, and fever were the most significant clinical features associated with hospitalization. In models built on the amino acid sequences of functional regions including spike, nucleocapsid, ORF3a, and ORF8 proteins, variants preceding the emergence of variants of concern (VOCs) or pre-VOC variants, were associated with hospitalization. Conclusions: Viral genomic features have limited utility in predicting hospitalization across SARS-CoV-2 diversity. Combining clinical and viral genomic datasets provides perspective on patient specific and virus-related factors that impact COVID-19 disease severity. Overall, clinical features had greater discriminatory power than viral genomic features in predicting hospitalization. © The Author(s) 2025.","COVID-19; Data integration; Disease severity; Machine learning; SARS-CoV-2; Viral genomics","Adult; Aged; Aged, 80 and over; Comorbidity; COVID-19; Female; Genome, Viral; Genomics; Hospitalization; Humans; Male; Middle Aged; Retrospective Studies; SARS-CoV-2; SARS-CoV-2 variants; Severity of Illness Index; Supervised Machine Learning; orf3a protein; orf8 protein; protein; unclassified drug; accuracy; adult; aged; ageusia; algorithm; amino acid sequence; anosmia; Article; asthma; cardiovascular disease; chill; clinical feature; clinical outcome; cohort analysis; comorbidity; coronavirus disease 2019; coughing; decision tree; demographics; diabetes mellitus; diagnostic test accuracy study; disease severity; dyspnea; female; fever; hospitalization; human; immune system; kidney disease; liver disease; lung disease; major clinical study; male; neuromuscular disease; open reading frame; phenotype; phylogenetic tree; phylogeny; polymerase chain reaction; prediction; prospective study; random forest; receiver operating characteristic; retrospective study; RNA extraction; Severe acute respiratory syndrome coronavirus 2; Shapley additive explanation; smoking; supervised machine learning; symptom; thorax pain; variant of concern; vascular disease; viral genomics; virus nucleocapsid; whole genome sequencing; clinical trial; epidemiology; genetics; genomics; middle aged; multicenter study; Severe acute respiratory syndrome coronavirus 2; severity of illness index; very elderly; virology; virus genome","","protein, 67254-75-5","MiSeq, Illumina, United States; MiniSeq, Illumina, United States; Nextera  DNA  Flex  Prep  Kit, Illumina, United States","Illumina, United States; Illumina, United States; Illumina, United States","Department of Pathology and Laboratory Medicine, Weill Cornell Medicine; McLaughlin Fund; University of Toronto, U of T; Canadian Institutes of Health Research, CIHR, (177701, 174925); Canadian Institutes of Health Research, CIHR","This work was supported by the Canadian Institutes of Health Research (CIHR no. 177701, no. 174925), the Canadian COVID-19 Genomics Network, the McLaughlin Fund, the Department of Laboratory Medicine and Pathobiology and the Institute for Pandemics at the University of Toronto. 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Kandel C.E., Banete A., Taylor M., Llanes A., McCready J., Crowl G., Et al., Similar duration of viral shedding of the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) delta variant between vaccinated and incompletely vaccinated individuals, Infect Control Hosp Epidemiol, 44, pp. 1002-1004, (2023); Kotwa J.D., Jamal A.J., Mbareche H., Yip L., Aftanas P., Barati S., Et al., Surface and air contamination with severe Acute Respiratory Syndrome Coronavirus 2 from hospitalized coronavirus disease 2019 patients in Toronto, Canada, March–May 2020, J Infect Dis, 225, pp. 768-776, (2022); Nasir J.A., Kozak R.A., Aftanas P., Raphenya A.R., Smith K.M., Maguire F., Et al., A comparison of whole genome sequencing of SARS-CoV-2 using amplicon-based sequencing, Random Hexamers, and bait capture, Viruses, 12, (2020); Josh Q., Ncov-2019 Sequencing Protocol V12020.; Rambaut A., Holmes E.C., O'Toole A., Hill V., McCrone J.T., Ruis C., Et al., A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology, Nat Microbiol, 5, pp. 1403-1407, (2020); Aksamentov I., Roemer C., Hodcroft E., Neher R., Nextclade: clade assignment, mutation calling and quality control for viral genomes, J Open Source Softw, 6, (2021); Hadfield J., Megill C., Bell S.M., Huddleston J., Potter B., Callender C., Et al., Nextstrain: real-time tracking of pathogen evolution, Bioinformatics, 34, pp. 4121-4123, (2018); Serna Garcia G., Al Khalaf R., Invernici F., Ceri S., Bernasconi A., CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning, Gigascience, 12, (2022); Johnson B.A., Xie X., Bailey A.L., Kalveram B., Lokugamage K.G., Muruato A., Et al., Loss of furin cleavage site attenuates SARS-CoV-2 pathogenesis, Nature, 591, pp. 293-299, (2021); Zhang J., Ejikemeuwa A., Gerzanich V., Nasr M., Tang Q., Simard J.M., Et al., Understanding the role of SARS-CoV-2 ORF3a in viral pathogenesis and COVID-19, Front Microbiol, 13, (2022); Jackson C.B., Farzan M., Chen B., Choe H., Mechanisms of SARS-CoV-2 entry into cells, Nat Rev Mol Cell Biol, 23, pp. 3-20, (2022); McCallum M., De Marco A., Lempp F.A., Tortorici M.A., Pinto D., Walls A.C., Et al., N-terminal domain antigenic mapping reveals a site of vulnerability for SARS-CoV-2, Cell, 184, pp. 2332-2330, (2021); de Silva T.I., Liu G., Lindsey B.B., Dong D., Moore S.C., Hsu N.S., Et al., The impact of viral mutations on recognition by SARS-CoV-2 specific T cells, IScience, 24, (2021); Arshad N., Laurent-Rolle M., Ahmed W.S., Hsu J.C.-C., Mitchell S.M., Pawlak J., Et al., SARS-CoV-2 accessory proteins ORF7a and ORF3a use distinct mechanisms to down-regulate MHC-I surface expression, Proceedings of the National Academy of Sciences, 120, (2023); Zhang Y., Chen Y., Li Y., Huang F., Luo B., Yuan Y., Et al., The ORF8 protein of SARS-CoV-2 mediates immune evasion through down-regulating MHC-Ι, Proc Natl Acad Sci, 118, (2021); Rodriguez-Perez R., Bajorath J., Interpretation of compound activity predictions from Complex Machine Learning models using local approximations and Shapley Values, J Med Chem, 63, pp. 8761-8777, (2020); Cheng M.-Y., Hsih W.-H., Ho M.-W., Lai Y.-C., Liao W.-C., Chen C.-Y., Et al., Younger adults with mild-to-moderate COVID-19 exhibited more prevalent olfactory dysfunction in Taiwan, J Microbiol Immunol Infect, 54, pp. 794-800, (2021); Fernandez-de-las-Penas C., Palacios-Cena D., Gomez-Mayordomo V., Florencio L.L., Cuadrado M.L., Plaza-Manzano G., Et al., Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: a systematic review and meta-analysis, Eur J Intern Med, 92, pp. 55-70, (2021); Nagy &#X.001;., Ligeti B., Szebeni J., Pongor S., Gyorffy B., COVIDOUTCOME—estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome, Database, 2021, (2021); Huang F., Chen L., Guo W., Zhou X., Feng K., Huang T., Et al., Identifying COVID-19 severity-related SARS-CoV-2 mutation using a machine learning method, Life, 12, (2022); Leary S., Gaudieri S., Parker M., Chopra A., James I., Pakala S., Et al., Generation of a Novel SARS-CoV-2 sub-genomic RNA due to the R203K/G204R variant in Nucleocapsid: homologous recombination has potential to change SARS-CoV-2 at both protein and RNA level, Pathog Immun, 6, pp. 27-49, (2021); Wu H., Xing N., Meng K., Fu B., Xue W., Dong P., Et al., Nucleocapsid mutations R203K/G204R increase the infectivity, fitness, and virulence of SARS-CoV-2, Cell Host Microbe, 29, pp. 1788-e18016, (2021); Zhang J., Li Q., Cruz Cosme R.S., Gerzanich V., Tang Q., Simard J.M., Et al., Genome-wide characterization of SARS-CoV-2 cytopathogenic proteins in the search of antiviral targets, MBio, 13, (2022); Canada H., Health Canada authorizes PAXLOVIDTM for patients with mild to moderate COVID-19 at high risk of developing serious disease, (2022); Marshall J.C., Murthy S., Diaz J., Adhikari N.K., Angus D.C., Arabi Y.M., Et al., A minimal common outcome measure set for COVID-19 clinical research, Lancet Infect Dis, 20, pp. e192-e197, (2020)","S. Mubareka; Sunnybrook Research Institute, Toronto, Canada; email: samira.mubareka@sunnybrook.ca; V.R. Duvvuri; Public Health Ontario, Toronto, 661 University Avenue, Canada; email: Venkata.Duvvuri@oahpp.ca","","BioMed Central Ltd","","","","","","14712334","","BIDMB","39875869","English","BMC Infect. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85217271749"
"Zhao Y.; He B.; Xu Z.; Zhang Y.; Zhao X.; Huang Z.-A.; Yang F.; Wang L.; Duan L.; Song J.; Yao J.","Zhao, Yu (59050877300); He, Bing (57198793266); Xu, Zhimeng (57202763349); Zhang, Yidan (57211160756); Zhao, Xuan (58082567200); Huang, Zhi-An (57193516151); Yang, Fan (58734071900); Wang, Liang (55637319700); Duan, Lei (26636918600); Song, Jiangning (56023619300); Yao, Jianhua (57693843200)","59050877300; 57198793266; 57202763349; 57211160756; 58082567200; 57193516151; 58734071900; 55637319700; 26636918600; 56023619300; 57693843200","Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire","2023","Briefings in Bioinformatics","24","1","bbac555","","","","1","10.1093/bib/bbac555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147044692&doi=10.1093%2fbib%2fbbac555&partnerID=40&md5=48730e9fbbe8f9557dde7a0c85206a3b","Ai Lab, Tencent, Shenzhen, China; School of Computer Science, Sichuan University, Chengdu, China; Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China; Monash Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, Vic 3800, Australia","Zhao Y., Ai Lab, Tencent, Shenzhen, China; He B., Ai Lab, Tencent, Shenzhen, China; Xu Z., Ai Lab, Tencent, Shenzhen, China; Zhang Y., Ai Lab, Tencent, Shenzhen, China, School of Computer Science, Sichuan University, Chengdu, China; Zhao X., Ai Lab, Tencent, Shenzhen, China; Huang Z.-A., Ai Lab, Tencent, Shenzhen, China, Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China; Yang F., Ai Lab, Tencent, Shenzhen, China; Wang L., Ai Lab, Tencent, Shenzhen, China; Duan L., School of Computer Science, Sichuan University, Chengdu, China; Song J., Ai Lab, Tencent, Shenzhen, China; Yao J., Ai Lab, Tencent, Shenzhen, China, Monash Biomedicine Discovery Institute and Monash Data Futures Institute, Monash University, Melbourne, Vic 3800, Australia","Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.  © 2022 The Author(s). Published by Oxford University Press. All rights reserved.","artificial intelligence; COVID-19; diagnosis; prognosis; TCR repertoire","Artificial Intelligence; Asthma; COVID-19; Humans; ROC Curve; Software; artificial intelligence; asthma; human; receiver operating characteristic; software","","","","","National Natural Science Foundation of China, NSFC, (61972268); National Natural Science Foundation of China, NSFC","National Natural Science Foundation of China (Grant No. 61972268). 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Rabbani G, Shariful Islam SM, Rahman MA, Et al., Pre-existing COPD is associated with an increased risk of mortality and severity in COVID-19: a rapid systematic review and meta-analysis, Expert Rev Respir Med, 15, pp. 705-716, (2021); Huang BZ, Chen Z, Sidell MA, Et al., Asthma disease status, COPD, and COVID-19 severity in a large multiethnic population, J Allergy Clin Immunol Pract, 9, pp. 3621-3628, (2021); JE la P, Rascon-Pacheco RA, Id eJ A-M, Et al., Hypertension, Diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico, Arch Med Res, 52, pp. 443-449, (2021); Woodsworth DJ, Castellarin M, Holt RA., Sequence analysis of T-cell repertoires in health and disease, Genome Med, 5, (2013); Fichtner AS, Ravens S, Prinz I., Human γ δ TCR repertoires in health and disease, Cell, 9, (2020); Li W, Yin Y, Quan X, Et al., Gene expression value prediction based on XGBoost algorithm, Front Genet, 10, (2019); Ogunleye A, Wang Q-G., XGBoost model for chronic kidney disease diagnosis, IEEE/ACM Trans Comput Biol Bioinform, 17, pp. 2131-2140, (2020); 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Hernandez DM, Valderrama S, Gualtero S, Et al., Loss of T-cell multifunctionality and TCR-Vβ repertoire against Epstein-Barr virus is associated with worse prognosis and clinical parameters in HIV+ patients, Front Immunol, 9, (2018); He J, Wu J, Jiao Y, Et al., IgH gene rearrangements as plasma biomarkers in non-Hodgkin’s lymphoma patients, Oncotarget, 2, pp. 178-185, (2011); Simnica D, Schultheiss C, Mohme M, Et al., Landscape of T-cell repertoires with public COVID-19-associated T-cell receptors in pre-pandemic risk cohorts, Clin Transl Immunol, 10, (2021)","J. Yao; Tencent Ai Lab, Guangdong, Tencent, Shenzhen, 518000, China; email: jianhua.yao@gmail.com","","Oxford University Press","","","","","","14675463","","","36567255","English","Brief. Bioinform.","Article","Final","","Scopus","2-s2.0-85147044692"
"Zhang B.; Wang J.; Ye Z.; Zhou L.; Xiong D.; Wang X.; Guo L.","Zhang, Bochao (57432311300); Wang, Jiping (55603246700); Ye, Zhipeng (59147787200); Zhou, Linfu (8657590200); Xiong, Daxi (36852664800); Wang, Xiaojun (59147756400); Guo, Liquan (36701256000)","57432311300; 55603246700; 59147787200; 8657590200; 36852664800; 59147756400; 36701256000","JASSNet: Heart and lung sound separation network based on joint attention mechanism and Semi-Supervised learning","2025","Biomedical Signal Processing and Control","104","","107525","","","","0","10.1016/j.bspc.2025.107525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216514364&doi=10.1016%2fj.bspc.2025.107525&partnerID=40&md5=e1a5c8b7499ea795497504b5ca75c048","School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230052, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Taizhou Institute of Science and Technology Nanjing University of Science and Technology, Taizhou, 225300, China; Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China; Neurology Department, Suzhou Xiangcheng People's Hospital, Suzhou, 215163, China","Zhang B., School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230052, China, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Wang J., School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230052, China, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Ye Z., Taizhou Institute of Science and Technology Nanjing University of Science and Technology, Taizhou, 225300, China; Zhou L., Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China, Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China; Xiong D., School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230052, China, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Wang X., Neurology Department, Suzhou Xiangcheng People's Hospital, Suzhou, 215163, China; Guo L., School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230052, China, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China","High-quality heart and lung sounds are essential for the automated diagnosis of cardiovascular or respiratory diseases. However, chest sounds recorded by stethoscopes often contain a mixture of heart and lung sounds, and the clinical measurement of high-quality cardiopulmonary sounds is costly, with labeled data being scarce. To address these challenges, this study proposes a heart–lung sound separation network based on a joint attention mechanism and semi-supervised learning (JASSNet). Specifically, a convolutional module is designed to extract fine-grained features, combined with both global and local attention mechanisms to enhance information interaction between features. A gating mechanism is employed to extract key heart and lung sound features. Finally, clinical validation is performed by comparing different semi-supervised learning strategies. The results demonstrate that the proposed model performs excellently on both simulated and clinical datasets. Notably, in clinical trials, the accuracy of heart rate (HR) and respiratory rate (RR) improved by over 10% and 15%, respectively. This study not only shows potential in the preprocessing steps of health monitoring systems but also holds significant clinical application prospects. © 2025 The Authors","Attention mechanism; Cardiopulmonary sound separation; Chronic obstructive pulmonary disease (COPD); Data augmentation; Deep learning; Semi-supervised learning (SSL)","Cardiology; Diagnosis; Heart; Pulmonary diseases; Self-supervised learning; Semi-supervised learning; Attention mechanisms; Cardiopulmonary sound separation; Chronic obstructive pulmonary disease; Data augmentation; Deep learning; Heart sounds; Semi-supervised learning; Sound separation; abnormal respiratory sound; Article; attention; background noise; breathing rate; chronic obstructive lung disease; controlled study; convolutional neural network; cross validation; feature extraction; filtration; Gaussian noise; heart rate; heart sound; human; major clinical study; masking; pitch; semi supervised machine learning; signal noise ratio; simulation; white noise; Lung cancer","","","","","National Key Research and Development Program of China, NKRDPC, (2022YFC0710800); Suzhou Science and Technology Plan, (LCZX202233); Nanjing University Medical School, (IRB202102006RI)","Funding text 1: This research was supported by the National Key Research and Development Program of China ( 2022YFC0710800 ), Suzhou Science and Technology Plan ( LCZX202233 ). ; Funding text 2: This research was supported by the National Key Research and Development Program of China (2022YFC0710800), Suzhou Science and Technology Plan (LCZX202233). Ethical statement. The retrospective study conducted in this research received ethical approval from the Ethics Committee of The Affiliated Suzhou Hospital of Nanjing University Medical School (Approval No.: IRB202102006RI). The study protocol followed the principles and guidelines outlined in the Helsinki Declaration and the International Conference on Harmonisation (ICH), ensuring adherence to good clinical. Informed consent statement. All subjects gave written consent and provided permission for educational and scientific purposes.","Labaki W.W., Han M.K., Chronic respiratory diseases: a global view, Lancet Respir. Med., 8, 6, pp. 531-533, (2020); Carter P., Et al., Association of Cardiovascular Disease With Respiratory Disease, J. Am. Coll. Cardiol., 73, 17, pp. 2166-2177, (2019); Yadollahi A., Moussavi Z.M.K., A Robust Method for Heart Sounds Localization Using Lung Sounds Entropy, IEEE Trans. Biomed. 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"Fakhraei R.; Matelski J.; Gershon A.; Kendzerska T.; Lapointe-Shaw L.; Kaneswaran L.; Wu R.","Fakhraei, Reza (57204008347); Matelski, John (56875394500); Gershon, Andrea (24760464400); Kendzerska, Tetyana (54383390000); Lapointe-Shaw, Lauren (55259845000); Kaneswaran, Lanujan (57204042189); Wu, Robert (10046340100)","57204008347; 56875394500; 24760464400; 54383390000; 55259845000; 57204042189; 10046340100","Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data","2023","COPD: Journal of Chronic Obstructive Pulmonary Disease","20","1","","274","283","9","1","10.1080/15412555.2023.2242493","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167370407&doi=10.1080%2f15412555.2023.2242493&partnerID=40&md5=7f6ea2fd11fca0923b8d1da1f0ddff3f","University of Toronto, Toronto, ON, Canada; Biostatistics Research Unit, University Health Network, Toronto, ON, Canada; Department of Medicine, University Health Network, Toronto, ON, Canada; Division of Respirology, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Division of Respirology, Department of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada; The Ottawa Hospital Research Institute, Ottawa, ON, Canada","Fakhraei R., University of Toronto, Toronto, ON, Canada; Matelski J., Biostatistics Research Unit, University Health Network, Toronto, ON, Canada; Gershon A., University of Toronto, Toronto, ON, Canada, Department of Medicine, University Health Network, Toronto, ON, Canada, Division of Respirology, Sunnybrook Health Sciences Center, Toronto, ON, Canada; Kendzerska T., Division of Respirology, Department of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada, The Ottawa Hospital Research Institute, Ottawa, ON, Canada; Lapointe-Shaw L., University of Toronto, Toronto, ON, Canada, Department of Medicine, University Health Network, Toronto, ON, Canada; Kaneswaran L., University of Toronto, Toronto, ON, Canada; Wu R., University of Toronto, Toronto, ON, Canada, Department of Medicine, University Health Network, Toronto, ON, Canada","Background: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission. Objectives: To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD. Methods: Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms. Results: Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year. Conclusion: We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.","acute exacerbations; COPD; machine learning; readmission","Disease Progression; Electronic Health Records; Hospitalization; Hospitals; Humans; Patient Readmission; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Risk Factors; antibiotic agent; antivirus agent; cholinergic receptor blocking agent; loop diuretic agent; methylprednisolone; prednisone; aged; Article; breathing rate; chronic obstructive lung disease; clinical article; cohort analysis; disease exacerbation; electronic medical record; female; heart failure; heart rate; hospital admission; hospital discharge; hospital readmission; human; least absolute shrinkage and selection operator; length of stay; male; medical history; pneumonia; prediction; recursive partitioning; retrospective study; sex difference; chronic obstructive lung disease; electronic health record; hospital; hospital readmission; hospitalization; risk factor","","methylprednisolone, 6923-42-8, 83-43-2; prednisone, 53-03-2","","","Canadian Institutes of Health Research, IRSC","This study was funded by Canadian Institutes of Health Research and the Canadian Lung Association Breathing as One Catalyst Grant.","All-cause readmission to acute care and return to the emergency department, (2012); Wilkinson T.M.A., Donaldson G.C., Hurst J.R., Et al., Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 169, 12, pp. 1298-1303, (2004); Sharpe I., Bowman M., Kim A., Et al., Strategies to prevent readmissions to hospital for COPD: a systematic review, COPD J Chron Obstruct Pulmon Dis, 18, 4, pp. 456-468, (2021); Gothe H., Rajsic S., Vukicevic D., Et al., Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review, BMC Health Serv Res, 19, 1, (2019); Kong C.W., Wilkinson T.M.A., Predicting and preventing hospital readmission for exacerbations of COPD, ERJ Open Res, 6, 2, pp. 00325-02019, (2020); 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Trajman A., Luiz R.R., McNemar chi2 test revisited: comparing sensitivity and specificity of diagnostic examinations, Scand J Clin Lab Invest, 68, 1, pp. 77-80, (2008); (2023); Bellou V., Belbasis L., Konstantinidis A.K., Et al., Prognostic models for outcome prediction in patients with chronic obstructive pulmonary disease: systematic review and critical appraisal, BMJ, 367, (2019); Press V.G., Myers L.C., Feemster L.C., Preventing COPD readmissions under the hospital readmissions reduction program: how far have We come?, Chest, 159, 3, pp. 996-1006, (2021)","R. Wu; University of Toronto, Toronto, Canada; email: robert.wu@uhn.ca","","Taylor and Francis Ltd.","","","","","","15412555","","","37555513","English","COPD J. Chronic Obstructive Pulm. Dis.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85167370407"
"Ramakrishnan S.; Beaufils F.; De Brandt J.; Viney K.; Bradley C.; Cottin V.; Maged Hassan G.; Cruz J.","Ramakrishnan, Sanjay (57214593873); Beaufils, Fabien (57213195808); De Brandt, Jana (57188870019); Viney, Kerri (22636273800); Bradley, Claire (57986296000); Cottin, Vincent (7004305225); Maged Hassan, G. (57671237800); Cruz, Joana (36715922000)","57214593873; 57213195808; 57188870019; 22636273800; 57986296000; 7004305225; 57671237800; 36715922000","European Respiratory Society International Congress 2021: Highlights from best-abstract awardees","2022","Breathe","18","1","210176","","","","1","10.1183/20734735.0176-2021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129582947&doi=10.1183%2f20734735.0176-2021&partnerID=40&md5=86ae434927d6459ef5ca94415eaf18c5","Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom; School of Medical and Health Sciences, Edith Cowan University, Perth, Australia; Univ. Bordeaux, Centre de Recherche Cardio-thoracique de Bordeaux, INSERM U1045, Bordeaux Imaging Center, Bordeaux, France; CHU Bordeaux, Service d’Exploration Fonctionnelle Respiratoire, Bordeaux, France; Faculty of Rehabilitation Sciences, Rehabilitation Research Center REVAL, Biomedical Research Institute BIOMED, Hasselt University, Hasselt, Belgium; Faculty of Medicine, Dept of Community Medicine and Rehabilitation, Section of Physiotherapy, Umeå University, Umeå, Sweden; Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland; Leeds Teaching Hospitals, Leeds, United Kingdom; National French Reference Coordinating Center for Rare Pulmonary Diseases, Louis Pradel Hospital and Hospices Civils de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, INRAE, member of ERN-LUNG, Lyon, France; Chest Diseases Dept, Alexandria University Faculty of Medicine, Alexandria, Egypt; Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences ESSLei, Polytechnic of Leiria, Leiria, Portugal","Ramakrishnan S., Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia; Beaufils F., Univ. Bordeaux, Centre de Recherche Cardio-thoracique de Bordeaux, INSERM U1045, Bordeaux Imaging Center, Bordeaux, France, CHU Bordeaux, Service d’Exploration Fonctionnelle Respiratoire, Bordeaux, France; De Brandt J., Faculty of Rehabilitation Sciences, Rehabilitation Research Center REVAL, Biomedical Research Institute BIOMED, Hasselt University, Hasselt, Belgium, Faculty of Medicine, Dept of Community Medicine and Rehabilitation, Section of Physiotherapy, Umeå University, Umeå, Sweden; Viney K., Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland; Bradley C., Leeds Teaching Hospitals, Leeds, United Kingdom; Cottin V., National French Reference Coordinating Center for Rare Pulmonary Diseases, Louis Pradel Hospital and Hospices Civils de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, INRAE, member of ERN-LUNG, Lyon, France; Maged Hassan G., Chest Diseases Dept, Alexandria University Faculty of Medicine, Alexandria, Egypt; Cruz J., Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences ESSLei, Polytechnic of Leiria, Leiria, Portugal","[No abstract available]","","Article; artificial intelligence; asthma; awards and prizes; behavior change; cancer screening; chronic obstructive lung disease; computer assisted tomography; continuous positive airway pressure; coronavirus disease 2019; cystic fibrosis; disease exacerbation; endoscopy; general practitioner; health care concepts; health care personnel; hospitalization; human; hyper IgE syndrome; imaging; immunophenotyping; immunotherapy; interstitial lung disease; learning algorithm; lung burden; lung cancer; lung disease; monitoring; non small cell lung cancer; outcome assessment; pediatrics; respiration control; respiratory system; respiratory tract infection; sickle cell anemia; telemedicine; telemonitoring; telerehabilitation; tobacco use","","","","","Belgian Respiratory Society; FWO-grant, (11B4718N); Fonds Wetenschappelijk Onderzoek, FWO; Vlaamse regering","Funding text 1: Conflict of interest: S. Ramakrishnan reports receiving grants or contracts outside the submitted work from National Institute of Health Research, and Australian Government Research Training Program. Support for attending meetings and/or travel from AstraZeneca, outside the submitted work. F. Beaufils has nothing to disclose. J. De Brandt reports receiving grants or contracts outside the submitted work from FWO Aspirant Mandate, ERS Best Abstract Grant 2020 and 2021, FWO long stay abroad grant 2019, and Belgian Respiratory Society short-term research fellowship. Speaker fee received from ERS PR course 2020, outside the submitted work. K. Viney has nothing to disclose. C. Bradley has nothing to disclose. V. Cottin reports receiving grants or contracts outside the submitted work from Boehringer Ingelheim. Consulting fees, outside the submitted work, received from Boehringer Ingelheim, Roche, Galapagos, Galecto Shionogi, Fibrogen, RedX, and PureTech. Payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events, as well as support for attending meetings and/or travel received from Boehringer Ingelheim and Roche, outside the submitted work. Participation on a Data Safety Monitoring Board or Advisory Board for Roche/Promedior, Celgene/ BMS, and Galapagos, outside the submitted work. M. Hassan has nothing to disclose. J. Cruz has nothing to disclose.; Funding text 2: Support statement: J. De Brandt is funded by the Flemish government. The Research of FWO (Research Foundation - Flanders) Aspirant J. De Brandt is sponsored by FWO-grant #11B4718N.","Abdel-Aal A, Jordan R, Barnard A, Et al., Prioritising respiratory research needs in primary care: Results from the International Primary Care Respiratory Group (IPCRG) global e-Delphi exercise, Eur Respir J, 58, (2021); Khan FA, Majidulla A, Tavaziva G, Et al., Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: A prospective study of diagnostic accuracy for culture-confirmed disease, Lancet Digit Health, 2, pp. E573-e581, (2020); Qin ZZ, Sander MS, Rai B, Et al., Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems, Sci Rep, 9, (2019); Topalovic M, Das N, Burgel P-R, Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur Respir J, 53, (2019); Verkleij M, Georgiopoulos AM, Friedman D., Development and evaluation of an internet-based cognitive behavioral therapy intervention for anxiety and depression in adults with cystic fibrosis (eHealth CF-CBT): An international collaboration, Internet Interv, 24, (2021); Zimmermann P, Curtis N., Why is COVID-19 less severe in children? 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Marillier M, Al Chikhanie Y, Veale D, Et al., Which severe COVID-19 patients may benefit the most from pulmonary rehabilitation?, Eur Respir J, 58, (2021); Xavier R, Godoy C, Silva EGE, Et al., Pulmonary rehabilitation in individuals post-acute COVID-19 infection: Preliminary results, Eur Respir J, 58, (2021); Wuyts M, Everaerts S, Vande Weygaerde Y, Et al., Late Breaking Abstract - Physical activity recovery in patients with COVID-19 infection included in pulmonary rehabilitation, Eur Respir J, 58, (2021); Mousing CA, Sorensen D., Living with the risk of being infected: COPD patients’ experiences during the coronavirus pandemic, J Clin Nurs, 30, pp. 1719-1729, (2021); Stilma W, Akerman E, Artigas A, Et al., Awake proning as an adjunctive therapy for refractory hypoxemia in non-intubated patients with COVID-19 acute respiratory failure: Guidance from an international group of healthcare workers, Am J Trop Med Hyg, 104, pp. 1676-1686, (2021); Houben-Wilke S, Goertz Y, Delbressine J, Et al., Impact of COVID-19 on mental health: A traumatic event, Eur Respir J, 58, (2021); Barmparessou Z, Pappa S, Pappas A, Et al., Sex differences in mental health of hospitalized patients with COVID-19, Eur Respir J, 58, (2021); Lopez Lopez L, Gonzalez-Duenas J, Torres-Sanchez I, Et al., In hospital NEMS for patients with coexisting physical fraility and cognitive impairment during acute exacerbation of COPD: A randomized controlled trial, Eur Respir J, 58, (2021); Gimenez-Moolhuyzen E, Sebio Garcia R, Martin-Garcia MM, Et al., Comparison between two endurance training programmes to increase functional capacity after lung transplantation, Eur Respir J, 58, (2021); De Brandt J, Derave W, Vandenabeele F, Et al., Effect of beta-alanine supplementation on muscle carnosine, oxidative and carbonyl stress, antioxidants and physical capacity in patients with COPD, Eur Respir J, 58, (2021); De Brandt J, Derave W, Vandenabeele F, Et al., Effect of oral beta-alanine supplementation on muscle carnosine in patients with COPD: A double blind, placebo-controlled, randomized trial, Eur Respir J, 56, (2020); Dacha S, Chuatrakoon B, Sornkaew K, Et al., Impact of wearing different facial masks on respiratory symptoms, oxygen saturation, and functional capacity during six-minute walk test (6MWT) in healthy young adults, Eur Respir J, 58, (2021); Oliveira A, Quach S, Alsubheen S, Et al., Rapid access rehabilitation after exacerbations of COPD - a qualitative study, Eur Respir J, 58, (2021); Global tuberculosis report 2021, (2021); Migliori GB, Thong PM, Akkerman O, Et al., Worldwide effects of coronavirus disease pandemic on tuberculosis services, January-April 2020, Emerg Infect Dis, 26, pp. 2709-2712, (2020); Motta I, Centis R, D'Ambrosio L, Et al., Tuberculosis, COVID-19 and migrants: Preliminary analysis of deaths occurring in 69 patients from two cohorts, Pulmonology, 26, pp. 233-240, (2020); Tadolini M, Codecasa LR, Garcia-Garcia J-M, Et al., Active tuberculosis, sequelae and COVID-19 co-infection: First cohort of 49 cases, Eur Respir J, 56, (2020); Tuberculosis and COVID-19 co-infection: Description of the global cohort, Eur Respir J, (2021); 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A prospective observational study, Eur Respir J, 58, (2021); Delestrain C, Jurdi HE, Guitton C, Et al., Late Breaking Abstract - Usefulness of lung ultrasound in the diagnosis and early detection of acute chest syndrome in children with sickle cell disease, Eur Respir J, 58, (2021); Pierrakos C, Lieveld A, Pisani L, Et al., Lung ultrasound aeration score for prognostication in invasively ventilated COVID-19 patients: Multicenter observational study, Eur Respir J, 58, (2021); Smargiassi A, Soldati G, Sofia C, Et al., Lung ultrasound and high-resolution CT-scan of the chest for COVID-19 pneumonia, Eur Respir J, 58, (2021); de Boer W, Veldman C, Steenbruggen I, Et al., Diaphragm strength in COVID-19 patients and breathlessness, Eur Respir J, 58, (2021); Levi G, Inciardi RM, Ciarfaglia M, Et al., Diagnosing pneumothorax through standardized bilateral ultrasound images comparison, Eur Respir J, 58, (2021); Hassan M, El-Shaarawy B, Al-Qaradawi MY, Et al., Ultrasound predictors of lung re-expansion following pleural effusion drainage, Eur Respir J, 58, (2021)","J. Cruz; Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences ESSLei, Polytechnic of Leiria, Leiria, Portugal; email: joana.cruz@ipleiria.pt","","European Respiratory Society","","","","","","18106838","","","","English","Breathe","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85129582947"
"Alanazi S.A.","Alanazi, Saad Awadh (57211018616)","57211018616","Melanoma Identification through X-ray Modality Using Inception-v3 Based Convolutional Neural Network","2022","Computers, Materials and Continua","72","1","","37","55","18","1","10.32604/cmc.2022.020118","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125714308&doi=10.32604%2fcmc.2022.020118&partnerID=40&md5=7b10442f4a55b54ba6c12611d5803ba7","College of Computer and Information Sciences, Jouf University, Aljouf, Sakaka, 72341, Saudi Arabia","Alanazi S.A., College of Computer and Information Sciences, Jouf University, Aljouf, Sakaka, 72341, Saudi Arabia","Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between lesions and surrounding skin, visual similarity between melanoma and non-melanoma lesions, and so on. Thus, reliable automatic detection of skin tumors is critical for pathologists’ effectiveness and precision. To take care of this issue, numerous research centers around the world are creating autonomous image processing-oriented frameworks. We suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing: we provided a Convolutional Neural Network (CNN) based framework using an Inception-v3 (INCP-v3) melanoma detection scheme and accomplished very high precision (98.96%) against melanoma detection. The classification framework of CNN is created utilizing TensorFlow and Keras in the backend (in Python). It likewise utilizes Transfer-Learning (TL) approach. It is prepared on the data gathered from the “International Skin Imaging Collaboration (ISIC)” repositories. The experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance. ©2022 National Information and Documentation Center (NIDOC)","Chronic bronchitis; Chronic obstructive pulmonary disease; Convolutional neural network; Deep learning; X-ray images","Convolution; Convolutional neural networks; Deep neural networks; Diagnosis; Image enhancement; Oncology; Pulmonary diseases; Chronic bronchitis; Chronic obstructive pulmonary disease; Convolutional neural network; Deep learning; Images processing; Malignant melanoma; Melanoma detection; Skin cancers; Skin disease; X-ray image; Dermatology","","","","","","","Barros W. K., Morais D. S., Lopes F. F., Torquato M. F., Barbosa R. d. M., Et al., Proposal of the cad system for melanoma detection using reconfigurable computing, Sensors, 20, 11, (2020); Jana E., Subban R., Saraswathi S., Research on skin cancer cell detection using image processing, 2017 IEEE Int. Conf. on Computational Intelligence and Computing Research, pp. 1-8, (2017); Pauline J., Abraham S., Bethanney Janney J., Detection of skin cancer by image processing techniques, Journal of Chemical and Pharmaceutical Research, 7, 2, pp. 148-153, (2015); Dorj U.-O., Lee K.-K., Choi J.-Y., Lee M., The skin cancer classification using deep convolutional neural network, Multimedia Tools and Applications, 77, 8, pp. 9909-9924, (2018); Masood A., Ali Al-Jumaily A., Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms, International Journal of Biomedical Imaging, 2013, pp. 1-22, (2013); Curiel-Lewandrowski C., Berry E., Leachman S., Artificial intelligence approach in melanoma, Melanoma, pp. 1-31, (2019); Brinker T. J., Hekler A., Hauschild A., Berking C., Schilling B., Et al., Comparing artificial intelligence algorithms to 157 German dermatologists: The melanoma classification benchmark, European Journal of Cancer, 111, pp. 30-37, (2019); Tariq M., Nisar S., Shah A., Akbar S., Khan M. A., Et al., Effect of hybrid reinforcement on the performance of filament wound hollow shaft, Composite Structures, 184, pp. 378-387, (2018); Chan S., Reddy V., Myers B., Thibodeaux Q., Brownstone N., Et al., Machine learning in dermatology: Current applications, opportunities, and limitations, Dermatology and Therapy, 10, 3, pp. 365-386, (2020); Verma A. K., Pal S., Kumar S., Comparison of skin disease prediction by feature selection using ensemble data mining techniques, Informatics in Medicine Unlocked, 16, (2019); AlJame M., Ahmad I., Imtiaz A., Mohammed A., Ensemble learning model for diagnosing covid-19 from routine blood tests, Informatics in Medicine Unlocked, 21, (2020); Vidya M., Karki M. V., Skin cancer detection using machine learning techniques, 2020 IEEE Int. Conf. on Electronics, Computing and Communication Technologies, pp. 1-5, (2020); Daghrir J., Tlig L., Bouchouicha M., Sayadi M., Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach, 2020 5th Int. Conf. on Advanced Technologies for Signal and Image Processing, pp. 1-5, (2020); Li Y., Shen L., Skin lesion analysis towards melanoma detection using deep learning network, Sensors, 18, 2, (2018); Vijayalakshmi M., Melanoma skin cancer detection using image processing and machine learning, International Journal of Trend in Scientific Research and Development, 3, 4, pp. 780-784, (2019); Bisla D., Choromanska A., Berman R. S., Stein J. A., Polsky D., Towards automated melanoma detection with deep learning: Data purification and augmentation, Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops, pp. 1-9, (2019); Almaraz-Damian J.-A., Ponomaryov V., Sadovnychiy S., Castillejos-Fernandez H., Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures, Entropy, 22, 4, (2020); Nahata H., Singh S. P., Deep learning solutions for skin cancer detection and diagnosis, Machine Learning with Health Care Perspective, pp. 159-182, (2020); Jinnai S., Yamazaki N., Hirano Y., Sugawara Y., Ohe Y., Et al., The development of a skin cancer classification system for pigmented skin lesions using deep learning, Biomolecules, 10, 8, (2020); Naeem A., Farooq M. S., Khelifi A., Abid A., Malignant melanoma classification using deep learning: Datasets, performance measurements, challenges and opportunities, IEEE Access, 8, pp. 110575-110597, (2020); Monika M. K., Vignesh N. A., Kumari C. U., Kumar M., Lydia E. L., Skin cancer detection and classification using machine learning, Materials Today: Proceedings, 33, pp. 4266-4270, (2020); Sagar A., Convolutional neural networks for classifying melanoma images, BioRxiv, (2020); Mustafa S., Dauda A. B., Dauda M., Image processing and SVM classification for melanoma detection, 2017 Int. Conf. on Computing Networking and Informatics, (2017); Zaqout I., Diagnosis of skin lesions based on dermoscopic images using image processing techniques, Pattern Recognition-Selected Methods and Applications, (2019); Hasan M. K., Elahi M. T. E., Alam M. A., Jawad M. T., Dermoexpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation, MedRxiv, (2021); Mehmood M., Ayub E., Ahmad F., Alruwaili M., Alrowaili Z. A., Et al., Machine learning enabled early detection of breast cancer by structural analysis of mammograms, Computers, Materials and Continua, 67, 1, pp. 641-657, (2021); Alanazi S. A., Alruwaili M., Ahmad F., Alaerjan A., Alshammari N., Estimation of organizational competitiveness by a hybrid of one-dimensional convolutional neural networks and self-organizing maps using physiological signals for emotional analysis of employees, Sensor, 21, 11, pp. 1-29, (2021)","S.A. Alanazi; College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf, 72341, Saudi Arabia; email: sanazi@ju.edu.sa","","Tech Science Press","","","","","","15462218","","","","English","Comput. Mater. Continua","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85125714308"
"Zhou X.; Ye C.; Iwao Y.; Okamoto T.; Kawata N.; Shimada A.; Haneishi H.","Zhou, Xingyu (58676418300); Ye, Chen (57158384200); Iwao, Yuma (59267308300); Okamoto, Takayuki (57204072110); Kawata, Naoko (23100411700); Shimada, Ayako (57201451381); Haneishi, Hideaki (7004884557)","58676418300; 57158384200; 59267308300; 57204072110; 23100411700; 57201451381; 7004884557","Respiratory Diaphragm Motion-Based Asynchronization and Limitation Evaluation on Chronic Obstructive Pulmonary Disease","2023","Diagnostics","13","20","3261","","","","1","10.3390/diagnostics13203261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175488832&doi=10.3390%2fdiagnostics13203261&partnerID=40&md5=4a5889624f86ddf5946774206332dccc","Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan; School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China; Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan; National Institutes for Quantum and Radiological Science and Technology, Chiba, 263-0024, Japan; Department of Respirology, Graduate School of Medicine, Chiba University, Chiba, 260-0856, Japan; Department of Respirology, Shin-Yurigaoka General Hospital, Kawasaki, 215-0026, Japan","Zhou X., Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan; Ye C., School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China, Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan; Iwao Y., Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan, National Institutes for Quantum and Radiological Science and Technology, Chiba, 263-0024, Japan; Okamoto T., Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan; Kawata N., Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan, Department of Respirology, Graduate School of Medicine, Chiba University, Chiba, 260-0856, Japan; Shimada A., Department of Respirology, Graduate School of Medicine, Chiba University, Chiba, 260-0856, Japan, Department of Respirology, Shin-Yurigaoka General Hospital, Kawasaki, 215-0026, Japan; Haneishi H., Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan","Background: Chronic obstructive pulmonary disease (COPD) typically causes airflow blockage and breathing difficulties, which may result in the abnormal morphology and motion of the lungs or diaphragm. Purpose: This study aims to quantitatively evaluate respiratory diaphragm motion using a thoracic sagittal magnetic resonance imaging (MRI) series, including motion asynchronization and limitations. Method: First, the diaphragm profile is extracted using a deep-learning-based field segmentation approach. Next, by measuring the motion waveforms of each position in the extracted diaphragm profile, obvious differences in the independent respiration cycles, such as the period and peak amplitude, are verified. Finally, focusing on multiple breathing cycles, the similarity and amplitude of the motion waveforms are evaluated using the normalized correlation coefficient (NCC) and absolute amplitude. Results and Contributions: Compared with normal subjects, patients with severe COPD tend to have lower NCC and absolute amplitude values, suggesting motion asynchronization and limitation of their diaphragms. Our proposed diaphragmatic motion evaluation method may assist in the diagnosis and therapeutic planning of COPD. © 2023 by the authors.","chronic obstructive pulmonary disease (COPD); field segmentation; magnetic resonance imaging (MRI); respiration cycle; respiratory diaphragm motion","adult; Article; breathing mechanics; breathing pattern; chronic obstructive lung disease; clinical article; controlled study; correlation coefficient; deep learning; diaphragm; female; human; image segmentation; male; motion; nuclear magnetic resonance imaging; quantitative analysis; retrospective study; waveform","","","Achieva dStream Release 5MR system, Philips Medical Systems, Netherlands; Ingenia CX, Philips Medical Systems, Netherlands","Philips Medical Systems, Netherlands; Philips Medical Systems, Netherlands","Japan Society for the Promotion of Science, KAKEN, (19K12816, 22K18181); Japan Society for the Promotion of Science, KAKEN","This research was funded by JSPS KAKENHI grant number [22K18181 and 19K12816].","Barnes P.J., Shapiro S.D., Pauwels R.A., Chronic obstructive pulmonary disease: Molecular and cellularmechanisms, Eur. 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Haneishi; Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan; email: haneishi@faculty.chiba-u.jp","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175488832"
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Commun., 10, 1, (2019); Kuo C.-H.S., Pavlidis S., Loza M., Baribaud F., Rowe A., Pandis I., Hoda U., Rossios C., Sousa A., Wilson S.J., Howarth P., Dahlen B., Dahlen S.-E., Chanez P., Shaw D., Krug N., Sandstrӧm T., De Meulder B., Lefaudeux D., Fowler S., Fleming L., Corfield J., Auffray C., Sterk P.J., Djukanovic R., Guo Y., Adcock I.M., Chung K.F., A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-biopred, Am J of Respir Crit, 195, 4, pp. 443-455, (2016); van der Burg N., Stenberg H., Ekstedt S., Diamant Z., Bornesund D., Ankerst J., Kumlien Georen S., Cardell L.O., Bjermer L., Erjefalt J., Tufvesson E., Neutrophil phenotypes in bronchial airways differentiate single from dual responding allergic asthmatics, Clin. Exp. Allergy, (2022); Spijkerman R., Hesselink L., Bertinetto C., Bongers C.C., Hietbrink F., Vrisekoop N., Leenen L.P., Hopman M.T., Jansen J.J., Koenderman L., Analysis of human neutrophil phenotypes as biomarker to monitor exercise-induced immune changes, J. Leukoc. Biol., 109, 4, pp. 833-842, (2021); Leckie M.J., Jenkins G.R., Khan J., Smith S.J., Walker C., Barnes P.J., Hansel T.T., Sputum T lymphocytes in asthma, COPD and healthy subjects have the phenotype of activated intraepithelial T cells (CD69+ CD103+), Thorax, 58, 1, (2003); Kim M.H., Kim T.B., Implication of cluster analysis in childhood asthma, Allergy Asthma Immunol Res, 13, 1, pp. 1-4, (2021)","N. van der Burg; Department of Clinical Sciences Lund, Respiratory Medicine and Allergology, Lund University, Lund, Sweden; email: nicole.van_der_burg@med.lu.se","","W.B. Saunders Ltd","","","","","","09546111","","RMEDE","36924848","English","Respir. Med.","Article","Final","","Scopus","2-s2.0-85151425170"
"Wang Y.; Huang Y.; Yeo Y.H.; Pang S.; Ramai D.; Zheng T.; Wang Y.; Yan Y.; DeVault K.R.; Francis D.; Antwi S.O.; Pang M.","Wang, Yichen (57220064020); Huang, Yuting (57214324745); Yeo, Yee Hui (57211772169); Pang, Songhan (58666128600); Ramai, Daryl (55319085600); Zheng, Ting (59586796000); Wang, Yiming (59550225700); Yan, Yan (57215280821); DeVault, Kenneth R. (7006660502); Francis, Dawn (23102672900); Antwi, Samuel O. (55805838700); Pang, Maoyin (57195397833)","57220064020; 57214324745; 57211772169; 58666128600; 55319085600; 59586796000; 59550225700; 57215280821; 7006660502; 23102672900; 55805838700; 57195397833","Eosinophilic Esophagitis-Related Food Impaction: Distinct Demographics, Interventions, and Promising Predictive Models","2025","Digestive Diseases and Sciences","70","2","","675","684","9","0","10.1007/s10620-024-08823-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217406309&doi=10.1007%2fs10620-024-08823-w&partnerID=40&md5=ffec31f7a461b63eab958c884af5d892","Division of Hospital Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States; Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States; Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, United States; University of Virginia College of Arts and Sciences, Charlottesville, VA, United States; Division of Gastroenterology and Hepatology, University of Utah, Salt Lake City, UT, United States; Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States; Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou, China; Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States","Wang Y., Division of Hospital Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States; Huang Y., Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States; Yeo Y.H., Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Pang S., University of Virginia College of Arts and Sciences, Charlottesville, VA, United States; Ramai D., Division of Gastroenterology and Hepatology, University of Utah, Salt Lake City, UT, United States; Zheng T., Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States; Wang Y., Department of Computing, Xi’an Jiaotong-Liverpool University, Suzhou, China; Yan Y., Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States; DeVault K.R., Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States; Francis D., Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States; Antwi S.O., Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, United States; Pang M., Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, United States","Background: Eosinophilic esophagitis (EoE) is an increasingly common cause of food impaction. Aims: This study aims to provide a nationwide analysis of food impaction in patients with or without EoE diagnosis, concentrating on patient demographics, interventions, outcomes, and development of predictive machine-learning models. Methods: A retrospective assessment was conducted using Nationwide Emergency Department Sample data from January 1, 2018, to December 31, 2019. We compared patients with food impaction with an associated EoE diagnosis to those without EoE and derived machine-learning models to predict EoE using International Classification of Diseases codes at discharge for identification. Results: Of 286,886,714 emergency department visits, 146,084 were for food impaction, with 7093 cases coinciding with an EoE diagnosis (4.9%). Patients with EoE were more commonly young men with fewer overall comorbidities but higher incidences of obesity, asthma, gastritis, and allergic rhinitis. A significantly larger proportion in the EoE group (89.6%) underwent esophagogastroduodenoscopy compared to the non-EoE group (51.1%; P < 0.001) and had a higher rate of biopsy during esophagogastroduodenoscopy in the emergency department (54.9% vs 13.4%; P < 0.001). Our machine-learning models, incorporating patient demographics, hospital attributes, and comorbidities, had a sensitivity of 86.1% and an area under the receiver operating characteristic curve of 0.828. Conclusions: This nationwide study demonstrates that EoE in food impaction is associated with specific patient demographics, comorbidities, and elevated interventions. Our machine-learning models hold promise as screening tools for EoE, aiding medical practitioners in determining the need for biopsy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.","Eosinophilic esophagitis; Food impaction; Machine learning; National database","Adolescent; Adult; Aged; Asthma; Emergency Service, Hospital; Endoscopy, Digestive System; Eosinophilic Esophagitis; Female; Food; Humans; Machine Learning; Male; Middle Aged; Obesity; Retrospective Studies; Rhinitis, Allergic; United States; Young Adult; adolescent; adult; aged; allergic rhinitis; asthma; complication; diagnosis; digestive tract endoscopy; eosinophilic esophagitis; epidemiology; female; food; hospital emergency service; human; machine learning; male; middle aged; obesity; retrospective study; United States; young adult","","","","","Mayo Clinic","The Scientific Publications staff at Mayo Clinic provided copyediting, proofreading, administrative, and clerical support.","Muir A., Falk G.W., Eosinophilic esophagitis: a review, JAMA, 326, pp. 1310-1318, (2021); O'Shea K.M., Aceves S.S., Dellon E.S., Gupta S.K., Spergel J.M., Furuta G.T., Et al., Pathophysiology of eosinophilic esophagitis, Gastroenterology, 154, pp. 333-345, (2018); Navarro P., Arias A., Arias-Gonzalez L., Laserna-Mendieta E.J., Ruiz-Ponce M., Lucendo A.J., Systematic review with meta-analysis: the growing incidence and prevalence of eosinophilic oesophagitis in children and adults in population-based studies, Aliment Pharmacol Ther, 49, pp. 1116-1125, (2019); Dellon E.S., Gibbs W.B., Fritchie K.J., Rubinas T.C., Wilson L.A., Woosley J.T., Et al., Clinical, endoscopic, and histologic findings distinguish eosinophilic esophagitis from gastroesophageal reflux disease, Clin Gastroenterol Hepatol, 7, pp. 1305-1313, (2009); Schupack D.A., Lenz C.J., Geno D.M., Tholen C.J., Leggett C.L., Katzka D.A., Et al., The evolution of treatment and complications of esophageal food impaction, United European Gastroenterol J, 7, pp. 548-556, (2019); Hiremath G.S., Hameed F., Pacheco A., Olive A., Davis C.M., Shulman R.J., Esophageal food impaction and eosinophilic esophagitis: a retrospective study, systematic review, and meta-analysis, Dig Dis Sci, 60, pp. 3181-3193, (2015); Overview of the Nationwide Emergency Department Sample (NEDS); Lam A.Y., Lee J.K., Coward S., Kaplan G.G., Dellon E.S., Bredenoord A.J., Et al., Epidemiologic burden and projections for eosinophilic esophagitis-associated emergency department visits in the United States: 2009–2030, Clin Gastroenterol Hepatol, (2023); Dellon E.S., Erichsen R., Baron J.A., Shaheen N.J., Vyberg M., Sorensen H.T., Et al., The increasing incidence and prevalence of eosinophilic oesophagitis outpaces changes in endoscopic and biopsy practice: national population-based estimates from Denmark, Aliment Pharmacol Ther, 41, pp. 662-670, (2015); Hahn J.W., Lee K., Shin J.I., Cho S.H., Turner S., Shin J.U., Et al., Global incidence and prevalence of eosinophilic esophagitis, 1976–2022: a systematic review and meta-analysis, Clin Gastroenterol Hepatol, (2023); Molina-Infante J., Gonzalez-Cordero P.L., Ferreira-Nossa H.C., Mata-Romero P., Lucendo A.J., Arias A., Rising incidence and prevalence of adult eosinophilic esophagitis in midwestern Spain (2007–2016), United Eur Gastroenterol J, 6, pp. 29-37, (2018); Syed A.A., Andrews C.N., Shaffer E., Urbanski S.J., Beck P., Storr M., The rising incidence of eosinophilic oesophagitis is associated with increasing biopsy rates: a population-based study, Aliment Pharmacol Ther, 36, pp. 950-958, (2012); Schoepfer A.M., Safroneeva E., Bussmann C., Kuchen T., Portmann S., Simon H.U., Et al., Delay in diagnosis of eosinophilic esophagitis increases risk for stricture formation in a time-dependent manner, Gastroenterology, 145, pp. e1231-e1232, (2013); Dellon E.S., Gonsalves N., Hirano I., Furuta G.T., Liacouras C.A., Katzka D.A., Et al., ACG clinical guideline: evidenced based approach to the diagnosis and management of esophageal eosinophilia and eosinophilic esophagitis (EoE), Am J Gastroenterol, 108, pp. 679-692, (2013); Ikenberry S.O., Jue T.L., Anderson M.A., Appalaneni V., Banerjee S., Et al., Management of ingested foreign bodies and food impactions, Gastrointest Endosc, 73, pp. 1085-1091, (2011); Yang S., Varghese P., Stephenson E., Tu K., Gronsbell J., Machine learning approaches for electronic health records phenotyping: a methodical review, J Am Med Inform Assoc, 30, pp. 367-381, (2023); Chubak J., Pocobelli G., Weiss N.S., Tradeoffs between accuracy measures for electronic health care data algorithms, J Clin Epidemiol, 65, (2012); Rybnicek D.A., Hathorn K.E., Pfaff E.R., Bulsiewicz W.J., Shaheen N.J., Dellon E.S., Administrative coding is specific, but not sensitive, for identifying eosinophilic esophagitis, Dis Esophagus, 27, pp. 703-708, (2014); Byrne K.R., Panagiotakis P.H., Hilden K., Thomas K.L., Peterson K.A., Fang J.C., Retrospective analysis of esophageal food impaction: differences in etiology by age and gender, Dig Dis Sci, 52, pp. 717-721, (2007); Kirchner G.I., Zuber-Jerger I., Endlicher E., Gelbmann C., Ott C., Ruemmele P., Et al., Causes of bolus impaction in the esophagus, Surg Endosc, 25, pp. 3170-3174, (2011); Sperry S.L., Crockett S.D., Miller C.B., Shaheen N.J., Dellon E.S., Esophageal foreign-body impactions: epidemiology, time trends, and the impact of the increasing prevalence of eosinophilic esophagitis, Gastrointest Endosc, 74, pp. 985-991, (2011)","M. Pang; Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, 4500 San Pablo Rd, United States; email: pang.maoyin@mayo.edu","","Springer","","","","","","01632116","","DDSCD","39779592","English","Dig. Dis. Sci.","Article","Final","","Scopus","2-s2.0-85217406309"
"Diab M.S.; Rodriguez-Villegas E.","Diab, Maha S. (57193699848); Rodriguez-Villegas, Esther (25031633700)","57193699848; 25031633700","Feature evaluation of accelerometry signals for cough detection","2024","Frontiers in Digital Health","6","","1368574","","","","1","10.3389/fdgth.2024.1368574","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189613379&doi=10.3389%2ffdgth.2024.1368574&partnerID=40&md5=5a71f4317c4a97225b0e7702e76375dd","Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom","Diab M.S., Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom; Rodriguez-Villegas E., Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom","Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and non-cough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The best-performing model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector. 2024 Diab and Rodriguez-Villegas.","accelerometer; cough; cough detection; feature evaluation; feature extraction; respiratory diseases; time-domain features; wearables","accelerometry; accuracy; adult; Article; asthma; chronic obstructive lung disease; clinical article; controlled study; correlation analysis; cough recording; coughing; cross validation; decision tree; deep learning; diagnostic test accuracy study; entropy; false discovery rate; female; human; kurtosis; learning algorithm; leave one out cross validation; machine learning; male; predictive value; principal component analysis; probability; random forest; recording; root mean squared error; sensitivity and specificity","","","","","European Research Council, ERC; Horizon 2020 Framework Programme, H2020, (724334); Engineering and Physical Sciences Research Council, EPSRC, (EP/P009794/1)","Funding text 1: This work was supported in part by the European Research Council (ERC) for the NOSUDEP project grant no 724334 and in part by the Engineering and Physical Sciences Research Council (EPSRC), UK/grant agreement no. EP/P009794/1. ; Funding text 2: The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported in part by the European Research Council (ERC) for the NOSUDEP project grant no 724334 and in part by the Engineering and Physical Sciences Research Council (EPSRC), UK/grant agreement no. EP/P009794/1.","Pramono R.X.A., Bowyer S., Rodriguez-Villegas E., Automatic adventitious respiratory sound analysis: a systematic review, PLoS One, 12, (2017); Rocha B.M., Mendes L., Couceiro R., Henriques J., Carvalho P., Paiva R.P., pp. 2761-2764, (2017); Pramono R.X.A., Imtiaz S.A., Rodriguez-Villegas E., A cough-based algorithm for automatic diagnosis of pertussis, PLoS One, 11, pp. 1-20, (2016); Pahar M., Klopper M., Reeve B., Warren R., Theron G., Niesler T., Automatic cough classification for tuberculosis screening in a real-world environment, Physiol Meas, 42, (2021); Chen X., Hu M., Zhai G.; Monge-Alvarez J., Hoyos-Barcelo C., San-Jose-Revuelta L.M., Casaseca-de-la Higuera P., A machine hearing system for robust cough detection based on a high-level representation of band-specific audio features, IEEE Trans Biomed Eng, 66, pp. 2319-2330, (2018); Sharan R.V., Abeyratne U.R., Swarnkar V.R., Porter P., Automatic croup diagnosis using cough sound recognition, IEEE Trans Biomed Eng, 66, pp. 485-495, (2018); Hoyos-Barcelo C., Monge-Alvarez J., Shakir M.Z., Alcaraz-Calero J.M., Casaseca-de La-Higuera P., Efficient k-NN implementation for real-time detection of cough events in smartphones, IEEE J Biomed Health Inform, 22, pp. 1662-1671, (2017); Monge-Alvarez J., Hoyos-Barcelo C., Lesso P., Casaseca-De-La-Higuera P., Robust detection of audio-cough events using local hu moments, IEEE J Biomed Health Inform, 23, pp. 184-196, (2019); Monge-Alvarez J., Hoyos-Barcelo C., Dahal K., Casaseca-de-la Higuera P., Audio-cough event detection based on moment theory, Appl Acoust, 135, pp. 124-135, (2018); Vhaduri S.; Rahman M.J., Nemati E., Rahman M., Vatanparvar K., Nathan V., Kuang J.; Mouawad P., Dubnov T., Dubnov S., Robust detection of COVID-19 in cough sounds: using recurrence dynamics and variable Markov model, SN Comput Sci, 2, (2021); Amrulloh Y., Abeyratne U., Swarnkar V., Triasih R.; Xu X., Nemati E., Vatanparvar K., Nathan V., Ahmed T., Rahman M.M., Listen2cough: leveraging end-to-end deep learning cough detection model to enhance lung health assessment using passively sensed audio, Proc ACM Interact Mob Wear Ubiquit Technol, 5, pp. 1-22, (2021); Lee G.T., Nam H., Kim S.H., Choi S.M., Kim Y., Park Y.H., Deep learning based cough detection camera using enhanced features, Expert Syst Appl, 206, (2022); Imran A., Posokhova I., Qureshi H.N., Masood U., Riaz M.S., Ali K., AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app, Inform Med Unlock, 20, (2020); Wei W., Wang J., Ma J., Cheng N., Xiao J.; Brown C., Chauhan J., Grammenos A., Han J., Hasthanasombat A., Spathis D.; Chowdhury M.E., Ibtehaz N., Rahman T., Mekki Y.M.S., Qibalwey Y., Mahmud S.; Morice A., Fontana G., Belvisi M., Birring S., Chung K., Dicpinigaitis P.V., ERS guidelines on the assessment of cough, Eur Respir J, 29, pp. 1256-1276, (2007); Lee K.K., Davenport P.W., Smith J.A., Irwin R.S., McGarvey L., Mazzone S.B., Global physiology and pathophysiology of cough: part 1: cough phenomenology—CHEST guideline and expert panel report, Chest, 159, pp. 282-293, (2021); Mohammadi H., Samadani A.A., Steele C., Chau T., Automatic discrimination between cough and non-cough accelerometry signal artefacts, Biomed Signal Process Control, 52, pp. 394-402, (2019); Doddabasappla K., Vyas R., Statistical and machine learning-based recognition of coughing events using triaxial accelerometer sensor data from multiple wearable points, IEEE Sens Lett, 5, pp. 1-4, (2021); Doddabasappla K., Vyas R., Spectral summation with machine learning analysis of tri-axial acceleration from multiple wearable points on human body for better cough detection, IEEE Sens Lett, 5, pp. 1-4, (2021); Vyas R., Doddabasappla K., FFT spectrum spread with machine learning (ML) analysis of triaxial acceleration from shirt pocket and torso for sensing coughs while walking, IEEE Sens Lett, 6, pp. 1-4, (2021); Otoshi T., Nagano T., Izumi S., Hazama D., Katsurada N., Yamamoto M., A novel automatic cough frequency monitoring system combining a triaxial accelerometer and a stretchable strain sensor, Sci Rep, 11, (2021); Liu J., Chen J., Jiang H., Jia W., Lin Q., Wang Z.; Diab M.S., Rodriguez-Villegas E.; Diab M.S., Rodriguez-Villegas E.","M.S. Diab; Wearable Technologies Lab, Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom; email: m.diab21@imperial.ac.uk","","Frontiers Media SA","","","","","","2673253X","","","","English","Front. Digit. Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85189613379"
"Atzeni M.; Cappon G.; Quint J.K.; Kelly F.; Barratt B.; Vettoretti M.","Atzeni, M. (58683196600); Cappon, G. (57189354010); Quint, J.K. (16507541000); Kelly, F. (7102252167); Barratt, B. (15831140000); Vettoretti, M. (57039045300)","58683196600; 57189354010; 16507541000; 7102252167; 15831140000; 57039045300","A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data","2025","Scientific Reports","15","1","2385","","","","0","10.1038/s41598-024-85089-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216439653&doi=10.1038%2fs41598-024-85089-2&partnerID=40&md5=81dc4f3b4a2b4b6de319590a94553865","Department of Information Engineering, University of Padova, Padova, Italy; Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, United Kingdom; School of Public Health, Imperial College London, London, United Kingdom","Atzeni M., Department of Information Engineering, University of Padova, Padova, Italy; Cappon G., Department of Information Engineering, University of Padova, Padova, Italy; Quint J.K., School of Public Health, Imperial College London, London, United Kingdom; Kelly F., Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, United Kingdom; Barratt B., Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, London, United Kingdom; Vettoretti M., Department of Information Engineering, University of Padova, Padova, Italy","Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. acute worsening of respiratory symptoms, may increase in frequency, leading to potentially life-threatening complications. Exposure to air pollutants may trigger COPD exacerbations. Literature predictive models for COPD exacerbations, while promising, may be constrained by their reliance on fixed air quality sensor data that may not fully capture individuals’ dynamic exposure to air pollution. To address this, we designed a machine learning (ML) framework that leverages data from personal air quality monitors, health records, lifestyle, and living condition information to build models that perform short-term prediction of COPD exacerbations. The framework employs (i) k-means clustering to uncover potentially distinct patient sub-types, (ii) supervised ML techniques (Logistic Regression, Random Forest, and eXtreme Gradient Boosting) to train and test predictive models for each patient sub-type and (iii) an explainable artificial intelligence technique (SHAP) to interpret the final models. The framework was tested on data collected in 101 COPD patients monitored for up to 6 months with occurrence of exacerbation in 10.7% of total samples. Two different patient sub-types have been identified, characterised by different disease severity. The best performing models were Random Forest in cluster 1, with area under the receiver operating characteristic curve (AUC) of 0.90, and area under the precision/recall curve (AUPRC) of 0.7; and Random Forest model in cluster 2, with AUC of 0.82 and AUPRC of 0.56. The model interpretability analysis identified previous symptoms and cumulative pollutant exposure as key predictors of exacerbations. The results of our study set a premise for a predictive framework in COPD exacerbations, particularly investigating the potential influence of environmental features. The SHAP analysis revealed that the contribution of environmental features is not uniform across all subjects. For instance, cumulative exposure to pollutants demonstrated greater predictive power in cluster 1. The SHAP analysis also shown that overall clinical factors and individual symptomatology play the most significant role in this setup to determine exacerbation risk. © The Author(s) 2025.","","Aged; Air Pollutants; Air Pollution; Disease Progression; Environmental Monitoring; Female; Humans; Life Style; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; adverse event; aged; air pollutant; air pollution; chronic obstructive lung disease; diagnosis; disease exacerbation; environmental monitoring; female; human; lifestyle; machine learning; male; middle aged; procedures","","Air Pollutants, ","","","Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (DM 1061)","M.A. acknowledges the support of MUR (Italian Ministry of University and Research) under the PON initiative ex DM 1061. ","Quaderi S., Hurst J., The unmet global burden of COPD, Glob. Health Epidemiol. Genom., 3, (2018); Barnes P., Chronic obstructive pulmonary disease, N. Engl. J. Med, 343, pp. 269-280, (2000); Pauwels R., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med, 163, pp. 1256-1276, (2001); Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med, 152, pp. S77-S121, (1995); Suissa S., Dell'Aniello S., Ernst P., Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality, Thorax, 67, pp. 957-963, (2012); Sutherland E., Cherniack R., Management of chronic obstructive pulmonary disease, N. Engl. J. Med, 350, pp. 2689-2697, (2004); Choi J., Et al., Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: particulate matter is hazardous, Int. J. Chron. Obstruct. Pulmon. Dis, 13, pp. 1053-1059, (2018); Evangelopoulos D., Et al., Personal exposure to air pollution and respiratory health of COPD patients in London, Eur. Respir. J, (2021); Foster W., Brown R., Macri K., Mitchell C., Bronchial reactivity of healthy subjects: 18–20 h postexposure to ozone, J. Appl. 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R package version 2, pp. 2-11, (2023); Jaccard P., Distribution de la florine alpine dans la bassin de dranses et dans quelques regiones voisines, Bull. Soc. Vaud. Sci. Nat, 37, pp. 241-272, (1901); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., Smote: synthetic minority over-sampling technique, J. Artif. Intell. Res, 16, pp. 321-357, (2002); Lundberg S., Lee S.-I., A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst, 30, pp. 1-10, (2017)","M. Vettoretti; Department of Information Engineering, University of Padova, Padova, Italy; email: martina.vettoretti@unipd.it","","Nature Research","","","","","","20452322","","","39827228","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216439653"
"Lu W.; Tong Y.; Zhao X.; Feng Y.; Zhong Y.; Fang Z.; Chen C.; Huang K.; Si Y.; Zou J.","Lu, Wei (58082156600); Tong, Yulan (58081981700); Zhao, Xiuxiu (58257391500); Feng, Yue (58367369700); Zhong, Yi (58655758900); Fang, Zhaojing (57950219200); Chen, Chen (57224917651); Huang, Kaizong (56571846100); Si, Yanna (55614799500); Zou, Jianjun (25642016600)","58082156600; 58081981700; 58257391500; 58367369700; 58655758900; 57950219200; 57224917651; 56571846100; 55614799500; 25642016600","Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool","2024","Postgraduate Medicine","136","1","","84","94","10","1","10.1080/00325481.2024.2313448","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184400273&doi=10.1080%2f00325481.2024.2313448&partnerID=40&md5=998060dc647297725e32fa9712b5f9a4","School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China; Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China","Lu W., School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China, Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Tong Y., School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China, Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Zhao X., Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Feng Y., Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Zhong Y., Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Fang Z., Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Chen C., Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China, Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China; Huang K., Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China, Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China; Si Y., Department of Anesthesiology, Periodic and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, China; Zou J., Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China, Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China","Objectives: Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. Methods: In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. Results: We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. Conclusion: Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently. © 2024 Informa UK Limited, trading as Taylor & Francis Group.","anesthesia; colonoscopy; Hypoxemia; machine learning; outpatient; prediction","Anesthesia; Colonoscopy; Humans; Hypoxia; Machine Learning; Outpatients; Sleep Apnea, Obstructive; etomidate; hemoglobin; propofol; remifentanil; accuracy; adult; aged; allergy; American Society of Anaesthesiologists score; anesthesia; area under the curve; Article; asthma; atrial fibrillation; blood pressure; body mass; bradycardia; brain infarction; chronic obstructive lung disease; classifier; colonoscopy; comorbidity; controlled study; coronary atherosclerosis; demographics; diabetes mellitus; drinking; electrocardiogram; electronic health record; feature selection; female; flow rate; gastrointestinal disease; heart block; height; high flow nasal cannula therapy; human; hypertension; hypoxemia; laboratory test; learning; least absolute shrinkage and selection operator; logistic regression analysis; machine learning; major clinical study; medical history; neck circumference; obstructive sleep apnea; outcome assessment; outpatient; overall response rate; oxygen saturation; peripheral lung lesion; prediction; predictive value; pregnancy; pulse oximetry; random forest; receiver operating characteristic; risk; risk factor; sedation; sensitivity and specificity; smoking; snoring; support vector machine; training; upper respiratory tract infection; anesthesia; colonoscopy; hypoxia; machine learning; sleep apnea syndromes","","etomidate, 15301-65-2, 33125-97-2, 51919-80-3; hemoglobin, 9008-02-0; propofol, 2078-54-8; remifentanil, 132539-07-2, 132875-61-7","XGBoost","","Jiangsu Pharmaceutical Association, (A2021024, H202108, JY202207, Q202202); Jiangsu Pharmaceutical Association; National Natural Science Foundation of China, NSFC, (81873954, 82173899); National Natural Science Foundation of China, NSFC; Six Talent Peaks Project in Jiangsu Province, (WSW-106); Six Talent Peaks Project in Jiangsu Province; Nanjing Medical Science and Technique Development Foundation, (ZKX22030); Nanjing Medical Science and Technique Development Foundation","This work was supported by National Natural Science Foundation of China [81873954, 82173899], the Six Talent Peaks Project of Jiangsu [WSW-106], Nanjing Medical Science and Technical Development Foundation [ZKX22030] and Jiangsu Pharmaceutical Association [H202108, A2021024, Q202202, JY202207]. The authors thank all the participants and their families.","Hazewinkel Y., Dekker E., Colonoscopy: basic principles and novel techniques, Nat Rev Gastroenterol Hepatol, 8, 10, pp. 554-564, (2011); Qadeer M.A., Lopez A.R., Dumot J.A., Et al., Hypoxemia during moderate sedation for gastrointestinal endoscopy: causes and associations, Digestion, 84, 1, pp. 37-45, (2011); Patterson K.W., Noonan N., Keeling N.W., Hypoxemia during outpatient gastrointestinal endoscopy: the effects of sedation and supplemental oxygen, J Clin Anesth, 7, 2, pp. 136-140, (1995); Hypoxemia in the ICU: prevalence, treatment, and outcome, Ann Intensive Care, 8, 1, (2018); Johnston S., McKenna A., Tham T., Silent myocardial ischaemia during endoscopic retrograde cholangiopancreatography, Endoscopy, 35, 12, pp. 1039-1042, (2003); Vargo J.J., Holub J.L., Faigel D.O., Et al., Risk factors for cardiopulmonary events during propofol-mediated upper endoscopy and colonoscopy, Aliment Pharmacol Ther, 24, 6, pp. 955-963, (2006); Froehlich F., Thorens J., Schwizer W., Et al., Sedation and analgesia for colonoscopy: patient tolerance, pain, and cardiorespiratory parameters, Gastrointest Endosc, 45, 1, pp. 1-9, (1997); Deng L., Cl L., Ge S.J., Et al., STOP questionnaire to screen for hypoxemia in deep sedation for young and middle-aged colonoscopy, Dig Endosc, 24, 4, pp. 255-258, (2012); Beitz A., Riphaus A., Meining A., Et al., Capnographic monitoring reduces the incidence of arterial oxygen desaturation and hypoxemia during propofol sedation for colonoscopy: a randomized, controlled study (ColoCap study), J Am Coll Gastroenterol, 107, 8, (2012); Froehlich F., Harris J.K., Wietlisbach V., Et al., Current sedation and monitoring practice for colonoscopy: an international observational study (EPAGE), Endoscopy, 38, 5, pp. 461-469, (2006); Wang D., Chen C., Chen J., Et al., The use of propofol as a sedative agent in gastrointestinal endoscopy: a meta-analysis, PLoS One, 8, 1, (2013); Lin Y., Zhang X., Li L., Et al., High-flow nasal cannula oxygen therapy and hypoxia during gastroscopy with propofol sedation: a randomized multicenter clinical trial, Gastrointest Endosc, 90, 4, pp. 591-601, (2019); Geng W., Tang H., Sharma A., Et al., An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy, J Int Med Res, 47, 5, pp. 2097-2103, (2019); Zhang Z., Zhao Y., Canes A., Et al., Predictive analytics with gradient boosting in clinical medicine, Ann Transl Med, 7, 7, (2019); Luo J.-C., Zhao Q.-Y., Tu G.-W., Clinical prediction models in the precision medicine era: old and new algorithms, Ann Transl Med, 8, 6, (2020); Suarez-Ibarrola R., Hein S., Reis G., Et al., Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer, World J Urol, 38, 10, pp. 2329-2347, (2020); Qadeer M.A., Rocio Lopez A., Dumot J.A., Et al., Risk factors for hypoxemia during ambulatory gastrointestinal endoscopy in ASA I–II patients, Dig Dis Sci, 54, 5, pp. 1035-1040, (2009); Laffin A.E., Kendale S.M., Huncke T.K., Severity and duration of hypoxemia during outpatient endoscopy in obese patients: a retrospective cohort study, Can J Anaesth, 67, pp. 1182-1189, (2020); Patel V.A., Romain P.S., Sanchez J., Et al., Obstructive sleep apnea increases the risk of cardiopulmonary adverse events associated with ambulatory colonoscopy independent of body mass index, Dig Dis Sci, 62, 10, pp. 2834-2839, (2017); Mehta P.P., Kochhar G., Kalra S., Et al., Can a validated sleep apnea scoring system predict cardiopulmonary events using propofol sedation for routine EGD or colonoscopy? 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Zou; Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China; email: zoujianjun100@126.com; Y. Si; Department of Anesthesiology, Perioperative and Pain Medicine (APPM), Nanjing First Hospital, Nanjing Medical University, Nanjing, No. 68 Changle road, Qinhuai district, 210006, China; email: siyanna@163.com","","Taylor and Francis Ltd.","","","","","","00325481","","POMDA","38314753","English","Postgrad. Med.","Article","Final","","Scopus","2-s2.0-85184400273"
"Sun W.; Wu G.; Ming M.; Zhang J.; Shi C.; Qin L.","Sun, Wenchao (59557950500); Wu, Gang (56531058500); Ming, Ming (59666131700); Zhang, Jiameng (59666319800); Shi, Chun (55263339600); Qin, Linlin (36818566400)","59557950500; 56531058500; 59666131700; 59666319800; 55263339600; 36818566400","Self-supervised learning for intelligent disease diagnosis using audio signals: beyond copd to a spectrum of diseases","2025","Applied Intelligence","55","6","487","","","","0","10.1007/s10489-024-06028-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219614007&doi=10.1007%2fs10489-024-06028-2&partnerID=40&md5=9c6cbee7b58e8725509a933a98f4b6d7","Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China; Hangzhou slan-health Co, Ltd, Xi Xing, Zhejiang, Hangzhou, 310051, China","Sun W., Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China; Wu G., Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China; Ming M., Hangzhou slan-health Co, Ltd, Xi Xing, Zhejiang, Hangzhou, 310051, China; Zhang J., Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China; Shi C., Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China; Qin L., Department of Automation, University of Science and Technology of China, Huangshan Road, Anhui, Hefei, 230022, China","Given the widespread prevalence and significant patient base of COPD (Chronic Obstructive Pulmonary Disease), the development of simple and rapid diagnostic methods has emerged as a key research focus. Through pathological studies, the medical community has identified the potential of cough sounds for diagnosing COPD, sparking interest in leveraging deep learning to analyze various disease-related sounds, including those associated with COVID-19 and cardiac conditions, etc. Yet, research specifically targeting COPD remains scarce, primarily due to two challenges: traditional models trained on small medical datasets often fall short of expectations due to stringent data privacy and collection requirements in healthcare; and the scarcity of publicly accessible COPD datasets, particularly those that could obviate the need for medical equipment. Addressing these challenges, our paper introduces a novel dataset of smartphone-recorded cough sounds, termed the CC (COPD-Cough) dataset. It comprises 221 recordings from COPD patients and 632 from healthy individuals, marking the first dataset explicitly curated for COPD cough sound analysis. The dataset, endorsed by clinical professionals and collected independently of medical devices, promises to propel advancements in straightforward COPD diagnostics. Furthermore, we propose a self-supervised learning model enhanced by unique data augmentation techniques and an efficient sound feature extractor, demonstrating superior performance across three distinct disease datasets and achieving state-of-the-art results. Comprehensive ablation studies affirm our model’s efficacy, while sensitivity analyses optimize its applicability to various tasks. For further engagement, the framework’s source code and dataset are available at https://github.com/auto-chao/COPD_Diagnosis and https://zenodo.org/records/10209837, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.","Axial Multi-Head Self-Attention Mechanism; COPD; Diagnosis; Self-Supervised Learning; Sound","Arthroplasty; Clinical research; COVID-19; Diagnosis; Attention mechanisms; Audio signal; Axial multi-head self-attention mechanism; Chronic obstructive pulmonary disease; Cough sounds; Diagnostic methods; Disease diagnosis; Research focus; Simple++; Spectra's; Pulmonary diseases","","","","","University of Science and Technology of China, USTC","This research was also supported by the advanced computing resources provided by the Supercomputing Center of the USTC. Thanks to Hangzhou slan-health Co, Ltd for their support in data access. ","Christenson S.A., Smith B.M., Bafadhel M., Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Safiri S., Carson-Chahhoud K., Noori M., Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990–2019: results from the global burden of disease study 2019, BMJ, 378, (2022); Wang C., Xu J., Yang L., Prevalence and risk factors of chronic obstructive pulmonary disease in china (the china pulmonary health cph study): a national cross-sectional study, Lancet, 391, (2018); Zhou M., Wang H., Zeng X., Mortality, morbidity, and risk factors in china and its provinces, 1990–2017: a systematic analysis for the global burden of disease study 2017, Lancet, 394, pp. 1145-1158, (2019); Liu M., Yin D., Wang Y., Comparing the performance of two screening questionnaires for chronic obstructive pulmonary disease in the chinese general population, Int J Chron Obstruct Pulmon Dis, 18, pp. 541-552, (2023); Ambrosino N., Bertella E., Lifestyle interventions in prevention and comprehensive management of copd, Breathe, 14, pp. 186-194, (2018); Wu C.-T., Li G.-H., Huang C.-T., Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: Development and cohort study, JMIR Mhealth Uhealth, 9, (2018); Chen A., Zhang J., Zhao L., Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors, Biosens. Bioelectron, 173, (2021); Davies H.J., Bachtiger P., Williams I., Wearable in-ear ppg: Detailed respiratory variations enable classification of copd, IEEE Trans Biomed Eng, 173, (2021); Xu X., Nemati E., Vatanparvar K., Listen2cough: Leveraging end-to-end deep learning cough detection model to enhance lung health assessment using passively sensed audio, Proc. ACM Interact Mob Wearable Ubiquitous Technol, 5, (2021); Rocha B.M., Filos D., Mendes L (2018) A Respiratory Sound Database for the Development of Automated Classification, (2018); Teresa Garcia-Ordas M., Alberto Benitez-Andrades J., Garcia-Rodriguez I., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, (2020); Shuvo S.B., Ali S.N., Swapnil S.I., A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram, IEEE JBHI, 25, pp. 2595-2603, (2021); Kuluozturk M., Kobat M.A., Barua P.D., Dkpnet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis, Med Eng Phys, 110, (2022); Dar J.A., Srivastava K.K., Ahmed Lone S., Design and development of hybrid optimization enabled deep learning model for covid-19 detection with comparative analysis with dcnn, biat-gru, xgboost, Comput Biol Med, 150, (2022); Laguarta J., Hueto F., Subirana B., Covid-19 artificial intelligence diagnosis using only cough recordings, IEEE Open J Eng Med Biol, 1, (2020); Alkhodari M., Fraiwan L., Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings, Comput Methods Programs Biomed, 200, (2021); Jamil S., Roy A.M., An efficient and robust phonocardiography (pcg)-based valvular heart diseases (vhd) detection framework using vision transformer (vit), Comput Biol Med, 158, (2023); Krishnan P.T., Balasubramanian P., Umapathy S., Automated heart sound classification system from unsegmented phonocardiogram (pcg) using deep neural network, Phys Eng Sci Med, 43, (2020); Pahar M., Klopper M., Warren R., Covid-19 cough classification using machine learning and global smartphone recordings, Comput Biol Med, 135, (2021); Chen X., He K (2021) Exploring Simple Siamese Representation Learning, (2021); Park D.S., Chan W., Zhang Y (2019) SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition, (2019); Sharma N., Krishnan P., Kumar R (2020) Coswara - A database of breathing, cough, and voice sounds for COVID-19 diagnosis, (2020); Liu C., Springer D., Li Q., An open access database for the evaluation of heart sound algorithms, Physiol Meas, 37, pp. 2181-2213, (2016); Fang H., Xie P., An end-to-end contrastive self-supervised learning framework for language understanding, TACL, 10, pp. 1324-1340, (2022); Chen T., Kornblith S., Norouzi M., A simple framework for contrastive learning of visual representations, (2020); Chen X., Xie S., He K., An Empirical Study of Training Self-Supervised Vision Transformers, (2021); Zhang H., Li F., Liu S., DINO: DETR with Improved Denoising Anchor Boxes for End-To-End Object Detection, (2022); Gao T., Yao X., Chen D (2021) SimCSE: Simple Contrastive Learning of Sentence Embeddings, (2021)","L. Qin; Department of Automation, University of Science and Technology of China, Hefei, Huangshan Road, Anhui, 230022, China; email: qinll@ustc.edu.cn","","Springer","","","","","","0924669X","","APITE","","English","Appl Intell","Article","Final","","Scopus","2-s2.0-85219614007"
"Salari M.; Sadati S.M.; Sedaghat A.; Abbasi B.; Zamanpour S.A.; Khodashahi R.; Davoudi M.","Salari, Maryam (57130425900); Sadati, Seyed Masoud (57218701577); Sedaghat, Alireza (56165530700); Abbasi, Bita (35787013900); Zamanpour, Seyed Amir (57222318045); Khodashahi, Rozita (57208403428); Davoudi, Mostafa (59546437500)","57130425900; 57218701577; 56165530700; 35787013900; 57222318045; 57208403428; 59546437500","Evaluating the Application of Machine Learning in Predicting the Mortality of Hospitalized COVID-19 Patients Using the Confusion Matrix and the Matthews Correlation Coefficient","2025","Archives of Clinical Infectious Diseases","20","2","e150150","","","","0","10.5812/archcid-150150","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217153919&doi=10.5812%2farchcid-150150&partnerID=40&md5=ed1d0b99a2e03d6a981914dd36cb73f9","Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran; Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran; Lung Disease Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Mashhad University of Medical Sciences, Mashhad, Iran; Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Transplant Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran","Salari M., Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran; Sadati S.M., Center of Statistics and Information Technology Management, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran; Sedaghat A., Lung Disease Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Abbasi B., Mashhad University of Medical Sciences, Mashhad, Iran; Zamanpour S.A., Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Khodashahi R., Transplant Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Davoudi M., Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran","Background: The COVID-19 pandemic, which occurred between 2019 and 2023, posed a significant threat to global health. Its high transmissibility, the emergence of new variants, and the novel nature of the disease made treatment and control highly challenging. Objectives: This study aimed to develop an algorithm for predicting the mortality of hospitalized COVID-19 patients using machine learning methods. Methods: This cross-sectional study was conducted on 581 hospitalized COVID-19 patients. The approach integrated multi-model features derived from computed tomography (CT) scans and electronic health record (EHR) data. High-resolution computed tomography (HRCT) images were initially processed using the Pulmonary Toolkit package in MATLAB software. Subsequently, the extracted variables were entered into the model as predictive factors, alongside demographic characteristics, underlying conditions, and laboratory results of the patients. The machine learning model was developed using the AdaBoost method by incorporating demographic and laboratory data with HRCT features. Results: In this study, 581 hospitalized COVID-19 patients were included. Among them, 199 (34.25%) patients died, while 382 (65.75%) recovered. According to the machine learning algorithm, the most effective variables for predicting COVID-19 mortality were lymphocyte variables, CRP, age, mean lung density, lung tissue percentage, RBC count, D-dimer levels, and emphysema. The MCC Index in this study was 0.73, and the area under the ROC curve was 0.96. Conclusions: According to our results, the three variables with the greatest impact on predicting mortality in COVID-19 patients were related to HRCT findings, laboratory results, and patient age. Therefore, it is recommended that, given the high cost of HRCT, this diagnostic test should only be performed if other risk factors are identified in laboratory results. If necessary, HRCT should be conducted promptly. © 2025, Salari et al.","Adaptive Boosting; COVID-19; HRCT; Laboratory Tests; Machine Learning","C reactive protein; D dimer; ferritin; aged; algorithm; area under the curve; Article; artificial intelligence; asthma; chronic kidney failure; clinical outcome; computer assisted tomography; coronavirus disease 2019; correlation coefficient; deep learning; diabetes mellitus; diagnostic accuracy; diagnostic test accuracy study; digital imaging and communications in medicine; emphysema; female; heart disease; hospitalization; human; machine learning; major clinical study; male; matthews correlation coefficient; measurement accuracy; measurement precision; mortality; nausea; predictive value; qualitative analysis; receiver operating characteristic; risk factor; very elderly","","C reactive protein, 9007-41-4; ferritin, 9007-73-2","","","Mashhad University of Medical Sciences, MUMS","This study was supported by the Mashhad University of Medical Sciences.","Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H., Comparing machine learning algorithms for predicting COVID-19 mortality, BMC Med Inform Decis Mak, 22, 1, (2022); 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Chu DKW, Pan Y, Cheng SMS, Hui KPY, Krishnan P, Liu Y, Et al., Molecular Diagnosis of a Novel Coronavirus (2019-nCoV) Causing an Outbreak of Pneumonia, Clin Chem, 66, 4, pp. 549-555, (2020); Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, Et al., Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study, Lancet Infect Dis, 20, 4, pp. 425-434, (2020); Song F, Shi N, Shan F, Zhang Z, Shen J, Lu H, Et al., Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia, Radiology, 297, 3, (2020); Causey JL, Guan Y, Dong W, Walker K, Qualls JA, Prior F, Et al., Lung cancer screening with low-dose CT scans using a deep learning approach, (2019); Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A., Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks, Comput Biol Med, 121, (2020); Liu H, Setiono R., A probabilistic approach to feature selection-a filter solution, Proceedings of the Thirteenth International Conference on International Conference on Machine Learning, pp. 319-327, (1996); 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Gong M., A Novel Performance Measure for Machine Learning Classification, Int. J. Manag. Inf. Technol, 13, 1, pp. 11-19, (2021)","M. Davoudi; Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran; email: davoudim5@mums.ac.ir","","Brieflands","","","","","","23452641","","","","English","Arch. Clin. Infect. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85217153919"
"Salman R.; Alzaatreh A.; Al Bataineh M.T.","Salman, Reem (57221960984); Alzaatreh, Ayman (55235053500); Al Bataineh, Mohammad T. (57217172834)","57221960984; 55235053500; 57217172834","Feature selection of the respiratory microbiota associated with asthma","2023","Journal of Big Data","10","1","90","","","","1","10.1186/s40537-023-00767-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160953997&doi=10.1186%2fs40537-023-00767-8&partnerID=40&md5=026b8ace59db33f04a417334eb445ecf","Department of Mathematics and Statistics, American University of Sharjah, Sharjah, 26666, United Arab Emirates; Center for Biotechnology, Department of Molecular Biology and Genetics, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, 127788, United Arab Emirates","Salman R., Department of Mathematics and Statistics, American University of Sharjah, Sharjah, 26666, United Arab Emirates; Alzaatreh A., Department of Mathematics and Statistics, American University of Sharjah, Sharjah, 26666, United Arab Emirates; Al Bataineh M.T., Center for Biotechnology, Department of Molecular Biology and Genetics, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, 127788, United Arab Emirates","The expanding development of data mining and statistical learning techniques have enriched recent efforts to understand and identify metagenomics biomarkers in airways diseases. In contribution to the growing microbiota research in respiratory contexts, this study aims to characterize respiratory microbiota in asthmatic patients (pediatrics and adults) in comparison to healthy controls, to explore the potential of microbiota as a biomarker for asthma diagonosis and prediction. Analysis of 16 S-ribosomal RNA gene sequences reveals that respiratory microbial composition and diversity are significantly different between asthmatic and healthy subjects. Phylum Proteobacteria represented the predominant bacterial communities in asthmatic patients in comparison to healthy subjects. In contrast, a higher abundance of Moraxella and Alloiococcus was more prevalent in asthmatic patients compared to healthy controls. Using a machine learning approach, 57 microbial markers were identified and used to characterize notable microbiota composition differences between the groups. Among the selected OTUs, Moraxella and Corynebacterium genera were found to be more enriched on the pediatric asthmatics (p-values < 0.01). In the era of precision medicine, the discovery of the respiratory microbiota associated with asthma can lead to valuable applications for individualized asthma care. © 2023, The Author(s).","Asthma; Machine learning; Metagenomics; Respiratory microbiota","Biomarkers; Data mining; Diseases; Pediatrics; Asthma; Features selection; Healthy controls; Healthy subjects; Machine-learning; Metagenomics; Microbiotas; Moraxella; Respiratory microbiota; Statistical learning techniques; Machine learning","","","","","American University of Sharjah, AUS","The authors are grateful for the comments and suggestions by the referees and the Editor. Their comments and suggestions have greatly improved the paper. The authors are also gratefully acknowledge that the work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah. 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Alzaatreh; Department of Mathematics and Statistics, American University of Sharjah, Sharjah, 26666, United Arab Emirates; email: aalzaatreh@aus.edu","","Springer Science and Business Media Deutschland GmbH","","","","","","21961115","","","","English","J. Big Data","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85160953997"
"Zolfagharnasab M.H.; Damari S.; Soltani M.; Ng A.; Karbalaeipour H.; Haghdadi A.; Saghayan M.H.; Matinfar F.","Zolfagharnasab, Mohammad Hossein (57219126128); Damari, Siavash (58526057200); Soltani, Madjid (41862498900); Ng, Artie (15048570800); Karbalaeipour, Hengameh (59468131700); Haghdadi, Amin (59468453500); Saghayan, Masood Hamed (57302465600); Matinfar, Farzam (36143301900)","57219126128; 58526057200; 41862498900; 15048570800; 59468131700; 59468453500; 57302465600; 36143301900","A novel rule-based expert system for early diagnosis of bipolar and Major Depressive Disorder","2025","Smart Health","35","","100525","","","","0","10.1016/j.smhl.2024.100525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211717071&doi=10.1016%2fj.smhl.2024.100525&partnerID=40&md5=d4cad054e853efb0efef4d03d9d4d846","Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Centre for Sustainable Business, International Business University, Toronto, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Department of Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran","Zolfagharnasab M.H., Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran; Damari S., Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran; Soltani M., Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran, Centre for Sustainable Business, International Business University, Toronto, Canada, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada, Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, Canada, Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, Canada; Ng A., Centre for Sustainable Business, International Business University, Toronto, Canada; Karbalaeipour H., Department of Psychology, Science and Research Branch, Islamic Azad University, Tehran, Iran; Haghdadi A., Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran; Saghayan M.H., Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran; Matinfar F., Department of Statistics, Mathematics, and Computer Science, University of Allameh Tabataba'i, Tehran, Iran","A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and C# languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period. © 2024","Bipolar disorder; Clinical Decision Support; Computer science; Expert system; Healthcare Informatics; Rule-based Reasoning","Article; asthma; atherosclerotic plaque; behavior; bipolar disorder; bipolar mania; breast cancer; clinical examination; data base; decision making; decision tree; diabetes mellitus; early diagnosis; expert system; extraction; eye disease; fuzzy logic; geriatric disorder; geriatrics; heart disease; human; hypertension; information processing; kidney disease; knowledge; low back pain; machine learning; major depression; mental disease; mental health; micturition disorder; nephrolithiasis; ontology; Parkinson disease; questionnaire; reliability; retina disease; rule based expert system; satisfaction; semantic web; semantic web rule language; software; student; workflow","","","","","","","Abu Naser S., AlDahdooh R., Lower back pain expert system diagnosis and treatment, Journal of Multidisciplinary Engineering Science Studies (JMESS), 2, 4, (2016); 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Soltani; Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; email: msoltani@uwaterloo.ca","","Elsevier B.V.","","","","","","23526483","","","","English","Smart Health","Article","Final","","Scopus","2-s2.0-85211717071"
"Godoy Mayoral R.; Benavent Núñez M.; Cruz Ruiz J.; López Yepes G.; Parralejo Jiménez A.; Callejas González F.J.; Izquierdo Alonso J.L.","Godoy Mayoral, R. (6507961433); Benavent Núñez, M. (58568694500); Cruz Ruiz, J. (55071066400); López Yepes, G. (58568556200); Parralejo Jiménez, A. (58568556100); Callejas González, F.J. (55071066700); Izquierdo Alonso, J.L. (7102685483)","6507961433; 58568694500; 55071066400; 58568556200; 58568556100; 55071066700; 7102685483","Smokers and risk of hospital death by COVID calculated with SAVANA's natural language processing in the Castilla-La Mancha area; [Fumadores y riesgo de muerte hospitalaria por COVID calculado con el procesamiento de lenguaje natural de SAVANA en el ámbito de Castilla-La Mancha]","2024","Revista Clinica Espanola","224","1","","35","42","7","1","10.1016/j.rce.2023.11.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182572491&doi=10.1016%2fj.rce.2023.11.007&partnerID=40&md5=ef19d3c665aec79e5760be13dde9321f","Servicio de Neumología, Complejo Hospitalario Universitario de Albacete, Albacete, Spain; Hospital Operations Specialist, Medsavana SL, Madrid, Spain; Junior Data Engineer, Medsavana SL, Madrid, Spain; Team Lead de Hospitales, Medsavana SL, Madrid, Spain; Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain","Godoy Mayoral R., Servicio de Neumología, Complejo Hospitalario Universitario de Albacete, Albacete, Spain; Benavent Núñez M., Hospital Operations Specialist, Medsavana SL, Madrid, Spain; Cruz Ruiz J., Servicio de Neumología, Complejo Hospitalario Universitario de Albacete, Albacete, Spain; López Yepes G., Junior Data Engineer, Medsavana SL, Madrid, Spain; Parralejo Jiménez A., Team Lead de Hospitales, Medsavana SL, Madrid, Spain; Callejas González F.J., Servicio de Neumología, Complejo Hospitalario Universitario de Albacete, Albacete, Spain; Izquierdo Alonso J.L., Servicio de Neumología, Hospital Universitario de Guadalajara, Guadalajara, Spain","Introduction: During the COVID pandemic, it was speculated that patients with the virus who were smoking-related might have a lower likelihood of disease exacerbation or death. To assess whether there is an association between smoking and risk of in-hospital mortality, SAVANA's big data and natural language processing (NLP) technology is used. Method: A retrospective, observational, non-interventional cohort study was conducted based on real-life data extracted from medical records throughout Castilla-La Mancha using natural language processing and artificial intelligence techniques developed by SAVANA. The study covered the entire population of this region with Electronic Medical Records in SESCAM presenting with a diagnosis of COVID from March 1, 2020 to February 28, 2021. Results: Smokers had a significantly higher percentage of cardiovascular risk factors (hypertension, dyslipidemia and diabetes), COPD, asthma, IDP, IC, CVD, PTE, cancer in general and lung cancer in particular, bronchiectasis, heart failure and a history of pneumonia (P < .0001). Former smokers, current smokers and non-smokers have a significant age difference. As for in-hospital deaths, they were more frequent in the case of ex-smokers, followed by smokers and then non-smokers (P < .0001). Conclusion: There is an increased risk of dying in hospital in SARS-CoV-2-infected patients who are active smokers or have smoked in the past. © 2023 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI)","Big Data; COVID; Death; SARS-CoV-2; Tobacco","Artificial Intelligence; Cohort Studies; COVID-19; Hospitals; Humans; Natural Language Processing; Retrospective Studies; RNA, Viral; SARS-CoV-2; Smokers; virus RNA; age; Article; artificial intelligence; asthma; big data; bronchiectasis; cardiovascular risk factor; chronic obstructive lung disease; cohort analysis; controlled study; coronavirus disease 2019; diabetes mellitus; dyslipidemia; electronic medical record; ex-smoker; heart failure; hospital mortality; human; hypertension; lung cancer; malignant neoplasm; medical history; mortality risk; natural language processing; non-smoker; observational study; pneumonia; population research; retrospective study; smoking; coronavirus disease 2019; hospital; natural language processing; Severe acute respiratory syndrome coronavirus 2; smoking","","RNA, Viral, ","","","","","Wang C., Horby P.W., Hayden F.G., Gao G.F., A novel coronavirus outbreak of global health concern, Lancet., 395, pp. 470-473, (2020); Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., Et al., A novel coronavirus from patients with pneumonia in China, 2019, N Engl J Med., 382, pp. 727-733, (2020); Callaway E., Time to use the p-word? Coronavirus enter dangerous new phase, Nature., 579, (2020); Remuzzi A., Remuzzi G., COVID-19 and Italy: What next?, Lancet., 395, pp. 1225-1228, (2020); Perez-Bermejo M., Murillo-Llorente M.T., The fast territorial expansion of the COVID-19 in Spain, J Epidemiol., 30, (2020); Raurell-Torreda M., Martinez-Estalella G., FradeMera M.J., Carrasco Rodriguez-Rey L.F., Romero de San Pio E., Reflections arising from the COVID-19 pandemic [Reflexiones derivadas de la pandemia COVID-19], Enferm Intensiva (Engl Ed)., 31, pp. 90-93, (2020); Perez R.C., Alvarez S., Llanos L., Ares A.N., Viedma E.C., Diaz-Perez D., Recomendaciones de consenso SEPAR y AEER sobre el uso de la broncoscopia y la toma de muestras de la vía respiratoria en pacientes con sospecha o con infección confirmada por COVID-19, Arch Bronconeumol., 56, pp. 19-26, (2020); Farsalinos K., Barbouni A., Niaura R., Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: Could nicotine be a therapeutic option?, Intern Emerg Med., 15, pp. 845-852, (2020); Vardavas C.I., Nikitara K.T., COVID-19 and smoking: A systematic review of the evidence, Induc Dis., 18, (2020); Izquierdo J.L., Almonacid C., Gonzalez Y., del Rio-Bermudez C., Ancochea J., Cardenas R., Et al., The impact of COVID-19 on patients with asthma, Eur Respir J., 57, (2021); Espinosa L., Tello J., Pardo A., Medrano I., Urena A., Salcedo I., Et al., Savana: A Global Information Extraction and Terminology Expansion Framework in the Medical Domain, Procesamiento del Lenguaje Natural., 57, pp. 23-30, (2016); Hernandez Medrano ITG J., Belda C., Urena A., Salcedo I., Espinosa-Anke L., Saggion H., Savana: Re-using electronic health records with artificial intelligence, International Journal of Interactive Multimedia and Artificial Intelligence., 4, pp. 8-12, (2017); Izquierdo J.L., Rodriguez J.M., Almonacid C., Benavent M., Arroyo-Espliguero R., Agusti A., Real-life burden of hospitalisations due to COPD exacerbations in Spain, ERJ Open Res., 8, pp. 00141-02022, (2022); Izquierdo J.L., Almonacid C., Campos C., Morena D., Benavent M., Gonzalez-de-Olano D., Et al., Systemic corticosteroids in patients with bronchial asthma: A real-life study, J Investig Allergol Clin Immunol., (2021); Patanavanich R., Siripoon T., Amponnavarat S., Glantz S.A., Active smokers are at higher risk of COVID-19 death: A systematic review and meta-analysis, Nicotine Tob Res., (2022); Soumagne T., Guillien A., Roche N., Annesi-Maesano I., Andujar P., Laurent L., Et al., In patients with mild-to-moderate COPD, tobacco smoking, and not COPD, is associated with a higher risk of cardiovascular comorbidity, Int J Chron Obstruct Pulmon Dis., 15, pp. 1545-1555, (2020); Dikalov S., Itani H., Richmond B., Vergeade A., Rahman S.M.J., Boutaud O., Et al., Tobacco smoking induces cardiovascular mitochondrial oxidative stress, promotes endothelial dysfunction, and enhances hypertension, Am J Physiol Heart Circ Physiol., 316, pp. H639-H646, (2019); Viswanath K., Herbst R.S., Land S.R., Leischow S.J., Shields P.G., Tobacco and cancer: an American Association for Cancer Research policy statement, Cancer Res., 70, pp. 3419-3430, (2010); Jimenez-Ruiz C.A., Lopez-Padilla D., Alonso-Arroyo A., Aleixandre-Benavent R., Solano-Reina S., de Granda-Orive J.I., COVID-19 y tabaquismo: revisión sistemática y metaanálisis de la evidencia [COVID-19 and smoking: A systematic review and meta-analysis of the evidence], Arch Bronconeumol., 57, pp. 21-34, (2021); Raines A.M., Tock J.L., McGrew S.J., Ennis C.R., Derania J., Jardak C.L., Et al., Correlates of death among SARS-CoV-2 positive veterans: The contribution of lifetime tobacco use, Addict Behav., 113, (2021); Navas Alcantara M.S., Montero Rivas L., Guisado Espartero M.E., Rubio-Rivas M., Ayuso Garcia B., Moreno Martinez F., Et al., Influence of smoking history on the evolution of hospitalized in COVID-19 positive patients: Results from the SEMI-COVID-19 registry, Med Clin (Barc)., 159, pp. 214-223, (2022); Heydari G., Arfaeinia H., COVID-19 and smoking: More severity and death — An experience from Iran, Lung India., 38, pp. S27-S30, (2021); Clift A.K., von Ende A., Tan P.S., Sallis H.M., Lindson N., Coupland C.A.C., Et al., Smoking and COVID-19 outcomes: An observational and Mendelian randomisation study using the UK Biobank cohort, Thorax., 77, pp. 65-73, (2022); Usman M.S., Siddiqi T.J., Khan M.S., Patel U.K., Shahid I., Ahmed J., Et al., Is there a smoker's paradox in COVID-19?, BMJ Evid Based Med., 26, pp. 279-284, (2021); Korzeniowska A., Reka G., Bilska M., Piecewicz-Szczesna H., The smoker's paradox during the COVID-19 pandemic? The influence of smoking and vaping on the incidence and course of SARS-CoV-2 virus infection as well as possibility of using nicotine in the treatment of COVID-19 — Review of the literature, Przegl Epidemiol., 75, pp. 27-44, (2021); Vogelmeier C.F., Criner G.J., Martinez F.J., Anzueto A., Barnes P.J., Bourbeau J., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary, Arch Bronconeumol., 53, pp. 128-149, (2017); Roughgarden K.L., Toll B.A., Tanner N.T., Frazier C.C., Silvestri G.A., Rojewski A.M., Tobacco treatment specialist training for lung cancer screening providers, Am J Prev Med., 61, pp. 765-768, (2021); Izquierdo J.L., Morena D., Gonzalez Y., Paredero J.M., Perez B., Graziani D., Et al., Clinical management of COPD in a real-world setting. A big data analysis, Arch Bronconeumol., 57, pp. 94-100, (2021); Izquierdo J.L., Godoy R., Manejo clínico de la EPOC en Castilla-La Mancha. Una oportunidad para mejorar, Rev SOCAMPAR., 5, pp. 31-32, (2020); Izquierdo J.L., Oeste C.L., Hernandez Medrano I., Artificial intelligence in pneumology: Diagnostic and prognostic utilities, Arch Bronconeumol., (2022)","R. Godoy Mayoral; Servicio de Neumología, Complejo Hospitalario Universitario de Albacete, Albacete, Spain; email: godoymayoral@gmail.com","","Sociedad Espanola de Medicina Interna (SEMI)","","","","","","00142565","","RCESA","38142978","English","Rev. Clin. Esp.","Article","Final","","Scopus","2-s2.0-85182572491"
"Curiale A.H.; San José Estépar R.","Curiale, Ariel Hernán (55785298900); San José Estépar, Raúl (57865245400)","55785298900; 57865245400","Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency","2025","Computers in Biology and Medicine","185","","109500","","","","0","10.1016/j.compbiomed.2024.109500","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211042237&doi=10.1016%2fj.compbiomed.2024.109500&partnerID=40&md5=682b3c367b9a26b95907f1395f3233ad","Applied Chest Imaging Laboratory, Department of Radiology and Medicine, Brigham and Women's Hospital, 399 Revolution Drive, Somerville, 02145, MA, United States; Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, United States","Curiale A.H., Applied Chest Imaging Laboratory, Department of Radiology and Medicine, Brigham and Women's Hospital, 399 Revolution Drive, Somerville, 02145, MA, United States, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, United States; San José Estépar R., Applied Chest Imaging Laboratory, Department of Radiology and Medicine, Brigham and Women's Hospital, 399 Revolution Drive, Somerville, 02145, MA, United States, Harvard Medical School, 25 Shattuck Street, Boston, 02115, MA, United States","Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R2= 0.36). Notably, it achieved a higher correlation coefficient of 0.68 for PiMZ heterozygous carriers, indicating its effectiveness in detecting early emphysema progression among smokers with mild to moderate AAT deficiency. The presented models could serve as a tool for monitoring disease progression and informing treatment strategies in carriers and subjects with AATD. Our code is available at github.com/acil-bwh/LobTe. © 2024","Alpha-1 Antitrypsin Deficiency; Attention mechanisms; COPD; CT; Deep learning; Emphysema progression; Prognostic markers; Transformers","Aged; alpha 1-Antitrypsin Deficiency; Disease Progression; Female; Humans; Lung; Male; Middle Aged; Pulmonary Emphysema; Tomography, X-Ray Computed; Diagnosis; Diseases; Endocrinology; Lung cancer; Alpha 1-antitrypsin; Alpha-1 antitrypsin deficiency; Attention mechanisms; COPD; CT; Deep learning; Emphysema progression; Prognostic markers; Transformer; Transformer modeling; adult; allele; alpha 1 antitrypsin deficiency; area under the curve; Article; autoencoder; cohort analysis; controlled study; convolutional neural network; correlation coefficient; current smoker; disease exacerbation; ex-smoker; follow up; genotype; heterozygote; human; lobe based transformer model; lung emphysema; major clinical study; middle aged; root mean squared error; x-ray computed tomography; aged; diagnostic imaging; disease exacerbation; female; genetics; lung; male; pathophysiology; Mean square error","","","","","National Institutes of Health, NIH, (1R01HL149877, 5R21LM013670); National Institutes of Health, NIH; Alpha-1 Foundation, A1F, (1037165); Alpha-1 Foundation, A1F","This work was supported by U.S. National Institutes of Health (NIH) grant 1R01HL149877 , 5R21LM013670 and Alpha-1 Foundation grant 1037165 .","Marin L., Colombo P., Bebawy M., Young P.M., Traini D., Chronic obstructive pulmonary disease: patho-physiology, current methods of treatment and the potential for simvastatin in disease management, Expert Opin. Drug Deliv., 8, 9, pp. 1205-1220, (2011); Campos M.A., Diaz A.A., The role of computed tomography for the evaluation of lung disease in alpha-1 antitrypsin deficiency, Chest, 153, 5, pp. 1240-1248, (2018); Stoller J.K., Aboussouan L.S., Alpha-1 antitrypsin deficiency, Lancet, 365, 9478, pp. 2225-2236, (2005); Seersholm N., Wilcke J., Kok-Jensen A., Dirksen A., Risk of hospital admission for obstructive pulmonary disease in alpha 1-antitrypsin heterozygotes of phenotype PiMZ, Am. J. Respir. Crit. Care Med., 161, 1, pp. 81-84, (2000); Seersholm N., Pi MZ and COPD: Will we ever know?, Thorax, 59, 10, pp. 823-825, (2004); Feld R.D., Heterozygosity of alpha 1-antitrypsin: A health risk?, Crit. Rev. Clin. Lab. Sci., 27, 6, pp. 461-481, (1989); Dahl M., Tybjaerg-Hansen A., Lange P., Vestbo J., Nordestgaard B.G., Change in lung function and morbidity from chronic obstructive pulmonary disease in alpha-1 antitrypsin MZ heterozygotes: A longitudinal study of the general population, Ann. Intern. Med., 136, 4, (2002); Foreman M.G., Wilson C., DeMeo D.L., Hersh C.P., Beaty T.H., Cho M.H., Ziniti J., Curran-Everett D., Criner G., Hokanson J.E., Brantly M., Rouhani F.N., Sandhaus R.A., Crapo J.D., Silverman E.K., Alpha-1 antitrypsin PiMZ genotype is associated with chronic obstructive pulmonary disease in two racial groups, Ann. Am. Thorac. Soc., 14, 8, pp. 1280-1287, (2017); Lynch D.A., Al-Qaisi M.A., Quantitative computed tomography in chronic obstructive pulmonary disease, J. Thorac. Imaging, 28, 5, pp. 284-290, (2013); Newell J., Hogg J., Snider G., Report of a workshop: quantitative computed tomography scanning in longitudinal studies of emphysema, Eur. Respir. J., 23, 5, pp. 769-775, (2004); Parr D.G., Sevenoaks M., Deng C., Stoel B.C., Stockley R.A., Detection of emphysema progression in α1-antitrypsin deficiency using CT densitometry; methodological advances, Respir. Res., 9, 1, (2008); Dirksen A., Piitulainen E., Parr D., Deng C., Wencker M., Shaker S., Stockley R., Exploring the role of CT densitometry: a randomised study of augmentation therapy in α1-antitrypsin deficiency, Eur. Respir. J., 33, 6, pp. 1345-1353, (2009); Stavngaard T., Shaker S.B., Dirksen A., Quantitative assessment of emphysema distribution in smokers and patients with alpha1-antitrypsin deficiency, Respir. Med., 100, 1, pp. 94-100, (2006); Castaldi P.J., San Jose Estepar R., Mendoza C.S., Hersh C.P., Laird N., Crapo J.D., Lynch D.A., Silverman E.K., Washko G.R., Distinct quantitative computed tomography emphysema patterns are associated with physiology and function in smokers, Am. J. Respir. Crit. Care Med., 188, 9, pp. 1083-1090, (2013); Curiale A.H., Estepar R.S.J., Detection of local emphysema progression using conditional CNN, Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262Z (2 April 2024), (2024); Serrano G.G., Washko G.R., Estepar R.S.J., Deep learning for biomarker regression: application to osteoporosis and emphysema on chest CT scans, Medical Imaging 2018: Image Processing, (2018); Singla S., Gong M., Ravanbakhsh S., Sciurba F., Poczos B., Batmanghelich K.N., Subject2Vec: Generative-discriminative approach from a set of image patches to a vector, Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, pp. 502-510, (2018); Humphries S.M., Notary A.M., Centeno J.P., Strand M.J., Crapo J.D., Silverman E.K., Lynch D.A., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, 2, pp. 434-444, (2020); Lynch D.A., Austin J.H.M., Hogg J.C., Grenier P.A., Kauczor H.-U., Bankier A.A., Barr R.G., Colby T.V., Galvin J.R., Gevenois P.A., Coxson H.O., Hoffman E.A., Newell J.D., Pistolesi M., Silverman E.K., Crapo J.D., CT-definable subtypes of chronic obstructive pulmonary disease: A statement of the fleischner society, Radiology, 277, 1, pp. 192-205, (2015); Regan E.A., Hokanson J.E., Murphy J.R., Make B., Lynch D.A., Beaty T.H., Curran-Everett D., Silverman E.K., Crapo J.D., Genetic epidemiology of COPD (COPDGene) study design, COPD: J. Chronic Obstr. Pulm. Dis., 7, 1, pp. 32-43, (2010); Vestbo J., Anderson W., Coxson H., Crim C., Dawber F., Edwards L., Hagan G., Knobil K., Lomas D., MacNee W., Silverman E., Tal-Singer R., Evaluation of COPD longitudinally to identify predictive surrogate end-points (ECLIPSE), Eur. Respir. J., 31, 4, pp. 869-873, (2008); Oh A.S., Baraghoshi D., Lynch D.A., Ash S.Y., Crapo J.D., Humphries S.M., Emphysema progression at CT by deep learning predicts functional impairment and mortality: Results from the copdgene study, Radiology, 304, 3, pp. 672-679, (2022); Ash S.Y., Choi B., Oh A., Lynch D.A., Humphries S.M., Deep learning assessment of progression of emphysema and fibrotic interstitial lung abnormality, Am. J. Respir. Crit. Care Med., 208, 6, pp. 666-675, (2023); Nam J.G., Kang H.-R., Lee S.M., Kim H., Rhee C., Goo J.M., Oh Y.-M., Lee C.-H., Park C.M., Deep learning prediction of survival in patients with chronic obstructive pulmonary disease using chest radiographs, Radiology, 305, 1, pp. 199-208, (2022); Woo S., Park J., Lee J., Kweon I.S., CBAM: Convolutional block attention module, (2018); Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Kaiser L., Polosukhin I., Attention is all you need, Adv. Neural Inf. Process. Syst., 30, (2017); Shamshad F., Khan S., Zamir S.W., Khan M.H., Hayat M., Khan F.S., Fu H., Transformers in medical imaging: A survey, Med. 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Intell. Med., 143, (2023); Li Y., Yao T., Pan Y., Mei T., Contextual transformer networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 45, 2, pp. 1489-1500, (2023); Huang Y., Wang Q., Jia W., Lu Y., Li Y., He X., See more than once: Kernel-sharing atrous convolution for semantic segmentation, Neurocomputing, 443, pp. 26-34, (2021); Boueiz A., Chang Y., Cho M.H., Washko G.R., San JoseEstepar R., Bowler R.P., Crapo J.D., DeMeo D.L., Dy J.G., Silverman E.K., Castaldi P.J., Et al., Lobar emphysema distribution is associated with 5-year radiological disease progression, Chest, 153, 1, pp. 65-76, (2018); Chen J., Cho M., Silverman E.K., Hokanson J.E., Kinney G.L., Crapo J.D., Rennard S., Dy J., Castaldi P., Turning subtypes into disease axes to improve prediction of COPD progression, Thorax, 74, 9, pp. 906-909, (2019)","A.H. Curiale; Department of Radiology and Medicine, Brigham and Women's Hospital, Somerville, 399 Revolution Drive, 02145, United States; email: acuriale@bwh.harvard.edu","","Elsevier Ltd","","","","","","00104825","","CBMDA","39644582","English","Comput. Biol. Med.","Article","Final","","Scopus","2-s2.0-85211042237"
"Yazdani A.; Bigdeli S.K.; Zahmatkeshan M.","Yazdani, Azita (57331464500); Bigdeli, Somayeh Kianian (58198787700); Zahmatkeshan, Maryam (57172886300)","57331464500; 58198787700; 57172886300","Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran","2023","Health Science Reports","6","4","e1212","","","","1","10.1002/hsr2.1212","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153752125&doi=10.1002%2fhsr2.1212&partnerID=40&md5=cbda890437730cef1d34a90ee9f5e7c6","Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran; School of Allied Medical Sciences, Fasa University of Medical Sciences, Fasa, Iran","Yazdani A., Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran, Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Bigdeli S.K., Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Zahmatkeshan M., Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran, School of Allied Medical Sciences, Fasa University of Medical Sciences, Fasa, Iran","Background and Aims: Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models. Methods: It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results: Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion: The development of software systems based on NB will be effective to predict the survival of COVID-19 patients. © 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.","COVID-19; decision tree; K-nearest neighbors; machine learning; Naive Bayes; random forest","acquired immune deficiency syndrome; anorexia; Article; asthma; Bayesian learning; blood pressure; breathing rate; computer assisted tomography; consciousness disorder; controlled study; coronavirus disease 2019; coughing; cross-sectional study; decision tree; diabetes mellitus; diagnostic test accuracy study; diarrhea; feature selection; female; fever; headache; heart disease; hematologic disease; hospital patient; human; Human immunodeficiency virus infection; immune deficiency; intubation; Iran; k nearest neighbor; kidney disease; learning algorithm; limb paralysis; lung disease; machine learning; male; measurement accuracy; measurement precision; mortality; multilayer perceptron; myalgia; nausea; neurologic disease; random forest; recall; receiver operating characteristic; respiratory distress; skin defect; smelling disorder; stomach pain; survival; taste disorder; thorax pain; vertigo; vomiting","","","","","Fasa University of Medical Sciences, FUMS","This study was extracted from a research supported financially by the Fasa University of Medical Sciences with the ethics code of IR.FUMS.REC.1399.059. https://ethics.research.ac.ir/ProposalCertificateEn.php?id=136619&Print=true&NoPrintHeader=true&NoPrintFooter=true&NoPrintPageBorder=true&LetterPrint=true ","Afrash M.R., Kazemi-Arpanahi H., Shanbehzadeh M., Nopour R., Mirbagheri E., Predicting hospital readmission risk in patients with COVID-19: a machine learning approach, Inform Med Unlocked, 30, (2022); Chowdhury S.F., Sium S.M.A., Anwar S., Research and management of rare diseases in the COVID-19 pandemic era: challenges and countermeasures, Front Public Health, 9, (2021); Sandhu R., Gill H.K., Sood S.K., Smart monitoring and controlling of pandemic influenza A (H1N1) using social network analysis and cloud computing, J Comput Sci, 12, pp. 11-22, (2016); Singh S., Bansal A., Sandhu R., Sidhu J., Fog computing and IoT based healthcare support service for dengue fever, Int J Pervasive Comput Commun, 14, pp. 197-207, (2018); 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Atiyah O.S., Thalij S.H., 18, 1, pp. 139-143, (2022); Suresh K., Severn C., Ghosh D., Survival prediction models: an introduction to discrete-time modeling, BMC Med Res Methodol, 22, 1, (2022); Sadoughi F., Sarsarshahi A., Eerfannia I., Firouzabad S.A., Ranking evaluation factors in hospital information systems, Hum Vet Med, 8, 2, pp. 92-97, (2016); Monjur O., Preo R.B., Shams A.B., Raihan M.M.S., Fairoz F., COVID-19 prognosis and mortality risk predictions from symptoms: a cloud-based smartphone application, BioMed, 1, 2, pp. 114-125, (2021)","M. Zahmatkeshan; Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran; email: m.zahmatkeshan@fums.ac.ir","","John Wiley and Sons Inc","","","","","","23988835","","","","English","Heal. Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85153752125"
"Kumari C.S.; Seethalakshmi K.","Kumari, C.S. (57200671742); Seethalakshmi, K. (57208321043)","57200671742; 57208321043","Synthesizing Radiological Insights: Enhancing Lung Disease Classification through Multimodal Imaging","2023","International Journal of Pharmaceutical Quality Assurance","14","4","","1126","1135","9","1","10.25258/ijpqa.14.4.47","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184437392&doi=10.25258%2fijpqa.14.4.47&partnerID=40&md5=4f10ea17b78a0c71bff8f567701f7c1b","Department of Computer Science and Engineering, Vel Tech Rangajaran Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India","Kumari C.S., Department of Computer Science and Engineering, Vel Tech Rangajaran Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India; Seethalakshmi K., Department of Computer Science and Engineering, Vel Tech Rangajaran Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India","Precisely categorizing lung diseases is essential for effective medical treatments. This paper presents a comprehensive analysis of advanced methods in lung disease classification, with a focus on integrating diverse imaging techniques like computerized tomography (CT), X-rays, and magnetic resonance imaging (MRI). These imaging approaches collectively enhance the understanding of pulmonary conditions, aiding in early detection and differential diagnosis. The paper initially explains the fundamental principles of CT, MRI, and X-rays, highlighting their unique characteristics and roles in elucidating lung structures. It explores state-of-the-art methodologies, encompassing both traditional machine learning using engineered features and the expanding domain of deep learning utilizing neural networks to classify intricate diseases. A wide range of prevalent lung ailments, spanning from pneumonia and lung cancer to chronic obstructive pulmonary disease (COPD), are covered. Each domain delves into the considerations for adapting imaging modalities, involving data pre-processing, feature extraction, and algorithmic orchestration. Comparative evaluations of performance metrics offer insights into the effectiveness and limitations of each approach. Furthermore, the paper outlines the challenges associated with classifying lung diseases, including limited annotated data, complexities in model interpretation, and the seamless integration of algorithmic outcomes into clinical practices. As for future research avenues, the paper suggests innovative directions such as data augmentation, integrating multi-modal imaging information, and advancing transparent artificial intelligence (AI) frameworks to enhance their acceptance in clinical settings. © 2023, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.","Deep learning; Neural networks; Object detection; Safety; Tensor flow; Transfer learning","accuracy; Article; artificial intelligence; chronic obstructive lung disease; classification; computer assisted tomography; controlled study; coronavirus disease 2019; data collection method; decision tree; deep learning; diagnostic test accuracy study; histopathology; human; image analysis; improvised crow search; improvised cuttle fish; include improvised grey wolf; information processing; k nearest neighbor; lung disease; machine learning; multimodal imaging; nuclear magnetic resonance imaging; pneumonia; positron emission tomography; radiology; thorax radiography","","","","","","","Pietrangelo, The top 10 deadliest diseases in the world, Healthline; The top 10 causes of death, World Health Organization; India ranks 67th among developing countries in doctor-population ratios, PharmaTutor; Midgley M. 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Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 710-713, (2018); Makaju S, Prasad PW, Alsadoon A, Singh AK, Elchouemi A., Lung cancer detection using CT scan images, Procedia Computer Science, 125, pp. 107-114; Moitra D, Mandal RK, Classification of Non-Small Cell Lung Cancer using One- Dimensional Convolutional Neural Network 2020 Expert Systems with Applications, (2020); Song Q, Zhao L, Luo X, Dou X, Using deep learning for classification of lung nodules on computed tomography images, Journal of healthcare engineering, (2017); Abiyev RH, Maaitah MKS, Deep convolutional neural networks for chest diseases detection, J Healthc Eng, (2018); Pham DT, Classification of COVID-19 chest X-rays with deep learning: new models or fine-tuning?, Health Inf Sci Syst, (2021); Turkoglu M, COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble, Appl Intell, (2021); Butt C, Gill J, Chun D, Babu BA, Deep learning system to screen coronavirus disease 2019 pneumonia, Appl Intell, (2020); Hira S, Bai A, Hira S, An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images, Appl Intell, (2020); Gianchandani N, Jaiswal A, Singh D, Kumar V, Kaur M, Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images, J Ambient Intell Human Comput, (2020); Choe Jooae, Hwang Hye Jeon, Seo Joon Beom, Lee Sang Min, Yun Jihye, Kim Min-Ju, Jeong Jewon, Et al., Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest CT, Radiology, 302, 1, pp. 187-197, (2022); Walsh Simon LF, Mackintosh John A., Calandriello Lucio, Silva Mario, Sverzellati Nicola, Larici Anna Rita, Humphries Stephen M., Et al., Deep Learning–based Outcome Prediction in Progressive Fibrotic Lung Disease Using High-Resolution Computed Tomography, American journal of respiratory and critical care medicine, 206, 7, pp. 883-891, (2022); Jasmine Pemeena Priyadarsini M., kotecha Ketan, Rajini G. K., Hariharan K., Utkarsh Raj K., Bhargav Ram K., Indragandhi V., Subramaniyaswamy V., Pandya Sharnil, Lung Diseases Detection Using Various Deep Learning Algorithms, Journal of Healthcare Engineering, 2023; Ravi Vinayakumar, Acharya Vasundhara, Alazab Mamoun, A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images, Cluster Computing, 26, 2, pp. 1181-1203, (2023); Azam Sami, Rafid AKM Rakibul Haque, Montaha Sidratul, Karim Asif, Jonkman Mirjam, De Boer Friso, Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases, Biomedicines, 11, 1, (2023); Podder Prajoy, Das Sanchita Rani, Mondal M. Rubaiyat Hossain, Bharati Subrato, Maliha Azra, Hasan Md Junayed, Piltan Farzin, LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases, Sensors, 23, 1, (2023); Karaddi Sahebgoud Hanamantray, Sharma Lakhan Dev, Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks, Expert Systems with Applications, 211, (2023)","C.S. Kumari; Department of Computer Science and Engineering, Vel Tech Rangajaran Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India; email: cshyamalakumari90@gmail.com","","Dr. Yashwant Research Labs Pvt. Ltd.","","","","","","09759506","","","","English","Int. J. Pharm. Qual. Assur.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85184437392"
"Kandhare P.G.; Ambalavanan N.; Travers C.P.; Carlo W.A.; Sirakov N.M.; Nakhmani A.","Kandhare, Pravinkumar G. (57207578620); Ambalavanan, Namasivayam (6701406113); Travers, Colm P. (57190743929); Carlo, Waldemar A. (35452965900); Sirakov, Nikolay M. (6603651328); Nakhmani, Arie (24725161500)","57207578620; 6701406113; 57190743929; 35452965900; 6603651328; 24725161500","Comparison metrics for multi-step prediction of rare events in vital sign signals","2023","Biomedical Signal Processing and Control","80","","104371","","","","1","10.1016/j.bspc.2022.104371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141923220&doi=10.1016%2fj.bspc.2022.104371&partnerID=40&md5=cb5baca0cfe5d7d41bc17d3260631ba8","Interdisciplinary Engineering, University of Alabama at Birmingham, Birmingham, AL, United States; Pediatrics, University of Alabama at Birmingham, Birmingham, AL, United States; Mathematics, Texas A & M University - Commerce, Commerce, TX, United States; Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, United States","Kandhare P.G., Interdisciplinary Engineering, University of Alabama at Birmingham, Birmingham, AL, United States; Ambalavanan N., Pediatrics, University of Alabama at Birmingham, Birmingham, AL, United States; Travers C.P., Pediatrics, University of Alabama at Birmingham, Birmingham, AL, United States; Carlo W.A., Pediatrics, University of Alabama at Birmingham, Birmingham, AL, United States; Sirakov N.M., Mathematics, Texas A & M University - Commerce, Commerce, TX, United States; Nakhmani A., Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL, United States","Prediction of changes in biomedical signals, such as vital signs, is useful for many clinical applications. Several signal prediction (forecasting) tools were developed, but their evaluation and applicability to a specific clinical use is context dependent. In this work, we propose a novel method to tackle the problem of evaluation and comparison of vital sign predictors for intervention based clinical studies. The proposed prediction quality measures are particularly well-suited for forecasting rare events scenarios. Specifically, using the novel metrics, we measure the prediction statistics and compare nine deep learning and autoregressive forecasting models for multi-step prediction of rare bradycardia events in preterm infants, however the new concepts allow applications to other biomedical signals. We validated the novel metrics with experimental results on testing sets with several days of vital sign recordings. Our results show that simple statistical predictors could outperform state-of-the-art deep learning architectures for low-dimensional signals. © 2022 Elsevier Ltd","Deep learning; Predictor evaluation; Time series forecasting","Bioelectric phenomena; Deep learning; Biomedical signal; Clinical application; Comparison metrics; Deep learning; Multi-step prediction; Predictor evaluation; Signal prediction; Time series forecasting; Vital sign; Vital sign signals; accuracy; algorithm; Article; artificial neural network; asthma; autoencoder; Bi-LSTM autoencoder; blood oxygen tension; bradycardia; breathing rate; controlled study; deep learning; electrocardiogram; evaluation study; feature extraction; finite difference predictor; gated recurrent unit network; heart disease; heart rate; human; last observation carried forward predictor; linear predictor; linear regression predictor; long short term memory network; neonatal intensive care unit; newborn; newborn monitoring; oxygen desaturation; predictive model; prematurity; seizure; sensitivity and specificity; time series analysis; transformer predictor; vital sign; Forecasting","","","","","Kaul Pediatric Research Institute of the Alabama Children's Hospital Foundation; Kaul Pediatric Research Institute of the Alabama Children’s Hospital Foundation, (U01 HL133708); National Institutes of Health, NIH, (05S1, U01HL133536); National Institutes of Health, NIH; U.S. Department of Health and Human Services, HHS; University of Virginia, UV","Funding text 1: This work was partially supported by NIH U01HL133536 grant, administrative supplement 05S1, the Kaul Pediatric Research Institute of the Alabama Children’s Hospital Foundation , and NIH U01 HL133708 grant ( University of Virginia ; Data Coordinating Center for NHLBI U01 Pre-Vent study). Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent those of the National Institutes of Health or the U.S. Department of Health and Human Services.; Funding text 2: This work was partially supported by NIH U01HL133536 grant, administrative supplement 05S1, the Kaul Pediatric Research Institute of the Alabama Children's Hospital Foundation, and NIH U01 HL133708 grant (University of Virginia; Data Coordinating Center for NHLBI U01 Pre-Vent study). Disclaimer: The views expressed in this article are those of the authors and do not necessarily represent those of the National Institutes of Health or the U.S. Department of Health and Human Services.","Aydin S., Demirtas S., Yetkin S., Cortical correlations in wavelet domain for estimation of emotional dysfunctions, Neural Comput. Appl., 30, 4, pp. 1085-1094, (2018); Aydin S., Tunga M.A., Yetkin S., Mutual information analysis of sleep eeg in detecting psycho-physiological insomnia, J. 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Rep., 10, 1, pp. 1-12, (2020); Lim K., Jiang H., Marshall A.P., Salmon B., Gale T.J., Dargaville P.A., (2020); Shirwaikar R.D., Acharya D., Makkithaya K., Surulivelrajan M., Srivastava S., Et al., Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction, Artif. Intell. Med., 98, pp. 59-76, (2019); Chantamit-o pas P., Goyal M., Prediction of stroke using deep learning model, Neural Information Processing, pp. 774-781, (2017); Tsiouris K.M., Pezoulas V.C., Zervakis M., Konitsiotis S., Koutsouris D.D., Fotiadis D.I., A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals, Comput. Biol. Med., 99, pp. 24-37, (2018); Wang B., Yi X., Gao J., Li Y., Xu W., Wu J., Han D., Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal, J. Clin. 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Syst., (2020); Laubscher R., Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks, Energy, 189, (2019); Zhang X., Kuehnelt H., De Roeck W., Traffic noise prediction applying multivariate bi-directional recurrent neural network, Appl. Sci., 11, 6, (2021); Siami-Namini S., Tavakoli N., Namin A.S., The performance of LSTM and biLSTM in forecasting time series, 2019 IEEE International Conference on Big Data, Big Data, pp. 3285-3292, (2019); Adonis S., Time serie transformer, (2020); Trivedi S., Simple transformer encoder in keras, (2019); Salkind N.J., Last observation carried forward, Encyclopedia of Research Design, pp. 687-688, (2012); Rencher A.C., Schaalje G.B., Linear Models in Statistics, (2008); Scheid F., Numerical Analysis, (1989); Wang F., Confidence interval for the mean of non-normal data, Qual. Reliab. Eng. Int., 17, 4, pp. 257-267, (2001); Murphy A.H., A new vector partition of the probability score, J. Appl. Meteorol. Climatol., 12, 4, pp. 595-600, (1973)","A. Nakhmani; Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, United States; email: anry@uab.edu","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85141923220"
"Reis J.S.D.; Costa R.L.; Silva F.D.D.S.; de Souza E.D.F.; Cortes T.R.; Coelho R.H.; Velasco S.R.M.; Neves D.J.D.; Sousa Filho J.F.; Barreto C.E.C.; Cabral Júnior J.B.; dos Reis H.S.; Mendes K.R.; Lins M.C.C.; Ferreira T.R.; Vanderlei M.H.G.D.S.; Alonso M.F.; Mariano G.L.; Gomes H.B.; Gomes H.B.","Reis, Jean Souza dos (57215490415); Costa, Rafaela Lisboa (56097009800); Silva, Fabricio Daniel dos Santos (57211584754); de Souza, Ediclê Duarte Fernandes (58640610800); Cortes, Taisa Rodrigues (55711178300); Coelho, Rachel Helena (57297925500); Velasco, Sofia Rafaela Maito (59605471300); Neves, Danielson Jorge Delgado (57192166934); Sousa Filho, José Firmino (57758135400); Barreto, Cairo Eduardo Carvalho (58944983100); Cabral Júnior, Jório Bezerra (57204453556); dos Reis, Herald Souza (57203112599); Mendes, Keila Rêgo (35574046700); Lins, Mayara Christine Correia (57944617800); Ferreira, Thomás Rocha (59512536500); Vanderlei, Mário Henrique Guilherme dos Santos (57252695900); Alonso, Marcelo Felix (36503553800); Mariano, Glauber Lopes (26032206700); Gomes, Heliofábio Barros (35219812800); Gomes, Helber Barros (57200365348)","57215490415; 56097009800; 57211584754; 58640610800; 55711178300; 57297925500; 59605471300; 57192166934; 57758135400; 58944983100; 57204453556; 57203112599; 35574046700; 57944617800; 59512536500; 57252695900; 36503553800; 26032206700; 35219812800; 57200365348","Predicting Asthma Hospitalizations from Climate and Air Pollution Data: A Machine Learning-Based Approach","2025","Climate","13","2","23","","","","0","10.3390/cli13020023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218501078&doi=10.3390%2fcli13020023&partnerID=40&md5=1ac651ab3001dfdc1a18e4c8dc7786cf","Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Center for Sci-Tech Research in Earth System and Energy-CREATE, Instituto de Investigação e Formação Avançada-IIFA, Earth Remote Sensing Laboratory (EaRS Lab), University of Évora, Évora, 7000-671, Portugal; Coordenação de Hidrologia, Centro Gestor e Operacional do Sistema de Proteção da Amazônia (Censipam), Belém, 66617-420, Brazil; Instituto de Geografia, Desenvolvimento e Meio Ambiente, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Laboratory of Sexually Transmitted Infections, Bacteriology and Mycology Section, Evandro Chagas Institute (IEC), Ananindeua, 67030-000, Brazil; Departamento de Ciências Climáticas e Atmosféricas, Universidade Federal do Rio Grande do Norte, Natal, 59078-970, Brazil; Faculdade de Meteorologia, Universidade Federal de Pelotas, Pelotas, 96010-610, Brazil","Reis J.S.D., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil, Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Costa R.L., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Silva F.D.D.S., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; de Souza E.D.F., Center for Sci-Tech Research in Earth System and Energy-CREATE, Instituto de Investigação e Formação Avançada-IIFA, Earth Remote Sensing Laboratory (EaRS Lab), University of Évora, Évora, 7000-671, Portugal; Cortes T.R., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Coelho R.H., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Velasco S.R.M., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Neves D.J.D., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Sousa Filho J.F., Centro de Integração de Dados e Conhecimentos para Saúde, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, 41745-715, Brazil; Barreto C.E.C., Coordenação de Hidrologia, Centro Gestor e Operacional do Sistema de Proteção da Amazônia (Censipam), Belém, 66617-420, Brazil; Cabral Júnior J.B., Instituto de Geografia, Desenvolvimento e Meio Ambiente, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; dos Reis H.S., Laboratory of Sexually Transmitted Infections, Bacteriology and Mycology Section, Evandro Chagas Institute (IEC), Ananindeua, 67030-000, Brazil; Mendes K.R., Departamento de Ciências Climáticas e Atmosféricas, Universidade Federal do Rio Grande do Norte, Natal, 59078-970, Brazil; Lins M.C.C., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Ferreira T.R., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Vanderlei M.H.G.D.S., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Alonso M.F., Faculdade de Meteorologia, Universidade Federal de Pelotas, Pelotas, 96010-610, Brazil; Mariano G.L., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Gomes H.B., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; Gomes H.B., Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil","This study explores the predictability of monthly asthma notifications using models built from different machine learning techniques in Maceió, a municipality with a tropical climate located in the northeast of Brazil. Two sets of predictors were combined and tested, the first containing meteorological variables and pollutants, called exp1, and the second only meteorological variables, called exp2. For both experiments, tests were also carried out incorporating lagged information from the time series of asthma records. The models were trained on 80% of the data and validated on the remaining 20%. Among the five methods evaluated—random forest (RF), eXtreme Gradient Boosting (XGBoost), Multiple Linear Regression (MLR), support vector machine (SVM), and K-nearest neighbors (KNN)—the RF models showed superior performance, notably those of exp1 when incorporating lagged asthma notifications as an additional predictor. Minimum temperature and sulfur dioxide emerged as key variables, probably due to their associations with respiratory health and pollution levels, emphasizing their role in asthma exacerbation. The autocorrelation of the residuals was assessed due to the inclusion of lagged variables in some experiments. The results highlight the importance of pollutant and meteorological factors in predicting asthma cases, with implications for public health monitoring. Despite the limitations presented and discussed, this study demonstrates that forecast accuracy improves when a wider range of lagged variables are used, and indicates the suitability of RF for health datasets with complex time series. © 2025 by the authors.","epidemiology; health data analysis; predictive modeling; respiratory diseases","Brazil; asthma; atmospheric pollution; epidemiology; hospital sector; pollution exposure; public health; respiratory disease; spatiotemporal analysis; sulfur dioxide","","","","","Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","The first and second authors thank the \""Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico\u2014(CNPq)\"" for the financial support during the conception of this study. ","Khanam U.A., Gao Z., Adamko D., Kusalik A., Rennie D.C., Goodridge D., Chu L., Lawson J.A., A scoping review of asthma and machine learning, J. 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Res, 200, (2021); Policies, Regulations & Legislation Promoting Healthy Housing: A Review, (2021); Chen B.-Y., Chen C.-H., Chuang Y.-C., Wu Y.-H., Pan S.-C., Guo Y.L., Changes in the relationship between childhood asthma and ambient air pollution in Taiwan: Results from a nationwide survey repeated 5 years apart, Pediatr. Allergy Immunol, 30, pp. 188-194, (2019); Deng Q., Deng L., Lu C., Li Y., Norback D., Parental stress and air pollution increase childhood asthma in China, Environ. Res, 165, pp. 23-31, (2018); Hwang B.-F., Traffic related air pollution as a determinant of asthma among Taiwanese school children, Thorax, 60, pp. 467-473, (2005); Laurent O., Pedrono G., Segala C., Filleul L., Havard S., Deguen S., Schillinger C., Riviere E., Bard D., Air pollution, asthma attacks, and socioeconomic deprivation: A small-area case-crossover study, Am. J. Epidemiol, 168, pp. 58-65, (2008); Li S., Batterman S., Wasilevich E., Wahl R., Wirth J., Su F.-C., Mukherjee B., Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: Time-series and time-stratified case-crossover analyses with threshold effects, Environ. Res, 111, pp. 1137-1147, (2011); Lovinsky-Desir S., Acosta L.M., Rundle A.G., Miller R.L., Goldstein I.F., Jacobson J.S., Chillrud S.N., Perzanowski M.S., Air pollution, urgent asthma medical visits and the modifying effect of neighborhood asthma prevalence, Pediatr. Res, 85, pp. 36-42, (2019); Samoli E., Nastos P.T., Paliatsos A.G., Katsouyanni K., Priftis K.N., Acute effects of air pollution on pediatric asthma exacerbation: Evidence of association and effect modification, Environ. Res, 111, pp. 418-424, (2011); Santus P., Russo A., Madonini E., Allegra L., Blasi F., Centanni S., Miadonna A., Schiraldi G., Amaducci S., How air pollution influences clinical management of respiratory diseases. A case-crossover study in Milan, Respir. Res, 13, (2012); Son J.-Y., Lee J.-T., Park Y.H., Bell M.L., Short-Term Effects of Air Pollution on Hospital Admissions in Korea, Epidemiology, 24, pp. 545-554, (2013)","F.D.D.S. Silva; Instituto de Ciências Atmosféricas, Universidade Federal de Alagoas, Maceió, 57072-900, Brazil; email: fabricio.santos@icat.ufal.br","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","22251154","","","","English","Clim.","Article","Final","","Scopus","2-s2.0-85218501078"
"Kohandel Gargari O.; Fathi M.; Rajai Firouzabadi S.; Mohammadi I.; Mahmoudi M.H.; Sarmadi M.; Shafiee A.","Kohandel Gargari, Omid (57395032400); Fathi, Mobina (57216507361); Rajai Firouzabadi, Shahryar (57213062369); Mohammadi, Ida (58085064700); Mahmoudi, Mohammad Hossein (58740571400); Sarmadi, Mehran (59564558300); Shafiee, Arman (57449054000)","57395032400; 57216507361; 57213062369; 58085064700; 58740571400; 59564558300; 57449054000","Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset","2025","Scientific Reports","15","1","4537","","","","0","10.1038/s41598-025-88345-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218268311&doi=10.1038%2fs41598-025-88345-1&partnerID=40&md5=f0c78975cff1ad29da7d06ccf9ea6baf","Alborz Artificial Intelligence Association, Alborz University of Medical Sciences, Alborz, Karaj, Iran; Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran, Iran; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Industrial Engineering Department, Sharif University of Technology, Tehran, Iran; Computer Engineering Department, Sharif University of Technology, Tehran, Iran","Kohandel Gargari O., Alborz Artificial Intelligence Association, Alborz University of Medical Sciences, Alborz, Karaj, Iran; Fathi M., Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran, Iran, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Rajai Firouzabadi S., School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Mohammadi I., School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Mahmoudi M.H., Industrial Engineering Department, Sharif University of Technology, Tehran, Iran; Sarmadi M., Computer Engineering Department, Sharif University of Technology, Tehran, Iran; Shafiee A., Alborz Artificial Intelligence Association, Alborz University of Medical Sciences, Alborz, Karaj, Iran","Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical data. In this study, we aim to compare different ML diagnostic models and obtain the most valuable features for asthma diagnosis using data from the National Health and Nutrition Examination Survey (NHANES) dataset. A total of 8,888 participants with available asthma diagnosis data from the 2017–2018 NHANES survey were included. After careful selection of variables related to asthma, various ML algorithms including Support Vector Machine (SVM), Random Forest (RF), AdaBoost (ADA), XGBoost (XGB), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Multi-Layer Perceptron (MLP) were evaluated. SVM and ADA emerged as top performers with the highest area under the curve (AUC) scores of 0.72 and 0.71, respectively. RF exhibited high accuracy but low precision. Feature interpretation using SHapley Additive exPlanations (SHAP) values identified significant predictors such as close relative asthma history, dietary fat intake, and chronic bronchitis. Feature reduction experiments showed promising results without significant loss in predictive performance. Our findings demonstrate the potential diagnosis ability of ML algorithms, particularly SVM and ADA, in asthma diagnosis by incorporating diverse clinical and demographic factors. In addition, close relative asthma history, dietary fat intake, and chronic bronchitis could be suggested as the valuable asthma diagnosis features. These outcomes can bring promising results in early diagnosis of asthma. © The Author(s) 2025.","Asthma; Bronchitis; Machine learning; Support vector machine","Adult; Aged; Algorithms; Asthma; Bayes Theorem; Female; Humans; Machine Learning; Male; Middle Aged; Nutrition Surveys; Support Vector Machine; United States; Young Adult; adult; aged; algorithm; asthma; Bayes theorem; diagnosis; epidemiology; female; human; machine learning; male; middle aged; nutrition; support vector machine; United States; young adult","","","","","","","Global strategy for asthma management and prevention: Global initiative for asthma, (2023); Wang Z., Et al., Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: a systematic analysis for the global burden of Disease Study 2019, Respir Res, 24, 1, (2023); Most recent national asthma data: Centers for disease control and prevention, (2023); Backer V., Et al., A 3-year longitudinal study of asthma quality of life in undiagnosed and diagnosed asthma patients, Int. J. Tuberc. Lung Dis, 11, 4, pp. 463-469, (2007); Aaron S.D., Boulet L.P., Reddel H.K., Gershon A.S., Underdiagnosis and overdiagnosis of asthma, Am. J. Respir. Crit Care Med, 198, 8, pp. 1012-1020, (2018); Reddel H.K., Et al., A summary of the new GINA strategy: a roadmap to asthma control, Eur. Respir. J, 46, 3, pp. 622-639, (2015); Aaron S.D., Et al., Reevaluation of diagnosis in adults with physician-diagnosed asthma, Jama, 317, 3, pp. 269-279, (2017); Trends, perspectives, and prospects, Science, 349, 6245, pp. 255-260, (2015); Kothalawala D.M., Et al., Prediction models for childhood asthma: a systematic review, Pediatr. Allergy Immunol, 31, 6, pp. 616-627, (2020); Topalovic M., Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur. Respir. J, 53, 4, (2019); Harvey J.L., Kumar S.A.P., Machine learning for predicting development of asthma in children, 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 6-9, (2019); Prasadl B., Ponnekanti K.P., Yeruva S., An Approach to Develop Expert Systems in Medical Diagnosis Using Machine Learning Algorithms (Asthma) and A Performance Study, International Journal on Soft Computing, 2, (2011); Schneider A., Et al., Diagnostic accuracy of spirometry in primary care, BMC Pulm. Med, 9, 1, pp. 1-10, (2009); Meneghini A.C., Paulino A.C.B., Pereira L.P., Vianna E.O., Accuracy of spirometry for detection of asthma: a cross-sectional study, Sao Paulo Med. J, 135, pp. 428-433, (2017); Tomita K., Et al., A scoring algorithm for predicting the presence of adult asthma: a prospective derivation study, Prim. Care Respir J, 22, 1, pp. 51-58, (2013); Hirsch S., Et al., Using a neural network to screen a population for asthma, Ann. Epidemiol, 11, 6, pp. 369-376, (2001); Li R., Wen Y., Association of body mass index with asthma occurrence and persistence in adolescents: a retrospective study of NHANES (2011–2018), Heliyon, 9, 9, (2023); Selya A.S., Thapa S., Mehta G., Earlier smoking after waking and the risk of asthma: a cross-sectional study using NHANES data, BMC Pulm. Med, 18, 1, (2018); Uong S.P., Hussain H., Thanik E., Lovinsky-Desir S., Stingone J.A., Urinary metabolites of polycyclic aromatic hydrocarbons and short-acting beta agonist or systemic corticosteroid asthma medication use within NHANES, Environ. Res, 220, (2023); Wu T., Et al., Visceral adiposity and respiratory outcomes in children and adults: a systematic review, Int. J. Obes. (Lond), 46, 6, pp. 1083-1100, (2022); Ekpo R.H., Osamor V.C., Azeta A.A., Ikeakanam E., Amos B.O., Machine learning classification approach for asthma prediction models in children, Health Technol, 13, 1, pp. 1-10, (2023); Berge G.T., Et al., Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital, BMC Med. Inf. Decis. Mak, 23, 1, (2023); Zipf G., Et al., National health and nutrition examination survey: plan and operations, 1999–2010, Vital Health Stat, 1, 56, pp. 1-37, (2013); Statistics CNCfH NHANES 2017–2018 overview; Williams D., Liao X., Xue Y., Carin L., Krishnapuram B., On classification with Incomplete Data, IEEE Trans. Pattern Anal. Mach. Intell, 29, 3, pp. 427-436, (2007); Keerthi S.S., Shevade S.K., Bhattacharyya C., Radha Krishna M.K., Improvements to platt’s smo algorithm for svm classifier design, Neural Comput, 13, 3, pp. 637-649, (2001); Breiman L., Random forests, Mach. Learn, 45, 1, pp. 5-32, (2001); Aha D.W., Kibler D., Albert M., Instance-based learning algorithms, Mach. Learn, 6, 1, pp. 37-66, (1991); Pedregosa F., Et al., Scikit-learn: machine learning in python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011); Freund Y., Schapire R.E., Experiments with a new boosting algorithm, Icml C, pp. 148-156, (1996); John G.H., Langley P., Estimating Continuous Distributions in Bayesian Classifiers., pp. 338-345, (1995); Lundberg S.M., Lee S., A unified approach to interpreting model predictions, Advances in Neural Information Processing Systems (NIPS), pp. 4765-4774, (2017); Tufail S., Riggs H., Tariq M., Sarwat A.I., Advancements and challenges in Machine Learning: a comprehensive review of models, libraries, applications, and algorithms, Electronics, 12, (2023); Boser B.E., Guyon I.M., Vapnik V.N., A training algorithm for optimal margin classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, (1992); Cortes C., Vapnik V., Smola A.J., Scholkopf B., Support-vector networks, Mach. Learn, 20, 3, pp. 273-297, (1995); Smola A.J., Scholkopf B., A tutorial on support vector regression, Stat. Comput, 14, 3, pp. 199-222, (2004); Encyclopedia of Machine Learning, (2011); Calders T., Jaroszewicz S., Efficient AUC Optimization for Classification, Knowledge Discovery in Databases: PKDD 2007, (2007); Kothalawala D.M., Et al., Development of childhood asthma prediction models using machine learning approaches, Clin. Translational Allergy, 11, 9, (2021); Magdon-Ismail M., No free lunch for noise prediction, Neural Comput, 12, 3, pp. 547-564, (2000); Farion K.J., Wilk S., Michalowski W., O'Sullivan D., Sayyad-Shirabad J., Comparing predictions made by a prediction model, clinical score, and physicians, Appl. Clin. Inf, 4, 3, pp. 376-391, (2013); Finkelstein J., Jeong I.C., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann. N. Y. Acad. Sci, 1387, 1, pp. 153-165, (2017); Chatzimichail E., Paraskakis E., Rigas A., An evolutionary two-objective genetic algorithm for asthma prediction, Uksim 15Th International Conference on Computer Modelling and Simulation, (2013); Ellison-Loschmann L., Et al., Socioeconomic status, asthma and chronic bronchitis in a large community-based study, Eur. Respir. J, 29, 5, pp. 897-905, (2007); Burke W., Fesinmeyer M., Reed K., Hampson L., Carlsten C., Family history as a predictor of asthma risk, Am. J. Prev. Med, 24, 2, pp. 160-169, (2003); Simpson J.L., Scott R., Boyle M.J., Gibson P.G., Inflammatory subtypes in asthma: assessment and identification using induced sputum, Respirology, 11, 1, pp. 54-61, (2006); Shi H., Et al., TLR4 links innate immunity and fatty acid–induced insulin resistance, J. Clin. Investig, 116, 11, pp. 3015-3025, (2006)","M.H. Mahmoudi; Industrial Engineering Department, Sharif University of Technology, Tehran, Iran; email: mahmoudi.mohammad.h@gmail.com","","Nature Research","","","","","","20452322","","","39915528","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218268311"
"Saad M.M.; Bayoumy A.A.; EL-Nisr M.M.; Zaki N.M.; Khalil T.H.; ELSerafi A.F.","Saad, Maha M. (57211286802); Bayoumy, Ahmed A. (57199145178); EL-Nisr, Magdy M. (58243961800); Zaki, Noha M. (58205069100); Khalil, Tarek H. (13905387700); ELSerafi, Ahmed F. (58937189500)","57211286802; 57199145178; 58243961800; 58205069100; 13905387700; 58937189500","Assessment of artificial intelligence-aided chest computed tomography in diagnosis of chronic obstructive airway disease: an observational study","2023","Egyptian Journal of Radiology and Nuclear Medicine","54","1","97","","","","1","10.1186/s43055-023-01043-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160608935&doi=10.1186%2fs43055-023-01043-8&partnerID=40&md5=6e7cf38b207fe639b4634cffc3706967","Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; Chest Unit, Department of Internal Medicine, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; Department of Radiodiagnosis, Faculty of Medicine, Ain Shams University, Cairo, Egypt","Saad M.M., Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; Bayoumy A.A., Chest Unit, Department of Internal Medicine, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; EL-Nisr M.M., Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; Zaki N.M., Department of Radiodiagnosis, Faculty of Medicine, Ain Shams University, Cairo, Egypt; Khalil T.H., Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; ELSerafi A.F., Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt","Background: The Global Initiative for Obstructive Lung Disease (GOLD) staging approach is frequently used to classify the severity of COPD by using spirometry. Recent advancements in artificial intelligence applications enable the automatic identification of COPD severity by chest computer tomography (CT). The goal of this study is to define the role of artificial intelligence in determining the severity of COPD. Methods: We used a non-contrast CT chest and a computer-aided detection system (Coreline Soft's AVIEW), which was conducted as a descriptive cross sectional study and involved 80 cases. For the diagnosis of parenchymal disease using density mask methods such as inspiratory low attenuation area-950% (%LAA-950 HUINS) and D-value (cluster-size analysis), the spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second (FEV1) to forced vital capacity was used to assess the severity of COPD. Results: Based on the results of the spirometry, the patients were divided into four groups: mild (n = 23), moderate (n = 39), severe (n = 17), and very severe (n = 1). Insp. LAA-950 (%) in GOLD group 3 was substantially greater than in GOLD groups 2 and 1. Additionally, when compared to groups 2 and 1, the D-value in the GOLD 3 group was significantly higher. Conclusions: Inspiratory LAA-950% and D-value were found to be significantly related to COPD severity as measured by dyspnea scale and spirometry. Inspiratory LAA-950% was effectively capable of distinguishing between patients with severe and moderate COPD. © 2023, The Author(s).","Artificial intelligence; Emphysema; Inspiratory low attenuation area","adult; Article; artificial intelligence; chronic obstructive lung disease; computer aided design; computer assisted tomography; cross-sectional study; descriptive research; disease severity; emphysema; female; forced expiratory volume; forced vital capacity; human; image processing; major clinical study; male; middle aged; observational study; spirometry; thorax radiography","","","Activion 16 model TSX-031A-2012, Toshiba Medical Systems; Coreline Soft AVIEW","Toshiba Medical Systems","","","Wang S., Summers R.M., Machine learning and radiology, Med Image Anal, 16, 5, pp. 933-951, (2012); Vestbo J., Hurd S.S., Agusti A.G., Jones P.W., Vogelmeier C., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am J Respir Crit Care Med, 187, 4, pp. 347-365, (2013); Chronic obstructive pulmonary disease (COPD): WHO; 2022 [updated 20 May 2022; Richter D.C., Joubert J.R., Nell H., Schuurmans M.M., Irusen E.M., Diagnostic value of post-bronchodilator pulmonary function testing to distinguish between stable, moderate to severe COPD and asthma, Int J Chronic Obstr Pulm Dis, 3, 4, (2008); Heussel C., Herth F., Kappes J., Hantusch R., Hartlieb S., Weinheimer O., Et al., Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values, Eur Radiol, 19, 10, pp. 2391-2402, (2009); Nambu A., Zach J., Kim S.S., Jin G., Schroeder J., Kim Y.-I., Et al., Significance of low-attenuation cluster analysis on quantitative CT in the evaluation of chronic obstructive pulmonary disease, Korean J Radiol, 19, pp. 139-146, (2018); Murray C.J., Lopez A.D., Evidence-based health policy–lessons from the Global Burden of Disease Study, Science, 274, 5288, pp. 740-743, (1996); Lynch D.A., Al-Qaisi M.L., Quantitative Ct in copd, J Thorac Imaging, 28, 5, (2013); Fan L., Zhou X., Xia Y., Guan Y., Zhang D., Li Z., Et al., Progress in the imaging of COPD: quantitative and functional evaluation, Chin J Acad Radiol, 1, pp. 1-6, (2019); Miller M.R., Hankinson J., Brusasco V., Burgos F., Casaburi R., Coates A., Et al., Standardisation of spirometry, Eur Respir J, 26, 2, pp. 319-338, (2005); Launois C., Barbe C., Bertin E., Nardi J., Perotin J.-M., Dury S., Et al., The modified Medical Research Council scale for the assessment of dyspnea in daily living in obesity: a pilot study, BMC Pulm Med, 12, (2012); Jimenez-Ruiz C.A., Masa F., Miravitlles M., Gabriel R., Viejo J.L., Villasante C., Et al., Smoking characteristics: differences in attitudes and dependence between healthy smokers and smokers with COPD, Chest, 119, 5, pp. 1365-1370, (2001); Anazawa R., Kawata N., Matsuura Y., Ikari J., Tada Y., Suzuki M., Et al., Longitudinal changes in structural lung abnormalities using MDCT in COPD with asthma-like features, Eur Respir Soc, (2019); Kumar I., Verma A., Jain A., Agarwal S., Performance of quantitative CT parameters in assessment of disease severity in COPD: a prospective study, Indian J Radiol Imaging, 28, 1, pp. 99-106, (2018); Ostridge K., Wilkinson T.M., Present and future utility of computed tomography scanning in the assessment and management of COPD, Eur Respir J, 48, pp. 216-228, (2016); Tanabe N., Muro S., Hirai T., Oguma T., Terada K., Marumo S., Et al., Impact of exacerbations on emphysema progression in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 183, 12, pp. 1653-1659, (2011); Madani A., Zanen J., De Maertelaer V., Gevenois P.A., Pulmonary emphysema: objective quantification at multi–detector row CT—comparison with macroscopic and microscopic morphometry, Radiology, 238, 3, pp. 1036-1043, (2006); Brillet P.-Y., Fetita C.I., Saragaglia A., Brun A.-L., Beigelman-Aubry C., Preteux F., Et al., Investigation of airways using MDCT for visual and quantitative assessment in COPD patients, Int J Chronic Obstr Pulm Dis, 3, (2008); Nakano Y., Muro S., Sakai H., Hirai T., Chin K., Tsukino M., Et al., Computed tomographic measurements of airway dimensions and emphysema in smokers: correlation with lung function, Am J Respir Crit Care Med, 162, 3, pp. 1102-1108, (2000); Dransfield M.T., Huang F., Nath H., Singh S.P., Bailey W.C., Washko G.R., CT emphysema predicts thoracic aortic calcification in smokers with and without COPD, COPD J Chronic Obstr Pulm Dis, 7, 6, pp. 404-410, (2010); Gietema H.A., Zanen P., Schilham A., van Ginneken B., van Klaveren R.J., Prokop M., Et al., Distribution of emphysema in heavy smokers: impact on pulmonary function, Respir Med, 104, pp. 76-82, (2010); Pauls S., Gulkin D., Feuerlein S., Muche R., Kruger S., Schmidt S.A., Et al., Assessment of COPD severity by computed tomography: correlation with lung functional testing, Clin Imaging, 34, 3, pp. 172-178, (2010); Tsushima K., Sone S., Fujimoto K., Kubo K., Morita S., Takegami M., Et al., Identification of occult parechymal disease such as emphysema or airway disease using screening computed tomography, COPD J Chronic Obstr Pulm Dis, 7, 2, pp. 117-125, (2010); Yamashiro T., Matsuoka S., Bartholmai B.J., Estepar R.S.J., Ross J.C., Diaz A., Et al., Collapsibility of lung volume by paired inspiratory and expiratory CT scans: correlations with lung function and mean lung density, Acad Radiol, 17, 4, pp. 489-495, (2010); 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Nakajima T., Sekine Y., Yamada Y., Suzuki H., Yasufuku K., Yoshida S., Et al., Long-term surgical outcome in patients with lung cancer and coexisting severe COPD, Thorac Cardiovasc Surg, 57, 6, pp. 339-342, (2009); Fan A., Grave E., Joulin A., Reducing transformer depth on demand with structured dropout, (2019); Mishima M., Hirai T., Itoh H., Nakano Y., Sakai H., Muro S., Et al., Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease, Proc Natl Acad Sci, 96, 16, pp. 8829-8834, (1999); Gietema H.A., Muller N.L., Fauerbach P.V.N., Sharma S., Edwards L.D., Camp P.G., Et al., Quantifying the extent of emphysema: factors associated with radiologists’ estimations and quantitative indices of emphysema severity using the ECLIPSE cohort, Acad RADIOL, 18, 6, pp. 661-671, (2011)","M.M. Saad; Department of Radiodiagnosis, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; email: mahamamdoh888@yahoo.com","","Institute for Ionics","","","","","","0378603X","","","","English","Egypt. J. Radiol. Nucl. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85160608935"
"Rao K.B.V.B.; Kar N.K.; Mehta K.K.; Awasthy M.; Konda S.; Patra R.K.","Rao, K.B.V.Brahma (58070596400); Kar, Naresh Kumar (57214146234); Mehta, Kamal K. (7201939546); Awasthy, Mohan (57194378993); Konda, Srinivas (59488843600); Patra, Raj Kumar (36952253500)","58070596400; 57214146234; 7201939546; 57194378993; 59488843600; 36952253500","A novel pulmonary emphysema detection using Seg-ResUnet-based abnormality segmentation and enhanced heuristic algorithm-aided deep learning","2025","Expert Systems with Applications","268","","126250","","","","0","10.1016/j.eswa.2024.126250","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213202582&doi=10.1016%2fj.eswa.2024.126250&partnerID=40&md5=859a8a7496d7724e230455e213cfd134","Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, 522 302, India; Department of Computer Science and Engineering, GITAM Deemed to be University, Hyderabad, India; Department Computer Science and Engineering (AIML), Bharati Vidyapeeth Deemed to be University, Department of Engineering and Technology, Navi Mumbai, India; Bharati Vidyapeeth Deemed to be University, Department of Engineering and Technology, Navi Mumbai, India; Department of Data Science, CMR Technical Campus, Hyderabad, India; Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, India","Rao K.B.V.B., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, 522 302, India; Kar N.K., Department of Computer Science and Engineering, GITAM Deemed to be University, Hyderabad, India; Mehta K.K., Department Computer Science and Engineering (AIML), Bharati Vidyapeeth Deemed to be University, Department of Engineering and Technology, Navi Mumbai, India; Awasthy M., Bharati Vidyapeeth Deemed to be University, Department of Engineering and Technology, Navi Mumbai, India; Konda S., Department of Data Science, CMR Technical Campus, Hyderabad, India; Patra R.K., Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, India","Pulmonary emphysema is a condition characterized by the chronic collapse of the lung alveoli. Hence, an efficient predictive mechanism is significant for diagnosing Chronic Obstructive Pulmonary Disease (COPD). Various diagnosis approaches employ bio-inspired mechanisms that determine the organism's behavior to rectify certain optimization issues. Modern improvements in deep learning have allowed a direct explanation of medical images without depending on corresponding radiographic features. Therefore, a deep learning-based pulmonary emphysema diagnosis system is presented in this work. Firstly, from the Computed Tomography Emphysema Database, the necessary images are fetched and then, these fetched images are given into the segmentation phase. Here, the Seg-ResUnet (SRUnet) is employed to segment the abnormalities within the images. Next, the abnormality-segmented images are subjected to the pulmonary emphysema classification stage, where the Parameter Tuning in Adaptive Multiscale Residual Densenet (PT-MRDNet) is utilized. Moreover, the Enhanced Good and Bad Groups-Based Optimizer (EGBGBO) algorithm is designed to optimize the parameters in PT-MRDNet that enhance the functionality of the model. Thus, an accurate pulmonary emphysema-classified outcome is achieved. Lastly, the developed pulmonary emphysema diagnosis framework is contrasted with several existing approaches and optimization models and shows the accuracy of the designed model is 7.92%, 3.61%, 4.99%, and 1.62% more than CNN, DenseNet, RAN, and MRDNet respectively at 8th batch size. Moreover, when evaluating the developed model with state-of-the-art approaches, it yields 6.63%, 15.37%, 6.13%, and 4.42% higher accuracy than A-ResNet, Conditional CNN, MHSONN, and CNN-FCNet. © 2024 Elsevier Ltd","Enhanced good and bad groups-based optimizer; Parameter tuning in adaptive multiscale residual densenet; Pulmonary emphysema detection; Seg-ResUnet; Semantic segmentation","Computerized tomography; Contrastive Learning; Deep learning; Diagnosis; Lung cancer; Pulmonary diseases; Enhanced good and bad group-based optimizer; Group-based; Heuristics algorithm; Optimizers; Parameter tuning in adaptive multiscale residual densenet; Parameters tuning; Pulmonary emphysema; Pulmonary emphysema detection; Seg-resunet; Semantic segmentation; Semantic Segmentation","","","","","","","4, (2022); Ajmera P., Kharat A., Seth J., Rathi S., Pant R., Gawali M., Kulkarni V., Maramraju R., Kedia I., Botchu R., Khaladkar S., A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography, BMC Medical Imaging, 22, (2022); Ali S., Doumari S.A., Dehghani M., Montazeri Z., Trojovsky P., Ashtiani H.J., A new Good and Bad Groups-Based Optimizer for solving various optimization problems, Applied Sciences, 11, 10, (2021); Ananthajothi K., Rajasekar P., Amanullah M., Enhanced U-Net-based segmentation and heuristically improved deep neural network for pulmonary emphysema diagnosis, Sādhanā, 48, (2023); Badrinarayanan V., Kendall A., Cipolla R., Segnet, A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, pp. 2481-2495, (2017); Chen J., Lu Y., Yu Q., Luo X., Adeli E., Wang Y., Lu L., Yuille A.L., Zhou Y., (2021); Chen S., Han Y., Lin J., Zha X., Kong P., Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection, Artificial Intelligence in Medicine, 107, (2020); Chen Y., Hou X., Yang Y., Ge Q., Zhou Y., Nie S., A novel deep learning model based on multi-scale and multi-view for detection of pulmonary nodules, Journal of Digital Imaging, 36, pp. 688-699, (2023); 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72, (2022); pp. 203-215, (2021); Joy D., Multistage ensemble learning model with weighted voting and genetic algorithm optimization strategy for detecting chronic obstructive pulmonary disease, IEEE Access, 9, pp. 48640-48657, (2021); Jung, Tony, Vij N., Early diagnosis and real-time monitoring of regional lung function changes to prevent chronic obstructive pulmonary disease progression to severe emphysema, Journal of Clinical Medicine, 10, 24, (2021); Kozuka T., Matsukubo Y., Kadoba T., Oda T., Suzuki A., Hyodo T., Im S., Kaida H., Yagyu Y., Tsurusaki M., Matsuki M., Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography, Japanese Journal of Radiology, 38, pp. 1052-1061, (2020); Kumar S., Bhagat V., Sahu P., Chaube M.K., Behera A.K., Guizani M., Gravina R., Dio D.M., Fortino G., Curry E., Alsamhi S.H., A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases, Computer Methods and Programs in Biomedicine, 243, (2024); 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Wang X., Zhang C., Zhang S., (2021); Xu Y., Hou S., Wang X., Li D., Lu L., A medical image segmentation method based on improved UNet 3+ network, Diagnostics, 13, (2023); Yildiz B.S., Bureerat S., Panagant N., Mehta P., Yildiz A.R., Reptile search algorithm and kriging surrogate model for structural design optimization with natural frequency constraints, Materials Testing, 64, pp. 1504-1511, (2022); Yoo H., Kim K.H., Singh R., Digumarthy S.R., Kalra M.K., Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs, JAMA Network Open, 3, pp. e2017135-e, (2020); You S., Park J.H., Park B., Shin H.B., Ha T., Yun J.S., Park K.J., Jung Y., Kim Y.N., Kim M., Sun J.S., The diagnostic performance and clinical value of deep learning-based nodule detection system concerning influence of location of pulmonary nodule, Insights into Imaging, 14, (2023); Zhang L., Jiang B., Wisselink H.J., Vliegenthart R., Xie X., COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images, The British Journal of Radiology, 95, 2021, (2022); Zhang Y., Tian Y., Kong Y., Zhong B., Fu Y., Residual dense network for image super-resolution, Proceedings of the IEEE Conference on computer vision and pattern recognition, pp. 2472-2481, (2018); (2024); Zhu X., Ye J., Zhou Z., Lee R., Shi B., Wang Z., Sun J., Huang W., Xia W., Characterization of different reconstruction techniques on computer-aided system for detection of pulmonary nodules in lung from low-dose CT protocol, Journal of Radiation Research and Applied Sciences, 15, pp. 212-217, (2022)","R.K. Patra; Department of Computer Science and Engineering, CMR Technical Campus, Hyderabad, India; email: rajkumarpatra.cse@cmrtc.ac.in","","Elsevier Ltd","","","","","","09574174","","ESAPE","","English","Expert Sys Appl","Article","Final","","Scopus","2-s2.0-85213202582"
"Lin L.; Liao Z.-H.; Li C.-Q.","Lin, Lu (59048446900); Liao, Zeng-hua (58895562600); Li, Chao-qian (7501680496)","59048446900; 58895562600; 7501680496","Insight into the role of mitochondrion-related gene anchor signature in mitochondrial dysfunction of neutrophilic asthma","2024","Journal of Asthma","61","9","","912","929","17","1","10.1080/02770903.2024.2311241","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185482635&doi=10.1080%2f02770903.2024.2311241&partnerID=40&md5=b4954489e600c5a28922d5ff56b353df","Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Guangxi Province, Nanning, China","Lin L., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Guangxi Province, Nanning, China; Liao Z.-H., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Guangxi Province, Nanning, China; Li C.-Q., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Guangxi Province, Nanning, China","Objective: At present, targeting molecular-pharmacological therapy is still difficult in neutrophilic asthma. The investigation aims to identify and validate mitochondrion-related gene signatures for diagnosis and specific targeting therapeutics in neutrophilic asthma. Methods: Bronchial biopsy samples of neutrophilic asthma and healthy people were identified from the GSE143303 dataset and then matched with human mitochondrial gene data to obtain mitochondria-related differential genes (MitoDEGs). Signature mitochondria-related diagnostic markers were jointly screened by support vector machine (SVM) analysis, least absolute shrinkage, and selection operator (LASSO) regression. The expression of marker MitoDEGs was evaluated by validation datasets GSE147878 and GSE43696. The diagnostic value was evaluated by receiver operating characteristic (ROC) curve analysis. Meanwhile, the infiltrating immune cells were analyzed by the CIBERSORT. Finally, oxidative stress level and mitochondrial functional morphology for asthmatic mice and BEAS-2B cells were evaluated. The expression of signature MitoDEGs was verified by qPCR. Results: 67 MitoDEGs were identified. Five signature MitoDEGs (SOD2, MTHFD2, PPTC7, NME6, and SLC25A18) were further screened out. The area under the curve (AUC) of signature MitoDEGs presented a good diagnostic performance (more than 0.9). There were significant differences in the expression of signature MitoDEGs between neutrophilic asthma and non-neutrophilic asthma. In addition, the basic features of mitochondrial dysfunction were demonstrated by in vitro and in vivo experiments. The expression of signature MitoDEGs in the neutrophilic asthma mice presented a significant difference from the control group. Conclusions: These MitoDEGs signatures in neutrophilic asthma may hold potential as anchor diagnostic and therapeutic targets in neutrophilic asthma. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.","airway inflammation; anchor diagnostic markers; bioinformatic analysis; mitochondrial damage/dysfunction; Neutrophilic asthma","Animals; Asthma; Female; Humans; Male; Mice; Mitochondria; Neutrophils; Oxidative Stress; complementary DNA; cytokine; gamma interferon; glutathione; immunoglobulin E; interleukin 17; interleukin 4; interleukin 5; malonaldehyde; microRNA; RNA; transcription factor; airway; allergic asthma; animal experiment; animal model; area under the curve; Article; asthma; BEAS-2B cell line; bronchus biopsy; controlled study; cytokine release; diagnostic value; differential gene expression; disorders of mitochondrial functions; enzyme linked immunosorbent assay; eosinophilic asthma; female; functional enrichment analysis; functional morphology; gene expression; goblet cell; histopathology; human; human cell; human tissue; immunocompetent cell; in vitro study; inflammatory cell; least absolute shrinkage and selection operator; lung lavage; lung parenchyma; machine learning; major clinical study; male; mitochondrial gene; mitochondrion; mouse; neutrophilic inflammation; nonhuman; oxidative stress; real time polymerase chain reaction; receiver operating characteristic; respiratory tract inflammation; scoring system; severe asthma; support vector machine; transmission electron microscopy; animal; asthma; genetics; immunology; metabolism; neutrophil; pathology","","gamma interferon, 82115-62-6; glutathione, 70-18-8; immunoglobulin E, 37341-29-0; malonaldehyde, 542-78-9; RNA, 63231-63-0","Cytoscape","","Natural Science Foundation of Guangxi Province, (2020GXNSFDA238003); Natural Science Foundation of Guangxi Province","This research has been financially supported under the Guangxi Natural Science Foundation (2020GXNSFDA238003). And we also thank the hard work of Gene Expression Omnibus (GEO) dataset, GSEA, Lasso, SVM-RFE team members. 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Li; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 6 Shuangyong Road, Guangxi Province, 530021, China; email: lichaoqiangood@163.com","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","38294718","English","J. Asthma","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85185482635"
"Mallick I.; Panchal P.; Kadam S.; Mohite P.; Scheele J.; Seiz W.; Agarwal A.; Sharma O.P.","Mallick, Ishita (58611775100); Panchal, Pradnya (58611775200); Kadam, Smita (57210467878); Mohite, Priyanka (58612250200); Scheele, Jürgen (58612090400); Seiz, Werner (6507325392); Agarwal, Amit (58611929900); Sharma, Om Prakash (57803162300)","58611775100; 58611775200; 57210467878; 58612250200; 58612090400; 6507325392; 58611929900; 57803162300","In-silico identification and prioritization of therapeutic targets of asthma","2023","Scientific Reports","13","1","15706","","","","1","10.1038/s41598-023-42803-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171888913&doi=10.1038%2fs41598-023-42803-w&partnerID=40&md5=83ab67ee3ccb826eb3682266b5fc23b2","Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Innoplexus AG, Frankfurter Str. 27, Eschborn, 65760, Germany","Mallick I., Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Panchal P., Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Kadam S., Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Mohite P., Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Scheele J., Innoplexus AG, Frankfurter Str. 27, Eschborn, 65760, Germany; Seiz W., Innoplexus AG, Frankfurter Str. 27, Eschborn, 65760, Germany; Agarwal A., Innoplexus Consulting Pvt. Ltd, 7th Floor, Midas Tower, Next to STPI Building, Phase 1, Hinjewadi Rajiv Gandhi Infotech Park, Hinjawadi, Maharashtra, Pune, 411057, India; Sharma O.P., Innoplexus AG, Frankfurter Str. 27, Eschborn, 65760, Germany","Asthma is a “common chronic disorder that affects the lungs causing variable and recurring symptoms like repeated episodes of wheezing, breathlessness, chest tightness and underlying inflammation. The interaction of these features of asthma determines the clinical manifestations and severity of asthma and the response to treatment"" [cited from: National Heart, Lung, and Blood Institute. Expert Panel 3 Report. Guidelines for the Diagnosis and Management of Asthma 2007 (EPR-3). Available at: https://www.ncbi.nlm.nih.gov/books/NBK7232/ (accessed on January 3, 2023)]. As per the WHO, 262 million people were affected by asthma in 2019 that leads to 455,000 deaths (https://www.who.int/news-room/fact-sheets/detail/asthma). In this current study, our aim was to evaluate thousands of scientific documents and asthma associated omics datasets to identify the most crucial therapeutic target for experimental validation. We leveraged the proprietary tool Ontosight® Discover to annotate asthma associated genes and proteins. Additionally, we also collected and evaluated asthma related patient datasets through bioinformatics and machine learning based approaches to identify most suitable targets. Identified targets were further evaluated based on the various biological parameters to scrutinize their candidature for the ideal therapeutic target. We identified 7237 molecular targets from published scientific documents, 2932 targets from genomic structured databases and 7690 dysregulated genes from the transcriptomics and 560 targets from genomics mutational analysis. In total, 18,419 targets from all the desperate sources were analyzed and evaluated though our approach to identify most promising targets in asthma. Our study revealed IL-13 as one of the most important targets for asthma with approved drugs on the market currently. TNF, VEGFA and IL-18 were the other top targets identified to be explored for therapeutic benefit in asthma but need further clinical testing. HMOX1, ITGAM, DDX58, SFTPD and ADAM17 were the top novel targets identified for asthma which needs to be validated experimentally. © 2023, Springer Nature Limited.","","Academies and Institutes; Asthma; Computational Biology; Dyspnea; Gene Expression Profiling; Humans; asthma; bioinformatics; dyspnea; gene expression profiling; genetics; human; organization","","","","","CEO of Innoplexus AG and Amit Ananpara","We would like to express our gratitude and appreciation to Holger Hoffmann, CEO of Innoplexus AG and Amit Ananpara, Director of Innoplexus Consulting Pvt. Ltd. for providing continuous encouragement and infrastructure facilities support for this publication. We would like to extend our sincere thanks to Innoplexus IT team for timely support and Md. Saifullah Khan for his contributions and support in reproducing high quality images of all the results and outputs.","Papi A., Brightling C., Pedersen S.E., Reddel H.K., Asthma, Lancet, 391, pp. 783-800, (2018); Rabe K.F., Schmidt D.T., Pharmacological treatment of asthma today, Eur. Respir. J. Suppl., 34, pp. 34s-40s, (2001); Heffler E., Madeira L.N.G., Ferrando M., Puggioni F., Racca F., Malvezzi L., Et al., Inhaled corticosteroids safety and adverse effects in patients with asthma, J. Allergy Clin. Immunol. Pract., (2018); Montuschi P., Peters-Golden M.L., Leukotriene modifiers for asthma treatment, Clin. Exp. Allergy., (2010); Barnes P.J., Theophylline, Am. J. Respir. Crit. 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(Lausanne)., 5, (2018); Hoffmann-Petersen B., Suffolk R., Petersen J.J.H., Petersen T.H., Brasch-Andersen C., Host A., Et al., Association of serum surfactant protein D and SFTPD gene variants with asthma in Danish children, adolescents, and young adults, Immun. Inflamm. Dis., 10, pp. 189-200, (2022); Chen J.Y., Cheng W.H., Lee K.Y., Kuo H.P., Chung K.F., Chen C.L., Et al., Abnormal ADAM17 expression causes airway fibrosis in chronic obstructive asthma, Biomed. Pharmacother., 140, (2021); Xepapadaki P., Adachi Y., Beltran C.F., El-Sayed Z.A., Gomez R.M., Hossny E., Filipovic I., Le Souef P., Morais-Almeida M., Miligkos M., Nieto A., Utility of biomarkers in the diagnosis and monitoring of asthmatic children, World Allergy Organ J., 16, (2023)","O.P. Sharma; Innoplexus AG, Eschborn, Frankfurter Str. 27, 65760, Germany; email: om.sharma@innoplexus.com","","Nature Research","","","","","","20452322","","","37735578","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85171888913"
"McHeick H.; Diab F.","McHeick, Hamid (6508070583); Diab, Farah (58189617800)","6508070583; 58189617800","Discrete separation of patients’ profiles for chronical obstructive pulmonary disease context-aware healthcare efficient systems","2023","IAES International Journal of Artificial Intelligence","12","3","","1508","1520","12","1","10.11591/ijai.v12.i3.pp1508-1520","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152938639&doi=10.11591%2fijai.v12.i3.pp1508-1520&partnerID=40&md5=3dfb8d875fdb12186e48830048812b74","Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada; Department of Computer Science-I, Ecole doctorale, Beirut, Lebanon","McHeick H., Department of Computer Science and Mathematics, University of Quebec at Chicoutimi, Chicoutimi, QC, Canada; Diab F., Department of Computer Science-I, Ecole doctorale, Beirut, Lebanon","According to the Public Health Agency of Canada (PHAC), the symptoms of chronic obstructive pulmonary disease (COPD) are shortness of breath, coughing, and sputum production. Many studies estimate that COPD will become the third-leading cause of death worldwide by 2030 (WHO, 2008). Pervasive healthcare systems cover healthcare issues, including chronic diseases; they help patients to manage their own health information and healthcare services at any time and in any place. We developed a COPD healthcare system based on a combination of the parameters of patients. The main goal is to avoid the severe phases of the disease by monitoring them. This combination of risk factors provides in total 600 profiles from data, with 88.5% accuracy. However, many studies have focused on and shown the issues of the effectiveness and accuracy of these systems. The problem is to apply a new classification model to detect the severe phases of the disease early. Therefore, instead of working on COPD parameters, we design and validate a profile-based classification model of patients. This model will facilitate the building of a rule-based framework. In addition, the accuracy of our extended COPD system is improved using the classification and separation of patients’ profiles. © 2023, Institute of Advanced Engineering and Science. All rights reserved.","Classification of profiles; Data combination of chronic; Efficiency of context-aware; Healthcare systems; Machine learning; obstructive pulmonary disease; Rule-based system; Separation of concerns","","","","","","Natural Sciences and Engineering Research Council of Canada, NSERC, (RGPIN-2017-05521)","This work was sponsored by Natural Sciences and Engineering Research Council of Canada (NSERC), and computer science department of the University of Quebec at Chicoutimi (Quebec), Canada (Research funding: RGPIN-2017-05521). Author Contributions: H.M. conceived the presented idea. H.M.","What is copd?; Sheet L. F., The lung association, lung fact sheet, (2015); Dumitrascu G. A., Chronic obstructive pulmonary disease (COPD), Decision Making in Anesthesiology: An Algorithmic Approach: Fourth Edition, pp. 100-101, (2007); Ajami H., McHeick H., Mustapha K., A pervasive healthcare system for COPD patients, Diagnostics, 9, 4, (2019); Gisler S., Fewer copd patients readmitted after video rehabilitation, study says, (2019); McHeick H., Sayegh J., A self-adaptive and efficient context-aware healthcare model for copd diseases, Informatics, 8, 3, (2021); Kang D. O., Lee H. J., Ko E. J., Kang K., Lee J., A wearable context aware system for ubiquitous healthcare, Annual International Conference of the IEEE Engineering in Medicine and Biology-Proceedings, pp. 5192-5195, (2006); Oliveira M., Et al., A context-aware framework for health care governance decision-making systems: A model based on the Brazilian digital TV, 2010 IEEE International Symposium on “A World of Wireless, Mobile and Multimedia Networks”, WoWMoM 2010-Digital Proceedings, (2010); Kim J., Chung K. Y., Ontology-based healthcare context information model to implement ubiquitous environment, Multimedia Tools and Applications, 71, 2, pp. 873-888, (2014); Lo C. C., Chen C. H., Cheng D. Y., Kung H. Y., Ubiquitous healthcare service system with context-awareness capability: Design and implementation, Expert Systems with Applications, 38, 4, pp. 4416-4436, (2011); McHeick H., Ajami H., Elkhaled Z., Survey of health care context models and prototyping of healthcare context framework, Simulation Series, 48, 9, pp. 422-429, (2016); Hickey S. J., Naive Bayes classification of public health data with greedy feature slection, Communications of the IIMA, 13, 2, pp. 87-98, (2013); Chen M., Hao Y., Hwang K., Wang L., Wang L., Disease prediction by machine learning over big data from healthcare communities, IEEE Access, 5, pp. 8869-8879, (2017); Ullah M. R., Bhuiyan M. A. R., Das A. K., IHEMHA: Interactive healthcare system design with emotion computing and medical history analysis, 2017 6th International Conference on Informatics, Electronics and Vision and 2017 7th International Symposium in Computational Medical and Health Technology, ICIEV-ISCMHT 2017, 2018, pp. 1-8, (2018); Himes B. E., Dai Y., Kohane I. S., Weiss S. T., Ramoni M. F., Prediction of chronic obstructive pulmonary disease (COPD) in Asthma patients using electronic medical records, Journal of the American Medical Informatics Association, 16, 3, pp. 371-379, (2009); Amalakuhan B., Kiljanek L., Parvathaneni A., Hester M., Cheriyath P., Fischman D., A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem, Journal of Community Hospital Internal Medicine Perspectives, 2, 1, (2012); Raghavan N., Et al., Components of the COPD assessment test (CAT) associated with a diagnosis of COPD in a random population sample, COPD: Journal of Chronic Obstructive Pulmonary Disease, 9, 2, pp. 175-183, (2012); Mcheick H., Saleh L., Mili H., Ajami H., HCES: Helper context engine system to predict relevant state of patients in COPD domain using Naïve Bayesian, ACM International Conference Proceeding Series, (2017); Mcheick H., Saleh L., Ajami H., Mili H., Context relevant prediction model for COPD domain using Bayesian belief network, Sensors (Switzerland), 17, 7, (2017); Cavailles A., Et al., Identification of patient profiles with high risk of hospital re-admissions for acute COPD exacerbations (AECOPD) in France using a machine learning model, International Journal of COPD, 15, pp. 949-962, (2020); Vora S., Chintan S., COPD classification using machine learning algorithms, International Research Journal of Engineering and Technology, 6, pp. 608-611, (2008); Garcia S., Luengo J., Herrera F., Data preprocessing in data mining, Intelligent Systems Reference Library, 72, (2015); Ahuja Y., Kumar Yadav S., Multiclass classification and support vector machine, Global Journal of Computer Science and Technology Interdisciplinary, 12, 11, pp. 14-19, (2012); Navlani A., Naïve bayes classifier tutorial: With python scikit-learn, (2018); Navlani A., Python decision tree classification with scikit-learn decision tree classifier-data camp, (2018)","H. McHeick; Department of Compter Science and Mathematics, University of Quebec at Chicoutimi, Quebec, 555 Boul De l’Universite, Chicoutimi, G7H-2B1, Canada; email: hamid_mcheick@uqac.ca","","Institute of Advanced Engineering and Science","","","","","","20894872","","","","English","IAES Int. J. Artif. Intell.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85152938639"
"Li S.; Zhu Q.; Huang A.; Lan Y.; Wei X.; He H.; Meng X.; Li W.; Lin Y.; Yang S.","Li, Sijun (55497721700); Zhu, Qingdong (57217142083); Huang, Aichun (57808662300); Lan, Yanqun (59514869700); Wei, Xiaoying (58977909900); He, Huawei (59362706800); Meng, Xiayan (58503379400); Li, Weiwen (59515258600); Lin, Yanrong (57211494935); Yang, Shixiong (58735754700)","55497721700; 57217142083; 57808662300; 59514869700; 58977909900; 59362706800; 58503379400; 59515258600; 57211494935; 58735754700","A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes","2025","BMC Medical Genomics","18","1","7","","","","0","10.1186/s12920-024-02076-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215071495&doi=10.1186%2fs12920-024-02076-2&partnerID=40&md5=8f0643527af69297685ee4d3be297748","Infectious Disease Laboratory, The Fourth People’s Hospital of Nanning, Nanning, China; Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Administrative Office, The Fourth People’s Hospital of Nanning, Nanning, China","Li S., Infectious Disease Laboratory, The Fourth People’s Hospital of Nanning, Nanning, China; Zhu Q., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Huang A., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Lan Y., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Wei X., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; He H., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Meng X., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Li W., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Lin Y., Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; Yang S., Administrative Office, The Fourth People’s Hospital of Nanning, Nanning, China","Background: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. Methods: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. Results: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. Conclusion: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease. © The Author(s) 2024.","Chronic obstructive pulmonary disease; Disulfidptosis; Disulfidptosis-related genes; Immune cells; Machine learning model","Gene Expression Profiling; Gene Regulatory Networks; Humans; Machine Learning; Pulmonary Disease, Chronic Obstructive; CD4 antigen; Article; bioinformatics; CD8+ T lymphocyte; cell infiltration; cell population; chronic obstructive lung disease; computer assisted tomography; controlled study; diagnostic test accuracy study; differential gene expression; disulfidptosis; forced vital capacity; gene expression; gene expression profiling; human; human tissue; immune response; immune-related gene; lung function; lung parenchyma; machine learning; nomogram; oxidative stress; prediction; protein expression; protein protein interaction; receiver operating characteristic; risk factor; upregulation; validation process; weighted gene co expression network analysis; gene regulatory network; genetics; immunology","","","R package","","Home for Researchers editorial team; Guangxi Zhuang Autonomous Region Health and Family Planning Commission, (ZA20231211); Guangxi Zhuang Autonomous Region Health and Family Planning Commission","Funding text 1: We thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.; Funding text 2: This study was supported by grants from Guangxi Zhuang Autonomous Region Health and Family Planning Commission ( Grant No.Z20211324 and No. ZA20231211). 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Lin; Department of Tuberculosis, The Fourth People’s Hospital of Nanning, Nanning, China; email: linyanrong2009@126.com; S. Yang; Administrative Office, The Fourth People’s Hospital of Nanning, Nanning, China; email: 13407719256@163.com","","BioMed Central Ltd","","","","","","17558794","","","39780155","English","BMC Med. Genomics","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85215071495"
"Sueaseenak D.; Boonsat P.; Tantisatirapong S.; Rujipong P.; Tulatamakit S.; Phokaewvarangkul O.","Sueaseenak, Direk (24734104500); Boonsat, Peeravit (59654202600); Tantisatirapong, Suchada (36246619100); Rujipong, Petcharat (57224091039); Tulatamakit, Sirapat (57204160301); Phokaewvarangkul, Onanong (57204085936)","24734104500; 59654202600; 36246619100; 57224091039; 57204160301; 57204085936","Early Diagnosis of Pneumonia and Chronic Obstructive Pulmonary Disease with a Smart Stethoscope with Cloud Server-Embedded Machine Learning in the Post-COVID-19 Era","2025","Biomedicines","13","2","354","","","","0","10.3390/biomedicines13020354","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218871658&doi=10.3390%2fbiomedicines13020354&partnerID=40&md5=260ec25e23b130afc9a37393afd794f0","Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Department of Adult and Gerontological Nursing, Faculty of Nursing, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Department of Medicine, Faculty of Medicine, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand","Sueaseenak D., Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Boonsat P., Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Tantisatirapong S., Department of Biomedical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Rujipong P., Department of Adult and Gerontological Nursing, Faculty of Nursing, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Tulatamakit S., Department of Medicine, Faculty of Medicine, Srinakharinwirot University, Nakhon Nayok, 26120, Thailand; Phokaewvarangkul O., Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand","Background/Objectives: Respiratory diseases are common and result in high mortality, especially in the elderly, with pneumonia and chronic obstructive pulmonary disease (COPD). Auscultation of lung sounds using a stethoscope is a crucial method for diagnosis, but it may require specialized training and the involvement of pulmonologists. This study aims to assist medical professionals who are non-pulmonologist doctors in early screening for pneumonia and COPD by developing a smart stethoscope with cloud server-embedded machine learning to diagnose lung sounds. Methods: The smart stethoscope was developed using a Micro-Electro-Mechanical system (MEMS) microphone to record lung sounds in the mobile application and then send them wirelessly to a cloud server for real-time machine learning classification. Results: The model of the smart stethoscope classifies lung sounds into four categories: normal, pneumonia, COPD, and other respiratory diseases. It achieved an accuracy of 89%, a sensitivity of 89.75%, and a specificity of 95%. In addition, testing with healthy volunteers yielded an accuracy of 80% in distinguishing normal and diseased lungs. Moreover, the performance comparison between the smart stethoscope and two commercial auscultation stethoscopes showed comparable sound quality and loudness results. Conclusions: The smart stethoscope holds great promise for improving healthcare delivery in the post-COVID-19 era, offering the probability of the most likely respiratory conditions for early diagnosis of pneumonia, COPD, and other respiratory diseases. Its user-friendly design and machine learning capabilities provide a valuable resource for non-pulmonologist doctors by delivering timely, evidence-based diagnoses, aiding treatment decisions, and paving the way for more accessible respiratory care. © 2025 by the authors.","chronic obstructive pulmonary disease; machine learning; mobile application; pneumonia; respiratory disease; respiratory sound","","","","","","National Research Council of Thailand, NRCT","This research was funded by the National Research Council of Thailand (NRCT).","Khaltaev N., Axelrod S., Chronic respiratory diseases global mortality trends, treatment guidelines, life style modifications, and air pollution: Preliminary analysis, J. Thorac Dis, 11, pp. 2643-2655, (2019); Methinee Jantiya P.C., Pussadee K., The Quality of Life in Patients with Chronic Obstructive Pulmonary Disease in Saraburi Hospital, Ramathibodi Nurs. 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Virol, 94, pp. 3698-3705, (2022); Jiao Z., Choi J.W., Halsey K., Tran T.M.L., Hsieh B., Wang D., Chang K., Wu J., Collins S.A., Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: A retrospective study, Lancet Digit. Health, 3, pp. e286-e294, (2021); Laguarta J., Hueto F., Subirana B., COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings, IEEE Open J. Eng. Med. Biol, 1, pp. 275-281, (2020); Khan S.I., Pachori R.B., Automated classification of lung sound signals based on empirical mode decomposition, Expert Syst. Appl, 184, (2021); Fraiwan L., Hassanin O., Fraiwan M., Khassawneh B., Ibnian A.M., Alkhodari M., Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers, Biocybern. Biomed. Eng, 41, pp. 1-14, (2021); Islam M.A., Bandyopadhyaya I., Bhattacharyya P., Saha G., Classification of Normal, Asthma and COPD Subjects Using Multichannel Lung Sound Signals, Proceedings of the 2018 International Conference on Communication and Signal Processing (ICCSP); Revathi A., Sasikaladevi N., Arunprasanth D., Amirtharajan R., Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models, Neural Comput. Appl, 34, pp. 8155-8172, (2022); Haider N.S., Behera A.K., Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybern. Biomed. 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Health, 11, pp. e1849-e1850, (2023); Zhang J., Wang H.S., Zhou H.Y., Dong B., Zhang L., Zhang F., Liu S.J., Wu Y.F., Yuan S.H., Tang M.Y., Et al., Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children, Front. Pediatr, 9, (2021); Diniz P., Simpson D., De Stefano A., Gismondi R., Digital Signal Processing with Applications in Medicine; in Encyclopedia of Life Support Systems (EOLSS), Developed under the Auspices of the UNESCO, (2003); Korenbaum V.I., Shiryaev A.D., Features of Sound Conduction in Human Lungs in the 80–1000 Hz and 10–19 KHz Frequency Ranges, Acoust. Phys, 66, pp. 548-558, (2020); Nowak D.J., Schamid P.E., Introduction to Digital Filters, IEEE Trans. Electromagn. Compat, EMC-10, pp. 210-220, (1968); Raihan M.J., Nahid A.-A., Chapter 3—Classification of histopathological colon cancer images using particle swarm optimization-based feature selection algorithm, Diagnostic Biomedical Signal and Image Processing Applications with Deep Learning Methods, pp. 61-82, (2023); Ostertagova E., Ostertag O., Kovac J., Methodology and Application of the Kruskal-Wallis Test, Appl. Mech. Mater, 611, pp. 115-120, (2014); Audevart A., Banachewicz K., Massaron L., Machine Learning Using TensorFlow Cookbook: Create Powerful Machine Learning Algorithms with TensorFlow, (2021); Pujolar G., Oliver-Angles A., Vargas I., Vazquez M.L., Changes in Access to Health Services during the COVID-19 Pandemic: A Scoping Review, Int. J. Environ. Res. Public Health, 19, (2022); Hirko K.A., Kerver J.M., Ford S., Szafranski C., Beckett J., Kitchen C., Wendling A.L., Telehealth in response to the COVID-19 pandemic: Implications for rural health disparities, J. Am. Med. Inform. Assoc, 27, pp. 1816-1818, (2020); Saeed S., Body R., Towards evidence based emergency medicine: Best BETs from the Manchester Royal Infirmary. Auscultating to diagnose pneumonia, Emerg. Med. J, 24, pp. 294-296, (2007); Soriano J.B., Zielinski J., Price D., Screening for and early detection of chronic obstructive pulmonary disease, Lancet, 374, pp. 721-732, (2009); Yang J., Soltan A.A.S., Clifton D.A., Machine learning generalizability across healthcare settings: Insights from multi-site COVID-19 screening, npj Digit. Med, 5, (2022)","O. Phokaewvarangkul; Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand; email: oji@chulapd.org","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","22279059","","","","English","Biomedicines","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85218871658"
"Hernández-Arango A.; Arias M.I.; Pérez V.; Chavarría L.D.; Jaimes F.","Hernández-Arango, Alejandro (59137220600); Arias, María Isabel (59422022800); Pérez, Viviana (59422442700); Chavarría, Luis Daniel (59421885600); Jaimes, Fabian (55891750800)","59137220600; 59422022800; 59422442700; 59421885600; 55891750800","Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases","2025","Journal of Medical Systems","49","1","19","","","","0","10.1007/s10916-025-02140-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217852645&doi=10.1007%2fs10916-025-02140-z&partnerID=40&md5=e888fd328caf43cb3715eed3739ad4b3","Department of Internal Medicine, University of Antioquia, Medellín, Colombia; Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia; Health Information Systems Professional Living Lab., Medellín, Colombia; Data Scientist, National University, Medellín, United States; Faculty of Medicine, Department of Internal Medicine, Hospital Alma Mater de Antioquia, University of Antioquia, University of Antioquia, Carrera 51 A # 62 – 42, Medellín, Colombia","Hernández-Arango A., Department of Internal Medicine, University of Antioquia, Medellín, Colombia, Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia, Faculty of Medicine, Department of Internal Medicine, Hospital Alma Mater de Antioquia, University of Antioquia, University of Antioquia, Carrera 51 A # 62 – 42, Medellín, Colombia; Arias M.I., Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia, Health Information Systems Professional Living Lab., Medellín, Colombia; Pérez V., Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia; Chavarría L.D., Hospital Alma Mater de Antioquia, University of Antioquia, Medellín, Colombia, Data Scientist, National University, Medellín, United States; Jaimes F., Department of Internal Medicine, University of Antioquia, Medellín, Colombia","Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. This cohort study evaluated three machine learning algorithms—XGBoost, Elastic Net logistic regression, and an Artificial Neural Network—to develop a prediction model for three outcomes: mortality, hospitalization, and emergency department visits. The objective was to build a clinical decision support system for patients with noncommunicable diseases treated at the Alma Mater Hospital complex in Medellín, Colombia. We collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71.83 years, with 63.8% women and 29.7% receiving home care. The most prevalent medical conditions were diabetes (52.9%), hypertension (67.2%), dyslipidemia (57.3%), and COPD (19.4%). For mortality prediction, the Elastic Net logistic regression model achieved an AUCROC of 0.883 (95% CI: 0.848–0.917), the XGBoost model reached an AUCROC of 0.896 (95% CI: 0.865–0.927), and the Neural Network achieved 0.886 (95% CI: 0.853–0.916). For hospitalization, the Elastic Net model had an AUCROC of 0.952 (95% CI: 0.937–0.965), the XGBoost model achieved 0.963 (95% CI: 0.952–0.974), and the Neural Network scored 0.932 (95% CI: 0.915–0.948). For emergency department visits, the AUCROC values were 0.980 (95% CI: 0.971–0.987) for Elastic Net, 0.977 (95% CI: 0.967–0.986) for XGBoost, and 0.976 (95% CI: 0.968–0.982) for the neural network. A dashboard was developed to interact with an ensemble risk categorization segmenting patient risk in the cohort to aid in clinical decision-making. A clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with Noncommunicable Diseases for effective decision-making. © The Author(s) 2025.","Artificial intelligence; Clinical decision support system; Emergency consultation; Hospitalization; Mortality; Predictive models","Aged; Aged, 80 and over; Algorithms; Colombia; Decision Support Systems, Clinical; Electronic Health Records; Emergency Service, Hospital; Female; Hospitalization; Humans; Logistic Models; Machine Learning; Male; Middle Aged; Neural Networks, Computer; Noncommunicable Diseases; Risk Assessment; adverse outcome; aged; Article; artificial intelligence; artificial neural network; clinical decision making; clinical decision support system; clinical feature; clinical outcome; cohort analysis; controlled study; diabetes mellitus; dyslipidemia; elastic tissue; electronic medical record; emergency department visit; emergency ward; female; health care system; home care; hospitalization; human; hypertension; logistic regression analysis; machine learning; major clinical study; male; mortality; non communicable disease; prediction; predictive model; retrospective study; risk assessment; XGBoost model; algorithm; artificial neural network; clinical decision support system; Colombia; electronic health record; epidemiology; hospital emergency service; hospitalization; middle aged; organization and management; procedures; risk assessment; statistical model; very elderly","","","","","","","Gallardo-Solarte K., Benavides-Acosta K.F.P., Rosales-Jimenez R., Costos de la enfermedad crónica no transmisible: la realidad colombiana, Rev. Cienc. Salud, 14, 1, pp. 103-114, (2016); Orueta Mendia J.F., Garcia-Alvarez A., Alonso-Moran E., Nuno-Solinis R., Desarrollo de un modelo de predicción de riesgo de hospitalizaciones no programadas en el País Vasco, Rev. Esp. Salud Publica, 88, 2, pp. 251-260, (2014); Gorbanev I., Cortes Martinez A.E., Agudelo S., Londono, Yepes Lujan F.J., Grupos relacionados por el diagnóstico: experiencia en tres hospitales de alta complejidad en Colombia, Univ. Médica, 57, 2, pp. 171-181, (2016); Nolte E., Assessing chronic disease management in European health systems, Europe, (2015); Stagg B.C., Et al., Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic, Ophthalmol Glaucoma, 4, 1, pp. 5-9, (2021); Garcia-Arango V., Osorio-Ciro J., Aguirre-Acevedo D., Vanegas-Vargas C., Clavijo-Usuga C., Gallo-Villegas J., Validación predictiva de un método de clasificación funcional en adultos mayores, Rev. Panam. Salud Publica, 45, (2021); Collins G.S., Et al., TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, BMJ, 385, (2024); Calderon-Larranaga A., Et al., Assessing and measuring chronic multimorbidity in the older population: A proposal for its operationalization, J. Gerontol. A Biol. Sci. Med. Sci, (2016); Faisal S., Tutz G., Multiple imputation using nearest neighbor methods, Inf. Sci. (Ny), 570, pp. 500-516, (2021); Pollard T.J., Johnson A.E.W., Raffa J.D., Mark R.G., tableone: An open source Python package for producing summary statistics for research papers, JAMIA Open, 1, 1, pp. 26-31, (2018); Gareth J., Daniela W., Trevor H., Robert T., An Introduction to Statistical Learning: With Applications in R, (2013); Friedman J., Hastie T., Tibshirani R., Regularization Paths for Generalized Linear Models via Coordinate Descent, J. Stat. Softw, 33, 1, pp. 1-22, (2010); Chen T., Guestrin C., XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, (2016); Ali Y.A., Awwad E.M., Al-Razgan M., Maarouf A., Hyperparameter search for machine learning algorithms for optimizing the computational complexity, Processes (Basel), 11, 2, (2023); DeLong E.R., DeLong D.M., Clarke-Pearson D.L., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Vasey B., Et al., Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI, Nat. Med, 28, 5, pp. 924-933, (2022); Gianfrancesco M.A., Tamang S., Yazdany J., Schmajuk G., Potential biases in machine learning algorithms using electronic health record data, JAMA Intern. Med, 178, 11, pp. 1544-1547, (2018); Li F., Xin H., Zhang J., Fu M., Zhou J., Lian Z., Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database, BMJ Open, 11, 7, (2021); Choi S.W., Ko T., Hong K.J., Kim K.H., Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients, Healthc. Inform. Res, 25, 4, pp. 305-312, (2019); Khera R., Et al., Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction, JAMA Cardiol, 6, 6, pp. 633-641, (2021); MacKay E.J., Et al., Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations, PLoS One, 16, 6, (2021)","A. Hernández-Arango; Department of Internal Medicine, University of Antioquia, Medellín, Colombia; email: alejandro.hernandeza@udea.edu.co","","Springer","","","","","","01485598","","JMSYD","39900784","English","J. Med. Syst.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85217852645"
"Narmadha A.P.; Gobalakrishnan N.","Narmadha, A.P. (59403246000); Gobalakrishnan, N. (57193202395)","59403246000; 57193202395","HET-RL: Multiple pulmonary disease diagnosis via hybrid efficient transformers based representation learning model using multi-modality data","2025","Biomedical Signal Processing and Control","100","","107157","","","","0","10.1016/j.bspc.2024.107157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208583909&doi=10.1016%2fj.bspc.2024.107157&partnerID=40&md5=b4e3ccd18e28f282b4e85a5a40aaff88","Department of Information Technology, Sri Venkateswara College of Engineering, Tamil Nadu, India","Narmadha A.P., Department of Information Technology, Sri Venkateswara College of Engineering, Tamil Nadu, India; Gobalakrishnan N., Department of Information Technology, Sri Venkateswara College of Engineering, Tamil Nadu, India","Pulmonary diseases, encompassing conditions such as chronic bronchitis, emphysema, asthma, and pulmonary fibrosis, involve intricate pathophysiological mechanisms affecting the respiratory system, necessitating precise diagnosis and tailored therapeutic approaches. Timely and accurate diagnosis of pulmonary diseases is crucial as it enables early intervention, optimal management, and prevention of complications, thereby improving patient outcomes and quality of life. The scarcity of multi-modality datasets and challenges in accurate diagnosis underscore the complexities faced by deep learning models in achieving comprehensive pulmonary diagnoses, emphasizing the need for enhanced data diversity and algorithmic robustness in addressing diagnostic issues. To overcome these challenges, we have proposed a hybrid efficient transformer based on representation learning named as HET-RL model for accurate various pulmonary disease using multi-modality. Primarily in our work, we utilized multiple data including radiograph, chief complaints and clinical parameters for enhancing the efficiency of model performance. We have performed dual-level pre-processing such as denoising and normalization for amplifying data quality using Steered Filter (SF) and Min-Max normalization, respectively. Then, we proposed HET-RL model which encompasses of Inter-Attention Transformer (IAT) and Text Analyzer Transformer (TAT) for data analyzing. In which, appropriate features are analyzed and extracted from radiograph (CT scan) by IAT and TAT with representation learning (RL) encode the text and extract significant information from both chief complaint clinical parameters. Finally, the extracted features from hybrid transformers are fused by adapting Fusion Network and then pulmonary disease are classified into multiple classes. Incorporating diverse data sources, including results of laboratory test and patient demographic characteristics, our model demonstrated superior performance compared to non-unified multimodal and an image-only model diagnosis model. It exhibited a 12% and 9% improvement in identifying pulmonary disease. Multimodal hybrid transformer-based models hold promise for streamlining patient triaging and enhancing the clinical decision-making process. © 2024 Elsevier Ltd","Hybrid Transformer; Multi-modality based diagnosis &Chest X-ray; Pulmonary Disease; Representation learning (RL)","Diagnostic radiography; Lung cancer; Pulmonary diseases; Clinical parameters; Condition; Disease diagnosis; Hybrid transformer; Learning models; Multi-modal; Multi-modality; Multi-modality based diagnose &chest X-ray; Representation learning; Text analyzers; Article; bronchiectasis; chronic obstructive lung disease; clinical assessment; clinical decision making; comparative study; computer assisted tomography; data analysis; data quality; data source; demographics; diagnostic accuracy; feature extraction; feature learning (machine learning); human; hybrid efficient transformer based representation learning; image analysis; image processing; inter attention transformer; interstitial lung disease; laboratory test; lung cancer; lung disease; major clinical study; patient triage; pleura effusion; pneumonia; pneumothorax; radiomics; text analyzer transformer; tuberculosis","","","","","","","Althuwaybi A.J.A., Ward C., Is the lung a complex organ to rebuild?, Lung Models for Regenerating Lung Tissue, 3D, pp. 1-17, (2022); Jee A.S., Sheehy R., Hopkins P., Corte T.J., Grainge C., Troy L.K., Keir G.J., Diagnosis and management of connective tissue disease-associated interstitial lung disease in Australia and New Zealand: a position statement from the Thoracic Society of Australia and New Zealand, Respirology, 26, 1, pp. 23-51, (2021); Jadhav S.P., Singh H., Hussain S., Gilhotra R., Mishra A., Prasher P., Gupta G., Introduction to lung diseases, Targeting Cellular Signalling Pathways in Lung Diseases, pp. 1-25, (2021); Alharbi K.S., Fuloria N.K., Fuloria S., Rahman S.B., Al-Malki W.H., Shaikh M.A.J., Gupta G., Nuclear factor-kappa B and its role in inflammatory lung disease, Chem. 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Inf., 177, (2023); Wang C., Ma J., Zhang S., Shao J., Wang Y., Zhou H.Y., Li W., Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases, npj Digital Med., 5, 1, (2022); Hughes N., Kalra D., Data Standards and Platform Interoperability, Real-World Evidence in Medical Product Development, pp. 79-107, (2023); Alyammahi S.K., Abdin S.M., Alhamad D.W., Elgendy S.M., Altell A.T., Omar H.A., The dynamic association between COVID-19 and chronic disorders: an updated insight into prevalence, mechanisms and therapeutic modalities, Infect. Genet. Evol., 87, (2021); Yadav S., Kaushik A., Sharma S., Simplify the difficult: artificial intelligence and cloud computing in healthcare, IoT and Cloud Computing for Societal Good, pp. 101-124, (2021); Islam S., Elmekki H., Elsebai A., Bentahar J., Drawel N., Rjoub G., Pedrycz W., A comprehensive survey on applications of transformers for deep learning tasks, Expert Syst. Appl., (2023); Zhou H.Y., Yu Y., Wang C., Zhang S., Gao Y., Pan J., Li W., A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics, Nat. Biomed. Eng., pp. 1-13, (2023); Ravi V., Acharya V., Alazab M., A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images, Clust. Comput., 26, 2, pp. 1181-1203, (2023); Shamrat F.J.M., Azam S., Karim A., Islam R., Tasnim Z., Ghosh P., De Boer F., LungNet22: a fine-tuned model for multiclass classification and prediction of lung disease using X-ray images, Journal of Personalized Medicine, 12, 5, (2022); Vieira P., Sousa O., Magalhaes D., Rabelo R., Silva R., Detecting pulmonary diseases using deep features in X-ray images, Pattern Recogn., 119, (2021); Fati S.M., Senan E.M., ElHakim N., Deep and hybrid learning technique for early detection of tuberculosis based on X-ray images using feature fusion, Appl. Sci., 12, 14, (2022); Alshmrani G.M.M., Ni Q., Jiang R., Pervaiz H., Elshennawy N.M., A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images, Alex. Eng. J., 64, pp. 923-935, (2023); Yadav P., Menon N., Ravi V., Vishvanathan S., Lung-GANs: unsupervised representation learning for lung disease classification using chest CT and X-ray images, IEEE Trans. Eng. Manag., (2021); Rajagopal R.D., Karthick R., Meenalochini P., Kalaichelvi T., Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images, Biomed. Signal Process. Control, 79, (2023); Brunese L., Mercaldo F., Reginelli A., Santone A., Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays, Comput. Methods Programs Biomed., 196, (2020); Wu J., Chen P., Li C., Kuo Y., Pai N., Lin C., Multilayer Fractional-Order Machine Vision Classifier for Rapid Typical Lung Diseases Screening on Digital Chest X-Ray Images, IEEE Access, 8, pp. 105886-105902, (2020); Tamal M., Alshammari M., Alabdullah M., Hourani R., Alola H.A., Hegazi T.M., An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray, Expert Syst. Appl., 180, (2020); Podder P., Das S., Mondal M.R., Bharati S., Maliha A., Hasan M.J., Piltan F., LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases, Sensors (Basel, Switzerland), (2023); Singh R.K., Pandey R., Babu R.N., COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays, Neural Comput. & Applic., 33, pp. 8871-8892, (2021); Willer K., Fingerle A.A., Noichl W., De Marco F., Frank M., Urban T., Pfeiffer F., X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study, The Lancet Digital Health, 3, 11, pp. e733-e744, (2021); Agrawal S., Chowdhary A., Agarwala S., Mayya V., Kamath S., S., Content-based medical image retrieval system for lung diseases using deep CNNs, Int. J. Inf. Technol., 14, 7, pp. 3619-3627, (2022); Janarthanan S., Rajendran M., Biju T.S., Ravi N., Sundaramoorthy K., pp. 55-72, (2021); Zhu X., Wolfgruber T.K., Leong L., Jensen M., Scott C., Winham S., Shepherd J.A., Deep learning predicts interval and screening-detected cancer from screening mammograms: a case-case-control study in 6369 women, Radiology, 301, 3, pp. 550-558, (2021); Wang J., Yang Z., Hu X., Li L., Lin K., Gan Z., (2022); Jaegle A., Gimeno F., Brock A., Vinyals O., Zisserman A., Carreira J.; Zhang Y., Deng L., Zhu H., Wang W., Ren Z., Zhou Q., Wang S., Deep learning in food category recognition, Inf. Fusion, 98, (2023); Lu S.Y., Nayak D.R., Wang S.H., Zhang Y.D., A cerebral microbleed diagnosis method via featurenet and ensembled randomized neural networks, Appl. Soft Comput., 109, (2021); Lu S., Zhu Z., Gorriz J.M., Wang S.H., Zhang Y.D., NAGNN: classification of COVID-19 based on neighboring aware representation from deep graph neural network, Int. J. Intell. Syst., 37, 2, pp. 1572-1598, (2022)","N. Gobalakrishnan; Department of Information Technology, Sri Venkateswara College of Engineering, Tamil Nadu, India; email: gobalakrishnanse@gmail.com","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85208583909"
"Hu W.; Yang X.; Wang L.; Zhu X.","Hu, Weixin (58171712600); Yang, Xiaoyu (57188557458); Wang, Lei (57070560700); Zhu, Xianyou (57188809045)","58171712600; 57188557458; 57070560700; 57188809045","MADGAN:A microbe-disease association prediction model based on generative adversarial networks","2023","Frontiers in Microbiology","14","","1159076","","","","1","10.3389/fmicb.2023.1159076","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151980356&doi=10.3389%2ffmicb.2023.1159076&partnerID=40&md5=7fb03ba4ec06c3e080f936e145f2ed9d","College of Computer Science and Technology, Hengyang Normal University, Hengyang, China; Institute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, China; Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China","Hu W., College of Computer Science and Technology, Hengyang Normal University, Hengyang, China; Yang X., Institute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, China; Wang L., Institute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, China, Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China; Zhu X., College of Computer Science and Technology, Hengyang Normal University, Hengyang, China","Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models. Copyright © 2023 Hu, Yang, Wang and Zhu.","computational prediction model; generative adversarial network; graph convolution neural network; microbe-disease associations; residual network","Actinomyces; aging; Alistipes; Article; asthma; Bacteroides; Bacteroides vulgatus; Bacteroidetes; Burkholderiales; chest tightness; chronic obstructive lung disease; Clostridia; Clostridiales; Clostridium innocuum; convolutional neural network; Corynebacterium; coughing; Cronobacter; disease association; dyspnea; Enterococcus; Enterococcus faecalis; Erwinia; Erysipelatoclostridium ramosum; Erysipelotrichales; Escherichia; Eubacteriaceae; Firmicutes; Fusobacterium; gene interaction; generative adversarial network; hemophilia; k fold cross validation; kernel method; Lachnospiraceae; machine learning; MADGAN; Mannheimia; microorganism; Mobiluncus; Moraxella; Neisseria; non insulin dependent diabetes mellitus; nonhuman; normal distribution; Oxalobacteraceae; Prevotellaceae; Rikenellaceae; smoking; Staphylococcus; Staphylococcus epidermidis; Stenotrophomonas maltophilia; Streptobacillus; Streptococcus parasanguinis; Verrucomicrobia; wheezing; Yersinia","","","","","Hunan Provincial Education Department Scientific Research Project, (20B080); National Natural Science Foundation of China, NSFC, (62272064); National Natural Science Foundation of China, NSFC; Natural Science Foundation of Hunan Province, (2022JJ50138); Natural Science Foundation of Hunan Province; Changsha Science and Technology Project, (KQ2203001); Changsha Science and Technology Project; Science and Technology Program of Hunan Province, (2016TP1020); Science and Technology Program of Hunan Province","This work was partly sponsored by the Hunan Provincial Natural Science Foundation of China (No. 2022JJ50138), the National Natural Science Foundation of China (No. 62272064), the Key project of Changsha Science and technology Plan (No. KQ2203001), the Science and Technology Innovation Program of Hunan Province (No. 2016TP1020), and the Hunan Provincial Education Department Scientific Research Project (No.20B080). ","Al-Moamary M.S., Alhaider S.A., Alangari A.A., Idrees M.M., Zeitouni M.O., Al Ghobain M.O., Et al., The Saudi initiative for asthma-2021 update: guidelines for the diagnosis and management of asthma in adults and children, Ann. Thorac. Med, 16, pp. 4-56, (2021); Arjovsky M., Chintala S., Bottou L., (2017); Caliskan M., Bochkov Y.A., Kreiner-Moller E., Bonnelykke K., Stein M.M., Du G., Et al., Rhinovirus wheezing illness and genetic risk of childhood-onset asthma, N. Engl. J. Med, 368, pp. 1398-1407, (2013); Cenit M.C., Sanz Y., Codoner-Franch P., Influence of gut microbiota on neuropsychiatric disorders, WJG, 23, pp. 5486-5498, (2017); Chen X., Huang Y.-A., You Z.-H., Yan G.Y., Wang X.S., A novel approach based on KATZ measure to predict associations of human microbiota with non-infectious diseases, Bioinformatics, 33, pp. 733-739, (2017); Cheng Y., Gong Y., Liu Y., Song B., Zou Q., Molecular design in drug discovery: a comprehensive review of deep generative models, Brief. Bioinform, 22, (2021); Cryan J.F., Dinan T.G., Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour, Nat. Rev. Neurosci, 13, pp. 701-712, (2012); Dai H., Chen C., Li Y., Yuan Y., GCNGAN: translating natural language to programming language based on GAN, J. Phys, 1873, (2021); Desbonnet L., Garrett L., Clarke G., Kiely B., Cryan J.F., Dinan T.G., Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression, Neuroscience, 170, pp. 1179-1188, (2010); Galiana A., Aguirre E., Rodriguez J.C., Mira A., Santibanez M., Candela I., Et al., Sputum microbiota in moderate versus severe patients with COPD, Eur. Respir. J, 43, pp. 1787-1790, (2014); Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Et al., Generative adversarial networks, Commun. ACM, 63, pp. 139-144, (2020); Guarner F., Malagelada J.-R., Gut flora in health and disease, Lancet, 361, pp. 512-519, (2003); Guilbert T.W., Mauger D.T., Lemanske R.F., Childhood asthma-predictive phenotype. The journal of allergy and clinical immunology, In Pract, 2, pp. 664-670, (2014); He B.S., Peng L.H., Li Z., Human microbe-disease association prediction with graph regularized non-negative matrix factorization, Front. Microbiol, 9, (2018); Huang Y.J., Asthma microbiome studies and the potential for new therapeutic strategies, Curr Allergy Asthma Rep, 13, pp. 453-461, (2013); Huang Y.-A., You Z.-H., Chen X., Huang Z.A., Zhang S., Yan G.Y., Prediction of microbe–disease association from the integration of neighbor and graph with collaborative recommendation model, J. Transl. Med, 15, pp. 1-11, (2017); Structure, function and diversity of the healthy human microbiome, Nature, 486, pp. 207-214, (2012); The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease, Cell Host Microbe, 16, pp. 276-289, (2014); James S.L., Abate D., Abate K.H., Abay S.M., Abbafati C., Abbasi N., Et al., Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017, Lancet, 392, pp. 1789-1858, (2018); Janssens Y., Nielandt J., Bronselaer A., Debunne N., Verbeke F., Wynendaele E., Et al., Disbiome database: linking the microbiome to disease, BMC Microbiol, 18, (2018); Karras T., Laine S., Aila T., (2019); Kau A.L., Ahern P.P., Griffin N.W., Goodman A.L., Gordon J.I., Human nutrition, the gut microbiome and the immune system, Nature, 474, pp. 327-336, (2011); Kim N., Yun M., Oh Y.J., Choi H.J., Mind-altering with the gut: modulation of the gut-brain axis with probiotics, J. Microbiol, 56, pp. 172-182, (2018); Lei K., Qin M., Bai B., Zhang G., Yang M., (2019); Li X., Watanabe K., Kimura I., Gut microbiota Dysbiosis drives and implies novel therapeutic strategies for diabetes mellitus and related metabolic diseases, Front. Immunol, 8, (2017); Long Y., Luo J., Zhang Y., Xia Y., Predicting human microbe–disease associations via graph attention networks with inductive matrix completion, Brief. Bioinform, 22, (2021); Luo J., Long Y., NTSHMDA: prediction of human microbe-disease association based on random walk by integrating network topological similarity, IEEE/ACM Trans. Comput. Biol. Bioinform, 17, pp. 1341-1351, (2018); Luo J., Xiao Q., A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network, J. Biomed. Inform, 66, pp. 194-203, (2017); Ma W., Zhang L., Zeng P., Huang C., Li J., Geng B., Et al., An analysis of human microbe–disease associations, Brief. Bioinform, 18, pp. 85-97, (2017); Quigley E.M.M., Gut bacteria in health and disease, Gastroenterol. Hepatol, 9, pp. 560-569, (2013); Schwabe R.F., Jobin C., The microbiome and cancer, Nat. Rev. Cancer, 13, pp. 800-812, (2013); Sender R., Fuchs S., Milo R., Revised estimates for the number of human and bacteria cells in the body, PLoS Biol, 14, (2016); Shen Z., Jiang Z., Bao W., CMFHMDA: collaborative matrix factorization for human microbe-disease association prediction, Intell. Comput. Theor. Appl, pp. 261-269, (2017); Sullivan A., Hunt E., MacSharry J., Murphy D.M., The microbiome and the pathophysiology of asthma, Respir. Res, 17, (2016); Uchiyama I., Mihara M., Nishide H., Chiba H., Kato M., MBGD update 2018: microbial genome database based on hierarchical orthology relations covering closely related and distantly related comparisons, Nucleic Acids Res, 47, pp. D382-D389, (2019); Wang D., Wang J., Lu M., Song F., Cui Q., Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases, Bioinformatics, 26, pp. 1644-1650, (2010); Wei H., Liu B., iCircDA-MF: identification of circRNA-disease associations based on matrix factorization, Brief. Bioinform, 21, pp. 1356-1367, (2020); Wu H., Feng J., Tian X., Xu F., Liu Y., Wang X.F., Et al., (2019); Xu J., Li Y., Discovering disease-genes by topological features in human protein–protein interaction network, Bioinformatics, 22, pp. 2800-2805, (2006); Zeng X., Tu X., Liu Y., Fu X., Su Y., Toward better drug discovery with knowledge graph, Curr. Opin. Struct. Biol, 72, pp. 114-126, (2022); Zhang W., Yang W., Lu X., Huang F., Luo F., The bi-direction similarity integration method for predicting microbe-disease associations, IEEE Access, 6, pp. 38052-38061, (2018); Zheng H., Li X., Li Y., Yan Z., Li T., GCN-GAN: integrating graph convolutional network and generative adversarial network for traffic flow prediction, IEEE Access, 10, pp. 94051-94062, (2022); Zhou T., Tan L., Cederquist G.Y., Fan Y., Hartley B.J., Mukherjee S., Et al., High-content screening in hPSC-neural progenitors identifies drug candidates that inhibit Zika virus infection in fetal-like organoids and adult brain, Cell Stem Cell, 21, pp. 274-283.e5, (2017); Zhu L., Duan G., Yan C., Wang J., Prediction of microbe-drug associations based on chemical structures and the KATZ measure, Curr. Bioinforma, 16, pp. 807-819, (2021); Zhu J.Y., Park T., Isola P., Efros A.A., (2017)","X. Zhu; College of Computer Science and Technology, Hengyang Normal University, Hengyang, China; email: zxy@hynu.edu.cn; L. Wang; Institute of Bioinformatics Complex Network Big Data, Changsha University, Changsha, China; email: wanglei@xtu.edu.cn","","Frontiers Media S.A.","","","","","","1664302X","","","","English","Front. Microbiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85151980356"
"Zhang L.; Wei J.; Wei J.; Zhang Z.; Zhang J.; Tang Q.; Wang Y.; Pan Y.; Qin X.","Zhang, Lin (58853354300); Wei, Jingpeng (58514025600); Wei, Jindou (58514251000); Zhang, Zhanman (57193089251); Zhang, Jiangfeng (57297326400); Tang, Qianhui (57211107417); Wang, Yue (58514924200); Pan, Yicong (58514479900); Qin, Xiao (14319326800)","58853354300; 58514025600; 58514251000; 57193089251; 57297326400; 57211107417; 58514924200; 58514479900; 14319326800","Identification of Clinical Heterogeneity and Construction of Prediction Models for Novel Subtypes in Patients with Abdominal Aortic Aneurysm: An Unsupervised Machine Learning Study","2024","Annals of Vascular Surgery","98","","","75","86","11","1","10.1016/j.avsg.2023.06.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166354447&doi=10.1016%2fj.avsg.2023.06.013&partnerID=40&md5=b5e0d6a013172abec112203f7510f1b0","Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China","Zhang L., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Wei J., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Wei J., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Zhang Z., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Zhang J., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Tang Q., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Wang Y., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Pan Y., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Qin X., Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China","Background: Abdominal aortic aneurysm (AAA) is one of the most common diseases in vascular surgery. Endovascular aneurysm repair (EVAR) can effectively treat AAA. It is essential to accurately classify patients with AAA who need EVAR. Methods: We enrolled 266 patients with AAA who underwent EVAR. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. To verify UMLA's accuracy, the operative and postoperative results of the 2 clusters were analyzed. Finally, a prediction model was developed using binary logistic regression analysis. Results: UMLAs could correctly classify patients based on their clinical characteristics. Patients in Cluster 1 were older, had a higher BMI, and were more likely than patients in Cluster 2 to develop pneumonia, chronic obstructive pulmonary disease, and cerebrovascular disease. The aneurysm diameter, neck angulation, diameter and angulation of bilateral common iliac arteries, and incidence of iliac artery aneurysm were significantly higher in cluster 1 patients than in cluster 2. Cluster 1 had a longer operative time, a longer length of stay in the intensive care unit and hospital, a higher medical expense, and a higher incidence of reintervention. A nomogram was established based on the BMI, neck angulation, left common iliac artery (LCIA) diameter and angulation, and right common iliac artery (RCIA) diameter and angulation. The nomogram was evaluated using receiver operating characteristic curve analysis, with an area under the curve of 0.933 (95% confidence interval, 0.902–0.963) and a C-index of 0.927. Conclusions: Our findings demonstrate that UMLAs can be used to rationally classify a heterogeneous cohort of patients with AAA effectively, and the analysis of postoperative variables also verified the accuracy of UMLAs. We established a prediction model for new subtypes of AAA, which can improve the quality of management of patients with AAA. © 2023 The Author(s)","","Aortic Aneurysm, Abdominal; Blood Vessel Prosthesis Implantation; Endovascular Procedures; Humans; Retrospective Studies; Risk Factors; Treatment Outcome; Unsupervised Machine Learning; abdominal aortic aneurysm; acute kidney failure; age; aged; aneurysm diameter; aneurysm rupture; artery dissection; Article; body mass; brain injury; cerebrovascular disease; chronic obstructive lung disease; clinical effectiveness; clinical feature; cohort analysis; common iliac artery; comparative study; embolism; endoleak; endovascular aneurysm repair; female; gastrointestinal hemorrhage; graft dysfunction; graft thrombosis; heart injury; hospital cost; human; iliac artery aneurysm; iliac artery dissection; incidence; independent variable; intensive care unit; intraoperative period; k means clustering; leg disease; leg embolism; length of stay; logistic regression analysis; lung complication; major clinical study; male; measurement accuracy; multiple organ failure; multivariate logistic regression analysis; nomogram; operation duration; patient coding; patient selection; pneumonia; postoperative period; postoperative thrombosis; predictive model; receiver operating characteristic; reoperation; retrospective study; risk factor; shock; surgical mortality; univariate analysis; univariate logistic regression analysis; unsupervised machine learning; wound infection; abdominal aortic aneurysm; blood vessel transplantation; diagnostic imaging; endovascular surgery; treatment outcome; unsupervised machine learning","","","","","National Natural Science Foundation of China, NSFC, (81960091)","Funding: This study was sponsored by the National Natural Science Foundation of China : 81960091 . Funding bodies had not participated in the design of the study, collection, interpretation, and analysis of the data or in writing the manuscript. ","Nordon I.M., Hinchliffe R.J., Loftus I.M., Et al., Pathophysiology and epidemiology of abdominal aortic aneurysms, Nat Rev Cardiol, 8, pp. 92-102, (2011); Oliveira-Pinto J., Sousa J., Mansilha A., Treatment of ruptured abdominal aortic aneurysms: state of the art, Acta Med Port, 31, pp. 213-218, (2018); Becquemin J.P., Pillet J.C., Lescalie F., Et al., A randomized controlled trial of endovascular aneurysm repair versus open surgery for abdominal aortic aneurysms in low- to moderate-risk patients, J Vasc Surg, 53, pp. 1167-1173.e1, (2011); Fatima M., Pasha M., Survey of machine learning algorithms for disease diagnostic, J Intell Learn Syst Appl, 9, pp. 1-16, (2017); Wang Z., Tang Z., Zhu Y., Et al., AD risk score for the early phases of disease based on unsupervised machine learning, Alzheimers Dement, 16, pp. 1524-1533, (2020); Maddali M.V., Churpek M., Pham T., Et al., Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis, Lancet Respir Med, 10, pp. 367-377, (2022); Kobayashi M., Huttin O., Magnusson M., Et al., Machine learning-derived echocardiographic phenotypes predict heart failure incidence in asymptomatic individuals, JACC Cardiovasc Imaging, 15, pp. 193-208, (2022); Wu S., Liu S., Chen N., Et al., Genome-wide identification of immune-related alternative splicing and splicing regulators involved in abdominal aortic aneurysm, Front Genet, 13, (2022); Sebastian A., Cistulli P.A., Cohen G., Et al., Association of snoring characteristics with predominant site of collapse of upper airway in obstructive sleep apnea patients, Sleep, 44, (2021); Ahlqvist E., Storm P., Karajamaki A., Et al., Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables, Lancet Diabetes Endocrinol, 6, pp. 361-369, (2018); Brusco M.J., Shireman E., Steinley D., A comparison of latent class, K-means, and K-median methods for clustering dichotomous data, Psychol Methods, 22, pp. 563-580, (2017); Rousseeuw P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, 20, pp. 53-65, (1987); Kent K.C., Clinical practice. Abdominal aortic aneurysms, N Engl J Med, 371, pp. 2101-2108, (2014); Bakker D.S., de Graaf M., Nierkens S., Et al., Unraveling heterogeneity in pediatric atopic dermatitis: identification of serum biomarker based patient clusters, J Allergy Clin Immunol, 149, pp. 125-134, (2022); Benito-Leon J., Del Castillo M.D., Estirado A., Et al., Using unsupervised machine learning to identify age- and sex-independent severity subgroups among patients with COVID-19: observational longitudinal study, J Med Internet Res, 23, (2021); Gavali H., Mani K., Tegler G., Et al., Editor's choice - prolonged ICU length of stay after AAA repair: analysis of time trends and long-term outcome, Eur J Vasc Endovasc Surg, 54, pp. 157-163, (2017); Piazza M., Gloviczki P., Huang Y., Et al., Evolution in management and outcome after repair of abdominal aortic aneurysms in the pre- and post-EVAR era, Perspect Vasc Surg Endovasc Ther, 25, pp. 11-19, (2013); Ferrel B., Patel S., Castillo A., Et al., The effect of abdominal aortic aneurysm size on endoleak, secondary intervention and overall survival following endovascular aortic aneurysm repair, Vasc Endovascular Surg, 55, pp. 467-474, (2021); Huang Y., Gloviczki P., Duncan A.A., Et al., Maximal aortic diameter affects outcome after endovascular repair of abdominal aortic aneurysms, J Vasc Surg, 65, pp. 1313-1322.e4, (2017); Schurink G.W., Aarts N.J., van Bockel J.H., Endoleak after stent-graft treatment of abdominal aortic aneurysm: a meta-analysis of clinical studies, Br J Surg, 86, pp. 581-587, (1999); Hobo R., Sybrandy J.E., Harris P.L., Et al., Endovascular repair of abdominal aortic aneurysms with concomitant common iliac artery aneurysm: outcome analysis of the EUROSTAR experience, J Endovasc Ther, 15, pp. 12-22, (2008); Mantas G.K., Antonopoulos C.N., Sfyroeras G.S., Et al., Factors predisposing to endograft limb occlusion after endovascular aortic repair, Eur J Vasc Endovasc Surg, 49, pp. 39-44, (2015); Ma J., Yang J., Cheng S., Et al., Prediction model of laparoendoscopic single-site surgery in gynecology using machine learning algorithm, Wideochir Inne Tech Maloinwazyjne, 16, pp. 587-596, (2021)","X. Qin; Department of Vascular Surgery Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, No. 6 of Shuangyong Road, 530021, China; email: dr_qinxiao@hotmail.com","","Elsevier Inc.","","","","","","08905096","","AVSUE","37380047","English","Ann. Vasc. Surg.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85166354447"
"Ananth S.; Navarra A.; Vancheeswaran R.","Ananth, Sachin (57217380611); Navarra, Alessio (57223156828); Vancheeswaran, Rama (55937093700)","57217380611; 57223156828; 55937093700","Obese, non-eosinophilic asthma: frequent exacerbators in a real-world setting","2022","Journal of Asthma","59","11","","2267","2275","8","1","10.1080/02770903.2021.1996598","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118343088&doi=10.1080%2f02770903.2021.1996598&partnerID=40&md5=b248acc416fc8f347716a913c9e2174b","West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, United Kingdom","Ananth S., West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, United Kingdom; Navarra A., West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, United Kingdom; Vancheeswaran R., West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, United Kingdom","Objective: In the UK, asthma deaths are at their highest level this century. Increased recognition of at-risk patients is needed. This study phenotyped frequent asthma exacerbators and used machine learning to predict frequent exacerbators. Methods: Patients admitted to a district general hospital with an asthma exacerbation between 1st March 2018 and 1st March 2020 were included. Patients were organized into two groups: “Infrequent Exacerbators” (1 admission in the previous 12 months) and “Frequent Exacerbators” (≥2 admissions in the previous 12 months). Patient data were retrospectively collected from hospital and primary care records. Machine learning models were used to predict frequent exacerbators. Results: 200 patients admitted for asthma exacerbations were randomly selected (73% female; mean age 47.8 years). Peripheral eosinophilia was uncommon in either group (21% vs 19%). More frequent exacerbators were being treated with high-dose ICS than infrequent exacerbators (46.5% vs 23.2%; P < 0.001), and frequent exacerbators used more SABA inhalers (10.9 vs 7.40; P = 0.01) in the year preceding the current admission. BMI was raised in both groups (34.2 vs 30.9). Logistic regression was the most accurate machine learning model for predicting frequent exacerbators (AUC = 0.80). Conclusions: Patients admitted for asthma are predominately female, obese and non-eosinophilic. Patients who require multiple admissions per year have poorer asthma control at baseline. Machine learning algorithms can predict frequent exacerbators using clinical data available in primary care. Instead of simply increasing the dose of corticosteroids, multidisciplinary management targeting Th2-low inflammation should be considered for these patients. © 2021 Taylor & Francis Group, LLC.","corticosteroids; machine learning; Phenotypes; prevention; Th2-low","Asthma; Disease Progression; Female; Hospitalization; Humans; Male; Middle Aged; Obesity; Phenotype; Retrospective Studies; beta adrenergic receptor stimulating agent; corticosteroid; long acting drug; short acting drug; adult; algorithm; Article; asthma; body mass; clinical feature; computer assisted tomography; controlled study; demographics; disease exacerbation; drug megadose; eosinophilia; female; general hospital; human; lung function test; machine learning; major clinical study; male; medical record; middle aged; observational study; patient assessment; phenotype; primary medical care; randomized controlled trial; retrospective study; asthma; disease exacerbation; hospitalization; obesity; phenotype","","","","","","","Asthma U.K., (2020); Rennard S.I., Exacerbations and progression of disease in asthma and chronic obstructive pulmonary disease, Proc Am Thorac Soc, 1, 2, pp. 88-92, (2004); O'Byrne P.M., Pedersen S., Lamm C.J., Tan W.C., Busse W.W., Severe exacerbations and decline in lung function in asthma, Am J Respir Crit Care Med, 179, 1, pp. 19-24, (2009); Miller M.K., Lee J.H., Miller D.P., Wenzel S.E., Recent asthma exacerbations: a key predictor of future exacerbations, Respir Med, 101, 3, pp. 481-489, (2007); Ivanova J.I., Bergman R., Birnbaum H.G., Colice G.L., Silverman R.A., McLaurin K., Effect of asthma exacerbations on health care costs among asthmatic patients with moderate and severe persistent asthma, J Allergy Clin Immunol, 129, 5, pp. 1229-1235, (2012); Kuruvilla M.E., Lee F.E.-H., Lee G.B., Understanding asthma phenotypes, endotypes, and mechanisms of disease, Clin Rev Allergy Immunol, 56, 2, pp. 219-233, (2019); Denlinger L.C., Phillips B.R., Ramratnam S., Ross K., Bhakta N.R., Cardet J.C., Castro M., Peters S.P., Phipatanakul W., Aujla S., Et al., Inflammatory and comorbid features of patients with severe asthma and frequent exacerbations, Am J Respir Crit Care Med, 195, 3, pp. 302-313, (2017); Moore W.C., Meyers D.A., Wenzel S.E., Teague W.G., Li H., Li X., D'Agostino R., Castro M., Curran-Everett D., Fitzpatrick A.M., Et al., Identification of asthma phenotypes using cluster analysis in the severe asthma research program, Am J Respir Crit Care Med, 181, 4, pp. 315-323, (2010); FitzGerald J.M., Barnes P.J., Chipps B.E., Jenkins C.R., O'Byrne P.M., Pavord I.D., Reddel H.K., The burden of exacerbations in mild asthma: a systematic review, ERJ Open Res, 6, 3, pp. 00359-2019, (2020); ten Brinke A., Risk factors of frequent exacerbations in difficult-to-treat asthma, Eur Respir J, 26, 5, pp. 812-818, (2005); Kupczyk M., Ten Brinke A., Sterk P.J., Bel E.H., Papi A., Chanez P., Nizankowska-Mogilnicka E., Gjomarkaj M., Gaga M., Brusselle G., The BIOAIR investigators, et al. 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Ananth; West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, United Kingdom; email: sachin.ananth@doctors.org.uk","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","34669527","English","J. Asthma","Article","Final","","Scopus","2-s2.0-85118343088"
"Roy A.; Gyanchandani B.; Oza A.; Singh A.","Roy, Abhinav (59407871500); Gyanchandani, Bhavesh (59407849000); Oza, Aditya (59469255100); Singh, Anurag (57194736417)","59407871500; 59407849000; 59469255100; 57194736417","TriSpectraKAN: a novel approach for COPD detection via lung sound analysis","2025","Scientific Reports","15","1","6296","","","","0","10.1038/s41598-024-82781-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218705516&doi=10.1038%2fs41598-024-82781-1&partnerID=40&md5=057fc92ba93a4f3699b380b65488681c","IIIT Naya Raipur, Naya Raipur, India","Roy A., IIIT Naya Raipur, Naya Raipur, India; Gyanchandani B., IIIT Naya Raipur, Naya Raipur, India; Oza A., IIIT Naya Raipur, Naya Raipur, India; Singh A., IIIT Naya Raipur, Naya Raipur, India","This study aims to create an automated, accessible, and cost-effective diagnostic tool for chronic obstructive pulmonary disease (COPD). Traditional diagnostic methods are expensive, time-consuming, and require specialized equipment. The proposed TriSpectraKAN model leverages audio-based lung sound features to improve early diagnosis. TriSpectraKAN is a hybrid model combining spectral features and the Kolmogorov–Arnold Network (KAN) to analyze lung sounds using Mel-frequency cepstral coefficients (MFCCs), chromagram, and Mel spectrograms. Each sub-model focuses on a different audio feature, capturing unique sonic signatures. These features are merged through a hybrid network for comprehensive analysis. The model, trained on a COPD dataset, was deployed on a Raspberry Pi for real-time use. TriSpectraKAN achieved 93% accuracy, an F1 score of 0.98, precision of 0.97, and recall of 0.98. This multimodal approach captured a broad range of lung sound features, improving diagnosis accuracy compared to traditional methods. The integration of multiple audio features in TriSpectraKAN enhances COPD diagnosis, demonstrating the potential of AI and machine learning to transform respiratory disease diagnosis through accessible tools. © The Author(s) 2024.","Chromagram (Croma); Mel-frequency cepstral coefficients (MFCC); Mel-spectrograms (MSpec); TriSpectraKAN","Aged; Female; Humans; Lung; Male; Pulmonary Disease, Chronic Obstructive; Respiratory Sounds; Sound Spectrography; abnormal respiratory sound; aged; chronic obstructive lung disease; diagnosis; female; human; lung; male; pathophysiology; procedures; sound detection","","","","","","","Ulrik C.S., Lokke A., Dahl R., Dollerup J., Hansen G., Cording P.H., Andersen K.K., Early detection of copd in general practice, Int. J. Chronic Obstruct. Pulm. Dis, 1, pp. 123-127, (2011); Liu R., Cai S., Zhang K., Hu N., Detection of adventitious respiratory sounds based on convolutional neural network, 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 298-303, (2019); Petmezas G., Et al., Automated lung sound classification using a hybrid cnn-lstm network and focal loss function, Sensors, 22, 3, (2022); Xu C., Qi S., Feng J., Xia S., Kang Y., Yao Y., Qian W., Dct-mil: Deep cnn transferred multiple instance learning for copd identification using ct images, Phys. Med. Biol, 65, 14, (2020); Wang K., Hao Y., Au W., Christiani D.C., Xia Z.L., A systematic review and meta-analysis on short-term particulate matter exposure and chronic obstructive pulmonary disease hospitalizations in china, J. Occup. Environ. Med, 61, 4, pp. e112-e124, (2019); Li J., Zhu L., Wei Y., Lv J., Guo Y., Bian Z., Du H., Yang L., Chen Y., Zhou Y., Et al., Association between adiposity measures and copd risk in Chinese adults, Eur. Respir. J, 55, (2020); Zhao Q., Li J., Zhao L., Zhu Z., Knowledge guided feature aggregation for the prediction of chronic obstructive pulmonary disease with Chinese emrs, IEEE/ACM Trans. Comput. Biol. Bioinf, 20, 6, pp. 3343-3352, (2023); Davies H.J., Bachtiger P., Williams I., Molyneaux P.L., Peters N.S., Mandic D.P., Wearable in-ear ppg: Detailed respiratory variations enable classification of copd, IEEE Trans. Biomed. Eng, 69, 7, pp. 2390-2400, (2022); Serbes G., Ulukaya S., Kahya Y.P., An automated lung sound preprocessing and classification system based onspectral analysis methods, Precision Medicine Powered by Phealth and Connected Health: ICBHI 2017, pp. 45-49, (2018); Jakovljevic N., Loncar-Turukalo T., Hidden Markov model based respiratory sound classification, Precision Medicine Powered by Phealth and Connected Health: ICBHI, 2017, pp. 39-43, (2018); Kochetov K., Putin E., Balashov M., Filchenkov A., Shalyto A., Noise masking recurrent neural network for respiratory sound classification, In Artificial Neural Networks and Machine Learning–ICANN 2018: 27Th International Conference on Artificial Neural Networks, Rhodes, Greece, 27, pp. 208-217; Chambres G., Hanna P., Desainte-Catherine M., Automatic detection of patient with respiratory diseases using lung sound analysis, 2018 International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1-6, (2018); Ma Y., Et al., Lungbrn: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm, In 2019 IEEE Biomedical Circuits and Systems Conference (Biocas), pp. 1-4, (2019); Acharya J., Basu A., Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning, IEEE Trans. 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Health Inform, 27, 10, pp. 4768-4779, (2023); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data Brief, 35, (2021); Pessoa D., Et al., Bracets: Bimodal repository of auscultation coupled with electrical impedance thoracic signals, Comput. Methods Progr. Biomed, 240, (2023); Shuvo S.B., Ali S.N., Swapnil S.I., Hasan T., Bhuiyan M.I.H., A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram, IEEE J. Biomed. Health Inform, 25, 7, pp. 2595-2603, (2021); Bharati S., Podder P., Mondal M.R.H., Hybrid deep learning for detecting lung diseases from X-ray images, Inform. Med. Unlock, 20, (2020); Ojala T., Pietikainen M., Harwood D., A comparative study of texture measures with classification based on featured distributions, Pattern Recogn, 29, 1, pp. 51-59, (1996); Chin C.Y., Weng M.Y., Lin T.C., Cheng S.Y., Yang Y.H.K., Tseng V.S., Mining disease risk patterns from nationwide clinical databases for the assessment of early rheumatoid arthritis risk, PLoS ONE, 10, 4, pp. 1-20, (2015); Rocha B.M., Filos D., Mendes L., Serbes G., Ulukaya S., Kahya Y.P., Jakovljevic N., Turukalo T.L., Vogiatzis I.M., Perantoni E., Et al., An open access database for the evaluation of respiratory sound classification algorithms, Physiol. Meas, 40, 3, (2019); Altan G., Kutlu Y., Garbi Y., Pekmezci A.O., Nural S., Multimedia respiratory database (respiratorydatabase@ tr): Auscultation sounds and chest X-rays, Nat. Eng. Sci, 2, 3, pp. 59-72, (2017); Liu Z., Et al., . Kan: Kolmogorov–Arnold Networks., (2024); Altan G., Deepoct: An explainable deep learning architecture to analyze macular edema on oct images, Eng. Sci. Technol. Int. J, 34, (2022); Hibino S., Suzuki C., Nishino T., Classification of singing insect sounds with convolutional neural network, Acoust. Sci. Technol, 42, 6, pp. 354-356, (2021)","A. Roy; IIIT Naya Raipur, Naya Raipur, India; email: abhinav21102@iiitnr.edu.in","","Nature Research","","","","","","20452322","","","39984500","English","Sci. Rep.","Article","Final","","Scopus","2-s2.0-85218705516"
"Carlin B.W.","Carlin, Brian W. (7006964538)","7006964538","Exacerbations of COPD","2023","Respiratory Care","68","7","","961","972","11","1","10.4187/respcare.10782","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162815281&doi=10.4187%2frespcare.10782&partnerID=40&md5=a0d2605ef9f6306489778622c4b000dc","Ingomar, PA, United States","Carlin B.W., Ingomar, PA, United States","COPD exacerbations are associated with significant morbidity, mortality, and increased health care expenditures. The recently published Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommendations have further refined the definition of an exacerbation. A better understanding of the risk factors associated with the development of an exacerbation exists, and improvements are being made in earlier detection approaches. Pharmacologic treatment strategies have been the cornerstone of effective therapy. In addition, both pharmacologic and non-pharmacologic strategies have been proven successful in the prevention of future exacerbations. Newer technologies, including the use of artificial intelligence and wearable monitoring devices, are now being used to help in the earlier detection of exacerbations. Such preventive and earlier detection strategies can help to develop a more personalized care model and improve outcomes for patients with COPD. © 2023 Daedalus Enterprises.","COPD; exacerbation of COPD","Artificial Intelligence; Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive; Risk Factors; amoxicillin plus clavulanic acid; biological marker; brain natriuretic peptide; bronchodilating agent; C reactive protein; cephalosporin; cotrimoxazole; cytokine; doxycycline; fibrinogen; glutathione; hypoxanthine; macrolide; methylprednisolone; oseltamivir; procalcitonin; quinoline derived antiinfective agent; ritonavir; surfactant; tetracycline; troponin; antibiotic therapy; arterial gas; Article; artificial intelligence; cell differentiation; chronic bronchitis; chronic obstructive lung disease; computer assisted tomography; disease exacerbation; dyspnea; electrocardiography; eosinophil count; female; gastroesophageal reflux; Haemophilus influenzae; health care personnel; health care utilization; heart failure; hospital readmission; hospitalization; human; male; morbidity; mortality; oxygen desaturation; oxygen therapy; Pseudomonas aeruginosa; pulse oximetry; quality of life; questionnaire; respiratory failure; risk factor; sensitivity and specificity; Streptococcus pneumoniae; tachycardia; tachypnea; thorax radiography; visual analog scale; artificial intelligence; chronic obstructive lung disease; disease exacerbation","","amoxicillin plus clavulanic acid, 74469-00-4, 79198-29-1; brain natriuretic peptide, 114471-18-0; C reactive protein, 9007-41-4; cephalosporin, 11111-12-9; cotrimoxazole, 8064-90-2; doxycycline, 10592-13-9, 17086-28-1, 564-25-0, 94088-85-4; fibrinogen, 9001-32-5; glutathione, 70-18-8; hypoxanthine, 68-94-0; methylprednisolone, 6923-42-8, 83-43-2; oseltamivir, 196618-13-0, 204255-09-4, 204255-11-8; procalcitonin, 56645-65-9; ritonavir, 155213-67-5; tetracycline, 23843-90-5, 60-54-8, 64-75-5, 8021-86-1","","","","","Lange P, Celli B, Agusti A, Boje G, Jensen M, Divo R, Et al., Lung-function trajectories leading to chronic obstructive pulmonary disease, N Engl J Med, 373, 2, pp. 111-122, (2015); Coler-Cataluna JJ, Martinez-Garcia MA, Sanchez RP, Salcedo E, Navarro M, Ochando R., Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease, Thorax, 60, pp. 925-931, (2005); Rodriguez-Roisin R., Toward a consensus definition for COPD exacerbations, Chest, 117, 5, pp. 398S-401S, (2000); Anthonisen NR, Mandreda J, Warren CP, Hershfield ES, Harding GK, Nelson NA., Antibiotic treatment in exacerbations of chronic obstructive pulmonary disease, Ann Intern Med, 106, 2, pp. 196-204, (1987); Celli BR, Fabbri LM, Aaron SD, Agusti A, Brook R, Criner GJ, Et al., An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations, Am J Respir Crit Care Med, 204, 11, pp. 1251-1258, (2021); Ritchie AL, Wedzicha JA., Definition, causes, pathogenesis, and consequences of chronic obstructive disease exacerbations, Clin Chest Med, 41, 3, pp. 421-438, (2020); Kunadharaju R, Sethi S., Treatment of acute exacerbations in chronic obstructive pulmonary disease, Clin Chest Med, 41, 3, pp. 439-451, (2020); Hurst JR, Vestbo J, Anzueto A, Locantore N, Mullerova H, Tal-Singer R, Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Wells JM, Washko GR, Han MK, Abbas N, Nath H, Mamary AJ, Et al., Pulmonary arterial enlargement and acute exacerbations of COPD, N Engl J Med, 367, 10, pp. 913-921, (2012); Stolz D, Kostikas K, Loefroth E, Fogel R, Gutzwiller FS, Conti V, Et al., Differences in COPD exacerbation risk between men and women, CHEST, 156, 4, pp. 674-684, (2019); Kim V, Zhao H, Regan E, Han MK, Make BJ, Crapo JD, Et al., The St. George’s Respiratory Questionnaire definition of chronic bronchitis may be a better predictor of COPD exacerbations compared with the classic definition, Chest, 156, 4, pp. 685-695, (2019); Thomashow B, Stiegler M, Criner GJ, Dransfield MT, Halpin DMG, Han ML, Et al., Higher COPD Assessment Test score associated with greater exacerbations risk: a post hoc analysis of the IMPACT trial, Chronic Obstr Pulm Dis, 9, 1, pp. 68-79, (2022); Yawn BP, Han ML, Make BM, Mannino D, Brown RW, Meldrum C, Et al., Protocol summary of the COPD assessment in primary care to identify undiagnosed respiratory disease and exacerbation risk (CAPTURE). 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Carlin; PO Box 174, Ingomar, 15127, United States; email: bwcmd@yahoo.com","","American Association for Respiratory Care","","","","","","00201324","","RECAC","37353338","English","Respir. Care","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85162815281"
"Deng J.; Wei L.; Chen Y.; Li X.; Zhang H.; Wei X.; Feng X.; Qiu X.; Liang B.; Zhang J.","Deng, Jiehua (57384169000); Wei, Lixia (58138011500); Chen, Yongyu (58752753000); Li, Xiaofeng (57383959000); Zhang, Hui (57190739966); Wei, Xuan (55654111600); Feng, Xin (59585040000); Qiu, Xue (57216510302); Liang, Bin (59558451400); Zhang, Jianquan (25029670300)","57384169000; 58138011500; 58752753000; 57383959000; 57190739966; 55654111600; 59585040000; 57216510302; 59558451400; 25029670300","Identification of benzo(a)pyrene-related toxicological targets and their role in chronic obstructive pulmonary disease pathogenesis: a comprehensive bioinformatics and machine learning approach","2025","BMC Pharmacology and Toxicology ","26","1","33","","","","0","10.1186/s40360-025-00842-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218444761&doi=10.1186%2fs40360-025-00842-1&partnerID=40&md5=10cf5a5d01e261ec8dc1e1145995e601","Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Guangdong Province, Shenzhen City, 518033, China; Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Gastroenterology and Respiratory Internal Medicine Department, The Afliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, China; Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China","Deng J., Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Guangdong Province, Shenzhen City, 518033, China; Wei L., Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Guangdong Province, Shenzhen City, 518033, China, Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Chen Y., Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Li X., Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Zhang H., Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Wei X., Department of Respiratory and Critical Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Feng X., Gastroenterology and Respiratory Internal Medicine Department, The Afliated Tumor Hospital of Guangxi Medical University, Nanning, 530021, China; Qiu X., Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Liang B., Department of Gastroenterology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Zhang J., Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Zhong Lu, Guangdong Province, Shenzhen City, 518033, China","Background: Chronic obstructive pulmonary disease (COPD) pathogenesis is influenced by environmental factors, including Benzo(a)pyrene (BaP) exposure. This study aims to identify BaP-related toxicological targets and elucidate their roles in COPD development. Methods: A comprehensive bioinformatics approach was employed, including the retrieval of BaP-related targets from the Comparative Toxicogenomics Database (CTD) and Super-PRED database, identification of differentially expressed genes (DEGs) from the GSE76925 dataset, and protein-protein interaction (PPI) network analysis. Functional enrichment and immune infiltration analyses were conducted using GO, KEGG, and ssGSEA algorithms. Feature genes related to BaP exposure were identified using SVM-RFE, Lasso, and RF machine learning methods. A nomogram was constructed and validated for COPD risk prediction. Molecular docking was performed to evaluate the binding affinity of BaP with proteins encoded by the feature genes. Results: We identified 72 differentially expressed BaP-related toxicological targets in COPD. Functional enrichment analysis highlighted pathways related to oxidative stress and inflammation. Immune infiltration analysis revealed significant increases in B cells, DC, iDC, macrophages, T cells, T helper cells, Tcm, and TFH in COPD patients compared to controls. Correlation analysis showed strong links between oxidative stress, inflammation pathway scores, and the infiltration of immune cells, including aDC, macrophages, T cells, Th1 cells, and Th2 cells. Seven feature genes (ACE, APOE, CDK1, CTNNB1, GATA6, IRF1, SLC1A3) were identified across machine learning methods. A nomogram based on these genes showed high diagnostic accuracy and clinical utility. Molecular docking revealed the highest binding affinity of BaP with CDK1, suggestive of its pivotal role in BaP-induced COPD pathogenesis. Conclusions: The study elucidates the molecular mechanisms of BaP-induced COPD, specifically highlighting the role of oxidative stress and inflammation pathways in promoting immune cell infiltration. The identified feature genes may serve as potential biomarkers and therapeutic targets. Additionally, the constructed nomogram demonstrates high accuracy in predicting COPD risk, providing a valuable tool for clinical application in BaP-exposed individuals. © The Author(s) 2025.","Benzo(a)pyrene; Bioinformatics; Chronic obstructive pulmonary disease; Immune score; KEGG; Polycyclic aromatic hydrocarbons","Benzo(a)pyrene; Computational Biology; Humans; Machine Learning; Molecular Docking Simulation; Nomograms; Protein Interaction Maps; Pulmonary Disease, Chronic Obstructive; benzo[a]pyrene; excitatory amino acid transporter 1; interferon regulatory factor 1; transcription factor GATA 6; ACE gene; APOE gene; Article; B lymphocyte; binding affinity; bioinformatics; CDK1 gene; cell infiltration; chronic obstructive lung disease; comparative study; controlled study; CTNNB1 gene; dendritic cell; diagnostic accuracy; differential gene expression; drug targeting; environmental exposure; functional enrichment analysis; GATA6 gene; gene; gene ontology; genetic code; helper cell; human; human cell; human tissue; immune response; inflammation; interstitial dendritic cell; IRF1 gene; KEGG; least absolute shrinkage and selection operator; machine learning; macrophage; memory T lymphocyte; molecular docking; nomogram; oxidative stress; pathogenesis; prediction; protein protein interaction; random forest; recursive feature elimination; risk factor; SLC1A3 gene; support vector machine; Tfh cell; toxicological parameters; bioinformatics; chronic obstructive lung disease; genetics; machine learning; molecular docking; procedures; protein protein interaction","","benzo[a]pyrene, 50-32-8; transcription factor GATA 6, 186434-31-1; Benzo(a)pyrene, ","","","Key clinical specialty of Shenzhen Futian District, (QZDZK-202406); Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi, (2023KY0121); Middle-aged and Young Teachers' Basic Ability Promotion Project of Guangxi; Beijing Norman Bethune Public Welfare Foundation, (BJ-RW2020022J); Shenzhen Science Technology Program, (JCYJ 20230807110914029); Futian Healthcare Research Project, (FTWS2022019); Natural Science Foundation of Guangdong Province, (2023A1515012987, 2024A1515011073); Natural Science Foundation of Guangdong Province; Science and Technology Department of Guangxi Zhuang Autonomous Foundation of Guangxi Key Research and Development Program, (FTWS2021055, 2024AB17061)","This work was supported by grants from the Natural Science Foundation of Guangdong Province (2024A1515011073), the Natural Science Foundation of Guangdong Province (2023A1515012987), the Shenzhen Science Technology Program (JCYJ 20230807110914029), the Futian Healthcare Research Project (FTWS2022019), the Key clinical specialty of Shenzhen Futian District (QZDZK-202406), Guangxi Young and Middle aged Teacher\u2019s Basic Ability Promoting Project (2023KY0121), the Science and Technology Department of Guangxi Zhuang Autonomous Foundation of Guangxi Key Research and Development Program (2024AB17061), the Futian Healthcare Research Project (FTWS2021055), the Beijing Norman Bethune Public Welfare Foundation, horizontal BJ-RW2020022J. 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Wei; Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen City, 3025 Shennan Zhong Lu, Guangdong Province, 518033, China; email: weilx004011@aliyun.com; J. Zhang; Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen City, 3025 Shennan Zhong Lu, Guangdong Province, 518033, China; email: jqzhang2002@126.com","","BioMed Central Ltd","","","","","","20506511","","","39962573","English","BMC Pharmacol. Toxicol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218444761"
"Saberi H.; Yousefi A.R.; Pouryousef M.; Birbaneh J.A.; Tokasi S.","Saberi, Hesan (57971674700); Yousefi, Ali Reza (36244924900); Pouryousef, Majid (23009876700); Birbaneh, Jafar Asghari (57971268700); Tokasi, Somayeh (56141299300)","57971674700; 36244924900; 23009876700; 57971268700; 56141299300","Response of invasive perennial western ragweed (Ambrosia psilostachya) to chemical and mechanical control","2022","Weed Biology and Management","22","4","","79","87","8","1","10.1111/wbm.12257","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142301731&doi=10.1111%2fwbm.12257&partnerID=40&md5=48c13ab26be16acb46d31854aa891c27","Department of Plant Production & Genetics, University of Zanjan, Zanjan, Iran; Department of Plant Production & Genetics, Faculty of Agriculture, University of Zanjan, Zanjan, Iran; Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran; Plant Protection Research Department, Guilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Guilan, Iran","Saberi H., Department of Plant Production & Genetics, University of Zanjan, Zanjan, Iran; Yousefi A.R., Department of Plant Production & Genetics, Faculty of Agriculture, University of Zanjan, Zanjan, Iran; Pouryousef M., Department of Plant Production & Genetics, Faculty of Agriculture, University of Zanjan, Zanjan, Iran; Birbaneh J.A., Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran; Tokasi S., Plant Protection Research Department, Guilan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Guilan, Iran","Western ragweed (Ambrosia psilostachya) is an invasive species in spring-sown crops that can also cause allergic rhinitis and asthma due to its allergenic pollen. In order to evaluate the chemical and mechanical control of western ragweed, two separate experiments were conducted in 2019 and 2020. Chemical treatments included non-treated plots, the combination of glyphosate and 2,4-D, glyphosate + ammonium sulfate, bentazon, imazethapyr + surfactant, picloram, 2,4-D, and mechanical treatments included: once mowing at 3–4 leaf stage, once mowing before male flowers' emergence, once mowing early female flowering stage, two mowings at 3–4 leaf stage, two mowings before male flowers' emergence. Results of these studies indicated that in both years, picloram at 0.96 kg ai ha−1 and the combination of 2,4-D and glyphosate at 1.23 + 0.72 kg ai ha−1 provided more than 90% control of western ragweed and reduced plant height, dry weight, and density. The application of imazethapyr and bentazon, respectively, at the rates of 0.1 and 0.96 kg ai ha−1 did not cause visual damage. The mowing shortly before flowering was the most effective mechanical treatment for western ragweed control. In order to the efficient management of the western ragweed, we suggest that the mowing treatments if appropriately timed and application of glyphosate plus 2, 4-D at 1.23 + 0.72 kg ai ha−1, and picloram at 0.96 kg ai ha−1 can prevent western ragweed from spreading by suppressing growth and reducing seed production. © 2022 Weed Science Society of Japan.","herbicide; invasive weeds; mowing; reproductive growth; weed management","","","","","","","","Ballard T.O., Foley M.E., Bauman T.T., Response of common ragweed (Ambrosia artemisiifolia) and giant ragweed (Ambrosia trifida) to postemergence imazethapyr, Weed Science, 44, 2, pp. 248-251, (1996); Barbour B., Meade J., The effects of cutting date and height on anthesis of common ragweed (Ambrosia artemisiifolia L.). 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Merr.], Canadian Journal of Plant Science, 92, 5, pp. 913-922, (2012); Vitalos M., Karrer G., Dispersal of Ambrosia artemisiifolia seeds along roads: the contribution of traffic and mowing machines, NeoBiota, 8, pp. 53-60, (2009); Webster T.M., Loux M.M., Regnier E.E., Harrison S.K., Giant ragweed (Ambrosia trifida) canopy architecture and interference studies in soybean (Glycine max), Weed Technology, 8, 3, pp. 559-564, (1994); Wopfner N., Gadermaier G., Egger M., Asero R., Ebner C., Jahn-Schmid B., Et al., The spectrum of allergens in ragweed and mugwort pollen, International Archives of Allergy and Immunology, 138, 4, pp. 337-346, (2005); Yin X., Martinez A.S., Sepulveda M.S., Christie M.R., Rapid genetic adaptation to recently colonized environments is driven by genes underlying life history traits, BMC Genomics, 22, 1, pp. 1-16, (2021)","A.R. Yousefi; Department of Plant Production Genetics, Faculty of Agriculture, University of Zanjan, Zanjan, 4537138791, Iran; email: yousefi.alireza@znu.ac.ir","","John Wiley and Sons Inc","","","","","","14446162","","WBMEA","","English","Weed Biol. Manage.","Article","Final","","Scopus","2-s2.0-85142301731"
"Verzellesi L.; Botti A.; Bertolini M.; Trojani V.; Carlini G.; Nitrosi A.; Monelli F.; Besutti G.; Castellani G.; Remondini D.; Milanese G.; Croci S.; Sverzellati N.; Salvarani C.; Iori M.","Verzellesi, Laura (57612247700); Botti, Andrea (26643766200); Bertolini, Marco (19933767200); Trojani, Valeria (57208558817); Carlini, Gianluca (57747457900); Nitrosi, Andrea (6506704805); Monelli, Filippo (57218261875); Besutti, Giulia (6505755263); Castellani, Gastone (55925089300); Remondini, Daniel (8988871200); Milanese, Gianluca (57200400450); Croci, Stefania (7003450132); Sverzellati, Nicola (10340134800); Salvarani, Carlo (7102462333); Iori, Mauro (8365087000)","57612247700; 26643766200; 19933767200; 57208558817; 57747457900; 6506704805; 57218261875; 6505755263; 55925089300; 8988871200; 57200400450; 7003450132; 10340134800; 7102462333; 8365087000","Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features","2023","Electronics (Switzerland)","12","18","3878","","","","1","10.3390/electronics12183878","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172811021&doi=10.3390%2felectronics12183878&partnerID=40&md5=62bbd2c6c658605b981b75dd3d151919","Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Department of Physics and Astronomy-DIFA, University of Bologna, Bologna, 40126, Italy; Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, 41124, Italy; Department of Experimental, Diagnostic and Specialty Medicine—DIMES, IRCCS-Policlinico di S.Orsola, Bologna, 40126, Italy; INFN-Sezione di Bologna, Bologna, 40127, Italy; Radiology Sciences, Department of Medicine and Surgery Unit, Azienda Ospedaliero-Universitaria di Parma, Parma, 43126, Italy; Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Rheumatology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy","Verzellesi L., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Botti A., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Bertolini M., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Trojani V., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Carlini G., Department of Physics and Astronomy-DIFA, University of Bologna, Bologna, 40126, Italy; Nitrosi A., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Monelli F., Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Besutti G., Radiology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, 41124, Italy; Castellani G., Department of Experimental, Diagnostic and Specialty Medicine—DIMES, IRCCS-Policlinico di S.Orsola, Bologna, 40126, Italy; Remondini D., Department of Physics and Astronomy-DIFA, University of Bologna, Bologna, 40126, Italy, INFN-Sezione di Bologna, Bologna, 40127, Italy; Milanese G., Radiology Sciences, Department of Medicine and Surgery Unit, Azienda Ospedaliero-Universitaria di Parma, Parma, 43126, Italy; Croci S., Clinical Immunology, Allergy and Advanced Biotechnologies Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Sverzellati N., Radiology Sciences, Department of Medicine and Surgery Unit, Azienda Ospedaliero-Universitaria di Parma, Parma, 43126, Italy; Salvarani C., Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, 41124, Italy, Rheumatology Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; Iori M., Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy","Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction. © 2023 by the authors.","COVID-19; deep learning; HRCT; imbalance dataset; machine learning; mortality; radiomics","","","","","","Ministero della Salute","This research was funded by the Italian Ministry of Health. The present study takes part in a major multi-center project, named “Endothelial, neutrophil, and complement perturbation linked to acute and chronic damage in COVID-19 pneumonitis coupled with machine learning approach”, whose code was COVID-2020-12371808. 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Inform, 9, (2021); Hu C., Liu Z., Jiang Y., Shi O., Zhang X., Xu K., Suo C., Wang Q., Song Y., Yu K., Et al., Early prediction of mortality risk among patients with severe COVID-19, using machine learning, Int. J. Epidemiol, 49, pp. 1918-1929, (2020); Ikemura K., Bellin E., Yagi Y., Billett H., Saada M., Simone K., Stahl L., Szymanski J., Goldstein D.Y., Reyes Gil M., Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study, J. Med. Internet. Res, 23, (2021); Tezza F., Lorenzoni G., Azzolina D., Barbar S., Leone L.A.C., Gregori D., Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques, J. Pers. Med, 11, (2021); Stachel A., Daniel K., Ding D., Francois F., Phillips M., Lighter J., Development and validation of a machine learning model to predict mortality risk in patients with COVID-19, BMJ Health Care Inform, 28, (2021)","V. Trojani; Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; email: valeria.trojani@ausl.re.it; M. Iori; Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, 42123, Italy; email: mauro.iori@ausl.re.it","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85172811021"
"Xie S.; Peng S.; Zhao L.; Yang B.; Qu Y.; Tang X.","Xie, Songquan (58193265300); Peng, Shuting (59540708800); Zhao, Long (57189625674); Yang, Binbin (57204444448); Qu, Yukun (59540672700); Tang, Xiaoping (55725163500)","58193265300; 59540708800; 57189625674; 57204444448; 59540672700; 55725163500","A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches","2025","Molecular Genetics and Genomics","300","1","18","","","","0","10.1007/s00438-024-02217-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216826320&doi=10.1007%2fs00438-024-02217-3&partnerID=40&md5=460eafa4e4fad8b5ebd46860bbe47d24","Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China","Xie S., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China; Peng S., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China; Zhao L., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China; Yang B., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China; Qu Y., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China; Tang X., Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, Sichuan, NanChong, 637000, China","Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk factors for stroke at an early stage is critical to improving patient outcomes and reducing the overall disease burden. However, the complexity of stroke risk factors requires advanced approaches for accurate prediction. The objective of this study is to identify key risk factors for stroke and develop a predictive model using machine learning techniques to enhance early detection and improve clinical decision-making. Data from the China Health and Retirement Longitudinal Study (2011–2020) were analyzed, classifying participants based on baseline characteristics. We evaluated correlations among 12 chronic diseases and applied machine learning algorithms to identify stroke-associated parameters. A dose–response relationship between these parameters and stroke was assessed using restricted cubic splines with Cox proportional hazards models. A refined predictive model, incorporating age, sex, and key risk factors, was developed. Stroke patients were significantly older (average age 69.03 years) and had a higher proportion of women (53%) compared to non-stroke individuals. Additionally, stroke patients were more likely to reside in rural areas, be unmarried, smoke, and suffer from various diseases. While the 12 chronic diseases were correlated (p < 0.05), the correlation coefficients were generally weak (r < 0.5). Machine learning identified nine parameters significantly associated with stroke risk: TyG-WC, WHtR, TyG-BMI, TyG, TMO, CysC, CREA, SBP, and HDL-C. Of these, TyG-WC, WHtR, TyG-BMI, TyG, CysC, CREA, and SBP exhibited a positive dose–response relationship with stroke risk. In contrast, TMO and HDL-C were associated with reduced stroke risk. In the fully adjusted model, elevated CysC (HR = 2.606, 95% CI 1.869–3.635), CREA (HR = 1.819, 95% CI 1.240–2.668), and SBP (HR = 1.008, 95% CI 1.003–1.012) were significantly associated with increased stroke risk, while higher HDL-C (HR = 0.989, 95% CI 0.984–0.995) and TMO (HR = 0.99995, 95% CI 0.99994–0.99997) were protective. A nomogram model incorporating age, sex, and the identified parameters demonstrated superior predictive accuracy, with a significantly higher Harrell’s C-index compared to individual predictors. This study identifies several significant stroke risk factors and presents a predictive model that can enhance early detection of high-risk individuals. Among them, CREA, CysC, SBP, TyG-BMI, TyG, TyG-WC, and WHtR were positively associated with stroke risk, whereas TMO and HDL-C were opposite. This serves as a valuable decision-support resource for clinicians, facilitating more effective prevention and treatment strategies, ultimately improving patient outcomes. © The Author(s) 2025.","Dose–response relationship; Feature parameters; Machine learning; Nomogram predictive modelling; Risk factors; Stroke","Aged; China; Female; Humans; Longitudinal Studies; Machine Learning; Male; Middle Aged; Proportional Hazards Models; Risk Factors; Stroke; C reactive protein; creatinine; cystatin C; glucose; high density lipoprotein cholesterol; low density lipoprotein cholesterol; nitrogen; triacylglycerol; urea; adult; aged; arthritis; Article; asthma; binary classification; body weight; calibration; cerebrovascular accident; chronic disease; clinical decision making; controlled study; correlation coefficient; creatinine blood level; cross validation; depression; diabetes mellitus; diastolic blood pressure; disease course; dyslipidemia; elastic tissue; feature selection; female; fivefold cross validation; follow up; gastrointestinal disease; glucose blood level; gradient boosting machine; heart disease; human; hypertension; k fold cross validation; kidney disease; least absolute shrinkage and selection operator; liver disease; logistic regression analysis; longitudinal study; lung disease; machine learning algorithm; major clinical study; male; mean corpuscular volume; memory disorder; mental disease; middle aged; neural network; nomogram; platelet count; predictive model; proportional hazards model; random forest; risk factor; risk reduction; rural area; rural population; sensitivity and specificity; stroke patient; support vector machine; systolic blood pressure; triacylglycerol blood level; triglyceride-glucose index; urea nitrogen blood level; waist circumference; waist to height ratio; xgboost; China; epidemiology; etiology; machine learning; risk factor","","C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; glucose, 50-99-7, 84778-64-3, 8027-56-3; nitrogen, 7727-37-9; urea, 57-13-6","","","Bureau of Science and Technology Nanchong Municipality, (20SXQT0316); Bureau of Science and Technology Nanchong Municipality","This study was supported by Nanchong Science and Technology Bureau (20SXQT0316) to Xiaoping Tang. 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Tang; Neurosurgery Department of North Sichuan Medical College Affiliated Hospital, NanChong, Sichuan, 637000, China; email: txping1971@163.com","","Springer Science and Business Media Deutschland GmbH","","","","","","16174615","","MGGOA","39853452","English","Mol. Genet. Genomics","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85216826320"
"Patel P.J.; Diwan D.; Patel K.A.; Ranga S.; Modi N.J.; Dumasia S.","Patel, Pinal J. (57214161132); Diwan, Daksha (58721988100); Patel, Kinjal A. (57667914100); Ranga, Shashi (57982954300); Modi, Niral J. (58695312500); Dumasia, Samay (58722780900)","57214161132; 58721988100; 57667914100; 57982954300; 58695312500; 58722780900","Multi feature fusion for COPD classification using Deep Learning algorithms","2024","Journal of Integrated Science and Technology","12","4","780","","","","1","10.62110/sciencein.jist.2024.v12.780","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184443372&doi=10.62110%2fsciencein.jist.2024.v12.780&partnerID=40&md5=126d19ff0fcacf9a2a0de3603e593846","Computer Engineering Department, Government Engineering College, Gujarat, Gandhinagar, India; General Department, Government Engineering College, Gujarat, Gandhinagar, India; Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Chemical Department, Government Engineering College, Gujarat, Valsad, India; Government Engineering College, Gandhinagar, Gujarat Technological University, Gujarat, India","Patel P.J., Computer Engineering Department, Government Engineering College, Gujarat, Gandhinagar, India; Diwan D., General Department, Government Engineering College, Gujarat, Gandhinagar, India; Patel K.A., Faculty of Information Technology, Monash University, Clayton, VIC, Australia; Ranga S., Chemical Department, Government Engineering College, Gujarat, Valsad, India; Modi N.J., Government Engineering College, Gandhinagar, Gujarat Technological University, Gujarat, India; Dumasia S., Computer Engineering Department, Government Engineering College, Gujarat, Gandhinagar, India","Machine learning (ML) and deep learning (DL) are becoming pivotal for providing solutions to healthcare issues. Due to their accurate and quick forecasting models and discoveries, ML and DL algorithms are being used for disease classification by healthcare experts. Along with life-threatening illnesses like cancer, respiratory problems such as Chronic Obstructive Pulmonary Disease (COPD) have been growing more prevalent and endangering the survival of human society. According to the World Health Organization, COPD will be the third-leading cause of death and the seventh-leading cause of illness globally by 2030. Therefore, early detection and fast treatment are essential. The primary methods for diagnosing COPD need inadequate and pricy spirometer and imaging equipment. In this paper, an attempt is made to determine the severity of COPD disease using ML and DL algorithms using the cough sound of the patient. To extract audio features like Mfcc, Chroma, Contract, Mel, and Tonnetz, we have used the Librosa Python Library. To address the issues of imbalanced dataset, we have used the SMOTE algorithm. To find the most effective multi feature fusion for classifying COPD, numerous experiments have been carried out using various fusions of audio features. For the purpose of evaluating the multi-feature fusion's performance, we have run MLP, CNN, RNN, and LSTM models on fusion of two audio features and three audio features. Results of experiments suggest that the LSTM model with Adam as an optimization function gives 100% training accuracy and 87% testing accuracy for fusion of Mfcc and Mel features. As a result of the fusion of the three features of Tonnetz, Chroma, and Mel, CNN model performs better with training accuracy of 90% and testing accuracy of 82%. © Authors.","Algorithms; Classification; COPD; Deep Learning; Multi Feature Fusion","","","","","","","","Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med, 3, 11, pp. 2011-2030, (2006); Ferrer M., Alonso J., Anto J.M., Relationship between chronic obstructive pulmonary disease stage and health-related quality of life, Cardiol. 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Sci, 2, 3, pp. 59-72, (2017); Hanifa R.M., Isa K., Mohamad M., Comparative Analysis on Different Cepstral Features for Speaker Identification Recognition, 2020 IEEE Student Conf. Res. Dev. SCOReD, 2020 2020, pp. 487-492; Ajibola Alim S., Khair Alang Rashid N., Some Commonly Used Speech Feature Extraction Algorithms, From Natural to Artificial Intelligence-Algorithms and Applications, (2018); Abraham J.V.T., Khan A.N., Shahina A., A deep learning approach for robust speaker identification using chroma energy normalized statistics and mel frequency cepstral coefficients, Int. J. Speech Technol, 26, 3, pp. 579-587, (2023); Garg U., Agarwal S., Gupta S., Dutt R., Singh D., Prediction of Emotions from the Audio Speech Signals using MFCC, MEL and Chroma, Proc.-2020 12th Int. Conf. Comput. Intell. Commun. Networks, CICN, 2020 2020, pp. 87-91; Kabra B., Nagar C., Attention-Emotion-Embedding BiLSTM-GRU network based sentiment analysis, J. Integr. Sci. Technol, 11, 4, (2023); Xie W., Fang Y., Yang G., Yu K., Li W., Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification, Biomolecules, 13, 9, (2023)","P.J. Patel; Computer Engineering Department, Government Engineering College, Gandhinagar, Gujarat, India; email: pinalpatel@gecg28.ac.in","","ScienceIn Publishing","","","","","","23214635","","","","English","J. Integr. Sci. Technol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85184443372"
"Ramgopal S.; Kapes J.; Alpern E.R.; Carroll M.S.; Heffernan M.; Simon N.-J.E.; Florin T.A.; Macy M.L.","Ramgopal, Sriram (55213304700); Kapes, Jack (58652232600); Alpern, Elizabeth R. (6701436373); Carroll, Michael S. (35933253500); Heffernan, Marie (52163787000); Simon, Norma-Jean E. (57203970898); Florin, Todd A. (9247229200); Macy, Michelle L. (7003782701)","55213304700; 58652232600; 6701436373; 35933253500; 52163787000; 57203970898; 9247229200; 7003782701","Perceptions of Artificial Intelligence-Assisted Care for Children With a Respiratory Complaint","2023","Hospital Pediatrics","13","9","","802","810","8","1","10.1542/hpeds.2022-007066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174347737&doi=10.1542%2fhpeds.2022-007066&partnerID=40&md5=19cccc141a5886e5ba4db3b08d214a71","Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States","Ramgopal S., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Kapes J., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Alpern E.R., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Carroll M.S., Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Heffernan M., Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Simon N.-J.E., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States; Florin T.A., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States; Macy M.L., Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States","OBJECTIVES: To evaluate caregiver opinions on the use of artificial intelligence (AI)-assisted medical decision-making for children with a respiratory complaint in the emergency department (ED). METHODS: We surveyed a sample of caregivers of children presenting to a pediatric ED with a respiratory complaint.We assessed caregiver opinions with respect to AI, defined as ""specialized computer programs""that ""help make decisions about the best way to care for children.""We performed multivariable logistic regression to identify factors associated with discomfort with AI-assisted decision-making. RESULTS: Of 279 caregivers who were approached, 254 (91.0%) participated. Most indicated they would want to know if AI was being used for their child's health care (93.5%) and were extremely or somewhat comfortable with the use of AI in deciding the need for blood (87.9%) and viral testing (87.6%), interpreting chest radiography (84.6%), and determining need for hospitalization (78.9%). In multivariable analysis, caregiver age of 30 to 37 years (adjusted odds ratio [aOR] 3.67, 95% confidence interval [CI] 1.43-9.38; relative to 18-29 years) and a diagnosis of bronchospasm (aOR 5.77, 95% CI 1.24-30.28 relative to asthma) were associated with greater discomfort with AI. Caregivers with children being admitted to the hospital (aOR 0.23, 95% CI 0.09-0.50) had less discomfort with AI. CONCLUSIONS: Caregivers were receptive toward the use of AI-assisted decision-making. Some subgroups (caregivers aged 30-37 years with children discharged from the ED) demonstrated greater discomfort with AI. Engaging with these subgroups should be considered when developing AI applications for acute care.  © 2023 by the American Academy of Pediatrics.","","adult; Article; artificial intelligence; asthma; bronchospasm; caregiver; child; child care; child health care; clinical decision making; controlled study; female; hospital admission; hospitalization; human; major clinical study; male; perception; respiratory tract disease; thorax radiography","","","","","","","Berner E, La Lande T., Overview of clinical decision support systems, Clinical Decision Support Systems, pp. 3-22, (2007); Shortliffe EH, Sep_ulveda MJ., Clinical decision support in the era of artificial intelligence, JAMA, 320, 21, pp. 2199-2200, (2018); Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA., Artificial intelligence-based clinical decision support in pediatrics, Pediatr Res, 93, 2, pp. 334-341, (2023); Bertsimas D, Dunn J, Steele DW, Trikalinos TA, Wang Y., Comparison of machine learning optimal classification trees with the pediatric emergency care applied research network head trauma decision rules, JAMA Pediatr, 173, 7, pp. 648-656, (2019); Singh D, Nagaraj S, Mashouri P, Et al., Assessment of machine learning-based medical directives to expedite care in pediatric emergency medicine, JAMA Netw Open, 5, 3, (2022); Mani S, Ozdas A, Aliferis C, Et al., Medical decision support using machine learning for early detection of late-onset neonatal sepsis, J Am Med Inform Assoc, 21, 2, pp. 326-336, (2014); Pennell C, Polet C, Arthur LG, Grewal H, Aronoff S., Risk assessment for intra-abdominal injury following blunt trauma in children: Derivation and validation of a machine learning model, J Trauma Acute Care Surg, 89, 1, pp. 153-159, (2020); Ramgopal S, Horvat CM, Yanamala N, Alpern ER., Machine learning to predict serious bacterial infections in young febrile infants, Pediatrics, 146, 3, (2020); Merrill C, Owens PL., Healthcare Cost and Utilization Project. 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Ramgopal; Division of Pediatric Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, 225 East Chicago Ave, 60611, United States; email: sramgopal@luriechildrens.org","","American Academy of Pediatrics","","","","","","21541663","","","37593809","English","Hosp. Pediatr.","Article","Final","","Scopus","2-s2.0-85174347737"
"Su X.; Li R.; Zhang Z.; Lu L.; Wang S.; Liu T.","Su, Xinxin (57958888800); Li, Runtian (57213626090); Zhang, Zhiguang (57193483278); Lu, Lin (57959990900); Wang, Siqi (58034391100); Liu, Tongxiang (56640169500)","57958888800; 57213626090; 57193483278; 57959990900; 58034391100; 56640169500","Mechanism of Marsdenia tenacissima in treating breast cancer by targeting the MAPK signaling pathway: Utilising metabolomics, network pharmacology, and In vivo experiments for verification","2025","Journal of Ethnopharmacology","343","","119477","","","","0","10.1016/j.jep.2025.119477","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217746962&doi=10.1016%2fj.jep.2025.119477&partnerID=40&md5=68d3bb5f3daba38bb4101d4125adffde","School of Pharmacy, Minzu University of China, Beijing, 100081, China; Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China","Su X., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China; Li R., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China; Zhang Z., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China; Lu L., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China; Wang S., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China; Liu T., School of Pharmacy, Minzu University of China, Beijing, 100081, China, Key Laboratory of Ethnomedicine (Minzu University of China), Ministry of Education, Beijing, 100081, China","Ethnopharmacological relevance: Marsdenia tenacissima dried stems have been used to treat asthma, trachitis, rheumatism, and carbuncles. M. Tenacissima extract is now available in China under the brand name “Xiao Ai Ping” and is commonly used in conjunction with chemotherapy to treat a number of diseases, including liver cancer, gastric cancer, colon cancer, and non-small cell lung cancer. Purpose of the study: The research focused on the potential mechanisms contributing to the in vivo therapeutic effects on breast cancer using the ethyl acetate portion of M. tenacissima extract (EMTE), demonstrating significant promise in treating lung cancer in our initial experiments. Materials and methods: We examined the impact of EMTE on the growth of breast cancer through experiments on homoplastic breast cancer mice. Moreover, we utilized UPLC-Q-TOF/MS analysis to identify the components of EMTE and anticipate its potential therapeutic targets. Through network pharmacology, we predicted the potential targets and pathways affected by EMTE in relation to breast cancer. Additionally, we analysed the metabolic changes induced by EMTE during its anti-breast cancer effects. Results: The MAPK pathway was identified as the most likely route by which EMTE could influence breast cancer through network pharmacological enrichment of pathways. Research on animals showed that EMTE could successfully inhibit the development of breast tumours in the homoplastic breast cancer mouse model. We observed that EMTE treatment affected the metabolism of breast cancer mice, particularly in the biosynthesis of phenylalanine, tyrosine, tryptophan, linoleic acid metabolism, and pyrimidine metabolism. These metabolic alterations may have contributed to the effects of glycolysis, tumour immune evasion, and pyrimidine de novo synthesis. Conclusion: Based on the results of network pharmacological and metabolomic analysis, we postulate that the inhibition of the MAPK/ERK pathway may have played a role in promoting apoptosis in breast cancer cells and confirmed relevant protein expression of the MAPK/ERK signaling pathway with Western blotting in tumour tissue of homoplastic breast cancer mice. © 2025 Elsevier B.V.","Breast cancer; MAPK pathway; Marsdenia tenacissima; Metabolomics; Network pharmacology","antineoplastic agent; linoleic acid; Marsdenia tenacissima extract; phenylalanine; plant extract; tenacigenin b; tenacigenin c; tryptophan; tyrosine; unclassified drug; 4T1 cell line; animal experiment; animal model; animal tissue; Article; breast cancer; cancer growth; condurango; controlled study; female; high performance liquid chromatography; in vivo study; lung cancer; MAPK signaling; Marsdenia tenacissima; metabolomics; mouse; nonhuman; pathway enrichment analysis; protein expression; pyrimidine metabolism; systems pharmacology; therapy effect; thin layer chromatography; tumor escape; tumor weight; ultra performance liquid chromatography","","linoleic acid, 1509-85-9, 2197-37-7, 60-33-3, 822-17-3; phenylalanine, 3617-44-5, 63-91-2; tryptophan, 6912-86-3, 73-22-3; tyrosine, 16870-43-2, 55520-40-6, 60-18-4","","","National Key Research and Development Program of China, NKRDPC, (2023YFC3504401); National Key Research and Development Program of China, NKRDPC; National Natural Science Foundation of China, NSFC, (81973977); National Natural Science Foundation of China, NSFC","This work was supported by National key research and development program of China (grant number 2023YFC3504401); National Natural Science Foundation of China (grant number 81973977) ","Adeyinka A., Nui Y., Cherlet T., Snell L., Watson P.H., Murphy L.C., Activated mitogen-activated protein kinase expression during human breast tumorigenesis and breast cancer progression, Clin. 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Canc, 1874, (2020); Teo B.S.-X., Lan Y., Jie J.C., Mingqiang J., Jiong Z., Research progress of xiaoaiping injection on anti-tumor, Scholastic: Journal of Natural and Medical Education, 2, pp. 159-168, (2023); To K.K.W., Wu X., Yin C., Chai S., Yao S., Kadioglu O., Efferth T., Ye Y., Lin G., Reversal of multidrug resistance by Marsdenia tenacissima and its main active ingredients polyoxypregnanes, J. Ethnopharmacol., 203, pp. 110-119, (2017); Wang K., Liu W., Xu Q., Gu C., Hu D., Tenacissoside G synergistically potentiates inhibitory effects of 5-fluorouracil to human colorectal cancer, Phytomedicine, 86, (2021); Wang P., Yang J., Zhu Z., Zhang X., Marsdenia tenacissima: a review of traditional uses, phytochemistry and pharmacology, The American journal of Chinese medicine, 46, pp. 1449-1480, (2018); Wang P., Yang J., Zhu Z., Zhang X., Marsdenia tenacissima: a review of traditional uses, phytochemistry and pharmacology, Am. J. Chin. Med., pp. 1-32, (2018); Wang W., Cui J., Ma H., Lu W., Huang J., Targeting pyrimidine metabolism in the era of precision cancer medicine, Front. Oncol., 11, (2021); Wang X., Wang N., Zhong L., Wang S., Zheng Y., Yang B., Zhang J., Lin Y., Wang Z., Prognostic value of depression and anxiety on breast cancer recurrence and mortality: a systematic review and meta-analysis of 282,203 patients, Mol Psychiatry, 25, pp. 3186-3197, (2020); Wang X., Wang Z.Y., Zheng J.H., Li S., TCM network pharmacology: a new trend towards combining computational, experimental and clinical approaches, Chin. J. Nat. Med., 19, pp. 1-11, (2021); Xiaohua L., Haitao L., Lingyu J., Leixi C., Yingfeng N., Yanhong G., Xiaojun M., Lixia Z., Ethyl acetate fraction in ethanol extract from root of “Dai-Bai-Jie”(Marsdenia tenacissima): anti-tumor activity in A549 cancer cells, J. Tradit. Chin. Med., 38, pp. 668-675, (2018); Yu F., Li Y., Zou J., Jiang L., Wang C., Tang Y., Gao B., Luo D., Jiang X., The Chinese herb Xiaoaiping protects against breast cancer chemotherapy-induced alopecia and other side effects: a randomized controlled trial, J. Int. Med. Res., 47, pp. 2607-2614, (2019); Zhan J., Shi L.L., Wang Y., Wei B., Yang S.L., In vivo study on the effects of xiaoaiping on the stemness of hepatocellular carcinoma cells, Evid Based Complement Alternat Med, 2019, (2019); Zhou Q.M., Wang S., Zhang H., Lu Y.Y., Wang X.F., Motoo Y., Su S.B., The combination of baicalin and baicalein enhances apoptosis via the ERK/p38 MAPK pathway in human breast cancer cells, Acta Pharmacol. Sin., 30, pp. 1648-1658, (2009)","T. Liu; School of Pharmacy, Minzu University of China, Beijing, No.27 Zhongguancun South Street, Haidian District, 100081, China; email: tongxliu123@muc.edu.cn","","Elsevier Ireland Ltd","","","","","","03788741","","JOETD","","English","J. Ethnopharmacol.","Article","Final","","Scopus","2-s2.0-85217746962"
"Wu Y.; Dai T.; Qin J.; Guo J.; Fan J.; Mei J.; Li X.; Liu F.","Wu, Yahui (57196068361); Dai, Tiansheng (59560212100); Qin, Jingwen (59559207200); Guo, Jian (59559608600); Fan, Jitao (59559410500); Mei, Jun (57202648557); Li, Xiaoli (59560212200); Liu, Fang (57091823300)","57196068361; 59560212100; 59559207200; 59559608600; 59559410500; 57202648557; 59560212200; 57091823300","Suppression of regulatory factor X 7 alleviates airway remodeling and inflammation in childhood asthma","2025","CytoJournal","22","","15","","","","0","10.25259/Cytojournal_138_2024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217963074&doi=10.25259%2fCytojournal_138_2024&partnerID=40&md5=22b5a9e6583f948e4ad77cb4c6c760e4","Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Department of Pediatrics, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China","Wu Y., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Dai T., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Qin J., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Guo J., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Fan J., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Mei J., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Li X., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China; Liu F., Department of Pediatrics, Ji’an Hospital, Shanghai East Hospital, Ji’an, China, Department of Pediatrics, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China","Objective: Childhood asthma is a chronic heterogeneous syndrome composed of distinct disease entities or phenotypes. This study was conducted to characterize regulatory factor X 7 (RFX7) in childhood asthma. Material and Methods: Two available transcriptome datasets (GSE65204 and GSE27011) were used to analyze regulatory factor X (RFX) family members in childhood asthma. Random forest, logistic regression, and linear support vector machine (SVM) analyses were performed to construct an RFX-based classification model. Airway smooth muscle cells (ASMCs) were induced through platelet-derived growth factor-BB (PDGF-BB) for an asthma in vitro model. RFX7 expression was measured through immunoblotting. RFX7 was knocked out by transfection of RFX7 small-interfering RNAs, and then airway remodeling and inflammation were assayed. Results: Among RFX family members, RFX3, RFX7, and RFX-associated protein displayed differential expression in childhood asthma versus healthy controls. Thus, SVM, logistic regression, and random forest-based machine learning models were built. The random forest model presented the best diagnostic efficacy (area under the curve [AUC] = 1 and 0.67 in discovery and verification sets). RFX7 was found to be effective in diagnosing childhood asthma (AUC = 0.724 and 0.775 in discovery and verification sets). In addition, RFX7 was overexpressed in PDGF-BB-stimulated ASMCs (**P < 0.01). Silencing RFX7 remarkably attenuated the proliferative and migrative capacities of ASMCs with PDGF-BB stimulation (**P < 0.01). In addition, RFX7 was positively related to neutrophil infiltration in childhood asthma, and its knockdown downregulated the levels of pro-inflammatory cytokines in PDGF-BB-stimulated ASMCs (**P < 0.01). Conclusion: The findings of this study indicate that RFX7 is a novel molecule that is correlated with airway remodeling and inflammation in childhood asthma, providing insights into the mechanism underlying this disease and its potential clinical importance. © 2025 The Author(s).","Airway remodeling; Airway smooth muscle cells; Asthma; Inflammation; Regulatory factor X transcription factors","becaplermin; complementary DNA; cytokine; interleukin 1beta; interleukin 6; platelet derived growth factor BB; regulatory factor X transcription factor; regulatory factor X7; RNA; small interfering RNA; transcription factor; transcriptome; tumor necrosis factor; unclassified drug; airway remodeling; airway smooth muscle cell; area under the curve; Article; asthma; cell culture; child; clinical article; controlled study; differential expression analysis; early onset asthma; female; functional enrichment analysis; gene set variation analysis; genetic transfection; human; human cell; immunoblotting; in vitro study; inflammation; KEGG; linear support vector machine; machine learning; male; model; neutrophil chemotaxis; phenotype; prediction; random forest; real time reverse transcription polymerase chain reaction; support vector machine; transient transfection; transwell assay; Western blotting; wound healing assay","","becaplermin, 165101-51-9; RNA, 63231-63-0","","","2021 Science and Technology Special Project and Social Development Project in Ji’an City, Jiangxi Province; Jiangxi Provincial Department of Science and Technology Applied Research Cultivation Plan Project, (20212BAG70005)","This project was supported by 2021 Jiangxi Provincial Department of Science and Technology Applied Research Cultivation Plan Project (No.20212BAG70005, Yahui Wu) and 2021 Science and Technology Special Project and Social Development Project in Ji\u2019an City, Jiangxi Province (No.20211-025242,Yahui Wu).","Hu Y, Chen Y, Liu S, Tan J, Yu G, Yan C, Et al., Residential greenspace and childhood asthma: An intra-city study, Sci Total Environ, 857, (2023); 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A placebo-controlled, double-blind study, Am J Respir Crit Care Med, 199, pp. 508-517, (2019); Lachowicz-Scroggins ME, Dunican EM, Charbit AR, Raymond W, Looney MR, Peters MC, Et al., Extracellular DNA, neutrophil extracellular traps, and inflammasome activation in severe asthma, Am J Respir Crit Care Med, 199, pp. 1076-1085, (2019); Maneechotesuwan K, Essilfie-Quaye S, Kharitonov SA, Adcock IM, Barnes PJ., Loss of control of asthma following inhaled corticosteroid withdrawal is associated with increased sputum interleukin-8 and neutrophils, Chest, 132, pp. 98-105, (2007); Chen Z, Fan N, Shen G, Yang J., Silencing lncRNA CDKN2BAS1 alleviates childhood asthma progression through inhibiting ZFP36 promoter methylation and promoting NR4A1 expression, Inflammation, 46, pp. 700-717, (2023); Akdis M, Aab A, Altunbulakli C, Azkur K, Costa RA, Crameri R, Et al., Interleukins (from IL-1 to IL-38), interferons, transforming growth factor β, and TNF-α: Receptors, functions, and roles in diseases, J Allergy Clin Immunol, 138, pp. 984-1010, (2016); Yu L, Ma W, Song B, Wang S, Li X, Wang Z., Hsa_circ_0030042 ameliorates oxidized low-density lipoprotein-induced endothelial cell injury via the MiR-616-3p/RFX7 axis, Int Heart J, 63, pp. 763-772, (2022)","F. Liu; Department of Pediatrics, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China; email: liufangsh30@163.com","","Scientific Scholar","","","","","","09745963","","","","English","CytoJournal","Article","Final","","Scopus","2-s2.0-85217963074"
"Bo N.; Jeong J.-H.; Forno E.; Ding Y.","Bo, Na (57205332407); Jeong, Jong-Hyeon (7402045818); Forno, Erick (25225322900); Ding, Ying (56389888100)","57205332407; 7402045818; 25225322900; 56389888100","Evaluating Meta-Learners to Analyze Treatment Heterogeneity in Survival Data: Application to Electronic Health Records of Pediatric Asthma Care in COVID-19 Pandemic","2025","Statistics in Medicine","44","3-4","e10333","","","","0","10.1002/sim.10333","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216113325&doi=10.1002%2fsim.10333&partnerID=40&md5=03408ec03bd7b9acbe50bb56dfd489ab","Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, United States; Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, United States; Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States; University of Pittsburgh, Pittsburgh, PA, United States","Bo N., Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, United States; Jeong J.-H., Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, United States, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, United States; Forno E., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States; Ding Y., Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, United States, University of Pittsburgh, Pittsburgh, PA, United States","An important aspect of precision medicine focuses on characterizing diverse responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE) or individualized treatment effects (ITE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a pseudo-ITE-based framework for analyzing HTE in survival data, which includes a group of meta-learners for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects. We evaluate the finite sample performance of the framework under various observational study settings. Furthermore, we applied the proposed methods to analyze the treatment heterogeneity of a written asthma action plan (WAAP) on time-to-ED (Emergency Department) return due to asthma exacerbation using a large asthma electronic health records dataset with visit records expanded from pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients with poorer asthma outcomes but enhanced benefits from WAAP and characterized patient profiles. Our research provides valuable insights for healthcare providers on the strategic distribution of WAAP, particularly during disruptive public health crises, ultimately improving the management and control of pediatric asthma. © 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.","COVID-19 pandemic; EHR data; heterogeneous treatment effects; meta-learner; precision asthma care; subgroup analysis","Asthma; Child; COVID-19; Electronic Health Records; Emergency Service, Hospital; Humans; Pandemics; SARS-CoV-2; Survival Analysis; adolescent; adult; Article; asthma; child; controlled study; coronavirus disease 2019; diagnostic test accuracy study; disease exacerbation; electronic health record; emergency ward; female; hospitalization; human; influenza vaccination; machine learning; male; mathematical analysis; median survival time; observational study; pandemic; patient care; predictive value; preschool child; sensitivity analysis; sensitivity and specificity; simulation; survival; treatment effect heterogeneity; electronic health record; hospital emergency service; mortality; pandemic; Severe acute respiratory syndrome coronavirus 2; survival analysis","","","","","","","Xu Y., Bechler K., Callahan A., Shah N., Principled Estimation and Evaluation of Treatment Effect Heterogeneity: A Case Study Application to Dabigatran for Patients With Atrial Fibrillation, Journal of Biomedical Informatics, 143, (2023); Xu Y., Ignatiadis N., Sverdrup E., Fleming S., Wager S., Shah N., Treatment Heterogeneity With Survival Outcomes, Handbook of Matching and Weighting Adjustments for Causal Inference, (2023); Bo N., Wei Y., Zeng L., Kang C., Ding Y., A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes, Journal of Data Science, 22, 4, pp. 505-523, (2024); Henderson N.C., Louis T.A., Rosner G.L., Varadhan R., Individualized Treatment Effects With Censored Data via Fully Nonparametric Bayesian Accelerated Failure Time Models, Biostatistics, 21, 1, pp. 50-68, (2018); Hu L., Ji J., Li F., Estimating Heterogeneous Survival Treatment Effect in Observational Data Using Machine Learning, Statistics in Medicine, 40, 21, pp. 4691-4713, (2021); Hu L., Ji J., Ennis R.D., Hogan J.W., A Flexible Approach for Causal Inference With Multiple Treatments and Clustered Survival Outcomes, Statistics in Medicine, 41, 25, pp. 4982-4999, (2022); Cui Y., Kosorok M.R., Sverdrup E., Wager S., Zhu R., Estimating Heterogeneous Treatment Effects With Right-Censored Data via Causal Survival Forests, Journal of the Royal Statistical Society Series B: Statistical Methodology, 85, 2, pp. 179-211, (2023); Zhu J., Gallego B., Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis, Journal of Biomedical Informatics, 107, (2020); Curth A., Lee C., dM S.V., SurvITE: Learning Heterogeneous Treatment Effects From Time-To-Event Data, Advances in Neural Information Processing Systems, 34, pp. 26740-26753, (2021); Alkhthlan A., “The Effects of an Asthma Action Plan and Asthma Self-Efficacy on Asthma Control,”; Shechter J., Roy A., Naureckas S., Estabrook C., Mohanty N., Variables Associated With Emergency Department Utilization by Pediatric Patients With Asthma in a Federally Qualified Health Center, Journal of Community Health, 44, 5, pp. 948-953, (2019); Teach S.J., Crain E.F., Quint D.M., Hylan M.L., Joseph J.G., Improved Asthma Outcomes in a High-Morbidity Pediatric Population: Results of an Emergency Department–Based Randomized Clinical Trial, Archives of Pediatrics & Adolescent Medicine, 160, 5, pp. 535-541, (2006); Brown M.D., Reeves M.J., Meyerson K., Korzeniewski S.J., Randomized Trial of a Comprehensive Asthma Education Program After an Emergency Department Visit, Annals of Allergy, Asthma & Immunology, 97, 1, pp. 44-51, (2006); Lundberg S.M., Lee S.I., A Unified Approach to Interpreting Model Predictions, Advances in Neural Information Processing Systems, pp. 4765-4774, (2017); Rubin D.B., Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies, Journal of Education & Psychology, 1972, 2, pp. i-31, (1972); Splawa-Neyman J., Dabrowska D., Speed T., On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9, Statistical Science, 5, 4, pp. 465-472, (1990); Kunzel S.R., Sekhon J.S., Bickel P.J., Yu B., Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning, Proceedings of the National Academy of Sciences of the United States of America, 116, 10, pp. 4156-4165, (2019); Horvitz D.G., Thompson D.J., A Generalization of Sampling Without Replacement From a Finite Universe, JASA, 47, 260, pp. 663-685, (1952); Kennedy E.H., Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects, Electronic Journal of Statistics, 17, 2, pp. 3008-3049, (2023); Tian L., Alizadeh A.A., Gentles A.J., Tibshirani R., A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates, JASA, 109, 508, pp. 1517-1532, (2014); Knaus M.C., Lechner M., Strittmatter A., Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence, Econometrics Journal, 24, 1, pp. 134-161, (2020); Chen S., Tian L., Cai T., Yu M., A General Statistical Framework for Subgroup Identification and Comparative Treatment Scoring, Biometrics, 73, 4, pp. 1199-1209, (2017); Nie X., Wager S., Quasi-Oracle Estimation of Heterogeneous Treatment Effects, Biometrika, 108, 2, pp. 299-319, (2020); Ishwaran H., Kogalur U., Random Survival Forests for R, R News, 24, 1, pp. 134-161, (2020); Sun T., Wei Y., Chen W., Ding Y., Genome-Wide Association Study-Based Deep Learning for Survival Prediction, Statistics in Medicine, 39, 30, pp. 4605-4620, (2020); Liaw A., Wiener M., Classification and Regression by Random Forest, R News, 2, pp. 18-22, (2002); Tibshirani R., Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society Series B: Statistical Methodology, 58, 1, pp. 267-288, (2018); Chernozhukov V., Chetverikov D., Demirer M., Et al., Double/Debiased Machine Learning for Treatment and Structural Parameters, Econometrics Journal, 21, 1, pp. C1-C68, (2018); Zhao L., Tian L., Cai T., Claggett B., Wei L.J., Effectively Selecting a Target Population for a Future Comparative Study, JASA, 108, 502, pp. 527-539, (2013); Crabbe J., Curth A., Bica I., dM S.V., “Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability,”, (2022); Wan K., Tanioka K., Shimokawa T., Rule Ensemble Method With Adaptive Group Lasso for Heterogeneous Treatment Effect Estimation, Statistics in Medicine, 42, 19, pp. 3413-3442, (2023); Wan K., Tanioka K., Shimokawa T., “Survival Causal Rule Ensemble Method Considering the Main Effect for Estimating Heterogeneous Treatment Effects,”, (2023); Candes E., Lei L., Ren Z., Conformalized Survival Analysis, Journal of the Royal Statistical Society Series B: Statistical Methodology, 85, 1, pp. 24-45, (2023); Lei L., Candes E.J., Conformal Inference of Counterfactuals and Individual Treatment Effects, Journal of the Royal Statistical Society Series B: Statistical Methodology, 83, 5, pp. 911-938, (2021)","Y. Ding; Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, United States; email: yingding@pitt.edu","","John Wiley and Sons Ltd","","","","","","02776715","","SMEDD","39853815","English","Stat. Med.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85216113325"
"Zhuan B.; Ma H.-H.; Zhang B.-C.; Li P.; Wang X.; Yuan Q.; Yang Z.; Xie J.","Zhuan, Bing (56835603600); Ma, Hong-Hong (58158285900); Zhang, Bo-Chao (57432311300); Li, Ping (59480528200); Wang, Xi (57604914600); Yuan, Qun (57221048452); Yang, Zhao (57221061955); Xie, Jun (57705667000)","56835603600; 58158285900; 57432311300; 59480528200; 57604914600; 57221048452; 57221061955; 57705667000","Identification of non-small cell lung cancer with chronic obstructive pulmonary disease using clinical symptoms and routine examination: a retrospective study","2023","Frontiers in Oncology","13","","1158948","","","","1","10.3389/fonc.2023.1158948","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167813329&doi=10.3389%2ffonc.2023.1158948&partnerID=40&md5=450454bf56346deba2d20e6544fa9428","Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Ningxia, Yinchuan, China; Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Ningxia, Yinchuan, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Jiangsu, Suzhou, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Jiangsu, Suzhou, China; Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China; Department of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China","Zhuan B., Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Ningxia, Yinchuan, China, Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Ningxia, Yinchuan, China; Ma H.-H., Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Ningxia, Yinchuan, China, Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Ningxia, Yinchuan, China; Zhang B.-C., School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Jiangsu, Suzhou, China, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Jiangsu, Suzhou, China; Li P., Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital, Ningxia, Yinchuan, China, Department of Respiratory Medicine, Ningxia Hui Autonomous Region People’s Hospital Affiliated to Ningxia Medical University, Ningxia, Yinchuan, China; Wang X., Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China; Yuan Q., Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China; Yang Z., Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China; Xie J., Department of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Jiangsu, Suzhou, China","Background: Patients with non-small cell lung cancer (NSCLC) and patients with NSCLC combined with chronic obstructive pulmonary disease (COPD) have similar physiological conditions in early stages, and the latter have shorter survival times and higher mortality rates. The purpose of this study was to develop and compare machine learning models to identify future diagnoses of COPD combined with NSCLC patients based on the patient’s disease and routine clinical data. Methods: Data were obtained from 237 patients with COPD combined with NSCLC as well as NSCLC admitted to Ningxia Hui Autonomous Region People’s Hospital from October 2013 to July 2022. Six machine learning algorithms (K-nearest neighbor, logistic regression, eXtreme gradient boosting, support vector machine, naïve Bayes, and artificial neural network) were used to develop prediction models for NSCLC combined with COPD. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1 score, Mathews correlation coefficient (MCC), Kappa, area under the receiver operating characteristic curve (AUROC)and area under the precision-recall curve (AUPRC) were used as performance indicators to evaluate the performance of the models. Results: 135 patients with NSCLC combined with COPD, 102 patients with NSCLC were included in the study. The results showed that pulmonary function and emphysema were important risk factors and that the support vector machine-based identification model showed optimal performance with accuracy:0.946, recall:0.940, specificity:0.955, precision:0.972, npv:0.920, F1 score:0.954, MCC:0.893, Kappa:0.888, AUROC:0.975, AUPRC:0.987. Conclusion: The use of machine learning tools combining clinical symptoms and routine examination data features is suitable for identifying the risk of concurrent NSCLC in COPD patients. Copyright © 2023 Zhuan, Ma, Zhang, Li, Wang, Yuan, Yang and Xie.","COPD; detection; emphysema; identification; machine learning; NSCLC; pulmonary function","bronchodilating agent; C reactive protein; CA 125 antigen; carcinoembryonic antigen; cytokeratin 19 fragment; fibrinogen; neuron specific enolase; tumor marker; accuracy; adult; algorithm; area under the curve; Article; artificial neural network; Bayesian learning; body mass; cancer staging; chronic obstructive lung disease; computer assisted tomography; controlled study; diagnostic test accuracy study; diffusing capacity for carbon monoxide; emphysema; emphysema index; eXtreme gradient boosting; female; forced expiratory volume; forced vital capacity; goddard score; human; k nearest neighbor; logistic regression analysis; lung cancer; lung function; lung function test; lymphocyte count; machine learning; major clinical study; male; medical examination; middle aged; multilayer perceptron; neutrophil count; non small cell lung cancer; performance indicator; platelet count; prediction; predictive value; recall; receiver operating characteristic; residual volume; respiratory tract parameters; retrospective study; risk factor; scoring system; sensitivity and specificity; smoking; squamous cell carcinoma; support vector machine; survival rate; symptom","","C reactive protein, 9007-41-4; fibrinogen, 9001-32-5","IntelliSpace Portal V9, Philips","Philips","Natural Science Foundation of Ningxia Hui Autonomous Region, (2022AAC03351); distinguished medical expert” in Jiangsu Province, (JSTPYXZJ2021006); National Natural Science Foundation of China, NSFC, (82160017); National Natural Science Foundation of China, NSFC; Government of Jiangsu Province, (JSSCRC2021568); Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province, (BK20201183); Natural Science Foundation of Jiangsu Province","This research was funded by The Natural Science Foundation of Jiangsu Province. China (Grant No. BK20201183); The “innovative and entrepreneurial talent” in Jiangsu Province (JSSCRC2021568); The “distinguished medical expert” in Jiangsu Province(JSTPYXZJ2021006); the National Natural Science Foundation of China(No. 82160017); Natural Science Foundation of Ningxia Hui Autonomous Region (No. 2022AAC03351). 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Yang; Department of Respiratory Medicine, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China; email: yangzhao0304@hotmail.com; J. Xie; Department of Thoracic Surgery, Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China; email: Xie9645216@163.com","","Frontiers Media SA","","","","","","2234943X","","","","English","Front. Oncol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85167813329"
"Susmann H.; Chambaz A.; Josse J.; Aegerter P.; Wargon M.; Bacry E.","Susmann, Herbert (57193161600); Chambaz, Antoine (14065709700); Josse, Julie (24461842500); Aegerter, Philippe (7003640892); Wargon, Mathias (24402239100); Bacry, Emmanuel (6701623085)","57193161600; 14065709700; 24461842500; 7003640892; 24402239100; 6701623085","Probabilistic prediction of arrivals and hospitalizations in emergency departments in Île-de-France","2025","International Journal of Medical Informatics","195","","105728","","","","0","10.1016/j.ijmedinf.2024.105728","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211132589&doi=10.1016%2fj.ijmedinf.2024.105728&partnerID=40&md5=05694d343a4674591cdc8b08b18b7cf1","CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France; Université Paris Cité, CNRS, MAP5, Paris, F-75006, France; Fédération Parisienne de Modélisation Mathématique, CNRS FR 2036, France; Inria PreMeDICaL team, Idesp, Université de Montpellier, France; Paris Area Emergency and Unscheduled Care Regional Observatory, Saint-Denis, France; Emergency Department, Saint-Denis Hospital, Saint-Denis, France; Epidemiology and Public Health Service, AP-HP, Hôpitaux Universitaires Paris-Saclay, Boulogne, France; University of Versailles Saint-Quentin, Versailles, France; INSERM CESP U1018, Université Paris-Saclay, Le Kremlin-Bicêtre, France","Susmann H., CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France; Chambaz A., Université Paris Cité, CNRS, MAP5, Paris, F-75006, France, Fédération Parisienne de Modélisation Mathématique, CNRS FR 2036, France; Josse J., Inria PreMeDICaL team, Idesp, Université de Montpellier, France; Aegerter P., Epidemiology and Public Health Service, AP-HP, Hôpitaux Universitaires Paris-Saclay, Boulogne, France, University of Versailles Saint-Quentin, Versailles, France, INSERM CESP U1018, Université Paris-Saclay, Le Kremlin-Bicêtre, France; Wargon M., Paris Area Emergency and Unscheduled Care Regional Observatory, Saint-Denis, France, Emergency Department, Saint-Denis Hospital, Saint-Denis, France; Bacry E., CEREMADE (UMR 7534), Université Paris-Dauphine PSL, Place du Maréchal de Lattre de Tassigny, Paris, 75016, France","Background: Forecasts of future demand is foundational for effective resource allocation in emergency departments (EDs). As ED demand is inherently variable, it is important for forecasts to characterize the range of possible future demand. However, extant research focuses primarily on producing point forecasts using a wide variety of prediction algorithms. In this study, our objective is to generate point and interval predictions that accurately characterize the variability in ED demand using ensemble methods that combine predictions from multiple base algorithms based on their empirical performance. Methods: Data consisted in daily arrivals and subsequent hospitalizations at 72 emergency departments in Île-de-France from 2014–2018. Additional explanatory variables were collected including public and school holidays, meteorological variables, and public health trends. One-day ahead point and 80% interval predictions of arrivals and hospitalizations were produced by predicting the 10%, 50%, and 90% quantiles of the forecast distribution. Quantile prediction algorithms included methods such as ARIMAX, variations of random forests, and generalized additive models. Ensemble predictions were then formed using Exponentially Weighted Averaging, Bernstein Online Aggregation, and Super Learning. Prediction intervals were post-processed using Adaptive Conformal Inference techniques. Point predictions were evaluated by their Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), and 80% interval predictions by their empirical coverage and mean interval width. Results: For point forecasts, ensemble methods achieved lower average MAE and MAPE than any of the base algorithms. All of the base algorithms and ensemble methods yielded prediction intervals with near optimal empirical coverage after conformalization. For hospitalizations, the shortest mean interval widths were achieved by the ensemble methods. Conclusions: Ensemble methods yield joint point and prediction intervals that adapt to individual EDs and achieve better performance than individual algorithms. Conformal inference techniques improve the performance of the prediction intervals. © 2024 Elsevier B.V.","Conformal inference; Emergency department; Ensemble learning; Machine learning; Time series forecasting","Algorithms; Emergency Service, Hospital; Forecasting; Hospitalization; Humans; Models, Statistical; Adversarial machine learning; Conformal inference; Emergency departments; Ensemble learning; Ensemble methods; Inference techniques; Interval prediction; Machine-learning; Prediction algorithms; Prediction interval; Time series forecasting; algorithm; Article; asthma; bronchiolitis; comparative study; controlled study; cross validation; electronic health record; emergency ward; femur fracture; forecasting; hospitalization; human; incidence; machine learning; mean absolute error; quantile regression; random forest; resource allocation; retrospective study; time series analysis; algorithm; forecasting; hospital emergency service; procedures; statistical model; Prediction models","","","","","Ministère de la Santé; Programme Hospitalier de Recherche Clinique, (PHQ15648); Agence Nationale de la Recherche, ANR, (ANR-19-P3IA-0001, PREPS-15-0648); Agence Nationale de la Recherche, ANR","Funding text 1: This research is supported by the Programme Hospitalier de Recherche Clinique \u2013 PHQ15648 (Minist\u00E8re de la Sant\u00E9). This research is partially supported by the Agence Nationale de la Recherche as part of the \u201CInvestissements d'avenir\u201D program (reference ANR-19-P3IA-0001; PRAIRIE 3IA Institute).We would like to thank St\u00E9phane Ga\u00EFffas, Karine Tribouley, Med Yasser Benigmim for previous work on the project and France Guyot, Nawal Derridj-Ait Younes, Reda Attia at Assistance Publique - H\u00F4pitaux de Paris. This work was sponsored by Assistance Publique - H\u00F4pitaux de Paris (D\u00E9l\u00E9gation \u00E0 la Recherche Clinique et \u00E0 l'Innovation; N\u2218 Dossier: PREPS-15-0648).; Funding text 2: Funding\u2003 This research is supported by the Programme Hospitalier de Recherche Clinique \u2013 PHQ15648 (Minist\u00E8re de la Sant\u00E9). This research is partially supported by the Agence Nationale de la Recherche as part of the \u201CInvestissements d'avenir\u201D program (reference ANR-19-P3IA-0001; PRAIRIE 3IA Institute). 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Learn., 106, 1, pp. 119-141, (2017); Zaffran M., Feron O., Goude Y., Josse J., Dieuleveut A., Adaptive conformal predictions for time series; Zhao X., Lai J.W., Ho A.F.W., Liu N., Ong M.E.H., Cheong K.H., Predicting hospital emergency department visits with deep learning approaches, Biocybern. Biomed. Eng., 42, 3, pp. 1051-1065, (2022); Zlotnik A., Gallardo-antolin A., Alfaro M.C., Perez M.C.P., Martinez J.M.M., Emergency department visit forecasting and dynamic nursing staff allocation using machine learning techniques with readily available open-source software, Comput. Inf. Nurs., 33, 8, (2015); Cevid D., Michel L., Naf J., Buhlmann P., Meinshausen N., Distributional random forests: heterogeneity adjustment and multivariate distributional regression, J. Mach. Learn. Res., 23, 333, pp. 1-79, (2022)","H. Susmann; Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York, 180 Madison Avenue, 10016, United States; email: susmah01@nyu.edu","","Elsevier Ireland Ltd","","","","","","13865056","","IJMIF","39657402","English","Int. J. Med. Informatics","Article","Final","","Scopus","2-s2.0-85211132589"
"Mei S.; Li X.; Zhou Y.; Xu J.; Zhang Y.; Wan Y.; Cao S.; Zhao Q.; Geng S.; Xie J.; Chen S.; Hong S.","Mei, Shuhao (59148449400); Li, Xin (59073696800); Zhou, Yuxi (57201494950); Xu, Jiahao (59445499500); Zhang, Yong (56004332100); Wan, Yuxuan (59148449500); Cao, Shan (59444780700); Zhao, Qinghao (57202021595); Geng, Shijia (57483202500); Xie, Junqing (57201493745); Chen, Shengyong (24491760700); Hong, Shenda (56245620000)","59148449400; 59073696800; 57201494950; 59445499500; 56004332100; 59148449500; 59444780700; 57202021595; 57483202500; 57201493745; 24491760700; 56245620000","Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series","2025","npj Systems Biology and Applications","11","1","18","","","","0","10.1038/s41540-025-00489-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218812903&doi=10.1038%2fs41540-025-00489-y&partnerID=40&md5=8393f35844d6e35322ed6ee8a35c2106","Department of Computer Science, Tianjin University of Technology, Tianjin, China; National Institute of Health Data Science, Peking University, Beijing, China; Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China; DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China; Department of Biotheraphy, The Second Hospital of Tianjin Medical University, Tianjin, China; Department of Cardiology, Peking University People’s Hospital, Beijing, China; HeartVoice Medical Technology, Hefei, China; Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, University of Oxford, Oxford, United Kingdom; Institute for Artificial Intelligence, Peking University, Beijing, China","Mei S., Department of Computer Science, Tianjin University of Technology, Tianjin, China, National Institute of Health Data Science, Peking University, Beijing, China; Li X., Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China; Zhou Y., Department of Computer Science, Tianjin University of Technology, Tianjin, China, DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China; Xu J., Department of Computer Science, Tianjin University of Technology, Tianjin, China; Zhang Y., DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China; Wan Y., Department of Computer Science, Tianjin University of Technology, Tianjin, China; Cao S., Department of Biotheraphy, The Second Hospital of Tianjin Medical University, Tianjin, China; Zhao Q., Department of Cardiology, Peking University People’s Hospital, Beijing, China; Geng S., HeartVoice Medical Technology, Hefei, China; Xie J., Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, University of Oxford, Oxford, United Kingdom; Chen S., Department of Computer Science, Tianjin University of Technology, Tianjin, China; Hong S., National Institute of Health Data Science, Peking University, Beijing, China, Institute for Artificial Intelligence, Peking University, Beijing, China","Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but they are not adept at predicting future COPD risk based on subtle data patterns. In this study, we introduce a novel deep learning-based approach, DeepSpiro, aimed at the early prediction of future COPD risk. DeepSpiro consists of four key components: SpiroSmoother for stabilizing the Volume-Flow curve, SpiroEncoder for capturing volume variability-pattern through key patches of varying lengths, SpiroExplainer for integrating heterogeneous data and explaining predictions through volume attention, and SpiroPredictor for predicting the disease risk of undiagnosed high-risk patients based on key patch concavity, with prediction horizons of 1–5 years, or even longer. Evaluated on the UK Biobank dataset, DeepSpiro achieved an AUC of 0.8328 for COPD detection and demonstrated strong predictive performance for future COPD risk (p-value < 0.001). In summary, DeepSpiro can effectively predict the long-term progression of COPD disease. © The Author(s) 2025.","","Deep Learning; Disease Progression; Early Diagnosis; Female; Humans; Male; Pulmonary Disease, Chronic Obstructive; Spirometry; chronic obstructive lung disease; deep learning; diagnosis; disease exacerbation; early diagnosis; female; human; male; pathophysiology; procedures; spirometry","","","","","Peking University, PKU; Natural Science Foundation of Beijing Municipality, (QY23040); Natural Science Foundation of Beijing Municipality; Fundamental Research Funds for the Central Universities, (PKU2024LCXQ030); Fundamental Research Funds for the Central Universities; National College Students Innovation and Entrepreneurship Training Program, (202410060109); National College Students Innovation and Entrepreneurship Training Program; National Natural Science Foundation of China, NSFC, (62376197, 92048301, 62020106004, 62202332, 62102008); National Natural Science Foundation of China, NSFC; PKU-OPPO Fund, (B0202301); Natural Science Foundation of Tianjin Municipality, (23JCYBJC00360); Natural Science Foundation of Tianjin Municipality","The authors gratefully acknowledge the financial supports by the National Natural Science Foundation of China under Grant 62202332, Grant 62102008, Grant 62376197, Grant 62020106004 and Grant 92048301; Clinical Medicine Plus X\u2014Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities (PKU2024LCXQ030); PKU-OPPO Fund (B0202301); Beijing Natural Science Foundations (QY23040); Natural Science Foundation of Tianjin City (23JCYBJC00360); College Student Innovation and Entrepreneurship Training Program (202410060109). 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Appl, 219, (2023); Raihan M.J., Khan M.A.-M., Kee S.-H., Nahid A.-A., Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP, Sci. Rep, 13, (2023); Sun Z., Et al., An improved random forest based on the classification accuracy and correlation measurement of decision trees, Expert Syst. Appl, 237, (2024); Mirsadraee M., Salarifar E., Attaran D., Evaluation of superiority of FEV1/VC over FEV1/FVC for classification of pulmonary disorders, J. Cardio-Thorac. Med, 3, pp. 355-359, (2015)","Y. Zhou; Department of Computer Science, Tianjin University of Technology, Tianjin, China; email: joy_yuxi@pku.edu.cn; S. Chen; Department of Computer Science, Tianjin University of Technology, Tianjin, China; email: csy@tjut.edu.cn; S. Hong; National Institute of Health Data Science, Peking University, Beijing, China; email: hongshenda@pku.edu.cn; Y. Zhang; DCST, BNRist, RIIT, Institute of Internet Industry, Tsinghua University, Beijing, China; email: zhangyong05@tsinghua.edu.cn","","Nature Research","","","","","","20567189","","","39955293","English","npj Syst. Bio.  Appl.","Article","Final","","Scopus","2-s2.0-85218812903"
"Gawlewicz-Mroczka A.; Pytlewski A.; Celejewska-Wójcik N.; Ćmiel A.; Gielicz A.; Sanak M.; Mastalerz L.","Gawlewicz-Mroczka, Agnieszka (18934647400); Pytlewski, Adam (57752696700); Celejewska-Wójcik, Natalia (55463144600); Ćmiel, Adam (6603056384); Gielicz, Anna (6505836857); Sanak, Marek (7004497201); Mastalerz, Lucyna (6603927132)","18934647400; 57752696700; 55463144600; 6603056384; 6505836857; 7004497201; 6603927132","Machine learning in the diagnosis of asthma phenotypes during coronavirus disease 2019 pandemic","2022","Clinical and Translational Allergy","12","10","e12201","","","","1","10.1002/clt2.12201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141172740&doi=10.1002%2fclt2.12201&partnerID=40&md5=34d5bd98abb6038302f887d92606ad51","Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland; University Hospital, Krakow, Poland; Department of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland","Gawlewicz-Mroczka A., Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland; Pytlewski A., University Hospital, Krakow, Poland; Celejewska-Wójcik N., Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland; Ćmiel A., Department of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland; Gielicz A., Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland; Sanak M., Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland; Mastalerz L., Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland","Background: During the coronavirus disease 2019 (COVID-19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin-tolerant asthma. Methods: We used a prospective database of 135 patients with non-steroidal anti-inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID-tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. Results: The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy-to-obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. Conclusions: ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID-19 pandemic. © 2022 The Authors. Clinical and Translational Allergy published by John Wiley & Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.","COVID-19 pandemic; machine learning; nonsteroidal anti-inflammatory drug (NSAID)–exacerbated respiratory disease (NERD); nonsteroidal anti-inflammatory drug tolerant asthma (NTA); oral aspirin challenge","acetylsalicylic acid; corticosteroid; fluticasone; icosanoid; immunoglobulin E; nonsteroid antiinflammatory agent; adult; area under the curve; Article; artificial neural network; aspirin exacerbated respiratory disease; asthma; clinical examination; clinical feature; coronavirus disease 2019; diagnostic accuracy; diagnostic test accuracy study; differential diagnosis; drug tolerability; eosinophil count; false positive result; female; forced expiratory volume; human; inflammation; laboratory test; machine learning; major clinical study; male; middle aged; pandemic; patient safety; phenotype; prick test; prospective study; receiver operating characteristic; sensitivity and specificity; sputum analysis; supernatant; urinalysis","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; fluticasone, 90566-53-3; immunoglobulin E, 37341-29-0","","","Narodowe Centrum Nauki, NCN, (N402 593040, UMO‐2013/11/B/NZ6/02034, UMO‐2015/19/B/NZ5/00096, UMO‐2018/31/B/NZ5/00806)","This work was supported by the National Science Centre (NCN), Poland (grant no.: UMO‐2018/31/B/NZ5/00806; UMO‐2015/19/B/NZ5/00096; UMO‐2013/11/B/NZ6/02034; and N402 593040). Funding source allowed to measure unique parameters described in Section 2.2 (Data collection). ","Kowalski L.M., Agache I., Bavbek S., Et al., Diagnosis and management of NSAID-exacerbated respiratory disease – a EAACI position paper, Allergy, 74, 1, pp. 28-39, (2019); Laidlaw T.M., Mullol J., Woessner K.M., Amin N., Mannent L.P., Chronic rhinosinusitis with nasal polyps and asthma, J Allergy Clin Immunol Pract, 9, 3, pp. 1133-1141, (2021); Ying S., Meng Q., Scadding G., Parikh A., Corrigan C.J., Lee T.H., Aspirin-sensitive rhinosinusitis is associated with reduced E-prostanoid 2 receptor expression on nasal mucosal inflammatory cells, J Allergy Clin Immunol, 117, pp. 312-318, (2006); Corrigan C.J., Napoli R.L., Meng Q., Et al., Reduced expression of the prostaglandin E2 receptor E-prostanoid 2 on bronchial mucosal leukocytes in patients with aspirin-sensitive asthma, J Allergy Clin Immunol, 129, 6, pp. 1636-1646, (2012); Nizankowska-Mogilnicka E., Bochenek G., Mastalerz L., Et al., EAACI/GA2LEN guideline: aspirin provocation test for diagnosis of aspirin hypersensitivity, Allergy, 62, 10, pp. 1111-1118, (2007); Kaplan A., Cao H., FitzGerald J.M., Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol, 9, 6, pp. 2255-2261, (2021); Tyrak K.E., Pajdzik K., Konduracka E., Et al., Artificial neural network identifies nonsteroidal antinflammatory drugs exacerbated respiratory disease (N-ERD) cohort, Allergy, 75, 7, pp. 1649-1658, (2020); 2021 Global strategy for asthma management and prevention report, (2021); Mastalerz L., Celejewska-Wojcik N., Wojcik K., Et al., Induced sputum supernatant bioactive lipid mediators can identify subtypes of asthma, Clin Exp Allergy, 45, 12, pp. 1779-1789, (2015); Mastalerz L., Tyrak K.E., Ignacak M., Et al., Prostaglandin E<sub>2</sub> decrease in induced sputum of hypersensitive asthmatics during oral challenge with aspirin, Allergy, 74, 5, pp. 922-932, (2019); Tyrak K.E., Kuprys-Lipinska I., Czarnobilska E., Et al., Sputum biomarkers during aspirin desensitization in nonsteroidal anti-inflammatory drugs exacerbated respiratory disease, Respir Med, 152, pp. 51-59, (2019); Djukanovic R., Sterk P.J., Fahy J.V., Hargreave F.E., Standardised methodology of sputum induction and processing, Eur Respir J Suppl, 20, pp. 1-55, (2002); Simpson J.L., Scott R., Boyle M.J., Gibson P.G., Inflammatory subtypes in asthma: assessment and identification using induced sputum, Respirology, 11, 1, pp. 54-61, (2006); Sze E., Bhalla A., Nair P., Mechanisms and therapeutic strategies for non-T2 asthma, Allergy, 75, 2, pp. 311-325, (2020); Arik S.O., Pfister T., TabNet: attentive interpretable tabular learning, (2019); Rajan J.P., Wineinger N.E., Stevenson D.D., White A.A., Prevalence of aspirin-exacerbated respiratory disease among asthmatic patients: a meta-analysis of the literature, J Allergy Clin Immunol, 135, 3, pp. 676-681.e1, (2015); Crespo-Lessmann A., Plaza V., Almonacid C., Et al., Multidisciplinary consensus on sputum induction biosafety during the COVID-19 pandemic, Allergy, 76, 8, pp. 2407-2419, (2021); Cahill K.N., Johns C.B., Cui J., Et al., Automated identification of an aspirin-exacerbated respiratory disease cohort, J Allergy Clin Immunol, 139, 3, pp. 819-825, (2017); Finkelstein J., Jeong I.C., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann N Y Acad Sci, 1387, 1, pp. 153-165, (2017); Lynch C.M., Abdollahi B., Fuqua J.D., Et al., Prediction of lung cancer patient survival via supervised machine learning classification techniques, Int J Med Inform, 108, pp. 1-8, (2017)","L. Mastalerz; Department of Internal Medicine, Jagiellonian University Medical College, Krakow, ul. Jakubowskiego 2, 30-688, Poland; email: lucyna.mastalerz@uj.edu.pl","","John Wiley and Sons Inc","","","","","","20457022","","","","English","Clin. Transl. Allergy","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85141172740"
"Nishibe T.; Iwasa T.; Kano M.; Akiyama S.; Iwahashi T.; Fukuda S.; Koizumi J.; Nishibe M.","Nishibe, Toshiya (7005984158); Iwasa, Tsuyoshi (59399845300); Kano, Masaki (57207982117); Akiyama, Shinobu (57212209169); Iwahashi, Toru (16402077700); Fukuda, Shoji (56645046900); Koizumi, Jun (35375235900); Nishibe, Masayasu (6603843325)","7005984158; 59399845300; 57207982117; 57212209169; 16402077700; 56645046900; 35375235900; 6603843325","Predicting Short-Term Mortality after Endovascular Aortic Repair Using Machine Learning–Based Decision Tree Analysis","2025","Annals of Vascular Surgery","111","","","170","175","5","0","10.1016/j.avsg.2024.10.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211711604&doi=10.1016%2fj.avsg.2024.10.009&partnerID=40&md5=8512fb7a6e5dbae5f36a96a4d2663b1c","Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan; Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Department of Radiology, Chiba University School of Medicine, Chiba, Japan; Department of Surgery, Eniwa Midorino Clinic, Eniwa, Japan","Nishibe T., Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan, Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Iwasa T., Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan; Kano M., Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Akiyama S., Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Iwahashi T., Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Fukuda S., Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan; Koizumi J., Department of Radiology, Chiba University School of Medicine, Chiba, Japan; Nishibe M., Department of Surgery, Eniwa Midorino Clinic, Eniwa, Japan","Background: Endovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)–based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data. Methods: This study analyzed 169 patients who underwent EVAR to identify predictors of short-term mortality (within 3 years) using DTA. Data included 23 variables such as age, gender, nutritional status, comorbidities, and surgical details. The Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the decision tree classifier. Results: DTA identified poor nutritional status as the most significant predictor, followed by chronic kidney disease, chronic obstructive pulmonary disease, and advanced age (octogenarian). The decision tree identified 6 terminal nodes with a risk of short-term mortality ranging from 0% to 79.9%. This model had 68.7% accuracy, 65.7% specificity, and 79.0% sensitivity. Conclusions: ML–based DTA is promising in predicting short-term mortality after EVAR, highlighting the need for comprehensive preoperative assessment and individualized management strategies. © 2024 Elsevier Inc.","","Aged; Aged, 80 and over; Aortic Aneurysm, Abdominal; Blood Vessel Prosthesis Implantation; Clinical Decision-Making; Decision Support Techniques; Decision Trees; Endovascular Aneurysm Repair; Endovascular Procedures; Female; Humans; Machine Learning; Male; Middle Aged; Nutritional Status; Predictive Value of Tests; Retrospective Studies; Risk Assessment; Risk Factors; Time Factors; Treatment Outcome; accuracy; adult; age; aged; aneurysm; Article; brain disease; chronic kidney failure; chronic obstructive lung disease; cohort analysis; comorbidity; computer language; decision tree; endovascular aneurysm repair; female; gender; heart disease; human; kidney disease; machine learning; major clinical study; male; malignant neoplasm; mortality; nutritional status; patient care; pneumonia; retrospective study; risk assessment; sensitivity and specificity; treatment outcome; type II endoleak; abdominal aortic aneurysm; adverse event; blood vessel transplantation; clinical decision making; decision support system; diagnostic imaging; endovascular aneurysm repair; endovascular surgery; middle aged; mortality; predictive value; risk factor; surgery; time factor; very elderly","","","","","","","Prinssen M., Verhoeven E.L., Buth J., Et al., Dutch Randomized Endovascular Aneurysm Management (DREAM)Trial Group. A randomized trial comparing conventional and endovascular repair of abdominal aortic aneurysms, N Engl J Med, 351, pp. 1607-1618, (2004); Meuli L., Zimmermann A., Menges A.L., Et al., Prognostic model for survival of patients with abdominal aortic aneurysms treated with endovascular aneurysm repair, Sci Rep, 12, (2022); Lim S., Halandras P.M., Park T., Et al., Outcomes of endovascular abdominal aortic aneurysm repair in high-risk patients, J Vasc Surg, 61, pp. 862-868, (2015); Xu Y., Liu X., Cao X., Et al., Artificial intelligence: a powerful paradigm for scientific research, Innovation (Camb), 2, (2021); Song Y.Y., Lu Y., Decision tree methods: applications for classification and prediction, Shanghai Arch Psychiatry, 27, pp. 130-135, (2015); Chahwan S., Comerota A.J., Pigott J.P., Et al., Elective treatment of abdominal aortic aneurysm with endovascular or open repair: the first decade, J Vasc Surg, 45, pp. 258-262, (2007); Nishibe T., Kano M., Matsumoto R., Et al., Prognostic value of nutritional markers for long-term mortality in patients undergoing endovascular aortic repair, Ann Vasc Dis, 16, pp. 124-130, (2023); Nishibe T., Kano M., Maekawa K., Et al., Association of neutrophils, lymphocytes, and neutrophil-lymphocyte ratio to overall mortality after endovascular abdominal aortic aneurysm repair, Int Angiol, 41, pp. 136-142, (2022); Bouillanne O., Morineau G., Dupont C., Et al., Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients, Am J Clin Nutr, 82, pp. 777-783, (2005); Lidoriki I., Schizas D., Frountzas M., Et al., GNRI as a prognostic factor for outcomes in cancer patients: a systematic review of the literature, Nutr Cancer, 73, pp. 391-403, (2021); Nishibe T., Iwahashi T., Kamiya K., Et al., Two-year outcome of the Endurant stent graft for endovascular abdominal aortic repair in Japanese patients: incidence of endoleak and aneurysm sac shrinkage, Int Angiol, 36, pp. 237-242, (2017); Nishibe T., Iwahashi T., Kamiya K., Et al., Clinical and morphological outcomes in endovascular aortic repair of abdominal aortic aneurysm using Gore C3 Excluder: comparison between patients treated within and outside instructions for use, Ann Vasc Surg, 59, pp. 54-62, (2019); Shirali G.A., Noroozi M.V., Malehi A.S., Predicting the outcome of occupational accidents by CART and CHAID methods at a steel factory in Iran, J Public Health Res, 7, (2018); Deo R.C., Machine learning in medicine, Circulation, 132, pp. 1920-1930, (2015); Kourou K., Exarchos T.P., Exarchos K.P., Et al., Machine learning applications in cancer prognosis and prediction, Comput Struct Biotechnol J, 13, pp. 8-17, (2015); Obermeyer Z., Emanuel E.J., Predicting the future - big data, machine learning, and clinical medicine, N Engl J Med, 375, pp. 1216-1219, (2016); Neal D., Beck A.W., Eslami M., Et al., Validation of a preoperative prediction model for mortality within 1 year after endovascular aortic aneurysm repair of intact aneurysms, J Vasc Surg, 70, pp. 449-461, (2019); Zidar D.A., Al-Kindi S.G., Liu Y., Et al., Association of lymphopenia with risk of mortality among adults in the US general population, JAMA Netw Open, 2, (2019); Xiong J., Wu Z., Chen C., Et al., Chronic obstructive pulmonary disease effect on the prevalence and postoperative outcome of abdominal aortic aneurysms: a meta-analysis, Sci Rep, 6, (2016); Ahn S., Min J.Y., Kim H.G., Et al., Outcomes after aortic aneurysm repair in patients with history of cancer: a nationwide dataset analysis, BMC Surg, 20, (2020); Patel V.I., Lancaster R.T., Mukhopadhyay S., Et al., Impact of chronic kidney disease on outcomes after abdominal aortic aneurysm repair, J Vasc Surg, 56, pp. 1206-1213, (2012); Correia M.I., Waitzberg D.L., The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis, Clin Nutr, 22, pp. 235-239, (2003); Ye S.L., Xu T.Z., Wang C., Et al., Controlling the nutritional risk score: a new tool for predicting postoperative mortality in patients with infrarenal abdominal aortic aneurysm treated with endovascular aneurysm repair, Front Nutr, 11, (2024)","T. Nishibe; Department of Medical Management and Informatics, Hokkaido Information University, Ebetsu, Nishi-Nopporo 59-2, Hokkaido, 069-8585, Japan; email: toshiyanishibe@yahoo.co.jp","","Elsevier Inc.","","","","","","08905096","","AVSUE","39580030","English","Ann. Vasc. Surg.","Article","Final","","Scopus","2-s2.0-85211711604"
"Rivai M.; Aulia D.; Aulia S.","Rivai, Muhammad (55847263000); Aulia, Dava (57252810100); Aulia, Sheva (58122191900)","55847263000; 57252810100; 58122191900","Reducing the Electronic Nose Sensor Array for Asthma Detection Using Firefly Algorithm","2024","International Journal of Intelligent Engineering and Systems","17","2","","700","714","14","1","10.22266/ijies2024.0430.56","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188149094&doi=10.22266%2fijies2024.0430.56&partnerID=40&md5=e130c225f1a91b80c69831fdd0494706","Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; Department of Informatics, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","Rivai M., Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; Aulia D., Department of Informatics, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; Aulia S., Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","Exhaled breath analysis comprises chemical compounds that can be utilized for diagnostic purposes, including asthma detection. An electronic nose can be offered as a means of monitoring patient circumstances. A significant problem often occurs when determining the appropriate number of gas sensors while maintaining high accuracy. The firefly algorithm (FA) is very effective because of its exploratory capabilities, presents theories that are easy to understand and has relatively fewer parameters. This study aims to reduce and determine the appropriate number of gas sensors for an electronic nose in differentiating healthy and asthmatic subjects using the FA and exhaled breath analysis. The experimental results indicate that the FA provides only four gas sensors that still maintain high performance. The convolutional neural network model was favored for its ability to classify the entire asthma dataset, making it the best machine learning model for the electronic nose, with an accuracy of 97.8%. © (2024), (Intelligent Network and Systems Society). All Rights Reserved.","Asthma; Diseases; Electronic nose; Firefly algorithm","","","","","","Indonesian Ministry of Education, Culture; Institut Teknologi Sepuluh Nopember, ITS; Indonesian Ministry of Education, Culture, Research","Funding text 1: This work was supported in part by the Indonesian Ministry of Education, Culture, Research, and Technology, and Institut Teknologi Sepuluh Nopember.; Funding text 2: This work was supported in part by the Indonesian Ministry of Education, Culture, Research,","Awal M. A., Hossain M. S., Debjit K., Ahmed N., Nath R. D., Habib G. M. M., Khan M. S., Islam M. A., Mahmud M. A. P., An early detection of asthma using BOMLA detector, IEEE Access, 9, pp. 58403-58420, (2021); Kim D., Cho S., Tamil L., Song D. J., Seo S., Predicting asthma attacks: Effects of indoor PM concentrations on peak expiratory flow rates of asthmatic children, IEEE Access, 8, pp. 8791-8797, (2020); Mattila T., Santonen T., Andersen H. R., Katsonouri A., Szigeti T., Uhl M., Wasowicz W., Lange R., Bocca B., Ruggieri F., Gehring M. K., Sarigiannis D. A., Tolonen H., Scoping review – The association between asthma and environmental chemicals, Int. J. Environ. Res. Public Health, 18, 1323, pp. 1-13, (2021); Chronic respiratory diseases : asthma, (2020); Tiotiu A., Biomarkers in asthma: state of the art, Asthma Res. Pract, 4, 10, pp. 1-10, (2018); Hendrick H., Hidayat R., Horng G. J., Wang Z. H., Non-invasive method for tuberculosis exhaled breath classification using electronic nose, IEEE Sens. J, 21, 9, pp. 11184-11191, (2021); Maciel M., Sankari S., Woollam M., Agarwal M., Optimization of metal oxide nanosensors and development of a feature extraction algorithm to analyze VOC profiles in exhaled breath, IEEE Sens. J, 23, 15, pp. 16571-16578, (2023); Savito L., Scarlata S., Bikov A., Carratu P., Carpagnano G. E., Exhaled volatile organic compounds for diagnosis and monitoring of asthma, World J. Clin. Cases, 11, 21, pp. 4996-5014, (2023); Binson V. A., Subramoniam M., Sunny Y., Mathew L., Prediction of Pulmonary Diseases with Electronic Nose Using SVM and XGBoost, IEEE Sens. J, 21, 18, pp. 20886-20895, (2021); Aulia D., Sarno R., Hidayati S. C., Rivai M., Optimization of the electronic nose sensor array for asthma detection based on genetic algorithm, IEEE Access, 11, pp. 74924-74935, (2023); Misbah M. Rivai, Kurniawan F., Diabetes detection using carbon nanomaterial coated QCM gas sensors and a convolutional neural network through urine sample, Int. J. Intell. Eng. Syst, 16, 5, pp. 417-427, (2023); Misbah M. Rivai, Kurniawan F., Muchidin Z., Aulia D., Identification of diabetes through urine using gas sensor and convolutional neural network, Int. J. Intell. Eng. Syst, 15, 1, pp. 520-529, (2022); Wang J., Zhang C., Chang M., He W., Lu X., Fei S., Lu G., Optimization of electronic nose sensor array for tea aroma detecting based on correlation coefficient and cluster analysis, Chemosensors, 9, 266, pp. 1-20, (2021); Tong J., Song C., Tong T., Zong X., Liu Z., Wang S., Tan L., Li Y., Chang Z., Design and optimization of electronic nose sensor array for real-time and rapid detection of vehicle exhaust pollutants, Chemosensors, 10, 496, pp. 1-12, (2022); Nguyen T. T., Quynh N. V., Dai L. V., Improved firefly algorithm: A novel method for optimal operation of thermal generating units, Complexity, 2018, (2018); Wang J., Zhang M., Song H., Cheng Z., Chang T., Bi Y., Sun K., Improvement and application of hybrid firefly algorithm, IEEE Access, 7, pp. 165458-165477, (2019); Khurshaid T., Wadood A., Farkoush S. G., Kim C. H., Yu J., Rhee S. B., Improved firefly algorithm for the optimal coordination of directional overcurrent relays, IEEE Access, 7, pp. 78503-78514, (2019); Ghasemi M., Mohammadi S. K., Zare M., Mirjalili S., Gil M., Hemmati R., A new firefly algorithm with improved global exploration and convergence with application to engineering optimization, Decis. Anal. J, 5, pp. 1-18, (2022); Attia K. A. M., Nassar M. W. I., Zeiny M. B. E., Serag A., Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study, Spectrochim. Acta – Part A Mol. Biomol. Spectrosc, 170, pp. 117-123, (2017); Ergun E., Aydemir O., Firefly algorithm based feature selection for EEG signal classification, Proc. of 2020 Medical Technologies Congress (TIPTEKNO), pp. 1-4, (2020); Deshmukh Y. S., Kumar P., Karan R., Singh S. K., Breast cancer detection-based feature optimization using firefly algorithm and ensemble classifier, Proc. of 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 1048-1054, (2021); Sujono H. A., Rivai M., Amin M., Asthma identification using gas sensors and support vector machine, Telkomnika (Telecommunication Comput. Electron. Control, 16, 4, pp. 1468-1480, (2018); Xie D., Chen D., Peng S., Yang Y., Xu L., Wu F., A low power cantilever-based metal oxide semiconductor gas sensor, IEEE Electron Device Lett, 40, 7, pp. 1178-1181, (2019); Yalsavar M., Karimaghaee P., Akbari A. S., Khooban M. H., Dehmeshki J., Majeed S. A., Kernel parameter optimization for support vector machine based on sliding mode control, IEEE Access, 10, pp. 17003-17017, (2022); Ding W., SVM-Based feature selection for differential space fusion and its application to diabetic fundus image classification, IEEE Access, 7, pp. 149493-149502, (2019); Sheykhmousa M., Mahdianpari M., Ghanbari H., Mohammadimanesh F., Ghamisi P., Homayouni S., Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, 13, pp. 6308-6325, (2020); Zhang G., Gionis A., Regularized impurity reduction: accurate decision trees with complexity guarantees, Data Min. Knowl. Discov, 37, 1, pp. 434-475, (2023); Jiang Y., Tong G., Yin H., Xiong N., A pedestrian detection method based on genetic algorithm for optimize XGBoost training parameters, IEEE Access, 7, pp. 118310-118321, (2019); Rivai M., Budiman F., Purwanto D., Baid M. S. A. A., Tukadi, Aulia D., Discrimination of durian ripeness level using gas sensors and neural network, Procedia Computer Science, pp. 677-684, (2022); Liu Y., Huang Y. X., Zhang X., Qi W., Guo J., Hu Y., Zhang L., Su H., Deep C-LSTM neural network for epileptic seizure and tumor detection using high-dimension EEG signals, IEEE Access, 8, pp. 37495-37504, (2020); Abbasi M. U., Rashad A., Basalamah A., Tariq M., Detection of epilepsy seizures in neo-natal EEG using LSTM architecture, IEEE Access, 7, pp. 179074-179085, (2019); Xiaoyan L., Raga R. C., BiLSTM model with attention mechanism for sentiment classification on chinese mixed text comments, IEEE Access, 11, pp. 26199-26210, (2023); Huang Z., Yang F., Xu F., Song X., Tsui K. L., Convolutional gated recurrent unit-recurrent neural network for state-of-charge estimation of lithium-ion batteries, IEEE Access, 7, pp. 93139-93149, (2019); Lent D. M. B., Novaes M. P., Carvalho L. F., Lloret J., Rodrigues J. J. P. C., Proenca M. L., A gated recurrent unit deep learning model to detect and mitigate distributed denial of service and portscan attacks, IEEE Access, 10, pp. 73229-73242, (2022); Roman I., Santana R., Mendiburu A., Lozano J. A., An experimental study in adaptive kernel selection for bayesian optimization, IEEE Access, 7, pp. 184294-184302, (2019); Cho H., Kim Y., Lee E., Choi D., Lee Y., Rhee W., Basic enhancement strategies when using bayesian optimization for hyperparameter tuning of deep neural networks, IEEE Access, 8, pp. 52588-52608, (2020); Sui G., Yu Y., Bayesian contextual bandits for hyper parameter optimization, IEEE Access, 8, pp. 42971-42979, (2020); Bazi S., Benzid R., Bazi Y., Rahhal M. M. A., A fast firefly algorithm for function optimization: Application to the control of bldc motor, Sensors, 21, 5267, pp. 1-23, (2021); Alomar K., Aysel H. I., Cai X., Data augmentation in classification and segmentation: A survey and new strategies, J. Imaging, 9, 46, pp. 1-26, (2023)","M. Rivai; Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia; email: muhammad_rivai@ee.its.ac.id","","Intelligent Network and Systems Society","","","","","","2185310X","","","","English","Int. J. Intelligent Eng. Syst.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85188149094"
"Rehman M.U.; Driss M.; Khakimov A.; Khalid S.","Rehman, Mujeeb Ur (59272855000); Driss, Maha (36952645100); Khakimov, Abdukodir (57194233776); Khalid, Sohail (53264151700)","59272855000; 36952645100; 57194233776; 53264151700","Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms","2022","Computers, Materials and Continua","72","3","","5681","5697","16","1","10.32604/cmc.2022.025840","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128692904&doi=10.32604%2fcmc.2022.025840&partnerID=40&md5=6acfb01fa8c612bed5426216fc2d6e9e","Department of Electrical Engineering, Riphah International University, Islamabad, 44000, Pakistan; Security Engineering Lab, Prince Sultan University, Riyadh, 12435, Saudi Arabia; RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, 2010, Tunisia; Peoples' Friendship University of Russia (RUDN University), Moscow, 117198, Russian Federation","Rehman M.U., Department of Electrical Engineering, Riphah International University, Islamabad, 44000, Pakistan; Driss M., Security Engineering Lab, Prince Sultan University, Riyadh, 12435, Saudi Arabia, RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, 2010, Tunisia; Khakimov A., Peoples' Friendship University of Russia (RUDN University), Moscow, 117198, Russian Federation; Khalid S., Department of Electrical Engineering, Riphah International University, Islamabad, 44000, Pakistan","Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based non-invasive and secured diagnosis for OLD using physiological and iris features. This research work implements different machine-learning-based techniques which classify various subjects, which are healthy and effective patients. The iris features include gray-level run-length matrix-based features, gray-level co-occurrence matrix, and statistical features. These features are extracted from iris images. Additionally, ten different classifiers and voting techniques, including hard and soft voting, are implemented and tested, and their performances are evaluated using several parameters, which are precision, accuracy, specificity, F-score, and sensitivity. Based on the statistical analysis, it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%. © 2022 Tech Science Press. All rights reserved.","machine learning; non-invasive diagnosis; Obstructive lung disease; physiological features; voting techniques","Biological organs; Image enhancement; Learning algorithms; Machine learning; Medical imaging; Early diagnosis; Human bodies; Iris features; Lung Cancer; Machine learning algorithms; Non-invasive diagnosis; Obstructive lung disease; Patient's suffering; Physiological features; Voting techniques; Diagnosis","","","","","Prince Sultan University, PSU","Acknowledgement: The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.","Harris P. E., Cooper K. L., Relton C., Thomas K. J., Prevalence of complementary and alternative medicine (CAM) use by the general population: A systematic review and update, International Journal of Clinical Practice, 66, 10, pp. 924-939, (2012); Sujitha R., Seenivasagam V., Classification of lung cancer stages with machine learning over big data healthcare framework, Journal of Ambient Intelligence and Humanized Computing, 12, pp. 5639-5649, (2021); Othman Z., Prabuwon A. S., Preliminary study on iris recognition system: Tissues of body organs in iridology, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 115-119, (2010); Ma L., Zhang D., Li N., Cai Y., Zuo W., Et al., Iris-based medical analysis by geometric deformation features, IEEE Journal of Biomedical and Health Informatics, 17, 1, pp. 223-231, (2012); Hussein S., Hassan O., Granat M., Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks, Biomedical Signal Processing and Control, 8, 6, pp. 534-541, (2013); Ramlee R., Aziz K., Ranjit S., Esro M., Automated detecting arcus senilis, symptom for cholesterol presence using iris recognition algorithm, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 3, 2, pp. 29-39, (2011); Bach D., Wachtel E., Phospholipid/cholesterol model membranes: Formation of cholesterol crystallites, Biomembrances, 1610, 2, pp. 187-197, (2003); Ramlee R. A., Ranjit S., Detecting cholesterol presence with iris recognition algorithm, Int. Conf. on Information Management and Engineering, pp. 714-717, (2009); Klein K., Buse J., The trials and tribulations of determining HbA targets for diabetes mellitus, Nature Reviews Endocrinology, 16, 4, pp. 717-730, (2020); Agarwal R., Sharma R., Determining diabetes using iris recognition system, International Journal of Diabetes in Developing Countries, 35, 4, pp. 432-438, (2015); Banzi J., Xue Z., An automated tool for non-contact, real-time early detection of diabetes by computer vision, International Journal of Machine Learning and Computing, 5, 3, (2015); Salles L., Silva M., The sign of the cross of andreas in the iris and diabetes mellitus: A longitudinal study, Revista Escola de Enfermagem USP, 49, 4, pp. 0626-0631, (2015); Battineni G., Sagaro G., Chinatalapudi N., Amenta F., Applications of machine learning predictive models in the chronic disease diagnosis, Journal of Personalized Medicine, 10, 2, (2020); Shafique A., Ahmed J., Boulila W., Ghandorh H., Ahmad J., Et al., Detecting the security level of various cryptosystems using machine learning models, IEEE Access, 9, pp. 9383-9383, (2020); Dwivedi A., Analysis of computational intelligence techniques for diabetes mellitus prediction, Neural Computing and Applications, 30, 12, pp. 3837-3845, (2018); Tama B., Rhee K., Tree-based classifier ensembles for early detection method of diabetes: An exploratory study, Artificial Intelligence Review, 51, 3, pp. 355-370, (2019); Xi J., Weizhong Z., Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning, PloS One, 14, 1, (2019); Rajeh A., Hurst J., Monitoring of physiological parameters to predict exacerbations of chronic obstructive pulmonary disease (COPD): A systematic review, Journal of Clinical Medicine, 5, 12, (2016); Polverino F., Hysinger E. B., Gupta N., Willmering M., Olin T., Et al., Lung MRI as a potential complementary diagnostic tool for early COPD, The American Journal of Medicine, 133, 6, pp. 757-760, (2020); Zarrin P. S., Pouya S., Roeckendorf N., Wenger C., In-vitro classification of saliva samples of COPD patients and healthy controls usingmachine learning tools, IEEE Access, 8, pp. 168053-168060, (2020); Meng X. H., Huang Y. X., Rao D. P., Zhang Q., Liu Q., Comparison of three data mining models for predicting diabetes or prediabetes by risk factors, The Kaohsiung Journal of Medical Sciences, 29, 2, pp. 93-99, (2013); Betancourt A. Y., Silvente M. G., A keypoints-based feature extraction method for iris recognition under variable image quality conditions, Knowledge-Based Systems, 92, pp. 169-182, (2016); Planger J., Schabhuttl P., Vuherer T., Enzinger N., CMT additive manufacturing of a high strength steel alloy for application in crane construction, Metals, 6, 9, (2019); Wildes R. P., Iris recognition: An emerging biometric technology, Proc. of the IEEE, 85, 9, pp. 1348-1363, (1997); Maad M., Implementation of machine learning techniques for the classification of lung X-ray images used to detect COVID-19 in humans, Iraqi Journal of Science, 62, 6, pp. 2099-2109, (2021); Saglani S., Custovic A., Childhood asthma: Advances using machine learning and mechanistic studies, American Journal of Respiratory and Critical Care Medicine, 199, 4, pp. 414-422, (2019); Jionglin W., Roy J., Walter F. S., Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches, Medical Care, pp. 106-113, (2010); Kairuddin W. N., Mahmud W. M., Texture feature analysis for different resolution level of kidney ultrasound images, IOP Conference Series:Materials Science and Engineering, 226, 1, (2017); Wang D., Zhang H., Liu R., Wang D., T-Test feature selection approach based on term frequency for text categorization, Pattern Recognition Letters, 45, pp. 1-10, (2014); Rehman M. U., Shafique A., Khalid S., Driss M., Rubaiee S., Future forecasting of COVID-19: A supervised learning approach, Sensors, 21, 10, (2021); Karamizadeh S., Abdullah S., Manaf A. A., Zamani M., Hooman A., An overview of principal component analysis, Journal of Signal and Information Processing, 4, 3, (2013); Gao Y., Zhou R., Lyu Q., Multiomics and machine learning in lung cancer prognosis, Journal of Thoracic Disease, 12, 8, (2020); Chabat F., Yang G. Z., Hansell D. M., Obstructive lung diseases: Texture classification for differentiation at CT, Radiology, 228, pp. 871-877, (2003); Amaral J. L., Lopes A. J., Veiga J., Faria A. C., Melo P. L., High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements, Computer Methods and Programs in Biomedicine, 144, pp. 113-125, (2017)","M.U. Rehman; Department of Electrical Engineering, Riphah International University, Islamabad, 44000, Pakistan; email: Mujeeb.rehman@riphah.edu.pk","","Tech Science Press","","","","","","15462218","","","","English","Comput. Mater. Continua","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85128692904"
"Reier-Nilsen T.; Stang J.Sø.; Flatsetøy H.; Isachsen M.; Ljungberg H.; Bahr R.; Nordlund B.","Reier-Nilsen, Tonje (26030568800); Stang, Julie Sørbø (55485003100); Flatsetøy, Hanne (58532060000); Isachsen, Martine (58532638300); Ljungberg, Henrik (6602838190); Bahr, Roald (7102647460); Nordlund, Björn (39161771200)","26030568800; 55485003100; 58532060000; 58532638300; 6602838190; 7102647460; 39161771200","Unsupervised field-based exercise challenge tests to support the detection of exercise-induced lower airway dysfunction in athletes","2023","BMJ Open Sport and Exercise Medicine","9","3","bmjsem-2023-001680","","","","1","10.1136/bmjsem-2023-001680","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167725796&doi=10.1136%2fbmjsem-2023-001680&partnerID=40&md5=1b36937eaf0717188693c4e5bd1b10e3","The Norwegian Olympic Sports Centre, Norwegian Olympic and Paralympic Committee and Confederation of Sports, Oslo, Norway; Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway; Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo, Norway; Division of Pediatric and Adolescent Medicine, Oslo University Hospital, Oslo, Norway; Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden; Astrid Lindgren Children's Hospital, Stockholm, Sweden","Reier-Nilsen T., The Norwegian Olympic Sports Centre, Norwegian Olympic and Paralympic Committee and Confederation of Sports, Oslo, Norway, Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway; Stang J.Sø., Department of Sports Medicine, Norwegian School of Sports Sciences, Oslo, Norway; Flatsetøy H., Division of Pediatric and Adolescent Medicine, Oslo University Hospital, Oslo, Norway; Isachsen M., Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden; Ljungberg H., Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden, Astrid Lindgren Children's Hospital, Stockholm, Sweden; Bahr R., The Norwegian Olympic Sports Centre, Norwegian Olympic and Paralympic Committee and Confederation of Sports, Oslo, Norway, Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway; Nordlund B., Department of Women's and Children's Health, Karolinska Institute, Stockholm, Sweden, Astrid Lindgren Children's Hospital, Stockholm, Sweden","Background Athletes are at risk for developing exercise-induced lower airway narrowing. The diagnostic assessment of such lower airway dysfunction (LAD) requires an objective bronchial provocation test (BPT). Objectives Our primary aim was to assess if unsupervised field-based exercise challenge tests (ECTs) could confirm LAD by using app-based spirometry. We also aimed to evaluate the diagnostic test performance of field-based and sport-specific ECTs, compared with established eucapnic voluntary hyperpnoea (EVH) and methacholine BPT. Methods In athletes with LAD symptoms, sensitivity and specificity analyses were performed to compare outcomes of (1) standardised field-based 8 min ECT at 85% maximal heart rate with forced expiratory volume in 1 s (FEV 1) measured prechallenge and 1 min, 3 min, 5 min, 10 min, 15 min and 30 min postchallenge, (2) unstandardised field-based sport-specific ECT with FEV 1 measured prechallenge and within 10 min postchallenge, (3) EVH and (4) methacholine BPT. Results Of 60 athletes (median age 17.5; range 16-28 years.; 40% females), 67% performed winter-sports, 43% reported asthma diagnosis. At least one positive BPT was observed in 68% (n=41/60), with rates of 51% (n=21/41) for standardised ECT, 49% (n=20/41) for unstandardised ECT, 32% (n=13/41) for EVH and methacholine BPT, while both standardised and unstandardised ECTs were simultaneously positive in only 20% (n=7/35). Standardised and unstandardised ECTs confirmed LAD with 54% sensitivity and 70% specificity, and 46% sensitivity and 68% specificity, respectively, using EVH as a reference, while EVH and methacholine BPT were both 33% sensitive and 85% specific, using standardised ECTs as reference. Conclusion App-based spirometry for unsupervised field-based ECTs may support the diagnostic process in athletes with LAD symptoms. Trial registration number NCT04275648.  © 2023 BMJ Publishing Group. All rights reserved.","app-based spirometer; asthma; athlete; bronchoprovocation tests; diagnosis; exercise testing; exercise-induced bronchoconstriction; field-based exercise challenge test; lower airway dysfunction","corticosteroid; methacholine; adolescent; adult; Article; asthma; bronchoscopy; chronic bronchitis; comparative study; controlled study; diagnostic test; diagnostic test accuracy study; exercise test; false negative result; female; forced expiratory volume; forced vital capacity; heart rate; human; hyperpnea; lower airway dysfunction; lung flow volume curve; lung function; major clinical study; male; practice guideline; provocation test; respiratory tract disease; sensitivity and specificity; spirometry; tracheobronchomalacia; unsupervised machine learning","","methacholine, 55-92-5","","","","","Price O.J., Ansley L., Menzies-Gow A., Et al., Airway dysfunction in elite athletes - An occupational lung disease, Allergy, 68, pp. 1343-1352, (2013); Price O.J., Sewry N., Schwellnus M., Et al., Prevalence of lower airway dysfunction in athletes: A systematic review and meta-analysis by a subgroup of the IOC consensus group on 'acute respiratory illness in the athlete, Br J Sports Med, 56, pp. 213-222, (2022); Hallstrand T.S., Moody M.W., Wurfel M.M., Et al., Inflammatory basis of exercise-induced Bronchoconstriction, Am J Respir Crit Care Med, 172, pp. 679-686, (2005); Anderson S.D., Kippelen P., Airway injury as a mechanism for exercise-induced Bronchoconstriction in elite athletes, J Allergy Clin Immunol, 122, pp. 225-235, (2008); Rundell K.W., Effect of air pollution on athlete health and performance, Br J Sports Med, 46, pp. 407-412, (2012); Couto M., Stang J., Horta L., Et al., Two distinct phenotypes of asthma in elite athletes identified by latent class analysis, J Asthma, 52, pp. 897-904, (2015); Schwellnus M., Adami P.E., Bougault V., Et al., International Olympic Committee (IOC) consensus statement on acute respiratory illness in athletes part 2: Non-infective acute respiratory illness, Br J Sports Med, (2022); Fitch K.D., Sue-Chu M., Anderson S.D., Et al., Asthma and the elite athlete: Summary of the International Olympic Committee's consensus conference, Lausanne, Switzerland, January 22-24, 2008, J Allergy Clin Immunol, 122, pp. 254-260, (2008); Simpson A.J., Romer L.M., Kippelen P., Self-reported symptoms after induced and inhibited Bronchoconstriction in athletes, Med Sci Sports Exerc, 47, pp. 2005-2013, (2015); Weiler J.M., Hallstrand T.S., Parsons J.P., Et al., Improving screening and diagnosis of exercise-induced Bronchoconstriction: A call to action, J Allergy Clin Immunol Pract, 2, pp. 275-280, (2014); Rundell K.W., Im J., Mayers L.B., Et al., Self-reported symptoms and exercise-induced asthma in the elite athlete, Med Sci Sports Exerc, 33, pp. 208-213, (2001); Anderson S.D., Argyros G.J., Magnussen H., Et al., Provocation by Eucapnic voluntary Hyperpnoea to identify exercise induced Bronchoconstriction, Br J Sports Med, 35, pp. 344-347, (2001); Global Strategy for Asthma Management and Prevention N.d. Available: https://ginasthma.org/wp-content/uploads/2021/05/GINA-Main-Report-2021-V2-WMS.pdf2021; Reier-Nilsen T., Sewry N., Chenuel B., Et al., Diagnostic approach to lower airway dysfunction in athletes: A systematic review and meta-analysis by a subgroup of the IOC consensus on ""acute respiratory illness in the athlete, Br J Sports Med, 57, pp. 481-489, (2023); Parsons J.P., Hallstrand T.S., Mastronarde J.G., Et al., An official American Thoracic society clinical practice guideline: Exercise-induced Bronchoconstriction, Am J Respir Crit Care Med, 187, pp. 1016-1027, (2013); Sandsund M., Faerevik H., Reinertsen R.E., Et al., Effects of breathing cold and warm air on lung function and physical performance in asthmatic and Nonasthmatic athletes during exercise in the cold, Ann N y Acad Sci, 813, pp. 751-756, (1997); Langdeau J.B., Turcotte H., Desagne P., Et al., Influence of Sympatho-vagal balance on airway responsiveness in athletes, Eur J Appl Physiol, 83, pp. 370-375, (2000); Hallstrand T.S., Leuppi J.D., Joos G., Et al., ERS technical standard on bronchial challenge testing: Pathophysiology and methodology of indirect airway challenge testing, Eur Respir J, 52, (2018); Quanjer P.H., Stanojevic S., Cole T.J., Et al., Multi-ethnic reference values for Spirometry for the 3-95-yr age range: The global lung function 2012 equations, Eur Respir J, 40, pp. 1324-1343, (2012); Miller M.R., Hankinson J., Brusasco V., Et al., Standardisation of Spirometry, Eur Respir J, 26, pp. 319-338, (2005); Brusasco V., Crapo R., Viegi G., Et al., Coming together: The ATS/ERS consensus on clinical pulmonary function testing, Eur Respir J, 26, pp. 1-2, (2005); Bjerg A., Ljungberg H., Dierschke K., Et al., Shorter time to clinical decision in work-related asthma using a Digital tool, ERJ Open Res, 6, (2020); Degryse J., Buffels J., Van Dijck Y., Et al., Accuracy of office Spirometry performed by trained primary-care physicians using the MIR Spirobank hand-held Spirometer, Respiration, 83, pp. 543-552, (2012); Pollard A.J., Mason N.P., Barry P.W., Et al., Effect of altitude on Spirometric parameters and the performance of peak flow meters, Thorax, 51, pp. 175-178, (1996); Ljungberg H., Carleborg A., Gerber H., Et al., Clinical effect on uncontrolled asthma using a novel Digital automated self-management solution: A physician-blinded randomised controlled crossover trial, Eur Respir J, 54, (2019); Carey D.G., Aase K.A., Pliego G.J., The acute effect of cold air exercise in determination of exercise-induced Bronchospasm in apparently healthy athletes, J Strength Cond Res, 24, pp. 2172-2178, (2010); Dickinson J.W., Whyte G.P., McConnell A.K., Et al., Screening elite winter athletes for exercise induced asthma: A comparison of three challenge methods, Br J Sports Med, 40, pp. 179-182, (2006); Durand F., Kippelen P., Ceugniet F., Et al., Undiagnosed exercise-induced Bronchoconstriction in ski-mountaineers, Int J Sports Med, 26, pp. 233-237, (2005); Kennedy M.D., Steele A.R., Parent E.C., Et al., Cold air exercise screening for exercise induced Bronchoconstriction in cold weather athletes, Respir Physiol Neurobiol, 269, (2019); Rundell K.W., Anderson S.D., Spiering B.A., Et al., Field exercise vs laboratory Eucapnic voluntary Hyperventilation to identify airway Hyperresponsiveness in elite cold weather athletes, Chest, 125, pp. 909-915, (2004); Stensrud T., Rossvoll O, Mathiassen M., Et al., Lung function and oxygen saturation after participation in Norseman Xtreme Triathlon, Scand J Med Sci Sports, 30, pp. 1008-1016, (2020); Sue-Chu M., Brannan J.D., Anderson S.D., Et al., Airway Hyperresponsiveness to Methacholine, adenosine 5-monophosphate, mannitol, Eucapnic voluntary Hyperpnoea and field exercise challenge in elite cross-country skiers, Br J Sports Med, 44, pp. 827-832, (2010); Hull J.H., Ansley L., Price O.J., Et al., Eucapnic voluntary Hyperpnea: Gold standard for diagnosing exercise-induced Bronchoconstriction in athletes, Sports Med, 46, pp. 1083-1093, (2016); Martin N., Lindley M.R., Hargadon B., Et al., Airway dysfunction and inflammation in Pool- and non-pool-based elite athletes, Medicine & Science in Sports & Exercise, 44, pp. 1433-1439, (2012); Coates A.L., Wanger J., Cockcroft D.W., Et al., ERS technical standard on bronchial challenge testing: General considerations and performance of Methacholine challenge tests, Eur Respir J, 49, (2017); Clemm H.H., Olin J.T., McIntosh C., Et al., Exercise-induced Laryngeal obstruction (EILO) in athletes: A narrative review by a subgroup of the IOC consensus on 'acute respiratory illness in the athlete, Br J Sports Med, 56, pp. 622-629, (2022); Bougault V., Turmel J., St-Laurent J., Et al., Asthma, airway inflammation and epithelial damage in swimmers and cold-air athletes, Eur Respir J, 33, pp. 740-746, (2009); Hankinson J.L., Eschenbacher B., Townsend M., Et al., Use of forced vital capacity and forced Expiratory volume in 1 second quality criteria for determining a valid test, Eur Respir J, 45, pp. 1283-1292, (2015); Helenius I.J., Tikkanen H.O., Haahtela T., Occurrence of exercise induced Bronchospasm in elite runners: Dependence on Atopy and exposure to cold air and pollen, Br J Sports Med, 32, pp. 125-129, (1998); Bougault V., Loubaki L., Joubert P., Et al., Airway remodeling and inflammation in competitive swimmers training in indoor chlorinated swimming pools, J Allergy Clin Immunol, 129, pp. 351-358, (2012); Bolger C., Tufvesson E., Anderson S.D., Et al., Effect of inspired air conditions on exercise-induced Bronchoconstriction and urinary Cc16 levels in athletes, J Appl Physiol (1985), 111, pp. 1059-1065, (2011); Dickinson J., McConnell A., Whyte G., Diagnosis of exercise-induced Bronchoconstriction: Eucapnic voluntary Hyperpnoea challenges identify previously Undiagnosed elite athletes with exercise-induced Bronchoconstriction, Br J Sports Med, 45, pp. 1126-1131, (2011); Leahy M.G., Peters C.M., Geary C.M., Et al., Diagnosis of exercise-induced Bronchoconstriction in swimmers: Context matters, Med Sci Sports Exerc, 52, pp. 1855-1861, (2020); Holzer K., Anderson S.D., Douglass J., Exercise in elite summer athletes: Challenges for diagnosis, J Allergy Clin Immunol, 110, pp. 374-380, (2002); Stensrud T., Mykland K.V., Gabrielsen K., Et al., Bronchial Hyperresponsiveness in skiers: Field test versus Methacholine provocation, Med Sci Sports Exerc, 39, pp. 1681-1686, (2007); Bohm P., Hecksteden A., Meyer T., Impact of a short-term water abstinence on airway Hyperresponsiveness in elite swimmers, Clin J Sport Med, 27, pp. 344-348, (2017)","T. Reier-Nilsen; The Norwegian Olympic Sports Centre, Norwegian Olympic and Paralympic Committee and Confederation of Sports, Oslo, Norway; email: tonjereiernilsen@icloud.com","","BMJ Publishing Group","","","","","","20557647","","","","English","BMJ Open Sport Exerc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85167725796"
"Huang F.; Tong X.; Hu C.; Zhang Q.; Wei Y.; Hu M.; Kong L.; Fu R.; Li X.; Xie Y.; Ming X.; Chen B.; Lin Y.; Xiong L.","Huang, Feng (57735721900); Tong, Xiaoyun (23390803900); Hu, Chunyan (58264473800); Zhang, Qiushi (58519731400); Wei, Yijie (57735554700); Hu, Min (57736215800); Kong, Lingqi (59314207500); Fu, Rongbing (57735883600); Li, Xiaohong (59292918200); Xie, Yuhuan (57218194286); Ming, Xi (59029925700); Chen, Bojun (59335112700); Lin, Yuping (57189996907); Xiong, Lei (57200270952)","57735721900; 23390803900; 58264473800; 58519731400; 57735554700; 57736215800; 59314207500; 57735883600; 59292918200; 57218194286; 59029925700; 59335112700; 57189996907; 57200270952","CAVO Inhibits Airway Inflammation and ILC2s in OVA-Induced Murine Asthma Mice","2023","BioMed Research International","2023","","8783078","","","","1","10.1155/2023/8783078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192495061&doi=10.1155%2f2023%2f8783078&partnerID=40&md5=03b379992ee4e5e662f2bff97f16a691","School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; The First Affiliated Hospital of Yunnan University of Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China; Department of Pharmacy, Tengchong Hospital of Chinese Medicine, Baoshan, China; Department of Ethnic Medicine, Youjiang Medical University for Nationalities, Baise, China; Basic Medical School, Yunnan University of Chinese Medicine, Kunming, China","Huang F., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Tong X., The First Affiliated Hospital of Yunnan University of Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China; Hu C., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Zhang Q., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Wei Y., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China, Department of Pharmacy, Tengchong Hospital of Chinese Medicine, Baoshan, China; Hu M., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Kong L., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Fu R., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China, Department of Ethnic Medicine, Youjiang Medical University for Nationalities, Baise, China; Li X., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Xie Y., Basic Medical School, Yunnan University of Chinese Medicine, Kunming, China; Ming X., The First Affiliated Hospital of Yunnan University of Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China; Chen B., Basic Medical School, Yunnan University of Chinese Medicine, Kunming, China; Lin Y., School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; Xiong L., Basic Medical School, Yunnan University of Chinese Medicine, Kunming, China","Cang-ai volatile oil (CAVO) is an aromatic Chinese medicine and is widely used to treat upper respiratory tract infections in children. However, the mechanism of CAVO in asthma treatment is unclear. In this study, we investigated the effects of CAVO on airway inflammation and the mechanism of inhibiting Group-2 innate lymphoid cells (ILC2s) in asthmatic mice, which was induced with Ovalbumin (OVA). CAVO improved AHR and airway inflammation in asthmatic mice. CAVO reduced the production of interleukin (IL)-2, IL-4, IL-5, IL-6, IL-7, IL-9, IL-13, IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) in the bronchoalveolar lavage fluid (BALF), while increased the production of IL-10, significantly. CAVO also inhibited the suppressor of tumorigenicity 2 (ST2) and IL-33 expressions in the lung tissue. Moreover, flow analyses demonstrated that CAVO inhibited ILC2s activation by reducing the sedimentation of its upstream cytokines, thus alleviating downstream cytokines. This could be because of the downregulated microRNA-155 and upregulated microRNA-146a. CAVO inhibits ILC2s activation, thus further attenuating airway inflammation and AHR in asthmatic mice. These effects may be related to the downregulation of microRNA-155 and upregulation of microRNA-146a.  © 2023 Feng Huang et al.","","antiinflammatory agent; cang ai volatile oil; CD19 antigen; CD4 antigen; CD5 antigen; Chinese medicinal formula; complementary DNA; dexamethasone; essential oil; interleukin 1 receptor like 1 protein; interleukin 10; interleukin 13; interleukin 2; interleukin 25; interleukin 33; interleukin 4; interleukin 5; interleukin 6; interleukin 7; interleukin 9; messenger RNA; microRNA; microRNA 146a; microRNA 155; mucin 5AC; ovalbumin; thymic stromal lymphopoietin; unclassified drug; essential oil; interleukin 10; interleukin 13; interleukin 2; interleukin 25; interleukin 33; interleukin 4; interleukin 5; interleukin 6; interleukin 7; interleukin 9; microRNA 146a; microRNA 155; ovalbumin; thymic stromal lymphopoietin; Acorus; Agastache rugosa; Ambrosia artemisiifolia; Amomum kravanh; animal cell; animal experiment; animal model; animal tissue; Article; asthma; Atractylodes lancea; controlled study; cytokine production; drug megadose; Elsholtzia ciliata; enzyme linked immunosorbent assay; Eupatorium; female; goblet cell; group 2 innate lymphoid cell; immunohistochemistry; Kaempferia galanga; low drug dose; lung lavage fluid; lung parenchyma; medicinal plant; mouse; nonhuman; ovalbumin-induced airway inflammation; protein expression; upregulation; Zanthoxylum bungeanum; article; bronchoalveolar lavage fluid; carcinogen testing; carcinogenicity; child; down regulation; drug therapy; flow measurement; group 2 innate lymphoid cell; intraperitoneal drug administration; neoplastic cell transformation; pharmacology; respiratory tract inflammation; sedimentation; upper respiratory tract infection","","dexamethasone, 50-02-2; interleukin 13, 148157-34-0; interleukin 2, 85898-30-2; ovalbumin, 77466-29-6","","","Yunnan Innovation Team of Application Research; Yunnan Provincial Science and Technology Department, (2019FF002-029, 2019FA035); Yunnan Provincial Science and Technology Department; Yunnan Key Laboratory of Formulated Granules, (202105AG070014); Yunnan Key Research and Development Program, (202103AC100005); Yunnan Key Research and Development Program; Yunnan University of TCM, (2017HC011); National Natural Science Foundation of China, NSFC, (82060751, 82060883, 82060884, 82074421); National Natural Science Foundation of China, NSFC","This research was supported by the National Natural Science Foundation of China (Nos. 82074421, 82060751, 82060884, and 82060883), the Yunnan Innovation Team of Application Research on TCM Theory of Prevention Disease at Yunnan University of TCM (No. 2017HC011), the Key Project of Yunnan Province Science and Technology Department (Nos. 2019FA035 and 2019FF002-029), the Yunnan Key Laboratory of Formulated Granules (No. 202105AG070014), and the Key Research and Development Program of Yunnan Province (No. 202103AC100005).","Brusselle G.G., Koppelman G.H., Biologic therapies for severe asthma, New England Journal of Medicine, 386, 2, pp. 157-171, (2022); 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Kabata H., Motomura Y., Kiniwa T., Kobayashi T., Moro K., Et al., ILCs and Allergy, Advances in Experimental Medicine and Biology, 1365, pp. 75-95, (2022); Walker J.A., McKenzie A.N., Development and function of group 2 innate lymphoid cells, Current Opinion Immunology, 25, pp. 148-155, (2013); Liu F., Xuan N.-X., Ying S.-M., Li W., Chen Z.-H., Shen H.-H., Herbal medicines for asthmatic inflammation: from basic researches to clinical applications, Mediators of Inflammation, 2016, (2016); Huang Y.F., Mao K., Chen X., Et al., S1P-dependent interorgan trafficking of group 2 innate lymphoid cells supports host defense, Science, 359, 6371, pp. 114-119, (2018); Han M., Rajput C., Hong J.Y., Et al., The innate cytokines IL-25, IL-33, and TSLP cooperate in the induction of type 2 innate lymphoid cell expansion and mucous metaplasia in rhinovirus-infected, Immature Mice Journal Immunology, 199, 4, pp. 1308-1318, (2017); Xu X., Ye L., Zhang Q., Et al., Group-2 innate lymphoid cells promote HCC progression through CXCL2-neutrophilinduced immunosuppression, Hepatology, 74, 5, pp. 2526-2543, (2021); Olguin-Martinez E., Munoz-Paleta O., Ruiz-Medina B.E., Ramos-Balderas J.L., Licona-Limon I., Licona-Limon P., IL-33 and the PKA pathway regulate ILC2 populations expressing IL-9 and ST2, Frontiers in Immunology, 13, (2022); Fonseca W., Rasky A.J., Ptaschinski C., Et al., Group 2 innate lymphoid cells (ILC2) are regulated by stem cell factor during chronic asthmatic disease, Mucosal Immunology, 12, 2, pp. 445-456, (2019); Yu Q.N., Guo Y.B., Li X., Et al., ILC2 frequency and activity are inhibited by glucocorticoid treatment via STAT pathway in patients with asthma, Allergy, 73, 9, pp. 1860-1870, (2018); Liu S.C., Verma M., Michalec L., Et al., Steroid resistance of airway type 2 innate lymphoid cells from patients with severe asthma: the role of thymic stromal lymphopoietin, Journal of Allergy and Clinical Immunology, 141, 1, pp. 257e6-268e6, (2018); Johansson K., Malmhall C., Ramos-Ramirez P., Radinger M., MicroRNA-155 is a critical regulator of type 2 innate lymphoid cells and IL-33 signaling in experimental models of allergic airway inflammation, Journal of Allergy and Clinical Immunology, 139, 3, pp. 1007e9-1016e9, (2017); Lyu B.L., Wei Z.C., Jiang L., Ma C., Yang G., Han S., MicroRNA-146a negatively regulates IL-33 in activated group 2 innate lymphoid cells by inhibiting IRAK1 and TRAF6, Genes and Immunity, 21, 1, pp. 37-44, (2020); Liu J.H., Wang J., Liu X., Shen H., The Role of Traditional Chinese Medicine in COVID-19: Theory, Initial Clinical Evidence, Potential Mechanisms, and Implications, Alternative Therapies Health and Medicine, 27, S1, pp. 210-227, (2021); Yang Z.H., Liu Y.X., Wang L., Et al., Traditional Chinese medicine against COVID-19: role of the gut microbiota, Biomedicine and Pharmacotherapy, 149, (2022); Chen B., Li J., Xie Y., Et al., Cang-ai volatile oil improves depressive-like behaviors and regulates DA and 5-HT metabolism in the brains of CUMS-induced rats, Journal of Ethnopharmacology, 15, 244, (2019); Zhang K.L., Lei N., Li M., Et al., Cang-ai volatile oil ameliorates depressive behavior induced by chronic stress through IDO-mediated tryptophan degradation pathway, Frontiers in Psychiatry, 12, (2021); Borghi S.M., Domiciano T.P., Rasquel-Oliveira F.S., Et al., Sphagneticola trilobata (L.) Pruski-derived kaurenoic acid prevents ovalbumin-induced asthma in mice: effect on Th2 cytokines, STAT6/GATA-3 signaling, NF κB/Nrf2 redox sensitive pathways, and regulatory T cell phenotype markers, Journal of Ethnopharmacology, 283, (2022); Wu P., Xu B.P., Shen A., Et al., The economic burden of medical treatment of children with asthma in China, BMC Pediatrics, 20, 1, (2020); Busse W., Corren J., Lanier B.Q., Et al., Omalizumab, anti-IgE recombinant humanized monoclonal antibody, for the treatment of severe allergic asthma, Journal of Allergy and Clinical Immunology, 108, 2, pp. 184-190, (2001); Lin C.C., Wang Y.Y., Chen S.M., Et al., Shegan-Mahuang Decoction ameliorates asthmatic airway hyperresponsiveness by downregulating Th2/Th17 cells but upregulating CD4 +FoxP3+ Tregs, Journal of Ethnopharmacology, 253, (2020); Yang Z.Y., Li X.H., Fu R.B., Et al., Therapeutic effect of Renifolin F on airway allergy in an ovalbumin-induced asthma mouse model in vivo, Molecules, 27, 12, (2022); Liu C.T., Song Y.C., Wu T.C., Et al., Targeting glycolysis in Th2 cells by pterostilbene attenuates clinical severities in an asthmatic mouse model and IL-4 production in peripheral blood from asthmatic patients, Immunology, 166, 2, pp. 222-237, (2022); Lloyd C.M., Snelgrove R.J., Type 2 immunity: expanding our view, Science Immunology, 3, (2018); Li B.W.S., Stadhouders R., De Bruijn M.J.W., Et al., Group 2 innate lymphoid cells exhibit a dynamic phenotype in allergic airway inflammation, Frontiers in Immunology, 8, (2017); Huang W.L., Song Y., Wang L.X., Wenshen decoction suppresses inflammation in IL-33-induced asthma murine model via inhibiting ILC2 activation, Annals of Translational Medicine, 7, 20, (2019); Lee H.Y., Rhee C.K., Kang J.Y., Et al., Blockade of IL-33/ST2 ameliorates airway inflammation in a murine model of allergic asthma, Experimental Lung Research, 40, 2, pp. 66-76, (2014); Kabata H., Moro K., Fukunaga K., Et al., Thymic stromal lymphopoietin induces corticosteroid resistance in natural helper cells during airway inflammation, Nature Communications, 4, 1, (2013); Cui J., Dong M., Yi L., Et al., Acupuncture inhibited airway inflammation and group 2 innate lymphoid cells in the lung in an ovalbumin-induced murine asthma model, Acupuncture in Medicine, 39, 3, pp. 217-225, (2021); Principe S., Porsbjerg C., Ditlev S.B., Et al., Treating severe asthma: targeting the IL-5 pathway, Clinical and Experimental Allergy, 51, 8, pp. 992-1005, (2021); Tamiya T., Ichiyama K., Kotani H., Et al., Smad2/3 and IRF4 play a cooperative role in IL-9-producing T cell induction, Journal of Immunology, 191, 5, pp. 2360-2371, (2013); Koch S., Sopel N., Finotto S., Th9 and other IL-9-producing cells in allergic asthma, Seminars in Immunopathology, 39, 1, pp. 55-68, (2017); Ingram J.L., Kraft M., IL-13 in asthma and allergic disease: asthma phenotypes and targeted therapies, Journal of Allergy and Clinical Immunology, 130, 4, pp. 829-842, (2012); Zhong M.F., Xu Z.S., Correlations of IL-10 gene polymorphisms with infantile asthma, Panminerva Medica, 63, 3, pp. 389-391, (2021); Surace L., Doisne J.M., Croft C.A., Et al., Dichotomous metabolic networks govern human ILC2 proliferation and function, Nature Immunology, 22, 11, pp. 1367-1374, (2021); Moro K., Ealey K.N., Kabata H., Koyasu S., Isolation and analysis of group 2 innate lymphoid cells in mice, Nature Protocols, 10, 5, pp. 792-806, (2015)","Y. Lin; School of Chinese Materia Medica, Yunnan Key Laboratory of Southern Medicine Utilization, Yunnan University of Chinese Medicine, Kunming, China; email: linyuping1221@163.com; L. Xiong; Basic Medical School, Yunnan University of Chinese Medicine, Kunming, China; email: xlluck@sina.com","","Hindawi Limited","","","","","","23146133","","","39282108","English","BioMed Res. Int.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85192495061"
"Ding C.; Lu R.; Kong Z.; Huang R.","Ding, Chao (59343651600); Lu, Renjie (59513318600); Kong, Zhiyu (59344402200); Huang, Rong (59344402300)","59343651600; 59513318600; 59344402200; 59344402300","Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support","2025","Journal of Affective Disorders","374","","","282","289","7","0","10.1016/j.jad.2025.01.051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214956875&doi=10.1016%2fj.jad.2025.01.051&partnerID=40&md5=a65a7aa8b714fb665d7f083593cee876","Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; South China University of Technology, Guangzhou, China","Ding C., Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Lu R., Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; Kong Z., South China University of Technology, Guangzhou, China; Huang R., Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China","Background: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG) index, body mass index (BMI), depression, and cognitive dysfunction in older adults, leveraging data from the NHANES survey and employing machine learning techniques. Methods: We analysed 1352 participants aged 60–79 from the 2011–2014 NHANES dataset, who underwent cognitive function testing, depression assessments, and TyG index measurements. Multivariate linear regression and subgroup analyses were conducted to examine associations between the TyG index and depression/cognitive impairment. Machine learning models evaluated the importance of predictive factors for depression, while Mendelian randomization (MR) was employed to explore the causal relationship between BMI and depression/cognitive function. Results: The TyG index was negatively associated with cognitive function scores and positively associated with depression scores in adjusted models (p < 0.001). In fully adjusted subgroup analyses, among obese individuals (BMI ≥ 28), a 100-unit increase in the TyG index was linked to a 3.79-point decrease in depression scores. Machine learning models (Xgboost, AUC = 0.960) identified BMI, TyG-BMI, gender, and comorbidities (e.g., asthma, stroke, emphysema) as key determinants of depression. MR analyses revealed a negative association between BMI and depression risk [OR: 0.9934; 95 % CI (0.9901–0.9968), p = 0.0001] and cognitive dysfunction risk [OR: 0.8514; 95 % CI (0.7929–0.9143), p < 0.05]. No evidence of heterogeneity or pleiotropy was detected. Limitations: Depression and cognitive impairments were self-reported, potentially introducing bias. The observed associations may be influenced by unmeasured confounders, necessitating further research into the underlying mechanisms. Conclusions: Our findings reveal associations between the TyG index and psychocognitive health in older adults. While these results highlight lipid metabolism as a potential factor in depression and cognitive dysfunction, further studies are needed to validate these findings and explore underlying mechanisms. © 2025","BMI; Cognitive function; Depression; NHANES; Older people; Triglyceride-glucose index","Aged; Blood Glucose; Body Mass Index; Cognitive Dysfunction; Depression; Female; Humans; Machine Learning; Male; Mendelian Randomization Analysis; Middle Aged; Nutrition Surveys; Obesity; Triglycerides; cholesterol; glucose; high density lipoprotein cholesterol; low density lipoprotein cholesterol; triacylglycerol; triacylglycerol; age; aged; Article; asthma; body mass; cerebrovascular accident; cholesterol blood level; cognition; cognition assessment; cognitive defect; cognitive function test; cohort analysis; comorbidity; cross-sectional study; deep neural network; depression; depression assessment; diabetes mellitus; digit symbol substitution test; educational status; emphysema; family income; female; heart failure; high density lipoprotein cholesterol level; human; hypertension; ischemic heart disease; light gradient boosting machine; low density lipoprotein cholesterol level; machine learning; major clinical study; male; Mendelian randomization analysis; multivariate logistic regression analysis; obese patient; obesity; obesity paradox; observational study; pleiotropy; prediction; random forest; risk factor; sex difference; Shapley additive explanation; smoking; socioeconomics; substance use; support vector machine; tobacco use; triglyceride-glucose index; United States; waist circumference; xtreme gradient boosting; blood; epidemiology; glucose blood level; middle aged; nutrition","","cholesterol, 57-88-5; glucose, 50-99-7, 84778-64-3, 8027-56-3; Blood Glucose, ; Triglycerides, ","","","","","Behnoush A.H., Mousavi A., Ghondaghsaz E., Shojaei S., Cannavo A., Khalaji A., The importance of assessing the triglyceride-glucose index (TyG) in patients with depression: a systematic review, Neurosci. Biobehav. Rev., 159, (2024); Blasco B.V., Garcia-Jimenez J., Bodoano I., Gutierrez-Rojas L., Obesity and depression: its prevalence and influence as a prognostic factor: a systematic review, Psychiatry Investig., 17, pp. 715-724, (2020); Blazquez E., Velazquez E., Hurtado-Carneiro V., Ruiz-Albusac J.M., Insulin in the brain: its pathophysiological implications for states related with central insulin resistance, type 2 diabetes and Alzheimer's disease, Front. Endocrinol., 5, (2014); Cabral D.A.R., Rego M.L.M., Fontes E.B., Tavares V.D.O., An overlooked relationship in recovery from substance use disorders: associations between body mass index and negative emotional states, Physiol. Behav., 273, (2024); Chamroonkiadtikun P., Ananchaisarp T., Wanichanon W., The triglyceride-glucose index, a predictor of type 2 diabetes development: a retrospective cohort study, Prim. Care Diabetes, 14, pp. 161-167, (2020); Chang J., Jiang T., Shan X., Zhang M., Li Y., Qi X., Bian Y., Zhao L., Pro-inflammatory cytokines in stress-induced depression: novel insights into mechanisms and promising therapeutic strategies, Prog. Neuro-Psychopharmacol. Biol. Psychiatry, 131, (2024); Chen X., Han P., Yu X., Zhang Y., Song P., Liu Y., Jiang Z., Tao Z., Shen S., Wu Y., Zhao Y., Zheng J., Chu L., Guo Q., Relationships between sarcopenia, depressive symptoms, and mild cognitive impairment in Chinese community-dwelling older adults, J. Affect. Disord., 286, pp. 71-77, (2021); Chen Y., Chang Z., Zhao Y., Liu Y., Fu J., Zhang Y., Liu Y., Fan Z., Association between the triglyceride-glucose index and abdominal aortic calcification in adults: a cross-sectional study, Nutrition, metabolism, and cardiovascular diseases : NMCD, 31, pp. 2068-2076, (2021); Clark L.J., Gatz M., Zheng L., Chen Y.L., McCleary C., Mack W.J., Longitudinal verbal fluency in normal aging, preclinical, and prevalent Alzheimer's disease, Am. J. Alzheimers Dis. Other Dement., 24, pp. 461-468, (2009); Dhillon S., Aducanumab: First Approval, Drugs, 81, pp. 1437-1443, (2021); Doraiswamy P.M., Krishnan K.R., Oxman T., Jenkyn L.R., Coffey D.J., Burt T., Clary C.M., Does antidepressant therapy improve cognition in elderly depressed patients? The journals of gerontology, Series A, Biol. Sci. Med. Sci., 58, pp. M1137-M1144, (2003); Er L.K., Wu S., Chou H.H., Hsu L.A., Teng M.S., Sun Y.C., Ko Y.L., Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals, PLoS One, 11, (2016); Fillenbaum G.G., Mohs R., CERAD (consortium to establish a registry for Alzheimer's disease) neuropsychology assessment battery: 35 years and counting, J. Alzheimer's Dis., 93, pp. 1-27, (2023); Godin O., Elbejjani M., Kaufman J.S., Body mass index, blood pressure, and risk of depression in the elderly: a marginal structural model, Am. J. Epidemiol., 176, pp. 204-213, (2012); Hong S., Han K., Park C.Y., The insulin resistance by triglyceride glucose index and risk for dementia: population-based study, Alzheimers Res. 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Psychiatry, 37, pp. 288-293, (2015); Kroenke K., Spitzer R.L., Williams J.B., The PHQ-9: validity of a brief depression severity measure, J. Gen. Intern. Med., 16, 9, pp. 606-613, (2001); Lachner C., Craver E.C., Babulal G.M., Lucas J.A., Ferman T.J., White R.O., Graff-Radford N.R., Day G.S., Disparate dementia risk factors are associated with cognitive impairment and rates of decline in African Americans, Ann. Neurol., 95, 3, pp. 518-529, (2024); Lee J.-H., Park S.K., Ryoo J.-H., Oh C.-M., Mansur R.B., Alfonsi J.E., Cha D.S., Lee Y., McIntyre R.S., Jung J.Y., The association between insulin resistance and depression in the Korean general population, J. Affect. Disord., 208, pp. 553-559, (2017); Lee J.W., Park S.H., Association between depression and nonalcoholic fatty liver disease: contributions of insulin resistance and inflammation, J. Affect. Disord., 278, pp. 259-263, (2021); Lim J., Kim J., Koo S.H., Kwon G.C., Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: an analysis of the 2007-2010 Korean National Health and nutrition examination survey, PLoS One, 14, (2019); Liu X., Li J., He D., Zhang D., Liu X., Association between different triglyceride glucose index-related indicators and depression in premenopausal and postmenopausal women: NHANES, 2013-2016, J. Affect. Disord., 360, pp. 297-304, (2024); Loprinzi P.D., Crush E., Joyner C., Cardiovascular disease biomarkers on cognitive function in older adults: joint effects of cardiovascular disease biomarkers and cognitive function on mortality risk, Prev. Med., 94, pp. 27-30, (2017); Merikangas A.K., Mendola P., Pastor P.N., Reuben C.A., Cleary S.D., The association between major depressive disorder and obesity in US adolescents: results from the 2001-2004 National Health and nutrition examination survey, J. 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Diabetol., 19, (2020); Sabiao T.D.S., Oliveira F.C., Bressan J., Pimenta A.M., Hermsdorff H.H.M., Oliveira F.L.P., Mendonca R.D., Carraro J.C.C., Fatty acid intake and prevalence of depression among Brazilian graduates and postgraduates (CUME study), J. Affect. Disord., 346, pp. 182-191, (2024); Salama I., Obesity and predictors affecting the occurrence of mild cognitive impairment, Res. J. Pharm., Biol. Chem. Sci., 9, (2018); Sanchez-Garcia A., Rodriguez-Gutierrez R., Mancillas-Adame L., Gonzalez-Nava V., Diaz Gonzalez-Colmenero A., Solis R.C., Alvarez-Villalobos N.A., Gonzalez-Gonzalez J.G., Diagnostic accuracy of the triglyceride and glucose index for insulin resistance: a systematic review, Int. J. Endocrinol., 2020, (2020); Shi P., Fang J., Lou C., Association between triglyceride-glucose (TyG) index and the incidence of depression in US adults with diabetes or pre-diabetes, Psychiatry Res., 344, (2024); Simental-Mendia L.E., Rodriguez-Moran M., Guerrero-Romero F., The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects, Metab. Syndr. Relat. Disord., 6, pp. 299-304, (2008); Yang Q., Xu H., Zhang H., Li Y., Chen S., He D., Yang G., Ban B., Zhang M., Liu F., Serum triglyceride glucose index is a valuable predictor for visceral obesity in patients with type 2 diabetes: a cross-sectional study, Cardiovasc. Diabetol., 22, (2023); Yi W., Chen F., Yuan M., Wang C., Wang S., Wen J., Zou Q., Pu Y., Cai Z., High-fat diet induces cognitive impairment through repression of SIRT1/AMPK-mediated autophagy, Exp. Neurol., 371, (2024); Zhang N., Chao J., Wu X., Chen H., Bao M., The role of cognitive function in the relationship between surrogate markers of visceral fat and depressive symptoms in general middle-aged and elderly population: a nationwide population-based study, J. Affect. Disord., 338, pp. 581-588, (2023); Zhong X., Ming J., Li C., Association between dyslipidemia and depression: a cross-sectional analysis of NHANES data from 2007 to 2018, BMC Psychiatry, 24, (2024)","R. Huang; Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China; email: ptn1724@shutcm.edu.cn","","Elsevier B.V.","","","","","","01650327","","JADID","39805501","English","J. Affective Disord.","Article","Final","","Scopus","2-s2.0-85214956875"
"Mahvash Mohammadi S.; Rumyantsev M.; Abdeeva E.; Baimukhambetova D.; Bobkova P.; El-Taravi Y.; Pikuza M.; Trefilova A.; Zolotarev A.; Andreeva M.; Iakovleva E.; Bulanov N.; Avdeev S.; Pazukhina E.; Zaikin A.; Kapustina V.; Fomin V.; Svistunov A.A.; Timashev P.; Avdeenko N.; Ivanova Y.; Fedorova L.; Kondrikova E.; Turina I.; Glybochko P.; Butnaru D.; Blyuss O.; Munblit D.","Mahvash Mohammadi, Sara (57416858800); Rumyantsev, Mikhail (57222560550); Abdeeva, Elina (57222553799); Baimukhambetova, Dina (57223893984); Bobkova, Polina (57221229540); El-Taravi, Yasmin (57222549640); Pikuza, Maria (57464146700); Trefilova, Anastasia (58856560400); Zolotarev, Aleksandr (58857019800); Andreeva, Margarita (57222558992); Iakovleva, Ekaterina (59655233600); Bulanov, Nikolay (43461093400); Avdeev, Sergey (7003292838); Pazukhina, Ekaterina (57728132000); Zaikin, Alexey (7103103296); Kapustina, Valentina (57222503555); Fomin, Victor (34769949900); Svistunov, Andrey A. (55578030700); Timashev, Peter (6507085058); Avdeenko, Nina (6506313689); Ivanova, Yulia (57223890334); Fedorova, Lyudmila (59654960200); Kondrikova, Elena (55003470900); Turina, Irina (6508014964); Glybochko, Petr (26435273000); Butnaru, Denis (15758889100); Blyuss, Oleg (56020531500); Munblit, Daniel (55233686800)","57416858800; 57222560550; 57222553799; 57223893984; 57221229540; 57222549640; 57464146700; 58856560400; 58857019800; 57222558992; 59655233600; 43461093400; 7003292838; 57728132000; 7103103296; 57222503555; 34769949900; 55578030700; 6507085058; 6506313689; 57223890334; 59654960200; 55003470900; 6508014964; 26435273000; 15758889100; 56020531500; 55233686800","Post-COVID-19 Condition Prediction in Hospitalised Cancer Patients: A Machine Learning-Based Approach","2025","Cancers","17","4","687","","","","0","10.3390/cancers17040687","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218878187&doi=10.3390%2fcancers17040687&partnerID=40&md5=34f805f2379c07a076df982972c425c0","Centre for Cancer Screening, Prevention and Early Detection, Wolfson Institute of Population Health, Queen Mary University of London, EC1M, London, 6BQ, United Kingdom; Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; University of British Columbia, Vancouver, V6T 1Z4, BC, Canada; Tareev Clinic of Internal Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Clinic of Pulmonology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Institute for Cognitive Neuroscience, University Higher School of Economics, Moscow, 101000, Russian Federation; Department of Mathematics and Women’s Cancer, University College London, London, WC1E 6BT, United Kingdom; Department of Internal Medicine No. 1, Institute of Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Division of Care in Long Term Conditions, Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King’s College London, London, WC2R 2LS, United Kingdom; Research and Clinical Center for Neuropsychiatry, Moscow, 119334, Russian Federation","Mahvash Mohammadi S., Centre for Cancer Screening, Prevention and Early Detection, Wolfson Institute of Population Health, Queen Mary University of London, EC1M, London, 6BQ, United Kingdom; Rumyantsev M., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Abdeeva E., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Baimukhambetova D., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Bobkova P., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; El-Taravi Y., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Pikuza M., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Trefilova A., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Zolotarev A., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Andreeva M., University of British Columbia, Vancouver, V6T 1Z4, BC, Canada; Iakovleva E., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Bulanov N., Tareev Clinic of Internal Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Avdeev S., Clinic of Pulmonology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Pazukhina E., Centre for Cancer Screening, Prevention and Early Detection, Wolfson Institute of Population Health, Queen Mary University of London, EC1M, London, 6BQ, United Kingdom; Zaikin A., Institute for Cognitive Neuroscience, University Higher School of Economics, Moscow, 101000, Russian Federation, Department of Mathematics and Women’s Cancer, University College London, London, WC1E 6BT, United Kingdom; Kapustina V., Department of Internal Medicine No. 1, Institute of Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Fomin V., Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Svistunov A.A., Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Timashev P., Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119435, Russian Federation; Avdeenko N., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Ivanova Y., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Fedorova L., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Kondrikova E., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Turina I., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Glybochko P., Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Butnaru D., Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Blyuss O., Centre for Cancer Screening, Prevention and Early Detection, Wolfson Institute of Population Health, Queen Mary University of London, EC1M, London, 6BQ, United Kingdom, Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation; Munblit D., Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, 119991, Russian Federation, Division of Care in Long Term Conditions, Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King’s College London, London, WC2R 2LS, United Kingdom, Research and Clinical Center for Neuropsychiatry, Moscow, 119334, Russian Federation","Background: The COVID-19 pandemic has led to widespread long-term complications, known as post-COVID conditions (PCC), particularly affecting vulnerable populations such as cancer patients. This study aims to predict the incidence of PCC in hospitalised cancer patients using the data from a longitudinal cohort study conducted in four major university hospitals in Moscow, Russia. Methods: Clinical data have been collected during the acute phase and follow-ups at 6 and 12 months post-discharge. A total of 49 clinical features were evaluated, and machine learning classifiers including logistic regression, random forest, support vector machine (SVM), k-nearest neighbours (KNN), and neural network were applied to predict PCC. Results: Model performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. KNN demonstrated the highest predictive performance, with an AUC of 0.80, sensitivity of 0.73, and specificity of 0.69. Severe COVID-19 and pre-existing comorbidities were significant predictors of PCC. Conclusions: Machine learning models, particularly KNN, showed some promise in predicting PCC in cancer patients, offering the potential for early intervention and personalised care. These findings emphasise the importance of long-term monitoring for cancer patients recovering from COVID-19 to mitigate PCC impact. © 2025 by the authors.","cancer patients; machine learning classifiers; post-COVID conditions; predictive modelling","abdominal pain; adult; aged; ageusia; anorexia; anosmia; area under the curve; arthralgia; Article; asplenia; asthma; bleeding; cancer patient; cancer therapy; chronic lung disease; cohort analysis; computer assisted tomography; confusion; conjunctivitis; coughing; dementia; diagnostic test accuracy study; diarrhea; disease severity; female; follow up; headache; hospitalization; human; intensive care unit; interview; invasive ventilation; long COVID; longitudinal study; lymphadenopathy; machine learning; machine learning algorithm; male; malignant neoplasm; malnutrition; neurologic disease; noninvasive ventilation; prospective study; quantitative analysis; rash; receiver operating characteristic; revascularization; reverse transcription polymerase chain reaction; risk factor; seizure; tuberculosis; vomiting; walking difficulty; wheezing","","","Microsoft Excel version 16.93.1, Microsoft, United States; R version 3.5.1, R Foundation; REDCap, Vanderbilt University, United States","Microsoft, United States; R Foundation; Vanderbilt University, United States","Barts Charity, (G-001522); Barts Charity; Engineering and Physical Sciences Research Council, EPSRC, (EDDCPJT/100022); Engineering and Physical Sciences Research Council, EPSRC; Russian Science Foundation, RSF, (24-68-00030); Russian Science Foundation, RSF","S.M.M. and O.B. acknowledge support from Barts Charity (G-001522). O.B. acknowledges support from UK and EPSRC joint award EDDCPJT/100022. A.Z. acknowledges support from Russian Science Foundation grant No. 24-68-00030.","Chen C., Haupert S.R., Zimmermann L., Shi X., Fritsche L.G., Mukherjee B., Global prevalence of post-coronavirus disease 2019 (COVID-19) condition or long COVID: A meta-analysis and systematic review, J. Infect. Dis, 226, pp. 1593-1607, (2022); Iqbal F.M., Lam K., Sounderajah V., Clarke J.M., Ashrafian H., Darzi A., Characteristics and predictors of acute and chronic post-COVID syndrome: A systematic review and meta-analysis, EClinicalMedicine, 36, (2021); Wise J., Long COVID: WHO calls on countries to offer patients more rehabilitation, BMJ, 372, (2021); Nalbandian A., Sehgal K., Gupta A., Madhavan M.V., McGroder C., Stevens J.S., Cook J.R., Nordvig A.S., Shalev D., Sehrawat T.S., Et al., Post-acute COVID-19 syndrome, Nat. Med, 27, pp. 601-615, (2021); Lancet T., Facing up to long COVID, Lancet, 396, (2020); Soriano J.B., Murthy S., Marshall J.C., Relan P., Diaz J.V., A clinical case definition of post-COVID-19 condition by a Delphi consensus, Lancet Infect. Dis, 22, pp. e102-e107, (2022); Brodin P., Immune determinants of COVID-19 disease presentation and severity, Nat. Med, 27, pp. 28-33, (2021); Carfi A., Bernabei R., Landi F., Persistent symptoms in patients after acute COVID-19, JAMA, 324, pp. 603-605, (2020); Huang C., Huang L., Wang Y., Li X., Ren L., Gu X., Kang L., Guo L., Liu M., Zhou X., Et al., RETRACTED: 6-month consequences of COVID-19 in patients discharged from hospital: A cohort study, Lancet, 397, pp. 220-232, (2021); Kingstone T., Taylor A.K., O'Donnell C.A., Atherton H., Blane D.N., Chew-Graham C.A., Finding the’right’GP: A qualitative study of the experiences of people with long-COVID, BJGP Open, 4, (2020); Ladds E., Rushforth A., Wieringa S., Taylor S., Rayner C., Husain L., Greenhalgh T., Persistent symptoms after COVID-19: Qualitative study of 114 “long Covid” patients and draft quality principles for services, BMC Health Serv. Res, 20, (2020); Tenforde M.W., Devine O.J., Reese H.E., Silk B.J., Iuliano A.D., Threlkel R., Vu Q.M., Plumb I.D., Cadwell B.L., Rose C., Et al., Point prevalence estimates of activity-limiting long-term symptoms among United States adults 1 month after reported severe acute respiratory syndrome coronavirus 2 infection, 1 November 2021, J. Infect. Dis, 227, pp. 855-863, (2023); Fankuchen O., Lau J., Rajan M., Swed B., Martin P., Hidalgo M., Yamshon S., Pinheiro L., Shah M.A., Long Covid in Cancer: A Matched Cohort Study of 1-year Mortality and Long COVID Prevalence Among Patients With Cancer Who Survived an Initial Severe SARS-CoV-2 Infection, Am. J. Clin. Oncol, 46, pp. 300-305, (2023); Cabrera Martimbianco A.L., Pacheco R.L., Bagattini A.M., Riera R., Frequency, signs and symptoms, and criteria adopted for long COVID-19: A systematic review, Int. J. Clin. Pract, 75, (2021); Fillmore N.R., La J., Szalat R.E., Tuck D.P., Nguyen V., Yildirim C., Do N.V., Brophy M.T., Munshi N.C., Prevalence and outcome of COVID-19 infection in cancer patients: A national Veterans Affairs study, JNCI J. Natl. Cancer Inst, 113, pp. 691-698, (2021); Lee L.Y., Cazier J.B., Angelis V., Arnold R., Bisht V., Campton N.A., Chackathayil J., Cheng V.W., Curley H.M., Fittall M.W., Et al., COVID-19 mortality in patients with cancer on chemotherapy or other anticancer treatments: A prospective cohort study, Lancet, 395, pp. 1919-1926, (2020); Finn O., Immuno-oncology: Understanding the function and dysfunction of the immune system in cancer, Ann. Oncol, 23, pp. viii6-viii9, (2012); Biswas S.K., Metabolic reprogramming of immune cells in cancer progression, Immunity, 43, pp. 435-449, (2015); Pazukhina E., Andreeva M., Spiridonova E., Bobkova P., Shikhaleva A., El-Taravi Y., Rumyantsev M., Gamirova A., Bairashevskaia A., Petrova P., Et al., Prevalence and risk factors of post-COVID-19 condition in adults and children at 6 and 12 months after hospital discharge: A prospective, cohort study in Moscow (StopCOVID), BMC Med, 20, (2022); Xu H., Lu T., Liu Y., Yang J., Ren S., Han B., Lai H., Ge L., Liu J., Prevalence and risk factors for long COVID among cancer patients: A systematic review and meta-analysis, Front. Oncol, 14, (2025); Debie Y., Palte Z., Salman H., Verbruggen L., Vanhoutte G., Chhajlani S., Raats S., Roelant E., Vandamme T., Peeters M., Et al., Long-term effects of the COVID-19 pandemic for patients with cancer, Qual. Life Res, 33, pp. 2845-2853, (2024); Dagher H., Chaftari A.M., Subbiah I.M., Malek A.E., Jiang Y., Lamie P., Granwehr B., John T., Yepez E., Borjan J., Et al., Long COVID in cancer patients: Preponderance of symptoms in majority of patients over long time period, Elife, 7, (2023); Munblit D., Bobkova P., Spiridonova E., Shikhaleva A., Gamirova A., Blyuss O., Nekliudov N., Bugaeva P., Andreeva M., DunnGalvin A., Et al., Incidence and risk factors for persistent symptoms in adults previously hospitalized for COVID-19, Clin. Exp. Allergy, 51, pp. 1107-1120, (2021); Osmanov I.M., Spiridonova E., Bobkova P., Gamirova A., Shikhaleva A., Andreeva M., Blyuss O., El-Taravi Y., DunnGalvin A., Comberiati P., Et al., Risk factors for post-COVID-19 condition in previously hospitalised children using the ISARIC Global follow-up protocol: A prospective cohort study, Eur. Respir. J, 59, (2022); Core Case Report Form Acute Respiratory Infection Clinical Characterisation Data Tool. 19 April 2020; Munblit D., Nekliudov N.A., Bugaeva P., Blyuss O., Kislova M., Listovskaya E., Gamirova A., Shikhaleva A., Belyaev V., Timashev P., Et al., Stop COVID cohort: An observational study of 3480 patients admitted to the Sechenov University Hospital Network in Moscow City for suspected coronavirus disease 2019 (COVID-19) infection, Clin. Infect. Dis, 73, pp. 1-11, (2021); Harris P.A., Taylor R., Thielke R., Payne J., Gonzalez N., Conde J.G., Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support, J. Biomed. Inform, 42, pp. 377-381, (2009); Harris P.A., Taylor R., Minor B.L., Elliott V., Fernandez M., O'Neal L., McLeod L., Delacqua G., Delacqua F., Kirby J., Et al., The REDCap consortium: Building an international community of software platform partners, J. Biomed. Inform, 95, (2019); Hosmer D.W., Lemeshow S., Sturdivant R.X., Applied Logistic Regression, (2013); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell. Res, 16, pp. 321-357, (2002); Gupta A., Jain V., Singh A., Stacking ensemble-based intelligent machine learning model for predicting post-COVID-19 complications, N. Gener. Comput, 40, pp. 987-1007, (2022); Shakhovska N., Yakovyna V., Chopyak V., A new hybrid ensemble machine-learning model for severity risk assessment and post-COVID prediction system, Math. Biosci. Eng, 19, pp. 6102-6123, (2022); Reme B.A., Gjesvik J., Magnusson K., Predictors of the post-COVID condition following mild SARS-CoV-2 infection, Nat. Commun, 14, (2023); Saito S., Shahbaz S., Osman M., Redmond D., Bozorgmehr N., Rosychuk R.J., Lam G., Sligl W., Cohen Tervaert J.W., Elahi S., Diverse immunological dysregulation, chronic inflammation, and impaired erythropoiesis in long COVID patients with chronic fatigue syndrome, J. Autoimmun, 147, (2024); Monroy-Iglesias M.J., Tremble K., Russell B., Moss C., Dolly S., Sita-Lumsden A., Cortellini A., Pinato D.J., Rigg A., Karagiannis S.N., Et al., Long-term effects of COVID-19 on cancer patients: The experience from Guy’s Cancer Centre, Future Oncol, 18, pp. 3585-3594, (2022)","O. Blyuss; Centre for Cancer Screening, Prevention and Early Detection, Wolfson Institute of Population Health, Queen Mary University of London, London, EC1M, 6BQ, United Kingdom; email: o.blyuss@qmul.ac.uk","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20726694","","","","English","Cancers","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218878187"
"Li N.; Chen Z.; Zhang W.; Li Y.; Huang X.; Li X.","Li, Na (59141918100); Chen, Zhaoyang (57994962700); Zhang, Wenhui (59553444800); Li, Yan (58609775400); Huang, Xin (55542932900); Li, Xiao (57196400010)","59141918100; 57994962700; 59553444800; 58609775400; 55542932900; 57196400010","Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability","2025","Journal of Hazardous Materials","489","","137575","","","","0","10.1016/j.jhazmat.2025.137575","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217648300&doi=10.1016%2fj.jhazmat.2025.137575&partnerID=40&md5=517c41e33b6d578ef36fdfb79fe24010","Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China","Li N., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China; Chen Z., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China; Zhang W., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China; Li Y., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China; Huang X., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China; Li X., Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, 250014, China","Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models always lack interpretability. In this study, we developed eight interpretable deep learning models to predict respiratory toxicity of chemicals, focusing on specific respiratory diseases such as pneumonia, pulmonary edema, respiratory infections, pulmonary embolism and pulmonary arterial hypertension, asthma, bronchospasm, bronchitis, and pulmonary fibrosis. In addition, we integrated data from eight respiratory toxicity endpoints into a comprehensive dataset and developed an overall respiratory system model. Model performance was evaluated using 5-fold cross-validation and external validation, with area under the curve (AUC) and accuracy (ACC) values exceeding 0.85 for all eight toxicity endpoints. To enhance model interpretability, we employed the frequency ratio method to identify key structural fragments in Klekota-Roth fingerprints (KRFP) and utilized SHAP (SHapley Additive exPlanations) game theory analysis to visualize critical features driving model predictions. This study demonstrates the role of interpretable deep learning models in predicting the respiratory toxicity of drugs and their environmental metabolites, offering valuable tools and information for early detection and risk assessment of pharmaceutical compounds and environmental pollutants with respiratory toxicity potential. © 2025 Elsevier B.V.","Deep learning; Environmental pollutants; Respiratory toxicity; Structural alert","Risk assessment; environmental chemical; Deep learning; Environmental pollutants; Interpretability; Learning models; Predictive models; Respiratory toxicity; Server-based; Structural alert; Toxicity endpoints; Web servers; chemical compound; data set; drug; game theory; machine learning; risk assessment; toxicity; area under the curve; Article; asthma; bronchitis; bronchospasm; chemical structure; computer prediction; convolutional neural network; cross validation; deep learning; dimensionality reduction; game; human; lung edema; lung embolism; lung fibrosis; lung toxicity; machine learning algorithm; molecular fingerprinting; pneumonia; pollutant; predictive model; pulmonary hypertension; quantitative structure activity relation; receiver operating characteristic; respiratory tract infection; risk assessment; support vector machine; Lung cancer","","","","","Shandong Pharmaceutical Association Hospital Rational Drug Use Young and Middle-aged Scientific Research; Shandong First Medical University & Shandong Provincial Qianfoshan Hospital; National Natural Science Foundation of China, NSFC, (81803433); National Natural Science Foundation of China, NSFC; Shandong Pharmaceutical Association Hospital, (hlyy-2024-01); Shandong Pharmaceutical Association, (ywjj-2024-01)","Funding text 1: This work was supported by the National Natural Science Foundation of China (grant 81803433), Shandong Pharmaceutical Association Hospital Rational Drug Use Young and Middle-aged Scientific Research (hlyy-2024\u201301), and Shandong Pharmaceutical Association Medical Institution Pharmacovigilance Young and Middle-aged Project (ywjj-2024\u201301). We would like to thank the staff at the Center for Big Data Research in Health and Medicine, The First Affliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital for their valuable contribution. We also appreciate Professor Kunal Roy team at Jadavpur University for the approved use of \u201CARKAdesc-v2.0 tool\u201D. The authors gratefully acknowledge the encouragement and support from Miss Chaoyue Yang and Miss Liying Zhao.; Funding text 2: This work was supported by the National Natural Science Foundation of China (grant 81803433), Shandong Pharmaceutical Association Hospital Rational Drug Use Young and Middle-aged Scientific Research (hlyy-2024-01), and Shandong Pharmaceutical Association Medical Institution Pharmacovigilance Young and Middle-aged Project (ywjj-2024-01). We would like to thank the staff at the Center for Big Data Research in Health and Medicine, The First Affliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital for their valuable contribution. We also appreciate Professor Kunal Roy team at Jadavpur University for the approved use of \u201CARKAdesc-v2.0 tool\u201D. The authors gratefully acknowledge the encouragement and support from Miss Chaoyue Yang and Miss Liying Zhao. 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Li; email: lixiao1688@163.com","","Elsevier B.V.","","","","","","03043894","","JHMAD","","English","J. Hazard. Mater.","Article","Final","","Scopus","2-s2.0-85217648300"
"Alotaibi M.; Alnajjar F.; Alsayed B.A.; Alhmiedat T.; Marei A.M.; Bushnag A.; Ali L.","Alotaibi, Mohammed (57188812127); Alnajjar, Fady (14526943700); Alsayed, Badr A. (57221227157); Alhmiedat, Tareq (35955680500); Marei, Ashraf M. (58120162800); Bushnag, Anas (56829435300); Ali, Luqman (56549598700)","57188812127; 14526943700; 57221227157; 35955680500; 58120162800; 56829435300; 56549598700","An Observational Pilot Study of a Tailored Environmental Monitoring and Alert System for Improved Management of Chronic Respiratory Diseases","2023","Journal of Multidisciplinary Healthcare","16","","","3799","3811","12","1","10.2147/JMDH.S435492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179317903&doi=10.2147%2fJMDH.S435492&partnerID=40&md5=cfd257479792841986ccb391fdf03a11","Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia; Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates; Department of Internal Medicine, Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia; Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia","Alotaibi M., Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia; Alnajjar F., Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates; Alsayed B.A., Department of Internal Medicine, Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia; Alhmiedat T., Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia; Marei A.M., Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia; Bushnag A., Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia, Faculty of Computers & Information Technology, University of Tabuk, Tabuk, Saudi Arabia; Ali L., Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates","Objective: Chronic lung-related diseases, with asthma being the most prominent example, characterized by diverse symptoms and triggers, present significant challenges in disease management and prediction of exacerbations across patients. This research aimed to devise a practical solution by introducing a personalized alert system tailored to individual lung function and environmental conditions, offering a holistic approach for the management of a range of chronic respiratory conditions. Methods: In response to these challenges, we developed a personalized alert system based on individual lung function tests conducted in diverse environmental conditions, as determined by air-quality sensors. Our research was substantiated through an observational pilot study involving twelve healthy participants. These participants were exposed to varying air quality, temperature, and humidity conditions, and their lung function, as indicated by peak expiratory flow (PEF) values, was monitored. Results: The study revealed pronounced variability in pulmonary responses across different environments. Leveraging these findings, we proposed a design of a personalized alarm system that monitors air quality in real-time and issues alerts under potentially unfavorable environmental conditions. Additionally, we investigated the use of basic machine learning techniques to predict PEF values in these varied environmental settings. Discussion: The proposed system offers a proactive approach for individuals, particularly those with asthma, to actively manage their respiratory health. By providing real-time monitoring and personalized alerts, it aims to minimize exposure to potential asthma triggers. Ultimately, our system seeks to empower individuals with the tools for timely intervention, potentially reducing discomfort and enhancing management of asthma symptoms. © 2023 Alotaibi et al.","alert system; lung functionality; real-time air-quality monitoring; respiratory health","carbon dioxide; volatile organic compound; adult; air monitoring; air pollution; air quality; Article; chronic respiratory tract disease; environmental  condition; environmental monitoring; environmental surveillance; human; human experiment; humidity; lung function; lung function test; normal human; observational study; particulate matter 10; particulate matter 2.5; pathophysiology; peak expiratory flow; pilot study; temperature; three dimensional bioprinting; weather; young adult","","carbon dioxide, 124-38-9, 58561-67-4","","","Deputyship for Research & Innovation; Ministry of Education in Saudi Arabia, (S-1442-0049)","This research was funded by Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (S-1442-0049).","Bateman ED, Hurd SS, Barnes PJ, Et al., Global strategy for asthma management and prevention: GINA executive summary, Eur Respir J, 31, 1, pp. 143-178, (2008); Vos T, Lim SS, Abbafati C, Et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019, Lancet, 396, pp. 1204-1222, (2020); GINA main report available online; Ibrahim NM, Almarzouqi FI, Al Melaih FA, Farouk H, Alsayed M, AlJassim FM., Prevalence of asthma and allergies among children in the united Arab emirates: a cross-sectional study, World Allergy Organ J, 14, 10, (2021); Alharbi SA, Kobeisy SAN, AlKhater SA, Et al., Childhood asthma awareness in Saudi Arabia: five-year follow-up study, J Asthma Allergy, 13, pp. 399-407, (2020); Tarraf H, Aydin O, Mungan D, Et al., Prevalence of asthma among the adult general population of five middle eastern countries: results of the SNAPSHOT program, BMC Pulm Med, 18, 1, (2018); Lotvall J, Akdis CA, Bacharier LB, Et al., Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome, J Allergy Clin Immunol, 127, 2, pp. 355-360, (2011); Janssens T, Ritz T., Perceived triggers of asthma: key to symptom perception and management, Clin Exp Allergy, 43, 9, pp. 1000-1008, (2013); Szefler SJ, Chipps B., Challenges in the treatment of asthma in children and adolescents, Ann Allergy Asthma Immunol, 120, 4, pp. 382-388, (2018); Miller LR., Trigger control to enhance asthma managements; Tibble H, Tsanas A, Horne E, Et al., Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model, BMJ Open, 9, 7, (2019); Ram S, Zhang W, Williams M, Pengetnze Y., Predicting asthma-related emergency department visits using big data, IEEE J Biomed Health Inform, 19, 4, pp. 1216-1223, (2015); Lee C-H, Chen JC-Y, Tseng VS., A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring, Comput Methods Programs Biomed, 101, 1, pp. 44-61, (2011); Levy ML, Bacharier LB, Bateman E, Et al., Key recommendations for primary care from the 2022 Global Initiative for Asthma (GINA) Update, NPJ Prim Care Respir Med, 33, 1, pp. 1-13, (2023); Papi A, Brightling C, Pedersen SE, Reddel HK., Asthma, Lancet, 391, 10122, pp. 783-800, (2018); Holguin F, Cardet JC, Chung KF, Et al., Management of severe asthma: a European respiratory society/American thoracic society guideline, Eur Respir J, 55, 1, (2020); Singh V, Meena P, Sharma BB., Asthma-like peak flow variability in various lung diseases, Lung India, 29, 1, pp. 15-18, (2012); Merchant RK, Inamdar R, Quade RC., Effectiveness of population health management using the propeller health asthma platform: a randomized clinical trial, J Allergy Clin Immunol Pract, 4, 3, pp. 455-463, (2016); Fleming L, Murray C, Bansal AT, Et al., The burden of severe asthma in childhood and adolescence: results from the paediatric U-BIOPRED cohorts, Eur Respir J, 46, 5, pp. 1322-1333, (2015); Davies MJ, Aroda VR, Collins BS, Et al., Management of hyperglycemia in type 2 diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD), Diabetes Care, 45, 11, pp. 2753-2786, (2022); Yoo JH, Kim JH., Advances in continuous glucose monitoring and integrated devices for management of diabetes with insulin-based therapy: improvement in glycemic control, Diabetes Metab J, 47, 1, pp. 27-41, (2023); Borrelli N, Grimaldi N, Papaccioli G, Fusco F, Palma M, Sarubbi B., Telemedicine in adult congenital heart disease: usefulness of digital health technology in the assistance of critical patients, Int J Environ Res Public Health, 20, 5775, (2023); Faragli A, Abawi D, Quinn C, Et al., The role of non-invasive devices for the telemonitoring of heart failure patients, Heart Fail Rev, 26, 5, pp. 1063-1080, (2021); Shan R, Sarkar S, Martin SS., Digital health technology and mobile devices for the management of diabetes mellitus: state of the art, Diabetologia, 62, 6, pp. 877-887, (2019); Sulaiman I, Greene G, MacHale E, Et al., A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma, Eur Respir J, 51, 1, (2018); D'Amato G, Holgate ST, Pawankar R, Et al., Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. a statement of the world allergy organization, World Allergy Organ J, 8, 1, (2015); Tiesler CMT, Thiering E, Tischer C, Et al., Exposure to visible mould or dampness at home and sleep problems in children: results from the lisaplus study, Environ Res, 137, pp. 357-363, (2015); Tiotiu A, Ioan I, Wirth N, Romero-Fernandez R, Gonzalez-Barcala F-J., The impact of tobacco smoking on adult asthma outcomes, Int J Environ Res Public Health, 18, 992, (2021); Bush A, Fleming L., Diagnosis and management of asthma in children, BMJ, 350, mar05 9, (2015); WHO Global Air Quality Guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide; Hankinson JL, Odencrantz JR, Fedan KB., Spirometric reference values from a sample of the general U.S. Population, Am J Respir Crit Care Med, 159, 1, pp. 179-187, (1999); Hall MA., Correlation-based feature subset selection for machine learning, (1998); Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KRK., Improvements to the SMO algorithm for SVM regression, IEEE Trans Neural Netw, 11, 5, pp. 1188-1193, (2000); Lewis SA, Weiss ST, Britton JR., Airway responsiveness and peak flow variability in the diagnosis of asthma for epidemiological studies, Eur Respir J, 18, 6, pp. 921-927, (2001)","M. Alotaibi; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, Saudi Arabia; email: mmalotaibi@ut.edu.sa; F. Alnajjar; Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, United Arab Emirates; email: fady.alnajjar@uaeu.ac.ae","","Dove Medical Press Ltd","","","","","","11782390","","","","English","J. Multidiscip.Healthc.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85179317903"
"Amin R.; Reza S.; Maniruzzaman; Mehedi Hasan A.L.; Lee H.-S.; Jang S.-W.; Shin J.","Amin, Ruhul (59070517800); Reza, Shamim (58750746400); Maniruzzaman (57221908945); Mehedi Hasan, A.L. (58750793400); Lee, Hyoun-Sup (58124416300); Jang, Si-Woong (22333899200); Shin, Jungpil (7402723945)","59070517800; 58750746400; 57221908945; 58750793400; 58124416300; 22333899200; 7402723945","Intensive Statistical Exploration to Identify Osteoporosis Predisposing Factors and Optimizing Recognition Performance With Integrated GP Kernels","2023","IEEE Access","11","","","131338","131350","12","1","10.1109/ACCESS.2023.3336422","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178063253&doi=10.1109%2fACCESS.2023.3336422&partnerID=40&md5=6e61d2667210894faee3e6e9ebcf9668","Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh; Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh; School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, 965-8580, Japan; Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh; Department of Applied Software Engineering, Dong-Eui University, Busanjin-gu, Busan, 47340, South Korea; Department of Computer Engineering, Dongeui University, Busan, 47340, South Korea","Amin R., Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh; Reza S., Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh; Maniruzzaman, Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh, School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, 965-8580, Japan; Mehedi Hasan A.L., Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh; Lee H.-S., Department of Applied Software Engineering, Dong-Eui University, Busanjin-gu, Busan, 47340, South Korea; Jang S.-W., Department of Computer Engineering, Dongeui University, Busan, 47340, South Korea; Shin J., School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, 965-8580, Japan","Osteoporosis, a common skeletal disorder, necessitates the identification of its risk factors to develop effective preventive measures. It is crucial to identify the underlying risk factors and their relationships with the response class attribute. Different machine learning (ML) algorithms and feature selection approaches are used to estimate the risk of osteoporosis. However, ML-based algorithms may struggle to detect risk factors as well as grading of osteoporosis due to different measurement scale of data and their probability distributional assumptions. Violation of these assumptions and results interpretation may be improper in the presence of heteroscedasticity, or unequal variance in data. In this study, we seek to overcome distribution assumption constraints and improve the interpretability of our results by using rigorous statistical approaches, ensuring a robust and trustworthy study of osteoporosis risk variables. The study dataset consists of 40 clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with osteoporosis. In the analysis, after confirming the normality assumption using Kolmogorov-Smirnov and Shapiro-Wilk tests, independent t-test assess the factor ALT, FBG, HDL-C, LDL-C, FNT, TL, TLT, and URIC has a substantial impact on the risk of developing osteoporosis. The Mann-Whitney U test for the non-normal FN variable likewise showed a p-value of less than 0.05, indicating that this variable has a significant effect on the likelihood of developing osteoporosis. Based on the chi-square test p-values for the categorical factors, gender, calcium, calcitriol, bisphosphonate, calcitonin, COPD, CAD, and drinking have a severe significant risk of osteoporosis. For developing the predictive Gaussian Process (GPs) model, we proposed two customized integrated GP kernels into the analysis to enhance the modeling of complex relationships within the data. The proposed GP kernel model (modified kernel 2) outperforms the other individual kernels in this experiment and has the best accuracy score of 86.64% and AUC score of 86.63% on osteoporosis data. Moreover, a simulation study is also conducted to robustify the proposed model, the results are improved by different evaluation matrices ranging in accuracy from 0.60-11.41% and AUC from 0.50-11.60%. ©2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.","Gaussian process kernel; Osteoporosis disease; prediction; risk factor; statistical analysis","Computer aided design; Computer aided diagnosis; Factor analysis; Gaussian distribution; Gaussian noise (electronic); Grading; Learning systems; Risk assessment; Risk perception; Statistical tests; Classification algorithm; Gaussian process kernel; Gaussian Processes; Kernel; Osteoporosis; Osteoporosis disease; P-values; Risk factors; Solid modelling; Support vectors machine; Diseases","","","","","Grand Information Technology Research Center Support Program; Institute for Information & Communications Technology Planning & Evaluation, (IITP-2023-2020-0-01791); Ministry of Science, ICT and Future Planning, MSIP; University of Aizu, UoA","This work was supported in part by the Ministry of Science and ICT (MSIT), South Korea, under the Grand Information Technology Research Center Support Program supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP) under Grant IITP-2023-2020-0-01791; and in part by the Competitive Research Fund of The University of Aizu, Japan.","Zhang B., Et al., Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study, Bone, 140; Smets J., Shevroja E., Hugle T., Leslie W.D., Hans D., Machine learning solutions for osteoporosis—A review, J. 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Med., 2020, pp. 1-6, (2020); Inoue D., Watanabe R., Okazaki R., COPD and osteoporosis: Links, risks, and treatment challenges, Int. J. Chronic Obstructive Pulmonary Disease, 11, pp. 637-648, (2016); Liu B., Liu J., Pan J., Zhao C., Wang Z., Zhang Q., The association of diabetes status and bone mineral density among US adults: Evidence from NHANES 2005–2018, BMC Endocrine Disorders, 23, 1, pp. 1-9, (2023); De Angelis P., Rella E., Manicone P.F., Gasparini G., Giovannini V., Liguori M.G., Camodeca F., De Rosa G., Cavalcanti C., D'Addona A., The effect of hyperlipidemia on peri-implant health: A clinical and radiographical prospective study, BioMed Res. Int., 2023, pp. 1-8; Lee J.W., Kwon B.C., Choi H.G., Analyses of the relationship between hyperuricemia and osteoporosis, Sci. Rep., 11, 1, (2021); Khandkar C., Vaidya K., Galougahi K.K., Patel S., Low bone mineral density and coronary artery disease: A systematic review and meta-analysis, IJC Heart Vasculature, 37, 2021; Duarte M.P., Ribeiro H.S., Neri S.G.R., Almeida L.S., Viana J.L., Lima R.M., Global prevalence of osteoporosis in chronic kidney disease: Protocol for a systematic review, Kidney Dialysis, 1, 1, pp. 47-52; Yuan S., Michaelsson K., Wan Z., Larsson S.C., Associations of smoking and alcohol and coffee intake with fracture and bone mineral density: A Mendelian randomization study, Calcified Tissue Int, 105, 6, pp. 582-588, (2019); Yeasmin R., Amin R., Reza M.S., Effect of integrated kernel PCA function on data sampling techniques for liver disease prediction, BAUET J, 3, 2, pp. 40-47; Elreedy D., Atiya A.F., A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance, Inf. 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Methods Programs Biomed., 152, pp. 23-34, (2017); Maniruzzaman M., Hasan M.A.M., Asai N., Shin J., Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques, IEEE Access, 11, pp. 33570-33583, (2023); Bhuyan H.K., Chakraborty C., Pani S.K., Ravi V., Feature and subfeature selection for classification using correlation coefficient and fuzzy model, IEEE Trans. Eng. Manag., 70, 5, pp. 1655-1669, (2023); Bui M.H., Dao P.T., Khuong Q.L., Le P.-A., Nguyen T.-T.-T., Hoang G.D., Le T.H., Pham H.T., Hoang H.-X.-T., Le Q.C., Dao X.T., Evaluation of community-based screening tools for the early screening of osteoporosis in postmenopausal Vietnamese women, PLoS ONE, 17, 4; Tanphiriyakun T., Rojanasthien S., Khumrin P., Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy, Sci. Rep., 11, 1","S.-W. Jang; Department of Computer Engineering, Dongeui University, Busan, 47340, South Korea; email: swjang@deu.ac.kr; J. Shin; School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, 965-8580, Japan; email: jpshin@u-aizu.ac.jp","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85178063253"
"Gorham T.J.; Tumin D.; Groner J.; Allen E.; Retzke J.; Hersey S.; Liu S.B.; Macias C.; Alachraf K.; Smith A.W.; Blount T.; Wall B.; Crickmore K.; Wooten W.I.; Jamison S.D.; Rust S.","Gorham, Tyler J. (56928327000); Tumin, Dmitry (55070889000); Groner, Judith (7004183449); Allen, Elizabeth (7202717477); Retzke, Jessica (58318434100); Hersey, Stephen (57206186343); Liu, Swan Bee (58319089700); Macias, Charlie (7003570632); Alachraf, Kamel (57557015300); Smith, Aimee W. (57203012251); Blount, Theresa (57207244582); Wall, Bennett (57262290700); Crickmore, Kim (6506696589); Wooten, William I. (49461820000); Jamison, Shaundreal D. (57206778621); Rust, Steve (57191569599)","56928327000; 55070889000; 7004183449; 7202717477; 58318434100; 57206186343; 58319089700; 7003570632; 57557015300; 57203012251; 57207244582; 57262290700; 6506696589; 49461820000; 57206778621; 57191569599","Predicting emergency department visits among children with asthma in two academic medical systems","2023","Journal of Asthma","60","12","","2137","2144","7","1","10.1080/02770903.2023.2225603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162233908&doi=10.1080%2f02770903.2023.2225603&partnerID=40&md5=9767428f738366036957442b7c8bef61","Information Technology Research & Innovation, Nationwide Children’s Hospital, Columbus, OH, United States; Department of Pediatrics, Brody School of Medicine at East Carolina University, Greenville, NC, United States; Division of Primary Care Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States; Department of Pediatrics, The Ohio State University, Columbus, OH, United States; Quality Improvement Services, Nationwide Children’s Hospital, Columbus, OH, United States; Brody School of Medicine at East Carolina University, Greenville, NC, United States; Department of Psychology, East Carolina University, Greenville, NC, United States; ECU Health, Greenville, NC, United States","Gorham T.J., Information Technology Research & Innovation, Nationwide Children’s Hospital, Columbus, OH, United States; Tumin D., Department of Pediatrics, Brody School of Medicine at East Carolina University, Greenville, NC, United States; Groner J., Division of Primary Care Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States, Department of Pediatrics, The Ohio State University, Columbus, OH, United States; Allen E., Division of Primary Care Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States, Department of Pediatrics, The Ohio State University, Columbus, OH, United States; Retzke J., Division of Primary Care Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States, Department of Pediatrics, The Ohio State University, Columbus, OH, United States; Hersey S., Division of Primary Care Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States, Department of Pediatrics, The Ohio State University, Columbus, OH, United States; Liu S.B., Information Technology Research & Innovation, Nationwide Children’s Hospital, Columbus, OH, United States; Macias C., Quality Improvement Services, Nationwide Children’s Hospital, Columbus, OH, United States; Alachraf K., Brody School of Medicine at East Carolina University, Greenville, NC, United States; Smith A.W., Department of Psychology, East Carolina University, Greenville, NC, United States; Blount T., ECU Health, Greenville, NC, United States; Wall B., ECU Health, Greenville, NC, United States; Crickmore K., ECU Health, Greenville, NC, United States; Wooten W.I., Department of Pediatrics, Brody School of Medicine at East Carolina University, Greenville, NC, United States; Jamison S.D., Department of Pediatrics, Brody School of Medicine at East Carolina University, Greenville, NC, United States; Rust S., Information Technology Research & Innovation, Nationwide Children’s Hospital, Columbus, OH, United States","Objective: To develop and validate a predictive algorithm that identifies pediatric patients at risk of asthma-related emergencies, and to test whether algorithm performance can be improved in an external site via local retraining. Methods: In a retrospective cohort at the first site, data from 26 008 patients with asthma aged 2–18 years (2012–2017) were used to develop a lasso-regularized logistic regression model predicting emergency department visits for asthma within one year of a primary care encounter, known as the Asthma Emergency Risk (AER) score. Internal validation was conducted on 8634 patient encounters from 2018. External validation of the AER score was conducted using 1313 pediatric patient encounters from a second site during 2018. The AER score components were then reweighted using logistic regression using data from the second site to improve local model performance. Prediction intervals (PI) were constructed via 10 000 bootstrapped samples. Results: At the first site, the AER score had a cross-validated area under the receiver operating characteristic curve (AUROC) of 0.768 (95% PI: 0.745–0.790) during model training and an AUROC of 0.769 in the 2018 internal validation dataset (p = 0.959). When applied without modification to the second site, the AER score had an AUROC of 0.684 (95% PI: 0.624–0.742). After local refitting, the cross-validated AUROC improved to 0.737 (95% PI: 0.676–0.794; p = 0.037 as compared to initial AUROC). Conclusions: The AER score demonstrated strong internal validity, but external validity was dependent on reweighting model components to reflect local data characteristics at the external site. © 2023 Taylor & Francis Group, LLC.","clinical decision support; Machine learning; pediatric asthma; population health; predictive modeling","Asthma; Child; Emergency Service, Hospital; Humans; Logistic Models; Neoplasms; Retrospective Studies; ROC Curve; corticosteroid; adult; ambulatory care; Article; asthma; Asthma Control Test; asthma emergency risk score; child; cohort analysis; controlled study; cross validation; emergency ward; female; human; ICD-10; ICD-9; major clinical study; male; multicenter study; predictive model; preschool child; prescription; prevalence; primary medical care; respiratory tract disease assessment; retrospective study; total quality management; young adult; asthma; hospital emergency service; neoplasm; receiver operating characteristic; statistical model","","","","","Eli Lilly and Company; Kate B. Reynolds Charitable Trust","Aimee W. Smith receives salary support from the Kate B. Reynolds Charitable Trust for an unrelated research project. Dmitry Tumin receives salary support from the Kate B. Reynolds Charitable Trust and Eli Lilly and Co. for unrelated research and quality improvement projects.","Most Recent National Asthma Data, (2021); Sullivan P.W., Ghushchyan V., Navaratnam P., Friedman H.S., Kavati A., Ortiz B., Lanier B., The national cost of asthma among school-aged children in the United States, Ann Allergy Asthma Immunol, 119, 3, pp. 246-252, (2017); Snyder D.A., Thomas O.W., Gleeson S.P., Stukus D.R., Jones L.M., Regan C., Shamansky A., Allen E.D., Reducing emergency department visits utilizing a primary care asthma specialty clinic in a high-risk patient population, J Asthma, 55, 7, pp. 785-794, (2018); Allen E.D., Montgomery T., Ayres G., Cooper J., Gillespie J., Gleeson S.P., Groner J., Hersey S., McGwire G., Rowe C., Et al., Quality improvement-driven reduction in countywide medicaid acute asthma health care utilization, Acad Pediatr, 19, 2, pp. 216-226, (2019); Alachraf K., Currie C., Wooten W., Tumin D., Social determinants of emergency department visits in mild compared to moderate and severe asthma, Lung, 200, 2, pp. 221-226, (2022); Hatoun J., Correa E.T., MacGinnitie A.J., Gaffin J.M., Vernacchio L., Development and validation of the asthma exacerbation risk score using claims data, Acad Pediatr, 22, 1, pp. 47-54, (2022); Das L.T., Abramson E.L., Stone A.E., Kondrich J.E., Kern L.M., Grinspan Z.M., Predicting frequent emergency department visits among children with asthma using EHR data, Pediatr Pulmonol, 52, 7, pp. 880-890, (2017); Martin A., Bauer V., Datta A., Masi C., Mosnaim G., Solomonides A., Rao G., Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data, J Asthma, 57, 12, pp. 1339-1346, (2020); Adibi A., Sadatsafavi M., Ioannidis J.P.A., Validation and utility testing of clinical prediction models: time to change the approach, JAMA, 324, 3, pp. 235-236, (2020); de Hond A.A.H., Kant I.M.J., Fornasa M., Cina G., Elbers P.W.G., Thoral P.J., Sesmu Arbous M., Steyerberg E.W., Predicting readmission or death after discharge from the ICU: external validation and retraining of a machine learning model, Crit Care Med, 51, 2, pp. 291-300, (2023); Barak-Corren Y., Chaudhari P., Perniciaro J., Waltzman M., Fine A.M., Reis B.Y., Prediction across healthcare settings: a case study in predicting emergency department disposition, NPJ Digital Med, 4, (2021); Kitamura G., Deible C., Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images, Clin Imaging, 61, pp. 15-19, (2020); Liu A.H., Zeiger R., Sorkness C., Mahr T., Ostrom N., Burgess S., Rosenzweig J.C., Manjunath R., Development and cross-sectional validation of the Childhood Asthma Control Test, J Allergy Clin Immunol, 119, 4, pp. 817-825, (2007); Schatz M., Sorkness C.A., Li J.T., Marcus P., Murray J.J., Nathan R.A., Kosinski M., Pendergraft T.B., Jhingran P., Asthma Control Test: reliability, validity, and responsiveness in patients not previously followed by asthma specialists, J Allergy Clin Immunol, 117, 3, pp. 549-556, (2006); Sangvai S., Hersey S.J., Snyder D.A., Allen E.D., Hafer C., Wickliffe J., Groner J.A., Implementation of the asthma control test in a large primary care network, Pediatr Qual Saf, 2, 5, (2017); Tibshirani R., Regression shrinkage and selection via the Lasso, J Royal Stat Society: Series B (Methodological), 58, 1, pp. 267-288, (1996); Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Soft, 33, 1, pp. 1-22, (2010); Stata Statistical Software: release 16, (2019); R: a language and environment for statistical computing, (2021); Canty A., Ripley B., boot: bootstrap R (S-Plus) functions, (2021); Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J.C., Muller M., pROC: an open-source package for R and S + to analyze and compare ROC curves, BMC Bioinformatics, 12, (2011); Wickham H., ggplot2: elegant Graphics for Data Analysis, (2016); Bridge J., Blakey J.D., Bonnett L.J., A systematic review of methodology used in the development of prediction models for future asthma exacerbation, BMC Med Res Methodol, 20, 1, (2020); Marwaha J.S., Kvedar J.C., Crossing the chasm from model performance to clinical impact: the need to improve implementation and evaluation of AI, NPJ Digit Med, 5, 1, (2022); Ulrich L., Macias C., George A., Bai S., Allen E., Unexpected decline in pediatric asthma morbidity during the coronavirus pandemic, Pediatr Pulmonol, 56, 7, pp. 1951-1956, (2021); Jamison S., Zheng Y., Nguyen L., Khan F., Tumin D., Simeonsson K., Telemedicine and disparities in visit attendance at a rural pediatric primary care clinic during the COVID-19 pandemic, J Health Care Poor Underserved, 34, 2, pp. 535-548","T.J. Gorham; Information Technology Research & Innovation, Nationwide Children’s Hospital, Columbus, United States; email: Tyler.Gorham@NationwideChildrens.org","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","37318283","English","J. Asthma","Article","Final","","Scopus","2-s2.0-85162233908"
"Alves Pegoraro J.; Guerder A.; Similowski T.; Salamitou P.; Gonzalez-Bermejo J.; Birmelé E.","Alves Pegoraro, Juliana (57209565141); Guerder, Antoine (55297894200); Similowski, Thomas (7005634009); Salamitou, Philippe (59658498900); Gonzalez-Bermejo, Jesus (8970434400); Birmelé, Etienne (16303290400)","57209565141; 55297894200; 7005634009; 59658498900; 8970434400; 16303290400","Detection of COPD exacerbations with continuous monitoring of breathing rate and inspiratory amplitude under oxygen therapy","2025","BMC Medical Informatics and Decision Making","25","1","101","","","","0","10.1186/s12911-025-02939-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219036387&doi=10.1186%2fs12911-025-02939-3&partnerID=40&md5=787f8f80300ade4bd5a2c0e0868458a0","Loewenstein Medical Technology GmbH+Co. KG, Karlsruhe, 76135, Germany; AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Hôpital Pitié-Salpêtrière, Département R3S (Respiration, Réanimation, Réadaptation respiratoire, Sommeil), Service de médecine de readaptation respiratoire, Paris, F-75013, France; INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Réhabilitation Respiratoire, Paris, F-75013, France; SRETT, 11 Rue Heinrich, Boulogne-Billancourt, 92100, France; Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS, 7 rue René-Descartes, Strasbourg, 67000, France","Alves Pegoraro J., Loewenstein Medical Technology GmbH+Co. KG, Karlsruhe, 76135, Germany; Guerder A., AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Hôpital Pitié-Salpêtrière, Département R3S (Respiration, Réanimation, Réadaptation respiratoire, Sommeil), Service de médecine de readaptation respiratoire, Paris, F-75013, France, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Réhabilitation Respiratoire, Paris, F-75013, France; Similowski T., AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Hôpital Pitié-Salpêtrière, Département R3S (Respiration, Réanimation, Réadaptation respiratoire, Sommeil), Service de médecine de readaptation respiratoire, Paris, F-75013, France, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Réhabilitation Respiratoire, Paris, F-75013, France; Salamitou P., SRETT, 11 Rue Heinrich, Boulogne-Billancourt, 92100, France; Gonzalez-Bermejo J., AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Hôpital Pitié-Salpêtrière, Département R3S (Respiration, Réanimation, Réadaptation respiratoire, Sommeil), Service de médecine de readaptation respiratoire, Paris, F-75013, France, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Réhabilitation Respiratoire, Paris, F-75013, France; Birmelé E., Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS, 7 rue René-Descartes, Strasbourg, 67000, France","Background: Chronic Obstructive Pulmonary Disease (COPD) is one of the main causes of morbidity and mortality worldwide. Its management represents real economic and public health burdens, accentuated by periods of acute disease deterioration, called exacerbations. Some researchers have studied the interest of monitoring patients’ breathing rate as an indicator of exacerbation, although achieving limited sensitivity and/or specificity. In this study, we look to improve the previously described method, by combining breathing variables, using multiple daily measures, and using an artificial intelligence-based novelty detection approach. Methods: Patients with COPD were monitored with a telemedicine device during their stay in a rehabilitation care center. Daily measures are compared to individually trained reference models based on: i. oxygen therapy duration ii. mean breathing rate, iii. mean inspiratory amplitude, iv. mean breathing rate and mean inspiratory amplitude, v. average distribution of breathing rate and inspiratory amplitude, vi. hidden Markov model (HMM) from a time series of breathing rate and inspiratory amplitude. Results: A set of 16 recordings with exacerbation and 23 recordings without exacerbation was obtained. When using a daily measure of breathing rate, pre-exacerbation periods were identified with a specificity of 50% and a sensitivity of 55.6%. The method based on daily oxygen therapy usage and the method based on time series obtain a sensitivity of 76.8% and 73.2%, respectively, for a fixed specificity of 50%. Conclusion: A single daily measure of breathing rate alone is not sufficient for the detection of pre-exacerbation periods. More complete models also achieve limited performance, equivalent to models based on changes in the duration of therapy usage. © The Author(s) 2025.","Chronic obstructive pulmonary disease (COPD); Classification; Exacerbation detection; Novelty detection; Respiratory pattern; Telemonitoring","","","","","","","","Global strategy for the diagnosis, management, and prevention of Chronic Obstructive Pulmonary Disease, (2021); Wilkinson T.M., Donaldson G.C., Hurst J.R., Seemungal T.A., Wedzicha J.A., Early therapy improves outcomes of exacerbations of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 169, 12, pp. 1298-1303, (2004); Iheanacho I., Zhang S., King D., Rizzo M., Ismaila A.S., Economic burden of Chronic Obstructive Pulmonary Disease (COPD): a systematic literature review, Int J Chronic Obstructive Pulm Dis, 15, (2020); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system, J Med Internet Res, 19, 3, (2017); Jensen M.H., Cichosz S.L., Dinesen B., Hejlesen O.K., Moving prediction of exacerbation in chronic obstructive pulmonary disease for patients in telecare, J Telemed Telecare, 18, 2, pp. 99-103, (2012); Orchard P., Agakova A., Pinnock H., Burton C.D., Sarran C., Agakov F., Et al., Improving prediction of risk of hospital admission in chronic obstructive pulmonary disease: application of machine learning to telemonitoring data, J Med Internet Res, 20, 9, (2018); Borel J.C., Pelletier J., Taleux N., Briault A., Arnol N., Pison C., Et al., Parameters recorded by software of non-invasive ventilators predict COPD exacerbation: a proof-of-concept study, Thorax, 70, 3, pp. 284-285, (2015); Blouet S., Sutter J., Fresnel E., Kerfourn A., Cuvelier A., Patout M., Prediction of severe acute exacerbation using changes in breathing pattern of COPD patients on home noninvasive ventilation, Int J Chronic Obstructive Pulm Dis, 13, (2018); Jiang W., Chao Y., Wang X., Chen C., Zhou J., Song Y., Day-to-Day Variability of Parameters Recorded by Home Noninvasive Positive Pressure Ventilation for Detection of Severe Acute Exacerbations in COPD, Int J Chronic Obstructive Pulm Dis, 16, (2021); Jiang W., Jin X., Du C., Gu W., Gao X., Zhou C., Et al., Internet of things-based management versus standard management of home noninvasive ventilation in COPD patients with hypercapnic chronic respiratory failure: a multicentre randomized controlled non-inferiority trial, Eclinicalmedicine, 70, (2024); des Solidarités et de la Santé M. La télésurveillance: ETAPES, (2021); Baptista B.R., Baptiste A., Granger B., Villemain A., Ohayon R., Rabec C., Et al., Growth of home respiratory equipment from 2006 to 2019 and cost control by health policies, Respir Med Res, 82, (2022); Yanez A.M., Guerrero D., de Alejo R.P., Garcia-Rio F., Alvarez-Sala J.L., Calle-Rubio M., Et al., Monitoring breathing rate at home allows early identification of COPD exacerbations, Chest, 142, 6, pp. 1524-1529, (2012); Soler J., Alves Pegoraro J., Le X., Nguyen D., Grassion L., Antoine R., Et al., Validation of respiratory rate measurements from remote monitoring device in COPD patients, Respir Med Res, 76, pp. 1-3, (2019); Alves Pegoraro J., Lavault S., Wattiez N., Similowski T., Gonzalez-Bermejo J., Birmele E., Machine-learning based feature selection for a non-invasive breathing change detection, BioData Min, 14, 1, pp. 1-16, (2021); Mackay A.J., Donaldson G.C., Patel A.R., Singh R., Kowlessar B., Wedzicha J.A., Detection and severity grading of COPD exacerbations using the exacerbations of chronic pulmonary disease tool (EXACT), Eur Respir J, 43, 3, pp. 735-744, (2014); Soyez F., Ninot G., Herkert A., Huyn S.P., Prosper M., Chinet T., Et al., Validation d’un questionnaire d’évaluation de l’exacerbation dans la BPCO: l’Exascore, Rev Mal Respir, 33, 1, pp. 17-24, (2016); Rabin J., Peyre G., Delon J., Bernot M., Wasserstein barycenter and its application to texture mixing, International Conference on Scale Space and Variational Methods in Computer Vision, pp. 435-446, (2011); Peyre G., Cuturi M., Et al., Computational optimal transport: With applications to data science, Found Trends Mach Learn, 11, 5-6, pp. 355-607, (2019); Aggarwal C.C., Outlier Analysis, (2017); Nishiyama M., Shibata T., Normalized scoring of hidden Markov models by on-line learning and its application to gesture-sequence perception, Proceedings of the 16th IEEE International Conference on Image Processing. ICIP’09, pp. 3529-3532, (2009)","J. Alves Pegoraro; Loewenstein Medical Technology GmbH+Co. KG, Karlsruhe, 76135, Germany; email: juliana.a.pegoraro@gmail.com","","BioMed Central Ltd","","","","","","14726947","","","","English","BMC Med. Informatics Decis. Mak.","Article","Final","","Scopus","2-s2.0-85219036387"
"Sagheb E.; Wi C.-I.; King K.S.; Agnikula Kshatriya B.S.; Ryu E.; Liu H.; Park M.A.; Seol H.Y.; Overgaard S.M.; Sharma D.K.; Juhn Y.J.; Sohn S.","Sagheb, Elham (57211215840); Wi, Chung-Il (58475009900); King, Katherine S. (57007705400); Agnikula Kshatriya, Bhavani Singh (57328337300); Ryu, Euijung (24077625100); Liu, Hongfang (7409753328); Park, Miguel A. (8293342300); Seol, Hee Yun (57208402180); Overgaard, Shauna M. (57813750300); Sharma, Deepak K. (55315729600); Juhn, Young J. (6507775791); Sohn, Sunghwan (7101646425)","57211215840; 58475009900; 57007705400; 57328337300; 24077625100; 7409753328; 8293342300; 57208402180; 57813750300; 55315729600; 6507775791; 7101646425","AI model for predicting asthma prognosis in children","2025","Journal of Allergy and Clinical Immunology: Global","4","2","100429","","","","0","10.1016/j.jacig.2025.100429","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218998115&doi=10.1016%2fj.jacig.2025.100429&partnerID=40&md5=e0e95cbeea9fe3686054d6877236ab04","Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn; Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn; Center for Digital Health, Mayo Clinic, Rochester, Minn; Department of Allergy and Immunology, Mayo Clinic, Rochester, Minn; UTHealth Houston, Houston, Tex; Department of Internal Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan-si, Korea","Sagheb E., Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn; Wi C.-I., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; King K.S., Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn; Agnikula Kshatriya B.S., Center for Digital Health, Mayo Clinic, Rochester, Minn; Ryu E., Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn; Liu H., Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn, UTHealth Houston, Houston, Tex; Park M.A., Department of Allergy and Immunology, Mayo Clinic, Rochester, Minn; Seol H.Y., Department of Internal Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital, Yangsan-si, Korea; Overgaard S.M., Center for Digital Health, Mayo Clinic, Rochester, Minn; Sharma D.K., Center for Digital Health, Mayo Clinic, Rochester, Minn; Juhn Y.J., Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minn; Sohn S., Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn","Background: Childhood asthma often continues into adulthood, but some children experience remission. Utilizing electronic health records (EHRs) to predict asthma prognosis can aid health care providers and patients in developing effective prioritized care plans. Objective: We aimed to develop artificial intelligence (AI) models using various clinical variables extracted from EHRs to predict childhood asthma prognosis (remission vs no remission) in different age groups. Methods: We developed AI models utilizing patients’ EHRs during the first 6, 9, or 12 years of their lives to predict their asthma prognosis status at ages 6 to 9, 9 to 12, or 12 to 15 years, respectively. We first developed the models based on a manually annotated birth cohort (n = 900). We then leveraged a larger birth cohort (n = 29,594) labeled automatically (with weak labels) by a previously validated natural language processing algorithm for asthma prognosis. Different models (logistic regression, random forest, and XGBoost [eXtreme Gradient Boosting]) were tested with diverse clinical variables from structured and unstructured EHRs. Results: The best AI models of each age group produced a prediction performance with areas under the receiver operating characteristic curve ranging from 0.85 to 0.93. The prediction model at age 12 showed the highest performance. Most of the AI models with weak labels showed enhanced performance, and models using the top 10 variables performed similarly to those using all of the variables. Conclusions: The AI models effectively predicted asthma prognosis for children by using EHRs with a relatively small number of variables. This approach demonstrates the potential to enhance prioritized care plans and patient education, improving disease management and quality of life for asthmatic patients. © 2025 The Author(s)","artificial intelligence; Asthma; asthma prognosis; dynamic variables; electronic health records; machine learning; natural language processing","","","","","","Genentech; GlaxoSmithKline, GSK; National Institutes of Health, NIH, (R01 HL126667, R21 AI142702, R21 AG065639); National Institutes of Health, NIH","Supported by the National Institutes of Health (grants R01 HL126667, R21 AI142702, and R21 AG065639).Disclosure of potential conflict of interest: Y. J. Juhn is principal investigator of the Respiratory Syncytial Virus Incidence Study, which was supported by GlaxoSmithKline, and principal investigator of the Artificial Intelligence Development Study for Asthma, which was supported by Genentech. The rest of the authors declare that they have relevant conflicts of interest.","Asthma in the US (May 2011); Asthma in the US (May 2011); Tai A., Tran H., Roberts M., Et al., Outcomes of childhood asthma to the age of 50 years, J Allergy Clin Immunol, 133, pp. 1572-1578.e1573, (2014); Savenije O.E., Kerkhof M., Koppelman G.H., Postma D.S., Predicting who will have asthma at school age among preschool children, J Allergy Clin Immunol, 130, pp. 325-331, (2012); Martin-Sanchez F., Verspoor K., Big data in medicine is driving big changes, Yearb Med Inform, 9, pp. 14-20, (2014); Kothalawala D.M., Murray C.S., Simpson A., Custovic A., Tapper W.J., Arshad S.H., Et al., Development of childhood asthma prediction models using machine learning approaches, Clin Transl Allergy, 11, (2021); Jeddi Z., Gryech I., Ghogho M., El Hammoumi M., Mahraoui C., machine learning for predicting the risk for childhood asthma using prenatal, perinatal, postnatal and environmental factors, Healthcare (Basel), 9, (2021); Yu G., Li Z., Li S., Liu J., Sun M., Liu X., Et al., The role of artificial intelligence in identifying asthma in pediatric inpatient setting, Ann Transl Med, 8, (2020); Patel D., Hall G.L., Broadhurst D., Smith A., Schultz A., Foong R.E., Does machine learning have a role in the prediction of asthma in children?, Paediatr Respir Rev, 41, pp. 51-60, (2022); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics J, 25, pp. 811-827, (2019); Chatzimichail E., Paraskakis E., Sitzimi M., Rigas A., An intelligent system approach for asthma prediction in symptomatic preschool children, Comput Math Methods Med, 2013, (2013); Lisspers K., Stallberg B., Larsson K., Janson C., Muller M., Luczko M., Et al., Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - based on the ARCTIC study, Respir Med, 185, (2021); Sills M.R., Ozkaynak M., Jang H., Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning, Int J Med Inform, 151, (2021); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, pp. 1747-1757, (2021); Hussain Z., Shah S.A., Mukherjee M., Sheikh A., Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort, BMJ Open, 10, (2020); Patel S.J., Chamberlain D.B., Chamberlain J.M., A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage, Acad Emerg Med, 25, pp. 1463-1470, (2018); Farion K.J., Wilk S., Michalowski W., O'Sullivan D., Sayyad-Shirabad J., Comparing predictions made by a prediction model, clinical score, and physicians: pediatric asthma exacerbations in the emergency department, Appl Clin Inform, 4, pp. 376-391, (2013); Lovric M., Banic I., Lacic E., Pavlovic K., Kern R., Turkalj M., Predicting treatment outcomes using explainable machine learning in children with asthma, Children (Basel), 8, (2021); Qin Y., Wang J., Han Y., Lu L., Deep learning algorithms-based CT images in glucocorticoid therapy in asthma children with small airway obstruction, J Healthc Eng, 2021, (2021); Kercsmar C.M., Sorkness C.A., Calatroni A., Gergen P.J., Bloomberg G.R., Gruchalla R.S., Et al., A computerized decision support tool to implement asthma guidelines for children and adolescents, J Allergy Clin Immunol, 143, pp. 1760-1768, (2019); Bhardwaj P., Tyagi A., Tyagi S., Antao J., Deng Q., Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization, J Asthma, 60, pp. 487-495, (2023); Ross M.K., Yoon J., van der Schaar A., van der Schaar M., Discovering pediatric asthma phenotypes on the basis of response to controller medication using machine learning, Ann Am Thorac Soc, 15, pp. 49-58, (2018); Juhn Y., Liu H., Artificial intelligence approaches using natural language processing to advance EHR-based clinical research, J Allergy Clin Immunol, 145, pp. 463-469, (2020); Seol H.Y., Rolfes M.C., Chung W., Sohn S., Ryu E., Park M.A., Et al., Expert artificial intelligence-based natural language processing characterises childhood asthma, BMJ Open Respir Res, 7, (2020); Kaur H., Sohn S., Wi C.I., Ryu E., Park M.A., Bachman K., Et al., Automated chart review utilizing natural language processing algorithm for asthma predictive index, BMC Pulm Med, 18, (2018); Wi C.I., Sohn S., Rolfes M.C., Seabright A., Ryu E., Voge G., Et al., Application of a natural language processing algorithm to asthma ascertainment. An automated chart review, Am J Respir Crit Care Med, 196, pp. 430-437, (2017); Wi C.I., Sohn S., Ali M., Krusemark E., Ryu E., Liu H., Et al., Natural language processing for asthma ascertainment in different practice settings, J Allergy Clin Immunol Pract, 6, pp. 126-131, (2018); Wu S.T., Sohn S., Ravikumar K.E., Wagholikar K., Jonnalagadda S.R., Liu H., Et al., Automated chart review for asthma cohort identification using natural language processing: an exploratory study, Ann Allergy Asthma Immunol, 111, pp. 364-369, (2013); Sohn S., Wi C.I., Wu S.T., Liu H., Ryu E., Krusemark E., Et al., Ascertainment of asthma prognosis using natural language processing from electronic medical records, J Allergy Clin Immunol, 141, pp. 2292-2294.e3, (2018); Sagheb E., Wi C.I., Yoon J., Seol H.Y., Shrestha P., Ryu E., Et al., Artificial intelligence assesses clinicians’ adherence to asthma guidelines using electronic health records, J Allergy Clin Immunol Pract, 10, pp. 1047-1056.e1, (2022); Juhn Y., Moon S., Wi C.I., Fu S., Weston J., Porcher J., Et al., Automated chart review for identifying pre- and peri-natal risk factors associated with childhood asthma, Am J Respir Crit Care Med, 197, (2018); Ho T.K., Random decision forests, Proceedings of the 3rd international conference on document analysis and recognition, pp. 278-282, (1995); Cox D.R., The regression analysis of binary sequences, J Roy Stat Soc B, 20, pp. 215-232, (1958); Chen T., Guestrin C.; Hanley J.A., McNeil B.J., The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, pp. 29-36, (1982); Louppe G., Wehenkel L., Sutera A., Geurts P., 2013. Understanding variable importances in forests of randomized trees, Proceedings of the 26th International Conference on Neural Information Processing Systems - Vol 1 (NIPS'13), pp. 431-439, (2013); RandomizedSearchCV, (2023); Juhn Y.J., Beebe T.J., Finnie D.M., Sloan J., Wheeler P.H., Yawn B., Et al., Development and initial testing of a new socioeconomic status measure based on housing data, J Urban Health, 88, pp. 931-944, (2011); Zhong W., Finnie D.M., Shah N.D., Wagie A.E., St. Sauver J.L., Jacobson D.J., Et al., Effect of multiple chronic diseases on health care expenditures in childhood, J Prim Care Community Health, 6, pp. 2-9, (2015); In distant supervision, we make use of an already existing database, such as Freebase or a domain-specific database, to collect examples for the relation we want to extract. We then use these examples to automatically generate our training data; Seol H.Y., Shrestha P., Muth J.F., Wi C.I., Sohn S., Ryu E., Et al., Artificial intelligence-assisted clinical decision support for childhood asthma management: a randomized clinical trial, PLoS One, 16, (2021)","S. Sohn; Department of Artificial Intelligence and Informatics, Rochester, Mayo Clinic, 200 First St SW, 55905; email: sohn.sunghwan@mayo.edu","","Elsevier B.V.","","","","","","27728293","","","","English","J. Allergy. Clin. Immunol. Glob.","Article","Final","","Scopus","2-s2.0-85218998115"
"Gupta S.; Agrawal M.; Deepak D.","Gupta, Sonia (57213186478); Agrawal, Monika (35854246100); Deepak, Desh (57217700826)","57213186478; 35854246100; 57217700826","Correlating spirometry findings with auscultation sounds for diagnosis of respiratory diseases","2024","Biomedical Signal Processing and Control","87","","105347","","","","1","10.1016/j.bspc.2023.105347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169064672&doi=10.1016%2fj.bspc.2023.105347&partnerID=40&md5=55de7ace95d33f1f0b9b4b5770266ad6","BSTT&M, Indian Institute of Technology, Delhi, India; Centre of Applied Research in Electronics, Indian Institute of Technology, Delhi, India; Department of Respiratory Medicine, Dr. Ram Manohar Lohia Hospital, Delhi, India","Gupta S., BSTT&M, Indian Institute of Technology, Delhi, India; Agrawal M., Centre of Applied Research in Electronics, Indian Institute of Technology, Delhi, India; Deepak D., Department of Respiratory Medicine, Dr. Ram Manohar Lohia Hospital, Delhi, India","Spirometry is the pulmonary function test (PFT) used for the diagnosis and severity measurement of respiratory illness. Although it is a non-invasive procedure still requires expertise, patient cooperation and repeated maneuver. Auscultation is the conventional method doctors use in the clinical environment. It can be used for early, efficient and remote diagnosis of respiratory diseases. In this paper, a method is proposed which can classify auscultation sounds collected from a simple stethoscope in OPD settings into three major categories, i.e., healthy, obstructive and restrictive which is traditionally diagnosed using spirometry. The technique proposed in this work is based on pre-processing respiratory sounds by enhancing them using specifically designed parametric equalizer filter. Spectro-temporal Gabor filter-bank-based features (GBFB) are extracted from pre-processed respiratory sounds to classify them using different machine learning models. Also, an exhaustive study is performed to determine most efficient features for classifying respiratory sounds. The proposed method is novel and gives state-of-the-art results with an F1 score of 97.95%, precision of 100% and recall of 96%. The method proposed can be used for automated diagnosis of respiratory illness and can circumvent spirometry test in some scenarios. It can even help people living in remote and rural areas where finding an expert to carry out the PFT is limited. © 2023 Elsevier Ltd","Auscultation; Equalization; Obstructive; Respiratory; Restrictive","Computer aided diagnosis; Equalizers; Gabor filters; Ophthalmology; Pulmonary diseases; Auscultation; Conventional methods; Equalisation; Measurements of; Obstructive; Pulmonary function test; Respiratory; Respiratory illness; Respiratory sounds; Restrictive; adult; aged; Article; asthma; chronic obstructive lung disease; controlled study; crackle; feature extraction; Gabor transform; human; lung auscultation; machine learning; major clinical study; pleural friction rub; respiratory tract disease; spirometry; stridor; wheezing; Noninvasive medical procedures","","","Littmann 3200","","","","Cruz A.A., Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach, (2007); Asher M.I., Keil U., Anderson H.R., Beasley R., Crane J., Martinez F., Mitchell E.A., Pearce N., Sibbald B., Stewart A.W., International study of asthma and allergies in childhood (ISAAC): rationale and methods, Eur. 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Physician, 89, 5, pp. 359-366, (2014); Gaillard E.A., Kuehni C.E., Turner S., Goutaki M., Holden K.A., de Jong C.C.M., Lex C., Et al., European Respiratory Society clinical practice guidelines for the diagnosis of asthma in children aged 5–16 years, Eur. Respir. J., 58, 5, (2021); Hafke-Dys H., Breborowicz A., Kleka P., Kocinski J., Biniakowski A., The accuracy of lung auscultation in the practice of physicians and medical students, PLoS One, 14, 8, (2019); Sokolovsky V., Furman E., Kalinina N.; Gelman A., Furman E.G., Kalinina N.M., Malinin S.V., Furman G.B., Sheludko V.S., Sokolovsky V.L., Computer-aided detection of respiratory sounds in bronchial asthma patients based on machine learning method. 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Control, 45, pp. 58-69, (2018); Altan G., Kutlu Y., Allahverdi N., Deep learning on computerized analysis of chronic obstructive pulmonary disease, IEEE J. Biomed. Health Inf., 24, 5, pp. 1344-1350, (2019); Gupta S., Agrawal M., Deepak D., Extraction of adventitious sounds from noisy lung sound using VMD-KLD and VMD-JSD, TENCON 2019-2019 IEEE Region 10 Conference, TENCON, pp. 1071-1075, (2019); Gupta S., Agrawal M., Deepak D., Gammatonegram based triple classification of lung sounds using deep convolutional neural network with transfer learning, Biomed. Signal Process. Control, 70, (2021); Sharan R.V., Abeyratne U.R., Swarnkar V.R., Claxton S., Hukins C., Porter P., Predicting spirometry readings using cough sound features and regression, Physiol. 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Gupta; BSTT&M, Indian Institute of Technology, Delhi, India; email: sonia.gupta@dbst.iitd.ac.in","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85169064672"
"Vohra B.; Mittal S.","Vohra, Bhavna (58298582200); Mittal, Sumit (57192177799)","58298582200; 57192177799","Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review","2023","Journal of Experimental Biology and Agricultural Sciences","11","2","","226","235","9","1","10.18006/2023.11(2).226.235","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160821324&doi=10.18006%2f2023.11%282%29.226.235&partnerID=40&md5=876e9b1a641cdf0cf42dd9fef82d33e1","M. M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Ambala, Haryana, Mullana, 133207, India","Vohra B., M. M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Ambala, Haryana, Mullana, 133207, India; Mittal S., M. M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Ambala, Haryana, Mullana, 133207, India","Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semisupervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy. © 2023, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.","Chest Radiographs; Computed Tomography; Deep Learning; Lung diseases; Machine learning; Prediction Models; Semi-supervised","","","","","","","","Abbas Q., Lung-deep: a computerized tool for detection of lung nodule patterns using deep learning algorithms Detection of Lung Nodules Patterns, International Journal of Advanced Computer Science and Applications, 8, 10, (2017); Abbas Q., Nodular-deep: classification of pulmonary nodules using deep neural network, International Journal of Medical Research & Health Sciences, 6, 8, pp. 111-118, (2017); Allemani C., Matsuda T., Di Carlo V., Et al., Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries, The Lancet, 391, 10125, pp. 1023-1075, (2018); Alshmrani G. M. M., Ni Q., Jiang R., Pervaiz H., Elshennawy N. 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Mittal; M. M. Institute of Computer Technology & Business Management, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, 133207, India; email: sumit.mittal@mmumullana.org","","Editorial board of Journal of Experimental Biology and Agricultural Sciences","","","","","","23208694","","","","English","J. Exp. Biol. Agric. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85160821324"
"Zhao X.; Wang Y.; Li J.; Liu W.; Yang Y.; Qiao Y.; Liao J.; Chen M.; Li D.; Wu B.; Huang D.; Wu D.","Zhao, Xuanna (56413183200); Wang, Yunan (59654732100); Li, Jiahua (59656056700); Liu, Weiliang (56651246600); Yang, Yuting (59654995300); Qiao, Youping (58931855500); Liao, Jinyu (57578696700); Chen, Min (57199451435); Li, Dongming (56668919900); Wu, Bin (56428243700); Huang, Dan (56413448300); Wu, Dong (53064665600)","56413183200; 59654732100; 59656056700; 56651246600; 59654995300; 58931855500; 57578696700; 57199451435; 56668919900; 56428243700; 56413448300; 53064665600","A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS","2025","Journal of Affective Disorders","377","","","284","293","9","0","10.1016/j.jad.2025.02.063","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218862976&doi=10.1016%2fj.jad.2025.02.063&partnerID=40&md5=84a0fe0c7a7cdbc119d2e6c35e2ec0aa","Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China","Zhao X., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Wang Y., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Li J., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Liu W., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Yang Y., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Qiao Y., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Liao J., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Chen M., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Li D., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Wu B., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Huang D., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China; Wu D., Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524013, China","Background: Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depression in COPD patients. Methods: This study included 2921 COPD patients from the 2018 China Health and Retirement Longitudinal Study (CHARLS), analyzing 36 behavioral, health, psychological, and socio-demographic indicators. LASSO regression filtered predictive factors, and six machine learning models—Logistic Regression, Support Vector Machine, Multilayer Perceptron, LightGBM, XGBoost, and Random Forest—were applied to identify the best model for predicting depression risk in COPD patients. Temporal validation used 2013 CHARLS data. We developed a personalized, interpretable risk prediction platform using SHAP. Results: A total of 2921 patients with COPD were included in the analysis, of whom 1451 (49.7 %) presented with depressive symptoms. 11 variables were selected to develop 6 machine learning models. Among these, the XGBoost model exhibited exceptional predictive performance in terms of discrimination, calibration, and clinical applicability, with an AUROC range of 0.747–0.811. In validation sets encompassing diverse population characteristics, XGBoost achieved the highest accuracy (70.63 %), sensitivity (59.05 %), and F1 score (63.17 %). Limitations: The target population for the model is COPD patients. And the clinical benefits of interventions based on the prediction results remain uncertain. Conclusion: We developed an online prediction platform for clinical application, allowing healthcare professionals to swiftly and efficiently evaluate the risk of depression in COPD patients, facilitating timely interventions and treatments. © 2025 The Authors","Chronic obstructive pulmonary disease; Depression; Machine learning; Prediction model; Shapley additive explanation","adult; aged; Article; calibration; chronic obstructive lung disease; cohort analysis; comparative study; controlled study; depression; female; human; longitudinal study; machine learning; major clinical study; male; multilayer perceptron; performance indicator; population parameters; predictive model; random forest; retrospective study; risk factor; sensitivity analysis; support vector machine","","","","","Projects of Zhanjiang City, (2021A05077, 2021A05082, 2021A05052); Guangdong Medical University, (LCYJ2022DL01, LCYT2017A003, LCYJ2023B003, LCYJ2020B008, LCYJ2021B007); Non-Funding Projects of Zhanjiang City, (2024B01328); Discipline Construction Project of Guangdong Medical University, (4SG21231G)","This work was supported by the Discipline Construction Project of Guangdong Medical University (grant number: 4SG21231G ), Clinical Research Projects of the Affiliated Hospital of Guangdong Medical University (grant numbers: LCYT2017A003 , LCYJ2020B008 , LCYJ2021B007 , LCYJ2023B003 and LCYJ2022DL01 ), and Projects of Zhanjiang City (grant numbers: 2021A05052 , 2021A05082 and 2021A05077 ), Non-Funding Projects of Zhanjiang City (grant number: 2024B01328 ). 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Neurobiol., 315, (2023); Ye S., Sun X., Kang B., Et al., The kinetic profile and clinical implication of SCC-Ag in squamous cervical cancer patients undergoing radical hysterectomy using the Simoa assay: a prospective observational study, BMC Cancer, 20, (2020); Yue S., Li S., Huang X., Et al., Machine learning for the prediction of acute kidney injury in patients with sepsis, J. Transl. Med., 20, (2022); Zheng Y., Zhang C., Liu Y., Risk prediction models of depression in older adults with chronic diseases, J. Affect. Disord., 359, pp. 182-188, (2024)","D. Wu; Zhanjiang, No.57, South of Renmin Road, Guangdong, 524013, China; email: wudong98@126.com","","Elsevier B.V.","","","","","","01650327","","JADID","","English","J. Affective Disord.","Article","Final","","Scopus","2-s2.0-85218862976"
"Qi X.; Chen H.","Qi, Xin (58849299800); Chen, Hong (58849066800)","58849299800; 58849066800","Recurrence Prediction and Risk Classification of COPD Patients Based on Machine Learning","2023","International Journal of Advanced Computer Science and Applications","14","12","","840","849","9","1","10.14569/IJACSA.2023.0141285","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183338609&doi=10.14569%2fIJACSA.2023.0141285&partnerID=40&md5=7598017995c62c71cc7aa371274de76f","Academic Affairs Office, Heilongjiang University of Chinese Medicine, Harbin, 150001, China; Chinese Pediatrics, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150040, China","Qi X., Academic Affairs Office, Heilongjiang University of Chinese Medicine, Harbin, 150001, China; Chen H., Chinese Pediatrics, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150040, China","In response to the frequent recurrence and readmission of patients with chronic obstructive pulmonary disease, a machine learning based recurrence risk prediction and risk classification model for patients with chronic obstructive pulmonary disease is studied and constructed. Approach: This model first utilizes the optimized long short-term memory network to recognize named entities in patient electronic medical records and extract entity features. Then, XGBoost is used to predict the probability of patient relapse and readmission, and its risk is classified. Results: These results confirm that the optimized bidirectional long short-term memory network has the best performance with an accuracy of 84.36% in electronic medical record named entity recognition. The accuracy of XGBoost is the highest on both the training and testing sets, with values of 0.8827 and 0.8514, respectively. XGBoost has the best predictive ability and effectiveness. By using k-means for layering, the workload of manual evaluation was reduced by 91%, and the overall simulation accuracy of the model was as high as 97.3% and 96.4%. Conclusions: These indicate that this method can be used to balance high-risk patients between risk, cost, and resources. © 2023, Science and Information Organization. All rights reserved.","BiLSTM; COPD; k-means; Machine learning; recurrence; risk classification; XGBoost","Brain; Forecasting; K-means clustering; Medical computing; Pulmonary diseases; BiLSTM; Chronic obstructive pulmonary disease; COPD; K-means; Machine-learning; Medical record; Memory network; Recurrence; Risk classification; Xgboost; Long short-term memory","","","","","","","Wang P X, Xu Y, Sun Y F, Cheng JW, Zhou K Q, Wu SY, Hu B, Zhang ZF, Guo W, Cao Y., Detection of circulating tumor cells enables early recurrence prediction in hepatocellular carcinoma patients undergoing liver transplantation, Liv. Int, 41, 3, pp. 562-573, (2021); Ye Z, Zhang Y, Liang Y, Lang J, Yang Cervical Cancer Metastasis and Recurrence Risk Prediction Based on Deep Convolutional Neural Network, Cur. Bio, 17, 2, pp. 164-173, (2022); Chen Z., Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm, Jou. Com. Cog. Eng, 1, 3, pp. 103-108, (2022); Kawahara D, Nishibuchi I, Kawamura M, Yoshida T, Nagata Y., Radiomic Analysis for Pretreatment Prediction of Recurrence after Radiotherapy in Locally Advanced Cervical Cancer, International Journal of Radiation Oncology, Biology, Physics, 111, 3S, pp. E93-E93, (2021); Xiaoke Z, Yu H, Liang Z, Tao L, Zhang M., A prognostic nomogram for predicting risk of recurrence in laryngeal squamous cell carcinoma patients after tumor resection to assist decision making for postoperative adjuvant treatment, Journal of Surgical Oncology, 120, 4, pp. 698-706, (2019); Chan L, Sadahiro S, Suzuki T, Okada K, Miyakita H, Yamamoto S, Kajiwara H., Tissue-Infiltrating Lymphocytes as a Predictive Factor for Recurrence in Patients with Curatively Resected Colon Cancer: A Propensity Score Matching Analysis, Oncology, 98, 10, pp. 680-688, (2020); Lafaie L, Celarier Thomas, Goethals L, Pozzetto B, Botelho㎞Evers E., Recurrence or Relapse of COVID-19 in Older Patients: A Description of Three Cases, Journal of the American Geriatrics Society, 68, 10, pp. 2179-2183, (2020); Dowsett M., Integration of Clinical Variables for the Prediction of Late Distant Recurrence in Patients with Estrogen Receptor- Positive Breast Cancer Treated With 5 Years of Endocrine Therapy: CTS5 (vol 14, pg 234, 2019), Journal of Clinical Oncology, 38, 6, pp. 656-656, (2020); Postiche H, Piccard M., BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named- Entity Recognition, IEEE transactions on big data, 8, 1, pp. 203-212, (2022); Benali B A, Mihi S, Moku A, Bazi I EI, Yakhouba N., Arabic named entity recognition in social media based on BiLSTM-CRF using an attention mechanism, Journal of Intelligent & Fuzzy Systems: App. Eng. Tec, 42, 6, pp. 5427-5436, (2022); Long R, Yang D, Liu Y., Disease Net: A Novel Disease Diagnosis Deep Framework via Fusing Medical Record Summarization, IAE. Int. Jou. Com. Sci, 49, 3, pp. 808-817, (2022); Puh K, Babac M B., Predicting sentiment and rating of tourist reviews using machine learning, Jou. Hos. Tou. Ins, 6, 3, pp. 1188-1204, (2023); Matheson A M, Parraga G., Machine Learning Predictions of COPD Mortality, Com. Hid. See. Che, 158, 3, pp. 846-847, (2020); AL khadar H, Meluskey M, White S, Ellis I, Gardner A., Comparison of machine learning algorithms for the prediction of five‐year survival in oral squamous cell carcinoma, Jou. Ora. Pat& Med, 50, 4, pp. 378-384, (2021); Wong N C, Lam C, Patterson L, Shay Egan B., Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy, BJU International, 123, 1, pp. 51-57, (2019); Paredes A Z, Hyer J M, Salimgarh D I, Moro A, Pawlik TM., A Novel Machine-Learning Approach to Predict Recurrence After Resection of Colorectal Liver Metastases, Ann. Sur. Onc, 27, 13, pp. 5139-5147, (2020); Zhang S, Zhu H, Xu H, Zhu G, Li K C., A named entity recognition method towards product reviews based on BiLSTM-attention-CRF, Int. Jou. Com. Sci. Eng, 25, 5, pp. 479-489, (2022); Li D, Dong C, Chen Z, Dong Y, Liu J., A combinatorial machine-learning-driven approach for predicting glass transition temperature based on numerous molecular descriptors, Mol. Sim, 49, 6, pp. 617-627, (2023); Guo Y, Mustafa Z, Kaunda D., Spam Detection Using Bidirectional Transformers and Machine Learning Classifier Algorithms, Jou. Com. Cog. Eng, 2, 1, pp. 5-9, (2022); Liu M, Stella F, Homeroom A, Lucas Peter J F, Lonneke B, Bischoff E., A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity, Art. Int. Med, 95, APR., pp. 104-117, (2019); Reito A, Karola K, Pekkanen L, Palomera J., 30-day recurrence, readmission rate, and clinical outcome after emergency lumbar discectomy, Spine, 45, 18, pp. 1253-1259, (2020); Lao Y, Yu V, Pham A, Wang T, Sheng K., Quantitative Characterization of Tumor Proximity to Stem Cell Niches: Implications on Recurrence and Survival in GBM Patients, Int. Jou. Rad. Ons. Bio. Phys, 110, 4, pp. 1180-1188, (2021); Meng T, Huang R, Hu P, Yin H, Song D., Novel Nomograms as Aids for Predicting Recurrence and Survival in Chordoma Patients: A Retrospective Multicenter Study in mainland China, Spine, 46, 1, pp. E37-E47, (2020); Lei M, Han Z, Wang S, Han T, Fang S, Lin F, Huang T., A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study, Injury, 54, 2, pp. 636-644, (2023); Holtkamp L H J, Lo S N, Thompson J F, Spillane A J, Stretch J R, Saw R P M, Shannon K F, Newegg O E, Hong A M., Adjuvant radiotherapy after salvage surgery for melanoma recurrence in a node field following a previous lymph node dissection, Jou. Sur. Ons, 128, 1, pp. 97-104, (2023); Fang Y, Luo B, Zhao T, He D, Jiang B, Liu Q., ST-SIGMA: Spatial-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting, CAAI Transactions on Intelligence Technology, 7, 4, pp. 744-757, (2022)","","","Science and Information Organization","","","","","","2158107X","","","","English","Intl. J. Adv.  Comput. Sci. Appl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85183338609"
"Qi Y.; Zhang J.; Lin J.; Yang J.; Guan J.; Li K.; Weng J.; Wang Z.; Chen C.; Xu H.","Qi, Yanhong (58705515900); Zhang, Jing (58705807600); Lin, Jiaying (58114067300); Yang, Jingwen (57996151800); Guan, Jiangan (58705957500); Li, Keying (58705807700); Weng, Jie (57203684750); Wang, Zhiyi (57192443216); Chen, Chan (37092588700); Xu, Hui (56342521000)","58705515900; 58705807600; 58114067300; 57996151800; 58705957500; 58705807700; 57203684750; 57192443216; 37092588700; 56342521000","Predicting the risk of acute respiratory failure among asthma patients-the A2- BEST2 risk score: a retrospective study","2023","PeerJ","11","","e16211","","","","1","10.7717/peerj.16211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177563828&doi=10.7717%2fpeerj.16211&partnerID=40&md5=e7a3038362489cde978d27b1c7aa9e4f","General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China; Geriatric Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; General Practice, Taizhou Women and Children's Hospital of Wenzhou Medical University, Taizhou, China; Wenzhou Medicial University, Sourthern Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, China","Qi Y., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China; Zhang J., Geriatric Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Lin J., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, General Practice, Taizhou Women and Children's Hospital of Wenzhou Medical University, Taizhou, China; Yang J., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, General Practice, Taizhou Women and Children's Hospital of Wenzhou Medical University, Taizhou, China; Guan J., Geriatric Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Li K., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China; Weng J., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, Wenzhou Medicial University, Sourthern Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, China; Wang Z., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, Wenzhou Medicial University, Sourthern Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, China; Chen C., Geriatric Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China, Wenzhou Medicial University, Sourthern Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, China; Xu H., General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China, Wenzhou Medicial University, Sourthern Zhejiang Institute of Radiation Medicine and Nuclear Technology, Wenzhou, China","Objectives. Acute respiratory failure (ARF) is a common complication of bronchial asthma (BA). ARF onset increases the risk of patient death. This study aims to develop a predictive model for ARF in BA patients during hospitalization. Methods. This was a retrospective cohort study carried out at two large tertiary hospitals. Three models were developed using three different ways: (1) the statistics- driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results. This study included 398 patients, with 298 constituting the modeling group and 100 constituting the validation group. Models A, B, and C yielded seven, seven, and eleven predictors, respectively. Finally, we chose the clinical knowledge-driven model, whose C-statistics and Brier scores were 0.862 (0.820_0.904) and 0.1320, respectively. The Hosmer-Lemeshow test revealed that this model had good calibration. The clinical knowledge-driven model demonstrated satisfactory C-statistics during external and internal validation, with values of 0.890 (0.815-0.965) and 0.854 (0.820- 0.900), respectively. A risk score for ARF incidence was created: The A2-BEST2 Risk Score (A2 (area of pulmonary infection, albumin), BMI, Economic condition, Smoking, and T2 (hormone initiation Time and long-term regular medication Treatment)). ARF incidence increased gradually from 1.37% (The A2-BEST2 Risk Score ≤ 4) to 90.32% (A2-BEST2 Risk Score ≥ 11.5). Conclusion. We constructed a predictive model of seven predictors to predict ARF in BA patients. This predictor's model is simple, practical, and supported by existing clinical knowledge.  © 2023 Qi et al.","Acute respiratory failure; Bronchial asthma; Predicted","albumin; C reactive protein; hormone; procalcitonin; transthyretin; acute respiratory failure; adult; Article; artificial ventilation; asthma; body mass; bronchiectasis; chest tightness; chronic obstructive lung disease; cohort analysis; dyspnea; female; heart rate; hospitalization; human; job stress; least absolute shrinkage and selection operator; lung infection; machine learning; major clinical study; male; mortality; oxygen saturation; predictive model; receiver operating characteristic; respiratory failure; retrospective study; risk assessment; risk factor; scoring system; smoking; socioeconomics; tertiary care center; upper respiratory tract infection; validation process; wheezing","","C reactive protein, 9007-41-4; procalcitonin, 56645-65-9","","","Wenzhou Municipal Science and Technology Bureau, WMSTB, (Y20210840)","This study was supported by the Wenzhou Municipal Science & Technology Bureau, China. (Grant No:Y20210840). 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Triantafyllidou C, Effraimidis P, Vougas K, Agholme J, Schimanke M, Cederquist K., The role of early warning scoring systems NEWS and MEWS in the acute exacerbation of COPD. Clinical Medicine Insights, Circulatory, Respiratory and Pulmonary Medicine, 17, (2023); Van Houwelingen JC, Le Cessie S., Predictive value of statistical models, Statistics in Medicine, 9, pp. 1303-1325, (1990); Weng J, Hou R, Zhou X, Xu Z, Zhou Z, Wang P, Wang L, Chen C, Wu J, Wang Z., Development and validation of a score to predict mortality in ICU patients with sepsis: a multicenter retrospective study, Journal of Translational Medicine, 19, (2021); Yan BD, Meng SS, Ren J, Lv Z, Zhang QH, Yu JY, Gao R, Shi CM, Wu CF, Liu CL, Zhang J, Ma ZS, Liu J., Asthma control and severe exacerbations in patients with moderate or severe asthma in Jilin Province, China: a multicenter cross-sectional survey, BMC Pulmonary Medicine, 16, (2016); Yii ACA, Tay TR, Puah SH, Lim HF, Li A, Lau P, Tan R, Neo LP, Chung KF, Koh MS., Blood eosinophil count correlates with severity of respiratory failure in life-threatening asthma and predicts risk of subsequent exacerbations, Clinical and Experimental Allergy, 49, pp. 1578-1586, (2019)","H. Xu; General Practice, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China; email: xuhui2580@126.com","","PeerJ Inc.","","","","","","21678359","","","","English","PeerJ","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85177563828"
"Swinnerton K.; Fillmore N.R.; Vo A.; La J.; Elbers D.; Brophy M.; Do N.V.; Monach P.A.; Branch-Elliman W.","Swinnerton, Kaitlin (57193422399); Fillmore, Nathanael R. (57209590729); Vo, Austin (57803536800); La, Jennifer (57956086400); Elbers, Danne (57210787367); Brophy, Mary (56772481600); Do, Nhan V. (57209588695); Monach, Paul A. (56932132500); Branch-Elliman, Westyn (6504102545)","57193422399; 57209590729; 57803536800; 57956086400; 57210787367; 56772481600; 57209588695; 56932132500; 6504102545","Leveraging near-real-time patient and population data to incorporate fluctuating risk of severe COVID-19: development and prospective validation of a personalised risk prediction tool","2025","eClinicalMedicine","81","","103114","","","","0","10.1016/j.eclinm.2025.103114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217884880&doi=10.1016%2fj.eclinm.2025.103114&partnerID=40&md5=4cba3b485a2a06ba1052dcac2ab04bf7","VA Boston Cooperative Studies Program, Boston, MA, United States; VA Boston Healthcare System, Department of Medicine, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; VA Boston Centre for Healthcare Optimisation and Implementation Research, Boston, MA, United States; Dana Farber Cancer Institute, Boston, MA, United States; Boston University School of Medicine, Boston, MA, United States; Greater Los Angeles VA Healthcare System, Department of Medicine, Section of Infectious Diseases and the Centre for Healthcare Innovation, Implementation, and Policy (CSHIIP), Los Angeles, CA, United States; University of California, Los Angeles David Geffen School of Medicine, Los Angeles, CA, United States","Swinnerton K., VA Boston Cooperative Studies Program, Boston, MA, United States; Fillmore N.R., VA Boston Cooperative Studies Program, Boston, MA, United States, VA Boston Healthcare System, Department of Medicine, Boston, MA, United States, Harvard Medical School, Boston, MA, United States, VA Boston Centre for Healthcare Optimisation and Implementation Research, Boston, MA, United States, Dana Farber Cancer Institute, Boston, MA, United States; Vo A., VA Boston Cooperative Studies Program, Boston, MA, United States; La J., VA Boston Cooperative Studies Program, Boston, MA, United States; Elbers D., VA Boston Cooperative Studies Program, Boston, MA, United States, Boston University School of Medicine, Boston, MA, United States; Brophy M., VA Boston Cooperative Studies Program, Boston, MA, United States, Harvard Medical School, Boston, MA, United States, Boston University School of Medicine, Boston, MA, United States; Do N.V., VA Boston Cooperative Studies Program, Boston, MA, United States, Harvard Medical School, Boston, MA, United States, Boston University School of Medicine, Boston, MA, United States; Monach P.A., VA Boston Cooperative Studies Program, Boston, MA, United States, VA Boston Healthcare System, Department of Medicine, Boston, MA, United States, Harvard Medical School, Boston, MA, United States; Branch-Elliman W., VA Boston Cooperative Studies Program, Boston, MA, United States, Greater Los Angeles VA Healthcare System, Department of Medicine, Section of Infectious Diseases and the Centre for Healthcare Innovation, Implementation, and Policy (CSHIIP), Los Angeles, CA, United States, University of California, Los Angeles David Geffen School of Medicine, Los Angeles, CA, United States","Background: Novel strategies that account for population-level changes in dominant variants, immunity, testing practices and changes in individual risk profiles are needed to identify patients who remain at high risk of severe COVID-19. The aim of this study was to develop and prospectively validate a tool to predict absolute risk of severe COVID-19 incorporating dynamic parameters at the patient and population levels that could be used to inform clinical care. Methods: A retrospective cohort of vaccinated US Veterans with SARS-CoV-2 from July 1, 2021, through August 25, 2023 was created. Models were estimated using logistic-regression-based machine learning with backward selection and included a variable with fluctuating absolute risk of severe COVID-19 to account for temporal changes. Age, sex, vaccine type, fully boosted status, and prior infection before vaccination were included a priori. Variations in individual risk over time, e.g., due to receipt of immune suppressive medications, were also potentially included. The model was developed using data from July 1, 2021, through August 31, 2022 and prospectively validated on a subsequent second cohort (September 1, 2022, through August 25, 2023). Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and calibration by Brier score. The final model was used to compare observed rates of severe disease to predicted rates among patients who received oral antivirals. Findings: 216,890 SARS-CoV-2 infections in Veterans not treated with oral antivirals were included (median age, 65; 88% male). The development cohort included 165,303 patients (66,121 in the training set, 49,591 in the tuning set, and 49,591 in the testing set) and the prospective validation cohort included 51,587 patients. The percentage of severe infections ranged from 5% to 25%. Model performance improved until 24 clinical predictor variables including age, co-morbidities, and immune-suppressive medications plus a 30-day rolling risk window were included (AUC in development cohort, 0.88 (95% CI, 0.87–0.88), AUC in prospective validation, 0.85 (95% CI, 0.84–0.85), Brier Score, 0.13). The most important variables for predicting severe disease included age, chronic kidney disease, chronic obstructive pulmonary disease, Alzheimer's disease, heart failure, and anaemia. Glucocorticoid use during the one-month prior to COVID-19 diagnosis was the next most important predictor. Models that included a near-real time fluctuating population risk variable performed better than models stratified by circulating variant and models with dominant variant included as a predictor. Patients with predicted severe disease risk >3% who received oral antivirals had approximately 4-fold lower rates of severe COVID-19 untreated patients at a similar risk level. Interpretation: Our novel risk prediction tool uses a simple method to adjust for temporal changes and can be implemented to facilitate uptake of evidence-based therapies. The study provides proof-of-concept for leveraging real-time data to support risk prediction that incorporates changing population-level trends and variation patient-level risk. Funding: This work was supported by the VA Boston Cooperative Studies Programme. WBE was supported by VA HSR&D IIR 20-076; VA HSR&D IIR 20-101; VA National Artificial Intelligence Institute. © 2025","Clinical decompensation scores; Clinical informatics; COVID-19; Dynamic sustainability; Learning health systems; Risk prediction; SARS-CoV-2","antivirus agent; glucocorticoid; adult; aged; Alzheimer disease; anemia; Article; body mass; chronic kidney failure; chronic obstructive lung disease; clinical outcome; cohort analysis; comorbidity; coronavirus disease 2019; disease severity; female; heart failure; hospitalization; human; immunosuppressive treatment; machine learning; major clinical study; male; mortality; near real time patient data; patient coding; personalized medicine; pharmacokinetics; prediction; predictive model; retrospective study; risk assessment; risk factor; Severe acute respiratory syndrome coronavirus 2; vaccination; validation study","","","","","VA Boston Cooperative; NAII; VA National Artificial Intelligence Institute.The; Gilead Sciences; VA National Artificial Intelligence Institute; VA Cooperative Studies Programme; Health Services Research and Development, HSR&D, (IIR 20-076, IIR 20-101)","Funding text 1: This work was supported by the VA Boston Cooperative Studies Programme. WBE was supported by VA HSR&D IIR 20-076; VA HSR&D IIR 20-101; VA National Artificial Intelligence Institute.The authors would like to thank Ms. Dipandita Basnet-Thapa for her assistance with references. This study was supported by the VA Cooperative Studies Programme, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. WBE was supported by the VA Health Services Research and Development Service (VA HSR&D IIR 20-076; VA HSR&D IIR 20\u2013101) and by the VA National Artificial Intelligence Institute (NAII). Views represented are those of the authors and do not necessarily represent those of the VA or the US Federal Government. The authors affirm that the manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.; Funding text 2: WBE and PM report receipt of grant funding from Gilead Sciences during the past 3 years; funds to institution. All other authors report no financial conflicts of interest. ; Funding text 3: Funding : This work was supported by the VA Boston Cooperative Studies Programme. WBE was supported by VA HSR&D IIR 20-076; VA HSR&D IIR 20-101; VA National Artificial Intelligence Institute. ; Funding text 4: The authors would like to thank Ms. Dipandita Basnet-Thapa for her assistance with references.This study was supported by the VA Cooperative Studies Programme, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. WBE was supported by the VA Health Services Research and Development Service (VA HSR&D IIR 20-076; VA HSR&D IIR 20-101) and by the VA National Artificial Intelligence Institute (NAII). Views represented are those of the authors and do not necessarily represent those of the VA or the US Federal Government. The authors affirm that the manuscript is an honest, accurate and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained. ","Chambers D.A., Feero W.G., Khoury M.J., Convergence of implementation science, precision medicine, and the learning health care system: a new model for biomedical research, JAMA, 315, 18, pp. 1941-1942, (2016); Greene S.M., Reid R.J., Larson E.B., Implementing the learning health system: from concept to action, Ann Intern Med, 157, 3, pp. 207-210, (2012); Branch-Elliman W., Elwy A.R., Chambers D.A., Embracing dynamic public health policy impacts in infectious diseases responses: leveraging implementation science to improve practice, Front Public Health, 11, (2023); Trottier C., La J., Li L.L., Et al., Maintaining the utility of coronavirus disease 2019 pandemic severity surveillance: evaluation of trends in attributable deaths and development and validation of a measurement tool, Clin Infect Dis, 77, 9, pp. 1247-1256, (2023); Trottier C.A., La J., Li L., Et al., Longitudinal trends in 30-day mortality attributable to SARS-CoV-2 among vaccinated and unvaccinated US veteran patients, Infect Control Hosp Epidemiol, 45, 3, pp. 393-395, (2024); Doron S., Monach P.A., Brown C.M., Branch-Elliman W., Improving COVID-19 disease severity surveillance measures: statewide implementation experience, Ann Intern Med, 176, 6, pp. 849-852, (2023); Corrigan J.K., La J., Fillmore N.R., Et al., Coronavirus disease 2019 (COVID-19) hospitalisation metrics that do not account for disease severity underestimate protection provided by severe acute respiratory coronavirus virus 2 (SARS-CoV-2) vaccination and boosting: a retrospective cohort study, Infect Control Hosp Epidemiol, 44, 1, pp. 149-151, (2023); Fillmore N.R., La J., Zheng C., Et al., The COVID-19 hospitalisation metric in the pre-and postvaccination eras as a measure of pandemic severity: a retrospective, nationwide cohort study, Infect Control Hosp Epidemiol, 43, 12, pp. 1767-1772, (2022); Hippisley-Cox J., Coupland C.A., Mehta N., Et al., Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study, BMJ, 374, (2021); Tang G., Luo Y., Lu F., Et al., Prediction of sepsis in COVID-19 using laboratory indicators, Front Cell Infect Microbiol, 10, (2020); Liang W., Liang H., Ou L., Et al., Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalised patients with COVID-19, JAMA Intern Med, 180, 8, pp. 1081-1089, (2020); Anand S.T., Vo A.D., La J., Et al., Risk of severe coronavirus disease 2019 despite vaccination in patients requiring treatment with immune-suppressive drugs: a nationwide cohort study of US Veterans, Transpl Infect Dis, 26, 1, (2024); Vo A.D., La J., Wu J.T., Et al., Factors associated with severe COVID-19 among vaccinated adults treated in US veterans affairs hospitals, JAMA Netw Open, 5, 10, (2022); Agrawal U., Bedston S., McCowan C., Et al., Severe COVID-19 outcomes after full vaccination of primary schedule and initial boosters: pooled analysis of national prospective cohort studies of 30 million individuals in England, Northern Ireland, Scotland, and Wales, Lancet, 400, 10360, pp. 1305-1320, (2022); Anand S.T., Vo A.D., La J., Et al., Risk of severe coronavirus disease 2019 despite vaccination in patients requiring treatment with immune-suppressive drugs: a nationwide cohort study of US Veterans, Transpl Infect Dis, 26, 1, (2024); Yan L., Bui D., Li Y., Et al., Identifying veterans who benefit from nirmatrelvir-ritonavir: a target trial emulation, Clin Infect Dis, 79, 3, pp. 643-651, (2024); Shah M.M., Joyce B., Plumb I.D., Et al., Paxlovid associated with decreased hospitalisation rate among adults with COVID-19 - United States, April-September 2022, MMWR Morb Mortal Wkly Rep, 71, 48, pp. 1531-1537, (2022); Branch-Elliman W., Monach P.A., Moving towards a precision approach for prevention of severe COVID-19, Lancet, 401, 10386, pp. 1423-1424, (2023); COVID-19:Shared data resource, (2024); Riley R.D., Ensor J., Snell K.I.E., Et al., Calculating the sample size required for developing a clinical prediction model, BMJ, 368, (2020); Blanche P., Dartigues J.F., Jacqmin-Gadda H., Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks, Stat Med, 32, 30, pp. 5381-5397, (2013); Heagerty P.J., Lumley T., Pepe M.S., Time-dependent ROC curves for censored survival data and a diagnostic marker, Biometrics, 56, 2, pp. 337-344, (2000); D'Agostino R.B., Nam B.-H., Evaluation of the performance of survival analysis models: discrimination and calibration measures, Handb Stat, 23, pp. 1-25, (2003); A brief on Brier scores, (2024); What is a brier score?, (2020); What is considered a good AUC score?, (2021); Monach P.A., Anand S.T., Fillmore N.R., La J., Branch-Elliman W., Underuse of antiviral drugs to prevent progression to severe COVID-19 - veterans health administration, March-September 2022, MMWR Morb Mortal Wkly Rep, 73, 3, pp. 57-61, (2024); Chambers D.A., Glasgow R.E., Stange K.C., The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change, Implement Sci, 8, 1, pp. 1-11, (2013); Appel K.S., Geisler R., Maier D., Miljukov O., Hopff S.M., Vehreschild J.J., A systematic review of predictor composition, outcomes, risk of bias, and validation of COVID-19 prognostic scores, Clin Infect Dis, 78, 4, pp. 889-899, (2024); Kamran F., Tang S., Otles E., Et al., Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study, BMJ, 376, (2022); Bellos I., Lourida P., Argyraki A., Et al., Development of a novel risk score for the prediction of critical illness amongst COVID-19 patients, Int J Clin Pract, 75, 4, (2021); Shi Y., Zheng Z., Wang P., Wu Y., Liu Y., Liu J., Development and validation of a predicted nomogramme for mortality of COVID-19: a multicentre retrospective cohort study of 4,711 cases in multiethnic, Front Med, 10, (2023); Anand S.T., Vo A.D., La J., Et al., Severe COVID-19 in vaccinated adults with hematologic cancers in the veterans health administration, JAMA Netw Open, 7, 2, (2024); Fillmore N.R., La J., Wu J.T.-Y., Et al., Inadequate sars-cov-2 vaccine effectiveness in patients with multiple myeloma: a large nationwide veterans affairs study, Blood, 138, (2021); Wu J.T., La J., Branch-Elliman W., Et al., Association of COVID-19 vaccination with SARS-CoV-2 infection in patients with cancer: a US nationwide veterans affairs study, JAMA Oncol, 8, 2, pp. 281-286, (2022); Hammond J., Fountaine R.J., Yunis C., Et al., Nirmatrelvir for vaccinated or unvaccinated adult outpatients with Covid-19, N Engl J Med, 390, 13, pp. 1186-1195, (2024); Huang R.J., Kwon N.S.-E., Tomizawa Y., Choi A.Y., Hernandez-Boussard T., Hwang J.H., A comparison of logistic regression against machine learning algorithms for gastric cancer risk prediction within real-world clinical data streams, JCO Clin Cancer Inform, 6, (2022); Yan L., Streja E., Li Y., Et al., Anti-SARS-CoV-2 pharmacotherapies among nonhospitalsed US veterans, January 2022 to January 2023, JAMA Netw Open, 6, 8, (2023)","W. Branch-Elliman; Greater Los Angeles VA Healthcare System, Section of Infectious Diseases, Los Angeles, 11301 Wilshire Blvd, 90073, United States; email: Westyn.Branch-Elliman@va.gov","","Elsevier Ltd","","","","","","25895370","","","","English","eClinicalMedicine","Article","Final","","Scopus","2-s2.0-85217884880"
"Tang W.; Zhang L.; Ai T.; Xia W.; Xie C.; Fan Y.; Chen S.; Chen Z.; Yao J.; Peng Y.","Tang, Wei (57217760278); Zhang, Lei (57215062291); Ai, Tao (57212794562); Xia, Wanmin (57219970573); Xie, Cheng (35099801800); Fan, Yinghong (57219970238); Chen, Sisi (57189844979); Chen, Zijin (58132388100); Yao, Jiawei (57729719100); Peng, Yi (57223860698)","57217760278; 57215062291; 57212794562; 57219970573; 35099801800; 57219970238; 57189844979; 58132388100; 57729719100; 57223860698","A pilot study exploring the association of bronchial bacterial microbiota and recurrent wheezing in infants with atopy","2023","Frontiers in Cellular and Infection Microbiology","13","","1013809","","","","1","10.3389/fcimb.2023.1013809","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149493137&doi=10.3389%2ffcimb.2023.1013809&partnerID=40&md5=b88f8534870b797bb05925af343a3643","Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; School of Clinical Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, China","Tang W., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Zhang L., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Ai T., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Xia W., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Xie C., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Fan Y., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Chen S., School of Clinical Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, China; Chen Z., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Yao J., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; Peng Y., Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China","Background: Differences in bronchial microbiota composition have been found to be associated with asthma; however, it is still unclear whether these findings can be applied to recurrent wheezing in infants especially with aeroallergen sensitization. Objectives: To determine the pathogenesis of atopic wheezing in infants and to identify diagnostic biomarkers, we analyzed the bronchial bacterial microbiota of infants with recurrent wheezing and with or without atopic diseases using a systems biology approach. Methods: Bacterial communities in bronchoalveolar lavage samples from 15 atopic wheezing infants, 15 non-atopic wheezing infants, and 18 foreign body aspiration control infants were characterized using 16S rRNA gene sequencing. The bacterial composition and community-level functions inferred from between-group differences from sequence profiles were analyzed. Results: Both α- and β-diversity differed significantly between the groups. Compared to non-atopic wheezing infants, atopic wheezing infants showed a significantly higher abundance in two phyla (Deinococcota and unidentified bacteria) and one genus (Haemophilus) and a significantly lower abundance in one phylum (Actinobacteria). The random forest predictive model of 10 genera based on OTU-based features suggested that airway microbiota has diagnostic value for distinguishing atopic wheezing infants from non-atopic wheezing infants. PICRUSt2 based on KEGG hierarchy (level 3) revealed that atopic wheezing-associated differences in predicted bacterial functions included cytoskeleton proteins, glutamatergic synapses, and porphyrin and chlorophyll metabolism pathways. Conclusion: The differential candidate biomarkers identified by microbiome analysis in our work may have reference value for the diagnosis of wheezing in infants with atopy. To confirm that, airway microbiome combined with metabolomics analysis should be further investigated in the future. Copyright © 2023 Tang, Zhang, Ai, Xia, Xie, Fan, Chen, Chen, Yao and Peng.","16S rRNA; atopy; infants; microbiome; wheezing","Bacteria; Bronchi; Humans; Infant; Pilot Projects; Respiratory Sounds; RNA, Ribosomal, 16S; biological marker; chlorophyll; cytoskeleton protein; genomic DNA; porphyrin; RNA 16S; Actinobacteria; agar gel electrophoresis; airway; area under the curve; Article; atopy; Bacteroidetes; birth weight; bronchoscopy; cesarean section; child; congenital heart disease; controlled study; cystic fibrosis; cytoskeleton; demographics; diagnostic test accuracy study; DNA extraction; female; Firmicutes; foreign body aspiration; gene sequence; glutamatergic synapse; Haemophilus; human; immune deficiency; infant; KEGG; learning algorithm; leukocyte count; lung dysplasia; lung lavage; machine learning; metabolism; metabolomics; microbial diversity; microbiome; neutrophil count; phylogenetic tree; phylum; pilot study; preschool child; principal component analysis; principal coordinate analysis; Proteobacteria; quality control; random forest; receiver operating characteristic; reference value; sample size; sensitivity and specificity; sequence alignment; sequence analysis; sequence homology; Stenotrophomonas; Streptococcus; taxonomy; wheezing; abnormal respiratory sound; bacterium; bronchus","","chlorophyll, 1406-65-1, 15611-43-5; porphyrin, 24869-67-8; RNA, Ribosomal, 16S, ","","","Chengdu Municipal Health Commission of Sichuan Province of China, (2022295); Health Commission of Sichuan Province, (16PJ067); Health Commission of Sichuan Province","This work was supported by the Chengdu Municipal Health Commission of Sichuan Province of China (grant number 2022295) and by the Health Commission of Sichuan Province of China (grant number 16PJ067). Acknowledgments ","Alba C., Aparicio M., Gonzalez-Martinez F., Gonzalez-Sanchez M.I., Perez-Moreno J., Toledo Del Castillo B., Et al., Nasal and fecal microbiota and immunoprofiling of infants with and without RSV bronchiolitis, Front. Microbiol, 12, (2021); An S.Q., Berg G., Stenotrophomonas maltophilia, Trends Microbiol, 26, 7, pp. 637-638, (2018); Berdah L., Taytard J., Leyronnas S., Clement A., Boelle P.Y., Corvol H., Stenotrophomonas maltophilia: A marker of lung disease severity, Pediatr. Pulmonol, 53, 4, pp. 426-430, (2018); Budden K.F., Shukla S.D., Rehman S.F., Bowerman K.L., Keely S., Hugenholtz P., Et al., Functional effects of the microbiota in chronic respiratory disease, Lancet Respir. Med, 7, 10, pp. 907-920, (2019); Caporaso J.G., Kuczynski J., Stombaugh J., Bittinger K., Bushman F.D., Costello E.K., Et al., QIIME allows analysis of high-throughput community sequencing data, Nat. 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Zhang; Respiratory Department, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China; email: zhanglei78322@163.com","","Frontiers Media S.A.","","","","","","22352988","","","36875523","English","Front. Cell. Infect. Microbiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85149493137"
"He H.; Zhao H.; Li L.; Yang H.; Yan J.; Yuan Y.; Hu X.; Zhang Y.","He, Hangzhi (57226531067); Zhao, Hui (56413263500); Li, Lifang (57205718755); Yang, Hong (57211859895); Yan, Jingjing (57211858258); Yuan, Yiwei (59347186600); Hu, Xiangwen (59541784000); Zhang, Yanbo (56645922300)","57226531067; 56413263500; 57205718755; 57211859895; 57211858258; 59347186600; 59541784000; 56645922300","Non-experimental rapid identification of lower respiratory tract infections in patients with chronic obstructive pulmonary disease using multi-label learning","2025","Computer Methods and Programs in Biomedicine","261","","108618","","","","0","10.1016/j.cmpb.2025.108618","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216843647&doi=10.1016%2fj.cmpb.2025.108618&partnerID=40&md5=7441e59f294abac66a69ae1eabb409ef","Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Department of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China","He H., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Zhao H., Department of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Li L., Department of Respiratory and Critical Care Medicine, The Second Hospital of Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Yang H., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Yan J., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Yuan Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Hu X., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China; Zhang Y., Department of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Shanxi Medical University, Shanxi Province, Taiyuan, 030001, China","Background and Objective: Microbiological culture is a standard diagnostic test that takes a long time to identify lower respiratory tract infections (LRTI) in patients with chronic obstructive pulmonary disease (COPD). This study entailed the development of an interactive decision-support system using multi-label machine learning. It is designed to assist clinical medical staff in the rapid and simultaneous diagnosis of various infections in these patients. Methods: Clinical health record data were collected from inpatients with COPD suspected of having a LRTI. Two major categories of multi-label learning frameworks were integrated with various machine learning algorithms to create 23 predictive models to identify four categories of infection: fungal, gram-negative bacterial, gram-positive bacterial, and multidrug-resistant organism infections. The predictive power of the individual models was tested. Subsequently, the model with the highest comprehensive performance was selected and integrated with SHAP technology to construct a decision support system. Results: Three-thousand-eight-hundred-one subjects participated in this study. LP-RF recorded the highest overall performance, with a Hamming loss of 0.158 (95 %CI: 0.157–0.159) and a samples-precision of 0.894 (95 %CI: 0.891–0.896). The developed diagnostic decision support system generates predicted probability output for each infection category in a specific patient and displays the interpreted output results. Conclusion: The developed multi-label decision support system enables effective prediction of four categories of infections in patients with a history of COPD, and has the potential to curb the overuse of antimicrobial drugs. This system is highly explainable and interactive, providing real-time support in the simultaneous diagnosis of multiple infection categories. © 2025 The Author(s)","Chronic obstructive pulmonary disease; Clinical decision support system; Lower respiratory tract infection; Multi-label machine learning","Aged; Algorithms; Decision Support Systems, Clinical; Female; Humans; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Respiratory Tract Infections; Contrastive Learning; Pulmonary diseases; Chronic obstructive pulmonary disease; Clinical decision support systems; Decision supports; Low respiratory tract infection; Machine-learning; Multi-label learning; Multi-label machine learning; Multi-labels; Respiratory tract infections; Support systems; age; aged; Article; body mass; chronic obstructive lung disease; clinical decision support system; controlled study; data completeness; data processing; data quality; diagnostic accuracy; diagnostic test accuracy study; differential diagnosis; female; Gram negative infection; Gram positive infection; human; learning algorithm; lower respiratory tract infection; lung mycosis; machine learning; major clinical study; male; medical history; medical staff; multidrug resistant infection; multilabel classification; prediction; predictive model; probability; algorithm; clinical decision support system; diagnosis; microbiology; middle aged; respiratory tract infection; Lung cancer","","","","","Elsevier Language Editing; Second Hospital of Tianjin Medical University, TMUSH, (2022YX123, ASLEPLUS1033883, ChiCTR2200064900); Second Hospital of Tianjin Medical University, TMUSH; Shanxi Province Science and Technology Cooperation and Exchange Special Project, (202204041101031); National Natural Science Foundation of China, NSFC, (82173631); National Natural Science Foundation of China, NSFC","Funding text 1: This work was supported by the National Natural Science Foundation of China [grant number 82173631], and Shanxi Province Science and Technology Cooperation and Exchange Special Project (Regional Cooperation Project) [grant number 202204041101031].Approval for this research was obtained from the Ethics Committee of the Second Hospital of Shanxi Medical University (Approval Number: 2022YX123), and it has been registered at the Chinese Clinical Trial Registry (Registration Number: ChiCTR2200064900). Informed consent was obtained from all participants.We appreciate the language editing service (ASLEPLUS1033883) provided by Elsevier Language Editing.; Funding text 2: This work was supported by the National Natural Science Foundation of China [grant number 82173631 ], and Shanxi Province Science and Technology Cooperation and Exchange Special Project (Regional Cooperation Project) [grant number 202204041101031 ]. 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Res., 2017, (2017); Goedhart M., Slot E., Pascutti M.F., Geerman S., Rademakers T., Nota B., Huveneers S., Buul J.D.V., MacNamara K.C., Voermans C., Nolte M.A., Bone marrow harbors a unique population of dendritic cells with the potential to boost neutrophil formation upon exposure to fungal antigen, Cells, 11, (2021); Cao Y., Chen X., Shu L., Shi L., Wu M., Wang X., Deng K., Wei J., Yan J., Feng G., Analysis of the correlation between BMI and respiratory tract microbiota in acute exacerbation of COPD, Front. Cell Infect. Microbiol., 13, (2023); Daubin C., Fournel F., Thiolliere F., Daviaud F., Ramakers M., Polito A., Flocard B., Valette X., Du Cheyron D., Terzi N., Fartoukh M., Allouche S., Parienti J.J., P. from the, B.P.s. group, Ability of procalcitonin to distinguish between bacterial and nonbacterial infection in severe acute exacerbation of chronic obstructive pulmonary syndrome in the ICU, Ann. Intens. Care, 11, (2021)","Y. Zhang; Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, 030001, China; email: sxmuzyb@126.com","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","39913996","English","Comput. Methods Programs Biomed.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85216843647"
"Liu G.; Hu J.; Yang J.; Song J.","Liu, Guanglei (58908705100); Hu, Jiani (58908705200); Yang, Jianzhe (58908395100); Song, Jie (58908549000)","58908705100; 58908705200; 58908395100; 58908549000","Predicting early-onset COPD risk in adults aged 20–50 using electronic health records and machine learning","2024","PeerJ","12","","e16950","","","","1","10.7717/peerj.16950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186088961&doi=10.7717%2fpeerj.16950&partnerID=40&md5=184f89c864d124ee77be7f858dcb827a","School of Information Science and Engineering, Yunnan University, Yunnan, Kunming, China; Ailurus Biotechnology Ltd., Guangdong, Shenzhen, China","Liu G., School of Information Science and Engineering, Yunnan University, Yunnan, Kunming, China; Hu J., Ailurus Biotechnology Ltd., Guangdong, Shenzhen, China; Yang J., Ailurus Biotechnology Ltd., Guangdong, Shenzhen, China; Song J., Ailurus Biotechnology Ltd., Guangdong, Shenzhen, China","Chronic obstructive pulmonary disease (COPD) is a major public health concern, affecting estimated 164 million people worldwide. Early detection and intervention strategies are essential to reduce the burden of COPD, but current screening approaches are limited in their ability to accurately predict risk. Machine learning (ML) models offer promise for improved accuracy of COPD risk prediction by combining genetic and electronic medical record data. In this study, we developed and evaluated eight ML models for primary screening of COPD utilizing routine screening data, polygenic risk scores (PRS), additional clinical data, or a combination of all three. To assess our models, we conducted a retrospective analysis of approximately 329,396 patients in the UK Biobank database. Incorporating personal information and blood biochemical test results significantly improved the model’s accuracy for predicting COPD risk, achieving a best performance of 0.8505 AUC, a specificity of 0.8539 and a sensitivity of 0.7584. These results indicate that ML models can be effectively utilized for accurate prediction of COPD risk in individuals aged 20 to 50 years, providing a valuable tool for early detection and intervention. Copyright 2024 Liu et al.","Chronic obstructive pulmonary disease; COPD; Early-onset; Electronic health records; Genetic data; Machine learning; Polygenic risk scores; Risk prediction; UK Biobank","adult; area under the curve; Article; artificial neural network; blood biochemistry; chronic obstructive lung disease; diagnostic accuracy; diagnostic test accuracy study; electronic health record; genetic risk score; genome-wide association study; genotype; human; information processing; machine learning; prediction; predictive value; quality control; receiver operating characteristic; retrospective study; sensitivity and specificity; single nucleotide polymorphism; UK Biobank; whole exome sequencing","","","","","Ailurus Biotechnology Co., Ltd","This work has received funding and technical support from the Ailurus Biotechnology Co., Ltd. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Akobeng AK., Understanding diagnostic tests 3: receiver operating characteristic curves, Acta Paediatrica, 96, 5, pp. 644-647, (2007); Alpaydin E., Introduction to machine learning, (2014); Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J., The UK Biobank resource with deep phenotyping and genomic data, Nature, 562, 7726, pp. 203-209, (2018); Chatterjee S., fastAdaboost: a fast implementation of Adaboost, (2016); Chen T, He T, Benesty M., XGBoost: extreme gradient boosting, (2016); Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An T-H, Lai D, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F., Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models, Nature Genetics, 55, 5, pp. 787-795, (2023); 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Song; Ailurus Biotechnology Ltd., Shenzhen, Guangdong, China; email: avec@ailurus.bio","","PeerJ Inc.","","","","","","21678359","","","","English","PeerJ","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85186088961"
"Jayamala R.; Shanmugapriya N.; Lalitha K.; Vijayarajan P.","Jayamala, R. (57201978780); Shanmugapriya, N. (57215218270); Lalitha, K. (57221819429); Vijayarajan, P. (57063295400)","57201978780; 57215218270; 57221819429; 57063295400","A deep learning model and optimization algorithm to forecasting environment monitoring of the air pollution","2023","Global Nest Journal","25","10","","47","55","8","1","10.30955/gnj.004759","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181466181&doi=10.30955%2fgnj.004759&partnerID=40&md5=651d040f8d5bed87d5bf950442fbf149","Department of Computer Science and Engineering, University College of Engineering, Anna University, BIT Campus, Trichy, India; Department of Computer Science and Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India; Department of Information Technology, Panimalar Engineering College, Chennai, India; Department of Electrical and Electronics Engineering, University College of Engineering, BIT Campus, Tiruchirapalli, India","Jayamala R., Department of Computer Science and Engineering, University College of Engineering, Anna University, BIT Campus, Trichy, India; Shanmugapriya N., Department of Computer Science and Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, Trichy, India; Lalitha K., Department of Information Technology, Panimalar Engineering College, Chennai, India; Vijayarajan P., Department of Electrical and Electronics Engineering, University College of Engineering, BIT Campus, Tiruchirapalli, India","Air pollution monitoring is becoming increasingly important, with an emphasis on the effects on human health. Because nitrogen dioxide (NO2) and sulphur dioxide (SO2) are the principal pollutants, many models for forecasting their potential harm have been created. Nonetheless, making precise predictions is nearly impossible. The prediction of air pollution enables researchers to understand how pollution affects human health. Deteriorating air quality can lead to respiratory diseases such as lung cancer and asthma. The effect of pollution on environmental degradation can also be predicted and reductions can be detected in the ozone layer. This study also focuses on and promotes the development of smart city environments by obtaining influential pollutants that affect the air, thereby reducing the source of specific pollutants An Artificial Neural Network (ANN) model is used as a forecast the pollution and the starling murmuration optimization (SMO) procedure is used to optimise the Artificial Neural Network strictures to achieve a lower forecasting error. Furthermore, in this research work, we used real time dataset as we have used Winsen ZPHS01B sensor module to collect the data, which is stored in cloud platform. After the composed data is used to train and test, after this process we will evaluate the results. To assess the performance of the suggested model. Furthermore, the perfect has been tested using two alternative kinds of input parameters: type as, which contains various lagged values of variables (NO2 and SO2), and type as, which only includes lagged values of the yield variables. The collected findings suggest that the projected model is more precise than existing joint forecasting benchmark models when different network input variables are considered. © 2023 Global NEST Printed in Greece. All rights reserved.","air pollution forecasting; Artificial neural network; deep learning model; sulfur dioxide","","","","","","","","Arumugam T., Kinattinkara S., Kannithottathil S., Velusamy S., Krishna M., Shanmugamoorthy M., Sivakumar V., Boobalakrishnan K.V., Comparative assessment of groundwater quality indices of Kannur District, Kerala, India using multivariate statistical approaches and GIS, Environmental Monitoring and Assessment, 195, 1, (2023); Arumugam T., Kinattinkara S., Velusamy S., Shanmugamoorthy M., Murugan S., GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala, Urban Climate, 49, (2023); Arumugam T., Ramachandran S., Kinattinkara S., Velusamy S., Shanmugamoorthy M., Shanmugavadivel S., Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region, Urban Climate, 45, (2022); Bai L., Wang J., Ma X., Lu H., Air pollution forecasts: An overview, International Journal of Environmental Research and Public Health, 15, 4, (2018); Cabaneros S.M., Calautit J.K, Hughes B.R., A review of artificial neural network models for ambient air pollution prediction, Environmental Modelling & Software, 1, 119, pp. 285-304, (2019); Chang Y.S., Chiao H.T., Abimannan S., Huang Y.P., Tsai Y.T., Lin K.M., An LSTM-based aggregated model for air pollution forecasting, Atmospheric Pollution Research, 11, 8, pp. 1451-1463, (2020); Feng X., Li Q., Zhu Y., Hou J., Jin L., Wang J., Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation, Atmospheric Environment, 107, pp. 118-128, (2015); Haviluddin F.A., Azhari M., Ahmar A.S., Artificial neural network optimized approach for improving spatial cluster quality of land value zone, International Journal of Engineering and Technologies, 7, 2, pp. 80-83, (2018); Kaimian H., Qi L., Chunlin W., Yanlin Q., Yuqin M., Gong C., Xianfeng Z., Sonali S., Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations, Aerosol Air Quality Researchy, 19, 6, pp. 1400-1410, (2019); 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Tao Q., Liu F., Li Y., Sidorov D., Air pollution forecasting using a deep learning model based on 1D convnets and bidirectional GRU, IEEE access, 7, pp. 76690-76698, (2019); Thiruppathi M., Vinoth Kumar K., Seagull Optimization-based Feature Selection with Optimal Extreme Learning Machine for Intrusion Detection in Fog Assisted WSN, Technical Gazette, 30, 5, pp. 1547-1553, (2023); Tiwari R., Upadhyay S., Singhal P., Garg U., Bisht S., Air pollution level prediction system, International Journal of Innovative Technology and Exploring Engineering, (2019); Yan R., Liao J., Yang J., Sun W., Nong M., Li F., Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering, Expert Systems with Applications, 1, 169, (2021); Zhang W., Wu Y., Calautit J.K., A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment, Renewable and Sustainable Energy Reviews, 1, 167, (2022); Zhao Z., Qin J., He Z., Li H., Yang Y., Zhang R., Combining forward with recurrent neural networks for hourly air quality prediction in Northwest of China, Environmental Science and Pollution Research, 27, 23, pp. 28931-28948, (2020); Zhou Y., Chang F.J., Chang L.C., Kao I.F., Wang Y.S., Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts, Journal of cleaner production, 1, 209, pp. 134-145, (2019); Zhou Y., De S., Ewa G., Perera C., Moessner K., Data-driven air quality characterisation for urban environments: A case study, (2021)","R. Jayamala; Department of Computer Science and Engineering, University College of Engineering, Anna University, Trichy, BIT Campus, India; email: jayamalar546@gmail.com","","Global NEST","","","","","","17907632","","","","English","Global Nest J.","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85181466181"
"Yu H.; Simpao A.F.; Ruiz V.M.; Nelson O.; Muhly W.T.; Sutherland T.N.; Galvez J.A.; Pushkar M.B.; Stricker P.A.; Tsui F.","Yu, Han (58773618400); Simpao, Allan F. (37038399900); Ruiz, Victor M. (57193823733); Nelson, Olivia (57193892483); Muhly, Wallis T. (55658268100); Sutherland, Tori N. (25824495000); Galvez, Julia A. (58529567900); Pushkar, Mykhailo B. (58401202000); Stricker, Paul A. (24367466900); Tsui, Fuchiang (7006389381)","58773618400; 37038399900; 57193823733; 57193892483; 55658268100; 25824495000; 58529567900; 58401202000; 24367466900; 7006389381","Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital","2023","JAMIA Open","6","4","ooad106","","","","1","10.1093/jamiaopen/ooad106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180301969&doi=10.1093%2fjamiaopen%2fooad106&partnerID=40&md5=ad10e4160f738aaf6d56d57a0108cf3d","Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States; Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, 02215, MA, United States; Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States; Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States; Department of Anesthesiology & Critical Care, Children’s Hospital & Medical Center, Omaha, 68114, NE, United States; Department of Anesthesiology, Intensive Care and Pediatric Anesthesiology, Kharkiv National Medical University, Kharkiv, 61022, Ukraine","Yu H., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, 02215, MA, United States; Simpao A.F., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States, Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States; Ruiz V.M., Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States; Nelson O., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States; Muhly W.T., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States; Sutherland T.N., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States; Galvez J.A., Department of Anesthesiology & Critical Care, Children’s Hospital & Medical Center, Omaha, 68114, NE, United States; Pushkar M.B., Department of Anesthesiology, Intensive Care and Pediatric Anesthesiology, Kharkiv National Medical University, Kharkiv, 61022, Ukraine; Stricker P.A., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States; Tsui F., Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, the Perelman School of Medicine, the University of Pennsylvania, Philadelphia, 19104, PA, United States, Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, 19104, PA, United States","Objectives: Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium. Materials and Methods: We performed a single-center retrospective cohort study using electronic health record data from February 2015 to December 2019. We built and evaluated 4 commonly used ML models for predicting emergence delirium: least absolute shrinkage and selection operator, ridge regression, random forest, and extreme gradient boosting. The primary outcome was the occurrence of emergence delirium, defined as a Watcha score of 3 or 4 recorded at any time during recovery. Results: The dataset included 54 776 encounters across 43 830 patients. The 4 ML models performed similarly with performance assessed by the area under the receiver operating characteristic curves ranging from 0.74 to 0.75. Notable variables associated with increased risk included adenoidectomy with or without tonsillectomy, decreasing age, midazolam premedication, and ondansetron administration, while intravenous induction and ketorolac were associated with reduced risk of emergence delirium. Conclusions: Four different ML models demonstrated similar performance in predicting postoperative emergence delirium using a large pediatric dataset. The prediction performance of the models draws attention to our incomplete understanding of this phenomenon based on the studied variables. The results from our modeling could serve as a first step in designing a predictive clinical decision support system, but further optimization and validation are needed. Clinical trial number and registry URL: Not applicable. # The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.","","dexmedetomidine; diazepam; ketamine; ketorolac; midazolam; ondansetron; adenoidectomy; ambulatory surgery; American Society of Anaesthesiologists score; anesthesia induction; Article; asthma; attention deficit hyperactivity disorder; autism; child; childhood; clinical decision support system; cohort analysis; controlled study; developmental delay; electronic health record; emergence agitation; emergency ward; epilepsy; extreme gradient boosting; female; gestational age; hospital patient; human; International Classification of Diseases; intraoperative period; least absolute shrinkage and selection operator; machine learning; major clinical study; male; medical history; obstructive sleep apnea; outcome assessment; patient history of surgery; pediatric hospital; predictive model; premedication; random forest; receiver operating characteristic; regional anesthesia; respiration control; retrospective study; ridge regression; secondary analysis; surgical technique; tonsillectomy","","dexmedetomidine, 113775-47-6, 145108-58-3; diazepam, 439-14-5, 11100-37-1, 53320-84-6; ketamine, 1867-66-9, 6740-88-1, 81771-21-3; ketorolac, 74103-06-3; midazolam, 59467-70-8, 59467-96-8; ondansetron, 103639-04-9, 116002-70-1, 99614-01-4","","","Children's Hospital of Philadelphia, CHOP","This research was supported by the Children’s Hospital of Philadelphia. ","Przybylo HJ, Martini DR, Mazurek AJ, Bracey E, Johnsen L, Cote CJ., Assessing behaviour in children emerging from anaesthesia: can we apply psychiatric diagnostic techniques?, Paediatr Anaesth, 13, 7, pp. 609-616, (2003); Eckenhoff JE, Kneale DH, Dripps RD., The incidence and etiology of postanesthetic excitment. A clinical survey, Anesthesiology, 22, pp. 667-673, (1961); Voepel-Lewis T, Malviya S, Tait AR., A prospective cohort study of emergence agitation in the pediatric postanesthesia care unit, Anesth Analg, 96, 6, pp. 1625-1630, (2003); Moore JK, Moore EW, Elliott RA, St Leger AS, Payne K, Kerr J., Propofol and halothane versus sevoflurane in paediatric day-case surgery: induction and recovery characteristics, Br J Anaesth, 90, 4, pp. 461-466, (2003); Aono J, Ueda W, Mamiya K, Takimoto E, Manabe M., Greater incidence of delirium during recovery from sevoflurane anesthesia in preschool boys, Anesthesiology, 87, 6, pp. 1298-1300, (1997); Cole JW, Murray DJ, McAllister JD, Hirshberg GE., Emergence behaviour in children: defining the incidence of excitement and agitation following anaesthesia, Paediatr Anaesth, 12, 5, pp. 442-447, (2002); Davis PJ, Greenberg JA, Gendelman M, Fertal K., Recovery characteristics of sevoflurane and halothane in preschool-aged children undergoing bilateral myringotomy and pressure equalization tube insertion, Anesth Analg, 88, 1, pp. 34-38, (1999); Murray DJ, Cole JW, Shrock CD, Snider RJ, Martini JA., Sevoflurane versus halothane: effect of oxycodone premedication on emergence behaviour in children, Paediatr Anaesth, 12, 4, pp. 308-312, (2002); Sikich N, Lerman J., Development and psychometric evaluation of the pediatric anesthesia emergence delirium scale, Anesthesiology, 100, 5, pp. 1138-1145, (2004); Jerome EH., Recovery of the pediatric patient from anesthesia, Pediatric Anesthesia, (1989); Malarbi S, Stargatt R, Howard K, Davidson A., Characterizing the behavior of children emerging with delirium from general anesthesia, Paediatr Anaesth, 21, 9, pp. 942-950, (2011); Olympio MA., Postanesthetic delirium: historical perspectives, J Clin Anesth, 3, 1, pp. 60-63, (1991); Kain ZN, Caldwell-Andrews AA, Maranets I, Et al., Preoperative anxiety and emergence delirium and postoperative maladaptive behaviors, Anesth Analg, 99, 6, pp. 1648-1654, (2004); Kim J, Byun SH, Kim JW, Et al., Behavioral changes after hospital discharge in preschool children experiencing emergence delirium after general anesthesia: a prospective observational study, Paediatr Anaesth, 31, 10, pp. 1056-1064, (2021); Hino M, Mihara T, Miyazaki S, Et al., Development and validation of a risk scale for emergence agitation after general anesthesia in children: a prospective observational study, Anesth Analg, 125, 2, pp. 550-555, (2017); Petre MA, Saha B, Kasuya S, Et al., Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review, BMJ Open, 11, 1, (2021); Georgiyants M A, Pushkar M B, Vysotska E V, Porvan A P., Optimization of current postoperative period after childrens’ adenotomy, Lik Sprava, 1-2, pp. 115-119, (2017); Beam AL, Kohane IS., Big data and machine learning in health care, JAMA, 319, 13, pp. 1317-1318, (2018); Bishara A, Chiu C, Whitlock EL, Et al., Postoperative delirium prediction using machine learning models and preoperative electronic health record data, BMC Anesthesiol, 22, 1, (2022); Neto PCS, Rodrigues AL, Stahlschmidt A, Helal L, Stefani LC., Developing and validating a machine learning ensemble model to predict postoperative delirium in a cohort of high-risk surgical patients: a secondary cohort analysis, Eur J Anaesthesiol, 40, 5, pp. 356-364, (2023); Song YX, Yang XD, Luo YG, Et al., Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: a retrospective study, CNS Neurosci Ther, 29, 1, pp. 158-167, (2023); Snell KIE, Levis B, Damen JAA, Et al., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMJ, 381, (2015); Watcha MF, Ramirez-Ruiz M, White PF, Jones MB, Lagueruela RG, Terkonda RP., Perioperative effects of oral ketorolac and acetaminophen in children undergoing bilateral myringotomy, Can J Anaesth, 39, 7, pp. 649-654, (1992); Bajwa SA, Costi D, Cyna AM., A comparison of emergence delirium scales following general anesthesia in children, Paediatr Anaesth, 20, 8, pp. 704-711, (2010); Uezono S, Goto T, Terui K, Et al., Emergence agitation after sevoflurane versus propofol in pediatric patients, Anesth Analg, 91, 3, pp. 563-566, (2000); Secondary Hmisc package reference; Ruiz VM, Goldsmith MP, Shi L, Et al., Early prediction of clinical deterioration using data-driven machine learning modeling of electronic health records, J Thorac Cardiovasc Surg, 164, 1, pp. 211-222, (2021); Shi L, Muthu N, Shaeffer GP, Sun Y, Ruiz Herrera VM, Tsui FR., Using data-driven machine learning to predict unplanned ICU transfers with critical deterioration from electronic health records, Stud Health Technol Inform, 290, pp. 660-664, (2022); Boyd K, Eng KH, Page CD., Area under the precision-recall curve: point estimates and confidence intervals, Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), 8190, (2013); DeLong ER, DeLong DM, Clarke-Pearson DL., Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 3, pp. 837-845, (1988); Zhang C, Li J, Zhao D, Wang Y., Prophylactic midazolam and clonidine for emergence from agitation in children after emergence from sevoflurane anesthesia: a meta-analysis, Clin Ther, 35, 10, pp. 1622-1631, (2013); Lapin SL, Auden SM, Goldsmith LJ, Reynolds AM., Effects of sevoflurane anaesthesia on recovery in children: a comparison with halothane, Paediatr Anaesth, 9, 4, pp. 299-304, (1999); Riker RR, Shehabi Y, Bokesch PM, Et al., Dexmedetomidine vs midazolam for sedation of critically ill patients: a randomized trial, JAMA, 301, 5, pp. 489-499, (2009); Zaal IJ, Devlin JW, Hazelbag M, Et al., Benzodiazepine-associated delirium in critically ill adults, Intensive Care Med, 41, 12, pp. 2130-2137, (2015); Traube C, Silver G, Gerber LM, Et al., Delirium and mortality in critically ill children: epidemiology and outcomes of pediatric delirium, Crit Care Med, 45, 5, pp. 891-898, (2017); Dervan LA, Di Gennaro JL, Farris RWD, Watson RS., Delirium in a tertiary PICU: risk factors and outcomes, Pediatr Crit Care Med, 21, 1, pp. 21-32, (2020); Haque N, Naqvi RM, Dasgupta M., Efficacy of ondansetron in the prevention or treatment of post-operative delirium—a systematic review, Can Geriatr J, 22, 1, pp. 1-6, (2019)","F. Tsui; Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, 2716 South St, 19146, United States; email: tsuif@chop.edu","","Oxford University Press","","","","","","25742531","","","","English","JAMIA Open","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85180301969"
"Park S.; Yi Y.; Han S.-S.; Kim T.-H.; Kim S.J.; Yoon Y.S.; Kim S.; Lee H.J.; Heo Y.","Park, SangJee (57963666700); Yi, Yehyeon (59655220900); Han, Seon-Sook (7405944640); Kim, Tae-Hoon (58805200700); Kim, So Jeong (59655745000); Yoon, Young Soon (8603697100); Kim, Suhyun (57221297787); Lee, Hyo Jin (57722530400); Heo, Yeonjeong (57215904827)","57963666700; 59655220900; 7405944640; 58805200700; 59655745000; 8603697100; 57221297787; 57722530400; 57215904827","Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry","2025","Diagnostics","15","4","449","","","","0","10.3390/diagnostics15040449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218870809&doi=10.3390%2fdiagnostics15040449&partnerID=40&md5=77d5b2b735ac188a4dc07cb11af4b8ca","Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, 24289, South Korea; Department of Internal Medicine, Seoul Medical Center, Seoul, 02053, South Korea; Department of Internal Medicine, Kangwon National University, Chuncheon, 24341, South Korea; Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, 18450, South Korea; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang, 10326, South Korea; Internal Medicine, Seoul National University Seoul Metropolitan Government Boramae Medical Center, Seoul, 07061, South Korea","Park S., Biomedical Research Institute, Kangwon National University Hospital, Chuncheon, 24289, South Korea; Yi Y., Department of Internal Medicine, Seoul Medical Center, Seoul, 02053, South Korea; Han S.-S., Department of Internal Medicine, Kangwon National University, Chuncheon, 24341, South Korea; Kim T.-H., Department of Internal Medicine, Kangwon National University, Chuncheon, 24341, South Korea; Kim S.J., Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, 18450, South Korea; Yoon Y.S., Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang, 10326, South Korea; Kim S., Department of Internal Medicine, Seoul Medical Center, Seoul, 02053, South Korea; Lee H.J., Internal Medicine, Seoul National University Seoul Metropolitan Government Boramae Medical Center, Seoul, 07061, South Korea; Heo Y., Department of Internal Medicine, Kangwon National University, Chuncheon, 24341, South Korea","Background/Objectives: The methacholine bronchial provocation test (MBPT) is a diagnostic test frequently used to evaluate airway hyper-reactivity. MBPT is essential for diagnosing asthma; however, it can be time-consuming and resource-intensive. This study aimed to develop an artificial intelligence (AI) model to predict the MBPT results using forced expiratory volume in one second (FEV1) and bronchodilator test measurements from spirometry. Methods: a dataset of spirometry measurements, including Pre- and Post-bronchodilator FEV1, was used to train and validate the model. Results: Among the evaluated models, the multilayer perceptron (MLP) achieved the highest area under the curve (AUC) of 0.701 (95% CI: 0.676–0.725), accuracy of 0.758, and an F1-score of 0.853. Logistic regression (LR) and a support vector machine (SVM) demonstrated comparable performance with AUC values of 0.688, while random forest (RF) and extreme gradient boost (XGBoost) achieved slightly lower AUC values of 0.669 and 0.672, respectively. Feature importance analysis of the MLP model identified key contributing features, including Pre-FEF25–75 (%), Pre-FVC (L), Post FEV1/FVC, Change-FEV1 (L), and Change-FEF25–75 (%), providing insight into the interpretability and clinical applicability of the model. Conclusions: These results highlight the potential of the model to utilize readily available spirometry data, particularly FEV1 and bronchodilator responses, to accurately predict MBPT results. Our findings suggest that AI-based prediction can improve asthma diagnostic workflows by minimizing the reliance on MBPT and enabling faster and more accessible assessments. © 2025 by the authors.","asthma; machine learning; methacholine bronchial provocation test","bronchodilating agent; methacholine; adult; Article; artificial intelligence; asthma; diagnostic accuracy; diagnostic test accuracy study; forced expiratory volume; forced vital capacity; human; inhalation test; machine learning; major clinical study; multilayer perceptron; predictive model; predictive value; random forest; retrospective study; spirometry; support vector machine","","methacholine, 55-92-5","","","Korea Health Industry Development Institute, KHIDI; Ministry of Health and Welfare, MOHW, (RS-2021-KH114109); Ministry of Health and Welfare, MOHW","This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: RS-2021-KH114109).","Corrao W.M., Braman S.S., Irwin R.S., Chronic cough as the sole presenting manifestation of bronchial asthma, N. 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Res, 14, pp. 170-176, (2009); Davis B.E., Blais C.M., Cockcroft D.W., Methacholine challenge testing: Comparative pharmacology, J. Asthma Allergy, 11, pp. 89-99, (2018); Crapo R.O., Casaburi R., Coates A.L., Enright P.L., Hankinson J.L., Irvin C.G., MacIntyre N.R., McKay R.T., Wanger J.S., Anderson S.D., Et al., Guidelines for methacholine and exercise challenge Testing-1999. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, July 1999, Am. J. Respir. Crit. Care Med, 161, pp. 309-329, (2000); Cockcroft D.W., Methacholine Challenge Testing in the Diagnosis of Asthma, Chest, 158, pp. 433-434, (2020); Louis R., Satia I., Ojanguren I., Schleich F., Bonini M., Tonia T., Rigau D., Ten Brinke A., Buhl R., Loukides S., Et al., European Respiratory Society guidelines for the diagnosis of asthma in adults, Eur. Respir. J, 60, (2022); Lung function testing: Selection of reference values and interpretative strategies, Am. Rev. Respir. Dis. Am. 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Med, 52, pp. 725-737, (1972); Siroux V., Boudier A., Dolgopoloff M., Chanoine S., Bousquet J., Gormand F., Just J., Le Moual N., Nadif R., Pison C., Et al., Forced midexpiratory flow between 25% and 75% of forced vital capacity is associated with long-term persis-tence of asthma and poor asthma outcomes, J. Allergy Clin. Immunol, 137, pp. 1709-1716.e6, (2016); Chiang C.H., Hsu K., Residual abnormalities of pulmonary function in asymptomatic Young adult asthmatics with childhood-onset asthma, J. Asthma, 34, pp. 15-21, (1997); Simon M.R., Chinchilli V.M., Phillips B.R., Sorkness C.A., Lemanske R.F., Szefler S.J., Taussig L., Bacharier L.B., Morgan W., Forced expiratory flow between 25% and 75% of vital capacity and FEV1/forced vital capacity ratio in rela-tion to clinical and physiological parameters in asthmatic children with normal FEV1 values, J. Allergy Clin. Immunol, 126, pp. 527-534.e8, (2010); Ciprandi G., Schiavetti I., Role of FEF25-75 in characterizing outpatients with asthma in clinical practice, Allergol. Sel, 8, pp. 12-17, (2024); Quanjer P.H., Weiner D.J., Pretto J.J., Brazzale D.J., Boros P.W., Measurement of FEF25–75% and FEF75% does not contribute to clinical decision making, Eur. Respir. J, 43, pp. 1051-1058, (2013); Seo H.-J., Lee P.-H., Kim B.-G., Lee S.-H., Park J.-S., Lee J., Park S.-W., Kim D.-J., Park C.-S., Jang A.-S., Methacholine Bronchial Provocation Test in Patients with Asthma: Serial Measurements and Clinical Significance, Korean J. Intern. Med, 33, pp. 807-814, (2018); Kang N., Lee K., Byun S., Lee J.-Y., Choi D.-C., Lee B.-J., Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests, Allergy Asthma Immunol. Res, 16, pp. 42-54, (2023); Kim H.A., Kwon J.E., Ahn J.Y., Choe J.Y., Kim D.S., Park S.H., Hyun M.C., Choi B.S., Analysis of PC20-FEF25%-75% and ΔFVC in the Methacholine Bronchial Provocation Test, Allergy Asthma Respir. Dis, 9, pp. 141-147, (2021); Feng Y., Wang Y., Zeng C., Mao H., Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease, Int. J. Med. Sci, 18, pp. 2871-2889, (2021); Tomita K., Nagao R., Touge H., Ikeuchi T., Sano H., Yamasaki A., Tohda Y., Deep Learning Facilitates the Diagnosis of Adult Asthma, Allergol. Int, 68, pp. 456-461, (2019); Browne M.W., Cross-Validation Methods, J. Math. Psychol, 44, pp. 108-132, (2000); Breiman L., Random forests, Mach. Learn, 45, pp. 5-32, (2001); Feng J., Xu H., Mannor S., Yan S., Robust logistic regression and classification, Advances in Neural Information Processing Systems, 27, (2014); Chen T., Guestrin C., XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Minding, pp. 785-794, (2016); Cortes C., Vapnik V., Support-vector networks, Mach. Learn, 20, pp. 273-297, (1995); Murtagh F., Multilayer Perceptrons for Classification and Regression, Neurocomputing, 2, pp. 183-197, (1991); He H., Garcia E.A., Learning from Imbalanced Data, IEEE Trans. Knowl. Data Eng, 21, pp. 1263-1284, (2009); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Et al., Scikit-Learn: Machine Learning in Python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011); McCracken J.L., Veeranki S.P., Ameredes B.T., Calhoun W.J., Diagnosis and Management of Asthma in Adults: A Review, JAMA, 318, pp. 279-290, (2017); Nair P., Martin J.G., Cockcroft D.C., Dolovich M., Lemiere C., Boulet L.-P., O'Byrne P.M., Airway Hyperresponsiveness in Asthma: Measurement and Clinical Relevance, J. Allergy Clin. Immunol. Pract, 5, pp. 649-659.e2, (2017); Lim K.H., Kim M.H., Yang M.S., Song W.J., Jung J.W., Lee J., Suh D., Shin Y., Kwon J.W., Kim S.H., Et al., The KAAACI Standardization Committee Report on the procedure and application of the bronchial provocation tests, Allergy Asthma Respir. Dis, 6, pp. 14-25, (2018); Birnbaum S., Barreiro T.J., Methacholine Challenge Testing: Identifying Its Diagnostic Role, Testing, Coding, and Reimbursement, Chest, 131, pp. 1932-1935, (2007); Jorres R.A., Nowak D., Kirsten D., Gronke L., Magnussen H., A Short Protocol for Methacholine Provocation Testing Adapted to the Rosenthal-Chai Dosimeter Technique, Chest, 111, pp. 866-869, (1997); Ahmed S.F., Alam M., Hassan M., Rozbu M.R., Ishtiak T., Rafa N., Mofijur M., Shawkat Ali A.B.M., Gandomi A.H., Deep Learning Modelling Techniques: Current Progress, Applications, Advantages, and Challenges, Artif. Intell. Rev, 56, pp. 13521-13617, (2023); Kaplan A., Cao H., FitzGerald J.M., Iannotti N., Yang E., Kocks J.W.H., Kostikas K., Price D., Reddel H.K., Tsiligianni I., Et al., Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis, J. Allergy Clin. Immunol. Pract, 9, pp. 2255-2261, (2021)","Y. Heo; Department of Internal Medicine, Kangwon National University, Chuncheon, 24341, South Korea; email: yonjong1954@naver.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218870809"
"Wang Y.; Wan S.; Li Y.; Li Z.; Tang W.; Yu J.","Wang, Yangfeng (57700295300); Wan, Shengpeng (12345385900); Li, Yujie (59518623200); Li, Zexin (59518962800); Tang, Wenjun (59519455600); Yu, Junsong (57193764466)","57700295300; 12345385900; 59518623200; 59518962800; 59519455600; 57193764466","Respiratory system disease recognition based on diaphragm fiber-optic F-P sensor","2025","Sensors and Actuators A: Physical","383","","116237","","","","0","10.1016/j.sna.2025.116237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215399325&doi=10.1016%2fj.sna.2025.116237&partnerID=40&md5=cef35a3b43afec9832579c5bead1c110","Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China; Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, Nanchang Hangkong University, Nanchang, 330063, China; ZHONGTIAN POWER CABLE Co. Ltd., Nantong, 226000, China","Wang Y., Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China; Wan S., Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China, Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, Nanchang Hangkong University, Nanchang, 330063, China; Li Y., Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China; Li Z., Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China, Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, Nanchang Hangkong University, Nanchang, 330063, China; Tang W., ZHONGTIAN POWER CABLE Co. Ltd., Nantong, 226000, China; Yu J., Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China, Key Laboratory of Nondestructive Testing (Nanchang Hangkong University), Ministry of Education, Nanchang Hangkong University, Nanchang, 330063, China","In this paper, the respiratory system disease recognition technology based on diaphragm fiber-optic F-P(EFPI) sensor and CNN-BiLSTM network was studied, a beneficial attempt has been made to apply the membrane type fiber optic F-P cavity sensor in the recognition of respiratory diseases. The diaphragm fiber-optic F-P acoustic sensor was fabricated and used to collect respiratory signals for the test set. The training sets were derived from the ICBHI Challenge database, which consists of breath signals from 128 subjects. These subjects included healthy individuals and patients with respiratory diseases such as Asthma, Pneumonia, Bronchiectasis, COPD, URTI, etc. A CNN-BiLSTM model was established by combining convolutional neural network and bidirectional long and short term memory network, the training sets after using data augmentation methods are imported into this model for training, and the test sets were imported into the trained model for testing. Four types of sample sets, Bronchiectasis, COPD, URTI, and Health, were selected for recognition experiments, and the results show that the accuracy of F-P acoustic sensor based on CNN-BiLSTM model is 92.1 % in respiratory disease recognition test. Compared with traditional CNN model and LSTM model, the test accuracy of CNN-BiLSTM model is improved by 9.7 % and 11.9 %, respectively. In real life, we collecting 50 COPD respiratory records and 50 healthy respiratory records by the EFPI sensor, the prediction accuracy in the CNN-BiLSTM model is 96 % and 94 %, respectively. © 2025 Elsevier B.V.","Deep Learning; Diaphragm fiber-optic F-P sensor; Optical fiber sensor; Respiratory system disease recognition","Diseases; Long short-term memory; Lung cancer; Optical fiber fabrication; Respiratory mechanics; Acoustic Sensors; Deep learning; Diaphragm fiber-optic F-P sensor; EFPI sensor; Fiber Sensor; Fiber-optics; Optical-; Respiratory system disease recognition; Test sets; Training sets; Fiber optic sensors","","","","","Natural Science Foundation of Jiangxi Province, (20202ACBL202002); The Academic and Technical Leader Plan of Jiangxi Provincial Major Disciplines, (20172BCB22012); National Natural Science Foundation of China, NSFC, (62105139, 61465009)","This work was supported in part by Natural Science Foundation of Jiangxi Province under Grant 20202ACBL202002, in part by The Academic and Technical Leader Plan of Jiangxi Provincial Major Disciplines under Grant 20172BCB22012, in part by the National Natural Science Foundation of China under Grant 62105139 and 61465009. 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Circuits Syst., 14, 3, pp. 535-544, (2020)","S. Wan; Key Laboratory for Optoelectronic Information Perception and Instrumentation of Jiangxi Province, Nanchang Hangkong University, Nanchang, 330063, China; email: sp_wan@163.com","","Elsevier B.V.","","","","","","09244247","","SAAPE","","English","Sens Actuators A Phys","Article","Final","","Scopus","2-s2.0-85215399325"
"López-Canay J.; Casal-Guisande M.; Pinheira A.; Golpe R.; Comesaña-Campos A.; Fernández-García A.; Represas-Represas C.; Fernández-Villar A.","López-Canay, Julia (59557039700); Casal-Guisande, Manuel (57211937492); Pinheira, Alberto (57221113439); Golpe, Rafael (6602269119); Comesaña-Campos, Alberto (55091165200); Fernández-García, Alberto (57223168491); Represas-Represas, Cristina (36091745500); Fernández-Villar, Alberto (55882055300)","59557039700; 57211937492; 57221113439; 6602269119; 55091165200; 57223168491; 36091745500; 55882055300","Predicting COPD Readmission: An Intelligent Clinical Decision Support System","2025","Diagnostics","15","3","318","","","","0","10.3390/diagnostics15030318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217790279&doi=10.3390%2fdiagnostics15030318&partnerID=40&md5=a379b9a25644d5eed3e1e991296386e5","Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain; NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain; Centro de Investigación Biomédica en Red, CIBERES ISCIII, Madrid, 28029, Spain; Department of Design in Engineering, University of Vigo, Vigo, 36208, Spain; Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain; Department of Computer Engineering, Superior Institute of Engineering of Porto, Porto, 4249-015, Portugal; Pulmonary Department, Hospital Lucus Augusti, Lugo, 27003, Spain; Servicio de Diagnóstico por Imagen, Hospital Ribera Povisa, Vigo, 36211, Spain; Pulmonary Department, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain","López-Canay J., Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain, NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain; Casal-Guisande M., Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain, NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain, Centro de Investigación Biomédica en Red, CIBERES ISCIII, Madrid, 28029, Spain; Pinheira A., Department of Design in Engineering, University of Vigo, Vigo, 36208, Spain, Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain, Department of Computer Engineering, Superior Institute of Engineering of Porto, Porto, 4249-015, Portugal; Golpe R., Pulmonary Department, Hospital Lucus Augusti, Lugo, 27003, Spain; Comesaña-Campos A., Department of Design in Engineering, University of Vigo, Vigo, 36208, Spain, Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain; Fernández-García A., Servicio de Diagnóstico por Imagen, Hospital Ribera Povisa, Vigo, 36211, Spain; Represas-Represas C., NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain, Centro de Investigación Biomédica en Red, CIBERES ISCIII, Madrid, 28029, Spain, Pulmonary Department, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain; Fernández-Villar A., NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, 36312, Spain, Centro de Investigación Biomédica en Red, CIBERES ISCIII, Madrid, 28029, Spain, Pulmonary Department, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain","Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2–3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms —Random Forest, Naïve Bayes, and Multilayer Perceptron—that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang–Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system’s future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity. © 2025 by the authors.","artificial intelligence; clinical decision-making; COPD; expert systems; fuzzy logic; intelligent systems; machine learning; Wang–Mendel algorithm","corticosteroid; Pneumococcus vaccine; ADL disability; adult; alcohol consumption; anemia; anxiety disorder; artery disease; Article; Bayesian learning; cardiovascular disease; case study; chronic obstructive lung disease; clinical decision support system; clinical feature; cohort analysis; current smoker; data base; data processing; demographics; depression; diabetes mellitus; disease exacerbation; drug abuse; dyspnea; electronic medical record; eosinophil count; expert system; feature selection; female; follow up; forced expiratory volume; home oxygen therapy; hospital discharge; hospital readmission; human; hypertension; influenza vaccination; information processing; interview; knowledge base; machine learning algorithm; major clinical study; male; malignant neoplasm; medical record review; multilayer perceptron; noninvasive ventilation; obstructive sleep apnea; preliminary data; proof of concept; random forest; residence characteristics; risk assessment; sensitivity and specificity; sputum culture; vaccination","","","","","","","Agusti A., Celli B.R., Criner G.J., Halpin D., Anzueto A., Barnes P., Bourbeau J., Han M.L.K., Martinez F.J., Montes de Oca M., Et al., Global Initiative for Chronic Obstructive Lung Disease 2023 Report: GOLD Executive Summary, Arch. 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Man Cybern, 22, pp. 1414-1427, (1992); Casal-Guisande M., Cerqueiro-Pequeno J., Bouza-Rodriguez J.-B., Comesana-Campos A., Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems, Mathematics, 11, (2023); Boughorbel S., Jarray F., El-Anbari M., Optimal Classifier for Imbalanced Data Using Matthews Correlation Coefficient Metric, PLoS ONE, 12, (2017); Chicco D., Jurman G., The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation, BMC Genom, 21, (2020); Guilford J.P., Psychometric Methods, (1954)","M. Casal-Guisande; Fundación Pública Galega de Investigación Biomédica Galicia Sur, Hospital Álvaro Cunqueiro, Vigo, 36312, Spain; email: manuel.casal.guisande@uvigo.es","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85217790279"
"Andrikopoulou E.; Thwaites T.; Vos R.D.","Andrikopoulou, Elisavet (57210190742); Thwaites, Thomas (58932762200); Vos, Ruth De (58933215500)","57210190742; 58932762200; 58933215500","Rapport and ethics in a digital world: impact on individuals","2023","ERS Monograph","2023","107","","131","","","1","10.1183/2312508X.10001223","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187141526&doi=10.1183%2f2312508X.10001223&partnerID=40&md5=a4c14b93569d754552f2bfc8b6ed912b","School of Computing, University of Portsmouth, Portsmouth, United Kingdom; General Practitioner (locum), NHS North West London, London, United Kingdom; Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom","Andrikopoulou E., School of Computing, University of Portsmouth, Portsmouth, United Kingdom; Thwaites T., General Practitioner (locum), NHS North West London, London, United Kingdom; Vos R.D., Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom","Technology and healthcare have now been closely interlinked for many decades, but since the coronavirus disease 2019 pandemic, technology has infiltrated even the most non-technological fields. Technology now dominates medical consultations and, although technological advancements have revolutionised healthcare delivery, the human elements involved in healthcare delivery remain critical to effective clinical interactions. The essence of healthcare continues to lie in human contact and in the “humanness” underpinning these interactions. The emotional support that emanates from “humanness” can significantly affect the patient’s well-being and help them cope with the psychological impact of their illness, improve their resilience, and increase their satisfaction with their care. This chapter discusses the technology element in the clinician–patient relationship, focusing on trust, empathy, ethical considerations and humanness, and also discusses the potential risk to the clinician–patient relationship when there are technological problems. © ERS 2023.","","Article; artificial intelligence; asthma; attitude to health; bibliometrics; caregiver; chronic obstructive lung disease; communication technology; continuous glucose monitoring; cost effectiveness analysis; data privacy; decision making; digital health technology; doctor patient relationship; electronic health record; emotional support; empathy; ethical dilemma; ethics; face-to-face consultation; general practitioner; glycemic control; health care personnel; health care quality; health care system; health insurance; health practitioner; health service; heart rate; human; information technology; Internet; medical informatics; medication compliance; outpatient; patient care; patient education; patient satisfaction; perception; psychological resilience; pulmonary rehabilitation; respiratory tract infection; telecare; telemedicine; telemonitoring; trust; video consultation","","","Tableau version 2022.3.6, Tableau, United States","Tableau, United States","","","Warren LR, Clarke J, Arora S, Et al., Improving data sharing between acute hospitals in England: an overview of health record system distribution and retrospective observational analysis of inter-hospital transitions of care, BMJ Open, 9, (2019); Kontopantelis E, Stevens RJ, Helms PJ, Et al., Spatial distribution of clinical computer systems in primary care in England in 2016 and implications for primary care electronic medical record databases: a cross-sectional population study, BMJ Open, 8, (2018); Sari Kundt F, Enthaler N, Dieplinger AM, Et al., Multiprofessional COPD care in Austria – challenges and approaches: results of a qualitative study, Wien Klin Wochenschr, 130, pp. 371-381, (2018); Andrikopoulou E, Scott PJ, Herrera H., Important design features of personal health records to improve medication adherence for patients with long-term conditions: protocol for a systematic literature review, JMIR Res Protoc, 7, (2018); Andrikopoulou E, Scott P, Herrera H, Et al., What are the important design features of personal health records to improve medication adherence for patients with long-term conditions? 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Data Protection Act 2018; Friedman CP, Wyatt JC, Ash JS., Ethics, safety, and closing thoughts, Evaluation Methods in Biomedical and Health Informatics, pp. 475-495, (2022); Li L, Qin L, Xu Z, Et al., Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy, Radiology, 296, pp. E65-E71, (2020); Wynants L, Van Calster B, Collins GS, Et al., Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal, BMJ, 369, (2020); Wolfe R, Carlin J., Statistical models for respiratory disease diagnosis and prognosis, Respirology, 20, pp. 541-547, (2015); Abramson MJ, Wolfe R., Prediction models in respiratory medicine, Respirology, 25, pp. 666-667, (2020); Wildes DM, Chisale M, Drew RJ, Et al., A systematic review of clinical prediction rules to predict hospitalisation in children with lower respiratory infection in primary care and their validation in a new cohort, EClinicalMedicine, 41, (2021); Liao W, Coupland CAC, Burchardt J, Et al., Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19.67 million people and evaluation of model performance against seven other risk prediction models, Lancet Respir Med, 11, pp. 685-697, (2023); Kang J, Kang J, Seo WJ, Et al., Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features, J Thorac Dis, 15, pp. 1506-1516, (2023); Honkoop P, Usmani O, Bonini M., The current and future role of technology in respiratory care, Pulm Ther, 8, pp. 167-179, (2022); Joumaa H, Sigogne R, Maravic M, Et al., Artificial intelligence to differentiate asthma from COPD in medico-administrative databases, BMC Pulm Med, 22, (2022); Bendavid I, Statlender L, Shvartser L, Et al., A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19, Sci Rep, 12, (2022); 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The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry, Comput Human Behav, 122, (2021); Srinivasan R, San Miguel Gonzalez B., The role of empathy for artificial intelligence accountability, J Responsible Technol, 9, (2022); Montemayor C, Halpern J, Fairweather A., In principle obstacles for empathic AI: why we can’t replace human empathy in healthcare, AI Soc, 37, pp. 1353-1359, (2022); Wu J., Empathy in artificial intelligence, Forbes, (2019); Shao R., An empathetic AI for mental health intervention: conceptualizing and examining artificial empathy, EMPATHICH ‘23: Proceedings of the 2nd Empathy-Centric Design Workshop, pp. 1-6, (2023); Kim H, Jung I, Lim YK., Understanding the negative aspects of user experience in human-likeness of voice-based conversational agents, DIS ‘2022: Proceedings of the 2022 ACM Designing Interactive Systems Conference, pp. 1418-1427, (2022); Saha S, Beach MC, Cooper LA., Patient centeredness, cultural competence and healthcare quality, J Natl Med Assoc, 100, pp. 1275-1285, (2008); Betancourt JR, Green AR, Carrillo JE, Et al., Defining cultural competence: a practical framework for addressing racial/ethnic disparities in health and health care, Public Health Rep, 118, pp. 293-302, (2003); Mougin F, Hollis KF, Soualmia LF., Inclusive digital health, Yearb Med Inform, 31, pp. 2-6, (2022); Dickert NW, Kass NE., Understanding respect: learning from patients, J Med Ethics, 35, pp. 419-423, (2009); Personalised Health and Care 2020: A Framework for Action, (2014); Five Year Forward View, (2014); Pinnock H, Noble M, Lo D, Et al., Personalised management and supporting individuals to live with their asthma in a primary care setting, Expert Rev Respir Med, 17, pp. 577-596, (2023); Pinnock H, Parke HL, Panagioti M, Et al., Systematic meta-review of supported self-management for asthma: a healthcare perspective, BMC Med, 15, (2017); Slevin P, Kessie T, Cullen J, Et al., Exploring the potential benefits of digital health technology for the management of COPD: a qualitative study of patient perceptions, ERJ Open Res, 5, pp. 00239-2018, (2019); Farmer A, Williams V, Velardo C, Et al., Self-management support using a digital health system compared with usual care for chronic obstructive pulmonary disease: randomized controlled trial, J Med Internet Res, 19, (2017); Jackson CL, Bolen S, Brancati FL, Et al., A systematic review of interactive computer-assisted technology in diabetes care. Interactive information technology in diabetes care, J Gen Intern Med, 21, pp. 105-110, (2006); 2018 Global Health Care Outlook: The Evolution of Smart Health Care, (2018); Andrikopoulou E, Scott P., Personal health records an approach to answer: what works for whom in what circumstances?, Stud Health Technol Inform, 294, pp. 725-729, (2022); NHS Long Term Plan, (2019); Delaney LJ., Patient-centred care as an approach to improving health care in Australia, Collegian, 25, pp. 119-123, (2018); Demography: Future Trends, (2012); Policy Paper State Pension Age Review 2023","E. Andrikopoulou; School of Computing, University of Portsmouth, Portsmouth, United Kingdom; email: elisavet.andrikopoulou@port.ac.uk","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85187141526"
"Yu Y.; Du N.; Zhang Z.; Huang W.; Li M.","Yu, Yongfu (58418368500); Du, Nannan (58317675700); Zhang, Zhongteng (58419720200); Huang, Weihong (57202800154); Li, Min (56908226400)","58418368500; 58317675700; 58419720200; 57202800154; 56908226400","Machine Learning-Assisted Diagnosis Model for Chronic Obstructive Pulmonary Disease","2023","International Journal of Information Technologies and Systems Approach","16","3","","","","","1","10.4018/IJITSA.324760","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163811567&doi=10.4018%2fIJITSA.324760&partnerID=40&md5=050c2e572f0e13411e04cd618d78b60d","School of Computer Science and Engineering, Central South University, China; Xiangya Hospital, Central South University, China; Central South University, China","Yu Y., School of Computer Science and Engineering, Central South University, China; Du N., Xiangya Hospital, Central South University, China; Zhang Z., Central South University, China; Huang W., Central South University, China; Li M., Xiangya Hospital, Central South University, China","Chronic obstructive pulmonary disease (COPD) is a long-term, irreversible, and progressive respiratory disease that often leads to lung function decline. Pulmonary function tests (PFTs) provide valuable information for diagnosing COPD; however, they are underutilised in clinical practice, with only a subset of test values being used for decision making. The final clinical diagnosis requires combining PFT results with patient information, symptoms, and other tests, such as imaging and blood analysis. This study aims to comprehensively utilise all the testing information in PFTs to assist in the diagnosis of COPD. Various machine learning models, such as logistic regression, support vector machine (SVM), k-nearest neighbour (KNN), random forest, decision tree, and XGBoost, have been employed to establish COPD diagnosis assistance models. The XGBoost model, trained with features extracted by the group LASSO algorithm, achieved the best performance, with an area under the receiver operating characteristic curve (ROC) of 0.90, 88.6% accuracy, and 98.5% sensitivity. This model can assist doctors in the clinical diagnosis and early prediction of COPD. © 2023 IGI Global. All rights reserved.","Chronic Obstructive Pulmonary Disease (COPD); Machine Learning; Pulmonary Function Test; Receiver Operating Characteristic Curve (ROC)","Decision trees; Diagnosis; Learning systems; Logistic regression; Nearest neighbor search; Support vector machines; Chronic obstructive pulmonary disease; Clinical diagnosis; Clinical practices; Diagnosis model; Lung function; Machine-learning; Pulmonary function test; Receiver operating characteristic curve; Receiver operating characteristic curves; Pulmonary diseases","","","","","National Key Research and Development Program of China, NKRDPC; Science and Technology Program of Hunan Province, (2022RC3013); Science and Technology Program of Hunan Province","This work is supported by Grant No.2022YFC2010200 from the National Key R&D Program of China and the science and technology innovation Program of Hunan Province (No. 2022RC3013).","Adeloye D., Chua S., Lee C., Basquill C., Papana A., Theodoratou E., Nair H., Gasevic D., Sridhar D., Campbell H., Chan K. 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A., Hsiao A., Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network, Radiology: Cardiothoracic Imaging, 3, 2, (2021); Ho T. T., Kim T., Kim W. J., Lee C. H., Chae K. J., Bak S. H., Kwon S. O., Jin G. Y., Park E. K., Choi S., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Scientific Reports, 11, 1, pp. 1-12, (2021); Jordan M. I., Mitchell T. M., Machine learning: Trends, perspectives, and prospects, Science, 349, 6245, pp. 255-260, (2015); Kononenko I., Machine learning for medical diagnosis: History, state of the art and perspective, Artificial Intelligence in Medicine, 23, 1, pp. 89-109, (2001); Lavalley M. P., Logistic regression, Circulation, 117, 18, pp. 2395-2399, (2008); Leidy N. K., Malley K. G., Steenrod A. W., Mannino D. M., Make B. J., Bowler R. P., Thomashow B. M., Barr R. G., Rennard S. I., Houfek J. F., Yawn B. P., Han M. K., Meldrum C. A., Bacci E. D., Walsh J. W., Martinez F., Insight into best variables for COPD case identification: A random forests analysis, Chronic Obstructive Pulmonary Diseases: Journal of the COPD Foundation, 3, 1, pp. 406-418, (2016); Li C., Liu W., Guo R., Yin X., Jiang K., Du Y., Du Y., Zhu L., Lai B., Hu X., Yu D., Ma Y., PP-OCRv3: More attempts for the improvement of ultra lightweight OCR system, (2022); Li X., Wang Y., Ruiz R., A survey on sparse learning models for feature selection, IEEE Transactions on Cybernetics, 52, 3, pp. 1642-1660, (2022); Liao M., Wan Z., Yao C., Chen K., Bai X., Real-time scene text detection with differentiable binarization, Proceedings AAAI, pp. 11474-11481, (2020); Lopez-Campos J. L., Tan W., Soriano J. B., Global burden of COPD, Respirology (Carlton, Vic.), 21, 1, pp. 14-23, (2016); Lozano R., Naghavi M., Foreman K., Lim S., Shibuya K., Aboyans V., Adair T., Aggarwal R., Ahn S., Alvarado M., Andrews K., Anderson H. 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D., Towards large-scale case-finding: Training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, The Lancet Digital Health, 2, 5, pp. e259-e267, (2020); Tibshirani R., Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B. Methodological, 58, 1, pp. 267-288, (1996); Tinkelman D. G., Price D. B., Nordyke R. J., Halbert R. J., Misdiagnosis of COPD and asthma in primary care patients 40 years of age and over, The Journal of Asthma, 43, 1, pp. 75-80, (2006); Topalovic M., Das N., Burgel P. R., Daenen M., Derom E., Haenebalcke C., Janssen R., Kerstjens H. A. M., Liistro G., Louis R., Ninane V., Pison C., Schlesser M., Vercauter P., Vogelmeier C. F., Wouters E., Wynants J., Janssens W., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, The European Respiratory Journal, 53, 4, (2019); Vestbo J., Hurd S. S., Agusti A. G., Jones P. W., Vogelmeier C., Anzueto A., Barnes P. J., Fabbri L. M., Martinez F. J., Nishimura M., Stockley R. A., Sin D. D., Rodriguez-Roisin R., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, American Journal of Respiratory and Critical Care Medicine, 187, 4, pp. 347-365, (2013); Vogelmeier C. F., Criner G. J., Martinez F. J., Anzueto A., Barnes P. J., Bourbeau J., Celli B. R., Chen R., Decramer M., Fabbri L. M., Frith P., Halpin D. M. G., Varela M. V., Nishimura M., Roche N., Rodriguez-Roisin R., Sin D. D., Singh D., Stockley R., Agusti A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report. GOLD executive summary, American Journal of Respiratory and Critical Care Medicine, 195, 5, pp. 557-582, (2017); Willer K., Fingerle A. A., Noichl W., De Marco F., Frank M., Urban T., Schick R., Gustschin A., Gleich B., Herzen J., Koehler T., Yaroshenko A., Pralow T., Zimmermann G. S., Renger B., Sauter A. P., Pfeiffer D., Makowski M. R., Rummeny E. J., Pfeiffer F., Et al., X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: A diagnostic accuracy study, The Lancet Digital Health, 3, 11, pp. e733-e744, (2021); Xu C., Qi S., Feng J., Xia S., Kang Y., Yao Y., Qian W., DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images, Physics in Medicine and Biology, 65, 14, (2020); Yuan M., Lin Y., Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society. Series B, Statistical Methodology, 68, 1, pp. 49-67, (2006)","","","IGI Global","","","","","","1935570X","","","","English","Int. J. Inf. Technol. Syst. Approach","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85163811567"
"Wu Y.-F.; Shu X.; Wang S.; Xu X.; Sun P.-L.","Wu, Yue-Fang (58752022900); Shu, Xin (59636858400); Wang, Shiqi (59543862800); Xu, Xiaojun (59544142100); Sun, Pei-Li (7202975953)","58752022900; 59636858400; 59543862800; 59544142100; 7202975953","Accurate identification of oxygen desaturation status in COPD by using classifier ensemble","2025","PLoS ONE","20","2 February","e0318837","","","","0","10.1371/journal.pone.0318837","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217069916&doi=10.1371%2fjournal.pone.0318837&partnerID=40&md5=33228eccc9d222d44e8752096d976951","Department of Internal Medicine, Nanjing University of Science, Technology Hospital, Jiangsu, Nanjing, China; School of Computer Science, Jiangsu University of Science and Technology, Jiangsu, Zhenjiang, China; Department of Respiratory Medicine, The First Affiliated Hospital with Nanjing Medical University, Jiangsu, Nanjing, China","Wu Y.-F., Department of Internal Medicine, Nanjing University of Science, Technology Hospital, Jiangsu, Nanjing, China; Shu X., School of Computer Science, Jiangsu University of Science and Technology, Jiangsu, Zhenjiang, China; Wang S., Department of Respiratory Medicine, The First Affiliated Hospital with Nanjing Medical University, Jiangsu, Nanjing, China; Xu X., Department of Respiratory Medicine, The First Affiliated Hospital with Nanjing Medical University, Jiangsu, Nanjing, China; Sun P.-L., Department of Respiratory Medicine, The First Affiliated Hospital with Nanjing Medical University, Jiangsu, Nanjing, China","The accurate identification of oxygen desaturation (OD) status plays critical role in the clinic diagnosis of chronic obstructive pulmonary disease (COPD), which is a common disease related to the lungs and respiratory tract of the human body. This paper focuses on a specific type of OD status, i.e., exercise-induced oxygen desaturation (EIOD) status in COPD, and try to further improve the performance of EIOD status identification. We propose a new and effective EIOD status identification method by using classifier ensemble strategy. In the proposed method, five different features of each data point from the time series of SpO2 and pulse are extracted and then combined to form the discriminative feature of the corresponding data point; then, multiple base classifiers with different balanced training subsets are trained and then integrated by using AdaBoost Algorithm. The comparative computational results on the 6-min walk test (6MWT) of the recruited participants show that the proposed method achieved the best global performance with AUC (Area Under Curve) value of 0.8532, indicating that the proposed method can be effectively used for the identification of EIOD and could assist the clinic diagnosis of COPD. © 2025 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Aged; Algorithms; Exercise; Female; Humans; Male; Middle Aged; Oximetry; Oxygen; Oxygen Saturation; Pulmonary Disease, Chronic Obstructive; Walk Test; oxygen; AdaBoost algorithm; area under the curve; Article; chronic obstructive lung disease; classifier; classifier ensemble; exercise-induced oxygen desaturation; human; long short term memory network; machine learning; multilayer perceptron; oxygen desaturation; oxygen saturation; pulse rate; six minute walk test; support vector machine; training; aged; algorithm; blood; diagnosis; exercise; female; male; metabolism; middle aged; oximetry; oxygen saturation; pathophysiology; physiology; procedures; walk test","","oxygen, 7782-44-7; Oxygen, ","CMS50S; pulse oximeter wristwatch model: ORANGER","","","","Agusti A, Celli BR, Criner GJ, Halpin D, Anzueto A, Barnes P, Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, American journal of respiratory and critical care medicine, 207, 7, pp. 819-837, (2023); Groneberg DA, Chung KF., Models of chronic obstructive pulmonary disease, Respiratory research, 5, pp. 1-16, (2004); Hogg JC., Pathophysiology of airflow limitation in chronic obstructive pulmonary disease, The Lancet, 364, 9435, pp. 709-721, (2004); Agusti A, Noguera A, Sauleda J, Sala E, Pons J, Busquets X., Systemic effects of chronic obstructive pulmonary disease, European Respiratory Journal, 21, 2, pp. 347-360, (2003); Burney P, Jithoo A, Kato B, Janson C, Mannino D, Nizankowska-Mogilnicka E, Et al., Chronic obstructive pulmonary disease mortality and prevalence: the associations with smoking and poverty—a BOLD analysis, Thorax, 69, 5, pp. 465-473, (2014); Halpin DM, Miravitlles M., Chronic obstructive pulmonary disease: the disease and its burden to society, Proceedings of the American Thoracic Society, 3, 7, pp. 619-623, (2006); Wu Y, Hu M, Sun P., Oxygen Depletion Status Identification in COPD by Using Attention-based LSTM, Journal of Nanjing University of Science and Technology, 47, 5, pp. 629-635, (2023); Hu M., Research on LSTM-based Oxygen Depletion Status Prediction in COPD, (2020); Perez T, Deslee G, Burgel PR, Caillaud D, Le Rouzic O, Zysman M, Et al., Predictors in routine practice of 6-min walking distance and oxygen desaturation in patients with COPD: impact of comorbidities, International journal of chronic obstructive pulmonary disease, pp. 1399-1410, (2019); Crisafulli E, Iattoni A, Venturelli E, Siscaro G, Beneventi C, Cesario A, Et al., Predicting walking-induced oxygen desaturations in COPD patients: a statistical model, Respiratory Care, 58, 9, pp. 1495-1503, (2013); Waatevik M, Johannessen A, Real FG, Aanerud M, Hardie JA, Bakke PS, Et al., Oxygen desaturation in 6-min walk test is a risk factor for adverse outcomes in COPD, European Respiratory Journal, 48, 1, pp. 82-91, (2016); Wu Y, Sun P, Shu X, Yu D., Identification of oxygen depletion status in chronic obstructive pulmonary disease based on supervised self-organized mapping, China Digital Medicine, 19, 3, pp. 86-91, (2024); Lan X, Wei R, Cai H, Guo Y, Hou M, Xin L, Et al., The application of machine learning algorithms in the medical field, Chinese Medical Equipment, 40, 3, pp. 93-97, (2019); Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, The American journal of emergency medicine, 36, 9, pp. 1650-1654, (2018); Kononenko I., Machine learning for medical diagnosis: history, state of the art and perspective, Artificial Intelligence in medicine, 23, 1, pp. 89-109, (2001); Garg A, Mago V., Role of machine learning in medical research: A survey, Computer science review, 40, (2021); He H, Garcia EA., Learning from imbalanced data, IEEE Transactions on knowledge and data engineering, 21, 9, pp. 1263-1284, (2009); Yu D-J, Hu J, Yang J, Shen H-B, Tang J, Yang J-Y., Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering, IEEE/ACM transactions on computational biology and bioinformatics, 10, 4, pp. 994-1008, (2013); Collins M, Schapire RE, Singer Y., Logistic regression, AdaBoost and Bregman distances, Machine Learning, 48, pp. 253-285, (2002); Baig MM, Awais MM, El-Alfy E-SM., AdaBoost-based artificial neural network learning, Neurocomputing, 248, pp. 120-126, (2017); Chaiprasittikul N, Thanathornwong B, Pornprasertsuk-Damrongsri S, Raocharernporn S, Maponthong S, Manopatanakul S., Application of a multi-layer perceptron in preoperative screening for orthognathic surgery, Healthcare Informatics Research, 29, 1, pp. 16-22, (2023); Suthaharan S, Suthaharan S., Support vector machine, Machine learning models and algorithms for big data classification: thinking with examples for effective learning, pp. 207-235, (2016); Sherstinsky A., Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network, Physica D: Nonlinear Phenomena, 404, (2020)","Y.-F. Wu; Department of Internal Medicine, Nanjing University of Science, Technology Hospital, Nanjing, Jiangsu, China; email: njwuyf@njust.edu.cn","","Public Library of Science","","","","","","19326203","","POLNC","39908245","English","PLoS ONE","Article","Final","","Scopus","2-s2.0-85217069916"
"Ding J.; Yue C.; Wang C.; Liu W.; Zhang L.; Chen B.; Shen S.; Piao Y.; Zhang L.","Ding, Jing (58464706200); Yue, Changli (37012287000); Wang, Chengshuo (57204929951); Liu, Wei (58242982400); Zhang, Libo (58243208000); Chen, Bo (59076269600); Shen, Shen (57204509753); Piao, Yingshi (35857262400); Zhang, Luo (36068675900)","58464706200; 37012287000; 57204929951; 58242982400; 58243208000; 59076269600; 57204509753; 35857262400; 36068675900","Machine learning method for the cellular phenotyping of nasal polyps from multicentre tissue scans","2023","Expert Review of Clinical Immunology","19","8","","1023","1028","5","1","10.1080/1744666X.2023.2207824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158814895&doi=10.1080%2f1744666X.2023.2207824&partnerID=40&md5=4ad2cca11ec24497a03a163c26d7715e","Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Head and Neck Molecular Pathological Diagnosis, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Department of Center for Translational Medicine, Keymed Biosciences Inc, Sichuan, Chengdu, China","Ding J., Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, China, Beijing Key Laboratory of Head and Neck Molecular Pathological Diagnosis, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Yue C., Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, China, Beijing Key Laboratory of Head and Neck Molecular Pathological Diagnosis, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Wang C., Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Liu W., Department of Center for Translational Medicine, Keymed Biosciences Inc, Sichuan, Chengdu, China; Zhang L., Department of Center for Translational Medicine, Keymed Biosciences Inc, Sichuan, Chengdu, China; Chen B., Department of Center for Translational Medicine, Keymed Biosciences Inc, Sichuan, Chengdu, China; Shen S., Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Piao Y., Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, China, Beijing Key Laboratory of Head and Neck Molecular Pathological Diagnosis, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Zhang L., Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China","Background: This study aimed to establish a convenient and accurate chronic rhinosinusitis evaluation platform CRSAI 1.0 according to four phenotypes of nasal polyps. Research design and methods: Tissue sections of a training (n = 54) and test cohort (n = 13) were sourced from the Tongren Hospital, and those for a validation cohort (n = 55) from external hospitals. Redundant tissues were automatically removed by the semantic segmentation algorithm of Unet++ with Efficientnet-B4 as backbone. After independent analysis by two pathologists, four types of inflammatory cells were detected and used to train the CRSAI 1.0. Dataset from Tongren Hospital were used for training and testing, and validation tests used the multicentre dataset. Results: The mean average precision (mAP) in the training and test cohorts for tissue eosinophil%, neutrophil%, lymphocyte%, and plasma cell% was 0.924, 0.743, 0.854, 0.911 and 0.94, 0.74, 0.839, and 0.881, respectively. The mAP in the validation dataset was consistent with that of the test cohort. The four phenotypes of nasal polyps varied significantly according to the occurrence of asthma or recurrence. Conclusions: CRSAI 1.0 can accurately identify various types of inflammatory cells in CRSwNP from multicentre data, which could enable rapid diagnosis and personalized treatment. © 2023 Informa UK Limited, trading as Taylor & Francis Group.","Accurate diagnosis; Inflammatory cell types; machine learning; multicentre; nasal polyps","Chronic Disease; Eosinophils; Humans; Nasal Polyps; Neutrophils; Rhinitis; Sinusitis; Article; asthma; chronic rhinosinusitis; cohort analysis; detection algorithm; eosinophil; human; human tissue; immunophenotyping; inflammatory cell; lymphocyte; machine learning; major clinical study; multicenter study; neutrophil; nose polyp; pathologist; plasma cell; recurrent disease; segmentation algorithm; semantics; tissue section; validation study; chronic disease; clinical trial; pathology; rhinitis; sinusitis","","","","","Capital’s Funds for Health Improvement and Research, (2022-2-2054); Chinese Academy of Meteorological Sciences, CAMS, (2019-I2M-5-022); Chinese Academy of Meteorological Sciences, CAMS; National Key Research and Development Program of China, NKRDPC, (2022YFC2504100); National Key Research and Development Program of China, NKRDPC; Program for Changjiang Scholars and Innovative Research Team in University, (IRT13082); Program for Changjiang Scholars and Innovative Research Team in University","This work was supported by grants from national key R&D program of China (2022YFC2504100), the program for the Changjiang scholars and innovative research team (IRT13082), CAMS innovation fund for medical sciences (2019-I2M-5-022) and the Capital’s Funds for Health Improvement and Research (2022-2-2054).","Shi J.B., Fu Q.L., Zhang H., Et al., Epidemiology of chronic rhinosinusitis: results from a cross-sectional survey in seven Chinese cities, Allergy, 70, 5, pp. 533-539, (2015); Polzehl D., Moeller P., Riechelmann H., Et al., Distinct features of chronic rhinosinusitis with and without nasal polyps, Allergy, 61, 11, pp. 1275-1279, (2006); Stevens W.W., Schleimer R.P., Kern R.C., Chronic rhinosinusitis with nasal polyps, J Allergy Clin Immunol Pract, 4, 4, pp. 565-572, (2016); Alobid I., Bernal-Sprekelsen M., Mullol J., Chronic rhinosinusitis and nasal polyps: the role of generic and specific questionnaires on assessing its impact on patient’s quality of life, Allergy, 63, 10, pp. 1267-1279, (2008); Tokunaga T., Sakashita M., Haruna T., Et al., Novel scoring system and algorithm for classifying chronic rhinosinusitis: the JESREC study, Allergy, 70, 8, pp. 995-1003, (2015); Ryu G., Kim D.K., Dhong H.J., Et al., Immunological characteristics in refractory chronic rhinosinusitis with nasal polyps undergoing revision surgeries, Allergy Asthma Immunol Res, 11, 5, pp. 664-676, (2019); Bachert C., Zhang N., Cavaliere C., Et al., Biologics for chronic rhinosinusitis with nasal polyps, J Allergy Clin Immunol, 145, 3, pp. 725-739, (2020); Bachert C., Bhattacharyya N., Desrosiers M., Et al., Burden of disease in chronic rhinosinusitis with nasal polyps, J Asthma Allergy, 11, pp. 127-134, (2021); Lou H., Meng Y., Piao Y., Et al., Cellular phenotyping of chronic rhinosinusitis with nasal polyps, Rhinology, 54, 2, pp. 150-159, (2016); Lou H., Meng Y., Piao Y., Et al., Predictive significance of tissue eosinophilia for nasal polyp recurrence in the Chinese population, Am J Rhinol Allergy, 29, 5, pp. 350-356, (2015); Smith K.A., Smith T.L., Mace J.C., Et al., Endoscopic sinus surgery compared to continued medical therapy for patients with refractory chronic rhinosinusitis, Int Forum Allergy Rhinol, 4, 10, pp. 823-827, (2014); Rudmik L., Soler Z.M., Smith T.L., Et al., Effect of continued medical therapy on productivity costs for refractory chronic rhinosinusitis, JAMA Otolaryngology–Head & Neck Surg, 141, 11, pp. 969-973, (2015); Wu Q., Chen J., Deng H., Et al., Expert-level diagnosis of nasal polyps using deep learning on whole-slide imaging, J Allergy Clin Immunol, 145, 2, pp. 698-701, (2020); Wu Q., Chen J., Ren Y., Et al., Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging, EBioMedicine, 66, (2021); Glenn J., Alex S., Jirka B., Et al., Ultralytics/Yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, supervise, Ly and YouTube Integrations, Apr, (2021); Ze L., Yutong L., Yue C., Et al., Swin transformer: hierarchical vision transformer using shifted windows, arXiv Preprint arXiv, pp. 9992-10002, (2021); Everingham M., Van Gool L., Christopher K.I., Et al., The PASCAL visual object classes (VOC) challenge, Int J Comput Vis, 88, 2, pp. 303-338, (2010)","Y. Piao; Department of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing, No. 1, Dong Jiao Min Xiang, Dongcheng, 100005, China; email: piaoyingshi2013@163.com; L. Zhang; Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, No. 1, Dong Jiao Min Xiang, Dongcheng, 100005, China; email: dr.luozhang@139.com","","Taylor and Francis Ltd.","","","","","","1744666X","","","37099717","English","Expert Rev. Clin. Immunol.","Article","Final","","Scopus","2-s2.0-85158814895"
"Li D.-D.; Chen T.; Ling Y.-L.; Jiang Y.; Li Q.-G.","Li, Dong-Dong (57917984900); Chen, Ting (57918576000); Ling, You-Liang (57917985000); Jiang, Yongan (57215596075); Li, Qiu-Gen (57226248666)","57917984900; 57918576000; 57917985000; 57215596075; 57226248666","A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification","2022","Computational and Mathematical Methods in Medicine","2022","","2679050","","","","1","10.1155/2022/2679050","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139398259&doi=10.1155%2f2022%2f2679050&partnerID=40&md5=7f8fe529630495a85f1e4dccf001a897","Nanchang University, Jiangxi, Nanchang, 330006, China; Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, Jiangxi, Nanchang, 330006, China; Department of Pulmonary and Critical Care Medicine, Wuhan Wuchang Hospital, Hubei, Wuhan, 430063, China","Li D.-D., Nanchang University, Jiangxi, Nanchang, 330006, China, Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, Jiangxi, Nanchang, 330006, China; Chen T., Department of Pulmonary and Critical Care Medicine, Wuhan Wuchang Hospital, Hubei, Wuhan, 430063, China; Ling Y.-L., Nanchang University, Jiangxi, Nanchang, 330006, China; Jiang Y., Nanchang University, Jiangxi, Nanchang, 330006, China; Li Q.-G., Nanchang University, Jiangxi, Nanchang, 330006, China, Department of Pulmonary and Critical Care Medicine, Jiangxi Provincial People's Hospital, Jiangxi, Nanchang, 330006, China","Background. Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN). Methods. Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model. Results. The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model. Conclusion. Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment. © 2022 Dong-Dong Li et al.","","Artificial Intelligence; Asthma; Computational Biology; DNA Methylation; Gene Expression Profiling; Gene Regulatory Networks; Humans; Neural Networks, Computer; Reproducibility of Results; Alkylation; Backpropagation; Decision trees; Diagnosis; Diseases; Gene expression; Methylation; Neural networks; Proteins; Random forests; Reliability analysis; Chronic disease; Clinical diagnosis; Diagnosis model; Diagnostic model; Gene ontology; Human health; Human lives; Model-based OPC; Neural-networks; Random forests; actin filament; Article; artificial neural network; asthma; cell adhesion; controlled study; decision tree; DNA methylation; early diagnosis; epigenetics; human; KEGG; random forest; sensitivity and specificity; serotoninergic nerve cell; shigellosis; support vector machine; transendothelial and transepithelial migration; artificial intelligence; biology; DNA methylation; gene expression profiling; gene regulatory network; genetics; procedures; reproducibility; Gene Ontology","","","","","","","Mims J.W., Asthma: Definitions and pathophysiology, International Forum of Allergy & Rhinology, 5, Supplement 1, pp. S2-S6, (2015); Ntontsi P., Photiades A., Zervas E., Xanthou G., Samitas K., Genetics and epigenetics in asthma, International Journal of Molecular Sciences, 22, 5, (2021); Miller R.L., Grayson M.H., Strothman K., Advances in asthma: New understandings of asthma's natural history, risk factors, underlying mechanisms, and clinical management, The Journal of Allergy and Clinical Immunology, 148, 6, pp. 1430-1441, (2021); Pelaia C., Crimi C., Vatrella A., Tinello C., Terracciano R., Pelaia G., Molecular targets for biological therapies of severe asthma, Frontiers in Immunology, 11, (2020); Weiss S.T., Emerging mechanisms and novel targets in allergic inflammation and asthma, Genome Medicine, 9, 1, (2017); Legaki E., Arsenis C., Taka S., Papadopoulos N.G., DNA methylation biomarkers in asthma and rhinitis: Are we there yet?, Clinical and Translational Allergy, 12, 3, (2022); 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Kawakami E., Tabata J., Yanaihara N., Ishikawa T., Koseki K., Iida Y., Saito M., Komazaki H., Shapiro J.S., Goto C., Akiyama Y., Saito R., Saito M., Takano H., Yamada K., Okamoto A., Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers, Clinical Cancer Research, 25, 10, pp. 3006-3015, (2019); Li H., Lai L., Shen J., Development of a susceptibility gene based novel predictive model for the diagnosis of ulcerative colitis using random forest and artificial neural network, Aging (Albany NY), 12, 20, pp. 20471-20482, (2020); Zhao D., Zhang Z., Wang Z., Du Z., Wu M., Zhang T., Zhou J., Zhao W., Meng Y., Diagnosis and prediction of endometrial carcinoma using machine learning and artificial neural networks based on public databases, Genes, 13, 6, (2022); Nicodemus-Johnson J., Myers R.A., Sakabe N.J., Sobreira D.R., Hogarth D.K., Naureckas E.T., Sperling A.I., Solway J., White S.R., Nobrega M.A., Nicolae D.L., Gilad Y., Ober C., DNA methylation in lung cells is associated with asthma endotypes and genetic risk, JCI Insight, 1, 20, (2016); Yang I.V., Pedersen B.S., Liu A., O'Connor G.T., Teach S.J., Kattan M., Misiak R.T., Gruchalla R., Steinbach S.F., Szefler S.J., Gill M.A., Calatroni A., David G., Hennessy C.E., Davidson E.J., Zhang W., Gergen P., Togias A., Busse W.W., Schwartz D.A., DNA methylation and childhood asthma in the inner city, The Journal of Allergy and Clinical Immunology, 136, 1, pp. 69-80, (2015); Ruzzin J., Petersen R., Meugnier E., Madsen L., Lock E.J., Lillefosse H., Ma T., Pesenti S., Sonne S.B., Marstrand T.T., Malde M.K., Du Z.Y., Chavey C., Fajas L., Lundebye A.K., Brand C.L., Vidal H., Kristiansen K., Froyland L., Persistent organic pollutant exposure leads to insulin resistance syndrome, Environmental Health Perspectives, 118, 4, pp. 465-471, (2010); Zafon C., Gil J., Perez-Gonzalez B., Jorda M., DNA methylation in thyroid cancer, Endocrine-Related Cancer, 26, pp. R415-r439, (2019); Pan Y., Liu G., Zhou F., Su B., Li Y., DNA methylation profiles in cancer diagnosis and therapeutics, Clinical and Experimental Medicine, 18, 1, pp. 1-14, (2018); Morgan A.E., Davies T.J., Mc Auley M.T., The role of DNA methylation in ageing and cancer, The Proceedings of the Nutrition Society, 77, 4, pp. 412-422, (2018); Clifford R.L., Yang C.X., Fishbane N., Patel J., Macisaac J.L., McEwen L.M., May S.T., Castellanos-Uribe M., Nair P., Obeidat M.'., Kobor M.S., Knox A.J., Hackett T.L., TWIST1 DNA methylation is a cell marker of airway and parenchymal lung fibroblasts that are differentially methylated in asthma, Clinical Epigenetics, 12, 1, (2020); Guo Y., Yuan X., Hong L., Wang Q., Liu S., Li Z., Huang L., Jiang S., Shi J., Promotor hypomethylation mediated upregulation of miR-23b-3p targets PTEN to promote bronchial epithelial-mesenchymal transition in chronic asthma, Frontiers in Immunology, 127, (2021); Kothalawala D.M., Murray C.S., Simpson A., Custovic A., Tapper W.J., Arshad S.H., Holloway J.W., Rezwan F.I., Investigators S., Development of childhood asthma prediction models using machine learning approaches, Clinical and Translational Allergy, 11, 9, (2021); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021); Kaplan A., Cao H., Fitzgerald J., Iannotti N., Yang E., Kocks J.W.H., Kostikas K., Price D., Reddel H.K., Tsiligianni I., Vogelmeier C.F., Pfister P., Mastoridis P., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, In Practice, 9, 6, pp. 2255-2261, (2021); Svitkina T.M., Ultrastructure of the actin cytoskeleton, Current Opinion in Cell Biology, 54, pp. 1-8, (2018); Zhao J., Manuchehrfar F., Liang J., Cell-substrate mechanics guide collective cell migration through intercellular adhesion: A dynamic finite element cellular model, Biomechanics and Modeling in Mechanobiology, 19, 5, pp. 1781-1796, (2020); Wu Z., Zhu M., Kang Y., Leung E.L.H., Lei T., Shen C., Jiang D., Wang Z., Cao D., Hou T., Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets, Briefings in Bioinformatics, 22, 4, (2021); Wang J., Prediction of postoperative recovery in patients with acoustic neuroma using machine learning and SMOTE-ENN techniques, Mathematical Biosciences and Engineering: MBE, 19, 10, pp. 10407-10423, (2022)","Y. Jiang; Nanchang University, Nanchang, Jiangxi, 330006, China; email: 1296918592@qq.com; Q.-G. Li; Nanchang University, Nanchang, Jiangxi, 330006, China; email: tchlqg2021@163.com","","Hindawi Limited","","","","","","1748670X","","","36213574","English","Comp. Math. Methods Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85139398259"
"Wändell P.; Carlsson A.C.; Eriksson J.; Wachtler C.; Ruge T.","Wändell, Per (55712339200); Carlsson, Axel C. (24448186700); Eriksson, Julia (57873201200); Wachtler, Caroline (6602668971); Ruge, Toralph (6507185354)","55712339200; 24448186700; 57873201200; 6602668971; 6507185354","A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care","2025","ESC Heart Failure","12","1","","613","621","8","0","10.1002/ehf2.15115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206640398&doi=10.1002%2fehf2.15115&partnerID=40&md5=03cd27347cbbe6b81476b0337abe7c2c","Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden; Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden; Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden; Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden","Wändell P., Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden; Carlsson A.C., Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden, Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden; Eriksson J., Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Wachtler C., Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden; Ruge T., Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden, Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden, Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden","Aims: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data. Methods: We used a sex- and age-matched case–control design. Cases of new CHF were identified across all outpatient care settings 2015–2022 (n = 9098). We included individuals 30 years and above, by sex and age groups of 30–65 years and >65 years. The controls (five per case) were sampled from the individuals in 2015–2022 without CHF at any time between 2010 and 2022, in total 45 490. From the stochastic gradient boosting (SGB) technique model, we obtained a rank of the 10 most important factors related to newly diagnosed CHF in individuals with diabetes, with the normalized relative influence (NRI) score and a corresponding odds ratio of marginal effects (ORME). Area under curve (AUC) was calculated. Results: For women 30–65 years and >65 years, we identified 488 and 3240 new cases of CHF, respectively, and men 30–65 years and >65 years 1196 and 4174 new cases. Among the 10 most important factors in the four groups (divided by sex and lower and higher age) for newly diagnosed CHF, we found the number of visits 12 months before diagnosis (NRI 44.3%–55.9%), coronary artery disease (NRI 2.9%–7.8%), atrial fibrillation and flutter (NRI 6.6%–12.2%) and ‘abnormalities of breathing’ (ICD-10 code R06) (NRI 2.6%–4.4%) were predictive in all groups. For younger women, a diagnosis of COPD (NRI 2.7%) contributed to the predictive effect, while for older women, oedema (NRI 3.1%) and number of years with diabetes (NRI 3.5%) contributed to the predictive effect. For men in both age groups, chronic renal disease had predictive effect (NRI 3.9%–5.1%) The model prediction of CHF among patients with diabetes was high, AUC around 0.85 for the four groups, and with sensitivity over 0.783 and specificity over 0.708 for all four groups. Conclusions: An SGB model using routinely collected data about diagnoses and number of visits in primary care, can accurately predict risk for diagnosis of heart failure in individuals with diabetes. Age and sex difference in predictive factors warrant further examination. © 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.","Cardiovascular diseases; Congestive heart failure; Diabetes mellitus; Machine learning; Primary care","Adult; Aged; Case-Control Studies; Diabetes Mellitus; Female; Heart Failure; Humans; Incidence; Machine Learning; Male; Middle Aged; Primary Health Care; Retrospective Studies; Risk Assessment; adult; aged; area under the curve; Article; atrial fibrillation; case control study; chronic kidney failure; cohort analysis; congestive heart failure; controlled study; coronary artery disease; diabetes mellitus; diagnostic test accuracy study; edema; female; groups by age; heart failure; human; ICD-10; machine learning; major clinical study; male; middle aged; outpatient care; phenotype; prediction; predictive model; primary health care; primary medical care; receiver operating characteristic; risk factor; sensitivity and specificity; support vector machine; clinical trial; complication; diabetes mellitus; diagnosis; epidemiology; heart failure; incidence; multicenter study; primary health care; procedures; retrospective study; risk assessment","","","R version 4.2.1","","Region Stockholm","The present study was funded by grants from Region Stockholm. 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Bozkurt B., Khalaf S., Heart failure in women, Methodist Debakey Cardiovasc J, 13, pp. 216-223, (2017); Taylor C.J., Ordonez-Mena J.M., Jones N.R., Et al., National trends in heart failure mortality in men and women, United Kingdom, 2000–2017, Eur J Heart Fail, 23, pp. 3-12, (2021); Pasea L., Dashtban A., Mizani M., Et al., Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study, Open Heart, 10, (2023); Rentsch C.T., Garfield V., Mathur R., Et al., Sex-specific risks for cardiovascular disease across the glycaemic spectrum: a population-based cohort study using the UK biobank, Lancet Reg Health Eur, 32, (2023); Williams B.A., Geba D., Cordova J.M., Shetty S.S., A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus, Clin Cardiol, 43, pp. 275-283, (2020); Pandey A., Vaduganathan M., Patel K.V., Et al., Biomarker-based risk prediction of incident heart failure in pre-diabetes and diabetes, JACC Heart Fail, 9, pp. 215-223, (2021); Hippisley-Cox J., Coupland C., Development and validation of risk prediction equations to estimate future risk of heart failure in patients with diabetes: a prospective cohort study, BMJ Open, 5, (2015); Segar M.W., Vaduganathan M., Patel K.V., Et al., Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score, Diabetes Care, 42, pp. 2298-2306, (2019); Fang Z., Raza U., Song J., Et al., Systemic aging fuels heart failure: molecular mechanisms and therapeutic avenues, ESC Heart Fail, (2024); Gaede P., Oellgaard J., Carstensen B., Et al., Years of life gained by multifactorial intervention in patients with type 2 diabetes mellitus and microalbuminuria: 21 years follow-up on the Steno-2 randomised trial, Diabetologia, 59, pp. 2298-2307, (2016); Dalsgaard N.B., Vilsboll T., Knop F.K., Effects of glucagon-like peptide-1 (GLP-1) receptor agonists on cardiovascular risk factors: a narrative review of head-to-head comparisons, Diabetes Obes Metab, 20, pp. 508-519, (2017); Abdin A., Wilkinson C., Aktaa S., Et al., European Society of Cardiology quality indicators update for the care and outcomes of adults with heart failure. The heart failure association of the ESC, Eur J Heart Fail, (2024); McDonagh T.A., Metra M., Adamo M., Et al., 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the heart failure association (HFA) of the ESC, Eur J Heart Fail, 24, pp. 4-131, (2022); McCormick N., Lacaille D., Bhole V., Avina-Zubieta J.A., Validity of heart failure diagnoses in administrative databases: a systematic review and meta-analysis, PLoS ONE, 9, (2014)","A.C. Carlsson; Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Solna, Sweden; email: axel.carlsson@ki.se","","John Wiley and Sons Inc","","","","","","20555822","","","39428319","English","ESC Heart Fail.","Article","Final","","Scopus","2-s2.0-85206640398"
"Hæsum L.K.E.; Cichosz S.L.; Hejlesen O.K.","Hæsum, Lisa Korsbakke Emtekær (56043957000); Cichosz, Simon Lebech (35224470300); Hejlesen, Ole Kristian (6603859869)","56043957000; 35224470300; 6603859869","Using machine learning to design a short test from a full-length test of functional health literacy in adults—The development of a short form of the Danish TOFHLA","2023","PLoS ONE","18","7 JULY","e0280613","","","","1","10.1371/journal.pone.0280613","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165881886&doi=10.1371%2fjournal.pone.0280613&partnerID=40&md5=51aa74db598da5fa0e23bc20bf36d3f0","Department of Nursing, University College of Northern Denmark, Aalborg, Denmark; Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark","Hæsum L.K.E., Department of Nursing, University College of Northern Denmark, Aalborg, Denmark, Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; Cichosz S.L., Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark; Hejlesen O.K., Faculty of Medicine, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark","Introduction Patients are compelled to become more involved in shared decision making with healthcare professionals in the self-management of chronic disease and general adherence to treatment. Therefore, it is valuable to be able to identify patients with low functional health literacy so they can be given special instructions about the management of chronic disease and medications. However, time spent by both patients and clinicians is a concern when introducing a screening instrument in the clinical setting, which raises the need for short instruments for assessing health literacy that can be used by patients without the involvement of healthcare personnel. This paper describes the development of a short version of the full-length Danish TOFHLA (DS-TOFHLA) that is easily applicable in the clinical context and where the use does not require a trained interviewer. Materials and methods Data were collected as a part of a large-scale telehomecare project (TeleCare North), which was a randomized controlled trial that included 1225 patients with chronic obstructive pulmonary disease. The DS-TOFHLA was developed solely using an algorithm-based selection of variables and multiple linear regression. A multiple linear regression model was developed using an exhaustive search strategy. Results The exhaustive search showed that the number of items in the full-length TOFHLA could be reduced from 17 numeracy items and 50 reading comprehension items to 20 reading comprehension items while maintaining a correlation of r = 0.90 between the scores from full-length and short versions. A generic model-based approach was developed, which is suitable for development of short versions of the TOFHLA in other languages, including the original American version. Conclusions This study demonstrated how a generic model-based approach could be applied in the development of a short version of the TOFHLA, thereby reducing the 67 items to 20 items in the short version. Furthermore, this study showed that the inclusion of numeracy items was not necessary. The development of the DS-TOFHLA presents an opportunity to reliably identify patients with inadequate functional health literacy in approximately 5 minutes without involvement of healthcare personnel. The approach may be used in the development of short versions of any scaling questionnaire. Copyright: © 2023 Hæsum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Adult; Denmark; Health Literacy; Humans; Language; Machine Learning; Reproducibility of Results; Surveys and Questionnaires; adult; aged; algorithm; Article; chronic obstructive lung disease; comprehension; controlled study; Danish language; DS TOFHLA; English (language); female; functional health literacy; health literacy; home care; human; intellectual assessment; language; machine learning; major clinical study; male; multiple linear regression analysis; numeracy; randomized controlled trial (topic); reading; telehealth; Denmark; language; machine learning; questionnaire; randomized controlled trial; reproducibility","","","","","","","Noncommunicable diseases, (2022); Jordan JE, Osborne RH., Chronic disease self-management education programs: challenges ahead, Medical Journal of Australia, 186, pp. 84-87, (2007); Coleman K, Austin BT, Brach C, Et al., Evidence on the Chronic Care Model in the new millennium, Health Aff, 28, pp. 75-85, (2009); Joosten EAG, DeFuentes-Merillas L, de Weert GH, Et al., Systematic review of the effects of shared decision-making on patient satisfaction, treatment adherence and health status, Psychother Psychosom, 77, pp. 219-226, (2008); Kickbusch I, Pelikan JM, Apfel F, Et al., Health literacy: the solid facts; Nutbeam D., Health promotion glossary, Health Promot, 1, pp. 113-127, (1998); Nutbeam D., Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century, Health Promot Int, 15, pp. 259-267, (2000); Duell P, Wright D, Renzaho AMN, Et al., Optimal health literacy measurement for the clinical setting: A systematic review, Patient Education and Counseling, 98, pp. 1295-1307, (2015); Nielsen-Bohlman L, Panzer AM, Kindig D a., Health Literacy: A Prescripton to End Confusion, (2004); Baker DW., The meaning and the measure of health literacy, J Gen Intern Med, 21, pp. 878-883, (2006); Sorensen K, van den Broucke S, Pelikan JM, Et al., Measuring health literacy in populations: illuminating the design and development process of the European Health Literacy Survey Questionnaire (HLS-EUQ), BMC Public Health, 13, (2013); Osborne RH, Batterham RW, Elsworth GR, Et al., The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ), BMC Public Health, 13, (2013); Haun J, McCormack L, Valerio M, Et al., Health Literacy Measurement: Health Literacy Measurement: An inventory and descriptive summary of 52 instruments, J Health Commun, (2014); Norman CD, Skinner HA., eHealth Literacy: Essential Skills for Consumer Health in a Networked World, J Med Internet Res, 8; Haesum LKE, Ehlers LH, Hejlesen OK., The long-term effects of using telehomecare technology on functional health literacy: results from a randomized trial, Public Health, 150, pp. 43-50, (2017); Emtekaer Haesum LK, Ehlers L, Hejlesen OK., Validation of the Test of Functional Health Literacy in Adults in a Danish population, Scand J Caring Sci, 29, (2015); Korsbakke Emtekaer Haesum L, Ehlers L, Hejlesen OK., Interaction between functional health literacy and telehomecare: Short-term effects from a randomized trial, Nurs Health Sci, 18, pp. 328-333, (2016); Sadeghi K., Cloze procedure: an Alternative in Language Testing Research, The Reading Matrix, 4, pp. 85-95, (2004); Parker RM, Baker DW, Williams M v, Et al., The test of functional health literacy in adults: a new instrument for measuring patients’ literacy skills, J Gen Intern Med, 10, pp. 537-541, (1995); Beaton DE, Bombardier C, Et al., Guidelines for the Process of Cross-Cultural Adaptation of Self-Report Measures; Baker DW, Williams M v, Parker RM, Et al., Development of a brief test to measure functional health literacy, Patient Educ Couns, 38, pp. 33-42, (1999); Refaeilzadeh P, Tang L, Liu H., Encyclopedia of Database Systems. Cross-Validation, (2016); Udsen FW, Lilholt PH, Hejlesen O, Et al., Effectiveness and cost-effectiveness of telehealthcare for chronic obstructive pulmonary disease: study protocol for a cluster randomized controlled trial, Trials, 15, (2014); Udsen FW, Lilholt PH, Hejlesen O, Et al., Cost-effectiveness of telehealthcare to patients with chronic obstructive pulmonary disease: Results from the Danish TeleCare North’ cluster-randomised trial, BMJ Open, 7, (2017); Houser J., Nursing research Reading, Using and Creating Evidence, (2011); Everitt B., The Cambridge Dictionary of Statistics, (2002)","L.K.E. Hæsum; Department of Nursing, University College of Northern Denmark, Aalborg, Denmark; email: lkeh@ucn.dk","","Public Library of Science","","","","","","19326203","","POLNC","37498890","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85165881886"
"Delpino F.M.; Figueiredo L.M.; Costa Â.K.; Carreno I.; da Silva L.N.; Flores A.D.; Pinheiro M.A.; da Silva E.P.; Marques G.Á.; de Oliveira Saes M.; Duro S.M.S.; Facchini L.A.; Vissoci J.R.N.; Flores T.R.; Demarco F.F.; Blumenberg C.; Filho A.D.P.C.; da Silva I.C.; Batista S.R.; Arcêncio R.A.; Nunes B.P.","Delpino, Felipe Mendes (57220929042); Figueiredo, Lílian Munhoz (57220922528); Costa, Ândria Krolow (57221246632); Carreno, Ioná (16030311500); da Silva, Luan Nascimento (57146248100); Flores, Alana Duarte (58144302400); Pinheiro, Milena Afonso (58143373700); da Silva, Eloisa Porciúncula (57901067600); Marques, Gabriela Ávila (57210019998); de Oliveira Saes, Mirelle (56022742200); Duro, Suele Manjourany Silva (56316314100); Facchini, Luiz Augusto (7004231120); Vissoci, João Ricardo Nickenig (36610859100); Flores, Thaynã Ramos (57189242652); Demarco, Flávio Fernando (57209275799); Blumenberg, Cauane (56102933600); Filho, Alexandre Dias Porto Chiavegatto (16416577700); da Silva, Inácio Crochemore (36631746500); Batista, Sandro Rodrigues (56526585100); Arcêncio, Ricardo Alexandre (16201857400); Nunes, Bruno Pereira (56022202600)","57220929042; 57220922528; 57221246632; 16030311500; 57146248100; 58144302400; 58143373700; 57901067600; 57210019998; 56022742200; 56316314100; 7004231120; 36610859100; 57189242652; 57209275799; 56102933600; 16416577700; 36631746500; 56526585100; 16201857400; 56022202600","Emergency department use and Artificial Intelligence in Pelotas: design and baseline results; [Uso serviços de serviços de urgência e emergência e Inteligência Artificial em Pelotas: protocolo e resultados iniciais]","2023","Revista Brasileira de Epidemiologia","26","","e230021","","","","1","10.1590/1980-549720230021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150153221&doi=10.1590%2f1980-549720230021&partnerID=40&md5=d3cffec4b457521a2b805c814747e785","Universidade Federal de Pelotas, RS, Pelotas, Brazil; Duke University, School of Medicine, Durham, NC, United States; Universidade de São Paulo, SP, São Paulo, Brazil; Universidade Federal de Goias, GO, Goiânia, Brazil","Delpino F.M., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Figueiredo L.M., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Costa Â.K., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Carreno I., Universidade Federal de Pelotas, RS, Pelotas, Brazil; da Silva L.N., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Flores A.D., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Pinheiro M.A., Universidade Federal de Pelotas, RS, Pelotas, Brazil; da Silva E.P., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Marques G.Á., Universidade Federal de Pelotas, RS, Pelotas, Brazil; de Oliveira Saes M., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Duro S.M.S., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Facchini L.A., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Vissoci J.R.N., Duke University, School of Medicine, Durham, NC, United States; Flores T.R., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Demarco F.F., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Blumenberg C., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Filho A.D.P.C., Universidade de São Paulo, SP, São Paulo, Brazil; da Silva I.C., Universidade Federal de Pelotas, RS, Pelotas, Brazil; Batista S.R., Universidade Federal de Goias, GO, Goiânia, Brazil; Arcêncio R.A., Universidade de São Paulo, SP, São Paulo, Brazil; Nunes B.P., Universidade Federal de Pelotas, RS, Pelotas, Brazil","Objective: To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil. Methods: The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year. Results: In total, 5,722 participants answered the survey, mostly females (66.8%), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around 30% of the sample has obesity, 14% diabetes, and 39% hypertension. Conclusion: The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state. © 2023.","Chronic diseases; Machine learning; Multimorbidity; Urgent and emergency care","Adult; Artificial Intelligence; Brazil; Emergency Service, Hospital; Female; Humans; Male; Middle Aged; Obesity; Socioeconomic Factors; adult; algorithm; Article; artificial intelligence; asthma; chronic bronchitis; chronic disease; cohort analysis; computer language; coronavirus disease 2019; data analysis; dependent variable; diabetes mellitus; emergency ward; female; household; human; hypertension; independent variable; information processing; learning algorithm; machine learning; male; malignant neoplasm; multiple chronic conditions; obesity; pandemic; prediction; prevalence; principal component analysis; quality control; questionnaire; resident; skin color; smoking; test retest reliability; training; urban area; Brazil; hospital emergency service; middle aged; obesity; socioeconomics","","","","","Foundation of Rio Grande do Sul, Brazil; Universidade Federal de Pelotas, UFPEL; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS, (21/2551-0000066-0); Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS","Research Support Foundation of Rio Grande do Sul, Brazil (FAPERGS) – grant number 21/2551-0000066-0 – Programa Pesquisa para o SUS: gestão compartilhada em saúde – PPSUS). Felipe Mendes Delpino received a doctoral fellowship from the National Council for Scientific and Technological Development (CNPq) during the writing of the manuscript. This work was supported by the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS) on the public edict 08/2020 – PPSUS (grant 21/2551-0000066-0). The study was conducted by researchers from the Postgraduate Program of Nursing and the Faculty of Nursing from the Federal University of Pelotas (UFPel).","Valentim IVL, Kruel AJ., The importance of interpersonal trust for the consolidation of Brazil’s Family Health Program, Cien Saude Colet, 12, 3, pp. 777-788, (2007); Política nacional de atenção às urgências, (2006); Guibu IA, Moraes JC, Guerra Junior AA, Costa EA, Acurcio FA, Costa KS, Et al., Main characteristics of patients of primary health care services in Brazil, Rev Saude Publica, 51, (2017); Paim J, Travassos C, Almeida C, Bahia L, Macinko J., The Brazilian health system: history, advances, and challenges, Lancet, 377, 9779, pp. 1778-1797, (2011); Castro MC, Massuda A, Almeida G, Menezes-Filho NA, Andrade MV, Noronha KVMS, Et al., Brazil’s unified health system: the first 30 years and prospects for the future, Lancet, 394, 10195, pp. 345-356, (2019); Agborsangaya CB, Lau D, Lahtinen M, Cooke T, Johnson JA., Health-related quality of life and healthcare utilization in multimorbidity: results of a cross-sectional survey, Qual Life Res, 22, 4, pp. 791-799, (2013); 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Carret MLV, Fassa AG, Domingues MR., Prevalência e fatores associados ao uso inadequado do serviço de emergência: uma revisão sistemática da literatura, Cad Saúde Pública, 25, 1, pp. 7-28, (2009); Carret MLV, Fassa AG, Kawachi I., Demand for emergency health service: Factors associated with inappropriate use, BMC Health Serv Res, 131, (2007); Alonso-Moran E, Nuno-Solinis R, Onder G, Tonnara G., Multimorbidity in risk stratification tools to predict negative outcomes in adult population, Eur J Intern Med, 26, 3, pp. 182-189, (2015); Rojas JC, Carey KA, Edelson DP, Venable LR, Howell MD, Churpek MM., Predicting intensive care unit readmission with machine learning using electronic health record data, Ann Am Thorac Soc, 15, 7, pp. 846-853, (2018); Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Et al., Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 22, pp. 2402-2410, (2016); Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Et al., Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis, Eur Heart J, 38, 7, pp. 500-507, (2017); Pan I, Nolan LB, Brown RR, Khan R, van der Boor P, Harris DG, Et al., Machine learning for social services: a study of prenatal case management in Illinois, Am J Public Health, 107, 6, pp. 938-944, (2017); Delpino FM, Costa AK, Farias SR, Chiavegatto Filho ADP, Arcencio RA, Nunes BP., Machine learning for predicting chronic diseases: a systematic review, Public Health, 205, pp. 14-25, (2022); Batista AFM., Machine Learning aplicado à saúde, (2019); Sahni N, Simon G, Arora R., Development and validation of machine learning models for prediction of 1-year mortality utilizing electronic medical record data available at the end of hospitalization in multicondition patients: a proof-of-concept study, J Gen Intern Med, 33, 6, pp. 921-928, (2018); Lima-Costa MF, Andrade FB, Souza PRB, Neri AL, Duarte YAO, Castro-Costa E, Et al., The brazilian longitudinal study of aging (ELSI-Brazil): objectives and design, Am J Epidemiol, 187, 7, pp. 1345-1353, (2018); Hallal PC, Barros FC, Silveira MF, Barros AJD, Dellagostin OA, Pellanda LC, Et al., EPICOVID19 protocol: repeated serological surveys on SARS-CoV-2 antibodies in Brazil, Cien Saude Colet, 25, 9, pp. 3573-3578, (2020)","F.M. Delpino; Pelotas, Rua Gomes Carneiro, 1, Centro, RS, CEP: 96010-610, Brazil; email: fmdsocial@outlook.com","","Assocaicao Brasileira de Pos, Gradacao em Saude Coletiva","","","","","","1415790X","","","36921129","English","Rev. Bras. Epidemiol.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85150153221"
"Hyun K.; Kim J.J.; Im K.S.; Kim Y.H.; Ryu J.H.","Hyun, Kwanyong (59661854000); Kim, Jae Jun (36068293800); Im, Kyong Shil (59661703300); Kim, Yoon Ho (58861271700); Ryu, Jeong Hwan (59229224600)","59661854000; 36068293800; 59661703300; 58861271700; 59229224600","Prediction of ipsilateral and contralateral pneumothorax using a simple chest X-ray","2025","Journal of Thoracic Disease","17","2","","898","907","9","0","10.21037/jtd-24-1729","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219219007&doi=10.21037%2fjtd-24-1729&partnerID=40&md5=c533ea7332debffbab86d9f62058783e","Department of Thoracic and Cardiovascular Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea; Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea; Department of Thoracic and Cardiovascular Surgery, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Gyeonggi-do, Uijeongbu, South Korea","Hyun K., Department of Thoracic and Cardiovascular Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea; Kim J.J., Department of Thoracic and Cardiovascular Surgery, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 271, Cheonbo-ro, Gyeonggi-do, Uijeongbu, South Korea; Im K.S., Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea; Kim Y.H., Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea; Ryu J.H., Department of Anesthesiology and Pain Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, South Korea","Background: Accurate prediction is essential for the effective management of spontaneous pneumothorax (SP). To improve prediction, this study primarily focuses on using simple chest X-rays to predict ipsilateral recurrence and contralateral occurrence of SP. Methods: All consecutive subjects diagnosed with SP from July 2017 to June 2023 were retrospectively reviewed. Ipsilateral recurrence and contralateral occurrence of SP within two years of completing treatment were analyzed. Using simple chest X-rays and clinical parameters such as age, sex, smoking, chronic obstructive pulmonary disease (COPD) and surgery, machine learning algorithms were applied to predict SP development. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to highlight the X-ray regions associated with SP development. Results: The study included 1,086 cases of SP, with 546 right-side and 540 left-side developments. Surgeries were performed in 243 right and 204 left cases. Ipsilateral recurrence occurred in 93 cases total, while contralateral occurrence occurred in 60 right and 34 left cases. For predicting ipsilateral recurrence in the young group, gradient boosting (GB) [area under curve (AUC) of 0.686, accuracy of 0.769, F1 score of 0.733, precision of 0.706, and recall of 0.769] for the right side and logistic regression (AUC of 0.628, accuracy of 0.781, F1 score of 0.753, precision of 0.737, and recall of 0.781) for the left side were the top-performing models. In the older group, K-nearest neighbors (KNN) (AUC of 0.615, accuracy of 0.801, F1 score of 0.760, precision of 0.735, and recall of 0.801) for the right side and logistic regression (AUC of 0.623, accuracy of 0.824, F1 score of 0.804, precision of 0.794, and recall of 0.824) for the left side were the best models. For predicting contralateral occurrence in the young group, random forest (RF) (AUC of 0.597, accuracy of 0.774, F1 score of 0.741, precision of 0.709, and recall of 0.774) for the right side and KNN (AUC of 0.650, accuracy of 0.893, F1 score of 0.849, precision of 0.809, and recall of 0.893) for the left side were the most effective models. In the older group, logistic regression (AUC of 0.630, accuracy of 0.935, F1 score of 0.914, precision of 0.894, and recall of 0.935) for the right side and neural network (NN) (AUC of 0.765, accuracy of 0.961, F1 score of 0.948, precision of 0.936, and recall of 0.961) for the left side were the top performers. Grad-CAM analysis revealed that apical lung portions were strongly associated with both ipsilateral recurrence and contralateral occurrence of SP. Conclusions: The results of this study suggest that machine learning algorithms using simple X-rays and basic clinical data can predict SP development with fair performance. The apical regions of the lung were strongly associated with SP development, consistent with clinical knowledge. © AME Publishing Company.","chest X-ray; machine learning; Prediction; spontaneous pneumothorax (SP)","","","","","","","","Kim IS, Kim JJ, Han JW, Et al., Conservative treatment for recurrent secondary spontaneous pneumothorax in patients with a long recurrence-free interval, J Thorac Dis, 12, pp. 2459-2466, (2020); Robinson PD, Cooper P, Ranganathan SC., Evidence-based management of paediatric primary spontaneous pneumothorax, Paediatr Respir Rev, 10, pp. 110-117, (2009); Mukhtar O, Shrestha B, Khalid M, Et al., Characteristics of 30-day readmission in spontaneous pneumothorax in the United States: a nationwide retrospective study, J Community Hosp Intern Med Perspect, 9, pp. 215-220, (2019); Huang TW, Lee SC, Cheng YL, Et al., Contralateral recurrence of primary spontaneous pneumothorax, Chest, 132, pp. 1146-1150, (2007); Park S, Jang HJ, Song JH, Et al., Do Blebs or Bullae on High-Resolution Computed Tomography Predict Ipsilateral Recurrence in Young Patients at the First Episode of Primary Spontaneous Pneumothorax?, Korean J Thorac Cardiovasc Surg, 52, pp. 91-99, (2019); Chang JM, Lai WW, Yen YT, Et al., Apex-to-Cupola Distance Following VATS Predicts Recurrence in Patients With Primary Spontaneous Pneumothorax, Medicine (Baltimore), 94, (2015); Zarfati A, Pardi V, Frediani S, Et al., Conservative and operative management of spontaneous pneumothorax in children and adolescents: Are we abusing of CT?, Pediatr Pulmonol, 59, pp. 41-47, (2024); Chiu CY, Chen TP, Wang CJ, Et al., Factors associated with proceeding to surgical intervention and recurrence of primary spontaneous pneumothorax in adolescent patients, Eur J Pediatr, 173, pp. 1483-1490, (2014); Riveiro-Blanco V, Pou-Alvarez C, Ferreiro L, Et al., Recurrence of primary spontaneous pneumothorax: Associated factors, Pulmonology, 28, pp. 276-283, (2022); Akkas Y, Peri NG, Kocer B, Et al., A novel structural risk index for primary spontaneous pneumothorax: Ankara Numune Risk Index, Asian J Surg, 40, pp. 249-253, (2017); Tsuboshima K, Matoba Y, Wakahara T., Contralateral bulla neogenesis associated with postoperative recurrences of primary spontaneous pneumothorax in young patients, J Thorac Dis, 11, pp. 5124-5129, (2019); Citak N, Ozdemir S, Kose S., Could the probability of surgical indication be determined after first episode of primary spontaneous pneumothorax?, Gen Thorac Cardiovasc Surg, 71, pp. 472-479, (2023); Primavesi F, Jager T, Meissnitzer T, Et al., First Episode of Spontaneous Pneumothorax: CT-based Scoring to Select Patients for Early Surgery, World J Surg, 40, pp. 1112-1120, (2016); Jang HJ, Lee JH, Nam SH, Et al., Fate of contralateral asymptomatic bullae in patients with primary spontaneous pneumothorax, Eur J Cardiothorac Surg, 58, pp. 365-370, (2020); Maniwa T, Saito Y, Saito T, Et al., Evaluation of chest computed tomography in patients after pneumonectomy to predict contralateral pneumothorax, Gen Thorac Cardiovasc Surg, 57, pp. 28-32, (2009); Jeong JY, Shin AY, Ha JH, Et al., Natural History of Contralateral Bullae/Blebs After Ipsilateral Video-Assisted Thoracoscopic Surgery for Primary Spontaneous Pneumothorax: A Retrospective Cohort Study, Chest, 162, pp. 1213-1222, (2022); Noh D, Keum DY, Park CK., Outcomes of Contralateral Bullae in Primary Spontaneous Pneumothorax, Korean J Thorac Cardiovasc Surg, 48, pp. 393-397, (2015); Kao CN, Chou SH, Tsai MJ, Et al., Male adolescents with contralateral blebs undergoing surgery for primary spontaneous pneumothorax may benefit from simultaneous contralateral blebectomies, BMC Pulm Med, 21, (2021); Jeon HW, Kim YD, Sim SB., Use of imaging studies to predict postoperative recurrences of primary spontaneous pneumothorax, J Thorac Dis, 12, pp. 2683-2690, (2020); Woo W, Kim CH, Kim BJ, Et al., Early Postoperative Pneumothorax Might Not Be 'True' Recurrence, J Clin Med, 10, (2021); Visuna L, Yang D, Garcia-Blas J, Et al., Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning, BMC Med Imaging, 22, (2022); Godec P, Pancur M, Ilenic N, Et al., Democratized image analytics by visual programming through integration of deep models and small-scale machine learning, Nat Commun, 10, (2019); Rahman MF, Tseng TB, Pokojovy M, Et al., Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images, Diagnostics (Basel), 14, (2024); Iyer TJ, Joseph Raj AN, Ghildiyal S, Et al., Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images, PeerJ Comput Sci, 7, (2021); Tragesser CJ, Hafezi N, Colgate CL, Et al., Early Surgery for Spontaneous Pneumothorax Associated With Reduced Recurrence, Resource Utilization, J Surg Res, 269, pp. 44-50, (2022); Liu YW, Chang PC, Chang SJ, Et al., Simultaneous bilateral thoracoscopic blebs excision reduces contralateral recurrence in patients undergoing operation for ipsilateral primary spontaneous pneumothorax, J Thorac Cardiovasc Surg, 159, pp. 1120-1127, (2020); Shin B, Kim SB, Kim CW, Et al., Risk factors related to the recurrence of pneumothorax in patients with emphysema, J Thorac Dis, 12, pp. 5802-5810, (2020)","J.J. Kim; Department of Thoracic and Cardiovascular Surgery, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu, 271, Cheonbo-ro, Gyeonggi-do, South Korea; email: medkjj@hanmail.net","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85219219007"
"Jiang S.; Qi B.; Xie S.; Xie Z.; Zhang H.; Jiang W.","Jiang, Sijie (57225914401); Qi, Bo (59476266500); Xie, Shaobing (57191482858); Xie, Zhihai (22137076300); Zhang, Hua (37078663100); Jiang, Weihong (57221919666)","57225914401; 59476266500; 57191482858; 22137076300; 37078663100; 57221919666","Development and Validation of an Explainable Prediction Model for Postoperative Recurrence in Pediatric Chronic Rhinosinusitis","2025","Otolaryngology - Head and Neck Surgery (United States)","172","3","","1044","1052","8","0","10.1002/ohn.1092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212262349&doi=10.1002%2fohn.1092&partnerID=40&md5=4decc2ce0f97cdeeafe5bca7dd2789b2","Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China; Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China; Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China; School of Computer Science and Engineering, Central South University, Changsha, China","Jiang S., Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China, Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China, Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China; Qi B., School of Computer Science and Engineering, Central South University, Changsha, China; Xie S., Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China, Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China, Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China; Xie Z., Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China, Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China, Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China; Zhang H., Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China, Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China, Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China; Jiang W., Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China, Hunan Province Key Laboratory of Otolaryngology Critical Diseases, Xiangya Hospital of Central South University, Changsha, China, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital of Central South University, Changsha, China, Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Xiangya Hospital of Central South University, Changsha, China","Objective: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS). Study Design: A decision analysis was performed with retrospective clinical data. Setting: Recurrent group and nonrecurrent group. Methods: This retrospective study included 148 pediatric CRS treated with functional endoscopic sinus surgery from January 2015 to January 2022. We collected demographic characteristics and peripheral blood inflammatory indices, and calculated inflammation indices. Models were trained with 3 ML algorithms and compared their predictive performance using the area under the receiver operating characteristic (AUC) curve. Shapley Additive Explanations and Ceteris Paribus profiles were used for model interpretation. The final model was transformed into a web for interactive visualization. Results: Among the 3 ML models, the Random Forest (RF) model demonstrated the best discriminative ability (AUC = 0.728). After reducing features based on importance and tuning parameters, the final RF model, including 4 features (systemic immune inflammation index (SII), pan-immune-inflammation value (PIV) and percentage of eosinophils (E%) and lymphocytes (L%)), showed good predictive performance in internal validation (AUC = 0.779). Global interpretation of the model suggested that L% and E% substantially contribute to the overall model. Local interpretation revealed a nonlinear relationship between the included features and model predictions. To enhance its clinical utility, the model was converted into a web (https://juice153.shinyapps.io/CRSRecurrencePrediction/). Conclusion: Our ML model demonstrated promising accuracy in predicting postoperative recurrence in pediatric CRS, revealing a complex nonlinear relationship between postoperative recurrence and the features SII, PIV, L%, and E%. © 2024 American Academy of Otolaryngology–Head and Neck Surgery Foundation.","functional endoscopic sinus surgery; machine learning; pediatrics; Random Forest","Adolescent; Child; Child, Preschool; Chronic Disease; Decision Support Techniques; Endoscopy; Female; Humans; Machine Learning; Male; Predictive Value of Tests; Recurrence; Retrospective Studies; Rhinitis; Rhinosinusitis; Sinusitis; antibiotic agent; corticosteroid; steroid; algorithm; allergic rhinitis; area under the curve; Article; asthma; blood cell count; child; chronic rhinosinusitis; cohort analysis; computer assisted tomography; controlled study; development; diagnostic test accuracy study; electronic medical record; electronic medical record system; endoscopic sinus surgery; endoscopy; eosinophil percentage; explainable machine learning; female; fungal sinusitis; human; inflammation; least absolute shrinkage and selection operator; lymphocyte monocyte ratio; machine learning; major clinical study; male; nasal endoscopy; neutrophil lymphocyte ratio; paranasal sinus tumor; pediatrics; physical examination; platelet count; prediction; predictive model; random forest; receiver operating characteristic; recurrent disease; retrospective study; Shapley additive explanation; systemic immune inflammation index; validation process; adolescent; chronic disease; decision support system; machine learning; predictive value; preschool child; procedures; recurrent disease; rhinitis; rhinosinusitis; sinusitis; surgery","","","R 4.3.0; SPSS version 26, IBM","IBM","Central South University, CSU; National Natural Science Foundation of China, NSFC, (82171118); National Natural Science Foundation of China, NSFC","Funding text 1: This work was carried out in part using computing resources at the High-Performance Computing Center of Central South University.; Funding text 2: This research was supported by the National Natural Science Foundation of China (No. 82171118). 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Watson D.S., Krutzinna J., Bruce I.N., Et al., Clinical applications of machine learning algorithms: beyond the black box, BMJ, 364, (2019); Su M., Feng G., Liu Z., Li Y., Wang R., Tapping on the Black Box: how is the scoring power of a machine-learning scoring function dependent on the training set?, J Chem Inf Model, 60, 3, pp. 1122-1136, (2020)","H. Zhang; Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China; email: entxy@126.com; W. Jiang; Department of Otolaryngology–Head and Neck Surgery, Xiangya Hospital of Central South University, Changsha, China; email: jiangwh68@126.com","","John Wiley and Sons Inc","","","","","","01945998","","OTOLD","39686801","English","Otolaryngol. Head Neck Surg.","Article","Final","","Scopus","2-s2.0-85212262349"
"Morena D.; Izquierdo J.L.; Rodríguez J.; Cuesta J.; Benavent M.; Perralejo A.; Rodríguez J.M.","Morena, Diego (57215197044); Izquierdo, José Luis (7102685483); Rodríguez, Juan (58831721700); Cuesta, Jesús (58782888300); Benavent, María (58458005900); Perralejo, Alejandro (58782888400); Rodríguez, José Miguel (58881557500)","57215197044; 7102685483; 58831721700; 58782888300; 58458005900; 58782888400; 58881557500","The Clinical Profile of Patients with COPD Is Conditioned by Age","2023","Journal of Clinical Medicine","12","24","7595","","","","1","10.3390/jcm12247595","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180692407&doi=10.3390%2fjcm12247595&partnerID=40&md5=500679bc0ba569372438376bb1425e89","Pulmonology Department, Respiratory Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain; Doctoral Program in Health Sciences, University of Alcalá, Alcalá de Henares, 28871, Spain; Department of Medicine and Medical Specialties, University of Alcalá, Alcalá de Henares, 28871, Spain; Geriatric Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain; SAVANA, Madrid, 28013, Spain; Respiratory Medicine, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, 28805, Spain","Morena D., Pulmonology Department, Respiratory Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain, Doctoral Program in Health Sciences, University of Alcalá, Alcalá de Henares, 28871, Spain; Izquierdo J.L., Pulmonology Department, Respiratory Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain, Department of Medicine and Medical Specialties, University of Alcalá, Alcalá de Henares, 28871, Spain; Rodríguez J., Geriatric Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain; Cuesta J., Department of Medicine and Medical Specialties, University of Alcalá, Alcalá de Henares, 28871, Spain; Benavent M., SAVANA, Madrid, 28013, Spain; Perralejo A., SAVANA, Madrid, 28013, Spain; Rodríguez J.M., Department of Medicine and Medical Specialties, University of Alcalá, Alcalá de Henares, 28871, Spain, Respiratory Medicine, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, 28805, Spain","In recent years, many studies have analyzed the importance of integrating time, or aging, into the equation that relates genetics and the environment to the development and origin of COPD. Under conditions of daily clinical practice, our study attempts to identify the differences in the clinical profile of patients with COPD according to age and the impact on the global burden of the disease. This study is non-interventional and observational, using artificial intelligence and data captured from electronic medical records. The study population included patients who were diagnosed with COPD between 2011 and 2021. A total of 73,901 patients had a diagnosis of COPD. The mean age was 73 years (95% CI: 72.9–73.1), and 56,763 were men (76.8%). We observed a specific prevalence of obesity, heart failure, depression, and hiatal hernia in women (p < 0.001), and ischemic heart disease and obstructive sleep apnea (OSA) in men (p < 0.001). In the analysis by age ranges, a progressive increase in cardiovascular risk factors was observed with age. In conclusion, in a real-life setting, COPD is a disease that primarily affects older subjects and frequently presents with comorbidities that are decisive in the evolutionary course of the disease. © 2023 by the authors.","age; artificial intelligence; big data; comorbidities; COPD; daily clinical practice","age; aged; Article; artificial intelligence; cardiovascular disease; cardiovascular risk factor; chronic obstructive lung disease; clinical practice; comorbidity; controlled study; convolutional neural network; data protection; depression; disease burden; electronic medical record; false negative result; female; genotype environment interaction; heart failure; hiatus hernia; hospital admission; hospital mortality; hospitalization; human; information processing; information retrieval; ischemic heart disease; machine learning; major clinical study; male; obesity; observational study; prevalence; quality control; retrospective study; sex difference; sleep apnea syndromes; Systematized Nomenclature of Medicine","","","","","Universidad de Alcalá, UAH","This project was funded by the Chair of Inflammatory Diseases of the Airways, University of Alcalá.","Soriano J.B., Kendrick P.J., Paulson K.R., Gupta V., Abrams E.M., Adedoyin R.A., Adhikari T.B., Advani S.M., Agrawal A., Ahmadian E., Et al., Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017, Lancet Respir. Med, 8, pp. 585-596, (2020); Lange P., Celli B., Agusti A., Jensen G.B., Divo M., Faner R., Guerra S., Marott J.L., Martinez F.D., Martinez-Camblor P., Et al., Lung-function trajectories leading to chronic obstructive pulmonary disease, N. Engl. J. Med, 373, pp. 111-122, (2015); Calverley P.M.A., Anderson J.A., Delli B., Ferguson G.T., Jenkings C., Jones P.W., Yates J.C., Vestbo J., Salmeterol and fluticasone propionate and survival in chronic obstructive pulmonary disease, N. Engl. J. Med, 356, pp. 775-789, (2007); Lipson D.A., Barnhart F., Brealey N., Brooks J., Criner G.J., Day N.C., Dransfield M.T., Halpin D.M.G., Han M.K., Jones C.E., Et al., Once-Daily Single-Inhaler Triple versus Dual Therapy in Patients with COPD, N. Engl. J. Med, 378, pp. 1671-1680, (2018); Rabe K.F., Martinez F.J., Ferguson G.T., Wang C., Singh D., Wedzicha J.A., Trivedi R., St Rose E., Ballal S., McLaren J., Et al., Triple Inhaled Therapy at Two Glucocorticoid Doses in Moderate-to-Very-Severe COPD, N. Engl. J. Med, 383, pp. 35-48, (2020); Tashkin D.P., Celli B., Senn S., Burkhart D., Kesten S., Menjogee S.M., Decramer M., A 4-year trial of tiotropium in chronic obstructive pulmonary disease, N. Engl. J. Med, 359, pp. 1543-1554, (2008); Wedzicha J.A., Banerji D., Chapman K.R., Vestbo J., Roche N., Ayers R.T., Thach C., Fogel R., Patalano F., Vogelmeier C.F., Et al., Indacaterol-Glycopyrronium versus Salmeterol-Fluticasone for COPD, N. Engl. J. Med, 374, pp. 2222-2234, (2016); Lucas-Ramos P., Izquierdo-Alonso J.L., Rodriguez-Gonzalez Moro J.M., Bellon-Cano J.M., Ancochea-Bermudez J., Calle-Rubio M., Calvo-Corbella E., Molina-Paris J., Perez-Rodriguez E., Pons S., Asociación de factores de riesgo cardiovascular y EPOC. Resultados de un estudio epidemiológico (estudio ARCE), Arch. Bronconeumol, 238, pp. 233-238, (2008); Agusti A., Calverley P.M.A., Celli B., Coxson H.O., Edwards L.D., Lomas D.A., MacNee W., Miller B.E., Rennard S., Silverman E.K., Characterisation of COPD heterogeneity in the ECLIPSE cohorte, Respir. Res, 11, (2010); Pozo-Rodriguez F., Alvarez C.J., Castro-Acosta A., Moreno C.M., Capelastegui A., Esteban C., Carcereny C.H., Lopez-Campos J.L., Alonso J.L.I., Quilez A.L., Clinical audit of patients admitted to hospital in Spain due to exacerbation of COPD (AUDIPOC study): Method and organisation, Arch. Bronconeumol, 46, pp. 349-357, (2010); Wells J.M., Criner G.J., Halpin D.M.G., Han M.K., Jain R., Lange P., Lipson D.A., Martinez F.J., Midwinter D., Singh D., Et al., Mortality risk and serious cardiopulmonary events in moderate-to-severe COPD: Post hoc analysis of the IMPACT trial, Chronic Obstr. Pulm. Dis, 10, pp. 33-45, (2023); Verbeken E.K., Cauberghs M., Mertens I., Clement J., Lauweryns J.M., Van de Woestijne K.P., The senile lung. Comparison with normal and emphysematous lungs. 2. Functional aspects, Chest, 101, pp. 800-809, (1992); Agusti A., Melen E., DeMeo D., Breyer-Kohansal R., Faner R., Pathogenesis of chronic obstructive pulmonary disease: Understanding the contributions of gene–environment interactions across the lifespan, Lancet, 10, pp. 512-524, (2022); von Elm E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P., The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies, Lancet, 370, pp. 1453-1457, (2007); Izquierdo J.L., Morena D., Gonzalez Y., Paredero J.M., Perez B., Graziani D., Gutierrez M., Rodriguez J.M., Clinical Management of COPD in a Real-World Setting. A Big Data Analysis, Arch. Bronconeumol, 57, pp. 94-100, (2021); Morena D., Fernandez J., Campos C., Castillo M., Lopez G., Benavent M., Izquierdo J.L., Clinical Profile of Patients with Idiopathic Pulmonary Fibrosis in Real Life, J. Clin. Med, 12, (2023); Izquierdo J.L., Rodriguez J.M., Almonacid C., Benavent M., Arroyo R., Agusti A., Real-life burden of hospitalizations due to copd exacerbations in Spain: A big-data analysis, ERJ Open Res, 8, pp. 00141-02022, (2022); Canales L., Menke S., Marchesseau S., D'Agostino A., del Rio-Bermudez C., Taberna M., Tello J., Assessing the performance of clinical natural language processing systems: Development of an evaluation methodology, JMIR Med. Inform, 9, (2021); Benson T., Principles of Health Interoperability HL7 and SNOMED, (2012); Agusti A., Edwards L.D., Rennard S.I., MacNee W., Tal-Singer R., Miller B.E., Vestbo J., Lomas D.A., Calverley P.M.A., Wouters E., Et al., Persistent systemic inflammation is associated with poor clinical outcomes in COPD: A novel phenotype, PLoS ONE, 7, (2012); Divo M., Cote C., de Torres J.P., Casanova C., Marin J.M., Pinto-Plata V., Zulueta J., Cabrera C., Zagaceta J., Hunninghake G., Et al., Comorbidities and risk of mortality in patients with chronic obstructive pulmonary disease, Am. J. Respir. Crit. Care Med, 186, pp. 155-161, (2012); Izquierdo J.L., Martinez A., Guzman E., De Lucas P., Rodriguez J.M., Lack of association of ischemic heart disease with COPD when taking into consideration classical cardiovascular risk factors, Int. J. Chronic Obstr. Pulm. Dis, 5, pp. 387-394, (2010); Mollica M., Aronne L., Paoli G., Flora M., Mazzeo G., Tartaglione S., Polito R., Tranfa C., Ceparano M., Komici K., Et al., Elderly with COPD: Comoborbitidies and systemic consequences, J. Gerontol. Geriatr, 69, pp. 32-44, (2021); Putcha N., Puhan M.A., Hansel N.N., Drummond M.B., Boyd C.M., Impact of comorbidities on self-rated health in self-reported COPD: An analysis of NHANES 2001–2008, COPD, 10, pp. 324-332, (2013); Van Manen J.G., Bindels P.J., Dekker E.W., Ijzermans C., Bottema B., Van Der Zee J., Schade E., Added value of co-morbidity in predicting health-related quality of life in COPD patients, Respir. Med, 95, pp. 496-504, (2001); Miller J., Edwards L.D., Agusti A., Bakke P., Calverley P.M., Celli B., Coxson H.O., Crim C., Lomas D.A., Miller B.E., Et al., Comorbidity, systemic inflammation and outcomes in the ECLIPSE cohort, Respir. Med, 107, pp. 1376-1384, (2013); Soler-Cataluna J.J., Martinez-Garcia M.A., Roman Sanchez P., Salcedo E., Navarro M., Ochando R., Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease, Thorax, 60, pp. 925-931, (2005); Huber M.B., Wacker M.E., Vogelmeier C.F., Leidl R., Excess costs of comorbidities in chronic obstructive pulmonary disease: A systematic review, PLoS ONE, 10, (2015); Corrao S., Santalucia P., Argano C., Djade C.D., Barone E., Tettamanti M., Pasina L., Franchi C., Eldin T.K., Marengoni A., Et al., Gender-differences in disease distribution and outcome in hospitalized elderly: Data from the REPOSI study, Eur. J. Intern. Med, 25, pp. 617-623, (2014); Schnell K., Weiss C.O., Lee T., Krishnan J.A., Leff B., Wolff J.L., Boyd C., The prevalence of clinically-relevant comorbid conditions in patients with physician-diagnosed COPD: A cross-sectional study using data from NHANES 1999–2008, BMC Pulm. Med, 12, (2012); Schane R.E., Walter L.C., Dinno A., Covinsky K.E., Walter L.C., Prevalence and risk factors for depressive symptoms in persons with chronic obstructive pulmonary disease, J. Gen. Intern. Med, 23, pp. 1757-1762, (2008); Soriano J.B., Alfageme I., Miravitlles M., de Lucas P., Soler-Cataluna J.J., Garcia-Rio F., Casanova C., Rodriguez Gonzalez-Moro J.M., Cosio B.G., Sanchez G., Et al., Prevalence and Determinants of COPD in Spain: EPISCAN II, Arch. Bronconeumol. Engl. Ed, 57, pp. 61-69, (2021); Fortin M., Stewart M., Poitras M.E., Almirall J., Maddocks H., A systematic review of prevalence studies on multimorbidity: Towards a more uniform methodology, Ann. Fam. Med, 10, pp. 142-151, (2012); Patient-centered care for older adults with multiple chronic conditions: A stepwise approach from the American Geriatrics Society, J. Am. Geriatr. Soc, 60, pp. 1957-1968, (2012)","D. Morena; Pulmonology Department, Respiratory Medicine, Hospital Universitario de Guadalajara, Guadalajara, 19002, Spain; email: diegomorenavalles6@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20770383","","","","English","J. Clin. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85180692407"
"Vasiliauskienė O.; Vasiliauskas D.; Kontrimienė A.; Jaruševičienė L.; Liseckienė I.","Vasiliauskienė, Olga (58724993500); Vasiliauskas, Dovydas (57994498100); Kontrimienė, Aušrinė (55831336600); Jaruševičienė, Lina (12802506800); Liseckienė, Ida (23975064000)","58724993500; 57994498100; 55831336600; 12802506800; 23975064000","Assessment of Quality of Life in Lithuanian Patients with Multimorbidity Using the EQ-5D-5L Questionnaire","2025","Medicina (Lithuania)","61","2","292","","","","0","10.3390/medicina61020292","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218915930&doi=10.3390%2fmedicina61020292&partnerID=40&md5=e1dc8a9ee3da72b6a0b7cc2db79640b1","Department of Family Medicine, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania; Department of Chemistry, The University of Chicago, Chicago, 60637, IL, United States; Department of Public Health, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania","Vasiliauskienė O., Department of Family Medicine, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania; Vasiliauskas D., Department of Chemistry, The University of Chicago, Chicago, 60637, IL, United States; Kontrimienė A., Department of Family Medicine, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania; Jaruševičienė L., Department of Family Medicine, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania; Liseckienė I., Department of Family Medicine, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania, Department of Public Health, Lithuanian University of Health Sciences, LT, Kaunas, 44307, Lithuania","Background and Objectives: Despite the critical importance of effective healthcare management for patients with multimorbidity, robust and reliable tools for assessing health-related quality of life in Lithuania remain scarce. We aim to identify trends in the quality of life of patients with multimorbidity and to evaluate the effectiveness of the Lithuanian version of the EuroQol EQ-5D-5L questionnaire. Materials and Methods: The study included patients between the ages of 40 and 85 (N = 498) who had at least two chronic conditions, arterial hypertension being a prerequisite. The participants completed a comprehensive set of questionnaires specifically prepared for the TELELISPA “Improved healthcare quality for patients with multimorbidity in Lithuania” project which included the translated EQ-5D-5L questionnaire. The predictive validity of the EQ-5D-5L questionnaire was assessed using correlations with the SF-36 and EQ-VAS scores, a random forest regression model. Reliability was evaluated using Cronbach’s alpha and inter-item correlations. Trends in the quality of life in different patient groups were assessed with Chi-square tests. Results: The EQ-5D-5L questionnaire demonstrated high reliability and validity with a Cronbach’s alpha value of 0.737, EQ-5D-5L random forest machine learning regression model RMSE value of 0.1396, and adequate scores from other measures. Lower quality of life was found in patients with multimorbidity who had chronic conditions such as angina pectoris, heart failure, atrial fibrillation, or joint diseases, as well as the patients who were older than 60 years of age, women, or unemployed. Different aspects of quality of life were also significantly negatively impacted by diabetes, asthma, and chronic kidney disease. Heart failure, joint diseases, and older age had the biggest negative effect on quality of life. Conclusions: It is found that the Lithuanian EQ-5D-5L questionnaire is suitable for the assessment of the quality of life in patients with multimorbidity and indicates lower quality of life among those with specific cardiovascular and joint disorder chronic conditions and, in particular, demographic groups. © 2025 by the authors.","chronic disease; EQ-5D-5L; multimorbidity; primary healthcare; quality of life; SF-36","","","","","","European Union Fund Investment Action Program, (08.4.2-ESFA-K-616-01-0003)","The study was funded by the European Union Fund Investment Action Program grant number \u201C08.4.2-ESFA-K-616-01-0003\u201D as a part of the TELELISPA project.","Salisbury C., Johnson L., Purdy S., Valderas J.M., Montgomery A.A., Epidemiology and impact of multimorbidity in primary care: A retrospective cohort study, Br. J. Gen. Pract, 61, pp. e12-e21, (2011); Navickas R., Visockiene Z., Puronaite R., Rukseniene M., Kasiulevicius V., Jureviciene E., Prevalence and structure of multiple chronic conditions in Lithuanian population and the distribution of the associated healthcare resources, Eur. J. Intern. Med, 26, pp. 160-168, (2015); Jureviciene E., Onder G., Visockiene Z., Puronaite R., Petrikonyte D., Gargalskaite U., Kasiulevicius V., Navickas R., Does multimorbidity still remain a matter of the elderly: Lithuanian national data analysis, Health Policy, 122, pp. 681-686, (2018); Soley-Bori M., Ashworth M., Bisquera A., Dodhia H., Lynch R., Wang Y., Fox-Rushby J., Impact of multimorbidity on healthcare costs and utilisation: A systematic review of the UK literature, Br. J. Gen. Pract, 71, pp. e39-e46, (2020); Vaitkaitiene E., Makari J., Zaborskis A., Conception of quality of life and health-related quality-of-life investigations in children population, Medicina, 43, (2007); Tran P.B., Kazibwe J., Nikolaidis G.F., Linnosmaa I., Rijken M., van Olmen J., Costs of multimorbidity: A systematic review and meta-analyses, BMC Med, 20, (2022); Makovski T.T., Schmitz S., Zeegers M.P., Stranges S., van den Akker M., Multimorbidity and quality of life: Systematic literature review and meta-analysis, Ageing Res. Rev, 53, (2019); Fortin M., Lapointe L., Hudon C., Vanasse A., Ntetu A.L., Maltais D., Multimorbidity and quality of life in primary care: A systematic review, Health Qual. Life Outcomes, 2, (2004); Lee J.E., Lee J., Shin R., Oh O., Lee K.S., Treatment burden in multimorbidity: An integrative review, BMC Prim. Care, 25, (2024); Hajek A., Kretzler B., Konig H.-H., Multimorbidity, Loneliness, and Social Isolation. A Systematic Review, Int. J. Environ. Res. Public Health, 17, (2020); Duncan P., Murphy M., Man M.-S., Chaplin K., Gaunt D., Salisbury C., Development and validation of the Multimorbidity Treatment Burden Questionnaire (MTBQ), BMJ Open, 8, (2020); Jenkinson C., Coulter A., Wright L., Short form 36 (SF36) health survey questionnaire: Normative data for adults of working age, BMJ, 306, pp. 1437-1440, (1993); Lins L., Carvalho F.M., SF-36 total score as a single measure of health-related quality of life: Scoping review, SAGE Open Med, 4, (2016); Herdman M., Gudex C., Lloyd A., Janssen M., Kind P., Parkin D., Bonsel G., Badia X., Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L), Qual. Life Res, 20, pp. 1727-1736, (2011); Devlin N., Pickard S., Busschbach J., The Development of the EQ-5D-5L and its Value Sets, Value Sets for EQ-5D-5L: A Compendium, Comparative Review & User Guide, (2022); Derkach M., Al Sayah F., Ohinmaa A., Svenson L.W., Johnson J.A., Comparative performance of the EuroQol EQ-5D-5L and the CDC healthy days measures in assessing population health, J. Patient-Rep. Outcomes, 6, (2022); Feng Y., Devlin N., Herdman M., Assessing the health of the general population in England: How do the three- and five-level versions of EQ-5D compare?, Health Qual. Life Outcomes, 13, (2015); Golicki D., Jakubczyk M., Graczyk K., Niewada M., Valuation of EQ-5D-5L Health States in Poland: The First EQ-VT-Based Study in Central and Eastern Europe, PharmacoEconomics, 37, pp. 1165-1176, (2019); Tavakol M., Dennick R., Making sense of Cronbach’s alpha, Int. J. Med. Educ, 2, pp. 53-55, (2011); Piedmont R.L., Inter-item Correlations, Encyclopedia of Quality of Life and Well-Being Research, pp. 3303-3304, (2014); Breiman L., Random Forests, Mach. Learn, 45, pp. 5-32, (2001); Saraiva P., On Shannon entropy and its applications, Kuwait J. Sci, 50, pp. 194-199, (2023); Mlynczak K., Golicki D., Validity of the EQ-5D-5L questionnaire among the general population of Poland, Qual. Life Res, 30, pp. 817-829, (2021); Ferreira P.L., Pereira L.N., Antunes P., Ferreira L.N., EQ-5D-5L Portuguese population norms, Eur. J. Health Econ, 24, pp. 1411-1420, (2023); Hernandez G., Garin O., Pardo Y., Vilagut G., Pont A., Suarez M., Neira M., Rajmil L., Gorostiza I., Ramallo-Farina Y., Et al., Validity of the EQ-5D-5L and reference norms for the Spanish population, Qual. Life Res, 27, pp. 2337-2348, (2018); Agborsangaya C.B., Lahtinen M., Cooke T., Johnson J.A., Comparing the EQ-5D 3L and 5L: Measurement properties and association with chronic conditions and multimorbidity in the general population, Health Qual. Life Outcomes, 12, (2014); Li C., Ford E.S., Mokdad A.H., Balluz L.S., Brown D.W., Giles W.H., Clustering of Cardiovascular Disease Risk Factors and Health-Related Quality of Life among US Adults, Value Health, 11, pp. 689-699, (2008); Ko H.-Y., Lee J.-K., Shin J.-Y., Jo E., Health-Related Quality of Life and Cardiovascular Disease Risk in Korean Adults, Korean J. Fam. Med, 36, pp. 349-356, (2015); de Visser C.L., Bilo H.J.G., Groenier K.H., de Visser W., Meyboom-de Jong B., The influence of cardiovascular disease on quality of life in type 2 diabetics, Qual. Life Res, 11, pp. 249-261, (2002); Kontoangelos K., Soulis D., Soulaidopoulos S., Antoniou C.K., Tsiori S., Martinaki S., Mourikis I., Tsioufis K., Papageorgiou C., Katsi V., Health Related Quality of Life and Cardiovascular Risk Factors, Behav. Med, 50, pp. 186-194, (2024); Mallamaci F., Pisano A., Tripepi G., Physical activity in chronic kidney disease and the EXerCise Introduction To Enhance trial, Nephrol. Dial. Transplant, 35, pp. ii18-ii22, (2020); Aldossari K.K., Shubair M.M., Al-Zahrani J., Alduraywish A.A., AlAhmary K., Bahkali S., Aloudah S.M., Almustanyir S., Al-Rizqi L., El-Zahaby S.A., Et al., Association between Chronic Pain and Diabetes/Prediabetes: A Population-Based Cross-Sectional Survey in Saudi Arabia, Pain Res. Manag, 2020, (2020); Iezzoni L.I., McCarthy E.P., Davis R.B., Siebens H., Mobility difficulties are not only a problem of old age, J. Gen. Intern. Med, 16, pp. 235-243, (2001); Garratt A.M., Hansen T.M., Augestad L.A., Rand K., Stavem K., Norwegian population norms for the EQ-5D-5L: Results from a general population survey, Qual. Life Res, 31, pp. 517-526, (2022); Bartley E.J., Fillingim R.B., Sex differences in pain: A brief review of clinical and experimental findings, Br. J. Anaesth, 111, pp. 52-58, (2013); Tinetti M.E., Naik A.D., Dindo L., Costello D.M., Esterson J., Geda M., Rosen J., Hernandez-Bigos K., Smith C.D., Ouellet G.M., Et al., Association of Patient Priorities-Aligned Decision-Making With Patient Outcomes and Ambulatory Health Care Burden Among Older Adults With Multiple Chronic Conditions: A Nonrandomized Clinical Trial, JAMA Intern. Med, 179, pp. 1688-1697, (2019)","O. Vasiliauskienė; Department of Family Medicine, Lithuanian University of Health Sciences, Kaunas, LT, 44307, Lithuania; email: olga.vasiliauskiene@lsmu.lt","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","1010660X","","","","English","Medicina","Article","Final","","Scopus","2-s2.0-85218915930"
"Staudner S.T.; Leininger S.B.; Vogel M.J.; Mustroph J.; Hubauer U.; Meindl C.; Wallner S.; Lehn P.; Burkhardt R.; Hanses F.; Zimmermann M.; Scharf G.; Hamer O.W.; Maier L.S.; Hupf J.; Jungbauer C.G.","Staudner, Stephan T. (57220057508); Leininger, Simon B. (57220052070); Vogel, Manuel J. (57220047726); Mustroph, Julian (56058007800); Hubauer, Ute (6507124366); Meindl, Christine (57191624973); Wallner, Stefan (56664140300); Lehn, Petra (57216873401); Burkhardt, Ralph (12773987000); Hanses, Frank (57321025200); Zimmermann, Markus (7201478639); Scharf, Gregor (56446538500); Hamer, Okka W. (6603345710); Maier, Lars S. (7006758541); Hupf, Julian (56063797300); Jungbauer, Carsten G. (35117825100)","57220057508; 57220052070; 57220047726; 56058007800; 6507124366; 57191624973; 56664140300; 57216873401; 12773987000; 57321025200; 7201478639; 56446538500; 6603345710; 7006758541; 56063797300; 35117825100","Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates","2023","Clinical and Experimental Medicine","23","8","","4919","4935","16","1","10.1007/s10238-023-01193-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171659035&doi=10.1007%2fs10238-023-01193-z&partnerID=40&md5=b5531a5524625e442dc855e0984e5e93","Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany; Emergency Department, University Hospital Regensburg, Regensburg, Germany; Department of Infection Prevention and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany; Department of Radiology, University Hospital Regensburg, Regensburg, Germany","Staudner S.T., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Leininger S.B., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Vogel M.J., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Mustroph J., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Hubauer U., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Meindl C., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Wallner S., Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany; Lehn P., Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany; Burkhardt R., Department of Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany; Hanses F., Emergency Department, University Hospital Regensburg, Regensburg, Germany, Department of Infection Prevention and Infectious Diseases, University Hospital Regensburg, Regensburg, Germany; Zimmermann M., Emergency Department, University Hospital Regensburg, Regensburg, Germany; Scharf G., Department of Radiology, University Hospital Regensburg, Regensburg, Germany; Hamer O.W., Department of Radiology, University Hospital Regensburg, Regensburg, Germany; Maier L.S., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; Hupf J., Emergency Department, University Hospital Regensburg, Regensburg, Germany; Jungbauer C.G., Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany","Coronavirus SARS-CoV-2 spread worldwide, causing a respiratory disease known as COVID-19. The aim of the present study was to examine whether Dipeptidyl-peptidase 3 (DPP3) and the inflammatory biomarkers IL-6, CRP, and leucocytes are associated with COVID-19 and able to predict the severity of pulmonary infiltrates in COVID-19 patients versus non-COVID-19 patients. 114 COVID-19 patients and 35 patients with respiratory infections other than SARS-CoV-2 were included in our prospective observational study. Blood samples were collected at presentation to the emergency department. 102 COVID-19 patients and 28 non-COVID-19 patients received CT imaging (19 outpatients did not receive CT imaging). If CT imaging was available, artificial intelligence software (CT Pneumonia Analysis) was used to quantify pulmonary infiltrates. According to the median of infiltrate (14.45%), patients who obtained quantitative CT analysis were divided into two groups (> median: 55 COVID-19 and nine non-COVID-19, ≤ median: 47 COVID-19 and 19 non-COVID-19). DPP3 was significantly elevated in COVID-19 patients (median 20.85 ng/ml, 95% CI 18.34–24.40 ng/ml), as opposed to those without SARS-CoV-2 (median 13.80 ng/ml, 95% CI 11.30–17.65 ng/ml; p < 0.001, AUC = 0.72), opposite to IL-6, CRP (each p = n.s.) and leucocytes (p < 0.05, but lower levels in COVID-19 patients). Regarding binary logistic regression analysis, higher DPP3 concentrations (OR = 1.12, p < 0.001) and lower leucocytes counts (OR = 0.76, p < 0.001) were identified as significant and independent predictors of SARS-CoV-2 infection, as opposed to IL-6 and CRP (each p = n.s.). IL-6 was significantly increased in patients with infiltrate above the median compared to infiltrate below the median both in COVID-19 (p < 0.001, AUC = 0.78) and in non-COVID-19 (p < 0.05, AUC = 0.81). CRP, DPP3, and leucocytes were increased in COVID-19 patients with infiltrate above median (each p < 0.05, AUC: CRP 0.82, DPP3 0.70, leucocytes 0.67) compared to infiltrate below median, opposite to non-COVID-19 (each p = n.s.). Regarding multiple linear regression analysis in COVID-19, CRP, IL-6, and leucocytes (each p < 0.05) were associated with the degree of pulmonary infiltrates, as opposed to DPP3 (p = n.s.). DPP3 showed the potential to be a COVID-19-specific biomarker. IL-6 might serve as a prognostic marker to assess the extent of pulmonary infiltrates in respiratory patients. © 2023, The Author(s).","Artificial intelligence; COVID-19; Dipeptidyl-peptidase 3; IL-6; Pulmonary infiltrates; SARS-CoV-2","Artificial Intelligence; Biomarkers; COVID-19; COVID-19 Testing; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases; Humans; Interleukin-6; SARS-CoV-2; angiotensin 1 receptor antagonist; antibiotic agent; antibody; azathioprine; beta adrenergic receptor blocking agent; biological marker; biological product; C reactive protein; dipeptidyl carboxypeptidase inhibitor; dipeptidyl peptidase; dipeptidyl peptidase III; diuretic agent; glucocorticoid; hydroxymethylglutaryl coenzyme A reductase inhibitor; immunosuppressive agent; insulin; interleukin 6; metformin; methotrexate; mycophenolate mofetil; procizumab; remdesivir; steroid; tacrolimus; unclassified drug; biological marker; dipeptidyl peptidase; interleukin 6; adult; anosmia; Article; artificial intelligence; asthma; atelectasis; blood sampling; chronic kidney failure; chronic obstructive lung disease; comorbidity; computer assisted tomography; congestive heart failure; controlled study; coronary artery disease; coronavirus disease 2019; coughing; diabetes mellitus; diagnostic value; disease severity; dysgeusia; dyspnea; emergency ward; extracorporeal oxygenation; fatigue; female; fever; ground glass opacity; high flow nasal cannula therapy; human; human cell; human tissue; hypertension; immunoassay; immunoluminometric assay; intensive care unit; invasive ventilation; leukocyte count; logistic regression analysis; lung embolism; lung infiltrate; major clinical study; male; mortality; noninvasive ventilation; obesity; observational study; outpatient; oxygen therapy; pleura effusion; pneumonia; prediction; prognostic assessment; prospective study; quantitative analysis; radiodiagnosis; receiver operating characteristic; respiratory tract infection; sensitivity and specificity; Severe acute respiratory syndrome coronavirus 2; artificial intelligence; coronavirus disease 2019; COVID-19 testing; Severe acute respiratory syndrome coronavirus 2","","azathioprine, 446-86-6, 55774-33-9; C reactive protein, 9007-41-4; dipeptidyl peptidase, 9032-67-1; insulin, 9004-10-8; metformin, 1115-70-4, 657-24-9; methotrexate, 15475-56-6, 59-05-2, 7413-34-5, 7532-09-4, 6745-93-3, 51865-79-3, 60388-53-6; mycophenolate mofetil, 116680-01-4, 128794-94-5, 115007-34-6; remdesivir, 1809249-37-3; tacrolimus, 104987-11-3, 109581-93-3; Biomarkers, ; Dipeptidyl-Peptidases and Tripeptidyl-Peptidases, ; Interleukin-6, ","sphingotest DPP3, sphingotec, Germany; syngo.via CT Pneumonia Analysis, Siemens Healthineers, Germany","Siemens Healthineers, Germany; sphingotec, Germany","Michael Schlossbauer and Bailey Marie Johnson","We especially thank Daniela Biermeier and Helga Staudner (Institute of Clinical Chemistry and Laboratory Medicine) for the asservation and analysis of samples. Furthermore, we acknowledge and emphasize the involvement of Michael Schlossbauer and Bailey Marie Johnson. Moreover, we would like to thank SphingoTec Gmbh and Roche Diagnostics International for their support regarding the sample measurements.","China: WHO Coronavirus Disease (COVID-19), (2023); Lee C.M., Snyder S.H., Dipeptidyl-aminopeptidase III of rat brain. Selective affinity for enkephalin and angiotensin, J Biol Chem, 257, pp. 12043-12050, (1982); Prajapati S.C., Chauhan S.S., Dipeptidyl peptidase III: a multifaceted oligopeptide N-end cutter, FEBS J, 278, pp. 3256-3276, (2011); Rehfeld L., Funk E., Jha S., Macheroux P., Melander O., Bergmann A., Novel methods for the quantification of dipeptidyl peptidase 3 (DPP3) concentration and activity in human blood samples, J Appl Lab Med, 3, pp. 943-953, (2019); Blet A., Deniau B., Santos K., Et al., Monitoring circulating dipeptidyl peptidase 3 (DPP3) predicts improvement of organ failure and survival in sepsis: a prospective observational multinational study, Crit Care, 25, (2021); Depret F., Amzallag J., Pollina A., Et al., Circulating dipeptidyl peptidase-3 at admission is associated with circulatory failure, acute kidney injury and death in severely ill burn patients, Crit Care, 24, (2020); Frigyesi A., Lengquist M., Spangfors M., Et al., Circulating dipeptidyl peptidase 3 on intensive care unit admission is a predictor of organ dysfunction and mortality, J Intensive Care, 9, (2021); Tipnis S.R., Hooper N.M., Hyde R., Karran E., Christie G., Turner A.J., A human homolog of angiotensin-converting enzyme. 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Homayounieh F., Bezerra Cavalcanti Rockenbach M.A., Ebrahimian S., Et al., Multicenter assessment of CT pneumonia analysis prototype for predicting disease severity and patient outcome, J Digit Imaging, 34, pp. 320-329, (2021); Gouda W., Yasin R., COVID-19 disease: CT pneumonia analysis prototype by using artificial intelligence, predicting the disease severity, Egypt J Radiol Nucl Med, 51, pp. 1-11, (2020); Huang L., Han R., Ai T., Et al., Serial quantitative chest CT assessment of COVID-19: a deep learning approach, Radiol Cardiothorac Imaging, 2, (2020); Ardali Duzgun S., Durhan G., Basaran Demirkazik F., Et al., AI-based quantitative CT analysis of temporal changes according to disease severity in COVID-19 pneumonia, J Comput Assist Tomogr, 45, pp. 970-978, (2021); Okuma T., Hamamoto S., Maebayashi T., Et al., Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results, Jpn J Radiol, 39, pp. 956-965, (2021); Karami H., Derakhshani A., Ghasemigol M., Et al., Weighted gene co-expression network analysis combined with machine learning validation to identify key modules and hub genes associated with SARS-CoV-2 infection, J Clin Med, (2021); Schlossbauer M.H., Hubauer U., Stadler S., Et al., The role of the tubular biomarkers NAG, kidney injury molecule-1 and neutrophil gelatinase-associated lipocalin in patients with chest pain before contrast media exposition, Biomark Med, 13, pp. 379-392, (2019); Gustine J.N., Jones D., Immunopathology of Hyperinflammation in COVID-19, Am J Pathol, 191, pp. 4-17, (2021); Magliocca A., Omland T., Latini R., Dipeptidyl peptidase 3, a biomarker in cardiogenic shock and hopefully much more, Eur J Heart Fail, 22, pp. 300-302, (2020); Takagi K., Blet A., Levy B., Et al., Circulating dipeptidyl peptidase 3 and alteration in haemodynamics in cardiogenic shock: results from the OptimaCC trial, Eur J Heart Fail, 22, pp. 279-286, (2020); Deniau B., Blet A., Santos K., Et al., Inhibition of circulating dipeptidyl-peptidase 3 restores cardiac function in a sepsis-induced model in rats: a proof of concept study, PLoS ONE, 15, (2020); Deniau B., Rehfeld L., Santos K., Et al., Circulating dipeptidyl peptidase 3 is a myocardial depressant factor: dipeptidyl peptidase 3 inhibition rapidly and sustainably improves haemodynamics, Eur J Heart Fail, 22, pp. 290-299, (2020); Miesbach W., Pathological role of angiotensin II in severe COVID-19, TH Open, 4, pp. e138-e144, (2020); Ekholm M., Kahan T., Jorneskog G., Broijersen A., Wallen N.H., Angiotensin II infusion in man is proinflammatory but has no short-term effects on thrombin generation in vivo, Thromb Res, 124, pp. 110-115, (2009); Kishimoto T., The biology of interleukin-6, Blood, 74, pp. 1-10, (1989); Kishimoto T., Akira S., Taga T., Interleukin-6 and its receptor: a paradigm for cytokines, Science, 258, pp. 593-597, (1992); Akira S., Taga T., Kishimoto T., Interleukin-6 in biology and medicine, Adv Immunol, 54, pp. 1-78, (1993); Gauldie J., Richards C., Harnish D., Lansdorp P., Baumann H., Interferon beta 2/B-cell stimulatory factor type 2 shares identity with monocyte-derived hepatocyte-stimulating factor and regulates the major acute phase protein response in liver cells, Proc Natl Acad Sci U S A, 84, pp. 7251-7255, (1987); Kishimoto T., Akira S., Narazaki M., Taga T., Interleukin-6 family of cytokines and gp130, Blood, 86, pp. 1243-1254, (1995); Jones S.A., Directing transition from innate to acquired immunity: defining a role for IL-6, J Immunol, 175, pp. 3463-3468, (2005); Damas P., Ledoux D., Nys M., Et al., Cytokine serum level during severe sepsis in human IL-6 as a marker of severity, Ann Surg, 215, pp. 356-362, (1992); Andrijevic I., Matijasevic J., Andrijevic L., Kovacevic T., Zaric B., Interleukin-6 and procalcitonin as biomarkers in mortality prediction of hospitalized patients with community acquired pneumonia, Ann Thorac Med, 9, pp. 162-167, (2014); Zobel K., Martus P., Pletz M.W., Et al., Interleukin 6, lipopolysaccharide-binding protein and interleukin 10 in the prediction of risk and etiologic patterns in patients with community-acquired pneumonia: results from the German competence network CAPNETZ, BMC Pulm Med, 12, (2012); Ramirez P., Ferrer M., Marti V., Et al., Inflammatory biomarkers and prediction for intensive care unit admission in severe community-acquired pneumonia, Crit Care Med, 39, pp. 2211-2217, (2011); Meduri G.U., Headley S., Kohler G., Et al., Persistent elevation of inflammatory cytokines predicts a poor outcome in ARDS. Plasma IL-1 beta and IL-6 levels are consistent and efficient predictors of outcome over time, Chest, 107, pp. 1062-1073, (1995); Bauer T.T., Monton C., Torres A., Et al., Comparison of systemic cytokine levels in patients with acute respiratory distress syndrome, severe pneumonia, and controls, Thorax, 55, pp. 46-52, (2000); Nishimoto N., Yoshizaki K., Miyasaka N., Et al., Treatment of rheumatoid arthritis with humanized anti-interleukin-6 receptor antibody: a multicenter, double-blind, placebo-controlled trial, Arthritis Rheum, 50, pp. 1761-1769, (2004); Yokota S., Imagawa T., Mori M., Et al., Efficacy and safety of tocilizumab in patients with systemic-onset juvenile idiopathic arthritis: a randomised, double-blind, placebo-controlled, withdrawal phase III trial, Lancet, 371, pp. 998-1006, (2008); Villaescusa L., Zaragoza F., Gayo-Abeleira I., Zaragoza C., A new approach to the management of COVID-19. Antagonists of IL-6: siltuximab, Adv Ther, 39, pp. 1126-1148, (2022); Du P., Geng J., Wang F., Chen X., Huang Z., Wang Y., Role of IL-6 inhibitor in treatment of COVID-19-related cytokine release syndrome, Int J Med Sci, 18, pp. 1356-1362, (2021); Ye Z., Zhang Y., Wang Y., Huang Z., Song B., Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review, Eur Radiol, 30, pp. 4381-4389, (2020); Pan Y., Guan H., Zhou S., Et al., Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China, Eur Radiol, 30, pp. 3306-3309, (2020); Song F., Shi N., Shan F., Et al., Emerging 2019 novel coronavirus (2019-nCoV) pneumonia, Radiology, 295, pp. 210-217, (2020); Bernheim A., Mei X., Huang M., Et al., Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection, Radiology, 295, (2020); Wu J., Wu X., Zeng W., Et al., Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features, Invest Radiol, 55, pp. 257-261, (2020); Shi H., Han X., Jiang N., Et al., Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study, Lancet Infect Dis, 20, pp. 425-434, (2020); Li K., Wu J., Wu F., Et al., The clinical and chest CT features associated with severe and critical COVID-19 pneumonia, Investig Radiol, 55, pp. 327-331, (2020)","S.T. Staudner; Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; email: stephan.staudner@gmail.com","","Springer Science and Business Media Deutschland GmbH","","","","","","15918890","","CEMLB","37733154","English","Clin. Exp. Med.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85171659035"
"Duffey H.; Leonard J.; Mistry R.D.","Duffey, Hannah (56778058400); Leonard, Jan (57209608523); Mistry, Rakesh D. (8337036500)","56778058400; 57209608523; 8337036500","Variation in diagnosis and management of allergic reactions among emergency medicine and allergy immunology providers","2023","Allergy and Asthma Proceedings","44","1","","51","58","7","1","10.2500/aap.2023.44.220088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145728188&doi=10.2500%2faap.2023.44.220088&partnerID=40&md5=ecb76b9b7115e7b3ccbc397c9c1462ac","Department of Dermatology, University of Utah, Salt Lake City, UT, United States; Section of Emergency Medicine, Children’s Hospital Colorado, Aurora, CO, United States","Duffey H., Department of Dermatology, University of Utah, Salt Lake City, UT, United States; Leonard J., Section of Emergency Medicine, Children’s Hospital Colorado, Aurora, CO, United States; Mistry R.D., Section of Emergency Medicine, Children’s Hospital Colorado, Aurora, CO, United States","Background: Children with anaphylaxis often emergently present for treatment. Providers' adherence to the principles of optimal management according to the most recent national guidelines is unknown. Objective: To assess the variation in management approaches for allergic reactions and anaphylaxis between allergy/immunology (AI) and emergency medicine (EM) providers. Methods: This was a cross-sectional survey study of AI and EM providers in the University of Colorado affiliated hospitals and Colorado Asthma and Allergy Society. The survey consisted of six cases of patients with allergic reactions, with four cases that represented patients with anaphylaxis that resolved by the time of discharge. For each vignette, the participants were asked about preferred initial therapy, adjunctive therapies, monitoring, outpatient prescription medications, and discharge instructions provided. Survey derivation and validation was accomplished by a multidisciplinary team of experts by using a modified Delphi process. Results: A total of 413 clinicians were contacted, of whom 194, (47%) responded, including 69 pediatric EM, 50 general EM, and 49 AI providers, and 26 did not identify a provider type. There were no statistically significant differences in correct recognition of anaphylaxis between the AI and EM providers. For each case, statistically significant differences were noted in the use of corticosteroids during and after resolution of anaphylaxis: AI providers reported giving fewer prescriptions than did the EM providers for corticosteroids in all cases of anaphylaxis (p < 0.001). The AI providers were less likely to prescribe scheduled antihistamines than were the EM providers in half of the cases (p < 0.02). Conclusion: Across the specialties, there were high rates of recognition of epinephrine as first-line treatment for anaphylaxis. The majority of the EM providers prescribed scheduled corticosteroids and antihistamines after resolution of anaphylaxis, whereas most of the AI providers did not prescribe scheduled corticosteroids. Analysis of the current data suggests against the routine use of corticosteroids in the management of anaphylaxis, particularly continued use after resolution of symptoms. AI involvement in the creation of EM and hospital protocols for allergic reactions could improve overall care. Copyright © 2023, OceanSide Publications, Inc., U.S.A.","","Adrenal Cortex Hormones; Anaphylaxis; Child; Cross-Sectional Studies; Emergency Medicine; Epinephrine; Histamine Antagonists; Humans; amoxicillin; antihistaminic agent; corticosteroid; epinephrine; histamine H2 receptor antagonist; antihistaminic agent; corticosteroid; epinephrine; allergy; anaphylaxis; Article; asthma; case report; clinical article; cross-sectional study; egg allergy; emergency medicine; hospital discharge; human; immunologist; internal medicine; Likert scale; major clinical study; nurse practitioner; osteopathic physician; outpatient; pediatric emergency medicine; prescription; rhinorrhea; swelling; tachypnea; urticaria; vomiting; anaphylaxis; child","","amoxicillin, 26787-78-0, 34642-77-8, 61336-70-7; epinephrine, 51-43-4, 55-31-2, 6912-68-1; Adrenal Cortex Hormones, ; Epinephrine, ; Histamine Antagonists, ","","","","","Wood RA, Camargo CA, Lieberman P, Et al., Anaphylaxis in America: the prevalence and characteristics of anaphylaxis in the United States, J Allergy Clin Immunol, 133, pp. 461-467, (2014); Lin RY, Anderson AS, Shah SN, Et al., Increasing anaphylaxis hospitalizations in the first 2 decades of life: New York State, 1990-2006, Ann Allergy Astha Immunol, 101, pp. 387-393, (2008); Poulos LM, Waters A-M, Correll PK, Et al., Trends in hospitalizations for anaphylaxis, angioedema, and urticaria in Australia, 1993–1994 to 2004–2005, J Allergy Clin Immunol, 120, pp. 878-884, (2007); Abstracts from the Eastern Allergy Conference May 31 - June 3, 2012, Palm Beach, Florida, Allergy Asthma Proc, 33, pp. 374-375, (2012); Alvarez-Perea A, Ameiro B, Morales C, Et al., Anaphylaxis in the pediatric emergency department: analysis of 133 cases after an allergy workup, J Allergy Clin Immunol Pract, 5, pp. 1256-1263, (2017); Lieberman PL., Recognition and first-line treatment of anaphylaxis, Am J Med, 127, pp. S6-S11, (2014); Sampson HA, Munoz-Furlong A, Campbell RL, Et al., Second symposium on the definition and management of anaphylaxis: summary report—Second National Institute of Allergy and Infectious Disease/Food Allergy and Anaphylaxis Network symposium, J Allergy and Clin Immunol, 117, pp. 391-397, (2006); Campbell RL, Hagan JB, Manivannan V, Et al., Evaluation of National Institute of Allergy and Infectious Diseases/Food Allergy and Anaphylaxis Network criteria for the diagnosis of anaphylaxis in emergency department patients, J Allergy Clin Immunol, 129, pp. 748-752, (2012); Lieberman P, Nicklas RA, Randolph C, Et al., Anaphylaxis–a practice parameter update 2015, Ann Allergy Asthma Immunol, 115, pp. 341-384, (2015); Shaker MS, Wallace DV, Golden DBK, Et al., Anaphylaxis—a 2020 practice parameter update, systematic review, and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) analysis, J Allergy Clin Immunol, 145, pp. 1082-1123, (2020); Alqurashi W, Ellis AK., Do corticosteroids prevent biphasic anaphylaxis?, J Allergy Clin Immunol Pract, 5, pp. 1194-1205, (2017); Hudgins JD, Monuteaux MC, Bourgeois FT, Et al., Complexity and severity of pediatric patients treated at United States emergency departments, J Pediatr, 186, pp. 145-149, (2017); Yao T-C, Wang J-Y, Chang S-M, Et al., Association of oral corticosteroid bursts with severe adverse events in children, JAMA Pediatr, 175, pp. 723-729, (2021); Aljebab F, Choonara I, Conroy S., Systematic review of the toxicity of short-course oral corticosteroids in children, Arch Dis Child, 101, pp. 365-370, (2016); Owusu-Ansah S, Badaki O, Perin J, Et al., Under prescription of epinephrine to Medicaid patients in the pediatric emergency department, Glob Pediatr Health, 6, (2019); Cohen JS, Agbim C, Hrdy M, Et al., Epinephrine autoinjector prescription filling after pediatric emergency department discharge, Allergy Asthma Proc, 42, pp. 142-146, (2021)","H. Duffey; Department of Dermatology, University of Utah, Salt Lake City, United States; email: hannah.duffey@hsc.utah.edu","","OceanSide Publications Inc.","","","","","","10885412","","AAPRF","36719699","English","Allergy Asthma Proc.","Article","Final","","Scopus","2-s2.0-85145728188"
"Ghamari S.-H.; Mohebi F.; Abbasi-Kangevari M.; Peiman S.; Rahimi B.; Ahmadi N.; Farzi Y.; Seyfi S.; Shahbal N.; Modirian M.; Azmin M.; Zokaei H.; Khezrian M.; Sherafat R.; Malekpour M.-R.; Roshani S.; Rezaei N.; Fallahi M.J.; Shoushtari M.H.; Akbaripour Z.; Khatibzadeh S.; Shahraz S.","Ghamari, Seyyed-Hadi (57207567632); Mohebi, Farnam (55633844600); Abbasi-Kangevari, Mohsen (57193543882); Peiman, Soheil (56245157800); Rahimi, Besharat (37861932100); Ahmadi, Naser (57211690973); Farzi, Yousef (57194268539); Seyfi, Shahedeh (57398454600); Shahbal, Nazila (57210992687); Modirian, Mitra (56071187600); Azmin, Mehrdad (36791414300); Zokaei, Hossein (57200648173); Khezrian, Maryam (57210460315); Sherafat, Roya (7801617957); Malekpour, Mohammad-Reza (57222048473); Roshani, Shahin (57218384088); Rezaei, Negar (57218820767); Fallahi, Mohammad Javad (56013804100); Shoushtari, Maryam Haddadzadeh (56288779100); Akbaripour, Zahra (58477175300); Khatibzadeh, Shahab (16550322300); Shahraz, Saeid (57215142060)","57207567632; 55633844600; 57193543882; 56245157800; 37861932100; 57211690973; 57194268539; 57398454600; 57210992687; 56071187600; 36791414300; 57200648173; 57210460315; 7801617957; 57222048473; 57218384088; 57218820767; 56013804100; 56288779100; 58477175300; 16550322300; 57215142060","Patient experience with chronic obstructive pulmonary disease: a nationally representative demonstration study on quality and cost of healthcare services","2023","Frontiers in Public Health","11","","1112072","","","","1","10.3389/fpubh.2023.1112072","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85164208892&doi=10.3389%2ffpubh.2023.1112072&partnerID=40&md5=48a366a003994840a71e4b0a72e70852","Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Haas School of Business, University of California, Berkeley, CA, United States; Department of Internal Medicine, AdventHealth Orlando Hospital, Orlando, FL, United States; Heller School of Social Policy and Management, Brandeis University, Waltham, MA, United States; The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands; Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Thoracic and Vascular Surgery Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Razi University Hospital, Guilan University of Medical Sciences, Guilan, Iran; Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, MA, United States","Ghamari S.-H., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Mohebi F., Haas School of Business, University of California, Berkeley, CA, United States; Abbasi-Kangevari M., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Peiman S., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Rahimi B., Department of Internal Medicine, AdventHealth Orlando Hospital, Orlando, FL, United States; Ahmadi N., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Farzi Y., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Seyfi S., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Shahbal N., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Modirian M., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Azmin M., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Zokaei H., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Khezrian M., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Sherafat R., Heller School of Social Policy and Management, Brandeis University, Waltham, MA, United States; Malekpour M.-R., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Roshani S., The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands; Rezaei N., Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Fallahi M.J., Thoracic and Vascular Surgery Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Shoushtari M.H., Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran; Akbaripour Z., Razi University Hospital, Guilan University of Medical Sciences, Guilan, Iran; Khatibzadeh S., Heller School of Social Policy and Management, Brandeis University, Waltham, MA, United States; Shahraz S., Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, MA, United States","Introduction: Due to insufficient data on patient experience with healthcare system among patients with chronic obstructive pulmonary disease (COPD), particularly in developing countries, this study attempted to investigate the journey of patients with COPD in the healthcare system using nationally representative data in Iran. Methods: This nationally representative demonstration study was conducted from 2016 to 2018 using a novel machine-learning based sampling method based on different districts’ healthcare structures and outcome data. Pulmonologists confirmed eligible participants and nurses recruited and followed them up for 3 months/in 4 visits. Utilization of various healthcare services, direct and indirect costs (including non-health, absenteeism, loss of productivity, and time waste), and quality of healthcare services (using quality indicators) were assessed. Results: This study constituted of a final sample of 235 patients with COPD, among whom 154 (65.5%) were male. Pharmacy and outpatient services were mostly utilized healthcare services, however, participants utilized outpatient services less than four times a year. The annual average direct cost of a patient with COPD was 1,605.5 USDs. Some 855, 359, 2,680, and 933 USDs were imposed annually on patients with COPD due to non-medical costs, absenteeism, loss of productivity, and time waste, respectively. Based on the quality indicators assessed during the study, the focus of healthcare providers has been the management of the acute phases of COPD as the blood oxygen levels of more than 80% of participants were documented by pulse oximetry devices. However, chronic phase management was mainly missed as less than a third of participants were referred to smoking and tobacco quit centers and got vaccinated. In addition, less than 10% of participants were considered for rehabilitation services, and only 2% completed four-session rehabilitation services. Conclusion: COPD services have focused on inpatient care, where patients experience exacerbation of the condition. Upon discharge, patients do not receive appropriate follow-up services targeting on preventive care for optimal controlling of pulmonary function and preventing exacerbation. Copyright © 2023 Ghamari, Mohebi, Abbasi-Kangevari, Peiman, Rahimi, Ahmadi, Farzi, Seyfi, Shahbal, Modirian, Azmin, Zokaei, Khezrian, Sherafat, Malekpour, Roshani, Rezaei, Fallahi, Shoushtari, Akbaripour, Khatibzadeh and Shahraz.","continuity of patient care; COPD; healthcare utilization; patient journey; quality of care; standard of care","Delivery of Health Care; Female; Hospitalization; Humans; Male; Patient Discharge; Patient Outcome Assessment; Pulmonary Disease, Chronic Obstructive; chronic obstructive lung disease; female; health care delivery; hospital discharge; hospitalization; human; male; outcome assessment","","","","","","","Momtazmanesh S., Moghaddam S.S., Ghamari S.H., Rad E.M., Rezaei N., Shobeiri P., Et al., Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019, eClinical Medicine, 59, (2023); Portegies M.L.P., Lahousse L., Joos G.F., Hofman A., Koudstaal P.J., Stricker B.H., Et al., Chronic obstructive pulmonary disease and the risk of stroke the Rotterdam study, Am J Respir Crit Care Med, 193, pp. 251-258, (2016); Byng D., Lutter J.I., Wacker M.E., Jorres R.A., Liu X., Karrasch S., Et al., Determinants of healthcare utilization and costs in COPD patients: first longitudinal results from the German COPD cohort COSYCONET, Int J COPD, 14, pp. 1423-1439, (2019); Wacker M.E., Hunger M., Karrasch S., Heinrich J., Peters A., Schulz H., Et al., Health-related quality of life and chronic obstructive pulmonary disease in early stages—longitudinal results from the population-based KORA cohort in a working age population, BMC Pulm Med, 14, (2014); Wacker M.E., Jorres R.A., Karch A., Wilke S., Heinrich J., Karrasch S., Et al., Assessing health-related quality of life in COPD: comparing generic and disease-specific instruments with focus on comorbidities, BMC Pulm Med, 16, (2016); COPD trends brief—burden, (2023); Heidari-Foroozan M., Aryan A., Esfahani Z., Shahrbaf M.A., Moghaddam S.S., Keykhaei M., Et al., National, subnational and risk attributed burden of chronic respiratory diseases in Iran from 1990 to 2019, Respir Res, 24, (2023); Rehman A.U., Ahmad Hassali M.A., Muhammad S.A., Shah S., Abbas S., Iab H., Et al., The economic burden of chronic obstructive pulmonary disease (COPD) in the USA, Europe, and Asia: results from a systematic review of the literature, Expert Rev Pharmacoecon Outcomes Res, 20, pp. 661-672, (2020); van der Schans S., Goossens L.M.A., Boland M.R.S., Kocks J.W.H., Postma M.J., van Boven J.F.M., Et al., Systematic review and quality appraisal of cost-effectiveness analyses of pharmacologic maintenance treatment for chronic obstructive pulmonary disease: methodological considerations and recommendations, Pharmacoeconomics, 35, pp. 43-63, (2017); Parsaeian M., Mahdavi M., Saadati M., Mehdipour P., Sheidaei A., Khatibzadeh S., Et al., Introducing an efficient sampling method for national surveys with limited sample sizes: Application to a national study to determine quality and cost of healthcare, BMC Public Health, 21, (2020); Abbasi-Kangevari M., Mohebi F., Ghamari S.H., Modirian M., Shahbal N., Ahmadi N., Et al., Quality and cost of healthcare services in patients with diabetes in Iran: results of a nationwide short-term longitudinal survey, Front Endocrinol, 14, (2023); GOLD reports—global Initiative for Chronic Obstructive Lung disease—GOLD., (2021); Rencher A.C., Schimek M.G., Methods of multivariate analysis. 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Shahraz; Tufts Medical Center, Institute for Clinical Research and Health Policy Studies, Boston, United States; email: shahraz@gmail.com","","Frontiers Media SA","","","","","","22962565","","","37397720","English","Front. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85164208892"
"Huang Y.; Wang T.; Li Y.; Wang Z.; Cai X.; Chen J.; Li R.; Li X.","Huang, Yang (57203417983); Wang, Tianqin (59461126300); Li, Yue (57193678599); Wang, Zhe (59466595900); Cai, Xiaoming (57201570573); Chen, Jingwen (57219050862); Li, Ruibin (57243354300); Li, Xuehua (7501697643)","57203417983; 59461126300; 57193678599; 59466595900; 57201570573; 57219050862; 57243354300; 7501697643","In Vitro-to-In Vivo Extrapolation on Lung Toxicity Induced by Metal Oxide Nanoparticles via Data-Mining","2025","Environmental Science and Technology","59","3","","1673","1682","9","0","10.1021/acs.est.4c06186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211643838&doi=10.1021%2facs.est.4c06186&partnerID=40&md5=caaf1314986e69a41f1cf5da71ba2afd","Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China; School of Chemistry and Materials Science, Ludong University, Yantai, 264025, China; School of Public Health, Soochow University, Jiangsu, Suzhou, 215123, China; State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Jiangsu, Suzhou, 215123, China","Huang Y., Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China, School of Chemistry and Materials Science, Ludong University, Yantai, 264025, China; Wang T., School of Chemistry and Materials Science, Ludong University, Yantai, 264025, China; Li Y., Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China; Wang Z., Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China; Cai X., School of Public Health, Soochow University, Jiangsu, Suzhou, 215123, China; Chen J., Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China; Li R., State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Soochow University, Jiangsu, Suzhou, 215123, China; Li X., Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian, 116024, China","While in silico analyses are commonly employed for chemical risk assessments, predicting chronic lung toxicity induced by engineered nanoparticles (ENMs) in vivo still faces many challenges due to complex interactions at multiple nanobio interfaces. In this study, we developed a rigorous method to compile published evidence on the in vivo lung toxicity of metal oxide nanoparticles (MeONPs) and revealed previously overlooked in vitro-to-in vivo extrapolation (IVIVE) relationships. A comprehensive multidimensional data set containing 1102 in vivo data points, 75 pulmonary toxicological biomarkers, and 20 features (covering in vitro effects, physicochemical properties, and exposure conditions) was constructed. An IVIVE approach that related effects at the cellular level to in vivo lung toxicity in rodent model was established with prediction accuracy reaching 89 and 80% in training and test sets. Experimental validation was conducted by testing chronic lung fibrosis of 8 new MeONPs in 32 independent mice, with prediction accuracy reaching 88%. The IVIVE model indicated that the proinflammatory cytokine IL-1β in THP-1 cells could serve as an in vitro marker to predict lung toxicity. The IVIVE model showed great promise for minimizing unnecessary animal tests and understanding toxicological mechanisms. © 2024 American Chemical Society.","computational toxicology; engineered nanomaterials; in vitro-to-in vivo extrapolation; lung toxicity; machine learning","Animals; Data Mining; Humans; Lung; Metal Nanoparticles; Mice; Oxides; Biological organs; Biomarkers; Extrapolation; Mammals; biological marker; cytokine; interleukin 1beta; metal oxide nanoparticle; nanoparticle; metal nanoparticle; oxide; Computational toxicology; Engineered nanomaterials; In vitro-to-in vivo extrapolation; In-silico; In-vitro; In-vivo; Lung toxicities; Machine-learning; Metal oxide nanoparticles; Prediction accuracy; chronic obstructive pulmonary disease; data mining; ecotoxicology; laboratory method; machine learning; nanomaterial; oxide; prediction; risk assessment; toxicity test; animal cell; animal experiment; animal model; animal tissue; Article; big data; chemical composition; computer model; controlled study; cross validation; data mining; IC50; in vitro study; in vivo study; lung fibrosis; lung parenchyma; lung toxicity; machine learning; mouse; nanobiotechnology; nonhuman; physical chemistry; prediction; rat; risk assessment; Shapley additive explanation; THP-1 cell line; Web of Science; animal; data mining; drug effect; human; lung; pathology; Lung cancer","","oxide, 16833-27-5; Oxides, ","","","National Natural Science Foundation of China, NSFC, (22176023, 22406080); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2022YFC3902104); National Key Research and Development Program of China, NKRDPC; Natural Science Foundation of Fujian Province, (ZR2024QB094); Natural Science Foundation of Fujian Province","This study was supported by the National Natural Science Foundation of China (22176023, 22406080), the National Key Research and Development Program of China (2022YFC3902104), and the Natural Science Foundation (ZR2024QB094) of Shandong Province. The authors thank Dr. Huifeng Wu from Yantai Inst Coastal Zone Res YIC, Chinese Acad Sci for the advice on data collection and language.","Garcia-Mouton C., Hidalgo A., Cruz A., Perez-Gil J., The Lord of the Lungs: The essential role of pulmonary surfactant upon inhalation of nanoparticles, Eur. J. Pharm. Biopharm., 144, pp. 230-243, (2019); Liu S., Xia T., Continued Efforts on Nanomaterial-Environmental Health and Safety Is Critical to Maintain Sustainable Growth of Nanoindustry, Small, 16, 21, (2020); Tsai S.J., Hofmann M., Hallock M., Ada E., Kong J., Ellenbecker M., Characterization and Evaluation of Nanoparticle Release during the Synthesis of Single-Walled and Multiwalled Carbon Nanotubes by Chemical Vapor Deposition, Environ. Sci. Technol., 43, 15, pp. 6017-6023, (2009); Vosburgh D.J.H., Boysen D.A., Oleson J.J., Peters T.M., Airborne Nanoparticle Concentrations in the Manufacturing of Polytetrafluoroethylene (PTFE) Apparel, J. Occup. Environ. 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"Jeong S.; Choi Y.J.","Jeong, Seungpil (58838346100); Choi, Yean Jung (7404776971)","58838346100; 7404776971","Association between household income levels and nutritional intake of allergic children under 6 years of age in Korea: 2019 Korea National Health and Nutrition Examination Survey and application of machine learning","2023","Frontiers in Public Health","11","","1287085","","","","1","10.3389/fpubh.2023.1287085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183018236&doi=10.3389%2ffpubh.2023.1287085&partnerID=40&md5=c3407299597dbef99063f8423471b6ff","Department of Medical Informatics, College of Medicine, Catholic University of Korea, Seoul, South Korea; Department of Food and Nutrition, Sahmyook University, Seoul, South Korea","Jeong S., Department of Medical Informatics, College of Medicine, Catholic University of Korea, Seoul, South Korea; Choi Y.J., Department of Food and Nutrition, Sahmyook University, Seoul, South Korea","Introduction: This study investigated the prevalence of allergic diseases in Korean children aged 6 and below, focusing on the interplay between nutritional status, household income levels, and allergic disease occurrence. Methods: This study used data from the 2019 Korea National Health and Nutrition Examination Survey, a nationwide comprehensive survey, and included a representative sample of 30,382 children under the age of 6 to investigate in detail the relationship between allergic diseases, nutritional intake, and socioeconomic factors. Logistic regression analysis was performed to identify factors associated with allergic diseases, including gender, BMI, eating habits, dietary supplement intake, and nutrient consumption. To predict childhood asthma, 14 machine learning models were compared using the ‘pycaret’ package in Python. Results: We discerned that 24.7% were diagnosed with allergic conditions like atopic dermatitis, asthma, and allergic rhinitis. Notably, household income exhibited a significant influence, with the lowest income quartile exhibiting higher prevalence rates of asthma, allergic rhinitis, and multiple allergic diseases. In contrast, the highest income quartile displayed lower rates of allergic rhinitis. Children diagnosed with allergic diseases demonstrated compromised intake of essential nutrients such as energy, dietary fiber, vitamin B1, sodium, potassium, and iron. Particularly noteworthy were the deficits in dietary fiber, vitamin A, niacin, and potassium intake among children aged 3–5 with allergies. Logistic regression analysis further elucidated that within low-income families, female children with higher BMIs, frequent dining out, dietary supplement usage, and altered consumption of vitamin B1 and iron faced an elevated risk of allergic disease diagnosis. Additionally, machine learning analysis pinpointed influential predictors for childhood asthma, encompassing BMI, household income, subjective health perception, height, and dietary habits. Discussion: Our findings underscore the pronounced impact of income levels on the intricate nexus between allergic diseases and nutritional status. Furthermore, our machine learning insights illuminate the multifaceted determinants of childhood asthma, where physiological traits, socioeconomic circumstances, environmental factors, and dietary choices intertwine to shape disease prevalence. This study emphasizes the urgency of tailored nutritional interventions, particularly in socioeconomically disadvantaged populations, while also underscoring the necessity for comprehensive longitudinal investigations to unravel the intricate relationship between allergic diseases, nutritional factors, and socioeconomic strata. Copyright © 2024 Jeong and Choi.","allergic diseases; children; household income; machine learning; nutritional intake","Asthma; Child; Child, Preschool; Dietary Fiber; Eating; Female; Humans; Iron; Nutrition Surveys; Potassium; Republic of Korea; Rhinitis, Allergic; Thiamine; iron; potassium; thiamine; allergic rhinitis; asthma; child; dietary fiber; eating; female; human; nutrition; preschool child; South Korea","","iron, 14093-02-8, 53858-86-9, 7439-89-6; potassium, 7440-09-7; thiamine, 59-43-8, 67-03-8; Dietary Fiber, ; Iron, ; Potassium, ; Thiamine, ","","","","","Sohn J.K., Keet C.A., McGowan E.C., Association between allergic disease and developmental disorders in the National Health and nutrition examination survey, J Allergy Clin Immunol Pract, 7, pp. 2481-2483.e1, (2019); Ha J., Lee S.W., Yon D.K., Ten-year trends and prevalence of asthma, allergic rhinitis, and atopic dermatitis among the Korean population, 2008-2017, Clin Exp Pediatr, 63, pp. 278-283, (2020); Lee H.S., Lee J.C., Hong S.C., Kim J.W., Kim S.Y., Lee K.H., Prevalence and risk factors for allergic diseases of preschool children living in Seogwipo, Jeju, Korea, J Nutr Health, 32, pp. 107-114, (2012); Hwang J.S., Im S.H., Probiotics as an immune modulator for allergic disorder, Allergy Asthma Respirat Dis, 22, pp. 325-335, (2021); Ferrante G., Carta M., Montante C., Notarbartolo V., Corsello G., Giuffre M., Current insights on early life nutrition and prevention of allergy, Front Pediatr, 8, pp. 1-8, (2020); Vandenplas Y., Early life and nutrition and allergy development, Nutrients, 14, pp. 282-285, (2022); Rueter K., Prescott S.L., Palmer D.J., Nutritional approaches for the primary prevention of allergic disease: an update, J Paediatr Child Health, 51, pp. 962-969, (2015); Miyake Y., Yura A., Iki M., Breastfeeding and the prevalence of symptoms of allergic disorders in Japanese adolescents, Clin Exp Allergy, 33, pp. 312-316, (2003); Dierick B.J.H., van der Molen T., Flokstra-de Blok B.M.J., Muraro A., Postma M.J., Kocks J.W.H., Et al., Burden and socioeconomics of asthma, allergic rhinitis, atopic dermatitis and food allergy, Expert Rev Pharmacoecon Outcomes Res, 20, pp. 437-453, (2020); Warren C.M., Brown E., Wang J., Matsui E.C., Increasing representation of historically marginalized populations in allergy, asthma, and immunologic research studies: challenges and opportunities, J Allergy Clin Immunol Pract, 10, pp. 929-935, (2022); Cortes A., Castillo A., Sciaraffia A., Food allergy: Children’s symptom levels are associated with mothers’ psycho-socio-economic variables, J Psychosom Res, 104, pp. 48-54, (2018); Whig P., Gupta K., Jiwani N., Jupalle H., Kouser S., Alam N., A novel method for diabetes classification and prediction with Pycaret, Microsyst Technol, 29, pp. 1479-1487, (2023); Stevens L.M., Mortazavi B.J., Deo R.C., Curtis L., Kao D.P., Recommendations for reporting machine learning analyses in clinical research, Circ Cardiovasc Qual Outcomes, 13, (2020); Kim B.K., Kim J.Y., Kang M.K., Yang M.S., Park H.W., Min K.U., Et al., Allergies are still on the rise? A 6-year nationwide population-based study in Korea, Allergol Int, 65, pp. 186-191, (2016); Shin Y.H., Hwang J., Kwon R., Lee S.W., Kim M.S., Et al., Global, regional, and national burden of allergic disorders and their risk factors in 204 countries and territories, from 1990 to 2019: a systematic analysis for the global burden of disease study 2019, Allergy, 78, pp. 2232-2254, (2013); Kang S.Y., Song W.J., Cho S.H., Chang Y.S., Time trends of the prevalence of allergic diseases in Korea: a systematic literature review, Asia Pac Allergy, 8, (2018); Her E.S., Seo B.Y., Relation of nutritional intake and allergic rhinitis in infants – using the Korea National Health and Nutrition Examination Survey (KNHANES) 2013~2016, Korean J Commun Nutr, 24, pp. 321-330, (2019); Lee H.J., Kim C.H., Lee J.S., Association between social economic status and asthma in Korean children: an analysis of the fifth Korea National Health and Nutrition Examination Survey (2010–2012), Allergy Asthma Respir Dis, 6, pp. 90-96, (2018); Kim H.W., Kim J.M., Evaluation of nutritional status and adequacy of energy and nutrient intakes among atopic dermatitis children under 12 years of age: based on Korea National Health and Nutrition Examination Survey data (2013–2015), J Nutr Health, 53, pp. 141-154, (2020); Yang S.H., Kim E.J., Kim Y.N., Seong K.S., Kim S.S., Han C.K., Et al., Comparison of eating habits and dietary intake patterns between people with and without allergy, J Nutr Health, 42, pp. 523-535, (2009); Jang M.J., Kim K.S., Risk factors for food allergy among children in Seoul: focusing on dietary habits and environmental factors, J Nutr Health, 52, pp. 559-568, (2019); Wyness L., Nutrition in early life and the risk of asthma and allergic disease, Br J Community Nurs, 19, pp. S28-S32, (2014); Jung J.H., Kim G.E., Park M.R., Kim S.Y., Kim M.J., Lee Y.J., Et al., Changes in allergen sensitization in children with allergic diseases in the 1980 to 2019, Allergy Asthma Respir Dis, 9, pp. 208-215, (2021); Kim S.Y., Sim S., Park B., Kim J.H., Choi H.G., High-fat and low-carbohydrate diets are associated with allergic rhinitis but not asthma or atopic dermatitis in children, PLoS One, 11, (2016); Choi D.J., Kim J.Y., Lee W.C., Association of allergic diseases with body mass index, waist circumference in Korean adolescents: the sixth Korea National Health and nutrition examination survey (2013, 2014), Korean J Family Pract, 7, pp. 858-863, (2017); Dietary reference intakes for Koreans 2020. Research Report, 2020, pp. 1-274; Kim E.K., Song B.C., Ju S.Y., Dietary status of young children in Korea based on the data of 2013~2015 Korea National Health and Nutrition Examination Survey, J Nutr Health, 51, pp. 330-339, (2018); Ogulur I., Pat Y., Ardicli O., Barletta E., Cevhertas L., Fernandez-Santamaria R., Et al., Advances and highlights in biomarkers of allergic diseases, Allergy, 76, pp. 3659-3686, (2021); Simon D., Recent advances in clinical allergy and immunology, Int Arch Allergy Immunol, 177, pp. 324-333, (2018); Banic I., Lovric M., Cuder G., Kern R., Rijavec M., Korosec P., Et al., Treatment outcome clustering patterns correspond to discrete asthma phenotypes in children, Asthma Res Pract, 7, (2021); Hur J.E., Park J.H., Kim Y.R., Kim H.K., Lee M.S., Kim J.H., Et al., Analysis of consumption status of calcium with related factors in a Korean population: based on data from the 2013∼2015 Korean National Health and nutritional examination survey (KNHANES), J Korean Soc Food Sci Nutr, 47, pp. 328-336, (2018); Miles E.A., Calder P.C., Can early omega-3 fatty acid exposure reduce risk of childhood allergic disease?, Nutrients, 9, pp. 784-800, (2017); Jeon Y.H., Ahn K., Kim J., Shin M., Hong S.J., Lee S.Y., Et al., Clinical characteristics of atopic dermatitis in Korean school-aged children and adolescents according to onset age and severity, J Korean Med Sci, 37, (2022); Ehlayel M.S., Bener A., Duration of breast-feeding and the risk of childhood allergic diseases in a developing country, Allergy Asthma Proc, 29, pp. 386-391, (2008); Hicke-Roberts A., Wennergren G.R., Hesselmar B., Late introduction of solids into infants’ diets may increase the risk of food allergy development, BMC Pediatr, 20, pp. 273-279, (2020); Kim J., Kim B., Kim D.H., Kim Y., Rajaguru V., Association between socioeconomic status and healthcare utilization for children with allergic diseases: Korean National Health and Nutritional Examination Survey (2015-2019), Healthcare, 11, (2023); Yoon J., Choi Y.J., Lee E., Cho H.J., Yang S.I., Kim Y.H., Et al., Allergic rhinitis in preschool children and the clinical utility of FeNO, Allergy Asthma Immunol Res, 9, pp. 314-321, (2017); Chen Y.C., Dong G.H., Lin K.C., Lee Y.L., Gender difference of childhood overweight and obesity in predicting the risk of incident asthma: a systematic review and meta-analysis, Obes Rev, 14, pp. 222-231, (2013); Cakmak S., Dales R.E., Judek S., Respiratory health effects of air pollution gases: modification by education and income, Arch Environ Occup Health, 61, pp. 5-10, (2006); Sundbom F., Malinovschi A., Lindberg E., Alving K., Janson C., Effects of poor asthma control, insomnia, anxiety and depression on quality of life in young asthmatics, J Asthma, 53, pp. 398-403, (2016); Pike K.C., Crozier S.R., Lucas J.S., Inskip H.M., Robinson S., Et al., Patterns of fetal and infant growth are related to atopy and wheezing disorders at age 3 years, Thorax, 65, pp. 1099-1106, (2010); Ellwood P., Asher M.I., Garcia-Marcos L., Williams H., Keil U., Robertson C., Et al., Do fast foods cause asthma, rhinoconjunctivitis and eczema? Global findings from the international study of asthma and allergies in childhood (ISAAC) phase three, Thorax, 68, pp. 351-360, (2013); Gehring U., Wijga A.H., Brauer M., Fischer P., de Jongste J.C., Kerkhof M., Et al., Traffic-related air pollution and the development of asthma and allergies during the first 8 years of life, Am J Respir Crit Care Med, 181, pp. 596-603, (2010); Almqvist C., Worm M., Leynaert B., Impact of gender on asthma in childhood and adolescence: a GA2LEN review, Allergy, 63, pp. 47-57, (2008)","Y.J. Choi; Department of Food and Nutrition, Sahmyook University, Seoul, South Korea; email: yjchoi@syu.ac.kr","","Frontiers Media SA","","","","","","22962565","","","38274515","English","Front. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85183018236"
"Kpene G.E.; Lokpo S.Y.; Darfour-Oduro S.A.","Kpene, Godsway Edem (57211189857); Lokpo, Sylvester Yao (57193259871); Darfour-Oduro, Sandra A. (56447053200)","57211189857; 57193259871; 56447053200","Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?","2025","BMC Endocrine Disorders","25","1","9","","","","0","10.1186/s12902-025-01831-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215351117&doi=10.1186%2fs12902-025-01831-5&partnerID=40&md5=7c64dc2e23594df4db7ef1b0054d12ae","Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana; Department of Public Health Studies, Elon University, Elon, NC, United States","Kpene G.E., Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana; Lokpo S.Y., Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana; Darfour-Oduro S.A., Department of Public Health Studies, Elon University, Elon, NC, United States","Background: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle – income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients. Method: The study was retrospective involving medical records of T2DM patients. Data extracted included mortality outcome (dead or alive), sociodemographic characteristics (age, sex, marital status, educational level, occupation and location), family history of diseases (diabetes, cardiovascular disease (CVD), or asthma), lifestyle (smoking and alcohol intake), comorbidities (such as skin infections, sickle cell disease, urinary tract infections, and pneumonia) and complications of diabetes (CVD, nephropathy, neuropathy, foot ulcers, and diabetic ketoacidosis) were analyzed using Stata version 16.0 and Python 3.6.1 programming language. Both descriptive and inferential statistics were done to describe and build predictive models respectively. The performance of machine learning (ML) techniques such as support vector machine (SVM), decision tree, k nearest neighbor (kNN), eXtreme Gradient Boosting (XGBoost) and logistic regression were evaluated using the best-fitting predictive model for T2DM mortality. Results: Of the 328 participants, 183 (55.79%) were female, and the percentage of mortality was 11.28%. A 100% mortality was recorded among the T2DM patients with sepsis (p-value = 0.012). T2DM in-patients were 3.83 times as likely to die [AOR = 3.83; 95% CI: (1.53–9.61)] if they had nephropathy compared to T2DM in-patients without nephropathy (p-value = 0.004). The full model which included sociodemographic characteristics, family history, lifestyle variables and complications of T2DM had the best prediction of T2DM mortality outcome (ROC = 72.97%). The accuracy for (test and train datasets) were as follows: (90% and 90%), (100% and 100%), (90% and 90%), (90% and 88%) and (88% and 90%) respectively for the various ML classification techniques: logistic regression, Decision tree classifier, kNN classifier, SVM and XGBoost. Conclusion: This study found that all in-patients with sepsis died. Nephropathy was the identified significant predictor of T2DM mortality. Decision tree classifier provided the best classifying potential. © The Author(s) 2025.","Machine learning techniques; Model; Mortality; Predictors; Type 2 diabetes Mellitus","Adult; Aged; Comorbidity; Diabetes Mellitus, Type 2; Female; Ghana; Humans; Machine Learning; Male; Middle Aged; Prognosis; Retrospective Studies; Risk Factors; Tertiary Care Centers; adult; age; alcohol consumption; Article; asthma; cardiovascular disease; cigarette smoking; comorbidity; controlled study; cross-sectional study; data extraction; decision tree; demography; descriptive research; diabetes mellitus; diabetic ketoacidosis; diagnostic accuracy; educational status; family history; female; foot ulcer; gender; geography; Ghana; human; k nearest neighbor; kidney disease; logistic regression analysis; machine learning; major clinical study; male; medical record; middle aged; mortality; mortality rate; neuropathy; non insulin dependent diabetes mellitus; occupation; outcome assessment; pneumonia; prediction; predictive value; retrospective study; sedentary lifestyle; sepsis; sickle cell anemia; single (marital status); skin infection; support vector machine; tertiary care center; urinary tract infection; aged; complication; epidemiology; mortality; non insulin dependent diabetes mellitus; prognosis; risk factor","","","","","","","Kabir A., Karim M.N., Islam R.M., Romero L., Billah B., Health system readiness for non-communicable diseases at the primary care level: a systematic review, BMJ Open, 12, 2, (2022); Khan M.A.B., Hashim M.J., King J.K., Govender R.D., Mustafa H., Al Kaabi J., Epidemiology of type 2 diabetes - global burden of Disease and Forecasted trends, J Epidemiol Glob Health, 10, 1, pp. 107-111, (2020); Ayah R., Joshi M.D., Wanjiru R., Njau E.K., Otieno C.F., Njeru E.K., Et al., A population-based survey of prevalence of diabetes and correlates in an urban slum community in Nairobi, Kenya, BMC Public Health, 13, 1, (2013); Gatimu S.M., Milimo B.W., Sebastian M.S., Prevalence and determinants of diabetes among older adults in Ghana, BMC Public Health, 16, 1, (2016); 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Adeloye D., Ige J.O., Aderemi A.V., Adeleye N., Amoo E.O., Auta A., Et al., Estimating the prevalence, hospitalisation and mortality from type 2 diabetes mellitus in Nigeria: a systematic review and meta-analysis, BMJ Open, 7, 5, (2017); Sarfo-Kantanka O., Sarfo F.S., Oparebea Ansah E., Eghan B., Ayisi-Boateng N.K., Acheamfour-Akowuah E., Secular trends in admissions and Mortality Rates from Diabetes Mellitus in the Central Belt of Ghana: a 31-Year review, PLoS ONE, 11, 11, (2016); Report on T2DM cases, (2021); Osei-Yeboah J., Owiredu W., Norgbe G., Obirikorang C., Lokpo S., Ashigbi E., Et al., Physical activity Pattern and its association with glycaemic and blood pressure control among people living with diabetes (PLWD) in the Ho Municipality, Ghana, Ethiop J Health Sci, 29, 1, pp. 819-830, (2019); Osei-Yeboah J., Lokpo S.Y., Owiredu W.K., Johnson B.B., Orish V.N., Botchway F., Et al., Medication adherence and its association with Glycaemic control, blood pressure control, glycosuria and proteinuria among people living with diabetes (PLWD) in the ho municipality, Ghana, The Open Public Health Journal, 11, 1, (2018); Lokpo S.Y., Owiredu W.K., Agordoh P., Agboli E., Amoo L.N.A., Noagbe M., Et al., Cardio-Metabolic Risk Profile of a Diabetic Population in the Ho Municipality, Asian J Res Rep Endocrinol, 1, 1, pp. 10-20, (2018); Cusick M., Meleth A.D., Agron E., Fisher M.R., Reed G.F., Knatterud G.L., Et al., Associations of Mortality and Diabetes complications in patients with type 1 and type 2 diabetes: Early Treatment Diabetic Retinopathy Study report 27, Diabetes Care, 28, 3, pp. 617-625, (2005); Huang D., He D., Gong L., Wang W., Yang L., Zhang Z., Et al., Clinical characteristics and risk factors associated with mortality in patients with severe community-acquired pneumonia and type 2 diabetes mellitus, Crit Care, 25, 1, (2021); Laurberg T., Witte D.R., Gudbjornsdottir S., Eliasson B., Bjerg L., Diabetes-related risk factors and survival among individuals with type 2 diabetes and breast, lung, colorectal, or prostate cancer, Sci Rep, 14, 1, (2024); Katsiki N., Banach M., Mikhailidis D.P., Is type 2 diabetes mellitus a coronary heart disease equivalent or not? Do not just enjoy the debate and forget the patient!, Arch Med Sci, 15, 6, pp. 1357-1364, (2019); Riihimaa P., Impact of machine learning and feature selection on type 2 diabetes risk prediction, J Med Artif Intell, 3, (2020); Ho Teaching Hospital [Internet, (2022); Cochran W.G., Sampling techniques, (1977); Zhang A., Lipton Z.C., Li M., Smola A.J., Dive into deep learning, (2023); Maniruzzaman M., Kumar N., Menhazul Abedin M., Shaykhul Islam M., Suri H.S., El-Baz A.S., Et al., Comparative approaches for classification of diabetes mellitus data: machine learning paradigm, Comput Methods Programs Biomed, 152, pp. 23-34, (2017); Muhammad L.J., Algehyne E.A., Usman S.S., Predictive supervised machine learning models for diabetes mellitus, SN Comput Sci, 1, 5, (2020); Chen T., He T., Benesty M., Khotilovich V., Package, ‘xgboost ’ R Version, 90, 1-66, (2019); Rao Kondapally Seshasai S., Kaptoge S., Thompson A., Di Angelantonio E., Gao P., Sarwar N., Et al., Diabetes mellitus, fasting glucose, and risk of cause-specific death, N Engl J Med, 364, 9, pp. 829-841, (2011); Asante V., Gariba B.B., Appiah-Brempong E., Sarpong H.L., Ghana J Sci, 64, (2015); Amponsah Kodom M., Health-seeking behavior of Diabetic and Hypertensive patients in Rural communities of Ghana, AJHES, 1, 2, pp. 1-16, (2022); Korsah K.A., Mensah G.P., Achempim-Ansong G., The influence of Social meanings on Treatment seeking behaviours of patients with type 2 diabetes Mellitus: a qualitative Enquiry in a Ghanaian Hospital, J Med, 3, 6, (2022); Reinhard H., Lajer M., Gall M.A., Tarnow L., Parving H.H., Rasmussen L.M., Et al., Osteoprotegerin and mortality in type 2 diabetic patients, Diabetes Care, 33, 12, pp. 2561-2566, (2010); Mulnier H.E., Seaman H.E., Raleigh V.S., Soedamah-Muthu S.S., Colhoun H.M., Lawrenson R.A., Mortality in people with type 2 diabetes in the UK, Diabet Med, 23, 5, pp. 516-521, (2006); Ang Y.G., Heng B.H., Saxena N., Liew S.T.A., Chong P.N., Annual all-cause mortality rate for patients with diabetic kidney disease in Singapore, J Clin Transl Endocrinol, 4, pp. 1-6, (2016); Yoo H., Choo E., Lee S., Study of hospitalization and mortality in Korean diabetic patients using the diabetes complications severity index, BMC Endocr Disorders, 20, 1, (2020); Gonzalez-Perez A., Saez M., Vizcaya D., Lind M., Garcia Rodriguez L., Incidence and risk factors for mortality and end-stage renal disease in people with type 2 diabetes and diabetic kidney disease: a population-based cohort study in the UK, BMJ Open Diabetes Res Care, 9, 1, (2021); Hsieh M.S., Hu S.Y., How C.K., Seak C.J., Hsieh V.C.R., Lin J.W., Et al., Hospital outcomes and cumulative burden from complications in type 2 diabetic sepsis patients: a cohort study using administrative and hospital-based databases, Ther Adv Endocrinol Metab, 10, (2019); Magliano D.J., Harding J.L., Cohen K., Huxley R.R., Davis W.A., Shaw J.E., Excess risk of dying from infectious causes in those with type 1 and type 2 diabetes, Diabetes Care, 38, 7, pp. 1274-1280, (2015); Kvistholm Jensen A., Nielsen E.M., Bjorkman J.T., Jensen T., Muller L., Persson S., Et al., Whole-genome sequencing used to investigate a nationwide outbreak of Listeriosis caused by ready-to-eat Delicatessen Meat, Denmark, 2014, Clin Infect Dis, 63, 1, pp. 64-70, (2016); Linkeviciute-Ulinskiene D., Kaceniene A., Dulskas A., Patasius A., Zabuliene L., Smailyte G., Increased mortality risk in people with type 2 diabetes Mellitus in Lithuania, Int J Environ Res Public Health, 17, (2020); Afkarian M., Sachs M.C., Kestenbaum B., Hirsch I.B., Tuttle K.R., Himmelfarb J., Et al., Kidney disease and increased mortality risk in type 2 diabetes, J Am Soc Nephrol, 24, 2, pp. 302-308, (2013); Lee S., Zhou J., Leung K.S.K., Wu W.K.K., Wong W.T., Liu T., Et al., Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong, BMJ Open Diabetes Res Care, 9, 1, (2021); Huang A.A., Huang S.Y., Use of machine learning to identify risk factors for coronary artery disease, PLoS ONE, 18, 4, (2023); Yue C., Xin L., Kewen X., Wls-Svm C.S., An Intelligent Diagnosis to Type 2 Diabetes Based on QPSO Algorithm and, 2008 International Symposium on Intelligent Information Technology Application Workshops, pp. 117-121, (2008); Georga E.I., Protopappas V.C., Ardigo D., Marina M., Zavaroni I., Polyzos D., Et al., Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression, IEEE J Biomed Health Inf, 17, 1, pp. 71-81, (2013); Chiu S.Y.H., Chen Y.I., Lu J.R., Ng S.C., Chen C.H., Developing a prediction model for 7-Year and 10-Year all-cause mortality risk in type 2 diabetes using a hospital-based prospective cohort study, J Clin Med, 10, 20, (2021); Barsasella D., Gupta S., Malwade S., Aminin, Susanti Y., Tirmadi B., Et al., Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension, Int J Med Inf, 154, (2021); Huang A.A., Huang S.Y., Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations, PLoS ONE, 18, 2, (2023)","G.E. Kpene; Department of Medical Laboratory Sciences, School of Allied Health Sciences, University of Health and Allied Sciences, Ho, Ghana; email: kpene96@gmail.com","","BioMed Central Ltd","","","","","","14726823","","BEDMA","39794757","English","BMC Endocr. Disord.","Article","Final","","Scopus","2-s2.0-85215351117"
"Patel P.J.; Yelve D.; Diwan D.; Ranga S.; Gandhi K.; Dumasia S.; Nayak R.","Patel, Pinal J. (57214161132); Yelve, Dhanashree (58721988000); Diwan, Daksha (58721988100); Ranga, Shashi (57982954300); Gandhi, Kinjal (57982482300); Dumasia, Samay (58722780900); Nayak, Rutu (58721988200)","57214161132; 58721988000; 58721988100; 57982954300; 57982482300; 58722780900; 58721988200","Performance analysis of deep learning algorithms for classifying chronic obstructive pulmonary disease","2024","Journal of Integrated Science and Technology","12","2","745","","","","1","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177858770&partnerID=40&md5=db4f0092eeed5e1791022d601a24fe55","Computer Engineering Department, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; General Department-Mathematics, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; Chemical Engineering Department, Government Engineering College, Gujarat, Valsad, India; Computer Science Engineering Department, ITM(SLS) Baroda University, Gujarat, Vadodara, India; Biomedical Engineering, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India","Patel P.J., Computer Engineering Department, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; Yelve D., Computer Engineering Department, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; Diwan D., General Department-Mathematics, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; Ranga S., Chemical Engineering Department, Government Engineering College, Gujarat, Valsad, India; Gandhi K., Computer Science Engineering Department, ITM(SLS) Baroda University, Gujarat, Vadodara, India; Dumasia S., Computer Engineering Department, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India; Nayak R., Biomedical Engineering, Government Engineering College, Sector-28, Gujarat, Gandhinagar, India","Nowadays, Deep learning (DL) and machine learning (ML) play a vital role in furnishing solutions to the medical problems. Owing to their accurate and timely forecasting models and results, ML and DL algorithms are being embraced by the medical professionals for early detection and prompt treatment of different diseases. The respiratory diseases like Chronic Obstructive Pulmonary Disease (COPD) are emerging and need an early diagnosis. The major methods for diagnosing COPD involve expensive and unsuitable spirometer and imaging equipment. In this paper, an analysis of cough sound of the patients and identification of COPD severity levels using ML and DL algorithms has been reported. The study includes experiments conducted using Librosa library and used CNN, RNN, LSTM, and MLP algorithms for detecting COPD severity levels. © Authors.","Algorithms; Classification; COPD; Deep Learning; Machine Learning","","","","","","","","Chhikara B.S., Parang K., Global Cancer Statistics 2022: the trends projection analysis, Chem. Biol. Lett, 10, 1, (2023); Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med, 3, 11, pp. 2011-2030, (2006); Price D., Chronic obstructive pulmonary disease, BMJ, 326, 7398, pp. 1046-1047, (2003); Shanwal V.K., Assessing impact of Air Pollution on behavior of school children in Greater Noida, India, J. Integr. Sci. Technol, 9, 1, pp. 1-8, (2021); Roche N., Bronchopneumopathie chronique obstructive, Rev. Mal. Respir, 25, 2, (2008); Mejza F., Gnatiuc L., Buist A.S., Et al., Prevalence and burden of chronic bronchitis symptoms: results from the BOLD study, Eur. Respir. J, 50, 5, (2017); Mehta B., Dave V., Detection of Cardiovascular Autonomic Neuropathy using Machine Learning Algorithms, J. Integr. Sci. Technol, 11, 4, (2023); Deshmukh P., Pawar V.R., Gaikwad A.N., Machine learning based approach for lesion segmentation and severity level classification of diabetic retinopathy, J. Integr. Sci. Technol, 11, 4, (2023); Kuriki K., Matsumoto R., Ijichi C., Taira J., Aoki S., Establishment of in silico prediction methods for potential bitter molecules using the human T2R14 homology-model structure, Chem. Biol. Lett, 9, 3, (2022); Patil R.R., Ruby A.U., Chaithanya B.N., Jain S., Geetha K., Review of fundamentals of Artificial Intelligence and application with medical data in healthcare, J. Integr. Sci. Technol, 10, 2, pp. 126-133, (2022); Khatri K.L., Tamil L.S., Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks, IEEE J. Biomed. Heal. Informatics, 22, 1, pp. 285-290, (2018); Goto T., Camargo C.A., Faridi M.K., Yun B.J., Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, Am. J. Emerg. Med, 36, 9, pp. 1650-1654, (2018); Ahmed J., Vesal S., Durlak F., Et al., COPD classification in CT images using a 3D convolutional neural network, Informatik aktuell, pp. 39-45, (2020); Du R., Qi S., Feng J., Et al., Identification of COPD from Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN, IEEE Access, 8, pp. 38907-38919, (2020); Srivastava A., Jain S., Miranda R., Et al., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput. Sci, 7, pp. 1-22, (2021); Hussain A., Ugli I.K.K., Kim B.S., Et al., Detection of Different stages of COPD Patients Using Machine Learning Techniques, International Conference on Advanced Communication Technology, ICACT, pp. 368-372, (2022); He Y., Chen F., Chen Q., Deep Learning-Based Analysis of the Effect of Cardiac Color Ultrasound on Chronic Obstructive Pulmonary Disease under Mask Region, Sci. Program, 2021, pp. 1-7, (2021); Panda B.S., Gopal K.M., Satapathy R.N., RRBCNN: Aircraft detection and classification using Bounding Box Regressor based on Scale Reduction module, J. Integr. Sci. Technol, 11, 1, (2023); Borde S., Ratnaparkhe V., Optimization in channel selection for EEG signal analysis of Sleep Disorder subjects, J. Integr. Sci. Technol, 11, 3, (2023); Bhosle K., Ahirwadkar B., Deep learning Convolutional Neural Network (CNN) for Cotton, Mulberry and Sugarcane Classification using Hyperspectral Remote Sensing Data, J. Integr. Sci. Technol, 9, 2, pp. 70-74, (2021); Tiwari J., Sadiwala R., Personality prediction from Five-Factor Facial Traits using Deep learning, J. Integr. Sci. Technol, 11, 4, (2023)","P.J. Patel; CE Department, Government Engineering College, Gandhinagar, Gujarat, India; email: pinalpatel@gecg28.ac.in","","ScienceIn Publishing","","","","","","23214635","","","","English","J. Integr. Sci. Technol.","Article","Final","","Scopus","2-s2.0-85177858770"
"Pakdehi M.; Ahmadisharaf E.; Azimi P.; Yan Z.; Keshavarz Z.; Caballero C.; Allen J.G.","Pakdehi, M. (57781151600); Ahmadisharaf, E. (56022989800); Azimi, P. (56344066500); Yan, Z. (57854676900); Keshavarz, Z. (57218099005); Caballero, C. (59550484900); Allen, J.G. (16834469500)","57781151600; 56022989800; 56344066500; 57854676900; 57218099005; 59550484900; 16834469500","Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms","2025","Environment International","196","","109319","","","","0","10.1016/j.envint.2025.109319","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217415922&doi=10.1016%2fj.envint.2025.109319&partnerID=40&md5=d50d8c0c7d26de544d55f180824da9b6","Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States; Civil and Environmental Engineering Department, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States; Harvard School of Public Health, Boston, 02115, MA, United States; Florida International University, Miami, 33199, FL, United States","Pakdehi M., Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States, Civil and Environmental Engineering Department, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States; Ahmadisharaf E., Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States, Civil and Environmental Engineering Department, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States; Azimi P., Harvard School of Public Health, Boston, 02115, MA, United States; Yan Z., Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States, Civil and Environmental Engineering Department, FAMU-FSU College of Engineering, Tallahassee, 32310, FL, United States; Keshavarz Z., Harvard School of Public Health, Boston, 02115, MA, United States; Caballero C., Harvard School of Public Health, Boston, 02115, MA, United States, Florida International University, Miami, 33199, FL, United States; Allen J.G., Harvard School of Public Health, Boston, 02115, MA, United States","Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human respiratory health (e.g. asthma) have received limited attention. Models can be used to predict these impacts and support development of mitigation and preventive actions. Despite the presence of models for some other impacts of flooding, quantitative models for estimating the impacts of flooding on indoor mold spores are lacking. In this article, we studied the aftermath of two recent hurricanes—Ida and Ian—in the United States and applied machine learning algorithms to develop the first quantitative model for predicting mold spores in buildings. A comprehensive fine-scale database (building level), consisting of flood characteristics, building properties, human indoor activities and existing mold spores, prepared through survey questionnaires, home inspections, laboratory analyses and flood hindcasting, from 60 homes was utilized. The modeling results suggested satisfactory performance for regression-based predictions of indoor mold spores (coefficient of determination or R2 of 0.83 and 0.38). This is the first quantitative model for predicting the impacts of flooding on mold spores. Our study provides a foundation for quantitative assessments of flood impacts on indoor mold spores in residential buildings. This supports insurance companies, public health officials and emergency managers to better assess the impacts of hurricanes and extreme flooding on human respiratory health. © 2025 The Authors","Extreme flooding; Hurricanes; Indoor air quality; Mold; Residential buildings","Air Pollution, Indoor; Algorithms; Floods; Fungi; Housing; Humans; Machine Learning; Models, Theoretical; Spores, Fungal; United States; Flood insurance; Health insurance; Indoor air pollution; Lung cancer; Building applications; Extreme flood; Extreme flooding; Floodings; Indoor air quality; Indoor molds; Latent impact; Machine learning algorithms; Quantitative models; Residential building; air quality; algorithm; building; extreme event; flooding; health geography; health impact; health status; hurricane event; indoor air; machine learning; quantitative analysis; research work; residential location; spore; Article; asthma; building; deep learning; extreme flooding; extreme gradient boosting; flooding; human; human activities; humidity; hurricane; indoor environment; machine learning; mitigation; mold; multilayer perceptron; nonhuman; prediction; questionnaire; random forest; residential building; respiratory system; United States; algorithm; fungus; fungus spore; housing; indoor air pollution; theoretical model; Air quality","","","","","Florida State University, FSU; United States’ National Science Foundation, (2203180); Gulf Research Program, (SCON-10001211); Gulf Research Program","Funding text 1: This study was financially supported through a research grant by United States\u2019 National Science Foundation (award number 2203180) and an Early-Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (grant number SCON-10001211). We are grateful for the Open Access Publishing Fund of Florida State University libraries. We also thank all participants of our surveys and those who helped us with recruiting volunteer families.; Funding text 2: This study was financially supported through a research grant by United States\u2019 National Science Foundation (award number 2203180) and an Early-Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (grant number SCON-10001211). We thank all participants of our surveys and those who helped us with recruiting volunteer families. 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Res., (2025); Rabby S.H., Sun X., Ibna Hafiz A.M., Yan Z., Imtiaz S.U., Pakdehi M., Moumouni A.S., Ahmadisharaf E., Alamdari N., Application of machine learning methods in water quality modeling, Machine learning and artificial intelligence in toxicology and environmental health, (2025); Rahimi L., Hoque M., Ahmadisharaf E., Alamdari N., Misra V., Maran A.C., Kao S.H., AghaKouchak A., Talchabhadel R., Future climate projections for South Florida: Improving the accuracy of air temperature and precipitation extremes with a hybrid statistical bias correction technique, Earth's Future, 12, 8, (2024); Riggs M.A., Rao C.Y., Brown C.M., Van Sickle D., Cummings K.J., Dunn K.H., Deddens J.A., Ferdinands J., Callahan D., Moolenaar R.L., Resident cleanup activities, characteristics of flood-damaged homes and airborne microbial concentrations in New Orleans, Louisiana, October 2005, Environ. Res., 106, pp. 401-409, (2008); Salvati P., Petrucci O., Rossi M., Bianchi C., Pasqua A.A., Guzzetti F., Gender, age and circumstances analysis of flood and landslide fatalities in Italy, Sci. Total Environ., 610-611, (2018); (2024); Shultz J.M., Trapido E.J., Kossin J.P., Fugate C., Nogueira L., Apro A., Patel M., Torres V.J., Ettman C.K., Espinel Z., (2022); Tellman B., Sullivan J.A., Kuhn C., Kettner A.J., Doyle C.S., Brakenridge G.R., Erickson T.A., Slayback D.A., Satellite imaging reveals increased proportion of population exposed to floods, Nature, 596, pp. 80-86, (2021); Verdonck T., Baesens B., Oskarsdottir M., vanden Broucke S., Special issue on feature engineering editorial, Mach. Learn., 113, pp. 3917-3928, (2024); Viitanen H., Vinha J., Salminen K., Ojanen T., Peuhkuri R., Paajanen L., Lahdesmaki K., Moisture and bio-deterioration risk of building materials and structures, J. Build. Phys., 33, pp. 201-224, (2010); Visitsunthorn N., Chaimongkol W., Visitsunthorn K., Pacharn P., Jirapongsananuruk O., Great flood and aeroallergen sensitization in children with asthma and/or allergic rhinitis, Asian Pac. J. Allergy Immunol., 36, pp. 69-76, (2018); Wing O.E.J., Lehman W., Bates P.D., Sampson C.C., Quinn N., Smith A.M., Neal J.C., Porter J.R., Kousky C., Inequitable patterns of US flood risk in the Anthropocene, Nat. Clim. Chang., 12, pp. 156-162, (2022); Yan Z., Kamanmalek S., Alamdari N., Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors, Sci. Total Environ., 912, (2024); Ying X., An overview of overfitting and its solutions, J. Phys. Conf. Ser., 1168, (2019); Zeng Z., Guan D., Steenge A.E., Xia Y., Mendoza-Tinoco D., Flood footprint assessment: a new approach for flood-induced indirect economic impact measurement and post-flood recovery, J. Hydrol., 579, (2019); Zhu J.-J., Yang M., Ren Z.J., Machine learning in environmental research: common pitfalls and best practices, Environ. Sci. Tech., 57, pp. 17671-17689, (2023)","E. Ahmadisharaf; Department of Civil and Environmental Engineering, Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, 32310, United States; email: eascesharif@gmail.com","","Elsevier Ltd","","","","","","01604120","","ENVID","39946930","English","Environ. Int.","Article","Final","","Scopus","2-s2.0-85217415922"
"Huang T.; Socrates V.; Ovchinnikova P.; Faustino I.; Kumar A.; Safranek C.; Chi L.; Wang E.A.; Puglisi L.; Wong A.H.; Wang K.H.; Taylor R.A.","Huang, Thomas (58068715100); Socrates, Vimig (57194574332); Ovchinnikova, Polina (58768220800); Faustino, Isaac (57322715800); Kumar, Anusha (58900651800); Safranek, Conrad (57202209015); Chi, Ling (58068758500); Wang, Emily A. (35774682200); Puglisi, Lisa (57200652169); Wong, Ambrose H. (36460490800); Wang, Karen H. (55389247600); Taylor, R. Andrew (57223661992)","58068715100; 57194574332; 58768220800; 57322715800; 58900651800; 57202209015; 58068758500; 35774682200; 57200652169; 36460490800; 55389247600; 57223661992","Characterizing Emergency Department Care for Patients With Histories of Incarceration","2025","JACEP Open","6","1","100022","","","","0","10.1016/j.acepjo.2024.100022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216857112&doi=10.1016%2fj.acepjo.2024.100022&partnerID=40&md5=c3b3f6d1e42de37cf3ef1e558587edb8","Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States; Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States; Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States; SEICHE Center for Health and Justice, Yale School of Medicine, New Haven, CT, United States; Equity Research and Innovation Center, Yale School of Medicine, Yale University, New Haven, CT, United States","Huang T., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States, Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States; Socrates V., Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States, Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States; Ovchinnikova P., Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States; Faustino I., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States; Kumar A., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States; Safranek C., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States, Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States; Chi L., Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States; Wang E.A., SEICHE Center for Health and Justice, Yale School of Medicine, New Haven, CT, United States; Puglisi L., SEICHE Center for Health and Justice, Yale School of Medicine, New Haven, CT, United States; Wong A.H., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States; Wang K.H., Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States, SEICHE Center for Health and Justice, Yale School of Medicine, New Haven, CT, United States, Equity Research and Innovation Center, Yale School of Medicine, Yale University, New Haven, CT, United States; Taylor R.A., Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States, Department for Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, United States","Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prevalent and subject to discrimination, in the health care system. © 2024 The Author(s)","artificial intelligence; emergency medicine; incarceration; large language models; quality of health care","acute heart infarction; adult; aged; agitation; anxiety; Article; artificial intelligence; asthma; bronchiectasis; chronic kidney failure; chronic obstructive lung disease; cohort analysis; comorbidity; controlled study; depression; diabetes mellitus; emergency medicine; emergency ward; employment status; encounter group; ethnicity; female; health behavior; health care; health care quality; health care system; history; housing instability; human; hypertension; incarceration; injury; insurance; language model; large language model; major clinical study; male; natural language processing; observational study; predictive value; psychosis; retrospective study; schizophrenia; sedation; smoking; sociodemographics; substance use; suicidal ideation; training","","","","","Yale School of Medicine, YSM","This publication was made possible by the Yale School of Medicine Fellowship for Medical Student Research. ","Frank J.W., Wang E.A., Nunez-Smith M., Lee H., Comfort M., Discrimination based on criminal record and healthcare utilization among men recently released from prison: a descriptive study, Health Justice, 2, 1, (2014); Nosrati E., Kang-Brown J., Ash M., McKee M., Marmot M., King L.P., Incarceration and mortality in the United States, SSM Popul Health, 15, (2021); Fazel S., Yoon I.A., Hayes A.J., Substance use disorders in prisoners: an updated systematic review and meta-regression analysis in recently incarcerated men and women, Addiction, 112, 10, pp. 1725-1739, (2017); Dumont D.M., Brockmann B., Dickman S., Alexander N., Rich J.D., Public health and the epidemic of incarceration, Annu Rev Public Health, 33, pp. 325-339, (2012); Fox A.D., Anderson M.R., Bartlett G., Valverde J., Starrels J.L., Cunningham C.O., Health outcomes and retention in care following release from prison for patients of an urban post-incarceration transitions clinic, J Health Care Poor Underserved, 25, 3, pp. 1139-1152, (2014); Massoglia M., Remster B., Linkages between incarceration and health, Public Health Rep, 134, 1_suppl, pp. 8S-14S, (2019); Wang E.A., Hong C.S., Shavit S., Sanders R., Kessell E., Kushel M.B., Engaging individuals recently released from prison into primary care: a randomized trial, Am J Public Health, 102, 9, pp. e22-e29, (2012); Binswanger I.A., Stern M.F., Deyo R.A., Et al., Release from prison--a high risk of death for former inmates, N Engl J Med, 356, 2, pp. 157-165, (2007); Eiting E., Korn C.S., Wilkes E., Ault G., Henderson S.O., Reduction in jail emergency department visits and closure after implementation of on-site urgent care, J Correct Health Care, 23, 1, pp. 88-92, (2017); Maher P.J., Adedipe A.A., Sanders B.L., Buck T., Craven P., Strote J., Emergency department utilization by a jail population, Am J Emerg Med, 36, 9, pp. 1631-1634, (2018); Martin R.A., Couture R., Tasker N., Et al., Emergency medical care of incarcerated patients: opportunities for improvement and cost savings, PLoS One, 15, 4, (2020); Formerly incarcerated reenter society transformed safely transitioning every person act. 115th Congress (2017-2018); Ferszt G.G., Palmer M., McGrane C., Where does your state stand on shackling of pregnant incarcerated women?, Nurs Womens Health, 22, 1, pp. 17-23, (2018); Knox D.K., Holloman G.H., Use and avoidance of seclusion and restraint: consensus statement of the American association for emergency psychiatry project beta seclusion and restraint workgroup, West J Emerg Med, 13, 1, pp. 35-40, (2012); Wong A.H., Ray J.M., Rosenberg A., Et al., Experiences of individuals who were physically restrained in the emergency department, JAMA Netw Open, 3, 1, (2020); Strout T.D., Perspectives on the experience of being physically restrained: an integrative review of the qualitative literature, Int J Ment Health Nurs, 19, 6, pp. 416-427, (2010); Huang T., Socrates V., Gilson A., Et al., Identifying incarceration status in the electronic health record using large language models in emergency department settings, J Clin Transl Sci, 8, 1, (2024); New Haven metro area looks most like ‘normal America’ study finds. New Haven Register, (2016); Kolko J., ‘Normal America’ is not a small town of White people. Fivethirtyeight, (2016); Huang T., Socrates V., Gilson A., Et al., Identifying incarceration status in the electronic health record using natural language processing in emergency department settings, medRxiv, (2023); Alahmary K., Kadasah S., Alsulami A., Alshehri A.M., Alsalamah M., Da'ar O.B., To admit or not to admit to the emergency department: the disposition question at a tertiary teaching and referral hospital, Healthcare (Basel), 11, 5, (2023); de Bruijn W., Daams J.G., van Hunnik F.J.G., Et al., Physical and pharmacological restraints in hospital care: protocol for a systematic review, Front Psychiatry, 10, (2020); Heinze C., Dassen T., Grittner U., Use of physical restraints in nursing homes and hospitals and related factors: a cross-sectional study, J Clin Nurs, 21, 7-8, pp. 1033-1040, (2012); Rich J.D., Wakeman S.E., Dickman S.L., Medicine and the epidemic of incarceration in the United States, N Engl J Med, 364, 22, pp. 2081-2083, (2011); Viglianti E.M., Iwashyna T.J., Winkelman T.N.A., Mass incarceration and pulmonary health: guidance for clinicians, Ann Am Thorac Soc, 15, 4, pp. 409-412, (2018); Udo T., Chronic medical conditions in U.S. adults with incarceration history, Health Psychol, 38, 3, pp. 217-225, (2019); Freudenberg N., Jails, prisons, and the health of urban populations: a review of the impact of the correctional system on community health, J Urban Health, 78, 2, pp. 214-235, (2001); Winkelman T.N.A., Ford B.R., Dunsiger S., Et al., Feasibility and acceptability of a smoking cessation program for individuals released from an urban, pretrial jail: a pilot randomized clinical trial, JAMA Netw Open, 4, 7, (2021); Zhao J., Star J., Han X., Et al., Incarceration history and access to and receipt of health care in the US, JAMA Health Forum, 5, 2, (2024); Wildeman C., Wang E.A., Mass incarceration, public health, and widening inequality in the USA, Lancet, 389, 10077, pp. 1464-1474, (2017); Steigerwald V.L., Rozek D.C., Paulson D., Depressive symptoms in older adults with and without a history of incarceration: A matched pairs comparison, Aging Ment Health, 26, 11, pp. 2179-2185, (2022); Stawinska-Witoszynska B., Czechowska K., Moryson W., Wieckowska B., The prevalence of generalised anxiety disorder among prisoners of the penitentiary institution in north-eastern Poland, Front Psychiatry, 12, (2021); Sunpuwan M., Thaweesit S., Tangchonlatip K., Perceived anxiety and depression and associated factors among women inmates with a long-term sentence in Thailand, PLoS One, 19, 3, (2024); Brumbles D., Meister A., Psychiatric elopement: using evidence to examine causative factors and preventative measures, Arch Psychiatr Nurs, 27, 1, pp. 3-9, (2013); Yasini M., Sedaghat M., Ghasemi Esfe A.R., Tehranidoost M., Epidemiology of absconding from an Iranian psychiatric centre, J Psychiatr Ment Health Nurs, 16, 2, pp. 153-157, (2009); Castle N.G., Engberg J., The health consequences of using physical restraints in nursing homes, Med Care, 47, 11, pp. 1164-1173, (2009)","R.A. Taylor; Department of Emergency Medicine, Yale School of Medicine, New Haven, New Haven, 464 Congress Ave. Suite 260, 06519, United States; email: richard.taylor@yale.edu","","Elsevier Inc.","","","","","","26881152","","","","English","JACEP Open","Article","Final","","Scopus","2-s2.0-85216857112"
"Bogart M.; Liu Y.; Oakland T.; Stiegler M.","Bogart, Michael (57191489150); Liu, Yuhang (57577416800); Oakland, Todd (57577192200); Stiegler, Marjorie (58581653500)","57191489150; 57577416800; 57577192200; 58581653500","Evaluating Triple Therapy Treatment Pathways in Chronic Obstructive Pulmonary Disease (COPD): A Machine-Learning Predictive Model","2022","International Journal of COPD","17","","","735","747","12","1","10.2147/COPD.S336297","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128332583&doi=10.2147%2fCOPD.S336297&partnerID=40&md5=e3a29b2daf3b3c738a316dbe5056bbe1","Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, United States; GNS Healthcare, Somerville, MA, United States; University of North Carolina Chapel Hill, Chapel Hill, NC, United States","Bogart M., Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, United States; Liu Y., GNS Healthcare, Somerville, MA, United States; Oakland T., GNS Healthcare, Somerville, MA, United States; Stiegler M., Value Evidence and Outcomes, GlaxoSmithKline, Research Triangle Park, NC, United States, University of North Carolina Chapel Hill, Chapel Hill, NC, United States","Purpose: Inhaled triple therapy (TT) comprising a long-acting muscarinic antagonist, long-acting β2 agonist, and inhaled corticosteroid is recommended for symptomatic chronic obstructive pulmonary disease (COPD) patients, or those at risk of exacerbation. However, it is not well understood which patient characteristics contribute most to future exacerbation risk. This study assessed patient predictors associated with future exacerbation time following initiation of TT. Patients and Methods: This retrospective cohort study used data from the Optum™ Clinformatics™ Data Mart, a large health claims database in the United States. COPD patients who initiated TT between January 2008 and March 2018 (index) were eligible. Patients were required to be aged ≥18 years at index and have continuous enrollment for the 12 months prior to index (baseline) and the 12 months following index (follow-up). Patients who had received TT during baseline were excluded. Data from eligible patients were analyzed using a reverse engineering forward simulation machine learning platform to predict future COPD exacerbation time. Results: Data from 73,625 patients were included. The model found that prior exacerbation was largely correlated with post-index exacerbation time; patients who had ≥4 exacerbation episodes during baseline had an average increase of 32.4 days post-index exacerbation, compared with patients with no exacerbations during baseline. Likewise, ≥2 inpatient visits (effect size 27.1 days), the use of xanthines (effect size 11.5 days), or rheumatoid arthritis (effect size 6.4 days) during baseline were associated with increased exacerbation time. Conversely, diagnosis of anemia (effect size –5.68 days), or oral corticosteroids in the past month (effect size –3.43 days) were associated with reduced exacerbation time. Conclusion: Frequent prior exacerbations, healthcare resource utilization, xanthine use, and rheumatoid arthritis were the strongest factors predicting the future increase of exacerbations. These results improve our understanding of exacerbation risk among COPD patients initiating triple therapy. © 2022 Bogart et al.","Bayesian modeling; chronic obstructive pulmonary disease; exacerbation; predictive modeling; triple therapy","Administration, Inhalation; Adolescent; Adrenal Cortex Hormones; Adrenergic beta-2 Receptor Agonists; Adult; Arthritis, Rheumatoid; Bronchodilator Agents; Drug Therapy, Combination; Humans; Machine Learning; Muscarinic Antagonists; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; United States; beta 2 adrenergic receptor stimulating agent; corticosteroid; muscarinic receptor blocking agent; xanthine; beta 2 adrenergic receptor stimulating agent; bronchodilating agent; corticosteroid; muscarinic receptor blocking agent; anemia; Article; chronic obstructive lung disease; cohort analysis; comorbidity; controlled study; disease exacerbation; drug efficacy; health care utilization; human; machine learning; major clinical study; outcome assessment; predictive value; retrospective study; rheumatoid arthritis; triplet chemotherapy; adolescent; adult; chronic obstructive lung disease; combination drug therapy; inhalational drug administration; machine learning; rheumatoid arthritis; United States","","xanthine, 69-89-6; Adrenal Cortex Hormones, ; Adrenergic beta-2 Receptor Agonists, ; Bronchodilator Agents, ; Muscarinic Antagonists, ","","","Fiona Goodwin and Rebecca Cunningham of Aura; American Thoracic Society, ATS, (A2313); GlaxoSmithKline, GSK, (213319)","Funding text 1: Editorial support (in the form of writing assistance, including preparation of the draft manuscript under the direction and guidance of the authors, collating and incorporating authors’ comments for each draft, assembling tables and figures, grammatical editing, and referencing) was provided by Fiona Goodwin and Rebecca Cunningham of Aura, a division of Spirit Medical Communications Group Limited, and was funded by GlaxoSmithKline. These data have been presented in abstract/poster form at the American Thoracic Society – 117th International Conference (Bogart M, Oakland T, Liu Y, Enev T. Triple therapy treatment pathways in chronic obstructive pulmonary disease (COPD): a real-world predictive model – American Thoracic Society – 117th International Conference. Am J Respir Crit Care Med. 2021;203:A2313.; Funding text 2: Editorial support (in the form of writing assistance, including preparation of the draft manuscript under the direction and guidance of the authors, collating and incorporating authors? comments for each draft, assembling tables and figures, grammatical editing, and referencing) was provided by Fiona Goodwin and Rebecca Cunningham of Aura, a division of Spirit Medical Communications Group Limited, and was funded by GlaxoSmithKline. These data have been presented in abstract/poster form at the American Thoracic Society ? 117th International Conference (Bogart M, Oakland T, Liu Y, Enev T. Triple therapy treatment pathways in chronic obstructive pulmonary disease (COPD): a real-world predictive model ? American Thoracic Society ? 117th International Conference. Am J Respir Crit Care Med. 2021;203:A2313. This study was funded by GlaxoSmithKline plc (study number 213319). The sponsor was involved in study conception and design, data interpretation, and the decision to submit the article for publication. The sponsor was also given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations.; Funding text 3: This study was funded by GlaxoSmithKline plc (study number 213319). The sponsor was involved in study conception and design, data interpretation, and the decision to submit the article for publication. The sponsor was also given the opportunity to review the manuscript for medical and scientific accuracy as well as intellectual property considerations.","Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, (2020); Blasi F, Cesana G, Conti S, Et al., The clinical and economic impact of exacerbations of chronic obstructive pulmonary disease: a cohort of hospitalized patients, PLoS One, 9, 6, (2014); Dalal AA, Shah M, D'Souza AO, Rane P., Costs of COPD exacerbations in the emergency department and inpatient setting, Respir Med, 105, 3, pp. 454-460, (2011); Guerra B, Gaveikaite V, Bianchi C, Puhan MA., Prediction models for exacerbations in patients with COPD, Eur Respir Rev, 26, 143, (2017); Hoogendoorn M, Feenstra TL, Boland M, Et al., Prediction models for exacerbations in different COPD patient populations: comparing results of five large data sources, Int J Chron Obstruct Pulmon Dis, 12, pp. 3183-3194, (2017); Hurst JR, Vestbo J, Anzueto A, Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Stallberg B, Lisspers K, Larsson K, Et al., Predicting hospitalization due to COPD exacerbations in Swedish primary care patients using machine learning – based on the ARCTIC study, Int J Chron Obstruct Pulmon Dis, 16, pp. 677-688, (2021); Stallberg B, Janson C, Larsson K, Et al., Real-world retrospective cohort study ARCTIC shows burden of comorbidities in Swedish COPD versus non-COPD patients, NPJ Prim Care Respir Med, 28, 1, (2018); Kaszuba E, Odeberg H, Rastam L, Halling A., Impact of heart failure and other comorbidities on mortality in patients with chronic obstructive pulmonary disease: a register-based, prospective cohort study, BMC Fam Pract, 19, 1, (2018); Westerik JA, Metting EI, van Boven JF, Tiersma W, Kocks JW, Schermer TR., Associations between chronic comorbidity and exacerbation risk in primary care patients with COPD, Respir Res, 18, 1, (2017); Tavakoli H, Chen W, Sin DD, FitzGerald JM, Sadatsafavi M., Predicting severe chronic obstructive pulmonary disease exacerbations. Developing a population surveillance approach with administrative data, Ann Am Thorac Soc, 17, 9, pp. 1069-1076, (2020); Zheng Y, Zhu J, Liu Y, Et al., Triple therapy in the management of chronic obstructive pulmonary disease: systematic review and meta-analysis, BMJ, 363, (2018); Bogart M, Glassberg MB, Reinsch T, Stanford RH., Impact of prompt versus delayed initiation of triple therapy post COPD exacerbation in a US-managed care setting, Respir Med, 145, pp. 138-144, (2018); Yu AP, Guerin A, Ponce de Leon D, Et al., Therapy persistence and adherence in patients with chronic obstructive pulmonary disease: multiple versus single long-acting maintenance inhalers, J Med Econ, 14, 4, pp. 486-496, (2011); Bogart M, Wu B, Germain G, Et al., Real-world adherence to single-inhaler vs multiple-inhaler triple therapy among patients with COPD in a commercially insured US population, Chest, 158, 4, pp. A1773-A1774, (2020); Xing H, McDonagh PD, Bienkowska J, Et al., Causal modeling using network ensemble simulations of genetic and gene expression data predicts genes involved in rheumatoid arthritis, PLoS Comput Biol, 7, 3, (2011); Iheanacho I, Zhang S, King D, Rizzo M, Ismaila AS., Economic burden of chronic obstructive pulmonary disease (COPD): a systematic literature review, Int J Chron Obstruct Pulmon Dis, 15, pp. 439-460, (2020); Brill SE, Wedzicha JA., Oxygen therapy in acute exacerbations of chronic obstructive pulmonary disease, Int J Chron Obstruct Pulmon Dis, 9, pp. 1241-1252, (2014); Ma Y, Tong H, Zhang X, Et al., Chronic obstructive pulmonary disease in rheumatoid arthritis: a systematic review and meta-analysis, Respir Res, 20, 1, (2019); Hyldgaard C, Bendstrup E, Pedersen AB, Et al., Increased mortality among patients with rheumatoid arthritis and COPD: a population-based study, Respir Med, 140, pp. 101-107, (2018); Mcguire K, Avina-Zubieta JA, Esdaile JM, Et al., Risk of incident chronic obstructive pulmonary disease in rheumatoid arthritis: a population-based cohort study, Arthritis Care Res (Hoboken), 71, 5, pp. 602-610, (2019); Terada K, Muro S, Ohara T, Et al., Abnormal swallowing reflex and COPD exacerbations, Chest, 137, 2, pp. 326-332, (2010); Falk JA, Minai OA, Mosenifar Z., Inhaled and systemic corticosteroids in chronic obstructive pulmonary disease, Proc Am Thorac Soc, 5, 4, pp. 506-512, (2008); McMahon TJ, Prybylowski AC., Anemia in the patient with chronic lung disease, Management of Anemia, pp. 143-155, (2018); Latourelle JC, Beste MT, Hadzi TC, Et al., Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson’s disease: a longitudinal cohort study and validation, Lancet Neurol, 16, 11, pp. 908-916, (2017); Berger JS, Haskell L, Ting W, Et al., Machine learning methodology predicts comorbidities are associated with increased total healthcare costs among patients with severe peripheral artery disease. Poster presented at: The American Heart Association Quality of Care and Outcomes Research Scientific Sessions; April 2–3, 2017, Arlington, Virginia, Circulation: Cardiovascular Quality and Outcomes, 10, (2017); Ivanov V, Torgovitsky R, Tchetgen ET, Et al., Using clinical trial and real world data to bridge efficacy to effectiveness of fingolimod in multiple sclerosis patients, Value Health, 19, 7, (2016); Anderson JP, Parikh JR, Shenfeld DK, Et al., Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: an application of machine learning using electronic health records, J Diabetes Sci Technol, 10, 1, pp. 6-18, (2016)","M. Bogart; GlaxoSmithKline, Five Moore Drive, PO Box 13398, Research Triangle Park, 27709-3398, United States; email: michael.r.bogart@gsk.com","","Dove Medical Press Ltd","","","","","","11769106","","","35418750","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85128332583"
"Zhang T.; Pang H.; Wu Y.; Xu J.; Liu L.; Li S.; Xia S.; Chen R.; Liang Z.; Qi S.","Zhang, Tiande (59415869600); Pang, Haowen (57781616300); Wu, Yanan (57394724800); Xu, Jiaxuan (57861994500); Liu, Lingkai (59416344800); Li, Shang (59415869700); Xia, Shuyue (7202893268); Chen, Rongchang (14017626800); Liang, Zhenyu (36010749700); Qi, Shouliang (36572483500)","59415869600; 57781616300; 57394724800; 57861994500; 59416344800; 59415869700; 7202893268; 14017626800; 36010749700; 36572483500","BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis","2025","Computer Methods and Programs in Biomedicine","259","","108516","","","","0","10.1016/j.cmpb.2024.108516","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209589981&doi=10.1016%2fj.cmpb.2024.108516&partnerID=40&md5=c24ab99d1cb8e0e0c5612ce51bdcaa4b","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Department of Respiratory and Critical Care Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Hetao Institute of Guangzhou National Laboratory, Guangzhou, China","Zhang T., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Pang H., School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China; Wu Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Xu J., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Liu L., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Li S., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Xia S., Department of Respiratory and Critical Care Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Chen R., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, Hetao Institute of Guangzhou National Laboratory, Guangzhou, China; Liang Z., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Qi S., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China, Department of Respiratory and Critical Care Medicine, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China","Background and objective: Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function. Methods: To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps. Results: BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD. Conclusions: BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available. © 2024","Chronic obstructive pulmonary disease; Deep learning; Generative adversarial network; Image translation; Parametric response mapping; Transformer","Algorithms; Exhalation; Humans; Image Processing, Computer-Assisted; Lung; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Respiratory Function Tests; Tomography, X-Ray Computed; Arthroplasty; Deep neural networks; Diagnosis; Forward error correction; Mean square error; Pulmonary diseases; Respirators; Sensory feedback; Adversarial networks; Chronic obstructive pulmonary disease; Computed tomography images; Deep learning; Functionals; Image translation; Parametric response mapping; Pulmonary function; Small airways; Transformer; Article; back propagation; breathvisionnet; chronic obstructive lung disease; classifier; clinical significance; comparative study; computer assisted tomography; controlled study; convolutional neural network; deep learning; diagnostic accuracy; edge aware discriminator; emphysema; external validity; forced expiratory volume; forced vital capacity; functional residual capacity; generative adversarial network; generative model; global context injection module; gradient penalty; human; image analysis; image quality; image registration; lung function; mean absolute error; mean squared error; parametric response mapping; phenotype; prediction; quantitative assay; radiation dose; radiation exposure; self supervised learning; signal noise ratio; small airway disease; total lung capacity; transformer block; Wasserstein generative adversarial network; algorithm; artificial neural network; chronic obstructive lung disease; diagnostic imaging; exhalation; image processing; lung; lung function test; pathophysiology; procedures; x-ray computed tomography; Computerized tomography","","","RTX 2080Ti GPU, NVIDIA","NVIDIA","National Natural Science Foundation of China, NSFC, (82072008, 82270044, 62271131); National Natural Science Foundation of China, NSFC; Fundamental Research Funds for the Central Universities, (N2424010-19); Fundamental Research Funds for the Central Universities","This work was partly supported by the National Natural Science Foundation of China (Nos. 82072008 , 82270044 , and 62271131 ) and the Fundamental Research Funds for the Central Universities ( N2424010-19 ).","Wu Y., Du R., Feng J., Qi S., Pang H., Xia S., Qian W., Deep CNN for COPD identification by multi-view snapshot integration of 3D airway tree and lung field, Biomed. 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Liang; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: 490458234@qq.com","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","39571504","English","Comput. Methods Programs Biomed.","Article","Final","","Scopus","2-s2.0-85209589981"
"Turcatel G.; Xiao Y.; Caveney S.; Gnacadja G.; Kim J.; Molfino N.A.","Turcatel, Gianluca (58772184100); Xiao, Yi (59414551500); Caveney, Scott (57225471245); Gnacadja, Gilles (6506422115); Kim, Julie (59413975500); Molfino, Nestor A. (7003898882)","58772184100; 59414551500; 57225471245; 6506422115; 59413975500; 7003898882","Predicting Asthma Exacerbations Using Machine Learning Models","2025","Advances in Therapy","42","1","e22796","362","374","12","0","10.1007/s12325-024-03053-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209384693&doi=10.1007%2fs12325-024-03053-y&partnerID=40&md5=f571b5fbcd43e04c226cc8e91a7fcd4e","Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA, United States; Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, 91320, CA, United States","Turcatel G., Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA, United States; Xiao Y., Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA, United States; Caveney S., Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, 91320, CA, United States; Gnacadja G., Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA, United States; Kim J., Digital Health and Innovation, Amgen Inc., Thousand Oaks, CA, United States; Molfino N.A., Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, 91320, CA, United States","Introduction: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and statistical methods to detect patterns for future disease events across large datasets from electronic health records (EHR). This study conducted training and fine-tuning of ML algorithms for the real-world prediction of asthma exacerbations in patients with physician-diagnosed asthma. Methods: Adults with ≥ 2 ICD9/10 asthma codes within 1 year and at least 30 days apart were identified from the Optum Panther EHR database between 2016 and 2023. An emergency department (ED), urgent care, or inpatient visit for asthma, while on systemic administration of corticosteroids, was considered an exacerbation. To predict factors associated with exacerbations in a 6-month study period, clinical information from patients was retrieved in the preceding 6-month baseline period. Clinical information included demographics, lab results, diagnoses, medications, immunizations, and allergies. Three models built using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Transformers algorithms were trained and tested on independent datasets. Predictions were explained using the SHAP (SHapley Additive exPlanations) library. Results: Of 1,331,934 patients with asthma, 16,279 (1.2%) experienced ≥ 1 exacerbation. XGBoost was the best predictive algorithm (area under the curve [AUC] = 0.964). Factors associated with exacerbations included a prior history of exacerbation, prednisone usage, high-dose albuterol usage, and elevated troponin I. Reduced probability of exacerbations was associated with receiving inhaled albuterol, vitamins, aspirin, statins, furosemide, and influenza vaccination. Conclusion: This ML-based study on asthma in the real world confirmed previously known features associated with increased exacerbation risk for asthma, while uncovering not entirely understood features associated with reduced risk of asthma exacerbations. These findings are hypothesis-generating and should contribute to ongoing discussion of the strengths and limitations of ML and other supervised learning models in patient risk stratification. © The Author(s) 2024.","Asthma; Electronic health records; Exacerbations; Machine learning; Physician-diagnosed asthma; Real-world prediction; XGBoost","Adult; Aged; Algorithms; Asthma; Disease Progression; Electronic Health Records; Female; Humans; Machine Learning; Male; Middle Aged; Prognosis; acetylsalicylic acid; furosemide; hydroxymethylglutaryl coenzyme A reductase inhibitor; influenza vaccine; prednisone; salbutamol; troponin I; adult; African American; algorithm; area under the curve; Article; asthma; cohort analysis; demographics; electronic health record; female; health care system; human; immunization; influenza; influenza vaccination; laboratory test; long short term memory network; machine learning; major clinical study; male; middle aged; Shapley additive explanation; short term memory; aged; asthma; disease exacerbation; drug therapy; pathophysiology; prognosis","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; furosemide, 54-31-9; prednisone, 53-03-2; salbutamol, 18559-94-9, 35763-26-9; troponin I, 77108-40-8","","","Amgen","This study was sponsored by Amgen Inc. The journal\u2019s Rapid Service and Open Access Fees were funded by Amgen Inc. ","Acosta J.N., Falcone G.J., Rajpurkar P., Topol E.J., Multimodal biomedical AI, Nat Med, 28, 9, pp. 1773-1784, (2022); Moor M., Banerjee O., Abad Z.S.H., Et al., Foundation models for generalist medical artificial intelligence, Nature, 616, 7956, pp. 259-265, (2023); Molfino N.A., Turcatel G., Riskin D., Machine learning approaches to predict asthma exacerbations: a narrative review, Adv Ther, 41, 2, pp. 534-552, (2024); Couillard S., Do W.I.H., Beasley R., Hinks T.S.C., Pavord I.D., Predicting the benefits of type-2 targeted anti-inflammatory treatment with the prototype Oxford asthma attack risk scale (ORACLE), ERJ Open Res, 8, 1, (2022); Couillard S., Petousi N., Smigiel K.S., Molfino N.A., Toward a predict and prevent approach in obstructive airway diseases, J Allergy Clin Immunol Pract, 11, 3, pp. 704-712, (2023); Tong Y., Messinger A.I., Wilcox A.B., Et al., Forecasting future asthma hospital encounters of patients with asthma in an academic health care system: predictive model development and secondary analysis study, J Med Internet Res, 23, 4, (2021); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021); Goto T., Camargo C.A., Faridi M.K., Yun B.J., Hasegawa K., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED, Am J Emerg Med, 36, 9, pp. 1650-1654, (2018); Wang Z., Li Y., Gao Y., Et al., Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: a systematic analysis for the global burden of disease study 2019, Respir Res, 24, 1, (2023); Fuhlbrigge A., Peden D., Apter A.J., Et al., Asthma outcomes: exacerbations, J Allergy Clin Immunol, 129, 3, pp. S34-S48, (2012); Agress A., Oprea Y., Roy S., Et al., The association between malignancy, immunodeficiency, and atopy in IgE-deficient patients, J Allergy Clin Immunol Pract, 12, 1, pp. 185-194, (2024); Price D., Wilson A.M., Chisholm A., Et al., Predicting frequent asthma exacerbations using blood eosinophil count and other patient data routinely available in clinical practice, J Asthma Allergy, 9, pp. 1-12, (2016); Venkatesan P., 2023 GINA report for asthma, Lancet Respir Med, 11, 7, (2023); Harvey M.G., Hancox R.J., Elevation of cardiac troponins in exacerbation of chronic obstructive pulmonary disease, Emerg Med Australas, 16, 3, pp. 212-215, (2004); Ikeda M., Ohshima N., Kawashima M., Shiina M., Kitani M., Suzukawa M., Severe asthma where eosinophilic granulomatosis with polyangiitis became apparent after the discontinuation of dupilumab, Intern Med, 61, 5, pp. 755-759, (2022); Carroll C.L., Coro M., Cowl A., Sala K.A., Schramm C.M., Transient occult cardiotoxicity in children receiving continuous beta-agonist therapy, World J Pediatr, 10, 4, pp. 324-329, (2014); Yalta K., Yalta T., Gurdogan M., Palabiyik O., Yetkin E., Cardiac biomarkers in the setting of asthma exacerbations: a review of clinical implications and practical considerations, Curr Allergy Asthma Rep, 20, 6, (2020); Pavasini R., d'Ascenzo F., Campo G., Et al., Cardiac troponin elevation predicts all-cause mortality in patients with acute exacerbation of chronic obstructive pulmonary disease: Systematic review and meta-analysis, Int J Cardiol, 191, pp. 187-193, (2015); Mullur J., Buchheit K.M., Aspirin-exacerbated respiratory disease: updates in the era of biologics, Ann Allergy Asthma Immunol, 131, 3, pp. 317-324, (2023); Kurth T., Barr R.G., Gaziano J.M., Buring J.E., Randomised aspirin assignment and risk of adult-onset asthma in the Women’s Health Study, Thorax, 63, 6, pp. 514-518, (2008); Barr R.G., Kurth T., Stampfer M.J., Buring J.E., Hennekens C.H., Gaziano J.M., Aspirin and decreased adult-onset asthma: randomized comparisons from the Physicians' Health Study, Am J Respir Crit Care Med, 175, 2, pp. 120-125, (2007); Park C., Jang J.H., Kim C., Et al., Real-world effectiveness of statin therapy in adult asthma, J Allergy Clin Immunol Pract, 12, 2, pp. 399-408.e396, (2024); Kim J.H., Wee J.H., Choi H.G., Et al., Association between statin medication and asthma/asthma exacerbation in a national health screening cohort, J Allergy Clin Immunol Pract, 9, 7, pp. 2783-2791, (2021); Inokuchi R., Aoki A., Aoki Y., Yahagi N., Effectiveness of inhaled furosemide for acute asthma exacerbation: a meta-analysis, Crit Care, 18, 6, (2014); Hodroge S.S., Glenn M., Breyre A., Et al., Adult patients with respiratory distress: current evidence-based recommendations for prehospital care, West J Emerg Med, 21, 4, pp. 849-857, (2020); Rodriguez Vazquez J.C., Pino Alfonso P.P., Gassiot Nuno C., Paez P.I., Usefulness of inhaled furosemide in a bronchial asthma attack, J Investig Allergol Clin Immunol, 8, 5, pp. 290-293, (1998); Tibrewal C., Modi N.S., Bajoria P.S., Et al., Therapeutic potential of vitamin D in management of asthma: a literature review, Cureus, 15, 7, (2023)","N.A. Molfino; Global Development, Amgen Inc., Thousand Oaks, One Amgen Center Dr, 91320, United States; email: nmolfino@amgen.com","","Adis","","","","","","0741238X","","ADTHE","39556295","English","Adv. Ther.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85209384693"
"Zhou K.; Wu F.; Lu L.; Tang G.; Deng Z.; Dai C.; Zhao N.; Wan Q.; Peng J.; Wu X.; Zeng X.; Cui J.; Yang C.; Chen S.; Huang Y.; Yu S.; Zhou Y.; Ran P.","Zhou, Kunning (57790557400); Wu, Fan (57218949883); Lu, Lifei (57338227200); Tang, Gaoying (58962444200); Deng, Zhishan (57201792489); Dai, Cuiqiong (57337771600); Zhao, Ningning (57338515900); Wan, Qi (58882263500); Peng, Jieqi (57217067920); Wu, Xiaohui (57790557300); Zeng, Xianliang (59540099900); Cui, Jiangyu (55587719600); Yang, Changli (58175914400); Chen, Shengtang (57948373000); Huang, Yongqing (59124457100); Yu, Shuqing (58175914500); Zhou, Yumin (8242343500); Ran, Pixin (55544866100)","57790557400; 57218949883; 57338227200; 58962444200; 57201792489; 57337771600; 57338515900; 58882263500; 57217067920; 57790557300; 59540099900; 55587719600; 58175914400; 57948373000; 59124457100; 58175914500; 8242343500; 55544866100","Association between impaired diffusion capacity and small airway dysfunction: a cross-sectional study","2025","ERJ Open Research","11","1","00910-2023","","","","0","10.1183/23120541.00910-2023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216762574&doi=10.1183%2f23120541.00910-2023&partnerID=40&md5=4d86ea680d0f10855632b1b500172c67","State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Guangzhou National Laboratory, Guangdong, Guangzhou, China; Department of Pulmonary and Critical Care Medicine, Wengyuan County People’s Hospital, Shaoguan, China; Medical Imaging Center, Wengyuan County People’s Hospital, Shaoguan, China; Lianping County People’s Hospital, Heyuan, China","Zhou K., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Wu F., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China, Guangzhou National Laboratory, Guangdong, Guangzhou, China; Lu L., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Tang G., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Deng Z., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Dai C., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Zhao N., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Wan Q., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Peng J., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China, Guangzhou National Laboratory, Guangdong, Guangzhou, China; Wu X., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Zeng X., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Cui J., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China; Yang C., Department of Pulmonary and Critical Care Medicine, Wengyuan County People’s Hospital, Shaoguan, China; Chen S., Medical Imaging Center, Wengyuan County People’s Hospital, Shaoguan, China; Huang Y., Lianping County People’s Hospital, Heyuan, China; Yu S., Lianping County People’s Hospital, Heyuan, China; Zhou Y., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China, Guangzhou National Laboratory, Guangdong, Guangzhou, China; Ran P., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangdong, Guangzhou, China, Guangzhou National Laboratory, Guangdong, Guangzhou, China","Background Small airway dysfunction (SAD) and impaired diffusion capacity of the lungs for carbon monoxide (DLCO) are positively associated with a worse prognosis. Individuals with both dysfunctions have been identified in clinical practice and it is unknown whether they have worse health status or need management. We conducted this study to explore the association between SAD and impaired DLCO, and the difference between the groups with two dysfunctions, with either one dysfunction and with no dysfunction. Methods This study involved subjects partly from those who had returned for the third-year follow-up (up to December 2022) of the Early Chronic Obstructive Pulmonary Disease study and those who newly participated. We assessed diffusion capacity, questionnaire, exacerbations, spirometry, impulse oscillometry (IOS) and computed tomography (CT). Impaired DLCO was defined as DLCO <80% predicted. Spirometry-defined SAD was defined using the percent predicted values of maximal mid-expiratory flow, and forced expiratory flow at 50% and 75% of forced vital capacity, at least two of these three values being <65% predicted after the use of a bronchodilator. IOS-defined SAD was defined when the difference in resistance at 5 and 20 Hz was >0.07 kPa·L−1·s. CT-defined SAD was defined when the percentage of expiratory low-attenuation areas <−856 HU comprised ⩾15% of the total lung volume. Covariate analyses and logistic regression were performed to assess the association between impaired DLCO and SAD. Results This study involved 581 subjects. The occurrence of both spirometry-and CT-defined SAD was significantly higher in subjects with impaired DLCO than normal DLCO. Subjects with two dysfunctions were associated with worse preceding year’s exacerbations than controls. Conclusions Impaired diffusion capacity is positively associated with SAD. Subjects with impaired diffusion capacity and SAD may have a worse health status and need additional management. © The authors 2025.","","bronchodilating agent; salbutamol; adult; airway obstruction; Article; artificial intelligence; artificial ventilation; body mass; chronic obstructive lung disease; clinical practice; computer assisted tomography; cross-sectional study; cystic fibrosis; diffusing capacity for carbon monoxide; disease exacerbation; female; follow up; forced expiratory flow; forced expiratory volume; forced vital capacity; health status; human; inspiratory capacity; interstitial lung disease; lung diffusion capacity; lung function; lung ventilation; lung volume; major clinical study; male; maximal mid expiratory flow; middle aged; peak expiratory flow; questionnaire; retinal nerve fiber layer thickness; six minute walk test; smoking; spirometry; thorax radiography; total lung capacity","","salbutamol, 18559-94-9, 35763-26-9","","","Foundation of Guangzhou National Laboratory, (SRPG22-016, SRPG22-018); State Key Laboratory of Respiratory Disease, SKLRD, (SKLRD-Z-202315, SKLRD-L-202402); State Key Laboratory of Respiratory Disease, SKLRD; Guangzhou Medical University, GMU, (2024SRP080, GMUCR2024-01012); Guangzhou Medical University, GMU","This study was supported by the Foundation of Guangzhou National Laboratory (SRPG22-016 and SRPG22-018), the State Key Laboratory of Respiratory Disease Clinical and Epidemiological Research Project (SKLRD-L-202402 and SKLRD-Z-202315), the Plan on Enhancing Scientific Research in Guangzhou Medical University (GMUCR2024-01012) and Research Capability Strengthening and Fundamental Improvement Project of Guangzhou Medical University (2024SRP080). Funding information for this article has been deposited with the Crossref Funder Registry.","Balasubramanian A, MacIntyre NR, Henderson RJ, Et al., Diffusing capacity of carbon monoxide in assessment of COPD, Chest, 156, pp. 1111-1119, (2019); Boutou AK, Shrikrishna D, Tanner RJ, Et al., Lung function indices for predicting mortality in COPD, Eur Respir J, 42, pp. 616-625, (2013); Kirby M, Owrangi A, Svenningsen S, Et al., On the role of abnormal DL<sub>CO</sub> in ex-smokers without airflow limitation: symptoms, exercise capacity and hyperpolarised helium-3 MRI, Thorax, 68, pp. 752-759, (2013); Balasubramanian A, Gearhart AS, Putcha N, Et al., Diffusing capacity as a predictor of hospitalizations in a clinical cohort of chronic obstructive pulmonary disease, Ann Am Thorac Soc, 21, pp. 243-250, (2024); de-Torres JP, O'Donnell DE, Marin JM, Et al., Clinical and prognostic impact of low diffusing capacity for carbon monoxide values in patients with Global Initiative for Obstructive Lung Disease I COPD, Chest, 160, pp. 872-878, (2021); Elbehairy AF, O'Donnell CD, Abd Elhameed A, Et al., Low resting diffusion capacity, dyspnea, and exercise intolerance in chronic obstructive pulmonary disease, J Appl Physiol, 127, pp. 1107-1116, (2019); Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease: 2024 Report; Hogg JC, Macklem PT, Thurlbeck WM, Et al., Site and nature of airway obstruction in chronic obstructive lung disease, N Engl J Med, 278, pp. 1355-1360, (1968); Lu L, Peng J, Wu F, Et al., Clinical characteristics of airway impairment assessed by impulse oscillometry in patients with chronic obstructive pulmonary disease: findings from the ECOPD study in China, BMC Pulm Med, 23, (2023); Sharpe AL, Reibman J, Oppenheimer BW, Et al., Role of small airway dysfunction in unexplained exertional dyspnoea, ERJ Open Res, 9, pp. 00603-2022, (2023); Criner RN, Hatt CR, Galban CJ, Et al., Relationship between diffusion capacity and small airway abnormality in COPDGene, Respir Res, 20, (2019); Wu F, Zhou Y, Peng J, Et al., Rationale and design of the Early Chronic Obstructive Pulmonary Disease (ECOPD) study in Guangdong, China: a prospective observational cohort study, J Thorac Dis, 13, pp. 6924-6935, (2021); Miller MR, Crapo R, Hankinson J, Et al., General considerations for lung function testing, Eur Respir J, 26, pp. 153-161, (2005); Miller MR, Hankinson J, Brusasco V, Et al., Standardisation of spirometry, Eur Respir J, 26, pp. 319-338, (2005); Niu Y, Yang T, Gu X, Et al., Long-term ozone exposure and small airway dysfunction: the China Pulmonary Health (CPH) study, Am J Respir Crit Care Med, 205, pp. 450-458, (2022); Xing Z, Sun T, Janssens JP, Et al., Airflow obstruction and small airway dysfunction following pulmonary tuberculosis: a cross-sectional survey, Thorax, 78, pp. 274-280, (2023); Lei J, Huang K, Wu S, Et al., Heterogeneities and impact profiles of early chronic obstructive pulmonary disease status: findings from the China Pulmonary Health study, Lancet Reg Health West Pac, 45, (2024); Xiao D, Chen Z, Wu S, Et al., Prevalence and risk factors of small airway dysfunction, and association with smoking, in China: findings from a national cross-sectional study, Lancet Respir Med, 8, pp. 1081-1093, (2020); Calverley PM, Albert P, Walker PP, Et al., Bronchodilator reversibility in chronic obstructive pulmonary disease: use and limitations, Lancet Respir Med, 1, pp. 564-573, (2013); Graham BL, Brusasco V, Burgos F, Et al., 2017 ERS/ATS standards for single-breath carbon monoxide uptake in the lung, Eur Respir J, 49, (2017); Harvey BG, Strulovici-Barel Y, Kaner RJ, Et al., Risk of COPD with obstruction in active smokers with normal spirometry and reduced diffusion capacity, Eur Respir J, 46, pp. 1589-1597, (2015); Quanjer PH, Tammeling GJ, Cotes JE, Et al., Lung volumes and forced ventilatory flows. 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Ran; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, National Center for Respiratory Medicine, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China; email: pxran@gzhmu.edu.cn","","European Respiratory Society","","","","","","23120541","","","","English","ERJ Open Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216762574"
"Heyman E.T.; Ashfaq A.; Ekelund U.; Ohlsson M.; Björk J.; Khoshnood A.M.; Lingman M.","Heyman, Ellen T. (57320171600); Ashfaq, Awais (57210769453); Ekelund, Ulf (57203258140); Ohlsson, Mattias (7006120856); Björk, Jonas (7005964271); Khoshnood, Ardavan M. (14045258300); Lingman, Markus (6504238326)","57320171600; 57210769453; 57203258140; 7006120856; 7005964271; 14045258300; 6504238326","A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system","2024","PLoS ONE","19","12","e0311081","","","","0","10.1371/journal.pone.0311081","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213417112&doi=10.1371%2fjournal.pone.0311081&partnerID=40&md5=5df54f4dfd3b2c8c6ca8a8271d5adee3","Department of Emergency Medicine, Halland Hospital, Region Halland, Sweden; Emergency Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Halland Hospital, Region Halland, Sweden; Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden; Skåne University Hospital, Lund, Sweden; Centre for Environmental and Climate Science, Lund University, Lund, Sweden; Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden; Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden; Emergency Medicine, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden; Skåne University Hospital, Malmö, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden","Heyman E.T., Department of Emergency Medicine, Halland Hospital, Region Halland, Sweden, Emergency Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Ashfaq A., Halland Hospital, Region Halland, Sweden, Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden; Ekelund U., Emergency Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden, Skåne University Hospital, Lund, Sweden; Ohlsson M., Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden, Centre for Environmental and Climate Science, Lund University, Lund, Sweden; Björk J., Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden, Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden; Khoshnood A.M., Emergency Medicine, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden, Skåne University Hospital, Malmö, Sweden; Lingman M., Halland Hospital, Region Halland, Sweden, Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden, Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden","Background Dyspnoea is one of the emergency department’s (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), pneumonia and “other diagnoses” by using deep learning and complete, unselected data from an entire regional health care system. Methods In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017–12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold standard diagnoses were defined as the subsequent in-hospital or ED discharge notes, and a subsample was manually reviewed by emergency medicine experts. A novel deep learning model, the clinical attention-based recurrent encoder network (CareNet), was developed. Cohort performance was compared to a simpler CatBoost model. A list of all variables and their importance for diagnosis was created. For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. AUROC, sensitivity and specificity were compared. Findings The most prevalent diagnoses among the 10,315 dyspnoeic ED visits were AHF (15.5%), eCOPD (14.0%) and pneumonia (13.3%). Median number of unique events, i.e., registered clinical data with time stamps, per ED visit was 1,095 (IQR 459–2,310). CareNet median AUROC was 87.0%, substantially higher than the CatBoost model´s (81.4%). CareNet median sensitivity for AHF, eCOPD, and pneumonia was 74.5%, 92.6%, and 54.1%, respectively, with a specificity set above 75.0, slightly inferior to that of the CatBoost baseline model. The model assembled a list of 1,596 variables by importance for diagnosis, on top were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life management difficulties and maternity care. Each patient visit received their own unique attention plot, graphically displaying important clinical events for the diagnosis. Interpretation We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system. © 2024 Heyman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","Adult; Aged; Aged, 80 and over; Cross-Sectional Studies; Deep Learning; Delivery of Health Care; Dyspnea; Emergency Service, Hospital; Female; Heart Failure; Humans; Male; Middle Aged; Pneumonia; Pulmonary Disease, Chronic Obstructive; Sweden; acute heart failure; aged; Article; atrial fibrillation; chronic obstructive lung disease; cohort analysis; cross-sectional study; deep learning; dyspnea; emergency department visit; emergency medicine; emergency ward; female; health care system; hidden Markov model; human; learning; machine learning; male; natural language processing; patient visit; pneumonia; receiver operating characteristic; sensitivity and specificity; smoking; adult; diagnosis; health care delivery; heart failure; hospital emergency service; middle aged; Sweden; very elderly","","","","","Vetenskapsrådet, VR, (2019-00198); Vetenskapsrådet, VR; Scientific Council of Region Halland, (979314, 980763); foundation Stiftelsen Landshövding Per Westlings minnesfond, (RMh2020-0007)","The work was supported by the Swedish Research Council under Grant no. 2019-00198 (JB); Scientific Council of Region Halland, Sweden under Grant no. 979314 (ETH); Sparbanksstiftelsen Varberg, Sweden under Grant no. 980763 (ETH); and the foundation Stiftelsen Landsh\u00F6vding Per Westlings minnesfond, Sweden under application no. RMh2020-0007 (ETH). The funders have played no role in study design, data collection, analysis, interpretation of data, or the writing of this manuscript. We wish to thank the adjudicating committee for their knowledgeable and diligent work.","Ibsen S, Lindskou TA, Nickel CH, Klojgard T, Christensen EF, Sovso MB., Which symptoms pose the highest risk in patients calling for an ambulance? A population-based cohort study from Denmark, Scand J Trauma Resusc Emerg Med, 29, 1, (2021); Jemt E, Ekstrom M, Ekelund U., Outcomes in Emergency Department Patients with Dyspnea versus Chest Pain: A Retrospective Consecutive Cohort Study, Emerg Med Int, 2022, (2022); Arvig MD, Mogensen CB, Skjot-Arkil H, Johansen IS, Rosenvinge FS, Lassen AT., Chief Complaints, Underlying Diagnoses, and Mortality in Adult, Non-trauma Emergency Department Visits: A Population-based, Multicenter Cohort Study, West J Emerg Med, 23, 6, pp. 855-863, (2022); Langlo NM, Orvik AB, Dale J, Uleberg O, Bjornsen LP., The acute sick and injured patients: an overview of the emergency department patient population at a Norwegian University Hospital Emergency Department, Eur J Emerg Med, 21, 3, pp. 175-180, (2014); Ray P, Birolleau S, Lefort Y, Becquemin M-H, Beigelman C, Isnard R, Et al., Acute respiratory failure in the elderly: etiology, emergency diagnosis and prognosis, Critical care, 10, 3, (2006); Kelly AM, Holdgate A, Keijzers G, Klim S, Graham CA, Craig S, Et al., Epidemiology, prehospital care and outcomes of patients arriving by ambulance with dyspnoea: an observational study, Scand J Trauma Resusc Emerg Med, 24, 1, (2016); Laribi S, Keijzers G, van Meer O, Klim S, Motiejunaite J, Kuan W, Et al., Epidemiology of patients presenting with dyspnea to emergency departments in Europe and the Asia-Pacific region, European journal of emergency medicine, 26, 5, pp. 345-349, (2019); Sporl P, Beckers SK, Rossaint R, Felzen M, Schroder H., Shedding light into the black box of out-of-hospital respiratory distress-A retrospective cohort analysis of discharge diagnoses, prehospital diagnostic accuracy, and predictors of mortality, PLoS One, 17, 8, (2022); Ovesen SH, Sorensen SF, Lisby M, Mandau MH, Thomsen IK, Kirkegaard H., Change in diagnosis from the emergency department to hospital discharge in dyspnoeic patients, Dan Med J, 69, 2, (2022); Hunold KM, Caterino JM., High Diagnostic Uncertainty and Inaccuracy in Adult Emergency Department Patients With Dyspnea: A National Database Analysis, Academic Emergency Medicine, 26, 2, pp. 267-271, (2019); Schewe JC, Kappler J, Dovermann K, Graeff I, Ehrentraut SF, Heister U, Et al., Diagnostic accuracy of physician-staffed emergency medical teams: a retrospective observational cohort study of prehospital versus hospital diagnosis in a 10-year interval, Scand J Trauma Resusc Emerg Med, 27, 1, (2019); Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K., Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review, Acad Emerg Med, 28, 2, pp. 184-196, (2021); Rapid Emergency Triage Treatment Scale (RETTS©) online version 2019, (2020); International Classification of Diseases (ICD) ICD-10 2019; Ashfaq A, Lonn S, Nilsson H, Eriksson JA, Kwatra J, Yasin ZM, Et al., Data resource profile: Regional healthcare information platform in Halland, Sweden, a dedicated environment for healthcare research, International journal of epidemiology, (2020); Ibm Corp N., IBM SPSS statistics for windows, (2022); Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E, Hierarchical attention networks for document classification, Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, (2016); Chung J, Gulcehre C, Cho K, Bengio Y., Empirical evaluation of gated recurrent neural networks on sequence modeling, (2014); Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J., Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, 26, (2013); Dorogush AV, Ershov V, Gulin A., CatBoost: gradient boosting with categorical features support, (2018); Blecker S, Sontag D, Horwitz LI, Kuperman G, Park H, Reyentovich A, Et al., Early Identification of Patients With Acute Decompensated Heart Failure, Journal of Cardiac Failure, 24, 6, pp. 357-362, (2018); Long B, Koyfman A, Gottlieb M., Diagnosis of Acute Heart Failure in the Emergency Department: An Evidence-Based Review, The western journal of emergency medicine, 20, 6, pp. 875-884, (2019); Swaminathan S, Qirko K, Smith T, Corcoran E, Wysham NG, Bazaz G, Et al., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PloS one, 12, 11, (2017); Long B, Long D, Koyfman A., Emergency Medicine Evaluation of Community-Acquired Pneumonia: History, Examination, Imaging and Laboratory Assessment, and Risk Scores, Journal of Emergency Medicine, 53, 5, pp. 642-652, (2017); Stekhoven DJ, Buhlmann P., MissForest—non-parametric missing value imputation for mixed-type data, Bioinformatics, 28, 1, pp. 112-118, (2011); Hunold KM, Caterino JM, Bischof JJ., Diagnostic Uncertainty in Dyspneic Patients with Cancer in the Emergency Department, West J Emerg Med, 22, 2, pp. 170-176, (2021); Fan FL, Xiong J, Li M, Wang G., On Interpretability of Artificial Neural Networks: A Survey, IEEE Transactions on Radiation and Plasma Medical Sciences, 5, 6, pp. 741-760, (2021); Tjoa E, Guan C., A survey on explainable artificial intelligence (xai): Toward medical xai, IEEE transactions on neural networks and learning systems, 32, 11, pp. 4793-4813, (2020); Newman-Toker DE, Peterson SM, Badihian S, Hassoon A, Nassery N, Parizadeh D, Et al., Diagnostic errors in the emergency department: a systematic review, (2022); Ghassemi M, Oakden-Rayner L, Beam AL., The false hope of current approaches to explainable artificial intelligence in health care, Lancet Digit Health, 3, 11, pp. e745-e50, (2021); Holmgren G, Andersson P, Jakobsson A, Frigyesi A., Artificial neural networks improve and simplify intensive care mortality prognostication: a national cohort study of 217,289 first-time intensive care unit admissions, Journal of intensive care, 7, 1, (2019)","E.T. Heyman; Department of Emergency Medicine, Halland Hospital, Region Halland, Sweden; email: ellen.tolestam-heyman@regionhalland.se","","Public Library of Science","","","","","","19326203","","POLNC","39729465","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85213417112"
"Hou L.; Min M.; Hou R.; Tan W.; Zhang M.; Liu Q.","Hou, Ling (59657678200); Min, Ming (59657459300); Hou, Rui (59658530400); Tan, Wei (59658530500); Zhang, Minghua (57749407200); Liu, Qianfei (59657886200)","59657678200; 59657459300; 59658530400; 59658530500; 57749407200; 59657886200","Prediction of clinical deterioration within one year in chronic obstructive pulmonary disease using the systemic coagulation-inflammation index: a retrospective study employing multiple machine learning method","2025","PeerJ","13","2","e18989","","","","0","10.7717/peerj.18989","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218987318&doi=10.7717%2fpeerj.18989&partnerID=40&md5=09abb812e32be645164f6ac4a3279d52","Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Hubei, China; Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; Hubei Enshi College, Enshi, China","Hou L., Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Hubei, China; Min M., Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; Hou R., Hubei Enshi College, Enshi, China; Tan W., Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; Zhang M., Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; Liu Q., Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China","Background. Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. Methods. A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results. Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. Conclusion. The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Copyright 2025 Hou et al.","Chronic obstructive pulmonary disease; Clinical deterioration; Machine learning; Predictor; Systemic coagulation-inflammation index","bilirubin; C reactive protein; D dimer; hemoglobin; procalcitonin; aged; area under the curve; Article; blood clotting; body mass; cellular distribution; chronic obstructive lung disease; cohort analysis; coronary artery disease; diabetes mellitus; elastic tissue; female; Fisher exact test; heart arrhythmia; human; hypertension; ICD-10; inflammation; k nearest neighbor; leukocyte count; logistic regression analysis; lymphocyte count; machine learning; major clinical study; male; neutrophil count; predictive value; random forest; receiver operating characteristic; red blood cell distribution width; respiratory failure; retrospective study; sensitivity and specificity; Sequential Organ Failure Assessment Score; support vector machine; systemic coagulation-inflammation index; systemic immune inflammation index","","bilirubin, 18422-02-1, 635-65-4; C reactive protein, 9007-41-4; hemoglobin, 9008-02-0; procalcitonin, 56645-65-9","R software 4.3.2","","","","Agusti A, Edwards LD, Rennard SI, MacNee W, Tal-Singer R, Miller BE, Vestbo J, Lomas DA, Calverley PM, Wouters E, Crim C, Yates JC, Silverman EK, Coxson HO, Bakke P, Mayer RJ, Celli B., Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype, PLOS ONE, 7, (2012); Barnes PJ., Inflammatory mechanisms in patients with chronic obstructive pulmonary disease, Journal of Allergy and Clinical Immunology, 138, pp. 16-27, (2016); Bazzan E, Casara A, Radu CM, Tine M, Biondini D, Faccioli E, Pezzuto F, Bernardinello N, Conti M, Balestro E, Calabrese F, Simioni P, Rea F, Turato G, Spagnolo P, Cosio MG, Saetta M., Macrophages-derived factor XIII links coagulation to inflammation in COPD, Frontiers in Immunology, 14, (2023); Chaurasia SN, Kushwaha G, Kulkarni PP, Mallick RL, Latheef NA, Mishra JK, Dash D., Platelet HIF-2α promotes thrombogenicity through PAI-1 synthesis and extracellular vesicle release, Haematologica, 104, pp. 2482-2492, (2019); 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Mkorombindo T, Dransfield MT., COPD: cOagulation-associated Pulmonary Disease?, Respirology, 26, pp. 290-291, (2021); Ozkan U, Gurdogan M., A novel potential biomarker for predicting the development of septic embolism in patients with infective endocarditis: systemic coagulation inflammation index, Turk Kardiyoloji Derneği Arşivi, 52, pp. 36-43, (2024); Pandey KC, De S, Mishra PK., Role of proteases in chronic obstructive pulmonary disease, Frontiers in Pharmacology, 8, (2017); Pantanowitz L, Pearce T, Abukhiran I, Hanna M, Wheeler S, Soong TR, Tafti AP, Pantanowitz J, Lu MY, Mahmood F, Gu Q, Rashidi HH., Non-generative artificial intelligence (AI) in medicine: advancements and applications in supervised and unsupervised machine learning, Modern Pathology, 38, 3, (2024); Papakonstantinou E, Karakiulakis G, Batzios S, Savic S, Roth M, Tamm M, Stolz D., Acute exacerbations of COPD are associated with significant activation of matrix metalloproteinase 9 irrespectively of airway obstruction, emphysema and infection, Respiratory Research, 16, (2015); 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Upadhyay P, Wu CW, Pham A, Zeki AA, Royer CM, Kodavanti UP, Takeuchi M, Bayram H, Pinkerton KE., Animal models and mechanisms of tobacco smoke-induced chronic obstructive pulmonary disease (COPD), Journal of Toxicology and Environmental Health: Part B, Critical Reviews, 26, pp. 275-305, (2023); Wang C, Xu J, Yang L, Xu Y, Zhang X, Bai C, Kang J, Ran P, Shen H, Wen F, Huang K, Yao W, Sun T, Shan G, Yang T, Lin Y, Wu S, Zhu J, Wang R, Shi Z, Zhao J, Ye X, Song Y, Wang Q, Zhou Y, Ding L, Yang T, Chen Y, Guo Y, Xiao F, Lu Y, Peng X, Zhang B, Xiao D, Chen CS, Wang Z, Zhang H, Bu X, Zhang X, An L, Zhang S, Cao Z, Zhan Q, Yang Y, Cao B, Dai H, Liang L, He J., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Welte T., Chronic obstructive pulmonary disease- a growing cause of death and disability worldwide, Deutsches Arzteblatt International, 111, pp. 825-826, (2014); Wu K, Tang H, Lin R, Carr SG, Wang Z, Babicheva A, Ayon RJ, Jain PP, Xiong M, Rodriguez M, Rahimi S, Balistrieri F, Rahimi S, Valdez-Jasso D, Simonson TS, Desai AA, Garcia JGN, Shyy JY, Thistlethwaite PA, Wang J, Makino A, Yuan JX., Endothelial platelet-derived growth factor-mediated activation of smooth muscle platelet-derived growth factor receptors in pulmonary arterial hypertension, Pulmonary Circulation, 10, (2020); Yu S, Zhang H, Wan L, Xue M, Zhang Y, Gao X., The association between the respiratory tract microbiome and clinical outcomes in patients with COPD, Microbiological Research, 266, (2023); Zengin I, Severgun K., Systemic coagulation inflammation index associated with bleeding in acute coronary syndrome, Kardiologiia, 63, pp. 72-77, (2023)","M. Zhang; Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; email: 1115295145@qq.com; Q. Liu; Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China; email: l879612253@163.com","","PeerJ Inc.","","","","","","21678359","","","","English","PeerJ","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218987318"
"Li H.; Zeng J.; Snyder M.P.; Zhang S.","Li, Han (36116844400); Zeng, Jianyang (33468010200); Snyder, Michael P. (57216999198); Zhang, Sai (56580715700)","36116844400; 33468010200; 57216999198; 56580715700","Modeling gene interactions in polygenic prediction via geometric deep learning","2025","Genome Research","35","1","","178","187","9","0","10.1101/gr.279694.124","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215951006&doi=10.1101%2fgr.279694.124&partnerID=40&md5=ab43f33335fa641d68be7deb643972ef","School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China; Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China; School of Engineering, Research Center for Industries of the Future, Westlake University, Zhejiang, Hangzhou, 310030, China; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, 94304, CA, United States; Department of Epidemiology, University of Florida, Gainesville, 32603, FL, United States; Departments of Biostatistics & Biomedical Engineering, UF Genetics Institute, University of Florida, Gainesville, 32603, FL, United States","Li H., School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China; Zeng J., School of Engineering, Research Center for Industries of the Future, Westlake University, Zhejiang, Hangzhou, 310030, China; Snyder M.P., Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, 94304, CA, United States; Zhang S., Department of Epidemiology, University of Florida, Gainesville, 32603, FL, United States, Departments of Biostatistics & Biomedical Engineering, UF Genetics Institute, University of Florida, Gainesville, 32603, FL, United States","Polygenic risk score (PRS) is a widely used approach for predicting individuals’ genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases. © 2025 Li et al.","","Deep Learning; Epistasis, Genetic; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Neural Networks, Computer; high density lipoprotein; protein; Alzheimer disease; Article; asthma; atrial fibrillation; autoimmune disease; biology; controlled study; coronary artery disease; deep learning; gene interaction; genetic risk; genetic variability; genotype phenotype correlation; heart infarction; human; low risk population; multiple sclerosis; prediction; protein protein interaction; rheumatoid arthritis; ulcerative colitis; artificial neural network; biological model; epistasis; genetic predisposition; genome-wide association study; multifactorial inheritance; procedures","","protein, 67254-75-5","","","Westlake University; New Cornerstone Science Foundation; National Natural Science Foundation of China, NSFC, (T2125007); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2021YFF1201300); National Key Research and Development Program of China, NKRDPC; Westlake Education Foundation, (41751)","This work was supported by the National Natural Science Foundation of China (T2125007 to J.Z.), the National Key Research and Development Program of China (2021YFF1201300 to J.Z.), the New Cornerstone Science Foundation through the XPLORER PRIZE (J.Z.), the Research Center for Industries of the Future (RCIF) at Westlake University (J.Z.), and the Westlake Education Foundation (J.Z.). 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Zeng; School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, 310030, China; email: zengjy@westlake.edu.cn; M.P. Snyder; Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, 94304, United States; email: mpsnyder@stanford.edu; S. Zhang; Department of Epidemiology, University of Florida, Gainesville, 32603, United States; email: sai.zhang@ufl.edu","","Cold Spring Harbor Laboratory Press","","","","","","10889051","","GEREF","39562137","English","Genome Res.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85215951006"
"Özbeyaz A.; Yıldırım M.; Tufaner F.","Özbeyaz, Abdurrahman (43261782000); Yıldırım, Mustafa (57886056400); Tufaner, Fatih (57190489143)","43261782000; 57886056400; 57190489143","Prediction of asthma outpatients using cumulative particulate matter and machine learning algorithms: a case study in Adiyaman, Turkey","2025","Discover Applied Sciences","7","1","19","","","","0","10.1007/s42452-024-06407-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212443307&doi=10.1007%2fs42452-024-06407-x&partnerID=40&md5=a58a5891583c424127778a26b73e86a4","Electrical-Electronics Engineering Department, Adıyaman University, Adıyaman, Turkey; Mechanical Engineering Department, University of Surrey, Guilford, United Kingdom; Environmental Engineering Department, Adıyaman University, Adıyaman, Turkey","Özbeyaz A., Electrical-Electronics Engineering Department, Adıyaman University, Adıyaman, Turkey; Yıldırım M., Mechanical Engineering Department, University of Surrey, Guilford, United Kingdom; Tufaner F., Environmental Engineering Department, Adıyaman University, Adıyaman, Turkey","Following the 2023 earthquake in Adıyaman, Turkey, particulate matter (PM10) levels saw a significant rise, prompting the need to develop a model linking these levels to the number of asthma cases in the city. Using PM10 data from the Adıyaman urban region, we built machine learning models to estimate asthma prevalence. The k-nearest neighbour, random forest, and linear regression algorithms were employed for this purpose, with random forest outperforming the others, achieving an R-value of 0.92. The k-nearest neighbour and linear regression techniques followed with R-values of 0.81 and 0.64, respectively. The model's performance was further enhanced by incorporating cumulative PM10 pollution data as input parameters. This study is notable as it is only the second in the literature to estimate the asthma prevalence using air pollution data, and it achieved a higher accuracy rate than the previous study. © The Author(s) 2024.","Asthma; k-NN; Linear regression; PM10; Random forest","Contrastive Learning; Diseases; Lung cancer; Nearest neighbor search; Random forests; Asthma; Case-studies; K-NN; Machine learning algorithms; Nearest-neighbour; Particulate Matter; Pm10; Pm10 levels; R value; Random forests; Adversarial machine learning","","","","","Ministry of Environment, MOE; Adıyaman University","Ad\u0131yaman University Education and Research Hospital supplied the asthma data for this study. We obtained the data on air pollution from the website of the Ministry of Environment, Urbanization, and Climate Change. This study did not contain any individual's data; it solely examined the number of events on particular days within a defined timeframe sourced from the hospital. 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Özbeyaz; Electrical-Electronics Engineering Department, Adıyaman University, Adıyaman, Turkey; email: aozbeyaz@adiyaman.edu.tr","","Springer Nature","","","","","","30049261","","","","English","Discov. appl. sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85212443307"
"Mulat Tebeje T.; Kindie Yenit M.; Gedlu Nigatu S.; Bizuneh Mengistu S.; Kidie Tesfie T.; Byadgie Gelaw N.; Moges Chekol Y.","Mulat Tebeje, Tsion (57981838600); Kindie Yenit, Melaku (59203702600); Gedlu Nigatu, Solomon (59203507000); Bizuneh Mengistu, Segenet (59203702700); Kidie Tesfie, Tigabu (59203507100); Byadgie Gelaw, Negalgn (59203507200); Moges Chekol, Yazachew (59203702800)","57981838600; 59203702600; 59203507000; 59203702700; 59203507100; 59203507200; 59203702800","Prediction of diabetic retinopathy among type 2 diabetic patients in University of Gondar Comprehensive Specialized Hospital, 2006–2021: A prognostic model","2024","International Journal of Medical Informatics","190","","105536","","","","0","10.1016/j.ijmedinf.2024.105536","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197479871&doi=10.1016%2fj.ijmedinf.2024.105536&partnerID=40&md5=2e59ab37cdccdb4e6b16ae1f415dd580","School of Public Health, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia; Department of Epidemiology and Biostatistics, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia; Department of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia; Department of Public Health, Mizan Aman College of Health Science, Mizan Aman, Southwest Ethiopia, Ethiopia; Department of Health Information Technology, Mizan Aman College of Health Science, Mizan Aman, Southwest Ethiopia, Ethiopia","Mulat Tebeje T., School of Public Health, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia; Kindie Yenit M., Department of Epidemiology and Biostatistics, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia; Gedlu Nigatu S., Department of Epidemiology and Biostatistics, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia; Bizuneh Mengistu S., Department of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia; Kidie Tesfie T., Department of Epidemiology and Biostatistics, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia; Byadgie Gelaw N., Department of Public Health, Mizan Aman College of Health Science, Mizan Aman, Southwest Ethiopia, Ethiopia; Moges Chekol Y., Department of Health Information Technology, Mizan Aman College of Health Science, Mizan Aman, Southwest Ethiopia, Ethiopia","Background: There has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient's demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients. Methods: A retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10-fold cross-validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR. Results: Logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients. Conclusions: The model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR. © 2024 Elsevier B.V.","Diabetic retinopathy; Ethiopia; Machine learning; Nomogram; Risk prediction; Type 2 diabetes","Adult; Aged; Algorithms; Bayes Theorem; Diabetes Mellitus, Type 2; Diabetic Retinopathy; Ethiopia; Female; Follow-Up Studies; Hospitals, Special; Humans; Logistic Models; Male; Middle Aged; Nomograms; Prognosis; Retrospective Studies; Risk Factors; Decision trees; Diagnosis; Eye protection; Forecasting; Logistic regression; Machine learning; Nearest neighbor search; Risk perception; albumin; creatinine; hemoglobin A1c; high density lipoprotein cholesterol; insulin; low density lipoprotein cholesterol; nitrogen; oral antidiabetic agent; triacylglycerol; urea; uric acid; Clinical practices; Diabetic retinopathy; Diabetics patients; Ethiopia; Logistics regressions; Machine-learning; Performance; Prognostic modeling; Risk predictions; Type-2 diabetes; 10 fold cross validation; acquired immune deficiency syndrome; adult; aged; albumin blood level; anemia; area under the curve; Article; asthma; Bayesian learning; cholesterol blood level; chronic kidney failure; cigarette smoking; clinical practice; combination drug therapy; comorbidity; controlled study; creatinine blood level; decision tree; diabetic heart disease; diabetic microangiopathy; diabetic nephropathy; diabetic neuropathy; diabetic patient; diabetic retinopathy; diastolic blood pressure; disease duration; dyslipidemia; female; follow up; glycemic control; human; hypertension; k nearest neighbor; least absolute shrinkage and selection operator; logistic regression analysis; major clinical study; male; mean arterial pressure; medication compliance; morbid obesity; nomogram; non insulin dependent diabetes mellitus; patient compliance; prediction; prognostic assessment; retrospective study; sensitivity and specificity; systolic blood pressure; thyroid disease; total cholesterol level; triacylglycerol blood level; urea nitrogen blood level; uric acid blood level; algorithm; Bayes theorem; complication; diabetic retinopathy; diagnosis; epidemiology; Ethiopia; hospital; middle aged; non insulin dependent diabetes mellitus; prognosis; risk factor; statistical model; Hospitals","","creatinine, 19230-81-0, 60-27-5; hemoglobin A1c, 62572-11-6; insulin, 9004-10-8; nitrogen, 7727-37-9; urea, 57-13-6; uric acid, 69-93-2","","","College of Medicine and Health Sciences, University of Gondar, CMHS","We would like to thank the University of Gondar College of Medicine and Health Sciences as well as Dilla University College of Health Sciences and Medicine. We also would like to acknowledge the data collectors and workers in the chart room.","Ghanchi F., Bailey C., Chakravarthy U., Cohen S., Dobson P., Gibson J., Et al.; Cheung N., Mitchell P., Wong T.Y., Diabetic retinopathy, Lancet, pp. 124-136, (2010); Teo Z.L., Edin M., Tham Y., chung, Yu M, Chee ML, Rim TH, Et al., Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045 Systematic Review and Meta-analysis, Ophthalmology [internet]., 128, 11, pp. 1580-1591, (2021); Sabanayagam C., Banu R., Chee M.L., Lee R., Wang Y.X., Tan G., Et al., Incidence and progression of diabetic retinopathy: a systematic review, LANCET Diabetes Endocrinol [internet]., 8587, 5, pp. 1-10, (2018); Murray C., Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study, Lancet, 2019, pp. 1990-2017, (2017); Peto T., Resnikoff S., Kempen J.H., Steinmetz J.D., Briant P.S., Wong T.Y., Et al.; Achigbu E.O.O., Agweye C.T.T., Achigbu K.I.I., Mbatuegwu A.I.I., Diabetic Retinopathy in Sub-Saharan Africa: A Review of Magnitude and Risk Factors, Niger. 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Rep., 13, 1, (2023); Nugawela M.D., Gurudas S., Prevost A.T., Mathur R., Robson J., Sathish T., Et al., Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings, eClinicalMedicine [internet]., (2022); Wang G.X., Hu X.Y., Zhao H.X., Li H.L., Chu S.F., Liu D.L., Development and validation of a diabetic retinopathy risk prediction model for middle-aged patients with type 2 diabetes mellitus, Front. Endocrinol. (lausanne) [internet]., (2023); Gong D., Fang L., Cai Y., Chong I., Guo J., Yan Z., Et al., Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy, Front. Endocrinol. (lausanne) [internet]., (2023)","T. Mulat Tebeje; School of Public Health, College of Health Science and Medicine, Dilla University, Dilla, Ethiopia; email: yemarina12@gmail.com","","Elsevier Ireland Ltd","","","","","","13865056","","IJMIF","38970878","English","Int. J. Med. Informatics","Article","Final","","Scopus","2-s2.0-85197479871"
"Bhattacharya D.; Becker B.T.; Behrendt F.; Beyersdorff D.; Petersen E.; Petersen M.; Cheng B.; Eggert D.; Betz C.; Schlaefer A.; Hoffmann A.S.","Bhattacharya, Debayan (57542498500); Becker, Benjamin Tobias (57189239188); Behrendt, Finn (57219519006); Beyersdorff, Dirk (55904083300); Petersen, Elina (57211677949); Petersen, Marvin (57211983486); Cheng, Bastian (36544764100); Eggert, Dennis (55750214200); Betz, Christian (23484112600); Schlaefer, Alexander (12784041700); Hoffmann, Anna Sophie (57843488700)","57542498500; 57189239188; 57219519006; 55904083300; 57211677949; 57211983486; 36544764100; 55750214200; 23484112600; 12784041700; 57843488700","Computer-Aided Diagnosis of Maxillary Sinus Anomalies: Validation and Clinical Correlation","2024","Laryngoscope","134","9","","3927","3934","7","0","10.1002/lary.31413","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189556264&doi=10.1002%2flary.31413&partnerID=40&md5=0bf57e21c1a9185a813719b04340b702","Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany; Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Clinic and Polyclinic for Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Population Health Research Department, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany","Bhattacharya D., Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany, Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Becker B.T., Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Behrendt F., Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany; Beyersdorff D., Clinic and Polyclinic for Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Petersen E., Population Health Research Department, University Heart and Vascular Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Petersen M., Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Cheng B., Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Eggert D., Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Betz C., Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Schlaefer A., Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany; Hoffmann A.S., Department of Otorhinolaryngology, Head and Neck Surgery and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany","Objective: Computer aided diagnostics (CAD) systems can automate the differentiation of maxillary sinus (MS) with and without opacification, simplifying the typically laborious process and aiding in clinical insight discovery within large cohorts. Methods: This study uses Hamburg City Health Study (HCHS) a large, prospective, long-term, population-based cohort study of participants between 45 and 74 years of age. We develop a CAD system using an ensemble of 3D Convolutional Neural Network (CNN) to analyze cranial MRIs, distinguishing MS with opacifications (polyps, cysts, mucosal thickening) from MS without opacifications. The system is used to find correlations of participants with and without MS opacifications with clinical data (smoking, alcohol, BMI, asthma, bronchitis, sex, age, leukocyte count, C-reactive protein, allergies). Results: The evaluation metrics of CAD system (Area Under Receiver Operator Characteristic: 0.95, sensitivity: 0.85, specificity: 0.90) demonstrated the effectiveness of our approach. MS with opacification group exhibited higher alcohol consumption, higher BMI, higher incidence of intrinsic asthma and extrinsic asthma. Male sex had higher prevalence of MS opacifications. Participants with MS opacifications had higher incidence of hay fever and house dust allergy but lower incidence of bee/wasp venom allergy. Conclusion: The study demonstrates a 3D CNN's ability to distinguish MS with and without opacifications, improving automated diagnosis and aiding in correlating clinical data in population studies. Level of Evidence: 3 Laryngoscope, 134:3927–3934, 2024. © 2024 The Authors. The Laryngoscope published by Wiley Periodicals LLC on behalf of The American Laryngological, Rhinological and Otological Society, Inc.","Convolutional Neural Network; deep learning; maxillary sinus; Paranasal sinus; Population study","Aged; Diagnosis, Computer-Assisted; Female; Humans; Magnetic Resonance Imaging; Male; Maxillary Sinus; Middle Aged; Neural Networks, Computer; Paranasal Sinus Diseases; Prospective Studies; Sensitivity and Specificity; bee venom; C reactive protein; wasp venom; adult; aged; alcohol consumption; Article; asthma; benchmarking; bronchitis; cohort analysis; computer assisted diagnosis; contact allergy; controlled study; convolutional neural network; deep learning; extrinsic asthma; female; food allergy; hair; house dust allergy; human; Hymenoptera venom allergy; incidence; intrinsic asthma; leukocyte count; major clinical study; male; maxillary sinus; nuclear magnetic resonance imaging; outcomes research; paranasal sinus; paranasal sinus disease; pollen allergy; prevalence; prospective study; questionnaire; smoking; training; validation process; artificial neural network; diagnosis; diagnostic imaging; epidemiology; middle aged; paranasal sinus disease; procedures; sensitivity and specificity","","C reactive protein, 9007-41-4","Skyra, Siemens, Germany","Siemens, Germany","Free and Hanseatic City of Hamburg; Arbeitsgemeinschaft industrieller Forschungsvereinigungen; Technische Universität Hamburg, TUHH; Zentrales Innovationsprogramm Mittelstand; University Medical Center Hamburg‐Eppendorf, (KK5208101KS0)","This work has not been submitted for publication anywhere else. This work is funded partially by the i3 initiative of the Hamburg University of Technology. The authors also acknowledge the partial funding by the Free and Hanseatic City of Hamburg (Interdisciplinary Graduate School) from University Medical Center Hamburg‐Eppendorf. This work was partially funded by Grant Number KK5208101KS0 (Zentrales Innovationsprogramm Mittelstand, Arbeitsgemeinschaft industrieller Forschungsvereinigungen). Open Access funding enabled and organized by Projekt DEAL.","Tarp B., Fiirgaard B., Christensen T., Jensen J.J., Black F.T., The prevalence and significance of incidental paranasal sinus abnormalities on MRI, Rhinology, 38, 1, pp. 33-38, (2000); Rak K.M., Newell J.D., Yakes W.F., Damiano M.A., Luethke J.M., Paranasal sinuses on MR images of the brain: significance of mucosal thickening, AJR Am J Roentgenol, 156, 2, pp. 381-384, (1991); Cooke L.D., Hadley D.M., MRI of the paranasal sinuses: incidental abnormalities and their relationship to symptoms, J Laryngol Otol, 105, 4, pp. 278-281, (1991); Rege I.C.C., Sousa T.O., Leles C.R., Mendonca E.F., Occurrence of maxillary sinus abnormalities detected by cone beam CT in asymptomatic patients, BMC Oral Health, 12, (2012); Hansen A.G., Helvik A.S., Nordgard S., Et al., Incidental findings in MRI of the paranasal sinuses in adults: a population-based study (HUNT MRI), BMC Ear Nose Throat Disord, 14, 1, (2014); Stec N., Arje D., Moody A.R., Krupinski E.A., Tyrrell P.N., A systematic review of fatigue in radiology: is it a problem?, Am J Roentgenol, 210, 4, pp. 799-806, (2018); 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Allergic rhinitis, N Engl J Med, 372, 5, pp. 456-463, (2015); Slavin R.G., Leipzig J.R., Goodgold H.M., “Allergic sinusitis” revisited, Ann Allergy Asthma Immunol, 85, 4, pp. 273-276, (2000); Albrecht T., Beule A.G., Hildenbrand T., Et al., Cross-cultural adaptation and validation of the 22-item sinonasal outcome test (SNOT-22) in German-speaking patients: a prospective, multicenter cohort study, Eur Arch Otorhinolaryngol, 279, 5, pp. 2433-2439, (2022)","D. Bhattacharya; Institute of Medical Technology and Intelligent Systems, Technische Universitaet Hamburg, Hamburg, Germany; email: debayan.bhattacharya@tuhh.de","","John Wiley and Sons Inc","","","","","","0023852X","","LARYA","38520698","English","Laryngoscope","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85189556264"
"Sun S.; Wang C.; Hu J.; Zhao P.; Wang X.; Balch W.E.","Sun, Shuhong (57215132626); Wang, Chao (57211638548); Hu, Junyan (59516803700); Zhao, Pei (57211634776); Wang, Xi (57190437336); Balch, William E. (7102039021)","57215132626; 57211638548; 59516803700; 57211634776; 57190437336; 7102039021","Spatial covariance reveals isothiocyanate natural products adjust redox stress to restore function in alpha-1-antitrypsin deficiency","2025","Cell Reports Medicine","6","1","101917","","","","0","10.1016/j.xcrm.2024.101917","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215211016&doi=10.1016%2fj.xcrm.2024.101917&partnerID=40&md5=595228b26f7558e6eea3f3df47d71556","Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Department of Nutrition and Food Hygiene, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China; Institute for Brain Tumors, Collaborative Innovation Center for Cancer Personalized Medicine, and Center for Global Health, Nanjing Medical University, Nanjing, 211166, China; Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China","Sun S., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States, Department of Nutrition and Food Hygiene, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China, Institute for Brain Tumors, Collaborative Innovation Center for Cancer Personalized Medicine, and Center for Global Health, Nanjing Medical University, Nanjing, 211166, China; Wang C., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States, Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China; Hu J., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Zhao P., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Wang X., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States; Balch W.E., Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States","Alpha-1 antitrypsin (AAT) deficiency (AATD) is a monogenic disease caused by misfolding of AAT variants resulting in gain-of-toxic aggregation in the liver and loss of monomer activity in the lung leading to chronic obstructive pulmonary disease (COPD). Using high-throughput screening, we discovered a bioactive natural product, phenethyl isothiocyanate (PEITC), highly enriched in cruciferous vegetables, including watercress and broccoli, which improves the level of monomer secretion and neutrophil elastase (NE) inhibitory activity of AAT-Z through the endoplasmic reticulum (ER) redox sensor protein disulfide isomerase (PDI) A4 (PDIA4). The intracellular polymer burden of AAT-Z can be managed by combination treatment of PEITC and an autophagy activator. Using Gaussian process (GP)-based spatial covariance (SCV) (GP-SCV) machine learning to map on a residue-by-residue basis at atomic resolution all variants in the worldwide AATD clinical population, we reveal a global rescue of monomer secretion and NE inhibitory activity for most variants triggering disease. We present a proof of concept that GP-SCV mapping of restoration of AAT variant function serves as a standard model to discover natural products such as the anti-oxidant PEITC that could potentially impact the redox/inflammatory environment of the ER to provide a nutraceutical approach to help minimize disease in AATD patients. © 2025 The Authors","AATD; alpha-1 antitrypsin deficiency; chronic obstructive pulmonary disease; COPD; Gaussian process machine learning; genetic variation; isothiocyanate; nutraceutical; oxidative stress; PDIA4; protein aggregation; protein disulfide isomerase A4; proteostasis","alpha 1-Antitrypsin; alpha 1-Antitrypsin Deficiency; Biological Products; Endoplasmic Reticulum; Humans; Isothiocyanates; Oxidation-Reduction; Oxidative Stress; cycloheximide; isothiocyanic acid; leukocyte elastase; phenethyl isothiocyanate; protein disulfide isomerase; protein disulfide isomerase A4; unclassified drug; alpha 1 antitrypsin; biological product; isothiocyanic acid; alpha 1 antitrypsin deficiency; Article; cell line; chronic obstructive lung disease; controlled study; DNA extraction; endoplasmic reticulum; enzyme linked immunosorbent assay; gaussian process based spatial covariance; Huh-7.5 cell line; human; human cell; IB3-1 cell line; IB3-Z cell line; immunoblotting; information processing; liver cell; machine learning; proof of concept; protein homeostasis; redox stress; unfolded protein response; drug effect; drug therapy; genetics; metabolism; oxidation reduction reaction; oxidative stress","","cycloheximide, 642-81-9, 66-81-9; isothiocyanic acid, 3129-90-6, 71048-69-6; leukocyte elastase, 109968-22-1; phenethyl isothiocyanate, 2257-09-2; protein disulfide isomerase, 37318-49-3; alpha 1 antitrypsin, 9041-92-3; alpha 1-Antitrypsin, ; Biological Products, ; Isothiocyanates, ; phenethyl isothiocyanate, ","ImageJ, National Institute of Health; RNeasy Mini, Qiagen","National Institute of Health; Qiagen","Ara Parseghian Medical Research Foundation, APMRF; Alpha-1 Foundation, A1F; National Institutes of Health, NIH, (AG049665, DK051870, HL141810, AG070209, HL095524); National Institutes of Health, NIH","Funding text 1: Grant support was provided by NIH HL095524, DK051870, AG070209, AG049665, and HL141810 to W.E.B. P.Z. was supported in part by an Ara Parseghian Medical Research Foundation Fellowship. C.W. was in part supported by an Alpha-1 Foundation Fellowship. We thank Dr. Mark Brantly for providing the Huh7.5 AAT\u2212/\u2212 knockout cell line. We thank Dr. Hugh Rosen, Dr. Steven Brown, and Dr. Sean Riley for the help on the high-throughput screening facility.; Funding text 2: Grant support was provided by NIH HL095524 , DK051870 , AG070209 , AG049665 , and HL141810 to W.E.B. P.Z. was supported in part by an Ara Parseghian Medical Research Foundation Fellowship. C.W. was in part supported by an Alpha One Foundation Fellowship . We thank Dr. Mark Brantly for providing the Huh7.5 AAT \u2212/\u2212 knockout cell line. We thank Dr. Hugh Rosen, Dr. Steven Brown, and Dr. Sean Riley for the help on the high-throughput screening facility. ","McElvaney O.F., Fraughen D.D., McElvaney O.J., Carroll T.P., McElvaney N.G., Alpha-1 antitrypsin deficiency: current therapy and emerging targets, Expet Rev. 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Sun; Department of Molecular Medicine, The Scripps Research Institute, La Jolla, United States; email: sunsh@njmu.edu.cn; C. Wang; Department of Molecular Medicine, The Scripps Research Institute, La Jolla, United States; email: chaowang@szbl.ac.cn; W.E. Balch; Department of Molecular Medicine, The Scripps Research Institute, La Jolla, United States; email: webalch@scripps.edu","","Cell Press","","","","","","26663791","","","39809267","English","Cell Rep. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85215211016"
"Golyak I.S.; Anfimov D.R.; Demkin P.P.; Berezhanskiy P.V.; Nebritova O.A.; Morozov A.N.; Fufurin I.L.","Golyak, Igor Semenovich (41661203200); Anfimov, Dmitriy Romanovich (57214136407); Demkin, Pavel Pavlovich (57214113393); Berezhanskiy, Pavel Vyacheslavovich (57189343626); Nebritova, Olga Aleksandrovna (57214117052); Morozov, Andrey Nikolaevich (55893915500); Fufurin, Igor Leonidovich (26632835500)","41661203200; 57214136407; 57214113393; 57189343626; 57214117052; 55893915500; 26632835500","A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases","2024","Journal of Biophotonics","17","10","e202400151","","","","0","10.1002/jbio.202400151","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199982185&doi=10.1002%2fjbio.202400151&partnerID=40&md5=99fe56d985a7780489719e1c9fec1c05","Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Sechenov First Moscow State Medical University, Moscow, Russian Federation","Golyak I.S., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Anfimov D.R., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Demkin P.P., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Berezhanskiy P.V., Sechenov First Moscow State Medical University, Moscow, Russian Federation; Nebritova O.A., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Morozov A.N., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation; Fufurin I.L., Department of Physics, Bauman Moscow State Technical University, Moscow, Russian Federation","Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm (780–1890 cm−1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid-infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations. © 2024 Wiley-VCH GmbH.","biomarker; breath analysis; deep learning; infrared spectroscopy; quantum cascade laser","Adult; Asthma; Breath Tests; Diabetes Mellitus, Type 1; Exhalation; Female; Humans; Machine Learning; Male; Optical Phenomena; Pneumonia; Spectrophotometry, Infrared; Support Vector Machine; Deep learning; Diagnosis; Diseases; Infrared spectroscopy; Laser spectroscopy; Learning systems; Pulsed lasers; Support vector machines; Breath analysis; Deep learning; Exhaled breaths; Hybrid approach; Hybrid learning approach; Infrared spectrum; Infrared: spectroscopy; Optical Diagnosis; Spectra's; Type 1 diabetes mellitus; adult; asthma; breath analysis; diagnosis; diagnostic imaging; exhalation; female; human; infrared spectrophotometry; insulin dependent diabetes mellitus; light related phenomena; machine learning; male; metabolism; pneumonia; procedures; support vector machine; Quantum cascade lasers","","","","","Ministry of Education and Science of the Russian Federation, Minobrnauka","The project was conducted within the \u201CPriority-2030\u201D program of The Ministry of Science and Higher Education of the Russian Federation.","Grant T., Croce E., Matsui E.C., Ann Allergy Asthma Immunol, 128, (2022); Uphoff E., Cabieses B., Pinart M., Valdes M., Anto J.M., Wright J., Europ Respirat J, 46, (2014); Rowley W.R., Bezold C., Arikan Y., Byrne E., Krohe S., Popul Health Manag, 20, (2017); Dixit K., Fardindoost S., Ravishankara A., Tasnim N., Hoorfar M., Biosensors, 11, (2021); Berezhanskiy P.V., Gutyrchik T., Vekshina Y., Gutyrchik N., Shapiev N., Ushina T., Med Pharmaceut J “Pulse”, 24, (2022); Maitland-van der Zee A.-H., Vijverberg, hilvering, raaijmakers, lammers, koenderman, biologics: targets and therapy, (2013); Lerminiaux N.A., Cameron A.D., Canad J Microbiol, 65, (2019); Liu G., Qin M., Evid-Based Complem Alter Med, 2022, (2022); de Lacy Costello B., Amann A., Al-Kateb H., Flynn C., Filipiak W., Khalid T., Osborne D., Ratcliffe N.M., J Breath Res, 8, (2014); Bajtarevic A., Ager C., Pienz M., Klieber M., Schwarz K., Ligor M., Ligor T., Filipiak W., Denz H., Fiegl M., Hilbe W., Weiss W., Lukas P., Jamnig H., Hackl M., Haidenberger A., Buszewski B., Miekisch W., Schubert J., Amann A., BMC Cancer, 9, (2009); Bos L.D.J., Sterk P.J., Schultz M.J., PLoS Pathog., 9, (2013); Alizadeh N., Jamalabadi H., Tavoli F., IEEE Sens. 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Soc., 29, (2017); Golyak I.S., Fufurin I.L., Kareva E.R., Anfimov D.R., Scherbakova A.V., Morozov A.N., Demkin P.P., Saratov fall meeting 2020: Optical and nanotechnologies for biology and medicine, (2021); Fufurin I., Berezhanskiy P., Golyak I., Anfimov D., Kareva E., Scherbakova A., Demkin P., Nebritova O., Morozov A., Materials, 15, (2022); Fufurin I.L., Anfimov D.R., Kareva E.R., Scherbakova A.V., Demkin P.P., Morozov A.N., Golyak I.S., Opt Eng, 60, (2021); Golyak I., Kareva E., Fufurin I., Anfimov D., Scherbakova A., Nebritova A., Demkin P., Morozov A., Comput Opt, 46, (2022); Kochikov I.V., Morozov A.N., Svetlichnyi S.I., Fufurin I.L., Opt. Spectrosc., 106, (2009); Lim J., Ryu S., Kim J.W., Kim W.Y., J Cheminform, 10, (2018); Portillo S.K.N., Parejko J.K., Vergara J.R., Connolly A.J., Astronom J, 160, (2020); Yang X., Deng C., Zheng F., Yan J., Liu W., 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), (2019); Houston J., Glavin F.G., Madden M.G., J. 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Acta, 515, (2021); Guntner A.T., Koren V., Chikkadi K., Righettoni M., Pratsinis S.E., ACS Sens, 1, (2016); Wilson A.D., Baietto M., Sensors, 11, (2011); Machado R.F., Laskowski D., Deffenderfer O., Burch T., Zheng S., Mazzone P.J., Mekhail T., Jennings C., Stoller J.K., Pyle J., Duncan J., Dweik R.A., Erzurum S.C., Am J Respirat Crit Care Med, 171, (2005); Ma M., He W., Zhao K., Xue L., Xia S., Zhang B., Front. Oncol., 12, (2022); Persaud K.C., Int J Lower Extrem Wounds, 4, (2005); Cikach F.S., Dweik R.A., Progr Cardiovasc Dis, 55, (2012); Maniscalco M., Paris D., Melck D.J., Molino A., Carone M., Ruggeri P., Caramori G., Motta A., Europ Respirat J, 51, (2018); Saasa V., Beukes M., Lemmer Y., Mwakikunga B., Diagnostics, 9, (2019); Lai S.Y., Deffenderfer O.F., Hanson W., Phillips M.P., Thaler E.R., The Laryngoscope, 112, (2002); Hibbard T., Killard A.J., Crit. Rev. Anal. Chem., 41, (2011); Held A., Henning D., Jiang C., Hoeschen C., Frodl T., Molecules, 28, (2023); Ross B.M., Maxwell R., Glen I., Progr Neuro-Psychopharmacol Biol Psychiatry, 35, (2011); Li Y., Wei X., Zhou Y., Wang J., You R., Microsyst Nanoeng, 9, (2023); Stepanov E.V., Ivashkin V.T., Laser Phys., 32, (2022); Maiti K.S., Fill E., Strittmatter F., Volz Y., Sroka R., Apolonski A., Spectrochim. Acta, Part A, 304, (2024); Caixeta D.C., Carneiro M.G., Rodrigues R., Alves D.C.T., Goulart L.R., Cunha T.M., Espindola F.S., Vitorino R., Sabino-Silva R., Diagnostics, 13, (2023); Bellantuono L., Tommasi R., Pantaleo E., Verri M., Amoroso N., Crucitti P., Di Gioacchino M., Longo F., Monaco A., Naciu A.M., Palermo A., Taffon C., Tangaro S., Crescenzi A., Sodo A., Bellotti R., Sci. Rep., 13, (2023); Ribeiro M.T., Singh S., Guestrin C., Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ‘16. ACM, (2016); Wang C., Ko T., Hsu C., Plasma Processes Polym., 18, (2021); Akulich F., Anahideh H., Sheyyab M., Ambre D., Chemom. Intell. Lab. Syst., 225, (2022)","I.S. Golyak; Department of Physics, Bauman Moscow State Technical University, Moscow, 105005, Russian Federation; email: igorgolyak@yandex.ru","","John Wiley and Sons Inc","","","","","","1864063X","","","39075328","English","J. Biophotonics","Article","Final","","Scopus","2-s2.0-85199982185"
"Oishee T.T.; Anjom J.; Mohammed U.; Hossain M.I.A.","Oishee, Tahiya Tasneem (58672311100); Anjom, Jareen (59333850700); Mohammed, Uzma (59509197600); Hossain, Md. Ishan Arefin (57207918793)","58672311100; 59333850700; 59509197600; 57207918793","Leveraging deep edge intelligence for real-time respiratory disease detection","2024","Clinical eHealth","7","","","207","220","13","0","10.1016/j.ceh.2025.01.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214699269&doi=10.1016%2fj.ceh.2025.01.001&partnerID=40&md5=1167d47ca22b91a9b4ae040b7623b91b","Department of Electrical and Computer Engineering, North South University, Dhaka, Bashundhara R/A, 1229, Bangladesh","Oishee T.T., Department of Electrical and Computer Engineering, North South University, Dhaka, Bashundhara R/A, 1229, Bangladesh; Anjom J., Department of Electrical and Computer Engineering, North South University, Dhaka, Bashundhara R/A, 1229, Bangladesh; Mohammed U., Department of Electrical and Computer Engineering, North South University, Dhaka, Bashundhara R/A, 1229, Bangladesh; Hossain M.I.A., Department of Electrical and Computer Engineering, North South University, Dhaka, Bashundhara R/A, 1229, Bangladesh","Detecting respiratory diseases such as COPD, bronchiolitis, URTI, and pneumonia is crucial for early medical intervention. This study utilizes the ICBHI dataset to train and evaluate deep learning architectures such as CNN-GRU, VGGish, YAMNet, CNN-LSTM, and basic CNN to automate this process. After a detailed analysis of the performance of these models, the CNN-LSTM model achieved an impressive accuracy and F1 score of 96% each. The model is also considerably lightweight, as its weights are further pruned and then quantized using TensorFlow Lite (TFLite), with the model being optimized at a significantly small size of 0.38 MB with only a loss of about 1% in performance. Subsequently, this was deployed to the smartphone application RespiScan. The application uses the prediction capabilities of the disease detection model on patients’ audio recordings. By providing a portable, cost-effective, and efficient, lightweight solution for respiratory health monitoring, this work contributes significantly to timely disease detection. It promotes proactive health management, thereby reducing the burden on healthcare systems. This work can be further validated in real-world conditions, such as for initial preliminary auscultation purposes, to ensure the proposed work's efficacy across different environmental settings. © 2025","Deep learning; Early diagnosis; Edge intelligence; ICBHI; Respiratory disease","","","","","","","","(2021); Kjelle E., Brandsaeter I.O., Andersen E.R., Et al., Cost of low-value imaging worldwide: a systematic review, Appl Health Econ Health Policy, 22, pp. 485-501, (2024); Tariq Z., Shah S.K., Lee Y., Feature-based fusion using CNN for lung and heart sound classification, Sensors, 22, 4, (2022); Brunese L., Mercaldo F., Reginelli A., Santone A., A neural network-based method for respiratory sound analysis and lung disease detection, Appl Sci, 12, 8, (2022); Chamberlain D.B., Kodgule R., Fletcher R.R., A mobile platform for automated screening of asthma and chronic obstructive pulmonary disease, In 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 5192-5195, (2016); Pillai A.S., Utilizing deep learning in medical image analysis for enhanced diagnostic accuracy and patient care: challenges, opportunities, and ethical implications, J Deep Learn Genom Data Anal, 1, 1, pp. 1-17, (2021); Adelaja O., Alkattan H., Operating artificial intelligence to assist physicians diagnose medical images: a narrative review, Mesopotamian J Artif Intell Healthcare, 2023, pp. 45-51, (2023); Bohadana A., Izbicki G., Kraman S.S., Fundamentals of lung auscultation, New England J Med, 370, 8, pp. 744-751, (2014); Reichert S., Gass R., Brandt C., Andres E.; Flietstra B., Markuzon N., Vyshedskiy A., Murphy R., Automated analysis of crackles in patients with interstitial pulmonary fibrosis, Pulmon Med, 2011, 1, (2011); Pramono R.X.A., Bowyer S., Rodriguez-Villegas E., Automatic adventitious respiratory sound analysis: a systematic review, PloS One, 12, 5, (2017); Srivastava A., Jain S., Miranda R., Patil S., Pandya S., Kotecha K., Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Comput Sci., 7, (2021); Kim Y., Hyon Y., Jung S.S., Lee S., Yoo G., Chung C., Ha T.Y., Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning, Scient Rep, 11, 1, (2021); Lal K.N., A lung sound recognition model to diagnose respiratory diseases by using transfer learning, Multimedia Tools Appl, 82, 23, pp. 36615-36631, (2023); Basu V., Rana S., Respiratory diseases recognition through respiratory sound with the help of deep neural network; Asatani N., Kamiya T., Mabu S., Kido S., Classification of respiratory sounds by generated image and improved CRNN, (1808); Xu L., Cheng J., Liu J., Kuang H., Wu F., Wang J., Arsc-net: Adventitious respiratory sound classification network using parallel paths with channel-spatial attention; Pham L., Phan H., Palaniappan R., Mertins A., McLoughlin I., CNN-MoE based framework for classification of respiratory anomalies and lung disease detection, IEEE J Biomed Health Inform, 25, 8, pp. 2938-2947, (2021); Fraiwan M., Fraiwan L., Alkhodari M., Hassanin O., Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory, J Ambient Intell Human Comput, 13, 10, pp. 4759-4771, (2021); Nathan V., Vatanparvar K., San Chun K., Kuang J., Utilizing deep learning on limited mobile speech recordings for detection of obstructive pulmonary disease; (2022); Abdul Z.K., Al-Talabani A.K., Mel frequency cepstral coefficient and its applications: a review, IEEE Access, 10, pp. 122136-122158, (2022); Zhao X., Wang D.; Rao K.S., Manjunath K.E., Speech recognition using articulatory and excitation source features, (2017); Feldman H.A., Kaiser N., Peacock J.A., Power spectrum analysis of three-dimensional redshift surveys, arXiv preprint, (1993); Yin H., Hohmann V., Nadeu C., Acoustic features for speech recognition based on Gammatone filterbank and instantaneous frequency, Speech Commun, 53, 5, pp. 707-715, (2011); Strang G., The discrete cosine transform, SIAM Rev, 41, 1, pp. 135-147, (1999); Ustubioglu A., Ustubioglu B., Ulutas G., Mel spectrogram-based audio forgery detection using CNN, Signal, Image Video Process, 17, pp. 2211-2219, (2023); Muller M., Short-time Fourier transform and chroma features, (2015); Dey R., Salem F.M., Gate-variants of gated recurrent unit (GRU) neural networks; Lian Z., Li Y., Tao J., Huang J.; Hershey S., Chaudhuri S., Ellis D.P., Gemmeke J.F., Jansen A., Moore R.C., Wilson K., CNN architectures for large-scale audio classification; Gemmeke J.F., Ellis D.P., Freedman D., Jansen A., Lawrence W., Moore R.C., Ritter M., Audio set: An ontology and human-labeled dataset for audio events; Mahum R., Irtaza A., Javed A., EDL-Det: A robust TTS synthesis detector using VGG19-based YAMNet and ensemble learning block, IEEE Access, 11, pp. 134701-134716, (2023); Haase D., Amthor M., Rethinking depthwise separable convolutions: How intra-kernel correlations lead to improved MobileNets; Alkhulaifi A., Alsahli F., Ahmad I., Knowledge distillation in deep learning and its applications, PeerJ Comput Sci, 7, (2021); Ferreira-Cardoso H., Jacome C., Silva S., Amorim A., Redondo M., Fontoura-Matias J., Vicente-Ferreira M., Vieira-Marques P., Valente J., Almeida R., Fonseca J., Azevedo I., Lung auscultation using the smartphone—feasibility study in real-world clinical practice, Sensors., 21, 14, (2021); Fraiwan M., Fraiwan L., Khassawneh B., Ibnian A., A dataset of lung sounds recorded from the chest wall using an electronic stethoscope, Data in Brief, 35, (2021)","M.I.A. Hossain; Department of Electrical and Computer Engineering, North South University, Bashundhara R/A, Dhaka, 1229, Bangladesh; email: ishan.hossain@northsouth.edu","","KeAi Communications Co.","","","","","","25889141","","","","English","Clin. eHealth","Article","Final","","Scopus","2-s2.0-85214699269"
"Suma K.V.; Koppad D.; Kumar P.; Kantikar N.A.; Ramesh S.","Suma, K.V. (56501696400); Koppad, Deepali (56372861300); Kumar, Preethi (59032828800); Kantikar, Neha A. (58164500900); Ramesh, Surabhi (59034863400)","56501696400; 56372861300; 59032828800; 58164500900; 59034863400","Multi-task Learning for Lung Sound and Lung Disease Classification","2025","SN Computer Science","6","1","51","","","","0","10.1007/s42979-024-03506-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213552112&doi=10.1007%2fs42979-024-03506-9&partnerID=40&md5=1e977290b95b678dcc946fb78bb96f54","Department of ECE, Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; M.V.J Medical College and Research Hospital, Karnataka, Bengaluru, 560054, India","Suma K.V., Department of ECE, Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Koppad D., Department of ECE, Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Kumar P., Department of ECE, Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Kantikar N.A., Department of ECE, Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Ramesh S., M.V.J Medical College and Research Hospital, Karnataka, Bengaluru, 560054, India","Recent advances in deep learning techniques have significantly increased the accuracy and efficacy of medical diagnosis. In this work, we propose a novel multitask learning (MTL) approach for concurrently classifying lung sounds and diseases. Our approach integrates MTL with four distinct deep learning architectures: 2D CNN, ResNet50, MobileNet, and DenseNet, to extract relevant features from lung sound recordings. The effectiveness of our proposed method is evaluated in this study using the ICBHI 2017 Respiratory Sound Database. The MTL for the MobileNet model outperformed the other models under consideration, achieving 74% accuracy for lung sound analysis and 91% accuracy for lung disease classification. The experiment's findings show how well our method works for simultaneously categorizing lung noises and lung illnesses. This study also computes the risk level for Chronic Obstructive Pulmonary Disease using the patient demographics from the database. Three machine learning algorithms as Random Forest classifiers, SVM, and logistic regressionwere used for this calculation. The Random Forest classifier achieved the greatest accuracy of 92% among three machine learning techniques.This activity significantly lessens the doctor's workload by assisting in both pathology diagnosis and good patient communication regarding potential causes or consequences. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.","Deep learning; MobileNet; Multi-task learning; Random forest; Resnet; Risk level; SVM","","","","","","Ramaiah Institute Of Technology, RIT; M.V.J Medical College & Research Hospital","The authors warmly acknowledged the Ramaiah Institute of Technology, Bengaluru, Karnataka, India and M.V.J Medical College & Research Hospital, Bengaluru, Karnataka, India. for providing the facilities required to carry out the research.","Kundu R., Das R., Geem Z.W., Han G.T., Sarkar R., Pneumonia detection in chest X-ray images using an ensemble of deep learning models, PLoS ONE, 16, 9, (2021); Bharati S., Podder P., Mondal M.R.H., Hybrid deep learning for detecting lung diseases from X-ray images, Inform Med Unlocked, 20, (2020); 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Li Y., Wu X., Yang P., Jiang G., Luo Y., Machine learning for lung cancer diagnosis, treatment, and prognosis, Genom Proteom Bioinform, 20, pp. 850-866, (2022); Kieu S.T.H., Bade A., Hijazi M.H.A., Kolivand H., A survey of deep learning for lung disease detection on medical images: State-of-the-art, taxonomy, issues and future directions, J Imaging, 6, 12, pp. 1-38, (2020); Tran-Anh D., Vu N.H., Nguyen-Trong K., Pham C., Multi-task learning neural networks for breath sound detection and classification in pervasive healthcare, Pervas Mob Comput, 86, pp. 1-13, (2022); Kordnoori S., Sabeti M., Mostafaei H., Banihashemi S.S.A., Analysis of lung scan imaging using deep multi-task learning structure for Covid-19 disease, IET Image Proc, 17, pp. 1534-1545, (2022); Ashwini S., Arunkumar J.R., Thandaiah Prabu R., Singh N., Singh N.P., Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network, Soft Comput, 28, 7, pp. 6219-6233, (2024); Li M., Li X., Jiang Y., Zhang J., Luo H., Yin S., Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images, Knowl Based Syst, 252, pp. 1-16, (2022); Dong Y., Hou L., Yang W., Han J., Wang J., Qiang Y., Zhao J., Hou J., Song K., Ma Y., Kazihise N.G.F., Cui Y., Ya X., Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images, Quant Imaging Med Surg, 11, 6, (2021); Zhang X., Han L., Sobeih T., Han L., Dempsey N., Lechareas S., Tridente A., Chen H., White S., Zhang D., CXR-Net: a multi-task deep learning network for explainable and accurate diagnosis of COVID-19 pneumonia from chest x-ray images, IEEE J Biomed Health Inform, 27, 2, pp. 980-991, (2022); Li J., Zhao G., Taoa Y., Zhai P., Chen H., He H., Cai T., Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19, Pattern Recognit, 114, (2021); Yimer F., Tessema A.W., Simegn G.L., Multiple lung diseases classification from chest X-ray images using deep learning approach, Int J Adv Trends Comput Sci Eng, 10, 5, (2021); Indumathi V., Siva R., An efficient lung disease classification from X-ray images using hybrid Mask-RCNN and BiDLSTM, Biomed Signal Process Control, 81, (2023); Yadav P., Menon N., Ravi V., Vishvanathan S., Lung-GANs: unsupervised representation learning for lung disease classification using chest CT and X-ray images, IEEE Trans Eng Manag, 70, 8, pp. 2774-2786, (2021); Xie J., Fonseca P., van Dijk J., Long S.O.X., A multi-task learning model using RR intervals and respiratory efort to assess sleep disordered breathing, Biomed Eng Online, 23, 45, pp. 1-16, (2024); Sabry A.H., Dallal Bashi O.I., Nik Ali N.H., Kubaisi Y.M.A., lung disease recognition methods using audio-based analysis with machine learning, Heliyon, 10, (2024); Huang D.-M., Huang J., Qiao K., Zhong N.-S., Lu H.-Z., Wang W.-J., Deep learning-based lung sound analysis for intelligent stethoscope, Milit Med Res, 10, pp. 1-23, (2023); Garcia-Mendez J.P., Lal A., Herasevich S., Tekin A., Pinevich Y., Lipatov K., Wang H.-Y., Qamar S., Ayala I.N., Khapov I., Gerberi D.J., Diedrich D., Pickering B.W., Herasevich V., Machine learning for automated classification of abnormal lung sounds obtained from public databases: A systematic review, Bioengineering, 10, pp. 1-19, (2023); 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Suma; Department of ECE, Ramaiah Institute of Technology, Bengaluru, Karnataka, 560054, India; email: sumakv@msrit.edu","","Springer","","","","","","2662995X","","","","English","SN COMPUT. SCI.","Article","Final","","Scopus","2-s2.0-85213552112"
"Trudzinski F.C.; Jörres R.A.; Alter P.; Watz H.; Vogelmeier C.F.; Kauczor H.-U.; Thangamani S.; Debic M.; Welte T.; Behr J.; Kahnert K.; Bals R.; Herr C.; Heußel C.P.; Biederer J.; von Stackelberg O.; Fähndrich S.; Wouters E.F.M.; Waschki B.; Rabe K.F.; Herth F.J.F.; Palm V.; Andreas S.; Kanerth K.; Bahmer T.; Bewig B.; Ewert R.; Stubbe B.; Ficker J.H.; Grohé C.; Held M.; Henke M.; Kirsten A.-M.; Koczulla R.; Kronsbein J.; Kropf-Sanchen C.; Herzmann C.; Pfeifer M.; Randerath W.J.; Seeger W.; Studnicka M.; Taube C.; Timmermann H.; Schmeck B.; Wirtz H.","Trudzinski, Franziska C. (25926206900); Jörres, Rudolf A. (7005686737); Alter, Peter (7003937302); Watz, Henrik (16508173900); Vogelmeier, Claus F. (7005604348); Kauczor, Hans-Ulrich (7102275418); Thangamani, Subasini (59221335600); Debic, Manuel (57962390100); Welte, Tobias (57223621683); Behr, Jürgen (57207894379); Kahnert, Kathrin (57190130517); Bals, Robert (7003340975); Herr, Christian (22034709700); Heußel, Claus Peter (7004889910); Biederer, Jürgen (7003612651); von Stackelberg, Oyunbileg (56610304000); Fähndrich, Sebastian (49762912800); Wouters, Emiel F. M. (35395980800); Waschki, Benjamin (23987158800); Rabe, Klaus F. (7102576614); Herth, Felix J. F. (57207907661); Palm, Viktoria (57200987221); Andreas, Stefan (55357138700); Kanerth, Kathrin (59317407900); Bahmer, Thomas (55843546500); Bewig, Burkhard (7003832988); Ewert, Ralf (35446517200); Stubbe, Beate (57210179737); Ficker, Joachim H. (7005948752); Grohé, Christian (55981368600); Held, Matthias (36522778900); Henke, Markus (55407625100); Kirsten, Anne-Marie (6603076087); Koczulla, Rembert (8741717400); Kronsbein, Juliane (23395051900); Kropf-Sanchen, Cornelia (55637940500); Herzmann, Christian (6602338851); Pfeifer, Michael (35316433100); Randerath, Winfried J. (7003853425); Seeger, Werner (55143401900); Studnicka, Michael (55611305900); Taube, Christian (7006506758); Timmermann, Hartmut (57211859930); Schmeck, Bernd (6603354847); Wirtz, Hubert (16424157100)","25926206900; 7005686737; 7003937302; 16508173900; 7005604348; 7102275418; 59221335600; 57962390100; 57223621683; 57207894379; 57190130517; 7003340975; 22034709700; 7004889910; 7003612651; 56610304000; 49762912800; 35395980800; 23987158800; 7102576614; 57207907661; 57200987221; 55357138700; 59317407900; 55843546500; 7003832988; 35446517200; 57210179737; 7005948752; 55981368600; 36522778900; 55407625100; 6603076087; 8741717400; 23395051900; 55637940500; 6602338851; 35316433100; 7003853425; 55143401900; 55611305900; 7006506758; 57211859930; 6603354847; 16424157100","Midregional Proatrial Natriuretic Peptide (MRproANP) is associated with vertebral fractures and low bone density in patients with chronic obstructive pulmonary disease (COPD)","2024","Respiratory Research","25","1","274","","","","0","10.1186/s12931-024-02902-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198689707&doi=10.1186%2fs12931-024-02902-2&partnerID=40&md5=a35b3d75dba152aad45bddee42a82d2f","Department of Pneumology and Critical Care Medicine), German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Thoraxklinik University of Heidelberg, Röntgenstrasse 1, Heidelberg, 69126, Germany; Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, German Center for Lung Research (DZL), LMU University Hospital, Ludwig-Maximilians-University (LMU), Comprehensive Pneumology Center Munich (CPC-M), Munich, Germany; Department of Medicine, Pulmonary and Critical Care Medicine, German Center for Lung Research (DZL), Philipps University of Marburg (UMR), Marburg, Germany; Pulmonary Research Institute at LungenClinic Grosshansdorf, Grosshansdorf, Germany; Airway Research Center North (ARCN), German Center for Lung Research (DZL), Woehrendamm 80, Grosshansdorf, 22927, Germany; Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Department of Pneumology, Hannover Medical School, Carl-Neuberg-Str. 1, Hannover, 30625, Germany; Department of Internal Medicine V, CPC Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), University Hospital, LMU Munich, Munich, Germany; MediCenterGermering, Germering, Germany; Department of Internal Medicine V - Pulmonology, Allergology, Critical Care Care Medicine, Saarland University Hospital, Homburg, Germany; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, University Medical Center Heidelberg, Heidelberg, Germany; Faculty of Medicine, University of Latvia, Riga, Latvia; Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, Kiel, Germany; Department of Pneumology, University Medical Centre Freiburg, Freiburg, Germany; Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, Netherlands; Medical Faculty, Sigmund Freud University, Vienna, Austria; Department of Internal Medicine, Sigmund Freud Private University, Vienna, Austria; LungenClinic Grosshansdorf, Airway Research Center North, Member of the German Center for Lung Research, Pulmonary Research Institute, Woehrendamm 80, Grosshansdorf, 22927, Germany; Department of Pneumology, Itzehoe Hospital, Itzehoe, Germany","Trudzinski F.C., Department of Pneumology and Critical Care Medicine), German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Thoraxklinik University of Heidelberg, Röntgenstrasse 1, Heidelberg, 69126, Germany; Jörres R.A., Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, German Center for Lung Research (DZL), LMU University Hospital, Ludwig-Maximilians-University (LMU), Comprehensive Pneumology Center Munich (CPC-M), Munich, Germany; Alter P., Department of Medicine, Pulmonary and Critical Care Medicine, German Center for Lung Research (DZL), Philipps University of Marburg (UMR), Marburg, Germany; Watz H., Pulmonary Research Institute at LungenClinic Grosshansdorf, Grosshansdorf, Germany, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Woehrendamm 80, Grosshansdorf, 22927, Germany; Vogelmeier C.F., Department of Medicine, Pulmonary and Critical Care Medicine, German Center for Lung Research (DZL), Philipps University of Marburg (UMR), Marburg, Germany; Kauczor H.-U., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Thangamani S., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Debic M., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Welte T., Department of Pneumology, Hannover Medical School, Carl-Neuberg-Str. 1, Hannover, 30625, Germany; Behr J., Department of Internal Medicine V, CPC Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), University Hospital, LMU Munich, Munich, Germany; Kahnert K., Department of Internal Medicine V, CPC Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), University Hospital, LMU Munich, Munich, Germany, MediCenterGermering, Germering, Germany; Bals R., Department of Internal Medicine V - Pulmonology, Allergology, Critical Care Care Medicine, Saarland University Hospital, Homburg, Germany; Herr C., Department of Internal Medicine V - Pulmonology, Allergology, Critical Care Care Medicine, Saarland University Hospital, Homburg, Germany; Heußel C.P., German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany, Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik, University Medical Center Heidelberg, Heidelberg, Germany; Biederer J., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany, Faculty of Medicine, University of Latvia, Riga, Latvia, Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, Kiel, Germany; von Stackelberg O., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Fähndrich S., Department of Pneumology, University Medical Centre Freiburg, Freiburg, Germany; Wouters E.F.M., Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, Netherlands, Medical Faculty, Sigmund Freud University, Vienna, Austria, Department of Internal Medicine, Sigmund Freud Private University, Vienna, Austria; Waschki B., LungenClinic Grosshansdorf, Airway Research Center North, Member of the German Center for Lung Research, Pulmonary Research Institute, Woehrendamm 80, Grosshansdorf, 22927, Germany, Department of Pneumology, Itzehoe Hospital, Itzehoe, Germany; Rabe K.F., LungenClinic Grosshansdorf, Airway Research Center North, Member of the German Center for Lung Research, Pulmonary Research Institute, Woehrendamm 80, Grosshansdorf, 22927, Germany; Herth F.J.F., Department of Pneumology and Critical Care Medicine), German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Thoraxklinik University of Heidelberg, Röntgenstrasse 1, Heidelberg, 69126, Germany; Palm V., Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany, German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Andreas S.; Kanerth K.; Bahmer T.; Bewig B.; Ewert R.; Stubbe B.; Ficker J.H.; Grohé C.; Held M.; Henke M.; Kirsten A.-M.; Koczulla R.; Kronsbein J.; Kropf-Sanchen C.; Herzmann C.; Pfeifer M.; Randerath W.J.; Seeger W.; Studnicka M.; Taube C.; Timmermann H.; Schmeck B.; Wirtz H.","Background: Patients with COPD are often affected by loss of bone mineral density (BMD) and osteoporotic fractures. Natriuretic peptides (NP) are known as cardiac markers, but have also been linked to fragility-associated fractures in the elderly. As their functions include regulation of fluid and mineral balance, they also might affect bone metabolism, particularly in systemic disorders such as COPD. Research question: We investigated the association between NP serum levels, vertebral fractures and BMD assessed by chest computed tomography (CT) in patients with COPD. Methods: Participants of the COSYCONET cohort with CT scans were included. Mean vertebral bone density on CT (BMD-CT) as a risk factor for osteoporosis was assessed at the level of TH12 (AI-Rad Companion), and vertebral compression fractures were visually quantified by two readers. Their relationship with N-terminal pro-B-type natriuretic peptide (NT-proBNP), Mid-regional pro-atrial natriuretic peptide (MRproANP) and Midregional pro-adrenomedullin (MRproADM) was determined using group comparisons and multivariable analyses. Results: Among 418 participants (58% male, median age 64 years, FEV1 59.6% predicted), vertebral fractures in TH12 were found in 76 patients (18.1%). Compared to patients without fractures, these had elevated serum levels (p ≤ 0.005) of MRproANP and MRproADM. Using optimal cut-off values in multiple logistic regression analyses, MRproANP levels ≥ 65 nmol/l (OR 2.34; p = 0.011) and age (p = 0.009) were the only significant predictors of fractures after adjustment for sex, BMI, smoking status, FEV1% predicted, SGRQ Activity score, daily physical activity, oral corticosteroids, the diagnosis of cardiac disease, and renal impairment. Correspondingly, MRproANP (p < 0.001), age (p = 0.055), SGRQ Activity score (p = 0.061) and active smoking (p = 0.025) were associated with TH12 vertebral density. Interpretation: MRproANP was a marker for osteoporotic vertebral fractures in our COPD patients from the COSYCONET cohort. Its association with reduced vertebral BMD on CT and its known modulating effects on fluid and ion balance are suggestive of direct effects on bone mineralization. Trial registration: ClinicalTrials.gov NCT01245933, Date of registration: 18 November 2010. © The Author(s) 2024.","","Aged; Atrial Natriuretic Factor; Biomarkers; Bone Density; Cohort Studies; Female; Humans; Male; Middle Aged; Osteoporotic Fractures; Protein Precursors; Pulmonary Disease, Chronic Obstructive; Spinal Fractures; adrenomedullin; atrial natriuretic factor; C reactive protein; carbon monoxide; copeptin; corticosteroid; interleukin 6; interleukin 8; osteopontin; troponin; tumor necrosis factor; biological marker; midregional pro-atrial natriuretic peptide, human; protein precursor; adult; aged; area under the curve; Article; body mass; bone density; bone mineralization; chronic obstructive lung disease; clinical article; computer assisted tomography; end stage renal disease; estimated glomerular filtration rate; female; fluid balance; forced expiratory volume; fragility fracture; heart disease; human; logistic regression analysis; major clinical study; male; middle aged; mineral balance; osteoporosis; physical activity; questionnaire; receiver operating characteristic; risk factor; smoking; spine fracture; thoracic spine; blood; cohort analysis; diagnosis; diagnostic imaging; epidemiology; physiology; spine fracture","","adrenomedullin, 148498-78-6; atrial natriuretic factor, 85637-73-6; C reactive protein, 9007-41-4; carbon monoxide, 630-08-0; interleukin 8, 114308-91-7; osteopontin, 106441-73-0; Atrial Natriuretic Factor, ; Biomarkers, ; midregional pro-atrial natriuretic peptide, human, ; Protein Precursors, ","","","Institut für Therapieforschung GmbH","The authors thank all patients of COSYCONET for their participation on and all study centers for their excellent work. COSYCONET Study Group Andreas, Stefan20; Bals, Robert11; Behr, J\u00FCrgen9; Kahnert, Kathrin9,10; Bahmer, Thomas21; Bewig, Burkhard22; Ewert, Ralf23 Stubbe, Beate23; Ficker, Joachim H.24; Groh\u00E9, Christian25; Held, Matthias26; Henke, Markus27; Herth, Felix1; Kirsten; Anne-Marie5; Watz, Henrik5; Koczulla, Rembert28; Kronsbein, Juliane29; 30Kropf-Sanchen, Cornelia; Herzmann, Christian31; Pfeifer, Michael32; Randerath, Winfried J.33; Seeger, Werner34; Studnicka, Michael35; Taube Christian36; Timmermann Hartmut37; Alter, Peter3; Schmeck, Bernd3; Vogelmeier, Claus3; 8\u2020Welte, Tobias; Wirtz, Hubert38. 20Lungenfachklinik, Immenhausen, Immenhausen Germany. 21Universit\u00E4tsklinikum Schleswig Holstein, Kiel, Germany22St\u00E4dtisches Krankenhaus Kiel. 23Universit\u00E4tsmedizin Greifswald, German. 24Klinikum N\u00FCrnberg, Paracelsus Medizinische Privatuniversit\u00E4t N\u00FCrnberg, Germany. 25Ev. Lungenklinik Berlin, Germany. 26Klinikum W\u00FCrzburg Mitte gGmbH, Standort Missioklinik. 27Asklepios Fachkliniken M\u00FCnchen-Gauting. 28Sch\u00F6n Klinik Berchtesgadener Land, Germany. 29Berufsgenossenschaftliches Universit\u00E4tsklinikum Bergmannsheil, Bochum, Germany. 30Universit\u00E4tsklinikum Ulm, Germany. 31Forschungszentrum Borstel, Germany. 32Klinik Donaustauf, Germany. 33Wissenschaftliches Institut Bethanien e. 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Trudzinski; Department of Pneumology and Critical Care Medicine), German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Thoraxklinik University of Heidelberg, Heidelberg, Röntgenstrasse 1, 69126, Germany; email: Franziska.trudzinski@med.uni-heidelberg.de","","BioMed Central Ltd","","","","","","14659921","","RREEB","39003487","English","Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85198689707"
"Rachel C.J.; Raman K.; Blessy T.; Ramalakshmi K.","Rachel, Carlin Jersha (59666204600); Raman, Kaushik (59666585100); Blessy, Trephena (59665827800); Ramalakshmi, Krishnan (59666020400)","59666204600; 59666585100; 59665827800; 59666020400","Redefining Respiratory Care with Digital Interventions in Children with Bronchial Asthma: Exploring the Efficacy of Game-based Breathing Training and the Buteyko Method","2024","Indian Journal of Respiratory Care","13","4","","243","247","4","0","10.5005/jp-journals-11010-1149","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219641979&doi=10.5005%2fjp-journals-11010-1149&partnerID=40&md5=ac99a8afb5cf9b2328039fb821ad70b9","Department of Physiotherapy, Dr MGR Educational and Research Institute, Tamil Nadu, Chennai, India","Rachel C.J., Department of Physiotherapy, Dr MGR Educational and Research Institute, Tamil Nadu, Chennai, India; Raman K., Department of Physiotherapy, Dr MGR Educational and Research Institute, Tamil Nadu, Chennai, India; Blessy T., Department of Physiotherapy, Dr MGR Educational and Research Institute, Tamil Nadu, Chennai, India; Ramalakshmi K., Department of Physiotherapy, Dr MGR Educational and Research Institute, Tamil Nadu, Chennai, India","Aim and background:This study aimed to evaluate smartphone game application with the Buteyko breathing technique to improve pulmonary function in asthma patients. Airway constriction and inflammation are the hallmarks of asthma. It is the most prevalent chronic illness in both adults and children. Although there are many recent studies showing the effectiveness of various methods, the Buteyko method and game therapy applications may reduce medication needs and symptoms. Methods: This was a quasi-experimental study of pre-and posttrial type carried out with 30 children diagnosed with asthma. Based on the inclusion and exclusion criteria, they were chosen using a simple random sampling method and split into two groups. Subjects of group A received the Buteyko breathing technique, and subjects of group B received the Buteyko breathing technique along with an interventional digital breathing technique, for 15 minutes, 5 sessions per week for 4 weeks. The pre-and posttest were analyzed using the Asthma Control Test and the Becker Asthma Score. Results: On comparison, a significant difference in means at p ≤ 0.05 was observed in group B when comparing the Academic Competence Test (ACT) and Behavioral Assessment Scale (BAS) scores of groups A and B between pre-and posttests. Conclusion: The Buteyko breathing technique along with the Digital Interventional Breathing Technique is more effective in improving pulmonary function in asthma patients compared to the Buteyko breathing technique. The findings of this study pave the way for depicting the role of games in the treatment protocol, by improving the interaction of the patient and thereby improving pulmonary function. Clinical significance: The evaluation of digital solutions for asthma, including artificial intelligence (AI)-assisted breathing techniques and the Buteyko method, holds significant clinical relevance. These innovative approaches offer potential advancements in personalized asthma management by improving breathing efficiency and reducing reliance on medication. Digital interventional breathing techniques can provide real-time feedback and tailored exercises, while the Buteyko method emphasizes controlled breathing to reduce symptoms. Together, these technologies promise to enhance patient outcomes, increase adherence to treatment protocols, and ultimately improve the quality of life for individuals with asthma. © The Author(s).","Asthma; Breathing games; Buteyko breathing; Digital interventional breathing technique; Pulmonary function","","","","","","","","Global strategy for asthma management and prevention, (2023); Mattiuzzi C, Lippi G., Worldwide asthma epidemiology: insights from the Global Health Data Exchange database, Int Forum Allergy Rhinol, 10, 1, pp. 75-80, (2020); Oksel C, Granell R, Haider S, Et al., Distinguishing wheezing phenotypes from infancy to adolescence. a pooled analysis of five birth cohorts, Ann Am Thorac Soc, 16, 7, pp. 868-876, (2019); Goodarzi E, Rashidi K, Zare Z, Et al., The Burden of Asthma in Children Aged 0-14 Years in Asia: A Systematic Analysis for the Global Burden of Disease Study 2019, J Pediatr Res, 9, 2, pp. 105-115, (2022); Medline Plus. Asthma in Children; Bunlam K, Rojnawee S, Pojsupap S, Et al., Enhancing respiratory muscle strength and asthma control in children with asthma: the impact of balloon-breathing exercise, Phys Act Health, 8, (2014); Hassan EEM, Abusaad FE, Mohammed BA., Effect of the Buteyko breathing technique on asthma severity control among school age children, Egypt J Bronchol, 16, 1, (2022); Laurino RA, Barnabe V, Saraiva-Romanholo BM, Et al., Respiratory rehabilitation: a physiotherapy approach to the control of asthma symptoms and anxiety, Clinics (Sao Paulo), 67, 11, pp. 1291-1297, (2012); Santino TA, Chaves GS, Freitas DA, Et al., Breathing exercises for adults with asthma, Cochrane Database Syst Rev, 2020, 3, (2020); Rosalba Courtney DO., Strengths, weakness, and possibilities of the Buteyko breathing method; Vagedes J, Helmert E, Kuderer S, Et al., The Buteyko breathing technique in children with asthma: a randomized controlled pilot study, Complement Ther Med, 56, (2021); Singh G, Ragavendra M., Buteyko breathing technique, JNPE, 7, 2, pp. 13-16, (2021); Hassan ZM, Riad NM, Ahmed FH., Effect of Buteyko breathing technique on patients with bronchial asthma, Eur J Clin Dent Ther, 61, 4, pp. 235-241, (2012); Joo S, Shin D, Song C., The effects of game-based breathing exercise on pulmonary function in stroke patients: a preliminary study, Med Sci Monit, 21, pp. 1806-1811, (2015); Hassan EEM, Abusaad FE, Mohammed BA., Buteyko breathing technique: The golden way for controlling asthma among children, MNJ, 8, 2, pp. 1-12, (2021); Jimcy M, Drisya G., Effect of Buteyko breathing technique on asthma control among children, Int J Pediatr Nurs, 7, 2, pp. 21-24, (2021); Bowler SD, Green A, Mitchell CA., Buteyko breathing techniques in asthma: a blinded randomized controlled trial, Med J Aust, 169, 11-12, (1998); Joo S, Lee K, Song C., A comparative study of smartphone game with spirometry for pulmonary function assessment in stroke patients, Biomed Res Int, 2018, (2018); Nguyen JD, Duong H., Pursed lip breathing, StatPearls [Internet], (2023); Leskovsek M, Lasic M, Ahlin D., Respiratory physiotherapy in a web browser, feasibility study, Open J Respir Dis, 3, 4, pp. 150-153, (2013)","C.J. Rachel; Department of Physiotherapy, Dr MGR Educational and Research Institute, Chennai, Tamil Nadu, India; email: carlinjersharachel.physio@drmgrdu.ac.in","","Jaypee Brothers Medical Publishers (P) Ltd","","","","","","22779019","","","","English","Indian J. Respir. Care","Article","Final","","Scopus","2-s2.0-85219641979"
"Ma Y.; Zhan Z.; Chen Y.; Zhang J.; Li W.; He Z.; Xie J.; Zhao H.; Xu A.; Peng K.; Wang G.; Zeng Q.; Yang T.; Chen Y.; Wang C.","Ma, Yiming (57212324623); Zhan, Zijie (57212323538); Chen, Yahong (7601429768); Zhang, Jing (54685583200); Li, Wen (56608287400); He, Zhiyi (24469755200); Xie, Jungang (7402994596); Zhao, Haijin (7404779548); Xu, Anping (58775998600); Peng, Kun (58775998700); Wang, Gang (58839424700); Zeng, Qingping (58775663300); Yang, Ting (57201495536); Chen, Yan (54794817300); Wang, Chen (58607519500)","57212324623; 57212323538; 7601429768; 54685583200; 56608287400; 24469755200; 7402994596; 7404779548; 58775998600; 58775998700; 58839424700; 58775663300; 57201495536; 54794817300; 58607519500","Machine learning-assisted construction of COPD self-evaluation questionnaire (COPD-EQ): a national multicentre study in China","2025","Journal of Global Health","15","","04052","","","","0","10.7189/JOGH.15.04052","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214492078&doi=10.7189%2fJOGH.15.04052&partnerID=40&md5=1ce55b3338869403aed2798eb1335c51","Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China; Research Unit of Respiratory Disease, Central South University, Changsha, China; Clinical Medical Research Center for Pulmonary and Critical Care Medicine in Hunan Province, Changsha, China; Diagnosis and Treatment Center of Respiratory Disease in Hunan Province, Changsha, China; Department of Radiology, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, China; Department of Respiratory and Critical Care Medicine, Third Hospital of Peking University, Beijing, China; Department of Respiratory and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, China; Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China; Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Respiratory and Critical Care Medicine, Yingcheng People’s Hospital, Yingcheng, China; Department of Respiratory and Critical Care Medicine, Sixth Hospital of Beijing, Beijing, China; Department of Respiratory and Critical Care Medicine, Anji People’s Hospital, Huzhou, China; Department of Intensive Care Unit, Longshan People’s Hospital, Xiangxi, China; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China; Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, China; National Clinical Research Center for Respiratory Disease, Beijing, China; National Center for Respiratory Medicine, Beijing, China; Chinese Alliance for Respiratory Diseases in Primary Care, Beijing, China; Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China; Department of Respiratory Medicine, Capital Medical University, Beijing, China","Ma Y., Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China, Research Unit of Respiratory Disease, Central South University, Changsha, China, Clinical Medical Research Center for Pulmonary and Critical Care Medicine in Hunan Province, Changsha, China, Diagnosis and Treatment Center of Respiratory Disease in Hunan Province, Changsha, China; Zhan Z., Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China, Research Unit of Respiratory Disease, Central South University, Changsha, China, Clinical Medical Research Center for Pulmonary and Critical Care Medicine in Hunan Province, Changsha, China, Diagnosis and Treatment Center of Respiratory Disease in Hunan Province, Changsha, China, Department of Radiology, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, China; Chen Y., Department of Respiratory and Critical Care Medicine, Third Hospital of Peking University, Beijing, China; Zhang J., Department of Respiratory and Critical Care Medicine, Zhongshan Hospital of Fudan University, Shanghai, China; Li W., Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; He Z., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, China; Xie J., Department of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Zhao H., Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China; Xu A., Department of Respiratory and Critical Care Medicine, Yingcheng People’s Hospital, Yingcheng, China; Peng K., Department of Respiratory and Critical Care Medicine, Sixth Hospital of Beijing, Beijing, China; Wang G., Department of Respiratory and Critical Care Medicine, Anji People’s Hospital, Huzhou, China; Zeng Q., Department of Intensive Care Unit, Longshan People’s Hospital, Xiangxi, China; Yang T., Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, China, National Clinical Research Center for Respiratory Disease, Beijing, China, National Center for Respiratory Medicine, Beijing, China, Chinese Alliance for Respiratory Diseases in Primary Care, Beijing, China; Chen Y., Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China, Research Unit of Respiratory Disease, Central South University, Changsha, China, Clinical Medical Research Center for Pulmonary and Critical Care Medicine in Hunan Province, Changsha, China, Diagnosis and Treatment Center of Respiratory Disease in Hunan Province, Changsha, China; Wang C., Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Beijing, China, National Clinical Research Center for Respiratory Disease, Beijing, China, National Center for Respiratory Medicine, Beijing, China, Chinese Alliance for Respiratory Diseases in Primary Care, Beijing, China, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China, Department of Respiratory Medicine, Capital Medical University, Beijing, China","Background Approximately 70% of chronic obstructive pulmonary disease (COPD) is underdiagnosed worldwide. We aimed to develop and validate a COPD self-evaluation questionnaire (COPD-EQ) that is better suited for COPD screening in China. Methods We developed a primary version of COPD-EQ based on the Delphi method. Then, we conducted a nationwide multicentre prospective to validate our novel COPDEQ screening ability. To improve the screening ability of COPD-EQ, we used a series of machine learning (ML)-based methods, including logistic regression, XgBoost, LightGBM, and CatBoost. These models were developed and then evaluated on a random 3:1 train/test split. Results Through the Delphi approach, we developed the primary version of COPD-EQ with nine items. In the following prospective multicentre study, we recruited 1824 outpatients from 12 sites, of whom 404 (22.1%) were diagnosed with COPD. After the score assignment assisted by ML models and the Shapley Additive Explanation method, six of nine items were retained for a briefer version of COPD-EQ. The scoring-based method achieves an AUC score of 0.734 at a threshold of 4.0. Finally, a novel six-item COPDEQ questionnaire was developed. Conclusions The COPD-EQ questionnaire was validated to be reliable and accurate in COPD screening for the Chinese population. The ML model can further improve the questionnaire’s screening ability. © 2025 The Author(s)","","Aged; China; Delphi Technique; Diagnostic Self Evaluation; Female; Humans; Machine Learning; Male; Mass Screening; Middle Aged; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Reproducibility of Results; Surveys and Questionnaires; aged; China; chronic obstructive lung disease; clinical trial; Delphi study; diagnosis; epidemiology; female; human; machine learning; male; mass screening; middle aged; multicenter study; procedures; prospective study; questionnaire; reproducibility; self evaluation","","","","","National Key Clinical Specialty Discipline Construction Program of China; CAMS Innovation Fund for Medical Science, (2021-I2M-1-049); National Natural Science Foundation of China, NSFC, (81873410, 81970043, 82070049); National Natural Science Foundation of China, NSFC","This study was supported by CAMS Innovation Fund for Medical Science (2021-I2M-1-049), National Nature Science Foundation of China (81873410, 81970043, 82070049) and the National Key Clinical Specialty Construction Projects of China.","Hurst JR, Siddiqui MK, Singh B, Varghese P, Holmgren U, de Nigris E., A Systematic Literature Review of the Humanistic Burden of COPD, Int J Chron Obstruct Pulmon Dis, 16, pp. 1303-1314, (2021); 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Proceedings of 31st Annual Conference on Neural Information Processing Systems (NIPS), pp. 3149-3157, (2017); Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A., CatBoost: unbiased boosting with categorical features, Proccedings of the 32 nd Conference on Neural Information Processing Systems (NeurIPS 2018), pp. 6639-6649, (2018); Hancock JT, Khoshgoftaar TM., CatBoost for big data: an interdisciplinary review, J Big Data, 7, (2020); Schober P, Vetter TR., Logistic Regression in Medical Research, Anesth Analg, 132, pp. 365-366, (2021); Kim SJ, Lee J, Park YS, Lee CH, Yoon HI, Lee SM, Et al., Age-related annual decline of lung function in patients with COPD, Int J Chron Obstruct Pulmon Dis, 11, pp. 51-60, (2015); Hancock DB., Compelling Interaction of Cigarette Smoking and Polygenetic Risk Emerges for Lung Function and COPD, JAMA Netw Open, 4, (2021); Wang B, Xiao D, Wang C., Smoking and chronic obstructive pulmonary disease in Chinese population: a meta-analysis, Clin Respir J, 9, pp. 165-175, (2015); Wheaton AG, Liu Y, Croft JB, VanFrank B, Croxton TL, Punturieri A, Et al., Chronic Obstructive Pulmonary Disease and Smoking Status - United States, 2017, MMWR Morb Mortal Wkly Rep, 68, pp. 533-538, (2019); Kamal R, Srivastava AK, Kesavachandran CN, Bihari V, Singh A., Chronic obstructive pulmonary disease (COPD) in women due to indoor biomass burning: a meta analysis, Int J Environ Health Res, 32, pp. 1403-1417, (2022); Regalado J, Perez-Padilla R, Sansores R, Paramo Ramirez JI, Brauer M, Pare P, Et al., The effect of biomass burning on respiratory symptoms and lung function in rural Mexican women, Am J Respir Crit Care Med, 174, pp. 901-905, (2006); Oishi K, Matsunaga K, Harada M, Suizu J, Murakawa K, Chikumoto A, Et al., A New Dyspnoea Evaluation System Focusing on Patients’ Perceptions of Dyspnoea and Their Living Disabilities: The Linkage between COPD and Frailty, J Clin Med, 9, (2020); Gruenberger JB, Vietri J, Keininger DL, Mahler DA., Greater dyspnoea is associated with lower health-related quality of life among European patients with COPD, Int J Chron Obstruct Pulmon Dis, 12, pp. 937-944, (2017); Guirguis-Blake JM, Senger CA, Webber EM, Mularski R, Whitlock EP., Screening for Chronic Obstructive Pulmonary Disease: Evidence Report and Systematic Review for the US Preventive Services Task Force, JAMA, 315, pp. 1378-1393, (2016); de Marco R, Accordini S, Cerveri I, Corsico A, Anto JM, Kunzli N, Et al., Incidence of chronic obstructive pulmonary disease in a cohort of young adults according to the presence of chronic cough and phlegm, Am J Respir Crit Care Med, 175, pp. 32-39, (2007); Omori H, Higashi N, Nawa T, Fukui T, Kaise T, Suzuki T., Chronic Cough and Phlegm in Subjects Undergoing Comprehensive Health Examination in Japan - Survey of Chronic Obstructive Pulmonary Disease Patients Epidemiology in Japan (SCOPE-J), Int J Chron Obstruct Pulmon Dis, 15, pp. 765-773, (2020); Choate R, Pasquale CB, Parada NA, Prieto-Centurion V, Mularski RA, Yawn BP., The Burden of Cough and Phlegm in People With COPD: A COPD Patient-Powered Research Network Study, Chronic Obstr Pulm Dis (Miami), 7, pp. 49-59, (2020); Gu Y, Zhang Y, Wen Q, Ouyang Y, Shen Y, Yu H, Et al., Performance of COPD population screener questionnaire in COPD screening: a validation study and meta-analysis, Ann Med, 53, pp. 1198-1206, (2021)","T. Yang; Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, 2 Yinghuayuan Eastern Road, China; email: zryyyangting@163.com; C. Wang; Department of Pulmonary and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, Changsha 139 Renmin Road, Hunan, China; email: chenyan99727@csu.edu.cn","","University of Edinburgh","","","","","","20472978","","","39749754","English","J. Glob. Health","Article","Final","","Scopus","2-s2.0-85214492078"
"Belza-Mai A.C.; Efta J.; Kenney R.; MacDonald N.; Stine J.; McCollom R.; Ratusznik M.; Patel N.","Belza-Mai, Ana Christine (59478744800); Efta, Jessica (57479585000); Kenney, Rachel (56395035700); MacDonald, Nancy (57189940593); Stine, John (57217687820); McCollom, Robert (58168882500); Ratusznik, Martin (58927759100); Patel, Nisha (57206931211)","59478744800; 57479585000; 56395035700; 57189940593; 57217687820; 58168882500; 58927759100; 57206931211","Optimizing discharge antimicrobial therapy: Evaluation of a transitions of care process and electronic scoring system for patients with community-acquired pneumonia or chronic obstructive pulmonary disease","2024","American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists","81","24","","1237","1244","7","0","10.1093/ajhp/zxae174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212457209&doi=10.1093%2fajhp%2fzxae174&partnerID=40&md5=f4dc33c1f10504267fcc14ab8388da5b","Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States","Belza-Mai A.C., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; Efta J., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; Kenney R., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; MacDonald N., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; Stine J., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; McCollom R., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; Ratusznik M., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States; Patel N., Department of Pharmacy Services, Henry Ford Hospital, Detroit, MI, United States","PURPOSE: Prescribing excess antibiotic duration at hospital discharge is common. A pharmacist-led Antimicrobial Stewardship Program Transition of Care (ASP TOC) intervention was associated with improved discharge prescribing. To improve the sustainability of this service, an electronic scoring system (ESS), which included the ASP TOC electronic variable, was implemented in the electronic medical record to prioritize pharmacist workload. The purpose of this study was to evaluate the implementation of the ASP TOC variable in the ESS in patients with community-acquired pneumonia (CAP) or chronic obstructive pulmonary disease (COPD). METHODS: This institutional review board-approved, retrospective quasi-experiment included patients discharged on oral antibiotics for CAP or COPD exacerbation (lower respiratory tract infection) from November 1, 2021, to March 1, 2022 (the preintervention period) and November 1, 2022, to March 1, 2023 (the postintervention period). The primary endpoint was optimized discharge antimicrobial regimen. A sample of at least 194 patients was required to achieve 80% power to detect a 20% difference in the frequency of optimized therapy. Multivariable logistic regression was used to identify factors associated with optimized regimens. RESULTS: Similar baseline characteristics were observed in both study groups (n = 100 for both groups). The frequency of optimized discharge regimens improved from 69% to 82% (P = 0.033). The percentage of ASP TOC interventions documented as completed by a pharmacist increased from 4% to 25% (P < 0.001). ASP TOC intervention, female gender, and COPD were independently associated with an optimized discharge regimen (adjusted odds ratios, 6.57, 1.61, and 3.89, respectively; 95% CI, 1.51-28.63, 0.81-3.17, and 1.85-8.20, respectively). CONCLUSION: After the launch of the ASP TOC variable, there was an increase in optimized discharge regimens and ASP TOC interventions completed. Pharmacists' use of the ASP TOC variable through an ESS can aid in improving discharge prescribing. © American Society of Health-System Pharmacists 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.","antimicrobial stewardship; artificial intelligence; chronic obstructive pulmonary disease; community-acquired pneumonia; lower respiratory tract infection; transitions of care","Aged; Aged, 80 and over; Anti-Bacterial Agents; Antimicrobial Stewardship; Community-Acquired Infections; Electronic Health Records; Female; Humans; Male; Middle Aged; Patient Discharge; Pharmacists; Pharmacy Service, Hospital; Pneumonia; Professional Role; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; antiinfective agent; aged; antimicrobial stewardship; chronic obstructive lung disease; community acquired infection; drug therapy; electronic health record; female; hospital discharge; hospital pharmacy; human; male; middle aged; organization and management; pharmacist; pneumonia; procedures; professional standard; retrospective study; very elderly","","Anti-Bacterial Agents, ","","","","","","","","","","","","","","15352900","","","38953520","English","Am J Health Syst Pharm","Article","Final","","Scopus","2-s2.0-85212457209"
"Wang F.; Jia K.; Li Y.","Wang, Feifei (57190750487); Jia, Ke (59165453100); Li, Yang (57218703695)","57190750487; 59165453100; 57218703695","Integrative deep learning with prior assisted feature selection","2024","Statistics in Medicine","43","20","","3792","3814","22","0","10.1002/sim.10148","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196652083&doi=10.1002%2fsim.10148&partnerID=40&md5=72cf148cfe34bcd01cf70d307b1e4725","Center for Applied Statistics, Renmin University of China, Beijing, China; School of Statistics, Renmin University of China, Beijing, China","Wang F., Center for Applied Statistics, Renmin University of China, Beijing, China, School of Statistics, Renmin University of China, Beijing, China; Jia K., School of Statistics, Renmin University of China, Beijing, China; Li Y., Center for Applied Statistics, Renmin University of China, Beijing, China, School of Statistics, Renmin University of China, Beijing, China","Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the “small (Formula presented.) and large (Formula presented.) ” challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify “prior information”. This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application. © 2024 John Wiley & Sons Ltd.","deep learning; ensemble learning; integrative analysis; prior information; variable selection","activating transcription factor 2; B Raf kinase; carcinoembryonic antigen related cell adhesion molecule 1; CD146 antigen; CD4 antigen; chemokine receptor CXCR4; cyclin dependent kinase 4; cyclin dependent kinase inhibitor 2A; cytotoxic T lymphocyte antigen 4; deubiquitinase; epidermal growth factor receptor; Hermes antigen; microphthalmia associated transcription factor; microRNA; osteonectin; phosphatidylinositol 3,4,5 trisphosphate 3 phosphatase; protein S100B; protein tyrosine kinase; STAT3 protein; telomerase reverse transcriptase; transcription factor EZH2; transcription factor Sox10; tumor necrosis factor; vitamin D receptor; accuracy; area under the curve; article; Article; artificial intelligence; artificial neural network; asthma; bioinformatics; breast cancer; cancer prognosis; convolutional neural network; cutaneous melanoma; diagnostic test accuracy study; DNA methylation; drug potentiation; entropy; follow up; gene expression; genotype; human; learning; medical research; melanoma; oncogene N ras; prediction; quantitative structure activity relation; recurrent neural network; sensitivity analysis; simulation; support vector machine; tumor growth; tumor suppressor gene","","chemokine receptor CXCR4, 188900-71-2; cyclin dependent kinase 4, 147014-97-9; epidermal growth factor receptor, 79079-06-4; osteonectin, 104052-78-0; phosphatidylinositol 3,4,5 trisphosphate 3 phosphatase, 210488-47-4; protein S100B, 357701-89-4; protein tyrosine kinase, 80449-02-1; telomerase reverse transcriptase, 120178-12-3","","","","","Ma S., Huang J., Song X., Integrative analysis and variable selection with multiple high-dimensional data sets, Biostatistics, 12, 4, pp. 763-775, (2011); Wu C., Cui Y., Ma S., Integrative analysis of gene–environment interactions under a multi-response partially linear varying coefficient model, Stat Med, 33, 28, pp. 4988-4998, (2014); Wang S., Wu M., Ma S., Integrative analysis of cancer omics data for prognosis modeling, Genes, 10, 8, (2019); Huang Y., Zhang Q., Zhang S., Huang J., Ma S., Promoting similarity of sparsity structures in integrative analysis with penalization, J Am Stat Assoc, 112, 517, pp. 342-350, (2017); Li Y., Wang F., Wu M., Ma S., Integrative functional linear model for genome-wide association studies with multiple traits, Biostatistics, 23, 2, pp. 574-590, (2022); Li Y., Li R., Qin Y., Wu M., Ma S., Integrative interaction analysis using threshold gradient directed regularization, Appl Stochast Models Bus Ind, 35, 2, pp. 354-375, (2019); Li Y., Huang C., Ding L., Li Z., Pan Y., Gao X., Deep learning in bioinformatics: introduction, application, and perspective in the big data era, Methods, 166, pp. 4-21, (2019); Tang B., Pan Z., Yin K., Khateeb A., Recent advances of deep learning in bioinformatics and computational biology, Front Genet, 10, (2019); Yu X., Zhou S., Zou H., Et al., Survey of deep learning techniques for disease prediction based on omics data, Human Gene, (2023); Mirza B., Wang W., Wang J., Choi H., Chung N.C., Ping P., Machine learning and integrative analysis of biomedical big data, Genes, 10, 2, (2019); Ding M.Q., Chen L., Cooper G.F., Young J.D., Lu X., Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics, Mol Cancer Res, 16, 2, pp. 269-278, (2018); Ma T., Zhang A., Multi-view factorization autoencoder with network constraints for multi-omic integrative analysis. IEEE, (2018); Liang M., Li Z., Chen T., Zeng J., Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach, IEEE/ACM Trans Comput Biol Bioinform, 12, 4, pp. 928-937, (2014); Sun D., Wang M., Li A., A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data, IEEE/ACM Trans Comput Biol Bioinform, 16, 3, pp. 841-850, (2018); Zhang T., Zhang L., Payne P.R., Li F., Synergistic drug combination prediction by integrating multiomics data in deep learning models, Transl Bioinform Therapeut Develop, pp. 223-238, (2021); Jiang Y., He Y., Zhang H., Variable selection with prior information for generalized linear models via the prior LASSO method, J Am Stat Assoc, 111, 513, pp. 355-376, (2016); Wang X., Xu Y., Ma S., Identifying gene-environment interactions incorporating prior information, Stat Med, 38, 9, pp. 1620-1633, (2019); Yi H., Zhang Q., Lin C., Ma S., Information-incorporated Gaussian graphical model for gene expression data, Biometrics, 78, 2, pp. 512-523, (2022); Wang F., Liang D., Li Y., Ma S., Prior information-assisted integrative analysis of multiple datasets, Bioinformatics, 39, 8, (2023); Lai S., Xu L., Liu K., Zhao J., Recurrent convolutional neural networks for text classification. Vol 29, (2015); Huang L., Ma D., Li S., Zhang X., Wang H., Text level graph neural network for text classification. arXiv preprint arXiv:1910.02356, (2019); Sun C., Qiu X., Xu Y., Huang X., How to fine-tune bert for text classification? Springer, (2019); Lee J., Yoon W., Kim S., Et al., BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36, 4, pp. 1234-1240, (2020); Veni C.K., Rani T.S., Ensemble based classification using small training sets: a novel approach. IEEE, (2014); Xinqin L., Tianyun S., Ping L., Wen Z., Application of bagging ensemble classifier based on genetic algorithm in the text classification of railway fault hazards. IEEE, (2019); Kim Y., Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, (2014); Zhang J., Li Y., Tian J., Li T., LSTM-CNN hybrid model for text classification. IEEE, (2018); Cheng Y., Yao L., Xiang G., Zhang G., Tang T., Zhong L., Text sentiment orientation analysis based on multi-channel CNN and bidirectional GRU with attention mechanism, IEEE Access, 8, pp. 134964-134975, (2020); Yang Z., Yang D., Dyer C., He X., Smola A., Hovy E., Hierarchical attention networks for document classification, (2016); Defferrard M., Bresson X., Vandergheynst P., Convolutional neural networks on graphs with fast localized spectral filtering, Adv Neural Inf Process Syst, 29, (2016); Onan A., Korukoglu S., Bulut H., A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification, Expert Syst Appl, 62, pp. 1-16, (2016); Dietterich T.G., Ensemble methods in machine learning. Springer, (2000); Bottou L., Large-scale machine learning with stochastic gradient descent. Springer, (2010); Chang A.E., Karnell L.H., Menck H.R., The National Cancer Data Base report on cutaneous and noncutaneous melanoma: a summary of 84,836 cases from the past decade, Cancer: Interdiscip Int J Am Cancer Soc, 83, 8, pp. 1664-1678, (1998); Puzanov I., Milhem M.M., Minor D., Et al., Talimogene laherparepvec in combination with ipilimumab in previously untreated, unresectable stage IIIB-IV melanoma, J Clin Oncol, 34, 22, (2016); Fan J., Lv J., Sure independence screening for ultrahigh dimensional feature space, J R Stat Soc Series B Stat Methodol, 70, 5, pp. 849-911, (2008); Li J., Das K., Fu G., Li R., Wu R., The Bayesian lasso for genome-wide association studies, Bioinformatics, 27, 4, pp. 516-523, (2011); Wang L., Wu Y., Li R., Quantile regression for analyzing heterogeneity in ultra-high dimension, J Am Stat Assoc, 107, 497, pp. 214-222, (2012); Shi X., Shen S., Liu J., Huang J., Zhou Y., Ma S., Similarity of markers identified from cancer gene expression studies: observations from GEO, Brief Bioinform, 15, 5, pp. 671-684, (2014); Ma S., Empirical study of supervised gene screening, BMC Bioinform, 7, pp. 1-14, (2006); Bhattacharjee M., Dhar S.K., Subramanian S., Recent Advances in Biostatistics: False Discovery Rates, Survival Analysis, and Related Topics, (2011); Sirota M., Schaub M.A., Batzoglou S., Robinson W.H., Butte A.J., Autoimmune disease classification by inverse association with SNP alleles, PLoS Genet, 5, 12, (2009); Helgadottir H., Olsson H., Tucker M.A., Yang X.R., Hoiom V., Goldstein A.M., Phenocopies in melanoma-prone families with germ-line CDKN2A mutations, Genet Med, 20, 9, pp. 1087-1090, (2018); Yajima I., Kumasaka M.Y., Thang N.D., Et al., Molecular network associated with MITF in skin melanoma development and progression, J Skin Cancer, (2011); Bosserhoff A.K., Melanoma inhibitory activity (MIA): an important molecule in melanoma development and progression, Pigment Cell Res, 18, 6, pp. 411-416, (2005); Pan J., Ruan W., Qin M., Et al., Intradermal delivery of STAT3 siRNA to treat melanoma via dissolving microneedles, Sci Rep, 8, 1, (2018); Lee J.H., Choi J.W., Kim Y.S., Frequencies of BRAF and NRAS mutations are different in histological types and sites of origin of cutaneous melanoma: a meta-analysis, Brit J Dermatol, 164, 4, pp. 776-784, (2011); Harpio R., Einarsson R., S100 proteins as cancer biomarkers with focus on S100B in malignant melanoma, Clin Biochem, 37, 7, pp. 512-518, (2004); Davies H., Bignell G.R., Cox C., Et al., Mutations of the BRAF gene in human cancer, Nature, 417, 6892, pp. 949-954, (2002); Ping S., Wang S., He J., Chen J., Identification and validation of immune-related lncRNA signature as a prognostic model for skin cutaneous melanoma, Pharmacogen Personaliz Med, pp. 667-681, (2021); Miko I., Phenotype variability: penetrance and expressivity, Nat Educ, 1, 1, (2008); Assmann V., Fieber C., Herrlich P., Et al., CD44 is the principal mediator of hyaluronic-acid-induced melanoma cell proliferation, J Investigat Dermatol, 116, 1, pp. 93-101, (2001); Zingg D., Debbache J., Schaefer S.M., Et al., The epigenetic modifier EZH2 controls melanoma growth and metastasis through silencing of distinct tumour suppressors, Nat Commun, 6, 1, (2015); Beckner M.E., AAMP (angio-associated, migratory cell protein), (2012); She Q., Dong Y., Li D., Et al., ABCB6 knockdown suppresses melanogenesis through the GSK3-β$$ \beta $$/β$$ \beta $$-catenin signaling axis in human melanoma and melanocyte cell lines, J Dermatol Sci, 106, 2, pp. 101-110, (2022); Zhu H., Zhao M., Chang C., Chan V., Lu Q., Wu H., The complex role of AIM2 in autoimmune diseases and cancers, Immun Inflammat Disease, 9, 3, pp. 649-665, (2021); Zeng N., Ma L., Cheng Y., Et al., Construction of a ferroptosis-related gene signature for predicting survival and immune microenvironment in melanoma patients, Int J General Med, pp. 6423-6438, (2021)","Y. Li; Center for Applied Statistics, Renmin University of China, Beijing, China; email: yang.li@ruc.edu.cn","","John Wiley and Sons Ltd","","","","","","02776715","","SMEDD","","English","Stat. Med.","Article","Final","","Scopus","2-s2.0-85196652083"
"Zhang Y.; Hai Y.; Song B.; Xu J.; Cao L.; Yasen R.; Xu W.; Zhang J.; Hu J.","Zhang, Yang (59662609100); Hai, Yang (56047769400); Song, Bangguo (59661729200); Xu, Jing (59662461800); Cao, Liangjia (58236214100); Yasen, Rukeye (59450815900); Xu, Wenjuan (57219423308); Zhang, Jiaxuan (59371546300); Hu, Jihong (55499495700)","59662609100; 56047769400; 59661729200; 59662461800; 58236214100; 59450815900; 57219423308; 59371546300; 55499495700","Screening and Validation of Potential Biomarkers of Immune Cells in Childhood Asthma Patients via Mendelian Randomization and Machine Learning","2025","Journal of Inflammation Research ","18","","","2583","2600","17","0","10.2147/JIR.S498017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219480005&doi=10.2147%2fJIR.S498017&partnerID=40&md5=630fbae6f77c5b28203b2dafc94f61d5","Scientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, China; Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Lanzhou, China; Research Center of Traditional Chinese Medicine, Gansu Province, Gansu University of Chinese Medicine, Lanzhou, China; Clinical College of Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China; College of Public Health, Gansu University of Chinese Medicine, Lanzhou, China; Laboratory and Simulation Training Center, Gansu University of Chinese Medicine, Lanzhou, China","Zhang Y., Scientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, China, Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Lanzhou, China, Research Center of Traditional Chinese Medicine, Gansu Province, Gansu University of Chinese Medicine, Lanzhou, China, Clinical College of Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China; Hai Y., Scientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, China, Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Lanzhou, China, Research Center of Traditional Chinese Medicine, Gansu Province, Gansu University of Chinese Medicine, Lanzhou, China; Song B., Clinical College of Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, China; Xu J., Scientific Research and Experimental Center, Gansu University of Chinese Medicine, Lanzhou, China, Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Lanzhou, China, Research Center of Traditional Chinese Medicine, Gansu Province, Gansu University of Chinese Medicine, Lanzhou, China; Cao L., College of Public Health, Gansu University of Chinese Medicine, Lanzhou, China; Yasen R., College of Public Health, Gansu University of Chinese Medicine, Lanzhou, China; Xu W., College of Public Health, Gansu University of Chinese Medicine, Lanzhou, China; Zhang J., College of Public Health, Gansu University of Chinese Medicine, Lanzhou, China; Hu J., Laboratory and Simulation Training Center, Gansu University of Chinese Medicine, Lanzhou, China","Purpose: Asthma is one of the most common chronic respiratory diseases affecting children, and there is currently no clear remedy. Immune cells play a key role in childhood asthma. Therefore, a deeper investigation of the correlation between immune cells and childhood asthma could lead to a better understanding of asthma’s origin, the identification of potential treatment targets, and the development of personalized treatment strategies. Patients and Methods: We used a two-sample Mendelian randomization (MR) analysis to investigate the possible causal relationship between childhood asthma and a total of 731 immune cells, including B cell (190), Maturation stages of T cell (79), Monocyte (43), Myeloid cell (64), TBNK (124), Treg (167), and CDC (64). LASSO logistic regression and SVM algorithms were used to identify key genes associated with childhood asthma. Specific signaling pathways associated with these key genes were further explored through gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Subsequently, the four key genes FCGR3A, TCTN3, ALOX5, and IL4R were verified in an established asthma mouse model using quantitative real-time PCR and Western blotting. Results: MR analysis showed that 60 immune cells were associated with childhood asthma, of which 32 were associated with high risk and 28 were associated with low risk. LASSO logistic regression and SVM algorithm identified six key genes that affect childhood asthma as ATF4, FCGR3A, GAS5, MGAT3, TAB1, and TCTN3. In addition, four genes, FCGR3A, TCTN3, ALOX5, and IL4R, were verified through animal experiments. Conclusion: Our findings confirmed that immune cells contribute to childhood asthma, highlighting the importance of key genes in the role of the immune microenvironment in this disease. These insights provide a new path for the exploration of the biological underpinnings of childhood asthma and the development of early intervention therapies. © 2025 Zhang et al.","biomarker; childhood asthma; immune cells; key genes; Mendelian randomization","","","","","","Traditional Chinese Medicine, Gansu Province, (ZYZX-2023-05); Scientific Research and Innovation Fund Project of Gansu University of Chinese Medicine, (2023KCYB-7); Gansu Provincial Joint Scientific Research Fund, (24JRRA880); National Natural Science Foundation of China, NSFC, (81960614); National Natural Science Foundation of China, NSFC; Gansu Provincial Administration of Traditional Chinese Medicine Scientific Research Project, (GZKP-2023-42); Key Laboratory of Dunhuang Medicine, Ministry of Education, (DHYX20-17)","This study was supported by grants from the National Natural Science Foundation of China (grant number 81960614); Open Subjects of the Key Laboratory of Dunhuang Medicine, Ministry of Education (DHYX20-17); Open Subjects of the Research Center of Traditional Chinese Medicine, Gansu Province (ZYZX-2023-05); Gansu Provincial Administration of Traditional Chinese Medicine Scientific Research Project (GZKP-2023-42); Scientific Research and Innovation Fund Project of Gansu University of Chinese Medicine (2023KCYB-7); General project of Gansu Provincial Joint Scientific Research Fund (No. 24JRRA880).","Asher MI, Garcia-Marcos L, Pearce NE, Strachan DP., Trends in worldwide asthma prevalence, Eur Respir J, 56, 6, (2020); Wu JHJ., Highlights of bronchial asthma in children guideline (2016 edition) for its diagnosis and treatment, World Clin Drugs, 39, 8, (2018); Pawankar R., Allergic diseases and asthma: a global public health concern and a call to action, World Allergy Organ J, 7, 1, (2014); (2024); El-Husseini ZW, Gosens R, Dekker F, Koppelman GH., The genetics of asthma and the promise of genomics-guided drug target discovery, Lancet Respir Med, 8, 10, pp. 1045-1056, (2020); Laubhahn K, Schaub B., From preschool wheezing to asthma: immunological determinants, Pediatr Allergy Immunol, 34, 10, (2023); 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Huang AA, Huang SY., Use of feature importance statistics to accurately predict asthma attacks using machine learning: a cross-sectional cohort study of the US population, PLoS One, 18, 11, (2023); Kaplan A, Cao H, FitzGerald JM, Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol Pract, 9, 6, pp. 2255-2261, (2021); Khanam U, Ulfat A, Adamko D, Et al., A scoping review of asthma and machine learning, J Asthma, 60, 2, pp. 213-226, (2023); Wang X, Meng L, Zhang J, Et al., Identification of angiogenesis-related genes in diabetic foot ulcer using machine learning algorithms, Heliyon, 9, 12, (2023); Wang Q, Liu D, Gao T, Et al., Experimental verification and identifying biomarkers related to insomnia, Front Neurol, 14, (2023); Xu B, Li H, Chen H, Et al., Identification and prediction of molecular subtypes of atherosclerosis based on m6A immune cell infiltration, Biochim Biophys Acta Gen Subj, 1868, 2, (2024); 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Wang C, Zhu D, Zhang D, Et al., Causal role of immune cells in schizophrenia: Mendelian randomization (MR) study, BMC Psychiatry, 23, 1, (2023); Li X, Howard TD, Zheng SL, Et al., Genome-wide association study of asthma identifies RAD50-IL13 and HLA-DR/DQ regions, J Allergy Clin Immunol, 125, 2, pp. 328-335, (2010); Zhao Y, Wang C., Review of CD4+ T cell subsets in asthma phenotypes: molecular mechanisms and biologic treatment options, J Shanghai Jiao Tong Univ, 43, 8, pp. 1064-1070, (2023); Luo W, Hu J, Xu W, Dong J., Distinct spatial and temporal roles for Th1, Th2, and Th17 cells in asthma, Front Immunol, 13, (2022); Coquet JM, Schuijs MJ, Smyth MJ, Et al., Interleukin-21-producing CD4(+) T cells promote type 2 immunity to house dust mites, Immunity, 43, 2, pp. 318-330, (2015); Tibbitt CA, Stark JM, Martens L, Et al., Single-Cell RNA sequencing of the T helper cell response to house dust mites defines a distinct gene expression signature in airway Th2 cells, Immunity, 51, 1, pp. 169-184, (2019); Cohn L, Homer RJ, Marinov A, Rankin J, Bottomly K., Induction of airway mucus production by T helper 2 (Th2) cells: a critical role for interleukin 4 in cell recruitment but not mucus production, J Exp Med, 186, 10, pp. 1737-1747, (1997); Ma CH, Ma ZQ, Fu Q, Ma SP., Ma Huang Tang ameliorates asthma through modulation of Th1/Th2 cytokines and inhibition of Th17 cells in ovalbumin-sensitized mice, Chin J Nat Med, 12, 5, pp. 361-366, (2014); Eldosoky MA, Hammad R, Rushdi A, Et al., MicroRNA-146a-5p and microRNA-210-3p correlate with T regulatory cells frequency and predict asthma severity in Egyptian pediatric population, J Asthma Allergy, 16, pp. 107-121, (2023); Kliem CV, Schaub B., The role of regulatory B cells in immune regulation and childhood allergic asthma, mol Cell Pediatr, 11, 1, (2024); Habener A, Happle C, Grychtol R, Et al., Regulatory B cells control airway hyperreactivity and lung remodeling in a murine asthma model, J Allergy Clin Immunol, 147, 6, pp. 2281-2294, (2021); Patel KR, Roberts JT, Subedi GP, Barb AW., Restricted processing of CD16a/Fc γ receptor IIIa N-glycans from primary human NK cells impacts structure and function, J Biol Chem, 293, 10, pp. 3477-3489, (2018); 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Hu; Laboratory and Simulation Training Center, Gansu University of Chinese Medicine, Lanzhou, Chengguan District, Gansu, 73000, China; email: hujihonghappy@163.com","","Dove Medical Press Ltd","","","","","","11787031","","","","English","J. Inflamm. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85219480005"
"Ottewill C.; Gleeson M.; Kerr P.; Hale E.M.; Costello R.W.","Ottewill, Ciara (57218855520); Gleeson, Margaret (58722608500); Kerr, Patrick (57743067800); Hale, Elaine Mac (57162774300); Costello, Richard W. (7101602656)","57218855520; 58722608500; 57743067800; 57162774300; 7101602656","Digital health delivery in respiratory medicine: adjunct, replacement or cause for division?","2024","European Respiratory Review","33","173","230251","","","","0","10.1183/16000617.0251-2023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204942264&doi=10.1183%2f16000617.0251-2023&partnerID=40&md5=5e083ff5677cbe8889f392efc7abe72d","Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland; Bon Secours Hospital, Dublin, Ireland","Ottewill C., Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland, Bon Secours Hospital, Dublin, Ireland; Gleeson M., Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland; Kerr P., Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland; Hale E.M., Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland; Costello R.W., Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland","Digital medicine is already well established in respiratory medicine through remote monitoring digital devices which are used in the day-to-day care of patients with asthma, COPD and sleep disorders. Image recognition software, deployed in thoracic radiology for many applications including lung cancer screening, is another application of digital medicine. Used as clinical decision support, this software will soon become part of day-to-day practice once concerns regarding generalisability have been addressed. Embodied in the electronic health record, digital medicine also plays a substantial role in the day-to-day clinical practice of respiratory medicine. Given the considerable work the electronic health record demands from clinicians, the next tangible impact of digital medicine may be artificial intelligence that aids administration, makes record keeping easier and facilitates better digital communication with patients. Future promises of digital medicine are based on their potential to analyse and characterise the large amounts of digital clinical data that are collected in routine care. Offering the potential to predict outcomes and personalise therapy, there is much to be excited by in this new epoch of innovation. However, these digital tools are by no means a silver bullet. It remains uncertain whether, let alone when, the promises of better models of personalisation and prediction will translate into clinically meaningful and cost-effective products for clinicians. © The authors 2024.","","Artificial Intelligence; Delivery of Health Care, Integrated; Diffusion of Innovation; Digital Health; Electronic Health Records; Humans; Predictive Value of Tests; Pulmonary Medicine; Telemedicine; oxygen; rifampicin; adult respiratory distress syndrome; Article; artificial intelligence; bacterial mutation; clinician; convolutional neural network; deep learning; digital health; digital therapy; electronic health record; health care delivery; hospital; human; large language model; lung cancer; lung cyst; lung fibrosis; lung tuberculosis; machine learning; mandibular prognathism; Mycobacterium tuberculosis; nonhuman; oxygen therapy; patient; patient engagement; patient triage; phenotype; prediction; predictive model; pulmonology; remote sensing; respiratory medicine; scientist; sleep disorder; sleep medicine; supervised machine learning; support vector machine; telehealth; telemedicine; therapy; treatment response; unsupervised machine learning; ward; diffusion of innovation; digital health; electronic health record; integrated health care system; organization and management; predictive value; pulmonology","","oxygen, 7782-44-7; rifampicin, 13292-46-1","","","","","Pattichis CS, Panayides AS., Connected health, Front Digit Health, 1, (2019); Elenko E, Underwood L, Zohar D., Defining digital medicine, Nat Biotechnol, 33, pp. 456-461, (2015); Cushen B, Sulaiman I, Greene G, Et al., The clinical impact of different adherence behaviors in patients with severe chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 197, pp. 1630-1633, (2018); Tsimberidou AM, Hong DS, Wheler JJ, Et al., Long-term overall survival and prognostic score predicting survival: the IMPACT study in precision medicine, J Hematol Oncol, 12, (2019); Duran CO, Bonam M, Bjork E, Et al., Implementation of digital health technology in clinical trials: the 6R framework, Nat Med, 29, pp. 2693-2697, (2023); 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Costello; Department of Respiratory Medicine, Beaumont Hospital and RCSI University of Medicine and Health Science, Dublin, Ireland; email: rcostello@rcsi.ie","","European Respiratory Society","","","","","","09059180","","EREWE","39322260","English","Eur. Respir. Rev.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85204942264"
"Jacquemyn X.; Chinni B.K.; Doshi A.N.; Kutty S.; Manlhiot C.","Jacquemyn, Xander (57282344200); Chinni, Bhargava K. (26321422900); Doshi, Ashish N. (27267577400); Kutty, Shelby (25650021600); Manlhiot, Cedric (22135585600)","57282344200; 26321422900; 27267577400; 25650021600; 22135585600","Phenotypic clustering of repaired Tetralogy of Fallot using unsupervised machine learning","2024","International Journal of Cardiology Congenital Heart Disease","17","","100524","","","","0","10.1016/j.ijcchd.2024.100524","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208516056&doi=10.1016%2fj.ijcchd.2024.100524&partnerID=40&md5=2e7f80e09e366eaeaf04c818a3ba9024","The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States; Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium","Jacquemyn X., The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States, Department of Cardiovascular Sciences, KU Leuven & Congenital and Structural Cardiology, UZ Leuven, Leuven, Belgium; Chinni B.K., The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States; Doshi A.N., The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States; Kutty S., The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States; Manlhiot C., The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, United States","Objective: Repaired Tetralogy of Fallot (rTOF), a complex congenital heart disease, exhibits substantial clinical heterogeneity. Accurate prediction of disease progression and tailored patient management remain elusive. We aimed to categorize rTOF patients into distinct phenotypes based on clinical variables and variables obtained from cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational cohort study of rTOF patients with at least two CMR assessments between 2005 and 2022 was performed. From patient records, clinical variables, CMR measurements, and electrocardiogram data were collected and processed. Baseline and follow-up variables between subsequent CMR studies were used to assess both inter- and intrapatient disease heterogeneity. Subsequently, unsupervised machine learning was performed, involving dimensionality reduction using principal component analysis and K-means clustering to identify different phenotypic clusters. Results: In total, 155 patients (54.2 % male, median 14.9 years) were included and followed for a median duration of 9.9 years. A total of 459 CMR studies were included in analysis for the identification of phenotypic clusters. Following analysis, we identified four distinct rTOF phenotypes: (1) stable/slow deteriorating, (2) deteriorating, structural remodeling, (3) deteriorated indicated for pulmonary valve replacement, and lastly (4) younger patients with coexisting anomalies. These phenotypes exhibited differential clinical profiles (p < 0.01), cardiac remodeling patterns (p < 0.01), and intervention rates (p < 0.01). Conclusions: Unsupervised machine learning analysis unveiled four discrete phenotypes within the rTOF population, elucidating the substantial disease heterogeneity on both a population- and patient-level. Our study underscores the potential of unsupervised machine learning as a valuable tool for characterizing complex congenital heart disease and potentially tailoring interventions. © 2024 The Authors","CMR; Machine learning; Phenotypic clustering; Risk stratification; Tetralogy of Fallot","angiotensin receptor antagonist; beta adrenergic receptor blocking agent; calcium channel blocking agent; digoxin; dipeptidyl carboxypeptidase inhibitor; diuretic agent; adolescent; adult; aged; angioplasty; Article; asthma; body mass; cardiovascular magnetic resonance; child; cluster analysis; cohort analysis; congenital heart disease; coronary stenting; demographics; DiGeorge syndrome; disease exacerbation; electrocardiogram; electrocardiography; Fallot tetralogy; female; follow up; heart left ventricle ejection fraction; heart rate; heart ventricle remodeling; human; inferior cava vein; machine learning; major clinical study; male; observational study; palliative therapy; patient care; persistent left superior vena cava; phenotypic clustering; prevalence; principal component analysis; pulmonary valve; pulmonary valve replacement; pulmonary valvuloplasty; retrospective study; scoliosis; unsupervised machine learning","","digoxin, 20830-75-5, 57285-89-9","1.5-T Siemens scanner, Siemens Medical Solutions, United States; Python version 3.11.3, Python Software Foundation; R Statistical Software version 4.1.1, R Foundation, Austria","Python Software Foundation; R Foundation, Austria; Siemens Medical Solutions, United States","","","Apitz C., Webb G.D., Redington A.N., Tetralogy of Fallot, Lancet (London, England), 374, pp. 1462-1471, (2009); Mai C.T., Isenburg J.L., Canfield M.A., Meyer R.E., Correa A., Alverson C.J., Et al., National population-based estimates for major birth defects, 2010-2014, Birth defects Res, 111, pp. 1420-1435, (2019); Smith C.A., McCracken C., Thomas A.S., Spector L.G., St Louis J.D., Oster M.E., Et al., Long-term outcomes of tetralogy of fallot: a study from the pediatric cardiac care consortium, JAMA Cardiol, 4, pp. 34-41, (2019); Stout K.K., Daniels C.J., Aboulhosn J.A., Bozkurt B., Broberg C.S., Colman J.M., Et al., 2018 AHA/ACC guideline for the management of adults with congenital heart disease: a report of the American college of cardiology/American heart association task force on clinical practice guidelines, Circulation, 139, pp. e698-e800, (2019); Baumgartner H., Backer J.D., Babu-Narayan S.V., Budts W., Chessa M., Diller G.-P., Et al., 2020 ESC Guidelines for the management of adult congenital heart disease: the Task Force for the management of adult congenital heart disease of the European Society of Cardiology (ESC). Endorsed by: association for European Paediatric and Congenital Card, Eur Heart J, 42, pp. 563-645, (2021); Hagdorn Q.A.J., Vos J.D.L., Beurskens N.E.G., Gorter T.M., Meyer S.L., Melle J.V., Et al., CMR feature tracking left ventricular strain-rate predicts ventricular tachyarrhythmia, but not deterioration of ventricular function in patients with repaired tetralogy of Fallot, Int J Cardiol, 295, pp. 1-6, (2019); Geva T., Mulder B., Gauvreau K., Babu-Narayan S.V., Wald R.M., Hickey K., Et al., Preoperative predictors of death and sustained ventricular tachycardia after pulmonary valve replacement in patients with repaired tetralogy of fallot enrolled in the INDICATOR cohort, Circulation, 138, pp. 2106-2115, (2018); Knauth A.L., Gauvreau K., Powell A.J., Landzberg M.J., Walsh E.P., Lock J.E., Et al., Ventricular size and function assessed by cardiac MRI predict major adverse clinical outcomes late after tetralogy of Fallot repair, Heart, 94, pp. 211-216, (2008); Jing L., Wehner G.J., Suever J.D., Charnigo R.J., Alhadad S., Stearns E., Et al., Left and right ventricular dyssynchrony and strains from cardiovascular magnetic resonance feature tracking do not predict deterioration of ventricular function in patients with repaired tetralogy of Fallot, J Cardiovasc Magn Reson Off J Soc Cardiovasc Magn Reson, 18, (2016); Atallah J., Gonzalez Corcia M.C., Walsh E.P., Ventricular arrhythmia and life-threatening events in patients with repaired tetralogy of fallot, Am J Cardiol, 132, pp. 126-132, (2020); Ghonim S., Gatzoulis M.A., Ernst S., Li W., Moon J.C., Smith G.C., Et al., Predicting survival in repaired tetralogy of fallot: a lesion-specific and personalized approach, JACC Cardiovasc Imaging, 15, pp. 257-268, (2022); Wald R.M., Valente A.M., Gauvreau K., Babu-Narayan S.V., Assenza G.E., Schreier J., Et al., Cardiac magnetic resonance markers of progressive RV dilation and dysfunction after tetralogy of Fallot repair, Heart, 101, pp. 1724-1730, (2015); Manlhiot C., Eynde J.V.V., Kutty S., Ross H.J., A primer on the present state and future prospects for machine learning and artificial intelligence applications in cardiology, Can J Cardiol, 38, pp. 169-184, (2022); Jacquemyn X., Kutty S., Manlhiot C., The lifelong impact of artificial intelligence and clinical prediction models on patients with Tetralogy of Fallot, CJC Pediat Cong Heart Dis, 2, pp. 440-452, (2023); Kawel-Boehm N., Hetzel S.J., Ambale-Venkatesh B., Captur G., Francois C.J., Jerosch-Herold M., Et al., Reference ranges (‘normal values’) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update, J Cardiovasc Magn Reson Off J Soc Cardiovasc Magn Reson, 22, (2020); Ringner M., What is principal component analysis?, Nat Biotechnol, 26, pp. 303-304, (2008); Celebi M.E., Kingravi H.A., Vela P.A., A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Syst Appl, 40, pp. 200-210, (2013); Arbelaitz O., Gurrutxaga I., Muguerza J., Perez J.M., Perona I., An extensive comparative study of cluster validity indices, Pattern Recogn, 46, pp. 243-256, (2013); Quer G., Arnaout R., Henne M., Arnaout R., Machine learning and the future of cardiovascular care: JACC state-of-the-art review, J Am Coll Cardiol, 77, pp. 300-313, (2021); Samad M.D., Wehner G.J., Arbabshirani M.R., Jing L., Powell A.J., Geva T., Et al., Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning, Eur Hear journal Cardiovasc Imaging, 19, pp. 730-738, (2018); Broberg C.S., Aboulhosn J., Mongeon F.-P., Kay J., Valente A.M., Khairy P., Et al., Prevalence of left ventricular systolic dysfunction in adults with repaired tetralogy of fallot, Am J Cardiol, 107, pp. 1215-1220, (2011); Andrade A.C., Jerosch-Herold M., Wegner P., Gabbert D.D., Voges I., Pham M., Et al., Determinants of left ventricular dysfunction and remodeling in patients with corrected tetralogy of fallot, J Am Heart Assoc, 8, (2019); Diller G.-P., Kempny A., Liodakis E., Alonso-Gonzalez R., Inuzuka R., Uebing A., Et al., Left ventricular longitudinal function predicts life-threatening ventricular arrhythmia and death in adults with repaired tetralogy of fallot, Circulation, 125, pp. 2440-2446, (2012); Valente A.M., Gauvreau K., Assenza G.E., Babu-Narayan S.V., Schreier J., Gatzoulis M.A., Et al., Contemporary predictors of death and sustained ventricular tachycardia in patients with repaired tetralogy of Fallot enrolled in the INDICATOR cohort, Heart, 100, pp. 247-253, (2014); Sullivan R.T., Frommelt P.C., Hill G.D., Earlier pulmonary valve replacement in down syndrome patients following tetralogy of fallot repair, Pediatr Cardiol, 38, pp. 1251-1256, (2017); Calcagni G., Calvieri C., Baban A., Bianco F., Barracano R., Caputo M., Et al., Syndromic and non-syndromic patients with repaired tetralogy of fallot: does it affect the long-term outcome?, J Clin Med, 11, (2022); Bokma J.P., Winter M.M., Oosterhof T., Vliegen H.W., Dijk A.V., Hazekamp M.G., Et al., Preoperative thresholds for mid-to-late haemodynamic and clinical outcomes after pulmonary valve replacement in tetralogy of Fallot, Eur Heart J, 37, pp. 829-835, (2016); Heng E.L., Gatzoulis M.A., Uebing A., Sethia B., Uemura H., Smith G.C., Et al., Immediate and midterm cardiac remodeling after surgical pulmonary valve replacement in adults with repaired tetralogy of fallot: a prospective cardiovascular magnetic resonance and clinical study, Circulation, 136, pp. 1703-1713, (2017); Bokma J.P., Geva T., Sleeper L.A., Babu Narayan S.V., Wald R., Hickey K., Et al., A propensity score-adjusted analysis of clinical outcomes after pulmonary valve replacement in tetralogy of Fallot, Heart, 104, pp. 738-744, (2018); Ouyang R., Leng S., Sun A., Wang Q., Hu L., Zhao X., Et al., Detection of persistent systolic and diastolic abnormalities in asymptomatic pediatric repaired tetralogy of Fallot patients with preserved ejection fraction: a CMR feature tracking study, Eur Radiol, 31, pp. 6156-6168, (2021)","C. Manlhiot; The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, 600 N. Wolfe Street, 1389 Blalock, 21287, United States; email: cmanlhi1@jhmi.edu","","Elsevier B.V.","","","","","","26666685","","","","English","Int. J. Cardiol. Congenit. Heart Dis.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85208516056"
"Zhan J.; Chen F.; Li Y.; Huang C.","Zhan, Jinshan (58108388400); Chen, Fangqi (57221313579); Li, Yanqiu (55719024800); Huang, Changzheng (25723346200)","58108388400; 57221313579; 55719024800; 25723346200","Risk prediction model for psoriatic arthritis: NHANES data and multi-algorithm approach","2025","Clinical Rheumatology","44","1","","277","289","12","0","10.1007/s10067-024-07244-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210149974&doi=10.1007%2fs10067-024-07244-4&partnerID=40&md5=d51d661b9114dde934a4e8775642cf54","Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Department of Dermatology, Hubei NO.3 People’s Hospital of Jiang Hang University, Hubei, Wuhan, China","Zhan J., Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Chen F., Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Li Y., Department of Dermatology, Hubei NO.3 People’s Hospital of Jiang Hang University, Hubei, Wuhan, China; Huang C., Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China","Objective: To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA). Results: The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737–0.826) for the training set and 0.780 (95% CI, 0.712–0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611–0.757) and 0.653 (95% CI, 0.535–0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets. Conclusion: The Borutamodel represents a promising tool for early risk assessment of PsA. (Table presented.) © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 2024.","Complications; Machine learning; Predictive models; Psoriasis; Psoriatic arthritis","Adult; Aged; Algorithms; Arthritis, Psoriatic; Female; Humans; Logistic Models; Male; Middle Aged; Nutrition Surveys; Psoriasis; Risk Assessment; Risk Factors; ROC Curve; United States; alanine aminotransferase; alkaline phosphatase; aspartate aminotransferase; bilirubin; calcium; cholesterol; creatinine; glucose; hemoglobin; high density lipoprotein; low density lipoprotein; phosphorus; triacylglycerol; uric acid; adult; age; alcohol consumption; Article; asthma; blood cell count; blood level; Boruta algorithm; cerebrovascular accident; chronic bronchitis; controlled study; data base; data processing; diabetes mellitus; educational status; fasting blood glucose level; female; heart infarction; human; hypertension; ischemic heart disease; laboratory test; least absolute shrinkage and selection operator; lifestyle; logistic regression analysis; major clinical study; male; malignant neoplasm; medical history; medical information; predictive model; psoriasis; psoriatic arthritis; random forest; receiver operating characteristic; risk assessment; smoking; stepwise regression; thyroid disease; aged; algorithm; diagnosis; epidemiology; middle aged; nutrition; procedures; risk factor; statistical model; United States","","alanine aminotransferase, 9000-86-6, 9014-30-6; alkaline phosphatase, 9001-78-9; aspartate aminotransferase, 9000-97-9; bilirubin, 18422-02-1, 635-65-4; calcium, 7440-70-2, 14092-94-5; cholesterol, 57-88-5; creatinine, 19230-81-0, 60-27-5; glucose, 50-99-7, 84778-64-3, 8027-56-3; hemoglobin, 9008-02-0; phosphorus, 7723-14-0; uric acid, 69-93-2","","","","","Michalek I.M., Loring B., John S.M., A systematic review of worldwide epidemiology of psoriasis, J Eur Acad Dermatol Venereol JEADV, 31, pp. 205-212, (2017); Ibrahim G., Waxman R., Helliwell P.S., The prevalence of psoriatic arthritis in people with psoriasis, Arthritis Rheum, 61, pp. 1373-1378, (2009); Kang Z., Zhang X., Du Y., Dai S.-M., Global and regional epidemiology of psoriatic arthritis in patients with psoriasis: A comprehensive systematic analysis and modelling study, J Autoimmun, 145, (2024); Mathew A.J., Chandran V., Depression in psoriatic arthritis: dimensional aspects and link with systemic inflammation, Rheumatol Ther, 7, pp. 287-300, (2020); Haroon M., Gallagher P., FitzGerald O., Diagnostic delay of more than 6 months contributes to poor radiographic and functional outcome in psoriatic arthritis, Ann Rheum Dis, 74, pp. 1045-1050, (2015); Coates L.C., Aslam T., Al Balushi F., Et al., Comparison of three screening tools to detect psoriatic arthritis in patients with psoriasis (CONTEST study), Br J Dermatol, 168, pp. 802-807, (2013); Tillett W., Charlton R., Nightingale A., Et al., Interval between onset of psoriasis and psoriatic arthritis comparing the UK Clinical Practice Research Datalink with a hospital-based cohort, Rheumatol Oxf Engl, 56, pp. 2109-2113, (2017); Zabotti A., Fagni F., Gossec L., Et al., Risk of developing psoriatic arthritis in psoriasis cohorts with arthralgia: exploring the subclinical psoriatic arthritis stage, RMD Open, 10, (2024); Ogdie A., Gelfand J.M., Clinical risk factors for the development of psoriatic arthritis among patients with psoriasis: a review of available evidence, Curr Rheumatol Rep, 17, (2015); Boehncke W.-H., Schon M.P., Psoriasis, Lancet Lond Engl, 386, pp. 983-994, (2015); Love T.J., Gudjonsson J.E., Valdimarsson H., Gudbjornsson B., Psoriatic arthritis and onycholysis – results from the cross-sectional Reykjavik psoriatic arthritis study, J Rheumatol, 39, pp. 1441-1444, (2012); Liu P., Kuang Y., Ye L., Et al., Predicting the risk of psoriatic arthritis in plaque psoriasis patients: development and assessment of a new predictive nomogram, Front Immunol, 12, (2021); Tey H.L., Ee H.L., Tan A.S.L., Et al., Risk factors associated with having psoriatic arthritis in patients with cutaneous psoriasis, J Dermatol, 37, pp. 426-430, (2010); Wilson F.C., Icen M., Crowson C.S., Et al., Incidence and clinical predictors of psoriatic arthritis in patients with psoriasis: a population-based study, Arthritis Rheum, 61, pp. 233-239, (2009); Ogdie A., Harrison R.W., McLean R.R., Et al., Prospective cohort study of psoriatic arthritis risk in patients with psoriasis in a real-world psoriasis registry, J Am Acad Dermatol, 87, pp. 1303-1311, (2022); Armstrong A.W., Read C., Pathophysiology, clinical presentation, and treatment of psoriasis: a review, JAMA, 323, pp. 1945-1960, (2020); ): A Step Towards Prevention - Pubmed.; Development of a Predictive Model for Screening Patients with Psoriasis at Increased Risk of Psoriatic Arthritis, (2024); Furue M., Kadono T., Inflammatory skin march” in atopic dermatitis and psoriasis, Inflamm Res Off J Eur Histamine Res Soc Al, 66, pp. 833-842, (2017); Husted J.A., Thavaneswaran A., Chandran V., Et al., Cardiovascular and other comorbidities in patients with psoriatic arthritis: a comparison with patients with psoriasis, Arthritis Care Res, 63, pp. 1729-1735, (2011); Xu J., Ou J., Li C., Et al., Multi-modality data-driven analysis of diagnosis and treatment of psoriatic arthritis, NPJ Digit Med, 6, (2023); Tinazzi I., McGonagle D., Aydin S.Z., Et al., Deep Koebner” phenomenon of the flexor tendon-associated accessory pulleys as a novel factor in tenosynovitis and dactylitis in psoriatic arthritis, Ann Rheum Dis, 77, pp. 922-925, (2018); Hsieh J., Kadavath S., Efthimiou P., Can traumatic injury trigger psoriatic arthritis? A review of the literature, Clin Rheumatol, 33, pp. 601-608, (2014); Eapi S., Chowdhury R., Lawal O.S., Et al., Etiological association between psoriasis and thyroid diseases, Cureus, 13, (2021); Wang C., Crapo L.M., The epidemiology of thyroid disease and implications for screening, Endocrinol Metab Clin North Am, 26, pp. 189-218, (1997); Gnanaraj P., Malligarjunan H., Dayalan H., Et al., Therapeutic efficacy and safety of propylthiouracil in psoriasis: an open-label study, Indian J Dermatol Venereol Leprol, 77, pp. 673-676, (2011); Kuchel J., Barakate M., Delbridge L., Et al., Short-term resolution of psoriasis after total thyroidectomy for euthyroid multinodular goitre, Australas J Dermatol, 43, pp. 214-217, (2002); Criswell L.A., Pfeiffer K.A., Lum R.F., Et al., Analysis of families in the multiple autoimmune disease genetics consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes, Am J Hum Genet, 76, pp. 561-571, (2005); Ruffilli I., Ragusa F., Benvenga S., Et al., Psoriasis, psoriatic arthritis, and thyroid autoimmunity, Front Endocrinol, 8, (2017); Antonelli A., Fallahi P., DelleSedie A., Et al., High values of alpha (CXCL10) and beta (CCL2) circulating chemokines in patients with psoriatic arthritis, in presence or absence of autoimmune thyroiditis, Autoimmunity, 41, pp. 537-542, (2008); Lee E.Y., Seo M., Juhnn Y.-S., Et al., Potential role and mechanism of IFN-gamma inducible protein-10 on receptor activator of nuclear factor kappa-B ligand (RANKL) expression in rheumatoid arthritis, Arthritis Res Ther, 13, (2011); Abji F., Pollock R.A., Liang K., Et al., Brief report: CXCL10 is a possible biomarker for the development of psoriatic arthritis among patients with psoriasis, Arthritis Rheumatol Hoboken NJ, 68, pp. 2911-2916, (2016); Frede N., Rieger E., Lorenzetti R., Et al., Respiratory tract infections and risk factors for infection in a cohort of 330 patients with axial spondyloarthritis or psoriatic arthritis, Front Immunol, 13, (2022); Nadeem A., Al-Harbi N.O., Ansari M.A., Et al., Psoriatic inflammation enhances allergic airway inflammation through IL-23/STAT3 signaling in a murine model, Biochem Pharmacol, 124, pp. 69-82, (2017); Damiani G., Radaeli A., Olivini A., Et al., Increased airway inflammation in patients with psoriasis, Br J Dermatol, 175, pp. 797-799, (2016); Scher J.U., Ogdie A., Merola J.F., Ritchlin C., Preventing psoriatic arthritis: focusing on patients with psoriasis at increased risk of transition, Nat Rev Rheumatol, 15, pp. 153-166, (2019); Li W., Han J., Qureshi A.A., Obesity and risk of incident psoriatic arthritis in US women, Ann Rheum Dis, 71, pp. 1267-1272, (2012); Love T.J., Zhu Y., Zhang Y., Et al., Obesity and the risk of psoriatic arthritis: a population-based study, Ann Rheum Dis, 71, pp. 1273-1277, (2012); Piche M.-E., Tchernof A., Despres J.-P., Obesity phenotypes, diabetes, and cardiovascular diseases, Circ Res, 126, pp. 1477-1500, (2020); Russolillo A., Iervolino S., Peluso R., Et al., Obesity and psoriatic arthritis: from pathogenesis to clinical outcome and management, Rheumatol Oxf Engl, 52, pp. 62-67, (2013); Agca R., Heslinga S.C., Rollefstad S., Et al., EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update, Ann Rheum Dis, 76, pp. 17-28, (2017); Haroon M., Gallagher P., Heffernan E., FitzGerald O., High prevalence of metabolic syndrome and of insulin resistance in psoriatic arthritis is associated with the severity of underlying disease, J Rheumatol, 41, pp. 1357-1365, (2014); Takeshita J., Grewal S., Langan S.M., Et al., Psoriasis and comorbid diseases: epidemiology, J Am Acad Dermatol, 76, pp. 377-390, (2017); Ahima R.S., Flier J.S., Adipose tissue as an endocrine organ, Trends Endocrinol Metab TEM, 11, pp. 327-332, (2000)","C. Huang; Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; email: hcz0501@126.com; Y. Li; Department of Dermatology, Hubei NO.3 People’s Hospital of Jiang Hang University, Wuhan, Hubei, China; email: 155212476@qq.com","","Springer Science and Business Media Deutschland GmbH","","","","","","07703198","","CLRHD","39585569","English","Clin. Rheumatol.","Article","Final","","Scopus","2-s2.0-85210149974"
"Shuzan M.N.I.; Chowdhury M.H.; Alam S.B.; Reaz M.B.I.; Khan M.S.; Murugappan M.; Chowdhury M.E.H.","Shuzan, Md Nazmul Islam (57211535233); Chowdhury, Moajjem Hossain (57206171550); Alam, Saadia Binte (55843585300); Reaz, Mamun Bin Ibne (6602752147); Khan, Muhammad Salman (59157592900); Murugappan, M. (25825367900); Chowdhury, Muhammad E. H. (8964151000)","57211535233; 57206171550; 55843585300; 6602752147; 59157592900; 25825367900; 8964151000","PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal","2024","Physical and Engineering Sciences in Medicine","47","4","","1705","1722","17","0","10.1007/s13246-024-01482-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204212664&doi=10.1007%2fs13246-024-01482-1&partnerID=40&md5=2e677c630431fb2eff79bb3a082bfede","Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia; Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar; Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh; Department of Electrical and Electronic Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh; Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha, 13133, Kuwait; Department of Electronics and Communication Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Tamilnadu, Chennai, 600117, India","Shuzan M.N.I., Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia, Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar; Chowdhury M.H., Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia, Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar; Alam S.B., Department of Computer Science and Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh; Reaz M.B.I., Department of Electrical and Electronic Engineering, Independent University, Bangladesh (IUB), Dhaka, 1229, Bangladesh; Khan M.S., Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar; Murugappan M., Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha, 13133, Kuwait, Department of Electronics and Communication Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Tamilnadu, Chennai, 600117, India; Chowdhury M.E.H., Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar","Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet’s performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates. © Australasian College of Physical Scientists and Engineers in Medicine 2024.","Convolutional neural network; Deep learning; Photoplethysmography (PPG); PPG2RepNet; Respiration rate; Respirational signal; Signal reconstruction","Deep Learning; Humans; Monitoring, Physiologic; Photoplethysmography; Respiration; Signal Processing, Computer-Assisted; Blood vessels; Deep neural networks; Image thinning; Noninvasive medical procedures; Pulmonary diseases; Signal to noise ratio; Sleep research; Convolutional neural network; Deep learning; Ground truth; Photoplethysmography; Ppg2repnet; Respiration rate; Respirational signal; Respiratory signals; Signal synthesis; Signals reconstruction; adolescent; adult; Article; artifact; artificial neural network; asthma; breathing rate; capnometry; child; convolutional neural network; correlation coefficient; deep learning; female; human; image segmentation; intensive care unit; kernel method; learning algorithm; machine learning; male; mean absolute error; normal human; outcome assessment; photoelectric plethysmography; root mean squared error; signal noise ratio; signal processing; sleep apnea syndromes; breathing; physiologic monitoring; Photoplethysmography","","","","","Qatar University, QU; Qatar National Library, QNL","This work was made possible by High Impact grant# QUHI-CENG-23/24\u2013216 from Qatar University. The statements made herein are solely the responsibility of the authors. The open-access publication cost is covered by the Qatar National Library.","Theerawit P., Sutherasan Y., Ball L., Pelosi P., Respiratory monitoring in adult intensive care unit, Expert Rev Respir Med, 11, 6, pp. 453-468, (2017); Boulding R., Stacey R., Niven R., Fowler S.J., Dysfunctional breathing: a review of the literature and proposal for classification, Eur Respiratory Rev, 25, 141, pp. 287-294, (2016); Bradley T.D., Floras J.S., Obstructive sleep apnoea and its cardiovascular consequences, Lancet, 373, 9657, pp. 82-93, (2009); Davies H.J., Mandic D.P., Rapid extraction of respiratory waveforms from photoplethysmography: a deep corr-encoder approach, Biomed Signal Process Control, 85, (2023); Charlton P.H., Bonnici T., Tarassenko L., Clifton D.A., Beale R., Watkinson P.J., An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram, Physiol Meas, 37, 4, (2016); Chowdhury M.H., Et al., Lightweight end-to-end deep learning solution for estimating the respiration rate from photoplethysmogram signal, Bioeng, 9, 10, (2022); Shuzan M.N.I., Et al., Machine learning-based respiration rate and blood oxygen saturation estimation using photoplethysmogram signals, Bioeng, 10, 2, (2023); Shuzan M.N.I., Et al., A novel non-invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model, IEEE Access, 9, pp. 96775-96790, (2021); Charlton P.H., Et al., Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review, IEEE Rev Biomed Eng, 11, pp. 2-20, (2017); Karlen W., Raman S., Ansermino J.M., Dumont G.A., Multiparameter respiratory rate estimation from the photoplethysmogram, IEEE Trans Biomed Eng, 60, 7, pp. 1946-1953, (2013); Shah S.A., Fleming S., Thompson M., Tarassenko L., Respiratory rate estimation during triage of children in hospitals, J Med Eng Technol, 39, 8, pp. 514-524, (2015); Zhang X., Ding Q., Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra fusion, Biomed Signal Process Control, 35, pp. 1-7, (2017); Pirhonen M., Peltokangas M., Vehkaoja A., Acquiring respiration rate from photoplethysmographic signal by recursive bayesian tracking of intrinsic modes in time-frequency spectra, Sensors, 18, 6, (2018); Prinable J.B., Jones P.W., Thamrin C., McEwan A., Using a recurrent neural network to derive tidal volume from a photoplethsmograph, IEEE Life Sciences Conference (LSC). IEEE, pp. 218-221, (2017); Lampier L.C., Coelho Y.L., Caldeira E.M.O., Bastos-Filho TF (2022) A deep learning approach to estimate the respiratory rate from photoplethysmogram, Ingenius, 27, pp. 96-104; Bian D., Mehta P., Selvaraj N., Respiratory rate estimation using PPG: A deep learning approach, 42Nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp. 5948-5952, (2020); Ravichandran V., Et al., RespNet: A deep learning model for extraction of respiration from photoplethysmogram, 41St Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 5556-5559, (2019); Aqajari S.A.H., Cao R., Zargari A.H.A., Rahmani A.M., An end-to-end and accurate ppg-based respiratory rate estimation approach using cycle generative adversarial networks, 43Rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp. 744-747, (2021); Roy B., Roy A., Chandra J.K., Gupta R., I-PRExT: Photoplethysmography derived respiration signal extraction and respiratory rate tracking using neural networks, IEEE Trans Instrum Meas, 70, pp. 1-9, (2021); Ary L.G., Et al., Physiobank physiotoolkit and physionet components of a new research resource for complex physiologic signals, Circulation, 101, 23, pp. e215-e220, (2000); Negi A., Raj A.N.J., Nersisson R., Et al., RDA-UNET-WGAN: an accurate breast ultrasound lesion segmentation using wasserstein generative adversarial networks, Arab J Sci Eng, 45, pp. 6399-6410, (2020); Lin T.-Y., Dollar P., Girshick R., He K., Hariharan B., Belongie S., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 2117-2125, (2017); Zhou Z., Rahman Siddiquee M.M., Tajbakhsh N., Liang J., Unet++: A Nested U-Net Architecture for Medical Image Segmentation In: Stoyanov, D., Et Al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support., pp. 3-11, (2018); Zhou Z., Siddiquee M.M.R., Tajbakhsh N., Liang N., Unet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans Med Imaging, 39, 6, pp. 1856-1867, (2020); Zhang Z., Liu Q., Wang Y., Road extraction by deep residual u-net, IEEE Geosci Remote Sens Lett, 15, 5, pp. 749-753, (2018); Rahman A., Et al., Fetal ECG extraction from maternal ECG using deeply supervised LinkNet + + model, Eng Appl Artif Intell, 123, (2023); Mahmud S., Hossain M.S., Chowdhury M.E., Reaz M.B.I., MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multilayer multi-resolution spatially pooled 1D signal reconstruction network, Neural Comput Appl, 35, 11, pp. 8371-8388, (2023); Ibtehaz N., Et al., PPG2ABP: translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms, Bioengineering, 9, 11, (2022); Mahmud S., Et al., NABNet: a nested attention-guided BiConvLSTM network for a robust prediction of blood pressure components from reconstructed arterial blood pressure waveforms using PPG and ECG signals, Biomed Signal Process Control, 79, (2023); Meyes R., Lu M., de Puiseau C.W., Meisen T., Ablation Studies in Artificial Neural Networks, (2019); Motin M.A., Karmakar C.K., Palaniswami M., Ensemble empirical mode decomposition with principal component analysis: A novel approach for extracting respiratory rate and heart rate from photoplethysmographic signal, IEEE J Biomedical Health Inf, 22, 3, pp. 766-774, (2017)","M.E.H. Chowdhury; Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar; email: mchowdhury@qu.edu.qa; M. Murugappan; Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Doha, Block 4, 13133, Kuwait; email: m.murugappan@kcst.edu.kw","","Springer Science and Business Media Deutschland GmbH","","","","","","26624729","","","39287773","English","Phys. Eng. Sci. Med.","Article","Final","","Scopus","2-s2.0-85204212664"
"Cai N.; Xie Y.; Cai Z.; Liang Y.; Zhou Y.; Wang P.","Cai, Nian (55620105400); Xie, Yiying (59296512300); Cai, Zijie (59426642400); Liang, Yuchen (59296637900); Zhou, Yinghong (58549438200); Wang, Ping (56510524500)","55620105400; 59296512300; 59426642400; 59296637900; 58549438200; 56510524500","Deep Learning Assisted Diagnosis of Chronic Obstructive Pulmonary Disease Based on a Local-to-Global Framework","2024","Electronics (Switzerland)","13","22","4443","","","","0","10.3390/electronics13224443","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210277513&doi=10.3390%2felectronics13224443&partnerID=40&md5=a568de98d5106ede7e550bdc8648ca0f","School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China","Cai N., School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Xie Y., School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Cai Z., School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Liang Y., School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Zhou Y., School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; Wang P., Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China","To aid the diagnosis of chronic obstructive pulmonary disease (COPD), a local-to-global deep framework with group attentions and slice-aware loss is designed in this paper, which utilizes the chest CT sequences of the patients as the network input. To fully mine the medical hints submerged in the CT slices, two types of group attentions are designed to extract local–global features of the grouped slices. Specifically, in each group, a group local attention block (GLAB) and a group global attention block (GGAB) are designed to extract local features in the CT slices and long-range dependencies among the grouped slices. To alleviate the influence of different numbers of CT slices in the chest CT sequences for different patients, a slice-aware loss is proposed by incorporating a normalized coefficient into the cross-entropy loss. Experimental results indicate that the designed deep model performs a good COPD identification on a real COPD dataset with 96.08% accuracy, 94.12% sensitivity, 97.06% specificity, and 95.32% AUC, which is superior to some existing deep learning methods. © 2024 by the authors.","chest CT sequence; chronic obstructive pulmonary disease; group global attention; group local attention; slice-aware loss","","","","","","Guangzhou Municipal Science and Technology Program key projects, (202102010251, 2024A03J1156); Guangzhou Municipal Science and Technology Program key projects","This research was funded by the grants from the Guangzhou Science and Technology Program, grant number Nos. 202102010251 and 2024A03J1156.","Bagdonas E., Raudoniute J., Bruzauskaite I., Aldonyte R., Novel aspects of pathogenesis and regeneration mechanisms in COPD, Int. J. Chronic Obstr. Pulm. Dis, 10, pp. 995-1013, (2015); Ko F.W., Chan K.P., Hui D.S., Goddard J.R., Shaw J.G., Reid D.W., Yang I.A., Acute exacerbation of COPD, Respirology, 21, pp. 1152-1165, (2016); Poh T.Y., Mac Aogain M., Chan A.K., Yii A.C., Yong V.F., Tiew P.Y., Koh M.S., Chotirmall S.H., Understanding COPD-overlap syndromes, Expert Rev. Respir. Med, 11, pp. 285-298, (2017); Wang Q., Liu S., The effects and pathogenesis of PM2. 5 and its components on chronic obstructive pulmonary disease, Int. J. Chronic Obstr. Pulm. Dis, 18, pp. 493-506, (2023); Negewo N.A., Gibson P.G., McDonald V.M., COPD and its comorbidities: Impact, measurement and mechanisms, Respirology, 20, pp. 1160-1171, (2015); Lozano R., Naghavi M., Foreman K., Lim S., Shibuya K., Aboyans V., Abraham J., Adair T., Aggarwal R., Ahn S.Y., Et al., Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: A systematic analysis for the Global Burden of Disease Study 2010, Lancet, 380, pp. 2095-2128, (2012); Yin P., Wu J., Wang L., Luo C., Ouyang L., Tang X., Liu J., Liu Y., Qi J., Zhou M., Et al., The burden of COPD in China and its provinces: Findings from the global burden of disease study 2019, Front. Public Health, 10, (2022); Singhvi D., Bon J., CT imaging and comorbidities in COPD: Beyond lung cancer screening, Chest, 159, pp. 147-153, (2021); Budoff M.J., Nasir K., Kinney G.L., Hokanson J.E., Barr R.G., Steiner R., Nath H., Lopez-Garcia C., Black-Shinn J., Casaburi R., Coronary artery and thoracic calcium on noncontrast thoracic CT scans: Comparison of ungated and gated examinations in patients from the COPDGene cohort, J. Cardiovasc. Comput. Tomogr, 5, pp. 113-118, (2011); Lynch D.A., Austin J.H., Hogg J.C., Grenier P.A., Kauczor H.U., Bankier A.A., Barr R.G., Colby T.V., Galvin J.R., Gevenois P.A., Et al., CT-definable subtypes of chronic obstructive pulmonary disease: A statement of the Fleischner Society, Radiology, 277, pp. 192-205, (2015); Lynch D.A., Moore C.M., Wilson C., Nevrekar D., Jennermann T., Humphries S.M., Austin J.H.M., Grenier P.A., Kauczor H.U., Han M.K., Et al., CT-based visual classification of emphysema: Association with mortality in the COPDGene study, Radiology, 288, pp. 859-866, (2018); Shen D., Wu G., Suk H.I., Deep learning in medical image analysis, Annu. Rev. Biomed. Eng, 19, pp. 221-248, (2017); Ramadoss R., Vimala C., Classification of Pulmonary Emphysema using Deep Learning, Proceedings of the 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC); Parui S., Parbat D., Chakraborty M., A deep learning paradigm for computer aided diagnosis of emphysema from lung HRCT images, Proceedings of the 2022 International Conference on Computing in Engineering & Technology (ICCET); Polat O., Salk I., Dogan O.T., Determination of COPD severity from chest CT images using deep transfer learning network, Multimed. Tools Appl, 81, pp. 21903-21917, (2022); Du R., Qi S., Feng J., Xia S., Kang Y., Qian W., Yao Y., Identification of COPD from multi-view snapshots of 3D lung airway tree via deep CNN, IEEE Access, 8, pp. 38907-38919, (2020); Wu Y., Du R., Feng J., Qi S., Pang H., Xia S., Qian W., Deep CNN for COPD identification by Multi-View snapshot integration of 3D airway tree and lung field, Biomed. Signal Process. Control, 79, (2023); Ho T.T., Kim T., Kim W.J., Lee C.H., Chae K.J., Bak S.H., Kwon S., Jin G., Park E., Choi S., Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci. Rep, 11, (2021); Ahmed J., Vesal S., Durlak F., Kaergel R., Ravikumar N., Remy-Jardin M., Maier A., COPD classification in CT images using a 3D convolutional neural network, Proceedings of the Bildverarbeitung für die Medizin 2020: Algorithmen–Systeme–Anwendungen; Xue M., Jia S., Chen L., Huang H., Yu L., Zhu W., CT-based COPD identification using multiple instance learning with two-stage attention, Comput. Methods Programs Biomed, 230, (2023); Xu C., Qi S., Feng J., Xia S., Kang Y., Yao Y., Qian W., DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images, Phys. Med. Biol, 65, (2020); Humphries S.M., Notary A.M., Centeno J.P., Strand M.J., Crapo J.D., Silverman E.K., Lynch D.A., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, pp. 434-444, (2020); Liu L., Li Y., Wu Y., Ren L., Wang G., LGI Net: Enhancing local-global information interaction for medical image segmentation, Comput. Biol. Med, 167, (2023); Zhou P., Shi W., Tian J., Qi B., Li B., Hao H., Xu B., Attention-based bidirectional long short-term memory networks for relation classification, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics; Ma C., Gu Y., Wang Z., TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation, J. Imaging Inform. Med, 1, pp. 1-13, (2024); Liu Z., Mao H., Wu C.Y., Feichtenhofer C., Darrell T., Xie S., A convnet for the 2020s, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Li J., Xia X., Li W., Li H., Wang X., Xiao X., Wang X., Xiao X., Wang R., Zhen M., Next-vit: Next generation vision transformer for efficient deployment in realistic industrial scenarios, arXiv, (2022); Shah V., Keniya R., Shridharani A., Punjabi M., Shah J., Mehendale N., Diagnosis of COVID-19 using CT scan images and deep learning techniques, Emerg. Radiol, 28, pp. 497-505, (2021); Kollias D., Arsenos A., Soukissian L., Kollias S., MIA-COV19D: COVID-19 detection through 3-D chest CT image analysis, Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV); Varchagall M., Nethravathi N., Chandramma R., Nagashree N., Athreya S.M., Using deep learning techniques to evaluate lung cancer using CT images, SN Comput. Sci, 4, (2023); Kienzle D., Lorenz J., Schon R., Ludwig K., Lienhart R., COVID detection and severity prediction with 3D-ConvNeXt and custom pretrainings, Proceedings of the 17th European Conference on Computer Vision (ECCV); Xie W., Jacobs C., Charbonnier J.P., Slebos D.J., van Ginneken B., Emphysema subtyping on thoracic computed tomography scans using deep neural networks, Sci. Rep, 13, (2023); Geng K., Shi Z., Zhao X., Wang J., Leader J., Pu J., BeyondCT: A deep learning model for predicting pulmonary function from chest CT scans, arXiv, (2024)","Y. Zhou; School of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, China; email: zhouyh@gdut.edu.cn","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","","Scopus","2-s2.0-85210277513"
"Lee A.N.; Hsiao A.; Hasenstab K.A.","Lee, Amanda N. (57940471900); Hsiao, Albert (53263994800); Hasenstab, Kyle A. (56462838200)","57940471900; 53263994800; 56462838200","Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning","2024","Radiology: Cardiothoracic Imaging","6","6","e240005","","","","0","10.1148/ryct.240005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213718904&doi=10.1148%2fryct.240005&partnerID=40&md5=8d47c6751466624fcf297d2cf09bd4a6","Computational Science Research Center, San Diego State University, San Diego, CA, United States; Department of Radiology, University of California San Diego, La Jolla, CA, United States; Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr, San Diego, 92182, CA, United States","Lee A.N., Computational Science Research Center, San Diego State University, San Diego, CA, United States; Hsiao A., Department of Radiology, University of California San Diego, La Jolla, CA, United States; Hasenstab K.A., Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr, San Diego, 92182, CA, United States","Purpose: To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)–based chronic obstructive pulmonary disease (COPD) staging. Materials and Methods: This retrospective study included inspiratory and expiratory lung CT images and spirometry measurements acquired between November 2007 and April 2011 from 8893 participants (mean age, 59.6 years ± 9.0 [SD]; 53.3% [4738 of 8893] male) in the COPDGene phase I cohort (ClinicalTrials.gov: NCT00608764). CNNs were trained to predict spirometry measurements (forced expiratory volume in 1 second [FEV1 ], FEV1 percent predicted, and ratio of FEV1 to forced vital capacity [FEV1 /FVC]) using clinical data and either single-phase or multiphase CT. Spirometry predictions were then used to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage. Agreement between CNN-predicted and reference standard spirometry measurements and GOLD stage was assessed using intraclass correlation coefficient (ICC) and compared using bootstrapping. Accuracy for predicting GOLD stage, within-one GOLD stage, and GOLD 0 versus 1–4 was calculated. Results: CNN-predicted and reference standard spirometry measurements showed moderate to good agreement (ICC, 0.66–0.79), which improved by inclusion of clinical data (ICC, 0.70–0.85; P ≤ .04), except for FEV1 /FVC in the inspiratory-phase CNN model with clinical data (P = .35) and FEV1 in the expiratory-phase CNN model with clinical data (P = .33). Single-phase CNN accuracies for GOLD stage, within-one stage, and diagnosis ranged from 59.8% to 84.1% (682–959 of 1140), with moderate to good agreement (ICC, 0.68–0.70). Accuracies of CNN models using inspiratory and expiratory images ranged from 60.0% to 86.3% (684–984 of 1140), with moderate to good agreement (ICC, 0.72). Inclusion of clinical data improved agreement and accuracy for both the single-phase CNNs (ICC, 0.72; P ≤ .001; accuracy, 65.2%–85.8% [743–978 of 1140]) and inspiratory-expiratory CNNs (ICC, 0.77–0.78; P ≤ .001; accuracy, 67.6%–88.0% [771–1003 of 1140]), except expiratory CNN with clinical data (no change in GOLD stage ICC; P = .08). Conclusion: CNN-based COPD diagnosis and staging using single-phase CT provides comparable accuracy with inspiratory-expiratory CT when provided clinical data relevant to staging. © RSNA, 2024.","Attention Map; Chronic Obstructive Pulmonary Disease; Convolutional Neural Network; CT; Severity Staging","adult; aged; Article; chronic obstructive lung disease; computer assisted tomography; controlled study; convolutional neural network; deep learning; diagnostic accuracy; disease severity; female; FEV1 FVC ratio; forced expiratory volume; forced vital capacity; Global Initiative for Chronic Obstructive Lung Disease stage; human; major clinical study; male; respiratory tract disease assessment; respiratory tract parameters; retrospective study; sensitivity and specificity; spirometry; staging","","","","","","","Chronic Obstructive Pulmonary Disease (COPD); Lin CH, Cheng SL, Chen CZ, Chen CH, Lin SH, Wang HC., Current Progress of COPD Early Detection: Key Points and Novel Strategies, Int J Chron Obstruct Pulmon Dis, 18, pp. 1511-1524, (2023); Kim WD., Phenotype of Chronic Obstructive Pulmonary Disease Based on Computed Tomography-Defined Underlying Pathology, Tuberc Respir Dis (Seoul), 85, 4, pp. 302-312, (2022); Wilgus ML, Abtin F, Markovic D, Et al., Panlobular emphysema is associated with COPD disease severity: A study of emphysema subtype by computed tomography, Respir Med, 192, (2022); Li Z, Liu L, Zhang Z, Et al., A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD, Acad Radiol, 29, 5, pp. 663-673, (2022); Hasenstab KA, Yuan N, Retson T, Et al., Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression andMortality with a Deep Learning Convolutional Neural Network, Radiol Cardiothorac Imaging, 3, 2, (2021); Ho TT, Kim T, Kim WJ, Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci Rep, 11, 1, (2021); Hasenstab KA, Tabalon J, Yuan N, Retson T, Hsiao A., CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT, Radiol Artif Intell, 4, 1, (2021); Hersh CP, Washko GR, Estepar RS, Et al., Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD, Respir Res, 14, 1, (2013); Gaeta M, Minutoli F, Girbino G, Et al., Expiratory CT scan in patients with normal inspiratory CT scan: a finding of obliterative bronchiolitis and other causes of bronchiolar obstruction, Multidiscip Respir Med, 8, 1, (2013); Salvatore M, Azour L, O'Connor M, Capaccione K, Mendelson D, Expiratory CT., What is Good Enough?, Int J Radiol Imaging Technol, 6, 2, (2020); Lynch DA, Al-Qaisi MA., Quantitative computed tomography in chronic obstructive pulmonary disease, J Thorac Imaging, 28, 5, pp. 284-290, (2013); Schroeder JD, McKenzie AS, Zach JA, Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease, AJR Am J Roentgenol, 201, 3, pp. W460-W470, (2013); Regan EA, Hokanson JE, Murphy JR, Et al., Genetic epidemiology of COPD (COPDGene) study design, COPD, 7, 1, pp. 32-43, (2010); Study Protocol: Genetic Epidemiology of Chronic Obstructive Pulmonary Disease; Adibi A, Sadatsafavi M., Looking at the COPD spectrum through “PRISm”, Eur Respir J, 55, 1, (2020); Chollet FC, Et al., Keras, (2015); Wang F, Jiang M, Qian C, Et al., Residual attention network for image classification, pp. 3156-3164; R: A Language and Environment for Statistical Computing, (2021); Hallgren KA., Computing Inter-Rater Reliability for Observational Data: An Overview and Tutorial, Tutor Quant Methods Psychol, 8, 1, pp. 23-34, (2012); Koo TK, Li MY., A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research, J Chiropr Med, 15, 2, pp. 155-163, (2016); Song L, Leppig JA, Hubner RH, Et al., Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function?, Int J Chron Obstruct Pulmon Dis, 15, pp. 1877-1886, (2020); Matsuoka S, Kurihara Y, Yagihashi K, Hoshino M, Nakajima Y., Airway dimensions at inspiratory and expiratory multisection CT in chronic obstructive pulmonary disease: correlation with airflow limitation, Radiology, 248, 3, pp. 1042-1049, (2008); Gawlitza J, Henzler T, Trinkmann F, Nekolla E, Haubenreisser H, Brix G., COPD Imaging on a 3rd Generation Dual-Source CT: Acquisition of Paired Inspiratory-Expiratory Chest Scans at an Overall Reduced Radiation Risk, Diagnostics (Basel), 10, 12, (2020); Hong JY, Han K, Jung JH, Kim JS., Association of Exposure to Diagnostic Low-Dose Ionizing Radiation With Risk of Cancer Among Youths in South Korea, JAMA Netw Open, 2, 9, (2019); Lee KH, Lee S, Park JH, Et al., Risk of Hematologic Malignant Neoplasms From Abdominopelvic Computed Tomographic Radiation in Patients Who Underwent Appendectomy, JAMA Surg, 156, 4, pp. 343-351, (2021); Bos D, Guberina N, Zensen S, Opitz M, Forsting M, Wetter A., Radiation Exposure inComputedTomography, DtschArztebl Int, 120, 9, pp. 135-141, (2023); Joyce S, O'Connor OJ, Maher MM, McEnteeMF. Strategies for dose reduction with specific clinical indications during computed tomography, Radiography, 26, pp. S62-S68, (2020); Cao X, Gao X, Yu N, Et al., Potential Value of Expiratory CT in Quantitative Assessment of Pulmonary Vessels in COPD, Front Med (Lausanne), 8, (2021); Gawlitza J, Trinkmann F, Scheffel H, Et al., Time to Exhale: Additional Value of Expiratory Chest CT in Chronic Obstructive Pulmonary Disease, Can Respir J, 2018, (2018); Charbonnier J-P, Pompe E, Moore C, Et al., Airway wall thickening on CT: Relation to smoking status and severity of COPD, Respir Med, 146, pp. 36-41, (2019); Wijnant SRA, De Roos E, Kavousi M, Et al., Trajectory and mortality of preserved ratio impaired spirometry: the Rotterdam Study, Eur Respir J, 55, 1, (2020); Du R, Qi S, Feng J, Et al., Identification of COPD FromMulti-View Snapshots of 3D Lung Airway Tree via Deep CNN, IEEE Access, 8, pp. 38907-38919, (2020); Xue M, Jia S, Chen L, Huang H, Yu L, Zhu W., CT-based COPD identification using multiple instance learning with two-stage attention, Comput Methods Programs Biomed, 230, (2023); Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, Et al., Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography, Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018)","K.A. Hasenstab; Department of Mathematics and Statistics, San Diego State University, San Diego, 5500 Campanile Dr, 92182, United States; email: kylehasenstab@gmail.com","","Radiological Society of North America Inc.","","","","","","26386135","","","","English","Radiol. Cardiothorac. Imaging.","Article","Final","","Scopus","2-s2.0-85213718904"
"Rogerson C.; Nelson Sanchez-Pinto L.; Gaston B.; Wiehe S.; Schleyer T.; Tu W.; Mendonca E.","Rogerson, Colin (57218300643); Nelson Sanchez-Pinto, L. (37038404800); Gaston, Benjamin (7005681408); Wiehe, Sarah (8607741000); Schleyer, Titus (55793336900); Tu, Wanzhu (7006479265); Mendonca, Eneida (7004308859)","57218300643; 37038404800; 7005681408; 8607741000; 55793336900; 7006479265; 7004308859","Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning","2024","Pediatric Pulmonology","59","12","","3313","3321","8","0","10.1002/ppul.27197","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199970692&doi=10.1002%2fppul.27197&partnerID=40&md5=73d2374e706a68f7bbe2843dab3a6453","Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States; Regenstrief Institute Center for Biomedical Informatics, Indianapolis, IN, United States; Anne & Robert H. Lurie Children's Hospital of Chicago, Northwestern University, Chicago, IL, United States; Regenstrief Institute Center for Health Services Research, Indianapolis, IN, United States; Department of Biostatistics, Indiana University, Indianapolis, IN, United States; Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, United States","Rogerson C., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States, Regenstrief Institute Center for Biomedical Informatics, Indianapolis, IN, United States; Nelson Sanchez-Pinto L., Anne & Robert H. Lurie Children's Hospital of Chicago, Northwestern University, Chicago, IL, United States; Gaston B., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States; Wiehe S., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States, Regenstrief Institute Center for Health Services Research, Indianapolis, IN, United States; Schleyer T., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States, Regenstrief Institute Center for Biomedical Informatics, Indianapolis, IN, United States; Tu W., Department of Biostatistics, Indiana University, Indianapolis, IN, United States; Mendonca E., Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, United States, Cincinnati Children's Hospital and Medical Center, Cincinnati, OH, United States","Rationale: More targeted management of severe acute pediatric asthma could improve clinical outcomes. Objectives: To identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization. Methods: We conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2–18 years admitted to the hospital for asthma were included. We used consensus k means clustering with patient demographics, vital signs, diagnostics, and laboratory data obtained in the first 12 h of hospitalization. Measurements and Main Results: The study population included 683 encounters divided into derivation (80%) and validation (20%) sets, and two distinct clusters were identified. Compared to Cluster 1 in the derivation set, Cluster 2 encounters (177 [32%]) were older (11 years [8; 14] vs. 5 years [3; 8]; p <.01) and more commonly males (63% vs. 53%; p =.03) of Black race (51% vs. 40%; p =.03) with non-Hispanic ethnicity (96% vs. 84%; p <.01). Cluster 2 encounters had smaller improvements in vital signs at 12-h including percent change in heart rate (−1.7 [−11.7; 12.7] vs. −7.8 [−18.5; 1.7]; p <.01), and respiratory rate (0.0 [−20.0; 22.2] vs. −11.4 [−27.3; 9.0]; p <.01). Encounters in Cluster 2 had lower percentages of neutrophils (70.0 [55.0; 83.0] vs. 85.0 [77.0; 90.0]; p <.01) and higher percentages of lymphocytes (17.0 [8.0; 32.0] vs. 9.0 [5.3; 14.0]; p <.01). Cluster 2 encounters had higher rates of invasive mechanical ventilation (23% vs. 5%; p <.01), longer hospital length of stay (4.5 [2.6; 8.8] vs. 2.9 [2.0; 4.3]; p <.01), and a higher mortality rate (7.3% vs. 0.0%; p <.01). The predicted cluster assignments in the validation set shared the same ratio (~2:1), and many of the same characteristics. Conclusions: We identified two clinical phenotypes of severe acute pediatric asthma which exhibited distinct clinical features and outcomes. © 2024 The Author(s). Pediatric Pulmonology published by Wiley Periodicals LLC.","asthma; informatics; machine learning; pediatrics","aminophylline; dexamethasone; methylprednisolone; steroid; Article; asthma; chi square test; child; clinical feature; clinical outcome; cohort analysis; electronic health record; ethnicity; female; fever; Fisher exact test; heart rate; Hispanic; hospitalization; human; intensive care unit; invasive ventilation; k means clustering; length of stay; leukocyte count; machine learning; male; mortality; mortality rate; pediatric intensive care unit; personalized medicine; phenotype; predictive value; rank sum test; retrospective study; tracheostomy; unsupervised machine learning; vital sign","","aminophylline, 317-34-0; dexamethasone, 50-02-2; methylprednisolone, 6923-42-8, 83-43-2","","","","","Rogerson C.M., Hogan A.H., Waldo B., White B.R., Carroll C.L., Shein S.L., Wide institutional variability in the treatment of pediatric critical asthma: a multicenter retrospective study, Pediatr Crit Care Med, 25, 1, pp. 37-46, (2024); Shah R., Saltoun C.A., Chapter 14: acute severe asthma (status asthmaticus), Allergy Asthma Proc, 33, pp. 47-50, (2012); Carroll C.L., Sala K.A., Pediatric status asthmaticus, Crit Care Clin, 29, 2, pp. 153-166, (2013); Avery C., Perrin E.M., Lang J.E., Updates to the pediatrics asthma management guidelines, JAMA Pediatr, 175, 9, pp. 966-967, (2021); Rogerson C.M., White B.R., Smith M., Et al., Institutional variability in respiratory support use for pediatric critical asthma: a multicenter retrospective study, Ann Am Thorac Soc, 21, 4, pp. 612-619, (2024); Russi B.W., Roberts A.R., Nievas I.F., Rogerson C.M., Morrison J.M., Sochet A.A., Noninvasive respiratory support for pediatric critical asthma: a multicenter cohort study, Respir Care, 69, 5, pp. 534-540, (2024); Moore W.C., Meyers D.A., Wenzel S.E., Et al., Identification of asthma phenotypes using cluster analysis in the severe asthma research program, Am J Respir Crit Care Med, 181, 4, pp. 315-323, (2010); Silkoff P.E., Moore W.C., Sterk P.J., Three major efforts to phenotype asthma: severe asthma research program, asthma disease endotyping for personalized therapeutics, and unbiased biomarkers for the prediction of respiratory disease outcome, Clin Chest Med, 40, 1, pp. 13-28, (2019); Gans M.D., Gavrilova T., Understanding the immunology of asthma: pathophysiology, biomarkers, and treatments for asthma endotypes, Paediatr Respir Rev, 36, pp. 118-127, (2020); Kuruvilla M.E., Lee F.E.H., Lee G.B., Understanding asthma phenotypes, endotypes, and mechanisms of disease, Clin Rev Allergy Immunol, 56, 2, pp. 219-233, (2019); Akar-Ghibril N., Casale T., Custovic A., Phipatanakul W., Allergic endotypes and phenotypes of asthma, J Allergy Clin Immunol Prac, 8, 2, pp. 429-440, (2020); von Elm E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P., The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies, Lancet, 370, 9596, pp. 1453-1457, (2007); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement, BMJ, 350, (2015); Maue D.K., Krupp N., Rowan C.M., Pediatric asthma severity score is associated with critical care interventions, World J Clin Pediatr, 6, 1, pp. 34-39, (2017); Okada S., Ohzeki M., Taguchi S., Efficient partition of integer optimization problems with one-hot encoding, Sci Rep, 9, 1, (2019); Zurca A.D., Suttle M.L., October T.W., An antiracism approach to conducting, reporting, and evaluating pediatric critical care research, Pediatr Crit Care Med, 23, 2, pp. 129-132, (2022); Urquhart A., Clarke P., US racial/ethnic disparities in childhood asthma emergent health care use: national health interview survey, 2013-2015, J Asthma, 57, 5, pp. 510-520, (2020); Hughes H.K., Matsui E.C., Tschudy M.M., Pollack C.E., Keet C.A., Pediatric asthma health disparities: race, hardship, housing, and asthma in a national survey, Acad Pediatr, 17, 2, pp. 127-134, (2017); Sammouda R., El-Zaart A., An optimized approach for prostate image segmentation using K-Means clustering algorithm with elbow method, Comput Intell Neurosci, 2021, (2021); (2022); guidelines for the diagnosis and management of asthma-summary report 2007, J Allergy Clin Immunol, 120, 5, pp. S94-S138, (2007); Grunwell J.R., Stephenson S.T., Tirouvanziam R., Brown L.A.S., Brown M.R., Fitzpatrick A.M., Children with neutrophil-predominant severe asthma have proinflammatory neutrophils with enhanced survival and impaired clearance, J Allergy Clin Immunol Prac, 7, 2, pp. 516-525.e6, (2019); Cottrill K.A., Rad M.G., Ripple M.J., Et al., Cluster analysis of plasma cytokines identifies two unique endotypes of children with asthma in the pediatric intensive care unit, Sci Rep, 13, 1, (2023); Fainardi V., Esposito S., Chetta A., Pisi G., Asthma phenotypes and endotypes in childhood, Minerva Med, 113, 1, pp. 94-105, (2022); Silkoff P.E., Strambu I., Laviolette M., Et al., Asthma characteristics and biomarkers from the airways disease endotyping for personalized therapeutics (ADEPT) longitudinal profiling study, Respir Res, 16, (2015); Konig I.R., Fuchs O., Hansen G., von Mutius E., Kopp M.V., What is precision medicine?, Eur Respir J, 50, 4, (2017); Stanski N.L., Wong H.R., Prognostic and predictive enrichment in sepsis, Nat Rev Nephrol, 16, 1, pp. 20-31, (2020)","C. Rogerson; MD, MPH, Department of Pediatrics, Division of Pediatric Critical Care Medicine, Riley Hospital for Children at Indiana University Health, Indianapolis, 705 Riley Hospital Dr, 46202, United States; email: crogerso@iu.edu","","John Wiley and Sons Inc","","","","","","87556863","","PEPUE","","English","Pediatr. Pulmonol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85199970692"
"Han T.-T.; Le Trung K.; Nguyen Anh P.; Nguyen Huu P.","Han, Trong-Thanh (57560466100); Le Trung, Kien (59515554600); Nguyen Anh, Phuong (58974022100); Nguyen Huu, Phat (59454927300)","57560466100; 59515554600; 58974022100; 59454927300","High performance method for COPD features extraction using complex network","2024","Biomedical Physics and Engineering Express","10","6","065045","","","","0","10.1088/2057-1976/ad8093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207594869&doi=10.1088%2f2057-1976%2fad8093&partnerID=40&md5=92e4a4557243f957192fa8615f42192f","School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam","Han T.-T., School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; Le Trung K., School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; Nguyen Anh P., School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; Nguyen Huu P., School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam","Objectives. The paper proposes a novel methodology for the classification of Chronic Obstructive Pulmonary Disease (COPD) utilizing respiratory sound attributes. Methods. The approach involves segmenting respiratory sounds into individual breaths and conducting extensive studies on this dataset. Spectral Transforms, various Wavelet Transforms are applied to capture distinct signal features. Complex Network is also employed to extract characteristic elements, generating novel representations of spectrogram data based on graph factors, including entropy, density, and position. The normalized and enriched data is then used to develop COPD classifiers using six machine learning algorithms, fine-tuning with appropriate training details and hyperparameter tuning. Results. Our results demonstrate robust performance, with ROC curves consistently exhibiting an Area Under the Curve (AUC) > 96% across different time-frequency transformations. Notably, the Random Forest algorithm achieves an AUC of 99.67%, outperforming other algorithms. Moreover, the Wavelet Daubechies 2 (Db2) consistently approaches 98% accuracy, particularly noteworthy in conjunction with the Naive Bayes algorithm. Conclusion. This study diagnosis patients through spectrogram images extracted from lung sounds. The application of Inverse Transforms, Complex Network, and Optimized Classification Algorithms yielded results beyond expectations. This methodology provides a promising approach for accurate COPD diagnosis, leveraging Machine Learning techniques applied to respiratory sound analysis. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","complex network; COPD; inverse transforms; machine learning","Algorithms; Area Under Curve; Bayes Theorem; Female; Humans; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Respiratory Sounds; ROC Curve; Signal Processing, Computer-Assisted; Sound Spectrography; Wavelet Analysis; Decision trees; Image coding; Image segmentation; Inverse problems; Inverse transforms; Lung cancer; Pulmonary diseases; Random forests; Wavelet transforms; Areas under the curves; Chronic obstructive pulmonary disease; Diseases features; Features extraction; High-performance methods; Machine-learning; Novel methodology; Respiratory sounds; Spectral transform; Spectrograms; abnormal respiratory sound; algorithm; area under the curve; Article; Bayesian learning; chronic obstructive lung disease; classification algorithm; classifier; data mining; entropy; feature extraction algorithm; human; learning algorithm; machine learning; random forest; receiver operating characteristic; sound analysis; support vector machine; wavelet transform; abnormal respiratory sound; algorithm; Bayes theorem; diagnosis; female; male; middle aged; pathophysiology; procedures; signal processing; sound detection; wavelet analysis; Spectrographs","","","","","","","Alom M Z, Taha T, Yakopcic C, Westberg S, Hasan M, Esesn B, Awwal A, Asari V, The history began from alexnet: A comprehensive survey on deep learning approaches, (2018); Anand K, Bianconi G, Entropy measures for networks: toward an information theory of complex topologies, Phys. 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Han; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; email: thanh.hantrong@set.hust.edu.vn; K. Le Trung; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; email: kien.lt203474@sis.hust.edu.vn; P. Nguyen Anh; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; email: phuong.na200480@sis.hust.edu.vn; P. Nguyen Huu; School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, 100000, Viet Nam; email: phat.nguyenhuu@hust.edu.vn","","Institute of Physics","","","","","","20571976","","","39332437","English","Biomed. Phys. Eng. Express","Article","Final","","Scopus","2-s2.0-85207594869"
"Coutu F.-A.; Iorio O.C.; Nabavi S.; Hadid A.; Jensen D.; Pamidi S.; Xia J.; Ross B.A.","Coutu, Felix-Antoine (58560592900); Iorio, Olivia C. (58560278900); Nabavi, Seyedfakhreddin (57190251035); Hadid, Amir (24175811600); Jensen, Dennis (24465450400); Pamidi, Sushmita (36647712000); Xia, Jianguo (59598303000); Ross, Bryan A. (35300440500)","58560592900; 58560278900; 57190251035; 24175811600; 24465450400; 36647712000; 59598303000; 35300440500","Continuous characterisation of exacerbation pathophysiology using wearable technologies in free-living outpatients with COPD: a prospective observational cohort study","2024","eBioMedicine","110","","105472","","","","0","10.1016/j.ebiom.2024.105472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209660442&doi=10.1016%2fj.ebiom.2024.105472&partnerID=40&md5=be0e9692f08782733340e57fa07545a0","Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine, McGill University, Montreal, QC, Canada; Clinical Exercise & Respiratory Physiology Laboratory, Department of Kinesiology & Physical Education, McGill University, Montreal, QC, Canada; Division of Respiratory Medicine, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada; Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada; Department of Parasitology, McGill University, Montreal, QC, Canada","Coutu F.-A., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, Department of Medicine, McGill University, Montreal, QC, Canada; Iorio O.C., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Nabavi S., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Hadid A., Clinical Exercise & Respiratory Physiology Laboratory, Department of Kinesiology & Physical Education, McGill University, Montreal, QC, Canada; Jensen D., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, Clinical Exercise & Respiratory Physiology Laboratory, Department of Kinesiology & Physical Education, McGill University, Montreal, QC, Canada; Pamidi S., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, Department of Medicine, McGill University, Montreal, QC, Canada, Division of Respiratory Medicine, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada, Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada; Xia J., Department of Parasitology, McGill University, Montreal, QC, Canada; Ross B.A., Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada, Department of Medicine, McGill University, Montreal, QC, Canada, Division of Respiratory Medicine, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada, Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada","Background: The most recent exacerbation of COPD (ECOPD) classification criteria relies in part on changes in respiratory rate (RR), heart rate (HR) and oxygen saturation (SpO2). Despite this paradigm shift, a thorough understanding of exacerbation patterns is still lacking, as is the identification of physiological exacerbation biomarkers. Methods: Using a convenience sampling approach, this prospective observational cohort study was conducted between February 2023 and January 2024. Continuous measurements of daytime/overnight respiratory (primary outcome), cardiovascular, autonomic, activity and sleep-related parameters were collected by a wearable biometric wristband and ring over 21 consecutive days in free-living outpatients experiencing and receiving treatment (≤3 days) for a current exacerbation from the home environment. The EXACT-PRO questionnaire served as the validated reference for daily symptom burden and to identify ‘recovered’ versus ‘persistent worsening’ participants. Unadjusted and adjusted (for age, sex, FEV1) linear mixed-effects models were fitted to estimate associations between each physiological parameter with daily EXACT-PRO score (points, pts), in all, ‘recovered’, and ‘persistent worsening’ participants. Results are presented as point estimates with 95% CIs. Findings: In 21 participants with COPD (43% female, mean age 66.8, BMI 27.7 kg/m2, FEV1 36.3% predicted; 85.7% with GOLD 3–4 disease), significant associations in unadjusted models with daily EXACT-PRO score included RR variability (−1.45 [−2.84, −0.073] pts/breath/min) but not RR, daily step count (−0.56 [−0.82, −0.31] pts/1000 steps), and sleep efficiency (−0.12 [−0.20, −0.037] pts/%asleep). In ‘recovered’ participants (n = 10), significant associations included nighttime HR, movement intensity and nightly SpO2. In ‘persistent worsening’ participants (n = 11), significant associations included HR variability, nightly RR variability, nightly SpO2, sleep efficiency, and skin temperature. Similar results were found in adjusted models. Interpretation: This study provides a prospective continuous characterisation of exacerbations of COPD using remotely collected, ambulatory/free-living data. The physiological patterns presented may contribute to the understanding of exacerbations and may enhance the development of effective remote monitoring solutions. Funding:University hospital (MUHC-CAS) grant. © 2024 The Author(s)","Chronic obstructive pulmonary disease; Exacerbations of COPD; Remote patient monitoring; Vital signs; Wearable electronic devices","Aged; Disease Progression; Female; Heart Rate; Humans; Male; Middle Aged; Outpatients; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Respiratory Rate; Wearable Electronic Devices; beta adrenergic receptor stimulating agent; biological marker; corticosteroid; macrolide; muscarinic receptor blocking agent; adult; aged; air pollution; Article; body mass; chronic obstructive lung disease; clinical article; clinical outcome; clinical trial; cohort analysis; energy expenditure; Exacerbation of COPD; female; forced expiratory volume; forced vital capacity; heart rate; heart rate variability; home environment; human; length of stay; longitudinal study; lung function; machine learning; male; middle aged; observational study; oxygen saturation; pathophysiology; physical activity; predictive value; prospective study; pulmonary rehabilitation; questionnaire; REM sleep; skin temperature; sleep quality; spirometry; telemonitoring; temperature; vital sign; wearable technology; breathing rate; diagnosis; disease exacerbation; outpatient; pathophysiology; prospective study; wearable computer","","","R Statistical Software version 4.3.2","","McGill University Health Centre, MUHC; Quebec Respiratory Health Network; Association des Pneumologues de la Province du Québec; American Thoracic Society, ATS; Alberta Kinesiology Association; MUHC-CAS; Ministère de l’Éducation et le Ministère de l’Enseignement Supérieur Innovation; Margaret M. and Albert B. Alkek Department of Medicine; 'Ahahui Koa Ānuenue, AKA; Canadian Thoracic Society, CTS; MUHC Foundation; Fonds de Recherche du Québec and Chest Foundation; APPQ; Montreal Chest Institute; Ministry of Communications and Information, Singapore, MCI; McGill University, MGU; GlaxoSmithKline, GSK; Canadian Institutes of Health Research, CIHR; QHRN; Thorasys, Inc.; American College of Chest Physicians","Funding text 1: University hospital (MUHC-CAS) grant.The present study was funded by the McGill University Health Centre (MUHC) Department of Medicine Contract Academic Staff (CAS) Research Award. We express our gratitude to all of the participants who dedicated their personal time to this study. We wish to thank immensely the Case Managers from the Montreal Chest Institute (MCI), and the MCI Day Hospital nurses, without whom this study would not have been possible. We also wish to acknowledge and thank Dany Malaeb for his support during recruitment.; Funding text 2: FAC, OCI, and SN declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. AH reports the following conflicts of interest: Co-Founder & CEO, SensifAI Health Inc. / SensifAI Sante Inc, Montreal, Quebec, Canada; Inflammatory response indicator (2023). US Patent, Pending. DJ reports the following conflicts of interest: Co-Founder & Chief Scientific Officer, SensifAI Health Inc. / SensifAI Sante Inc, Montreal, Quebec, Canada; Inflammatory response indicator (2023). US Patent, Pending. SP reports the following conflicts of interest: Chair of Planning Committee, American Thoracic Society, Assembly of Sleep, Respiratory and Neurobiology; grants from the Canadian Institutes of Health Research (CIHR), Fonds de Recherche du Qu\u00E9bec and Chest Foundation Grant; Sleep and Breathing Conference in New Zealand 2023\u2013 travel paid for (flight and hotel). JX declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. BAR reports the following conflicts of interest: Honoraria from the Canadian Thoracic Society (CTS - COPD Educational Event (Speaker) and Content Creator (educational materials)), CHEST (Content Creator (educational materials)), Respiplus non-profit (Content Creator (educational materials)), Alberta Kinesiology Association (AKA - Content Creator (educational materials)), Association des Pneumologues de la Province du Qu\u00E9bec (APPQ - presenter), McGill University Continuing Professional Development (CPD - COPD Educational Event (Speaker)), GSK (Speaker and Moderator), AZ (Speaker and Moderator), and COVIS (COPD Educational Event (Speaker)); Research funding as Principal Investigator from the McGill University Health Centre (MUHC) Department of Medicine Contract Academic Staff (CAS) Research Award, the Quebec Respiratory Health Network (QHRN), the Minist\u00E8re de l\u2019\u00C9ducation et le Minist\u00E8re de l\u2019Enseignement Sup\u00E9rieur Innovation and Partnership Program (McGill University and Thorasys, Inc.), and the MUHC Foundation/MCI Respiratory Research Campaign Innovation Grant; and reception of in-kind support (placebo and intervention) for research from Amazentis, and reception of in-kind support (diagnostic device(s)) for research from Thorasys Inc., Spire Health, and Restech. ","Diagnosis and management of COPD, (2024); Hawkins N.M., Nordon C., Rhodes K., Et al., Heightened long-term cardiovascular risks after exacerbation of chronic obstructive pulmonary disease, Heart, 110, 10, pp. 702-709, (2024); Amegadzie J.E., Lee T.Y., Sadatsafavi M., Lynd L.D., Sin D.D., Johnson K.M., Trends in hospital admissions for chronic obstructive pulmonary disease over 16 years in Canada, Can Med Assoc J, 195, 35, pp. E1172-E1179, (2023); Aaron S.D., Donaldson G.C., Whitmore G.A., Hurst J.R., Ramsay T., Wedzicha J.A., Time course and pattern of COPD exacerbation onset, Thorax, 67, 3, pp. 238-243, (2012); Hurst J.R., Donaldson G.C., Quint J.K., Goldring J.J., Baghai-Ravary R., Wedzicha J.A., Temporal clustering of exacerbations in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 179, 5, pp. 369-374, (2009); Coutu F.A., Iorio O.C., Ross B.A., Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease, Front Med, 10, (2023); Celli B.R., Fabbri L.M., Aaron S.D., Et al., An updated definition and severity classification of chronic obstructive pulmonary disease exacerbations: the Rome Proposal, Am J Respir Crit Care Med, 204, 11, pp. 1251-1258, (2021); Langsetmo L., Platt R.W., Ernst P., Bourbeau J., Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort, Am J Respir Crit Care Med, 177, 4, pp. 396-401, (2008); MacLeod M., Papi A., Contoli M., Et al., Chronic obstructive pulmonary disease exacerbation fundamentals: diagnosis, treatment, prevention and disease impact, Respirology, 26, 6, pp. 532-551, (2021); Polsky M., Moraveji N., Hendricks A., Teresi R.K., Murray R., Maselli D.J., Use of remote cardiorespiratory monitoring is associated with a reduction in hospitalizations for subjects with COPD, Int J Chronic Obstr Pulm Dis, 18, pp. 219-229, (2023); Fan K.G., Mandel J., Agnihotri P., Tai-Seale M., Remote patient monitoring technologies for predicting chronic obstructive pulmonary disease exacerbations: review and comparison, JMIR Mhealth Uhealth, 8, 5, (2020); Borel J.C., Pelletier J., Taleux N., Et al., Parameters recorded by software of non-invasive ventilators predict COPD exacerbation: a proof-of-concept study, Thorax, 70, 3, pp. 284-285, (2015); Yanez A.M., Guerrero D., Perez de Alejo R., Et al., Monitoring breathing rate at home allows early identification of COPD exacerbations, Chest, 142, 6, pp. 1524-1529, (2012); Rubio N., Parker R.A., Drost E.M., Et al., Home monitoring of breathing rate in people with chronic obstructive pulmonary disease: observational study of feasibility, acceptability, and change after exacerbation, Int J Chronic Obstr Pulm Dis, 12, pp. 1221-1231, (2017); O'Donnell D.E., Parker C.M., COPD exacerbations: pathophysiology, Thorax, 61, 4, pp. 354-361, (2006); Iorio O.C., Coutu F.-A., Malaeb D., Ross B.A., Feasibility, functionality, and user experience with wearable technologies for acute exacerbation monitoring in patients with severe COPD, Front Signal Process (Lausanne), 4, (2024); Guidance for industry on patient reported outcome measures: use in medical product development to support labeling claims, (2009); Leidy N.K., Wilcox T.K., Jones P.W., Roberts L., Powers J.H., Sethi S., Standardizing measurement of chronic obstructive pulmonary disease exacerbations. Reliability and validity of a patient-reported diary, Am J Respir Crit Care Med, 183, 3, pp. 323-329, (2011); Mannino D.M., Clerisme-Beaty E.M., Franceschina J., Ting N., Leidy N.K., Exacerbation recovery patterns in newly diagnosed or maintenance treatment-naïve patients with COPD: secondary analyses of TICARI 1 trial data, Int J Chronic Obstr Pulm Dis, 13, pp. 1515-1525, (2018); Murray L.T., Leidy N.K., The short-term impact of symptom-defined COPD exacerbation recovery on health status and lung function, Chronic Obstr Pulm Dis, 5, 1, pp. 27-37, (2018); Choi H.S., Park Y.B., Shin K.C., Et al., Exacerbations of chronic obstructive pulmonary disease tool to assess the efficacy of acute treatment, Int J Chronic Obstr Pulm Dis, 14, pp. 471-478, (2019); Mackay A.J., Kostikas K., Murray L., Et al., Patient-reported outcomes for the detection, quantification, and evaluation of chronic obstructive pulmonary disease exacerbations, Am J Respir Crit Care Med, 198, 6, pp. 730-738, (2018); Jones P.W., Lamarca R., Chuecos F., Et al., Characterisation and impact of reported and unreported exacerbations: results from ATTAIN, Eur Respir J, 44, 5, pp. 1156-1165, (2014); The exacerbations of chronic pulmonary disease tool (exact) patient-reported outcome (pro) user manual (version 8.0), (2016); R: The R project for statistical computing, (2024); Park S.-C., Saiphoklang N., Jung D., Et al., Use of a wearable biosensor to study heart rate variability in chronic obstructive pulmonary disease and its relationship to disease severity, Sensors, 22, 6, (2022); L'Her E., N'Guyen Q.-T., Pateau V., Bodenes L., Lellouche F., Photoplethysmographic determination of the respiratory rate in acutely ill patients: validation of a new algorithm and implementation into a biomedical device, Ann Intensive Care, 9, 1, (2019); Hawthorne G., Greening N., Esliger D., Et al., Usability of wearable multiparameter technology to continuously monitor free-living vital signs in people living with chronic obstructive pulmonary disease: prospective observational study, JMIR Hum Factors, 9, 1, (2022); Shah S.A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system, J Med Internet Res, 19, 3, (2017); Alahmari A.D., Patel A.R., Kowlessar B.S., Et al., Daily activity during stability and exacerbation of chronic obstructive pulmonary disease, BMC Pulm Med, 14, (2014); Shorofsky M., Bourbeau J., Kimoff J., Et al., Impaired sleep quality in COPD is associated with exacerbations: the CanCOLD cohort study, Chest, 156, 5, pp. 852-863, (2019); Chen Y.W., Leung J.M., Sin D.D., A systematic review of diagnostic biomarkers of COPD exacerbation, PLoS One, 11, 7, (2016); Franciosi L.G., Page C.P., Celli B.R., Et al., Markers of exacerbation severity in chronic obstructive pulmonary disease, Respir Res, 7, 1, (2006); Heindl S., Lehnert M., Criee C.P., Hasenfuss G., Andreas S., Marked sympathetic activation in patients with chronic respiratory failure, Am J Respir Crit Care Med, 164, 4, pp. 597-601, (2001)","B.A. Ross; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montréal, 5252 De Maisonneuve, Suite 3D.57, QC H4A 3S5, Canada; email: bryan.ross@mcgill.ca","","Elsevier B.V.","","","","","","23523964","","","39579617","English","eBioMedicine","Article","Final","","Scopus","2-s2.0-85209660442"
"Liu G.-H.; Li C.-L.; Yang C.-Y.; Liu S.-F.","Liu, Guan-Heng (58121718600); Li, Chin-Ling (57215023531); Yang, Chih-Yuan (57211789950); Liu, Shih-Feng (7409463386)","58121718600; 57215023531; 57211789950; 7409463386","Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice","2025","Computational and Structural Biotechnology Journal","27","","","541","547","6","0","10.1016/j.csbj.2025.01.015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216526328&doi=10.1016%2fj.csbj.2025.01.015&partnerID=40&md5=bd92b49e7297f577194df3c68abc096f","Department of Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan; Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan; Medical Department, College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan","Liu G.-H., Department of Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan; Li C.-L., Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan; Yang C.-Y., Department of Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan; Liu S.-F., Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan, Medical Department, College of Medicine, Chang Gung University, Taoyuan, 333, Taiwan","Background: Chronic Obstructive Pulmonary Disease (COPD) is a major contributor to global morbidity and healthcare costs. Accurately predicting these costs is crucial for resource allocation and patient care. This study developed and validated an AI-driven COPD Medical Cost Prediction Index (MCPI) to forecast healthcare expenses in COPD patients. Methods: A retrospective analysis of 396 COPD patients was conducted, utilizing clinical, demographic, and comorbidity data. Missing data were addressed through advanced imputation techniques to minimize bias. The final predictors included interactions such as Age × BMI, alongside Tumor Presence, Number of Comorbidities, Acute Exacerbation frequency, and the DOSE Index. A Gradient Boosting model was constructed, optimized with Recursive Feature Elimination (RFE), and evaluated using 5-fold cross-validation on an 80/20 train-test split. Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Results: On the training set, the model achieved an MSE of 0.049, MAE of 0.159, MAPE of 3.41 %, and R² of 0.703. On the test set, performance metrics included an MSE of 0.122, MAE of 0.258, MAPE of 5.49 %, and R² of 0.365. Tumor Presence, Age, and BMI were identified as key predictors of cost variability. Conclusions: The MCPI demonstrates strong potential for predicting healthcare costs in COPD patients and enables targeted interventions for high-risk individuals. Future research should focus on validation with multicenter datasets and the inclusion of additional socioeconomic variables to enhance model generalizability and precision. © 2025 The Authors","5-fold cross-validation; COPD; Gradient boosting model; MCPI; Recursive Feature Elimination","Cost benefit analysis; Risk assessment; 5-fold cross-validation; Chronic obstructive pulmonary disease; Cost prediction; Cross validation; Gradient boosting; Gradient boosting model; Mean squared error; Medical cost prediction index; Prediction indices; Recursive feature elimination; aged; Article; artificial intelligence; body mass; Charlson Comorbidity Index; chronic obstructive lung disease; clinical practice; cohort analysis; comorbidity; cross validation; female; health care cost; human; major clinical study; male; mean absolute error; mean squared error; Medical Research Council Dyspnea Scale; morbidity; patient care; performance indicator; prediction; recursive feature elimination; retrospective study; Pulmonary diseases","","","","","","","Halpin D.M., Mortality of patients with COPD, Expert Rev Respir Med, 18, 6, pp. 381-395, (2024); Owusuaa C., Dijkland S.A., Nieboer D., van der Rijt C.C., van der Heide A., Predictors of mortality in chronic obstructive pulmonary disease: a systematic review and meta-analysis, BMC Pulm Med, 22, 1, (2022); Organization W.H.; Polosukhin V.V., Gutor S.S., Du R.-H., Richmond B.W., Massion P.P., Wu P., Et al., Small airway determinants of airflow limitation in chronic obstructive pulmonary disease, Thorax, 76, 11, pp. 1079-1088, (2021); Song Q., Zhao Y.-Y., Zeng Y.-Q., Liu C., Cheng W., Deng M.-H., Et al., The characteristics of airflow limitation and future exacerbations in different GOLD groups of COPD patients, Int J Chronic Obstr Pulm Dis, pp. 1401-1412, (2021); Aghapour M., Ubags N.D., Bruder D., Hiemstra P.S., Sidhaye V., Rezaee F., Et al., Role of air pollutants in airway epithelial barrier dysfunction in asthma and COPD, Eur Respir Rev, 31, 163, (2022); Evangelopoulos D., Chatzidiakou L., Walton H., Katsouyanni K., Kelly F.J., Quint J.K., Et al., Personal exposure to air pollution and respiratory health of COPD patients in London, Eur Respir J, 58, 1, (2021); Silver S.R., Alarcon W.A., Li J., Incident chronic obstructive pulmonary disease associated with occupation, industry, and workplace exposures in the Health and Retirement Study, Am J Ind Med, 64, 1, pp. 26-38, (2021); Czarnecka-Chrebelska K.H., Mukherjee D., Maryanchik S.V., Rudzinska-Radecka M., Biological and genetic mechanisms of COPD, its diagnosis, treatment, and relationship with lung cancer, Biomedicines, 11, 2, (2023); Hurst J.R., Vestbo J., Anzueto A., Locantore N., Mullerova H., Tal-Singer R., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Whittaker H., Rubino A., Mullerova H., Morris T., Varghese P., Xu Y., Et al., Frequency and severity of exacerbations of COPD associated with future risk of exacerbations and mortality: a UK routine health care data study, Int J Chronic Obstr Pulm Dis, pp. 427-437, (2022); 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Kaliappan J., Srinivasan K., Mian Qaisar S., Sundararajan K., Chang C.-Y., Performance evaluation of regression models for the prediction of the COVID-19 reproduction rate, Front Public Health, 9, (2021); Liu M., Chen H., Wei D., Wu Y., Li C., Nonlinear relationship between urban form and street-level PM2. 5 and CO based on mobile measurements and gradient boosting decision tree models, Build Environ, 205, (2021); Mateo J., Rius-Peris J., Marana-Perez A., Valiente-Armero A., Torres A., Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis, Biocybern Biomed Eng, 41, 2, pp. 792-801, (2021); Demir I., Kirisci M., Forecasting COVID-19 disease cases using the SARIMA-NNAR hybrid model, Univers J Math Appl, 5, 1, pp. 15-23, (2022); Naeem M., Yu J., Aamir M., Khan S.A., Adeleye O., Khan Z., Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak, PeerJ Comput Sci, 7, (2021); Little R.J., Carpenter J.R., Lee K.J., A comparison of three popular methods for handling missing data: complete-case analysis, inverse probability weighting, and multiple imputation, Sociol Methods Res, 53, 3, pp. 1105-1135, (2024); Austin P.C., White I.R., Lee D.S., van Buuren S., Missing data in clinical research: a tutorial on multiple imputation, Can J Cardiol, 37, 9, pp. 1322-1331, (2021); Faisal S., Tutz G., Multiple imputation using nearest neighbor methods, Inf Sci, 570, pp. 500-516, (2021)","C.-Y. Yang; Department of Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan; email: cyyang@cgu.edu.tw; S.-F. Liu; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, 833, Taiwan; email: liuphysico@yahoo.com.tw","","Elsevier B.V.","","","","","","20010370","","","","English","Comput. Struct. Biotechnol. J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216526328"
"Lee H.; Yoon H.-Y.","Lee, Hyewon (57190295653); Yoon, Hee-Young (56966016000)","57190295653; 56966016000","Inhaled corticosteroid increased the risk of adrenal insufficiency in patients with chronic airway diseases: a nationwide population-based study","2024","Scientific Reports","14","1","28831","","","","0","10.1038/s41598-024-78298-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209749572&doi=10.1038%2fs41598-024-78298-2&partnerID=40&md5=55abacbba67160fd7293f6440e4341c5","Department of Health Administration and Management, College of Medical Sciences, Soonchunhyang University, Asan, South Korea; Department of Software Convergence, Soonchunhyang University Graduate School, Asan, South Korea; Division of Allergy and Respiratory Diseases, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, 59 Daesagwanro, Yongsan-gu, Seoul, 04401, South Korea","Lee H., Department of Health Administration and Management, College of Medical Sciences, Soonchunhyang University, Asan, South Korea, Department of Software Convergence, Soonchunhyang University Graduate School, Asan, South Korea; Yoon H.-Y., Division of Allergy and Respiratory Diseases, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, 59 Daesagwanro, Yongsan-gu, Seoul, 04401, South Korea","Inhaled corticosteroids (ICS) are commonly used for airway disease, but concerns about adrenal insufficiency (AI) have arisen. This retrospective observational study investigated the link between ICS use and AI risk using data from the National Health Insurance Service-National Sample Cohort, analyzing 66,631 patients with COPD (Korean Standard Classification of Diseases [KCD] codes J42-J44) or asthma (KCD codes J45-J46). ICS use, daily dosage, and AI cases (hospitalization or ≥ 2 outpatient visits with KCD code E27) were identified via diagnostic codes. Cox proportional hazard survival analysis and inverse probability of treatment weighting (IPTW) addressed baseline differences between ICS and non-ICS users. In total 66,631 patients, the mean age was 57.3 years, 42.6% were male, and 42.2% had a Charlson comorbidity index (CCI) of 2 or higher. Among the patients, 15.5% used ICS, with a mean daily dose of 404.2 µg/day. The incidence of AI was higher in ICS users (1.69 per 1000) than in non-users (0.54 per 1000). ICS use independently increased AI risk (HR: 3.06, 95% CI: 1.82–5.14, p < 0.001). Each 100 µg/day increase in ICS was associated with a 3% increase in AI incidence (HR: 1.03, 95% CI: 1.02–1.04, p < 0.001). Quartile analysis indicated a significant AI risk increase across all ICS dosage quartiles compared with non-users. Subgroup analysis showed consistent associations with age, sex, and smoking, with stronger links in systemic steroid users (HR: 3.54, 95% CI: 2.10–5.96, p < 0.001) and those with higher CCI (HR: 2.61, 95% CI: 1.64–4.12, p < 0.001). ICS may use increases AI risk in chronic airway disease patients, particularly among systemic steroid users and those with higher CCI. Close monitoring of high-risk patients is advised, and further research is needed to clarify mechanisms and optimize safe ICS use. © The Author(s) 2024.","Adrenal dysfunction; Chronic Airway diseases; Glucocorticoids; Inhaled Corticosteroid Safety; Inverse probability treatment weighting; Respiratory Tract diseases; Retrospective cohort study","Administration, Inhalation; Adrenal Cortex Hormones; Adrenal Insufficiency; Adult; Aged; Asthma; Female; Humans; Incidence; Male; Middle Aged; Proportional Hazards Models; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Risk Factors; corticosteroid; adrenal insufficiency; adult; aged; asthma; chronic obstructive lung disease; drug therapy; epidemiology; female; human; incidence; inhalational drug administration; male; middle aged; proportional hazards model; retrospective study; risk factor","","Adrenal Cortex Hormones, ","","","Soonchunhyang University Research Fund; National Research Foundation of Korea, NRF; Ministry of Science and ICT, (NRF-2022R1C1C1010045)","This study was supported by the Soonchunhyang University Research Fund and the Young Researcher Program through the National Research Foundation of Korea, funded by the Ministry of Science and ICT [grant number NRF-2022R1C1C1010045; H Lee].","Minov J., Stoleski S., Chronic obstructive airways diseases: Where are we now?, Open Respir Med J, 9, pp. 37-38, (2015); Al Wachami N., Et al., Estimating the global prevalence of chronic obstructive pulmonary disease (COPD): A systematic review and meta-analysis, BMC Public Health, 24, (2024); Li H.Y., Et al., Global, regional and national burden of chronic obstructive pulmonary disease over a 30-year period: Estimates from the 1990 to 2019 Global Burden of Disease Study, Respirology, 28, pp. 29-36, (2023); Boers E., Et al., Global burden of chronic obstructive pulmonary disease through 2050, JAMA Netw Open, 6, (2023); Wang Z., Et al., Global, regional, and national burden of asthma and its attributable risk factors from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019, Respir Res, 24, (2023); Phua G.C., Macintyre N.R., Inhaled corticosteroids in obstructive airway disease, Respir Care, 52, pp. 852-858, (2007); Shang W., Wang G., Wang Y., Han D., The safety of long-term use of inhaled corticosteroids in patients with asthma: A systematic review and meta-analysis, Clin Immunol, 236, (2022); Miravitlles M., Et al., Systematic review on long-term adverse effects of inhaled corticosteroids in the treatment of COPD, Eur Respir Rev, 30, (2021); Pandya D., Puttanna A., Balagopal V., Systemic effects of inhaled corticosteroids: An overview, Open Respir Med J, 8, pp. 59-65, (2014); Patel R., Naqvi S.A., Griffiths C., Bloom C.I., Systemic adverse effects from inhaled corticosteroid use in asthma: A systematic review, BMJ Open Respir Res, 7, (2020); Sannarangappa V., Jalleh R., Inhaled corticosteroids and secondary adrenal insufficiency, Open Respir Med J, 8, pp. 93-100, (2014); Mortimer K.J., Et al., Oral and inhaled corticosteroids and adrenal insufficiency: A case-control study, Thorax, 61, pp. 405-408, (2006); Lapi F., Kezouh A., Suissa S., Ernst P., The use of inhaled corticosteroids and the risk of adrenal insufficiency, Eur Respir J, 42, pp. 79-86, (2013); Broersen L.H., Pereira A.M., Jorgensen J.O., Dekkers O.M., Adrenal insufficiency in corticosteroids use: Systematic review and meta-analysis, J Clin Endocrinol Metab, 100, pp. 2171-2180, (2015); Todd G.R., Et al., Survey of adrenal crisis associated with inhaled corticosteroids in the United Kingdom, Arch Dis Child, 87, pp. 457-461, (2002); Lipworth B.J., Et al., Effect of ciclesonide and fluticasone on hypothalamic-pituitary-adrenal axis function in adults with mild-to-moderate persistent asthma, Ann Allergy Asthma Immunol, 94, pp. 465-472, (2005); Skoner D.P., Et al., Effects of inhaled mometasone furoate on growth velocity and adrenal function: A placebo-controlled trial in children 4–9 years old with mild persistent asthma, J Asthma, 48, pp. 848-859, (2011); Kowalski M.L., Wojciechowski P., Dziewonska M., Rys P., Adrenal suppression by inhaled corticosteroids in patients with asthma: A systematic review and quantitative analysis, Allergy Asthma Proc, 37, pp. 9-17, (2016); Eichenhorn M.S., Et al., Lack of long-term adverse adrenal effects from inhaled triamcinolone: Lung Health Study II, Chest, 124, pp. 57-62, (2003); Fahim A., Faruqi S., Wright C.E., Kastelik J.A., Morice A.H., Comparison of the effect of high-dose inhaled budesonide and fluticasone on adrenal function in patients with severe chronic obstructive pulmonary disease, Ann Thorac Med, 7, pp. 140-144, (2012); Lee J., Lee J.S., Park S.H., Shin S.A., Kim K., Cohort Profile: The National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol, 46, (2017); Boulet L.; Ernst P., Gonzalez A.V., Brassard P., Suissa S., Inhaled corticosteroid use in chronic obstructive pulmonary disease and the risk of hospitalization for pneumonia, Am J Respir Crit Care Med, 176, pp. 162-166, (2007); Shin J., Yoon H.Y., Lee Y.M., Ha E., Lee J.H., Inhaled corticosteroids in COPD and the risk for coronary heart disease: A nationwide cohort study, Sci Rep, 10, (2020); Austin P.C., Stuart E.A., Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies, Stat Med, 34, pp. 3661-3679, (2015); Austin P.C., An introduction to propensity score methods for reducing the effects of confounding in observational studies, Multivariate Behav Res, 46, pp. 399-424, (2011); Yoon E.C., Lee H., Yoon H.Y., Inhaled corticosteroids and the risk of nontuberculous mycobacterial infection in chronic airway disease: A nationwide population-based study, Tuberc Respir Dis (Seoul; Yu S.Y., Et al., Low-dose aspirin and incidence of lung carcinoma in patients with chronic obstructive pulmonary disease in Hong Kong: A cohort study, PLoS Med, 19, (2022); Chesnaye N.C., Et al., An introduction to inverse probability of treatment weighting in observational research, Clin Kidney J, 15, pp. 14-20, (2022); Austin P.C., Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples, Stat Med, 28, pp. 3083-3107, (2009); Suissa S., Patenaude V., Lapi F., Ernst P., Inhaled corticosteroids in COPD and the risk of serious pneumonia, Thorax, 68, pp. 1029-1036, (2013); Buttgereit F., Et al., Standardised nomenclature for glucocorticoid dosages and glucocorticoid treatment regimens: Current questions and tentative answers in rheumatology, Ann Rheum Dis, 61, pp. 718-722, (2002); Goldstein L.B., Samsa G.P., Matchar D.B., Horner R.D., Charlson Index comorbidity adjustment for ischemic stroke outcome studies, Stroke, 35, pp. 1941-1945, (2004); Baptist A.P., Reddy R.C., Inhaled corticosteroids for asthma: Are they all the same?, J Clin Pharm Ther, 34, pp. 1-12, (2009); Daley-Yates P.T., Inhaled corticosteroids: Potency, dose equivalence and therapeutic index, Br J Clin Pharmacol, 80, pp. 372-380, (2015); Loscalzo J.; Pelewicz K., Miskiewicz P., Glucocorticoid Withdrawal-An Overview on When and How to Diagnose Adrenal Insufficiency in Clinical Practice, Diagnostics (Basel), 11, (2021); Dinsen S., Et al., Why glucocorticoid withdrawal may sometimes be as dangerous as the treatment itself, Eur J Intern Med, 24, pp. 714-720, (2013); Kachroo P., Et al., Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma, Nat Med, 28, pp. 814-822, (2022); Issa-El-Khoury K., Kim H., Chan E.S., Vander Leek T., Noya F., CSACI position statement: Systemic effect of inhaled corticosteroids on adrenal suppression in the management of pediatric asthma, Allergy Asthma Clin Immunol, 11, (2015); Ahmet A., Kim H., Spier S., Adrenal suppression: A practical guide to the screening and management of this under-recognized complication of inhaled corticosteroid therapy, Allergy Asthma Clin Immunol, 7, (2011); Agusti A., Et al., Inhaled corticosteroids in COPD: Friend or foe?, Eur Respir J, 52, (2018); Mkorombindo T., Dransfield M.T., Inhaled corticosteroids in chronic obstructive pulmonary disease: Benefits and risks, Clin Chest Med, 41, pp. 475-484, (2020); Goldbloom E.B., Et al., Symptomatic adrenal suppression among children in Canada, Arch Dis Child, 102, pp. 338-339, (2017); Schuetz P., Et al., Effect of a 14-day course of systemic corticosteroids on the hypothalamic-pituitary-adrenal-axis in patients with acute exacerbation of chronic obstructive pulmonary disease, BMC Pulm Med, 8, (2008); Bornstein S.R., Predisposing factors for adrenal insufficiency, New England Journal of Medicine, 360, pp. 2328-2339, (2009)","H.-Y. Yoon; Division of Allergy and Respiratory Diseases, Department of Internal Medicine, Soonchunhyang University Seoul Hospital, Seoul, 59 Daesagwanro, Yongsan-gu, 04401, South Korea; email: yhyoung85@gmail.com","","Nature Research","","","","","","20452322","","","39572602","English","Sci. Rep.","Article","Final","","Scopus","2-s2.0-85209749572"
"Huang J.; Zhou X.; Xu Y.; Yu C.; Zhang H.; Qiu J.; Wei J.; Luo Q.; Xu Z.; Lin Y.; Qiu P.; Li C.","Huang, Junhao (57417923800); Zhou, Xiaojie (57144869000); Xu, Yueling (58570564500); Yu, Chenshi (59386300400); Zhang, Huanhuan (57033942300); Qiu, Jiang (58124631200); Wei, Jiale (57418872000); Luo, Qihan (57202806776); Xu, Zhiwei (57201373743); Lin, Yiyou (57217026923); Qiu, Ping (57204418083); Li, Changyu (56245425300)","57417923800; 57144869000; 58570564500; 59386300400; 57033942300; 58124631200; 57418872000; 57202806776; 57201373743; 57217026923; 57204418083; 56245425300","Shen Qi Wan regulates OPN/CD44/PI3K pathway to improve airway inflammation in COPD: Network pharmacology, bioinformatics, and experimental validation","2025","International Immunopharmacology","144","","113624","","","","0","10.1016/j.intimp.2024.113624","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209538543&doi=10.1016%2fj.intimp.2024.113624&partnerID=40&md5=a27b5562f0c872d1092b0b3e3d22809f","School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xian, 710032, China; Department of Medicine, Hangzhou Normal University, Hangzhou, 311121, China; Jinhua Academy, Zhejiang Chinese Medical University, Jinhua, 321000, China; School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China","Huang J., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Zhou X., Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Xu Y., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Yu C., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Zhang H., Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xian, 710032, China; Qiu J., Department of Medicine, Hangzhou Normal University, Hangzhou, 311121, China; Wei J., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Luo Q., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Xu Z., Jinhua Academy, Zhejiang Chinese Medical University, Jinhua, 321000, China; Lin Y., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Qiu P., School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; Li C., School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China, Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China","Background: Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases with undefined pathogenesis and unsatisfactory therapeutic options. Shenqi Wan (SQW), a traditional Chinese medicinal compound, has demonstrated certain preventive and therapeutic effects on COPD. However, the underlying molecular mechanisms remain incompletely understood. In this study, we used weighted gene co-expression network analysis (WGCNA) and machine learning to identify biomarkers for COPD, combined with network pharmacology and experimental validation to evaluate how SQW reduces airway inflammation in COPD. Methods: Targets of SQW in treating COPD and its network regulation mechanism were predicted via network pharmacology. Meanwhile, potential biomarkers were predicted using WGCNA and machine learning algorithms and validated in COPD patients. The relationship between the core pathway and key target was analyzed by ingenuity pathway analysis (IPA) to reveal the regulatory mechanism of SQW. We evaluated the efficacy of SQW treatment in LPS/MS-induced COPD mice by evaluating lung function, histopathological parameters, and levels of inflammatory markers and oxidative stress. The distribution and expression of OPN/CD44/PI3K loop-related proteins were examined through immunofluorescence staining and Western Blotting. In vitro, we added LPS to BEAS-2B cells to mimic the inflammatory microenvironment and transfected the cells with OPN overexpression plasmid to observe the improvement induced by SQW. Results: GO and KEGG analyses demonstrated that SQW inhibited inflammation and oxidative stress via the PI3K/Akt pathway, thereby improving COPD. Machine learning algorithms identified OPN as a potential biomarker, with elevated expression observed in the lung tissue of COPD patients. IPA indicated that OPN may modulate the CD44-mediated activation of the PI3K/AKT pathway, forming a positive feedback regulatory mechanism. SQW ameliorated lung function and pathological injury in mice; further, it reduced inflammation, oxidative stress, and OPN/CD44/PI3K positive feedback loop-related protein expression in both mice and cells. After OPN overexpression, the levels of inflammatory factors and ROS were significantly increased, and the OPN/CD44/PI3K signal was further activated, weakening the ameliorative effect of the SQW drug-containing serum. Conclusion: Overall, SQW contributed to ameliorating COPD by reducing airway inflammation and oxidative stress through inhibiting the OPN/CD44/PI3K positive feedback loop. © 2024","Airway inflammation; COPD; OPN/CD44/PI3K positive feedback loop; SQW","Animals; Anti-Inflammatory Agents; Cell Line; Computational Biology; Disease Models, Animal; Drugs, Chinese Herbal; Humans; Hyaluronan Receptors; Lipopolysaccharides; Lung; Male; Mice; Mice, Inbred C57BL; Network Pharmacology; Osteopontin; Phosphatidylinositol 3-Kinases; Pulmonary Disease, Chronic Obstructive; Signal Transduction; B cell maturation antigen; Chinese medicinal formula; cigarette smoke; dexamethasone; dpp6 protein; glutathione peroxidase; herbaceous agent; Hermes antigen; hypoxia inducible factor 1; interleukin 1beta; interleukin 6; kiaa0125 protein; lipopolysaccharide; malonaldehyde; messenger RNA; osteopontin; phosphatidylinositol 3 kinase; phosphoinositide dependent protein kinase 1; shen qi wan; steroid hormone; superoxide dismutase; tumor necrosis factor; unclassified drug; antiinflammatory agent; herbaceous agent; hyaluronic acid binding protein; osteopontin; phosphatidylinositol 3 kinase; shenqi; adult; animal experiment; animal model; Article; BEAS-2B cell line; bioinformatics; chronic obstructive lung disease; clinical article; clinical evaluation; controlled study; drug efficacy; drug megadose; enzyme activation; enzyme linked immunosorbent assay; gene expression profiling; gene overexpression; histopathology; human; human cell; human tissue; immunofluorescence; in vitro study; ingenuity pathway analysis; KEGG; low drug dose; lung function; lung parenchyma; machine learning; male; mouse; nonhuman; oxidative stress; pathway analysis; Pi3K/Akt signaling; plasmid; positive feedback; protein expression; regulatory mechanism; respiratory tract inflammation; staining; systems pharmacology; therapy effect; weighted gene co expression network analysis; Western blotting; animal; bioinformatics; C57BL mouse; cell line; disease model; drug effect; drug therapy; genetics; lung; metabolism; pathology; signal transduction","","dexamethasone, 50-02-2; glutathione peroxidase, 9013-66-5; malonaldehyde, 542-78-9; osteopontin, 106441-73-0; phosphatidylinositol 3 kinase, 115926-52-8; superoxide dismutase, 37294-21-6, 9016-01-7, 9054-89-1; Anti-Inflammatory Agents, ; Drugs, Chinese Herbal, ; Hyaluronan Receptors, ; Lipopolysaccharides, ; Osteopontin, ; Phosphatidylinositol 3-Kinases, ; shenqi, ","","","","","Christenson S.A., Smith B.M., Bafadhel M., Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Halpin D.M.G., Celli B.R., Criner G.J., Frith P., Lopez Varela M.V., Salvi S., Vogelmeier C.F., Chen R., Mortimer K., Montes de Oca M., Aisanov Z., Obaseki D., Decker R., Agusti A., The GOLD Summit on chronic obstructive pulmonary disease in low- and middle-income countries, Int. 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Qiu; School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China; email: dongguacha126@126.com","","Elsevier B.V.","","","","","","15675769","","IINMB","39577218","English","Int. Immunopharmacol.","Article","Final","","Scopus","2-s2.0-85209538543"
"Hu C.-Y.; Gutierrez-Avila I.; He M.Z.; Lavigne É.; Alcala C.S.; Yitshak-Sade M.; Lamadrid-Figueroa H.; Tamayo-Ortiz M.; Mercado-Garcia A.; Just A.C.; Gennings C.; Téllez-Rojo M.M.; Wright R.O.; Wright R.J.; Rosa M.J.","Hu, Cheng-Yang (57201882052); Gutierrez-Avila, Ivan (57205564334); He, Mike Z. (57192544766); Lavigne, Éric (15520780100); Alcala, Cecilia S. (57192663970); Yitshak-Sade, Maayan (56416385900); Lamadrid-Figueroa, Hector (6505665297); Tamayo-Ortiz, Marcela (57202319882); Mercado-Garcia, Adriana (6506651026); Just, Allan C. (57219155650); Gennings, Chris (7004630085); Téllez-Rojo, Martha M (57203583920); Wright, Robert O. (7403912299); Wright, Rosalind J. (7403911777); Rosa, Maria José (24171671600)","57201882052; 57205564334; 57192544766; 15520780100; 57192663970; 56416385900; 6505665297; 57202319882; 6506651026; 57219155650; 7004630085; 57203583920; 7403912299; 7403911777; 24171671600","Windows of susceptibility and joint effects of prenatal and postnatal ambient air pollution and temperature exposure on asthma and wheeze in Mexican children","2024","Environment International","193","","109122","","","","0","10.1016/j.envint.2024.109122","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208573934&doi=10.1016%2fj.envint.2024.109122&partnerID=40&md5=fa7a2062b9bab93d5e4899cf9e46cacd","Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, 230032, China; Population Studies Division, Health Canada, 269 Laurier Avenue West, Ottawa, K1A 0K9, ON, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada; Department of Perinatal Health, Center for Population Health Research, National Institute of Public Health (INSP), Av. Universidad #655 Col. Santa Maria Ahuacatitlan C.P. 62100, Cuernavaca, Morelos, Mexico; Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, 10032, NY, United States; Center for Nutrition and Health Research, National Institute of Public Health, Av. Universidad #655 Col. Santa Maria Ahuacatitlan C.P. 62100, Cuernavaca, Morelos, Mexico; Department of Epidemiology, Brown University School of Public Health, 121 S Main St, Providence, 02903, RI, United States; Department of Public Health, Icahn School of Medicine at Mount Sinai, 1184 Fifth Avenue, New York, 10029, NY, United States; Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States","Hu C.-Y., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States, Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, 230032, China; Gutierrez-Avila I., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; He M.Z., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Lavigne É., Population Studies Division, Health Canada, 269 Laurier Avenue West, Ottawa, K1A 0K9, ON, Canada, School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada; Alcala C.S., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Yitshak-Sade M., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Lamadrid-Figueroa H., Department of Perinatal Health, Center for Population Health Research, National Institute of Public Health (INSP), Av. Universidad #655 Col. Santa Maria Ahuacatitlan C.P. 62100, Cuernavaca, Morelos, Mexico; Tamayo-Ortiz M., Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, 10032, NY, United States; Mercado-Garcia A., Center for Nutrition and Health Research, National Institute of Public Health, Av. Universidad #655 Col. Santa Maria Ahuacatitlan C.P. 62100, Cuernavaca, Morelos, Mexico; Just A.C., Department of Epidemiology, Brown University School of Public Health, 121 S Main St, Providence, 02903, RI, United States; Gennings C., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Téllez-Rojo M.M., Center for Nutrition and Health Research, National Institute of Public Health, Av. Universidad #655 Col. Santa Maria Ahuacatitlan C.P. 62100, Cuernavaca, Morelos, Mexico; Wright R.O., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States, Department of Public Health, Icahn School of Medicine at Mount Sinai, 1184 Fifth Avenue, New York, 10029, NY, United States, Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Wright R.J., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States, Department of Public Health, Icahn School of Medicine at Mount Sinai, 1184 Fifth Avenue, New York, 10029, NY, United States, Institute for Climate Change, Environmental Health, and Exposomics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States; Rosa M.J., Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1057, New York, 10029, NY, United States","Introduction: Prenatal and early-life exposure to air pollution and extreme temperatures are associated with childhood asthma and wheeze. However, potential windows of susceptibility and their sex-specific and interactive effects have not been fully elucidated. We aimed to identify critical windows of susceptibility and evaluate sex-specific effects in these associations, and evaluate exposure interactions. Methods: We analyzed data from 468 mother–child pairs enrolled in the PROGRESS birth cohort in Mexico City. Daily residential levels of PM2.5, NO2, and temperature were generated from our validated spatiotemporally resolved models from conception to age 4 years. Childhood asthma and wheeze outcomes were collected at 4–6 and 7–8 years. Distributed lag nonlinear models (DLNMs) were used to identify susceptible windows for prenatal weekly-specific and postnatal monthly-specific associations of air pollution and temperature with respiratory outcomes adjusting for covariates. To evaluate sex-specific effects, DLNMs were stratified. Joint effects were assessed using relative excess risk due to interaction and attributable proportion. Results: Mid-gestation was a critical window for both PM2.5 (weeks 20–28, cumulative OR: 1.18 [95% CI: 1.01, 1.37]; weeks 19–26, cumulative OR: 1.18 [95% CI: 1.02, 1.36]) and NO2 (weeks 18–25, cumulative OR: 1.16 [95% CI: 1.02, 1.31]) exposure, associated with higher odds of wheeze. Postnatal exposure to PM2.5 and NO2 during the first year of life was also linked to higher odds of wheeze. The warmer and colder temperatures showed mixed effects on respiratory outcomes. We observed a synergistic interaction between high PM2.5 and high temperature exposure during the first year of life, associated with higher odds of current wheeze. The associations of prenatal air pollution and temperature exposure with respiratory outcomes were more pronounced in males. Conclusions: Early-life air pollution exposure contributes to the development of childhood asthma and wheeze, while exposure to temperature showed mixed associations with respiratory outcomes. © 2024 The Author(s)","Air pollution; Asthma; Susceptible window; Temperature; Wheeze","Air Pollutants; Air Pollution; Asthma; Child; Child, Preschool; Disease Susceptibility; Environmental Exposure; Female; Humans; Male; Mexico; Particulate Matter; Pregnancy; Prenatal Exposure Delayed Effects; Respiratory Sounds; Temperature; Mexico [North America]; Critical temperature; Diseases; nitrogen dioxide; tobacco smoke; Air pollution exposures; Asthma; Earliest life; Joint effect; NO  2; PM 2.5; Specific effects; Susceptible window; Temperature exposure; Wheeze; ambient air; asthma; atmospheric pollution; child care; health care; pollution exposure; temperature anomaly; adult; air pollution; airway resistance; ambient air; Article; asthma; attributable risk; body mass; child; cohort analysis; cross validation; directed acyclic graph; early onset asthma; education; environmental exposure; environmental factor; female; gestational age; human; lung development; machine learning; male; menstrual cycle; Mexican; outcome assessment; particulate matter 2.5; perinatal exposure; prediction; prenatal care; prenatal exposure; preschool child; prospective study; questionnaire; school child; seasonal variation; social status; spatiotemporal analysis; synergistic effect; temperature; wheezing; abnormal respiratory sound; adverse event; air pollutant; disease predisposition; epidemiology; Mexico; particulate matter; pregnancy; prenatal exposure delayed effect; temperature; Risk assessment","","nitrogen dioxide, 10102-44-0; Air Pollutants, ; Particulate Matter, ","","","National Institute of Public Health; Instituto Nacional de Perinatología, INPer; Ministry of Health Mexico; National Center for Advancing Translational Sciences, NCATS; American British Cowdray Medical Center; National Institute of Child Health and Human Development, NICHD, (R01ES014930, R01ES021357, P30ES023515, T32HD049311, R01ES013744, R24ES028522); National Institute of Child Health and Human Development, NICHD; Icahn School of Medicine at Mount Sinai, ISMMS, (UL1TR004419); Icahn School of Medicine at Mount Sinai, ISMMS","Funding text 1: We are grateful to the PROGRESS participants and staff at the National Institute of Public Health/Ministry of Health of Mexico and the National Institute of Perinatology. We thank the ABC (American British Cowdray Medical Center) in Mexico for providing some of the needed research facilities.; Funding text 2: This work was supported by the National Institute of Child Health and Human Development grant T32HD049311 (Alcala, CS) and the Excellence in Doctoral Education and Training Enhancement Program for visiting researcher role at the Icahn School of Medicine at Mount Sinai (Hu, C-Y). The National Institute of Environmental Health Sciences grants, R00ES027496 and R01ES033245 (Rosa MJ, PI). The PROGRESS project has been supported by the following grants; R01ES014930, R01ES013744, R24ES028522, P30ES023515 (Wright RO, PI) and R01ES021357 (Baccarelli A and Wright RO, MPI). This work was also supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Award (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. ","Agache I., Et al., The impact of outdoor pollution and extreme temperatures on asthma-related outcomes: a systematic review for the EAACI guidelines on environmental science for allergic diseases and asthma, Allergy, (2024); Aguilera I., Et al., Early-life exposure to outdoor air pollution and respiratory health, ear infections, and eczema in infants from the INMA study, Environ. Health Perspect., 121, pp. 387-392, (2013); Aguilera J., Et al., Editorial: the impact of climate change on allergic disease, Front. Allergy, 4, (2023); Althouse A.D., Adjust for multiple comparisons? it's not that simple, Ann. Thorac. 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"Beriwal S.; Ayeelyan J.","Beriwal, Snehlata (57443690300); Ayeelyan, John (59157815000)","57443690300; 59157815000","Decoding Pollution: A Federated Learning-Based Pollution Prediction Study with Health Ramifications Using Causal Inferences","2025","Electronics (Switzerland)","14","2","350","","","","0","10.3390/electronics14020350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215966000&doi=10.3390%2felectronics14020350&partnerID=40&md5=94c46fe401dc90f7b5e694a8796ead43","School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida, 201310, India","Beriwal S., School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida, 201310, India; Ayeelyan J., School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida, 201310, India","Unprecedented levels of air pollution in our cities due to rapid urbanization have caused major health concerns, severely affecting the population, especially children and the elderly. A steady loss of ecological balance, without remedial measures like phytoremediation, coupled with alarming vehicular and industrial pollution, have pushed the Air Quality Index (AQI) and particulate matter (PM) to dangerous levels, especially in the metropolitan cities of India. Monitoring and accurate prediction of inhalable Particulate Matter 2.5 (PM2.5) and Particulate Matter 10 (PM10) levels, which cause escalations in and increase the risks of asthma, respiratory inflammation, bronchitis, high blood pressure, compromised lung function, and lung cancer, have become more critical than ever. To that end, the authors of this work have proposed a federated learning (FL) framework for monitoring and predicting PM2.5 and PM10 across multiple locations, with a resultant impact analysis with respect to key health parameters. The proposed FL approach encompasses four stages: client selection for processing and model updates, aggregation for global model updates, a pollution prediction model with necessary explanations, and finally, the health impact analysis corresponding to the PM levels. This framework employs a VGG-19 deep learning model, and leverages Causal Inference for interpretability, enabling accurate impact analysis across a host of health conditions. This research has employed datasets specific to India, Nepal, and China for the purposes of model prediction, explanation, and impact analysis. The approach was found to achieve an overall accuracy of 92.33%, with the causal inference-based impact analysis producing an accuracy of 84% for training and 72% for testing with respect to PM2.5, and an accuracy of 79% for training and 74% for testing with respect to PM10. Compared to previous studies undertaken in this field, this proposed approach has demonstrated better accuracy, and is the first of its kind to analyze health impacts corresponding to PM2.5 and PM10 levels. © 2025 by the authors.","AQI; federated learning framework; health analysis; impact analysis of PM2.5 and PM10; pollutant prediction model","","","","","","","","Messan S., Shahud A., Anis A., Kalam R., Ali S., Aslam M.I., Air-MIT: Air Quality Monitoring Using Internet of Things, Eng. Proc, 20, (2022); Beriwal S., John A., A review of Various Techniques for Forecasting Pollution and Air Quality Indexing, Proceedings of the 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 1680-1686; Gilik A., Ogrenci A.S., Ozmen A., Air quality prediction using CNN+LSTM-based hybrid deep learning architecture, Environ. Sci. Pollut. 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Prod, 209, pp. 134-145, (2018); Beriwal S., A J., K S., Spatial and Temporal based Pollution Forecasting using Hybrid Model, Proceedings of the 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), pp. 991-998; Huang Y., Ying J.J.-C., Tseng V.S., Spatio-attention embedded recurrent neural network for air quality prediction, Knowledge-Based Syst, 233, (2021); Zhang K., The J., Xie G., Yu H., Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone, J. Clean. Prod, 277, (2020); Zhang Q., Han Y., Li V.O.K., Lam J.C.K., Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-Grained Air Pollution Estimation and Forecast in Metropolitan Cities, IEEE Access, 10, pp. 55818-55841, (2022); Wang Q., Liu Y., Pan X., Atmosphere pollutants and mortality rate of respiratory diseases in Beijing, Sci. Total Environ, 391, pp. 143-148, (2008); Abe K.C., Miraglia S.G.E.K., Health impact assessment of air pollution in São Paulo, Brazil, Int. J. Environ. Res. Public Health, 13, (2016); Olstrup H., An Air Quality Health Index (AQHI) with Different Health Outcomes Based on the Air Pollution Concentrations in Stockholm during the Period of 2015–2017, Atmosphere, 11, (2020); Xu K., Cui K., Young L.-H., Wang Y.-F., Hsieh Y.-K., Wan S., Zhang J., Air Quality Index, Indicatory Air Pollutants and Impact of COVID-19 Event on the Air Quality near Central China, Aerosol Air Qual. Res, 20, pp. 1204-1221, (2020); Abelsohn A., Stieb D.M., Health effects of outdoor air pollution: Approach to counseling patients using the Air Quality Health Index, Can. Fam. Physician, 57, pp. 881-887, (2011); Jalili M., Ehrampoush M.H., Mokhtari M., Ebrahimi A.A., Mazidi F., Abbasi F., Karimi H., Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran, Sci. Rep, 11, (2021); Fei Z., Ryeznik Y., Sverdlov A., Tan C.W., Wong W.K., An overview of healthcare data analytics with applications to the COVID-19 pandemic, IEEE Trans. Big Data, 8, pp. 1463-1480, (2021); Li L., Fan Y., Tse M., Lin K., A review of applications in federated learning, Comput. Ind. Eng, 149, (2020); Niknam S., Dhillon H.S., Reed J.H., Federated learning for wireless communications: Motivation, opportunities, and challenges, IEEE Commun. Mag, 58, pp. 46-51, (2020); Jiang J.C., Kantarci B., Oktug S., Soyata T., Federated Learning in Smart City Sensing: Challenges and Opportunities, Sensors, 20, (2020); Nguyen D.-V., Zettsu K., Spatially-distributed Federated Learning of Convolutional Recurrent Neural Networks for Air Pollution Prediction, Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), pp. 3601-3608; Abimannan S., A J., Shukla S., Satheesh D., Federated Learning for Improved Air Pollution Prediction: A Combined LSTM-SVR Approach, Proceedings of the 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), pp. 1-7; Neo E.X., Hasikin K., Mokhtar M.I., Lai K.W., Azizan M.M., Razak S.A., Hizaddin H.F., Towards Integrated Air Pollution Monitoring and Health Impact Assessment Using Federated Learning: A Systematic Review, Front. Public Health, 10, (2022); Smuha N.A., The EU Approach to Ethics Guidelines for Trustworthy Artificial Intelligence, Comput. Law Rev. Int, 20, pp. 97-106, (2019); Liu H., Wang Y., Fan W., Liu X., Li Y., Jain S., Tang J., Trustworthy ai: A computational perspective, ACM Trans. Intell. Syst. Technol, 14, pp. 1-59, (2022); Ho C.W.L., Ali J., Caals K., Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance, Bull. World Health Organ, 98, (2020); Putra M.A.P., Karna N., Alief R.N., Zainudin A., Kim D.-S., Lee J.-M., Sampedro G.A., PureFed: An Efficient Collaborative and Trustworthy Federated Learning Framework Based on Blockchain Network, IEEE Access, 1, pp. 82413-82426, (2024); Lee W., Reward-based participant selection for improving federated reinforcement learning, ICT Express, 9, pp. 803-808, (2022); Rouniyar A., Utomo S., John A., Hsiung P.A., Air Pollution Image Dataset from India and Nepal, (2023); Li T., Sahu A.K., Zaheer M., Sanjabi M., Talwalkar A., Smith V., Federated optimization in heterogeneous networks, Proc. Mach. Learn. Syst, 2, pp. 429-450, (2020); Ayeelyan J., Utomo S., Rouniyar A., Hsu H.C., Hsiung P.A., Federated learning design and functional models: Survey, Artif. Intell. Rev, 58, (2025); Li M., Using the propensity score method to estimate causal effects: A review and practical guide, Organ. Res. Methods, 16, pp. 188-226, (2013); Liu C., Tsow F., Zou Y., Tao N., Particle Pollution Estimation Based on Image Analysis, PLoS ONE, 11, (2016); Bo Q., Yang W., Rijal N., Xie Y., Feng J., Zhang J., Particle Pollution Estimation from Images Using Convolutional Neural Network and Weather Features, Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3433-3437; Wang X., Wang M., Liu X., Zhang X., Li R., A PM2.5 concentration estimation method based on multi-feature combination of image patches, Environ. Res, 211, (2022); Zhang Q., Fu F., Tian R., A deep learning and image-based model for air quality estimation, Sci. Total. Environ, 724, (2020); Zhang Q., Tian L., Fu F., Wu H., Wei W., Liu X., Real-Time and Image-Based AQI Estimation Based on Deep Learning, Adv. Simul, 5, (2022); Kow P.-Y., Hsia I.-W., Chang L.-C., Chang F.-J., Real-time imagebased air quality estimation by deep learning neural networks, J. Environ. Manag, 307, (2022)","J. Ayeelyan; School of Computer Science and Engineering, Galgotias Univeristy, Greater Noida, 201310, India; email: johnmtech@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20799292","","","","English","Electronics (Switzerland)","Article","Final","","Scopus","2-s2.0-85215966000"
"Kong H.; Li Y.; Shen Y.; Pan J.; Liang M.; Geng Z.; Zhang Y.","Kong, Haobo (57220051500); Li, Yong (57222657939); Shen, Ya (57226042421); Pan, Jingjing (57697509400); Liang, Min (59501551100); Geng, Zhi (57210820624); Zhang, Yanbei (36116119700)","57220051500; 57222657939; 57226042421; 57697509400; 59501551100; 57210820624; 36116119700","Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms","2024","European journal of medical research","29","1","","618","","","0","10.1186/s40001-024-02218-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214185930&doi=10.1186%2fs40001-024-02218-3&partnerID=40&md5=f09024c510d2b54fa33fbae7d6e08a86","Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China; Department of Respiratory Intensive Care Unit, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230022, China; Department of Tuberculosis, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230000, Anhui, China; Department of Respiratory and Critical Care Medicine, Fuyang Infectious Disease Clinical College of Anhui Medical University, Fuyang, Anhui, China; Department of Neurology, First Affiliated Hospital of Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China; Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China","Kong H., Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China, Department of Respiratory Intensive Care Unit, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230022, China; Li Y., Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China, Department of Tuberculosis, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230000, Anhui, China; Shen Y., Department of Respiratory and Critical Care Medicine, Fuyang Infectious Disease Clinical College of Anhui Medical University, Fuyang, Anhui, China; Pan J., Department of Respiratory Intensive Care Unit, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230022, China; Liang M., Department of Tuberculosis, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230000, Anhui, China; Geng Z., Department of Neurology, First Affiliated Hospital of Anhui Medical University, Hefei, China, Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, China, Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China; Zhang Y., Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China","BACKGROUND: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms. METHODS: Data were collected from two centers and categorized into development and validation cohorts. Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method. Five machine learning algorithms, logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM), were utilized to construct the predictive models. Model performance was evaluated through nested cross-validation and area under the curve (AUC) metrics, supplemented by interpretations using Shapley Additive explanations (SHAP) and line charts of AUC values. Models were subjected to external validation using an independent validation group, facilitating the early identification and management of pulmonary embolism risks in tuberculosis patients. RESULTS: Data from 694 patients were used for model development, and 236 patients from the validation group met the enrollment criteria. The optimal subset of variables identified included D-dimer, smoking status, dyspnea, age, sex, diabetes, platelet count, cough, fibrinogen, hemoglobin, hemoptysis, hypertension, chronic obstructive pulmonary disease (COPD), and chest pain. The RF model outperformed others, achieving an AUC of 0.839 (95% CI 0.780-0.899) and maintaining the highest average performance in external fivefold cross-validation (AUC: 0.906 ± 0.041). CONCLUSIONS: The RF model demonstrates high and consistent effectiveness in predicting pulmonary embolism risk in tuberculosis patients. © 2024. The Author(s).","Machine learning; Pulmonary embolism; Pulmonary tuberculosis; Risk prediction","Adult; Aged; Algorithms; Female; Humans; Machine Learning; Male; Middle Aged; Pulmonary Embolism; Risk Assessment; Risk Factors; Tuberculosis; adult; aged; algorithm; complication; epidemiology; etiology; female; human; lung embolism; machine learning; male; middle aged; procedures; risk assessment; risk factor; tuberculosis","","","","","","","","","","","","","","","","2047783X","","","39710777","English","Eur J Med Res","Article","Final","","Scopus","2-s2.0-85214185930"
"Sinha R.; Sahu K.K.","Sinha, Rashmi (59344275400); Sahu, Kishor Kumar (59343828600)","59344275400; 59343828600","Developing A Machine Learning Model to Predict Medication Adherence in Chronic Disease Management","2025","Journal of Neonatal Surgery","14","1","","10","16","6","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219510450&partnerID=40&md5=4067349ef8cef523c122f82a527f9352","Department of Pharmacy, Kalinga University, Raipur, India","Sinha R., Department of Pharmacy, Kalinga University, Raipur, India; Sahu K.K., Department of Pharmacy, Kalinga University, Raipur, India","In the healthcare industry, chronic disease prediction is crucial. It is crucial to diagnose the illness early. Large amounts of data are generated in computer science as a result of significant technological advancements. Many medical databases are created as clinical information networks advance. Data mining, the process of managing vast amounts of diverse data and extracting insights from it, has emerged as a crucial area of study. Early illness detection, patient treatment, and community services from huge data creation in the biomedical and healthcare communities are all benefited by the accurate analysis of medical data. Nowadays, one of the main areas of research is the management and extraction of knowledge from vast amounts of diverse data. Accurate processing of health data helps the biomedical and healthcare communities by improving patient care, early illness detection, and community services. However, analytical precision is reduced if medical data is not sufficiently consistent. For the domains of biomedical pattern recognition and master learning, the perception and diagnosis of chronic disease are guaranteed to be consistent. Additionally, the decision-making approach's goal is pushed. The study of high-dimensional, multi-modal biomedical data can be effectively addressed by machine learning. In computer science, chronic disease prediction is crucial. Early detection and prediction of chronic disease is crucial. The dataset for chronic obstructive pulmonary disease is used for analysis by the suggested model. Using supervised machine learning techniques such as Random Forest, Multiplayer Perceptron, Logistic Regression, Stochastic Gradient Descent, and XG boost, we provide a chronic obstructive pulmonary disease prediction system. Next, we examine classification techniques for predicting chronic diseases using a variety of criteria, such as accuracy, precision, sensitivity, ROC, and AUC. © 2025 EL-MED-Pub. All rights reserved.","decision making; Disease; WHO","","","","","","","","Wang L, Fan R, Zhang C, Hong L, Zhang T, Chen Y, Liu K, Wang Z, Zhong J., Applying machine learning models to predict medication nonadherence in Crohn’s disease maintenance therapy, Patient preference and adherence, 3, pp. 917-926, (2020); Menon PA, Gunasundari R., Deep Feature Extraction and Classification of Alzheimer's Disease: A Novel Fusion of Vision Transformer-DenseNet Approach with Visualization; Zullig LL, Jazowski SA, Wang TY, Hellkamp A, Wojdyla D, Thomas L, Egbuonu-Davis L, Beal A, Bosworth HB., Novel application of approaches to predicting medication adherence using medical claims data, Health services research, 54, 6, pp. 1255-1262, (2019); Claycomb WR, Huth CL, Flynn L, McIntire DM, Lewellen TB, Center CI., Chronological examination of insider threat sabotage: Preliminary observations, J. Wirel. Mob. Networks Ubiquitous Comput. 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Neonatal. Surg.","Article","Final","","Scopus","2-s2.0-85219510450"
"Li X.; Vaghi E.; Pasi G.; Coulson N.S.; De Simoni A.; Viviani M.; Panzarasa P.; Karampatakis G.; Wood H.E.; Griffiths C.J.","Li, Xiancheng (58481783300); Vaghi, Emanuela (59559301900); Pasi, Gabriella (7003307397); Coulson, Neil S. (7003467289); De Simoni, Anna (57200327390); Viviani, Marco (13410043100); Panzarasa, Pietro (6602241741); Karampatakis, Georgios (57205421092); Wood, Helen E. (57206025237); Griffiths, Chris J. (55228809500)","58481783300; 59559301900; 7003307397; 7003467289; 57200327390; 13410043100; 6602241741; 57205421092; 57206025237; 55228809500","Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning–Based Sentiment Analysis","2025","Journal of Medical Internet Research","27","","J Med Internet Res 2025 | vol. 27 | e56038","","","","0","10.2196/56038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217916158&doi=10.2196%2f56038&partnerID=40&md5=204be2714d9b6d6b187320afa9474234","School of Business and Management, Queen Mary University of London, London, United Kingdom; Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; School of Medicine, University of Nottingham, Nottingham, United Kingdom; Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom; School of Business and Management, Queen Mary University of London, London, United Kingdom; Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom","Li X., School of Business and Management, Queen Mary University of London, London, United Kingdom; Vaghi E., Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Pasi G., Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Coulson N.S., School of Medicine, University of Nottingham, Nottingham, United Kingdom; De Simoni A., Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom; Viviani M., Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy; Panzarasa P., School of Business and Management, Queen Mary University of London, London, United Kingdom; Karampatakis G., Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom; Wood H.E., Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom; Griffiths C.J., Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom","Background: Online health communities (OHCs) enable people with long-term conditions (LTCs) to exchange peer self-management experiential information, advice, and support. Engagement of “superusers,” that is, highly active users, plays a key role in holding together the community and ensuring an effective exchange of support and information. Further studies are needed to explore regular users’ interactions with superusers, their sentiments during interactions, and their ultimate impact on the self-management of LTCs. Objective: This study aims to gain a better understanding of sentiment distribution and the dynamic of sentiment of posts from 2 respiratory OHCs, focusing on regular users’ interaction with superusers. Methods: We conducted sentiment analysis on anonymized data from 2 UK respiratory OHCs hosted by Asthma UK (AUK), and the British Lung Foundation (BLF) charities between 2006-2016 and 2012-2016, respectively, using the Bio-Bidirectional Encoder Representation from Transformers (BioBERT), a pretrained language representation model. Given the scarcity of health-related labeled datasets, BioBERT was fine-tuned on the COVID-19 Twitter Dataset. Positive, neutral, and negative sentiments were categorized as 1, 0, and –1, respectively. The average sentiment of aggregated posts by regular users and superusers was then calculated. Superusers were identified based on a definition already used in our previous work (ie, “the 1% users with the largest number of posts over the observation period”) and VoteRank, (ie, users with the best spreading ability). Sentiment analyses of posts by superusers defined with both approaches were conducted for correlation. Results: The fine-tuned BioBERT model achieved an accuracy of 0.96. The sentiment of posts was predominantly positive (60% and 65% of overall posts in AUK and BLF, respectively), remaining stable over the years. Furthermore, there was a tendency for sentiment to become more positive over time. Overall, superusers tended to write shorter posts characterized by positive sentiment (63% and 67% of all posts in AUK and BLF, respectively). Superusers defined by posting activity or VoteRank largely overlapped (61% in AUK and 79% in BLF), showing that users who posted the most were also spreaders. Threads initiated by superusers typically encouraged regular users to reply with positive sentiments. Superusers tended to write positive replies in threads started by regular users whatever the type of sentiment of the starting post (ie, positive, neutral, or negative), compared to the replies by other regular users (62%, 51%, 61% versus 55%, 45%, 50% in AUK; 71%, 62%, 64% versus 65%, 56%, 57% in BLF, respectively; P<.001, except for neutral sentiment in AUK, where P=.36). Conclusions: Network and sentiment analyses provide insight into the key sustaining role of superusers in respiratory OHCs, showing they tend to write and trigger regular users’ posts characterized by positive sentiment. ©Xiancheng Li, Emanuela Vaghi, Gabriella Pasi, Neil S Coulson, Anna De Simoni, Marco Viviani, AD HOC Group.","asthma; bio-bidirectional encoder representations from transformers; chronic obstructive pulmonary disease; online health communities; sentiment analysis; social media; social network analysis","Asthma; COVID-19; Deep Learning; Humans; Self-Management; Social Media; United Kingdom; anonymised data; Article; asthma; clinical outcome; community; coronavirus disease 2019; deep learning; demographics; human; identifiable information; Internet; natural language processing; online health community; self care; sentiment analysis; social network analysis; procedures; psychology; social media; United Kingdom","","","","","HealthUnlocked; National Health Service; National Institute for Health and Care Research, NIHR, (202037); National Institute for Health and Care Research, NIHR","The authors would like to thank AUK, BLF, and HealthUnlocked for granting them permission to conduct the study. The study was partly funded by the National Institute for Health and Care Research Program Grant for Applied Research (reference 202037; effectiveness and cost-effectiveness of a digital social intervention for people with troublesome asthma promoted by primary care clinicians). The views expressed are those of the author or authors and not necessarily those of the National Health Service, National Institute for Health and Care Research, or the Department of Health and Social Care.","Hossain SN, Jaglal SB, Shepherd J, Perrier L, Tomasone JR, Sweet SN, Et al., Web-based peer support interventions for adults living with chronic conditions: scoping review, JMIR Rehabil Assist Technol, 8, 2, (2021); van Uden-Kraan CF, Drossaert CHC, Taal E, Seydel ER, van de Laar MAFJ., Self-reported differences in empowerment between lurkers and posters in online patient support groups, J Med Internet Res, 10, 2, (2008); Petkovic J, Duench S, Trawin J, Dewidar O, Pardo Pardo J, Simeon R, Et al., Behavioural interventions delivered through interactive social media for health behaviour change, health outcomes, and health equity in the adult population, Cochrane Database Syst Rev, 5, 5, (2021); Allen C, Vassilev I, Kennedy A, Rogers A., Long-term condition self-management support in online communities: a meta-synthesis of qualitative papers, J Med Internet Res, 18, 3, (2016); 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Bakshy E, Hofman JM, Mason WA, Watts DJ., Everyone's an influencer: quantifying influence on twitter, Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65-74, (2011); Dezso Z, Barabasi AL., Halting viruses in scale-free networks, Phys Rev E Stat Nonlin Soft Matter Phys, 65, (2002); Pastor-Satorras R, Vespignani A., Immunization of complex networks, Phys Rev E Stat Nonlin Soft Matter Phys, 65, (2002); Paul G, Sreenivasan S, Stanley HE., Resilience of complex networks to random breakdown, Phys Rev E Stat Nonlin Soft Matter Phys, 72, (2005); Zhang J, Chen D, Dong Q, Zhao Z., Identifying a set of influential spreaders in complex networks, Sci Rep, 6, 1, (2016); Pozzi FA, Fersini E, Messina E, Liu B., Sentiment Analysis in Social Networks, (2016); Zunic A, Corcoran P, Spasic I., Sentiment analysis in health and well-being: systematic review, JMIR Med Inform, 8, 1, (2020); Yue L, Chen W, Li X, Zuo W, Yin M., A survey of sentiment analysis in social media, Knowl Inf Syst, 60, 2, pp. 617-663, (2018); Crocamo C, Viviani M, Famiglini L, Bartoli F, Pasi G, Carra G., Surveilling COVID-19 emotional contagion on Twitter by sentiment analysis, Eur Psychiatry, 64, 1, (2021); Viviani M, Crocamo C, Mazzola M, Bartoli F, Carra G, Pasi G., Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content, Future Gener Comput Syst, 125, pp. 446-459, (2021); Crocamo C, Viviani M, Bartoli F, Carra G, Pasi G., Detecting binge drinking and alcohol-related risky behaviours from Twitter's users: an exploratory content- and topology-based analysis, Int J Environ Res Public Health, 17, 5, (2020); Gabarron E, Dorronzoro E, Rivera-Romero O, Wynn R., Diabetes on Twitter: a sentiment analysis, J Diabetes Sci Technol, 13, 3, pp. 439-444, (2019); Cabling ML, Turner JW, Hurtado-de-Mendoza A, Zhang Y, Jiang X, Drago F, Et al., Sentiment analysis of an online breast cancer support group: communicating about tamoxifen, Health Commun, 33, 9, pp. 1158-1165, (2018); Effectiveness and ost-effectiveness of a digital social intervention for people with troublesome asthma promoted by primary care clinicians (AD-HOC); Petersen CL, Li X, Stevens CJ, Gooding TL, Carpenter-Song EA, Batsis JA., Adapting natural language processing and sentiment analysis methods for an intervention in older adults: positive perceptions of health and technology, Gerontechnology, 22, 1, pp. 1-6, (2023); Pearson JL, Amato MS, Papandonatos GD, Zhao K, Erar B, Wang X, Et al., Exposure to positive peer sentiment about nicotine replacement therapy in an online smoking cessation community is associated with NRT use, Addict Behav, 87, pp. 39-45, (2018); Health Unlocked; Naseem U, Razzak I, Khan SK, Prasad M., A comprehensive survey on word representation models: from classical to state-of-the-art word representation language models, ACM Trans Asian Low Resour Lang Inf Process, 20, 5, pp. 1-35, (2021); Devlin J, Chang MW, Lee K, Toutanova K., BERT: pre-training of deep bidirectional transformers for language understanding, (2018); Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Et al., Attention is all you need, (2017); Lee J, Yoon W, Kim S, Kim D, Kim S, So C, Et al., BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36, 4, pp. 1234-1240, (2020); Chakraborty AK, Das S, Kolya AK., Sentiment analysis of COVID-19 tweets using evolutionary classification-based LSTM model, Proceedings of Research and Applications in Artificial Intelligence: RAAI 2020, (2021); Kaggle; Hugging Face; Kingma DP, Ba J., Adam: a method for stochastic optimization, (2014); Sparse categorical crossentropy; Sparse categorical accuracy; Cinelli M, De Francisci Morales G, Galeazzi A, Quattrociocchi W, Starnini M., The echo chamber effect on social media, Proc Natl Acad Sci U S A, 118, 9, (2021)","X. Li; School of Business and Management, Queen Mary University of London, London, Mile End Road, Bethnal Green, E14NS, United Kingdom; email: x.l.li@qmul.ac.uk","","JMIR Publications Inc.","","","","","","14388871","","","39946690","English","J. Med. Internet Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85217916158"
"Lovelace T.C.; Ryu M.H.; Jia M.; Castaldi P.; Sciurba F.C.; Hersh C.P.; Benos P.V.","Lovelace, Tyler C. (57221470243); Ryu, Min Hyung (57204857117); Jia, Minxue (57226177317); Castaldi, Peter (26323082900); Sciurba, Frank C. (55755090900); Hersh, Craig P. (6701515147); Benos, Panayiotis V. (7003299594)","57221470243; 57204857117; 57226177317; 26323082900; 55755090900; 6701515147; 7003299594","Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling","2024","eClinicalMedicine","75","","102786","","","","0","10.1016/j.eclinm.2024.102786","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201680833&doi=10.1016%2fj.eclinm.2024.102786&partnerID=40&md5=9e569b7fc0761b5c4b16ad6c025251fc","Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Department of Epidemiology, University of Florida, Gainesville, FL, United States","Lovelace T.C., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States; Ryu M.H., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Jia M., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States; Castaldi P., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Sciurba F.C., Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Hersh C.P., Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, United States; Benos P.V., Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States, Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, United States, Department of Epidemiology, University of Florida, Gainesville, FL, United States","Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. Methods: In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45–80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45–80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Findings: Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Interpretation: Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. Funding: This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion. © 2024 The Authors","COPD mortality; Graphical models; Machine learning","adult; aged; airway obstruction; all cause mortality; area under the curve; Article; asthma; benchmarking; bioinformatics; body mass; cause of death; chronic obstructive lung disease; clinical outcome; comorbidity; computer assisted tomography; controlled study; coughing; cross validation; cross-sectional study; cystic fibrosis; demographics; diagnostic test accuracy study; dyspnea; ex-smoker; exercise; exertional dyspnea; female; follow up; forced expiratory volume; forced vital capacity; gene expression; heart rate; high risk patient; human; internet access; leukocyte differential count; lung artery pressure; lung function; machine learning; major clinical study; male; mMRC dyspnea score; mortality; mortality risk; mortality risk score; oxygen saturation; pneumonia; prediction; probability; pulmonary hypertension; receiver operating characteristic; retrospective study; risk factor; scoring system; six minute walk test; smoking; smoking cessation; social aspect; social status; spirometry; walking distance","","","","","","","Collaborators GBDCRD, Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015, Lancet Respir Med, 5, 9, pp. 691-706, (2017); Celli B.R., Cote C.G., Marin J.M., Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, 10, pp. 1005-1012, (2004); Puhan M.A., Garcia-Aymerich J., Frey M., Et al., Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index, Lancet, 374, 9691, pp. 704-711, (2009); Soler-Cataluna J.J., Martinez-Garcia M.A., Sanchez L.S., Tordera M.P., Sanchez P.R., Severe exacerbations and BODE index: two independent risk factors for death in male COPD patients, Respir Med, 103, 5, pp. 692-699, (2009); Jones R.C., Donaldson G.C., Chavannes N.H., Et al., Derivation and validation of a composite index of severity in chronic obstructive pulmonary disease: the DOSE Index, Am J Respir Crit Care Med, 180, 12, pp. 1189-1195, (2009); Guerra B., Haile S.R., Lamprecht B., Et al., Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease, BMC Med, 16, 1, (2018); Moll M., Qiao D., Regan E.A., Et al., Machine learning and prediction of all-cause mortality in COPD, Chest, 158, 3, pp. 952-964, (2020); Strand M., Austin E., Moll M., Et al., A risk prediction model for mortality among smokers in the COPDGene® study, Int J Chronic Obstr Pulm Dis, 7, 4, pp. 346-361, (2020); Cox D.R., Regression models and life-tables, J Roy Stat Soc B, 34, 2, pp. 187-202, (1972); Klein J.P., Moeschberger M.L., Survival analysis: techniques for censored and truncated data, 1230, (2003); Ishwaran H., Kogalur U.B., Blackstone E.H., Lauer M.S., Random survival forests, Ann Appl Stat, 2, 3, pp. 841-860, (2008); Glymour C., Zhang K., Spirtes P., Review of causal discovery methods based on graphical models, Front Genet, 10, (2019); Raghu V.K., Poon A., Benos P.V., Evaluation of causal structure learning methods on mixed data types. 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Tanigawa T., Araki S., Nakata A., Et al., Increase in memory (CD4+CD29+ and CD4+CD45RO+) T and naive (CD4+CD45RA+) T-cell subpopulations in smokers, Arch Environ Health, 53, 6, pp. 378-383, (1998); Nakata A., Takahashi M., Irie M., Fujioka Y., Haratani T., Araki S., Relationship between cumulative effects of smoking and memory CD4+ T lymphocyte subpopulations, Addict Behav, 32, 7, pp. 1526-1531, (2007); Starkey M.R., Plank M.W., Casolari P., Et al., IL-22 and its receptors are increased in human and experimental COPD and contribute to pathogenesis, Eur Respir J, 54, 1, (2019); Paats M.S., Bergen I.M., Hoogsteden H.C., van der Eerden M.M., Hendriks R.W., Systemic CD4+ and CD8+ T-cell cytokine profiles correlate with GOLD stage in stable COPD, Eur Respir J, 40, 2, pp. 330-337, (2012); MacLeod M.K.L., Kappler J.W., Marrack P., Memory CD4 T cells: generation, reactivation and re-assignment, Immunology, 130, 1, pp. 10-15, (2010); Daniels H., van Schilfgaarde M., Jansen H.M., Et al., Characterization of CD4+ memory T cell responses directed against common respiratory pathogens in peripheral blood and lung, J Infect Dis, 195, 11, pp. 1718-1725, (2007)","P.V. Benos; University of Florida, Gainesville, 2004 Mowry Rd, 32610, United States; email: pbenos@ufl.edu","","Elsevier Ltd","","","","","","25895370","","","","English","eClinicalMedicine","Article","Final","","Scopus","2-s2.0-85201680833"
"Feng Q.; Lv Z.; Ba C.X.; Zhang Y.Q.","Feng, Qi (59309666200); Lv, ZiWen (59417518600); Ba, Chun Xiao (59309835500); Zhang, Ying Qian (58176712600)","59309666200; 59417518600; 59309835500; 58176712600","Predictive value of triglyceride-glucose index for the occurrence of acute respiratory failure in asthmatic patients of MIMIC-IV database","2024","Scientific Reports","14","1","28631","","","","0","10.1038/s41598-024-74294-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209680816&doi=10.1038%2fs41598-024-74294-8&partnerID=40&md5=d6c4c723d837921766f758a63f9b22cf","Hebei North University, Hebei, Zhangjiakou, 075031, China; Hebei Medical University, Hebei, Shijiazhuang, 050031, China; Three Departments of Respiration, Hebei Children’s Hospital, Hebei, Shijiazhuang, 050031, China","Feng Q., Hebei North University, Hebei, Zhangjiakou, 075031, China, Three Departments of Respiration, Hebei Children’s Hospital, Hebei, Shijiazhuang, 050031, China; Lv Z., Hebei North University, Hebei, Zhangjiakou, 075031, China; Ba C.X., Hebei Medical University, Hebei, Shijiazhuang, 050031, China, Three Departments of Respiration, Hebei Children’s Hospital, Hebei, Shijiazhuang, 050031, China; Zhang Y.Q., Three Departments of Respiration, Hebei Children’s Hospital, Hebei, Shijiazhuang, 050031, China","This study aims to investigate the association between the triglyceride-glucose (TyG) index and the occurrence of acute respiratory failure in asthma patients. This retrospective observational cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database. The primary outcome was the development of acute respiratory failure in asthma patients. Initially, the Boruta algorithm and SHapley Additive exPansions were applied to preliminarily determine the feature importance of the TyG index, and a risk prediction model was constructed to evaluate its predictive ability. Secondly, Logistic regression proportional hazards models were employed to assess the association between the TyG index and acute respiratory failure in asthma patients. Finally, subgroup analyses were conducted for sensitivity analyses to explore the robustness of the results. A total of 751 asthma patients were included in the study. When considering the TyG index as a continuous variable, logistic regression analysis revealed that in the unadjusted Model 1, the odds ratio (OR) was 2.381 (95% CI: 1.857–3.052; P < 0.001), in Model II, the OR was 2.456 (95% CI: 1.809–3.335; P < 0.001), and in the multivariable-adjusted model, the OR was 1.444 (95% CI: 1.029–2.028; P = 0.034). A consistent association was observed between the TyG index and the risk of acute respiratory failure in asthma patients. No significant interaction was found between the TyG index and various subgroups (P > 0.05). Furthermore, machine learning results indicated that an elevated TyG index was a significant feature predictive of respiratory failure in asthma patients. The baseline risk model achieved an AUC of 0.743 (95% CI: 0.679–0.808; P < 0.05), whereas the combination of the baseline risk model with the TyG index yielded an AUC of 0.757 (95% CI: 0.694–0.821; P < 0.05). The TyG index can serve as a predictive indicator for acute respiratory failure in asthma patients, albeit confirmation of these findings requires larger-scale prospective studies. © The Author(s) 2024.","Acute respiratory failure; Asthma; Insulin resistance; MIMIC-IV database; TyG","Adult; Aged; Asthma; Blood Glucose; Databases, Factual; Female; Humans; Male; Middle Aged; Predictive Value of Tests; Respiratory Insufficiency; Retrospective Studies; Risk Factors; Triglycerides; triacylglycerol; adult; aged; asthma; blood; complication; diagnosis; etiology; factual database; female; glucose blood level; human; male; middle aged; predictive value; respiratory failure; retrospective study; risk factor","","Blood Glucose, ; Triglycerides, ","","","Medical Information Mart for Intensive Care","The authors thank all the participants and staff in the Medical Information Mart for Intensive Care (MIMIC-IV) database for their substantial contributions.","Han P., Et al., Analysis of risk factors for acute attacks complicated by respiratory failure in children with asthma, Front. Pediatr, 11, (2024); Ogawa W., Et al., New classification and diagnostic criteria for insulin resistance syndrome, Endocr. J, 69, 2, pp. 107-113, (2022); Tahapary D.L., Et al., Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and tryglyceride/glucose index, Diabetes Metab. Syndr, 16, 8, (2022); Goyal J.P., Et al., Effect of insulin resistance on lung function in asthmatic children, J. Pediatr. Endocrinol. Metab, 35, 2, pp. 217-222, (2021); Bartziokas K., Et al., Unraveling the link between Ιnsulin Resistance and Bronchial Asthma, Biomedicines, 12, 2, (2024); Mathioudakis N.N., Et al., Development and validation of a machine learning model to predict near-term risk of iatrogenic hypoglycemia in hospitalized patients, JAMA Netw. Open, 4, 1, (2021); Johnson A.E.W., . MIMIC-IV, a freely accessible electronic health record dataset, Sci Data, 10, 1; Al Huneiti R., Et al., National clinical guidelines: The diagnosis and management of asthma in adults, Qatar Med. J, 2022, 2, (2022); Sterne J.A., Et al., Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls, BMJ, 338, (2009); Heymans M.W., Twisk J.W.R., Handling missing data in clinical research, J. Clin. Epidemiol, 151, pp. 185-188, (2022); Degenhardt F., Seifert S., Szymczak S., Evaluation of variable selection methods for random forests and omics data sets, Brief. Bioinform, 20, 2, pp. 492-503, (2019); Lundberg S.M., Et al., From local explanations to Global understanding with explainable AI for trees, Nat. Mach. Intell, 2, 1, pp. 56-67, (2020); Nohara Y., Et al., Explanation of machine learning models using shapley additive explanation and application for real data in hospital, Comput. Methods Programs Biomed, 214, (2022); Ye Z., Et al., Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit, Cardiovasc. Diabetol, 22, 1, (2023); Chen T., Qian Y., Deng X., Triglyceride glucose index is a significant predictor of severe disturbance of consciousness and all-cause mortality in critical cerebrovascular disease patients, Cardiovasc. Diabetol, 22, 1, (2023); Cai W., Et al., Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: Analysis of the MIMIC-IV database, Cardiovasc. Diabetol, 22, 1, (2023); Zhang R., Et al., Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: Analysis of the MIMIC-III database, Cardiovasc. Diabetol, 22, 1, (2023); Jonsson E.N., Nyberg J., Using forest plots to interpret covariate effects in pharmacometric models, CPT Pharmacomet. Syst. Pharmacol.; Sadatsafavi M., Saha-Chaudhuri P., Petkau J., Model-based ROCcurve: Examining the effect of case mix and model calibration onthe ROC plot, Med. Decis. Mak, 42, 4, pp. 487-499, (2022); Ahmadizar F., Et al., Asthma related medication use and exacerbations in children and adolescents with type 1 diabetes, Pediatr. Pulmonol, 51, 11, pp. 1113-1121, (2016); Ferreira S.S., Et al., Insulin modulates the immune cell phenotype in pulmonary allergic inflammation and increases pulmonary resistance in diabetic mice, Front. Immunol, 11, (2020); Di Filippo P., Et al., Insulin resistance and lung function in obese asthmatic pre-pubertal children, J. Pediatr. Endocrinol. Metab, 31, 1, pp. 45-51, (2018); Lockhart S.M., Et al., The excess insulin requirement in severe COVID-19 compared to non-COVID-19 viral pneumonitis is related to the severity of respiratory failure and pre-existing diabetes, Endocrinol. Diabetes Metab, 4, 3, (2021); Blouquit S., Et al., Effects of endothelin-1on epithelial ion transport in human airways, Am. J. Respir. Cell. Mol. Biol, 29, 2, pp. 245-251, (2023); Gras D., Et al., Bronchial epithelium as a target for innovative treatments in asthma, Pharmacol.Ther, 140, 3, pp. 290-305, (2013); Wu T.D., Et al., Association of triglyceride-glucose index and lung health: A population-based study, Chest, 160, 3, pp. 1026-1034, (2021); Staggers K.A., Et al., Metabolic dysfunction, triglyceride-glucose index, and risk of severe asthma exacerbation, J. Allergy Clin. Immunol. Pract, 11, 12, pp. 3700-3705e2, (2023); Cottrell L., Et al., Metabolic abnormalities in children with asthma, Am. J. Respir Crit. Care Med, 183, 4, pp. 441-448, (2011); Bartziokas K., Papaioannou A.I., Drakopanagiotakis F., Gouveri E., Papanas N., Steiropoulos P., Unraveling the link between insulin resistance and bronchial asthma, Biomedicines, 12, 2, (2024); Skrgat S., Harlander M., Janic M., Obesity and insulin resistance in asthma pathogenesis and clinical outcomes, Biomedicines, 12, 1, (2024); Husemoen L.L., Et al., Association of obesity and insulin resistance with asthma and aeroallergen sensitization, Allergy, 63, 5, pp. 575-582, (2008); Jiang J., Et al., Relationship of obesity to adipose tissue insulin resistance, BMJ Open. Diabetes Res. Care, 8, 1, (2020); Zheng R., Et al., Association between triglyceride-glucose index and in-hospital mortality in critically ill patients with sepsis: Analysis of the MIMIC-IV database, Cardiovasc. Diabetol, 22, 1, (2023); Fowler, Effect of vitamin C infusion on organ failure and biomarkers of inflammation and vascular injury in patients with sepsis and severe acute respiratory failure: The CITRIS-ALI Randomized Clinical Trial, JAMA, 322, 13, pp. 1261-1270; Nejatifar F., Et al., Association of Metabolic Syndrome and Asthma Status: A prospective study from Guilan Province, Iran. Endocr. Metab. Immune, 22; Nygaard U.C., Et al., Improved diet quality is associated with decreased concentrations of inflammatory markers in adults with uncontrolled asthma, Am. J. Clin. Nutr, 114, 3, pp. 1012-1027, (2021)","Y.Q. Zhang; Three Departments of Respiration, Hebei Children’s Hospital, Shijiazhuang, Hebei, 050031, China; email: zhangyingqian666@163.com","","Nature Research","","","","","","20452322","","","39562796","English","Sci. Rep.","Article","Final","","Scopus","2-s2.0-85209680816"
"Li Q.; Liu Y.; Wang X.; Xie C.; Mei X.; Cao W.; Guan W.; Lin X.; Xie X.; Zhou C.; Yi E.","Li, Qingyang (59207710300); Liu, Yu (59247399000); Wang, Xiaoyu (58039621400); Xie, Chengshu (57214877960); Mei, Xinyue (57218574748); Cao, Weitao (57212505656); Guan, Wenhui (58141768700); Lin, Xinqing (36096849800); Xie, Xiaohong (57193688896); Zhou, Chengzhi (57209046130); Yi, Erkang (57195987112)","59207710300; 59247399000; 58039621400; 57214877960; 57218574748; 57212505656; 58141768700; 36096849800; 57193688896; 57209046130; 57195987112","The influence of CLEC5A on early macrophage-mediated inflammation in COPD progression","2024","Cellular and Molecular Life Sciences","81","1","330","","","","0","10.1007/s00018-024-05375-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200456576&doi=10.1007%2fs00018-024-05375-0&partnerID=40&md5=d196cd4d973fb321a6c70ae34d05bf5c","State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Guangzhou National Laboratory, Guangzhou International BioIsland, No.9 XingDaoHuanBei Road, Guangdong, Guangzhou, 510005, China; Department of Pulmonary and Critical Care Medicine, Guangzhou First People’s Hospital, South China University of Technology Guangzhou, Guangdong, Guangzhou, 510180, China","Li Q., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Liu Y., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Wang X., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Xie C., Guangzhou National Laboratory, Guangzhou International BioIsland, No.9 XingDaoHuanBei Road, Guangdong, Guangzhou, 510005, China; Mei X., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Cao W., Department of Pulmonary and Critical Care Medicine, Guangzhou First People’s Hospital, South China University of Technology Guangzhou, Guangdong, Guangzhou, 510180, China; Guan W., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Lin X., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Xie X., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Zhou C., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China; Yi E., State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, 195 Dongfeng Xi Road, Guangdong, Guangzhou, 510182, China, Guangzhou National Laboratory, Guangzhou International BioIsland, No.9 XingDaoHuanBei Road, Guangdong, Guangzhou, 510005, China","Chronic obstructive pulmonary disease (COPD) is a complex syndrome with poorly understood mechanisms driving its early progression (GOLD stages 1–2). Elucidating the genetic factors that influence early-stage COPD, particularly those related to airway inflammation and remodeling, is crucial. This study analyzed lung tissue sequencing data from patients with early-stage COPD (GSE47460) and smoke-exposed mice. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning to identify potentially pathogenic genes. Further analyses included single-cell sequencing from both mice and COPD patients to pinpoint gene expression in specific cell types. Cell–cell communication and pseudotemporal analyses were conducted, with findings validated in smoke-exposed mice. Additionally, Mendelian randomization (MR) was used to confirm the association between candidate genes and lung function/COPD. Finally, functional validation was performed in vitro using cell cultures. Machine learning analysis of 30 differentially expressed genes identified 8 key genes, with CLEC5A emerging as a potential pathogenic factor in early-stage COPD. Bioinformatics analyses suggested a role for CLEC5A in macrophage-mediated inflammation during COPD. Two-sample Mendelian randomization linked CLEC5A single nucleotide polymorphisms (SNPs) with Forced Expiratory Volume in One Second (FEV1), FEV1/Forced Vital Capacity (FVC) and early/later on COPD. In vitro, the knockdown of CLEC5A led to a reduction in inflammatory markers within macrophages. Our study identifies CLEC5A as a critical gene in early-stage COPD, contributing to its pathogenesis through pro-inflammatory mechanisms. This discovery offers valuable insights for developing early diagnosis and treatment strategies for COPD and highlights CLEC5A as a promising target for further investigation. © The Author(s) 2024.","Chronic obstructive pulmonary disease; CLEC5A; Macrophages; Mendelian randomization; ScRNA-seq","Animals; Disease Progression; Female; Humans; Inflammation; Lectins, C-Type; Lung; Machine Learning; Macrophages; Male; Mendelian Randomization Analysis; Mice; Mice, Inbred C57BL; Polymorphism, Single Nucleotide; Pulmonary Disease, Chronic Obstructive; Receptors, Cell Surface; chemokine receptor CCR1; chemokine receptor CCR5; cigarette smoke; complementary DNA; CXCL1 chemokine; CXCL13 chemokine; interleukin 1beta; interleukin 6; interleukin 8; leukocyte surface antigen CD53; macrophage elastase; macrophage inflammatory protein 1alpha; matrix metalloproteinase 14; matrix metalloproteinase 19; messenger RNA; monocyte chemotactic protein 1; monocyte chemotactic protein 5; phosphatidylinositol 3,4,5 trisphosphate 3 phosphatase; triggering receptor expressed on myeloid cells 2; tumor necrosis factor; cell surface receptor; CLEC5A protein, human; lectin; animal experiment; animal model; Article; bioinformatics; CD8+ T lymphocyte; cdh16 gene; cell communication; cell culture; cell proliferation; chronic bronchitis; chronic obstructive lung disease; clec5a gene; clinical article; cohort analysis; ctss gene; dendritic cell; differential gene expression; disease exacerbation; down regulation; early diagnosis; emphysema; enzyme linked immunosorbent assay; fcer1g gene; fcgr2b gene; forced expiratory volume; forced vital capacity; gene; gene expression; gene knockdown; gene ontology; genetic transfection; genome-wide association study; hk3 gene; human; human cell; human tissue; immunocompetent cell; immunofluorescence assay; in vitro study; inflammation; itgb2 gene; iyd gene; lung function; lung parenchyma; machine learning; macrophage; macrophage inflammation; male; Mendelian randomization analysis; mouse; mrc1 gene; natural killer cell; nonhuman; pathogenesis; pleiotropy; protein expression level; protein protein interaction; real time polymerase chain reaction; recursive feature elimination; RNA extraction; RNA isolation; single cell RNA seq; single nucleotide polymorphism; smooth muscle cell; THP-1 cell line; upregulation; weighted gene co expression network analysis; Western blotting; animal; C57BL mouse; female; genetics; lung; macrophage; metabolism; pathology; single nucleotide polymorphism","","chemokine receptor CCR1, 265970-50-1; interleukin 8, 114308-91-7; macrophage elastase, ; macrophage inflammatory protein 1alpha, 155075-84-6; matrix metalloproteinase 14, ; phosphatidylinositol 3,4,5 trisphosphate 3 phosphatase, 210488-47-4; CLEC5A protein, human, ; Lectins, C-Type, ; Receptors, Cell Surface, ","CFX Connect, Biorad, United States","Biorad, United States","Postdoctoral Startup Foundation of Guangzhou City; National Natural Science Foundation of China, NSFC, (82200045); National Natural Science Foundation of China, NSFC; China Postdoctoral Science Foundation, (2022M710900); China Postdoctoral Science Foundation; National Key Laboratory of Respiratory Diseases, (SKLRD-Z-202326); National Key Research and Development Program of China, NKRDPC, (2021YFC2301101); National Key Research and Development Program of China, NKRDPC","The National Natural Science Foundation of China (82200045), China Postdoctoral Science Foundation (2022M710900), Youth Foundation of the National Key Laboratory of Respiratory Diseases (SKLRD-Z-202326), National Key R&D Program of China (2021YFC2301101), Postdoctoral Startup Foundation of Guangzhou City (No. E.K.Y., No. Q.Y.L.) supported this study. 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Wortham B.W., Et al., Cutting edge: CLEC5A mediates macrophage function and chronic obstructive pulmonary disease pathologies, J Immunol, 196, pp. 3227-3231, (2016); Barnes P.J., Inflammatory mechanisms in patients with chronic obstructive pulmonary disease, J Allergy Clin Immunol, 138, pp. 16-27, (2016); Zhang Z., Et al., A macrophage-related gene signature for identifying COPD based on bioinformatics and ex vivo experiments, J Inflamm Res, 16, pp. 5647-5665, (2023)","C. Zhou; State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 195 Dongfeng Xi Road, Guangdong, 510182, China; email: doctorzcz@163.com; E. Yi; State Key Laboratory of Respiratory Diseases, National Clinical Research Center for Respiratory Diseases, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 195 Dongfeng Xi Road, Guangdong, 510182, China; email: erkangyi@gzhmu.edu.cn","","Springer Science and Business Media Deutschland GmbH","","","","","","1420682X","","CMLSF","39097839","English","Cell. Mol. Life Sci.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85200456576"
"Akduman S.; Yilmaz K.","Akduman, Seha (56146379000); Yilmaz, Kadir (57863673500)","56146379000; 57863673500","Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis","2024","Medicine (United States)","103","29","","e38998","","","0","10.1097/MD.0000000000038998","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199172493&doi=10.1097%2fMD.0000000000038998&partnerID=40&md5=50378e113f4a9652a3853bfaab022e84","Department of Pulmonary Diseases, Yeditepe University, Faculty of Medicine, Istanbul, Türkiye; Istanbul Commerce University, Social Sciences Institute, Industrial Policies and Technology Management Program (DR), Istanbul, Türkiye","Akduman S., Department of Pulmonary Diseases, Yeditepe University, Faculty of Medicine, Istanbul, Türkiye; Yilmaz K., Istanbul Commerce University, Social Sciences Institute, Industrial Policies and Technology Management Program (DR), Istanbul, Türkiye","This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) outpatient treatment support in terms of patient health and public costs. The data obtained in the research using semiotic analysis, content analysis and trend analysis methods were analyzed with strengths, weakness, opportunities, threats (SWOT) analysis. In this context, 18 studies related to asthma, COPD and artificial intelligence were evaluated. The strengths of artificial intelligence applications in asthma and COPD outpatient treatment stand out as early diagnosis, access to more patients and reduced costs. The points that stand out among the weaknesses are the acceptance and use of technology and vulnerabilities related to artificial intelligence. Opportunities arise in developing differential diagnoses of asthma and COPD and in examining prognoses for the diseases more effectively. Malicious use, commercial data leaks and data security issues stand out among the threats. Although artificial intelligence applications provide great convenience in the outpatient treatment process for asthma and COPD diseases, precautions must be taken on a global scale and with the participation of international organizations against weaknesses and threats. In addition, there is an urgent need for accreditation for the practices to be carried out in this regard.  Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.","artificial intelligence; asthma; COPD; outpatient treatment","Ambulatory Care; Artificial Intelligence; Asthma; Humans; Pulmonary Disease, Chronic Obstructive; Article; artificial intelligence; asthma; awareness; big data; chronic obstructive lung disease; clinical evaluation; content analysis; controlled study; diagnostic error; differential diagnosis; early diagnosis; health care access; health care cost; health service; human; information security; outpatient care; systematic error; ambulatory care; diagnosis; economics; procedures; therapy","","","","","","","Barnes P.J., Mechanisms in COPD: differences from asthma, Chest., 117, pp. 4S-10S, (2000); Gibson P.G., McDonald V.M., Asthma-COPD overlap 2015: now we are six, Thorax., 70, pp. 683-691, (2015); Postma D.S., Rabe K.F., The asthma-COPD overlap syndrome, N Engl J Med., 373, pp. 1241-1249, (2015); Cosio B.G., Soriano J.B., Lopez-Campos J.L., Et al., Defining the asthma-COPD overlap syndrome in a COPD cohort, Chest., 149, pp. 45-52, (2016); 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Bazarbayev M., Et al., Digital medical ecosystem: Transformation and development prospects, Sci Innovation., 2, pp. 64-69, (2023); Ter-Akopov G.N., Kosinova N.N., Knyazev S.A., Digital technologies in healthcare: Achievements and prospects, 1st International Scientific Conference"" Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth""(MTDE 2019), (2019); Armitage P., Berry G., Matthews J.N.S., Statistical methods in medical research, (2008); Hanson J.L., Balmer D.F., Giardino A.P., Qualitative research methods for medical educators, Acad Pediatr., 11, pp. 375-386, (2011); Yilmaz K., Turanli M., A multi-disciplinary investigation of linearization deviations in different regression models, Asian J Probab Stat., 22, pp. 15-19, (2023); Kaplan A., Cao H., FitzGerald J.M., Et al., Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis, J Allergy Clin Immunol., 9, pp. 2255-2261, (2021); 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IEEE, (2021); Yu G., Li Z., Li S., Et al., The role of artificial intelligence in identifying asthma in pediatric inpatient setting, Ann Translat Med., 8, pp. 1367-1367, (2020); Fernandez-Granero M.A., Sanchez-Morillo D., Leon-Jimenez A., An artificial intelligence approach to early predict symptom-based exacerbations of COPD, Biotechnol Biotechnol Equip., 32, pp. 778-784, (2018); Messinger A.I., Luo G., Deterding R.R., The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma, J Allergy Clin Immunol., 145, pp. 476-478, (2020); Badnjevic A., Gurbeta L., Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Sci Rep., 8, (2018); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics J., 25, pp. 811-827, (2019); Mekov E., Miravitlles M., Petkov R., Artificial intelligence and machine learning in respiratory medicine, Expert Rev Respirat Med., 14, pp. 559-564, (2020); Antao J., De Mast J., Marques A., Et al., Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases, Expert Rev Respirat Med., 17, pp. 1-13, (2024); Gelman A., Sokolovsky V., Furman E., Et al., Artificial intelligence in the respiratory sounds analysis and computer diagnostics of bronchial asthma, medRxiv., 17, pp. 2021-2111, (2021); Hashimoto D.A., Witkowski E., Gao L., Et al., Artificial Intelligence in anesthesiology: current techniques, clinical applications, and limitations, Anesthesiology., 132, pp. 379-394, (2020); Hunter B., Hindocha S., Lee R.W., The role of Artificial Intelligence in Early cancer diagnosis, Cancers (Basel)., 14, (2022); Ji Y., Ji Y., Liu Y., Et al., Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images, Front Cell Dev Biol., 11, (2023)","S. Akduman; Department of Pulmonary Diseases, Yeditepe University, Faculty of Medicine, İstanbul, Koşuyolu, Koşuyolu Cd. No: 168, Kadiköy, 34718, Türkiye; email: sehaakduman42@gmail.com","","Lippincott Williams and Wilkins","","","","","","00257974","","MEDIA","39029048","English","Medicine","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85199172493"
"Rifas-Shiman S.L.; Aris I.M.; Switkowski K.M.; Young J.; Fleisch A.F.; Perng W.; Chavarro J.E.; Cardenas A.; Gold D.R.; Zhang M.; James P.; Whooten R.C.; Kleinman K.P.; Oken E.; Hivert M.-F.","Rifas-Shiman, Sheryl L. (57216597735); Aris, Izzuddin M. (57204016104); Switkowski, Karen M. (55383033000); Young, Jessica (35112182200); Fleisch, Abby F. (15729108700); Perng, Wei (55183448400); Chavarro, Jorge E. (57222996585); Cardenas, Andres (55315594400); Gold, Diane R. (58895084900); Zhang, Mingyu (57202875633); James, Peter (7402640931); Whooten, Rachel C. (57200522487); Kleinman, Ken P. (57217692960); Oken, Emily (57216595262); Hivert, Marie-France (57216594945)","57216597735; 57204016104; 55383033000; 35112182200; 15729108700; 55183448400; 57222996585; 55315594400; 58895084900; 57202875633; 7402640931; 57200522487; 57217692960; 57216595262; 57216594945","Cohort Profile Update: Project Viva Offspring","2024","International Journal of Epidemiology","53","6","dyae162","","","","0","10.1093/ije/dyae162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211984934&doi=10.1093%2fije%2fdyae162&partnerID=40&md5=3cd8e65af6e858f64548c32baf56c059","Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Pediatric Endocrinology and Diabetes, Maine Medical Center, Portland, ME, United States; Center for Interdisciplinary Population and Health Research, MaineHealth Institute for Research, Portland, ME, United States; Department of Epidemiology, the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, United States; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States; Department of Epidemiology and Population Health, Stanford Medicine, Stanford, CA, United States; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Division of Pediatric Endocrinology, Department of Pediatrics, Massachusetts General Hospital for Children, Boston, MA, United States; Department of Biostatistics, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States; Diabetes Unit, Massachusetts General Hospital, Boston, MA, United States","Rifas-Shiman S.L., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Aris I.M., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Switkowski K.M., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Young J., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Fleisch A.F., Pediatric Endocrinology and Diabetes, Maine Medical Center, Portland, ME, United States, Center for Interdisciplinary Population and Health Research, MaineHealth Institute for Research, Portland, ME, United States; Perng W., Department of Epidemiology, the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, United States; Chavarro J.E., Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, United States, Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States; Cardenas A., Department of Epidemiology and Population Health, Stanford Medicine, Stanford, CA, United States; Gold D.R., Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States; Zhang M., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; James P., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States; Whooten R.C., Division of Pediatric Endocrinology, Department of Pediatrics, Massachusetts General Hospital for Children, Boston, MA, United States; Kleinman K.P., Department of Biostatistics, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States; Oken E., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States, Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, United States; Hivert M.-F., Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, United States, Diabetes Unit, Massachusetts General Hospital, Boston, MA, United States","[No abstract available]","adolescent; allergy; asthma; behavioural health; cardiometabolic health; child; Cohort; neurodevelopment; obesity; respiratory health","adiponectin; biological marker; black carbon; bronchodilating agent; glucose; hemoglobin A1c; high density lipoprotein cholesterol; insulin; leptin; nitric oxide; triacylglycerol; adolescence; asthma; child health; cohort analysis; obesity; reproduction; respiratory disease; adolescent; adult; Article; artificial intelligence; birth cohort; cardiometabolic risk; cohort analysis; controlled study; fasting blood glucose level; fasting insulin level; female; follow up; gender identity; homeostasis model assessment; human; lifespan; major clinical study; male; medical research; metabolomics; obesity; ovary polycystic disease; overnutrition; perinatal period; physical activity; prenatal exposure; progeny; project management; quality of life; residence characteristics; risk factor; sexual orientation; young adult; allergy; article; asthma; behavior; child; nerve cell differentiation; obesity; special situation for pharmacovigilance","","adiponectin, 283182-39-8; glucose, 50-99-7, 84778-64-3, 8027-56-3; hemoglobin A1c, 62572-11-6; insulin, 9004-10-8; nitric oxide, 10102-43-9","","","National Institutes of Health, NIH, (3R01HD096032-04S1, R01 ES031065, UH3 OD023286, R01 HD034568, R24 ES030894, K23DK13132, R01ES031259, P42ES004705, R01 ES024765); National Institutes of Health, NIH","Project Viva and its team of co-investigators have been funded by grants from the National Institutes of Health (grant numbers R01 HD034568, UH3 OD023286, R01 ES031065, R24 ES030894, 3R01HD096032-04S1, R01 ES024765, R01ES031259, P42ES004705, K23DK13132). We appreciate the Project Viva mothers, children and families for their ongoing participation and are grateful to the dozens of Project Viva staff, past and present, who collected such high-quality data. We are indebted to the vision of Matthew W Gillman, the founding PI of Project Viva.","Oken E, Baccarelli AA, Gold DR, Et al., Cohort profile: Project Viva, Int J Epidemiol, 44, pp. 37-48, (2015); Rifas-Shiman SL, Aris IM, Switkowski KM, Et al., Cohort profile update: Project Viva mothers, Int J Epidemiol, 52, pp. e332-e339, (2023); Perng W, Rahman ML, Aris IM, Et al., Metabolite profiles of the relationship between Body Mass Index (BMI) milestones and metabolic risk during early adolescence, Metabolites, 10, (2020); Tylavsky FA, Ferrara A, Catellier DJ, Et al., Understanding childhood obesity in the US: the NIH environmental influences on child health outcomes (ECHO) program, Int J Obes (Lond), 44, pp. 617-627, (2020); Aris IM, Rifas-Shiman SL, Jimenez MP, Et al., Neighborhood Child Opportunity Index and Adolescent Cardiometabolic Risk, Pediatrics, 147, (2021); Minguez-Alarcon L, Rifas-Shiman SL, Mitchell C, Et al., Cesarean delivery and metabolic health and inflammation biomarkers during mid-childhood and early adolescence, Pediatr Res, 91, pp. 672-680, (2022); Janis JA, Rifas-Shiman SL, Seshasayee SM, Et al., Plasma concentrations of per- and polyfluoroalkyl substances and body composition from mid-childhood to early adolescence, J Clin Endocrinol Metab, 106, pp. e3760-e3770, (2021); Rokoff LB, Seshasayee SM, Carwile JL, Et al., Associations of urinary metabolite concentrations of phthalates and phthalate replacements with body composition from mid-childhood to early adolescence, Environ Res, 226, (2023); Thilakaratne R, Lin PD, Rifas-Shiman SL, Et al., Cross-sectional and prospective associations of early childhood circulating metals with early and mid-childhood cognition in the Project Viva cohort, Environ Res, 246, (2024); Fleisch AF, Aris IM, Rifas-Shiman SL, Et al., Prenatal exposure to traffic pollution and childhood body mass index trajectory, Front Endocrinol (Lausanne), 9, (2018); Jimenez MP, Oken E, Gold DR, Et al., Early life exposure to green space and insulin resistance: an assessment from infancy to early adolescence, Environ Int, 142, (2020); Wu AJ, Aris IM, Rifas-Shiman SL, Oken E, Taveras EM, Hivert MF., Longitudinal associations of fruit juice intake in infancy with DXA-measured abdominal adiposity in mid-childhood and early adolescence, Am J Clin Nutr, 114, pp. 117-123, (2021); Zhang M, Aris IM, Lin PD, Et al., Prenatal and childhood per- and Polyfluoroalkyl Substance (PFAS) exposures and blood pressure trajectories from birth to late adolescence in a prospective US Prebirth Cohort, J Am Heart Assoc, 12, (2023); Zhang M, Rifas-Shiman SL, Aris IM, Et al., Associations of Prenatal Per- and Polyfluoroalkyl Substance (PFAS) exposures with offspring adiposity and body composition at 16-20 years of age: project viva, Environ Health Perspect, 131, (2023); Monthe-Dreze C, Rifas-Shiman SL, Aris IM, Et al., Maternal diet in pregnancy is associated with differences in child body mass index trajectories from birth to adolescence, Am J Clin Nutr, 113, pp. 895-904, (2021); Plows JF, Aris IM, Rifas-Shiman SL, Goran MI, Oken E., Associations of maternal non-nutritive sweetener intake during pregnancy with offspring body mass index and body fat from birth to adolescence, Int J Obes (Lond), 46, pp. 186-193, (2022); Aris IM, Rifas-Shiman SL, Li LJ, Belfort MB, Hivert MF, Oken E., Early-life predictors of systolic blood pressure trajectories from infancy to adolescence: findings from project viva, Am J Epidemiol, 188, pp. 1913-1922, (2019); Aris IM, Rifas-Shiman SL, Li LJ, Et al., Patterns of body mass index milestones in early life and cardiometabolic risk in early adolescence, Int J Epidemiol, 48, pp. 157-167, (2019); Perng W, Rifas-Shiman SL, Hivert MF, Chavarro JE, Sordillo J, Oken E., Metabolic trajectories across early adolescence: differences by sex, weight, pubertal status and race/ethnicity, Ann Hum Biol2019, 46, pp. 205-214; Perng W, Rifas-Shiman SL, Sordillo J, Hivert MF, Oken E., Metabolomic profiles of overweight/obesity phenotypes during adolescence: a cross-sectional study in project viva, Obesity (Silver Spring), 28, pp. 379-387, (2020); Perng W, Rifas-Shiman SL, Hivert MF, Chavarro JE, Oken E., Branched chain amino acids, androgen hormones, and metabolic risk across early adolescence: a prospective study in project viva, Obesity (Silver Spring), 26, pp. 916-926, (2018); Sordillo JE, Coull BA, Rifas-Shiman SL, Et al., Characterization of longitudinal wheeze phenotypes from infancy to adolescence in Project Viva, a prebirth cohort study, J Allergy Clin Immunol, 145, pp. 716-719, (2020); Hanson C, Rifas-Shiman SL, Shivappa N, Et al., Associations of prenatal dietary inflammatory potential with childhood respiratory outcomes in project viva, J Allergy Clin Immunol Pract, 8, pp. 945-952, (2020); Aris IM, Sordillo JE, Rifas-Shiman SL, Et al., Childhood patterns of overweight and wheeze and subsequent risk of current asthma and obesity in adolescence, Paediatr Perinat Epidemiol, 35, pp. 569-577, (2021); Cardenas A, Sordillo JE, Rifas-Shiman SL, Et al., The nasal methylome as a biomarker of asthma and airway inflammation in children, Nat Commun, 10, (2019); Zhou JC, Rifas-Shiman SL, Haines J, Jones K, Oken E., Adolescent overeating and binge eating behavior in relation to subsequent cardiometabolic risk outcomes: a prospective cohort study, J Eat Disord, 10, (2022); Whooten RC, Rifas-Shiman SL, Perng W, Et al., Associations of childhood adiposity and cardiometabolic biomarkers with adolescent PCOS, Pediatrics, 153, (2024); Chiu Y-H, Rifas-Shiman SL, Kleinman K, Oken E, Young JG., Effects of intergenerational exposure interventions on adolescent outcomes: an application of inverse probability weighting to longitudinal pre-birth cohort data, Paediatr Perinat Epidemiol, 34, pp. 366-375, (2020); Aris IM, Sarvet AL, Stensrud MJ, Et al., Separating algorithms from questions and causal inference with unmeasured exposures: an application to birth cohort studies of early body mass index rebound, Am J Epidemiol, 190, pp. 1414-1423, (2021); (2000)","S.L. Rifas-Shiman; Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, 401 Park Drive Suite 401E, 02215, United States; email: sheryl_rifas@hphci.harvard.edu","","Oxford University Press","","","","","","03005771","","IJEPB","39657066","English","Int. J. Epidemiol.","Article","Final","","Scopus","2-s2.0-85211984934"
"De Filippo M.; Fasola S.; De Matteis F.; Gorone M.S.P.; Preda L.; Votto M.; Malizia V.; Marseglia G.L.; La Grutta S.; Licari A.","De Filippo, Maria (57196003143); Fasola, Salvatore (37037266700); De Matteis, Federica (59231115500); Gorone, Maria Sole Prevedoni (8708120100); Preda, Lorenzo (7005579364); Votto, Martina (57208747336); Malizia, Velia (16432530700); Marseglia, Gian Luigi (26422377200); La Grutta, Stefania (6701854558); Licari, Amelia (15057979700)","57196003143; 37037266700; 59231115500; 8708120100; 7005579364; 57208747336; 16432530700; 26422377200; 6701854558; 15057979700","Machine learning-enhanced HRCT analysis for diagnosis and severity assessment in pediatric asthma","2024","Pediatric Pulmonology","59","12","","3268","3277","9","0","10.1002/ppul.27183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199411631&doi=10.1002%2fppul.27183&partnerID=40&md5=2e790493668c1f3872101cfa9257f872","Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Institute of Translational Pharmacology (IFT), National Research Council of Italy (CNR), Palermo, Italy; Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy","De Filippo M., Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Fasola S., Institute of Translational Pharmacology (IFT), National Research Council of Italy (CNR), Palermo, Italy; De Matteis F., Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Gorone M.S.P., Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Preda L., Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Votto M., Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Malizia V., Institute of Translational Pharmacology (IFT), National Research Council of Italy (CNR), Palermo, Italy; Marseglia G.L., Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; La Grutta S., Institute of Translational Pharmacology (IFT), National Research Council of Italy (CNR), Palermo, Italy; Licari A., Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy","Objectives: Chest high-resolution computed tomography (HRCT) is conditionally recommended to rule out conditions that mimic or coexist with severe asthma in children. However, it may provide valuable insights into identifying structural airway changes in pediatric patients. This study aims to develop a machine learning-based chest HRCT image analysis model to aid pediatric pulmonologists in identifying features of severe asthma. Methods: This retrospective case-control study compared children with severe asthma (as defined by ERS/ATS guidelines) to age- and sex-matched controls without asthma, using chest HRCT scans for detailed imaging analysis. Statistical analysis included classification trees, random forests, and conventional ROC analysis to identify the most significant imaging features that mark severe asthma from controls. Results: Chest HRCT scans differentiated children with severe asthma from controls. Compared to controls (n = 21, mean age 11.4 years), children with severe asthma (n = 20, mean age 10.4 years) showed significantly greater bronchial thickening (BT) scores (p < 0.001), airway wall thickness percentage (AWT%, p < 0.001), bronchiectasis grading (BG) and bronchiectasis severity (BS) scores (p = 0.016), mucus plugging, and centrilobular emphysema (p = 0.009). Using AWT% as the predictor in conventional ROC analysis, an AWT% ≥ 38.6 emerged as the optimal classifier for discriminating severe asthmatics from controls, with 95% sensitivity, specificity, and overall accuracy. Conclusion: Our study demonstrates the potential of machine learning-based analysis of chest HRCT scans to accurately identify features associated with severe asthma in children, enhancing diagnostic evaluation and contributing to the development of more targeted treatment approaches. © 2024 The Author(s). Pediatric Pulmonology published by Wiley Periodicals LLC.","artificial intelligence; chest high-resolution computed tomography; Children; machine learning; severe asthma","corticosteroid; fluticasone; formoterol; immunoglobulin E; montelukast; nitric oxide; omalizumab; salmeterol; airway obstruction; allergic rhinitis; Alternaria; Alternaria alternata; Article; artificial intelligence; Aspergillus fumigatus; asthma; atopic dermatitis; body mass; bronchiectasis; case control study; Chest high resolution computed tomography; child; chronic obstructive lung disease; clinical article; clinical trial; controlled study; cross-sectional study; cystic fibrosis; decision tree; diagnostic accuracy; disease severity; disease severity assessment; dyskinesia; emergency ward; emphysema; eosinophil count; female; forced expiratory volume; forced vital capacity; hospitalization; human; lung emphysema; lung volume; machine learning; male; mucus plugging; passive smoking; prevalence; pulmonologist; radiation exposure; random forest; receiver operating characteristic; retrospective study; school child; severe asthma; spirometry; total lung capacity","","fluticasone, 90566-53-3; formoterol, 73573-87-2; immunoglobulin E, 37341-29-0; montelukast, 151767-02-1, 158966-92-8; nitric oxide, 10102-43-9; omalizumab, 242138-07-4; salmeterol, 89365-50-4","R statistical software version 4.0.2, R Foundation, Australia","R Foundation, Australia","Università degli Studi di Pavia; Ricerca Corrente Fondazione IRCCS Policlinico San Matteo, (5×1000-2018-cod.08073521)","The authors wish to acknowledge the \u201CBREATHE\u201D study group, including academic clinicians and researchers such as Prof. Andrea Albarelli, Dr. Giuseppe Roberto Marseglia, Dr. Mara De Amici, and Dr. Giorgia Testa, for their invaluable contributions to this research project. This work was conducted as part of the \u201CIntegrating deep learning CT-scan model, biological and clinical variaBles to pRedict sEverity of asTHma in childrEn (BREATHE)\u201D project, funded by the Ricerca Corrente Fondazione IRCCS Policlinico San Matteo (5\u00D71000-2018-cod.08073521) research grant. Open access publishing facilitated by Universita degli Studi di Pavia, as part of the Wiley - CRUI-CARE agreement.","Pijnenburg M.W., Fleming L., Advances in understanding and reducing the burden of severe asthma in children, Lancet Respir Med, 8, 10, pp. 1032-1044, (2020); Szefler S.J., Zeiger R.S., Haselkorn T., Et al., Economic burden of impairment in children with severe or difficult-to-treat asthma, Ann Allergy Asthma Immunol, 107, 2, pp. 110-119.e1, (2011); Licari A., Manti S., Castagnoli R., Leonardi S., Marseglia G.L., Measuring inflammation in paediatric severe asthma: Biomarkers in clinical practice, Breathe, 16, 1, (2020); Difficult-to-treat and severe asthma in adolescent and adult patients: Diagnosis and management. A GINA pocket guide for health professionals. Version 3. Fontana, WI: Global Initiative for Asthma, Global Initiative for Asthma, (2021); Chung K.F., Wenzel S.E., Brozek J.L., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, 2, pp. 343-373, (2014); Aysola R.S., Hoffman E.A., Gierada D., Et al., Airway remodeling measured by multidetector CT is increased in severe asthma and correlates with pathology, Chest, 134, 6, pp. 1183-1191, (2008); Matsuoka S., Yamashiro T., Washko G.R., Kurihara Y., Nakajima Y., Hatabu H., Quantitative CT assessment of chronic obstructive pulmonary disease, Radiographics, 30, 1, pp. 55-66, (2010); Matsumoto H., Niimi A., Tabuena R.P., Et al., Airway wall thickening in patients with cough variant asthma and nonasthmatic chronic cough, Chest, 131, 4, pp. 1042-1049, (2007); Perez-Rovira A., Kuo W., Petersen J., Tiddens H.A.W.M., de Bruijne M., Automatic airway-artery analysis on lung CT to quantify airway wall thickening and bronchiectasis, Med Phys, 43, 10, pp. 5736-5744, (2016); Weikert T., Friebe L., Wilder-Smith A., Et al., Automated quantification of airway wall thickness on chest CT using retina U-Nets - performance evaluation and application to a large cohort of chest CTs of COPD patients, Eur J Radiol, 155, (2022); von Elm E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P., The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies, The Lancet, 370, 9596, pp. 1453-1457, (2007); Reddel H.K., Taylor D.R., Bateman E.D., Et al., An official American thoracic Society/European respiratory society statement: Asthma control and exacerbations: Standardizing endpoints for clinical asthma trials and clinical practice, Am J Respir Crit Care Med, 180, 1, pp. 59-99, (2009); Walker C., Gupta S., Hartley R., Brightling C.E., Computed tomography scans in severe asthma: Utility and clinical implications, Curr Opin Pulm Med, 18, 1, pp. 42-47, (2012); Schroeder J.D., McKenzie A.S., Zach J.A., Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease, Am J Roentgenol, 201, 3, (2013); Ketai L., Coutsias C., Williamson S., Coutsias V., Thin-Section CT evidence of bronchial thickening in Children with stable asthma, Academic Radiol, 8, 3, pp. 257-264, (2001); Lo D., Maniyar A., Gupta S., Gaillard E., 19, 1; Marchac V., Emond S., Mamou-Mani T., Et al., Thoracic CT in pediatric patients with difficult-to-treat asthma, Am J Roentgenol, 179, 5, pp. 1245-1252, (2002); de Blic J., Tillie-Leblond I., Emond S., Mahut B., Dang Duy T.L., Scheinmann P., High-resolution computed tomography scan and airway remodeling in children with severe asthma, J Allergy Clin Immunol, 116, 4, pp. 750-754, (2005); SilvaZanon T.K.B., Zanon M., Altmayer S., Et al., High-resolution CT pulmonary findings in children with severe asthma, J Pediatr, 97, 1, pp. 37-43, (2021); van den Bosch W.B., Lv Q., Andrinopoulou E.R., Et al., Children with severe asthma have substantial structural airway changes on computed tomography, ERJ Open Res, 10, 1, (2024); Barker A.F., Bronchiectasis, N Engl J Med, 346, 18, pp. 1383-1393, (2002); Saglani S., Papaioannou G., Khoo L., Et al., Can HRCT be used as a marker of airway remodelling in children with difficult asthma?, Respir Res, 7, 1, (2006); Gupta S., Siddiqui S., Haldar P., Et al., Qualitative analysis of high-resolution CT scans in severe asthma, Chest, 136, 6, pp. 1521-1528, (2009); Dunican E.M., Elicker B.M., Gierada D.S., Et al., Mucus plugs in patients with asthma linked to eosinophilia and airflow obstruction, J Clin Invest, 128, 3, pp. 997-1009, (2018); Tamura K., Shirai T., Hirai K., Et al., Mucus plugs and small airway dysfunction in asthma, COPD, and asthma-COPD overlap, Allergy, Asthma Immunol Res, 14, 2, pp. 196-209, (2022); Takahashi M., Classification of centrilobular emphysema based on CT-pathologic correlations, Open Respir Med J, 6, pp. 155-159, (2012); Silva C.I.S., Colby T.V., Muller N.L., Asthma and associated conditions: High-resolution CT and pathologic findings, Am J Roentgenol, 183, 3, pp. 817-824, (2004); Hong K.Y., Lee J.H., Park S.W., Et al., Evaluation of emphysema in patients with asthma using high-resolution CT, Korean J Intern Med, 17, 1, pp. 24-30, (2002)","A. Licari; MD, Pediatric Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Fondazione IRCCS Policlinico San Matteo, Pavia, p.le C. Golgi 19, 27100, Italy; email: amelia.licari@unipv.it","","John Wiley and Sons Inc","","","","","","87556863","","PEPUE","","English","Pediatr. Pulmonol.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85199411631"
"Figueroa R.; Taramasco C.; Lagos M.E.; Martínez F.; Rimassa C.; Godoy J.; Pino E.; Navarrete J.; Pinto J.; Nazar G.; Pérez C.; Herrera D.","Figueroa, Rosa (14631693800); Taramasco, Carla (35085484500); Lagos, María Elena (56957194000); Martínez, Felipe (54585693700); Rimassa, Carla (56346948500); Godoy, Julio (55567072800); Pino, Esteban (23028926500); Navarrete, Jean (56536233700); Pinto, Jose (57985059700); Nazar, Gabriela (55587709300); Pérez, Cristhian (54973844200); Herrera, Daniel (59253775800)","14631693800; 35085484500; 56957194000; 54585693700; 56346948500; 55567072800; 23028926500; 56536233700; 57985059700; 55587709300; 54973844200; 59253775800","A Technological Framework to Support Asthma Patient Adherence Using Pictograms","2024","Applied Sciences (Switzerland)","14","15","6410","","","","0","10.3390/app14156410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200855992&doi=10.3390%2fapp14156410&partnerID=40&md5=2bffabbde92c1625e736528fea084f15","Department of Electrical Engineering, Universidad de Concepción, Concepción, 4070409, Chile; School of Engineering, Andres Bello University, Viña del Mar 2531015, Chile; Department of Nursing and Public Health, Universidad de Concepcion, Concepción, 4070409, Chile; Faculty of Medicine, Andres Bello University, Viña del Mar, 2531015, Chile; Faculty of Medicine, University of Valparaíso, Valparaíso, 2340000, Chile; Department of Computer Science, Universidad de Concepción, Concepción, 4070409, Chile; Department of Industrial Engineering, Universidad de Concepción, Concepción, 4070409, Chile; Department of Psychology, Universidad de Concepción, Concepción, 4070409, Chile; Department of Medical Education, Universidad de Concepción, Concepción, 4070409, Chile","Figueroa R., Department of Electrical Engineering, Universidad de Concepción, Concepción, 4070409, Chile; Taramasco C., School of Engineering, Andres Bello University, Viña del Mar 2531015, Chile; Lagos M.E., Department of Nursing and Public Health, Universidad de Concepcion, Concepción, 4070409, Chile; Martínez F., Faculty of Medicine, Andres Bello University, Viña del Mar, 2531015, Chile; Rimassa C., Faculty of Medicine, University of Valparaíso, Valparaíso, 2340000, Chile; Godoy J., Department of Computer Science, Universidad de Concepción, Concepción, 4070409, Chile; Pino E., Department of Electrical Engineering, Universidad de Concepción, Concepción, 4070409, Chile; Navarrete J., Department of Industrial Engineering, Universidad de Concepción, Concepción, 4070409, Chile; Pinto J., Department of Electrical Engineering, Universidad de Concepción, Concepción, 4070409, Chile; Nazar G., Department of Psychology, Universidad de Concepción, Concepción, 4070409, Chile; Pérez C., Department of Medical Education, Universidad de Concepción, Concepción, 4070409, Chile; Herrera D., Faculty of Medicine, University of Valparaíso, Valparaíso, 2340000, Chile","Background: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health. © 2024 by the authors.","machine learning; n-gram; natural language processing; pictograms; tokenization","Computational linguistics; Diseases; Iterative methods; Learning algorithms; Natural language processing systems; Patient treatment; Language processing; Machine-learning; Medical treatment; N-grams; Natural language processing; Natural languages; Pictogrammes; Technological components; Technological framework; Tokenization; Machine learning","","","","","Agencia Nacional de Investigación y Desarrollo, ANID; Fondo de Fomento al Desarrollo Científico y Tecnológico, FONDEF; Universidad de Concepción, UdeC, (219.092.053-M); Universidad de Concepción, UdeC; National Research and Development Agency, (ID19I10120); National Center on Health Information Systems, (CTI230006 CENS); Millennium Nucleus on Sociomedicine, (ANID–MILENIO–NCS2021_013)","This research was funded by Universidad de Concepci\u00F3n grant number 219.092.053-M, FONDEF, ANID (National Research and Development Agency, Chile, Funder URL https://anid.cl/, accessed on 17 July 2024), grant number ID19I10120 (RLFI, CT, ML, CR, JG, EP, JN, JP, FM, GN), the National Center on Health Information Systems (CTI230006 CENS), and the Millennium Nucleus on Sociomedicine, grant ANID\u2013MILENIO\u2013NCS2021_013 (CT, RLFI, ML). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Shelmerdine S.C., Martin H., Shirodkar K., Shamshuddin S., Weir-McCall J.R., Can artificial intelligence pass the Fellowship of the Royal College of Radiologists examination? Multi-reader diagnostic accuracy study, BMJ, 379, (2022); Hilton C.B., Milinovich A., Felix C., Vakharia N., Crone T., Donovan C., Proctor A., Nazha A., Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence, npj Digit. Med, 3, (2020); Nilsen P., Reed J., Nair M., Savage C., Macrae C., Barlow J., Svedberg P., Larsson I., Lundgren L., Nygren J., Realizing the potential of artificial intelligence in healthcare: Learning from intervention, innovation, implementation and improvement sciences, Front. Health Serv, 2, (2022); Pucchio A., Papa J.D., de Moraes F.Y., Artificial intelligence in the medical profession: Ready or not, here AI comes, Clinics, 77, (2022); Fuller R., Landrigan P.J., Balakrishnan K., Bathan G., Bose-O'Reilly S., Brauer M., Caravanos J., Chiles T., Cohen A., Corra L., Et al., Pollution and health: A progress update, Lancet Planet. Health, 6, pp. e535-e547, (2022); Park J.H., Moon J.H., Kim H.J., Kong M.H., Oh Y.H., Sedentary Lifestyle: Overview of Updated Evidence of Potential Health Risks, Korean J. Fam. Med, 41, pp. 365-373, (2020); Global Burden of Disease. Global Health Metrics. Asthma-Level 3 Cause [Internet], 396, pp. 108-109, (2020); Makela M.J., Backer V., Hedegaard M., Larsson K., Adherence to inhaled therapies, health outcomes and costs in patients with asthma and COPD, Respir. Med, 107, pp. 1481-1490, (2013); Granda P., Villamanan E., Carpio C., Laorden D., Sobrino C., Herrero A., Quirce S., Alvarez-Sala R., Adherence to inhalers in patients with severe asthma treated with anti-interleukin-5 biologics, Farm. Hosp, 46, pp. 203-207, (2022); Aslam A.M., Kennedy J., Seghol H., Khisty N., Nicols T.A., Adie S., A randomized controlled trial of patient recall after detailed written consent versus standard verbal consent in adults with routine orthopaedic trauma, Bone Jt. Open, 4, pp. 104-109, (2023); Merks P., Cameron J., Bilmin K., Swieczkowski D., Chmielewska-Ignatowicz T., Harezlak T., Bialoszewska K., Sola K.F., Jaguszewski M.J., Vaillancourt R., Medication Adherence and the Role of Pictograms in Medication Counselling of Chronic Patients: A Review, Front. Pharmacol, 12, (2021); Ramzan S., Iqbal M.M., Kalsum T., Text-to-Image Generation Using Deep Learning, Eng. Proc, 20, (2022); Ku H., Lee M., TextControlGAN: Text-to-Image Synthesis with Controllable Generative Adversarial Networks, Appl. Sci, 13, (2023); Zhou R., Jiang C., Xu Q., A survey on generative adversarial network-based text-to-image synthesis, Neurocomputing, 451, pp. 316-336, (2021); Norre M., Vandeghinste V., Bouillon P., Francois T., Extending a text-to-pictograph system to French and to Arasaac, Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021); Ben Mesmia F., Mouhoub M., A Web-Based Communication Tool for Arabic-Speaking Newcomers to Canada, Proceedings of the 14th International Conference on Advances in Computer-Human Interactions; Mutal J., Bouillon P., Norre M., Gerlach J., Ormaechea-Grijalba L., A Neural Machine Translation Approach to Translate Text to Pictographs in a Medical Speech Translation System-The BabelDr Use Case, Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas, 1: Research Track; Schubbe D., Cohen S., Yen R.W., Muijsenbergh M.V., Scalia P., Saunders C.H., Durand M.-A., Does pictorial health information improve health behaviours and other outcomes? A systematic review protocol, BMJ Open, 8, (2018); Figueroa R., Taramasco C., Flores C., Ortiz L., Vasquez-Venegas C., Salas P., Zeng-Treilter Q., A Physician’s Perspective on the Incorporation of Pictograms as a Supplement to Medical Instructions in Chile: A Pilot Study, IRBM, 44, (2023); Heyns J., van Huyssteen M., Bheekie A., The effectiveness of using text and pictograms on oral rehydration, dry-mixture sachet labels, Afr. J. Prim. Health Care Fam. Med, 13, (2021); Rungsriwattana V., Ngamchaliew P., Buathong N., Effects of Pharmaceutical Pictograms on Medication Adherence in Elderly Patients with Chronic Diseases at Primary Health Care Center in Hat Yai, Songkhla, J. Med. Assoc. Thail, 104, pp. 482-488, (2021); Leong M., Tam V., Xu T., Peters M., Understanding medication schedules: Do pictograms help?, J. 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Res, 12, pp. 607-614, (2021); Graphical Symbols—Test Methods—Part 3: Method for Testing Symbol Referent Association; Graphical Symbols—Test Methods—Part 1: Method for Testing Comprehensibility, (2014); Graphical Symbols—Test Methods—Part 2: Method for Testing Perceptual Quality, (2008); Graphical Symbols—Test Methods—Part 3: Method for Testing Symbol Referent Association, (2014); Maipradit R., Hata H., Matsumoto K., Sentiment classification using N-gram inverse document frequency and automated machine learning, IEEE Softw, 36, pp. 65-70, (2019); Espinoza-Navarro O., Rivera-Gutierrez S., Ética y Jurisprudencia Administrativa de los Derechos de los Sujetos de Investigación en Pandemia (COVID-19): Función de los Comités Éticos Científicos: Chile, Int. J. Morphol, 39, pp. 785-788, (2021); Venegas C.P.V., Desarrollo De Algoritmo Para Asociación Automática De Pictogramas a Indicaciones Médicas, Doctoral Dissertation, (2017); Hopewell S., Boutron I., Chan A.-W., Collins G.S., de Beyer J.A., Hrobjartsson A., Nejstgaard C.H., Ostengaard L., Schulz K.F., Tunn R., Et al., An update to SPIRIT and CONSORT reporting guidelines to enhance transparency in randomized trials, Nat. Med, 28, pp. 1740-1743, (2022)","R. Figueroa; Department of Electrical Engineering, Universidad de Concepción, Concepción, 4070409, Chile; email: rosa.figueroa@biomedica.udec.cl; C. Taramasco; School of Engineering, Andres Bello University, Viña del Mar 2531015, Chile; email: carla.taramasco@unab.cl","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85200855992"
"Linginani I.; Lakshmi M.A.","Linginani, Indira (57211476182); Lakshmi, Muddana A. (56440168100)","57211476182; 56440168100","An intelligent model of pulmonary emphysema detection using adaptive image segmentation and multi-dilated densenet with attention mechanism","2025","Applied Soft Computing","168","","112483","","","","0","10.1016/j.asoc.2024.112483","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210358572&doi=10.1016%2fj.asoc.2024.112483&partnerID=40&md5=2b67cd6cfd7dfcb172cd3325d1001d51","Department of Computer Science and Engineering, GITAM (Deemed to be University) Hyderabad, Telangana, Rudraram, 502329, India","Linginani I., Department of Computer Science and Engineering, GITAM (Deemed to be University) Hyderabad, Telangana, Rudraram, 502329, India; Lakshmi M.A., Department of Computer Science and Engineering, GITAM (Deemed to be University) Hyderabad, Telangana, Rudraram, 502329, India","Pulmonary emphysema is a significant factor in lung cancer and Chronic Obstructive Pulmonary Disease (COPD). Traditional pulmonary emphysema detection models may have difficulty in accurately detecting and diagnosing the severity of the disease. So, this work developed a novel pulmonary emphysema detection system with the help of deep learning frameworks. Originally, the significant images are accumulated from the benchmark sources, and fed into the Adaptive Trans-DenseUnet (ATDUnet)-based segmentation model. The ATDUnet model is highly effective in accurately segmenting pulmonary emphysema from the gathered images. Moreover, to enhance the segmentation process, the parameters are tuned in the ATDUnet using the Statistical Solution of the Osprey Optimization Algorithm (SSOOA). Subsequently, the segmented image is given to the pulmonary emphysema classification phase, where the Multi-Dilated DenseNet with Attention Mechanism (MDDNet-AM) is employed. By incorporating an attention mechanism, MDDNet-AM can focus on important features within the image for improved accuracy and efficiency in diagnosis. Finally, the developed model offered the pulmonary emphysema classified outcome. Then, the outcome of the developed model is compared against conventional pulmonary emphysema detection methods, and given the accuracy to be 93.10. Therefore, the result proved that the use of developed advanced technology in pulmonary emphysema detection has shown promising results in the field of respiratory health. © 2024 Elsevier B.V.","Adaptive Trans-DenseUnet; Multi-Dilated Densenet with Attention Mechanism; Pulmonary Emphysema Detection; Statistical Solution of Osprey Optimization Algorithm","Image segmentation; Pulmonary diseases; Adaptive image segmentation; Adaptive trans-denseunet; Attention mechanisms; Developed model; Intelligent models; Multi-dilated densenet with attention mechanism; Optimization algorithms; Pulmonary emphysema; Pulmonary emphysema detection; Statistical solution of osprey optimization algorithm; Lung cancer","","","","","","","Zhou Z., Gou F., Tan Y., Wu J., A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing countries, IEEE J. Biomed. 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Lett., 16, 12, pp. 1949-1953, (2019); Zhang K., Guo Y., Wang X., Yuan J., Ding Q., Multiple feature reweight densenet for image classification, IEEE Access, 7, pp. 9872-9880, (2019); Suri J.S., Et al., UNet deep learning architecture for segmentation of vascular and non-vascular images: a microscopic look at unet components buffered with pruning, explainable artificial intelligence, and bias, IEEE Access, 11, pp. 595-645, (2023); Zhou Z., Siddiquee M.M.R., Tajbakhsh N., Liang J., UNet++: Redesigning skip connections to exploit multiscale features in image segmentation, IEEE Trans. Med. Imaging, 39, 6, pp. 1856-1867, (2020); Zhao Z., Et al., Cloud identification and properties retrieval of the fengyun-4A satellite using a resunet model, IEEE Trans. Geosci. Remote Sens., 61, pp. 1-18, (2023)","I. Linginani; Department of Computer Science and Engineering, GITAM (Deemed to be University) Hyderabad, Rudraram, Telangana, 502329, India; email: indiralingineni@gmail.com","","Elsevier Ltd","","","","","","15684946","","","","English","Appl. Soft Comput.","Article","Final","","Scopus","2-s2.0-85210358572"
"Shen J.; Zhang X.; Lu Y.; Ye P.; Zhang P.; Yan Y.","Shen, Jiakun (57393506500); Zhang, Xueshuai (57221089544); Lu, Yu (58946502800); Ye, Pengfei (57217864905); Zhang, Pengyuan (55491517000); Yan, Yonghong (7404586597)","57393506500; 57221089544; 58946502800; 57217864905; 55491517000; 7404586597","Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification","2024","Computers in Biology and Medicine","179","","108843","","","","0","10.1016/j.compbiomed.2024.108843","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198995607&doi=10.1016%2fj.compbiomed.2024.108843&partnerID=40&md5=8ffb4072e0630071848537693e804848","Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China; Children's Hospital Capital Institute of Pediatrics, Beijing, China","Shen J., Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Zhang X., Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Lu Y., Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China; Ye P., Children's Hospital Capital Institute of Pediatrics, Beijing, China; Zhang P., Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China; Yan Y., Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China","Respiratory diseases are one of the major health problems worldwide. Early diagnosis of the disease types is of vital importance. As one of the main symptoms of many respiratory diseases, cough may contain information about different pathological changes in the respiratory system. Therefore, many researchers have used cough sounds to diagnose different diseases through artificial intelligence in recent years. The acoustic features and data augmentation methods commonly used in speech tasks are used to achieve better performance. Although these methods are applicable, previous studies have not considered the characteristics of cough sound signals. In this paper, we designed a cough-based respiratory disease classification system and proposed audio characteristic-dependent feature extraction and data augmentation methods. Firstly, according to the short durations and rapid transition of different cough stages, we proposed maximum overlapping mel-spectrogram to avoid missing inter-frame information caused by traditional framing methods. Secondly, we applied various data augmentation methods to mitigate the problem of limited labeled data. Based on the frequency energy distributions of different diseased cough audios, we proposed a parameter-independent self-energy-based augmentation method to enhance the differences between different frequency bands. Finally, in the model testing stage, we leveraged test-time augmentation to further improve the classification performance by fusing the test results of the original and multiple augmented audios. The proposed methods were validated on the Coswara dataset through stratified four-fold cross-validation. Compared to the baseline model using mel-spectrogram as input, the proposed methods achieved an average absolute performance improvement of 3.33% and 3.10% in macro Area Under the Receiver Operating Characteristic (macro AUC) and Unweighted Average Recall (UAR), respectively. The visualization results through Gradient-weighted Class Activation Mapping (Grad-CAM) showed the contributions of different features to model decisions. © 2024 Elsevier Ltd","Cough; Data augmentation; Mel-spectrogram; Model interpretability; Respiratory disease classification; Test-time augmentation","Adult; Cough; Female; Humans; Male; Middle Aged; Signal Processing, Computer-Assisted; Sound Spectrography; Classification (of information); Data mining; Diagnosis; Extraction; Pulmonary diseases; Spectrographs; Cough; Data augmentation; Disease classification; Interpretability; Mel-spectrogram; Model interpretability; Respiratory disease classification; Spectrograms; Test time; Test-time augmentation; Article; asthma; audio recording; calculation; controlled study; coronavirus disease 2019; coughing; cross validation; data visualization; disease classification; feature extraction; human; pneumonia; receiver operating characteristic; respiratory tract disease; adult; classification; female; male; middle aged; pathophysiology; procedures; signal processing; sound detection; Feature extraction","","","","","China Postdoctoral Science Foundation, (2022M723325); China Postdoctoral Science Foundation","This work is funded by China Postdoctoral Science Foundation (NO. 2022M723325 ). ","World Health Organization, The top 10 causes of death, (2020); Organization W.H., Et al., COVID-19 Weekly Epidemiological Update, Edition 150, 6 July 2023, (2023); Bach P.B., Jett J.R., Pastorino U., Tockman M.S., Swensen S.J., Begg C.B., Computed tomography screening and lung cancer outcomes, Jama, 297, 9, pp. 953-961, (2007); Chung K.F., Pavord I.D., Prevalence, pathogenesis, and causes of chronic cough, Lancet, 371, 9621, pp. 1364-1374, (2008); Morice A., Fontana G., Belvisi M., Birring S., Chung K., Dicpinigaitis P.V., Kastelik J., McGarvey L., Smith J., Tatar M., Et al., ERS guidelines on the assessment of cough, Eur. Respir. J., 29, 6, pp. 1256-1276, (2007); Knocikova J., Korpas J., Vrabec M., Javorka M., Wavelet analysis of voluntary cough sound in patients with respiratory diseases, J. Physiol. 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Eng., 66, 2, pp. 485-495, (2018); Yadav S., Kausthubha N., Gope D., Krishnaswamy U.M., Ghosh P.K., Comparison of cough, wheeze and sustained phonations for automatic classification between healthy subjects and asthmatic patients, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 1400-1403, (2018); Pahar M., Klopper M., Reeve B., Warren R., Theron G., Diacon A., Niesler T., Automatic tuberculosis and COVID-19 cough classification using deep learning, 2022 International Conference on Electrical, Computer and Energy Technologies, ICECET, pp. 1-9, (2022); Aytekin I., Dalmaz O., Gonc K., Ankishan H., Saritas E.U., Bagci U., Celik H., Cukur T., Covid-19 detection from respiratory sounds with hierarchical spectrogram transformers, IEEE J. Biomed. Health Inf., (2023); Shen J., Zhang X., Zhang P., Yan Y., Zhang S., Huang Z., Tang Y., Wang Y., Zhang F., Sun A., Piecewise position encoding in convolutional neural network for cough-based Covid-19 detection, ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 1-5, (2023); Dentamaro V., Giglio P., Impedovo D., Moretti L., Pirlo G., AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath, Pattern Recognit., 127, (2022); Sharan R.V., Berkovsky S., Navarro D.F., Xiong H., Jaffe A., Detecting pertussis in the pediatric population using respiratory sound events and CNN, Biomed. Signal Process. Control, 68, (2021); Bagad P., Dalmia A., Doshi J., Nagrani A., Bhamare P., Mahale A., Rane S., Agarwal N., Panicker R., Cough against covid: Evidence of covid-19 signature in cough sounds, (2020); He K., Zhang X., Ren S., Sun J., Deep residual learning for image recognition, pp. 770-778; Muller R., Kornblith S., Hinton G.E., When does label smoothing help?, Adv. Neural Inf. Process. Syst., 32, (2019); Laguarta J., Hueto F., Subirana B., COVID-19 artificial intelligence diagnosis using only cough recordings, IEEE Open J. Eng. Med. Biol., 1, pp. 275-281, (2020); Imran A., Posokhova I., Qureshi H.N., Masood U., Riaz M.S., Ali K., John C.N., Hussain M.I., Nabeel M., AI4covid-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app, Inform. Med. Unlocked, 20, (2020); Lella K.K., Pja A., Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice, and breath, Alex. Eng. J., 61, 2, pp. 1319-1334, (2022); Schluter J., Grill T., Exploring data augmentation for improved singing voice detection with neural networks, pp. 121-126; Khurana A., Paul S., Rai P., Biswas S., Aggarwal G., Sita: Single image test-time adaptation, (2021); Zhang M., Levine S., Finn C., Memo: Test time robustness via adaptation and augmentation, Adv. Neural Inf. Process. Syst., 35, pp. 38629-38642, (2022); Sharma N., Krishnan P., Kumar R., Ramoji S., Chetupalli S., Nirmala R., Kumar Ghosh P., Ganapathy S., Coswara-a database of breathing, cough, and voice sounds for COVID-19 diagnosis, Proceedings of the Annual Conference of the International Speech Communication Association, Vol. 2020, INTERSPEECH, pp. 4811-4815, (2020); Selvaraju R.R., Cogswell M., Das A., Vedantam R., Parikh D., Batra D., Grad-cam: Visual explanations from deep networks via gradient-based localization, pp. 618-626; Park D.S., Chan W., Zhang Y., Chiu C.-C., Zoph B., Cubuk E.D., Le Q.V., SpecAugment: A simple data augmentation method for automatic speech recognition, Interspeech 2019, (2019); Wold S., Esbensen K., Geladi P., Principal component analysis, Chemometr. Intell. Lab. Syst., 2, 1-3, pp. 37-52, (1987); Korpas J., Sadlonova J., Vrabec M., Analysis of the cough sound: an overview, Pulmon. 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Syst., 32, (2019); Kingma D.P., Ba J., Adam: A method for stochastic optimization, (2014); Han J., Xia T., Spathis D., Bondareva E., Brown C., Chauhan J., Dang T., Grammenos A., Hasthanasombat A., Floto A., Et al., Sounds of COVID-19: exploring realistic performance of audio-based digital testing, NPJ Digit. Med., 5, 1, (2022); Pahar M., Klopper M., Warren R., Niesler T., COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features, Comput. Biol. Med., 141, (2022); Chetupalli S.R., Krishnan P., Sharma N., Muguli A., Kumar R., Nanda V., Pinto L.M., Ghosh P.K., Ganapathy S., Multi-modal point-of-care diagnostics for COVID-19 based on acoustics and symptoms, IEEE J. Transl. Eng. 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Meas., 40, 3, (2019); Mo A., Gui E., Fletcher R.R., Use of voluntary cough sounds and deep learning for pulmonary disease screening in low-resource areas, 2022 IEEE Global Humanitarian Technology Conference, GHTC, pp. 242-249, (2022); Windmon A., Et al., pp. 329-338; Mahanta S.K., Jain S., Kaushik D., (2021); Magni C., Chellini E., Lavorini F., Fontana G.A., Widdicombe J., Voluntary and reflex cough: similarities and differences, Pulmon. Pharmacol. Ther., 24, 3, pp. 308-311, (2011)","X. Zhang; Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China; email: zhangxueshuai@hccl.ioa.ac.cn","","Elsevier Ltd","","","","","","00104825","","CBMDA","39029433","English","Comput. Biol. Med.","Article","Final","","Scopus","2-s2.0-85198995607"
"Zhu D.; Feng J.; Guo J.; Duan J.; Yang Y.; Leng J.","Zhu, Dongping (59239933000); Feng, Junfei (59240017700); Guo, Jie (59239753600); Duan, Jixian (59239933100); Yang, Yan (59239753700); Leng, Jing (59239576300)","59239933000; 59240017700; 59239753600; 59239933100; 59239753700; 59239576300","Establishing a risk prediction model for residual pulmonary vascular obstruction after regular anticoagulant therapy for non-high-risk pulmonary embolism","2024","Journal of Thoracic Disease","16","7","","4447","4459","12","0","10.21037/jtd-23-1876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199995278&doi=10.21037%2fjtd-23-1876&partnerID=40&md5=2b68648665fe410bd1042573eb9df7bf","School of Clinical Medicine, Dali University, Dali, China; Department of Respiratory and Critical Care, the Third People’s Hospital of Yunnan Province, Kunming, China; Department of Reflection Imaging, the Third People’s Hospital of Yunnan Province, Kunming, China","Zhu D., School of Clinical Medicine, Dali University, Dali, China; Feng J., Department of Respiratory and Critical Care, the Third People’s Hospital of Yunnan Province, Kunming, China; Guo J., Department of Respiratory and Critical Care, the Third People’s Hospital of Yunnan Province, Kunming, China; Duan J., Department of Respiratory and Critical Care, the Third People’s Hospital of Yunnan Province, Kunming, China; Yang Y., Department of Reflection Imaging, the Third People’s Hospital of Yunnan Province, Kunming, China; Leng J., Department of Respiratory and Critical Care, the Third People’s Hospital of Yunnan Province, Kunming, China","Background: The incidence of pulmonary embolism (PE) has been on the rise annually. Despite receiving regular sequential anticoagulation therapy, some patients with non-high-risk acute PE (APE) continue to experience residual pulmonary vascular obstruction (RPVO). This study sought to identify the risk factors for RPVO following 3 months of sequential anticoagulation therapy for non-high-risk PE. Machine learning techniques were utilized to construct a clinical prediction model for predicting the occurrence of RPVO. Methods: A total of 254 acute non-high-risk PE patients were included in this study, all of whom were admitted to the Third People’s Hospital of Yunnan Province between 2020 and 2023. After 3 months of regular anticoagulant treatment, computed tomography pulmonary angiography (CTPA) were reviewed to identify the presence of RPVO. Patients were then categorized into either the thrombolysis group or the thrombosis residue group. Throughout the study period, 49 patients were excluded due to missing data, irregular treatment, or loss to follow-up. Clinical symptoms, physical signs, and laboratory results of 205 PE patients were recorded. Correlation and collinearity analyses were conducted on relevant risk factors, and significance tests were performed. Heat maps illustrating the relationships between influencing factors were generated. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate logistic regression analysis to create a predictive model. Internal validation of the model was also carried out. Results: By searching the literature to understand all the clinical indicators that may affect the efficacy of anticoagulation therapy. A total of 205 patients with non-high-risk acute pulmonary thromboembolism were evaluated for various risk factors. Five independent factors were identified by multivariable analysis—age, chronic obstructive pulmonary disease (COPD), acratia, pulmonary systolic blood pressure (PASP), and major arterial embolism—and their P value, odds ratio (OR) and confidence interval (CI) were as follows: (P=0.012, OR =1.123; 95% CI: 1.026–1.23), (P=0.002, OR =13.30; 95% CI: 2.673–66.188), (P=0.001, OR =14.009; 95% CI: 2.782–70.547), (P=0.003, OR =1.061; 95% CI: 1.020–1.103) and (P<0.001, OR =18.128; 95% CI: 3.853–85.293), which may indicate a poor prognosis after standard anticoagulant therapy. A nomogram was constructed using these variables and internally validated. The receiver operating characteristic (ROC) curves of the model demonstrated strong predictive accuracy, with an area under the curve (AUC) of 0.94 (95% CI: 0.89–0.96) for the training set and 0.93 (95% CI: 0.88–0.95) for the validation set. Calibration curves were utilized to assess the practicality of the nomogram. Conclusions: A novel predictive model was developed based on a single-center retrospective study to identify patients with RPVO following anticoagulant therapy for acute non-high-risk PE. This model may aid in the early detection of patients, prompt adjustment of treatment, and ultimately lead to a decrease in adverse outcomes. © Journal of Thoracic Disease. All rights reserved.","anticoagulant therapy; computed tomography pulmonary angiography (CTPA); machine learning; nomogram; Non-high-risk pulmonary thromboembolism","alanine aminotransferase; albumin; aspartate aminotransferase; C reactive protein; creatinine; D dimer; globulin; hemoglobin; heparin; rivaroxaban; activated partial thromboplastin time; adult; adverse outcome; anticoagulant therapy; Article; blood clot lysis; blood vessel occlusion; brain infarction; chronic obstructive lung disease; cohort analysis; computed tomography pulmonary angiography; confidence interval; controlled study; demographics; diabetes mellitus; dyspnea; female; follow up; high risk patient; human; hypertension; international normalized ratio; ischemic heart disease; kidney failure; kidney function; laboratory test; least absolute shrinkage and selection operator; lung embolism; major clinical study; male; middle aged; multivariate logistic regression analysis; nomogram; prediction; predictive model; pulmonary artery systolic pressure; receiver operating characteristic; residual pulmonary vascular obstruction; retrospective study; stomach disease","","alanine aminotransferase, 9000-86-6, 9014-30-6; aspartate aminotransferase, 9000-97-9; C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5; hemoglobin, 9008-02-0; heparin, 37187-54-5, 8057-48-5, 8065-01-8, 9005-48-5, 9041-08-1; rivaroxaban, 366789-02-8","","","","","Pulmonary embolism and pulmonary vascular disease Working Committee of Respiratory Physician Branch of Chinese Medical Doctor Association, National Cooperation group on prevention and treatment of pulmonary embolism and pulmonary vascular disease. Guidelines for diagnosis, treatment and prevention of pulmonary thromboembolism, National Medical Journal of China, 98, pp. 1060-1087, (2018); Konstantinides SV, Meyer G, Becattini C, Et al., 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS), Eur Heart J, 41, pp. 543-603, (2020); Stein PD, Fowler SE, Goodman LR, Et al., Multidetector computed tomography for acute pulmonary embolism, N Engl J Med, 354, pp. 2317-2327, (2006); Streiff MB, Holmstrom B, Angelini D, Et al., NCCN Guidelines Insights: Cancer-Associated Venous Thromboembolic Disease, Version 2.2018, J Natl Compr Canc Netw, 16, pp. 1289-1303, (2018); Xiong W, Zhao Y, Liu S, Et al., Sequential Therapy of Nadroparin and Rivaroxaban in the Initial Treatment of Patients With Acute Pulmonary Embolism, Front Pharmacol, 13, (2022); Kearon C, Akl EA, Ornelas J, Et al., Antithrombotic Therapy for VTE Disease: CHEST Guideline and Expert Panel Report, Chest, 149, pp. 315-352, (2016); Key NS, Khorana AA, Kuderer NM, Et al., Venous Thromboembolism Prophylaxis and Treatment in Patients With Cancer: ASCO Clinical Practice Guideline Update, J Clin Oncol, 38, pp. 496-520, (2020); Konstantinides SV, Meyer G, Becattini C, Et al., 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS): The Task Force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC), Eur Respir J, 54, (2019); Crush J, Seah M, Chou D, Et al., Sequential low molecular weight heparin and rivaroxaban for venous thromboprophylaxis in pelvic and acetabular trauma, Arch Orthop Trauma Surg, 142, pp. 3271-3277, (2022); Tang Y, Wang K, Shi Z, Et al., A RCT study of Rivaroxaban, low-molecular-weight heparin, and sequential medication regimens for the prevention of venous thrombosis after internal fixation of hip fracture, Biomed Pharmacother, 92, pp. 982-988, (2017); LE Gal G, Carrier M, Kovacs MJ, Et al., Residual vein obstruction as a predictor for recurrent thromboembolic events after a first unprovoked episode: data from the REVERSE cohort study, J Thromb Haemost, 9, pp. 1126-1132, (2011); Barsam SJ, Patel JP, Roberts LN, Et al., The impact of body weight on rivaroxaban pharmacokinetics, Res Pract Thromb Haemost, 1, pp. 180-187, (2017); Moore KT, Wong P, Zhang L, Et al., Influence of age on the pharmacokinetics, pharmacodynamics, efficacy, and safety of rivaroxaban, Curr Med Res Opin, 34, pp. 2053-2061, (2018); Jarboe L, Dadlani A, Bandikatla S, Et al., Drug Use Evaluation of Direct Oral Anticoagulants (DOACs) in Patients With Advanced Cirrhosis, Cureus, 14, (2022); Liu YY, Li XC, Duan Z, Et al., Correlation between the embolism area and pulmonary arterial systolic pressure as an indicator of pulmonary arterial hypertension in patients with acute pulmonary thromboembolism, Eur Rev Med Pharmacol Sci, 18, pp. 2551-2555, (2014); Lang IM, Simonneau G, Pepke-Zaba JW, Et al., Factors associated with diagnosis and operability of chronic thromboembolic pulmonary hypertension. A case-control study, Thromb Haemost, 110, pp. 83-91, (2013); Zhang Z., Univariate description and bivariate statistical inference: the first step delving into data, Ann Transl Med, 4, (2016); Wendelboe AM, Raskob GE., Global Burden of Thrombosis: Epidemiologic Aspects, Circ Res, 118, pp. 1340-1347, (2016); Jimenez D, de Miguel-Diez J, Guijarro R, Et al., Trends in the Management and Outcomes of Acute Pulmonary Embolism: Analysis From the RIETE Registry, J Am Coll Cardiol, 67, pp. 162-170, (2016); Igneri LA, Hammer JM., Systemic Thrombolytic Therapy for Massive and Submassive Pulmonary Embolism, J Pharm Pract, 33, pp. 74-89, (2020); Pires I, Santos JM, Neto V, Et al., A new ratio with PaO2/ FiO2 and pulmonary arterial systolic pressure in the prognosis of intermediate high risk pulmonary embolism, European Heart Journal, 42, (2021); McIntyre KM, Sasahara AA., The hemodynamic response to pulmonary embolism in patients without prior cardiopulmonary disease, Am J Cardiol, 28, pp. 288-294, (1971); Wang GX., Correlation between obstructive area of acute pulmonary embolism and hemodynamic changes, China Journal of Modern Medicine, 14, pp. 34-46, (2007); Pesavento R, Filippi L, Palla A, Et al., Impact of residual pulmonary obstruction on the long-term outcome of patients with pulmonary embolism, Eur Respir J, 49, (2017); Picart G, Robin P, Tromeur C, Et al., Predictors of residual pulmonary vascular obstruction after pulmonary embolism: Results from a prospective cohort study, Thromb Res, 194, pp. 1-7, (2020); Rali PM, Criner GJ., Submassive Pulmonary Embolism, Am J Respir Crit Care Med, 198, pp. 588-598, (2018); Ruzicic DP, Dzudovic B, Matijasevic J, Et al., Signs and symptoms of acute pulmonary embolism and their predictive value for all-cause hospital death in respect of severity of the disease, age, sex and body mass index: retrospective analysis of the Regional PE Registry (REPER), BMJ Open Respir Res, 10, (2023)","J. Leng; Department of Respiratory and Critical Care, The Third People’s Hospital of Yunnan Province, Kunming, 292 Beijing Road, Guandu District, 650011, China; email: zdp.jscy@outlook.com","","AME Publishing Company","","","","","","20721439","","","","English","J. Thorac. Dis.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85199995278"
"Patel R.J.; Willie-Permor D.; Fan A.; Zarrintan S.; Malas M.B.","Patel, Rohini J. (57463729900); Willie-Permor, Daniel (57915823100); Fan, Austin (57204023219); Zarrintan, Sina (15019929400); Malas, Mahmoud B. (57202686331)","57463729900; 57915823100; 57204023219; 15019929400; 57202686331","30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology","2024","Annals of Vascular Surgery","109","","","63","76","13","0","10.1016/j.avsg.2024.05.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200589176&doi=10.1016%2fj.avsg.2024.05.016&partnerID=40&md5=174d85c7021dff70477954360ee2caa8","Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States","Patel R.J., Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States; Willie-Permor D., Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States; Fan A., Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States; Zarrintan S., Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States; Malas M.B., Center for Learning and Excellence in Vascular & Endovascular Research (CLEVER), Division of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA, United States","Background: The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. Methods: Patients with a computed tomographic angiography head/neck between 2010 and 2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each computed tomography was used to create a three-dimensional image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with area under the receiver operating characteristic curves (AUCs). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. Results: We created 3 models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, peripheral arterial disease, hyperlipidemia, body mass index (BMI), chronic obstructive pulmonary disease (COPD), and history of transient ischemia attack (TIA)/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue (PVAT) volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, PVAT, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. Conclusions: Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model has a 20% chance of stroke/death. Further prospective studies are needed to validate our findings. © 2024 Elsevier Inc.","","Aged; Aged, 80 and over; Artificial Intelligence; Carotid Stenosis; Computed Tomography Angiography; Decision Support Techniques; Female; Humans; Male; Middle Aged; Plaque, Atherosclerotic; Predictive Value of Tests; Prognosis; Radiographic Image Interpretation, Computer-Assisted; Retrospective Studies; Risk Assessment; Risk Factors; Stroke; Time Factors; acetylsalicylic acid; clopidogrel; hydroxymethylglutaryl coenzyme A reductase inhibitor; aged; Article; artificial intelligence; atrial fibrillation; body mass; carotid artery occlusion; cerebrovascular accident; chronic obstructive lung disease; computed tomographic angiography; computer assisted tomography; coronary artery disease; female; fibromuscular dysplasia; heart failure; human; hyperlipidemia; hypertension; major clinical study; male; morphology; mortality rate; obstructive lung disease; peripheral arterial disease; perivascular adipose tissue; predictive model; receiver operating characteristic; tertiary care center; transient ischemic attack; transitional blindness; artificial intelligence; atherosclerotic plaque; carotid stenosis; cerebrovascular accident; complication; computed tomographic angiography; computer assisted diagnosis; decision support system; diagnostic imaging; etiology; middle aged; mortality; predictive value; prognosis; retrospective study; risk assessment; risk factor; time factor; very elderly","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1; clopidogrel, 113665-84-2, 120202-66-6, 90055-48-4, 94188-84-8, 120202-65-5, 120202-67-7, 894353-16-3, 744256-69-7","aspirin","","U.S. National Library of Medicine, NLM, (T15LM011271); U.S. National Library of Medicine, NLM","Rohini J. Patel is funded through the National Library of Medicine, T15 Postdoctoral Training Grant Fellowship Program in Biomedical Informatics (Grant T15LM011271) ","Heron M., Deaths: leading causes for 2017, Natl Vital Stat Rep, 68, pp. 1-77, (2019); Mozaffarian D., Benjamin E.J., Go A.S., Et al., Heart disease and stroke statistics-2016 update: a report from the American heart association, Circulation, 133, pp. e38-e360, (2016); Murphy S.J.X., Werring D.J., Stroke: causes and clinical features, Medicine (Abingdon), 48, pp. 561-566, (2020); Kernan W.N., Ovbiagele B., Black H.R., Et al., Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association, Stroke, 45, pp. 2160-2236, (2014); Barnett H.J.M., Taylor D.W., Haynes R.B., Et al., Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenosis, N Engl J Med, 325, pp. 445-453, (1991); Executive Committee for the asymptomatic carotid atherosclerosis study, JAMA, 273, pp. 1421-1428, (1995); Randomised trial of endarterectomy for recently symptomatic carotid stenosis: final results of the MRC European Carotid Surgery Trial (ECST), Lancet, 351, pp. 1379-1387, (1998); AbuRahma A.F., Avgerinos E.M., Chang R.W., Et al., Society for vascular surgery clinical Practice guidelines for management of extracranial Cerebrovascular disease, J Vasc Surg, 75, pp. 4S-22S, (2022); Ferguson G.G., Eliasziw M., Barr H.W., Et al., The North American symptomatic carotid endarterectomy trial : surgical results in 1415 patients, Stroke, 30, pp. 1751-1758, (1999); Saha S.P., Whayne T.F., Mukherjee D., Evidence-based management of carotid artery disease, Int J Angiol, 19, pp. e21-e24, (2010); Vincent S., Eberg M., Eisenberg M.J., Et al., Meta-analysis of randomized Controlled trials comparing the long-term outcomes of carotid artery stenting versus endarterectomy, Circ Cardiovasc Qual Outcomes, 8, pp. S99-S108, (2015); Inzitari D., Eliasziw M., Gates P., Et al., The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis. North American Symptomatic Carotid Endarterectomy Trial Collaborators, N Engl J Med, 342, pp. 1693-1700, (2000); Dharmakidari S., Bhattacharya P., Chaturvedi S., Carotid artery stenosis: medical therapy, surgery, and stenting, Curr Neurol Neurosci Rep, 17, (2017); Orion K.C., Ruppert J., Call D., Et al., The role of advanced diagnostic technology in the selection of a patient with symptomatic but hemodynamically insignificant disease for carotid endarterectomy, J Vasc Surg, 1, pp. 90-93, (2015); Gupta A., Baradaran H., Schweitzer A.D., Et al., Carotid plaque MRI and stroke risk: a systematic review and meta-analysis, Stroke, 44, pp. 3071-3077, (2013); Adla T., Adlova R., Multimodality imaging of carotid stenosis, Int J Angiol, 24, pp. 179-184, (2015); Kolossvary M., Szilveszter B., Merkely B., Et al., Plaque imaging with CT-a comprehensive review on coronary CT angiography based risk assessment, Cardiovasc Diagn Ther, 7, pp. 489-506, (2017); Samarzija K., Milosevic P., Jurjevic Z., Et al., Grading of carotid artery stenosis with computed tomography angiography: whether to use the narrowest diameter or the cross-sectional area, Insights Imaging, 9, pp. 527-534, (2018); Anderson G.B., Ashforth R., Steinke D.E., Et al., Angiography for the detection and Characterization of carotid artery bifurcation disease, Stroke, 31, pp. 2168-2174, (2000); Hemmati H.R., Alizadeh M., Kamali-Asl A., Et al., Semi-automated carotid lumen segmentation in computed tomography angiography images, J Biomed Res, 31, pp. 548-558, (2017); Saba L., Gao H., Acharya U.R., Et al., Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model, Neuroradiology, 54, pp. 1207-1214, (2012); Zhu G., Li Y., Ding V., Et al., Semiautomated Characterization of carotid artery plaque features from computed tomography angiography to predict atherosclerotic cardiovascular disease risk score, J Comput Assist Tomogr, 43, pp. 452-459, (2019); Chrencik M.T., Khan A.A., Luther L., Et al., Quantitative assessment of carotid plaque morphology (geometry and tissue composition) using computed tomography angiography, J Vasc Surg, 70, pp. 858-868, (2019); Karlof E., Buckler A., Liljeqvist M.L., Et al., Carotid plaque Phenotyping by correlating plaque morphology from computed tomography angiography with transcriptional Profiling, Eur J Vasc Endovasc Surg, 62, pp. 716-726, (2021); Ibrahimi P., Jashari F., Nicoll R., Et al., Coronary and carotid atherosclerosis: how useful is the imaging?, Atherosclerosis, 231, pp. 323-333, (2013); DeMarco J.K., Huston J., Imaging of high-risk carotid artery plaques: current status and future directions, Neurosurg Focus, 36, (2014); Sheahan M., Ma X., Paik D., Et al., Atherosclerotic plaque tissue: noninvasive quantitative assessment of characteristics with software-aided measurements from conventional CT angiography, Radiology, 286, pp. 622-631, (2018); Lal B.K., Khan A.A., Kashyap V.S., Et al., Computed tomography angiographic biomarkers help identify vulnerable carotid artery plaque, J Vasc Surg, 75, pp. 1311-1322, (2022); Varga-Szemes A., Schoepf U.J., Maurovich-Horvat P., Et al., Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve, Int J Cardiol, 331, pp. 307-315, (2021); St Pierre S., Siegelman J., Obuchowski N.A., Et al., Measurement accuracy of atherosclerotic plaque structure on CT using phantoms to establish ground truth, Acad Radiol, 24, pp. 1203-1215, (2017); Calvillo-King L., Xuan L., Zhang S., Et al., Predicting risk of perioperative death and stroke after carotid endarterectomy in asymptomatic patients: derivation and validation of a clinical risk score, Stroke, 41, pp. 2786-2794, (2010); Dakour-Aridi H., Faateh M., Kuo P.L., Et al., The Vascular Quality Initiative 30-day stroke/death risk score calculator after transfemoral carotid artery stenting, J Vasc Surg, 71, pp. 526-534, (2020); Liang P., O'Donnell T.F.X., Cronenwett J.L., Et al., Vascular Quality Initiative risk score for 30-day stroke or death following transcarotid artery revascularization, J Vasc Surg, 73, pp. 1665-1674, (2021); Malas M.B., Arhuidese I.J., Qazi U., Et al., Predicting stroke risk in patients with carotid artery stenosis using contrast enhanced carotid duplex ultrasound to quantify plaque vasa-vasorum volume: results of a Pilot study, J Vasc Endovasc Surg, 2, (2017); Katsuda S., Kaji T., Atherosclerosis and extracellular matrix, J Atheroscler Thromb, 10, pp. 267-274, (2003); Chistiakov D.A., Sobenin I.A., Orekhov A.N., Vascular extracellular matrix in atherosclerosis, Cardiol Rev, 21, pp. 270-288, (2013); Lin A., Dey D., Wong D.T.L., Et al., Perivascular adipose tissue and coronary atherosclerosis: from biology to imaging Phenotyping, Curr Atheroscler Rep, 21, (2019); Liu Y., Sun Y., Hu C., Et al., Perivascular adipose tissue as an indication, Contributor to, and therapeutic target for atherosclerosis, Front Physiol, 11, (2020); Amundson D.E., Djurkovic S., Matwiyoff G.N., The obesity paradox, Crit Care Clin, 26, pp. 583-596, (2010); Ades P.A., Savage P.D., The obesity paradox: perception vs knowledge, Mayo Clin Proc, 85, pp. 112-114, (2010); Elagizi A., Kachur S., Lavie C.J., Et al., An overview and update on obesity and the obesity paradox in cardiovascular diseases, Prog Cardiovasc Dis, 61, pp. 142-150, (2018); Arhuidese I.J., Holscher C.M., Elemuo C., Et al., Impact of body mass index on outcomes of autogenous fistulas for hemodialysis access, Ann Vasc Surg, 68, pp. 192-200, (2020); Quinones-Ossa G.A., Lobo C., Garcia-Ballestas E., Et al., Obesity and stroke: does the paradox apply for stroke?, Neurointervention, 16, pp. 9-19, (2021); Yousufuddin M., Takahashi P.Y., Major B., Et al., Association between hyperlipidemia and mortality after incident acute myocardial infarction or acute decompensated heart failure: a propensity score matched cohort study and a meta-analysis, BMJ Open, 9, (2019); Wang T.Y., Newby L.K., Chen A.Y., Et al., Hypercholesterolemia paradox in relation to mortality in acute coronary syndrome, Clin Cardiol, 323, pp. E22-E28, (2009); Velavan P., Huan Loh P., Clark A., Et al., The cholesterol paradox in heart failure, Congest Heart Fail, 1, pp. 336-341, (2007)","M.B. Malas; Professor in Residence, Chief, Division of Vascular and Endovascular Surgery, Vice Chair of Surgery for Clinical Research, University of California San Diego, La Jolla, 92093, United States; email: mmalas@health.ucsd.edu","","Elsevier Inc.","","","","","","08905096","","AVSUE","39009122","English","Ann. Vasc. Surg.","Article","Final","","Scopus","2-s2.0-85200589176"
"Cheema C.; Baldwin J.; Rodeghero J.; Werneke M.W.; Mioduski J.E.; Jeffries L.; Kucksdorf J.; Shepherd M.; Dionne C.; Randall K.","Cheema, Carolyn (57469573400); Baldwin, Jonathan (57193228767); Rodeghero, Jason (15045791400); Werneke, Mark W (6602103386); Mioduski, Jerry E (8592298300); Jeffries, Lynn (26633862900); Kucksdorf, Joseph (57226764336); Shepherd, Mark (57208431449); Dionne, Carol (14321536200); Randall, Ken (7006933787)","57469573400; 57193228767; 15045791400; 6602103386; 8592298300; 26633862900; 57226764336; 57208431449; 14321536200; 7006933787","Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis","2025","Journal of Manual and Manipulative Therapy","33","1","","63","72","9","0","10.1080/10669817.2024.2424619","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209582763&doi=10.1080%2f10669817.2024.2424619&partnerID=40&md5=b6a560a2563cd7fdfcd703dead44fc44","College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, United States; College of Allied Health, Department of Medical Imaging and Radiation Sciences, The University of Oklahoma Health Sciences Center, Oklahoma, OK, United States; Department of Public Health & Community Medicine, School of Medicine, Tufts University, Boston, MA, United States; Net Health Systems, Inc, Pittsburgh, PA, United States; Bellin Health, Orthopedics and Sports Medicine, Green Bay, WI, United States; Physical Therapy Department Bellin College, Green Bay, WI, United States","Cheema C., College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, United States; Baldwin J., College of Allied Health, Department of Medical Imaging and Radiation Sciences, The University of Oklahoma Health Sciences Center, Oklahoma, OK, United States; Rodeghero J., Department of Public Health & Community Medicine, School of Medicine, Tufts University, Boston, MA, United States; Werneke M.W., Net Health Systems, Inc, Pittsburgh, PA, United States; Mioduski J.E., Net Health Systems, Inc, Pittsburgh, PA, United States; Jeffries L., College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, United States; Kucksdorf J., Bellin Health, Orthopedics and Sports Medicine, Green Bay, WI, United States; Shepherd M., Physical Therapy Department Bellin College, Green Bay, WI, United States; Dionne C., College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, United States; Randall K., College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, OK, United States","Objectives: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed ‘Holy Grail’ questions, one being: ‘What are the most important (preventable) predictors of chronicity’ for patients with LBP? Subsequently, prognostic factors influencing outcomes for CLBP have been described, however results remain conflicting due to methodological weaknesses. Methods: This retrospective observational cohort study examined prognostic risk factors for PT outcomes in CLBP treatment using a sub-type of AI. Bootstrap random forest supervised machine learning analysis was employed to identify the outcomes-associated variables. Results: The top variables identified as predictive were: FOTO™ predicted functional status (FS) change score; FOTO™ predicted number of visits; initial FS score, age; history of jogging/walking, obesity, and previous treatments; provider education level; medication use; gender. Conclusion: This article presents how AI can be used to predict risk prognostic factors in healthcare research. Improving predictive accuracy helps clinicians predict outcomes and determine most appropriate plans of care and may impact research attrition rates. © 2024 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; chronic low back pain; Machine learning; physical therapy; prediction","Adult; Chronic Pain; Female; Humans; Low Back Pain; Machine Learning; Male; Middle Aged; Physical Therapy Modalities; Prognosis; Retrospective Studies; Risk Factors; Treatment Outcome; accuracy; adult; algorithm; angina pectoris; arthritis; Article; artificial intelligence; asthma; body mass; body weight; cerebrovascular accident; chronic obstructive lung disease; clinical outcome; cohort analysis; comorbidity; congestive heart failure; coronavirus disease 2019; data analysis; depression; diabetes mellitus; exercise; headache; hearing impairment; heart infarction; hepatitis; human; hypertension; low back pain; machine learning; major clinical study; observational study; osteoporosis; outcome assessment; peripheral vascular disease; random forest; retrospective study; risk factor; seizure; smoking; visual impairment; chronic pain; female; male; middle aged; physiotherapy; prognosis; therapy; treatment outcome","","","R 4.1.1, R Foundation; SAS 9.4, SAS","R Foundation; SAS","University of Oklahoma Health Sciences Center, OUHSC; Hudson College of Public Health, University of Oklahoma Health Sciences Center; Department of Biostatistics and Epidemiology, Biostatistics and Epidemiology Research Design and Analysis Center","This project was partially supported by a student research grant at the University of Oklahoma Health Sciences Center as the lead author was a Doctor of Science Candidate at the time of the investigation, and this project was the major work of the author\u2019s doctoral thesis. Dr Tabitha Garwe, University of Oklahoma Health Sciences Center College of Public Health, Department of Biostatistics and Epidemiology, Biostatistics and Epidemiology Research Design and Analysis Center (BSE RDAC).","Brooks G., Dolphin M., Vanbeveren P., Et al., Referral source and outcomes of physical therapy care in patients with low back pain, J Orthopaedic Sports Phys Ther, 42, 8, pp. 705-715, (2012); Deutscher D., Werneke M.W., Hayes D., Et al., Impact of risk adjustment on provider ranking for patients with low back pain receiving physical therapy, J Orthopaedic Sports Phys Ther, 48, 8, pp. 637-648, (2018); Searle A., Spink M., Ho A., Et al., Exercise interventions for the treatment of chronic low back pain: a systematic review and meta-analysis of randomised controlled trials, Clin Rehabil, 29, 12, pp. 1155-1167, (2015); Deyo R.A., Dworkin S.F., Amtmann D., Et al., Report of the NIH task force on research standards for chronic low back pain, Phys Ther, 95, 2, pp. e1-e18, (2015); Hoy D., Brooks P., Blyth F., Et al., The epidemiology of low back pain, Best Pract Res Clin Rheumatol, 24, 6, pp. 769-781, (2010); Martin B.I., Deyo R.A., Mirza S.K., Et al., Expenditures and health status among adults with back and neck problems, JAMA, 299, 6, pp. 656-664, (2008); Shmagel A., Foley R., Ibrahim H., Epidemiology of chronic low back pain in US adults: data from the 2009–2010 National health and nutrition examination survey, Arthritis Care Res, 68, 11, pp. 1688-1694, (2016); Bouter L.M., Pennick V., Bombardier C., Cochrane back review group, (2003); Cheema C., Baldwin J., Rodeghero J., Et al., The effectiveness of post‐professional physical therapist training in the treatment of chronic low back pain using a propensity score approach with machine learning, Musculoskeletal Care, 20, 3, pp. 625-640, (2022); Farin E., Gramm L., Schmidt E., The patient–physician relationship in patients with chronic low back pain as a predictor of outcomes after rehabilitation, J Behav Med, 36, 3, pp. 246-258, (2013); Garcia A.N., Costa L.O., Costa L.D.C.M., Et al., Do prognostic variables predict a set of outcomes for patients with chronic low back pain: a long-term follow-up secondary analysis of a randomized control trial, J Man Manipulative Ther, 27, 4, pp. 197-207, (2019); Gardner T., Refshauge K., Smith L., Et al., Physiotherapists’ beliefs and attitudes influence clinical practice in chronic low back pain: a systematic review of quantitative and qualitative studies, J Physiother, 63, 3, pp. 132-143, (2017); Haukoos J.S., Lewis R.J., The propensity score, JAMA, 314, 15, pp. 1637-1638, (2015); O'Sullivan P., Caneiro J.P., O'Keeffe M., Et al., Unraveling the complexity of low back pain, J Orthopaedic Sports Phys Ther, 46, 11, pp. 932-937, (2016); Peterson C.K., Bolton J., Humphreys B.K., Predictors of improvement in patients with acute and chronic low back pain undergoing chiropractic treatment, J Manipulative Physiol Ther, 35, 7, pp. 525-533, (2012); Hayden J., Chou R., Hogg-Johnson S., Et al., Systematic reviews of low back pain prognosis had variable methods and results—guidance for future prognosis reviews, J Clin Epidemiol, 62, 8, pp. 781-796, (2009); Nieminen L.K., Pyysalo L.M., Kankaanpaa M.J., Prognostic factors for pain chronicity in low back pain: a systematic review, Pain Rep, 6, 1, (2021); Brugnara G., Neuberger U., Mahmutoglu M.A., Et al., Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning, Stroke, 51, 12, pp. 3541-3551, (2020); Chekroud A.M., Zotti R.J., Shehzad Z., Et al., Cross-trial prediction of treatment outcome in depression: a machine learning approach, Lancet Psychiatry, 3, 3, pp. 243-250, (2016); Chu C.S., Lee N.P., Adeoye J., Et al., Machine learning and treatment outcome prediction for oral cancer, J Oral Pathol Med, 49, 10, pp. 977-985, (2020); Riquelme D., Akhloufi M.A., Deep learning for lung cancer nodules detection and classification in CT scans, AI, 1, 1, pp. 28-67, (2020); Salehi H., Burgueno R., Emerging artificial intelligence methods in structural engineering, Eng Struct, 171, pp. 170-189, (2018); Spooner A., Chen E., Sowmya A., Et al., A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction, Sci Rep, 10, 1, pp. 1-10, (2020); Zhao L., Chen Y., Schaffner D.W., Comparison of logistic regression and linear regression in modeling percentage data, Appl Environ Microbiol, 67, 5, pp. 2129-2135, (2001); Hart D.L., Werneke M.W., Wang Y.-C., Et al., Computerized adaptive test for patients with lumbar spine impairments produced valid and responsive measures of function, Spine (Phila Pa 1976), 35, 24, pp. 2157-2164, (2010); Hart D.L., Stratford P.W., Werneke M.W., Et al., Lumbar computerized adaptive test and modified Oswestry low back pain disability questionnaire: relative validity and important change, J Orthopaedic Sports Phys Ther, 42, 6, pp. 541-551, (2012); Wang Y.-C., Hart D.L., Werneke M., Et al., Clinical interpretation of outcome measures generated from a lumbar computerized adaptive test, Phys Ther, 90, 9, pp. 1323-1335, (2010); Friedman J., The elements of statistical learning: data mining, inference, and prediction, (2009); ElShawi R., Sherif Y., Al-Mallah M., Et al., Interpretability in healthcare: a comparative study of local machine learning interpretability techniques, Comput Intel, 37, 4, pp. 1633-1650, (2020); Emanet N., Oz H.R., Bayram N., Et al., A comparative analysis of machine learning methods for classification type decision problems in healthcare, Decis Anal, 1, 1, pp. 1-20, (2014); Rodeghero J., Cook C., Cleland J., Et al., Risk stratification of patients with low back pain seen in physical therapy practice, Man Ther, 20, 6, pp. 855-860, (2015); Verkerk K., Luijsterburg P.A., Miedema H.S., Et al., Prognostic factors for recovery in chronic nonspecific low back pain: a systematic review, Phys Ther, 92, 9, pp. 1093-1108, (2012); Gill R.S., Karmali S., Hadi G., Et al., Predictors of attrition in a multidisciplinary adult weight management clinic, Can J Surg, 55, 4, (2012); Jancey J., Lee A., Howat P., Et al., Reducing attrition in physical activity programs for older adults, J Aging Phys Act, 15, 2, pp. 152-165, (2007); Kuijer W., Groothoff J.W., Brouwer S., Et al., Prediction of sickness absence in patients with chronic low back pain: a systematic review, J Occup Rehabil, 16, 3, pp. 430-458, (2006)","C. Cheema; College of Allied Health, Department of Rehabilitation Sciences, The University of Oklahoma Health Sciences Center, Tulsa, 41St, Suite 2F09, 4502 E, United States; email: Carolyn-Cheema@ouhsc.edu","","Taylor and Francis Ltd.","","","","","","10669817","","JMMTF","39540649","English","J. Man. Manip. Ther.","Article","Final","","Scopus","2-s2.0-85209582763"
"Liu Y.; Zhang W.; Sun M.; Liang X.; Wang L.; Zhao J.; Hou Y.; Li H.; Yang X.","Liu, Yanhui (59138006600); Zhang, Wenxiu (57434647700); Sun, Mengzhou (59138158000); Liang, Xiaoyun (7401735901); Wang, Lu (59138158100); Zhao, Jiaqi (59137384700); Hou, Yongquan (59138006700); Li, Haina (59138006800); Yang, Xiaoguang (59137232500)","59138006600; 57434647700; 59138158000; 7401735901; 59138158100; 59137384700; 59138006700; 59138006800; 59137232500","The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model","2024","BMC Pulmonary Medicine","24","1","515","","","","0","10.1186/s12890-024-03333-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206275931&doi=10.1186%2fs12890-024-03333-x&partnerID=40&md5=f40c570d67592f4503b6e259ac4e590c","Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; Respiratory and Critical Care Medicine Department, Hohhot First Hospital, Inner Mongolia, China; Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China; Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China","Liu Y., Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; Zhang W., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China; Sun M., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Beijing, China; Liang X., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd, Shanghai, China; Wang L., Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; Zhao J., Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; Hou Y., Respiratory and Critical Care Medicine Department, Hohhot First Hospital, Inner Mongolia, China; Li H., Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; Yang X., Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China","Background: Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD. Purpose: The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients. Methods: A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time. Results: COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time. Conclusion: The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice. © The Author(s) 2024.","COVID-19 with COPD; Deep learning method; Nucleic acid turning-negative time; Pulmonary involvement; Radiomics features","Aged; COVID-19; Deep Learning; Female; Humans; Lung; Male; Middle Aged; Prognosis; Pulmonary Disease, Chronic Obstructive; Pulmonary Emphysema; Retrospective Studies; SARS-CoV-2; Severity of Illness Index; Tomography, X-Ray Computed; C reactive protein; procalcitonin; adult; Article; bronchitis; chronic obstructive lung disease; cohort analysis; computer assisted tomography; coronavirus disease 2019; deep learning; disease severity; emphysema; female; hospitalization; human; leukocyte count; lymphocyte count; machine learning; major clinical study; male; measurement accuracy; measurement precision; middle aged; nasopharyngeal swab; neutrophil lymphocyte ratio; nucleic acid analysis; pneumonia; radiomics; real time reverse transcription polymerase chain reaction; retrospective study; sensitivity and specificity; training; vaccination; aged; complication; diagnostic imaging; lung; lung emphysema; pathophysiology; prognosis; Severe acute respiratory syndrome coronavirus 2; severity of illness index; x-ray computed tomography","","C reactive protein, 9007-41-4; procalcitonin, 56645-65-9","Discovery CT 750, Discovery; Discovery CT 760, Discovery; SPSS 27, IBM, United States","Discovery; Discovery; IBM, United States","Science and Technology Major Project of Inner Mongolia Autonomous Region of China, (2023YFSH0015)","This work was supported by the Inner Mongolia Autonomous Region Science and Technology Plan Project (2023YFSH0015). 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Yang; Medical Imaging Department, Hohhot First Hospital, Inner Mongolia, China; email: 13347113579@163.com","","BioMed Central Ltd","","","","","","14712466","","BPMMB","39402509","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85206275931"
"Albiges T.; Sabeur Z.; Arbab-Zavar B.","Albiges, Timothy (58100026900); Sabeur, Zoheir (6603062212); Arbab-Zavar, Banafshe (23396128800)","58100026900; 6603062212; 23396128800","Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases","2025","Intelligence-Based Medicine","11","","100217","","","","0","10.1016/j.ibmed.2025.100217","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217798549&doi=10.1016%2fj.ibmed.2025.100217&partnerID=40&md5=5783cd019250e471929266c337e2516a","Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom","Albiges T., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; Sabeur Z., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; Arbab-Zavar B., Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom","Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life. Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities. This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance. © 2025 The Authors","Artificial intelligence; COPD; Eigenspaces; Eigenvalue spectral densities; Machine learning; Projection; Signal analysis; Transfer learning","abnormal respiratory sound; accuracy; area under the curve; Article; artificial intelligence; Bayesian learning; chronic obstructive lung disease; classification; classifier; data base; diagnostic test accuracy study; disease severity; information processing; logistic regression analysis; lung auscultation; machine learning; personalized medicine; quality of life; random forest; receiver operating characteristic; sensitivity and specificity; sound analysis; spectral density; support vector machine; transfer learning (machine learning)","","","","","","","Research Brief: support for people with chronic obstructive pulmonary disease, (2021); GOLD report 2023. 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Tharwat A., Classification assessment methods, Appl Comput Inform, 17, 1, pp. 168-192, (2020); Sokolova M., Lapalme G., A systematic analysis of performance measures for classification tasks, Inf Process Manag, 45, 4, pp. 427-437, (2009)","Z. Sabeur; Department of Computing and Informatics, Bournemouth University, Bournemouth, BH12 5BB, United Kingdom; email: zsabeur@bournemouth.ac.uk","","Elsevier B.V.","","","","","","26665212","","","","English","Intell. Based Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85217798549"
"Galvis Ruiz G.E.; Benavides-Cruz J.; Corredor D.M.; Morales-Mendoza E.; Cotrino Palma H.D.A.; Cely-Jiménez A.","Galvis Ruiz, Germán Enrique (58294172900); Benavides-Cruz, Johana (57404356900); Corredor, Daniela Muñoz (59451518300); Morales-Mendoza, Esteban (58795057400); Cotrino Palma, Héctor Daniel Alejandro (59450253600); Cely-Jiménez, Andrés (58794092500)","58294172900; 57404356900; 59451518300; 58795057400; 59450253600; 58794092500","Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography","2025","Informatics in Medicine Unlocked","52","","101605","","","","0","10.1016/j.imu.2024.101605","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210615279&doi=10.1016%2fj.imu.2024.101605&partnerID=40&md5=2f6239f728ee25fa3bea53938f1efb46","Radiology and Diagnostic Imaging Program, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C., 110151, Colombia; Research Unit, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C., 110151, Colombia; Healthcare Management Institute, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C, 110151, Colombia; Department of Data Management, Keralty, Street 100 #11b – 67, Bogotá D.C., 110221, Colombia","Galvis Ruiz G.E., Radiology and Diagnostic Imaging Program, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C., 110151, Colombia; Benavides-Cruz J., Research Unit, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C., 110151, Colombia; Corredor D.M., Healthcare Management Institute, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C, 110151, Colombia; Morales-Mendoza E., Healthcare Management Institute, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C, 110151, Colombia; Cotrino Palma H.D.A., Radiology and Diagnostic Imaging Program, Fundación Universitaria Sanitas, Street 170 #8-41, Bogotá D.C., 110151, Colombia; Cely-Jiménez A., Department of Data Management, Keralty, Street 100 #11b – 67, Bogotá D.C., 110221, Colombia","Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (n=500), atelectasis (n=499); and images without opacities (n=298). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results. © 2024","Artificial intelligence; Children; Deep learning; Lung diseases; Pulmonary atelectasis; Radiography","adolescent; Article; asthma; atelectasis; brightness; bronchiolitis; child; contrast enhancement; controlled study; convolutional neural network; deep learning; false negative result; false positive result; female; foreign body; human; image analysis; image quality; learning curve; lung consolidation; lung disease; major clinical study; male; pediatrician; pneumonia; prediction; radiologist; thorax radiography","","","","","","","Speets A., van der graaf Y., Hoes W.A., Kalmijn S., Sachs A., Chest radiography in general practice: indications, diagnostic yield, and consequences for patient management, Br J Gen Pract, 56, pp. 574-578, (2006); Westra S.J., Wallace E.C., Imaging evaluation of pediatric chest trauma, Radiol Clin, 43, pp. 267-281, (2005); Salehi M., Mohammadi R., Ghaffari H., Sadighi N., Reiazi R., Automated detection of pneumonia cases using deep transfer learning with pediatric chest X-ray images, Br J Radiol, 94, (2021); Jabber B., Lingampalli J., Basha C.Z., Krishna A., Detection of COVID-19 patients using chest X-ray images with convolution neural network and Mobile Net, 2020 3rd International conference on intelligent sustainable systems (ICISS), Thoothukudi, pp. 1032-1035, (2020); Karaci A., VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm, Neural Comput Appl, 34, pp. 8253-8274, (2022); Sharma S., Guleria K., A deep learning-based model for the detection of pneumonia from chest X-Ray images using VGG-16 and neural networks, Procedia Comput Sci, 218, pp. 357-366, (2023); Murphy K., Smits H., Knoops A.J.G., Korst M.B.J.M., Samson T., Scholten E.T., Et al., COVID-19 on chest radiographs: a multireader evaluation of an artificial intelligence system, Radiology, 296, pp. E166-E172, (2020); Saha P., Sadi M.S., Islam M., EMCNet: automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers, Inform Med Unlocked, 22, (2021); Chen K.-C., Yu H.-R., Chen W.-S., Lin W.-C., Lee Y.-C., Chen H.-H., Et al., Diagnosis of common pulmonary diseases in children by X-ray images and deep learning, Sci Rep, 10, (2020); Mujahid M., Rustam F., Alvarez R., Luis Vidal Mazon J., Diez I., dela T., Ashraf I., Pneumonia classification from X-ray images with inception-V3 and convolutional neural network, Diagnostics, 12, (2022); Longjiang E., Zhao B., Liu H., Zheng C., Song X., Cai Y., Et al., Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays, Pediatr Pulmonol, 56, pp. 1036-1044, (2021); Schalekamp S., Klein W.M., van Leeuwen K.G., Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective, Pediatr Radiol, 52, pp. 2120-2130, (2022); Gielczyk A., Marciniak A., Tarczewska M., Lutowski Z., Pre-processing methods in chest X-ray image classification 2022, PLoS One, 17, (2022); Xie Y., Richmond D., Pre-training on grayscale ImageNet improves medical image classification, Proceedings of the European conference on computer vision (ECCV) workshops, (2018); Hu Y., Zhong Z., Wang R., Liu H., Tan Z., Zheng W.S., Data augmentation in logit space for medical image classification with limited training data, Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), LNCS, pp. 469-479, (2021); Alashban A., Alsadan A., Alhussainan N.F., Ouni R., Single convolutional neural network with three layers model for crowd density estimation, IEEE Access, 10, pp. 63823-63833, (2022); Thomaz R.L., Carneiro P.C., Patrocinio A.C., Feature extraction using convolutional neural network for classifying breast density in mammographic images, (2017); Dey N., Zhang Y.-D., Rajinikanth V., Pugalenthi R., Raja N.S.M., Customized VGG19 architecture for pneumonia detection in chest X-rays, Pattern Recogn Lett, 143, (2021); Chouhan V., Singh S.K., Khamparia A., Gupta D., Tiwari P., Moreira C., Et al., A novel transfer learning-based approach for pneumonia detection in chest X-ray images, Appl Sci, 10, (2020); Neshat M., Ahmed M., Askari H., Thilakaratne M., Mirjalili S., Hybrid inception architecture with residual connection: fine-tuned Inception-ResNet deep learning model for lung inflammation diagnosis from chest radiographs, Procedia Comput Sci, 235, pp. 1841-1850, (2024); Cinar A., Yildirim M., Eroglu Y., Classification of pneumonia cell images using improved ResNet50 model, Trait Du Signal, 38, pp. 165-173, (2021); Kesuma L.I., Rudiansyah R., Classification of COVID-19 diseases through lung CT-scan image using the ResNet-50 architecture, Comput Eng Appl J, 12, pp. 11-30, (2023); Reshan M.A., Gill K.S., Anand V., Gupta S., Alshahrani H., Sulaiman A., Et al., Detection of pneumonia from chest x-ray images utilizing MobileNet model, Healthcare, 11, (2023); Behzadi-khormouji H., Rostami H., Salehi S., Derakhshande-Rishehri T., Masoumi M., Salemi S., Et al., Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images, Comput Methods Progr Biomed, 185, (2020); Ladds M.A., Thompson A.P., Kadar J.P., Slip D., Hocking D., Harcourt R., Super machine learning: improving the accuracy and reducing variance of behavior classification from accelerometry, Animal Biotelemetry, 5, pp. 1-9, (2017); Ramezan C.A., Warner T.A., Maxwell A.E., Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification, Rem Sens, 11, (2019); Tougui I., Jilbab A., El M.J., Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications, Healthc Inform Res, 27, (2021); Monshi M.M.A., Poon J., Chung V., Monshi F.M., CovidXrayNet: optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR, Comput Biol Med, 133, (2021); Manaswi N.K., Deep learning with applications using Python, pp. 31-43, (2018); Pang B., Nijkamp E., Wu Y.N., Deep learning with tensor flow: a review, J Educ Behav Stat, 45, pp. 227-248, (2020); Salvat Navarro A., Aplicacionesde machine learning en el diagnostico del cancer de pulmon, (2023); Huang X., Li B., Huang T., Yuan S., Wu W., Yin H., Et al., External validation based on transfer learning for diagnosing atelectasis using portable chest X-rays, Front Med, 9, (2022); Zhang Q., Bai C., Liu Z., Yang L.T., Yu H., Zhao J., Et al., A GPU-based residual network for medical image classification in smart medicine, Inf Sci, 536, pp. 91-100, (2020); McBee M.P., Awan O.A., Colucci A.T., Ghobadi C.W., Kadom N., Kansagra A.P., Et al., Deep learning in radiology, Acad Radiol, 25, pp. 1472-1480, (2018); Alanazi A., Using machine learning for healthcare challenges and opportunities, Inform Med Unlocked, 30, (2022); Velasco J., Identification of normal and diseased lungs using X-ray images through transfer learning, Int J Adv Trends Comput Sci Eng, 9, pp. 6227-6231, (2020); Calli E., Sogancioglu E., van Ginneken B., van Leeuwen K.G., Murphy K., Deep learning for chest X-ray analysis: a survey, Med Image Anal, 72, (2021); Jaiswal A., Li T., Zander C., Han Y., Rousseau J.F., Peng Y., Et al., Scalp - supervised contrastive learning for cardiopulmonary disease classification and localization in chest X-rays using patient metadata, IEEE international conference on data mining, ICDM, Auckland, pp. 1132-1137, (2021); Ke A., Ellsworth W., Banerjee O., Ng A.Y., Rajpurkar P., CheXtransfer: performance and parameter efficiency of ImageNet models for chest X-Ray interpretation, ACM CHIL 2021 -Proceedings of the 2021 ACM conference on health, Inference, and learning, pp. 116-124, (2021); Lakhani P., Deep Convolutional neural networks for endotracheal tube position and x-ray image classification: challenges and opportunities, J Digit Imag, 30, pp. 460-468, (2017); Chen B., Li J., Lu G., Yu H., Zhang D., Label cooccurrence learning with graph convolutional networks for multilabel chest X-ray image classification, IEEE J Biomed Health Inform, 24, pp. 2292-2302, (2020); Bhosale Y.H., Patnaik K.S., PulDi-COVID: chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates, Biomed Signal Proces, 81, (2023); Mohn S.F., Law M., Koleva M., Lee B., Berg A., Murray N., Et al., Machine learning model for chest radiographs: using local data to enhance performance, Can Assoc Radiol J, 74, pp. 548-556, (2023); Astudillo Delgado V.M., Revelo Luna D.A., Apoyo al diagnóstico de neumonía y detección de opacidades pulmonares usando segmentación e instancias semánticas en imágenes de rayos X de tórax, Ing Desarro, 39, pp. 259-274, (2022); Kim J.H., Improvement of the inceptionV3 model classification performance using chest X-ray images, J Mech Med Biol, 22, (2022); Shadin N.S., Sanjana S., Lisa N.J., COVID-19 diagnosis from chest X-ray images using convolutional neural network (CNN) and InceptionV3, International conference on information technology (ICIT), Amman, pp. 799-804, (2021); Bermejo Pelaez D., San R., Estepar J., Ledesma-Carbayo M.J., Detección y clasificación de enfisema pulmonar en imágenes de TAC mediante Redes Neuronales Convolucionales Multiescala. Poster session presented at: XXXIV Congreso Anual de la Sociedad Española de Ingeniería Biomédica; 23/11/2016 - 25/11/2016; Valencia, España","J. Benavides-Cruz; Bogotá D.C, Street 170 8-41, 110151, Colombia; email: jbenavidescr@unisanitas.edu.co","","Elsevier Ltd","","","","","","23529148","","","","English","Inform. Med. Unlocked","Article","Final","","Scopus","2-s2.0-85210615279"
"Cheng W.; Yu C.; Liu X.","Cheng, Wenwen (57741666500); Yu, Chen (59648046600); Liu, Xiaohui (59648490400)","57741666500; 59648046600; 59648490400","Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model","2025","Frontiers in Artificial Intelligence","8","","1473223","","","","0","10.3389/frai.2025.1473223","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218703469&doi=10.3389%2ffrai.2025.1473223&partnerID=40&md5=1df9703fddd1200012bfabdcfc7a095a","Military Preventive Medicine School, Air Force Medical University, Xi'an, China; The 986th Hospital of PLAAF, Air Force Medical University, Xi'an, China","Cheng W., Military Preventive Medicine School, Air Force Medical University, Xi'an, China; Yu C., Military Preventive Medicine School, Air Force Medical University, Xi'an, China; Liu X., The 986th Hospital of PLAAF, Air Force Medical University, Xi'an, China","Background: Assessing cognitive function in patients with chronic obstructive pulmonary disease (COPD) is crucial for ensuring treatment efficacy and avoiding moderate cognitive impairment (MCI) or dementia. We aimed to build better machine learning models and provide useful tools to provide better guidance and assistance for COPD patients' treatment and care. Methods: A total of 863 COPD patients from a local general hospital were collected and screened, and they were separated into two groups: cognitive impairment (356 patients) and cognitively normal (507 patients). The Montreal Cognitive Assessment (MoCA) was used to test cognitive function. The swarm intelligence optimization algorithm (SIOA) was used to direct feature weighting and hyperparameter optimization, which were considered simultaneous activities. A self-assigning feature weights and residual evolution (SAFWRE) algorithm was built on the concept of linear and nonlinear information fusion. Results: The best method in SIOA was the circle search algorithm. On the training set, SAFWRE's ROC-AUC was 0.9727, and its PR-AUC was 0.9663; on the test set, SAFWRE's receiver operating characteristic-area under curve (ROC-AUC) was 0.9243, and its precision recall-area under curve (PR-AUC) was 0.9059, and its performance was much superior than that of the control technique. In terms of external data, the classification and prediction performance of various models are comprehensively evaluated. SAFWRE has the most excellent classification performance, with ROC-AUC of 0.8865 and pr-auc of 0.8299. Conclusion: This work develops a practical visualization system based on these weight attributes which has strong application importance and promotion value. Copyright © 2025 Cheng, Yu and Liu.","AI; chronic obstructive pulmonary disease; mild cognitive impairment; ML; Montreal","","","","","","Key Research and Development Projects of Shaanxi Province, (2023-YBSF-348); Key Research and Development Projects of Shaanxi Province","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Key Research and Development Projects of Shaanxi Province, China (Program No. 2023-YBSF-348). ","Abdollahzadeh B., Khodadadi N., Barshandeh S., Trojovsky P., Gharehchopogh F.S., El-kenawy E.S.M., Et al., Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning, Cluster Comput, 27, pp. 5235-5283, (2024); Anstey K.J., Von Sanden C., Salim A., O'Kearney R., Smoking as a risk factor for dementia and cognitive decline: a meta-analysis of prospective studies, Am. J. 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"Kang H.Y.J.; Ko M.; Ryu K.S.","Kang, Ha Ye Jin (57220114930); Ko, Minsam (37104441600); Ryu, Kwang Sun (50263181500)","57220114930; 37104441600; 50263181500","Prediction model for survival of younger patients with breast cancer using the breast cancer public staging database","2024","Scientific Reports","14","1","25723","","","","0","10.1038/s41598-024-76331-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208081551&doi=10.1038%2fs41598-024-76331-y&partnerID=40&md5=c1cbd1746346f82868e2ab7e119c1528","Department of Applied Artificial Intelligence, Hanyang University, Gyeonggi- do, Ansan-si, South Korea; Department of Cancer AI & amp; Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Goyang-si, South Korea","Kang H.Y.J., Department of Applied Artificial Intelligence, Hanyang University, Gyeonggi- do, Ansan-si, South Korea, Department of Cancer AI & amp; Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Goyang-si, South Korea; Ko M., Department of Applied Artificial Intelligence, Hanyang University, Gyeonggi- do, Ansan-si, South Korea; Ryu K.S., Department of Cancer AI & amp; Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, Goyang-si, South Korea","Breast cancer (BC) is a major contributor to female mortality worldwide, particularly in young women with aggressive tumors. Despite the need for accurate prognosis in this demographic, existing studies primarily focus on broader age groups, often using the SEER database, which has limitations in variable selection. This study aimed to develop an ML-based model to predict survival outcomes in young BC patients using the BC public staging database. A total of 3,401 patients with BC were included in the study. Patients were categorized as younger (n = 1574) and older (n = 1827). We applied several survival models—Random Survival Forest, Gradient Boosting Survival, Extra Survival Trees (EST), and penalized Cox models (Lasso and ElasticNet)—to compare mortality characteristics. The EST model outperformed others in predicting mortality for both age groups. Older patients exhibited a higher prevalence of comorbidities compared to younger patients. Tumor stage was the primary variable used to train the model for mortality prediction in both groups. COPD was a significant variable only in younger patients with BC. Other variables exhibited varying degrees of consistency in each group. These findings can help identify high-risk young female patients with BC who require aggressive treatment by predicting the risk of mortality. © The Author(s) 2024.","Breast cancer; Breast cancer in young women; Machine learning; Survival prediction model","Adult; Age Factors; Aged; Breast Neoplasms; Databases, Factual; Female; Humans; Middle Aged; Neoplasm Staging; Prognosis; Proportional Hazards Models; SEER Program; Survival Analysis; Young Adult; adult; age; aged; breast tumor; cancer registry; cancer staging; factual database; female; human; middle aged; mortality; pathology; prognosis; proportional hazards model; survival analysis; young adult","","","","","National Cancer Center of Korea; National Research Foundation of Korea, NRF; Ministry of Education, MOE, (NRF-2022R1F1A107504); Ministry of Education, MOE","This study was supported by a Grant (no: 2310440-2) from the National Cancer Center of Korea, Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. NRF-2022R1F1A107504).","Anderson B.O., Et al., The global breast Cancer Initiative: a strategic collaboration to strengthen health care for non-communicable diseases, Lancet Oncol, 22, pp. 578-581, (2021); Fernandes U., Et al., Breast cancer in young women: a rising threat: a 5-year follow-up comparative study, Porto Biomed. J, 8, (2023); DeSantis C.E., Et al., Breast cancer statistics, 2019, CA Cancer J. Clin, 69, pp. 438-451, (2019); Shah A.N., Et al., Circulating tumor cells, circulating tumor DNA, and disease characteristics in young women with metastatic breast cancer, Breast Cancer Res. Treat, 187, pp. 397-405, (2021); Pruessmann J., Et al., Conditional disease-free and overall survival of 1858 Young women with non-metastatic breast Cancer and with participation in a post-therapeutic Rehab Programme according to clinical subtypes, Breast Care, 16, pp. 163-172, (2020); Sun Y., Nomograms for prediction of overall and cancer-specific survival in young breast cancer, Breast Cancer Res. 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Ryu; Department of Cancer AI & amp; Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, South Korea; email: niceplay13@ncc.re.kr","","Nature Research","","","","","","20452322","","","39468113","English","Sci. Rep.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85208081551"
"Zhu Z.; Zeng Z.; Song B.; Chen H.; Zeng H.","Zhu, Zirui (57221058489); Zeng, Zhuo (59344456600); Song, Baichen (59349307100); Chen, Huishan (59349307200); Zeng, Huiqing (36791900300)","57221058489; 59344456600; 59349307100; 59349307200; 36791900300","Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning","2024","Journal of Cellular and Molecular Medicine","28","18","e70107","","","","0","10.1111/jcmm.70107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205336490&doi=10.1111%2fjcmm.70107&partnerID=40&md5=46073f37b48f1c6b407fdd96c2702cc8","Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China; Xiamen University Tan Kah Kee College, Zhangzhou, China","Zhu Z., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China; Zeng Z., Xiamen University Tan Kah Kee College, Zhangzhou, China; Song B., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Chen H., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Zeng H., Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China","This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8+ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation. © 2024 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.","bioinformatics; COPD; gene biomarkers; immune infiltration; machine learning","Adenylyl Cyclases; Aged; Biomarkers; Chromogranins; Computational Biology; Female; Gene Expression Profiling; GTP-Binding Protein alpha Subunits, Gs; Humans; Killer Cells, Natural; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Transcriptome; Adducin 3; biological marker; BTG3 Associated Nuclear Protein; cytoskeleton associated protein 2; DNA mismatch repair protein MSH2; GNAS Complex Locus; nuclear protein; nucleic acid binding protein; unclassified drug; Zinc finger protein 681; adenylate cyclase; biological marker; chromogranin; GNAS protein, human; stimulatory guanine nucleotide binding protein; transcriptome; adaptive immunity; adult; aged; Article; bioinformatics; CD8+ T lymphocyte; cell infiltration; chronic obstructive lung disease; cohort analysis; controlled study; cytotoxicity; diagnostic test accuracy study; disease severity; female; gene expression; human; immune response; immunocompetent cell; machine learning; macrophage; major clinical study; male; middle aged; natural killer cell; overall survival; protein protein interaction; receiver operating characteristic; regulatory T lymphocyte; retrospective study; RNA sequence; support vector machine; transcriptomics; diagnosis; gene expression profiling; genetics; immunology; metabolism; procedures","","DNA mismatch repair protein MSH2, 153700-72-2; adenylate cyclase, 9012-42-4; Adenylyl Cyclases, ; Biomarkers, ; Chromogranins, ; GNAS protein, human, ; GTP-Binding Protein alpha Subunits, Gs, ","","","","","Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (copd) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, 5, pp. 447-458, (2022); 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Kapellos T.S., Bassler K., Aschenbrenner A.C., Fujii W., Schultze J.L., Dysregulated functions of lung macrophage populations in copd, J Immunol Res, 2018, pp. 1-19, (2018); Song W., Yue Y., Zhang Q., Imbalance of gut microbiota is involved in the development of chronic obstructive pulmonary disease: a review, Biomed Pharmacother, 165, (2023); Barratt S.L., Flower V.A., Pauling J.D., Millar A.B., Vegf (vascular endothelial growth factor) and fibrotic lung disease, Int J Mol Sci, 19, 5, (2018); Czumaj A., Sledzinski T., Biological role of unsaturated fatty acid desaturases in health and disease, Nutrients, 12, 2, (2020); Albano G.D., Gagliardo R.P., Montalbano A.M., Profita M., Overview of the mechanisms of oxidative stress: impact in inflammation of the airway diseases, Antioxidants, 11, 11, (2022); Hikichi M., Mizumura K., Maruoka S., Gon Y., Pathogenesis of chronic obstructive pulmonary disease (copd) induced by cigarette smoke, J Thorac Dis, 11, pp. S2129-S2140, (2019); Cong J., Wei H., Natural killer cells in the lungs, Front Immunol, 10, (2019); Kiang K.M.-Y., Leung G.K.-K., A review on adducin from functional to pathological mechanisms: future direction in cancer, Biomed Res Int, 2018, pp. 1-14, (2018); Liu C.-M., Hsu W.-H., Lin W.-Y., Chen H.-C., Adducin family proteins possess different nuclear export potentials, J Biomed Sci, 24, pp. 1-11, (2017); Bastepe M., The gnas locus: quintessential complex gene encoding gsα, xlαs, and other imprinted transcripts, Curr Genomics, 8, 6, pp. 398-414, (2007); Zhou Y., Lin Z., Xie S., Et al., Interplay of chronic obstructive pulmonary disease and colorectal cancer development: unravelling the mediating role of fatty acids through a comprehensive multi-omics analysis, J Transl Med, 21, 1, (2023); Bagdonas E., Raudoniute J., Bruzauskaite I., Aldonyte R., Novel aspects of pathogenesis and regeneration mechanisms in copd, Int J Chron Obstruct Pulmon Dis, 10, pp. 995-1013, (2015); Richmond B.W., Du R.H., Han W., Et al., Bacterial-derived neutrophilic inflammation drives lung remodeling in a mouse model of chronic obstructive pulmonary disease, Am J Respir Cell Mol Biol, 58, 6, pp. 736-744, (2018)","H. Zeng; Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; email: 13606080893@139.com","","John Wiley and Sons Inc","","","","","","15821838","","","39344484","English","J. Cell. Mol. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85205336490"
"Koyama J.; Morise M.; Furukawa T.; Oyama S.; Matsuzawa R.; Tanaka I.; Wakahara K.; Yokota H.; Kimura T.; Shiratori Y.; Kondoh Y.; Hashimoto N.; Ishii M.","Koyama, Junji (57193393437); Morise, Masahiro (55525126200); Furukawa, Taiki (57192231447); Oyama, Shintaro (57204363623); Matsuzawa, Reiko (57163388200); Tanaka, Ichidai (55089187500); Wakahara, Keiko (6602438755); Yokota, Hideo (7203053778); Kimura, Tomoki (56623703400); Shiratori, Yoshimune (7102556025); Kondoh, Yasuhiro (7103041898); Hashimoto, Naozumi (7403228654); Ishii, Makoto (57194711085)","57193393437; 55525126200; 57192231447; 57204363623; 57163388200; 55089187500; 6602438755; 7203053778; 56623703400; 7102556025; 7103041898; 7403228654; 57194711085","Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer","2024","BMC Cancer","24","1","1417","","","","0","10.1186/s12885-024-13190-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209559072&doi=10.1186%2fs12885-024-13190-w&partnerID=40&md5=c5e35e274d2da897ad1f2ccd2701141a","Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Medical IT Center, Nagoya University Hospital, Nagoya, Japan; Innovative Research Center for Preventive Medical Engineering (PME), Nagoya University, Nagoya, Japan; Image Processing Research Team, RIKEN Center for Advanced Photonics, Wako, Japan; Advanced Data Science Project, RIKEN Information R & amp;D and Strategy Headquarters, Wako, Japan; Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan; Department of Respiratory Medicine, Fujita Health University, Toyoake, Japan","Koyama J., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Morise M., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Furukawa T., Medical IT Center, Nagoya University Hospital, Nagoya, Japan; Oyama S., Innovative Research Center for Preventive Medical Engineering (PME), Nagoya University, Nagoya, Japan, Image Processing Research Team, RIKEN Center for Advanced Photonics, Wako, Japan; Matsuzawa R., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Tanaka I., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Wakahara K., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan; Yokota H., Image Processing Research Team, RIKEN Center for Advanced Photonics, Wako, Japan, Advanced Data Science Project, RIKEN Information R & amp;D and Strategy Headquarters, Wako, Japan; Kimura T., Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan; Shiratori Y., Medical IT Center, Nagoya University Hospital, Nagoya, Japan; Kondoh Y., Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan; Hashimoto N., Department of Respiratory Medicine, Fujita Health University, Toyoake, Japan; Ishii M., Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Aichi, Nagoya, 4668550, Japan","Background: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection. Methods: The prediction model was built based on random survival forest (RSF) algorithm using patient characteristics, anticancer treatment histories, and radiomics features of the primary tumor. The predictive accuracy was validated with external test data and compared with that of cox proportional hazard (CPH) model. Results: A total of 459 patients (training, n = 299; test, n = 160) with advanced NSCLC were enrolled. The algorithm identified following features as significant factors associated with survival: age, sex, performance status, Brinkman index, comorbidity of chronic obstructive pulmonary disease, histology, stage, driver oncogene status, tumor PD-L1 expression, administered anticancer agent, six markers of blood test (sodium, lactate dehydrogenase, etc.), and three radiomics features associated with tumor texture, volume, and shape. The C-index of RSF model for test data was 0.841, which was higher than that of CPH model (0.775, P < 0.001). Furthermore, the RSF model enabled to identify poor survivor treated with pembrolizumab because of tumor PD-L1 high expression and those treated with driver oncogene targeted therapy according to driver oncogene status. Conclusions: The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine. Trial registration: The trial design was retrospectively registered study performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Nagoya University Graduate School of Medicine (approval: 2020 − 0287). © The Author(s) 2024.","Artificial intelligence; Machine learning; Non-small cell lung cancer; Precision medicine; Random survival forest","Adult; Aged; Aged, 80 and over; Algorithms; Artificial Intelligence; B7-H1 Antigen; Biomarkers, Tumor; Carcinoma, Non-Small-Cell Lung; Female; Humans; Lung Neoplasms; Male; Middle Aged; Precision Medicine; Prognosis; Radiomics; Retrospective Studies; antineoplastic agent; B Raf kinase; epidermal growth factor receptor; lactate dehydrogenase; pembrolizumab; programmed death 1 ligand 1; protein tyrosine kinase inhibitor; tumor marker; aged; algorithm; Article; artificial intelligence; bone metastasis; brain metastasis; chronic obstructive lung disease; clinical feature; clinical practice; cohort analysis; comorbidity; computer assisted tomography; controlled study; creatinine clearance; diagnostic test accuracy study; female; first-line treatment; follow up; gene mutation; histology; human; immunotherapy; learning; linear regression analysis; liver metastasis; lung cancer; lung metastasis; machine learning; major clinical study; male; molecularly targeted therapy; neutrophil lymphocyte ratio; non small cell lung cancer; observational study; overall survival; personalized medicine; primary tumor; principal component analysis; probability; radiomics; random survival forest; receiver operating characteristic; retrospective study; survival prediction; training; tumor proportion score; tumor volume; adult; diagnostic imaging; lung tumor; metabolism; middle aged; mortality; pathology; personalized medicine; procedures; prognosis; radiomics; very elderly","","epidermal growth factor receptor, 79079-06-4; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; pembrolizumab, 1374853-91-4; B7-H1 Antigen, ; Biomarkers, Tumor, ","","","Japan Society for the Promotion of Science, JSPS, (19K07910); Japan Society for the Promotion of Science, JSPS","This work was supported by the Kaken (Grants-in-Aid from Japan Society for the Promotion of Science, 19K07910). The research did not receive any other specific grant from funding agencies in commercial, or not-for-profit sectors. ","Soria J.C., Ohe Y., Vansteenkiste J., Et al., Osimertinib in untreated EGFR-Mutated Advanced Non-small-cell Lung Cancer, N Engl J Med, 378, pp. 113-125, (2018); Hida T., Nokihara H., Kondo M., Et al., Alectinib versus Crizotinib in patients with ALK-positive non-small-cell lung cancer (J-ALEX): an open-label, randomised phase 3 trial, Lancet, 390, pp. 29-39, (2017); Peters S., Camidge D.R., Shaw A.T., Et al., Alectinib versus Crizotinib in untreated ALK-Positive non-small-cell Lung Cancer, N Engl J Med, 377, pp. 829-838, (2017); Reck M., Rodriguez-Abreu D., Robinson A.G., Et al., Pembrolizumab versus Chemotherapy for PD-L1-Positive non-small-cell Lung Cancer, N Engl J Med, 375, pp. 1823-1833, (2016); Herbst R.S., Giaccone G., de Marinis F., Et al., Atezolizumab for First-Line treatment of PD-L1-Selected patients with NSCLC, N Engl J Med, 383, pp. 1328-1339, (2020); Hellmann M.D., Paz-Ares L., Bernabe Caro R., Et al., Nivolumab plus Ipilimumab in Advanced Non-small-cell Lung Cancer, N Engl J Med, 381, pp. 2020-2031, (2019); Gandhi L., Rodriguez-Abreu D., Gadgeel S., Et al., Pembrolizumab plus Chemotherapy in Metastatic Non-small-cell Lung Cancer, N Engl J Med, 378, pp. 2078-2092, (2018); Paz-Ares L., Luft A., Vicente D., Et al., Pembrolizumab plus Chemotherapy for squamous non-small-cell Lung Cancer, N Engl J Med, 379, pp. 2040-2051, (2018); Socinski M.A., Jotte R.M., Cappuzzo F., Et al., Atezolizumab for First-Line treatment of metastatic nonsquamous NSCLC, N Engl J Med, 378, pp. 2288-2301, (2018); West H., McCleod M., Hussein M., Et al., Atezolizumab in combination with carboplatin plus nab-paclitaxel chemotherapy compared with chemotherapy alone as first-line treatment for metastatic non-squamous non-small-cell lung cancer (IMpower130): a multicentre, randomised, open-label, phase 3 trial, Lancet Oncol, 20, pp. 924-937, (2019); Nishio M., Barlesi F., West H., Et al., Atezolizumab Plus Chemotherapy for First-Line treatment of Nonsquamous NSCLC: results from the Randomized phase 3 IMpower132 trial, J Thorac Oncol, 16, pp. 653-664, (2021); Paz-Ares L., Ciuleanu T.E., Cobo M., Et al., First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (CheckMate 9LA): an international, randomised, open-label, phase 3 trial, Lancet Oncol, 22, pp. 198-211, (2021); Mok T.S.K., Wu Y.L., Kudaba I., Et al., Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial, Lancet, 393, pp. 1819-1830, (2019); Lambin P., Rios-Velazquez E., Leijenaar R., Et al., Radiomics: extracting more information from medical images using advanced feature analysis, Eur J Cancer, 48, pp. 441-446, (2012); Kumar V., Gu Y., Basu S., Et al., Radiomics: the process and the challenges, Magn Reson Imaging, 30, pp. 1234-1248, (2012); Ardila D., Kiraly A.P., Bharadwaj S., Et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nat Med, 25, pp. 954-961, (2019); Aerts H.J., Velazquez E.R., Leijenaar R.T., Et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat Commun, 5, (2014); Huang Y., Liu Z., He L., Et al., Radiomics signature: a potential biomarker for the prediction of Disease-Free Survival in early-stage (I or II) Non-small Cell Lung Cancer, Radiology, 281, pp. 947-957, (2016); Hosny A., Parmar C., Coroller T.P., Et al., Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study, PLoS Med, 15, (2018); Sun R., Limkin E.J., Vakalopoulou M., Et al., A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, Lancet Oncol, 19, pp. 1180-1191, (2018); Tang C., Hobbs B., Amer A., Et al., Development of an Immune-Pathology Informed Radiomics Model for Non-small Cell Lung Cancer, Sci Rep, 8, (2018); He B., Dong D., She Y., Et al., Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker, J Immunother Cancer, 8, (2020); Trebeschi S., Drago S.G., Birkbak N.J., Et al., Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers, Ann Oncol, 30, pp. 998-1004, (2019); Tunali I., Gray J.E., Qi J., Et al., Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report, Lung Cancer, 129, pp. 75-79, (2019); Vaidya P., Bera K., Patil P.D., Et al., Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade, J Immunother Cancer, 8, (2020); Barabino E., Rossi G., Pamparino S., Et al., Exploring response to Immunotherapy in Non-small Cell Lung Cancer using Delta-Radiomics, Cancers (Basel), 14, (2022); van Griethuysen J.J.M., Fedorov A., Parmar C., Et al., Computational Radiomics System to Decode the Radiographic phenotype, Cancer Res, 77, pp. e104-e107, (2017); Ishwaran H., Kogalur U.B., Blackstone E.H., Et al., Random survival forests, Annals Appl Stat, 2, (2008); Jaiyesimi I.A., Leighl N.B., Ismaila N., Et al., Therapy for stage IV Non-small Cell Lung Cancer without driver alterations: ASCO Living Guideline, Version 2023.3, J Clin Oncol, 42, pp. e23-e43, (2024); Owen D.H., Ismaila N., Freeman-Daily J., Et al., Therapy for stage IV Non-small Cell Lung Cancer with driver alterations: ASCO Living Guideline, Version 2024.1, J Clin Oncol, 42, pp. e44-e59, (2024); Hendriks L.E., Kerr K.M., Menis J., Et al., Non-oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up, Ann Oncol, 34, pp. 358-376, (2023); Hendriks L.E., Kerr K.M., Menis J., Et al., Oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up, Ann Oncol, 34, pp. 339-357, (2023); Kang L., Chen W., Petrick N.A., Et al., Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach, Stat Med, 34, pp. 685-703, (2015); Kanda Y., Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics, Bone Marrow Transpl, 48, pp. 452-458, (2013); Polsterl S., scikit-survival: a Library for Time-to-event analysis built on Top of scikit-learn, J Mach Learn Res, 21, pp. 1-6, (2020); Tanaka I., Furukawa T., Morise M., The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence, Cancer Cell Int, 21, (2021); Song J., Shi J., Dong D., Et al., A New Approach to predict progression-free survival in Stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy, Clin Cancer Res, 24, pp. 3583-3592, (2018); Song J., Wang L., Ng N.N., Et al., Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant-positive Non-small Cell Lung Cancer, JAMA Netw Open, 3, (2020); Mu W., Jiang L., Zhang J., Et al., Non-invasive decision support for NSCLC treatment using PET/CT radiomics, Nat Commun, 11, (2020); Tian P., He B., Mu W., Et al., Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images, Theranostics, 11, pp. 2098-2107, (2021); Mu W., Jiang L., Shi Y., Et al., Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images, J Immunother Cancer, 9, (2021); Castillo J.J., Vincent M., Justice E., Diagnosis and management of hyponatremia in cancer patients, Oncologist, 17, pp. 756-765, (2012); Petrelli F., Cabiddu M., Coinu A., Et al., Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies, Acta Oncol, 54, pp. 961-970, (2015); Gu X.B., Tian T., Tian X.J., Et al., Prognostic significance of neutrophil-to-lymphocyte ratio in non-small cell lung cancer: a meta-analysis, Sci Rep, 5, (2015); Leung E.Y., Scott H.R., McMillan D.C., Clinical utility of the pretreatment glasgow prognostic score in patients with advanced inoperable non-small cell lung cancer, J Thorac Oncol, 7, pp. 655-662, (2012); Goldstraw P., Chansky K., Crowley J., Et al., The IASLC Lung Cancer Staging Project: proposals for revision of the TNM Stage groupings in the Forthcoming (Eighth) Edition of the TNM classification for Lung Cancer, J Thorac Oncol, 11, pp. 39-51, (2016); Dercle L., Ammari S., Champiat S., Et al., Rapid and objective CT scan prognostic scoring identifies metastatic patients with long-term clinical benefit on anti-PD-1/-L1 therapy, Eur J Cancer, 65, pp. 33-42, (2016); Sakata Y., Kawamura K., Ichikado K., Et al., Comparisons between tumor burden and other prognostic factors that influence survival of patients with non-small cell lung cancer treated with immune checkpoint inhibitors, Thorac Cancer, 10, pp. 2259-2266, (2019); Isensee F., Jaeger P.F., Kohl S.A.A., Et al., nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation, Nat Methods, 18, pp. 203-211, (2021)","M. Morise; Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, 65 Tsurumai-cho, Showa-ku, Aichi, 4668550, Japan; email: morise.masahiro.u1@f.mail.nagoya-u.ac.jp","","BioMed Central Ltd","","","","","","14712407","","BCMAC","39558311","English","BMC Cancer","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85209559072"
"Feng L.; Wu Z.; Jia X.; Yang L.; Wang M.; Huang M.; Ma Y.","Feng, Ling (59323250800); Wu, Zhenzhen (57212058008); Jia, Xinyu (57211856595); Yang, Lan (57205414961); Wang, Min (58838732400); Huang, Mao (7404260072); Ma, Yuan (56719926800)","59323250800; 57212058008; 57211856595; 57205414961; 58838732400; 7404260072; 56719926800","Screening, identification and targeted intervention of necroptotic biomarkers of asthma","2024","Biochemical and Biophysical Research Communications","735","","150674","","","","0","10.1016/j.bbrc.2024.150674","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203650942&doi=10.1016%2fj.bbrc.2024.150674&partnerID=40&md5=1667cc39b646d0c629b8cc872b24b7b6","Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China","Feng L., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Wu Z., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Jia X., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Yang L., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Wang M., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Huang M., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Ma Y., Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China","Background: As a pivotal pathway of programmed cell death, necroptosis significantly contributes to the pathogenesis of respiratory disorders. However, its role in asthma is not yet fully elucidated. Therefore, this study aimed to identify markers associated with necroptosis, evaluate their functions in asthma, and explore potential therapeutic agents targeting necroptosis for the management of asthma. Methods: Firstly, machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were utilized to identify necroptosis-related differentially expressed genes (NRDEGs) in asthma patients compared to healthy controls. Concurrently, the expression of NRDEGs was validated using external datasets, Western blot, and quantitative real-time polymerase chain reaction (qPCR). Secondly, the clinical relevance of NRDEGs was assessed through Receiver Operating Characteristic (ROC) curve analysis and correlation with clinical indicators. Thirdly, the relationship between NRDEGs and pulmonary immune cell infiltration, as well as the signaling interactions between different cells types, were analyzed through immune infiltration and single-cell analysis. Fourthly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), were conducted to elucidate the functional roles of NRDEGs. Finally, compounds targeting NRDEGs were screened, and their binding affinities were evaluated using molecular docking studies. Results: In asthma, necroptosis is activated, leading to the identification of four NRDEGs: NLRP3, PYCARD, ALOX15, and VDAC3. Among these, NLRP3, PYCARD, and ALOX15 are upregulated, whereas VDAC3 is downregulated in asthma. Comprehensive clinical evaluations indicated that NRDEGs hold diagnostic value for asthma. Specifically, NLRP3 was inversely correlated with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), while VDAC3 showed an inverse correlation with sputum neutrophils. Conversely, ALOX15 expression was positively correlated with fractional exhaled nitric oxide (FeNO) levels, as well as sputum eosinophils, blood eosinophils, and blood IgE levels. Subsequent immune infiltration analysis revealed associations between NRDEGs and activated dendritic cells, mast cells, and eosinophils. Single-cell RNA sequencing (scRNA-seq) further confirmed the communication signals between myeloid dendritic cells, fibroblasts, neutrophils, and helper T cells, predominantly related to fibrosis and immune-inflammatory responses. Pathway enrichment analysis demonstrated that NRDEGs are involved in ribosomal function, oxidative phosphorylation, and fatty acid metabolism. Finally, resveratrol and triptonide were identified as potential therapeutic agents targeting the proteins encoded by NRDEGs for asthma treatment. Conclusions: The necroptosis pathway is activated in asthma, with NRDEGs—namely PYCARD, NLRP3, ALOX15, and VDAC3—correlated with declines in lung function and airway inflammation. These genes serve as reliable predictors of asthma risk and are involved in the regulation of the immune-inflammatory microenvironment. Resveratrol and triptolide have been identified as promising therapeutic candidates due to their potential to target the proteins encoded by these genes. © 2024 The Authors","Asthma; Biomarkers; CellChat; Immunoinfiltration analysis; Machine learning; Molecular docking; Necroptosis","Adult; Asthma; Biomarkers; Female; Humans; Machine Learning; Male; Middle Aged; Necroptosis; biological marker; immunoglobulin E; RANTES; transcriptome; biological marker; apoptosis; Article; asthma; binding affinity; bioinformatics; CD4+ T lymphocyte; CD8+ T lymphocyte; cell infiltration; decision tree; down regulation; drug mechanism; forced expiratory volume; fractional exhaled nitric oxide; gene expression; gene ontology; inflammation; machine learning; macrophage; molecular docking; necroptosis; oxidative phosphorylation; polymerase chain reaction; predictive value; principal component analysis; protein expression; quantitative structure activity relation; real time polymerase chain reaction; receiver operating characteristic; screening; signal transduction; single cell analysis; Th1 cell; upregulation; adult; female; genetics; human; immunology; male; metabolism; middle aged","","immunoglobulin E, 37341-29-0; Biomarkers, ","","","National Natural Science Foundation of China, NSFC, (82170031, 81970031); National Natural Science Foundation of China, NSFC; Postgraduate Research & Practice Innovation Program of Jiangsu Province, (JX10214178)","This work was supported by the National Natural Science Foundation of China (NSFC, No.82170031, No.81970031) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (JX10214178). 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Huang; Department of Pulmonary & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 300 Guangzhou Road, Jiangsu, 210029, China; email: hm6114@163.com","","Elsevier B.V.","","","","","","0006291X","","BBRCA","39270557","English","Biochem. Biophys. Res. Commun.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85203650942"
"Zhang H.; Qian D.; Zhang X.; Meng P.; Huang W.; Gu T.; Fan Y.; Zhang Y.; Wang Y.; Yu M.; Yuan Z.; Chen X.; Zhao Q.; Ruan Z.","Zhang, Hang (57196154975); Qian, Dewei (57457931300); Zhang, Xiaomiao (56494893000); Meng, Peize (58768267000); Huang, Weiran (59262968100); Gu, Tongtong (57316972900); Fan, Yongliang (55938422000); Zhang, Yi (59071905100); Wang, Yuchen (58855485200); Yu, Min (55866199900); Yuan, Zhongxiang (8279040200); Chen, Xin (57112846300); Zhao, Qingnan (55969366900); Ruan, Zheng (36885197800)","57196154975; 57457931300; 56494893000; 58768267000; 59262968100; 57316972900; 55938422000; 59071905100; 58855485200; 55866199900; 8279040200; 57112846300; 55969366900; 36885197800","Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study","2024","Journal of Translational Medicine","22","1","772","","","","0","10.1186/s12967-024-05395-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201401579&doi=10.1186%2fs12967-024-05395-1&partnerID=40&md5=4795acb29f57ed0281e12801e825f4ec","Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85 Wujin Road, Shanghai, 200080, China; Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, China; Department of Pharmacy, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600 Yishan Road, Shanghai, 200233, China; Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, China; Department of Clinical Pharmacy, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China","Zhang H., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Qian D., Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85 Wujin Road, Shanghai, 200080, China; Zhang X., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Meng P., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Huang W., Qing Yuan Research Institute, SEIEE, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, China; Gu T., Department of Pharmacy, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600 Yishan Road, Shanghai, 200233, China; Fan Y., Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85 Wujin Road, Shanghai, 200080, China; Zhang Y., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Wang Y., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Yu M., Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85 Wujin Road, Shanghai, 200080, China; Yuan Z., Department of Cardiovascular Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 85 Wujin Road, Shanghai, 200080, China; Chen X., Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, No. 68 Changle Road, Nanjing, 210006, China; Zhao Q., Department of Clinical Pharmacy, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China; Ruan Z., Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 Xinsongjiang Road, Shanghai, 201620, China","Background: Acute respiratory distress syndrome (ARDS) after cardiac surgery is a severe respiratory complication with high mortality and morbidity. Traditional clinical approaches may lead to under recognition of this heterogeneous syndrome, potentially resulting in diagnosis delay. This study aims to develop and external validate seven machine learning (ML) models, trained on electronic health records data, for predicting ARDS after cardiac surgery. Methods: This multicenter, observational cohort study included patients who underwent cardiac surgery in the training and testing cohorts (data from Nanjing First Hospital), as well as those patients who had cardiac surgery in a validation cohort (data from Shanghai General Hospital). The number of important features was determined using the sliding windows sequential forward feature selection method (SWSFS). We developed a set of tree-based ML models, including Decision Tree, GBDT, AdaBoost, XGBoost, LightGBM, Random Forest, and Deep Forest. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and Brier score. The SHapley Additive exPlanation (SHAP) techinque was employed to interpret the ML model. Furthermore, a comparison was made between the ML models and traditional scoring systems. ARDS is defined according to the Berlin definition. Results: A total of 1996 patients who had cardiac surgery were included in the study. The top five important features identified by the SWSFS were chronic obstructive pulmonary disease, preoperative albumin, central venous pressure_T4, cardiopulmonary bypass time, and left ventricular ejection fraction. Among the seven ML models, Deep Forest demonstrated the best performance, with an AUC of 0.882 and a Brier score of 0.809 in the validation cohort. Notably, the SHAP values effectively illustrated the contribution of the 13 features attributed to the model output and the individual feature's effect on model prediction. In addition, the ensemble ML models demonstrated better performance than the other six traditional scoring systems. Conclusions: Our study identified 13 important features and provided multiple ML models to enhance the risk stratification for ARDS after cardiac surgery. Using these predictors and ML models might provide a basis for early diagnostic and preventive strategies in the perioperative management of ARDS patients. © The Author(s) 2024.","Acute respiratory distress syndrome; Cardiac surgery; Machine learning; Prediction model; SHAP value","Aged; Area Under Curve; Cardiac Surgical Procedures; Cohort Studies; Female; Humans; Machine Learning; Male; Middle Aged; Respiratory Distress Syndrome; ROC Curve; albumin; adult; adult respiratory distress syndrome; albumin level; Article; artificial ventilation; blood gas analysis; cardiac patient; cardiopulmonary bypass; central venous pressure; chronic obstructive lung disease; cohort analysis; coronary artery bypass graft; electronic health record; female; forced expiratory volume; heart left ventricle ejection fraction; heart surgery; hospitalization; human; length of stay; leukocyte count; machine learning; male; middle aged; mortality; multicenter study; observational study; positive end expiratory pressure ventilation; prediction; receiver operating characteristic; retrospective study; sensitivity and specificity; transfusion; adverse event; aged; area under the curve; clinical trial; etiology; respiratory distress syndrome","","","Python version 3.8; R version 4.0.3","","Nanjing First Hospital Hospital; Nanjing Medical University, NMU; Home for Researchers editorial team; Fundamental Research Funds for the Central Universities, (YG2024QNA30, YG2023LC08); Fundamental Research Funds for the Central Universities; Shanghai Municipal Hospital Development Center, SHDC, (SHDC2020CR1021B); Shanghai Municipal Hospital Development Center, SHDC","Funding text 1: We thank the data administrators for data collection and management of this work: Dr. Hong Lang, who worked in Zhongda Hospital, Dongnan University; Dr. Wuwei Wang and Dr. Yunzhang Wu, who worked in Nanjing First Hospital Hospital, Nanjing Medical University. We thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.; Funding text 2: This work was sponsored by \u201Cthe Fundamental Research Funds for the Central Universities\u201D (No. YG2024QNA30 to Hang Zhang, and No. YG2023LC08 to Zheng Ruan) and Clinical Research Plan of Shanghai Hospital Development Center (No. SHDC2020CR1021B to Zheng Ruan). 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Ruan; Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, No. 650 Xinsongjiang Road, 201620, China; email: ruanzheng002245@126.com; X. Chen; Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, No. 68 Changle Road, 210006, China; email: stevecx@njmu.edu.cn; Q. Zhao; Department of Clinical Pharmacy, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, No. 650 Xinsongjiang Road, 201620, China; email: zhaoqingnan2010@126.com","","BioMed Central Ltd","","","","","","14795876","","","39148090","English","J. Transl. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85201401579"
"Feng S.; Zhang R.; Zhang W.; Yang Y.; Song A.; Chen J.; Wang F.; Xu J.; Liang C.; Liang X.; Chen R.; Liang Z.","Feng, Shengchuan (57994913200); Zhang, Ran (59522537700); Zhang, Wenxiu (57434647700); Yang, Yuqiong (57196011558); Song, Aiqi (57965026400); Chen, Jiawei (59522487800); Wang, Fengyan (57204322098); Xu, Jiaxuan (57861994500); Liang, Cuixia (57893414700); Liang, Xiaoyun (7401735901); Chen, Rongchang (14017626800); Liang, Zhenyu (36010749700)","57994913200; 59522537700; 57434647700; 57196011558; 57965026400; 59522487800; 57204322098; 57861994500; 57893414700; 7401735901; 14017626800; 36010749700","Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning","2025","Respiration","104","1","","1","14","13","0","10.1159/000540383","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215647437&doi=10.1159%2f000540383&partnerID=40&md5=0d5308d9e7b82ceae4afb79b3eab66d9","State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Neusoft Medical Systems Co. Ltd., Shenyang, China; Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China; Nanshan School, Guangzhou Medical University, Guangzhou, China; First Clinical School, Guangzhou Medical University, Guangzhou, China","Feng S., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Zhang R., Neusoft Medical Systems Co. Ltd., Shenyang, China; Zhang W., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China; Yang Y., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Song A., Nanshan School, Guangzhou Medical University, Guangzhou, China; Chen J., First Clinical School, Guangzhou Medical University, Guangzhou, China; Wang F., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Xu J., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Liang C., Neusoft Medical Systems Co. Ltd., Shenyang, China; Liang X., Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China; Chen R., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Liang Z., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China","Introduction: Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deeplearning (DL) features. Methods: We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results. Results: The model 3- B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively. Conclusions: DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.  © 2024 S. Karger AG, Basel.","Acute exacerbation; Chronic obstructive pulmonary disease; High-resolution computed tomography; Quantitative computed tomography; VGG-16","Aged; Deep Learning; Disease Progression; Female; Humans; Male; Middle Aged; Phenotype; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Tomography, X-Ray Computed; aged; area under the curve; Article; chronic obstructive lung disease; clinical feature; clinical trial; cohort analysis; computer assisted tomography; deep learning; disease exacerbation; emphysema; female; human; major clinical study; male; observational study; phenotype; prediction; receiver operating characteristic; retrospective study; risk factor; diagnostic imaging; middle aged; pathophysiology; predictive value; x-ray computed tomography","","","","","State Key Laboratory of Respiratory Disease, SKLRD; China Postdoctoral Science Foundation, (2022M720915); China Postdoctoral Science Foundation; Technology Program of Guangzhou, (202201020451); National Key Research and Development Program of China, NKRDPC, (2017YFC1310600, 2022YFF0710802); National Key Research and Development Program of China, NKRDPC; Guangzhou Medical University, GMU, (SKLRD-Z-202317, SKLRD-OP-202401); Guangzhou Medical University, GMU; National Natural Science Foundation of China, NSFC, (82300059, 82200044, 82270044, 82170042); National Natural Science Foundation of China, NSFC; Science, Technology and Innovation Commission of Shenzhen Municipality, (JCYJ20210324114400002); Science, Technology and Innovation Commission of Shenzhen Municipality","This study was funded by the National Natural Science Foundation of China (82270044, 82170042, 82200044, 82300059), the National Key Research and Development Program of China (2017YFC1310600, 2022YFF0710802), Technology Program of Guangzhou (202201020451), Shenzhen Science and Technology Program (JCYJ20210324114400002), State Key Laboratory of Respiratory Disease, Guangzhou Medical University (SKLRD-Z-202317, SKLRD-OP-202401), and China Postdoctoral Science Foundation (2022M720915). ","Zhong N., Wang C., Yao W., Chen P., Kang J., Huang S., Et al., Prevalence of chronic obstructive pulmonary disease in China: a large, population-based survey, Am J Respir Crit Care Med., 176, 8, pp. 753-760, (2007); Ferrera M.C., Labaki W.W., Han M.K., Advances in chronic obstructive pulmonary disease, Annu Rev Med., 72, pp. 119-134, (2021); Celli B., Fabbri L., Criner G., Martinez F.J., Mannino D., Vogelmeier C., Et al., Definition and nomenclature of chronic obstructive pulmonary disease: time for its revision, Am J Respir Crit Care Med., 206, 11, pp. 1317-1325, (2022); Wang C., Xu J., Yang L., Xu Y., Zhang X., Bai C., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study, Lancet., 391, 10131, pp. 1706-1717, (2018); Miller M.R., Hankinson J., Brusasco V., Burgos F., Casaburi R., Coates A., Et al., Standardisation of spirometry, Eur Respir J., 26, 2, pp. 319-338, (2005); Yang T., Chen C., Chen Z., The CT pulmonary vascular parameters and disease severity in COPD patients on acute exacerbation: a correlation analysis, BMC Pulm Med., 21, 1, (2021); Zhang W., Zhao Y., Tian Y., Liang X., Piao C., Early diagnosis of high-risk chronic obstructive pulmonary disease based on quantitative high-resolution computed tomography measurements, Int J Chron Obstruct Pulmon Dis., 18, pp. 3099-3114, (2023); Han M.K., Kim M.G., Mardon R., Renner P., Sullivan S., Diette G.B., Et al., Spirometry utilization for COPD: how do we measure up?, Chest., 132, 2, pp. 403-409, (2007); Martinez C.H., Mannino D.M., Jaimes F.A., Curtis J.L., Han M.K., Hansel N.N., Et al., Undiagnosed obstructive lung disease in the United States. 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Chaudhary M.F.A., Hoffman E.A., Guo J., Comellas A.P., Newell J.D., Nagpal P., Et al., Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study, Lancet Digit Health., 5, 2, pp. e83-e92, (2023); Thevenot J., Lopez M.B., Hadid A., A survey on computer vision for assistive medical diagnosis from faces, IEEE J Biomed Health Inform., 22, 5, pp. 1497-1511, (2018); Hasan M.K., Islam M.M., Hashem M.M.A., Mathematical model development to detect breast cancer using multigene genetic programming, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)., pp. 574-579, (2016); Islam M.M., Iqbal H., Haque M.R., Hasan M.K., Prediction of breast cancer using support vector machine and K-Nearest neighbors, 2017 IEEE region 10 humanitarian Technology conference (R10-HTC)., pp. 226-229, (2017); Haque M.R., Islam M.M., Iqbal H., Reza M.S., Hasan M.K., Performance evaluation of random forests and artificial neural networks for the classification of liver disorder, 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2)., pp. 1-5, (2018); 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Lu Y., Liang H., Shi S., Fu X., Lung cancer detection using a dilated CNN with VGG16, Proceedings of the 2021 4th International Conference on signal processing and machine learning, pp. 45-51, (2021); Han Y., Ma Y., Wu Z., Zhang F., Zheng D., Liu X., Et al., Histologic subtype classification of nonsmall cell lung cancer using PET/CT images, Eur J Nucl Med Mol Imaging., 48, 2, pp. 350-360, (2021); Ran J., Cao R., Cai J., Yu T., Zhao D., Wang Z., Development and validation of a nomogram for preoperative prediction of lymph node metastasis in lung adenocarcinoma based on radiomics signature and deep learning signature, Front Oncol., 11, (2021); Agusti A., Celli B.R., Criner G.J., Halpin D., Anzueto A., Barnes P., Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, Arch Bronconeumol., 59, 4, pp. 232-248, (2023); Gawlitza J., Trinkmann F., Scheffel H., Fischer A., Nance J.W., Henzler C., Et al., Time to exhale: additional value of expiratory chest CT in chronic obstructive pulmonary disease, Can Respir J., 2018, (2018); Hua Q., Chen G., Yang Y., Leng S., Zhao Z., Bai F., Et al., Quantitative evaluation of chronic obstructive pulmonary disease and risk prediction of acute exacerbation by high-resolution computed tomography, Evid Based Complement Alternat Med., 2022, (2022); Ercan S., Canturk A., Avci E.R., Gezer N.S., Ozuygur Ermis S.S., Tokatli G., Et al., Radiologic features of COPD exacerbations: quantitative analysis of thorax computerised tomography, Eur Respir J., 62, (2023); McDonough J.E., Yuan R., Suzuki M., Seyednejad N., Elliott W.M., Sanchez P.G., Et al., Small-airway obstruction and emphysema in chronic obstructive pulmonary disease, N Engl J Med., 365, 17, pp. 1567-1575, (2011); Maselli D.J., Yen A., Wang W., Okajima Y., Dolliver W.R., Mercugliano C., Et al., Small airway disease and emphysema are associated with future exacerbations in smokers with CT-derived bronchiectasis and COPD: results from the COPDGene cohort, Radiology., 300, 3, pp. 706-714, (2021); Yin C., Udrescu M., Gupta G., Cheng M., Lihu A., Udrescu L., Et al., Fractional dynamics foster deep learning of COPD stage prediction, Adv Sci., 10, 12, (2023)","R. Chen; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: chenrc@vip.163.com; Z. Liang; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, Department of Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: 490458234@qq.com","","S. Karger AG","","","","","","00257931","","RESPB","39047695","English","Respiration","Article","Final","","Scopus","2-s2.0-85215647437"
"Biswas S.; Aizan L.N.B.; Mathieson K.; Neupane P.; Snowdon E.; MacArthur J.; Sarkar V.; Tetlow C.; Joshi George K.","Biswas, Sayan (57968261500); Aizan, Luqman Naim Bin (58758787800); Mathieson, Katie (59146615300); Neupane, Prashant (59146615400); Snowdon, Ella (58621809800); MacArthur, Joshua (57968415100); Sarkar, Ved (57967953700); Tetlow, Callum (58758946600); Joshi George, K. (57208514134)","57968261500; 58758787800; 59146615300; 59146615400; 58621809800; 57968415100; 57967953700; 58758946600; 57208514134","Clinicosocial determinants of hospital stay following cervical decompression: A public healthcare perspective and machine learning model","2024","Journal of Clinical Neuroscience","126","","","1","11","10","0","10.1016/j.jocn.2024.05.032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194378989&doi=10.1016%2fj.jocn.2024.05.032&partnerID=40&md5=336d3c0d42c77854f86d736abc2601b2","Faculty of Biology, Medicine and Health, University of Manchester, England, Manchester, M13 9PL, United Kingdom; Department of General Surgery, Warrington and Halton Foundation Trust, Warrington, United Kingdom; Department of Vascular Surgery, Manchester Vascular Centre, Manchester Royal Infirmary, Manchester, M13 9WL, United Kingdom; College of Letters and Sciences, University of California, Berkeley, 94720, CA, United States; Division of Data Science, The Northern Care Alliance NHS Group, England, Manchester, M6 8HD, United Kingdom; Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal Hospital, England, Manchester, M6 8HD, United Kingdom","Biswas S., Faculty of Biology, Medicine and Health, University of Manchester, England, Manchester, M13 9PL, United Kingdom; Aizan L.N.B., Department of General Surgery, Warrington and Halton Foundation Trust, Warrington, United Kingdom; Mathieson K., Faculty of Biology, Medicine and Health, University of Manchester, England, Manchester, M13 9PL, United Kingdom; Neupane P., Department of Vascular Surgery, Manchester Vascular Centre, Manchester Royal Infirmary, Manchester, M13 9WL, United Kingdom; Snowdon E., Faculty of Biology, Medicine and Health, University of Manchester, England, Manchester, M13 9PL, United Kingdom; MacArthur J., Faculty of Biology, Medicine and Health, University of Manchester, England, Manchester, M13 9PL, United Kingdom; Sarkar V., College of Letters and Sciences, University of California, Berkeley, 94720, CA, United States; Tetlow C., Division of Data Science, The Northern Care Alliance NHS Group, England, Manchester, M6 8HD, United Kingdom; Joshi George K., Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal Hospital, England, Manchester, M6 8HD, United Kingdom","Objective: Post-operative length of hospital stay (LOS) is a valuable measure for monitoring quality of care provision, patient recovery, and guiding hospital resource management. But the impact of patient ethnicity, socio-economic deprivation as measured by the indices of multiple deprivation (IMD), and pre-existing health conditions on LOS post-anterior cervical decompression and fusion (ACDF) is under-researched in public healthcare settings. Methods: From 2013 to 2023, a retrospective study at a single center reviewed all ACDF procedures. We analyzed 14 non-clinical predictors—including demographics, comorbidities, and socio-economic status—to forecast a categorized LOS: short (≤2 days), medium (2–3 days), or long (>3 days). Three machine learning (ML) models were developed and assessed for their prediction reliability. Results: 2033 ACDF patients were analyzed; 79.44 % had a LOS ≤ 2 days. Significant predictors of LOS included patient sex (HR:0.81[0.74–0.88], p < 0.005), IMD decile (HR:1.38[1.24–1.53], p < 0.005), smoking (HR:1.24[1.12–1.38], p < 0.005), DM (HR:0.70[0.59–0.84], p < 0.005), and COPD (HR:0.66, p = 0.01). Asian patients had the highest mean LOS (p = 0.003). Testing on 407 patients, the XGBoost model achieved 80.95 % accuracy, 71.52 % sensitivity, 85.76 % specificity, 71.52 % positive predictive value, and a micro F1 score of 0.715. This model is available at: https://acdflos.streamlit.app. Conclusions: Utilizing non-clinical pre-operative parameters such as patient ethnicity, socio-economic deprivation index, and baseline comorbidities, our ML model effectively predicts postoperative LOS for patient undergoing ACDF surgeries. Yet, as the healthcare landscape evolves, such tools will require further refinement to integrate peri and post-operative variables, ensuring a holistic decision support tool. © 2024 The Author(s)","ACDF; Index of multiple deprivation; Machine learning","Adult; Aged; Cervical Vertebrae; Decompression, Surgical; Female; Humans; Length of Stay; Machine Learning; Male; Middle Aged; Retrospective Studies; Spinal Fusion; accuracy; adult; African; alcohol consumption; Article; Asian; Caribbean; Caucasian; cerebrovascular accident; cervical myelopathy; cervicobrachial neuralgia; chronic obstructive lung disease; cigarette smoking; comorbidity; convalescence; decision support system; dementia; demography; diabetes mellitus; ethnicity; female; forecasting; health care delivery; health care quality; health status; heart failure; heart infarction; holistic care; human; hypertension; k nearest neighbor; length of stay; machine learning; major clinical study; male; malignant neoplasm; middle aged; people of mixed ancestry; posterior cervical decompression and fusion; postoperative period; prediction; predictive value; preoperative evaluation; process monitoring; public health service; random forest; reliability; resource management; retrospective study; sensitivity and specificity; sex difference; social determinants of health; social isolation; social status; aged; cervical vertebra; decompression surgery; procedures; spine fusion; surgery","","","XGBoost","","","","Bible J.E., Kang J.D., Anterior cervical discectomy and fusion: Surgical indications and outcomes, Semin Spine Surg, 28, 2, pp. 80-83, (2016); Robinson R., Anterolateral cervical disc removal and interbody fusion for cervical disc syndrome, Bull Johns Hopkins Hosp, 96, pp. 223-224, (1955); Smith G.W., Robinson R.A., The treatment of certain cervical-spine disorders by anterior removal of the intervertebral disc and interbody fusion, JBJS, 40, 3, (1958); Papadopoulos E.C., Huang R.C., Girardi F.P., Synnott K., Cammisa F.P.J., Three-level anterior cervical discectomy and fusion with plate fixation: radiographic and clinical results, Spine (Phila Pa 1976), 31, 8, (2006); Gore D.R., Sepic S.B., Anterior cervical fusion for degenerated or protruded discs: a review of one hundred forty-six patients, Spine (Phila Pa 1976), 9, 7, (1984); Riley L.H.I., Robinson R.A., Johnson K.A., Walker A.E., The results of anterior interbody fusion of the cervical spine. 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Biswas; The University of Manchester, Faculty of Biology, Medical and Health Sciences, Manchester, M13 9PL, United Kingdom; email: sayan.biswas@nca.nhs.uk","","Churchill Livingstone","","","","","","09675868","","JCNUE","38821028","English","J. Clin. Neurosci.","Article","Final","","Scopus","2-s2.0-85194378989"
"Bilancia M.; Nigri A.; Cafarelli B.; Di Bona D.","Bilancia, Massimo (6602477889); Nigri, Andrea (57195275298); Cafarelli, Barbara (36238676300); Di Bona, Danilo (6602682612)","6602477889; 57195275298; 36238676300; 6602682612","An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma","2024","International Journal of Biostatistics","20","2","","361","388","27","0","10.1515/ijb-2023-0061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197472622&doi=10.1515%2fijb-2023-0061&partnerID=40&md5=62ab996fa600aa28e642a5d6bcc6c1d5","Department of Precision and Regenerative Medicine and Jonian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy; Department of Economics, Management and Territory (DEMeT), University of Foggia, Foggia, 18972, Italy; Department of Medical and Surgical Sciences (DSMC), University of Foggia, Foggia, Italy","Bilancia M., Department of Precision and Regenerative Medicine and Jonian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy; Nigri A., Department of Economics, Management and Territory (DEMeT), University of Foggia, Foggia, 18972, Italy; Cafarelli B., Department of Economics, Management and Territory (DEMeT), University of Foggia, Foggia, 18972, Italy; Di Bona D., Department of Medical and Surgical Sciences (DSMC), University of Foggia, Foggia, Italy","Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.  © 2024 Walter de Gruyter GmbH, Berlin/Boston.","clustering algorithms; group-Lasso; logistic regression with interactions; partitioning around medoids (PAM); severe eosinophilic asthma","Adult; Anti-Asthmatic Agents; Antibodies, Monoclonal; Asthma; Cluster Analysis; Female; Humans; Interleukin-5; Logistic Models; Male; Middle Aged; Phenotype; benralizumab; corticosteroid; immunoglobulin E antibody; mepolizumab; antiasthmatic agent; interleukin 5; monoclonal antibody; Article; Asthma Control Test; body mass; bootstrapping; bronchiectasis; chronic rhinosinusitis; clinical examination; clustering algorithm; controlled study; data interpretation; deep learning; eosinophil count; eosinophilic asthma; female; follow up; forced expiratory volume; heredity; hospital admission; human; k means clustering; least absolute shrinkage and selection operator; logistic regression analysis; major clinical study; male; medical history; monoclonal antibody therapy; odds ratio; physical examination; prick test; probability; reproducibility; retrospective study; severe asthma; sinonasal polyp; smoking; treatment response; adult; asthma; cluster analysis; drug therapy; immunology; middle aged; phenotype; statistical model","","benralizumab, 1044511-01-4; mepolizumab, 196078-29-2; Anti-Asthmatic Agents, ; Antibodies, Monoclonal, ; Interleukin-5, ","","","","","Reddel H.K., Bacharier L.B., Bateman E.D., Brightling C.E., Brusselle G.G., Buhl R., Et al., Global initiative for asthma strategy 2021: executive summary and rationale for key changes, Eur Respir J, 59, (2022); Cao Y., Chen S., Chen X., Zou W., Liu Z., Wu Y., Et al., Global trends in the incidence and mortality of asthma from 1990 to 2019: an age-period-cohort analysis using the global burden of disease study 2019, Front Public Health, 10, (2022); Reddel H.K., Taylor D.R., Bateman E.D., Boulet L.P., Boushey H.A., Busse W.W., Et al., An official American thoracic society/European respiratory society statement: asthma control and exacerbations, Am J Respir Crit Care Med, 180, pp. 59-99, (2009); 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Bilancia; Department of Precision and Regenerative Medicine and Jonian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy; email: massimo.bilancia@uniba.it","","Walter de Gruyter GmbH","","","","","","15574679","","","38910330","English","Int. J. Biostat.","Article","Final","","Scopus","2-s2.0-85197472622"
"Mutlu A.; Aydın Keskin G.; Çıldır İ.","Mutlu, Atilla (57833799200); Aydın Keskin, Gülşen (59231280700); Çıldır, İhsan (59231053000)","57833799200; 59231280700; 59231053000","Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors","2024","Environmental Monitoring and Assessment","196","8","759","","","","0","10.1007/s10661-024-12908-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199352598&doi=10.1007%2fs10661-024-12908-4&partnerID=40&md5=2685fe1a938a5d2ac1f35efc55f98659","Department of Environmental Engineering, College of Engineering, Balikesir University, Balikesir, Turkey; Department of Industrial Engineering, College of Engineering, Balikesir University, Balikesir, Turkey; Ministry of Health Edremit State Hospital, Edremit, Balikesir, Turkey","Mutlu A., Department of Environmental Engineering, College of Engineering, Balikesir University, Balikesir, Turkey; Aydın Keskin G., Department of Industrial Engineering, College of Engineering, Balikesir University, Balikesir, Turkey; Çıldır İ., Ministry of Health Edremit State Hospital, Edremit, Balikesir, Turkey","This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM10 and SO2 pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg–Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (N = 181), sinusitis (N = 83), and upper respiratory infections (N = 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using R2 values, demonstrated a high level of predictive accuracy. Specifically, the R2 value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.","Air pollution; Artificial neural networks; Hospital admissions; Multilayer perceptron; Respiratory diseases","Adolescent; Adult; Aged; Air Pollutants; Air Pollution; Child; Child, Preschool; Environmental Monitoring; Female; Hospitalization; Humans; Male; Meteorological Concepts; Middle Aged; Neural Networks, Computer; Particulate Matter; Respiratory Tract Diseases; Respiratory Tract Infections; Sulfur Dioxide; Young Adult; Air quality; Hospitals; Multilayers; Pulmonary diseases; sulfur dioxide; Air pollutants; Artificial neural network approach; Hospital admissions; Meteorological condition; Meteorological factors; Multilayers perceptrons; PM 10; Respiratory disorders; Upper respiratory tract; Well being; air quality; artificial neural network; asthma; atmospheric pollution; health impact; health policy; hospital sector; meteorology; pollution effect; prediction; adolescent; adult; air pollutant; air pollution; air quality; algorithm; Article; artificial intelligence; artificial neural network; asthma; back propagation; bronchitis; burnout; child; clinical audit; data analysis; demographics; female; hospital admission; hospitalization; human; humidity; incidence; learning algorithm; machine learning; major clinical study; male; mathematical model; meteorology; particulate matter; perceptron; pollution; respiratory tract disease; scoliosis; sea surface temperature; seasonal variation; sinusitis; topography; training; upper respiratory tract; workload; aged; air pollutant; environmental monitoring; epidemiology; hospitalization; meteorological phenomena; middle aged; preschool child; procedures; respiratory tract disease; respiratory tract infection; young adult; Multilayer neural networks","","sulfur dioxide, 7446-09-5; Air Pollutants, ; Particulate Matter, ; Sulfur Dioxide, ","","","Provincial Environmental Agency; Provincial Public Health Agency; Division of Scientific Research Projects of Balikesir University, (BAP.2023/054)","Funding text 1: We express our appreciation to the Provincial Environmental Agency as well as the Provincial Public Health Agency for their invaluable support throughout the course of this study. For this study, preliminary data were acquired from \u0130hsan \u00C7\u0131ld\u0131r\u2019s master\u2019s thesis.; Funding text 2: We wish to express our sincere gratitude to the Division of Scientific Research Projects of Balikesir University (Project No: BAP.2023/054) for their invaluable support. 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Zhang Y., Ding Z., Xiang Q., Wang W., Huang L., Mao F., Short-term effects of ambient PM(1) and PM(2.5) air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China, International Journal of Hygiene and Environmental Health, 224, (2020); Zhang Y., Ni H., Bai L., Cheng Q., Zhang H., Wang S., Xie M., Zhao D., Su H., The short-term association between air pollution and childhood asthma hospital admissions in urban areas of Hefei City in China: A time-series study, Environmental Research, 169, pp. 510-516, (2019); Zhou R., Wu D., Fang L., Xu A., Lou X., A Levenberg–Marquardt backpropagation neural network for predicting forest growing stock based on the least-squares equation fitting parameters, Forests, 9, 12, (2018)","A. Mutlu; Department of Environmental Engineering, College of Engineering, Balikesir University, Balikesir, Turkey; email: amutlu@balikesir.edu.tr","","Springer Science and Business Media Deutschland GmbH","","","","","","01676369","","EMASD","39046576","English","Environ. Monit. Assess.","Article","Final","","Scopus","2-s2.0-85199352598"
"Luo X.; Zeng W.; Tang J.; Liu W.; Yang J.; Chen H.; Jiang L.; Zhou X.; Huang J.; Zhang S.; Du L.; Shen X.; Chi H.; Wang H.","Luo, Xiufang (58066069700); Zeng, Wei (59351352800); Tang, Jingyi (58927366000); Liu, Wang (59351300700); Yang, Jinyan (57969502500); Chen, Haiqing (58541522900); Jiang, Lai (58542178400); Zhou, Xuancheng (55743209800); Huang, Jinbang (58542178500); Zhang, Shengke (58541851400); Du, Linjuan (59210843500); Shen, Xiang (57221284345); Chi, Hao (57447178800); Wang, Huachuan (59210258800)","58066069700; 59351352800; 58927366000; 59351300700; 57969502500; 58541522900; 58542178400; 55743209800; 58542178500; 58541851400; 59210843500; 57221284345; 57447178800; 59210258800","Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease","2024","Scientific Reports","14","1","22699","","","","0","10.1038/s41598-024-71773-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205447159&doi=10.1038%2fs41598-024-71773-w&partnerID=40&md5=91ac03786bc6668af816843b129e5312","Geriatric Department, Dazhou Central Hospital, Dazhou, 635000, China; Oncology Department, Second People’s Hospital of Yaan City, Yaan, 625000, China; Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Department of General Surgery, Cheng Fei Hospital, Chengdu, 610000, China; School of Stomatology, Southwest Medical University, Luzhou, 646000, China; Oncology Department, Dazhou Central Hospital, Dazhou, 635000, China; Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan, Luzhou, 646000, China; Department of Thoracic Surgery, Dazhou Central Hospital, Dazhou, 635000, China","Luo X., Geriatric Department, Dazhou Central Hospital, Dazhou, 635000, China; Zeng W., Oncology Department, Second People’s Hospital of Yaan City, Yaan, 625000, China; Tang J., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Liu W., Department of General Surgery, Cheng Fei Hospital, Chengdu, 610000, China; Yang J., School of Stomatology, Southwest Medical University, Luzhou, 646000, China; Chen H., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Jiang L., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Zhou X., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Huang J., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Zhang S., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Du L., Oncology Department, Dazhou Central Hospital, Dazhou, 635000, China; Shen X., Department of Respiratory and Critical Care Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan, Luzhou, 646000, China; Chi H., Department of Clinical Medicine, Clinical Medical College, Southwest Medical University, Luzhou, 646000, China; Wang H., Department of Thoracic Surgery, Dazhou Central Hospital, Dazhou, 635000, China","Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes—EP300, MTOR, NFE2L1, TXN—exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD. © The Author(s) 2024.","COPD; Diagnostic models; Dysregulation; Metabolic pathways; Omics technologies; Senescence-related genes; Therapeutic targets","Cellular Senescence; Computational Biology; Gene Expression Profiling; Gene Regulatory Networks; Humans; Male; Pulmonary Disease, Chronic Obstructive; Single-Cell Analysis; Transcriptome; transcriptome; bioinformatics; cell aging; chronic obstructive lung disease; gene expression profiling; gene regulatory network; genetics; human; male; metabolism; procedures; single cell analysis","","","","","Dazhou Central Hospital; Southwest Medical University, SWMU","We sincerely thank Dazhou Central Hospital and Southwest Medical University for supporting this study.","Huang Q., Wang Y., Zhang L., Qian W., Shen S., Wang J., Wu S., Xu W., Chen B., Lin M., Wu J., Single-cell transcriptomics highlights immunological dysregulations of monocytes in the pathobiology of COPD, Respir. 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"Khanmohammadi S.; Arashpour M.; Bazli M.; Farzanehfar P.","Khanmohammadi, Sadegh (57220342275); Arashpour, Mehrdad (55520813200); Bazli, Milad (57192808241); Farzanehfar, Parisa (57191076909)","57220342275; 55520813200; 57192808241; 57191076909","Data-Driven PM2.5 Exposure Prediction in Wildfire-Prone Regions and Respiratory Disease Mortality Risk Assessment","2024","Fire","7","8","277","","","","0","10.3390/fire7080277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202628998&doi=10.3390%2ffire7080277&partnerID=40&md5=846ae50b418fdf41a790938a489d0419","Department of Civil Engineering, Monash University, Melbourne, 3800, VIC, Australia; College of Engineering, IT and Environment, Charles Darwin University, Melbourne, 3000, VIC, Australia; Northern Health Hospital, Melbourne, 3076, VIC, Australia","Khanmohammadi S., Department of Civil Engineering, Monash University, Melbourne, 3800, VIC, Australia; Arashpour M., Department of Civil Engineering, Monash University, Melbourne, 3800, VIC, Australia; Bazli M., College of Engineering, IT and Environment, Charles Darwin University, Melbourne, 3000, VIC, Australia; Farzanehfar P., Northern Health Hospital, Melbourne, 3076, VIC, Australia","Wildfires generate substantial smoke containing fine particulate matter (PM2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health impacts to provide a holistic understanding of smoke exposure risks. Various data-driven models including Support Vector Regression, Multi-layer Perceptron, and three tree-based ensemble algorithms (Random Forest, Extreme Gradient Boosting (XGBoost), and Natural Gradient Boosting (NGBoost)) are evaluated in this study. Ensemble models effectively predict PM2.5 levels based on temperature, humidity, wind, and fuel moisture, revealing the significant roles of radiation, temperature, and moisture. Further modelling links smoke exposure to deaths from chronic obstructive pulmonary disease (COPD) and lung cancer using age, sex, and pollution type as inputs. Ambient pollution is the primary driver of COPD mortality, while age has a greater influence on lung cancer deaths. This research advances atmospheric and health impact understanding, aiding forest fire prevention and management. © 2024 by the authors.","air pollution; artificial intelligence; bronchus; chronic obstructive pulmonary disease (COPD); lung cancer (TB&L); machine learning; tracheal; wildfires","","","","","","Australian Research Council, ARC, (LP180101080)","The authors are grateful for support from the Australian Research Council (ARC) through the Linkage Project funding scheme (LP180101080).","Bhowmik R.T., Jung Y.S., Aguilera J.A., Prunicki M., Nadeau K., A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework, J. Environ. Manag, 341, (2023); Rodrigues M., Cunill Camprubi A., Balaguer-Romano R., Coco Megia C.J., Castanares F., Ruffault J., Fernandes P.M., Resco de Dios V., Drivers and implications of the extreme 2022 wildfire season in Southwest Europe, Sci. 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Softw, 173, (2024); Li Y., Li G., Wang K., Wang Z., Chen Y., Forest Fire Risk Prediction Based on Stacking Ensemble Learning for Yunnan Province of China, Fire, 7, (2023); Susantoro T.M., Wikantika K., Suliantara S., Setiawan H.L., Harto A.B., Sakti A.D., Applying random forest to oil and gas exploration in Central Sumatra basin Indonesia based on surface and subsurface data, Remote Sens. Appl. Soc. Environ, 32, (2023); Satpathy P., Boopathy R., Gogoi M.M., Suresh Babu S., Das T., Machine learning techniques to predict atmospheric black carbon in a tropical coastal environment, Remote Sens. Appl. Soc. Environ, 34, (2024); Islam M.D., Islam K.S., Ahasan R., Mia M.R., Haque M.E., A data-driven machine learning-based approach for urban land cover change modeling: A case of Khulna City Corporation area, Remote Sens. Appl. Soc. Environ, 24, (2021); Patton A., Datta A., Zamora M.L., Buehler C., Xiong F., Gentner D.R., Koehler K., Non-linear probabilistic calibration of low-cost environmental air pollution sensor networks for neighborhood level spatiotemporal exposure assessment, J. Expo Sci. Environ. Epidemiol, 32, pp. 908-916, (2022); Callaghan M., Schleussner C.-F., Nath S., Lejeune Q., Knutson T.R., Reichstein M., Hansen G., Theokritoff E., Andrijevic M., Brecha R.J., Et al., Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies, Nat. Clim. Change, 11, pp. 966-972, (2021); Hartonen T., Jermy B., Sonajalg H., Vartiainen P., Krebs K., Vabalas A., Metspalu A., Esko T., Nelis M., Hudjashov G., Et al., Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland, Nat. Hum. Behav, 7, pp. 1069-1083, (2023); Wright D.P., Thyer M., Westra S., Renard B., McInerney D., A generalised approach for identifying influential data in hydrological modelling, Environ. Model. Softw, 111, pp. 231-247, (2019); Davis K.L., Colefax A.P., Tucker J.P., Kelaher B.P., Santos I.R., Global coral reef ecosystems exhibit declining calcification and increasing primary productivity, Commun. Earth Environ, 2, (2021); Basheer M., Nechifor V., Calzadilla A., Gebrechorkos S., Pritchard D., Forsythe N., Gonzalez J.M., Sheffield J., Fowler H.J., Harou J.J., Cooperative adaptive management of the Nile River with climate and socio-economic uncertainties, Nat. Clim. Change, 13, pp. 48-57, (2023); Lundberg S.M., Erion G., Chen H., DeGrave A., Prutkin J.M., Nair B., Katz R., Himmelfarb J., Bansal N., Lee S.-I., From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell, 2, pp. 56-67, (2020); Romero G.Q., Goncalves-Souza T., Kratina P., Marino N.A.C., Petry W.K., Sobral-Souza T., Roslin T., Global predation pressure redistribution under future climate change, Nat. Clim. Change, 8, pp. 1087-1091, (2018); Algavi Y.M., Borenstein E., A data-driven approach for predicting the impact of drugs on the human microbiome, Nat. Commun, 14, (2023); Wang J., Ogawa S., Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan, Int J Environ. Res Public Health, 12, pp. 9089-9101, (2015); Arashpour M., AI explainability framework for environmental management research, J. Environ. Manag, 342, (2023); Chalian H., Khoshpouri P., Assari S., Patients’ age and discussion with doctors about lung cancer screening: Diminished returns of Blacks, Aging Med, 2, pp. 35-41, (2019); Waskom M.L., Seaborn: Statistical data visualization, J. Open Source Softw, 6, (2021); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011)","S. Khanmohammadi; Department of Civil Engineering, Monash University, Melbourne, 3800, Australia; email: sadegh.khanmohammadi@monash.edu","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","25716255","","","","English","Fire","Article","Final","","Scopus","2-s2.0-85202628998"
"Slagboom T.N.A.; van Bunderen C.C.; van der Lely A.J.; Drent M.L.","Slagboom, Tessa N. A. (57217383625); van Bunderen, Christa C. (15074413000); van der Lely, Aart Jan (57195071885); Drent, Madeleine L. (16635432300)","57217383625; 15074413000; 57195071885; 16635432300","Cardiovascular risk and glucocorticoids: a Dutch National Registry of growth hormone treatment in adults with growth hormone deficiency analysis","2024","Pituitary","27","5","","590","604","14","0","10.1007/s11102-024-01448-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202749061&doi=10.1007%2fs11102-024-01448-2&partnerID=40&md5=dbd77d5051eebfb0761362e02b891df6","Department of Endocrinology & amp; Metabolism, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, Netherlands; Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, Netherlands; Division of Endocrinology and Metabolism, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Netherlands","Slagboom T.N.A., Department of Endocrinology & amp; Metabolism, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands, Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, Netherlands; van Bunderen C.C., Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, Netherlands; van der Lely A.J., Division of Endocrinology and Metabolism, Department of Internal Medicine, Erasmus Medical Center, Rotterdam, Netherlands; Drent M.L., Department of Endocrinology & amp; Metabolism, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, Netherlands, Amsterdam Gastroenterology Endocrinology and Metabolism, Amsterdam, Netherlands","Purpose: Patients with hypopituitarism are at increased cardiovascular risk, in part because of growth hormone deficiency (GHD), but probably also because of the overuse of glucocorticosteroids in concomitant adrenal insufficiency (AI). We hypothesized that patients with hypopituitarism that were on glucocorticosteroid replacement therapy for concomitant AI would have worse cardiovascular outcomes than those without. Methods: Retrospective nationwide cohort study. GHD patients from the Dutch National Registry of Growth Hormone Treatment in adults were grouped by the presence (AI; N = 1836) or absence (non-AI; N = 750) of concomitant AI, and differences between groups were analyzed for baseline characteristics and cardiovascular risk, at baseline and during GHRT. Results: At baseline, AI patients had higher levels of total and LDL cholesterol (both p < 0.01). During GHRT, AI patients were more likely to use cardiovascular drugs (p ≤ 0.01), but we did not find worse outcomes for blood pressure, body composition, lipid and glucose metabolism. The risk of developing peripheral arterial disease (HR 2.22 [1.06–4.65]) and non-fatal cerebrovascular events (HR 3.47 [1.60–7.52]) was higher in AI patients, but these differences disappeared in the models adjusted for baseline differences. Conclusion: We found no clear evidence to support our hypothesis that patients with hypopituitarism and concomitant AI have worse cardiovascular outcomes than non-AI patients. This suggests that glucocorticoid replacement therapy in AI may be safer than previously thought. However, cardiovascular burden, events and medication use at baseline and during GHRT (in unadjusted models) were higher in AI; so the lack of power, the important role of (adjusting for) other risk factors, and the inability to distinguish between glucocorticoid treatment regimens may have influenced the outcomes. © The Author(s) 2024.","Adrenal insufficiency; Cardiovascular; Cerebrovascular; Glucocorticosteroids; Growth hormone replacement therapy; Hypopituitarism","Adrenal Insufficiency; Adult; Aged; Cardiovascular Diseases; Female; Glucocorticoids; Heart Disease Risk Factors; Hormone Replacement Therapy; Human Growth Hormone; Humans; Hypopituitarism; Male; Middle Aged; Netherlands; Registries; Retrospective Studies; Risk Factors; antiarrhythmic agent; anticoagulant agent; antihypertensive agent; antilipemic agent; cholesterol; corticosteroid; glucocorticoid; growth hormone; insulin; low density lipoprotein cholesterol; oral antidiabetic agent; glucocorticoid; human growth hormone; adrenal insufficiency; adult; Article; asthma; blood pressure; body composition; cardiovascular risk; cerebrovascular disease; cohort analysis; controlled study; diabetes mellitus; eczema; female; glucose metabolism; growth hormone deficiency; hazard ratio; heart arrhythmia; human; hypercholesterolemia; hypertension; hypopituitarism; lipid metabolism; major clinical study; male; Netherlands; peripheral arterial disease; prognosis; retrospective study; rheumatic disease; risk factor; thrombosis; adrenal insufficiency; aged; cardiovascular disease; drug therapy; epidemiology; heart disease risk factor; hormone substitution; hypopituitarism; middle aged; register","","cholesterol, 57-88-5; growth hormone, 36992-73-1, 37267-05-3, 66419-50-9, 9002-72-6; insulin, 9004-10-8; human growth hormone, 12629-01-5; Glucocorticoids, ; Human Growth Hormone, ","","","","","Jullien N., Et al., Clinical lessons learned in constitutional hypopituitarism from two decades of experience in a large international cohort, Clin Endocrinol, 94, 2, pp. 277-289, (2021); Etiology of hypopituitarism in adult patients: The experience of a single center database in the Serbian population, International Journal of Endocrinology, (2017); Tanriverdi F., Et al., Etiology of hypopituitarism in tertiary care institutions in Turkish population: analysis of 773 patients from Pituitary Study Group database, Endocrine, 47, pp. 198-205, (2014); Ntali G., Et al., Mortality in patients with non-functioning pituitary adenoma is increased: systematic analysis of 546 cases with long follow-up, Eur J Endocrinol, 174, 2, pp. 137-145, (2016); Fernandez A., Et al., Radiation-induced hypopituitarism, Endocrine-related Cancer, 16, 3, pp. 733-772, (2009); Gupta V., Adult growth hormone deficiency, Indian J Endocrinol Metabol, 15, Suppl3, pp. S197-S202, (2011); Chap. 1 5 - Hypertension in growth hormone excess and deficiency, , in Endocrine Hypertension, pp. 217-247, (2023); Lombardi G., Et al., The cardiovascular system in growth hormone excess and growth hormone deficiency, J Endocrinol Investig, 35, pp. 1021-1029, (2012); Simpson H., Et al., Growth hormone replacement therapy for adults: into the new millennium, Growth Horm IGF Res, 12, 1, pp. 1-33, (2002); Arlt W., Allolio B., Adrenal insufficiency, Lancet, 361, 9372, pp. 1881-1893, (2003); Increased cardiovascular risk in patients with adrenal insufficiency: A short review, . Biomed Research International, (2017); Ngaosuwan K., Et al., Cardiovascular Disease in patients with primary and secondary adrenal insufficiency and the role of comorbidities, J Clin Endocrinol Metabolism, 106, 5, pp. 1284-1293, (2021); van Nieuwpoort I.C., Et al., Dutch National Registry of GH treatment in adults: patient characteristics and diagnostic test procedures, Eur J Endocrinol, 164, 4, pp. 491-497, (2011); Society G.H.R., Consensus guidelines for the diagnosis and treatment of adults with growth hormone deficiency: summary statement of the Growth Hormone Research Society Workshop on adult growth hormone Deficiency, J Clin Endocrinol Metab, 83, pp. 379-381, (1998); Filipsson H., Et al., The impact of glucocorticoid replacement regimens on metabolic outcome and comorbidity in Hypopituitary patients, J Clin Endocrinol Metabolism, 91, 10, pp. 3954-3961, (2006); Danilowicz K., Et al., Correction of cortisol overreplacement ameliorates morbidities in patients with hypopituitarism: a pilot study, Pituitary, 11, 3, pp. 279-285, (2008); Chifu I., Et al., Morbidity in patients with chronic adrenal insufficiency – Cardiovascular Risk factors and hospitalization rate compared to Population based controls, Horm Metab Res, 56, 1, pp. 20-29, (2024); Weaver J.U., Et al., The effect of growth hormone replacement on cortisol metabolism and glucocorticoid sensitivity in hypopituitary adults, Clin Endocrinol, 41, 5, pp. 639-648, (1994); Rodriguez-Arnao J., Perry L., Besser G.M., Rosst R.J.M., Growth hormone treatment in hypopituitary GH deficient adults reduces circulating cortisol levels during hydrocortisone replacement therapy, Clin Endocrinol, 45, 1, pp. 33-37, (1996); Clayton R.N., Cardiovascular complications of Cushings syndrome: impact on morbidity and mortality, J Neuroendocrinol, 34, 8, (2022); Coulden A., Hamblin R., Wass J., Karavitaki N., Cardiovascular health and mortality in Cushing’s disease, Pituitary, 25, 5, pp. 750-753, (2022); Walker B.R., Glucocorticoids and Cardiovascular Disease*, Eur J Endocrinol, 157, 5, pp. 545-559, (2007); Husebye E.S., Pearce S.H., Krone N.P., Kampe O., Adrenal insufficiency, Lancet, 397, pp. 613-629, (2021); Besser G., Jeffcoate W., Endocrine and metabolic diseases. Adrenal diseases, BMJ, 1, 6007, (1976)","T.N.A. Slagboom; Department of Endocrinology & amp; Metabolism, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, De Boelelaan 1117, Netherlands; email: t.slagboom@amsterdamumc.nl","","Springer","","","","","","1386341X","","PITUF","39215905","English","Pituitary","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85202749061"
"Harmon I.; Brailsford J.; Sanchez-Cano I.; Fishe J.","Harmon, Ira (57266060200); Brailsford, Jennifer (57219470722); Sanchez-Cano, Isabel (59134327400); Fishe, Jennifer (57160424400)","57266060200; 57219470722; 59134327400; 57160424400","Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters","2025","Prehospital Emergency Care","29","1","","10","21","11","0","10.1080/10903127.2024.2352583","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193622337&doi=10.1080%2f10903127.2024.2352583&partnerID=40&md5=820c09765e0e0523afaee6b3780d5b77","Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States; Department of Emergency Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States","Harmon I., Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States; Brailsford J., Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States; Sanchez-Cano I., Department of Emergency Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States; Fishe J., Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States, Department of Emergency Medicine, University of Florida College of Medicine–Jacksonville, Jacksonville, FL, United States","Introduction: Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study’s objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations. Methods: This is a retrospective observational study of patient encounters for ages 2–18 years from the ESO Data Collaborative between 2018 and 2021. We modified two existing rule-based pediatric asthma CPs and created three new CPs (one rule-based and two machine learning-based). Two pediatric emergency medicine physicians independently reviewed encounters to assign labels of asthma exacerbation or not. Taking that labeled encounter data, a 50/50 train/test split was used to create training and test sets from the labeled data. A 90/10 split was used to create a small validation set from the training set. We used specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and macro F1 to compare performance across all CP models. Results: After applying the inclusion and exclusion criteria, 24,283 patient encounters remained. The machine-learning models exhibited the best performance for the identification of pediatric asthma exacerbations. A multi-layer perceptron-based model had the best performance in all metrics, with an F1 score of 0.95, specificity of 1.00, sensitivity of 0.91, negative predictive value of 0.98, and positive predictive value of 1.00. Conclusion: We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest. © 2024 National Association of EMS Physicians.","","Adolescent; Asthma; Child; Child, Preschool; Emergency Medical Services; Female; Humans; Machine Learning; Male; Phenotype; Retrospective Studies; adolescent; asthma; child; diagnosis; emergency health service; female; human; machine learning; male; phenotype; preschool child; procedures; retrospective study; therapy","","","","","National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI, (K23HL149991)","Dr. Fishe\u2019s activities were supported by a career development award from NIH/NHLBI K23HL149991. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.","Fishe J.N., Gautam S., Hendry P., Blake K.V., Hendeles L., Emergency medical services administration of systemic corticosteroids for pediatric asthma: a statewide study of emergency department outcomes, Acad Emerg Med, 26, 5, pp. 549-551, (2019); Pate C.A., Zahran H.S., Qin X., Johnson C., Hummelman E., Malilay J., Asthma surveillance—United States, 2006–2018, MMWR Surveill Summ, 70, 5, pp. 1-32, (2021); Glick A.F., Tomopoulos S., Fierman A.H., Trasande L., Disparities in mortality and morbidity in pediatric asthma hospitalizations, 2007 to 2011, Acad Pediatr, 16, 5, pp. 430-437, (2016); McCaig L.F., Nawar E.W., National hospital ambulatory medical care survey: 2004 emergency department summary, Adv Data, 372, pp. 1-29, (2006); Shah M.N., Cushman J.T., Davis C.O., Bazarian J.J., Auinger P., Friedman B., The epidemiology of emergency medical services use by children: an analysis of the National Hospital Ambulatory Medical Care Survey, Prehosp Emerg Care, 12, 3, pp. 269-276, (2008); Lerner E.B., Dayan P.S., Brown K., Fuchs S., Leonard J., Borgialli D., Babcock L., Hoyle J.D., Kwok M., Lillis K., Et al., Characteristics of the pediatric patients treated by the pediatric emergency care applied research network’s affiliated EMS agencies, Prehosp Emerg Care, 18, 1, pp. 52-59, (2014); Fishe J.N., Palmer E., Finlay E., Smotherman C., Gautam S., Hendry P., Hendeles L., A statewide study of the epidemiology of emergency medical services’ management of pediatric asthma, Pediatr Emerg Care, 37, 11, pp. 560-569, (2021); Nassif A., Ostermayer D.G., Hoang K.B., Claiborne M.K., Camp E.A., Shah M.I., Implementation of a prehospital protocol change for asthmatic children, Prehosp Emerg Care, 22, 4, pp. 457-465, (2018); Riney L., Palmer S., Finlay E., Bertrand A., Burcham S., Hendry P., Shah M., Kothari K., Ashby D., Ostermayer D., Et al., EMS administration of systemic corticosteroids to pediatric asthma patients: an analysis by severity and transport interval, Prehosp Emerg Care, 27, 7, pp. 900-907, (2023); Ramgopal S., Mazzarini A., Martin-Gill C., Owusu-Ansah S., Prehospital management of pediatric asthma patients in a large emergency medical services system, Pediatr Pulmonol, 55, 1, pp. 83-89, (2020); Riney L.C., Schwartz H., Kurowski E.M., Collett L., Florin T.A., Improving administration of prehospital corticosteroids for pediatric asthma, Pediatr Qual Saf, 6, 3, (2021); Fishe J.N., Garvan G., Bertrand A., Burcham S., Hendry P., Shah M., Kothari K., Ashby D.W., Ostermeyer D., Riney L., Et al., Early administration of steroids in the ambulance setting: an observational design trial (EASI-AS-ODT), Acad Emerg Med, 31, 1, pp. 49-60, (2023); Cheetham A.L., Navanandan N., Leonard J., Spaur K., Markowitz G., Adelgais K.M., Impact of prehospital pediatric asthma management protocol adherence on clinical outcomes, J Asthma, 59, 5, pp. 937-945, (2022); Browne L.R., Shah M.I., Studnek J.R., Farrell B.M., Mattrisch L.M., Reynolds S., Ostermayer D.G., Brousseau D.C., Lerner E.B., 2015 pediatric research priorities in prehospital care, Prehosp Emerg Care, 20, 3, pp. 311-316, (2016); Foltin G.L., Dayan P., Tunik M., Marr M., Leonard J., Brown K., Hoyle J., Lerner E.B., Priorities for pediatric prehospital research, Pediatr Emerg Care, 26, 10, pp. 773-777, (2010); (2023); (2022); Cameron C.B., Users Guide to Computable Phenotypes, (2016); Afshar M., Press V.G., Robison R.G., Kho A.N., Bandi S., Biswas A., Avila P.C., Kumar H.V.M., Yu B., Naureckas E.T., Et al., A computable phenotype for asthma case identification in adult and pediatric patients: external validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN), J Asthma, 55, 9, pp. 1035-1042, (2018); Vazquez L., Connolly J., (2013); Wu S.T., Sohn S., Ravikumar K.E., Wagholikar K., Jonnalagadda S.R., Liu H., Juhn Y.J., Automated chart review for asthma cohort identification using natural language processing: an exploratory study, Ann Allergy Asthma Immunol, 111, 5, pp. 364-369, (2013); Munikar M., Shakya S., Shrestha A., Fine-grained sentiment classification using BERT, IEEE, 1, (2019); Wei X.-S., Wu J., Cui Q., (2019); Cloutier M.M., Baptist A.P., Blake K.V., Brooks E.G., Bryant-Stephens T., DiMango E., Dixon A.E., Elward K.S., Hartert T., Krishnan J.A., Et al., 2020 focused updates to the asthma management guidelines: a report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group, J Allergy Clin Immunol, 146, 6, pp. 1217-1270, (2020); Buderer N.M.F., Statistical methodology: I. 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Fishe; Center for Data Solutions, University of Florida College of Medicine–Jacksonville, Jacksonville, United States; email: Jennifer.Fishe@jax.ufl.edu","","Taylor and Francis Ltd.","","","","","","10903127","","PEMCF","38713633","English","Prehosp. Emerg. Care","Article","Final","","Scopus","2-s2.0-85193622337"
"Karla R.; Yalavarthi R.","Karla, Raghuram (59337148500); Yalavarthi, Radhika (56035933300)","59337148500; 56035933300","Hybrid Deep Learning Segmentation Method on Chest Radiograph Images for Lung Cancer Detection","2024","SSRG International Journal of Electrical and Electronics Engineering","11","8","","81","90","9","0","10.14445/23488379/IJEEE-V11I8P108","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204532014&doi=10.14445%2f23488379%2fIJEEE-V11I8P108&partnerID=40&md5=5439c310f93f69db091b4f2f2f49a9cf","Department of Computer Science and Engineering, Gitam University, Andhra Pradesh, India","Karla R., Department of Computer Science and Engineering, Gitam University, Andhra Pradesh, India; Yalavarthi R., Department of Computer Science and Engineering, Gitam University, Andhra Pradesh, India","Chronic pulmonary diseases and lung cancer have become major respiratory concerns in the past decade. Their growing importance emphasizes their impact on public health and the need for better understanding, identification, and control. They increased deaths in India and abroad. High teen and adult smoking rates cause these events. Saving lives requires identifying lung cancer and COPD. Fast and effective diagnosis and treatment of the two disorders. This study employs chest radiographs, neural networks with artificial intelligence, machine learning algorithms, and deep learning techniques to accurately detect the two most lethal thoracic illnesses. Residual neural networks (ResNets) improve picture feature extraction and sickness classification. This approach analyzes chest radiograph imaging scan datasets with anomalies like tiny lobes or larger respiratory system capillaries better than lung imaging. Advanced AI and DL can provide healthcare monitoring systems with accurate insights and results. The dynamic field of oncology uses deep learning techniques. The research focuses on deep learning segmentation models. A model was created to improve chest radiography and lung cancer detection. Investigations using RID data. Model sensitivity and mean false positive are assessed independently. Compared to leading methods, RadiographNet has improved significantly. © 2024 Seventh Sense Research Group.","Arterial infection; Artificial Intelligence; Lobes; Pulmonology; Smoking","","","","","","","","Han Yong, Et al., Histologic Subtype Classification of Non-Small Cell Lung Cancer Using PET/CT Images, European Journal of Nuclear Medicine and Molecular Imaging, 48, pp. 350-360, (2021); Shiri Isaac, Et al., Impact of Feature Harmonization on Radio Genomics Analysis: Prediction of EGFR and KRAS Mutations from Non- Small Cell Lung Cancer PET/CT Images, Computers in Biology and Medicine, 142, pp. 1-12, (2022); Zhang Tiening, Et al., Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radionics, Cancers, 13, 8, pp. 1-14, (2021); Heuvelmans Marjolein A., Et al., Lung Cancer Prediction by Deep Learning to Identify Benign Lung Nodules, Lung Cancer, 154, pp. 1-4, (2021); Ahn Beung-Chul, Et al., Clinical Decision Support Algorithm Based on Machine Learning to Assess the Clinical Response to Anti-Programmed Death-1 Therapy in Patients with Non-Small-Cell Lung Cancer, European Journal of Cancer, 153, pp. 179-189, (2021); Kirienko Margarita, Et al., Radiomics and Gene Expression Profiles to Characterise the Disease and Predict Outcomes in Patients with Lung Cancer, European Journal of Nuclear Medicine and Molecular Imaging, 48, pp. 3643-3655, (2021); Chaturvedi Pragya, Et al., Prediction and Classification of Lung Cancer Using Machine Learning Techniques, IOP Conference Series: Materials Science and Engineering, 1099, (2021); Singh Apurva, Chitalia Rhea, Kontos Despina, Radiogenomics in Brain, Breast, and Lung Cancer: Opportunities and Challenges, Journal of Medical Imaging, 8, 3, (2021); Tanaka Ichidai, Furukawa Taiki, Morise Masahiro, The Current Issues and Future Perspective of Artificial Intelligence for Developing New Treatment Strategy in Non-Small Cell Lung Cancer: Harmonization of Molecular Cancer Biology and Artificial Intelligence, Cancer Cell International, 21, 1, pp. 1-14, (2021); Marentakis Panagiotis, Et al., Lung Cancer Histology Classification from CT Images Based on Radiomics and Deep Learning Models, Medical & Biological Engineering & Computing, 59, pp. 215-226, (2021); Ladbury Colton, Et al., Integration of Artificial Intelligence in Lung Cancer: Rise of the Machine, Cell Reports Medicine, pp. 1-11, (2023); El-Brolsy Hanaa Mohammed Elsayed Mohammed, Et al., Fighting Non-Small Lung Cancer Cells Using Optimal Functionalization of Targeted Carbon Quantum Dots Derived from Natural Sources Might Provide Potential Therapeutic and Cancer Bio-Image Strategies, International Journal of Molecular Sciences, 23, 21, pp. 1-23, (2022); Tunali Ilke, Gillies Robert J., Schabath Matthew B., Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine, Cold Spring Harbor Perspectives in Medicine, 14, 8, pp. 1-25, (2021); Tejaswini Chintakayala, Et al., CNN Architecture for Lung Cancer Detection, 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), pp. 346-350, (2022); Zhu Nanhang, Et al., A Light-Up Fluorescence Resonance Energy Transfer Magnetic Aptamer Sensor for Ultra-Sensitive Lung Cancer Exosome Detection, Journal of Materials Chemistry B, 9, pp. 2483-2493, (2021); Murugesan Malathi, Et al., A Hybrid Deep Learning Model for Effective Segmentation and Classification of Lung Nodules from CT Images, Journal of Intelligent & Fuzzy Systems, 42, 3, pp. 2667-2679, (2022); Alanazi Saad Awadh, Et al., Boosting Breast Cancer Detection Using Convolutional Neural Network, Journal of Healthcare Engineering, 2021, pp. 1-11, (2021); Masud Mehedi, Et al., A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using A Deep Learning-Based Classification Framework, Sensors, 21, 3, pp. 1-20, (2021); Amini Mehdi, Et al., Overall Survival Prognostic Modelling of Non-Small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radionics Features: the Quest for the Optimal Machine Learning Algorithm, Clinical Oncology, 34, 2, pp. 114-127, (2022); Wankhade Shalini, Vigneshwari S., A Novel Hybrid Deep Learning Method for Early Detection of Lung Cancer Using Neural Networks, Healthcare Analytics, 3, pp. 1-13, (2023); Wallace G.M.F., Et al., Chest X-Rays in COPD Screening: Are they Worthwhile?, Respiratory Medicine, 103, 12, pp. 1862-1865, (2009); Agazzi Giorgio Maria, Et al., CT Texture Analysis for Prediction of EGFR Mutational Status and ALK Rearrangement in Patients with Non-Small Cell Lung Cancer, La Radiologia Medica, 126, pp. 786-794, (2021); Ueda Daiju, Et al., Artificial Intelligence-Supported Lung Cancer Detection by Multi-Institutional Readers with Multi-Vendor Chest Radiographs: A Retrospective Clinical Validation Study, BMC Cancer, 21, pp. 1-8, (2021); Samuel G. Samuel G., Armato III, Et al., The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans, The International Journal of Medical Physics Research and Practice, 38, 2, pp. 915-931, (2011); Chaunzwa Tafadzwa L., Et al., Deep Learning Classification of Lung Cancer Histology Using CT Images, Scientific Reports, 11, pp. 1-12, (2021)","R. Karla; Department of Computer Science and Engineering, Gitam University, Andhra Pradesh, India; email: rkarla@gitam.edu","","Seventh Sense Research Group","","","","","","23488379","","","","English","SSRG. Int. J. Electr. Electron. Eng.","Article","Final","","Scopus","2-s2.0-85204532014"
"Fu Y.; Liu Y.; Zhong C.; Heidari A.A.; Liu L.; Yu S.; Chen H.; Wu P.","Fu, Yujie (59120091900); Liu, Yining (58748342500); Zhong, Chuyue (58283585300); Heidari, Ali Asghar (56541062900); Liu, Lei (57219872566); Yu, Sudan (57407552800); Chen, Huiling (36865973700); Wu, Peiliang (56763169100)","59120091900; 58748342500; 58283585300; 56541062900; 57219872566; 57407552800; 36865973700; 56763169100","An enhanced machine learning-based prognostic prediction model for patients with AECOPD on invasive mechanical ventilation","2024","iScience","27","12","111230","","","","0","10.1016/j.isci.2024.111230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209676118&doi=10.1016%2fj.isci.2024.111230&partnerID=40&md5=16b1bbc7e37d406cd102c963c2a5e444","Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China; School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran; College of Computer Science, Sichuan University, Sichuan, Chengdu, 610065, China; Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China; Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China","Fu Y., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Liu Y., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China; Zhong C., The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China; Heidari A.A., School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran; Liu L., College of Computer Science, Sichuan University, Sichuan, Chengdu, 610065, China; Yu S., Department of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China; Chen H., Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China; Wu P., Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China","Chronic obstructive pulmonary disease (COPD) causes irreversible airflow limitations, increasing global morbidity and mortality. Acute exacerbations (AECOPDs) worsen symptoms and may require mechanical ventilation, leading to complications. Understanding factors affecting AECOPD prognosis during mechanical ventilation is crucial. Inspired by rime ice physics, the RIME algorithm has been proposed but it had limitations in feature selection and solution space exploration. We improve RIME by adding a dispersed foraging mechanism and differential crossover operator, creating DDRIME. Our study analyzes patient data to identify factors related to invasive mechanical ventilation in AECOPD. DDRIME's performance is tested against RIME on 83 functions and 12 public datasets for feature selection. It outperformed most algorithms, with bDDRIME_KNN showing high accuracy in predicting AECOPD outcomes. Key indicators—chronic heart failure (CHF), D-dimer (D-D), fungal infection (FI), and pectoral muscle area (PMA)—predicted prognosis with >0.98 accuracy. bDDRIME is thus a valuable tool for predicting AECOPD patients’ outcomes on mechanical ventilation. © 2024 The Author(s)","Machine learning; Pathophysiology","","","","","","Department of Education of Zhejiang Province, ZPDE, (Y202353056); Department of Education of Zhejiang Province, ZPDE","This study was supported by the Scientific Research Fund of Zhejiang Provincial Education Department (no. Y202353056).","McDonagh T.A., Metra M., Adamo M., Gardner R.S., Baumbach A., Bohm M., Burri H., Butler J., Celutkiene J., Chioncel O., Et al., 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure, Eur. 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"Koziel S.; Pietrenko-Dabrowska A.; Wojcikowski M.; Pankiewicz B.","Koziel, Slawomir (57204542925); Pietrenko-Dabrowska, Anna (16023085900); Wojcikowski, Marek (6508250690); Pankiewicz, Bogdan (6507648091)","57204542925; 16023085900; 6508250690; 6507648091","High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling","2024","Scientific Reports","14","1","26120","","","","0","10.1038/s41598-024-77214-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208167353&doi=10.1038%2fs41598-024-77214-y&partnerID=40&md5=bf741fbff32662af6bbcf8285e0eec09","Engineering Optimization & amp; Modeling Center, Reykjavik University, Reykjavík, 102, Iceland; Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland","Koziel S., Engineering Optimization & amp; Modeling Center, Reykjavik University, Reykjavík, 102, Iceland, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland; Pietrenko-Dabrowska A., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland; Wojcikowski M., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland; Pankiewicz B., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdańsk, 80-233, Poland","Accurate tracking of harmful gas concentrations is essential to swiftly and effectively execute measures that mitigate the risks linked to air pollution, specifically in reducing its impact on living conditions, the environment, and the economy. One such prevalent pollutant in urban settings is nitrogen dioxide (NO2), generated from the combustion of fossil fuels in car engines, commercial manufacturing, and food processing. Its elevated levels have adverse effects on the human respiratory system, exacerbating asthma and potentially causing various lung diseases. However, precise monitoring of NO2 requires intricate and costly equipment, prompting the need for more affordable yet dependable alternatives. This paper introduces a new method for reliably calibrating cost-effective NO2 sensors by integrating machine learning with neural network surrogates, global data scaling, and an expanded set of correction model inputs. These inputs encompass differentials of environmental parameters (such as temperature, humidity, atmospheric pressure), as well as readings from both primary and supplementary low-cost NO2 detectors. The methodology was showcased using a purpose-built platform housing NO2 and environmental sensors, electronic control units, drivers, and a wireless communication module for data transmission. Comparative experiments utilized NO2 data acquired during a five-month measurement campaign in Gdansk, Poland, from three independent high-precision reference stations, and low-cost sensor data gathered by the portable measurement platforms at the same locations. The numerical experiments have been carried out using several calibration scenarios using various sets of calibration input, as well as enabling/disabling the use of differentials, global data scaling, and NO2 readings from the primary sensor. The results validate the remarkable correction quality, exhibiting a correlation coefficient exceeding 0.9 concerning reference data, with a root mean squared error below 3.2 µg/m3. This level of performance positions the calibrated sensor as a dependable and cost-effective alternative to expensive stationary equipment for NO2 monitoring. © The Author(s) 2024.","Affine transformation; Air pollution monitoring; Environmental monitoring; Low-cost sensors; Monitoring platform; Nitrogen dioxide sensors; Sensor calibration","nitrogen dioxide; affine transform; air pollution; article; asthma; atmospheric pressure; calibration; car; combustion; controlled study; correlation coefficient; economic aspect; environmental monitoring; environmental parameters; environmental surveillance; food processing; fossil fuel; gas; housing; human; humidity; lung disease; machine learning; nerve cell network; Poland; pollution monitoring; respiratory system; root mean squared error; sensor; temperature; wireless communication","","nitrogen dioxide, 10102-44-0","","","UK Research and Innovation, UKRI, (104887)","","Chen T.-M., Kuschner W.G., Gokhale J., Shofer S., Outdoor air pollution: Nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects, Am. 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Res, 180, (2020); van Zoest V., Osei F.B., Stein A., Hoek G., Calibration of low-cost NO<sub>2</sub> sensors in an urban air quality network, Atmos. Environ, 210, pp. 66-75, (2019); de Vito S., Veneri P.D., Esposito E., Salvato M., Bright V., Jones R.L., Popoola O., Dynamic multivariate regression for on-field calibration of high speed air quality chemical multi-sensor systems, In XVIII AISEM Annual Conference, Trento, Italy, pp. 1-3, (2015); Masson N., Piedrahita R., Hannigan M., Quantification method for electrolytic sensors in long-term monitoring of ambient air quality, Sensors, 15, pp. 27283-27302, (2015); Esposito E., De Vito S., Salvato M., Bright V., Jones R.L., Popoola O., Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems, Sens. 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Environ, 38, pp. 1943-1953, (2004); Breysse P.N., Et al., Indoor exposures to air pollutants and allergens in the homes of asthmatic children in inner-city Baltimore, Environ. Res, 98, pp. 167-176, (2005); The Math Works Inc., MATLAB, Version 2021A, Natick, MA, (2021); Map Data from Openstreetmap","S. Koziel; Engineering Optimization & amp; Modeling Center, Reykjavik University, Reykjavík, 102, Iceland; email: koziel@ru.is","","Nature Research","","","","","","20452322","","","39478115","English","Sci. Rep.","Article","Final","","Scopus","2-s2.0-85208167353"
"Yellepeddi S.S.K.A.; Kuppusamy P.","Yellepeddi, Samba Siva Krishna Assish (59408203400); Kuppusamy, P. (55574186541)","59408203400; 55574186541","OPTIMIZED EFFICIENTNET WITH GENETIC EXPRESS PROCESSING FOR ACCURATE LUNG DISEASE CLASSIFICATION","2024","Journal of Theoretical and Applied Information Technology","102","22","","8202","8220","18","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211125137&partnerID=40&md5=804b8b917b4778256499b8f95131fba1","School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, 522237, India","Yellepeddi S.S.K.A., School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, 522237, India; Kuppusamy P., School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, 522237, India","The ongoing COVID-19 pandemic underscores the urgency for rapid and precise diagnostic tools. This study presents an innovative approach for classifying lung diseases COVID-19, asthma, and pneumothorax using Computed Tomography (CT) lung images. The EfficientNet B4 is proposed to classify lung diseases accurately using compound scaling features including depth, width, and resolution. By integrating the EfficientNet model with a Genetic Express Processing Algorithm (GEP) for hyperparameter tuning, the proposed method focuses on optimizing dropout, learning rate, and batch size. Fine-tuning the EfficientNet B4 model through compound scaling and hyperparameter optimization led to a classification accuracy of 96.5%. Visualizing lung-infected regions using Class Activation Maps (CAMs) provides insights into classification decisions. This research work incorporates Generative Adversarial Networks (GANs) to generate synthetic images that enhances data diversity and model generalization. This method combines Deep Learning (DL) models with Genetic Algorithms (GA) and GANs, demonstrating substantial improvements in disease detection accuracy. The proposed approach offers medical professionals efficient diagnostic tools for early and reliable disease diagnosis. Code can be available at https://github.com/YellepeddiSambaSivaKrishnaAssish/Optimized-EfficientNet-using-GEP-for-Lungdiseases.git. © Little Lion Scientific","Artificial Intelligence; Computed Tomography; COVID-19; Deep Learning; Genetic Express Processing; Optimization","","","","","","","","Xie Xingzhi, Zhong Zheng, Zhao Wei, Zheng Chao, Wang Fei, Liu Jun, Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing, Radiology, 296, 2, pp. E41-E45, (2020); Kanne Jeffrey P., Little Brent P., Chung Jonathan H., Elicker Brett M., Ketai Loren H., Essentials for radiologists on COVID-19: an update—radiology scientific expert panel, Radiology, 296, 2, pp. E113-E114, (2020); Tariq Zeenat, Shah Sayed Khushal, Lee Yugyung, Lung disease classification using deep convolutional neural network, 2019 IEEE international conference on bioinformatics and biomedicine (BIBM), pp. 732-735, (2019); Bansal Shrey, Singh Mukul, Dubey R. K., Panigrahi Bijaya Ketan, Multi-objective genetic algorithm based deep learning model for automated covid-19 detection using medical image data, Journal of Medical and Biological Engineering, 41, 5, pp. 678-689, (2021); Bhargava Anuja, Bansal Atul, Goyal Vishal, Machine learning-based automatic detection of novel coronavirus (COVID-19) disease, Multimedia Tools and Applications, 81, 10, pp. 13731-13750, (2022); Roser Max, Ritchie Hannah, Ortiz-Ospina Esteban, Hasell Joe, Coronavirus disease (COVID-19)–Statistics and research, Our World in data, 4, pp. 1-45, (2020); Su Yuanjie, Chen Guorui, Chen Chunxu, Gong Qichen, Xie Guangzhong, Yao Mingliang, Tai Huiling, Jiang Yadong, Chen Jun, Self‐powered respiration monitoring enabled by a triboelectric nanogenerator, Advanced Materials, 33, 35, (2021); Loey Mohamed, El-Sappagh Shaker, Mirjalili Seyedali, Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data, Computers in Biology and Medicine, 142, (2022); Siva Krishna A., Brahmaji Rao KN, Soora Narasimha Reddy, Shailaja Kotte, Santosh Kumar NC, Sridharan Abel, Uthayakumar J., Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images, Multimedia Tools and Applications, 82, 8, pp. 12653-12677, (2023); Pasha Akram, Latha P. H., Bio-inspired dimensionality reduction for Parkinson’s disease (PD) classification, Health information science and systems, 8, 1, (2020); Ayyar Meghna P., Benois-Pineau Jenny, Zemmari Akka, A hierarchical classification system for the detection of Covid-19 from chest X-ray images, Proceedings of the IEEE/CVF international conference on computer vision, pp. 519-528, (2021); Wang Linda, Lin Zhong Qiu, Wong Alexander, Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images, Scientific reports, 10, 1, (2020); Qi Xiao, Foran David J., Nosher John L., Hacihaliloglu Ilker, Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis, (2023); Goyal Shimpy, Singh Rajiv, Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques, Journal of Ambient Intelligence and Humanized Computing, 14, 4, pp. 3239-3259, (2023); Bougourzi Fares, Contino Riccardo, Distante Cosimo, Taleb-Ahmed Abdelmalik, CNR-IEMN: A Deep Learning based approach to recognise COVID-19 from CT-scan, ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8568-8572, (2021); Shah Vruddhi, Keniya Rinkal, Shridharani Akanksha, Punjabi Manav, Shah Jainam, Mehendale Ninad, Diagnosis of COVID-19 using CT scan images and deep learning techniques, Emergency radiology, 28, pp. 497-505, (2021); Afshar Parnian, Heidarian Shahin, Naderkhani Farnoosh, Rafiee Moezedin Javad, Oikonomou Anastasia, Plataniotis Konstantinos N., Mohammadi Arash, Hybrid deep learning model for diagnosis of COVID-19 using CT scans and clinical/demographic data, 2021 IEEE International Conference on Image Processing (ICIP), pp. 180-184, (2021); Demir Fatih, Demir Kursat, Sengur Abdulkadir, DeepCov19Net: automated COVID-19 disease detection with a robust and effective technique deep learning approach, New Generation Computing, 40, 4, pp. 1053-1075, (2022); Chouhan Vikash, Singh Sanjay Kumar, Khamparia Aditya, Gupta Deepak, Tiwari Prayag, Moreira Catarina, Damasevicius Robertas, De Albuquerque Victor Hugo C., A novel transfer learning based approach for pneumonia detection in chest X-ray images, Applied Sciences, 10, 2, (2020); Nayak Soumya Ranjan, Nayak Deepak Ranjan, Sinha Utkarsh, Arora Vaibhav, Pachori Ram Bilas, Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study, Biomedical Signal Processing and Control, 64, (2021); Yang Hang, Wang Liyang, Xu Yitian, Liu Xuhua, CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images, International journal of machine learning and cybernetics, 14, 3, pp. 973-987, (2023); Godbin, A. Beena A. Beena, Graceline Jasmine S., Screening of COVID-19 based on GLCM features from CT images using machine learning classifiers, SN Computer Science, 4, 2, (2022); Tiwari Ravi Shekhar, Das Tapan Kumar, Srinivasan Kathiravan, Chang Chuan-Yu, Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images, Scientific Reports, 12, 1, (2022); Hassan Esraa, Shams Mahmoud Y., Hikal Noha A., Elmougy Samir, Detecting COVID-19 in chest CT images based on several pre-trained models, Multimedia Tools and Applications, pp. 1-21, (2024); Hermawati Fajar Astuti, Trilaksono Bambang Riyanto, Nugroho Anto Satriyo, Imah Elly Matul, Kamelia Telly, LER Mengko Tati, Handayani Astri, Et al., Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study, MethodsX, 12, (2024); Mittal Vasu, Kumar Akhil, COVINet: A hybrid model for classification of COVID and non-COVID pneumonia in CT and X-Ray imagery, International Journal of Cognitive Computing in Engineering, 4, pp. 149-159, (2023); Siddiqua Morseda, Ferdousee Zannatul, Deep Transfer Learning Based Approaches for Classification of Chest CT Scans: Normal, COVID-19, and Pneumonia, 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), pp. 841-846, (2024); Hossain Md Sabbir, Faruk Md Farukuzzaman, Srizon Azmain Yakin, Mahedy Hasan SM, Chowdhury Md Shariful, Hossain Md Rakib, Islam Md Nazmul, Hossain Md Faruk, A Customized 3D CNN Integrated with Convolutional Block Attention Module for Precise Diagnosis of COVID-19 and Pneumonia from CT Scan Images, 2023 26th International Conference on Computer and Information Technology (ICCIT), pp. 1-6, (2023); Zhao Aite, Wu Huimin, Chen Ming, Wang Nana, A multi-level feature attention network for COVID-19 detection based on multi-source medical images, Multimedia Tools and Applications, pp. 1-32, (2024); Gao Zilin, Xie Jiangtao, Wang Qilong, Li Peihua, Global second-order pooling convolutional networks, Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp. 3024-3033, (2019); Rafi Taki Hasan, Shubair Raed M., Farhan Faisal, Hoque Md Ziaul, Quayyum Farhan Mohd, Recent advances in computer-aided medical diagnosis using machine learning algorithms with optimization techniques, IEEE Access, 9, pp. 137847-137868, (2021); Gifani Parisa, Shalbaf Ahmad, Vafaeezadeh Majid, Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans, International journal of computer assisted radiology and surgery, 16, pp. 115-123, (2021); Ju Hong, Cui Yanyan, Su Qiaosen, Juan Liran, Manavalan Balachandran, CODE-NET: A deep learning model for COVID-19 detection, Computers in Biology and Medicine, (2024); Yang Xingyi, He Xuehai, Zhao Jinyu, Zhang Yichen, Zhang Shanghang, Xie Pengtao, COVID-CT-dataset: a CT scan dataset about COVID-19, (2020); Angelov Plamen, Soares Eduardo, Explainable-by-design approach for covid-19 classification via ct-scan, (2020); Zhang Mengting, Tian Xiuxia, Transformer architecture based on mutual attention for image-anomaly detection, Virtual Reality & Intelligent Hardware, 5, 1, pp. 57-67, (2023); Kulkarni Ajay, Chong Deri, Batarseh Feras A., Foundations of data imbalance and solutions for a data democracy, Data democracy, pp. 83-106, (2020); Raji Ismail Damilola, Bello-Salau Habeeb, Umoh Ime Jarlath, Onumanyi Adeiza James, Adegboye Mutiu Adesina, Salawudeen Ahmed Tijani, Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models, Applied Sciences, 12, 3, (2022); Alibrahim Hussain, Ludwig Simone A., Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization, 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1551-1559, (2021); Sen Shibaprasad, Saha Soumyajit, Chatterjee Somnath, Mirjalili Seyedali, Sarkar Ram, A bi-stage feature selection approach for COVID-19 prediction using chest CT images, Applied Intelligence, 51, pp. 8985-9000, (2021); Pradhan Kanchan, Chawla Priyanka, Rawat Sanyog, A deep learning-based approach for detection of lung cancer using self adaptive sea lion optimization algorithm (SASLnO), Journal of Ambient Intelligence and Humanized Computing, 14, 9, pp. 12933-12947, (2023); Liu Weili, Wang Bo, Song Yucheng, Liao Zhifang, Radiological image analysis using effective channel extension and fusion network based on COVID CT images, Journal of Radiation Research and Applied Sciences, 17, 3, (2024); Visuna Lara, Yang Dandi, Garcia-Blas Javier, Carretero Jesus, Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning, BMC Medical Imaging, 22, 1, (2022); Agarwal Saurabh, Arya K. V., Meena Yogesh Kumar, CNN-O-ELMNet: Optimized Lightweight and Generalized Model for Lung Disease Classification and Severity Assessment, IEEE Transactions on Medical Imaging, (2024); Farjana Afia, Liza Fatema Tabassum, Al Mamun Miraz, Das Madhab Chandra, Hasan Md Maruf, SARS CovidAID: Automatic detection of SARS CoV-19 cases from CT scan images with pretrained transfer learning model (VGG19, RESNet50 and DenseNet169) architecture, 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1-6, (2023); Yellepeddi, Samba Siva Krishna Assish Samba Siva Krishna Assish, Kuppusamy P., An Automatic Detection and Severity Levels of COVID-19 Using Convolutional Neural Network Models, Data Science in the Medical Field, pp. 15-24, (2025)","P. Kuppusamy; School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh, 522237, India; email: drpkscse@gmail.com","","Little Lion Scientific","","","","","","19928645","","","","English","J. Theor. Appl. Inf. Technol.","Article","Final","","Scopus","2-s2.0-85211125137"
"Luo L.; Liu K.; Deng L.; Wang W.; Lai T.; Li X.","Luo, Lianxiang (57193721943); Liu, Kangdi (58316532000); Deng, Liyan (58021138700); Wang, Wenjian (58813791200); Lai, Tianli (58548640100); Li, Xiaoling (57213194801)","57193721943; 58316532000; 58021138700; 58813791200; 58548640100; 57213194801","Chicoric acid acts as an ALOX15 inhibitor to prevent ferroptosis in asthma","2024","International Immunopharmacology","142","","113187","","","","0","10.1016/j.intimp.2024.113187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204082321&doi=10.1016%2fj.intimp.2024.113187&partnerID=40&md5=35abf65eed16f548e375dc0546bc017f","The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Experimental Animal Center, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China","Luo L., The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Liu K., The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Deng L., The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Wang W., The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Lai T., The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China; Li X., Experimental Animal Center, Guangdong Medical University, Guangdong, Zhanjiang, 524023, China","Background: Chicoric acid (CA) is a crucial immunologically active compound found in chicory and echinacea, possessing a range of biological activities. Ferroptosis, a type of iron-dependent cell death induced by lipid peroxidation, plays a key role in the development and advancement of asthma. Targeting ferroptosis could be a potential therapeutic strategy for treating asthma. Purpose: The purpose of this study was to explore the screening of ALOX15, a pivotal target of ferroptosis in asthma, and potential therapeutic agents, as well as to investigate the promising potential of CA as an ALOX15 inhibitor for modulating ferroptosis in asthma. Methods: Through high-throughput data processing of bronchial epithelial RNA from asthma patients using bioinformatics and machine learning, the key target of ferroptosis in asthma, ALOX15, was identified. An inhibitor of ALOX15 was then obtained through high-throughput molecular docking and molecular dynamics simulation tests. In vitro experiments were conducted using a 16HBE cell model induced by house dust mite (HDM) and lipopolysaccharide (LPS), which were treated with the ALOX15 inhibitor (PD146176), CA treatment, or ALOX15 knockdown. In vivo experiments were also carried out using a mouse model induced by HDM and LPS. Results: The composite model of ALOX15 and CA in molecular dynamics simulations shows good stability and flexibility. Network pharmacological analysis reveals that CA regulates ferroptosis through ALOX15 in treating asthma. In vitro studies show that ALOX15 is highly expressed in HDM and LPS treatments, while CA inhibits HDM and LPS-induced ferroptosis in 16HBE cells by reducing ALOX15 expression. Knockdown of ALOX15 has the opposite effect. Metabolomics analysis identifies key compounds associated with ferroptosis, including L-Targinine, eicosapentaenoic acid, 16-hydroxy hexadecanoic acid, and succinic acid. In vivo experiments demonstrate that CA suppresses ALOX15 expression, inhibits ferroptosis, and improves asthma symptoms in mice. Conclusion: Our research initially identified CA as a promising asthma treatment that effectively blocks ferroptosis by specifically targeting ALOX15. This study not only highlights CA as a potential therapeutic agent for asthma but also introduces novel targets and treatment options for this condition, along with innovative approaches for utilizing natural compounds to target diseases associated with ferroptosis. © 2024 Elsevier B.V.","ALOX15; Asthma; Chicoric acid (CA); Ferroptosis; Ferroptosis inhibitor","Animals; Anti-Asthmatic Agents; Arachidonate 12-Lipoxygenase; Arachidonate 15-Lipoxygenase; Asthma; Caffeic Acids; Cell Line; Disease Models, Animal; Female; Ferroptosis; Humans; Lipopolysaccharides; Lipoxygenase Inhibitors; Male; Mice; Mice, Inbred BALB C; Molecular Docking Simulation; Pyroglyphidae; Succinates; 16 hydroxy hexadecanoic acid; 4 guanidinobutanoic acid; aspartic acid; calcium ion; cichoric acid; citrulline; ferrous ion; icosapentaenoic acid; iron; levo targinine; lipid; lipid peroxide; lipopolysaccharide; malic acid; marmesin; n(g) methylarginine; phospholipid hydroperoxide glutathione peroxidase; protein inhibitor; pseudouridine; pyroglutamic acid; RNA; succinic acid; succinic anhydride; unclassified drug; ALOX15 protein, human; Alox15 protein, mouse; antiasthmatic agent; arachidonate 12 lipoxygenase; arachidonate 15 lipoxygenase; caffeic acid derivative; cichoric acid; lipoxygenase inhibitor; succinic acid derivative; 16HBE14o- cell line; animal experiment; animal model; animal tissue; Article; asthma; binding affinity; bioinformatics; bronchospasm; cell infiltration; cell lysate; citric acid cycle; controlled study; Dermatophagoides; down regulation; female; ferroptosis; flow cytometry; functional enrichment analysis; gene expression; gene expression profiling; high throughput technology; histopathology; human; human cell; in vitro study; in vivo study; inflammatory cell; KEGG; lipid peroxidation; liquid chromatography-mass spectrometry; machine learning; metabolomics; molecular docking; molecular dynamics; mouse; NETosis; nonhuman; oxidative stress; phenotype; protein structure; random forest; smoking; therapy effect; thermostability; upregulation; weighted gene co expression network analysis; Western blotting; animal; Bagg albino mouse; cell line; disease model; drug effect; drug therapy; genetics; immunology; male; metabolism; Pyroglyphidae","","aspartic acid, 56-84-8, 6899-03-2; calcium ion, 14127-61-8; cichoric acid, 6537-80-0; citrulline, 372-75-8; ferrous ion, 15438-31-0; icosapentaenoic acid, 10417-94-4, 1553-41-9, 25378-27-2, 32839-30-8; iron, 14093-02-8, 53858-86-9, 7439-89-6; lipid, 66455-18-3; malic acid, 149-61-1, 6915-15-7; marmesin, 13849-08-6; n(g) methylarginine, 156706-47-7, 17035-90-4, 53308-83-1; phospholipid hydroperoxide glutathione peroxidase, 97089-70-8; pseudouridine, 1445-07-4; pyroglutamic acid, 16891-48-8, 28874-51-3, 98-79-3; RNA, 63231-63-0; succinic acid, 110-15-6; succinic anhydride, 108-30-5; arachidonate 12 lipoxygenase, 82391-43-3; arachidonate 15 lipoxygenase, 82249-77-2; ALOX15 protein, human, ; Alox15 protein, mouse, ; Anti-Asthmatic Agents, ; Arachidonate 12-Lipoxygenase, ; Arachidonate 15-Lipoxygenase, ; Caffeic Acids, ; chicoric acid, ; Lipopolysaccharides, ; Lipoxygenase Inhibitors, ; Succinates, ","","","College Students' Innovative Entrepreneurial Training Plan Program; National Natural Science Foundation of China, NSFC, (82370564); National Natural Science Foundation of China, NSFC; Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province, (202410571002, 202410571040); Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province; Guangdong Medical University, (ZZZF006)","Funding text 1: This research was funded by the National Natural Science Foundation of China (82370564); College Students' Innovative Entrepreneurial Training Plan Program (202410571002, 202410571040); Guangdong Medical University students innovative experiment project (ZZZF006).; Funding text 2: This research was funded by the National Natural Science Foundation of China ( 82370564 ); The Special Fund for Science and Technology Innovation Strategy of Guangdong province (202410571002, 202410571040). 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Sci., 8, (2021)","L. Luo; The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang City, No. 2 Wenming East Road, Xiashan District, Guangdong Province, China; email: luolianxiang321@gdmu.edu.cn","","Elsevier B.V.","","","","","","15675769","","IINMB","39298822","English","Int. Immunopharmacol.","Article","Final","","Scopus","2-s2.0-85204082321"
"Martin C.; Mahan K.S.; Wiggen T.D.; Gilbertsen A.J.; Hertz M.I.; Hunter R.C.; Quinn R.A.","Martin, Christian (57221345030); Mahan, Kathleen S. (57210259137); Wiggen, Talia D. (57216644270); Gilbertsen, Adam J. (56405558300); Hertz, Marshall I. (7102692035); Hunter, Ryan C. (9237108900); Quinn, Robert A. (57210822939)","57221345030; 57210259137; 57216644270; 56405558300; 7102692035; 9237108900; 57210822939","Microbiome and metabolome patterns after lung transplantation reflect underlying disease and chronic lung allograft dysfunction","2024","Microbiome","12","1","196","","","","0","10.1186/s40168-024-01893-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205996075&doi=10.1186%2fs40168-024-01893-y&partnerID=40&md5=a863e9501a17a5e5da18a8a82a5af375","Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824, MI, United States; Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, 14051, NY, United States","Martin C., Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824, MI, United States; Mahan K.S., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Wiggen T.D., Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Gilbertsen A.J., Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Hertz M.I., Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, 55455, MN, United States; Hunter R.C., Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, 55455, MN, United States, Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, 14051, NY, United States; Quinn R.A., Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824, MI, United States","Background: Progression of chronic lung disease may lead to the requirement for lung transplant (LTx). Despite improvements in short-term survival after LTx, chronic lung allograft dysfunction (CLAD) remains a critical challenge for long-term survival. This study investigates the molecular and microbial relationships between underlying lung disease and the development of CLAD in bronchoalveolar lavage fluid (BALF) from subjects post-LTx, which is crucial for tailoring treatment strategies specific to allograft dysfunctions. Methods: Paired 16S rRNA gene amplicon sequencing and untargeted LC–MS/MS metabolomics were performed on 856 BALF samples collected over 10 years from LTx recipients (n = 195) with alpha-1-antitrypsin disease (AATD, n = 23), cystic fibrosis (CF, n = 47), chronic obstructive pulmonary disease (COPD, n = 78), or pulmonary fibrosis (PF, n = 47). Data were analyzed using random forest (RF) machine learning and multivariate statistics for associations with underlying disease and CLAD development. Results: The BALF microbiome and metabolome after LTx differed significantly according to the underlying disease state (PERMANOVA, p = 0.001), with CF and AATD demonstrating distinct microbiome and metabolome profiles, respectively. Uniqueness in CF was mainly driven by Pseudomonas abundance and its metabolites, whereas AATD had elevated levels of phenylalanine and a lack of shared metabolites with the other underlying diseases. BALF microbiome and metabolome composition were also distinct between those who did or did not develop CLAD during the sample collection period (PERMANOVA, p = 0.001). An increase in the average abundance of Veillonella (AATD, COPD) and Streptococcus (CF, PF) was associated with CLAD development, and decreases in the abundance of phenylalanine-derivative alkaloids (CF, COPD) and glycerophosphorylcholines (CF, COPD, PF) were signatures of the CLAD metabolome. Although the relative abundance of Pseudomonas was not associated with CLAD, the abundance of its virulence metabolites, including siderophores, quorum-sensing quinolones, and phenazines, were elevated in those with CF who developed CLAD. There was a positive correlation between the abundance of these molecules and the abundance of Pseudomonas in the microbiome, but there was no correlation between their abundance and the time in which BALF samples were collected post-LTx. Conclusions: The BALF microbiome and metabolome after LTx are particularly distinct in those with underlying CF and AATD. These data reflect those who developed CLAD, with increased virulence metabolite production from Pseudomonas, an aspect of CF CLAD cases. These findings shed light on disease-specific microbial and metabolic signatures in LTx recipients, offering valuable insights into the underlying causes of allograft rejection. 9jCt4jk1gKgtvTcVFjuR9mVideo Abstract © The Author(s) 2024.","Bronchioalveolar lavage fluids; Chronic lung allograft dysfunction; Cystic fibrosis; Lung diseases; Metabolome; Microbiome; Pseudomonas aeruginosa virulence factors","Adult; Aged; Allografts; Bacteria; Bronchoalveolar Lavage Fluid; Cystic Fibrosis; Female; Humans; Lung; Lung Diseases; Lung Transplantation; Male; Metabolome; Metabolomics; Microbiota; Middle Aged; Pulmonary Disease, Chronic Obstructive; RNA, Ribosomal, 16S; alkaloid; alpha 1 antitrypsin; glycerophospholipid; glycerophosphorylcholine; phenazine derivative; phenylalanine; quinolone derivative; rhamnolipid; RNA 16S; siderophore; steroid; virulence factor; RNA 16S; Actinomyces; adult; Article; bronchoalveolar lavage fluid; bronchoscopy; chronic lung allograft dysfunction; chronic obstructive lung disease; clinical article; computer assisted tomography; cystic fibrosis; DNA extraction; female; gene sequence; Haemophilus; human; liquid chromatography-mass spectrometry; lung fibrosis; lung function; lung transplantation; machine learning; male; metabolome; metabolomics; microbiome; middle aged; plethysmography; Prevotella; principal coordinate analysis; Pseudomonas; quorum sensing; random forest; Streptococcus; ultra performance liquid chromatography; Veillonella; adverse event; aged; allograft; bacterium; bronchoalveolar lavage fluid; chemistry; classification; genetics; isolation and purification; lung; lung disease; metabolism; microbiology; microflora; surgery","","alpha 1 antitrypsin, 9041-92-3; glycerophosphorylcholine, 4217-84-9, 563-24-6; phenylalanine, 3617-44-5, 63-91-2; RNA, Ribosomal, 16S, ","DNeasy PowerSoil Pro kit, Qiagen; Illumina MiSeq, Illumina; QExactive mass spectrometer, Thermo; R version 4. 2.1; Vanquish ultra high performance liquid  chromatography system, Thermo; ggplot2  version  3.5.0; random  forest version 4.7.1.1; vegan version 2.6.4","Illumina; Qiagen; Thermo; Thermo","Michigan State University, MSU; Cystic Fibrosis Foundation, CFF, (HUNTER18ABO); Cystic Fibrosis Foundation, CFF; National Institute of Allergy and Infectious Diseases, NIAID, (R01AI145925); National Institute of Allergy and Infectious Diseases, NIAID","We thank Anthony Schilmiller for his constant support during sample processing in the mass spectrometry and metabolomics core laboratory at Michigan State University. We thank the staff at the University of Minnesota Genomics Center for their guidance on sample preparation and sequencing. We thank Bonnie Holme for maintenance of the O\u2019Brien BALF Specimen database. The CF Foundation grant HUNTER18ABO and the National Institute of Allergy and Infectious Diseases (R01AI145925) provided funding.","Afonso J.E., Werebe E De C., Carraro R.M., Teixeira Rh De O.B., Fernandes L.M., Abdalla L.G., Et al., . 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Quinn; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, 48824, United States; email: quinnrob@msu.edu; R.C. Hunter; Department of Microbiology and Immunology, University of Minnesota Medical School, Minneapolis, 55455, United States; email: rhunter2@buffalo.edu","","BioMed Central Ltd","","","","","","20492618","","","39385282","English","Microbiome","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85205996075"
"Vieira R.; de Sousa K.A.; Castro-Gamboa I.","Vieira, Rafael (57222596862); de Sousa, Kally Alves (29067614700); Castro-Gamboa, Ian (6507983527)","57222596862; 29067614700; 6507983527","LUMIOS – Label using machine in organic samples – A software for dereplication, molecular docking, and combined machine and deep learning","2024","Expert Systems with Applications","248","","123447","","","","0","10.1016/j.eswa.2024.123447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185283290&doi=10.1016%2fj.eswa.2024.123447&partnerID=40&md5=261e34d1992b8550fcdde0adafb1a527","Federal Institute of Education, Science, and Technology of Rondônia – IFRO, Rio Amazonas Street, 151, Ji-Paraná, 76900-730, RO, Brazil; Federal Institute of Education, Science, and Technology of Rondônia – IFRO, 15 de Novembro Street, 4849, Guajará-Mirim, 76850-000, RO, Brazil; São Paulo State University – UNESP, Professor Francisco Degni Avenue, 55, Araraquara, 14800-900, SP, Brazil","Vieira R., Federal Institute of Education, Science, and Technology of Rondônia – IFRO, Rio Amazonas Street, 151, Ji-Paraná, 76900-730, RO, Brazil; de Sousa K.A., Federal Institute of Education, Science, and Technology of Rondônia – IFRO, 15 de Novembro Street, 4849, Guajará-Mirim, 76850-000, RO, Brazil; Castro-Gamboa I., São Paulo State University – UNESP, Professor Francisco Degni Avenue, 55, Araraquara, 14800-900, SP, Brazil","LUMIOS, short for Label Using Machine In Organic Samples, is a versatile Python-based software designed to assist professionals and students in organic chemistry with computational exploration of natural products (NPs). Offering user-friendly and NO-CODE application, LUMIOS utilizes three mass spectra categories: catechin, theobromine, and caffeine. Notably, its machine and deep learning models boast accuracy rates exceeding 90%, with a focus on modulating respiratory diseases, particularly through catechin and theobromine. LUMIOS functions by preprocessing molecular information from mass spectra, dereplicating molecules through comparison with a collection of chemical database. The software ensures alignment with the NP class, safeguarding the dereplication process. Annotated structures are then tested in ML and DL models, directing users swiftly to biomacromolecular targets linked to respiratory diseases like asthma and SARS-CoV-2. This enables a streamlined computational screening of the input data. © 2024 Elsevier Ltd","Data processing; Molecular annotations; Natural products; Respiratory diseases","Computational chemistry; Computer software; Data handling; Diagnosis; Flavonoids; Molecular dynamics; Phenols; Pulmonary diseases; Computational exploration; Dereplication; Learning models; Mass spectra; Molecular annotation; Molecular docking; Natural products; Organic Chemistry; Organic samples; User friendly; Deep learning","","","","","Instituto Federal de Educação, Ciência e Tecnologia da Paraíba, IFPB; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS, (005/2021); Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS; Iranian Fisheries Research Organization, IFRO, (18/2019/REIT); Iranian Fisheries Research Organization, IFRO","The authors (specially R.V) expresses gratitude for the financial support provided by the Fundação de Amparo à Pesquisa do Estado de Rondônia (FAPERO) (PAP/Universal: 005/2021 ) and the Instituto Federal de Educação, Ciência e Tecnologia de Rondônia (IFRO) (Call Notice No. 18/2019/REIT – PROPESP/IFRO , dated December 2, 2019) for enabling the research associated with the completion of the doctoral studies (2019–2023). 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Vieira R., de Sousa K.A., da Silva G.S., Silva D.H.S., Castro-Gamboa I., CHEIC: Chemical image classificator. An intelligent system for identification of volatiles compounds with potential for respiratory diseases using deep learning, Expert Systems with Applications, 234, (2023); vonRanke N.L., Ribeiro M.M.J., Miceli L.A., de Souza N.P., Abrahim-Vieira B.A., Castro H.C., Teixeira V.L., Rodrigues C.R., Souza A.M.T., Structure-activity relationship, molecular docking, and molecular dynamic studies of diterpenes from marine natural products with anti-HIV activity, Journal of Biomolecular Structure and Dynamics, 40, 7, pp. 3185-3195, (2022); William E.W., Arun S.M., NIST Mass Spectrometry Data Center standard reference libraries and software tools: Application to seized drug analysis, Journal of Forensic Science, 68, pp. 1484-1493, (2023); Wyner A.J., Olson M., Bleich J., Mease D., Explaining the success of adaboost and random forests as interpolating classifiers, The Journal of Machine Learning Research, 18, 1, pp. 1558-1590, (2017); Zhang C., Jia D., Wang L., Wang W., Liu F., Yang A., Comparative research on network intrusion detection methods based on machine learning, Computers & Security, (2022); Zhang L., Tan J., Han D., Zhu H., From machine learning to deep learning: progress in machine intelligence for rational drug discovery, Drug Discovery Today, 22, 11, pp. 1680-1685, (2017)","R. Vieira; Federal Institute of Education, Science, and Technology of Rondônia – IFRO, Ji-Paraná, Rio Amazonas Street, 151, 76900-730, Brazil; email: rafael.vieira@ifro.edu.br","","Elsevier Ltd","","","","","","09574174","","ESAPE","","English","Expert Sys Appl","Article","Final","","Scopus","2-s2.0-85185283290"
"YADAV S.; SEHRAWAT H.; JAGLAN V.; SINGH S.I.M.A.; KANTHA P.; GOYAL P.; DALAL S.","YADAV, SUDHA (59529166700); SEHRAWAT, HARKESH (57203900839); JAGLAN, VIVEK (57217603496); SINGH, S.I.M.A. (43661719800); KANTHA, PRAVEEN (58105964000); GOYAL, PARUL (57741151700); DALAL, SURJEET (57190939535)","59529166700; 57203900839; 57217603496; 43661719800; 58105964000; 57741151700; 57190939535","A NOVEL EFFECTIVE FORECASTING MODEL DEVELOPED USING ENSEMBLE MACHINE LEARNING FOR EARLY PROGNOSIS OF ASTHMA ATTACK AND RISK GRADE ANALYSIS","2025","Scalable Computing","26","1","","398","414","16","0","10.12694/scpe.v26i1.3758","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216108279&doi=10.12694%2fscpe.v26i1.3758&partnerID=40&md5=d6218468b54ed7bfad575c3cb41bf989","Department of Computer Science & Engineering, Maharshi Dayanand University, Haryana, Rohtak, India; Department of Computer Science and Engineering, Maharshi Dayanand University, Haryana, Rohtak, India; Department of Computer Science and Engineering, Amity University Madhya Pradesh, Gwalior, India; Department of Planning and Architecture, Dada Lakshmi Chand State University of Performing and Visual Arts, Rohtak, India; Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India; Computer Science & Engineering Department, M. M. Engineering College, Maharishi Markandeshwar Deemed to be University, Mullana, Haryana, Ambala, 133207, India","YADAV S., Department of Computer Science & Engineering, Maharshi Dayanand University, Haryana, Rohtak, India; SEHRAWAT H., Department of Computer Science and Engineering, Maharshi Dayanand University, Haryana, Rohtak, India; JAGLAN V., Department of Computer Science and Engineering, Amity University Madhya Pradesh, Gwalior, India; SINGH S.I.M.A., Department of Planning and Architecture, Dada Lakshmi Chand State University of Performing and Visual Arts, Rohtak, India; KANTHA P., Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India; GOYAL P., Computer Science & Engineering Department, M. M. Engineering College, Maharishi Markandeshwar Deemed to be University, Mullana, Haryana, Ambala, 133207, India; DALAL S., Department of Computer Science and Engineering, Amity University Madhya Pradesh, Gwalior, India","Research curiosity enlarging the concern of clinician and researchers towards combination of medical science together with artificial intelligence to develop cost effective predictive model for asthma exacerbation. To accumulate the classification consequences, extensively known ensemble machine learning methods pivotal to artificial intelligence techniques are investigated and novel predictive model developed using catboost classifier that produced comparatively improved outcomes to predict the occurrence of asthma and asthma risk grade. Proposed model result is compared with other classifiers which are Support vector machine (SVM), K-Nearest neighbors (KNN), Logistic regression, Adaboost classifier, Gradient boosting classifier, Random forest, Decision tree. Model regulated classification accuracy as high as 93% with datasets selected for formation of early prognosis model of asthma disease by embracing only 20% of the features in the reduced feature set. © (2025), (West University of Timisoara). All rights reserved.","Asthma; Ensemble learning classifiers; Machine learning; Predictive model; Risk grade","Contrastive Learning; Diseases; Federated learning; k-nearest neighbors; Prediction models; Risk analysis; Risk assessment; Support vector machines; Asthma; Cost effective; Ensemble learning; Ensemble learning classifier; Forecasting models; Learning classifiers; Machine-learning; Medical science; Predictive models; Risk grade; Adversarial machine learning","","","","","","","Siddiquee J., Roy A., Datta A., Sarkar P., Saha S., Biswas S. S., Smart asthma attack prediction system using Internet of Things, Proceedings of the 7th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEEE IEMCON 2016, pp. 1-4, (2016); Achuth Rao M. V., Kausthubha N. K., Yadav S., Gope D., Krishnaswamy U. M., Ghosh P. K., Automatic predict tion of spirometry readings from cough and wheeze for monitoring of asthma severity, Proceedings of the 25th European Signal Processing Conference, EUSIPCO 2017, pp. 41-45, (2017); Do Q. T., Doig A. K., Son T. C., Chaudri J. M., Personalized Prediction of Asthma Severity and Asthma Attack for a Personalized Treatment Regimen, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 1-5, (2018); Do Q. T., Doig A. K., Son T. C., Chaudri J. M., Personalized Prediction of Asthma Severity and Asthma Attack for a Personalized Treatment Regimen, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 1-5, (2018); Luo G., Stone B. L., Fassl B., Maloney C. G., Gesteland P. H., Yerram S. R., Nkoy F. L., Predicting asthma control deterioration in children, BMC Medical Informatics and Decision Making, 15, 1, pp. 1-8, (2015); Gold D. R., Damokosh A. I., Dockery D. W., Berkey C. S., Body-mass index as a predictor of incident asthma in a prospective cohort of children, Pediatric Pulmonology, 36, 6, pp. 514-521, (2003); Do Q. T., Doig A. K., Son T. C., Deep Q-learning for Predicting Asthma Attack with Considering Personalized Environmental Triggers’ Risk Scores, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 562-565, (2019); Kocsis O., Lalos A., Arvanitis G., Moustakas K., Multi-model Short-term Prediction Schema for mHealth Empowering Asthma Self-management, Electronic Notes in Theoretical Computer Science, 343, pp. 3-17, (2019); Hoq M. N., Alam R., Amin A., Prediction of possible asthma attack from air pollutants: Towards a high density air pollution map for smart cities to improve living, Proceedings of the 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, pp. 1-5, (2019); Do Q., Tran S., Doig A., Reinforcement Learning Framework to Identify Cause of Diseases-Predicting Asthma Attack Case, Proceedings of the 2019 IEEE International Conference on Big Data, Big Data, 2019, pp. 4829-4838, (2019); Luo J., Long Y., NTSHMDA: Prediction of Human Microbe-Disease Association Based on Random Walk by Integrating Network Topological Similarity, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17, 4, pp. 1341-1351, (2020); Priya C. K., Sudhakar M., Lingampalli J., Basha C. Z., An Advanced Fog based Health Care System Using ANN for the prediction of Asthma, Proceedings of the 5th International Conference on Computing Methodologies and Communication, ICCMC, 2021, pp. 1138-1145, (2021); Lisspers K., Stallberg B., Larsson K., Janson C., Muller M., Luczko M., Bjerregaard B. K., Bacher G., Holzhauer B., Goyal P., Johansson G., Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning - Based on the ARCTIC study, Respiratory Medicine, 185, (2021); Aditya Narayan S., Nair A. Y., Veni S., Determining the Effect of Correlation between Asthma/Gross Domestic Product and Air Pollution, Proceedings of the 2022 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET, 2022, pp. 44-48, (2022); Tong Y., Wang Y., Zhang Q., Zhang Z., Chen G., A Reliability-constrained Association Rule Mining Method for Explaining Machine Learning Predictions on Continuity of Asthma Care, Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM, 2022, pp. 1219-1226, (2022); Mahammad A. B., Kumar R., Machine Learning Approach to Predict Asthma Prevalence with Decision Trees, Proceedings of the International Conference on Technological Advancements in Computational Sciences, ICTACS, 2022, pp. 263-267, (2022); Lilhore U. K., Dalal S., Faujdar N., Margala M., Chakrabarti P., Chakrabarti T., Velmurugan H., Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease, Scientific Reports, 13, 1, (2023); Kroes J. A., Zielhuis S. W., Van Roon E. N., Ten Brinke A., Prediction of response to biological treatment with monoclonal antibodies in severe asthma, Biochemical Pharmacology, 179, (2020); Dalal S., Lilhore U. K., Simaiya S., Jaglan V., Mohan A., Ahuja S., Chakrabarti P., A precise coronary artery disease prediction using Boosted C5. 0 decision tree model, Journal of Autonomous Intelligence, 6, 3, (2023); Saha C., Riner M. E., Liu G., Individual and neighborhood-level factors in predicting asthma, Archives of Pediatrics & Adolescent Medicine, 159, 8, pp. 759-763, (2005); Castro-Rodriguez J. A., Cifuentes L., Martinez F. D., Predicting asthma using clinical indexes, Frontiers in Pediatrics, 7, (2019); Deshwal D, Sangwan P, Dahiya N, Et al., COVID-19 Detection using Hybrid CNN-RNN Architecture with Transfer Learning from X-Rays, Current Medical Imaging, (2023); Ram S., Zhang W., Williams M., Pengetnze Y., Predicting asthma-related emergency department visits using big data, IEEE Journal of Biomedical and Health Informatics, 19, 4, pp. 1216-1223, (2015); Mrazek D. A., Klinnert M., Mrazek P. J., Brower A., McCormick D., Rubin B., Jones J., Prediction of early-onset asthma in genetically at-risk childre, (1999); Monadi M., Firouzjahi A., Hosseini A., Javadian Y., Sharbatdaran M., Heidari B., Serum C-reactive protein in asthma and its ability in predicting asthma control, a case-control study, Caspian Journal of Internal Medicine, 7, 1, (2016); Jaiswal V., Saurabh P., Lilhore U. K., Pathak M., Simaiya S., Dalal S., A breast cancer risk predication and classification model with ensemble learning and big data fusion, Decision Analytics Journal, (2023); Forno E., Celedon J. C., Epigenomics and transcriptomics in the prediction and diagnosis of childhood asthma: are we there yet?, Frontiers in Pediatrics, 7, (2019); Priya C. K., Sudhakar M., Lingampalli J., Basha C. Z., An advanced fog based health care system using ann for the prediction of asthma, Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1138-1145, (2021); Kaan A., Dimich-Ward H., Manfreda J., Becker A., Watson W., Ferguson A., Chan-Yeung M., Cord blood IgE: its determinants and prediction of development of asthma and other allergic disorders at 12 months, Annals of Allergy, Asthma & Immunology, 84, 1, pp. 37-42, (2000)","","","West University of Timisoara","","","","","","18951767","","","","English","Scalable Comput. Pract. Exp.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216108279"
"Scherr R.; Spina A.; Dao A.; Andalib S.; Halaseh F.F.; Blair S.; Wiechmann W.; Rivera R.","Scherr, Riley (58737617900); Spina, Aidin (58737210600); Dao, Allen (59656923100); Andalib, Saman (58738017600); Halaseh, Faris F. (58105124700); Blair, Sarah (59656784400); Wiechmann, Warren (8338408600); Rivera, Ronald (57223918686)","58737617900; 58737210600; 59656923100; 58738017600; 58105124700; 59656784400; 8338408600; 57223918686","Novel Evaluation Metric and Quantified Performance of ChatGPT-4 Patient Management Simulations for Early Clinical Education: Experimental Study","2025","JMIR Formative Research","9","","e66478","","","","0","10.2196/66478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218967562&doi=10.2196%2f66478&partnerID=40&md5=546dc8f7717086fb462fc240cd9452b1","School of Medicine, University of California, Irvine School of Medicine, Irvine, CA, United States; Department of Medicine, Stanford Medicine, Stanford, CA, United States; School of Medicine, Tufts University, Boston, MA, United States; Department of Emergency Medicine, University of California Irvine, Orange, CA, United States","Scherr R., School of Medicine, University of California, Irvine School of Medicine, Irvine, CA, United States; Spina A., School of Medicine, University of California, Irvine School of Medicine, Irvine, CA, United States; Dao A., Department of Medicine, Stanford Medicine, Stanford, CA, United States; Andalib S., School of Medicine, University of California, Irvine School of Medicine, Irvine, CA, United States; Halaseh F.F., School of Medicine, University of California, Irvine School of Medicine, Irvine, CA, United States; Blair S., School of Medicine, Tufts University, Boston, MA, United States; Wiechmann W., Department of Emergency Medicine, University of California Irvine, Orange, CA, United States; Rivera R., Department of Emergency Medicine, University of California Irvine, Orange, CA, United States","Background: Case studies have shown ChatGPT can run clinical simulations at the medical student level. However, no data have assessed ChatGPT’s reliability in meeting desired simulation criteria such as medical accuracy, simulation formatting, and robust feedback mechanisms. Objective: This study aims to quantify ChatGPT’s ability to consistently follow formatting instructions and create simulations for preclinical medical student learners according to principles of medical simulation and multimedia educational technology. Methods: Using ChatGPT-4 and a prevalidated starting prompt, the authors ran 360 separate simulations of an acute asthma exacerbation. A total of 180 simulations were given correct answers and 180 simulations were given incorrect answers. ChatGPT was evaluated for its ability to adhere to basic simulation parameters (stepwise progression, free response, interactivity), advanced simulation parameters (autonomous conclusion, delayed feedback, comprehensive feedback), and medical accuracy (vignette, treatment updates, feedback). Significance was determined with χ2 analyses using 95% CIs for odds ratios. Results: In total, 100% (n=360) of simulations met basic simulation parameters and were medically accurate. For advanced parameters, 55% (200/360) of all simulations delayed feedback, while the Correct arm (157/180, 87%) delayed feedback was significantly more than the Incorrect arm (43/180, 24%; P<.001). A total of 79% (285/360) of simulations concluded autonomously, and there was no difference between the Correct and Incorrect arms in autonomous conclusion (146/180, 81% and 139/180, 77%; P=.36). Overall, 78% (282/360) of simulations gave comprehensive feedback, and there was no difference between the Correct and Incorrect arms in comprehensive feedback (137/180, 76% and 145/180, 81%; P=.31). ChatGPT-4 was not significantly more likely to conclude simulations autonomously (P=.34) and provide comprehensive feedback (P=.27) when feedback was delayed compared to when feedback was not delayed. Conclusions: These simulations have the potential to be a reliable educational tool for simple simulations and can be evaluated by a novel 9-part metric. Per this metric, ChatGPT simulations performed perfectly on medical accuracy and basic simulation parameters. It performed well on comprehensive feedback and autonomous conclusion. Delayed feedback depended on the accuracy of user inputs. A simulation meeting one advanced parameter was not more likely to meet all advanced parameters. Further work must be done to ensure consistent performance across a broader range of simulation scenarios. © 2025 JMIR Publications Inc.. All rights reserved.","AI in medical education; ChatGPT; ChatGPT-4; clinical education; feedback; medical education; medical school simulations; medical simulation; medical student; multimedia; patient management; pilot study; preclinical curriculum; simulation","","","","","","","","Gilson A, Safranek CW, Huang T, Et al., How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of large language models for medical education and knowledge assessment, JMIR Med Educ, 9, (2023); Nakao T, Miki S, Nakamura Y, Et al., Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: evaluation study, JMIR Med Educ, 10, (2024); Kung TH, Cheatham M, Medenilla A, Et al., Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models, PLOS Digital Health, 2, 2, (2023); Ali R, Tang OY, Connolly ID, Et al., Performance of ChatGPT and GPT-4 on Neurosurgery written board examinations, Neurosurgery, 93, 6, pp. 1353-1365, (2023); Brin D, Sorin V, Vaid A, Et al., Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments, Sci Rep, 13, 1, (2023); Mohammad B, Supti T, Alzubaidi M, Et al., The pros and cons of using ChatGPT in medical education: a scoping review, Stud Health Technol Inform, 305, pp. 644-647, (2023); Boscardin CK, Gin B, Golde PB, Hauer KE., ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity, Acad Med, 99, 1, pp. 22-27, (2024); Khan RA, Jawaid M, Khan AR, Sajjad M., ChatGPT—reshaping medical education and clinical management, Pak J Med Sci, 39, 2, pp. 605-607, (2023); Scherr R, Halaseh FF, Spina A, Andalib S, Rivera R., ChatGPT interactive medical simulations for early clinical education: case study, JMIR Med Educ, 9, (2023); Halaseh FF, Yang JS, Danza CN, Halaseh R, Spiegelman L., ChatGPT’s role in improving education among patients seeking emergency medical treatment, West J Emerg Med, 25, 5, pp. 845-855, (2024); Wu Y, Zheng Y, Feng B, Yang Y, Kang K, Zhao A., Embracing ChatGPT for medical education: exploring its impact on doctors and medical students, JMIR Med Educ, 10, (2024); Araujo SM, Cruz-Correia R., Incorporating ChatGPT in medical informatics education: mixed methods study on student perceptions and experiential integration proposals, JMIR Med Educ, 10, (2024); Sallam M., ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns, Healthcare (Basel), 11, 6, (2023); Alkhaaldi SMI, Kassab CH, Dimassi Z, Et al., Medical student experiences and perceptions of chatgpt and artificial intelligence: cross-sectional study, JMIR Med Educ, 9, (2023); Skryd A, Lawrence K., ChatGPT as a tool for medical education and clinical decision-making on the wards: case study, JMIR Form Res, 8, (2024); Kim HW, Hong JW, Nam EJ, Kim KY, Kim JH, Kang JI., Medical students’ perceived stress and perceptions regarding clinical clerkship during the COVID-19 pandemic, PLoS ONE, 17, 10, (2022); Heston TF, Khun C., Prompt engineering in medical education, IME, 2, 3, pp. 198-205, (2023); Musallam E, Alhaj Ali A, Alkhafaji M., OpenAI’s ChatGPT clinical simulation: an innovative teaching strategy for clinical nursing education, Nurse Educ, 49, 6, pp. E361-E362, (2024); Lucas HC, Upperman JS, Robinson JR., A systematic review of large language models and their implications in medical education, Med Educ, 58, 11, pp. 1276-1285, (2024); Jeyaraman M, SP K, Jeyaraman N, Nallakumarasamy A, Yadav S, Bondili SK., ChatGPT in medical education and research: a boon or a bane?, Cureus, 15, 8, (2023); van de Ridder JMM, Shoja MM, Rajput V., Finding the place of ChatGPT in medical education, Acad Med, 98, 8, (2023); Clark RC., Multimedia learning in e-courses, The Cambridge Handbook of Multimedia Learning, pp. 842-882, (2014); Wylie R, Chi MTH., The self-explanation principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 413-432, (2014); Luetner D, Schmeck A., The generative activity principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 433-448, (2014); Mayer RE., Cognitive theory of multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 43-71, (2014); Low R, Sweller J., The modality principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 227-246, (2014); Plass JL, Schwartz RN., Multimedia learning with simulations, The Cambridge Handbook of Multimedia Learning, pp. 729-761, (2014); Mayer RE, Fiorella L., Principles for reducing extraneous processing in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 279-315, (2014); Johnson CI, Priest HA., The feedback principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 449-463, (2014); Stefanidis D, Cook D, Kalantar-Motamedi SM, Et al., Society for simulation in healthcare guidelines for simulation training, Simul Healthc, 19, 1S, pp. S4-S22, (2024); van Gog T., The signaling (or cueing) principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 263-278, (2014); Feldon DF, Jeong S, Clark RE., Fifteen common but questionable principles of multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 25-40, (2021); Dabbs W, Bradley MH, Chamberlin SM., Acute asthma exacerbations: management strategies, Am Fam Physician, 109, 1, pp. 43-50, (2024); Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW., Large language models in medicine, Nat Med, 29, 8, pp. 1930-1940, (2023); Alkaissi H, McFarlane SI., Artificial hallucinations in ChatGPT: implications in scientific writing, Cureus, 15, 2, (2023); Hattie J, Timperley H., The power of feedback, Rev Educ Res, 77, 1, pp. 81-112, (2007); Chamberland M, Setrakian J, St-Onge C, Bergeron L, Mamede S, Schmidt HG., Does providing the correct diagnosis as feedback after self-explanation improve medical students diagnostic performance?, BMC Med Educ, 19, 1, (2019); Kalyuga S., The expertise reversal principle in multimedia learning, The Cambridge Handbook of Multimedia Learning, pp. 576-597, (2014); Csikszentmihalyi M, Abuhamdeh S, Nakamura J., Csikszentmihalyi M, Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihaly, pp. 227-238, (2014)","R. Scherr; School of Medicine University of California, Irvine School of Medicine, Irvine, 1001 Health Sciences Road, 92617, United States; email: rscherr@hs.uci.edu","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85218967562"
"Kim S.; Qin Y.; Park H.J.; Bohn R.I.C.; Yue M.; Xu Z.; Forno E.; Chen W.; Celedón J.C.","Kim, Soyeon (57211484256); Qin, Yidi (57221341881); Park, Hyun Jung (57211141734); Bohn, Rebecca I. Caldino (58911060900); Yue, Molin (57728906000); Xu, Zhongli (57217159738); Forno, Erick (25225322900); Chen, Wei (57203581701); Celedón, Juan C. (7004338434)","57211484256; 57221341881; 57211141734; 58911060900; 57728906000; 57217159738; 25225322900; 57203581701; 7004338434","MOSES: a methylation-based gene association approach for unveiling environmentally regulated genes linked to a trait or disease","2024","Clinical Epigenetics","16","1","161","","","","0","10.1186/s13148-024-01776-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209564997&doi=10.1186%2fs13148-024-01776-x&partnerID=40&md5=8ac5e1e8c189c4adfba1da03ca6926e5","Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; School of Medicine, Tsinghua University, Beijing, China","Kim S., Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Qin Y., Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Park H.J., Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Bohn R.I.C., Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Yue M., Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Xu Z., School of Medicine, Tsinghua University, Beijing, China; Forno E., Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Chen W., Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Celedón J.C., Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States","Background: DNA methylation is a critical regulatory mechanism of gene expression, influencing various human diseases and traits. While traditional expression quantitative trait loci (eQTL) studies have helped elucidate the genetic regulation of gene expression, there is a growing need to explore environmental influences on gene expression. Existing methods such as PrediXcan and FUSION focus on genotype-based associations but overlook the impact of environmental factors. To address this gap, we present MOSES (methylation-based gene association), a novel approach that utilizes DNA methylation to identify environmentally regulated genes associated with traits or diseases without relying on measured gene expression. Results: MOSES involves training, imputation, and association testing. It employs elastic-net penalized regression models to estimate the influence of CpGs and SNPs (if available) on gene expression. We developed and compared four MOSES versions incorporating different methylation and genetic data: (1) cis-DNA methylation within 1 Mb of promoter regions, (2) both cis-SNPs and cis-CpGs, 3) both cis- and a part of trans- CpGs (±5Mb away) from promoter regions), and 4) long-range DNA methylation (±10 Mb away) from promoter regions. Our analysis using nasal epithelium and white blood cell data from the Epigenetic Variation and Childhood Asthma in Puerto Ricans (EVA-PR) study demonstrated that MOSES, particularly the version incorporating long-range CpGs (MOSES-DNAm 10 M), significantly outperformed existing methods like PrediXcan, MethylXcan, and Biomethyl in predicting gene expression. MOSES-DNAm 10 M identified more differentially expressed genes (DEGs) associated with atopic asthma, particularly those involved in immune pathways, highlighting its superior performance in uncovering environmentally regulated genes. Further application of MOSES to lung tissue data from idiopathic pulmonary fibrosis (IPF) patients confirmed its robustness and versatility across different diseases and tissues. Conclusion: MOSES represents an innovative advancement in gene association studies, leveraging DNA methylation to capture the influence of environmental factors on gene expression. By incorporating long-range CpGs, MOSES-DNAm 10 M provides superior predictive accuracy and gene association capabilities compared to traditional genotype-based methods. This novel approach offers valuable insights into the complex interplay between genetics and the environment, enhancing our understanding of disease mechanisms and potentially guiding therapeutic strategies. The user-friendly MOSES R package is publicly available to advance studies in various diseases, including immune-related conditions like asthma. © The Author(s) 2024.","Asthma; DNA methylation; eQTM (expression quantitative trait methylation); Gene expression prediction; Gene-level association tests; Machine learning; MOSES (methylation-based gene association method); Nasal epithelium; PrediXcan; TWAS (transcriptome-wide association studies)","Asthma; CpG Islands; DNA Methylation; Epigenesis, Genetic; Female; Gene Expression Regulation; Gene-Environment Interaction; Genetic Association Studies; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Idiopathic Pulmonary Fibrosis; Male; Polymorphism, Single Nucleotide; Promoter Regions, Genetic; Quantitative Trait Loci; allergic asthma; Article; Biomethyl; child; cohort analysis; controlled study; CpG island; differential gene expression; diseases; DNA methylation; early onset asthma; environment; epigenetics; expression quantitative trait locus; female; fibrosing alveolitis; gene; gene expression; genetic analysis; genetic association; genetic trait; genetic variation; human; human tissue; immune system; leukocyte; machine learning; major clinical study; male; MethylXcan; MOSES; nose epithelium; pathogenesis; prediction; PrediXcan; promoter region; Puerto Rican; single nucleotide polymorphism; asthma; fibrosing alveolitis; gene expression regulation; genetic association study; genetic epigenesis; genetic predisposition; genetics; genome-wide association study; genotype environment interaction; procedures; quantitative trait locus; single nucleotide polymorphism","","","","","University of Pittsburgh, (SCR_022735, S10OD028483); University of Pittsburgh; National Institutes of Health, NIH, (HL150431, Hl149693); National Institutes of Health, NIH; Medical Center, University of Pittsburgh, UPMC, (P30CA047904, P50 CA254865-01, R01GM108618); Medical Center, University of Pittsburgh, UPMC; National Science Foundation, NSF, (2225775); National Science Foundation, NSF","This work was supported by grants HL079966, HL117191, and MD011764 (to J.C.C.), U54 MD007587 (to E.A-P. and G.C.), and K01 HL153792 (to S.K.) from the U.S. National Institutes of Health (NIH). The contribution of E.F. was supported by NIH grant Hl149693, and that of W.C. was supported by NIH grant HL150431 and NSF grant 2225775. H.J.P. was supported by the UPMC Hillman Cancer Center Biostatistics Shared Resource that is supported in part by award P30CA047904 and R01GM108618 at the NIH. H.J.P. is also supported by the Hillman Cancer Center Career Enhancement Program Award (P50 CA254865-01). This research was supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. 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Celedón; Division of Pulmonary Medicine, Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, United States; email: juan.celedon@chp.edu","","BioMed Central Ltd","","","","","","18687075","","","39558360","English","Clin. Epigenetics","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85209564997"
"Dai T.; Bao M.; Zhang M.; Wang Z.; Tang J.; Liu Z.","Dai, Tian (57790663300); Bao, Manzhen (59254219200); Zhang, Miao (57203862934); Wang, Zonggui (59254665600); Tang, JingJing (57219454829); Liu, Zeyan (56805863400)","57790663300; 59254219200; 57203862934; 59254665600; 57219454829; 56805863400","A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy","2024","BMC Medical Informatics and Decision Making","24","1","224","","","","0","10.1186/s12911-024-02627-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200877946&doi=10.1186%2fs12911-024-02627-8&partnerID=40&md5=3f6e2edc431e67acc93492653b51911a","Department of General Surgery (Ward one), The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Nursing Department, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Department of Orthopedics (Ward two), The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Department of Emergency Internal Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China","Dai T., Department of General Surgery (Ward one), The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China, Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Bao M., Nursing Department, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China, Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Zhang M., Nursing Department, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Wang Z., Department of Orthopedics (Ward two), The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China, Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Tang J., Department of General Surgery (Ward one), The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China, Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China; Liu Z., Department of Emergency Internal Medicine, The Second Affiliated Hospital of Anhui Medical University, Anhui, Hefei, 230601, China","Objective: To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care strategies. Methods: A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary classification prediction models were established using Logistic Regression (LR), Support Vector Classification (SVC), K Nearest Neighbor (KNN), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XgBoost). The binary classification label denoted 1 for PSH occurrence and 0 for no PSH occurrence. Parameters were optimized via 5-fold cross-validation. Model performance was evaluated using Area Under Curve (AUC), specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and F1-score. Clinical utility was evaluated using decision curve analysis (DCA), model explanation was enhanced using shapley additive explanation (SHAP), and model visualization was achieved using a nomogram. Results: The incidence of PSH within 1 year was 29.1% (144 patients). Among the models tested, the RF model demonstrated the highest discrimination capability with an AUC of 0.888 (95% CI: 0.881–0.935), along with superior specificity, accuracy, sensitivity, and F1 score. It also showed the highest clinical net benefit on the DCA curve. SHAP analysis identified the top 10 influential variables associated with PSH risk: body mass index (BMI), operation duration, history and status of chronic obstructive pulmonary disease (COPD), prealbumin, tumor node metastasis (TNM) staging, stoma site, thickness of rectus abdominis muscle (TRAM), C-reactive protein CRP, american society of anesthesiologists physical status classification (ASA), and stoma diameter. These insights from SHAP plots illustrated how these factors influence individual PSH outcomes. The nomogram was used for model visualization. Conclusion: The Random Forest model demonstrated robust predictive performance and clinical relevance in forecasting colonic PSH. This model aids in early identification of high-risk patients and guides preventive care. © The Author(s) 2024.","Machine learning; Parastomal hernia; Predictive model","Aged; Algorithms; Case-Control Studies; Colorectal Neoplasms; Colostomy; Female; Humans; Incisional Hernia; Machine Learning; Male; Middle Aged; Postoperative Complications; Risk Assessment; adverse event; aged; algorithm; case control study; colorectal tumor; colostomy; etiology; female; human; incisional hernia; machine learning; male; middle aged; postoperative complication; risk assessment; surgery","","","","","Anhui Nursing Society Scientific Research Program Youth Project, (AHHL a202116); 2020 Anhui Medical University Scientific Research Fund Youth Project, (2020xkj136)","This research was supported by the 2021 Anhui Nursing Society Scientific Research Program Youth Project (AHHL a202116), and the 2020 Anhui Medical University Scientific Research Fund Youth Project (2020xkj136). 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Liu; Department of Emergency Internal Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China; email: jy02893741@163.com","","BioMed Central Ltd","","","","","","14726947","","","39118122","English","BMC Med. Informatics Decis. Mak.","Article","Final","","Scopus","2-s2.0-85200877946"
"Mochizuki H.; Hirai K.; Furuya H.; Niimura F.; Suzuki K.; Okino T.; Ikeda M.; Noto H.","Mochizuki, Hiroyuki (7202006042); Hirai, Kota (37034145400); Furuya, Hiroyuki (35573303700); Niimura, Fumio (6602149017); Suzuki, Kenta (59261714900); Okino, Tsuyoshi (59261171000); Ikeda, Miki (59261171100); Noto, Hironori (59260811700)","7202006042; 37034145400; 35573303700; 6602149017; 59261714900; 59261171000; 59261171100; 59260811700","The analysis of lung sounds in infants and children with a history of wheezing/asthma using an automatic procedure","2024","BMC Pulmonary Medicine","24","1","394","","","","0","10.1186/s12890-024-03210-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201282274&doi=10.1186%2fs12890-024-03210-7&partnerID=40&md5=9070cbeaa2243dfdb1b6599d516de3c2","Department of Pediatrics, Tokai University Hachioji Hospital, Hachioji, Japan; Department of Pediatrics, Tokai University School of Medicine, Shimokasuya 143, Kanagawa, Isehara, 259-1193, Japan; Department of Basic Clinical Science and Public Health, Tokai University School of Medicine, Isehara, Japan; Murata Manufacturing Co., Ltd, Nagaokakyo, Japan","Mochizuki H., Department of Pediatrics, Tokai University Hachioji Hospital, Hachioji, Japan, Department of Pediatrics, Tokai University School of Medicine, Shimokasuya 143, Kanagawa, Isehara, 259-1193, Japan; Hirai K., Department of Pediatrics, Tokai University Hachioji Hospital, Hachioji, Japan, Department of Pediatrics, Tokai University School of Medicine, Shimokasuya 143, Kanagawa, Isehara, 259-1193, Japan; Furuya H., Department of Basic Clinical Science and Public Health, Tokai University School of Medicine, Isehara, Japan; Niimura F., Department of Pediatrics, Tokai University Hachioji Hospital, Hachioji, Japan, Department of Pediatrics, Tokai University School of Medicine, Shimokasuya 143, Kanagawa, Isehara, 259-1193, Japan; Suzuki K., Murata Manufacturing Co., Ltd, Nagaokakyo, Japan; Okino T., Murata Manufacturing Co., Ltd, Nagaokakyo, Japan; Ikeda M., Murata Manufacturing Co., Ltd, Nagaokakyo, Japan; Noto H., Murata Manufacturing Co., Ltd, Nagaokakyo, Japan","Background: Lung sound analysis parameters have been reported to be useful biomarkers for evaluating airway condition. We developed an automatic lung sound analysis software program for infants and children based on lung sound spectral curves of frequency and power by leveraging machine learning (ML) technology. Methods: To put this software program into clinical practice, in Study 1, the reliability and reproducibility of the software program using data from younger children were examined. In Study 2, the relationship between lung sound parameters and respiratory flow (L/s) was evaluated using data from older children. In Study 3, we conducted a survey using the ATS-DLD questionnaire to evaluate the clinical usefulness. The survey focused on the history of wheezing and allergies, among healthy 3-year-old infants, and then measured lung sounds. The clinical usefulness was evaluated by comparing the questionnaire results with the results of the new lung sound parameters. Results: In Studies 1 and 2, the parameters of the new software program demonstrated excellent reproducibility and reliability, and were not affected by airflow (L/s). In Study 3, infants with a history of wheezing showed lower FAP0 and RPF75p (p < 0.001 and p = 0.025, respectively) and higher PAP0 (p = 0.001) than healthy infants. Furthermore, infants with asthma/asthma-like bronchitis showed lower FAP0 (p = 0.002) and higher PAP0 (p = 0.001) than healthy infants. Conclusions: Lung sound parameters obtained using the ML algorithm were able to accurately assess the respiratory condition of infants. These parameters are useful for the early detection and intervention of childhood asthma. © The Author(s) 2024.","Asthma; Infants; Lung sound analysis; Machine learning; Software","Asthma; Child; Child, Preschool; Female; Humans; Infant; Machine Learning; Male; Reproducibility of Results; Respiratory Sounds; Software; Surveys and Questionnaires; abnormal respiratory sound; adolescent; Article; asthma; bronchitis; child; clinical practice; female; hospitalization; human; infant; lung function test; machine learning; major clinical study; male; questionnaire; reproducibility; respiratory syncytial virus infection; retrospective study; sensitivity and specificity; spirometry; wheezing; abnormal respiratory sound; diagnosis; pathophysiology; preschool child; software","","","SPSS, IBM","IBM","Environmental Restoration and Conservation Agency, ERCA","This study was supported by the Environmental Restoration and Conservation Agency of Japan in 2009\u20132019, No. 1\u20131. ","Global Initiative for Asthma. Global Strategy for Asthma Management and Prevention; Guidelines for the treatment and management of pediatric bronchial asthma 2023 (Japanese), (2023); McFadden E.R., Kiser R., DeGroot W.J., Acute bronchial asthma. 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Imamura T., Enseki M., Murayama Y., Furuya H., Mochizuki H., Characteristics of breath sounds during methacholine-induced bronchoconstriction in children with asthma, Tokai J Exp Clin Med, 47, 3, pp. 125-130, (2022); Nukaga M., Tabata H., Enseki M., Hirai K., Furuya H., Kato M., Et al., Changes in the breath sound spectrum with bronchodilation in children with asthma, Respir Investig, 56, 5, pp. 392-398, (2018); Sanchez I., Pasterkamp H., Tracheal sound spectra depend on body height, Am Rev Respir Dis, 148, 4 pt 1, pp. 1083-1087, (1993); Grzywalski T., Piecuch M., Szajek M., Breborowicz A., Hafke-Dys H., Kocinski J., Et al., Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination, Eur J Pediatr, 178, 6, pp. 883-890, (2019); Islam M.A., Bandyopadhyaya I., Bhattacharyya P., Saha G., Multichannel lung sound analysis for asthma detection, Comput Methods Progr Biomed, 159, pp. 111-123, (2018); Ntalianis V., Fakotakis N.D., Nousias S., Lalos A.S., Birbas M., Zacharaki E.I., Et al., Deep CNN sparse coding for real time inhaler sounds classification, Sensors (Basel), 20, 8, (2020); 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Kagawa T., Imamura T., Enseki M., Tabata H., Furuya H., Niimura F., Et al., Effect of inspiratory flow on breath sound analysis in children with asthma, Arerugi (Japanese), 69, 3, pp. 184-191, (2020); Castro-Rodriguez J.A., Holberg C.J., Wright A.L., Martinez F.D., A clinical index to define risk of asthma in young children with recurrent wheezing, Am J Respir Crit Care Med, 162, 4 pt 1, pp. 1403-1406, (2000); Guilbert T.W., Morgan W.J., Krawiec M., Lemanske R.F., Sorkness C., Szefler S.J., Et al., The prevention of early asthma in kids study: design, rationale and methods for the Childhood Asthma Research and Education network, Control Clin Trials, 25, 3, pp. 286-310, (2004); Mochizuki H., Hirai K., Tabata H., Forced oscillation technique and childhood asthma, Allergol Int, 61, 3, pp. 373-383, (2012); McGeachie M.J., Yates K.P., Zhou X., Guo F., Sternberg A.L., Van Natta M.L., Et al., Patterns of growth and decline in lung function in persistent childhood asthma, N Engl J Med, 374, 19, pp. 1842-1852, (2016); Berry C.E., Billheimer D., Jenkins I.C., Lu Z.J., Stern D.A., Gerald L.B., Et al., A distinct low lung function trajectory from childhood to the fourth decade of life, Am J Respir Crit Care Med, 194, 5, pp. 607-612, (2016)","H. Mochizuki; Department of Pediatrics, Tokai University Hachioji Hospital, Hachioji, Japan; email: mochihi@tokai.ac.jp","","BioMed Central Ltd","","","","","","14712466","","BPMMB","39143523","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85201282274"
"Lukhumaidze L.; Hogg J.C.; Bourbeau J.; Tan W.C.; Kirby M.","Lukhumaidze, Leila (59299897400); Hogg, James C. (7201452328); Bourbeau, Jean (34567907500); Tan, Wan C. (13403886200); Kirby, Miranda (35174507500)","59299897400; 7201452328; 34567907500; 13403886200; 35174507500","Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning","2025","Academic Radiology","32","1","","543","555","12","0","10.1016/j.acra.2024.08.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202175420&doi=10.1016%2fj.acra.2024.08.030&partnerID=40&md5=2c33fb2a83c7a21001e31a0f45505b6d","Toronto Metropolitan University, Toronto, ON, Canada; Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada","Lukhumaidze L., Toronto Metropolitan University, Toronto, ON, Canada; Hogg J.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Bourbeau J., Montreal Chest Institute of the Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada, Respiratory Epidemiology and Clinical Research Unit, Research Institute of McGill University Health Centre, Montreal, QC, Canada; Tan W.C., Center for Heart, Lung Innovation, University of British Columbia, Vancouver, BC, Canada; Kirby M., Toronto Metropolitan University, Toronto, ON, Canada","Rationale and Objectives: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features. Materials and Methods: A total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n = 274 with normal lung function (stable control), n = 22 stable PRISm, and n = 300 stable COPD. Investigated features included: quantitative CT (QCT) features (n = 34), such as total lung volume (%TLCCT) and percentage of ground glass and reticulation (%GG+Reticulationtexture), as well as Radiomic (n = 102) features, including varied intensity zone distribution grainy texture (GLDZMZDV). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values. Results: Machine learning models for all feature combinations achieved AUCs between 0.63–0.84 for stable PRISm vs. stable control, and 0.65–0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p < 0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLCCT and increased %GG+Reticulationtexture and GLDZMZDV. Conclusion: These findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm. © 2024 The Association of University Radiologists","Fibrosis; ILA; PRISm; Radiomic; Texture Features","Aged; Female; Humans; Lung; Machine Learning; Male; Middle Aged; Pulmonary Disease, Chronic Obstructive; Radiographic Image Interpretation, Computer-Assisted; Spirometry; Tomography, X-Ray Computed; aged; Article; chronic obstructive lung disease; clinical feature; computer assisted tomography; controlled study; disease classification; female; follow up; human; lung function; lung volume; machine learning; major clinical study; male; preserved ratio impaired spirometry; Shapley additive explanation; spirometry; total lung capacity; chronic obstructive lung disease; computer assisted diagnosis; diagnostic imaging; lung; middle aged; procedures; spirometry; x-ray computed tomography","","","","","Université de Sherbrooke, UdeS; UBC James Hogg Research Center; Francois Maltais; University of Saskatchewan, UOS; University of Toronto, U of T; Dalhousie University; Darcy Marciniuk, Ron Clemens; University of Calgary, U of C; John Hopkins School of Public Health, Baltimore; Yvan Fortier and Mina Dligui; Keck School of Medicine of USC","The authors would also like to thank the men and women who participated in the study and individuals in the *CanCOLD Collaborative research Group: Jonathon Samet (the Keck School of Medicine of USC, California, USA); Milo Puhan (John Hopkins School of Public Health, Baltimore, USA); Qutayba Hamid, Carolyn Baglole, Palmina Mancino, Pei-Zhi Li, Zhi Song, Dennis Jensen, Benjamin Mcdonald Smith (McGill University, Montreal, QC, Canada); Yvan Fortier and Mina Dligui (Sherbrooke University, Sherbrooke, QC, Canada); Kenneth Chapman, Jane Duke, Andrea S Gershon, Teresa To, (University of Toronto, Toronto, ON Canada); J Mark Fitzgerald, Mohsen Sadatsafavi (University of British Columbia, Vancouver, BC); Christine Lo, Sarah Cheng, Elena Un, Michael Cheng, Cynthia Fung, Nancy Haynes, Liyun Zheng, LingXiang Zou, Joe Comeau, Jonathon Leipsic, Cameron Hague (UBC James Hogg Research Center, Vancouver, BC, Canada); Brandie L Walker, Curtis Dumonceaux, (University of Calgary, Calgary, AB, Canada); Paul Hernandez, Scott Fulton, (University of Dalhousie, Halifax, NS, Canada); Shawn Aaron, Kathy Vandemheen, (University of Ottawa, Ottawa, ON, Canada); Denis O'Donnell, Matthew McNeil, Kate Whelan (Queen's University, Kingston, ON, Canada); Francois Maltais, Cynthia Brouillard (University of Laval, Quebec City, QC, Canada); Darcy Marciniuk, Ron Clemens, Janet Baran (University of Saskatchewan, Saskatoon, SK, Canada).","Guerra S., Sherrill D.L., Venker C., Ceccato C.M., Halonen M., Martinez F.D., Morbidity and mortality associated with the restrictive spirometric pattern: a longitudinal study, Thorax, 65, 6, pp. 499-504, (2010); Mannino D.M., Buist A.S., Petty T.L., Enright P.L., Redd S.C., Lung function and mortality in the United States: data from the First National Health and Nutrition Examination Survey follow up study, Thorax, 58, 5, pp. 388-393, (2003); 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Kim S.S., Yagihashi K., Stinson D.S., Zach J.A., McKenzie A.S., Curran-Everett D., Et al.; Fortis S., Comellas A., Kim V., Et al., Low FVC/TLC in preserved ratio impaired spirometry (PRISm) is associated with features of and progression to obstructive lung disease, Sci Rep, 10, 1, (2020); Gevenois P.A., De Vuyst P., de Maertelaer V., Et al., Comparison of computed density and microscopic morphometry in pulmonary emphysema, Am J Respir Crit Care Med, 154, 1, pp. 187-192, (1996); Podolanczuk A.J., Oelsner E.C., Barr R.G., Et al., High-attenuation areas on chest computed tomography and clinical respiratory outcomes in community-dwelling adults, Am J Respir Crit Care Med, 196, 11, pp. 1434-1442, (2017); Zwanenburg A., Vallieres M., Abdalah M.A., Et al., The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping, Radiology, 295, 2, pp. 328-338, (2020); Nienhuis M.; Lynch D.A., Godwin J.D., Safrin S., Et al., High-resolution computed tomography in idiopathic pulmonary fibrosis: diagnosis and prognosis, Am J Respir Crit Care Med, 172, 4, pp. 488-493, (2005); 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Guerra S., Carsin A.E., Keidel D., Et al., Health-related quality of life and risk factors associated with spirometric restriction, Eur Respir J, 49, 5, (2017); Tan W.C., Sin D.D., Bourbeau J., Et al., Characteristics of COPD in never-smokers and ever-smokers in the general population: results from the CanCOLD study, Thorax, 70, 9, pp. 822-829, (2015); Bourbeau J., Tan W.C., Benedetti A., Et al., Canadian cohort obstructive lung disease (CanCOLD): fulfilling the need for longitudinal observational studies in COPD, COPD, 11, 2, pp. 125-132, (2014); Vestbo J., Hurd S.S., Rodriguez-Roisin R., The 2011 revision of the global strategy for the diagnosis, management and prevention of COPD (GOLD)–why and what?, Clin Respir J, 6, 4, pp. 208-214, (2012); MacIntyre N., Crapo R.O., Viegi G., Et al., Standardisation of the single-breath determination of carbon monoxide uptake in the lung, Eur Respir J [Internet], 26, 4, pp. 720-735, (2005); Wanger J., Clausen J.L., Coates A., Et al., Standardisation of the measurement of lung volumes, Eur Respir J [Internet], 26, 3, pp. 511-522, (2005); Miller M.R., Hankinson J., Brusasco V., Et al., Standardisation of spirometry, Eur Respir J [Internet], 26, 2, pp. 319-338, (2005); Palagyi K., Tschirren J., Hoffman E.A., Sonka M., Quantitative analysis of pulmonary airway tree structures, Comput Biol Med, 36, 9, pp. 974-996, (2006); Tschirren J., Yavarna T., Reinhardt J.M.; Smith B.M., Hoffman E.A., Rabinowitz D., Et al., Comparison of spatially matched airways reveals thinner airway walls in COPD. 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Official Statement of The European Respiratory Society, Eur Respir J, 8, 3, pp. 492-506, (1995); Tanizawa K., Handa T., Nagai S., Et al., Clinical impact of high-attenuation and cystic areas on computed tomography in fibrotic idiopathic interstitial pneumonias, BMC Pulm Med [Internet], 15, 1, (2015); Best A.C., Meng J., Lynch A.M., Et al., Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality, Radiology, 246, 3, pp. 935-940, (2008); Best A.C., Lynch A.M., Bozic C.M., Miller D., Grunwald G.K., Lynch D.A., Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment, Radiology [Internet], 228, 2, pp. 407-414, (2003); Kim H.J., Brown M.S., Chong D., Et al., Comparison of the quantitative CT imaging biomarkers of idiopathic pulmonary fibrosis at baseline and early change with an interval of 7 months, Acad Radiol [Internet], 22, 1, pp. 70-80, (2015); Wu X., Kim G.H., Salisbury M.L., Et al., Computed tomographic biomarkers in idiopathic pulmonary fibrosis. the future of quantitative analysis, Am J Respir Crit Care Med [Internet], 199, 1, pp. 12-21, (2018); Kirby M., Tanabe N., Tan W.C., Et al., Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. findings from a population-based study, Am J Respir Crit Care Med, 197, 1, pp. 56-65, (2018); Cordasco E.M., Beerel F.R., Vance J.W., Wende R.W., Toffolo R.R., Newer aspects of the pulmonary vasculature in chronic lung disease: a comparative study, Angiology [Internet], 19, 7, pp. 399-407, (1968); Estepar R.S.J., Kinney G.L., Black-Shinn J.L., Et al., Computed tomographic measures of pulmonary vascular morphology in smokers and their clinical implications, Am J Respir Crit Care Med [Internet], 188, 2, pp. 231-239, (2013); Rahaghi F.N., Argemi G., Nardelli P., Et al., Pulmonary vascular density: comparison of findings on computed tomography imaging with histology, Eur Respir J [Internet], 54, 2, (2019); Jain N., Covar R.A., Gleason M.C., Newell J.D., Gelfand E.W., Spahn J.D., Quantitative computed tomography detects peripheral airway disease in asthmatic children, Pediatr Pulmonol [Internet], 40, 3, pp. 211-218, (2005); Schroeder J.D., McKenzie A.S., Zach J.A., Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease, AJR Am J Roentgenol [Internet], 201, 3, pp. W460-W470, (2013); Galban C.J., Han M.K., Boes J.L., Et al., Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression, Nat Med [Internet], 18, 11, pp. 1711-1715, (2012); Amelon R., Cao K., Ding K., Christensen G.E., Reinhardt J.M., Raghavan M.L., Three-dimensional characterization of regional lung deformation, Journal of Biomechanics [Internet], 44, 13, pp. 2489-2495, (2011); Au R.C., Tan W.C., Bourbeau J., Hogg J.C., Kirby M., Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease, Phys Med Biol, 66, 24, (2021); Thibault G., Angulo J., Meyer F., Advanced statistical matrices for texture characterization: application to cell classification, IEEE Trans Biomed Eng, 61, 3, pp. 630-637, (2014); Galloway M.M., Texture analysis using gray level run lengths, Comput Graph Image Process [Internet], 4, 2, pp. 172-179, (1975); Amadasun M., King R., Textural features corresponding to textural properties, IEEE Trans Syst Man Cybernet, 19, 5, pp. 1264-1274, (1989); Sun C., Wee W.G., Neighboring gray level dependence matrix for texture classification, Comput Graph Image Process [Internet], 20, 3, (1982); Lundberg S.M., Lee S.I., A unified approach to interpreting model predictions, Adv Neural Inf Process Syst [Internet], (2017)","M. Kirby; Toronto Metropolitan University, Toronto, Canada; email: Miranda.Kirby@torontomu.ca","","Elsevier Inc.","","","","","","10766332","","ARADF","39191563","English","Acad. Radiol.","Article","Final","","Scopus","2-s2.0-85202175420"
"Liu J.; Bo N.; Zhou X.; Forno E.; Ding Y.","Liu, Jiaqian (59298622700); Bo, Na (57205332407); Zhou, Xueping (59298473900); Forno, Erick (25225322900); Ding, Ying (56389888100)","59298622700; 57205332407; 59298473900; 25225322900; 56389888100","PREDICTING PEDIATRIC ASTHMA SEVERE OUTCOMES USING MACHINE LEARNING METHODS FOR EHR DATA WITH REPEATED CLINIC VISITS","2024","Journal of Statistical Research","58","1","","131","149","18","0","10.3329/jsr.v58i1.75419","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202054074&doi=10.3329%2fjsr.v58i1.75419&partnerID=40&md5=b4317f6239d03c979e35f1553ad1157d","Department of Biostatistics, University of Pittsburgh, United States; Department of Pediatrics, Indiana University, United States","Liu J., Department of Biostatistics, University of Pittsburgh, United States; Bo N., Department of Biostatistics, University of Pittsburgh, United States; Zhou X., Department of Biostatistics, University of Pittsburgh, United States; Forno E., Department of Pediatrics, Indiana University, United States; Ding Y., Department of Biostatistics, University of Pittsburgh, United States","Asthma is the most common multifactorial chronic disease among children. Identifying children at high risk of severe asthma outcomes, such as emergency department (ED) visits and hospitalizations due to asthma exacerbation, is essential in asthma care and clinical management. Existing studies have employed different machine learning methods to predict pediatric asthma occurrence or progression using electronic health records (EHR) data. However, these studies often neglected the correlated nature of EHR data (e.g., repeated clinic visits of the same patients). To address this issue, this research applied and evaluated two types of machine learning-based methods for longitudinal or clustered data, including random forests with mixed effects and generalized neural networks with mixed effects. We applied these methods to the real-world large asthma EHR data obtained from the Children’s Hospital of Pittsburgh in a four-year period expanded from pre to post-COVID-19 pandemic, focusing on predicting the chance of having ED visits due to asthma exacerbation and the length of stay (LOS) when hospitalized. Moreover, we characterized the importance of predictors using the kernel SHAP metric and identified vulnerable patient groups that are more likely to experience asthma exacerbation or have a longer LOS. Our findings provide valuable guidance to improve pediatric asthma care by prioritizing the protection of these vulnerable patients, especially when a disruptive health crisis occurs. © Institute of Statistical Research and Training (ISRT), University of Dhaka, Dhaka 1000, Bangladesh.","COVID-19 pandemic; EHR data; mixed effects model; neural networks; pediatric asthma; random forests","","","","","","Indiana Clinical and Translational Sciences Institute, CTSI; University of Pittsburgh","This work has been supported by the CTSI Public Health Trans-Disciplinary Collaboration Pilot grant by the University of Pittsburgh, USA.","Ahlem Hajjem F. B., Larocque D., Mixed-effects random forest for clustered data, Journal of Statistical Computation and Simulation, 84, pp. 1313-1328, (2014); AlSaad R., Malluhi Q., Janahi I., Boughorbel S., Predicting emergency department utilization among children with asthma using deep learning models, Healthcare Analytics, 2, (2022); Das L. T., Abramson E. L., Stone A. E., Kondrich J. E., Kern L. M., Grinspan Z. M., “Predicting frequent emergency department visits among children with asthma using EHR data,”, Pediatric Pulmonology, 52, pp. 880-890, (2017); Fokkema M., Smits N., Zeileis A., Hothorn T., Kelderman H., Detecting treatmentsubgroup interactions in clustered data with generalized linear mixed-effects model trees, Behavior research methods, 50, pp. 2016-2034, (2018); Fontana L., Masci C., Ieva F., Paganoni A. M., Performing Learning Analytics via Generalised Mixed-Effects Trees, Data, 6, (2021); Hajjem A., Bellavance F., Larocque D., “Mixed effects regression trees for clustered data,”, Statistics & Probability Letters, 81, pp. 451-459, (2011); Hajjem A., Larocque D., Bellavance F., “Generalized mixed effects regression trees,”, Statistics & Probability Letters, 126, pp. 114-118, (2017); Lizzo J. M., Cortes S., Pediatric asthma, StatPearls, (2019); Lundberg S. M., Lee S.-I., A unified approach to interpreting model predictions, Advances in neural information processing systems, 30, (2017); Maity T. K., Pal A. K., Subject specific treatment to neural networks for repeated measures analysis, Proc Int MultiConf Eng Comput Sci, 1, pp. 60-65, (2013); Mandel F., Ghosh R. P., Barnett I., Neural Networks for Clustered and Longitudinal Data Using Mixed Effects Models, Biometrics, 79, (2021); Patel S. J., Chamberlain D. B., Chamberlain J. M., “A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage,”, Academic Emergency Medicine, 25, pp. 1463-1470, (2018); Pellagatti M., Masci C., Ieva F., Paganoni A. M., “Generalized mixed-effects random forest: A flexible approach to predict university student dropout, Statistical Analysis and Data Mining: The ASA Data Science Journal, 14, pp. 241-257, (2021); Sela R. J., Simonoff J. S., RE-EM trees: a data mining approach for longitudinal and clustered data, Machine learning, 86, pp. 169-207, (2012); Sills M. R., Ozkaynak M., Jang H., Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning, International Journal of Medical Informatics, 151, (2021); Simchoni G., Rosset S., Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks, Advances in Neural Information Processing Systems, 34, pp. 25111-25122, (2021); Speiser J. L., Wolf B. J., Chung D., Karvellas C. J., Koch D. G., Durkalski V. L., BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes, Chemometrics and Intelligent Laboratory Systems, 185, pp. 122-134, (2019); Tandon R., Adak S., Kaye J. A., “Neural networks for longitudinal studies in Alzheimer’s disease, Artificial intelligence in medicine, 36, pp. 245-255, (2006); Wang X., Wang Z., Pengetnze Y. M., Lachman B. S., Chowdhry V., Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits, (2019); Wortwein T., Allen N. B., Sheeber L. B., Auerbach R. P., Cohn J. F., Morency L.-P., Neural Mixed Effects for Nonlinear Personalized Predictions, Proceedings of the 25th International Conference on Multimodal Interaction, pp. 445-454, (2023)","Y. Ding; Department of Biostatistics, University of Pittsburgh, United States; email: yingding@pitt.edu","","Institute of Statistical Research and Training (ISRT), University of Dhaka","","","","","","0256422X","","","","English","J. Stat. Res.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85202054074"
"Nkuhairwe I.N.; Esterhuizen T.M.; Sigwadhi L.N.; Tamuzi J.L.; Machekano R.; Nyasulu P.S.","Nkuhairwe, Ivan Nicholas (58686853800); Esterhuizen, Tonya Marianne (6506241847); Sigwadhi, Lovemore Nyasha (57220593371); Tamuzi, Jacques Lukenze (57210897062); Machekano, Rhoderick (6602151366); Nyasulu, Peter S. (36700333500)","58686853800; 6506241847; 57220593371; 57210897062; 6602151366; 36700333500","Estimating the causal effect of dexamethasone versus hydrocortisone on the neutrophil- lymphocyte ratio in critically ill COVID-19 patients from Tygerberg Hospital ICU using TMLE method","2024","BMC Infectious Diseases","24","1","1365","","","","0","10.1186/s12879-024-10112-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211114141&doi=10.1186%2fs12879-024-10112-w&partnerID=40&md5=ede6a1ade8a5a9e13ce0afdaa6717a27","Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa; Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Biostatistics Research Group (BRG), Mikro Park, Kuilsriver, Cape Town, South Africa","Nkuhairwe I.N., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa; Esterhuizen T.M., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa; Sigwadhi L.N., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa, Biostatistics Research Group (BRG), Mikro Park, Kuilsriver, Cape Town, South Africa; Tamuzi J.L., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa; Machekano R., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa; Nyasulu P.S., Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, Cape Town, 7500, South Africa, Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa","Background: Causal inference from observational studies is an area of interest to researchers, advancing rapidly over the years and with it, the methods for causal effect estimation. Among them, Targeted Maximum Likelihood estimation (TMLE) possesses arguably the most outstanding statistical properties, and with no outright treatment for COVID-19, there was an opportunity to estimate the causal effect of dexamethasone versus hydrocortisone upon the neutrophil-lymphocyte ratio (NLR), a vital indicator for disease progression among critically ill COVID-19 patients. Methods: TMLE variations were used in the analysis. Super Learner (SL), Bayesian Additive Regression Trees (BART) and parametric regression (PAR) were implemented to estimate the average treatment effect (ATE). Results: The study had 168 participants, 128 on dexamethasone and 40 on hydrocortisone. The mean causal difference in NLR on day 5; ATE [95% CI]: from SL-TMLE was − 0.309 [-3.800, 3.182] BART-TMLE 0.246 [-3.399, 3.891] and PAR-TMLE 1.245 [-1.882, 4372]. The ATE of dexamethasone versus hydrocortisone on NLR was not statistically significant since the confidence interval included zero. Conclusion: The effect of dexamethasone is not significantly different from that of hydrocortisone on NLR in critically ill COVID-19 patients admitted to ICU. This implies that the difference in effect on NLR between the two drugs is due to random chance. TMLE remains an outstanding approach for causal analysis of observational studies with the ability to be augmented with multiple prediction approaches. © The Author(s) 2024.","Corticosteroids; COVID-19; Dexamethasone; Hydrocortisone; Neutrophil-lymphocyte ratio; Targeted learning; Targeted maximum likelihood estimation","Adult; Aged; Anti-Inflammatory Agents; Bayes Theorem; COVID-19; COVID-19 Drug Treatment; Critical Illness; Dexamethasone; Female; Humans; Hydrocortisone; Intensive Care Units; Likelihood Functions; Lymphocytes; Male; Middle Aged; Neutrophils; SARS-CoV-2; dexamethasone; hydrocortisone; antiinflammatory agent; dexamethasone; hydrocortisone; adult; Article; asthma; Bayesian additive regression tree; chronic kidney failure; cohort analysis; coronavirus disease 2019; critically ill patient; diabetes mellitus; disease exacerbation; female; human; Human immunodeficiency virus infection; hyperlipidemia; hypertension; intensive care unit; machine learning; male; maximum likelihood method; middle aged; neutrophil lymphocyte ratio; observational study; retrospective study; super learner; aged; Bayes theorem; blood; coronavirus disease 2019; COVID-19 pharmacotherapy; critical illness; drug effect; immunology; lymphocyte; neutrophil; Severe acute respiratory syndrome coronavirus 2; statistical model","","dexamethasone, 50-02-2; hydrocortisone, 50-23-7; Anti-Inflammatory Agents, ; Dexamethasone, ; Hydrocortisone, ","R  software  version  4.2.0","","Division of Epidemiology and Biostatistics; Universiteit Stellenbosch, US","I would like to acknowledge the Division of Epidemiology and Biostatistics, Stellenbosch University for the opportunity to pursue my master\u2019s degree. Secondly, I would like to thank Professor Tonya Esterhuizen, Lovemore Nyasha and Professor Rhoderick Machekano for the expert input, knowledge and guidance given to me towards this research work.","Rothman K.J., Greenland S., Causation and Causal Inference in Epidemiology. 95. Epub ahead of Print 10, (2011); Hernan M., Robins J., Causalinference: What If, (2020); Schuler M.S., Rose S., Targeted maximum likelihood estimation for causal inference in observational studies, Am J Epidemiol, 185, pp. 65-73, (2017); Pinzon M.A., Ortiz S., Holguin H., Dexamethasone vs methylprednisolone high dose for Covid-19 pneumonia, Plos One, 16; Du Plessis E.M., Lalla U., Allwood B.W., Et al., Corticosteroids in critical COVID-19: are all corticosteroids equal?, South Afr Med J, 111, pp. 550-553, (2021); Mehta P., McAuley D.F., Brown M., Et al., COVID-19: consider cytokine storm syndromes and immunosuppression, Lancet, 395, pp. 1033-1034, (2020); Chen H., Xie J., Su N., Et al., Corticosteroid therapy is Associated with Improved Outcome in critically ill patients with COVID-19 with Hyperinflammatory phenotype, Chest, 159, pp. 1793-1802, (2021); Crisan Dabija R., Antohe I., Trofor A., Et al., Corticosteroids in SARS-COV2 infection: certainties and uncertainties in clinical practice, Expert Rev Anti Infect Ther, 19, pp. 1553-1562, (2021); Welte T., Ambrose L.J., Sibbring G.C., Et al., Current Evidence for COVID-19 Therapies: A Systematic Literature Review, (2021); Liu L., Zheng Y., Cai L., Et al., Neutrophil-to-lymphocyte ratio, a critical predictor for assessment of disease severity in patients with COVID-19, Int J Lab Hematol, 43, pp. 329-335, (2021); Cai J., Li H.H., Zhang C., Et al., The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19, Cell Metab, 33, pp. 258-e2693, (2021); Liu Y., Du X., Chen J., Et al., Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19, J Infect, 81, pp. e6-e12, (2020); Li X., Liu C., Mao Z., Et al., Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis, Crit Care, 24, (2020); Allocation of Scarce Critical Care Resources During the COVID-19 Public Health Emergency in South Africa; Zemlin A.E., Allwood B., Erasmus R.T., Et al., Prognostic value of biochemical parameters among severe COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa, IJID Reg, 2, pp. 191-197, (2022); Chapanduka Z.C., Abdullah I., Allwood B., Et al., Haematological predictors of poor outcome among COVID-19 patients admitted to an intensive care unit of a tertiary hospital in South Africa, PLoS ONE, 17, (2022); van Der Laan M.J., Rose S., Targeted Learning - Preface, (2011); Ren J., Cislo P., Cappelleri J.C., Et al., Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study, BMC Med Res Methodol, 23, pp. 1-11, (2023); Rosenblum M., Laander M., Estimating Causal Eff Using Target Maximum Likelihood Estimation, pp. 1-6, (2010); van Der Laan M., Coyle J., Hejazi N., Et al., Introduction | Targeted Learning in R. 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Tmle3: The Extensible TMLE Framework, Epub ahead of Print 2024; van Buuren S., Groothuis-Oudshoorn K., Mice: multivariate imputation by chained equations in R, J Stat Softw, 45, pp. 1-67, (2011); R: A Language and Environment for Statistical Computing, (2024); RStudio: Integrated Development Environment for R, Posit Team., (2024); Ifferent Steroid Replacement Medications - CSRF - Cushing’s Support & Research Foundation; Lo Y.T., Lim V.Y., Ng M., Et al., A Prognostic Model Using Post-Steroid Neutrophil-Lymphocyte Ratio Predicts Overall Survival in Primary Central Nervous System Lymphoma. Cancers (Basel); 14. Epub ahead of Print 2022; Dorie V., Hill J., Shalit U., Et al., Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition, Stat Sci, 34, pp. 43-68, (2017); Sullivan T.R., Lee K.J., Ryan P., Et al., Multiple imputation for handling missing outcome data when estimating the relative risk, BMC Med Res Methodol, 17, pp. 1-10, (2017); Berkeley U.C., Gruber S., van Der Laan M.J., Targeted Maximum Likelihood Estimation: A Gentle Introduction, UC Berkley Div Biostat Work Pap Ser, (2009)","P.S. Nyasulu; Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, 3rd Floor, Education Building, Francie Van Zijl Drive, Parow, 7500, South Africa; email: pnyasulu@sun.ac.za","","BioMed Central Ltd","","","","","","14712334","","BIDMB","39609735","English","BMC Infect. Dis.","Article","Final","","Scopus","2-s2.0-85211114141"
"Anita G.; Singarapu S.","Anita, Gurijala (59449373300); Singarapu, Sunil (59377640600)","59449373300; 59377640600","Automated Detection and Classification of Pneumonia using Deep Learning and Convolutional Neural Networks","2025","Journal of Intelligent Systems and Internet of Things","14","2","","91","102","11","0","10.54216/JISIoT.140208","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210595041&doi=10.54216%2fJISIoT.140208&partnerID=40&md5=a822363f83db1895477bcde97b12b3f3","Department of ECE, Chaitanya deemed to be University, Hyderabad, India","Anita G., Department of ECE, Chaitanya deemed to be University, Hyderabad, India; Singarapu S., Department of ECE, Chaitanya deemed to be University, Hyderabad, India","Lung disease is considerable deprivation from health standpoint. These include chronic obstructive pulmonary illnesses, asthma, lung fibrosis, lung parenchyma illnesses, and tuberculosis among others. It is highly critical in the early phase of lung illnesses when they are the most treatable. Many of these were made for the purpose of applying machine learning and image processing. Many types of DL methods including CNN, VNN, VGG networks, capsule networks are used during lung illness prediction process. Following the release of the book on Pandemic Covid-19, many projects have been carried out at international level intending to study the feasibility of such work for prediction of future events. Pneumonia is a lung infection that starts earlier in the disease course and is closely associated with the virus (pneumonia condition), which was responsible for considerable chest infection in some covid-positive individuals. While doctors are no strangers to lung diseases and their complicated nature, many will find it difficult in some of them to make distinctions between common pneumonia and the Covid-19. X-ray imaging of the chest provides the highest degree of accuracy in suffem lung diseases. In this work, a novel approach for the calculation of lung illnesses such as pneumonia and COVID-19 is proposed. The data source for this method is Chest X-ray pictures taken from patients. The system includes characteristics such as the extraction of features, the prediction of illnesses, and the precise and adaptive evaluation of ROI, the collecting of datasets, and the enhancement of image quality. In future, this research can be extended with IOT devices for the recognition of COVID-19 and pneumonia. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.","Classification; CNN; DL; Hybrid Clustering; Internet of Things; Lung Illnesses; Pneumonia","","","","","","","","Bharati S., Podder P., Mondal R., Mahmood A., Raihan-Al-masud M., Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer, Advances in Intelligent Systems and Computing, 941, pp. 447-457, (2020); Coudray N., Ocampo P.S., Sakellaropoulos T., Et al., Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nat Med, 24, pp. 1559-1567, (2018); Mondal M.R.H., Bharati S., Podder P., Podder P., Data Analytics for Novel Coronavirus Disease"", Informatics in Medicine Unlocked, 20, (2020); Kuan K., Ravaut M., Manek G., Chen H., Lin J., Nazir B., Chen C., Howe T.C., Zeng Z., Chandrasekhar V., Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge, (2017); Sun W., Zheng B., Qian W., Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis, Comput Biol Med, 89, pp. 530-539, (2017); Song Q., Zhao L., Luo X., Dou X., Using deep learning for classification of lung nodules on computed tomography images, Journal of Healthcare Engineering, (2017); Sun W., Zheng B., Qian W., Computer aided lung cancer diagnosis with deep learning algorithms, Proc SPIE. Medical Imaging, (2016); NIH Sample Chest X-Rays Dataset; Abbas A., Abdelsamea M.M., Gaber M.M., Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, Appl Intell, (2020); Abiyev R.H., Maaitah M.K.S., Deep convolutional neural networks for chest diseases detection, J Healthc Eng, (2018); Angeline R., Mrithika M., Raman A., Warrier P., Pneumonia detection and classification using chest X-ray images with convolutional neural network, New Trends in Computational Vision and Bio-Inspired Computing. ICCVBIC, (2020); Apostolopoulos I.D., Mpesiana A., Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys Eng Sci Med, 43, pp. 635-640, (2020); Asuntha A., Srinivasan A., Deep learning for lung cancer detection and classification, Multimed Tools Appl, (2020); Avni U., Greenspan H., Konen E., Sharon M., Goldberger J., X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words, IEEE Trans Med Imaging, 30, 3, pp. 733-746, (2011); Bentivegna E., Luciani M., Spuntarelli V., Speranza M.L., Guerritore L., Sentimentale A., Martelletti P., Extremely severe case of COVID-19 pneumonia recovered despite bad prognostic indicators: A didactic report, SN Compr Clin Med, 2, pp. 1204-1207, (2020); Butt C., Gill J., Chun D., Babu B.A., Deep learning system to screen coronavirus disease 2019 pneumonia, Appl Intell, (2020); Chen X., Laurent S., Onur O.A., Kleineberg N.N., Fink G.R., Schweitzer F., Warnke C., A systematic review of neurological symptoms and complications of COVID-19, J Neurol, (2021); Dansana D., Kumar R., Bhattacharjee A., Hemanth D.J., Gupta D., Khanna A., Castillo A., Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm, Soft Comput, (2020)","G. Anita; Department of ECE, Chaitanya deemed to be University, Hyderabad, India; email: gurijalaanitha.cdu@gmail.com","","American Scientific Publishing Group (ASPG)","","","","","","2769786X","","","","English","J. Intell. Syst. Internet. Thing.","Article","Final","","Scopus","2-s2.0-85210595041"
"Zhao M.; Wu Y.; Li Y.; Zhang X.; Xia S.; Xu J.; Chen R.; Liang Z.; Qi S.","Zhao, Meng (59442050400); Wu, Yanan (57394724800); Li, Yifu (58888903100); Zhang, Xiaoyu (59185096400); Xia, Shuyue (7202893268); Xu, Jiaxuan (57861994500); Chen, Rongchang (14017626800); Liang, Zhenyu (36010749700); Qi, Shouliang (36572483500)","59442050400; 57394724800; 58888903100; 59185096400; 7202893268; 57861994500; 14017626800; 36010749700; 36572483500","Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images","2024","BMC Pulmonary Medicine","24","1","294","","","","0","10.1186/s12890-024-03109-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196714741&doi=10.1186%2fs12890-024-03109-3&partnerID=40&md5=2583c44153d9ae93c9a49fb3df4585ef","College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People’s Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China","Zhao M., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Wu Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Li Y., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Zhang X., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Xia S., Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China; Xu J., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Chen R., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, Key Laboratory of Respiratory Disease of Shenzhen, Shenzhen Institute of Respiratory Disease, Shenzhen People’s Hospital (Second Affiliated Hospital of Jinan University, First Affiliated Hospital of South University of Science and Technology of China), Shenzhen, China; Liang Z., State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Qi S., College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China, Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China","Background: Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. Methods: The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. Results: 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. Conclusions: The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole. © The Author(s) 2024.","Chronic obstructive pulmonary disease; Computed tomography; Pulmonary lobe; Radiomics; Severity staging","Aged; Female; Humans; Lung; Male; Middle Aged; Neural Networks, Computer; Pulmonary Disease, Chronic Obstructive; Radiomics; Retrospective Studies; Severity of Illness Index; Support Vector Machine; Tomography, X-Ray Computed; accuracy; aged; algorithm; area under the curve; Article; artificial neural network; chronic obstructive lung disease; classifier; controlled study; decision making; diagnostic accuracy; diagnostic test accuracy study; disease severity; female; forced expiratory volume; forced vital capacity; human; image segmentation; learning; least absolute shrinkage and selection operator; low-dose computed tomography; lung lobe; machine learning; major clinical study; male; prevalence; radiomics; receiver operating characteristic; retrospective study; sensitivity and specificity; staging; supervised machine learning; support vector machine; treatment planning; classification; diagnostic imaging; lung; middle aged; pathology; procedures; radiomics; severity of illness index; support vector machine; x-ray computed tomography","","","","","National Natural Science Foundation of China, NSFC, (82072008, 82270044); National Natural Science Foundation of China, NSFC; Fundamental Research Funds for the Central Universities, (N2424010-19); Fundamental Research Funds for the Central Universities","This work was partly supported by the National Natural Science Foundation of China (Nos. 82072008, 82270044), and the Fundamental Research Funds for the Central Universities (N2424010-19). ","Roth G.A., Abate D., Abate K.H., Et al., Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of Disease Study 2017, Lancet, 392, pp. 1736-1788, (2018); Halpin D.M.G., Criner G.J., Papi A., Et al., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. 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Qi; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; email: qisl@bmie.neu.edu.cn; Z. Liang; State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; email: 490458234@qq.com","","BioMed Central Ltd","","","","","","14712466","","BPMMB","38915049","English","BMC Pulm. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196714741"
"Oviesi S.; Tarokh M.J.; Momeni M.K.","Oviesi, Safura (57218326209); Tarokh, Mohamad Jafar (14036613400); Momeni, Mohamad kazem (57221044648)","57218326209; 14036613400; 57221044648","Quantum neural network-assisted learning for small medical datasets: a case study in emphysema detection","2025","Journal of Supercomputing","81","1","308","","","","0","10.1007/s11227-024-06740-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212440777&doi=10.1007%2fs11227-024-06740-3&partnerID=40&md5=0b7ef2901eb698a664303e4496aeb9c2","Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran; Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran","Oviesi S., Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran; Tarokh M.J., Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran; Momeni M.K., Faculty of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran","Advancement in AI and deep learning has transformed medical image diagnosis; however, approaches to disease diagnosis, such as the detection of emphysema, one of the severest forms of COPD, stand to benefit from these technologies more. The main problem with the detection of emphysema from CT scans is the lack of very large, annotated datasets to train deep learning models. Classic models, like CNNs, are usually underfitting to small datasets and hence have very poor diagnostic accuracy. To address this challenge, we consider an integrated hybrid quantum–classical neural network model by combining the quantum variational circuits with CNNs. This new approach uses the power of quantum computing to identify subtle patterns in small datasets and could solve one of the key problems of deep learning. The model is pre-trained on large chest X-ray datasets and fine-tuned on a smaller emphysema dataset, which allows it to generalize more when data is limited. The experimental results confirm that the proposed approach is effective; namely, the quantum-assisted model reaches an accuracy of 0.5690 and F1-score of 0.5990, outperforming the traditional CNN models. This work points to the novelty of quantum computing in diagnosis with limited amounts of data, a very important challenge in this area of medical AI. Given that our research will conform to the limitation and work on small datasets, this work opens a new frontier in medical image analysis and shows ways in which QNN can substantially outperform traditional methods in detecting subtle markers of diseases; this indeed contributes to the growing body of knowledge in quantum-enhanced AI and opens up new frontiers toward some potential applications in the field of diagnosis of rare diseases and health diagnostics. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.","Emphysema; Machine learning; Medical image; Quantum neural network","Computerized tomography; Deep learning; Diagnosis; Oncology; Positron emission tomography; Quantum electronics; Case-studies; Disease diagnosis; Emphysema; Machine-learning; Medical data sets; Medical image; Medical image diagnosis; Quantum Computing; Quantum neural networks; Small data set; Diseases","","","","","","","Mall P.K., Singh P.K., Srivastav S., Narayan V., Paprzycki M., Jaworska T., Ganzha M., A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities, Healthcare Analytics, (2023); Houshmand M., Khorrampanah M., Alkhudhari A.H.M., Optimized quantum computing technique to encrypt medical images, Opt Quant Electron, 56, 3, (2024); Varoquaux G., Cheplygina V., Machine learning for medical imaging: methodological failures and recommendations for the future, NPJ Digital Med, 5, 1, (2022); Davila A., Colan J., Hasegawa Y., Comparison of fine-tuning strategies for transfer learning in medical image classification, Image Vis Comput, 146, (2024); Ali H., Wang M., Xie J., Cil-net: Densely connected context information learning network for boosting thyroid nodule segmentation using ultrasound images, Cogn Comput, 16, 3, pp. 1176-1197, (2024); Zhong Y., Liu Y., Gao E., Wei C., Wang Z., Yan C., Deep learning solutions for pneumonia detection: Performance comparison of custom and transfer learning models., pp. 2024-2106, (2024); Ali H., Wang M., Xie J., Emtl-net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy, Eng Sci Technol Intern J, 51, (2024); Ali H., Haq I.U., Cui L., Feng J., Msal-net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network, BMC Med Inform Decis Mak, 22, 1, (2022); Kshatri S.S., Singh D., Convolutional neural network in medical image analysis: A review, Archiv Comput Methods Eng, 30, 4, pp. 2793-2810, (2023); Salehi A.W., Khan S., Gupta G., Alabduallah B.I., Almjally A., Alsolai H., Siddiqui T., Mellit A., A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope, Sustainability, 15, 7, (2023); Sistaninejhad B., Rasi H., Nayeri P., A review paper about deep learning for medical image analysis, Comput Math Methods Med, 2023, 1, (2023); Li M., Jiang Y., Zhang Y., Zhu H., Medical image analysis using deep learning algorithms, Front Public Health, 11, (2023); Gaur L., Bhatia U., Jhanjhi N., Muhammad G., Masud M., Medical image-based detection of covid-19 using deep convolution neural networks, Multimedia Syst, 29, 3, pp. 1729-1738, (2023); Yuan F., Zhang Z., Fang Z., An effective cnn and transformer complementary network for medical image segmentation, Pattern Recogn, 136, (2023); Ding W., Wang H., Huang J., Hengrong J., Geng Y., Lin C.-T., Pedrycz W., Ftranscnn: Fusing transformer and a cnn based on fuzzy logic for uncertain medical image segmentation, Inf Fusion, 99, (2023); Xin W., Feng Y., Hong X., Lin Z., Chen T., Li S., Qiu S., Liu Q., Ma Y., Zhang S., Ctranscnn: Combining transformer and cnn in multilabel medical image classification, Knowl-Based Syst, 281, (2023); Ashwath V.A., Sikha O.K., Raul B., Ts-cnn: A three-tier self-interpretable cnn for multi-region medical image classification, IEEE Access, (2023); Krichen M., Convolutional neural networks: A survey, Computers, 12, 8, (2023); Derry A., Krzywinski M., Altman N., Convolutional neural networks, Nat Methods, 20, 9, pp. 1269-1270, (2023); Atasever S., Azginoglu N., Terzi D.S., Terzi R., A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning, Clin Imaging, 94, pp. 18-41, (2023); Ozturk C., Tasyurek M., Turkdamar M.U., Transfer learning and fine-tuned transfer learning methods’ effectiveness analyse in the cnn-based deep learning models, Concurr Comput Practice Exp, 35, 4, (2023); Athar A., Asif R.N., Saleem M., Munirmral S., Momani AM (2023) Improving pneumonia detection in chest x-rays using transfer learning approach (alexnet) and adversarial training, . In: 2023 International Conference on Business Analytics for Technology and Security (ICBATS), pp. 1-7; Kundur N.C., Anil B.C., Dhulavvagol P.M., Ganiger R., Ramadoss B., Pneumonia detection in chest x-rays using transfer learning and tpus, Eng Technol Appl Sci Res, 13, 5, pp. 11878-11883, (2023); Schuld M., Sinayskiy I., Petruccione F., An introduction to quantum machine learning, Contemp Phys, 56, 2, pp. 172-185, (2015); Biamonte J., Wittek P., Pancotti N., Rebentrost P., Wiebe N., Lloyd S., Quantum machine learning, Nature, 549, 7671, pp. 195-202, (2017); Ciliberto C., Herbster M., Ialongo A.D., Pontil M., Rocchetto A., Severini S., Wossnig L., Quantum machine learning: a classical perspective, Proc Royal Soc A Math Phys Eng Sci, 474, 2209, (2018); Gupta S., Zia R.K.P., Quantum neural networks, J Comput Syst Sci, 63, 3, pp. 355-383, (2001); Suchara M., Alexeev Y., Chong F., Finkel H., Hoffmann H., Larson J., Osborn J., Smith G., Hybrid Quantum-Classical Computing Architectures., (2018); Gong L.-H., Pei J.-J., Zhang T.-F., Zhou N.-R., Quantum convolutional neural network based on variational quantum circuits, Optics Commun, 550, (2024); Cerezo M., Arrasmith A., Babbush R., Benjamin S.C., Endo S., Fujii K., McClean J.R., Mitarai K., Yuan X., Cincio L., Et al., Variational quantum algorithms, Nature Rev Phys, 3, 9, pp. 625-644, (2021); Liu J., Lim K.H., Wood K.L., Huang W., Guo C., Huang H.-L., Hybrid quantum-classical convolutional neural networks, Sci China Phys Mech Astron, 64, 9, (2021); Arthur D., Date P., Hybrid quantum-classical neural networks, 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 49-55, (2022); Fan F., Shi Y., Guggemos T., Zhu Xiao X., Hybrid quantum-classical convolutional neural network model for image classification, IEEE Transactions on Neural Networks and Learning Systems, (2023); Li W., Chu P.-C., Liu G.-Z., Tian Y.-B., Qiu T.-H., Wang S.-M., An image classification algorithm based on hybrid quantum classical convolutional neural network, Quantum Eng, 2022, 1, (2022); Ren Z., Lan Q., Zhang Y., Wang S., Exploring simple triplet representation learning, Comput Struct Biotechnol J, 23, pp. 1510-1521, (2024); Ren Z., Zhang Y., Wang S., A hybrid framework for lung cancer classification, Electronics, 11, 10, (2022); Cao H., Wang Y., Chen J., Jiang D., Zhang X., Tian Q., Wang M., Swin-unet: Unet-like pure transformer for medical image segmentation, European Conference on Computer Vision, pp. 205-218, (2022); Perera S., Navard P., Yilmaz A., Segformer3d: An efficient transformer for 3d medical image segmentation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4981-4988, (2024); Kermany D.S., Goldbaum M., Cai W., Valentim C.C.S., Liang H., Baxter S.L., McKeown A., Yang G., Wu X., Yan F., Et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, 5, pp. 1122-1131, (2018); Sorensen L., Shaker S.B., De Bruijne M., Quantitative analysis of pulmonary emphysema using local binary patterns, IEEE Trans Med Imaging, 29, 2, pp. 559-569, (2010); Shaker S.B., von Wachenfeldt K.A., Larsson S., Mile I., Persdotter S., Dahlback M., Broberg P., Stoel B., Bach K.S., Hestad M., Et al., Identification of patients with chronic obstructive pulmonary disease (copd) by measurement of plasma biomarkers, Clin Respir J, 2, 1, pp. 17-25, (2008); Ioffe S., Szegedy C., Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning, pp. 448-456, (2015); Glorot X., Bengio Y., Understanding the difficulty of training deep feedforward neural networks, In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249-256, (2010); Ansel J., Yang E., He H., Gimelshein N., Jain A., Voznesensky M., Bao B., Bell P., Berard D., Burovski E., Et al., Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation, In: Proceedings of the 29Th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2, pp. 929-947, (2024); Javadi-Abhari A., Treinish M., Krsulich K., Wood C.J., Lishman J., Gacon J., Martiel S., Nation P.D., Bishop L.S., Cross A.W., Johnson B.R., Gambetta J.M., Quantum Computing with Qiskit, (2024)","M.J. Tarokh; Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran; email: mjtarokh@kntu.ac.ir","","Springer","","","","","","09208542","","JOSUE","","English","J Supercomput","Article","Final","","Scopus","2-s2.0-85212440777"
"Gore S.; Meche B.; Shao D.; Ginnett B.; Zhou K.; Azad R.K.","Gore, Steven (57737421300); Meche, Bailey (58928209800); Shao, Danyang (58636756600); Ginnett, Benjamin (58928364800); Zhou, Kelly (58928518300); Azad, Rajeev K. (7007181772)","57737421300; 58928209800; 58636756600; 58928364800; 58928518300; 7007181772","DiseaseNet: a transfer learning approach to noncommunicable disease classification","2024","BMC Bioinformatics","25","1","107","","","","0","10.1186/s12859-024-05734-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187216385&doi=10.1186%2fs12859-024-05734-5&partnerID=40&md5=3928a6a86556acb883c7f7a176bc9f1b","Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, United States; Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, United States; Department of Engineering, Eastern Arizona College, Thatcher, AZ, United States; Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States","Gore S., Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, United States; Meche B., Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, United States; Shao D., Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, United States; Ginnett B., Department of Engineering, Eastern Arizona College, Thatcher, AZ, United States; Zhou K., Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States; Azad R.K., Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, TX, United States","As noncommunicable diseases (NCDs) pose a significant global health burden, identifying effective diagnostic and predictive markers for these diseases is of paramount importance. Epigenetic modifications, such as DNA methylation, have emerged as potential indicators for NCDs. These have previously been exploited in other contexts within the framework of neural network models that capture complex relationships within the data. Applications of neural networks have led to significant breakthroughs in various biological or biomedical fields but these have not yet been effectively applied to NCD modeling. This is, in part, due to limited datasets that are not amenable to building of robust neural network models. In this work, we leveraged a neural network trained on one class of NCDs, cancer, as the basis for a transfer learning approach to non-cancer NCD modeling. Our results demonstrate promising performance of the model in predicting three NCDs, namely, arthritis, asthma, and schizophrenia, for the respective blood samples, with an overall accuracy (f-measure) of 94.5%. Furthermore, a concept based explanation method called Testing with Concept Activation Vectors (TCAV) was used to investigate the importance of the sample sources and understand how future training datasets for multiple NCD models may be improved. Our findings highlight the effectiveness of transfer learning in developing accurate diagnostic and predictive models for NCDs. © The Author(s) 2024.","Classification; Deep learning; DNA methylation; Machine learning; Noncommunicable diseases; Transfer learning","Humans; Machine Learning; Neural Networks, Computer; Noncommunicable Diseases; Alkylation; Deep learning; Diagnosis; Learning systems; Methylation; Neural network models; Deep learning; Disease classification; Disease models; DNA Methylation; Learning approach; Machine-learning; Neural network model; Neural-networks; Non-communicable disease; Transfer learning; artificial neural network; human; machine learning; non communicable disease; Diseases","","","","","","","Vos T., Lim S.S., Abbafati C., Et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019, Lancet, 396, pp. 1204-1222, (2020); Roth G.A., Global burden of disease collaborative network: global burden of disease study 2017 (GBD 2017) results, Lancet, 392, pp. 1736-1788, (2018); Walton E., Hass J., Liu J., Et al., Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research, Schizophr Bull, 42, 2, pp. 406-414, (2016); Wockner L.F., Noble E.P., Lawford B.R., Et al., Genome-wide DNA methylation analysis of human brain tissue from schizophrenia patients, Transl Psychiatry, 4, 1, (2014); Grayson D.R., Guidotti A., The dynamics of DNA methylation in schizophrenia and related psychiatric disorders, Neuropsychopharmacology, 38, 1, pp. 138-166, (2013); Hanson M., Godfrey K.M., Lillycrop K.A., Burdge G.C., Gluckman P.D., Developmental plasticity and developmental origins of non-communicable disease: theoretical considerations and epigenetic mechanisms, Prog Biophys Mol Biol, 106, 1, pp. 272-280, (2011); Liu L., Wu J., Qing L., Et al., DNA methylation analysis of the NR3C1 gene in patients with schizophrenia, J Mol Neurosci, 70, 8, pp. 1177-1185, (2020); Yang I.V., Pedersen B.S., Liu A., Et al., DNA methylation and childhood asthma in the inner city, J Allergy Clin Immunol, 136, 1, pp. 69-80, (2015); Auta J., Smith R.C., Dong E., Et al., DNA-methylation gene network dysregulation in peripheral blood lymphocytes of schizophrenia patients, Schizophr Res, 150, 1, pp. 312-318, (2013); Liu Y., Aryee M.J., Padyukov L., Et al., Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis, Nat Biotechnol, 31, 2, (2013); Richardson B., Scheinbart L., Strahler J., Gross L., Hanash S., Johnson M., Evidence for impaired T cell DNA methylation in systemic lupus erythematosus and rheumatoid arthritis, Arthritis Rheum, 33, 11, pp. 1665-1673, (1990); Manolio T.A., Collins F.S., Cox N.J., Et al., Finding the missing heritability of complex diseases, Nature, 461, 7265, pp. 747-753, (2009); Gunasekara C.J., Hannon E., MacKay H., Et al., A machine learning case–control classifier for schizophrenia based on DNA methylation in blood, Transl Psychiatry, 11, 1, pp. 1-10, (2021); Zhang M., Pan C., Liu H., Zhang Q., Li H., An attention-based deep learning method for schizophrenia patients classification using DNA methylation data, Arxiv:Quant-Ph, (2020); Moghadam B.T., Etemadikhah M., Rajkowska G., Et al., Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods, J Psychiatr Res, 114, pp. 41-47, (2019); Gore S., Azad R.K., CancerNet: a unified deep learning network for pan-cancer diagnostics, BMC Bioinform, 23, 1, pp. 1-17, (2022); Zhou J., Chen Q., Braun P.R., Et al., Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders, Proc Natl Acad Sci, 119, 34, (2022); Tang M., Huang T., Yang J., Guo C., Integrative multi-omics for diagnosis, treatments, and drug discovery of aging-related neuronal diseases, (2022); Mao W., Zaslavsky E., Hartmann B.M., Sealfon S.C., Chikina M., Pathway-level information extractor (PLIER) for gene expression data, Nat Methods, 16, 7, pp. 607-610, (2019); Hudon Thibeault A., Laprise C., Cell-specific DNA methylation signatures in asthma, Genes, 10, 11, (2019); Perera F., Tang W., Herbstman J., Et al., Relation of DNA methylation of 5′-CpG island of ACSL3 to transplacental exposure to airborne polycyclic aromatic hydrocarbons and childhood asthma, PLOS ONE, 4, 2, (2009); Cribbs A., Feldmann M., Oppermann U., Towards an understanding of the role of DNA methylation in rheumatoid arthritis: therapeutic and diagnostic implications, Ther Adv Musculoskelet Dis, 7, 5, pp. 206-219, (2015); Nakano K., Boyle D.L., Firestein G.S., Regulation of DNA methylation in rheumatoid arthritis synoviocytes, J Immunol, 190, 3, pp. 1297-1303, (2013); Liebold I., Grutzkau A., Gockeritz A., Et al., Peripheral blood mononuclear cells are hypomethylated in active rheumatoid arthritis and methylation correlates with disease activity, Rheumatology, 60, 4, pp. 1984-1995, (2021); Zhu H., Wu L., Mo X., Et al., Rheumatoid arthritis–associated DNA methylation sites in peripheral blood mononuclear cells, Ann Rheum Dis, 78, 1, pp. 36-42, (2019); Ai R., Hammaker D., Boyle D.L., Et al., Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes, Nat Commun, 7, 1, pp. 1-9, (2016); de la Rica L., Urquiza J.M., Gomez-Cabrero D., Et al., Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression, J Autoimmun, 41, pp. 6-16, (2013); Alfimova M.V., Kondratiev N.V., Golov A.K., Golimbet V.E., Methylation of the reelin gene promoter in peripheral blood and its relationship with the cognitive function of schizophrenia patients, Mol Biol (NY), 52, 5, pp. 676-685, (2018); Mak M., Samochowiec J., Frydecka D., Et al., First-episode schizophrenia is associated with a reduction of HERV-K methylation in peripheral blood, Psychiatry Res, 271, pp. 459-463, (2019); Nishioka M., Bundo M., Koike S., Et al., Comprehensive DNA methylation analysis of peripheral blood cells derived from patients with first-episode schizophrenia, J Hum Genet, 58, 2, pp. 91-97, (2013); Murata Y., Ikegame T., Koike S., Et al., Global DNA hypomethylation and its correlation to the betaine level in peripheral blood of patients with schizophrenia, Prog Neuropsychopharmacol Biol Psychiatry, 99, (2020); Hu M., Xia Y., Zong X., Et al., Risperidone-induced changes in DNA methylation in peripheral blood from first-episode schizophrenia patients parallel changes in neuroimaging and cognitive phenotypes, Psychiatry Res, 317, (2022); Li M., Li Y., Qin H., Et al., Genome-wide DNA methylation analysis of peripheral blood cells derived from patients with first-episode schizophrenia in the Chinese Han population, Mol Psychiatry, 26, 8, pp. 4475-4485, (2021); Gautam Y., Johansson E., Mersha T.B., Multi-omics profiling approach to asthma: an evolving paradigm, J Pers Med, 12, 1, (2022); Fikri R.M.N., Norlelawati A.T., El-Huda A.R.N., Et al., Reelin (RELN) DNA methylation in the peripheral blood of schizophrenia, J Psychiatr Res, 88, pp. 28-37, (2017); Zhuo C., Wang D., Zhou C., Et al., Double-edged sword of tumour suppressor genes in schizophrenia, Front Mol Neurosci, 12, (2019); Pan D., Kocherginsky M., Conzen S.D., Activation of the glucocorticoid receptor is associated with poor prognosis in estrogen receptor-negative breast cancer, Cancer Res, 71, 20, pp. 6360-6370, (2011); Guidotti A., Auta J., Davis J.M., Et al., Toward the identification of peripheral epigenetic biomarkers of schizophrenia, J Neurogenet, 28, 1-2, pp. 41-52, (2014); Bozinovski S., Fulgosi A., The influence of pattern similarity and transfer learning upon training of a base perceptron b2, Int J Syst Sci, 3, pp. 121-126, (1976); Dietterich T.G., Pratt L., Thrun S., Special issue on inductive transfer, Mach Learn, 28, 1, pp. 215-220, (1997); Pratt L.Y., Discriminability-based transfer between neural networks, In: Advances in Neural Information Processing Systems, 5, (1992); West J., Ventura D., Warnick S., Spring research presentation: A theoretical foundation for inductive transfer. Brigham Young University, College of Physical and Mathematical Sciences, 1, no. 08, (2007); Yosinski J., Clune J., Bengio Y., Lipson H., How transferable are features in deep neural networks, ? In: Advances in Neural Information Processing Systems, 27, (2014); Taroni J.N., Grayson P.C., Hu Q., Et al., MultiPLIER: a transfer learning framework for transcriptomics reveals systemic features of rare disease, Cell Syst, 8, 5, pp. 380-394, (2019); Chollet F., Keras, . Arxiv, 1512, (2015); Kim B., Gilmer J., Wattenberg M., Viegas F., Tcav: Relative concept importance testing with linear concept activation vectors, Arxiv, 1711, (2018); Hart S., Shapley value, Game Theory, pp. 210-216, (1989); Sohl-Dickstein J., Weiss E., Maheswaran N., Ganguli S., Deep unsupervised learning using nonequilibrium thermodynamics, Advances in Neural Information Processing Systems, pp. 2256-2265, (2015); Yang Y., Su X., Zhao B., Li G., Fuzzy-based deep attributed graph clustering, IEEE Trans Fuzzy Syst, 99, PP, pp. 1-14, (2023)","R.K. Azad; Department of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, United States; email: Rajeev.Azad@unt.edu","","BioMed Central Ltd","","","","","","14712105","","BBMIC","38468193","English","BMC Bioinform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85187216385"
"Wu C.-P.; Sleiman J.; Fakhry B.; Chedraoui C.; Attaway A.; Bhattacharyya A.; Bleecker E.R.; Erdemir A.; Hu B.; Kethireddy S.; Meyers D.A.; Rashidi H.H.; Zein J.G.","Wu, Chao-Ping (57216976563); Sleiman, Joelle (57897012400); Fakhry, Battoul (57869299700); Chedraoui, Celine (59004077000); Attaway, Amy (55630114300); Bhattacharyya, Anirban (55158685700); Bleecker, Eugene R. (7004832308); Erdemir, Ahmet (7006275388); Hu, Bo (55484497200); Kethireddy, Shravan (22938490800); Meyers, Deborah A. (35379965900); Rashidi, Hooman H. (6506820780); Zein, Joe G. (9734318000)","57216976563; 57897012400; 57869299700; 59004077000; 55630114300; 55158685700; 7004832308; 7006275388; 55484497200; 22938490800; 35379965900; 6506820780; 9734318000","Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data","2024","Journal of Allergy and Clinical Immunology: In Practice","12","8","","2084","2091.e4","","0","10.1016/j.jaip.2024.04.035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194061858&doi=10.1016%2fj.jaip.2024.04.035&partnerID=40&md5=50cad77ac28fe7ba0777d4ec7939f96a","Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States; Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Department of Medicine, Mayo Clinic, Jacksonville, Fla, United States; Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz, United States; Pathology and Laboratory Medicine Institute, Cleveland Clinic, Ohio, United States","Wu C.-P., Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States; Sleiman J., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Fakhry B., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Chedraoui C., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Attaway A., Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Bhattacharyya A., Department of Medicine, Mayo Clinic, Jacksonville, Fla, United States; Bleecker E.R., Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz, United States; Erdemir A., Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States; Hu B., Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States; Kethireddy S., Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States; Meyers D.A., Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz, United States; Rashidi H.H., Pathology and Laboratory Medicine Institute, Cleveland Clinic, Ohio, United States; Zein J.G., Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz, United States","Background: Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. Objectives: To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. Methods: We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. Results: Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/μL) (34.8%). Conclusions: Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed. © 2024 American Academy of Allergy, Asthma & Immunology","Asthma; Asthma phenotypes; Cluster analysis; Machine learning","Adult; Aged; Asthma; Cluster Analysis; Electronic Health Records; Female; Humans; Machine Learning; Male; Middle Aged; Phenotype; Spirometry; Young Adult; budesonide; corticosteroid; sinonasal polyp; unclassified drug; adult; area under the curve; Article; asthma; body mass; chronic obstructive lung disease; classification algorithm; cluster analysis; cohort analysis; comorbidity; depression; dyspnea; emergency ward; eosinophil count; eosinophilia; female; forced expiratory volume; forced vital capacity; gastroesophageal reflux; hospital readmission; hospitalization; human; lung function test; machine learning; major clinical study; male; obesity; personalized medicine; phenotype; polysomnography; practice guideline; pregnancy rate; sinonasal polyp; smoking; spirometry; aged; cluster analysis; diagnosis; drug therapy; electronic health record; epidemiology; middle aged; pathophysiology; young adult","","budesonide, 51333-22-3, 51372-29-3","","","National Institutes of Health, NIH; National Institutes of Health-National Heart, Lung, and Blood Institute, (R01 HL161674); National Heart, Lung, and Blood Institute, NHLBI, (R01 HL161674); National Heart, Lung, and Blood Institute, NHLBI","This study was funded by the National Institutes of Health-National Heart, Lung, and Blood Institute (grant no. R01 HL161674, PI: J.Z.).","Chung K.F., Asthma phenotyping: a necessity for improved therapeutic precision and new targeted therapies, J Intern Med, 279, pp. 192-204, (2016); Global Initiative for Asthma; Cisternas M.G., Blanc P.D., Yen I.H., Katz P.P., Earnest G., Eisner M.D., Et al., A comprehensive study of the direct and indirect costs of adult asthma, J Allergy Clin Immunol, 111, pp. 1212-1218, (2003); Murphy K.R., Solis J., National Asthma Education and Prevention Program 2020 Guidelines: What's Important for Primary Care, J Fam Pract, 70, pp. S19-S28, (2021); Wenzel S., Severe asthma: from characteristics to phenotypes to endotypes, Clin Exp Allergy, 42, pp. 650-658, (2012); Modena B.D., Bleecker E.R., Busse W.W., Erzurum S.C., Gaston B.M., Jarjour N.N., Et al., Gene expression correlated with severe asthma characteristics reveals heterogeneous mechanisms of severe disease, Am J Respir Crit Care Med, 195, pp. 1449-1463, (2017); Jones A.C., Bosco A., Using network analysis to understand severe asthma phenotypes, Am J Respir Crit Care Med, 195, pp. 1409-1411, (2017); Georas S.N., Wright R.J., Ivanova A., Israel E., LaVange L.M., Akuthota P., Et al., The Precision Interventions for Severe and/or Exacerbation-Prone (PrecISE) asthma network: an overview of network organization, procedures, and interventions, J Allergy Clin Immunol, 149, pp. 488-516.e9, (2022); Teague W.G., Phillips B.R., Fahy J.V., Wenzel S.E., Fitzpatrick A.M., Moore W.C., Et al., Baseline features of the Severe Asthma Research Program (SARP III) cohort: differences with age, J Allergy Clin Immunol Pract, 6, pp. 545-554.e4, (2018); Wenzel S.E., Schwartz L.B., Langmack E.L., Halliday J.L., Trudeau J.B., Gibbs R.L., Et al., Evidence that severe asthma can be divided pathologically into two inflammatory subtypes with distinct physiologic and clinical characteristics, Am J Respir Crit Care Med, 160, pp. 1001-1008, (1999); Hastie A.T., Mauger D.T., Denlinger L.C., Coverstone A., Castro M., Erzurum S., Et al., Mixed sputum granulocyte longitudinal impact on lung function in the Severe Asthma Research Program, Am J Respir Crit Care Med, 203, pp. 882-892, (2021); Hastie A.T., Moore W.C., Li H., Rector B.M., Ortega V.E., Pascual R.M., Et al., Biomarker surrogates do not accurately predict sputum eosinophil and neutrophil percentages in asthmatic subjects, J Allergy Clin Immunol, 132, pp. 72-80.e12, (2013); Peters M.C., McGrath K.W., Hawkins G.A., Hastie A.T., Levy B.D., Israel E., Et al., Plasma interleukin-6 concentrations, metabolic dysfunction, and asthma severity: a cross-sectional analysis of two cohorts, Lancet Respir Med, 4, pp. 574-584, (2016); Moore W.C., Meyers D.A., Wenzel S.E., Teague W.G., Li H., Li X., Et al., Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program, Am J Respir Crit Care Med, 181, pp. 315-323, (2010); Lefaudeux D., De Meulder B., Loza M.J., Peffer N., Rowe A., Baribaud F., Et al., U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics, J Allergy Clin Immunol, 139, pp. 1797-1807, (2017); Denton E., Price D.B., Tran T.N., Canonica G.W., Menzies-Gow A., FitzGerald J.M., Et al., Cluster analysis of inflammatory biomarker expression in the International Severe Asthma Registry, J Allergy Clin Immunol Pract, 9, pp. 2680-2688.e7, (2021); Haldar P., Pavord I.D., Shaw D.E., Berry M.A., Thomas M., Brightling C.E., Et al., Cluster analysis and clinical asthma phenotypes, Am J Respir Crit Care Med, 178, pp. 218-224, (2008); Schatz M., Hsu J.W.Y., Zeiger R.S., Chen W., Dorenbaum A., Chipps B.E., Et al., Phenotypes determined by cluster analysis in severe or difficult-to-treat asthma, J Allergy Clin Immunol, 133, pp. 1549-1556, (2014); Mani S., Shankle W.R., Dick M.B., Pazzani M.J., Two-stage machine learning model for guideline development, Artif Intell Med, 16, pp. 51-71, (1999); Kogan E., Didden E.M., Lee E., Nnewihe A., Stamatiadis D., Mataraso S., Et al., A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records, Int J Cardiol, 374, pp. 95-99, (2023); Nouraei H., Nouraei H., Rabkin S.W., Comparison of unsupervised machine learning approaches for cluster analysis to define subgroups of heart failure with preserved ejection fraction with different outcomes, Bioengineering, 9, (2022); Tsoi K.K.F., Chan N.B., Yiu K.K.L., Poon S.K.S., Lin B., Ho K., Machine learning clustering for blood pressure variability applied to systolic blood pressure intervention trial (SPRINT) and the Hong Kong community cohort, Hypertension, 76, pp. 569-576, (2020); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, pp. 1747-1757, (2021); Razavi-Termeh S.V., Sadeghi-Niaraki A., Choi S.M., Asthma-prone areas modeling using a machine learning model, Sci Rep, 11, (2021); HCUP clinical classifications software; Wang E., Wechsler M.E., Tran T.N., Heaney L.G., Jones R.C., Menzies-Gow A.N., Et al., Characterization of severe asthma worldwide: data from the International Severe Astham Registry, Chest, 157, pp. 790-804, (2020); Kim J.T., Kim N.R., Choi S.H., Oh S., Park M.-S., Lee S.-H., Et al., Neural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes, Sci Rep, 12, (2022); Thorndike R.L., Who belongs in the family?, Psychometrika, 18, pp. 267-276, (1953); Michael E., Ma H., Li H., Qi S., An optimized framework for breast cancer classification using machine learning, BioMed Res Int, 2022, pp. 1-18, (2022); Lung function testing: selection of reference values and interpretative strategies, Am Rev Respir Dis, 144, pp. 1202-1218, (1991); Matabuena M., Salgado F.J., Nieto-Fontarigo J.J., Alvarez-Puebla M.J., Arismendi E., Barranco P., Et al., Identification of asthma phenotypes in the Spanish MEGA Cohort Study using cluster analysis, Arch Bronconeumol, 59, pp. 223-231, (2023); Loureiro C.C., Sa-Couto P., Todo-Bom A., Bousquet J., Cluster analysis in phenotyping a Portuguese population, Rev Port Pneumol Engl Ed, 21, pp. 299-306, (2015); Trivedi A.P., Hall C., Goss C.W., Lew D., Krings J.G., McGregor M.C., Et al., Quantitative CT characteristics of cluster phenotypes in the Severe Asthma Research Program cohorts, Radiology, 304, pp. 450-459, (2022); Wu W., Bang S., Bleecker E.R., Castro M., Denlinger L., Erzurum S.C., Et al., Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma, Am J Respir Crit Care Med, 199, pp. 1358-1367, (2019); He L.X., Deng K., Wang J., Zhang X., Wang L., Zhang H.P., Et al., Clinical subtypes of neutrophilic asthma: a cluster analysis from Australasian Severe Asthma Network, J Allergy Clin Immunol Pract, 12, pp. 686-698.e8, (2024); Bourdin A., Molinari N., Vachier I., Varrin M., Marin G., Gamez A.S., Et al., Prognostic value of cluster analysis of severe asthma phenotypes, J Allergy Clin Immunol, 134, pp. 1043-1050, (2014); Chung K.F., Wenzel S.E., Brozek J.L., Bush A., Castro M., Sterk P.J., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, pp. 343-373, (2014); FitzGerald J.M., Barnes P.J., Chipps B.E., Jenkins C.R., O'Byrne P.M., Pavord I.D., Et al., The burden of exacerbations in mild asthma: a systematic review, ERJ Open Res, 6, pp. 00359-02019, (2020); Zein J.G., Erzurum S.C., Asthma is different in women, Curr Allergy Asthma Rep, 15, (2015); White M.J., Risse-Adams O., Goddard P., Contreras M.G., Adams J., Hu D., Et al., Novel genetic risk factors for asthma in African American children: Precision Medicine and the SAGE II Study, Immunogenetics, 68, pp. 391-400, (2016); Barnes K.C., Grant A.V., Hansel N.N., Gao P., Dunston G.M., African Americans with asthma: genetic insights, Proc Am Thorac Soc, 4, pp. 58-68, (2007); Stubbs M.A., Clark V.L., Gibson P.G., Yorke J., McDonald V.M., Associations of symptoms of anxiety and depression with health-status, asthma control, dyspnoea, dysfunction breathing and obesity in people with severe asthma, Respir Res, 23, (2022); Johnson O., Gerald L.B., Harvey J., Roy G., Hazucha H., Large C., Et al., An online weight loss intervention for people with obesity and poorly controlled asthma, J Allergy Clin Immunol Pract, 10, pp. 1577-1586.e3, (2022); Zhang P., Zein J., Novel insights on sex-related differences in asthma, Curr Allergy Asthma Rep, 19, (2019); Bettelli E., Carrier Y., Gao W., Korn T., Strom T.B., Oukka M., Et al., Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells, Nature, 441, pp. 235-238, (2006); Newcomb D.C., Cephus J.Y., Boswell M.G., Fahrenholz J.M., Langley E.W., Feldman A.S., Et al., Estrogen and progesterone decrease let-7f microRNA expression and increase IL-23/IL-23 receptor signaling and IL-17A production in patients with severe asthma, J Allergy Clin Immunol, 136, pp. 1025-1034.e11, (2015); Juniper E.F., Daniel E.E., Roberts R.S., Kline P.A., Hargreave F.E., Newhouse M.T., Improvement in airway responsiveness and asthma severity during pregnancy: a prospective study, Am Rev Respir Dis, 140, pp. 924-931, (1989); Peters M.C., Mauger D., Ross K.R., Phillips B., Gaston B., Cardet J.C., Et al., Evidence for exacerbation-prone asthma and predictive biomarkers of exacerbation frequency, Am J Respir Crit Care Med, 202, pp. 973-982, (2020); Zein J.G., Dweik R.A., Comhair S.A., Bleecker E.R., Moore W.C., Peters S.P., Et al., Asthma is more severe in older adults, PLoS One, 10, (2015); Menzies-Gow A., Bafadhel M., Busse W.W., Casale T.B., Kocks J.W.H., Pavord I.D., Et al., An expert consensus framework for asthma remission as a treatment goal, J Allergy Clin Immunol, 145, pp. 757-765, (2020); Hough K.P., Curtiss M.L., Blain T.J., Liu R.-M., Trevor J., Deshane J.S., Et al., Airway remodeling in asthma, Front Med (Lausanne), 7, (2020); Israel E., Reddel H.K., Severe and difficult-to-treat asthma in adults, N Engl J Med, 377, pp. 965-976, (2017); Ortega H.G., Liu M.C., Pavord I.D., Brusselle G.G., FitzGerald J.M., Chetta A., Et al., Mepolizumab treatment in patients with severe eosinophilic asthma, N Engl J Med, 371, pp. 1198-1207, (2014)","J.G. Zein; Mayo Clinic Arizona, Scottsdale, 13400 E Shea Blvd, 85259, United States; email: Zein.Joe@mayo.edu","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","38685479","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85194061858"
"Lee H.; Park M.-B.; Won Y.-J.","Lee, Hocheol (57211534531); Park, Myung-Bae (56591059800); Won, Young-Joo (7102129118)","57211534531; 56591059800; 7102129118","AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis","2025","JMIR Formative Research","9","","e57874","","","","0","10.2196/57874","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215866222&doi=10.2196%2f57874&partnerID=40&md5=4c51e66e57e5a175f80b8083096522f3","Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea","Lee H., Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea; Park M.-B., Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea; Won Y.-J., Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea","Background: Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population. Objective: This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model. Methods: This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023. Data were collected using a mobile app (Gosufit) that measured depression, stress, anxiety, basal metabolic rate, oxygen saturation, heart rate, and average daily step count. Health coordinators recorded data on diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, percent body fat, and percent muscle. The presence of diabetes was the target variable, with various health indicators as predictors. Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. The dataset was split into 70% training and 30% testing sets. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Shapley additive explanations (SHAPs) were used for model interpretability. Results: Significant predictors of diabetes included hypertension (X21=197.294; P<.001), hyperlipidemia (X21=47.671; P<.001), age (mean: diabetes group 72.66 years vs nondiabetes group 71.81 years), stress (mean: diabetes group 42.68 vs nondiabetes group 41.47; t3082=-2.858; P=.004), and heart rate (mean: diabetes group 75.05 beats/min vs nondiabetes group 73.14 beats/min; t3082=-7.948; P<.001). The extreme gradient boosting model (XGBM) demonstrated the best performance, with an accuracy of 84.88%, precision of 77.92%, recall of 66.91%, F1 score of 72.00, and AUC of 0.7957. The SHAP analysis of the top-performing XGBM revealed key predictors for diabetes: hypertension, age, percent body fat, heart rate, hyperlipidemia, basal metabolic rate, stress, and oxygen saturation. Hypertension strongly increased diabetes risk, while advanced age and elevated stress levels also showed significant associations. Hyperlipidemia and higher heart rates further heightened diabetes probability. These results highlight the importance and directional impact of specific features in predicting diabetes, providing valuable insights for risk stratification and targeted interventions. Conclusions: This study focused on modifiable risk factors, providing crucial data for establishing a system for the automated collection of health information and lifelog data from older adults using digital devices at service facilities. © Hocheol Lee, Myung-Bae Park, Young-Joo Won.","aging; artificial intelligence; diabetes; extreme gradient boosting model; geriatrics; machine learning; older adults; prediction model; super-aging population","","","","","","National Research Foundation of Korea, NRF; Ministry of Education, MOE, (NRF-2021R1C1C2005464); Ministry of Education, MOE","This research was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2021R1C1C2005464).","World population prospects 2022: summary of results, (2022); Chen C, Ding S, Wang J., Digital health for aging populations, N Med, 29, 7, pp. 1623-1630, (2023); Kim KW, Kim OS., Super aging in South Korea unstoppable but mitigatable: a sub-national scale population projection for best policy planning, Spat Demogr, 8, 2, pp. 155-173, (2020); Kwon HS., Prevalence and treatment status of diabetes mellitus in Korea, J Korean Med Assoc, 66, 7, pp. 404-407, (2023); de Boer IH, Bangalore S, Benetos A, Et al., Diabetes and hypertension: a position statement by the American Diabetes Association, Diabetes Care, 40, 9, pp. 1273-1284, (2017); Ha KH, Lee KA, Han KD, Moon MK, Kim DJ., Diabetes screening in South Korea: a new estimate of the number needed to screen to detect diabetes, Korean J Intern Med, 38, 1, pp. 93-100, (2023); Flack KD, Davy KP, Hulver MW, Winett RA, Frisard MI, Davy BM., Aging, resistance training, and diabetes prevention, J Aging Res, 2011, (2010); Rooney MR, Rawlings AM, Pankow JS, Et al., Risk of progression to diabetes among older adults with prediabetes, JAMA Intern Med, 181, 4, pp. 511-519, (2021); Kramer MK, Vanderwood KK, Arena VC, Et al., Evaluation of a diabetes prevention program lifestyle intervention in older adults: a randomized controlled study in three senior/community centers of varying socioeconomic status, Diabetes Educ, 44, 2, pp. 118-129, (2018); Caprani N, Gurrin C, O'Connor N., I like to log: a questionnaire study towards accessible lifelogging for older users, 12th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 263-264, (2010); Wilson PWF, Kannel WB., Obesity, diabetes, and risk of cardiovascular disease in the elderly, Am J Geriatr Cardiol, 11, 2, pp. 119-123, (2002); Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S., Machine learning approaches in cardiovascular imaging, Circ Cardiovasc Imaging, 10, 10, (2017); Google Play; Pudjihartono N, Fadason T, Kempa-Liehr AW, O'Sullivan JM., A review of feature selection methods for machine learning-based disease risk prediction, Front Bioinform, 2, (2022); May RJ, Maier HR, Dandy GC., Data splitting for artificial neural networks using SOM-based stratified sampling, Neural Netw, 23, 2, pp. 283-294, (2010); Lundberg SM, Su-In L., A unified approach to interpreting model predictions, 31st International Conference on Neural Information Processing Systems, pp. 4768-4777, (2017); Lai H, Huang H, Keshavjee K, Guergachi A, Gao X., Predictive models for diabetes mellitus using machine learning techniques, BMC Endocr Disord, 19, 1, (2019); Yuvaraj N, SriPreethaa KR., Diabetes prediction in healthcare systems using machine learning algorithms on Hadoop cluster, Cluster Comput, 22, S1, pp. 1-9, (2019); Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I., Machine learning and data mining methods in diabetes research, Comput Struct Biotechnol J, 15, pp. 104-116, (2017); Tang Y., Deep learning using linear support vector machines, (2015); Ye C, Fu T, Hao S, Et al., Prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning, J Med Internet Res, 20, 1, (2018); Farran B, Channanath AM, Behbehani K, Thanaraj TA., Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait-a cohort study, BMJ Open, 3, 5, (2013); Lee DH, de Rezende LFM, Hu FB, Jeon JY, Giovannucci EL., Resting heart rate and risk of type 2 diabetes: a prospective cohort study and meta-analysis, Diabetes Metab Res Rev, 35, 2, (2019); Sullivan PW, Ghushchyan VH, Ben-Joseph R., The impact of obesity on diabetes, hyperlipidemia and hypertension in the United States, Qual Life Res, 17, 8, pp. 1063-1071, (2008); Du J, Yang S, Zeng Y, Ye C, Chang X, Wu S., Visualization obesity risk prediction system based on machine learning, Sci Rep, 14, 1, (2024); Kyrou I, Tsigos C., Obesity in the elderly diabetic patient: is weight loss beneficial? No, Diabetes Care, 32, pp. S403-S409, (2009); Newman AB, Lee JS, Visser M, Et al., Weight change and the conservation of lean mass in old age: the Health, Aging and Body Composition Study, Am J Clin Nutr, 82, 4, pp. 872-878, (2005); Harrison TA, Hindorff LA, Kim H, Et al., Family history of diabetes as a potential public health tool, Am J Prev Med, 24, 2, pp. 152-159, (2003)","Y.-J. Won; Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Changjogwan, Yonseidae-gil 1, 26493, South Korea; email: youngwon@yonsei.ac.kr","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","","Scopus","2-s2.0-85215866222"
"Malviya A.; Dixit R.; Shukla A.; Kushwaha N.","Malviya, Anjali (58413372600); Dixit, Rahul (57169857000); Shukla, Anupam (57188965158); Kushwaha, Nagendra (55838774600)","58413372600; 57169857000; 57188965158; 55838774600","A Novel Approach to Detection of COVID-19 and Other Respiratory Diseases Using Autoencoder and LSTM","2025","SN Computer Science","6","1","27","","","","0","10.1007/s42979-024-03546-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212767393&doi=10.1007%2fs42979-024-03546-1&partnerID=40&md5=96b62184605257ad11f92aadb5e82ba8","Indian Institute of Information Technology, Pune, India; Sardar Vallabhbhai National Institute of Technology, Surat, India","Malviya A., Indian Institute of Information Technology, Pune, India; Dixit R., Sardar Vallabhbhai National Institute of Technology, Surat, India; Shukla A., Sardar Vallabhbhai National Institute of Technology, Surat, India; Kushwaha N., Indian Institute of Information Technology, Pune, India","Innumerable approaches of deep learning-based COVID-19 detection systems have been suggested by researchers in the recent past, due to their ability to process high-dimensional, complex data, leading to more accurate prediction of the COVID-19 infected patients. There is a visible dominance of Convolutional Neural Network (CNN) based models analysing chest images like X-rays and Computed Tomography (CT) scans for prediction, while the utilization of audio data for the same is less prevalent. Considering the respiratory system is one of the primary means by which the SARS-CoV-2 virus spreads, respiratory sounds are a potential biomarker for determining the presence of COVID-19. In this paper, we propose a novel approach for the detection of COVID-19 from amidst a dataset comprising of respiratory sound samples of healthy, COVID-19, and other lung diseases which are often misinterpreted as COVID-19. The approach employs an autoencoder for anomaly detection and a Long Short-Term Memory (LSTM) network for the detection of COVID-19 from amongst other lung diseases. The first stage of the model comprises an encoder-decoder-based autoencoder model with baseline reconstruction error, trained in an unsupervised environment, to reconstruct “healthy” audio signals. An LSTM based multi-class classifier is proposed for the second stage to classify the infected samples into the five classes: COVID-19, Bronchiolitis, COPD, Pneumonia and URTI. The experimental results demonstrate the efficacy of our proposed approach in detecting COVID-19 from a 5-class test set of audio samples of patients suffering from respiratory disease, with an accuracy of 98.7%, and an AUC of 1. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.","Autoencoder; COVID-19; Deep learning; Long short-term memory (LSTM); Respiratory-diseases classification","","","","","","","","WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data; Rahman T., Ibtehaz N., Khandakar A., Et al., QUCoughScope: an intelligent application to detect COVID-19 patients using cough and breath sounds, Diagnostics, 12, (2022); Laguarta J., Hueto F., Subirana B., Covid-19 Artificial intelligence diagnosis using only cough recordings, IEEE Open J Eng Med Biol, 1, pp. 275-281, (2020); Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput, 9, 8, pp. 1735-1780, (1997); Panwar H., Gupta P.K., Siddiqui M., Application of deep learning for fast detection of covid-19 in x-rays using Ncovnet, Chaos Solitons Fract, 138, pp. 1391-1405, (2020); Vaid S., Kalantar R., Bhandari M., Deep learning covid-19 detection bias: accuracy through artificial intelligence, Int Orthop (SICOT), 44, pp. 1539-1542, (2020); Jain G., Mittal D., Thakur D., A deep learning approach to detect covid-19 coronavirus with x-ray images, Biocybernet Biomed Eng, 40, pp. 1391-1405, (2020); Akter S., Shamrat F.M.J.M., Chakraborty S., Karim A., Azam S., Covid-19 detection using deep learning algorithm on chest x-ray images, Biology, 10, (2021); Tulin O., Muhammed T., Eylul A.Y., Ulas B.B., Ozal Y., Rajendra A.U., Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput Biol Med, (2020); Chouat I., Echtioui A., Khemakhem R., Et al., COVID-19 detection in CT and CXR images using deep learning models, Biogerontology, 23, pp. 65-84, (2022); Aslani S., Jacob J., Utilisation of deep learning for covid-19 diagnosis, Clin Radiol, 78, pp. 150-157, (2023); Hassan A., Shahin I., Alsabek M.B., COVID-19 detection system using recurrent neural networks, In: 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), pp. 1-5, (2020); Pahar M., Klopper M., Warren R., Niesler T.R., COVID-19 cough classification using machine learning and global smartphone recordings, Comput Biol Med, 135, (2020); Pahar M., Klopper M., Warren R., Niesler T.R., COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features, Comput Biol Med, 141, (2021); Lella K.K., Pia A., Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: cough, voice and breath, Alexand Eng J, 61, pp. 1319-1334, (2022); Malviya A., Dixit R., Shukla A., Et al., Long short-term memory-based deep learning model for COVID-19 detection using coughing sound, SN Comput Sci, 4, (2023); Aytekin I., Et al., COVID-19 detection from respiratory sounds with hierarchical spectrogram transformers, IEEE J Biomed Health Inform, 28, 3, pp. 1273-1284, (2024); Chatterjee S., Maity S., Bhattacharjee M., Et al., Variational autoencoder based imbalanced COVID-19 detection using chest X-ray images, New Gener Comput, 41, pp. 25-60, (2023); Ullah Z., Usman M., Gwak J., MTSS-AAE: multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images, Expert Syst Appl, (2023); Baccarelli E., Scarpiniti M., Momenzadeh A., Twinned residual auto-encoder (TRAE)—a new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images, Expert Syst Appl, (2023); Addo D., Zhou S., Jackson J., Nneji G.U., Monday H.N., Sarpong K., Patamia R.A., Ekong F., Owusu-Agyei C.A., Evae-net: an ensemble variational autoencoder deep learning network for covid-19 classification based on chest x-ray images, Diagnostics, 12, 2022, (2022); Demir F., Demir K., Sengur A., DeepCov19Net: automated COVID-19 disease detection with a robust and effective technique deep learning approach, New Gener Comput, 40, pp. 1053-1075, (2022); Hamdi S., Moussaoui A., Oussalah M., Saidi M., Autoencoders and Ensemble-Based Solution for COVID-19 Diagnosis from Cough Sound, Modelling and Implementation of Complex Systems MISC 2022. 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OSF, (2018); McFee B., Raffel C., Liang D., Ellis D., McVicar M., Battenberg E., Nieto O., Librosa: Audio and music signal analysis in python, In: Proceedings of the 14Th Python in Science Conference, (2015)","A. Malviya; Indian Institute of Information Technology, Pune, India; email: anjalimalviya@iiitp.ac.in","","Springer","","","","","","2662995X","","","","English","SN COMPUT. SCI.","Article","Final","","Scopus","2-s2.0-85212767393"
"Labyad M.; Draiss G.; El Fakiri K.; Ouzennou N.; Bouskraoui M.","Labyad, Maryem (59180594000); Draiss, Ghizlane (36019837100); El Fakiri, Karima (36025094400); Ouzennou, Nadia (57205682563); Bouskraoui, Mohamed (7004108384)","59180594000; 36019837100; 36025094400; 57205682563; 7004108384","The Use of Chatgpt by Pediatric Physicians in the Management of Childhood Asthma in the City of Marrakech (Morocco)","2025","OnLine Journal of Biological Sciences","25","1","","232","243","11","0","10.3844/ojbsci.2025.232.243","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217217914&doi=10.3844%2fojbsci.2025.232.243&partnerID=40&md5=ebf80ee99011ed55c3057f9fc120c133","Infectious Disease Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, Morocco; Department of Pediatric, Faculty of Medicine and Pharmacy of Marrakech, University Hospital Mohamed VI, Cadi Ayyad University, Morocco; Department of Biology, Faculty of Sciences Semlalia, Pharmacology, Neurobiology, Anthropobiology, and Environment Laboratory, Cadi Ayyad University, Marrakech, Morocco; ISPITS, Higher Institute of Nursing and Technical Health, Marrakech, Morocco","Labyad M., Infectious Disease Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, Morocco; Draiss G., Department of Pediatric, Faculty of Medicine and Pharmacy of Marrakech, University Hospital Mohamed VI, Cadi Ayyad University, Morocco; El Fakiri K., Department of Pediatric, Faculty of Medicine and Pharmacy of Marrakech, University Hospital Mohamed VI, Cadi Ayyad University, Morocco; Ouzennou N., Department of Biology, Faculty of Sciences Semlalia, Pharmacology, Neurobiology, Anthropobiology, and Environment Laboratory, Cadi Ayyad University, Marrakech, Morocco, ISPITS, Higher Institute of Nursing and Technical Health, Marrakech, Morocco; Bouskraoui M., Department of Pediatric, Faculty of Medicine and Pharmacy of Marrakech, University Hospital Mohamed VI, Cadi Ayyad University, Morocco","Pediatric asthma is a chronic disease requiring continuous management, where digital tools, such as ChatGPT, are beginning to play a vital role, in optimizing physicians' efficiency, reducing their workload, and improving communication with patients. However, its integration raises questions about the accuracy of the information provided and data security. In addition, the use of ChatGPT in clinical practice remains little explored compared to its use in the field of researsch. In this context, this study aims to describe the use of ChatGPT by pediatricians, assess its advantages and limitations, and suggest recommendations for optimal integration of this tool in the context of pediatric asthma. A prospective survey was conducted for one month at the mother-child hospital in Marrakech. Data were collected online using a structured questionnaire from all pediatric doctors working in this establishment. Data analysis was performed using descriptive methods in addition to bivariate and multivariate analyses, with SPSS software. The study included 53 pediatric physicians, represented mainly by women (94.3%). Approximately 56,6% of doctors used ChatGPT in the management of children with asthma, especially for abstracting (41.5%), translating (35.84%), and asking medical questions (22.64%). The main reasons for use were; simplification of administrative tasks (44.11%) and the rapid access to information (35.29%). Although 57% of physicians were satisfied with ChatGPT’s use, limitations such as lack of customization (26.6%) and reliance on technology (31.3%) were noted. User doctors were the ones who were familiar with this technology and who encountered more difficulties in the management of pediatric asthma (p<0.001). Although recent, ChatGPT is already widely adopted by pediatricians for some specific tasks in the management of childhood asthma, such as writing summaries, translating, and answering questions, while leaving clinical decisions under the control of physicians. This highlights both the effectiveness of the tool and the need for strict human supervision and enhanced security measures. © 2025 Maryem Labyad, Ghizlane Draiss, Karima El Fakiri, Nadia Ouzennou and Mohamed Bouskraoui.","Artificial Intelligence; Asthma; Chatbot; ChatGPT; Child; Pediatrician","","","","","","","","Adamopoulou E., Moussiades L., An Overview of Chatbot Technology, Artificial Intelligence Applications and Innovations, 584, (2020); Adnani E., Haounani A., L’intelligence Artificielle au Maroc: Entre éthique et réglementation, Revue Internationale de La Recherche Scientifique (Revue-IRS, 2, 3, pp. 1234-1252, (2024); Archibald M. M., Caine V., Ali S., Hartling L., Scott S. D., What Is Left Unsaid: An Interpretive Description of the Information Needs of Parents of Children with Asthma, Research in Nursing & Health, 38, 1, pp. 19-28, (2015); Ben Ameur S., Elasmar K., Jdidi J., Belhadj R., Aloulou H., Maaloul I., Damak J., Kammoun T. 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"Christina Dally E.; Banu Rekha B.","Christina Dally, E. (59152314000); Banu Rekha, B. (57486161700)","59152314000; 57486161700","Automated Chronic Obstructive Pulmonary Disease (COPD) detection and classification using Mayfly optimization with deep belief network model","2024","Biomedical Signal Processing and Control","96","","106488","","","","0","10.1016/j.bspc.2024.106488","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194967733&doi=10.1016%2fj.bspc.2024.106488&partnerID=40&md5=3a85ad8b7c135255cb461b57485fcf33","SNS College of Technology, Coimbatore, India; PSG College of Technology, Coimbatore, India","Christina Dally E., SNS College of Technology, Coimbatore, India; Banu Rekha B., PSG College of Technology, Coimbatore, India","Chronic Obstructive Pulmonary Disease (COPD) is a progressive and debilitating respiratory condition affecting millions worldwide. Respiratory disease affects quality of life and poses a substantial economic burden on patients and families. Diagnosis of COPD is unreliable as the test depends on the effort made by the tester and testee. Routine healthcare data collection from patients enables the identification of COPD subtypes so that physicians can define the disease severity and progression. Selecting optimal features from a large volume of healthcare data increases the computation burden and may lead to misclassification. In this research work, the Mayfly optimization algorithm is used for optimal feature selection from the COPD Patients Dataset, and the Deep Belief Network is then used for classification. The proposed Mayfly Optimized Deep Belief Network (MODBN) performance is experimentally verified using a benchmark dataset, and the performances are comparatively analyzed with traditional machine learning algorithms. A maximum classification accuracy of 96.89 % was attained by the proposed model in the classification of COPD compared to traditional machine learning algorithms. © 2024 Elsevier Ltd","Chronic obstructive pulmonary disease; Deep belief network; Machine learning; Mayfly optimization algorithm","Benchmarking; Deep learning; Diagnosis; Health care; Learning algorithms; Optimization; Pulmonary diseases; Chronic obstructive pulmonary disease; Deep belief networks; Disease classification; Disease detection; Machine learning algorithms; Machine-learning; Mayfly optimization algorithm; Network models; Optimisations; Optimization algorithms; Article; chronic obstructive lung disease; controlled study; deep belief network; disease classification; feature selection; human; learning algorithm; machine learning; Classification (of information)","","","","","","","MathewosTessema D.D., Tassew C.M., Yingling K.D., Picchi M.A., Guodong W.U., Petersen H., Randell S., Lin Y., Belinsky S.A., YohannesTesfaigzi, Identification of novel epigenetic abnormalities as sputum biomarkers for lung cancer risk among smokers and COPD patients, Lung Cancer, 146, pp. 189-196, (2020); Hoesterey D., Das N., Janssens W., Buhr R.G., Martinez F.J., Cooper C.B., Tashkin D.P., Barjaktarevic I., Spirometric indices of early airflow impairment in individuals at risk of developing COPD: spirometry beyond FEV1/FVC, Respir. 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Proteomics, 206, (2019); ArchanaKanwade, Bairagi V.K., Classification of COPD and normal lung airways using feature extraction of electromyographic signals, Journal of King Saud University - Computer and Information Sciences, 31, 4, pp. 506-513, (2019); Lin S., Zhang Q., Chen F., Luo L.I., Chen L., Zhang W., Smooth Bayesian network model for the prediction of future high-cost patients with COPD, Int. J. Med. Inf., 126, pp. 147-155, (2019); Lanclus M., Clukers J., Van Holsbeke C., WimVos G.L., Holbrechts B., Barboza K., De Backer W., De Backer J., Machine learning algorithms utilizing functional respiratory imaging may predict COPD exacerbations, Acad. Radiol., 26, 9, pp. 1191-1199, (2019); Rodriguez-Aguilar M., Diaz L., de Leon-Martinez P., Gorocica-Rosete R.P., Padilla I.-R., Ornelas-Rebolledo O., Flores-Ramirez R., Identification of breath-prints for the COPD detection associated with smoking and household air pollution by electronic nose, Respir. Med., 163, (2020); Binson V.A., Subramoniam M., Mathew L., Detection of COPD and Lung Cancer with electronic nose using ensemble learning methods, ClinicaChimicaActa, 523, pp. 231-238, (2021); ShahnajHaider N., Behera A.K., Computerized lung sound-based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybernetics and Biomedical Engineering, 42, 1, pp. 42-59, (2022); Li Z., Liu L., Zhang Z., Yang X., Li X., YanliGao K.H., A novel CT-based radiomics features analysis for identification and severity staging of COPD, Acad. Radiol., 29, 5, pp. 663-673, (2022); Yanan W., Qi S., Feng J., Chang R., Pang H., JieHou M.L., Wang Y., Xia S., Qian W., Attention-guided multiple instance learning for COPD identification: to combine the intensity and morphology, Biocybernetics and Biomedical Engineering, 43, 3, pp. 568-585, (2023); Zarrin P.S., Roeckendorf N., Wenger C., In-vitro classification of saliva samples of COPD patients and healthy controls using machine learning tools, IEEE Access, 8, pp. 168053-168060, (2020); Weng Y., Fang Y., Yan H., Yang Y., Hong W., Bayesian non-parametric classification with tree-based feature transformation for NIPPV efficacy prediction in COPD patients, IEEE Access, 7, pp. 177774-177783, (2019); Fang Y., Wang H., Wang L., Di R., Song Y., Diagnosis of COPD based on a knowledge graph and integrated model, IEEE Access, 7, pp. 46004-46013, (2019); Mohamed I., Fouda M.M., Hosny K.M., Machine learning algorithms for COPD patients readmission prediction: a data analytics approach, IEEE Access, 10, pp. 15279-15287, (2022); 22, 5, pp. 1486-1496; Sen I., Saraclar M., Kahya Y.P., Differential diagnosis of asthma and COPD based on multivariate pulmonary sounds analysis, IEEE Trans. Biomed. Eng., 68, 5, pp. 1601-1610, (2021); Davies H.J., Bachtiger P., Williams I., Molyneaux P.L., Peters N.S., Mandic D.P., Wearable in-ear PPG: detailed respiratory variations enable classification of COPD, IEEE Trans. Biomed. Eng., 69, 7, pp. 2390-2400, (2022); Blanco-Almazan D., Groenendaal W., Lijnen L., Onder R., Smeets C., Ruttens D., Catthoor F., Jane R., Breathing pattern estimation using wearable bioimpedance for assessing COPD severity, IEEE J. Biomed. Health Inform., 26, 12, pp. 5983-5991, (2022); Wang Q., Wang H., Wang L., Fengping Y., Diagnosis of chronic obstructive pulmonary disease based on transfer learning, IEEE Access, 8, pp. 47370-47383, (2020); Roy A., Satija U., A novel melspectrogram snippet representation learning framework for severity detection of chronic obstructive pulmonary diseases, IEEE Trans. Instrum. Meas., 72, pp. 1-11, (2023); Dhar J., Multistage ensemble learning model with weighted voting and genetic algorithm optimization strategy for detecting chronic obstructive pulmonary disease, IEEE Access, 9, pp. 48640-48657, (2021); Altan G., Kutlu Y., Pekmezci A.O., Nural S., Deep learning with 3D-second order difference plot on respiratory sounds, Biomed. Signal Process. Control, 45, pp. 58-69, (2018); altan G., kutlu Y., gokcen A., Chronic obstructive pulmonary disease severity analysis using deep learning onmulti-channel lung sounds, Turk. J. Electr. Eng. Comput. Sci., 28, 5, pp. 2979-2996, (2020); Zhang L., Jiang B., Wisselink H.J., Vliegenthart R., Xie X., COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images, Br. J. Radiol., 95, 1133, pp. 1-9, (2022); Sun J., Liao X., Yan Y., Zhang X., Sun J., Tan W., Liu B., Jiangfen W.U., Guo Q., Gao S., Li Z., Wang K., Li Q., Detection and staging of chronic obstructive pulmonary disease using a computed tomography–based weakly supervised deep learning approach, Eur. Radiol., 32, pp. 5319-5329, (2022); Almeida S.D., Norajitra T., Luth C.T., Wald T., Weru V., Nolden M., Jager P.F., von Stackelberg O., Heussel C.P., Weinheimer O., Biederer J., Kauczor H.-U., Maier-Hein K., pp. 1-14","E. Christina Dally; SNS College of Technology, Coimbatore, India; email: christinadallysns@gmail.com","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85194967733"
"Richie R.C.","Richie, R.C. (7004184245)","7004184245","Assessing the Pathophysiology, Morbidity, and Mortality of Obstructive Sleep Apnea","2024","Journal of insurance medicine (New York, N.Y.)","51","3","","143","162","19","0","10.17849/insm-51-3-1-20.2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211393205&doi=10.17849%2finsm-51-3-1-20.2&partnerID=40&md5=6436ed7bbc7f38c94af407fb423973c0","Journal of Insurance Medicine","Richie R.C., Journal of Insurance Medicine","The basic definitions of obstructive sleep apnea (OSA), its epidemiology, its clinical features and complications, and the morbidity and mortality of OSA are discussed. Included in this treatise is a discussion of the various symptomatic and polysomnographic phenotypes of COPD that may enable better treatment and impact mortality in persons with OSA. The goal of this article is to serve as a reference for life and disability insurance company medical directors and underwriters when underwriting an applicant with probable or diagnosed sleep apnea. It is well-referenced (133 ref.) allowing for more in-depth investigation of any aspect of sleep apnea being queried. Copyright © 2024 Journal of Insurance Medicine.","AHI (Apnea hypopnea index); AI (Arousal Index); Apnea; COMISA (comorbid insomnia and sleep apnea).; CSA (central sleep apnea); EES (Epworth sleepiness scale); HSAT (home sleep apnea test); Hypopnea; PLMS (periodic limb movement of sleep); RDI (respiratory disturbance index); REI (respiratory event index); RERA (respiratory-event related arousal); STOP-bang questionnaire; T90 (time spent with oxygen saturation <90%); UARS (upper airway resistance syndrome)","Humans; Insurance, Life; Polysomnography; Pulmonary Disease, Chronic Obstructive; Sleep Apnea, Obstructive; chronic obstructive lung disease; human; life insurance; mortality; obstructive sleep apnea; pathophysiology; polysomnography","","","","","","","","","","","","","","","","07436661","","","39471830","English","J Insur Med","Article","Final","","Scopus","2-s2.0-85211393205"
"Ahmadiankalati M.; Boury H.; Subbarao P.; Lou W.; Lu Z.","Ahmadiankalati, Mojtaba (57217296429); Boury, Himani (58260085400); Subbarao, Padmaja (57373689300); Lou, Wendy (7006030594); Lu, Zihang (56867892000)","57217296429; 58260085400; 57373689300; 7006030594; 56867892000","Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study","2024","BMC Medical Research Methodology","24","1","262","","","","0","10.1186/s12874-024-02376-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208291676&doi=10.1186%2fs12874-024-02376-2&partnerID=40&md5=81834c36e0961070935c2f3f21dfd8f2","Department of Public Health Sciences, Queen’s University, Kingston, K7L 3N6, ON, Canada; Department of Pediatrics and Translational Medicine, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada","Ahmadiankalati M., Department of Public Health Sciences, Queen’s University, Kingston, K7L 3N6, ON, Canada; Boury H., Department of Public Health Sciences, Queen’s University, Kingston, K7L 3N6, ON, Canada; Subbarao P., Department of Pediatrics and Translational Medicine, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lou W., Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lu Z., Department of Public Health Sciences, Queen’s University, Kingston, K7L 3N6, ON, Canada, Department of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada","Background: Asthma is a heterogeneous disease that affects millions of children and adults. There is a lack of objective gold standard diagnosis that spans the ages; instead, diagnoses are made by clinician assessment based on a cluster of signs, symptoms and objective tests dependent on age. Yet, there is a clear morbidity associated with chronic asthma symptoms. Machine learning has become a popular tool to improve asthma diagnosis and classification. There is a paucity of literature on the use of Bayesian machine learning algorithms to predict asthma diagnosis in children. This paper develops a prediction model using the Bayesian additive regression trees (BART) and compares its performance to various machine learning algorithms in predicting the diagnosis of childhood asthma. Methods: Clinically relevant variables collected at or before 3 years of age from 2794 participants in the CHILD Cohort Study were used to predict physician-diagnosed asthma at age 5. BART and six other commonly used machine learning algorithms, namely adaptive boosting, logistic regression, decision tree, neural network, random forest, and support vector machine were trained. Measures of performance including sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were calculated. The confidence intervals were calculated using Bootstrapping samples. Important predictors and interaction effects associated with asthma were also identified using BART. Results: BART, logistic regression and random forest showed the highest area under the ROC curve compared to other machine learning algorithms. Based on BART, recurrent wheeze, respiratory infection and food sensitization at 3 years of age were the most important predictors. The three most important interaction effects were found to be interaction terms of respiratory infection at 3 years and recurrent wheezing at 3 years, maternal asthma and paternal asthma, and maternal wheezing and inhalant sensitization of child at 3 years. Conclusions: BART demonstrated promising prediction performance when compared to other machine learning algorithms. Future research could validate the BART in an external cohort to evaluate its reliability and generalizability. © The Author(s) 2024.","Asthma; Bayesian additive regression tree; CHILD Cohort Study; Machine learning; Prediction","Algorithms; Asthma; Bayes Theorem; Child; Child, Preschool; Cohort Studies; Decision Trees; Female; Humans; Logistic Models; Machine Learning; Male; Respiratory Sounds; ROC Curve; Sensitivity and Specificity; abnormal respiratory sound; algorithm; asthma; Bayes theorem; child; cohort analysis; decision tree; diagnosis; female; human; machine learning; male; pathophysiology; preschool child; receiver operating characteristic; sensitivity and specificity; statistical model","","","","","; ","","Subbarao P., Mandhane P.J., Sears M.R., Asthma: epidemiology, etiology and risk factors, CMAJ, 181, 9, pp. E181-E190, (2009); Patel D., Hall G.L., Broadhurst D., Smith A., Schultz A., Foong R.E., Does machine learning have a role in the prediction of asthma in children?, Paediatr Respir Rev, 41, pp. 51-60, (2022); Kothalawala D.M., Murray C.S., Simpson A., Et al., Development of childhood asthma prediction models using machine learning approaches, Clinical and Translational Allergy, 11, 9, (2021); Prosperi M.C., Marinho S., Simpson A., Custovic A., Buchan I.E., Predicting phenotypes of asthma and eczema with machine learning, BMC Med Genomics, 7, pp. 1-10, (2014); Bose S., Kenyon C.C., Masino A.J., Personalized prediction of early childhood asthma persistence: a machine learning approach, PLoS ONE, 16, 3, (2021); Kothalawala D.M., Kadalayil L., Weiss V.B., Et al., Prediction models for childhood asthma: a systematic review, Pediatr Allergy Immunol, 31, 6, pp. 616-627, (2020); Chipman H.A., George E.I., McCulloch R.E., BART: Bayesian additive regression trees, Ann Appl Stat, 4, 1, pp. 266-298, (2010); Kapelner A., Bleich J., BartMachine: Machine Learning with Bayesian Additive Regression Trees, J Stat Softw, 70, 4, pp. 1-40, (2016); Subbarao P., Anand S.S., Becker A.B., Et al., The Canadian Healthy Infant Longitudinal Development (CHILD) Study: examining developmental origins of allergy and asthma, Thorax, 70, 10, pp. 998-1000, (2015); Tse S.M., Rifas-Shiman S.L., Coull B.A., Litonjua A.A., Oken E., Gold D.R., Sex-specific risk factors for childhood wheeze and longitudinal phenotypes of wheeze, J Allergy Clin Immunol, 138, pp. 1561-1568, (2016); Jaakkola J., Nafstad P., Magnus P., Environmental tobacco smoke, parental atopy, and childhood asthma, Environ Health Perspect, 109, pp. 579-582, (2001); Lodge C.J., Allen K.J., Lowe A.J., Et al., Perinatal cat and dog exposure and the risk of asthma and allergy in the urban environment: A systematic review of longitudinal studies, Clin Dev Immunol, (2011); Melen E., Wickman M., Nordvall S., van Hage-Hamsten M., Lindfors A., Influence of early and current environmental exposure factors on sensitization and outcome of asthma in pre‐school children, Allergy, 56, pp. 646-652, (2001); Pividori M., Schoettler N., Nicolae D.L., Ober C., Im H.K., Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: Genome-wide and transcriptome-wide studies, Lancet Respir Med, 7, pp. 509-522, (2019); Tan Y.V., Roy J., Bayesian additive regression trees and the General BART model, Stat Med, 38, 25, pp. 5048-5069, (2019); Hill J., Linero A., Murray J., Bayesian additive regression trees: A review and look forward, Ann Rev Stat Appl, 7, pp. 251-278, (2020); Bleich J., Kapelner A., George E.I., Jensen S.T., Variable selection for BART: An application to gene regulation, Ann Appl Stat, 8, 3, pp. 1750-1781, (2014); Twala B.E., Jones M., Hand D.J., Good methods for coping with missing data in decision trees, Pattern Recogn Lett, 29, 7, pp. 950-956, (2008); Alfaro E., Gamez M., Garcia N., adabag: An R package for classification with boosting and bagging, J Stat Softw, 54, pp. 1-35, (2013); Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Softw, 33, 1, (2010); Therneau T., Atkinson B., Ripley B., Ripley M.B., Package ‘rpart’, (2015); Gunther F., Fritsch S., Neuralnet: training of neural networks, R J, 2, 1, (2010); RColorBrewer S., Liaw M.A., Package ‘randomforest’, (2018); Karatzoglou A., Meyer D., Hornik K., Support vector machines in R, J Stat Softw, 15, pp. 1-28, (2006); Van Buuren S., Groothuis-Oudshoorn K., mice: Multivariate imputation by chained equations in R, J Stat Softw, 45, pp. 1-67, (2011); Chatzimichail E., Paraskakis E., Rigas A., An evolutionary two-objective genetic algorithm for asthma prediction, In: 2013 Uksim 15Th International Conference on Computer Modelling and Simulation, pp. 90-94, (2013); Chatzimichail E., Paraskakis E., Sitzimi M., Rigas A., An intelligent system approach for asthma prediction in symptomatic preschool children, Comput Math Methods Med, 2013, (2013); Smolinska A., Klaassen E.M., Dallinga J.W., Et al., Profiling of volatile organic compounds in exhaled breath as a strategy to find early predictive signatures of asthma in children, PLoS ONE, 9, 4, (2014); AlSaad R., Malluhi Q., Janahi I., Boughorbel S., Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma, BMC Med Inform Decis Mak, 19, pp. 1-11, (2019); Harvey J.L., Kumar S.A.P., Machine learning for predicting development of asthma in children, pp. 596-603; Fragoso T.M., Bertoli W., Louzada F., Bayesian model averaging: A systematic review and conceptual classification, Int Stat Rev, 86, 1, pp. 1-28, (2018); Lu Z., Lou W., Bayesian approaches to variable selection: a comparative study from practical perspectives, Int J Biostat, 18, 1, pp. 83-108, (2022); Wenzel F., Galy-Fajou T., Deutsch M., Kloft M., Machine learning and knowledge discovery in databases, Bayesian Nonlinear Support Vector Machines for Big Data, pp. 447-463, (2017)","Z. Lu; Department of Public Health Sciences, Queen’s University, Kingston, K7L 3N6, Canada; email: zihang.lu@queensu.ca","","BioMed Central Ltd","","","","","","14712288","","","39487434","English","BMC Med. Res. Methodol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85208291676"
"Bae W.D.; Alkobaisi S.; Horak M.; Bankar S.; Bhuvaji S.; Kim S.; Park C.-S.","Bae, Wan D. (14826508800); Alkobaisi, Shayma (14826556600); Horak, Matthew (24401453800); Bankar, Siddheshwari (59299541800); Bhuvaji, Sartaj (59300275600); Kim, Sungroul (57218664381); Park, Choon-Sik (17233894400)","14826508800; 14826556600; 24401453800; 59299541800; 59300275600; 57218664381; 17233894400","Synthetic Data Generation and Evaluation Techniques for Classifiers in Data Starved Medical Applications","2025","IEEE Access","13","","","16584","16602","18","0","10.1109/ACCESS.2025.3532222","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216278673&doi=10.1109%2fACCESS.2025.3532222&partnerID=40&md5=171c136861d620b3ac1cfe3b5adc68d7","Seattle University, Department of Computer Science, Seattle, 98122, WA, United States; United Arab Emirates University, College of Information Technology, Al Ain, United Arab Emirates; Amazon AWS Lambda, Seattle, 98108, WA, United States; Soonchunhyang University, Graduate School, Department of ICT Environmental Health System, Asan, 31538, South Korea; Soonchunhyang University Bucheon Hospital, Department of Internal Medicine, Bucheon, 14584, South Korea","Bae W.D., Seattle University, Department of Computer Science, Seattle, 98122, WA, United States; Alkobaisi S., United Arab Emirates University, College of Information Technology, Al Ain, United Arab Emirates; Horak M., Amazon AWS Lambda, Seattle, 98108, WA, United States; Bankar S., Seattle University, Department of Computer Science, Seattle, 98122, WA, United States; Bhuvaji S., Seattle University, Department of Computer Science, Seattle, 98122, WA, United States; Kim S., Soonchunhyang University, Graduate School, Department of ICT Environmental Health System, Asan, 31538, South Korea; Park C.-S., Soonchunhyang University Bucheon Hospital, Department of Internal Medicine, Bucheon, 14584, South Korea","With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. However, prediction models are sensitive to the size and distribution of the data they are trained on. ML algorithms rely heavily on vast quantities of training data to make accurate predictions. Ideally, the dataset should have an equal number of samples for each label to encourage the model to make predictions based on the input data rather than the distribution of the training data. In medical applications, class imbalance is a common issue because the occurrence of a disease or risk episode is often rare. This leads to a training dataset where healthy cases outnumber unhealthy ones, resulting in biased prediction models that struggle to detect the minority, unhealthy cases effectively. This paper addresses the problem of class imbalance, given the scarcity of training datasets by improving the quality of generated data. We propose an incremental synthetic data generation system that improves data quality over iterations by gradually adjusting to the data distribution and thus avoids overfitting in classifiers. Through extensive experimental assessments on real asthma patients' datasets, we demonstrate the efficiency and applicability of our proposed system for individual-based health risk prediction models. Incremental SMOTE methods were compared to the original SMOTE variants as well as various architectures of autoencoders. Our incremental data generation system enhances selected state-of-the-art SMOTE methods, resulting in sensitivity improvements for deep transfer learning (TL) classifiers ranging from 4.01% to 7.79%. Compared with the performance of TL without oversampling, the improvement achieved by the incremental SMOTE methods ranged from 27.18% to 40.97%. These results highlight the effectiveness of our technique in predicting asthma risk and their applicability to imbalanced, data-starved medical contexts.  © 2025 The Authors.","Autoencoders; class imbalance problem; control coefficient; data starved contexts; rare event prediction; synthetic minority oversampling technique; transfer learning","Diseases; Electronic health record; Health risks; Network security; Auto encoders; Class imbalance problems; Controls coefficient; Data starved context; Event prediction; Prediction modelling; Rare event prediction; Synthetic data generations; Synthetic minority over-sampling techniques; Transfer learning; Prediction models","","","","","United Arab Emirates University, UAEU; Soonchunhyang University, SCH; Ministry of Science and ICT; UAEU-NFRP, (G00004281); National Research Foundation of Korea, NRF, (NRF-2022R1A2C1010172); National Research Foundation of Korea, NRF; Seattle University, (11-0-1-480530)","This work was supported in part by United Arab Emirates University, United Arab Emirates, under UAEU-NFRP under Grant G00004281; in part by the Ministry of Science and ICT, South Korea, under the National Research Foundation of Korea under Grant NRF-2022R1A2C1010172; in part by Seattle University, USA, under the Thomas Bannan Chair Engineering Award under Grant 11-0-1-480530; and in part by Soonchunhyang University, South Korea. This work involved human subjects or animals in its research. The authors confirm that all human/animal subject research procedures and protocols are exempt from review board approval.","Kelly C.S., Morrow A.L., Shults J., Nakas N., Strope G.L., Adelman R.D., Outcomes evaluation of a comprehensive intervention program for asthmatic children enrolled in medicaid, Pediatrics, 105, 5, pp. 1029-1035, (2000); Raghupathi W., Raghupathi V., Big data analytics in healthcare: Promise and potential, Health Inf. Sci. Syst., 2, 1, pp. 1-10, (2014); Forno E., Celedon J.C., Predicting asthma exacerbations in children, Current Opinion Pulmonary Med., 18, 1, pp. 63-69, (2012); Thabtah F., Hammoud S., Kamalov F., Gonsalves A., Data imbalance in classification: Experimental evaluation, Inf. Sci., 513, pp. 429-441, (2020); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: Synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, pp. 321-357, (2002); The Synthetic Data Vault, (2023); Bae W.D., Kim S., Park C.-S., Alkobaisi S., Lee J., Seo W., Park J.S., Park S., Lee S., Lee J.W., Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data, PLoS ONE, 16, 1, (2021); Lopez V., Fernandez A., Garcia S., Palade V., Herrera F., An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Inf. Sci., 250, pp. 113-141, (2013); Zhao Y., Wong Z.S.-Y., Tsui K.L., A framework of rebalancing imbalanced healthcare data for rare events' classification: A case of look-alike sound-alike mix-up incident detection, J. Healthcare Eng., 2018, pp. 1-11, (2018); Hoens T.R., Chawla N.V., Imbalanced datasets: From sampling to classifiers, Imbalanced Learning: Foundations, Algorithms, and Applications., (2013); Kamalov F., Denisov D., Gamma distribution-based sampling for imbalanced data, Knowl.-Based Syst., 207, (2020); Lee H., Kim J., Kim S., Gaussian-based SMOTE algorithm for solving skewed class distributions, Int. J. FUZZY Log. Intell. Syst., 17, 4, pp. 229-234, (2017); Wan Q., Deng X., Li M., Yang H., SDDSMOTE: Synthetic minority oversampling technique based on sample density distribution for enhanced classification on imbalanced microarray data, Proc. 6th Int. Conf. Compute Data Anal., pp. 35-42, (2022); Fernandez A., Garcia S., Herrera F., Chawla N.V., SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary, J. Artif. Intell. Res., 61, pp. 863-905, (2018); Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., Generative adversarial networks, Commun. ACM, 63, 11, pp. 139-144, (2020); Xu L., Skoularidou M., Cuesta-Infante A., Veeramachaneni K., Modeling tabular data using conditional GAN, Proc. Adv. Neural Inf. Process. Syst., 32, (2019); Gong X., Tang B., Zhu R., Liao W., Song L., Data augmentation for electricity theft detection using conditional variational auto-encoder, Energies, 13, 17, (2020); Khozeimeh F., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Alizadehsani R., Gorriz J.M., Hussain S., Sani Z.A., Moosaei H., Khosravi A., Nahavandi S., Islam S.M.S., Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients, Sci. Rep., 11, 1, (2021); Jeong J., Jeong H., Kim H.-J., An autoencoder-based numerical training data augmentation technique, Proc. IEEE Int. Conf. Big Data (Big Data), pp. 5944-5951, (2022); Islam Z., Abdel-Aty M., Cai Q., Yuan J., Crash data augmentation using variational autoencoder, Accident Anal. Prevention, 151, (2021); Nguyen T.-T.-D., Nguyen D.-K., Ou Y.-Y., Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network, Briefings Bioinf., 22, 6, (2021); Fang J., Tang C., Cui Q., Zhu F., Li L., Zhou J., Zhu W., Semisupervised learning with data augmentation for tabular data, Proc. 31st ACM Int. Conf. Inf. Knowl. Manage., pp. 3928-3932, (2022); Wewer C.R., Iosifidis A., Improving online non-destructive moisture content estimation using data augmentation by feature space interpolation with variational autoencoders, Proc. IEEE 21st Int. Conf. Ind. Informat. (INDIN), pp. 1-7, (2023); Chawla N.V., Lazarevic A., Hall L., Bowyer K.W., SMOTEBoost: Improving prediction of the minority class in boosting, Proc. 7th Eur. Conf. Princ. Pract. Knowl. Discovery Databases., pp. 107-119, (2003); Moniz N., Ribeiro R., Cerqueira V., Chawla N., SMOTEBoost for regression: Improving the prediction of extreme values, Proc. IEEE 5th Int. Conf. Data Sci. Adv. Anal. (DSAA), pp. 150-159, (2018); Wahyu Pratama R.F., Purnami S.W., Rahayu S.P., Boosting support vector machines for imbalanced microarray data, Proc. Comput. Sci., 144, pp. 174-183, (2018); Wu Y., Ding Y., Feng J., SMOTE-Boost-based sparse Bayesian model for flood prediction, EURASIP J. Wireless Commun. Netw., 2020, 1, pp. 1-12, (2020); Saglam F., Cengiz M.A., A novel SMOTE-based resampling technique trough noise detection and the boosting procedure, Expert Syst. Appl., 200, (2022); Woo J., Rudasingwa G., Kim S., Assessment of daily personal PM2. 5 exposure level according to four major activities among children, Appl. Sci., 10, 1, (2019); Menegola A., Fornaciali M., Pires R., Bittencourt F.V., Avila S., Valle E., Knowledge transfer for melanoma screening with deep learning, Proc. IEEE 14th Int. Symp. Biomed. Imag. (ISBI), pp. 297-300, (2017); Gretel, (2023)","S. Alkobaisi; United Arab Emirates University, College of Information Technology, Al Ain, United Arab Emirates; email: shayma.alkobaisi@uaeu.ac.ae","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216278673"
"Dai Z.; Zheng X.; Lin J.; Huo Z.; You W.; Miao Q.","Dai, Zhaoji (58656502800); Zheng, Xianwei (56972874700); Lin, Junqi (58914574800); Huo, Zixuan (58656502900); You, Weijie (58656185300); Miao, Qing (57195608736)","58656502800; 56972874700; 58914574800; 58656502900; 58656185300; 57195608736","DAB-LSTMNN: A novel respiratory disease diagnosis deep CNN framework based on attention scheme and BLSTM neural networks","2025","International Journal of Wavelets, Multiresolution and Information Processing","23","1","2450049","","","","0","10.1142/S0219691324500498","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208015913&doi=10.1142%2fS0219691324500498&partnerID=40&md5=a7e685f4abb91c1eda9d46a23d404765","School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China","Dai Z., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China; Zheng X., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China; Lin J., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China; Huo Z., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China; You W., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China; Miao Q., School of Mathematics, Foshan University, Guangdong, Foshan, 528000, China","Respiratory diseases have a significant impact on modern society, posing considerable public health risks. With the emergence of the COVID-19 pandemic, addressing respiratory issues has become increasingly urgent and important. Recently, artificial intelligence-based methods utilizing the acoustic recordings of suspected patients have shown promise in the diagnosis of various respiratory diseases, enabling localized treatment and containment of their spread. As the existing methods cannot efficiently extract subtle differences between the sound samples, thereby limiting their generalization ability. To improve the diagnosis accuracy, this paper proposes a novel multi-channel, multi-modal deep learning architecture based on the attention mechanism. The proposed framework combines a deep convolutional neural network (DCNN) with a bidirectional long short-term memory (BLSTM) network, and also utilizes the attention scheme to extract temporal and spectral features from different modalities of speech data (e.g. coughs, counting sounds and sustained vowel articulation). The proposed method effectively classifies COVID-19 patients, asthma patients and healthy individuals, with a test accuracy of 89.27 ± 0.1%, and an F1 score of 85.42 ± 0.2%. The experimental results validate the feasibility of our method, and also indicate that it is competitive with the existing deep networks.  © 2025 World Scientific Publishing Company.","attention mechanism; BLSTM; DCNN; multichannel; Respiratory diseases","Audio recordings; Convolutional neural networks; COVID-19; Deep neural networks; Diagnosis; Long short-term memory; Patient treatment; Pulmonary diseases; Sound recording; Attention mechanisms; Bidirectional long short-term memory; Convolutional neural network; Disease diagnosis; Localized treatment; Multi channel; Neural-networks; Public health risks; Short term memory; Sound sample; Lung cancer","","","","","","","Abdel-Hamid O., Mohamed A.-R., Jiang H., Deng L., Penn G., Yu D., Convolutional neural networks for speech recognition, IEEE/ACM Trans. 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Computing, Power and Communication Technologies (GUCON), pp. 604-608, (2020); Bhattacharya D., Et al., Coswara: A website application enabling COVID-19 screening by analysing respiratory sound samples and health symptoms, (2022); Brabenec L., Mekyska J., Galaz Z., Rektorova I., Speech disorders in Parkinson's disease: Early diagnostics and effects of medication and brain stimulation, J. Neural Transm, 124, pp. 303-334, (2017); Casper J. K., Leonard R., Understanding Voice Problems: A Physiological Perspective for Diagnosis and Treatment, (2006); Chaudhari G., Jiang X., Fakhry A., Han A., Xiao J., Shen S., Khanzada A., Virufy: Global applicability of crowdsourced and clinical datasets for AI detection of COVID-19 from cough, (2020); Chen C.-F. R., Fan Q., Panda R., CrossViT: Cross-attention multi-scale vision transformer for image classification, Proc. IEEE/CVF Int. Conf. Computer Vision, pp. 357-366, (2021); Das S., A machine learning model for detecting respiratory problems using voice recognition, 2019 IEEE 5th Int. Conf. Convergence in Technology (I2CT), pp. 1-3, (2019); Dave N., Feature extraction methods LPC, PLP and MFCC in speech recognition, Int. J. Adv. Res. Eng. Technol, 1, 6, pp. 1-4, (2013); Erdogdu Sakar B., Serbes G., Sakar C. O., Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease, PLOS One, 12, 8, (2017); Giannakopoulos T., A method for silence removal and segmentation of speech signals, implemented in MATLAB, 2, 2, 17, (2009); Godino-Llorente J. I., Gomez-Vilda P., Blanco-Velasco M., Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters, IEEE Trans. Biomed. Eng, 53, 10, pp. 1943-1953, (2006); Hamdi S., Oussalah M., Moussaoui A., Saidi M., Attention-based hybrid CNNLSTM and spectral data augmentation for COVID-19 diagnosis from cough sound, J. Intell. Inf. Syst, 59, 2, pp. 367-389, (2022); Haritaoglu E. D., Et al., Using deep learning with large aggregated datasets for COVID-19 classification from cough, (2022); Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput, 9, 8, pp. 1735-1780, (1997); Husain M., Simpkin A., Gibbons C., Talkar T., Low D. M., Bonato P., Ghosh S., Quatieri T., OKeeffe D. T., Artificial intelligence for detecting COVID-19 with the aid of human cough, breathing and speech signals: Scoping review, IEEE Open J. Eng. Med. Biol, 3, pp. 235-241, (2022); Islam R., Abdel-Raheem E., Tarique M., A study of using cough sounds and deep neural networks for the early detection of COVID-19, Biomed. Eng. Adv, 3, (2022); Ittichaichareon C., Suksri S., Yingthawornsuk T., Speech recognition usingMFCC, Int. Conf. Computer Graphics, Simulation and Modeling, 9, pp. 136-137, (2012); Khanna V. V., Chadaga K., Sampathila N., Prabhu S., Chadaga R., Umakanth S., Diagnosing COVID-19 using artificial intelligence: A comprehensive review, Network Model. Anal. Health Inform. Bioinform, 11, 1, (2022); Kingma D. P., Ba J., Adam: A method for stochastic optimization, (2014); Kosasih K., Abeyratne U. R., Swarnkar V., Triasih R., Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Trans. Biomed. Eng, 62, 4, pp. 1185-1194, (2014); Lella K. K., Pja A., Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: Cough, breath, and voice, AIMS Public Health, 8, 2, (2021); Lella K. K., Pja A., A literature review on COVID-19 disease diagnosis from respiratory sound data, (2021); Lella K. K., Pja A., Automatic diagnosis of COVID-19 disease using deep convolutional neural network with multi-feature channel from respiratory sound data: Cough, voice, and breath, Alex. Eng. J, 61, 2, pp. 1319-1334, (2022); Low L.-S. A., Maddage N. C., Lech M., Sheeber L., Allen N., Content based clinical depression detection in adolescents, 2009 17th European Signal Processing Conf, pp. 2362-2366, (2009); Mallol-Ragolta A., Cuesta H., Gomez E., Schuller B. W., Cough-based COVID-19 detection with contextual attention convolutional neural networks and gender information, INTERSPEECH 2021, pp. 941-945, (2021); Mao A., Mohri M., Zhong Y., Cross-entropy loss functions: Theoretical analysis and applications, (2023); Maruf S., Martins A. F., Haffari G., Selective attention for context-aware neural machine translation, (2019); Moritz N., Hori T., Le Roux J., Triggered attention for end-to-end speech recognition, ICASSP 2019 - 2019 IEEE Int. Conf. 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Artificial Intelligence Circuits and Systems (AICAS), pp. 1-4, (2021); Shahid Z., Et al., COVID-19 and older adults: What we know, J. Am. Geriatr. Soc, 68, 5, pp. 926-929, (2020); Sharma J., Granmo O.-C., Goodwin M., Environment sound classification using multiple feature channels and attention based deep convolutional neural network, Interspeech 2020, pp. 1186-1190, (2020); Sharma N., Et al., Coswara - A database of breathing, cough, and voice sounds for COVID-19 diagnosis, (2020); Shi J., Zheng X., Li Y., Zhang Q., Ying S., Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease, IEEE J. Biomed. Health Inform, 22, 1, pp. 173-183, (2017); Soriano J. B., Et al., Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: A systematic analysis for the global burden of disease study 2017, Lancet Respir. Med, 8, 6, pp. 585-596, (2020); Tian S., Et al., Characteristics of COVID-19 infection in Beijing, J. Infect, 80, 4, pp. 401-406, (2020); Tokuda K., Kobayashi T., Masuko T., Imai S., Mel-generalized cepstral analysis - A unified approach to speech spectral estimation, 3rd Int. Conf. Spoken Language Processing (ICSLP), pp. 18-22, (1994); Vos T., Et al., Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: A systematic analysis for the global burden of disease study 2019, Lancet, 396, pp. 1204-1222, (2020); Wang F., Jiang M., Qian C., Yang S., Li C., Zhang H., Wang X., Tang X., Residual attention network for image classification, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 3156-3164, (2017); Zhang H., Cisse M., Dauphin Y. N., Lopez-Paz D., mixup: Beyond empirical risk minimization, (2017)","X. Zheng; School of Mathematics, Foshan University, Foshan, Guangdong, 528000, China; email: alex.w.zheng@hotmail.com","","World Scientific","","","","","","02196913","","","","English","Int. J. Wavelets Multiresolution Inf. Process.","Article","Final","","Scopus","2-s2.0-85208015913"
"Honchar O.; Ashcheulova T.; Chumachenko T.; Chumachenko D.","Honchar, Oleksii (57492942500); Ashcheulova, Tetiana (59411770000); Chumachenko, Tetyana (57209537448); Chumachenko, Dmytro (58194260300)","57492942500; 59411770000; 57209537448; 58194260300","Early prediction of long COVID-19 syndrome persistence at 12 months after hospitalisation: a prospective observational study from Ukraine","2025","BMJ Open","15","1","e084311","","","","0","10.1136/bmjopen-2024-084311","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214564983&doi=10.1136%2fbmjopen-2024-084311&partnerID=40&md5=2ded1d18a29aeb1e5ba70b34d952341a","Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine; Department of Epidemiology, Kharkiv National Medical University, Kharkiv, Ukraine; Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University Kharkiv Aviation Institute, Kharkiv, Ukraine","Honchar O., Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine; Ashcheulova T., Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine; Chumachenko T., Department of Epidemiology, Kharkiv National Medical University, Kharkiv, Ukraine; Chumachenko D., Department of Mathematical Modelling and Artificial Intelligence, National Aerospace University Kharkiv Aviation Institute, Kharkiv, Ukraine","Objective To identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development. Design A combined cross-sectional and prospective observational study. Setting A tertiary care hospital. Participants 221 patients hospitalised for COVID-19 who have undergone comprehensive clinical, sonographic and survey-based evaluation predischarge and at 1 month with subsequent 12-month follow-up. The final cohort included 166 patients who had completed the final visit at 12 months. Main outcome measure A self-reported persistence of LCS at 12 months after discharge. Results Self-reported LCS was detected in 76% of participants at 3 months and in 43% at 12 months after discharge. Patients who reported incomplete recovery at 1 year were characterised by a higher burden of comorbidities (Charlson index of 0.69±0.96 vs 0.31±0.51, p=0.001) and residual pulmonary consolidations (1.56±1.78 vs 0.98±1.56, p=0.034), worse blood pressure (BP) control (systolic BP of 138.1±16.2 vs 132.2±15.8 mm Hg, p=0.041), renal (estimated glomerular filtration rate of 59.5±14.7 vs 69.8±20.7 mL/min/1.73 m 2, p=0.007) and endothelial function (flow-mediated dilation of the brachial artery of 10.4±5.4 vs 12.4±5.6%, p=0.048), higher in-hospital levels of liver enzymes (alanine aminotransferase (ALT) of 76.3±60.8 vs 46.3±25.3 IU/L, p=0.002) and erythrocyte sedimentation rate (ESR) (34.3±12.1 vs 28.3±12.6 mm/h, p=0.008), slightly higher indices of ventricular longitudinal function (left ventricular (LV) global longitudinal strain (GLS) of 18.0±2.4 vs 17.0±2.3%, p=0011) and higher levels of Hospital Anxiety and Depression Scale anxiety (7.3±4.2 vs 5.6±3.8, p=0.011) and depression scores (6.4±3.9 vs 4.9±4.3, p=0.022) and EFTER-COVID study physical symptoms score (12.3±3.8 vs 9.2±4.2, p<0.001). At 1 month postdischarge, the persisting differences included marginally higher LV GLS, mitral E/e' ratio and significantly higher levels of both resting and exertional physical symptoms versus patients who reported complete recovery. Logistic regression and machine learning-based binary classification models have been developed to predict the persistence of LCS symptoms at 12 months after discharge. Conclusions Compared with post-COVID-19 patients who have completely recovered by 12 months after hospital discharge, those who have subsequently developed 'very long' COVID were characterised by a variety of more pronounced residual predischarge abnormalities that had mostly subsided by 1 month, except for steady differences in the physical symptoms levels. A simple artificial neural networks-based binary classification model using peak ESR, creatinine, ALT and weight loss during the acute phase, predischarge 6-minute walk distance and complex survey-based symptoms assessment as inputs has shown a 92% accuracy with an area under receiver-operator characteristic curve 0.931 in prediction of LCS symptoms persistence at 12 months.  © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.","Follow-Up Studies; Post-Acute COVID-19 Syndrome; Prognosis","Aged; COVID-19; Cross-Sectional Studies; Female; Hospitalization; Humans; Male; Middle Aged; Post-Acute COVID-19 Syndrome; Prognosis; Prospective Studies; SARS-CoV-2; Self Report; Ukraine; alanine aminotransferase; creatinine; dexamethasone; liver enzyme; methylprednisolone; remdesivir; adult; alanine aminotransferase blood level; anxiety; Article; asthma; binary classification; body weight loss; brachial artery; Charlson Comorbidity Index; chronic kidney failure; controlled study; convalescence; coronary artery disease; coronavirus disease 2019; creatinine blood level; cross-sectional study; depression; disease burden; dyspnea; endothelial dysfunction; erythrocyte sedimentation rate; estimated glomerular filtration rate; female; flow-mediated dilation test; follow up; heart left ventricle ejection fraction; Hospital Anxiety and Depression Scale-Anxiety; Hospital Anxiety and Depression Scale-Depression; hospital discharge; hospital patient; hospitalization; human; hypertension; left ventricular global longitudinal strain; logistic regression analysis; long COVID; lung consolidation; major clinical study; male; Medical Research Council Dyspnea Scale; middle aged; mitral annular plane systolic excursion; modified Borg dyspnea scale; obesity; observational study; oxygen desaturation; oxygen saturation; prediction; predictive value; prevalence; prognosis; prospective study; self report; sensitivity and specificity; six minute walk test; systolic blood pressure; tertiary care center; transthoracic echocardiography; Ukraine; vasodilatation; aged; complication; coronavirus disease 2019; epidemiology; long COVID; prognosis; self report; Severe acute respiratory syndrome coronavirus 2","","alanine aminotransferase, 9000-86-6, 9014-30-6; creatinine, 19230-81-0, 60-27-5; dexamethasone, 50-02-2; methylprednisolone, 6923-42-8, 83-43-2; remdesivir, 1809249-37-3","","","","","Angarita-Fonseca A., Torres-Castro R., Benavides-Cordoba V., Et al., Exploring long COVID condition in Latin America: Its impact on patients' activities and associated healthcare use, Front Med (Lausanne), 10, (2023); Carfi A., Bernabei R., Landi F., Et al., Persistent Symptoms in Patients After Acute COVID-19, JAMA, 324, pp. 603-605, (2020); COVID-19 rapid guideline: managing the long-term effects of COVID-19, COVID-19 rapid guideline: managing the long-term effects of COVID-19, (2020); Shah W., Hillman T., Playford E.D., Et al., Managing the long term effects of covid-19: summary of NICE, SIGN, and RCGP rapid guideline, BMJ, 372, (2021); Torrell G., Puente D., Jacques-Avino C., Et al., Characterisation, symptom pattern and symptom clusters from a retrospective cohort of Long COVID patients in primary care in Catalonia, BMC Infect Dis, 24, (2024); Nittas V., Gao M., West E.A., Et al., Long COVID Through a Public Health Lens: An Umbrella Review, Public Health Rev, 43, (2022); Gyongyosi M., Alcaide P., Asselbergs F.W., Et al., Long COVID and the cardiovascular system-elucidating causes and cellular mechanisms in order to develop targeted diagnostic and therapeutic strategies: a joint Scientific Statement of the ESC Working Groups on Cellular Biology of the Heart and Myocardial and Pericardial Diseases, Cardiovasc Res, 119, pp. 336-356, (2023); Tejerina F., Catalan P., Rodriguez-Grande C., Et al., Post-COVID-19 syndrome. 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Bujang M.A., Sa'at N., Sidik T.M.I.T.A.B., Et al., Sample Size Guidelines for Logistic Regression from Observational Studies with Large Population: Emphasis on the Accuracy Between Statistics and Parameters Based on Real Life Clinical Data, Malays J Med Sci, 25, pp. 122-130, (2018); Juhola M., Laurikkala J., Missing values: how many can they be to preserve classification reliability?, Artif Intell Rev, 40, pp. 231-245, (2013); Honchar O., Ashcheulova T., Spontaneous physical functional recovery after hospitalization for COVID-19: insights from a 1 month follow-up and a model to predict poor trajectory, Front Med (Lausanne), 10, (2023); Honchar O., Ashcheulova T., Chumachenko T., Et al., A prognostic model and pre-discharge predictors of post-COVID-19 syndrome after hospitalization for SARS-CoV-2 infection, Front Public Health, 11, (2023)","O. Honchar; Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine; email: oleksiygonchar@gmail.com","","BMJ Publishing Group","","","","","","20446055","","","39762090","English","BMJ Open","Article","Final","","Scopus","2-s2.0-85214564983"
"Lin Q.; Zheng Z.; Ni H.; Xu Y.; Nie H.","Lin, Qibin (57210213805); Zheng, Zhishui (55262455200); Ni, Haiyang (57541114000); Xu, Yaqing (36191480100); Nie, Hanxiang (7103200385)","57210213805; 55262455200; 57541114000; 36191480100; 7103200385","Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients","2024","Gene","919","","148502","","","","0","10.1016/j.gene.2024.148502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191347932&doi=10.1016%2fj.gene.2024.148502&partnerID=40&md5=c844282db5a7a7d00a59273b078b0ccf","Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China; Department of Geriatric Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China","Lin Q., Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China; Zheng Z., Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China; Ni H., Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China; Xu Y., Department of Geriatric Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China; Nie H., Department of Respiratory and Critical Care Medicine, Renmin Hospital of Wuhan University, Hubei, Wuhan, 430060, China","Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients. © 2024 Elsevier B.V.","Cellular senescence; Immune microenvironment; Molecular biological characteristics; Senescence subtypes; Severe asthma; Treatment","Adult; Animals; Asthma; Cellular Senescence; Disease Models, Animal; Female; Humans; Male; Mice; Proto-Oncogene Protein c-ets-1; peroxisome proliferator activated receptor agonist; tachykinin receptor antagonist; transcription factor Ets 1; adult; animal experiment; animal model; animal tissue; Article; asthma; Bagg albino mouse; CD4+ T lymphocyte; cell aging; cell infiltration; controlled study; dendritic cell; differential gene expression; eosinophil; female; gene; gene expression profiling; gene ontology; gene set enrichment analysis; gene set variation analysis; human; human tissue; immune microenvironment; immune response; KEGG; least absolute shrinkage and selection operator; male; microenvironment; middle aged; mouse; neutrophil; nonhuman; oxidative phosphorylation; pathogenesis; predictive model; protein expression; protein expression level; protein protein interaction; random forest; receiver operating characteristic; regulatory T lymphocyte; unsupervised machine learning; weighted gene co expression network analysis; Western blotting; animal; disease model; genetics; immunology; metabolism","","","","","National Natural Science Foundation of China, NSFC, (82170021); National Natural Science Foundation of China, NSFC","This study was supported by grants from the National Natural Science Foundation of China (No. 82170021). 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Xu; Department of Geriatric Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China; email: 15392885220@163.com","","Elsevier B.V.","","","","","","03781119","","GENED","38670389","English","Gene","Article","Final","","Scopus","2-s2.0-85191347932"
"Arnold M.; Liou L.; Boland M.R.","Arnold, Monique (57218630337); Liou, Lathan (57216160621); Boland, Mary Regina (55235603000)","57218630337; 57216160621; 55235603000","Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis","2024","BioData Mining","17","1","35","","","","0","10.1186/s13040-024-00387-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203839203&doi=10.1186%2fs13040-024-00387-9&partnerID=40&md5=9288a21453ea09f4b964c929a10aeb8b","Department of Emergency Medicine, The Mount Sinai Hospital at the Icahn School of Medicine, 306 E 96th Street, #4A, New York, 10128, NY, United States; Icahn School of Medicine at Mount Sinai Hospital, New York City, NY, United States; Data Science, Department of Mathematics, Herbert W. Boyer School of Natural Sciences, Mathematics, and Computing, Saint Vincent College, Latrobe, PA, United States","Arnold M., Department of Emergency Medicine, The Mount Sinai Hospital at the Icahn School of Medicine, 306 E 96th Street, #4A, New York, 10128, NY, United States; Liou L., Icahn School of Medicine at Mount Sinai Hospital, New York City, NY, United States; Boland M.R., Data Science, Department of Mathematics, Herbert W. Boyer School of Natural Sciences, Mathematics, and Computing, Saint Vincent College, Latrobe, PA, United States","Background: Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models. Results: We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and extreme gradient boosting) to predict total in-hospital cost for admission for each condition. Our models had good predictive performance, with testing R-squared values of 0.701-0.750 (mean of 0.713) for CHF; 0.694-0.724 (mean 0.709) for COPD; and 0.615-0.729 (mean 0.694) for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures, and elective/nonelective admission. Conclusions: ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions. © The Author(s) 2024.","Algorithms; Clinical informatics; Health informatics; Healthcare costs; Machine learning","adult; age; aged; algorithm; Article; chronic obstructive lung disease; clinical assessment; clinical evaluation; cohort analysis; comparative study; congestive heart failure; controlled study; diabetic ketoacidosis; disease exacerbation; female; health care cost; hospital admission; hospital discharge; hospital patient; human; length of stay; linear regression analysis; machine learning; major clinical study; male; medical record; middle aged; prediction; random forest; retrospective study; ridge regression; sample size; support vector machine","","","","","University of Pennsylvania","The research reported in this manuscript is supported, in part, by the Institutional Clinical and Translational Science Award (CTSA) with Dr. Boland as a coinvestigator (UL1-TR-001878) with Dr. Garret Fitzgerald as the PI. Generous funding was also provided by the University of Pennsylvania. 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Arnold; Department of Emergency Medicine, The Mount Sinai Hospital at the Icahn School of Medicine, New York, 306 E 96th Street, #4A, 10128, United States; email: Moniquearnold247@gmail.com","","BioMed Central Ltd","","","","","","17560381","","","","English","BioData Min.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85203839203"
"Mayr W.; Triantafyllopoulos A.; Batliner A.; Schuller B.W.; Berghaus T.M.","Mayr, Wolfgang (59193757400); Triantafyllopoulos, Andreas (57211644900); Batliner, Anton (6602152015); Schuller, Björn W. (6603767415); Berghaus, Thomas M. (6602726611)","59193757400; 57211644900; 6602152015; 6603767415; 6602726611","Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation","2025","International Journal of COPD","20","","","137","147","10","0","10.2147/COPD.S480842","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216598666&doi=10.2147%2fCOPD.S480842&partnerID=40&md5=647e31980c14a4901b9944c395f9f582","Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany; Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany; Group on Language Audio, & Music (GLAM), Imperial College, London, United Kingdom; Munich Data Science Institute (MDSI), Munich, Germany; Medical Faculty, Ludwig Maximilians University of Munich, Munich, Germany","Mayr W., Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany; Triantafyllopoulos A., Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, Munich Center for Machine Learning (MCML), Munich, Germany; Batliner A., Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, Munich Center for Machine Learning (MCML), Munich, Germany; Schuller B.W., Health Informatics (CHI), Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, Munich Center for Machine Learning (MCML), Munich, Germany, Group on Language Audio, & Music (GLAM), Imperial College, London, United Kingdom, Munich Data Science Institute (MDSI), Munich, Germany; Berghaus T.M., Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany, Medical Faculty, Ludwig Maximilians University of Munich, Munich, Germany","Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD. Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation. We extracted a set of spectral, prosodic, and temporal variability features, which were used as input to a support vector machine (SVM). Our baseline for predicting patient state was an SVM model using self-reported BORG and COPD Assessment Test (CAT) scores. Results: In 50 COPD patients (52% males, 22% GOLD II, 44% GOLD III, 32% GOLD IV, all patients group E), speech analysis was superior in distinguishing during and after exacerbation status compared to BORG and CAT scores alone by achieving 84% accuracy in prediction. CAT scores correlated with reading rhythm, and BORG scales with stability in articulation. Pulmonary function testing (PFT) correlated with speech pause rate and speech rhythm variability. Conclusion: Speech analysis may be a viable technology for classifying COPD status, opening up new opportunities for remote disease monitoring. © 2025 Mayr et al.","COPD; digital health; feature interpretation; pathological speech; personalization","Aged; Disease Progression; Female; Functional Status; Humans; Lung; Male; Middle Aged; Predictive Value of Tests; Prognosis; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Reproducibility of Results; Respiratory Function Tests; Severity of Illness Index; Signal Processing, Computer-Assisted; Speech Acoustics; Speech Production Measurement; Support Vector Machine; Time Factors; Voice Quality; aged; Article; chronic obstructive lung disease; clinical assessment; COPD assessment test; demographics; digital health; disease exacerbation; disease severity; female; forced expiratory volume; functional status; hospitalization; human; loudness; lung function; machine learning; male; modified Borg dyspnea scale; oxygen supply; oxygen therapy; pathology; plethysmography; prediction; prospective study; quality of life; questionnaire; speech; speech analysis; spirometry; support vector machine; total lung capacity; chronic obstructive lung disease; comparative study; diagnosis; lung; lung function test; middle aged; pathophysiology; predictive value; procedures; prognosis; reproducibility; severity of illness index; signal processing; time factor; voice","","","IBM  Statistical  Package  for  Social Science SPSS Version 29.0.1.0, IBM","IBM","","","Sethi S, Evans N, Grant B, Murphy T., New strains of bacteria and exacerbations of chronic obstructive pulmonary disease, N Engl J Med, 347, 7, pp. 465-471, (2002); Sethi S, Sethi R, Eschberger K, Et al., Airway bacterial concentrations and exacerbations of chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 176, 4, pp. 356-361, (2007); Agusti A, Celli BR, Criner GJ, Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, Am J Respir Crit Care Med, 207, 7, pp. 819-837, (2023); 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Triantafyllopoulos A, Fendler M, Batliner A, Et al., Distinguishing between pre-and post-treatment in the speech of patients with chronic obstructive pulmonary disease, Proceedings of Interspeech, pp. 3623-3627, (2022); Borg AG., Psychophysical bases of perceived exertion, Med Sci Sports Exerc, 14, 5, pp. 377-381, (1982); Williams N., The Borg rating of perceived exertion (RPE) scale, J Occup Med, 67, 5, pp. 404-405, (2017); Jones PW, Harding G, Berry P, Wiklund I, Chen WH, Leidy NK., Development and first validation of the COPD Assessment Test, Eur Respir J, 34, 3, pp. 648-654, (2009); Hareendran A, Leidy NK, Monz BU, Winnette R, Becker K, Mahler DA., Proposing a standardized method for evaluating patient report of the intensity of dyspnea during exercise testing in COPD, Int J Thron Obstruct Pulmon Dis, 7, pp. 345-355, (2012); Schuller BW, Batliner A., Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing, (2013); Wasserstein RL, Lazar NA., The ASA’s statement on p-values: context, process, and purpose, AM STAT, 70, 2, pp. 129-133, (2016); Nallanthighal VS, Herma A, Strik H., Detection of COPD exacerbation from speech: comparison of acoustic features and deep learning based speech breathing models, ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9097-9101, (2022); Schiel F., Automatic Phonetic Transcription of Non-Prompted Speech, Proceedings of ICPhS14, pp. 607-610, (1999); Eyben F, Wollmer M, Schuller BW., Opensmile: the Munich versatile and fast open-source audio feature extractor, Proceedings of 18th ACM international conference on Multimedia, pp. 1459-1462, (2010); Crooks MG, Brinker AD, Hayman Y, Et al., Continuous cough monitoring using ambient sound recording during convalescence from a COPD exacerbation, Lung, 195, 3, pp. 289-294, (2017); Bartl-Pokorny KD, Pokorny FB, Batliner A, Et al., The voice of COVID-19: acoustic correlates of infection in sustained vowels, J Acoust Soc Am, 149, 6, (2021); Van Bemmel L, Harmsen W, Cucchiarini C, Strik H., Automatic selection of the most characterizing features for detecting COPD in speech, Speech and Computer: 23rd International Conference, SPECOM 2021, pp. 737-748; Kisler T, Reichel U, Schiel F., Multilingual processing of speech via web services, Comput Speech Langu Virtual Special Issues, 45, pp. 326-347, (2017); Ishikawa K, Webster J., The Formant bandwidth as a measure of vowel intelligibility in dysphonic speech, J Voice, 37, 2, pp. 173-177, (2023); De Cheveigne A., Formant bandwidth affects the identification of competing vowels, Proceedings of ICPhS14, pp. 2093-2096, (1999); Eyben F., Real-Time Speech and Music Classification by Large Audio Feature Space Extraction, (2015); Honig F, Batliner A, Bocklet T, Et al., Are men more sleepy than women or does it only look like – automatic analysis of sleepy speech, Proceedings of ICASSP, pp. 995-999, (2014); Merkus J, Hubers F, Cucchiarini C, Strik H., Digital eavesdropper – acoustic speech characteristics as markers of exacerbations in COPD patients, Proceedings of RRaPID workshop of the 12th International Conference on Language Resources and Evaluation (LREC2020), (2020)","W. Mayr; Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Stenglinstrasse 2, D-86156, Germany; email: wolfgang.mayr@uk-augsburg.de; A. Triantafyllopoulos; Health Informatics, Department of Clinical Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Ismaninger Straße 22, 81675, Germany; email: andreas.triantafyllopoulos@tum.de","","Dove Medical Press Ltd","","","","","","11769106","","","39867993","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85216598666"
"Rueda R.; Fabello E.; Silva T.; Genzor S.; Mizera J.; Stanke L.","Rueda, Ramón (57195982156); Fabello, Esteban (59195699100); Silva, Tatiana (57225131401); Genzor, Samuel (57713787500); Mizera, Jan (57279189400); Stanke, Ladislav (55644160600)","57195982156; 59195699100; 57225131401; 57713787500; 57279189400; 55644160600","Machine learning approach to flare-up detection and clustering in chronic obstructive pulmonary disease (COPD) patients","2024","Health Information Science and Systems","12","1","50","","","","0","10.1007/s13755-024-00308-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207525359&doi=10.1007%2fs13755-024-00308-4&partnerID=40&md5=d1f0ecbcd03f32f6e3053390ba5c1be6","Tree Technology, Asturias, Spain; Department of Pulmonary Diseases and Tuberculosis, University Hospital Olomouc, Zdravotníků 248/7, Olomuc, 77900, Czech Republic; Czech National e-Health Center, University Hospital Olomouc, Olomuc, Czech Republic","Rueda R., Tree Technology, Asturias, Spain; Fabello E., Tree Technology, Asturias, Spain; Silva T., Tree Technology, Asturias, Spain; Genzor S., Department of Pulmonary Diseases and Tuberculosis, University Hospital Olomouc, Zdravotníků 248/7, Olomuc, 77900, Czech Republic; Mizera J., Department of Pulmonary Diseases and Tuberculosis, University Hospital Olomouc, Zdravotníků 248/7, Olomuc, 77900, Czech Republic; Stanke L., Czech National e-Health Center, University Hospital Olomouc, Olomuc, Czech Republic","Purpose : Chronic obstructive pulmonary disease (COPD) is a prevalent and preventable condition that typically worsens over time. Acute exacerbations of COPD significantly impact disease progression, underscoring the importance of prevention efforts. This observational study aimed to achieve two main objectives: (1) identify patients at risk of exacerbations using an ensemble of clustering algorithms, and (2) classify patients into distinct clusters based on disease severity. Methods : Data from portable medical devices were analyzed post-hoc using hyperparameter optimization with Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest, and Support Vector Machine (SVM) algorithms, to detect flare-ups. Principal Component Analysis (PCA) followed by KMeans clustering was applied to categorize patients by severity. Results : 25 patients were included within the study population, data from 17 patients had the required reliability. Five patients were identified in the highest deterioration group, with one clinically confirmed exacerbation accurately detected by our ensemble algorithm. Then, PCA and KMeans clustering grouped patients into three clusters based on severity: Cluster 0 started with the least severe characteristics but experienced decline, Cluster 1 consistently showed the most severe characteristics, and Cluster 2 showed slight improvement. Conclusion : Our approach effectively identified patients at risk of exacerbations and classified them by disease severity. Although promising, the approach would need to be verified on a larger sample with a larger number of recorded clinically verified exacerbations. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.","Clustering; COPD; Data analysis; Machine learning","adult; aged; air pollution; air quality; algorithm; Article; artificial intelligence; artificial neural network; blood oxygen tension; blood pressure monitoring; caregiver; chest tightness; chronic obstructive lung disease; clinical article; cluster analysis; coughing; data analysis; decision tree; diabetes mellitus; diastolic blood pressure; disease exacerbation; disease severity; dyspnea; exercise tolerance; female; health care cost; heart rate; heart rhythm; human; machine learning; male; middle aged; multinomial logistic regression; observational study; oximetry; particulate matter 10; particulate matter 2.5; principal component analysis; questionnaire; respiratory tract disease; sleep quality; spirometry; support vector machine; systolic blood pressure","","","","","Horizon 2020 Framework Programme, H2020, (857159)","","Venkatesan P., Gold copd report: 2024 update, Lancet Respir Med, 12, 1, pp. 15-16, (2024); He Y., Qian D., Diao J., Cho M., Silverman E., Gusev A., Manrai A., Martin A., Patel C., Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors, Nat Commun, 14, 1, (2023); Lee S., Lee I., Kim S., Predicting development of chronic obstructive pulmonary disease and its risk factor analysis, Annu Int Conf IEEE Eng Med Biol Soc, (2023); Mah J., Ritchie A., Finney L., Selected updates on chronic obstructive pulmonary disease, Curr Opin Pulm Med, (2023); Hurst J., Vestbo J., Anzueto A., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N Engl J Med, 363, 12, pp. 1128-1138, (2010); Langsetmo L., Platt R., Ernst P., Bourbeau J., Underreporting exacerbation of chronic obstructive pulmonary disease in a longitudinal cohort, Am J Respir Crit Care Med, 177, 4, pp. 396-401, (2008); Zatloukal J., Brat K., Neumannova K., Volakova E., Hejduk K., Kocova E., Kudela O., Kopecky M., Plutinsky M., Koblizek V., Chronic obstructive pulmonary disease - diagnosis and management of stable disease; a personalized approach to care, using the treatable traits concept based on clinical phenotypes. Position paper of the Czech pneumological and phthisiological society, Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub, 164, 4, pp. 325-356, (2020); Adibi A., Sin D., Safari A., Johnson K., Aaron S., FitzGerald J., Sadatsafavi M., The acute copd exacerbation prediction tool (accept): a modelling study, Lancet Respir Med, 8, 10, pp. 1013-1021, (2020); Fernandez-Granero M., Sanchez-Morillo D., Lopez-Gordo M., Leon A., A machine learning approach to prediction of exacerbations of chronic obstructive pulmonary disease, Artificial Computation in Biology and Medicinescience, 9107, pp. 305-311, (2015); Wu Y., Lan C., Tzeng I., Wu C., The copd-readmission (core) score: a novel prediction model for one-year chronic obstructive pulmonary disease readmissions, J Formos Med Assoc, 120, 3, pp. 1005-1013, (2021); Goto T., Camargo C., Faridi M., Yun B., Hasegawa K., Machine learning approaches for predicting disposition of asthma and copd exacerbations in the ed, Am J Emerg Med, 36, 9, pp. 1650-1654, (2018); Peng J., Chen C., Zhou M., Xie X., Zhou Y., Luo C., A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators, Sci Rep, 10, 1, (2020); Newandee D.A., Reisman S.S., Bartels A.N., de Meersman R.E., Copd Severity Classification Using Principal Component and Cluster Analysis on Hrv Parameters, (2003); Merone M., Et al., Discovering Copd Phenotyping via Simultaneous Feature Selection and Clustering., (2018); Bellos C., Papadopoulos A., Rosso R., Fotiadis D.I., Categorization of patients’ health status in copd disease using a wearable platform and random forests methodology, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, (2012); Hussain A., Et al., Detection of different stages of copd patients using machine learning techniques, 2021 23Rd International Conference on Advanced Communication Technology (ICACT), (2021); ECMWF Projects: ECMWF Projects: Copernicus training—CAMS; Catestonline.; Costa M., Goldberger A., Peng C., Multiscale entropy analysis of biological signals, Phys Rev E, 71, 2 Pt 1, (2005); Higuchi T., Approach to an irregular time series on the basis of the fractal theory, Physica D, 31, 2, pp. 277-283, (1988); He S., Cistulli P., Chazal P., A review of novel oximetry parameters for the prediction of cardiovascular disease in obstructive sleep apnoea, Diagnostics, 13, 21, (2023); Alowiwi H., Watson S., Jetmalani K., Et al., Relationship between concavity of the flow-volume loop and small airway measures in smokers with normal spirometry, BMC Pulm Med, 22, 1, (2022); Kohonen T., The self-organizing map, Proc IEEE, 78, 9, pp. 1464-1480, (1990); Hearst M.A., Dumais S.T., Osuna E., Platt J., Scholkopf B., Support vector machines, IEEE Intell Syst Their Appl, 13, 4, pp. 18-28, (1998); Liu F.T., Ting K.M., Zhou Z.H., Isolation forest, 2008 Eighth IEEE International Conference on Data Mining, pp. 413-422, (2008); Schubert E., Sander J., Ester M., Kriegel H.P., Xu X., Dbscan revisited, revisited: why and how you should (still) use dbscan, ACM Trans Database Syst (TODS), 42, 3, pp. 1-21, (2017); Arthur D., Vassilvitskii S., K-means++: The advantages of careful seeding, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035, (2007); Smith L., Oakden-Rayner L., Bird A., Zeng M., To M., Mukherjee S., Palmer L., Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis, Lancet Digit Health, 5, 12, pp. 872-881, (2023); Johns D., Walters J., Walters E., Diagnosis and early detection of copd using spirometry, J Thorac Dis, 6, 11, pp. 1557-1569, (2014); Hoesterey D., Das N., Janssens W., Et al., Spirometric indices of early airflow impairment in individuals at risk of developing copd: spirometry beyond fev1/fvc, Respir Med, 156, pp. 58-68, (2019); Kollert F., Tippelt A., Muller C., Et al., Hemoglobin levels above anemia thresholds are maximally predictive for long-term survival in copd with chronic respiratory failure, Respir Care, 58, 7, pp. 1204-1212, (2013); Toft-Petersen A., Torp-Pedersen C., Weinreich U., Rasmussen B., Association between hemoglobin and prognosis in patients admitted to hospital for copd, Int J Chron Obstruct Pulm Dis, 11, pp. 2813-2820, (2016); Deep A., Behera P., Subhankar S., Rajendran A., Rao C., Serum electrolytes in patients presenting with acute exacerbation of chronic obstructive pulmonary disease (copd) and their comparison with stable copd patients, Cureus, 15, 4, (2023); Lindner G., Herschmann S., Funk G., Et al., Sodium and potassium disorders in patients with copd exacerbation presenting to the emergency department, BMC Emerg Med, 22, 1, (2022)","R. Rueda; Tree Technology, Asturias, Spain; email: ramon.rueda@treelogic.com","","Springer","","","","","","20472501","","","","English","Health Inf. Sci. Syst.","Article","Final","","Scopus","2-s2.0-85207525359"
"Despotovic V.; Elbéji A.; Fünfgeld K.; Pizzimenti M.; Ayadi H.; Nazarov P.V.; Fagherazzi G.","Despotovic, Vladimir (35572969800); Elbéji, Abir (57657890100); Fünfgeld, Kevser (59024950000); Pizzimenti, Mégane (57217294849); Ayadi, Hanin (58560568100); Nazarov, Petr V. (56261780400); Fagherazzi, Guy (35069333800)","35572969800; 57657890100; 59024950000; 57217294849; 58560568100; 56261780400; 35069333800","Digital voice-based biomarker for monitoring respiratory quality of life: findings from the colive voice study","2024","Biomedical Signal Processing and Control","96","","106555","","","","0","10.1016/j.bspc.2024.106555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196011266&doi=10.1016%2fj.bspc.2024.106555&partnerID=40&md5=10d18b7d5f36a339a66f7221e9b6f00e","Bioinformatics & AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, Luxembourg; Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg; Multi-Omics Data Science, Department of Cancer Research, Luxembourg Institute of Health, Strassen, Luxembourg","Despotovic V., Bioinformatics & AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, Luxembourg; Elbéji A., Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg; Fünfgeld K., Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg; Pizzimenti M., Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg; Ayadi H., Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg; Nazarov P.V., Bioinformatics & AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, Luxembourg, Multi-Omics Data Science, Department of Cancer Research, Luxembourg Institute of Health, Strassen, Luxembourg; Fagherazzi G., Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg","Regular monitoring of respiratory quality of life (RQoL) is essential in respiratory healthcare, facilitating prompt diagnosis and tailored treatment for chronic respiratory diseases. Voice alterations resulting from respiratory conditions create unique audio signatures that can potentially be utilized for disease screening or monitoring. Analyzing data from 1908 participants from the Colive Voice study, which collects standardized voice recordings alongside comprehensive demographic, epidemiological, and patient-reported outcome data, we evaluated various strategies to estimate RQoL from voice, including handcrafted acoustic features, standard acoustic feature sets, and advanced deep audio embeddings derived from pretrained convolutional neural networks. We compared models using clinical features alone, voice features alone, and a combination of both. The multimodal model combining clinical and voice features demonstrated the best performance, achieving an accuracy of 70.8% and an area under the receiver operating characteristic curve (AUROC) of 0.77; an improvement of over 5% in terms of accuracy and 7% in terms of AUROC compared to model utilizing voice features alone. Incorporating vocal biomarkers significantly enhanced the predictive capacity of clinical variables across all acoustic feature types, with a net classification improvement (NRI) of up to 0.19. Our digital voice-based biomarker is capable of accurately predicting RQoL, either as an alternative to or in conjunction with clinical measures, and could be used to facilitate rapid screening and remote monitoring of respiratory health status. © 2024","Audio processing; Deep learning; Respiratory quality of life; Voice biomarker","Audio acoustics; Biomarkers; Convolutional neural networks; Diagnosis; Diseases; E-learning; Acoustic features; Audio processing; Audio signature; Condition; Deep learning; Quality of life; Receiver operating characteristic curves; Respiratory quality of life; Voice biomarker; Voice study; acoustics; adult; arterial gas; Article; asthma; body mass; chronic obstructive lung disease; clinical feature; cohort analysis; controlled study; convolutional neural network; coronavirus disease 2019; coughing; deep learning; demography; diagnostic accuracy; diagnostic test accuracy study; diffusing capacity for carbon monoxide; feature extraction; female; fibrosing alveolitis; human; lung volume; major clinical study; male; paralanguage; phonation; quality of life; questionnaire; remote sensing; smoking habit; sore throat; thorax pain; voice; Deep learning","","","","","Luxembourg Institute of Health","Colive Voice study is funded by the Luxembourg Institute of Health. The funder played no role in the study design, data collection, analysis and interpretation of data, or the writing of this manuscript. We would like to thank all participants that contributed to Colive Voice study, as well as our partners for their help in recruiting new participants. Special thanks go to Aur\u00E9lie Fischer, Philippe Kayser, Luigi De Giovanni, Michael Schnell and Aurore Dobosz for their substantial contribution to the Colive Voice study.","Jones P.W., Quirk F.H., Baveystock C.M., Littlejohns P., A self-complete measure of health status for chronic airflow limitation. the St. George's respiratory questionnaire, Am. Rev. Respir. Dis., 145, 6, pp. 1321-1327, (1992); Chauvin A., Rupley L., Meyers K., Johnson K., Eason J., Research corner outcomes in cardiopulmonary physical therapy: Chronic respiratory disease questionnaire (CRQ):, Cardiopulm. Phys. Ther. 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Voice, (2022); Nallanthighal V.S., Harma A., Strik H., Detection of COPD exacerbation from speech: Comparison of acoustic features and deep learning based speech breathing models, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9097-9101, (2022); Vertigan A.E., Kapela S.L., Gibson P.G., Laryngeal dysfunction in severe asthma: A cross-sectional observational study, J. Allergy Clin. Immunol.: Pract., 9, 2, pp. 897-905, (2021); Alam M.Z., Simonetti A., Brillantino R., Tayler N., Grainge C., Siribaddana P., Nouraei S.A.R., Batchelor J., Rahman M.S., Mancuzo E.V., Holloway J.W., Holloway J.A., Rezwan F.I., Predicting pulmonary function from the analysis of voice: A machine learning approach, Front. Digital Health, 4, (2022); Sara J.D.S., Maor E., Borlaug B., Lewis B.R., Orbelo D., Lerman L.O., Lerman A., Non-invasive vocal biomarker is associated with pulmonary hypertension, PLoS One, 15, (2020); Tracey B., Patel S., Zhang Y., Chappie K., Volfson D., Parisi F., Adans-Dester C., Bertacchi F., Bonato P., Wacnik P., Voice biomarkers of recovery from acute respiratory illness, IEEE J. Biomed. Health Inf., 26, 6, pp. 2787-2795, (2022); Han J., Xia T., Spathis D., Bondareva E., Brown C., Chauhan J., Dang T., Grammenos A., Hasthanasombat A., Floto A., Cicuta P., Mascolo C., Sounds of COVID-19: exploring realistic performance of audio-based digital testing, npj Digit. Med., 5, 1, pp. 1-9, (2022); Pah N.D., Indrawati V., Kumar D.K., Voice features of sustained phoneme as COVID-19 biomarker, IEEE J. Transl. Eng. Health Med., 10, pp. 1-9, (2022); Al Ismail M., Deshmukh S., Singh R., Detection of Covid-19 through the analysis of vocal fold oscillations, pp. 1035-1039, (2021); Despotovic V., Ismael M., Cornil M., Call R.M., Fagherazzi G., Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results, Comput. Biol. Med., 138, (2021); Triantafyllopoulos A., Semertzidou A., Song M., Pokorny F.B., Schuller B.W., Introducing the COVID-19 YouTube (COVYT) speech dataset featuring the same speakers with and without infection, Biomed. Signal Process. Control, 88, (2024); Fagherazzi G., Zhang L., Elbeji A., Higa E., Despotovic V., Ollert M., Aguayo G.A., Nazarov P.V., Fischer A., A voice-based biomarker for monitoring symptom resolution in adults with COVID-19: Findings from the prospective Predi-COVID cohort study, PLoS Digit. Health, 1, 10, (2022); Anane I., Guezguez F., Knaz H., Ben Saad H., How to stage airflow limitation in stable chronic obstructive pulmonary disease male patients?, Am. J. Men's Health, 14, 3, (2020); Zysman M., Rubenstein J., Le Guillou F., Colson R.M.H., Pochulu C., Grassion L., Escamilla R., Piperno D., Pon J., Khan S., Raherison-Semjen C., COPD burden on sexual well-being, Respir. Res., 21, 1, (2020); Yasien D.G., Hassan E.S., Mohamed H.A., Phonatory function and characteristics of voice in recovering COVID-19 survivors, Eur. Arch. Oto-Rhino-Laryngol., 279, 9, pp. 4485-4490, (2022); Lenain R., Weston J., Shivkumar A., Fristed E., Surfboard: Audio feature extraction for modern machine learning, (2020); Jadoul Y., Thompson B., de Boer B., Introducing Parselmouth: A Python interface to Praat, J. Phonetics, 71, pp. 1-15, (2018); Eyben F., Scherer K.R., Schuller B.W., Sundberg J., Andre E., Busso C., Devillers L.Y., Epps J., Laukka P., Narayanan S.S., Truong K.P., The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing, IEEE Trans. Affect. Comput., 7, 2, pp. 190-202, (2016); Eyben F., Weninger F., Gross F., Schuller B., Recent developments in openSMILE, the Munich open-source multimedia feature extractor, Proceedings of the 21st ACM International Conference on Multimedia, MM ’13, pp. 835-838, (2013); Hershey S., Chaudhuri S., Ellis D.P.W., Gemmeke J.F., Jansen A., Moore C., Plakal M., Platt D., Saurous R.A., Seybold B., Slaney M., Weiss R., Wilson K., CNN Architectures for Large-Scale Audio Classification, (2017); Cramer J., Wu H.-H., Salamon J., Bello J.P., Look, listen, and learn more: Design choices for deep audio embeddings, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3852-3856, (2019); Niizumi D., Takeuchi D., Ohishi Y., Harada N., Kashino K., BYOL for audio: Exploring pre-trained general-purpose audio representations, IEEE/ACM Trans. Audio, Speech, Lang. Process., 31, pp. 137-151, (2023); de Hond A.A.H., Shah V.B., Kant I.M.J., Van Calster B., Steyerberg E.W., Hernandez-Boussard T., Perspectives on validation of clinical predictive algorithms, npj Digit. Med., 6, 1, pp. 1-3, (2023); Pati S., Swain S., Patel S.K., Chauhan A.S., Panda N., Mahapatra P., Pati S., An assessment of health-related quality of life among patients with chronic obstructive pulmonary diseases attending a tertiary care hospital in Bhubaneswar city, India, J. Family Med. Primary Care, 7, 5, pp. 1047-1053, (2018); Bove D.G., Lavesen M., Lindegaard B., Characteristics and health related quality of life in a population with advanced chronic obstructive pulmonary disease, a cross-sectional study, BMC Palliat. Care, 19, 1, (2020); Gonzalez-Barcala F.-J., de la Fuente-Cid R., Tafalla M., Nuevo J., Caamano-Isorna F., Factors associated with health-related quality of life in adults with asthma. A cross-sectional study, Multidiscip. Respir. Med., 7, (2012); Cox I.A., Arriagada N.B., Graaff B.D., Corte T.J., Glaspole I., Lartey S., Walters E.H., Palmer A.J., Health-related quality of life of patients with idiopathic pulmonary fibrosis: A systematic review and meta-analysis, Eur. Respir. Rev., 29, 158, (2020); Meys R., Delbressine J.M., Goertz Y.M., Vaes A.W., Machado F.V., Van Herck M., Burtin C., Posthuma R., Spaetgens B., Franssen F.M., Spies Y., Vijlbrief H., van't Hul A.J., Janssen D.J., Spruit M.A., Houben-Wilke S., Generic and respiratory-specific quality of life in non-hospitalized patients with COVID-19, J. Clin. Med., 9, 12, (2020); Cappa V., Marcon A., Di Gennaro G., Chamitava L., Cazzoletti L., Bombieri C., Nicolis M., Perbellini L., Sembeni S., de Marco R., Spelta F., Ferrari M., Zanolin M.E., Health-related quality of life varies in different respiratory disorders: A multi-case control population based study, BMC Pulm. Med., 19, (2019); Njoroge M.W., Mjojo P., Chirwa C., Rylance S., Nightingale R., Gordon S.B., Mortimer K., Burney P., Balmes J., Rylance J., Obasi A., Niessen L.W., Devereux G., Changing lung function and associated health-related quality-of-life: A five-year cohort study of Malawian adults, eClinicalMedicine, 41, (2021); Huber M.B., Kurz C., Kirsch F., Schwarzkopf L., Schramm A., Leidl R., The relationship between body mass index and health-related quality of life in COPD: real-world evidence based on claims and survey data, Respir. Res., 21, (2020); Sergeeva G., Emelyanov A., Body mass index and quality of life in patients with asthma, Eur. Respir. J., 38, (2011); Fischer A., Elbeji A., Aguayo G., Fagherazzi G., Recommendations for successful implementation of the use of vocal biomarkers for remote monitoring of COVID-19 and long COVID in clinical practice and research, Interact. J. Med. Res., 11, (2022); Muzammel M., Salam H., Othmani A., End-to-end multimodal clinical depression recognition using deep neural networks: A comparative analysis, Comput. Methods Programs Biomed., 211, (2021); Rohanian M., Hough J., Purver M., Detecting depression with word-level multimodal fusion, Interspeech 2019, pp. 1443-1447, (2019); Rohanian M., Hough J., Purver M., Multi-modal fusion with gating using audio, lexical and disfluency features for alzheimer's dementia recognition from spontaneous speech, Interspeech 2020, pp. 2187-2191, (2020); Vasquez-Correa J.C., Arias-Vergara T., Orozco-Arroyave J.R., Eskofier B., Klucken J., Noth E., Multimodal assessment of Parkinson's disease: A deep learning approach, IEEE J. Biomed. Health Inf., 23, 4, pp. 1618-1630, (2019)","V. Despotovic; Bioinformatics & AI Unit, Department of Medical Informatics, Luxembourg Institute of Health, Strassen, Luxembourg; email: vladimir.despotovic@lih.lu","","Elsevier Ltd","","","","","","17468094","","","","English","Biomed. Signal Process. Control","Article","Final","","Scopus","2-s2.0-85196011266"
"Salvi S.; Ghorpade D.; Nair S.; Pinto L.; Singh A.K.; Venugopal K.; Dhar R.; Talwar D.; Koul P.; Prabhudesai P.","Salvi, Sundeep (7005258777); Ghorpade, Deesha (57204812288); Nair, Sanjeev (7402726229); Pinto, Lancelot (25628330900); Singh, Ashok K. (57212409810); Venugopal, K. (57210284705); Dhar, Raja (7006700626); Talwar, Deepak (24449863700); Koul, Parvaiz (58944144000); Prabhudesai, Pralhad (6602951966)","7005258777; 57204812288; 7402726229; 25628330900; 57212409810; 57210284705; 7006700626; 24449863700; 58944144000; 6602951966","A 7-point evidence-based care discharge protocol for patients hospitalized for exacerbation of COPD: consensus strategy and expert recommendation","2024","npj Primary Care Respiratory Medicine","34","1","44","","","","0","10.1038/s41533-024-00378-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212684331&doi=10.1038%2fs41533-024-00378-7&partnerID=40&md5=1cb3e4c2fdde5bdaa5b8107fc7651ed9","Pulmocare Research and Education Foundation, Pune, India; Symbiosis Medical College for Women and Symbiosis University Hospital and Research Centre, Symbiosis International (Deemed University), Pune, India; Department of Pulmonary Medicine, Government Medical College, Thrissur, India; Department Respiratory of Medicine, PD Hinduja Hospital, Mumbai, India; Department of Pulmonary and Critical Care Medicine, Regency Hospital Kanpur, Kanpur, India; Department of Pulmonology Sooriya Hospital, Chennai, India; Department of Respiratory Medicine, CK Birla Hospitals, Kolkata, India; Metro Respiratory Center, Metro Hospitals and Heart Institute, Noida, India; Sher-i-Kashmir Institute of Medical Sciences University, Ganderbal, India; Department of Respiratory Medicine, Lilavati Hospital and Research Centre, Mumbai, India","Salvi S., Pulmocare Research and Education Foundation, Pune, India, Symbiosis Medical College for Women and Symbiosis University Hospital and Research Centre, Symbiosis International (Deemed University), Pune, India; Ghorpade D., Pulmocare Research and Education Foundation, Pune, India; Nair S., Department of Pulmonary Medicine, Government Medical College, Thrissur, India; Pinto L., Department Respiratory of Medicine, PD Hinduja Hospital, Mumbai, India; Singh A.K., Department of Pulmonary and Critical Care Medicine, Regency Hospital Kanpur, Kanpur, India; Venugopal K., Department of Pulmonology Sooriya Hospital, Chennai, India; Dhar R., Department of Respiratory Medicine, CK Birla Hospitals, Kolkata, India; Talwar D., Metro Respiratory Center, Metro Hospitals and Heart Institute, Noida, India; Koul P., Sher-i-Kashmir Institute of Medical Sciences University, Ganderbal, India; Prabhudesai P., Department of Respiratory Medicine, Lilavati Hospital and Research Centre, Mumbai, India","Acute exacerbations of COPD (ECOPD) are an important event in the life of a COPD patient as it causes significant deterioration of physical, mental, and social health, hastens disease progression, increases the risk of dying and causes a huge economic loss. Preventing ECOPD is therefore one of the most important goals in the management of COPD. Before the patient is discharged after hospitalization for ECOPD, it is crucial to offer an evidence-based care bundle protocol that will help minimize the future risk of readmissions and death. To develop the content of this quality care bundle, an Expert Working Group was formed, which performed a systematic review of literature, brainstormed, and debated on key clinical issues before arriving at a consensus strategy that could help physicians achieve this goal. A 7-point consensus strategy was prepared, which included: (1) enhancing awareness and seriousness of ECOPD, (2) identifying patients at risk for future exacerbations, (3) optimizing pharmacologic treatment of COPD, (4) identifying and treating comorbidities, (5) preventing bacterial and viral infections, (6) pulmonary rehabilitation, and (7) palliative care. Physicians may find this 7-point care bundle useful to minimize the risk of future exacerbations and reduce morbidity and mortality. © The Author(s) 2024.","","Consensus; Disease Progression; Evidence-Based Medicine; Hospitalization; Humans; Patient Care Bundles; Patient Discharge; Pulmonary Disease, Chronic Obstructive; acetylcysteine; antibiotic agent; antioxidant; arformoterol; beta adrenergic receptor stimulating agent; bronchodilating agent; budesonide; corticosteroid; dipeptidyl carboxypeptidase inhibitor; formoterol; glycopyrronium; hydroxymethylglutaryl coenzyme A reductase inhibitor; immunoglobulin E; ipratropium bromide; ipratropium bromide plus salbutamol; levalbuterol; mucolytic agent; muscarinic receptor blocking agent; placebo; revefenacin; salbutamol; adjuvant therapy; ambient air; antibiotic therapy; Article; artificial intelligence; awareness; bacterial infection; behavior change; cardiovascular disease; care bundle; chronic obstructive lung disease; clinical protocol; comorbidity; consensus; diet supplementation; disease exacerbation; evidence based practice; frailty; general condition deterioration; grip strength; high risk patient; home care; hospital discharge; hospital patient; hospital readmission; human; infection prevention; long term care; low socioeconomic status; lung function; lung volume; machine learning; maintenance therapy; medication compliance; mental health; mortality risk; nebulization; noninvasive ventilation; obstructive sleep apnea; oxygen therapy; palliative therapy; pharmaceutical care; physician; predictive model; pulmonary hypertension; pulmonary rehabilitation; quality of life; risk factor; sit-to-stand test; six minute walk test; vaccination; virus infection; walking speed; care bundle; chronic obstructive lung disease; evidence based medicine; hospitalization; procedures; therapy","","acetylcysteine, 616-91-1, 89344-48-9; arformoterol, 200815-49-2, 67346-49-0; budesonide, 51333-22-3, 51372-29-3; formoterol, 73573-87-2; glycopyrronium, 596-51-0, 1624259-25-1, 13283-82-4, 51186-83-5, 740028-90-4, 873295-46-6; immunoglobulin E, 37341-29-0; ipratropium bromide, 22254-24-6, 66985-17-9; levalbuterol, 50293-90-8; revefenacin, 864750-70-9; salbutamol, 18559-94-9, 35763-26-9","","","Cipla","This project was supported by Cipla Ltd, but did not participate in literature search, debates, discussions, manuscript writing and final manuscript evaluation.","Li H.Y., Et al., Global, regional and national burden of chronic obstructive pulmonary disease over a 30-year period: estimates from the 1990 to 2019 Global Burden of Disease Study, Respirology, 28, pp. 29-36, (2023); Venkatesan P., GOLD COPD report: 2023 update, Lancet Respir. 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"Bettacchioli E.; Foulquier J.-B.; Chevet B.; Cornec-Le Gall E.; Hanrotel C.; Lanfranco L.; De Moreuil C.; Lambert Y.; Dueymes M.; Foulquier N.; Cornec D.","Bettacchioli, Eleonore (57204074055); Foulquier, Jean-Baptiste (57204696713); Chevet, Baptiste (57484090700); Cornec-Le Gall, Emilie (55314361300); Hanrotel, Catherine (6507469783); Lanfranco, Luca (57191429723); De Moreuil, Claire (55444128900); Lambert, Yannick (59314741000); Dueymes, Maryvonne (7003453230); Foulquier, Nathan (57202736836); Cornec, Divi (26641101300)","57204074055; 57204696713; 57484090700; 55314361300; 6507469783; 57191429723; 55444128900; 59314741000; 7003453230; 57202736836; 26641101300","Dual MPO/PR3 ANCA positivity and vasculitis: Insights from a 7-cases study and an AI-powered literature review","2024","Rheumatology (United Kingdom)","63","9","","2557","2568","11","0","10.1093/rheumatology/keae170","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203201254&doi=10.1093%2frheumatology%2fkeae170&partnerID=40&md5=993cc3e2fd78261ce035daa0a0b97fb9","Immunology and Immunotherapy Laboratory, CHU de Brest, Brest, France; LBAI Inserm UMR 1227, Univ Brest, Brest, France; Medical Biology Laboratory, Morlaix Hospital Centre, Morlaix, France; Rheumatology Department, CHU de Brest, Brest, France; Nephrology Department, CHU de Brest, Brest, France; GGB Inserm UMR 1078, Univ Brest, Brest, France; Internal Medicine Department, CHU de Brest, Brest, France; GETBO Inserm UMR 1304, Univ Brest, Brest, France; Internal Medicine Department, Morlaix Hospital Centre, Morlaix, France","Bettacchioli E., Immunology and Immunotherapy Laboratory, CHU de Brest, Brest, France, LBAI Inserm UMR 1227, Univ Brest, Brest, France; Foulquier J.-B., Medical Biology Laboratory, Morlaix Hospital Centre, Morlaix, France; Chevet B., Rheumatology Department, CHU de Brest, Brest, France; Cornec-Le Gall E., Nephrology Department, CHU de Brest, Brest, France, GGB Inserm UMR 1078, Univ Brest, Brest, France; Hanrotel C., Nephrology Department, CHU de Brest, Brest, France; Lanfranco L., Nephrology Department, CHU de Brest, Brest, France; De Moreuil C., Internal Medicine Department, CHU de Brest, Brest, France, GETBO Inserm UMR 1304, Univ Brest, Brest, France; Lambert Y., Internal Medicine Department, Morlaix Hospital Centre, Morlaix, France; Dueymes M., Immunology and Immunotherapy Laboratory, CHU de Brest, Brest, France, LBAI Inserm UMR 1227, Univ Brest, Brest, France; Foulquier N., LBAI Inserm UMR 1227, Univ Brest, Brest, France; Cornec D., LBAI Inserm UMR 1227, Univ Brest, Brest, France, Rheumatology Department, CHU de Brest, Brest, France","Objectives: Anti-neutrophil cytoplasm antibodies (ANCA)-associated vasculitides (AAV) are rare conditions characterized by inflammatory cell infiltration in small blood vessels, leading to tissue necrosis. While most patients with AAV present antibodies against either myeloperoxidase (MPO) or proteinase 3 (PR3), rare cases of dual positivity for both antibodies (DP-ANCA) have been reported, and their impact on the clinical picture remains unclear. The goal of this study was to investigate the clinical implications, phenotypic profiles and outcomes of patients with DP-ANCA. Methods: A retrospective screening for DP-ANCA cases was conducted at Brest University Hospital's immunology laboratory (France), analysing ANCA results from March 2013 to March 2022. Clinical, biological, imaging, and histological data were collected for each DP-ANCA case. Additionally, a comprehensive literature review on DP-ANCA was performed, combining an artificial intelligence (AI)-based search using BIBOT software with a manual PUBMED database search. Results: The report of our cases over the last 9 years and those from the literature yielded 103 described cases of patients with DP-ANCA. We identified four distinct phenotypic profiles: (i) idiopathic AAV (∼30%); (ii) drug-induced AAV (∼25%); (iii) autoimmune disease associated with a low risk of developing vasculitis (∼20%); and (iv) immune-disrupting comorbidities (infections, cancers, etc) not associated with AAV (∼25%). Conclusion: This analysis of over a hundred DP-ANCA cases suggests substantial diversity in clinical and immunopathological presentations. Approximatively 50% of DP-ANCA patients develop AAV, either as drug-induced or idiopathic forms, while the remaining 50%, characterized by pre-existing dysimmune conditions, demonstrates a remarkably low vasculitis risk. These findings underscore the complex nature of DP-ANCA, its variable impact on patient health, and the necessity for personalized diagnostic and management approaches in these cases.  © 2024 The Author(s). Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved.","ANCA-associated vasculitis; double positivity; MPO; PR3","Adult; Aged; Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis; Antibodies, Antineutrophil Cytoplasmic; Artificial Intelligence; Female; Humans; Male; Middle Aged; Myeloblastin; Peroxidase; Retrospective Studies; antibiotic agent; benralizumab; corticosteroid; creatinine; cyclophosphamide; myeloblastin; myeloperoxidase; neutrophil cytoplasmic antibody; omalizumab; rituximab; myeloblastin; neutrophil cytoplasmic antibody; peroxidase; abdominal pain; acute kidney failure; aged; ANCA associated vasculitis; anorexia; arthralgia; Article; artificial intelligence; asthenia; asthma; body weight loss; case report; clinical article; clinical feature; comorbidity; conduction deafness; corticosteroid therapy; coughing; creatinine blood level; diagnostic imaging; diarrhea; drug substitution; drug withdrawal; epistaxis; female; fever; France; glomerulonephritis; hematuria; hemoptysis; histopathology; human; hypereosinophilia; immunoassay; immunofluorescence; immunopathology; infection; joint cancer; keratitis; kidney biopsy; livedo reticularis; low drug dose; low risk patient; male; malignant neoplasm; Medline; muscle weakness; myalgia; myocarditis; outcome assessment; patient history of colectomy; phenotype; plasma exchange; pneumonia; proctocolectomy; proteinuria; rapidly progressive glomerulonephritis; rectum hemorrhage; remission; sarcoma; ulcerative colitis; very elderly; adult; ANCA associated vasculitis; diagnosis; immunology; middle aged; retrospective study","","benralizumab, 1044511-01-4; creatinine, 19230-81-0, 60-27-5; cyclophosphamide, 50-18-0, 6055-19-2; myeloblastin, 128028-50-2; myeloperoxidase, ; omalizumab, 242138-07-4; rituximab, 174722-31-7; peroxidase, 9003-99-0; Antibodies, Antineutrophil Cytoplasmic, ; Myeloblastin, ; Peroxidase, ","","","","","Kitching AR, Anders HJ, Basu N, Et al., ANCA-associated vasculitis, Nat Rev Dis Primer, 6, pp. 1-27, (2020); Suppiah R, Robson JC, Grayson PC, Et al., 2022 American College of Rheumatology/ European Alliance of Associations for Rheumatology classification criteria for microscopic polyangiitis, Ann Rheum Dis, 81, pp. 321-326, (2022); Robson JC, Grayson PC, Ponte C, Et al., 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for granulomatosis with polyangiitis, Ann Rheum Dis, 81, pp. 315-320, (2022); Grayson PC, Ponte C, Suppiah R, Et al., 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification criteria for eosinophilic granulomatosis with polyangiitis, Ann Rheum Dis, 81, pp. 309-314, (2022); Gao Y, Zhao MH., Review article: drug-induced anti-neutrophil cytoplasmic antibody-associated vasculitis, Nephrology, 14, pp. 33-41, (2009); Pendergraft WF, Niles JL., Trojan horses: drug culprits associated with antineutrophil cytoplasmic autoantibody (ANCA) vasculitis, Curr Opin Rheumatol, 26, pp. 42-49, (2014); Cornec D, Gall ECL, Fervenza FC, Specks U., ANCA-associated vasculitis–clinical utility of using ANCA specificity to classify patients, Nat Rev Rheumatol, 12, pp. 570-579, (2016); Orgeolet L, Foulquier N, Misery L, Et al., Can artificial intelligence replace manual search for systematic literature? 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Weiner M, Segelmark M., The clinical presentation and therapy of diseases related to anti-neutrophil cytoplasmic antibodies (ANCA), Autoimmun Rev, 15, pp. 978-982, (2016); Roth AJ, Ooi JD, Hess JJ, Et al., Epitope specificity determines pathogenicity and detectability in ANCA-associated vasculitis, J Clin Invest, 123, pp. 1773-1783, (2013); Holle JU, Hellmich B, Backes M, Gross WL, Csernok E., Variations in performance characteristics of commercial enzyme immunoassay kits for detection of antineutrophil cytoplasmic antibodies: what is the optimal cut off?, Ann Rheum Dis, 64, pp. 1773-1779, (2005); Trevisin M, Pollock W, Dimech W, Et al., Antigen-specific ANCA ELISAs have different sensitivities for active and treated vasculitis and for nonvasculitic disease, Am J Clin Pathol, 129, pp. 42-53, (2008); Stone JH, Merkel PA, Spiera R, Rituximab versus cyclophosphamide for ANCA-associated vasculitis, N Engl J Med, 363, pp. 221-232, (2010); Sada K, Yamamura M, Harigai M, Et al., Classification and characteristics of Japanese patients with antineutrophil cytoplasmic antibody-associated vasculitis in a nationwide, prospective, inception cohort study, Arthritis Res Ther, 16, (2014); Lionaki S, Blyth ER, Hogan SL, Et al., Classification of antineutrophil cytoplasmic autoantibody vasculitides: the role of antineutrophil cytoplasmic autoantibody specificity for myeloperoxidase or proteinase 3 in disease recognition and prognosis, Arthritis Rheum, 64, pp. 3452-3462, (2012); Wechsler ME, Nair P, Terrier B, Et al., Mepolizumab or placebo for eosinophilic granulomatosis with polyangiitis, N Engl J Med, 390, pp. 911-921, (2017); Greco A, Rizzo MI, De Virgilio A, Et al., Churg-Strauss syndrome, Autoimmun Rev, 14, pp. 341-348, (2015); Guillevin L, Pagnoux C, Seror R, The five-factor score revisited: assessment of prognoses of systemic necrotizing vasculitides based on the French vasculitis study group (FVSG) cohort, Medicine (Baltimore), 90, pp. 19-27, (2011); Lyons PA, Rayner TF, Trivedi S, Et al., Genetically distinct subsets within ANCA-associated vasculitis, N Engl J Med, 367, pp. 214-223, (2012); Nakazawa D, Masuda S, Tomaru U, Ishizu A., Pathogenesis and therapeutic interventions for ANCA-associated vasculitis, Nat Rev Rheumatol, 15, pp. 123-01, (2019)","D. Cornec; B Lymphocytes, Autoimmunity and Immunotherapies, UMR 1227, Univ Brest, Inserm, 9, Brest, rue Felix Le Dantec, 29200, France; email: divi.cornec@chu-brest.fr","","Oxford University Press","","","","","","14620324","","RUMAF","38552316","English","Rheumatology","Article","Final","","Scopus","2-s2.0-85203201254"
"Thiruvengadam K.; Doddamani D.; Krishnan R.","Thiruvengadam, Kannan (57208210497); Doddamani, Dadakhalandar (56215778500); Krishnan, Rajendran (59454581200)","57208210497; 56215778500; 59454581200","Performance of the Classical Model in Feature Selection Across Varying Database Sizes of Healthcare Data","2024","International Journal of Statistics in Medical Research","13","","","228","237","9","0","10.6000/1929-6029.2024.13.21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208180303&doi=10.6000%2f1929-6029.2024.13.21&partnerID=40&md5=2a646cd960d9f8b5e24c350a3b650aae","ICMR, National Institute for Research in Tuberculosis, Chennai, India; ICMR – Regional Medical Research Centre, Port Blair, Port Blair, India","Thiruvengadam K., ICMR, National Institute for Research in Tuberculosis, Chennai, India; Doddamani D., ICMR – Regional Medical Research Centre, Port Blair, Port Blair, India; Krishnan R., ICMR, National Institute for Research in Tuberculosis, Chennai, India","Machine learning is increasingly being applied to medical research, particularly in selecting predictive modelling variables. By identifying relevant variables, researchers can improve model accuracy and reliability, leading to better clinical decisions and reduced overfitting. Efficient utilization of resources and the validity of medical research findings depend on selecting the right variables. However, few studies compare the performance of classical and modern methods for selecting characteristics in health datasets, highlighting the need for a critical evaluation to choose the most suitable approach. We analysed the performance of six different variable selection methods, which includes stepwise, forward and backward selection using p-value and AIC, LASSO, and Elastic Net. Health-related surveillance data on behaviors, health status, and medical service usage were used across ten databases, with sizes ranging from 10% to 100%, maintaining consistent outcome proportions. Varying database sizes were utilized to assess their impact on prediction models, as they can significantly influence accuracy, overfitting, generalizability, statistical power, parameter estimation reliability, computational complexity, and variable selection. The stepwise and backward AIC model showed the highest accuracy with an Area under the ROC Curve (AUC) of 0.889. Despite its sparsity, the Lasso and Elastic Net model also performed well. The study also found that binary variables were considered more crucial by the Lasso and Elastic Net model. Importantly, the significance of variables remained consistent across different database sizes. The study shows that no major variations in results between the fitness metric of the model and the number of variables in stepwise and backward p-value models, irrespective of the database’s size. LASSO and Elastic Net models surpassed other models throughout various database sizes, and with fewer variables. © (2024), (Lifescience Global). All rights reserved.","data interpretation; machine learning; Models; regression analysis; variable selection","adult; aged; alcohol consumption; arthritis; Article; asthma; blindness; body mass; cerebrovascular accident; chronic obstructive lung disease; data base; data interpretation; depression; diabetes mellitus; diagnostic test accuracy study; feature selection; female; health data; health status; heart disease; human; least absolute shrinkage and selection operator; machine learning; major clinical study; male; malignant neoplasm; medical research; medical service; physical activity; predictive model; receiver operating characteristic; regression analysis; reliability; sleep time; smoking","","","R version 4.2.2, R Foundation, Austria; STATA version 16.0, StatCorp","R Foundation, Austria; StatCorp","","","Evans RS., Electronic Health Records: Then, Now, and in the Future, Yearb Med Inform, 25, (2016); Brnabic A, Hess LM., Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making, BMC Medical Informatics and Decision Making, 21, 1, pp. 1-19, (2021); van der Ploeg T, Austin PC, Steyerberg EW., Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints, BMC Medical Research Methodology, 14, 1, pp. 137-137, (2014); Hossain E, Hossain E, Khan A, Moni MA, Uddin S., Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18, 2, pp. 745-758, (2019); Bagherzadeh-Khiabani F, Ramezankhani A, Azizi F, Hadaegh F, Steyerberg EW, Khalili D., A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results, Journal of Clinical Epidemiology, 71, pp. 76-85, (2016); Chowdhury MZI, Turin TC., Variable selection strategies and its importance in clinical prediction modelling, Family Medicine and Community Health, 8, 1, (2020); He L, Lingjun He, Levine RA, Fan J, Beemer J, Stronach J., Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research, Practical Assessment, Research and Evaluation, 23, 1, (2018); Steyerberg EW., Clinical Prediction Models, (2009); Demsar J., Statistical Comparisons of Classifiers over Multiple Data Sets, Journal of Machine Learning Research, 7, 1, pp. 1-30, (2006); Rodriguez D, Catal C, Catal Cagatay, Diri B., Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem, Information Sciences, 179, 8, pp. 1040-1058, (2009); Murtaugh PA., In defense of P values, Ecology, 95, 3, pp. 611-617, (2014); Portet S., A primer on model selection using the Akaike Information Criterion, Infectious Disease Modelling, 5, pp. 111-128, (2020); Tibshirani R., Regression shrinkage and selection via the lasso: a retrospective, Journal of The Royal Statistical Society Series B-statistical Methodology, 73, 3, pp. 273-282, (2011); Zou H, Hastie T., Regularization and variable selection via the elastic net, Journal of The Royal Statistical Society Series B-statistical Methodology, 67, 2, pp. 301-320, (2005); Behavioral risk factor surveillance system survey questionnaire, pp. 22-23, (2022); Control C for D, Prevention, others. Behavioral risk factor surveillance system survey data; Hastie T, Tibshirani R, Friedman J., The elements of statistical learning: data mining, inference and prediction, Math Intell, (2005); R: A language and environment for statistical computing, (2022); Mukherjee T, Mukherjee T, Duckett M, Kumar P, Paquet J, Paquet JD, Et al., RSSI-Based Supervised Learning for Uncooperative Direction-Finding, ECML/PKDD, pp. 216-227, (2017); Guyon I, Weston Jason, Weston J, Barnhill S, Vapnik Vladimir, Vapnik V., Gene Selection for Cancer Classification using Support Vector Machines, Machine Learning, 46, 1, pp. 389-422, (2002); Deng H, Runger George C., Runger GC., Feature Selection via Regularized Trees, (2012); Gelman A, Carlin John B., Carlin John B., Carlin JB, Stern Hal S., Stern HS, Et al., Bayesian data analysis, (2013); Box GEP, Tiao George C., Tiao GC, Tiao GC., Bayesian Inference in Statistical Analysis: Box/Bayesian, (1992); Smith G, Campbell Frank, Campbell F., A Critique of Some Ridge Regression Methods, Journal of the American Statistical Association, 75, 369, pp. 74-81, (1980); Way TW, Sahiner Berkman, Sahiner B, Hadjiiski LM, Chan HP., Effect of finite sample size on feature selection and classification: a simulation study, Medical Physics, 37, 2, pp. 907-920, (2010)","R. Krishnan; The Department of Epidemiology, ICMR-National Institute for Research in Tuberculosis, Chennai, No. 1, Mayor Sathyamoorthy Road, 600 031, India; email: tkannan1985@gmail.com","","Lifescience Global","","","","","","19296029","","","","English","Int. J. Stats. Med. Res.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85208180303"
"Prathibha T.P.; Arabi P.M.","Prathibha, T.P. (57191228009); Arabi, Punal M (26427948200)","57191228009; 26427948200","Computer Aided Classification of Lung Cancer, Ground Glass Lung and Pulmonary Fibrosis Using Machine Learning and KNN Classifier","2024","International Journal of Advanced Computer Science and Applications","15","7","","1145","1151","6","0","10.14569/IJACSA.2024.01507111","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201859033&doi=10.14569%2fIJACSA.2024.01507111&partnerID=40&md5=4a4903615de834687d27477de2cd9322","Department of Biomedical Engineering, ACS College of Engineering, Visvesvaraya Technological University, Bangalore, India","Prathibha T.P., Department of Biomedical Engineering, ACS College of Engineering, Visvesvaraya Technological University, Bangalore, India; Arabi P.M., Department of Biomedical Engineering, ACS College of Engineering, Visvesvaraya Technological University, Bangalore, India","Respiratory diseases are one of the most prevalent acute and chronic ailments worldwide. According to a recent survey, there were around 545 million cases of chronic respiratory diseases worldwide. Chronic respiratory diseases such as chronic obstructive pulmonary disease (COPD), pneumoconioses, asthma, interstitial lung disease and pulmonary sarcoidosis are significant public health problems across the world. The most significant CRD (Chronic Respiratory Disease) risks have been identified including smoking, contact with indoor and outdoor pollutants, allergies, occupational exposure, poor nutrition, obesity, inactivity and other factors. Interstitial lung diseases are diagnosed on high-resolution computed tomography (HRCT) using a variety of different interstitial pattern namely such as reticular, nodular, reticulonodular, ground-glass lung, cystic, ground-glass with reticular, cystic with ground-glass. If the lung diseases are identified at an early stage life span could be increased. Computer aided diagnosis could play a crucial role in identifying lung diseases at an early stage, disease management and treatment planning. In this paper a novel method is proposed to identify and classify HRCT images of cancerous lung using ML (Machine Learning) and to identify and classify ground glass lung, pulmonary fibrosis lung and healthy lung HRCT images using LBP (Local Binary Pattern) and KNN (K-Nearest Neighbor) classifier. Experimenting the proposed method on 996 images yielded 94% accuracy. © (2024), (Science and Information Organization). All Rights Reserved.","Ground glass; healthy; KNN; LBP; LBP; lung cancer; lung diseases classification; ML and pulmonary fibrosis","Computerized tomography; Diagnosis; Indoor air pollution; Nutrition; Pulmonary diseases; Disease classification; Ground glass; Healthy; K-near neighbor; Local binary patterns; Lung Cancer; Lung disease classification; Machine learning and pulmonary fibrose; Machine-learning; Nearest-neighbour; Pulmonary fibrosis; Lung cancer","","","","","","","Labaki W., Han M., Chronic respiratory diseases: a global view, The Lancet of Respiratory medicine, 8, 6, pp. 531-533, (2020); Miller W., Shah R., Isolated Diffuse Ground-Glass Opacity in Thoracic CT: Cause and Clinical Presentations, AJR, 184, pp. 612-622, (2005); Arakawa H., Honma K., Honeycomb Lung: History and Current Concepts, American Journal of Roentgenlogy, 196, 4, pp. 773-782, (2011); Swensen SJ, Aughenbaugh GL, Brown LR, High-resolution computed tomography of the lung, Mayo Clin Proc, 64, pp. 1284-1294, (1989); Muhammed Anshad PY, Kumar S.S., Recent methods for the Detection of Tumour Using Computer Aided Diagnosis – A Review, International Conference on Control Instrumentation Communication and Computational Technologies, pp. 1014-1019, (2014); Anthimopoulos M., Christodoulidis S., Ebner L., Christe A., Mougiakakou S., Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network, IEEE Transactions on Medical Imaging, 35, 5, pp. 1207-1216, (2016); Lakshmi Narayanan A, Jeeva J.B, A Computer Aided Diagnosis for detection and classification of lung nodules, IEEE Sponsored 9th International Conference on Intelligent Systems and Control, (2015); Dai Shuangfeng, Lu Ke, Dong Jiyong, Lung Segmentation with Improved Graph Cuts on Chest CT Images, 3rd IAPR Asian Conference on Pattern Recognition, pp. 241-245, (2015); Chunran Yang, Yuanyuan Wang, Yi Guo, Automatic Detection and Segmentation of Lung Nodule on CT Images, 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, (2018); Boban Binila Mariyam, Megalingam Rajesh Kannan, Lung Diseases Classification Based on Machine Learning Algorithms and Performance Evaluation, International Conference on Communication and Signal Processing, pp. 0315-0320, (2020); Agarwala Sunita, Nandi Debashis, Kumar Abhishek, Dhara Ashis Kumar, Basu Thakur Sumitra, Sadhu Anup, Bhadra Ashok Kumar, Automated Segmentation of Lung Field in HRCT Images using Active Shape Model, Proc. of the 2017 IEEE Region 10 Conference, pp. 2516-2020, (2017); Nadkarni Nidhi S., Borkar Sangam, Detection of Lung Cancer in CT Images using Image Processing, Proceedings of the Third International Conference on Trends in Electronics and Informatics, pp. 863-866, (2019); Anthimopoulos M., Christodoulidis S., Christe A., Mougiakakou S., Classification of Interstitial Lung Disease Patterns Using Local DCT Features and Random Forest, Annual-International-Conference-of-the-IEEE-Engineering-in-Medicine-and-Biology-Society-IEEE-Engineering-in-Medicine-and-Biology-Society-Conference, pp. 6040-6043, (2014); Yang Bingqian, Feng Xiufang, Dong Yunyun, An Efficient Honeycomb Lung Segmentation Network Combining Multi-Paradigms Representation and Cascade Attention, International Journal of Advanced Computer Science and Applications, 14, 12, (2023); Dudhane A., Shingadkar G., Sanghavi P., Jankharia B., Talbar S., Interstitial Lung Disease Classification Using Feed Forward Neural Networks, Advances in Intelligent Systems Research, 137, pp. 515-521, (2016); Ming Joel Than Chia, Rijal Omar Mohd, Kassim Rosminah M., Yunus Ashari, Noor Norliza Mohd, Texture-based Classification for Reticular Pattern and Ground Glass Opacity in High Resolution Computed Tomography Thorax Images, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 230-234, (2016); Orozco Hiram Madero, Villegas Osslan Osiris Vergara, Cruz Sanchez Vianey Guadalupe, de Jesus Ochoa Dominguez Humberto, de Jesus Nandayapa Alfaro Manuel, Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine, BioMedical Engineering OnLine, 14, 9, pp. 1-20, (2015); EGRIBOZ Emre, Kaynar Furkan, VARLI Songul, MUSELLIM Benan, SELCUK Tuba, Finding and Following of Honeycombing Regions in Computed Tomography Lung Images by Deep Learning, (2019); Senthil Kumar K., Venkatalakshmi K., Karthikeyan K., Lung Cancer Detection Using Image Segmentation by means of Various Evolutionary Algorithms, Computational and Mathematical Methods in Medicine, 2019, pp. 1-16, (2019); Gite Shilpa, Mishra Abhinav, Kotecha Ketan, Enhanced lung image segmentation using deep learning, Neural Computing and Applications, 35, pp. 22839-22853, (2022); Ojala Timo, Pietikainen Matti, Harwood David, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, 29, 1, pp. 51-59, (1996); Narain Ponraj D., Christy Esther, Aneesha G, Susmitha G, Sharu Monica, Analysis of LBP and LOOP Based Textural Feature Extraction for the Classification of CT Lung Images, Fourth International Conference on Devices, Circuits and Systems, pp. 309-312, (2018)","","","Science and Information Organization","","","","","","2158107X","","","","English","Intl. J. Adv.  Comput. Sci. Appl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85201859033"
"Tuluri F.; Remata R.; Walters W.L.; Tchounwou P.B.","Tuluri, Francis (26656408700); Remata, Reddy (7202835111); Walters, Wilbur L. (16044271800); Tchounwou, Paul B. (7003492960)","26656408700; 7202835111; 16044271800; 7003492960","Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning","2023","International Journal of Environmental Research and Public Health","20","11","6022","","","","0","10.3390/ijerph20116022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161638265&doi=10.3390%2fijerph20116022&partnerID=40&md5=50530d7233b1ec4bf7d2a437e8a133bb","Department of Industrial Systems & Technology, Jackson State University, Jackson, 39217, MS, United States; Department of Atmospheric Sciences, Jackson State University, Jackson, 39217, MS, United States; College of Sciences, Engineering & Technology, Jackson State University, Jackson, 39217, MS, United States; RCMI Center for Health Disparities Research, Jackson State University, Jackson, 39217, MS, United States; RCMI Center for Urban Health Disparities Research and Innovation, Morgan State University, Baltimore, 21251, MD, United States","Tuluri F., Department of Industrial Systems & Technology, Jackson State University, Jackson, 39217, MS, United States; Remata R., Department of Atmospheric Sciences, Jackson State University, Jackson, 39217, MS, United States; Walters W.L., College of Sciences, Engineering & Technology, Jackson State University, Jackson, 39217, MS, United States; Tchounwou P.B., RCMI Center for Health Disparities Research, Jackson State University, Jackson, 39217, MS, United States, RCMI Center for Urban Health Disparities Research and Innovation, Morgan State University, Baltimore, 21251, MD, United States","Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants—particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)—were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by −4.1 ppb and −0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by −50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected. © 2023 by the authors.","air quality; COVID-19; machine learning modeling; python programming; statistical descriptive analysis; transportation and mobility","Air Pollutants; Air Pollution; Communicable Disease Control; COVID-19; Environmental Monitoring; Humans; Mississippi; Nitric Oxide; Nitrogen Dioxide; Particulate Matter; Mississippi; United States; carbon monoxide; nitrogen dioxide; nitrogen oxide; ozone; sulfur dioxide; nitric oxide; nitrogen dioxide; air quality; carbon monoxide; COVID-19; health policy; machine learning; metropolitan area; mobility; ozone; particulate matter; sulfur dioxide; air quality; Article; controlled study; coronavirus disease 2019; deterioration; hazard; humidity; lockdown; machine learning; Mississippi; particulate matter 10; particulate matter 2.5; precipitation; prediction; pressure; temperature; time series analysis; validity; wind; wind direction; wind speed; air pollutant; air pollution; communicable disease control; coronavirus disease 2019; environmental monitoring; human; particulate matter; procedures","","carbon monoxide, 630-08-0; nitrogen dioxide, 10102-44-0; nitrogen oxide, 11104-93-1; ozone, 10028-15-6; sulfur dioxide, 7446-09-5; nitric oxide, 10102-43-9; Air Pollutants, ; Nitric Oxide, ; Nitrogen Dioxide, ; Particulate Matter, ","","","RCMI Center for Urban Health Disparities Research and Innovation at Morgan State University; National Institutes of Health, NIH, (U54MD015929); National Institutes of Health, NIH; National Institute on Minority Health and Health Disparities, NIMHD, (U54MD013376); National Institute on Minority Health and Health Disparities, NIMHD; Jackson State University, JSU","This research was supported by the National Institutes of Health NIMHD Grant No. U54MD015929 through the RCMI Center for Health Disparities Research at Jackson State University, and NIMHD Grant No. U54MD013376 through the RCMI Center for Urban Health Disparities Research and Innovation at Morgan State University.","Air Quality, and Health; Air Pollution and Health; Zhou X., Josey K., Kamareddine L., Caine M.C., Dominici F., Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States, Sci. Adv, 7, (2021); Yao Y., Pan J., Liu Z., Meng X., Wang W., Kan H., Wang W., Temporal association between particulate matter pollution and case fatality rate of COVID-19 in Wuhan, Environ. Res, 189, (2020); Onyeaka H., Anumudu C.K., Al-Sharify Z.T., Egele-Godswill E., Mbaegbu P., COVID-19 pandemic: A review of the global lockdown and its far-reaching effects, Sci. Prog, 104, (2021); Lewis D., What scientists have learnt from COVID lockdowns, Nature, 609, pp. 236-239, (2022); Connerton P., de Assuncao J.V., de Miranda M.R., Slovic A.D., Perez-Martinez P.J., Helena Ribeiro H., Air Quality during COVID-19 in Four Megacities: Lessons and Challenges for Public Health, Int. J. Environ. Res. Public Health, 17, (2020); Venter Z.S., Aunan K., Chowdhury S., Lelieveld J., COVID-19 lockdowns cause global air pollution declines, Earth Atmos. Planet. Sci, 117, pp. 18984-18990, (2020); Sarmadi M., Rahimi S., Rezaei M., Sanaei D., Dianatinasab M., Air quality index variation before and after the onset of COVID-19 pandemic: A comprehensive study on 87 capital, industrial and polluted cities of the world, Environ. Sci. Eur, 33, (2021); Shi Z., Song C., Liu B., Lu G., Xu J., Van Vu T., Elliott R.J., Li W., Bloss W.J., Harrison R.M., Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns, Sci. Adv, 7, (2021); Jiang Z., Shi H., Zhao B., Gu Y., Zhu Y., Miyazaki K., Zhang Y., Bowman K.W., Sekiya T., Kuo-Nan Liou K.N., Modeling the Impact of COVID-19 on Air Quality in Southern California: Implications for Future Control Policies, Atmos. Chem. Phys, 21, pp. 8693-8708, (2021); Campbell P.C., Tong D., Tang Y., Baker B., Lee P., Saylor R., Stein A., Ma S., Lamsal L., Qu Z., Impacts of the COVID-19 economic slowdown on ozone pollution in the U.S, Atmos. Environ, 264, (2021); Hammer M.S., Donkelaar A.V., Martin V.R., Mcduffie E., Lyapustin A., Sayer A.M., Hsu N.C., Levy R.C., Garay M.J., Kahn R.A., Effects of COVID-19 lockdowns on fine particulate matter concentrations, Sci. Adv, 7, (2021); Lipsitt J., Chan-Golston A.M., Liu J., Su J., Zhu Y., Jerrett M., Spatial analysis of COVID-19 and traffic-related air pollution in Los Angeles, Environ. Int, 153, (2021); Ellis S., The Effects of COVID-19 Lockdown on Air Pollution in LA County; Yumin L., Shiyuan L., Ling H., Ziyi L., Yonghui Z., Li L., Yangjun W., Kangjuan L., The casual effects of COVID-19 lockdown on air quality and short-term health impacts in China, Environ. Pollut, 290, (2021); Grange S.K., Carslaw D.C., Lewis A.C., Boleti E., Hueglin C., Random forest meteorological normalization models for Swiss PM10 trend analysis, Atmos. Chem. Phys, 18, pp. 6223-6239, (2018); Grange S., Rmweather Package; Grange S.K., Lee J.D., Drysdale W.S., Lewis A.C., Hueglin C., Emmenegger L., Carslaw D.C., COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas, Atmos. Chem. Phys, 21, pp. 4169-4185, (2021); Gonzalez-Pardo J., Ceballos-Santos S., Manzanas R., Santibanez M., Fernandez-Olmo I., Estimating changes in air pollution levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case study for urban traffic sites in Spain, Sci. Total Environ, 823, (2022); Vu T.V., Shi Z., Cheng J., Zhang Q., He K., Wang S., Harrison M.R., Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique, Atmos. Chem. Phys, 19, pp. 11303-11314, (2019); Lovric M., Antunovic M., Sunic I., Vukovic M., Kecorius S., Kroll M., Beslic I., Godec R., Pehnec G., Geiger B.C., Et al., Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia, Int. J. Environ. Res. Public Health, 19, (2022); Chen X., Zhang F., Zhang D., Xu L., Liu R., Teng X., Zhang X., Wang S., Li W., Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau, Environ. Sci. Atmos, 3, pp. 708-716, (2023); Farhadi Z., Bevrani H., Feizi-Derakhshi M., Kim W., Ijaz M.F., An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods, Appl. Sci, 12, (2022); RStudio: Integrated Development for R, (2019); Breiman L., Random Forests, Mach. Learn, 45, pp. 5-32, (2001); Muhammad M., Long X., Salman M., COVID-19 pandemic and environmental pollution: A blessing in disguise?, Sci. Total Environ, 728, (2021); Chen K., Wang M., Huang C., Kinney P.L., Anastas P.T., Air pollution reduction and mortality benefit during the COVID-19 outbreak in China, Lancet Planet Health, 4, (2020); Ghahremanloo M., Lops Y., Choi Y., Jung J., Mousavinezhad S., Hammond D., A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach, Atmos Environ, 272, (2022); Fenech S., Aquilina N.J., Vella R., COVID-19-Related Changes in NO<sub>2</sub> and O<sub>3</sub> Concentrations and Associated Health Effects in Malta, Front. Sustain. Cities, 3, (2021)","F. Tuluri; Department of Industrial Systems & Technology, Jackson State University, Jackson, 39217, United States; email: francis.tuluri@jsums.edu; P.B. Tchounwou; RCMI Center for Health Disparities Research, Jackson State University, Jackson, 39217, United States; email: paul.tchounwou@morgan.edu","","MDPI","","","","","","16617827","","","37297626","English","Int. J. Environ. Res. Public Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85161638265"
"Lei P.; Li J.; Yi J.; Chen W.","Lei, Pengyu (59141757900); Li, Jie (56027276700); Yi, Jizheng (55880863500); Chen, Wenjie (58461773100)","59141757900; 56027276700; 55880863500; 58461773100","Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet","2024","Biomedicines","12","5","1061","","","","0","10.3390/biomedicines12051061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194077389&doi=10.3390%2fbiomedicines12051061&partnerID=40&md5=8ac6f4c416176e7ef02bdde46b01f6a0","College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha, 410000, China","Lei P., College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; Li J., College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; Yi J., College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China, Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center, Changsha, 410000, China; Chen W., College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China","The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale. Thus, this paper introduces a ConvBiGRU model for localizing lung slices and a multi-module UNet-based model for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), contributing to the analysis of lung adipose tissue and the auxiliary diagnosis of lung diseases. In this study, we propose a bidirectional gated recurrent unit (BiGRU) structure for precise lung slice localization and a modified multi-module UNet model for accurate SAT and VAT segmentations, incorporating an additive weight penalty term for model refinement. For segmentation, we integrate attention, competition, and multi-resolution mechanisms within the UNet architecture to optimize performance and conduct a comparative analysis of its impact on SAT and VAT. The proposed model achieves satisfactory results across multiple performance metrics, including the Dice Score (92.0% for SAT and 82.7% for VAT), F1 Score (82.2% for SAT and 78.8% for VAT), Precision (96.7% for SAT and 78.9% for VAT), and Recall (75.8% for SAT and 79.1% for VAT). Overall, the proposed localization and segmentation framework exhibits high accuracy and reliability, validating its potential application in computer-aided diagnosis (CAD) for medical tasks in this domain. © 2024 by the authors.","deep learning (DL); localization; segmentation; subcutaneous adipose tissue (SAT); visceral adipose tissue (VAT)","","","","","","Natural Science Foundation of Hunan Province, (2022JJ31022); Natural Science Foundation of Hunan Province; National Natural Science Foundation of China, NSFC, (62202505); National Natural Science Foundation of China, NSFC","This work was supported in part by the Hunan Provincial Natural Science Foundation of China (grant no. 2022JJ31022) and the National Natural Science Foundation of China (grant no. 62202505).","Padwal R., Leslie W.D., Lix L.M., Majumdar S.R., Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study, Ann. Intern. Med, 164, pp. 532-541, (2016); Kuda O., Rossmeisl M., Kopecky J., Omega-3 fatty acids and adipose tissue biology, Mol. Asp. Med, 64, pp. 147-160, (2018); Lee M.J., Wu Y., Fried S.K., Adipose Tissue Heterogeneity: Implication of Depot Differences in Adipose Tissue for Obesity Complications, Mol. Asp. Med, 34, pp. 1-11, (2013); He L., Ai Z., Xiang Z., The application value of human component analysis technique in the evaluation of nonalcoholic fatty liver disease, Mod. Hosp, 19, pp. 1157-1165, (2019); Su B., Tian S., Wang H., Correlation study of CT images of fatty liver and abdominal fat distribution, J. Aerosp. Med, 28, pp. 1043-1047, (2017); Zhang J., Qi Y., Correlation between visceral obesity index and pathology of chronic hepatitis C, Hebei Pharm, 34, pp. 369-370, (2012); Castro O., Systemic fat embolism and pulmonary hypertension in sickle cell disease, Hematol.-Oncol. Clin. N. 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J, 17, (2018); Tong Y., Udupa J.K., Torigian D.A., Fat quantification and analysis of lung transplant patients on unenhanced chest CT images based on standardized anatomic space, Proceedings of the Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; Anderson M.R., Udupa J.K., Edwin E., Adipose tissue quantification and primary graft dysfunction after lung transplantation: The Lung Transplant Body Composition study, J. Heart Lung Transplant, 38, pp. 1246-1256, (2019); Cho Y.H., Do K.H., Chae E.J., Choi S.H., Jo K.W., Lee S.O., Hong S.B., Association of Chest CT-Based Quantitative Measures of Muscle and Fat with Post-Lung Transplant Survival and Morbidity: A Single Institutional Retrospective Cohort Study in Korean Population, Korean J. Radiol, 20, pp. 522-530, (2019); Dudeja V., Misra A., Pandey R.M., BMI does not accurately predict overweight in Asian Indians in northern India, Br. J. Nutr, 86, pp. 105-112, (2001); Burkhauser R.V., Cawley J., Beyond BMI: The value of more accurate measures of fatness and obesity in social science research, J. Health Econ, 27, pp. 519-529, (2008); Heker M., Greenspan H., Joint liver lesion segmentation and classification via transfer learning, arXiv, (2020); Ferdian E., Suinesiaputra A., Dubowitz D.J., Zhao D., Wang A., Cowan B., Young A.A., 4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics, Front. Phys, 8, (2020); Bottigli U., Cerello P., Cheran S.C., Delogu P., Fantacci M.E., Fauci F., Golosio B., Lauria A., Torres E.L., Magro R., Et al., GPCALMA: A Tool For Mammography with A GRID-Connected Distributed Database, Med. Phys, 682, pp. 67-72, (2003); Jafari M., Auer D.P., Francis S.T., Garibaldi J.M., Chen X., DRU-Net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation, Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1144-1148; Zhang P., Zhong Y., Li X., ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation, arXiv, (2020); Usman M., Lee B.D., Byon S.S., Kim S.H., Lee B.I., Shin Y.G., Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning, Sci. Rep, 10, (2020); Peng Y., Wang M., Liu C., Jia H., A preliminary study on the value of CT texture analysis combined with machine learning in auxiliary diagnosis of vertebral occult fractures, J. Jinan Univ. (Nat. Sci. Med.), 3, pp. 1-8, (2020); Estrada S., Lu R., Conjeti S., Orozco-Ruiz X., Panos-Willuhn J., Breteler M.M.B., Reuter M., FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI, Magn. Reson. Med, 83, pp. 1471-1483, (2020); Cao H., Sheng B., Wu W., Automatic quantitative detection algorithm of abdominal fat based on improved K-Means, J. Comput. Aided Des. Graph, 29, pp. 575-583, (2017); Hussein S., Green A., Watane A., Papadakis G.Z., Osman M.M., Bagci U., Context Driven Label Fusion for Segmentation of Subcutaneous and Visceral Fat in CT Volumes, arXiv, (2015); Irmakci I., Hussein S., Savran A., Kalyani R.R., Reiter D., Chia C.W., Fishbein K.W., Spencer R.G., Ferrucci L., Bagci U., A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation, IEEE Trans. Biomed. Eng, 66, pp. 1069-1081, (2019); Amer R., Nassar J., Bendahan D., Greenspan H., Ben-Eliezer N., Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images, Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention; Rundo L., Han C., Nagano Y., Zhang J., Hataya R., Militello C., Tangherloni A., Nobile M.S., Ferretti C., Besozzi D., Et al., USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets, Neurocomputing, 365, pp. 31-43, (2019); Koitka S., Kroll L., Malamutmann E., Oezcelik A., Nensa F., Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks, Eur. Radiol, 31, pp. 1795-1804, (2021); Langner T., Hedstrom A., Morwald K., Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI, Magn. Reson. Med, 81, pp. 2736-2745, (2019); Masoudi S., Anwar S.M., Harmon S.A., Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation, arXiv, (2020); Cho K., Van Merrienboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv, (2014); Schlemper J., Oktay O., Schaap M., Heinrich M., Kainz B., Glocker B., Rueckert D., Attention gated networks: Learning to leverage salient regions in medical images, Med. Image Anal, 53, pp. 197-207, (2019); Tan C., Feng X., Long J., Geng L., FORECAST-CLSTM: A New Convolutional LSTM Network for Cloudage Nowcasting, Proceedings of the IEEE Visual Communications and Image Processing (VCIP); Yan K., Wang X., Lu L., Summers R.M., DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning, J. Med. Imaging, 5, (2018); Russell B.C., Torralba A., Murphy K.P., Freeman W.T., LabelMe: A Database and Web-Based Tool for Image Annotation, Int. J. Comput. Vis, 77, pp. 157-173, (2008); Ronneberger O., Fischer P., Brox T., U-net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, pp. 234-241, (2015); Petit O., Thome N., Rambour C., Themyr L., Collins T., Soler L., U-net transformer: Self and cross attention for medical image segmentation, Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, 27 September 2021, Proceedings 12, pp. 267-276, (2021); Liu Q., Chen C., Qin J., Dou Q., Heng P., FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1013-1023; Zhang Y., Liu H., Hu Q., TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation, Medical Image Computing and Computer Assisted Intervention—MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September–1 October 2021, Proceedings, Part I 24, pp. 14-24, (2021)","J. Li; College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; email: jieli.jsj@csuft.edu.cn; J. Yi; College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China; email: t20152279@csuft.edu.cn","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","22279059","","","","English","Biomedicines","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194077389"
"D'Auria D.; Bettini F.; Tognarelli S.; Calvanese D.; Menciassi A.","D'Auria, Daniela (57191490802); Bettini, Fabio (57194005384); Tognarelli, Selene (26430120100); Calvanese, Diego (7004220724); Menciassi, Arianna (7004244810)","57191490802; 57194005384; 26430120100; 7004220724; 7004244810","Technologies and main functionalities of the telemonitoring application reCOVeryaID","2024","Frontiers in Big Data","7","","1360092","","","","0","10.3389/fdata.2024.1360092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200202008&doi=10.3389%2ffdata.2024.1360092&partnerID=40&md5=8caf16617e2cf01883457edf369fcf9f","Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Computing Science, Umeå University, Umeå, Sweden","D'Auria D., Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy; Bettini F., Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy; Tognarelli S., The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Calvanese D., Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy, Department of Computing Science, Umeå University, Umeå, Sweden; Menciassi A., The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy","The COVID-19 pandemic has highlighted the need to take advantage of specific and effective patient telemonitoring platforms, with specific reference to the constant monitoring of vital parameters of patients most at risk. Among the various applications developed in Italy, certainly there is reCOVeryaID, a web application aimed at remotely monitoring patients potentially, currently or no longer infected with COVID-19. Therefore, in this paper we present a system model, consisting of a multi-platform intelligent telemonitoring application, that enables remote monitoring and provision of integrated home care to both patients symptomatic, asymptomatic and pre-symptomatic with severe acute respiratory infectious disease or syndrome caused by viruses belonging to the Coronavirus family, as well as simply to people with respiratory problems and/or related diseases (chronic obstructive pulmonary disease or asthma). In fact, in this paper we focus on exposing the technologies and various functionalities offered by the system, which constitute the practical implementation of the theoretical framework described in detail in another paper. Specifically, the reCOVeryaID telemonitoring application is a stand-alone, knowledge base-supported application that can promptly react and inform physicians if dangerous trends in a patient's short- and long-term vital signs are detected, thus enabling them to be monitored continuously, both in the hospital and at home. The paper also reports an evaluation of user satisfaction, carried out by actual patients and medical doctors. Copyright © 2024 D'Auria, Bettini, Tognarelli, Calvanese and Menciassi.","artificial intelligence; coronavirus; COVID-19; eHealth; long-term monitoring; rule-based system; telehealth; telemedicine","","","","","","UK Research and Innovation, UKRI, (103949); Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR, (FISR2020IP 01767)","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by a national grant from the Italian Ministry of University and Research under FISR2020IP 01767 (reCOVeryaID - Una applicazione di telemonitoraggio intelligente per pazienti sintomatici, asintomatici e pre-sintomatici al Coronavirus). ","Ali N.A., Khoja A., Telehealth: an important player during the COVID-19 pandemic, Ochsner J, 20, pp. 113-114, (2020); Bertoncelli C.M., Costantini S., Persia F., Bertoncelli D., D'Auria D., Predictmed-epilepsy: a multi-agent based system for epilepsy detection and prediction in neuropediatrics, Comput. Methods Programs Biomed, 236, (2023); Burdick H., Lam C., Mataraso S., Siefkas A., Braden G., Dellinger R.P., Et al., Prediction of respiratory decompensation in COVID-19 patients using machine learning: the READY trial, Comput. Biol. Med, 124, (2020); Caricchio R., Gallucci M., Dass C., Zhang X., Gallucci S., Fleece D., Et al., Preliminary predictive criteria for COVID-19 cytokine storm, Ann. Rheum. Dis, 80, pp. 88-95, (2021); Charles B.L., Telemedicine can lower costs and improve access, Healthc. Financ. Manag, 54, (2000); Chauhan V., Galwankar S., Arquilla B., Garg M., Di Somma S., El-Menyar A., Et al., Novel coronavirus (COVID-19): leveraging telemedicine to optimize care while minimizing exposures and viral transmission, J. Emerg. Trauma Shock, 13, pp. 20-24, (2020); Coupet S., Nicolas G., Louder C., Meyer M., When public health messages become stressful: managing chronic disease during COVID-19, Soc. Sci. Humanit. Open, 4, (2021); Danhieux K., Buffel V., Pairon A., Benkheil A., Remmen R., Wouters E., Et al., The impact of COVID-19 on chronic care according to providers: a qualitative study among primary care practices in Belgium, BMC Fam. Pract, 21, (2020); D'Auria D., Moscato V., Postiglione M., Romito G., Sperli G., Improving graph embeddings via entity linking: a case study on Italian clinical notes, Intell. Syst. Appl, 17, (2023); D'Auria D., Russo R., Fedele A., Addabbo F., Calvanese D., An intelligent telemonitoring application for coronavirus patients: recoveryaid, Front. Big Data, 6, (2023); De Lauretis L., Persia F., Costantini S., D'Auria D., How to leverage intelligent agents and complex event processing to improve patient monitoring, J. Log. Comput, 33, pp. 900-935, (2023); Dimitroulas T., Bertsias G., Practical issues in managing systemic inflammatory disorders during the COVID-19 pandemic, Mediterr. J. Rheumatol, 31, pp. 253-256, (2020); Elliott M., Baird J., Pulse oximetry and the enduring neglect of respiratory rate assessment: a commentary on patient surveillance, Br. J. Nurs, 28, (2019); Gao Y., Cai G.Y., Fang W., Li H.Y., Wang S.Y., Chen L., Et al., Machine learning based early warning system enables accurate mortality risk prediction for COVID-19, Nat. Commun, 11, (2020); Hacker K.A., Briss P.A., Richardson L., Wright J., Petersen R., COVID-19 and chronic disease: the impact now and in the future, Prev. Chronic Dis, 18, (2021); Han Z., Wei B., Hong Y., Li T., Cong J., Zhu X., Et al., Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning, IEEE Trans. Med. Imaging, 39, pp. 2584-2594, (2020); Hollander J.E., Carr B.G., Virtually perfect? Telemedicine for COVID-19, N. Engl. J. Med, 382, pp. 1679-1681, (2020); Ko S., Wang Z., Premkumar A., Qi T., Shuhua K., Lim Y.W., Et al., Continuous vital signs monitoring in patients hospitalized at home: burden or benefit?, J. Am. Med. Dir. Assoc, 24, pp. 759-760, (2023); Li W.T., Ma J., Shende N., Castaneda G., Chakladar J., Tsai J.C., Et al., Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis, BMC Med. Inform. Decis. Mak, 20, (2020); Mason A., Rose E., Edwards C.J., Clinical management of Lupus patients during the COVID-19 pandemic, Lupus, 29, pp. 1661-1672, (2020); Monaghesh E., Hajizadeh A., The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence, BMC Public Health, 20, (2020); Murali S., 14, pp. 56814-56820, (2023); Persia F., Costantini S., Ferri C., Lauretis L.D., D'Auria D., “A smart framework for automatically analyzing electrocardiograms,”, 2021 Third International Conference on Transdisciplinary AI (TransAI), pp. 64-67, (2021); Scott D.A., McDougall R., The effective introduction of Lifebox pulse oximetry to Malawi, Anaesthesia, 72, pp. 675-677, (2017); Smith M., Withnall R., Blackadder-Coward J., Taylor N., Developing a multimodal biosensor for remote physiological monitoring, BMJ Military Health, (2021); Sole D., Komatsu M.K., Carvalho K.V.T., Naspitz C.K., Pulse oximetry in the evaluation of the severity of acute asthma and/or wheezing in children, J. Asthma, 36, pp. 327-333, (2009); Taguchi O., Hida W., Kikuchi Y., Miki H., Iijima H., Homma M., Et al., Bronchial asthma and desaturation assessment by pulse oximetry, Nihon Kyobu Shikkan Gakkai Zasshi, 32, pp. 115-120, (1994); Takei R., Yamano Y., Kataoka K., Yokoyama T., Matsuda T., Kimura T., Et al., Pulse oximetry saturation can predict prognosis of idiopathic pulmonary fibrosis, Respir. Investig, 58, pp. 190-195, (2020); Totuk A., Bayramoglu B., Tayfur I., Reliability of smartphone measurements of peripheral oxygen saturation and heart rate in hypotensive patients measurement of vital signs with smartphones, Heliyon, 9, (2023); Wiffen L., Brown T., Brogaard Maczka A., Kapoor M., Pearce L., Chauhan M., Et al., Measurement of vital signs by lifelight software in comparison to standard of care multisite development (VISION-MD): protocol for an observational study, JMIR Res. Protoc, 12, (2023); Wright A., Salazar A., Mirica M., Volk L.A., Schiff G.D., The invisible epidemic: neglected chronic disease management during COVID-19, J. Gen. Intern. Med, 35, pp. 2816-2817, (2020)","D. D'Auria; Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy; email: daniela.dauria@unibz.it","","Frontiers Media SA","","","","","","2624909X","","","","English","Frontiers. Big. Data.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85200202008"
"Isangula K.G.; Haule R.J.","Isangula, Kahabi Ganka (56780415700); Haule, Rogers John (59120576600)","56780415700; 59120576600","Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania","2024","JMIR Research Protocols","13","1","e54388","","","","0","10.2196/54388","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192702293&doi=10.2196%2f54388&partnerID=40&md5=e0b7f241eb7be3a8e797bca442db65a2","School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, Tanzania","Isangula K.G., School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, Tanzania; Haule R.J., School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, Tanzania","Background: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. Objective: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. Methods: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. Results: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Conclusions: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. ©Kahabi Ganka Isangula, Rogers John Haule.","Africa; analysis; artificial intelligence; asthma; chronic obstructive pulmonary disease; cough; cough classifiers; cough sound; cross-sectional research; detecting respiratory disease; diagnostic study; machine learning; management; mobile phone; noninvasive; respiratory diseases; rural; Tanzania; treatment; tuberculosis; user-friendly","","","","","","Francis Crick Institute, FCI; School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences; Bacha Khan University","The author gratefully acknowledges the technical support received from Amy Strange and Luke Nightingale of the Crick Institute (United Kingdom), as well as Professor Stephen Cose of the Medical Research Council and London School of Hygiene and Tropical Medicine Uganda Research Unit. Additionally, the author expresses sincere gratitude to the leadership of the School of Nursing and Midwifery at the Aga Khan University for offering protected time for the development of this protocol. Finally, we acknowledge using generative artificial intelligence to refine and edit sentences for clarity, while specifying its nonuse in content generation (a sample transcript is provided in Multimedia Appendix 5).","Ngari MM, Rashid MA, Sanga D, Mathenge H, Agoro O, Mberia JK, Et al., Burden of HIV and treatment outcomes among TB patients in rural Kenya: a 9-year longitudinal study, BMC Infect Dis, 23, 1, (2023); Chhabra P, Sharma G, Kannan AT., Prevalence of respiratory disease and associated factors in an urban area of Delhi, Indian J Community Med, 33, 4, pp. 229-232, (2008); Duan KI, Birger M, Au DH, Spece LJ, Feemster LC, Dieleman JL., Health care spending on respiratory diseases in the United States, 1996-2016, Am J Respir Crit Care Med, 207, 2, pp. 183-192, (2023); Early detection of tuberculosis: an overview of approaches, guidelines and tools: an overview of approaches, guidelines and tools, (2011); Ozoh OB, Ngahane BHM, Zar HJ, Masekela R, Chakaya J, Aluoch J, Et al., Pan African Thoracic Society. 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SensiML Toolkit; Pahar M, Klopper M, Warren R, Niesler T., COVID-19 cough classification using machine learning and global smartphone recordings, Comput Biol Med, 135, (2021); Pahar M, Miranda I, Diacon A, Niesler T., Automatic non-invasive cough detection based on accelerometer and audio signals, J Signal Process Syst, 94, 8, pp. 821-835, (2022); Tacchetti M., User guide for ELAN linguistic annotator version 5.0.0; Hegde S, Sreeram S, Alter IL, Shor C, Valdez TA, Meister KD, Et al., Cough sounds in screening and diagnostics: a scoping review, Laryngoscope, 134, 3, pp. 1023-1031, (2024); Moschovis PP, Sampayo EM, Cook A, Doros G, Parry BA, Lombay J, Et al., The diagnosis of respiratory disease in children using a phone-based cough and symptom analysis algorithm: the smartphone recordings of cough sounds 2 (SMARTCOUGH-C 2) trial design, Contemp Clin Trials, 101, (2021); Sharan RV, Rahimi-Ardabili H., Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: a systematic review, Int J Med Inform, 176, (2023); Nikhil K., Introduction to PyTorch, Deep Learning with Python, pp. 195-208, (2017); Han W, Chan CF, Choy CS, Pun KP., An efficient MFCC extraction method in speech recognition, 2006 IEEE International Symposium on Circuits and Systems. 2006. 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Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity, Acad Emerg Med, 3, 9, pp. 895-900, (1996); Xiong Y, Ba X, Hou A, Zhang K, Chen L, Li T., Automatic detection of mycobacterium tuberculosis using artificial intelligence, J Thorac Dis, 10, 3, pp. 1936-1940, (2018); Arifin WN., Sample size calculator. github, (2023); Meremo AJ, Kidenya BR, Mshana SE, Kabangila R, Kataraihya JB., High prevalence of tuberculosis among adults with fever admitted at a tertiary hospital in north-western Tanzania, Tanzan J Health Res, 14, 3, pp. 183-188, (2012); Asthma: diagnosis; COPD: diagnosis; Braun V, Clarke V., Using thematic analysis in psychology, Qual Res Psychol, 3, 2, pp. 77-101, (2006); Louart S, Hedible GB, Ridde V., Assessing the acceptability of technological health innovations in sub-Saharan Africa: a scoping review and a best fit framework synthesis, BMC Health Serv Res, 23, 1, (2023); Germain CB., Human Behavior in the Social Environment, (1991); Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, Et al., An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals, NPJ Digit Med, 6, 1, (2023); Yeracaris CA., Social factors associated with the acceptance of medical innovations: a pilot study, J Health Hum Behav, 3, pp. 193-198, (1962)","K.G. Isangula; School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, Salama House, 344 Urambo St PO Box 125, 255, Tanzania; email: kahabi.isangula@aku.edu","","JMIR Publications Inc.","","","","","","19290748","","","","English","JMIR Res. Prot.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85192702293"
"Jamshidi E.; Asgary A.; Setareh S.; Casutt A.; Gonzalez M.; Bianchi M.P.; Lovis A.; De Palma M.; von Garnier C.; Mansouri N.","Jamshidi, E. (57217795317); Asgary, A. (57222399046); Setareh, S. (57725186600); Casutt, A. (56540023600); Gonzalez, M. (36027940900); Bianchi, M.P. (55948983900); Lovis, A. (23089015600); De Palma, M. (57215694182); von Garnier, C. (55965013500); Mansouri, N. (9238064700)","57217795317; 57222399046; 57725186600; 56540023600; 36027940900; 55948983900; 23089015600; 57215694182; 55965013500; 9238064700","Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis","2023","Clinical Oncology","35","6","","e362","e375","13","0","10.1016/j.clon.2023.03.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150857301&doi=10.1016%2fj.clon.2023.03.003&partnerID=40&md5=3a1b473f5a07c2a2cc68082b2b00f8ec","Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Centre of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran; Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Division of Thoracic Surgery, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland; Agora Cancer Research Center, Lausanne, Switzerland","Jamshidi E., Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Centre of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Asgary A., Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran; Setareh S., Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran; Casutt A., Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Gonzalez M., Division of Thoracic Surgery, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Bianchi M.P., Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Lovis A., Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; De Palma M., Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland, Agora Cancer Research Center, Lausanne, Switzerland; von Garnier C., Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Mansouri N., Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland, Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Switzerland, Agora Cancer Research Center, Lausanne, Switzerland","Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk of lung metastasis using readily available predictors. Materials and methods: We retrospectively analysed a cohort of 11 164 oncological patients, with clinical records gathered between 2000 and 2020. The input data consisted of 94 parameters, including age, body mass index (BMI), sex, social history, 81 primary cancer types, underlying lung disease and diabetes mellitus. The strongest underlying predictors were discovered with the analysis of the highest performing method among four distinct machine learning methods. Results: Lung metastasis was present in 958 of 11 164 oncological patients. The median age and BMI of the study population were 63 (±19) and 25.12 (±5.66), respectively. The random forest method had the most robust performance among the machine learning methods. Feature importance analysis revealed high BMI as the strongest predictor. Advanced age, smoking, male gender, alcohol dependence, chronic obstructive pulmonary disease and diabetes were also strongly associated with lung metastasis. Among primary cancers, melanoma and renal cancer had the strongest correlation. Conclusions: Using a machine learning-based approach, we revealed new correlations between personal and medical characteristics of patients with cancer and lung metastasis. This study highlights the previously unknown impact of predictors such as obesity, advanced age and underlying lung disease on the occurrence of lung metastasis. This prediction model can assist physicians with preventive risk factor control and treatment strategies. © 2023","Asthma; BMI; cancer; diabetes; lung metastasis; machine learning","Diabetes Mellitus; Humans; Lung Neoplasms; Male; Retrospective Studies; Risk Factors; adult; age; alcoholism; Article; body mass; cancer prognosis; cancer risk; chronic obstructive lung disease; clinical feature; cohort analysis; diabetes mellitus; female; gender; human; kidney cancer; lung metastasis; major clinical study; male; melanoma; middle aged; obesity; prediction; retrospective study; risk factor; smoking; trend study; diabetes mellitus; lung tumor","","","","","","","Massague J., Obenauf A.C., Metastatic colonization by circulating tumour cells, Nature, 529, pp. 298-306, (2016); Qian C.-N., Mei Y., Zhang J., Cancer metastasis: issues and challenges, Chin J Cancer, 36, pp. 1-4, (2017); Anderson R.L., Balasas T., Callaghan J., Coombes R.C., Evans J., Hall J.A., Et al., A framework for the development of effective anti-metastatic agents, Nat Rev Clin Oncol, 16, pp. 185-204, (2018); Stella G.M., Kolling S., Benvenuti S., Bortolotto C., Lung-seeking metastases, Cancers, 11, (2019); Jamil A., Kasi A., Lung metastasis. StatPearls, (2022); Xie S., Wu Z., Wu B., Zhu X., The metastasizing mechanisms of lung cancer: recent advances and therapeutic challenges, Biomed Pharmacother, 138, (2021); Li X.J., Gangadaran P., Kalimuthu S., Oh J.M., Zhu L., Jeong S.Y., Et al., Role of pulmonary macrophages in initiation of lung metastasis in anaplastic thyroid cancer, Int J Cancer, 139, pp. 2583-2592, (2016); Jordens M.S., Labuhn S., Luedde T., Hoyer L., Kostev K., Loosen S.H., Et al., Prevalence of lung metastases among 19,321 metastatic colorectal cancer patients in eight countries of Europe and Asia, Curr Oncol, 28, pp. 5035-5040, (2021); Ballester B., Milara J., Cortijo J., Idiopathic pulmonary fibrosis and lung cancer: mechanisms and molecular targets, Int J Mol Sci, 20, (2019); Yoo H., Jeong B.-H., Chung M.J., Lee K.S., Kwon O.J., Chung M.P., Risk factors and clinical characteristics of lung cancer in idiopathic pulmonary fibrosis: a retrospective cohort study, BMC Pulm Med, 19, pp. 1-8, (2019); Durham A.L., Adcock I.M., The relationship between COPD and lung cancer, Lung Cancer, 90, pp. 121-127, (2015); MacEachern S.J., Forkert N.D., Machine learning for precision medicine, Genome, 64, (2020); Collins G.S., Reitsma J.B., Altman D.G., Moons K.G.M., Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement, Br J Surg, 102, pp. 148-158, (2015); Hancock J.T., Khoshgoftaar T.M., Survey on categorical data for neural networks, J Big Data, 7, pp. 1-41, (2020); Emmanuel T., Maupong T., Mpoeleng D., Semong T., Mphago B., Tabona O., A survey on missing data in machine learning, J Big Data, 8, pp. 1-37, (2021); Harper P.R., A review and comparison of classification algorithms for medical decision making, Health Policy, 71, pp. 315-331, (2005); Garreta R., Moncecchi G., Learning scikit-learn: machine learning in Python, (2013); Wong T.-T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation, Pattern Recog, 48, pp. 2839-2846, (2015); Raschka S., Model evaluation, model selection, and algorithm selection in machine learning, arXiv [csLG], (2018); Kim H.-Y., Statistical notes for clinical researchers: chi-squared test and Fisher's exact test, Restor Dent Endod, 42, (2017); Kaplan E.L., Meier P., Nonparametric estimation from incomplete observations, pp. 319-337, (1992); Chen R.-C., Dewi C., Huang S.-W., Caraka R.E., Selecting critical features for data classification based on machine learning methods, J Big Data, 7, pp. 1-26, (2020); Chen H., Stoltzfus K.C., Lehrer E.J., Horn S.R., Siva S., Trifiletti D.M., Et al., The epidemiology of lung metastases, Front Med, 8, (2021); Bohr A., Memarzadeh K., Artificial intelligence in healthcare, (2020); Saxena A., Chandra S., Artificial intelligence and machine learning in healthcare, (2021); Liu W., Wang S., Ye Z., Xu P., Xia X., Guo M., Prediction of lung metastases in thyroid cancer using machine learning based on SEER database, Cancer Med, 11, pp. 2503-2515, (2022); Fan Y., Cai M., Xia L., Distinction and potential prediction of lung metastasis in patients with malignant primary osseous spinal neoplasms, Spine, 45, pp. 921-929, (2020); Quail D.F., Olson O.C., Bhardwaj P., Walsh L.A., Akkari L., Quick M.L., Et al., Obesity alters the lung myeloid cell landscape to enhance breast cancer metastasis through IL5 and GM-CSF, Nat Cell Biol, 19, pp. 974-987, (2017); Dalamaga M., Diakopoulos K.N., Mantzoros C.S., The role of adiponectin in cancer: a review of current evidence, Endocr Rev, 33, pp. 547-594, (2012); Kelesidis I., Kelesidis T., Mantzoros C.S., Adiponectin and cancer: a systematic review, Br J Cancer, 94, (2006); Wang Y., Zeng Z., Tang M., Zhang M., Bai Y., Cui H., Et al., Sex disparities in the clinical characteristics, synchronous distant metastasis occurrence and prognosis: a pan-cancer analysis, J Cancer, 12, (2021); Abrams J.A., Lee P.C., Port J.L., Altorki N.K., Neugut A.I., Cigarette smoking and risk of lung metastasis from esophageal cancer, Cancer Epidemiol Biomarkers Prev, 17, (2008); Makino A., Tsuruta M., Okabayashi K., Ishida T., Shigeta K., Seishima R., Et al., The impact of smoking on pulmonary metastasis in colorectal cancer, OncoTargets Ther, 13, pp. 9623-9629, (2020); El Rayes T., Catena R., Lee S., Stawowczyk M., Joshi N., Fischbach C., Et al., Lung inflammation promotes metastasis through neutrophil protease-mediated degradation of Tsp-1, Proc Natl Acad Sci U S A, 112, pp. 16000-16005, (2015); Bekaert S., Rocks N., Vanwinge C., Noel A., Cataldo D., Asthma-related inflammation promotes lung metastasis of breast cancer cells through CCL11–CCR3 pathway, Respir Res, 22, (2021); Ferguson R.D., Novosyadlyy R., Fierz Y., Alikhani N., Sun H., Yakar S., Et al., Hyperinsulinemia enhances c-Myc-mediated mammary tumor development and advances metastatic progression to the lung in a mouse model of type 2 diabetes, Breast Cancer Res, 14, (2012)","N. Mansouri; Division of Pulmonary Medicine, Department of Medicine, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; email: nahal.mansouri@chuv.ch","","Elsevier Ltd","","","","","","09366555","","CLIOE","36967312","English","Clin. Oncol.","Article","Final","","Scopus","2-s2.0-85150857301"
"Zhao C.; Xiang B.; Zhang J.; Yang P.; Liu Q.; Wang S.","Zhao, Chunxiu (59391509000); Xiang, Bingbing (57287591500); Zhang, Jie (57929952300); Yang, Pingliang (15073606300); Liu, Qiaoli (58558288700); Wang, Shun (57215014531)","59391509000; 57287591500; 57929952300; 15073606300; 58558288700; 57215014531","Predicting postoperative pulmonary infection risk in patients with diabetes using machine learning","2024","Frontiers in Physiology","15","","1501854","","","","0","10.3389/fphys.2024.1501854","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212286381&doi=10.3389%2ffphys.2024.1501854&partnerID=40&md5=f317352582db9a28d25bb3fd5f734e1e","Department of Critical Care Medicine, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Sichuan, Chengdu, China; Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China; Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China","Zhao C., Department of Critical Care Medicine, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu, Sichuan, Chengdu, China; Xiang B., Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China; Zhang J., Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China; Yang P., Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China; Liu Q., Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China; Wang S., Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Sichuan, Chengdu, China","Background: Patients with diabetes face an increased risk of postoperative pulmonary infection (PPI). However, precise predictive models specific to this patient group are lacking. Objective: To develop and validate a machine learning model for predicting PPI risk in patients with diabetes. Methods: This retrospective study enrolled 1,269 patients with diabetes who underwent elective non-cardiac, non-neurological surgeries at our institution from January 2020 to December 2023. Predictive models were constructed using nine different machine learning algorithms. Feature selection was conducted using Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Model performance was assessed via the Area Under the Curve (AUC), precision, accuracy, specificity and F1-score. Results: The Ada Boost classifier (ADA) model exhibited the best performance with an AUC of 0.901, Accuracy of 0.91, Precision of 0.82, specificity of 0.98, PPV of 0.82, and NPV of 0.82. LASSO feature selection identified six optimal predictive factors: postoperative transfer to the ICU, Age, American Society of Anesthesiologists (ASA) physical status score, chronic obstructive pulmonary disease (COPD) status, surgical department, and duration of surgery. Conclusion: Our study developed a robust predictive model using six clinical features, offering a valuable tool for clinical decision-making and personalized prevention strategies for PPI in patients with diabetes. Copyright © 2024 Zhao, Xiang, Zhang, Yang, Liu and Wang.","Ada Boost classifier; diabetes mellitus; machine learning; postoperative pulmonary infection; risk prediction","adult; age; aged; area under the curve; Article; blood glucose monitoring; blood pressure; body mass; chronic obstructive lung disease; clinical feature; controlled study; cross-sectional study; diabetes mellitus; diagnostic test accuracy study; diastolic blood pressure; disease duration; female; gender; general anesthesia; glucose blood level; heart rate; human; hypertension; insulin treatment; intensive care unit; ischemic heart disease; least absolute shrinkage and selection operator; lung infection; machine learning; major clinical study; male; mean arterial pressure; New York Heart Association class; non insulin dependent diabetes mellitus; operation duration; perioperative period; postoperative pulmonary infection; receiver operating characteristic; retrospective study; risk factor; sensitivity and specificity; support vector machine; systolic blood pressure","","","R version 4.2.2 software","","Sichuan University, SCU; Science and Technology Department of Sichuan Province, SPDST, (2022NSFSCO710); Science and Technology Department of Sichuan Province, SPDST; Chengdu Science and Technology Bureau, (2022-YF05-01343-SN); Chengdu Science and Technology Bureau","The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Science and Technology Department of Sichuan Province (grant 2022NSFSCO710), Chengdu Science and Technology Bureau (grant 2022-YF05-01343-SN), and 1\u00B735 projects for disciplines of excellence, West China Hospital, Sichuan University (grantZYJC21008). ","Abbott T., Fowler A.J., Pelosi P., Gama de Abreu M., Moller A.M., Canet J., Et al., A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications, Br. J. 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Surg, 270, 1, pp. 147-157, (2019); Shin B., Lee H., Kang D., Jeong B.H., Chon H.R., Koh W.J., Et al., Airflow limitation severity and post-operative pulmonary complications following extra-pulmonary surgery in COPD patients, Respirology, 22, 5, pp. 935-941, (2017); Short H.L., Fevrier H.B., Meisel J.A., Santore M.T., Heiss K.F., Wulkan M.L., Et al., Defining the association between operative time and outcomes in children's surgery, J. Pediatr. Surg, 52, 10, pp. 1561-1566, (2017); Sidey-Gibbons J.A.M., Sidey-Gibbons C.J., Machine learning in medicine: a practical introduction, BMC Med. Res. Methodol, 19, 1, (2019); Song K., Rong Z., Yang X., Yao Y., Shen Y., Shi D., Et al., Early pulmonary complications following total knee arthroplasty under general anesthesia: a prospective cohort study using ct scan, Biomed. Res. Int, 2016, (2016); Verkoulen K., Laven I., Daemen J., Degens J.H.R.J., Hendriks L.E.L., Hulsewe K.W.E., Et al., The (un)lucky seven-how can we mitigate risk factors for postoperative pneumonia after lung resections?, Transl. Lung Cancer Res, 13, 8, pp. 1763-1767, (2024); Vestbo J., Hurd S.S., Agusti A.G., Jones P.W., Vogelmeier C., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am. J. Respir. Crit. Care Med, 187, 4, pp. 347-365, (2013); von E.L.M.E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P., Et al., Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies, BMJ, 335, 7624, pp. 806-808, (2007); Wang B., Liang H.S., Shen J.W., An Y.Z., Feng Y., A nomogram for predicting postoperative pulmonary complications in critical patients transferred to ICU after abdominal surgery, J. Intensive Care Med, (2024); Wu Y., Mo Q., Xie Y., Zhang J., Jiang S., Guan J., Et al., A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo, Urolithiasis, 51, 1, (2023); Zhou Z., Wang H., Tan S., Zhang H., Zhu Y., The alterations of innate immunity and enhanced severity of infections in diabetes mellitus, Immunology, 171, 3, pp. 313-323, (2024)","Q. Liu; Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China; email: 1176620592@qq.com; S. Wang; Department of Anesthesiology, Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China; email: wangshun@cmc.edu.cn","","Frontiers Media SA","","","","","","1664042X","","","","English","Front. Physiol.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85212286381"
"Moradi N.; Habibirad A.; Panahi H.","Moradi, Nasrin (57210185775); Habibirad, Arezou (57190287229); Panahi, Hanieh (55963780200)","57210185775; 57190287229; 55963780200","Leveraging neural networks for robust survival estimation in cox proportional hazards model with censored data","2024","Communications in Statistics Case Studies Data Analysis and Applications","10","3-4","","349","364","15","0","10.1080/23737484.2024.2439847","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214668099&doi=10.1080%2f23737484.2024.2439847&partnerID=40&md5=c434003b09c77fd9029b185bce1431c4","Department of Statistics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran; Department of Mathematics and Statistics, Lahijan Branch, Islamic Azad University, Lahijan, Iran","Moradi N., Department of Statistics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran; Habibirad A., Department of Statistics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran; Panahi H., Department of Mathematics and Statistics, Lahijan Branch, Islamic Azad University, Lahijan, Iran","Survival analysis, traditionally dominated by models like the Cox Proportional Hazards, faces significant challenges in handling high-dimensional and complex datasets common in modern medical and biological research. This paper introduces an innovative approach to address these challenges by leveraging Variational Autoencoders (VAE) for feature extraction before survival analysis. We propose a framework where VAE compress high-dimensional input data into a lower-dimensional, yet informative latent space. Also, we identify key factors associated with mortality. We use publicly available Covid-19 data and analyze the survival characteristics of a large sample of 566,602 patients. Various machine learning techniques compared to improve prediction accuracy. The performance of the models has been evaluated using metrics like the concordance index and accuracy. The proposed algorithm performs the best with an accuracy of 0.94. Also, it shows good concordance, with training and test concordance index values of 0.91559 and 0.91299, respectively, while other algorithms range from 70% to 91% accuracy. The findings suggest that most features have a significant impact on reducing mortality, except for COPD, asthma, inmsupr, cardiovascular, obesity, renal chronic and tobacco. This model can help identify patients at high risk of death, so they can be prioritized for critical care. © 2025 Taylor & Francis Group, LLC.","Coronavirus (Covid-19); Cox Proportional Hazard Model; feature selection; neural network; survival analysis; variational autoencoders","","","","","","","","Antolini L., Boracchi P., Biganzoli E., “A Time-Dependent Discrimination Index for Survival Data.”, Statistics in Medicine, 24, 24, pp. 3927-3944, (2005); Baek E.T., Yang H.J., Kim S.H., Lee G.S., Oh I.J., Kang S.R., Min J.J., “Survival Time Prediction by Integrating Cox Proportional Hazards Network and Distribution Function Network.”, BMC Bioinformatics, 22, 1, (2021); Bartlett P., Freund Y., Lee W.S., Schapire R.E., “Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods.”, The Annals of Statistics, 26, 5, pp. 1651-1686, (1998); Buhlmann P., Yu B., “Boosting with the L 2 Loss: Regression and Classification, Journal of the American Statistical Association, 98, 462, pp. 324-339, (2003); Chen Y., Jia Z., Mercola D., Xie X., “A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index.”, Computational and Mathematical Methods in Medicine, 2013, pp. 873595-98, (2013); Cox D.R., “Regression Models and Life-Tables.”, Journal of the Royal Statistical Society: Series B (Methodological), 34, 2, pp. 187-202, (1972); Fathi M., Nemati M., Mohammadi S.M., Abbasi-Kesbi R., “A Machine Learning Approach Based on SVM for Classification of Liver Diseases.”, Biomedical Engineering: Applications, Basis and Communications, 32, 3, (2020); Harrell F.E., Califf R.M., Pryor D.B., Lee K.L., Rosati R.A., “Evaluating the Yield of Medical Tests.”, JAMA, 247, 18, pp. 2543-2546, (1982); Hu S., Wang Y.G., Drovandi C., Cao T., “Predictions of Machine Learning with Mixed-Effects in Analyzing Longitudinal Data under Model Misspecification.”, Statistical Methods & Applications, 32, 2, pp. 681-711, (2023); Ishwaran H., Kogalur U.B., “Random Survival Forests for R.”, R News, 7, 2, pp. 25-31, (2007); Ishwaran H., Kogalur U.B., Blackstone E.H., Lauer M.S., pp. 841-860, (2008); Katzman J.L., Shaham U., Cloninger A., Bates J., Jiang T., Kluger Y., “DeepSurv: Personalized Treatment Recommender System Using a Cox Proportional Hazards Deep Neural Network.”, BMC Medical Research Methodology, 18, 1, (2018); Khan F.M., Zubek V.B., Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis, 2008 Eighth IEEE International Conference on Data Mining, pp. 863-868, (2008); Khozeimeh F., Sharifrazi D., Izadi N.H., Joloudari J.H., Shoeibi A., Alizadehsani R., Gorriz J.M., Hussain S., Sani Z.A., Moosaei H., Et al., “Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients.”, Scientific Reports, 11, 1, (2021); Kingma D.P., Welling M., (2013); Kourou K., Exarchos T.P., Exarchos K.P., Karamouzis M.V., Fotiadis D.I., “Machine Learning Applications in Cancer Prognosis and Prediction.”, Computational and Structural Biotechnology Journal, 13, pp. 8-17, (2015); Kvamme H., Borgan O., Scheel I., “Time-to-Event Prediction with Neural Networks and Cox Regression.”, Journal of Machine Learning Research, 20, 129, pp. 1-30, (2019); Ma B., Yan G., Chai B., Hou X., “XGBLC: An Improved Survival Prediction Model Based on XGBoost.”, Bioinformatics (Oxford, England), 38, 2, pp. 410-418, (2022); Mallett S., Royston P., Waters R., Dutton S., Altman D.G., “Reporting Performance of Prognostic Models in Cancer: A Review.”, BMC Medicine, 8, 1, (2010); Mittal S., Madigan D., Burd R.S., Suchard M.A., “High-Dimensional, Massive Sample-Size Cox Proportional Hazards Regression for Survival Analysis.”, Biostatistics (Oxford, England), 15, 2, pp. 207-221, (2014); Molaris V.A., Triantafyllopoulos K., Papadakis G., Economou P., Bersimis S., “The Effect of COVID-19 on Minor Dry Bulk Shipping: A Bayesian Time Series and a Neural Networks Approach.”, Communications in Statistics: Case Studies, Data Analysis and Applications, 7, 4, pp. 624-638, (2021); Moncada-Torres A., van Maaren M.C., Hendriks M.P., Siesling S., Geleijnse G., “Explainable Machine Learning Can Outperform Cox Regression Predictions and Provide Insights in Breast Cancer Survival.”, Scientific Reports, 11, 1, (2021); Polsterl S., Navab N., Katouzian A., (2016); Polsterl S., Navab N., Katouzian A., Fast Training of Support Vector Machines for Survival Analysis, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2015, pp. 243-259, (2015); Punn N.S., Sonbhadra S.K., Agarwal S., COVID-19 Epidemic Analysis Using Machine Learning and Deep Learning Algorithms, Health Sciences, (2020); Shrubsole M.J., Jin F., Dai Q., Shu X.O., Potter J.D., Hebert J.R., Gao Y.T., Zheng W., “Dietary Folate Intake and Breast Cancer Risk: Results from the Shanghai Breast Cancer Study.”, Cancer Research, 61, 19, pp. 7136-7141, (2001); Wang P., Li Y., Reddy C.K., “Machine Learning for Survival Analysis: A Survey.”, ACM Computing Surveys, 51, 6, pp. 1-36, (2019); Wang J., Liu C., Li J., Yuan C., Zhang L., Jin C., Xu J., Wang Y., Wen Y., Lu H., Et al., “iCOVID: Interpretable Deep Learning Framework for Early Recovery-Time Prediction of COVID-19 Patients.”, NPJ Digital Medicine, 4, 1, (2021); Wang J., Yu H., Hua Q., Jing S., Liu Z., Peng X., Cao C., Luo Y., “A Descriptive Study of Random Forest Algorithm for Predicting COVID-19 Patients Outcome.”, PeerJ, 8, (2020); Yin Q., Chen W., Zhang C., Wei Z., “A Convolutional Neural Network Model for Survival Prediction Based on Prognosis-Related Cascaded Wx Feature Selection.”, Laboratory Investigation; a Journal of Technical Methods and Pathology, 102, 10, pp. 1064-1074, (2022); Zhang S., Guo M., Duan L., Wu F., Hu G., Wang Z., Huang Q., Liao T., Xu J., Ma Y., Et al., “Development and Validation of a Risk Factor-Based System to Predict Short-Term Survival in Adult Hospitalized Patients with COVID-19: A Multicenter, Retrospective, Cohort Study.”, Critical Care, 24, 1, pp. 1-13, (2020)","A. Habibirad; Department of Statistics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran; email: ahabibi@um.ac.ir","","Taylor and Francis Ltd.","","","","","","23737484","","","","English","Commun. Stat., Case Stud. Data Anal. Appl.","Article","Final","","Scopus","2-s2.0-85214668099"
"Sander M.D.; Madsen R.L.; Laursen S.H.; Hangaard S.","Sander, Mathilde D. (58676581600); Madsen, Rikke L. (58676511100); Laursen, Sisse H. (57044401100); Hangaard, Stine (57196475233)","58676581600; 58676511100; 57044401100; 57196475233","A Study on Optimization and Evaluation of the Visualization of Complex Algorithm Results in Remote Monitoring of COPD","2023","Studies in health technology and informatics","309","","","23","27","4","0","10.3233/SHTI230732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175496734&doi=10.3233%2fSHTI230732&partnerID=40&md5=cef9828c230bc17679eaba5c7509f5eb","Aalborg University, Department of Health and Science, Aalborg, Denmark; Research Unit for Clinical Nursing, Aalborg University Hospital, Aalborg, Denmark","Sander M.D., Aalborg University, Department of Health and Science, Aalborg, Denmark; Madsen R.L., Aalborg University, Department of Health and Science, Aalborg, Denmark; Laursen S.H., Aalborg University, Department of Health and Science, Aalborg, Denmark, Research Unit for Clinical Nursing, Aalborg University Hospital, Aalborg, Denmark; Hangaard S., Aalborg University, Department of Health and Science, Aalborg, Denmark","BACKGROUND: Artificial intelligence (AI) can potentially increase the quality of telemonitoring in chronic obstructive pulmonary disease (COPD). However, the output from AI is often difficult for clinicians to understand due to the complexity. This challenge may be accommodated by visualizing the AI results, however it hasn't been studied how this could be done specifically, i.e., considering which visual elements to include. AIM: To investigate how complex results from a predictive algorithm for patients with COPD can be translated into easily understandable data for the clinicians. METHODS: Semi-structured interviews were conducted to explore clinicians' needs when visualizing the results of a predictive algorithm. This formed a basis for creating a prototype of an updated user interface. The user interface was evaluated using usability tests through the ""Think aloud"" method. RESULTS: The clinicians pointed out the need for visualization of exacerbation alerts and the development in patients' data. Furthermore, they wanted the system to provide more information about what caused exacerbation alerts. Elements such as color and icons were described as particularly useful. The usability of the prototype was primarily assessed as easily understandable and advantageous in connection to the functions of the predictive algorithm. CONCLUSION: Predictive algorithm use in telemonitoring of COPD can be optimized by clearly visualizing the algorithm's alerts, clarifying the reasons for algorithm output, and by providing a clear overview of the development in the patient's data. This can contribute to clarity when the clinicians should act and why they should act on alerts from predictive algorithms.","Artificial Intelligence; COPD; Predictive Algorithm; Telemonitoring; Usability; User Interface","Algorithms; Artificial Intelligence; Humans; Pulmonary Disease, Chronic Obstructive; Telemedicine; algorithm; artificial intelligence; chronic obstructive lung disease; human; procedures; telemedicine","","","","","","","","","","","","","","","","18798365","","","37869799","English","Stud Health Technol Inform","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85175496734"
"Liu W.; Wang K.; Guan H.; Ma L.; Cui Y.; Liu C.; Shi J.; Fan Y.; Sun Y.","Liu, Wenqin (59154451500); Wang, Kanghua (57210379363); Guan, Haoyan (57212680194); Ma, Ling (59487324300); Cui, Yueming (58507334100); Liu, Chang (57211498155); Shi, Jianbo (26025986700); Fan, Yunping (36622813700); Sun, Yueqi (54399260000)","59154451500; 57210379363; 57212680194; 59487324300; 58507334100; 57211498155; 26025986700; 36622813700; 54399260000","Blood transcriptomics reveal systemic eosinophilic and neutrophilic inflammation patterns in patients with nasal polyps","2024","Rhinology","62","6","","739","749","10","0","10.4193/Rhin24.248","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211503961&doi=10.4193%2fRhin24.248&partnerID=40&md5=844c840123b66eee865a9cd3da42b76a","Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Otorhinolaryngology, the University of Hong Kong-Shenzhen Hospital, Shenzhen, China","Liu W., Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Wang K., Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Guan H., Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Ma L., Department of Otorhinolaryngology, the University of Hong Kong-Shenzhen Hospital, Shenzhen, China; Cui Y., Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Liu C., Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Shi J., Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Fan Y., Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Sun Y., Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China","Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic sinonasal disease characterized by heterogeneous inflammation. However, the presence of systemic inflammation heterogeneity in CRSwNP patients remains unknown. This study aims to profile transcriptomic alterations in the blood of CRSwNP patients and characterize the CRSwNP heterogeneity based on blood transcriptomic biomarkers. Methodology: Patients with CRSwNP were prospectively recruited from three hospitals and chronologically divided into exploratory (n=123) and independent validation (n=46) cohorts. Transcriptomic profiles were generated by whole blood mRNA sequencing and subjected to patient clustering, differential expression, and pathway analysis. Differences in immune pattern and clinicopathologic features between clusters were assessed. A transcriptomic signature was defined and applied to an independent cohort to validate the findings. Results: CRSwNP patients showed diverse blood transcriptomic profiles versus healthy controls, or when stratified by tissue and blood eosinophils and asthma comorbidity. Transcriptome-wide correlation analysis revealed a transcriptional signature associated with blood eosinophil levels, consisting of nine T2-related genes (CLC, SIGLEC8, ALOX15, IL5RA, PTGDR2, CCL23, CCR3, EPX and IL1RL1). Three distinct clusters with differing systemic eosinophilic and neutrophilic inflammation patterns and asthma comorbidity were identified based on transcriptomic profiling of T2 and T1/3-related blood biomarkers. A 36-gene signature was developed by machine learning and accurately predicted the three CRSwNP subtypes. Validation on an independent cohort confirmed the prediction robustness. Conclusions: There is heterogeneous systemic inflammation associated with eosinophilic and neutrophilic patterns in patients with CRSwNP. Endotyping based on blood transcriptomic biomarkers might lead to more personalized treatment strategies for CRSwNP in the future. © 2024, International Rhinologic Society. All rights reserved.","endotype; nasal polyp; rhinosinusitis; transcriptome","Adult; Biomarkers; Chronic Disease; Eosinophils; Female; Gene Expression Profiling; Humans; Inflammation; Male; Middle Aged; Nasal Polyps; Neutrophils; Prospective Studies; Rhinitis; Sinusitis; Transcriptome; biological marker; transcriptome; adult; blood; chronic disease; eosinophil; female; gene expression profiling; genetics; human; inflammation; male; metabolism; middle aged; neutrophil; prospective study; rhinitis; sinonasal polyp; sinusitis","","Biomarkers, ","","","","","Fokkens WJ, Lund VJ, Hopki ns C, Et al., European Position Paper on Rhinosinusitis and Nas al Pol yps 2020, Rhi nol ogy, 58, pp. 1-464, (2020); Orlandi RR, Kingdom TT, Smith TL, Et al., International consensus statement on allergy and rhinology: rhinosinusitis 2021, Int Forum Allergy Rhinol, 11, 3, pp. 213-739, (2021); Kato A, Peters AT, Stevens WW, Schleimer RP, Tan BK, Kern RC., Endotypes of chronic rhinosinusitis: relationships to disease phenotypes, pathogenesis, clinical find-ings, and treatment approaches, Allergy, 77, 3, pp. 812-826, (2022); Chapurin N, Wu J, Labby AB, Chandra RK, Chowdhury NI, Turner JH., Current insight into treatment of chronic rhinosinusitis: phenotypes, endotypes, and implications for targeted therapeutics, J Allergy Clin Immunol, 150, 1, pp. 22-32, (2022); Schl ei mer RP., I mmunopathogenesi s of chronic rhinosinusitis and nasal polyposis, Annu Rev Pathol, 12, pp. 331-357, (2017); Ko YG, Kim MH, Park JY, Et al., Chronic rhinos-inusitis endotypes associate with distinct local cytokine milieus that shape the dis- tribution of innate lymphoid cells, Allergy, 77, 7, pp. 2246-2250, (2022); Jonstam K, Westman M, Hol tappel s G, Holweg CTJ, Bachert C., Serum periostin, IgE, and SE-IgE can be used as biomarkers to identify moderate to severe chronic rhi-nosinusitis with nasal polyps, J Allergy Clin Immunol, 140, 6, pp. 1705-1708, (2017); Drake VE, Rafaels N, Kim J., Peripheral blood eosi nophi l i a correl ates wi th hyperpl as-tic nasal polyp growth, Int Forum Allergy Rhinol, 6, 9, pp. 926-934, (2016); Zhong B, Yuan T, Du J, Et al., The role of preoperative blood eosinophil counts in distinguishing chronic rhinosinusitis with nasal polyps phenotypes, Int Forum Allergy Rhinol, 11, 1, pp. 16-23, (2021); Pa pr oc k a-Zjawiona M, Merecz-Sadowska A, Zajdel R, Blizniewska-Kowalska K, Malinowska K., Serum IL-5, POSTN and IL-33 levels in chronic rhinosinusitis with nasal polyposis cor-relate with clinical severity, BMC Immunol, 23, 1, (2022); Bigler J, Boedigheimer M, Schofield JPR, Et al., A severe asthma disease signature from gene expression profiling of peripheral blood from U-BIOPRED cohorts, Am J Respir Crit Care Med, 195, 10, pp. 1311-1320, (2017); Zeng X, Qing J, Li CM, Et al., Blood transcrip-tomic signature in type-2 biomarker-low severe asthma and asthma control, J Allergy Clin Immunol, 152, 4, pp. 876-886, (2023); Mol l M, Bouei z A, Ghos h AJ, Et al., Development of a blood-based transcriptional risk score for chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 205, 2, pp. 161-170, (2022); Mobus L, Rodriguez E, Harder I, Et al., Blood transcriptome profiling identifies 2 can-didate endotypes of atopic dermatitis, J Allergy Clin Immunol, 150, 2, pp. 385-395, (2022); Erwin EA, Jaramillo LM, Smith B, Et al., Sex differences in blood transcriptional profiles and clinical phenotypes in pediatric patients with eosinophilic esophagitis, J Allergy Clin Immunol Pract, 9, 9, pp. 3350-3358e8, (2021); Bacher t C, Marple B, Schlosser RJ, Et al., Adult chronic rhinosinusitis, Nat Rev Dis Primers, 6, 1, (2020); Bachert C, Zhang N, Hellings PW, Bousquet J., Endot ype-dr i ven care pat hways i n pati ents wi th chroni c r hi nosi nusi ti s, J Allergy Clin I mmunol, 141, 5, pp. 1543-1551, (2018); Asano T, Kanemitsu Y, Takemura M, Et al., Serum periostin as a biomarker for comor-bi d chroni c r hi nosi nusi ti s i n pat i ent s wi t h as t hma, Ann Am Thor ac Soc, 14, 5, pp. 667-675, (2017); Tsai PC, Lee TJ, Chang PH, Fu CH., Role of serum eosinophil cationic protein in distinct endotypes of chronic rhinosinusitis, Rhinology, 62, 1, pp. 111-118, (2024); Li u Z, Fan Y, Zhou A, Li u J, J i ao Q., Assessment of serum soluble CD40 ligand levels in patients with chronic rhinosinusitis, World Allergy Organ J, 17, 3, (2024); Niu Y, Cao S, Luo M, Ning J, Wen N, Wu H., Serum proteomics identify CSF1R as a novel biomarker for postoperative recurrence in chronic rhinosinusitis with nasal polyps, World Allergy Organ J, 17, 3, (2024); Tattersall MC, Jarjour NN, Busse PJ., Systemic inflammation in asthma: what are the risks and impacts outside the airway?, J Allergy Clin Immunol Pract, 12, 4, pp. 849-862, (2024); King PT., Inflammation in chronic obstructive pulmonary disease and its role in car-diovascular disease and lung cancer, Clin Transl Med, 4, 1, (2015); Obling N, Backer V, Hurst JR, Bodtger U., Nasal and systemic inflammation in Chronic Obstructive Pulmonar y Disease (COPD), Respir Med, 195, (2022); Yang JO, Zi nter MS, Pel l egri ni M, Et al., Whole blood transcriptomics identifies sub-classes of pediatric septic shock, Crit Care, 27, 1, (2023); Beretta L, Bar turen G, Vi gone B, Et al., Genome-wide whole blood transcriptome profiling in a large European cohort of systemic sclerosis patients, Ann Rheum Dis, 79, 9, pp. 1218-1226, (2020); 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Orlandi RR, Kingdom TT, Smith TL, Et al., International consensus statement on allergy and rhinology: rhinosinusitis 2021, Int Forum Allergy Rhinol, 11, 3, pp. 213-739, (2021); Bousquet J, Khal taev N, Cruz AA, Et al., Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA(2)LEN and AllerGen), Allergy, 63, pp. 8-160, (2008); Bateman ED, Hurd SS, Barnes PJ, Et al., Global strategy for asthma management and prevention: GINA executive summary, Eur Respir J, 31, 1, pp. 143-178, (2008); He H, Pan L, Cui Z, Et al., Smok i ng Pr eval ence, Pat t er ns, and Ces s at i on Among Adults in Hebei Province, Central China: I mplications From China National Health Survey (CNHS), Front Public Health, 8, (2020); Cui Y, Wang K, Shi J, Sun Y., Endotyping di f f i cul t-t o-t reat chr oni c r hi nosi nusitis with nasal polyps by structured histo-pathol ogy, I nt Arch Al l ergy I mmunol, 184, 10, pp. 1036-1046, (2023); Psal ti s AJ, Li G, Vaezeafshar R, Cho KS, Hwang PH., Modi f i cati on of the Lund-Kennedy endoscopi c scor i ng syst em i mproves i ts rel i abi l i ty and correl ati on with patient-reported outcome measures, Laryngoscope, 124, 10, pp. 2216-2223, (2014); Lund VJ, Kennedy DW., Quantification for staging sinusitis. The Staging and Therapy Group, Ann Otol Rhinol Lar yngol Suppl, 167, pp. 17-21, (1995); Chapurin N, Wu J, Labby AB, Chandra RK, Chowdhury NI, Turner JH., Current insight into treatment of chronic rhinosinusitis: Phenotypes, endotypes, and implications for targeted therapeutics, J Allergy Clin Immunol, 150, 1, pp. 22-32, (2022); Hong HY, Chen FH, Sun YQ, Et al., Local I L-25 contri butes to Th2-bi ased i nfl am-mator y profiles in nasal polyps, Allergy, 73, 2, pp. 459-469, (2018); Ma L, Deng Y, Wang K, Shi J, Sun Y., Rel ati onshi p between eosi nophi l i c and neutrophi l i c i nf l ammati on i n Chi nese chronic rhinosinusitis with nasal polyps, Int Arch Allergy Immunol, 184, 6, pp. 576-586, (2023); Yu G, Wang LG, Han Y, He QY., clusterPro-filer: an R package for comparing biological themes among gene clusters, Omics, 16, 5, pp. 284-287, (2012); Bachert C, Zhang N, Hellings PW, Bousquet J., Endot ype-dr i ven care pat hways i n pati ents wi th chroni c r hi nosi nusi ti s, J Allergy Clin Immunol, 141, 5, pp. 1543-1551, (2018); Cardenas A, Sordillo JE, Rifas-Shiman SL, Et al., The nasal methylome as a biomarker of asthma and airway inflammation in chil-dren, Nat Commun, 10, 1, (2019); Dunn JLM, Shoda T, Cal dwel l JM, Et al., Esophageal type 2 cytokine expression heterogeneity in eosinophilic esophagitis in a multisite cohort, J Allergy Clin Immunol, 145, 6, pp. 1629-1640, (2020); Garcia-Sanchez A, Estravis M, Martin MJ, Et al., PTGDR2 Expression in Peripheral Blood as a Potential Biomarker in Adult Patients with Asthma, J Pers Med, 11, 9, (2021); J i ang Y, Gr uzi eva O, Wang T, Et al., Transcriptomics of atopy and atopic asthma in white blood cells from children and ado-lescents, Eur Respir J, 53, 5, (2019); Khalfaoui L, Symon FA, Couillard S, Et al., Airway remodelling rather than cellular infil-tration characterizes both type2 cytokine biomarker-high and-low severe asthma, Allergy, 77, 10, pp. 2974-2986, (2022); Okano M, Hi rahara K, Ki uchi M, Et al., I nt er l euk i n-33-a c t i v a t ed neur opep-tide CGRP-producing memor y Th2 cells cooperate with somatosensor y neurons to i nduce conj uncti val i tch, I mmuni ty, 55, 12, pp. 2352-2368, (2022); Wang W, Xu Y, Wang L, Et al., Single-cell profiling identifies mechanisms of inflammatory heterogeneity in chronic rhinosinusitis, Nat Immunol, 23, 10, pp. 1484-1494, (2022); Yuan J, Liu Y, Yu J, Et al., Gene knockdown of CCR3 reduces eosinophilic inflammation and the Th2 immune response by inhibit-ing the PI3K/AKT pathway in allergic rhinitis mice, Sci Rep, 12, 1, (2022); Gittler JK, Shemer A, Suarez-Farinas M, Et al., Progressive activation of T(H)2/T(H)22 cytoki nes and selective epi dermal pro-tei ns character i zes acute and chroni c atopic dermatitis, J Allergy Clin Immunol, 130, 6, pp. 1344-1354, (2012); Jovic S, Linge HM, Shikhagaie MM, Et al., The neutrophil-recruiting chemokine GCP-2/ CXCL6 is expressed in cystic fibrosis airways and retains its functional properties after binding to extracellular DNA, Mucosal Immunol, 9, 1, pp. 112-123, (2016); Mattos MS, Ferrero MR, Kraemer L, Et al., CXCR1 and CXCR2 Inhibition by Ladarixin I mproves Neutrophil-Dependent Air way I nfl ammati on i n Mi ce, Front I mmunol, 11, (2020); Wang C, Zhou W, Su G, Hu J, Yang P., Progranu lin Suppressed Autoi mmune Uveitis and Autoi mmune Neuroi nf l ammation by Inhibiting Th1/ Th17 Cells and Promoting Treg Cells and M2 Macrophages, Neurol Neuroimmunol Neuroinflamm, 9, 2, (2022); Mejias A, Dimo B, Suarez NM, Et al., Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection, PLoS Med, 10, 11, (2013); Berr y MP, Graham CM, McNab FW, Et al., An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis, Nature, 466, 7309, pp. 973-977, (2010); Wi ttkowski KM, Song T., Nonparametri c methods for molecular biology, Methods Mol Biol, 620, pp. 105-153, (2010); Wi t t kowsk i KM, Lee E, Nussbaum R, Chamian FN, Krueger JG., Combining sev-eral ordinal measures in clinical studies, Stat Med, 23, 10, pp. 1579-1592, (2004); Aran D, Hu Z, Butte AJ., xCell: digitally por- traying the tissue cellular heterogeneity landscape, Genome Biol, 18, 1, (2017); Zielinska-Blizni ewska H, Paprock a-Zjawiona M, Merecz-Sadowska A, Zajdel R, Blizniewska-Kowalska K, Malinowska K., Serum IL-5, POSTN and IL-33 levels in chronic rhinosinusitis with nasal polyposis cor-relate with clinical severity, BMC Immunol, 23, 1, (2022)","J. Shi; Department of Otolaryngology, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 58 Zhongshan Road II, Guangdong, 510080, China; email: tsjbent@163.com; Y. Fan; Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, No.628, Zhenyuan Road Guangming District, Guangdong, 518107, China; email: zhfanyp@163.com; Y. Sun; Department of Otolaryngology, the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, No.628, Zhenyuan Road Guangming District, Guangdong, 518107, China; email: aqi1733@163.com","","International Rhinologic Society","","","","","","03000729","","RNGYA","39365558","English","Rhinology","Article","Final","","Scopus","2-s2.0-85211503961"
"Huyut M.T.; Velichko A.; Belyaev M.; Karaoğlanoğlu Ş.; Sertogullarindan B.; Demir A.Y.","Huyut, Mehmet Tahir (57188572324); Velichko, Andrei (35320758400); Belyaev, Maksim (56411794800); Karaoğlanoğlu, Şebnem (58871271800); Sertogullarindan, Bunyamin (23996019200); Demir, Abdussamed Yasin (57209583584)","57188572324; 35320758400; 56411794800; 58871271800; 23996019200; 57209583584","Detection of Right Ventricular Dysfunction Using LogNNet Neural Network Model Based on Pulmonary Embolism Data Set","2024","Eastern Journal of Medicine","29","1","","118","128","10","0","10.5505/ejm.2024.54775","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184432539&doi=10.5505%2fejm.2024.54775&partnerID=40&md5=1aad27d1e74a2429ad1bee6336154bcc","Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, 4000, Turkey; Petrozavodsk State University, Institute of Physics And Technology, Petrozavodsk, 185910, Russian Federation; İzmir Katip Çelebi University, Medical Faculty, Department of Pulmonary Medicine, Izmir, Turkey; Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Genetics, Erzincan, 24000, Turkey","Huyut M.T., Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Biostatistics and Medical Informatics, Erzincan, 4000, Turkey; Velichko A., Petrozavodsk State University, Institute of Physics And Technology, Petrozavodsk, 185910, Russian Federation; Belyaev M., Petrozavodsk State University, Institute of Physics And Technology, Petrozavodsk, 185910, Russian Federation; Karaoğlanoğlu Ş., İzmir Katip Çelebi University, Medical Faculty, Department of Pulmonary Medicine, Izmir, Turkey; Sertogullarindan B., İzmir Katip Çelebi University, Medical Faculty, Department of Pulmonary Medicine, Izmir, Turkey; Demir A.Y., Erzincan Binali Yıldırım University, Faculty of Medicine, Department of Genetics, Erzincan, 24000, Turkey","The high association of right ventricular dysfunction (RVD) with mortality in patients with acute pulmonary embolism (PE) remains an important health problem. In this respect, rapid, economical and highly-accurate detection of risk factors for early diagnosis of RVD in patients with PE is expected to greatly benefit the diagnosis and treatment of the disease and contribute significantly to the reduction of mortality. The aim of this study is to identify the most effective features from the PE dataset for RVD diagnosis, using a special-algorithm for the LogNNet reservoir neural-network. The cohort of patients diagnosed with acute PE in the last five years in our hospital was retrospectively analyzed and the data in accordance with our criteria were recorded. A total of 163 patients' data were acces sed and the patients had 20 characteristics. RVD was diagnosed in 27 of these patients. 78-79 years of age was found to be an important threshold for the diagnosis of RVD. The LogNNet model revealed that older age, comorbidities and coronary-heart disease greatly increased the risk of RVD. The model also found that individuals with diabetes and COPD were at higher risk of RVD, while individuals with malignancies were at lower risk of RVD. However, the model found that unilateral-thrombus increased the risk of RVD more than bilateral-thrombus. The risk of RVD is high in PE patients with unilateral-thrombus. In addition, PE patients with comorbidities such as coronary heart disease, diabetes and COPD are at high-risk for RVD and should be followed closely. © 2024, Yuzuncu Yil Universitesi Tip Fakultesi. All rights reserved.","artificial intelligence; LogNNet; pulmonary embolism; Right ventricular dysfunction; supervised machine learning models; thrombosis","adult; aged; algorithm; Article; artificial neural network; chronic obstructive lung disease; cohort analysis; comorbidity; deep vein thrombosis; diabetes mellitus; Doppler flowmetry; Doppler ultrasonography; early diagnosis; echocardiography; feature selection; female; heart right ventricle failure; human; inferior cava vein; interventricular septum; ischemic heart disease; LogNNet neural network; lung embolism; major clinical study; male; mortality; multidetector computed tomography; retrospective study; right ventricular enlargement; risk factor; thrombus; tissue Doppler imaging; transthoracic echocardiography; tricuspid annular plane systolic excursion; very elderly","","","","","","","Beckman MG, Hooper WC, Critchley SE, Et al., Venous thromboembolism. A public health concern, Am J Prev Med, 38, 4, pp. 495-501, (2010); Kasper W., Konstantinides S, Geibel A, Et al., Management strategies and determinants of outcome in acute major pulmonary embolism: results of a multicenter registry, J Am Coll Cardiol, 30, 5, pp. 1165-1171, (1997); Cires-Drouet R, LaRocco A, Soldin D, Et al., Left ventricular systolic dysfunction during acute pulmonary embolism, Thrombosis Research, 223, pp. 1-6, (2023); Lualdi JC, Goldhaber SZ., Right ventricular dysfunction after acute pulmonary embolism: pathophysiologic factors, detection, and therapeutic implications, Am Heart J, 130, 6, pp. 1276-1282, (1995); Heit JA, Spencer FA, White RH., The epidemiology of venous thromboembolism, J Thromb Thrombolysis, 41, 1, pp. 3-14, (2016); Raskob GE, Angchaisuksiri P, Blanco AN, Et al., Thrombosis: a major contributor to global diseaseburden, Arterioscler Thromb Vasc Biol, 34, pp. 2363-2371, (2014); Cho JH, Kutti-Sridharan G, Kim SH, Et al., Right ventricular dysfunction as an echocardiographicprognostic factor in hemodynamically stable patientswith acute pulmonary embolism: a meta-analysis, BMC Cardiovasc Disord, 14, 64, pp. 2-9, (2014); Crager SE, Humphreys C., Right Ventricular Failure and Pulmonary Hypertension, Emerg Med Clin N Am, 40, pp. 519-537, (2022); Vallabhajosyula S, Shankar A, Vojjini R, Et al., Impact of right ventricular dysfunction on short-term and long-term mortality in sepsis: a meta-analysis of 1,373 patients, CHEST, 159, pp. 2254-2263, (2021); Konstantinides SV, Meyer G, Becattini C, Et al., 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with t he European Respiratory Society (ERS): The Task Force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC), Eur Heart J, 41, pp. 543-603, (2019); Lang RM, Badano LP, Mor-Avi V, Et al., Recommendations for cardiac chamber quantificationby echocardiography in adults: an update from theAmerican Society of Echocardiography and the European Association of Cardiovascular Imaging, Eur Heart J Cardiovasc Imaging, 17, 4, (2016); Piazza G, Goldhaber SZ., Pulmonary embolism in heart failure, Circulation, 118, 15, pp. 1598-1601, (2008); Huyut MT, Huyut Z., Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19, Int Immuno, 100, (2021); Huyut MT, Ustundag H., Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: A retrospective observational study, Med Gas Res, 12, pp. 60-66, (2022); Huyut MT, Velichko A., Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network, Sensors, 22, (2022); Huyut MT, Velichko A, Belyaev M., Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers, Appl Sci, 12, (2022); Huyut MT, Velichko A, Belyaev M, Izotov Y, Korzun D., Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application, Sensors, 22, (2022); Huyut MT, Velichko A., LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19, MethodsX, 10, (2023); Huyut MT., Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models, IRBM, 44, 1, (2023); Huyut MT, Huyut Z., Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees, Heliyon, 9, 1, (2023); Velichko A., Neural network for low-memory IoT devices and MNIST image recognition using kernels based on logistic map, Electronics, 9, (2020); Velichko A., A method for medical data analysis using the lognnet for clinical decision support systems and edge computing in healthcare, Sensors, 21, (2021); Velichko A, Heidari H., A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks, Entropy, 23, (2021); Izotov Y, Velichko A, Boriskov PP., Method for fast classification of MNIST digits on Arduino UNO board using LogNNet and linear congruential generator, J Phys Conf Ser, 2094, (2021); Matthews Correlation Coefficient | Encyclopedia MDPI; Chicco D, Jurman G., The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation, BMC Genomics, 21, 6, pp. 2-13, (2020); Barco S, Mahmoudpour SH, Planquette B, Et al., Prognostic value of right ventricular dysfunction orelevated cardiac biomarkers in patients with low-risk pulmonary embolism: a systematic review and meta-analysis, Eur Heart J, 40, 11, pp. 902-910, (2019); Kobayashi S, Muto M, Yabe H, Et al., A retrospectiveobservational study investigating the factorsassociated with right heart failure in patients withprimary acute pulmonary embolism and deep veinthrombosis, J Gen Fam Med, 21, 3, pp. 63-70, (2020); Becattini C, Maraziti G, Vinson DR, Et al., Right ventricle assessment in patients with pulmonary embolism at low risk for death based on clinical models: an individual patient data meta-analysis, Eur Heart J, 42, pp. 3190-3199, (2021); Kosmala W, Przewlocka-Kosmala M, Mazurek W., Subclinical right ventricular dysfunction in diabetes mellitus—an ultrasonic strain/strain rate study, Diabetic Medicine, 24, 1, pp. 656-663, (2007); Martin KA, Molsberry R, Cuttica MJ, Et al., Time trends in pulmonary embolism mortality rates in the United States, 1999 to 2018, J Am Heart Assoc, 9, (2020); Barco S, Valerio L, Ageno W, Et al., Age–sex specific pulmonary embolism-related mortality in the USA and Canada, 2000–18: an analysis of the WHO Mortality Database and of the CDC Multiple Cause of Death database, Lancet Respir Med, 9, pp. 33-42, (2021); Sanchez O, Trinquart L, Colombet I, Et al., Prognostic value of right ventricular dysfunction in patients with haemodynamically stable pulmonary embolism: a systematic review, Eur Heart J, 29, pp. 1569-1577, (2008); Coutance G, Cauderlier E, Ehtisham J, Et al., The prognostic value of markers of right ventricular dysfunction in pulmonary embolism: a meta-analysis, Crit Care, 15, (2011); Rennebaum S, Schneider SW, Henzler T, Et al., Incidence of pulmonary embolism and impact on mortality in patients with malignant melanoma, Clinical Imaging, 83, pp. 72-76, (2022); Di-Minno MND, Ambrosino P, Ambrosini F, Et al., Prevalence of deep vein thrombosis and pulmonary embolism in patients with superficial vein thrombosis: a systematic review and meta-analysis, J Tromb Haemost, 14, 5, pp. 964-972, (2016); Kurnicka K, Lichodziejewska B, Goliszek S, Echocardiographic pattern of acute pulmonaryembolism: analysis of 511 consecutive patients, J Am Soc Echocardiogr, 29, 9, pp. 907-913, (2016)","M.T. Huyut; Erzincan Binali Yıldırım Universitesi, Tıp Fakültesi, Biyoistatistik ve Tıbbi Bilişim AD, Erzincan, 2400, Turkey; email: mehmettahirhuyut@gmail.com","","Yuzuncu Yil Universitesi Tip Fakultesi","","","","","","13010883","","EJMAA","","English","East. J. Med.","Article","Final","","Scopus","2-s2.0-85184432539"
"ElDahshan K.; Hefny H.; ElSayed I.A.","ElDahshan, Kamal (36967841100); Hefny, Hesham (6603134048); ElSayed, Iman Ahmed (57195973920)","36967841100; 6603134048; 57195973920","HMGD: A High-Accuracy Model for Detection and Prediction of Respiratory Genetic Diseases","2024","Journal of Computer Science","20","6","","649","657","8","0","10.3844/jcssp.2024.649.657","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190648946&doi=10.3844%2fjcssp.2024.649.657&partnerID=40&md5=16c4a26c9172fb0448399288636d8137","Department of Computer Science, El Azhar Univeristy, Cairo, Egypt; Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo, Egypt","ElDahshan K., Department of Computer Science, El Azhar Univeristy, Cairo, Egypt; Hefny H., Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo, Egypt; ElSayed I.A., Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo, Egypt","Respiratory genetic diseases are considered a major participant in the reasons of death worldwide nowadays and were one of the major participants in helping in increasing the numbers of COVID-19 patients. It is considered one of the most alarming diseases affecting in particular the respiratory system. The journey of early detection of respiratory genetic diseases is considered to be very challenging today to assist in lessening the percentage rate of death since people with these diseases are more vulnerable to being infected by COVID-19 and other dangerous diseases than others. Also, it is considered a very difficult mission for medical practitioners because of the high requirement for expertise and knowledgeable practitioners. While, predicting or detecting respiratory genetic disease in an early phase has many gaps and lacks accuracy accommodated with speed as well; as a result any slight update in the accuracy accommodating speed will be considered of great improvement and importance which will later result in the reduction of the increasing number of genetically diseased patients as the well-known diseases of Alpha-1 antitrypsin deficiency, Cystic fibrosis, Kartagener syndrome and many other respiratory genetic diseases. In this study we will introduce a new hybrid-model approach (HMGD) based on merging two outstanding soft computing optimization algorithms which weren’t used before in neither detection nor prediction of diseases which are Extended Compact Genetic Algorithm (ECGA) and Compact Co-Evolutionary Algorithm (CCoEA); one for which ECGA will act for the feature selection phase and output will be fed to the CCoEA for feature optimization resulting in the certainty factor of the detected/predicted respiratory genetic disease. The model will be used through a graphical user-friendly interface built up especially for the model to analyze data, learn from that output data, and result in a tactile and touchable prediction/detection for the respiratory genetic disease. The HMGD model proved its reliability and outstanding performance over other known computational models by an accuracy of 98.27% for respiratory genetic diseases’ prediction in 1.03 sec, while an accuracy of 97.89% for respiratory genetic diseases’ detection in 1.4 sec. The model proved to achieve a higher level of accuracy in the detection or prediction of respiratory genetic diseases than other machine learning models. © 2024 Kamal ElDahshan, Hesham Hefny and Iman Ahmed ElSayed. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.","Co-Evolutionary Algorithms; COPD Disease; COVID-19; Detection; Disease Prediction; Evolutionary Computation; Genetic Algorithm; Machine Learning Models; Protein Sequence; Respiratory Genetic Diseases","","","","","","","","Amaral J. L., Lopes A. J., Jansen J. M., Faria A. C., Melo P. L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 105, 3, pp. 183-193, (2012); Breuza L., Poux S., Estreicher A., Famiglietti M. L., Magrane M., Tognolli M., The UniProtKB guide to the human proteome, Database, 2016, (2016); Esteban C., Arostegui I., Moraza J., Aburto M., Quintana J. M., Perez-Izquierdo J., Capelastegui A., Development of a decision tree to assess the severity and prognosis of stable COPD, European Respiratory Journal, 38, 6, pp. 1294-1300, (2011); Fleming S., These are the top 10 global causes of death - but two diseases are in decline, World Economic Forum Global Health, (2021); Jayaraj D., Sathiamoorthy S., Deep neural network based classifier model for lung cancer diagnosis and prediction system in healthcare informatics, Intelligent Data Communication Technologies and Internet of Things: ICICI 2019, pp. 492-499, (2020); Koppad S. H., Kumar S. A., Rao M. K. N., Contemplation of Computational Methods and Techniques to Predict COPD, pp. 538-545, (2019); Perna D., Tagarelli A., Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks, 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMf Contemplation of Computational Methods anS), pp. 50-55, (2019); Priya T., Meyyappan T., Disease prediction by machine learning over big data lung cancer, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 16-24, (2021); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics Journal, 25, 3, pp. 811-827, (2019); UniProt C., UniProt, (2021)","I.A. ElSayed; Department of Computer Science, Faculty of Graduate Studies for Statistical Research, Cairo, Egypt; email: support@thescipub.com","","Science Publications","","","","","","15493636","","","","English","J. Comput. Sci.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85190648946"
"Goto R.; Inoue K.; Osawa I.; Baicker K.; Fleming S.L.; Tsugawa Y.","Goto, Ryunosuke (57209652604); Inoue, Kosuke (57191528031); Osawa, Itsuki (57212303434); Baicker, Katherine (6603361567); Fleming, Scott L. (57208627380); Tsugawa, Yusuke (34972388900)","57209652604; 57191528031; 57212303434; 6603361567; 57208627380; 34972388900","Machine learning for detection of heterogeneous effects of Medicaid coverage on depression","2024","American Journal of Epidemiology","193","7","","951","958","7","0","10.1093/aje/kwae008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197966473&doi=10.1093%2faje%2fkwae008&partnerID=40&md5=cbecb4a2b3b08e00cc0b0a533cac81b3","Department of Pediatrics, University of Tokyo Hospital, Tokyo, 113-8655, Japan; Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan; Department of Emergency and Critical Care Medicine, University of Tokyo Hospital, Tokyo, 113-8655, Japan; University of Chicago, Chicago, 60637, IL, United States; Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, United States; Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, 90024, CA, United States; Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, 90095, CA, United States","Goto R., Department of Pediatrics, University of Tokyo Hospital, Tokyo, 113-8655, Japan; Inoue K., Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan; Osawa I., Department of Emergency and Critical Care Medicine, University of Tokyo Hospital, Tokyo, 113-8655, Japan; Baicker K., University of Chicago, Chicago, 60637, IL, United States; Fleming S.L., Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, United States; Tsugawa Y., Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, 90024, CA, United States, Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, 90095, CA, United States","In 2008, Oregon expanded its Medicaid program using a lottery, creating a rare opportunity to study the effects of Medicaid coverage using a randomized controlled design (Oregon Health Insurance Experiment). Analysis showed that Medicaid coverage lowered the risk of depression. However, this effect may vary between individuals, and the identification of individuals likely to benefit the most has the potential to improve the effectiveness and efficiency of the Medicaid program. By applying the machine learning causal forest to data from this experiment, we found substantial heterogeneity in the effect of Medicaid coverage on depression; individuals with high predicted benefit were older and had more physical or mental health conditions at baseline. Expanding coverage to individuals with high predicted benefit generated greater reduction in depression prevalence than expanding to all eligible individuals (21.5 vs 8.8 percentage-point reduction; adjusted difference = +12.7 [95% CI, +4.6 to +20.8]; P = 0.003), at substantially lower cost per case prevented ($16 627 vs $36 048; adjusted difference = −$18 598 [95% CI, −156 953 to −3120]; P = 0.04). Medicaid coverage reduces depression substantially more in a subset of the population than others, in ways that are predictable in advance. Targeting coverage on those most likely to benefit could improve the effectiveness and efficiency of insurance expansion. © The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.","causal forest; causal inference; depression; generalized random forest; machine learning; Medicaid; mental health; Oregon Health Insurance Experiment","Adult; Depression; Female; Humans; Insurance Coverage; Machine Learning; Male; Medicaid; Middle Aged; Oregon; United States; Young Adult; Oregon; United States; cost analysis; detection method; experimental study; insurance system; machine learning; mental health; adult; Article; asthma; Black person; Caucasian; chronic obstructive lung disease; congestive heart failure; controlled study; depression; diabetes mellitus; education; emphysema; female; health care cost; health care utilization; health insurance; heart infarction; high school; Hispanic; human; hypertension; interview; kidney failure; machine learning; major clinical study; medicaid; mental health; mood disorder; outcome assessment; Patient Health Questionnaire 8; prediction; prevalence; randomization; self report; substance abuse; therapy effect; insurance; male; middle aged; Oregon; United States; young adult","","","","","Eli Lilly and Mayo Clinic; National Institute for Health Care Management Foundation, NIHCM; Congressional Budget Office, CBO; Toranomon Hospital; National Defense Science and Engineering Graduate, NDSEG; Japanese Endocrine Society; National Institutes of Health, NIH; Ministry of Education, Culture, Sports, Science and Technology, MEXT; Chernobyl-Fukushima Medical Fund; National Institute on Minority Health and Health Disparities, NIMHD, (R01MD013913); Japan Foundation for Pediatric Research, (22-001); Stanford University, SU, (R01AG068633, R01AG082991); National Institute on Aging, NIA, (R01AG034151, P01AG005842); Japan Society for the Promotion of Science, JSPS, (22 K17392, 21 K20900)","Funding text 1: R.G. receives funding from the Japan Foundation for Pediatric Research (22-001) and the Chernobyl-Fukushima Medical Fund for other work not related to this study. K.I. receives funding from the Japan Society for the Promotion of Science (21 K20900 and 22 K17392), the Japanese Endocrine Society, and the Program for the Development of Next-generation Leading Scientists with Global Insight (L-INSIGHT) sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, for other work not related to this study. I.O. receives funding from the Toranomon Hospital, Tokyo, Japan, for other work not related to this study. K.B. receives funding from the National Institutes of Health (NIH)/National Institute on Aging (P01AG005842, R01AG034151) for other work not related to this study, and serves on the boards of directors of Eli Lilly and Mayo Clinic and on advisory panels of the Congressional Budget Office and National Institute for Health Care Management Foundation. S.L.F. receives funding from a National Defense Science and Engineering Graduate Fellowship and a Stanford Graduate Fellowship for other work not related to this study. Y.T. receives funding from the NIH/National Institute on Aging (R01AG068633, R01AG082991), the NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten GRoW @ Annenberg for other work not related to this study, and serves on the board of directors of M3, Inc. ; Funding text 2: This study was presented at the Society for Epidemiologic Research Annual Meeting, June 13-16, 2023, Portland, OR. R.G. receives funding from the Japan Foundation for Pediatric Research (22-001) and the Chernobyl-Fukushima Medical Fund for other work not related to this study. K.I. receives funding from the Japan Society for the Promotion of Science (21 K20900 and 22 K17392), the Japanese Endocrine Society, and the Program for the Development of Next-generation Leading Scientists with Global Insight (L-INSIGHT) sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, for other work not related to this study. I.O. receives funding from the Toranomon Hospital, Tokyo, Japan, for other work not related to this study. K.B. receives funding from the National Institutes of Health (NIH)/National Institute on Aging (P01AG005842, R01AG034151) for other work not related to this study, and serves on the boards of directors of Eli Lilly and Mayo Clinic and on advisory panels of the Congressional Budget Office and National Institute for Health Care Management Foundation. S.L.F. receives funding from a National Defense Science and Engineering Graduate Fellowship and a Stanford Graduate Fellowship for other work not related to this study. Y.T. receives funding from the NIH/National Institute on Aging (R01AG068633, R01AG082991), the NIH/National Institute on Minority Health and Health Disparities (R01MD013913), and Gregory Annenberg Weingarten GRoW @ Annenberg for other work not related to this study, and serves on the board of directors of M3, Inc.","Finkelstein A, Taubman S, Wright B, Et al., The Oregon Health Insurance Experiment: evidence from the first year, Q J Econ, 127, 3, pp. 1057-1106, (2012); Taubman SL, Allen HL, Wright BJ, Et al., Medicaid increases emergency-department use: evidence from Oregon’s Health Insurance Experiment, Science, 343, 6168, pp. 263-268, (2014); Baicker K, Taubman SL, Allen HL, Et al., The Oregon experiment—effects of Medicaid on clinical outcomes, N J Engl J Med, 368, 18, pp. 1713-1722, (2013); Baicker K, Allen HL, Wright BJ, Et al., The effect of Medicaid on management of depression: evidence from the Oregon Health Insurance Experiment, Milbank Q, 96, 1, pp. 29-56, (2018); Mokdad AH, Ballestros K, Echko M, Et al., The state of US health, 1990-2016, JAMA, 319, 14, pp. 1444-1472, (2018); Poisal JA, Sisko AM, Cuckler GA, Et al., National Health Expenditure Projections, 2021–30: growth to moderate as COVID-19 impacts wane: study examines national health expenditure projections, 2021-30 and the impact of declining federal supplemental spending related to the COVID-19 pandemic, Health Aff, 41, 4, pp. 474-486, (2022); Sommers BD, Gawande AA, Baicker K., Health insurance coverage and health—what the recent evidence tells us, N Engl J Med, 377, 6, pp. 586-593, (2017); Wager S, Athey S., Estimation and inference of heterogeneous treatment effects using random forests, J Am Stat Assoc, 113, 523, pp. 1228-1242, (2018); Inoue K, Athey S, Tsugawa Y., Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management, Int J Epidemiol, 52, 4, pp. 1243-1256, (2023); Kroenke K, Strine TW, Spitzer RL, Et al., The PHQ-8 as a measure of current depression in the general population, J Affect Disord, 114, 1-3, pp. 163-173, (2009); Kent DM, Steyerberg E, van Klaveren D., Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects, BMJ, 363, (2018); Davis JMV, Heller SB., Using causal forests to predict treatment heterogeneity: an application to summer jobs, Am Econom Rev, 107, 5, pp. 546-550, (2017); Chernozhukov V, Chetverikov D, Demirer M, Et al., Double/ debiased machine learning for treatment and structural parameters, EconomJ, 21, 1, pp. C1-C68, (2018); Chernozhukov V, Chetverikov D, Demirer M, Et al., Double/ debiased/Neyman machine learning of treatment effects, Am Econom Rev, 107, 5, pp. 261-265, (2017); Semenova V, Chernozhukov V., Debiased machine learning of conditional average treatment effects and other causal functions, Econom J, 24, 2, pp. 264-289, (2021); Athey S, Tibshirani J, Wager S., Generalized random forests, Ann Statist, 47, 2, pp. 1148-1178, (2019); Tibshirani J, Athey S, Wager S, Et al., Package ‘grf’, (2018); Rose G., Sick individuals and sick populations, Int J Epidemiol, 14, 1, pp. 32-38, (1985); Everson SA, Maty SC, Lynch JW, Et al., Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes, J Psychosom Res, 53, 4, pp. 891-895, (2002); Caron A, Baio G, Manolopoulou I., Estimating individual treatment effects using non-parametric regression models: a review, J R Stat Soc Ser A Stat Soc, 185, 3, pp. 1115-1149, (2022); Kunzel SR, Sekhon JS, Bickel PJ, Et al., Metalearners for estimating heterogeneous treatment effects using machine learning, Proc Natl Acad Sci, 116, 10, pp. 4156-4165, (2019); Hahn PR, Murray JS, Carvalho CM., Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion), Bayesian Anal, 15, 3, pp. 965-1056, (2020); Oprescu M, Syrgkanis V, Wu ZS., Orthogonal random forest for causal inference, PMLR, 15, pp. 4932-4941, (2019); Athey S, Wager S., Estimating treatment effects with causal forests: an application, Observational Studies, 5, 2, pp. 37-51, (2019); Mhasawade V, Zhao Y, Chunara R., Machine learning and algorithmic fairness in public and population health, Nat Mach Intell, 3, 8, pp. 659-666, (2021); Parikh RB, Teeple S, Navathe AS., Addressing bias in artificial intelligence in health care, JAMA, 322, 24, (2019); Cintron DW, Adler NE, Gottlieb LM, Et al., Heterogeneous treatment effects in social policy studies: an assessment of contemporary articles in the health and social sciences, Ann Epidemiol, 70, pp. 79-88, (2022)","R. Goto; Department of Pediatrics, University of Tokyo Hospital Address, Tokyo, 7-3-1 Bunkyo-ku, Hongo, 113-8655, Japan; email: rgoto@m.u-tokyo.ac.jp","","Oxford University Press","","","","","","00029262","","AJEPA","38400644","English","Am. J. Epidemiol.","Article","Final","","Scopus","2-s2.0-85197966473"
"Ferraro V.A.; Zanconato S.; Carraro S.","Ferraro, Valentina Agnese (55809916800); Zanconato, Stefania (7003785103); Carraro, Silvia (6701552362)","55809916800; 7003785103; 6701552362","Metabolomics Applied to Pediatric Asthma: What Have We Learnt in the Past 10 Years?","2023","Children","10","9","1452","","","","0","10.3390/children10091452","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172787384&doi=10.3390%2fchildren10091452&partnerID=40&md5=c7eec38f6f39dc024dd7fe727b16ee9e","Unit of Pediatric Allergy and Respiratory Medicine, Women’s and Children’s Health Department, University of Padova, Padova, 35122, Italy","Ferraro V.A., Unit of Pediatric Allergy and Respiratory Medicine, Women’s and Children’s Health Department, University of Padova, Padova, 35122, Italy; Zanconato S., Unit of Pediatric Allergy and Respiratory Medicine, Women’s and Children’s Health Department, University of Padova, Padova, 35122, Italy; Carraro S., Unit of Pediatric Allergy and Respiratory Medicine, Women’s and Children’s Health Department, University of Padova, Padova, 35122, Italy","Background: Asthma is the most common chronic condition in children. It is a complex non-communicable disease resulting from the interaction of genetic and environmental factors and characterized by heterogeneous underlying molecular mechanisms. Metabolomics, as with the other omic sciences, thanks to the joint use of high-throughput technologies and sophisticated multivariate statistical methods, provides an unbiased approach to study the biochemical–metabolic processes underlying asthma. The aim of this narrative review is the analysis of the metabolomic studies in pediatric asthma published in the past 10 years, focusing on the prediction of asthma development, endotype characterization and pharmaco-metabolomics. Methods: A total of 43 relevant published studies were identified searching the MEDLINE/Pubmed database, using the following terms: “asthma” AND “metabolomics”. The following filters were applied: language (English), age of study subjects (0–18 years), and publication date (last 10 years). Results and Conclusions: Several studies were identified within the three areas of interest described in the aim, and some of them likely have the potential to influence our clinical approach in the future. Nonetheless, further studies are needed to validate the findings and to assess the role of the proposed biomarkers as possible diagnostic or prognostic tools to be used in clinical practice. © 2023 by the authors.","asthma endotypes; metabolomics; pediatric asthma; pharmaco-metabolomics; predictive medicine","biological marker; Article; asthma; child; clinical practice; disease severity; energy balance; feeding behavior; human; immune response; liquid chromatography-mass spectrometry; machine learning; mass spectrometry; medical decision making; metabolomics; metagenomics; microbial metabolism; oxidative stress; pediatrics; personalized medicine; pharmacometabolomics; predictive value; principal component analysis; proteomics; risk factor","","","","","","","Pietzner M., Stewart I.D., Raffler J., Khaw K.-T., Michelotti G.A., Kastenmuller G., Wareham N.J., Langenberg C., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity, Nat. 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Rev, 36, pp. 118-127, (2020); Brustad N., Olarini A., Kim M., Chen L., Ali M., Wang T., Cohen A.S., Ernst M., Hougaard D., Schoos A.-M., Et al., Diet-associated vertically transferred metabolites and risk of asthma, allergy, eczema, and infections in early childhood, Pediatr. Allergy Immunol, 34, (2023); Moya B., Riggioni C., Kalayci O., Eigenmann P., Editorial comment on: Diet-associated vertically transferred metabolites and risk of asthma, allergy, eczema, and infections in early childhood, Pediatr. Allergy Immunol, 34, (2023); Carraro S., Ferraro V.A., Maretti M., Giordano G., Pirillo P., Stocchero M., Zanconato S., Baraldi E., Metabolomic Profile at Birth, Bronchiolitis and Recurrent Wheezing: A 3-Year Prospective Study, Metabolites, 11, (2021); Chawes B.L., Giordano G., Pirillo P., Rago D., Rasmussen M.A., Stokholm J., Bonnelykke K., Bisgaard H., Baraldi E., Neonatal Urine Metabolic Profiling and Development of Childhood Asthma, Metabolites, 9, (2019); Rago D., Pedersen C.-E.T., Huang M., Kelly R.S., Gurdeniz G., Brustad N., Knihtila H., Lee-Sarwar K.A., Morin A., Rasmussen M.A., Et al., Characteristics and Mechanisms of a Sphingolipid-associated Childhood Asthma Endotype, Am. J. Respir. Crit. Care Med, 203, pp. 853-863, (2021); Chiu C.-Y., Lin G., Cheng M.-L., Chiang M.-H., Tsai M.-H., Su K.-W., Hua M.-C., Liao S.-L., Lai S.-H., Yao T.-C., Et al., Longitudinal urinary metabolomic profiling reveals metabolites for asthma development in early childhood, Pediatr. 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Sci, 21, (2020); Kelly R.S., Sordillo J.E., Lutz S.M., Avila L., Soto-Quiros M., Celedon J.C., McGeachie M.J., Dahlin A., Tantisira K., Huang M., Et al., Pharmacometabolomics of Bronchodilator Response in Asthma and the Role of Age-Metabolite Interactions, Metabolites, 9, (2019); Carraro S., di Palmo E., Licari A., Barni S., Caldarelli V., De Castro G., Di Marco A., Fenu G., Giordano G., Lombardi E., Et al., Metabolomics to identify omalizumab responders among children with severe asthma: A prospective study, Allergy, 77, pp. 2852-2856, (2022); Ferraro V.A., Carraro S., Pirillo P., Gucciardi A., Poloniato G., Stocchero M., Giordano G., Zanconato S., Baraldi E., Breathomics in Asthmatic Children Treated with Inhaled Corticosteroids, Metabolites, 10, (2020); Kachroo P., Stewart I.D., Kelly R.S., Stav M., Mendez K., Dahlin A., Soeteman D.I., Chu S.H., Huang M., Cote M., Et al., Metabolomic profiling reveals extensive adrenal suppression due to inhaled corticosteroid therapy in asthma, Nat. Med, 28, pp. 814-822, (2022)","V.A. Ferraro; Unit of Pediatric Allergy and Respiratory Medicine, Women’s and Children’s Health Department, University of Padova, Padova, 35122, Italy; email: valentinaagnese.ferraro@unipd.it","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","22279067","","","","English","Child.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85172787384"
"Jung Y.J.; Ahn J.; Park S.; Sun J.-M.; Lee S.-H.; Ahn J.S.; Ahn M.-J.; Cho S.Y.; Jung H.A.","Jung, Ye Ji (58642671200); Ahn, Joonghyun (55224100800); Park, Sehhoon (56521848600); Sun, Jong-Mu (8423851500); Lee, Se-Hoon (58376934900); Ahn, Jin Seok (58854694800); Ahn, Myung-Ju (7103352186); Cho, Sun Young (56645282200); Jung, Hyun Ae (35272249400)","58642671200; 55224100800; 56521848600; 8423851500; 58376934900; 58854694800; 7103352186; 56645282200; 35272249400","Machine learning prediction of the case-fatality of COVID-19 and risk factors for adverse outcomes in patients with non-small cell lung cancer","2024","Translational Cancer Research","13","6","","2587","2595","8","0","10.21037/tcr-23-2188","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197616912&doi=10.21037%2ftcr-23-2188&partnerID=40&md5=b749a667de4c237602292918d8b4c4bf","Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Biomedical Statistics Center, Data Science Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea","Jung Y.J., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Ahn J., Biomedical Statistics Center, Data Science Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Park S., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Sun J.-M., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Lee S.-H., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Ahn J.S., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Ahn M.-J., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Cho S.Y., Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Division of Infectious Diseases, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea; Jung H.A., Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea","Background: Since the emergence of coronavirus disease 2019 (COVID-19) across the globe, patients with cancer have been found to have an increased risk of infection with COVID-19 and are highly likely to experience a severe disease course. This study analyzed the clinical outcomes of COVID-19 in patients with non-small cell lung cancer (NSCLC) and identified the risk factors for adverse outcomes. Methods: The study included patients diagnosed with COVID-19 between January 2020 and April 2022 at the Samsung Medical Center in Seoul, Republic of Korea, who have a history of NSCLC. The case-fatality rate and risk factors for COVID-19 were analyzed using a machine-learning prediction method. Additionally, the study investigated the effect of COVID-19 on the systemic treatment of patients with advanced-stage NSCLC. Results: Overall, 1,127 patients were included in this study, with 10.3% of the patients being older than 75 years; of these patients, 51.8% were ex- or current smokers. Among the 584 patients cured after surgery, 91 had stable disease after concurrent chemo-radiotherapy, and 452 had recurrent or metastatic NSCLC. Among 452 patients with recurrent or metastatic NSCLC, 387 received systemic treatment in a palliative setting during COVID-19. Of these, 188 received targeted therapy, 111 received cytotoxic chemotherapy, 63 received immunotherapy +/− chemotherapy, and 26 received other agents. Among them, 94.6% of patients continued systemic treatment after the COVID-19 infection. Only one patient discontinued treatment because of complications of the COVID-19 infection, and 18 patients changed their systemic treatment because of disease progression. The case fatality rates were 0.86% for patients with early-stage NSCLC, 4.4% for patients with locally advanced NSCLC, and 9.96% for patients with advanced NSCLC. The factors associated with fatalities included palliative chemotherapy, type of palliative chemotherapy, age (≥75 years), diabetes, smoking history, history of lung radiotherapy, hypertension, sex, and chronic obstructive pulmonary disease (COPD). The predictive model using logistic regression and eXtreme Gradient Boosting (XGB) performed well [area under the curve (AUC) for logistic regression =0.84 and AUC for XGB =0.84]. Conclusions: The case fatality rate in patients with NSCLC was 4.8%, while most patients with advanced NSCLC continued to receive systemic treatment. However, patients with risk factors require careful management of COVID-19 complications. © Translational Cancer Research. All rights reserved.","case-fatality rate; Coronavirus disease 2019 outbreak (COVID-19 outbreak); non-small cell lung cancer (NSCLC)","anaplastic lymphoma kinase inhibitor; epidermal growth factor receptor kinase inhibitor; SARS-CoV-2 vaccine; adverse outcome; aged; area under the curve; Article; cancer growth; cancer immunotherapy; cancer patient; cancer surgery; cardiovascular disease; case fatality rate; chemoradiotherapy; chronic obstructive lung disease; clinical outcome; controlled study; coronavirus disease 2019; current smoker; diabetes mellitus; drug withdrawal; ECOG Performance Status; ex-smoker; female; human; hypertension; machine learning; major clinical study; male; non small cell lung cancer; palliative chemotherapy; patient history of chemotherapy; patient history of radiotherapy; post infection complication; predictive model; retrospective study; risk factor; risk model; sex; systemic therapy; vaccination","","","","","National Research Foundation of Korea, NRF; Korean Society of Medical Oncology; Ministry of Science and ICT, South Korea, MSIT; ICT, (NRF-2021R1F1A1054782)","The abstract in this article has been previously published at ESMO Congress 2023. Funding: This study was supported by grants from the Korean Society of Medical Oncology (KSMO) 2021 and the National Research Foundation of Korea (NRF) funded by the Ministry of Science and Information and Communication Technology (ICT) (No. NRF-2021R1F1A1054782).","WHO Coronavirus Disease (COVID-19) Dashboard; Estimating mortality from COVID-19; Grasselli G, Zangrillo A, Zanella A, Et al., Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy, JAMA, 323, pp. 1574-1581, (2020); Lee LYW, Cazier JB, Starkey T, Et al., COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study, Lancet Oncol, 21, pp. 1309-1316, (2020); Deng G, Yin M, Chen X, Et al., Clinical determinants for fatality of 44,672 patients with COVID-19, Crit Care, 24, (2020); Wang Q, Berger NA, Xu R., Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID-19 Infection, JAMA Oncol, 7, pp. 220-227, (2021); Al-Quteimat OM, Amer AM., The Impact of the COVID-19 Pandemic on Cancer Patients, Am J Clin Oncol, 43, pp. 452-455, (2020); Kuderer NM, Choueiri TK, Shah DP, Et al., Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study, Lancet, 395, pp. 1907-1918, (2020); Garassino MC, Whisenant JG, Huang LC, Et al., COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study, Lancet Oncol, 21, pp. 914-922, (2020); Jee J, Foote MB, Lumish M, Et al., Chemotherapy and COVID-19 Outcomes in Patients With Cancer, J Clin Oncol, 38, pp. 3538-3546, (2020); Lee LY, Cazier JB, Angelis V, Et al., COVID-19 mortality in patients with cancer on chemotherapy or other anticancer treatments: a prospective cohort study, Lancet, 395, pp. 1919-1926, (2020); Richardson S, Hirsch JS, Narasimhan M, Et al., Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area, JAMA, 323, pp. 2052-2059, (2020); Liaw A, Wiener M., Classification and Regression by randomForest, R News, 2, pp. 18-22, (2002); Chen T, He T, Benesty M, Et al., xgboost: Extreme Gradient Boosting, (2022); Friedman J, Hastie T, Tibshirani R., Regularization Paths for Generalized Linear Models via Coordinate Descent, J Stat Softw, 33, pp. 1-22, (2010); Luo J, Rizvi H, Preeshagul IR, Et al., COVID-19 in patients with lung cancer, Ann Oncol, 31, pp. 1386-1396, (2020); Passaro A, Bestvina C, Velez Velez M, Et al., Severity of COVID-19 in patients with lung cancer: evidence and challenges, J Immunother Cancer, 9, (2021); Liang W, Guan W, Chen R, Et al., Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China, Lancet Oncol, 21, pp. 335-337, (2020); Bakouny Z, Hawley JE, Choueiri TK, Et al., COVID-19 and Cancer: Current Challenges and Perspectives, Cancer Cell, 38, pp. 629-646, (2020); Luo J, Rizvi H, Egger JV, Et al., Impact of PD-1 Blockade on Severity of COVID-19 in Patients with Lung Cancers, Cancer Discov, 10, pp. 1121-1128, (2020); Rajula HSR, Verlato G, Manchia M, Et al., Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment, Medicina (Kaunas), 56, (2020)","H.A. Jung; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 81 Irwon-ro, Gangnam-gu, 06351, South Korea; email: hyunae.jung@samsung.com","","AME Publishing Company","","","","","","2218676X","","","","English","Transl. Cancer Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85197616912"
"Zhang S.; Duan H.; Yan J.","Zhang, Shuaiyang (59456115500); Duan, Hangyu (58697572800); Yan, Jun (57216604362)","59456115500; 58697572800; 57216604362","Identifying biomarkers of endoplasmic reticulum stress and analyzing immune cell infiltration in chronic obstructive pulmonary disease using machine learning","2024","Frontiers in Medicine","11","","1462868","","","","0","10.3389/fmed.2024.1462868","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210914971&doi=10.3389%2ffmed.2024.1462868&partnerID=40&md5=1bc31ee967761a1860a2f4413b1370d7","Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China; Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China","Zhang S., Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China; Duan H., Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China; Yan J., Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China","Background: Endoplasmic reticulum stress (ERS) is a crucial factor in the progression of chronic obstructive pulmonary disease (COPD). However, the key genes associated with COPD and immune cell infiltration remain to be elucidated. Therefore, this study aimed to identify biomarkers pertinent to the diagnosis of ERS in COPD and delve deeper into the association between pivotal genes and their possible interactions with immune cells. Methods: We selected the genetic data of 189 samples from the Gene Expression Omnibus database, including 91 control and 98 COPD samples. First, we identified the differentially expressed genes between patients with COPD and controls and then screened the ERS genes associated with COPD. Second, 22 core ERS genes associated with COPD were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model and Support Vector Machine Recursive Feature Elimination (SVM-RFE), and the predictive effects of the screened core genes in COPD were evaluated. Third, we explored immune cell infiltration associated with COPD and conducted an in-depth analysis to explore the possible connections between the identified key genes and their related immune cells. Results: A total of 66 differentially expressed endoplasmic reticulum stress–related genes (DE-ERGs) were identified in this study, among which 12 were upregulated and 54 were downregulated. The 22 key genes screened were as follows: AGR3, BCHE, CBY1, CHRM3, CYP1B1, DCSTAMP, DDHD1, DMPK, EDEM3, EDN1, FKBP10, HSPA2, KPNA2, LGALS3, MAOB, MMP9, MPO, MTTP, PIK3CA, PTGIS, PURA, and TMCC1. Their expression was significantly different between COPD and healthy samples, and the difference between the groups was significant. Receiver operating characteristic curve analysis revealed that CBY1 (area under the curve [AUC] = 0.800), BCHE (AUC = 0.773), EDEM3 (AUC = 0.768), FKBP10 (AUC = 0.760), MAOB (AUC = 0.736), and MMP9 (AUC = 0.729) showed a strong ability to distinguish COPD samples from normal samples. Immune cell infiltration results associated with the three key genes were also obtained. Conclusion: The insights of our study have the potential to present new evidence for exploring emerging diagnostic signs of COPD while also contributing to a better understanding of its developmental mechanisms. Copyright © 2024 Zhang, Duan and Yan.","BCHE; CBY1; chronic obstructive pulmonary disease; EDEM3; endoplasmic reticulum stress; immune cell infiltration; machine learning","biological marker; cytochrome P450 1B1; gelatinase B; microsomal triglyceride transfer protein inhibitor; myeloperoxidase; agr3 gene; Article; bche gene; cby1 gene; cell infiltration; chrm3 gene; chronic obstructive lung disease; controlled study; core gene; data base; dcstamp gene; ddhd1 gene; differential gene expression; edem3 gene; edn1 gene; endoplasmic reticulum stress; fkbp10 gene; gene; gene expression; hspa2 gene; human; human cell; immunocompetent cell; kpna2 gene; least absolute shrinkage and selection operator; lgals3 gene; machine learning; major clinical study; maob gene; mpo gene; pik3ca gene; ptgis gene; pura gene; receiver operating characteristic; recursive feature elimination; support vector machine; tmcc1 gene","","gelatinase B, 146480-36-6; myeloperoxidase, ","","","Sepsis Research Institute; Capital Health Development Science and Technology Special Project, (2020-2-4192)","The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Sepsis Research Institute (University-Affiliated Type III Institute), and the Capital Health Development Science and Technology Special Project (Shouzhi 2020-2-4192).","Bhatt S., Washko G., Hoffman E., Newell J., Bodduluri S., Diaz A., Et al., Imaging advances in chronic obstructive pulmonary disease. 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Maselli D., Bhatt S., Anzueto A., Bowler R., DeMeo D., Diaz A., Et al., Clinical epidemiology of COPD: insights from 10 years of the COPDGene study, Chest, 156, pp. 228-238, (2019); Huertas A., Palange P., COPD: a multifactorial systemic disease, Ther Adv Respir Dis, 5, pp. 217-224, (2011); Czarnecka-Chrebelska K., Mukherjee D., Maryanchik S., Rudzinska-Radecka M., Biological and genetic mechanisms of COPD, its diagnosis, treatment, and relationship with lung cancer, Biomedicines, 11, 448, (2023); Rodrigues S., Cunha C., Soares G., Silva P., Silva A., Goncalves-de-Albuquerque C., Mechanisms, pathophysiology and currently proposed treatments of chronic obstructive pulmonary disease, Pharmaceuticals (Basel), 14, 979, (2021); Wang C., Zhou J., Wang J., Li S., Fukunaga A., Yodoi J., Et al., Progress in the mechanism and targeted drug therapy for COPD, Signal Transduct Target Ther, 5, 248, (2020); Peng H., Zhou Q., Liu J., Wang Y., Mu K., Zhang L., Et al., Endoplasmic reticulum stress: a vital process and potential therapeutic target in chronic obstructive pulmonary disease, Inflamm Res, 72, pp. 1761-1772, (2023); Chen G., Wei T., Ju F., Li H., Protein quality control and aggregation in the endoplasmic reticulum: from basic to bedside, Front Cell Dev Biol, 11, (2023); Daverkausen-Fischer L., Draga M., Prols F., Regulation of translation, translocation, and degradation of proteins at the membrane of the endoplasmic reticulum, Int J Mol Sci, 23, 5576, (2022); Sicari D., Delaunay-Moisan A., Combettes L., Chevet E., Igbaria A., A guide to assessing endoplasmic reticulum homeostasis and stress in mammalian systems, FEBS J, 287, pp. 27-42, (2020); Yu Y., Yang A., Yu G., Wang H., Endoplasmic reticulum stress in chronic obstructive pulmonary disease: mechanisms and future perspectives, Biomolecules, 12, 1637, (2022); Wang H., Chen F., Wu L., Ephedrine ameliorates Chronic Obstructive Pulmonary Disease (COPD) through restraining Endoplasmic Reticulum (ER) stress in vitro and in vivo, Int Immunopharmacol, 103, (2022); Sarker I., Machine learning: algorithms, real-world applications and research directions, SN Comput Sci, 2, 160, (2021); Shehab M., Abualigah L., Shambour Q., Abu-Hashem M., Shambour M., Alsalibi A., Et al., Machine learning in medical applications: a review of state-of-the-art methods, Comput Biol Med, 145, (2022); Ahsan M., Luna S., Siddique Z., Machine-learning-based disease diagnosis: a comprehensive review, Healthcare (Basel), 10, 541, (2022); Johnson W., Li C., Rabinovic A., Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics, 8, pp. 118-127, (2007); Kanehisa M., Goto S., Sato Y., Furumichi M., Tanabe M., KEGG for integration and interpretation of large-scale molecular data sets, Nucleic Acids Res, 40, pp. D109-D114, (2012); Friedman J., Hastie T., Tibshirani R., Regularization paths for generalized linear models via coordinate descent, J Stat Softw, 33, pp. 1-22, (2010); Craven K., Gokmen-Polar Y., Badve S., CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer, Sci Rep, 11, 4691, (2021); Ferrera M., Labaki W., Han M., Advances in chronic obstructive pulmonary disease, Annu Rev Med, 72, pp. 119-134, (2021); Decramer M., Janssens W., Miravitlles M., Chronic obstructive pulmonary disease, Lancet, 379, pp. 1341-1351, (2012); Sakhatskyy P., Gabino Miranda G., Newton J., Lee C., Choudhary G., Vang A., Et al., Cigarette smoke-induced lung endothelial apoptosis and emphysema are associated with impairment of FAK and eIF2α, Microvasc Res, 94, pp. 80-89, (2014); Tao S., Jing J., Wang Y., Li F., Ma H., Identification of genes related to Endoplasmic Reticulum Stress (ERS) in Chronic Obstructive Pulmonary Disease (COPD) and clinical validation, Int J Chron Obstruct Pulmon Dis, 18, pp. 3085-3097, (2023); Lockridge O., Review of human butyrylcholinesterase structure, function, genetic variants, history of use in the clinic, and potential therapeutic uses, Pharmacol Ther, 148, pp. 34-46, (2015); Ben Anes A., Ben Nasr H., Garrouch A., Bennour S., Bchir S., Hachana M., Et al., Alterations in acetylcholinesterase and butyrylcholinesterase activities in chronic obstructive pulmonary disease: relationships with oxidative and inflammatory markers, Mol Cell Biochem, 445, pp. 1-11, (2018); Sicinska P., Bukowska B., Pajak A., Koceva-Chyla A., Pietras T., Nizinkowski P., Et al., Decreased activity of butyrylcholinesterase in blood plasma of patients with chronic obstructive pulmonary disease, Arch Med Sci, 13, pp. 645-651, (2017); Gu Y., Chow M., Kapoor A., Mei W., Jiang Y., Yan J., Et al., Biphasic Alteration of Butyrylcholinesterase (BChE) during prostate cancer development, Transl Oncol, 11, pp. 1012-1022, (2018); Zengin T., Onal-Suzek T., Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma, BMC Bioinform, 21Suppl. 14, 368, (2020); Mancini M., Soverini S., Gugliotta G., Santucci M., Rosti G., Cavo M., Et al., Chibby 1: a new component of β-catenin-signaling in chronic myeloid leukemia, Oncotarget, 8, pp. 88244-88250, (2017); Epting D., Senaratne L., Ott E., Holmgren A., Sumathipala D., Larsen S., Et al., Loss of CBY1 results in a ciliopathy characterized by features of Joubert syndrome, Hum Mutat, 41, pp. 2179-2194, (2020); Cyge B., Voronina V., Hoque M., Kim E., Hall J., Bailey-Lundberg J., Et al., Loss of the ciliary protein Chibby1 in mice leads to exocrine pancreatic degeneration and pancreatitis, Sci Rep, 11, (2021); Schuierer M., Graf E., Takemaru K., Dietmaier W., Bosserhoff A., Reduced expression of beta-catenin inhibitor Chibby in colon carcinoma cell lines, World J Gastroenterol, 12, pp. 1529-1535, (2006); Yu S., Ito S., Wada I., Hosokawa N., ER-resident protein 46 (ERp46) triggers the mannose-trimming activity of ER degradation-enhancing α-mannosidase-like protein 3 (EDEM3), J Biol Chem, 293, pp. 10663-10674, (2018); Xu Y., Peloso G., Nagai T., Mizoguchi T., Deik A., Bullock K., Et al., EDEM3 modulates plasma triglyceride level through its regulation of LRP1 expression, iScience, 23, (2020); Scott E., Garnham R., Cheung K., Duxfield A., Elliott D., Munkley J., Pro-survival factor EDEM3 confers therapy resistance in prostate cancer, Int J Mol Sci, 23, 8184, (2022)","J. Yan; Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China; email: dzmyyyj@126.com","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","","Scopus","2-s2.0-85210914971"
"Salet N.; Gökdemir A.; Preijde J.; van Heck C.H.; Eijkenaar F.","Salet, N. (57203753941); Gökdemir, A. (59227086300); Preijde, J. (57191578840); van Heck, C.H. (57194598473); Eijkenaar, F. (39261242800)","57203753941; 59227086300; 57191578840; 57194598473; 39261242800","Using machine learning to predict acute myocardial infarction and ischemic heart disease in primary care cardiovascular patients","2024","PLoS ONE","19","7 July","e0307099","","","","0","10.1371/journal.pone.0307099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199126545&doi=10.1371%2fjournal.pone.0307099&partnerID=40&md5=74370825257ffb25ba69337895d6a9b3","Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands; Esculine b.v., Capelle aan den IJssel, Netherlands; DrechtDokters, Hendrik-Ido-Ambacht, Netherlands","Salet N., Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands; Gökdemir A., Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands, Esculine b.v., Capelle aan den IJssel, Netherlands; Preijde J., Esculine b.v., Capelle aan den IJssel, Netherlands; van Heck C.H., DrechtDokters, Hendrik-Ido-Ambacht, Netherlands; Eijkenaar F., Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands","Background Early recognition, which preferably happens in primary care, is the most important tool to combat cardiovascular disease (CVD). This study aims to predict acute myocardial infarction (AMI) and ischemic heart disease (IHD) using Machine Learning (ML) in primary care cardiovascular patients. We compare the ML-models’ performance with that of the common SMART algorithm and discuss clinical implications. Methods and results Patient-level medical record data (n = 13,218) collected between 2011–2021 from 90 GP-practices were used to construct two random forest models (one for AMI and one for IHD) as well as a linear model based on the SMART risk prediction algorithm as a suitable comparator. The data contained patient-level predictors, including demographics, procedures, medications, biometrics, and diagnosis. Temporal cross-validation was used to assess performance. Furthermore, predictors that contributed most to the ML-models’ accuracy were identified. The ML-model predicting AMI had an accuracy of 0.97, a sensitivity of 0.67, a specificity of 1.00 and a precision of 0.99. The AUC was 0.96 and the Brier score was 0.03. The IHD-model had similar performance. In both ML-models anticoagulants/antiplatelet use, systolic blood pressure, mean blood glucose, and eGFR contributed most to model accuracy. For both outcomes, the SMART algorithm was substantially outperformed by ML on all metrics. Conclusion Our findings underline the potential of using ML for CVD prediction purposes in primary care, although the interpretation of predictors can be difficult. Clinicians, patients, and researchers might benefit from transitioning to using ML-models in support of individualized predictions by primary care physicians and subsequent (secondary) prevention. Copyright: © 2024 Salet et al.","","Adult; Aged; Algorithms; Female; Humans; Machine Learning; Male; Middle Aged; Myocardial Infarction; Myocardial Ischemia; Primary Health Care; Risk Assessment; antithrombocytic agent; C reactive protein; creatinine; high density lipoprotein; low density lipoprotein; accuracy; acute heart infarction; adult; algorithm; aortic aneurysm; Article; artificial neural network; asthma; atherosclerosis; body mass; cardiovascular disease; cardiovascular mortality; cardiovascular risk; cerebrovascular accident; chronic obstructive lung disease; coronary atherosclerosis; decision tree; diabetes mellitus; diagnostic accuracy; diastolic blood pressure; electronic health record; estimated glomerular filtration rate; female; human; hypertension; intermittent claudication; ischemic heart disease; learning; learning algorithm; machine learning; major clinical study; male; obesity; physical activity; primary medical care; receiver operating characteristic; risk factor; risk management; sensitivity and specificity; systolic blood pressure; transient ischemic attack; aged; diagnosis; heart infarction; heart muscle ischemia; middle aged; primary health care; procedures; risk assessment","","C reactive protein, 9007-41-4; creatinine, 19230-81-0, 60-27-5","","","","","WHO reveals leading causes of death and disability worldwide: 2000–2019; Walker I. F., Et al., The Economic Costs of Cardiovascular Disease, Diabetes Mellitus, and Associated Complications in South Asia: A Systematic Review, Value in Health Regional Issues, 15, pp. 12-26, (2018); Gheorghe A., Et al., The economic burden of cardiovascular disease and hypertension in low- and middle-income countries: A systematic review, BMC Public Health, 18, (2018); Barton P., Andronis L., Briggs A., McPherson K., Capewell S., Effectiveness and cost effectiveness of cardiovascular disease prevention in whole populations: Modelling study, BMJ, 343, (2011); Damen J. A. A. G., Et al., Prediction models for cardiovascular disease risk in the general population: Systematic review, BMJ (Online), 353, (2016); Stewart J., Manmathan G., Wilkinson P., Primary prevention of cardiovascular disease: A review of contemporary guidance and literature, JRSM Cardiovasc. 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Neurol, (2023); Li C., Et al., Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records, Eur. Hear. J. - Digit. Heal, (2024); Kasim S., Et al., Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians, PLoS One, (2024); Yang L., Et al., Study of cardiovascular disease prediction model based on random forest in eastern China, Sci. Reports, 101 10, pp. 1-8, (2020); Li R., Et al., Cardiovascular Disease Risk Prediction Based on Random Forest, Lect. Notes Electr. Eng, 536, pp. 31-43, (2018); Song X., Liu X., Liu F., Wang C., Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis, International Journal of Medical Informatics, (2021); Klooster C. C., Et al., Predicting 10-year risk of recurrent cardiovascular events andcardiovascular interventions in patients with established cardiovascular disease: results from UCC-SMART and REACH, Int. J. Cardiol, 325, pp. 140-148, (2021); Nies L. M. E., Et al., The impact of the new Dutch guideline on cardiovascular risk management in patients with COPD: a retrospective study, BJGP open, 5, pp. 1-10, (2021); Van 't Klooster C. C., Et al., Supplemental material Predicting 10-year risk of recurrent cardiovascular events and cardiovascular interventions in patients with established cardiovascular disease: results from UCC-SMART and REACH; Troyanskaya O., Et al., Missing value estimation methods for DNA microarrays, Bioinformatics, 17, pp. 520-525, (2001); Kramer O., K-Nearest Neighbors, pp. 13-23, (2013); Gao Z., Chen Z., Sun A., Deng X., Gender differences in cardiovascular disease, Med. Nov. Technol. Devices, 4, (2019); Rodgers J. L., Et al., Cardiovascular Risks Associated with Gender and Aging, J. Cardiovasc. Dev. Dis, 6, (2019); HM L., Et al., Relation between COPD severity and global cardiovascular risk in US adults, Chest, 142, pp. 1118-1125, (2012); Rothnie K. J., Quint J. K., Chronic obstructive pulmonary disease and acute myocardial infarction: effects on presentation, management, and outcomes, Eur. Hear. Journal. Qual. Care Clin. Outcomes, 2, (2016); MC T., Et al., Asthma predicts cardiovascular disease events: the multi-ethnic study of atherosclerosis, Arterioscler. Thromb. Vasc. Biol, 35, pp. 1520-1525, (2015); Leon B. M., Maddox T. M., Diabetes and cardiovascular disease: Epidemiology, biological mechanisms, treatment recommendations and future research, World J. Diabetes, 6, (2015); Cardiovascular risk in post-myocardial infarction patients: nationwide real world data demonstrate the importance of a long-term perspective, Eur. Heart J, 36, pp. 1163-1170a, (2015); Whelton S. P., Et al., Association of Normal Systolic Blood Pressure Level With Cardiovascular Disease in the Absence of Risk Factors, JAMA Cardiol, 5, pp. 1011-1018, (2020); MS D., RS V. & V X Trajectories of Blood Lipid Concentrations Over the Adult Life Course and Risk of Cardiovascular Disease and All-Cause Mortality: Observations From the Framingham Study Over 35 Years, J. Am. Heart Assoc, 8, (2019); CM L., Et al., Blood glucose and risk of cardiovascular disease in the Asia Pacific region, Diabetes Care, 27, pp. 2836-2842, (2004); Mann J. F. E., Gerstein H. C., Dulau-Florea I., Lonn E., Cardiovascular risk in patients with mild renal insufficiency, Kidney Int, 63, pp. S192-S196, (2003); E L., Joints effects of BMI and smoking on mortality of all-causes, CVD, and cancer, Cancer Causes Control, 30, (2019); Winzer E. B., Woitek F., Linke A., Physical Activity in the Prevention and Treatment of Coronary Artery Disease, J. Am. Heart Assoc, 7, (2018); C, I., IV, T., MK, M., E, S. & MD, E. Adult asthma and risk of coronary heart disease, cerebrovascular disease, and heart failure: a prospective study of 2 matched cohorts, Am. J. Epidemiol, 176, pp. 1014-1024, (2012); McKay A. J., Et al., Is the SMART risk prediction model ready for real-world implementation? A validation study in a routine care setting of approximately 380 000 individuals, Eur. J. Prev. Cardiol, 29, pp. 654-663, (2022); Weng S. F., Reps J., Kai J., Garibaldi J. M., Qureshi N., Can machine-learning improve cardiovascular risk prediction using routine clinical data?, PLoS One, 12, (2017); Xu S., Et al., Cardiovascular risk prediction method based on CFS subset evaluation and random forest classification framework, 2017 IEEE 2nd Int. Conf. Big Data Anal. ICBDA, pp. 228-232, (2017); Breiman L., Random Forests, Mach. Learn, 451 45, pp. 5-32, (2001); Tandel G. S., Tiwari A., Kakde O. G., Performance optimisation of deep learning models using majority voting algorithm for brain tumour classification, Comput. Biol. Med, 135, (2021); Jung Y., Multiple predicting K-fold cross-validation for model selection, J. Nonparametr. Stat, (2018); Jung Y., Hu J., A K-fold averaging cross-validation procedure, J. Nonparametr. Stat, (2015); Rahimian F., Et al., Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records, PLoS Med, (2018); Rose S., A Machine Learning Framework for Plan Payment Risk Adjustment, Health Serv. Res, (2016); Amin M. S., Chiam Y. K., Varathan K. D., Identification of significant features and data mining techniques in predicting heart disease, Telemat. Informatics, 36, pp. 82-93, (2019); Hosmer D. W., Lemeshow S., Applied logistic regression, (2000); Steyerberg E. W., Et al., Assessing the performance of prediction models: A framework for traditional and novel measures, Epidemiology, 21, pp. 128-138, (2010); Dorresteijn J. A. N., Et al., Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score, Heart, 99, pp. 866-872, (2013); Kangwanariyakul Y., Nantasenamat C., Tantimongcolwat T., Naenna T., Data mining of magnetocardiograms for prediction of ischemic heart disease, EXCLI J, 9, (2010); Quesada J. A., Pineda A. L., Durazo-Arvizu R. A., Orozco-Beltran D., Machine learning to predict cardiovascular risk, Artic. Int. J. Clin. Pract, (2019); Dwivedi A. K., Performance evaluation of different machine learning techniques for prediction of heart disease, Neural Comput. Appl, 29, pp. 685-693, (2018); Rajkomar A., Et al., Scalable and accurate deep learning with electronic health records, npj Digit. Med, (2018); Couronne R., Probst P., Boulesteix A. L., Random forest versus logistic regression: A large-scale benchmark experiment, BMC Bioinformatics, 19, pp. 1-14, (2018); Smith P. F., Ganesh S., Liu P., A comparison of random forest regression and multiple linear regression for prediction in neuroscience, J. Neurosci. Methods, 220, pp. 85-91, (2013); Deo R. C., Machine Learning in Medicine, Circulation, 132, pp. 1920-1930, (2015); Smits G. H. J. M., van Doorn S., Bots M. L., Hollander M., Cardiovascular risk reduction with integrated care: results of 8 years follow up, BMC Prim. Care, 24, pp. 1-9, (2023); Soltani S., Et al., Community-based cardiovascular disease prevention programmes and cardiovascular risk factors: a systematic review and meta-analysis, Public Health, 200, pp. 59-70, (2021); Frederix I., Dendale P., Schmid J. P., Who needs secondary prevention?, Eur. J. Prev. Cardiol, 24, pp. 8-13, (2017); Bansilal S., Castellano M., The global cardiovascular disease pandemic, current status and future projections, IJCA, 201, pp. S1-S7, (2015)","N. Salet; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, Netherlands; email: Salet@eshpm.eur.nl","","Public Library of Science","","","","","","19326203","","POLNC","39024245","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85199126545"
"Mbous Y.P.V.; Siddiqui Z.A.; Bharmal M.; Lemasters T.; Kolodney J.; Kelley G.A.; Kamal K.M.; Sambamoorthi U.","Mbous, Yves Paul Vincent (57190763249); Siddiqui, Zasim Azhar (57200257397); Bharmal, Murtuza (10142850200); Lemasters, Traci (53871446900); Kolodney, Joanna (55320893400); Kelley, George A. (7005338683); Kamal, Khalid M. (7006870429); Sambamoorthi, Usha (7004156073)","57190763249; 57200257397; 10142850200; 53871446900; 55320893400; 7005338683; 7006870429; 7004156073","Predictive and Interpretable Machine Learning of Economic Burden: The Role of Chronic Conditions Among Elderly Patients with Incident Primary Merkel Cell Carcinoma (MCC)","2024","ClinicoEconomics and Outcomes Research ","16","","","847","868","21","0","10.2147/CEOR.S456968","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212398149&doi=10.2147%2fCEOR.S456968&partnerID=40&md5=b48b7dbfe4daacb72e66ebc75913c0f1","School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, United States; AstraZeneca Oncology Outcomes Research, AstraZeneca, Boston, MA, United States; School of Medicine, Department of Hematology/Oncology, West Virginia University, Morgantown, WV, United States; School of Public Health, Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV, United States; College of Pharmacy, Department of Pharmacotherapy, University of North Texas Health Science Center, Fort Worth, TX, United States","Mbous Y.P.V., School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, United States; Siddiqui Z.A., School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, United States; Bharmal M., AstraZeneca Oncology Outcomes Research, AstraZeneca, Boston, MA, United States; Lemasters T., School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, United States; Kolodney J., School of Medicine, Department of Hematology/Oncology, West Virginia University, Morgantown, WV, United States; Kelley G.A., School of Public Health, Department of Epidemiology and Biostatistics, West Virginia University, Morgantown, WV, United States; Kamal K.M., School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Morgantown, WV, United States; Sambamoorthi U., College of Pharmacy, Department of Pharmacotherapy, University of North Texas Health Science Center, Fort Worth, TX, United States","Objective: To evaluate chronic conditions as leading predictors of economic burden over time among older adults with incident primary Merkel Cell Carcinoma (MCC) using machine learning methods. Methods: We used a retrospective cohort of older adults (age ≥ 67 years) diagnosed with MCC between 2009 and 2019. For these elderly MCC patients, we derived three phases (pre-diagnosis, during-treatment, and post-treatment) anchored around cancer diagnosis date. All three phases had 12 months baseline and 12-months follow-up periods. Chronic conditions were identified in baseline and follow-up periods, whereas annual total and out-of-pocket (OOP) healthcare expenditures were measured during the 12-month followup. XGBoost regression models and SHapley Additive exPlanations (SHAP) methods were used to identify leading predictors and their associations with economic burden. Results: Congestive heart failure (CHF), chronic kidney disease (CKD) and depression had the highest average incremental total expenditures during pre-diagnosis, treatment, and post-treatment phases, respectively ($25,004, $24,221, and $16,277 (CHF); $22,524, $19,350, $20,556 (CKD); and $21,645, $22,055, $18,350 (depression)), whereas the average incremental OOP expenditures during the same periods were $3703, $3,013, $2,442 (CHF); $2,457, $2,518, $2,914 (CKD); and $3,278, $2,322, $2,783 (depression). Except for hypertension and HIV, all chronic conditions had higher expenditures compared to those without the chronic conditions. Predictive models across each of phases of care indicated that CHF, CKD, and heart diseases were among the top 10 leading predictors; however, their feature importance ranking declined over time. Although depression was one of the leading drivers of expenditures in unadjusted descriptive models, it was not among the top 10 predictors. Conclusion: Among older adults with MCC, cardiac and renal conditions were the leading drivers of total expenditures and OOP expenditures. Our findings suggest that managing cardiac and renal conditions may be important for cost containment efforts. © 2024 Mbous et al.","chronic conditions; healthcare expenditures; Merkel cell carcinoma; SEER-Medicare; SHAP; XGBoost","prescription drug; aged; alcohol consumption; anxiety; arthritis; Article; asthma; cancer staging; cerebrovascular accident; chemotherapy; chronic kidney failure; chronic obstructive lung disease; congestive heart failure; depression; education; emergency ward; female; follow up; health care; health workforce; hepatitis; human; Human immunodeficiency virus; Human immunodeficiency virus infection; hyperlipidemia; income; machine learning; male; marriage; merkel cell carcinoma; outpatient; prediction; retrospective study; sensitivity analysis; Shapley additive explanation; smoking; thyroid disease; very elderly","","","","","University of Southern California, USC; Cytel; National Cancer Institute’s Surveillance, Epidemiology and End Results Program; Massachusetts Department of Public Health, DPH; National Cancer Institute, NCI; Pfizer; Cerevel Therapeutics; Centers for Disease Control and Prevention, CDC, (1NU58DP007156); Centers for Disease Control and Prevention, CDC","The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention\u2019s (CDC) National Program of Cancer Registries, under cooperative agreement 1NU58DP007156; the National Cancer Institute\u2019s Surveillance, Epidemiology and End Results Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the author(s) and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The authors would like to thank Dr Murtuza Bharmal for his advice, involvement, and assistance towards the performance of the study. The abstract of this paper was presented at the International Society for Pharmacoeconomics and Outcomes Research conference 2024 as a poster with interim findings. The poster\u2019s abstract was published in \u2018ISPOR Abstracts 2024\u2019 in the journal \u2018Value in Health\u2019. This study was funded by the EMD Serono Research & Development Institute, Inc. Dr. Murtuza Bharmal was an employee of EMD Serono at the time the study was conducted. Dr Khalid Kamal reports grants from Cerevel Therapeutics, Pfizer/Cytel, and honorarium from Pharmacy Times and Continuing Education, outside the submitted work. The authors report no other conflicts of interest in this work.","Pozzi V, Molinelli E, Campagna R, Et al., Knockdown of nicotinamide N-methyltransferase suppresses proliferation, migration, and chemoresistance of Merkel cell carcinoma cells in vitro, Hum Cell, 37, 3, pp. 729-738, (2024); Mazziotta C, Badiale G, Cervellera CF, Et al., All-trans retinoic acid exhibits anti-proliferative and differentiating activity in Merkel cell carcinoma cells via retinoid pathway modulation, J Eur Acad Dermatol Venereol, 38, 7, pp. 1419-1431, (2024); Zhan S, Nguyen M, Hollsten J., Immune checkpoint inhibition therapy as first-line treatment for localized eyelid Merkel cell carcinoma in a nonsurgical candidate, Can J Ophthalmol, 59, 2, pp. e183-e184, (2024); Callahan R, Darzi A., Five policy levers to meet the value challenge in cancer care, Health Aff, 34, 9, pp. 1563-1568, (2015); Mariotto AB, Enewold L, Zhao J, Zeruto CA, Robin Yabroff K., Medical care costs associated with cancer survivorship in the United States, Canc Epide Biomarkers Prev; 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McGarvey N, Gitlin M, Fadli E, Chung KC., Increased healthcare costs by later stage cancer diagnosis, BMC Health Serv Res, 22, 1, (2022); Chen S, Cao Z, Prettner K, Et al., Estimates and Projections of the Global Economic Cost of 29 Cancers in 204 Countries and Territories From 2020 to 2050, JAMA Oncol, 9, 4, pp. 465-472, (2023); Pisu M, Henrikson NB, Banegas MP, Yabroff KR., Costs of cancer along the care continuum: what we can expect based on recent literature, Cancer, 124, 21, pp. 4181-4191, (2018); Ren K, Yin X, Zhou B., Effects of surgery on survival of patients aged 75 years or older with Merkel cell carcinoma, Cancer Med; Bahar F, DeCaprio JA., Why do we distinguish between virus-positive and virus-negative Merkel cell carcinoma?, Br J Dermatol, 190, 6, pp. 785-786, (2024); Kearney M, Thokagevistk K, Boutmy E, Bharmal M., Treatment patterns, comorbidities, healthcare resource use, and associated costs by line of chemotherapy and level of comorbidity in patients with newly-diagnosed Merkel cell carcinoma in the United States, J Med Econ, 21, 12, pp. 1159-1171, (2018); McEvoy AM, Lachance K, Hippe DS, Et al., Recurrence and Mortality Risk of Merkel Cell Carcinoma by Cancer Stage and Time From Diagnosis, JAMA Dermatol, 158, 4, pp. 382-389, (2022); Cancer Comorbidities and Complications: proposals for a, (2021); George M, Smith A, Sabesan S, Ranmuthugala G., Physical Comorbidities and Their Relationship with Cancer Treatment and ItsOutcomes in Older Adult Populations: systematic Review, JMIR Cancer, 7, 4, (2021); Gurney J, Sarfati D, Stanley J., The impact of patient comorbidity on cancer stage at diagnosis, Br J Cancer, 113, 9, pp. 1375-1380, (2015); Duthie K, Strohschein FJ, Loiselle CG., Living with cancer and other chronic conditions: patients’ perceptions of their healthcare experience, Can Oncol Nurs J, 27, 1, pp. 43-48, (2017); Tsevat J, Moriates C., Value-Based Health Care Meets Cost-Effectiveness Analysis, Ann Intern Med, 169, 5, pp. 329-332, (2018); Balasubramanian BA, Higashi RT, Rodriguez SA, Sadeghi N, Santini NO, Lee SC., Thematic Analysis of Challenges of Care Coordination for Underinsured and Uninsured Cancer Survivors With Chronic Conditions, JAMA Network Open, 4, 8, (2021); Home CPI US Bureau of Labor Statistics, (2019); Definitions of “Cost” in Medicare Utilization Files | resDAC; Andersen RM., National Health Surveys and the Behavioral Model of Health Services Use, Med Care, 46, 7, pp. 647-653, (2008); Bice TW, Boxerman SB., A quantitative measure of continuity of care, Med Care, 15, 4, pp. 347-349, (1977); Liu CW, Einstadter D, Cebul RD., Care fragmentation and emergency department use among complex patients with diabetes, Am J Manag Care, 16, 6, pp. 413-420, (2010); Hunter DJ, Holmes C, Drazen JM, Kohane IS, Leong T-Y., Where Medical Statistics Meets Artificial Intelligence, N Engl J Med, 389, 13, pp. 1211-1219, (2023); Workflow of a Machine Learning Project | By Ayush Pant | Towards Data Science. Towards Data Science; Shi X, Wong YD, Li MZF, Palanisamy C, Chai C., A feature learning approach based on XGBoost for driving assessment and risk prediction, Accid Anal Prev, 129, pp. 170-179, (2019); Ogunleye A, Wang QG., XGBoost Model for Chronic Kidney Disease Diagnosis, IEEE/ACM Trans Comput Biol Bioinform, 17, 6, pp. 2131-2140, (2020); Lundberg SM, Allen PG, Lee SI., A Unified Approach to Interpreting Model Predictions, Adv Neural Inf Process Syst; Bluethmann SM, Mariotto AB, Rowland JH., Anticipating the “Silver Tsunami”: prevalence Trajectories and Comorbidity Burden among Older Cancer Survivors in the United States, Cancer Epidemiol Biomark Prev, 25, 7, pp. 1029-1036, (2016); Steuten L, Garmo V, Phatak H, Sullivan SD, Nghiem P, Ramsey SD., Treatment Patterns, Overall Survival, and Total Healthcare Costs of Advanced Merkel Cell Carcinoma in the USA, Appl Health Econ Health Policy, 17, 5, pp. 733-740, (2019); Brown ML, Riley GF, Schussler N, Etzioni R., Estimating Health Care Costs Related to Cancer Treatment from SEER-Medicare Data, Med Care, 40, 8, pp. IV104-IV117, (2002); Yabroff KR, Mariotto A, Tangka F, Et al., Annual Report to the Nation on the Status of Cancer, Part 2: patient Economic Burden Associated With Cancer Care, JNCI J National Cancer Inst, 113, 12, pp. 1670-1682, (2021); Seidler AM, Pennie ML, Veledar E, Culler SD, Chen SC., Economic Burden of Melanoma in the Elderly Population: population-Based Analysis of the Surveillance, Epidemiology, and End Results (SEER)–Medicare Data, Arch Dermatol, 146, 3, pp. 249-256, (2010); Jiang C, Deng L, Karr MA, Et al., Chronic comorbid conditions among adult cancer survivors in the United States: results from the National Health Interview Survey, 2002-2018, Cancer, 128, 4, pp. 828-838, (2022); Guy GP, Yabroff KR, Ekwueme DU, Rim SH, Li R, Richardson LC., Economic Burden of Chronic Conditions Among Survivors of Cancer in the United States, J clin oncol, 35, 18, pp. 2053-2061, (2017); Davis-Ajami ML, Lu ZK, Wu J., Multiple chronic conditions and associated health care expenses in US adults with cancer: a 2010–2015 Medical Expenditure Panel Survey study, BMC Health Serv Res, 19, 1, (2019); Rim SH, Guy GPJ, Yabroff KR, McGraw KA, Ekwueme DU., The impact of chronic conditions on the economic burden of cancer survivorship: a systematic review, Expert Rev Pharmacoecon Outcomes Res, 16, 5, pp. 579-589, (2016); Subramanian S, Tangka FKL, Sabatino SA, Et al., Impact of chronic conditions on the cost of cancer care for Medicaid beneficiaries, Medicare Medic Res Rev, 2, 4, pp. E1-E21, (2012); Fowler H, Belot A, Ellis L, Et al., Comorbidity prevalence among cancer patients: a population-based cohort study of four cancers, BMC Cancer, 20, 1, (2020); Sogaard M, Thomsen RW, Bossen KS, Sorensen HT, Norgaard M., The impact of comorbidity on cancer survival: a review, Clin Epidemiol, 5, sup1, pp. 3-29, (2013); Warren JL, Yabroff KR, Meekins A, Topor M, Lamont EB, Brown ML., Evaluation of trends in the cost of initial cancer treatment, J Natl Cancer Inst, 100, 12, pp. 888-897, (2008); Chandra S, Zheng Y, Pandya S, Et al., Real-world outcomes among US Merkel cell carcinoma patients initiating immune checkpoint inhibitors or chemotherapy, Future Oncol, 16, 31, pp. 2521-2536, (2020); Zheng Y, Yu T, Mackey RH, Et al., Clinical Outcomes, Costs, and Healthcare Resource Utilization in Patients with Metastatic Merkel Cell Carcinoma Treated with Immune Checkpoint Inhibitors vs Chemotherapy, Clinicoecon Outcomes Res, 13, pp. 213-226, (2021); Alexandrescu DT., Melanoma costs: a dynamic model comparing estimated overall costs of various clinical stages, Dermatol Online J, 15, 11, (2009); SEER-Medicare Linked Data Resource, (2023)","K.M. Kamal; School of Pharmacy, Department of Pharmaceutical Systems and Policy, West Virginia University, Robert C. Byrd Health Sciences Center [North], Morgantown, P.O. Box 9510, United States; email: kkamal@hsc.wvu.edu","","Dove Medical Press Ltd","","","","","","11786981","","","","English","Clin. Outcomes Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85212398149"
"Yang Z.; Zheng Y.; Zhang L.; Zhao J.; Xu W.; Wu H.; Xie T.; Ding Y.","Yang, Zehua (58677765500); Zheng, Yamei (58140656800); Zhang, Lei (57221979418); Zhao, Jie (57206579639); Xu, Wenya (59329362500); Wu, Haihong (57221981088); Xie, Tian (57221981520); Ding, Yipeng (56453697900)","58677765500; 58140656800; 57221979418; 57206579639; 59329362500; 57221981088; 57221981520; 56453697900","Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms","2024","International Journal of COPD","19","","","2397","2414","17","0","10.2147/COPD.S478634","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209193685&doi=10.2147%2fCOPD.S478634&partnerID=40&md5=c127a1b4d8f3af5a62d69bb1dfc93903","Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China","Yang Z., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Zheng Y., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Zhang L., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Zhao J., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Xu W., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Wu H., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Xie T., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China; Ding Y., Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Hainan, Haikou, 570311, China","Background and Purpose: Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors, and genetic factors are important determinants of COPD. This study focuses on screening the best predictive models for assessing COPD-associated SNPs and then using the best models to predict potential risk factors for COPD. Methods: Healthy subjects (n=290) and COPD patients (n=233) were included in this study, the Agena MassARRAY platform was applied to genotype the subjects for SNPs. The selected sample loci were first screened by logistic regression analysis, based on which the key SNPs were further screened by LASSO regression, RFE algorithm and Random Forest algorithm, and the ROC curves were plotted to assess the discriminative performance of the models to screen the best prediction model. Finally, the best prediction model was used for the prediction of risk factors for COPD. Results: One-way logistic regression analysis screened 44 candidate SNPs from 146 SNPs, on the basis of which 44 SNPs were screened or feature ranked using LASSO model, RFE-Caret, RFE-Lda, RFE-lr, RFE-nb, RFE-rf, RFE-treebag algorithms and random forest model, respectively, and obtained ROC curve values of 0.809, 0.769, 0.798, 0.743, 0.686, 0.766, 0.743, 0.719, respectively, so we selected the lasso model as the best model, and then constructed a column-line graph model for the 25 SNPs screened in it, and found that rs12479210 might be the potential risk factors for COPD. Conclusion: The LASSO model is the best predictive model for COPD and rs12479210 may be a potential risk locus for COPD. © 2024 Yang et al.","COPD; LASSO; machine learning; predictive model; SNP","Aged; Algorithms; Case-Control Studies; Female; Genetic Association Studies; Genetic Predisposition to Disease; Humans; Logistic Models; Lung; Machine Learning; Male; Middle Aged; Phenotype; Polymorphism, Single Nucleotide; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Risk Assessment; Risk Factors; ROC Curve; genomic DNA; Article; body mass; case control study; chronic obstructive lung disease; controlled study; cross validation; diagnostic test accuracy study; disease predisposition; DNA extraction; forced expiratory volume; forced vital capacity; gene locus; genotype; genotyping; human; learning algorithm; least absolute shrinkage and selection operator; logistic regression analysis; major clinical study; nomogram; predictive model; questionnaire; random forest; receiver operating characteristic; respiratory tract infection; risk factor; risk model; single nucleotide polymorphism; thorax pain; wheezing; aged; algorithm; diagnosis; female; genetic association study; genetic predisposition; genetics; lung; machine learning; male; middle aged; pathophysiology; phenotype; predictive value; risk assessment; statistical model","","","","","Innovation Platform for Academicians of Hainan Province; National Natural Science Foundation of China, NSFC; Hainan Province Science and Technology Special Fund, (ZDYF2024SHFZ094); Hainan Province, (YSPTZX202312); Key Research and Development Program of National Natural Science Foundation of China, (81860015)","Funding text 1: This study was supported by Hainan Province Science and Technology Special Fund (No. ZDYF2024SHFZ094), the research project of Innovation Platform for Academicians of Hainan Province (YSPTZX202312), the Innovation Platform for Academicians of Hainan Province and the Key Research and Development Program of National Natural Science Foundation of China (No. 81860015).; Funding text 2: We thank all members of our research team for their contributions to this study, as well as Hainan Provincial People\u2019s Hospital and all participants for their support to this study. We also thank National Natural Science Foundation of China for funding this study.","regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the global burden of disease study 2015, Lancet, 388, 10053, pp. 1545-1602, (2016); Adeloye D, Chua S, Lee C, Et al., Global and regional estimates of COPD prevalence: systematic review and meta-analysis, J Global Health, 5, 2, (2015); Anees Ur R, Ahmad Hassali MA, Muhammad SA, Et al., The economic burden of chronic obstructive pulmonary disease (COPD) in the USA, Europe, and Asia: results from a systematic review of the literature, Expert Rev Pharmacoecon Outcomes Res, 20, 6, pp. 661-672, (2020); AS B, MA M, WM V, Et al., International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study, Lancet, 370, 9589, pp. 741-750, (2007); Mannino DM, Buist AS., Global burden of COPD: risk factors, prevalence, and future trends, Lancet, 370, 9589, pp. 765-773, (2007); KF R, Hurd S, Anzueto A, Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary, Am J Respir Crit Care Med, 176, 6, pp. 532-555, (2007); Wang CD, Chen N, Huang L, Et al., Impact of CYP1A1 polymorphisms on susceptibility to chronic obstructive pulmonary disease: a meta-analysis, Biomed Res Int, 2015, (2015); Humbert M, Montani D, Perros F, Dorfmuller P, Adnot S, Eddahibi S., Endothelial cell dysfunction and cross talk between endothelium and smooth muscle cells in pulmonary arterial hypertension, Vasc Pharmacol, 49, 4–6, pp. 113-118, (2008); Yuksel H, Yilmaz O, Karaman M, Et al., Role of vascular endothelial growth factor antagonism on airway remodeling in asthma, Annals Allergy Asthma Immunol, 110, 3, pp. 150-155, (2013); Marciniak SJ, Lomas DA., Genetic susceptibility, Clinics Chest Med, 35, 1, pp. 29-38, (2014); BD H, de Jong K, Lamontagne M, Et al., Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis, Nature Genet, 49, 3, pp. 426-432, (2017); Sakornsakolpat P, Prokopenko D, Lamontagne M, Et al., Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations, Nature Genet, 51, 3, pp. 494-505, (2019); Shrine N, AL G, AM E, Et al., New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries, Nat Genet, 51, 3, pp. 481-493, (2019); Wu MC, Kraft P, Epstein MP, Et al., Powerful SNP-set analysis for case-control genome-wide association studies, Am J Hum Genet, 86, 6, pp. 929-942, (2010); Omranian N, Eloundou-Mbebi JM, Mueller-Roeber B, Nikoloski Z., Gene regulatory network inference using fused LASSO on multiple data sets, Sci Rep, 6, (2016); Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W., Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics Proteomics, 15, 1, pp. 41-51, (2018); Yu X, Zeng Q., Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes, Aquatic Toxicol, 251, (2022); Lamontagne M, JC B, Obeidat M, Et al., Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations, Human Mol Gene, 27, 10, pp. 1819-1829, (2018); Ma X, Wu Y, Zhang L, Et al., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, J Transl Med, 18, 1, (2020); Gim J, An J, Sung J, Silverman EK, Cho MH, Won S., A between ethnicities comparison of chronic obstructive pulmonary disease genetic risk, Front Genet, 11, (2020); Zhou Y, Chen J, Bai F, Et al., Suggestive evidence of genetic association of IL23R polymorphisms with chronic obstructive pulmonary disease risk in the Chinese population, J Gene Med, 25, 5, (2023); Zhou JJ, Cho MH, Castaldi PJ, Hersh CP, Silverman EK, Laird NM., Heritability of chronic obstructive pulmonary disease and related phenotypes in smokers, Am J Respir Crit Care Med, 188, 8, pp. 941-947, (2013); Moll M, Sakornsakolpat P, Shrine N, Et al., Chronic obstructive pulmonary disease and related phenotypes: polygenic risk scores in population-based and case-control cohorts, Lancet Respir Med, 8, 7, pp. 696-708, (2020); Shrine N, AG I, Chen J, Et al., Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk, Nat Genet, 55, 3, pp. 410-422, (2023); Zhang J, Xu H, Qiao D, Et al., A polygenic risk score and age of diagnosis of COPD, Eur Respir J, 60, 3, (2022); Kang J, Choi YJ, Kim IK, Et al., LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer, Cancer Res Treat, 53, 3, pp. 773-783, (2021); Feng ZZ, Yang X, Subedi S, McNicholas PD., The LASSO and sparse least square regression methods for SNP selection in predicting quantitative traits, IEEE/ACM trans comput biol bioinfo, 9, 2, pp. 629-636, (2012); Yang C, Wan X, Yang Q, Xue H, Yu W., Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso, BMC Bioinf, 1, 1, (2010); Wang H, Zhang Y, Chen L, Et al., Identification of clinical prognostic features of esophageal cancer based on m6A regulators, Front Immunol, 13, (2022); Beck MW., NeuralNetTools: visualization and analysis tools for neural networks, J Stat Software, 85, 11, pp. 1-20, (2018); TM D, Dankers F, Valdes G, Et al., Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers, Med Phys, 45, 7, pp. 3449-3459, (2018); QA H, SM R, MV P, Et al., Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics, Cardiovasc diabetol, 18, 1, (2019); Elbeltagi A, Pande CB, Kumar M, Et al., Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models, Environ Sci Pollut Res Int, 30, 15, pp. 43183-43202, (2023); Botta V, Louppe G, Geurts P, Wehenkel L., Exploiting SNP correlations within random forest for genome-wide association studies, PLoS One, 9, 4, (2014); Cibulka M, Brodnanova M, Grendar M, Et al., Alzheimer’s disease-associated SNP rs708727 in SLC41A1 may increase risk for parkinson’s disease: report from enlarged Slovak study, Int J Mol Sci, 23, 3, (2022); Deo RC., Machine learning in medicine, Circulation, 132, 20, pp. 1920-1930, (2015); Cibulka M, Brodnanova M, Grendar M, Et al., SNPs rs11240569, rs708727, and rs823156 in SLC41A1 do not discriminate between Slovak patients with idiopathic parkinson’s disease and healthy controls: statistics and machine-learning evidence, Int J Mol Sci, 20, 19, (2019); Ding Y, Li Q, Feng Q, Et al., CYP2B6 genetic polymorphisms influence chronic obstructive pulmonary disease susceptibility in the Hainan population, Int J Chronic Obstr, 14, pp. 2103-2115, (2019); Li Q, Zhang C, Cheng Y, Et al., IL1RL1 polymorphisms rs12479210 and rs1420101 are associated with increased lung cancer risk in the Chinese Han population, Front Genetics, 14, (2023); Saikumar Jayalatha AK, Hesse L, Ketelaar ME, Koppelman GH, Nawijn MC., The central role of IL-33/IL-1RL1 pathway in asthma: from pathogenesis to intervention, Pharmacol Ther, 225, (2021); Forder A, Zhuang R, VGP S, Et al., Mechanisms contributing to the comorbidity of COPD and lung cancer, Int J Mol Sci, 24, 3, (2023)","T. Xie; Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, 19 Xiuhua Road, Xiuying District, Hainan, 570311, China; email: hpphxietian@163.com; Y. Ding; Department of Respiratory and Critical Care Medicine, Hainan Affiliated Hospital of Hainan Medical University, Hainan General Hospital, Haikou, 19 Xiuhua Road, Xiuying District, Hainan, 570311, China; email: yipengding2024@163.com","","Dove Medical Press Ltd","","","","","","11769106","","","39525518","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85209193685"
"Rezaeiahari M.; Brown C.C.; Eyimina A.; Perry T.T.; Goudie A.; Boyd M.; Tilford J.M.; Jefferson A.A.","Rezaeiahari, Mandana (35180472600); Brown, Clare C. (57201579590); Eyimina, Arina (58516089200); Perry, Tamara T. (57541717600); Goudie, Anthony (25031013900); Boyd, Melanie (58608618800); Tilford, J. Mick (6603922271); Jefferson, Akilah A. (57193622813)","35180472600; 57201579590; 58516089200; 57541717600; 25031013900; 58608618800; 6603922271; 57193622813","Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model","2024","Journal of Asthma","61","3","","203","211","8","0","10.1080/02770903.2023.2260881","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171791882&doi=10.1080%2f02770903.2023.2260881&partnerID=40&md5=54c4becd135314fe293ab6cdeb2ffa58","College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Department of Pediatrics, Allergy & Immunology Division, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Arkansas Children’s Research Institute, Little Rock, AR, United States","Rezaeiahari M., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Brown C.C., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Eyimina A., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Perry T.T., Department of Pediatrics, Allergy & Immunology Division, University of Arkansas for Medical Sciences, Little Rock, AR, United States, Arkansas Children’s Research Institute, Little Rock, AR, United States; Goudie A., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Boyd M., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Tilford J.M., College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, United States; Jefferson A.A., Department of Pediatrics, Allergy & Immunology Division, University of Arkansas for Medical Sciences, Little Rock, AR, United States, Arkansas Children’s Research Institute, Little Rock, AR, United States","Objective: Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes. Methods: Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5–18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5–11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model. Results: The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications. Conclusions: Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy. © 2023 Taylor & Francis Group, LLC.","claims data; conditional random forest; machine learning; Random forest; variable importance","beclometasone; beta adrenergic receptor stimulating agent; budesonide plus formoterol; corticosteroid; fluticasone; fluticasone propionate plus salmeterol; formoterol; leukotriene; leukotriene receptor blocking agent; mometasone furoate; montelukast; salbutamol; adolescent; adult; area under the curve; Article; Black person; Caucasian; child; child hospitalization; cohort analysis; data base; diagnostic test accuracy study; disease exacerbation; emergency ward; ethnic group; feature selection; female; health care utilization; health insurance; Hispanic; human; ICD-10; major clinical study; male; medicaid; outcome assessment; pediatrics; predictive model; preschool child; race; random forest; risk assessment; risk factor; rural area; self report; sensitivity and specificity; severe asthma; urban area","","beclometasone, 4419-39-0; budesonide plus formoterol, 150693-37-1, 150693-38-2; fluticasone, 90566-53-3; fluticasone propionate plus salmeterol, 136112-01-1; formoterol, 73573-87-2; mometasone furoate, 83919-23-7, 105102-22-5; montelukast, 151767-02-1, 158966-92-8; salbutamol, 18559-94-9, 35763-26-9","","","ACHI; National Institutes of Health, NIH, (KL2 T R003108, UL1 T R003107); National Institutes of Health, NIH; United States Agency for International Development, USAID; National Center for Advancing Translational Sciences, NCATS; National Institute on Minority Health and Health Disparities, NIMHD, (1K01MD018072); National Institute on Minority Health and Health Disparities, NIMHD; Arkansas Biosciences Institute, ABI","Research reported in this publication was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under award number KL2 T R003108 and UL1 T R003107. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Partial support for this project was provided by the AID/ABI/ACHI All Payer Claims Database. Cooperative Agreement. CCB was supported by the National Institute on Minority Health and Health Disparities (NIMHD) of the National Institutes of Health [1K01MD018072].","Reddel H.K., Taylor D.R., Bateman E.D., Boulet L.-P., Boushey H.A., Busse W.W., Casale T.B., Chanez P., Enright P.L., Gibson P.G., Et al., An Official American Thoracic Society/European Respiratory Society Statement: asthma control and exacerbations, Am J Respir Crit Care Med, 180, 1, pp. 59-99, (2009); Navanandan N., Hatoun J., Celedon J.C., Liu A.H., Predicting severe Asthma exacerbations in children: blueprint for today and tomorrow, J Allergy Clin Immunol Pract, 9, 7, pp. 2619-2626, (2021); Lalloo U.G., Malolepszy J., Kozma D., Krofta K., Ankerst J., Johansen B., Thomson N.C., Budesonide and formoterol in a single inhaler improves asthma control compared with increasing the dose of corticosteroid in adults with mild-to-moderate asthma, Chest, 123, 5, pp. 1480-1487, (2003); Reddel H.K., Jenkins C.R., Marks G.B., Ware S.I., Xuan W., Salome C.M., Badcock C.A., Woolcock A.J., Optimal asthma control, starting with high doses of inhaled budesonide, Eur Respir J, 16, 2, pp. 226-235, (2000); 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Miller M.K., Lee J.H., Miller D.P., Wenzel S.E., Recent asthma exacerbations: a key predictor of future exacerbations, Respir Med, 101, 3, pp. 481-489, (2007); Chipps B.E., Zeiger R.S., Borish L., Wenzel S.E., Yegin A., Hayden M.L., Miller D.P., Bleecker E.R., Simons F.E.R., Szefler S.J., Et al., Recent asthma exacerbations predict future exacerbations in children with severe or difficult-to-treat asthma, J Allergy Clin Immunol, 130, 2, pp. 332-342, (2012); Suissa S., Ernst P., Boivin J.F., Horwitz R.I., Habbick B., Cockroft D., Blais L., McNutt M., Buist A.S., Spitzer W.O., A cohort analysis of excess mortality in asthma and the use of inhaled beta-agonists, Am J Respir Crit Care Med, 149, 3, pp. 604-610, (1994); Frequent use of quick-relief medication among persons with active asthma | CDC; Farber H.J., Chi F.W., Capra A., Jensvold N.G., Finkelstein J.A., Lozano P., Quesenberry C.P., Lieu T.A., Use of asthma medication dispensing patterns to predict risk of adverse health outcomes: a study of Medicaid-insured children in managed care programs, Ann Allergy Asthma Immunol, 92, 3, pp. 319-328, (2004); Schatz M., Zeiger R.S., Vollmer W.M., Mosen D., Apter A.J., Stibolt T.B., Leong A., Johnson M.S., Mendoza G., Cook E.F., Validation of a β-agonist long-term asthma control scale derived from computerized pharmacy data, J Allergy Clin Immunol, 117, 5, pp. 995-1000, (2006); Heidari E., Zalmai R., Richards K., Sakthisivabalan L., Brown C., Z-code documentation to identify social determinants of health among Medicaid beneficiaries, Res Social Adm Pharm, 19, 1, pp. 180-183, (2023)","M. Rezaeiahari; College of Public Health, University of Arkansas for Medical Sciences, Little Rock, 4301 W. Markham St. Slot 820, 72205, United States; email: mrezaeiahari@uams.edu","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","37725084","English","J. Asthma","Article","Final","","Scopus","2-s2.0-85171791882"
"Cambiaso E.; Narteni S.; Baiardini I.; Braido F.; Paglialonga A.; Mongelli M.","Cambiaso, Enrico (55418311400); Narteni, Sara (57220574684); Baiardini, Ilaria (6603202824); Braido, Fulvio (8314050300); Paglialonga, Alessia (23668671800); Mongelli, Maurizio (7005882346)","55418311400; 57220574684; 6603202824; 8314050300; 23668671800; 7005882346","Advancements on IoT and AI applied to Pneumology","2024","Microprocessors and Microsystems","108","","105062","","","","0","10.1016/j.micpro.2024.105062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193857397&doi=10.1016%2fj.micpro.2024.105062&partnerID=40&md5=3f03d72b7ae4c078eb62ad8c8c98b86b","Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Corso F. M. Perrone 24, Genoa, 16152, Italy; University of Genova, Respiratory Critical Care Unit and Sleep Breathing Disorders, Largo R. Benzi 10, Genova, 16132, Italy; Politecnico di Torino - DAUIN Department, Corso Duca degli Abruzzi 24, Torino, 10129, Italy","Cambiaso E., Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Corso F. M. Perrone 24, Genoa, 16152, Italy; Narteni S., Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Corso F. M. Perrone 24, Genoa, 16152, Italy, Politecnico di Torino - DAUIN Department, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Baiardini I., University of Genova, Respiratory Critical Care Unit and Sleep Breathing Disorders, Largo R. Benzi 10, Genova, 16132, Italy; Braido F., University of Genova, Respiratory Critical Care Unit and Sleep Breathing Disorders, Largo R. Benzi 10, Genova, 16132, Italy; Paglialonga A., Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Corso F. M. Perrone 24, Genoa, 16152, Italy; Mongelli M., Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Corso F. M. Perrone 24, Genoa, 16152, Italy","The objective of this work is the design of a technological platform for remote monitoring of patients with Chronic Obstructive Pulmonary Disease (COPD). The concept of the framework is a breakthrough in the state of medical, scientific and technological art, aimed at engaging patients in the treatment plan and supporting interaction with healthcare professionals. The proposed platform is able to support a new paradigm for the management of patients with COPD, by integrating clinical data and parameters monitored in daily life using Artificial Intelligence algorithms. Therefore, the doctor is provided with a dynamic picture of the disease and its impact on lifestyle and vice versa, and can thus plan more personalized diagnostics, therapeutics, and social interventions. This strategy allows for a more effective organization of access to outpatient care and therefore a reduction of emergencies and hospitalizations because exacerbations of the disease can be better prevented and monitored. Hence, it can result in improvements in patients’ quality of life and lower costs for the healthcare system. © 2024 The Author(s)","Cyber-security; Healthcare; Intelligible analytics; Internet of Things; Machine learning; Statistical validation","Diagnosis; Internet of things; Patient treatment; Pulmonary diseases; Remote control; Chronic obstructive pulmonary disease; Cyber security; Healthcare; Intelligible analytic; Machine-learning; Pneumology; Remote monitoring; Statistical validation; Technological platform; Treatment plans; Machine learning","","","","","European Space Agency, ESA; Selex Communications S.p.A.; Consorzio Nazionale Interuniversitario per le Telecomunicazioni, CNIT; European Commission, EC; German Aerospace Center in Munich; Compagnia di San Paolo, CSP, (101112286); Compagnia di San Paolo, CSP","Funding text 1: Maurizio Mongelli (Member, IEEE) received the Ph.D. degree in electronics and computer engineering from the University of Genoa (UNIGE), in 2004. The doctorate was funded by Selex Communications S.p.A. (Selex). He worked for both Selex and the Italian Telecommunications Consortium (CNIT) from 2001 until 2010. During his doctorate and in the following years, he worked on the quality of service for military networks with Selex. From 2007 to 2008, he coordinated a joint laboratory between UniGe and Selex, dedicated to the study and prototype implementation of Ethernet resilience mechanisms. He was the CNIT Technical Coordinator of a research project concerning satellite emulation systems, funded by the European Space Agency; spent three months working on the project at the German Aerospace Center in Munich. Since 2012 he is a Researcher at the Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) of the National Research Council (CNR), where he deals with machine learning, bioinformatics and cyber security, having the responsibility and coordination, for the CNR part, of several funded projects in those sectors. He is co-author of over 100 international scientific papers and 2 patents.; Funding text 2: The work was supported by : Compagnia di San Paolo, scientific research call 2019 (Bando 2019\u20132020 per progetti di ricerca scientifica presentati da enti genovesi): project \u201CAdvances in pneumology via ICT and data analytics\u201D (PNEULYTICS); Bando incentivazione della progettazione europea 2021 - Mission \u201CPromoting Competitiveness\u201D (DR nr. 3386 of 26/07/2021 ); LoLiPoP-IoT project, nr. 101112286 funded by European Commission . 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Health Econ., 15, pp. 117-132, (2014); Mongelli M., Orani V., Cambiaso E., Vaccari I., Paglialonga A., Braido F., Catalano C.E., Challenges and opportunities of IoT and AI in pneumology, 2020 23rd Euromicro Conference on Digital System Design, DSD, pp. 285-292, (2020); Wang Y., Nekovee M., Khatib E.J., Barco R., Machine learning/AI as IoT enablers, Wirel. Netw. Ind. IoT Appl. Chall. 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Cambiaso; Cnr-Istituto di Elettronica, Ingegneria dell'Informazione e delle Telecomunicazioni (CNR-IEIIT), Genoa, Corso F. M. Perrone 24, 16152, Italy; email: enrico.cambiaso@cnr.it","","Elsevier B.V.","","","","","","01419331","","MIMID","","English","Microprocessors Microsyst","Article","Final","","Scopus","2-s2.0-85193857397"
"Feng X.; Wang D.; Pan Q.; Yan M.; Liu X.; Shen Y.; Fang L.; Cai G.; Ning G.","Feng, Xue (57226123148); Wang, Daoyuan (57824367900); Pan, Qing (57200677881); Yan, Molei (35770142900); Liu, Xiaoqing (57221422548); Shen, Yanfei (56856262500); Fang, Luping (16443890800); Cai, Guolong (25652845100); Ning, Gangmin (7006493943)","57226123148; 57824367900; 57200677881; 35770142900; 57221422548; 56856262500; 16443890800; 25652845100; 7006493943","Reinforcement Learning Model for Managing Noninvasive Ventilation Switching Policy","2023","IEEE Journal of Biomedical and Health Informatics","27","8","","4120","4130","10","0","10.1109/JBHI.2023.3274568","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159846357&doi=10.1109%2fJBHI.2023.3274568&partnerID=40&md5=c167b0c44adcabbde51201d0185d6bab","Zhejiang University, Department of Biomedical Engineering, Hangzhou, 310027, China; Zhejiang University of Technology, College of Information Engineering, Hangzhou, 310023, China; Zhejiang Hospital, Department of Intensive Care Unit, Hangzhou, 310009, China; Deepwise AI LAB, Beijing, 100080, China; Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China","Feng X., Zhejiang University, Department of Biomedical Engineering, Hangzhou, 310027, China; Wang D., Zhejiang University, Department of Biomedical Engineering, Hangzhou, 310027, China; Pan Q., Zhejiang University of Technology, College of Information Engineering, Hangzhou, 310023, China; Yan M., Zhejiang Hospital, Department of Intensive Care Unit, Hangzhou, 310009, China; Liu X., Deepwise AI LAB, Beijing, 100080, China; Shen Y., Zhejiang Hospital, Department of Intensive Care Unit, Hangzhou, 310009, China; Fang L., Zhejiang University of Technology, College of Information Engineering, Hangzhou, 310023, China; Cai G., Zhejiang Hospital, Department of Intensive Care Unit, Hangzhou, 310009, China; Ning G., Zhejiang University, Department of Biomedical Engineering, Hangzhou, 310027, China, Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, 311121, China","Noninvasive ventilation (NIV) has been recognized as a first-line treatment for respiratory failure in patients with chronic obstructive pulmonary disease (COPD) and hypercapnia respiratory failure, which can reduce mortality and burden of intubation. However, during the long-term NIV process, failure to respond to NIV may cause overtreatment or delayed intubation, which is associated with increased mortality or costs. Optimal strategies for switching regime in the course of NIV treatment remain to be explored.For the goal of reducing 28-day mortality of the patients undergoing NIV, Double Dueling Deep Q Network (D3QN) of offline-reinforcement learning algorithm was adopted to develop an optimal regime model for making treatment decisions of discontinuing ventilation, continuing NIV, or intubation. The model was trained and tested using the data from Multi-Parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) and evaluated by the practical strategies. Furthermore, the applicability of the model in majority disease subgroups (Catalogued by International Classification of Diseases, ICD) was investigated. Compared with physician's strategies, the proposed model achieved a higher expected return score (4.25 vs. 2.68) and its recommended treatments reduced the expected mortality from 27.82% to 25.44% in all NIV cases. In particular, for these patients finally received intubation in practice, if the model also supported the regime, it would warn of switching to intubation 13.36 hours earlier than clinicians (8.64 vs. 22 hours after the NIV treatment), granting a 21.7% reduction in estimated mortality. In addition, the model was applicable across various disease groups with distinguished achievement in dealing with respiratory disorders. The proposed model is promising to dynamically provide personalized optimal NIV switching regime for patients undergoing NIV with the potential of improving treatment outcomes. © 2013 IEEE.","NIV switching regime; offline-reinforcement learning; optimal policies","Critical Care; Humans; Noninvasive Ventilation; Policy; Pulmonary Disease, Chronic Obstructive; Respiratory Insufficiency; Treatment Outcome; Intensive care units; Learning algorithms; Pulmonary diseases; Reinforcement learning; Respiratory therapy; Ventilation; Intubation; Non-invasive ventilations; Noninvasive ventilation switching regime; Offline; Offline-reinforcement learning; Optimal policies; Predictive models; Reinforcement learnings; Respiratory failure; Article; artificial neural network; bootstrapping; chronic obstructive lung disease; data mining; diastolic blood pressure; disease burden; first-line treatment; heart rate; human; hypercapnia; intensive care; International Classification of Diseases; intubation; learning algorithm; machine learning; Markov decision process; mean arterial pressure; mortality; noninvasive ventilation; outcome assessment; oxygen saturation; physician; reinforcement (psychology); respiratory failure; support vector machine; systolic blood pressure; tidal volume; chronic obstructive lung disease; policy; respiratory failure; treatment outcome; Data mining","","","","","Key Research and Development Program of Zhejiang Province, (2020C03073); National Natural Science Foundation of China, NSFC, (31870938, 81871454)","This work was supported in part by the Zhejiang Province Key Research and Development Program under Grant 2020C03073, and in part by the National Natural Science Foundation of China under Grants 81871454 and 31870938.","Ko B.S., Ahn S., Lim K.S., Kim W.Y., Lee Y.-S., Lee J.H., Early failure of noninvasive ventilation in chronic obstructive pulmonary disease with acute hypercapnic respiratory failure, Intern. 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Learn, pp. 759-766, (2000); Thomas P., Brunskill E., Data-efficient off-policy policy evaluation for reinforcement learning, Proc. Int. Conf. Mach. Learn, pp. 2139-2148, (2016); Farajtabar M., Chow Y., Ghavamzadeh M., More robust doubly robust off-policy evaluation, Proc. Int. Conf. Mach. Learn, pp. 1447-1456, (2018); Jiang N., Li L., Doubly robust off-policy value evaluation for reinforcement learning, Proc. Int. Conf. Mach. Learn, pp. 652-661, (2016); Purushotham S., Meng C., Che Z., Liu Y., Benchmarking deep learning models on large healthcare datasets, J. Biomed. Inform, 83, pp. 112-134, (2018); Stefan M.S., Et al., Comparative effectiveness of noninvasive and invasive ventilation in critically ill patients with acute exacerbation of chronic obstructive pulmonary disease, Crit. Care Med, 43, 7, pp. 1386-1394, (2015); Lipton Z.C., Kale D.C., Elkan C., Wetzel R., Learning to diagnose with LSTM recurrent neural networks, Comput. Ence, (2015); Balami J.S., Packham S.M., Gosney M.A., Non-invasive ventilation for respiratory failure due to acute exacerbations of chronic obstructive pulmonary disease in older patients, Age Ageing, 35, 1, pp. 75-79, (2006); Sweet D.G., Et al., European consensus guidelines on the management of respiratory distress syndrome-2019 update, Neonatology, 115, 4, pp. 432-450, (2019)","G. Ning; Zhejiang University, Department of Biomedical Engineering, Hangzhou, 310027, China; email: gmning@zju.edu.cn; G. Cai; Zhejiang Hospital, Department of Intensive Care Unit, Hangzhou, 310009, China; email: caiguolong@126.com","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","37159312","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85159846357"
"Bogacz K.; Szczegielniak A.; Czekaj Ł.; Jarynowski A.; Kitłowski R.; Maksymowicz S.; Lietz-Kijak D.; Pańczyszak B.; Łuniewski J.; Krajczy E.; Lenczuk M.; Sahajdak J.; Kassolik K.; Kaliciński S.; Szczegielniak J.","Bogacz, Katarzyna (7801496459); Szczegielniak, Anna (55586445900); Czekaj, Łukasz (26425022300); Jarynowski, Andrzej (55341326800); Kitłowski, Robert (57349857400); Maksymowicz, Stanisław (57205165691); Lietz-Kijak, Danuta (13610982400); Pańczyszak, Bartosz (57201552538); Łuniewski, Jacek (25634266500); Krajczy, Edyta (56541641700); Lenczuk, Mirosław (58922306900); Sahajdak, Jacek (58923597200); Kassolik, Krzysztof (17135215500); Kaliciński, Szymon (58922949600); Szczegielniak, Jan (8645792200)","7801496459; 55586445900; 26425022300; 55341326800; 57349857400; 57205165691; 13610982400; 57201552538; 25634266500; 56541641700; 58922306900; 58923597200; 17135215500; 58922949600; 8645792200","Assessment of rehabilitation effectiveness in patients with COPD as part of the project “PulmoRehab – Access to healthcare services through a personalized care system for patients with COPD, including remote monitoring and tele-rehabilitation based on Artificial Intelligence methods” – Fizjoterapia Polska; [Ocena skuteczności rehabilitacji u pacjentów z POChP w ramach projektu „PulmoRehab – dostęp do usług zdrowotnych poprzez spersonalizowany system opieki nad pacjentami z POChP, który obejmuje zdalny monitoring i telerehabilitację opartą na metodach sztucznej inteligencji”]","2024","Fizjoterapia Polska","2024","1","","6","11","5","0","10.56984/8ZG2EF8D9D","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186858904&doi=10.56984%2f8ZG2EF8D9D&partnerID=40&md5=cf170100c47001aa80f9ae3fa26305b1","Physiotherapy Department, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Poland; Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland; Department of Psychoprophylaxis, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland; Aidmed, Poland; Independent Unit of Propaedeutic and Dental Physical Diagnostics, Pomeranian Medical University in Szczecin, Poland; Jan Długosz University in Częstochowa, Poland; Stobrawskie Medical Center, Poland; Rehabilitation Center in Nysa, Poland; Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Wroclaw, Poland","Bogacz K., Physiotherapy Department, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Poland, Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland; Szczegielniak A., Department of Psychoprophylaxis, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Poland; Czekaj Ł., Aidmed, Poland; Jarynowski A., Aidmed, Poland; Kitłowski R., Aidmed, Poland; Maksymowicz S., Aidmed, Poland; Lietz-Kijak D., Independent Unit of Propaedeutic and Dental Physical Diagnostics, Pomeranian Medical University in Szczecin, Poland; Pańczyszak B., Jan Długosz University in Częstochowa, Poland; Łuniewski J., Stobrawskie Medical Center, Poland; Krajczy E., Rehabilitation Center in Nysa, Poland; Lenczuk M., Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland; Sahajdak J., Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland; Kassolik K., Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Wroclaw, Poland; Kaliciński S., Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland; Szczegielniak J., Physiotherapy Department, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Poland, Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, Poland","Introduction. In the project “PulmoRehab – Access to Healthcare Services through a Personalized Patient Care System for COPD including Remote Monitoring and Tele-rehabilitation Based on Artificial Intelligence Methods,” SP ZOZ Specialized Hospital Ministry of Internal Affairs and Administration in Głuchołazy and 10 partners conducted research on tele-rehabilitation for people suffering from Chronic Obstructive Pulmonary Disease (COPD). The tele-rehabilitation program, based on personalized approach and technology, allowed patients to use therapy at home or a convenient location. The project aimed to reduce social inequalities in healthcare by providing remote access to health services for COPD patients. Materials and Methods. The study involved 80 patients aged 50 to 76 years, hospitalized at Ministry of Internal Affairs and Administration’s Specialist Hospital of St. John Paul II, meeting specific criteria. Qualification for the tele-rehabilitation program took into account the assessment of exercise tolerance, dyspnea, fatigue, lung ventilatory function, and saturation. The program included exercises adapted to the individual needs of the patients. Results. Analysis of the results conducted using XLSTAT software 2021.2.2 showed statistically significant differences between spirometric values before and after rehabilitation. Similarly, significant improvement was observed in the results of the 6MWT test and blood saturation. The tele-rehabilitation program proved effective in improving respiratory health parameters in COPD patients. Conclusions. 1. The tele-rehabilitation program improves lung ventilatory function in people with COPD. It reduces the feeling of dyspnea, decreases fatigue, improves SpO2, and increases exercise tolerance, demonstrating the beneficial effects of the rehabilitation applied. 2. The obtained results encourage further research on a representative group with the use of randomization. © 2024, DJ Studio. All rights reserved.","COPD; Tele-rehabilitation","","","","","","","","Czekaj t, Domaszewicz J, Radziriski t, Jarynowski A, Kittowski R, Doboszyriska A., Validation and usability of AIDMED-telemedical system for cardiological and pulmonary diseases, E-methodology, 7, 7, pp. 125-139, (2020); Czekaj L, Jarynowski A, Prusiak K., Enhancing Physical Activity Motivation in Pulmonary Patients with Artificial Intelligence (A Collaborative Approach to Telerehabilitation), E-methodology, (2023); Maksymowicz S, Jarynowski A, Czekaj t, Gesicki S, Romaszko-Wojtowicz A, Wojta-Kempa M, Doboszyriska A., Telemedicine as a socio-medical process. Experiences from remote monitoring of long-COVID patients in Poland, E-methodology, 8, 8, pp. 65-78, (2021); Romaszko-Wojtowicz A, Maksymowicz S, Jarynowski A, Jaskiewicz t, Czekaj t, Doboszyriska A., Telemonitoring in Long-COVID Patients—Preliminary Findings, International Journal of Environmental Research and Public Health, 19, 9, (2022); tuniewski J, Szczegielniak J, Krajczy M, Bogacz K., A New way of interpreting the results of the six-minute walk test with the use of the genetic algorithm, Physiotherapy, 97, (2011); tuniewski J, Szczegielniak J, Bogacz K, Migata M., Nowatorski projekt szpitalnej rehabilitacji osöb po przebytej chorobie COVID-19 w Szpitalu MSWiA w Gtuchotazach, Praktyczna fizjoterapia & rehabilitacja, 121, pp. 72-74, (2020); Rutkowska A, Rutkowski S, Wrzeciono A, Czech O, Szczegielniak J, Jastrzebski D., Short-Term Changes in Quality of Life in Patients with Advanced Lung Cancer during InHospital Exercise Training and Chemotherapy Treatment: A Randomized Controlled Trial, J Clin Med, 10, (2021); Rutkowska A, Kacperak K, Rutkowski S, Cacciante L, Kiper P, Szczegielniak J., The impact of isolation due to COVID-19 on physical activity levels in adult students, Sustainability, 13, (2021); Rutkowski S, Buekers J, Anna Rutkowska A, Cieslik B, Szczegielniak J., Monitoring Physical Activity with a Wearable Sensor in Patients with COPD during In-Hospital Pulmonary Rehabilitation Program: A Pilot Study, Sensors, 21, (2021); Rutkowski S, Rutkowska A, Jastrzebski D, Racheniuk H, Pawetczyk W, Szczegielniak J., Effect of Virtual Reality-Based Rehabilitation on Physical Fitness in Patients with Chronic Obstructive Pulmonary Disease, J Hum Kinet, 69, pp. 149-157, (2019); Rutkowski S, Rutkowska A, Kiper P, Jastrzebski D, Racheniuk H, Turolla A, Szczegielniak J, Casaburi R., Virtual Reality Rehabilitation in Patients with Chronic Obstructive Pulmonary Disease: A Randomized Controlled Trial, International Journal of Chronic Obstructive Pulmonary Disease, 15, pp. 117-124, (2020); Rutkowski S, Szczegielniak J, Szczepariska-Gieracha J., Evaluation of The Efficacy of Immersive Virtual Reality Therapy as a Method Supporting Pulmonary Rehabilitation: A Randomized Controlled Trial, J Clin Med, 10, 2, (2021); Sieroi A, Szczegielniak J., Postepowanie pocovidowe, Rehabilitacja w Praktyce, 3, pp. 9-10, (2021); Szczegielniak J, Latawiec K, tuniewski J, Stanislawski R, Bogacz K, Krajczy M, Rydel M., A study on nonlinear estimation of submaximal effort tolerance based on the generalized MET concept and the 6MWT in pulmonary rehabilitation, PLoS ONE, 13, 2, (2018); Szczegielniak J., Rekomendacje postepowania fizjoterapeutycznego, (2020); Szczegielniak J, Bogacz K, Krajczy M, tuniewski J., Testy wysitkowe stosowane w rehabilitacji chorych na POCHP, Fizjoterapia Polska, 3, 19, pp. 24-30, (2019); Szczegielniak J, Bogacz K, Majorczyk E, Szczegielniak A, tuniewski J., Post-COVID-19 rehabilitation - a Polish pilot program, Medycyna Pracy, 72, (2021); Szczegielniak J, tuniewski J, Krajczy M, Bogacz K., Modele rehabilitacji chorych na POCHP, Fizjoterapia Polska, 3, 19, pp. 126-137, (2019); Cox NS, Dal Corso S, Hansen H, McDonald CF, Hill CJ, Zanaboni P, Alison JA, O'Halloran P, Macdonald H, Holland AE., Telerehabilitation for chronic respiratory disease; Hansen H, Bieler T, Beyer N, Kallemose T, Wlcke JT, 0stergaard LM, Frost Andeassen H, Martinez G, Lavesen M, Fr0lich A, Godtfredsen NS., Supervised pulmonary tele-rehabilitation versus pulmonary rehabilitation in severe COPD: a randomised multicentre trial; Simony C, Riber C, Bodtger U, Birkelund R., Striving for Confidence and Satisfaction in Everyday Life with Chronic Obstructive Pulmonary Disease: Rationale and Content of the Tele-Rehabilitation Programme; Ergan B, Oczkowski S, Rochwerg B, Carlucci A, Chatwin M, Clini E, Elliott M, Gonzalez-Bermejo J, Hart N, Lujan M, Nasilowski J, Nava S, Pepin JL, Pisani L, Storre JH, Wijkstra P, Tonia T, Boyd J, Scala R, Windisch W., European Respiratory Society guidelines on long-term home non-invasive ventilation for management of COPD","K. Bogacz; Physiotherapy Department, Faculty of Physical Education and Physiotherapy, Opole University of Technology, Poland; email: k.bogacz@interia.pl","","DJ Studio","","","","","","16420136","","FPIOB","","English","Fizjoter. Pol.","Article","Final","","Scopus","2-s2.0-85186858904"
"Alfonso A.F.; Suárez K.M.G.; Bautista C.X.P.; Cantos R.M.C.; Cañar G.N.C.","Alfonso, Annabel Fernández (58991237700); Suárez, Katherine María Guevara (58991372100); Bautista, Cristian Xavier Proaño (58990541800); Cantos, Rosa Mercerdes Cantos (58991237800); Cañar, Gissela Nicool Calvache (58990818500)","58991237700; 58991372100; 58990541800; 58991237800; 58990818500","New perspectives on advances in diagnosis through imaging in chronic respiratory diseases: a systematic literature review; [Novas perspectivas sobre avanços no diagnóstico por imagem em doenças respiratórias crônicas: uma revisão sistemática da literatura]; [Nuevas perspectivas sobre los avances en el diagnóstico por imagen en enfermedades respiratorias crónicas: una revisión sistemática de la literatura]","2024","Sapienza","5","1","e24019","","","","0","10.51798/sijis.v5i1.717","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190687048&doi=10.51798%2fsijis.v5i1.717&partnerID=40&md5=3089854fc32ab6f4ecdb0a737717bfae","Universidad de Ciencias Médicas de la Habana, Cuba; Universidad de las Américas, Ecuador; Universidad Nacional de Chimborazo, Ecuador; Universidad Católica de Cuenca, Ecuador; Hospital Yarovi Makuar, Ecuador","Alfonso A.F., Universidad de Ciencias Médicas de la Habana, Cuba; Suárez K.M.G., Universidad de las Américas, Ecuador; Bautista C.X.P., Universidad Nacional de Chimborazo, Ecuador; Cantos R.M.C., Universidad Católica de Cuenca, Ecuador; Cañar G.N.C., Hospital Yarovi Makuar, Ecuador","Background: Chronic respiratory diseases, such as asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease, and cystic fibrosis, present substantial global health challenges. This systematic review explores recent advances in imaging-based diagnostic methods for these conditions, emphasizing high-resolution computed tomography (HRCT), magnetic resonance imaging (MRI), positron emission tomography (PET), and artificial intelligence (AI). Methodology: A systematic search of databases identified studies published in the last five years, focusing on innovative imaging techniques for chronic respiratory diseases. Inclusion criteria emphasized diagnostic accuracy and advancements in imaging modalities. Results: Seven studies were included, covering interventions in intensive care, mesenchymal stem cell therapy for COVID-19-induced acute respiratory distress syndrome (ARDS), endovascular treatment for aortic arch aneurysms, and lung cancer screening. MSC therapy demonstrated positive outcomes in ARDS patients, while endovascular repair showed technical success. LungSEARCH highlighted the effectiveness of lung cancer screening in high-risk populations. Discussion: Recent imaging technologies, including HRCT, MRI, PET, and AI, have revolutionized chronic respiratory disease diagnosis. The review emphasizes their clinical applications, impact on patient outcomes, and potential for personalized medicine. AI enhances image analysis accuracy, yet challenges like cost and interpretation discrepancies persist. Conclusion: Imaging technologies, particularly HRCT, MRI, PET, and AI, show promise in improving diagnostic accuracy and personalized treatment for chronic respiratory diseases. Collaboration among healthcare professionals, researchers, and industry stakeholders is crucial for addressing challenges and ensuring widespread access to advanced diagnostic tools. Future directions involve refining imaging methods for routine clinical integration, advancing patient care, and reducing the burden of respiratory diseases. © 2024, Sapienza Grupo Editorial. All rights reserved.","Artificial intelligence; Chronic respiratory diseases; Diagnosis; Imaging technologies; Treatment","","","","","","","","Almalki W. H., Introduction to Lung Disease, Microbiome in Inflammatory Lung Diseases, pp. 1-12, (2022); Bartholmai B. J., Raghunath S., Karwoski R. A., Moua T., Rajagopalan S., Maldonado F., Robb R. A. J. J. o. t. i., Quantitative CT imaging of interstitial lung diseases, 28, 5, (2013); Cohade C., Wahl R. L., Applications of positron emission tomography/computed tomography image fusion in clinical positron emission tomography— clinical use, interpretation methods, diagnostic improvements, (2003); Dupuis J., Harel F., Nguyen Q. T. J. C., Imaging T., Molecular imaging of the pulmonary circulation in health and disease, 2, pp. 415-426, (2014); Fekete M., Fazekas-Pongor V., Balazs P., Tarantini S., Nemeth A. N., Varga J. T. J. W. K. 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L., Hansell D. M., High-resolution CT of interstitial lung disease: a continuous evolution, (2014); Washko G. R., Parraga G. J. E. R. J., COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging, 52, 5, (2018); Weatherley N. D., Eaden J. A., Stewart N. J., Bartholmai B. J., Swift A. J., Bianchi S. M., Wild J. M. J. T., Experimental and quantitative imaging techniques in interstitial lung disease, (2019); Wong J., Tenorio E. R., Lima G., Dias-Neto M., Baghbani-Oskouei A., Mendes B., Radiology I., Early Feasibility of Endovascular Repair of Distal Aortic Arch Aneurysms Using Patient-Specific Single Retrograde Left Subclavian Artery Branch Stent Graft, 46, 2, pp. 249-254, (2023); Yu Y., Jain B., Anand G., Heidarian M., Lowe A., Kalra A. J. B., Technologies for non-invasive physiological sensing: Status, challenges, and future horizons, (2023); Zhou S. K., Greenspan H., Davatzikos C., Duncan J. S., Van Ginneken B., Madabhushi A., Summers R. M. J. P. o. t. I., A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises, 109, 5, pp. 820-838, (2021)","A.F. Alfonso; Universidad de Ciencias Médicas de la Habana, Cuba; email: annabelfdez1832@gmail.com","","Sapienza Grupo Editorial","","","","","","26759780","","","","English","Sapienza","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85190687048"
"Pan Z.; Liao S.; Sun W.; Zhou H.; Lin S.; Chen D.; Jiang S.; Long H.; Fan J.; Deng F.; Zhang W.; Chen B.; Wang J.; Huang Y.; Li J.; Chen Y.","Pan, Zihan (57202907408); Liao, Sha (57668138600); Sun, Wanlu (57213928646); Zhou, Haoyi (57196123986); Lin, Shuo (58914010500); Chen, Dian (57211445379); Jiang, Simin (57669138600); Long, Huanyu (57219575584); Fan, Jing (57202666941); Deng, Furong (7102493923); Zhang, Wenlou (57209331688); Chen, Baiqi (57208555286); Wang, Junyi (57557029100); Huang, Yongwei (57216166097); Li, Jianxin (55720560100); Chen, Yahong (7601429768)","57202907408; 57668138600; 57213928646; 57196123986; 58914010500; 57211445379; 57669138600; 57219575584; 57202666941; 7102493923; 57209331688; 57208555286; 57557029100; 57216166097; 55720560100; 7601429768","Screening and early warning system for chronic obstructive pulmonary disease with obstructive sleep apnoea based on the medical Internet of Things in three levels of healthcare: Protocol for a prospective, multicentre, observational cohort study","2024","BMJ Open","14","2","","","","","0","10.1136/bmjopen-2023-075257","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186445780&doi=10.1136%2fbmjopen-2023-075257&partnerID=40&md5=ca310a4d8748156e35a495b425c37e54","Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; General Practice Medicine, Peking University, First Hospital, Beijing, China; Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Beijing, China; School of Software, Beihang University, Beijing, China; Air Liquide Healthcare (Beijing), Beijing, China; Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China; Sleep Monitoring Center, Peking University, Third Hospital, Beijing, China; School of Computer Science and Engineering, Beihang University, Beijing, China","Pan Z., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China, General Practice Medicine, Peking University, First Hospital, Beijing, China; Liao S., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; Sun W., Department of Pulmonary and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Beijing, China; Zhou H., School of Software, Beihang University, Beijing, China; Lin S., Air Liquide Healthcare (Beijing), Beijing, China; Chen D., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; Jiang S., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; Long H., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; Fan J., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; Deng F., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China; Zhang W., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China; Chen B., Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China; Wang J., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China, Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China; Huang Y., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China, Sleep Monitoring Center, Peking University, Third Hospital, Beijing, China; Li J., School of Computer Science and Engineering, Beihang University, Beijing, China; Chen Y., Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China","Introduction Chronic obstructive pulmonary disease (COPD) and obstructive sleep apnoea (OSA) are prevalent respiratory diseases in China and impose significant burdens on the healthcare system. Moreover, the co-occurrence of COPD and OSA exacerbates clinical outcomes significantly. However, comprehensive epidemiological investigations in China remain scarce, and the defining characteristics of the population affected by COPD and OSA, alongside their intrinsic relationship, remain ambiguous. Methods and analysis We present a protocol for a prospective, multicentre, observational cohort study based on a digital health management platform across three different healthcare tiers in five sites among Chinese patients with COPD. The study aims to establish predicative models to identify OSA among patients with COPD and to predict the prognosis of overlap syndrome (OS) and acute exacerbations of COPD through the Internet of Things (IoT). Moreover, it aims to evaluate the feasibility, effectiveness and cost-effectiveness of IoT in managing chronic diseases within clinical settings. Participants will undergo baseline assessment, physical examination and nocturnal oxygen saturation measuring. Specific questionnaires screening for OSA will also be administered. Diagnostic lung function tests and polysomnography will be performed to confirm COPD and OSA, respectively. All patients will undergo scheduled follow-ups for 12 months to record the changes in symptoms, lung functions and quality of life. Primary outcomes include the prevalence and characteristics of OS, while secondary outcomes encompass OS prognosis and the feasibility of the management model in clinical contexts. A total of 682 patients with COPD will be recruited over 12-24 months. Ethics and dissemination The study has been approved by Peking University Third Hospital, and all study participants will provide written informed consent. Study results will be published in an appropriate journal and presented at national and international conferences, as well as relevant social media and various stakeholder engagement activities. Trial registration number NCT04833725.  © 2023. Sociedade Brasileira de Neurocirurgia. All rights reserved.","Chronic airways disease; Clinical Trial; Primary Care; Pulmonary Disease, Chronic Obstructive; SLEEP MEDICINE; Telemedicine","Cohort Studies; Delivery of Health Care; Humans; Internet of Things; Multicenter Studies as Topic; Observational Studies as Topic; Prospective Studies; Pulmonary Disease, Chronic Obstructive; Quality of Life; Sleep Apnea, Obstructive; adult; aged; Article; blood oxygen tension; China; Chinese; chronic obstructive lung disease; clinical outcome; clinical trial; cohort analysis; community care; controlled study; cost effectiveness analysis; digital health; evaluation study; female; follow up; forced expiratory volume; health care facility; health care management; health care personnel; health care system; hospital admission; human; information processing; information storage; informed consent; internet of things; lung function; lung function test; machine learning; major clinical study; male; medical research; multicenter study; neck circumference; observational study; obstructive sleep apnea; oxygen saturation; physical examination; polysomnography; prevalence; primary medical care; prospective study; quality of life; quality of life assessment; sample size; secondary care center; sleep medicine; social media; stakeholder engagement; telemedicine; chronic obstructive lung disease; complication; health care delivery; multicenter study (topic); sleep apnea syndromes","","","","","BOE Technology Group and Air Liquide's VitalAire; Beijing Science and Technology New Star Program, (20220484157); Capital Health Development Research Project, (2020-2Z-40917); Peking University, PKU, (BYSYDL2021013, HDCXZHKC2021206); Peking University, PKU","Funding text 1: We gratefully acknowledge the research centres and patients involved in the study and the support from BOE Technology Group and Air Liquide's VitalAire (Beijing). The funders did not have a role in the design, data collection and analysis, decision to publish, or manuscript preparation. ; Funding text 2: The study was funded by the Capital Health Development Research Project (2020-2Z-40917), Beijing Science and Technology New Star Program (20220484157), Proof of Concept Program of Zhongguancun Science City and Peking University Third Hospital (HDCXZHKC2021206), and Clinical Cohort Construction Program of Peking University Third Hospital (BYSYDL2021013). ","Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease, (2020); World Health Statistics 2017-monitoring Health for the SDGs [EB/OL] N.d. 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Chen; Pulmonary and Critical Care Medicine, Peking University, Third Hospital, Beijing, China; email: chenyahong@vip.sina.com; J. Li; School of Computer Science and Engineering, Beihang University, Beijing, China; email: chenyahong@vip.sina.com","","BMJ Publishing Group","","","","","","20446055","","","38418236","English","BMJ Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85186445780"
"Zheng H.; Wang G.; Wang Y.; Wang Q.; Sun T.","Zheng, Huiyan (57739689600); Wang, Guifeng (58934792000); Wang, Yunlai (58934490600); Wang, Qixian (58934490700); Sun, Ting (57739561700)","57739689600; 58934792000; 58934490600; 58934490700; 57739561700","Combined analysis of bulk RNA and single-cell RNA sequencing to identify pyroptosis-related markers and the role of dendritic cells in chronic obstructive pulmonary disease","2024","Heliyon","10","6","e27808","","","","0","10.1016/j.heliyon.2024.e27808","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187573070&doi=10.1016%2fj.heliyon.2024.e27808&partnerID=40&md5=2f40e19661d30c1069949e6e837770b9","Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China","Zheng H., Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Wang G., Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Wang Y., Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Wang Q., Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China; Sun T., Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, Hangzhou, China","Chronic obstructive pulmonary disease (COPD) is characterized by dyspnea caused by airflow limitation. Further development may lead to decreased lung function and other lung diseases. Pyroptosis is a type of programmed cell death that involves multiple pathways. For example, the pathway induced by the NLR family pyrin domain containing 3 (NLRP3) inflammasome is closely associated with COPD exacerbation. Therefore, in this study, various machine learning algorithms were applied to screen for diagnostically relevant pyroptosis-related genes from the GEO dataset, and the results were verified using external datasets. The results showed that deep neural networks and logistic regression algorithms had the highest AUC of 0.91 and 0.74 in the internal and external test sets, respectively. Here, we explored the immune landscape of COPD using diagnosis-related genes. We found that the infiltrating abundance of dendritic cells significantly differed between the COPD and control groups. Finally, the communication patterns of each cell type were explored based on scRNA-seq data. The critical role of significant pathways involved in communication between DCS and other cell populations in the occurrence and progression of COPD was identified. © 2024 The Authors","Biomarkers; Chronic obstructive pulmonary disease; Dendritic cell; Diagnostic model; Immune infiltration; Machine learning","","","","","","Basic Public Welfare Research Program of Zhejiang Province, (LGF22H070001)","The study was supported by Zhejiang Basic Public Welfare Research Program (LGF22H070001).","Sun S., Shen Y., Wang J., Li J., Cao J., Zhang J., Identification and validation of autophagy-related genes in chronic obstructive pulmonary disease, Int. J. Chronic Obstr. Pulm. Dis., 16, pp. 67-78, (2021); Lin Z., Xu Y., Guan L., Qin L., Ding J., Zhang Q., Zhou L., Seven ferroptosis-specific expressed genes are considered as potential biomarkers for the diagnosis and treatment of cigarette smoke-induced chronic obstructive pulmonary disease, Ann. Transl. 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Soc., 7, pp. 84-90, (2010); Maghsoudloo M., Azimzadeh Jamalkandi S., Najafi A., Masoudi-Nejad A., Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis, Mol. Med. (Camb.), 26, (2020); Tsai K.Y.F., Hirschi Budge K.M., Llavina S., Davis T., Long M., Bennett A., Sitton B., Arroyo J.A., Reynolds P.R., RAGE and AXL expression following secondhand smoke (SHS) exposure in mice, Exp. Lung Res., 45, pp. 297-309, (2019); Vasudevan S., Vasquez J.J., Chen W., Aguilar-Rodriguez B., Niemi E.C., Zeng S., Tamaki W., Nakamura M.C., Arjomandi M., Lower PDL1, PDL2, and AXL expression on lung myeloid cells suggests inflammatory bias in smoking and chronic obstructive pulmonary disease, Am. J. Respir. Cell Mol. Biol., 63, pp. 780-793, (2020)","T. Sun; Department of Health Management Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; email: 1195037@zju.edu.cn","","Elsevier Ltd","","","","","","24058440","","","","English","Heliyon","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85187573070"
"Gunawardana J.R.N.A.; Viswakula S.D.; Rannan-Eliya R.P.; Wijemunige N.","Gunawardana, J.R.N.A. (57221543719); Viswakula, S.D. (59157919700); Rannan-Eliya, Ravindra P (14919790700); Wijemunige, Nilmini (57202706438)","57221543719; 59157919700; 14919790700; 57202706438","Machine learning approaches for asthma disease prediction among adults in Sri Lanka","2024","Health Informatics Journal","30","3","","","","","0","10.1177/14604582241283968","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204036956&doi=10.1177%2f14604582241283968&partnerID=40&md5=25100b70dd75ad4289a43988361fecc0","Institute for Health Policy, Sri Lanka; Department of Statistics, University of Colombo, Sri Lanka; Robert Gordon University, United Kingdom","Gunawardana J.R.N.A., Institute for Health Policy, Sri Lanka, Robert Gordon University, United Kingdom; Viswakula S.D., Department of Statistics, University of Colombo, Sri Lanka; Rannan-Eliya R.P., Institute for Health Policy, Sri Lanka; Wijemunige N., Institute for Health Policy, Sri Lanka","Objectives: Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases. © The Author(s) 2024.","asthma; classification; disease prediction; LightGBM; logistic regression; machine learning","Adult; Aged; Algorithms; Asthma; Female; Humans; Logistic Models; Machine Learning; Male; Middle Aged; Sri Lanka; adult; aged; algorithm; asthma; diagnosis; female; human; machine learning; male; middle aged; Sri Lanka; statistical model","","","","","Kotelawala Defence University; Direktion für Entwicklung und Zusammenarbeit, DEZA; Ministry of Health, MOH; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF, (400640_160374); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Institute for Health Policy Public Interest Research Fund, (PIRF-2018-02)","Funding text 1: The authors thank their colleagues in the SLHAS consortium, consisting of the Institute for Health Policy, University of Colombo, University of Ruhuna and University of Peradeniya, for their input to the design of the survey tools and support of data collection, in particular Dr Renuka Jayatissa (Medical Research Institute) whose staff provided training in anthropometric measurement; colleagues in the Ministry of Health, who facilitated the SLHAS, especially Dr S Sridharan, Deputy Director General (Planning); and Dr Anuji Gamage (Kotelawala Defence University). The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Swiss Agency for Development Cooperation (SDC) and the Swiss National Science Foundation (SNSF) through the Swiss Programme for Research on Global Issues for Development (r4d programme) by the grant \u201CInclusive Social Protection for Chronic Health Problems\u201D (Grant number 400640_160374), and the Institute for Health Policy Public Interest Research Fund (Grant number PIRF-2018-02).; Funding text 2: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Swiss Agency for Development Cooperation (SDC) and the Swiss National Science Foundation (SNSF) through the Swiss Programme for Research on Global Issues for Development (r4d programme) by the grant \u201CInclusive Social Protection for Chronic Health Problems\u201D (Grant number 400640_160374), and the Institute for Health Policy Public Interest Research Fund (Grant number PIRF-2018-02). 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Gunawardana; Institute for Health Policy, Sri Lanka; email: nishaniamalka@gmail.com","","SAGE Publications Ltd","","","","","","14604582","","HIJEA","39262121","English","Health Informatics J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85204036956"
"Wu J.; Lu Y.; Dong S.; Wu L.; Shen X.","Wu, Ji (57221109002); Lu, Yao (59188009900); Dong, Sunbin (59187397100); Wu, Luyang (59187808500); Shen, Xiping (58023028300)","57221109002; 59188009900; 59187397100; 59187808500; 58023028300","Predicting COPD exacerbations based on quantitative CT analysis: an external validation study","2024","Frontiers in Medicine","11","","1370917","","","","0","10.3389/fmed.2024.1370917","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196849974&doi=10.3389%2ffmed.2024.1370917&partnerID=40&md5=be8f72a1785f52510ce91b63fc78fd79","Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China; Department of Anesthesia, Fifth People's Hospital of Wujiang District, Suzhou, China; Department of General Medicine, Municipal Hospital, Suzhou, China","Wu J., Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China; Lu Y., Department of Anesthesia, Fifth People's Hospital of Wujiang District, Suzhou, China; Dong S., Department of General Medicine, Municipal Hospital, Suzhou, China; Wu L., Department of General Medicine, Municipal Hospital, Suzhou, China; Shen X., Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China","Purpose: Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Methods: Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. Results: A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82–0.92) in the internal cohort and 0.86 (95%CI, 0.81–0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. Conclusion: The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations. Copyright © 2024 Wu, Lu, Dong, Wu and Shen.","body composition; chronic obstructive pulmonary disease; deep learning system; image analysis; skeletal muscle","biological marker; aged; airway obstruction; Article; body composition; body mass; chronic obstructive lung disease; cohort analysis; computer assisted tomography; controlled study; deep learning; dyspnea; exercise; female; follow up; human; image analysis; intra-abdominal fat; lung volume; major clinical study; male; pectoral muscle; prediction; receiver operating characteristic; retrospective study; risk factor; skeletal muscle; survival analysis; thin layer chromatography; thorax deformity; total lung capacity; validation study","","","","","Scientific Research Foundation of Suzhou Ninth Hospital Affiliated to Soochow University, (YK202330)","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the Scientific Research Foundation of Suzhou Ninth Hospital Affiliated to Soochow University (no. YK202330). ","Oelsner E.C., Balte P.P., Bhatt S.P., Cassano P.A., Couper D., Folsom A.R., Et al., Lung function decline in former smokers and low-intensity current smokers: a secondary data analysis of the NHLBI pooled cohorts study, Lancet Respir Med, 8, pp. 34-44, (2020); Christenson S.A., Smith B.M., Bafadhel M., Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, pp. 2227-2242, (2022); Mathioudakis A.G., Janssens W., Sivapalan P., Singanayagam A., Dransfield M.T., Jensen J.U.S., Et al., Acute exacerbations of chronic obstructive pulmonary disease: in search of diagnostic biomarkers and treatable traits, Thorax, 75, pp. 520-527, (2020); MacLeod M., Papi A., Contoli M., Beghe B., Celli B.R., Wedzicha J.A., Et al., Chronic obstructive pulmonary disease exacerbation fundamentals: diagnosis, treatment, prevention and disease impact, Respirology (Carlton, Vic), 26, pp. 532-551, (2021); Dmg H., Criner G.J., Papi A., Singh D., Anzueto A., Martinez F.J., Et al., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. 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Oh A.S., Baraghoshi D., Lynch D.A., Ash S.Y., Crapo J.D., Humphries S.M., Et al., Emphysema progression at CT by deep learning predicts functional impairment and mortality: results from the COPDGene study, Radiology, 304, pp. 672-679, (2022); Lopez K., Li H., Lipkin-Moore Z., Kay S., Rajeevan H., Davis J.L., Et al., Deep learning prediction of hospital readmissions for asthma and COPD, Respir Res, 24, (2023); Smith L.A., Oakden-Rayner L., Bird A., Zeng M., To M.S., Mukherjee S., Et al., Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis, Lancet Digit Health, 5, pp. e872-e881, (2023); Moor M., Bennett N., Plecko D., Horn M., Rieck B., Meinshausen N., Et al., Predicting sepsis using deep learning across international sites: a retrospective development and validation study, EClinicalMedicine, 62, (2023); Ohliger M.A., Body composition at CT and risk of future disease, Radiology, 306, (2023); Keyl J., Hosch R., Berger A., Ester O., Greiner T., Bogner S., Et al., Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer, J Cachexia Sarcopenia Muscle, 14, pp. 545-552, (2023); Salman R., Sammer M.B., Serrallach B.L., Sangi-Haghpeykar H., Annapragada A.V., Paul Guillerman R., Lower skeletal muscle mass on CT body composition analysis is associated with adverse clinical course and outcome in children with COVID-19, Radiol Med, 127, pp. 440-448, (2022); Makimoto K., Hogg J.C., Bourbeau J., Tan W.C., Kirby M., CT imaging with machine learning for predicting progression to COPD in individuals at risk, Chest, 164, pp. 1139-1149, (2023); Moll M., Qiao D., Regan E.A., Hunninghake G.M., Make B.J., Tal-Singer R., Et al., Machine learning and prediction of all-cause mortality in COPD, Chest, 158, pp. 952-964, (2020); Shimizu K., Tanabe N., Tho N.V., Suzuki M., Makita H., Sato S., Et al., Per cent low attenuation volume and fractal dimension of low attenuation clusters on CT predict different long-term outcomes in COPD, Thorax, 75, pp. 116-122, (2020); Celli B.R., Cote C.G., Marin J.M., Casanova C., Montes de Oca M., Mendez R.A., Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, pp. 1005-1012, (2004); Sanchez-Salcedo P., de Torres J.P., BODE index: a good quality of life marker in chronic obstructive pulmonary disease patients, Archivos de bronconeumologia, 51, pp. 311-312, (2015); Ko F.W., Chan K.P., Hui D.S., Goddard J.R., Shaw J.G., Reid D.W., Et al., Acute exacerbation of COPD, Respirology (Carlton, Vic), 21, pp. 1152-1165, (2016); Schroeder J.D., McKenzie A.S., Zach J.A., Wilson C.G., Curran-Everett D., Stinson D.S., Et al., Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease, AJR Am J Roentgenol, 201, pp. W460-W470, (2013); Negroni D., Zagaria D., Paladini A., Falaschi Z., Arcoraci A., Barini M., Et al., COVID-19 CT scan lung segmentation: how we do it, J Digit Imaging, 35, pp. 424-431, (2022); Hofmanninger J., Prayer F., Pan J., Rohrich S., Prosch H., Langs G., Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem, Eur Radiol Exp, 4, (2020); Viertel M., Bock C., Reich M., Loser S., Plauth M., Performance of CT-based low skeletal muscle index, low mean muscle attenuation, and bioelectric impedance derived low phase angle in the detection of an increased risk of nutrition related mortality, Clin Nutr, 38, pp. 2375-2380, (2019); Huang Y., Lu Y., Huang Y.-M., Wang M., Ling W., Sui Y., Et al., Obesity in patients with COVID-19: a systematic review and meta-analysis, Metabolism, 113, (2020); Bear D.E., MacGowan L., Elstad M., Puthucheary Z., Connolly B., Wright R., Et al., Relationship between skeletal muscle area and density and clinical outcome in adults receiving Venovenous extracorporeal membrane oxygenation, Crit Care Med, 49, pp. e350-e359, (2021); Lee M.H., Zea R., Garrett J.W., Graffy P.M., Summers R.M., Pickhardt P.J., Abdominal CT body composition thresholds using automated AI tools for predicting 10-year adverse outcomes, Radiology, 306, (2023); Xu K., Khan M.S., Li T.Z., Gao R., Terry J.G., Huo Y., Et al., AI body composition in lung Cancer screening: added value beyond lung Cancer detection, Radiology, 308, (2023); Franssen F.M.E., O'Donnell D.E., Goossens G.H., Blaak E.E., Schols A.M.W.J., Obesity and the lung: 5, Obesity COPD Thorax, 63, pp. 1110-1117, (2008); Wouters E.F.M., Obesity and metabolic abnormalities in chronic obstructive pulmonary disease, Ann Am Thorac Soc, 14, pp. S389-S394, (2017); Kisiel M.A., Arnfelt O., Lindberg E., Jogi O., Malinovschi A., Johannessen A., Et al., Association between abdominal and general obesity and respiratory symptoms, asthma and COPD. Results from the RHINE study, Respir Med, 211, (2023); Tolonen A., Pakarinen T., Sassi A., Kytta J., Cancino W., Rinta-Kiikka I., Et al., Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: a review, Eur J Radiol, 145, (2021); van Bakel S.I.J., Gietema H.A., Stassen P.M., Gosker H.R., Gach D., van den Bergh J.P., Et al., CT scan-derived muscle, but not fat, area independently predicts mortality in COVID-19, Chest, 164, pp. 314-322, (2023); Celli B.R., Anderson J.A., Cowans N.J., Crim C., Hartley B.F., Martinez F.J., Et al., Pharmacotherapy and lung function decline in patients with chronic obstructive pulmonary disease. A systematic review, Am J Respir Crit Care Med, 203, pp. 689-698, (2021); Marin J.M., Carrizo S.J., Casanova C., Martinez-Camblor P., Soriano J.B., Agusti A.G., Et al., Prediction of risk of COPD exacerbations by the BODE index, Respir Med, 103, pp. 373-378, (2009); Han M.K., Quibrera P.M., Carretta E.E., Barr R.G., Bleecker E.R., Bowler R.P., Et al., Frequency of exacerbations in patients with chronic obstructive pulmonary disease: an analysis of the SPIROMICS cohort, Lancet Respir Med, 5, pp. 619-626, (2017)","X. Shen; Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China; email: shenxiping2022@163.com","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196849974"
"Mekov E.; Miravitlles M.; Topalovic M.; Singanayagam A.; Petkov R.","Mekov, Evgeni (56708921200); Miravitlles, Marc (57203200679); Topalovic, Marko (55931197500); Singanayagam, Aran (58326971200); Petkov, Rosen (8702203400)","56708921200; 57203200679; 55931197500; 58326971200; 8702203400","Stepping Up the Personalized Approach in COPD with Machine Learning","2023","Current Respiratory Medicine Reviews","19","3","","165","169","4","0","10.2174/1573398X19666230607115316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172346416&doi=10.2174%2f1573398X19666230607115316&partnerID=40&md5=9c62dead08b34262994d6d0d31bd8091","Department of Occupational Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria; Pneumology Department, Hospital Universitari Vall d´Hebron/Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Campus, CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain; ArtiQ NV, Leuven, Belgium; Centre for Molecular Bacteriology and Infection, Imperial College London, United Kingdom; Department of Respiratory Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria","Mekov E., Department of Occupational Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria; Miravitlles M., Pneumology Department, Hospital Universitari Vall d´Hebron/Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Hospital Campus, CIBER de Enfermedades Respiratorias (CIBERES), Barcelona, Spain; Topalovic M., ArtiQ NV, Leuven, Belgium; Singanayagam A., Centre for Molecular Bacteriology and Infection, Imperial College London, United Kingdom; Petkov R., Department of Respiratory Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria","Introduction: There is increasing interest in the application of artificial intelligence (AI) and machine learning (ML) in all fields of medicine to facilitate greater personalisation of management. Methods: ML could be the next step of personalized medicine in chronic obstructive pulmonary disease (COPD) by giving the exact risk (risk for exacerbation, death, etc.) of every patient (based on his/her parameters like lung function, clinical data, demographics, previous exacerbations, etc.), thus providing a prognosis/risk for the specific patient based on individual characteristics (individu-al approach). Result: ML algorithm might utilise some traditional risk factors along with some others that may be location-specific (e.g. the risk of exacerbation thatmay be related to ambient pollution but that could vary massively between different countries, or between different regions of a particular country). Conclusion: This is a step forward from the commonly used assignment of patients to a specific group for which prognosis/risk data are available (group approach). © 2023 Bentham Science Publishers.","COPD; Exacerbations; GOLD; Machine learning; Mortality; Prediction","acetylsalicylic acid; anticoagulant agent; bronchodilating agent; accuracy; algorithm; Article; artificial intelligence; asthma; blood cell count; chronic bronchitis; chronic obstructive lung disease; controlled study; coronary artery disease; decision tree; demographics; diagnostic test accuracy study; diaphragm movement; disease duration; disease exacerbation; disease severity; dyslipidemia; forced expiratory volume; forced vital capacity; health care personnel; heart failure; heart rate; hematocrit; human; learning algorithm; lung function; machine learning; mortality; neutrophil lymphocyte ratio; oxygen consumption; oxygen saturation; personalized medicine; physical capacity; pollution; prediction; prognosis; risk assessment; risk factor; smoking; spirometry; telemonitoring; training; vaccination","","acetylsalicylic acid, 493-53-8, 50-78-2, 53663-74-4, 53664-49-6, 63781-77-1","","","","","Global strategy for the diagnosis, management and prevention of COPD, (2023); Mekov E, Miravitlles M, Petkov R., Artificial intelligence and machine learning in respiratory medicine, Expert Rev Respir Med, 14, 6, pp. 559-564, (2020); James G, Witten D, Hastie T, Tibshirani R., An introduction to statistical learning: With applications in R, (2013); Sethi S., Personalised medicine in exacerbations of copd: The be-ginnings, Eur Respir J, 40, 6, pp. 1318-1319, (2012); Agusti A., The path to personalised medicine in COPD, Thorax, 69, 9, pp. 857-864, (2014); McDonald VM, Fingleton J, Agusti A, Et al., participants of the Treatable Traits Down Under International Workshop; Treatable Traits Down Under International Workshop participants. Treatable traits: A new paradigm for 21st century management of chronic airway diseases: Treatable traits down under international workshop report, Eur Respir J, 53, 5, (2019); Franssen FME, Alter P, Bar N, Et al., Perlth COPD: Where are we?, Int J Chron Obstruct Pulmon Dis, 14, pp. 1465-1484, (2019); Verstraete K, Das N, Gyselinck I, De Vos M, Janssens W., Machine learning for estimating individual treatment effects in randomized controlled trials, Eur Respir J, 58, 65, (2021); Gonem S, Janssens W, Das N, Topalovic M., Applications of artificial intelligence and machine learning in respiratory medicine, Thorax, 75, 8, pp. 695-701, (2020); Rokach L., Ensemble-based classifiers, Artif Intell Rev, 33, 1-2, pp. 1-39, (2010); Mekov E, Nunez A, Sin DD, Et al., Update on asthma–copd overlap (aco): A narrative review, Int J Chron Obstruct Pulmon Dis, 16, pp. 1783-1799, (2021); Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A, Crespo LF., Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms, Biomed Mater Eng, 24, 6, pp. 3825-3832, (2014); Kor CT, Li YR, Lin PR, Lin SH, Wang BY, Lin CH., Explainable machine learning model for predicting first-time acute exacerbation in patients with chronic obstructive pulmonary disease, J Pers Med, 12, 2, (2022); Hussain A, Choi HE, Kim HJ, Aich S, Saqlain M, Kim HC., Fore-cast the exacerbation in patients of chronic obstructive pulmonary disease with clinical indicators using machine learning techniques, Diagnostics, 11, 5, (2021); Zhudenkov K, Palmer R, Jauhiainen A, Et al., Longitudinal FEV<sub>1</sub> and exacerbation risk in copd: Quantifying the association using joint modelling, Int J Chron Obstruct Pulmon Dis, 16, pp. 101-111, (2021); Dong H, Hao Y, Li D, Et al., Risk factors for acute exacerbation of chronic obstructive pulmonary disease in industrial regions of chi-na: A multicenter cross-sectional study, Int J Chron Obstruct Pulmon Dis, 15, pp. 2249-2256, (2020); Zhang H, Wu F, Yi H, Et al., Gender differences in chronic obstructive pulmonary disease symptom clusters, Int J Chron Obstruct Pulmon Dis, 16, pp. 1101-1107, (2021); Fermont JM, Masconi KL, Jensen MT, Et al., biomarkers and clinical outcomes in COPD: A systematic review and meta-analysis, Thorax, 74, 5, pp. 439-446, (2019); Cavailles A, Brinchault-Rabin G, Dixmier A, Et al., Comorbidities of COPD, Eur Respir Rev, 22, 130, pp. 454-475, (2013); Nunez A, Marras V, Harlander M, Et al., Association between rou-tine blood biomarkers and clinical phenotypes and exacerbations in chronic obstructive pulmonary disease, Int J Chron Obstruct Pulmon Dis, 15, pp. 681-690, (2020); Skoczynski S, Krzyzak D, Studnicka A, Et al., Chronic obstructive pulmonary disease and platelet count, Adv Exp Med Biol, 1160, pp. 19-23, (2019); Lu FY, Chen R, Li N, Et al., Neutrophil-to-lymphocyte ratio pre-dicts clinical outcome of severe acute exacerbation of COPD in frequent exacerbators, Int J Chron Obstruct Pulmon Dis, 16, pp. 341-349, (2021); Celli BR, Cote CG, Marin JM, Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, 10, pp. 1005-1012, (2004); Waatevik M, Johannessen A, Gomez Real F, Et al., Oxygen desatu-ration in 6-min walk test is a risk factor for adverse outcomes in COPD, Eur Respir J, 48, 1, pp. 82-91, (2016); Agrawal RK, Gupta NK, Srivastav AB, Ved ML., Echocardiograph-ic evaluation of heart in chronic obstructive pulmonary disease patient and its co-relation with the severity of disease, Lung India, 28, 2, pp. 105-109, (2011); Mekov E, Yanev N, Kurtelova N, Et al., Diaphragmatic movement at rest and after exertion: A non-invasive and easy to obtain prog-nostic marker in COPD, Int J Chron Obstruct Pulmon Dis, 17, pp. 1041-1050, (2022); Rasch-Halvorsen O, Hassel E, Brumpton BM, Et al., Lung function and peak oxygen uptake in chronic obstructive pulmonary disease phenotypes with and without emphysema, PLoS One, 16, 5, (2021)","E. Mekov; Department of Occupational Diseases, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria; email: evgeni.mekov@gmail.com","","Bentham Science Publishers","","","","","","1573398X","","","","English","Curr. Respir. Med. Rev.","Article","Final","","Scopus","2-s2.0-85172346416"
"Chen S.; Wang K.; Wang C.; Fan Z.; Yan L.; Wang Y.; Liu F.; Shi J.; Guo Q.; Dong N.","Chen, Shiqi (57221856665); Wang, Kan (57208081999); Wang, Chen (55520505900); Fan, Zhengfeng (58156100000); Yan, Lizhao (57215723992); Wang, Yixuan (57195410608); Liu, Fayuan (58347585400); Shi, JiaWei (55859764900); Guo, QianNan (56477829500); Dong, NianGuo (7005861079)","57221856665; 57208081999; 55520505900; 58156100000; 57215723992; 57195410608; 58347585400; 55859764900; 56477829500; 7005861079","Prediction of postoperative stroke in patients experienced coronary artery bypass grafting surgery: a machine learning approach","2024","Frontiers in Cardiovascular Medicine","11","","1448740","","","","0","10.3389/fcvm.2024.1448740","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213064250&doi=10.3389%2ffcvm.2024.1448740&partnerID=40&md5=696db97da8a6617d86f78195ff0a34b3","Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China","Chen S., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Wang K., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Wang C., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Fan Z., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Yan L., Department of Hand Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Wang Y., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Liu F., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Shi J., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Guo Q., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China; Dong N., Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan, China","Background: Coronary artery bypass grafting (CABG) surgery has been a widely accepted method for treating coronary artery disease. However, its postoperative complications can have a significant effect on long-term patient outcomes. A retrospective study was conducted to identify before and after surgery that contribute to postoperative stroke in patients undergoing CABG, and to develop predictive models and recommendations for single-factor thresholds. Materials and methods: We utilized data from 1,200 patients who undergone CABG surgery at the Wuhan Union Hospital from 2016 to 2022, which was divided into a training group (n = 841) and a test group (n = 359). 33 preoperative clinical features and 4 postoperative complications were collected in each group. LASSO is a regression analysis method that performs both variable selection and regularization to enhance model prediction accuracy and interpretability. The LASSO method was used to verify the collected features, and the SHAP value was used to explain the machine model prediction. Six machine learning models were employed, and the performance of the models was evaluated by area under the curve (AUC) and decision curve analysis (DCA). AUC, or area under the receiver operating characteristic curve, quantifies the ability of a model to distinguish between positive and negative outcomes. Finally, this study provided a convenient online tool for predicting CABG patient post-operative stroke. Results: The study included a combined total of 1,200 patients in both the development and validation cohorts. The average age of the participants in the study was 60.26 years. 910 (75.8%) of the patients were men, and 153 (12.8%) patients were in NYHA class III and IV. Subsequently, LASSO model was used to identify 11 important features, which were mechanical ventilation time, preoperative creatinine value, preoperative renal insufficiency, diabetes, the use of an intra-aortic balloon pump (IABP), age, Cardiopulmonary bypass time, Aortic cross-clamp time, Chronic Obstructive Pulmonary Disease (COPD) history, preoperative arrhythmia and Renal artery stenosis in descending order of importance according to the SHAP value. According to the analysis of receiver operating characteristic (ROC) curve, AUC, DCA and sensitivity, all seven machine learning models perform well and random forest (RF) machine model was found to perform best (AUC-ROC = 0.9008, Accuracy: 0.9008, Precision: 0.6905; Recall: 0.7532, F1: 0.7205). Finally, an online tool was established to predict the occurrence of stroke after CABG based on the 11 selected features. Conclusion: Mechanical ventilation time, preoperative creatinine value, preoperative renal insufficiency, diabetes, the use of an intra-aortic balloon pump (IABP), age, Cardiopulmonary bypass time, Aortic cross-clamp time, Chronic Obstructive Pulmonary Disease (COPD) history, preoperative arrhythmia and Renal artery stenosis in the preoperative and intraoperative period was associated with significant postoperative stroke risk, and these factors can be identified and modeled to assist in implementing proactive measures to protect the brain in high-risk patients after surgery. 2024 Chen, Wang, Wang, Fan, Yan, Wang, Liu, Shi, Guo and Dong.","coronary artery bypass grafting (CABG); machine learning (ML); postoperative complications; preoperative clinical features; random forest; stroke","creatinine; adult; area under the curve; Article; artificial intelligence; artificial ventilation; cardiopulmonary bypass; cerebrovascular accident; chronic obstructive lung disease; coronary artery bypass graft; coronary artery disease; decision tree; diabetes mellitus; diagnostic accuracy; extracorporeal oxygenation; heart arrest; heart arrhythmia; heart infarction; heart surgery; heart ventricle aneurysm; hemodialysis; hospitalization; human; hyperlipidemia; hypertension; logistic regression analysis; machine learning; major clinical study; middle aged; percutaneous coronary intervention; postoperative complication; random forest; receiver operating characteristic; renal artery stenosis; retrospective study; sensitivity and specificity","","creatinine, 19230-81-0, 60-27-5","","","National Natural Science Foundation of China, NSFC, (81770387, 82200410); National Natural Science Foundation of China, NSFC; National Key Research and Development Program of China, NKRDPC, (2021YFA1101900); National Key Research and Development Program of China, NKRDPC","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (grants 81770387; grants 82200410) and the National Key Research and Development Plan of China (2021YFA1101900). Study sponsors have not been involved in any aspect of decision making concerning the present study. Acknowledgments ","Gilboa S.M., Devine O.J., Kucik J.E., Oster M.E., Riehle-Colarusso T., Nembhard W.N., Et al., Congenital heart defects in the United States: estimating the magnitude of the affected population in 2010, Circulation, 134, pp. 101-109, (2016); Laakso M., Heart in diabetes: a microvascular disease, Diabetes Care, 34, 2, pp. S145-S149, (2011); Toribio M., Fitch K.V., Stone L., Zanni M.V., Lo J., de Filippi C., Et al., Assessing statin effects on cardiovascular pathways in HIV using a novel proteomics approach: analysis of data from INTREPID, a randomized controlled trial, Ebiomedicine, 35, pp. 58-66, (2018); Benstoem C., Stoppe C., Liakopoulos O.J., Ney J., Hasenclever D., Meybohm P., Et al., Remote ischaemic preconditioning for coronary artery bypass grafting (with or without valve surgery), Cochrane Database Syst Rev, 5, (2017); Zembala M., Michler R.E., Rynkiewicz A., Huynh T., She L., Lubiszewska B., Et al., Clinical characteristics of patients undergoing surgical ventricular reconstruction by choice and by randomization, J Am Coll Cardiol, 56, pp. 499-507, (2010); 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Mathew G., Agha R., Albrecht J., Goel P., Mukherjee I., Pai P., Et al., STROCSS 2021: strengthening the reporting of cohort, cross-sectional and case-control studies in surgery, Int J Surg, 96, (2021); Wang K., Yan L.Z., Li W.Z., Jiang C., Wang N.N., Zheng Q., Et al., Comparison of four machine learning techniques for prediction of intensive care unit length of stay in heart transplantation patients, Front Cardiovasc Med, 9, (2022); Zhou J., Shao G., Chen X., Yang X., Huang X., Peng P., Et al., miRNA 206 and miRNA 574-5p are highly expression in coronary artery disease, Biosci Rep, 36, (2015); Pan X.X., Ruan C.C., Liu X.Y., Kong L.R., Ma Y., Wu Q.H., Et al., Perivascular adipose tissue-derived stromal cells contribute to vascular remodeling during aging, Aging Cell, 18, (2019); Ozmen R., Bozguney M., Tekin A.I., Eroglu T., Tuncay A., Impact of single versus double clamp technique on blood lactate levels and postoperative complications in coronary artery bypass Surgery, Braz J Cardiovasc Surg, 37, pp. 55-64, (2022); Wang S., Ran Y., Cheng S., Lyu Y., Liu J., Determinants and clinical outcomes of stroke following revascularization among patients with reduced ejection fraction, Brain Behav, 13, (2023); Raman N., Al-Robaidi K., Jadhav A., Thirumala P.D., Perioperative stroke and readmissions rates in noncardiac non-neurologic surgery, J Stroke Cerebrovasc Dis, 29, (2020); Howard B.V., Metzger J.S., Koller K.R., Jolly S.E., Asay E.D., Wang H., Et al., All-cause, cardiovascular, and cancer mortality in western Alaska native people: western Alaska tribal collaborative for health (WATCH), Am J Public Health, 104, pp. 1334-1340, (2014); Cao C.C., Chen D.W., Li J., Ma M.Q., Chen Y.B., Cao Y.Z., Et al., Community-acquired versus hospital-acquired acute kidney injury in patients with acute exacerbation of COPD requiring hospitalization in China, Int J Chron Obstruct Pulmon Dis, 13, pp. 2183-2190, (2018); Xu X., Mishra G.D., Dobson A.J., Jones M., Progression of diabetes, heart disease, and stroke multimorbidity in middle-aged women: a 20-year cohort study, PLoS Med, 15, (2018); Jia H., Huang B., Kang L., Lai H., Li J., Wang C., Et al., Preoperative and intraoperative risk factors of postoperative stroke in total aortic arch replacement and stent elephant trunk implantation, Eclinicalmedicine, 47, (2022); Kim Y.R., Hwang I.C., Lee Y.J., Ham E.B., Park D.K., Kim S., Stroke risk among patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis, Clinics, 73, (2018); Cai W., Hu J., Wang H., Chen S., Zhu G., Chen X., Valve surgery in combination with cryoablation in the treatment of atrial fibrillation, Pak J Med Sci, 34, pp. 1402-1407, (2018); Aissa I., Elkoundi A., Andalousi R., Benakrout A., Chlouchi A., Moutaoukil M., Et al., Unusual localization of bleeding under acenocoumarol: spinal subdural hematoma, Int J Surg Case Rep, 59, pp. 15-18, (2019)","J. Shi; Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; email: shijiawei@21cn.com; Q. Guo; Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; email: 936163754@qq.com; N. Dong; Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China; email: dongnianguo@hotmail.com","","Frontiers Media SA","","","","","","2297055X","","","","English","Front. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85213064250"
"Sur S.; Ghosh M.; Rai R.","Sur, Saubashya (23490822000); Ghosh, Mritunjoy (59233954500); Rai, Ritu (59233954600)","23490822000; 59233954500; 59233954600","In silico immunoinformatics based prediction and designing of multi-epitope construct against human rhinovirus C","2023","Acta Biologica Szegediensis","67","1","","11","23","12","0","10.14232/abs.2023.1.11-23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199525739&doi=10.14232%2fabs.2023.1.11-23&partnerID=40&md5=28fa6fb94482209feb28d3ff4bbaaf77","Postgraduate Department of Botany, Life Sciences Block, Ramananda College, West Bengal, Bishnupur, 722122, India; Department of Botany, Parimal Mitra Smriti Mahavidyalaya, West Bengal, Malbazar, 735221, India","Sur S., Postgraduate Department of Botany, Life Sciences Block, Ramananda College, West Bengal, Bishnupur, 722122, India; Ghosh M., Postgraduate Department of Botany, Life Sciences Block, Ramananda College, West Bengal, Bishnupur, 722122, India; Rai R., Department of Botany, Parimal Mitra Smriti Mahavidyalaya, West Bengal, Malbazar, 735221, India","Human rhinovirus C (HRV-C) is an RNA virus infecting human respiratory tract. It is associated with complexities like asthma, chronic obstructive pulmonary disease, and respiratory damage. HRV-C has many serotypes. Till date there is no vaccine. Despite some limitations, corticosteroids, bronchodilators, and common cold medicines are used to treat HRV-C infections. Here, we have used immunoinformatics approach to predict suitable cytotoxic T-cell, helper T-cell and linear B-cell epitopes from the most antigenic protein. VP2 protein of Rhinovirus C53 strain USA/CO/2014-20993 was found to be most antigenic. The multi-epitope construct was designed using the best CTL, HTL and linear B-cell epitopes and attaching them with adjuvant and linkers. Interferon-gamma inducing epitopes and conformational B-cell epitopes were also pre-dicted from the construct. Physicochemical and structural properties of the construct were satisfactory. Binding pockets were identified that could be the targets for designing effective inhibitors. Molecular docking revealed strong binding affinity of the construct with human Toll-like receptors 2 and 4. Normal mode analysis divulged stability of the docked complex. Codon optimization, in silico cloning and immune simulation analysis demonstrated suitability of the construct. These findings are likely to aid in vitro studies for developing vaccine against HRV-C. © 2023, University of Szeged. All rights reserved.","Human rhinovirus C; immunoinformatics; linker; molecular docking; multi-epitope; toll-like receptors","epitope; gamma interferon; protein VP1; protein VP2; protein VP3; protein VP4; toll like receptor 2; toll like receptor 4; allergenicity; alpha helix; amino acid sequence; antigenicity; Article; B lymphocyte; beta sheet; binding affinity; computer model; controlled study; cytotoxic T lymphocyte; helper cell; Human rhinovirus; human rhinovirus c; immune response; immunoinformatics; ligand binding; machine learning; molecular docking; nonhuman; protein secondary structure; protein tertiary structure; toxicity","","gamma interferon, 82115-62-6; protein VP1, 92354-95-5; toll like receptor 2, 203811-81-8; toll like receptor 4, 203811-83-0","","","","","Abdellrazeq GS, Fry LM, Elnaggar MM, Bannantine JP, Schneider DA, Chamberlin WM, Mahmoud AHA, Park KT, Hulubei V, Davis WC, Simultaneous cognate epitope recognition by bovine CD4 and CD8 T cells is essential for primary expansion of antigen-specific cytotoxic T-cells following ex vivo stimulation with a candidate Mycobacterium avium subsp. paratuberculosis peptide vaccine, Vaccine, 38, pp. 2016-2025, (2020); 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Jakiela B, Brockman-Schneider R, Amineva S, Lee WM, Gern JE, Basal cells of differentiated bronchial epithelium are more susceptible to rhinovirus infection, Am J Respir Cell Mol Biol, 38, pp. 517-523, (2008); Jendele L, Krivak R, Skoda P, Novotny M, Hoksza D, PrankWeb: a web server for ligand binding site prediction and visualization, Nucleic Acids Res, 47, W1, pp. W345-W349, (2019); Jin J, Hjerrild KA, Silk SE, Brown RE, Labbe GM, Marshall JM, Wright KE, Bezemer S, Clemmensen SB, Biswas S, Li Y, El-Turabi A, Douglas AD, Hermans P, Detmers FJ, de Jongh WA, Higgins MK, Ashfield R, Draper SJ, Accelerating the clinical development of protein-based vaccines for malaria by efficient purification using a four amino acid C-terminal 'C-tag, Int J Parasitol, 47, pp. 435-446, (2017); Jones DT, Cozzetto D, DISOPRED3: precise disor-dered region predictions with annotated protein-binding activity, Bioinformatics, 31, pp. 857-863, (2015); Lopez-Blanco JR, Aliaga JI, Quintana-Orti ES, Chacon P, iMODS: internal coordinates normal mode analysis server, Nucleic Acids Res, 42, pp. W271-W276, (2014); Khatoon N, Pandey RK, Prajapati VK, Exploring Leishmania secretory proteins to design B and T cell multi-epitope subunit vaccine using immunoinformatics approach, Sci Rep, 7, (2017); Korber B, LaBute M, Yusim K, Immunoinformatics comes of age, PloS Comput Biol, 2, 6, (2006); Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S, The ClusPro web server for protein-protein docking, Nat Protoc, 12, pp. 255-278, (2017); Larsen MV, Lundegaard C, Lamberth K, Buus A, Lund O, Nielsen M, Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction, BMC Bioinform, 8, (2007); Lau SK, Yip CC, Woo PC, Yuen KY, Human rhinovirus C: a newly discovered human rhinovirus species, Emerg Health Threats J, 3, (2010); Lee S, Nguyen MT, Currier MG, Jenkins JB, Strobert EA, Kajon AE, Madan-Lala R, Bochkov YA, Gern JE, Roy K, Lu X, Erdman DD, Spearman P, Moore ML, A polyvalent inactivated rhinovirus vaccine is broadly im-munogenic in rhesus macaques, Nat Commun, 7, (2016); Majid M, Andleeb S, Designing a multi-epitopic vaccine against the enterotoxigenic Bacteroides fragilis based on immunoinformatics approach, Sci Rep, 9, (2019); Mak RK, Tse LY, Lam WY, Wong GWK, Chan PKS, Leung TF, Clinical spectrum of human rhinovirus infections in hospitalized Hong Kong children, Pediatr Infect Dis J, 30, pp. 749-753, (2011); McLean GR, Developing a vaccine for human rhino-viruses, J Vaccines Immun, 2, pp. 16-20, (2014); McGuffin LJ, The PSIPRED protein structure prediction server, Bioinformatics, 16, pp. 404-405, (2000); Mittal A, Sasidharan S, Raj S, Balaji SN, Saudagar P, Exploring the Zika genome to design a potential multi-epitope vaccine using an immunoinformatics approach, Int J Pept Res Ther, 26, pp. 2231-2240, (2020); Nair DT, Singh K, Siddiqui Z, Nayak BP, Rao KV, Salunke DM, Epitope recognition by diverse antibodies suggests conformational convergence in an antibody response, J Immun, 168, pp. 2371-2382, (2002); Nielsen M, Lundegaard C, Lund O, Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method, BMC Bioinform, 8, (2007); Nielsen M, Lund O, NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction, BMC Bioinform, 10, (2009); Olejnik J, Hume AJ, Muhlberger E, Toll-like receptor 4 in acute viral infection: too much of a good thing, PLoS Pathog, 14, (2018); Palmenberg AC, Rathe JA, Liggett SB, Analysis of the complete genome sequences of human rhinovirus, J Allergy Clin Immunol, 125, pp. 1190-1201, (2010); Papi A, Contoli M, Rhinovirus vaccination: the case against, Eur Respir J, 37, pp. 5-7, (2011); Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Pono-marenko JV, Sathiamurthy M, Schoenberger SP, Stewart S, Surko P, Way S, Wilson S, Sette A, The design and implementation of the immune epitope database and analysis resource, Immunogenetics, 57, pp. 326-336, (2005); Ponomarenko J, Bui HH, Li W, Fusseder N, Bourne PE, Sette A, Peters B, ElliPro: a new structure-based tool for the prediction of antibody epitopes, BMC Bio-inform, 9, (2008); Purcell AW, McCluskey J, Rossjohn J, More than one reason to rethink the use of peptides in vaccine design, Nat Rev Drug Discov, 6, pp. 404-414, (2006); Rapin N, Lund O, Bernaschi M, Castiglione F, Com-putational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system, PLoS One, 5, (2010); Ras-Carmona A, Pelaez-Prestel HF, Lafuente EM, Reche PA, BCEPS: A Web server to predict linear B cell epitopes with enhanced immunogenicity and cross-reactivity, Cells, 10, (2021); Sami SA, Marma KKS, Mahmud S, Khan MAN, Albogami S, El-Shehawi AM, Rakib A, Chakraborty A, Mohiud-din M, Dhama K, Uddin MMN, Hossain MK, Tallei TE, Emran TB, Designing of a multi-epitope vaccine against the structural proteins of Marburg virus ex-ploiting the immunoinformatics approach, ACS Omega, 6, pp. 32043-32071, (2021); Scully EJ, Basnet S, Wrangham RW, Muller MN, Otali E, Hyeroba D, Grindle KA, Pappas TE, Thompson ME, Machanda Z, Watters KE, Palmenberg AC, Gern JE, Goldberg TL, Lethal Respiratory Disease Associated with Human Rhinovirus C in Wild Chimpanzees, Uganda, 2013, Emerg Infect Dis, 24, pp. 267-274, (2018); Seib KL, Zhao X, Rappuoli R, Developing vaccines in the era of genomics: a decade of reverse vaccinology, Clin Microbiol Infect, 18, pp. 109-116, (2012); Stepanova E, Isakova-Sivak I, Rudenko L, Overview of human rhinovirus immunogenic epitopes for rational vaccine design, Expert Rev Vaccines, 18, pp. 877-880, (2019); Stone CA, Miller EK, Understanding the association of human rhinovirus with asthma, Clin Vaccine Im-munol, 23, pp. 6-10, (2016); Wallner B, Elofsson A, Can correct protein models be identified?, Protein Sci, 12, pp. 1073-1086, (2003); Wiederstein M, Sippl MJ, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins, Nucleic Acids Res, 35, pp. W407-W410, (2007); Wei L, Ye X, Sakurai T, Mu Z, Wei L, ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning, Bioinformatics, 38, pp. 1514-1524, (2022)","S. Sur; Postgraduate Department of Botany, Life Sciences Block, Ramananda College, Bishnupur, West Bengal, 722122, India; email: saubashya@gmail.com","","University of Szeged","","","","","","1588385X","","ABSCC","","English","Acta Biol. Szegediensis","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85199525739"
"Persson A.P.; Måneheim A.; Economou Lundeberg J.; Fedorowski A.; Healey J.S.; Sundström J.; Engström G.; Johnson L.S.B.","Persson, Anders Paul (57210736270); Måneheim, Alexandra (57759080800); Economou Lundeberg, Johan (57210980188); Fedorowski, Artur (6602745793); Healey, Jeff S (8084299100); Sundström, Johan (56702246400); Engström, Gunnar (7004836666); Johnson, Linda S B (57198981606)","57210736270; 57759080800; 57210980188; 6602745793; 8084299100; 56702246400; 7004836666; 57198981606","Reference ranges for ambulatory heart rate measurements in a middle-Aged population","2024","Heart","110","12","","831","837","6","0","10.1136/heartjnl-2023-323681","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189967515&doi=10.1136%2fheartjnl-2023-323681&partnerID=40&md5=f78a49b38e21e1e97cd83faeb6cb5014","Department of Clinical Sciences, Lund University, Malmö, Sweden; Department of Clinical Physiology, Skånes Universitetssjukhus Malmö, Malmö, Sweden; Department of Clinical Physiology, Skånes Universitetssjukhus Lund, Lund, Sweden; Department of Clinical Sciences, Lund University Faculty of Medicine, Malmö, Sweden; Department of Medicine, Karolinska Institute, Solna, Sweden; Population Health Research Institute, Hamilton, ON, Canada; Department of Medicine, McMaster University, Hamilton, ON, Canada; Department of Medical Sciences, Uppsala University, Uppsala, Sweden; The George Institute for Global Health, Newtown, NSW, Australia","Persson A.P., Department of Clinical Sciences, Lund University, Malmö, Sweden, Department of Clinical Physiology, Skånes Universitetssjukhus Malmö, Malmö, Sweden; Måneheim A., Department of Clinical Sciences, Lund University, Malmö, Sweden, Department of Clinical Physiology, Skånes Universitetssjukhus Malmö, Malmö, Sweden; Economou Lundeberg J., Department of Clinical Sciences, Lund University, Malmö, Sweden, Department of Clinical Physiology, Skånes Universitetssjukhus Lund, Lund, Sweden; Fedorowski A., Department of Clinical Sciences, Lund University Faculty of Medicine, Malmö, Sweden, Department of Medicine, Karolinska Institute, Solna, Sweden; Healey J.S., Population Health Research Institute, Hamilton, ON, Canada, Department of Medicine, McMaster University, Hamilton, ON, Canada; Sundström J., Department of Medical Sciences, Uppsala University, Uppsala, Sweden, The George Institute for Global Health, Newtown, NSW, Australia; Engström G., Department of Clinical Sciences, Lund University, Malmö, Sweden; Johnson L.S.B., Department of Clinical Sciences, Lund University, Malmö, Sweden, Population Health Research Institute, Hamilton, ON, Canada","Background Elevated heart rate (HR) predicts cardiovascular disease and mortality, but there are no established normal limits for ambulatory HR. We used data from the Swedish CArdioPulmonary Imaging Study to determine reference ranges for ambulatory HR in a middle-Aged population. We also studied clinical correlates of ambulatory HR. Methods A 24-hour ECG was registered in 5809 atrial fibrillation-free individuals, aged 50-65 years. A healthy subset (n=3942) was used to establish reference values (excluding persons with beta-blockers, cardiovascular disease, hypertension, heart failure, anaemia, diabetes, sleep apnoea or chronic obstructive pulmonary disease). Minimum HR was defined as the lowest 1-minute HR. Reference ranges are reported as means±SDs and 2.5th-97.5th percentiles. Clinical correlates of ambulatory HR were analysed with multivariable linear regression. Results The average mean and minimum HRs were 73±9 and 48±7 beats per minute (bpm) in men and 76±8 and 51±7 bpm in women; the reference range for mean ambulatory HR was 57-90 bpm in men and 61-92 bpm in women. Average daytime and night-Time HRs are also reported. Clinical correlates, including age, sex, height, body mass index, physical activity, smoking, alcohol intake, diabetes, hypertension, haemoglobin level, use of beta-blockers, estimated glomerular filtration rate, per cent of predicted forced expiratory volume in 1 s and coronary artery calcium score, explained <15% of the interindividual differences in HR. Conclusion Ambulatory HR varies widely in healthy middle-Aged individuals, a finding with relevance for the management of patients with a perception of tachycardia. Differences in ambulatory HR between individuals are largely independent of common clinical correlates.  © 2024 BMJ Publishing Group. All rights reserved.","Bradycardia; Electrocardiography; Epidemiology","Age Factors; Aged; Electrocardiography, Ambulatory; Female; Heart Rate; Humans; Male; Middle Aged; Reference Values; Sweden; beta adrenergic receptor blocking agent; creatinine; salbutamol; adult; aged; alcohol consumption; algorithm; Article; artificial intelligence; body mass; bronchodilatation; cardiovascular disease; chronic kidney failure; cohort analysis; computer assisted tomography; coronary artery calcium score; coronary artery disease; diabetes mellitus; electrocardiogram; expiratory reserve volume; female; glomerulus filtration rate; heart failure; heart rate; heart rate measurement; height; hemoglobin blood level; human; human experiment; hypertension; major clinical study; male; mortality; physical activity; sleep apnea syndromes; smoking; spirometry; age; ambulatory electrocardiography; epidemiology; heart rate; middle aged; physiology; procedures; reference value; Sweden","","creatinine, 19230-81-0, 60-27-5; salbutamol, 18559-94-9, 35763-26-9","CardioSpy ECG analysis software; Jaeger MasterScreen PFT, CareFusion, Germany; Stata  V.15.1, StataCorp, United States","CareFusion, Germany; StataCorp, United States","Verket för innovationssystem; Knut och Alice Wallenbergs Stiftelse; Svenska Sällskapet för Medicinsk Forskning, SSMF; Hjärt-Lungfonden; Vetenskapsrådet, VR","The main funding body of SCAPIS (Swedish CArdioPulmonary Imaging Study) is the Swedish Heart and Lung Foundation. SCAPIS was also supported by grants from the Knut and Alice Wallenberg Foundation, the Swedish Research Council and Verket f\u00F6r innovationssystem (Sweden\u2019s innovation agency). LSBJ and GE are supported by the Swedish Heart and Lung Foundation. LSBJ is supported by the Swedish Research Council and the Swedish Society for Medical Research. ","Aune D., Sen A., O'Hartaigh B., Et al., Resting heart rate and the risk of cardiovascular disease, total cancer, and all-cause mortality-a systematic review and dose-response meta-Analysis of prospective studies, Nutr Metab Cardiovasc Dis, 27, pp. 504-517, (2017); Dyer A.R., Persky V., Stamler J., Et al., Heart rate as a prognostic factor for coronary heart disease and mortality: Findings in three chicago epidemiologic studies, Am J Epidemiol, 112, pp. 736-749, (1980); Kannel W.B., Kannel C., Paffenbarger R.S., Et al., Heart rate and cardiovascular mortality: The framingham study, Am Heart J, 113, pp. 1489-1494, (1987); Greenland P., Daviglus M.L., Dyer A.R., Et al., Resting heart rate is a risk factor for cardiovascular and Noncardiovascular mortality: The chicago heart association detection project in industry, Am J Epidemiol, 149, pp. 853-862, (1999); Kolloch R., Legler U.F., Champion A., Et al., Impact of resting heart rate on outcomes in hypertensive patients with coronary artery disease findings from the International verapamil-SR/Trandolapril study (INVEST), Eur Heart J, 29, pp. 1327-1334, (2008); Bohm M., Schumacher H., Teo K.K., Et al., Resting heart rate and cardiovascular outcomes in diabetic and non-diabetic individuals at high cardiovascular risk analysis from the ONTARGET/TRANSCEND trials, Eur Heart J, 41, pp. 231-238, (2020); Warnier M.J., Rutten F.H., De Boer A., Et al., Resting heart rate is a risk factor for mortality in chronic obstructive pulmonary disease, but not for exacerbations or pneumonia, PLoS One, 9, (2014); Diaz A., Bourassa M.G., Guertin M.-C., Et al., Long-Term prognostic value of resting heart rate in patients with suspected or proven coronary artery disease, Eur Heart J, 26, pp. 967-974, (2005); Bohm M., Swedberg K., Komajda M., Et al., Heart rate as a risk factor in chronic heart failure (SHIFT): The association between heart rate and outcomes in a randomised placebo-controlled trial, Lancet, 376, pp. 886-894, (2010); Morseth B., Graff-Iversen S., Jacobsen B.K., Et al., Physical activity, resting heart rate, and atrial fibrillation: The tromsø study, Eur Heart J, 37, pp. 2307-2313, (2016); 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Klein D.A., Katz D.H., Beussink-Nelson L., Et al., Association of chronic kidney disease with chronotropic incompetence in heart failure with preserved ejection fraction, Am J Cardiol, 116, pp. 1093-1100, (2015); Palatini P., Resting heart rate as a cardiovascular risk factor in hypertensive patients: An update, Am J Hypertens, 34, pp. 307-317, (2021); Giannoglou G.D., Chatzizisis Y.S., Zamboulis C., Et al., Elevated heart rate and Atherosclerosis: An overview of the pathogenetic mechanisms, Int J Cardiol, 126, pp. 302-312, (2008); Custodis F., Schirmer S.H., Baumhakel M., Et al., Vascular pathophysiology in response to increased heart rate, J Am Coll Cardiol, 56, pp. 1973-1983, (2010); Eppinga R.N., Hagemeijer Y., Burgess S., Et al., Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality, Nat Genet, 48, pp. 1557-1563, (2016)","A.P. Persson; Department of Clinical Sciences, Lund University, Malmö, Sweden; email: anders_p.persson@med.lu.se","","BMJ Publishing Group","","","","","","13556037","","HEARF","38580434","English","Heart","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85189967515"
"Mihaicuta S.; Udrescu L.; Militaru A.; Nadasan V.; Tiotiu A.; Bikov A.; Ursoniu S.; Birza R.; Popa A.M.; Frent S.","Mihaicuta, Stefan (6602901111); Udrescu, Lucretia (36467301400); Militaru, Adrian (57347182600); Nadasan, Valentin (57035383600); Tiotiu, Angelica (23499586600); Bikov, Andras (20336515300); Ursoniu, Sorin (26435634900); Birza, Romina (57721172700); Popa, Alina Mirela (58138203300); Frent, Stefan (26435260100)","6602901111; 36467301400; 57347182600; 57035383600; 23499586600; 20336515300; 26435634900; 57721172700; 58138203300; 26435260100","Multivariate analysis and data mining help predict asthma exacerbations","2024","Journal of Asthma","61","6","","608","618","10","0","10.1080/02770903.2023.2297366","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181215142&doi=10.1080%2f02770903.2023.2297366&partnerID=40&md5=43818c5cb15b6e4fc9476ecc0625be4c","Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Department I-Drug Analysis, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Department of Computer and Information Technology, Politehnica University Timisoara, Timisoara, Romania; Department of Hygiene, “G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, Targu Mures, Romania; Department of Pulmonology, Nancy University Hospital, Nancy, France; Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; Division of Infection, Immunity & Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Department of Public Health and Health Management, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Center for Translational Research and Systems Medicine, Timisoara, Romania","Mihaicuta S., Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Udrescu L., Department I-Drug Analysis, Faculty of Pharmacy, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Militaru A., Department of Computer and Information Technology, Politehnica University Timisoara, Timisoara, Romania; Nadasan V., Department of Hygiene, “G.E. Palade” University of Medicine, Pharmacy, Science and Technology of Targu Mures, Targu Mures, Romania; Tiotiu A., Department of Pulmonology, Nancy University Hospital, Nancy, France; Bikov A., Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom, Division of Infection, Immunity & Respiratory Medicine, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom; Ursoniu S., Department of Public Health and Health Management, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania, Center for Translational Research and Systems Medicine, Timisoara, Romania; Birza R., Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Popa A.M., Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania; Frent S., Center for Research and Innovation in Precision Medicine of Respiratory Diseases, Department of Pulmonology, “Victor Babes” University of Medicine and Pharmacy Timisoara, Timisoara, Romania","Background: Work-related asthma has become a highly prevalent occupational lung disorder. Objective: Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation. Method: We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations. Results: We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19–85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p <.001), occupational exposure (OR 4.65, p <.001), and lung function impairment (FEV1 < 80%) (OR 1.15, p =.011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT. Conclusions: Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation. © 2024 Taylor & Francis Group, LLC.","asthma exacerbation; data mining; ensemble learning; Occupational exposure; predictor; work-related asthma","Adult; Aged; Aged, 80 and over; Asthma; Asthma, Occupational; Data Mining; Disease Progression; Female; Humans; Logistic Models; Male; Middle Aged; Multivariate Analysis; Occupational Exposure; Retrospective Studies; Young Adult; adult; age; aged; airflow; algorithm; Article; asthma; Asthma Control Test; bakery worker; body mass; calculation; cement industry; clinical feature; cohort analysis; current smoker; data mining; decision tree; demographics; disease exacerbation; electronics industry; family history; feature ranking; female; forced expiratory volume; gender; hairdresser; human; industry; logistic regression analysis; lung function; maintenance therapy; major clinical study; male; multivariate analysis; occupational exposure; pathogenesis; plastic industry; prediction; preliminary data; prick test; random forest; retrospective study; Romania; spirometry; treatment duration; univariate analysis; asthma; disease exacerbation; middle aged; multivariate analysis; occupational asthma; pathophysiology; statistical model; very elderly; young adult","","","","","","","Ye Q., Liao A., D'Urzo A., FEV1 reversibility for asthma diagnosis: a critical evaluation, Expert Rev Respir Med, 12, 4, pp. 265-267, (2018); Louis R., Satia I., Ojanguren I., Schleich F., Bonini M., Tonia T., Rigau D., Ten Brinke A., Buhl R., Loukides S., Et al., European Respiratory Society guidelines for the diagnosis of asthma in adults, Eur Respir J, 60, 3, (2022); Boulet L.P., Reddel H.K., Bateman E., Pedersen S., FitzGerald J.M., O'Byrne P.M., The Global Initiative for Asthma (GINA): 25 years later, Eur Respir J, 54, 2, (2019); Haughney J., Price D., Kaplan A., Chrystyn H., Horne R., May N., Moffat M., Versnel J., Shanahan E.R., Hillyer E.V., Et al., Achieving asthma control in practice: understanding the reasons for poor control, Respir Med, 102, 12, pp. 1681-1693, (2008); 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Mapp C.E., Boschetto P., Maestrelli P., Fabbri L.M., Occupational asthma, Am J Respir Crit Care Med, 172, 3, pp. 280-305, (2005); de Vocht F., Zock J.-P., Kromhout H., Sunyer J., Anto J.M., Burney P., Kogevinas M., Comparison of self‐reported occupational exposure with a job exposure matrix in an international community‐based study on asthma, Am J Ind Med, 47, 5, pp. 434-442, (2005)","L. Udrescu; Faculty of Pharmacy, Timisoara, Timisoara, 2 Eftimie Murgu Square, 30004, Romania; email: udrescu.lucretia@umft.ro","","Taylor and Francis Ltd.","","","","","","02770903","","JOUAD","38112563","English","J. Asthma","Article","Final","","Scopus","2-s2.0-85181215142"
"Sri Lalitha Y.; Samson M.; Akunuri R.; Devi G.; Singh N.; Bisht M.S.; Adnan M.M.","Sri Lalitha, Y. (55941257700); Samson, Mamatha (56257264600); Akunuri, Roshini (57863654900); Devi, Gayatri (59522462900); Singh, Navdeep (57218540796); Bisht, Manbir Singh (58994922600); Adnan, Myasar Mundher (57221874677)","55941257700; 56257264600; 57863654900; 59522462900; 57218540796; 58994922600; 57221874677","An efficient pulmonary carcinoma nodule detection model","2024","Cogent Engineering","11","1","2406377","","","","0","10.1080/23311916.2024.2406377","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205962154&doi=10.1080%2f23311916.2024.2406377&partnerID=40&md5=063f8ffc70e5bfee7fa8e2067803b1f7","Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, India; School of Engineering, Lovely Professional University, Phagwara, India; Department of Engineering, Uttaranchal University, Dehradun, India; Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq","Sri Lalitha Y., Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, India; Samson M., Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, India; Akunuri R., Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, India; Devi G., Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, India; Singh N., School of Engineering, Lovely Professional University, Phagwara, India; Bisht M.S., Department of Engineering, Uttaranchal University, Dehradun, India; Adnan M.M., Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq","Lung cancer is among the top causes of death globally, significantly impacting global health due to its high incidence and mortality rates. Breathing difficulties and reduced quality of life are common in people with respiratory illnesses such as asthma, interstitial lung disease, and chronic obstructive pulmonary disease (COPD). Doctors diagnose and establish the stages of cancer, which may not always be correct. The only way to increase the chances of human survival is to recognize them early. The average survival rate increased from 14% to 49% when lung cancer was diagnosed early. Although computed tomography (CT) is significantly more effective than radiography, a complete diagnosis requires a combination of imaging methods. Machine learning technology has been developed and tested for lung cancer detection using CT images. Image processing and machine learning techniques for lung cancer identification were used to a dataset of CT scans in order to categorize the presence of lung cancer. From kaggle, we obtained a lungs imaging dataset. Fuzzy C-means, its variants and K-means algorithms were considered for image segmentation. Aberrant images were segmented to concentrate on the tumor. SVM, KNN, RF, and CNN were employed to study the classification efficiency and to determine if the CT image of the patient is normal or abnormal. The study reveals that the EnFCM method of Segmentation followed by CNN showed 99% accuracy, EnFCM segmentation followed by classification in tumor detection and identification has a good impact. The accuracy obtained with this model is more efficient. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Algorithms & Complexity; Artificial Intelligence; Computation; computed tomography; Computer Graphics & Visualization; Computer Science (General); Deep Learning; Lung cancer; machine learning; segmentation; Systems & Computer Architecture","","","","","","","","Abdullah D.M., Ahmed N.S., A review of most recent lung cancer detection techniques using machine learning, International Journal of Science and Business, IJSAB International, 5, 3, pp. 159-173, (2021); Akay M.F., Support vector machines combined with feature selection for breast cancer diagnosis, Expert Systems with Applications, 36, 2, pp. 3240-3247, (2009); Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A., Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: A Cancer Journal for Clinicians, 68, 6, pp. 394-424, (2018); Chakraborty S., Chakraborty S., Applications of artificial neural networks in machining processes: a comprehensive review, International Journal on Interactive Design and Manufacturing (IJIDeM), 18, pp. 1917-1948, (2024); Chalkidou A., O'Doherty M.J., Marsden P.K., False discovery rates in PET and CT studies with texture features: A systematic review, PloS One, 10, 5, (2015); Chen H., A hybrid deep learning model for accurate segmentation of lung cancer lesions in chest CT images, IEEE Transactions on Medical Imaging, 42, 5, pp. 1245-1255, (2023); Farag A.A., El Munim H.E., Graham J.H., Farag A.A., A novel approach for lung nodules segmentation in chest CT using level sets, IEEE Transactions on Image Processing, 22, 12, (2013); Joshua E.S.N., Chakkravarthy M., Bhattacharyya D., An extensive review on lung cancer detection using machine learning techniques, Journal of Critical Reviews, 7, 14, pp. 351-359, (2020); Kalidindi A., Kompalli P.L., Bandi S., Anugu S.R.R., CT image classification of human brain using deep learning, International Journal of Online and Biomedical Engineering (iJOE), 17, pp. 51-62, (2021); Kanungo T., Mount D.M., Netanyahu N.S., Piatko C.D., Silverman R., Wu A.Y., An efficient k-means clustering algorithm: Analysis and implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 7, pp. 881-892, (2002); Kirienko M., Sollini M., Silvestri G., Mognetti S., Voulaz E., Antunovic L., Rossi A., Antiga L., Chiti A., Convolutional neural networks promising in lung cancer T-parameter assessment on baseline FDG-PET/CT, Contrast Media & Molecular Imaging, (2018); Kumaresan S., Jai Aultrin K.S., Kumar S.S., Dev Anand M., Deep learning-based weld defect classification using VGG16 transfer learning adaptive fine-tuning, International Journal on Interactive Design and Manufacturing (IJIDeM), 17, 6, pp. 2999-3010, (2023); Lim S.X., Abdul Jalil M.M., Virgiyanti W., Yunus F., Lung cancer detection on CT scan images with deep learning methods: Sugeno fuzzy integral-based CNN ensemble method, 2023 International Conference on Informatics Engineering, Science & Technology (INCITEST), pp. 1-8, (2023); Naik D.A., Madana Mohana R., Ramu G., Sri Lalitha Y., SureshKumar M., Raghavender K.V., Analyzing histopathological images by using machine learning techniques, Applied Nano Science Springer, 13, pp. 2507-2513, (2022); Nisha Jenipher V., Radhika S., A study on early prediction of lung cancer using machine learning techniques, 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 911-916, (2020); P.r R., Nair R.A.S., A comparative study of lung cancer detection using machine learning algorithms, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), (2019); Rahane W., Dalvi H., Magar Y., Kalane A., Jondhaleet S., Lung cancer detection using image processing and machine learning healthcare, International Conference on Current Trends towards Converging Technologies, pp. 1-5, (2018); Salaken S.M., Khosravi A., Khatami A., Nahavandi S., Hosen M.A., Lung cancer classification using deep learned features on low population dataset, IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), (2017); Sri Lalitha Y., Manognya K., Efficient tumor detection in MRI brain images, International Journal of Online and Biomedical Engineering, 16, 13, pp. 122-131, (2020); Zhao Y., A deep learning framework for automated lung cancer detection in low-dose CT scans using multi-scale feature fusion, Medical Image Analysis, 83, (2023)","Y. Sri Lalitha; Gokaraju Rangaraju Institute of Engineering and Technology, JNTUH, Hyderabad, Telangana, India; email: srilalitham.y@gmail.com","","Cogent OA","","","","","","23311916","","","","English","Cogent Eng.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85205962154"
"Hwang Y.-S.; Lee O.E.-K.; Kim W.-J.; Jo H.-S.","Hwang, Yu-Seong (57221854284); Lee, Othelia Eun-Kyoung (57190219272); Kim, Woo-Jin (56560422500); Jo, Heui-Sug (7005809256)","57221854284; 57190219272; 56560422500; 7005809256","Designing a Socially Assistive Robot to Assist Older Patients with Chronic Obstructive Pulmonary Disease in Managing Indoor Air Quality","2024","Applied Sciences (Switzerland)","14","13","5647","","","","0","10.3390/app14135647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198444560&doi=10.3390%2fapp14135647&partnerID=40&md5=f4836d74a530109f945d1bf0f5d900e7","Department of Health Policy and Management, Kangwon National University School of Medicine, Gangwon, Chuncheon-si, 24341, South Korea; School of Social Work, University of North Carolina at Charlotte, Charlotte, 28223, NC, United States; Department of Internal Medicine, Kangwon National University School of Medicine, Gangwon, Chuncheon-si, 24341, South Korea","Hwang Y.-S., Department of Health Policy and Management, Kangwon National University School of Medicine, Gangwon, Chuncheon-si, 24341, South Korea; Lee O.E.-K., School of Social Work, University of North Carolina at Charlotte, Charlotte, 28223, NC, United States; Kim W.-J., Department of Internal Medicine, Kangwon National University School of Medicine, Gangwon, Chuncheon-si, 24341, South Korea; Jo H.-S., Department of Health Policy and Management, Kangwon National University School of Medicine, Gangwon, Chuncheon-si, 24341, South Korea","Chronic obstructive pulmonary disease (COPD) stems from airflow blockage and lung damage, and indoor air pollution exacerbates COPD, underscoring the necessity for proactive management. Older COPD patients, prone to respiratory and heat-related issues, require crucial assistance, yet their reduced awareness necessitates ongoing education to identify and enhance indoor air quality. To tackle this challenge, we developed a socially assistive robot (SAR) integrating IoT air quality sensors to guide patients in improving indoor air quality (IAQ). This study evaluated IAQ enhancement among older COPD patients using this technology, uncovering a significant reduction in ‘poor air quality alerts’ with a clear linear trend. Although ‘good alerts’ remained consistent, machine learning models predicted improved air quality post-alerts. Consistent alerts serve as a motivating factor for patients to maintain IAQ standards. However, barriers to SAR utilization, such as psychological and operational hurdles, need to be addressed in future research endeavors. © 2024 by the authors.","chronic obstructive pulmonary disease; indoor air quality; Internet of things; self-management; socially assistive robot","","","","","","National Research Foundation of Korea, NRF; Ministry of Science and ICT; MSIT, (RS-2023-00279534)","This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00279534) * MSIT: Ministry of Science and ICT.","Pauwels R.A., Rabe K.F., Burden and clinical features of chronic obstructive pulmonary disease (COPD), Lancet, 364, pp. 613-620, (2004); Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: A systematic review and modelling analysis, Lancet Respir. 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Promot, 36, pp. 43-51, (2019); Cooper S., Di Fava A., Vivas C., Marchionni L., Ferro F., ARI: The social assistive robot and companion, Proceedings of the 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 745-751; Yu C., Sommerlad A., Sakure L., Livingston G., Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life, Ageing Res. Rev, 78, (2022); Cano S., Gonzalez C.S., Gil-Iranzo R.M., Albiol-Perez S., Affective communication for socially assistive robots (sars) for children with autism spectrum disorder: A systematic review, Sensors, 21, (2021); Getson C., Nejat G., Socially assistive robots helping older adults through the pandemic and life after COVID-19, Robotics, 10, (2021); Feil-Seifer D., Mataric M.J., Defining socially assistive robotics, Proceedings of the 9th International Conference on Rehabilitation Robotics (ICORR 2005), pp. 465-468; Meinert E., Van Velthoven M., Brindley D., Alturkistani A., Foley K., Rees S., Wells G., de Pennington N., The internet of things in health care in oxford: Protocol for proof-of-concept projects, JMIR Res. Protoc, 7, (2018); Hello, We Are HYODOL ‘How It Works?’; Lee O.E., Lee H., Park A., Choi N.G., My precious friend: Human-robot interactions in home care for socially isolated older adults, Clin. 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Geosci, 144, (2020); Winkle K., McMillan D., Arnelid M., Harrison K., Balaam M., Johnson E., Leite I., Feminist human-robot interaction: Disentangling power, principles and practice for better, more ethical HRI, Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, pp. 72-82; Yu S.-H., Guo A.-M., Zhang X.-J., Effects of self-management education on quality of life of patients with chronic obstructive pulmonary disease, Int. J. Nurs. Sci, 1, pp. 53-57, (2014); Simoni-Wastila L., Wei Y.-J., Qian J., Zuckerman I.H., Stuart B., Shaffer T., Dalal A.A., Bryant-Comstock L., Association of chronic obstructive pulmonary disease maintenance medication adherence with all-cause hospitalization and spending in a Medicare population, Am. J. Geriatr. Pharmacother, 10, pp. 201-210, (2012); Gasteiger N., Loveys K., Law M., Broadbent E., Friends from the future: A scoping review of research into robots and computer agents to combat loneliness in older people, Clin. Interv. Aging, 16, pp. 941-971, (2021); Lazar A., Thompson H.J., Piper A.M., Demiris G., Rethinking the design of robotic pets for older adults, Proceedings of the 2016 ACM Conference on Designing Interactive Systems, pp. 1034-1046; Zhu H., Wu C.K., Koo C.H., Tsang Y.T., Liu Y., Chi H.R., Tsang K.-F., Smart healthcare in the era of internet-of-things, IEEE Consum. Electron. Mag, 8, pp. 26-30, (2019)","H.-S. Jo; Department of Health Policy and Management, Kangwon National University School of Medicine, Chuncheon-si, Gangwon, 24341, South Korea; email: joheuisug@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85198444560"
"Nakamoto K.; Horimasu Y.; Yamaguchi K.; Sakamoto S.; Masuda T.; Miyamoto S.; Nakashima T.; Iwamoto H.; Ohshimo S.; Sadamori T.; Fujitaka K.; Hamada H.; Shime N.; Hattori N.","Nakamoto, Kanako (57216415977); Horimasu, Yasushi (19336838200); Yamaguchi, Kakuhiro (56814086600); Sakamoto, Shinjiro (56419303500); Masuda, Takeshi (46961249900); Miyamoto, Shintaro (54794320600); Nakashima, Taku (15763036000); Iwamoto, Hiroshi (26121202700); Ohshimo, Shinichiro (15731705200); Sadamori, Takuma (23393851900); Fujitaka, Kazunori (58439156900); Hamada, Hironobu (7402815518); Shime, Nobuaki (58437743400); Hattori, Noboru (9278195000)","57216415977; 19336838200; 56814086600; 56419303500; 46961249900; 54794320600; 15763036000; 26121202700; 15731705200; 23393851900; 58439156900; 7402815518; 58437743400; 9278195000","Usefulness of Quantification and Serial Monitoring of Fine Crackles for Early Detection of Treatment-related Lung Injury: A Report of Two Cases","2024","Internal Medicine","63","12","","1783","1787","4","0","10.2169/internalmedicine.2387-23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196261820&doi=10.2169%2finternalmedicine.2387-23&partnerID=40&md5=132b6e120e992a6831b2cd0a46fd872e","Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; De-partment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan","Nakamoto K., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Horimasu Y., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Yamaguchi K., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Sakamoto S., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Masuda T., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Miyamoto S., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Nakashima T., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Iwamoto H., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Ohshimo S., De-partment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan; Sadamori T., De-partment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan; Fujitaka K., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Hamada H., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; Shime N., De-partment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Japan; Hattori N., Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan","Early detection and appropriate management of treatment-related interstitial lung disease (ILD) are important in cancer treatment. We established an algorithm for quantifying fine crackles using machine learning and reported that the fine crackle quantitative value (FCQV) calculated by this algorithm was more sensitive than chest radiography for detecting interstitial changes. Using this algorithm, we periodically analyzed respiratory sounds in two patients with lung cancer who developed treatment-related ILDs and found that the FCQV was elevated before the diagnosis of ILD. These cases may indicate the usefulness of the FCQV in the early diagnosis of treatment-related ILDs. © 2024 Japanese Society of Internal Medicine. All rights reserved.","auscultation; lung neoplasms; pneumonia; stethoscopes","Aged; Algorithms; Early Diagnosis; Female; Humans; Lung Diseases, Interstitial; Lung Injury; Lung Neoplasms; Male; Middle Aged; Respiratory Sounds; carboplatin; fluorodeoxyglucose f 18; ipilimumab; nivolumab; paclitaxel; prednisolone; abnormal respiratory sound; adult; aged; algorithm; Article; auscultation; cancer chemotherapy; case report; chemoradiotherapy; chronic obstructive lung disease; clinical article; computer assisted tomography; COPD assessment test; coughing; crackle; fever; fine crackle quantitative value; ground glass opacity; human; interstitial lung disease; machine learning; male; middle aged; pneumonia; positron emission tomography-computed tomography; radiation pneumonia; respiratory tract disease assessment; squamous cell lung carcinoma; thorax radiography; treatment related interstitial lung disease; very elderly; abnormal respiratory sound; diagnosis; diagnostic imaging; early diagnosis; etiology; female; interstitial lung disease; lung injury; lung tumor","","carboplatin, 41575-94-4; fluorodeoxyglucose f 18, 63503-12-8; ipilimumab, 477202-00-9; nivolumab, 946414-94-4; paclitaxel, 33069-62-4; prednisolone, 50-24-8","MSS-U11C, Pioneer, Japan","Pioneer, Japan","Japan Agency for Medical Research and Development, AMED","This work was supported by the Japan Agency for Medical Research and Development (AMED).","Satouchi M, Nosaki K, Takahashi T, Et al., First-line pembrolizumab vs chemotherapy in metastatic non-small-cell lung cancer: KEYNOTE-024 Japan subset, Cancer Sci, 111, pp. 4480-4489, (2020); Antonia SJ, Borghaei H, Ramalingam SS, Et al., Four-year survival with nivolumab in patients with previously treated advanced non-small-cell lung cancer: a pooled analysis, Lancet Oncol, 20, pp. 1395-1408, (2019); Gandhi L, Rodriguez-Abreu D, Gadgeel S, Et al., Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer, N Engl J Med, 378, pp. 2078-2092, (2018); Horinouchi H, Nogami N, Saka H, Et al., Pembrolizumab plus pemetrexed-platinum for metastatic nonsquamous non-small-cell lung cancer: KEYNOTE-189 Japan Study, Cancer Sci, 112, pp. 3255-3265, (2021); Antonia SJ, Villegas A, Daniel D, Et al., Durvalumab after chemoradiotherapy in stagef III non-small-cell lung cancer, N Engl J Med, 377, pp. 1919-1929, (2017); Murray N, Coy P, Pater JL, Et al., Importance of timing for thoracic irradiation in the combined modality treatment of limited-stage small-cell lung cancer, J Clin Oncol, 11, pp. 336-344, (1993); Kaku S, Horinouchi H, Watanabe H, Et al., Incidence and prognostic factors in severe drug-induced interstitial lung disease caused by antineoplastic drug therapy in the real world, J Cancer Res Clin Oncol, 148, pp. 1737-1746, (2022); Sellares J, Hernandez-Gonzalez F, Lucena CM, Et al., Auscultation of velcro crackles is associated with usual iinterstitial pneumonia, Medicine (Baltimore), 95, (2016); Melbye H, Garcia-Marcos L, Brand P, Et al., Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians’ classification of lung sounds from video recordings, BMJ Open Respir Res, 3, (2016); Horimasu Y, Ohshimo S, Yamaguchi K, Et al., A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: a proof-of-concept study, Medicine (Baltimore), 100, (2021); Hanania AN, Mainwaring W, Ghebre YT, Et al., Radiation-induced lung injury, Chest, 156, pp. 150-162, (2019); Tarantino P, Modi S, Tolaney SM, Et al., Interstitial lung disease induced by anti-ERBB2 antibody-drug conjugates: a review, JAMA Oncol, 7, pp. 1873-1881, (2021)","Y. Horimasu; Department of Molecular and Internal Medicine, Hiroshima University, Graduate School of Biomedical and Health Sciences, Japan; email: yasushi17@hiroshima-u.ac.jp","","Japanese Society of Internal Medicine","","","","","","09182918","","IEDIE","37866919","English","Intern. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196261820"
"Lee H.; Song M.J.; Cho Y.-J.; Kim D.J.; Hong S.-B.; Jung S.Y.; Lim S.Y.","Lee, Haeun (57395055700); Song, Myung Jin (57195597849); Cho, Young-Jae (57050711200); Kim, Dong Jung (56521858700); Hong, Sang-Bum (10439962800); Jung, Se Young (56660153400); Lim, Sung Yoon (57218269883)","57395055700; 57195597849; 57050711200; 56521858700; 10439962800; 56660153400; 57218269883","Supervised machine learning model to predict mortality in patients undergoing venovenous extracorporeal membrane oxygenation from a nationwide multicentre registry","2023","BMJ Open Respiratory Research","10","1","e002025","","","","0","10.1136/bmjresp-2023-002025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181165064&doi=10.1136%2fbmjresp-2023-002025&partnerID=40&md5=424e88fdbded2701d8586eb3a7ea94e6","Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam, South Korea; Devision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Cardiovascular and Thoracic Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Pulmonary and Critical Care Medicine, Asan Medical Center, Seoul, South Korea; Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea","Lee H., Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam, South Korea; Song M.J., Devision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Cho Y.-J., Devision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Kim D.J., Department of Cardiovascular and Thoracic Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea; Hong S.-B., Department of Pulmonary and Critical Care Medicine, Asan Medical Center, Seoul, South Korea; Jung S.Y., Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam, South Korea, Department of Family Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; Lim S.Y., Devision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea","Background Existing models have performed poorly when predicting mortality for patients undergoing venovenous extracorporeal membrane oxygenation (VV-ECMO). This study aimed to develop and validate a machine learning (ML)-based prediction model to predict 90-day mortality in patients undergoing VV-ECMO. Methods This study included 368 patients with acute respiratory failure undergoing VV-ECMO from 16 tertiary hospitals across South Korea between 2012 and 2015. The primary outcome was the 90-day mortality after ECMO initiation. The inputs included all available features (n=51) and those from the electronic health record (EHR) systems without preprocessing (n=40). The discriminatory strengths of ML models were evaluated in both internal and external validation sets. The models were compared with conventional models, such as respiratory ECMO survival prediction (RESP) and predicting death for severe acute respiratory distress syndrome on VV-ECMO (PRESERVE). Results Extreme gradient boosting (XGB) (areas under the receiver operating characteristic curve, AUROC 0.82, 95% CI (0.73 to 0.89)) and light gradient boosting (AUROC 0.81 (95% CI 0.71 to 0.88)) models achieved the highest performance using EHR's and all other available features. The developed models had higher AUROCs (95% CI 0.76 to 0.82) than those of RESP (AUROC 0.66 (95% CI 0.56 to 0.76)) and PRESERVE (AUROC 0.71 (95% CI 0.61 to 0.81)). Additionally, we achieved an AUROC (0.75) for 90-day mortality in external validation in the case of the XGB model, which was higher than that of RESP (0.70) and PRESERVE (0.67) in the same validation dataset. Conclusions ML prediction models outperformed previous mortality risk models. This model may be used to identify patients who are unlikely to benefit from VV-ECMO therapy during patient selection. © Author(s) (or their employer(s)) 2023.","","Extracorporeal Membrane Oxygenation; Hospital Mortality; Humans; Respiratory Distress Syndrome; Retrospective Studies; Supervised Machine Learning; acute respiratory failure; adult; adult respiratory distress syndrome; area under the curve; Article; artificial ventilation; asthma; bacterial pneumonia; brain disease; brain embolism; chronic obstructive lung disease; cohort analysis; diagnostic test accuracy study; epilepsy; extracorporeal oxygenation; female; human; Human immunodeficiency virus infection; interstitial lung disease; laboratory test; liver cirrhosis; major clinical study; male; middle aged; mortality; multicenter study; nervous system injury; observational study; predictive value; receiver operating characteristic; RESP score; retrospective study; seizure; sensitivity and specificity; supervised machine learning; support vector machine; tertiary care center; veno-venous ECMO; virus pneumonia; clinical trial; hospital mortality; respiratory distress syndrome; supervised machine learning","","","Python version 3.8.8, Python Software Foundation, United States; R studio V.4.1.0, R studio, United States","Python Software Foundation, United States; R studio, United States","Chi Ryang Chung; Jae-Seung Jung","We thank all the medical staff and ECMO centres participating in the ECMO registry for their contribution: Chi Ryang Chung, Jae-Seung Jung, Jin Young Oh, Jung-Hyun Kim, Jung-Wan Yoo, Sang-Min Lee, Seung Yong Park, So Hee Park, So-My Koo, Sunghoon Park, Woo Hyun Cho, Youjin Chang and Yun Su Sim.","Bellani G., Laffey J.G., Pham T., Et al., Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries, JAMA, 315, pp. 788-800, (2016); Thompson B.T., Chambers R.C., Liu K.D., Acute respiratory distress syndrome, N Engl J Med, 377, pp. 1904-1905, (2017); Combes A., Hajage D., Capellier G., Et al., Extracorporeal membrane oxygenation for severe acute respiratory distress syndrome, N Engl J Med, 378, pp. 1965-1975, (2018); Li X., Hu M., Zheng R., Et al., Delayed initiation of ECMO is associated with poor outcomes in patients with severe COVID-19: A multicenter retrospective cohort study, Front Med (Lausanne), 8, (2021); Tonna J.E., Abrams D., Brodie D., Et al., Management of adult patients supported with Venovenous Extracorporeal membrane oxygenation (VV ECMO): guideline from the Extracorporeal life support Organization (ELSO), ASAIO J, 67, pp. 601-610, (2021); Badulak J., Antonini M.V., Stead C.M., Et al., Extracorporeal membrane oxygenation for COVID-19: updated 2021 guidelines from the Extracorporeal life support organization, ASAIO J, 67, pp. 485-495, (2021); Harnisch L.O., Moerer O., Contraindications to the initiation of veno-venous ECMO for severe acute respiratory failure in adults: A systematic review and practical approach based on the current literature, Membranes, 11; MacLaren G., When to initiate ECMO with low likelihood of success, Crit Care, 22, (2018); Shaefi S., Brenner S.K., Gupta S., Et al., Extracorporeal membrane oxygenation in patients with severe respiratory failure from COVID-19, Intensive Care Med, 47, pp. 208-221, (2021); Tabatabai A., Ghneim M.H., Kaczorowski D.J., Et al., Mortality risk assessment in COVID-19 Venovenous Extracorporeal membrane oxygenation, Ann Thorac Surg, 112, pp. 1983-1989, (2021); Enger T., Philipp A., Videm V., Et al., Prediction of mortality in adult patients with severe acute lung failure receiving veno-venous Extracorporeal membrane oxygenation: a prospective observational study, Crit Care, 18, (2014); Schmidt M., Zogheib E., Roze H., Et al., The PRESERVE mortality risk score and analysis of long-term outcomes after Extracorporeal membrane oxygenation for severe acute respiratory distress syndrome, Intensive Care Med, 39, pp. 1704-1713, (2013); Schmidt M., Bailey M., Sheldrake J., Et al., Predicting survival after Extracorporeal membrane oxygenation for severe acute respiratory failure. The respiratory Extracorporeal membrane oxygenation survival prediction (RESP) score, Am J Respir Crit Care Med, 189, pp. 1374-1382, (2014); Hilder M., Herbstreit F., Adamzik M., Et al., Comparison of mortality prediction models in acute respiratory distress syndrome undergoing Extracorporeal membrane oxygenation and development of a novel prediction score, Crit Care, 21, (2017); Nielsen A.B., Thorsen-Meyer H.-C., Belling K., Et al., Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish national patient Registry and electronic patient records, Lancet Digit Health, 1, pp. e78-e89, (2019); Baek M.S., Lee S.-M., Chung C.R., Et al., Improvement in the survival rates of Extracorporeal membrane oxygenation-supported respiratory failure patients: a multicenter retrospective study in Korean patients, Crit Care, 23, (2019); Baek M.S., Chung C.R., Kim H.J., Et al., Age is major factor for predicting survival in patients with acute respiratory failure on Extracorporeal membrane oxygenation: a Korean multicenter study, J Thorac Dis, 10, pp. 1406-1417, (2018); Ayers B., Wood K., Gosev I., Et al., Predicting survival after Extracorporeal membrane oxygenation by using machine learning, Ann Thorac Surg, 110, pp. 1193-1200, (2020); Lundberg S.M., Lee S.-I., A unified approach to interpreting model predictions, Adv Neural Inf Process Syst, (2017); Chen T., Guestrin C., Xgboost: A Scalable tree boosting system, Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, (2016); Ke G., Meng Q., Finley T., Et al., Lightgbm: A highly efficient gradient boosting decision tree, Adv Neural Inf Process Syst, (2017); Waskom M., Botvinnik O., O'Kane D., Et al., Mwaskom/Seaborn: V0. 8.1 (September 2017), Zenodo, (2017); Virtanen P., Gommers R., Oliphant T.E., Et al., Scipy 1.0: fundamental Algorithms for scientific computing in python, Nat Methods, 17, 352, pp. 261-272, (2020); Van G., The python library reference, release 3.8. 2, (2020); Hunter J.D., Matplotlib: A 2d Graphics environment, Comput Sci Eng, 9, pp. 90-95, (2007); Reback J., McKinney W., Van Den Bossche J., Et al., Pandas-Dev/Pandas: Pandas 1.0. 5. Zenodo, (2020); Harris C.R., Millman K.J., van der Walt S.J., Et al., Array programming with Numpy, Nature, 585, pp. 357-362, (2020); R: A language and environment for statistical computing, (2013); Yoon J.H., Pinsky M.R., Clermont G., Artificial intelligence in critical care medicine, Crit Care, 26, (2022); Kang M.W., Kim J., Kim D.K., Et al., Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy, Crit Care, 24, (2020); Ozenne B., Subtil F., Maucort-Boulch D., The precision-recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases, J Clin Epidemiol, 68, pp. 855-859, (2015); Fitzgerald M., Saville B.R., Lewis R.J., Decision curve analysis, JAMA, 313, pp. 409-410, (2015)","S.Y. Jung; Department of Digital Healthcare, Seoul National University Bundang Hospital, Seongnam, South Korea; email: syjung@snubh.org; S.Y. Lim; Devision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea; email: nucleon727@gmail.com","","BMJ Publishing Group","","","","","","20524439","","","38154913","English","BMJ Open Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85181165064"
"Lee J.-H.; Hong C.; Oh J.S.; Kim T.-B.","Lee, Ji-Hyang (57208203071); Hong, Chaelin (57225953337); Oh, Ji Seon (35590419200); Kim, Tae-Bum (57206927697)","57208203071; 57225953337; 35590419200; 57206927697","Electronic medical record–based machine learning predicts the relapse of asthma exacerbation","2023","Annals of Allergy, Asthma and Immunology","131","2","","270","271","1","0","10.1016/j.anai.2023.04.025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160047098&doi=10.1016%2fj.anai.2023.04.025&partnerID=40&md5=65f42be3b9a9ba6d589f4727aa3c4e3b","Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Asan Institute of Life Sciences, Big Data Research Center, Asan Medical Center, Seoul, South Korea; Department of Information Medicine, Big Data Research Center, Asan Medical Center, Seoul, South Korea","Lee J.-H., Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Hong C., Asan Institute of Life Sciences, Big Data Research Center, Asan Medical Center, Seoul, South Korea; Oh J.S., Department of Information Medicine, Big Data Research Center, Asan Medical Center, Seoul, South Korea; Kim T.-B., Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea","[No abstract available]","","Asthma; Chronic Disease; Electronic Health Records; Humans; Machine Learning; Recurrence; beta adrenergic receptor stimulating agent; corticosteroid; leukotriene receptor blocking agent; muscarinic receptor blocking agent; Article; asthma; bronchiectasis; chronic sinusitis; clinical feature; clinical outcome; comorbidity; computer prediction; disease exacerbation; electronic medical record; forced expiratory volume; health care utilization; human; lung function; machine learning; relapse; rhinitis; tertiary care center; asthma; chronic disease; electronic health record; machine learning; recurrent disease","","","","","Korea Health Technology Research and Development; Ministry of Trade, Industry and Energy, MOTIE; Ministry of Health and Welfare, MOHW, (2021IP0047, HC20C0076, HI19C0481); Korea Health Industry Development Institute, KHIDI; Asan Institute for Life Sciences, Asan Medical Center, (20004927)","Funding: This research was supported by a grant of the Korea Health Technology Research and Development (R&D) Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0481, HC20C0076). This study was also supported by a grant (2021IP0047) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea, and the Technology Innovation Program (20004927, upgrade of Common Data Model (CDM)–based distributed Biohealth Data Platform and Development of Verification Technology) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea). ","Song W.J., Lee J.H., Kang Y., Joung W.J., Chung K.F., Future risks in patients with severe asthma, Allergy Asthma Immunol Res, 11, 6, pp. 763-778, (2019); Sears M.R., Can we predict exacerbations of asthma?, Am J Respir Crit Care Med, 199, 4, pp. 399-400, (2019); Bloom C.I., Palmer T., Feary J., Quint J.K., Cullinan P., Exacerbation patterns in adults with asthma in England. A population-based study, Am J Respir Crit Care Med, 199, 4, pp. 446-453, (2019); Martin A., Bauer V., Datta A., Masi C., Mosnaim G., Solomonides A., Et al., Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data, J Asthma, 57, 12, pp. 1339-1346, (2020); Jeffery M.M., Inselman J.W., Maddux J.T., Lam R.W., Shah N.D., Rank M.A., Asthma patients who stop asthma biologics have a similar risk of asthma exacerbations as those who continue asthma biologics, J Allergy Clin Immunol Pract, 9, 7, pp. 2742-2750, (2021); (2022); Zein J.G., Wu C.P., Attaway A.H., Zhang P., Nazha A., Novel machine learning can predict acute asthma exacerbation, Chest, 159, 5, pp. 1747-1757, (2021)","","","American College of Allergy, Asthma and Immunology","","","","","","10811206","","ALAIF","37100278","English","Ann. Allergy Asthma Immunol.","Article","Final","","Scopus","2-s2.0-85160047098"
"Yazdani A.; Erfannia L.; Farzaneh A.; Ali O.","Yazdani, Azita (57331464500); Erfannia, Leila (55986093000); Farzaneh, Ali (58888439500); Ali, Omar (56498722200)","57331464500; 55986093000; 58888439500; 56498722200","Survival analysis of patients with COVID-19 using deep neural network and random forest techniques","2024","Frontiers in Health Informatics","13","","186","","","","0","10.30699/fhi.v13i0.512","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185148530&doi=10.30699%2ffhi.v13i0.512&partnerID=40&md5=73b2c41991bfd5fab85e9747fd35ebfd","Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Department of Information Systems, American University of the Middle East, Kuwait","Yazdani A., Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Erfannia L., Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Farzaneh A., Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands; Ali O., Department of Information Systems, American University of the Middle East, Kuwait","Introduction: The prediction of the survival chance of coronavirus disease 2019 (COVID-19) patients is as important as the early detection of the coronavirus. Since patient mortality, factors may differ by location, this study concentrated on identifying the influential factors and predicting survival for COVID-19 patients using machine learning methods in Fars province, Iran. Material and Methods: The research dataset was extracted in the period January 21, 2020, to September 25, 2020, and contains 25858 hospitalized patients’ records with 51 features. These records were classified into two categories: death (label 1) and survival (label 0). The methodology of this research is CRISP standard. A comparison was made between the efficiency of two deep neural network and random forest algorithms in predicting survival. Modeling steps were done with Python language in the Google Colab environment. Results: Experimental results demonstrated that the deep neural network algorithm had better performance than random forest with accuracy, precision, recall, F-score, and receiver operating characteristic of 97.2%, 100%, 93.54%, 96.66%, and 97.9%, respectively. Based on the results of the random forest model, history of hypertension, chronic neurological disorders, chronic lung diseases, asthma, chronic kidney disease and, heart disease were the most important risk factors related to death. Conclusion: Deployment of our proposed model allows medical professionals to exercise greater caution during the treatment of patients who are most likely to die due to their medical conditions. © 2023, Published by Frontiers in Health Informatics.","COVID-19; Deep Learning; Machine Learning; Survival","","","","","","","","Moulaei K, Shanbehzadeh M, Mohammadi-Taghiabad Z, Kazemi-Arpanahi H., Comparing machine learning algorithms for predicting COVID-19 mortality, BMC Med Inform Decis Mak, 22, 1, (2022); Alyasseri ZAA, Al-Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Et al., Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches, Expert Syst, 39, 3, (2022); Rezaee R, Asadi S, Yazdani A, Rezvani A, Kazeroon AM., Development, usability and quality evaluation of the resilient mobile application for women with breast cancer, Health Sci Rep, 5, 4, (2022); Yazdani A, Sharifian R, Ravangard R, Zahmatkeshan M., COVID-19 and information communication technology: a conceptual model, Journal of Advanced Pharmacy Education and Research, 11, pp. 83-97, (2021); Afrash MR, Erfannia L, Amraei M, Mehrabi N, Jelvay S, Shanbehzadeh M., Machine learning-based clinical decision support system for automatic diagnosis of COVID-19 based on the routine blood test, Journal of Biostatistics and Epidemiology, 8, 1, pp. 77-89, (2022); Almalki YE, Qayyum A, Irfan M, Haider N, Glowacz A, Alshehri FM, Et al., A novel method for COVID-19 diagnosis using artificial intelligence in chest X-ray images, Healthcare, 9, 5, (2021); Irfan M, Iftikhar MA, Yasin S, Draz U, Ali T, Hussain S, Et al., Role of hybrid deep neural networks (HDNNs), computed tomography, and chest X-rays for the detection of COVID-19, Int J Environ Res Public Health, 18, 6, (2021); Meng L, Dong D, Li L, Niu M, Bai Y, Wang M, Et al., A deep learning prognosis model help alert for COVID-19 patients at high-risk of death: a multi-center study, IEEE J Biomed Health Inform, 24, 12, pp. 3576-3584, (2020); Ikemura K, Bellin E, Yagi Y, Billett H, Saada M, Simone K, Et al., Using automated machine learning to predict the mortality of patients with COVID-19: Prediction model development study, J Med Internet Res, 23, 2, (2021); 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Hossin M, Sulaiman MN., A review on evaluation metrics for data classification evaluations, International Journal of Data Mining & Knowledge Management Process, 5, 2, pp. 1-11, (2015); Bradley AP., The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recognition, 30, 7, pp. 1145-1159, (1997); Hajian-Tilaki K., Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation, Caspian J Intern Med, 4, 2, pp. 627-635, (2013); Ahmed TU, Jamil MN, Hossain MS, Islam RU, Andersson K., An integrated deep learning and belief rule base intelligent system to predict survival of COVID-19 patient under uncertainty, Cognit Comput, 14, 2, pp. 660-676, (2022)","L. Erfannia; Department of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; email: Leila.erfannia@gmail.com","","Iranian Medical Informatics Association (IrMIA)","","","","","","26767104","","","","English","Front. Health. Inform.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85185148530"
"Pour J.N.; Pourmina M.A.; Moghaddasi M.N.","Pour, Javad Nouri (57850075300); Pourmina, Mohammad Ali (35311746300); Moghaddasi, Mohammad Naser (57210111578)","57850075300; 35311746300; 57210111578","Improving Breast Cancer Detection with Convolutional Neural Networks and Modified ResNet Architecture","2024","Current Medical Imaging Reviews","20","1","e15734056290499","","","","0","10.2174/0115734056290499240402102301","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196435227&doi=10.2174%2f0115734056290499240402102301&partnerID=40&md5=d5aeae0f89acb5e29cdeb82d99464adb","Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","Pour J.N., Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; Pourmina M.A., Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; Moghaddasi M.N., Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran","Background: The pathogenesis of breast cancer is characterized by dysregulated cell proliferation, leading to the formation of a neoplastic mass. Conventional methodologies for analyzing carcinomatous distal areas within whole-slide images (WSIs) tissue regions may lack comprehensive insights. Purpose: This study aims to introduce an innovative methodology based on convolutional neural networks (CNN), specifically employing a CNN Modified ResNet architecture for breast cancer detection. The research seeks to address the limitations of existing approaches and provide a robust solution for the comprehensive analysis of tissue regions. Methods: The dataset utilized in this study comprises approximately 275,000 RGB image patches, each standardized at 50x50 pixels. The CNN Modified ResNet architecture is implemented, and a comparative evaluation against diverse architectures is conducted. Rigorous validation tests employing established performance metrics are carried out to assess the proposed methodology. Results: The proposed architecture achieves a notable 89% accuracy in breast cancer detection, surpassing alternative methods by 2%. The results signify the efficacy and superiority of the CNN Modified ResNet model in analyzing carcinomatous distal areas within WSIs tissue regions. Conclusion: In conclusion, this study demonstrates the potential of the CNN Modified ResNet architecture as an effective tool for breast cancer detection. The enhanced accuracy and comprehensive analysis capabilities make it a promising approach for advancing the understanding of neoplastic masses in WSIs tissue regions. Further research and validation could solidify its role in clinical applications and diagnostic procedures. © 2024 The Author(s).","Breast cancer; Clinical applications; Convolutional neural network; ResBlock; ResNet; WSIs tissue","Breast Neoplasms; Female; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted; Neural Networks, Computer; Cell proliferation; Clinical research; Convolution; Convolutional neural networks; Diseases; Network architecture; gelatinase B; microRNA; Breast Cancer; Breast cancer detection; Clinical application; Comprehensive analysis; Convolutional neural network; Innovative methodologies; Resblock; Resnet; Whole slide images; Whole-slide image tissue; accuracy; architecture; Article; artificial intelligence; artificial neural network; asthma; bioinformatics; breast cancer; cancer classification; cancer diagnosis; cancer risk; celiac disease; cell proliferation; convolutional neural network; decision tree; deep learning; disease severity; drug interaction; endoscopy; female; health care system; human; human tissue; information processing; lung cancer; machine learning; outcome assessment; particulate matter; patient monitoring; retina blood vessel; seizure; statistical concepts; support vector machine; telecommunication; uterine cervix cancer; artificial neural network; breast tumor; computer assisted diagnosis; diagnostic imaging; image processing; procedures; Tissue","","gelatinase B, 146480-36-6","Intel Core  i7  processor, Intel; RTX  3070  graphics  card, Intel","Intel; Intel","","","Morgan E, Arnold M, Gini A, Et al., Global burden of colorectal cancer in 2020 and 2040: Incidence and mortality estimates from GLOBOCAN, Gut, 72, 2, pp. 338-344, (2023); Frick C, Rumgay H, Vignat J, Et al., Quantitative estimates of preventable and treatable deaths from 36 cancers worldwide: A population-based study, Lancet Glob Health, 11, 11, pp. e1700-e1712, (2023); Huang J, Chan SC, Ngai CH, Et al., Global incidence, mortality and temporal trends of cancer in children: A joinpoint regression analysis, Cancer Med, 12, 2, pp. 1903-1911, (2023); 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Med. Imaging Rev.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85196435227"
"Zhang H.","Zhang, Honghao (58914772100)","58914772100","Portable oxygen breathing apparatus integrated with biosensors: Enabling intelligent monitoring and optimal oxygen provision for biomechanical homeostasis","2024","MCB Molecular and Cellular Biomechanics","21","4","535","","","","0","10.62617/mcb535","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213291631&doi=10.62617%2fmcb535&partnerID=40&md5=0c241eae9058d071f01c746d75482f85","Northwestern Polytechnical University, Shanxi, Xi’an, 710072, China","Zhang H., Northwestern Polytechnical University, Shanxi, Xi’an, 710072, China","A lightweight, compact, inconspicuous gadget that provides extra oxygen when traveling is called a portable oxygen breathing apparatus. Cells rely on oxygen to drive the oxidative phosphorylation process within mitochondria, where adenosine triphosphate (ATP) is synthesized. Adequate ATP is essential for maintaining the muscle contraction and cell motility. With these portable devices, patients can maintain their oxygen therapy while going about their daily lives, enhancing their quality of life (QoL) and encouraging more mobility. An incorrect assessment could result in a low oxygen supply during exercise. Hypoxia-induced changes can also trigger intracellular signaling pathways that may lead to cell damage and, in the long term, contribute to the progression of various pathologies. Promising resolution to the difficulties can be discovered by incorporating machine learning (ML) algorithms and sophisticated monitoring systems into portable oxygen delivery devices. In this study, we propose a novel intelligent portable oxygen breathing apparatus integrated with biosensors (IPOBAB) that has revolutionized the treatment of long-term respiratory disorders, particularly severe hypoxemia and chronic obstructive pulmonary disease (COPD). IPOBAB system deployed with the Dynamic Gradient Boosting Machine (DGBM) classifier to classify the physical activities into low, moderate, and high exertion levels to ensure oxygen delivery is repeatedly adjusted based on the patient’s current requirements. Inertial Measurement Unit (IMU) sensor data, blood oxygen saturation (SpO2), and cardiovascular rate are just a few of the vital physiological features that biological sensors continuously monitor. This data lets doctors perform real-time assessments of a patient’s health status. To eliminate noise, the information was processed using a median filter. The Fast Fourier Transform (FFT), which displays dominating frequency components, divides the electrical signal into individual frequencies to extract features. The results demonstrated that the IPOBAB model exhibits a high weighted accuracy of 98.4% in mechanically adjusting oxygen flow according to medical criteria compared to existing algorithms. This indicates that the system is effective in optimizing oxygen delivery, which is essential for maintaining the proper cell and molecular biomechanical functions in patients with long-term respiratory disorders. In conclusion, the IPOBAB represents a significant advancement in portable oxygen therapy as it combines adaptive oxygen delivery and comprehensive monitoring, thereby optimizing the care for patients with long-term respiratory conditions and safeguarding the integrity and functionality of cells and tissues at the molecular level. Copyright © 2024 by author(s).","biosensors; dynamic gradient boosting machine (DGBM); inertial measurement unit (IMU); oxygen supply; portable oxygen breathing apparatus","Adenosinetriphosphate; Brain; Breath controlled devices; Cell signaling; Gas bearings; Hemodialyzers; Magnetic couplings; Metabolic engineering; Nonmetallic bearings; Oxygen regulators; Physiological models; Pulmonary diseases; Respiratory therapy; Screws; Sensory feedback; Speed reducers; Adenosine triphosphate; Breathing apparatus; Dynamic gradient boosting machine; Gradient boosting; Inertial measurement unit; Inertial measurements units; Intelligent monitoring; Oxygen delivery; Portable oxygen breathing apparatus; Respiratory disorders; Median filters","","","","","","","Sanchez-Morillo D., Lara-Dona A., Priego-Torres B., Morales-Gonzalez M., Montoro-Ballesteros F., Leon-Jimenez A., Portable Oxygen Therapy: Is the 6-Minute Walking Test Overestimating the Actual Oxygen Needs?, Journal of Clinical Medicine, 9, 12, (2020); Lellouche F., L'Her E., Conventional Oxygen Therapy: Technical and Physiological Issues, High Flow Nasal Cannula: Physiological Effects and Clinical Applications, pp. 1-36, (2021); Pillai S., Upadhyay A., Sayson D., Nguyen B.H., Tran S.D., Advances in medical wearable biosensors: Design, fabrication and materials strategies in healthcare monitoring, Molecules, 27, 1, (2021); Strapazzon G., Gatterer H., Falla M., Dal Cappello T., Malacrida S., Turner R., Schenk K., Paal P., Falk M., Schweizer J., Brugger H., Hypoxia and hypercapnia effects on cerebral oxygen saturation in avalanche burial: A pilot human experimental study, Resuscitation, 158, pp. 175-182, (2021); Verma D., Singh K.R., Yadav A.K., Nayak V., Singh J., Solanki P.R., Singh R.P., Internet of Things (IoT) in nano-integrated wearable biosensor devices for healthcare applications, Biosensors and Bioelectronics: X, 11, (2022); Wang L., Lou Z., Jiang K., Shen G., Bio‐multifunctional smart wearable sensors for medical devices, Advanced Intelligent Systems, 1, 5, (2019); Swapna M., Viswanadhula U.M., Aluvalu R., Vardharajan V., Kotecha K., Bio-signals in medical applications and challenges using artificial intelligence, Journal of Sensor and Actuator Networks, 11, 1, (2022); Vakhter V., Kahraman B., Bu G., Foroozan F., Guler U., A prototype wearable device for noninvasive monitoring of transcutaneous oxygen, IEEE Transactions on Biomedical Circuits and Systems, 17, 2, pp. 323-335, (2023); Kanna R.K., Banappagoudar S.B., Menezes F.R., Sona P.S., Patient Monitoring System for COVID Care Using Biosensor Application, International Conference on Communication, Networks and Computing, pp. 310-321, (2022); Kumar S.A., Gopinath B., Kavinraj A., Sasikala S., Towards improving patient health monitoring systems using machine learning and the Internet of Things, 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), pp. 1-5, (2021); Mia M.M.H., Mahfuz N., Habib M.R., Hossain R., An Internet of Things application on continuous remote patient monitoring and diagnosis, 2021 4th international conference on bio-engineering for smart technologies (BioSMART), pp. 1-6, (2021); Lavric A., Petrariu A.I., Mutescu P.M., Coca E., Popa V., Internet of Things concept in the context of the COVID-19 pandemic: a multi-sensor application design, Sensors, 22, 2, (2022); Stella K., Menaka M., Jeevitha R., Jenila S.J., Devi A., Vethapackiam K., Patient Pulse Rate and Oxygen Level Monitoring System Using IoT, International Conference on IoT Based Control Networks and Intelligent Systems, pp. 343-355, (2023); Bhattacharya S., Pandey M., System for Remote Health Monitoring using Biosensors with IoT Cloud Convergence, NeuroQuantology, 20, 16, (2022); Contardi U.A., Morikawa M., Brunelli B., Thomaz D.V., Max30102 photometric biosensor coupled to esp32-webserver capabilities for continuous point-of-care oxygen saturation and heart rate monitoring, Engineering Proceedings, 16, 1, (2021); Wong D.L.T., Yu J., Li Y., Deepu C.J., Ngo D.H., Zhou C., Singh S.R., Koh A., Hong R., Veeravalli B., Motani M., An integrated wearable wireless vital signs biosensor for continuous inpatient monitoring, IEEE Sensors Journal, 20, 1, pp. 448-462, (2019); Jin X., Liu C., Xu T., Su L., Zhang X., Artificial intelligence biosensors: Challenges and prospects, Biosensors and Bioelectronics, 165, (2020); Nwibor C., Haxha S., Ali M.M., Sakel M., Haxha A.R., Saunders K., Nabakooza S., Remote health monitoring system for the estimation of blood pressure, heart rate, and blood oxygen saturation level, IEEE Sensors Journal, 23, 5, pp. 5401-5411, (2023); Madevska Bogdanova A., Koteska B., Vicentic T., . Ilic S., Tomic M., Spasenovic M., Blood Oxygen Saturation Estimation with Laser‐Induced Graphene Respiration Sensor, Journal of Sensors, 2024, 1, (2024); Mondal A., Dutta D., Chanda N., Mandal N., Mandal S., RESPIPulse: Machine learning-assisted sensory device for pulsed mode delivery of oxygen bolus using surface electromyography (sEMG) signals, Sensors and Actuators A: Physical, 369, (2024); Nathan V., Vatanparvar K., Kuang J., Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze Sensing Using Mobile Sensors, (2020)","H. Zhang; Northwestern Polytechnical University, Xi’an, Shanxi, 710072, China; email: leozhh@yeah.net","","Sin-Chn Scientific Press","","","","","","15565297","","","","English","MCB Mol. Cell. Biomech.","Article","Final","","Scopus","2-s2.0-85213291631"
"Li L.; Meng J.; Chen J.","Li, Lu (59300592300); Meng, Jiaqi (59467012500); Chen, Jiquan (8985856500)","59300592300; 59467012500; 8985856500","Longitudinal Analysis of Risk Factors for Pulmonary Function Decline in Chronic Lung Diseases Over Five Years","2024","International Journal of COPD","19","","","2639","2650","11","0","10.2147/COPD.S487178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212245142&doi=10.2147%2fCOPD.S487178&partnerID=40&md5=9c41e3182629f6e6a4e9fa72d44401f6","Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, China","Li L., Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, China; Meng J., Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, China; Chen J., Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, China","Objective: Chronic lung diseases (CLDs) are a major global health concern, characterized by a progressive decline in pulmonary function that severely impacts quality of life. It is essential to identify and predict the primary risk factors for CLDs. This study aims to establish a predictive model to assist healthcare providers in the early identification of high-risk patients and timely interventions and treatment options. Methods: This study utilized questionnaire data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011, 2013, and 2015. A latent class growth model (LCGM) was established using CLDs as the baseline sample. This model stratified the patients based on the extent of the decline in Δpeak expiratory flow (ΔPEF), which served as the target variable. Independent variables included age, gender, smoking status, body mass index, education level, and comorbidities. A random forest model was developed using Python, and the importance of the feature was visualized through the SHAP method. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis. Results: After screening, a total of 553 patients with CLDs were included in the study. The random forest model pinpointed grip strength, age, education level, gender, and asthma as the top five risk factors for pulmonary function decline. Specifically, the model demonstrated robust predictive performance with an area under the ROC curve (AUC) value of 0.77, affirming its accuracy and clinical applicability. Both calibration and decision curves further substantiated the reliability of the model in identifying patients at increased risk for pulmonary function decline. Conclusion: The predictive model developed in this study serves as a valuable tool for clinicians to target early interventions and optimize treatment strategies to enhance the quality of care and patient outcomes in the management of CLDs. © 2024 Li et al.","chronic lung diseases; health services; latent class growth modeling; machine learning in healthcare; pulmonary function decline; random forest model","Age Factors; Aged; Asthma; China; Chronic Disease; Comorbidity; Decision Support Techniques; Disease Progression; Educational Status; Female; Hand Strength; Humans; Latent Class Analysis; Longitudinal Studies; Lung; Lung Diseases; Male; Middle Aged; Predictive Value of Tests; Prognosis; Respiratory Function Tests; Risk Assessment; Risk Factors; Sex Factors; Time Factors; adult; area under the curve; Article; blood pressure; body mass; bootstrapping; Center for Epidemiological Studies Depression Scale; chronic lung disease; cognition; cognitive impairment assessment; controlled study; cross validation; daily life activity; depression; diagnostic test accuracy study; grip strength; health care personnel; health service; hospitalization; human; latent class growth model; longitudinal study; lung function; machine learning; major clinical study; middle aged; peak expiratory flow; questionnaire; random forest; receiver operating characteristic; risk factor; sleep time; statistical concepts; telephone interview for cognitive status; age; aged; asthma; China; chronic disease; comorbidity; decision support system; diagnosis; disease exacerbation; educational status; epidemiology; female; hand strength; latent class analysis; lung; lung disease; lung function test; male; pathophysiology; predictive value; prognosis; risk assessment; risk factor; sex factor; time factor","","","","","","","Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019, EClinicalMedicine, 59, (2023); Adeloye D, Song P, Zhu Y, Et al., NIHR RESPIRE Global Respiratory Health Unit. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, Lancet Respir Med, 10, 5, pp. 447-458, (2022); Zhao Y, Hu Y, Smith JP, Et al., Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS), Int J Epidemiol, 43, 1, pp. 61-68, (2014); Soriano JB, Polverino F, Cosio BG., What is early COPD and why is it important?, Eur Respir J, 52, 6, (2018); Christenson SA, Smith BM, Bafadhel M, Putcha N., Chronic obstructive pulmonary disease, Lancet, 399, 10342, pp. 2227-2242, (2022); Yang IA, Jenkins CR, Salvi SS., Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment, Lancet Respir Med, 10, 5, pp. 497-511, (2022); Cavailles A, Brinchault-Rabin G, Dixmier A, Et al., Comorbidities of COPD, Eur Respir Rev, 22, 130, pp. 454-475, (2013); Hansen EF, Vestbo J, Phanareth K, Et al., Peak flow as predictor of overall mortality in asthma and chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 163, 3, pp. 690-693, (2001); Lundberg SM, Erion G, Chen H, Et al., From local explanations to global understanding with explainable AI for trees, Nat Mach Intell, 2, 1, pp. 56-67, (2020); Nguena Nguefack HL, Page MG, Katz J, Et al., Trajectory modelling techniques useful to epidemiological research: a comparative narrative review of approaches, Clin Epidemiol, 12, pp. 1205-1222, (2020); Zhu L, Wang Y, Li J, Et al., Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases, Front Public Health, 12, (2024); Katz S., The index of ADL: a standardized measure of biological and psychosocial function, JAMA, 185, (1963); Lawton MP, Brody EM., Assessment of older people: self-maintaining and instrumental activities of daily living, Gerontologist, 9, pp. 179-186, (1969); Li H, Li C, Wang A, Et al., Associations between social and intellectual activities with cognitive trajectories inChinese middle-aged and older adults: anationa lly representative cohort study, Alzheimers Res Ther, 12, 1, (2020); Chen S, Ling Y, Zhou F, Et al., Trajectories of cognitive function among people aged 45 years and older living with diabetes in China: results from a nationally representative longitudinal study (2011–2018), PLoS One, 19, 5, (2024); Herle M, Micali N, Abdulkadir M, Et al., Identifying typical trajectories in longitudinal data: modelling strategies and interpretations, Eur J Epidemiol, 35, 3, pp. 205-222, (2020); Norman K, Stobaus N, Gonzalez MC, Et al., Hand grip strength: outcome predictor and marker of nutritional status, Clin Nutr, 30, 2, pp. 135-142, (2011); Leong DP, Teo KK, Rangarajan S, Et al., Prospective Urban Rural Epidemiology (PURE) study investigators. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study, Lancet, 386, 9990, pp. 266-273, (2015); Celis-Morales CA, Welsh P, Lyall DM, Et al., Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants, BMJ, 361, (2018); Raherison C, Girodet PO., Epidemiology of COPD, Eur Respir Rev, 18, 114, pp. 213-221, (2009); Lopez-Campos JL, Tan W, Soriano JB., Global burden of COPD, Respirology, 21, 1, pp. 14-23, (2016); Fang L, Gao P, Bao H, Et al., Chronic obstructive pulmonary disease in China: a nationwide prevalence study, Lancet Respir Med, 6, 6, pp. 421-430, (2018); Sloth MMB, Neble Larsen E, Godtfredsen NS, Et al., Impact of offspring and their educational level on readmission and death among older adults with chronic obstructive pulmonary disease: a nationwide cohort study using multistate survival models, J Epidemiol Community Health, 77, 9, pp. 558-564, (2023); Chowdhury NU, Guntur VP, Newcomb DC, Wechsler ME., Sex and gender in asthma, Eur Respir Rev, 30, 162, (2021); Scicluna V, Han M., COPD in women: future challenges, Arch Bronconeumol, 59, 1, pp. 3-4, (2023); Tho NV, Park HY, Nakano Y., Asthma-COPD overlap syndrome (ACOS): a diagnostic challenge, Respirology, 21, 3, pp. 410-418, (2016); Alshabanat A, Zafari Z, Albanyan O, Et al., Asthma and COPD Overlap Syndrome (ACOS): a systematic review and meta analysis, PLoS One, 10, 9, (2015); Spruit MA, Pitta F, McAuley E, Et al., Pulmonary rehabilitation and physical activity in patients with chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 192, 8, pp. 924-933, (2015); Neunhauserer D, Reich B, Mayr B, Et al., Impact of exercise training and supplemental oxygen on submaximal exercise performance in patients with COPD, Scand J Med Sci Sports, 31, 3, pp. 710-719, (2021)","J. Chen; Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, China; email: chenjq9932@163.com","","Dove Medical Press Ltd","","","","","","11769106","","","39659774","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85212245142"
"Gao Y.; Lu H.; Zhou H.; Tan J.","Gao, Ying (59223370300); Lu, Han (59223721100); Zhou, Huan (57221743058); Tan, Jiaxing (57208408782)","59223370300; 59223721100; 57221743058; 57208408782","Exploring the impact of polychlorinated biphenyls on comorbidity and potential mitigation strategies","2024","Frontiers in Public Health","12","","1474994","","","","0","10.3389/fpubh.2024.1474994","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208800543&doi=10.3389%2ffpubh.2024.1474994&partnerID=40&md5=722432ebd7c0062ec1ef59eaa74064ef","Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Sichuan, Chengdu, China; West China School of Medicine, Sichuan University, Sichuan, Chengdu, China; Computational Mathematics and Machine Learning, School of Mathematics, Sichuan University, Sichuan, Chengdu, China; Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, United States","Gao Y., Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Sichuan, Chengdu, China, West China School of Medicine, Sichuan University, Sichuan, Chengdu, China; Lu H., Computational Mathematics and Machine Learning, School of Mathematics, Sichuan University, Sichuan, Chengdu, China; Zhou H., Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Sichuan, Chengdu, China; Tan J., Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Sichuan, Chengdu, China, Department of Pediatrics, Pennsylvania State University College of Medicine, Hershey, PA, United States","Introduction: Polychlorinated Biphenyls (PCBs) persist in the environment and accumulate in humans. Currently, there is a lack of understanding about the overall impact of PCBs on human health, and effective interventions for exposed populations are insufficient. Methods: Our study aimed to assess the impact of PCBs on various diseases and mortality risks using data from the National Health and Nutrition Examination Survey, while proposing lifestyle adjustments, particularly dietary modifications, to mitigate mortality risk. Statistical analyses employed principal component analysis, multifactorial logistic regression, multifactorial Cox regression, comorbidity network analysis, and machine learning prediction models. Results: Results indicated significant associations between 7 types of PCBs and 12 diseases (p < 0.05), with 6 diseases showing significant positive correlations (OR > 1, p < 0.05), along with listing the 25 most relevant diseases, such as asthma and chronic bronchitis (OR [95% CI] = 5.85 [4.37, 7.83], p < 0.0001), arthritis and osteoporosis (OR [95% CI] = 6.27 [5.23, 7.55], p < 0.0001). This suggested that PCBs may be intimately involved in the development and progression of multiple diseases. By constructing multidimensional machine learning models and conducting multiple iterations for precision and error measurement, PCBs may have the potential to become specific biomarkers for certain diseases in the future. Building upon this, we further suggested that controlling dietary intake to reduce dietary inflammatory index (DII) could lower mortality and disease risks. Discussion: While PCBs were independent risk factors for mortality, substantial evidence suggested that adjusting DII might mitigate the adverse effects of PCBs to some extent. Further physiological mechanisms require deeper exploration through additional research. Copyright © 2024 Gao, Lu, Zhou and Tan.","comorbidities; DII; machine learning; mortality; polychlorinated biphenyls","Adult; Aged; Comorbidity; Environmental Exposure; Environmental Pollutants; Female; Humans; Machine Learning; Male; Middle Aged; Nutrition Surveys; Polychlorinated Biphenyls; polychlorinated biphenyl; adult; aged; comorbidity; environmental exposure; female; human; machine learning; male; middle aged; nutrition; pollutant","","Environmental Pollutants, ; Polychlorinated Biphenyls, ","","","National Natural Science Foundation of China, NSFC, (82300797); National Natural Science Foundation of China, NSFC; Sichuan Provincial Science and Technology Support Program, (2024NSFSC1500); Sichuan Provincial Science and Technology Support Program","The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was partially funded by grants from the National Natural Science Foundation of China (Grant No. 82300797) and the Sichuan Science and Technology Program (Grant No. 2024NSFSC1500). 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Tan J., Liu N., Sun P., Tang Y., Qin W., A proinflammatory diet may increase mortality risk in patients with diabetes mellitus, Nutrients, 14, (2022); Li W., Xiao H., Wu H., Pan C., Deng K., Xu X., Et al., Analysis of environmental chemical mixtures and nonalcoholic fatty liver disease: NHANES 1999-2014, Environ Pollut, 311, (2022); Li L., Huang Q., Yang L., Zhang R., Gao L., Han X., Et al., The association between non-alcoholic fatty liver disease (NAFLD) and advanced fibrosis with serological vitamin B12 markers: results from the NHANES 1999-2004, Nutrients, 14, (2022); Yang Z., Gong D., He X., Huang F., Sun Y., Hu Q., Association between daidzein intake and metabolic associated fatty liver disease: a cross-sectional study from NHANES 2017-2018, Front Nutr, 10, (2023); Li Z., Zhu G., Chen G., Luo M., Liu X., Chen Z., Et al., Distribution of lipid levels and prevalence of hyperlipidemia: data from the NHANES 2007-2018, Lipids Health Dis, 21, (2022); Tan Y., Fu Y., Yao H., Wu X., Yang Z., Zeng H., Et al., Relationship between phthalates exposures and hyperuricemia in U.S. general population, a multi-cycle study of NHANES 2007-2016, Sci Total Environ, 859, (2023); Liu H., Tan X., Liu Z., Ma X., Zheng Y., Zhu B., Et al., Association between diet-related inflammation and COPD: findings from NHANES III, Front Nutr, 8, (2021); Zhao L., Sun Y., Liu Y., Yan Z., Peng W., A J-shaped association between Dietary Inflammatory Index (DII) and depression: a cross-sectional study from NHANES 2007-2018, J Affect Disord, 323, pp. 257-263, (2023); Mahemuti N., Jing X., Zhang N., Liu C., Li C., Cui Z., Et al., Association between systemic immunity-inflammation index and hyperlipidemia: a population-based study from the NHANES (2015-2020), Nutrients, 15, (2023); He K., Pang T., Huang H., The relationship between depressive symptoms and BMI: 2005-2018 NHANES data, J Affect Disord, 313, pp. 151-157, (2022); Zhang Y.B., Chen C., Pan X.F., Guo J., Li Y., Franco O.H., Et al., Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies, BMJ, 373, (2021)","J. Tan; Division of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China; email: xingest@foxmail.com","","Frontiers Media SA","","","","","","22962565","","","39540082","English","Front. Public Health","Article","Final","","Scopus","2-s2.0-85208800543"
"Barrett J.S.","Barrett, Jeffrey S. (7403498331)","7403498331","Artificial Intelligence Opportunities to Guide Precision Dosing Strategies","2024","Journal of Pediatric Pharmacology and Therapeutics","29","4","","434","440","6","0","10.5863/1551-6776-29.4.434","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202030893&doi=10.5863%2f1551-6776-29.4.434&partnerID=40&md5=717b5c20ea8963aa1117c6fac0b64239","Aridhia Digital Research Environment, Glasgow, United Kingdom","Barrett J.S., Aridhia Digital Research Environment, Glasgow, United Kingdom","[No abstract available]","AI; EHR; individualized pharmacotherapy; MIPD; precision dosing; RWD; RWE","anticoagulant agent; hemoglobin A1c; immunoglobulin E; omalizumab; algorithm; anticoagulation; Article; artificial intelligence; asthma; bioinformatics; clinical decision making; clinical outcome; consultation; diet; disease severity; dose calculation algorithm; drug monitoring; electronic health record; epigenome; genome-wide association study; genomics; health care personnel; health insurance; human; hypoglycemia; machine learning; metabolomics; microarray analysis; patient care; personalized medicine; pharmacodynamics; pharmacogenomics; pharmacokinetic parameters; phenotype; polypharmacy; proteomics; support vector machine; tuberculosis; uniformity of dosage; volume of distribution; whole exome sequencing","","hemoglobin A1c, 62572-11-6; immunoglobulin E, 37341-29-0; omalizumab, 242138-07-4","","","","","Wang Y, Goswami S., Understanding FDA’s perspective on precision dosing, (2022); Derendorf H, Peloquin C., Roger W. Jelliffe, M.D. (1929– 2020), Clin Pharmacokinet, 59, (2020); Jelliffe RW, Iglesias T, Hurst A, Et al., Individualizing drug dosage regimens: comparison of two types or pharmacokinetic models of gentamicin, three methods of fitting serum level data, and several monitoring strategies, Clin Pharmacokinet, 21, 6, pp. 461-478, (1991); Jelliffe RW, Schumitzky A, Van Guilder M., A simulation study of factors affecting aminoglycoside therapeutic precision, Drug Invest, 4, 1, pp. 20-29, (1992); Ashley E., Towards precision medicine, Nat Rev Genet, 17, pp. 507-522, (2016); Kosorok MR, Laber EB., Precision medicine, Annu Rev Stat Appl, 6, pp. 263-286, (2019); Johnson KB, Wei WQ, Weeraratne D, Et al., Precision medicine, AI, and the future of personalized health care, Clin Transl Sci, 14, 1, pp. 86-93, (2021); Ashby WR., An Introduction to Cybernetics, (1956); Rheingold H., Tools for Thought: The History and Future of Mind-Expanding Technology, (2000); Improving care: priorities to improve electronic health records; Chang A., AIMed: artificial intelligence in medicine: analytics, big data, cloud and cognitive computing, databases, and deep learning in healthcare and medicine, (2017); Miotto R, Li L, Kidd B, Et al., Deep patient: an unsupervised representation to predict the future of patients from the electronic health records, Sci Rep, 6, (2016); Ravitz AD., Big data, artificial intelligence, and the promise of precision medicine: a Johns Hopkins collaboration to develop the precision medicine analytics platform, Johns Hopkins APL Technical Digest, 35, 4, (2021); Sahu M, Gupta R, Ambasta RK, Kumar P., Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis, Prog Mol Biol Transl Sci, 190, 1, pp. 57-100, (2022); Furey TS, Cristianini N, Duffy N, Et al., Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics, 16, 10, pp. 906-914, (2000); Shah SJ, Katz DH, Selvaraj S, Et al., Phenomapping for novel classification of heart failure with preserved ejection fraction, Circulation, 131, 3, pp. 269-279, (2015); Adir O, Poley M, Chen G, Et al., Integrating artificial intelligence and nanotechnology for precision cancer medicine, Adv Mater, 32, 13, (2020); Kermany DS, Goldbaum M, Cai W, Et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, 5, pp. 1122-1131, (2018); Arafah A, Khatoon S, Rasool I, Et al., The future of precision medicine in the cure of Alzheimer’s disease, Biomedicines, 11, 2, (2023); Islam MS, Rahman W, Abdelkader A, Et al., Using AI to measure Parkinson’s disease severity at home, Digit Med, 6, (2023); Gavan SP, Thompson AJ, Payne K., The economic case for precision medicine, Expert Rev Precis Med Drug Dev, 3, 1, pp. 1-9, (2018); Basu A, Carlson JJ, Veenstra DL., A framework for prioritizing research investments in precision medicine, Med Decis Making, 36, 5, pp. 567-580, (2016); Garrison LP, Towse A., A strategy to support efficient development and use of innovations in personalized medicine and precision medicine, J Manag Care Spec Pharm, 25, 10, pp. 1082-1087, (2019); Poweleit EA, Vinks AA, Mizuno T., Artificial intelligence and machine learning approaches to facilitate therapeutic drug management and model-informed precision dosing, Ther Drug Monit, 45, 2, pp. 143-150, (2023); Barrett JS., Pediatric models in motion: requirements for model-based decision support at the bedside, Br J Clin Pharmacol, 79, 1, pp. 85-96, (2015); Alfaro-Ponce M, Chairez I., Bioinformatics-inspired non-parametric modelling of pharmacokineticspharmacodynamics systems using differential neural networks, 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1-6; Gobburu JV, Chen EP., Artificial neural networks as a novel approach to integrated pharmacokinetic—pharmacodynamic analysis, J Pharm Sci, 85, 5, pp. 505-510, (1996); Wicha SG, Martson AG, Nielsen EI, Et al., International Society of Anti-Infective Pharmacology (ISAP); the PK/PD study group of the European Society of Clinical Microbiology, Infectious Diseases (EPASG). From therapeutic drug monitoring to model-informed precision dosing for antibiotics, Clin Pharmacol Ther, 109, 4, pp. 928-941, (2021); Jager NGL, Chai MG, van Hest RM, Et al., Precision dosing software to optimize antimicrobial dosing: a systematic search and follow-up survey of available programs, Clin Microbiol Infect, 28, 9, pp. 1211-1224, (2022); Scheetz MH, Lodise TP, Downes KJ, Et al., The case for precision dosing: medical conservatism does not justify inaction, J Antimicrob Chemother, 76, 7, pp. 1661-1665, (2021); Azuaje F., Artificial intelligence for precision oncology: beyond patient stratification, NPJ Precis Oncol, 3, (2019); Saykin AJ, Shen L, Foroud TM, Et al., Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans, Alzheimers Dement, 6, 3, pp. 265-273, (2010); Thompson PM, Stein JL, Medland SE, Et al., The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data, Brain Imaging Behav, 8, 2, pp. 153-182, (2014); Wu J, Chen Y, Wang P, Et al., Integrating transcriptomics, genomics, and imaging in alzheimer’s disease: a federated model, Front Radiol, 1, (2022); Ratwani R., Electronic health records and improved patient care: opportunities for applied psychology, Curr Dir Psychol Sci, 4, pp. 359-365, (2017); Jamison DT, Breman JG, Measham AR, Et al., The International Bank for Reconstruction and Development/ The World Bank, (2006); HITECH Act of 2009, 42 USC sec 139w-4(0)(2) (February 2009), part 2, subtitle C, sec 13301, subtitle B, sec 3014: Competitive grants to States and Indian tribes for the development of loan programs to facilitate the widespread adoption of certified EHR technology; Gonzalez D, Rao GG, Bailey SC, Et al., Precision dosing: public health need, proposed framework, and anticipated impact, Clin Transl Sci, 10, 6, pp. 443-454, (2017)","J.S. Barrett; Aridhia Digital Research Environment, Glasgow, United Kingdom; email: jeff.barrett@aridhia.com","","Pediatric Pharmacy Advocacy Group, Inc.","","","","","","15516776","","","","English","J. Pediatr. Pharmacol. Ther.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85202030893"
"Zhang W.; Liu H.; Li T.; Jiang Y.; Cao X.; Chen L.; Zhou L.","Zhang, Wei (59325614000); Liu, Haiqing (58907713400); Li, Tuantuan (57216322622); Jiang, Ying (59326137400); Cao, Xiaoyu (59325784000); Chen, Li (59325960900); Zhou, Lili (59326137500)","59325614000; 58907713400; 57216322622; 59326137400; 59325784000; 59325960900; 59326137500","The Study of Associated Factors for Non-Tuberculous Mycobacterial Pulmonary Disease Compared to Pulmonary Tuberculosis: A Propensity Score Matching Analysis","2024","Infection and Drug Resistance","17","","","3189","3197","8","0","10.2147/IDR.S467257","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203840345&doi=10.2147%2fIDR.S467257&partnerID=40&md5=bab9928e85083fc34599d33c15c7285e","The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China","Zhang W., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Liu H., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Li T., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Jiang Y., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Cao X., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Chen L., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China; Zhou L., The Second People’s Hospital of Fuyang City, Anhui, Fuyang, China","Investigate the differences in clinical manifestations, imaging features, and associated inflammatory markers between Nontuberculous Mycobacterial Pulmonary Disease (NTM-PD) and Pulmonary Tuberculosis (PTB), identify potential risk factors for NTM-PD, and establish a logistic regression model to evaluate its diagnostic value. Methods: Baseline data were collected from 145 patients with NTM-PD and 206 patients with PTB. Propensity score matching (PSM) was utilized to achieve a 1:1 match between the two groups, resulting in 103 matched pairs. The differences in comorbidities, imaging features, and inflammatory markers were compared between the two groups. Multivariate binary logistic regression analysis was conducted to identify independent influencing factors, and the diagnostic value of the established model was evaluated. Results: After matching, significant differences were observed between the NTM-PD group and the PTB group in terms of diabetes, bronchiectasis, chronic obstructive pulmonary disease(COPD), cystic and columnar changes, lung cavity presentation, and monocyte percentage (MONO%), lymphocyte count (LYMPH#), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) (P<0.05). Logistic regression analysis confirmed that diabetes, bronchiectasis, COPD, and lung cavities were risk factors for NTMPD. The established regression analysis model was analyzed by the Receiver Operating Characteristic (ROC) curve, the Area Under the Curve (AUC) was obtained as 0.795 (P<0.001, 95% CI 0.734–0.857). At a Youden index of 0.505, the sensitivity was 84.5% and the specificity was 66.6%. The Hosmer-Lemeshow test was used to evaluate the model’s calibration, with a chi-square value of 11.023 and P=0.200>0.05, indicating no significant difference between predicted and observed values. Conclusion: For patients without diabetes but with bronchiectasis, COPD, and imaging characteristics of lung cavities, a high level of vigilance and active differential diagnosis for NTM-PD should be exercised. Given that the clinical manifestations of NTM-PD are similar to those of PTB, a detailed differential diagnosis is necessary during the diagnostic process to avoid misdiagnosis. © 2024 Zhang et al.","nontuberculous mycobacterial pulmonary disease; propensity score matching; pulmonary tuberculosis","C reactive protein; serum amyloid A; adult; aged; alcohol consumption; antibiotic resistance; area under the curve; Article; atelectasis; atypical mycobacteriosis; bronchiectasis; calibration; chronic obstructive lung disease; cohort analysis; comorbidity; computer assisted tomography; controlled study; diabetes mellitus; diagnostic error; diagnostic test accuracy study; diagnostic value; differential diagnosis; dyspnea; female; fever; forced vital capacity; hematological parameters; hemoptysis; hospitalization; human; liver fibrosis; lung cavity; lung disease; lung tuberculosis; lymphocyte count; lymphocyte monocyte ratio; machine learning; major clinical study; male; methodology; middle aged; monocyte; monocyte percentage; Mycobacterium tuberculosis; neutrophil lymphocyte ratio; platelet lymphocyte ratio; predictive value; propensity score; risk factor; sensitivity and specificity; smoking; tuberculosis; venous thromboembolism; Youden index","","C reactive protein, 9007-41-4","7600 Automated Biochemistry Analyzer, Hitachi; SPSS  Statistics  version  26.0, IBM; XE2100, Sysmex","Hitachi; IBM; Sysmex","Fuyang Municipal Health Commission, (FY2021-052)","This work was supported by Scientific Research Project of Fuyang Municipal Health Commission (FY2021-052).","Sharma SK, Upadhyay V., Epidemiology, diagnosis, and treatment of non-tuberculous mycobacterial diseases, Indian J Med Res, 152, 3, pp. 185-226, (2020); Gopalaswamy R, Shanmugam S, Mondal R, Et al., Tuberculosis and non-tuberculous mycobacterial infections- a comparative analysis of epidemiology, diagnosis, and treatment, J Biomed Sci, 27, 1, (2020); Chen PR, Tan SY., The clinical characteristics of 89 cases of non-tuberculous mycobacterium pulmonary disease complicated with tracheobronchial lesions, Chin J Tubercul Respirat Dis, 43, 11, pp. 947-952, (2020); Winthrop KL, Marras TK, Adjemian J, Et al., Incidence and prevalence of nontuberculous mycobacterial lung disease in a large US managed care health plan, 2008–2015, Ann Am Thoracic Soc, 17, 2, pp. 178-185, (2020); Lee H, Myung W, Koh WJ, Et al., Epidemiology of nontuberculous mycobacterial infection, South Korea, 2007–2016, Emerging Infectious Diseases, 25, 3, (2019); Furuuchi K, Morimoto K, Yoshiyama T, Et al., Interrelational changes in the epidemiology and clinical features of nontuberculous mycobacterial pulmonary disease and tuberculosis in a referral hospital in Japan, Respir Med, 152, pp. 74-80, (2019); The Office of the Fifth National TB Epidemiological survey. The fifth tuberculosis epidemiological survey in 2010, Chin J Antituberculosis, 34, 8, pp. 485-508, (2012); Tanaka G, Jo T, Tamiya H, Et al., Factors affecting in-hospital mortality of non-tuberculous mycobacterial pulmonary disease, BMC Infect Dis, 21, 1, (2021); Izumi K, Morimoto K, Hasegawa N, Et al., Epidemiology of adults and children treated for nontuberculous mycobacterial pulmonary disease in Japan, Ann Am Thoracic Soc, 16, 3, pp. 341-347, (2019); Global Tuberculosis Report 2023, (2023); Tan Y, Deng Y, Yan X, Et al., Nontuberculous mycobacterial pulmonary disease and associated risk factors in China: a prospective surveillance study, J Infect, 83, 1, pp. 46-53, (2021); Ji S, Xu W, Sun J, Et al., Retrospective analysis of patients with non-tuberculous mycobacteria from a primary hospital in Southeast China, Sci Rep, 10, 1, (2020); Guidelines for the diagnosis and treatment of nontuberculous mycobacteria Disease (2020 Edition), Chin J Tubercul Breath, 43, 11, pp. 918-946, (2020); WS288-2017 diagnosis of pulmonary tuberculosis, (2017); Globaltuberculosisreport 2022[Eb], (2022); Zheng Y, Zhou H, Zhou JY., Clinical analysis of bronchiectasis co-infected with nontuberculous mycobacteria, Chin J Infect Chemoth, 19, 3, pp. 253-258, (2019); Chen H, Chen PR, Tan SY., Clinical epidemiological analysis of bronchiectasis coinfected with nontuberculous mycobacteria, Chinese Med J, 51, 3, pp. 43-46, (2016); Andrejak C, Nielsen R, O TV, Et al., Chronic respiratory disease, inhaled corticosteroids and risk of non-tuberculous mycobacteriosis, Thorax, 68, 3, pp. 256-262, (2013); Brode SK, Campitelli MA, Kwong JC, Et al., The risk of mycobacterial infections associated with inhaled corticosteroid use, Eur Respir J, 50, 3, (2017); Liu VX, Winthrop KL, Lu Y, Et al., Association between inhaled corticosteroid use and pulmonary nontuberculous mycobacterial infection, Ann Am Thorac Soc, 15, 10, pp. 1169-1176, (2018); Getahun H, Matteelli A, Chaisson RE, Et al., Latent mycobacterium tuberculosis infection, N Engl J Med, 372, 22, pp. 21-35, (2015); Zhang Y, Wei H., Analysis of detection results of nontuberculous mycobacteria among patients visiting a tuberculosis control institution in Nanyang City, Chin J Health Laborat Technol, 31, 13, pp. 1574-1576, (2021); He HQ., Clinical characteristics analysis of nontuberculous mycobacterial pulmonary infections in patients at a general hospital, Fujian Med Univer, (2021)","L. Zhou; The Second People’s Hospital of Fuyang City, Fuyang, Anhui, China; email: zw2314160@163.com","","Dove Medical Press Ltd","","","","","","11786973","","","","English","Infect. Drug Resist.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85203840345"
"Pooja M.R.; Ravi V.; Mazroa A.A.; Ravi P.","Pooja, M.R. (57190388894); Ravi, Vinayakumar (56755324000); Mazroa, Alanoud Al (58894388100); Ravi, Pradeep (58955034000)","57190388894; 56755324000; 58894388100; 58955034000","Deployment of a Phenotypic Characterization System for Effective Identification of the Onset of Asthma Disease","2024","Open Public Health Journal","17","","e18749445285615","","","","0","10.2174/0118749445285615240402072009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193992678&doi=10.2174%2f0118749445285615240402072009&partnerID=40&md5=6c905f2e443268b9bbefdae6654a663e","Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, 57002, India; Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Karnataka, Mysuru, India","Pooja M.R., Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, 57002, India; Ravi V., Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; Mazroa A.A., Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Ravi P., Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Karnataka, Mysuru, India","Background: Essentially, machine learning techniques help with clinical decision-making by forecasting prediction results based on recent and historical data, which are frequently found in carefully chosen clinical data repositories. In order to uncover hidden patterns in the data, machine learning applies sophisticated analytical techniques that conduct an exploratory analysis while constructing prediction models to support clinical judgment. Objective: To effectively identify asthmatics in two distinct cohorts representing India's rural and urban populations by adopting a phenotypic characterization approach. Methods: Cross-sectional and categorical in design, the data represent the two populations, with clinical history information emphasizing clinical symptoms and patterns defining the condition. The method adopts a hybrid approach since it uniquely blends the unsupervised and supervised learning techniques to explore the advantages of both. The clustering data emphasizing the phenotypic characteristics of asthma is input to the classifier, and the performance of the classifier was continuously monitored for significant improvement in the results. Results: Asthma disease outcome predictions made by the hybrid decision support system were quite accurate, with classification accuracy reaching up to 85.1% and 95.3% for the two datasets, respectively. Conclusion: Since asthma is a heterogeneous disease with multiple subtypes, employing clustering information in the form of cluster evaluation scores as an input parameter to the classifiers can effectively predict disease outcomes. © 2024 The Author(s).","Asthma; Clustering; Correlation; Healthcare; Hybrid clustering; Phenotypic","Article; asthma; Asthma Control Questionnaire; child; classification; classification accuracy; classifier; clinical decision support system; clinical outcome; cohort analysis; cross validation; cross-sectional study; disease course; disease onset; fuzzy c means clustering; human; machine learning; phenotype; phenotypic characterization; predictive model; risk factor; supervised machine learning; unsupervised machine learning","","","","","","","Patel SJ, Chamberlain DB, Chamberlain JM., A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage, Acad Emerg Med, 25, 12, pp. 1463-1470, (2018); Lai CKW, Beasley R, Crane J, Foliaki S, Shah J, Weiland S., Global variation in the prevalence and severity of asthma symptoms: Phase three of the international study of asthma and allergies in childhood (ISAAC), Thorax, 64, 6, pp. 476-483, (2009); Chakraborty C, Mitra T, Mukherjee A, Ray AK., CAIDSA: Computer-aided intelligent diagnostic system for bronchial asthma, Expert Syst Appl, 36, 3, pp. 4958-4966, (2009); Turcatel G., Machine learning models to predict asthma exacerbations, CHEST, 164, 4, (2023); Lovric M., Predicting treatment outcomes using explainable machine learning in children with asthma, Children, 8, 5, (2021); Rohankumar V., A smart health care ecosystem, Recent Trends in Computational Sciences, pp. 339-345, (2024); Louis G, Schleich F, Guillaume M, Et al., Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis, ERJ Open Res, 9, 1, pp. 00451-2022, (2023); Shivade C, Raghavan P, Lussier FE, Et al., A review of approaches to identifying patient phenotype cohorts using electronic health records, J Am Med Inform Assoc, 21, 2, pp. 221-230, (2014); Prosperi MCF, Sahiner UM, Belgrave D, Et al., Challenges in identifying asthma subgroups using unsupervised statistical learning techniques, Am J Respir Crit Care Med, 188, 11, pp. 1303-1312, (2013); Lee CH, Chen JCY, Tseng VS., A novel data mining mechanism considering bio-signal and environmental data with applications on asthma monitoring, Comput Methods Programs Biomed, 101, 1, pp. 44-61, (2011); Moore WC, Meyers DA, Wenzel SE, Et al., Identification of asthma phenotypes using cluster analysis in the Severe Asthma Research Program, Am J Respir Crit Care Med, 181, 4, pp. 315-323, (2010); Tapia R, de Jesus S., Early prediction of Asthma, J Clin Med, 12, 16, (2023); Salau AO, Pooja MR, Hasani NF, Braide SL., Model based risk assessment to evaluate lung functionality for early prognosis of asthma using neural network approach, Math Model Eng Probl, 9, 4, pp. 1053-1060, (2022); Pooja MR, Pushpalatha MP., Cluster analysis to characterize the patterns of complementary and alternative medicines usage in asthma controls, Open Public Health J, 13, (2020); Pooja MR, Pushpalatha MP., A predictive framework for the assessment of asthma control level, Int J Eng Adv Technol, 8, pp. 239-245, (2019); Farion K, Michalowski W, Wilk S., Developing a decision model for asthma exacerbations: Combining rough sets and expert-driven selection of clinical attributes, Rough Sets and Current Trends in Computing, pp. 428-437, (2006); Pooja MR., A predictive model for the early prognosis and characterization of asthma, J Pharm Negat Results, 13, 4, pp. 1-9, (2022); Pooja MR, Pushpalatha MP., An empirical analysis of machine learning classifiers for clinical decision making in asthma, Cognitive Computing and Information Processing, pp. 105-117, (2017); Wu W, Bleecker E, Moore W, Et al., Unsupervised phenotyping of severe asthma research program participants using expanded lung data, J Allergy Clin Immunol, 133, 5, pp. 1280-1288, (2014); Opina MTD, Moore WC., Phenotype-driven therapeutics in severe asthma, Curr Allergy Asthma Rep, 17, pp. 1-13, (2017); Tsang KCH, Pinnock H, Wilson AM., Application of machine learning algorithms for asthma management with mHealth: A clinical review, J Asthma Allergy, 15, pp. 855-873, (2022); Pooja MR, Pushpalatha MP., A hybrid decision support system for the identification of asthmatic subjects in a cross-sectional study, 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 288-293; Schmidt S, Li G, Chen Y-P P., Medical knowledge discovery from a regional asthma dataset, Advanced Intelligent Computing Theories and Applications With Aspects of Artificial Intelligence, pp. 888-895, (2008); Palmu HH, Jaakkola MS, Makikyro EMS., Subtypes of asthma and cold weather-related respiratory symptoms, Int J Environ Res Public Health, 19, 14, (2022); Pushpalatha MP, Pooja MR., A predictive model for the effective use of asthma severity indicators, 2017 International Conference on Computer Communication and Informatics (ICCCI); AlSaad R, Malluhi Q, Janahi I, Boughorbel S., Predicting emergency department utilization among children with asthma using deep learning models, Healthc Anal, 2, (2022); Patel D, Hall GL, Broadhurst D, Smith A, Schultz A, Foong RE., Does machine learning have a role in the prediction of asthma in children?, Paediatr Respir Rev, 41, pp. 51-60, (2022); Molfino NA, Turcatel G, Riskin D., Machine learning approaches to predict asthma exacerbations: A narrative review, Adv Ther, 41, 2, pp. 534-552, (2023); Pushpalatha MP, Pooja MR., A predictive model for the effective prognosis of Asthma using Asthma severity indicators, 2017 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6","V. Ravi; Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia; email: vinayakumarr77@gmail.com","","Bentham Science Publishers","","","","","","18749445","","","","English","Open Public Health J.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85193992678"
"Bourdin A.; Bardin P.; Chanez P.","Bourdin, Arnaud (7801311848); Bardin, Phil (7006391749); Chanez, Pascal (7102979861)","7801311848; 7006391749; 7102979861","Imagining the severe asthma decision trees of the future","2024","Expert Review of Respiratory Medicine","18","8","","561","567","6","0","10.1080/17476348.2024.2390987","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201644423&doi=10.1080%2f17476348.2024.2390987&partnerID=40&md5=48da9f4ee16031d4995bf72b78960b3d","Département de Pneumologie et Addictologie, PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, Montpellier, France; Monash Lung and Sleep Allergy Immunology, Monash Hospital, Monash Health and University, Hudson Institute, Melbourne, VIC, Australia; APHM, Clinique des bronches allergies et sommeil, Marseille, France; Aix Marseille Univ, INSERM U1263, INRA 1260 (C2VN), Marseille, France","Bourdin A., Département de Pneumologie et Addictologie, PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, Montpellier, France; Bardin P., Monash Lung and Sleep Allergy Immunology, Monash Hospital, Monash Health and University, Hudson Institute, Melbourne, VIC, Australia; Chanez P., APHM, Clinique des bronches allergies et sommeil, Marseille, France, Aix Marseille Univ, INSERM U1263, INRA 1260 (C2VN), Marseille, France","Introduction: There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories. Areas covered: The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted. Expert opinion: Asthma severity is currently defined a priori, thereby precluding a role for early interventions aiming to prevent outcomes such as exacerbations (systemic corticosteroids exposure) and lung function decline. Asthma ‘at-risk’ might represent the ultimate paradigm but merits longitudinal studies considering modern interventions. Real exacerbations, severe airway hyperresponsiveness, excessive T2-related biomarkers, noxious environments and patient behaviors, harms of OCS and high-doses inhaled corticosteroids (ICS), and low adherence-to-effectiveness ratios of ICS-containing inhalers are predictors of future risks. New tools such as imaging, genetic, and epigenetic signatures should be used. Logical and numerical artificial intelligence may be used to generate a consistent risk score. A pragmatic definition of response to treatments will allow development of a validated and applicable algorithm. Biologics have the best potential to minimize the risks, but cost remains an issue. We propose a simplified six-step algorithm for decision-making that is ultimately aiming to achieve asthma remission. © 2024 Informa UK Limited, trading as Taylor & Francis Group.","biomarkers; decision tree; imaging; prediction; Severe asthma","Administration, Inhalation; Adrenal Cortex Hormones; Algorithms; Anti-Asthmatic Agents; Artificial Intelligence; Asthma; Clinical Decision-Making; Decision Support Techniques; Decision Trees; Humans; Risk Factors; Severity of Illness Index; biological marker; corticosteroid; monoclonal antibody; nonsteroid antiinflammatory agent; antiasthmatic agent; corticosteroid; adulthood; Article; artificial intelligence; birth; clinical assessment; clinical practice; decision tree; diagnostic imaging; disease exacerbation; drug megadose; early intervention; epigenetics; follow up; genetics; high risk patient; human; knowledge gap; longitudinal study; lung function; medical decision making; medication compliance; personalized medicine; remission; respiratory tract allergy; risk reduction; severe asthma; severe persistent asthma; shared decision making; treatment response; algorithm; asthma; clinical decision making; decision support system; diagnosis; drug therapy; inhalational drug administration; pathophysiology; risk factor; severity of illness index","","Adrenal Cortex Hormones, ; Anti-Asthmatic Agents, ","","","","","Holguin F., Cardet J.C., Chung K.F., Et al., Management of severe asthma: a European Respiratory Society/American Thoracic Society guideline, Eur Respir J, 55, 1, (2020); Lommatzsch M., Brusselle G.G., Levy M.L., Et al., A2BCD: a concise guide for asthma management, Lancet Respir Med, 11, 6, pp. 573-576, (2023); Bourdin A., Bjermer L., Brightling C., Et al., ERS/EAACI statement on severe exacerbations in asthma in adults: facts, priorities and key research questions, Eur Respir J, 54, 3, (2019); Meyers D.A., Bleecker E.R., Holloway J.W., Et al., Asthma genetics and personalised medicine, Lancet Respir Med, 2, 5, pp. 405-415, (2014); McGeachie M.J., Yates K.P., Zhou X., Et al., Patterns of growth and decline in lung function in persistent childhood asthma, N Engl J Med, 374, 19, pp. 1842-1852, (2016); Bui D.S., Lodge C.J., Perret J.L., Et al., Trajectories of asthma and allergies from 7 years to 53 years and associations with lung function and extrapulmonary comorbidity profiles: a prospective cohort study, Lancet Respir Med, 9, 4, pp. 387-396, (2021); Reddel H., Ware S., Marks G., Et al., Differences between asthma exacerbations and poor asthma control, Lancet Lond Engl, 353, 9150, pp. 364-369, (1999); Bateman E.D., Reddel H.K., Eriksson G., Et al., Overall asthma control: the relationship between current control and future risk, J Of Allergy Clin Immunol, 125, 3, pp. 600-608, (2010); Bourdin A., Charriot J., Boissin C., Et al., Will the asthma revolution fostered by biologics also benefit adult ICU patients?, Allergy, 76, 8, pp. 2395-2406, (2020); Bateman E.D., Buhl R., O'Byrne P.M., Et al., Development and validation of a novel risk score for asthma exacerbations: the risk score for exacerbations, J Allergy Clin Immunol, 135, 6, pp. 1457-1464, (2015); Couillard S., Laugerud A., Jabeen M., Et al., Derivation of a prototype asthma attack risk scale centred on blood eosinophils and exhaled nitric oxide, Thorax, 77, 2, pp. 199-202, (2022); FitzGerald J.M., Bleecker E.R., Menzies-Gow A., Et al., Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies, Lancet Respir Med, 6, 1, pp. 51-64, (2018); Suehs C.M., Menzies-Gow A., Price D., Et al., Expert consensus on the tapering of oral corticosteroids for the treatment of asthma. A delphi study, Am J Respir Crit Care Med, 203, 7, pp. 871-881, (2021); Volmer T., Effenberger T., Trautner C., Et al., Consequences of long-term oral corticosteroid therapy and its side-effects in severe asthma in adults: a focused review of the impact data in the literature, Eur Respir J, 52, 4, (2018); Blakey J., Chung L.P., McDonald V.M., Et al., Oral corticosteroids stewardship for asthma in adults and adolescents: a position paper from the thoracic society of Australia and New Zealand, Respirol Carlton Vic, 26, 12, pp. 1112-1130, (2021); Price D.B., Trudo F., Voorham J., Et al., Adverse outcomes from initiation of systemic corticosteroids for asthma: long-term observational study, J Asthma Allergy, 11, pp. 193-204, (2018); Maijers I., Kearns N., Harper J., Et al., Oral steroid-sparing effect of high-dose inhaled corticosteroids in asthma, Eur Respir J, 55, 1, (2020); Bourdin A., Suehs C., Charriot J., Integrating high dose inhaled corticosteroids into oral corticosteroids stewardship, Eur Respir J, 55, 1, (2020); Bloechliger M., Reinau D., Spoendlin J., Et al., Adverse events profile of oral corticosteroids among asthma patients in the UK: cohort study with a nested case-control analysis, Respir Res, 19, 1, (2018); Brown P., Pratt A.G., Hyrich K.L., Therapeutic advances in rheumatoid arthritis, BMJ, 384, (2024); Nair P., Wenzel S., Rabe K.F., Et al., Oral glucocorticoid–sparing effect of benralizumab in severe asthma, N Engl J Med, 376, 25, pp. 2448-2458, (2017); Bel E.H., Wenzel S.E., Thompson P.J., Et al., Oral glucocorticoid-sparing effect of mepolizumab in eosinophilic asthma, N Engl J Med, 371, 13, pp. 1189-1197, (2014); Rabe K.F., Nair P., Brusselle G., Et al., Efficacy and safety of Dupilumab in glucocorticoid-dependent severe asthma, N Engl J Med, 378, 26, pp. 2475-2485, (2018); Foster J.M., McDonald V.M., Guo M., Et al., “I have lost in every facet of my life”: the hidden burden of severe asthma, Eur Respir J, 50, 3, (2017); Khaleva E., Rattu A., Brightling C., Et al., Development of core outcome measures sets for paediatric and adult severe asthma (COMSA), Eur Respir J, 61, 4, (2023); Canonica G.W., Colombo G.L., Bruno G.M., Et al., Shadow cost of oral corticosteroids-related adverse events: a pharmacoeconomic evaluation applied to real-life data from the severe asthma network in Italy (SANI) registry, World Allergy Organ J, 12, 1, (2019); Rakkar K., Pang Y.L., Rajasekar P., Et al., Mepolizumab induced changes in Nasal Methylome and transcriptome to predict response in asthma, Am J Respir Crit Care Med, 209, 10, pp. 1268-1272, (2024); Khatri S., Moore W., Gibson P.G., Et al., Assessment of the long-term safety of mepolizumab and durability of clinical response in patients with severe eosinophilic asthma, J Allergy Clin Immunol, 143, 5, pp. 1742-1751, (2019); Korn S., Bourdin A., Chupp G., Et al., Integrated safety and efficacy among patients receiving benralizumab for up to 5 years, J Allergy Clin Immunol Pract, 9, 12, pp. 4381-4392, (2021); Bardin P.G., Price D., Chanez P., Et al., Managing asthma in the era of biological therapies, Lancet Respir Med, 5, 5, pp. 376-378, (2017); Jackson D.J., Heaney L.G., Humbert M., Et al., Reduction of daily maintenance inhaled corticosteroids in patients with severe eosinophilic asthma treated with benralizumab (SHAMAL): a randomised, multicentre, open-label, phase 4 study, Lancet Lond Engl, 403, pp. 271-281, (2024); Jyssum I., Gehin J.E., Sexton J., Et al., Adalimumab serum levels and anti-drug antibodies: associations to treatment response and drug survival in inflammatory joint diseases, Rheumatology (Oxford), 63, 6, pp. 1746-1755, (2024); Vaisman-Mentesh A., Gutierrez-Gonzalez M., DeKosky B.J., Et al., The molecular mechanisms that underlie the immune biology of anti-drug antibody formation following treatment with monoclonal antibodies, Front Immunol, 11, (2020); Conrad M.L., Ferstl R., Teich R., Et al., Maternal TLR signaling is required for prenatal asthma protection by the nonpathogenic microbe Acinetobacter lwoffii F78, J Exp Med, 206, 13, pp. 2869-2877, (2009); Ruckwardt T.J., Morabito K.M., Phung E., Et al., Safety, tolerability, and immunogenicity of the respiratory syncytial virus prefusion F subunit vaccine DS-Cav1: a phase 1, randomised, open-label, dose-escalation clinical trial, Lancet Respir Med, 9, 10, pp. 1111-1120, (2021); Conde E., Bertrand R., Balbino B., Et al., Dual vaccination against IL-4 and IL-13 protects against chronic allergic asthma in mice, Nat Commun, 12, 1, (2021); Dunican E.M., Elicker B.M., Gierada D.S., Et al., Mucus plugs in patients with asthma linked to eosinophilia and airflow obstruction, J Clin Invest, 128, 3, pp. 997-1009, (2018); Huang B.K., Elicker B.M., Henry T.S., Et al., Persistent mucus plugs in proximal airways are consequential for airflow limitation in asthma, JCI Insight, 9, (2024); Tang M., Elicker B.M., Henry T., Et al., Mucus plugs persist in asthma, and changes in mucus plugs associate with changes in airflow over time, Am J Respir Crit Care Med, 205, 9, pp. 1036-1045, (2022); Nordenmark L.H., Hellqvist A., Emson C., Et al., Tezepelumab and mucus plugs in patients with moderate-to-severe asthma, NEJM Evid, 2, 10, (2023); Jung Y., Jean T., Morphew T., Et al., Peripheral airway impairment and dysanapsis define risk of uncontrolled asthma in obese asthmatic children, J Allergy Clin Immunol Pract, 10, 3, pp. 759-767, (2022); Postma D.S., Brightling C., Baldi S., Et al., Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study, Lancet Respir Med, 7, 5, pp. 402-416, (2019); Gautier V., Redier H., Pujol J.L., Et al., Comparison of an expert system with other clinical scores for the evaluation of severity of asthma, Eur Respir J, 9, 1, pp. 58-64, (1996); Begne C., Justet A., Dupin C., Et al., Evaluation in a severe asthma expert center improves asthma outcomes regardless of step-up in asthma therapy, J Allergy Clin Immunol Pract, 8, 4, pp. 1439-1442, (2020); Tran T.N., Heatley H., Rowell J., Et al., Longitudinal patterns of intermittent oral corticosteroid therapy for asthma in the United Kingdom, J Allergy Clin Immunol Glob, 3, 2, (2024); Siddiqui S., Wenzel S.E., Bozik M.E., Et al., Safety and efficacy of dexpramipexole in eosinophilic asthma (EXHALE): a randomized controlled trial, J Allergy Clin Immunol, 152, 5, pp. 1121-1130, (2023); Lobet S., Caulet M., Paintaud G., Et al., Confounding mitigation for the exposure-response relationship of bevacizumab in colorectal cancer patients, Br J Clin Pharmacol, 90, 4, pp. 976-986, (2024)","A. Bourdin; Département de Pneumologie et Addictologie, PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, Montpellier, France; email: a-bourdin@chu-montpellier.fr","","Taylor and Francis Ltd.","","","","","","17476348","","","39120156","English","Expert Rev. Respir. Med.","Article","Final","","Scopus","2-s2.0-85201644423"
"Liao K.-M.; Cheng K.-C.; Sung M.-I.; Shen Y.-T.; Chiu C.-C.; Liu C.-F.; Ko S.-C.","Liao, Kuang-Ming (37117397700); Cheng, Kuo-Chen (56701506000); Sung, Mei-I (57188997456); Shen, Yu-Ting (57226430472); Chiu, Chong-Chi (56666562200); Liu, Chung-Feng (39861635300); Ko, Shian-Chin (16202964800)","37117397700; 56701506000; 57188997456; 57226430472; 56666562200; 39861635300; 16202964800","Machine learning approaches for practical predicting outpatient near-future AECOPD based on nationwide electronic medical records","2024","iScience","27","4","109542","","","","0","10.1016/j.isci.2024.109542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189499829&doi=10.1016%2fj.isci.2024.109542&partnerID=40&md5=c9300b7828fe20eb8727a904450ae62c","Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan, 722013, Taiwan; Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, 73658, Taiwan; Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung, 82445, Taiwan; School of Medicine, College of Medicine, I-Shou University, Kaohsiung, 82445, Taiwan; Department of Medical Education and Research, E-Da Cancer Hospital, I-Shou University, Kaohsiung, 82445, Taiwan; Department of Pulmonary Medicine, Chi Mei Medical Center, Tainan, 710402, Taiwan","Liao K.-M., Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan, 722013, Taiwan, Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, 73658, Taiwan; Cheng K.-C., Department of Pulmonary Medicine, Chi Mei Medical Center, Tainan, 710402, Taiwan; Sung M.-I., Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; Shen Y.-T., Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; Chiu C.-C., Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung, 82445, Taiwan, School of Medicine, College of Medicine, I-Shou University, Kaohsiung, 82445, Taiwan, Department of Medical Education and Research, E-Da Cancer Hospital, I-Shou University, Kaohsiung, 82445, Taiwan; Liu C.-F., Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; Ko S.-C., Department of Pulmonary Medicine, Chi Mei Medical Center, Tainan, 710402, Taiwan","In this research, we aimed to harness machine learning to predict the imminent risk of acute exacerbation in chronic obstructive pulmonary disease (AECOPD) patients. Utilizing retrospective data from electronic medical records of two Taiwanese hospitals, we identified 26 critical features. To predict 3- and 6-month AECOPD occurrences, we deployed five distinct machine learning algorithms alongside ensemble learning. The 3-month risk prediction was best realized by the XGBoost model, achieving an AUC of 0.795, whereas the XGBoost was superior for the 6-month prediction with an AUC of 0.813. We conducted an explainability analysis and found that the episode of AECOPD, mMRC score, CAT score, respiratory rate, and the use of inhaled corticosteroids were the most impactful features. Notably, our approach surpassed predictions that relied solely on CAT or mMRC scores. Accordingly, we designed an interactive prediction system that provides physicians with a practical tool to predict near-term AECOPD risk in outpatients. © 2024 The Author(s)","Health sciences; Machine learning","","","","","","Chi Mei Medical Center, (CMFHR11139); Chi Mei Medical Center","This research was funded by Chi Mei Medical Center , grant number CMFHR11139 . ","(2023); Chen S., Kuhn M., Prettner K., Yu F., Yang T., Barnighausen T., Bloom D.E., Wang C., The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020-50: a health-augmented macroeconomic modelling study, Lancet. Glob. Health, 11, pp. e1183-e1193, (2023); MacLeod M., Papi A., Contoli M., Beghe B., Celli B.R., Wedzicha J.A., Fabbri L.M., Chronic obstructive pulmonary disease exacerbation fundamentals: Diagnosis, treatment, prevention and disease impact, Respirology, 26, pp. 532-551, (2021); Ho T.W., Tsai Y.J., Ruan S.Y., Huang C.T., Lai F., Yu C.J., In-hospital and one-year mortality and their predictors in patients hospitalized for first-ever chronic obstructive pulmonary disease exacerbations: a nationwide population-based study, PLoS One, 9, (2014); Lin C.H., Yu C.J., Wang H.C., Lin M.C., Cheng S.L., The beneficial effect of COPD pay-for-performance program in Taiwan, International Perspectives on Pulmonary and Critical Care Medicine, (2020); Cheng K.C., Lai C.C., Wang C.Y., Wang C.M., Ho C.H., Sung M.I., Hsing S.C., Liao K.M., Ko S.C., The Impact of the Pay-for-Performance Program on the Outcome of COPD Patients in Taiwan after One Year, Int. J. Chron. Obstruct. Pulmon. Dis., 17, pp. 883-891, (2022); Chen S., Shi Y., Hu B., Huang J., A Prediction Model for In-Hospital Mortality of Acute Exacerbations of Chronic Obstructive Pulmonary Disease Patients Based on Red Cell Distribution Width-to-Platelet Ratio, Int. J. Chron. Obstruct. Pulmon. Dis., 18, pp. 2079-2091, (2023); Lundberg S.M., Lee S.I., A Unified Approach to Interpreting Model Predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), pp. 4768-4777, (2017); Suissa S., Dell'Aniello S., Ernst P., Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality, Thorax, 67, pp. 957-963, (2012); Mullerova H., Maselli D.J., Locantore N., Vestbo J., Hurst J.R., Wedzicha J.A., Bakke P., Agusti A., Anzueto A., Hospitalized exacerbations of COPD: risk factors and outcomes in the ECLIPSE cohort, Chest, 147, pp. 999-1007, (2015); Soler-Cataluna J.J., Martinez-Garcia M.A., Roman Sanchez P., Salcedo E., Navarro M., Ochando R., Severe acute exacerbations and mortality in patients with chronic obstructive pulmonary disease, Thorax, 60, pp. 925-931, (2005); Wedzicha J.A., Calverley P.M.A., Seemungal T.A., Hagan G., Ansari Z., Stockley R.A., The prevention of chronic obstructive pulmonary disease exacerbations by salmeterol/fluticasone propionate or tiotropium bromide, Am. J. Respir. Crit. Care Med., 177, pp. 19-26, (2008); Bestall J.C., Paul E.A., Garrod R., Garnham R., Jones P.W., Wedzicha J.A., Usefulness of the medical research council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease, Thorax, 54, pp. 581-586, (1999); Jones P.W., Harding G., Berry P., Wiklund I., Chen W.H., Kline Leidy N., Development and first validation of the COPD Assessment Test, Eur. Respir. J., 34, pp. 648-654, (2009); Liu C.F., Huang C.C., Wang J.J., Kuo K.M., Chen C.J., The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center, Healthcare (Basel), 9, (2021); Liao K.M., Liu C.F., Chen C.J., Shen Y.T., Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease, Diagnostics, 11, (2021); Mullerova H., Shukla A., Hawkins A., Quint J., Risk factors for acute exacerbations of COPD in a primary care population: a retrospective observational cohort studyBMJ, Open, 4, (2014); Hurst J.R., Vestbo J., Anzueto A., Locantore N., Mullerova H., Tal-Singer R., Miller B., Lomas D.A., Agusti A., Macnee W., Et al., Susceptibility to exacerbation in chronic obstructive pulmonary disease, N. Engl. J. Med., 363, pp. 1128-1138, (2010); Jenkins C.R., Celli B., Anderson J.A., Ferguson G.T., Jones P.W., Vestbo J., Yates J.C., Calverley P.M.A., Seasonality and determinants of moderate and severe COPD exacerbations in the TORCH study, Eur. Respir. J., 39, pp. 38-45, (2012); Byng D., Lutter J.I., Wacker M.E., Jorres R.A., Liu X., Karrasch S., Schulz H., Vogelmeier C., Holle R., Determinants of healthcare utilization and costs in COPD patients: first longitudinal results from the german COPD cohort COSYCONET, COPD, 14, pp. 1423-1439, (2019); Lee S.J., Lee S.H., Kim Y.E., Cho Y.J., Jeong Y.Y., Kim H.C., Kim J.H., You J.J., Yoon C.H., Lee J.D., Hwang Y.S., Clinical Features according to the Frequency of Acute Exacerbation in COPD, Tuberc. Respir. Dis., 72, pp. 367-373, (2012); Cote C.G., Dordelly L.J., Celli B.R., Impact of COPD exacerbations on patient-centered outcomes, Chest, 131, pp. 696-704, (2007); Koc C., Sahin F., What Are the Most Effective Factors in Determining Future Exacerbations, Morbidity Weight, and Mortality in Patients with COPD Attack?, Medicina (Kaunas), 58, (2022); Kim J.K., Lee S.H., Lee B.H., Lee C.Y., Kim D.J., Min K.H., Kim S.K., Yoo K.H., Jung K.S., Hwang Y.I., Factors associated with exacerbation in mild- to-moderate COPD patients, Int. J. Chron. Obstruct. Pulmon. Dis., 11, pp. 1327-1333, (2016); Lee S.-D., Huang M.-S., Kang J., Lin C.H., Park M.J., Oh Y.M., Kwon N., Jones P.W., Sajkov D., The COPD assessment test (CAT) assists prediction of COPD exacerbations in high-risk patients, Respir. Med., 108, pp. 600-608, (2014); Karloh M., Fleig Mayer A., Maurici R., Pizzichini M.M.M., Jones P.W., Pizzichini E., The COPD Assessment Test: What Do We Know So Far?: A Systematic Review and Meta-Analysis About Clinical Outcomes Prediction and Classification of Patients Into GOLD Stages, Chest, 149, pp. 413-425, (2016); Bansode P.M., Kumar C., M S., Usefulness of Cat Score in Patients with Stable Copd and Acute Exacerbation of Copd and it's Co-Relation with PFT, J. Assoc. Physicians India, 70, pp. 11-12, (2022); Rovina N., Symiakakis M., Tsioka A., Travlos A., Vlachos K., Koutsoukou A., Koulouris N., COPD assessment test (CAT) in acute exacerbation of COPD and in the long term follow up of COPD patients, Eur. Respir. J., 42, (2013); Lee S.D., Huang M.S., Kang J., Lin C.H., Park M.J., Oh Y.M., Kwon N., Jones P.W., Sajkov D., The COPD assessment test (CAT) assists prediction of COPD exacerbations in high-risk patients, Respir. Med., 108, pp. 600-608, (2014); Kor C.T., Li Y.R., Lin P.R., Lin S.H., Wang B.Y., Lin C.H., Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease, J. Pers. Med., 12, (2022); R: A Language and Environment for Statistical Computing, (2021); Buuren S.V., Groothuis-Oudshoorn K., Mice: multivariate imputation by chained equations in R, J. Stat. Soft., 45, pp. 1-67, (2011); Chawla N.V., Bowyer K.W., Hall L.O., Kegelmeyer W.P., SMOTE: Synthetic minority over-sampling technique, J. Artif. Int. Res., 16, pp. 321-357, (2002); Zhang L., Wang Z., Zhou Z., Li S., Huang T., Yin H., Lyu J., Developing an ensemble machine learning model for early prediction of sepsis-associated acute kidney injury, iScience, 25, (2022)","C.-F. Liu; Department of Medical Research, Chi Mei Medical Center, Tainan, 710402, Taiwan; email: chungfengliu@gmail.com; S.-C. Ko; Department of Pulmonary Medicine, Chi Mei Medical Center, Tainan, 710402, Taiwan; email: 737005@mail.chimei.org.tw","","Elsevier Inc.","","","","","","25890042","","","","English","iScience","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85189499829"
"Luo Y.; Song X.; Tong R.","Luo, Yi (58543636000); Song, Xuewu (57900184500); Tong, Rongsheng (36159054200)","58543636000; 57900184500; 36159054200","Developing a Machine Learning Model to Predict 180-day Readmission for Elderly Patients with Angina","2024","Reviews in Cardiovascular Medicine","25","6","","","","","0","10.31083/j.rcm2506203","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196856648&doi=10.31083%2fj.rcm2506203&partnerID=40&md5=099809ea7db9d6a0307e3ff31a67d627","Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Sichuan, Chengdu, 610072, China; Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Sichuan, Chengdu, 610072, China","Luo Y., Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Sichuan, Chengdu, 610072, China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Sichuan, Chengdu, 610072, China; Song X., Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Sichuan, Chengdu, 610072, China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Sichuan, Chengdu, 610072, China; Tong R., Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Sichuan, Chengdu, 610072, China, Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Sichuan, Chengdu, 610072, China","Background: Readmission of elderly angina patients has become a serious problem, with a dearth of available prediction tools for readmission assessment. The objective of this study was to develop a machine learning (ML) model that can predict 180-day all-cause readmission for elderly angina patients. Methods: The clinical data for elderly angina patients was retrospectively collected. Five ML algorithms were used to develop prediction models. Area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and the Brier score were applied to assess predictive performance. Analysis by Shapley additive explanations (SHAP) was performed to evaluate the contribution of each variable. Results: A total of 1502 elderly angina patients (45.74% female) were enrolled in the study. The extreme gradient boosting (XGB) model showed good predictive performance for 180-day readmission (AUROC = 0.89; AUPRC = 0.91; Brier score = 0.21). SHAP analysis revealed that the number of medications, hematocrit, and chronic obstructive pulmonary disease were important variables associated with 180-day readmission. Conclusions: An ML model can accurately identify elderly angina patients with a high risk of 180-day readmission. The model used to identify individual risk factors can also serve to remind clinicians of appropriate interventions that may help to prevent the readmission of patients. © 2024 The Author(s). Published by IMR Press.","angina; elderly; machine learning; predict; readmission","albumin; alkaline phosphatase; aspartate aminotransferase; bile acid; bilirubin; brain natriuretic peptide; cholesterol; cholinesterase; creatine kinase; creatinine; fibrinogen; globulin; glycated hemoglobin; hemoglobin; hemoglobin A1c; high density lipoprotein cholesterol; lactate dehydrogenase; lipoprotein; low density lipoprotein cholesterol; myoglobin; triacylglycerol; uric acid; aged; algorithm; angina pectoris; area under the receiver operating characteristic curve; Article; basophil count; Charlson Comorbidity Index; chronic obstructive lung disease; decision tree; eosinophil count; female; follow up; heart failure; hematocrit; hospital readmission; hospitalization; length of stay; machine learning; machine learning model; male; medical record; model; neutrophil count; osmotic pressure; predictive value; prospective study; random forest; receiver operating characteristic; retrospective study; risk factor; sensitivity and specificity; Shapley additive explanation; support vector machine","","alkaline phosphatase, 9001-78-9; aspartate aminotransferase, 9000-97-9; bilirubin, 18422-02-1, 635-65-4; brain natriuretic peptide, 114471-18-0; cholesterol, 57-88-5; cholinesterase, 9001-08-5; creatine kinase, 9001-15-4; creatinine, 19230-81-0, 60-27-5; fibrinogen, 9001-32-5; glycated hemoglobin, 9062-63-9; hemoglobin, 9008-02-0; hemoglobin A1c, 62572-11-6; lactate dehydrogenase, 9001-60-9; lactate dehydrogenase A, ; uric acid, 69-93-2","Python version 3.7.0; SPSS software version 25, IBM, United States","IBM, United States","Sichuan Academy of Medical Sciences; Sichuan Provincial People’s Hospital; National Key Research and Development Program of China, NKRDPC, (2020YFC2005506)","Funding text 1: We greatly appreciate Sichuan Academy of Medical Sciences and Sichuan Provincial People\u2019s Hospital for providing the data. This research was funded by the National Key Research and Development Program of China (Grant No.2020YFC2005506).; Funding text 2: This research was funded by the National Key Research and Development Program of China (Grant No.2020YFC2005506).","Kloner RA, Chaitman B., Angina and Its Management, Journal of Cardiovascular Pharmacology and Therapeutics, 22, pp. 199-209, (2017); Hermiz C, Sedhai YR., Angina, (2023); Balla C, Pavasini R, Ferrari R., Treatment of Angina: Where Are We?, Cardiology, 140, pp. 52-67, (2018); Ferrari R, Camici PG, Crea F, Danchin N, Fox K, Maggioni AP, Et al., Expert consensus document: A’diamond’ approach to personalized treatment of angina, Nature Reviews. Cardiology, 15, pp. 120-132, (2018); Quashie NT, D'Este C, Agrawal S, Naidoo N, Kowal P., Prevalence of angina and co-morbid conditions among older adults in six low- and middle-income countries: Evidence from SAGE Wave 1, International Journal of Cardiology, 285, pp. 140-146, (2019); Doll JA, Tang F, Cresci S, Ho PM, Maddox TM, Spertus JA, Et al., Change in Angina Symptom Status After Acute Myocardial Infarction and Its Association With Readmission Risk: An Analysis of the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients’ Health Status (TRIUMPH) Registry, Journal of the American Heart Association, 5, (2016); Edmondson D, Green P, Ye S, Halazun HJ, Davidson KW., Psychological stress and 30-day all-cause hospital readmission in acute coronary syndrome patients: an observational cohort study, PLoS ONE, 9, (2014); Gonzalez-Fernandez RA, Baez J, Fernandez-Martinez J, Lugo JE, Altieri PI., Readmission in unstable angina, Puerto Rico Health Sciences Journal, 14, pp. 7-10, (1995); Murphy A, Mahal A, Richardson E, Moran AE., The economic burden of chronic disease care faced by households in Ukraine: a cross-sectional matching study of angina patients, International Journal for Equity in Health, 12, (2013); Alam K, Mahal A., The economic burden of angina on households in South Asia, BMC Public Health, 14, (2014); Htet S, Alam K, Mahal A., Economic burden of chronic conditions among households in Myanmar: the case of angina and asthma, Health Policy and Planning, 30, pp. 1173-1183, (2015); Rajaguru V, Kim TH, Han W, Shin J, Lee SG., LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital, Frontiers in Cardiovascular Medicine, 9, (2022); Padhukasahasram B, Reddy CK, Li Y, Lanfear DE., Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization, PloS One, 10, (2015); Rohr R., Rehospitalizations among patients in the Medicare fee-for-service program, The New England Journal of Medicine, 361, pp. 311-312, (2009); Liu X, Hu P, Yeung W, Zhang Z, Ho V, Liu C, Et al., Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation, The Lancet. Digital Health, 5, pp. e657-e667, (2023); Doudesis D, Lee KK, Boeddinghaus J, Bularga A, Ferry AV, Tuck C, Et al., Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations, Nature Medicine, 29, pp. 1201-1210, (2023); Kahan T, Forslund L, Held C, Bjorkander I, Billing E, Eriksson SV, Et al., Risk prediction in stable angina pectoris, European Journal of Clinical Investigation, 43, pp. 141-151, (2013); Li YH, Sheu WHH, Yeh WC, Chang YC, Lee IT., Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach, Diagnostics (Basel, Switzerland), 11, (2021); Hallert C., Patient readmission. Medical diagnosis and risk factors of emergency readmission within 14 days, Nordisk Medicin, 113, pp. 198-201, (1998); The Standard for Healthy Chinese Older Adults, Biomedical and Environmental Sciences: BES, 36, pp. 666-667, (2023); Dakota I, Munawar M, Pranata R, Raffaello WM, Sukmawan R., Diagnostic prediction model in subjects with low-risk unstable angina pectoris/non-ST segment Elevation Myocardial Infarction, European Review for Medical and Pharmacological Sciences, 25, pp. 5145-5152, (2021); Reeh J, Therming CB, Heitmann M, Hojberg S, Sorum C, Bech J, Et al., Prediction of obstructive coronary artery disease and prognosis in patients with suspected stable angina, European Heart Journal, 40, pp. 1426-1435, (2019); Dritsas E, Trigka M., Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction, Sensors (Basel, Switzerland), 23, (2023); Okere AN, Sanogo V, Alqhtani H, Diaby V., Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach, Expert Review of Pharmacoeconomics & Outcomes Research, 21, pp. 1043-1048, (2021); Alzeer AH, Althemery A, Alsaawi F, Albalawi M, Alharbi A, Alzahrani S, Et al., Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia, International Journal of Medical Informatics, 154, (2021); Cholack G, Garfein J, Krallman R, Feldeisen D, Montgomery D, Kline-Rogers E, Et al., Predictors of Early (0-7 Days) and Late (8-30 Days) Readmission in a Cohort of Acute Coronary Syndrome Patients, International Journal of Medical Students, 10, pp. 38-48, (2022); Izadnegahdar M, Mackay M, Lee MK, Sedlak TL, Gao M, Bairey Merz CN, Et al., Sex and Ethnic Differences in Outcomes of Acute Coronary Syndrome and Stable Angina Patients With Obstructive Coronary Artery Disease, Circulation. Cardiovascular Quality and Outcomes, 9, pp. S26-S35, (2016); Lam L, Ahn HJ, Okajima K, Schoenman K, Seto TB, Shohet RV, Et al., Gender Differences in the Rate of 30-Day Readmissions after Percutaneous Coronary Intervention for Acute Coronary Syndrome, Women’s Health Issues: Official Publication of the Jacobs Institute of Women’s Health, 29, pp. 17-22, (2019); Kim HS, Kim Y, Kwon H., Health-related quality of life and readmission of patients with cardiovascular disease in South Korea, Perspectives in Public Health, 141, pp. 28-36, (2021); Takase H, Toriyama T, Sugiura T, Ueda R, Dohi Y., Brain natriuretic peptide in the prediction of recurrence of angina pectoris, European Journal of Clinical Investigation, 34, pp. 79-84, (2004); Ephrem G., Red blood cell distribution width is a predictor of readmission in cardiac patients, Clinical Cardiology, 36, pp. 293-299, (2013); Li YF, Li WH, Li ZP, Feng XH, Xu WX, Chen SM, Et al., Left atrial area index predicts adverse cardiovascular events in patients with unstable angina pectoris, Journal of Geriatric Cardiology: JGC, 13, pp. 652-657, (2016); Reynolds K, Butler MG, Kimes TM, Rosales AG, Chan W, Nichols GA., Relation of Acute Heart Failure Hospital Length of Stay to Subsequent Readmission and All-Cause Mortality, The American Journal of Cardiology, 116, pp. 400-405, (2015); DiConti-Gibbs A, Chen KY, Coffey CE, Polypharmacy in the Hospitalized Older Adult: Considerations for Safe and Effective Treatment, Clinics in Geriatric Medicine, 38, pp. 667-684, (2022); Safstrom E, Arestedt K, Liljeroos M, Nordgren L, Jaarsma T, Stromberg A., Associations between continuity of care, perceived control and self-care and their impact on health-related quality of life and hospital readmission-A structural equation model, Journal of Advanced Nursing, 79, pp. 2305-2315, (2023); Woodend AK, Sherrard H, Fraser M, Stuewe L, Cheung T, Struthers C., Telehome monitoring in patients with cardiac disease who are at high risk of readmission, Heart & Lung: the Journal of Critical Care, 37, pp. 36-45, (2008)","R. Tong; Department of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610072, China; email: 318004031@qq.com","","IMR Press Limited","","","","","","15306550","","RCMEC","","English","Rev. Cardiovasc. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85196856648"
"Jayamini W.K.D.; Mirza F.; Bidois-Putt M.-C.; Naeem M.A.; Chan A.H.Y.","Jayamini, Widana Kankanamge Darsha (57930112500); Mirza, Farhaan (52664014200); Bidois-Putt, Marie-Claire (58839515200); Naeem, M. Asif (57213518795); Chan, Amy Hai Yan (55337510300)","57930112500; 52664014200; 58839515200; 57213518795; 55337510300","Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Maori Views","2024","JMIR Formative Research","8","","e59811","","","","0","10.2196/59811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208397386&doi=10.2196%2f59811&partnerID=40&md5=a1b90f14bdf9b1693666d90b32d835ad","Department of Computer Science, School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand; Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, Sri Lanka; School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand; Department of Data Science & Artificial Intelligence, National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan","Jayamini W.K.D., Department of Computer Science, School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand, Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, Sri Lanka; Mirza F., Department of Computer Science, School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand; Bidois-Putt M.-C., School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand; Naeem M.A., Department of Data Science & Artificial Intelligence, National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan; Chan A.H.Y., School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand","Background: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Maori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. Objective: This study aimed to explore the views and perceptions that Maori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Maori are represented in ML models without worsening existing health inequities. Methods: Semistructured interviews were conducted with 20 Maori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. Results: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Maori experience of colonization. Most of the participants were interested in using technology to support their asthma management, and we gained insights into user preferences regarding computer-based health care applications. Participants discussed their experiences, highlighting problems with health care quality and limited access to resources. They also mentioned the factors that trigger their asthma control level. Conclusions: The exploration revealed that there is a need for greater information about AI and technology for Maori communities and a need to address trust issues relating to the use of technology. Expectations in relation to computer-based applications for health purposes were expressed. The research outcomes will inform future investigations on AI and technology to enhance the health of people with asthma, in particular those designed for Indigenous populations in New Zealand. © 2024 JMIR Publications Inc.. All rights reserved.","artificial intelligence; asthma risk prediction; health system development; machine learning; maori perceptions; mobile phone","","","","","","Health Research Council of New Zealand, HRC; Auckland University of Technology, New Zealand, AUT, (22/925); Auckland University of Technology, New Zealand, AUT","Funding text 1: The authors would like to acknowledge the participants who contributed to this study. In addition, we extend our gratitude to the New Zealand Health Research Council and Auckland University of Technology for funding this research. This research was funded by the New Zealand Health Research Council (22/925) and the Auckland University of Technology. The views expressed in this publication are those of the authors and not necessarily those of the funding bodies. The funding bodies did not play any roles in the design of the study and the collection, analysis, interpretation of data, and in writing the manuscript; Funding text 2: The authors would like to acknowledge the participants who contributed to this study. In addition, we extend our gratitude to the New Zealand Health Research Council and Auckland University of Technology for funding this research. This research was funded by the New Zealand Health Research Council (22/925) and the Auckland University of Technology. The views expressed in this publication are those of the authors and not necessarily those of the funding bodies. The funding bodies did not play any roles in the design of the study and the collection, analysis, interpretation of data, and in writing the manuscript.","Hargreave FE, Nair P., The definition and diagnosis of asthma, Clin Exp Allergy, 39, 11, pp. 1652-1658, (2009); Chan AHY, Tomlin A, Beyene K, Harrison J., Asthma exacerbations in New Zealand 2010-2019: a national population-based study, Respir Med, 217, (2023); Levy ML, Winter R., Asthma deaths: what now?, Thorax, 70, 3, pp. 209-210, (2015); van Boven JF, Drummond D, Chan AH, Hew M, Hui CY, Adejumo I, Et al., ERS ""CONNECT"" Clinical research collaboration - moving multiple digital innovations towards connected respiratory care: addressing the over-arching challenges of whole systems implementation, Eur Respir J, 62, 5, (2023); Chan AHY, Stewart AW, Harrison J, Camargo CA, Black PN, Mitchell EA., The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: a randomised controlled trial, Lancet Respir Med, 3, 3, pp. 210-219, (2015); Chan A, De Simoni A, Wileman V, Holliday L, Newby CJ, Chisari C, Et al., Digital interventions to improve adherence to maintenance medication in asthma, Cochrane Database Syst Rev, 6, 6, (2022); Chan AHY, Reddel HK, Apter A, Eakin M, Riekert K, Foster JM., Adherence monitoring and e-health: how clinicians and researchers can use technology to promote inhaler adherence for asthma, J Allergy Clin Immunol Pract, 1, 5, pp. 446-454, (2013); Bosnic-Anticevich S, Bakerly ND, Chrystyn H, Hew M, van der Palen J., Advancing digital solutions to overcome longstanding barriers in Asthma and COPD management, Patient Prefer Adherence, 17, pp. 259-272, (2023); Jacome C, Almeida R, Pereira AM, Araujo L, Correia MA, Pereira M, Et al., INSPIRERS group. Asthma app use and interest among patients with asthma: a multicenter study, J Investig Allergol Clin Immunol, 30, 2, pp. 137-140, (2020); Pinnock H, Hui CY, van Boven JFM., Implementation of digital home monitoring and management of respiratory disease, Curr Opin Pulm Med, 29, 4, pp. 302-312, (2023); Finkelstein J, Jeong IC., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann N Y Acad Sci, 1387, 1, pp. 153-165, (2017); Tsang KC, Pinnock H, Wilson AM, Shah SA., Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review, J Asthma Allergy, 15, pp. 855-873, (2022); Obermeyer Z, Powers B, Vogeli C, Mullainathan S., Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366, 6464, pp. 447-453, (2019); O'Neil C., Weapons of math destruction: How big data increases inequality and threatens democracy, (2017); Molfino NA, Turcatel G, Riskin D., Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review, Adv Ther, 41, 2, pp. 534-552, (2024); Chen IY, Pierson E, Rose S, Joshi S, Ferryman K, Ghassemi M., Ethical Machine Learning in Healthcare, Annu Rev Biomed Data Sci, 4, pp. 123-144, (2021); Asthma; Schlichting D, Fadason T, Grant CC, O'Sullivan JM., Childhood asthma in New Zealand: the impact of on-going socioeconomic disadvantage (2010-2019), N Z Med J, 134, 1533, pp. 80-95, (2021); Jayamini WKD, Mirza F, Naeem MA, Chan AHY., State of Asthma-Related Hospital Admissions in New Zealand and Predicting Length of Stay Using Machine Learning, Applied Sciences, 12, 19, (2022); Paul AK, Schaefer M., Safeguards for the use of artificial intelligence and machine learning in global health, Bull World Health Organ, 98, 4, pp. 282-284, (2020); Komene E, Pene B, Gerard D, Parr J, Aspinall C, Wilson D., Whakawhanaungatanga-Building trust and connections: A qualitative study indigenous Maori patients and whanau (extended family network) hospital experiences, J Adv Nurs, 80, 4, pp. 1545-1558, (2024); Morse JM., Determining Sample Size, Qual Health Res, 10, 1, pp. 3-5, (2000); Hika K, Harwood M, Ritchie S, Chan AHY., Maori Experiences and Beliefs about Antibiotics and Antimicrobial Resistance for Acute Upper Respiratory Tract Symptoms: A Qualitative Study, Antibiotics (Basel), 11, 6, (2022); Beard RL, Fetterman DJ, Wu B, Bryant L., The two voices of Alzheimer's: attitudes toward brain health by diagnosed individuals and support persons, Gerontologist, 49, pp. S40-S49, (2009); Clarke V, Braun V., Thematic analysis, The Journal of Positive Psychology, 12, 3, pp. 297-298, (2016); Charmaz K, Belgrave LL., Qualitative interviewing and grounded theory analysis, The SAGE Handbook of Interview Research: The Complexity of the Craft, 2, pp. 347-365, (2012); Devlin J, Chang M-W, Lee K, Toutanova K., BERT: pre-training of deep bidirectional transformers for language understanding, pp. 1-16, (2018); Hugging Face; Siau K, Wang W., Building trust in artificial intelligence, machine learning, and robotics, Cutter Business Technol J, 31, 2, pp. 47-53, (2018); Cibangu SK., Marginalization of indigenous voices in the information age: a case study of cell phones in the rural Congo, Information Technology for Development, 26, 2, pp. 234-267, (2019); Koh J, Tuazon G., A proposal to include maori perspectives in AIed. 2023, ASCILITE 2023 Conference Proceedings: People, Partnerships and Pedagogies, pp. 462-466; Gordon NP, Hornbrook MC., Differences in Access to and Preferences for Using Patient Portals and Other eHealth Technologies Based on Race, Ethnicity, and Age: A Database and Survey Study of Seniors in a Large Health Plan, J Med Internet Res, 18, 3, (2016); Glikson E, Woolley AW., Human trust in artificial intelligence: review of empirical research, Acad Manag Ann, 14, 2, pp. 627-660, (2020); Gasteiger N, Anderson A, Day K., Rethinking engagement: exploring women's technology use during the perinatal period through a Kaupapa Maori consistent approach, J N Z Coll Midwives J, 55, pp. 20-26, (2019); Yap A, Wilkinson B, Chen E, Han L, Vaghefi E, Galloway C, Et al., Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening, Asia Pac J Ophthalmol (Phila), 11, 3, pp. 287-293, (2022); Wikaire E, Harwood M, Wikaire-Mackey K, Crengle S, Brown R, Anderson A, Et al., Reducing healthcare inequities for Maori using Telehealth during COVID-19, N Z Med J, 135, pp. 112-119, (2022); Te Morenga L, Pekepo C, Corrigan C, Matoe L, Mules R, Goodwin D, Et al., Co-designing an mHealth tool in the New Zealand Maori community with a ""Kaupapa Maori"" approach, AlterNative: An International Journal of Indigenous Peoples, 14, 1, pp. 90-99, (2018); Whitehead L, Talevski J, Fatehi F, Beauchamp A., Barriers to and Facilitators of Digital Health Among Culturally and Linguistically Diverse Populations: Qualitative Systematic Review, J Med Internet Res, 25, (2023); Hughson JAP, Daly JO, Woodward-Kron R, Hajek J, Story D., The Rise of Pregnancy Apps and the Implications for Culturally and Linguistically Diverse Women: Narrative Review, JMIR Mhealth Uhealth, 6, 11, (2018); Moore N., Reducing seclusion use for tangata whai i te ora through integration of Maori culture into practice, Scope (Health and Wellbeing), 6, pp. 57-61, (2021); Te reo Maori proficiency and support continues to grow; Graham R, Masters-Awatere B., Experiences of Maori of Aotearoa New Zealand's public health system: a systematic review of two decades of published qualitative research, Aust N Z J Public Health, 44, 3, pp. 193-200, (2020); Gilmour J, Huntington A, Robson B., Oral health experiences of Maori with dementia and whanau perspectives - oranga waha mo nga iwi katoa, Nurs Prax NZ, 32, 1, pp. 20-27, (2006); Ethnic profile; Alharbi ET, Nadeem F, Cherif A., Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review, BMC Med Inform Decis Mak, 21, 1, (2021); Bose S, Kenyon CC, Masino AJ., Personalized prediction of early childhood asthma persistence: A machine learning approach, PLoS One, 16, 3, (2021); Jenkins CR, Boulet LP, Lavoie KL, Raherison-Semjen C, Singh D., Personalized Treatment of Asthma: The Importance of Sex and Gender Differences, J Allergy Clin Immunol Pract, 10, 4, pp. 963-971, (2022); Gillies A, Tinirau R, Mako N., Whakawhanaungatanga - extending the networking concept, He pukenga korero : a journal of Maori studies, 8, 2, pp. 29-37, (2007); Reilly MPJ., Maori Studies, Past and Present: A Review, 23, 2, pp. 340-370, (2011)","W.K.D. Jayamini; Department of Computer Science, School of Engineering Computer and Mathematical Sciences, Auckland University of Technology, Auckland, Building WZ, Level 8, 6th St Paul Street, 1010, New Zealand; email: darsha.jayamini@autuni.ac.nz","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85208397386"
"Abdul Sattar Shaikh A.; Bhargavi M.S.; Kumar C P.","Abdul Sattar Shaikh, Abdullah (58617295200); Bhargavi, M.S. (57221859827); Kumar C, Pavan (56963400900)","58617295200; 57221859827; 56963400900","Weighted aggregation through probability based ranking: An optimized federated learning architecture to classify respiratory diseases","2023","Computer Methods and Programs in Biomedicine","242","","107821","","","","0","10.1016/j.cmpb.2023.107821","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172171863&doi=10.1016%2fj.cmpb.2023.107821&partnerID=40&md5=898b3779e1ca47c2225c021e667accd6","Department of Computer Science and Engineering, Bangalore Institute of Technology, Karnataka, Bangalore, 560004, India; Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Karnataka, Dharwad, 580009, India","Abdul Sattar Shaikh A., Department of Computer Science and Engineering, Bangalore Institute of Technology, Karnataka, Bangalore, 560004, India; Bhargavi M.S., Department of Computer Science and Engineering, Bangalore Institute of Technology, Karnataka, Bangalore, 560004, India; Kumar C P., Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Karnataka, Dharwad, 580009, India","Background and Objective Respiratory Diseases are one of the leading chronic illnesses in the world according to the reports by World Health Organization. Diagnosing these respiratory diseases is done through auscultation where a medical professional listens to sounds of air in the lungs for anomalies through a stethoscope. This method necessitates extensive experience and can also be misinterpreted by the medical professional. To address this issue, we introduce an AI-based solution that listens to the lung sounds and classifies the respiratory disease detected. Since the research work deals with medical data that is tightly under wraps due to privacy concerns in the medical field, we introduce a Deep learning solution to classify the diseases and a custom Federated learning (FL) approach to further improve the accuracy of the deep learning model and simultaneously maintain data privacy. Federated Learning architecture maintains data privacy and facilitates a distributed learning system for medical infrastructures. Methods The approach utilizes Generative Adversarial Networks (GAN) based Federated learning approach to ensure data privacy. Generative Adversarial Networks generate new data by synthesizing new lung sounds. This new synthesized data is then converted to spectrograms and trained on a neural network to classify four lung diseases, Heart Attack and Normal breathing patterns. Furthermore, to address performance loss during FL, we also propose a new “Weighted Aggregation through Probability-based Ranking (FedWAPR)” algorithm for optimizing the FL aggregation process. The FedWAPR aggregation takes inspiration from exponential distribution function and ranks better performing clients according to it. Results and Conclusion A test accuracy of about 92% was achieved by the trained model while classifying various respiratory diseases and heart failure. Additionally, we developed a novel FedWAPR approach that significantly outperformed the FedAVG approach for the FL aggregate function. A patient can be checked for respiratory diseases using this improved learning approach without the need for extensive sensitive data recording or for making sure the data sample obtained is secure. In a decentralized training runtime, the trained model successfully classifies various respiratory diseases and heart failure using lung sounds with a test accuracy on par with a centralized model. © 2023 Elsevier B.V.","Federated learning; GANs; Respiratory disease classification","Algorithms; Heart Failure; Humans; Probability; Respiratory Sounds; Respiratory Tract Diseases; Biological organs; Cardiology; Deep learning; Distribution functions; Entropy; Learning systems; Network architecture; Pulmonary diseases; Sensitive data; Disease classification; Federated learning; GAN; Learning approach; Learning architectures; Lung sounds; Medical professionals; Probability-based ranking; Respiratory disease classification; Test accuracy; abnormal respiratory sound; algorithm; Article; artificial neural network; asthma; breathing pattern; chronic obstructive lung disease; classifier; controlled study; data privacy; deep learning; disease classification; fibrosing alveolitis; heart failure; heart infarction; human; information security; learning; lung auscultation; lung disease; major clinical study; measurement accuracy; respiratory tract disease; waveform; weighted aggregation through probability based ranking; heart failure; probability; Diagnosis","","","","","","","Leroyer C., Perfetti L., Trudeau C., L'ARCHEVEQUE J., Chan-Yeung M., Malo J.-L., Comparison of serial monitoring of peak expiratory flow and fev1 in the diagnosis of occupational asthma, Am. J. Respir. Crit. Care Med., 158, 3, pp. 827-832, (1998); van Aalderen W.M., Childhood asthma: diagnosis and treatment, Scientifica, (2012); Mathers C.D., Loncar D., Projections of global mortality and burden of disease from 2002 to 2030, PLoS Med., 3, 11, (2006); Bohadana A., Izbicki G., Kraman S.S., Fundamentals of lung auscultation, N. Engl. J. Med., 370, 8, pp. 744-751, (2014); Pramono R.X.A., Imtiaz S.A., Rodriguez-Villegas E., Evaluation of features for classification of wheezes and normal respiratory sounds, PLoS ONE, 14, 3, (2019); Pasterkamp H., Kraman S.S., Wodicka G.R., Respiratory sounds: advances beyond the stethoscope, Am. J. Respir. Crit. Care Med., 156, 3, pp. 974-987, (1997); Reichert S., Gass R., Brandt C., Andres E., Analysis of respiratory sounds: state of the art, Clin. Med. Circ. Respirat. Pulm. 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Eng., 41, 1, pp. 1-14, (2021); Khan M.U., Mobeen A., Samer S., Samer A., Embedded system design for real-time detection of asthmatic diseases using lung sounds in cepstral domain, 6th International Electrical Engineering Conference (IEEC 2021), April, 2021 at NEDUET, pp. 1-5, (2021); Garcia-Ordas M.T., Benitez-Andrades J.A., Garcia-Rodriguez I., Benavides C., Alaiz-Moreton H., Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data, Sensors, 20, 4, (2020); Rocha B., Filos D., Mendes L., Vogiatzis I., Perantoni E., Kaimakamis E., Natsiavas P., Oliveira A., Jacome C., Marques A., Et al., A respiratory sound database for the development of automated classification, International Conference on Biomedical and Health Informatics, pp. 33-37, (2017); Basu V., Rana S., Respiratory diseases recognition through respiratory sound with the help of deep neural network, 2020 4th International Conference on Computational Intelligence and Networks (CINE), pp. 1-6, (2020); Tariq Z., Shah S.K., Lee Y., Lung disease classification using deep convolutional neural network, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 732-735, (2019); Dou Q., So T.Y., Jiang M., Liu Q., Vardhanabhuti V., Kaissis G., Li Z., Si W., Lee H.H., Yu K., Et al., Federated deep learning for detecting Covid-19 lung abnormalities in ct: a privacy-preserving multinational validation study, npj Digit. Med., 4, 1, pp. 1-11, (2021); Feki I., Ammar S., Kessentini Y., Muhammad K., Federated learning for Covid-19 screening from chest x-ray images, Appl. Soft Comput., 106, (2021); Tong F., Liu L., Xie X., Hong Q., Li L., Respiratory sound classification: from fluid-solid coupling analysis to feature-band attention, IEEE Access, 10, pp. 22018-22031, (2022); Cetinkaya A.E., Akin M., Sagiroglu S., A communication efficient federated learning approach to multi chest diseases classification, 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 429-434, (2021); Fraiwan M., Fraiwan L., Alkhodari M., Hassanin O., Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory, J. Ambient Intell. Humaniz. Comput., 13, 10, pp. 4759-4771, (2022); Alkhodari M., Khandoker A.H., Detection of Covid-19 in smartphone-based breathing recordings: a pre-screening deep learning tool, PLoS ONE, 17, 1, (2022); Saldanha J., Chakraborty S., Patil S., Kotecha K., Kumar S., Nayyar A., Data augmentation using variationalautoencoders for improvement of respiratory disease classification, PLoS ONE, 17, 8, (2022); Tasar B., Yaman O., Tuncer T., Accurate respiratory sound classification model based on piccolo pattern, Appl. Acoust., 188, (2022); Haider N.S., Behera A., Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (copd), Biocybern. Biomed. Eng., 42, 1, pp. 42-59, (2022); Kochetov K., Filchenkov A., Generative adversarial networks for respiratory sound augmentation, Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System, pp. 106-111, (2020); Jayalakshmy S., Sudha G.F., Conditional gan based augmentation for predictive modeling of respiratory signals, Comput. Biol. Med., 138, (2021); Malygina T., Ericheva E., Drokin I., Gans' n lungs: improving pneumonia prediction, (2019); Hsu F.-S., Huang S.-R., Huang C.-W., Huang C.-J., Cheng Y.-R., Chen C.-C., Hsiao J., Chen C.-W., Chen L.-C., Lai Y.-C., Et al., Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—hf_lung_v1, PLoS ONE, 16, 7, (2021); Deng L., The mnist database of handwritten digit images for machine learning research [best of the web], IEEE Signal Process. Mag., 29, 6, pp. 141-142, (2012); Wang H., Yurochkin M., Sun Y., Papailiopoulos D., Khazaeni Y., Federated learning with matched averaging, (2020); Chen H.-Y., Chao Fedbe W.-L., Making Bayesian model ensemble applicable to federated learning, (2020); Li T., Sahu A.K., Zaheer M., Sanjabi M., Talwalkar A., Smith V., Federated optimization in heterogeneous networks, Proc. Mach. Learn. Syst., 2, pp. 429-450, (2020)","P. Kumar C; Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Dharwad, Karnataka, 580009, India; email: pavan@iiitdwd.ac.in","","Elsevier Ireland Ltd","","","","","","01692607","","CMPBE","37776709","English","Comput. Methods Programs Biomed.","Article","Final","","Scopus","2-s2.0-85172171863"
"Siddiqui T.; Latif M.; Farooq M.U.; Baig M.A.; Hassan Y.S.","Siddiqui, Taskeena (59174456100); Latif, Mustafa (58181632000); Farooq, Muhammad Umer (59596480600); Baig, Mirza Adnan (57322239600); Hassan, Yusuf Sharif (59174304800)","59174456100; 58181632000; 59596480600; 57322239600; 59174304800","Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers","2024","Engineering, Technology and Applied Science Research","14","3","","14741","14746","5","0","10.48084/etasr.7106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196064570&doi=10.48084%2fetasr.7106&partnerID=40&md5=26dc3aa47fbc5a0884f34808df6d6e5e","Department of Computer Science and Information Technology, NED University of Engineering and Technology, Pakistan; Department of Software Engineering, NED University of Engineering and Technology, Pakistan; Department of Computer Science, IQRA University, Pakistan; Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Saudi Arabia","Siddiqui T., Department of Computer Science and Information Technology, NED University of Engineering and Technology, Pakistan; Latif M., Department of Software Engineering, NED University of Engineering and Technology, Pakistan; Farooq M.U., Department of Computer Science and Information Technology, NED University of Engineering and Technology, Pakistan; Baig M.A., Department of Computer Science, IQRA University, Pakistan; Hassan Y.S., Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Saudi Arabia","Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care. © by the authors.","artificial neural networks; bagging; chronic diseases; COPD; deep learning; ensemble learning","","","","","","","","Gibson G. J., Loddenkemper R., Lundback B., Sibille Y., Respiratory health and disease in Europe: the new European Lung White Book, European Respiratory Journal, 42, 3, pp. 559-563, (2013); Aggarwal A. N., Prasad K. T., Muthu V., Obstructive lung diseases burden and COVID-19 in developing countries: a perspective, Current Opinion in Pulmonary Medicine, 28, 2, pp. 84-92, (2022); Biabani S. A. A., Tayyib N. A., A Review on the Use of Machine Learning Against the Covid-19 Pandemic, Engineering, Technology & Applied Science Research, 12, 1, pp. 8039-8044, (2022); Sharma S., Salibi D., Tzenios N., Modern approaches of rehabilitation in COPD patients, Special journal of the Medical Academy and other Life Sciences, 1, (2023); Wu Y. K., Su W. L., Yang M. C., Chen S. Y., Wu C. W., Lan C. C., Associations Between Physical Activity, Smoking Status, and Airflow Obstruction and Self-Reported COPD: A Population-Based Study, International Journal of Chronic Obstructive Pulmonary Disease, 17, pp. 1195-1204, (2022); Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, The Lancet Respiratory Medicine, 10, 5, pp. 447-458, (2022); Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: A systematic analysis for the global burden of disease study 2017, The Lancet Respiratory Medicine, 8, 6, pp. 585-596, (2020); Obira J. O., Sinde R., Development of a Sensor-Based Heartbeat and Body Temperature Monitoring System for Remote Chronic Patients, Engineering, Technology & Applied Science Research, 11, 4, pp. 7375-7380, (2021); Rehman A., Shafiq H., Jawed S., Behram F., Chronic Obstructive Pulmonary Disease (COPD) Screening is Still a Challenge in Pakistan: COPD in Pakistan, Journal of Aziz Fatimah Medical & Dental College, 1, 1, pp. 18-23, (2019); Kousalya K., Dinesh K., Krishnakumar B., Kavyapriya J. G., Kowsika C., Ponmathi K., Prediction of Optimal Algorithm For Diagnosis of Chronic Obstructive Pulmonary Disease, 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), pp. 1-8, (2023); Khan M. A., Et al., Feasibility of delivering integrated COPD-asthma care at primary and secondary level public healthcare facilities in Pakistan: a process evaluation, BJGP Open, 3, 1, (2019); Khan M. A., Ahmed M., Anil S., Walley J., Strengthening the delivery of asthma and chronic obstructive pulmonary disease care at primary health-care facilities: study design of a cluster randomized controlled trial in Pakistan, Global Health Action, 8, 1, (2015); Wang Q., Wang H., Wang L., Yu F., Diagnosis of Chronic Obstructive Pulmonary Disease Based on Transfer Learning, IEEE Access, 8, pp. 47370-47383, (2020); Adeloye D., Song P., Zhu Y., Campbell H., Sheikh A., Rudan I., Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis, The Lancet Respiratory Medicine, 10, 5, pp. 447-458, (2022); Boban B. M., Megalingam R. K., Lung Diseases Classification based on Machine Learning Algorithms and Performance Evaluation, 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0315-0320, (2020); An N., Ding H., Yang J., Au R., Ang T. F. A., Deep ensemble learning for Alzheimer’s disease classification, Journal of Biomedical Informatics, 105, (2020); Zarrin P. S., Roeckendorf N., Wenger C., In-Vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools, IEEE Access, 8, pp. 168053-168060, (2020); Vora S., Shah C., COPD Classification using Machine Learning Algorithms, International Research Journal of Engineering and Technology (IRJET), (2019); Bugajski A., Lengerich A., Koerner R., Szalacha L., Utilizing an Artificial Neural Network to Predict Self-Management in Patients With Chronic Obstructive Pulmonary Disease: An Exploratory Analysis, Journal of Nursing Scholarship, 53, 1, pp. 16-24, (2021); Safar A. A., Salih D. M., Murshid A. M., Pattern recognition using the multi-layer perceptron (MLP) for medical disease: A survey, International Journal of Nonlinear Analysis and Applications, 14, 1, pp. 1989-1998, (2023); Huang Z., Et al., Predicting the morbidity of chronic obstructive pulmonary disease based on multiple locally weighted linear regression model with K-means clustering, International Journal of Medical Informatics, 139, (2020); Anuradha G., Jamal D. N., Classification of Dementia in EEG with a Two-Layered Feed Forward Artificial Neural Network, Engineering, Technology & Applied Science Research, 11, 3, pp. 7135-7139, (2021); Lin S., Zhang Q., Chen F., Luo L., Chen L., Zhang W., Smooth Bayesian network model for the prediction of future high-cost patients with COPD, International Journal of Medical Informatics, 126, pp. 147-155, (2019); Nuanmeesri S., Sriurai W., Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods, Engineering, Technology & Applied Science Research, 11, 5, pp. 7714-7719, (2021)","T. Siddiqui; Department of Computer Science and Information Technology, NED University of Engineering and Technology, Pakistan; email: siddiqui.pg3600982@cloud.neduet.edu.pk","","Dr D. Pylarinos","","","","","","22414487","","","","English","Eng. Technol. Appl. Sci. Res.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85196064570"
"Qin Z.-M.; Liang S.-Q.; Long J.-X.; Deng J.-M.; Wei X.; Yang M.-L.; Tang S.-J.; Li H.-L.","Qin, Zan-Mei (58891427800); Liang, Si-Qiao (55653582300); Long, Jian-Xiong (55176898600); Deng, Jing-Min (7402612865); Wei, Xuan (55654111600); Yang, Mei-Ling (56108841600); Tang, Shao-Jie (55320005000); Li, Hai-Li (57226697313)","58891427800; 55653582300; 55176898600; 7402612865; 55654111600; 56108841600; 55320005000; 57226697313","Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches","2024","Combinatorial Chemistry and High Throughput Screening","27","3","","400","407","7","0","10.2174/1386207326666230602161939","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185290787&doi=10.2174%2f1386207326666230602161939&partnerID=40&md5=008aa96adfa679f78e80727a03c2734a","Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Department of Epidemiology and Health Statistics, School of Public Health of Guangxi Medical University, Guangxi, Nanning, China; School of Automation, Xi'an University of Posts and Telecommunications, Shanxi, Xi'an, 710121, China; Xi'an Key Laboratory of Advanced Controlling and Intelligent Processing (ACIP), Shanxi, Xi'an, 710121, China","Qin Z.-M., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Liang S.-Q., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Long J.-X., Department of Epidemiology and Health Statistics, School of Public Health of Guangxi Medical University, Guangxi, Nanning, China; Deng J.-M., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Wei X., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Yang M.-L., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China; Tang S.-J., School of Automation, Xi'an University of Posts and Telecommunications, Shanxi, Xi'an, 710121, China, Xi'an Key Laboratory of Advanced Controlling and Intelligent Processing (ACIP), Shanxi, Xi'an, 710121, China; Li H.-L., Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Guangxi, Nanning, China","Introduction: To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches. Methods: A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma. Results: A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respec-tively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index. Conclusion: Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis. © 2024 Bentham Science Publishers.","Asthma; AUC; clinical data; GWAS-supported loci; machine learning; pathogenesis","Adult; Asthma; Case-Control Studies; Female; Genetic Loci; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Machine Learning; Male; Middle Aged; Polymorphism, Single Nucleotide; Risk Factors; adult; aged; area under the curve; Article; asthma; body mass; case control study; clinical study; controlled study; cross validation; DNA isolation; family history; female; genome-wide association study; genotype; genotyping; Guangxi; human; machine learning; major clinical study; male; pathogenesis; polymerase chain reaction; prediction; risk factor; asthma; gene locus; genetic predisposition; genetics; middle aged; single nucleotide polymorphism","","","","","Young and Middle Teachers Basic Capacity Improve-ment Project of Guangxi Higher Education Institution; Young and Middle Teachers Basic Capacity Improvement Project of Guangxi Higher Education Institution, (2017KY0101, 2018KY0137); Natural Science Foundation of Guangxi Province, (2017GXNSFAA198104); Natural Science Foundation of Guangxi Province","Funding text 1: This work was supported by the Guangxi Natural Science Foundation (Grant no. 2017GXNSFAA198104) and the Young and Middle Teachers Basic Capacity Improve-ment Project of Guangxi Higher Education Institution (Grant no. 2017KY0101 and Grant no. 2018KY0137). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the article.; Funding text 2: This work was supported by the Guangxi Natural Science Foundation (Grant no. 2017GXNSFAA198104) and the Young and Middle Teachers Basic Capacity Improvement Project of Guangxi Higher Education Institution (Grant no. 2017KY0101 and Grant no. 2018KY0137). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the article.","The global strategy for asthma management and prevention, (2019); Los H., Koppelman G.H., Postma D.S., The importance of genetic influences in asthma, Eur. Respir. J, 14, 5, pp. 1210-1227, (1999); Kim K.W., Ober C., Lessons learned from GWAS of asthma, Allergy Asthma Immunol. 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Genet, 10, (2019); Ding W., Chen G., Shi T., Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prog-nosis, Epigenetics, 14, 1, pp. 67-80, (2019); Fu B., Liu P., Lin J., Deng L., Hu K., Zheng H., Predicting invasive disease-free survival for early-stage breast cancer patients using follow-up clinical data, IEEE Trans. Biomed. Eng, (2018)","J.-M. Deng; Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China; email: ldyyy666@163.com","","Bentham Science Publishers","","","","","","13862073","","CCHSF","37278039","English","Comb. Chem. High Throughput Screen.","Article","Final","All Open Access; Green Open Access","Scopus","2-s2.0-85185290787"
"Yoshida A.; Kai C.; Futamura H.; Oochi K.; Kondo S.; Sato I.; Kasai S.","Yoshida, Akifumi (57408256900); Kai, Chiharu (58297877700); Futamura, Hitoshi (24764879600); Oochi, Kunihiko (58945481100); Kondo, Satoshi (35956448800); Sato, Ikumi (58945481200); Kasai, Satoshi (57483366100)","57408256900; 58297877700; 24764879600; 58945481100; 35956448800; 58945481200; 57483366100","Spirometry test values can be estimated from a single chest radiograph","2024","Frontiers in Medicine","11","","1335958","","","","0","10.3389/fmed.2024.1335958","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188089656&doi=10.3389%2ffmed.2024.1335958&partnerID=40&md5=94cfe8e365427e0f9622b9cb6fa56060","Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan; Major in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Japan; Konica Minolta, Inc, Tokyo, Japan; Kyoto Industrial Health Association, Kyoto, Japan; Graduate School of Engineering, Muroran Institute of Technology, Muroran, Japan; Department of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata, Japan","Yoshida A., Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan; Kai C., Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan, Major in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Japan; Futamura H., Konica Minolta, Inc, Tokyo, Japan; Oochi K., Kyoto Industrial Health Association, Kyoto, Japan; Kondo S., Graduate School of Engineering, Muroran Institute of Technology, Muroran, Japan; Sato I., Major in Health and Welfare, Graduate School of Niigata University of Health and Welfare, Niigata, Japan, Department of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata, Japan; Kasai S., Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan","Introduction: Physical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning. Methods: Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC as spirometry measurements as well as the corresponding chest radiographs of 11,837 participants were used in this study. The data were randomly allocated to the training, validation, and evaluation datasets at an 8:1:1 ratio. A deep learning network was pretrained using ImageNet. The input and output information were CXRs and spirometry test values, respectively. The training and evaluation of the deep learning network were performed separately for each parameter. The mean absolute error rate (MAPE) and Pearson’s correlation coefficient (r) were used as the evaluation indices. Results: The MAPEs between the spirometry measurements and AI estimates for FVC, FEV1 and FEV1/FVC were 7.59% (r = 0.910), 9.06% (r = 0.879) and 5.21% (r = 0.522), respectively. A strong positive correlation was observed between the measured and predicted indices of FVC and FEV1. The average accuracy of >90% was obtained in each estimation of spirometry indices. Bland–Altman analysis revealed good agreement between the estimated and measured values for FVC and FEV1. Discussion: Frontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility. Copyright © 2024 Yoshida, Kai, Futamura, Oochi, Kondo, Sato and Kasai.","artificial intelligence; chest radiography; deep learning; pulmonary function test; spirometry","adult; aged; Article; artificial intelligence; Bland Altman analysi; chronic obstructive lung disease; controlled study; correlation analysis; correlation coefficient; deep learning; diagnostic accuracy; diagnostic test accuracy study; evaluation study; expiratory reserve volume; female; forced expiratory volume; forced vital capacity; human; lung function test; major clinical study; male; mathematical model; mean absolute error; middle aged; residual neural network; spirometry; thorax radiography; validation study","","","MATLAB 2022a, Mathworks","Mathworks","","","Buist A.S., McBurnie M.A., Vollmer W.M., Gillespie S., Burney P., Mannino D.M., Et al., International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study, Lancet, 370, pp. 741-750, (2007); Agusti A., Celli B.R., Criner G.J., Halpin D., Anzueto A., Barnes P., Et al., Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary, Eur Respir J, 61, (2023); Fouka E., Papaioannou A.I., Hillas G., Steiropoulos P., Asthma-COPD overlap syndrome: recent insights and unanswered questions, J Pers Med, 12, (2022); Celli B., Fabbri L., Criner G., Martinez F.J., Mannino D., Vogelmeier C., Et al., Definition and nomenclature of chronic obstructive pulmonary disease: time for its revision, Am J Respir Crit Care Med, 206, pp. 1317-1325, (2022); Celli B.R., Cote C.G., Marin J.M., Casanova C., Montes de Oca M., Mendez R.A., Et al., The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease, N Engl J Med, 350, pp. 1005-1012, (2004); Hurst J.R., Anzueto A., Vestbo J., Susceptibility to exacerbation in COPD, Lancet Respir Med, 5, (2017); Lindberg A., Jonsson A.C., Ronmark E., Lundgren R., Larsson L.G., Lundback B., Ten-year cumulative incidence of COPD and risk factors for incident disease in a symptomatic cohort, Chest, 127, pp. 1544-1552, (2005); Kalhan R., Dransfield M.T., Colangelo L.A., Cuttica M.J., Jacobs D.R., Thyagarajan B., Et al., Respiratory symptoms in young adults and future lung disease. 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Yoshida; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan; email: akifumi-yoshida@nuhw.ac.jp; S. Kasai; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan; email: satoshi-kasai@nuhw.ac.jp","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85188089656"
"Motarjem K.; Moghimbeygi M.","Motarjem, K. (57194585769); Moghimbeygi, M. (57190026630)","57194585769; 57190026630","Prediction of Anoxic Tonic Seizures due to Asthma in Children Using Machine Learning Methods","2024","Journal of Sciences, Islamic Republic of Iran","35","1","","63","69","6","0","10.22059/jsciences.2024.372994.1007850","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210377452&doi=10.22059%2fjsciences.2024.372994.1007850&partnerID=40&md5=326b59903ca8e1c3a752d2921d076f1f","Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran; Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran","Motarjem K., Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran; Moghimbeygi M., Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran","The objective of this study is to investigate the factors influencing asthma attacks in children under six years old using machine learning (ML) methods. There are many statistical methods for data classification that can be used to classify medical data. But using the data itself as well as a set of different methods in machine learning can provide vast and more comparable results. Hence, this study applied ML approaches to predict asthma and second anoxic tonic seizures due to asthma (ATSA) based on variables such as first ATSA, age, region of residence, parent smoking status, and parents' asthma history. The results revealed that children's age and place of residence significantly affected the duration of asthma attacks, with children living in certain areas of Tehran experiencing shorter intervals between attacks due to high air pollution. Machine learning techniques proved useful in predicting ATSA based on age, gender, living region, parents' smoking status, and asthma history, with the AdaBoost method highlighting the importance of the child's age and living area in predicting ATSA. © 2024 University of Tehran. All rights reserved.","Asthma; Childhood; Machine Learning; Prediction Model","","","","","","","","Malveaux FJ., The state of childhood asthma: introduction, Pediatrics, 123, pp. S129-S130, (2009); Roemer M., Health care expenditures for the five most common children’s conditions. 2008: estimates for U.S. civilian non institutionalized children, ages 0–17, (2011); Yunginger JW, Reed CE, O'Connell EJ, Melton LJ, O'Fallon WM, Silverstein MD., A community-based study of the epidemiology of asthma. 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Moghimbeygi; Department of Mathematics, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran; email: m.moghimbeygi@yahoo.com","","University of Tehran","","","","","","10161104","","","","English","J. Sci. Islam. Repub. Iran","Article","Final","","Scopus","2-s2.0-85210377452"
"Lou Z.; Li M.; Kong N.; Campbell N.L.; Tu W.","Lou, Zhouyang (57196003209); Li, Mingyang (56900064900); Kong, Nan (7005055833); Campbell, Noll L. (18036473900); Tu, Wanzhu (7006479265)","57196003209; 56900064900; 7005055833; 18036473900; 7006479265","An Improved Statistical Modeling Approach to Individual Anticholinergic Drug Use Trend Analysis","2024","IEEE Journal of Biomedical and Health Informatics","28","2","","1122","1133","11","0","10.1109/JBHI.2023.3332598","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177057171&doi=10.1109%2fJBHI.2023.3332598&partnerID=40&md5=9ef0bd0e1a1cbd29c61a3998cd554894","Purdue University, School of Industrial Engineering, West Lafayette, 47907-2050, IN, United States; University of South Florida, Department of Industrial and Management Systems Engineering, Tampa, 33620-9951, FL, United States; Purdue University, Weldon School of Biomedical Engineering, West Lafayette, 47907-2050, IN, United States; Purdue University, Regenstrief Institute, College of Pharmacy, West Lafayette, 47907-2050, IN, United States; Indiana University, School of Medicine, Regenstrief Institute, Eskenazi Health, Indianapolis, 46202-5114, IN, United States","Lou Z., Purdue University, School of Industrial Engineering, West Lafayette, 47907-2050, IN, United States; Li M., University of South Florida, Department of Industrial and Management Systems Engineering, Tampa, 33620-9951, FL, United States; Kong N., Purdue University, Weldon School of Biomedical Engineering, West Lafayette, 47907-2050, IN, United States; Campbell N.L., Purdue University, Regenstrief Institute, College of Pharmacy, West Lafayette, 47907-2050, IN, United States; Tu W., Indiana University, School of Medicine, Regenstrief Institute, Eskenazi Health, Indianapolis, 46202-5114, IN, United States","Anticholinergic (AC) drugs are commonly prescribed to older adults for treating diseases and chronic conditions, such as chronic obstructive pulmonary disease, urinary incontinence, gastrointestinal disorder, or simply pain and allergy. The high prevalence of AC drug use can have a detrimental effect on the mental health of older adults. We aim to improve the prediction of future trends of AC drug use at the individual level, with pharmacy refill data. The individual drug use data presents challenges in the modeling, such as data being discrete-valued with excess zeros and having significant unobserved heterogeneity in the trend pattern. To address these challenges, we propose a statistical model of hierarchical structure and an EM scheme for the model parameter estimation. We evaluate the proposed modeling approach through a numerical study with synthetic data and a case study with real-world pharmacy refill data. The simulation study show that our analysis method outperforms the existing ones (e.g., reducing MSE significantly), particularly in terms of accurately predicting the trend pattern. The real-world case study further verifies the out-performance and demonstrate the advantageous features of our method. We expect the prediction tool developed based on our study can assist pharmacists' decision on initiating or strengthening behavioral interventions with the hope of discontinuing AC drug misuse.  © 2013 IEEE. ","Drug use trend modeling; hierarchical structure; pharmacy refill data; prediction model","Aged; Cholinergic Antagonists; Computer Simulation; Humans; Models, Statistical; Pharmaceutical Services; Urinary Incontinence; Commerce; Market Research; Pulmonary diseases; cholinergic receptor blocking agent; cyclobenzaprine; diphenoxylate; fentanyl; cholinergic receptor blocking agent; Anticholinergic drugs; Drug; Drug use trend modeling; Hierarchical structures; Market researches; Older adults; Pharmacy refill data; Prediction modelling; Predictive models; Trend model; aged; Anticholinergic Cognitive Burden Scale; Article; case study; chronic disease; confusion matrix; decision making; diarrhea; disease severity; drug misuse; drug potency; drug use; expectation-maximization algorithm; health care system; histogram; human; machine learning; major clinical study; mathematical analysis; mathematical parameters; muscle rigidity; myalgia; pharmacist; pharmacy practice; Poisson regression; predictive model; prescription; statistical analysis; statistical model; trend analysis; trend study; urban health; computer simulation; pharmacy (shop); statistical model; urine incontinence; Forecasting","","cyclobenzaprine, 303-53-7, 6202-23-9; diphenoxylate, 3810-80-8, 915-30-0; fentanyl, 437-38-7, 1443-54-5; Cholinergic Antagonists, ","","","","","Jessen F., Et al., Anticholinergic drug use and risk for dementia: Target for dementia prevention, Eur. Arch. Psychiatry Clin. Neurosci., 260, 2, pp. 111-115, (2010); Lechevallier-Michel N., Molimard M., Dartigues J.-F., Fabrigoule C., Fourrier-Reglat A., Drugs with anticholinergic properties and cognitive performance in the elderly: Results from the paquid study, Brit. J. Clin. Pharmacol., 59, 2, pp. 143-151, (2005); Boustani M.A., Et al., Enhancing care for hospitalized older adults with cognitive impairment: A randomized controlled trial, J. Gen. Intern. Med., 27, 5, pp. 561-567, (2012); Ness J., Hoth A., Barnett M.J., Shorr R.I., Kaboli P.J., Anticholinergic medications in community-dwelling older veterans: Prevalence of anticholinergic symptoms, symptom burden, and adverse drug events, Amer. J. Geriatr. Pharmacother., 4, 1, pp. 42-51, (2006); Buhrich N., Weller A., Kevans P., Misuse of anticholinergic drugsby people with serious mental illness, Psychiatr. Serv., 51, 7, pp. 928-929, (2000); Campbell N., Et al., The cognitive impact of anticholinergics: A clinical review, Clin. Interv. Aging, 4, pp. 225-233, (2009); Gray S.L., Et al., Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study, JAMA Intern. Med., 175, 3, pp. 401-407, (2015); Cai X., Campbell N., Khan B., Callahan C., Boustani M., Long-term anticholinergic use and the aging brain, Alzheimer’s Dement, 9, 4, pp. 377-385, (2013); Carriere I., Et al., Drugs with anticholinergic properties, cognitive decline, and dementia in an elderly general population: The 3-city study, Arch. Intern. Med., 169, 14, pp. 1317-1324, (2009); Riordan D.O., Et al., Pharmacist-led academic detailing intervention in primary care: A mixed methods feasibility study, Int. J. Clin. Pharm., 41, 2, pp. 574-582, (2019); Campbell N.L., Et al., Association of anticholinergic burden with cognitive impairment and health care utilization among a diverse ambulatory older adult population, Pharmacother.: J. Hum. Pharmacol. Drug Ther., 36, 11, pp. 1123-1131, (2016); Campbell N.L., Et al., Use of anticholinergics and the risk of cognitive impairment in an African American population, Neurology, 75, 2, pp. 152-159, (2010); Campbell N.L., Lane K.A., Gao S., Boustani M.A., Unverzagt F., Anticholinergics influence transition from normal cognition to mild cognitive impairment in older adults in primary care, Pharmacother.: J. Hum. Pharmacol. Drug Ther., 38, 5, pp. 511-519, (2018); Richardson K., Et al., Anticholinergic drugs and risk of dementia: Case-control study, BMJ, 361, (2018); Buu A., Li R., Tan X., Zucker R.A., Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field, Statist. Med., 31, 29, pp. 4074-4086, (2012); DeSantis S.M., Bandyopadhyay D., Hidden Markov models for zero-inflated Poisson counts with an application to substance use, Statist. Med., 30, 14, pp. 1678-1694, (2011); Dillon P., Stewart D., Smith S.M., Gallagher P., Cousins G., Group-based trajectory models: Assessing adherence to antihypertensive medication in older adults in a community pharmacy setting, Clin. Pharmacol. Therapeutics, 103, 6, pp. 1052-1060, (2018); Franklin J.M., Et al., Group-based trajectory models: A new approach to classifying and predicting long-term medication adherence, Med. Care, 51, 9, pp. 789-796, (2013); Hargrove J.L., Et al., Antihypertensive adherence trajectories among older adults in the first year after initiation of therapy, Amer. J. Hypertension, 30, 10, pp. 1015-1023, (2017); MacEwan J.P., Silverstein A.R., Shafrin J., Lakdawalla D.N., Hatch A., Forma F.M., Medication adherence patterns among patients with multiple serious mental and physical illnesses, Adv. Ther., 35, 5, pp. 671-685, (2018); Kaur N., Gonzales M., Alcaraz C.G., Barnes L.E., Wells K.J., Gong J., Theory-guided randomized neural networks for decoding medication-taking behavior, Proc. IEEE EMBS Int. Conf. Biomed. Health Inform., 2021, pp. 1-4; Neelon B., O'Malley A.J., Smith V.A., Modeling zero-modified count and semicontinuous data in health services research part 1: Background and overview, Statist. Med., 35, 27, pp. 5070-5093, (2016); Perumean-Chaney S.E., Morgan C., McDowall D., Aban I., Zero-inflated and overdispersed: What’s one to do ?, J. Stat. Comput. Simul., 83, 9, pp. 1671-1683, (2013); Lambert D., Zero-inflated poisson regression, with an application to defects in manufacturing, Technometrics, 34, 1, pp. 1-14, (1992); Cheung Y.B., Zero-inflated models for regression analysis of count data: A study of growth and development, Statist. Med., 21, 10, pp. 1461-1469, (2002); Glassman P.A., Et al., The utility of adding retrospective medication profiling to computerized provider order entry in an ambulatory care population, J. Amer. Med. Inform. Assoc.: JAMIA, 14, 4, pp. 424-431, (2007); Vest J.R., Health information exchange and healthcare utilization, J. Med. Syst., 33, 3, pp. 223-231, (2008); Famoye F., Singh K.P., Zero-inflated generalized poisson regression model with an application to domestic violence data, J. Data Sci., 4, 1, pp. 117-130; Park B.-J., Lord D., Application of finite mixture models for vehicle crash data analysis, Accident Anal. Prevention, 41, 4, pp. 683-691, (2009); Lim H.K., Li W.K., Yu P.L.H., Zero-inflated poisson regression mixture model, Comput. Statist. Data Anal., 71, pp. 151-158, (2014); Campbell N., Maidment I., Fox C., Khan B., Boustani M., The 2012 update to the anticholinergic cognitive burden scale: 2013 AGS annual scientific meeting, J. Amer. Geriatrics Soc., 61, S1, pp. S142-S143, (2013); Carroll R.J., Ruppert D., Transfor Mation and Weightingin Regression, (2019)","N. Kong; Purdue University, Weldon School of Biomedical Engineering, West Lafayette, 47907-2050, United States; email: nkong@purdue.edu","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21682194","","ITIBF","37963002","English","IEEE J. Biomedical Health Informat.","Article","Final","","Scopus","2-s2.0-85177057171"
"Burns S.; Cushing A.; Taylor A.; Lowe D.J.; Carlin C.","Burns, Shane (57222399287); Cushing, Andrew (57728401300); Taylor, Anna (57212317902); Lowe, David J. (57139650800); Carlin, Christopher (22133701900)","57222399287; 57728401300; 57212317902; 57139650800; 22133701900","Supporting long-term condition management: a workflow framework for the co-development and operationalization of machine learning models using electronic health record data insights","2024","Frontiers in Artificial Intelligence","7","","1458508","","","","0","10.3389/frai.2024.1458508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210163671&doi=10.3389%2ffrai.2024.1458508&partnerID=40&md5=6546c7ff3112708991f9a34f6a8a8002","Lenus Health Ltd., Edinburgh, United Kingdom; Departments of Respiratory and Emergency Medicine, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom","Burns S., Lenus Health Ltd., Edinburgh, United Kingdom; Cushing A., Lenus Health Ltd., Edinburgh, United Kingdom; Taylor A., Departments of Respiratory and Emergency Medicine, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom; Lowe D.J., Departments of Respiratory and Emergency Medicine, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom; Carlin C., Departments of Respiratory and Emergency Medicine, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom","The prevalence of long-term conditions such as cardiovascular disease, chronic obstructive pulmonary disease (COPD), asthma, and diabetes mellitus is rising. These conditions are leading sources of premature mortality, hospital admission, and healthcare expenditure. Machine learning approaches to improve the management of these conditions have been widely explored, with data-driven insights demonstrating the potential to support earlier diagnosis, triage, and treatment selection. The translation of this research into tools used in live clinical practice has however been limited, with many projects lacking clinical involvement and planning beyond the initial model development stage. To support the move toward a more coordinated and collaborative working process from concept to investigative use in a live clinical environment, we present a multistage workflow framework for the co-development and operationalization of machine learning models which use routine clinical data derived from electronic health records. The approach outlined in this framework has been informed by our multidisciplinary team’s experience of co-developing and operationalizing risk prediction models for COPD within NHS Greater Glasgow & Clyde. In this paper, we provide a detailed overview of this framework, alongside a description of the development and operationalization of two of these risk-prediction models as case studies of this approach. Copyright © 2024 Burns, Cushing, Taylor, Lowe and Carlin.","electronic health record data; long-term condition management; machine learning in healthcare; model operationalization; risk prediction; workflow framework","","","","","","ResMed","The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The funding for the co-development and operationalization of the models described in the case studies came from an Accelerated Access Collaborative/NIHR Artificial Intelligence in Health and Care award is: AI_AWARD02283. The NHS GG&C team are additionally supported by an unrestricted investigator-initiated award from ResMed.","Alqahtani J.S., Aquilina J., Bafadhel M., Bolton C.E., Burgoyne T., Holmes S., Et al., Research priorities for exacerbations of COPD, Lancet Respir. Med, 9, pp. 824-826, (2021); Atella V., Piano Mortari A., Kopinska J., Belotti F., Lapi F., Cricelli C., Et al., Trends in age-related disease burden and healthcare utilization, Aging Cell, 18, (2019); Bastian G., Baker G.H., Limon A., Bridging the divide between data scientists and clinicians, Int. Based Med, 6, (2022); Breiman L., Random forests, Mach. Learn, 45, pp. 5-32, (2001); Cardet J.C., Louisias M., King T.S., Castro M., Codispoti C.D., Dunn R., Et al., Income is an independent risk factor for worse asthma outcomes, J. Allergy Clin. Immunol, 141, pp. 754-760.e3, (2018); Chen T., Guestrin C., (2016); Cortes C., Vapnik V., Support-vector networks, Mach. 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Asthma, 46, pp. 392-398, (2009); Fung P.L., Zaidan M.A., Timonen H., Niemi J.V., Kousa A., Kuula J., Et al., Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration, J. Aerosol Sci, 152, (2021); Gianfrancesco M.A., Tamang S., Yazdany J., Schmajuk G., Potential biases in machine learning algorithms using electronic health record data, JAMA Intern. Med, 178, pp. 1544-1547, (2018); Gill S.K., Karwath A., Uh H.-W., Cardoso V.R., Gu Z., Barsky A., Et al., Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare, Eur. Heart J, 44, pp. 713-725, (2023); Habehh H., Gohel S., Machine learning in healthcare. Curr, Genomics, 22, pp. 291-300, (2021); Head T., Kumar M., Nahrstaedt H., Louppe G., Shcherbatyi I., Scikit-Optimize, (2020); What approvals and decisions do I need?, (2024); Holman H.R., The relation of the chronic disease epidemic to the health care crisis, ACR Open Rheumatol, 2, pp. 167-173, (2020); Javaid M., Haleem A., Pratap Singh R., Suman R., Rab S., Significance of machine learning in healthcare: features, pillars and applications, Int. J. Int. Netw, 3, pp. 58-73, (2022); Juhn Y.J., Ryu E., Wi C.-I., King K.S., Malik M., Romero-Brufau S., Et al., Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index, J. Am. Med. Inform. Assoc, 29, pp. 1142-1151, (2022); Khan Z., The emerging challenges and strengths of the National Health Services: a physician perspective, Cureus, 15, (2016); Koh D.-M., Papanikolaou N., Bick U., Illing R., Kahn C.E., Kalpathi-Cramer J., Et al., Artificial intelligence and machine learning in cancer imaging, Commun. Med, 2, (2022); la Roi-Teeuw H.M., van Royen F.S., de Hond A., Zahra A., de Vries S., Bartels R., Et al., Don’t be misled: three misconceptions about external validation of clinical prediction models, J. Clin. Epidemiol, 172, (2024); Li Y., Wang H., Luo Y., Improving fairness in the prediction of heart failure length of stay and mortality by integrating social determinants of health, Circ. Heart Failure, 15, (2022); Lundberg S.M., Erion G., Chen H., DeGrave A., Prutkin J.M., Nair B., Et al., From local explanations to global understanding with explainable AI for trees, Nature Machine Int, 2, pp. 56-67, (2020); Lundberg S.M., Lee S.I., (2017); Software and artificial intelligence (AI) as a medical device, (2024); Musbahi O., Syed L., Le Feuvre P., Cobb J., Jones G., Public patient views of artificial intelligence in healthcare: a nominal group technique study, Digital Health, 7, (2021); Briefing paper- Chronic obstructive pulmonary disease (COPD) update, (2019); The NHS Long Term Plan, (2019); Long Term Physical Health Condition, (2023); Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Et al., Scikit-learn: machine learning in Python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011); Pierce R.L., Van Biesen W., Van Cauwenberge D., Decruyenaere J., Sterckx S., Explainability in medicine in an era of AI-based clinical decision support systems, Front. Genet, 13, (2022); Scottish Atlas of Healthcare Variation, (2022); Ribeiro M.T., Singh S., Guestrin C., pp. 1135-1144, (2016); Sauer C.M., Chen L.-C., Hyland S.L., Girbes A., Elbers P., Celi L.A., Leveraging electronic health records for data science: common pitfalls and how to avoid them, Lancet Digital Health, 4, pp. e893-e898, (2022); Scottish Index of Multiple Deprivation 2020, (2023); Snell N., Strachan D., Hubbard R., Gibson J., Gruffydd-Jones K., Jarrold I., S32 epidemiology of chronic obstructive pulmonary disease (COPD) in the UK: findings from the British lung foundation’s ‘respiratory health of the nation’ project, Thorax, 71, (2016); Snoek J., Larochelle H., Adams R.P., Practical Bayesian optimization of machine learning algorithms, Adv. Neural Inf. Proces. Syst, 25, pp. 2951-2959, (2012); Straw I., Wu H., Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction, BMJ Health Care Inform, 29, (2022); Sunarti S., Fadzlul Rahman F., Naufal M., Risky M., Febriyanto K., Masnina R., Artificial intelligence in healthcare: opportunities and risk for future, 1st Int. Conf. Safety Pub. Health, 35, pp. S67-S70, (2021); Taylor A., (2024); Weerts H., Dudik M., Edgar R., Jalali A., Lutz R., Madaio M., Fairlearn: assessing and improving fairness of AI systems, J. Mach. Learn. Res, 24, pp. 1-8, (2023); Noncommunicable diseases, (2023); Young A.T., Amara D., Bhattacharya A., Wei M.L., Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review, Lancet Digital Health, 3, pp. e599-e611, (2021)","S. Burns; Lenus Health Ltd., Edinburgh, United Kingdom; email: shane.burns@lenushealth.com","","Frontiers Media SA","","","","","","26248212","","","","English","Frontier. Artif. Intell.","Article","Final","","Scopus","2-s2.0-85210163671"
"Seong H.; Lee K.-S.; Choi Y.; Na D.; Kim J.; Shin H.J.; Ahn K.H.","Seong, Hyunyoung (57216892298); Lee, Kwang-Sig (57221177656); Choi, Yumin (58904087200); Na, Donghyun (58617316000); Kim, Jaewoo (58904034600); Shin, Hyeon Ju (56484958000); Ahn, Ki Hoon (26031248300)","57216892298; 57221177656; 58904087200; 58617316000; 58904034600; 56484958000; 26031248300","Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: Machine learning analysis using national health insurance data","2024","Medicine (United States)","103","8","","E36909","","","0","10.1097/MD.0000000000036909","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185858732&doi=10.1097%2fMD.0000000000036909&partnerID=40&md5=5ab606468df64c910b4453c474142be5","Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea; AI Center, Korea University, College of Medicine, Seoul, South Korea; Korea University, School of Mechanical Engineering, Seoul, South Korea; Department of Obstetrics & Gynecology, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea","Seong H., Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea; Lee K.-S., AI Center, Korea University, College of Medicine, Seoul, South Korea; Choi Y., Korea University, School of Mechanical Engineering, Seoul, South Korea; Na D., Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea; Kim J., Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea; Shin H.J., Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea; Ahn K.H., Department of Obstetrics & Gynecology, Anam Hospital, Korea University, College of Medicine, Seoul, South Korea","This study uses machine learning and population data to analyze major determinants of blood transfusion among patients with hip arthroplasty. Retrospective cohort data came from Korea National Health Insurance Service claims data for 19,110 patients aged 65 years or more with hip arthroplasty in 2019. The dependent variable was blood transfusion (yes vs no) in 2019 and its 31 predictors were included. Random forest variable importance and Shapley Additive Explanations were used for identifying major predictors and the directions of their associations with blood transfusion. The random forest registered the area under the curve of 73.6%. Based on random forest variable importance, the top-10 predictors were anemia (0.25), tranexamic acid (0.17), age (0.16), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.04), dementia (0.03), iron (0.02), and congestive heart failure (0.02). These predictors were followed by their top-20 counterparts including cardiovascular disease, statin, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, peripheral vascular disease, liver disease, solid tumor, myocardial infarction and hypertension. In terms of max Shapley Additive Explanations values, these associations were positive, e.g., anemia (0.09), tranexamic acid (0.07), age (0.09), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.02), dementia (0.03), iron (0.04), and congestive heart failure (0.03). For example, the inclusion of anemia, age, tranexamic acid or spinal anesthesia into the random forest will increase the probability of blood transfusion among patients with hip arthroplasty by 9%, 7%, 9% or 5%. Machine learning is an effective prediction model for blood transfusion among patients with hip arthroplasty. The high-risk group with anemia, age and comorbid conditions need to be treated with tranexamic acid, iron and/or other appropriate interventions. © 2024 Lippincott Williams and Wilkins. All rights reserved.","blood transfusion; hip arthroplasty; machine learning","Aged; Anemia; Antifibrinolytic Agents; Arthroplasty, Replacement, Hip; Artificial Intelligence; Blood Loss, Surgical; Dementia; Erythrocyte Transfusion; Female; Heart Failure; Humans; Iron; Machine Learning; National Health Programs; Retrospective Studies; Tranexamic Acid; iron; tranexamic acid; antifibrinolytic agent; tranexamic acid; age; aged; anemia; Article; artificial intelligence; cardiovascular disease; chronic kidney failure; chronic obstructive lung disease; cohort analysis; congestive heart failure; controlled study; dementia; diabetes mellitus; erythrocyte transfusion; female; general anesthesia; geriatric patient; heart infarction; high risk population; hip arthroplasty; human; hypertension; liver disease; machine learning; major clinical study; male; national health insurance; peripheral vascular disease; prediction; random forest; retrospective study; risk factor; sex difference; social status; solid tumor; spinal anesthesia; very elderly; anemia; artificial intelligence; dementia; erythrocyte transfusion; heart failure; hip replacement; machine learning; operative blood loss; public health","","iron, 14093-02-8, 53858-86-9, 7439-89-6; tranexamic acid, 1197-18-8, 701-54-2; Antifibrinolytic Agents, ; Iron, ; Tranexamic Acid, ","","","Korea University, KU, (K2327361); Korea University, KU; Ministry of Health and Welfare, MOHW, (HI22C1463); Ministry of Health and Welfare, MOHW; Korea Health Industry Development Institute, KHIDI","This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI22C1463) and technically by 4P Lab, Co., Ltd. for data analysis, and also supported by Korea University Grant (K2327361). ","Spahn D.R., Anemia and patient blood management in hip and knee surgery: A systematic review of the literature., Anesthesiology, 113, pp. 482-495, (2010); Jang S.Y., Cha Y.H., Yoo J.I., Blood transfusion for elderly patients with hip fracture: A nationwide cohort study., J Korean Med Sci, 35, (2020); Montroy J., Lavallee L.T., Zarychanski R., The top 20 surgical procedures associated with the highest risk for blood transfusion., Br J Surg, 107, pp. e642-e643, (2020); Gupta P.B., Demario V.M., Amin R.M., Patient blood management program improves blood use and clinical outcomes in orthopedic surgery., Anesthesiology, 129, pp. 1082-1091, (2018); Arshi A., Lai W.C., Iglesias B.C., Blood transfusion rates and predictors following geriatric hip fracture surgery., Hip Int, 31, pp. 272-279, (2021); To J., Sinha R., Kim S.W., Predicting perioperative transfusion in elective hip and knee arthroplasty: A validated predictive model., Anesthesiology, 127, pp. 317-325, (2017); Hou G., Zhou F., Tian Y., Predicting the need for blood transfusions in elderly patients with pertrochanteric femoral fractures., Injury, 45, pp. 1932-1937, (2014); Yoshihara H., Yoneoka D., Predictors of allogeneic blood transfusion in total hip and knee arthroplasty in the United States, 2000-2009., J Arthroplasty, 29, pp. 1736-1740, (2014); Bian F.C., Cheng X.K., An Y.S., Preoperative risk factors for postoperative blood transfusion after hip fracture surgery: Establishment of a nomogram., J Orthop Surg Res, 16, (2021); Huang Z., Huang C., Xie J., Analysis of a large data set to identify predictors of blood transfusion in primary total hip and knee arthroplasty., Transfusion, 58, pp. 1855-1862, (2018); Huang Z., Martin J., Huang Q., Predicting postoperative transfusion in elective total HIP and knee arthroplasty: Comparison of different machine learning models of a case-control study., Int J Surg, 96, (2021); Lee K.-S., Kim E.S., Explainable artificial intelligence in the early diagnosis of gastrointestinal disease., Diagnostics (Basel), 12, (2022); Lundberg S.M., Erion G., Chen H., From local explanations to global understanding with explainable AI for trees., Nat Mach Intell, 2, pp. 56-67, (2020); Iban M.C., An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants., Habitat Int, 128, (2022); Slover J., Lavery J.A., Schwarzkopf R., Incidence and risk factors for blood transfusion in total joint arthroplasty: Analysis of a statewide database., J Arthroplasty, 32, pp. 2684-2684, (2017); Sim Y.E., Sim S.D., Seng C., Preoperative anemia, functional outcomes, and quality of life after hip fracture surgery., J Am Geriatr Soc, 66, pp. 1524-1531, (2018); Carson J.L., Stanworth S.J., Dennis J.A., Transfusion thresholds for guiding red blood cell transfusion., Cochrane Database Syst Rev, 12, (2021); Luo X., Huang H., Tang X., Efficacy and safety of tranexamic acid for reducing blood loss in elderly patients with intertrochanteric fracture treated with intramedullary fixation surgery: A meta-analysis of randomized controlled trials., Acta Orthop Traumatol Turc, 54, pp. 4-14, (2020); Xing F., Chen W., Long C., Postoperative outcomes of tranexamic acid use in geriatric trauma patients treated with proximal femoral intramedullary nails: A systematic review and meta-analysis., Orthop Traumatol Surg Res, 106, pp. 117-126, (2020); Augustinus S., Mulders M.A.M., Gardenbroek T.J., Tranexamic acid in hip hemiarthroplasty surgery: A systematic review and meta-analysis., Eur J Trauma Emerg Surg, 49, pp. 1247-1258, (2023); Mauermann W.J., Shilling A.M., Zuo Z., A comparison of neuraxial block versus general anesthesia for elective total hip replacement: A meta-analysis., Anesth Analg, 103, pp. 1018-1025, (2006); Hu S., Zhang Z.Y., Hua Y.Q., A comparison of regional and general anaesthesia for total replacement of the hip or knee: A meta-analysis., J Bone Joint Surg Br, 91, pp. 935-942, (2009); Macfarlane A.J., Prasad G.A., Chan V.W., Does regional anaesthesia improve outcome after total hip arthroplasty? A systematic review., Br J Anaesth, 103, pp. 335-345, (2009); Guay J., The effect of neuraxial blocks on surgical blood loss and blood transfusion requirements: A meta-analysis., J Clin Anesth, 18, pp. 124-128, (2006); Rodgers A., Walker N., Schug S., Reduction of postoperative mortality and morbidity with epidural or spinal anaesthesia: Results from overview of randomised trials., BMJ, 321, (2000); Sinha R., Gurwara A.K., Gupta S.C., Laparoscopic cholecystectomy under spinal anesthesia: A study of 3492 patients., J Laparoendosc Adv Surg Tech A, 19, pp. 323-327, (2009); Biboulet P., Jourdan A., Van Haevre V., Hemodynamic profile of target-controlled spinal anesthesia compared with 2 target-controlled general anesthesia techniques in elderly patients with cardiac comorbidities., Reg Anesth Pain Med, 37, pp. 433-440, (2012)","H.J. Shin; Department of Anesthesiology and Pain Medicine, Anam Hospital, Korea University, College of Medicine, Seoul, 73, Goryeodae-ro, Seongbuk-gu, 02841, South Korea; email: may335@naver.com","","Lippincott Williams and Wilkins","","","","","","00257974","","MEDIA","38394543","English","Medicine","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85185858732"
"Chen C.; Yuan F.; Meng X.; Peng F.; Shao X.; Wang C.; Shen Y.; Du H.; Lv D.; Zhang N.; Wang X.; Wang T.; Wang P.","Chen, Cai (57209307140); Yuan, Fenglong (59350109200); Meng, Xiangwei (57216349375); Peng, Fulai (56124731300); Shao, Xuekun (58662592900); Wang, Cheng (59349765900); Shen, Yang (59349880600); Du, Haitao (57219668397); Lv, Danyang (57783449800); Zhang, Ningling (57563379300); Wang, Xiuli (57923672800); Wang, Tao (59349995000); Wang, Ping (57213429786)","57209307140; 59350109200; 57216349375; 56124731300; 58662592900; 59349765900; 59349880600; 57219668397; 57783449800; 57563379300; 57923672800; 59349995000; 57213429786","Genetic biomarker prediction based on gender disparity in asthma throughout machine learning","2024","Frontiers in Medicine","11","","1397746","","","","0","10.3389/fmed.2024.1397746","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205358399&doi=10.3389%2ffmed.2024.1397746&partnerID=40&md5=8328de121c63cf585bbe88268cf005e1","Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China; Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, China; Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, China; School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China; Shandong Academy of Chinese Medicine, Jinan, China; Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Jinan, China","Chen C., Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China; Yuan F., Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, China; Meng X., Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan, China; Peng F., Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China; Shao X., School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China; Wang C., Shandong Academy of Chinese Medicine, Jinan, China; Shen Y., Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Du H., Shandong Academy of Chinese Medicine, Jinan, China; Lv D., Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China; Zhang N., Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan, China; Wang X., Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, China; Wang T., Neck-Shoulder and Lumbocrural Pain Hospital of Shandong First Medical University, Jinan, China; Wang P., Shandong Academy of Chinese Medicine, Jinan, China","Background: Asthma is a chronic respiratory condition affecting populations worldwide, with prevalence ranging from 1–18% across different nations. Gender differences in asthma prevalence have attracted much attention. Purpose: The aim of this study was to investigate biomarkers of gender differences in asthma prevalence based on machine learning. Method: The data came from the gene expression omnibus database (GSE69683, GSE76262, and GSE41863), which involved in a number of 575 individuals, including 240 males and 335 females. Theses samples were divided into male group and female group, respectively. Grid search and cross-validation were employed to adjust model parameters for support vector machine, random forest, decision tree and logistic regression model. Accuracy, precision, recall, and F1 score were used to evaluate the performance of the models during the training process. After model optimization, four machine learning models were utilized to predict biomarkers of sex differences in asthma. In order to validate the accuracy of our results, we performed Wilcoxon tests on the genes expression. Result: In datasets GSE76262 and GSE69683, support vector machine, random forest, logistic regression, and decision tree all achieve 100% accuracy, precision, recall, and F1 score. Our findings reveal that XIST serves as a common biomarker among the three samples, comprising a total of 575 individuals, with higher expression levels in females compared to males (p < 0.01). Conclusion: XIST serves as a genetic biomarker for gender differences in the prevalence of asthma. Copyright © 2024 Chen, Yuan, Meng, Peng, Shao, Wang, Shen, Du, Lv, Zhang, Wang, Wang and Wang.","asthma; biomarker; gender disparity; machine learning; prevalence","biological marker; accuracy; adult; Article; asthma; cross validation; decision tree; female; gender inequality; gene expression; human; logistic regression analysis; machine learning; major clinical study; male; prevalence; random forest; regression model; sex difference; support vector machine","","","","","","","Huang K., Yang T., Xu J., Yang L., Zhao J., Zhang X., Et al., Prevalence, risk factors, and management of asthma in China: a national cross-sectional study, Lancet, 394, pp. 407-418, (2019); Papi A., Brightling C., Pedersen S.E., Reddel H.K., Asthma, Lancet, 391, pp. 783-800, (2018); Agache I., Akdis C.A., Precision medicine and phenotypes, endotypes, genotypes, regiotypes, and theratypes of allergic diseases, J Clin Invest, 129, pp. 1493-1503, (2019); Kuruvilla M.E., Lee F.E., Lee G.B., Understanding asthma phenotypes, endotypes, and mechanisms of disease, Clin Rev Allergy Immunol, 56, pp. 219-233, (2019); 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Li B., Li X., Jiang Z., Zhou D., Feng Y., Chen G., Et al., Lnc RNA XIST modulates mi R-328-3p ectopic expression in lung injury induced by tobacco-specific lung carcinogen NNK both in vitro and in vivo, Br J Pharmacol, 181, pp. 2509-2527, (2024); Li J., Xue L., Wu Y., Yang Q., Liu D., Yu C., Et al., STAT3-activated lnc RNA XIST accelerates the inflammatory response and apoptosis of LPS-induced acute lung injury, J Cell Mol Med, 25, pp. 6550-6557, (2021); Fagerberg L., Hallstrom B.M., Oksvold P., Kampf C., Djureinovic D., Odeberg J., Et al., Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics, Mol Cell Proteomics, 13, pp. 397-406, (2014); She C., Yang Y., Zang B., Yao Y., Liu Q., Leung P.S.C., Et al., Effect of Lnc RNA XIST on immune cells of primary biliary cholangitis, Front Immunol, 13, (2022); Yu B., Qi Y., Li R., Shi Q., Satpathy A.T., Chang H.Y., B cell-specific XIST complex enforces X-inactivation and restrains atypical B cells, Cell, 184, pp. 1790-1803.e17, (2021); 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Habener A., Grychtol R., Gaedcke S., DeLuca D., Dittrich A.M., Happle C., Et al., IgA<sup>+</sup> memory B-cells are significantly increased in patients with asthma and small airway dysfunction, Eur Respir J, 60, (2022); Zhou P., Xiang C.X., Wei J.F., The clinical significance of spondin 2 eccentric expression in peripheral blood mononuclear cells in bronchial asthma, J Clin Lab Anal, 35, (2021); Jiang Y., Yan Q., Zhang M., Lin X., Peng C., Huang H.T., Et al., Identification of molecular markers related to immune infiltration in patients with severe asthma: a comprehensive bioinformatics analysis based on the human bronchial epithelial transcriptome, Dis Markers, 2022, pp. 1-20, (2022)","X. Wang; Department of Pulmonary and Critical Care Medicine, Yantai Yeda Hospital, Yantai, China; email: 469944924@qq.com; ; ","","Frontiers Media SA","","","","","","2296858X","","","","English","Front. Med.","Article","Final","","Scopus","2-s2.0-85205358399"
"Choi J.Y.; Rhee C.K.","Choi, Joon Young (56039939900); Rhee, Chin Kook (59397800300)","56039939900; 59397800300","Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models","2024","Journal of Asthma and Allergy","17","","","783","789","6","0","10.2147/JAA.S471964","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204947766&doi=10.2147%2fJAA.S471964&partnerID=40&md5=3ed33dd3d134d08304c742eac7f8f2be","Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea","Choi J.Y., Department of Internal Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea; Rhee C.K., Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea","Asthma is a chronic inflammatory airway disease with significant burden; exacerbations can severely affect quality of life and healthcare costs. Advances in big data analysis and artificial intelligence have made it easier to predict future exacerbations more accurately. This study used an integrated dataset of Korean National Health Insurance, meteorological, air pollution, and viral data from national public databases to develop a model to predict asthma exacerbations on a daily basis in South Korea. We merged these sources and applied random forest, AdaBoost, XGBoost, and LightGBM machine learning models to compare their performances at predicting future exacerbations. Of the models, XGBoost (AUROC of 0.68 and accuracy of 0.96) and LightGBM (AUROC of 0.67 and accuracy of 0.96) were the most promising. Common important variables were the number of visits and exacerbations per year, and medical resource utilization, including the prescription of asthma medications. Comorbid diabetes, hypertension, gastroesophageal reflux, arthritis, metabolic syndrome, osteoporosis, and ischemic heart disease were also associated with elevated exacerbation risk. The models examined in this study highlight the importance of previous exacerbations, use of medical resources, and comorbidities in the prediction of future exacerbations in patients with asthma. © 2024, Dove Medical Press Ltd. All rights reserved.","asthma; big data analysis; LightGBM; machine learning; South Korea; XGBoost","bronchodilating agent; carbon monoxide; corticosteroid; leukotriene receptor blocking agent; methylxanthine; nitrogen dioxide; sulfur dioxide; Adenoviridae; adult; air pollution; arthritis; Article; artificial intelligence; asthma; blood pressure; chronic inflammation; chronic obstructive lung disease; cohort analysis; comorbidity; computer assisted tomography; Coronavirinae; diabetes mellitus; diastolic blood pressure; disease surveillance; gastroesophageal reflux; health care cost; health data; human; hypertension; ICD-10; Influenza A virus; ischemic heart disease; machine learning; major clinical study; metabolic syndrome X; metagenomics; multiplex reverse transcription polymerase chain reaction; osteoporosis; Paramyxovirinae; particulate matter; pollution; polymerase chain reaction; polysomnography; prediction; quality of life; receiver operating characteristic; respiratory syncytial virus infection; respiratory tract disease; Rhinovirus; South Korea","","carbon monoxide, 630-08-0; methylxanthine, 28109-92-4; nitrogen dioxide, 10102-44-0; sulfur dioxide, 7446-09-5","","","Korea Environmental Industry and Technology Institute, KEITI; Ministry of Environment, MOE, (2022003310008); Ministry of Environment, MOE","This study was supported by grants from the Korean Environment Industry and Technology Institute through the Core Technology Development Project for Environmental Disease Prevention and Management, funded by the Korea Ministry of Environment (Grant number 2022003310008).","Momtazmanesh S, Moghaddam SS, Ghamari S-H., GBD 2019 Chronic Respiratory Diseases Collaborators. Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the global burden of disease study 2019, EClinicalMedicine, 59, (2023); Wisnivesky J, Federmann E, Eckert L, Et al., Impact of exacerbations on lung function, resource utilization, and productivity: results from an observational, prospective study in adults with uncontrolled asthma, J Asthma, 60, pp. 1072-1079, (2023); Jiao T, Schnitzer ME, Forget A, Blais L., Identifying asthma patients at high risk of exacerbation in a routine visit: a machine learning model, Respir Med, 198, (2022); Joo H, Lee D, Lee SH, Kim YK, Rhee CK., Increasing the accuracy of the asthma diagnosis using an operational definition for asthma and a machine learning method, BMC Pulm Med, 23, (2023); Hwang H, Jang JH, Lee E, Park HS, Lee JY., Prediction of the number of asthma patients using environmental factors based on deep learning algorithms, Respir Res, 24, (2023); Jo YS, Han S, Lee D, Et al., Development of a daily predictive model for the exacerbation of chronic obstructive pulmonary disease, Sci Rep, 13, (2023); Lee J, Jung HM, Kim SK, Et al., Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis, Sci Rep, 9, (2019); He S, Lin W, Zhong J, Zheng X, Jin Y, Cao C., Independent risk factors of asthma exacerbations: 3-year follow-up in a single-center prospective cohort study, Ann Transl Med, 10, 24, (2022); Kraft M, Brusselle G, FitzGerald JM, Et al., Patient characteristics, biomarkers and exacerbation risk in severe, uncontrolled asthma, Euro Respir J, 2021, (2021); Grossman NL, Ortega VE, King TS, Et al., Exacerbation-prone asthma in the context of race and ancestry in Asthma clinical research network trials, J Allergy Clin Immunol, 144, pp. 1524-1533, (2019); Tiotiu AI, Novakova P, Nedeva D, Et al., Impact of air pollution on asthma outcomes, Int J Environ Res Public Health, 18, (2020); Yu HR, Lin CR, Tsai JH, Et al., A multifactorial evaluation of the effects of air pollution and meteorological factors on asthma exacerbation, Int J Environ Res Public Health, 18, (2020); Jo EJ, Choi MH, Kim CH, Et al., Patterns of medical care utilization according to environmental factors in asthma and chronic obstructive pulmonary disease patients, Korean J Intern Med, 36, pp. 1146-1156, (2021); Mikhail I, Grayson MH., Asthma and viral infections: an intricate relationship, Ann Allergy Asthma Immunol, 123, pp. 352-358, (2019)","C.K. Rhee; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 222 Banpo-daero, Seocho-gu, 06591, South Korea; email: chinkook77@gmail.com","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85204947766"
"Levinson R.T.; Paul C.; Meid A.D.; Schultz J.-H.; Wild B.","Levinson, Rebecca T. (56289490900); Paul, Cinara (58701604400); Meid, Andreas D. (55218414400); Schultz, Jobst-Hendrik (7401897932); Wild, Beate (12802902500)","56289490900; 58701604400; 55218414400; 7401897932; 12802902500","Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study","2024","JMIR Cardio","8","","e54994","","","","0","10.2196/54994","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202661102&doi=10.2196%2f54994&partnerID=40&md5=c651ab63e8ffb394c1e9279f33ff87d2","Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany; Medical Faculty of Heidelberg, Internal Medicine IX - Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany","Levinson R.T., Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany; Paul C., Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany; Meid A.D., Medical Faculty of Heidelberg, Internal Medicine IX - Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany; Schultz J.-H., Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany; Wild B., Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany","Background: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. Objective: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. Methods: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. Results: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. Conclusions: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission. ©Rebecca T Levinson, Cinara Paul, Andreas D Meid, Jobst-Hendrik Schultz, Beate Wild.","all cause; cardiac; cardiology; heart; heart failure; hospitalization; insurance; machine learning; predict; prediction; predictions; predictive; predictor; predictors; readmission; statutory health insurance","","","","","","Klaus Tschira Stiftung, KTS; Universität Heidelberg","The authors would like to acknowledge Jan D Lanzer for his insightful comments in discussions that shaped this project. This study was founded by the German Innovation Funds project PREMISE (grant 01VSF18019), and no authors received personal funding. The funding body did not play any role in the design of the study, collection, analyses, and interpretation of data or the manuscript. RTL is funded by the Klaus Tschira Stiftung through the Informatics for Life Consortium. For the publication fee, we acknowledge financial support by Heidelberg University.","Ziaeian B, Fonarow GC., Epidemiology and aetiology of heart failure, Nat Rev Cardiol, 13, 6, pp. 368-378, (2016); Ruff C, Gerharz A, Groll A, Stoll F, Wirbka L, Haefeli WE, Et al., Disease-dependent variations in the timing and causes of readmissions in Germany: a claims data analysis for six different conditions, PLoS One, 16, 4, (2021); Curtis LH, Greiner MA, Hammill BG, Kramer JM, Whellan DJ, Schulman KA, Et al., Early and long-term outcomes of heart failure in elderly persons, 2001-2005, Arch Intern Med, 168, 22, pp. 2481-2488, (2008); Liao L, Allen LA, Whellan DJ., Economic burden of heart failure in the elderly, Pharmacoeconomics, 26, 6, pp. 447-462, (2008); Arundel C, Lam PH, Khosla R, Blackman MR, Fonarow GC, Morgan C, Et al., Association of 30-day all-cause readmission with long-term outcomes in hospitalized older Medicare beneficiaries with heart failure, Am J Med, 129, 11, pp. 1178-1184, (2016); Saito M, Negishi K, Marwick TH., Meta-analysis of risks for short-term readmission in patients with heart failure, Am J Cardiol, 117, 4, pp. 626-632, (2016); Kreis K, Neubauer S, Klora M, Lange A, Zeidler J., Status and perspectives of claims data analyses in Germany-a systematic review, Health Policy, 120, 2, pp. 213-226, (2016); Statutory health insurance; Busse R, Blumel M, Knieps F, Barnighausen T., Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition, Lancet, 390, pp. 882-897, (2017); Klabunde CN, Potosky AL, Legler JM, Warren JL., Development of a comorbidity index using physician claims data, J Clin Epidemiol, 53, 12, pp. 1258-1267, (2000); Madelaire C, Gustafsson F, Kristensen SL, D'Souza M, Stevenson LW, Kober L, Et al., Burden and causes of hospital admissions in heart failure during the last year of life, JACC Heart Fail, 7, 7, pp. 561-570, (2019); Lee CS, Tkacs NC, Riegel B., The influence of heart failure self-care on health outcomes: hypothetical cardioprotective mechanisms, J Cardiovasc Nurs, 24, 3, pp. 179-187, (2009); Rajkomar A, Dean J, Kohane I., Machine learning in medicine, N Engl J Med, 380, 14, pp. 1347-1358, (2019); Bazoukis G, Stavrakis S, Zhou J, Bollepalli SC, Tse G, Zhang Q, Et al., Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review, Heart Fail Rev, 26, 1, pp. 23-34, (2021); Yilmaz A, Hayiroglu M, Salturk S, Pay L, Demircali AA, Coskun C, Et al., Machine learning approach on high risk treadmill exercise test to predict obstructive coronary artery disease by using P, QRS, and T waves' features, Curr Probl Cardiol, 48, 2, (2023); Hayiroglu M, Altay S., The role of artificial intelligence in coronary artery disease and atrial fibrillation, Balkan Med J, 40, 3, pp. 151-152, (2023); Shin S, Austin PC, Ross HJ, Abdel-Qadir H, Freitas C, Tomlinson G, Et al., Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality, ESC Heart Fail, 8, 1, pp. 106-115, (2021); van der Galien OP, Hoekstra RC, Gurgoze MT, Manintveld OC, van den Bunt MR, Veenman CJ, Et al., Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach, BMC Med Inform Decis Mak, 21, 1, (2021); Chamberlain AM, Dunlay SM, Gerber Y, Manemann SM, Jiang R, Weston SA, Et al., Burden and timing of hospitalizations in heart failure: a community study, Mayo Clin Proc, 92, 2, pp. 184-192, (2017); Thünen Landatlas; R: A Language and Environment for Statistical Computing, (2013); Wickham H, Averick M, Bryan J, Chang W, McGowan LD, Francois R, Et al., Welcome to the tidyverse, J Open Source Softw, 4, 43, (2019); Dowle M, Srinivasan A., data.table: Extension of ‘data.frame’, Data.table, (2023); Wickham H., ggplot2: Elegant Graphics for Data Analysis, (2009); Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Et al., mlr3: a modern object-oriented machine learning framework in R, J Open Source Softw, 4, 44, (2019); Kuhn M., Building predictive models in R using the caret package, J Stat Soft, 28, 5, pp. 1-26, (2008); Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Et al., pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics, 12, (2011); (1988); Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, Et al., A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data, BMC Med Inform Decis Mak, 18, 1, (2018); Allam A, Nagy M, Thoma G, Krauthammer M., Neural networks versus logistic regression for 30 days all-cause readmission prediction, Sci Rep, 9, 1, (2019); Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, Et al., Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches, JAMA Cardiol, 2, 2, pp. 204-209, (2017); Gerharz A, Ruff C, Wirbka L, Stoll F, Haefeli WE, Groll A, Et al., Predicting hospital readmissions from health insurance claims data: a modeling study targeting potentially inappropriate prescribing, Methods Inf Med, 61, 1-2, pp. 55-60, (2022); Schwabe U, Paffrath D, Ludwig WD, Klauber J., Arzneiverordnungs-Report 2018 [Drug Prescription Report 2018], (2018); Katz PO, Dunbar KB, Schnoll-Sussman FH, Greer KB, Yadlapati R, Spechler SJ., ACG clinical guideline for the diagnosis and management of gastroesophageal reflux disease, Am J Gastroenterol, 117, 1, pp. 27-56, (2022); Forgacs I, Loganayagam A., Overprescribing proton pump inhibitors, BMJ, 336, 7634, pp. 2-3, (2008); Fraser LA, Leslie WD, Targownik LE, Papaioannou A, Adachi JD, The effect of proton pump inhibitors on fracture risk: report from the Canadian Multicenter Osteoporosis Study, Osteoporos Int, 24, 4, pp. 1161-1168, (2013); Ramsay EN, Pratt NL, Ryan P, Roughead EE., Proton pump inhibitors and the risk of pneumonia: a comparison of cohort and self-controlled case series designs, BMC Med Res Methodol, 13, (2013); Dunlay SM, Redfield MM, Weston SA, Therneau TM, Long KH, Shah ND, Et al., Hospitalizations after heart failure diagnosis a community perspective, J Am Coll Cardiol, 54, 18, pp. 1695-1702, (2009); Hoang-Kim A, Parpia C, Freitas C, Austin PC, Ross HJ, Wijeysundera HC, Et al., Readmission rates following heart failure: a scoping review of sex and gender based considerations, BMC Cardiovasc Disord, 20, 1, (2020); Zheng PP, Yao SM, He W, Wan YH, Wang H, Yang JF., Frailty related all-cause mortality or hospital readmission among adults aged 65 and older with stage-B heart failure inpatients, BMC Geriatr, 21, 1, (2021); van Dis J., Where We Live: Health Care in Rural vs Urban America, JAMA, 287, 1, (2002); Holstiege J, Akmatov MK, Stork S, Steffen A, Batzing J., Higher prevalence of heart failure in rural regions: a population-based study covering 87% of German inhabitants, Clin Res Cardiol, 108, 10, pp. 1102-1106, (2019); Al-Omary MS, Khan AA, Davies AJ, Fletcher PJ, Mcivor D, Bastian B, Et al., Outcomes following heart failure hospitalization in a regional Australian setting between 2005 and 2014, ESC Heart Fail, 5, 2, pp. 271-278, (2018); Melbye H, Stylidis M, Solis JCA, Averina M, Schirmer H., Prediction of chronic heart failure and chronic obstructive pulmonary disease in a general population: the Tromsø study, ESC Heart Fail, 7, 6, pp. 4139-4150, (2020); Khan MS, Tahhan AS, Vaduganathan M, Greene SJ, Alrohaibani A, Anker SD, Et al., Trends in prevalence of comorbidities in heart failure clinical trials, Eur J Heart Fail, 22, 6, pp. 1032-1042, (2020); Su A, Al'Aref SJ, Beecy AN, Min JK, Karas MG., Clinical and socioeconomic predictors of heart failure readmissions: a review of contemporary literature, Mayo Clin Proc, 94, 7, pp. 1304-1320, (2019); Sawicki OA, Mueller A, Klaassen-Mielke R, Glushan A, Gerlach FM, Beyer M, Et al., Strong and sustainable primary healthcare is associated with a lower risk of hospitalization in high risk patients, Sci Rep, 11, 1, (2021); Desai RJ, Wang SV, Vaduganathan M, Evers T, Schneeweiss S., Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes, JAMA Netw Open, 3, 1, (2020); Gemeinsamer Bundesausschuss-Innovationsfonds; AOK Baden-Württemberg","R.T. Levinson; Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Im Neuenheimer Feld 410, 69120, Germany; email: rebeccaterrall.levinson@med.uni-heidelberg.de","","JMIR Publications Inc.","","","","","","25611011","","","","English","JMIR Cardio","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85202661102"
"Ramirez-Bautista J.A.; Chaparro-Cárdenas S.L.; Gamboa-Contreras W.; Guerrero-Salazar W.; Huerta-Ruelas J.A.","Ramirez-Bautista, Julian Andres (57194122336); Chaparro-Cárdenas, Silvia L. (57194117689); Gamboa-Contreras, Wilson (55628489000); Guerrero-Salazar, William (57948919100); Huerta-Ruelas, Jorge Adalberto (8416877800)","57194122336; 57194117689; 55628489000; 57948919100; 8416877800","Classification of COVID-19 associated symptomatology using machine learning; [Clasificación de la sintomatología asociada a la COVID-19 mediante aprendizaje automático]","2023","DYNA (Colombia)","90","226","","36","43","7","0","10.15446/dyna.v90n226.105616","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163220001&doi=10.15446%2fdyna.v90n226.105616&partnerID=40&md5=033fff6b5864255984daf24c273c78be","Departamento de Investigación, Fundación Universitaria de San Gil-Unisangil, San Gil, Colombia; Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada-Instituto Politécnico Nacional, Querétaro, Mexico","Ramirez-Bautista J.A., Departamento de Investigación, Fundación Universitaria de San Gil-Unisangil, San Gil, Colombia; Chaparro-Cárdenas S.L., Departamento de Investigación, Fundación Universitaria de San Gil-Unisangil, San Gil, Colombia; Gamboa-Contreras W., Departamento de Investigación, Fundación Universitaria de San Gil-Unisangil, San Gil, Colombia; Guerrero-Salazar W., Departamento de Investigación, Fundación Universitaria de San Gil-Unisangil, San Gil, Colombia; Huerta-Ruelas J.A., Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada-Instituto Politécnico Nacional, Querétaro, Mexico","The health situation caused by the SARS-Cov2 coronavirus, posed major challenges for the scientific community. Advances in artificial intelligence are a very useful resource, but it is important to determine which symptoms presented by positive cases of infection are the best predictors. A machine learning approach was used with data from 5,434 people, with eleven symptoms: breathing problems, dry cough, sore throat, running nose, history of asthma, chronic lung, headache, heart disease, hypertension, diabetes, and fever. Based on public data from Kaggle with WHO standardized symptoms. A model was developed to detect COVID-19 positive cases using a simple machine learning model. The results of 4 loss functions and by SHAP values, were compared. The best loss function was Binary Cross Entropy, with a single hidden layer configuration with 10 neurons, achieving an F1 score of 0.98 and the model was rated with an area under the curve of 0.99 aucROC. © The author;.","artificial neural networks; computer-aided diagnosis: COVID-19; disease diagnosis; machine learning","","","","","","Fundación Universitaria de San Gil-UNISANGIL; Instituto Politécnico Nacional, IPN; Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, CICATA-IPN, Querétaro","The authors would like to thank the Fundación Universitaria de San Gil-UNISANGIL, Colombia and the Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, unit Querétaro from the Instituto Politécnico Nacional, México, for their support for this work.","Pena-Reyes C. A., Sipper M., Evolutionary Computation in medicine: an overview, Artif. Intell. Med, 19, 1, pp. 1-23, (2000); Tan K.C., Yu Q.C., Heng M., Lee T.H., Evolutionary computing for knowledge discovery in medical diagnosis, Artif. Intell. Med, 27, 2, pp. 129-154, (2003); Li Z., Chen W., Wang J., Liu J., An automatic recognition system for patients with movement disorders based on wearable sensors,  IEEE Conf. Ind. Electron. Appl. ICIEA 2014, pp. 1948-1953, (2014); Andrikopoulou M., Et al., Symptoms and critical illness among obstetric patients with coronavirus disease 2019 (COVID-19) infection, Obstet. Gynecol, 136, 2, pp. 291-299, (2020); Amenta E.M., Spallone A., Rodriguez-Barradas M.C., El--Sahly H.M., Atmar R.L., Kulkarni P.A., Postacute COVID-19: an overview and approach to classification, Open Forum Infect. Dis, 7, 12, pp. 1-7, (2020); Maghdid H.S., Ghafoor K.Z., Sadiq A.S., Curran K., Rawat D.B., Rabie K., A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study, pp. 1-7, (2020); Alimadadi A., Aryal S., Manandhar I., Munroe P.B., Joe B., Cheng X., Artificial intelligence and machine learning to fight Covid-19, Physiol. Genomics, 52, 4, pp. 200-202, (2020); Zoabi Y., Shomron N., COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach, npj Digital Medicine, (2020); Alafif T., Bajaba S., Machine and deep learning towards COVID-19 diagnosis and treatment: survey, Challenges, (2020); Zoabi Y., Deri-Rozov S., Shomron N., Machine learning-based prediction of COVID-19 diagnosis based on symptoms, npj Digit. Med, 4, 1; Chen Y., Et al., An interpretable machine learning framework for accurate severe vs non-severe COVID-19 clinical type classification, medRxiv, (2020); Ahamad M.M., Et al., A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients, Expert Syst. Appl, 160, (2020); Khanday A.M.U.D., Rabani S.T., Khan Q.R., Rouf N., Mohi Ud Din M., Machine learning based approaches for detecting COVID-19 using clinical text data, Int. J. Inf. Technol, 12, 3, pp. 731-739, (2020); Smarr B.L., Et al., Feasibility of continuous fever monitoring using wearable devices, Sci. Rep, 10, 1, (2020); Usha-Ruby A., Theerthagiri P., Jeena-Jacob I., Vamsidhar Y., Binary cross entropy with deep learning technique for image classification, Int. J. Adv. Trends Comput. Sci. Eng, 9, 4, pp. 5393-5397, (2020); Valencia A.M., Construcción de la distribución de pérdidas y el problema de agregación de riesgo operativo bajo modelos LDA: una revisión, Revista Ingenierías Universidad de Medellín, 12, 23, pp. 71-82, (2013); Wang Z., Bovik A.C., Mean squared error: Love it or leave it?. A new look at signal fidelity measures, IEEE Signal Process. Mag, 6, 1, pp. 98-117, (2009); Meyer G.P., An alternative probabilistic interpretation of the huber loss, pp. 5261-5269, (2019); Lundberg S., Lee S.-I., A Unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst, 2017, pp. 4766-4775, (2017); Mangalathu S., Hwang S.H., Jeo J.S., Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach, Eng. Struct, 219, (2020); Strumbelj E., Kononenko I., Explaining prediction models and individual predictions with feature contributions, Knowl. Inf. Syst, 41, 3, pp. 647-665, (2014)","","","Universidad Nacional de Colombia","","","","","","00127353","","","","English","DYNA","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85163220001"
"Borghetti P.; Costantino G.; Santoro V.; Mataj E.; Singh N.; Vitali P.; Greco D.; Volpi G.; Sepulcri M.; Guida C.; Tomasi C.; Buglione M.; Nardone V.","Borghetti, Paolo (37012031100); Costantino, Gianluca (57218497381); Santoro, Valeria (59052546000); Mataj, Eneida (57214792122); Singh, Navdeep (57218562620); Vitali, Paola (54584532900); Greco, Diana (57191586572); Volpi, Giulia (57217082989); Sepulcri, Matteo (57201252307); Guida, Cesare (7003336823); Tomasi, Cesare (26636146200); Buglione, Michela (57204128613); Nardone, Valerio (56565165300)","37012031100; 57218497381; 59052546000; 57214792122; 57218562620; 54584532900; 57191586572; 57217082989; 57201252307; 7003336823; 26636146200; 57204128613; 56565165300","Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer","2024","In Vivo","38","3","","1359","1366","7","0","10.21873/invivo.13576","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191921655&doi=10.21873%2finvivo.13576&partnerID=40&md5=89fb1aabd68ae2c77eb1d7ffec8293b6","Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Radiation Oncology Department, Humanitas-Gavazzeni, Bergamo, Italy; Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy; Radiotherapy Unit, Veneto Institute of Oncology IOV – IRCCS, Padua, Italy; Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1, Naples, Italy; D.S.M.C, University of Brescia, Brescia, Italy; Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy; Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia, Piazzale Spedali Civili, 1, Brescia, 25123, Italy","Borghetti P., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Costantino G., Radiation Oncology Department, Humanitas-Gavazzeni, Bergamo, Italy; Santoro V., Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy; Mataj E., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy, Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia, Piazzale Spedali Civili, 1, Brescia, 25123, Italy; Singh N., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Vitali P., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Greco D., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Volpi G., Azienda Ospedaliera Universitaria Integrata Verona, Radiation Oncology, Verona, Italy; Sepulcri M., Radiotherapy Unit, Veneto Institute of Oncology IOV – IRCCS, Padua, Italy; Guida C., Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1, Naples, Italy; Tomasi C., D.S.M.C, University of Brescia, Brescia, Italy; Buglione M., Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; Nardone V., Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy","Background/Aim: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a predictive model of OS in this setting. Patients and Methods: Clinical variables of patients treated in three Institutions with SBRT for stage I NSCLC were retrospectively collected into a reference cohort A (107 patients) and 2 comparative cohorts B1 (32 patients) and B2 (38 patients). A predictive model was built using Cox regression (CR) and artificial neural networks (ANN) on reference cohort A and then tested on comparative cohorts. Results: Cohort B1 patients were older and with worse chronic obstructive pulmonary disease (COPD) than cohort A. Cohort B2 patients were heavier smokers but had lower Charlson Comorbidity Index (CCI). At CR analysis for cohort A, only ECOG Performance Status 0-1 and absence of previous neoplasms correlated with better OS. The model was enhanced combining ANN and CR findings. The reference cohort was divided into prognostic Group 1 (0-2 score) and Group 2 (3-9 score) to assess model’s predictions on OS: grouping was close to statistical significance (p=0.081). One and 2-year OS resulted higher for Group 1, lower for Group 2. In comparative cohorts, the model successfully predicted two groups of patients with divergent OS trends: higher for Group 1 and lower for Group 2. Conclusion: The produced model is a relevant tool to clinically stratify SBRT candidates into prognostic groups, even when applied to different cohorts. ANN are a valuable resource, providing useful data to build a prognostic model that deserves to be validated prospectively. © 2024 International Institute of Anticancer Research. All rights reserved.","artificial intelligence; artificial neural networks; Early lung cancer; predictive model; stereotactic radiotherapy","Aged; Aged, 80 and over; Artificial Intelligence; Carcinoma, Non-Small-Cell Lung; Female; Humans; Lung Neoplasms; Male; Middle Aged; Neoplasm Staging; Neural Networks, Computer; Prognosis; Radiosurgery; Retrospective Studies; fluorodeoxyglucose f 18; adult; aged; Article; artificial intelligence; artificial neural network; cancer prognosis; cancer survival; Charlson Comorbidity Index; chronic obstructive lung disease; cigarette smoking; cohort analysis; controlled study; diagnostic test accuracy study; early cancer; ECOG Performance Status; female; high risk patient; human; human tissue; intermediate risk patient; low risk patient; major clinical study; male; non small cell lung cancer; overall survival; positron emission tomography-computed tomography; predictive model; prognostic assessment; proportional hazards model; retrospective study; stereotactic body radiation therapy; survival prediction; cancer staging; lung tumor; middle aged; mortality; non small cell lung cancer; pathology; procedures; prognosis; radiosurgery; very elderly","","fluorodeoxyglucose f 18, 63503-12-8","","","","","Wong MCS, Lao XQ, Ho KF, Goggins WB, Tse SLA, Incidence and mortality of lung cancer: global trends and association with socioeconomic status, Sci Rep, 7, 1, (2017); Chang JY, Senan S, Paul MA, Mehran RJ, Louie AV, Balter P, Groen HJ, McRae SE, Widder J, Feng L, van den Borne BE, Munsell MF, Hurkmans C, Berry DA, van Werkhoven E, Kresl JJ, Dingemans AM, Dawood O, Haasbeek CJ, Carpenter LS, De Jaeger K, Komaki R, Slotman BJ, Smit EF, Roth JA, Stereotactic ablative radiotherapy versus lobectomy for operable stage I non-small-cell lung cancer: a pooled analysis of two randomised trials, Lancet Oncol, 16, 6, pp. 630-637, (2015); NCT00840749: Randomized Study to Compare CyberKnife to Surgical Resection In Stage I Non-small Cell Lung Cancer; NCT00687986: Trial of Either Surgery or Stereotactic Radiotherapy for Early Stage (IA) Lung Cancer; Dautruche A, Filion E, Mathieu D, Bahig H, Roberge D, Lambert L, Vu T, Campeau MP, To biopsy or not to biopsy?: A matched cohort analysis of early-stage lung cancer treated with stereotactic radiation with or without histologic confirmation, Int J Radiat Oncol Biol Phys, 107, 1, pp. 88-97, (2020); Ball D, Mai GT, Vinod S, Babington S, Ruben J, Kron T, Chesson B, Herschtal A, Vanevski M, Rezo A, Elder C, Skala M, Wirth A, Wheeler G, Lim A, Shaw M, Schofield P, Irving L, Solomon B, Nedev N, Le H, Stereotactic ablative radiotherapy versus standard radiotherapy in stage 1 non-small-cell lung cancer (TROG 09.02 CHISEL): a phase 3, open-label, randomised controlled trial, Lancet Oncol, 20, 4, pp. 494-503, (2019); Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, D'Amico TA, Dilling TJ, Dowell J, Gettinger S, Gubens MA, Hegde A, Hennon M, Lackner RP, Lanuti M, Leal TA, Lin J, Loo BW, Lovly CM, Martins RG, Massarelli E, Morgensztern D, Ng T, Otterson GA, Patel SP, Riely GJ, Schild SE, Shapiro TA, Singh AP, Stevenson J, Tam A, Yanagawa J, Yang SC, Gregory KM, Hughes M, NCCN Guidelines insights: Non-small cell lung cancer, Version 2.2021, J Natl Compr Canc Netw, 19, 3, pp. 254-266, (2021); Nanda RH, Liu Y, Gillespie TW, Mikell JL, Ramalingam SS, Fernandez FG, Curran WJ, Lipscomb J, Higgins KA, Stereotactic body radiation therapy versus no treatment for early stage non-small cell lung cancer in medically inoperable elderly patients: A National Cancer Data Base analysis, Cancer, 121, 23, pp. 4222-4230, (2015); Charlson ME, Pompei P, Ales KL, MacKenzie C, A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation, J Chronic Dis, 40, 5, pp. 373-383, (1987); Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, Carbone PP, Toxicity and response criteria of the Eastern Cooperative Oncology Group, Am J Clin Oncol, 5, pp. 649-655, (1982); Sultan S, Ullman K, Ester E, Melzer A, Anthony M, Kelly RF, Landsteiner A, Stampe C, Thiboutot J, Wilt TJ, Non-surgical therapies for early-stage non-small cell lung cancer: a systematic review, (2023); Stokes WA, Bronsert MR, Meguid RA, Blum MG, Jones BL, Koshy M, Sher DJ, Louie AV, Palma DA, Senan S, Gaspar LE, Kavanagh BD, Rusthoven CG, Post-treatment mortality after surgery and stereotactic body radiotherapy for early-stage non-small-cell lung cancer, J Clin Oncol, 36, 7, pp. 642-651, (2018); Yang H, Wang L, Shao G, Dong B, Wang F, Wei Y, Li P, Chen H, Chen W, Zheng Y, He Y, Zhao Y, Du X, Sun X, Wang Z, Wang Y, Zhou X, Lai X, Feng W, Shen L, Qiu G, Ji Y, Chen J, Jiang Y, Liu J, Zeng J, Wang C, Zhao Q, Yang X, Hu X, Ma H, Chen Q, Chen M, Jiang H, Xu Y, A combined predictive model based on radiomics features and clinical factors for disease progression in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy, Front Oncol, 12, (2022); Luo LM, Huang BT, Chen CZ, Wang Y, Su CH, Peng GB, Zeng CB, Wu YX, Wang RH, Huang K, Qiu ZH, A combined model to improve the prediction of local control for lung cancer patients undergoing stereotactic body radiotherapy based on radiomic signature plus clinical and dosimetric parameters, Front Oncol, 11, (2022); Bertolini M, Trojani V, Botti A, Cucurachi N, Galaverni M, Cozzi S, Borghetti P, La Mattina S, Pastorello E, Avanzo M, Revelant A, Sepulcri M, Paronetto C, Ursino S, Malfatti G, Giaj-Levra N, Falcinelli L, Iotti C, Iori M, Ciammella P, Novel harmonization method for multi-centric radiomic studies in non-small cell lung cancer, Curr Oncol, 29, 8, pp. 5179-5194, (2022); Klement RJ, Belderbos J, Grills I, Werner-wasik M, Hope A, Giuliani M, Ye H, Sonke J, Peulen H, Guckenberger M, Prediction of early death in patients with early-stage NSCLC—can we select patients without a potential benefit of SBRT as a curative treatment approach?, J Thorac Oncol, 11, 7, pp. 1132-1139, (2016); Kopek N, Paludan M, Petersen J, Hansen AT, Grau C, Hoyer M, Co-morbidity index predicts for mortality after stereotactic body radiotherapy for medically inoperable early-stage non-small cell lung cancer, Radiother Oncol, 93, 3, pp. 402-407, (2009); Louie AV, Haasbeek CJ, Mokhles S, Rodrigues GB, Stephans KL, Lagerwaard FJ, Palma DA, Videtic GM, Warner A, Takkenberg JJ, Reddy CA, Maat AP, Woody NM, Slotman BJ, Senan S, Predicting overall survival after stereotactic ablative radiation therapy in early-stage lung cancer: development and external validation of the Amsterdam Prognostic Model, Int J Radiat Oncol Biol Phys, 93, 1, pp. 82-90, (2015); Hanazawa H, Matsuo Y, Takeda A, Tsurugai Y, Iizuka Y, Kishi N, Takehana K, Mizowaki T, Development and validation of a prognostic model for non-lung cancer death in elderly patients treated with stereotactic body radiotherapy for non-small cell lung cancer, J Radiat Res, (2021); Kann BH, Hosny A, Aerts HJWL, Artificial intelligence for clinical oncology, Cancer Cell, 39, 7, pp. 916-927, (2021); Zhang K, Chen K, Artificial intelligence: opportunities in lung cancer, Curr Opin Oncol, 34, 1, pp. 44-53, (2022); Mazaki J, Katsumata K, Ohno Y, Udo R, Tago T, Kasahara K, Kuwabara H, Enomoto M, Ishizaki T, Nagakawa Y, Tsuchida A, A novel prediction model for colon cancer recurrence using auto-artificial intelligence, Anticancer Res, 41, 9, pp. 4629-4636, (2021); Akazawa M, Hashimoto K, Artificial intelligence in ovarian cancer diagnosis, Anticancer Res, 40, 8, pp. 4795-4800, (2020); Mazaki J, Katsumata K, Ohno Y, Udo R, Tago T, Kasahara K, Kuwabara H, Enomoto M, Ishizaki T, Nagakawa Y, Tsuchida A, A novel predictive model for anastomotic leakage in colorectal cancer using auto-artificial intelligence, Anticancer Res, 41, 11, pp. 5821-5825, (2021); de Margerie-Mellon C, Chassagnon G, Artificial intelligence: A critical review of applications for lung nodule and lung cancer, Diagn Interv Imaging, 104, 1, pp. 11-17, (2023); Kriegeskorte N, Golan T, Neural network models and deep learning, Curr Biol, 29, 7, pp. R231-R236, (2019)","E. Mataj; Radiation Oncology Department, Spedali Civili, University of Brescia, Brescia, Italy; email: e.mataj@unibs.it","","International Institute of Anticancer Research","","","","","","0258851X","","IVIVE","38688600","English","In Vivo","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191921655"
"Lu K.Y.-F.; Alqaderi H.; Hasan S.B.; Alhazmi H.; Alghounaim M.; Devarajan S.; Freire M.; Altabtbaei K.","Lu, Korina Yun-Fan (59092103500); Alqaderi, Hend (56255370300); Hasan, Saadoun Bin (59235705600); Alhazmi, Hesham (57218421197); Alghounaim, Mohammad (57191544312); Devarajan, Sriraman (57211852165); Freire, Marcelo (52463592100); Altabtbaei, Khaled (57221558934)","59092103500; 56255370300; 59235705600; 57218421197; 57191544312; 57211852165; 52463592100; 57221558934","Sputum production and salivary microbiome in COVID-19 patients reveals oral-lung axis","2024","PLoS ONE","19","7 July","e0300408","","","","0","10.1371/journal.pone.0300408","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199650098&doi=10.1371%2fjournal.pone.0300408&partnerID=40&md5=c6007981387bb386b4ec7bca93963071","Harvard School of Dental Medicine, Boston, MA, United States; Tufts University, School of Dental Medicine, Boston, MA, United States; Dasman Diabetes Institute, Dasman, Kuwait; Kuwait Ministry of Health, Sulaibikhat, Kuwait; Department of Preventive Dentistry, Division of Pediatric Dentistry, Umm Al-Qura University, Mekkah, Saudi Arabia; Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States; Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, La Jolla, San Diego, CA, United States; School of Dentistry, University of Alberta, Edmonton, AB, Canada","Lu K.Y.-F., Harvard School of Dental Medicine, Boston, MA, United States; Alqaderi H., Tufts University, School of Dental Medicine, Boston, MA, United States, Dasman Diabetes Institute, Dasman, Kuwait; Hasan S.B., Kuwait Ministry of Health, Sulaibikhat, Kuwait; Alhazmi H., Harvard School of Dental Medicine, Boston, MA, United States, Department of Preventive Dentistry, Division of Pediatric Dentistry, Umm Al-Qura University, Mekkah, Saudi Arabia; Alghounaim M., Kuwait Ministry of Health, Sulaibikhat, Kuwait; Devarajan S., Dasman Diabetes Institute, Dasman, Kuwait; Freire M., Department of Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States, Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California, La Jolla, San Diego, CA, United States; Altabtbaei K., School of Dentistry, University of Alberta, Edmonton, AB, Canada","SARS-CoV-2, a severe respiratory disease primarily targeting the lungs, was the leading cause of death worldwide during the pandemic. Understanding the interplay between the oral microbiome and inflammatory cytokines during acute infection is crucial for elucidating host immune responses. This study aimed to explore the relationship between the oral microbiome and cytokines in COVID-19 patients, particularly those with and without sputum production. Saliva and blood samples from 50 COVID-19 patients were subjected to 16S ribosomal RNA gene sequencing for oral microbiome analysis, and 65 saliva and serum cytokines were assessed using Luminex multiplex analysis. The Mann-Whitney test was used to compare cytokine levels between individuals with and without sputum production. Logistic regression machine learning models were employed to evaluate the predictive capability of oral microbiome, salivary, and blood biomarkers for sputum production. Significant differences were observed in the membership (Jaccard dissimilarity: p = 0.016) and abundance (PhILR dissimilarity: p = 0.048; metagenomeSeq) of salivary microbial communities between patients with and without sputum production. Seven bacterial genera, including Prevotella, Streptococcus, Actinomyces, Atopobium, Filifactor, Leptotrichia, and Selenomonas, were more prevalent in patients with sputum production (p<0.05, Fisher’s exact test). Nine genera, including Prevotella, Megasphaera, Stomatobaculum, Selenomonas, Leptotrichia, Veillonella, Actinomyces, Atopobium, and Corynebacteria, were significantly more abundant in the sputum-producing group, while Lachnoanaerobaculum was more prevalent in the non-sputum-producing group (p<0.05, ANCOM-BC). Positive correlations were found between salivary IFN-gamma and Eotaxin2/CCL24 with sputum production, while negative correlations were noted with serum MCP3/CCL7, MIG/CXCL9, IL1 beta, and SCF (p<0.05, Mann-Whitney test). The machine learning model using only oral bacteria input outperformed the model that included all data: blood and saliva biomarkers, as well as clinical and demographic variables, in predicting sputum production in COVID-19 subjects. The performance metrics were as follows, comparing the model with only bacteria input versus the model with all input variables: precision (95% vs. 75%), recall (100% vs. 50%), F1-score (98% vs. 60%), and accuracy (82% vs. 66%). Copyright: © 2024 Lu et al.","","Actinomyces; Adult; Aged; Biomarkers; COVID-19; Cytokines; Female; Humans; Leptotrichia; Lung; Machine Learning; Male; Microbiota; Middle Aged; Mouth; Prevotella; RNA, Ribosomal, 16S; Saliva; SARS-CoV-2; Sputum; Streptococcus; biological marker; chemokine; CXCL9 chemokine; cytokine; eotaxin; gamma interferon; growth factor; monocyte chemotactic protein 3; stem cell factor; biological marker; cytokine; RNA 16S; accuracy; Actinomyces; adult; amplicon; Article; asthma; Atopobium; blood sampling; clinical article; controlled study; coronavirus disease 2019; Corynebacterium; DNA extraction; DNA library; DNA sequencing; female; gene sequence; Gram negative anaerobic bacteria; growth regulation; heart disease; human; Leptotrichia; male; Megasphaera; microbiome; middle aged; mouth flora; nasopharyngeal swab; pandemic; Prevotella; real time reverse transcription polymerase chain reaction; respiratory tract disease; salivary microbiome; Selenomonas; serum; Severe acute respiratory syndrome coronavirus 2; sputum; Streptococcus; thorax pain; type II interferon signaling; vein puncture; aged; blood; genetics; isolation and purification; lung; machine learning; metabolism; microbiology; microflora; mouth; saliva; Severe acute respiratory syndrome coronavirus 2; virology","","gamma interferon, 82115-62-6; Biomarkers, ; Cytokines, ; RNA, Ribosomal, 16S, ","FastPrep-24, MP Biomedicals; Illumina MiSeq platform, Illumina; Luminex 200 system, Luminex, United States; Qiagen’s AllPrep Bacterial DNA/RNA/Protein Kit, Qiagen, Germany; STATA SE 17.0, StataCorp, United States; e Immune Monitoring 65-Plex Human ProcartaPlex Panel, Thermo, Austria","Illumina; Luminex, United States; MP Biomedicals; Qiagen, Germany; StataCorp, United States; Thermo, Austria","Dasman Diabetes Institute, DDI; Kuwait Ministry of Health; National Institutes of Health, NIH, (R21DE029625); Conrad Prebys Foundation, (1U54GH009824, 1R01AI170111-01)","Funding: This study was funded by J Craig Venter Institute, CA, USA; Kuwait Ministry of Health; Dasman Diabetes Institute, Kuwait; and L\u2019Or\u00E9alUNESCO. MF and HJ were funded by NIH R21DE029625 and Conrad Prebys Foundation grant #53.CLD and JLE were funded by NIH 1U54GH009824 and 1R01AI170111-01 to CLD.","Chow EJ, Uyeki TM, Chu HY., The effects of the COVID-19 pandemic on community respiratory virus activity, Nat Rev Microbiol, 21, 3, pp. 195-210, (2023); Shenoy S., Gut microbiome, Vitamin D, ACE2 interactions are critical factors in immune-senescence and inflammaging: key for vaccine response and severity of COVID-19 infection, Inflammation Research, 71, 1, pp. 13-26, (2021); Wang LL, Yang JW, Xu JF., Severe acute respiratory syndrome coronavirus 2 causes lung inflammation and injury, Clinical Microbiology and Infection, 28, 4, pp. 513-520, (2022); Gupta A, Bhanushali S, Sanap A, Shekatkar M, Kharat A, Raut C, Et al., Oral dysbiosis and its linkage with SARS-CoV-2 infection, Microbiological Research, 261, (2022); Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP., DADA2: High-resolution sample inference from Illumina amplicon data, Nature Methods, 13, 7, pp. 581-583, (2016); Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2, Nature Biotechnology, 37, 8, pp. 852-857, (2019); Espinoza JL, Dupont CL., VEBA: a modular end-to-end suite for in silico recovery, clustering, and analysis of prokaryotic, microeukaryotic, and viral genomes from metagenomes, BMC Bioinformatics, 23, 1, (2022); Lin H, Peddada SD., Analysis of compositions of microbiomes with bias correction, Nature Communications, 11, 1, (2020); Russel J, Thorsen J, Brejnrod AD, Bisgaard H, Sorensen SJ, Burmolle M., DAtest: a framework for choosing differential abundance or expression method, (2018); Larsen JM., The immune response to Prevotella bacteria in chronic inflammatory disease, Immunology, 151, 4, pp. 363-374, (2017); Aja E, Mangar M, Fletcher HM, Mishra A., Filifactor alocis: Recent Insights and Advances, Journal of Dental Research, (2021); Kononen E, Wade WG., Actinomyces and Related Organisms in Human Infections, Clinical Microbiology Reviews, 28, 2, pp. 419-442, (2015); Couturier MR, Slechta ES, Goulston C, Fisher MA, Hanson KE., Leptotrichia Bacteremia in Patients Receiving High-Dose Chemotherapy, Journal of Clinical Microbiology, 50, 4, pp. 1228-1232, (2012); Kumar PS, Griffen AL, Moeschberger ML, Leys EJ., Identification of Candidate Periodontal Pathogens and Beneficial Species by Quantitative 16S Clonal Analysis, Journal of Clinical Microbiology, 43, 8, pp. 3944-3955, (2005); Marouf N, Cai W, Said KN, Daas H, Diab H, Chinta VR, Et al., Association between periodontitis and severity of COVID-19 infection: A case–control study, Journal of Clinical Periodontology, 48, 4, pp. 483-491, (2021); Loayza D, Lafebre M., Periodontal disease and COVID-19: Prognosis and potential pathways of association in their pathogenesis, Can J Dent Hyg, 57, 1, pp. 44-51, (2023); Abranches J, Zeng L, Kajfasz JK, Palmer SR, Chakraborty B, Wen ZT, Et al., Biology of Oral Streptococci, Microbiology Spectrum, 6, 5, (2018); Haldar K, George L, Wang Z, Mistry V, Ramsheh MY, Free RC, Et al., The sputum microbiome is distinct between COPD and health, independent of smoking history, Respiratory Research, 21, 1, (2020); Chen C, Shen T, Tian F, Lin P, Li Q, Cui Z, Et al., New microbiota found in sputum from patients with community-acquired pneumonia, Acta Biochimica et Biophysica Sinica, 45, 12, pp. 1039-1048, (2013); Alqaderi H, Altabtbaei K, Espinoza JL, Bin-Hasan S, Alghounaim M, Alawady A, Et al., Host-Microbiome Associations in Saliva Predict COVID-19 Severity, (2023); Huang X, Huang X, Huang Y, Zheng J, Lu Y, Mai Z, Et al., The oral microbiome in autoimmune diseases: friend or foe?, Journal of Translational Medicine, 21, 1, (2023); Altabtbaei K, Maney P, Ganesan SM, Dabdoub SM, Nagaraja HN, Kumar PS., Anna Karenina and the subgingival microbiome associated with periodontitis, Microbiome, 9, 1, (2021); Hajishengallis G, Chavakis T, Lambris JD., Current understanding of periodontal disease pathogenesis and targets for host-modulation therapy, Periodontology 2000, 84, 1, pp. 14-34, (2020); Gadotti AC, de Castro Deus M, Telles JP, Wind R, Goes M, Garcia Charello Ossoski R, Et al., IFN-γ is an independent risk factor associated with mortality in patients with moderate and severe COVID-19 infection, Virus Research, 289, (2020); Castro F, Cardoso AP, Goncalves RM, Serre K, Oliveira MJ., Interferon-Gamma at the Crossroads of Tumor Immune Surveillance or Evasion, Frontiers in Immunology, 9, (2018); Forssmann U, Uguccioni M, Loetscher P, Dahinden CA, Langen H, Thelen M, Et al., Eotaxin-2, a Novel CC Chemokine that Is Selective for the Chemokine Receptor CCR3, and Acts Like Eotaxin on Human Eosinophil and Basophil Leukocytes, The Journal of Experimental Medicine, 185, 12, pp. 2171-2176, (1997); Soffritti I, D'Accolti M, Fabbri C, Passaro A, Manfredini R, Zuliani G, Et al., Oral Microbiome Dysbiosis Is Associated With Symptoms Severity and Local Immune/Inflammatory Response in COVID-19 Patients: A Cross-Sectional Study, Frontiers in Microbiology, 12, (2021); Luo Ling Xu McVicar DW, Adit Ben-Baruch Kuhns DB, Johnston JA, Oppenheim JJ, Et al., Monocyte chemotactic protein-3 (MCP3) interacts with multiple leukocyte receptors: binding and signaling of MCP3 through shared as well as unique receptors on monocytes and neutrophils, European Journal of Immunology, 25, 9, pp. 2612-2617, (1995); Whiting D, Hsieh G, Yun JJ, Banerji A, Yao W, Fishbein MC, Et al., Chemokine Monokine Induced by IFN-γ/CXC Chemokine Ligand 9 Stimulates T Lymphocyte Proliferation and Effector Cytokine Production, The Journal of Immunology, 172, 12, pp. 7417-7424, (2004); Lopez-Castejon G, Brough D., Understanding the mechanism of IL-1β secretion, Cytokine & Growth Factor Reviews, 22, 4, pp. 189-195, (2011); Tayel SI, El-Hefnway SM, Abd El Gayed EM, Abdelaal GA., Association of stem cell factor gene expression with severity and atopic state in patients with bronchial asthma, Respiratory Research, 18, 1, (2017)","K. Altabtbaei; School of Dentistry, University of Alberta, Edmonton, Canada; email: altabtba@ualberta.ca","","Public Library of Science","","","","","","19326203","","POLNC","39052548","English","PLoS ONE","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85199650098"
"Nicolò M.; Adraman A.; Risoli C.; Menta A.; Renda F.; Tadiello M.; Palmieri S.; Lechiara M.; Colombi D.; Grazioli L.; Natale M.P.; Scardino M.; Demeco A.; Foresti R.; Montanari A.; Barbato L.; Santarelli M.; Martini C.","Nicolò, Marco (57219976737); Adraman, Altin (57764969000); Risoli, Camilla (57219668724); Menta, Anna (59143181700); Renda, Francesco (7004661017); Tadiello, Michele (59143672100); Palmieri, Sara (58597291900); Lechiara, Marco (57209246364); Colombi, Davide (55671588900); Grazioli, Luigi (7005249218); Natale, Matteo Pio (57766483800); Scardino, Matteo (59144330400); Demeco, Andrea (57215046453); Foresti, Ruben (57193006434); Montanari, Attilio (59143509800); Barbato, Luca (57225391117); Santarelli, Mirko (59143509900); Martini, Chiara (25626401200)","57219976737; 57764969000; 57219668724; 59143181700; 7004661017; 59143672100; 58597291900; 57209246364; 55671588900; 7005249218; 57766483800; 59144330400; 57215046453; 57193006434; 59143509800; 57225391117; 59143509900; 25626401200","Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs’ Parenchymal Involvement Quantification in COVID-19 Patients","2024","Diagnostics","14","10","985","","","","0","10.3390/diagnostics14100985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194138657&doi=10.3390%2fdiagnostics14100985&partnerID=40&md5=56ab277490a83ae89a83d6d4534db515","Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Department of Neuroradiology, University Hospital of Padova, Via Giustiniani 2, Padova, 35128, Italy; Department of Radiological Function, “Guglielmo da Saliceto” Hospital, Via Taverna 49, Piacenza, 29121, Italy; Department of Radiology—Diagnostic Imaging, ASST Rhodense, Viale Forlanini 95, Garbagnate Milanese, 20024, Italy; Department of Respiratory Disease, University of Foggia, Via Antonio Gramsci 89, Foggia, 71122, Italy; Department of Radiology, A.O.U. Città della Salute e della Scienza di Torino, Via Zuretti 29, Torino, 10126, Italy; Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43126, Italy; Diagnostics for Images Unit and Interventional Radiology, AST Pesaro Urbino, Piazzale Cinelli 1, San Salvatore, 61121, Italy; Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy; Medical Physics Unit, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy; Diagnostic Department, Parma University Hospital, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, Parma, 43126, Italy","Nicolò M., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Adraman A., Department of Neuroradiology, University Hospital of Padova, Via Giustiniani 2, Padova, 35128, Italy; Risoli C., Department of Radiological Function, “Guglielmo da Saliceto” Hospital, Via Taverna 49, Piacenza, 29121, Italy; Menta A., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Renda F., Department of Radiology—Diagnostic Imaging, ASST Rhodense, Viale Forlanini 95, Garbagnate Milanese, 20024, Italy; Tadiello M., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Palmieri S., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Lechiara M., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Colombi D., Department of Radiological Function, “Guglielmo da Saliceto” Hospital, Via Taverna 49, Piacenza, 29121, Italy; Grazioli L., Department of Diagnostic Imaging, Spedali Civili di Brescia, Piazzale Spedali Civili 1, Brescia, 25123, Italy; Natale M.P., Department of Respiratory Disease, University of Foggia, Via Antonio Gramsci 89, Foggia, 71122, Italy; Scardino M., Department of Radiology, A.O.U. Città della Salute e della Scienza di Torino, Via Zuretti 29, Torino, 10126, Italy; Demeco A., Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43126, Italy; Foresti R., Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43126, Italy; Montanari A., Diagnostics for Images Unit and Interventional Radiology, AST Pesaro Urbino, Piazzale Cinelli 1, San Salvatore, 61121, Italy; Barbato L., Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy; Santarelli M., Medical Physics Unit, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy; Martini C., Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43126, Italy, Diagnostics for Images Unit and Interventional Radiology, AST Pesaro Urbino, Piazzale Cinelli 1, San Salvatore, 61121, Italy, Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy, Medical Physics Unit, “Sapienza” University of Rome, Sant’Andrea University Hospital, Via Di Grottarossa, 1035-1039, Rome, 00189, Italy, Diagnostic Department, Parma University Hospital, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, Parma, 43126, Italy","(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients’ age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients. © 2024 by the authors.","artificial intelligence; chest CT; COVID-19; deep learning; software-based score; visual score","contrast medium; adult; Article; artificial intelligence; artificial neural network; case study; chronic obstructive lung disease; clinical study; computer assisted tomography; controlled study; coronavirus disease 2019; correlation coefficient; coughing; deep learning; diagnostic accuracy; disease assessment; dyspnea; female; fever; follow up; ground glass opacity; human; image quality; internal validity; lung emphysema; lung parenchyma; machine learning; major clinical study; male; obstructive lung disease; oxygen saturation; radiation dose; real time polymerase chain reaction; residual volume; retrospective study; Severe acute respiratory syndrome coronavirus 2; software based quantitative assessment score; thorax radiography; visual quantitative assessment score; x-ray computed tomography","","","Brilliance 64, Philips, Netherlands; IntelliSpace Portal, Philips; iDose4","Philips; Philips, Netherlands","","","Revel M.-P., Parkar A.P., Prosch H., Silva M., Sverzellati N., Gleeson F., Brady A., COVID-19 Patients and the Radiology Department—Advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI), Eur. Radiol, 30, pp. 4903-4909, (2020); Mir M., Boike S., Benedict T., Olson H., Jama A.B., Anwer U., Khan S.A., The Role of Computed Tomography in the Management of Hospitalized Patients with COVID-19, Cureus, 15, (2023); Cozzi D., Cavigli E., Moroni C., Smorchkova O., Zantonelli G., Pradella S., Miele V., Ground-Glass Opacity (GGO): A Review of the Differential Diagnosis in the Era of COVID-19, Jpn. J. Radiol, 39, pp. 721-732, (2021); Pontone G., Scafuri S., Mancini M.E., Agalbato C., Guglielmo M., Baggiano A., Muscogiuri G., Fusini L., Andreini D., Mushtaq S., Et al., Role of Computed Tomography in COVID-19, J. Cardiovasc. Comput. Tomogr, 15, pp. 27-36, (2021); Hansell D.M., Bankier A.A., MacMahon H., McLoud T.C., Muller N.L., Remy J., Fleischner Society: Glossary of Terms for Thoracic Imaging, Radiology, 246, pp. 697-722, (2008); Homayounieh F., Holmberg O., Al Umairi R., Aly S., Basevicius A., Costa P.R., Darweesh A., Gershan V., Ilves P., Kostova-Lefterova D., Et al., Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study, Radiology, 298, pp. E141-E151, (2021); Suliman I.I., Khouqeer G.A., Ahmed N.A., Abuzaid M.M., Sulieman A., Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose, Life, 13, (2023); Risoli C., Nicolo M., Colombi D., Moia M., Rapacioli F., Anselmi P., Michieletti E., Ambrosini R., Di Terlizzi M., Grazioli L., Et al., Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients, Diagnostics, 12, (2022); Laqmani A., Veldhoen S., Dulz S., Derlin T., Behzadi C., Schmidt-Holtz J., Wassenberg F., Sehner S., Nagel H.-D., Adam G., Et al., Reduced-Dose Abdominopelvic CT Using Hybrid Iterative Reconstruction in Suspected Left-Sided Colonic Diverticulitis, Eur. Radiol, 26, pp. 216-224, (2016); Laqmani A., Avanesov M., Butscheidt S., Kurfurst M., Sehner S., Schmidt-Holtz J., Derlin T., Behzadi C., Nagel H.D., Adam G., Et al., Comparison of Image Quality and Visibility of Normal and Abnormal Findings at Submillisievert Chest CT Using Filtered Back Projection, Iterative Model Reconstruction (IMR) and IDose 4 <sup>TM</sup>, Eur. J. Radiol, 85, pp. 1971-1979, (2016); Sverzellati N., Milanese G., Milone F., Balbi M., Ledda R.E., Silva M., Integrated Radiologic Algorithm for COVID-19 Pandemic, J. Thorac. Imaging, 35, pp. 228-233, (2020); Elmokadem A.H., Mounir A.M., Ramadan Z.A., Elsedeiq M., Saleh G.A., Comparison of Chest CT Severity Scoring Systems for COVID-19, Eur. Radiol, 32, pp. 3501-3512, (2022); Colombi D., Petrini M., Maffi G., Villani G.D., Bodini F.C., Morelli N., Milanese G., Silva M., Sverzellati N., Michieletti E., Comparison of Admission Chest Computed Tomography and Lung Ultrasound Performance for Diagnosis of COVID-19 Pneumonia in Populations with Different Disease Prevalence, Eur. J. Radiol, 133, (2020); Caruso D., Zerunian M., Polici M., Pucciarelli F., Guido G., Polidori T., Rucci C., Bracci B., Tremamunno G., Laghi A., Diagnostic Performance of CT Lung Severity Score and Quantitative Chest CT for Stratification of COVID-19 Patients, Radiol. Med, 127, pp. 309-317, (2022); Li K., Fang Y., Li W., Pan C., Qin P., Zhong Y., Liu X., Huang M., Liao Y., Li S., CT Image Visual Quantitative Evaluation and Clinical Classification of Coronavirus Disease (COVID-19), Eur. Radiol, 30, pp. 4407-4416, (2020); Esposito G., Ernst B., Henket M., Winandy M., Chatterjee A., Van Eyndhoven S., Praet J., Smeets D., Meunier P., Louis R., Et al., AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs, Diagnostics, 12, (2022); Pan F., Ye T., Sun P., Gui S., Liang B., Li L., Zheng D., Wang J., Hesketh R.L., Yang L., Et al., Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19), Radiology, 295, pp. 715-721, (2020); Granata V., Fusco R., Villanacci A., Magliocchetti S., Urraro F., Tetaj N., Marchioni L., Albarello F., Campioni P., Cristofaro M., Et al., Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center, J. Pers. Med, 12, (2022); Durhan G., Ardali Duzgun S., Basaran Demirkazik F., Irmak I., Idilman I., Akpinar M.G., Akpinar E., Ocal S., Telli G., Topeli A., Et al., Visual and Software-Based Quantitative Chest CT Assessment of COVID-19: Correlation with Clinical Findings, Diagn. Interv. Radiol, 26, pp. 557-564, (2020); Saba L., Agarwal M., Patrick A., Puvvula A., Gupta S.K., Carriero A., Laird J.R., Kitas G.D., Johri A.M., Balestrieri A., Et al., Six Artificial Intelligence Paradigms for Tissue Characterisation and Classification of Non-COVID-19 Pneumonia against COVID-19 Pneumonia in Computed Tomography Lungs, Int. J. Comput. Assist. Radiol. Surg, 16, pp. 423-434, (2021); Suri J., Agarwal S., Chabert G., Carriero A., Pasche A., Danna P., Saba L., Mehmedovic A., Faa G., Singh I., Et al., COVLIAS 2.0-CXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans, Diagnostics, 12, (2022); Guiot J., Vaidyanathan A., Deprez L., Zerka F., Danthine D., Frix A.-N., Thys M., Henket M., Canivet G., Mathieu S., Et al., Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19, Diagnostics, 11, (2020); Jungmann F., Muller L., Hahn F., Weustenfeld M., Dapper A.-K., Mahringer-Kunz A., Graafen D., Duber C., Schafigh D., Pinto dos Santos D., Et al., Commercial AI Solutions in Detecting COVID-19 Pneumonia in Chest CT: Not yet Ready for Clinical Implementation?, Eur. Radiol, 32, pp. 3152-3160, (2022); Prokop M., van Everdingen W., van Rees Vellinga T., Quarles van Ufford H., Stoger L., Beenen L., Geurts B., Gietema H., Krdzalic J., Schaefer-Prokop C., Et al., CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19—Definition and Evaluation, Radiology, 296, pp. E97-E104, (2020)","C. Martini; Department of Medicine and Surgery, University of Parma, Parma, Via Gramsci 14, 43126, Italy; email: martinic@ao.pr.it","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85194138657"
"Mazumder R.; Sandeep Kumar C.; Upadhye V.","Mazumder, Rupa (7005832705); Sandeep Kumar, C. (58714061300); Upadhye, Vijay (57542796000)","7005832705; 58714061300; 57542796000","Pulmonary function testing in lung cancer: Analysis and implications","2024","Onkologia i Radioterapia","18","7","","","","","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201567123&partnerID=40&md5=0b82b8d1e315047334f1dedb7dc2fb6b","Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), Uttar Pradesh, Greater Noida, India; Department of Genetics, School of Sciences, JAIN (Deemed-to-be University), Karnataka, India; Department of Microbiology, Parul University, Gujarat, Vadodara, India","Mazumder R., Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), Uttar Pradesh, Greater Noida, India; Sandeep Kumar C., Department of Genetics, School of Sciences, JAIN (Deemed-to-be University), Karnataka, India; Upadhye V., Department of Microbiology, Parul University, Gujarat, Vadodara, India","Objective: International norms and professional judgment are the main foundations for the use of pulmonary function testing. The description of a common pattern is currently described using predetermined cut-offs. Based on the ATS/ERS (American Thoracic Society/European Respiratory Society) interpretation approach, we sought to investigate the anticipated illness outcome. Then, we looked at whether different decision trees that integrated lung function with clinical characteristics may lead to a more precise diagnosis using an impartial machine learning framework. Materials and methods: Data from 968 participants who were first admitted to a pulmonary clinic were included in our research. Complete pulmonary function and studies that were chosen at the doctor's discretion formed the basis of the final clinical diagnosis. Clinical diagnoses were divided into ten categories and approved by a panel of experts. Results: The ATS/ERS algorithm correctly diagnosed 38%. Only Chronic Obstructive Pulmonary Disease (COPD) was accurately diagnosed (74%). After 10-fold cross-validation, the new data-based decision tree raised detection accuracy to 68% for the most common lung disorders, with COPD, asthma, interstitial lung disease, and neuromuscular condition having considerably better positive predictive values and sensitivity. Conclusion: Our findings demonstrate that computer-based selection of lung function and clinical variables and associated decision-making criteria may enhance the present algorithms for lung function interpretation. © 2024, Medical Project Poland. All rights reserved.","drug-induced sleep endoscopy; obstructive sleep apnea; sleep disorder","adult; airway resistance; algorithm; anxiety; Article; cancer chemotherapy; cancer radiotherapy; carbon monoxide diffusion capacity; chronic obstructive lung disease; clinical decision making; cohort analysis; computer assisted tomography; controlled study; cross validation; decision making; decision tree; diagnostic accuracy; diagnostic test accuracy study; disease severity; dyspnea; fatigue; female; forced expiratory volume; forced vital capacity; hospitalization; human; hyperventilation; interstitial lung disease; lung cancer; lung function test; lung parenchyma; lung transplantation; lung ventilation; lung volume; machine learning; major clinical study; male; peak expiratory flow; physical activity; plethysmography; predictive value; prevalence; receiver operating characteristic; sensitivity and specificity; spirometry; support vector machine; thorax radiography; total lung capacity; training; validation study","","","","","","","Schabath MB, Cote ML., Cancer progress and priorities: lung cancer, Cancer Epidemiol Biomarkers Prev, 28, pp. 1563-1579, (2019); Polanski J, Jankowska-Polanska B, Mazur G., Relationship between nutritional status and quality of life in patients with lung cancer, Cancer Manag Res, 13, pp. 1407-1416, (2021); Yang M, Shen Y, Tan L, Li W., Prognostic value of sarcopenia in lung cancer: a systematic review and meta-analysis, Chest, 156, pp. 101-111, (2019); Watanabe Y, Hattori A, Nojiri S, Matsunaga T, Takamochi K, Et al., Clinical impact of a small component of ground-glass opacity in solid-dominant clinical stage IA non–small cell lung cancer, J Thorac Cardiovasc Surg, 163, pp. 791-801, (2022); Ring AM, Carlens J, Bush A, Castillo-Corullon S, Fasola S, Et al., Pulmonary function testing in children's interstitial lung disease, Eur Respir Rev, 29, (2020); Bhakta NR, Kaminsky DA, Bime C, Thakur N, Hall GL, Et al., Addressing race in pulmonary function testing by aligning intent and evidence with practice and perception, Chest, 161, pp. 288-297, (2022); Johansen MB, Bendstrup E, Davidsen JR, Shaker SB, Martin HM., The diagnostic trajectories of Danish patients with autoimmune rheumatologic diseases associated interstitial lung disease: an interview-based study, Eur Clin Respir J, 10, (2023); Avancini A, Sartori G, Gkountakos A, Casali M, Trestini I, Et al., Physical activity and exercise in lung cancer care: will promises be fulfilled?, Oncologist, 25, (2020); Liu Z, Qiu T, Pei L, Zhang Y, Xu L, Et al., Two-week multimodal prehabilitation program improves perioperative functional capability in patients undergoing thoracoscopic lobectomy for lung cancer: a randomized controlled trial, Anesth Analg, 131, pp. 840-849, (2020); Forster C, Doucet V, Perentes JY, Abdelnour-Berchtold E, Zellweger M, Et al., Impact of an enhanced recovery after surgery pathway on thoracoscopic lobectomy outcomes in non-small cell lung cancer patients: a propensity score-matched study, Transl Lung Cancer Res, 10, (2021); Cadranel J, Canellas A, Matton L, Darrason M, Parrot A, Et al., Pulmonary complications of immune checkpoint inhibitors in patients with nonsmall cell lung cancer, Eur Respir Rev, 28, (2019); Chaft JE, Rimner A, Weder W, Azzoli CG, Kris MG, Et al., Evolution of systemic therapy for stages I–III non-metastatic non-small-cell lung cancer, Nat Rev Clin Oncol, 18, pp. 547-557, (2021); Chang JY, Mehran RJ, Feng L, Verma V, Liao Z, Et al., Stereotactic ablative radiotherapy for operable stage I non-small-cell lung cancer (revised STARS): long-term results of a single-arm, prospective trial with prespecified comparison to surgery, Lancet Oncol, 22, pp. 1448-1457, (2021); Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Et al., Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests, Eur Respir J, 53, (2019); Luo J, Rizvi H, Egger JV, Preeshagul IR, Wolchok JD, Et al., Impact of PD-1 Blockade on Severity of COVID-19 in Patients with Lung CancersPD-1 Blockade and Severity of COVID-19, Cancer Discov, 10, pp. 1121-1128, (2020); Rolfo C, Meshulami N, Russo A, Krammer F, Garcia-Sastre A, Et al., Lung cancer and severe acute respiratory syndrome coronavirus 2 infection: identifying important knowledge gaps for investigation, J Thorac Oncol, 17, pp. 214-227, (2022); Blanco JR, Cobos-Ceballos MJ, Navarro F, Sanjoaquin I, de Las Revillas FA, Et al., Pulmonary long-term consequences of COVID-19 infections after hospital discharge, Clin Microbiol Infect, 27, pp. 892-896, (2021); Bourke SJ., Lung Function in Specific Respiratory and Systemic Diseases: A Compendium, Cotes’ Lung Function, pp. 697-728, (2020); Ciancio N, Pavone M, Torrisi SE, Vancheri A, Sambataro D, Et al., Contribution of pulmonary function tests (PFTs) to the diagnosis and follow up of connective tissue diseases, Multidiscip Respir Med, 14, pp. 1-11, (2019); Stanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Et al., ERS/ ATS technical standard on interpretive strategies for routine lung function tests, Eur Respir J, 60, (2022); Al Yamani WH, Ghunimat DM, Bisharah MM., Modeling and predicting the sensitivity of high-performance concrete compressive strength using machine learning methods, Asian J Civ Eng, 24, pp. 1-13, (2023); Kakavas S, Kotsiou OS, Perlikos F, Mermiri M, Mavrovounis G, Et al., Pulmonary function testing in COPD: looking beyond the curtain of FEV1, NPJ Prim Care Respir Med, 31, (2021); Kraemer R, Gardin F, Smith HJ, Baty F, Barandun J, Et al., Functional Predictors Discriminating Asthma–COPD Overlap (ACO) from Chronic Obstructive Pulmonary Disease (COPD), Int J Chron Obstruct Pulmon Dis, 17, pp. 2723-2743, (2022); Wang J, Chu Y, Li J, Zeng F, Wu M, Et al., Development of a prediction model with serum tumor markers to assess tumor metastasis in lung cancer; Hernandez-Gonzalez F, Prieto-Gonzalez S, Brito-Zeron P, Cuerpo S, Sanchez M, Et al., Impact of a systematic evaluation of connective tissue disease on diagnosis approach in patients with interstitial lung diseases, Medicine (Baltimore), 99, (2020); Haider NS, Behera AK., Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybern Biomed Eng, 42, pp. 42-59, (2022)","R. Mazumder; Department of Pharmacy, Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, Uttar Pradesh, India; email: rupa_mazumder@rediffmail.com","","Medical Project Poland","","","","","","18968961","","","","English","Onkol. Radioter.","Article","Final","","Scopus","2-s2.0-85201567123"
"Chan A.H.Y.; Te Ao B.; Baggott C.; Cavadino A.; Eikholt A.A.; Harwood M.; Hikaka J.; Gibbs D.; Hudson M.; Mirza F.; Naeem M.A.; Semprini R.; Chang C.L.; Tsang K.C.H.; Shah S.A.; Jeremiah A.; Abeysinghe B.N.; Roy R.; Wall C.; Wood L.; Dalziel S.; Pinnock H.; Van Boven J.F.M.; Roop P.; Harrison J.","Chan, Amy Hai Yan (55337510300); Te Ao, Braden (55480837300); Baggott, Christina (36964257800); Cavadino, Alana (55246086700); Eikholt, Amber A. (58105210500); Harwood, Matire (7005799967); Hikaka, Joanna (55014230900); Gibbs, Dianna (59140641300); Hudson, Mariana (57490758300); Mirza, Farhaan (52664014200); Naeem, Muhammed Asif (57213518795); Semprini, Ruth (57193226570); Chang, Catherina L. (38861156600); Tsang, Kevin C.H. (57219008423); Shah, Syed Ahmar (56424513100); Jeremiah, Aron (58492071000); Abeysinghe, Binu Nisal (57856406800); Roy, Rajshri (56784967200); Wall, Clare (7102194943); Wood, Lisa (55859741700); Dalziel, Stuart (7003830957); Pinnock, Hilary (6701815935); Van Boven, Job F.M. (53464198500); Roop, Partha (6602395877); Harrison, Jeff (55471524100)","55337510300; 55480837300; 36964257800; 55246086700; 58105210500; 7005799967; 55014230900; 59140641300; 57490758300; 52664014200; 57213518795; 57193226570; 38861156600; 57219008423; 56424513100; 58492071000; 57856406800; 56784967200; 7102194943; 55859741700; 7003830957; 6701815935; 53464198500; 6602395877; 55471524100","DIGIPREDICT: Physiological, behavioural and environmental predictors of asthma attacks - A prospective observational study using digital markers and artificial intelligence - Study protocol","2024","BMJ Open Respiratory Research","11","1","e002275","","","","0","10.1136/bmjresp-2023-002275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194017053&doi=10.1136%2fbmjresp-2023-002275&partnerID=40&md5=7681bbd4a25a6c13e6556025e598e03b","School of Pharmacy, The University of Auckland, Faculty of Medical and Health Sciences, Region, Auckland, New Zealand; School of Population Health, University of Auckland, Auckland, New Zealand; Department of Respiratory Medicine and Respiratory Research Unit, Waikato Hospital, Hamilton, New Zealand; University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands; Medication Adherence Expertise Center of the Northern Netherlands (MAECON), Groningen, Netherlands; Te Kupenga Hauora Maori, University of Auckland, Auckland, New Zealand; Pinnacle Midlands Health Network, Hamilton, New Zealand; Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand; National University of Computer and Emerging Sciences, Islamabad, Pakistan; Medical Research Institute of New Zealand, Wellington, New Zealand; University College London, London, United Kingdom; The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, United Kingdom; Department of Electrical Computer and Software Engineering, University of Auckland, Auckland, New Zealand; Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand; Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia; Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand","Chan A.H.Y., School of Pharmacy, The University of Auckland, Faculty of Medical and Health Sciences, Region, Auckland, New Zealand; Te Ao B., School of Population Health, University of Auckland, Auckland, New Zealand; Baggott C., Department of Respiratory Medicine and Respiratory Research Unit, Waikato Hospital, Hamilton, New Zealand; Cavadino A., School of Population Health, University of Auckland, Auckland, New Zealand; Eikholt A.A., University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands, Medication Adherence Expertise Center of the Northern Netherlands (MAECON), Groningen, Netherlands; Harwood M., School of Population Health, University of Auckland, Auckland, New Zealand; Hikaka J., Te Kupenga Hauora Maori, University of Auckland, Auckland, New Zealand; Gibbs D., Pinnacle Midlands Health Network, Hamilton, New Zealand; Hudson M., School of Pharmacy, The University of Auckland, Faculty of Medical and Health Sciences, Region, Auckland, New Zealand; Mirza F., Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand; Naeem M.A., Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand, National University of Computer and Emerging Sciences, Islamabad, Pakistan; Semprini R., Medical Research Institute of New Zealand, Wellington, New Zealand; Chang C.L., Department of Respiratory Medicine and Respiratory Research Unit, Waikato Hospital, Hamilton, New Zealand; Tsang K.C.H., University College London, London, United Kingdom, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, United Kingdom; Shah S.A., The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, United Kingdom; Jeremiah A., Department of Electrical Computer and Software Engineering, University of Auckland, Auckland, New Zealand; Abeysinghe B.N., Department of Electrical Computer and Software Engineering, University of Auckland, Auckland, New Zealand; Roy R., Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand; Wall C., Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand; Wood L., Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, NSW, Australia; Dalziel S., Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand; Pinnock H., The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, United Kingdom; Van Boven J.F.M., University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands, Medication Adherence Expertise Center of the Northern Netherlands (MAECON), Groningen, Netherlands; Roop P., Department of Electrical Computer and Software Engineering, University of Auckland, Auckland, New Zealand; Harrison J., School of Pharmacy, The University of Auckland, Faculty of Medical and Health Sciences, Region, Auckland, New Zealand","Introduction Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. Methods and analysis A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. Ethics and dissemination Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. Trial registration number Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.  © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.","Asthma; Clinical Epidemiology; Inhaler devices; Surveys and Questionnaires; Telemedicine","Adolescent; Adult; Artificial Intelligence; Asthma; Child; Female; Humans; Male; Nebulizers and Vaporizers; New Zealand; Observational Studies as Topic; Prospective Studies; bronchodilating agent; corticosteroid; algorithm; area under the curve; Article; artificial intelligence; artificial neural network; asthma; behavior change; breathing rate; cohort analysis; controlled study; diagnostic test accuracy study; food frequency questionnaire; forced vital capacity; health care personnel; heart rate; hospitalization; human; ICD-10; long term memory; machine learning; major clinical study; observational study; outcome assessment; peak expiratory flow; physiological adaptation; prospective study; questionnaire; recurrent neural network; sensitivity analysis; sensitivity and specificity; sociodemographics; adolescent; adult; child; female; male; nebulizer; New Zealand; prospective study","","","","","John Hunter Hospital Charitable Trust; University of Auckland, UoA; University of Newcastle Australia, UON; European Respiratory Society, ERS; National Institute for Health and Care Research, NIHR; Noumi Foods Pty Ltd; National Institutes of Health, NIH; Auckland Medical Research Foundation, AMRF; Life Corp AI Limited; National Health and Medical Research Council, NHMRC; Lottery Grants Board, Cure Kids New Zealand; Medical Research Futures Fund; Starship Foundation; University of Hong Kong, HKU; Asthma and Lung UK; Health Research Council of New Zealand, HRC, (22/540); Health Research Council of New Zealand, HRC; European Cooperation in Science and Technology, COST, (CA19132); European Cooperation in Science and Technology, COST","Funding text 1: AHYC reports research grants from Health Research Council of New Zealand, Auckland Medical Research Foundation, Asthma UK, University of Auckland, Oakley Mental Health Foundation, Chorus Ltd, World Health Organisation, and Hong Kong University, outside the submitted work and all paid to her institution (the University of Auckland). She is the previous holder of a Robert Irwin Postdoctoral Fellowship. AHYC also reports consultancy fees from AcademyeX and Spoonful of Sugar Ltd, and is also a Board member of Asthma NZ., member of Respiratory Effectiveness Group (REG) and working group lead for the European Respiratory Society Clinical Research Collaboration 'CONNECT'. JFMvB reports grants from Aardex, grants and personal fees from AstraZeneca, grants and personal fees from Chiesi, grants from European Commission COST Action 'ENABLE' (CA19132), grants from European Respiratory Society for the Clinical Research Collaboration 'CONNECT', personal fees from GSK, grants and personal fees from Novartis, personal fees from Teva, grants and personal fees from Trudell Medical and personal fees from Vertex, outside the submitted work and all paid to his institution (UMCG).JH reports research grants from Health Research Council of New Zealand, Auckland Medical Research Foundation, University of Auckland, outside the submitted work and all paid to her institution (the University of Auckland). HP reports research grants from National Institute for Health and Care Research, European Respiratory Society and UK Medical Research Council paid to her University for applied and implementation research on models of care for non-communicable respiratory disease. Within the last 3 years, she has contributed to sponsored symposia (Teva Pharmaceuticals and Sandoz UK) on topics related to delivery of care and digital health. CB reports personal fees and honorarium from AstraZeneca and GSK outside of the submitted work.LW reports research grants from National Health and Medical Research Council of Australia, Medical Research Futures Fund, National Institutes of Health USA, John Hunter Hospital Charitable Trust, Hunter Medical Research Institute, Noumi Foods Pty Ltd, Lifecykel Labs Pty Ltd, Department of Industry, Innovation and Science, outside of the submitted work and all paid to her institution (University of Newcastle, Australia). Within the last 3 years, she has contributed to sponsored symposia (Sanofi, Boehringer-Ingelheim) on topics related to nutrition and respiratory disease. SD reports research grants from Health Research Council of New Zealand, Auckland Medical Research Foundation, Starship Foundation, Lottery Grants Board, Cure Kids New Zealand, National Health and Medical Research Council of Australia, Medical Research Futures Fund outside the submitted work and all paid to his institutions (the University of Auckland/Starship Children\u2019s Hospital). All other authors have no interests to disclose. ; Funding text 2: This work is supported by the Health Research Council New Zealand, grant number 22/540, Auckland Medical Research Foundation via a Senior Research Fellowship award and Life Corp AI Limited. The funders had no influence on the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit for publication. 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Chan A.H.Y., Stewart A.W., Harrison J., Et al., Electronic adherence monitoring device performance and patient acceptability: A randomized control trial, Expert Rev Med Devices, 14, pp. 401-411, (2017); Dierick B.J.H., Achterbosch M., Eikholt A.A., Et al., Electronic monitoring with a Digital smart spacer to support personalized Inhaler use education in patients with asthma: The randomized controlled OUTERSPACE trial, Respiratory Medicine, 218, (2023); Fleming L., Asthma exacerbation prediction: Recent insights, Curr Opin Allergy Clin Immunol, 18, pp. 117-123, (2018); Bhat G.S., Shankar N., Kim D., Et al., Machine learning-based asthma risk prediction using iot and Smartphone applications, IEEE Access, 9, pp. 118708-118715, (2021); De Boer C., Ghomrawi H., Many B., Et al., Utility of Wearable sensors to assess postoperative recovery in pediatric patients after Appendectomy, J Surg Res, 263, pp. 160-166, (2021); Angelucci A., Greco M., Canali S., Et al., Fitbit data to assess functional capacity in patients before elective surgery: Pilot prospective observational study, J Med Internet Res, 25, (2023); De Diego-Alonso C., Alegre-Ayala J., Buesa A., Et al., Multidimensional analysis of sedentary behaviour and participation in Spanish stroke survivors (Part&Sed-stroke): A protocol for a longitudinal Multicentre study, BMJ Open, 13, (2023); Ringeval M., Wagner G., Denford J., Et al., Fitbit-based interventions for healthy lifestyle outcomes: Systematic review and meta-analysis, J Med Internet Res, 22, (2020); Feehan L.M., Geldman J., Sayre E.C., Et al., Accuracy of Fitbit devices: Systematic review and narrative syntheses of quantitative data, JMIR Mhealth Uhealth, 6, (2018); Tibble H., Tsanas A., Horne E., Et al., Predicting asthma attacks in primary care: Protocol for developing a machine learning-based prediction model, BMJ Open, 9, (2019); Hochreiter S., Schmidhuber J., Long short-term memory, Neural Comput, 9, pp. 1735-1780, (1997); Deng C., Ji X., Rainey C., Et al., Integrating machine learning with human knowledge, IScience, 23, (2020); Hosmer D.W., Lemeshow S., Sturdivant R.X., Applied Logistic Regression., 398, (2013); Moullin J.C., Sabater-Hernandez D., Fernandez-Llimos F., Et al., A systematic review of implementation frameworks of innovations in Healthcare and resulting generic implementation framework, Health Res Policy Syst, 13, (2015); Holtrop J.S., Estabrooks P.A., Gaglio B., Et al., Understanding and applying the RE-AIM framework: Clarifications and resources, J Clin Transl Sci, 5, (2021); Greenhalgh T., Wherton J., Papoutsi C., Et al., Beyond adoption: A new framework for theorizing and evaluating Nonadoption, abandonment, and challenges to the scale-up, spread, and Sustainability of health and care Technologies, J Med Internet Res, 19, (2017); McCambridge J., Witton J., Elbourne D.R., Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects, J Clin Epidemiol, 67, pp. 267-277, (2014); Sutton S., Kinmonth A.-L., Hardeman W., Et al., Does electronic monitoring influence adherence to medication? randomized controlled trial of measurement reactivity, Ann Behav Med, 48, pp. 293-299, (2014); 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Reilly M.C., Zbrozek A.S., Dukes E.M., The validity and reproducibility of a work productivity and activity impairment instrument, Pharmacoeconomics, 4, pp. 353-365, (1993); Brakema E.A., Tabyshova A., Van Der Kleij Rmjj, Et al., The socioeconomic burden of chronic lung disease in low-resource settings across the globe - An observational FRESH AIR study, Respir Res, 20, (2019); Bangor A., Kortum P.T., Miller J.T., An empirical evaluation of the system usability scale, International Journal of Human-Computer Interaction, 24, pp. 574-594, (2008); Holden R.J., Karsh B.-T., The technology acceptance model: Its past and its future in health care, J Biomed Inform, 43, pp. 159-172, (2010); Abu-Dalbouh H.M., A questionnaire approach based on the technology acceptance model for mobile tracking on patient progress applications, Journal of Computer Science, 9, pp. 763-770, (2013)","A.H.Y. Chan; School of Pharmacy, The University of Auckland, Faculty of Medical and Health Sciences, Auckland, Region, New Zealand; email: a.chan@auckland.ac.nz","","BMJ Publishing Group","","","","","","20524439","","","38777583","English","BMJ Open Respir. Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194017053"
"Raje N.; Jadhav A.","Raje, Nikhil (57670405100); Jadhav, Ashish (57223382096)","57670405100; 57223382096","Hybrid DL Models for Improved Accuracy in Diagnosing Chronic Obstructive Pulmonary Disease","2024","Advances in Nonlinear Variational Inequalities","27","4","","385","391","6","0","10.52783/anvi.v27.1605","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209403124&doi=10.52783%2fanvi.v27.1605&partnerID=40&md5=b88c4ea654aea8622b9ac545ea5a4ff8","Department of Computer Engineering, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, India; Department of Information Technology, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, India","Raje N., Department of Computer Engineering, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, India; Jadhav A., Department of Information Technology, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, India","Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disorder marked by enduring airflow obstruction, leading to considerable illness and death rates. Timely and precise diagnosis is essential for proper management and treatment. In this study, we present a novel hybrid deep learning (DL) model leveraging an Autoencoder-GAN (Generative Adversarial Network) architecture to improve the accuracy of COPD diagnosis. Our approach incorporates a cutting-edge preprocessing method, Adaptive Histogram Equalization with Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance the contrast and detail in CXR images, facilitating more precise feature extraction. The proposed Autoencoder-GAN Hybrid Model significantly outperforms traditional models, achieving an impressive accuracy of 98.3%. By enhancing image quality and focusing on key features through CLAHE preprocessing, our model is able to better distinguish between healthy and COPD-affected lungs. We compared the performance of our model with standard DL models, including CNN, SVM and Random Forest Classifiers, demonstrating superior results across various evaluation metrics. This study highlights the potential of advanced DL techniques and innovative preprocessing methods to enhance the accuracy of COPD diagnosis, offering a promising tool for healthcare professionals in the early detection and management of this chronic disease. © 2024, International Publications. All rights reserved.","Autoencoder-GAN; CLAHE; COPD Diagnosis; CXR; DL; Medical Image Processing","","","","","","","","Zhang G., Luo L., Zhang L., Liu Z., Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence, Diagnostics, 13, 3, (2023); Asnaoui K.E., Chawki Y., Idri A., Automated Methods for Detection and Classification Pneumonia Based on X-Ray Images Using DL, (2021); Chandra T.B., Verma K., Pneumonia Detection on CXR Using Machine Learning Paradigm, 1022, (2020); Agrawal H., Pneumonia Detection Using Image Processing and DL, Proc.-Int. Conf. Artif. Intell. Smart Syst. ICAIS 2021, pp. 67-73, (2021); Bhandari M., Shahi T.B., Siku B., Neupane A., Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using DL and XAI, Comput. Biol. Med., 150, (2022); Bhosale Y.H., Patnaik K.S., Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: A Meta-analysis & systematic review, Multimedia Tools and Applications, (2023); Hammoudi K., Et al., DL on CXR Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19, J. Med. Syst, 45, 7, (2021); Gu X., Pan L., Liang H., Yang R., “Classification of bacterial and viral childhood pneumonia using DL in chest radiography, ACM Int. Conf. Proceeding Ser., pp. 88-93, (2018); Ibrahim D.M., Elshennawy N.M., Sarhan A.M., Deep-chest: Multi-classification DL model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases, Comput. Biol. Med, 132, (2021); Islam S.R., Maity S.P., Ray A.K., Mandal M., Automatic Detection of Pneumonia on Compressed Sensing Images using DL, 2019 IEEE Can. Conf. Electr. Comput. Eng. CCECE 2019, pp. 9-12, (2019); Agarwal V., Lohani M.C., Bist A.S., Harahap E.P., Khoirunisa A., Analysis Of DL Techniques For CXR Classification In Context Of Covid-19, ADI J. Recent Innov, 3, 2, pp. 208-216, (2022); O'Quinn W., Haddad R.J., Moore D.L., Pneumonia Radiograph Diagnosis Utilizing DL Network, Proc. 2019 IEEE 2nd Int. Conf. Electron. Inf. Commun. Technol. ICEICT 2019, pp. 763-767, (2019); Paul M., CXR Images (Pneumonia), CXR Images (Pneumonia), (2018); Ullah N., Marzougui M., Ahmad I., Chelloug S.A., DeepLungNet: An Effective DL-Based Approach for Lung Disease Classification Using CRIs, Electron, 12, 8, (2023); Nahiduzzaman M., Et al., Parallel CNN-ELM: A multiclass classification of CXR images to identify seventeen lung diseases including COVID-19, Expert Syst. Appl, 229, (2023); Alshmrani G.M.M., Ni Q., Jiang R., Pervaiz H., Elshennawy N.M., A DL architecture for multi-class lung diseases classification using CXR (CXR) images, Alexandria Eng. J, 64, pp. 923-935, (2023); Rao G.V.E., B R., Srinivasu P.N., Ijaz M.F., Wozniak M., Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from CXRs, Biomed. Signal Process. Control, 88, (2024)","","","International Publications","","","","","","1092910X","","","","English","Adv. Nonlinear Var. Inequalities","Article","Final","","Scopus","2-s2.0-85209403124"
"Zarate-Tamames B.; Garin N.; Calvin-Lamas M.; Jornet S.; Martinez-Simon J.J.; Garcia-Gil S.; Garcia-Rebolledo E.M.; Morillo-Verdugo R.","Zarate-Tamames, Borja (58171343300); Garin, Noe (37361248200); Calvin-Lamas, Marta (6508337893); Jornet, Sonia (58974434400); Martinez-Simon, Jose J. (57207958367); Garcia-Gil, Sara (57912660100); Garcia-Rebolledo, Eva M. (6504203124); Morillo-Verdugo, Ramon (23395170100)","58171343300; 37361248200; 6508337893; 58974434400; 57207958367; 57912660100; 6504203124; 23395170100","Transforming respiratory diseases management: a CMO-based hospital pharmaceutical care model","2024","Frontiers in Pharmacology","15","","1461473","","","","0","10.3389/fphar.2024.1461473","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208609276&doi=10.3389%2ffphar.2024.1461473&partnerID=40&md5=880a1a1e8f1686647354f098a5c20d0f","Department of Pharmacy, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain; School of Health Science Blanquerna, Universitat Ramon Llull, Barcelona, Spain; Department of Pharmacy, Complexo Hospitalario Universitario A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain; Departament of Pharmacy, Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain; Department of Pharmacy, Hospital Universitario Fundación Alcorcón, Madrid, Spain; Department of Pharmacy, Complejo Hospitalario Universitario de Canarias, Santa Cruz de Tenerife, Spain; Department of Pharmacy, Hospital Universitario de Fuenlabrada, Madrid, Spain; Department of Pharmacy, Hospital Universitario de Valme, Área de Gestión Sanitaria Sur de Sevilla, Sevilla, Spain","Zarate-Tamames B., Department of Pharmacy, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain, Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain; Garin N., Department of Pharmacy, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain, School of Health Science Blanquerna, Universitat Ramon Llull, Barcelona, Spain; Calvin-Lamas M., Department of Pharmacy, Complexo Hospitalario Universitario A Coruña (CHUAC), Instituto de Investigación Biomédica de A Coruña (INIBIC), A Coruña, Spain; Jornet S., Departament of Pharmacy, Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain; Martinez-Simon J.J., Department of Pharmacy, Hospital Universitario Fundación Alcorcón, Madrid, Spain; Garcia-Gil S., Department of Pharmacy, Complejo Hospitalario Universitario de Canarias, Santa Cruz de Tenerife, Spain; Garcia-Rebolledo E.M., Department of Pharmacy, Hospital Universitario de Fuenlabrada, Madrid, Spain; Morillo-Verdugo R., Department of Pharmacy, Hospital Universitario de Valme, Área de Gestión Sanitaria Sur de Sevilla, Sevilla, Spain","Introduction: Respiratory diseases encompass a diverse range of conditions that significantly impact global morbidity and mortality. While common diseases like asthma and COPD exhibit moderate symptoms, less prevalent conditions such as pulmonary hypertension and cystic fibrosis profoundly affect quality of life and mortality. The prevalence of these diseases has surged by approximately 40% over the past 3 decades. Despite advancements in pharmacotherapy, challenges in drug administration, adherence, and adverse effects persist. This study aimed to develop and perform an interim validation of a Capacity-Motivation-Opportunity (CMO) model tailored for respiratory outpatients to enhance pharmaceutical care, which is the direct, responsible provision of medication-related care for the purpose of achieving definite outcomes that improve a patient’s quality of life, and overall wellbeing. Methodology: This cross-sectional, multicenter study was conducted from March 2022 to March 2023. It comprised four phases: 1) forming an expert panel of 15 hospital pharmacists, 2) selecting respiratory pathologies based on prevalence and severity, 3) developing the CMO model’s pillars, and 4) integrating and conducting an interim validation of the model. The Capacity pillar focused on patient stratification and personalized care; the Motivation pillar aligned therapeutic goals through motivational interviewing; and the Opportunity pillar promoted the use of information and communication technologies (ICTs) for telemedicine. Results: The model included eight respiratory diseases based on expert assessment. For the Capacity pillar, 22 variables were defined for patient stratification, leading to three priority levels for personalized pharmaceutical care. In a preliminary test involving 201 patients across six hospitals, the stratification tool effectively classified patients according to their needs. The Motivation pillar adapted motivational interviewing techniques to support patient adherence and behavior change. The Opportunity pillar established teleconsultation protocols and ICT tools to enhance patient monitoring and care coordination. Conclusion: The CMO model, tailored for respiratory patients, provides a comprehensive framework for improving pharmaceutical care. By focusing on patient-centered care, aligning therapeutic goals, and leveraging technology, this model addresses the multifaceted needs of individuals with respiratory conditions. Future studies are necessary to validate this model in other healthcare systems and ensure its broad applicability. Copyright © 2024 Zarate-Tamames, Garin, Calvin-Lamas, Jornet, Martinez-Simon, Garcia-Gil, Garcia-Rebolledo and Morillo-Verdugo.","adherence; behavior; hospital pharmacy; innovation; pharmaceutical care; respiratory diseases","adult; article; Article; artificial intelligence; asthma; behavior change; body mass; capacity motivation opportunity model; chronic obstructive lung disease; conceptual framework; cross-sectional study; cystic fibrosis; Delphi study; disease severity; doctor patient relationship; drug safety; drug therapy; electronic health record; extraction; female; glycemic control; health care; health care cost; health care planning; health care system; hospital medicine; hospital pharmacist; hospital pharmacy; human; hypertension; hypoglycemia; medication compliance; middle aged; morbidity; mortality; motivational interviewing; multicenter study; non insulin dependent diabetes mellitus; obesity; oxygen therapy; patient compliance; patient monitoring; person centered care; personality; personalized medicine; pharmaceutical care; pharmacist; polypharmacy; prevalence; pulmonary hypertension; quality of life; questionnaire; respiratory tract disease; risk factor; teleconsultation; telemedicine","","","","","","","Almahdi F.B., Hashim A.H., Albaba E.A.M., Salih O.N., Alkasam R.J., Mosli M.H., Et al., The impact of the pharmaceutical care management model of hepatitis C medications on the cost at health insurance level, Value Health Reg. 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"Li T.; Li X.; XU H.; Wang Y.; Ren J.; Jing S.; Jin Z.; chen G.; Zhai Y.; Wu Z.; Zhang G.; Wang Y.","Li, Teng (58155307900); Li, Xueke (59328774900); XU, Haoran (59329584700); Wang, Yanyan (57725619200); Ren, Jingyu (59254884600); Jing, Shixiang (58018054900); Jin, Zichen (58829632800); chen, Gang (59329584800); Zhai, Youyou (59329176900); Wu, Zeyu (57541330000); Zhang, Ge (57759490800); Wang, Yuying (55938861500)","58155307900; 59328774900; 59329584700; 57725619200; 59254884600; 58018054900; 58829632800; 59329584800; 59329176900; 57541330000; 57759490800; 55938861500","Machine learning approaches for predicting frailty base on multimorbidities in US adults using NHANES data (1999–2018)","2024","Computer Methods and Programs in Biomedicine Update","6","","100164","","","","0","10.1016/j.cmpbup.2024.100164","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204031778&doi=10.1016%2fj.cmpbup.2024.100164&partnerID=40&md5=95445c10a51d26c82869a6a424b66c9e","Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Department of Geriatric Endocrinology, The First Affiliated Hospital of Zhengzhou University, China; National Cancer Center, Chinese Academy of Medical Sciences & Peking Union Medical College, China; Medical School, Fudan University, China; Department of Nursing, The First Affiliated Hospital of Zhengzhou University, China; Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, China; Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, China; Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, China","Li T., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Li X., Department of Geriatric Endocrinology, The First Affiliated Hospital of Zhengzhou University, China; XU H., National Cancer Center, Chinese Academy of Medical Sciences & Peking Union Medical College, China; Wang Y., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Ren J., Medical School, Fudan University, China; Jing S., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Jin Z., Department of Nursing, The First Affiliated Hospital of Zhengzhou University, China; chen G., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Zhai Y., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; Wu Z., Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, China, Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, China; Zhang G., Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, China; Wang Y., Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China","Background: The global increase in an aging population has led to more common age-related health challenges, particularly multimorbidity and frailty, but there is a significant gap. Methods: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (1999–2018). The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted multivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested cross-validation and tested with various algorithms including decision tree, Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AU-PRC) to evaluate six algorithms, select the optimal model, and test the discrimination and consistency of the optimal model. Results: The study included 46,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cross-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration. Conclusions: We found 49 years maintain the balance of physiological reserve and external aggression. In addition, chronic diseases are trigger factor of frailty, while acute diseases are contributing factor that exacerbates the body's rapid decline. Last, the XGBoost frailty prediction model, with its simplicity, high performance and high clinical value holds potential for clinical application. © 2024","Frailty; Machine learning; Multimorbidity","adult; algorithm; ankle brachial index; area under the curve; arthritis; Article; artificial neural network; asthma; cerebrovascular accident; chronic disease; chronic kidney failure; chronic obstructive lung disease; congenital heart disease; controlled study; cross-sectional study; diabetes mellitus; diagnostic test accuracy study; dyspnea; electronic health record; epilepsy; ethnicity; exercise; female; frailty; glomerulus filtration rate; glucose blood level; health care personnel; heart catheterization; heart failure; hospital readmission; human; hypertension; insulin resistance; k nearest neighbor; limb weakness; logistic regression analysis; lung transplantation; machine learning; major clinical study; male; middle aged; muscle mass; nonalcoholic fatty liver; phlebotomy; prediction; questionnaire; receiver operating characteristic; risk factor; seizure; systolic blood pressure; wheezing","","","","","","","Kennedy B.K., Berger S.L., Brunet A., Campisi J., Cuervo A.M., Epel E.S., Et al., Geroscience: linking aging to chronic disease, Cell, 159, 4, pp. 709-713, (2014); Radner H., Yoshida K., Smolen J.S., Solomon D.H., Multimorbidity and rheumatic conditions-enhancing the concept of comorbidity, Nat. 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Learn., 2023, pp. 1-6, (2023); von Elm E., Altman D.G., Egger M., Pocock S.J., Gotzsche P.C., Vandenbroucke J.P., Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies, BMJ, 335, 7624, pp. 806-808, (2007); Hakeem F.F., Bernabe E., Sabba H.W., Association between oral health and frailty among American older adults, J. Am. Med. Dir. Assoc., 22, s1, (2020); (2023); Reboussin D.M., Allen N.B., Griswold M.E., Guallar E., Hong Y., Lackland D.T., Et al., Systematic Review for the 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: a Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines, Circulation, 138, 17, pp. e595-e616, (2018); 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022, Diabetes Care, 45, pp. S17-s38, (2022); Sun D.Q., Jin Y., Wang T.Y., Zheng K.I., Rios R.S., Zhang H.Y., Et al., MAFLD and risk of CKD, Metabolism., 115, (2021); Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report, Circulation, 106, 25, pp. 3143-3421, (2002); Mahemuti N., Jing X., Zhang N., Liu C., Li C., Cui Z., Et al., Association between systemic immunity-inflammation index and hyperlipidemia: a population-based study from the NHANES (2015-2020), Nutrients, 15, 5, (2023); Karlas T., Petroff D., Sasso M., Fan J.G., Mi Y.Q., de Ledinghen V., Et al., Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis, J. Hepatol., 66, 5, pp. 1022-1030, (2017); Blodgett J., Theou O., Kirkland S., Andreou P., Rockwood K., Frailty in NHANES: comparing the frailty index and phenotype, Arch. Gerontol. Geriatr., 60, 3, pp. 464-470, (2015); O'Neill D.E., Graham M.M., Anemia, cardiovascular disease, and frailty in the older adult, Can. J. Cardiol., 38, 6, pp. 715-717, (2022); Hanlon P., Morton F., Siebert S., Jani B.D., Nicholl B.I., Lewsey J., Et al., Frailty in rheumatoidrmdopen-2021-002111 arthritis and its relationship with disease activity, hospitalisation and mortality: a longitudinal analysis of the Scottish Early Rheumatoid Arthritis cohort and UK Biobank, RMD Open, 8, 1, (2022); Assar M.E., Laosa O., Rodriguez Manas L., Diabetes and frailty, Curr. Opin. Clin. Nutr. Metab. Care, 22, 1, pp. 52-57, (2019); Bielecka-Dabrowa A., Ebner N., Dos Santos M.R., Ishida J., Hasenfuss G., von Haehling S., Cachexia, muscle wasting, and frailty in cardiovascular disease, Eur. J. Heart. Fail., 22, 12, pp. 2314-2326, (2020); Burton J.K., Stewart J., Blair M., Oxley S., Wass A., Taylor-Rowan M., Et al., Prevalence and implications of frailty in acute stroke: systematic review & meta-analysis, Age Ageing, 51, 3, (2022); Liu P., Li Y., Zhang Y., Mesbah S.E., Ji T., Ma L., Frailty and hypertension in older adults: current understanding and future perspectives, Hypertens. Res., 43, 12, pp. 1352-1360, (2020); Qu J., Liang Y., Rao Y., Pei Y., Li D., Zhang Y., Et al., Causal relationship between frailty and chronic obstructive pulmonary disease or asthma: a two sample bidirectional Mendelian randomization study, Arch. Gerontol. Geriatr., 118, (2023); Malik A., Brito D., Vaqar S., Chhabra L., Congestive Heart Failure. StatPearls. Treasure Island (FL) Ineligible companies. Disclosure: Daniel Brito Declares No Relevant Financial Relationships With Ineligible companies. Disclosure: Sarosh Vaqar Declares No Relevant Financial Relationships With Ineligible companies. Disclosure: Lovely Chhabra Declares No Relevant Financial Relationships With Ineligible Companies, (2023); Jamthikar A., Gupta D., Saba L., Khanna N.N., Araki T., Viskovic K., Et al., Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models, Cardiovasc. Diagn. Ther., 10, 4, pp. 919-938, (2020); Liu H., Jiao J., Zhu M., Wen X., Jin J., Wang H., Et al., An early predictive model of frailty for older inpatients according to nutritional risk: protocol for a cohort study in China, BMC. Geriatr., 21, 1, (2021); Miron-Mombiela R., Ruiz-Espana S., Moratal D., Borras C., Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques, Mech. Ageing Dev., 215, (2023); Clegg A., Bates C., Young J., Ryan R., Nichols L., Ann Teale E., Et al., Development and validation of an electronic frailty index using routine primary care electronic health record data, Age Ageing, 45, 3, pp. 353-360, (2016); Mijwel M.M., Esen A., Shamil A., Overview of neural networks, Babylonian J. Mach. Learn., 2023, pp. 42-45, (2023)","T. Li; Department of OphthalmologyCardiology, The First Affiliated Hospital of Zhengzhou University, China; email: liteng981120@126.com","","Elsevier B.V.","","","","","","26669900","","","","English","Comput. Methods Programs Biomed. Update","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85204031778"
"Zhong Y.; Wu Q.; Cai L.; Chen Y.; Shen Q.","Zhong, Yukai (58970471200); Wu, Qiong (59041722400); Cai, Li (58970162700); Chen, Yuanjing (58970777400); Shen, Qi (57189358475)","58970471200; 59041722400; 58970162700; 58970777400; 57189358475","CDC167 exhibits potential as a biomarker for airway inflammation in asthma","2024","Mammalian Genome","35","2","","135","148","13","0","10.1007/s00335-024-10037-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189474606&doi=10.1007%2fs00335-024-10037-4&partnerID=40&md5=82d0f2cefab440a111bf81daec725b83","Department of Pediatrics, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, 200093, China; Department of Respiratory, Kongjiang Hospital of Shanghai Yangpu District, No. 480 Shuang Yang Road, Yangpu District, Shanghai, 200093, China; Department of Colorectal Surgery, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, 200093, China; Department of Geriatric Medicine, Tongji University Affiliated Yangpu Hospital, No. 450 Teng Yue Road, Yangpu District, Shanghai, 200090, China","Zhong Y., Department of Pediatrics, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, 200093, China; Wu Q., Department of Respiratory, Kongjiang Hospital of Shanghai Yangpu District, No. 480 Shuang Yang Road, Yangpu District, Shanghai, 200093, China; Cai L., Department of Colorectal Surgery, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, 200093, China; Chen Y., Department of Respiratory, Kongjiang Hospital of Shanghai Yangpu District, No. 480 Shuang Yang Road, Yangpu District, Shanghai, 200093, China; Shen Q., Department of Geriatric Medicine, Tongji University Affiliated Yangpu Hospital, No. 450 Teng Yue Road, Yangpu District, Shanghai, 200090, China","Current asthma treatments have been discovered to decrease the risk of disease progression. Herein, we aimed to characterize novel potential therapeutic targets for asthma. Differentially expressed genes (DEGs) for GSE64913 and GSE137268 datasets were characterized. Weighted correlation network analysis (WGCNA) was used to identify trait-related module genes within the GSE67472 dataset. The intersection of the module genes of interest, as well as the DEGs, comprised the key module genes that underwent additional candidate gene screening using machine learning. In addition, a bioinformatics-based approach was used to analyze the relative expression levels, diagnostic values, and reverently enriched pathways of the screened candidate genes. Furthermore, the candidate genes were silenced in asthmatic mice, and the inflammation and lung injury in the mice were validated. A total of 1710 DEGs were characterized in GSE64913 and GSE137268 for asthma patients. WGCNA identified 2367 asthma module genes, of which 285 overlapped with 1710 DEGs. Four candidate genes, CDC167, POSTN, SEC14L1, and SERPINB2, were validated using the intersection genes of three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machine. All the candidate genes were significantly upregulated in asthma patients and demonstrated diagnostic utility for asthma. Furthermore, silencing CDC167 reduced the levels of inflammatory cytokines significantly and alleviated lung injury in ovalbumin (OVA)-induced asthmatic mice. Our study demonstrated that CDC167 exhibits potential as diagnostic markers and therapeutic targets for asthma patients. © The Author(s) 2024.","","Animals; Asthma; Biomarkers; Computational Biology; Disease Models, Animal; Female; Gene Expression Profiling; Gene Regulatory Networks; Humans; Inflammation; Mice; immunoglobulin E; interleukin 13; interleukin 4; interleukin 5; proteasome; biological marker; adult; animal experiment; animal model; Article; asthma; bronchoalveolar lavage fluid; cdc167 gene; controlled study; diagnostic value; differential gene expression; extracellular matrix; female; gene; gene ontology; gene set enrichment analysis; gene silencing; hierarchical clustering; least absolute shrinkage and selection operator; lung injury; machine learning; male; mouse; nonhuman; random forest; respiratory tract inflammation; support vector machine; upregulation; weighted gene co expression network analysis; animal; bioinformatics; disease model; gene expression profiling; gene regulatory network; genetics; human; inflammation; metabolism; procedures","","immunoglobulin E, 37341-29-0; interleukin 13, 148157-34-0; proteasome, 140879-24-9; Biomarkers, ","","","UK Research and Innovation, UKRI, (103690)","","Abdel-Aziz M.I., Neerincx A.H., Vijverberg S.J., Kraneveld A.D., Maitland-van der Zee A.H., Omics for the future in asthma, Semin Immunopathol, 42, pp. 111-126, (2020); Aegerter H., Lambrecht B.N., The pathology of asthma: what is obstructing our view?, Annu Rev Pathol, 18, pp. 387-409, (2023); Alhamzawi R., Ali H.T.M., The Bayesian adaptive lasso regression, Math Biosci, 303, pp. 75-82, (2018); Asadi S., Roshan S., Kattan M.W., Random forest swarm optimization-based for heart diseases diagnosis, J Biomed Inform, 115, (2021); Asher M.I., Rutter C.E., Bissell K., Chiang C.Y., El Sony A., Ellwood E., Ellwood P., Garcia-Marcos L., Marks G.B., Morales E., Mortimer K., Perez-Fernandez V., Robertson S., Silverwood R.J., Strachan D.P., Pearce N., Worldwide trends in the burden of asthma symptoms in school-aged children: Global Asthma Network Phase I cross-sectional study, Lancet, 398, pp. 1569-1580, (2021); Bakakos P., Schleich F., Alchanatis M., Louis R., Induced sputum in asthma: from bench to bedside, Curr Med Chem, 18, pp. 1415-1422, (2011); Botia J.A., Vandrovcova J., Forabosco P., Guelfi S., D'Sa K., Hardy J., Lewis C.M., Ryten M., Weale M.E., An additional k-means clustering step improves the biological features of WGCNA gene co-expression networks, BMC Syst Biol, 11, (2017); Bradley B.L., Azzawi M., Jacobson M., Assoufi B., Collins J.V., Irani A.M., Schwartz L.B., Durham S.R., Jeffery P.K., Kay A.B., Eosinophils, T-lymphocytes, mast cells, neutrophils, and macrophages in bronchial biopsy specimens from atopic subjects with asthma: comparison with biopsy specimens from atopic subjects without asthma and normal control subjects and relationship to bronchial hyperresponsiveness, J Allergy Clin Immunol, 88, pp. 661-674, (1991); Burgess J.K., Jonker M.R., Berg M., Ten Hacken N.T.H., Meyer K.B., van den Berge M., Nawijn M.C., Heijink I.H., Periostin: contributor to abnormal airway epithelial function in asthma?, Eur Respir J, 57, (2021); Busse W.W., Melen E., Menzies-Gow A.N., Holy Grail: the journey towards disease modification in asthma, Eur Respir Rev, 31, (2022); Calven J., Ax E., Radinger M., The airway epithelium-A central player in asthma pathogenesis, Int J Mol Sci, 21, (2020); Cao Y., Chen S., Chen X., Zou W., Liu Z., Wu Y., Hu S., Global trends in the incidence and mortality of asthma from 1990 to 2019: an age-period-cohort analysis using the global burden of disease study 2019, Front Public Health, 10, (2022); Chen P.S., Hsu H.P., Phan N.N., Yen M.C., Chen F.W., Liu Y.W., Lin F.P., Feng S.Y., Cheng T.L., Yeh P.H., Omar H.A., Sun Z., Jiang J.Z., Chan Y.S., Lai M.D., Wang C.Y., Hung J.H., CCDC167 as a potential therapeutic target and regulator of cell cycle-related networks in breast cancer, Aging (Albany NY), 13, pp. 4157-4181, (2021); Ding X., Qin J., Huang F., Feng F., Luo L., The combination of machine learning and untargeted metabolomics identifies the lipid metabolism -related gene CH25H as a potential biomarker in asthma, Inflamm Res, (2023); Du L., Xu C., Shi J., Tang L., Xiao L., Lei C., Liu H., Liang Y., Guo Y., Tang K., Elevated CXCL14 in induced sputum was associated with eosinophilic inflammation and airway obstruction in patients with asthma, Int Arch Allergy Immunol, 183, pp. 1216-1225, (2022); Fuhlbrigge A.L., Sharma S., Oral corticosteroid use in asthma: a wolf in sheep’s clothing, J Allergy Clin Immunol Pract, 9, pp. 347-348, (2021); Global and regional burden of chronic respiratory disease in 2016 arising from non-infectious airborne occupational exposures: a systematic analysis for the Global Burden of Disease Study 2016, Occup Environ Med, 77, pp. 142-150, (2020); Guo Y., Xing Y., Weighted gene co-expression network analysis of pneumocytes under exposure to a carcinogenic dose of chloroprene, Life Sciences, 151, pp. 339-347, (2016); Habib N., Pasha M.A., Tang D.D., Current understanding of asthma pathogenesis and biomarkers, Cells, 11, (2022); He L.L., Xu F., Zhan X.Q., Chen Z.H., Shen H.H., Identification of critical genes associated with the development of asthma by co-expression modules construction, Mol Immunol, 123, pp. 18-25, (2020); Izuhara K., Matsumoto H., Ohta S., Ono J., Arima K., Ogawa M., Recent developments regarding periostin in bronchial asthma, Allergol Int, 64, pp. S3-S10, (2015); Lam H.C., Li A.M., Chan E.Y., Goggins W.B., The short-term association between asthma hospitalisations, ambient temperature, other meteorological factors and air pollutants in Hong Kong: a time-series study, Thorax, 71, pp. 1097-1109, (2016); Langfelder P., Horvath S., WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics, 9, (2008); Li M., Zhu W., Wang C., Zheng Y., Sun S., Fang Y., Luo Z., Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma, BMC Pulm Med, 21, (2021); Li Y., Li L., Zhao H., Gao X., Li S., The identification and clinical value evaluation of CYCS related to asthma through bioinformatics analysis and functional experiments, Dis Markers, 2023, (2023); Maestrelli P., Saetta M., Di Stefano A., Calcagni P.G., Turato G., Ruggieri M.P., Roggeri A., Mapp C.E., Fabbri L.M., Comparison of leukocyte counts in sputum, bronchial biopsies, and bronchoalveolar lavage, Am J Respir Crit Care Med, 152, pp. 1926-1931, (1995); Mo Y., Zhang K., Feng Y., Yi L., Liang Y., Wu W., Zhao J., Zhang Z., Xu Y., Hu Q., He J., Zhen G., Epithelial SERPINB10, a novel marker of airway eosinophilia in asthma, contributes to allergic airway inflammation, Am J Physiol Lung Cell Mol Physiol, 316, pp. L245-l254, (2019); Moore W.C., Kornmann O., Humbert M., Poirier C., Bel E.H., Kaneko N., Smith S.G., Martin N., Gilson M.J., Price R.G., Bradford E.S., Liu M.C., Stopping versus continuing long-term mepolizumab treatment in severe eosinophilic asthma (COMET study), Eur Respir J, 59, (2022); Ne E.L., Abdel-Latif R.S., El-Hady H.A., Association between SERPINB2 gene expression by real time PCR in respiratory epithelial cells and atopic bronchial asthma severity, Egypt J Immunol, 24, pp. 165-181, (2017); Nwaru B.I., Ekstrom M., Hasvold P., Wiklund F., Telg G., Janson C., Overuse of short-acting β(2)-agonists in asthma is associated with increased risk of exacerbation and mortality: a nationwide cohort study of the global SABINA programme, Eur Respir J, 55, (2020); 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How to discuss safety of commonly used medications with parents, J Allergy Clin Immunol Pract, 10, pp. 3064-3072, (2022); Skov I.R., Madsen H., Henriksen D.P., Andersen J.H., Pottegard A., Davidsen J.R., Low-dose oral corticosteroids in asthma associates with increased morbidity and mortality, Eur Respir J, 60, (2022); Strauss R.H., McFadden E.R., Ingram R.H., Deal E.C., Jaeger J.J., Influence of heat and humidity on the airway obstruction induced by exercise in asthma, J Clin Invest, 61, pp. 433-440, (1978); Uddin S., Khan A., Hossain M.E., Moni M.A., Comparing different supervised machine learning algorithms for disease prediction, BMC Med Inform Decis Mak, 19, (2019); Wu Q., Liu J., Deng J., Chen Y., Long non-coding RNA HOTTIP induces inflammation in asthma by promoting EFNA3 transcription by CCCTC-binding factor, Am J Transl Res, 14, pp. 8903-8917, (2022); Yang Q., Wang R., Wei B., Peng C., Wang L., Hu G., Kong D., Du C., Candidate biomarkers and molecular mechanism investigation for glioblastoma multiforme utilizing WGCNA, BioMed Res Int, (2018); Zayed H., Novel comprehensive bioinformatics approaches to determine the molecular genetic susceptibility profile of moderate and severe asthma, Int J Mol Sci, 21, (2020); Zhang Z., Wang J., Chen O., Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis, BMC Med Genomics, 14, (2021); Zhou J., Lu Y., Wu W., Feng Y., HMSC-derived exosome inhibited Th2 cell differentiation via regulating miR-146a-5p/SERPINB2 pathway, J Immunol Res, 2021, (2021)","Y. Chen; Department of Respiratory, Kongjiang Hospital of Shanghai Yangpu District, Shanghai, No. 480 Shuang Yang Road, Yangpu District, 200093, China; email: chenyujish@163.com; Q. Shen; Department of Geriatric Medicine, Tongji University Affiliated Yangpu Hospital, Shanghai, No. 450 Teng Yue Road, Yangpu District, 200090, China; email: shenqi301@163.com","","Springer","","","","","","09388990","","MAMGE","38580753","English","Mamm. Genome","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85189474606"
"Amiri S.; Li Y.-C.; Buchwald D.; Pandey G.","Amiri, Solmaz (57203016323); Li, Yan-Chak (57219099611); Buchwald, Dedra (7005705871); Pandey, Gaurav (12752834100)","57203016323; 57219099611; 7005705871; 12752834100","Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA","2024","Science of the Total Environment","921","","171102","","","","0","10.1016/j.scitotenv.2024.171102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185888424&doi=10.1016%2fj.scitotenv.2024.171102&partnerID=40&md5=ab1281a2dfc318cb91ec75c2a4aee8c4","Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, United States; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States","Amiri S., Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, United States; Li Y.-C., Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Buchwald D., Institute for Research and Education to Advance Community Health (IREACH), Elson S. Floyd College of Medicine, Washington State University, Seattle, WA, United States; Pandey G., Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States","Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017–2019), and the last five years (2014–2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health. © 2024 Elsevier B.V.","Air toxic; Childhood asthma; Geographic information systems; Machine learning; Socioeconomic status","Air Pollutants; Air Pollution; Asthma; Child; Environmental Exposure; Humans; Retrospective Studies; Trichloroethanes; Washington; Spokane; United States; Washington [United States]; Diagnosis; Diseases; Environmental Protection Agency; Machine learning; 1,1,1 trichloroethane; 2 nitropropane; 2,4 dinitrophenol; 2,4,6 trichlorophenol; arsenic; lead; phthalic acid 2 ethylhexyl monoester; tetrachloroethane; toxic substance; 1,1,1-trichloroethane; trichloroethane; Air toxics; Atmospheric pollutants; Childhood asthma; Elementary schools; Exposure period; Hazardous effects; Machine-learning; Socio-economic status; Trichloroethane; Washington; air quality; asthma; child health; GIS; identification method; machine learning; primary education; socioeconomic status; toxicity; air pollution; air toxicity; Article; asthma; chest tightness; child; coughing; dyspnea; elementary student; environmental exposure; false discovery rate; female; geographic information system; human; machine learning; male; retrospective study; school child; social status; toxicity; wheezing; air pollutant; analysis; asthma; Washington; Geographic information systems","","1,1,1 trichloroethane, 71-55-6; 2 nitropropane, 79-46-9; 2,4 dinitrophenol, 25550-58-7, 51-28-5; 2,4,6 trichlorophenol, 88-06-2; arsenic, 7440-38-2; lead, 7439-92-1, 13966-28-4; phthalic acid 2 ethylhexyl monoester, 4376-20-9; tetrachloroethane, 25322-20-7, 79-34-5; trichloroethane, 25323-89-1; 1,1,1-trichloroethane, ; Air Pollutants, ; Trichloroethanes, ","XGBoost","","National Institutes of Health, NIH, (1OT2OD032581-01, R01HG011407); National Institutes of Health, NIH; Rambøll Fonden","This research was funded by the National Institutes of Health ( 1OT2OD032581-01 and R01HG011407 ) and Ramboll Foundation . ","Adgent M.A., Carroll K.N., Hazlehurst M.F., Et al., A combined cohort analysis of prenatal exposure to phthalate mixtures and childhood asthma, Environ. Int., 143, (2020); Agency for Toxic Substances and Disease Registry (ATSD), Toxicological Profile for Chlorophenols, (2022); Akinbami L.J., Schoendorf K.C., Trends in childhood asthma: prevalence, health care utilization, and mortality, Pediatrics, 110, 2, pp. 315-322, (2002); Alexandersson R., Hedenstierna G., Pulmonary function after long-term exposure to trichlorophenol, Int. Arch. Occup. Environ. Health, 49, 3-4, pp. 275-280, (1982); Ashizawa A., Cronin D., Harper C., Ingerman L., Roney N., Tucker P.G., Toxicological Profile for 1, 3-Butadiene, (2012); Bably M., Arif A.A., Post A., Prenatal use of cleaning and scented products and its association with childhood asthma, asthma symptoms, and mental health and developmental comorbidities, J. 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Med., 134, pp. 47-53, (2018); Ekerljung L., Bossios A., Lotvall J., Et al., Multi-symptom asthma as an indication of disease severity in epidemiology, Eur. Respir. J., 38, 4, pp. 825-832, (2011); Environmental US, Agency P, Assessing Outdoor Air Near Schools; EPA, Why Indoor Air Quality is Important to Schools, (2022); Forno E., Celedon J.C., Asthma and ethnic minorities: socioeconomic status and beyond, Curr. Opin. Allergy Clin. Immunol., 9, 2, pp. 154-160, (2009); Gass K., Klein M., Chang H.H., Flanders W.D., Strickland M.J., Classification and regression trees for epidemiologic research: an air pollution example, Environ. Health, 13, 1, (2014); Grineski S.E., Collins T.W., Geographic and social disparities in exposure to air neurotoxicants at U.S. public schools, Environ. Res., 161, pp. 580-587, (2018); Hall H., Nielsen E.; Han K., Ran Z., Wang X., Et al., Traffic-related organic and inorganic air pollution and risk of development of childhood asthma: a meta-analysis, Environ. 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Floyd College of Medicine, Washington State University, Seattle, 1100 Olive Way, Ste 1200, 98101, United States; email: solmaz.amiri@wsu.edu","","Elsevier B.V.","","","","","","00489697","","STEVA","38387571","English","Sci. Total Environ.","Article","Final","","Scopus","2-s2.0-85185888424"
"Jiang H.; Fu C.-Y.","Jiang, Hui (58981045100); Fu, Chang-yong (57699655600)","58981045100; 57699655600","Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning","2024","International Immunopharmacology","133","","112064","","","","0","10.1016/j.intimp.2024.112064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190118150&doi=10.1016%2fj.intimp.2024.112064&partnerID=40&md5=ea9d5ee1eeef7f3f719286b12ddbed39","Department of Respiratory Medicine, Shanghai East hospital,School of Medicine, Tongji university, Shanghai, China; Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China","Jiang H., Department of Respiratory Medicine, Shanghai East hospital,School of Medicine, Tongji university, Shanghai, China; Fu C.-Y., Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China","Background: There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression. Methods: Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies. Results: The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation. Conclusion: Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions. © 2024","Asthma; Diagnosis learning; Immune infiltration; Major depressive disorder","Animals; Asthma; Biomarkers; Computational Biology; Databases, Genetic; Gene Expression Profiling; Gene Regulatory Networks; Humans; Machine Learning; Mice; protein serine threonine kinase; small interfering RNA; biological marker; animal experiment; Article; asthma; bioinformatics; blood; cell activation; cell infiltration; centrifugation; clinical article; controlled study; diagnostic value; differential expression analysis; differential gene expression; functional enrichment analysis; gene expression; genetic screening; hierarchical clustering; histopathology; human; immune cell infiltration analysis; immunocompetent cell; learning algorithm; least absolute shrinkage and selection operator; M1 macrophage; machine learning; macrophage activation; major depression; male; microarray analysis; mouse; natural killer cell; nomogram; nonhuman; pathophysiology; peripheral blood mononuclear cell; real time polymerase chain reaction; receiver operating characteristic; support vector machine; THP-1 cell line; upregulation; weighted gene co expression network analysis; Western blotting; animal; asthma; gene expression profiling; gene regulatory network; genetic database; genetics; immunology","","protein serine threonine kinase, ; Biomarkers, ","","","Tongji University","This work was supported by the Clinical Research Project of Tongji Hospital of Tongji University (Grant No. ITJ(QN)2314). 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Chem., 29, pp. 5758-5781, (2022); Wei Y., Feng J., Ma J., Chen D., Chen J., Neutrophil/lymphocyte, platelet/lymphocyte and monocyte/lymphocyte ratios in patients with affective disorders, J. Affect Disord., 309, pp. 221-228, (2022); Dey A., Hankey Giblin P.A., Insights into macrophage heterogeneity and cytokine-induced neuroinflammation in major depressive disorder, Pharmaceuticals (Basel), 11, (2018)","C.-Y. Fu; Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China; email: shouyi2011@163.com","","Elsevier B.V.","","","","","","15675769","","IINMB","38608447","English","Int. Immunopharmacol.","Article","Final","","Scopus","2-s2.0-85190118150"
"Zheng L.; Ohde J.W.; Overgaard S.M.; Brereton T.A.; Jose K.; Wi C.-I.; Peterson K.J.; Juhn Y.J.","Zheng, Lu (57813765500); Ohde, Joshua W. (57217094677); Overgaard, Shauna M. (57813750300); Brereton, Tracey A. (57813727300); Jose, Kristelle (58068694200); Wi, Chung-Il (56182827700); Peterson, Kevin J. (55439369000); Juhn, Young J. (6507775791)","57813765500; 57217094677; 57813750300; 57813727300; 58068694200; 56182827700; 55439369000; 6507775791","Clinical Needs Assessment of a Machine Learning–Based Asthma Management Tool: User-Centered Design Approach","2024","JMIR Formative Research","8","","e45391","","","","0","10.2196/45391","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194941471&doi=10.2196%2f45391&partnerID=40&md5=e9f9533d57030884e0560dc47d3d1737","Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Precision Population Science Lab, Mayo Clinic, Rochester, MN, United States; Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, United States; Mayo Clinic Health System Research, Mayo Clinic, Rochester, MN, United States","Zheng L., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Ohde J.W., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Overgaard S.M., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Brereton T.A., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Jose K., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Wi C.-I., Precision Population Science Lab, Mayo Clinic, Rochester, MN, United States, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, United States; Peterson K.J., Center for Digital Health, Mayo Clinic, Rochester, MN, United States; Juhn Y.J., Precision Population Science Lab, Mayo Clinic, Rochester, MN, United States, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, United States, Mayo Clinic Health System Research, Mayo Clinic, Rochester, MN, United States","Background: Personalized asthma management depends on a clinician’s ability to efficiently review patient’s data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. Objective: We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians’ experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. Methods: At the “discovery” phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the “define” phase, a synthesis analysis was conducted to converge key results from interviewees’ insights into themes, eventually forming critical “how might we” research questions to guide model development and implementation. Results: We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients’ high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. Conclusions: As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design. ©Lu Zheng, Joshua W Ohde, Shauna M Overgaard, Tracey A Brereton, Kristelle Jose, Chung-Il Wi, Kevin J Peterson, Young J Juhn.","artificial intelligence (AI); asthma; formative research; machine learning (ML); qualitative; user needs; user-centered design","","","","","","Center for Digital Health; Precision Population Science Lab; UK Research and Innovation, UKRI, (104196)","We would like to deeply thank Dr Manuel Arteta, Dr Valeria Cristiani, Joy Fladager Muth, Dr Gregory Garrison, Dr Margret Gill, Dr Jason Greenwood, Darcy Hall, Dr Martha Hartz, Dr Julian Lynaugh, Dr Donnchadh O\u2019Sullivan, Dr Sarah Scherger, Dr Eric Schnaith, and Dr David Soma for finding their time for interview and sharing their valuable insights into usability of A-GPS in clinical practice setting. This project was supported by the Center for Digital Health, Precision Population Science Lab, the AI Program of Department of Pediatric and Adolescent Medicine. We acknowledge no generative AI usage in the preparation of this paper.","Aristidou A, Jena R, Topol EJ., Bridging the chasm between AI and clinical implementation, Lancet, 399, 10325, (2022); Duclos C, Bouaud J, Pragmatic considerations on clinical decision support from the 2019 literature, Yearb Med Inform, 29, 1, pp. 155-158, (2020); Amann J, Blasimme A, Vayena E, Frey D, Madai VI, Explainability for artificial intelligence in healthcare: a multidisciplinary perspective, BMC Med Inform Decis Mak, 20, 1, (2020); Mhasawade V, Zhao Y, Chunara R., Machine learning and algorithmic fairness in public and population health, Nat Mach Intell, 3, 8, pp. 659-666, (2021); Watson DS, Krutzinna J, Bruce IN, Griffiths CE, McInnes IB, Barnes MR, Et al., Clinical applications of machine learning algorithms: beyond the black box, BMJ, 364, (2019); Babione JN, Ocampo W, Haubrich S, Yang C, Zuk T, Kaufman J, Et al., Human-centred design processes for clinical decision support: a pulmonary embolism case study, Int J Med Inform, 142, (2020); Pencina MJ, Goldstein BA, D'Agostino RB., Prediction models—development, evaluation, and clinical application, N Engl J Med, 382, 17, pp. 1583-1586, (2020); Teal G, French T., Spaces for participatory design innovation, Proceedings of the 16th Participatory Design Conference 2020 - Participation(s) Otherwise, pp. 64-74, (2020); Seol HY, Rolfes MC, Chung W, Sohn S, Ryu E, Park MA, Et al., Expert artificial intelligence-based natural language processing characterises childhood asthma, BMJ Open Respir Res, 7, 1, (2020); Martin-Sanchez F, Verspoor K., Big data in medicine is driving big changes, Yearb Med Inform, 9, 1, pp. 14-20, (2014); Park Y, Jackson GP, Foreman MA, Gruen D, Hu J, Das AK., Evaluating artificial intelligence in medicine: phases of clinical research, JAMIA Open, 3, 3, pp. 326-331, (2020); Brereton T, Overgaard S, Jose K., A proposed model documentation framework to facilitate translation of AI models in healthcare, AMIA Clinical Informatics Conference Proceedings, (2022); Hansen NB, Dindler C, Halskov K, Iversen OS, Bossen C, Basballe DA, Et al., How participatory design works: mechanisms and effects, Proceedings of the 31st Australian Conference on Human-Computer-Interaction, pp. 30-41, (2019); Banbury A, Pedell S, Parkinson L, Byrne L., Using the Double Diamond model to co-design a dementia caregivers telehealth peer support program, J Telemed Telecare, 27, 10, pp. 667-673, (2021); Dan N., How to apply a design thinking, HCD, UX or any creative process from scratch, Medium, (2016); Design Council, (2022); What is the framework for innovation? Design council's evolved double diamond; Basadur M, Ellspermann SJ, Evans GW., A new methodology for formulating ill-structured problems, Omega, 22, 6, pp. 627-645, (1994); Berger W., Innovation: the secret phrase top innovators use, Harvard Business Review, (2012); Nesbitt K, Beleigoli A, Du H, Tirimacco R, Clark R., User Experience (UX) design as a co-design methodology: lessons learned during the development of a web-based portal for cardiac rehabilitation, Eur J Cardiovasc Nurs, 21, 2, pp. 178-183, (2022); Seol HY, Shrestha P, Muth JF, Wi CI, Sohn S, Ryu E, Et al., Artificial intelligence-assisted clinical decision support for childhood asthma management: a randomized clinical trial, PLoS One, 16, 8, (2021); Overgaard SM, Peterson KJ, Wi CI, Kshatriya BSA, Ohde JW, Brereton T, Et al., A technical performance study and proposed systematic and comprehensive evaluation of an ML-based CDS solution for pediatric asthma, AMIA Jt Summits Transl Sci Proc, 2022, pp. 25-35, (2022); Tactivos Inc. MURAL; Gordon JE, Belford SM, Aranguren DL, Blair D, Fleming R, Gajarawala NM, Et al., Outcomes of Mayo Clinic reBoot camps for postimplementation training in the electronic health record, J Am Med Inform Assoc, 29, 9, pp. 1518-1524, (2022); Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl KD, Et al., MI in Healthcare Workshop Working Group. Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency, NPJ Digit Med, 3, (2020); Faes L, Liu X, Wagner SK, Fu DJ, Balaskas K, Sim DA, Et al., A clinician's guide to artificial intelligence: how to critically appraise machine learning studies, Transl Vis Sci Technol, 9, 2, (2020)","L. Zheng; Center for Digital Health, Mayo Clinic, Rochester, 200 1st Street South West, United States; email: zheng.lu@mayo.edu","","JMIR Publications Inc.","","","","","","2561326X","","","","English","JMIR Form.  Res.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85194941471"
"Zhou X.; Pu Y.; Zhang D.; Guan Y.; Lu Y.; Zhang W.; Fu C.-C.; Fang Q.; Zhang H.; Liu S.; Fan L.","Zhou, Xiuxiu (57204916826); Pu, Yu (57205611901); Zhang, Di (57206456966); Guan, Yu (55524064000); Lu, Yang (57850888900); Zhang, Weidong (57917992900); Fu, Chi-Cheng (57217491423); Fang, Qu (57217493486); Zhang, Hanxiao (58184304900); Liu, Shiyuan (9232762100); Fan, Li (56611135400)","57204916826; 57205611901; 57206456966; 55524064000; 57850888900; 57917992900; 57217491423; 57217493486; 58184304900; 9232762100; 56611135400","Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort","2023","Journal of Applied Clinical Medical Physics","24","11","e14171","","","","0","10.1002/acm2.14171","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173520696&doi=10.1002%2facm2.14171&partnerID=40&md5=77a16eebd3b39cb5e70c4f414630e28a","Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Shanghai Aitrox Technology Corporation Limited, Shanghai, China","Zhou X., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Pu Y., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Zhang D., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Guan Y., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Lu Y., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Zhang W., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Fu C.-C., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Fang Q., Shanghai Aitrox Technology Corporation Limited, Shanghai, China; Zhang H., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Liu S., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; Fan L., Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China","Purpose: To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results. Materials and methods: A total of 615 subjects from a community-based screening population (40–74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions. Results: The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively. Conclusions: The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD. © 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.","chronic obstructive; pulmonary disease; pulmonary function test; quantitative imaging; tomography; X-ray computed","Adult; Aged; Forced Expiratory Volume; Humans; Lung; Middle Aged; Pulmonary Disease, Chronic Obstructive; Retrospective Studies; Tomography, X-Ray Computed; adult; aged; chronic obstructive lung disease; diagnostic imaging; forced expiratory volume; human; lung; middle aged; physiology; procedures; retrospective study; x-ray computed tomography","","","","","Clinical Innovation Project of Shanghai Changzheng Hospital, (2020YLCYJ‐Y24); National Natural Science Foundation of China, NSFC, (81871321, 81930049, 82171926); National Natural Science Foundation of China, NSFC; Science and Technology Commission of Shanghai Municipality, STCSM, (21DZ2202600); Science and Technology Commission of Shanghai Municipality, STCSM; National Key Research and Development Program of China, NKRDPC, (2022YFC2010000, 2022YFC2010002, 2022YFC2010005, YXFSC2022JJSJ002); National Key Research and Development Program of China, NKRDPC","We thank the investigators and participants at the investigative sites for their support during the conduct of the study. We acknowledge Prof. Rui Wang and Prof. Qian He from the Department of statistics of Second Affiliated Hospital of PLA Naval Medical University for their help in statistics. They agreed with the data analysis of this study. This work was supported by the National Natural Science Foundation of China [grants number 81871321, 82171926 and 81930049]; the program of Science and Technology Commission of Shanghai Municipality [grant number 21DZ2202600]; National Key R&D Program of China [grant number 2022YFC2010000, 2022YFC2010002, 2022YFC2010005]; Construction of CT standardized database for chronic obstructive pulmonary disease [grant number YXFSC2022JJSJ002]; Clinical Innovation Project of Shanghai Changzheng Hospital [grant number 2020YLCYJ‐Y24] ","Wender R., Fontham E.T., Barrera E., Et al., American Cancer Society lung cancer screening guidelines, CA Cancer J Clin, 63, 2, pp. 107-117, (2013); Ritchie A.I., Martinez F.J., The challenges of defining early chronic obstructive pulmonary disease in the general population, Am J Respir Crit Care Med, 203, 10, pp. 1209-1210, (2021); Meghji J., Mortimer K., Agusti A., Et al., Improving lung health in low-income and middle-income countries: from challenges to solutions, Lancet, 397, 10277, pp. 928-940, (2021); Lowe K.E., Regan E.A., Anzueto A., Et al., COPDGene(®) 2019: redefining the diagnosis of chronic obstructive pulmonary disease, Chronic Obstr Pulm Dis, 6, 5, pp. 384-399, (2019); Park S.W., Lim M.N., Kim W.J., Bak S.H., Quantitative assessment the longitudinal changes of pulmonary vascular counts in chronic obstructive pulmonary disease, Respir Res, 23, 1, (2022); Deepak D., Prasad A., Atwal S.S., Agarwal K., Recognition of small airways obstruction in asthma and COPD—the road less travelled, J Clin Diagn Res, 11, 3, pp. Te01-te05, (2017); Cho J., Lee C.H., Kim D.K., Et al., Impact of gender on chronic obstructive pulmonary disease outcomes: a propensity score-matched analysis of a prospective cohort study, Korean J Intern Med, 35, 5, pp. 1154-1163, (2020); Baron R., Kadlecek S., Loza L., Et al., deriving regionally specific biomarkers of emphysema and small airways disease using variable threshold parametric response mapping on volumetric lung CT images, Acad Radiol, 29, 2, pp. S127-s136, (2022); Hwang H.J., Seo J.B., Lee S.M., Et al., New method for combined quantitative assessment of air-trapping and emphysema on chest computed tomography in chronic obstructive pulmonary disease: comparison with parametric response mapping, Korean J Radiol, 22, 10, pp. 1719-1729, (2021); Bhatt S.P., Soler X., Wang X., Et al., Association between functional small airway disease and FEV1 decline in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 194, 2, pp. 178-184, (2016); Schiwek M., Triphan S.M.F., Biederer J., Et al., Quantification of pulmonary perfusion abnormalities using DCE-MRI in COPD: comparison with quantitative CT and pulmonary function, Eur Radiol, 32, 3, pp. 1879-1890, (2022); Galban C.J., Han M.K., Boes J.L., Et al., Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression, Nat Med, 18, 11, pp. 1711-1715, (2012); Martini K., Frauenfelder T., Advances in imaging for lung emphysema, Ann Transl Med, 8, 21, (2020); Occhipinti M., Paoletti M., Bartholmai B.J., Et al., Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD, Respir Res, 20, 1, (2019); Kuo-Lung L., Yeun-Chung C., Chong-Jen Y., Et al., Bullous Parametric Response Map For Functional Localization of COPD, J Digit Imaging, 35, 2, pp. 115-126, (2022); Lv R., Xie M., Jin H., Et al., A preliminary study on the relationship between high-resolution computed tomography and pulmonary function in people at risk of developing chronic obstructive pulmonary disease, Front Med (Lausanne), 9, (2022); Konietzke P., Wielputz M.O., Wagner W.L., Et al., Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval, Eur Radiol, 30, 5, pp. 2502-2512, (2020); Gomes P., Bastos H.N.E., Carvalho A., Et al., Pulmonary emphysema regional distribution and extent assessed by chest computed tomography is associated with pulmonary function impairment in patients with COPD, Front Med (Lausanne), 8, (2021); Martinez C.H., Diaz A.A., Meldrum C., Et al., Age and small airway imaging abnormalities in subjects with and without airflow obstruction in SPIROMICS, Am J Respir Crit Care Med, 195, 4, pp. 464-472, (2017); Pompe E., Galban C.J., Ross B.D., Et al., Parametric response mapping on chest computed tomography associates with clinical and functional parameters in chronic obstructive pulmonary disease, Respir Med, 123, pp. 48-55, (2017); Capaldi D.P., Zha N., Guo F., Et al., Pulmonary imaging biomarkers of gas trapping and emphysema in COPD: (3)He MR imaging and CT parametric response maps, Radiology, 279, 2, pp. 597-608, (2016); Ho T.T., Kim T., Kim W.J., Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci Rep, 11, 1, (2021); Humphries S.M., Notary A.M., Centeno J.P., Et al., Deep learning enables automatic classification of emphysema pattern at CT, Radiology, 294, 2, pp. 434-444, (2020); Li F., Choi J., Zou C., Et al., Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images, Sci Rep, 11, 1, (2021); Schabdach J., Wells W.M., Cho M., Batmanghelich K.N., A likelihood-free approach for characterizing heterogeneous diseases in large-scale studies, Inf Process Med Imaging, 10265, pp. 170-183, (2017); Singla S., Gong M., Ravanbakhsh S., Sciurba F., Poczos B., Batmanghelich K.N., Subject2Vec: generative-discriminative approach from a set of image patches to a vector, Med Image Comput Comput Assist Interv, 11070, pp. 502-510, (2018); Chen S., Wang C., Li B., Et al., Risk factors for FEV(1) decline in mild COPD and high-risk populations, Int J Chron Obstruct Pulmon Dis, 12, pp. 435-442, (2017); Koo H.K., Vasilescu D.M., Booth S., Et al., Small airways disease in mild and moderate chronic obstructive pulmonary disease: a cross-sectional study, Lancet Respir Med, 6, 8, pp. 591-602, (2018); Postma D.S., Brightling C., Baldi S., Et al., Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): baseline data from a prospective cohort study, Lancet Respir Med, 7, 5, pp. 402-416, (2019); Kirby M., Tanabe N., Tan W.C., Et al., Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression. Findings from a population-based study, Am J Respir Crit Care Med, 197, 1, pp. 56-65, (2018)","X. Zhou; Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; email: sunaay@126.com; S. Liu; Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; email: radiology_cz@163.com; L. Fan; Department of Radiology, Second Affiliated Hospital of PLA Naval Medical University, Shanghai, China; email: fanli0930@163.com","","John Wiley and Sons Ltd","","","","","","15269914","","","37782241","English","J. Appl. Clin. Med. Phys.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85173520696"
"Wu M.; Liu D.; Zhu F.; Yu Y.; Ye Z.; Xu J.","Wu, Ming (57801394700); Liu, Danru (55622222200); Zhu, Fenhua (56397485900); Yu, Yeheng (8616266200); Ye, Zhicheng (57263373000); Xu, Jin (57213432266)","57801394700; 55622222200; 56397485900; 8616266200; 57263373000; 57213432266","Diagnostic Value of Immunological Biomarkers in Children with Asthmatic Bronchitis and Asthma","2023","Medicina (Lithuania)","59","10","1765","","","","0","10.3390/medicina59101765","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175259485&doi=10.3390%2fmedicina59101765&partnerID=40&md5=a47b15f0ca3e596d46f4790b6caca599","Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China","Wu M., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; Liu D., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; Zhu F., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; Yu Y., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; Ye Z., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; Xu J., Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China","Background and Objectives: This study aimed to investigate the diagnostic value of immunological biomarkers in children with asthmatic bronchitis and asthma and to develop a machine learning (ML) model for rapid differential diagnosis of these two diseases. Materials and Methods: Immunological biomarkers in peripheral blood were detected using flow cytometry and immunoturbidimetry. The importance of characteristic variables was ranked and screened using random forest and extra trees algorithms. Models were constructed and tested using the Scikit-learn ML library. K-fold cross-validation and Brier scores were used to evaluate and screen models. Results: Children with asthmatic bronchitis and asthma exhibit distinct degrees of immune dysregulation characterized by divergent patterns of humoral and cellular immune responses. CD8+ T cells and B cells were more dominant in differentiating the two diseases among many immunological biomarkers. Random forest showed a comprehensive high performance compared with other models in learning and training the dataset of immunological biomarkers. Conclusions: This study developed a prediction model for early differential diagnosis of asthmatic bronchitis and asthma using immunological biomarkers. Evaluation of the immune status of patients may provide additional clinical information for those children transforming from asthmatic bronchitis to asthma under recurrent attacks. © 2023 by the authors.","asthma; asthmatic bronchitis; immunological biomarkers; machine learning; random forest","Asthma; Biomarkers; Bronchitis; CD8-Positive T-Lymphocytes; Child; Diagnosis, Differential; Humans; biological marker; asthma; bronchitis; CD8+ T lymphocyte; child; complication; differential diagnosis; human","","Biomarkers, ","","","Fudan University, (EK2022ZX05)","This study was supported by grants from the Key Development Program of the Children’s Hospital at Fudan University (EK2022ZX05).","Eg K.P., Thomas R.J., Masters I.B., McElrea M.S., Marchant J.M., Chang A.B., Development and validation of a bronchoscopically defined bronchitis scoring tool in children, Pediatr. Pulmonol, 55, pp. 2444-2451, (2020); Morice A.H., Millqvist E., Bieksiene K., Birring S.S., Dicpinigaitis P., Ribas C.D., Boon M.H., Kantar A., Lai K., McGarvey L., Et al., ERS guidelines on the diagnosis and treatment of chronic cough in adults and children, Eur. Respir. J, 55, (2020); Bian F., Wu Y.-E., Zhang C.-L., Use of aerosol inhalation treatment with budesonide and terbutaline sulfate on acute pediatric asthmatic bronchitis, Exp. Ther. Med, 14, pp. 1621-1625, (2017); Breiteneder H., Peng Y., Agache I., Diamant Z., Eiwegger T., Fokkens W.J., Traidl-Hoffmann C., Nadeau K., O'Hehir R.E., O'Mahony L., Et al., Biomarkers for diagnosis and prediction of therapy responses in allergic diseases and asthma, Allergy, 75, pp. 3039-3068, (2020); Hammad H., Lambrecht B.N., The basic immunology of asthma, Cell, 184, pp. 2521-2522, (2021); Handelman G.S., Kok H.K., Chandra R.V., Razavi A.H., Lee M.J., Asadi H., eDoctor: Machine learning and the future of medicine, J. Intern. Med, 284, pp. 603-619, (2018); Choi R.Y., Coyner A.S., Kalpathy-Cramer J., Chiang M.F., Campbell J.P., Introduction to Machine Learning, Neural Networks, and Deep Learning, Transl. Vis. Sci. Technol, 9, (2020); Jiang Z.F., Shen K.L., Zhu F., Practice of Pediatrics, (2015); Reddel H.K., Bacharier L.B., Bateman E.D., Brightling C.E., Brusselle G.G., Buhl R., Cruz A.A., Duijts L., Drazen J.M., FitzGerald J.M., Et al., Global Initiative for Asthma Strategy 2021: Executive Summary and Rationale for Key Changes, Am. J. Respir. Crit. Care Med, 205, pp. 17-35, (2021); Global Initiative for Asthma–GINA; ATS 2024 International Conference, New York, NY, USA; Angraal S., Mortazavi B.J., Gupta A., Khera R., Ahmad T., Desai N.R., Jacoby D.L., Masoudi F.A., Spertus J.A., Krumholz H.M., Machine Learning Prediction of Mortality and Hospitalization in Heart Failure with Preserved Ejection Fraction, JACC Hear. Fail, 8, pp. 12-21, (2020); Poldrack R.A., Huckins G., Varoquaux G., Establishment of Best Practices for Evidence for Prediction: A Review, JAMA Psychiatry, 77, pp. 534-540, (2020); Vu H.L., Ng K.T.W., Richter A., An C., Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation, J. Environ. Manag, 311, (2022); Gans M.D., Gavrilova T., Understanding the immunology of asthma: Pathophysiology, biomarkers, and treatments for asthma endotypes, Paediatr. Respir. Rev, 36, pp. 118-127, (2020); Kuruvilla M.E., Lee F.E.-H., Lee G.B., Understanding Asthma Phenotypes, Endotypes, and Mechanisms of Disease, Clin. Rev. Allergy Immunol, 56, pp. 219-233, (2019); Su Q., Jiang L., Chai J., Dou Z., Rong Z., Zhao X., Yu B., Wang Y., Wang X., Changes of Peripheral Blood Lymphocyte Subsets and Immune Function in Children with Henoch-Schonlein Purpura Nephritis, Iran. J. Immunol. IJI, 18, pp. 259-267, (2021); Du H., Dong X., Zhang J., Cao Y., Akdis M., Huang P., Chen H., Li Y., Liu G., Akdis C.A., Et al., Clinical characteristics of 182 pediatric COVID-19 patients with different severities and allergic status, Allergy, 76, pp. 510-532, (2021); Kurachi M., CD8+ T cell exhaustion, Semin. Immunopathol, 41, pp. 327-337, (2019); Kalish R.S., Askenase P.W., Molecular mechanisms of CD8+ T cell–mediated delayed hypersensitivity: Implications for allergies, asthma, and autoimmunity, J. Allergy Clin. Immunol, 103, pp. 192-199, (1999); Bryant N., Muehling L.M., T-cell responses in asthma exacerbations, Ann. Allergy, Asthma Immunol, 129, pp. 709-718, (2022); Lourenco O., Fonseca A.M., Taborda-Barata L., Human CD8+ T Cells in Asthma: Possible Pathways and Roles for NK-Like Subtypes, Front. Immunol, 7, (2016); Li H., Wang H., Sokulsky L., Liu S., Yang R., Liu X., Zhou L., Li J., Huang C., Li F., Et al., Single-cell transcriptomic analysis reveals key immune cell phenotypes in the lungs of patients with asthma exacerbation, J. Allergy Clin. Immunol, 147, pp. 941-954, (2021); Habener A., Happle C., Grychtol R., Skuljec J., Busse M., Daluge K., Obernolte H., Sewald K., Braun A., Meyer-Bahlburg A., Et al., Regulatory B cells control airway hyperreactivity and lung remodeling in a murine asthma model, J. Allergy Clin. Immunol, 147, pp. 2281-2294.e7, (2021); Fang L., Sun Q., Roth M., Immunologic and Non-Immunologic Mechanisms Leading to Airway Remodeling in Asthma, Int. J. Mol. Sci, 21, (2020); Boonpiyathad T., Sozener Z.C., Satitsuksanoa P., Akdis C.A., Immunologic mechanisms in asthma, Semin. Immunol, 46, (2019); Newman R., Tolar P., Chronic calcium signaling in IgE+ B cells limits plasma cell differentiation and survival, Immunity, 54, (2021); Gould H.J., Sutton B.J., Beavil A.J., Beavil R.L., McCloskey N., Coker H.A., Fear D., Smurthwaite L., The biology of IGE and the basis of allergic disease, Annu. Rev. Immunol, 21, pp. 579-628, (2003); Turner D.P., Deng H., Houle T.T., Statistical Hypothesis Testing: Overview and Application, Headache: J. Head Face Pain, 60, pp. 302-308, (2020); Farell B., Hypothesis testing, attention, and ‘Same’-‘Different’ judgments, Cogn. Psychol, 132, (2022); Haug C.J., Drazen J.M., Artificial Intelligence and Machine Learning in Clinical Medicine, 2023, New Engl. J. Med, 388, pp. 1201-1208, (2023); Gottesman O., Johansson F., Komorowski M., Faisal A., Sontag D., Doshi-Velez F., Celi L.A., Guidelines for reinforcement learning in healthcare, Nat. Med, 25, pp. 16-18, (2019); Li D., Liu Z., Armaghani D.J., Xiao P., Zhou J., Novel ensemble intelligence methodologies for rockburst assessment in complex and variable environments, Sci. Rep, 12, (2022); Srivastava R., Kumar S., Kumar B., Classification model of machine learning for medical data analysis, Statistical Modeling in Machine Learning, pp. 111-132, (2023); Czarnecki W.M., Podlewska S., Bojarski A.J., Extremely Randomized Machine Learning Methods for Compound Activity Prediction, Molecules, 20, pp. 20107-20117, (2015)","J. Xu; Department of Clinical Laboratory, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102, China; email: jinxu_125@163.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","1010660X","","","37893483","English","Medicina","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85175259485"
"Hui C.Y.; Shenton A.V.; Martin C.; Weatherill D.; Moylan D.; Hayes M.; Rienda L.G.; Kinley E.; Eck S.; Pinnock H.","Hui, Chi Yan (57194330405); Shenton, Ann Victoria (59280272700); Martin, Claire (59278208800); Weatherill, David (57786941800); Moylan, Dianna (59280272800); Hayes, Morag (59279751700); Rienda, Laura Gonzalez (59281311300); Kinley, Emma (57239613600); Eck, Stefanie (57990220100); Pinnock, Hilary (6701815935)","57194330405; 59280272700; 59278208800; 57786941800; 59280272800; 59279751700; 59281311300; 57239613600; 57990220100; 6701815935","Patient and public involvement workshop to shape artificial intelligence-supported connected asthma self-management research","2024","PLOS Digital Health","3","5","e0000521","","","","0","10.1371/journal.pdig.0000521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201587102&doi=10.1371%2fjournal.pdig.0000521&partnerID=40&md5=6ea2795fa9d7327d2605fe7be5a3983d","Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; School of Psychology, Faculty of Health, Liverpool John Moore’s University, United Kingdom; Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University of Munich (TUM), Germany","Hui C.Y., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Shenton A.V., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Martin C., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Weatherill D., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Moylan D., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Hayes M., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Rienda L.G., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; Kinley E., School of Psychology, Faculty of Health, Liverpool John Moore’s University, United Kingdom; Eck S., Institute of General Practice and Health Services Research, TUM School of Medicine, Technical University of Munich (TUM), Germany; Pinnock H., Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom","Digital interventions with artificial intelligence (AI) can potentially support people with asthma to reduce the risk of exacerbation. Engaging patients throughout the development process is essential to ensure usability of the intervention for the end-users. Using our Connected for Asthma (C4A) intervention as an exemplar, we explore how patient involvement can shape a digital intervention. Seven Patient and Public Involvement (PPI) colleagues from the Asthma UK Centre for Applied Research participated in four advisory workshops to discuss how they would prefer to use/interact with AI to support living with their asthma, the benefit and caveats to use the AI that incorporated asthma monitoring and indoor/outdoor environmental data. Discussion focussed on the three most wanted use cases identified in our previous studies. PPI colleagues wanted AI to support data collection, remind them about self-management tasks, teach them about asthma environmental triggers, identify risk, and empower them to confidently look after their asthma whilst emphasising that AI does not replace clinicians. The discussion informed the key components in the next C4A interventions, including the approach to interacting with AI, the technology features and the research topics. Attendees highlighted the importance of considering health inequities, the presentation of data, and concerns about data accuracy, data privacy, security and ownership. We have demonstrated how patient roles can shift from that of ‘user’ (the traditional ‘tester’ of a digital intervention), to a co-design partner who shapes the next iteration of the intervention. Technology innovators should seek practical and feasible strategies to involve PPI colleagues throughout the development cycle of a digital intervention; supporting researchers to explore the barriers, concerns, enablers and advantages of implementing digital healthcare. © 2024 Hui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.","","","","","","","Medical Research Council, MRC; Tactuum Ltd; Confidence in Concept fund, (MRC-CIC7-71)","This work was supported by the Medical Research Council (Confidence in Concept fund (MRC-CIC7-71) to CYH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank you for all the PPI colleagues to contribute their views in the workshop; the University of Edinburgh IoT service (https://www.ed.ac.uk/information-services/iot), Mr Richard Jackaman, Mr Simon Chapple and Mr Matthew Hodson for providing the information about the latest IoT environmental sensor, and for arranging an indoor sensor to support the discussion in the workshop; and the Tactuum Ltd, Mr Vinh Tran and Mr Mark Buchner to support the connection between the Edinburgh LoRaWan IoT environment sensors and the C4A app. We also thank you for the funder, Medical Research Council (MRC) to support this work. The views expressed in this publication are those of the authors and not necessarily those of the MRC.","SIGN 158: British guideline on the management of asthma, (2019); Global strategy for asthma management and prevention 2023, (2023); Pinnock H, Parke HL, Panagioti M, Daines L, Pearce G, Taylor SJ, Et al., Systematic meta-review of supported self-management for asthma: a healthcare perspective, BMC medicine, 15, 1, pp. 1-32, (2017); WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, (2021); Pinnock H, Hui CY, van Boven JF., Implementation of digital home monitoring and management of respiratory disease, Current Opinion in Pulmonary Medicine, 29, 4, (2023); Ying W, Wimalasiri JS, Ray P, Chattopadhyay S, Wilson CS., An Ontology Driven Multi-Agent Approach to Integrated eHealth Systems, International Journal of e-Health and Medical Communications, 1, 1, pp. 28-40, (2010); Ndayishimiye C, Lopes H, Middleton J., A systematic scoping review of digital health technologies during COVID-19: a new normal in primary health care delivery, Health and Technology, 13, 2, pp. 273-284, (2023); Dal Mas F, Massaro M, Rippa P, Secundo G., The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda, Technovation, 123, (2023); Masterson D, Areskoug Josefsson K, Robert G, Nylander E, Kjellstrom S., Mapping definitions of co-production and co-design in health and social care: A systematic scoping review providing lessons for the future, Health Expectations, 25, 3, pp. 902-913, (2022); Involve patients, (2019); Baines R, Bradwell H, Edwards K, Stevens S, Prime S, Chatterjee A., Meaningful patient and public involvement in digital health innovation, implementation and evaluation: a systematic review, Health Expectations, 25, 4, pp. 1232-1245, (2022); Arias FD, Navarro M, Elfanagely Y, Elfanagely O., Biases in research studies, Translational Surgery, pp. 191-194, (2023); UK Standards for Public Involvement, (2019); Agyei-Manu E, Atkins N, Lee B, Rostron J, Dozier M, McQuillan R., The benefits, challenges, and best practice for patient and public involvement in evidence synthesis: A systematic review and thematic synthesis, Health Expectations, (2023); Hui CY, McKinstry B, Fulton O, Buchner M, Pinnock H., Patients’ and clinicians’ perceived trust in internet-of-things systems to support asthma self-management: qualitative interview study, JMIR mHealth and uHealth, 9, 7, (2021); Greenhalgh T, Hinton L, Finlay T, Macfarlane A, Fahy N, Chant A., Frameworks for supporting patient and public involvement in research: systematic review and co-design pilot, Health expectations, 22, 4, pp. 785-801, (2019); Hui CY, McKinstry B, Fulton O, Buchner M, Pinnock H., Patients’ and clinicians’ visions of a future internet-of-things system to support asthma self-management: mixed methods study, Journal of medical Internet research, 23, 4, (2021); Staniszewska S, Brett J, Simera I, Seers K, Mockford C, Entwistle A., GRIPP2 reporting checklists: tools to improve reporting of patient and public involvement in research, 358, (2017); Rogers EM, Singhal A, Quinlan MM., Diffusion of innovations, InAn integrated approach to communication theory and research, pp. 432-448, (2014); Young AT, Amara D, Bhattacharya A, Wei ML., Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review, The Lancet Digital Health, 3, 9, pp. e599-e611, (2021); Li F, Ruijs N, Lu Y., Ethics & AI: A systematic review on ethical concerns and related strategies for designing with AI in healthcare, AI, 4, 1, pp. 28-53, (2022); Wu X, Xiao L, Sun Y, Zhang J, Ma T, He L., A survey of human-in-the-loop for machine learning, Future Generation Computer Systems, 135, pp. 364-381, (2022); Mosqueira-Rey E, Hernandez-Pereira E, Alonso-Rios D, Bobes-Bascaran J, Fernandez-Leal A., Human-in-the-loop machine learning: A state of the art, Artificial Intelligence Review, 56, 4, pp. 3005-3054, (2023); Tang L, Li J, Fantus S., Medical artificial intelligence ethics: A systematic review of empirical studies, Digital Health, 9, (2023); Rizi MH, Seno SA., A systematic review of technologies and solutions to improve security and privacy protection of citizens in the smart city, Internet of Things, 20, (2022); Connected Things, (2023); UK GDPR guidance and resources, (2023); Indoor Air Quality Guidance: Assessment, Monitoring, Modelling and Mitigation, (2021); McCarron A, Semple S, Braban CF, Swanson V, Gillespie C, Price HD., Public engagement with air quality data: using health behaviour change theory to support exposure-minimising behaviours, Journal of Exposure Science & Environmental Epidemiology, 28, pp. 1-1, (2022); Hunter RF, Rodgers SE, Hilton J, Clarke M, Garcia L, Lovell R., GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health–a new initiative, Wellcome Open Research, 7, (2022); Shearer E, Cho M, Magnus D., Regulatory, social, ethical, and legal issues of artificial intelligence in medicine, Artificial Intelligence in Medicine, pp. 457-477, (2021); Chen Y, Hosin AA, George MJ, Asselbergs FW, Shah AD., Digital technology and patient and public involvement (PPI) in routine care and clinical research—A pilot study, Plos one, 18, 2, (2023)","H. Pinnock; Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, United Kingdom; email: hilary.pinnock@ed.ac.uk","","Public Library of Science","","","","","","27673170","","","","English","PLOS Digit. Health","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85201587102"
"Abineza C.; Balas V.E.; Nsengiyumva P.","Abineza, Claudia (57754302300); Balas, Valentina E. (9279071000); Nsengiyumva, Philibert (57192167208)","57754302300; 9279071000; 57192167208","Deep Learning Model for Early Subsequent COPD Exacerbation Prediction","2023","Studies in Informatics and Control","32","3","","99","107","8","0","10.24846/v32i3y202309","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184873458&doi=10.24846%2fv32i3y202309&partnerID=40&md5=e0a939560f14e5147b9cb4ec27d51321","African Center of Excellence in Internet of Things, University of Rwanda, KN 67 Street, Kigali, 3900, Rwanda; Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, 77 Revoluţiei Boulevard, Arad, 310130, Romania; Academy of Romanian Scientists, 3 Ilfov Street, Bucharest, 050044, Romania","Abineza C., African Center of Excellence in Internet of Things, University of Rwanda, KN 67 Street, Kigali, 3900, Rwanda; Balas V.E., Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, 77 Revoluţiei Boulevard, Arad, 310130, Romania, Academy of Romanian Scientists, 3 Ilfov Street, Bucharest, 050044, Romania; Nsengiyumva P., African Center of Excellence in Internet of Things, University of Rwanda, KN 67 Street, Kigali, 3900, Rwanda","Chronic Obstructive Pulmonary Disease (COPD) patients have a burden of frequent exacerbations during daily life. Automatic solutions for early COPD exacerbation prediction could promote COPD healthcare and reduce hospital readmissions. Previous works didn’t consider symptoms change patterns which might not be effective for timely and personalized therapy. When using a pulse oximeter for COPD diagnosis, arterial oxygen saturation (SPO2) levels are targeted, depending on whether a patient is stable, hospitalized, or being recovered from exacerbation states. However, the timely management of COPD is a problem, due to the manual monitoring of individual measurements. This research investigates whether the Long Short-Term Memory (LSTM) model can predict early COPD subsequent exacerbation by prompting therapy depending on COPD symptoms patterns and SPO2 burden levels. Time-stamped Electronic Health Record (EHR) from COPD patients’ data time series were examined, over subsequent days, with the aim to evaluate a short-time window which a monitoring system for an accurate and early prediction of subsequent exacerbations could be based on. Therefore, the LSTM model was evaluated by varying a window of one to six prior time-steps, to forecast a subsequent day. The window of 1 day showed a good performance of a training accuracy of 87%, a testing accuracy of 85% and an area under the curve (AUC) of 0.83, by employing the training and testing model on only 54 patients. © 2012-2023. All rights reserved","Data-time series; Early prediction; LSTM; Monitoring system; Subsequent COPD exacerbation","","","","","","African Centre of Excellence in the Internet of Things; Artificial Intelligence for Development in Africa; Styrelsen för Internationellt Utvecklingssamarbete, Sida; International Development Research Centre, IDRC","The research reported in this paper has been funded firstly, by The International Development Research Centre (IDRC) and The Swedish International Development Cooperation Agency (SIDA), under ‘The Artificial Intelligence for Development in Africa (AI4D Africa) program with the management of The African Center for Technology Studies (ACTS)’ and secondly by The African Centre of Excellence in the Internet of Things (ACEIoT). Kindly thanks are also given to Prof. Dr. Peter Lucas (University of Twente, Enschede, the Netherlands), for his motivational ideas during this research.","Aaron S. D., COPD Exacerbations: Predicting the Future from the Recent Past, American Journal of Respiratory and Critical Care Medicine, 179, 5, pp. 335-336, (2009); Athilakshmi R., Jacob S. G., Rajavel R., Automatic Detection of Biomarker Genes through Deep Learning Techniques: A Research Perspective, Studies in Informatics and Control, 32, 2, pp. 51-61, (2023); Balas E. V., Sanjiban S. R., Dharmendra S., Pijush S., Handbook of Deep Learning Applications, Smart Innovation, Systems and Technologies, 136, (2019); Boer L., van der Heijden M., van Kuijk N., Lucas P., Vercoulen J., Assendelft W., Bischof E., Schermer T., Validation of ACCESS: an automated tool to support self-management of COPD exacerbations, International Journal of Chronic Obstructive Pulmonary Disease, 13, pp. 3255-3267, (2018); Buekers J., Theunis J., De Boever P., Vaes A. W., Koopman M., Janssen E. V., Wouters E. F., Spruit M. A., Aerts J., Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients with Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study, JMIR mHealth and uHealth, 7, 6, (2019); Claxton S., Porter P., Brisbane J., Bear N., Wood J., Peltonen V., Della P., Smith C., Abeyratne U., Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis, NPJ Digital Medicine, 4, 1, (2021); Treatment and Medications for COPD, (2020); Dumitrescu C.-M., Dumitrache I., Combining Deep Learning Technologies with Multi-Level Gabor Features for Facial Recognition in Biometric Automated Systems, Studies in Informatics and Control, 28, 2, pp. 221-230, (2019); Esteban C., Moraza J., Sancho F., Aburto M., Aramburu A, Goiria B., Garcia-Loizaga A., Capelastegui A., Machine learning for COPD exacerbation prediction, European Respiratory Journal, 46, 59, (2015); Fernandez-Granero M. A., Sanchez-Morillo D., Leon-Jimenez A., Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD, Sensors, 15, 10, pp. 26978-26996, (2015); Fernandez-Granero M. A., Sanchez-Morillo D., Leon-Jimenez A., An artificial intelligence approach to early predict symptom-based exacerbations of COPD, Biotechnology & Biotechnological Equipment, 32, 3, pp. 778-784, (2018); Fernandez-Granero M. A., Sanchez-Morillo D., Leon-Jimenez A., Crespo L. F., Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms, Bio-Medical Materials and Engineering, 24, 6, pp. 3825-3832, (2014); At-A-Glance Outpatient Management Reference for Chronic Obstructive Pulmonary Disease (COPD), (2017); Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Pulmonary Disease, (2020); Huang W. C., Wu M. F., Chen H. C., Hsu J. Y., Features of COPD patients by comparing CAT with mMRC: a retrospective, cross-sectional study, NPJ Primary Care Respiratory Medicine, 25, (2015); Hurst J. R., Donaldson G. C., Quint J. K., Goldring J. J. P., Baghai-Ravary R., Wedzicha J. A., Temporal clustering of exacerbations in chronic obstructive pulmonary disease, American Journal of Respiratory and Critical Care Medicine, 179, 5, pp. 369-374, (2009); Kerkhof M., Freeman D., Jones R., Chisholm A., Price D., Predicting frequent COPD exacerbations using primary care data, International Journal of Chronic Obstructive Pulmonary Disease, 10, pp. 2439-2450, (2015); Liu D., Peng S., Zhang J., Bai S., Liu H. X., Qu J. M., Prediction of short term re-exacerbation in patients with acute exacerbation of chronic obstructive pulmonary disease, International Journal of Chronic Obstructive Pulmonary Disease, 10, pp. 1265-1273, (2015); Liu M., Stella F., Hommersom A., Lucas P. J. F., Boer L., Bischof E., A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity, Artificial Intelligence in Medicine, 95, pp. 104-117, (2019); Min X., Yu B., Wang F., Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD, Scientifc Reports, 9, 1, (2019); Nunavath V., Goodwin M., Fidje J. T., Moe C. E., Deep Neural Networks for Prediction of Exacerbations of Patients with Chronic Obstructive Pulmonary Disease, Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, 893, pp. 217-228, (2018); Polsky M. B., Moraveji N., Early identification and treatment of COPD exacerbation using remote respiratory monitoring, Respiratory Medicine Case Reports, 34, 5, (2021); Press V. G., Myers L. C., Feemster L. C., Preventing COPD Readmissions Under the Hospital Readmissions Reduction Program: How Far Have We Come?, Chest, 159, 3, pp. 996-1006, (2020); Quintana J. M., Anton-Ladislao A., Orive M., Aramburu A., Iriberri M., Sanchez R., Jimenez-Puente A., de-Miguel-Diez J., Esteban C., Predictors of short-term COPD readmission, Internal and Emergency Medicine, 17, 5, pp. 1481-1490, (2022); Shah S. A., Velardo C., Farmer A., Tarassenko L., Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital Health system, Journal of Medical Internet Research, 19, 3, (2017); Sun J., Ma X., Kazi M., Comparison of Decline Curve Analysis DCA with Recursive Neural Networks RNN for Production Forecast of Multiple Wells, Proceedings of the SPE Western Regional Meeting, (2018); Swaminathan S., Qirko K., Smith T., Corcoran E., Wysham N. G., Bazaz G., Kappel G., Gerber A. N., A machine learning approach to triaging patients with chronic obstructive pulmonary disease, PLOS ONE, 12, 11, (2017); Clinical use of Pulse Oximetry Pocket Reference 2010, (2010); van der Heijden M., Lucas P. J., Lijnse B., Heijdra Y. F., Schermer T. R., An autonomous mobile system for the management of COPD, Journal of Biomedical Informatics, 46, 3, pp. 458-469, (2013); Van Houdt G., Mosquera C., Napoles G., A Review on the Long Short-Term Memory Model, Artificial Intelligence Review, 53, 8, pp. 5929-5955, (2020); Varsamopoulos S., Bertels K., Almudever C. G., Designing neural network-based decoders for surface codes, Quantum Machine Intelligence, 2, pp. 1-12, (2018); Wageck B., Cox N. S., Holland A. E., Recovery Following Acute Exacerbations of Chronic Obstructive Pulmonary Disease – A Review, COPD: Journal of Chronic Obstructive Pulmonary Disease, 16, 1, pp. 93-103, (2019); Wu C. T., Li G. H., Huang C. T., Cheng Y. C., Chen C. H., Chien J. Y., Kuo P. H., Kuo L. C., Lai F., Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study, JMIR mHealth and uHealth, 9, 5, (2021); Wu Y. K., Lan C. C., Tzeng I. S., Wu C. W., The COPD-readmission (CORE) score: A novel prediction model for one-year chronic obstructive pulmonary disease readmissions, Journal of the Formosan Medical Association, 120, 3, pp. 1005-1013, (2020); Yawn B. P., Mintz M. L., Doherty D. E., GOLD in Practice: Chronic Obstructive Pulmonary Disease Treatment and Management in the Primary Care Setting, International Journal of Chronic Obstructive Pulmonary Disease, 16, pp. 289-299, (2021)","V.E. Balas; Academy of Romanian Scientists, Bucharest, 3 Ilfov Street, 050044, Romania; email: balas@drbalas.ro","","National Institute for R and D in Informatics","","","","","","12201766","","","","English","Stud. Inform. Control","Article","Final","All Open Access; Bronze Open Access","Scopus","2-s2.0-85184873458"
"Moztarzadeh O.; Liska J.; Liskova V.; Skalova A.; Topolcan O.; Jamshidi A.; Hauer L.","Moztarzadeh, Omid (57190307831); Liska, Jan (57521598400); Liskova, Veronika (57381158000); Skalova, Alena (55942644700); Topolcan, Ondrej (7005911152); Jamshidi, Alireza (57218589959); Hauer, Lukas (55875669800)","57190307831; 57521598400; 57381158000; 55942644700; 7005911152; 57218589959; 55875669800","Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning","2023","Clinics and Practice","13","6","","1335","1351","16","0","10.3390/clinpract13060120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180639408&doi=10.3390%2fclinpract13060120&partnerID=40&md5=343b9258fe0157e6ec6edacb8cae22d2","Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, Pilsen, 30460, Czech Republic; Department of Anatomy, Faculty of Medicine in Pilsen, Charles University, Pilsen, 32300, Czech Republic; Sikl’s Department of Pathology, Faculty of Medicine in Pilsen, Charles University, Ed. Beneše 13, Pilsen, 30599, Czech Republic; Biopticka Laboratory, Mikulasske namesti 628, Pilsen, 32600, Czech Republic; Central Laboratory of Immunoanalysis, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Ed. Beneše 13, Pilsen, 30599, Czech Republic; Dentistry School, Babol University of Medical Sciences, Babol, 4717647745, Iran","Moztarzadeh O., Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, Pilsen, 30460, Czech Republic, Department of Anatomy, Faculty of Medicine in Pilsen, Charles University, Pilsen, 32300, Czech Republic; Liska J., Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, Pilsen, 30460, Czech Republic; Liskova V., Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, Pilsen, 30460, Czech Republic; Skalova A., Sikl’s Department of Pathology, Faculty of Medicine in Pilsen, Charles University, Ed. Beneše 13, Pilsen, 30599, Czech Republic, Biopticka Laboratory, Mikulasske namesti 628, Pilsen, 32600, Czech Republic; Topolcan O., Central Laboratory of Immunoanalysis, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Ed. Beneše 13, Pilsen, 30599, Czech Republic; Jamshidi A., Dentistry School, Babol University of Medical Sciences, Babol, 4717647745, Iran; Hauer L., Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Alej Svobody 80, Pilsen, 30460, Czech Republic","Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white–red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies. © 2023 by the authors.","candidosis/chronic hyperplastic candidosis; chronic mucosal lesions; digital health; leukoplakia; machine learning; oral intraepithelial neoplasia; oral squamous cell carcinoma","corticosteroid; adult; anatomical location; anxiety; Article; artificial neural network; asthma; buccal mucosa; Candida albicans; candidiasis; cellular immunity; chronic hyperplastic candidiasis; clinical decision making; colony forming unit; decision tree; diabetes mellitus; diagnostic accuracy; diagnostic test accuracy study; female; flow rate; fuzzy system; gastroesophageal reflux; health service; histology; human; hypertension; intraepithelial neoplasia; keratinization; leukoplakia; lichen planus; machine learning; male; mean absolute error; mean squared error; middle aged; mouth squamous cell carcinoma; oral mucosal disease; personalized medicine; pH; retrospective study; risk factor; root mean squared error; signal processing; smoking","","","CHROMagar","","Ministerstvo Zdravotnictví Ceské Republiky, MZCR, (00669806)","This study was supported by the grant of the Ministry of Health of the Czech Republic–Conceptual Development of Research Organization (Faculty Hospital in Pilsen–FNPl, 00669806).","Lamey P.J., Darwazeh A., Muirhead J., Rennie J., Samaranayake L., MacFarlane T., Chronic hyperplastic candidosis and secretor status, J. Oral Pathol. Med, 20, pp. 64-67, (1991); Zhang W., Wu S., Wang X., Wei P., Yan Z., Combination treatment with photodynamic therapy and laser therapy in chronic hyperplastic candidiasis: A case report, Photodiagnosis Photodyn. Ther, 38, (2022); Williams A., Rogers H., Williams D., Wei X.-Q., Farnell D., Wozniak S., Jones A., Higher Number of EBI3 Cells in Mucosal Chronic Hyperplastic Candidiasis May Serve to Regulate IL-17-Producing Cells, J. Fungi, 7, (2021); Zhang W., Wu S., Wang X., Gao Y., Yan Z., Malignant Transformation and Treatment Recommendations of Chronic Hyperplastic Candidiasis—A Six-year Retrospective Cohort Study, Mycoses, 64, pp. 1422-1428, (2021); Li B., Fang X., Hu X., Hua H., Wei P., Successful treatment of chronic hyperplastic candidiasis with 5-aminolevulinic acid photodynamic therapy: A case report, Photodiagnosis Photodyn. Ther, 37, (2022); Farah C., Concurrent chronic hyperplastic candidosis and oral lichenoid lesion as adverse events of secukinumab therapy, Aust. Dent. J, 66, pp. 340-345, (2021); Sitheeque M., Samaranayake L., Chronic hyperplastic candidosis/candidiasis (candidal leukoplakia), Crit. Rev. Oral Biol. Med, 14, pp. 253-267, (2003); Pina P.S.S., Custodio M., Sugaya N.N., de Sousa S.C.O.M., Histopathologic aspects of the so-called chronic hyperplastic candidiasis: An analysis of 36 cases, J. Cutan. Pathol, 48, pp. 66-71, (2021); Di Cosola M., Cazzolla A.P., Charitos I.A., Ballini A., Inchingolo F., Santacroce L., Candida albicans and oral carcinogenesis. A brief review, J. Fungi, 7, (2021); Sharma A., Oral candidiasis: An opportunistic infection: A review, Int. J. Appl. Dent. Sci, 5, pp. 23-27, (2019); Lorenzo-Pouso A.I., Perez-Jardon A., Caponio V.C.A., Spirito F., Chamorro-Petronacci C.M., Alvarez-Calderon-Iglesias O., Gandara-Vila P., Lo Muzio L., Perez-Sayans M., Oral chronic hyperplastic candidiasis and its potential risk of malignant transformation: A systematic review and prevalence meta-analysis, J. Fungi, 8, (2022); Achararit P., Manaspon C., Jongwannasiri C., Phattarataratip E., Osathanon T., Sappayatosok K., Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks, Eur. J. Dent, (2023); Fu C., Zhang X., Veri A.O., Iyer K.R., Lash E., Xue A., Yan H., Revie N.M., Wong C., Lin Z.-Y., Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets, Nat. Commun, 12, (2021); Varoquaux G., Cheplygina V., Machine learning for medical imaging: Methodological failures and recommendations for the future, NPJ Digit. Med, 5, (2022); Daneshfar F., Jamshidi M.B., An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse, Neural Netw, 163, pp. 108-121, (2023); Shehab M., Abualigah L., Shambour Q., Abu-Hashem M.A., Shambour M.K.Y., Alsalibi A.I., Gandomi A.H., Machine learning in medical applications: A review of state-of-the-art methods, Comput. Biol. Med, 145, (2022); Keshmiri Neghab H., Jamshidi M., Keshmiri Neghab H., Digital twin of a magnetic medical microrobot with stochastic model predictive controller boosted by machine learning in cyber-physical healthcare systems, Information, 13, (2022); Jamshidi M.B., Jamshidi M., Rostami S., An intelligent approach for nonlinear system identification of a li-ion battery, Proceedings of the 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 98-103; Jamshidi M.B., Alibeigi N., Lalbakhsh A., Roshani S., An ANFIS approach to modeling a small satellite power source of NASA, Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), pp. 459-464; Moztarzadeh O., Jamshidi M., Sargolzaei S., Jamshidi A., Baghalipour N., Malekzadeh Moghani M., Hauer L., Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer, Bioengineering, 10, (2023); Jamshidi M.B., Daneshfar F., A hybrid echo state network for hypercomplex pattern recognition, classification, and big data analysis, Proceedings of the 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), pp. 007-012; Li X., Li W., Xu Y., Human age prediction based on DNA methylation using a gradient boosting regressor, Genes, 9, (2018); Jamshidi M.B., Ebadpour M., Moghani M.M., Cancer digital twins in metaverse, Proceedings of the 2022 20th International Conference on Mechatronics-Mechatronika (ME), pp. 1-6; Jamshidi M., Moztarzadeh O., Jamshidi A., Abdelgawad A., El-Baz A.S., Hauer L., Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning, Future Internet, 15, (2023); Jamshidi M.B., Talla J., Lalbakhsh A., Sharifi-Atashgah M.S., Sabet A., Peroutka Z., A conceptual deep learning framework for COVID-19 drug discovery, Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 00030-00034; Shafiei A., Jamshidi M., Khani F., Talla J., Peroutka Z., Gantassi R., Baz M., Cheikhrouhou O., Hamam H., A hybrid technique based on a genetic algorithm for fuzzy multiobjective problems in 5G, internet of things, and mobile edge computing, Math. Probl. Eng, 2021, (2021); Moztarzadeh O., Jamshidi M., Sargolzaei S., Keikhaee F., Jamshidi A., Shadroo S., Hauer L., Metaverse and Medical Diagnosis: A Blockchain-Based Digital Twinning Approach Based on MobileNetV2 Algorithm for Cervical Vertebral Maturation, Diagnostics, 13, (2023); Prettenhofer P., Louppe G., Gradient boosted regression trees in scikit-learn, PyData 2014, (2014); Keprate A., Ratnayake R.C., Using gradient boosting regressor to predict stress intensity factor of a crack propagating in small bore piping, Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1331-1336; Khan M.S.I., Islam N., Uddin J., Islam S., Nasir M.K., Water quality prediction and classification based on principal component regression and gradient boosting classifier approach, J. King Saud Univ.-Comput. Inf. Sci, 34, pp. 4773-4781, (2022); Khalaj O., Jamshidi M.B., Saebnoori E., Masek B., Stadler C., Svoboda J., Hybrid machine learning techniques and computational mechanics: Estimating the dynamic behavior of oxide precipitation hardened steel, IEEE Access, 9, pp. 156930-156946, (2021); Jamshidi M., Yahya S.I., Nouri L., Hashemi-Dezaki H., Rezaei A., Chaudhary M.A., A High-Efficiency Diplexer for Sustainable 5G-Enabled IoT in Metaverse Transportation System and Smart Grids, Symmetry, 15, (2023); Jamshidi M., Yahya S.I., Nouri L., Hashemi-Dezaki H., Rezaei A., Chaudhary M.A., A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse, Sensors, 23, (2023); Jamshidi M., Dehghaniyan Serej A., Jamshidi A., Moztarzadeh O., The Meta-Metaverse: Ideation and Future Directions, Future Internet, 15, (2023)","O. Moztarzadeh; Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, Pilsen, Alej Svobody 80, 30460, Czech Republic; email: omid.moztarzadeh@lfp.cuni.cz","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20397283","","","","English","Clin. Pract.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85180639408"
"Zhu Y.; Liu Y.; Wang Q.; Niu S.; Wang L.; Cheng C.; Chen X.; Liu J.; Zhao S.","Zhu, Yanfei (58666951800); Liu, Yuan (57215927668); Wang, Qi (59514555400); Niu, Sen (58315349300); Wang, Lanyu (58182376500); Cheng, Chao (56734887700); Chen, Xujin (58667420900); Liu, Jinhui (57193916921); Zhao, Songyun (57929940400)","58666951800; 57215927668; 59514555400; 58315349300; 58182376500; 56734887700; 58667420900; 57193916921; 57929940400","Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis","2023","Journal of Cancer Research and Clinical Oncology","149","19","","17479","17493","14","0","10.1007/s00432-023-05472-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175090751&doi=10.1007%2fs00432-023-05472-w&partnerID=40&md5=a17fd7f204e8c561824ce02d17e24af2","Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China; Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China","Zhu Y., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Liu Y., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Wang Q., Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, China; Niu S., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Wang L., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Cheng C., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Chen X., Wuxi Medical Center of Nanjing Medical University, Wuxi, China; Liu J., Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; Zhao S., Wuxi Medical Center of Nanjing Medical University, Wuxi, China","Introduction: Osteoporosis that emerges subsequent to gastrectomy poses a significant threat to the long-term health of patients. The primary objective of this investigation was to formulate a machine learning algorithm capable of identifying substantial preoperative, intraoperative, and postoperative risk factors. This algorithm, in turn, would enable the anticipation of osteoporosis occurrence after gastrectomy. Methods: This research encompassed a cohort of 1125 patients diagnosed with gastric cancer, including 108 individuals with low bone density or osteoporosis. A total of 40 distinct variables were collected, comprising patient demographics, pertinent medical history, medication records, preoperative examination attributes, surgical procedure specifics, and intraoperative details. Four distinct machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN)—were employed to establish the predictive model. Evaluation of the models involved receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Shapley additive explanation (SHAP) was employed for visualization and analysis. Results: Among the four prediction models employed, the XGBoost algorithm demonstrated exceptional performance. The ROC analysis yielded excellent predictive accuracy, showcasing area under the curve (AUC) values of 0.957 and 0.896 for training and validation sets, respectively. The calibration curve further confirmed the robust predictive capacity of the XGBoost model. The DCA demonstrated a notably higher benefit rate for patients undergoing intervention based on the XGBoost model. Moreover, the AUC value of 0.73 for the external validation set indicated favorable extrapolation of the XGBoost prediction model. SHAP analysis outcomes unveiled numerous high-risk factors for osteoporosis development after gastrectomy, including a history of chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), hypoproteinemia, postoperative neutrophil-to-lymphocyte ratio (NLR) exceeding 3, steroid usage history, advanced age, and absence of calcitonin use. Conclusion: The osteoporosis prediction model derived through the XGBoost machine learning algorithm in this study displays remarkable predictive precision and carries significant clinical applicability. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Gastrectomy; Gastric tumor; Machine learning; Osteoporosis; Risk factor","Algorithms; Bone Diseases, Metabolic; Gastrectomy; Humans; Machine Learning; Osteoporosis; calcitonin; aged; area under the curve; Article; bone density; cancer patient; chronic obstructive lung disease; clinical outcome; cohort analysis; controlled study; demographics; diagnostic accuracy; diagnostic test accuracy study; female; gastrectomy; human; hypoproteinemia; inflammatory bowel disease; k nearest neighbor; learning algorithm; machine learning; major clinical study; male; medical history; neutrophil lymphocyte ratio; osteoporosis; preoperative evaluation; random forest; receiver operating characteristic; retrospective study; risk factor; sensitivity and specificity; stomach cancer; support vector machine; algorithm; clinical trial; gastrectomy; machine learning; metabolic bone disease; multicenter study; osteoporosis","","calcitonin, 12321-44-7, 21215-62-3, 9007-12-9","","","Wuxi Taihu Lake Talent Plan, (2020THRC-DJ-SNW)","This work was supported by Wuxi Taihu Lake Talent Plan, Supports for Leading Talents in Medical and Health Profession (2020THRC-DJ-SNW). ","Agidigbi T.S., Kim C., Reactive oxygen species in osteoclast differentiation and possible pharmaceutical targets of ros-mediated osteoclast diseases, Int J Mol Sci, 20, 14, (2019); An Y., Zhang H., Wang C., Jiao F., Xu H., Wang X., Et al., Activation of Ros/Mapks/Nf-Κb/Nlrp3 and Inhibition of Efferocytosis in Osteoclast-Mediated Diabetic Osteoporosis, Faseb J, 33, 11, pp. 12515-12527, (2019); Arceo-Mendoza R.M., Camacho P.M., Postmenopausal osteoporosis: latest guidelines, Endocrinol Metab Clin North Am, 50, 2, pp. 167-178, (2021); Azuma Y., Kaji K., Katogi R., Takeshita S., Kudo A., Tumor necrosis factor-alpha induces differentiation of and bone resorption by osteoclasts, J Biol Chem, 275, 7, pp. 4858-4864, (2000); Balyen L., Peto T., Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology, Asia Pac J Ophthalmol (phila), 8, 3, pp. 264-272, (2019); Binkley N., Bolognese M., Sidorowicz-Bialynicka A., Vally T., Trout R., Miller C., Et al., A phase 3 trial of the efficacy and safety of oral recombinant calcitonin: the oral calcitonin in postmenopausal osteoporosis (Oracal) trial, J Bone Miner Res, 27, 8, pp. 1821-1829, (2012); Buckley L., Guyatt G., Fink H.A., Cannon M., Grossman J., Hansen K.E., Et al., American college of rheumatology guideline for the prevention and treatment of glucocorticoid-induced osteoporosis, Arthritis Rheumatol, 69, 8, pp. 1521-1537, (2017); Delitala A.P., Scuteri A., Doria C., Thyroid Hormone Diseases and Osteoporosis, 9, 4, (2020); den Uyl D., Bultink I.E., Lems W.F., Advances in glucocorticoid-induced osteoporosis, Curr Rheumatol Rep, 13, 3, pp. 233-240, (2011); Ferizi U., Honig S., Chang G., Artificial intelligence, osteoporosis and fragility fractures, Curr Opin Rheumatol, 31, 4, pp. 368-375, (2019); Gatta A., Verardo A., Bolognesi M., Hypoalbuminemia, Intern Emerg Med, 7, pp. S193-S199, (2012); Gregson C.L., Dennison E.M., Compston J.E., Adami S., Adachi J.D., Anderson, Disease-specific perception of fracture risk and incident fracture rates: Glow cohort study, Osteoporos Int, 25, 1, pp. 85-95, (2014); Grover M.L., Edwards F.D., Chang Y.H., Cook C.B., Behrens M.C., Dueck A.C., Fracture risk perception study: patient self-perceptions of bone health often disagree with calculated fracture risk, Womens Health Issues, 24, 1, pp. e69-e75, (2014); Ito A., Takeda M., Yoshimura T., Komatsu T., Ohno T., Kuriyama H., Et al., Anti-hyperalgesic effects of calcitonin on neuropathic pain interacting with its peripheral receptors, Mol Pain, 8, (2012); Johnston C.B., Dagar M., Osteoporosis in older adults, Med Clin North Am, 104, 5, pp. 873-884, (2020); Kanis J.A., Cooper C., Rizzoli R., Reginster J.Y., European guidance for the diagnosis and management of osteoporosis in postmenopausal women, Osteoporos Int, 30, 1, pp. 3-44, (2019); Karimi P., Islami F., Anandasabapathy S., Freedman N.D., Kamangar F., Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention, Cancer Epidemiol Biomarkers Prev, 23, 5, pp. 700-713, (2014); Lal R.A., Hoffman A.R., Perspectives on long-acting growth hormone therapy in children and adults, Arch Endocrinol Metab, 63, 6, pp. 601-607, (2019); Li S., Chen B., Chen H., Hua Z., Shao Y., Yin H., Et al., Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning, PLoS ONE, 16, 9, (2021); Liu J., Zhang D., Cao Y., Zhang H., Li J., Xu J., Et al., Screening of crosstalk and pyroptosis-related genes linking periodontitis and osteoporosis based on bioinformatics and machine learning, Front Immunol, 13, (2022); Machlowska J., Baj J., Sitarz M., Maciejewski R., Sitarz R., Gastric cancer: Epidemiology, risk factors, classification, genomic characteristics and treatment strategies, Int J Mol Sci, 21, 11, (2020); Munoz-Torres M., Alonso G., Raya M.P., Calcitonin therapy in osteoporosis, Treat Endocrinol, 3, 2, pp. 117-132, (2004); Nakamura T., Imai Y., Matsumoto T., Sato S., Takeuchi K., Igarashi K., Et al., Estrogen prevents bone loss via estrogen receptor alpha and induction of fas ligand in osteoclasts, Cell, 130, 5, pp. 811-823, (2007); Pinto D., Alshahrani M., Chapurlat R., Chevalley T., Dennison E., Camargos B.M., Et al., The Global approach to rehabilitation following an osteoporotic fragility fracture: a review of the rehabilitation working group of the international osteoporosis foundation (Iof) committee of scientific advisors, Osteoporos Int, 33, 3, pp. 527-540, (2022); Raisz L.G., Pathogenesis of osteoporosis: concepts, conflicts, and prospects, J Clin Invest, 115, 12, pp. 3318-3325, (2005); Semenov V.V., Kriachkova L.V., Shestakova N., Khanov V., Donchenko H., Balashova O., Et al., Gastric cancer epidemiology from 2009 to 2019 in Dnipro Region, Ukraine. Cancer Epidemiol, 82, (2023); Smets J., Shevroja E., Hugle T., Leslie W.D., Hans D., Machine learning solutions for osteoporosis-a review, J Bone Miner Res, 36, 5, pp. 833-851, (2021); Spada F., Barnes T.M., Greive K.A., Comparative safety and efficacy of topical mometasone furoate with other topical corticosteroids, Australas J Dermatol, 59, 3, pp. e168-e174, (2018); Trovas G.P., Lyritis G.P., Galanos A., Raptou P., Constantelou E., A randomized trial of nasal spray salmon calcitonin in men with idiopathic osteoporosis: effects on bone mineral density and bone markers, J Bone Miner Res, 17, 3, pp. 521-527, (2002); Tu K.N., Lie J.D., Wan C.K.V., Cameron M., Austel A.G., Nguyen J.K., Et al., Osteoporosis: a review of treatment options, P t, 43, 2, pp. 92-104, (2018); Udagawa N., Koide M., Nakamura M., Nakamichi Y., Yamashita T., Uehara S., Et al., Osteoclast differentiation by Rankl and Opg signaling pathways, J Bone Miner Metab, 39, 1, pp. 19-26, (2021); Wang L., Heckmann B.L., Yang X., Long H., Osteoblast autophagy in glucocorticoid-induced osteoporosis, J Cell Physiol, 234, 4, pp. 3207-3215, (2019); Wang M., Zhou H., Cui W., Wang Z., Zhu G., Chen X., Et al., Nomogram to predict cadmium-induced osteoporosis and fracture in a chinese female population, Biol Trace Elem Res, 199, 11, pp. 4028-4035, (2021); Watts N.B., Adler R.A., Bilezikian J.P., Drake M.T., Eastell R., Orwoll E.S., Et al., Osteoporosis in men: an endocrine society clinical practice guideline, J Clin Endocrinol Metab, 97, 6, pp. 1802-1822, (2012); Weinstein R.S., Jilka R.L., Parfitt A.M., Manolagas S.C., Inhibition of osteoblastogenesis and promotion of apoptosis of osteoblasts and osteocytes by glucocorticoids. Potential mechanisms of their deleterious effects on bone, J Clin Invest, 102, 2, pp. 274-282, (1998); Yokota K., Sato K., Miyazaki T., Kitaura H., Kayama H., Miyoshi F., Et al., Combination of tumor necrosis factor α and interleukin-6 induces mouse osteoclast-like cells with bone resorption activity both in vitro and in vivo, Arthritis Rheumatol, 66, 1, pp. 121-129, (2014)","S. Zhao; Wuxi Medical Center of Nanjing Medical University, Wuxi, China; email: 2021122190@stu.njmu.edu.cn; J. Liu; Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China; email: jinhuiliu@njmu.edu.cn","","Springer Science and Business Media Deutschland GmbH","","","","","","01715216","","JCROD","37897658","English","J. Cancer Res. Clin. Oncol.","Article","Final","","Scopus","2-s2.0-85175090751"
"Chen X.; Wang X.; Huang S.; Luo W.; Luo Z.; Chen Z.","Chen, Xiaodong (26030199300); Wang, Xiangyuan (58965050700); Huang, Shangqing (58965591800); Luo, Wenxuan (58965322900); Luo, Zebin (57188739427); Chen, Zipan (58965591900)","26030199300; 58965050700; 58965591800; 58965322900; 57188739427; 58965591900","Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics","2024","Journal of Asthma and Allergy","17","","","291","303","12","0","10.2147/JAA.S448064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189134657&doi=10.2147%2fJAA.S448064&partnerID=40&md5=c36fbb02fe92edf29804e1d08d04c3c8","Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Health Management Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China","Chen X., Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Wang X., Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Huang S., Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Luo W., Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Luo Z., Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; Chen Z., Health Management Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China","Objective: To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA). Methods: Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve. Results: A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2–86 years old, mean: 53.89 ±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4–79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808–0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608–0.976), 0.792 and 0.80, respectively. Conclusion: The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients. © 2024 Chen et al.","BA; bronchial asthma; computed tomography; CT; Radiomics","adolescent; adult; aged; area under the curve; Article; artificial intelligence; asthma; calibration; cancer staging; child; computer assisted tomography; controlled study; diagnostic test accuracy study; feature extraction; female; human; image quality; least absolute shrinkage and selection operator; logistic regression analysis; major clinical study; male; radiomics; receiver operating characteristic; retrospective study; sensitivity and specificity; supine position; training","","","","","Guangdong Medical University, (LCYJ2020B010); Zhanjiang Science and Technology Development Special Fund, (2019A01026, 2020A01024); University Affiliated Hospital, (BJ201521)","The work was supported by the Clinical Research Project of the Affiliated Hospital of Guangdong Medical University (LCYJ2020B010); Zhanjiang Science and Technology Development Special Fund Competitive Allocation Project (2019A01026; 2020A01024); Guangdong Medical University Doctoral Fund of the University Affiliated Hospital (BJ201521).","Reddel Helen K, Bacharier Leonard B, Bateman Eric D, Et al., Global initiative for asthma strategy 2021. executive summary and rationale for key changes, Arch Bronconeumol, 58, 1, pp. 35-51, (2022); Thomas R, Spagnolo P, Pierre-Olivier B, Et al., Diagnosis and management of asthma-the Swiss guidelines, Respiration, 95, 5, pp. 364-380, (2018); Aaron Shawn D, Vandemheen Katherine L, FitzGerald JM., Reevaluation of diagnosis in adults with physician-diagnosed asthma, JAMA, 317, 3, pp. 269-279, (2017); King-Biggs MB., Asthma, Ann Intern Med, 171, 7, pp. ITC49-ITC64, (2019); Ravdeep K, Chupp G., Phenotypes and endotypes of adult asthma: moving toward precision medicine, J Allergy Clin Immunol, 144, 1, pp. 1-12, (2019); Nam BD, Ko S, Hwang JH., Quantitative evaluation of computed tomography findings in patients with bronchial asthma: prediction of therapeutic response, J Med Imaging Radiat Oncol, 65, 6, pp. 663-671, (2021); 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Winkler T, Venegas Jose G., Complex airway behavior and paradoxical responses to bronchoprovocation, J Appl Physiol, 103, 2, pp. 655-663, (2007); Won Wha K, Chang Hyun L, Goo Jin M, Et al., Xenon-enhanced dual-energy CT of patients with asthma: dynamic ventilation changes after methacholine and salbutamol inhalation, AJR Am J Roentgenol, 199, 5, pp. 975-981, (2012); Joonwoo P, Sujeong K, Jae-Kwang L, Et al., Quantitative CT image-based structural and functional changes during asthma acute exacerbations, J Appl Physiol, 131, 3, pp. 1056-1066, (2021); Yingli S, Cheng L, Liang J, Et al., Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction, Eur Radiol, 30, 7, pp. 3650-3659, (2020); Chung-Il W, Sunghwan S, Rolfes Mary C, Et al., Application of a natural language processing algorithm to asthma ascertainment an automated chart review, Am J Respir Crit Care Med, 196, 4, pp. 430-437, (2017); Chung-Il W, Sunghwan S, Mir A, Et al., Natural language processing for asthma ascertainment in different practice settings, J Allergy Clin Immunol Pract, 6, 1, pp. 126-131, (2018); 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Yang K, Yang Y, Kang Y, Et al., The value of radiomic features in chronic obstructive pulmonary disease assessment: a prospective study, Clin Radiol, 77, 22, pp. e466-e472, (2022); Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018); Manti S, Licari A, Salvatore L, Et al., Management of asthma exacerbations in the paediatric population: a systematic review, Eur Respir Rev, 30, 161, (2021); Guo-Qiang Z, Ermis Saliha Selin O, Madeleine R, Et al., Sex disparities in asthma development and clinical outcomes: implications for treatment strategies, J Asthma Allergy, 15, pp. 231-247, (2022)","Z. Luo; Radiology Imaging Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; email: gdmcfsjd@qq.com; Z. Chen; Health Management Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang City, China; email: 41881014@qq.com","","Dove Medical Press Ltd","","","","","","11786965","","","","English","J. Asthma Allerg.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85189134657"
"Xuan S.; Zhang J.; Guo Q.; Zhao L.; Yao X.","Xuan, Shurui (57221967911); Zhang, Jiayue (57216656844); Guo, Qinxing (58196314600); Zhao, Liang (55340935900); Yao, Xin (57222335355)","57221967911; 57216656844; 58196314600; 55340935900; 57222335355","A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach","2023","Diagnostics","13","8","1440","","","","0","10.3390/diagnostics13081440","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153771780&doi=10.3390%2fdiagnostics13081440&partnerID=40&md5=c264dcda68a82df6ee68931e817137b2","Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China; Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China; Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China","Xuan S., Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China; Zhang J., Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China; Guo Q., Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China; Zhao L., Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, China; Yao X., Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, China","Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening. © 2023 by the authors.","COPD; diagnostic model; machine learning; miRNA","BH3 protein; circulating microRNA; complementary DNA; epidermal growth factor; microRNA; microRNA 1180 3p; microRNA 1233 3p; microRNA 133b; microRNA 139 5p; microRNA 142 5p; microRNA 145 5p; microRNA 148a 3p; microRNA 150 5p; microRNA 184; microRNA 186 5p; microRNA 193b 3p; microRNA 205 5p; microRNA 214 3p; microRNA 221 3p; microRNA 26b 5p; microRNA 29a 3p; microRNA 331 3p; microRNA 339 5p; microRNA 345 5p; microRNA 409 3p; microRNA 502 3p; microRNA 576 3p; microRNA 744 5p; unclassified drug; area under the curve; Article; Bayesian learning; biological functions; chronic obstructive lung disease; classifier; clinical effectiveness; cohort analysis; controlled study; decision tree; diagnostic test; diagnostic test accuracy study; diagnostic value; extreme gradient boosting; feature selection; feature selection algorithm; gene expression profiling; human; human tissue; k nearest neighbor; light gradient boosting machine; machine learning; major clinical study; mRNA expression level; predictive value; random forest; real time polymerase chain reaction; signal transduction; support vector machine","","epidermal growth factor, 59459-45-9, 62229-50-9","","","National Natural Science Foundation of China, NSFC, (81870039); National Natural Science Foundation of China, NSFC","This research was funded by the National Natural Science Foundation of China (No. 81870039).","Halpin D.M.G., Celli B.R., Criner G.J., Frith P., Varela L., Salvi S., Vogelmeier C.F., Chen R., Mortimer K., Montes de Oca M., The GOLD Summit on Chronic Obstructive Pulmonary Disease in Low-and Middle-Income Countries, Int. 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Ther, 5, (2020); Kim H.-T., Yin W., Nakamichi Y., Panza P., Grohmann B., Buettner C., Guenther S., Ruppert C., Kobayashi Y., Guenther A., Et al., WNT/RYK Signaling Restricts Goblet Cell Differentiation during Lung Development and Repair, Proc. Natl. Acad. Sci. USA, 116, pp. 25697-25706, (2019); Saito A., Horie M., Nagase T., TGF-β Signaling in Lung Health and Disease, Int. J. Mol. Sci, 19, (2018); Hagstad S., Bjerg A., Ekerljung L., Backman H., Lindberg A., Ronmark E., Lundback B., Passive Smoking Exposure Is Associated With Increased Risk of COPD in Never Smokers, Chest, 145, pp. 1298-1304, (2014); Liu Y., Pleasants R.A., Croft J.B., Wheaton A.G., Heidari K., Malarcher A.M., Ohar J.A., Kraft M., Mannino D.M., Strange C., Smoking Duration, Respiratory Symptoms, and COPD in Adults Aged ≥45 Years with a Smoking History, Int. J. Chronic Obstruct. Pulm. Dis, 10, pp. 1409-1416, (2015); Cho S.J., Stout-Delgado H.W., Aging and Lung Disease, Annu. Rev. Physiol, 82, pp. 433-459, (2020)","X. Yao; Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 300 Guangzhou Road, 210029, China; email: yaoxin@njmu.edu.cn; L. Zhao; Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 264 Guangzhou Road, 210029, China; email: zhaoliang0302@gmail.com","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754418","","","","English","Diagn.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85153771780"
"Zhao D.; Mou X.; Li Y.; Yao Y.; Du L.; Li Z.; Wang P.; Li X.; Chen X.; Li X.; Li Y.; Fang Z.; Xia J.","Zhao, Dongfang (58811338700); Mou, Xiuying (57447693900); Li, Yueqi (57212385786); Yao, Yicheng (57323074800); Du, Lidong (24450055100); Li, Zhenfeng (58758808900); Wang, Peng (57222164710); Li, Xiaoran (57215794485); Chen, Xianxiang (8722171000); Li, Xiaopan (59152182200); Li, Yong (57221628139); Fang, Zhen (55533717900); Xia, Jingen (35340277200)","58811338700; 57447693900; 57212385786; 57323074800; 24450055100; 58758808900; 57222164710; 57215794485; 8722171000; 59152182200; 57221628139; 55533717900; 35340277200","The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease","2024","Physiological Measurement","45","5","055022","","","","0","10.1088/1361-6579/ad3d24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194887219&doi=10.1088%2f1361-6579%2fad3d24&partnerID=40&md5=775ae877a527e0b38461eb4b7b6d1335","Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China Japan Friendship Hospital, Beijing, China; Beijing Friendship Hospital, Capital Medical University, Beijing, China; Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China","Zhao D., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Mou X., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Li Y., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Yao Y., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Du L., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Li Z., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; Wang P., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; Li X., Beijing Friendship Hospital, Capital Medical University, Beijing, China; Chen X., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China; Li X., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China Japan Friendship Hospital, Beijing, China; Li Y., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China Japan Friendship Hospital, Beijing, China; Fang Z., Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China, School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100094, China, Research Unit of Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China; Xia J., National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China Japan Friendship Hospital, Beijing, China","Objective. Diagnosing chronic obstructive pulmonary disease (COPD) using impulse oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors, which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results. Approach. Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier’s performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China-Japan Friendship Hospital for training and validating the model. Main results. The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy = 0.920, specificity = 0.941, precision = 0.875, recall = 0.875). Significance. The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS. © 2024 Institute of Physics and Engineering in Medicine.","COPD; feature derivation; K-nearest neighbors; machine learning; respiratory system mechanics","Aged; Algorithms; Female; Humans; Machine Learning; Male; Middle Aged; Oscillometry; Pulmonary Disease, Chronic Obstructive; Classification (of information); Diagnosis; Learning algorithms; Nearest neighbor search; Pulmonary diseases; Respiratory system; Chronic obstructive pulmonary disease; Feature derivation; Impulse oscillometry; K-near neighbor; Machine learning algorithms; Machine-learning; Nearest-neighbour; On-machines; Performance; Respiratory system mechanic; aged; algorithm; chronic obstructive lung disease; diagnosis; female; human; machine learning; male; middle aged; oscillometry; pathophysiology; procedures; Feature Selection","","","","","National Key Research and Development Program of China, NKRDPC, (2020YFC2003703, 2021YFC3002204, 2020YFC1512304); National Key Research and Development Program of China, NKRDPC; Chinese Academy of Meteorological Sciences, CAMS, (2019-I2M-5-019); Chinese Academy of Meteorological Sciences, CAMS; National Natural Science Foundation of China, NSFC, (62071451); National Natural Science Foundation of China, NSFC","This work was funded by the National Key Research and Development Project 2020YFC2003703, 2021YFC3002204, 2020YFC1512304 National Natural Science Foundation of China (Grant 62071451), and CAMS Innovation Fund for Medical Sciences (2019-I2M-5-019).","Amaral J L M, Lopes A J, Faria A C D, Melo P L, Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease, Comput. Methods Programs Biomed, 118, pp. 186-197186, (2014); Amaral J L M, Lopes A J, Jansen J M, Faria A C D, Melo P L, Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Comput. Methods Programs Biomed, 105, pp. 183-193183, (2011); Badnjevic A, Cifrek M, Koruga D, Osmankovic D, Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease, BMC Med. Inf. Decis. Making, 15, (2015); Barua M, Nazeran H, Nava P, Diong B, Goldman M, Classification of impulse oscillometric patterns of lung function in asthmatic children using artificial neural networks, Conf. Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. Annual Conf., 2006 IEEE Engineering in Medicine and Biology Society, 327 331, (2005); Bicke S, Popler J, Lesnick B, Eid N, Impulse oscillometry: interpretation and practical applications, Chest, 146, pp. 841-847841, (2014); Bshier B, Salvi S, Measuring lung function using sound waves: role of the forced oscillation technique and impulse oscillometry system, Breathe, 11, pp. 57-6557, (2015); Christenson S A, Smith B M, Bafadhel M, Putcha N, Chronic obstructive pulmonary disease, Lancet, 399, (2022); Chronic obstructive pulmonary disease (COPD), (2023); Di Mango A M G T, Lopes A J, Jansen J M, Melo P L, Changes in respiratory mechanics with increasing degrees of airway obstruction in COPD: detection by forced oscillation technique, Respiratory Med, 100, pp. 399-410399, (2005); Frerichs I, Lasarow L, Strodthoff C, Vogt B, Zhao Z, Weiler N, Spatial ventilation inhomogeneity determined by electrical impedance tomography in patients with chronic obstructive lung disease, Front. Physiol, 12, (2021); Frerichs I, Paradiso R, Kilintzis V, Rocha B M, Braun F, Rapin M, Wacker J, Wearable pulmonary monitoring system with integrated functional lung imaging and chest sound recording: a clinical investigation in healthy subjects, Physiol. Meas, 44, (2023); Kastelik J A, Aziz I, Ojoo J C, Morice A H, Evaluation of impulse oscillation system: comparison with forced oscillation technique and body plethysmography, Eur. Respiratory J, 19, pp. 1214-1220, (2002); Kraemer H C, Periyakoil V S, Noda A, Kappa coefficients in medical research, Stat. Med, 21, pp. 2109-21292109, (2002); Labaki Wassim W., Rosenberg Sharon R., Chronic obstructive pulmonary disease, Annals of Internal Medicine, 173, (2020); Laurent H, Aubreton S, Pereira B, Merle P, Richard R, Costes F, Preoperative respiratory muscle endurance training improves ventilatory capacity and prevents pulmonary postoperative complications after lung surgery, Eur. J. Phys. Rhabilitation Med, 56, pp. 73-8173, (2019); Liang X, Zheng J, Gao Y, Zhang Z, Han W, Clinical application of oscillometry in respiratory diseases: an impulse oscillometry registry, ERJ Open Research, 8 80, (2022); Nakahara Y, Mabu S, Hirano T, Murata Y, Doi K, Fukatsu-Chikumoto A, Matsunaga K, Neural network approach to investigating the importance of test items for predicting physical activity in chronic obstructive pulmonary disease, J. Clin. Med, 12, (2023); Niewoehner D E, Clinical practice. Outpatient management of severe COPD, New Engl. J. Med, 362, (2010); Nright P L, Crapo R O, Controversies in the use of spirometry for early recognition and diagnosis of chronic obstructive pulmonary disease in cigarette smokers, Clin. Chest Med, 21, pp. 645-652645, (2000); Oostveen E, MacLeod D, Lorino H, Farre R, Hantos Z, The forced oscillation technique in clinical practice: methodology, recommendations and future developments, Eur. Respiratory J, 22, pp. 1026-1041, (2003); Piorunek T, Kostrzewska M, Cofta S, Batura-Gabryel H, Andrzejczak P, Impulse oscillometry in the diagnosis of airway resistance in chronic obstructive pulmonary disease, Adv. Exp. Medi. Biol, 838, (2015); Raherison C, Girodet P-O, Epidemiology of COPD, Eur. Respiratory Rev, 18, pp. 213-221213, (2009); Rodriguez-Galiano V F, Luque-Espinar J A, Chica-Olmo M, Mendes M P, Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods, Sci. Total Environ, 624, pp. 661-672661, (2017); Sheikh K, Coxson H O, Coxson H O, Parraga G, This is what COPD looks like, Respirology, 21, pp. 224-236224, (2015); Smith H J, Reinhold P, Goldman M D, Forced oscillation technique and impulse oscillometry Lung Function Testing 31 Gosselink R Stam H European Respiratory Society, (2005); Spathis D, Vlamos P, Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Inform. J, 25, pp. 811-827811, (2017); Ubois A B, Brody A W, Lewis D H, Burgess B F, Oscillation mechanics of lungs and chest in man, J. Appl. Physiol, 8, pp. 587-594587, (1956); Yamagami H, Tanaka A, Kishino Y, Mikuni H, Kawahara T, Association between respiratory impedance measured by forced oscillation technique and exacerbations in patients with COPD, Int. J. Chronic Obstructive Pulmonary Dis, 13, pp. 79-8979, (2017)","J. Xia; National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China, Japan Friendship Hospital, Beijing, China; email: xiajingen2016@buaa.edu.cn","","Institute of Physics","","","","","","09673334","","PMEAE","38599216","English","Physiol. Meas.","Article","Final","","Scopus","2-s2.0-85194887219"
"Mishra S.; Maheshwarappa H.M.","Mishra, Shivangi (57683010700); Maheshwarappa, Harish M. (57213196906)","57683010700; 57213196906","Precision Medicine in Respiratory Care: Where do We Stand Now?","2023","Indian Journal of Respiratory Care","12","3","","207","210","3","0","10.5005/jp-journals-11010-1068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219609147&doi=10.5005%2fjp-journals-11010-1068&partnerID=40&md5=f5a1d82a7864f880506640ff473447b1","Department of Critical Care Medicine, Manipal Hospital Whitefield, Karnataka, Bengaluru, India; Department of Critical Care Medicine, Kauvery Hospitals, Karnataka, Bengaluru, India","Mishra S., Department of Critical Care Medicine, Manipal Hospital Whitefield, Karnataka, Bengaluru, India; Maheshwarappa H.M., Department of Critical Care Medicine, Kauvery Hospitals, Karnataka, Bengaluru, India","Precision medicine also known as “personalized medicine” is a healthcare delivery system based on identifying biomarkers using genomics to link endotypes with phenotypes. Significant overlap exists between different phenotypes. Endotyping helps in giving a more precise definition of the phenotypes. However, it has proved to be most helpful in the development of therapeutics in oncology. Precision medicine has been brought to much more common use with COVID-19 with various drugs developed targeting interleukins. Precision medicine is now being actively developed for the management of infectious diseases and chronic and lifestyle diseases. Various challenges still exist in the path of future development of precision medicine such as cost, ethics, incorporation of machine learning, and availability of trained manpower to manage the data and algorithms. In this review, we will discuss the growth and challenges precision medicine faces in the field of respiratory care. © The Author(s). 2023.","Acute respiratory distress syndromes; Asthma; Biomarkers; Phenotypes; Precision medicine","","","","","","","","Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease [Internet], (2011); Sackett DL, Rosenberg WMC, Gray JAM, Et al., Evidence based medicine: what it is and what it isn’t, BMJ, 312, pp. 71-72, (1996); Rennard SI, Vestbo J., The many “small COPDs”: COPD should be an orphan disease, Chest, 134, 3, pp. 623-627, (2008); Jameson JL, Longo DL., Precision medicine–personalized, problematic, and promising, N Engl J Med, 372, 23, pp. 2229-2234, (2015); Barta JA, Powell CA, Wisnivesky JP., Global epidemiology of lung cancer, Ann Glob Health, 85, 1, (2019); Howlader N, Forjaz G, Mooradian MJ, Et al., The effect of advances in lung-cancer treatment on population mortality, N Engl J Med, 383, 7, pp. 640-649, (2020); Mok TS, Wu YL, Thongprasert S, Et al., Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma, N Engl J Med, 361, 10, pp. 947-957, (2009); Shaw AT, Kim DW, Nakagawa K, Et al., Crizotinib versus chemotherapy in advanced ALK-positive lung cancer, N Engl J Med, 368, 25, pp. 2385-2394, (2013); Teramoto A, Tsukamoto T, Kiriyama Y, Et al., Automated classification of lung cancer types from cytological images using deep convolutional neural networks, Biomed Res Int, 2017, (2017); Onoi K, Chihara Y, Uchino J, Et al., Immune checkpoint inhibitors for lung cancer Treatment: a review, J Clin Med, 9, 5, (2020); Rizvi NA, Hellmann MD, Snyder A, Et al., Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer, Science, 348, 6230, pp. 124-128, (2015); Steuer CE, Papadimitrakopoulou V, Herbst RS, Et al., Innovative clinical trials: the Lung-MAP study, Clin Pharmacol Ther, 97, 5, pp. 488-491, (2015); Guilleminault L, Ouksel H, Belleguic C, Et al., Personalised medicine in asthma: from curative to preventive medicine, Eur Respir Rev, 26, 143, (2017); Haldar P, Pavord ID, Shaw DE, Et al., Cluster analysis and clinical asthma phenotypes, Am J Respir Crit Care Med, 178, 3, pp. 218-224, (2008); Wilson S, Ward J, Sousa A, Et al., The U-BIOPRED severe asthma study: immunopathological characterisation, Eur Respir J, 44, (2014); Lai T, Wang S, Xu Z, Et al., Long-term efficacy and safety of omalizumab in patients with persistent uncontrolled allergic asthma: a systematic review and meta-analysis, Sci Rep, 5, (2015); Wenzel S, Ford L, Pearlman D, Et al., Dupilumab in persistent asthma with elevated eosinophil levels, N Engl J Med, 368, 26, pp. 2455-2466, (2013); Wenzel SE, Schwartz LB, Langmack EL, Et al., Evidence that severe asthma can be divided pathologically into two inflammatory subtypes with distinct physiologic and clinical characteristics, Am J Respir Crit Care Med, 160, 3, pp. 1001-1008, (1999); Jatakanon A, Uasuf C, Maziak W, Et al., Neutrophilic inflammation in severe persistent asthma, Am J Respir Crit Care Med, 160, 5, pp. 1532-1539, (1999); Corren J, Menzies-Gow A, Chupp G, Et al., Efficacy of tezepelumab in severe, uncontrolled asthma: pooled analysis of the pathway and navigator clinical trials, Am J Respir Crit Care Med, 208, 1, pp. 13-24, (2023); Pavord ID, Chanez P, Criner GJ, Et al., Mepolizumab for eosinophilic chronic obstructive pulmonary disease, N Engl J Med, 377, 17, pp. 1613-1629, (2017); Atherton HC, Jones G, Danahay H., IL-13-induced changes in the goblet cell density of human bronchial epithelial cell cultures: MAP kinase and phosphatidylinositol 3-kinase regulation, Am J Physiol Lung Cell Mol Physiol, 285, 3, pp. L730-L739, (2003); Bhatt SP, Rabe KF, Hanania NA, Et al., Dupilumab for COPD with type 2 inflammation indicated by eosinophil counts, N Eng J Med, 389, 3, pp. 205-214, (2023); Skov M, Hansen CR, Pressler T., Cystic fibrosis-an example of personalized and precision medicine, APMIS, 127, 5, pp. 352-360, (2019); Beitler JR, Thompson BT, Baron RM, Et al., Advancing precision medicine for acute respiratory distress syndrome, Lancet Respir Med, 10, 1, pp. 107-120, (2022); Calfee CS, Delucchi K, Parsons PE, Et al., Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials, Lancet Respir Med, 2, 8, pp. 611-620, (2014); Calfee CS, Delucchi KL, Sinha P, Et al., Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial, Lancet Respir Med, 6, 9, pp. 691-698, (2018); Rautanen A, Mills TC, Gordon AC, Et al., Genome-wide association study of survival from sepsis due to pneumonia: an observational cohort study, Lancet Respir Med, 3, 1, pp. 53-60, (2015); Jabaudon M, Blondonnet R, Pereira B, Et al., Plasma sRAGE is independently associated with increased mortality in ARDS: a meta-analysis of individual patient data, Intensive Care Med, 44, 9, pp. 1388-1399, (2018); Tsangaris I, Tsantes A, Vrigkou E, Et al., Angiopoietin-2 levels as predictors of outcome in mechanically ventilated patients with acute respiratory distress syndrome, Dis Markers, 2017, (2017); Jabaudon M, Blondonnet R, Ware LB., Biomarkers in acute respiratory distress syndrome, Curr Opin Crit Care, 27, 1, pp. 46-54, (2021); Guillen-Guio B, Lorenzo-Salazar JM, Ma SF, Et al., Sepsis-associated acute respiratory distress syndrome in individuals of European ancestry: a genome-wide association study, Lancet Respir Med, 8, 3, pp. 258-266, (2020)","H.M. Maheshwarappa; Department of Critical Care Medicine, Kauvery Hospitals, Bengaluru, Karnataka, India; email: dr.harishmm@rocketmail.com","","Jaypee Brothers Medical Publishers (P) Ltd","","","","","","22779019","","","","English","Indian J. Respir. Care","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85219609147"
"Pamarapa C.; Kemlek S.; Sukumwattana W.; Sitthikul P.; Khuanrubsuan S.; Chaikhampa A.; Wongtrakool P.; Chuajak A.; Phonlakrai M.; Keerativittayayut R.","Pamarapa, Chayanon (58706136900); Kemlek, Salisa (59005111100); Sukumwattana, Wichasa (59004283100); Sitthikul, Pharinda (59004283200); Khuanrubsuan, Sichon (59003667900); Chaikhampa, Akkarawat (59003873200); Wongtrakool, Paritt (59004078800); Chuajak, Ammarut (57201466066); Phonlakrai, Monchai (57208238040); Keerativittayayut, Ruedeerat (57202011113)","58706136900; 59005111100; 59004283100; 59004283200; 59003667900; 59003873200; 59004078800; 57201466066; 57208238040; 57202011113","AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images","2024","Journal of Associated Medical Sciences","57","2","","149","156","7","0","10.12982/JAMS.2024.037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191459690&doi=10.12982%2fJAMS.2024.037&partnerID=40&md5=ab94a933106047f8fd62c6c97eaa20f2","School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; Queen Savang Vadhana Memorial Hospital, Chonburi Province, Thailand","Pamarapa C., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Kemlek S., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Sukumwattana W., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Sitthikul P., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Khuanrubsuan S., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Chaikhampa A., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Wongtrakool P., Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; Chuajak A., Queen Savang Vadhana Memorial Hospital, Chonburi Province, Thailand; Phonlakrai M., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; Keerativittayayut R., School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand","Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection. © 2024, Faculty of Associated Medical Sciences, Chiang Mai University. All rights reserved.","Chronic obstructive pulmonary disease; convolutional neural network; Low dose computed tomography; ResNet","","","","","","Chulabhorn Royal Academy, CRA, (RAA2564/036, RCP2555/002)","We would like to express our sincere gratitude to Chulabhorn Royal Academy for the generous support through grant number RAA2564/036 and RCP2555/002. In addition, we would like to extend our appreciation to all the participants in Chulabhorn Royal Academy Integrated Lung Cancer Screening Program.","Gonzalez G, Ash SY, Vegas-Sanchez-Ferrero G, Onieva JO, Rahaghi FN, Ross JC, Et al., Disease staging and prognosis in smokers using deep learning in chest computed tomography, Am J Respir Crit Care Med, 197, 2, pp. 193-203, (2018); MacNee W., Pathology, pathogenesis, and patho-physiology, BMJ, 332, 7551, pp. 1202-1204, (2006); Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, (2024); Lamprecht B, Soriano JB, Studnicka M, Kaiser B, Vanfleteren LE, Gnatiuc L, Et al., Determinants of underdiagnosis of COPD in national and international surveys, Chest, 148, 4, pp. 971-985, (2015); Johns DP, Walters JA, Walters EH, Et al., Diagnosis and early detection of COPD using spirometry, J Thorac Dis, 6, 11, pp. 1557-1569, (2014); Coates AL, Graham BL, McFadden RG., Spirometry in primary care, Can Respir J, 20, 1, pp. 13-22, (2013); Cooper BG., An update on contraindications for lung function testing, Thorax, 66, 8, pp. 714-723, (2011); Gierada DS, Black WC, Chiles C, Pinsky PF, Yankelevitz DF, Et al., Low-dose CT screening for lung cancer: Evidence from 2 decades of study, Radiol Imaging Cancer, 2, 2, (2020); Nawa T., Low-dose CT screening for lung cancer reduced lung cancer mortality in Hitachi City, Int J Radiat Biol, 95, 10, pp. 1441-1446, (2019); Larke FJ, Kruger RL, Cagnon CH, Flynn M, McNitt-Gray MM, Wu X, Et al., Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial, AJR Am J Roentgenol, 197, 5, pp. 1165-1169, (2011); Bailey KL., The importance of the assessment of pulmonary function in COPD, Med Clin North Am, 96, 4, pp. 745-752, (2012); Ronneberger O, Fischer P, Brox T., U-Net: Convolutional Networks for Biomedical Image Segmentation, pp. 234-241, (2015); Tan W, Huang P, Li X, Ren G, Chen Y, Yang. J. Analysis of segmentation of lung parenchyma based on deep learning methods, J Xray Sci Technol, 29, 6, pp. 945-959, (2021); Nemec SF, Bankier AA, Eisenberg RL., Upper lobe-predominant diseases of the lung, AJR Am J Roentgenol, 200, 3, pp. W222-W237, (2013); Tang LYW, Coxson HO, Lam S, Leipsic j, Sin D., Towards large-scale case-finding: Training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT, Lancet Digit Health, 2, 5, pp. e259-e267, (2020); Bradley P, Fuhrman M, Zimmerman M., Pediatric critical care (Fourth Edition), (2011); He K, Zhang X, Ren S, Sun J, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016); Ho TT, Kim T, Kim WJ, Lee CH, chae KJ, Bak SH, Et al., A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects, Sci Rep, 11, (2021); Gierada DS, Bierhals AJ, Choong CK, Bartel ST, Ritter JH, Das NA., Et al., Effects of CT section thickness and reconstruction kernel on emphysema quantification relationship to the magnitude of the CT emphysema index, Acad Radiol, 17, 2, pp. 146-156, (2010); Selim M, Zhang J, Fei B, Zhang GQ, Chen J., STAN-CT: Standardizing CT Image using Generative Adversarial Networks, AMIA Annu Symp Proc, 2020, pp. 1100-1109, (2020); Ait Skourt B, El Hassani A, Majda A., Lung CT Image Segmentation Using Deep Neural Networks, Procedia Computer Science, 127, pp. 109-113, (2018); Murugappan M, Bourisly AK, Prakash NB, Sumithra MG, Acharya UR., Et al., Automated semantic lung segmentation in chest CT images using deep neural network, Neural Comput Appl, 35, 21, pp. 15343-15364, (2023); Rodriguez JD, Perez A, Lozano JA., Sensitivity analysis of k-fold cross validation in prediction error estimation, IEEE Trans Pattern Anal Mach Intell, 32, 3, pp. 569-575, (2010); Anguita D, Ghelardoni L, Ghio A, Oneto L, Ridella S., The ‘K’ in K-fold cross validation, ESANN 2012 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (2012)","R. Keerativittayayut; School of Radiological Technology, Faculty of Health Science Technology, Chulabhorn Royal Academy, Bangkok, Thailand; email: ruedeerat.kee@cra.ac.th","","Faculty of Associated Medical Sciences, Chiang Mai University","","","","","","25396056","","","","English","J. Assoc. Med. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85191459690"
"Ko E.; Kim Y.; Shokoohi F.; Mersha T.B.; Kang M.","Ko, Euiseong (57194782567); Kim, Youngsoon (57201741884); Shokoohi, Farhad (55253025100); Mersha, Tesfaye B. (56147975000); Kang, Mingon (37013429600)","57194782567; 57201741884; 55253025100; 56147975000; 37013429600","SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis","2024","Briefings in Bioinformatics","25","4","bbae239","","","","0","10.1093/bib/bbae239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194868977&doi=10.1093%2fbib%2fbbae239&partnerID=40&md5=711decb9907dd47c6883b245859b1e43","Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, United States; Department of Information and Statistics, Department of Bio&Medical Bigdata (BK21 Four program), Gyeongsang National University, Jinju, South Korea; Department of Mathematical Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States; Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States","Ko E., Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, United States; Kim Y., Department of Information and Statistics, Department of Bio&Medical Bigdata (BK21 Four program), Gyeongsang National University, Jinju, South Korea; Shokoohi F., Department of Mathematical Sciences, University of Nevada, Las Vegas, Las Vegas, NV, United States; Mersha T.B., Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States; Kang M., Department of Computer Science, University of Nevada, Las Vegas, Las Vegas, NV, United States","Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual’s characteristics. © The Author(s) 2024.","Asthma; Cancer; Interpretable deep learning; Sexual dimorphism analysis; SPIN","Asthma; Deep Learning; Female; Genetic Predisposition to Disease; Humans; Male; Neoplasms; Neural Networks, Computer; Sex Characteristics; artificial neural network; asthma; deep learning; female; genetic predisposition; genetics; human; male; metabolism; neoplasm; sexual characteristics","","","","","Centers for Medicare and Medicaid Services, CMS; National Science Foundation Major Research Instrumentation; National Science Foundation, NSF, (2117941); National Research Foundation of Korea, NRF, (NRF-2021R1I1A3048029); National Institutes of Health, NIH, (R01 HG011411)","This work was supported by the National Science Foundation Major Research Instrumentation (NSF MRI) (Grant#:2117941), the Centers for Medicare & Medicaid Services (CMS) Minority Research Grant Program, the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2021R1I1A3048029) and the National Institutes of Health (NIH) R01 HG011411 grant support.","Ober C, Loisel DA, Gilad Y., Sex-specific genetic architecture of human disease, Sex-specific genetic architecture of human disease, 9, pp. 911-922, (2008); 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Bourquard T, Lee K, Al-Ramahi I, Et al., Functional variants identify sex-specific genes and pathways in alzheimer’s disease, Nat Commun, 14, 1, (2023); Hao J, Kim Y, Kim T-K, Kang M., Pasnet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data, BMC Bioinformatics, 19, 1, pp. 1-13, (2018); Hao J, Kim Y, Mallavarapu T, Jung Hun O, Kang M., Coxpasnet: pathway-based sparse deep neural network for survival analysis, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 381-386, (2018); Elmarakeby HA, Hwang J, Arafeh R, Et al., Biologically informed deep neural network for prostate cancer discovery, Nature, 598, 7880, pp. 348-352, (2021); Park C, Kim B, Park T., Deephiscom: deep learning pathway analysis using hierarchical structural component models, Brief Bioinform, 23, 5, (2022); Yin Q, Chen W, Zhang C, Wei Z., A convolutional neural network model for survival prediction based on prognosis-related cascaded wx feature selection, Lab Invest, 102, 10, pp. 1064-1074, (2022); Chen J, Yang H, Min AS, Et al., Genomic landscape of lung adenocarcinoma in east asians, Nat Genet, 52, 2, pp. 177-186, (2020); Banerjee P, Balraj P, Ambhore NS, Et al., Network and co-expression analysis of airway smooth muscle cell transcriptome delineates potential gene signatures in asthma, Sci Rep, 11, 1, (2021); Wang Y, Zhao W, Xiao Z, Et al., A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme, J Cell Mol Med, 24, 7, pp. 3807-3821, (2020); Xia X, Li X, Li F, Et al., A novel tumor suppressor protein encoded by circular akt3 rna inhibits glioblastoma tumorigenicity by competing with active phosphoinositide-dependent kinase-1, Mol Cancer, 18, pp. 1-16, (2019); Zhou S, Niu R, Sun H, Et al., The map3k1/c-Jun signaling axis regulates glioblastoma stem cell invasion and tumor progression, Biochem Biophys Res Commun, 612, pp. 188-195, (2022); Ji H, Ba Y, Ma S, Et al., Construction of interferon-gamma-related gene signature to characterize the immune-inflamed phenotype of glioblastoma and predict prognosis, efficacy of immunotherapy and radiotherapy, Front Immunol, 12, (2021); Xiong D-D, Wen-Qing X, He R-Q, Et al., In silico analysis identified mirna-based therapeutic agents against glioblastoma multiforme, Oncol Rep, 41, 4, pp. 2194-2208, (2019); Li T, Yang Z, Li H, Et al., Phospholipase cγ1 (plcg1) overexpression is associated with tumor growth and poor survival in idh wild-type lower-grade gliomas in adult patients, Lab Invest, 102, 2, pp. 143-153, (2022); Vignoli A, Lesma E, Alfano RM, Et al., Glioblastoma multiforme in a child with tuberous sclerosis complex, Am J Med Genet A, 167, 10, pp. 2388-2393, (2015); Akcay S, Guven E, Afzal M, Kazmi I., Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme, Gene, 824, (2022); Yi L, Cui Y, Qingfu X, Jiang Y., Stabilization of lsd1 by deubiquitinating enzyme usp7 promotes glioblastoma cell tumorigenesis and metastasis through suppression of the p53 signaling pathway, Oncol Rep, 36, 5, pp. 2935-2945, (2016); Huan R, Yue J, Lan J, Et al., Hypocretin-1 suppresses malignant progression of glioblastoma cells through notch1 signaling pathway, Brain Res Bull, 196, pp. 46-58, (2023); Yi G-Z, Xiang W, Feng W-Y, Et al., Identification of key candidate proteins and pathways associated with temozolomide resistance in glioblastoma based on subcellular proteomics and bioinformatical analysis, Biomed Res Int, 2018, pp. 1-12, (2018); Qian Y, Sun Y, Chen Y, Et al., Nrf2 regulates downstream genes by targeting mir-29b in severe asthma and the role of grape seed proanthocyanidin extract in a murine model of steroid-insensitive asthma, Pharm Biol, 60, 1, pp. 347-358, (2022); Li J, Hao Y, Li W, Et al., Hla-g in asthma and its potential as an effective therapeutic agent, Allergol Immunopathol, 51, 1, pp. 22-29, (2023); Alves CC, Arruda LKP, Oliveira FR, Et al., Human leukocyte antigen-g 3’untranslated region polymorphisms are associated with asthma severity, Mol Immunol, 101, pp. 500-506, (2018); Esposito S, Ierardi V, Daleno C, Et al., Genetic polymorphisms and risk of recurrent wheezing in pediatric age, BMC Pulm Med, 14, 1, pp. 1-10, (2014); Dragicevic S, Milosevic K, Nestorovic B, Nikolic A., Influence of the polymorphism c-509t in the tgfb1 gene promoter on the response to montelukast, Pediatr Allergy Immunol Pulmonol, 30, 4, pp. 239-245, (2017); Hur GY, Broide DH., Genes and pathways regulating decline in lung function and airway remodeling in asthma, Allergy Asthma Immunol Res, 11, 5, pp. 604-621, (2019); Xia T, Ma J, Sun Y, Sun Y., Androgen receptor suppresses inflammatory response of airway epithelial cells in allergic asthma through mapk1 and mapk14, Hum Exp Toxicol, 41, (2022); Sanchez-Ovando S, Baines KJ, Barker D, Et al., Six gene and th2 signature expression in endobronchial biopsies of participants with asthma, Immunity Inflammation Dis, 8, 1, pp. 40-49, (2020); Baines KJ, Negewo NA, Gibson PG, Et al., A sputum 6 gene expression signature predicts inf lammatory phenotypes and future exacerbations of copd, Int J Chron Obstruct Pulmon Dis, pp. 1577-1590, (2020); Song W, Zheng S, Li M, Et al., Linking endotypes to omics profiles in difficult-to-control asthma using the diagnostic chinese medicine syndrome differentiation algorithm, J Asthma, 57, 5, pp. 532-542, (2020); Han L, Wang L, Tang S, Et al., Itgb4 deficiency in bronchial epithelial cells directs airway inflammation and bipolar disorder-related behavior, J Neuroinflammation, 15, 1, pp. 1-14, (2018); Min Z, Zhou J, Mao R, Et al., Pyrroloquinoline quinone administration alleviates allergic airway inf lammation in mice by regulating the jak-stat signaling pathway, Mediators Inflamm, 2022, pp. 1-18, (2022); Yang M, Li L-Y, Qin X-D, Et al., Perf luorooctanesulfonate and per-f luorooctanoate exacerbate airway inf lammation in asthmatic mice and in vitro, Sci Total Environ, 766, (2021); Quinn KD, Schedel M, Nkrumah-Elie Y, Et al., Dysregulation of metabolic pathways in a mouse model of allergic asthma, Allergy, 72, 9, pp. 1327-1337, (2017); Zou W, Niu C, Zhou F, Gong C., Pns-r1 inhibits dex-induced bronchial epithelial cells apoptosis in asthma through mitochondrial apoptotic pathway, Cell Biosci, 9, 1, pp. 1-10, (2019); Huang Z-J, Shen Q-H, Yan-Sheng W, Huang Y-L., A gibbs sampling method to determine biomarkers for asthma, Comput Biol Chem, 67, pp. 255-259, (2017); Gunawardhana LP, Gibson PG, Simpson JL, Et al., Characteristic dna methylation profiles in peripheral blood monocytes are associated with inf lammatory phenotypes of asthma, Epigenetics, 9, 9, pp. 1302-1316, (2014); Lundberg SM, Lee S-I., A unified approach to interpreting model predictions, Advances in neural information processing systems, 30, (2017); Nguyen KD, Vanichsarn C, Fohner A, Nadeau KC., Selective deregulation in chemokine signaling pathways of cd4+ cd25hicd127lo/− regulatory t cells in human allergic asthma, J Allergy Clin Immunol, 123, 4, pp. 933-939, (2009); Soares AG, Muscara MN, Costa SKP., Molecular mechanism and health effects of 1, 2-naphtoquinone, EXCLI J, 19, (2020)","T.B. Mersha; Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, 3333 Burnet Avenue MLC 7037, 45229, United States; email: tesfaye.mersha@cchmc.org; M. Kang; Department of Computer Science, University of Nevada, Las Vegas, 4505 S. Maryland Pkwy., 89154-4022, United States; email: mingon.kang@unlv.edu","","Oxford University Press","","","","","","14675463","","","38807262","English","Brief. Bioinform.","Article","Final","All Open Access; Green Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85194868977"
"Huang C.-H.; Chou K.-T.; Perng D.-W.; Hsiao Y.-H.; Huang C.-W.","Huang, Chien-Hua (55905083200); Chou, Kun-Ta (16836002700); Perng, Diahn-Warng (7003478845); Hsiao, Yi-Han (55208527100); Huang, Chien-Wen (56389707800)","55905083200; 16836002700; 7003478845; 55208527100; 56389707800","Using Machine Learning with Impulse Oscillometry Data to Develop a Predictive Model for Chronic Obstructive Pulmonary Disease and Asthma","2024","Journal of Personalized Medicine","14","4","398","","","","0","10.3390/jpm14040398","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191553960&doi=10.3390%2fjpm14040398&partnerID=40&md5=5e2e05523a6623f6cdba27c5fa37c1e0","Department of Eldercare, College of Nursing, Central Taiwan University of Science and Technology, Taichung, 406053, Taiwan; Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, 112201, Taiwan; Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, 112304, Taiwan; Division of Chest Medicine, Department of Internal Medicine, Asia University Hospital, Taichung, 413505, Taiwan; Department of Medical Laboratory Science and Biotechnology, College of Medical and Health Science, Asia University, Taichung, 413305, Taiwan","Huang C.-H., Department of Eldercare, College of Nursing, Central Taiwan University of Science and Technology, Taichung, 406053, Taiwan; Chou K.-T., Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, 112201, Taiwan, Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, 112304, Taiwan; Perng D.-W., Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, 112201, Taiwan, Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, 112304, Taiwan; Hsiao Y.-H., Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, 112201, Taiwan, Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, 112304, Taiwan; Huang C.-W., Division of Chest Medicine, Department of Internal Medicine, Asia University Hospital, Taichung, 413505, Taiwan, Department of Medical Laboratory Science and Biotechnology, College of Medical and Health Science, Asia University, Taichung, 413305, Taiwan","We aimed to develop and validate a machine learning model using impulse oscillometry system (IOS) profiles for accurately classifying patients into three assessment-based categories: no airflow obstruction, asthma, and chronic obstructive pulmonary disease (COPD). Our research questions were as follows: (1) Can machine learning methods accurately classify obstructive disease states based solely on multidimensional IOS data? (2) Which IOS parameters and modeling algorithms provide the best discrimination? We used data for 480 patients (240 with COPD and 240 with asthma) and 84 healthy individuals for training. Physiological and IOS parameters were combined into six feature combinations. The classification algorithms tested were logistic regression, random forest, neural network, k-nearest neighbor, and support vector machine. The optimal feature combination for identifying individuals without pulmonary obstruction, with asthma, or with COPD included 15 IOS and physiological features. The neural network classifier achieved the highest accuracy (0.786). For discriminating between healthy and unhealthy individuals, two combinations of twenty-three features performed best in the neural network algorithm (accuracy of 0.929). When distinguishing COPD from asthma, the best combination included 15 features and the neural network algorithm achieved an accuracy of 0.854. This study provides compelling technical evidence and clinical justifications for advancing IOS data-driven models to aid in COPD and asthma management. © 2024 by the authors.","COPD; impulse oscillometry; machine learning","accuracy; algorithm; Article; artificial neural network; asthma; chronic obstructive lung disease; classification algorithm; controlled study; diagnostic accuracy; diagnostic test accuracy study; forced expiratory volume; forced vital capacity; homeostasis model assessment; human; impulse oscillometry; k nearest neighbor; logistic regression analysis; machine learning; major clinical study; oscillometry; predictive model; receiver operating characteristic; sensitivity and specificity; spirometry; support vector machine","","","","","Asia University Hospital, AUH, (11051007); Asia University Hospital, AUH","This research was funded by the Asia University Hospital, Taiwan, grant number \u201C11051007\u201D.","Huang P., Lin C.T., Li Y., Tammemagi M.C., Brock M.V., Garner M., Ettinger D.S., Atkar-Khattra S., Xu Y., Bhujwalla Z.M., Et al., Deep Machine Learning Predicts Cancer Risk in Follow-Up Lung Screening with Low-Dose CT: A Training and Validation Study of a Deep Learning Method; Walsh S.L.F., Calandriello L., Silva M., Sverzellati N., Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: A case-cohort study, Lancet Respir. Med, 6, pp. 837-845, (2018); Hwang E.J., Park S., Jin K.-N., Kim J.I., Choi S.Y., Lee J.H., Goo J.M., Aum J., Yim J.J., Cohen J.G., Et al., Development and Validation of a Deep Learning-Based Automated Detection Algorithm for Major Thoracic Diseases on Chest Radiographs, JAMA Netw. Open, 2, (2019); Yates E.J., Yates L.C., Harvey H., Machine learning “red dot”: Open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification, Clin. Radiol, 73, pp. 827-831, (2018); Lu M.T., Ivanov A., Mayrhofer T., Hosny A., Aerts H.J.W.L., Hoffmann U., Deep learning to assess long-term mortality from chest radiographs, JAMA Netw. Open, 2, (2019); Ardila D., Kiraly A.P., Bharadwaj S., Choi B., Reicher J.J., Peng L., Tse D., Etemadi M., Ye W., Corrado G., Et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nat. Med, 25, pp. 954-961, (2019); Chung F., Barnes N., Allen M., Angus R., Corris P., Knox A., Miles J., Morice A., O'Driscoll B., Richardson M., Assessing Asthma Control, A Guide for Clinicians and Patients, (2019); Miravitlles M., Kraan J., Wedzicha J.A., van der Molen T., Beier J., Soriano J.B., Strandberg E., Brun M., Cegla U., Gerken F., New horizons in the diagnosis and pharmacotherapy of chronic obstructive pulmonary disease, Eur. Respir. J, 54, (2019); Wang C., Xu J., Yang L., Xu X., Zhang X., Bai C., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): A national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Miravitlles M., Soler-Cataluna J.J., Calle M., Soriano J.B., Treatment of COPD by clinical phenotypes: Putting old evidence into clinical practice, Eur. Respir. 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Med, 49, pp. 23-30, (2010); Liu Z., Lin L., Liu X., Clinical application value of impulse oscillometry in geriatric patients with COPD, Int. J. Chronic Obstr. Pulm. Dis, 12, pp. 897-905, (2017); Li L.Y., Yan T.S., Yang J., Li Y.Q., Fu L.X., Lan L., Liang B.M., Wang M.Y., Luo F.M., Impulse oscillometry for detection of small airway dysfunction in subjects with chronic respiratory symptoms and preserved pulmonary function, Respir. Res, 22, (2021); Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease: 2023 Report, (2023); Global Strategy for Asthma Management and Prevention, 2023 Update, (2023); Demsar J., Curk T., Erjavec A., Gorup C., Hocevar T., Milutinovic M., Mozina M., Polajnar M., Toplak M., Staric A., Et al., Orange: Data Mining Toolbox in Python, J. Mach. Learn. Res, 14, pp. 2349-2353, (2013); Porojan-Suppini N., Fira-Mladinescu O., Marc M., Tudorache E., Oancea C., Lung Function Assessment by Impulse Oscillometry in Adults, Ther. Clin. Risk Manag, 16, pp. 1139-1150, (2020); Shirai T., Kurosawa H., Clinical Application of the Forced Oscillation Technique, Intern. Med, 55, pp. 559-566, (2016); Ma X., Wu Y., Zhang L., Yuan W., Yan L., Fan S., Lian Y., Zhu X., Gao J., Zhao J., Et al., Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population, J. Transl. Med, 18, (2020); Zhang B., Wang J., Chen J., Ling Z., Ren Y., Xiong D., Guo L., Machine learning in chronic obstructive pulmonary disease, Chin. Med. J, 136, pp. 536-538, (2023); Amaral J.L., Lopes A.J., Jansen J.M., Faria A.C., Melo P.L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Comput. Methods Programs Biomed, 105, pp. 183-193, (2012)","C.-W. Huang; Division of Chest Medicine, Department of Internal Medicine, Asia University Hospital, Taichung, 413505, Taiwan; email: 108184@ctust.edu.tw","","Multidisciplinary Digital Publishing Institute (MDPI)","","","","","","20754426","","","","English","J. Pers. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85191553960"
"Stempel D.; Nemala S.K.; Lynch B.; Gardner D.D.; Winders T.","Stempel, David (35263153000); Nemala, Sridhar Krishna (58108346300); Lynch, Briana (58108346400); Gardner, Donna D. (8973347200); Winders, Tonya (57194740752)","35263153000; 58108346300; 58108346400; 8973347200; 57194740752","Application of passive monitoring of nighttime respiratory symptoms in chronic asthma management","2023","Journal of Allergy and Clinical Immunology: In Practice","11","5","","1559","1561","2","0","10.1016/j.jaip.2022.12.050","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148369230&doi=10.1016%2fj.jaip.2022.12.050&partnerID=40&md5=21933f74bd9a474d427040a0e4f631e5","CurieAi, Inc., Santa Clara, Calif, United States; Allergy & Asthma Network, Vienna, Va","Stempel D., CurieAi, Inc., Santa Clara, Calif, United States; Nemala S.K., CurieAi, Inc., Santa Clara, Calif, United States; Lynch B., CurieAi, Inc., Santa Clara, Calif, United States; Gardner D.D., Allergy & Asthma Network, Vienna, Va; Winders T., Allergy & Asthma Network, Vienna, Va","[No abstract available]","","Asthma; Humans; Article; artificial intelligence; asthma; decision making; emergency ward; follow up; health care personnel; hospitalization; human; monitoring; oximetry; personalized medicine; physical activity; pulse oximetry; respiratory tract disease; spirometry; virus infection; wheezing; asthma","","","","","Sanofi Genzyme","The study was funded by a Corporate Social Responsibility research grant from Sanofi Genzyme. ","Globe G., Martin M., Schatz M., Wiklund I., Lin J., von Maltzahn R., Et al., Symptoms and markers of symptom severity in asthma—content validity of the asthma symptom diary, Health Qual Life Outcomes, 13, (2015); (2021); Cloutier M.M., Baptist A.P., Blake K.V., Brooks E.G., Bryant-Stephens T., DiMango E., 2020 focused updates to the Asthma Management Guidelines: a report from the National Asthma Education and Prevention Program Coordinating Committee Expert Panel Working Group, J Allergy Clin Immunol, 146, pp. 1217-1270, (2020); Nathan R.A., Sorkness C.A., Kosinski M., Schatz M., Li J.T., Marcus P., Et al., Development of the asthma control test: a survey for assessing asthma control, J Allergy Clin Immunol, 113, pp. 59-65, (2004); Jones P.W., Harding G., Berry P., Wiklund I., Chen W.-H., Kline Leidy N., Development and first validation of the COPD assessment test, Eur Respir J, 34, pp. 648-654, (2009); Mallia P., Johnston S.L., How viral infections cause exacerbation of airway diseases, Chest, 130, pp. 1203-1210, (2006); Kew K.M., Malik P., Aniruddhan K., Normansell R., Shared decision-making for people with asthma, Cochrane Database Syst Rev, 10, (2017); Expert panel report 3: guidelines for the diagnosis and management of asthma, (2007); Rhee H., Miner S., Sterling M., Halterman J.S., Fairbanks E., The development of an automated device for asthma monitoring for adolescents: methodologic approach and user acceptability, JMIR Mhealth Uhealth, 2, (2014)","B. Lynch; Santa Clara, 3979 Freedom Circle, Mission Towers, Suite 340, 95054; email: briana.lynch@curieai.com","","American Academy of Allergy, Asthma and Immunology","","","","","","22132198","","","36720385","English","J. Allergy Clin. Immunol. Pract.","Article","Final","","Scopus","2-s2.0-85148369230"
"Xu J.; Talankar S.; Pan J.; Harmon I.; Wu Y.; Fedele D.A.; Brailsford J.; Fishe J.N.","Xu, Jie (58966963300); Talankar, Sankalp (59227878000); Pan, Jinqian (59227728600); Harmon, Ira (57266060200); Wu, Yonghui (55645924700); Fedele, David A. (34871707100); Brailsford, Jennifer (57219470722); Fishe, Jennifer Noel (57160424400)","58966963300; 59227878000; 59227728600; 57266060200; 55645924700; 34871707100; 57219470722; 57160424400","Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study","2024","JMIR Research Protocols","13","","e57981","","","","0","10.2196/57981","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199180471&doi=10.2196%2f57981&partnerID=40&md5=c87536c7e076aeabec4facebb296e770","Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States; Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States; Department of Clinical and Health Psychology, University of Florida College of Public Health and Health Professions, Gainesville, FL, United States; Department of Emergency Medicine, Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States","Xu J., Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States; Talankar S., Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States; Pan J., Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States; Harmon I., Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States; Wu Y., Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States; Fedele D.A., Department of Clinical and Health Psychology, University of Florida College of Public Health and Health Professions, Gainesville, FL, United States; Brailsford J., Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States; Fishe J.N., Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States, Department of Emergency Medicine, Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, FL, United States","Background: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice. Objective: The aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time. Methods: This mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma. Results: OneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project. Conclusions: Pediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML to large, multicenter RWD data. © 2024 JMIR Publications Inc.. All rights reserved.","federated learning; machine learning; pediatric asthma; qualitative research","","","","","","National Institutes of Health, NIH; UK Research and Innovation, UKRI, (103526); National Heart, Lung, and Blood Institute, NHLBI, (1R01HL169277)","This study obtained funding from the National Institutes of Health/National Heart, Lung, and Blood Institute on September 1, 2023 (1R01HL169277). In September 2024, we began data abstraction. The OneFlorida+ data trust contains approximately 21.29 million patients. Between 2011 and 2023, OneFlorida+ recorded data for 411,628 patients aged 2-18 years and contained 11,156,148 clinical notes.","Zhang D, Zheng J., The burden of childhood asthma by age group, 1990-2019: a systematic analysis of Global Burden of Disease 2019 data, Front Pediatr, 10, (2022); Most recent national asthma data; Healthcare use data 2020; Healthcare Cost and Utilization Project National (Nationwide) Inpatient Sample (NIS); Sullivan PW, Ghushchyan V, Navaratnam P, Friedman HS, Kavati A, Ortiz B, Et al., The national cost of asthma among school-aged children in the United States, Ann Allergy Asthma Immunol, 119, 3, pp. 246-252, (2017); Wardlaw AJ, Silverman M, Siva R, Pavord ID, Green R., Multi-dimensional phenotyping: towards a new taxonomy for airway disease, Clin Exp Allergy, 35, 10, pp. 1254-1262, (2005); Akar-Ghibril N, Casale T, Custovic A, Phipatanakul W., Allergic endotypes and phenotypes of asthma, J Allergy Clin Immunol Pract, 8, 2, pp. 429-440, (2020); Social determinants of health; Liao M, Li Y, Kianifard F, Obi E, Arcona S., Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis, BMC Nephrol, 17, 1, (2016); Xu J, Glicksberg BS, Su C, Walker P, Bian J, Wang F., Federated learning for healthcare informatics, J Healthc Inform Res, 5, 1, pp. 1-19, (2021); Identifying pediatric asthma subtypes using novel privacy-preserving federated machine learning methods; Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D., Key challenges for delivering clinical impact with artificial intelligence, BMC Med, 17, 1, (2019); Brownson RC, Jacobs JA, Tabak RG, Hoehner CM, Stamatakis KA., Designing for dissemination among public health researchers: findings from a national survey in the United States, Am J Public Health, 103, 9, pp. 1693-1699, (2013); OneFlorida+ Consortium; PHI on HiPerGator process; Martinez J, Piersol CV, Lucas K, Leland NE., Operationalizing stakeholder engagement through the Stakeholder-Centric Engagement Charter (SCEC), J Gen Intern Med, 37, pp. 105-108, (2022); Expert Panel Report 3: Guidelines for the diagnosis and management of asthma, (2017); Richesson RL, Smerek MM, Blake Cameron C., A framework to support the sharing and reuse of computable phenotype definitions across health care delivery and clinical research applications, EGEMS, 4, 3, (2016); Ross MK, Zheng H, Zhu B, Lao A, Hong H, Natesan A, Et al., Accuracy of asthma computable phenotypes to identify pediatric asthma at an academic institution, Methods Inf Med, 59, 6, pp. 219-226, (2020); Al Sallakh MA, Vasileiou E, Rodgers SE, Lyons RA, Sheikh A, Davies GA., Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review, Eur Respir J, 49, 6, (2017); Nissen F, Quint JK, Wilkinson S, Mullerova H, Smeeth L, Douglas IJ., Validation of asthma recording in electronic health records: a systematic review, Clin Epidemiol, 9, pp. 643-656, (2017); Buderer NMF., Statistical methodology: I. 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PheKB; Tang M, Goldstein BA, He J, Hurst JH, Lang JE., Performance of a computable phenotype for pediatric asthma using the problem list, Ann Allergy Asthma Immunol, 125, 5, pp. 611-613, (2020); Peer K, Adams WG, Legler A, Sandel M, Levy JI, Boynton-Jarrett R, Et al., Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records, J Allergy Clin Immunol, 147, 6, pp. 2162-2170, (2021)","J.N. Fishe; Department of Emergency Medicine, Center for Data Solutions, University of Florida College of Medicine - Jacksonville, Jacksonville, 655 W 8th St., 32209, United States; email: Jennifer.Fishe@jax.ufl.edu","","JMIR Publications Inc.","","","","","","19290748","","","","English","JMIR Res. Prot.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85199180471"
"Almaliki A.H.; Khattak A.","Almaliki, Abdulrazak H. (57209395620); Khattak, Afaq (56668489200)","57209395620; 56668489200","Synergizing TabNet and SHAP for PM10 Forecasting: Insights From Makkah, Saudi Arabia","2024","IEEE Access","12","","","195528","195543","15","0","10.1109/ACCESS.2024.3520815","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213064283&doi=10.1109%2fACCESS.2024.3520815&partnerID=40&md5=f0c9d89075b67c328d7ea2a7759488ae","Taif University, College of Engineering, Department of Civil Engineering, Taif, 21974, Saudi Arabia; Trinity College Dublin, Department of Civil, Structural and Environmental Engineering, Dublin, D02 PN40, Ireland","Almaliki A.H., Taif University, College of Engineering, Department of Civil Engineering, Taif, 21974, Saudi Arabia; Khattak A., Trinity College Dublin, Department of Civil, Structural and Environmental Engineering, Dublin, D02 PN40, Ireland","Particulate matter (PM10) poses a serious threat to public health by increasing the risk of respiratory issues like asthma and bronchitis, as well as cardiovascular problems such as heart attacks and strokes. In Makkah, Saudi Arabia, the combined impact of dense vehicular traffic, large-scale construction projects, and an arid climate contributes to elevated PM10 concentrations, posing substantial challenges to air quality management and urban sustainability. This study utilizes the TabNet model to estimate PM10 concentrations, taking advantage of its ability to perform sparse feature selection and sequential decision-making to uncover complex relationships among different environmental variables. To improve the predictive accuracy of proposed model, hyperparameter tuning was carried out using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The dataset containing meteorological and atmospheric parameters was collected from the Haram station in Makkah over the period from January 2016 to December 2018. TabNet outperformed other machine learning models, achieving a Mean Absolute Error (MAE) of 8.27 and coefficient of determination (R2) of 0.872 on the training set, while attaining an MAE of 9.05 and an R2 of 0.805 on the testing set. Afterwards, SHAP analysis illustrated the relative contributions of various features to PM10 concentrations, identifying atmospheric pressure as the most significant factor, closely followed by humidity and temperature. Lower to medium atmospheric pressure was found to substantially elevate PM10 levels, whereas medium to high humidity and elevated temperatures were likewise associated with increased PM10 concentrations. Furthermore, SHAP interaction plots revealed a moderating effect of atmospheric pressure on the influence of temperature on PM10 levels. These insights highlight the importance of considering both individual and interactive environmental factors when developing air quality models, leading to a deeper understanding of air pollution dynamics in Makkah and supporting more effective mitigation strategies.  © 2013 IEEE.","air quality; Makkah; PM10; SHAP; TabNet","Air quality; Diseases; Health risks; Public risks; Risk assessment; Makkah; Mean absolute error; Particulate Matter; Pm10; PM10 concentration; Pm10 levels; Respiratory disorders; Saudi Arabia; SHAP; Tabnet; Covariance matrix","","","","","Taif University, TU, (TU-DSPP-2024-173); Taif University, TU","This work was supported by Taif University, Saudi Arabia, under Project TU-DSPP-2024-173.","Manisalidis I., Stavropoulou E., Stavropoulos A., Bezirtzoglou E., Environmental and health impacts of air pollution: A review, Frontiers Public Health, 8, (2020); Zhang X., Han L., Wei H., Tan X., Zhou W., Li W., Qian Y., Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development, J. 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Syst., pp. 1-9, (2017); Schapire R.E., Explaining AdaBoost, Empirical Inference: Festschrift in Honor of Vladimir, pp. 37-52, (2013); Natekin A., Knoll A., Gradient boosting machines, a tutorial, Frontiers Neurorobotics, 7, (2013); Feng D.-C., Wang W.-J., Mangalathu S., Taciroglu E., Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls, J. Structural Eng., 147, 11, (2021); Aas K., Jullum M., Loland A., Explaining individual predictions when features are dependent: More accurate approximations to Shapley values, Artif. Intell., 298, (2021)","A. Khattak; Trinity College Dublin, Department of Civil, Structural and Environmental Engineering, Dublin, D02 PN40, Ireland; email: akhattak@tcd.ie","","Institute of Electrical and Electronics Engineers Inc.","","","","","","21693536","","","","English","IEEE Access","Article","Final","","Scopus","2-s2.0-85213064283"
"Sangisetti B.R.; Pabboju S.","Sangisetti, Bhagya Rekha (57210827802); Pabboju, Suresh (56440253100)","57210827802; 56440253100","Deep fit_predic: a novel integrated pyramid dilation EfficientNet-B3 scheme for fitness prediction system","2024","Computer Methods in Biomechanics and Biomedical Engineering","27","14","","2009","2023","14","0","10.1080/10255842.2023.2269287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174536444&doi=10.1080%2f10255842.2023.2269287&partnerID=40&md5=fde2a15a44127ff33b3a5b2291bda1eb","Department of Computer Science & Engineering, University College of Engineering, Osmania University, Telangana, Hyderabad, India; Department of Computer Science & Engineering, Anurag University, Telangana, Hyderabad, India; Department of Information Technology, Chaitanya Bharathi Institute of Technology, Telangana, Hyderabad, India","Sangisetti B.R., Department of Computer Science & Engineering, University College of Engineering, Osmania University, Telangana, Hyderabad, India, Department of Computer Science & Engineering, Anurag University, Telangana, Hyderabad, India; Pabboju S., Department of Information Technology, Chaitanya Bharathi Institute of Technology, Telangana, Hyderabad, India","This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person’s health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%. © 2023 Informa UK Limited, trading as Taylor & Francis Group.","EfficientNetB3; enhanced chameleon swarm optimizer; Fitness prediction system; individual health profiles; one-hot encoding; pyramid dilation module","Algorithms; Cluster Analysis; Deep Learning; Female; Humans; Male; Physical Fitness; Clustering algorithms; Deep learning; Encoding (symbols); Signal encoding; Efficientnetb3; Encodings; Enhanced chameleon swarm optimizer; Fitness prediction system; Individual health profile; Learning techniques; One-hot encoding; Prediction systems; Pyramid dilation module; Swarm optimizer; accuracy; air quality; altitude; arthritis; Article; artificial intelligence; artificial neural network; asthma; body weight; cardiovascular disease; clustering algorithm; deep learning; deep reinforcement learning; diabetes mellitus; diet; EfficientNet-B3 scheme; exercise; feature extraction; fitness; gravity; heart rate; influenza; latitude; longitude; Minkowski integrated gravity center clustering; motivation; neurology; physical activity; prediction; recurrent neural network; rowing; running; training; urinary tract infection; walking; algorithm; cluster analysis; female; fitness; human; male; physiology; Forecasting","","","","","","","Abdulaziz M., Al-Motairy B., Al-Ghamdi M., Al-Qahtani N., Building a personalized fitness recommendation application based on sequential information, Int J Adv Comput Sci Appl, 12, 1, (2021); Alanazi A., Using machine learning for healthcare challenges and opportunities, Inf Med Unlocked, 30, (2022); Alkhalaf M., Yu P., Shen J., Deng C., A review of the application of machine learning in adult obesity studies, Appl Comput Intelligence, 2, 1, pp. 32-48, (2022); Bhimavarapu U., Sreedevi M., Chintalapudi N., Battineni G., Physical activity recommendation system based on deep learning to prevent respiratory diseases, Computers, 11, 10, (2022); Bijalwan V., Semwal V.B., Singh G., Gonzalez Crespo R., Heterogeneous computing model for post-injury walking pattern restoration and postural stability rehabilitation exercise recognition, Expert Systems, 39, 6, (2022); Boukhennoufa I., Zhai X., Utti V., Jackson J., McDonald-Maier K.D., Wearable sensors and machine learning in post-stroke rehabilitation assessment: a systematic review, Biomed Signal Process Control, 71, (2022); Chen Y., Ning Y., Chai Z., Rangwala H., Federated multi-task learning with hierarchical attention for sensor data analytics, 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow, UK: IEEE, pp. 1-8, (2020); Ferreira B., Ferreira P.M., Pinheiro G., Figueiredo N., Carvalho F., Menezes P., Batista J., Deep learning approaches for workout repetition counting and validation, Pattern Recog Lett, 151, pp. 259-266, (2021); Freschlin C.R., Fahlberg S.A., Romero P.A., Machine learning to navigate fitness landscapes for protein engineering, Curr Opin Biotechnol, 75, (2022); Ghassemi M., Naumann T., Schulam P., Beam A.L., Chen I.Y., Ranganath R., A review of challenges and opportunities in machine learning for health, AMIA Summits on Translational Science Proceedings, (2020); Huang S.C., Pareek A., Seyyedi S., Banerjee I., Lungren M.P., Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines, NPJ Digit Med, 3, 1, (2020); Jiang X., On T., Phan N., Mohammadi H., Mayyuri V.D., Chen A., Borcea C., Zone-based Federated Learning for Mobile Sensing Data, 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom). Atlanta, GA: IEEE, pp. 141-148, (2023); Jossa-Bastidas O., Zahia S., Fuente-Vidal A., Ferez N.S., Noguera O.R., Montane J., Garcia-Zapirain B., Predicting physical exercise adherence in fitness apps using a deep learning approach, Int J Environ Res Public Health, 18, 20, (2021); Kulkarni A., Seetharam A., Ramesh A., Deepfit: deep learning based fitness center equipment use modeling and prediction, Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. Houston, TX: Association for Computing Machinery;, pp. 394-403, (2019); Liu X., Gao B., Suleiman B., You H., Ma Z., Liu Y., Anaissi A., Privacy-preserving personalized fitness recommender system P3FitRec: a multi-level deep learning approach, ACM Trans Knowl Discov Data, 17, 6, pp. 1-24, (2023); Mahyari A., Pirolli P., Physical exercise recommendation and success prediction using interconnected recurrent neural networks, 2021 IEEE International Conference on Digital Health (ICDH). Chicago, IL: IEEE, pp. 148-153, (2021); Mekruksavanich S., Jitpattanakul A., CNN-based deep learning network for human activity recognition during physical exercise from accelerometer and photoplethysmographic sensors, Computer networks, big data and IoT: proceedings of ICCBI 2021, pp. 531-542, (2022); Mekruksavanich S., Jitpattanakul A., RNN-based deep learning for physical activity recognition using smartwatch sensors: a case study of simple and complex activity recognition, Math Biosci Eng, 19, 6, pp. 5671-5698, (2022); Mogaveera D., Mathur V., Waghela S., e-Health monitoring system with diet and fitness recommendation using machine learning, 2021 6th International Conference on Inventive Computation Technologies (ICICT). Coimbatore, India: IEEE;, pp. 694-700, (2021); Mulani J., Heda S., Tumdi K., Patel J., Chhinkaniwala H., Patel J., Deep reinforcement learning based personalized health recommendations, Deep learning techniques for biomedical and health informatics, pp. 231-255, (2020); Ni J., Muhlstein L., McAuley J., Modeling heart rate and activity data for personalized fitness recommendation, 1343–1353, (2019); Notin P., Dias M., Frazer J., Hurtado J.M., Gomez A.N., Marks D., Gal Y., Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval, International Conference on Machine Learning PMLR;, pp. 16990-17017, (2022); Skolik A., Jerbi S., Dunjko V., Quantum agents in the gym: a variational quantum algorithm for deep q-learning, Quantum, 6, (2022); Smyth B., Lawlor A., Berndsen J., Feely C., Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners, User Model User-Adapt Interact, 32, 5, pp. 787-838, (2022); Stromback D., Huang S., Radu V., Mm-fit: multi-modal deep learning for automatic exercise logging across sensing devices, Proc ACM Interact Mob Wearable Ubiquitous Technol, 4, 4, pp. 1-22, (2020); Sujith A.V.L.N., Sajja G.S., Mahalakshmi V., Nuhmani S., Prasanalakshmi B., Systematic review of smart health monitoring using deep learning and Artificial intelligence, Neurosci Informatics, 2, 3, (2022); Vellido A., The importance of interpretability and visualization in machine learning for applications in medicine and health care, Neural Comput Appl, 32, 24, pp. 18069-18083, (2020); Wang J., Wu B., Jiang Y., Yuan Y., Research on prediction of physical fitness test results in colleges and universities based on deep learning, Mathematical problems in engineering., (2022); Zhao Z., Arya A., Orji R., Chan G., Physical activity recommendation for exergame player modeling using machine learning approach, 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH). Vancouver, BC: IEEE;, pp. 1-9, (2020)","B.R. Sangisetti; Department of Computer Science & Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India; email: bhagyarekha2001@gmail.com","","Taylor and Francis Ltd.","","","","","","10255842","","","37865927","English","Comput. Methods Biomech. Biomed. Eng.","Article","Final","","Scopus","2-s2.0-85174536444"
"Chin W.-C.; Huang S.-Y.; Liu F.-Y.; Wang C.-H.; Tang I.; Hsiao I.-T.; Huang Y.-S.","Chin, Wei-Chih (57193379888); Huang, Sheng-Yao (57202921343); Liu, Feng-Yuan (26643015200); Wang, Chih-Huan (57192708652); Tang, I. (57312045500); Hsiao, Ing-Tsung (7004692799); Huang, Yu-Shu (57133257700)","57193379888; 57202921343; 26643015200; 57192708652; 57312045500; 7004692799; 57133257700","The application of machine learning on brain imaging features of different narcolepsy subtypes","2024","Sleep","47","2","zsad328","","","","0","10.1093/sleep/zsad328","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184659099&doi=10.1093%2fsleep%2fzsad328&partnerID=40&md5=c224adb2f165a237efc8f44edf0f18be","Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan; College of Life Sciences and Medicine, National Tsing Hua University, Hsinchu, Taiwan; Department of Mathematics, Soochow University, Taipei, Taiwan; Department of Medical Imaging and Radiological Sciences, College of Medicine and Healthy Aging Center, Chang Gung University, Taoyuan, Taiwan; Department of Nuclear Medicine, Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan; Department of Psychology, Zhejiang Normal University, Zhejiang, China","Chin W.-C., Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan, College of Life Sciences and Medicine, National Tsing Hua University, Hsinchu, Taiwan; Huang S.-Y., Department of Mathematics, Soochow University, Taipei, Taiwan; Liu F.-Y., Department of Medical Imaging and Radiological Sciences, College of Medicine and Healthy Aging Center, Chang Gung University, Taoyuan, Taiwan, Department of Nuclear Medicine, Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan; Wang C.-H., Department of Psychology, Zhejiang Normal University, Zhejiang, China; Tang I., Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan; Hsiao I.-T., Department of Medical Imaging and Radiological Sciences, College of Medicine and Healthy Aging Center, Chang Gung University, Taoyuan, Taiwan, Department of Nuclear Medicine, Molecular Imaging Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan; Huang Y.-S., Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan, Taiwan","Study Objectives: Narcolepsy is a central hypersomnia disorder, and differential diagnoses between its subtypes can be difficult. Hence, we applied machine learning to analyze the positron emission tomography (PET) data of patients with type 1 or type 2 narcolepsy, and patients with type 1 narcolepsy and comorbid schizophrenia, to construct predictive models to facilitate the diagnosis. Methods: This is a retrospective and prospective case–control study of adolescent and young adult patients with type 1 or type 2 narcolepsy, and type 1 narcolepsy and comorbid schizophrenia. All participants received 18-F-fluorodeoxy glucose PET, sleep studies, neurocognitive tests, sleep questionnaires, and human leukocyte antigen typing. The collected PET data were analyzed by feature selections and classification methods in machine learning to construct predictive models. Results: A total of 314 participants with narcolepsy were enrolled; 204 had type 1 narcolepsy, 90 had type 2 narcolepsy, and 20 had type 1 narcolepsy and comorbid schizophrenia. We used three filter methods for feature selection followed by a comparative analysis of classification methods. To apply a small number of regions of interest (ROI) and high classification accuracy, the Naïve Bayes classifier with the Term Variance as feature selection achieved the goal with only three ROIs (left basal ganglia, left Heschl, and left striatum) and produced an accuracy of higher than 99%. Conclusions: The accuracy of our predictive model of PET data are promising and can aid clinicians in the diagnosis of narcolepsy subtypes. Future research with a larger sample size could further refine the predictive model of narcolepsy. © 2024 Oxford University Press. All rights reserved.","feature selection; machine learning; PET; type 1 narcolepsy; type 2 narcolepsy","fluorodeoxyglucose f 18; glucose; adolescent; adult; Article; Asperger syndrome; asthma; attention deficit hyperactivity disorder; auditory hallucination; basal ganglion; Bayesian learning; bipolar disorder; case control study; cataplexy; comorbidity; controlled study; corpus striatum; decision tree; delusion; diabetes mellitus; disease classification; feature selection; female; generalized anxiety disorder; Heschl gyrus; HLA typing; human; hypersomnia; hypertension; hypnagogic hallucination; hypnopompic hallucination; insomnia; k nearest neighbor; machine learning; major clinical study; major depression; male; multiclass classification; narcolepsy; neuroimaging; nose allergy; obesity; obstructive sleep apnea; parasomnia; periodic limb movement disorder; polysomnography; positron emission tomography; predictive model; prospective study; questionnaire; REM sleep; retrospective study; schizophrenia; sleep latency; sleep study; support vector machine; thyroid disease; tic; visual hallucination; young adult; article; classifier; diagnosis; differential diagnosis; drug therapy; feature selection; narcolepsy with cataplexy; narcolepsy without cataplexy; sleep questionnaire; special situation for pharmacovigilance","","fluorodeoxyglucose f 18, 63503-12-8; glucose, 50-99-7, 84778-64-3, 8027-56-3","","","Chang Gung Memorial Hospital, CGMH, (3L0291, 3L0292, 3M0051); Chang Gung Memorial Hospital, CGMH; Ministry of Science and Technology, Taiwan, MOST, (107-2314-B-182A-128-MY2, 109-2314-B-182A-112-MY3); Ministry of Science and Technology, Taiwan, MOST","This study was partially supported by Taiwan’s Ministry of Science and Technology grant # MOST 107-2314-B-182A-128-MY2 and 109-2314-B-182A-112-MY3 and Chang Gung Memorial Hospital Research Grants #CMRPG 3M0051 to YS Huang and #CMRPG 3L0291 and 3L0292 to Wei-Chih Chin. ","Silber MH, Krahn LE, Olson EJ, Pankratz VS., The epidemiology of narcolepsy in Olmsted County, Minnesota: a population-based study, Sleep, 25, 2, pp. 197-202, (2002); The International Classification of Sleep Disorders: Diagnostic and Coding Manual, (2005); International Classification of Sleep Disorders, (2014); Nishino S, Okuro M, Kotorii N, Et al., Hypocretin/orexin and narcolepsy: New basic and clinical insights, Acta Physiol (Oxf), 198, 3, pp. 209-222, (2010); Juji T, Satake M, Honda Y, Doi Y., HLA antigens in Japanese patients with narcolepsy All the patients were DR2 positive, Tissue Antigens, 24, pp. 316-319, (1984); Thannickal TC, Moore RY, Nienhuis R, Et al., Reduced number of hypocretin neurons in human narcolepsy, Neuron, 27, pp. 469-474, (2000); Tandon R, Keshavan MS, Nasrallah HAS., “just the facts” what weknow in 2008 2 Epidemiology and etiology, Schizophr Res, 102, 1-3, pp. 1-18, (2008); Ruoff CM, Reaven NL, Funk SE, Et al., High rates of psychiatric comorbidity in narcolepsy: Findings from the Burden of Narcolepsy Disease (BOND) study of 9,312 patients in the United States, J Clin Psychiatry, 78, 2, pp. 171-176, (2017); Douglass AB, Hays P, Pazderka F, Russell JM., Florid refractory schizophrenias that turn out to be treatable variants of HLA-associated narcolepsy, J Nerv Ment Dis, 179, 1, pp. 12-17, (1991); Huang YS, Hsiao T, Liu FY, Et al., Neurocognition, sleep, and PET findings in type 2 vs type 1 narcolepsy, Neurology, 90, 17, pp. e1478-e1487, (2018); Huang YS, Liu FY, Lin CY, Hsiao IT, Guilleminault C., Brain imaging and cognition in young narcoleptic patients, Sleep Med, 24, pp. 137-144, (2016); Chin WC, Liu FY, Huang YS, Hsiao IT, Wang CH, Chen YC., Different positron emission tomography findings in schizophrenia and narcolepsy type 1 in adolescents and young adults: A preliminary study, J Clin Sleep Med, 17, 4, pp. 739-748, (2021); Diagnostic and statistical manual of mental disorders, 5th ed (DSM-5), (2013); Joshi A, Koeppe RA, Fessler JA., Reducing between scanner differences in multi-center PET studies, Neuroimage, 46, 1, pp. 154-159, (2009); Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Et al., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, 15, 1, pp. 273-289, (2002); Quinlan JR., Induction of decision trees, Mach Learn, 1, 1, pp. 81-106, (1986); Pudjihartono N, Fadason T, Kempa-Liehr AW, O'Sullivan JM., A review of feature selection methods for machine learning-based disease risk prediction, Front Bioinform, 2, (2022); Malan NS, Sharma S., Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals, Comput Biol Med, 107, pp. 118-126, (2019); Altman NS., An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician, 46, 3, pp. 175-185, (1992); Mozina M, Demsar J, Kattan M, Zupan B., Nomograms for visualization of naive Bayesian classifier, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 337-348, (2004); Zhang GP., Neural networks for classification: A survey, IEEE Trans Syst Man Cybern C Appl Rev, 30, 4, pp. 451-462, (2000); Daqi G., Tao Z., Support vector machine classifiers using RBF kernels with clustering-based centers and widths, 2007 International Joint Conference on Neural Networks, (2007); Snoek J, Larochelle H, Adams RP., Practical bayesian optimization of machine learning algorithms, Adv Neural Info Process, 25, pp. 2960-2968, (2012); Passos D, Mishra P., A tutorial on automatic hyperparameter tuning of deep spectral modelling for regression and classification tasks, Chemometr Intell Lab Syst, 223, (2022); Isabona J, Imoize AL, Kim Y., Machine learning-based boosted regression ensemble combined with hyperparameter tuning for optimal adaptive learning, Sensors (Basel), 22, 10, (2022); Stephansen JB, Olesen AN, Olsen M, Et al., Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy, Nat Commun, 9, 1, pp. 1-15, (2018); Zhang Z, Mayer G, Dauvilliers Y, Et al., Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning, Sci Rep, 8, 1, pp. 1-11, (2018); Dauvilliers Y, Siegel JM, Lopez R, Torontali ZA, Peever JH., Cataplexy—clinical aspects, pathophysiology and management strategy, Nat Rev Neurol, 10, 7, pp. 386-395, (2014); Kanayashi T, Yano T, Ishiguro H, Et al., Hypocretin (orexin) levels in human lumbar CSF in different age groups: Infants to elderly persons, Sleep, 25, pp. 337-339, (2002); Huang YS, Guilleminault C, Chen CH, Lai PC, Hwang FM., Narcolepsycataplexy and schizophrenia in adolescents, Sleep Med, 15, 1, pp. 15-22, (2014); Fang F, Sun H, Wang Z, Ren M, Calabrese JR, Gao K., Antipsychotic drug induced somnolence: Incidence, mechanisms, and management, CNS Drugs, 30, 9, pp. 845-867, (2016); Okura M, Riehl J, Mignot E, Nishino S., Sulpiride, a D2/D3 blocker, reduces cataplexy but not REM sleep in canine narcolepsy, Neuropsychopharmacology, 23, 5, pp. 528-538, (2000); Dauvilliers Y, Tafti M, Landolt HP., Catechol-O-methyltransferase, dopamine, and sleep-wake regulation, Sleep Med Rev, 22, pp. 47-53, (2015); Gent TC, Bassetti CL, Adamantidis AR., Sleep-wake control and the thalamus, Curr Opin Neurobiol, 52, pp. 188-197, (2018); Lazarus M, Chen JF, Urade Y, Huang ZL., Role of the basal ganglia in the control of sleep and wakefulness, Curr Opin Neurobiol, 23, 5, pp. 780-785, (2013); Blouin AM, Thannickal TC, Worley PF, Baraban JM, Reti IM, Siegel JM., Nar immunostaining of human hypocretin (orexin) neurons: Loss in narcolepsy, Neurology, 65, 8, pp. 1189-1192, (2005); Gautam R, Sharma M., Prevalence and diagnosis of neurological disorders using different deep learning techniques: A meta-analysis, J Med Syst, 44, 2, (2020); Vieira S, Pinaya WH, Mechelli A., Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications, Neurosci Biobehav Rev, 74, pp. 58-75, (2017); Syu HY, Prediction Model of Narcolepsy Based on Ensemble Learning Approach, (2018); Iglesias G, Talavera E, Gonzalez-Prieto A, Mozo A, Gomez-Canaval S., Data augmentation techniques in time series domain: A survey and taxonomy, Neural Comput & Applic, 35, pp. 10123-10145, (2023); Wei J, Zou K., Eda: Easy data augmentation techniques for boosting performance on text classification tasks, 1901, (2019)","Y.-S. Huang; Department of Child Psychiatry and Sleep Center, Chang Gung Memorial Hospital Taoyuan, Taiwan, Taoyuan, No.5, Fuxing St., 333, Taiwan; email: yushuhuang1212@gmail.com; I.-T. Hsiao; Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, No. 259 Wen-hua 1st Road, 333, Taiwan; email: ihsiao@mail.cgu.edu.tw","","Oxford University Press","","","","","","01618105","","SLEED","38183289","English","Sleep","Article","Final","","Scopus","2-s2.0-85184659099"
"Hirons N.; Allen A.; Matsuyoshi N.; Su J.; Kaye L.; Barrett M.A.","Hirons, Nicholas (58700311800); Allen, Angier (57211532103); Matsuyoshi, Noah (58699519200); Su, Jason (11840142100); Kaye, Leanne (49961411000); Barrett, Meredith A. (23567765400)","58700311800; 57211532103; 58699519200; 11840142100; 49961411000; 23567765400","Prediction of short-acting beta-agonist usage in patients with asthma using temporal-convolutional neural networks","2023","JAMIA Open","6","4","ooad091","","","","0","10.1093/jamiaopen/ooad091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177052249&doi=10.1093%2fjamiaopen%2fooad091&partnerID=40&md5=f2a1d0b07d2740c2b407ac6047511abb","Propeller Health, San Francisco, CA, United States; ResMed Science Center, San Diego, CA, United States; School of Public Health, University of California Berkeley, Berkeley, CA, United States","Hirons N., Propeller Health, San Francisco, CA, United States; Allen A., ResMed Science Center, San Diego, CA, United States; Matsuyoshi N., Propeller Health, San Francisco, CA, United States; Su J., School of Public Health, University of California Berkeley, Berkeley, CA, United States; Kaye L., ResMed Science Center, San Diego, CA, United States; Barrett M.A., ResMed Science Center, San Diego, CA, United States","Objective: Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy. Methods: Participants with self-reported asthma enrolled in a digital health platform (Propeller Health, WI), which included a smartphone application and inhaler sensors that collected the date and time of SABA use. Linear regression, random forests, and temporal convolutional networks (TCN) were applied to predict expected SABA puffs/person/day from SABA usage and environmental triggers. The models were compared with a simple baseline model using explained variance (R2), as well as using average precision (AP) and area under the receiving operator characteristic curve (ROC AUC) for predicting days with ≥1-10 puffs. Results: Data included 1.2 million days of data from 13 202 participants. A TCN outperformed other models in predicting puff count (R2 = 0.562) and day-over-day change in puff count (R2 = 0.344). The TCN predicted days with ≥10 puffs with an ROC AUC score of 0.952 and an AP of 0.762 for predicting a day with ≥1 puffs. SABA use over the preceding 7 days had the highest feature importance, with a smaller but meaningful contribution from air pollutant features. Conclusion: Predicted SABA use may serve as a valuable forward-looking signal to inform early clinical intervention and self-management. Further validation with known exacerbation events is needed.  © 2023 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association.","asthma; computer; neural networks; supervised machine learning; telemetry","beta adrenergic receptor stimulating agent; short acting drug; accuracy; adult; air pollutant; area under the curve; Article; asthma; caregiver; clinician; comparative study; controlled study; convolutional neural network; drug use; female; human; major clinical study; male; medical information; prediction; predictive model; random forest; receiver operating characteristic; retrospective study; self report; temporal convolutional neural network","","","","","","","Enilari O, Sinha S., The global impact of asthma in adult populations, Ann Glob Health, 85, 1, (2019); Barnett SBL, Nurmagambetov TA., Costs of asthma in the United States: 2002-2007, J Allergy Clin Immunol, 127, 1, pp. 145-152, (2011); Akinbami LJ, Moorman JE, Liu X., Asthma prevalence, health care use, and mortality: United States, 2005-2009, Natl Health Stat Report, 32, pp. 1-14, (2011); Anderson WC, Anderson WC, Gondalia R, Et al., Assessing asthma control: comparison of electronic-recorded short-acting betaagonist rescue use and self-reported use utilizing the asthma control test, J Asthma, 58, 2, pp. 271-275, (2021); Patel M, Pilcher J, Reddel HK, Et al., Predictors of severe exacerbations, poor asthma control, and b-agonist overuse for patients with asthma, J Allergy Clin Immunol Pract, 2, 6, pp. 751-758, (2014); Kaplan A, Mitchell PD, Cave AJ, Et al., Effective asthma management: is it time to let the AIR out of SABA?, J Clin Med, 9, 4, (2020); Stanford RH, Shah MB, D'Souza AO, Et al., Short-acting b-agonist use and its ability to predict future asthma-related outcomes, Ann Allergy Asthma Immunol, 109, 6, pp. 403-407, (2012); Jarrin R, Barrett MA, Kaye L, Et al., Need for clarifying remote physiologic monitoring reimbursement during the COVID-19 pandemic: a respiratory disease case study, NPJ Digit Med, 4, 1, (2021); Messinger AI, Luo G, Deterding RR., The doctor will see you now: how machine learning and artificial intelligence can extend our understanding and treatment of asthma, J Allergy Clin Immunol, 145, 2, pp. 476-478, (2020); County Health Rankings&Roadmaps 2022; Guarnieri M, Balmes JR., Outdoor air pollution and asthma, Lancet, 383, 9928, pp. 1581-1592, (2014); Parikh RB, Manz C, Chivers C, Et al., Machine learning approaches to predict 6-month mortality among patients with cancer, JAMA Netw Open, 2, 10, (2019); Desai RJ, Wang SV, Vaduganathan M, Et al., Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes, JAMA Network Open, 3, 1, (2020); Obermeyer Z, Powers B, Vogeli C, Et al., Dissecting racial bias in an algorithm used to manage the health of populations, Science, 366, 6464, pp. 447-453, (2019); Gilpin LH, Bau D, Yuan BZ, Et al., Explaining explanations: an overview of interpretability of machine learning, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80-89, (2018); Lundberg SC, Lee S-I., A unified approach to interpreting model predictions, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768-4777, (2017); Merchant R, Szefler SJ, Bender BG, Et al., Impact of a digital health intervention on asthma resource utilization, World Allergy Organ J, 11, 1, (2018); Merchant RK, Inamdar R, Quade RC., Effectiveness of population health management using the propeller health asthma platform: a randomized clinical trial, J Allergy Clin Immunol Pract, 4, 3, pp. 455-463, (2016); Remy P., Temporal convolutional networks for keras, (2020); van den Oord A, Dieleman S, Zen H, Et al., WaveNet: a generative model for raw audio, (2016); Bai S, Kolter JZ, Koltun V., An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, (2018); Su JG, Barrett MA, Combs V, Et al., Identifying impacts of air pollution on subacute asthma symptoms using digital medication sensors, Int J Epidemiol, 51, 1, pp. 213-224, (2022); Vaswani A, Shazeer N, Parmar N, Et al., Attention is All You Need, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6000-6010, (2017); Luo G, He S, Stone BL, Et al., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, 1, (2020); Finkelstein J, Jeong IC., Machine learning approaches to personalize early prediction of asthma exacerbations, Ann N Y Acad Sci, 1387, 1, pp. 153-165, (2017); Min X, Yu B, Wang F., Predictive modeling of the hospital readmission risk from patients' claims data using machine learning: a case study on COPD, Sci Rep, 9, 1, (2019); Davis J, Goadrich M., The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd international conference on Machine learning, pp. 233-240, (2006); Sheffield PE, Zhou J, Shmool JLC, Et al., Ambient ozone exposure and children's acute asthma in New York city: a case-crossover analysis, Environ Health, 14, (2015); Delamater PL, Finley AO, Banerjee S., An analysis of asthma hospitalizations, air pollution, and weather conditions in Los Angeles county, California, Sci Total Environ, 425, pp. 110-118, (2012); Pepper JR, Barrett MA, Su JG, Et al., Geospatial-temporal analysis of the impact of ozone on asthma rescue inhaler use, Environ Int, 136, (2020); Integrated Science Assessment (ISA) for Ozone and Related Photochemical Oxidants (External Review Draft, Sep 2019), (2019); Pope CA, Dockery DW., Acute health effects of PM10Pollution on symptomatic and asymptomatic children, Am Rev Respir Dis, 145, 5, pp. 1123-1128, (1992); Donaldson K, Gilmour MI, MacNee W., Asthma and PM10, Respir Res, 1, 1, pp. 12-15, (2000); Pope CA, Dockery DW, Spengler JD, Et al., Respiratory health and PM10Pollution: a daily time series analysis, Am Rev Respir Dis, 144, 3, pp. 668-674, (1991); Weinmayr G, Romeo E, De Sario M, Et al., Short-term effects of PM 10 and NO 2 on respiratory health among children with asthma or asthma-like symptoms: a systematic review and Meta-Analysis, Environ Health Perspect, 118, 4, pp. 449-457, (2010); Forno E, Celed-on JC., Asthma and ethnic minorities: socioeconomic status and beyond, Curr Opin Allergy Clin Immunol, 9, 2, pp. 154-160, (2009); Becker J., Building trust through transparency? FDA regulation of AI/ML-based software, (2021)","A. Allen; ResMed Science Center, San Diego, United States; email: angier.allen@resmed.com","","Oxford University Press","","","","","","25742531","","","","English","JAMIA Open","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85177052249"
"Sangana R.; Xu Y.; Shah B.; Tian X.; Zack J.; Shakeri-Nejad K.; Kalluri S.; Jones I.; Ligueros-Saylan M.; Taylor A.F.; Jain D.K.; Scosyrev E.; Uddin A.; Laurent N.; Paganoni P.","Sangana, Ramachandra (57203147726); Xu, Yan (58899621800); Shah, Bharti (57222965928); Tian, Xianbin (57195631556); Zack, Julia (53265622300); Shakeri-Nejad, Kasra (12809008900); Kalluri, Sampath (55846096800); Jones, Ieuan (7401689520); Ligueros-Saylan, Monica (7801546431); Taylor, Angel Fowler (55655956600); Jain, Devendra Kumar (57548081700); Scosyrev, Emil (23499172700); Uddin, Alkaz (57224802569); Laurent, Nathalie (56229678600); Paganoni, Paola (58899236000)","57203147726; 58899621800; 57222965928; 57195631556; 53265622300; 12809008900; 55846096800; 7401689520; 7801546431; 55655956600; 57548081700; 23499172700; 57224802569; 56229678600; 58899236000","Bioequivalence Between a New Omalizumab Prefilled Syringe With an Autoinjector or with a Needle Safety Device Compared with the Current Prefilled Syringe: A Randomized Controlled Trial in Healthy Volunteers","2024","Clinical Pharmacology in Drug Development","13","6","","611","620","9","0","10.1002/cpdd.1373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185683168&doi=10.1002%2fcpdd.1373&partnerID=40&md5=2e52c9d2d286e11a96057c11a1b17407","Novartis Institutes for Biomedical Research, Cambridge, MA, United States; Genentech Research and Early Development, South San Francisco, CA, United States; Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Novartis Pharma AG, Basel, Switzerland; Novartis Healthcare Pvt, Hyderabad, India","Sangana R., Novartis Institutes for Biomedical Research, Cambridge, MA, United States; Xu Y., Genentech Research and Early Development, South San Francisco, CA, United States; Shah B., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Tian X., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Zack J., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Shakeri-Nejad K., Novartis Pharma AG, Basel, Switzerland; Kalluri S., Novartis Healthcare Pvt, Hyderabad, India; Jones I., Novartis Pharma AG, Basel, Switzerland; Ligueros-Saylan M., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Taylor A.F., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Jain D.K., Novartis Pharma AG, Basel, Switzerland; Scosyrev E., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Uddin A., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States; Laurent N., Novartis Pharma AG, Basel, Switzerland; Paganoni P., Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States","Omalizumab is an anti-IgE monoclonal antibody currently approved for the treatment of asthma, nasal polyps/chronic rhinosinusitis with nasal polyps, and chronic spontaneous urticaria. Omalizumab is available as an injection in a prefilled syringe (PFS) with a needle safety device (NSD). New product configurations were developed to reduce the number of injections per dose administration, improve patient convenience and treatment compliance. The objective of this randomized open-label 12-week study was to demonstrate pharmacokinetic bioequivalence between (1) new PFS with autoinjector (PFS-AI), (2) new PFS-NSD configuration, and (3) current PFS-NSD configuration. Each new configuration was considered bioequivalent to the current configuration if the confidence intervals (CIs) for the geometric mean ratios (GMR) were contained in the 0.80-1.25 range for maximum concentration (Cmax), area under the concentration-time curve until the last quantifiable measurement (AUClast), and AUC extrapolated to infinity (AUCinf). Safety was assessed throughout the study. In total, 193 healthy volunteers were randomized at 1:1:1 ratio to omalizumab 1×300 mg/2 mL via new PFS-AI (n = 66), omalizumab 1×300 mg/2 mL via new PFS-NSD (n = 64), or omalizumab 2×150 mg/1 mL via current PFS-NSD (n = 63). Comparing new PFS-AI versus current PFS-NSD, the GMRs were: Cmax, 1.085; AUClast, 1.093; AUCinf, 1.100. Comparing new PFS-NSD versus current PFS-NSD, the GMRs were: Cmax, 1.006; AUClast, 1.016; AUCinf, 1.027. The 95% CIs for all GMR parameters were contained within the 0.80-1.25 range. Safety findings were consistent with the known safety profile of omalizumab. Single-dose omalizumab administered as the new PFS-AI or new PFS-NSD was bioequivalent to the current PFS-NSD. © 2024 Novartis Pharmaceuticals. Clinical Pharmacology in Drug Development published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.","autoinjector; bioequivalence; Omalizumab; pharmacokinetics","Adult; Area Under Curve; Female; Healthy Volunteers; Humans; Injections, Subcutaneous; Male; Middle Aged; Needles; Omalizumab; Syringes; Therapeutic Equivalency; Young Adult; immunoglobulin E; immunoglobulin Fc fragment; immunoglobulin G antibody; omalizumab; omalizumab; adult; antibody titer; area under the curve; Arizona; Article; bioequivalence; controlled study; creatine kinase blood level; disease severity; drug clearance; drug half life; drug safety; drug tolerability; female; headache; hemoglobin blood level; human; human experiment; immunogenicity; immunoglobulin blood level; incidence; injection site induration; injection site pain; limit of quantitation; male; maximum concentration; Nebraska; normal human; phase 1 clinical trial; pruritus; randomized controlled trial; single drug dose; time to maximum plasma concentration; volume of distribution; area under the curve; comparative study; middle aged; needle; normal human; subcutaneous drug administration; syringe; therapeutic equivalence; young adult","","immunoglobulin E, 37341-29-0; omalizumab, 242138-07-4; Omalizumab, ","","","Novartis","This study was funded by Novartis Pharma AG, Basel, Switzerland.","Busse W., Corren J., Lanier B.Q., Et al., Omalizumab, anti-IgE recombinant humanized monoclonal antibody, for the treatment of severe allergic asthma, J Allergy Clin Immunol, 108, 2, pp. 184-190, (2001); Zheng L., Li B., Qian W., Et al., Fine epitope mapping of humanized anti-IgE monoclonal antibody omalizumab, Biochem Biophys Res Commun, 375, 4, pp. 619-622, (2008); Gevaert P., Calus L., Van Zele T., Et al., Omalizumab is effective in allergic and nonallergic patients with nasal polyps and asthma, J Allergy Clin Immunol, 131, 1, pp. 110-116, (2013); Gevaert P., Omachi T.A., Corren J., Et al., Efficacy and safety of omalizumab in nasal polyposis: 2 randomized phase 3 trials, J Allergy Clin Immunol, 146, 3, pp. 595-605, (2020); Kaplan A., Ferrer M., Bernstein J.A., Et al., Timing and duration of omalizumab response in patients with chronic idiopathic/spontaneous urticaria, J Allergy Clin Immunol, 137, 2, pp. 474-481, (2016); Eckman J.A., Sterba P.M., Kelly D., Et al., Effects of omalizumab on basophil and mast cell responses using an intranasal cat allergen challenge, J Allergy Clin Immunol, 125, 4, pp. 889-895, (2010); Lin H., Boesel K.M., Griffith D.T., Et al., Omalizumab rapidly decreases nasal allergic response and FcepsilonRI on basophils, J Allergy Clin Immunol, 113, 2, pp. 297-302, (2004); Plosker G.L., Keam S.J., Omalizumab: a review of its use in the treatment of allergic asthma, BioDrugs, 22, 3, pp. 189-204, (2008); Menzella F., Just J., Sauerbeck I.S., Et al., Omalizumab for the treatment of patients with severe allergic asthma with immunoglobulin E levels above >1500 IU/mL, World Allergy Organ J, 16, 6, (2023); Somerville L., Bardelas J., Viegas A., D'Andrea P., Blogg M., Peachey G., Immunogenicity and safety of omalizumab in pre-filled syringes in patients with allergic (IgE-mediated) asthma, Curr Med Res Opin, 30, 1, pp. 59-66, (2014); Riviere G.J.K.P., Jaffer J., Yeh C.M., Reynolds C., Brookman L., Bioequivalence of a novel omalizumab solution for injection compared with the standard lyophilized powder formulation, J. Bioequivalence Bioavailab, 3, 6, pp. 144-150, (2011); Holgate S.T., Chuchalin A.G., Hebert J., Et al., Efficacy and safety of a recombinant anti-immunoglobulin E antibody (omalizumab) in severe allergic asthma, Clin Exp Allergy, 34, 4, pp. 632-638, (2004); Hanania N.A., Alpan O., Hamilos D.L., Et al., Omalizumab in severe allergic asthma inadequately controlled with standard therapy: a randomized trial, Ann Intern Med, 154, 9, pp. 573-582, (2011); Saini S.S., Bindslev-Jensen C., Maurer M., Et al., Efficacy and safety of omalizumab in patients with chronic idiopathic/spontaneous urticaria who remain symptomatic on H1 antihistamines: a randomized, placebo-controlled study, J Invest Dermatol, 135, 1, pp. 67-75, (2015); Odajima H., Ebisawa M., Nagakura T., Et al., Long-term safety, efficacy, pharmacokinetics and pharmacodynamics of omalizumab in children with severe uncontrolled asthma, Allergol Int, 66, 1, pp. 106-115, (2017); Menzella F., Ferrari E., Ferrucci S.M., Et al., Self-administration of omalizumab: why not? A literature review and expert opinion, Expert Opin Biol Ther, 21, 4, pp. 499-507, (2021); Murphy K.R., Winders T., Smith B., Millette L., Chipps B.E., Identifying patients for self-administration of omalizumab, Adv Ther, 40, 1, pp. 19-24, (2023); Tornero Molina J., Lopez Robledillo J.C., Casamira Ruiz N., Potential benefits of the self-administration of subcutaneous methotrexate with autoinjector devices for patients: a review, Drug Healthc Patient Saf, 13, pp. 81-94, (2021); Sigurgeirsson B., Browning J., Tyring S., Et al., Secukinumab demonstrates efficacy, safety, and tolerability upon administration by 2 ml autoinjector in adult patients with plaque psoriasis: 52-week results from MATURE, a randomized, placebo-controlled trial, Dermatol Ther, 35, 3, (2022); Rekaya N., Vicik S.M., Hulesch B.T., McDonald L.L., Enhancement of an auto-injector device for self-administration of etanercept in-patients with rheumatoid arthritis confers emotional and functional benefits, Rheumatol Ther, 7, 3, pp. 537-552, (2020); Chow S.C., Bioavailability and bioequivalence in drug development, Wiley Interdiscip Rev Comput Stat, 6, 4, pp. 304-312, (2014); Moghadam-Kia S., Oddis C.V., Aggarwal R., Approach to asymptomatic creatine kinase elevation, Cleve Clin J Med, 83, 1, pp. 37-42, (2016); King C., Cox F., Sloan A., McCrea P., Edgar J.D., Conlon N., Rapid transition to home omalizumab treatment for chronic spontaneous urticaria during the COVID-19 pandemic: a patient perspective, World Allergy Organ J, 14, 10, (2021); Wiuff A.C., Knudsgaard Wiis M.A., Heilskov S., Et al., The impact of home treatment and self-administration of omalizumab on chronic urticaria, World Allergy Organ J, 15, 12, (2022)","R. Sangana; Novartis Institutes for Biomedical Research, Cambridge, United States; email: ramachandra.sangana@novartis.com","","John Wiley and Sons Inc","","","","","","2160763X","","","38389387","English","Clin. Pharmacol. Drug Dev.","Article","Final","All Open Access; Hybrid Gold Open Access","Scopus","2-s2.0-85185683168"
"Ogunbiyi T.E.; Adegoke M.A.; Adetunji A.O.; Ojo J.A.","Ogunbiyi, Temitope Elizabeth (59125095000); Adegoke, Michael Abejide (56669735600); Adetunji, Abe Oluwatobi (59420243100); Ojo, Joseph Ayodele (59420689200)","59125095000; 56669735600; 59420243100; 59420689200","Feature analytics of asthma severity levels for bioinformatics improvement using Gini importance","2024","International Journal of Bioinformatics Research and Applications","20","6","","584","607","23","0","10.1504/IJBRA.2024.142547","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209948007&doi=10.1504%2fIJBRA.2024.142547&partnerID=40&md5=65954f85ee426d8a2a987bbc43c89067","Department of Computer Science and Information Technology, Bells University of Technology, Ota, Nigeria; Department of Biological Science, Anchor University, Lagos, Nigeria; Department of Biological Sciences, Bells University of Technology, Ota, Nigeria","Ogunbiyi T.E., Department of Computer Science and Information Technology, Bells University of Technology, Ota, Nigeria; Adegoke M.A., Department of Computer Science and Information Technology, Bells University of Technology, Ota, Nigeria; Adetunji A.O., Department of Biological Science, Anchor University, Lagos, Nigeria; Ojo J.A., Department of Biological Sciences, Bells University of Technology, Ota, Nigeria","In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics. © 2024 Inderscience Enterprises Ltd.","asthma; bioinformatics; feature importance; machine learning; severity prediction","","","","","","","","Cardenas Rodriguez N., Carmona Aparicio L., Perez Lozano D.L., Genetic variations associated with pharmacoresistant epilepsy, Molecular Medicine Reports, 21, 4, pp. 1685-1701, (2020); Chen Z., Pang M., Zhao Z., Li S., Miao R., Zhang Y., Feature selection may improve deep neural networks for the bioinformatics problems, Bioinformatics, 36, 5, pp. 1542-1552, (2020); Custovic A., Siddiqui S., Saglani S., Considering biomarkers in asthma disease severity, Journal of Allergy and Clinical Immunology, 149, 2, pp. 480-487, (2022); Gu Z., Complex heatmap visualization, Imeta, 1, 3, (2022); Ilesanmi S., JonhBosco A., Ahiara W.C., Akiode J., Udeani U.H., Olaleye T., Okewale O.A., An ensemble statistical evaluation of medical image embedding with SqueezeNet neural network, 2022 5th Information Technology for Education and Development (ITED), pp. 1-6, (2022); Jeddi Z., Gryech I., Ghogho M., Hammoumi M.E., Machine learning for predicting the risk for childhood asthma using prenatal, perinatal, postnatal and environmental factors, Healthcare, 9, 11, pp. 1-11, (2021); Kothalawala D.M., Murray C.S., Simpson A., Custovic A., Tapper W.J., Arshad S.H., Rezwan F.I., Development of childhood asthma prediction models using machine learning approaches, Clinical and Translational Allergy, 11, 9, (2021); Lson R.D., Assaf R., Brettin T., Conrad N., Cucinell C., Introducing the bacterial and viral bioinformatics resource center (BV-BRC): a resource combining PATRIC, IRD and ViPR, Nucleic Acids Research, 51, D1, pp. D678-D689, (2023); Merino N., Jackson T.R., Campbell J.H., Kersting A.B., Subsurface microbial communities as a tool for characterizing regional-scale groundwater flow, Science of The Total Environment, 842, 14, (2022); Nembrini S., Konig I.R., Wright M.N., The revival of the Gini importance?, Bioinformatics, 34, 21, pp. 3711-3718, (2018); Olaleye T.O., Okewale A.O., Solanke I., Alomaja O.V., Adebayo O.F., Akintunde S.M., Evaluation of deep image embedders for healthcare informatics improvement using visualized performance metrics, Computational Intelligence in Healthcare, pp. 115-135, (2023); Pooja M.R., Pushpalatha M.P., A predictive framework for the assessment of asthma control level, International Journal of Engineering and Advanced Technology (IJEAT), 8, 3, pp. 2249-8958, (2019); Rahman T., Khandakar A., Hoque M.E., Ibtehaz N., Development and validation of an early scoring system for prediction of disease severity in COVID-19 using complete blood count parameters, IEEE Access, 9, 1, pp. 120422-120441, (2021); Rao M.A., Kausthuba N.K., Yadav S., Gope D., Krishnaswamy U.M., Ghosh P.K., Automatic prediction of spirometry readings from cough and wheeze for monitoring of asthma severity, 2017 25th European Signal Processing Conference (EUSIPCO), pp. 41-45, (2017); Salman R., Alzaatreh A., Bataineh M.T., Feature selection of the respiratory microbiota associated with asthma, J. Big Data, 10, 90, (2023); Shah S.P., Grunwell J., Shih J., Stephenson S., Fitzpatrick A.M., Exploring the utility of noninvasive type 2 inflammatory markers for prediction of severe asthma exacerbations in children and adolescents, The Journal of Allergy and Clinical Immunology: In Practice, 7, 8, pp. 2624-2633, (2019); Shen Y., Predicting protein structure from single sequences, Nature Computational Science, 2, 12, pp. 775-776, (2022); Surendro K., Predictive analytics for predicting customer behavior, 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), (2019); Thakur D., Asthma Disease Prediction, (2023); Ullah R., Khan S., Ali H., Chaudhary I.I., Bilal M., Ahmad I., A comparative study of machine learning classifiers for risk prediction of asthma disease, Photodiagnosis and Photodynamic Therapy, 28, pp. 292-296, (2019); Yan Z., Liu L., Jiao L., Wen X., Bioinformatics analysis and identification of underlying biomarkers potentially linking allergic rhinitis and asthma, Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 26, pp. e924934-1, (2020); Yokoyama A., Okazaki H., Makita N., Fukui A., Regional differences in the incidence of asthma exacerbations in Japan: a heat map analysis of healthcare insurance claims data, Allergology International, 71, 1, pp. 47-54, (2022); Yu B., Chen F., Chen H., NPP estimation using random forest and impact feature variable importance analysis, Journal of Spatial Science, 64, 1, pp. 173-192, (2019); Zhou C., McCarthy S.A., Durbin R., YaHS: yet another Hi-C scaffolding tool, Bioinformatics, 39, 1, pp. 1-3, (2023); Zhou H., Wang X., Zhu R., Feature selection based on mutual information with correlation coefficient, Applied Intelligence, 52, 5, pp. 1-18, (2022)","T.E. Ogunbiyi; Department of Computer Science and Information Technology, Bells University of Technology, Ota, Nigeria; email: elizatope_20005@yahoo.com","","Inderscience Publishers","","","","","","17445485","","","","English","Int. J. Bioinformatics Res. Appl.","Article","Final","","Scopus","2-s2.0-85209948007"
"Lakshmi P.V.N.; Vedavathi K.","Lakshmi, P. V. Naga (37077395200); Vedavathi, K. (57211392361)","37077395200; 57211392361","Deep Learning and Probabilistic Neural Networks Based Detection and Classification of Lung Diseases for Pneumonia","2024","International Journal of Intelligent Systems and Applications in Engineering","12","12s","","708","713","5","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185309868&partnerID=40&md5=0349d500c31036bca02f32ae02c8e22e","GITAM Institute of science, GITAM University, Andhra Pradesh, Vishakapatnam, India; Department of Computer Science Loyola Academy, Telangana, Secunderabad, India; Department of Computer Science, GITAM Institute of Science, GITAM University, Andhra Pradesh, Vishakapatnam, India","Lakshmi P.V.N., GITAM Institute of science, GITAM University, Andhra Pradesh, Vishakapatnam, India, Department of Computer Science Loyola Academy, Telangana, Secunderabad, India; Vedavathi K., Department of Computer Science, GITAM Institute of Science, GITAM University, Andhra Pradesh, Vishakapatnam, India","Lung illness is a widespread problem in every region of the globe. Among these are asthma, pneumonia, chronic obstructive pulmonary disease (COPD), fibrosis, and tuberculosis. Detecting lung disease early on is crucial. Several models have been created that use a combination of machine learning and image processing to achieve this goal. Convolutional neural networks (CNN), vanilla neural networks (NN), visual geometry group-based neural networks (VGG), and capsule networks are only some of the well-known deep learning approaches used to predict lung illnesses. Since the publication of the novel Covid-19, numerous research projects focusing on the novel's ability to accurately foresee the future have been started all around the world. Since a number of individuals died from severe chest congestion, the early lung disease known as pneumonia is likely to have a tight connection to Covid-19 (pneumonic condition). The distinction between COVID-19 and other lung disorders, such as pneumonia, can be difficult for medical professionals to make. The most precise method of predicting lung disease is by X-ray imaging of the chest. Using patient chest X-ray images as the data source, we provide a novel framework in this study for the prediction of lung disorders including pneumonia and Covid-19. The system collects data, improves images, analyses regions of interest (ROIs) adaptively and precisely, extracts characteristics, and predicts diseases. © 2024, Ismail Saritas. All rights reserved.","Classification; Deep Learning; Hybrid Clustering; Pneumonia; Probabilistic Neural Networks Lung Diseases","","","","","","","","Bharati S, Podder P, Mondal R, Mahmood A, Raihan-Al-Masud M., Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer, Advances in intelligent systems and computing, 941, pp. 447-457, (2020); Coudray N, Ocampo PS, Sakellaropoulos T, Et al., Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning, Nat Med, 24, pp. 1559-1567, (2018); Mondal MRH, Bharati S, Podder P, Podder P., Data analytics for novel coronavirus disease, informatics in medicine unlocked, 20, (2020); Kuan K, Ravaut M, Manek G, Chen H, Lin J, Nazir B, Chen C, Howe TC, Zeng Z, Chandrasekhar V., Deep learning for lung cancer detection: tackling the Kaggle data science bowl 2017 challenge, (2017); Sun W, Zheng B, Qian W., Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis, Comput Biol Med, 89, pp. 530-539, (2017); Song Q, Zhao L, Luo X, Dou X., Using deep learning for classification of lung nodules on computed tomography images, Journal of healthcare engineering, (2017); Sun W, Zheng B, Qian W., Computer aided lung cancer diagnosis with deep learning algorithms, Proc SPIE. Medical Imaging, 9785, (2016); NIH sample Chest X-rays dataset; Abbas A, Abdelsamea MM, Gaber MM, Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, Appl Intell, (2020); Abiyev RH, Maaitah MKS, Deep convolutional neural networks for chest diseases detection, J Healthc Eng, (2018); Angeline R, Mrithika M, Raman A, Warrier P, Pneumonia detection and classification using chest X-ray images with convolutional neural network, New trends in computational vision and bio-inspired computing, (2020); Apostolopoulos ID, Mpesiana A, Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys Eng Sci Med, 43, pp. 635-640, (2020); Asuntha A, Srinivasan A, Deep learning for lung cancer detection and classification, Multimed Tools Appl, (2020); Avni U, Greenspan H, Konen E, Sharon M, Goldberger J, X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words, IEEE Trans Med Imaging, 30, 3, pp. 733-746, (2011); Bentivegna E, Luciani M, Spuntarelli V, Speranza ML, Guerritore L, Sentimentale A, Martelletti P, Extremely severe case of COVID-19 pneumonia recovered despite bad prognostic indicators: a didactic report, SN Compr Clin Med, 2, pp. 1204-1207, (2020); Butt C, Gill J, Chun D, Babu BA, Deep learning system to screen coronavirus disease 2019 pneumonia, Appl Intell, (2020); Chen X, Laurent S, Onur OA, Kleineberg NN, Fink GR, Schweitzer F, Warnke C, A systematic review of neurological symptoms and complications of COVID-19, J Neurol, (2021); Dansana D, Kumar R, Bhattacharjee A, Hemanth DJ, Gupta D, Khanna A, Castillo A, Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm, Soft Comput, (2020)","P.V.N. Lakshmi; GITAM Institute of science, GITAM University, Vishakapatnam, Andhra Pradesh, India; email: nagmtechloyola@gmail.com","","Ismail Saritas","","","","","","21476799","","","","English","Internat. J. Intel. Syst. Appl. Eng.","Article","Final","","Scopus","2-s2.0-85185309868"
"Cao Z.; Zhao S.; Hu S.; Wu T.; Sun F.; Li S.","Cao, Zhenghua (59166151600); Zhao, Shengkun (59300611200); Hu, Shaodan (57386350000); Wu, Tong (59166541500); Sun, Feng (59166151700); Li, Shi (58793623300)","59166151600; 59300611200; 57386350000; 59166541500; 59166151700; 58793623300","Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning","2024","International Journal of COPD","19","","","2073","2095","22","0","10.2147/COPD.S476808","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205335827&doi=10.2147%2fCOPD.S476808&partnerID=40&md5=101dc274c8999a942fea669d2a2f4726","Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Geriatric Department, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Jiangsu, Suzhou, China","Cao Z., Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Zhao S., Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Hu S., Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Wu T., Geriatric Department, Suzhou Hospital of Integrated Traditional Chinese and Western Medicine, Jiangsu, Suzhou, China; Sun F., Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China; Li S., Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Jilin, Changchun, China","Purpose: To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory response and ferroptosis in chronic obstructive pulmonary disease (COPD) patients and to aid in the development of targeted traditional Chinese medicine (TCM). Mendelian randomization analysis elucidates the causal relationships among immune cells, genes, and COPD, offering novel insights for the early diagnosis, prevention, and treatment of COPD. This approach also provides a fresh perspective on the use of traditional Chinese medicine for treating COPD. Methods: R software was used to extract COPD-related data from the Gene Expression Omnibus (GEO) database, differentially expressed genes were identified for enrichment analysis, and WGCNA was used to pinpoint genes within relevant modules associated with COPD. This analysis included determining genes linked to the inflammatory response in COPD patients and analyzing their correlation with ferroptosis. Further steps involved filtering core genes, constructing TF-miRNA‒mRNA network diagrams, and employing three types of machine learning to predict the core miRNAs, key immune cells, and characteristic genes of COPD patients. This process also delves into their correlations, single-gene GSEA, and diagnostic model predictions. Reverse inference complemented by molecular docking was used to predict compounds and traditional Chinese medicines for treating COPD; Mendelian randomization was applied to explore the causal relationships among immune cells, genes, and COPD. Results: We identified 2443 differential genes associated with COPD through the GEO database, along with 8435 genes relevant to WGCNA and 1226 inflammation-related genes. A total of 141 genes related to the inflammatory response in COPD patients were identified, and 37 core genes related to ferroptosis were selected for further enrichment analysis and analysis. The core miRNAs predicted for COPD include hsa-miR-543, hsa-miR-181c, and hsa-miR-200a, among others. The key immune cells identified were plasma cells, activated memory CD4 T cells, gamma delta T cells, activated NK cells, M2 macrophages, and eosinophils. Characteristic genes included EGF, PLG, PTPN22, and NR4A1. A total of 78 compounds and 437 traditional Chinese medicines were predicted. Mendelian randomization analysis revealed a causal relationship between 36 types of immune cells and COPD, whereas no causal relationship was found between the core genes and COPD. Conclusion: A definitive causal relationship exists between immune cells and COPD, while the prediction of core miRNAs, key immune cells, characteristic genes, and targeted traditional Chinese medicines offers novel insights for the early diagnosis, prevention, and treatment of COPD. © 2024 Cao et al.","bioinformatics analysis; characteristic genes; COPD; early diagnosis; machine learning; Mendelian randomization; targeted traditional Chinese medicine","Biomarkers; Computational Biology; Databases, Genetic; Drugs, Chinese Herbal; Ferroptosis; Gene Expression Profiling; Gene Regulatory Networks; Genetic Markers; Genetic Predisposition to Disease; Humans; Lung; Machine Learning; Medicine, Chinese Traditional; Mendelian Randomization Analysis; MicroRNAs; Molecular Docking Simulation; Phenotype; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive; Transcriptome; biological marker; interleukin 17; interleukin 1beta; microRNA; programmed death 1 ligand 1; reactive oxygen metabolite; stromelysin; toll like receptor 4; toll like receptor 7; tumor necrosis factor; biological marker; herbaceous agent; microRNA; transcriptome; algorithm; Article; bioinformatics; CD4+ T lymphocyte; cell differentiation; Chinese medicine; cholesterol metabolism; chronic obstructive lung disease; computer language; controlled study; data base; data mining; down regulation; eosinophil; ferroptosis; gamma delta T lymphocyte; gene; gene expression; gene ontology; gene set enrichment analysis; genetic susceptibility; Ginkgo biloba; human; human cell; immune cell function assay; immune response; inflammation; information processing; interstitial lung disease; KEGG; least absolute shrinkage and selection operator; lymphoid cell; machine learning; macrophage; MAPK signaling; Mendelian randomization analysis; molecular docking; natural killer cell; non small cell lung cancer; Notch signaling; passive smoking; Perilla; phylogenetic tree; pleiotropy; prevalence; protein protein interaction; receiver operating characteristic; regulatory T lymphocyte; risk factor; RNA sequence; screening; sensitivity analysis; signal transduction; single nucleotide polymorphism; Th17 cell; upregulation; Wnt signaling; blood; diagnosis; drug effect; drug therapy; gene expression profiling; gene regulatory network; genetic database; genetic marker; genetic predisposition; genetics; immunology; lung; Mendelian randomization analysis; metabolism; pathophysiology; phenotype; predictive value; procedures","","stromelysin, 79955-99-0; toll like receptor 4, 203811-83-0; Biomarkers, ; Drugs, Chinese Herbal, ; Genetic Markers, ; MicroRNAs, ","R programming language","","","","Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease (2024 Report); Labaki WW, Rosenberg SR., Chronic obstructive pulmonary disease, Ann Intern Med, 173, 3, pp. ITC17-ITC32, (2020); Divo MJ, Liu C, Polverino F, Castaldi PJ, Celli BR, Tesfaigzi Y., From pre-COPD to COPD: a Simple, Low cost and easy to IMplement (SLIM) risk calculator, Eur Respir J, 62, 3, (2023); Celli BR, Wedzicha JA., Update on clinical aspects of chronic obstructive pulmonary disease, N Engl J Med, 381, 13, pp. 1257-1266, (2019); Meghji J, Mortimer K, Agusti A, Et al., Improving lung health in low-income and middle-income countries: from challenges to solutions, Lancet, 397, 10277, pp. 928-940, (2021); Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet Respir Med, 8, 6, pp. 585-596, (2020); Global health estimates: leading causes of death. 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Brusselle GG, Joos GF, Bracke KR., New insights into the immunology of chronic obstructive pulmonary disease, Lancet, 378, 9795, pp. 1015-1026, (2011); Nunez B, Sauleda J, Anto JM, Et al., Anti-tissue antibodies are related to lung function in chronic obstructive pulmonary disease, Am J Respir Crit Care Med, 183, 8, pp. 1025-1031, (2011); Hong X, Xiao Z., Changes in peripheral blood TBNK lymphocyte subsets and their association with acute exacerbation of chronic obstructive pulmonary disease, J Int Med Res, 51, 6, (2023); Booth S, Hsieh A, Mostaco-Guidolin L, Et al., A single-cell atlas of small airway disease in chronic obstructive pulmonary disease: a cross-sectional study, Am J Respir Crit Care Med, 208, 4, pp. 472-486, (2023); Xu F, Vasilescu DM, Kinose D, Et al., The molecular and cellular mechanisms associated with the destruction of terminal bronchioles in COPD, Eur Respir J, 59, 5, (2022); Habener A, Grychtol R, Gaedcke S, Et al., IgA(+) memory B-cells are significantly increased in patients with asthma and small airway dysfunction, Eur Respir J, 60, 5, (2022); Kim WD, Sin DD., Granzyme B may act as an effector molecule to control the inflammatory process in COPD, COPD, 21, 1, pp. 1-11, (2024); Li Y, Shen D, Wang K, Et al., Mogroside V ameliorates broiler pulmonary inflammation via modulating lung microbiota and rectifying Th17/ Treg dysregulation in lipopolysaccharides-induced lung injury, Poult Sci, 102, 12, (2023); Wohnhaas CT, Bassler K, Watson CK, Et al., Monocyte-derived alveolar macrophages are key drivers of smoke-induced lung inflammation and tissue remodeling, Front Immunol, 15, (2024); Wang Y, Shumansky K, Sin DD, Et al., Associations of interleukin-1 gene cluster polymorphisms with C-reactive protein concentration and lung function decline in smoking-induced chronic obstructive pulmonary disease, Int J Clin Exp Pathol, 8, 10, pp. 13125-13135, (2015); Osei ET, Noordhoek JA, Hackett TL, Et al., Interleukin-1alpha drives the dysfunctional cross-talk of the airway epithelium and lung fibroblasts in COPD, Eur Respir J, 48, 2, pp. 359-369, (2016); Usman K, Fouadi M, Nwozor KO, Et al., Interleukin-1alpha inhibits transforming growth factor-beta1 and beta2-induced extracellular matrix production, remodeling and signaling in human lung fibroblasts: master regulator in lung mucosal repair, Matrix Biol, 132, pp. 47-58, (2024); Chen G, Sun L, Kato T, Et al., IL-1beta dominates the promucin secretory cytokine profile in cystic fibrosis, J Clin Invest, 129, 10, pp. 4433-4450, (2019); Zhang N, Zhang Q, Zhang Z, Et al., IRF1 and IL1A associated with PANoptosis serve as potential immune signatures for lung ischemia reperfusion injury following lung transplantation, Int Immunopharmacol, 139, (2024); Gao F, Zhang T, Zhang H, Et al., Explore bioactive ingredients and potential mechanism of Houpo Mahuang decoction for chronic bronchitis based on UHPLC-Q exactive orbitrap HRMS, network pharmacology, and experiment verification, J Ethnopharmacol, 303, (2023); Dong Y, Liu Y, Tang J, Et al., Zhisou powder displays therapeutic effect on chronic bronchitis through inhibiting PI3K/Akt/HIF-1alpha/VEGFA signaling pathway and reprograming metabolic pathway of arachidonic acid, J Ethnopharmacol, 319, (2024); Bonnelykke K, Matheson MC, Pers TH, Et al., Meta-analysis of genome-wide association studies identifies ten loci influencing allergic sensitization, Nat Genet, 45, 8, pp. 902-906, (2013); Nguyen J, Armstrong BS, Cowman S, Et al., Immunophenotyping of acute inflammatory exacerbations of lung injury driven by mutant surfactant protein-c: a role for inflammatory eosinophils, Front Pharmacol, 13, (2022)","S. Li; Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin, China; email: shili0648@163.com","","Dove Medical Press Ltd","","","","","","11769106","","","39346628","English","Int. J. COPD","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85205335827"
"Shou L.; He H.; Wei Y.; Xu X.; Wang W.; Zheng J.","Shou, Lu (59415414200); He, Haidong (57221468453); Wei, Yi (59415331600); Xu, Xianrong (55924048000); Wang, Wenmin (59203097200); Zheng, Jisheng (57219180727)","59415414200; 57221468453; 59415331600; 55924048000; 59203097200; 57219180727","Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis","2024","Autoimmunity","57","1","2427085","","","","0","10.1080/08916934.2024.2427085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209477550&doi=10.1080%2f08916934.2024.2427085&partnerID=40&md5=f58178f10468d385c77aa9a7d19a4de4","Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China; The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Zhejiang, Hangzhou, China","Shou L., Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China; He H., Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China; Wei Y., Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China; Xu X., Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China; Wang W., The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Zhejiang, Hangzhou, China; Zheng J., Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Zhejiang, Hangzhou, China","Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by downloading the training set GSE69683 and validation set GSE137268 from the GEO dataset. SVM-RFE analysis and the LASSO regression model were used to screen key genes, and CIBERSORT was used to assess immune infiltration in the samples. A total of 20 DEGs were identified in this study, mainly enriched for lymph node-like receptors, b-cell receptors, and neutrophil extracellular trap pathway. Comparative validation set GSE137268 identified thioredoxin (TXN) and coagulation factor V (F5) were identified as diagnostic markers of severe asthma. CIBERSORT analysis revealed that TXN and F5 are associated with multiple immune cell infiltrates. In addition, we identified miRNA and TF at the transcriptional level that may regulate F5 and TXN, and found that several commonly used drugs may exert therapeutic effects by targeting F5 and TXN. Taken together, TXN and F5 may be key genes in the development of severe asthma and are associated with immune infiltration. Our study can help to better understand the pathogenesis of asthma and provide new ideas for clinical treatment. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","F5; Immunoinfiltration; Machine learning; Severe asthma; TXN","Asthma; Biomarkers; Computational Biology; Gene Expression Profiling; Gene Expression Regulation; Humans; Machine Learning; MicroRNAs; Severity of Illness Index; Thioredoxins; Transcriptome; B lymphocyte receptor; blood clotting factor 5; budesonide; methylprednisolone; microRNA; montelukast; nucleotide binding oligomerization domain like receptor; salbutamol; thioredoxin; transcription factor; biological marker; transcriptome; TXN protein, human; Article; BCR signaling; bioinformatics; cohort analysis; comparative study; controlled study; correlation analysis; differential expression analysis; differential gene expression; drug targeting; functional enrichment analysis; gene identification; gene ontology; genetic association; genetic marker; immune infiltration; immunity; immunocompetent cell; immunopathogenesis; KEGG; least absolute shrinkage and selection operator; machine learning; molecular docking; NETosis; pathway enrichment analysis; recursive feature elimination; severe asthma; signal transduction; support vector machine; therapy effect; transcription regulation; validation study; asthma; diagnosis; gene expression profiling; gene expression regulation; genetics; human; immunology; metabolism; procedures; severity of illness index","","blood clotting factor 5, 9001-24-5, 9013-23-4; budesonide, 51333-22-3, 51372-29-3; methylprednisolone, 6923-42-8, 83-43-2; montelukast, 151767-02-1, 158966-92-8; salbutamol, 18559-94-9, 35763-26-9; thioredoxin, 52500-60-4; Biomarkers, ; MicroRNAs, ; Thioredoxins, ; TXN protein, human, ","","","Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province, (2021AX003)","Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (grant numbers: 2021AX003). The 7th National Traditional Chinese Medicine Experts Academic Experience Inheritance Project. Chai Xiujuan Famous old TCM expert inheritance studio construction project of Zhejiang Province. Not applicable.","Chung K.F., Dixey P., Abubakar-Waziri H., Et al., Characteristics, phenotypes, mechanisms and management of severe asthma, Chin Med J, 135, 10, pp. 1141-1155, (2022); Chung K.F., Wenzel S.E., Brozek J.L., Et al., International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma, Eur Respir J, 43, 2, pp. 343-373, (2014); Groopman J.E., Chen F.W., Hope J.A., Et al., Serological characterization of HTLV-III infection in aids and related disorders, J Infect Dis, 153, 4, pp. 736-742, (1986); Shaw D.E., Sousa A.R., Fowler S.J., Et al., Clinical and inflammatory characteristics of the European u-biopred adult severe asthma ­cohort, Eur Respir J, 46, 5, pp. 1308-1321, (2015); Ji T., Li H., T-helper cells and their cytokines in pathogenesis and treatment of asthma, Front Immunol, 14, (2023); Gandhi N.A., Bennett B.L., Graham N.M., Et al., Targeting key proximal drivers of type 2 inflammation in disease, Nat Rev Drug Discov, 15, 1, pp. 35-50, (2016); Bryant V.L., Ma C.S., Avery D.T., Et al., Cytokine-mediated regulation of human b cell differentiation into Ig-secreting cells: predominant role of IL-21 produced by CXCR5+ T follicular helper cells, J Immunol, 179, 12, pp. 8180-8190, (2007); Xie Y., Abel P.W., Casale T.B., Et al., T(h)17 cells and corticosteroid insensitivity in severe asthma, J Allergy Clin Immunol, 149, 2, pp. 467-479, (2022); Britt R.D., Ruwanpathirana A., Ford M.L., Et al., Macrophages orchestrate airway inflammation, remodeling, and resolution in asthma, Int J Mol Sci, 24, 13, (2023); Yang Y., Cao Y., Han X., Et al., Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis, Comput Biol Med, 154, (2023); Liang Y., Lin F., Huang Y., Identification of biomarkers associated with diagnosis of osteoarthritis patients based on bioinformatics and machine learning, J Immunol Res, 2022, (2022); Zhang Y., Xia R., Lv M., Et al., Machine-learning algorithm-based prediction of diagnostic gene biomarkers related to immune infiltration in patients with chronic obstructive pulmonary disease, Front Immunol, 13, (2022); Antoniak S., Owens A.P., Baunacke M., Et al., Par-1 contributes to the innate immune response during viral infection, J Clin Invest, 123, 3, pp. 1310-1322, (2013); Zhou G., Soufan O., Ewald J., Et al., Networkanalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis, Nucleic Acids Res, 47, W1, pp. W234-W241, (2019); Luo L., Zhu J., Guo Y., Et al., Mitophagy and immune infiltration in vitiligo: evidence from bioinformatics analysis, Front Immunol, 14, (2023); Lommatzsch M., Virchow J.C., Severe asthma: definition, diagnosis and treatment, Dtsch Arztebl Int, 111, 50, pp. 847-855, (2014); Nawaz S.F., Ravindran M., Kuruvilla M.E., Asthma diagnosis using patient-reported outcome measures and objective diagnostic tests: now and into the future, Curr Opin Pulm Med, 28, 3, pp. 251-257, (2022); Chen S.T., Yang N., Constructing ferroptosis-related competing ­endogenous RNA networks and exploring potential biomarkers correlated with immune infiltration cells in asthma using combinative bioinformatics strategy, BMC Genomics, 24, 1, (2023); Sun D., Yang H., Fan L., Et al., M6a regulator-mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma, J Cell Mol Med, 25, 21, pp. 10236-10247, (2021); Jiang Y., Yan Q., Zhang M., Et al., Identification of molecular markers related to immune infiltration in patients with severe asthma: a comprehensive bioinformatics analysis based on the human bronchial epithelial transcriptome, Dis Markers, 2022, (2022); Yu H., Huang X., Xie C., Et al., Transcriptomics reveals apigenin alleviates airway inflammation and epithelial cell apoptosis in ­allergic asthma via MAPK pathway, Phytother Res, 37, 9, pp. 4002-4017, (2023); Tan Y.Y., Zhou H.Q., Lin Y.J., Et al., FGF2 is overexpressed in asthma and promotes airway inflammation through the FGFR/MAPK/NF-kappaB pathway in airway epithelial cells, Mil Med Res, 9, 1, (2022); Hammad H., Lambrecht B.N., The basic immunology of asthma, Cell, 184, 6, pp. 1469-1485, (2021); Han Y., Xu X., Tang C., Et al., Reactive oxygen species promote tubular injury in diabetic nephropathy: the role of the mitochondrial ROS-TXNIP-NLRP3 biological axis, Redox Biol, 16, pp. 32-46, (2018); Zhang W., Zhu Y., Yu H., Et al., Libertellenone H, a natural pimarane diterpenoid, inhibits thioredoxin system and induces ROS-mediated apoptosis in human pancreatic cancer cells, Molecules, 26, 2, (2021); Bai L., Yan F., Deng R., Et al., Thioredoxin-1 rescues MPP(+)/MPTP-induced ferroptosis by increasing glutathione peroxidase 4, Mol Neurobiol, 58, 7, pp. 3187-3197, (2021); Sang J., Li W., Diao H.J., Et al., Jolkinolide B targets thioredoxin and glutathione systems to induce ROS-mediated paraptosis and apoptosis in bladder cancer cells, Cancer Lett, 509, pp. 13-25, (2021); Palacionyte J., Januskevicius A., Vasyle E., Et al., Novel serum biomarkers for patients with allergic asthma phenotype, Biomedicines, 12, 1, (2024); Ito W., Kobayashi N., Takeda M., Et al., Thioredoxin in allergic ­inflammation, Int Arch Allergy Immunol, 155 Suppl 1, pp. 142-146, (2011); Yamada Y., Nakamura H., Adachi T., Et al., Elevated serum levels of thioredoxin in patients with acute exacerbation of asthma, Immunol Lett, 86, 2, pp. 199-205, (2003); Hori K., Hirashima M., Ueno M., Et al., Regulation of eosinophil migration by adult t cell leukemia-derived factor, J Immunol, 151, 10, pp. 5624-5630, (1993); Popovic M., Smiljanic K., Dobutovic B., Et al., Thrombin and vascular inflammation, Mol Cell Biochem, 359, 1-2, pp. 301-313, (2012); Li Y., Liu H., Ye S., Et al., The effects of coagulation factors on the risk of endometriosis: a mendelian randomization study, BMC Med, 21, 1, (2023); Gobel K., Eichler S., Wiendl H., Et al., The coagulation factors ­fibrinogen, thrombin, and factor xii in inflammatory disorders–a systematic review, Front Immunol, 9, (2018); Mast A.E., Ruf W., Regulation of coagulation by tissue factor pathway inhibitor: implications for hemophilia therapy, J Thromb Haemost, 20, 6, pp. 1290-1300, (2022); de Boer J.D., Majoor C.J., van 't Veer C., Et al., Asthma and coagulation, Blood, 119, 14, pp. 3236-3244, (2012); Swystun L.L., Liaw P.C., The role of leukocytes in thrombosis, Blood, 128, 6, pp. 753-762, (2016); Soccio P., Moriondo G., Lacedonia D., Et al., Mirna and exosomal miRNA as new biomarkers useful to phenotyping severe asthma, Biomolecules, 13, 10, (2023); Roffel M.P., Boudewijn I.M., van Nijnatten J.L.L., Et al., Identification of asthma-associated microRNAs in bronchial biopsies, Eur Respir J, 59, 3, (2022)","J. Zheng; Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, 310012, China; email: wamlxj@163.com","","Taylor and Francis Ltd.","","","","","","08916934","","AUIME","39531229","English","Autoimmunity","Article","Final","","Scopus","2-s2.0-85209477550"
"Kabir F.; Akter N.; Hasan M.K.; Ahmed D.M.T.; Akter M.","Kabir, Fatema (59499175700); Akter, Nahida (57210669591); Hasan, Md. Kamrul (59498492400); Ahmed, Dr. Md. Tofael (59498149000); Akter, Mariam (57485830900)","59499175700; 57210669591; 59498492400; 59498149000; 57485830900","Predicting Chronic Obstructive Pulmonary Disease Using ML and DL Approaches and Feature Fusion of X-Ray Image and Patient History","2024","International Journal of Advanced Computer Science and Applications","15","12","","149","158","9","0","10.14569/IJACSA.2024.0151216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213965382&doi=10.14569%2fIJACSA.2024.0151216&partnerID=40&md5=675f09fe95b7e58a8aa4d50d3f19af15","Computer Science and Engineering Department, CCN University of Science and Technology, Cumilla, Bangladesh; Department of Information and Communication Technology, Cumilla University, Cumilla, Bangladesh; Department of Computer Science and Engineering, Northern University, Dhaka, Bangladesh","Kabir F., Computer Science and Engineering Department, CCN University of Science and Technology, Cumilla, Bangladesh; Akter N., Computer Science and Engineering Department, CCN University of Science and Technology, Cumilla, Bangladesh; Hasan M.K., Computer Science and Engineering Department, CCN University of Science and Technology, Cumilla, Bangladesh; Ahmed D.M.T., Department of Information and Communication Technology, Cumilla University, Cumilla, Bangladesh; Akter M., Department of Computer Science and Engineering, Northern University, Dhaka, Bangladesh","By 2030, chronic obstructive pulmonary disease (COPD) is expected to become one of the top three causes of death and a leading contributor to illness globally. Chronic Obstructive Pulmonary Disease (COPD) is a debilitating respiratory disease and lung ailment caused by smoking-related airway inflammation, leading to breathing difficulties. Our COPD Healthcare Monitoring System for COPD Early Detection addresses this critical need by leveraging advanced Machine Learning (ML) and Deep Learning (DL) technologies. Unlike previous studies that predominantly rely on image datasets alone, our advanced monitoring system utilizes both image and text datasets, offering a more comprehensive approach. Importantly, we manually curated our dataset, ensuring its uniqueness and reliability, a feature lacking in existing literature. Despite the utilization of popular models like nnUnet, Cx-Net, and V-net by other papers, our model outperformed them, achieving superior accuracy. XGBoost led with an impressive 0.92 score. Additionally, deep learning models such as VGG16, VGG19, and ResNet50 delivered scores ranging from 0.85 to 0.89, showcasing their efficacy in COPD detection. By amalgamating these techniques, our system revolutionizes COPD care, offering real-time patient data analysis for early detection and management. This innovative approach, coupled with our meticulously curated dataset, promises improved patient outcomes and quality of life. Overall, our study represents a significant advancement in COPD research, paving the way for more accurate diagnosis and personalized treatment strategies. © (2024), (Science and Information Organization). All Rights Reserved.","advanced monitoring system; Chronic obstructive pulmonary disease; COPD; COPD early detection; COPD healthcare; deep learning; machine learning; respiratory disease","Diagnosis; Personalized medicine; Pulmonary diseases; Advanced monitoring; Advanced monitoring system; Chronic obstructive pulmonary disease; Chronic obstructive pulmonary disease early detection; Chronic obstructive pulmonary disease healthcare; Deep learning; Machine-learning; Monitoring system; Lung cancer","","","","","","","Qureshi H., Sharafkhaneh A., Hanania N. 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M., Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors, (2024); Almeida S. D., Et al., cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations, (2023); Victor Ikechukwu A., Murali S., CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis, Mach Learn Sci Technol, 4, 2, (2023); Bosnic-Anticevich S., Bakerly N. D., Chrystyn H., Hew M., van der Palen J., Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management, (2023); Yin C., Et al., Fractional dynamics foster deep learning of COPD stage prediction, (2023); Ries A., Et al., Improving Image Quality of Sparse-view Lung Cancer CT Images with a Convolutional Neural Network, (2023); Wang X., Et al., Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data, Comput Methods Programs Biomed, 230, (2023); Tyagi A., Rao A., Rao S., Singh R. K., COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations, (2023); Lee S., Lee I. S., Kim S., Predicting Development of Chronic Obstructive Pulmonary Disease and its Risk Factor Analysis, (2023); Wu Y., Et al., Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images, (2022); Erken O., Fazla B., Romano F., Grotberg J. B., Izbassarov D., Muradoglu M., Effects of elastoviscoplastic properties of mucus on airway closure in healthy and pathological conditions, (2022); Shen Y., Et al., Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism, (2022); Triantafyllopoulos A., Et al., Distinguishing between pre- and post-treatment in the speech of patients with chronic obstructive pulmonary disease, (2022); Davies H. J., Hammour G., Xiao H., Mandic D. P., An Apparatus for the Simulation of Breathing Disorders: Physically Meaningful Generation of Surrogate Data, (2021); Song Z., Et al., Supervised multi-specialist topic model with applications on large-scale electronic health record data, (2021); Martell M. B., Chen M., Linton-Reid K., Posma J. M., Copley S. J., Aboagye E. O., Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs","","","Science and Information Organization","","","","","","2158107X","","","","English","Intl. J. Adv.  Comput. Sci. Appl.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85213965382"
"Amose J.; Vairavan M.","Amose, John (57196425029); Vairavan, Manimegalai (57750651300)","57196425029; 57750651300","Optimal chest position of auscultation for chronic obstructive pulmonary disease diagnosis using machine learning","2023","Indonesian Journal of Electrical Engineering and Computer Science","32","3","","1417","1425","8","0","10.11591/ijeecs.v32.i3.pp1417-1425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178270659&doi=10.11591%2fijeecs.v32.i3.pp1417-1425&partnerID=40&md5=43c11c38c92ac5f6edf9adcc1eb2105b","Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India; Department of Biomedical Engineering, KPR Institute of Engineering and Technology, Coimbatore, India","Amose J., Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India, Department of Biomedical Engineering, KPR Institute of Engineering and Technology, Coimbatore, India; Vairavan M., Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India","Digital stethoscopes over recent years have gained acceptance among pulmonologists to perform auscultations due to their advantages over traditional stethoscopes. During the previous decade, researchers have prominently contributed to the development of algorithms aimed at enabling objective diagnosis of respiratory sounds and conditions, thereby affording individuals lacking medical expertise the capability to auscultate themselves. However, auscultation requires the personnel to be aware of the optimal chest position to place the device for a reliable diagnosis as well. This study aims to identify the optimal chest position to place a digital stethoscope’s diaphragm to objectively diagnose chronic obstructive pulmonary disease (COPD). Lung sound recordings from seven chest positions with data available in the ICBHI 2017 database namely, anterior left (Al), anterior right (Ar), lateral left (Ll), lateral right (Lr), posterior left (Pl), posterior right (Pr) and trachea (Tc), were analyzed in this study. COPD+ and COPD- at diagnosis, each chest position was done objectively using mel frequency cepstral coefficients (MFCC) features and machine learning models namely support vector machine (SVM) and decision tree (DT). The results indicate that the Pr chest position offers superior precision, recall, and F1-score, with a recognition accuracy of 99.7% in COPD screening. © 2023 Institute of Advanced Engineering and Science. All rights reserved.","Chest position; COPD; Lung sounds; Machine learning; Stethoscope","","","","","","","","Murphy S. L., Kochanek K. D., Xu J., Arias E., Mortality in the United States, 2020 key findings data from the national vital statistics system, NCHS Data Brief, (2021); Wheaton A. G., Cunningham T. J., Ford E. S., Croft J. B., Employment and activity limitations among adults with chronic obstructive pulmonary disease-United States, MMWR Morb Mortal Wkly Rep, 64, 11, pp. 289-295, (2015); Mannino D. M., Gagnon R. C., Petty T. L., Lydick E., Obstructive lung disease and low lung function in adults in the United States: Data from the national health and nutrition examination survey, 1988- 1994, Archives of Internal Medicine, 160, 11, pp. 1683-1689, (2000); Wheaton A. G., Et al., Chronic obstructive pulmonary disease and smoking status-United States, 2017, MMWR. Morbidity and Mortality Weekly Report, 68, 24, pp. 533-538, (2019); Liu Y., Wheaton A. G., Chapman D. P., Cunningham T. J., Lu H., Croft J. B., Prevalence of healthy sleep duration among adults-United States, 2014, MMWR. Morbidity and Mortality Weekly Report, 65, 6, pp. 137-141, (2016); Wheaton A. G., Ford E. S., Cunningham T. J., Croft J. B., Chronic obstructive pulmonary disease, hospital visits, and comorbidities: National survey of residential care facilities, 2010, Journal of Aging and Health, 27, 3, pp. 480-499, (2015); Cunningham T. J., Ford E. S., Rolle I. V., Wheaton A. G., Croft J. B., Associations of self-reported cigarette smoking with chronic obstructive pulmonary disease and co-morbid chronic conditions in the United States, COPD: Journal of Chronic Obstructive Pulmonary Disease, 12, 3, pp. 281-291, (2015); Qaseem A., Et al., Disease: A clinical practice guideline update from the american college of physicians, american college of chest physicians, american thoracic society, and european respiratory society, Annals of Internal Medicine, 155, 3, pp. 179-191, (2011); Ghafarian P., Jamaati H., Hashemian S. M., A review on human respiratory modeling, Tanaffos, 15, 2, pp. 61-69, (2016); Fernandes J. T., Rocha B. M., Paiva R. P., Cruz T. J., Using low cost embedded systems for respiratory sounds auscultation, Proceedings of the International Symposium on Wireless Communication Systems, 2018, (2018); Rocha B. M., Pessoa D., Marques A., Carvalho P., Paiva R. P., Automatic classification of adventitious respiratory sounds: A (un)solved problem?, Sensors (Switzerland), 21, 1, pp. 1-19, (2021); Grzywalski T., Belluzzo R., Drgas S., Cwalinska A., Hafke-Dys H., Interactive lungs auscultation with reinforcement learning agent, ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2, pp. 824-832, (2019); Jones A., Jones R. D., Kwong K., Burns Y., Effect of positioning on recorded lung sound intensities in subjects without pulmonary dysfunction, Physical Therapy, 79, 7, pp. 682-690, (1999); Royston T. J., Zhang X., Mansy H. A., Sandler R. H., Modeling sound transmission through the pulmonary system and chest with application to diagnosis of a collapsed lung, The Journal of the Acoustical Society of America, 111, 4, pp. 1931-1946, (2002); Harper P., Kraman S. S., Pasterkamp H., Wodicka G. R., An acoustic model of the respiratory tract, IEEE Transactions on Biomedical Engineering, 48, 5, pp. 543-550, (2001); Rocha B. M., Et al., A respiratory sound database for the development of automated classification, IFMBE Proceedings, 66, pp. 33-37, (2018); Rocha B. M., Et al., An open access database for the evaluation of respiratory sound classification algorithms, Physiological Measurement, 40, 3, (2019); Hult P., Wranne B., Ask P., A bioacoustic method for timing of the different phases of the breathing cycle and monitoring of breathing frequency, Medical Engineering and Physics, 22, 6, pp. 425-433, (2000); Mondal A., Bhattacharya P. S., Saha G., Reduction of heart sound interference from lung sound signals using empirical mode decomposition technique, Journal of Medical Engineering and Technology, 35, 6–7, pp. 344-353, (2011); Nersisson R., Noel M. M., Heart sound and lung sound separation algorithms: a review, Journal of Medical Engineering and Technology, 41, 1, pp. 13-21, (2017); Davis S. B., Mermelstein P., Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, IEEE Transactions on Acoustics, Speech, and Signal Processing, 28, 4, pp. 357-366, (1980); Moore R. K., Systems for isolated and connected word recognition, New Systems and Architectures for Automatic Speech Recognition and Synthesis, pp. 73-143, (1985); Mogran N., Bourlard H., Hermansky H., Automatic speech recognition: An auditory perspective, Speech Processing in the Auditory System, pp. 309-338, (2006); Young S., Et al., The HTK book, (1995); Rocha B. M., Mendes L., Chouvarda I., Carvalho P., Paiva R. P., Detection of cough and adventitious respiratory sounds in audio recordings by internal sound analysis, IFMBE Proceedings, 66, pp. 51-55, (2018); Wang L., Support vector machines: theory and applications, Springer Science & Business Media, 177, (2005); Buhmann M. D., Radial basis functions: theory and implementations, (2003); Drineas P., Mahoney M. W., Approximating a gram matrix for improved kernel-based learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3559, pp. 323-337, (2005); Shashua A., Introduction to machine learning: Class notes 67577, (2009); Fraiwan L., Hassanin O., Fraiwan M., Khassawneh B., Ibnian A. M., Alkhodari M., Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers, Biocybernetics and Biomedical Engineering, 41, 1, pp. 1-14, (2021)","J. Amose; Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India; email: johnnyamose@gmail.com","","Institute of Advanced Engineering and Science","","","","","","25024752","","","","English","Indones. J. Electrical Eng. Comput. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85178270659"
"Chowdhury U.; Chowdhury M.","Chowdhury, Upol (58812500400); Chowdhury, Mahfuzulhoq (55604498000)","58812500400; 55604498000","A cough type chronic disease prediction scheme using machine learning and diagnosis support system using a mobile application","2023","International Journal of Electronic Healthcare","13","3","","209","230","21","0","10.1504/IJEH.2023.135798","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182363999&doi=10.1504%2fIJEH.2023.135798&partnerID=40&md5=1511d3c4fdab23f31d3e9f14b4cf2bb5","Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh","Chowdhury U., Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh; Chowdhury M., Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh","Machine learning has been found to considerably lower the probability of inaccurate diagnoses when incorporated into modern diagnostic procedures. Different from the literature works, this paper proposes a method for diagnosing the three most similarly symptomised cough-type chronic diseases: COPD, bronchial asthma, and pneumonia. The symptoms of cough-type chronic disease patients admitted to the hospital were collected from eight medical colleges spread out over Bangladesh to construct the classifying model. This paper proposes a set of 27 attributes for appropriately classifying cough-type chronic disease instances. Several machine learning methods are tested using the dataset. Our research suggests gradient tree boosting to be the most effective, with a classification accuracy of 91%, despite the fact that previous research has identified support vector machine and random forest to be the most efficient models in these kinds of classification tasks. This paper also developed a mobile application for the diagnostic support systems.  © 2023 Inderscience Enterprises Ltd.","cough type chronic disease; evaluation; healthcare; machine learning; mobile application; prediction","","","","","","","","Bhardwaj R., Et al., A study of machine learning in healthcare, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 2, pp. 236-241, (2017); Callahan A., Shah N.H., Machine learning in healthcare, Key Advances in Clinical Informatics, pp. 279-291, (2017); Cavallazzi R., Ramirez J., Community-acquired pneumonia in chronic obstructive pulmonary disease, Current Opinion in Infectious Diseases, 33, 2, pp. 173-181, (2020); Ferdous F., Et al., Pneumonia mortality and healthcare utilization in young children in rural Bangladesh: a prospective verbal autopsy study, Tropical Medicine and Health, 46, 1, (2018); Finkelstein J., Et al., Machine learning approaches to personalize early prediction of asthma exacerbations, Annals of the New York Academy of Sciences, 1387, 1, pp. 153-165, (2017); Goto T., Et al., Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ed, The American Journal of Emergency Medicine, 36, 9, pp. 1650-1654, (2018); Groneberg D.A., Et al., Chronic cough due to occupational factors, Journal of Occupational Medicine and Toxicology, 1, 1, pp. 1-10, (2006); Irwin R.S., Madison J.M., The diagnosis and treatment of cough, New England Journal of Medicine, Psychiatry, 343, 23, pp. 1715-1721, (2000); Shukla R.K., Et al., Comparative study of GST polymorphism in relation to age in COPD and lung cancer, Tuberk Toraks, 61, 4, pp. 275-282, (2013); Spathis D., Vlamos P., Diagnosing asthma and chronic obstructive pulmonary disease with machine learning, Health Informatics Journal, 25, 3, pp. 811-827, (2019); Stokes K., Et al., A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings, Biocybernetics and Biomedical Engineering, 41, 4, pp. 1288-1302, (2021); Togacar M., Et al., A deep feature learning model for pneumonia detection applying a combination of MRMR feature selection and machine learning models, IRBM, 41, 4, pp. 212-222, (2020); Torres A., Et al., Which individuals are at increased risk of pneumococcal disease and why?. Impact of COPD, asthma, smoking, diabetes, and/or chronic heart disease on community-acquired pneumonia and invasive pneumococcal disease, Thorax, 70, 10, pp. 984-989, (2015); Vora S., Shah C., COPD classification using machine learning algorithms, Int. Res. J. Eng. Technol, 6, 4, pp. 608-611, (2019); Vu H.L., Et al., Analysis of input set characteristics and variances on k-fold cross validation for a recurrent neural network model on waste disposal rate estimation, Journal of Environmental Management, 311, pp. 114-869, (2022); 6 Most Common Diseases during Monsoon: Dengue, Malaria, Typhoid, Cholera and More - Symptoms and Prevention, (2022); Wiens J., Shenoy E.S., Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology, Foundation of Clinical Infectious Diseases, 66, 1, pp. 149-153, (2018); World Health Rankings, Bangladesh: Influenza and Pneumonia, (2022); Yahyaoui A., Yumusak N., Deep and machine learning towards pneumonia and asthma detection, International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 494-497, (2021); Zaidi S.R., Blakey J.D., Why are people with asthma susceptible to pneumonia?. A review of factors related to upper airway bacteria, Respirology, 24, 5, pp. 423-430, (2019)","M. Chowdhury; Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh; email: mahfuz@cuet.ac.bd","","Inderscience Publishers","","","","","","17418453","","","","English","Int. J. Electron. Healthc.","Article","Final","","Scopus","2-s2.0-85182363999"
"Cui S.; Shu Z.; Ma Y.; Lin Y.; Wang H.; Cao H.; Liu J.; Gong X.","Cui, Sijia (57209319495); Shu, Zhenyu (57193356344); Ma, Yanqing (57196458187); Lin, Yi (57813896900); Wang, Haochu (57196432447); Cao, Hanbo (57191657810); Liu, Jing (57640582900); Gong, Xiangyang (9279856600)","57209319495; 57193356344; 57196458187; 57813896900; 57196432447; 57191657810; 57640582900; 9279856600","A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development","2022","Frontiers in Medicine","9","","944294","","","","0","10.3389/fmed.2022.944294","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138891287&doi=10.3389%2ffmed.2022.944294&partnerID=40&md5=b61de591c157d856fb2a0da3986e32a9","Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Hangzhou Medical College, Institute of Artificial Intelligence and Remote Imaging, Hangzhou, China","Cui S., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Shu Z., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Ma Y., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Lin Y., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Wang H., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Cao H., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Liu J., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China; Gong X., Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Zhejiang, Hangzhou, China, Hangzhou Medical College, Institute of Artificial Intelligence and Remote Imaging, Hangzhou, China","The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress. The patients were divided into fSAD and non-fSAD groups and divided randomly into a training group (n = 190) and a validation group (n = 82) at a 7:3 ratio. Lung kit software was used for automatic delineation of regions of interest (ROI) on chest CT images. The most valuable imaging features were selected and a radiomic score was established for risk assessment. Multivariate logistic regression analysis showed that age, radiomic score, smoking, and history of asthma were significant predictors of fSAD (P < 0.05). Results suggested that the radiomic nomogram model provides clinicians with useful data and could represent a reliable reference to form fSAD clinical treatment strategies. Copyright © 2022 Cui, Shu, Ma, Lin, Wang, Cao, Liu and Gong.","nomogram; pulmonary function test; radiomics; risk factor; small airway dysfunction","salbutamol; adult; age; Article; asthma; bronchitis; cigarette smoking; cohort analysis; computer assisted tomography; controlled study; feature selection; female; human; image processing; image segmentation; lung; major clinical study; male; medical history; middle aged; nomogram; predictive model; radiomics; risk assessment; risk factor; small airway disease; smoking; thorax radiography; tuberculosis; validation process","","salbutamol, 18559-94-9, 35763-26-9","Artificial Intelligence Kit Version 3.0.1.A, GE Healthcare; LK Version V1.0.0.R, GE Healthcare; Pyradiomics version 2.12; Vmax22, Sensormedics","GE Healthcare; GE Healthcare; Sensormedics","","","Bommart S., Marin G., Bourdin A., Revel M.P., Klein F., Hayot M., Et al., Computed tomography quantification of airway remodelling in normal ageing subjects: A cross-sectional study, Eur Respir J, 45, pp. 1167-1170, (2015); Alfieri V., Aiello M., Pisi R., Tzani P., Mariani E., Marangio E., Et al., Small airway dysfunction is associated to excessive bronchoconstriction in asthmatic patients, Respir Res, 15, 86, (2014); Macklem P.T., The physiology of small airways, Am J Respir Crit Care Med, 157, pp. S181-S183, (1998); Lee J.E., Choe K.W., Lee S.W., Clinical and radiological characteristics of 2009 H1N1 influenza associated pneumonia in young male adults, Yonsei Med J, 54, pp. 927-934, (2013); Izquierdo-Alonso J.L., Rodriguez-Gonzalezmoro J.M., de Lucas-Ramos P., Unzueta I., Ribera X., Anton E., Et al., Prevalence and characteristics of three clinical phenotypes of chronic obstructive pulmonary disease (COPD), Respir Med, 107, pp. 724-731, (2013); Xiao D., Chen Z., Wu S., Huang K., Xu J., Yang L., Et al., Prevalence and risk factors of small airway dysfunction, and association with smoking, in China: Findings from a national cross-sectional study, Lancet Respir Med, 8, pp. 1081-1093, (2020); Konstantinos Katsoulis K., Kostikas K., Kontakiotis T., Techniques for assessing small airways function: Possible applications in asthma and COPD, Respir Med, 119, pp. e2-e9, (2016); van den Berge M., Ten Hacken N.H.T., Cohen J., Douma W.R., Postma D.S., Small airway disease in asthma and COPD: Clinical implications, Chest, 139, pp. 412-423, (2011); Usmani O.S., Singh D., Spinola M., Bizzi A., Barnes P.J., The prevalence of small airways disease in adult asthma: A systematic literature review, Respir Med, 116, pp. 19-27, (2016); Skylogianni E., Triga M., Douros K., Bolis K., Priftis K.N., Fouzas S., Et al., Small-airway dysfunction precedes the development of asthma in children with allergic rhinitis, Allergol Immunopathol, 46, pp. 313-321, (2018); Karimi R., Tornling G., Forsslund H., Mikko M., Wheelock A., Nyren S., Et al., Lung density on high resolution computer tomography (HRCT) reflects degree of inflammation in smokers, Respir Res, 15, 23, (2014); Lafata K.J., Zhou Z., Liu J.G., Hong J., Kelsey C.R., Yin F.F., An exploratory radiomics approach to quantifying pulmonary function in CT images, Sci Rep, 9, (2019); Occhipinti M., Paoletti M., Bartholmai B.J., Rajagopalan S., Karwoski R.A., Nardi C., Et al., Spirometric assessment of emphysema presence and severity as measured by quantitative CT and CT-based radiomics in COPD, Respir Res, 20, 101, (2019); Cho Y.H., Seo J.B., Lee S.M., Kim N., Yun J., Hwang J.E., Et al., Radiomics approach for survival prediction in chronic obstructive pulmonary disease, Eur Radiol, 31, pp. 7316-7324, (2021); Miller M.R., Structural and physiological age-associated changes in aging lungs, Semin Respir Crit Care Med, 31, pp. 521-527, (2010); Piorunek T., Kostrzewska M., Stelmach-Mardas M., Mardas M., Michalak S., Gozdzik-Spychalska J., Et al., Small airway obstruction in chronic obstructive pulmonary disease: Potential parameters for early detection, Adv Exp Med Biol, 980, pp. 75-82, (2017); Janssens J.P., Pache J.C., Nicod L.P., Physiological changes in respiratory function associated with ageing, Eur Respir J, 13, pp. 197-205, (1999); Huang K., Rabold R., Schofield B., Mitzner W., Tankersley C.G., Age-dependent changes of airway and lung parenchyma in C57BL/6J mice, J Appl Physiol, 2007, pp. 200-206, (1985); Yuan R., Hogg J.C., Pare P.D., Sin D.D., Wong J.C., Nakano Y., Et al., Prediction of the rate of decline in FEV(1) in smokers using quantitative Computed Tomography, Thorax, 64, pp. 944-949, (2009); Wang C., Xu J., Yang L., Xu Y., Zhang X., Bai C., Et al., Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China pulmonary health [CPH] study): A national cross-sectional study, Lancet, 391, pp. 1706-1717, (2018); Llontop C., Garcia-Quero C., Castro A., Dalmau R., Casitas R., Galera R., Et al., Small airway dysfunction in smokers with stable ischemic heart disease, PLoS One, 12, e0182858, (2017); Huang K., Yang T., Xu J., Yang L., Zhao J., Zhang X., Et al., Prevalence, risk factors, and management of asthma in China: A national cross-sectional study, Lancet, 394, pp. 407-418, (2019); Wang M., Luo X., Xu S., Liu W., Ding F., Zhang X., Et al., Trends in smoking prevalence and implication for chronic diseases in China: Serial national cross-sectional surveys from 2003 to 2013, Lancet Respir Med, 7, pp. 35-45, (2019); Collaborators G.B.D.T., Smoking prevalence and attributable disease burden in 195 countries and territories, 1990-2015: A systematic analysis from the global burden of disease study 2015, Lancet, 389, pp. 1885-1906, (2017); Postma D.S., Brightling C., Baldi S., Van den Berge M., Fabbri L.M., Gagnatelli A., Et al., Exploring the relevance and extent of small airways dysfunction in asthma (ATLANTIS): Baseline data from a prospective cohort study, Lancet Respir Med, 7, pp. 402-416, (2019); Nagarajan S., Ahmad S., Quinn M., Agrawal S., Manilich E., Concepcion E., Et al., Allergic sensitization and clinical outcomes in urban children with asthma, 2013-2016, Allergy Asthma Proc, 39, pp. 281-288, (2018); Chen H., Zeng Q.S., Zhang M., Chen R.C., Xia T.T., Wang W., Et al., Quantitative low-dose computed tomography of the lung parenchyma and airways for the differentiation between chronic obstructive pulmonary disease and asthma patients, Respiration, 94, pp. 366-374, (2017); Littleton S.W., Tulaimat A., The effects of obesity on lung volumes and oxygenation, Respir Med, 124, pp. 15-20, (2017); Juel C.T., Ali Z., Nilas L., Ulrik C.S., Asthma and obesity: Does weight loss improve asthma control? A systematic review, J Asthma Allergy, 5, pp. 21-26, (2012); Eneli I.U., Skybo T., Camargo C.A., Weight loss and asthma: A systematic review, Thorax, 63, pp. 671-676, (2008)","X. Gong; Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China; email: cjr.gxy@hotmail.com","","Frontiers Media S.A.","","","","","","2296858X","","","","English","Front. Med.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85138891287"
"Bonella F.; James W.E.; Spagnolo P.","Bonella, Francesco (57194852075); James, W. Ennis (57189524057); Spagnolo, Paolo (14822544700)","57194852075; 57189524057; 14822544700","Sarcoidosis","2023","ERS Monograph","2023","100","","293","309","16","0","10.1183/2312508X.10019122","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172896027&doi=10.1183%2f2312508X.10019122&partnerID=40&md5=d383fadd34f2a3f82cc235e92a2fa4d3","Pneumology Dept, Center for Interstitial and Rare Lung Diseases, Ruhrlandklinik University Hospital, University of Duisburg-Essen, Essen, Germany; Division of Pulmonary and Critical Care Medicine, Susan Pearlstine Sarcoidosis Center of Excellence, Medical University of South Carolina, Charleston, SC, United States; Respiratory Disease Unit, Dept of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy","Bonella F., Pneumology Dept, Center for Interstitial and Rare Lung Diseases, Ruhrlandklinik University Hospital, University of Duisburg-Essen, Essen, Germany; James W.E., Division of Pulmonary and Critical Care Medicine, Susan Pearlstine Sarcoidosis Center of Excellence, Medical University of South Carolina, Charleston, SC, United States; Spagnolo P., Respiratory Disease Unit, Dept of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy","Sarcoidosis has highly variable clinical presentations and outcomes, which make the diagnosis and management challenging. The lung is the most frequently involved organ, but radiological appearance, functional impairment and respiratory symptoms are not specific. Multiple tools are available to assist clinicians in excluding alternative causes of respiratory symptoms and assessing disease activity that may respond to treatment. Cardiac, neurological and renal involvements are life-threatening manifestations of sarcoidosis requiring a close follow-up and prompt changes in the treatment strategy if patients continue to deteriorate. New imaging and molecular biomarkers are in development to better characterise the extent of sarcoidosis in different organs and possibly guide treatment decisions. The most recent guideline on sarcoidosis treatment recommends steroids as the first-line therapy in several organ manifestations, although evidence is limited. Further immunosuppressive treatment should be considered to spare steroids or in those patients with contraindications. Despite several negative clinical trials in the last decade, promising compounds mainly targeting immunological mechanisms underlying sarcoidosis pathogenesis are currently being tested in phase II and III trials, the results of which are expected in the next few years. © 2023, European Respiratory Society. All rights reserved.","","abatacept; adalimumab; azathioprine; baricitinib; biological marker; calcitriol; corticosteroid; creatinine; crizotinib; cyclophosphamide; cyclosporine; dexamethasone; etanercept; gadolinium; inflammasome; infliximab; interleukin 17; interleukin 18; interleukin 1beta; methotrexate; methylprednisolone; mycophenolate mofetil; neuropilin 2; octreotide; osteoclast differentiation factor; parathyroid hormone; prednisolone; prednisone; rituximab; ruxolitinib; sarilumab; secukinumab; steroid; tofacitinib; tumor necrosis factor; vitamin D; adaptive immunity; adrenal insufficiency; Article; artificial intelligence; asthma; atrial fibrillation; autoimmune disease; cardiac sarcoidosis; cardiovascular magnetic resonance; chronic obstructive lung disease; chronic rhinosinusitis; contraindication; cyanosis; cystic fibrosis; diabetes insipidus; disease activity; disease severity; dyspnea; echocardiography; endobronchial ultrasonography; first-line treatment; follow up; forced expiratory volume; forced vital capacity; functional disease; glycolysis; granulomatous inflammation; heart; heart atrium flutter; heart failure; heart muscle biopsy; heart muscle fibrosis; human; hypercalcemia; hypercalciuria; hyperprolactinemia; hypopituitarism; immunoglobulin G4 related disease; immunosuppressive treatment; interstitial lung disease; interstitial nephritis; interstitial pneumonia; kidney; kidney biopsy; kidney calcification; lung; lung alveolitis; lung artery pressure; lung biopsy; lung function; lung function test; lung perfusion; lung sarcoidosis; lung transplantation; lupus pernio; lymphadenopathy; lymphocyte count; mediastinoscopy; morbidity; multimodal imaging; multiple sclerosis; myelooptic neuropathy; myocarditis; nephrolithiasis; nervous system; nose polyp; nuclear magnetic resonance imaging; optic neuritis; osteolysis; outcome assessment; paroxysmal supraventricular tachycardia; phase 2 clinical trial (topic); plasmapheresis; pleocytosis; pleura effusion; pneumothorax; positron emission tomography; positron emission tomography-computed tomography; practice guideline; predictive value; presyncope; prevalence; psoriatic arthritis; ptosis (eyelid); pulmonary hypertension; quality of life; radiation dose; radiation exposure; rheumatoid arthritis; scoring system; seizure; Sjoegren syndrome; skin sarcoidosis; spontaneous pneumothorax; sudden cardiac death; supraventricular tachycardia; systemic lupus erythematosus; tachycardia; thorax pain; thorax radiography; transthoracic echocardiography","","abatacept, 332348-12-6; adalimumab, 331731-18-1, 1446410-95-2; azathioprine, 446-86-6, 55774-33-9; baricitinib, 1187594-09-7; calcitriol, 32222-06-3, 32511-63-0, 66772-14-3; creatinine, 19230-81-0, 60-27-5; crizotinib, 877399-52-5; cyclophosphamide, 50-18-0, 6055-19-2; cyclosporine, 59865-13-3, 63798-73-2, 79217-60-0; dexamethasone, 50-02-2; etanercept, 185243-69-0, 200013-86-1, 2055118-96-0; gadolinium, 7440-54-2; infliximab, 170277-31-3; interleukin 18, 189304-55-0; methotrexate, 15475-56-6, 59-05-2, 7413-34-5, 7532-09-4, 6745-93-3, 51865-79-3, 60388-53-6; methylprednisolone, 6923-42-8, 83-43-2; mycophenolate mofetil, 116680-01-4, 128794-94-5, 115007-34-6; neuropilin 2, 227018-38-4; octreotide, 83150-76-9, 1607842-55-6, 135467-16-2, 79517-01-4; osteoclast differentiation factor, 200145-93-3; parathyroid hormone, 12584-96-2, 68893-82-3, 9002-64-6; prednisolone, 50-24-8; prednisone, 53-03-2; rituximab, 174722-31-7; ruxolitinib, 1092939-17-7, 941678-49-5; sarilumab, 1189541-98-7; secukinumab, 875356-43-7, 875356-44-8, 1229022-83-6; tofacitinib, 477600-75-2, 540737-29-9","","","","","Arkema EV, Cozier YC., Sarcoidosis epidemiology: recent estimates of incidence, prevalence and risk factors, CurrOpinPulm Med, 26, pp. 527-534, (2020); Chen ES., Innate immunity in sarcoidosis pathobiology, CurrOpinPulm Med, 22, pp. 469-475, (2016); Baughman RP, Teirstein AS, Judson MA, Et al., Clinical characteristics of patients in a case control study of sarcoidosis, Am J Respir Crit Care Med, 164, pp. 1885-1889, (2001); Judson M, Boan A, Lackland D., The clinical course of sarcoidosis: presentation, diagnosis, and treatment in a large white and black cohort in the United States, Sarcoidosis Vasc Diff Lung Dis, 29, pp. 119-127, (2012); 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Mena-Vazquez N, Rojas-Gimenez M, Fuego-Varela C, Et al., Safety and effectiveness of abatacept in a prospective cohort of patients with rheumatoid arthritis-associated interstitial lung disease, Biomedicines, 10, (2022); Frye BC, Rump IC, Uhlmann A, Et al., Safety and efficacy of abatacept in patients with treatment-resistant SARCoidosis (ABASARC) - protocol for a multi-center, single-arm phase IIa trial, Contemp Clin Trials Commun2020, 19; Huppertz C, Jager B, Wieczorek G, Et al., The NLRP3 inflammasome pathway is activated in sarcoidosis and involved in granuloma formation, Eur Respir J, 55, (2020); Sahashi K, Ina Y, Takada K, Et al., Significance of interleukin 6 in patients with sarcoidosis, Chest, 106, pp. 156-160, (1994); Sharp M, Donnelly SC, Moller DR., Tocilizumab in sarcoidosis patients failing steroid sparing therapies and anti-TNF agents, Respir Med X, 1, (2019)","F. Bonella; Pneumology Dept, Center for Interstitial and Rare Lung Diseases, Ruhrlandklinik University Hospital, University of Duisburg-Essen, Essen, Germany; email: francesco.bonella@rlk.uk-essen.de","","European Respiratory Society","","","","","","2312508X","","","","English","ERS Monogr.","Article","Final","","Scopus","2-s2.0-85172896027"
"Salamat M.R.; Salamat A.H.; Sattari M.; Saeedbakhsh S.; Asgari M.","Salamat, Mohammad Reza (26040949300); Salamat, Amir Hossein (6507398816); Sattari, Mohammad (57198173782); Saeedbakhsh, Saeed (55622815200); Asgari, Mehdi (56652412700)","26040949300; 6507398816; 57198173782; 55622815200; 56652412700","Identifying the Most Important Factors in Determining the Osteoporosis in Women Using Data Mining Techniques","2023","Acta Medica Iranica","61","4","","229","237","8","0","10.18502/acta.v61i4.13174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165991714&doi=10.18502%2facta.v61i4.13174&partnerID=40&md5=927b296fbdacb8cf8a99a8bb5295509c","Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Research and Development Division, Osteoporosis Diagnosis Center, Isfahan, Iran; Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Nursing, School of Nursing, Larestan University of Medical Sciences, Larestan, Iran","Salamat M.R., Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Salamat A.H., Research and Development Division, Osteoporosis Diagnosis Center, Isfahan, Iran; Sattari M., Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Saeedbakhsh S., Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Asgari M., Department of Nursing, School of Nursing, Larestan University of Medical Sciences, Larestan, Iran","Osteoporosis is one of the primary causes of disability and mortality in the elderly. If osteoporosis's significant features can be identified, the risk of developing this disease will be reduced. In recent years, data mining approaches have become a suitable tool for medical researchers. This study applied data mining methods to identify osteoporosis’s significant features. This study applied data from women having osteoporosis or osteopenia in the period 2011-2019 in the Osteoporosis Diagnosis Center, Isfahan, Iran. Data mining methods such as linear regression, naïve bayes, decision tree, support vector machine, random forest, and neural network were implemented on the dataset. This study consisted of 8258 patients’ information, of which 1482 had osteoporosis. The results showed that the support vector machine, decision tree, neural network are the best method based on accuracy, precision, and AUC measures. Six candidate features were age, weight, back pain, low activity, menopause date, and previous fracture. Support vector machine, decision tree, and neural network are the best candidate techniques for predicting osteoporosis. Thin older people are more at risk of osteoporosis than other people. Yet, people with middleweight and middle age are at lower risk of osteoporosis. © 2023 Tehran University of Medical Sciences. All rights reserved.","Data mining; Osteoporosis; Women","calcium; corticosteroid; accuracy; adult; aged; area under the curve; Article; asthma; backache; Bayesian learning; body height; body weight; controlled study; data collection method; data mining; data preprocessing; data processing; decision tree; diagnostic test accuracy study; feature selection; female; fracture; hormone substitution; human; independent variable; insulin dependent diabetes mellitus; kidney disease; linear regression analysis; machine learning; measurement precision; menopause; menstrual irregularity; mortality; nerve cell network; non insulin dependent diabetes mellitus; osteoporosis; prediction; probability; random forest; RapidMiner; rheumatoid arthritis; software; stomach disease","","calcium, 7440-70-2, 14092-94-5","","","","","Camacho PM, Petak SM, Binkley N, Diab DL, Eldeiry LS, Farooki A, Et al., American Association of Clinical Endocrinologists and American College of Endocrinology Clinical Practice guidelines for the diagnosis and tr4eatment of postmenopausal osteoporosis—2016, Endocr Pract, 22, pp. 1-4, (2016); Rachner TD, Khosla S, Hofbauer LC., Osteoporosis: now and the future, Lancet, 377, pp. 1276-1287, (2011); Sugimoto T, Sato M, Dehle FC, Brnabic AJ, Weston A, Burge R., Lifestyle-related metabolic disorders, osteoporosis, and fracture risk in Asia: A systematic review, Value Health Reg Issues, 9, pp. 49-56, (2016); Marshall D, Johnell O, Wedel H., Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures, BMJ, 312, pp. 1254-1259, (1996); Mirzaie M, Darabi S., Population Aging in Iran and Rising Health Care Costs, Salmand, 12, pp. 156-169, (2017); Lindsay R, Cosman F., Harrison’s Principles of Internal Medicine, Osteoporosis, pp. 3131-3136, (2012); Facts and Statistics, (2015); Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA., Mortality after all major types of osteoporotic fracture in men and women: an observational study, Lancet, 353, pp. 878-882, (1999); Tapak L, Shirmohammadi-Khorram N, Amini P, Alafchi B, Hamidi O, Poorolajal J., Prediction of survival and metastasis in breast cancer patients using machine learning classifiers, Clin Epidemiol Glob Health, 7, pp. 293-299, (2019); Moeinzadeh F, Rouhani MH, Mortazavi M, Sattari M., Prediction of chronic kidney disease in Isfahan with extracting association rules using data mining techniques, Tehran University Medical Journal TUMS Publications, 79, 6, pp. 459-467, (2021); Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA., Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm, Comput Methods Programs Biomed, 141, pp. 19-26, (2017); Moudani W, Shahin A, Chakik F, Rajab D., Intelligent decision support system for osteoporosis prediction, Int J Intell Inf Technol, 8, pp. 26-45, (2012); Yoo TK, Kim SK, Kim DW, Choi JY, Lee WH, Park EC., Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning, Yonsei Med J, 54, pp. 1321-1330, (2013); Mona S, Somayeh A, Abbasi M, Ameri H., Providing a model for predicting the risk of osteoporosis using decision tree algorithms, J Mazandaran Univ Med Sci, 24, pp. 110-118, (2014); Li H, Li X, Ramanathan M, Zhang A., Identifying informative risk features and predicting bone disease progression via deep belief networks, Methods, 69, pp. 257-265, (2014); Guannoni N, Sassi R, Bedhiafi W, Elloumi M., A Comparison Between Classification Algorithms for Postmenopausal Osteoporosis Prediction in Tunisian Population, InInternational Conference on Information Technology in Bio-and Medical Informatics, pp. 234-248, (2016); Pedrassani de Lira C, Toniazzo de Abreu LL, Veiga Silva AC, Mazzuchello LL, Rosa MI, Comunello E, Et al., Comput Inform Nurs, 34, pp. 369-375, (2016); Iliou T, Anagnostopoulos CN, Stephanakis IM, Anastassopoulos G., A novel data preprocessing method for boosting neural network performance: a case study in osteoporosis prediction, Inf Sci, 380, pp. 92-100, (2017); Langarizade M, Owji L, Orooji A., Developing a decision support system for osteoporosis Prediction, J Health Adm, 21, pp. 87-100, (2019); A Mowafy M., Osteoporosis Risk Prediction Among a Group of Postmenopausal Females: A Case-Control Study, Egypt Family Med J, 3, pp. 65-82, (2019); Genuer R, Poggi JM, Tuleau-Malot C., Variable selection using random forests, Pattern Recognit Lett, 31, pp. 2225-2236, (2010); Friedl MA, Brodley CE., Decision tree classification of land cover from remotely sensed data, Remote Sens Environ, 61, pp. 399-409, (1997); Montgomery DC, Peck EA, Vining GG., Introduction to linear regression analysis, (2012); Rish I., An empirical study of the naive Bayes classifier, workshop on empirical methods in artificial intelligence, 3, pp. 41-46, (2001); Babinec T., Neural Networks and Statistical Models, Sawtooth Software Conference, (1997); Auria L, Moro RA., Support Vector Machines (SVM) as a Technique for Solvency Analysis, (2009); Mastrogiannis N, Boutsinas B, Giannikos I., A method for improving the accuracy of data mining classification algorithms, Comput Oper Res, 36, pp. 2829-2839, (2009); Alvarez SA., An exact analytical relation among recall, precision, and classification accuracy in information retrieval, pp. 1-22, (2002); Huang J, Ling CX., Using AUC and accuracy in evaluating learning algorithms, IEEE Trans Knowl Data Eng, 17, pp. 299-310, (2005); Loeber R, Keenan K., Interaction between conduct disorder and its comorbid conditions: Effects of age and gender, Clin Psychol Rev, 14, pp. 497-523, (1994); Shahid Z, Kalayanamitra R, McClafferty B, Kepko D, Ramgobin D, Patel R, Et al., COVID‐19 and older adults: what we know, J Am Geriatr Soc, 68, pp. 926-929, (2020); Miller MM, Allison A, Trost Z, De Ruddere L, Wheelis T, Goubert L, Et al., Differential effect of patient weight on pain-related judgements about male and female chronic low back pain patients, J Pain, 19, pp. 57-66, (2018); Zhao LJ, Liu YJ, Liu PY, Hamilton J, Recker RR, Deng HW., Relationship of obesity with osteoporosis, J Clin Endocrinol Metab, 92, pp. 1640-1646, (2007); Vuori IM., Dose–response of physical activity and low back pain, osteoarthritis, and osteoporosis, Med Sci Sports Exerc, 33, pp. S551-S586, (2001); Chilibeck PD, Vatanparast H, Cornish SM, Abeysekara S, Charlesworth S., Evidence-based risk assessment and recommendations for physical activity: arthritis, osteoporosis, and low back pain, Appl Physiol Nutr Metab, 36, pp. S49-S79, (2011); Watanabe R, Tanaka T, Aita K, Hagiya M, Homma T, Yokosuka K, Et al., Osteoporosis is highly prevalent in Japanese males with chronic obstructive pulmonary disease and is associated with deteriorated pulmonary function, J Bone Miner Metab, 33, pp. 392-400, (2015); Stampfer MJ, Colditz GA, Willett WC., Menopause and heart disease, Ann N Y Acad Sci, 592, pp. 193-203, (1990); Tang MX, Jacobs D, Stern Y, Marder K, Schofield P, Gurland B, Et al., Effect of oestrogen during menopause on risk and age at onset of Alzheimer's disease, Lancet, 348, pp. 429-432, (1996)","M. Sattari; Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; email: msattarimng.mui@gmail.com","","Medical Sciences University of Teheran","","","","","","00446025","","AMEIA","","English","Acta Med. Iran.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85165991714"
"Cao L.; Xie Z.; Liu T.; Wang Z.; Fan C.","Cao, Lei (36967879100); Xie, Zhiheng (58627165200); Liu, Tianyu (57213687529); Wang, Zijian (56074709600); Fan, Chunjiang (57210154524)","36967879100; 58627165200; 57213687529; 56074709600; 57210154524","A DEEP LEARNING FRAMEWORK-BASED EXERCISE ASSESSMENT FOR REHABILITATION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE","2023","Journal of Mechanics in Medicine and Biology","23","9","2340089","","","","0","10.1142/S0219519423400894","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175473161&doi=10.1142%2fS0219519423400894&partnerID=40&md5=34644b82685b167653ebe053cdb0dfda","Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, 201306, China; Department of Artificial Intelligence, Donghua University, Shanghai, 200051, China; Department of Rehabilitation Medicine, Rehabilitation Hospital Wuxi, Wuxi, 214001, China","Cao L., Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, 201306, China; Xie Z., Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, 201306, China; Liu T., Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, 201306, China; Wang Z., Department of Artificial Intelligence, Donghua University, Shanghai, 200051, China; Fan C., Department of Rehabilitation Medicine, Rehabilitation Hospital Wuxi, Wuxi, 214001, China","Chronic obstructive pulmonary disease (COPD), which has a high prevalence and mortality rate, is an irreversible condition marked by airflow restriction with different degrees of reversible damage. Notably, there is no cure for COPD, whose treatment primarily relies on rehabilitation exercises to improve airflow limitation. In this paper, a vision-based rehabilitation exercise efficacy prediction system is proposed to assess the efficacy of rehabilitation training for COPD patients. A camera was utilized to capture rehabilitation training videos of COPD patients, and we also collected various physical indicators. In addition, we used clustering algorithm to divide patients with different rehabilitation effects for subsequent progression analysis. Our model achieved a classification of rehabilitation progress accuracy of 90.6%, making it possible to effectively obtain favorable rehabilitation training results without physician supervision. It was meaningful for helping COPD patients get effective feedback when training alone. © The Author(s)","adjunctive therapy; Artificial intelligence; chronic obstructive pulmonary disease; medical applications","Clustering algorithms; Deep learning; Disease control; Patient rehabilitation; Pulmonary diseases; Adjunctive therapy; Chronic obstructive pulmonary disease; Condition; Learning frameworks; Mortality rate; Prediction systems; Prevalence rates; Rehabilitation exercise; Rehabilitation training; Vision based; Medical applications","","","","","National Natural Science Foundation of China, NSFC, (62102242); Shanghai Education Research Program, (C2022152); Project of Jiangxu Health Commission, (Z2022012)","The work was supported by the National Natural Science Young Foundation of China (Grant No. 62102242), the Shanghai Education Research Program (Grant No. C2022152) and the Project of Jiangxu Health Commission (No. Z2022012).","Agusti A, Hogg JC, Update on the pathogenesis of chronic obstructive pulmonary disease, N Engl J Med, 381, pp. 1248-1256, (2019); Raherison C, Girodet PO, Epidemiology of COPD, Eur Respir Rev, 18, pp. 213-221, (2009); Celli BR, Pulmonary rehabilitation in patients with COPD, Am J Respir Crit Care Med, 152, pp. 861-864, (1995); Agusti A, Vogelmeier C, Faner R, COPD 2020: Changes and challenges, Am J Physiol Lung Cell Mol Physiol, 319, pp. 879-883, (2020); Matera MG, Cazzola M, Page C, Prospects for COPD treatment, Curr Opin Pharmacol, 56, pp. 74-84, (2021); Neder JA, Et al., Exercise ventilatory inefficiency adds to lung function in predicting mortality in COPD, COPD: J Chronic Obstr Pulm Dis, 13, pp. 416-424, (2016); COPD: Current therapeutic interventions and future approaches-European Respiratory Society, Barnes PJ, Stockley RA, COPD: Current therapeutic interventions and future approaches, Eur Respir J, 25, pp. 1084-1106, (2005); Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A, An artificial intelligence approach to early predict symptom-based exacerbations of COPD, Biotechnol Biotechnol Equipment, pp. 1-7, (2018); Vourganas I, Stankovic V, Stankovic L, Individualised responsible artificial intelligence for home-based rehabilitation, Sensors, 21, 2, (2021); Huo C-C, Et al., Prospects for intelligent rehabilitation techniques to treat motor dysfunction, Neural Regen Res, 16, pp. 264-269, (2020); Lu Y, Et al., Effects of home-based breathing exercises in subjects with COPD, Respir Care, 65, pp. 377-387, (2020); Bayona NA, Et al., The role of task-specific training in rehabilitation therapies, Top Stroke Rehabil, 12, pp. 58-65, (2005); Liao Y, Vakanski A, Xian M, A deep learning framework for assessing physical rehabilitation exercises, IEEE Trans Neural Syst Rehabil Eng, 28, pp. 468-477, (2020); Barry DT, Adaptation, artificial intelligence, and physical medicine and rehabilitation, PM R, 10, pp. 31-143, (2018); Vamsikrishna KM, Dogra DP, Desarkar MS, Computer-vision-assisted palm rehabilitation with supervised learning, IEEE Trans Biomed Eng, 63, pp. 991-1001, (2016); Huang Q, Wang F, Prevention and detection research of intelligent sports rehabilitation under the background of artificial intelligence, Appl Bionics Biomech, (2022); Qiu Y, Et al., Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training, Biomed Signal Process Control, 72, (2022); Velez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S, Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: A review, Sensors, 21, (2021); Kaku A, Et al., Towards data-driven stroke rehabilitation via wearable sensors and deep learning, Proc 5th Machine Learning for Healthcare Conf, pp. 143-171, (2020); Capecci M, Et al., A hidden semi-markov model based approach for rehabilitation exercise assessment, J Biomed Inf, 78, pp. 1-11, (2018); Ahad MdAR, Antar AD, Shahid O, Vision-based action understanding for assistive healthcare: A short review, IEEE Conf Computer Vision and Pattern Recognition (CVPR) Workshops IEEE, pp. 1-11, (2019); Debnath B, Et al., A review of computer vision-based approaches for physical rehabilitation and assessment, Multimed Syst, 28, pp. 209-239, (2022); Kanungo T, Et al., An efficient k-means clustering algorithm: Analysis and implementation, IEEE Trans Pattern Anal Mach Intell, 24, pp. 881-892, (2002); Likas A, Vlassis N, Verbeek JJ, The global k-means clustering algorithm, Pattern Recognit, 36, pp. 451-461, (2003); Shen J, Et al., Real-time superpixel segmentation by DBSCAN clustering algorithm, IEEE Trans Image Process, 25, pp. 5933-5942, (2016); Rousseeuw PJ, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J Comput Appl Math, 20, pp. 53-65, (1987); He K, Et al., Deep residual learning for image recognition, 2016 IEEE Conf Computer Vision and Pattern Recognition, pp. 770-778, (2016); Ng JY-H, Choi J, Neumann J, Davis LS, ActionFlowNet: Learning motion representation for action recognition, 2018 IEEE Winter Conf Applications of Computer Vision, pp. 1616-1624, (2018); Gu J, Et al., Recent advances in convolutional neural networks, Pattern Recognit, 77, pp. 354-377, (2018); Hochreiter S, Schmidhuber J, Long short-term memory, Neural Comput, 9, pp. 1735-1780, (1997); Yu Y, Si X, Hu C, Zhang J, A review of recurrent neural networks: LSTM cells and network architectures, Neural Comput, 31, pp. 1235-1270, (2019)","L. Cao; Department of Artificial Intelligence, Shanghai Maritime University, Shanghai, 201306, China; email: lcao@shmtu.edu.cn","","World Scientific","","","","","","02195194","","","","English","J. Mech. Med. Biol.","Article","Final","","Scopus","2-s2.0-85175473161"
"Rout N.K.; Parida N.; Rout R.K.; Sahoo K.S.; Jhanjhi N.Z.; Masud M.; AlZain M.A.","Rout, Narendra Kumar (57211462985); Parida, Nirjharinee (57211457439); Rout, Ranjeet Kumar (56416176900); Sahoo, Kshira Sagar (57091357900); Jhanjhi, N.Z. (36088700700); Masud, Mehedi (17338820600); AlZain, Mohammed A. (37048572200)","57211462985; 57211457439; 56416176900; 57091357900; 36088700700; 17338820600; 37048572200","Analysis of Breath-Holding Capacity for Improving Efficiency of COPD Severity-Detection Using Deep Transfer Learning","2023","Applied Sciences (Switzerland)","13","1","507","","","","0","10.3390/app13010507","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145710306&doi=10.3390%2fapp13010507&partnerID=40&md5=e7d825179ef19419565af98e93d5f826","Department of Computer Science, Rajendra University, Balangir, 767002, India; School of Computer Science & Engineering, Lovely Professional University, Phagwara, 144411, India; Computer Science and Engineering, National Institute of Technology Srinagar, Srinagar, 190006, India; Department of CSE, SRM University, Amaravati, 522240, India; School of Computer Science, SCS Taylors University, Subang, 47500, Malaysia; Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia; Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia","Rout N.K., Department of Computer Science, Rajendra University, Balangir, 767002, India; Parida N., School of Computer Science & Engineering, Lovely Professional University, Phagwara, 144411, India; Rout R.K., Computer Science and Engineering, National Institute of Technology Srinagar, Srinagar, 190006, India; Sahoo K.S., Department of CSE, SRM University, Amaravati, 522240, India; Jhanjhi N.Z., School of Computer Science, SCS Taylors University, Subang, 47500, Malaysia; Masud M., Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia; AlZain M.A., Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia","Air collection around the lung regions can cause lungs to collapse. Conditions like emphysema can cause chronic obstructive pulmonary disease (COPD), wherein lungs get progressively damaged, and the damage cannot be reversed by treatment. It is recommended that these conditions be detected early via highly complex image processing models applied to chest X-rays so that the patient’s life may be extended. Due to COPD, the bronchioles are narrowed and blocked with mucous, and causes destruction of alveolar geometry. These changes can be visually monitored via feature analysis using effective image classification models such as convolutional neural networks (CNN). CNNs have proven to possess more than 95% accuracy for detection of COPD conditions for static datasets. For consistent performance of CNNs, this paper presents an incremental learning mechanism that uses deep transfer learning for incrementally updating classification weights in the system. The proposed model is tested on 3 different lung X-ray datasets, and an accuracy of 99.95% is achieved for detection of COPD. In this paper, a model for temporal analysis of COPD detected imagery is proposed. This model uses Gated Recurrent Units (GRUs) for evaluating lifespan of patients with COPD. Analysis of lifespan can assist doctors and other medical practitioners to take recommended steps for aggressive treatment. A smaller dataset was available to perform temporal analysis of COPD values because patients are not advised continuous chest X-rays due to their long-term side effects, which resulted in an accuracy of 97% for lifespan analysis. © 2022 by the authors.","CNN; COPD; deep learning; disease; GRU; lifespan; lung","","","","","","Taif University, TU","Taif University Researchers Supporting Project number (TURSP-2020/98), Taif University, Taif, Saudi Arabia.","Rajasenbagam T., Jeyanthi S., Pandian J.A., Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques, J. Ambient Intell. Humaniz. Comput, pp. 1-8, (2021); Chagas J.V., Rodrigues D., Ivo R.F., Hassan M.M., de Albuquerque V.H., Filho P.P., A new approach for the detection of pneumonia in children using CXR images based on an real-time IOT system, J. 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Sci, 11, (2021); Rout R.K., Choudhury P.P., Sahoo S., Ray C., Partitioning 1-variable boolean functions for various classification of n-variable boolean functions, Int. J. Comput. Math, 92, pp. 2066-2090, (2015); Umer S., Mondal R., Pandey H.M., Rout R.K., Deep features based convolutional neural network model for text and non-text region segmentation from document images, Appl. Soft Comput, 113, (2021); Rout R.K., Hassan S.S., Sheikh S., Umer S., Sahoo K.S., Gandomi A.H., Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences, Comput. Biol. Med, 141, (2022); Hassan S.S., Rout R.K., Sahoo K.S., Jhanjhi N., Umer S., Tabbakh T.A., Almusaylim Z.A., A Vicenary analysis of SARS-CoV-2 genomes, CMC-Comput. Mater. Contin, pp. 3477-3493, (2021); Sitaula C., Hossain M.B., Attention-based VGG-16 model for COVID-19 chest X-ray image classification, Appl. Intell, 51, pp. 2850-2863, (2021)","N.Z. Jhanjhi; School of Computer Science, SCS Taylors University, Subang, 47500, Malaysia; email: noorzaman.jhanjhi@taylors.edu.my","","MDPI","","","","","","20763417","","","","English","Appl. Sci.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85145710306"
"Chen Y.; Tannous C.; Birling Y.","Chen, Yilan (57931289000); Tannous, Caterina (6701657332); Birling, Yoann (57201183738)","57931289000; 6701657332; 57201183738","Constitution in Chinese Medicine Clinical Reasoning","2022","Journal of Chinese Medicine","2022","130","","29","41","12","0","","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139985492&partnerID=40&md5=0992e72037b738cf92886ffeda1b48f1","Sydney Institute of Traditional Chinese Medicine, Australia; Director of Undergraduate Health Science at the School of Health Science at Western Sydney University, Australia; wisdom of Chinese medicine in Beijing, China","Chen Y., Sydney Institute of Traditional Chinese Medicine, Australia; Tannous C., Director of Undergraduate Health Science at the School of Health Science at Western Sydney University, Australia; Birling Y., wisdom of Chinese medicine in Beijing, China","The systemic study of Chinese medicine constitution has progressed greatly in recent years. However, a clear and comprehensive understanding of how to apply the theory of constitution to the practice of Chinese medicine is still lacking. In order to obtain an in-depth understanding of the integration of constitution in Chinese medicine clinical reasoning, we qualitatively analysed clinical experience reports and clinical cases in Chinese-language literature on insomnia, asthma, constipation, eczema, infertility and bi syndrome. We identified four major themes: the influence of socio-demographic information on clinical reasoning, constitution identification and pattern diagnosis, integration of constitution in active treatment, and continued management of constitution. This article presents our findings and includes practical examples including formulas, techniques and clinical cases. The findings of our study provide practical and comprehensive guidance on the clinical application of constitution theory in Chinese medicine practice. © 2022, Journal of Chinese Medicine. All rights reserved.","Chinese medicine; clinical experience; clinical reasoning; Constitution; pattern","ai ye; Angelica sinensis extract; antihistaminic agent; Atractylodes macrocephala extract; bai jiang cao; bai mao gen; bai xian pi; bakumondoto; bo he; chao gu ya; chao mai ya; chenpi; Chinese drug; chuan xiong; dai zhe shi; dan nan xing; dang shen; duan mu li; Evodia fruit extract; fa ban xia; fu ling; gan jiang; glucocorticoid; gou teng; guang yu jin; he huan pi; huang qin; ji nei jin; jiang zhi ban xia; jiangcan; keishi bukuryo gan; Leonurus japonicus extract; liu yi san; nu zhen zi; Paeonia suffruticosa extract; radix bupleuri; radix paeoniae alba; radix paeoniae rubra; shao yao; sheng di huang; sheng gan cao; shuang huang lian; Sophora flavescens extract; topical agent; unclassified drug; xia ku cao; xiaochaihu tang; xu chang qing; xuan fu dai zhe tang; xuan fu hua; ye jiao teng; ze lan; zhi fu zi; zhi gan cao; zhi shi; zhu ru; zi bei chi; adult; age; Article; asthma; case report; child; Chinese; Chinese (language); Chinese medicine; clinical article; clinical feature; clinical reasoning; constipation; eczema; female; gender; geographic distribution; geography; human; infertility; insomnia; Leonurus japonicus; lifestyle modification; middle aged; professional practice; qualitative analysis; school child; sociodemographics","","xiaochaihu tang, 63364-01-2","","","","","Cao W., Zhang X., Constipation Treated by Therapy of Moistening Dryness and Dispersing Body Fluids, Acta Chinese Medicine and Pharmacology, 47, 2, pp. 101-103, (2019); Chen G., Huang L., Chen Jifan’s experience of using Tong method in the treatment of Bi syndrome, Jiangxi Journal of Traditional Chinese Medicine, 31, 4, pp. 5-6, (2000); Chen S., Chen Y., Yu H., Clinical experience on the treatment of senile functional constipation from the perspective of Spleen yin deficiency, Chinese Journal of Ethnomedicine and Ethnopharmacy, 29, 13, pp. 80-82, (2020); Chen Y., Qu S., Zhang J., Et al., A brief analysis of treatment of Bi syndrome in Zhen Jiu Jia Yi Jing, Journal of Clinical Acupuncture and Moxibustion, 36, 3, pp. 78-82, (2020); Ding C., Zhan H., Wang H., Et al., Treatment of 30 cases of slow-transit constipation with the ‘treating the fu organ from the zang organ’ reasoning and the qi-tonififying, yin-nurturing and intestines-humecting method, Liaoning Journal of Traditional Chinese Medicine, 43, 2, pp. 298-300, (2016); Fan C., Treatment of 32 cases of eczema with Quan Xie Tang, Journal of Sichuan of Traditional Chinese Medicine, 25, 7, pp. 85-86, (2007); Han Z., Zhang B., Ni Y., Et al., Discussion on the Prevention and Treatment of Eczema with Constitution Theory and Preventive Treatment of Disease Theory of Traditional Chinese Medicine, Chinese Medicine Modern Distance Education of China, 18, 1, pp. 49-51, (2020); Huang J., Lu Y., Experience of the treatment of child insomnia by professor Lu Yanfang, Chinese Journal of Ethnomedicine and Ethnopharmacy, 23, 7, (2014); Jiang D., Zhou L., Chen X., Clinical experience of Chen Xinghe of Long Jiang school in stasis-based treatment of Bi syndrome, Jiangsu Journal of Traditional Chinese Medicine, 52, 11, pp. 14-17, (2020); Jiang F., Chen Y., Chen Yugen’s clinical experience in the treatment of chronic constipation, Jiangsu Journal of Traditional Chinese Medicine, 49, 3, pp. 30-31, (2017); Kuang D., Discussion pattern and constitution differentiation, Chinese Journal of Basic Medicine in Traditional Chinese Medicine, 8, 2, pp. 1-5, (2002); Experience in pattern-based treatment of chronic functional constipation, Beijing Journal of Traditional Chinese Medicine, 31, pp. 580-581, (2012); Li G., Yang S., Ouyang H., Difficulties in the treatment of eczema and strategies in Chinese medicine, Chin J Dermato Venerol Integ Trad W Med, 9, 4, pp. 253-255, (2010); Li X., Gao S., Treatment of 25 cases of smoldering eczema with Fu Fang Dan Shen Ye and Dang Gui Tang modified, Lishizhen Medicine and Materia Medica Research, 12, 4, (2001); Li X., Wu H., Wu Huatang’s experience in the treatment of functional constipation in seniors due to dryness in intestines and insufficient fluids, Hunan Journal of Traditional Chinese Medicine, 32, 6, pp. 31-32, (2016); Discussion on the treatment of functional constipation using Chuan Dao Tong You Tang, , Traditional Chinese Medicine Journal, 12, 4, pp. 5-11, (2013); Liao C., Clinical experience in TCM treatment of Bi syndrome, Journal of Traditional Chinese Medicine, 50, (2009); Liu J., Professor Yuan Zhanying’s experience in deficiency-based treatment of Bi syndrome, China Journal of Chinese Medicine, 28, 9, pp. 1305-1306, (2013); Liu J., Zhou A., Approaches of pattern differentiation in asymptomatic male infertility, China Journal of Traditional Chinese Medicine and Pharmacy, 22(10), 691-693.Liu, Q. & Chen, Z. (2001). 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Chin. Med.","Article","Final","","Scopus","2-s2.0-85139985492"
"Zhang X.; Luo G.","Zhang, Xiaoyi (59649527800); Luo, Gang (7401536289)","59649527800; 7401536289","Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study","2022","JMIR Medical Informatics","10","6","e38220","","","","0","10.2196/38220","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133522463&doi=10.2196%2f38220&partnerID=40&md5=a7a996d3f42f71d41811961515987e45","Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Zhang X., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Luo G., Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States","Background: Asthma hospital visits, including emergency department visits and inpatient stays, are a significant burden on health care. To leverage preventive care more effectively in managing asthma, we previously employed machine learning and data from the University of Washington Medicine (UWM) to build the world’s most accurate model to forecast which asthma patients will have asthma hospital visits during the following 12 months. Objective: Currently, two questions remain regarding our model’s performance. First, for a patient who will have asthma hospital visits in the future, how far in advance can our model make an initial identification of risk? Second, if our model erroneously predicts a patient to have asthma hospital visits at the UWM during the following 12 months, how likely will the patient have ≥1 asthma hospital visit somewhere else or ≥1 surrogate indicator of a poor outcome? This work aims to answer these two questions. Methods: Our patient cohort included every adult asthma patient who received care at the UWM between 2011 and 2018. Using the UWM data, our model made predictions on the asthma patients in 2018. For every such patient with ≥1 asthma hospital visit at the UWM in 2019, we computed the number of days in advance that our model gave an initial warning. For every such patient erroneously predicted to have ≥1 asthma hospital visit at the UWM in 2019, we used PreManage and the UWM data to check whether the patient had ≥1 asthma hospital visit outside of the UWM in 2019 or any surrogate indicators of poor outcomes. Such surrogate indicators included a prescription for systemic corticosteroids during the following 12 months, any type of visit for asthma exacerbation during the following 12 months, and asthma hospital visits between 13 and 24 months later. Results: Among the 218 asthma patients in 2018 with asthma hospital visits at the UWM in 2019, 61.9% (135/218) were given initial warnings of such visits ≥3 months ahead by our model and 84.4% (184/218) were given initial warnings ≥1 day ahead. Among the 1310 asthma patients in 2018 who were erroneously predicted to have asthma hospital visits at the UWM in 2019, 29.01% (380/1310) had asthma hospital visits outside of the UWM in 2019 or surrogate indicators of poor outcomes. Conclusions: Our model gave timely risk warnings for most asthma patients with poor outcomes. We found that 29.01% (380/1310) of asthma patients for whom our model gave false-positive predictions had asthma hospital visits somewhere else during the following 12 months or surrogate indicators of poor outcomes, and thus were reasonable candidates for preventive interventions. There is still significant room for improving our model to give more accurate and more timely risk warnings. ©Xiaoyi Zhang, Gang Luo.","asthma; clinical decision support; emergency department; forecasting; health outcome; healthcare outcome; machine learning; patient care management; prediction model","","","","","","National Institutes of Health, NIH, (R01HL142503); National Institutes of Health, NIH; National Heart, Lung, and Blood Institute, NHLBI","We thank Brian Kelly for the helpful discussions. GL was partially supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (award number R01HL142503). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","Chronic respiratory diseases: asthma, (2021); Centers for Disease Control and Prevention, (2021); Nurmagambetov T, Kuwahara R, Garbe P., The economic burden of asthma in the United States, 2008-2013, Ann Am Thorac Soc, 15, 3, pp. 348-356, (2018); Lieu TA, Quesenberry CP, Sorel ME, Mendoza GR, Leong AB., Computer-based models to identify high-risk children with asthma, Am J Respir Crit Care Med, 157, 4, pp. 1173-1180, (1998); Mays GP, Claxton G, White J., Managed care rebound? 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Loymans RJB, Debray TPA, Honkoop PJ, Termeer EH, Snoeck-Stroband JB, Schermer TRJ, Et al., Exacerbations in adults with asthma: a systematic review and external validation of prediction models, J Allergy Clin Immunol Pract, 6, 6, pp. 1942-1952, (2018); Lieu TA, Capra AM, Quesenberry CP, Mendoza GR, Mazar M., Computer-based models to identify high-risk adults with asthma: is the glass half empty of half full?, J Asthma, 36, 4, pp. 359-370, (1999); Schatz M, Nakahiro R, Jones CH, Roth RM, Joshua A, Petitti D., Asthma population management: development and validation of a practical 3-level risk stratification scheme, Am J Manag Care, 10, 1, pp. 25-32, (2004); Forno E, Fuhlbrigge A, Soto-Quiros ME, Avila L, Raby BA, Brehm J, Et al., Risk factors and predictive clinical scores for asthma exacerbations in childhood, Chest, 138, 5, pp. 1156-1165, (2010); Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Et al., Asthma exacerbation prediction and risk factor analysis based on a time-sensitive, attentive neural network: retrospective cohort study, J Med Internet Res, 22, 7, (2020); Longman JM, Passey ME, Ewald DP, Rix E, Morgan GG., Admissions for chronic ambulatory care sensitive conditions - a useful measure of potentially preventable admission?, BMC Health Serv Res, 15, (2015); Johnston JJ, Longman JM, Ewald DP, Rolfe MI, Diez Alvarez S, Gilliland AHB, Et al., Validity of a tool designed to assess the preventability of potentially preventable hospitalizations for chronic conditions, Fam Pract, 37, 3, pp. 390-394, (2020); Zhang X, Luo G., Error analysis of machine learning predictions on asthma hospital encounters, J Allergy Clin Immunol, 149, 2, (2022); Howell D, Rogers L, Kasarskis A, Twyman K., Comparison and validation of algorithms for asthma diagnosis in an electronic medical record system, Ann Allergy Asthma Immunol, 128, 6, pp. 667-681, (2022); Collective Medical and Consonus Healthcare announce partnership to improve postacute transitions of care, (2018); Tong Y, Messinger AI, Luo G., Testing the generalizability of an automated method for explaining machine learning predictions on asthma patients' asthma hospital visits to an academic healthcare system, IEEE Access, 8, pp. 195971-195979, (2020); Zhang X, Luo G., Ranking rule-based automatic explanations for machine learning predictions on asthma hospital encounters in patients with asthma: retrospective cohort study, JMIR Med Inform, 9, 8, (2021); Wiens J, Guttag JV, Horvitz E., Patient risk stratification with time-varying parameters: a multitask learning approach, J Mach Learn Res, 17, 79, pp. 1-23, (2016); Wang T, Jin F, Hu Y, Cheng Y., Early predictions for medical crowdfunding: a deep learning approach using diverse inputs, (2019); Guan Y, Wang X, Chen X, Yi D, Chen L, Jiang X., Assessment of the timeliness and robustness for predicting adult sepsis, iScience, 24, 2, (2021); Lauritsen SM, Kalor ME, Kongsgaard EL, Lauritsen KM, Jorgensen MJ, Lange J, Et al., Early detection of sepsis utilizing deep learning on electronic health record event sequences, Artif Intell Med, 104, (2020); Luo G, Nau CL, Crawford WW, Schatz M, Zeiger RS, Rozema E, Et al., Developing a predictive model for asthma-related hospital encounters in patients with asthma in a large, integrated health care system: secondary analysis, JMIR Med Inform, 8, 11, (2020); Luo G, He S, Stone BL, Nkoy FL, Johnson MD., Developing a model to predict hospital encounters for asthma in asthmatic patients: secondary analysis, JMIR Med Inform, 8, 1, (2020); Luo G., A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling, Glob Transit, 1, pp. 61-82, (2019); Luo G, Stone BL, Koebnick C, He S, Au DH, Sheng X, Et al., Using temporal features to provide data-driven clinical early warnings for chronic obstructive pulmonary disease and asthma care management: protocol for a secondary analysis, JMIR Res Protoc, 8, 6, (2019)","G. Luo; Department of Biomedical Informatics and Medical Education University of Washington UW Medicine South Lake Union, Seattle, 850 Republican Street, Building C, Box 358047, 98195, United States; email: gangluo@cs.wisc.edu","","JMIR Publications Inc.","","","","","","22919694","","","","English","JMIR Med. Inform.","Article","Final","All Open Access; Gold Open Access; Green Open Access","Scopus","2-s2.0-85133522463"
"Li H.L.; Feng X.; Wu Z.","Li, Hao Liang (58318037700); Feng, Xuehua (58318201700); Wu, Zhujiao (58318858300)","58318037700; 58318201700; 58318858300","Research on the application of nonverbal suggestion psychological counseling in patients with tracheal intubation in ICU","2022","Revista de Psiquiatria Clinica","49","5","","1","7","6","0","10.15761/0101-60830000000465","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162223195&doi=10.15761%2f0101-60830000000465&partnerID=40&md5=eb8f94c75275b574ac046735ebe6f504","People's Hospital of Wanning, Hainan ICU, 571500, China; Haikou People's Hospital, 570208, China","Li H.L., People's Hospital of Wanning, Hainan ICU, 571500, China; Feng X., Haikou People's Hospital, 570208, China; Wu Z., People's Hospital of Wanning, Hainan ICU, 571500, China","In the intensive-care unit (ICU), tracheal intubation (TI) is a regular treatment that frequently saves lives. But a sizable percentage of operations have life-threatening complications, making TI maybe one of the most frequent but unappreciated airway crises in the ICU. The client care paradigm in healthcare communication and behaviour has paid less attention to the significance of nonverbal communication. This is a result of the approach's very nature. Our objective was to determine what effect nonverbal communication research o ver the past 25 years has had on various health outcomes. We first discuss the functions of a nonverbal performance and its significance in treatment before demonstrating how a doctor's nonverbal behaviour affects a patient's satisfaction, trust, or adherence. The skills for assessing nonverbal behaviour are then provided. Therefore, the use of an Intubation Bundle is required for normal airway care in the ICU. Following a strategy for managing difficult airways that includes the utilization of tracheal aids and oral rescue tools and techniques is beneficial. The impact of interpersonal sensitivity on patient outcomes has also been demonstrated. © 2022, Universidade de Sao Paulo. Museu de Zoologia. All rights reserved.","COPD; Correlation; Inflammatory reaction; RDW; Severity of illness","etomidate; ketamine; propofol; suxamethonium; thiopental; anorexia nervosa; Article; aspiration; body mass; chronic obstructive lung disease; cognition; decision making; difficult airway management; dyspnea; electroencephalogram; electromyography; endotracheal intubation; epistaxis; esophagus intubation; follow up; health care personnel; health care quality; heart arrest; heart arrhythmia; human; hyperkalemia; hypotension; hypoxemia; inflammation; insomnia; intensive care unit; life expectancy; lung insufficiency; machine learning; nonverbal communication; obesity; patient care; pneumothorax; psychological counseling; psychotherapy; respiration control; risk factor; tracheostomy; vomiting","","etomidate, 15301-65-2, 33125-97-2, 51919-80-3; ketamine, 1867-66-9, 6740-88-1, 81771-21-3; propofol, 2078-54-8; suxamethonium, 306-40-1, 71-27-2; thiopental, 71-73-8, 76-75-5","","","","","Tan M., Et al., Comparison of diagnostic accuracy of early screening for pre‐eclampsia by NICE guidelines and a method combining maternal factors and biomarkers: results of SPREE, Ultrasound in Obstetrics & Gynecology, 51, 6, pp. 743-750, (2018); Cuijpers P., Reijnders M., Huibers M. J., The role of common factors in psychotherapy outcomes, Annual review of clinical psychology, 15, pp. 207-231, (2019); Cuijpers P., Berking M., Andersson G., Quigley L., Kleiboer A., Dobson K. S., A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments, The Canadian Journal of Psychiatry, 58, 7, pp. 376-385, (2013); Schramm E., Hautzinger M., Zobel I., Kriston L., Berger M., Harter M., Comparative efficacy of the cognitive behavioral analysis system of psychotherapy versus supportive psychotherapy for early onset chronic depression: design and rationale of a multisite randomized controlled trial, BMC psychiatry, 11, 1, pp. 1-9, (2011); Frank J. D., Frank J. B., Persuasion and healing: A comparative study of psychotherapy, (1993); Wampold B. E., Imel Z. E., The great psychotherapy debate: The evidence for what makes psychotherapy work, (2015); Cronbach L. J., Snow R. E., Aptitudes and instructional methods: A handbook for research on interactions, (1977); Dance K. A., Neufeld R. W., Aptitude-treatment interaction research in the clinical setting: A review of attempts to dispel the"" patient uniformity"" myth, Psychological Bulletin, 104, 2, (1988); Smith B., Sechrest L., Treatment of aptitude X treatment interactions, (1992); Cohen Z. D., DeRubeis R. J., Treatment selection in depression, Annual Review of Clinical Psychology, 14, pp. 209-236, (2018); Snow R. E., Aptitude-treatment interaction as a framework for research on individual differences in psychotherapy, Journal of consulting and clinical psychology, 59, 2, (1991); Miglietta M. A., Bochicchio G., Scalea T. M., Computer-assisted communication for critically ill patients: a pilot study, Journal of Trauma and Acute Care Surgery, 57, 3, pp. 488-493, (2004); Mitchell T. M., Machine learning, (2007); Bishop C. M., Nasrabadi N. M., Pattern recognition and machine learning, 4, (2006); Yarkoni T., Westfall J., Choosing prediction over explanation in psychology: Lessons from machine learning, Perspectives on Psychological Science, 12, 6, pp. 1100-1122, (2017); Gilpin L. H., Bau D., Yuan B. Z., Bajwa A., Specter M., Kagal L., Explaining explanations: An approach to evaluating interpretability of machine learning, (2018); Ayodele T. O., Types of machine learning algorithms, New advances in machine learning, 3, pp. 19-48, (2010); Booth B. M., Hickman L., Subburaj S. K., Tay L., Woo S. E., D'Mello S. K., Bias and fairness in multimodal machine learning: A case study of automated video interviews, Proceedings of the 2021 International Conference on Multimodal Interaction, pp. 268-277, (2021); Thompson T. L., Encyclopedia of health communication, (2014); Rahman M. M., Chakraborty S., Paul A., Jobayer A. M., Hossain M. A., Wheel therapy chair: A smart system for disabled person with therapy facility, 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 630-635, (2017)","H.L. Li; People's Hospital of Wanning, Hainan ICU, 571500, China; email: haoliangl2022@163.com","","Universidade de Sao Paulo. Museu de Zoologia","","","","","","01016083","","RPCLF","","English","Rev. Psiquiatr. Clin.","Article","Final","","Scopus","2-s2.0-85162223195"
"Shrivastava P.; Tripathi N.; Singh B.K.; Dewangan B.K.","Shrivastava, Poonam (57761944700); Tripathi, Neeta (56957901200); Singh, Bikesh Kumar (55480042200); Dewangan, Bhupesh Kumar (57193869672)","57761944700; 56957901200; 55480042200; 57193869672","Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters","2023","Archives of Acoustics","48","1","","13","24","11","0","10.24425/aoa.2022.142905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166472858&doi=10.24425%2faoa.2022.142905&partnerID=40&md5=671e9dd696f044856071ee88c643ec13","Department of Electronics and Telecommunication, SSTC, Bhilai, India; Department of Biomedical Engineering, National Institute of Technology, Raipur, India; Department of Computer Science and Engineering, School of Engineering, OP Jindal University, Raigarh, India","Shrivastava P., Department of Electronics and Telecommunication, SSTC, Bhilai, India; Tripathi N., Department of Electronics and Telecommunication, SSTC, Bhilai, India; Singh B.K., Department of Biomedical Engineering, National Institute of Technology, Raipur, India; Dewangan B.K., Department of Computer Science and Engineering, School of Engineering, OP Jindal University, Raigarh, India","Non-invasive techniques for the assessment of respiratory disorders have gained increased importance in recent years due to the complexity of conventional methods. In the assessment of respiratory disorders, machine learning may play a very essential role. Respiratory disorders lead to variation in the production of speech as both go hand in hand. Thus, speech analysis can be a useful means for the pre-diagnosis of respiratory disorders. This article aims to develop a machine learning approach to differentiate healthy speech from speech corresponding to different respiratory disorders (affected). Thus, in the present work, a set of 15 relevant and efficient features were extracted from acquired data, and classification was done using different classifiers for healthy and affected speech. To assess the performance of different classifiers, accuracy, specificity (Sp), sensitivity (Se), and area under the receiver operating characteristic curve (AUC) was used by applying both multi-fold cross-validation methods (5-fold and 10-fold) and the holdout method. Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC> 82%, and accuracy > 99%). The conclusion was that the proposed classifiers may provide an aid in the simple assessment of respiratory disorders utilising speech parameters with high efficiency. In the future, the proposed approach can be evaluated for the detection of specific respiratory disorders such as asthma, COPD, etc.  © 2023 The Author(s).","affected speech; classification techniques; healthy speech; machine learning; respiratory disorders; speech analysis","Classification (of information); Learning systems; Nearest neighbor search; Speech analysis; Support vector machines; Affected speech; Classification technique; Comparative analyzes; Conventional methods; Healthy speech; Machine learning approaches; Machine-learning; Noninvasive technique; Performance; Respiratory disorders; Decision trees","","","","","","","Alghowinem S., Et al., A comparative study of different classifiers for detecting depression from spontaneous speech, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8022-8026, (2013); Amaral J.L., Lopes A.J., Jansen J.M., Faria A.C., Melo P.L., Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease, Computer Methods and Programs in Biomedicine, 105, 3, pp. 183-193, (2012); Amaral J.L., Lopes A.J., Jansen J.M., Faria A.C., Melo P.L., An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms, Computer Methods and Programs in Biomedicine, 112, 3, pp. 441-454, (2013); Byun H., Lee S.W., Applications of support vector machines for pattern recognition: A survey, International Workshop on Support Vector Machines, pp. 213-236, (2002); Calverley P.M.A., Defining airflow obstruction: More data, further clarity, American Journal of Respiratory and Critical Care Medicine, 202, 5, pp. 649-650, (2020); Caruana R., Niculescu-Mizil A., An empirical comparison of supervised learning algorithms, Proceedings of the 23rd International Conference on Machine Learning, pp. 161-168, (2006); Chun K.S., Et al., Towards passive assessment of pulmonary function from natural speech recorded using a mobile phone, 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1-10, (2020); Dixit Mittal V., Sharma Y., Voice parameter analysis for the disease detection, IOSR Journal of Electronics and Communication Engineering, 9, 3, pp. 48-55, (2014); Dogan M., Eryuksel E., Kocak I., Celikel T., Sehitoglu M.A., Subjective and objective evaluation of voice quality in patients with asthma, Journal of Voice, 21, 2, pp. 224-230, (2007); Fawcett T., An introduction to ROC analysis, Pattern Recognition Letters, 27, 8, pp. 861-874, (2006); Gore S.M., Salunke M.M., Patil S.A., Kemalkar A.K., Disease detection using voice analysis, International Research Journal of Engineering and Technology, 7, 5, pp. 7655-7659, (2020); Gurbuz E., Kilic E., A new adaptive support vector machine for diagnosis of diseases, Expert Systems, 31, 5, pp. 389-397, (2014); Halpin D.M., Et al., Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. The 2020 GOLD science committee report on COVID-19 and chronic obstructive pulmonary disease, American Journal of Respiratory and Critical Care Medicine, 203, 1, pp. 24-36, (2021); Jain D., Singh V., Feature selection and classification systems for chronic disease prediction: A re view, Egyptian Informatics Journal, 19, 3, pp. 179-189, (2018); Kocsis O., Et al., Assessing machine learning algorithms for self-management of asthma, 2017 E-Health and Bioengineering Conference (EHB), pp. 571-574, (2017); Kuncheva L.I., Combining Pattern Classifiers: Methods and Algorithms, (2014); Mohamed E.E., El Maghraby R.A., Voice changes in patients with chronic obstructive pulmonary disease, Egyptian Journal of Chest Diseases and Tuberculosis, 63, 3, pp. 561-567, (2014); Refaeilzadeh P., Tang L., Liu H., Cross-validation, Encyclopedia of Database Systems, (2009); Saloni Sharma R.K., Gupta A.K., Disease detection using voice analysis: A review, International Journal of Medical Engineering and Informatics, 6, 3, pp. 189-209, (2014); Sapankevych N.I., Sankar R., Time series prediction using support vector machines: A survey, IEEE Computational Intelligence Magazine, 4, 2, pp. 24-38, (2009); Sonu Sharma R.K., Disease detection using analysis of voice parameters, 5th IEEE International Conference on Advanced Computing and Communication Technologies (ICACCT-2011), pp. 416-420, (2011); Teixeira J.P., Fernandes P.O., Jitter, shimmer and HNR classification within gender, tones and vowels in healthy voices, Procedia Technology, 16, pp. 1228-1237, (2014); Tsang K.C., Pinnock H., Wilson A.M., Shah S.A., Application of machine learning to support self-management of asthma with mHealth, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 5673-5677, (2020); Walia G.S., Sharma R.K., Level of asthma: mathematical formulation based on acoustic parameters, 2016 Conference on Advances in Signal Processing (CASP), pp. 24-27, (2016); Wiechern B., Liberty K.A., Pattemore P., Lin E., Effects of asthma on breathing during reading aloud, Speech, Language and Hearing, 21, 1, pp. 30-40, (2018)","B.K. Dewangan; Department of Computer Science and Engineering, School of Engineering, OP Jindal University, Raigarh, India; email: bhupesh.dewangan@gmail.com","","Polska Akademia Nauk","","","","","","01375075","","","","English","Arch. Acoust.","Article","Final","All Open Access; Gold Open Access","Scopus","2-s2.0-85166472858"
"Sehgal S.; Agarwal M.; Gupta D.; Bashambu A.","Sehgal, Shallu (57209969805); Agarwal, Manisha (34879541600); Gupta, Deepak (56985108600); Bashambu, Arun (57221952266)","57209969805; 34879541600; 56985108600; 57221952266","Improvised grasshopper algorithm for automatic lung disease detection","2022","Intelligent Decision Technologies","16","2","","285","298","13","0","10.3233/IDT-210066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133304660&doi=10.3233%2fIDT-210066&partnerID=40&md5=67e85268325fee2c4b00ab86a25a9bb1","Banasthali Vidyapeeth, Banasthali, Jaipur, India; Maharaja Agrasen Institute of Technology, Rohini, Delhi, India","Sehgal S., Banasthali Vidyapeeth, Banasthali, Jaipur, India; Agarwal M., Banasthali Vidyapeeth, Banasthali, Jaipur, India; Gupta D., Maharaja Agrasen Institute of Technology, Rohini, Delhi, India; Bashambu A., Banasthali Vidyapeeth, Banasthali, Jaipur, India","Chronic obstructive pulmonary disease (COPD) has been impacting a large population. It has a higher fatality rate than that of lung cancer. Diagnosis of this disease is quite challenging. Medical images analysis has been able to solve this challenge by early and accurate diagnosis of pulmonary disease. This analysis technique helps in pre-diagnosis and providing timely medical treatment thus reducing the mortality rate. The goal of this study is to establish an accurate process for classifying CT scan images into healthy lungs, COPD and Fibrosis impacted lung images. This classifying process has three steps. In the first step, lung scan is used for feature extraction. Then second and third step of feature selection and lung disease identification are carried using Machine Learning (ML) classifier. Haralick texture features with Gray Level Co-occurrence Matrix (GLCM), Zernike's moments, Gabor features and spatial domain features are used for feature extraction from the segmented lung CT images. For feature selection, our proposed evolutionary algorithm is the Improvised Grasshopper Algorithm (IGOA). After feature extraction from CT scan medical images, IGOA selects an optimal set of features that increases the classification accuracy and decreases the cost of computation. Lastly, three ML classifiers viz. Decision Tree Classifier, k-Nearest Neighbor (KNN), Random Forest Classifier are applied to every feature set chosen by IGOA. The research results show that IGOA filtered out the maximum number of unimportant features of about 71.01%. IGOA eliminates 28.99% of the total extracted features. IGOA gave a better accuracy of 99.8%. Research results imply that the introduced feature selection method is appropriate for disease classification from CT scan images. IGOA method can be used for real-time applications as it has a less computational cost and has better accuracy.  © 2022 - IOS Press. All rights reserved.","CT scan images; feature selection; lung diseases identification; Machine learning classifier","Biological organs; Classification (of information); Computer aided diagnosis; Computerized tomography; Decision trees; Evolutionary algorithms; Extraction; Image classification; Medical imaging; Nearest neighbor search; Pulmonary diseases; Textures; Chronic obstructive pulmonary disease; CT-scan images; Disease detection; Features extraction; Features selection; Learning classifiers; Lung disease identification; Machine learning classifier; Machine-learning; Research results; Feature Selection","","","","","","","Campos H., Lemos A., Asthma and COPD from a pulmonologist's point of view, Brazilian Journal of Pulmonology, 35, 4, pp. 301-309, (2009); Vestbo J., Hurd S., Agusti A., Jones P., Vogelmeier C., Anzueto A., Et al., Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease, American Journal of Respiratory and Critical Care Medicine, 187, 4, pp. 347-365, (2013); 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Senthil Kumar K., Venkatalakshmi K., Karthikeyan K., Lung cancer detection using image segmentation by means of various evolutionary algorithms, Computational and Mathematical Methods in Medicine, 2019, pp. 1-16, (2019); Badnjevic A., Gurbeta L., Custovic E., An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings, Scientific Reports, 8, 1, (2018); Makaju S., Prasad P., Alsadoon A., Singh A., Elchouemi A., Lung cancer detection using CT scan images, Procedia Computer Science, 125, pp. 107-114, (2018); Al Mohammad B., Brennan P., Mello-Thoms C., A review of lung cancer screening and the role of computer-aided detection, Clinical Radiology, 72, 6, pp. 433-442, (2017); Qin C., Yao D., Shi Y., Song Z., Computer-aided detection in chest radiography based on artificial intelligence: A survey, BioMedical Engineering OnLine, 17, 1, (2018); Parsa Hosseini M., Soltanian-Zadeh H., Akhlaghpoor S., Detection and severity scoring of chronic obstructive pulmonary disease using volumetric analysis of lung CT images, Iranian Journal of Radiology, 9, 1, pp. 22-27, (2012); 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Askarzadeh A., A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm, Computers & Structures, 169, pp. 1-12, (2016); Mirjalili S., Lewis A., The whale optimization algorithm, Advances in Engineering Software, 95, pp. 51-67, (2016); Eesa A., Brifcani A.M.A., Orman Z., Cuttlefish algorithm-A novel bio-inspired optimization algorithm, International Journal of Scientific & Engineering Research, 37, 4, pp. 1978-1986, (2013); Gupta D., Rodrigues J., Sundaram S., Khanna A., Korotaev V., De Albuquerque V., Usability feature extraction using modified crow search algorithm: A novel approach, Neural Computing and Applications, 32, 15, pp. 10915-10925, (2018); Sweetlin J., Nehemiah H., Kannan A., Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images, Computer Methods and Programs in Biomedicine, 145, pp. 115-125, (2017); De Carvalho Filho A., De Sampaio W., Silva A., De Paiva A., Nunes R., Gattass M., Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index, Artificial Intelligence in Medicine, 60, 3, pp. 165-177, (2014); 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Sehgal; Banasthali Vidyapeeth, Jaipur, Banasthali, India; email: shallubashambu@mait.ac.in","","IOS Press BV","","","","","","18724981","","","","English","Intelligent Decis. Technol","Article","Final","","Scopus","2-s2.0-85133304660"